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517493 | Automatic annotation of protein motif function with Gene Ontology terms | Background Conserved protein sequence motifs are short stretches of amino acid sequence patterns that potentially encode the function of proteins. Several sequence pattern searching algorithms and programs exist foridentifying candidate protein motifs at the whole genome level. However, amuch needed and importanttask is to determine the functions of the newly identified protein motifs. The Gene Ontology (GO) project is an endeavor to annotate the function of genes or protein sequences with terms from a dynamic, controlled vocabulary and these annotations serve well as a knowledge base. Results This paperpresents methods to mine the GO knowledge base and use the association between the GO terms assigned to a sequence and the motifs matched by the same sequence as evidence for predicting the functions of novel protein motifs automatically. The task of assigning GO terms to protein motifsis viewed as both a binary classification and information retrieval problem, where PROSITE motifs are used as samples for mode training and functional prediction. The mutual information of a motif and aGO term association isfound to be a very useful feature. We take advantageof the known motifs to train a logistic regression classifier, which allows us to combine mutual information with other frequency-based features and obtain a probability of correctassociation. The trained logistic regression model has intuitively meaningful and logically plausible parameter values, and performs very well empirically according to our evaluation criteria. Conclusions In this research, different methods for automatic annotation of protein motifs have been investigated. Empirical result demonstrated that the methods have a great potential for detecting and augmenting information about thefunctions of newly discovered candidate protein motifs. | Background With the completion of many genome sequencing projects and advances in the methods of automatic discovery of sequence patterns (see Brazma [ 1 ] and Brejova et al [ 2 ] for reviews), it is now possible to search or discover protein sequence motifs at the genome level. If one regards protein sequences as "sentences" of the biological language with amino acids as the alphabet, then protein motifs can be considered as words or phrases of that language and determining the function of a motif is equivalent to determining the sense of a word. Identifying biological sequence motifs has been a fundamental task of bioinformatics, which has led to the development of several motif (pattern) databases, such as PROSITE, BLOCKS, SMART and Pfam [ 3 - 6 ]. These databases are usually constructed by studying the set of protein sequences that are known to have certain functions and extracting the conserved sequence motifs that are believed to be responsible for their functions. However, the number of motifs that can be extracted in this way is quite limited, and it has been a major challenge to discover new motifs. With the advent of algorithms and programs that can automatically discover sequence motifs from any given set of sequences [ 1 , 2 , 7 - 9 ], it is possible to mine a large number of sequences to find novel motifs without necessarily knowing their functions and to compile a dictionary of biological language accordingly. An essential task involved in the compilation of such a dictionary is to determine the function (the meaning) of newly identified protein motifs. Here, we report development of general methods that can be used to predict the function of protein motifs by mining the knowledge in the Gene Ontology. The Gene Ontology™ (GO) project [ 10 ] is a concerted effort by the bioinformatics community to develop a controlled vocabulary (GO terms) and to annotate biological sequences with the vocabulary. A biological sequence is described in three different aspects, namely, biological process, cellular component, and molecular function. The standardized annotation with a controlled vocabulary is the main advantage of Gene Ontology, which facilitates both communications among scientists and information management. Both the number of annotated sequences and the number of GO terms associated with individual sequences in the Gene Ontology database are increasing very rapidly. Moreover, natural language processing techniques are also being used to automatically annotate gene products with GO terms [ 11 , 12 ]. Thus, it can be foreseen that the annotations of protein sequences in the Gene Ontology database will become more and more detailed, and have a great potential to be used as an enriched knowledge base of proteins. The basic approach for determining the function of a motif is to study all the sequences that contain the motif (pattern). Intuitively, if all the functional aspects of the sequences matching a motif are known, we should be able to learn which function is most likely encoded by the motif, based on the assumption that every protein function is encoded by an underlying motif. This means that we would need a knowledge base of protein sequences, in which the functions of a sequence are annotated as detailed as possible. In addition, we would also need prediction methods that can work on a given set of protein sequences and their functional descriptions to reliably attribute one of the functions to the motif that matches these sequences. To determine the function of any novel motif, we would first search the protein knowledge base to retrieve all the functional descriptions of the proteins containing the motif, and then use such prediction methods to decide which function is encoded by the motif. In this research, we use the Gene Ontology database as our protein knowledge base and explore statistical methods that can learn to automatically assign biological functions (in the form of GO terms) to a protein motif. Our approach is based on the observation that the Gene Ontology database contains protein sequences and the GO terms associated with the sequences. In addition, the database also contains information of known protein motifs, e.g. the PROSITE patterns that match the sequences. Thus, the protein sequences in the database provide a sample of potential associations of GO term with motifs, among which some are correct (i.e., the GO term definition matches the functional description of the motif) and some are not. This provides us an opportunity to perform supervised learning to identify discriminative features and use these features to predict whether a new association is correct or not. Current Gene Ontology database is implemented with relational database system, which allows one to perform queries like "retrieve all GO terms associated with the sequences that matches a given motif" and vice versa . However, the database usually returns more than one GO terms that may or may not describe the function of the motif in the query. Thus, we need methods to disambiguate which GO term describe the function of the motif (assign a GO term to a motif) and determine how confident we are as the assignment is concerned. We use statistical approaches to learn from known examples and cast disambiguation task into a classification problem. Furthermore, the probability output by the classifier can be used to represent its confidence for the assignment. Recently, Schug et al [ 13 ] published their result of automatically associating GO terms with protein domains from two motif databases – ProDom and CDD [ 14 , 15 ]. Their approach is to use protein domains to BLAST [ 16 ] search against GO database and assign the molecular functional GO term from the sequence matching the domains with most significant p -value. They found that, in the database they worked with, most sequences only had one functional GO term. Therefore, they could assign the GO term of a sequence to the motif that matched with highest score with fairly good accuracy. However, due to restrictive assumption that each sequence has only one GO term, their approach can not address the potential problem that a sequence matching a motif has multiple associated GO terms, which is common case now, and how to resolve such ambiguity. Results The data set We use the May 2002 release of the Gene Ontology sequence database (available online [ 17 ]), which contains 37,331 protein sequences. For each sequence, a set of GO terms assigned to the sequence is identified, and a set of PROSITE patterns that match the same sequence is also retrieved. If both sets are nonempty, all the possible pattern-term combinations formed by the two sets are produced. Table 1 shows an example association of GO terms with PROSITE motifs. The protein MGI|MGI:97380 from the database is assigned seven GO terms and the sequence also matches two PROSITE patterns. Thus, as cross product of two sets, 14 distinct associations are produced. Note that the same pattern-term association may be observed multiple times within the database. A total of 4,135 GO terms, 1,282 PROSITE motifs, and 2,249 distinct PROSITE-GO associations have been obtained from this database. Using the information stored in the Gene Ontology and PROSITE, we manually judged a set of 1,602 cases of distinct PROSITE-GO associations to determine whether the association is correct or not. The PROSITE-GO association set has been judged in two different ways. One way is to label an association as correct if and only if the definition of the GO term and the PROSITE motif match perfectly according to the annotator. Gene Ontology has the structure of a directed acyclic graph (DAG) to reflect the relations among the terms. Most terms (nodes in the graph) have parent, sibling and child terms to reflect the relation of "belonging to" or "subfamily". The second way of judging GO-PROSITE association is to label an association as correct if the GO term and the PROSITE motif are either exact match or the definitions of GO term and PROSITE motif are within one level difference in the tree, i.e., the definition of GO term and the PROSITE motif have either a parent-child relation or a sibling relation according to the GO structure. Thus we have two sets of labeled PROSITE-GO associations, the perfect match set and the relaxed match set (with neighbors). Both sets are further randomly divided into training (1128 distinct associations) and test (474 distinct associations) sets. Since the test sample size is fairly large, the variance of the prediction accuracy can be expected to be small. Thus we have not considered any alternative split of training and test sets. Measuring term-motif associations Intuitively, we may think of the GO terms assigned to a protein as one description of the function of a protein in one language (human understandable) while the motifs contained in the protein sequence as another description of the same function in a different language (biological). We would like to discover the "translation rules" between these two languages. Looking at a large number of annotated sequences, we hope to find which terms tend to co-occur with a given motif pattern. Imagine that, if the sequences that match a motif are all assigned a term T , and none of the sequences that do not match the motif is assigned the term T , then it is very likely that the motif pattern is encoding the function described by term T . Of course, this is only an ideal situation; in reality, we may see that most of, but not all of the proteins matching a motif pattern would be assigned the same pattern, and also some proteins that do not match the motif may also have the same term. Thus, we want to have a quantitative measure of such correlation between GO terms and motif patterns. A commonly used association measure is mutual information (M.I.), which measures the correlation between two discrete random variables X and Y [ 18 ]. It basically compares the observed joint distribution p ( X = x , Y = y ) with the expected joint distribution under the hypothesis that X and Y are independent, which is given by p ( X = x ) p ( Y = y ). A larger mutual information indicates a stronger association between X and Y , and I ( X;Y ) = 0 if and only if X and Y are independent. For our purpose, we regard the assignment of a term T to a sequence and the matching of a sequence with a motif M as two binary random variables. The involved probabilities can then be empirically estimated based on the number of sequences matching motif M ( NM ), the number of sequences assigned term T ( NT ), the number of sequences both matching M and assigned T ( NT - M ), and the total number of sequences in the database. Table 2 shows the top five terms that have the highest mutual information with PROSITE motif PS00109, which is the specific active-site signature of protein tyrosine kinases, along with the related counts. We set out to test whether we can use mutual information as a criterion to assign a GO term to a PROSITE motif. One approach is to use a mutual information cutoff value c to define a simple decision rule: assign term T to motif M , if and only if I ( T;M ) ≥ c . For a given cutoff c , the precision of term assignment is defined as the ratio of the number of correct assignments to that of the total assignments according to the cutoff c . In Figure 1 , we plot the precision at different mutual information cutoff values. It is easy to see that, in general, using a higher (i.e., stricter) cutoff, the precision is higher; indeed, the Pearson correlation coefficient between the precision and the cutoff is 0.837. This suggests that mutual information is indeed a good indicator of the correlation However, a drawback of such an approach is that, given a motif, sometimes, many observed motif-term associations can have mutual information above the cutoff value, making it difficult to decide which pair is correct. While in other cases, the mutual information of the observed motif-term pairs may all be below the cutoff value, but we still would like to predict what terms are most likely to be appropriate for the motif. To address this problem, we can use a different cutoff strategy, and adopt a decision rule that assigns a GO term to a motif based on the ranking of mutual information, which is a common technique used in information retrieval text categorization [ 19 ]. More specifically, for each PROSITE motif M in the annotated data set, all observed motif-term associations containing M are retrieved and ranked according to mutual information, then the term that has highest mutual information is assigned to M . Alternatively, if we use this approach to facilitate human annotation, we can relax the rule to include GO terms that have lower ranks, thus allowing multiple potential GO terms to be assigned to a motif, assuming that a human annotator would be able to further decide which is correct. In this method, the key in making a decision is to select a cutoff rank that covers as many correct associations as possible (high sensitivity) while also retrieves as fewer incorrect associations as possible (high specificity). The optimal cutoff can be determined by the desired utility function. Figure 2 shows the Receiver Operating Characteristic (ROC) curve [ 20 ] of assigning GO terms to PROSITE motifs in our data set according to the rank of motif-term associations. The two curves are for the two different labeled association sets (i.e., perfect match and relaxed match) respectively. The areas under the two curves are 0.782 and 0.735 respectively, which can be considered as fairly good. We also plot the precision, also referred to as positive predictive value, in panel B. The precision is calculated as the percent of predicted assignments that are truly correct. As shown in panel B, if we assign the GO terms at the top rank for all PROSITE motifs, 50–70% of the cases will be predicted correctly. As we loosen the threshold to include lower ranked terms, we would assign more terms to a motif, and as expected, precision would decline. But even at rank 5, we still have a precision of about 50%. Also shown in Table 2 , with respect to the PROSITE pattern of tyrosine kinase (PS00109), most of the top five associated GO terms are related to kinase activity and the term with the highest rank is the most specific. Predicting motif functions using logistic regression While the mutual information measure appears to give reasonable results, there are three motivations for exploring more sophisticated methods. First, the mutual information value is only meaningful when we compare two candidate terms for a given motif pattern; it is hard to interpret the absolute value. While a user can empirically tune the cutoff based on some utility preferences, it would be highly desirable to attach some kind of confidence value or probability of correctness to all the potential candidate motif-term associations. Second, there may be other features that can also help predict the function (term) for a motif. We hope that the additional features may help a classifier to further separate correct motif-term assignment from wrong ones. Third, there exist many motifs with known functions (e.g., those in the PROSITE database), and it is desirable to take advantage of such information to help predict the functions of unknown motifs. This means that we need methods that can learn from such information. In this section, we show that the use of logistic regression can help achieve all three goals. Specifically, we use logistic regression to combine the mutual information with other features, and produce a probability of correct assignment. The motifs with known functions serve as training examples that are needed for estimating the parameters of the regression function. Feature extraction and parameter estimation We now discuss the features to be used in logistic regression, in addition to the mutual information discussed in the previous section. The goal is to identify a set of features that is helpful to determine whether association of any pair of a GO term and a motif is correct or not, without requiring specific information regarding the function of GO term and motif. For a distinct motif-term pair, we collect following frequency-based features: (1) The number of sequences in which the GO term ( T ) and PROSITE motif ( M ) co-occur ( NT-M ). (2) The number of sequences in which T occurs ( NT ). (3) The number of sequences in which M occurs ( NM ). (4) The number of distinct GO terms ( G ) seen associated with M ( NG|M ). (5) The number of distinct PROSITE patterns ( P ) seen associated with T ( NP|T ). In addition, we also consider, as a feature, the similarity of the sequences that support a motif-term pair. Intuitively, if a motif is conserved among a set of diverse sequences, it is more likely that the motif is used as a building block in proteins with different functions. Thus, the average pair-wise sequence similarity of the sequence set can potentially be used as a heuristic feature in the logistic regression classifier. Given a set of sequences, we use a BLAST search engine to perform pair-wise sequence comparisons. We devised a metric AvgS to measure the averaged pair-wise sequence similarity per 100 amino acids (see methods) and use it as an input feature for classifier. To cast the prediction problem as a binary classification problem, we augment our data set of motif-term pairs with a class label variable Y , so that Y = 1 means correct assignment and 0 means incorrect. We represent a motif-term pair by a vector of features X = ( X 1 ,..., X k ), where k is the number of features. The seven features/variables used in our experiments are NT-M, NT, NM, NG|M, NP|T, AvgS , and M.I. . Suppose we have observed n motif-term pairs, then we have n samples of ( y i , x i ), i = 1, 2, ..., n , where, y i is the correctness label and x i is the feature vector for the corresponding motif-term pair. Our goal is to train a classifier which, when given a motif-term pair and feature vector X , would output a label Y with value 1 or 0. Alternatively, we can also consider building a classifier which outputs a probability that Y = 1 instead of a deterministic label. Thus, our task is now precisely a typical supervised learning problem; many supervised learning techniques can potentially be applied. Here, we choose to use logistic regression as our classification model because it has a sound statistical foundation, gives us a probability of correct assignment, and can combine our features naturally without any further transformation. In order to build a model only with the truly discriminative features, it is a common practice to perform feature selection for logistic regression. We use a combined forward and backward feature selection algorithm. Starting from the intercept, we sequentially add features into the model and test if the log-likelihood increases significantly; we keep the current feature if it does. After the forward selection, we sequentially drop features from the model, to see if dropping a feature would significantly reduce the log-likelihood of the model; if it does, we exclude the feature from the model, otherwise continue. When testing the significance, we use the likelihood ratio statistic G , given by 2 l ( D|β f )/ l ( D|β - f ), where, l ( D|β f ) and l ( D|β - f ) are the log-likelihood of the model with feature f and the model without feature f , respectively. Since we add or drop one feature at a time, G follows χ 2 distribution with degree of freedom of 1 [ 21 ]. We use the p -value of 0.1 as a significant threshold. Figure 3 illustrates the procedure of feature selection. We found that the average pair-wise similarity of supporting sequence set does not contribute to the model significantly and so excluded it; all other variables contribute to the model significantly. The results of parameters estimation are show in the Table 3 . Logistic regression classification After fitting the model using the training set, we tested the model on the test set, i.e., we used the model to compute an output p ( Y i = 1| X i ) for each test case. Table 4 shows an example of computed conditional probability of correct assignment for the GO terms associated with the protein motif possible the motif "PS00383", which is the "tyrosine specific protein phosphatases signature and profiles". The table 4 lists top 5 GO terms, which are observed to be associated with the motif and ranked according to the conditional probability returned by logistic regression. As the results from the logistic regression are the conditional probability that an association of a GO term with a given motif is correct, we need to decide the cut off threshold for making decision. We calculate the sensitivity and specificity for a different threshold from 0.1 to 0.9 with a step of 0.1 and plotted the ROC curves as shown in Figure 4 . The areas under the logistic regression ROC curves are 0.875 and 0.871 for perfect match and relaxed match test set respectively. The precision of the rules is plotted in panel B, where we see that, as the rule becomes more stringent (using a higher threshold), predictions generally become more accurate. We noticed that the precision on the perfect match test set is more variable. This is probably due to the fact that this data set has fewer cases with Y = 1, thus, a small change in the number of cases introduces a large change in percentage. For example, when the threshold is set at 0.9, only three cases are covered by the rule and two of them are correct, thus percent correct drop to 66%. To see whether the additional features are useful, we also performed ROC analysis using different mutual information cutoff threshold on the perfect match test set. The result is shown in Figure 4 panels C and D . We see that using mutual information alone performs almost as well as logistic regression with additional features. However, the area under the curve (0.816) is smaller than that of logistic regression (0.875), indicating that logistic regression does take advantage of other features and has more discriminative power than mutual information alone. The coefficients β 1 , β 2 and β 3 for the three features NT-M , NT and NM , which are also involved in the calculation of mutual information, have a very interesting interpretation – they indicate that the roles of these three variables in the logistic regression model actually are to compromise the effect of mutual information! Indeed, according to the formula of the mutual information, a strong correlation corresponds to a high NT-M , low NT , and low NM , but the coefficients shown in Table 3 clearly suggest the opposite. We believe that this actually corrects one drawback of mutual information – over-emphasizing the correlation but ignoring the support or the strength of evidence. For example, if a term is rare, say occurs only once in the data set, then it would have a very high mutual information value (due to an extremely low NT ) with respect to any pattern matched by the sequence to which the term is assigned. But, intuitively, one occurrence is very weak evidence, and at least should be regarded as weaker than when we have a term occurring 10 times in total and co-occurring 9 times with the same motif. The key issue here is that mutual information only reflects the correlation between variables, but does not take into account the strength of evidence, therefore, tends to over-favor the situation where there is a perfect correlation but very little evidence. However, the number of sequences in which the co-occurrence happens, which is called the "support" for the association, is also very important. The coefficients for the other two parameters, NG|M and NP|T , are also meaningful. Their negative signs indicate that the more terms a motif co-occurs with or the more motifs a term co-occurs with, the less likely a particular association is correct. This also makes sense intuitively, since all those co-occurring terms can be regarded as "competing" for a candidate description of the motif's function, so the more terms a motif is associated with, the competition is stronger, and thus the chance that any particular term is a correct description of function should be smaller. Thus, the logistic regression model not only performs well in terms of prediction accuracy but also gives meaningful and logically plausible coefficient values. Discussion In this paper, we explore the use of the Gene Ontology knowledge base to predict the functions of protein motifs. We find that the mutual information can be used as an important feature to capture the association between a motif and a GO term. Evaluation indicates that, even used alone, the mutual information could be useful for ranking terms for any given motif. We further use logistic regression to combine mutual information with several other statistical features and to learn a probabilistic classifier from a set of motifs with known functions. Our evaluation shows that, with the addition of new features and with the extra information provided by the motifs with known functions, logistic regression can perform better than using the mutual information alone. This is encouraging, as it shows that we can potentially learn from the motifs with known functions to better predict the functions of unknown motifs. This means that our prediction algorithm can be expected to further improve, as we accumulate more and more known motifs. Although we have so far only tested our methods on the known motifs, which is necessary for the purpose of evaluation, the method is most useful for predicting the functions of new and unknown motifs. For the future work, we can build a motif function prediction system and apply our algorithm to many candidate new motifs e.g., those discovered using TEIRESIAS, SPLASH or other programs. This would further enable us to perform data mining from the Gene Ontology database in several ways. For example, we can hypothesize the functions of a large number of novel motifs probabilistically, then we will be able to answer a query, such as "finding the five patterns that are most likely associated with the GO term tyrosine kinase". This is potentially very useful because it is not uncommon that substantial knowledge about the functions and sub-cellular location of a given protein is available even though a structural explanation for the functions remains obscure. On the other hand, we believe that our methods will facilitate identifying potentially biological meaningful patterns among the millions of patterns returned by pattern searching programs. A sequence pattern that associates with certain GO term with high M.I. or probability is more like to be a meaningful pattern that that with low scores. Furthermore, our methods can also be used in automatic annotation of novel protein sequences as suggested in Schug et al and Rigoutsos et al [ 9 , 13 , 22 ]. Our methods provide different approaches to associate sequence patterns with functional descriptions. After associating functional descriptions (in the form of GO term) to motifs, we can determine what motifs a novel protein sequence matches and correspondingly transfer the functional descriptions associated with motifs to the sequence. One key advantage of our methods is that the probability of correctness for a GO-motif association can be considered as confidence or uncertainty. This enables one to optimize the automatic annotation according to Bayesian decision theory and minimize the risk of incorrect annotation. Having stated the potential uses of our approaches, we also realize that there exist some limitations for our methods. For example, in order to predict the function of a newly identified sequence pattern correctly, we would require functional annotations of the sequences of GO database be complete and accurate, which may not always be the case. In this paper, we mainly used the motifs with known function to evaluate the capability of the methods developed in this research. Our result shows that the methods work well with known sequences patterns. Currently, the annotation of motif function with GO term is carried out manually at the European Bioinformatics Institute (the GOA project). Such approach is warranted because human annotation is more accurate than automatic ones. However, as the amount of information regarding protein functions accumulates and a large number of new potential motifs are discovered, it will be very labor intensive to annotate the potential association of protein function and protein patterns. By then, the methods studied in this research will potentially prove to be useful to discover the underlying protein motifs that are responsible for the newly annotated function. For example, the methods can be used as prescreening to narrow down to the most possible associations of protein function and motifs, thus facilitate human annotation. Conclusions In summary, we have developed methods that disambiguate the associations between of Gene Ontology terms and protein motifs. These methods can be used to mine the knowledge contained in the Gene Ontology database to predict the function of novel motifs, discover the basis of a molecular function at primary sequence level and automatically annotated the function of novel proteins. Methods Mutual information Mutual information is defined as follows In which the probabilities p ( X = x , Y = y ), p ( X = x ) and p ( Y = y ) can be empirically estimated from the data by counting occurrence/co-occurrence followed by normalization. Sensitivity and specificity The sensitivity and specificity of the rules are calculated as where TP (True Positive) is the number of associations labeled as correct among the retrieved motif-term pairs meeting the ranking cutoff criteria, FN (False Negative) is the number of associations labeled as correct but not retrieved, TN (True Negative) is the number of associations labeled as incorrect and not retrieved, and FP (False Positive) is the number of associations labeled incorrect but are retrieved. Averaged sequence similarity Calculation of the average pair-wise sequence similarity per 100 amino acids ( AvgS ) of a sequence set is as follows Where S ij is raw BLAST pair-wise similarity scores between the sequence i and sequence j ; L i and L j are the lengths of sequences i and j , respectively; n is the number of sequences in the set; and δ ( i , j ) is a delta function which equals 1 if i = j and 0 otherwise. Logistic regression The logistic regression model is a conditional model that assumes the following linear relationship between p ( Y = 1| X ) and X 1 , ..., X k : where, β = ( β 0 , β 1 , ..., β k ) is the parameter vector. We can fit the logistic regression model (i.e., estimate the parameters) using the Maximum Likelihood method – essentially setting the parameters to values at which the likelihood of the observed data is maximized (Hosmer and Lemeshow 1989, Hastie et al 2001). In our experiments, we use iteratively reweighted least squares (IRLS) algorithm [ 23 ] to fit the logistic regression model. All features are normalized to zero mean and unit variance before training. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC517493.xml |
528844 | Are the effects of nicotinic acid on insulin resistance precipitated by abnormal phosphorous metabolism? | Nicotinic acid is a unique cholesterol modifying agent that exerts favorable effects on all cholesterol parameters. It holds promise as one of the main pharmacological agents to treat mixed dyslipidemia in metabolic syndrome and diabetic patients. The use of nicotinic acid has always been haunted with concerns that it might worsen insulin resistance and complicate diabetes management. We will discuss the interaction between phosphorous metabolism and carbohydrate metabolism and the possibility that worsening of insulin resistance could be related to adrug induced alteration in phosphorous metabolism, and the implications of that in medical management of diabetes and metabolic syndrome patients with mixed dyslipidemia. | Background Nicotinic acid functions in the body after conversion to nicotinamide adenine dinucleotide (NAD) in the NAD coenzyme system. Niacin reduces total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), apolipoprotein B-100 (Apo B) and Lipoprotein (a) (Lp (a)). Niacin increases high-density lipoprotein cholesterol (HDL-C), apolipoprotein A-I (Apo A-I), lipoprotein A-I, and HDL2: HDL3 ratio. The severity and type of underlying lipid abnormality determines the extent of response to niacin treatment [ 1 ]. There is evidence suggesting that niacin reduces the risk of a coronary artery or cerebrovascular disease in the setting of secondary prevention, and when combined with a bile acid-sequestrant, promotes regression and decreases progression of atherosclerotic cardiovascular disease [ 2 , 3 ]. Niacin is known to cause transient, small but statistically significant dose-related reductions in phosphorous levels. These effects were studied for extended release niacin in placebo-controlled trials, mean percentage changes from baseline (± standard error) in phosphorous were (-4.0 ± 1.3) for the 500 mg once daily dose, (-7.2 ± 1.1) for the 1000 mg once daily dose, (-9.1 ± 1.1) for the 1500 mg once daily dose, and (-12.6 ± 1.6) for the 2000 mg once daily dose [ 4 ]. Presentation of the hypothesis It has been long known that niacin induces impaired fasting glucose. I was also suggested that this effect is most likely transient. The mechanism behind this phenomenon has not been fully elucidated [ 5 ]. Multiple studies were conducted to study effects of nicotinic acid on exacerbating insulin resistance. One retrospective study suggested that the use of moderate doses of extended release nicotinic acid (average, 1000 mg/d) in reasonably controlled diabetics was associated with improved glycemic control with HbA1c levels decreased by 0.5% ± 0.3% due to aggressive hypoglycemic therapy; as most of these patients had their insulin or oral diabetes regimen intensified [ 6 ]. Another retrospective study using unmodified niacin concluded similar results [ 7 ]. The (ADMIT) trial, was one of the first studies to demonstrate the safety of nicotinic acid in patients with diabetes. Diabetic patients (468 participants, 125 with diabetes and peripheral arterial disease) were randomized to treatment with either placebo or niacin (2500 mg/d) for 18 weeks. Patients randomized to niacin therapy had modest increases in fasting glucose (+8 mg/dL). There was no significant change in HbA1c levels or in diabetes treatment regimen in the niacin-treated diabetic patients group [ 8 ]. Another prospective Trial (ADVENT), was a 16-week, double-blind, placebo-controlled trial, 148 patients were randomized to placebo (n = 49) or 1000 (n = 45) or 1500 mg/d (n = 52) of extended release niacin. Fasting plasma glucose levels in both treatment groups were unchanged from placebo at the study's end. Meanwhile, the mean HbA1c levels were only slightly elevated in the 1500 mg arm, compared with placebo. The 1500 mg nicotinic acid arm required a small increase in anti-diabetic treatment regimen [ 9 ]. It appears from the clinical data presented above, that nicotinic acid at higher doses could results in alterations in glycemic control of patients with an already abnormal glucose metabolism. Phosphate is primarily an intracellular anion. Insulin is implicated in the transport of glucose and phosphate from the intra-cellular to the extra-cellular space. Studies have suggested a link between decreased serum phosphorous and disturbed carbohydrate metabolism, eventually leading to hyperglycemia [ 10 , 11 ]. Chronic hypophosphatemia inhibits glucose transport. Further more, reduced serum phosphorous levels significantly decrease the phosphorylation of carbohydrate intermediates in glycolysis and glycogenesis. It seems intuitive to suspect that the transient decrease in plasma phosphorous level might explain the transient disturbance in carbohydrate metabolism and hyperglycemia induced by nicotinic acid. Also, studies suggest the existence of a dose response relationship in both instances. One way to test this hypothesis will be to administer nicotinic acid to both healthy subjects, and to those with impaired fasting glucose or subjects with diabetes, then measure simultaneously at different doses serum phosphorous, and correlate the changes to changes in serum glucose and serum insulin either individually or combined as a measure of insulin resistance. It will be of interest to study if such a relationship exists in patients with the metabolic syndrome diagnosis, since literature suggests the existence of an abnormal phosphorous metabolism in these patients [ 10 , 12 ]. Implications of the hypothesis This hypothesis has several important insights and implications: 1. If valid, it suggests the possibility of a simple clinical way to prevent nicotinic acid induced worsening of glycemic control by dietary phosphorous supplementation, eliminating the need to intensify expensive diabetic regimens in diabetic patients that need nicotinic acid treatment. 2. If valid, it will aid in lessening the theoretical anxiety of the possibility of precipitating diabetes in glucose intolerant or metabolic syndrome patients in need of nicotinic acid treatment for the management of dyslipidemia. 3. Metabolic syndrome and diabetics stand to benefit greatly from the new evidence implicating the importance of increasing HDL-cholesterol in cardiovascular event reduction. 4. This hypothesis offers new insights into the mechanism of development of glucose intolerance. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC528844.xml |
520821 | 17beta-estradiol induced vitellogenesis is inhibited by cortisol at the post-transcriptional level in Arctic char (Salvelinus alpinus) | This study was performed to investigate stress effects on the synthesis of egg yolk precursor, vitellogenin (Vtg) in Arctic char ( Salvelinus alpinus ). In particular the effect of cortisol (F) was determined since this stress hormone has been suggested to interfere with vitellogenesis and is upregulated during sexual maturation in teleosts. Arctic char Vtg was purified and polyclonal antibodies were produced in order to develop tools to study regulation of vitellogenesis. The Vtg antibodies were used to develop an enzyme-linked immunosorbent assay. The corresponding Vtg cDNA was cloned from a hepatic cDNA library in order to obtain DNA probes to measure Vtg mRNA expression. Analysis of plasma from juvenile Arctic char, of both sexes, exposed to different steroids showed that production of Vtg was induced in a dose dependent fashion by 17β-estradiol (E2), estrone and estriol. Apart from estrogens a high dose of F also upregulated Vtg. In addition, F, progesterone (P) and tamoxifen were tested to determine these compounds ability to modulate E2 induced Vtg synthesis at both the mRNA and protein level. Tamoxifen was found to inhibit E2 induced Vtg mRNA and protein upregulation. P did not alter the Vtg induction while F reduced the Vtg protein levels without affecting the Vtg mRNA levels. Furthermore the inhibition of Vtg protein was found to be dose dependent. Thus, the inhibitory effect of F on Vtg appears to be mediated at the post-transcriptional level. | Introduction The major proteinaceous egg yolk precursor vitellogenin (Vtg) is a large complex lipoglycophosphoprotein produced under estrogenic control in the liver of sexually maturing female oviparous animals. The estrogenic control of Vtg is mediated by binding of the most potent estrogen, 17-β-estradiol (E2), to the hepatic estrogen receptor (ER) [ 1 ]. The ER-E2 complex activates the transcription of the Vtg-genes by binding to estrogen responsive elements [ 1 ]. Vtg is transported from the liver as a dimer via the circulation to the oocytes, where it is taken up by receptor mediated endocytosis [ 2 , 3 ] and proteolytically cleaved into the smaller yolk units lipovitelin, phosvitin [ 4 , 5 ] and phosvettes [ 6 ], which serve as a nutritional source for the growing embryos [ 7 ]. Studies have shown that Vtg bind metal-ions such as zinc, calcium [ 8 , 9 ] and magnesium [ 10 ]. It has been suggested that Vtg is involved in the transport of metal-ions, crucial for embryonic development, into the growing oocyte [ 11 ]. A number of Vtg genes have been characterized in a wide variety of oviparous species and it has been shown that the Vtg genes are highly conserved [ 12 , 13 ]. The Vtg-genes belong to a small gene family where the number of genes varies depending on species [ 7 , 14 , 15 ]. The different genes give rise to multiple forms of the protein, which are expressed at different times during oogenesis. This indicates that Vtg isoforms may have different roles during oocyte maturation and embryonic development [ 5 ]. Vitellogenin genes are present in both females and males but the lack of estrogens in the males prevents the expression of the protein under normal conditions [ 16 ]. In teleosts, cortisol (F) is released from interrenal cells in response to stress. It has been shown that F affects reproduction by decreasing the amount of gonadotropins produced by the pituitary, the amount steroids present in the plasma and by reducing gamete quality [ 17 ]. Earlier studies on stress responses on teleost reproduction are ambiguous. In some studies F does not interact with E2 systems [ 16 , 18 ], while other studies indicate that F interferes with the binding of E2 to ER, thereby decreasing hepatic Vtg production [ 19 ]. It has been proposed that this ambiguity is due to species-specific responses to F thereby giving rise to different stress responses in different species. Many manmade substances with endocrine disrupting properties (EDS) are present in the environment. It has been observed that stress responses are induced in organisms when exposed to EDS. Numerous EDS have been shown to impair reproductive function in teleost fish [ 18 ]. It is therefore important to examine how stress responses interfere with the expression of commonly used biomarkers. Exposure of male or juvenile fish to estrogenic substances results in stimulation of Vtg production [ 20 , 21 ]. Vtg is therefore widely used as a biomarker for estrogenicity [ 22 , 23 ]. In the present study Arctic char Vtg was characterized and the effect of F on E2 induced vitellogenesis was investigated. Materials and methods Experimental animals and rearing conditions Juvenile Arctic char with an average weight of 18.4 ± 10.7 g were obtained from the National Swedish Board of Fisheries Research Station, Kälarne, Sweden. They were kept in indoor 50 l tanks with a continuous flow-through water system with temperature and photoperiods as close to the natural conditions as possible. The fish were allowed to acclimatize for 1 week prior to initiating the experiments. No food was administered to the fish during the experiments. Fish treatment and sampling Vtg synthesis was induced by intraperitoneally ( i.p .) injection of Arctic char with 10 -6 M E2. Peanut oil was used, as a carrier and control injections were made with carrier alone. The fish were kept for four days prior to sampling. Plasma was collected by centifugation and used to purify Vtg in order to develop polyclonal antibodies. Juvenile Arctic char were injected i.p . with different doses of E2, estriol and estrone (end-concentrations ranging between 10 -9 to 10 -6 M) and F, corticosterone, cortisone, 11-ketotestosterone and progesterone (P) (end-concentrations ranging between 10 -8 to 10 -5 M) to determine the effect of these 8 hormones on Vtg expression. Four days after injection the fish were sacrificed, bled and the livers were removed. The obtained blood was centrifuged at 5000 × g for 1 minute in order to separate the blood cells from the plasma. The plasma and livers were immediately frozen in liquid nitrogen and stored at -80°C until analyzed. To further investigate the effects of steroids on Vtg production, different doses of E2 (end-concentration ranging between 10 -8 to 10 -6 M) was administered i.p . with or without co-injection of F (end-concentration ranging between 10 -8 to 10 -4 M), P (10 -5 M) or tamoxifen (Tam) (10 -5 M). After four days the fish were sacrificed, the liver and plasma were collected and stored as described above. Hormone determinations E2 and F plasma levels were determined by radioimmunoassay according to manufacturers instructions (E2-Coat-a-Count, DPC, USA, F-Spectria Cortisol RIA, Orion Diagnostica, Espoo, Finland). The measurements were made in triplicates. Isolation of vitellogenin Prior to chromatography, the Vtg in the plasma was concentrated by selective precipitation as described by [ 24 ]. 0.5 ml of plasma were mixed with 2 ml of 20 mM EDTA, and precipitation was obtained by subsequently adding 0.1 ml 0.5 M MgCl 2 . The precipitate was collected by centrifugation at 5000 × g for 15 minutes at +4°C, and the supernatant was discarded. The obtained precipitate was re-dissolved in 1 ml of 1 M NaCl prior to a second precipitation, performed by lowering the ionic strength of the sample by adding 10 ml of ultrapure deionized water (MQ). The precipitate was collected by centrifugation at 5000 g for 15 minutes +4°C and the pellet was dissolved in 1 ml of 1 M NaCl prior to fast performance liquid chromatography (FPLC). All solutions used for FPLC contained aprotinin (0.5% v/v) and were filtered through 0.22 μm filters and degassed. The column used was a Resource Q (Pharmacia, Sweden), which was equilibrated with five volumes of 20 mM Tris-HCl pH 8.0 (Buffer A). The plasma was diluted 50 times and 0.5 ml of the diluted sample was loaded onto the equilibrated column. Unbound plasma-proteins were eluted with 5 ml of buffer A. The bound proteins were separated by a 15 ml linear gradient from 0.00 M to 0.50 M NaCl. The column was washed with 5 ml of 1.0 M NaCl to ensure that no other proteins remained bound. The flow-rate was 1 ml min -1 and 1 ml fractions were collected. The obtained Vtg was stored in 50% (v/v) glycerol until further analysis. In order to control the efficiency of the different purification steps, 10 μg of total protein from each of the steps were run onto an 8% discontinuous polyacrylamide gel (SDS-PAGE) and stained with Coomassie brilliant blue. The FPLC purified Vtg was used to immunize rabbits (AgriSera, Vindeln, Sweden). Western blot analysis To identify Vtg present in the plasma of sampled fish total protein was loaded onto a discontinuous polyacrylamide gel with a 2 or 4% stacking gel and an 8% separating gel [ 25 ]. Following electrophoresis, the proteins were blotted onto nitrocellulose membrane (Hybond-ECL™) or PVDF membrane (Amersham) using either semi-dry or tank transfer system (Bio-Rad Laboratories). To block non-specific antibody binding, the membranes were incubated with fat-free milk powder (5% in Tris-buffered saline, pH 7.4, containing 0.5% Tween 20; TBS-T). The membranes were incubated with primary antibody for 1 hour at room temperature (RT) or over night at 4°C. The primary antibodies were directed against Arctic char Vtg and diluted 1:5000 in TBS-T. The membranes were washed 3 × 5 minutes in TBS-T and incubated for 1 hour with the secondary antibody (Horseradish Peroxidase-conjugated anti-rabbit Ig, DAKO A/S Denmark), diluted 1:5000 in TBS-T. Prior to detection, the membranes were washed 3 × 5 minutes in TBS-T. The detection was performed using the ECL™ detection system (Amersham Pharmacia Biotech, Uppsala, Sweden) Two-dimensional polyacrylamide gel electrophoresis analysis Two-dimensional poloyacrylamide gel electrophoresis (2D-PAGE) of plasma proteins was run on Multiphor II electrophoretic unit (Pharmacia Biotech) according to the manufactures manual. Separation in the first dimension (IEF) was performed using linear pH 4–7 gradient immobiline DryStrips (Amersham Biosciences), 40μg protein was loaded per strip. In the second dimension an 8–18% gradient polyacrylamide gel (ExcelGel SDS, Amersham Biosciences) was used. The gels were either stained with Coomassie Brilliant Blue or the proteins was transferred to PVDF-membrane. The blot was blocked with fat-free milk powder (5%) in TBS, pH 7.4, followed by anti-Vtg (diluted 1:3000) incubation over night at 4°C. After 3 × 10 minutes washes in TBS-T the membrane was incubated for 2 hours with the secondary antibody (HRP-conjugated anti-rabbit Ig, Amersham Biosciences), diluted 1:3000. Prior to detection, the membranes were washed 2 × 15 minutes in TBS-T and 1 × 5 minutes in TBS. For detection of antibody staining ECL™ reagents was used and the chemiluminescent signal was detected on Hyperfilm MP (Amersham Biosciences). ELISA procedure Quantification of plasma Vtg was performed by enzyme-linked immunosorbent assay (ELISA), prepared by coating 96 well microtiter plates (Nunc A/S, Roskilde, Denmark) with plasma-samples diluted in coating buffer (0.1 M Na 2 CO 3 , pH 9.6). A standard curve made from purified Arctic char Vtg was also loaded onto each plate as a control. The plates were incubated at RT for 1 hour prior to blocking non-specific binding by adding phosphate buffered saline, pH 7.6 (PBS) containing 1% dry milk to each well. The plates were washed in PBS containing 0.05% Tween 20 (PBS-T) before addition of primary antibody. The polyclonal primary antibodies against Arctic char Vtg were diluted 1:10000 in PBS-T, added to the plates and incubated in RT for 1 hour. After washing the plates with PBS-T, a secondary antibody incubation was performed by adding HRP-conjugated goat-antirabbit polyclonal antibodies (DAKO A/S Denmark) diluted 1:5000 in PBS-T. The plates were incubated for 1 hour at RT prior to PBS-T-wash and detection. The detection was performed using a peroxidase substrate kit (Horseradish peroxidase substrate kit, BIO-RAD, Hercules, CA, USA). The plates were read at 415 nm, using a microplate reader (BIO-RAD microplate reader Model 550). All samples were analyzed in triplicates. To establish the titer of the polyclonal Vtg antibodies an ELISA with the wells loaded with equal amount VTG and various antibody concentrations were used. The detection limit of the ELISA procedure was determined by loading a standard curve of pure Vtg and using a fixed antibody concentration. cDNA cloning A ZAP Express cDNA library (Stratagene, La Jolla, CA, USA) from E2 induced Arctic char liver was used. The library was screened using a probe constructed from the rainbow trout pSG Vg 5.09 cDNA clone [ 26 ]. The isolated phage DNA clones were subjected to in vivo excision prior to sequencing. Positive clones from the screening were selected for sequencing by dot blot and Northern blot analysis (data not shown) and sequencing was performed using Thermo Sequenase (Amersham). RNA extraction and slot blot procedure Total RNA was isolated from Arctic char livers according to Chomczynski and Sacci [ 27 ]. Slot blot analysis was used to quantify Vtg mRNA levels. Nylon membranes (Hybond N, Amersham) were soaked in 20 × SSC (1 × SSC, 0.15 M NaCl; 15 mM sodium citrate buffer, pH 7.0). RNA samples were prepared by mixing 10 μg of total RNA with 6 × SSC and 7.5% formaldehyde and heating to 68°C for 15 min. The RNA samples were immediately cooled down on ice prior application onto the slot blot. Following slot blot the membranes were washed twice with 2 × SSC and cross-linked on both sides before hybridization against a single stranded digoxigenin (DIG) labeled cRNA Arctic char Vtg probe. Hybridization and detection of Vtg was performed as described previously [ 28 ]. Quantification of the mRNA was performed with Quantity One version 4.2.3 (BIO-RAD Laboratories AB, Sundbyberg, Sweden). In order to normalize the amount of total RNA in each slot, a slot blot membrane was hybridized with a DIG-labeled probe complementary to Arctic char 18S rRNA The probe was made as follow: total RNA from liver was used for first-strand cDNA synthesis according to the manual of Amersham. 18S fragments was PCR amplified by 30 cycles of 94°C for 30 seconds, 57°C for 30 seconds and 72°C for 30 seconds, using Quantum RNA classic 18S PCR primer pair (Ambion). The PCR fragment was cloned into pGEM-T vector (Promega). The purified plasmid was used as DNA-template in a PCR reaction (as above) to synthesize the DIG-labeled 18S DNA probe (DIG-11-dUTP was obtained from Roche). The Vtg mRNA levels in liver from control fish was arbitrarily set to 1. Statistics Significance was calculated using one-way ANOVA followed by Bonferroni's multiple comparison test with a P < 0.05. All statistical analysis was performed using GraphPad Prism version 3.02 for Windows (GraphPad Software, San Diego California USA). Results Administration of E2 to juvenile Arctic char led to a rapid increase in plasma protein concentrations from 6.2 ± 0.3 mg/ml in control fish to 21.4 ± 0.5 mg/ml in E2 injected fish. The plasma contained low molecular weight proteins that were excluded from the preparation by sequential precipitations. The final pellet was re-dissolved in 1 M NaCl and subjected to FPLC purification. A single absorbance peak containing Vtg was identified at an ion concentration of 0.37 M (Fig. 1 ). This peak was not present in plasma from untreated juvenile fish (data not shown). SDS-PAGE analysis showed that the purified Vtg had a molecular mass of 185 kDa. Figure 1 Elution profiles from Resource Q-chromatography of E2 treated Arctic char plasma proteins following selective precipitation. The linear gradient used was between 0.00–0.50 M NaCl. The absorbance was measured at 280 nm. The pure Vtg gave rise to one homogenous absorbance peak at an ion concentration of 0.37 M. The purified Vtg was used to produce polyclonal Vtg antibodies. The specificity of the polyclonal rabbit antiserum against Arctic char Vtg was determined using western blot analysis. A single band with a molecular mass of 185 kDa was detected only in the plasma of sexually mature females or E2 exposed fish (Fig. 2 ). To determine if the antibodies could be used quantitatively, plasma from E2 injected fish was separated on SDS-PAGE and detected by western blot analysis. The western blot displayed an increase in plasma Vtg from fish injected with increasing E2 concentrations, further confirming the specificity of the antibodies (Fig. 3 ). In order to develop an ELISA, the antibodies were tested both at increasing concentrations of antibodies with fixed antigen concentrations and at fixed concentrations of antibodies with increasing concentrations of antigen. The results show that the produced antisera have a high titer allowing dilution up to 10.000 fold without increasing the detection limit (Fig. 4 ). From these experiments the detection limit of the ELISA was determined to be 5 ng Vtg/well. Figure 2 Western blot analyses using a polyclonal antibody against Arctic char Vtg, on plasma from A) untreated juveniles. B) E2 exposed juveniles. C) male fish. D) female fish. Figure 3 Western blot of plasma from Arctic char exposed to different concentrations of E2 using a polyclonal antibody against Arctic char Vtg. Figure 4 ELISA titration curves. A) Titration; A maximum dilution of the antisera was determined to be 10.000×. B) Detection limit; An antibody dilution of 1:10.000 was used and the detection limit was determined to 5 ng Vtg/well. Screening of the Arctic char hepatic cDNA library revealed several positive clones. The longest clones were selected and sequenced to completion (clone 1 and clone 3). Sequencing of clone 1 and clone 3 revealed that the Arctic char Vtg mRNA displayed high homology to rainbow trout Vtg mRNA, both at the nucleotide level (89% and 83% respectively) and at the protein level (85% and 82% respectively). Clone 1 and clone 3 showed high similarity (94% on both nucleotide and protein level). In addition, clone 1 was found to contain a second polyadenlyation site and a 116 bases longer 3'UTR. Even though no full-length clones were obtained, these features imply that the clones are products of different genes. Eight substances were injected into juvenile Arctic char to determine their potency at inducing Vtg synthesis. ELISA analysis of plasma revealed that only the three estrogens and F induced Vtg synthesis (Fig. 5 ). The most potent estrogen, E2, was found to be 3 times more effective at inducing Vtg synthesis than estrone and 7 times more potent than the weakest estrogen, estriol. All estrogens induced a dose dependent induction of Vtg. The ability of F to induce Vtg was approximately 70 times lower than E2 and was only observed at the highest dose. Slot blot analysis of Vtg mRNA levels revealed a dose dependent induction corresponding to the induction pattern observed with the ELISA. E2 was the strongest inducer, with both estrone and estriol being weaker but equally potent inducers of Vtg mRNA (Fig. 6 ). In agreement with the ELISA determinations, F induced Vtg mRNA only at the highest dose. None of the other substances tested displayed any effects on Vtg mRNA. Figure 5 Plasma Vtg concentrations in fish exposed to estrogens and cortisol. Control fish (C) were injected i.p . with peanutoil. All values are presented as a mean of 10 fish ± SEM. a denotes P < 0.05 when compared with control and b denotes P < 0.05 when compared to the highest concentration of each substance. Figure 6 Relative Vtg mRNA levels in fish subjected to i.p . administration of estrogens and cortisol. Each bar represents a mean value of three fish ± SEM. Significant differences are marked with a and b . a denotes P < 0.05 when compared with control (C) and b denotes P < 0.05 when compared to the highest concentration of each substance. Arctic char were co-injected with E2 and F, P or tamoxifen in order to determine if other compounds could inhibit Vtg production. Plasma hormone determinations were performed on all groups of fish and the mean plasma levels of E2 and F are shown in table 1 . The known antiestrogen tamoxifen was used as a control substance and was found to inhibit the E2 dependent upregulation of both Vtg mRNA and protein levels (Fig. 7 ). However, while P did not affect the E2 dependent Vtg induction, F co-injection resulted in lowered Vtg protein levels without affecting the Vtg mRNA levels. A second experiment was therefore performed to determine the dose-response effect of co-injection of F with the three different estrogens. ELISA analysis of plasma from co-injected fish reveled dose-dependent inhibition of estrogen induced Vtg levels in plasma (Fig. 8 ). Western blot of plasma proteins from fish treated with a combination of E2 and F confirmed that F was able to decrease the level of Vtg that are expected in the plasma from an E2-injected fish (Fig. 9 ). Table 1 Plasma levels of E2 and F following intraperitoneal injections. Treatment Plasma levels* Cortisol control nd Cortisol 10 -7 M 42.3 ± 6.4 Cortisol 10 -6 M 132.1 ± 51.8 Cortisol 10 -5 M 2593 ± 697 Cortisol 10 -4 M 15015 ± 3051 17β-estradiol control nd 17β-estradiol 10 -8 M 10.1 ± 2.8 17β-estradiol 10 -7 M 76.7 ± 12.6 17β-estradiol 10 -6 M 687 ± 75 * The plasma levels are presented as mean (ng/ml) ± S.E. nd: non detectable levels, below detection limit Figure 7 Vtg mRNA and protein levels following co-injection of E2 and P, F and tamoxifen. The dark bar indicates the relative hepatic Vtg mRNA levels while the light bars displays Vtg protein levels present in the plasma. All bars represent a mean value from 5 fish ± SEM. a denotes P < 0.05 when compared with control and b denotes P < 0.05 when compared to Vtg protein levels in E2 induced fish. c denotes P < 0.05 when compared with control (C) and d denotes P < 0.05 when compared to Vtg mRNA levels in E2 induced fish. Figure 8 Dose dependent effects of F and E2 on plasma Vtg levels in juvenile Arctic char. All values are presented as a mean value of 5 fish ± SEM. a denotes P < 0.05 when compared with control (C). b denotes P < 0.05 when compared with each E2 concentrations positive control. c denotes P < 0.05 when compared with each E2 + F 10 -7 control. Figure 9 Plasma proteins, 20 μg per lane, from Arctic char treated with 17-β-estradiol (E2, 10 -7 M) or/and cortisol (F, 10 -5 M) separated on 8% SDS-PAGE. Coomassie-stained gel and corresponding Western blot using a polyclonal antibody against Arctic char vitellogenin. Lane 1: control, lane 2: E2, lane 3: F, lane 4: E2 + F. Molecular weight (Da) are shown to the left. The polyclonal antibody directed against a 185 kDa Vtg recognized several high and low molecular weight spots of Vtg and Vtg-derivatives as shown by 2D-PAGE analysis (Fig. 10 ). Since Vtg is transported in the plasma as a dimmer it migrates as a large complex on 2D-PAGE. There is less of both high and low molecular Vtg-isoforms in the plasma from co-injected fish compared to E2 injected fish. Figure 10 Immunoblots of Arctic char plasma proteins from control, E2- (10 -7 M), and E2 + F- (10 -7 M and 10 -5 M) treated fish separated by two-dimensional electrophoresis. 40 μg total protein was separated by isoelectric focusing in the first dimension using a pH gradient 4–7, followed by SDS-PAGE using 8–18% acrylamide gradient. Polyclonal anti-Arctic char vitellogenin was used. Figure show a part of the PVDF-membrane, spots recognized by the vitellogenin antibody are circled. Discussion In this study Arctic char Vtg was purified and polyclonal antibodies was made in order to use Vtg protein determinations as a marker of F effects on egg yolk formation. The purification was performed following the procedure outlined by Silversand and Haux [ 24 ]. The chromatographic profile of the purified Arctic char Vtg displayed large similarities when compared to turbot ( Schophthalmus maximus ) [ 24 ]. Elution of the protein was obtained at a Cl - concentration of 0.37 M, a value comparable to those earlier reported [ 29 ]. The purified Vtg was used to obtain polyclonal antisera from rabbits. The antisera displayed a high specificity for the 185 kDa Vtg, and also recognized Vtg dimers and derivatives as observed by 2D PAGE. Vtg was only detected in females or E2 exposed juvenile Arctic char. It has been found that teleost Vtg, even though highly conserved, may differ in size between 120 – 300 kDa, and are present in the blood plasma mainly as a 300 – 600 kDa dimer [ 30 ]. It was also found that the E2 induced Vtg production was dose dependent, as described earlier in many species [ 31 - 33 ]. ELISA procedures have been developed for Vtg from many teleost species [ 34 , 35 ]. This method requires a high specificity of the antibody and a low inter-assay variability. During the evaluation of the antibodies it was found that the antisera contained a high titer of specific Vtg antibodies giving the ELISA a low detection limit of 5 ng Vtg. Low intra and inter assay variability (3%, data not shown) was observed. Eight substances were tested for their ability to induce Vtg production in Arctic char. It has earlier been shown that Vtg production in teleost fish is under dose dependent estrogenic control [ 16 ] and this was also evident in the Arctic char. Presence of Vtg in plasma was detected by the ELISA procedure revealing that Vtg protein was only present in fish exposed to the three estrogens and F. E2 was found to be the most potent estrogen, followed by estrone and estriol. Estriol was the weakest inducer of Vtg synthesis both at mRNA and protein level. These results are in accordance with earlier studies on different species, including human, mouse and rainbow trout [ 36 , 37 ]. The results reported here demonstrate that cortisol acts as a partial antagonist on Vtg expression. The plasma levels of E2 and F following hormone injections showed that the resulting plasma levels covered the range normally observed for Arctic char and other salmonids [ 38 , 39 ]. Exposure of Arctic char to high F levels (10 -5 M) resulted in elevated plasma Vtg levels. While F alone induced a low level of Vtg mRNA expression the co exposure of Arctic char to estrogens and F resulted in a reduction in circulating Vtg levels while the Vtg mRNA levels were not affected. These results suggest that F acts at a post-transcriptional level in Arctic char. Our results are in contrast to earlier in vitro studies that indicate that F can down regulate Vtg mRNA levels in rainbow trout hepatocytes [ 18 , 40 ], but are supported by a study on Xenopus that showed F upregulation of hepatic Vtg production [ 41 ]. In Xenopus it was suggested that the C/EBPβ-like protein is involved in upregulation of Vtg by increasing the ER levels [ 41 ]. Reduced binding of E2 to ER has been observed following F exposure in rainbow trout liver [ 17 ]. F has been suggested to interfere with ER transcription by destabilizing ER mRNA, thereby decreasing the mRNA half-life. ER and the glucocorticoid receptor (GR) interact in the liver through C/EBPβ-like protein, and it has been suggested that GR suppress C/EBPβ-like protein binding to the rainbow trout ER promoter, thereby reducing the ER expression [ 40 ]. It is known that stress factors are species specific and it cannot be ruled out at the present time that the differences observed between rainbow trout and Arctic char are due to such species differences. However, it should be noted that the earlier studies were conducted on in vitro systems as opposed to the whole animal model used in the present study, and that no determination of circulating Vtg levels was performed in the previous studies. Adding to the complexity of F involvement in reproduction we have recently shown that F potentiates the E2 mediated expression of eggshell protein in Arctic char [ 38 ]. F is upregulated during final oocyte maturation and spawning in teleost fish [ 42 ]. Thus, it is conceivable that the increase in circulating F levels in maturing female fish is involved in the regulation of eggshell proteins. However, the present results indicate that this involvement is limited to the eggshell proteins as the circulating Vtg levels are reduced under the same conditions. In the present study the main effect of F was observed at the circulating Vtg level. We hypothesize that the co-treatment of Arctic char with glucocorticoids and estrogens results in upregulation of both stress induced systems, such as metallothionein (MT), and estrogen responsive genes, such as eggshell proteins and vitellogenin. MT has been shown to be upregulated by cortisol [ 43 ] in rainbow trout primary cultures and has a main function to sequester zinc [ 44 ]. The involvement of MT in fish reproduction has been shown previously for rainbow trout and Arctic char [ 38 , 39 ]. In both species MT is upregulated towards the end of vitellogenesis [ 38 , 39 ] and is believed to sequester Zn from the liver in order to control the Zn homeostasis once vitellogenesis is over [ 45 ]. It has also been shown that E2 functions as an antagonist of MT induction in both rainbow trout [ 28 ] and Arctic char [ 46 ] further supporting the involvement of MT in reproduction. If Vtg requires Zn for proper tertiary folding, then upregulation of MT by cortisol could lead to a redistribution of Zn from Vtg to MT with degradation of Vtg as a consequence. As egg shell proteins do not use Zn as a structural motif the upregulation of MT would not have the same effect on eggshell proteins. This is in part confirmed by our previous study showing that F potentiates estrogenic induction of eggshell proteins. Further studies are underway to determine the cause of the reduction in circulating Vtg levels. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC520821.xml |
544357 | Single-track sequencing for genotyping of multiple SNPs in the N-acetyltransferase 1 (NAT1) gene | Background Fast, cheap and reliable methods are needed to identify large populations, which may be at risk in relation to environmental exposure. Polymorphisms in NAT1 (N-acetyl transferase) may be suitable markers to identify individuals at risk. Results A strategy allowing to address simultaneously 24 various genetic variants in the NAT1 gene using the single sequencing reaction method on the same PCR product is described. A modified automated DNA sequencing using only one of the sequence terminators was used to genotype PCR products in single-track sequencing reactions of NAT1 and was shown to be universal for both DNA sequencing using labeled primers and labeled nucleotides. By this method we detected known SNPs at site T640G, which confers the NAT1*11 allele with frequency of 0.036, further T1088A and C1095A with frequency of 0.172 and 0.188, respectively and a deletion of TAATAATAA in the poly A signal area with a frequency 0.031. All observed frequencies were in Hardy Weinberg equilibrium and comparable to those in Caucasian population. The single-track signatures of the variant genotypes were verified on samples previously genotyped by RLFP. Conclusions The method could be of great help to scientists in the field of molecular epidemiology of screening of large populations for known informative biomarkers of susceptibility, such as NAT1. | Background We have previously described the single sequencing reaction (SSR) protocol for assessment of a known polymorphism in the 3'utr region of CYP19 (aromatase) [ 1 , 2 ]. Here we have extended and optimized the use of the method for multiple polymorphisms in the NAT1 gene. The increasing number of detected mutations in the NAT1 , makes genotyping using conventional restriction fragment length polymorphism (RFLP) or allele-specific amplification complicated. We developed a rapid and universal strategy based on single-track DNA sequencing analysis of a unique PCR product encompassing the entire NAT1 coding region (contains no introns) along with the flanking 5' and 3' untranslated regions. It allows a rapid and economic characterization of NAT1 alleles. Our method brings reproducible results on both Alf Express™ (Pharmacia) and ABI310 PRISM sequencing instruments and may be adopted for majority of epidemiological studies with relevance of NAT1 in environmentally related diseases. N-acetyltransferases (NAT, EC 2.3.1.5) are implicated in the biotransformation of primary arylamines (e.g. 2-naphthylamine and aminobiphenyls), heterocyclic amines, hydrazines, and their N-hydroxylated metabolites present in tobacco smoke and food [ 3 - 7 ]. An increased activity for p-aminobenzoic acid acetylation (marker for NAT1 activity) was observed in the breast malignant tissues compared to benign and control tissues [ 8 , 9 ]. Human NATs may have adapted a common catalytic mechanism from cysteine proteases for acetyl-transfer reactions [ 10 , 11 ]. The NAT1 gene is highly polymorphic (for listing of known variant alleles ). Expression of NAT1*16 but not NAT1*10 and NAT1*11 caused a 2-fold decrease in the amount and catalytic activity of NAT1 in COS-1 cell cytosol [ 12 - 14 ]. All available data suggest that slow NAT1 phenotype results from NAT1 allelic variants that encode reduced expression of NAT1 and/or less-stable NAT1 protein [ 15 ]. Epidemiological studies suggest that the NAT1 and NAT2 acetylation polymorphisms modify the risk of developing urinary bladder, colorectal, breast, head and neck, lung, and possibly prostate cancers [ 16 - 18 ]. Interactions between NAT2*4 and NAT1*10 were suggested by the increased frequency of the NAT2*4/NAT1*10 haplotype [ 19 - 21 ]. The individual risks associated with NAT1 acetylator phenotypes/genotypes are usually small, but they increase when considered in conjunction with other susceptibility genes and/or aromatic and heterocyclic amine carcinogen exposures. Because of the relatively high frequency of the variant NAT1 genotypes in the population, the attributable cancer risk may be high. Large-scale molecular epidemiological studies that investigate the role of NAT1 genotypes and/or phenotypes together with other genetic susceptibility gene polymorphisms and biomarkers of carcinogen exposure are necessary to expand our current understanding of the role of NAT1 acetylation polymorphisms in cancer risk [ 22 ]. Results We outlined a strategy allowing to address simultaneously 13 genetic variants in the coding area of NAT1 with two sequencing tracks: A and G and 11 variants in the 3' flanking area with two tracks: A and T (Table 1 ). Using single-track sequencing of the NAT1 coding region, we have detected 11 individuals carrying polymorphisms at site T640G , which characterizes NAT1*11 alleles (Figure 1C,1D,1E ). Identical results were obtained when performing the single-track sequencing on an Alf Express™ system as on ABI310 PRISM (Figure 2E,2F ). The frequency of this polymorphism, 0.036 found by single-track sequencing in our study was comparable to the frequency 0.032 found by PCR-RFLP method in 396 German control individuals [ 23 ]. Polymorphisms T1088A and C1095A (Figure 3A,3B,3C and Figure 4A,4B,4C , representing each genotype analyzed by both Alf Express™ and ABI310 PRISM) were in perfect linkage disequilibrium. The frequency of these polymorphisms was 0.172 and 0.188 respectively and was comparable to frequency 0.206 reported by Bruhn et al . [ 24 ]. A-track sequencing of the 3'-untranslated region of NAT1 revealed a deletion of TAATAATAA (Figure 3D,3E on Alf Express™ and Figure 4E on ABI310 PRISM). It was found in 7 individuals accounting for the frequency 0.031, which is comparable to the frequency 0.033 observed in the study on 314 control individuals by Bruhn et al . [ 24 ]. This change is also unique for the NAT1*11 alleles and co-segregates with T640G polymorphism. Very good co-segregation of T640G and 1067-1090delTAATAATAA polymorphisms in 7 informative samples was observed (4 heterozygotes and 3 homozygotes). Despite the different chemistry, Figures 1 , 2 , 3 , 4 demonstrate perfect concordance between the results obtained by Alf Express™ and ABI310 PRISM analysis, verified on samples with previously characterized genotypes by RFLP. Discussion A modified automated DNA sequencing with a fluorescent label was used to genotype PCR products spanning through the whole NAT1 gene by single-track sequencing reactions in 192 control individuals. Previously, we have shown that single-track sequencing reactions performed on PCR products, with subsequent analysis on an Alf Express™ DNA Sequencer, can be as informative, sensitive, and accurate as complete sequencing reactions but more economical option for the genotyping of known polymorphisms in the human aromatase gene [ 1 ]. Compared to analysis by full gel-based sequencing, four times more samples can be analyzed per gel in considerably shorter time. The method could be particularly efficient in cases of high density of polymorphic sites residing in a common area as in the case of NAT1 . Conclusions This paper shows that single-track sequencing reactions can be used as a tool for screening for all types of known polymorphisms in field laboratories with limited or overloaded sequence capacity. Compared to standard sequencing, this single track sequencing may have better signal to noise ratio. This approach is time and cost effective and can be accommodated for high-throughput analyses in epidemiology studies. Methods Materials Chemicals for PCR and sequencing were purchased from ABI (Applied Biosystems, Foster City, CA, USA) and Amersham Biosciences (Uppsala, Sweden). Subjects The study included 192 DNA samples from Norwegian healthy individuals obtained through Norwegian Population Registry as a population-based series of residents in the Oslo area. Test samples from healthy Norwegian controls with known genotypes (previously assessed by RFLP) were available. PCR amplification of NAT1 A single PCR amplification of the entire coding region and the 3' flanking area of NAT1 (923 bp) using forward primer: 5'-tactgggctctgaccactat-3' and reverse primer: 5'-tgctttctagcataaatcacc-3' was performed. PCR mix contained 16.9 μl dH 2 O, 10 × PCR buffer (2.5 μl), 1.25 mM MgCl 2 (4 μl), 2.0 mM dNTP (1.0 μl mixture of each), 10 μM oligonucleotide primer, 0.2 μl Taq DNA polymerase (Perkin Elmer, 5 U/μl) and 1.0 μl of genomic DNA (100 ng/μl) in a final volume of 25 μl. Thermal cycling (GeneAmp 2400, PE, Foster city, CA) included initial denaturation 2 min at 94°C, 10 cycles of 30 sec at 94°C, 30 sec at decreasing annealing temperature 58 to 48°C, 1 min at 72°C, and 25 cycles of 30 sec at 94°C, 30 sec at 50°C and 1 min at 72°C. Quality of PCR products was checked on 6% acrylamide gels. Single-track sequencing The SNPs studied by this assay are summarized in Table 1 . Single A-, G- and T- track sequencing using 1 given terminator at a time was performed using both Alf Express™ system (Pharmacia Biotech, Uppsala, Sweden) and ABI310 PRISM capillary sequencer. Alf Express™ Cy5-labeled primers NAT1A-F-CY5: 5'-gggagggtatgtttacagca-3' and NAT1A-POLYA-CY5: 5'-gcataaatcaccaatttcca-3' were used for genotyping the coding region and 3'untranslated area of NAT1 , respectively. The single sequencing reactions (7 μl) contained: 1.25 μl of dH 2 O, 0.5 μl of 10-times concentrated FS polymerase buffer (125 mM Tris-HCl, pH 9.5, 50 mM (NH 4 ) 2 SO 4 , 150 mM MgCl 2 ), 2 μl of ddNTP termination mix (containing 10 mM dNTPs and either ddATP or ddTTP (10 μM) for A-track or T-track sequencing respectively, 1 μl of 2 μM primer, 0.25 μl of FS polymerase (5 U/μl), and 2 μl of NAT1 PCR product. The following conditions were used for thermal cycling: initial denaturation for 5 min at 95°C, 35 cycles of 30 sec at 95°C, 30 sec at 50°C, and 1 min at 68°C. The single-tracks were evaluated using conventional software AlfWin, Fragment Analyzer, v. 1.02 supplied with the sequencer (Alf Express™). For the single-track sequencing by ABI310 PRISM we used the same sequencing primers as for Alf Express™ system however labeled with the fluorophor 6-FAM. Sequencing reactions (10 μl) contained: 5.0 μl dH 2 O, 1.0 μl of 10-times concentrated Thermo Sequenase buffer (260 mM Tris-HCl, pH 9.5, 65 mM MgCl 2 ), 2.0 μl of ddNTP termination mix (containing dNTPs and either ddATP or ddGTP for A-track or G-track sequencing respectively – for composition see Table 3), 0.5 μl of 1 μM primer, 0.5 μl of Thermo Sequenase DNA polymerase (with pyrophosphatase) 3.2 U/μl and 1.0 μl NAT1 PCR product (described above). Thermal cycling conditions were as follows: initial denaturation 20 sec at 95°C, 25 cycles of 20 sec at 95°C, 20 sec at 55°C and 1 min at 72°C, and then 10 cycles of 20 sec at 95°C, 1 min at 72°C. Results were evaluated using GeneScan Analysis software, v. 3.7. Abbreviations ABPs – aminobiphenyls, NAT1 – N-acetyltransferase 1, SSR (Single Sequencing Reaction) Author's contributions PS performed the SSR analysis on an Alf Express™ and prepared the first draft of the paper, CFS and MS optimized the SSR analysis for an ABI system in the lab of EHK, TK designed primers and contributed with reagents, materials, and advice, VNK brought the idea, organized the study and was responsible for the revisions of the paper. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC544357.xml |
533861 | Omega-3 fatty acids and major depression: A primer for the mental health professional | Omega-3 fatty acids play a critical role in the development and function of the central nervous system. Emerging research is establishing an association between omega-3 fatty acids (alpha-linolenic, eicosapentaenoic, docosahexaenoic) and major depressive disorder. Evidence from epidemiological, laboratory and clinical studies suggest that dietary lipids and other associated nutritional factors may influence vulnerability and outcome in depressive disorders. Research in this area is growing at a rapid pace. The goal of this report is to integrate various branches of research in order to update mental health professionals. | Introduction Major depressive disorder (MDD) is a recurrent, debilitating, and potentially life threatening illness. Over the last 100 years, the age of onset of major depression has decreased, and its overall incidence has increased in Western countries. The increases in depression, up to 20-fold higher post 1945, cannot be fully explained by changes in attitudes of health professionals or society, diagnostic criteria, reporting bias, institutional or other artifacts [ 1 , 2 ] Despite advances in pharmacotherapy, and the increasing sophistication of cognitive/behavioral interventions, there are many patients with MDD who remain treatment resistant [ 3 ]. Depression is undoubtedly an extremely complex and heterogeneous condition. This is reflected by the non-universal results obtained using cognitive-behavior and antidepressant medications. As research continues to mount, it is becoming clear that neurobiology/physiology, genetics, life stressors, and environmental factors can all contribute to vulnerability to depression. While much attention has been given to genetics and life stressors, only a small group of international researchers have focused on nutritional influences on depressive symptoms. Collectively, the results of this relatively small body of research indicate that nutritional influences on MDD are currently underestimated [ 4 ]. Omega-3 fatty acids in particular represent an exciting area of research, with eicosapentaenoic acid (EPA) emerging as a new potential agent in the treatment of depression [ 5 ]. Omega-3 fatty acids Omega-3 fatty acids are long-chain, polyunsaturated fatty acids (PUFA) of plant and marine origin. Because these essential fatty acids cannot be synthesized by the human body, they must be derived from dietary sources. Flaxseed, hemp, canola and walnut oils are all generally rich sources of the parent omega-3, alpha linolenic acid (ALA). Dietary ALA can be metabolized in the liver to the longer-chain omega-3 eicosapentaenoic (EPA) and docosahexaenoic acid (DHA). This conversion is limited in human beings, it is estimated that only 5–15% of ALA is ultimately converted to DHA [ 6 ]. Aging, illness and stress, as well as excessive amounts of omega-6 rich oils (corn, safflower, sunflower, cottonseed) can all compromise conversion [ 7 ]. Dietary fish and seafood provide varying amounts of pre-formed EPA and DHA as highlighted in Table 1 . Table 1 Various Sources of EPA and DHA Fish/Seafood Total EPA/DHA (mg/100 g) Mackerel 2300 Chinook salmon 1900 Herring 1700 Anchovy 1400 Sardine 1400 Coho salmon 1200 Trout 600 Spiny lobster 500 Halibut 400 Shrimp 300 Catfish 300 Sole 200 Cod 200 USDA Nutrient Database http://www.nal.usda.gov/fnic/foodcomp/search/ The dietary intake of omega-3 fatty acids has dramatically declined in Western countries over the last century, the North American diet currently has omega-6 fats outnumbering omega-3 by a ratio of up to 20:1. There are a number of reasons for this skewed ratio, most notably the mass introduction of the aforementioned omega-6 rich oils into the food supply, either directly or through animal rearing practices [ 8 ]. The ideal dietary ratio of omega-6 to omega-3 has been recommended by an international panel of lipid experts to be approximately 2:1 [ 9 ]. Given that approximately 20% of the dry weight of the brain is made up of PUFA and that one out of every three fatty acids in the central nervous system (CNS) are PUFA, the importance of these fats cannot be argued [ 7 ]. Considering that highly-consumed vegetable oils have significant omega-6 to omega-3 ratios (see Table 2 ), it is quite plausible that, for some individuals, inadequate intake of omega-3 fatty acids may have neuropsychiatric consequences. While far from robust at this time, emerging research suggests that omega-3 fatty acids may be of therapeutic value in the treatment of depression. Table 2 Omega-6 and Omega-3 Content (%) of Dietary Oils Oil Omega-6 Omega-3 Safflower 75 0 Sunflower 65 0 Corn 54 0 Cottonseed 50 0 Sesame 42 0 Peanut 32 0 Soybean 51 7 Canola 20 9 Walnut 52 10 Flax 14 57 USDA Nutrient Database http://www.nal.usda.gov/fnic/foodcomp/search/ Epidemiological Data A number of epidemiological studies support a connection between dietary fish/seafood consumption and a lower prevalence of depression. Significant negative correlations have been reported between worldwide fish consumption and rates of depression [ 10 ]. Examination of fish/seafood consumption throughout nations has also been correlated with protection against post-partum depression [ 11 ], bipolar disorder [ 12 ] and seasonal affective disorder [ 13 ]. Separate research involving a random sample within a nation confirms the global findings, as frequent fish consumption in the general population is associated with a decreased risk of depression and suicidal ideation [ 14 ]. In addition, a cross-sectional study from New Zealand found that fish consumption is significantly associated with higher self-reported mental health status [ 15 ]. Not all studies support a connection between omega-3 intake and mood. A recent cross-sectional study of male smokers, using data collected between 1985 and 1988, indicated that subjects reporting anxiety or depressed mood had higher intakes of both omega-3 and omega-6 fatty acids [ 16 ]. In a large population-based study of older males aged 50–69, there was no association between dietary intake of omega-3 fatty acids or fish consumption and depressed mood, major depressive episodes, or suicide [ 17 ]. The epidemiological studies which support a connection between dietary fish and depression clearly do not prove causation. There are a number of cultural, economic and social factors which may confound the results. Most significantly, those who do consume more fish may generally have healthier lifestyle habits, including exercise and stress management. Despite the limitations, the epidemiological data certainly justify a closer examination of omega-3 fatty acids in those actually with depression. Omega-3 status in MDD There are a number of methods used to determine EFA status in the human body, notably the plasma and red blood cell (RBC) phospholipids. These are a reflection of dietary fatty acid intake within the preceding few weeks. While not identical, significant correlations exist between blood and brain phospholipids. A number of studies have found decreased omega-3 content in the blood of depressed patients [ 18 - 21 ]. Furthermore, the EPA content in RBC phospholipids is negatively correlated with the severity of depression, and the omega-6 arachidonic acid to EPA ratio positively correlates with the clinical symptoms of depression [ 18 ]. More recently, investigators have been utilizing adipose tissue as a longer term measurement of EFA intake (1–3 years). In a study of 150 elderly males from Crete, the parent omega-3 ALA adipose tissue stores were negatively correlated with depression [ 22 ]. A separate study found a negative correlation between adipose tissue DHA and rates of depression. In this case, mildly depressed adults had 34.6 percent less DHA in adipose tissue than non-depressed subjects [ 23 ]. Relationships between omega-3 status and post-partum depression have also been investigated. In a cohort of 380 Australian women, plasma DHA was investigated at 6 months post-partum. Logistic regression analysis indicated that a 1% increase in plasma DHA was associated with a 59% reduction in the reporting of depressive symptoms [ 24 ]. It is well known that during pregnancy there is a significant transfer (up to 2.2 g/day) EFAs to the developing fetus [ 7 ]. Increased risk of post-partum depressive symptoms has recently been associated with a slower normalization of DHA levels after pregnancy [ 25 ]. Suicide attempts have also been associated with low levels of RBC EPA. In a study involving 100 suicide attempt cases in China compared to 100 hospital admission controls, there was an eightfold difference in suicide attempt risk between the lowest and highest RBC EPA level quartiles [ 26 ]. The seasonality of depression and suicide has been described by investigators, with more deaths in spring and summer vs.autumn and winter. Total serum cholesterol has been highly significantly synchronized with the annual rhythms in violent suicide deaths [ 27 ]. Recently, investigators found that EFA levels also vary by season, with peaks of EPA and DHA from August to September. The parent omega-3 and 6 levels did not have a seasonal variation, suggesting a seasonal interference with delta-5-desaturase conversion. The authors of this study suggest that the seasonal variation in EPA or DHA may, in part, explain seasonality of violent suicide occurrence [ 28 ]. The overlap between cardiovascular disease and depression has also been noted, with omega-3 status emerging as a common thread. Indeed, major depression in acute coronary syndrome patients is associated with significantly lower plasma levels of omega-3 fatty acids, particularly DHA [ 29 ]. In addition, elevated homocysteine levels, a known risk factor for cardiovascular disease, has been associated with the excess omega-6 fatty acids found in the Western diet [ 30 ]. Finally, lowered intake of the parent omega-3 ALA has been associated with depression in 771 Japanese patients with newly diagnosed lung cancer [ 31 ]. It is important to note that not every study supports an association between lowered omega-3 status and depression. Two studies have actually shown significant increases in plasma and RBC omega-3 status among depressed patients [ 32 , 33 ]. A recent study involving depressed adolescent patients found no significant relationship between adipose tissue EFA levels and depression [ 34 ]. Possible mechanisms of omega-3 EFA Detailed reviews of the possible neurobehavioral mechanisms of omega-3 fatty acids have been previously published and are beyond the scope of this review [ 35 , 36 ]. The influence of omega-3 fatty acids within the CNS is far from completely understood, and our current knowledge is largely based on the consequences of omega-3 deficiency within animal models. There are two major areas of omega-3 fatty acid influence worthy of further discussion. The first is the importance of omega-3 fatty acids in neuronal membranes. Omega-3 fatty acids are an essential component of CNS membrane phospholipid acyl chains and are therefore critical to the dynamic structure and function of neuronal membranes [ 37 ]. Proteins are embedded in the lipid bi-layer of the cell and the conformation or quaternary structure of these proteins is sensitive to the lipid components. The proteins in the bi-layer have critical cellular functions as they act as transporters and receptors. Omega-3 fatty acids can alter membrane fluidity by displacing cholesterol from the membrane [ 38 ]. An optimal fluidity, influenced by EFAs, is required for neurotransmitter binding and the signaling within the cell [ 39 ]. EFAs can act as sources for second messengers within and between neurons [ 35 ]. The second area where omega-3 fatty acids may exert significant influence in major depression is via cytokine modulation. A growing body of research has documented an association between depression and the production these proinflammatory immune chemicals. These cytokines, including interleukin-1 beta (IL-1β), -2 and -6, interferon-gamma, and tumor necrosis factor alpha (TNFα), can have direct and indirect effects on the CNS. Some of the documented activities of these cytokines include lowered neurotransmitter precursor availability, activation of the hypothalamic-pituitary axis, and alterations of the metabolism of neurotransmitters and neurotransmitter mRNA [ 40 ]. Researchers have found elevations of IL-1β, and TNFα are associated with the severity of depression [ 41 ]. Psychological stress can cause an elevation of these cytokines. It is worth noting that various tricyclic and selective serotonin re-uptake inhibiting antidepressants can inhibit the release of these inflammatory cytokines [ 40 ]. Omega-3 fatty acids, and EPA in particular, are well documented inhibitors of proinflammatory cytokines such as IL-1 β and TNFα. In addition, it has recently been suggested that the anti-inflammatory role of omega-3 fatty acids may influence brain derived neurotrophic factor (BDNF) in depression [ 36 ]. BDNF is a polypeptide that supports the survival and growth of neurons through development and adulthood. Serum BDNF has been found to be negatively correlated with the severity of depressive symptoms [ 42 ]. Antidepressant medications and voluntary exercise can enhance BDNF, while diets high in saturated fat and sucrose, and psychological stress inhibit BDNF production [ 36 ]. Clinical evidence The epidemiological and laboratory studies, along with the research which shows depressed patients appear to have lowered omega-3 status, have naturally led to clinical investigations. A number of case reports have appeared in the literature, the first of which was over 20 years ago. In this initial series of case reports, flaxseed oil (source of the parent omega-3 ALA) at various dosages, was reported to improve the symptoms of bipolar depression and agoraphobia [ 43 ]. An additional case report documented an improvement in depressive symptoms during pregnancy with the use of 4 g EPA/2 g DHA per day. Interestingly, improvements in symptoms (measured via the Hamilton Rating Scale for depression – HRDS) occurred at four weeks, and with the exception of insomnia and anxious thoughts, all symptoms resolved at six weeks [ 44 ]. Despite the interesting results, there are major scientific problems with case reports, most notably the placebo response. A recently published case report published took advantage of modern brain imaging to corroborate clinical improvements. In this case a patient with treatment resistant depression was placed on a daily dose of 4 g pure EPA, and after one month there were significant improvements, including a co-morbid social phobia. After nine months the patient was reportedly symptom free. It was found that over the course of the nine months of treatment, there was a 53 percent increase in cerebral phosphomonoesters and the ratio of cerebral phosphomonoesters to phosphodiesters increased 79 percent, indicating reduced neuronal phospholipid turnover. Utilizing MRI technology, the researchers found that the EPA treatment was associated with structural brain changes, including a reduction in lateral ventricular volume. This is likely to be a result of increased phospholipid biosynthesis and reduced phospholipid breakdown [ 45 ]. Given the recent research indicating a decrease in volume in various areas of the brain of depressed patients, this is certainly an important case study [ 46 ]. A series of case reports also suggest that 1 – 4 g of pure EPA may be helpful in anorexia nervosa, a condition with the highest risk of morbidity and mortality among psychiatric disorders [ 47 ]. In all six of the cases, EPA was reported to improve mood to varying degrees. For some, discontinuing EPA therapy resulted in deteriorations in mood and other psychiatric symptoms. An interesting study examined fish oil vs.marine oil extracted from Antarctic krill in premenstrual syndrome. Krill is similar to fish oil, with the exception that it contains naturally-occurring phospholipids, and contains more EPA per gram than standard fish oil capsules (240 mg/g EPA in krill vs.180 mg/g in standard fish oil). In the 3-month trial, patients (n = 70) received 2 g of krill oil or 2 g fish oil daily for one month, then for eight days prior to, and two days during, menstruation for the following two months. Evaluation at 45 days and three months showed that krill oil significantly improved depressive symptoms of premenstrual syndrome. The absence of significant effects of fish oil on mood suggests that the presence of the phospholipids and/or higher amounts of EPA may be responsible for the therapeutic effect of krill oil [ 48 ]. There have been some controlled studies that have examined omega-3 fatty acids and a placebo intervention in depression. The first small clinical study (n = 30) showed that four months of treatment with 9.6 g of omega-3 fatty acids (6.2 g EPA/3.4 g DHA) was of therapeutic value in bipolar disorder. Specifically, this study showed a highly significant effect in treating depression (p < 0.001 HRSD scores) [ 49 ]. In a separate double-blind, placebo-controlled study (n = 22), the addition of 2 g of pure EPA to standard antidepressant medication enhanced the effectiveness of that medication vs.medication and placebo. This 3-week study, involving patients with treatment-resistant depression, showed that EPA had an effect on insomnia, depressed mood, and feelings of guilt and worthlessness. There were no clinically relevant side effects noticed [ 50 ]. In a small pilot study (n = 30), Harvard researchers found that just 1 g of EPA could reduce aggression (modified Overt Aggression Scale) and depressive symptom scores (Montgomery-Asberg Depression Rating Scale) among borderline personality disorder patients. The results of this 2-month, placebo-controlled study are encouraging, given the difficulty in treating borderline personality disorder. It is also of note that 90 percent of participants remained in the study and no clinically relevant side effects were noticed with EPA [ 51 ]. In a double-blind, placebo-controlled trial over two months, high dose fish oil (9.6 g/day) was added to standard antidepressant therapy in 28 patients with MDD. In this study the patients who received the omega-3 fish oil capsules had a significantly decreased score on the HRSD compared to those taking the placebo. Once again, the fish oil, even at this high dose, was well tolerated with no adverse events reported [ 52 ]. Various doses of pure EPA have also been investigated in depression. In a 12-week, randomized, double-blind, placebo-controlled study, patients (n = 70) were given ethyl-EPA at doses of 1 g, 2 g or 4 g. The patients in this case had experienced persistent depression, despite ongoing standard antidepressant pharmacotherapy at adequate does. Interestingly, in this study, "less was more." Those in the 1 g per day group had the best outcome. The patients who received 1 g per day of EPA were the only group to show statistically significant improvements. Among the 1 g/day group, 53 percent achieved a 50 percent reduction in HRSD scores. The 1 g EPA led to improvements in depression, anxiety, sleep, lassitude, libido, and suicidal ideation. These findings suggest that omega-3 fatty acids can augment antidepressant pharmacotherapy and/or alleviate depression by entirely different means than standard medications [ 53 ]. A large study examining the effects of omega-3 or placebo added to cognitive-behavior therapy would be of interest. To date, the published data on supplementation with pure EPA on MDD or depressive symptoms have been positive. With regard to DHA or a combination of EPA and DHA, there have been three negative reports. A trial on DHA alone as monotherapy in the treatment of MDD was recently reported. In this study, 2 g pure DHA or placebo was administered to 36 patients with depression for six weeks. The response differences between the groups, as measured by scores on the Montgomery-Asberg Depression Rating Scale did not reach statistical significance [ 54 ]. In an open label pilot study, the combination of 1.7 g of EPA and 1.2 g of DHA failed to show benefits among seven women with a past history of post-partum depression. The omega-3 monotherapy was initiated between the 34 th – 36 th week of pregnancy and was assessed through 12 weeks post-partum. In these women the fish oil combination did not reduce the risk of relapse [ 55 ]. Finally, a pure DHA supplement, at low doses of 200 mg per day for 4 months post-partum, did not improve self-rated or diagnostic measures of depression over placebo. However, the women enrolled (n = 89) in this study were not clinically depressed as a group, which precludes interpretation that DHA is ineffective in post-partum depression [ 56 ]. Other dietary considerations It is important to consider the nutrients which can ultimately influence omega-3 status. Among them, four important dietary factors also relate to MDD: zinc, selenium, folic acid and dietary antioxidants. A number of studies have shown that zinc levels are lower among patients with depression and a recent study found that 25 mg zinc supplementation may improve depressive symptoms [ 57 ]. Interestingly, 25 mg of zinc supplemented for two months has also been shown to significantly increase omega-3 status in the plasma phospholipids at the expense of saturated fat [ 58 ]. Lowered levels of selenium have been associated with negative mood scores in at least 5 studies [ 59 ]. Selenium plays a significant role in the human antioxidant defense system. In addition, selenium deficiency can interfere with the normal conversion of ALA into EPA and DHA, and results in an increase in the omega-6:omega-3 ratio [ 60 ]. Regarding folic acid, a growing body of research has documented the low levels of folic acid among patients with depression [ 61 ]. In addition, there are small clinical trials showing a beneficial effect of folic acid in depression, and its ability to enhance the effectiveness of antidepressant medications at just 500 mcg [ 61 , 62 ]. It is of relevance here because folic acid has been shown to increase omega-3 status when supplemented, and decrease omega-3 status when it is in deficiency in the animal model [ 63 ]. In addition, a folic acid deficient diet can enhance lipid peroxidation [ 64 ]. In patients with MDD there are in fact signs of oxidative stress and lipid peroxidation, and antidepressant medications may reverse the severity of oxidative stress in depressed patients [ 65 ]. A recent human study found that depressive symptoms are independently correlated with lipid peroxidation [ 66 ]. Patients with obsessive compulsive disorder (OCD) and co-morbid depression have higher levels of lipid peroxidation than those with OCD alone [ 67 ]. Dietary antioxidants are known to influence the antioxidant defense system, and new research suggests that dietary antioxidants can influence omega-3 status. Specifically, a diet devoid of antioxidants lowered essential fatty acid levels in the plasma of trained athletes, even though the amount and types of fats were not altered [ 68 ]. Omega-3 fatty acids have been shown to decrease lipid peroxidation in vivo [ 69 ], and antioxidant supplementation can prevent the negative influence of saturated fat on BDNF levels and cognitive function in animals [ 70 ]. Conclusion While far from robust, there is enough epidemiological, laboratory and clinical evidence to suggest that omega-3 fatty acids may play a role in certain cases of depression. Fish oil supplements are well tolerated, and have been shown to be without significant side effects over large scale, 3-year research [ 71 ]. Generally, omega-3 supplements are inexpensive, which makes them an attractive option as an adjuvant to standard care. At this time, however, the routine use of omega-3 fatty acids for the treatment of MDD cannot be recommended. The research reviewed here shows that the data is far from unequivocal. Large trials are warranted to truly determine efficacy, appropriate dosing and the potentially active components – EPA, DHA, or both. It is also clear that omega-3 intake occurs in dietary context, one that includes other important nutrients. Future research should consider the influence of zinc, selenium, folic acid and dietary antioxidant status to determine who may be a successful candidate for omega-3 supplementation. In the meantime, given the current excess intake of omega-6 rich oils, and the emerging research on omega-3 fatty acids and MDD, all mental health professionals should at least ensure adequate intake of omega-3 fatty acids among patients with MDD. The current average North American intake of EPA and DHA is approximately 130 mg per day, well short of the minimum 650 mg recommended by the international panel of lipid experts [ 6 ]. While it is not necessary for mental health professionals to become clinical nutritionists, consideration of a patient's dietary quality may be worthwhile. Hopefully future research will determine if dietary modifications or supplementation can influence the outcome of standard care. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC533861.xml |
545075 | Identification of cardiac malformations in mice lacking Ptdsr using a novel high-throughput magnetic resonance imaging technique | Background Congenital heart defects are the leading non-infectious cause of death in children. Genetic studies in the mouse have been crucial to uncover new genes and signaling pathways associated with heart development and congenital heart disease. The identification of murine models of congenital cardiac malformations in high-throughput mutagenesis screens and in gene-targeted models is hindered by the opacity of the mouse embryo. Results We developed and optimized a novel method for high-throughput multi-embryo magnetic resonance imaging (MRI). Using this approach we identified cardiac malformations in phosphatidylserine receptor ( Ptdsr ) deficient embryos. These included ventricular septal defects, double-outlet right ventricle, and hypoplasia of the pulmonary artery and thymus. These results indicate that Ptdsr plays a key role in cardiac development. Conclusions Our novel multi-embryo MRI technique enables high-throughput identification of murine models for human congenital cardiopulmonary malformations at high spatial resolution. The technique can be easily adapted for mouse mutagenesis screens and, thus provides an important new tool for identifying new mouse models for human congenital heart diseases. | Background Congenital malformations are a major cause of death in childhood, and are typically characterized by lesions that do not compromise fetal survival. For instance, congenital heart disease (CHD) typically consists of lesions such as ventricular and atrial septal defects, which are compatible with fetal hemodynamics [ 1 ]. Although human genetic studies have identified some genes that cause congenital cardiac malformations, the molecular and developmental mechanisms underlying most of these defects remain largely unknown. Despite the high incidence of CHD (~1% of live births), only a handful of genes have been identified that when mutated, result in congenital heart disease [ 2 - 4 ]. The mouse is a particularly good model for studying mechanisms of cardiac diseases as its anatomy and development resembles that of the human more closely than any other genetically tractable organism. Importantly, the mouse is amenable to genotype-driven approaches such as transgenic knockouts of defined candidate genes [ 5 ], genome wide mutagenesis approaches using gene-trap [ 6 ] or transposon insertion screens [ 7 ], and high-throughput phenotype-driven screens that rely, for instance, on N -ethyl- N -nitrosourea (ENU) mutagenesis [ 8 , 9 ]. However, high-throughput cardiovascular genomic approaches in the mouse have been hampered by the paucity of phenotyping tools that allow efficient identification of complex cardiac malformations. As mouse embryos are opaque, late developmental defects are particularly difficult to identify. For instance, cardiac septal defects or outflow tract abnormalities can only be confidently identified after 14.5 days post coitum (dpc) when cardiac and outflow tract septation are completed in normal embryos. The identification of malformations in late gestation embryos typically relies on serial histological sectioning, which is extremely labor intensive. Furthermore, this often results in the irretrievable loss of 3D information, which is essential for the interpretation of complex cardiac malformations. In addition, standard pathological analysis is not amenable to high-throughput phenotype screening protocols that are required for any mutagenesis screen aiming at the functional dissection of the developmental biology of cardiac diseases. Therefore, new technological approaches must be harnessed that allow an efficient phenotyping of heart defects and also of subtle cardiac abnormalities that are at danger of being overseen in traditional histopathology screens. This is even more important in the light of upcoming new endeavors in functional mouse genome annotation [ 10 ]. Currently, new large-scale mouse mutagenesis screens are being set up in the US and in Europe that aim to produce heritable mutations in every gene in the mouse genome [ 11 - 13 ]. To make full use of these new mouse mutant resources more precise and efficient phenotyping methods are urgently needed [ 12 - 14 ]. The success of genome wide saturation mutagenesis screens depends therefore on improved phenotyping, and new high-resolution imaging approaches for mouse mutants are one of the most important which need to be established. We previously reported the development of fast gradient-echo MRI of single mouse embryos [ 15 - 17 ]. This resulted in the acquisition of a 3D dataset in under 9 hours, with an experimental image resolution of 25 × 25 × 26 μm/voxel. We showed that MRI is capable of accurately identifying normal embryonal structures, and cardiac and adrenal malformations in knockout mouse embryos, and we have validated this technique by performing in depth histological examinations of imaged embryos [ 15 - 17 ]. These experiments showed that single-embryo MRI could correctly identify all cardiac lesions (atrial septal defects, ventricular septal defects, outflow tract defects such as double-outlet right ventricle, and aortic arch defects) except those under 20 μm – which is below the resolution of the MRI technique. As this method images single embryos in overnight runs, it still lacks the throughput required for phenotype-driven mutagenesis screens. For instance, in a typical recessive ENU mutagenesis screen, to screen 50 ENU mutant lines using a 3-generation breeding scheme would require the analysis of ~1200 embryos [ 18 ]. We now report the development of a method of imaging up to 32 embryos simultaneously in a single unattended overnight run, at high spatial resolution. Allowing ~30 minutes per embryo, the analysis of 1200 embryos would take 75 working days for a single trained individual. We show that this high-throughput multi-embryo MRI technique can be used to rapidly identify unsuspected embryonal cardiac and visceral malformations. Using this technique we could identify a novel, and hitherto unsuspected role for the phosphatidylserine receptor ( Ptdsr ) in controlling ventricular septal, outflow tract and pulmonary artery development. In addition, we found thymus hypoplasia in Ptdsr -deficient embryos. These findings suggest that a novel Ptdsr -mediated pathway is required for cardiac and thymus development. Results Multi-embryo imaging We modified our previously described fast gradient echo magnetic resonance imaging technique [ 15 - 17 ] to image embryos embedded in four to eight layers (16 – 32 embryos total) in 28 mm nuclear magnetic resonance tubes using a single quadrature driven birdcage coil (Figure 1a ). Preparation and embedding of embryos typically took less than an hour. In initial experiments we imaged up to 16 embryos simultaneously in overnight runs of <9 hours, with an experimental resolution of 51 × 51 × 39 μm. Subsequently, we used a custom made optimized probe with an increased sensitivity range to enhance imaging throughput. This allowed us to image 32 embryos simultaneously (Figure 1a,b ), but with a larger matrix size and an increased field-of-view in the long axis of the tube. For these experiments we imaged embryos for ~12 hours, and achieved an improved experimental resolution of 43 × 43 × 36 μm. The optimized coil used for 32 embryos MRI (Figure 1 ) has a sensitivity range in z-direction of close to 50 mm. The artefacts seen at both ends in the longitudinal image (Figure 1b ) are caused by B1-inhomogeneities at the end-rings of the coil. However, accurate image analysis for the entire data set remains possible if the height of the embryo stack does not exceed approximately 47 mm as demonstrated in the corresponding axial views of the top and the bottom layer (Figure 1c,d ). The resolution achieved with the multi-embryo MRI technique allowed us to visualize the heart, cardiac septa, central nervous system, and visceral organs in fine detail (Figure 1d–f ), in embryos taken from each layer. The data are permanently archived on DVDs, for subsequent analysis. Construction of 3D reconstructions of the heart typically takes ~4 hours, but was not necessary for the identification of cardiovascular defects. Figure 1 High-throughput high-resolution magnetic resonance microscopy. ( a ) Stack of 32 embryos embedded in a NMR tube. ( b ) Section through the long axis of the NMR tube showing embryos in eight layers. ( c ) Sagittal section through layer 8 showing the four embryos in this layer. ( d–f ) Transverse, sagittal, and coronal sections through individual embryos in layers 5, 1 and 4 respectively. The voxel size is 25.4 × 25.4 × 24.4 μm. Structures indicated are the spinal cord (sc), the right and left lungs, atria and ventricles (rl, ll, ra, la, rv, lv), primary atrial and interventricular septa (pas, ivs), mitral valve (mv), midbrain roof (mbr), midbrain (mb), mesencephalic vesicle (mes), thalamus (tha), hypothalamus (hy), pons (po), cerebellum (c), medulla oblongata (mo), pituitary (pit), tongue (t), thymus (th), left superior vena cava and main bronchus (lsvc, lmb), aorta (ao), liver (li), stomach (s), left adrenal and kidney (lad, lk), pancreas (pa), intestines (i), umbilical hernia (uh), aqueduct of Sylvius (aq), fourth ventricle (fv), inner ear (ie), larynx (lar), right ventricular outflow tract (rvot), spleen (sp), and testes (te). Scale bars = 500 μm; axes: d – dorsal; v – ventral; r – right; l – left; a – anterior, p – posterior. Sensitivity and specificity of multi-embryo imaging To assess the sensitivity and specificity of multi-embryo imaging in comparison to single-embryo imaging, we used a model of Cited2 deficiency [ 19 ]. Embryos lacking Cited2 ( Cited2 -/- ) have diverse cardiac malformations, including atrial and ventricular septal defects, outflow tract and aortic arch malformations, and adrenal agenesis [ 15 , 17 , 19 ]. As the Trp53 -repressor gene Cdkn2a P19ARF is a target of Cited2 [ 20 ], we also examined embryos lacking both Cited2 and Trp53 to determine if this would rescue the heart and adrenal defects in Cited2 -/- mice. We imaged 50 embryos using the multi-embryo technique in the 16-embryo mode. Embryonal genotypes included 12 wild-type, 13 Cited2 +/- , 14 Cited2 -/- , three Cited2 -/- : Trp53 -/- , four Cited2 -/- : Trp53 +/- , two Trp53 +/- , and two Trp53 -/- . We analyzed the data from each embryo for cardiac malformations without knowledge of the genotype. This typically took a maximum of 30 minutes per embryo. We scored each embryo for atrial and ventricular septal defects (ASD, VSD), outflow tract (e.g. double-outlet right ventricle, common arterial trunk), and aortic arch malformations (e.g. right-sided or bilateral aortic arch, retroesophageal subclavian artery), and for adrenal agenesis. Each embryo was then re-imaged singly at high resolution, and the data re-analyzed as before. In this group we identified 20 embryos with ASD, 19 with VSD, 18 with outflow tract defects, 11 with aortic arch defects, and 21 with bilateral adrenal agenesis, using high-resolution single embryo imaging. In comparison to single embryo imaging, the overall sensitivity and specificity of multi-embryo imaging for cardiac malformations was 88% and 92% respectively. For ASD (18 identified by single embryo imaging) the sensitivity and specificity was 85% and 95%; for VSD 94% and 94%; for outflow tract malformations 94% and 100%; and for aortic arch malformations 91% and 100% respectively (Figure 2 ). For bilateral adrenal agenesis, the sensitivity was 100% and specificity was 95% (Figure 3 ). Embryos lacking both Cited2 and Trp53 had cardiovascular defects and adrenal agenesis, indicating that Trp53 does not play a major role in the genesis of these defects in mice lacking Cited2 . These results indicate that multi-embryo MRI is a potentially powerful high-throughput tool for efficiently characterizing cardiovascular malformations and identifying other defects in organogenesis. Figure 2 Identification of septal, outflow tract, and aortic arch malformations using multi-embryo MRI ( a – e' ) Images of transverse sections from 5 Cited2 -/- embryos obtained using the multi-embryo technique ( a–e ) compared with images from the same embryos obtained subsequently using the single embryo technique ( a'–e' ). ( a, a' ) Section showing left and right atria and ventricles (la, ram, live, rave). The atria are separated by the primary atria septum (pas), which is deficient at its ventral margin creating an osmium premium type of atria septal defect (ASD-P). ( b, b' ) Section showing a ventricular septal defect (VSD) in the interventricular septum (ivs). ( c, c' ) Section showing double outlet right ventricle, wherein the ascending aorta (a-ao) and the pulmonary artery (pa) both arise from the right ventricle (rv). The aortic valve (ao-v) is indicated. ( d, d' ) Section showing a right-sided aortic arch (ao-a) passing to the right of the trachea (tr) and the esophagus (es). ( e, e' ) Section showing bilateral aortic arches (ao-a) forming a vascular ring around the trachea (tr) and the esophagus (es). Also indicated are the thymus (th) and the right superior vena cava (r-svc). ( f – j ) Serial transverse sections through a wild-type heart obtained using single embryo MRI, demonstrating corresponding normal structures, including the systemic venous sinus (svs), left superior vena cava (l-svc), pulmonary vein (pvn), descending aorta (d-ao), mitral and tricuspid valves (mv, tv), the secondary atrial septum (sas), left and right ventricular outflow tracts (lvot, rvot), pulmonary valve (pv), and arterial duct (ad) of the pulmonary artery. Scale bars = 635 μm for multi-embryo, and 317 μm for single embryo images; axes: d – dorsal; v – ventral; r – right; l – left. Figure 3 Identification of adrenal agenesis using multi-embryo MRI Images of coronal sections from 2 embryos obtained using the multi-embryo technique ( a, b ) compared with images from the same embryos obtained subsequently using the single embryo technique ( a', b' ). ( a, a' ) Normal right adrenal gland (rad) anterior to the right kidney (rk) in a wild-type embryo. The right lung (rl) is indicated. ( b, b' ) Agenesis of right adrenal gland in a Cited2 -/- embryo. Scale bars = 635 μm for multi-embryo, and 317 μm for single embryo images; axes: d – dorsal; v – ventral; a – anterior, p – posterior. Cardiac malformations in mice lacking Ptdsr We next evaluated the role of multi-embryo MRI in analyzing unexplained lethality in embryos generated in collaborating laboratories. Recently, we have generated mice lacking the phosphatidylserine receptor ( Ptdsr -/- ) on a C57BL/6J background, by gene targeting in embryonic stem cells [ 21 ]. Ptdsr is a nuclear protein of unknown function, which is essential for the development and differentiation of multiple organs during embryogenesis [ 21 - 24 ]. Ablation of Ptdsr function in knockout mice causes perinatal lethality, growth retardation [ 21 , 22 , 24 ] and a delay in terminal differentiation of the kidney, intestine, liver and lungs during embryogenesis [ 21 ]. In addition, Ptdsr -/- embryos develop complex ocular lesions [ 21 ] as well as haematopoietic defects [ 24 ]. However, as many malformations have been described in Ptdsr mutants, none of those detected could explain the observed perinatal lethality of Ptdsr -/- mice. In the process of phenotypical characterization of our Ptdsr -deficient mouse line, we frequently observed subcutaneous edema of varying sizes in Ptdsr -/- embryos by gross inspection ([ 21 ] and Figure 4 ). As the development of edema in various mouse mutants is frequently associated with cardiovascular defects [ 25 ] we started to investigate if this also holds true for Ptdsr -deficient mice. We examined 8 embryos lacking Ptdsr , and 8 littermate wild-type or heterozygous controls using multi-embryo MRI. We found that 5 of 8 Ptdsr -/- embryos had cardiac malformations, which included ventricular septal defects, double outlet right ventricle, and pulmonary artery hypoplasia (Figure 5 ). None of the wild-type or Ptdsr +/- embryos had cardiac malformations. These findings were confirmed on single embryo imaging (Figure 6 ). Furthermore, to verify the identified cardiac defects in the Ptdsr -/- mice we performed serial transverse sectioning of all analyzed embryos. In all cases, we could recognize again the same heart defects that were identified before using the multi-embryo MRI technique (Figure 7 ). In addition, we analyzed by multi-embryo MRI a second Ptdsr -knockout mouse line ( Ptdsr tm1.1 Gbf ), which is identical to the initially analyzed Ptdsr mutant except that the loxP -flanked neomycin selection cassette was removed by breeding the original Ptdsr tm1 Gbf knockout line [ 21 ] to a CMV-Cre deleter mouse line [ 26 ]. From this Ptdsr tm1.1 Gbf knockout mouse line we analyzed 8 Ptdsr -/- embryos, and as littermate controls, 3 Ptdsr +/+ and 1 Ptdsr +/- embryos. We found ventricular septal defects in five out of the eight Ptdsr -/- embryos (data not shown). Again we found no evidence for cardiac malformations in wild-type or heterozygous littermate control embryos. Figure 4 Edema in Ptdsr -/- mice ( a ) The Ptdsr -/- mutant (15.5 dpc) is growth retarded and the severe edema along the back of the embryo is visible. ( b, c ) Sagital sections of embryos at 16.5 dpc. The mutant embryo (c) exhibits massive subcutaneous edema compared to a wild-type (b) littermate. Scale bar = 100 μm in (b) and (c). Figure 5 Identification of cardiac malformations in Ptdsr -/- embryos using multi-embryo MRI ( a–e ) Transverse thoracic sections showing the heart of heterozygous or wild-type control embryos from each litter. The left and right ventricles (lv, rv) are separated by the interventricular septum (ivs). The left and right atria (la, ra) are also indicated, separated by the primary atrial septum (pas). ( f–i ) Corresponding sections through littermate Ptdsr -/- embryos, showing ventricular septal defects (VSD). Scale bar = 635 μm; axes: d – dorsal; v – ventral; r – right; l – left; a – anterior, p – posterior. Individual embryos are indicated by number. Figure 6 Cardiac malformations and thymus hypoplasia in Ptdsr -/- embryos . ( a–c ) Transverse and oblique (through the plane of the ascending aorta) sections, and 3D reconstruction (left-ventral oblique view) of a heart of a wild-type embryo at 15.5 dpc. The left and right ventricles (lv, rv) are separated by the interventricular septum (ivs). The left and right atria (la, ra), and the trachea (tr) are also indicated. The ascending aorta (a-ao) arises from the left ventricular outflow tract (lvot), via the aortic valve (ao-v), and continues on as the aortic arch (ao-a), which joins the descending aorta (d-ao). The pulmonary artery (pa) arises from the right ventricular outflow tract (rvot), and continues as the arterial duct (ad), which joins the descending aorta. ( d–f ) Corresponding images of a Ptdsr -/- embryo, showing a smaller heart with a ventricular septal defect (VSD). The aorta arises from the right ventricle. The pulmonary artery is small and its connection to the descending aorta (arterial duct) could not be identified. ( g–i ) Corresponding images of another Ptdsr -/- embryo, showing a ventricular septal defect (VSD). The aorta overrides the VSD resulting in a double-outlet right ventricle. ( j, k ) Coronal sections of Ptdsr +/+ and Ptdsr -/- embryos, showing the two lobes of the thymus (th). The arterial duct of the pulmonary artery in the Ptdsr -/- embryo is narrowed. ( l ) Correlation between embryo weight and volume. Scattergram of embryo weight versus embryo volume measured from multi-embryo MRI datasets for 16 embryos using Amira. The co-efficient of regression (r) is indicated. ( m, n ,) Absolute embryo and thymus volumes (μl) were measured from the MRI datasets from 5 wild-type (wt), 3 heterozygote (h), and 8 Ptdsr -/- (m) embryos at 15.5 dpc. There was no significant difference in the wild-type and heterozygote data, which were therefore pooled together (wt/h). ( o ) Relative thymus volumes (% of embryo volume) were calculated as Ptdsr -/- embryos were slightly smaller than littermate wild-type embryos. The data are represented as mean ± S.D. The probability of a type I error ( P ) is indicated. Scale bars = 317 μm; axes: r – right; l – left; d – dorsal; v – ventral; a – anterior, p – posterior. Individual embryos are indicated by number. Figure 7 Cardiac malformations in Ptdsr -/- embryos: analysis using histology Embryos analyzed by MRI (Figure 3) were sectioned transversely and stained with hematoxylin and eosin. (a–c) Serial caudal to cranial sections of the wild-type embryo showing normal cardiac and vascular anatomy. The left and right ventricles (lv, rv) are separated by the interventricular septum (ivs). The ascending aorta (a-ao) arises from the left ventricle, continues on as the aortic arch (ao-a), which joins the descending aorta (d-ao). The pulmonary artery (pa) arises from the right ventricle via the pulmonary valve (pv) and continues as the arterial duct (ad), which joins the descending aorta. The left and right atria (la, ra), trachea (tr), right main bronchus (rmb) and esophagus (es) are indicated. (d) Section through embryo 33 indicating the ventricular septal defect (VSD). (e, f) Sections through embryo 55 showing that both aorta and pulmonary artery arise from the right ventricle (double outlet right ventricle), and that the arterial duct of the pulmonary artery is narrow in comparison to the aorta – indicating pulmonary artery hypoplasia. The aortic valve (aov) is indicated. (g–i) Serial caudal to cranial sections through embryo 35 showing a VSD, aorta arising from the right ventricle (double outlet right ventricle), and a severely narrowed arterial duct. Scale bars = 500 μm; axes: r – right; l – left; d – dorsal; v – ventral. Individual embryos are indicated by number. We also observed a modest degree of thymic hypoplasia in Ptdsr -/- embryos (Figure 6k ). To confirm this, thymus and embryo volumes were measured in 8 Ptdsr -/- embryos and 8 wild-type or heterozygous control embryos at 15.5 dpc. Embryo volume measured from the MRI datasets correlated very strongly with embryo weight (Figure 6l ), and was modestly reduced in Ptdsr -/- embryos (Figure 6m ). The volume of the thymus was significantly reduced in Ptdsr -/- embryos, even after correction for embryo volume, to 58% of the control value (Figure 6n,o ). To correlate the identified cardiopulmonary malformations in Ptdsr -/- embryos with expression of the Ptdsr gene during heart development, we made use of our Ptdsr gene-trap reporter mouse line [ 21 ]. Using X-Gal staining in heterozygous embryos staged between 9.5 dpc and 12.5 dpc we found specific Ptdsr expression in the heart starting at 10.5 dpc and getting more defined to the compact zone and the trabeculi from 11.5 dpc onwards (Figure 8 ). Furthermore, when we analyzed Ptdsr -/- hearts by histopathology at 16.5 dpc we observed a severe differentiation defect in the compact zone as well as in the trabeculi (Figure 9 ), thus demonstrating that Ptdsr is in addition required for heart muscle differentiation at later stages of development. Taken together these results indicate that Ptdsr plays a hitherto unsuspected role in cardiovascular development as well as in cardiac muscle differentiation. Figure 8 Analysis of Ptdsr expression in the embryonic heart ( a, b ) Staining of heterozygous Ptdsr-βgeo -embryos [21] using X-Gal at 10.5 dpc (a) and 11.5 dpc (b). (a) At 10.5 dpc Ptdsr expression can be seen throughout the heart. (b) Transverse sections of X-Gal stained embryos at 11.5 dpc showed an increased expression of Ptdsr in the myocardial wall and a beginning decrease of the expression in the trabeculation. Scale bar = 100 μm in (b). Figure 9 Myocardial wall malformations in Ptdsr -/- embryos ( a, b ) Sagital sections of wild-type (a) and homozygous mutant (b) embryos at 16.5 dpc revealed a thinning of the myocardial wall (compact zone) and an increased myocardial trabeculation (b) in the mutant heart. Scale bar = 100 μm. Discussion Utility of MRI in identifying mouse models of human malformations Our results show that it is possible to efficiently identify and quantitate relatively subtle cardiac and visceral malformations in late gestation mouse embryos using multi-embryo MRI. We have deliberately optimized our technique at late gestation in order to identify those congenital defects that allow survival through most of gestation. Importantly, these defects resemble human congenital malformations, and would provide mouse models for the study of these diseases. Our method represents a simpler alternative to the multi-coil approach published recently [ 27 , 28 ] in which up to eight fixed mouse embryos were imaged simultaneously, with a resolution of 200 μm. In comparison, the multi-embryo method described here has a higher experimental resolution of 43 μm. Notably, it requires substantial developmental and financial effort to equip an experimental MR-system with multiple coil and receive capability. Role of MRI in investigating murine embryonal or perinatal lethality Many mouse gene knockouts display late gestational lethality, but incomplete analysis and loss of 3D information consequent to histological sectioning, results in major developmental malformations being frequently missed. As shown here, Ptdsr -/- embryos on a C57BL/6J background develop heart defects. This was not noted in recent reports [ 22 , 24 ], and emphasizes the need not only for completely examining several mutant embryos, but also repeating these examinations in different genetic backgrounds. A major advantage of MRI is the ability to easily ship fixed embryos from referring laboratories to the laboratory that performs the MRI analysis. This minimizes the expense of animal relocation, re-derivation, and breeding required to generate the embryos, and significantly reduces animal experimentation. Role of MRI in high-throughput phenotype driven screens MRI screens performed at late gestation would be expected to identify genes that affect later aspects of development, and identify hypomorphic and haploinsufficient alleles of genes that affect earlier steps of development. Published data from genome-wide ENU mutagenesis screens in the mouse indicate that ~30% progeny carry a heritable recessive phenotype, making 3-generation recessive screens the method of choice for identifying developmental malformations [ 18 ]. At least 24 3 rd generation progeny per 1 st generation mutant are typically screened, resulting in a >78% probability of identifying at least one fully penetrant recessive homozygous mutant. A typical recessive screen (e.g. 50 – 100 first generation mutants per year) would require the analysis of ~1200 – 2400 embryos per year. Our results show that multi-embryo MRI is eminently suitable for such throughput. Although, screening of 32 embryos overnight requires typically about 16 hours of data analysis, multi-embryo MRI mutagenesis screens can be easily performed at a reasonable scale if multiple operators analyze the data in parallel. As the data are permanently stored on DVDs, and can be analyzed easily by commercially available software, they can be without difficulty disseminated to specialists for further and more detailed analysis. Another powerful application of multi-embryo MRI will likely be the investigation and screening of potentially teratogenic drugs. Functional role of Ptdsr in heart development Our results presented here indicate a new, and hitherto unsuspected role for the phosphatidylserine receptor in controlling ventricular septal, outflow tract, pulmonary artery, and thymus development. This finding suggests that a novel Ptdsr -mediated pathway is required for cardiac and thymus development. Recently, we have demonstrated that in contrast to previously reported hypothetical Ptdsr functions, the Ptdsr protein is not required for the clearance of apoptotic cells [ 21 ]. Moreover, detailed analysis of apoptosis induction and apoptotic cell clearance in Ptdsr +/+ and Ptdsr -/- embryos during heart development did not reveal any difference in the number and location of apoptotic cells between the genotypes (J.B., A.D.G. and A.L. unpublished observations). This further excludes that Ptdsr has any function in apoptotic cell clearance and points to other developmental mechanisms that are affected by Ptdsr ablation. The neural crest plays an important role in the development of the cardiac outflow tract, aortic arches, and the thymus [ 29 ]. As Ptdsr -deficient embryos lack intestinal ganglia [ 21 ] which are also derived from the neural crest, these results suggest that Ptdsr -/- mice may have an underlying neural crest defect. Importantly, dysfunction of these Ptdsr -mediated pathways during development could also potentially result in heart defects in humans. Conclusions Our results validate the utility of multi-embryo MRI for high-throughput identification of murine models for human congenital cardiac malformations, and using this technique we have shown that Ptdsr is essential for normal cardiac development. Further experiments are needed to define exactly in which pathways Ptdsr is involved during heart development. We expect that multi-embryo MRI will be an important technology for future phenotype-driven mouse mutagenesis screens. The technology can be easily implemented at standard MRI imaging centers, thus allowing by collaboration with individual researchers or mouse mutagenesis centers, a high-throughput functional genetic dissection of mechanisms underlying cardiac development and congenital heart diseases. Methods Mice Cited2 -/- [ 19 ], Trp53 -/- [ 30 ], and Ptdsr -/- mice [ 21 ] have been described previously. All embryos were harvested at 15 days after detection of the vaginal plug. Embryo preparation Embryos were fixed in 4% paraformaldehyde at 4°C for ~1 week, and then embedded in 1% agarose (Seakem) containing 2 mM gadolinium-diethylenetriamine pentaacetic anhydride (Gd-DTPA, Magnevist, Schering UK) in 28 mm nuclear magnetic resonance tubes (Figure 1 ). The left forelimb was removed from each embryo to facilitate the identification of the left side. In addition, embryos had other limbs and/or tails removed before embedding so that each embryo in a given layer (of four embryos) could be unequivocally identified. Magnetic resonance imaging Single embryo imaging was performed as described previously [ 15 - 17 ]. For multi-embryo imaging, we used the same 11.7 Tesla (500 MHz) vertical magnet (Magnex Scientific, Oxon, UK). This was interfaced to a Bruker Avance console (Bruker Medical, Ettlingen, Germany) equipped with a shielded gradient system with a maximal gradient strength of 548 mTesla/m (Magnex Scientific, Oxon, UK), and quadrature-driven birdcage type coils with an inner diameter of 28 mm (Rapid Biomedical, Würzburg, Germany). Compressed air at room temperature was used to reduce the heating induced by the gradients. A 3D spoiled gradient echo sequence (echo time 10 ms), a π/2 excitation pulse with rectangular pulse shape, (π/2 = 100 μs), was used with a short repetition time (30 ms) to obtain strong T 1 contrast. A matrix size of 512 × 512 × 768 (bandwidth: 130 Hz/pixel) at a field of view of 26 × 26 × 30 mm achieved an experimental resolution of 51 × 51 × 39 μm when imaging up to 16 specimens. In case of 32 embryos, a matrix size of 608 × 608 × 1408 at field of view of 26 × 26 × 50 mm, yielded an experimental resolution of 43 × 43 × 36 μm. The total experimental time was ~8.75 hours for 16 embryos, and ~12.3 hours for 32 embryos (typically overnight runs) whereby each phase encoding step was averaged four times. Data reconstruction and analysis The raw MR data were reconstructed into a stack of 1024 (for 16 embryos), or 2048 (for 32 embryos) 2D TIFF files (16 bit pixel resolution, 2 or 4 GB total size) using purpose-written software as described previously [ 16 ]. The TIFF files were analyzed using Amira 3.1(TGS Europe, Mérignac Cedex, France). 3D reconstructions were performed using the Image Segmentation Editor, and tissue volumes for morphometric analysis were measured using the Measure Tissue Statistics tool available in Amira 3.1. The probability (p) of a Type I error was calculated using a 2-sample equal variance 2-tailed t -test in Microsoft Excel. Histology Embryos were dehydrated in ethanol, embedded in paraffin wax, and sections were stained with hematoxylin and eosin. X-Gal staining of embryos Embryos were dissected free of extraembryonic membranes and then fixed in 4% paraformaldehyde at 4°C. Expression of Ptdsr was detected by staining the embryos overnight in X-Gal according to standard protocols. The embryos were postfixed in 4% paraformaldehyde and processed for documentation or histology. Authors' contribution J.E.S. developed the multi-embryo MRI technique, J.B. generated both Ptdsr knockout lines and harvested embryos for MRI and histopathological analysis, S.D.B developed the sample preparation for embryonic MRI, A.D.G. carried out the histopathological analysis of Ptdsr -/- mutants, C.B. prepared the embryos for MRI, K.C. and S.N. assisted the experimental development, S.B. analyzed the MRI and histopathological data, A.L. and S.B. were responsible for the co-ordination of the study and the drafting of the paper. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC545075.xml |
514574 | Conflicts of interest in translational research | Translational research requires a team approach to scientific inquiry and product development. Translational research teams consist of basic and clinical scientists who can be members of both academic and industrial communities. The conception, pre-clinical testing, and clinical evaluation of a diagnostic or therapeutic approach demands an intense interaction between investigators with diverse backgrounds. As the barriers between industry and academia are removed, issues of potential conflict of interest become more complex. Translational researchers must become aware of the situations which constitute conflict of interest and understand how such conflicts can impact their research programs. Finally, the translational research community must participate in the dialogue ongoing in the public and private sectors and help shape the rules that will govern conflicts that arise during the evolution of their research programs. | Introduction By its nature, translational research crosses boundaries between basic science and clinical application. It places researchers in new contexts and ushers in a range of new contacts and relationships. Crossing these boundaries contributes directly to the creativity and social impact of translational medicine. But crossing these boundaries also gives rise to new and often conflicting obligations between researchers, their employers, and their industry sponsors. The public is rightfully concerned that the financial interests of researchers, their institutions, and their corporate sponsors may bias research. Yet history also teaches us that industry collaboration is often essential in realizing the promise of translational research. Industry collaboration has figured prominently in many translational research successes including recombinant growth hormone, angioplasty, stenting for coronary artery disease, and many new medications and diagnostic devices [ 1 ]. Translational researchers must, therefore, understand what financial conflicts of interest are and how they are managed. Their industry partners must understand the constraints placed on researchers by federal and university policies as well as state laws. Relevant policies in the United States include the regulations issued by the Public Health Service and published as part of the Code of Federal Regulations (42 CFR 50.601–50.607) and in the National Science Foundation Grant Policy Guide (Section 510) [ 2 , 3 ]. Laws governing the use of state-owned resources may also be relevant for those working at or with public universities. What triggers financial conflicts of interest? Conflicts may arise whenever researchers' outside, personal financial interests have the potential to compromise an investigator's professional judgment and independence in the design, conduct, or publication of research. The most commonly regulated financial interests include consulting fees or compensation for personal services, equity or other ownership interests, royalties, and intellectual property rights. A researcher may, for example, receive consulting income or equity in exchange for service on a scientific advisory board of a company that then sponsors clinical research in her lab. Another researcher may be paid for talks to physician groups about an approved medication while simultaneously conducting research on potential off-label uses of the drug. The investigator or an immediate family member may hold stock in the research sponsor. These examples are all common cases and most institutions have relatively standardized ways of managing such common conflicts. Greater challenges are created when the financial relationships between commercial interests and investigators are either ambiguous or complex. Ambiguity can result in a number of ways, but one of the most frequent occurs when investigators approach consulting as an extension of discussions among academic colleagues. Thinking that talking to a corporate representative is the same as talking to an academic colleague, for example, may lead the investigator to make inappropriate disclosures that compromise intellectual property rights or contractual obligations. Often it is not any one financial interest, but rather the combination of multiple financial interests that makes a situation unmanageable. It is extraordinarily difficult, for example, to manage situations in which an investigator is the founder of a startup company, an inventor on a patent licensed to the company, a consultant to the company, and the recipient of other government and industry grants for closely related research. How institutions manage potential conflicts of financial interest Spurred by a combination of bad experiences and new regulations, most research institutions now have policies in place for the management of some aspects of personal financial interests in research. One strategy is simply to prohibit personal financial interests in research. The Association of American Universities, for example, advocates outright prohibition in cases involving research on human subjects unless there are "compelling circumstances" that justify an exception [ 4 ]. Prohibition forces financially interested researchers to either divest their interests or to remove themselves from the research. Although effective, prohibitions are blunt tools and in our opinion should be used only as a last resort. We say this not only because prohibiting financial interests may leave any number of other equally biasing interests in place [ 5 ], but also because, when properly managed, financial interests may play a positive role in the development of a translational research program. Access to company resources and sharing investigator knowledge are often critical to timely translations of basic science to clinical practice. The complex research enterprise needed to develop clinical products is simply beyond the scope of what many investigators can achieve on their own in academic institutions. Fortunately, there are usually less draconian alternatives to outright prohibition. These strategies seek to ensure the integrity of the research, guarantee public scrutiny and access, and, of course, to protect human participants [ 6 ]. One of most common is to assign key research activities such as recruiting, consenting, and data analysis to team members who have no financial stake in the results. Multi-center designs ensure that the biases of any one investigator are less likely to influence the final results. Independent data safety monitoring boards or other oversight committees may also check the influence of personal financial interests. So, too, will requirements to disclose financial interests to publishers, conference organizers, and institutional review boards. Research integrity is further protected by a vigilant stance regarding publication restrictions. Industry partners have a legitimate interest in protecting proprietary information, but this can usually be honored by providing a short period for review prior to submitting a manuscript for publication. No contract or agreement, however, should give the sponsor the right to control publication. Work that requires such control is more appropriately done in industry rather than in academic laboratories. The close attention to publication restrictions is particularly important when a researcher may have a student working on an industry sponsored project. Junior and student scientists working in a research program, who may not have any relationship with a company, must be able to have freedom in pursuing aspects of projects outside the bounds of the research agreement and publishing data in a timely fashion. Translational research that results in the development of a new company presents particular opportunities and challenges. Because of the importance of small companies to economic growth, public research universities often view the number of university-related startups as an index of their contribution to the state economy. More specific institutional interests are created when universities take equity in startups through licensing agreements. Unlike more established firms, start-up companies are often highly dependent on obtaining favorable research outcomes from a particular project. In many cases, prohibition may be the only way to manage the tangle of institutional and individual interests than can result in this situation. Universities may create "firewalls" between the management of equity and researchers [ 7 ]. They may require divestiture or bar researchers from receiving grants back from companies to which their inventions have been licensed. Consulting and other company contacts may be restricted when the investigator or the university has a significant financial interest in the startup. Although universities are still coming to grips with their own institutional financial interests, there is an emerging consensus that they should not conduct clinical research on their own inventions unless a strong case for locating the research at the university can be made on clinical grounds [ 8 , 9 ]. For novel biologic therapies, the investigator who invented the approach may be ideally suited to complete the clinical translation. The failure of many novel agents to demonstrate activity in Phase I may be linked to a disconnect between the scientist developing the agent and the physician running the clinical trial. Translational research is defined as that person being one and the same. Recently, the American Association of Medical Colleges published suggested guidelines for the management of conflict of interest based on input from a number of medical schools [ 10 ]. Such documents not only encourage dialogue concerning these issues, but also provide some guidance for individual institutions establishing their internal policies. How financial conflicts of interest can affect your research program Unrecognized and unmanaged conflicts of financial interest represent major risk factors for programs of translational research. Even when properly disclosed and managed, however, outside financial interests may limit your research activities in a variety of ways, ranging from mild to severe. If nothing else, time and staff resources must be allocated to institutional and extra-institutional review processes. Because approval is required before funding is released or human subjects are enrolled, the pace of research is slowed by outside financial interests. Scientists with personal financial interests in the research will usually find themselves barred from participating in sensitive research activities, especially those involving direct contact with human subjects. This increases the costs of research as additional staff or consultants are hired to do the work that the financially interested party would have otherwise performed. But even this strategy may prove difficult if key staff or alternative expert consultants also have financial interests. There is a tipping point beyond which so many potential team members are financially involved that the interests simply can not be managed and outright prohibition becomes the only option. In the most extreme circumstances it is possible for researchers to research themselves out of a job. Their personal financial interests in a research sponsor may be so great or so complex that their employers are unable to accept further funding from that sponsor. A line of translational research may simply end for a researcher when he or she is barred from carrying the work into the clinical arena as a result of individual or institutional financial interests. This can occur even when the researcher is not actively seeking financial gain. Early basic science work, for example, may lead to an invention that the university decides to patent and license. In most universities the researcher is entitled to a share of whatever revenue is produced. The researcher now has a personal financial interest that requires management and may disqualify her or him from participation in later clinical work designed to translate the basic science into practice. This is an extreme case, of course, but it does happen and it illustrates the often unseen and unintended implications of how financial interests are usually managed. Dealing intelligently with financial interests in research Public concern about personal and institutional interests in research is not going to go away. Nor is the need for industry collaboration in translational research. Indeed the need for collaborations between industry and academia is only going to grow as we move beyond a sequential "bench to bedside" model to acknowledge the benefits of combining clinical and basic biological studies [ 11 ]. If financial interests can not be avoided, we can at least be more thoughtful about how we manage them-as individual researchers, as industry collaborators, and as academic research institutions. Individual investigators should recognize that disclosure of personal financial interests, while perhaps uncomfortable, is vital to continued public confidence in science. They must also balance their wish for personal gain against the additional oversight and management of their activities that will inevitably result. They should recognize that some financial interests are more easily managed than others. Consulting income paid as cash, for example, is much more easily managed than consulting income paid with equity in the company. The latter creates a long-term financial interest and may imply management influence. The understandable desire to "share the wealth" with team members by assisting them in obtaining outside financial interests of their own backfires when it then becomes necessary to remove them from the tasks that they were hired to do in the first place. The decision to create a company in order to further a translational research agenda is appealing, often appropriate, but always more complex than researchers typically appreciate. It is a risky conceit to believe that one's success in obtaining research grants and managing a research team is adequate preparation to launch a business. This is borne out by the fact that technologies licensed to companies founded by faculty inventors are generally less successful than those licensed to companies with non-academic founders [ 12 ]. Even when the researcher has the requisite business experience, however, company formation may result in profound conflicts of commitment, inappropriate use of university resources in support of the company, and confusion over intellectual property. These difficulties combine to increase costs and slow, if not block, progress on translational research efforts. In spite of a growing entrepreneurial spirit within academia, the culture gap between industry and academia remains large. Industries seeking to partner with academic researchers must understand that universities are not simply laboratories for hire. They should not assume that they will own or control publication of the research they support. Universities are not set up to guarantee the same level of security and secrecy than can be obtained in industry laboratories. The open character of universities is in fact an asset- it is the foundation on which the creative engine rests. Finally it is useful to appreciate that "indirect costs" are real costs for universities. Indeed, even at the full rate, universities subsidize research contracts [13]. Efforts to avoid indirect costs, like efforts to negotiate contracts that contain publication restrictions and overreaching intellectual property clauses, ultimately have the effect of disrupting working relationships and slowing the pace of research. By the same token, industry collaborators should recognize that efforts to "build relationships" with academic researchers and their staff by providing extra financial incentives and benefits are often counterproductive, not only because they fail to buy loyalty, but also because they create unacceptable financial conflicts of interest for academic personnel. Universities, too, have much to learn about managing financial interests and collaborating creatively with their industry partners. Although a consensus regarding institutional conflicts of interest is emerging, universities still have much to do in terms of implementation, particularly with regard to credible external review of clinical research opportunities in which the institution holds a financial interest. Managing or avoiding institutional conflicts of interest will also require a more realistic view of technology transfer opportunities. Although "big hits" do occur, they occur only rarely and revenue from technology transfer operations is typically only a tiny fraction of revenue from sponsored research. Universities run the risk of disrupting their larger research mission by overly aggressive efforts to capture and commercialize intellectual property. Open publication and teaching should always be the most significant "knowledge transfer" functions of a research university. The increasing complexity of translational research also challenges universities to devise new kinds of collaboration with industry. Mankoff and her colleagues, for example, recently proposed the creation of centers that obtain revenue from existing therapies, while simultaneously providing material for biological studies and supporting experimental therapies [ 11 ]. Before these more complex collaborations can occur, however, there is much work to be done to simplify material transfer practices between laboratories and to create more straightforward ways for facilities and personnel to be shared. Individual researchers, industries, and universities have much to learn about the management of financial interests. Given the potential for financial interests to disrupt or even end programs of translational research, we believe that all parties would benefit from greater attention and greater creativity in the management of conflicts of interest. A dialogue must be established between the individual investigator, industry, academic institutions, and the public to define the issues and develop rational solutions. Such a dialogue could be initiated by the development of a focus on such topics at national meetings, by individual organizations whose membership is involved in translational research developing interdisciplinary panels to discuss the issues and attempt to develop guidelines, and by an integration of conflict of interest topics in the training of junior scientists. Solutions should enhance, not impede, translational research. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC514574.xml |
193607 | New Genomic Approach Predicts True Evolutionary History of Bacterial Genomes | null | Bacteria are an indiscriminate lot. While most organisms tend to pass their genes on to the next generation of their species, bacteria often exchange genetic material with totally unrelated species. That is why skeptics doubted that bacteria researchers could ever hope to map a reliable history of cell lineages in bacteria over time. But now, thanks to the availability of sequenced genomes for groups of related bacteria, researchers at the University of Arizona demonstrate that constructing a bacterial family tree is indeed possible. Previous efforts to trace the ancestry of bacteria were constrained by a dearth of related bacterial genomes, which, among other things, prevented scientists from successfully accounting for bacteria's tendency to exchange genes with unrelated organisms. In this process, called lateral gene transfer , organisms acquire genetic material not from their ancestors, the most prevalent route, but from unrelated organisms. Lateral gene transfer greatly complicates the issue of who descended from whom, because two organisms could appear closely related based on the similarity of some genes but distantly related based on other genes. The problem is to determine which genes have been faithfully vertically transmitted—from parent cell to offspring—and thus reflect the history of the bacterial cell lineages. In this issue, Nancy Moran, Emmanuelle Lerat, and Vincent Daubin propose an approach that solves this problem by identifying a set of genes that serve as reliable indicators of the vertical transfer of bacterial cell lineages. This method has important implications for biologists studying the evolutionary history of organisms by establishing a foundation for charting the evolutionary events, such as lateral gene transfer, that shape the structure and substance of genomes. With this method, scientists can begin to understand how bacteria have evolved and how their genomes have changed. Bacteria promise to reveal the most information about genomic evolution, because so many clusters of related bacterial genomes have been sequenced—allowing for broad comparative analysis among species—and their genomes are small and relatively compact. In this study, the researchers chose the ancient bacteria group Proteobacteria, an ecologically diverse group (including Escherichia coli and Salmonella species) with the most documented cases of lateral gene transfer and the highest number of species with sequenced genomes. The researchers identified a set of likely single-copy orthologs (homologous genes that diverged due to the speciation of ancestral lineages) with widespread distribution in the different species of Proteobacteria that could be used to trace the history of the cell lineages. Surprisingly, they found that almost all of the 205 ortholog gene families they selected supported the same evolutionary branching pattern. Only two did not, which the researchers then investigated and found to be the result of lateral gene transfer. These results, the researchers say, support the ability of their method to reconstruct the important evolutionary events affecting genomes. By mapping out the evolutionary path of genetic information on a genomic level, their approach promises to elucidate not only the evolution of bacterial genomes but also the diversification of species. Electron micrograph of Proteobacteria in eukaryotic cell | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC193607.xml |
535551 | Permissive human cytomegalovirus infection of a first trimester extravillous cytotrophoblast cell line | Human cytomegalovirus (HCMV) is the leading cause of congenital viral infection in the United States and Europe. Despite the significant morbidity associated with prenatal HCMV infection, little is known about how the virus infects the fetus during pregnancy. To date, primary human cytotrophoblasts (CTBs) have been utilized to study placental HCMV infection and replication; however, the minimal mitotic potential of these cells restricts experimentation to a few days, which may be problematic for mechanistic studies of the slow-replicating virus. The aim of this study was to determine whether the human first trimester CTB cell line SGHPL-4 was permissive for HCMV infection and therefore could overcome such limitations. HCMV immediate early (IE) protein expression was detected as early as 3 hours post-infection in SGHPL-4 cells and progressively increased as a function of time. HCMV growth assays revealed the presence of infectious virus in both cell lysates and culture supernatants, indicating that viral replication and the release of progeny virus occurred. Compared to human fibroblasts, viral replication was delayed in CTBs, consistent with previous studies reporting delayed viral kinetics in HCMV-infected primary CTBs. These results indicate that SGHPL-4 cells are fully permissive for the complete HCMV replicative cycle. Our findings suggest that these cells may serve as useful tools for future mechanistic studies of HCMV pathogenesis during early pregnancy. | Findings Human cytomegalovirus (HCMV) is a ubiquitous beta-herpesvirus that is the leading cause of congenital viral infection in the United States and Europe. Intrauterine transmission of the virus occurs in approximately 40% of pregnant women with primary HCMV infection, and the incidence of congenital HCMV infection is an estimated 1% of newborns [ 1 - 3 ]. Although the pathogenesis of HCMV transmission to the fetus during pregnancy is unclear, the placenta has been implicated as an important determining factor [ 4 - 8 ]. Primary first trimester extravillous cytotrophoblasts (CTBs), which are specialized placental epithelial cells that invade and remodel the uterine wall during placentation, have been previously shown to be fully permissive for HCMV infection in vitro [ 7 , 9 ]. Additionally, using an in vitro coculture system, Maidji and colleagues demonstrated that infected uterine microvascular endothelial cells transmit HCMV to differentiating invading CTBs, suggesting that placental HCMV infection can occur in a retrograde fashion that initiates in the maternal endothelium [ 8 ]. Despite these reports, the minimal mitotic potential of primary CTBs restricts experimentation to a few days, which may be problematic for mechanistic studies of the slow-replicating virus. Alternatively, the utilization of trophoblast cell lines would provide an easily manipulative in vitro model for the study of HCMV infection of the placenta. In the present study, we used a first trimester human extravillous CTB cell line, termed SGHPL-4, to investigate HCMV replication. SGHPL-4 cells were derived from first trimester chorionic villous tissue and have been described previously. Importantly, these cells share many characteristics with isolated primary cells, including the expression of cytokeratin-7, HLA class I antigen, HLA-G, BC-1, CD9, human chorionic gonadotrophin, and human placental lactogen[ 10 - 12 ]. The lytic replication cycle of HCMV is a temporally regulated cascade of events that is initiated when the virus binds to host cell receptors. Upon entry into the cell, the viral DNA translocates to the nucleus where viral gene expression occurs in a stepwise fashion beginning with the expression of immediate early (IE) genes (reviewed in [ 13 ]). To initiate studies of HCMV infection in the SGHPL-4 cell line, placental CTBs and human foreskin fibroblasts (HFFs) were infected with HCMV and the nuclear HCMV IE proteins (IE 1/2; Chemicon, Temecula, CA) were examined by immunofluorescence at various intervals after viral infection. At 3 h p.i., IE 1/2 was present in SGHPL-4 cells in similar numbers to that of HFFs. In fact, the percentages of IE-positive cells initially did not differ between CTBs and HFFs, suggesting that viral entry into the cells and IE transcription occurred at similar rates between the cell types (Figure 1A,1B,1C ). Characteristic cytopathic effects of HCMV infection including swollen cells with nuclear inclusions were observed in both SGHPL-4 and HFF cells by 48 h p.i. (data not shown), and throughout a 6 day culture period, the numbers of IE-positive cells increased continuously in both cell types (Figure 1A ). Interestingly, the rate of IE 1/2 protein expression in SGHPL-4 cells as compared to HFFs appeared to differ beginning at 72 h p.i. By 72 h p.i., there was a 40% increase in the percentage of IE-positive HFFs over SGHPL-4 cells. While nearly 100% of HFFs stained positive for IE 1/2 120 h (5 days) p.i., the maximum fraction of IE-positive SGHPL-4 cells did not exceed 60% (Figure 1A,1D,1E ), suggesting that subsequent viral gene expression and thus cell-to-cell viral spread may be kinetically delayed. These findings are consistent with other reports demonstrating delayed kinetics of viral gene expression in primary CTBs as compared to primary fibroblasts [ 14 ]. Figure 1 Productive HCMV infection in SGHPL-4 and HFF cells. (A-E) HCMV IE protein expression in human cytotrophoblasts. SGHPL-4 (□) or HFF (■) cells were infected with HCMV strain RVdlMwt-GFP [17] at a MOI of 2.5 PFU per cell and incubated at 37°C for 1, 4, 8, 12, 24, 48, 72, 96,120 or 144 h. At the indicated times, cells were fixed and stained for HCMV IE 1/2 and DAPI (Molecular Probes) and visualized on a Zeiss Axio Plan II microscope (Thornwood, NY). To determine the number of HCMV-infected cells, three fields of view were considered and the percent of IE-positive cells was calculated as: (average number of IE-stained cells/average number of DAPI-stained cells) × 100. The graph demonstrates an increase in the percentage of SGHPL-4 and HFF cells expressing IE 1/2 over a period of time. Representative images of HCMV IE 1/2 are depicted at 8 h p.i in (B) CTBs and (C) HFFs and at 120 h p.i. in (D) CTBs and (E) HFFs; IE 1/2-red, DAPI-blue, overlaid-purple. (F) Infected CTBs produce and release infectious virions. SGHPL-4 or HFF cells were inoculated with HCMV at a MOI of 0.1 PFU per cell. At the indicated times, cells or culture medium were harvested, freeze-thawed three times, and titers of infectious virus in SGHPL-4 cell lysates (○) and supernatants (△) and HFF cell lysates (●) and supernatants (▲) were determined by a microtiter plaque assay on HFFs [18]. Infectious progeny virus was detected in both cell lysates and culture supernatants of SGHPL-4 and HFF cells. The dashed line represents the lower limit of detection of the plaque assay used to measure viral titers. Although several studies have shown that first trimester primary trophoblasts can be permissively infected with HCMV, some reports have demonstrated that progression through the replicative cycle was slow and progeny virus remained predominantly cell associated [ 9 , 15 , 16 ]. To determine whether SGHPL-4 cells support productive HCMV replication, 9 day viral growth assays were performed (Figure 1F ). SGHPL-4 and HFF cells were inoculated with HCMV at a MOI of 0.1 PFU per cell, and both culture lysates and supernatants were titered for infectious virus at various days p.i. While viral titers in infected HFFs were detectable as early as 2 days p.i., viral replication was undetectable or below the lower limit of detection of the assay in SGHPL-4 lysates up to 3 days p.i. However, at days 5–9 p.i., HCMV replicated to titers of ≥ 5000 and 3600 PFU/ml in SGHPL-4 cell lysates and supernatants, respectively. Relative to HFF-infected control cultures, viral titers recovered from SGHPL-4 culture lysates and supernatants were reduced by ~20- and ~200-fold, respectively (Figure 1F ). While viral titers were decreased in infected SGHPL-4 cells as compared to infected HFFs, placental CTBs effectively supported productive viral replication as measured by infectious intracellular and extracellular virions. Moreover, when SGHPL-4 cells were infected with another laboratory-derived strain of HCMV (strain AD169), similar results were obtained (data not shown) suggesting that viral replication was not virus-strain specific. Collectively, these data indicate that SGHPL-4 cells support productive HCMV replication. In the present study, we demonstrate that the first trimester extravillous CTB cell line SGHPL-4 is fully permissive for HCMV replication. The utilization of a CTB cell line, rather than primary CTBs and explant cultures that are short-lived cultures, may provide an experimental advantage for in vitro studies of placental HCMV infection. We propose that the permissiveness for HCMV replication in SGHPL-4 cells may allow for future studies in elucidating the molecular mechanism(s) of HCMV infection and pathogenesis at the maternal-fetal interface during early pregnancy. List of abbreviations human cytomegalovirus (HCMV), cytotrophoblast (CTB), human foreskin fibroblasts (HFFs), immediate early (IE), hours (h), post-infection (p.i.), multiplicity of infection (MOI), plaque forming unit (PFU), 4', 6-diamidino-2-phenylindole, dihydrochloride(DAPI) Competing interests The authors declare that they have no competing interests. Authors' contributions HL participated in the experimental design, performed all experiments and drafted the manuscript. BS participated in the experimental design and assisted with viral propagation and viral replication assays. CM conceived of the study and participated in its design and coordination. All authors read and approved the final manuscript. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC535551.xml |
517928 | GDR (Genome Database for Rosaceae): integrated web resources for Rosaceae genomics and genetics research | Background Peach is being developed as a model organism for Rosaceae, an economically important family that includes fruits and ornamental plants such as apple, pear, strawberry, cherry, almond and rose. The genomics and genetics data of peach can play a significant role in the gene discovery and the genetic understanding of related species. The effective utilization of these peach resources, however, requires the development of an integrated and centralized database with associated analysis tools. Description The Genome Database for Rosaceae (GDR) is a curated and integrated web-based relational database. GDR contains comprehensive data of the genetically anchored peach physical map, an annotated peach EST database, Rosaceae maps and markers and all publicly available Rosaceae sequences. Annotations of ESTs include contig assembly, putative function, simple sequence repeats, and anchored position to the peach physical map where applicable. Our integrated map viewer provides graphical interface to the genetic, transcriptome and physical mapping information. ESTs, BACs and markers can be queried by various categories and the search result sites are linked to the integrated map viewer or to the WebFPC physical map sites. In addition to browsing and querying the database, users can compare their sequences with the annotated GDR sequences via a dedicated sequence similarity server running either the BLAST or FASTA algorithm. To demonstrate the utility of the integrated and fully annotated database and analysis tools, we describe a case study where we anchored Rosaceae sequences to the peach physical and genetic map by sequence similarity. Conclusions The GDR has been initiated to meet the major deficiency in Rosaceae genomics and genetics research, namely a centralized web database and bioinformatics tools for data storage, analysis and exchange. GDR can be accessed at . | Background In the United States and temperate regions throughout the world, the family Rosaceae ranks third in economic importance. The most important fruit producing crops include apple ( Malus ), pear ( Pyrus ), raspberries/blackberries ( Rubus ), strawberries ( Fragaria ), and stone fruits ( Prunus ) such as peach/nectarine, apricot, plum, cherry and almond [ 1 ]. Additionally, Rosaceae contains a wide variety of ornamental plants including roses, flowering cherry, crabapple, quince and pear. Peach is being developed as a model species for Rosaceae because of its small genome size [haploid size of 300 Mb [ 3 ]], approximately twice that of Arabidopsis and other characteristics: a relatively short juvenile period (2–3 yrs) and extensive genetics and genomics resources such as molecular marker maps, interesting mutants and clone library resources [ 1 ]. In addition, it has been demonstrated that molecular marker tools developed in peach are easily applied to other species in the family [ 5 - 8 ]. Developing genetic resources for a model organism can greatly accelerate the genetic understanding of the individual member species in the same family. The utilization of rice physical mapping resources in the study of other crops within Poaceae is an excellent example of the usefulness of a comparative genomics approach [ 2 ]. Two major efforts to develop peach as a model for genomics of Rosaceae have been initiated: (1) Structural genomics – the development of a complete physical map of the peach genome and the anchoring of the genetic markers of many of the economically important Rosaceae species maps on this physical map (2) Functional genomics – the development of an extensive EST database for fruit, shoot and seed tissues and integration of the tentative unigene set onto the physical and genetic maps of peach. The volume and the complexity of the data being produced by these peach genomics projects, in addition to the rapidly accumulating genomics and genetics data for other important rosaceous species, necessitate the development of a properly curated and integrated scientific database. Such a database will help scientists to efficiently access, analyze, integrate and apply the data to their own research in a timely manner. RoseDB, which focused on apple genetics and cherry genomics, has been decommissioned and now only exists as a mirror site at INRA. To meet the major need for a centralized and integrated web-database for genomics and genetics research in Rosaceae, the Genome Database for Rosaceae (GDR; ) has been initiated. The goals of the GDR are (1) to develop an organized and integrated web resource for peach genomics data to facilitate the gene discovery in other member species by a comparative mapping approach, (2) to collect and integrate all Rosaceae genomics data, (3) to develop online tools and resources for the Rosaceae community. In this paper, we describe the structure and content of the database, we review the database access utility and tools and we report a case study where we mapped publicly available Rosaceae sequences to the peach physical and genetic maps by sequence similarity. Construction and content GDR data EST data source and annotation An international cooperative project is in progress to develop an extensive peach EST database from a variety of vegetative and reproductive tissues of peach and almond. Currently, GDR contains 9984 ESTs from developing fruit of peach and 2794 ESTs from developing seed of almond. This translates into 3843 tentative unigenes for peach and 773 for almond. The peach and almond ESTs are being processed at the Clemson University Genomics Institute (CUGI) utilizing publicly available software, integrated in a fully automated in-house developed script (CUGIEST). The processing occurs in three stages: trace file processing to identify a filtered, high quality clone library, assembly of high quality sequences to produce longer transcripts and reduce redundancy, and sequence annotation. Annotation consists of pairwise comparison of both the filtered clone library and the EST contig consensus sequences against the GenBank nr protein database using the fastx3.4 algorithm [ 9 ]. The ten most significant matches with the expectation values (EXP) less than 1 × e -9 , for each contig and individual clone in the library are recorded. The unigene data set is then derived by selecting the clone that best represents each contig (the clone with the most significant EXP for the homology search) and the singletons that have either unique protein matches or no known significant matches. Peach ESTs were further annotated by Gene Ontology (GO) assignment based on the single "best hit" match against the SWISS-PROT database. Of the 1552 sequences from the putative peach unigene set that had matches with the SWISS-PROT database, 1439 could tentatively be assigned GO classifications. Additional sequence annotation includes computational analysis for simple sequence repeats (SSR) and open reading frame (ORF) on both the filtered clone library and the contig library. SSR analysis was preformed using a modified version (CUGISSR) of a Perl script SSRIT [ 10 ] with parameters set to detect di- to pentanucleotide with length greater than 18 bp. To examine the location of SSRs in the EST sequences in relation to the putative coding region, CUGISSR uses the FLIP [ 11 ] program which is available at the OGMP (Organelle Genome Megasequencing Project), Biochemistry department, University of Montreal . FLIP is a UNIX C program that finds/translates ORFs (open reading frames) in sequences. Using the FLIP output, CUGISSR selects the longest ORF as the putative coding region and reports the location of SSRs in relation to the putative coding region. In addition to the peach and almond ESTs processed by CUGI, all the publicly available Rosaceae EST data are daily downloaded from GenBank dbEST and annotated with the top ten most significant matches (EXP < 1 × e -9 ) following a monthly homology search of GenBank nr protein database using the fastx34 algorithm. Genetically anchored physical map and transcript map data The genetically anchored physical map for peach is under development using peach BAC libraries [ 1 ]. It is being constructed using an approach that employs a combination of hybridization of mapped markers and BAC fingerprinting [ 12 ]. For BAC fingerprinting, FPC [ 13 ] is used for automatic assembly of the bands. Hybridization of mapped markers to BAC clones aids in the physical mapping process and also enables researchers to identify the BACs containing these markers. To date, over 250 genetic markers such as RFLPs, AFLPs and SSRs from several molecular genetic maps have been hybridized to BAC clones. Through this, the peach physical map has been anchored on the general Prunus map [ 5 ]. Additional 3,000 peach ESTs from the unigene set are currently being mapped to the peach physical map to develop a transcript map of peach fruit ESTs. The EST hybridization is being done as an international cooperative project to develop a functional genomic database for peach. The EST hybridization results are sent to the peach physical mapping team in Clemson University. The overall BAC fingerprinting results and EST/marker hybridization data, stored in an FPC output file and/or spread sheets, are submitted to GDR by the Clemson peach physical mapping team. Database and software design and implementation The GDR is a relational database implemented using Oracle Database Management System version 9.2.0. Currently, the database is composed of 28 tables which store all the data for EST processing, assembly and annotation, SSR analysis, BAC clones, libraries, genetic markers and maps that are used for BAC hybridization, results of the hybridization of markers and ESTs to BAC clones, contact information, and publications. EST data processing, annotation, and uploading of the database are fully automated using a series of scripts written in Perl version 5.8.2. Daily download, annotation and upload of GenBank Rosaceae ESTs are also automatically performed by a series of Perl scripts. Data for BAC hybridization, genetic maps and markers which are submitted from researchers are examined by a curator for any potential errors and then uploaded using Perl scripts. BAC contig data for the developing peach physical map are uploaded directly from the FPC output file to our oracle database using Perl scripts. Web interfaces for database query and the query result pages are mostly developed using Java Server Page (JSP). JSPs are more efficient, easier to use, more powerful and more portable than traditional CGI and many alternative CGI [ 4 ]. BAC contigs of the developing peach physical maps are displayed using WebFPC and WebChrom which are downloaded from an Arizona Institute of Genomics web site . We have developed a map viewer to provide users with a convenient access to the integrated genetic, physical and transcript map information. Our map viewer is developed using Scalable Vector Graphics (SVG). The SVG viewer plug-in can be freely downloaded from the Adobe Website and the system requirements can be found at their website . Our map viewer program accesses our underlying relational database to dynamically generate an integrated genetic and transcript map with a direct link to the WebFPC physical map from each marker. Utility and discussion Database access and tools The GDR website is composed of general information pages, database query/browse interfaces and other tools such as map viewer and sequence similarity server. The GDR web pages are extensively linked such that users can easily access the data of interest regardless of the navigation starting point. For example, the EST detail pages have links to the BAC detail pages, marker detail pages or map viewer for the ESTs that are anchored to BACs, markers or maps. Similarly, the BAC detail pages have links to EST detail page, marker detail page, WebFPC and map viewer. Users can also access the data detail pages from the map viewer or WebFPC/WebChrom. Instead of displaying the entire EST or BAC data in one page, we used the right hand side navigation bars to help users find specific information easily and quickly. A general GDR navigation tool bar is also included in each page to help provide a more user-friendly interface. Database search interface The generic search site allows users to select data types such as EST, BAC and Marker, and search by name. Users can also follow the link to perform more detailed search for each data type. In the EST search site, users can search either the CUGI peach EST database or Rosaceae EST database downloaded from GenBank. ESTs can be searched by their name(s) and annotation features such as whether the EST belongs to a contig, is a unigene, is used as a probe and has SSRs, or any combination thereof. The EST details page, instead of displaying all the details in one page, initially displays the clone information and the sequence with a side bar containing links to library detail, assembly/unigene information, sequence homology, SSR information, Map position and anchored BACs. Each page linked from this side bar has the same side bar for easy navigation between the features. The Sequence homology page shows the most significant matches (EXP < 1e-9) in the Genbank nr protein database from the fastx sequence similarity search. The SSR information page shows the sequence along with the computationally derived SSRs to help users in the primer design for SSR marker development. The longest putative open reading frame (ORF) is also marked in color in the sequence along with the SSRs. SSRs in the non-coding region tend to be more polymorphic and those in the coding region tend to be more transferable among species, so the information of SSR position in a gene structure will be useful for marker development. The Map position page allows users to view the ESTs' map position using our Map Viewer. Users can retrieve the anchored BAC clones for the EST of interest and all the other ESTs and markers that hybridized to the same BAC through the anchored BACs page. The assembly/unigene information page displays the assembly results which include the contig name and the unigene clone that best represents the contig. The contig name is linked to a contig page that displays the contig sequence with a side bar containing links to the comprising ESTs, sequence homology and SSR information. As ESTs with no match to the GenBank proteins or with no SSRs can still assemble into contigs with a match or SSRs, users may get further annotation results of their ESTs of interest by visiting the contig detail site. In addition, contigs may have longer sequences surrounding the SSRs, allowing more flexibility in the primer design for marker development. In the BAC search site, users can search BACs by name or by probe specifications used for BAC hybridization. The search results site and the linked sites provide users with all the data about the BAC, such as the BAC contigs that the BAC belongs to, other BACs in the contig, probes that hybridized to the BACs, the detailed data about the probes, and link to the WebFPC physical map. Markers can be searched by name or features such as map name, type, and source organism. Similarly with the BAC search result sites, the maker search results sites leads users to pages with the marker information, anchored BACs, other markers and ESTs hybridized to the same BACs and link to the Map Viewer and the WebFPC physical map. Graphical interface to maps GDR hosts peach WebFPC and WebChrom to allow users to view the developing peach FPC contigs. Peach WebChrom displays the eight linkage groups of the general Prunus map and each linkage group has a link to a page where the developing contigs are located by the linkage group. Each contig has a direct link to WebFPC in which the individual BAC clones are displayed (Figure 1A ). GDR also provides a graphical tool for users to access the integrated genetic, transcript and physical map information. The map viewer displays the general Prunus map [ 5 ] with the number of ESTs anchored to each locus (Figure 1B ). The EST details page is linked from the map so that users can get all the data about the ESTs that are anchored to the loci of interest. When a locus is selected, a box appears to display marker type, the number of anchored BACs and the number of other probes that share the same BACs. Each number entry has links to a page where the detailed information is displayed. Also shown in the box are BAC contig names that are anchored to the loci. The BAC contig names have links to WebFPC so that users can directly access the physical map via this route (Figure 1B ). Sequence similarity server GDR also has a BLAST and FASTA sequence similarity search server that allows users to conduct homology searches between their sequences of interest and the various sequence data sets including annotated sequences in GDR. Users can select the database (e.g. peach ESTs, peach unigenes, mapped peach unigenes, GenBank Rosaceae ESTs, GenBank Rosaceae proteins etc) and various search parameters. Users can upload batch sequence files and the parsed results from the search are formatted in a spread sheet and sent to users by email. When the query sequence has a match to the annotated GDR sequences, users can retrieve all the information such as putative function, SSRs, and the anchored map positions via a hyperlink in the excel spreadsheet. Our sequence similarity server, specifically designed for Rosaceae researchers, will help users utilize the developing peach resources in the studies of other Rosaceae species. For example, as described in the case study below, sequences derived from other Rosaceae species could be immediately anchored to the various Rosaceae maps and the physical map when the query sequences show significant similarity to the mapped peach ESTs. Case study: Mapping of Rosaceae sequences onto Rosaceae maps by sequence similarity We report here a case study illustrating the utility of our sequence similarity server and other integrated GDR web resources. In this study, we performed a sequence similarity search using the FASTA algorithm with the non-peach Rosaceae sequences against the mapped peach ESTs to annotate Rosaceae sequences with map positions. A fasta formatted file with a total of 16258 publicly available non peach Rosaceae sequences was uploaded to the GDR FASTA server. We selected the mapped peach database and used the default parameters. The search results returned from the server are formatted in a spread sheet for easy browsing and the match names are hyperlinked to both GenBank and the GDR web site (Figure 2 ). By following the GDR link, users can get the anchored position in the genetic and physical maps as well as other annotation results such as putative function (Figure 2 ). To summarize our results, we used 259 query/match pairs which have a percent identity over 95 and an align-length greater than 100 nucleotides. The majority of the query sequences that showed high similarity to peach ESTs were Prunus sequences such as apricot ( Prunus armeniaca ), almond ( Prunus dulcis ) and sour cherry ( Prunus cerasus ). This was expected as peach also belongs to the genera Prunus . The 259 query/match pairs consisted of 209 Rosaceae sequences and 61 mapped peach ESTs. The matching of multiple Rosaceae sequences to single peach sequences was expected since the Rosaceae sequences were not assembled and therefore potentially contains multiple sequences representing the same gene. The 209 Rosaceae sequences were anchored to 38 different loci in four different Rosaceae maps. The number of sequences from each Rosaceae species that anchored to each map is shown in Figure 3 . This study demonstrates the usefulness of applying a comparative genomics approach to Rosaceae genomics using the GDR as a data mining tool. The entire data for the mapped Rosaceae sequences with anchored map positions are available at . Future development We plan to incorporate more Rosaceae genomics and genetics data from researchers worldwide as well as data from the ongoing Prunus genomics projects. Data to be added in the near future include apple ESTs, strawberry ESTs, rose ESTs and apricot map/marker data from collaborators. When the genomics projects from Prunus are finished, we will host 10–15,000 unique ESTs from a variety of vegetative and reproductive tissues of peach and almond, the complete peach physical map with anchored genetic markers and unique ESTs. In addition to adding new data, future development efforts will focus on improving the tools and functionality of the web interface such as an advanced search site with options for search/display categories, full sequence processing facilities for Rosaceae researchers, a newsgroup for the Rosaceae community, a site for Rosaceae literature, and more analysis tools such as an interactive contig viewer and a comparative map viewer. Conclusions The GDR is initiated to support the genomics and genetics research in Rosaceae, which contains numerous economically important fruit trees and horticultural plants. Currently GDR contains all the genomics data for the Rosaceae model peach, maps and markers of Rosaceae species and all the publicly available Rosaceae ESTs. Our integrated database provides users with easy access and retrieval of the annotated data, and the web tools enable them to further analyze their data. With future plans, including more data acquisition and tool developments, GDR will play an important role in the timely and efficient analysis of the data, the exchange of results and ideas among researchers worldwide, the support of Rosaceae labs worldwide with Bioinformatics tools and the utilization of the data from the model species in the study of other Rosaceae species. The methodology and tools applied to develop GDR should be easily applied to develop other comparative genomic databases for different families. Availability The GDR is publicly available and can be accessed at . List of abbreviations used AFLP: Amplified Fragment Length Polymorphism BAC: Bacterial Artificial Chromosome CGI: Common Gateway Interface EST: Expressed Sequence Tag EXP: EXpectation Value HTML: HyperText Markup Language HTTP: HyperText Transfer Protocol INRA: Institut National de la Recherche Agronomique RFLP: Restriction Fragment Length Polymorphism SSR: Simple Sequence Repeat XML: eXtensible Markup Language Authors' contributions SJ performed web interface design and programming for html pages and JSP search pages, participated in the database construction, carried out database curation, designed the Map Viewer and performed the case study. CJ wrote scripts for database upload and sequence processing, programmed Map Viewer and implemented WebFPC and WebChrom. MS participated in the database construction and wrote scripts for data upload and search pages for Genbank Rosaceae sequences. SF performs database administration. IC developed the application prototype for servlet-database connection and participated in the database construction. AA conceived of the project and participated in its design and coordination. ZD developed the sequence similarity server application. JT participated in the coordination of the project. DM conceived and supervised the project, participated in the database construction, carried out EST analysis, and provided input in the web interface design. All authors read and approved the final manuscript. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC517928.xml |
449897 | Migratory Sleeplessness in the White-Crowned Sparrow (Zonotrichia leucophrys gambelii) | Twice a year, normally diurnal songbirds engage in long-distance nocturnal migrations between their wintering and breeding grounds. If and how songbirds sleep during these periods of increased activity has remained a mystery. We used a combination of electrophysiological recording and neurobehavioral testing to characterize seasonal changes in sleep and cognition in captive white-crowned sparrows (Zonotrichia leucophrys gambelii) across nonmigratory and migratory seasons. Compared to sparrows in a nonmigratory state, migratory sparrows spent approximately two-thirds less time sleeping. Despite reducing sleep during migration, accuracy and responding on a repeated-acquisition task remained at a high level in sparrows in a migratory state. This resistance to sleep loss during the prolonged migratory season is in direct contrast to the decline in accuracy and responding observed following as little as one night of experimenter-induced sleep restriction in the same birds during the nonmigratory season. Our results suggest that despite being adversely affected by sleep loss during the nonmigratory season, songbirds exhibit an unprecedented capacity to reduce sleep during migration for long periods of time without associated deficits in cognitive function. Understanding the mechanisms that mediate migratory sleeplessness may provide insights into the etiology of changes in sleep and behavior in seasonal mood disorders, as well as into the functions of sleep itself. | Introduction Seasonal behaviors occur in virtually all organisms, ranging from insects to mammals ( Goldman et al. 2004 ). Just as circadian rhythms allow organisms to anticipate daily cycles of light and dark, circannual behaviors such as migration allow them to predict and respond to seasonal changes in environmental conditions. Like circadian rhythms, seasonal migratory behavior is both endogenously generated and shaped by external factors such as photoperiod length, weather, and food availability ( Gwinner and Helm 2003 ). The most extraordinary examples of seasonal migration occur in birds, many species of which regularly migrate thousands of kilometers. Given the long distances traversed during migration, much research has focused on the timing of migratory flights, navigation during migration, and the energetic costs of migration ( Berthold 1996 ; Gauthreaux 1996 ). One aspect of migration that is likely to impact all others, however, remains a complete mystery: Do birds sleep during migration and if so, how ( Moore 1999 ; Schwilch et al. 2002 ; Jenni and Schaub 2003 )? In many bird species, migration proceeds at a pace that does not seem to allow much time for sleep. The apparent conflict between migratory behavior and sleep may be particularly extreme for songbirds. In the nonmigratory seasons, songbirds sleep at night and are active during the day. In the migratory seasons, however, many songbirds undergo a profound behavioral shift and begin to fly at night while still remaining active during the day ( Berthold 1996 ). In the only study to directly observe migratory behavior using telemetry, a Swainson's thrush (Catharus ustulatus) flew on six of seven nights and traveled 1,500 km, with flights that occurred under favorable weather conditions lasting up to7 h ( Cochran 1987 ; see also, Cochran and Wikelski 2004 ; Cochran et al. 2004 ); however, daytime activity was not reported. Although some studies have observed brief periods of sleep behavior in the evening prior to the initiation of a nocturnal flight ( Eyster 1954 ; Berthold and Querner 1988 ; Berthold 1996 ; Ramenofsky et al. 2003 ), the overall increase in activity during migration suggests a marked reduction in time available for sleep. Despite their apparent sleep loss, migrating songbirds are capable of engaging in adaptive waking behaviors, including prolonged flight, navigation, foraging, and evading predators in novel environments. The preservation of cognitive and physical performance during migration is surprising because sleep restriction in other animals causes profound deficits in neurobehavioral and physiological function. In humans, as little as one night of sleep deprivation adversely affects alertness, working memory, cognitive throughput ( Van Dongen et al. 2003 ), divergent thinking ( Wimmer et al. 1992 ; Harrison and Horne 1999 ), insight ( Wagner et al. 2004 ), and memory consolidation ( Karni et al. 1994 ; Stickgold et al. 2000 , 2001 ; Maquet 2001 ; Fischer et al. 2002 ; Fenn et al. 2003 ), but see Siegel (2001) . Even partial sleep restriction can have adverse effects on neurobehavioral function in humans; limiting sleep to 6 h per night (75% of the normal requirement) for ten nights decreases alertness to a level comparable to that following 24 h of total sleep deprivation ( Van Dongen et al. 2003 ). The most prolonged sleep deprivation studies have been performed in rats, where near-total (>90%) sleep deprivation leads to physiological impairment culminating in death after as little as 2–3 wk ( Rechtschaffen et al. 1983 ; Rechtschaffen and Bergmann 2002 ). Similarly, fruit flies (Drosophila melanogaster) deprived of sleep also die, suggesting that sleep serves a function vital to survival in all animals ( Shaw et al. 2002 ). Given the dramatic effects of sleep deprivation in other animals, the preservation of adaptive waking function in spite of the apparent reduction in sleep during migration in songbirds seems paradoxical. Songbirds might have found a way to obtain sufficient amounts of sleep by either engaging in short but intense bouts of sleep or by sleeping in flight. Alternatively, songbirds might possess a capacity unprecedented in the animal kingdom to circumvent or withstand the effects of sleep loss during migration. We have performed long-term electrophysiological recordings of sleep and wakefulness during the nonmigratory and migratory seasons in a songbird, the white-crowned sparrow (Zonotrichia leucophrys gambelii), which migrates 4,300 km twice a year between Alaska and southern California ( DeWolfe 1968 ; Chilton et al. 1995 ). We chose to study sleep in the white-crowned sparrow since migration has been investigated for over 50 years in this species, both in the wild and in captivity ( Farner 1950 ; Ramenofsky et al. 2003 ). Furthermore, we measured cognitive function across an entire year to determine whether sleep loss has a differential effect on cognitive function during migratory and nonmigratory seasons. Results Demonstration of Migratory Restlessness in the Laboratory Setting The sleep patterns of birds in a migratory state were recorded in captivity, where migratory behavior manifests itself as migratory restlessness (Zugunruhe), i.e., nocturnal activity, including hopping and wing flapping ( Berthold and Querner 1988 ; Berthold et al. 2000 ). Migratory restlessness is genetically controlled ( Berthold and Querner 1981 ; Berthold 1990 ) and appears to reflect the natural migratory urge, since the number of nights during which birds exhibit episodes of migratory restlessness is positively correlated with the duration of migration in the wild ( Gwinner 1986 ; Berthold and Querner 1988 ; Berthold 1996 ). Starting in June 2002, we established colonies of white-crowned sparrows at the University of Wisconsin-Madison. Seasonal changes in activity were continuously recorded with an infrared activity monitoring system. As shown in a representative activity plot spanning one calendar year ( Figure 1 ) activity in the dark phase, as well as in the light phase, increased dramatically during the migratory seasons in the spring (March–June) and fall (August–December), when these birds would normally be migrating between California and Alaska ( Chilton et al. 1995 ). As indicated in previous reports using similar techniques, the increase in activity was more pronounced in the spring than in the fall ( Berthold 1996 ). The activity data demonstrate that the typical patterns of seasonal changes in migratory restlessness could be reproduced reliably in our laboratory setting. Figure 1 Activity Across the Year The purple line traces the (smoothed) percentage of 30-s epochs at night during which a bird (no. 38) broke an infrared beam by crossing the cage. The blue line shows the same information for the hours of light. Note the broad peak in activity between March and June and the broader and lower peak in activity between August and December, corresponding to the spring and fall migrations, respectively. The low levels of activity during July and December through March correspond to the summer and winter nonmigratory periods. The sharp high peaks (orange ovals) in early August of 2003 and early February of 2004 mark brief periods of experimenter-induced sleep restriction. To further characterize seasonal changes in behavior, we used infrared-sensitive cameras to record the sparrows' behavior. As reported previously ( Berthold and Querner 1988 ; Berthold et al. 2000 ), the video recordings revealed that the infrared activity monitoring system underestimated the amount of time that birds were active. In addition to hopping back and forth across the infrared beam, the birds also spent a significant amount of time either hopping over or under the infrared beam. Occasionally, birds also flapped their wings with their heads raised while holding onto a perch, as if attempting to initiate flight ( Video 1 ) a behavior that was restricted to the night and virtually never occurred in nonmigratory birds. The video recordings thus confirmed that the birds were displaying migratory behaviors consistent with previous reports in songbirds ( Berthold and Querner 1988 ; Berthold et al. 2000 ), including white-crowned sparrows ( Ramenofsky et al. 2003 ), and indicated that they were spending even more time active and behaviorally awake during migration than was indicated by the infrared activity monitoring system. Electrophysiological Correlates of Sleep and Wakefulness in Sparrows Although infrared activity and video monitoring suggested a reduction in the amount of sleep during migration, electrophysiological recordings are required to confirm behavioral state, as well as quantify potential changes in sleep stages and intensity. To characterize sleep patterns during the nonmigratory and migratory seasons, eight sparrows were instrumented for recording electroencephalographic activity (EEG) from both hemispheres, as well as electromyographic activity (EMG) (see Materials and Methods ). To control for potential seasonal changes in sleep patterns related to the acute effects of photoperiod, all recordings were performed under a constant 12:12 light/dark (LD) cycle. Consequently, the seasonal changes in sleep patterns reported below can be attributed to endogenous changes in migratory state, rather than to the combined effects of migratory state and changes in photoperiod on behavior. All of the birds instrumented for EEG and EMG recording exhibited episodes of migratory restlessness starting between mid-July and mid-August, in synchrony with the other birds in the laboratory. Their behavior was indistinguishable from that displayed by the sparrows without electrophysiological implants. In three birds, bouts of migratory restlessness started prior to surgery or during postoperative recovery. The electrophysiological correlates of sleep and wakefulness in nonmigrating and migrating white-crowned sparrows were similar to each other, as well as to those previously described in other species of birds ( Rattenborg and Amlaner 2002 ). Wakefulness Behavior during wakefulness included hopping and flying around the cage, feeding, drinking, feather preening, and actively scanning the room. Although movement artifacts obscured the EEG and EMG during gross movements (e.g., hopping and wing flapping), the recordings occurring immediately before and after such behaviors showed a low-amplitude, activated EEG in both hemispheres, and high EMG activity typical of alert wakefulness ( Figure 2 ). Figure 2 Examples of Electrophysiological Correlates of Behavioral States This figure shows four 1-min samples each containing three EEG recordings (left hemisphere vs. right hemisphere [L v. R], left hemisphere vs. neutral reference [L], and right hemisphere vs. neutral reference [R]) and one EMG from one sparrow (no. 65) depicting the typical electrophysiological correlates of each behavioral state. (A) Transition from SWS (blue) to wakefulness (black). (B) Drowsiness (gray). (C) Transition from SWS (blue) to a bout of REM sleep (red), and then a brief awakening (black), followed by a return to SWS (blue). (D) Wakefulness during a period of migratory restlessness. High-amplitude artifacts associated with gross movements are shaded with a gray background. (A–C) were recorded on 11 August 2003, during the summer while the bird was in a nonmigratory state, and (D) was recorded on 11 October 2003, during the fall migratory period. Drowsiness Upon ceasing active wakeful behaviors, the sparrows entered drowsiness, a mixed state with behavioral and EEG characteristics of both wakefulness and slow-wave sleep (SWS) ( Campbell and Tobler 1984 ). Sparrows in this state typically stood on the floor facing the front of the cages with their heads facing toward the center of the room ( Video 2 ). Their heads were held close to their bodies, and the position of the eyelids fluctuated between open, partially closed, and completely closed states, while their heads moved from side to side. This behavior was intermittently interrupted by the birds opening their eyes completely and raising their heads, apparently attending to the activities of other birds in the room. The fact that the sparrows selected optimal vantage points in their cages from which to monitor the room, moved their heads from side to side in a scanning manner, and intermittently responded to stimuli in the environment indicates that several behavioral aspects of wakefulness were intact during this state. Although artifact from the head movements precluded reliable spectral analysis of the EEG during drowsiness, as in other studies of songbirds ( Szymczak et al. 1993 ), visual assessments of the EEG showed activity intermediate between that of wakefulness and SWS (i.e., increased amplitude in the low-frequency range relative to wakefulness; Figure 2 B). The EMG typically showed brief bursts of activity associated with head movements during drowsiness. The EEG amplitude and frequency approached that of SWS, but unlike unequivocal SWS, which is followed by rapid eye movement (REM) sleep in birds and mammals ( Amlaner and Ball 1994 ; Zepelin 2000 ), REM sleep never occurred directly following drowsiness. Based on the mixture of behavioral and electrophysiological features of wakefulness and SWS, drowsiness was not included in either the calculation of time in wakefulness or sleep, but rather it constituted a separate behavioral category. Slow-wave sleep In contrast to the vigilant behaviors exhibited by sparrows in drowsiness, during SWS the birds were motionless, usually with closed eyes; the head was either pulled in toward the body and facing forward or resting on the bird's back ( Video 3 ). The amplitude of low-frequency EEG reached its highest levels during SWS (see Figure 2 A and 2 C). As in other birds ( Spooner 1964 ; Ookawa and Gotoh 1965 ; Ball et al. 1988 ; Rattenborg et al. 1999b ), sparrows occasionally showed interhemispheric asymmetries in EEG low-frequency activity during SWS. However, such asymmetries were restricted to periods of immobility, and they never occurred during active behaviors. REM sleep An activated EEG pattern similar to that occurring during wakefulness characterized REM sleep ( Figure 2 C). Unlike most mammals, but similar to other birds, EMG activity recorded from the nuchal muscles rarely declined during REM sleep. Nevertheless, behavioral signs of reduced muscle tone made REM sleep readily distinguishable from brief awakenings; in contrast to wakefulness, during REM sleep the eyes were closed and the head either rolled to one side or fell forward, occasionally dropping to the bird's feet. In extreme cases, birds would sway and briefly lose their balance on the perch. Episodes of REM sleep typically lasted a maximum of 10 s, but often occurred in clusters separated by only a few seconds of SWS. Migratory restlessness During episodes of migratory restlessness, defined as active hopping and wing flapping at night interrupted by only brief pauses in motor activity, both eyes were completely open, and the EEG recorded from both hemispheres showed an activated pattern, indistinguishable from alert wakefulness ( Figure 2 D). There was no sign of either drowsiness or an interhemispheric asymmetry in SWS-related EEG, indicating that sparrows exhibiting migratory restlessness were completely awake. Comparisons of Sleep Patterns in Nonmigrating and Migrating Sparrows In nonmigrating sparrows, sleep and wakefulness patterns across the day were typical of those previously described in a songbird ( Szymczak et al. 1993 ). The proportion of time spent in each state varied in a consistent manner across the LD cycle ( Figure 3 A). Wakefulness and drowsiness encompassed all of the time during the light phase and a small proportion of time during the dark phase. At night, SWS was the predominant sleep stage during all hours of the night. In a pattern similar to humans and other mammals ( Borbély and Achermann 2000 ; Tobler 2000 ), the proportion of time spent in SWS declined across the night, whereas REM sleep increased. Figure 3 Changes in Sleep during Fall Migration Behavioral state was scored across 24-h (noon to noon) periods using a combination of video and electrophysiological recordings for birds in a nonmigratory ( n = 5) and migratory ( n = 8) state. The plots and table reflect the average for all birds in each group. All recordings were performed under a 12:12 LD photoperiod with lights turned off at 18:00 and on at 06:00. (A) Proportion of time in each behavioral state for nonmigrating (top) and migrating (bottom) birds. The proportion of every 10-min period spent in each sleep/wakefulness state was calculated for each bird and then averaged across all birds: wakefulness (black), drowsiness (gray), SWS (blue), and REM sleep (red). Note that overall sleep propensity in migrating birds is greatly diminished between approximately 22:30 and 06:00. Note also the increased propensity for REM sleep from 18:00 to 20:00 as compared to the same time period when not migrating. (B) Sleep and REM sleep latencies. Sleep latency was calculated as the length of time from lights out until the first occurrence of sleep (in all cases SWS) for birds in a nonmigratory and migratory state; average sleep latency did not differ significantly between nonmigrating and migrating birds. REM sleep latency was calculated as the length of time from sleep onset to the first occurrence of REM sleep. Note that REM sleep occurred earlier in sleep during migration for all five birds that were recorded in both a nonmigratory and migratory state ( t = 3.3, paired, two-tailed, p < 0.05). Note also that the REM sleep latencies for the three birds recorded only in a migratory state were shorter than the shortest REM sleep latency in nonmigrating birds. (C) Sleep percentages. Average daily percentages of sleep and wakefulness states for birds in a nonmigrating ( n = 5) and migrating ( n = 8) state. Total sleep is the sum of SWS and REM sleep. For all states of vigilance, values for the migrating condition differed significantly from the nonmigrating condition ( p < 0.01, after Bonferroni correction). The proportion of total sleep occupied by REM sleep was not significantly different between migratory states. The migratory state was marked by a dramatic change in both the amount and pattern of sleep across the day. The most striking difference was a large reduction in total amount of sleep in migrating birds. Figure 3 A shows the average hypnogram for all birds recorded during nonmigratory ( n = 5) and migratory ( n = 8) conditions. Total time spent sleeping was reduced by an average of 63% in migrating birds compared to nonmigrating birds ( Figure 3 C). In the most extreme case, sleep time decreased from 9.05 h on a nonmigrating night to 1.39 h on a migrating night, representing a sleep reduction of about 85%. In all but one bird, most of the sleep obtained on migratory nights occurred during the first few hours of the night. One bird, however, obtained more sleep during the second half of the night, although it had a brief episode of sleep at the beginning of the night, followed by episodes of migratory restlessness. In addition to the restriction of sleep to the first few hours of the night in most migrating birds, there was also a shift in the timing of REM sleep. Although SWS latency was not affected by migratory status, the latency from SWS onset to REM sleep onset changed from 24.2 ± 6.9 min on nonmigratory nights to 10.3 ± 5.9 min on migratory nights ( Figure 3 B). Even in the bird that slept more during the second half of the night, REM sleep still occurred unusually early during the brief initial bout of sleep on its migratory night. In addition, REM sleep latencies for the three birds recorded only in a migratory state were all shorter than the shortest REM sleep latency for the five birds recorded in a nonmigratory state. Moreover, REM sleep as a proportion of total sleep time was elevated early in migratory nights (i.e., 18:00–20:00) when compared to the corresponding hours on nonmigrating nights (10.0% vs. 2.8%, t = 2.5, p < 0.05) ( Figure 4 ). Although the migratory state significantly influenced the timing of REM sleep, the overall proportion of total sleep time spent in REM sleep was similar on nonmigratory (16.3%) and migratory (14.8%) nights ( t = 0.56, p > 0.1) (see Figure 3 C). Figure 4 REM Sleep across the Dark Phase REM sleep as a proportion of total sleep time is plotted for every 10-min period during the dark phase for birds in nonmigrating ( n = 5) and migrating ( n = 8) states. The individual dots represent the average for each 10-min period; the solid line is a spline fit to these data. The dashed line represents the absolute amount of REM sleep, as a percentage of recording time. Note that the fit for the migrating birds is truncated, not only because very few periods after midnight had any REM sleep, but also because no point after midnight was based on more than one bird. Finally, despite the marked reduction in nocturnal sleep during migration, sleep did not occur during the light phase in migrating birds; as in nonmigrating birds, SWS and REM sleep were restricted to the dark phase of the LD cycle. Nonetheless, time spent in drowsiness increased significantly during the light phase (38.4% vs. 27.7%, t = 2.68, p < 0.05) in migrating birds; drowsiness also increased during the dark phase, but this did not reach statistical significance (12.9% vs. 8.1%, t = 1.73, p > 0.1). On migratory nights, drowsiness usually occurred during the later half of night between bouts of migratory restlessness. Spectral Analysis of the SWS EEG in Nonmigrating and Migrating Sparrows In addition to determining changes in the amount and type of sleep, we also compared EEG activity during SWS on nonmigratory and migratory nights for evidence of changes in sleep intensity using fast Fourier transform (FFT) spectral analysis of the EEG. In mammals, SWS-related slow-wave activity (SWA) of 0.75- to 4.5-Hz appears to reflect sleep intensity, since arousal thresholds are positively correlated with the amount of SWA ( Franken et al. 1991 ; Neckelmann and Ursin 1993 ). SWA in the 0.75- to 4.5-Hz band also increases as a function of prior time awake and shows a progressive decline across the sleep period in mammals, suggesting that SWA is an EEG marker of sleep-related homeostatic processes ( Borbély 1982 ; Tobler 2000 ). We first examined the time course of SWA across the night on nonmigratory nights using the 0.75- to 4.5-Hz frequency band typically employed in mammals, but we were unable to detect a significant decline in spectral power. When we examined changes in spectral power across all frequencies, however, a significant and pronounced decline across the night was apparent in the 1.25- to 2.5-Hz band for both the left and right hemispheres ( Figure 5 ). Assuming that EEG in this frequency range reflects homeostatic processes in sparrows, and given the marked reduction in sleep during migration, we predicted that sparrows might compensate for decrements in total amounts of sleep with increased spectral power in the 1.25- to 2.5-Hz range during SWS on migratory nights. In the five birds recorded on both nonmigratory and migratory nights, however, we did not detect a significant increase in 1.25- to 2.5-Hz spectral power during SWS on the migratory night when compared to the corresponding hours of the nonmigratory night; three birds showed an increase and two birds showed a decrease in 1.25- to 2.5-Hz spectral power. Figure 5 Time Course of EEG Power Density in the 1.5- to 2.5-Hz Band in SWS This figure shows the time course of EEG power density in the 1.5- to 2.5-Hz band in SWS during the dark phase for the left (blue) and right (red) hemisphere in nonmigrating sparrows. Curves represent mean 2-h values with standard error of the mean ( n = 5). The EEG power density in the 1.5- to 2.5-Hz band of each 2-h interval is expressed as a percentage of the average EEG power in the 1.5- to 2.5-Hz band over all SWS epochs (dashed line = 100% of average 1.5- to 2.5-Hz power). The last 2-h interval is excluded since not all birds exhibited SWS during this time. A two-way, repeated measures ANOVA, with “hemisphere” and “2-h intervals” as factors, revealed a significant effect of the 2-h interval ( F = 5.60, p < 0.05 with the Greenhouse–Geisser correction); neither an effect of hemisphere, nor an interaction between hemisphere and interval reached statistical significance ( p > 0.1). Assessing Cognitive Function in Sparrows Given the reduction in time spent sleeping and the apparent lack of a compensatory increase in sleep intensity during migration, we were interested in determining whether sparrows in a migratory state showed associated changes in cognitive function. Because we were interested in detecting seasonal changes in cognition, we used a test that could be administered repeatedly to the sparrows over long periods of time and without the potential confound of “practice” effects, a repeated-acquisition task; it has been widely used in humans and animals to provide repeated measures of the acute and chronic effects of neurotoxic insult on the ability to acquire a new sequence of operant responses ( Winsauer et al. 2002 ). When combined with a performance component that simply requires memory of a previously learned sequence of operant responses, these two tasks can be used to determine whether changes in responding and accuracy during acquisition are related to direct effects on learning or global effects on psychomotor performance. The repeated-acquisition procedure can also be used to assess the effects of sleep deprivation on both the quality (i.e., accuracy) and quantity (i.e., number of responses) of behavior ( Cohn et al. 1992 ). We trained a group of eight sparrows not instrumented for electrophysiological recordings to respond under a multiple schedule of repeated-acquisition and performance in standard operant testing chambers (see Materials and Methods ). In the acquisition component, birds learned a different three-response sequence of key pecks (e.g., left-right-center) during each session under a second-order fixed-ratio (FR) 3 schedule. In contrast, during the performance component, birds responded on the same three-response sequence each session under the same schedule of reinforcement. Despite being wild-caught, the sparrows adapted well to the testing apparatus and readily learned to respond in both tasks ( Video 4 ). Effects of Sleep Restriction on Cognitive Function during the Nonmigratory Season To determine whether the task was sensitive to the effects of sleep restriction, as well as to provide a comparison for the effects of sleep loss occurring spontaneously during migration, sleep was restricted to the first 3 h of the dark phase on three consecutive nights during the nonmigratory (winter) season (see Materials and Methods ). A 3-h sleep period at the start of the dark phase was chosen to mimic the general sleep pattern of sparrows during migration. Sleep restriction reduced accuracy (percentage correct responses) on both the acquisition (repeated measures analysis of variance [ANOVA], F = 12.65, p < 0.001) and performance (F = 3.12, p < 0.05) components of the task ( Figure 6 ). The total number of responses also decreased following sleep restriction in both the acquisition (F = 33.91, p < 0.0001) and performance (F = 10.25, p < 0.0001) components of the task (see Figure 6 ). These effects of sleep restriction were evident following the first night and persisted following subsequent nights of sleep restriction. Figure 6 Operant Behavior and Sleep Restriction Average values for accuracy and the total number of responses on the acquisition and performance tasks are shown for the 3 d preceding sleep restriction, the 3 d of sleep restriction, and the 3 d following sleep restriction; boxes represent the 25th to 75th percentile of data with the median indicated by the line across the box. The “whiskers” extend from the quartiles to the most extreme value less than 1.5 times the interquartile range. Points outside the whiskers are plotted with small circles. Accuracy and total number of responses decreased significantly in both the acquisition and performance tasks following sleep restriction ( n = 7). Comparison of Cognitive Function during Migratory and Nonmigratory Seasons The same group of eight sparrows was tested under the multiple schedule of repeated-acquisition and performance for one year, encompassing both spring and fall migrations. Figure 7 shows accuracy, the total number of responses in the repeated-acquisition component of the task, and nocturnal activity across the year for the entire group of birds. During periods of increased nocturnal activity corresponding to the spring (March–June) and fall (August–December) migratory periods, accuracy on the repeated-acquisition task remained stable. The number of responses was lowest during the winter, intermediate during the spring migration and summer, and actually reached the highest level during the fall migration, before returning to the low winter levels. Figure 7 Comparison of Operant Responding and Migratory Behavior The average value for all birds of nighttime (purple) and daytime (blue) activity (percentage of 30-s epochs containing at least one infrared beam break) and the accuracy (green) and total number of responses (red) on the acquisition task ( n = 8). As in Figure 1 , the sharp high peaks (orange ovals) in early August of 2003 and early February of 2004 mark brief periods of experimenter-induced sleep restriction. Because the sparrows did not all migrate at exactly the same time, we also examined the effect of migratory status on accuracy and responding more specifically by selecting for each bird two 3-wk periods with maximal nocturnal activity during the spring and fall migrations, and two 3-wk periods with minimal nocturnal activity during the summer and winter nonmigratory periods (see Materials and Methods ). We chose a 3-wk window for analysis, as this was the longest period of relative nocturnal quiescence that could be found in all birds during the summer. Accuracy on the repeated-acquisition task was virtually unaffected by migratory status. The total number of responses during the repeated-acquisition task was lowest during the winter, intermediate during spring and summer, and highest during the fall ( Figure 8 ). The preservation of accuracy and responding during migration is thus unlike the decline in accuracy and responding observed following three nights of sleep restriction during the nonmigratory season. Figure 8 Seasonal Aspects of Operant Behavior Accuracy and the total number of responses during the acquisition task are shown for the 3 wk of spring and fall during which each bird was most active (orange) at night and for the 3 wk during summer and winter during which each bird was the least active (purple) at night. Note that the data for winter are plotted twice to facilitate comparison. In contrast to sleep restriction imposed by the experimenters (see Figure 6 ), sparrows maintained high levels of accuracy on the acquisition and performance tasks during periods of sleep restriction associated with migration. Furthermore, the total number of responses reached the highest values during the fall migration, in contrast to the decline in responding following experimenter-imposed sleep restriction. Discussion Each spring and fall, songbirds switch from sleeping at night to migrating at night. Whether migrating songbirds sleep during flight, forgo sleep altogether, or compensate for night-time sleep loss by sleeping during the day has remained a mystery. Our EEG recordings of the white-crowned sparrow demonstrate a marked reduction in sleep, as well as distinct changes in sleep architecture during migration, including a shift in the timing of REM sleep to earlier in the night. Although an increase in drowsiness and a corresponding decrease in wakefulness were observed during the day, migrating sparrows did not compensate for sleep loss at night by sleeping more during the day or by increasing SWS intensity on migratory nights. Despite the apparent reduction in sleep occurring during migration, observations of songbirds in the wild suggest that they are fully capable of maintaining a high level of cognitive and physical function, including navigation during long-distance flights, foraging, and evading predators in novel environments. Our results from the repeated-acquisition and performance tasks also suggest that songbirds are not cognitively or physically impaired during episodes of migratory restlessness in the laboratory. Unlike sleep restriction during the nonmigratory season, which caused a decrease in accuracy and responding in both the acquisition and performance components of the task, accuracy and responding did not decrease during migratory periods (spring and fall), when compared to nonmigratory periods (summer and winter). In fact, responding was highest during the fall migration and lowest during the winter. We saw no evidence of sleep in active sparrows during periods of migratory restlessness in the laboratory setting, suggesting that songbirds do not sleep during migratory flights in the wild. Moreover, if birds have evolved the capacity to sleep while flying and depend upon it to avoid sleep deprivation, we would then expect them to be vulnerable to the effects of the sleep restriction resulting from migratory restlessness in the laboratory. The fact that birds exhibit migratory restlessness in captivity for periods of time similar to the duration of migration in the wild indicates an ability to withstand the effects of sleep restriction ( Van Dongen et al. 2003 ). Furthermore, as discussed below, we did not see evidence of decrements in cognitive function similar to those observed following even a single night of sleep restriction during the nonmigratory season. Despite the evidence against sleep in flight, our results do not rule out the possibility that some sleep might occur during flight in the wild. In the laboratory, migratory behavior is characterized by hopping and attempts to initiate flight, a time when sleep is not likely to occur. In the wild, however, it is conceivable that once birds have initiated a nocturnal flight, SWS could occur either unihemispherically or bihemispherically. Precedent for the former is found in bottlenose dolphins (Tursiops truncates), northern fur seals (Callorhinus ursinus), and cape fur seals (Arctocephalus pussilus) that swim in a coordinated manner while exhibiting unihemispheric SWS, a state characterized by SWS-related EEG in one hemisphere and EEG indistinguishable from wakefulness in the other hemisphere ( Mukhametov et al. 1977 ; Lyamin and Chetyrbok 1992 ; Rattenborg et al. 2000 ). Birds also show interhemispheric asymmetries in SWS-related EEG when sedentary, although the asymmetry is less pronounced than that in aquatic mammals ( Rattenborg et al. 2001 ). SWS may even occur simultaneously in both hemispheres during flight because the motor control of flight is mediated by spinal reflexes and can persist in decerebrated birds ( Cohen and Karten 1974 ; Steeves et al. 1987 ); REM sleep during flight seems unlikely, however, given the associated reduction in skeletal muscle tone ( Heller et al. 1983 ; Dewasmes et al. 1985 ). The fact that nocturnal flights occur high in the generally unobstructed night sky where constant visual assessment of the environment may not be necessary also makes SWS in flight seem feasible. In such a scenario, navigational assessments and corrections could be made during brief awakenings. Ultimately, recordings from birds migrating in the wild are needed to determine whether any sleep occurs during migratory flights. The mechanisms that orchestrate the endogenous circannual rhythm of migratory behavior and associated changes in sleep remain largely unknown ( Wingfield et al. 1990 ; Berthold 1996 ). Nevertheless, research into the neuroendocrine and circadian control of migratory behavior suggests possible interrelationships between migratory behavior and associated sleep patterns. Several studies suggest that migration is associated with increased hypothalamic–pituitary–adrenal (HPA) axis function ( Meier and Fivizzani 1975 ; Ramenofsky et al. 1999 ; Wingfield 2003 ; Landys et al. 2004 ). In mammals, increased HPA function is associated with sleep disruption and REM sleep abnormalities. For example, plasma glucocorticoids are increased in rats ( Meerlo et al. 2002 ) and humans ( Spiegel et al. 1999 ) following sleep deprivation, and in patients with insomnia ( Vgontzas et al. 2001 ). In depressed patients, increased plasma cortisol levels are correlated with reduced REM sleep latency ( Poland et al. 1992 ). The reduction in sleep and shift in REM sleep timing observed during migration could therefore be related to activation of the HPA axis. Changes in sleep may also be linked to alterations in the circadian rhythm during migration. In particular, since the occurrence of REM sleep is closely tied to the circadian rhythm in humans ( Czeisler et al. 1980 ; Dijk and Czeisler 1995 ), the shift toward more REM sleep early in the night during migration may reflect a phase advance in the circadian propensity for REM sleep. In garden warblers (Sylvia borin), however, rather than being phase advanced, the amplitude of the circadian rhythm is reduced during migration ( Gwinner et al. 1993 ). Nonetheless, a link between the timing of REM sleep and changes in the circadian rhythm may exist, since depressed humans with short REM sleep latencies show circadian patterns similar to migrating songbirds; the amplitude of the circadian temperature rhythm is reduced while the phase remains unchanged compared to subjects with normal REM sleep latencies ( Schulz and Lund 1983 ). Gwinner suggested that a dampened melatonin rhythm allows the phase relationship of coupled activity rhythms to shift, thereby resulting in activity during the day and night ( Gwinner 1996 ), a mechanism that may also contribute to the changes in total sleep time and REM sleep timing observed during migration. Finally, the reduced latency to REM sleep during migratory nights might reflect a homeostatic response to prior REM sleep deprivation. In mammals, REM sleep deprivation or restriction leads to an increase in REM sleep during recovery sleep ( Tobler 2000 ). As in mammals, pigeons (Columba livia) deprived of sleep also show an increase in REM sleep during recovery sleep ( Tobler and Borbély 1988 ). Although the increase in REM sleep during the early portions of migratory nights is suggestive of a homeostatic response to prior REM sleep loss, the overall proportion of total sleep time spent in REM sleep was not consistently elevated on migratory nights. Consequently, the changes in REM sleep timing in migration are more suggestive of a shift in the circadian timing of REM sleep than a homeostatic response to REM sleep deprivation. Regardless of the mechanism, the changes in sleep architecture during the night in migratory sparrows are reminiscent of those seen in individuals with mood disorders. Like migrating sparrows, both depressed and manic patients show reduced latency to REM sleep, loss of SWS, and reduced amounts of total sleep, often with early morning awakening ( Benca et al. 1992 ); sleep decrements are most profound during mania. Given the aspects of bipolar illness, such as increased energy, activity, and creativity, that may be adaptive under certain circumstances ( Andreasen 1987 ; Jamison 1993 ; Wilson 1998 ; Brody 2001 ), and its many parallels with migratory behavior, including seasonality, it is possible that similar mechanisms may be involved in both migration and bipolar disorder. Despite the marked reduction in sleep during migration, we did not detect a significant increase in SWA during SWS on migratory nights, when compared to the corresponding hours of nonmigratory nights, in the five sparrows recorded during both nonmigratory and migratory states. This may indicate that migrating sparrows respond differently to sleep deprivation or that birds in general, unlike mammals, do not show increases in SWA during SWS following deprivation. The few studies that have examined sleep homeostasis in birds have produced conflicting results. Pigeons did not show a progressive decline in SWA (0.75- to 4.5-Hz) across the normal sleep period (SWS and REM sleep combined) when SWS was the predominant behavioral state, or an increase in SWA following 24 h of total sleep deprivation, suggesting a fundamental difference between sleep homeostasis in birds and mammals ( Tobler and Borbély 1988 ). Unlike pigeons, however, blackbirds (Turdus merula) did show a decline in SWS-related SWA (0.5- to 4.0-Hz) across the major sleep period, indicating that aspects of mammalian SWS regulation may be present in some birds ( Szymczak et al. 1996 ). In nonmigrating sparrows, we did not detect a decline in SWA during SWS across the night using the 0.75- to 4.5-Hz frequency range studied in pigeons and mammals, but a progressive decline in SWA was apparent in the 1.25- to 2.5-Hz frequency range, suggesting that this frequency band may reflect SWS homeostasis in sparrows. Nevertheless, even when this 1.25- to 2.5-Hz band was examined, migrating sparrows failed to show a consistent increase in spectral power. Assuming that the decline in 1.25- to 2.5-Hz power in nonmigrating sparrows reflects SWS homeostasis, the apparent absence of an increase in this band during migration suggests that, unlike nonmigrating sparrows, migrating sparrows may require less SWS. A reduced need for SWS during migration may also be reflected in the shorter REM sleep latencies on migratory nights, since REM sleep latency is positively correlated with SWS need in humans ( Feinberg et al. 1992 ). Alternatively, the increase in drowsiness occurring during the light phase in migrating sparrows may have compensated for SWS loss during the previous night, thereby accounting for the absence of an increase in SWS-related SWA during the subsequent night. Deprivation of daytime drowsiness may clarify whether this state contributes to SWS homeostasis in nonmigrating and migrating sparrows. In addition, a link between the declining trend in 1.25- to 2.5-Hz spectral power and SWS homeostasis in nonmigrating sparrows will need to be established with additional studies of SWS deprivation in both nonmigrating and migrating sparrows. The results from the repeated-acquisition task suggest that songbirds appear resistant to the effects of sleep restriction during migration, although sleep restriction during the nonmigratory seasons appeared to impair accuracy and responding. It is possible that stress associated with the experimenter-induced sleep deprivation procedure may have contributed to the decrements observed; however, these birds were well acclimated to daily handling, and the methods used to deprive the birds of sleep (i.e., walking past the cage) were minimally intrusive. Regardless, birds in a migratory state were clearly able to maintain high levels of accuracy and responding during periods of spontaneous sleep loss occurring during migration. The only previous study to assess cognition in a migratory songbird compared the closely related nonmigratory Sardinian warbler (Sylvia melanocephala momus) to migratory garden warblers (S. borin); the migratory species performed better on a long-term memory task simulating habitat selection, despite being trained during a period of migratory restlessness, when sleep was presumably restricted ( Mettke-Hofmann and Gwinner 2003 ). Although based only on a between-species comparison of one migratory and one nonmigratory species of songbird, these results and those from the white-crowned sparrow suggest that cognition is not impaired, and may even be enhanced, in migrating songbirds. Studies using other forms of neurobehavioral testing will be needed to determine whether migrating songbirds show a generalized resistance to the adverse effects of sleep restriction on specific cognitive functions known to be sensitive to sleep deprivation. In particular, the recent evidence suggesting that sleep is required for memory consolidation ( Karni et al. 1994 ; Stickgold et al. 2000 , 2001 ; Maquet 2001 ; Fischer et al. 2002 ; Fenn et al. 2003 ), a process not assessed with the repeated-acquisition task used herein, raises the question as to how birds consolidate memories during periods of migratory sleeplessness. The apparent resistance to the effects of sleep restriction in migrating songbirds is unprecedented and clearly needs to be confirmed with further neurobehavioral testing. Future studies aimed at understanding the mechanisms underlying migratory sleeplessness may provide insight into the etiology and treatment of certain sleep disorders, as well as psychiatric disorders such as bipolar disorder, where similar seasonal bouts of sleeplessness with high levels of cognitive function are diagnostic of hypomania. Furthermore, an understanding of the mechanisms involved in migratory sleeplessness may lead to the development of methods to temporarily mitigate the effects of sleep deprivation that otherwise compromise performance in humans engaged in sustained operations where the maintenance of high levels of cognitive and physical function is critical. Finally, revealing the mechanisms through which migratory songbirds resist the effects of sleep deprivation may yield important clues as to the function of sleep in general. Materials and Methods Birds Sparrows used for the operant testing were captured in Alaska (lat 64°49′ N, long 147°52′ E) during June 2002. Sparrows used for the EEG recordings were captured on their wintering grounds in the Sacramento valley in California (lat 39°00′ N, long 122°00′ E) during November 2002. All birds were collected using mist nets under the authority of a United States Fish and Wildlife Service permit. Birds were transported to the University of Wisconsin-Madison where they were individually housed in galvanized wire cages (L: 35 cm × W: 25 cm × H: 32 cm) in environmentally controlled rooms (L: 4.0 m × W: 2.7 m × H: 2.7 m; 22.0–24.5 °C, 40% relative humidity). Each bird was in visual and auditory contact with other birds in the room. To simulate seasonal changes in photoperiod, operant birds were exposed to photoperiods ranging from 9.5:14.5 LD to 16.5:7.5 LD. Sparrows used for the EEG recordings were maintained under a 12:12 LD schedule; lights went on at 06:00 with an illuminance level of 540–640 lux measured at the level of the cage floor. Illuminance during the dark phase was less than 0.5 lux. Birds were fed a mixed-seed and provided water ad libitum, and their diet was supplemented daily with lettuce, dried insects, live mealworms, and grit. Birds involved in operant testing were food restricted as described below. All experimental protocols were approved by the University of Wisconsin-Madison Animal Care and Use Committee. Activity monitoring As in previous studies ( Wikelski et al. 1999 ), gross activity was measured using an infrared motion detector (no. 49–312, Radio Shack) connected to a system (VitalView version 4.0, Mini Mitter, Bend, Oregon, United States; http://www.minimitter.com ) that logged the number of times that the bird crossed the infrared beam aimed across the center of the cage each 30-s interval. Although the infrared activity monitoring system may underestimate overall activity because it fails to quantify activity that does not result in a beam break, it nevertheless provides a rapid method for assessing gross seasonal changes in behavior. For the birds in which EEG was recorded, behavior was also continuously recorded using 16 infrared-sensitive cameras (two per bird) connected to a digital video storage system (Salient Systems, Austin, Texas, United States; http://www.salientsys.com ). Infrared illuminators provided lighting for the cameras during the dark phase. Surgery In July 2003, eight adult white-crowned sparrows approximately 13–14 mo of age were randomly selected from our captive population and surgically instrumented for chronic EEG and EMG recordings. All surgical procedures were performed under isoflurane anesthesia (1.0%–3.5% isoflurane with 500 ml/min O 2 ). The bird's head was stabilized in a Kopf Instruments (Tujunga, California, United States) stereotaxic device, using an adaptor developed for use with birds. A temperature-regulated heat pad set at 40 °C reduced heat loss during the procedure. After establishing a suitable anesthetic plane, the feathers overlying the cranium were clipped and the scalp was cleaned with 70% isopropyl alcohol. A longitudinal incision was made along the midline of the head to expose the cranium. After cleaning with 3% hydrogen peroxide and drying the cranium, four small holes were drilled through the cranium to the dura: two for the EEG electrodes, one for the reference electrode, and one for the ground electrode. To record the EEG from the left and right cerebral hemispheres, two holes were drilled 2 mm lateral of the midline, one over the left and one over the right Wulst, a brain region homologous to portions of the mammalian neocortex ( Medina and Reiner 2000 ). A third hole for the reference electrode was positioned over the midline of the cerebellum. The fourth hole for the ground electrode was drilled over the right hemisphere. Stainless steel electrodes (no. AS 633, Cooner Wire, Chatsworth, California, United States) were inserted through the holes to the level of the dura and held in place using surgical glue. A final electrode was positioned over the nuchal muscles for recording EMG activity. Each electrode was connected to a lightweight, flexible, and electrically shielded recording cable (Dragonfly, Ridgeley, West Virginia, United States; http://www.dragonflyinc.com ). The cable was attached to the bird's cranium using dental acrylic (Justi Products, Oxnard, California, United States). To form a strong adhesion, the acrylic was allowed to infiltrate the porous cavity between the inner and outer layers of the cranium through small holes drilled only through the dorsal layer of the cranium ( Dave et al. 1999 ). Finally, the incision was closed around the acrylic with surgical adhesive (Tissuemend II, Veterinary Products Laboratories, Phoenix, Arizona, United States; http://www.vpl.com ). Electrophysiological recording After surgery, each bird was placed in the recording cage (L: 35 cm × W: 25 cm × H: 32 cm) for at least 10 d of postoperative recovery and adaptation to the recording cable. The recording cable was attached to a low-torque, six-channel mercury commutator (Dragonfly) designed for use with small birds ( Dave et al. 1999 ), and the weight of the recording cable was counterbalanced with a spring; these recording conditions allowed the sparrows to move unimpeded throughout the cage. The EEG and EMG signals were referenced to the cerebellar electrode, amplified, and band-pass filtered (0.3- to 30-Hz and 10- to 90-Hz, respectively), using Grass-Telefactor amplifiers (model 12 Neurodata and 7P511, Grass-Telefactor, West Warwick, Rhode Island, United States; http://www.grass-telefactor.com ), digitized at 100 Hz, and visualized using Somnologica 3 software (Medcare, Reykjavik, Iceland; http://www.medcare.com ). Sleep–wakefulness scoring Representative 24-h periods occurring prior to and during migration were selected for behavioral state scoring. The migratory nights selected for scoring were preceded by several nights with similar amounts of migratory restlessness. The behavioral state was scored visually, using both electrophysiological (i.e., EEG and EMG) and video recordings, and categorized as either wakefulness, drowsiness, SWS, or REM sleep. For accurate detection of REM sleep, the duration of scoring epochs was set at 4 s, since episodes of REM sleep may be as brief as only several seconds in birds ( Rattenborg and Amlaner 2002 ). As in previous studies of sleep in birds ( Rattenborg et al. 1999 a, 1999 b, 2001 ), the behavioral state was sampled across the 24-h period by scoring the first 4 s of each minute, resulting in a total of 1,440 samples per day. In addition, to determine the latency to SWS and REM sleep onsets precisely, every 4-s epoch was scored from lights out until the first unequivocal episodes of both SWS and REM sleep had occurred. Since SWS and REM sleep were restricted to the dark phase of the LD cycle in all birds during both migratory and nonmigratory seasons, sleep latency was calculated as the elapsed time from lights out to the first epoch of either SWS or REM sleep, and REM sleep latency was the elapsed time from the first epoch of SWS to the first epoch of REM sleep. Spectral analysis of the EEG EEG power spectra of the left and right hemisphere derivation were computed for all 4-s epochs, as described previously, by a FFT routine (Matlab function, Mathworks, Natick, Massachusetts, United States; using a Hanning window) within the frequency range of 0.25–25.0 Hz ( Huber et al. 2000 ). Values were collapsed into 0.5-Hz bins. For the analysis of the time course of EEG power, artifact-free SWS epochs were selected. Cognitive testing To encourage responding by the sparrows, their food was restricted to maintain 90% of their free-feeding weights during nonmigratory periods. In practice, however, it was difficult to maintain birds at this weight, especially during periods of premigratory fattening, when even food-restricted birds gained weight. Sparrows were tested for 60-min sessions once per day on 5–7 d per week from 1 February 2003 through 15 February 2004. Testing was always performed between 09:30 and 16:00, and the testing order was counterbalanced across days for each bird. Activity levels in the home cages were measured using the infrared activity monitoring system. The amount of active time during the dark phase was used to select for analysis two 3-wk periods when the birds were migrating (spring and fall) and two 3-wk periods when they were not migrating (winter and summer). Multiple schedule of repeated-acquisition and performance Preliminary training for the repeated-acquisition task was described previously ( Winsauer et al. 1995 ) and included shaping the approach to the food trough, shaping the response (key peck), and then reinforcing responses on each key when it was illuminated. To train repeated acquisition in all the sparrows, all three response keys were illuminated simultaneously with white light, but only one of the three response keys was chosen to be correct for a particular session, and each response emitted on that key resulted in the delivery of mixed-seed. Responding on either of the other two illuminated keys was considered an error and resulted in a 5-s time-out during which the key lights were extinguished and responding had no programmed consequence. For each daily session during this stage of training, the position for the correct response was varied pseudorandomly. After the sparrows acquired this task reliably, regardless of key position, a second response was added to the sequence or chain so that two correct responses were necessary to obtain seed. This type of sequential responding is procedurally defined as a “chain” because each response except the last produces a discriminative stimulus controlling the response that follows ( Kelleher 1966 ). The key positions for the correct responses varied both within the two-response sequence and across sessions. The color of the key lights changed after each correct response. A third response was added to the sequence when stable responding was obtained under the two-response sequence. The average number of sessions required to train repeated acquisition of the first, second, and third member of the sequence was 38, 65, and 35, respectively. A second component was then added to the schedule so that sparrows responded under a multiple schedule of repeated acquisition and performance of response chains. During acquisition components, the three response keys were illuminated at the same time with one of three colors: green, red, or white. Responding on the correct key in the presence of one color (e.g., keys green, center correct; keys red, left correct; keys white, right correct) changed the color of the key lights as well as the position for the next correct response. When the subject completed the response sequence by emitting three correct responses (i.e., one correct response in the presence of each color), the key lights were extinguished, and the stimulus light in the mixed-seed trough was illuminated for 0.05 s. Subsequently, the response keys were illuminated with the first color (i.e., green), and the sequence was reset. Within a given session, the correct response that was associated with a particular color did not change, and the same sequence (in this case, center-left-right [C-L-R]) was repeated during all acquisition components of a given session. Responding on this sequence was maintained by food presentation under a second-order FR3 schedule such that every third completion of the sequence resulted in the presentation of 5 s of access to mixed-seed. When sparrows responded on an incorrect key (in the example, the left or right key when the green lights were illuminated), the error was followed by a 5-s time-out. An incorrect response did not reset the three-response sequence (i.e., the stimuli and the position of the correct response were the same before and after a time-out). To establish a steady state of repeated acquisition, the sequence was changed from session to session. An example of sequences for five consecutive sessions was C-L-R, L-R-C, C-R-L, R-L-C, and L-C-R. The sequences were carefully selected to be equivalent in several ways, and there were restrictions on their ordering across sessions. Briefly, each sequence was scheduled with equal frequency, and consecutive correct responses within a sequence were scheduled on different keys. Occasionally, a correct sequence position for a given color was the same for two consecutive sessions (in the list of sequences above, L-R-C and C-R-L). During performance components, the response keys and the houselights were illuminated, and the sequence remained the same (R-C-L) from session to session. In all other aspects (color of the stimuli for each response in the sequence, second-order FR3 schedule of food presentation, 5-s time-out, etc.), the performance components were identical to the acquisition components. Experimental sessions always began with an acquisition component, which then alternated with a performance component after 20 reinforcers or 20 min, whichever occurred first. The performance component alternated back to the acquisition component after 10 reinforcers or 20 min, whichever occurred first. Each session terminated after 60 min. Sleep deprivation To determine whether accuracy and response rate on the repeated-acquisition and performance task were affected by sleep restriction during the nonmigratory season, sleep was restricted to the first 3 h of the dark phase (18:00–21:00) for three consecutive nights starting on 10 February 2004. Birds were deprived of sleep starting at 21:00 until the following day at 18:00 by experimenters who entered the housing room at least once every 5 min or sooner if behavioral signs of sleep were observed via closed-circuit cameras. Walking quietly past the cages was always sufficiently stimulating to keep the birds awake; we never had to handle the birds to induce wakefulness. Statistics Comparisons were made using either Student's t -test, with Welch correction for sample size, or ANOVA. All tests were performed using “R” ( http://www.r-project.org ). Prior to analysis two procedures were performed on the data for the cognitive testing. One consequence of counterbalancing the order of testing the birds was that the length of time from withdrawal of food to the onset of testing (and presumably one component of food motivation) varied on a 3-d schedule. For this reason a 3-d running average of the cognitive testing variables was computed. There was also a linear trend across the year, particularly in acquisition percentage correct ( r 2 varied from 0.31 to as high as 0.67). This trend was removed before making seasonal comparisons. The spring and fall migratory periods and the summer and winter nonmigratory periods were determined as follows. For each bird, each date during the study was used to compute the average nighttime activity for the following 21 d. The periods in the spring (21 February–20 May) and fall (21 August–20 November) with the highest average activity for each bird were designated as peak migration times, and the periods in the summer and winter with the lowest activity for each bird were chosen as nonmigratory times. Video 1 Migratory Restlessness in a White-Crowned Sparrow Wing whirring while holding the perch (which occurred only at night) and perch hopping. Video from infrared camera. Bright object at the center of the screen is the source for the infrared motion detection beam. Video 2 Drowsiness in a White-Crowned Sparrow A brief example of active wakefulness followed by about 50 sec of drowsy behavior. Captured during the daytime. Video 3 SWS in a White-Crowned Sparrow Captured on surveillance camera. Bright object at the center of the screen is the source for the infrared motion detection beam. Video 4 Acquisition Component of the Operant Task A correct sequence of three presses must be repeated three times. Feedback is provided by the lighted keys. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC449897.xml |
533875 | "Any other comments?" Open questions on questionnaires – a bane or a bonus to research? | Background The habitual "any other comments" general open question at the end of structured questionnaires has the potential to increase response rates, elaborate responses to closed questions, and allow respondents to identify new issues not captured in the closed questions. However, we believe that many researchers have collected such data and failed to analyse or present it. Discussion General open questions at the end of structured questionnaires can present a problem because of their uncomfortable status of being strictly neither qualitative nor quantitative data, the consequent lack of clarity around how to analyse and report them, and the time and expertise needed to do so. We suggest that the value of these questions can be optimised if researchers start with a clear understanding of the type of data they wish to generate from such a question, and employ an appropriate strategy when designing the study. The intention can be to generate depth data or 'stories' from purposively defined groups of respondents for qualitative analysis, or to produce quantifiable data, representative of the population sampled, as a 'safety net' to identify issues which might complement the closed questions. Summary We encourage researchers to consider developing a more strategic use of general open questions at the end of structured questionnaires. This may optimise the quality of the data and the analysis, reduce dilemmas regarding whether and how to analyse such data, and result in a more ethical approach to making best use of the data which respondents kindly provide. | Background The survey is a key method in health services research [ 1 ]. The majority of survey questionnaires consist of closed questions where respondents are asked to choose from a fixed number of options. These are considered to be efficient because data are easy to collect, code and analyse [ 2 ]. Efficiency is important in survey methodology where researchers attempt to obtain the attitudes or experiences of a representative sample for generalisation to a wider population, and may need to gather information from large numbers to ensure precision of estimates. In addition to closed questions, it is not uncommon to include an 'open' question where respondents are invited to provide information in free text format, for example 'Is there anything else you would like to say' at the end of a questionnaire. When the questionnaires are returned and being prepared for analysis, the researcher may face the dilemma of whether or not to analyse and report the written responses to this open question. In this paper we draw on expert opinion in key texts, and examples of the use of open questions in predominantly closed question questionnaires, to consider whether there is value in including such questions, and if so, how best to optimise the quality of the data and analysis. Discussion Different types of open questions in surveys There are four types of questions which might require an open rather than closed response (see Figure 1 ). The general open question, typically 'any other comments?', used at the end of a structured questionnaire is the type we focus on in this paper. We believe that the use of this type of open question is common, and we consider it to be the type that is most likely to pose a dilemma for researchers around whether and how to analyse any responses. Figure 1 Different types of open questions in surveys The potential benefits of general open questions General open questions offer a number of benefits when piloting a questionnaire. Responses to them can reassure the researcher that all relevant issues have been covered [ 3 - 5 ]. Responses may also be used to corroborate answers to closed questions, offering reassurance to the researcher that the questionnaire is valid, or highlighting problems with particular questions. The benefits of using general open questions in the main study are less clear. They have been recommended to help make a dull statistical report more interesting [ 6 ], by providing the reader with quotes to illustrate important points, and in self administered questionnaires because there is some evidence that they increase response rates [ 5 ]. Increasing the response rate is a considerable benefit in survey methodology, but it is not necessarily the issue which drives researchers to use general open questions. Researchers may use general open questions without giving much thought to why they are doing so, simply including the question because it is usual practice. Or they may be driven by a desire to offer respondents an opportunity to voice their opinion. Closed questions represent the researchers' agenda, even if they have been developed through listening to people's views in focus groups and depth interviews. The use of 'any other comments' may redress the power balance between researchers and research participants. Respondents may take this opportunity to ask for clarification or information about a health issue or health service, or voice concern about the research. If researchers include a general open question for this reason then they will need to consider how best to respond to individuals about such queries and concerns. Another possible driver for including a general open question is a concern about missing an important issue, even if the questionnaire has been developed using a considerable amount of qualitative research and piloting. There may be issues which respondents want to give more details about than the structured questions allow. There may be issues which qualitative methods and piloting fail to uncover because they affect a small number of people only, or they are specific to sub groups which have not been included in the development work, or they have occurred since the design of the questionnaire. Thus general open questions may act as a 'safety net' and help the researcher to identify issues not covered by the closed questions, either by elaborating and explaining some of the findings from closed questions, or identifying new issues. For example, the purpose of a survey of NHS Direct nurses was to describe the nurses' qualifications, experience and reasons for joining the service [ 7 ]. A small number of closed questions asked about nurses' views of working for this new service, and respondents used the general open question to expand in considerable detail on this issue [ 8 ]. In other studies, responses to general open questions have elaborated on answers to a closed question, identifying the aspects of the service which contributed to NHS Direct users feeling reassured by the advice offered [ 9 ], explaining why junior doctors felt that training had not prepared them for their job [ 10 ], and illuminating why people were more satisfied with an emergency ambulance call taker making use of a priority dispatch system [ 11 ]. An example of a new issue emerging after the design of the questionnaire was the emergence of media criticism as a concern of doctors in one of a series of annual surveys [ 12 ]. Why are general open questions a problem? Having asked a general open question, researchers may face the dilemma of whether to analyse responses or not. Practical constraints may contribute to a decision not to do so because data input and analysis require considerable resources [ 5 , 6 ] and these may not have not been allocated during the study design. However, ignoring this data can feel unethical and it has been recommended that researchers should not ask open questions unless they are prepared to analyse the responses [ 13 ]. Another barrier may be the lack of clarity around the status of the responses. They tend to fall between two stools, being neither strictly qualitative nor quantitative data, and this can make them uncomfortable to work with. This lack of clarity of status may result in them not being analysed, or being analysed and published in the body or appendix of a report but not within any peer reviewed articles emerging from the study. Are responses to general open questions qualitative or quantitative data? Some researchers consider responses to general open questions to be qualitative data [ 14 , 15 ], some do not [ 16 ], and others describe them as 'quasi-qualitative data' [ 17 ]. General open questions have some of the features of qualitative approaches: they appear to allow respondents to write whatever they want in their own words, with little structure imposed by the researcher; the output is words rather than numbers or ticks; the analysis may use techniques associated with qualitative research; and publication can involve the display of verbatim quotes so that it looks like qualitative data. However, data from general open questions can lack some of the key strengths of qualitative research. One could argue that the closed questions indicate the legitimate agenda for the responses to the general open question, and thus may impose constraints on responses. More importantly, there is a lack of attention to context, and a lack of conceptual richness, because the data on each case often consist of a few sentences or less. Typically, recipients are asked non-directive questions such as 'Is there anything else you would like to say' or 'Any other comments?', with a small amount of space for responses. The key to determining the status of data derived from general open questions may therefore be their depth; both the amount recipients are prompted to write (either through the instructions given or the amount of space allocated), and the amount they actually write. Thus researchers may be able to determine the status of a general open question at the design stage of a study by having a strategy to generate depth and treat the data qualitatively, or by having a strategy to generate shorter responses as a 'safety net' for complementary or new issues. Having such a strategy may help researchers to devise a strategy for analysis and publication. Generating qualitative data by design Researchers can determine the status of a general open question at the design stage of a study by having a strategy to generate depth and treat the data qualitatively. For example, at the end of a structured questionnaire about use of Chinese medicine, one researcher invited respondents to tell the 'story' of their use of Chinese medicine, leaving a full one and a half pages of white space, and offering a example of the detail required. The following instructions were given: 'Now tell us your own story, using the space on the next page. We've provided one true patient story to give you an idea of the kinds of details we need. The important subjects are repeated in the list above the space we've provided for you to write in. Also use the back of the page if you wish. Please remember to write clearly.' [ 18 ]. This approach produced 460 accounts from 575 respondents (80%). These 'handwritten stories' were treated as qualitative data, and analysis focused on the language used by respondents, as well as emerging themes, to show the holistic nature of the health care delivery as experienced by the respondents. In the above example, the 80% response suggests that the stories obtained could be viewed as representative of the population surveyed. However, in qualitative research the validity of the study does not rest on the researcher's ability to demonstrate representativeness with respect to the total population. Rather, it rests on transferability whereby the researcher offers detailed description of the setting in which the research was undertaken [ 19 ]. Thus what is required is that the characteristics of the sample are clearly presented, such that the reader is informed about the likely transferability of the beliefs and experiences expressed. With data obtained from a structured survey, it is always possible to use the quantitative responses to characterise the nature of the group providing comments, and to make their relationship to the wider population apparent. This means that comments from a subset of responders are still valuable data even when they do not represent the entire sample. One important corollary of this is that open questions can be designed expressly to elicit comments from a subset of the population surveyed, using the principles of purposive sampling [ 20 ]. An example of this would be to encourage all respondents reporting a particular type of experience in a closed question to tell their story. An alternative approach would be to sample post hoc from the full range of responses received, for example sampling information rich cases or extreme cases. If the open question is used to generate qualitative data, then researchers will need to use qualitative analysis techniques and possibly qualitative software as used in the Chinese medicine example discussed previously [ 18 ], and will need to consider issues important to good quality qualitative research, such as clear exposition of data collection and analysis, the search for disconfirming evidence and reflexivity [ 17 , 21 , 22 ]. Qualitative researchers expect analysis to be challenging and time consuming and will ensure that they have the resources required to undertake it if the intention to collect such data is explicit in the research proposal. When reporting research findings from studies using face-to-face interviews, it is good practice to indicate the length of the interviews. Similarly, when reporting the data from these open questions, it might be helpful to indicate the potential depth of data to the reader by detailing the average number of lines of text available from respondents [ 18 ]. Generating quantifiable data by design General open questions may produce little more than the closed questions on the questionnaire [ 23 ] and rather than considering it unethical to analyse these responses, a more appropriate strategy might be preliminary analysis involving reading the responses so that the researcher can consider the contribution they make to the study overall. If the comments merely corroborate or slightly elaborate upon the answers to closed questions, then formal analysis may not be worthwhile [ 23 ]. It may be good practice to report within publications that the responses to the general open question did not provide additional information to the closed questions. It is where they offer insights or issues not available in the closed questions that formal analysis could be considered good practice, even if the role of this analysis is to identify hypotheses or questions for further study. Formal analysis may be prompted by either the strength of numbers making particular comments, or the strength of feeling within a small number of the comments. For example, in a survey of NHS Direct nurses, the large number of detailed comments and the emotional content of some of them, prompted a formal analysis [ 8 ], and in a survey of junior doctors, the strength of feeling expressed by a small number of doctors around one issue prompted formal analysis [ 12 ]. From a quantitative perspective, the strength of a survey approach is representativeness, and thus non-response bias should be a concern. Respondents are less likely to complete a general open question than a closed one on a postal questionnaire [ 4 ]: 81% of the 71% of respondents to a survey of NHS Direct users, that is 58% of the sample [ 9 ]; 67% of the 74% of respondents to a survey of NHS Direct nurses, that is 50% of the sample [ 8 ]; and 40% of the 74% of respondents to a survey of junior doctors, that is 30% overall [ 12 ]. Those who choose to answer the general open question could be different from respondents overall, either being more articulate or having a greater interest in the survey topic. It is important to consider and report on who has made written comments so that bias can be considered. In a patient satisfaction survey, females were more likely to make comments than males but interestingly there were no significant differences by age group or educational status [ 23 ]. In a survey of NHS Direct nurses, the proportion of nurses making written comments varied by their job satisfaction levels, with nurses who felt that their job satisfaction had 'not really changed' under-represented in the written comments and those who felt it had 'worsened a lot' over-represented in the written comments [ 8 ]. The comments were reported in this context. Formal analysis must be rigorous so that the findings are useful and convincing. Content analysis may be undertaken [ 2 , 3 ], where the researcher takes the following steps: 1. Reads a sub-set of the comments. 2. Devises a coding frame to describe the thematic content of the comments. 3. Assigns the codes to all the comments. The coding frame can be applied using software designed for this purpose [ 24 ] or manually. Two coders may be needed to test the reliability of assigning codes [ 2 ]. 4. The codes can be entered into a statistical package alongside the data from the closed questions and treated as variables in a quantitative analysis. The coding process is time consuming and requires expertise [ 2 , 4 , 6 ]. The skills of a qualitative researcher are not needed, but the coding is similar to the early stages of qualitative analysis [ 25 , 26 ] and researchers may wish to seek the advice of a qualitative researcher. Any decisions made will affect the results and thus the coding process is suited to a skilled researcher. When reporting the responses to general open questions it is important that the numbers of respondents making each comment are displayed, with recognition that although a specific number of people mentioned an issue it might be relevant to many more who did not choose to mention it. Although numbers rather than percentages tend to be used when reporting responses to open questions [ 8 , 12 , 15 , 26 ], percentages will sometimes be the most appropriate way of presenting the results, for example, in a before and after design with different numbers of comments in each time period [ 11 ]. Verbatim comments can be displayed to illustrate the themes [ 8 , 26 ], because it is the comments themselves which have convinced the researcher of the importance of the dissemination of the information [ 8 ]. When doing this, attention to confidentiality is important, taking care not to report comments which might identify an individual. Publication of responses to open questions Publication of responses to open questions can occur within a paper reporting the main findings of the questionnaire or as a separate publication. Where comments elaborate and explain findings from closed questions, it may be most appropriate to publish them in the same paper [ 9 ]. Where a new issue emerges [ 8 , 12 ], a separate publication may be appropriate. For publication, both the data and analysis need to be robust enough to stand up to scrutiny and peer review. Advantages and disadvantages of having an explicit strategy An explicit strategy requires that researchers consider the role of a general open question in the context of their survey, and its status in terms of generating either qualitative or quantifiable data. If the role of the question is to give a voice to participants then the researcher can ensure that comments will be read to identify any concerns and queries expressed by individuals, and that appropriate action is taken with individual comments. If the role is to generate qualitative data then attention can be paid to generating depth of data and the quality issues associated with qualitative research. If the role is to act as a safety net and generate quantifiable data then resources will need to be allocated for reading the comments, and if there appears to be added value, for formally analysing the data with attention to non-response bias and reliability of coding. Having a strategy may reduce any dilemma faced by researchers about whether and how to analyse these questions, may help the researcher to allocate the appropriate time and expertise to this data, and may produce an analysis robust enough for publication in peer reviewed journals. A potential disadvantage of having such a strategy may be that some flexibility is lost and that some important issues are missed. Finally, researchers cannot assume that they know how best to facilitate a respondent to complete a general open question and may need to consider using cognitive aspects of survey methodology to construct the question [ 27 ]. Summary • General open questions at the end of structured questionnaires can present a problem to researchers who may face the dilemma of whether or not to analyse them. • They are necessary when piloting questionnaires because they identify further issues for inclusion in the survey, and may be a bonus in the main study because they may increase response rates and may identify issues which complement responses to closed questions. • The value of such questions, and the quality of the data and analysis, may be optimised if researchers make more strategic use of them by being clear about their role, and understanding the type of data they wish to generate when they design their study. • An explicit strategy for generating qualitative data will encourage attention to depth of data and issues important to the analysis of qualitative data such as reflexivity. • An explicit strategy for generating quantifiable 'safety net' data, that is important issues missed by the closed questions, will encourage attention to non-response bias and reliability of coding. Competing interests The author(s) declare that they have no competing interests. Authors' contributions AOC conceived the paper and wrote the first draft. KJT reviewed it critically, and developed the sections on qualitative research. Both authors produced the final draft and read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC533875.xml |
514560 | Fitness benefits of trypsin proteinase inhibitor expression in Nicotiana attenuata are greater than their costs when plants are attacked. | Background The commonly invoked cost-benefit paradigm, central to most of functional biology, explains why one phenotype cannot be optimally fit in all environments; yet it is rarely tested. Trypsin proteinase inhibitors (TPIs) expression in Nicotiana attenuata is known to decrease plant fitness when plants compete with unattacked conspecifics that do not produce TPIs and also to decrease the performance of attacking herbivores. Results In order to determine whether the putative benefits of TPI production outweigh its cost, we transformed N. attenuata to silence endogenous TPI production or restore it in a natural mutant that was unable to produce TPIs. We compared the lifetime seed production of N. attenuata genotypes of the same genetic background with low or no TPI to that of genotypes with high TPI levels on which M. sexta larvae were allowed to feed freely. Unattacked low TPI-producing genotypes produced more seed capsules than did plants with high TPI levels. Caterpillar attack reduced seed capsule production in all genotypes and reversed the pattern of seed capsule production among genotypes. M. sexta larvae attacking genotypes with high TPI activity consumed more TPI, less protein, and move later to the young leaves. Larval masses were negatively correlated (R 2 = 0.56) with seed capsule production per plant. Conclusions Our results demonstrate that the fitness benefits of TPI production outweigh their costs in greenhouse conditions, when plants are attacked and that despite the ongoing evolutionary interactions between plant and herbivore, TPI-mediated decreases in M. sexta performance translates into a fitness benefit for the plant. | Background The cost-benefit paradigm is central to functional biology and to ecological and evolutionary theory because fitness costs and benefits associated with a trait determine its equilibrium value in a population. If the trait offers fitness benefits to the population rather than costs, then selection should lead to beneficial allele(s) being fixed, which reduces variability [ 1 ]. Alternatively, when the fitness benefit of the trait also has a cost, intermediate frequencies of the trait may be favored because the benefit varies [ 1 - 3 ]. For example, resistance against natural enemies has costs as well as obvious benefits for fitness, as has been shown in insect-parasite, insect-parasitoid, plant-pathogen and plant-insect systems [ 4 - 7 ]. Herbivores can reduce seed production and other correlates of plant fitness, and this reduction can result in natural selection for either constitutively expressed or inducible plant defenses [ 8 - 10 ]. Current theory predicts that one benefit of induced defenses is to allow a plant to optimize its allocation of limiting resources to defense, growth, and reproduction [ 9 ]. Although defenses might benefit plants in the presence of herbivores, plant resistance to herbivores can be costly in the absence of enemies, and inducible expression of resistance traits allow plants to forgo or, to pay the potential fitness cost of resistance traits when they are needed [ 3 , 5 , 11 - 14 ]. Evidence for the existence of resistance costs and benefits from studies using plant species with constitutive and inducible defenses is increasing [ 3 , 14 - 16 ]. Experiments on natural populations of plants as diverse as Arabidopsis , Ipomea , Pastinaca and Trifolium have provided evidence for costs [ 2 , 17 - 20 ]. These experiments typically use quantitative genetic approaches to determine whether, in the absence of enemies, fitness and resistance are inversely correlated. However, attribution of fitness consequences to the expression of a particular defense trait in an environment either with or without herbivory is difficult, because genes that control the expression of defensive traits may have pleiotropic effects [ 21 ]. Ideally, one should assess the costs and benefits of inducible defenses in plants that differ only in the expression of genes that control (induced) resistance but are otherwise genetically identical [ 15 ]. Transformation technology provides a means of manipulating traits with unparalleled precision. Although the benefits of plant traits that provide resistance against herbivores are expected to equal or exceed their cost when the system is at evolutionary equilibrium [ 22 - 25 ], very few direct tests have been done. While costs and putative benefits of defense traits have been studied in separate experiments, their currencies are usually not comparable (i.e., plant fitness for the cost; herbivore performance for the benefits). Tests of the cost-benefit model using the same currency are few [ 5 ] and these studies do not consider the heterogeneity of the plant. Ecological interactions can be viewed as the net outcome of a series of cost-benefit optimizations in which both players respond to the variability in each others' defense traits. For example, there is enormous within-plant heterogeneity of defensive secondary metabolites. This heterogeneity could motivate within-plant movement of herbivores, so that they eat leaves of low fitness value rather than leaves of high fitness value, or it could motivate herbivores to move off plants and onto neighboring competitors [ 26 , 27 ]. Herbivores, in turn, can both readjust their metabolism to cope with the secondary metabolites as well as adjust their feeding positions to maximize their performance [ 27 - 29 ]. We present here a cost-benefit analysis of a plant-insect interaction in which the costs and benefits of a defensive protein are evaluated in the currency of plant fitness. Nicotiana attenuata [Torr. Ex Wats. (synonymous with Nicotiana torreyana Nelson and Macbr.)], a postfire annual native tobacco inhabiting the Great Basin Desert, has a number of well-described herbivore-induced direct and indirect defenses [ 30 ], which increase the fitness of plants under attack in natural populations [ 5 , 31 ]. Trypsin proteinase inhibitors (TPI) play an important defensive role in addition to nicotine [ 30 ]. We isolated cDNA from N. attenuata that coded for a TPI precursor belonging to the potato PI-II family with a 7-repeat TPI domain. The normal constitutive expression of this gene increases 4-fold after herbivore attack [ 32 , 33 ]. The elicitation of TPI expression in N. attenuata varies with ontogeny and leaf age [ 34 ], as is true for nicotine [ 35 ] and volatile emissions [ 36 ]. The within-plant pattern of systemic TPI induction at the rosette stage of growth suggests that the signal(s) triggering remote TPI induction follows a source-sink relationship; regardless of ontogenetic stage, if young sink leaves are damaged, TPI levels increase only in the attacked leaf, while older leaves are less sensitive to leaf damage and produce a less intense response in the attacked leaf, the systemic responses in young leaves is dramatic [ 34 ]. The spatial and temporal variability in N. attenuata 's ability to deploy certain defenses against herbivores can be correlated with the relative fitness values of leaves growing at particular nodes. Removal of young and mature leaves at the elongation stage in N. sylvestris had a greater negative effect on fitness than did the removal of old leaves, but not at either the rosette or flowering stages, demonstrating the different fitness values of leaves growing at different nodes on a plant. Damage to younger leaves increases nicotine contents more than damage to older leaves does, suggesting that defense allocation is proportional to the fitness value of the tissue, as predicted by Optimal Defense (OD) theory [ 10 , 23 , 35 , 37 ]. Manduca sexta , a specialized lepidopteran herbivore, prefers elongating N. attenuata plants to rosette-stage plants for oviposition and places eggs on leaves in the middle section of the stem (from S1 to S3; Figure 1 ; [ 38 ]). TPIs of N. attenuata leaves reduce the growth of M. sexta larvae [ 32 , 33 ]. However, insects may adapt to high TPI levels, replacing the inhibited trypsin with the secretion of trypsins that are insensitive to the particular TPIs of the diet [ 29 , 39 ]. Intra-plant movement of the first instar larvae is very rare but common in the second-to fourth-larval instars [ 38 ]. Larger instars are heavier and more difficult to handle by insect predators, and also better able to defend themselves against an attack in their natural environment by vigorous movement and regurgitation (A. Kessler, personal communication). Larvae are particularly sensitive to jasmonate-induced defenses during the third instar (approx. 11 days after hatching), and can be motivated to move between adjacently growing plants [ 26 ] by the plant's induced defense. When M. sexta larvae were placed on MeJA-induced plants, larvae left the induced plants 1–3 days earlier than did larvae placed on uninduced plants, which dramatically reduced the leaf area consumed and larval weight gain [ 40 ]. Figure 1 Sketch of Nicotiana attenuata plant showing different leaf positions on either the rosette or the stem [38] and larval location. Larva depicts the leaf growing at node S1 on which a single M. sexta neonate was placed. TPI expression in N. attenuata is known to decrease lifetime seed production in unattacked but competing plants [ 32 ] and to decrease M. sexta performance in attacked plants [ 32 ]. Whether the TPI-mediated decrease in herbivore performance translates into a fitness benefit for the plant is unknown. In other systems, plants expressing high PI levels caused herbivores to grow more slowly, but they compensated by eating more tissue, a potential fitness detriment for the plant [ 41 ]. Here we provide a critical test of whether the fitness benefits of TPI expression outweigh their costs. We compare lifetime seed production of N. attenuata genotypes with either low or no TPI production to that of TPI-producing genotypes on which M. sexta larvae were allowed to feed freely for 11 days. TPI and protein content were measured in all genotypes at all leaf positions. M. sexta larval mass and movement were recorded, and we calculated and simulated the amount of TPI and protein consumed by the larvae from the larval movement and the TPI and protein concentration at each leaf position from each genotype. We used two independently transformed N. attenuata lines in which the expression of the pi gene was down-regulated by antisense expression of a 175 bp fragment of the N. attenuata pi gene (AS –, AS-), and untransformed wildtype plants (WT) of the same genetic background (an inbred line collected from Utah). In addition, we used a natural N. attenuata genotype collected from Arizona, which has a mutation in the endogenous 7-domain pi gene and does not produce pi transcripts or TPI activity (A). We transformed this genotype with the full-length cDNA of the 7-domain pi gene in a sense orientation under control of a constitutive promotor (S++), so that after 11 days of caterpillar attack it produced TPIs at 74 % of the level found in the stem leaves of the wildtype Utah genotype. Our analysis demonstrates that the fitness benefits of TPIs production outweigh their cost when plants are attacked. Results Spatial and temporal distribution of plant TPI/protein contents In order to determine the effect of caterpillar attack on TPI activity, measurements were made from all rosette and stem leaves before, and 4 and 11 d after larvae started to feed on S1 leaves (Fig. 1 ) from transformed (AS –, AS-, and S++) and untransformed (WT and A) genotypes (Fig. 2 and Fig. 1-4/Appendix 1 [see Additional file 1 ]). All genotypes had high within-plant heterogeneity of TPI activity and protein contents. Constitutive TPI levels in all genotypes on day 0 (before larvae started to feed) were higher in rosette leaves than in stem leaves (F 1,70-AS – = 217.13; P <0.0001; F 1,70-AS- = 357.76; P <0.0001; F 1,70-WT = 209.8; P <0.0001; F 1,70-S++ = 4.27; P = 0.04), while protein content showed the opposite pattern, with higher levels in stem leaves than in rosette leaves (F 1,70-AS – = 331.8; P <0.0001; F 1,70-AS- = 256.6; P <0.0001; F 1,70-WT = 289.6; P <0.0001; F 1,70-S++ = 1.87.1; P <0.0001; Fig. 1-4/Appendix 1) which persisted through the samplings performed on day 4 and 11 (data not shown). A-genotype plants had a similar pattern in protein content (data not shown; F 1,70-A = 245.5; P <0.0001). Caterpillar attack increased levels and within-plant heterogeneity of TPI activity. Larval damage to WT plants increased TPI activity 2.5-fold in S1 leaves (F 1,14 = 197.0; P <0.0001) and 1.7-fold in unattacked (F 1,110 = 17.3; P <0.0001) stem leaves, and did not alter TPI activity in older rosette leaves 4 d after neonates started to feed (F 1,62 = 0.04; P = 0.8; Fig. 2 and Fig. 1/Appendix 1). By day 11, TPI activity had increased in WT S1 leaves 4-fold (F 1,22 = 183.3; P <0.0001), 2.5-fold in the stem leaves (S avg; F 1,334 = 337.0; P <0.0001; Fig. 2 ), and also marginally (1.1-fold) on the rosette leaves (F 1,94 = 8.6; P <0.004; Fig. 1/Appendix 1). Figure 2 TPI activity (mean ± SEM) from stem leaves and the leaf growing at node S1 of untransformed wild type Nicotiana attenuata plants of the Utah genotype (WT); two homozygous T 3 independently transformed lines of the Utah genotype that had been transformed with a construct containing a 175 bp pi gene fragment in an antisense orientation (AS –, AS-); plants of a homozygous T 3 transformed line of the Arizona genotype transformed with a construct containing the full-length pi gene in a sense (S++) orientation before attack (day 0); and either unattacked or attacked by M. sexta larvae 4 and 11 d after neonates started to feed on the leaf at S1 position. Thin bars indicate ± SEM. Levels and within-plant heterogeneity of TPI activity were either intermediate or low in AS compared to WT plants after larval damage. After 4 days of caterpillar attack, TPI levels in AS – and AS-genotypes were 60 % and 40 % lower than those of unattacked WT (F 1,190-AS–total = 62.4; P < 0.0001; F 1,190-AS-total = 23.4; P < 0.0001; Figs 2 and 3/Appendix 1). Caterpillar attack increased TPI activity 2.4-fold in S1 leaves in AS plants, attaining values that were 19% and 48% in AS – and AS-plants, respectively of that in attacked WT plants (F 1,14-AS–S1 = 630.3; P < 0.0001; F 1,14-AS-S1 = 193.2; P < 0.0001; Fig. 1 ); TPI levels in the stem leaves were 37% and 55% of that found in induced WT plants (F 1,190-AS– = 89.8; P < 0.0001; F 1,190-AS- = 42.1; P < 0.0001; Fig. 2 ). By day 11, TPI levels in stem leaves were 22% in AS – and 65% in AS-of the WT levels (F 2,501 = 225.5; P < 0.0001; Fig. 2 ). As expected, caterpillar attack did not affect either levels or within-plant heterogeneity of TPI activity of S++ plants. Compared to the constitutive levels of TPI activity in the WT, levels in S++ plants on day 4 were 30% higher (F 1,190-total = 23.8; P < 0.0001; Fig. 4/Appendix 1). Caterpillar attack did not alter TPI activity in S++ plants (F 1,430-total = 0.1; P = 0.06; Fig. 4/Appendix 1) which remained at approximately 90% of the induced WT plants in the S1 leaf and 1.1-fold at the plant level (averaged across all measured leaf positions; F 1,14-S++-S1 = 3.8; P = 0.06; F 1,190-total = 0.8; P = 0.3; Fig. 2 ). By day 11 d, TPI activity in S++ plants were 56% in the S1 leaf (F 1,22 = 48.8; P < 0.0001) and 74% in stem leaves of the induced WT levels (F 1,430 = 72.3; P < 0.0001; Fig. 2 ). As expected, the untransformed A genotype showed no TPI activity even after caterpillars had fed on the plant for 4 or 11 d. Protein levels did not differ significantly among genotypes. In summary, TPI levels in AS – and AS-genotypes in S1 and stem leaves were lower than in WT plants without differences in protein contents. Absolute TPI levels were substantially lower in the AS genotypes after caterpillar attack and S++ genotype produced TPI levels that were 74% of the activity found in induced WT plants. Within-plant movement of M. sexta larvae To determine the effect of TPI on within-plant movement of M. sexta larvae, we measured the position of each larva on each plant daily. Caterpillars on low TPI genotypes left the S1 leaf and moved to the BOTTOM of the plant earlier than those feeding on high TPI genotypes (Fig. 3 ). While larvae on WT plants started to move from the S1 leaf to the BOTTOM of the plant after 5 d, larvae on AS – plants started to move 2 d earlier (day 3), and those that fed on AS-plants started to move 1 d earlier (day 4; Fig. 3 ). This early larval movement resulted in more larvae on the TOP and MIDDLE parts of plants from the AS – genotype (51 %, 77 %, and 80%) than on WT plants (11 %, 37 %, and 49 %) during subsequent days (days 6–8; Mann-Whitney U -test; P <0.0001; Fig. 3 ). If caterpillars prefer to feed on leaves with low TPI levels, then we would expect to have higher defoliation levels of plants with either no or low TPI compared to those with high TPI levels, increasing the number of caterpillars on the BOTTOM after some days. By day 11, 65 % of the larvae on WT plants were on the top and 19 % were on the BOTTOM, while on AS – plants, 30 % were on the TOP and 48 % on the BOTTOM, and on AS-plants 44 % were on the TOP and 36 % on the BOTTOM (Mann-Whitney U -test; P WT-AS–TOP = 0.001; P WT-AS–BOTTOM = 0.03; P WT-AS-TOP = 0.01; P WT-AS-BOTTOM = 0.3; Fig. 3 ). Figure 3 Relative number of M. sexta larvae on different plant locations on WT, AS –, AS-, S++, and Arizona (A) genotypes during 11 days on leaves growing at node S1, or the bottom, middle or top part of the plant (Figure 1). A single M. sexta neonate was placed on the leaf growing at node S1 and larval movement was monitored. Similar movement patterns were found in larvae on S++ and A genotypes. Larvae on A plants moved earlier (day 4) from the leaf at node S1 and toward the MIDDLE and TOP of the plant compared to larvae on S++ plants (Fig. 3 ). This earlier movement was reflected in the number of larvae on the MIDDLE and TOP of the plant from day 6 to 9 with a greater percentage in A (23 %, 65 %, 69 %, and 84 %) than in S++ (8 %, 16 %, 28 %, and 56 %) genotypes (Mann-Whitney U -test; P <0.0001; Fig. 3 ). On day 11, there were no differences in the number of caterpillars between A and S++ plants at the BOTTOM and at TOP of the plant (Mann-Whitney U -test; P A-S++TOP = 0.35; P A-S++BOTTOM = 0.1; Fig. 3 ). In summary, lighter caterpillars moved later than heavier caterpillars upward within the plant during the first days, and on day 11 they moved later to the BOTTOM of the plant. Calculated and simulated TPI and protein consumed by M. sexta larvae We calculated the amount of TPI and protein consumed by M. sexta larvae during the first, second, and third instars from each larvae's instar-specific feeding site, the concentration of leaf protein and TPI at the feeding site, and the instar-specific consumption from literature values (Tables 1a and b/Appendix 1). Plant genotype strongly influenced the calculated amount of TPI and protein consumed. Calculated total TPI and TPI consumed during the first, second and third instars were the highest for larvae on WT (16.7 g total) and the lowest for larvae on AS – (2.9 g total) plants (F 2,76-Total = 888.6; P < 0.0001; F 2,76-First = 28419.3; P < 0.0001; F 2,76-Second = 442.8; P < 0.0001; F 2,76-Third = 671.2; P < 0.0001; Fig. 5/Appendix 1). During the second instar, larvae on AS – plants consumed the highest calculated amount of protein (1.5 g), larvae on WT plants, the lowest (0.8 g), but no differences were found between genotypes during the first and second instars (F 2,76-First = 1.8; P = 0.1; F 2,76-Second = 87.14; P < 0.0001; F 2,76-Third = 2.1; P = 0.1; Fig. 5/Appendix 1). As expected, the calculated total amount of protein consumed was higher on larvae fed on AS – (7.0 g) than those fed on either AS-(6.6 g) or WT (6.3 g) genotypes (F 2,76-Total = 11.6; P < 0.0001; Fig. 5/Appendix 1). Larval mass of caterpillars fed on WT, AS –, and AS-genotypes was affected by the amount of TPI (F 2,76-11d = 10.2; P = 0.0001) but not by protein consumed. Similar results were found when larvae fed on S++ and A genotypes. Second instar larvae on A consumed more protein (1.3 g) than those on S++ (0.8 g) plants, but no differences were found during the first and third instars (F 1,49-Second = 152.9; P < 0.0001; F 1,49-Third = 1.0; P = 0.3; Fig. 5/Appendix 1). The calculated total amount of protein consumed was higher for larvae on A (7.0 g) than on S++ (6.3 g) genotypes (F 1,49-Total = 7.8; P = 0.007; Fig. 5/Appendix 1). Larval mass of caterpillars on A and S++ genotypes was affected by the amount of TPI and protein consumed (F 1,49-11d = 49.8; P < 0.0001). In summary, caterpillar fed on high TPI-genotypes consumed more TPI and less protein than those larvae fed on low TPI-genotypes. We estimated the effect of the differences in larval movement by simulating TPI and protein consumption by transposing movement and consumption patterns from untransformed (WT and A) to transformed (AS –, AS-, and S++) plants as explained in the supplemental section (Fig. 6 and Tables 1a and b/Appendix 1). Patterns of larval movement on WT plants (S WT ) did not alter TPI consumed on the AS (AS – and AS-) genotypes when WT movement data were transposed to larvae on AS genotypes (P AS – = 0.9; P AS- = 0.09); the highest values were found in the calculated WT genotype (F 4,126 = 545.5; P < 0.0001; Fig. 6/Appendix 1). WT daily movement patterns decreased S WT protein consumed from AS – genotype plants (F 4,126 = 11.7; P < 0.0001; Fig. 6/Appendix 1). Larval movement on AS – plants increased TPI and protein consumed on WT plants (F 4,127-TPI = 473.8; P < 0.0001; F 4,127-Protein = 8.1; P < 0.0001; Fig. 6/Appendix 1). Figure 6 Seed capsule production per plant of N. attenuata genotypes (WT, AS –, AS-, S++ and A), regressed against M. sexta larvae mass (g) 11 d after neonates started to feed on the leaf at S1 position (elongation stage). Line represents a regression fitted to the points (Y = -12.773 (g) + 42.229; R 2 = 0.5642). Larval movement on A plants did not change the amount of TPI consumed on S++ genotype plants (F 1,49 = 1.5; P = 0.2) but did increase the amount of protein consumed (F 2,74 = 7.4; P = 0.001; Fig. 6/Appendix 1); larval movement on S++ plants did not change protein consumed on A genotype (F 2,73 = 3.8; P = 0.2; Fig. 6/Appendix 1). In summary, when larval movement patterns on low TPI plants were transposed to high TPI genotypes, protein and TPI consumption increased. Transposing WT movement patterns to AS – genotype decreased the amount of protein consumed. Fitness consequences of TPI expression for plants attacked by M. sexta larvae To determine whether expression of TPIs increases N. attenuata' s fitness when plants are attacked by M. sexta larvae, we measured caterpillar mass on and capsule number per plant from transformed and untransformed genotypes with either low or no TPI activity (A, AS –, and AS-) and high TPI activity (WT and S++). Larval mass of caterpillars fed on low TPI genotypes were higher (45-21 %) than those fed on genotypes with high TPI activity (F 4,35-4d = 20.0; P < 0.0001; F 4,195-11d = 8.6; P < 0.0001; Fig. 4 ), those that fed on either WT or S++ (F 1,14-4d = 0.02; P = 0.9; F 1,78-11d = 0.1; P = 0.6) or AS – or A (F 1,14-4d = 0.01; P = 0.9; F 1,78-11d = 1.6; P = 0.2) did not differ (Fig. 4 ). Figure 4 M. sexta mass (mean ± SEM) at 4 and 11d after neonates started to feed on leaves at S1 position (elongation stage) from WT, AS –, AS-, S++, and Arizona (A) genotypes. Bars with the same letter are not significantly different at P < 0.05 determined by one-way ANOVA. Thin bars indicate ± SEM. We measured lifetime seed capsule number per plant on unattacked and attacked plants and calculated the mean differences and the percentage mean differences between treatments in order to estimate fitness consequences of constitutive and inducible TPI production. As expected, mean capsule number in unattacked plants was higher on genotypes with either low or no TPI activity (A and AS –) than on genotypes with intermediate and high TPI activity (WT, S++, and AS-; Fig. 5 ), which reflects the fitness cost of TPI production. Eleven days of caterpillar attack reduced seed capsule production per plant in all genotypes and reversed the pattern of seed capsule production among high and low TPI-containing genotypes. Within the group of transformed (AS – and AS-) and untransformed (WT) unattacked plants from the Utah genotype, mean capsule number was higher (22–25 %) on the genotype with low TPI activity (AS –) than on genotypes with intermediate and high TPI activity (AS-and WT; F 2,81 = 8.6; P = 0.004; Fig. 5 ); however after 11 d of caterpillar attack, mean capsule number, absolute and relative mean difference in capsule number were the highest on WT (15 capsules) and the lowest on AS – (4 capsules) genotypes (F 2,81 = 25.3; P < 0.0001; Fig. 5 and Table 1 ). Within the Arizona genotypes, mean capsule number of unattacked plants was higher on the genotype with no TPI activity (A; 49 capsules) than on the genotype with high TPI activity (S++; 35 capsules; F 1,54 = 16.4; P = 0.0002; Fig. 5 ). However, when plants were attacked, mean capsule number as well as absolute and relative mean difference in capsule number were higher on S++ (23 capsules) than on A genotypes (17 capsules; F 1,54 = 7.9; P = 0.006; Fig. 5 and Table 1 ). Figure 5 Mean capsule number from WT, AS –, AS-, S++, and A genotypes that were either unattacked or attacked by Manduca sexta larvae for 11 days. Bars with the same letter within a group are not significantly different at P < 0.01 determined by one-way ANOVA. Thin bars indicate ± SEM. Table 1 Absolute and relative mean differences between treatments in seed capsule production and TPI levels from either untransformed wildtype (WT) or homozygous T 3 independently transformed lines of a WT genotype of Nicotiana attenuata which had been transformed with constructs containing the pi gene in an anti-sense orientation (AS –, AS-); absolute and relative mean differences between untransformed plants of the Arizona (A) genotype and plants of the Arizona genotype transformed with constructs containing the full-length pi gene in a sense (S++) orientation, that were either unattacked or attacked by Manduca sexta larvae for 11 days. Genotypes Mean diff. in capsule number % Mean diff. in capsule number P TPI levels WT 18.04 54.18 <0.0001 High AS-- 40.36 91.35 <0.0001 Low AS- 23.14 68.14 <0.0001 Intermediate S++ 12.64 35.47 <0.0001 High A 32.25 65.58 <0.0001 No TPI P-values are from one-way ANOVAs between treatments. In order to determine the effect of caterpillar attack on seed capsule production per plant, we regressed caterpillar mass against seed capsule production per plant from transformed and untransformed genotypes and found that a linear equation (Y = -12.7 (g) + 42.2; R 2 = 0.5; Fig. 6 ; P < 0.0001) represented the best fit. The relationship suggests that the higher the M. sexta larvae mass, the lower the seed capsule number production per plant. Discussion Our experiments demonstrate that the benefits of TPI expression in N. attenuata grown in greenhouse conditions outweigh their costs when plants are attacked by M. sexta larvae. Unattacked plants with low constitutive TPI levels produced more seed capsules (AS–: 44, AS-: 34 and A: 49 capsules) than did plants with high TPI levels (WT: 33 and S++: 35 capsules), and 11 days of M. sexta attack reduced seed capsule production per plant in all genotypes and reversed the pattern of seed capsule production with higher reductions in AS (AS–: 91 % and AS-: 68 %) and A (65 %) than in WT (54 %) and S++ (35 %) plants (Fig. 5 and Table 1 ). This differential reduction in seed capsule production amongst genotypes correlated negatively with larval mass. Across all genotypes, the larger the larval mass, the lower the number of capsules per plant (Fig. 6 ). This result is consistent with previous demonstrations that endogenous TPIs decrease the performance of M. sexta [ 33 ] and with the central prediction of the Optimal Defense theory, namely that defense is costly [ 23 , 35 , 37 ]. Moreover, the results highlight the heuristic value of the cost-benefit paradigm for functional studies. However, conclusive evidence that TPI expression in N. attenuata outweigh their costs when plants are attacked will require field experiments in which both ecological and allocation costs of defense can arise. For example, constitutive and inducible TPI production incurs large fitness costs in N. attenuata when plants where grown with competitors [ 32 , 42 ], one of the dominant selective factors for this species [ 30 ]. In addition, other factors such as temperature and M. sexta predators can affect feeding damage [ 38 ]. Despite the central role of the cost-benefit model of inducible defenses, the vast majority of research in this area examines how inducible defenses influence either herbivore performance or plant fitness in separate experiments and their currencies are usually not comparable (i.e., plant fitness for the cost; herbivore performance for the benefits). Few studies have tested the cost-benefit model by measuring both costs and benefits in the same currency (plant fitness for both the costs and benefits) and have elicted plant defenses by either applying methyl jasmonate or damaging leaves [ 5 , 11 ]. However, because of the pleiotorpic effects of the elicitors, the observed fitness differences do not arise solely from the expression of the resistant trait [ 30 , 43 ], and therefore these studies are likely to overestimate the fitness cost of resistance. Direct genetic manipulation of TPI expression allowed us measure the costs and benefits of a defensive protein in a plant-insect interaction in the common currency of plant fitness. Transformation technology gave us the means to manipulate TPI expression with high precision. Antisense expression of the pi gene reduced constitutive and caterpillar induced TPI levels in AS – and AS-genotypes (by 35–80% of the activity of WT) in S1 and stem leaves without influencing protein contents. Caterpillar attack increased TPI levels 2–2.5-fold in either WT or AS genotypes (Fig. 2 ; Figs 1-3/Appendix 1) but the absolute levels were substantially lower in the AS genotypes. Transformation of the A genotype with a functional TPI gene under the control of a constitutive promoter (S++ genotype) produced TPI levels that were 74% of the activity found in caterpillar attacked WT plants (Fig. 2 ; Fig. 4/Appendix 1). Because these transformed lines did not differ in any other measured defense traits [ 42 ], they allowed us to examine the defensive function of TPIs by constraining plant responses to herbivore attack and observe unconstrained herbivore behavior in response to these constrained plant responses. In this way, the dynamics of the plant responses, or the lack thereof, are reflected in the herbivore behavior. Low constitutive TPI expression in the host plant may increase proteolytic enzyme activity in the guts of neonates, digestion efficiency and the growth rates [ 44 ] (Fig. 4 ). This early increase in larval growth rate translates into increases in pupal mass, which is an accurate proxy for fecundity in Lepidoptera [ 45 , 46 ], but may also profoundly influence larval movement. Given the large within-plant heterogeneity in food quality, it is reasonable to expect a complex resource-oriented larval behavior that changes with instars [ 27 , 47 ]. Moving has been shown to be costly during the first 3 instars [ 26 , 38 ], but these costs are thought to decrease with size [ 48 ]. Larvae with larger mass (on either low or no TPI-producing genotypes) left the S1 leaf 1–2 days earlier than did those with lower mass (on high TPI-producing genotypes; Fig. 3 ). The heavier larvae moved earlier than lighter larvae to young leaves which typically have higher levels of protein and water contents [ 27 , 48 , 49 ]. Based on the calculations, larvae fed on high TPI genotypes consumed 3–4 fold more TPI and 10 % less protein than did larvae feeding low TPI genotypes over the 11d of the experiment (Fig. 3 and 4 ; Fig. 5/Appendix 1). Since we did not measure the amount of leaf consumed by larvae and the values used for the calculation of protein consumption are from plants with natural TPI levels, the calculations likely underestimate the amount of protein consumed. These results suggest that a high TPI content keeps caterpillars from feeding on the high-protein younger leaves at the TOP of the plants possibly by decreasing larval mass and thereby their ability to move. Larval movement influences the caterpillar's ability to compensate for variation in diet quality. By moving, caterpillars can exploit the high within-plant heterogeneity in food quality to compensate for nutritional imbalances. For example, Helicoverpa zea larvae feed on multiple plant structures to balance their amino acid requirements [ 50 ]. M. sexta larvae fed low protein and nutritionally unbalanced diets compensated not only for the decreased protein intake [ 51 ] but also for unbalanced nutrition by selecting diets high in the missing nutrients which increased larval growth rates [ 52 , 53 ]. Growth depends on nutrient ratios, and insects may use behavioral and post-ingestive mechanisms to compensate for nutrient imbalances [ 54 , 55 ]. To estimate the consequence of higher caterpillar mass on movement, we transposed the larval location data of caterpillars from those observed on low-to high-TPI genotypes, and found increased larval protein (10 %) and TPI (12 %) consumption (Fig. 6/Appendix 1). Transposing daily larval location data in the opposite direction decreased (by 10 %) protein consumed but did not influence TPI consumption (Fig. 6/Appendix 1). These calcuations suggest that M. sexta caterpillars may adjust their feeding positions to minimize TPI consumption and maximize protein intake. Hence the naturally occurring high TPI levels delay larval growth and prevent caterpillars from feeding on high-quality younger leaves, which may have a high fitness value for the plant [ 35 , 50 , 56 ]. The interaction between N. attenuata and M. sexta starts with moths ovipositing on leaves at the bottom of the plant; oviposition is influenced by temperature, food quality and quantity, and predation risk [ 38 ]. Plants respond by increasing TPI levels, which decreases larval mass and survivorship [ 33 ], and by increasing the emission of volatile organic compounds, which alters oviposition choices and attracts the generalist predator Geocoris pallens to feeding larvae [ 31 ]. Geocoris is size selective and preferentially attacks eggs and larvae in the first three instars. The up-regulation of TPIs by herbivore attack slows larval growth and keeps larvae in stages that are more vulnerable to the predator, thus increasing larval mortality [ 57 ]. Interestingly, the volatile signals that function as indirect defenses by attracting Geocoris to feeding larvae are elicited by the same signals that elicit TPI production [ 34 , 36 , 58 ], providing the mechanism of coordination among these defense system. Once larvae reach a mass that can compensate for the cost of movement, they leave the leaf with high TPI levels and move upward within the host plant and feed preferentially on young leaves with high levels of protein and nicotine, which increases larval mass and decreases plant fitness [ 35 , 38 , 51 ]. A starvation period during the firsts instars was found to reduce M. sexta larval development more than feeding on fully JA-induced (high TPIs) N. attenuata leaves [ 40 ]. Thus for these larval instars, the costs of movement, which include increases in starvation and predation risks are likely greater than the costs of coping with a plant's induced defenses. Other generalist herbivores on N. attenuata , namely noctuid larvae and weevil beetles, usually attack older leaves that are lower in nutrients as well as nicotine [ 30 , 35 , 38 ]. Nicotine is not an efficient defense against M. sexta , because this insect is adapted to feed on N. attenuata and larvae can detoxify nicotine [ 59 - 61 ]. Moreover, its attack down-regulates nicotine production which could be sequestered by the herbivore and maybe co-opted and used as a defense against parasitoids [ 30 , 62 , 63 ]. Hence the plant relies on other defenses when attacked by M. sexta larvae: TPIs, for example, decrease larval mass and prevent caterpillars from feeding on leaves with high fitness value for the plant. This delayed in caterpillar movement upward within the plant, maybe a result of larvae adaptation to high leaf-TPI levels by increasing the production of insensitive gut proteases to TPIs [ 29 ]. Eliciting only those defenses that confer resistance to the attacking herbivore (targeting), rather than the entire defensive repertoire, may minimize the cost of resistance [ 14 ]. Conclusions We conclude that despite the ongoing evolutionary interaction between N. attenuata and M. sexta , TPI-mediated decreases in herbivore performance translates into a fitness benefit for the plant. Methods Plant material and transformation N. attenuata used in this study were grown from seeds collected from either Utah [ 5 ] or Arizona [ 32 ] and inbred 10 and 4 generations, respectively. In order to silence the expression of N. attenuata 's pi gene in the genotype collected in Utah (WT), WT was transformed by an Agrobacterium -mediated transformation procedure with pNATPI1, which contains 175 bp of N. attenuata 's 7-repeat domain pi gene in an anti-sense orientation (AS), as described in [ 32 ]. Southern gel blot analysis confirmed that all T 3 lines were single-copy independent transformants [ 42 ]. In this study, we used a genotype of N. attenuata collected from Arizona (A), with methyl jamonate (MeJA)-inducible nicotine levels identical to that found in WT plants, but completely lacking the ability to produce TPIs or accumulate TPI mRNA [ 32 ]. More recently, the mutation in the 7-domain repeat pi of A plants has been characterized and found to be located in the 5'signal peptide, resulting in a premature stop codon (J. Wu and I.T. Baldwin, unpublished data). Because we never detected TPI activity with radial diffusion assay in A genotype [ 34 ], nor have we detected TPI mRNA transcript with either northern blot analysis or reverse transcriptase-PCR, we suggest that this transcript is rapidly silenced [ 33 ]. Plants of the A genotype were transformed with a binary transformation vector pRESC2PIA2 containing the full-length 7-domain N. attenuata pi gene from the WT genotype in the sense orientation under control of the constitutive CaMV 35S promotor [ 42 ]. Several T 3 lines harboring a single copy of the transgene [ 42 ] were screened for TPI activity, and all had TPI activity comparable to that of elicited WT plants. One of these A lines (S++) with 60% of the activity of MeJA-elicited WT plants was selected for study. Arizona non-transformed plants (A) had no detectable TPI activity. All of these transformed and untransformed genotypes were used in the experiments and the quality of the seeds that these genotypes produce do not differ from the seed quality of the WT. Bioassay experiments and plant fitness determination In order to determine the effect of M. sexta herbivory on the fitness of N. attenuata 's genotypes using either down-regulation or restored expression of the pi gene, a single M. sexta neonate was placed on the leaf growing at node S1 (Fig. 1 ) of 48 soil-grown plants in elongation stage of AS lines (AS – and AS-), on A line transformed to express the functional pi (S++), and on untransformed genotypes (WT and A). Larvae were allowed to move and feed freely on plants for 11 days. Their mass was determined 4 and 11 days after hatching. Larval movement on the plant during this time was monitored, and larval location on the plant classified as follows: S1 (leaf where larvae started to feed), BOTTOM (from 0 to S3 leaf position), MIDDLE (from S4 to S5 leaf position), and TOP (from S6 to S9 leaf position; Fig. 1 ). Eggs of Manduca sexta L. (Lepidoptera: Sphingidae) were obtained from Carolina Biological Supply Company (Burlington, North Carolina, USA) and placed in plastic containers (200 mL) on a moist tissue. The containers were kept in climate chambers at 28°C and 65 % relative humidity under a 16:8 h light:dark photoperiod until the eggs hatched. Seeds were germinated in diluted liquid smoke solutions as described in [ 64 ]. Seedlings were transplanted in 1-L pots in a glasshouse under the conditions described in [ 42 ] with 1000 – 1300 μmol m -2 s -1 PPFD supplied by 450 W Na-vapor HID bulbs. To compare the lifetime reproductive performance among genotypes after being either unattacked or attacked by M. sexta larvae, we recorded the number of seed capsules per plant from 28 plants (8 + 12 plants were used to TPI determination) of each genotype and treatment combination two weeks after last watering day. Daily watering stopped 21 d after neonates started to feed on the leaf, in order to mimic the drying and termination of growth in the plant's natural habitat, the Great Basin Desert. The number of capsules per plant reflects the lifetime reproductive output (seeds) in N. attenuata under natural or glasshouse conditions [ 5 , 65 ]. Constitutive and TPI activity induced by caterpillar damage were determined from stem and rosette leaves before the larvae were placed on the leaf at node S1 (8 plants; 4 rosette leaves; 5 stem leaves; Fig. 1 ), and 4 (8 plants; 4 rosette leaves; 8 stem leaves) and 11 (12 plants; 4 rosette leaves; 14 stem leaves) days after the larvae started to feed. During the last harvest TPI activity was also determined on axillary leaves from S1 to S4 nodes. Protein concentrations and TPI activity were measured and expressed as nmol mg -1 as described in [ 34 ]. Larvae TPI and protein consumption were calculated and simulated as explained in the supplemental section. Statistical analysis Data were analyzed with Stat View, Version 5.0 (SAS, 1998). The TPI, protein and larval mass, and calculated and simulated values were analyzed by ANOVAs followed by Fisher's protected LSD post-hoc comparisons in all experiments. Differences in larval number on plants were analyzed with the Mann-Whitney U -test. Author's contributions JAZ carried out the experiments and analyzed the data, while planning of the experiment and writing of the manuscript was a joint effort by JAZ and ITB. Supplementary Material Additional File 1 Calculation of TPI and protein consumed by M. sexta larvae. Table 1. Combination of TPI and protein from different N. attenuata genotypes and larval location of different genotypes that have either calculated (C) or simulated (S) values. Fig. 1: TPI activity (mean ± SEM) and protein content from different leaf positions of WT plants at the elongation stage. Fig. 2: TPI activity (mean ± SEM) and protein content from different leaf positions of AS – plants at the elongation stage. Fig. 3: TPI activity (mean ± SEM) and protein content from different leaf positions of AS-plants at the elongation stage. Fig. 4: TPI activity (mean ± SEM) and protein content from different leaf positions of S++ plants at the elongation stage. Fig. 5: Calculated TPI and protein consumed by M. sexta larvae fed on WT, AS–, AS-, S++, and A genotypes during the first, second and third instars. Fig. 6: Calculated and simulated TPI and protein consumed by M. sexta larvae. Click here for file | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC514560.xml |
516028 | Cross-species global and subset gene expression profiling identifies genes involved in prostate cancer response to selenium | Background Gene expression technologies have the ability to generate vast amounts of data, yet there often resides only limited resources for subsequent validation studies. This necessitates the ability to perform sorting and prioritization of the output data. Previously described methodologies have used functional pathways or transcriptional regulatory grouping to sort genes for further study. In this paper we demonstrate a comparative genomics based method to leverage data from animal models to prioritize genes for validation. This approach allows one to develop a disease-based focus for the prioritization of gene data, a process that is essential for systems that lack significant functional pathway data yet have defined animal models. This method is made possible through the use of highly controlled spotted cDNA slide production and the use of comparative bioinformatics databases without the use of cross-species slide hybridizations. Results Using gene expression profiling we have demonstrated a similar whole transcriptome gene expression patterns in prostate cancer cells from human and rat prostate cancer cell lines both at baseline expression levels and after treatment with physiologic concentrations of the proposed chemopreventive agent Selenium. Using both the human PC3 and rat PAII prostate cancer cell lines have gone on to identify a subset of one hundred and fifty-four genes that demonstrate a similar level of differential expression to Selenium treatment in both species. Further analysis and data mining for two genes, the Insulin like Growth Factor Binding protein 3, and Retinoic X Receptor alpha, demonstrates an association with prostate cancer, functional pathway links, and protein-protein interactions that make these genes prime candidates for explaining the mechanism of Selenium's chemopreventive effect in prostate cancer. These genes are subsequently validated by western blots showing Selenium based induction and using tissue microarrays to demonstrate a significant association between downregulated protein expression and tumorigenesis, a process that is the reverse of what is seen in the presence of Selenium. Conclusions Thus the outlined process demonstrates similar baseline and selenium induced gene expression profiles between rat and human prostate cancers, and provides a method for identifying testable functional pathways for the action of Selenium's chemopreventive properties in prostate cancer. | Background Gene expression profiling, along with other methods to evaluate the global changes in genomes, provides the opportunity to understand whole scale changes present in human biology. Yet the sheer mass of data presented by these techniques often makes subsequent analysis difficult. Techniques such as gene expression profiling may result in the identification of hundreds if not thousands of differentially expressed genes that may be associated with the biological process, but may also represent noise related to the biological and technical variation. In an economic environment where limited resources are available for the follow-up and validation of potential target genes methods must be provided for the prioritization and sorting of data. Previous methods have relied heavily on the mapping of metabolic pathways or transcription factor binding sites [ 1 - 5 ]. These processes rely on the premise that the metabolic pathways associated with a given disease are well delineated, or that groups of proteins with very similar structural or functional design are involved in the disease process. In situations where these assumptions may not be true, alternative methods for the sorting of the data are needed. Here we demonstrate an alternative approach using comparative genomics and animal models of human prostate cancer to sort and identify genes involved in the response of prostate cancer cells to the proposed chemopreventive agent Selenium [ 6 , 7 ]. This process takes advantage of the continued sequencing of multiple animal genomes and the ability to produce gene expression profiles in multiple species. Through the use of these techniques one can leverage established animal models to identify genes associated with human disease processes, as is demonstrated here with the identification of Insulin-like growth factor-2 Binding protein 3 (IGFBP3) and retinoid-X-receptor alpha (RXRalpha). Results Generation of common genes and homologs Sequence validated gene libraries for both the rat and human DNAs were obtained from Research Genetics (Huntsville, AL), and were supplemented with additional DNA samples obtained from the University of Iowa rat clone sequencing program [ 8 ]. The majority of the rat DNAs, and a subset of the human DNAs were resequenced by Dr. J. Quackenbush at TIGR through a joint Program in Genomic Applications consortium. The GeneBank accession numbers for the 19,200 individual human or rat clones present in the recent slide printings were used to query the NCBI Unigene database to return the associated Unigene IDs. Unigene IDs were returned for virtually all identified clones, and were placed in an Oracle database where they were compared with the downloaded NCBI Homologene dataset (build 106) of rat, mouse, and human homologues. Of the 19,200 clones, 5740 genes were identified with homologues present on both the rat and human slides. This homologue set was used for the subsequent comparisons across species. Similar global and prostate gene expression profiles between rat and human prostate cancer cell lines We have sought to compare the rat and human prostate cancer transcriptomes in an effort to judge the degree of similarity between the two cell types. Because the use of differentially expressed genes would bias the comparison by eliminating the majority of genes that do not show any difference, we used the absolute level of expression for each gene and compared the rat and human genes for significant differences in absolute expression levels. In order to derive the absolute level of expression for individual genes in human or rat prostate cancer cells we used expression values derived from the associated self-self hybridizations performed for each cell line. The experiments were facilitated by the use of slides that have been quality controlled for the quantity of spotted target DNA through the use of a FITC label third dye [ 9 ]. These slides were subsequently imaged for FITC fluorescence and sorted based on the similar amounts of target DNA present on each slide [ 10 ]. Using the third dye quality control correlation coefficients of greater than 0.80 are routinely achieved between slide replicates [ 9 ]. In this manner comparisons of bound hybridized probe can be made across slides with a degree of confidence. RNA samples from cells were harvested, labeled, and homotypically hybridized to establish the baseline level of consistency within the hybridizations. Performing slice analysis on the normalized homotypic gene expression data across all the self-self hybridization slides within a species and retaining genes that demonstrated consistent expression patterns within two standard deviations of the mean expression value was performed to remove a degree of error from the technical replicates. Using the third dye as a baseline for comparison, these common expressed genes were then broken down into their component Cy3 or Cy5 expression vectors and used to build the transcriptomes for each gene using their absolute expression values. These transcriptomes were then used to compare expression values between the rat and human cell lines. These genes were annotated and gene homologues identified from the NCBI Homologene[ 11 ] dataset of rat-human homologues. Thus from a dataset of 5740 homologues, 2883 genes were found that were present within this experimental dataset and expressed in both the rat and human prostate cancer cell lines, and thus could be used for comparative genomics. These samples were processed using the Multiexperiment Viewer mircoarray statistical analysis and visualization program developed by TIGR [ 12 ]. Files were loaded and visualized for comparison across the 2883 common expressed genes in a self-organizing tree algorhythm [ 13 ] (figure 1 ) and analyzed for similarities in global expression patterns. The hierarchical clustering in self-organizing trees failed to demonstrate a pattern of clustering between species. T-test analysis [ 12 , 14 ] between the human and rat cell lines identified 58 genes (2%) which demonstrated significantly different expression patterns between species (p < 0.01 with Bonferroni correction). Thus in these comparisons, 2826 genes, or 98% of the genes examined, failed to demonstrate a statistically significant difference in expression between the human and rat prostate cancer cell lines. Using principle components analysis (figure 2 , [ 12 ]) these studies can be visualized, and demonstrate while there is some clustering of the rat and human prostate cancer cell lines, the differences are not significant. Thus when comparing gene expression patterns in rat and human cell lines one will detect significant species-specific differences in expression in 1 out of every 50 genes, with the majority of the genes demonstrating similar expression patterns. Figure 1 Gene expression profiles for human and rat prostate cancer cells. Clustering of the expressed genes in the human (LNCAP, DU145, PRO4, LN4, and PC3 derivatives) and rat (AT3, MatLyLu, and PAIII) prostate cancer cell lines based on the common homologs as defined within to NCBI Homologene database. Raw data files are available for review from the corresponding author. Figure 2 Principal Components Analysis of Rat and Human Prostate Cancer Cell Lines. There is a clustering of the human (Pro4-purple, LN4-dark-blue, PC3S-light blue, PC3US-yellow) and rat (MatLyLu-red, AT3-magenta, PAIII-green) prostate cancer cell lines in the same quadrant. The degree of separation within the quadrant was not significant by T-testing. Each sample is presented in duplicate based on independent Cy3 and Cy5 vector profiles. The presence of a large quantity of genes whose expression may be related to general cellular functions, as opposed to prostate specific metabolism, could infuse a significant amount of homogeneity to the data. In the presence of such homogeneity it may be impossible to identify the true differences that are related to prostate cellular function, and thus the perceived similarities may be artifactual. To address this issue we sought to repeat the analysis using only prostate related genes. To generate a list of such genes we used cDNAs in eight normal human prostate cDNA libraries present in the NCI Cancer Genome Anatomy Project [ 15 ]. Generation of a list of common genes proved impossible, as the combination of more than four of the cDNA lists resulted in the number of common genes being reduced to zero. A similar result was obtained when one attempted to generate a list of commonly expressed genes across multiple different cancer cDNA libraries. As an alternative approach we developed a list of 12,008 expressed genes were identified based on their presence in at least one of the eight normal human prostate cDNA libraries. The human Unigene IDs for each of the expressed genes were then used to identify the associated rat homologues from Homologene [ 11 ] and yielded 2,269 homologous rat genes (18.9%), of which 1,319 (58.1%) had associated prostate cancer gene expression data. These 1,319 prostate expressed genes were then used to repeat the comparative genomics. Similar visual and clustering results were identified for the prostate transcriptomes. T-test analysis [ 12 , 14 ] between the human and rat cell lines identified 30 prostate expressed genes (2%) which demonstrated significant differential expression between species (p < 0.01 with Bonferroni correction, while 1,289 genes (98%) failed to demonstrate a significant difference in expression across species. Thus even when only prostate expressed genes are considered, similar results were obtained. Between the rat and human prostate cancer cell lines the patterns of expression are similar for 49 of 50 genes examined. Comparison of global and prostate specific differential gene expression profiles between rat and human prostate cancer cell lines treated with selenium While global gene expression profiles appear to be similar between rat and human prostate cancer cell lines one wonders whether the response to specific physiologic stimuli may elicit similar transcriptional changes. If so, one may be able to infer a degree of homology in their biological response to the stimuli. This has already been observed on a physiological level for the rat models of prostate cancer. For example, rat and human prostate cancers respond very similarly to chemotheraputic and environmental agents including hormonal agents (both respond), cyclophosphamide (neither respond), high fat diets (increased incidence), and soy isoflavones (decreased incidence) [ 16 - 22 ]. In an effort to evaluate these similar biological responses we have compared the transcriptomes between rat and human prostate cancer cell lines treated with the proposed prostate cancer chemopreventive agent Selenium. Samples from the human PC3 and rat PA-III cell lines were treated with Selenium and examined for differential gene expression profiling. These two cell lines were chosen based on their similar biologic characteristics, as both cell lines were derived from androgen independent metastatic tumors, and thus represent tumors with similar biologic potential [ 23 , 24 ]. The cells were treated with twenty-five micromolar Selenium for either 6 hours or 5 days, to identify both immediate changes in gene transcription or changes related to the long term exposure to Selenium. Due to our interest in prostate cancer we have attempted to choose a form and concentration of Selenium that would be reflected in the ongoing prevention trials such as the SELECT prostate cancer prevention trial [ 25 , 26 ]. In this trial patients receive Selenium in the form of Selenized baker's yeast. Previous HPLC and electrospray mass spectroscopy studies have demonstrated that 85% of the Selenium in yeast is present as selenomethionine [ 27 ]. Selenomethionine has previously been used in in-vitro studies of prostate cancer cells[ 28 , 29 ]. These studies demonstrated an inhibition of prostate cancer cell proliferation over a broad range of concentrations, while an IC50 and/or decreased expression was seen at concentrations above 70 micromolar selenomethionine. To avoid the general effects of cell inhibition or cell death while focusing on the effect of Selenium we chose a lower concentration of 25 micromolar selenomethionine. These changes, while not resulting in increased cell death, did cause decreased cell division and increased doubling time in both species (data not shown). Common rat and human homologous genes demonstrating differential expression by greater than two standard deviations were identified and included 1123 genes after 6 hours and 1053 genes after 5 days of exposure to Selenium. When the expression patterns of these genes were compared across species by T-test and principle component analysis as outlined above 713 genes (25%) were found to have statistically significant differences in expression between species (p < 0.01 with Bonferroni correction). Thus when comparing rat and human samples, while the majority of the gene expression changes are similar, in at least one in four genes (p = 0.75) one can detect significant species specific differences in expression alteration when cells are treated with Selenium. Yet similar physiologic changes (decreased cellular proliferation, increased cell death) were observed in both species. These changes represent the desired physiologic changes one would expect for the chemopreventive effects of Selenium, and could be dissected by examining the common transcriptional changes seen in both species with respect to Selenium. Combined differential expression patterns for selenium responsive genes identify common gene pathways Because some of the differences in the rat and human prostate cancer cell line transcriptomes may be related to confounding variables such as culture methods, cell passage number, or time in culture, an effort was made to focus on genes that are common, and as such may define the similar Selenium based cell proliferative changes. The subsets of 1123 and 1053 differentially expressed genes (6 hours and 5 days respectively) were analyzed for genes that demonstrate similar changes in expression with respect to Selenium across species. Of these differentially expressed genes, 291 and 309 demonstrated up-regulation in rat and human cells at 6 hours and 5 days respectively. Likewise, 261 (6 hours) and 216 (5 days) demonstrated down-regulation in the presence of Selenium. When these subsets were further analyzed to identify genes with similar levels of up or down-regulation (defined as ratio differences within 0.2 units of each other) 81 genes were identified at 6 hours and 73 at 5 days (table 1-see additional file 1 ). These genes included 40 ESTs or genes with limited associated data, and 90 defined genes with associated gene data. Twenty-four of the genes were common to Selenium treatment at both 6 hours and 5 days. Additional information related to these genes was obtained using the GeneInfo data mining tool. This tool was developed by the authors (MWD, XW, HL, GZ) to allow for the rapid identification of supplemental data from the biomedical literature related to genes of interest. In brief, the tool allows one to cut and paste a list of genes based on either Unigene or Genebank IDs and search PubMed for associated references based on annotations of the associated gene names. Additional search terms can be stipulated by the user based on their knowledge of the biological process or in response to results received from the previous search. Results are returned in a table that lists the number of references that met the search criteria and provides a hyperlink to the associated references for either downloading or viewing. In this way the user is allowed to direct queries in an open manner based on their own experience or unpublished data. In this manner searches were conducted using the list of genes and the search terms "prostate cancer", "Selenium", and "apoptosis" (table 1-see additional file 1 ). IGFBP3 and RXR-alpha are expressed in the prostate, induced by selenium, and downregulated in prostate cancer Of the 154 genes identified with similar cross-species differential expression changes with respect to Selenium, two genes were identified that had unique features based on their associated references and interrelated functions. These genes, IGFBP3 and RXR-alpha were both up-regulated with respect to Selenium and could be used to suggest a model for Selenium action in prostate cancer. PXR-alpha is upregulated in both rat and human prostate cancer cells at 5 days in response to Selenium. Likewise, IGFBP3 is upregulated after six hours of Selenium treatment in both species. These two genes both contained Medline references with respect to prostate cancer, but had not yet been implicated in Selenium action. Western blotting performed on the human prostate cancer cell line PC3 with respect to Selenium validated the bioinformatically identified expression data (figure 3 ). To confirm the role of these two proteins in the prostate immunohistochemical studies on prostate cancer tissue microarrays were performed to identify IGFBP3 and RXR-alpha in both normal, nodular hyperplasia (benign prostatic hypertrophy), high grade prostatic intraepithelial neoplasia (HGPIN), invasive carcinoma, and metastatic prostatic carcinoma (table 2 ). These studies demonstrate that both IGFBP3 and RXR-alpha are expressed in the normal human prostatic epithelium (figure 4 , table 2 ). IGFBP3 is also expressed in the prostatic basal cells. Patterns of expression were predominantly nuclear, a finding that has been described for both proteins [ 30 ]. In addition, staining for IGFBP3 was also noted in the prostatic stroma, consistent with IGFBP3's associated function as a secreted protein. Decreased levels of IGFBP3 was noted in prostatic cancers when compared to normal prostate epithelium (p = 0.0044). Along with this decreased expression there was a distinct shift in the protein localization nuclear to cytoplasmic was observed (p < 0.00001), and in cases where expression was still present, there were decreased numbers and intensity of cell staining. IGFBP3 expression was similar in HGPIN, invasive carcinoma, and metastatic carcinoma. The level and pattern of IGFBP3 expression in nodular hyperplasia was similar to that seen in normal prostate tissues, and significantly different from the expression seen in cancer samples (p = 0.0036 and p < 0.00001 respectively). RXR-alpha expression was also significantly downregulated in prostate cancer when compared to normal prostate epithelium or nodular hyperplasia (p < 0.0001), and was similar to that seen in HGPIN and metastatic carcinoma. RXR-alpha expression was consistently nuclear in the samples studied, and while the intensity of staining was similar, in the remaining positive cancer cases there were decreased numbers of cells staining (8.6 +/- 12.6% in malignant epithelium vs 20.0 +/- 25.5% in normal epithelium). Figure 3 Expression of IGFBP3 and RXR-alpha with respect to Selenium. Western blotting reveals an induction of RXR-alpha or IGFBP-3 protein after Selenium treatment of human PC3 prostate cancer cells (arrows, upper row). Western blotting of immunoprecipitations from rat PAIII cells (bottom row) reveal RXR-alpha in immunoprecipitated IGFBP3 extracts (right panel) and IGFBP-3 in immunoprecipitated RXR-alpha extracts confirming and extending the reported interactions between the human proteins[40]. Table 2 Expression of IGFBP3 and RXRalpha in Prostatic Epithelium Normal Prostate Nodular Hyperplasia HGPIN Prostate Cancer Metastatic Cancer IGFBP3 Positive cases 105 62 49 202 25 Negative cases 5 1 9 36 8 Statistics (comparison) p = 0.0036 (cancer) N.S. (cancer) p = 0.0044 (normal) N.S. (cancer) IGFBP3 Intensity (avg+/-std) 2.47 +/- 0.70 2.49 +/- 0.65 2.57 +/- 0.82 2.74 +/- 0.56 2.79 +/- 0.49 Percentage cells (avg+/- std) 8.3 +/- 13.5 7.5 +/- 12.5 8.8 +/- 15.2 4.4 +/- 6.6 8.5 +/- 12.6 Nuclear cases 92 59 40 94 8 Cytoplasmic cases 22 6 18 152 11 Statistics (comparison) p < 0.00001 (cancer) p = 0.065 (cancer) p < 0.00001 (normal) N.S. (cancer) RXRalpha Positive cases 92 58 35 112 16 Negative cases 10 3 31 125 19 Statistics (comparison) p < 0.00001 (cancer) N.S. (cancer) p < 0.00001 (normal) N.S. (cancer) RXRalpha Intensity (avg+/-std) 2.73 +/- 0.51 2.78 +/- 0.50 2.83 +/- 0.38 2.76 +/- 0.49 3 +/- 0 Percentage cells (avg+/- std) 20.0 +/- 25.5 23.2 +/- 25.7 8.4 +/- 12.5 8.6 +/- 12.6 4.2 +/- 4.6 Nuclear cases 92 58 35 107 16 Cytoplasmic cases 2 0 6 9 0 Statistics (comparison) N.S. (cancer) N.S. (cancer) N.S. (normal) N.S. (cancer) Figure 4 Expression of IGFBP3 and RXRalpha in human prostate tissues. Immunohistochemical staining for IGFBP3 is present as brown staining in normal prostate (A) and prostate cancer (C). Similarly RXRalpha expression is present in normal prostate (B) and lost in prostate cancer (D). All images recorded at 100× magnification. Discussion Leveraging cross-species bioinformatics in the prioritization of gene data Through the use of cross-species comparisons of the number of differentially expressed genes to be examined after 6 hours and 5 days of Selenium treatment was dropped from 9453 and 7768 to 1123 and 1053 respectively, an 87–89 percent reduction of the sample size. Even with the use of multiple timepoints, the number of differentially expressed genes was only reduced in a single species study to 5934, less than half. By using comparative genomics the final dataset was reduced to 154 genes, providing a greater than 100 fold enrichment of the data. Thus by leveraging the additional biological species the ability to reduce the final analysis pool was substantial. This process only works if the species used have biological relevance to the disease in question. The choice of rat prostate cancer cell lines was made based on their use as an animal model for the study of prostate cancer [ 31 ]. The animal systems have been extensively used in the study of hormonal carcinogenesis, and in particular have been of value as a model of environmental and dietary effects on prostate cancer [ 18 - 20 ]. Previous studies have identified similar effects of rat animal models and prostate cancer cell lines to soy based diets [ 17 - 19 ], high fat diets [ 20 - 22 ], hormonal chemotherapeutics (Pollard, personal communication) and standard chemotherapy [ 32 , 33 ]. While comparative gene expression profiling has been performed, this has usually been through cross-species hybridizations to leverage RNA studies in species where sufficient expressed transcripts in a given species have not been identified for the production of species-specific gene expression slides, in particular for microbial genomes [ 34 - 37 ]. Thus the approach taken here leverages the production of species-specific gene expression profiles along with the increasing amount of gene homolog data generated by the sequencing of additional animal genomes. It is expected that with future genome efforts additional cross-species studies will be possible that leverage the knowledge of additional animal models in the study of disease. Similarities in prostate cancer transcriptomes across species For both overall and prostate expressed genes, we have failed to identify a significant difference in the transcriptomes between rat and human prostate cancer cell lines. This general similarity in transcriptomes may be due to the inherent biological similarities of the cell lines and/or their underlying biological origin. While the studies sought to utilize prostate cancer cell lines with similar biological potentials (established cell lines all derived from metastases) the degree of diversity present within the samples may account for some of the residual differences still identified. In addition, the extended period of time that these cell lines have been used has allowed for the continued in-vitro evolution of the cells, and could possibly extend those genomic differences. Yet the common clustering of the rat and human cell lines together suggests there are still significant similarities in their biological potential. This is also demonstrated by the similar biological potential of the cell lines when treated with a given stimulus, in this example Selenium. This parallels the similar physiological properties observed in the rat models of human prostate cancer. Based on these features we demonstrate that it is possible to identify functionally significant genes related to Selenium response by using comparative genomics. These findings also support the use of animal models in the study of human prostate cancer by suggesting that there is enough inherent genomic similarity that valuable insights may be gained from animal systems. Comparative genomics identifies functionally significant genes with respect to selenium chemoprevention A true test of the profiling method is the identification of genes that have a functional significance to the experimental system. In this case we have identified a series of genes, which when examined with additional data mining techniques, identifies genes with associated roles related to apoptosis (IGFBP3, RXRalpha, dynamin-2), antioxidant protection (selenoprotein N, peroxiredoxin I, zinc metalloprotease, glutathione S transferase), cell cycle (CDC26-anaphase promoting complex, kinetochore associated protein), and protein balance (proteasome subunit beta-4, ubiquitin conjugating enzyme). In addition, the ability to sort the identified genes by their associated biomedical literature allowed the focus to shift to IGFBP3 and RXRalpha. Retinoids, through the retinoid X receptor, have been shown to induce the expression of IGFBP3 [ 38 ]. In concert these two proteins act to induce apoptosis in cancer cell lines [ 39 ]. In particular, recent data has shown that these proteins work in synergy to enhance apoptosis in prostate cancer, and that there is a physical interaction between these two proteins in prostate cancer cells[ 40 ]. Further validation and confirmatory data is presented here that demonstrates the selenium induced expression and interaction between both RXRalpha and IGFBP3 in prostate cancer cells, along with their expression in normal prostate epithelium and subsequent down-regulation in malignant prostatic epithelium. This allows one to pose a model by which the restoration of IGFBP3 and RXRalpha levels by Selenium treatment may lead to the disruption of prostate tumorigenesis. This model is testable, and if validated, would present not only a mechanism by which Selenium may exert its effect, but provide a biomarker for assaying the effect of Selenium supplementation in the ongoing prostate cancer prevention clinical trials. Conclusions Using gene profiling on highly controlled spotted cDNA arrays we have demonstrated that similar baseline and selenium induced gene expression profiles can be identified between rat and human prostate cancer cells. This has allowed us to filter our gene expression data to identify genes whose transcriptional response to Selenium is similar across species, and by so doing focus our discovery process on specific common physiologic pathways. Two such proteins, RXR-alpha and IGFBP-3, which may be located in a common pathway, have been identified as dysregulated in human prostate cancers. This provides further support that the cross-species methods employed here can identify genes with roles in human prostate cancer. Methods Cell culture and selenium treatment Cell lines were received from ATCC, Rockford, MD, (LNCap, DU-145, MatLyLu, AT3), from Drs. Paul Lindholm and Andre Kadjacsy-Balla (LN4, Pro4, PC3, PC3-NI(PC3US), PC3-I(PC3-S)), or Dr. Morris Pollard and Mark Suckow (PA-III). These cells were cultured in RPMI (DU-145) or DME medium supplemented with 10% fetal calf serum, 10 mM glutamine, and 10 mM sodium pyruvate, and passaged 1:8 or 1:10 when the cells reached 70–80% confluence with trypsin-EDTA. For the Selenium studies PC3 or PAIII cells from a single cell stock were seeded at 1 × 10EE4 cells per ml and grown to 50% confluence at which time the culture medium was changed to either standard growth medium (above) or medium supplemented with twenty-five micromolar Selenium (Seleno-DL-methionine, Sigma cat# S3875, St. Louis MO). The cells were then cultured for an additional 6 hours or 5 days. Cells that reached 80% confluence prior to the five day timepoint were split using trypsin-EDTA and replated in either control or selenium-containing medium for the duration of the experiment. Cells were monitored for viability and cell growth with parallel growth curves conducted in triplicate, this data demonstrated the previously described [ 41 , 42 ] decrease in cellular proliferation (data not shown) observed in the presence of Selenium. RNA isolation and quantitation RNA was isolated from cells using Trizol (Invitrogen cat # 15596018, Carlsbad, CA) and subsequently examined for quality using agarose gel electrophoresis and Gelstar nucleic acid stain against known RNA standards and failed to demonstrate significant degradation based on the presence of high molecular weight RNA species, and intact 28s and 18s ribosomal RNA bands. DNA library preparation and amplification Sequence-verified rat and human libraries (Research Genetics, Huntsville, AL, and University of Iowa cDNA clone set, IA), consisting of 41,472 human clones and 36,000 rat clones were used as a source of probe DNA. A subset of 200 randomly selected clones were chosen from these libraries, resequenced locally, and demonstrated clone accuracy of 92%. We have opted to reformat libraries from 96 to 384-format for culture growth/archiving, PCR, purification, and printing. This has reduced the number of plates of our 41,472 human clone library from 432 to a more manageable 108, and the rat clone library from 375 to 94. The library was reformatted and subsequently manipulated using slot pin replicator tools (VP Scientific, San Diego, CA). Cultures were grown in 150 ul Terrific Broth (Sigma, St. Louis, MO) supplemented with 100 mg/ml ampicillin in 384 deep-well plates (Matrix Technologies, Hudson, NH) sealed with air pore tape sheets (Qiagen, Valencia, CA) and incubated with shaking for 14–16 hr. Clone inserts were amplified in duplicate in 384-well format from 0.5 μl bacterial culture diluted 1:8 in sterile distilled water or from 0.5 μl purified plasmid (controls only) using 0.26 μM of each vector primer {SK865 5'-fluorescein-GTC CGT ATG TTG TGT GGA A-3' and SK536: 5'-fluorescein-GCG AAA GGG GGA TGT GCT G-3'} (Integrated DNA Technologies, Coralville, IA) in a 20 μl reaction consisting of 10 mM Tris-HCl pH8.3, 3.0 mM MgCl 2 , 50 mM KCl, 0.2 mM each dNTP (Amersham, Piscataway, NJ), 1 M betaine, and 0.50 U Taq polymerase (Roche, Indianapolis IN). Reactions were amplified with a touchdown thermal profile consisting of 94°C for 5 min; 20 cycles of 94°C for 1 min, 60°C for 1 min (minus 0.5° per cycle), 72°C for 1 min; and 15 cycles of 94°C for 5 min; 20 cycles 94°C for 1 min, 55°C for 1 min, 72°C for 1 min; terminated with a 7 min hold at 72°. PCR reactions analyzed for single products by 1% agarose gel electrophoresis analysis. Products from replicate plates were pooled and then purified by size exclusion filtration using the Multiscreen 384 PCR filter plates (Millipore, Bedford, MA) to remove unincorporated primer and PCR reaction components. Forty wells of each 384-well probe plate were quantified by the PicoGreen assay (Molecular Probes, Eugene, OR) according to the manufacturers instructions. After quantification, all plates were dried down, and reconstituted at 150 ng/μl in 3% DMSO/1.5 M betaine. Array slide fabrication A single printing array containing 19,200 elements (human) or 2 arrays of 9,600 (rat), were printed on poly-L-lysine coated slides prepared in-house (1–2 arrays/slide) as previously described [ 9 ]. Printing was conducted with a GeneMachines Omni Grid printer (San Carlos, CA) with 16 or 32 Telechem International SMP3 pins (Sunnyvale, CA) at 40% humidity and 22°C. To control pin contact force and duration, the instrument was set with the following Z motion parameters, velocity: 7 cm/sec, acceleration: 100 cm/sec 2 , deceleration: 100 cm/sec 2 . All slides were post-processed using the previously described nonaqueous protocol[ 9 ]. Slide coating was performed as described previously [ 43 ]. Image files on all arrays were collected after blocking (fluorescein), and again after hybridization (Cy3 and Cy5) with a ScanArray 5000 (GSI Lumonics, Billerica, MA). Experimental design and bioinformatics based data analysis The experimental design utilized two biological replicates for each comparison with each replicate incorporating a Cy3/Cy5 dye flip. In addition, self-self hybridizations were performed for each sample to ensure experimental accuracy and evaluate expression bias. Comparisons were organized in a loop design for either human or rat prostate cancer cell lines, or were run as two-sample comparisons of baseline untreated control and Selenium treated cells. Array image TIFF files were analyzed with Gleams software (Nutec Sciences, Atlanta, GA). Additional TIFF file analysis, data normalization, clustering, and principle components analysis was performed using the Spotfinder, MIDAS and MultiExperiment Viewer Software from The Institute for Genomic Research (TIGR, Rockville, MD, [ 44 ], [ 12 ]) and used default values set in the MCW Practical Guide to TIGR Software Use (M. Datta, unpublished). In brief, image expression data was used as channel intensity minus background and intensity thresholds were set at a value of 300. Images were analyzed as dye flip pairs normalized using MIDAS with LocFit based LOWESS normalization and slice analysis set at two standard deviation cutoffs and a sample data population of 500 [ 45 ]. Samples were then averaged across two dye flip replicate pairs with removal of zero/dropped values using locally developed averaging software from the BEAR microarray suite (M. Datta, submitted). These final averaged values were subsequently annotated using the BEAR suite annotator and used for pattern identification and correlation with gene homologs. Homologous genes were identified from the NCBI homologene database ftp files and parsed using local scripts and databases present in the Bioinformatics Program,[ 46 ]. Additional data mining to identify references in the biomedical literature associated with specific genes and user chosen search terms was performed using the locally developed GeneInfo data tool (M. Datta, submitted). Raw data files, along with analyzed data subsets are available for use and study and can be obtained via a secure ftp site after contacting the corresponding author mdatta@mcw.edu . Protein purification, western blotting, and immunoprecipitation Protein extracts were prepared and immunoprecipitations and/or western blots made from five day twenty-five micromolar Selenium treated or control PC3 or PAIII prostate cancer cell lines as described previously[ 47 ]. In brief, ten micrograms of total protein were run on pre-cast 12% reducing SDS PAGE gels (Bio-Rad Labs, Hurcules, CA) and transferred to PVDF membranes. After blocking with caseine blocking buffer (Bio-Rad Labs, Hurcules, CA) the PVDF membranes were incubated with either anti-RXR-alpha or anti-IGFBP-3 antibodies (Santa Cruz Biotechnology, Santa Cruz, CA) at 200 μg/ml dilutions, washed, and incubated with anti-rabbit secondary antibody (2 μg/ml) and developed with ECL Chemiluminescence (cat. RPN2108, Amersham Biosciences, Piscataway, New Jersey). Immunoprecipitations were carried out using 200 microgram samples of total cellular protein, which after preclearing with protein A agarose beads was sequentially incubated with either anti-RXR-alpha (1 μg/ml) or anti-IGFBP-3 (1 μg/ml) antibodies, washed, incubated with anti-rabbit protein A agarose beads, washed, and the protein pellet western blotted with the complimentary antibody (anti-IGFBP-3 or anti-RXR-alpha, respectively), and developed with ECL Chemiluminescence. Tissue microarray production, immunohistochemistry, and analysis After expedited institutional review board approval normal prostate tissues and prostate cancer samples were obtained from de-identified discarded patient specimens. The formalin-fixed paraffin embedded specimens were prepared as 5 micron sections. Tissue microarrays were prepared from donor tissue blocks as 0.6 mm cores in 12 (4 × 4) or (5 × 5) grids with between 192 to 300 samples and used in the preparation of 5 micron sections. Immunohistochemistry was performed using primary rabbit polyclonal antibodies to the insulin-like growth factor binding protein 3 (IGFBP3, 1:300, Santa Cruz Biotechnology, Santa Cruz, CA), or retinoic-X-receptor alpha (RXR-alpha, 1:800, Santa Cruz Biotechnology, Santa Cruz, CA) using methods previously described [ 48 , 49 ]. In brief, endogenous peroxidase from deparaffinized sections were blocked with Methanol/Acetic acid, and after treatment with blocking serum (ABC kit, Pierce Biotechnology, Rockford, IL) samples were incubated for 30 minutes with either anti-IGFBP3 (1:300) or anti-RXRalpha (1:600). Sections were subsequently washed, and incubated with anti-rabbitt secondary antibody conjugated to horseradish peroxidase and counterstained with Mayers hematoxalin. Antigen retrieval (90 C waterbath for 10 minutes) was used for RXRalpha. Positive controls for each antibody included nuclear staining in Sertoli cells [ 50 ] and lymphocytes[ 51 ]. Positive staining was recorded and scored on a 0–2 scale (0 = no staining, 1 = staining that does not obscure the hematoxalyn counterstain, 2 = staining that obscures the hematoxalyn counterstain). Evidence of positive staining was recorded as presence of staining (yes/no) or percent of epithelial or basal cells staining (number of cells staining over total number of cells). Patterns of staining (nuclear, cytoplasmic, membranous, diffuse extracellular) were also recorded. All samples were analyzed and recorded by two separate personnel, including a trained urologic pathologist (MWD, BM). Statistical analysis was performed using Chi-squared probability analysis. Abbreviations None declared. Authors contributions M.W.D. was responsible for the conception and implementation of this project in association with P.J.T., M.S., and M.P. H.L., X.W., and G.Z. were actively involved in the programming, database construction, and testing of the software. M.S. and M.H. were responsible for spotted cDNA construction, hybridization, and experimental analysis along with M.W.D. Cell culture, western blots, immunoprecipitations, and selenium treatments were performed by M.S. with assistance by B.M. Tissue microarray staining and analysis was performed by M.W.D., R.D., T.B., and B.M. All the authors reviewed and accepted the final version of the paper. Supplementary Material Additional File 1 Table 1, Word document, Table of the genes identified in the selenium gene expression studies. Click here for file | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC516028.xml |
546226 | Construction of predictive promoter models on the example of antibacterial response of human epithelial cells | Background Binding of a bacteria to a eukaryotic cell triggers a complex network of interactions in and between both cells. P. aeruginosa is a pathogen that causes acute and chronic lung infections by interacting with the pulmonary epithelial cells. We use this example for examining the ways of triggering the response of the eukaryotic cell(s), leading us to a better understanding of the details of the inflammatory process in general. Results Considering a set of genes co-expressed during the antibacterial response of human lung epithelial cells, we constructed a promoter model for the search of additional target genes potentially involved in the same cell response. The model construction is based on the consideration of pair-wise combinations of transcription factor binding sites (TFBS). It has been shown that the antibacterial response of human epithelial cells is triggered by at least two distinct pathways. We therefore supposed that there are two subsets of promoters activated by each of them. Optimally, they should be "complementary" in the sense of appearing in complementary subsets of the (+)-training set. We developed the concept of complementary pairs, i.e., two mutually exclusive pairs of TFBS, each of which should be found in one of the two complementary subsets. Conclusions We suggest a simple, but exhaustive method for searching for TFBS pairs which characterize the whole (+)-training set, as well as for complementary pairs. Applying this method, we came up with a promoter model of antibacterial response genes that consists of one TFBS pair which should be found in the whole training set and four complementary pairs. We applied this model to screening of 13,000 upstream regions of human genes and identified 430 new target genes which are potentially involved in antibacterial defense mechanisms. | Background Promoter model construction is a way to utilize information about coexpressed genes; this kind of information becomes more and more available with the advent of gene expression mass data, mainly from microarray experiments. Having a promoter model at hand, one has (i) an explanatory model that and how the coexpressed gene may be coregulated, and (ii) a means to scan the whole genome for additional genes that may belong to the same "regulon". The field of searching for regulatory elements in silico and promoter modeling is already well-cultivated. In spite of numerous sophisticated approaches devoted to this subject [ 1 - 9 ], we still lack a standard method which would enable us to produce promoter models. This may indicate that the existing approaches have their distinct shortcomings and that, thus, the field is still open for new ideas. The biological system we consider in this work is the transcriptional regulation of the response of lung epithelial cells to infection with Pseudomonas aeruginosa . Binding of bacteria to a eukaryotic cell triggers a complex network of interactions within and between both cells. P. aeruginosa is a pathogen that causes acute and chronic lung infections affecting pulmonary epithelial cells [ 10 , 11 ]. We use this example for examining the ways in which the response of the eukaryotic cell(s) is triggered, leading us to a better understanding of the details of the inflammatory process in general. After adhesion of P. aeruginosa to the epithelial cells, the response of these cells is triggered by at least two distinct agents: bacterial lipopolysaccharides [ 12 ] and/or bacterial pilins or flaggelins [ 13 ]. Both pathways lead to the activation of the transcription factor NF-κB. It has also been shown that transcription factors AP-1 and C/EBP participate in this response [ 14 , 15 ]; pronounced hints on the participation of Elk-1 [ 16 ] have been reported as well. However, it is a commonly accepted view that transcription factors which are involved in a certain cellular response cooperate and in most cases act in a synergistic manner. Therefore, their binding sites are organized in a non-random manner [ 2 , 3 , 8 , 9 ]. We use this consideration as a basis for constructing a predictive promoter model. We searched for combinations of potential transcription factor binding sites (TFBS), considering those transcription factors (TFs) that are known to be involved in antibacterial responses. Some of the found combinations could be predicted from the fact that they may constitute well-known composite elements, like those containing NF-κB and C/EBP or NF-κB and Sp1 binding sites [TRANSCompel, [ 17 ]]. We start with a search for pairwise combinations of TFBS in a set of human genes published to be induced during antibacterial response, considering that combinations of the higher orders can be constructed from them later on. We suggest a simple, but exhaustive method for searching for TFBS pairs which characterize the whole training set, and combinations of mutually exclusive pairs (complementary pairs). The idea of starting the analysis with a "seed" of sequences allows a very biology-driven way of initial filtering of information.To enhance the statistical reliability and to get additional evidence in TFBS combination search, we applied the principal idea of phylogenetic footprinting (using orthologous mouse promoters), yet proposing a different view on applicability of this approach. Finally we came up with a promoter model which we applied to screening of 13,000 upstream regions human genes. We identified 430 new target genes which are potentially involved in antibacterial defense mechanisms. Results Development of the approach In every step of our investigations we tried to combine purely computational approaches with the preexisting experiment-based knowledge, as it is represented in corresponding databases and literature, and with our own biological expertise. To develop a promoter model, the first task is to select those transcription factors, the binding sites of which shall consitute the model. The overwhelming majority of methods and tools estimating the relevance of predicted TF binding sites in promoter regions are based on their over- and underrepresentation in a positive (+) training set in comparison with some negative (-) training set. If, however, a binding site is ubiquitous, or very degenerate, so that it can be found frequently in any sequence, the comparison with basically any (-)-training would not reveal any significance for its occurrence. That tells nothing about their functionality in any specific case, which may be dependent on some additional factors and/or other conditions. Therefore, basing the decision about the relevance of a transcription factor for a certain cellular response solely on whether its predicted binding sites are overrepresented in the responding promoters may lead to a loss of important information. Thus, we did not rely on this kind of evidence but rather chose the candidate transcription factors according to available experimental data. We found 5 factors reported in literature as taking part in anti-bacterial or similar responses and selected them as candidate TFs [ 11 , 12 , 15 , 18 - 29 ]. Not all of these candidate TFs are overrepresented in the (+)-training set used in this analysis (Table 1 ; see also Methods). For instance, no overrepresentation has been found for important factors such as NF-κB, AP-1 and C/EBP. Nevertheless, these factors were included in the model, because not the binding sites themselves, but their combinations may be overrepresented. Table 1 The genes of the (+)-training set (without orthologs). Marked with asterisks are those included in the "seed" set. No Gene name Accessin no. And LocusLinkID Experimental evidence Additional information Participation in anti-Pseudomonas response 1 Monocyte chemoattractant protein-1, MCP-1* EMBL: D26087 Microarray [66], other experiments [20,21,38] Is well know as expressed in antibacterial response 100% 2 β-defensin* LocusLinkID: 1673 [15,18,19,39,40] Is well known as expressed in antibacterial response; important target gene in innate immunity 100% 3 Interferon regulatory factor 1, IRF-1* LocusLinkID: 3659 Microarray [66] Known to be expressed in epithelial cells probable 4 Equilibrate nucleoside transporter 1, SLC29a1 LocusLinkID: 2030 Microarray [66] 5 Proteinkinase C η type, PKCη* LocusLinkID: 5583 Microarray [66] TRANSPATH ® Important link in Ca 2+ -connected pathways probable 6 Folypolyglutamate synthase, FPGS Ensembl : ENSG00000136877 Microarray [66] 7 RhoB* LocusLinkID: 388 Microarray [66] is induced as part of the immediate early response in different systems probable 8 Origin recognition complex subunit 2, hORC2L LocusLinkID: 4999 Microarray [66] 9 Transcription factor TEL2* LocusLinkID: 51513 Microarray [66] Transcription factor probable 10 Interleukin 8, IL8* EPD: EP73083 LocusLinkID: 3576 [10,11,26,44,45] Is well know as expressed in antibacterial response 100% 11 Transcription factor ELF3* LocusLinkID: 1999 Microarray [66] Transcription factor probable 12 Mucin 1(mouse gene), MUC1* RefSeq: NM_013605 [17,27,28,36,47] Different mucins are shown as expressed in antibacterial response 100% 13 NF-kappaB inhibitor alpha, IkBa* LocusLinkID: 4792 EPD: EP73215 Microarray [66] NF-kB inhibitor, the main link in NF-kB-targeting pathways Very high 14 Tissue Factor Pathway Inhibitor 2, TFPI LocusLinkID: 7980 EPD: EP73430 Microarray [66] 15 Urokinase-type plasminogen activator precursor, PLAU LocusLinkID: 5328 Microarray [66] 16 c-jun* Microarray [66] Transcription factor probable 17 Cytochrom P450 dioxin-inducible* LocusLinkID: 1545 Microarray [66] Stress-inducible probable 18 Dyphtheria toxin resistance protein, DPH2L2 EPD: EP74285 Microarray [66] On the other hand, some of the factors, which have also been mentioned in literature as potentially relevant (e.g., SRF [ 30 ]) or might be of a certain interest because of their participation in relevant pathways (CREB, according to the TRANSPATH database [ 31 ]) were not included in the model because we could not adjust the thresholds for their detection according to our requirements (see Methods). SRF were of special interest, because it is known that it tends to cooperate with Elk-1 [ 30 ], but to identify 80% of TP we had to lower the matrix similarity threshold to 0.65, which is unacceptably low and would provide too many false positives. Finally, we constructed our promoter model of binding sites of 5 TFs (NF-κB, C/EBP, AP-1, Elk-1, Sp1), considering their pairwise combinations and some combinations of higher order (complementary pairs, see below). In several steps of the model construction we had to estimate overrepresentation of a feature in the (+)-training set compared with the (-)-training set. We operated with the number of sequences that possess the considered feature, in our case a pair of TFBS, at least once. Otherwise, mere enrichment of a feature in the (+)-training set may be due to strong clustering in a few members of that set which would not lead to a useful prediction model. At the first step the T-test has been performed (the normality of distribution has been demonstrated before (data no shown)), but it appeared to be a weak filter: for example, we could find several pairs which showed, if estimated with T-test, a remarkable overrepresentation (p < 0.001), but with a difference of 97% in the (+)-training set versus 85% in the (-)-training set, which is of no practical use to construct a predictive model, since it is also important to have minimal occurrence of a discriminating feature in the (-)-training set. In the further work we considered all pairs with p < 0.005, but as this did not reasonably restrict the list of considered pairs, we had to apply an additional filtering approach. For this purpose we used a simple characteristic such as the percentage of sequences in (+)- and (-)-training sets. By operating directly with percentages we could easily filter out those pairs which would identify too many false positive sequences, thus getting rid of a substantial part of useless information. This procedure allows to estimate immediately the applicability of the model to identify further candidate genes that may be involved in the cellular response under consideration (see Methods ). The main problem of promoter model construction are the numerous false positives. Developing our approaches we applied some anti-false-positives measures : • distance assumptions • identification of "seed" sequences • phylogenetic conservation • subclassification into complementary sequence sets. In the following, we will comment on each item in more details. Distance assumptions The commonly accepted view that functionally cooperating transcription factors may physically interact with each other triggered us to introduce certain assumptions concerning the distances between the considered TFBS. Transcription factors can interact either immediately with each other or through some (often conjectural) mediator proteins (co-factors). Principally there can be many ways of taking this into account, since our knowledge about the mechanisms of interaction is limited. In this work we used two different approaches to consider distances in the promoter model development. In the first case we based our assumptions on the structure of known composite elements. We assumed that the binding sites of interacting TFs should occur in a distance of not more than 150 bp to each other (which is the case for most of the reported composite elements [ 17 ]; 150 bp is even an intended overestimation). To be on the safe side and not to overlook some potentially interesting interactions we allowed the upper threshold of 250 bp. Also by analogy with composite elements, for which it is relevant that the pair occurs not at a certain distance, but within a certain distance range, we considered the pairs occurring in segments of a certain length. The second approach was based on more abstract considerations. Thinking of TF interaction, we can imagine three different situations: (a) Directly interacting factors should have the binding sites at a close distance. (b) The factors interacting through some co-factor may have binding sites on some medium distance, depending on the size and other properties of the co-factor (and the factors themselves). (c) We can also expect direct interaction of another type, when the two factors are not located in the nearest neighborhood, but their interaction requires the DNA to bend or even to loop. This means that the distance is no longer a close one, although we cannot estimate the distance range for this case; thus, we allowed different ranges of distances, excluding only the closest ones. We searched for pairs in three distance ranges, roughly called "close", "middle" and "far", all with adjustable borders, so that moving them we could get the best proportion of percentages in (+)- and (-)-training sets. We used the search in the distance ranges as a starting point, but some of the found pairs required optimization of the borders, so that they finally did not fit into any of the predefined ranges. The initial "close" range was taken as 5–20 bp, to exclude the overlapping of the sites, but to allow close interaction; however, the border had to be shifted in many cases up to 50 bp. The initial "middle" range was chosen from 21 to 140 bp (the number of nucleotides wrapping around the core particle of the nucleosome); the "long" range had its upper border at 250 bp. "Seed" sequences Initially the idea of "seed" sequences was exploited because of the desire to make use of preexisting biological knowledge about the expressed genes and also because of doubts in the reliability of the available data set. Different experimental approaches differ in their reliability. The microarray analysis is not absolutely reliable [ 31 , 34 - 36 ], so we could expect that not all of the reported genes may be relevant for the antibacterial response. On the other hand, some genes are already known to be relevant according to additional published evidence. We thus decided to search for distinguishing features first in these "trustable" genes, and then to spread the obtained results to the whole set. Therefore, we started our analysis with a group of "seed" sequences, which we considered for distinct reasons more reliable and preferable. Choosing a seed group, we took into consideration two kinds of evidence; the first was the source of information, i. e. the methods with which the gene has been shown to participate in the response. We took the promoter sequences of those genes which have been reported by other methods but microarray analysis [ 11 , 13 , 15 , 18 - 22 , 27 - 29 , 38 - 47 , 47 ], and which have been independently reported by at least two different groups. The second kind of evidence was whether we could find any additional biological reasoning for the gene to participate in this kind of reply. For instance, a well-known participant of the NF-κB-activating pathway such as IκBα, or participants of different pathways which are likely to be triggered here as well, like c-Jun or PKC, were estimated as the first candidates for the "seed" group. Finally, the "seed" contained 12 human sequences (Table 1 ). We could retrieve all mouse orthologs constituting a separate mouse "seed". We then run our analysis in either "seed" separately and in the combined human/mouse "seed" and compared the results. First, we identified all TFBS pairs that are present in all sequences of this "seed" group (see Methods ) (Fig. 1 , step 2). Further on, we searched for the found pairs in the whole (+)-training set (Fig. 1 , step 3). In the next step we made a search in the (-)-training set for those pairs that were found in at least 80% of the (+)-training set (Fig. 1 , step 4), choosing only those which showed the lowest percentages in the (-)-training set (Fig. 1 , step 6). Figure 1 Algorithm of the search for common pairs using seed sets. Step 1. Selection of a "seed" set. Step 2. Identification of all pairs in the "seed" set; only those, which are found in 100% of the "seed" sequences, are taken into further consideration. Step 3. Search for the selected pairs in the whole (+)-training set. Step 4. Only those which are found in more than 80% of sequences of the (+)-training set are taken for into the further consideration. Step 5. Search for the "survived" pairs in the negative training set. Only those which are present in less than 40% of sequences are left. Step 6. The list of the common pairs is ready for the next analysis. Using this approach, we could avoid being drowned by a flood of pairs, most of which would be of minor importance. The huge number of nearly 37,000 pairs in different intervals which can be found in the whole (+)-training set was reduced by at least two orders of magnitude: depending on the "seed" the number of considered pairs varied from 50 to 400. In the next steps this number was reduced by another order of magnitude (Table 2 ). Table 2 Stepwise filtering of pairs. Pairs found on different steps of the search No of found pairs Pairs found in the whole training set in all distance intervals ~37000 Pairs found in the "seed" set in all distance intervals (step 2 on the fig. 1) ~400 "Seed" pairs in more than 80% of the training set (step 4 on the fig. 1) ~180 "Seed" pairs in more than 80% of the training set and less than 40% of the negative training set (step 6 on the fig. 1) 4 Each "seed" is characterized by its own set of pairs. To ensure the robustness of the obtained results, we undertook the "leave-one-out" test, removing consecutively one sequence of the "seed" set (for the combined "seed" sets which included human and mouse orthologs we excluded simultaneously both orthologous sequences). This has been repeated for each sequence (or ortholog pair). Only the robust pairs have been taken into further consideration. Phylogenetic conservation Evolutionary conservation of a (potential) TFBS is generally accepted as an additional criterion for a predicted site to be functional (phylogenetic footprinting; [ 49 - 52 ]). However, some recent analysis of the human genome reported by Levy and Hannenhalli [ 50 , 53 ] and our own observations made for short promoter regions have shown that only about 50% [ 50 ], 64 % [ 53 ] or 70 % (Sauer et al., in preparation) of the experimentally proven binding sites are conserved. Missing between 30 and 50 % of all true positives may seem to be acceptable when analyzing single TFBS, but if one constituent of a relevant combination of TFBS belongs to a non-conserved region, we will loose the whole combination from all further analyses. The observed fact is that functional features are not necessarily bound to conserved regions, as long as we speak about primary sequence conservation. Dealing with such degenerate objects as TF binding sites, one should not expect an absolute conservation of their binding sequences. From the functional point of view, it seems to be more reasonable to expect that not the sequences, but the mere occurrence of binding sites and/or their combinations as well as (perhaps) their spatial arrangement would be preserved among evolutionarily related genomes. That is the approach that we use in the present work, completely refraining from sequence alignments. We search for those pairs of TFBS which can be found in human and corresponding mouse orthologous promoter regions, considering the promoter as a metastring of TFBS. We took a feature (the pair of TFBS) into account only if we could identify it in both orthologous promoters, not taking into consideration in what region of the promoter it appeared; we also did not try to align metastrings of TFBS symbols, since they may be interrupted by many additional predicted TFBS (no matter whether they are true or false positives). While this work was in progress, we found a very similar approach in the work of Eisen and coworkers [ 54 , 55 ], who searched for conserved "word templates" in the transcription control regions of yeast. We believe that switching from primary sequence preservation to the conservation of higher-order features like clusters of TFBS is the next step in development of the approaches of comparative genomics. Complementary pairs (pairs of pairs) The idea that combinations or clusters of regulatory sites in upstream regions provide specific transcriptional control is not new [ 1 , 8 , 56 ]. Nevertheless, the problem of detecting such combinations is still under active development. As mentioned before, due to the complexity of the regulatory mechanisms in eukaryotes the computational prediction of functional regulatory sites remains a difficult task, and the spatial organization of the sites is the problem of the next level of complexity. To facilitate the search for combinations we tried to exploit the concept that subsets of principally co-regulated promoters may be subject to differential regulation. If the response of the cell is mediated through at least two distinct pathways, it is logical to suppose that there are subsets of promoters activated by each of them. The subsets may not be obvious from the expression data or from any other observations, but in some cases (as in ours, when we have two different pathways triggering the same response) one can presuppose the existence of two or more subsets, each of them possessing an own combination of TFBS. These combinations will be complementary in the sense of their occurrence in the set (Fig. 2 ). For simplicity we considered only pairs of TFBS, but the search for combinations of higher order would make the model more specific. Moreover, detection of complementary pairs enables to identify corresponding complementary subsets of sequences, thus to shed light on some features of the ascending regulatory network. Figure 2 Complementary pairs A, B, C and D are transcription factor binding sites, which form two sorts of pairs (A-B and C-D). These pairs are complementary in the sense of occurring in complementary subsets of the whole set. Formalization of the approach In the following, we will formalize our approach and describe the logics of our investigation. All procedures are described for the example of pairwise combinations, but principally all of them can be applied to combinations of higher orders. We restricted our attempt to pairs for sake of computational feasibility. Identification of pairs We consider all possible pairwise combinations of TFBS in each sequence, as described in Methods . A pair is taken into account if it has been found in a sequence at least once. Let us consider two TFBS m and n located in a distance range from r 1 to r 2 (where r 1 ≤ r 2 ) on either strand of DNA (+ or -). We can denote the sets of sequences containing pairs in different relative orientation as, . To allow inversions of DNA segments containing pairs, we consider three classes of combinations (Fig. 3 ): Figure 3 Pair classes When grouping different combinations of transcription factor binding sites according to mutual orientation, we allow inversions of the whole module. This gives rise to a total of three classes as shown. In more general form for i = 1,...3 represents the set of sequences with a pair of i -th class m , n ( i ) ( r 1 , r 2 ). Let be a fraction of the sequences in the (+)-training set, and the fraction of sequences in the (-)-training (control) set. We have to solve now the optimization problem to maximize the difference by choosing appropriate values for m , n , i and r 1 , r 2 . Also, we are interested only in pairs, which are present in at least a minimum fraction of (+)training sequences ( C 1 ) and in a defined maximum fraction of (-)-training sequences ( C 2 ). They can be filtered in advance. Thus, we search for such for which where 0 ≤ C 1,2 ≤ 1 are adjustable parameters. For single pairs we chose C 1 = 0.8 and C 2 = 0.4. We could not find pairs which would satisfy more stringent parameters, i. e. either higher C 1 or lower C 2 ; on the other hand, requirement (1) was found to be satisfied by a lot of different combinations which gave rise to the same P t and P c . To make the analysis more specific, we can consider combinations of pairs instead of single pairs. For sake of simplicity, we will omit furtheron ( r 1 , r 2 ) from the expression (but it should be kept in mind that is always a function of ( r 1 , r 2 )). Each possible type of pair is determined by values of m , n and i . We can list all types of pairs and assign a number j to each pair in this list. Then each type of pair is characterized by m j , n j , i j : Then the sequences with the pair can be represented as . For simplicity, let us call For two different j 1 and j 2 ( j 1 ≠ j 2 ) we can identify and , which appear in the (+)training set simultaneously: A triple or a combination of a higher order can be represented in the same way. Defining complementary pairs (pairs of pairs) The antibacterial response of the cell is triggered by at least two distinct pathways, and it may be therefore supposed that there are subsets of promoters activated by each of them. Optimally, they should be "complementary" in the sense of appearing in complementary subsets of the (+)-training set (Fig 2 ). Complementary pairs were searched first in a "seed" subset of the (+)-training set of sequences (Fig 4 , step 1). It comprises those 12 human genes for which the most reliable evidence is available that they are involved in the antibacterial response (as discussed in the subsection Seed sequences ; Table 1 ). We considered all possible pairs which could be found in this subset (Fig. 4 , step 2). Further on, we considered all pairwise combinations, calling pairs complementary, if: Figure 4 Algorithm of the search for complementary pairs using "seed" sets Step 1. Selection of a "seed" set; Step 2. Selection of complementary pairs in the human "seed"; every combination is checked in the (-) training set and only those, which are found in less than 40% of sequences, are taken into further consideration. Step 3. Selection of complementary pairs in the "seed" of orthologs or in the joint "human + orthologs" "seed". (Step 2 may be omitted and substituted by Step 3) Step 4. Search for the selected pairs in the whole (+)-training set. After that the final choice is made. (a) they together cover the whole subset ( C 1 is therefore always set to 1, ); (b) each of them can be found in not more and not less than a certain number of sequences (defined by adjustable parameters C 3 and C 4 , see below), with an allowed overlap (defined by the parameter C 5 ). Thus, the requirement for complementary pairs is: where 0 ≤ C 3,4,5 ≤ 1 are adjustable parameters. We chose C 3 = 0.3, C 4 = 0.7 and C 5 = 0.2. As we had no means to estimate the expected proportion of complementary pairs in the subsets, we started with these rather unrestrictive parameter settings. Finally the chosen pairs were found in the proportion 0.4/0.6 for C 3 / C 4 . In the next step we repeated the search including the orthologous sequences to the "seed" set (Fig. 4 , step 3). We looked for those pair combinations which were found in the first step (in the human "seed" sequences). (The second and the third steps may be combined in one). In the last step we repeated the search in the whole (+)-training set of 33 sequences, looking only for the combinations found in the second step (i.e., in the 12 "seed" and their orthologous sequences) (Fig. 4 , step 4). The percentage of the pair occurrence in the (-)-training set has been counted on the first step with the subsequent filtering of pairs. Results of the pair search A rather large number of combinations satisfied the requirements described in the previous section. However, when we selected those that were robust in a "leave-one-out" test for the "seed" sets, the final list of potential model constituents was shortened down to only 2 ubiquitous and 12 complementary pairs. We found one satisfactory pair which should be found in all promoters of target genes: AP - 1, NF - κB (1) (10,93) ( AP-1 , NF - κB , class 1, distance from 10 to 93 bp; see Fig. 3 for pair classes). The search for the combination of two or more pairs, which should be found in the whole set simultaneously, did not give any significant improvement of the results. Among the complementary pairs we found, several of them appeared to be interchangeable: each pair of pairs or any combination of them resulted in the selection of the same subsets from the (+)-training set (52%) (Fig. 5 ). Fig. 5 shows only those pairs which have been chosen for the final model, but there were several more which identified the same subset of the (+)-training set. The large number of complementary pairs may indicate that they are parts of more complex TFBS combinations, consisting of 4, 5 or more TFBS. Figure 5 Seven pairs, which are combined in four complementary combinations, and the results of their simultaneous application Each of the complementary pairs searches for nearly the same portion of the training set, while in the negative training set their intersection appears to be very small. Here, only those pairs are shown that have been chosen for the final model, but there were several more, which searched for the same subset of the training set and gave altogether 1,7% in the negative training set. Note that the circles are not exactly drawn to scale. The false positive rate depended on the number of applied pairs; when we used all of them together, they gave only 1.7% of FP (i. e., only 1.7% of the sequences in the (-)-training set revealed the presence of all pairs under consideration). But the simultaneous usage of all the pairs could overfit the model, so we did not apply them all, sacrificing a bit of specificity for sake of a higher sensitivity. Finally, we came up with 4 complementary pairs (Fig. 5 ) composed of 7 different TFBS pairs. Four of these TFBS pairs together are indicative for one subset of sequences, the remaining three for the other. As it has been mentioned before, the discovery of complementary pairs entails automatically the discovery of the corresponding subsets of sequences. We analyzed the distribution of the constituents of the found complementary pairs across the (+)-training set, which enabled us to assign the genes either to one or to the other subset, or to both (Table 3 ). Note that one of the subsets (subset 1) is in good agreement with the experimental data: MCP1, IL-8, β-defensin and MUC1 are known to be regulated by LPS, whereas IκBa is an important participant of this pathway; thus, these genes could be expected to belong to one pathway and, therefore, to one subset. Here, they all belong to the subset 1. This observation provides good support for the concept of complementary pairs which we applied here. Table 3 Assignment of training sequences to two subsets. Genes marked with asterisk are known to be activated through LPS-dependent pathway; note that they all belong to one subset. Subset 1(LPS-dependent pathway) Subset 2 Complementary pairs Elk-1, NF-κB (2) (11–124) Elk-1, Sp1 (1) (14–96) C/EBP, Sp1 (2) (22–87) C/EBP, NF-κB (1) (4–97) AP-1, Elk-1 (3) (28–39) NF-κB, Sp1 (2) (86–219) Regulated genes (in the training set) MCP1* IL8* β-Defensin* MUC1* ELF3 cytochrome p450 IkBa* PKC, proteinkinase C TEL2 c-jun(?) TFPI-2 RhoB, PLAU, IRF-1, hORC2L Not assigned SLC29, DPH2L2, FPGS, In order to avoid the overfitting of the model and to demonstrate the significance of our results, we performed a permutation test. For that, we conducted 2000 iterations of random permutation of (+) and (-) labels in the training sets and tried to rebuild the model using the procedure described above. The rate of correct classification on this random selection was estimated. The cases of common and complementary pairs were considered separately. The analysis was made for different C 1 , C 2 (0.7< C 1 <0.8, 0.4< C 2 <0.5) for common pairs; for complementary pairs we considered the case with C 3 = 0.3 C 4 = 0.7 C 5 = 0.2. The probability to find by chance a "seed" of 12 sequences which would produce at least one pair common for the random selection of 33 sequences (including the "seed") depends on the chosen C 1 , C 2 and is found to vary between p < 0.0005 ( C 1 = 0. 8 , C 2 = 0.4, the parameters used for our model construction) and p = 0.02 ( C 1 = 0.7, C 2 = 0.4). We failed to find any complementary pairs after 1000 iterations of the permutation test with the parameters used for the "real" (not permuted) model construction. These results suggest that the success of the model construction based on the search for combinations of TFBS is strictly dependent on the selected training set (thus, on our prior biological knowledge) and that the significance of the findings, depending on the correct choice of the adjustable parameters, is high enough to claim their non-randomness. Thus, we can say that in the described case the pairs found in the given (+)-training set with the given parameters are the real characteristics of this set. Promoter model The model consists of two kinds of combinations of pairs: ubiquitous pairs (which should be found in all promoters of the target genes), and complementary pairs. We can divide the model into two modules, one for each kind of combination. Let M 1 and M 2 be modules comprising ubiquitous pairs and complementary pairs, respectively. Module M1 comprises the pair AP -1, NF-κB (1) (10,93). Module M2 comprises all complementary combinations listed in the Fig. 5 . Each complementary pair can be taken as a submodule ( m ) in M2 . To apply the model means to search for sequences containing all these combinations. Let us call S(M) the set of sequences which possess the whole model M ; then we can also consider S(M1) and S(M2) (the sets possesing the modules M1 and M2 , respectively), and S(m) – the set with a submodule m . Then Module M2 consists of submodules ( m ); in this case we consider four submodules, so the sequences containing M2 can be found as: S ( M 2) = S ( m 1 ) ∩ S ( m 2 ) ∩ S ( m 3 ) ∩ S ( m 4 ), where the set with each submodule we must consider as a union of sequence sets containing the complementary pairs: The final result of application of the model M can be presented as S ( M ) = S ( M 1) ∩ S ( M 2) The model gives 3.4% of false positives and re-identifies 52% of the whole (+)-training set, but these 52% comprise all most reliable sequences of the set (remember that we must allow for some reduction because the set is not absolutely reliable). Identification of potential target genes Applying our promoter model to screening of 13000 upstream regions from a collection of human 5'-flanking sequences [ 57 ], we identified about 580 genes as harboring this combination of TFBS. After erasing all those that encode hypothetical products, we came up with a list of 430 potential target genes, which can be checked for plausibility. More than 60% of these genes encode different representatives of the immune system, which can be expected to participate in the cells' response, as well as transcription factors and other regulatory proteins. Some of the most interesting potential target genes are shown on the Table 4 . The whole data set one can find in the Additional files. Table 4 Selection of candidate genes identified by the promoter model. The whole list one can find in Additional files. TNFRSF14 tumor necrosis factor receptor superfamily TNFAIP6 tumor necrosis factor, alpha-induced protein 6 PPP3CA protein phosphatase 3 (calcineurin A) NLI-IF nuclear LIM interactor-interacting factor WISP1 WNT1 inducible signaling pathway protein 1 IL8 interleukin 8 TFPI2 tissue factor pathway inhibitor 2 DEFB2 defensin, beta 2 POU2F1 POU domain, class 2, transcription factor 1 MAP2K1IP1 mitogen-activated protein kinase kinase 1 interacting protein 1 CSF2 colony stimulating factor 2 (granulocyte-macrophage) TAF2F TATA box binding protein (TBP)-associated factor RNA polymerase II, F, 55 kD ABT1 TATA-binding protein-binding protein CALN1 calneuron 1 TRAF1 TNF receptor-associated factor 1 FPGS folylpolyglutamate synthase RENT2 regulator of nonsense transcripts 2 CYP26A1 cytochrome P450, subfamily XXVIA EHF ets homologous factor, MAP3K11 mitogen-activated prot. kinase kinase kinase 11 IRAK-M interleukin-1 receptor-associated kinase M ARHGDIA Rho GDP dissociation inhibitor (GDI) alpha HSY11339 GalNAc alpha-2, 6-sialyltransferase I, long form HCNGP transcriptional regulator protein CYP4F11 cytochrome P450, subfamily IVF IRF3 interferonregulatory factor 3 ICAM3 intercellular adhesion molecule 3 PPARA peroxisome proliferative activated receptor, alpha IKBKG inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase gamma ELK1 ELK1, member of ETS oncogene family STK31 serine/threonine kinase 31 SERPING1 serine (or cysteine) proteinase inhibitor GPR4 G protein-coupled receptor 4 RAB5B RAB5B, member RAS oncogene family RAB7 RAB7, member RAS oncogene family NFKB1 nuclear factor of kappa light polypeptide gene enhancer in B-cells NFKBIB nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, beta CEBPE CCAAT/enhancer binding protein (C/EBP), ε ELK1 ELK1, member of ETS oncogene family EHF ets homologous factor 15 Zinc finger proteins small inducible cytokine subfamily A (Cys-Cys), members 5,11, 20 and 23 Interleukins: IL1, IL1delta, IL8, IL12A, IL12B, IL13, IL23, Discussion We have proposed some approaches to promoter model construction and show how these approaches work in the particular case of antibacterial response of a eukaryotic cell, namely the reaction of human lung epithelial cell to P. aeruginosa binding. One of the results of our work is a list of potential target genes, enriched with different regulatory proteins, including transcription factors and known participants of the ascending pathways. This theoretical result must have two practical consequences: first, it allows to restrict further experimental research to a manageable number of candidate genes; second, it enables to understand or to clarify some uncertain details concerning the triggering pathways, and thus to make some new predictions based on this information. There is a number of published tools for searching for regulatory modules (i.e., "sequence elements that modulate transcription", following the definition given by Bailey and Noble [ 1 ] following [ 7 , 58 ]) [ 7 , 53 , 59 - 63 ]. The used algorithms may be devided in three classes (sliding window approach, hidden Markov models, discriminative technique), as briefly reviewed in [ 1 ]. Any of the approaches, independent of which algorithm it is based on, encounters the same problems arising from the biological nature (and extreme complexity) of the object: (i) scarcity of knowledge about exact location of promoters and enhancers and of experimentally proven binding sites (information used for constructing (+)-training sets); (ii) the fact that statistical significance of a feature (TFBS or a cluster of them) does not necessarily tell anything about the biological functionality of this feature; analogously, the insignificance can not be taken as a proof of the lack of function; (iii) usually weak reasoning for grouping genes (their promoters) in sets according to their function, co-regulation, functional occurrence in the same cell types, etc. The latter has some lucky exceptions, like sets of muscle genes [ 58 ] or cell-cycle regulated genes [ 64 ], and the situation will obviously improve with further development of microarray technique. In the present work we tried to address the listed problems. We could not, of course, improve the situation with the paucity of experimental data, only endeavored to make our data searches as accurate and exhaustive as possible. In principle we developed our approaches basing them, whenever possible, on biological reasoning. We find it extremely important to use as much experimental evidence as it is available at the moment. In our approach we alternated two different kinds of steps – expanding the data and restricting it: exhaustive data search – "seed" and distance constraints – exhaustive enumeration of all possible pairs – complementary pair constraints. To avoid the problem of low confidence in the (+)-training set (which may occur not only in our specific case), we developed the approach of "seed" sequences. The difference from the "seeds" used in cluster analysis is that in our approach the choice of the "seed" is biologically based. Although the "seed" approach is, obviously, a restrictive measure, moreover, a pre-process restriction, which may result in missing potentially relevant additional sequence features, we find it useful and appropriate when the choice of the "seed" is made on a solid biological basis. After having applied the restrictive "seed" technique and distance assumptions, we undertake an exhaustive, complete enumeration of all possible pairs of potential TF binding sites that can be found in the (+)-training set, which in turn reveals a large number of combinations. This list of all found pairs is processed under a new kind of constraints imposed by the search for complementary pairs. The search for complementary pairs is a completely new approach, which supplies us with a new kind of information. It enables to identify subsets of the (+)-training set which possess different regulatory modules, thus suggesting their triggering by different regulatory pathways. This kind of information becomes extremely important in two cases: (i) when two or more pathways are presupposed to be triggered in the cellular response, like in the case considered in this work; (ii) when the (+)-training set consists of not really co-regulated, but of co-expressed genes, without precise information about which of them are regulated by the same mechanism. The identification of complementary pairs and, consequently, groups of sequences enables to better define the co-regulated genes thus providing a partial, although only predicted, confirmation of the co-regulation, and at the same time to better understand the ascending pathways. The final result of our search supported the idea of complementary pairs. There is a lot of evidence in literature that interleukin 8, β-defensin, monocyte chemoattractant protein and different mucins are regulated through LPS-triggered pathway(s) [ 12 , 15 , 38 ]. On the other hand, it is also well-known that LPS is one of the "gates" through which the antibacterial response is triggered [ 24 , 65 ]. We know, that in the particular case of interaction with P. aeruginosa this pathway is not the only one [ 13 ], but we do not know in advance which of the genes in the (+)-training set belongs to which pathway (except for several genes as listed above). We had no means to include our pre-knowledge in the search. With the complementary pair approach we could re-identify the LPS subset in good agreement with our expectations (Table 3 ), confirming the efficiency of the method. Our approach, as any other, has its limits. It has been shown for the genuine composite elements of certain types (for instance, NF-AT and AP-1) [ 66 ] that one of the two constituents of a composite element could be rather degenerate, as compared with its canonical consensus sequence or when scored with a positional weight matrices (PWM). This means, that our requirement for all binding sites to be found with rather high PWM thresholds may be too restrictive. We are running risk to overlook those constituents of pairs which possess weak consensi. We could not find a solution to this problem. We have no information about which of the TFs could be represented by such low-threshold consensus, and if we take from the very beginning the lower thresholds for all considered matrices, we will be drowned in potential binding sites, nearly all of them probably being false positives. Nevertheless, we find that the PWM approach is better than string identification, which even with allowed mismatches can not provide the same flexibility as PWMs. The next source of limitations we see in the preselection of factors according to published data. Obviously, we can not expect that the experimental data is exhaustive; some of the transcription factors may be not reported just because their participance in a certain process has not yet been investigated. On the other hand, statistical overrepresentation, as it has already been mentioned before, can not be taken by itself as proof of biological functionality or its lack; some TFBS cannot be overrepresented due to their degenerate nature. We had no other idea of how to take into account those TFBS which are not overrepresented, but to rely on published experimental data. We find that the usual methods based on statistical overrepresentaion are even more restrictive, but maybe the best solution could be found in merging both approaches – i.e., using the experimental evidence along with statistical ones, for instance using Bayesian techniques. We see the perspectives of this work in two different fields: further investigation of regulatory networks triggered by P. aeruginosa binding, and further development of the methodological approaches, making them more flexible and applicable to any similar task. The list of predicted target genes has to be evaluated experimentally, but may have its value for further research already on the present step. The future work on reconstructing the intracellular pathways triggering the genetic program of the antibacterial cell response will be well supported with the information picked up from this list. It may give some hints for the next steps of experimental research, for instance providing information about the first candidates to be checked. The information about the complementary subsets of regulated genes helps to better understand the triggering pathways, and the complementarity of their function is a subject for further consideration. The methodological approaches presented in this paper can be, of course, applied to other objects. In this work we focused on the experimentally proven basis for the initial choice of transcription factors. This kind of evidence is stronger than any prediction, but it can work only when this information is available, which may be not the case for some other sets of genes or cellular situations. In the next step of development we would like to allow also an exhaustive computational search through the whole list of known TFs for potential constituents of the models. The usage of Bayesian techniques, as mentioned in the previous paragraph, would be also appropriate for this kind of predictions. Conclusions We suggest a methodology for promoter model construction based on the search of TFBS pairs and show how it works in the particular case of antibacterial response of human lung epithelial cells. We show that the method allows to identify and predict subsets of target genes potentially triggered by different regulatory pathways and thus possessing different regulatory modules. The methodology is easily applicable to any similar task and does not depend on the number of included TFs and/or number of investigated sequences, which only should not be too low for statistical reasons. Methods Databases Eukaryotic Promoter Database , release 77-1. DBTSS, the database of transcription start sites , release 3.0 TRANSCompel ® Professional release 7.1 TRANSFAC ® Professional release 7.1 TRANSPATH ® Professional release 4.1 Training sets The positive (+) training set comprises: 1. Promoters of human genes shown to be expressed in epithelial cells after interaction with P. aeruginosa by means of: a. microarray analysis [ 67 ], b. other methods [ 11 , 13 , 15 , 27 , 28 , 37 , 38 ]. (Table 1 ) 2. Orthologous mouse promoters. The sequences were derived either from Eukaryotic Promoter Database , or from DBTSS, the database of proven transcription start sites . The length of the sequences was 600 bp (-500/+100). This region comprises most of then known upstream elements and corresponds to the upstream region used by Davuluri et al . as "proximal promoters" for promoter recognition [ 69 ], plus a 100 bp proximal downstream region which also contains many known regulatory elements documented in the TRANSFAC database [ 70 ]. The "seed" set is a subset of the positive training set selected for highest experimental reliability (see Table 1 ). The negative (-) training set was composed of randomly chosen 5'-upstream sequences derived from the TRANSGENOME information resource of annotated human genome features [ 57 ]. The set was manually cleaned from all genes which potentially could be involved in the same or similar cellular responses. The set comprised 2040 sequences. Defining the set of transcription factors (potential constituents of the model) We based our selection of TFs on experimental evidence. For that we undertook an extended literature search, looking for the TFs which have been shown to take part either directly in the response of epithelial cells to P. aeruginosa binding or in the pathways triggered during similar responses. The search revealed 5 candidate factors: NF-κB [ 11 , 12 , 15 , 18 , 21 , 23 , 24 , 26 ], C/EBP [ 21 , 24 , 25 , 27 ], AP-1 [ 24 , 25 ], Elk-1 [ 16 , 24 ] and Sp1 [ 28 , 29 , 48 ]. Including C/EBP and Sp1 in the list was additionally reasoned by the fact that these factors are known to be second constituents in the most frequent NF-κB-containing composite elements as they are compiled in the TRANSCompel ® database [ 17 ]. Moreover, these are the types of composite elements known to participate in different kinds of immune response. Search for the potential transcription factor binding sites We made this search with the weight matrix approach using the Match™ tool [ 68 ]; the matrices were chosen from the library collected in TRANSFAC ® [ 70 ]. For the model construction, the thresholds for the matrix search have been defined individually for each matrix and in such a way that (i) it should yield not less than 80% TP (true positive set, here the set of experimentally proven TFBS from TRANSFAC ® ); (ii) at least one hit for every searched transcription factor could be found in every sequence of the (+)-training set. The lower border for the thresholds was predefined as 0.80/0.79 (core similarity/ matrix similarity). Identification of pairs We considered all the coordinates (with strand information) of all potential TF binding sites found by Match™ for each transcription factor. Further on, we examined all possible combinations of the coordinates, thus revealing all possible pairs in the sequence. We worked under two different kinds of distance assumptions as described in Formalization of the approach , choosing the most promising results achieved with either of them. We considered all pairs of TFs within these segments. All the pairs of one type found within one distance range were merged. We considered a pair only if it appeared in the sequence at least once (within a certain distance), not taking in account the number of pairs in each sequence. Authors' contributions ES developed the methodological approaches as well as statistical analysis and conducted the data analysis. EW conceived the study and participated in its design and coordination. Both authors drafted the manuscript. Both authors read and approved the manuscript. Appendix 1 Estimation of the validity of model construction algorithm The question is, if we choose by chance a subset of sequences, will our algorithm be able to define a model, specific to such a random subset? In other words, will this algorithm allow to make a model of anything, without dependence on the preselection of the sets ((+)-training set and/or the "seed" set)? We tried to prove the validity of the algorithm theoretically. Our algorithm is based on the definition of biologically relevant "seed" sets, in which we search for the candidate pairs (normal and complementary ones). Therefore, in order to answer the question, it is reasonable to estimate the probability to come across a "seed" set of k sequences, 100% of which possess the required common feature: a pair, a combination of pairs or complementary pairs, just by chance. Note that this estimation is written not for the whole model construction process, but only for the first step of it, where we consider only the "seed" sequences. Let us consider the frequencies of predicted single sites ( f ) of the TFs included in the model and the frequencies of all possible pairs ( F ), constructed of these sites. If the frequencies of single sites and the pairs of them satisfy the equation F ij = f i f j , (1) we can interpret F ij as the probabilities of independent events, which is a prerequisite for the following formalism. We measured the frequencies of predicted single sites and the frequencies of all possible pairs in the (-)-training set (see Methods ). We did not take into consideration distances and orientations; the probability estimated for the general case will decrease further with the addition of new constraints. The frequencies f i and F ij of single sites and pairs, respectively, were measured directly as f i = m i / N F ij = M ij / N where N is the number of all sequences of the (-)-training set, m i is the number of sequences possessing the i -th site, and M ij is the number of sequences possessing pairs of the i -th and j -th sites. F ij was then calculated as (1) and compared with the measured value. For all cases investigated in this work, the difference between the calculated and measured values did not exceed standard deviation (σ), only in one case getting to 1,5 σ (data not shown). This confirms the correctness of using pair frequencies as probabilities in this case. Let us estimate the probability P pair to find a set of k sequences in N with any (at least one) pair, same in all k . We can enumerate all possible pairs of sites of the considered TFs, considering only the cases of the independent sites ( i < j ). Let U be the number of all possible pairs, then we can call F ij = F u , u ∈ {1,..., U } It is easy to show, that the probability P pair can be calculated as: Let us estimate the probability P 2 pairs to find k sequences with any common pairwise combination of pairs (pair of pairs). The pairs of pairs may consist either of 3 (when one site is shared) or of 4 different sites (thus leaving out combinations of identical pairs); their probabilities therefore are: and where f i , f j , f l , f o are the frequencies of the single sites of the considered TFs, i < j < l < o . We can enumerate all possible pairs of pairs (notating them as Q): Let V be the number of all possible pairs of pairs, V = t + s . Analogously to (2), the probability to find k sequences each possessing a pair of pairs of one type, is: where v ∈ {1,..., V }. Let us estimate the probability to find k sequences with any complementary pair (complementary combinations). We consider pairs as complementary, if two of them are found in the seed set in not more than 60% of the sequences and not less than 40 %, the allowed overlap being 20%. The two complementary pairs together must cover the whole seed set. In the case studied here, comprising the 12 sequences of the seed set, we fixed that each of the pairs should be present in at least 5, but not more than 7 sequences, and they are allowed to co-occur in 0–2 sequences. The probability that we choose 12 sequences, possessing any one pair of complementary pairs in accordance with these requirements can be calculated as: where u , w ∈ {1,..., U }, and are the binomial coefficients (note that this formula implies that P compl reaches the maximum when the frequencies of both pairs are 0.5). All the probabilities were calculated for the (-)training set of 2040 5'-upstream sequences and for the set of 5 selected transcription factors (see Methods ). The results are: P pair = 0.44 ± 0.02 P 2 pairs = 0.13 ± 0.01 P compl = 0.013± 0.003 We have estimated the simplest variant, considering each time only one feature (1 pair, 2 pairs, or complementary pairs). In this case it can be seen that the simultaneous occurrence of 1 or 2 pairs in 12 randomly chosen sequences has a rather high probability, and thus we can not base our model construction on the search of only these features. (An increase of the number of "normal" pairs in the search will not dramatically improve the situation: the formula (3) describes the probability to find any combination of 3 or 4 sites, therefore, up to 6 pairs;obviously, the simultaneous search of more than 6 pairs will definitely overfit the model, so we do not consider this case). The probability to find 12 sequences sharing complementary pairs is much lower, so the consideration of a complementary combination makes the model much more specific, and the probability of finding a model with a complementary pair "by chance" is sufficiently low for us to claim that the proposed algorithm is valid. Note that this is a very rough estimation, considering only the upper borders; we would like to emphasize once more, that the probabilities were calculated without considering orientation and distance constraints, and that this is the estimation made for only the very first step of analysis: choosing of a seed set with needed properties. Obviously, this value depends on the number of the sequences in the "seed". Note that when we spread our requirements for simultaneous search on the whole (+)-training set (which is the next step of the model construction) the probability of constructing a model "by chance" will drop dramatically. Supplementary Material Additional File 1 The whole list of genes found with the promoter model when applying it to the collection of 13000 human 5'-upstream sequences. This list is not cleaned from hypothetical genes. Click here for file Additional File 2 The list of genes (found with the promoter model when applying it to the collection of 13000 human 5'-upstream sequences) cleaned from hypothetical genes. Click here for file | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC546226.xml |
524474 | A Structural Analysis of Eukaryotic Membrane Evolution | null | It took nearly 200 years for biologists to redefine the plant/animal dichotomy set up by Linnaeus in 1758. Among the defining traits used in the new five kingdom model of the 20th century was the presence of a nucleus. Possession of a nucleus is one of the chief characteristics that earns an organism, even a single-celled organism, the name of eukaryote. Those not similarly blessed are prokaryotes. Biologists today classify life into three domains (of which microbes lay claim to two), yet the evolution of many fundamental features of eukaryotic biology remains a mystery. A pivotal moment in the evolution of early eukaryotes was the emergence of elaborate, interconnected membrane-bound compartments that make up the Golgi apparatus, endoplasmic reticulum, and nuclear envelope. The nuclear envelope, with its inner and outer membrane, forms a barrier between the cytoplasm and nucleus. Embedded in this envelope are nuclear pore complexes (NPCs), massive (over 400 subunits), cylindrically shaped protein assemblies that connect the outer and inner nuclear membranes via sharply curved sections of pore membranes. The NPC's central ring-like structure is sandwiched between a cytoplasmic ring, with fibrils extending into the cytoplasm, and a nuclear ring, with a “basket” extending into the nucleoplasm. NPCs police traffic flow between the nucleus and cytoplasm, routinely allowing entry to small molecules while providing only selective passage to macromolecules. Early eukaryote? How eukaryotes evolved complex membrane-mediated trafficking systems from their stripped down prokaryotic contemporaries is a fundamental question in biology. Michael Rout's team investigates one aspect of eukaryotic evolution—the origin and evolution of NPC proteins (nups)—by examining the structure of nups. In a new study, Rout and colleagues report the structure of a core building block of the NPC in yeast, and propose how the complex could have evolved from organisms with no such system. The researchers first tackled the structures of the seven protein components of a core NPC subcomplex, called the yNup84 subcomplex in yeast (and the vNup107-160 subcomplex in vertebrates). Rout and colleagues used algorithms that predict secondary structures to generate three-dimensional models of the component nups. Each nup, they found, consists mostly of either repeating alpha helixes (in an alpha-solenoid fold), zigzagging beta sheets (in a beta-propeller fold), or a distinctive arrangement of an amino-terminal beta-propeller followed by a long stretch of alpha-solenoid. Next, the authors compared the structural conformations of the homologous nups found in humans and plants, and showed that the overall architecture of the subcomplex has been conserved throughout eukaryotic evolution. A search for evidence of the distinctive propeller/solenoid arrangement in other organisms shed light on the function and origin of the yNup87/vNup107-160 subcomplex. Neither bacterial nor archaebacterial proteins contain such an arrangement; it appears to exist only in eukaryotes. Moreover, proteins containing this arrangement function only as components of the coated vesicle complexes that operate in intracellular vesicular transport systems or as part of the NPC. That these complexes are linked by common architecture, the authors argue, suggests an “intimate connection between vesicle coating complexes and the yNup87/vNup107-160 subcomplex.” It's likely that both complexes function in curving membranes: when components of this subcomplex are disrupted in yeast, NPCs form abnormal clusters that impair nuclear membrane interactions. How did this shared molecular architecture evolve? Rout and colleagues propose that both nups and vesicle coating complexes developed from a common early eukaryotic ancestor—a primitive coating component with a simplified version of the repetitive folds described here. This molecular carpenter specialized in carving and remodeling membranes, and was repurposed to support the many specialized functions that facilitate molecular transport through the elaborately connected, highly specialized internal membrane systems of the modern eukaryote. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC524474.xml |
546232 | Identification of amplified and highly expressed genes in amplicons of the T-cell line huT78 detected by cDNA microarray CGH | Background Conventional Comparative Genomic Hybridization (CGH) has been widely used for detecting copy number alterations in cancer and for identifying regions containing candidate tumor responsible genes. Recently, several studies have shown the utility of cDNA microarray CGH for studing gene copy changes in various types of tumors. However, no such studies on T-cell lymphomas have been performed. To date T-cell lymphomas analyzed by the use of chromosome CGH have revealed only slight copy number alterations and not gene amplifications. Results In the present study, we describe the characterization of three amplicons of the T-cell line huT78 located at 2q34-q37, 8q23-q24 and 20p, where new amplified and overexpressed genes are found. The use of a cDNA microarray containing 7.657 transcripts allowed the identification of certain genes, such as BCLX , PCNA , FKBP1A , IGFBP2 and cMYC , that are amplified, highly expressed, and also contained in the amplicons on 20p and 2q. The expresion of these genes was analyzed in 39 T-cell lymphomas and 3 other T-cell lines. Conclusion By the use of conventional CGH and CGH and expression cDNA microarrays we defined three amplicons in the T-cell line huT78 and identified several novel gene amplifications ( BCLX , PCNA, FKBP1A, IGFBP2 and cMYC ). We showed that overexpression of the amplified genes could be attributable to gene dosage. We speculate that deregulation of those genes could be important in the development of T-cell lymphomas and/or in the maintenance of T-cell lines. | Background Gene amplification plays an important role in the progression and initiation of many solid tumors, as is the case of breast cancer where amplification of the genes ERBB2 (17q12), c MYC (8q24), and CCND1 (11q13) are found in 10–25% of breast tumors. Amplifications are revealed by Comparative Genomic Hybridization (CGH) in small chromosome areas (restricted to 2–10 Mb) where DNA copy number increases from more than 5 to 10-fold. Within these amplicons it is possible to identify critical amplified genes that are also overexpressed: this is the case for cMYC (8q24.12), ERBB2 (17q12-q21), MDM2 (12q14.3-q15) or BCL2 (18q21.3) [ 1 - 3 ]. Recently array-based CGH on cDNA microarrays has been used to investigate the genomic alterations with high resolution. Using this technique exhaustive analysis of the 17q12 and 17q23 amplicons in breast cancer has led to the identification of other genes that are also contained in the amplicons and whose overexpression could also be attributable to gene amplification [ 4 - 8 ]. Other recent studies in prostate cell lines [ 9 ] and in neuroblastoma tumors [ 10 , 11 ] have also shown the utility of cDNA microarray CGH in defining amplicon boundaries and in analyzing the complexity of the amplicons. Tumor-related genes contained in amplicons are identified in these works due to the posibility of correlating gene expression to gene dosage data. To date, however, no amplifications have been described in primary tumors or cell lines derived from T-cell lymphomas [ 12 , 13 ]. Methods The cutaneous T-cell line huT78 was obtained from American Type Culture Collection (Rockville, MD) and cells were grown under recommended culture conditions. High molecular weight DNA was extracted and CGH was performed as described previously [ 12 , 14 ]. In order to define regions of high-level amplification and to identify amplified and highly expressed genes contained in the amplicons, we performed cDNA microarray experiments on T-cell line huT78 to obtain expression and genomic profiles of the amplicons. We used the CNIO Oncochip (v1.1a) containing 7.657 different sequence-validated I.M.A.G.E cDNA clones (Research Genetics; Huntsville, AL) -some of them duplicated to reach a total of 11.718 spots- that represent known genes and expressed sequence tags (ESTs) related to the tumoral process, and tissue specific genes. A complete list can be found at . Genomic DNA hybridizations on microarrays were performed using DNA extracted from the T-cell line huT78 and from the blood of a control donor used as a reference. Genomic DNAs were Alu I and Rsa I digested, labeled with Cy5 (huT78) and Cy3 (control) using BioPrime labeling kit (Life Technologies, Inc., Gaithersburg, MD), and hybridized on microarrays at 50°C for 14–16 h, as previously described [ 15 ]. Post-hybridization washes were performed and microarrays were scanned using a GenePix scanner (Axon Instruments, Foster City, CA). Fluorescence ratios Cy5/Cy3 were obtained and normalized by adjusting these ratios to a normalized factor so that the median of the ratios of all spots in the array equals 1. Only measurements with fluorescence intensities higher than two times the sum of the background averages' of both fluorochromes (Cy3 and Cy5) were considered reliable. Logarithms of the fluorescence ratios (log 2 values) were calculated and used for the analysis. The CNIO Oncochip contains 7.657 different clones, of which 3.079 are replicated at least twice: thus, we removed and averaged the replicates by using an in-house developed preprocessing tool [ 16 ]. Expression data was also obtained from huT78 cells and from magnetically isolated T lymphocytes obtained from the pooled peripheral blood of 5 anonymous donors that were used as a control. T lymphocytes were isolated by using either magnetic microbeads conjugated to monoclonal mouse anti-human CD3 antibodies purchased from Miltenyi Biotec Inc. (Auburn, CA), or magnetic depletion of non-T-cells with a cocktail of antibodies using the Pan T-cell Isolation Kit (Miltenyi Biotec Inc.). Total RNAs were extracted with Tri Reagent (Molecular Research Center, Cincinnati, OH) following the manufacturer's instructions and amplified using a T7-based method, as previously described [ 17 , 18 ]. Briefly, 5 μgr of total RNA were used to produce double-stranded cDNA (Superscript Choice System, Life technologies Inc.) and amplification of mRNAs was performed using the Megascript T7 in vitro transcription kit (Ambion, Austin, TX) following manufacturer's recommendations. A pool of aRNAs obtained from the Universal Human RNA (Stratagene, La Jolla, CA) was used as a standard reference in all hybridizations. Test or reference amplified RNAs (aRNAs) were labeled with fluorescent Cy5 and Cy3, respectively, as reported [ 18 ] and hybridized on microarrays at 42°C for 15 hours. Fluorescence ratios (Cy5/Cy3) were normalized and filtered for genomic data. The Cy5/Cy3 ratios obtained in the cell line hybridization were then compared to those obtained in control T lymphocytes hybridization. In order to analyze if the candidate genes contained in the amplicons are altered in primary tumors and other cell lines, we analyzed expression data obtained in a previous study [ 19 ] using tumor samples from 39 primary T-cell lymphomas and 3 cell lines (Jurkat, Molt 16 and Karpas 45). Sample description and clinical details are specified in the work from Martinez-Delgado et al. [ 19 ]. All the tumors were diagnosed according to the World Health Organization classification criteria, and all individuals had given official consent. Results CGH studies CGH carried out in huT78 cells revealed three high-level gains at 2q34-q37, 8q23-q24 and 20p, showing copy number gains higher than 5 fold (Figure 1 ). Low-level gains and losses of whole chromosomes/chromosomal regions were also detected in other chromosomes, with the exception of chromosomes 1, 21 and 22 that did not show copy number alterations. Figure 1 CGH and genomic profiles of chromosomes 1, 2, 8 and 20 of the huT78 cell line . Average of the log2 genomic values over 3 neighbouring genes are plotted in the figure as a function of the location of the clones according to EnsEMBL database. On the right of each graph, CGH profiles show the number of chromosomes analysed (n) and the average profile of the metaphases studied with a 99% interval of confidence. Red and green bars at both sides of each ideogram indicate gains or losses. Only 4 chromosomes are shown in the figure, chromosome 1 did not present DNA amplification, neither by CGH nor by microarray experiments, whilst chromosomes 2, 8 and 20 showed high-level DNA amplification at 2q34-q37, 8q23-q24 and 20p. On the bottom of each graph amplicons are represented (green bars) and the gain or loss regions (green and red bars, respectively). p and q arms are also indicated. Expression and copy number profiling Expression and copy number profiling across each chromosome were performed using data from a total of 4.229 tumor-related genes or ESTs that had a map position and an identity confirmed by in-house sequencing. Map positions of the cDNA clones were obtained from the EnsEMBL database . According to this database, clones were ordered along the chromosomes and their expression and genomic values (See additional data file 1 for the raw data used to perform this analysis) were plotted as a function of their location to obtain chromosomal genomic and expression profiles (Figure 2 ). Figure 2 Genomic and expression profiles of chromosomes 2, 8 and 20 of the huT78 cell line . Chromosomes presenting regions of high level amplification are shown in the figure. Genomic and expression microarray data (averages of log 2 values over 3 neighbouring genes) are plotted as a function of the location of the clones. At the foot of each graph amplicons are represented (gross green bars) along with the gain or loss regions (green and red bars, respectively). p and q arms are also indicated. Only thirty genes that showed high-level genomic gains were also highly expressed in the cutaneous T-cell line huT78 (Table 1 ). Genes were defined as significantly up-regulated (or down-regulated) if the difference in ratio to the control was at least two-fold (log 2 [ratio expression data] ≥ +/-1). Cut-off levels for genomic data were defined at more than 1.7, a significantly high value to assure that the gene is gained at least 4-fold (log 2 [genomic data] ≥ 0,8) (data from FISH studies). Within these thirty genes that were gained and overexpressed in the cell line, 5 of them were located in amplicons of chromosome 2 ( XRCC5 , IGFBP2 and PSMB3 ) and chromosome 20 ( FKBP1A and BCL2L1 ) revealed by conventional CGH. Table 1 Genes highly gained and overexpressed in huT78 cell line. Unigene ID Gene Symbol G E Log 2 (G) Log 2 (E) Cytogenetic Location Gene Hs.76884 ID3 1,857 2,343 0,893 1,228 1p36.13-p36.12 inhibitor of DNA binding 3, dominant negative helix-loop-helix protein Hs.84981 XRCC5 2,04 4,044 1,029 2,016 2q35 X-ray repair complementing defective repair in Chinese hamster cells 5 Hs.162 IGFBP2 2,563 4,544 1,358 2,184 2q33-34 insulin-like growth factor binding protein 2 Hs.82793 PSMB3 1,794 3,874 0,843 1,954 2q35 proteasome subunit, beta type, 3 Hs.174007 VHL 3,119 2,044 1,641 1,031 3p26-p25 von Hippel-Lindau syndrome Hs.55173 CELSR3 1,862 2,647 0,897 1,404 3p24.1-p21.2 cadherin, EGF LAG seven-pass G-type receptor 3 Hs.180145 HSPC030 1,777 2,947 0,829 1,559 3 HSPC030 protein Hs.350266 ARGBP2 1,823 2,028 0,866 1,020 4q35.1 Arg/Abl-interacting protein ArgBP2 Hs.179565 MCM3 1,791 4,378 0,841 2,130 6p12 MCM3 minichromosome maintenance deficient 3 Hs.278589 GTF2I 1,886 2,379 0,915 1,251 7q11.23 general transcription factor II, i Hs.167246 POR 2,007 2,609 1,005 1,384 7q11.2 P450 (cytochrome) oxidoreductase Hs.61762 HIG2 1,885 3,152 0,915 1,656 7q32.2 hypoxia-inducible protein 2 Hs.274424 SAS 1,743 4,944 0,802 2,306 9p24.1-p23 N-acetylneuraminic acid phosphate synthase; sialic acid synthase Hs.184793 DKFZP434F195 1,78 2,125 0,832 1,087 9 DKFZP434F195 protein Hs.18910 POV1 1,78 3,702 0,832 1,888 11p11.2-p11.1 prostate cancer overexpressed gene 1 Hs.91877 THRSP 6,491 3,095 2,698 1,630 11q13.5 thyroid hormone responsive Hs.180628 DNM1L 1,758 2,715 0,814 1,441 12p12.3 dynamin 1-like Hs.76294 CD63 1,747 6,165 0,805 2,624 12q12-q13 CD63 antigen (melanoma 1 antigen) Hs.247888 - 2,284 3,201 1,192 1,679 16 Hs.12303 SUPT6H 1,742 2,819 0,801 1,495 17q11.2 suppressor of Ty 6 homolog (S. cerevisiae) Hs.296281 ILF1 1,754 3,146 0,811 1,653 17q25 interleukin enhancer binding factor 1 Hs.75716 SERPINB2 2,358 3,767 1,238 1,913 18q21.3 serine (or cysteine) proteinase inhibitor, clade B (ovalbumin), member 2 Hs.8372 UQCR 2,046 2,916 1,033 1,544 19p13.3 ubiquinol-cytochrome c reductase subunit Hs.661 NDUFB7 1,845 2,416 0,884 1,272 19p13.12-p13.11 NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 7 Hs.36992 - 4,848 3,213 2,277 1,684 19 Hs.250615 CYP2A7 1,778 3,718 0,830 1,895 19q13.2 cytochrome P450, subfamily IIA, polypeptide 7 Hs.356727 FKBP1A 1,918 4,022 0,940 2,008 20p13 FK506 binding protein 1A Hs.305890 BCL2L1 6,941 11,508 2,795 3,525 20q11.21 BCL2-like 1, Bcl-X Hs.273385 GNAS 1,764 2,059 0,819 1,042 20q13.2-q13.3 GNAS complex locus Hs.284380 GGT1 1,784 2,762 0,835 1,466 22q11.23 gamma-glutamyltransferase 1 Relation of 30 genes presenting genomic values ≥ 1.7 [log 2 (G) ≥ 0,8] and expression values ≥ 2 [log 2 (E) ≥ 1]; it represents gains above 4-fold and overexpression above 2-fold. In bold, genes located in amplicons. G , Genomic values; E , Expression values. Amplicons on chromosomes 20, 2 and 8 Focusing on chromosome 20, the genomic structure of amplicon at 20p is not continuous, showing a peak of amplification around 30 Mb on the genes ID1 and BCL2L1 ( BCLX ) located at 20q11 (Table 2 , Figure 3a ). Both genes are close together (60 Kb apart) and are contained in the same BAC (RP11-243J16). This BAC was labeled and used as a probe for FISH on metaphase spreads of the cell line showing an extremely high amplification of this genomic fragment (figure 3b ). Surprisingly, not all genes that are highly gained are also overexpressed when compared to the expression of normal T-lymphocytes. For example, although ID1 and BCLX show amplification (4 and 7 times respectively), only BCLX is highly expressed (Figure 3c ). Other genes that are contained in the amplicon (as defined by conventional CGH) and that showed an increased copy number (some of them also overexpressed), such as FKBP1A , CDC25B and PCNA , were also probed by FISH using BACs RP11-314N13, RP5-1009E24 and RP4-746J20, respectively (Figure 3d ). These genes are also gained but not in such a high amplification level as BCLX . These data are observed in their genomic values (Table 2 ), thus FISH with the corresponding BACs confirmed these results. Table 2 Genes in amplicons of chromosome 2, 8 and 20. Genes in amplicon of Chromosome 20 Unigene ID Gene symbol Position (Kb)* Genomic values Expression values Genetic data** Expression data** 20p13 Hs.356727 FKBP1A - 1,918 4,022 0,940 2,008 Hs.155140 CSNK2A1 451 n.d. 1,678 n.d 0,747 Hs.156114 PTPNS1 1,505 1,635 0,868 0,709 -0,205 Hs.26045 PTPRA 2,832 1,279 0,251 0,355 -1,997 Hs.89648 AVP 3,051 n.d. 1,274 n.d 0,349 Hs.85004 CENPB 3,753 1,703 1,169 0,768 0,226 Hs.153752 CDC25B 3,765 2,122 1,761 1,079 0,811 Hs.80905 RASSF2 4,748 1,25 0,981 0,322 -0,028 Hs.78996 PCNA 5,083 1,694 8,187 0,760 3,033 Hs.2281 CHGB 5,880 n.d. 0,855 n.d -0,226 Hs.73853 BMP2 6,737 2,694 n.d. 1,430 n.d Hs.91143 JAG1 10,606 1,89 0,953 0,918 -0,069 Hs.44296 FLJ22324 13,761 1,543 2,292 0,626 1,197 Hs.82306 ADF 17,538 1,43 0,475 0,516 -1,075 Hs.268281 CRN 20,003 1,458 1,034 0,544 0,048 Hs.2030 THBD 23,016 1,655 0,572 0,727 -0,806 Hs.1872 PCK1 24,899 1,482 0,824 0,568 -0,279 Hs.274264 VSX1 25,044 1,67 1,682 0,740 0,750 Hs.75424 ID1 29,981 3,825 1,640 1,935 0,714 20q11 Hs.305890 BCL2L1 30,040 6,941 11,508 2,795 3,525 Genes in amplicon of chromosome 2 Unigene ID Gene symbol Position (Kb)* Genomic values Expression values Genomic data** Expression data** 2q34 Hs.54089 BARD1 218,722 0,734 1,646 -0,446 0,719 Hs.84981 XRCC5 220,094 1,823 2,815 0,856 1,341 Hs.162 IGFBP2 220,592 2,563 4,544 1,358 2,184 Hs.107169 IGFBP5 220,635 2,034 0,820 1,024 -0,285 Hs.38125 IFI75 224,000 1,342 1,664 0,413 0,647 Hs.309943 SP140 224,057 1,45 0,646 0,536 -0,631 Hs.83583 ARPC2 226,590 1,058 0,648 0,081 -0,626 Hs.166068 VIL1 226,796 1,025 1,463 0,036 0,549 Hs.82568 CYP27A1 227,154 1,676 0,764 0,745 -0,388 Hs.88049 PRKAG3 227,195 2,091 0,436 1,064 -1,196 Hs.153003 STK16 227,618 1,657 1,272 0,729 0,347 Hs.75318 TUBA1 227,622 1,631 5,622 0,706 2,491 Hs.77768 DNAJB2 227,652 1,82 0,841 0,864 -0,250 Hs.89655 PTPRN 227,662 1,659 1,447 0,730 0,533 Hs.48291 PDE6D 227,901 1,122 0,520 0,166 -0,943 Hs.1734 INHA 228,352 1,187 0,849 0,247 -0,237 Hs.198 PAX3 230,984 1,677 1,504 0,746 0,589 Hs.78946 CUL3 233,252 1,18 0,997 0,239 -0,004 Hs.75498 SCYA20 236,823 1,347 0,293 0,430 -1,769 Hs.91400 HDAC4 243,313 1,26 0,543 0,333 -0,881 Hs.36587 PPP1R7 244,757 0,907 1,859 -0,141 0,895 2q37 Hs.5345 RNPEPL1 245,348 1,488 0,949 0,573 -0,076 2q35 Hs.82793 PSMB3 - 1,794 3,874 0,843 1,954 Genes in amplicon of chromosome 8 Unigene ID Gene symbol Position (Kb)* Genomic values Expression values Genomic data** Expression data** 8q23 Hs.114218 FZD6 105,144 0,980 0,621 -0,030 -0,717 Hs.86905 ATP6C 105,407 1,067 0,918 0,094 -0,123 Hs.82173 TIEG 105,820 1,277 0,842 0,353 -0,248 Hs.94262 p53R2 106,237 1,324 0,564 0,369 -0,831 Hs.2463 ANGPT1 109,654 1,745 n.d 0,803 n.d. Hs.106673 EIF3S6 111,104 1.170 0.402 0.227 -1.314 Hs.58189 EIF3S3 119,873 1,097 0,566 0,130 -0,885 Hs.184161 EXT1 121,031 1,260 3,592 0,333 1,845 Hs.174185 ENPP2 122,788 1,223 0,891 0,290 -0,167 Hs.12940 ZHX1 127,568 1,212 1,309 0,277 0,389 Hs.61661 - 127,822 1,101 1,318 0,139 0,399 Hs.181107 ANXA13 127,989 1,333 0,655 0,415 -0,611 Hs.344478 KIAA0196 129,211 1,302 n.d 0,381 n.d Hs.79070 MYC 132,293 1,883 1,597 0,853 0,630 Hs.305916 TG 136,747 1,441 1,711 0,527 0,775 Hs.75789 NDRG1 137,117 1,617 1,140 0,693 0,190 Hs.157240 MGC4737 143,611 1,722 0,828 0,784 -0,273 Hs.740 PTK3 144,545 1,896 0,939 0,914 -0,487 Hs.77667 LY6E 146,427 1,595 2,410 0,674 1,269 Hs.301118 CYP11B1 146,498 1,619 1,227 0,695 0,295 Hs.348605 - 146,918 1,309 0,826 0,388 -0,276 Hs.264428 TSTA3 147,413 1,365 0,693 0,449 -0,530 Hs.223241 EEF1D 147,485 1,554 3,142 0,636 1,651 Hs.323834 NFKBIL2 147,600 1,375 4,436 0,459 2,149 Hs.339697 LOC51160 147,616 1,418 1,546 0,504 0,629 Hs.31442 RECQL4 147,653 1,458 3,419 0,544 1,774 Hs.12271 - 147,813 1,640 2,034 0,714 1,024 Hs.92679 - 147,876 1,178 1,510 0,236 0,595 8q24 Hs.331601 - 147,927 2,138 1,077 1,096 0,107 Areas of genomic gain presenting the highest values are shown in bold. Note that in 20p amplicon, the entire region is gained, but only the highest values are indicated. Underlined are gained and overexpressed genes. * Map location according to the EnsMBL database (with exception of FKBP1A and PSMB3 genes that are FISH mapped); ** Log 2 values; n.d. No data. Figure 3 a) Microarray genomic values for chromsome 20. Note that the entire chromosome 20 has been gained and that, as CGH reveals, a peak of amplification is found at 20p. b) FISH with BAC RP11-243J16 (labeled in green) containing ID1 and BCLX genes to confirm genomic data; c) Graphical representation of microarray genomic (blue) and expression (pink) values as a function of gene position; d) FISH with BAC RP11-314N13 (labeled in red) containing FKBP1A gene on chromosome 20. Metaphase spreads from huT78 cells were prepared by standard cytogenetic methods. Gene-specific BAC clones were selected from the EnsEMBL database. Clones were labeled with SpectrumGreen-dUTP or SpectrumOrange-dUTP (Vysis) by nick translation. Dual-color hybridizations were performed at 37°C for 14–16 h and slides were washed and examined using an Olimpus AX60 epifluorescence microscope. The specificity and location of each probe was previously confirmed by FISH on normal metaphases prior to hybridization on huT78 cells. Using CGH, amplicon in chromosome 2 is narrower than amplicon in chromosome 20 (Figure 1 ). In fact it seems to be restricted to a few genes if genomic data are examined (Table 2 ). Only two areas, one around 220 and the other around 227 Mb, present the highest genomic values (shadowed in table 2 ), with 4 genes overexpressed: XRCC5 (2q35) and IGFBP2 (2q33-24) around 220 Mb, TUBA1 (tubulin, alpha 1) around 227 Mb and PSMD3 mapped by FISH at 2q35. Amongst these, IGFBP2 is the most highly overexpressed (Table 2 ). Amplicon in chromosome 8 is located at 8q23-q24, where the gene cMYC is located. This gene was found as amplified in other tumors and in this study we show that it is also amplified in the cell line huT78. cMYC is not however highly expressed in this cell line (Table 2 ). Chromosome 8 amplicon affects a wide region, from 132 to 146 Mb (shadowed in table 2 ), but only one gene located within this area is overexpressed, LY6E ( lymphocyte antigen 6 complex , locus E ). This gene is upregulated by all-trans-retinoic acid (ATRA), a differentiation inducer capable of causing clinical remission in about 90% of patients with acute promyelocytic leukemia. Other genes located in the amplicon that have not been analyzed in this study could also be important. Analyses of the amplicon candidate genes in primary T-cell lymphomas and cell lines Expression of the relevant genes found in the amplicons that were gained in this T-cell line were examined in a series of 39 primary T-cell lymphomas and 3 T-cell lines (Jurkat, Molt 16 and Karpas 45) [ 19 ]. Three genes located in 20q amplicon were overexpressed in the majority of primary tumors and stablish cell lines. FKBP1A and PCNA were overexpressed in 89,7 and 71,8% respectively (35 and 28 out of 39) of the primary tumors and in all the three cell lines analyzed. In addition, BCLX was overexpressed in 64,1% (25/39) of the primary tumors and in Jurkat cell line. Regarding genes in amplicons of chromosome 2 and 8, IGFBP2 was only overexpressed in 15,4% (6/39) of the tumors and cMYC is not highly expressed in any of them. However, both genes were overexpressed in the three cell lines. Discussion Conventional chromosome CGH of the T-cell line huT78 showed genomic patterns of copy number changes affecting most of the chromosomes. Three highly amplified regions were detected using this technique. Nevertheless, resolution of chromosome CGH is no less than 2 Mb for copy number gains and is a function of both amplicon size and copy number gains [ 20 ]. In order to precisely define regions of high-level amplification and to identify amplified and highly expressed genes contained in the amplicons of the T-cell line huT78, we performed cDNA microarray CGH and expression profiling of the cell line. For this purpose, we used the CNIO Oncochip containing 7.657 different sequence-validated cDNA clones that represent known genes and expressed sequence tags (ESTs) related to the tumoral process, and tissue specific genes. Recently, several studies have shown the utility of cDNA microarray CGH for studing gene copy changes in various types of tumors, and the usefulness of defining amplicon boundaries at high resolution (gene level) to assist in locating and identifying candidate oncogenes [ 4 - 11 ]. However, to date, no such studies on T-cell lymphomas have been performed. In the present study, we determined the precise locations of gene amplifications in the T-cell line huT78 by the use of array-based CGH on cDNA microarrays. We observed that, although chromosome CGH detected amplicons of different size (as for example the amplicons on chromosomes 20 and 2), the structure of those amplicons is more complex when it is analyzed at high resolution. Amplicon in chromosome 20 extends along all p arm when detected by use of chromosome CGH. Instead, several peaks of amplification, different in magnitude, appear at 3, 5–6, 10 and 29–30 Mbs when analyzed at gene-by-gene level. Amplicon in chromosome 2, although showing a narrower shape by chromosome CGH than that of chromosome 20, also presented a complex structure at high resolution, with two peaks of amplification around 220 and 227 Mbs. By analyzing mRNA levels in parallel, we observed that not all genes that are amplified are also overexpressed. Thus, by analyzing the expression of the genes contained in the amplicons, we selected several genes whose expression seemed to respond to gene copy number gains. We showed that only a few of the genes contained in the amplicon are highly gained and also overexpressed. In this way we could select the candidate genes to study further that could be involved in the tumorogenesis of the T-cell lymphomas. As stated before [ 7 ], however, the elevated expression of an amplified gene cannot alone be considered strong independent evidence of a candidate oncogene's role in tumorigenesis. Thus, we examined the expression of the selected candidate genes in a series of 39 primary T-cell lymphomas and 3 T-cell lines (Jurkat, Molt 16 and Karpas 45) [ 19 ]. Three genes located in 20q amplicon were overexpressed in the majority of primary tumors and stablish cell lines. FKBP1A , PCNA and BCLX were overexpressed in 90–65% of the primary tumors. FKBP1A is a receptor for rapamicin as well as for FK506, a powerful immunosuppressant in T-cells, and PCNA is involved in the control of eukaryotic DNA replication and may have a role in DNA repair synthesis. BCLX is involved in both positive and negative regulation of programmed cell death. Thus, these three genes, could be significant in the development of T-cell lymphomas. When the candidate genes located in amplicons of chromosome 2 and 8 were examined, we observed that IGFBP2 was only overexpressed in a low proportion of the primary tumors (15%) and cMYC is not highly expressed in any of them. Curiously, both genes were overexpressed in the three cell lines suggesting that they could be of relevance in the maintenance of the cell lines. Conclusions We have identified 3 amplicons by CGH in the T-cell line huT78. By cDNA microarray CGH. We have narrowed down the regions and selected some amplified and overexpressed genes: BCLX , PCNA and FKBP1A . These genes are also overexpressed in 65–90% of a set of 39 T-cell lymphomas and 3 cell lines, while IGFBP2 and cMYC are only overexpressed in T-cell lines. Our data suggest a different role of these genes in such processes. New studies with more clones covering these amplicons can help us to better identify relevant genes in T-cell lymphomas. Authors' contributions BM carried out the genomic and RNA hybridizations of the cell line onto the microarrays, performed FISH experiments, analyzed the results and drafted the manuscript. BM-D participated in the design of the study and in the analysis and discussion of the results. MC performed the amplification of the RNA and hybridization onto the microarray to carry out expression studies of primary tumors. VF performed nucleic acid extractions for hybridizations onto the microarray, grew and label the BAC clones for FISH assays and performed CGH. RD-U carried out statistical analysis of the microarray results. JB conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript. Supplementary Material Additional File 1 This file contains row genomic and expression data for each gene. Each gene is identified by a unique clone identification (Clone ID), the corresponding gene symbol (Gene Symbol) and the Unigene number (Unigene ID). Localization of all clones are also shown (map positions of the cDNA clones were obtained from the EnsEMBL database, as referred in the text). Click here for file | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC546232.xml |
539343 | How Bacteria Stopped Worrying and Learned to Love…Formaldehyde | null | Poring over the vast amount of sequence and genetic information now available for many organisms, scientists frequently encounter what appear to be redundant biochemical pathways. Redundant pathways take the same starting material and transform it into the same product, but through different routes. Why should cells maintain redundant pathways? An interesting case is that of Methylobacterium extorquens , a bacterium that can grow on organic molecules with a single carbon atom such as the alcohol methanol. Bacteria in this species first oxidize methanol into formaldehyde, then use formaldehyde to make serine—the entry point for the synthesis of many of the cell's building blocks—via two apparently redundant pathways. The short pathway is a direct (non-enzymatic) reaction of formaldehyde with tetrahydrofolate to make methylene-tetrahydrofolate, which donates a single carbon atom for serine synthesis. A hypothesized long pathway could also lead to methylene-tetrahydrofolate through a long series of enzymatic reactions, one of which consumes energy. Christopher Marx and colleagues now demonstrate that the bacteria modulate their use of each pathway during the course of acclimation to growth on methanol. In the process the authors offer new insights into the bacteria's rapid disposal of formaldehyde, a toxic chemical that would pickle cells in a minute if it was allowed to accumulate. The short and long (pathways) of formaldehyde metabolism The authors noticed that the short pathway transferred both hydrogen atoms of formaldehyde to the serine molecule while the long pathway transferred only one. If they fed bacteria methanol in which hydrogen had been replaced with its heavier form, deuterium: the resulting serine was slightly heavier than normal, as a result of acquiring one—or two—deuterium atoms. By measuring the ratio of the two serine forms, Marx and colleagues inferred the relative contribution of the two pathways to serine synthesis. The long pathway dominated, accounting for eight times more serine than the short pathway, when cells first encountered methanol. But the situation was reversed after the cells acclimated to methanol: then the short pathway produced 15 times more serine than the long pathway. The authors also measured absolute amounts of formaldehyde processed by each pathway, using methanol marked with a heavy form of carbon ( 14 C). Although the relative contribution of the long pathway decreased during ramping-up to methanol growth, the absolute amount of formaldehyde that flowed through it increased 8-fold within the first half of the transition, and then decreased. The authors generated a mathematical model based on known reaction rates from the short and long pathways. When they simulated methanol exposure, the model predicted a switch from long to short pathway very similar to what they had observed experimentally. The authors conclude that the pathways are not in fact redundant, but fulfill different functions. The long pathway is not an efficient means of serine synthesis from formaldehyde; in fact, the small amount of serine it produces is at some energy cost. But it allows the cell to spend ATP—the molecular fuel—to jump-start formaldehyde assimilation while avoiding formaldehyde accumulation when the cells first experience methanol. The short pathway is a direct and efficient (energy-free) route to serine production (and hence growth), but one that is slower to reach its cruising speed. Thus, the cells use these two formaldehyde assimilation routes like a driver uses the transmission of a car: start with powerful low gears when first accelerating, and shift to more efficient gears once hurtling down the road. The combination of both pathways represents an elegant solution to the problem of growth in toxic environments and provides a useful paradigm for detoxification in medical and environmental contexts. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC539343.xml |
545049 | Dietary exposure to soy or whey proteins alters colonic global gene expression profiles during rat colon tumorigenesis | Background We previously reported that lifetime consumption of soy proteins or whey proteins reduced the incidence of azoxymethane (AOM)-induced colon tumors in rats. To obtain insights into these effects, global gene expression profiles of colons from rats with lifetime ingestion of casein (CAS, control diet), soy protein isolate (SPI), and whey protein hydrolysate (WPH) diets were determined. Results Male Sprague Dawley rats, fed one of the three purified diets, were studied at 40 weeks after AOM injection and when tumors had developed in some animals of each group. Total RNA, purified from non-tumor tissue within the proximal half of each colon, was used to prepare biotinylated probes, which were hybridized to Affymetrix RG_U34A rat microarrays containing probes sets for 8799 rat genes. Microarray data were analyzed using DMT (Affymetrix), SAM (Stanford) and pair-wise comparisons. Differentially expressed genes (SPI and/or WPH vs. CAS) were found. We identified 31 induced and 49 repressed genes in the proximal colons of the SPI-fed group and 44 induced and 119 repressed genes in the proximal colons of the WPH-fed group, relative to CAS. Hierarchical clustering identified the co-induction or co-repression of multiple genes by SPI and WPH. The differential expression of I-FABP (2.92-, 3.97-fold down-regulated in SPI and WPH fed rats; P = 0.023, P = 0.01, respectively), cyclin D1 (1.61-, 2.42-fold down-regulated in SPI and WPH fed rats; P = 0.033, P = 0.001, respectively), and the c-neu proto-oncogene (2.46-, 4.10-fold down-regulated in SPI and WPH fed rats; P < 0.001, P < 0.001, respectively) mRNAs were confirmed by real-time quantitative RT-PCR. SPI and WPH affected colonic neuro-endocrine gene expression: peptide YY (PYY) and glucagon mRNAs were down-regulated in WPH fed rats, whereas somatostatin mRNA and corresponding circulating protein levels, were enhanced by SPI and WPH. Conclusions The identification of transcripts co- or differentially-regulated by SPI and WPH diets suggests common as well as unique anti-tumorigenesis mechanisms of action which may involve growth factor, neuroendocrine and immune system genes. SPI and WPH induction of somatostatin, a known anti-proliferative agent for colon cancer cells, would inhibit tumorigenesis. | Background Colorectal cancer (CRC) is the third most common cancer and the third leading cause of cancer-related mortality in the U.S. [ 1 , 2 ]. Estimated new cases of colon cancer were 79,650 for men and 73,530 for women in 2004 [ 1 ]; approximately $6.3 billion is spent in the United States each year on treatment of CRC [ 2 ]. Accumulating evidence suggests that diet is an important environmental factor in the etiology of CRC. High consumption of red meats, animal fats, chocolate, alcohol and refined cereals are linked to higher incidence of these cancers in Western societies [ 3 - 5 ], whereas protective effects of fruits, vegetables and whole grains have been suggested [ 5 ]. Soy foods and soybean constituents have received considerable attention for their potential role in reducing cancer risk [ 6 , 7 ]. Our laboratories reported the protective effects of lifetime ingestion of soy protein isolate (SPI) on azoxymethane (AOM)-induced colon cancer in rats [ 8 ]. Similarly, the effect of whey protein hydrolysate (WPH) in the diet to reduce colon tumor incidence has been reported by us and others [ 9 - 11 ]. Several hypotheses have been proposed to account for soy and whey protein-induced anti-tumorigenesis. For example, soy isoflavones have been proposed to play a key role in soy's anti-cancer functions [ 12 ]. Yanagihara et al ., among others, reported that genistein inhibits colon cancer cell proliferation and stimulates apoptosis in vitro [ 13 - 15 ]. However, subcutaneous administration of genistein to mice did not confirm these in vitro effects [ 16 ]. Holly et al . reported that soy sphingolipids inhibit colonic cell proliferation, and suggested that this may partially account for its anticancer benefits [ 17 ]. Other reports indicate that soy diets inhibit tumorigenesis by regulating the synthesis or activities of specific proteins. For example, Rowlands et al . reported that dietary soy and whey proteins down-regulate expression of liver and mammary gland phase I enzymes involved in carcinogen activation [ 18 ]. Elevated activities of phase II detoxification enzymes were reported in soy-fed rats [ 19 , 20 ]. Such dietary effects may result in lower tissue concentrations of activated carcinogen. The anticancer properties of whey proteins have been ascribed to their ability to elevate cellular levels of the antioxidant glutathione [ 21 , 22 ]. Moreover, the whey protein, α-lactalbumin, inhibits proliferation of mammary epithelial cells in vitro [ 11 ]. The anticancer properties of whey may also relate to its immune system-enhancing actions [ 23 ]. Despite extensive research, there is no consensus for anti-cancer mechanism(s) of soy and whey, which will undoubtedly involve multiple interrelated processes, pathways and many components. Many of the same molecular and biochemical changes underlying human colon cancer are observed in the azoxymethane (AOM)-induced rat colon cancer model [ 24 ]. Moreover, previous studies suggest a different molecular etiology for tumors of the proximal and distal colon in this model and in human colon [ 24 , 25 ]. Differential dietary effects on proximal vs. distal colon DNA damage were noted [ 26 ] and Westernization of the human diet is thought to have favored a shift of tumors from distal to more proximal locations [ 27 ]. Thus, region-specific localization of dietary effects on colon tumorigenesis is an important factor to consider in any molecular analysis of CRC. Here, we use Affymetrix high-density oligonucleotide microarrays to determine the expression profiles of non-tumor (i.e., normal) tissue in proximal colons (PC) of rats, subjected to lifetime diets containing casein (CAS, control diet), soy protein isolate (SPI), or whey protein hydrolysate (WPH) and which were administered AOM to induce tumors. We hypothesized that genes whose expression contributes to anti-tumorigenesis would be regulated in parallel by SPI and WPH; in addition, changes unique to each diet might also be apparent. Results Validation of the microarray approach Quality control steps ensured that the RNA used for microarray and real-time RT-PCR analysis was of high quality. These steps included evaluation of the RNA with the RNA 6000 Nano Assay and assessment of the cRNA hybridization to GeneChips by comparison of data obtained for probe sets representative of 5' and 3' ends of control genes. All RNA samples had an A260/280 absorbance ratio between 1.9 and 2.1. The ratio of 28S to 18S rRNA was very close to 2 on RNA electropherograms, and signal ratios below 3 were noted for 3' vs. 5' probe sets for β-actin and glyceraldehyde-3-phosphate dehydrogenase (per Affymetrix user guidelines) after hybridization. Total false change rates (TFC) were determined following Affymetrix-recommended guidelines [ 28 ], except that the inter-chip comparisons used cRNA targets made in parallel starting from the same RNA pool. Inter-chip variability, measured as TFC%, was 0.25% – 0.6% and well below the suggested 2% cutoff (Table 1 ). These values confirmed the fidelity and reproducibility of the microarray procedures used. Unsupervised nearest-neighbor hierarchical clustering identified differences in proximal colon gene expression profiles of CAS, SPI and WPH groups (Figs. 1 and 2 ), indicating that the type of dietary protein has a major effect on gene expression in normal proximal colon tissue of AOM-treated rats. Interestingly, the overall gene expression profiles for SPI and WPH groups were more similar to each other than each was to the CAS group (Fig. 1A ). Table 1 Inter-chip variability Diet group Number of arrays TFC (%)* CAS 3 0.252 ± 0.138 WPH 3 0.369 ± 0.025 SPI 3 0.570 ± 0.165 *TFC (Total false change) = false change rate (decreased category) + false change rate (increased category), as described in ref. 28; TFC reported as mean ± SEM, TFC should be no more than 2% (Affymetrix). Figure 1 Hierarchical clustering of proximal colon gene expression profiles. A. Clustering of nine PC global gene expression profiles (8799 genes); n = 3 profiles each for CAS, SPI and WPH. Each cell represents the expression level of an individual gene in each sample (green = low expression, black = middle expression, red = high expression). The dendrogram reflects the extent of relatedness of different profiles; the shorter branch-point of the SPI and WPH trees indicates the greater similarity between these profiles. B. Clustering of 18 global comparative expression profiles including 9 of SPI vs. CAS and 9 of WPH vs. CAS profiles. Each row in the heat map represents the relative expression level of a given gene across all comparisons (red = up regulated, black = unchanged, green = down regulated). Figure 2 Hierarchical clustering of 211 differentially expressed genes in either SPI or WPH. The differential expression data are taken only from the pairwise comparison analysis, with CAS profiles used as baseline. Each cell in the heat map represents the relative expression level of a given gene in an individual comparison analysis (red = up regulated, black = unchanged, green = down regulated). The dendrogram reflects the relatedness of different profiles. Differentially expressed genes Multiple filtering criteria were applied to the microarray data set so as to identify differentially expressed colon transcripts in rats fed SPI, WPH or CAS; results are reported only for transcripts that passed all three analytical filters used: DMT t-test, SAM and pair-wise comparison survival methods. Among the 8799 genes and ESTs examined with the rat U34A array, we identified 31 induced and 49 repressed genes in proximal colons of SPI-fed rats, whereas 44 induced and 119 repressed genes were detected in WPH-fed rats (Tables 2 , 3 , 4 , 5 ). Interestingly, more down- than up-regulated genes were noted for both SPI and WPH. Additionally, 37 genes were co-repressed, whereas only two were co-induced by SPI and WPH (Table 6 ). More than 90% of identified genes in WPH and SPI animals showed the same direction of change relative to CAS. This is visually apparent in the hierarchical clustering output (Fig. 2 ). Table 2 Down-regulated genes in rats fed with WPH diet* Category and Gene Name Probe Set GB Accession No. Fold Change P value Cell adhesion Embigin AJ009698 -6.57 0 Cadherin 17 L46874 -4.8 0.036 Cadherin X78997 -3.36 0.004 Protein tyrosine phosphatase M60103 -2.64 0.004 Cytokeratin-8 S76054 -2.71 0 Trans-Golgi network integral membrane protein TGN38 X53565 -4.92 0.012 Tumor-associated calcium signal transducer 1 AJ001044 -9.37 0.001 Claudin-3 AJ011656 -7.55 0.02 Claudin-9 AJ011811 -5.12 0 Cell cycle/growth control Mapk6 M64301 -2.61 0.003 Epithelial membrane protein 1 Z54212 -4.67 0.015 Glucagon K02813 -7.73 0.005 Peptide tyrosine-tyrosine (YY) M17523 -4.56 0.001 Src related tyrosine kinase U09583 -3.31 0.033 FGF receptor activating protein U57715 -4.25 0.002 Cyclin D1 D14014 -1.97 0.001 Neu oncogene X03362 -2.61 0.017 Defense/immunity protein Seminal vesicle secretion protein iv J00791 -5.35 0.001 Putative cell surface antigen U89744 -5.22 0.008 Decay accelerating factor GPI AF039583 -6.12 0 Beta defensin-1 AF093536 -26.78 0.001 Detoxification/antioxidation Glutathione S-transferase J02810 -5.17 0 Glutathione S-transferase Yb X04229 -9.33 0 J03914 -2.43 0.002 Glutathione S-transferase, alpha 1 K01932 -3.07 0.002 Glutathione transferase, subunit 8 X62660 -6.42 0.001 Glutathione S-transferase Yc1 S72505 -3.69 0.004 Glutathione S-transferase Yc2 S72506 -21.38 0.008 N-acetyltransferase 1 U01348 -4.64 0.003 Cytochrome P450CMF1b J02869 -8.23 0.001 Cytochrome P450 4F4 U39206 -6.43 0.004 Cytochrome P450 monooxygenase U39943 -2.82 0.011 Cytochrome P450 pseudogene U40004 -2.87 0 Cytochrome P450 3A9 U46118 -6.91 0 Cytochrome P450IVF M94548 -5.78 0.002 Cytochrome P450, subfamily 51 U17697 -2.07 0.005 Alcohol dehydrogenase M15327 -2.06 0.025 Aldehyde dehydrogenase M23995 -10.56 0.035 AF001898 -2.72 0.004 D-amino-acid oxidase AB003400 -13.69 0 3-methylcholanthrene-inducible UDP-glucuronosyltransferase S56937 -9 0 UDP-glucuronosyltransferase D38062 -3.17 0.005 D38065 -3.29 0.002 UDP glycosyltransferase 1 D83796 -6.87 0 J02612 -6.58 0 J05132 -4.03 0 Metabolism Meprin 1 alpha S43408 -3.82 0.014 Brain serine protease bsp1 AJ005641 -4.42 0.007 Cystathionine gamma-lyase D17370 -3.05 0.002 Cathepsin S L03201 -2.62 0 Meprin beta-subunit M88601 -5 0.004 Disintegrin and metalloprotease domain 7 X66140 -11.91 0 Fucosyltransferase 1 AB006137 -4.96 0.001 Fucosyltransferase 2 AB006138 -7.97 0.017 UDP-glucose:ceramide glycosyltransferase AF047707 -2.86 0.007 Type II Hexokinase D26393 -2.7 0.001 Hexokinase II S56464 -4.45 0.007 CDP-diacylglycerol synthase AB009999 -4.66 0 Carboxylesterase precursor AB010635 -5.29 0.002 Fatty acid Coenzyme A ligase AB012933 -2.5 0.041 3beta-HSD L17138 -3.27 0 11-beta-hydroxylsteroid dehydrogenase type 2 U22424 -3 0.001 Peroxiredoxin 6 AF014009 -3.55 0.01 Platelet phospholipase A2 X51529 -3.25 0.001 Ligand binding/carrier Carnitine transporter AB017260 -3.95 0.005 Chloride channel (ClC-2) AF005720 -5.69 0.002 Putative potassium channel AF022819 -4.84 0 Mitochondrial dicarboxylate carrier AJ223355 -3.55 0.009 Aquaporin 3 D17695 -7.83 0 Na_H_Exchanger L11236 -9.81 0.003 Angiotensin/vasopressin receptor (AII/AVP) M85183 -3.3 0.002 H+, K+-ATPase M90398 -13.87 0 Intestinal fatty acid binding protein K01180 -7.29 0.001 Apolipoprotein A-I precursor M00001 -3.45 0.023 Apolipoprotein A-I J02597 -2.47 0.003 Sodium-hydrogen exchange protein-isoform 3 M85300 -7.36 0.004 Liver fatty acid binding protein V01235 -2.62 0 Sodium transporter X59677 -3.8 0 Cation transporter X78855 -3.62 0.003 ATP-binding cassette AB010467 -3.89 0.004 Methionine adenosyltransferase II, alpha J05571 -2.91 0.007 Phenylalanine hydroxylase M12337 -7.43 0 Carbonic anhydrase IV S68245 -4.28 0.011 Signal transduction B7 antigen X76697 -170.95 0.002 CD24 antigen U49062 -3.08 0 Chemokine CX3C AF030358 -5.04 0.011 Itmap1 AF022147 -7.5 0.001 HCNP E05646 -2.5 0.001 Brain glucose-transporter protein M13979 -2.97 0.019 Protein kinase C delta M18330 -2.48 0.002 Guanylate cyclase 2C M55636 -4.58 0.003 A2b-adenosine receptor M91466 -2.8 0.04 Guanylate cyclase activator 2A M95493 -4.18 0.005 Phospholipase C beta-3 M99567 -2.57 0.018 Tm4sf3 Y13275 -3.33 0 Phospholipase D AB000778 -2.71 0.009 BEM-2 D45413 -6.41 0.015 Sgk L01624 -3.93 0 Stress response/apoptosis Prostaglandin D synthetase J04488 -43.11 0.009 GTP cyclohydrolase I M58364 -3.26 0.014 Structure proteins Chromogranin B (Chgb) AF019974 -2.56 0.005 Intestinal mucin M76740 -5.09 0.002 Muc3 U76551 -11.07 0.006 Mucin-like protein M81920 -11.97 0.001 Myosin 5B U60416 -3.94 0 Keratin 18 X81448 -3.23 0.004 Keratin 19 X81449 -2.69 0.001 ZG-16p protein Z30584 -4.43 0.002 Plasmolipin Z49858 -7.2 0 Cytokeratin 21 M63665 -4.96 0 Syndecan S61865 -3.3 0.006 Claudin 3 M74067 -6.68 0.01 Transcription factor/regulator Hepatocyte nuclear factor 3 gamma AB017044 -6.96 0 Apolipoprotein B mRNA editing protein L07114 -2.34 0 DNA-binding inhibitor L23148 -4.1 0.01 Kruppel-like factor 4 (gut) L26292 -3.08 0.017 Others Prolactin receptor M74152 -3.26 0.014 LOC286964 U89280 -2.96 0.003 Ckmt1 X59737mRNA -2.65 0.025 Arginase II U90887 -23.69 0 Deleted in malignant brain tumors 1 U32681 -3.47 0.002 3' end GAA-triplet repeat L13025 -2.73 0.001 Polymeric immunoglobulin receptor L13235 -2.93 0.004 *Changes in gene expression were determined by t-test (DMT), comparative analysis (MAS 5.0), and SAM (Stanford). Gene expression profiles from CAS animals were used as control. P value and fold change are based on DMT analysis; whereas final genes listed met all of the analytical criteria as described in Methods. Table 3 Up-regulated genes in rats fed with WPH diet* Category and Gene Name Probe Set GB Accession No. Fold Change P value Cell adhesion Fibronectin X05834 2.3 0 EGF-containing fibulin-like extracellular matrix protein 1 D89730 2.17 0.004 Cell cycle/growth control Somatostatin M25890 2.72 0.001 Somatostatin-14 K02248 3.87 0.009 APEG-1 U57097 3.24 0.002 Defense/immunity protein IgG gamma heavy chain M28670 2.21 0.009 T-cell receptor beta chain X14319 2.14 0 Adipsin M92059 3.21 0 Ligand binding/carrier Angiotensin receptor M86912 2.75 0.017 Calretinin X66974 2.52 0.005 Purkinje cell protein 4 M24852 3.06 0.001 Secretogranin III U02983 2.77 0.005 Secretogranin II M93669 2.84 0.001 Aquaporin 1 X67948 3.4 0.008 Cacna2d1 M86621 2.84 0 Retinol-binding protein M10934 2.17 0.018 Metabolism Lipoprotein lipase L03294 2.72 0 Ubiquitin carboxyl-terminal hydrolase D10699 3 0.003 Signal transduction Thy-1 protein X02002 2.89 0.002 CD3 gamma-chain S79711 3.28 0.002 Synapsin M27925 3.94 0.001 Alpha-actinin-2 associated LIM protein AF002281 2.74 0.009 RESP18 L25633 2.74 0.033 T3 delta protein X53430 2.75 0.003 Protein phosphatase inhibitor-1 J05592t 2.6 0.009 CART protein U10071 2.16 0.001 Neuroendrocrine protein M63901 3.7 0.006 Protein kinase C-binding protein Zeta1 U63740 3.14 0.003 cannabinoid receptor 1 X55812 2.17 0.002 Guanylyl cyclase A J05677 3.18 0.007 Tachykinin 1 X56306 2.36 0.036 Protein tyrosine phosphatase L19180 2.47 0.041 Argininosuccinate synthetase X12459 4.69 0.004 Stress response/apoptosis Small inducible cytokine Y08358 3.35 0.029 Structure proteins Fast myosin alkali light chain L00088 4.52 0.03 Light molecular-weight neurofilament AF031880 2.41 0 Neurofilament protein middle Z12152 2.97 0.006 Alpha-tubulin V01227 2.25 0 Peripherin AF031878 2.82 0.007 Transcription factor/regulator snRNP M29293 2.11 0.004 snRNP-associated polypeptide X73411 3.33 0.002 Others C1-13 gene product X52817 3.17 0 ND5, ND6 S46798 2.31 0.015 Sensory neuron synuclein X86789 2.84 0 *Changes in gene expression were determined by t-test (DMT), comparative analysis (MAS 5.0), and SAM (Stanford). Gene expression profiles from CAS animals were used as control. P value and fold change are based on the DMT analysis; whereas listed genes met all of the analytical criteria as described in Methods. Table 4 Down-regulated genes in rats fed with SPI diet* Category and Gene Name Probe Set GB Accession No. Fold Change P value Cell adhesion Embigin AJ009698 -5.13 0.001 Cell Cycle/growth control FGF receptor activating protein 1 U57715 -5.59 0.002 BEST5 protein Y07704 -2.37 0.003 Peptide tyrosine-tyrosine (YY) M17523 -3.91 0.002 Glucagon gene K02813 -6.58 0.002 Epithelial membrane protein-1 Z54212 -3.47 0.017 Neu oncogene X03362 -1.58 0.05 Defense/immunity protein Beta defensin-1 AF068860 -42.16 0.001 AF093536 -10.2 0 Detoxification/antioxidation Glutathione S-transferase J02810 -7.14 0 Glutathione S-transferase Yb X04229 -11.71 0.001 Glutathione S-transferase, alpha 1 K01932 -4.18 0.004 Glutathione S-transferase Yc1 S72505 -5.23 0.001 Glutathione S-transferase Yc2 S72506 -5.27 0.012 S82820 -3.45 0.006 Cytochrome P450 4F4 (CYP4F4) U39206 -6.52 0.002 Cytochrome P450CMF1b J02869 -4.12 0.002 Cytochrome P450 (CYP4F1) M94548 -2.88 0.002 1-Cys peroxiredoxin Y17295 -2.55 0.002 Metallothionein M11794 -2.92 0.006 D-amino-acid oxidase AB003400 -5.42 0 Peroxiredoxin 6 AF014009 -3.07 0.008 Phenylalanine hydroxylase M12337 -10.99 0.001 Metabolism Dipeptidase L07315 -3.08 0.001 Meprin beta-subunit M88601 -3.27 0.001 Disintegrin and metalloprotease domain 7 X66140 -14.03 0 Ligand binding/carrier Carnitine transporter AB017260 -3.81 0.003 Chloride channel (ClC-2) AF005720 -3.26 0.001 Putative potassium channel AF022819 -2.69 0.001 Mitochondrial dicarboxylate carrier AJ223355 -2.54 0.01 Aquaporin 3 D17695 -4.13 0 Intestinal fatty acid binding protein K01180 -4.43 0.005 Na_H_Exchanger L11236 -4.47 0.002 H+, K+-ATPase M90398 -2.52 0.001 Carbonic anhydrase IV S68245 -4.28 0.005 Sodium transporter X59677 -3.4 0 Phosphatidylethanolamine binding protein X75253 -2.69 0 Signal transduction B7 antigen X76697 -170.95 0.002 HCNP E05646 -3.38 0 Itmap1 AF022147 -7.97 0.005 Guanylate cyclase activator 2A M95493 -3.28 0.006 Sgk L01624 -2.76 0 Stress response/apoptosis Prostaglandin D synthetase J04488 -45.8 0.01 Structure proteins Muc3 U76551 -3.56 0.01 Intestinal mucin M76740 -3.31 0.006 Mucin-like protein M81920 -3 0 Plasmolipin Z49858 -2.92 0.003 Transcription factor/regulator Testis specific X-linked gene X99797 -6.91 0.003 Others Arginase II U90887 -3.22 0 3-phosphoglycerate dehydrogenase X97772 -4.15 0.017 Aldehyde dehydrogenase family 1 AF001898 -3.93 0.004 *Changes in gene expression were determined by t-test (DMT), comparative analysis (MAS 5.0), and SAM (Stanford). Gene expression profiles from CAS animals were used as control. P value and fold-change are based on the DMT analysis; whereas genes listed above met all of the analytical criteria as described in Methods. Table 5 Up-regulated genes in rats fed with SPI diet* Category and Gene Name Probe Set GB Accession No. Fold Change P value Cell adhesion Collagen alpha1 type I Z78279 2.49 0 Secreted phosphoprotein 1 M14656 111.39 0.006 Matrix metalloproteinase 13 M60616 24.34 0.002 Regenerating islet M62930 193.08 0.011 Defense/immunity protein Ig gamma-2a chain L22654 115.17 0.001 Ig gamma heavy chain M28670 3.22 0 Ig germline kappa-chain C-region M18528 2.48 0.038 Ig light-chain U39609 2.63 0.021 Fc-gamma M32062 4.72 0.017 Detoxification Glutathione S-transferase 1 J03752 2.86 0 Glutathione-S-transferase,alpha type2 K00136 2.56 0.009 UDP glucuronosyltransferase D38066 2.83 0.014 Metabolism Matrix metalloproteinase 7 L24374 3.63 0.02 lysozyme rc_AA892775 2.77 0 Matrix metalloproteinase 12 X98517 11.8 0.013 Mitochondrial carbamyl phosphate synthetase I M12335 59.25 0.001 Aldolase B, exon 9 X02291 8.7 0.01 Aldolase B, exon 2 X02284 2.71 0.001 Signal transduction MHC class II antigen RT1.B-1 beta-chain X56596 2.55 0.001 CD3 gamma-chain S79711 4.51 0.001 Ligand binding/carrier Intracellular calcium-binding protein L18948 28.29 0.014 Retinol binding protein II M13949 5.11 0.001 Apolipoprotein B M27440 6.47 0.024 Apolipoprotein A-I J02597 2.49 0.004 Iron ion transporter AF008439 18.78 0.008 Stress response/apoptosis Heme oxygenase J02722 9.66 0.002 JE product X17053 3.52 0.001 Pancreatitis-associated protein M98049 68.39 0.004 Pancreatitis associated protein III L20869 15.35 0 Reg protein E01983 30.25 0.001 Others Histamine N-tele-methyltransferase S82579 6.17 0.04 *Changes in gene expression were determined by t-test (DMT), comparative analysis (MAS 5.0), and SAM (Stanford). Gene expression profiles from CAS animals were used as control. P value and fold-change are based on the DMT analysis; whereas final listed genes met all of the analytical criteria as described in the Methods. Table 6 Genes co-regulated with WPH and SPI diet* Category and Gene Name Probe Set GB Accession No. Fold Change in WPH P value Fold Change in SPI P value Down-regulated genes Embigin AJ009698 -6.57 0 -5.13 0.001 Epithelial membrane protein 1 Z54212 -4.67 0.015 -3.47 0.017 Glucagon K02813 -7.73 0.005 -6.58 0.002 Peptide tyrosine-tyrosine (YY) M17523 -4.56 0.001 -3.91 0.002 FGF receptor activating protein U57715 -4.25 0.002 -5.59 0.002 Neu oncogene X03362 -2.61 0.017 -1.58 0.05 CD52 antigen X76697 -170.95 0.002 -170.95 0.002 Beta defensin-1 AF068860 -54.48 0.001 -42.16 0.001 Glutathione S-transferase J02810 -5.17 0 -7.14 0 Glutathione S-transferase Yb X04229 -9.33 0 -11.71 0.001 Glutathione S-transferase, alpha 1 K01932 -3.07 0.002 -4.18 0.004 Glutathione S-transferase Yc1 S72505 -3.69 0.004 -5.23 0.001 Glutathione S-transferase Yc2 S72506 -21.38 0.008 -5.27 0.012 Cytochrome P450CMF1b J02869 -8.23 0.001 -4.12 0.002 Cytochrome P450 4F4 U39206 -6.43 0.004 -6.52 0.002 Cytochrome P450IVF M94548 -5.78 0.002 -2.88 0.002 D-amino-acid oxidase AB003400 -13.69 0 -5.42 0 Meprin beta-subunit M88601 -5 0.004 -3.27 0.001 Disintegrin and metalloprotease domain 7 X66140 -11.91 0 -14.03 0 Carnitine transporter AB017260 -3.95 0.005 -3.81 0.003 Chloride channel (ClC-2) AF005720 -5.69 0.002 -3.26 0.001 Putative potassium channel AF022819 -4.84 0 -2.69 0.001 Mitochondrial dicarboxylate carrier AJ223355 -3.55 0.009 -2.54 0.01 Aquaporin 3 D17695 -7.83 0 -4.13 0 Na_H_Exchanger L11236 -9.81 0.003 -4.47 0.002 H+, K+-ATPase M90398 -13.87 0 -2.52 0.001 Fatty acid binding protein 1 K01180 -7.29 0.001 -4.43 0.005 Sodium transporter X59677 -3.8 0 -3.4 0 Carbonic anhydrase IV S68245 -4.28 0.011 -4.28 0.005 Itmap1 AF022147 -7.5 0.001 -7.97 0.005 HCNP E05646 -2.5 0.001 -3.38 0 Guanylate cyclase activator 2A M95493 -4.18 0.005 -3.28 0.006 Sgk L01624 -3.93 0 -2.76 0 Prostaglandin D synthetase J04488 -43.11 0.009 -45.8 0.01 Mucin 3 M76740 -5.09 0.002 -3.31 0.006 Mucin-like protein M81920 -11.97 0.001 -3 0 Plasmolipin Z49858 -7.2 0 -2.92 0.003 Up-regulated genes Ig gamma heavy chain M28670 2.21 0.009 3.22 0 CD3 gamma-chain S79711 3.28 0.002 4.51 0.001 *Changes in gene expression were determined by t-test (DMT), comparative analysis (MAS 5.0), and SAM (Stanford). Gene expression profiles from CAS animals were used as control. P value and fold change are based on the DMT analysis; whereas final listed genes met all of the analytical criteria described in Methods. Gene expression: effects of WPH As based on Gene Ontology (GO) annotations, the 44 up-regulated and 119 down-regulated genes of the WPH group belong to multiple functional categories including cell adhesion (n = 10), cell cycle and growth control (n = 10), detoxification (n = 17), defense and immunity (n = 7), signal transduction (n = 29), transcriptional regulation (n = 6), metabolism (n = 19), ligands and carriers (n = 27), cell death (n = 3), structural proteins (n = 16), and others (Tables 2 & 3 ). The fold change for up-regulated genes ranged between 2.1 [small nuclear ribonucleoparticle-associated protein (snRNP)] to 4.7 (argininosuccinate synthetase), whereas down-regulated genes exhibited fold changes between 2.0 (cyclin D1) and 171 (CD52 antigen). Lifetime ingestion of WPH affected the expression of xenobiotic metabolism-related enzymes including several of the cytochrome P450s and glutathione S-transferases, alcohol dehydrogenase (ADH), and UDP-glucuronosyltransferase. Cytochrome P450 enzymes and ADH are considered to play key roles in activation of the proximate carcinogen from AOM [ 29 ]. Down-regulation of expression of Phase I detoxification enzymes by WPH might therefore diminish AOM-induced DNA adducts and genomic instability. Consistent with results from a study in which whey proteins inhibited cell proliferation in vitro [ 11 ], lifetime feeding of WPH was associated with changes in expression of genes involved in cell cycle control and proliferation; cyclin D1, neu oncogene, mapk6, glucagon, and peptide YY (PYY) genes were down-regulated, whereas the expression of somatostatin, a growth-inhibitory peptide was induced. WPH altered expression of genes involved in cellular defense. Induced genes included Ig gamma heavy chain, adipsin, and T-cell receptor beta chain, whereas expression of the antibacterial peptide beta defensin-1 and seminal vesicle secretion protein IV (SVS IV) were down-regulated. About 20% of WPH-affected genes are involved in cell signaling; these include guanylate cyclase 2C, protein kinase C delta, and synapsin. Additionally, genes encoding ligands or membrane channels [i.e., chloride channel, intestinal fatty acid binding protein (I-FABP), apoliprotein A-I (Apo-AI), Na+, K+-ATPase, and sodium transporter] were down-regulated by WPH, whereas calretinin and retinol binding protein (RBP) levels were increased. Gene expression: effects of SPI Colon genes, whose mRNA expression was affected by ingestion of SPI, fell into multiple functional categories including cell adhesion (n = 4), cell cycle and growth control (n = 6), detoxification (n = 18), defense and immunity (n = 6), signal transduction (n = 4), transcriptional regulation (n = 1), metabolism (n = 8), ligands and carrier proteins (n = 17), cell death proteins (n = 5), and structural proteins (n = 3) (Tables 4 & 5 ). Relative abundance of numerous transcripts was changed in the same direction by WPH and SPI (Fig. 2 ). However, some exceptions were noted. For example, mRNA encoding Apo-AI was down-regulated by WPH, but elevated by SPI. Apo-AI is the major determinant of the capacity of HDL particles to promote cholesterol efflux and this protein is associated with the inhibition of atherosclerosis [ 30 ]. However, the impact of differential response of Apo-AI to WPH and SPI on anti-tumorigenesis is unknown. Confirmation of differential gene expression We performed quantitative real-time RT-PCR on selected genes to confirm the microarray results. Based upon known associations with cell proliferation or differentiation, 14 genes were chosen for further study. Included in this group was BTEB2; this gene was not present on the microarrays but was included in RT-PCR analysis due to its significant expression in intestine and involvement in cell proliferation [see discussion]. As shown in Figure 3 , eight genes were confirmed to be differentially expressed: these included the gastrointestinal hormone genes PYY (12.9-fold down-regulated in WPH fed rats; P = 0.004), glucagon (17.8-fold down-regulated in WPH fed rats; P = 0.005), and somatostatin (3.92-, 2.65-fold up-regulated in SPI and WPH fed rats; P = 0.05, P = 0.025, respectively); cyclin D1 (1.6-, 2.4-fold down-regulated in SPI and WPH fed rats; P = 0.033, P = 0.001, respectively); BTEB2 (1.9-, 6.7-fold down-regulated in SPI and WPH fed rats; P = 0.024, P < 0.001, respectively); c-neu proto-oncogene (2.5-, 4.1-fold down-regulated in SPI and WPH fed rats; P < 0.001, P < 0.001, respectively); the colonocyte differentiation marker I-FABP (2.9-, 4.0-fold down-regulated in SPI and WPH fed rats; P = 0.023, P = 0.01, respectively); and the mucin, MUC3 (2.78-, 4.05-fold down-regulated in SPI and WPH fed rats; P < 0.001, P < 0.001, respectively). Differential expression of five other genes was not confirmed statistically, due to individual animal variation in the transcript levels; however, the mean-fold changes for mRNA abundance were greater than two and in agreement with the corresponding microarray results for these genes. Only one of the selected genes – retinol binding protein (RBP), failed to exhibit greater than a 2-fold change (in the predicted direction) at the mRNA level by real-time RT-PCR. Figure 3 Quantitative real-time RT-PCR verification of microarray results. RNA used for real-time RT-PCRs was from the same animals (n = 7 per diet group) whose RNAs comprised the pools for microarray analysis. Values are mean ± SEM and were analyzed by one-way ANOVA, *P < 0.05, SPI or WPH vs. CAS. Serum somatostatin (Sst) As shown in Fig. 4 , circulating Sst concentration was significantly higher in rats fed WPH and SPI. Colonic Sst protein content in colon homogenates was below the limit of detection of the assay used (data not shown). Figure 4 Diet effects on serum Sst concentration. Values are mean ± SEM. One-way ANOVA. *P < 0.05, SPI or WPH vs. CAS. Discussion The type of dietary protein(s) can markedly affect the onset and/or progression of CRC [ 31 ]. Epidemiological and animal studies have found that dietary soy and whey proteins decrease the incidence of certain tumors, including those of the colon and rectum [ 6 , 7 , 32 - 35 ]. Using the AOM-treated male Sprague Dawley rat model, we previously found that lifetime feeding of SPI led to a ~ 76% lower incidence of AOM-induced colon tumors compared to rats lifetime-fed CAS [ 8 ]. Additionally, in the same studies, a ~ 46% lower incidence of colon tumors was found in WPH-fed compared to CAS-fed rats [ 9 ]. The molecular mechanism(s) by which these dietary proteins reduce the incidence of chemically-induced colon tumors is unclear, although several mechanisms and putative bio-active factors have been proposed [ 11 - 24 ]. The present study has now identified genes that are differentially expressed as a function of these diets and which serve to highlight potential pathways for dietary protection from carcinogenesis. The ability to simultaneously analyze a large number of different mRNAs makes microarrays very appealing for identifying genes and gene families whose expression is altered by diet [ 36 , 37 ]. We focused on the 'normal' colon tissue since we are interested in genes that are differentially regulated by diet and which act in anti-oncogenic fashion in pre-cancerous tissues. We limited our analysis to the proximal colon since several studies have suggested that the molecular etiology of proximal and distal colon tumors differs [ 25 , 26 ] and proximal colon tumors have become more prevalent with Westernization of the diet and aging of the population [ 27 ]. We chose to include colonic smooth muscle with the mucosa since: a) the former tissue layer interacts with the latter to influence its growth and function, and b) we could monitor all colonic genes affected by diet. However, one potential caveat to this strategy is the 'dilution' effect that may have been imposed on the more rare mucosal transcripts. Another caveat is that no information is obtained regarding where the differentially expressed transcripts occur. In this regard, however, we have confirmed by immuno-histochemistry that I-FABP is expressed predominantly in the inter-cryptal surface epithelium of colons from AOM-treated rats (Fig. 5 ). Our study used a sample size of three (per diet group) which balanced the costs for the experimental reagents with the minimum number required for statistical analysis. The quantitative PCR analyses provided confirmation that the filtering strategies used yielded bona-fide differentially expressed transcripts. Figure 5 Immuno-histochemistry for I-FABP in colons from AOM-treated rats. Panels A and B are sections from CAS and WPH-fed animals, respectively. Arrows point to the strong areas of staining for I-FABP in the inter-cryptal surface epithelium (overall intensity of staining is greater for CAS than for WPH). Only two transcripts were induced by both SPI and WPH; whereas 37 transcripts were repressed by both SPI and WPH. This suggests that the cancer-protective actions of the two diets are generally associated with repression of colonic genes that facilitate tumorigenesis. An alternative explanation is that CAS induces genes that facilitate colon cancer development when compared to SPI and WPH. It is also likely that SPI and WPH diets act in unique ways to inhibit tumorigenesis. Irregardless, our results indicate that the nature of the dietary protein can profoundly affect colon gene expression profiles. Thus, gene expression profiling studies of colons should account for potential confounding effects of diet. Dietary factors in SPI or WPH inhibit cell proliferation and induce apoptosis among other biological actions [ 11 , 13 ]. In the present study, we identified cyclin D1 gene and the neu proto-oncogene as being repressed in proximal colon by SPI and WPH. Cyclin D1 is a key regulator of cell cycle progression [ 38 ], and a target of β-catenin, a protein whose abnormal accumulation in the nucleus is strongly linked to the development of multiple tumor types, including those of the colon [ 39 ]. Aberrantly increased expression of cyclin D1 in colon epithelial cells contributes to their abnormal proliferation and tumorigenicity [ 40 , 41 ]. Similarly, the oncogenic and cellular growth-promoting activities of the HER-2/neu proto-oncogene are well known [ 42 ]. HER-2/neu, a tyrosine kinase receptor for neu-differentiation factor, is expressed in normal colonic epithelium and is up-regulated in adenomatous polyps of the colon [ 43 ]. The down regulation of cyclin D1 and c-neu mRNA abundance by SPI and WPH may at least partly explain their anti-tumorigenic properties. Similarly, Krüppel-like transcription factors have been linked to cell growth and tumorigenesis. BTEB2 (also known as Krüppel-like factor 5, KLF5, or intestinal KLF) was reported to enhance intestinal epithelia cell colony formation, cyclin D1 transcription, and cell proliferation [ 44 ]. Consistent with our results for cyclin D1, colonic BTEB2 mRNA expression was down-regulated by SPI and WPH. Aquaporin 3, a water channel highly expressed in colonic epithelium, was down-regulated by SPI and WPH. Aquaporins are thought to be induced early in colon cancer and to facilitate oncogenesis [ 45 ], therefore, dietary repression of such genes may additionally contribute to anti-tumorigenesis. The results for I-FABP and MUC3 indicated 3–4 fold decreases in transcript abundance in proximal colons of rats on SPI or WPH diets. These particular results are not easily reconciled with decreased tumorigenesis in SPI and WPH groups, since both genes are highly expressed in the normal differentiated colonic epithelium and are likely to be under-expressed in adenomas and adenocarcinomas [ 46 ]. Perhaps, these represent diet-modulated genes that are not direct participants in anti-tumorigenesis. Gastrointestinal hormones regulate a myriad of intestinal functions including motility, absorption, digestion, cell proliferation and death, and immune response [ 47 ]. The microarray and real-time RT-PCR assays identified inductive effects of SPI and WPH on somatostatin mRNA and protein abundance. These results implicate this gene product in autocrine and paracrine mechanisms underlying colon cancer protection by SPI and WPH since somatostatin is a well-known anti-proliferative agent for colon tumor cells [ 48 , 49 ]. This hormone is also a negative regulator of angiogenesis [ 50 ]; this is predicted to counter tumorigenesis. It is possible that the small decrements in rat growth rates observed with lifetime SPI or WPH diets [ 8 , 9 ] are a consequence of this increased circulating somatostatin level. We also found decreased abundance of mRNAs encoding peptide YY (PYY) and glucagon in colons of WPH-fed rats. PYY gene expression in human colon tumors is much reduced relative to the adjacent normal tissue [ 51 ]; however, chemically-induced colon tumors in rats generally exhibit higher overall expression of PYY due to increased prevalence of PYY-positive cells, compared to normal mucosa [ 52 , 53 ]. PYY stimulates proliferation of intestinal epithelium [ 54 ]; therefore, an inhibition of PYY expression by dietary WPH may contribute to colon cancer-protective actions. A similar scenario might apply to colon glucagon gene expression, as this growth stimulatory peptide for colon cancer cells [ 55 ] was inhibited by WPH at the level of colon mRNA abundance. Our data highlighted other aspects of diet and colon gene expression that warrant further study. For example, the B7 antigen (also known as CD52) mRNA was strongly down-regulated by SPI or WPH. The corresponding protein is normally expressed at high levels on cell membranes of T and B lymphocytes and monocytes; infusion with anti-CD52 antibody leads to systemic depletion of T cells [ 56 ]. The lower abundance of this transcript in non-tumor colon tissue of rats on SPI or WPH diets may reflect fewer numbers of immune cells in this tissue, as compared to CAS-fed animals. One possible interpretation of this data is that the 'normal' tissue of the CAS group has manifested an immune response, perhaps in response to increased tumorigenicity relative to SPI or WPH groups. Such an interpretation raises the prospect of a functional immuno-editing mechanism [ 57 ] occurring in this model of colon cancer and an indirect effect of diet on lymphocyte populations (via presence of tumors or tumor precursors) in the colon. An alternative mechanism is that dietary protein can directly affect the populations of lymphocytes resident in the colon, which in turn, may affect tumorigenesis. A related observation was the enhanced abundance of CD3 gamma chain transcripts in colons of SPI and WPH animals. The protein encoded by this transcript helps mediate T cell antigen receptor engagement and signaling [ 58 ]; its decreased abundance in colonic T cells of CAS-fed animals may indicate a specific immune defect [ 59 ] occurring in the CAS-fed animals after exposure to carcinogen and thereby contributing to enhanced tumorigenesis in this group. Several microarray studies of human paired normal colon vs. colon tumors have been published [ 60 - 64 ]. Comparison of the present results for normal colon tissue of AOM-treated rats on different diets to the published studies for human CRC identified only a small number of common differentially expressed genes and/or gene families in common (data not shown). This small number is probably due to the fact that our study did not examine colon tumors; rather we focused on 'normal' colon tissue. In this regard, it will be interesting to examine the expression profiles of colons from animals not treated with AOM so as to more specifically correlate transcripts with diet and cancer phenotype. This study has illuminated a number of genes and gene families that may act as dietary protein-dependent modulators of oncogenesis in the rat colon. Additional studies that specifically address the functional involvement of these genes in cancer-prevention via dietary means are required to confirm the postulated roles. Conclusions We have identified genes in rat colon that are differentially expressed, as a consequence of altered dietary protein, during AOM-induced oncogenesis. These are candidates for genes that sub-serve the anti-cancer effects of dietary SPI and WPH in this tissue. Methods Rats, diets and carcinogen treatment The animals whose colons were used in the present study have been previously described [ 8 , 9 ]. Time-mated [gestation day (GD) 4] Sprague-Dawley rats were purchased from Harlan Industries (Indianapolis, IN), housed individually and allowed ad libitum access to water and pelleted food. Rats were randomly assigned to one of three semi-purified isocaloric diets made according to the AIN-93G diet formula [ 65 ] and which differed only by protein source: a) CAS (New Zealand Milk Products, Santa Rosa, CA), b) WPH (New Zealand Milk Products, Santa Rosa, CA) or c) SPI (Dupont Protein Technologies, St. Louis, MO). Offspring were weaned to the same diet as their mothers and were fed the same diets throughout the study. At 90 days of age, male offspring received s.c. injections of 15 mg/kg AOM (Ash Stevens, Detroit, MI) in saline once a week for 2 weeks. Forty weeks later, rats were euthanized, and the colon (cecum to anus) was divided into two equal segments (proximal and distal), opened longitudinally, and examined for tumors. We found that both WPH and SPI significantly decreased the colon tumor incidence [data published in [ 8 , 9 ]]. A representative non-tumor segment of each proximal colon (PC) was frozen in liquid nitrogen and stored at -80°C for later use. Animal care and handling were in accordance with the Institutional Animal Care & Use Committee guidelines of the University of Arkansas for Medical Sciences. RNA processing Total RNA was isolated from rat proximal colons (n = 7 for each of CAS, SPI and WPH diets) using TRIzol reagent (Invitrogen, Carlsbad, CA), and further purified with the RNeasy Mini Kit (QIAGEN, Valencia, CA). To remove contaminating DNA, on-column DNA digestion with RNase-Free DNase (QIAGEN) was performed. Integrity of isolated RNAs was confirmed using the RNA 6000 Nano LabChip kit with the Agilent 2100 Bioanalyzer System (Agilent Biotechnologies, Palo Alto, CA). To reduce errors due to biological variability, RNA samples were pooled as proposed by Bakay et al [ 66 ]. Pooled RNA (equal amounts of RNA from each of 7 animals; 8 ug total) was used for cDNA synthesis using a T7-(deoxythymidine) 24 primer and Superscript II (Life Technologies, Inc., Gaithersburg, MD). The resulting cDNA was used with the ENZO BioArray High Yield RNA Transcript labeling kit (ENZO, Farmingdale, NY) to synthesize biotin-labeled cRNA. The cRNA was purified on RNeasy spin columns (QIAGEN) and subjected to chemical fragmentation (size range of 35 to 200 bp). Three replicate cRNA targets were made in parallel starting from each RNA pool. Microarray procedures Ten ug of cRNA was hybridized for 16 hours to an Affymetrix (Santa Clara, CA) rat U34A GeneChip (3 chips used per diet group), followed by incubations with streptavidin-conjugated phycoerythrin, and then with polyclonal anti-streptavidin antibody coupled to phycoerythrin. Following washing, GeneChips were scanned using an Agilent GeneArray laser scanner. Images were analyzed using Affymetrix MAS 5.0 software. Bacterial sequence-derived probes on the arrays served as external controls for hybridization, whereas the housekeeping genes β-actin and GAPDH served as endogenous controls and for monitoring the quality of the RNA target. To compare array data between GeneChips, we scaled the average of the fluorescent intensities of all probes on each array to a constant target intensity of 500. Bioinformatics To validate the microarray procedure for our samples, unsupervised nearest-neighbor hierarchical clustering (Spotfire, Somerville, MA) was performed on gene expression data. The inter-chip variability test also was performed as specified in the Affymetrix data analysis manual [ 28 ]. To identify colon genes differentially expressed with SPI or WPH (control: CAS diet), multiple criteria were applied; final results are reported only for transcripts that passed all three analytical steps described below. Firstly, the t-test feature of DMT (Affymetrix) was used to identify genes whose expression was regulated (induced/repressed with P < 0.05) by SPI or WPH, and signal fold changes (FC) for these genes were calculated. Secondly, microarray data were analyzed using 'Significance of Analysis of Microarrays' (SAM, Stanford) to identify significant changes in gene expression among diet groups [ 67 ], using a false discovery rate (FDR) cutoff of 0.5%. Lastly, a pair-wise comparison survival (3 × 3) method was used to identify differentially expressed transcripts [ 68 ]. In brief, the three replicate expression profiles obtained for SPI colons were iteratively compared with the three CAS profiles (latter as baseline) in MAS 5.0 (Affymetrix), generating nine comparisons in total. Transcripts with a log ratio greater than or equal to 1 (≥2 fold change), which increased (I) in nine of nine comparisons, and which were expressed above background (i.e., called as Present) in all three SPI GeneChips, were considered to be up-regulated by SPI. Transcripts with a log ratio less than or equal to -1, were decreased (D) in nine of nine comparisons, and expressed above background (Present) in all three CAS chips were considered to be down-regulated by SPI. WPH-regulated genes were similarly identified. Genes that were independently identified by all three approaches comprised the final reported lists of differentially expressed genes (Tables 2 , 3 , 4 , 5 , 6 ). Validation of gene expression by quantitative real-time RT-PCR One μg of total RNA from each of the 21 individual proximal colons (which comprised the original pools for the microarray experiment) was reverse-transcribed using random hexamers and MultiScribe Reverse Transcriptase in a two-step RT-PCR reaction (Applied Biosystems, Foster City, CA). Primers (Table 7 ) were designed using 'Primer Express' (Applied Biosystems) and were selected to yield a single amplicon; this was verified by dissociation curves and/or analysis in agarose gels. SYBR Green real-time PCR was performed with an ABI Prism 7000 Sequence Detector. Thermal cycling conditions included pre-incubation at 50°C for 2 min, 95°C for 10 min followed by 40 PCR cycles at 95°C for 15 sec and 60°C for 1 min. The relative transcript levels for each gene were calculated using the relative standard curve method (User Bulletin #2, Applied Biosystems) and normalized to the house-keeping gene β-actin. Data are reported as mean ± SEM of n = 7 animals per dietary group. Significant differences between diet groups were determined by one-way ANOVA ( P < 0.05). Table 7 Primer sequences for real-time RT-PCR Gene Forward primer Reverse primer Accession no. Beta-actin 5'-GACGGTCAGGTCATCACTATCG-3' 5'-ACGGATGTCAACGTCACACTTC-3' NM_031144 I-FABP 5'-AGGAAGCTTGGAGCTCATGACA-3' 5'-TCCTTCCTGTGTGATCGTCAGTT-3' K01180 Neu Oncogene 5'-GTGGTCGTTGGAATCCTAATCAA-3' 5'-CCTTCCTTAGCTCCGTCTCTTTTA-3' X03362 PYY 5'-AGGAGCTGAGCCGCTACTATGC-3' 5'-TTCTCGCTGTCGTCTGTGAAGA-3' M17523 Glucagon 5'-TGGTGAAAGGCCGAGGAAG-3' 5'-TGGTGGCAAGGTTATCGAGAA-3' K02813 Somatostatin 5'-GGAAACAGGAACTGGCCAAGT-3' 5'-TGCAGCTCCAGCCTCATCTC-3' K02248 PAP III 5'-AAGAGGCCATCAGGACACCTT-3' 5'-CACTCCCATCCACCTCTGTTG-3' L20869 CYP4F1 5'-CCAAGTGGAAACGGTTGATTTC-3' 5'-TCCTGGCAGTTGCTGTCAAAG-3' M94548 GST 5'-ACTTCCCCAATCTGCCCTACTTA-3' 5'-CGAATCCGCTCCTCCTCTGT-3' X04229 Cyclin D1 5'-TCAAGTGTGACCCGGACTGC-3' 5'-ACTTCCCCTTCCTCCTCGGT-3' D14014 Beta defensin-1 5'-TCTTGGACGCAGAACAGATCAATA-3' 5'-TCCTGCAACAGTTGGGCTATC-3' AF093536 H+, K+-ATPase 5'-ATTCCGCATCCCTAGACAACG-3' 5'-TCTTACTAAAGCTGGCCATGATGTT-3' M90398 Prostaglandin D synthetase 5'-CAAGCTGGTTCCGGGAGAAG-3' 5'-TTGGTCTCACACTGGTTTTTCCTTA-3' J04488 RBP 5'-TCGTTTCTCTGGGCTCTGGTAT- 3' 5'-TTCCCAGTTGCTCAGAAGACG-3' M10934 Muc3 5'-AAGGTGTGAGGAAGTGATGGAGA-3' 5'-GCAGAGACCGTCGGCTTTATC-3' U76551 BTEB1 5'-ACACTGGTCACCATCGCCAA-3' 5'-GGACTCGACCCAGATTCGGT-3' NM_057211 BTEB2 5'-CTACTTTCCCCCATCACCACC-3' 5'-GAATCGCCAGTTTCGAAGCA-3' AB096709 Serum Sst Rat serum Sst content (15 animals from each diet) was determined using the somatostatin-28 EIA kit purchased from Phoenix Pharmaceuticals Corporation (Belmont, California). Authors' contributions RX performed the microarray and real-time PCR experiments, conducted the data analysis, and participated in drafting the manuscript. TMB designed and oversaw the animal component of the study. FAS designed the analytical and overall approaches to the study, supervised the project, and drafted the manuscript. All authors read and approved the final manuscript. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC545049.xml |
449868 | Gene Duplication: The Genomic Trade in Spare Parts | The duplication of genes and their subsequent diversification has had a key role in evolution. A range of fates can befall a duplicated gene | If necessity is the mother of invention, then its father is an inveterate tinkerer, with a large garage full of spare parts. Innovation (like homicide) requires motive and opportunity. Clearly, the predominant ‘motive’ during the evolution of a novel gene function is to gain a selective advantage. To understand why gene duplications represent the major ‘opportunities’ from which new genes evolve, we must first consider what constrains genic evolution. The vast majority of genes in every genome are selectively constrained, in that most nucleotide changes that alter the fitness of the organism are deleterious. How do we know this? Comparisons between genomes clearly demonstrate that coding sequences diverge at slower rates than non-coding regions, largely due to a deficit of mutations at positions where a base change would cause an amino-acid change. Gene duplication provides opportunities to explore this forbidden evolutionary space more widely by generating duplicates of a gene that can ‘wander’ more freely, on condition that between them they continue to supply the original function. Susumu Ohno was the first to comprehensively elucidate the potential of gene duplication, in his book Evolution by Gene Duplication , published more than 30 years ago ( Ohno 1970 ). The prescience of Ohno's book is highlighted by the fact that his book has almost certainly been cited more times in the past five years than in the first five years after its publication. What Is the Evidence for the Importance of Gene Duplication? The primary evidence that duplication has played a vital role in the evolution of new gene functions is the widespread existence of gene families. Members of a gene family that share a common ancestor as a result of a duplication event are denoted as being paralogous, distinguishing them from orthologous genes in different genomes, which share a common ancestor as a result of a speciation event. Paralogous genes can often be found clustered within a genome, although dispersed paralogues, often with more diverse functions, are also common. Whole genome sequences of closely related organisms have allowed us to identify changes in the gene complements of species over relatively short evolutionary distances. These comparisons typically reveal dramatic expansions and contractions of gene families that can be related to underlying biological differences. For example, humans and mice differ in their sensory reliance on sight and smell respectively; colour vision in humans has been significantly enhanced by the duplication of an Opsin gene that allows us to distinguish light at three different wavelengths, while mice can distinguish only two. By contrast, a much higher proportion of the large gene family of olfactory receptors have retained their functionality in mice, as compared to humans. Given the apparent importance of gene duplication for the evolution of new biological functions over all evolutionary timescales, it is of great interest to be able to comprehensively document the duplicative differences that exist between our own species and our closest relatives, the great apes. The study by Fortna et al. (2004) in this issue of PLoS Biology identifies over 3% of around 30,000 genes as having undergone lineage-specific copy number changes among five hominoid (humans plus the great apes) species. This is the first time that copy number changes among apes have been assayed for the vast majority of human genes, and we can expect that the biological consequences of the 140 human-specific copy number changes identified in this study will be heavily investigated over the coming years. How Do Duplications Arise? The various mechanisms by which genes become duplicated are often classified on the basis of the size of duplication generated, and whether they involve an RNA intermediate ( Figure 1 ). Figure 1 Mechanism of Gene Duplication A two-exon gene is flanked by two Alu elements and a neighbouring replication termination site. Recombination between the two Alu elements leads to a tandem duplication event, as does a replication error instigated by the replication termination site. Retrotransposition of the mRNA of the gene leads to the random integration of an intron-less paralogue at a distinct genomic location. ‘Retrotransposition’ describes the integration of reverse transcribed mature RNAs at random sites in a genome. The resultant duplicated genes (retrogenes) lack introns and have poly-A tails. Separated from their regulatory elements, these integrated sequences rarely give rise to expressed full-length coding sequences, although functional retrogenes have been identified in most genomes. Tandem duplication of a genomic segment (segmental duplication) is one of the possible outcomes of ‘unequal crossing over’, which results from homologous recombination between paralogous sequences. These recombination events can also give rise to the deletion or inversion of intervening sequences. Recent evidence suggests that the explosion of segmental duplications in recent primate evolution has been caused in part by the rapid proliferation of Alu elements about 40 MYA. Alu elements are derived from the 7SL RNA gene and represent the most frequent dispersed repeat in the human genome, with the approximately 1 million copies of the 300-bp Alu element representing around 10% of the entire genome. The striking enrichment of Alu elements at the junctions between duplicated and single copy sequences implicates unequal crossing over between these repeats in the generation of segmental duplications ( Bailey et al. 2003 ). The observation of segmental duplication events with no evidence for homology-driven unequal crossing over suggests that segmental duplications can also arise through non-homologous mechanisms. A recent screen for spontaneous duplications in yeast suggests that replication-dependent chromosome breakages also play a significant role in generating tandem duplications, because duplication breakpoints are enriched at replication termination sites ( Koszul et al. 2004 ). Genome duplication events generate a duplicate for every gene in the genome, representing a huge opportunity for a step-change in organismal complexity. However, genome duplication presents significant problems for the faithful transmission of a genome from one generation to the next, and is consequently a rare event, at least in Metazoa. In principle, genome duplications should be easily identified through the coincident emergence within a phylogeny of many gene families. Unfortunately, this signal is complicated by subsequent piecemeal loss and gain of gene family members. Consequently, there is heated debate over possible ancient genome duplication events in early vertebrate evolution and more recently in teleost fish, both of which must have occurred hundreds of millions of years ago ( McLysaght et al. 2002 ; Van de Peer et al. 2003 ). So what are the relative contributions of these different mechanisms? Not all interspersed duplicate genes are generated by retrotransposition. The initially tandem arrangement of segmental duplications can be broken up by subsequent rearrangements. In keeping with this hypothesis, duplicated genes in a tandem arrangement typically represent more recent duplication events ( Friedman and Hughes 2003 ). Recent analyses suggest that 70% of non-functional duplicated genes (pseudogenes) in the human genome result from retrotransposition rather than any DNA-based process ( Torrents et al. 2003 ). What Fates Befall a Recently Duplicated Gene? A duplicated gene newly arisen in a single genome must overcome substantial hurdles before it can be observed in evolutionary comparisons. First, it must become fixed in the population, and second, it must be preserved over time. Population genetics tells us that for new alleles, fixation is a rare event, even for new mutations that confer an immediate selective advantage. Nevertheless, it has been estimated that one in a hundred genes is duplicated and fixed every million years ( Lynch and Conery 2000 ), although it should be clear from the duplication mechanisms described above that it is highly unlikely that duplication rates are constant over time. However, once fixed, three possible fates are typically envisaged for our gene duplication. Despite the slackened selective constraints, mutations can still destroy the incipient functionality of a duplicated gene: for example, by introducing a premature stop codon or a mutation that destroys the structure of a major protein domain. These degenerative mutations result in the creation of a pseudogene (nonfunctionalization). Over time, the likelihood of such a mutation being introduced increases. Recent studies suggest that there is a relatively narrow time window for evolutionary exploration before degradation becomes the most likely outcome, typically of the order of 4 million years ( Lynch and Conery 2000 ). During the relatively brief period of relaxed selection following gene duplication, a new, advantageous allele may arise as a result of one of the gene copies gaining a new function (neofunctionalization). This can be revealed by an accelerated rate of amino-acid change after duplication in one of the gene copies. This burst of selection is necessarily episodic—once a new function is attained by one of the duplicates, selective constraints on this gene are reasserted. These patterns of selection can be observed in real data: most recently duplicated gene pairs in the human genome have diverged at different rates from their ancestral amino-acid sequence ( Zhang et al. 2003 ). A convincing instance of neofunctionalization is the evolution of antibacterial activity in the ECP gene in Old World Monkeys and hominoids after a burst of amino-acid changes following the tandem duplication of the progenitor gene EDN (a ribonuclease) some 30 MYA ( Zhang et al. 1998 ). The divergence of duplicated genes over time can be also monitored in genome-wide functional studies. In both yeast and nematodes, the ability of a gene to buffer the loss of its duplicate declines over time as their functional overlap decreases. Rather than one gene duplicate retaining the original function, while the other either degrades or evolves a new function, the original functions of the single-copy gene may be partitioned between the duplicates (subfunctionalization). Many genes perform a multiplicity of subtly distinct functions, and selective pressures have resulted in a compromise between optimal sequences for each role. Partitioning these functions between the duplicates may increase the fitness of the organism by removing the conflict between two or more functions. This outcome has become associated with a population genetic model known as the Duplication–Degeneration–Complementation (DDC) model, which focuses attention on the regulatory changes after duplication ( Force et al. 1999 ). In this model, degenerative changes occur in regulatory sequences of both duplicates, such that these changes complement each other, and the union of the expression patterns of the two duplicates reconstitutes the expression pattern of the original ( Figure 2 ). Figure 2 Fates of Duplicate Genes A new duplication in a gene (blue) with two tissue-specific promoters (arrows) arises in a population of single copy genes. Fixation within the population results in a minority of cases. After fixation, one gene is inactivated (degradation) or assumes a new function (neofunctionalization), or the expression pattern of the original gene is partitioned between the two duplicates as one promoter is silenced in each duplicate in a complementary manner (subfunctionalization). A recent study by Dorus and colleagues ( Dorus et al. 2003 ) investigated the retrotransposition (since the existence of a human–mouse common ancestor) of one of the two autosomal copies of the CDYL gene to Y chromosome (forming CDY ). In the mouse, both Cdyl genes produce two distinct transcripts, one of which is expressed ubiquitously while the other is testis-specific. By contrast, in humans both CDYL genes produce a single ubiquitously expressed transcript, and CDY exhibits testis-specific expression. As CDY is a retrogene (see above) that has not been duplicated together with its ancestral regulatory sequences, it is clear that the DDC model is not the only route by which to achieve spatial partitioning of ancestral expression patterns. Subfunctionalization can also lead to the partitioning of temporal as well as spatial expression patterns. In humans, the β-globin cluster of duplicated genes contains three genes with coordinated but distinct developmental expression patterns. One gene is expressed in embryos, another in foetuses, and the third from neonates onwards. In addition, coding sequence changes have co-evolved with the regulatory changes so that the O 2 binding affinity of haemoglobin is optimised for each developmental stage. This coupling between coding and regulatory change is similarly noted at a genomic level when expression differences between many duplicated genes pairs are correlated with their coding sequence divergence ( Makova and Li 2003 ). Other Evolutionary Consequences of Gene Duplication If duplication results in the formation of a novel function as a result of interaction between the two diverged duplicates, which of the above categories of evolutionary outcome does this innovation fall into? Not all new biological functions resulting from gene duplications can be ascribed to individual genes. Protein–protein interactions often occur between diverged gene duplicates. This is especially true for ligand–receptor pairs, which are often supposed to coevolve after a gene duplication event, and thus progress from homophilic to heterophilic interactions. This emergent function of the new gene pair does not fit comfortably into any of the scenarios outlined above: both genes are functional yet neither retains the original function, nor has the original function been partitioned. This mode of ‘duplicate co-evolution’ is likely to be especially prevalent in signalling pathways. Earlier, we saw that homologous recombination between paralogous sequences can result in rearrangements, including tandem duplications. Such recombination events need not cause rearrangements, but can also result in the nonreciprocal transfer of sequence from one paralogue to the other—a process known as gene conversion. Gene conversion homogenizes paralogous sequences, retarding their divergence, and consequently obscuring their antiquity. This leads to the observation of ‘concerted evolution’ whereby duplicates within a species can be highly similar and yet continue to diverge between species ( Figure 3 ). Once gene duplicates have diverged sufficiently so that they differ in their functionality (or non-functionality), gene conversion events can become deleterious—for example, by introducing disrupting mutations from a pseudogene into its functional duplicate. A substantial proportion of disease alleles in Gaucher disease result from the introduction of mutations into the glucocerebrosidase gene from a tandemly repeated pseudogene ( Tayebi et al. 2003 ). These kinds of recombinatorial interactions only occur between paralogues that are minimally diverged. Thus, while selective interactions and functional overlap between duplicates declines relatively slowly over evolutionary time, the potential for recombinatorial interactions between paralogues is relatively short-lived. Figure 3 Concerted Evolution Different gene conversion events homogenize minimally diverged duplicate genes in each daughter species (A and B), with the result that while paralogues are highly similar, orthologues diverge over time. For some genes, duplication confers an immediate selective advantage by facilitating elevated expression, or as Ohno put it, ‘duplication for the sake of producing more of the same’. This has clearly been the case for histones and ribosomal RNA genes. In this scenario, gene conversion is of potential benefit in maintaining homogeneity between copies. Certainly both histone and rDNA genes are commonly found in arrays of duplicates: structures that facilitate array homogenization by both gene conversion and repeated unequal crossing over. Mechanisms of segmental duplication are oblivious to where genes begin and end, and so are additionally capable of duplicating parts of genes or several contiguous genes. The intragenic duplication of individual exons or enhancer elements also presents new opportunities for the evolution of new functions or greater regulatory complexity. Conclusions The likelihood that newly duplicated genes will both remain functional clearly relates to their inherent potential to undergo subfunctionalization or neofunctionalization. Under the DDC model, greater regulatory complexity bestows greater potential for subfunctionalization ( Force et al. 1999 ), whereas neofunctionalization is more likely to occur in genes that are necessarily rapidly evolving, such as those involved in reproduction, immunity, and host defence ( Emes et al. 2003 ). This is not to say that these biases are deterministic, there are plenty of ‘successful’ gene family clusters that contain associated pseudogenes. Duplicate gene evolution has most likely played a substantial role in both the rapid changes in organismal complexity apparent in deep evolutionary splits and the diversification of more closely related species. The rapid growth in the number of available genome sequences presents diverse opportunities to address important outstanding questions in duplicate gene evolution. For those interested in patterns of selection following duplication, the transient nature of the evolutionary window of opportunity following duplication will focus attention on recently duplicated genes. In this regard it will be important to document copy number variation not only among species, as Fortna et al. have, but within species as well. In addition, it has been, and will continue to be, a lot easier to identify copy number changes between genomes than it is to identify their biological consequences (if any). Extensive functional studies targeted at duplicated genes are required if we are to more fully understand the range of evolutionary outcomes. Moreover, collaborations between the proteomics and evolutionary genetics communities would facilitate investigation of the potential role of gene duplication during the evolution of the protein–protein and cell–cell interactions that are fundamental to the biology of multicellular organisms. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC449868.xml |
545934 | Involvement of RhoA-mediated Ca2+ sensitization in antigen-induced bronchial smooth muscle hyperresponsiveness in mice | Background It has recently been suggested that RhoA plays an important role in the enhancement of the Ca 2+ sensitization of smooth muscle contraction. In the present study, a participation of RhoA-mediated Ca 2+ sensitization in the augmented bronchial smooth muscle (BSM) contraction in a murine model of allergic asthma was examined. Methods Ovalbumin (OA)-sensitized BALB/c mice were repeatedly challenged with aerosolized OA and sacrificed 24 hours after the last antigen challenge. The contractility and RhoA protein expression of BSMs were measured by organ-bath technique and immunoblotting, respectively. Results Repeated OA challenge to sensitized mice caused a BSM hyperresponsiveness to acetylcholine (ACh), but not to high K + -depolarization. In α-toxin-permeabilized BSMs, ACh induced a Ca 2+ sensitization of contraction, which is sensitive to Clostridium botulinum C3 exoenzyme, indicating that RhoA is implicated in this Ca 2+ sensitization. Interestingly, the ACh-induced, RhoA-mediated Ca 2+ sensitization was significantly augmented in permeabilized BSMs of OA-challenged mice. Moreover, protein expression of RhoA was significantly increased in the hyperresponsive BSMs. Conclusion These findings suggest that the augmentation of Ca 2+ sensitizing effect, probably via an up-regulation of RhoA protein, might be involved in the enhanced BSM contraction in antigen-induced airway hyperresponsiveness. | Background Increased airway narrowing in response to nonspecific stimuli is a characteristic feature of human obstructive diseases, including bronchial asthma. This abnormality is an important symptom of the disease, although the pathophysiological variations leading to the hyperresponsiveness are unclear now. Several mechanisms have been suggested to explain the airway hyperresponsiveness (AHR), such as alterations in the neural control of airway smooth muscle [ 1 ], increased mucosal secretions [ 2 ], and mechanical factors related to remodeling of the airways [ 3 ]. In addition, it has also been suggested that one of the factors that contribute to the exaggerated airway narrowing in asthmatics is an abnormality of the nature of airway smooth muscle [ 4 , 5 ]. Rapid relief from airway limitation in asthmatic patients by β-stimulant inhalation may also suggest an involvement of augmented airway smooth muscle contraction in the airway obstruction. Thus, it may be important for development of asthma therapy to understand changes in the contractile signaling of airway smooth muscle cells associated with the disease. Smooth muscle contraction including airways is mainly regulated by an increase in cytosolic Ca 2+ concentration in myocytes. Recently, additional mechanisms have been suggested in agonist-induced smooth muscle contraction by studies in which the simultaneous measurements of force development and intracellular Ca 2+ concentration, and chemically permeabilized preparations in various types of smooth muscles were used. It has been demonstrated that agonist stimulation increases myofilament Ca 2+ sensitivity in permeabilized smooth muscles of the rat coronary artery [ 6 ], guinea pig vas deferens [ 7 ], canine trachea [ 8 ] and rat bronchus [ 9 ]. Although the detailed mechanism is not fully understood, a participation of RhoA, a monomeric GTP binding protein, in the agonist-induced Ca 2+ sensitization has been suggested by many investigators [ 10 ]. Moreover, an augmented RhoA-mediated Ca 2+ sensitization in smooth muscle contraction has been reported in experimental animal models of diseases such as hypertension [ 11 - 13 ], coronary [ 14 - 16 ] and cerebral [ 17 - 19 ] vasospasm. It is thus possible that RhoA-mediated signaling is the key for understanding the abnormal contraction of diseased smooth muscles. Here, we show an increased acetylcholine (ACh)-induced contraction of bronchial smooth muscle (BSM) isolated from repeatedly ovalbumin (OA)-challenged BALB/c mice, which have been reported to have in vivo AHR [ 20 ]. A participation of RhoA-mediated Ca 2+ sensitization in the augmented ACh-induced contraction of BSM was demonstrated in this animal model of AHR. Methods Sensitization and antigenic challenge Male BALB/c mice (6-week old, specific pathogen-free; Charles River Japan, Inc., Kanagawa, Japan) were used. All experiments were approved by the Animal Care Committee at the Hoshi University (Tokyo, Japan). Preparation of a murine model of allergic bronchial asthma, which has in vivo airway hyperresponsiveness (AHR), was performed as described by Kato et al . [ 20 ]. In brief, mice were actively sensitized by intraperitoneal injections of 8 μg ovalbumin (OA; Seikagaku Co., Tokyo, Japan) with 2 mg Imject Alum (Pierce Biotechnology, Inc., Rockfold, IL, USA) on day 0 and day 5. The sensitized mice were challenged with aerosolized OA-saline solution (5 mg/ml) for 30 min on days 12, 16 and 20. A control group of mice received the same immunization procedure but inhaled saline aerosol instead of OA challenge. The aerosol was generated with an ultrasonic nebulizer (Nihon Kohden, Tokyo, Japan) and introduced to a Plexiglas chamber box (130 × 200 mm, 100 mm height) in which the mice were placed. Determination of intact bronchial smooth muscle (BSM) responsiveness Twenty-four h after the last antigen challenge, the mice were sacrificed by exsanguination from abdominal aorta under urethane (1.6 g/kg, i.p .) anesthesia. Then the airway tissues under the larynx to lungs were immediately removed. About 3 mm length of the left main bronchus (about 0.5 mm diameter) was isolated and epithelium was removed by gently rubbing with keen-edged tweezers [ 21 ]. The resultant tissue ring preparation was then suspended in a 5 ml-organ bath by two stainless-steel wires (0.2 mm diameter) passed through the lumen. For all tissues, one end was fixed to the bottom of the organ bath while the other was connected to a force-displacement transducer (TB-612T, Nihon Kohden) for the measurement of isometric force. A resting tension of 0.5 g was applied. The buffer solution contained modified Krebs-Henseleit solution with the following composition (mM); NaCl 118.0, KCl 4.7, CaCl 2 2.5, MgSO 4 1.2, NaHCO 3 25.0, KH 2 PO 4 1.2 and glucose 10.0. The buffer solution was maintained at 37°C and oxygenated with 95% O 2 -5% CO 2 . The BSM responsiveness to exogenously applied Ca 2+ in acetylcholine (ACh)-stimulated or high K + -depolarized muscle was determined as previously [ 22 ]. In brief, after an equilibration period, the organ bath solution was replaced with Ca 2+ -free solution containing 10 -6 M nicardipine with the following composition (mM); NaCl 122.4, KCl 4.7, MgSO 4 1.2, NaHCO 3 25.0, KH 2 PO 4 1.2, glucose 10.0 and EGTA 0.05. Fifteen min later, 1 mM ACh was added and, after attainment of a plateau (almost baseline level) response to ACh, a cumulative concentration-response curve for Ca 2+ (0.1–6.0 mM) was made. A higher concentration of Ca 2+ was added after the response to the previous concentration reached a plateau. In another series of experiments, bronchial smooth muscles were depolarized with 60 mM K + , instead of ACh, in the presence of 10 -6 M atropine and in the absence of nicardipine in the Ca 2+ -free solution. All these functional studies were performed in the presence of 10 -6 M indomethacin. The concentration of indomethacin had no effect both on baseline tension and on the ACh- and high K + -induced constrictions of BSMs (data not shown). BSM permeabilized fiber experiments To determine the change in Ca 2+ sensitization of BSM contraction, permeabilized BSMs were prepared as described previously [ 21 ] with minor modification. In brief, 24 h after the last antigen challenge, the left main bronchus was isolated as described above and cut into ring strips (about 200 μm width, 500 μm diameter). The epithelium was removed by gently rubbing with keen-edged tweezers. The ring strips were then permeabilized by a 30-min treatment with 83.3 μg/ml α-toxin (Sigma, St. Louis, MO, USA) in the presence of Ca 2+ ionophore A23187 (10 μM, Sigma) at room temperature in relaxing solution. Relaxing solution contained: 20 mM PIPES, 7.1 mM Mg 2+ -dimethanesulfonate, 108 mM K + -methanesulfonate, 2 mM EGTA, 5.875 mM Na 2 ATP, 2 mM creatine phosphate, 4 U/ml creatine phosphokinase, 1 μM carbonyl cyanide p-trifluoromethoxyphenylhydrazone (FCCP) and 1 μg/ml E-64 (pH 6.8) containing 10 μM A23187. Free Ca 2+ concentration was changed by adding an appropriate amount of CaCl 2 . The apparent binding constant of EGTA for Ca 2+ was considered to be 10 6 M -1 [ 23 ]. The permeabilized muscle strip was then suspended in a 400-μL organ bath at room temperature. The contractile force developed was measured by an isometric transducer (T7-8-240; Orientec, Tokyo, Japan) under a resting tension of 50 mg. To determine the involvement of RhoA in the ACh-induced myofilament Ca 2+ sensitization, the α-toxin-permeabilized muscle strips were treated with Clostridium botulinum C3 exoenzyme (10 μg/ml; Calbiochem-Novabiochem Corp., La Jolla, CA) in the presence of 100 μM NAD for 20 min at room temperature. Determination of RhoA protein level in BSM Protein samples of BSMs were prepared as previously [ 21 ]. In breif, the airway tissues below the main bronchi to lungs were removed and immediately soaked in ice-cold, oxygenated Krebs-Henseleit solution. The airways were carefully cleaned of adhering connective tissues, blood vessels and lung parenchyma under a stereomicroscopy. The epithelium was removed as much as possible by gently rubbing with keen-edged tweezers [ 21 ]. Then the bronchial tissue (containing the main and intrapulmonary bronchi) segments were quickly frozen with liquid nitrogen, and the tissue was crushed to pieces by CryopressTM (CP-100W; Microtec, Co. Ltd., Chiba, Japan: 15 sec × 3). The tissue powder was homogenized in ice-cold tris(hydroxymethyl)aminomethane (Tris, 10 mM; pH 7.5) buffer containing 5 mM MgCl 2 , 2 mM EGTA, 250 mM sucrose, 1 mM dithiothreitol, 1 mM 4-(2-aminoethyl)benzenesulfonyl fluoride, 20 μg/ml leupeptin, 20 μg/ml aprotinin, 1% Triton X-100 and 1% sodium cholate. The tissue homogenate was then centrifuged (3,000 g, 4°C for 15 min) and the resultant supernatant was stored at -85°C until use. To determine the level of RhoA protein in BSMs, the samples (10 μg of total protein per lane) were subjected to 15% SDS-PAGE and the proteins were then electrophoretically transferred to a PVDF membrane. After blocking with 3% gelatin, the PVDF membrane was incubated with polyclonal rabbit anti-RhoA antibody (1:3,000; Santa Cruz Biotechnology, Inc., Santa Cruz, CA, USA). Then the membrane was incubated with horseradish peroxidase-conjugated goat anti-rabbit IgG (1:2,500 dilution; Amersham Biosciences, Co., Piscataway, NJ, USA), detected by an enhanced chemiluminescent system (Amersham Biosciences, Co.) and analyzed by a densitometry system. Thereafter, the primary and secondary antibodies were stripped and the membrane was reprobed by using monoclonal mouse anti-glyceraldehyde-3-phosphate dehydrogenase (GAPDH; 1:3,000 dilution; Chemicon International, Inc., Temecula, CA, USA) to confirm the same amount of proteins loaded. Determination of active form of RhoA in BSM The active form of RhoA, GTP-bound RhoA, in BSMs was measured by RhoA pull down assay. In brief, bronchial tissues containing the main and intrapulmonary bronchi were isolated as described above. The isolated bronchial tissues were equilibrated in oxygenated Krebs-Henseleit solution at 37°C for 1 hr. After the equilibration period, the tissues were stimulated by ACh (10 -3 M for 10 min) and were quickly frozen with liquid nitrogen. The tissues were then lysed in lysis buffer with the following composition (mM); HEPES 25.0 (pH 7.5), NaCl 150, IGEPAL CA-630 1%, MgCl 2 10.0, EDTA 1.0, glycerol 10%, NaF 25.0, sodium orthovanadate 1.0 and peptidase inhibitors. Active RhoA in tissue lysates (200 μg protein) was precipitated with 25 μg GST-tagged Rho binding domain (amino acids residues 7–89 of mouse rhotekin; Upstate, Lake Placid, NY, USA), which was expressed in Escherichia coli and bound to glutathione-agarose beads. The precipitates were washed three times in lysis buffer, and after adding the SDS loading buffer and boiling for 5 min, the bound proteins were resolved in 15% polyacrylamide gels, transferred to nitrocellulose membranes, and immunoblotted with anti-RhoA antibody as described above. Determination of phosphorylation of myosin phosphatase and myosin light chain in BSM Phosphorylated proteins were detected by using the fluorescent Pro-Q-Diamond dye (Molecular Probes, Eugene, OR, USA), which can directly detect phosphate groups attached to tyrosine, serine or threonine residues in gels [ 24 ]. In brief, bronchial tissue lysates (50 μg protein) with SDS loading buffer prepared as described above were resolved in 10 – 20% gradient polyacrylamid gels (Atto Co., Tokyo, Japan). Proteins were fluorescently stained by fixing the gels in 50% methanol and 10% acetic acid for 1 h. The gels were washed with deionised water for 20 min, stained with Pro-Q-Diamond for 1.5 h and destained by three washes in 4% acetonitrile in 50 mM sodium acetate, pH 4.0, for 2 h. Gels were scanned with a fluorimager, a Typhoon 9410 laser scanner (Amersham Biosciences, Co.), with excitation at 532 nm and a 580 nm band pass emission filter for Pro-Q-diamond dye detection. Phosphorylated proteins were quantified densitometrically with the ImageQuant software (Amersham Biosciences, Co.). After scanning, the gels were washed with deionised water for 30 min and incubated in 0.7% glycine-0.2% SDS in 0.3% Tris buffer for 15 min. The proteins were then electrophoretically transferred to a PVDF membrane and immunoblottings for myosin phosphatase target subunit 1 (MYPT1; polyclonal goat anti-MYPT1 antibody; 1:1000; Santa Cruz Biotechnology, Inc.), GAPDH and myosin light chain (MLC; polyclonal rabbit anti-MLC2 antibody; 1:3000; Santa Cruz Biotechnology, Inc) were performed as described above. Statistical analyses All the data are expressed as the mean ± S.E. Statistical significance of difference was determined by unpaired Student's t -test, Bonferroni/Dunn's test or two-way analysis of variance (ANOVA). Results Contractile response of intact BSM preparations Under Ca 2+ -free condition (in the presence of 10 -6 M nicardipine and 0.05 mM EGTA), ACh (10 -3 M) generated a transient phasic contraction in all BSM preparations used. The generated tension of BSM from the repeatedly OA-challenged mice (69 ± 12 mg, N = 6) was significantly greater than that from the sensitized control animals (20 ± 12 mg, N = 6; P < 0.05). The concentration of nicardipine used in the present study completely blocked high K + (10–90 mM)-induced BSM contraction in Ca 2+ (2.5 mM) containing normal Krebs-Henseleit solution (data not shown), indicating that voltage-dependent Ca 2+ channels were completely blocked in this condition. The tension returned to baseline level within 5 min after the ACh application, and then the contraction induced by cumulatively administered Ca 2+ was measured. Figure 1A shows the concentration-response curves to Ca 2+ of murine BSMs that were preincubated with nicardipine (10 -6 M) and ACh (10 -3 M) under Ca 2+ -free (0.05 mM EGTA) condition. Addition of Ca 2+ induced a concentration-dependent BSM contraction in both the sensitized control and OA-challenged groups. The contractile response to Ca 2+ of the ACh-stimulated BSMs from the repeatedly OA-challenged mice was markedly augmented as compared to that from the sensitized control animals. By contrast, no significant difference in the response to Ca 2+ of BSMs depolarized with 60 mM K + (in the absence of nicardipine and presence of 10 -6 M atropine) was observed between groups (Fig. 1B ). Likewise, the ACh (10 -7 –10 -3 M) concentration-response curve determined in normal Krebs-Henseleit solution (2.5 mM Ca 2+ ) was significantly shifted upward in BSMs from the OA-challenged mice as compared with that from the sensitized control animals, whereas no significant difference in the contractile response induced by isotonic high K + (10–90 mM) was observed between groups (data not shown). Figure 1 Cumulative concentration-response curves to Ca 2+ of bronchial rings obtained from sensitized control (Control; open circles ) and repeatedly ovalbumin-challenged (OA-challenged; closed circles ) mice. Bronchial rings were preincubated with 10 -3 M acetylcholine (ACh) in the presence of 10 -6 M nicardipine ( A ) or isotonic 60 mM K + in the presence of 10 -6 M atropine ( B ) in Ca 2+ -free, 0.05 mM EGTA solution. Each point represents the mean ± S.E. from 6 experiments. The Ca 2+ -induced contraction of the ACh-stimulated bronchial smooth muscles was significantly augmented in the OA-challenged group ( A ; P < 0.05 by ANOVA), whereas no significant change in the Ca 2+ -induced contraction of the high K + -depolarized muscles was observed between groups ( B ). Contractile response of α-toxin-permeabilized BSM preparations The BSM contractility was also determined by using α-toxin-permeabilized BSM preparations. In all BSM preparations treated with a-toxin, application of free Ca 2+ (pCa = 6.5, 6.3, 6.0, 5.5 and 5.0) induced a concentration-dependent reproducible contractile response, indicating successful permeabilization. In the α-toxin-permeabilized BSM, no significant difference in the Ca 2+ responsiveness or the maximal contractile response induced by pCa 5.0 (Emax) was observed between the sensitized control (pEC 50 [Ca 2+ (M)] = 5.67 ± 0.04, Emax = 26.7 ± 1.2 mg; N = 6) and repeatedly OA-challenged (pEC 50 [Ca 2+ (M)] = 5.78 ± 0.15, Emax = 22.8 ± 5.9 mg; N = 6) groups. In both groups, when the Ca 2+ concentration was clamped at pCa 6.0, application of ACh (10 -5 –10 -3 M) in the presence of GTP (10 -4 M) caused a further contraction, i.e ., ACh-induced Ca 2+ sensitization, in an ACh concentration-dependent manner (Fig. 2 ). The ACh-induced Ca 2+ sensitization was significantly greater in the repeatedly OA-challenged group (Fig. 2B ). Figure 2 Acetylcholine (ACh)-induced Ca 2+ sensitization of murine bronchial smooth muscle. ( A ) A typical recording of contraction induced by Ca 2+ (pCa 6.0 and 5.0) and ACh (10 -5 –10 -3 M) with guanosine triphosphate (GTP; 10 -4 M) in α-toxin-permeabilized bronchial smooth muscle isolated from a sensitized control mouse. In the presence of GTP, ACh induced further contractions even in the constant Ca 2+ concentration at pCa 6.0, i.e ., ACh-induced Ca 2+ sensitization, in an ACh-concentration-dependent manner. ( B ) Concentration-response curves for ACh (10 -5 –10 -3 M)-induced Ca 2+ sensitization in α-toxin-permeabilized bronchial smooth muscle isolated from sensitized control (Control; open circles ) and repeatedly ovalbumin-challenged (OA-challenged; closed circles ) mice. The data are expressed as percentage increase in tension induced by ACh (10 -5 –10 -3 M) in the presence of Ca 2+ (pCa 6.0) and GTP (10 -4 M) from the sustained contraction induced by pCa 6.0. Each point represents the mean ± S.E. from 6 experiments. The ACh-induced Ca 2+ sensitization of bronchial smooth muscle contraction was significantly augmented in the OA-challenged mice (*P < 0.05 vs. Control group by unpaired Student's t -test). To determine an involvement of RhoA protein in the ACh-induced Ca 2+ sensitization, the effect of pretreatment with C3 exoenzyme on the contractile response of the α-toxin-permeabilized BMS was also investigated. The C3 treatment alone had no significant effect on the Ca 2+ responsiveness of α-toxin-permeabilized BSMs in any groups (data not shown). However, the ACh (10 -3 M, in the presence of 10 -4 M GTP)-induced Ca 2+ sensitizing effect was inhibited by treatment with C3 in both the sensitized control and OA-challenged groups (Fig. 3 ). Interestingly, the remaining C3-insensitive component of the ACh-induced Ca 2+ sensitization was the same level between groups, whereas the Ca 2+ sensitization before treatment with C3 was significantly greater in BSMs of the OA-challenged mice (Fig. 3B ). These findings indicate that the C3-sensitive Ca 2+ sensitization, probably mediated by RhoA [ 25 , 26 ], might be augmented in BSMs of the OA-challenged AHR mice. Figure 3 Effect of Clostridium botulinum C3 exoenzyme, an inhibitor of RhoA protein, on the acetylcholine (ACh)-induced Ca 2+ sensitization of the α-toxin-permeabilized bronchial smooth muscle of mice. ( A ) Typical recordings of contraction induced by Ca 2+ (pCa 6.0 and 5.0) and ACh (10 -3 M) with guanosine triphosphate (GTP; 10 -4 M) in α-toxin-permeabilized bronchial smooth muscle isolated from a sensitized control mouse. In the presence of GTP, ACh induced a further contraction even in the constant Ca 2+ concentration at pCa 6.0, i.e ., ACh-induced Ca 2+ sensitization ( a ). The ACh-induced Ca 2+ sensitization was re-estimated after treatment with C3 exoenzyme (10 μg/mL, for 20 min; b ). ( B ) Summary of the effects of C3 exoenzyme on the ACh-induced Ca 2+ sensitization of bronchial smooth muscle contraction in the sensitized control (Control) and repeatedly ovalbumin (OA)-challenged (OA-challenged) mice. The data are expressed as percentage increase in tension induced by ACh (in the presence of Ca 2+ and GTP) from the sustained contraction induced by pCa 6.0. Each column represents the mean ± S.E. from 6 experiments. *P < 0.05 vs. Control group (Before C3) and #P < 0.05 vs. respective Before C3 group by Bonferroni/Dunn's test. Upregulation of RhoA protein in BSMs of OA-challenged mice The expression of RhoA protein in BSM homogenates was assessed by using immunoblotting. As shown in Fig. 4A , immunoblotting with the antibody against RhoA gave a single 21 kD band, indicating the expression of RhoA protein in murine BSM. The level of RhoA protein in samples of the OA-challenged mice was significantly increased as compared with that of the sensitized control animals. Moreover, the GTP-bound active form of RhoA in ACh-stimulated BSMs was markedly increased in the OA-challenged mice (Fig. 5 ). Figure 4 The levels of RhoA protein in the bronchi obtained from the sensitized control (Control) and repeatedly ovalbumin (OA)-challenged (OA-challenged) mice. ( A ) Typical immunoblots. Lane 1 ; Control, lane 2 ; OA-challenged, and GAPDH; glyceraldehyde-3-phosphate dehydrogenase. The bands were analyzed by a densitometer and normalized by the intensity of corresponding GAPDH band, and the data are summarized in B . Each column represents the mean ± S.E. from 5 experiments. The expression level of RhoA protein in the bronchi was significantly increased in the OA-challenged group (*P < 0.001 vs. Control group by unpaired Student's t -test). Figure 5 Representative immunoblots showing activation of RhoA in acetylcholine (ACh)-stimulated bronchi obtained from the sensitized control (Control) and repeatedly ovalbumin (OA)-challenged (Challenged) mice. Isolated bronchial tissues were incubated for 10 min in the absence (-) or presence (+) of 10 -3 M ACh ( see Methods ). Tissues were then rapidly lysed, GTP-bound active form of RhoA was pulled down with GST-tagged Rho binding domain of rhotekin, and RhoA was visualized by Western blotting. The respective blot of total RhoA in each sample is also shown. The GTP-bound RhoA in ACh-stimulated bronchi was markedly increased in the OA-challenged mice. Augmented ACh-induced phosphorylation of MLC in BSMs of OA-challenged mice Figure 6 shows the levels of total and phosphorylated MLCs in BSMs determined by immunoblotting and Pro-Q Diamond dye staining, respectively. Immunoblotting with the antibody against MLC protein revealed a single 20 kD band, which contains both phosphorylated and non-phosphorylated MLC proteins (total MLC). The levels of total MLC were the same between groups (Fig. 6 , middle panel ). In the Pro-Q Diamond dye-stained gels, there were several positive bands, i.e ., phosphorylated proteins [ 24 ], in the ACh-stimulated BSM samples. Among them, a 20 kD band corresponding to MLC was distinctly found (Fig. 6 , bottom panel ). The ACh-induced phosphorylation of MLC in BSMs of OA-challenged mice was markedly augmented as compared with those of control animals. A Pro-Q Diamond dye-positive 140 kD band probably corresponding to MYPT1, i.e ., phosphorylated MYPT1, was also found in the ACh-stimulated BSM samples and was increased in the OA-challenged group (data not shown). Figure 6 Representative photographs showing phosphorylation of myosin light chain (MLC) in acetylcholine (ACh)-stimulated bronchi obtained from the sensitized control (Cont) and repeatedly ovalbumin-challenged (OA) mice. Isolated bronchial tissues were incubated for 10 min in the absence (non-stimulated; NS) or presence of 10 -3 M ACh ( see Methods ). The electrophoretically separated proteins on gels were stained by Pro-Q Diamond dye, which can detect phosphorylated proteins specifically and quantitatively. After detection of phosphorylated proteins, immunoblotting for MLC was performed to detect total (phosphorylated and non-phosphorylated) MLC. The respective Pro-Q Diamond dye-positive band ( bottom panel ), which has same molecular weight with MLC visualized by immunoblotting ( middle panel ), in each sample was determined as phosphorylated MLC (p-MLC). The ACh-induced phosphorylation of MLC was augmented in the OA-challenged mice whereas the total MLC levels were equal to the control. Discussion An in vivo AHR accompanied by increased IgE production and pulmonary eosinophilia has been demonstrated in the actively sensitized and repeatedly OA-challenged BALB/c strain of mice [ 20 ]. By using the same sensitization and challenge protocol in BALB/c mice, the current study demonstrated an increased BSM contractility in ACh-stimulated, but not in high K + -depolarized (without receptors stimulation), intact muscle strips of the repeatedly OA-challenged mice (Fig. 1 ). Likewise, the ACh-induced, C3-sensitive Ca 2+ sensitization of BSM contraction was significantly augmented in α-toxin-permeabilized BSMs of the OA-challenged mice (Figs. 2 and 3 ), whereas the contraction induced by Ca 2+ itself was the same as the control level (see Results section). These findings suggest that the C3-sensitive, RhoA-mediated Ca 2+ sensitization might be augmented in BSMs of the OA-challenged AHR mice. Indeed, the current study also demonstrated a marked increase in the expression and activation of RhoA protein in BSMs of the AHR mice (Fig. 4 and 5 ). In the present study, no significant difference in the Ca 2+ -induced contraction (in the absence of ACh and GTP) of α-toxin-permeabilized BSMs was observed between groups (see Result section), indicating that the contents of typical contractile elements such as calmodulin, myosin light chain (MLC; Fig. 6 ) and SM α-actin might be the same as control even in the BSMs of the OA-challenged mice. Moreover, the results also indicate that the downstream signaling activated by Ca 2+ -calmodulin complex, including phosphorylation of MLC via activation of MLC kinase, might be in an analogous fashion between groups. The results that the contractile response of intact (non-permeabilized) BSMs induced by high K + depolarization was not changed after OA challenge also support our speculation. Thus, the baseline Ca 2+ sensitivity of contractile elements themselves in BSM cells is unlikely to change in AHR. By contrast with the contraction induced by Ca 2+ itself, the ACh-stimulated contraction of intact BSM strips from the OA-challenged mice was significantly augmented as compared to that from the sensitized control animals (Fig. 1 ). BSMs are predominantly innervated by vagal efferent nerves, which release ACh when stimulated leading to an activation of muscarinic ACh receptors. The activation of muscarinic receptors existing on BSM, which are mainly thought to be of the M 3 subtype [ 27 ], results in BSM contraction by increasing intracellular Ca 2+ concentration through Ca 2+ release from sarcoplasmic reticulum and Ca 2+ influx from voltage-dependent (nicardipine-sensitive) and receptor-operated (nicardipine-insensitive) Ca 2+ channels [ 28 ]. Therefore, one possible explanation for the increased response to ACh of OA-challenged BSMs may be attributable to an enhanced Ca 2+ mobilization in BSM cells. However, the possibility might be denied by the current result that the ACh-induced contraction of α-toxin-permeabilized BSMs from the OA-challenged mice was significantly augmented as compared with that from the control animals even at a constant Ca 2+ concentration (pCa 6.0; Fig. 2B ). Moreover, it has also been reported that there is no difference between normal and antigen-induced AHR animals in ACh-induced increase in intracellular Ca 2+ concentration in BSMs, irrespective of a great difference in ACh-induced BSM contraction [ 29 , 30 ]. In addition to the classical Ca 2+ -mediated contractile signaling in smooth muscle, it has been demonstrated that agonist stimulation increases myofilament Ca 2+ sensitivity in various types of smooth muscles including airways [ 8 , 10 , 21 , 31 ]. Recent studies suggest a participation of RhoA in the agonist-induced Ca 2+ sensitization of smooth muscle contraction [ 10 ]. Hirata et al . [ 32 ] firstly reported an involvement of RhoA in the mechanism for the increase in Ca 2+ sensitization in smooth muscle. It was then shown that RhoA is responsible for the inhibition of MLC phosphatase through the activation of Rho-associated kinases [ 33 ]. The present study demonstrated an ACh-induced Ca 2+ sensitization in murine BSM contraction (Fig. 2 ),which is sensitive to C3 exoenzyme (Fig. 3 ), in the α-toxin-permeabilized BSMs. Furthermore, western blot analysis clearly demonstrated a distinct expression of RhoA protein in BSMs of mice (Fig. 4 ). Collectively, these findings firstly demonstrated a participation of RhoA-mediated Ca 2+ sensitization in ACh-induced BSM contraction in mice. One of the important findings in the present study is that the C3-sensitive, RhoA-mediated Ca 2+ sensitization in ACh-induced contraction was significantly augmented in BSMs of the repeatedly OA-challenged AHR mice (Figs. 2 and 3 ). Moreover, the protein level of RhoA in BSMs of the AHR mice was significantly increased (Fig. 4 ). Thus, the current study demonstrated an augmentation of ACh-induced, RhoA-mediated Ca 2+ sensitization of BSM contraction, which coincides with enhanced protein expression of RhoA, in antigen-induced AHR. Although the mechanism(s) of up-regulation of RhoA in OA-challenged BSMs is not known here, inflammatory cytokines such as tumor necrosis factor-α [ 34 ], which is also demonstrated in airways of this murine model of asthma (unpublished data), may be involved in. On the other hand, it has been reported that an introduction of active forms of RhoA to permeabilized smooth muscle induced contractile response [ 32 , 35 ]. It is thus likely that ACh stimulation activates the upregulated RhoA (Fig. 5 ), resulting in a greater phosphorylation of MLC (Fig. 6 ) and contraction of BSMs in AHR mice. An increase in responsiveness to muscarinic agonists of airway smooth muscle has been demonstrated in animal models of AHR [ 21 , 22 , 36 , 37 ] and asthmatic patients [ 38 ], although no change in the levels of plasma membrane receptors was observed [ 36 , 37 , 39 ]. Moreover, the agonist-induced increase in cytosolic Ca 2+ level was within normal level even in the hyperresponsive BSMs [ 29 , 30 ]. Taken together with our current findings, it is likely that the enhanced contractility to agonists reflects, at least in part, the augmentation of muscarinic receptor- and RhoA-mediated Ca 2+ sensitization, although the mechanism(s) for activation of RhoA by ACh is still unclear. If RhoA proteins are activated by receptors other than muscarinic receptor, it might account for the 'non-specific' AHR, which is a common feature of allergic asthmatics. Indeed, the BSMs of the OA-challenged mice also have hyperresponsiveness to endothelin-1 [ 40 ], which has been reported to activate RhoA via its own receptors [ 41 ]. An upregulation of RhoA/Rho-kinase associated with the augmented smooth muscle contractility has also been reported in rat myomertium during pregnancy [ 42 , 43 ], arterial smooth muscle of spontaneously hypertensive rats [ 12 ], coronary vasospasm in pigs [ 16 ], dog femoral artery in heart failure [ 44 ], and BSMs in rat experimental asthma [ 21 ]. Thus, the upregulation of RhoA might be widely involved in the enhanced contraction of the diseased smooth muscles including the BSMs in AHR over species. Conclusions In conclusion, the current study demonstrated an ACh-induced, RhoA-mediated Ca 2+ sensitization in murine BSM contraction. An augmentation of the Ca 2+ sensitizing effect, probably by the upregulation of RhoA protein, might be involved in the enhanced BSM contraction observed in the antigen-induced AHR in mice. Authors' contributions YC conceived of the study, participated in its design and coordination, and drafted the manuscript. AU carried out the intact smooth muscle studies. KS, HT and HS carried out the skinned fiber studies and immunoblot analysis. SN carried out the analysis of active RhoA. MM participated in the direction of the study as well as writing and preparing the manuscript. All authors read and approved the final manuscript. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC545934.xml |
509294 | A Focused and Efficient Genetic Screening Strategy in the Mouse: Identification of Mutations That Disrupt Cortical Development | Although the mechanisms that regulate development of the cerebral cortex have begun to emerge, in large part through the analysis of mutant mice ( Boncinelli et al. 2000 ; Molnar and Hannan 2000 ; Walsh and Goffinet 2000 ), many questions remain unanswered. To provide resources for further dissecting cortical development, we have carried out a focused screen for recessive mutations that disrupt cortical development. One aim of the screen was to identify mutants that disrupt the tangential migration of interneurons into the cortex. At the same time, we also screened for mutations that altered the growth or morphology of the cerebral cortex. We report here the identification of thirteen mutants with defects in aspects of cortical development ranging from the establishment of epithelial polarity to the invasion of thalamocortical axons. Among the collection are three novel alleles of genes for which mutant alleles had already been used to explore forebrain development, and four mutants with defects in interneuron migration. The mutants that we describe here will aid in deciphering the molecules and mechanisms that regulate cortical development. Our results also highlight the utility of focused screens in the mouse, in addition to the large-scale and broadly targeted screens that are being carried out at mutagenesis centers. | Introduction The cerebral cortex is the seat of consciousness and the means by which we carry out abstract reasoning. Understanding how the cortex is assembled during embryonic development will give deeper insights into how this marvelous machine functions and provide the basis for therapy and repair. Although a diversity of approaches will be needed to answer all of our questions, an important starting point in studying events in development is often the careful analysis of mutant phenotypes. Much of what we know about cortical development has emerged through the study of mutations in mice and humans. For example, spontaneous mutations in mice such as reeler and scrambler have helped to tease apart the regulation of the radial migrations that create the cortical layers. Other important insights have come from the study of spontaneous mutations that cause radial migration defects and lead to lissencephaly and similar cortical defects in humans. Our understanding of radial migration and many other aspects of cortical development have also benefited enormously from the application of gene knockout approaches in mice. Despite this progress, many aspects of cortical development remain to be explored and would benefit enormously from additional mutant resources. The tangential migrations of cortical interneurons, for example, are regulated differently from the radial migrations of projection neurons, and only a few mutations have been described that disrupt interneuron migration. Forward genetic approaches in the mouse, although technically feasible for many years, have become increasingly attractive with the availability of a dense genetic map and a nearly complete genomic sequence. These tools allow the process of gene identification, which was once very cumbersome, to be relatively straightforward. With the initial resurgence of interest in genetic screens, large-scale screens aimed at identifying mutations in broadly defined phenotypic categories were established at mutagenesis centers in Germany, the United Kingdom, the United States, and other countries ( Hrabe de Angelis et al. 2000 ; Nolan et al. 2000 ). More recently, smaller, more focused screening efforts have had notable success ( Vitaterna et al. 1994 ; Eggenschwiler et al. 2001 ; Kapfhamer et al. 2002 ; Garcia-Garcia and Anderson 2003 ; Hoebe et al. 2003 ), generally in situations where the effects of mutation on a specific cellular or biochemical process can be readily ascertained. Further development of forward genetics in the mouse as an approach with general utility will require the validation of focused screening strategies that allow for the identification of mutations disrupting specific processes in diverse situations, as has been done in other model organisms. In Drosophila, deletion strains and chromosomal inversions have been used to identify mutations within a specific region of the genome, and this approach has been elegantly adapted for use in the mouse ( Juriloff et al. 1985 ; Kile et al. 2003 ). The development of screening strategies for the mouse that focus on the identification of mutations affecting a biological process rather than mapping to a certain genomic region will further expand the questions that can be addressed. Here we describe a focused genetic screen in the mouse that takes advantage of a transgenic reporter line that labels the ganglionic eminences of the ventral forebrain and migrating cortical interneurons with β-galactosidase. The use of this reporter gene allows the isolation of mutations that alter growth and morphogenesis of the cortex very efficiently and, more specifically, allows the identification of mutations disrupting interneuron migration from the ganglionic eminences into the cortex. In this screen, we isolated four mutations that affect tangential migration. We also isolated nine novel mutations affecting other aspects of cortical development; these mutations include three that represent novel alleles of genes that have been shown to have a role in cortical development. These results illustrate the power of a forward genetic approach, borrowed from other model organisms, that can be applied to various questions of mammalian biology. Results The Dlx-LacZ Transgene Labels the Ganglionic Eminences and Migrating Interneurons The expression of Dlx family homeodomain transcription factors is necessary for differentiation of neurons in the striatum and for migration of many, if not all, of the interneuron precursors that arise in the ganglionic eminences of the embryonic telencephalon ( Marin and Rubenstein 2001 , 2003 ). A transgenic line in which the expression of β-galactosidase is driven by transcriptional control sequences from the Dlx5/6 intergenic region faithfully recapitulates most aspects of Dlx5 expression in the central nervous system, including strong expression in the subventricular zone (SVZ; a secondary progenitor zone) of the ganglionic eminences, in immature interneurons as they migrate tangentially through the intermediate zone (IZ) or marginal zone (MZ) of the cortex ( Figure 1 A– 1 C), and in mature cortical GABAergic interneurons ( Stuhmer et al. 2002 ). The dispersed interneuron precursors label the cerebral cortex, and the underlying high-level expression in the ganglionic eminences provides a useful adjunct to the morphological landmarks, allowing cortical defects to be identified in whole-mount stained embryos. We took advantage of the Dlx-LacZ line as a background upon which to screen for mutations. Mutations were induced using ethyl-nitroso-urea (ENU), a mutagen that induces primarily single-base substitutions with very little bias ( Russell et al. 1979 ; Vrieling et al. 1988 ; Nivard et al. 1992 ), and animals were bred in order to detect recessive mutations that disrupted the distribution of Dlx-LacZ–labeled cells in the developing cortex at embryonic day 14.5 (E14.5) ( Figure 1 D). In total, 705 litters with an average of seven embryos each, and representing 305 lines of mice, were stained using β-galactosidase histochemistry, and then examined in whole mount; higher-resolution analysis on 100-μm coronal vibratome sections of brains was also performed on a third (225) of the litters. Figure 1 The Strategy for Isolation of Recessive Mutations Disrupting Cortical Development (A) Migrating interneuron precursors appear to migrate tangentially (yellow arrows) through both the MZ and the IZ/SVZ. Precursors also migrate radially (pink arrows) to reach the developing cortical plate (CP). (B) Migrating interneuron precursors expressing β-galactosidase can be seen in a whole-mount preparation of an E14.5 Dlx-LacZ mouse as diffuse cortical staining. (C) In coronal section, the densely labeled SVZ of the LGE can be seen, as can the streams of migrating interneuron precursors in the cortex. (D) To identify recessive mutations, male C57BL6/J mice were treated with ENU and then crossed to FVBN/J females that were homozygous for the Dlx-LacZ transgene. Male offspring of this cross were backcrossed to produce female offspring that were then backcrossed to their fathers. Embryonic litters from these backcrosses were stained and examined for defects at E13.5 or E14.5. Dlx-LacZ Allows Efficient Identification of Cortical Mutants During the course of screening we identified eight mutants with defective growth or patterning of the cerebral cortex, four mutants with defects in the migration of interneuron precursors into the cortex, and one mutant in which thalamocortical axons fail to invade the cortex ( Table 1 ). Lines of mice carrying each mutation were established, in which the phenotype was propagated as a recessive trait with Mendelian inheritance. Preliminary mapping, together with the overt phenotype, pointed us toward a likely locus for three of the mutants ( Figure 2 ). The other ten mutations appear to identify loci whose role in forebrain development has not previously been described. Figure 2 Novel Alleles of scribble, megalin, and Rfx4 (A and B) Comparison of E12.5 WT (A) and mutant (B) embryos showing the severe craniorachischisis of line 90 embryos. (C) Sequencing chromatograms show the thymine in the WT sequence (top) that is an adenine in line 90 (bottom). This thymine-to-adenine substitution changes an isoleucine codon to a lysine codon within a conserved LRR domain of SCRIBBLE. (D and E) Ribbon diagrams of an LRR fold, illustrating the predicted position (red arrowheads) of the line 90 amino acid substitution. The substitution is in a linker region between two stretches of β-sheet. An LRR fold has a β-sheet on the inside, and linker regions on the outside, of a broad half-circular curve. (F) The predicted domain structure of SCRIBBLE indicating where the line 90 and the Circletail alleles alter the protein relative to the three LRR and four PDZ domains. (G and H) Dorsal views of the cortex of E17.5 WT (G) and line 267 mutant (H) embryos stained to reveal the expression of the Dlx-LacZ transgene. The red arrowheads point to the choroid plexus of the third ventricle, which is stained because of its endogenous β-galactosidase expression, and which is greatly enlarged in the mutant. The cortex of the mutant is longer along the rostrocaudal axis and is altered in shape in the caudal portion (yellow arrows). The olfactory bulbs (blue arrowheads) are also altered in shape. (I) The choroid plexus persists in its hypertrophic state after birth and can be seen as a pinkish lump on the head of this postnatal day 14 (P14) pup. (J) A coronal section through the dorsal portion of the diencephalon of a P0 pup. (K) Sequencing chromatograms show the thymine in the WT sequence (top) that is an adenine in line 267 (bottom). (L) The predicted domain structure of MEGALIN indicating where the new stop codon is introduced by the line 267 mutation. (M and N) Dorsal views of the cerebral cortex of E14.5 WT (M) and line 269 mutant (N) embryos stained to reveal the expression of the Dlx-LacZ transgene. (O and P) Coronal sections of E14.5 WT (O) and line 269 mutant (P) forebrains. Red arrowheads indicate the apparent span over which dorsal midline structures are lost in the mutant. (Q) Sequence chromatograms showing the thymine in the WT sequence (top) that is a cytosine in line 269 (bottom). (R) The predicted domain structure of RFX4 indicating where the line 269 substitution alters the protein relative to the DNA binding and the extended (B, C, and Dim) dimerization domains. Table 1 Summary of Cortical Mutants a See supporting information b Scored on the basis of polydactyly c These two mutants fail to complement and are likely to be allelic d Scored on the basis of migration defects e These two mutants have very different phenotypes. Since the interval is large the two mutants seem likely to represent two different genes A Novel scribble Allele The three mutations in previously characterized loci produce alleles that differ from the existing ones in informative ways ( Figure 2 ). Mice homozygous for the line 90 mutation have an open neural tube in the spinal cord and hindbrain region, or craniorachischisis, and a disorganized and hyperplastic neuroepithelium in the cortex and other parts of the central nervous system ( Figure 2 A and 2 B). An essentially identical phenotype is seen in homozygous Loop-tail and Circletail mutants ( Kibar et al. 2001 ; Murdoch et al. 2001 a, 2001 b, 2003 ), both of which also have dominant tail defects, a phenotype that is not seen in line 90. Both Loop-tail and Circletail mice have mutations in genes that regulate planar cell polarity. Loop-tail mice carry a mutation in the strabismus-1 gene (Str-1, also known as Ltap/Lpp1), and Circletail mice have a mutation in the scribble gene (Scrb1). Mapping of the line 90 mutation places it on Chromosome 15 in the region of Scrb1. Scrb1 encodes a protein of 1,665 amino acids with three leucine-rich repeat (LRR) domains near the amino terminus and four centrally located PDZ domains ( Figure 2 F). Sequencing of RT-PCR products from line 90 mice identified a missense mutation in Scrb1 that causes an isoleucine-to-lysine substitution in the third LRR domain ( Figure 2 C– 2 F). In Drosophila, a critical role for a Scrb1 homolog, scribbled, in establishment of apical basal polarity in epithelia has been described ( Bilder and Perrimon 2000 ; Bilder et al. 2000 , 2003 ). Analysis of mice carrying the Circletail allele of Scrb1 show a loss of planar cell polarity in the hair cells of the inner ear, indicating that Scrb1 is required for the establishment of planar cell polarity rather than apical-basal polarity in the mouse ( Montcouquiol et al. 2003 ). Interestingly, a strong genetic interaction has been observed between the Circletail allele of Scrb1 and Loop-tail mutants ( Murdoch et al. 2001b ). The interaction is strong enough that compound heterozygotes for the two mutations show a severe phenotype that is indistinguishable from the individual homozygous phenotypes. The molecular nature of the Circletail allele of Scrb1, with a frameshift mutation that causes a premature stop codon after the first two PDZ domains ( Murdoch et al. 2003 ), is substantially different from that of line 90. To see whether interaction between the two loci was a unique attribute of the Circletail allele, we crossed line 90 with Loop-tail mice. As with the Circletail allele, a strong genetic interaction was seen between the line 90 allele of Scrb1 and the Ltap/Lpp1 mutation, such that embryos indistinguishable from homozygotes of the individual mutations were recovered (unpublished data). This indicates quite clearly that the integrity of the LRR domains is critical for the tight coordination between Scrb1 and Ltap/Lpp1 in establishment of planar cell polarity. A Novel megalin Allele We also identified a mutation that caused an enlarged cortex, hypertrophy of the choroid plexus of the third ventricle, and abnormalities in the dorsal diencephalon ( Figure 2 G– 2 J). Mapping of this mutation places it on Chromosome 2 in a region containing the megalin gene (also known as Lrp2 ). Prolapse of the third ventricle choroid plexus was described in a knockout allele of megalin ( Willnow et al. 1996 ), suggesting that this might be the responsible locus in this case. On this basis we sequenced RT-PCR products from the megalin gene and identified a base substitution producing a premature stop codon rather than the tyrosine codon at residue 2721 ( Figure 2 K and 2 L). The ENU-induced allele is predicted to express a truncated MEGALIN protein consisting of the amino-terminal portion of the extracellular domain, whereas the knockout is a null. Unlike the case of Scrb1, where the phenotypes produced by the two alleles are essentially identical, the two megalin alleles show phenotypic differences. Although both alleles produce a hypertrophic choroid plexus, this defect is more pronounced with the ENU-induced allele. We have found that the choroid plexus defects are also associated with an expansion and inhibition of differentiation in the dorsal neuroepithelium of the diencephalon that would ordinarily form the subcommissural organ and of the pineal gland just caudal to the defective choroid plexus (see Figure 2 I– 2 J; A. Ashique, unpublished data). It is not possible to say from the published characterization of the knockout allele whether a similar condition occurs there, but it is clear that the knockout allele causes holoprosencephaly ( Willnow et al. 1996 ), a phenotype that we have not seen in line 267. Indeed, the ENU-induced allele causes an enlarged cortex ( Figure 2 G and 2 H) without any obvious deficiency of midline structures such as would be expected in even mild holoprosencephaly. A Novel Rfx4 Allele A morphologically identifiable dorsal midline is absent from the cerebral cortex of line 269 homozygotes ( Figure 2 M– 2 P). This is an unusual defect that is similar to that seen in a transgene insertion mutation that disrupts the Rfx4 gene ( Blackshear et al. 2003 ). The line 269 mutation was mapped to Chromosome 10 in the region containing Rfx4 . Sequencing of Rfx4 revealed a base substitution that changes an evolutionarily conserved leucine residue to proline ( Figure 2 Q and 2 R). RFX4 is a member of the Rfx subfamily of winged-helix transcription factors ( Emery et al. 1996 ) and can form dimers with two of the other family members, RFX2 and RFX3 ( Morotomi-Yano et al. 2002 ). The amino acid substitution affects the conserved C domain of RFX4's large dimerization domain ( Katan-Khaykovich et al. 1999 ). The transgene insertion mutation, on the other hand, selectively eliminates the expression of a neural-specific transcript of the gene, and so behaves as a tissue-specific null ( Blackshear et al. 2003 ). Dimerization is not necessary for DNA binding activity of Rfx-class transcription factors ( Katan et al. 1997 ; Katan-Khaykovich and Shaul 1998 ). Instead, dimerization appears to determine whether the bound transcription factor mediates transcriptional activation or repression. The fact that the line 269 allele produces a phenotype that is apparently identical to that of a null allele provides clear evidence that RFX4-containing dimers regulate important transcriptional regulatory events during formation of the cortical midline. Dlx-LacZ Allows Efficient Identification of Tangential Migration Mutants Four mutants were identified in which the morphology of the cortex was normal at E14.5, but in which the distribution of LacZ-expressing cells in the cortex was altered. Three mutants, 154, 239, and 275, were identified on the basis of defects that are apparent in whole-mount preparations. Defects in the fourth mutant, 251, were only apparent upon examining sections. All four mutations are, for unknown reasons, perinatal lethal when homozygous, and this has prevented us from analyzing adult phenotypes. In addition, the line 154, 239, and 275 mutations appear to cause an increase in spontaneous seizures and mortality in young adult heterozygotes. Because the seizures are sporadic, their basis has not yet been studied. Line 154 has the most severe defects. Affected embryos often have fewer labeled cells in the cortex except in the most rostral regions, where they form abnormal aggregates ( Figure 3 A and 3 B). Abnormalities in the LacZ expression pattern that are associated with the failure of cells to invade the developing cortex can also be found in the subcortical telencephalon of line 154 embryos ( Figure 3 C– 3 H). Here, prominent, radially oriented columns of cells form near or at boundaries between subcortical subdivisions: the dorsal LGE, the ventral LGE, and the medial ganglionic eminence. To confirm that the abnormal pattern of LacZ expression was due to a perturbation in the distribution of interneurons in the cortex and not the result of ectopic expression of the transgene, we examined the expression of an interneuron marker, glutamic acid decarboxylase 1 (Gad67). Interneurons are GABAergic and so express GAD67, an enzyme involved in GABA synthesis. Glutamatergic projection neurons do not express GAD67. Whole-mount in situ analysis of GAD67 at E15.5 showed that the distribution of interneurons was abnormal, as predicted by the aberrant expression of the transgene ( Figure 3 I and 3 J). Figure 3 Severe Disruption of Interneuron Migration in Line 154 Mutants (A) Disseminated immature interneurons are seen as diffuse cortical (Cx) staining in WT embryos. Subcortical expression in the SVZ of the LGE can be seen as a darkly stained, inverted crescent. (B) Embryos homozygous for the line 154 mutation have little or no cortical staining, and the subcortical staining has aberrant streaks (pink arrowheads) and spots (white arrowhead), particularly in the frontal cortex. (C, E, and G) In this rostral-to-caudal series of coronal sections from WT embryos, the normal MZ and IZ/SVZ migratory streams are diffusely labeled (red arrowheads), and a sharp cortical-subcortical boundary (black arrowheads) is marked by the abrupt transition between the densely stained SVZ of the LGE and the diffuse staining of the migrating interneuron precursors. (D, F, and H) A rostral-to-caudal series of coronal sections from a line 154 mutant embryo shows the rostral spots (white arrowheads) visible in whole mount to be aggregates of cells in the cortex, and the streaky subcortical staining to be radially directed linear aggregates (pink arrowheads). The SVZ of the LGE is also noticeably thinner, and there is not a well-defined cortical-subcortical transition in the staining pattern. (I and J) Rostral views of Gad67 whole-mount in situ hybridization show the pattern of migration abnormalities in the cortex and significant defects in population of the olfactory bulb by GABAergic neurons. Three Migration Mutants Have Defects in a Common Process Lines 239, 251, and 275 share features that suggest that the loci involved may have roles in a common regulatory process. All three mutations produce defects that are most apparent in the rostral cortex at E14.5, and all three cause anterior polydactyly ( Figure 4 A– 4 C; unpublished data). The limb defects might be only superficially similar or they could have a common developmental basis. If the latter explanation were true, it would strongly suggest that the neuronal migration phenotypes of the three mutants are similar because the same regulatory mechanism is defective in all of them. Initial mapping uncovered linkage to Chromosome 7 for the line 251 mutation and to Chromosome 10 for the other two, indicating that at least two distinct loci are involved ( Table 1 ). To determine the number of loci involved, complementation tests were carried out using crosses between all three lines. The line 251 mutation complemented the other two as expected, but crosses between line 239 and line 275 produced mutant embryos with both limb and forebrain defects that were indistinguishable from either parental line. Further study will be required to determine whether the two lines carry the same or two independent mutations. Figure 4 Limb Patterning Defects in Tangential Migration Mutants Anterior is to the right for all limbs. (A–C) Left hindlimbs are shown from E14.5 WT (A), line 251 (B), and line 239 (C). Yellow arrowheads point to extra digits on the anterior (thumb) side of the limb in mutants. Line 275 mutants also have anterior polydactyly. The mutants have a slight developmental delay that causes some differences in appearance of the limb buds at E14.5, when the limbs are growing rapidly. (D) This diagram illustrates components of the Shh/Fgf feedback loop that maintains Fgf4 expression in the AER. (E–J) In situ hybridization on E11.5 limb buds. Unlike in WT (E), expression of the posterior patterning gene Hoxd13 in left forelimb buds extends ectopically into an anterior domain (yellow arrowheads) in 251 (F) and 239 (G) mutants. Fgf4 expression in left hindlimb buds, restricted to the posterior AER in WT (H), has an ectopic expression domain at the far anterior edge of the AER (yellow arrowheads) in 251 (I) and 239 (J) mutants. To identify the developmental basis of the limb defects, we examined the expression of genes involved in limb patterning. A regulatory network involving Shh and Fgf4 regulates the expression of Hoxd13 and controls patterning of the limb ( Figure 4 D). We first investigated the expression of Hoxd13 as a molecular marker of anterior-posterior (A-P) pattern in the limb at early stages. Hoxd13 is ectopically expressed in the anterior portion of line 239 and line 251 mutant limbs ( Figure 4 E– 4 G), indicating that the anterior polydactyly is caused by a defect in A-P patterning. We next examined the expression of the elements of the network that regulate Hoxd13 expression ( Figure 4 D) to see whether their expression was perturbed. The expression of Shh , Ptc1, gremlin (also known as Cktsf1b1 ), and Bmp4 are not distinguishable from wild-type (WT) (unpublished data). In contrast, an ectopic domain of FGF4 persists in the anterior apical ectodermal ridge (AER) ( Figure 4 H– 4 J). Thus the defects in limb patterning result from disruption of a step downstream of Shh, Ptc1, gremlin, and Bmp4, and upstream of Fgf4 . This does not allow a specific molecular mechanism to be invoked, yet the combination of similar defects in both the limb and in the pattern of interneuron migration is strong evidence that the same molecular mechanism is disrupted in all three mutants. Migrating Cells Persist Abnormally in the IZ of 239, 251, and 275 The 239, 251, and 275 mutants have clusters of LacZ-expressing cells in the IZ and/or SVZ of the rostral cortex, whereas more caudally, defects appear milder ( Figure 5 ). In WT animals there is a clear demarcation, at the corticostriatal junction, between the SVZ of the dorsal LGE and the stream of cells migrating into the cortex (black arrowheads in Figure 5 C– 5 E). The crispness of this boundary is lost in the 239 and 275 lines (yellow ovals in Figure 5 F– 5 H and 5 L– 5 N), although not in 251. In all three mutants, occasional linear aggregates of LacZ-expressing cells extend from the IZ/SVZ to the MZ (red arrowheads in Figure 5 I and 5 N). Figure 5 Line 239, 251, and 275 Mutants Have Similar Migration Defects (A) Frontal view of an E14.5 WT embryo. (B) Frontal view of an E14.5 embryo homozygous for the line 239 mutation. The stream of cells leaving the SVZ of the LGE is streaky and aggregated in the rostral cortex of the mutant. (C–E) A rostral-to-caudal series of coronal sections from an E14.5 WT embryo. WT embryos have diffuse cortical staining and a sharp boundary (black arrowheads) between the subcortical and the cortical telencephalon. (F–H) Coronal sections from E14.5 line 239 mutant forebrains. Yellow circles indicate the area of the cortical-subcortical boundary where a large excess of migrating cells can be seen in the IZ/SVZ area. Orange arrowhead indicates aggregated cells in the IZ/SVZ of the lateral wall of the cortex. White arrowheads indicate aggregates in the medial wall. The staining in the MZ appears normal. Defects become less apparent in the more caudal sections. (I–K) Sections from an E14.5 line 251 mutant embryo. Orange arrowheads indicate aggregates in the lateral wall of the cortex. In (I), a linear aggregate of stained cells can be seen extending from the IZ/SVZ to the MZ (red arrowhead). The cortical-subcortical boundary is well defined in line 251 mutants. (L–N) Sections from an E14.5 line 275 mutant embryo. Yellow circles indicate aberrant staining in the cortical-subcortical boundary region. The white arrowhead indicates aggregates in the medial wall of the cortex, and the red arrowhead points to a radially directed aggregate of cells extending from the IZ/SVZ to the MZ. To study the effects of the mutations at later stages of cortical interneuron development, we examined near-term (E18.5) animals ( Figure 6 ). We focused our analysis on line 251 and evaluated the expression of the Dlx-LacZ transgene and Gad67 . As at E14.5, aggregation of Gad67 + ; LacZ + cells in the IZ/SVZ is a prominent feature of the phenotype (black arrowheads, Figure 6 B, 6 F, and 6 J). Despite the severity of the defects observed in the IZ/SVZ, Dlx-LacZ–expressing interneuron precursors in the MZ and in the cortical plate do not form aggregates (yellow and blue arrowheads in Figure 6 I and 6 J), suggesting that the mutation inhibits the ability of migrating interneurons to leave the IZ/SVZ, but does not significantly impact other aspects of their migration. Figure 6 Dlx-LacZ and GAD67 Expression Show that Interneuron Precursors Persist in the IZ/SVZ of 251 Mutant Cortices (A) Coronal section through the forebrain of an E18.5 WT embryo stained for β-galactosidase and counterstained with nuclear fast red. (B) A similar section from a line 251 mutant embryo. Unusual accumulations of stained cells can be seen in the cortex just dorsal to the striatum (white arrowheads). Aggregates of cells in the IZ/SVZ can also be seen (black arrowhead). (C and D) Sections adjacent to those in (A) and (B) hybridized with a probe for Gad67 mRNA. Arrowheads in (D) point to the same features that are seen in (B). (E–H) Higher-magnification views of the dorsal portions of the sections in (A–D). (I and J) Higher-magnification view of cortex. In the WT cortex (I), cells can be seen dispersed through the cortical plate (yellow arrowheads) and scattered through the MZ (blue arrowheads). Similar distributions of labeled cells can be seen in the mutant cortex (J). In contrast to the WT, however, aggregates of cells are found in IZ/SVZ (black arrowheads). A Mutant with Defects in Invasion of the Cortex by Thalamocortical Axons In addition to the seven mutants described in the previous sections, six other mutants were isolated. Most of these have not been characterized in detail, but one, line 412, illustrates the range of phenotypes that the Dlx-LacZ transgene allowed us to identify. In line 412, the mutant phenotype was detectable in whole mount as a subtle but consistent defect near the cortical-subcortical boundary. Upon sectioning, this defect was revealed to be a delamination in the region of the external capsule ( Figure 7 A– 7 D). Thalamocortical fibers ordinarily enter the cortex at this point, and this defect can occur when fewer axon tracts cross this zone ( Hevner et al. 2002 ). Indeed, the 412 mutant has fewer thalamocortical fibers traversing the corticostriatal boundary at E14.5, as evidenced by the reduced density of L1 + axons in this location ( Figure 7 E and 7 F). Examination of late-stage embryos shows that the lack of cortical invasion by thalamocortical fibers at E14.5 is due to a persistent inhibition and not merely a delay in innervation (unpublished data). The IZ of the cortex often appears thinner in mutants, consistent with the absence of thalamocortical axons. Interestingly, TAG1 + corticofugal axons are still present in the cortex ( Figure 7 G and 7 H). Analysis of markers for the dorsal thalamus, where the thalamocortical axons originate, and for the striatum, which they must traverse, does not reveal any obvious defects (A. Ashique, personal communication). This, together with the observation that corticofugal fibers appear to be intact, suggests that the mutation may disrupt a molecule that is directly involved in pathfinding by thalamocortical axons. Figure 7 Corticostriatal Delamination and Lack of the Thalamocortical Projection in Line 412 Mutants (A–D) Coronal hemisections of E15.5 WT (A and C) and line 412 (B and D) mutant embryos stained for the Dlx-LacZ transgene. The cortex is thinner in 412 mutants, as can be seen in both the rostral (B) and the caudal (D) sections. Delamination of the corticostriatal boundary can be seen in the region between the red arrowheads in (D). (E and F) Immunostaining for L1 antigen labels the thalamocortical fibers in coronal hemisections from E14.5 WT (E) and mutant (F) embryos. The striatum and corticostriatal areas are shown. In the WT section, the thalamocortical fibers can be seen traversing the striatum through the internal capsule and coursing into the cortex. In the mutant section, a few fibers enter the striatum but do not traverse it to reach the cortex. Corticostriatal delamination can be seen as a hole in the right side of the section. (G and H) Immunostaining for TAG1 antigen (blue arrowheads) reveals corticofugal fibers. Discussion Novel ENU-Induced Alleles ENU overwhelmingly induces single-basepair substitution mutations. The mutant alleles that are produced often have relatively selective effects on protein function, and so provide valuable probes of the function of different protein domains. This is illustrated by two of the alleles described here, Rfx4 and Scrb1 . Each of these alleles is a missense mutation that appears to disrupt specific domains in a selective fashion. The SCRB1 protein has two sets of recognizable domains: three amino-terminal LRRs and four, more carboxy-terminal, PDZ domains. The previously described allele of Scrb1, the Circletail allele, has a stop codon that terminates translation after the first two PDZ domains. In contrast, the line 90 allele of Scrb1 is predicted to encode a charged lysine in place of a hydrophobic isoleucine on the outside of the third LRR domain (see Figure 2 D and 2 E). The homozygous phenotypes produced by the line 90 and the Circletail alleles are apparently identical, and both show strong genetic interactions with a mutation in Str-1, a cell-surface protein that regulates planar cell polarity in both flies and mammals. From this we can conclude that both the LRR and the PDZ domains are required for SCRB1's role in the establishment of epithelial polarity. The missense allele of Rfx4 is predicted to have very subtle effects on the structure of the protein. The mutation causes a proline to be substituted for a leucine residue in a dimerization domain of the protein. Proline residues are not compatible with α-helices, but in this case the substitution appears to be at the beginning of a β-turn that links two helices. Presumably, the substitution causes the linkage between the α-helices to be stiffened or abnormally constrained in some way. Whatever the exact molecular consequences are, the nature of the allele and the genetics suggest that the mutation prevents dimerization. Ordinarily RFX4 can dimerize with itself and with the related transcription factors RFX2 and RFX3 ( Morotomi-Yano et al. 2002 ). If inactive dimers were produced by the missense allele, it would seem likely to cause a dominant phenotype, which it does not. Studies are underway that will test these ideas as well as determine whether the failure of the dorsal midline to form is the result of a loss of a dorsal signaling center or an inability of the cortical neuroepithelium to respond to those signals. The megalin allele provides a contrast to these first two cases. Despite its molecular severity as a premature stop codon, its phenotypic consequences are less pronounced than the knockout allele. The knockout mutation produces early-head-fold–stage embryos with reduced neuroepithelium in the anterior midline ( Willnow et al. 1996 ). These defects originate during gastrulation, and by mid-gestation result in holoprosencephaly. The absence of these early defects in the ENU-induced allele could be interpreted to mean that the defective protein retains some function and supplies the MEGALIN activity that is required during gastrulation. Alternatively, it is possible that the deleted region in the knockout is required for the proper expression of a neighboring gene and that the gastrulation phenotype does not reflect an early role for megalin . In each of these three cases, the ENU-induced allele provides novel information about the role of the gene in cortical development and suggests avenues for further exploration. As we determine the molecular nature of the other mutations, it is likely that the selective nature of the ENU-induced alleles will provide important insights into the function of other proteins that regulate cortical development. Nature of the Tangential Migration Defects The migration of immature interneurons has been followed using lipophilic dyes, tissue chimeras, transfection with a green fluorescent protein (GFP) expression vector, and, recently, using a Gad67-GFP knockin mouse ( de Carlos et al. 1996 ; Anderson et al. 1997 , 2001 ; Tamamaki et al. 1997 ; Denaxa et al. 2001 ; Polleux et al. 2002 ; Ang et al. 2003 ). These studies have identified tangentially migrating cells in both the IZ/SVZ and the MZ. Some studies have concluded that the cells in the IZ/SVZ and the MZ are two independent migratory streams ( Lavdas et al. 1999 ). In contrast, the use of a Gad67-GFP knockin allele to follow interneuron precursors ( Tanaka et al. 2003 ) led to the conclusion that the general pattern of cell migration is tangentially through the IZ and thence radially outward from the IZ to the MZ. Cells in the MZ also migrate tangentially but at a slower rate than those in the IZ ( Polleux et al. 2002 ; Tanaka et al. 2003 ), in a process that may be a search for the proper location at which to invade the cortical plate ( Ang et al. 2003 ). These two models for tangential migration produce different interpretations of the pattern of defects that we see in our novel mutants. In the first model, obvious defects in the IZ but not the MZ would imply that the mutations are disrupting a process used to regulate migration through the IZ but not through the independently regulated MZ. In the second model, the predominance of defects in the IZ/SVZ would indicate an inhibition at an early step of the migratory process. The presence of cell aggregates extending from the IZ to the MZ is consistent with this being the step that is defective, whereas the presence of properly dispersed cells in the MZ and cortical plate would indicate that the mutations inhibit, but do not completely block, this early step. Concordance of Limb Patterning and Migration Defects Three out of the four tangential migration defects also result in anterior polydactyly. The association of limb patterning and neuronal migration defects has not been previously reported and, given that a great deal more is known about limb patterning than about tangential migration, it is tempting to speculate on what this may imply about the migration mutants. One possibility is that shared mechanisms are used to pattern the telencephalon and the limb. It is equally possible that genes that regulate patterning in the limb regulate cell migration decisions more directly. Mutations in an arista-less homolog, Arx, cause profound defects in interneuron migrations, and mutations in another arista-less homolog, Alx4, cause anterior polydactyly. However, as the limbs of Alx4 mutants, unlike the 239, 251, and 275 mutants, have ectopic Shh expression in the anterior limb bud, it is unlikely that the mutations described here disrupt an arista-less pathway. Shh maintains the expression of Fgf4 in the posterior AER of WT limb buds in a process that requires gremlin . Genes that act in this process downstream of Bmp4 are plausible candidates for the line 239, 251, and 275 mutations. General Implications for Genetic Screens in the Mouse The work described here demonstrates clearly that genetic screening strategies in the mouse need not be limited to general or broad-based phenotyping approaches; a focused genetic screening strategy can provide a powerful means of dissecting a specific aspect of mammalian biology. The strategy of broad-based approaches has been an effective one for the past several years as the collection of mutants using chemically induced mutations resurged in popularity. In other genetic systems, strategies have evolved from general toward focused screens that allow mutations affecting a specific process to be identified, and it is likely that mouse genetics will progress in a similar fashion. An additional concern with mice, however, is the cost of breeding and housing, which is higher than that for other model genetic organisms. We have shown, nonetheless, that a laboratory-based, focused screening strategy is a productive pursuit. The costs of such a screen could be shared by the careful combination of reporters so that screening for several distinct processes could be carried out at once. This general idea is highlighted by our isolation of mutations in which the growth and patterning of the cortex is defective. With the exception of line 90, the identification of these mutants benefited from the easy visualization of cortical size and structure by expression of the transgene. In several cases, the interneuron migration mutants being a good example, the identification of the mutant phenotype without the transgene would have been very unlikely. Independent reporters could be used to pursue the simultaneous identification of several different classes of mutants more directly. For example, reporters that label migrating neurons with GFP, and thalamocortical axons with β-galactosidase or alkaline phosphatase, would allow both migration and axonal pathfinding mutations to be identified in the same screen. Given the large number of reporter and indicator strains that have been made over the last few years and the powerful tools that exist for gene identification, it is clear that focused screens in the mouse will provide the resources to address many questions in the coming years. Materials and Methods Animals and breeding. Male C57BL/6J mice were obtained from Jackson Laboratories (Bar Harbor, Maine, United States) and treated with three intraperitoneal injections of 100 mg/kg ENU (Sigma, St. Louis, Missouri, United States) spaced at 7-d intervals. Eight weeks after the last injection, the ENU-treated males were set up in breeding pairs with FVB/NJ females homozygous for the Dlx-LacZ transgene. Male offspring of this cross (G1 males) were backcrossed to the transgenic line, and female offspring (G2 females) were saved. For each line, one to six of the G2 females were backcrossed to their fathers to generate timed pregnancies. Embryos were harvested either 13 d (the first 100 lines) or 14 d (all subsequent lines) after the vaginal plug was identified. Screening. Embryos were dissected in phosphate-buffered saline (PBS) and fixed for 45 min at room temperature in 4% paraformaldehyde (PFA) in PBS. Subsequently, embryos were washed in detergent rinse (0.1 M phosphate buffer [pH 7.3], 2 mM MgCl 2 , 0.01% sodium deoxycholate and 0.02% Nonidet P-40). Embryos were then stained for 48–72 h at room temperature on a rocking platform using X-gal as a substrate for the detection of β-galactosidase activity. Staining was terminated after visual inspection by repeated washing in PBS. Embryos were then fixed again and stored in 4% PFA in PBS until further examined. All litters were examined in whole mount and approximately one-half were selected for sectioning on a vibratome (VT1000S; Leica, Wetzlar, Germany). For sectioning, the heads of all pups from a litter were separated from the torsos and mounted aligned in the same orientation in an agarose block. Sections 100 μm thick were collected in PBS and mounted in Kaiser's glycerol gelatin (Merck, Darmstadt, Germany) on slides. All phenotypes reported here were seen in more than ten litters resulting from both G1 male × G2 female and G2 male × G2 female crosses. Histology. Whole-mount in situ hybridization was carried out according to Henrique et al. (1995) . Gad67 in situ hybridization on sections was carried out following standard protocols ( Dagerlind et al. 1992 ) with a digoxigenin-labeled antisense RNA probe generated by in vitro transcription using a plasmid obtained from Brian G. Condie ( Maddox and Condie 2001 ). In brief, embryos were dissected in diethylpyrocarbonate-treated PBS, and the heads were removed and fresh-frozen on dry ice. Tissue was sectioned at 20 μm on a cryostat (Leica CM3050). Slide-mounted cryosections were warmed to room temperature and fixed in 4% PFA in PBS. Deacetylation was performed for 10 min by immersion in 0.1 M triethanolamine containing 25 mM acetic anhydride followed by rinsing in 2× saline sodium citrate and dehydration through an increasing alcohol series (60%, 75%, 95%, and 100%). Sections were hybridized with the riboprobe under stringent conditions (50% formamide, 10% dextran sulfate, 20 mM Tris-HCl, 0.3 M NaCl, 5 mM EDTA, 0.02% Ficoll 400, 0.02% polyvinylpyrrolidone, 0.02% BSA, 0.5 mg/ml tRNA, 0.2 mg/ml carrier DNA, and 200 mM DTT) for 16–20 h at 63 °C. After hybridization, sections were washed four times in 4× saline sodium citrate solution and incubated for 30 min in RNase buffer (10 mM Tris-HCl [pH 7.5], 0.5 M NaCl, and 5 mM EDTA [pH 8.0]) containing 20 μg/ml RNase A at 37 °C. High-stringency washes were performed twice for 30 min at 63 °C. Incubation with the anti-digoxigenin antibody (Roche, Basel, Switzerland) was carried out in maleic acid buffer (100 mM maleic acid, 150 mM NaCl, and 1% blocking reagent [Roche]). Finally, staining was performed with BM purple substrate (Roche) terminated by repeated washes in PBS, and sections were coverslipped using Kaiser's glycerol gelatin. Nuclear fast red counterstaining for X-gal–stained sections was performed with pre-made staining solution (Vector Laboratories, Burlingame, California, United States). Staining was differentiated in 70% ethanol. Immunohistochemistry using anti-TAG1 (Developmental Studies Hybridoma Bank, University of Iowa, Iowa City, Iowa, United States) and anti-L1 (Chemikon, Mönchengladback, Germany) antibodies was performed on 100-μm free-floating sections following the instructions and using the reagents of the Vectastain ABC Kit (Vector). Primary antibodies were used at a dilution of 1:500. Horseradish peroxidase activity from secondary antibodies was revealed by diaminobenzidine as a substrate in staining buffer (0.5 mg/ml diaminobenzidine, 20 mM sodium cacodylate, and 30 mN acetic acid). Mapping. Initial linkage was established using 12 DNA samples from both carriers and mutant embryos. We scored a set of 82 simple sequence repeat markers. The panel of markers was selected from the set at the Center for Inherited Disease Research Website ( http://www.cidr.jhmi.edu/mouse/mouse.html ). Markers were chosen that could be scored easily on agarose gels. All chromosomal assignments reported are the result of LOD scores significantly greater than three. The intervals that are listed in Table 1 were derived following the initial establishment of linkage by haplotype analysis that included additional recombinant chromosomes from mutant animals or obligate carriers. The phenotypes of 239 and 275 are very similar, and the fact that they map to the same interval likely reflects allelism. The localization of both 351 and 357 to the same interval seems more likely to be coincidental, because the phenotypes are quite different. Sequencing. For sequencing of Rfx4 and Scrb1 genes, RT-PCR samples were prepared from cDNA prepared using E10.5 to E16.5 embryos. For megalin, exons were amplified using genomic DNA samples. Primer sequences are available upon request. In both cases sequencing was done by the University of California, Berkeley, DNA Sequencing Facility. Supporting Information Figure S1 Cortical Size Is Altered in Lines 152 and 351 (A) Dorsal views of the cortex of WT (left) and line 152 mutant (right) embryos stained for expression of the Dlx-LacZ transgene. The line 152 mutation reduces the size of the cortex. (B and C) Coronal sections through the cortex of E14.5 WT (B) and line 351 mutant (C) embryos. The cortex has a characteristic high-domed shape and is thinner in the mutant. (D and E) Dorsal (D) and ventral (E) views of adult brains of WT (left) and line 351 mutant (right) brains. The cortex is overall larger in the mutant than in the WT. The olfactory bulbs are present in the mutant but are tucked under the cortex, making them less visible. (9.1 MB TIF). Click here for additional data file. Figure S2 Central Nervous System Defects and Cleft Upper Jaw in Lines 366 and 357 (A) Lateral view of an E14.5 WT embryo. (B) The cleft upper jaw of a line 366 mutant is visible at the left. The telencephalon, including the cortex, is significantly shortened along the rostrocaudal axis. (C and D) Lateral and front views of the cleft and reduced upper jaw of an E18.5 embryo homozygous for the line 366 mutation. (E and F) Lateral views of WT (E) and line 357 homozygote (F) embryos at E13.5 showing the reduced telencephalon and relatively expanded midbrain. (G) A front view of the embryo in (F) shows the cleft upper jaw. (H and I) Sagittal sections through E13.5 WT (H) and line 357 (I) embryos. The overgrown midbrain in the mutant embryo has forced the neuroepithelium into folds. (9.9 MB TIF). Click here for additional data file. Figure S3 Line 407 Mutants Have Dorsoventral Defects in the Cortical Primordia and Facial Midline Defects (A) Lateral view of an E14.5 mutant embryo showing edema and hemorrhage suggestive of vascular defects. Frontal views of WT (B) and mutant (C) embryos illustrate the narrowed frontonasal process, maxilla, and mandible of the mutant. (D) shows coronal hemisections in a WT embryo. (E) shows the accumulation of Dlx-LacZ–positive cells in a SVZ-like area dorsal to the LGE. (4.3 MB TIF). Click here for additional data file. Accession Numbers Accession numbers of the genes discussed in this paper are available at LocusLink ( http://www.ncbi.nih.gov/LocusLink , and are as follows: Alx4 (11695), Arx (11878), Bmp4 (12159), Dlx5/6 (13395/13396), Fgf4 (14175), Gad67 (14415), gremlin (23892), Hoxd13 (15433), L1 (16728), Lrp2 (14725), Ltap/Lpp1 (93840), Ptc1 (19206), Rfx4 (71137), Scrb1 (105782), scribbled (44448), Shh (20423), and Tag1 (21367). | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC509294.xml |
534115 | Microarray and comparative genomics-based identification of genes and gene regulatory regions of the mouse immune system | Background In this study we have built and mined a gene expression database composed of 65 diverse mouse tissues for genes preferentially expressed in immune tissues and cell types. Using expression pattern criteria, we identified 360 genes with preferential expression in thymus, spleen, peripheral blood mononuclear cells, lymph nodes (unstimulated or stimulated), or in vitro activated T-cells. Results Gene clusters, formed based on similarity of expression-pattern across either all tissues or the immune tissues only, had highly significant associations both with immunological processes such as chemokine-mediated response, antigen processing, receptor-related signal transduction, and transcriptional regulation, and also with more general processes such as replication and cell cycle control. Within-cluster gene correlations implicated known associations of known genes, as well as immune process-related roles for poorly described genes. To characterize regulatory mechanisms and cis-elements of genes with similar patterns of expression, we used a new version of a comparative genomics-based cis -element analysis tool to identify clusters of cis -elements with compositional similarity among multiple genes. Several clusters contained genes that shared 5–6 cis -elements that included ETS and zinc-finger binding sites. cis -Elements AP2 EGRF ETSF MAZF SP1F ZF5F and AREB ETSF MZF1 PAX5 STAT were shared in a thymus-expressed set; AP4R E2FF EBOX ETSF MAZF SP1F ZF5F and CREB E2FF MAZF PCAT SP1F STAT cis-clusters occurred in activated T-cells; CEBP CREB NFKB SORY and GATA NKXH OCT1 RBIT occurred in stimulated lymph nodes. Conclusion This study demonstrates a series of analytic approaches that have allowed the implication of genes and regulatory elements that participate in the differentiation, maintenance, and function of the immune system. Polymorphism or mutation of these could adversely impact immune system functions. | Background The immune system is composed of a multiplicity of individual cell types that derive from a relatively small number of immuno-hematopoietic progenitors that undergo complex developmental and exposure-driven differentiation and activation. Cell-type specific gene expression is driven to a large measure by complex transcriptional regulation that orchestrates differential expression of a wide variety of genes necessary to accomplish immune effector functions. A number of specific transcription factors (TFs) which regulate gene expression in immune system cell types have been identified, largely through gene knockout experiments and isolation of protein complexes that bind to regulatory regions of target genes. Examples include PU.1/Ets, Ikaros, E2A, EBF, PAX5, GATA3, NFAT, cMYB, and OCT-2 [ 1 - 4 ]. These proteins bind to clusters of cis -regulatory elements in multiple diverse combinations to give rise to specific patterns of gene expression [ 5 ]. However, the layout of regulatory and coding regions is not known for most genes that are preferentially expressed in lymphocytes and immune tissues (see, for examples, [ 6 - 11 ]). Based on the nearly completed nucleotide sequences of the mouse and human genomes ( [ 12 ]; [ 13 ]), we have sought to expand our knowledge of the structure and function of compartment-specific genes, and in particular, to find clusters of cis -elements that bind TFs and regulate gene expression during biological processes. DNA sequences of both coding regions and non-coding regions which harbor cis -elements that govern expression, are phylogenetically conserved [ 14 - 16 ]. This conservation of functionally important regions of DNA underpins current methods of identifying putative regulatory regions by comparative sequence analysis. In practice, finding relevant clusters of cis -elements is difficult and computationally intensive. High-throughput gene expression profiling provides a powerful approach to the investigation of relative transcriptional activity as a function of biological differentiation across a variety of cells and tissues. Published examples that probe a wide variety of distinct, differentiated materials include the Human Gene Expression (HuGE) Index database [ 17 ] and the GNF (Genomics Institute of the Novartis Research Foundation; ) database of human and mouse gene expression [ 18 ]. These resources provide access to patterns of expression of a significant fraction (15–25%) of all mouse and human genes in several dozen tissues and cell types. We have created a large database locally, which has permit investigators from our campus to profile gene expression in mouse tissues and cell types specific to their interests [ 19 ]. To do this, we used the Incyte Mouse GEM1 microarray, an 8638 element spotted cDNA gene expression platform and a universal reference design that employed poly A+ mRNA was prepared from whole day-1 postnatal mouse. Two channel Cy3-Cy5 microarray hybridization technology was used to identify relative strength of signals from each element of the array for a specific tissue. From this database, we identified 360 cDNAs on the microarray that exhibit preferential expression in immune tissues such as lymph nodes, thymus, and activated T-cells relative to most other types of tissues. We identified 333 genes that encode these sequences and have grouped them by biological functions and by patterns of expression. Cis -element clusters that are conserved in pairs of orthologs are strong predictors of regulatory regions within mammalian genes [ 15 , 20 , 21 ]. We have used this method to identify putative regulatory modules, which are clusters of conserved cis -regulatory elements that occur in coordinately regulated genes of the immune system and may play a role in controlling their expression during development or mature cell function. Several of the modules identified through this approach contain cis -elements whose biological relevance has been experimentally validated in previous studies. Other computationally identified modules from this immunomic database have not been studied in detail, but the results and a tool to analyze them further, are provided at the website [ 22 ]. Taken together these data provide valuable guidance to the design of experiments that seek to identify regulatory modules in genes with specific patterns of expression. Results Selection of set of immune genes Our goal is to identify genes, which are essential for the differentiation, maintenance, and function of the immune system, and their associated regulatory elements. Polymorphisms or mutation in these might underlie well-known variation among individuals in effectiveness of their immune response. Mouse immune genes were identified from our gene expression database constructed using the 8638 element microarray and probed with mRNA prepared from 65 normal adult and fetal tissues. We chose to select relevant genes by collecting those expressed above a threshold value rather than by statistical analysis of variance. Given the small number of replicates and the large number of comparisons being made, we would not have enough statistical power to detect differentially expressed genes by using traditional statistical tests with appropriate specificity. In addition, with the reduced specificity of statistical tests, the biologically non-significant, but somewhat reproducible differences in gene expression will obscure changes that are of biologically significant magnitude, but vary from replicate to replicate. Expression of genes is not discontinuous from tissue to tissue, but varies quantitatively over a wide range. The threshold to distinguish expressed from non-expressed genes was set to identify the hundred or so most highly expressed gene in each relevant tissue. Genes were considered to be "immune genes" if they were more highly expressed in one or more of 6 immune tissues (lymph nodes from normal and antigen stimulated mice, thymus, activated T-cells, spleen, peripheral blood mononuclear cells) than in most other normal adult and fetal mouse tissues. 680 genes were identified where the amount of cDNA hybridized from one or more immune tissues was 3 or more times greater than hybridization of cDNAs from the reference whole mouse (Figure 1A ). To increase specificity, the 680 genes were then filtered to remove those with 2-fold or greater expression in normal brain, spinal cord, heart, kidney, pancreas or stomach. These tissues were chosen because they do not play a role in the immune response, contain very few cells of the immune system, and should not express immune-specific genes. By contrast, no effort was made to remove genes expressed in the intestinal tract, lung, or fetus where cells of the immune system might be expected. The resulting set of 483 genes was examined by hierarchical cluster analysis. Spleen and peripheral blood mononuclear cells were noted to express genes encoding proteins of immature erythroid cells and polymorphonuclear leukocytes. To remove these, the set was restricted to genes that were expressed 2 fold or greater in at least one of stimulated and unstimulated lymph nodes, activated T cells, or thymus. The end result is a set of 360 expressed sequences, which we call "immune" genes (Figure 1A and 1B ). 265 of the expressed sequences were linked to specific genes and gene symbols, using the Mouse Genome Database (MGD) [ 23 ] and NCBI-LocusLink [ 24 ]. The remainders were analyzed using MouseBLAST and BLAT [ 12 ] to find sequence homologies with known genes. An additional 78 sequences could be linked to specific genes, 9 (seven occurring twice and one occurring thrice) of these were redundant, so that a total of 333 previously known unique genes were represented by the 360 expressed sequences. 292 of these genes were assigned a probable function, using criteria described in Methods. 5 sequences were repetitive elements and 12 sequences could not be linked to a known gene or function. Gene symbols, names, functions, and extensive additional annotations are provided in the supplementary materials ( Additional file 1 ). Human orthologs of these mouse immune genes were sought by sequence homology. Where found, pairs of mouse-human orthologs were annotated with regard to function and were analyzed for phylogenetically conserved regulatory regions. Figure 1 Expression profiles of sequences across tissues: Hierarchical tree clustering of genes and tissues was carried out using Pearson correlation and the log of the average of the relative expression ratio for each gene, as measured in replicate arrays. Sequences with similar expression patterns across all tissues are clustered together in the resulting trees, the closeness of the sequences in sub trees is a measure of how closely correlated their expression is. (A) Hierarchical tree clustering of genes across 65 normal adult and fetal tissues. 680 sequences were identified that were highly expressed in thymus, unstimulated and stimulated lymph nodes, spleen, peripheral blood mononuclear cells, and in vitro activated T-cells. To increase specificity, 320 sequences were removed because they were also highly expressed in one or more non-lymphoid tissues, as described in the text. The pattern of expression of the remaining 360 "immune genes" across tissues is shown. (B) Hierarchical tree clustering of 360 immune genes across 18 normal adult and fetal tissues. There are 3 major groups of tissues that show clusters of highly expressed "immune genes" These include the 6 immune tissues, various segments of adult intestine, and fetal day 16.5 lung and intestine. Less prominent clusters are seen in adult lung and liver. Genes in these clusters are described in the text. While the band of high expression extends across all genes for the 6 immune tissues, relative expression of each gene within the immune tissues shows distinct patterning. Hierarchical clustering of genes and tissues Hierarchical tree clustering of the 360 sequences and 65 normal adult and fetal tissues was carried out by Pearson correlation using the log of the average of the relative expression ratio for each gene as measured in replicate arrays (Figure 1A ). While the band of high expression extends across the 6 immune tissues, relative expression of each gene within the immune tissues shows distinct patterning (Figure 1B ). For intestinal and fetal tissues, the areas of high expression are localized and do not include the majority of the immune genes. Function of the genes expressed in these tissues will be described. Function of the immune genes A putative function could be assigned to 298 expressed sequences ( Additional file 1 ) based on one or more known functional annotation or sequence analysis-based structural classifiers. This annotation is independent of pattern of expression and gives an overview of the types of functions carried out by immune genes. Six functional groups derived from these annotations are shown in Table 1 . The HGNC [ 25 ] and MGI [ 23 ] approved gene symbols are used in the table, although many of these genes are better known by their aliases as provided in supplementary materials. Table 1 shows 59 genes that have functions associated with defense-immune or defense response (immune is a subcategory of defense in GO annotations). Defense-immune genes were more directly related to antigen recognition and receptor signaling of T- and B-lymphocytes than defense genes, although the separation of defense and immune is somewhat arbitrary. 47 genes in Table 1 are involved in cell signaling, 14 in apoptosis, 8 in chemotaxis, and 6 in lysosomes. Additional lists of genes grouped by function and shown in the supplementary materials include 39 in transcription, 23 in DNA replication/cell cycle control, 20 in protein synthesis, 13 in transport, and 10 in adhesion. Smaller groups of genes that are important in function of the immune system include protein trafficking and degradation, and maintenance of the cytoskeleton. Functions carried out by some of the genes that are highly expressed in immune tissues are common to cells and tissues that are actively proliferating and synthesizing proteins. These include, for example, genes involved in DNA synthesis and the cell cycle such as the minichromosome maintenance proteins, Mcm2 through Mcm7 ; the DNA polymerases and primase, Pola2 , Cdc6 , Prim1 ; the processivity factor Pcna , and cyclin E1, Ccne1 . They play a role in regulation of chromosomal replication in many types of cells [ 26 ]. In the immune tissues, high expression of these genes is characteristic of activated T-cells, which are proliferating. Similarly, other immune genes are involved in protein synthesis and are not specific to the immune system. Twelve immune genes encode ribosomal proteins. Table 1 Six sets of genes that are highly expressed in immune tissues, grouped by function. Gene symbol and GenBank accession number identify genes Defense – Immune – 38 Genes Defense – 21 Genes Signal – 47 Genes 1190001G19Rik NM_026875 5830443L24Rik BC031475 1200013B08Rik NM_028773 A630096C01Rik BB629669 Arl6ip2 BC006934 2410118I19Rik AK004869 AI789751 AI789751 Bst1 NM_009763 2610207I05Rik AK011909 B2m NM_009735 C1qg NM_007574 Adcy7 NM_007406 BB219290 NM_145141 C1s NM_144938 AI325941 BF181435 Btla-interim BM240873 Camp NM_009921 Arhh AK017885 Cd14 NM_009841 Daf1 NM_010016 Cd37 NM_007645 Cd79b NM_008339 Gbp2 NM_010260 Cd53 NM_007651 Cd86 BC013807 Gzmb NM_013542 Cd97 NM_011925 Cxcl9 NM_008599 Klra24-pending AA288274 Clecsf12 NM_020008 Fcgr3 NM_010188 Klrd1 NM_010654 Clecsf5 NM_021364 Gp49a NM_008147 Ncf2 NM_010877 Clk3 AF033565 H2-Aa NM_023145 Ncf4 NM_008677 Coro1a NM_009898 H2-Ab1 NM_010379 Oas2 NM_145227 D530020C15Rik BC027196 H2-DMa NM_010386 Oasl2 NM_011854 Dgkz BC014860 H2-Eb1 NM_010382 Ocil-pending NM_053109 Dok2 NM_010071 H2-K U47328 Prg NM_011157 E430019B13Rik AA881918 H2-L M34961 Tnfrsf13b AK004668 G431001E03Rik AA387272 H2-Oa NM_008206 Tnfrsf4 NM_011659 Gnb2-rs1 NM_008143 H2-Ob NM_010389 Tnfrsf9 NM_011612 Gpcr25 NM_008152 H2-Q7 NM_010394 Zbp1 AA175243 Gprk6 NM_011938 Igh-4 L36938 Hck NM_010407 Igj BC006026 Apoptosis – 14 Genes Iigp-pending NM_021792 Igl AK008551 Il2rg NM_013563 Igsf7 AF251705 5630400E15Rik AK017464 Il4ra NM_010557 Lst1 AF000427 AI447904 BF179348 Jak1 BC031297 Ly86 NM_010745 Axud1 BC029720 Lck BC011474 Mpa2 NM_008620 Biklk BC010510 Lcp2 BC006948 Mpeg1 L20315 Birc2 NM_007464 Lyn BC031547 Ms4a1 NM_007641 Casp4 NM_007609 Lypla1 BF160555 Ms4a4b NM_021718 Dnase1l3 NM_007870 Map3k1 AF117340 Ms4a6c NM_028595 Ian4 NM_031247 Map4k1 BC005433 Sema4d NM_013660 Ifi203 AA174447 Mbc2 BC011482 Tactile-pending NM_032465 Ripk3 NM_019955 P2y5 AK011967 Tcrd AI530748 Scotin-pending NM_025858 Pilra AJ400844 Tcrg NM_011558 Stk17b NM_133810 Pip5k2a AK012196 Tlr1 NM_030682 Stk4 W77521 Ptpn2 NM_008977 Trygn16 M97158 Trp53inp1 NM_021897 Ptpn8 NM_008979 Ptprc NM_011210 Lysosomes – 6 Genes Chemotaxis – 8 Genes Ptprcap NM_016933 Rac2 NM_009008 Acp5 AA002801 Ccl19 NM_011888 Stat1 NM_009283 Ctsl NM_009984 Ccl22 NM_009137 Stat3 BC003806 Ctss NM_021281 Ccl4 NM_013652 Stat4 NM_011487 Ctsz NM_022325 Ccl6 NM_009139 Stk10 NM_009288 Man1a NM_008548 Ccr2 NM_009915 Syk NM_011518 Man2b1 NM_010764 Cxcl13 NM_018866 Tln NM_011602 Cxcr4 NM_009911 S100a8 NM_013650 There are sets of genes that work together to produce the cellular and humoral immune responses. For example, molecules of the major histocompatibility complex present foreign peptides to T cells. They are encoded by genes such H2-Aa , H2-Ab1 , H2-DMa , H2-Eb1 , H2-K , H2-L , H2-Oa , H2-Ob , H2-Q7 , and B2m (Table 1 , Defense – Immune). Signal transduction pathways are abundant and play critical roles in the function of lymphocytes. They link the recognition of antigens or chemokines by receptors on the cell surface to the transcription of genes required for cell division and new protein synthesis. This process of lymphocyte activation requires an intracellular signaling cascade with participation of protein kinases, G-proteins, and products of cleavage of membrane phospholipids [ 27 - 29 ] (Table 1 , Signal). Janus kinases, encoded by genes such as Jak1 , phosphorylate both signal transducers and activators of transcription ( Stat1 , Stat3 , and Stat4 ) as part of the lymphocytes' response to cytokines. The product of Rac2 is a G protein that participates in the cascade of kinases leading to activation of TFs. Chemokines are a family of small proteins that activate cells such as lymphocytes as part of the host response to infection. Genes that encode the chemokines ( Ccl4 , Ccl6 , Ccl19 , Ccl22 , Cxcl13 ) and chemokine receptors ( Cxcr4 and Ccr2 ) (Table 1 , Chemotaxis) are highly expressed in immune tissues. Twenty-one sequences representing 19 known genes were highly expressed in gastrointestinal tissue (Figure 1B ). Of these, 5 were classified as "Defense – Immune", including B2m , H2-Q7 , Tcrg , Tlr1 , and H2-K . Of the 51 genes expressed in fetal tissues (Figure 1B ), 46 are annotated. Sixteen genes functioned in protein synthesis and 13 in cell cycle/DNA synthesis. No "Defense" or "Defense-Immune" genes were highly expressed in fetal tissues. Genes expressed in fetal tissues reflect active growth and proliferation of cells. In immune tissues, these same genes are particularly well expressed in activated T-cells and thymus, where cell proliferation is occurring. Cis-regulatory elements of MHC class I genes Regulatory modules predicted by comparative analyses of DNA sequences must be validated by genomic footprinting and other biochemical techniques, which prove that the predicted TF binding sites are biologically relevant. Because extensive data are available, we compared the structure of the promoter elements of the H2-K and HLA-A genes (MHC class I) as predicted by computational and biochemical studies. Experimentally identified, conserved, regulatory elements within the promoter of MHC class I genes include: an enhancer A element (two NFKB sites), an interferon-stimulated response element (ISRE), site α (cAMP-response element), enhancer B (inverted CCAAT), CCAAT, and TATA elements [ 30 ]. Computationally predicted arrangements of conserved cis -elements in the promoters of H2-K and its human ortholog, HLA-A , are shown in Figure 2 . FASTA sequences and corresponding coordinates of the regions used in the analysis are given in Additional file 10 . The predicted arrangements are in close agreement with results of genomic footprinting, electrophoretic mobility shift assays, and other techniques. For HLA-A , computationally identified binding sites previously found by biochemical analyses include: IRFF, CREB, ECAT, PCAT, TBPF and two NFKB. The enhancer-A element of the MHC class I promoter encompasses two NFKB binding sites and plays an important role in the constitutive and cytokine-induced expression of MHC class I genes. Our IRFF site is the reported ISRE and can bind interferon regulatory factor 1 to activate MHC class I transcription. Site α of the MHC Class I promoter corresponds to our CREB binding site and plays an important role in regulation of expression of Class I genes. Our PCAT and ECAT sites include sequences consistent with the CCAAT site and our TBPF is a TATA binding site, as reported in the MHC class I promoter immediately upstream of the transcription start site [ 30 ]. Computational analysis identifies additional potential binding sites that have not yet been tested for biological relevance. These include families IKRS, WHZF, EKLF, EGRF, and AHRR. Several of these may play a specific role in the immune system. For instance, the IKRS family of sites bind Ikaros zinc finger transcription factors, which are regulators of lymphocyte differentiation; the WHZ family of TFs includes members that are critical to the proper expression of genes during development of the thymus [ 31 ]; the EGR family of zinc finger transcription factors is induced as a consequence of activation of the mitogen-activated protein kinase (MAPK) signaling pathway during positive selection in the thymus [ 32 ]; and AhR is known to effect immunosuppression by inducing bone marrow stromal cells to deliver a death signal to lymphocytes [ 33 ]. We conclude that computational analyses both identify previously reported TF binding sites and predict phylogenetically conserved sites that should be examined for biological relevance in future biochemical studies. Figure 2 Computationally predicted clusters of cis -elements in the promoter region of mouse H2-K and its human ortholog HLA-A : The ATG of human HLA-A is at position 10,001 while that of mouse H2-K is at 10,463. Thus, the region represented relative to ATG is -305 to +97 (human) and -653 to -293 (mouse). Additionally, these regions correspond to chr 6: 30,015,866–30,016,268 (+) of the Human Genome July 2003 Assembly and chr17: 33,638,839–33,639,199 (-) of the Mouse October 2003 Assembly . Families of transcription factor binding sites and the relative positions of the sites in the nucleotide sequences of the two genes are represented as different colored bars stretching across the ortholog gene pair. Cis-Regulatory elements in genes grouped by patterns of expression Locally developed programs, TraFaC and CisMols, were used to identify and display putative regulatory modules in genes grouped by patterns of expression. The algorithms use a moving 200 bp window to scan regions of DNA for specific sequences characteristic of TF binding sites (Figure 3 ). Figure 3 Example of a CisMols display of location and composition of clusters of cis -elements that are putative regulatory modules. The genes are those with high expression in thymus. The algorithm used by TraFac and CisMols to display regulatory modules uses a moving 200 bp window to scan regions of DNA for specific sequences characteristic of TF binding sites ( cis -elements). Clusters of these cis -elements are not generally distributed evenly across a segment of DNA, but are highly localized to specific segments which are likely to play a role in regulation of gene expression. Because the scanning window is limited to 200 bp and the scan changes the frame of sequences within the window, a regulatory module that contains multiple cis -elements may not be displayed as one list of multiple elements, but rather as a list of several modules of different composition and arrangement within one small segment of DNA. Each colored cube indicates a cluster of 3 or more cis -elements with at least one "lymphoid element". The region searched is upstream 3 kb and downstream 100 bp of transcription start site (as defined by the respective mRNAs from NCBI's RefSeq database). The legend in the lower left half indicates the composition of each of the modules and the genes that share them. In the lower right hand panel is the Trafac image of one of the cis -element dense region with multiple shared modules of Arid1a gene. Cluster analysis of genome wide expression data from microarrays permits the grouping together of genes with similar patterns of expression across cells, tissues or experimental conditions. Clustering of genes by patterns of expression was first applied on a large scale to yeast [ 34 ], where control of important variables like genotype, phase of cell cycle, and growth conditions permits precise identification of coordinately regulated genes. Clustering has also been used to catalog mammalian genes that are differentially expressed in normal and malignant immune cells [ 35 , 36 ]. While yeast genes with similar patterns of expression have been found to share regulatory elements [ 37 ], identification of such elements in clustered genes of mammals is complex and not very successful [ 38 , 39 ]. Conservation of functionally important regions of DNA underpins current methods of identifying putative regulatory regions by computational analysis of nucleotide sequences [ 14 - 16 ]. Using K-means clustering in GeneSpring (Version 4.2.1), 160 genes, which had been annotated using SOURCE [ 40 ] early in our studies, were divided into distinct sets based on similarity of expression patterns across 15 tissues. Tissues were given equal weight, the number of clusters was set at 20, and similarity was measured by standard correlation. For technical reasons, GeneSpring did not assign 4 genes to clusters. The cluster sets are shown in Additional file 2 . K-cluster set 15 K-cluster set 15 contained 14 genes. While these genes had similarities in patterns of expression across a group of 15 tissues, their most prominent shared characteristic was preferential expression in thymus. They were diverse in function. For instance, the group comprised transcription factors Ets1 and Tcf12 , chromatin matrix associated protein Smarcf1 (recently renamed Arid1a ), the ATP-binding cassette transporter Abcg1 (transports peptides during antigen processing), the 2'-5'-oligoadenylate synthetase Oasl2 that is induced by interferon, and the histocompatibility antigen H2-K that plays a role in antigen presentation and processing. Sequences of both the mouse gene and its human ortholog were available for seven genes ( Abcg1 , Ctsl , Man2b1 , Sgpl1 , Arid1a , Tcf12 , and Zfp162 ). The 3 kb upstream regions of all 7 genes were compared to identify modules of shared cis -elements. The search criteria were limited by (1) requiring modules to contain at least 3 TF binding sites, one of which is a lymphoid element (see this list of lymphoid elements in Methods), (2) to be evolutionarily conserved, that is, to occur within the phylogenetic footprints in the aligned mouse-human orthologs, and (3) to be located within 3 kb upstream and 100 bp downstream of the first bp of exon 1 (transcription start). Examples of modules of cis -elements are shown in Table 2 . Arid1a , Abcg1 and Sgpl1 are most similar to one another. They also have the most similar patterns of expression across tissues, when clustered in hierarchical trees. One module, AP2F EGRF MAZF SP1F ZBPF, contains 5 cis -elements within a 200 bp window and is present within 3 kb upstream of transcription start in Arid1a , Abcg1 and Sgpl1 . The conserved modules containing multiple transcription factor binding sites (Table 2 and Figures 3 and 4 ; Additional file 11 gives fasta sequences, list of binding sites and coordinates) are likely to play a role in regulation of expression of these genes, but this hypothesis must be experimentally verified. Ctsl , Man2b1 , Zfp162 and Tcf12 did not share modules (within upstream 3 kb region and having at least one "lymphoid element") with the other genes. Table 2 Examples of modules of shared cis -elements in K-cluster, set 15 genes. All elements of a module are within a 200 bp window and are present in both the human and mouse orthologs. Modules are located within 3-kb upstream of the transcription start site. Genes Modules of shared cis -elements Arid1a Abcg1 Sgpl1 Man2b1 Ctsl Zfp162 Tcf12 AP2F EGRF MAZF SP1F ZBPF + + + - - - - AP2F EGRF SP1F ZBPF + + + - - - - AP2F MAZF SP1F ZBPF + + + - - - - AP2F HESF MAZF SP1F + + + - - - - MAZF SP1F ZBPF + + + - - - - AP2F MAZF SP1F + + + - - - - AP2F SP1F ZBPF + + - - - - - EGRF MAZF ZBPF + + + - - - - ETSF SP1F ZBPF + + + - - - - AP2F EGRF ETSF SP1F ZBPF + + + - - - - AP2F MAZF MZF1 SP1F ZBPF + + - - - - - AP2F MAZF MZF1 SP1F + + - - - - - AP2F MZF1 SP1F ZBPF + + - - - - - ETSF MAZF SP1F + - + - - - - EGRF ETSF ZBPF + - + - - - - Figure 4 Clusters of TF binding sites immediately upstream of the transcription start site in 3 genes of cluster set 15 (co-expressed genes with high expression in thymus) : The upper panels compare the location of TF binding sites surrounding and upstream regions of transcription start site (based on the corresponding mRNA annotations from NCBI's RefSeq database) of human and mouse Arid1a , Abcg1 and Zfp162 genes in GenomeTrafac database . The bottom panels list the gene descriptions and each of the binding sites in their order of occurrence from distal (top) to proximal (bottom) to exon 1 of the human gene, which is on the left within each panel. Binding sites in bold are known "lymphoid elements". The first nucleotide of exon 1 is at bp 40,001. Each of the colored bars represents a class of TF binding sites and connects homologous binding sites in genes of the two species. The orthologous genes may differ in the number and location of specific TF's binding sites. The corresponding coordinates of the regions on human (NCBI Build 35, May 2004) genome assembly are: chr1: 26,706,590–26,706,986 (+), chr21: 42,512,113
42,512,317 (+) and chr11: 64,302,720–64,302,924 (-) for human ARID1A , ABCG1 and SF1 respectively. Coordinates in the mouse genome assemblies (Build 33 Mouse Assembly, May 2004) are: chr4: 132,206,952–132,207,348 (-), chr19: 6,151,958–6,152,162 (+) and chr17: 29,663,342–29,663,546 (+) for Arid1a , Abcg1 and Zfp162 respectively. Figure 4 shows the computationally predicted arrangement of cis -elements immediately upstream of the transcription start site (promoters) of specific individual genes: Arid1a , Abcg1 , and Zfp162 . Elements were required to be within 500 bp of transcription start to be shown in Figure 4 , which focuses on sequence conservation in classical promoters of pairs of orthologs and does not require that elements be shared with other genes. Modules in Table 2 were within 3 kb of transcription start, which could include both classical promoters and upstream enhancers, and were shared by more than one pair of orthologs. A number of the elements of modules listed in Table 2 are also present in the predicted promoters. For example, MAZF and SP1F are also present in the promoters of Arid1a , Abcg1 and Zfp162 . K-cluster Set 7 K-Cluster set 7 includes 19 genes. As a group the genes were better expressed in stimulated lymph nodes and activated T-cells than in the other tissues. Expression was characteristically low in peripheral blood mononuclear cells and in other non-immune adult and fetal tissues. Among the genes in set 7 are the integral surface membrane protein CD72 found on B-cells, the transcription regulators I rf5 and Icsbp1 , the tyrosine kinases Hck , Stk10 , and Lyn that are a part of the intracellular signaling cascade, the mitogen activated protein kinases Map3k1 and Map4k1 that participate in the very earliest steps of induction of new gene expression after lymphocytes are exposed to antigen, and the ATP-binding cassette transporters Abca7 and Tap1 of the type that transport peptides during antigen processing. Other less well-characterized genes in these sets may have functions similar to the genes that are better annotated. Sequences of 11 genes from Set 7 and their human orthologs were examined for the presence of clusters of TF binding sites, at least one of which is a lymphoid element, as defined in Methods. The 11 genes shared relatively few clusters of TF binding sites. There were 7 clusters shared by 3 genes. The largest cluster contained 6 elements, AP2F CDEF EGRF SP1F ZBPF ZF5F and was shared by Irf5 and Stk10 . There were 21 clusters shared by 2 genes and containing 3 to 6 TF binding sites. The composition and location of these are shown in images from CisMols (Additional files 3 , 4 , 5 , 6 , 7 , 8 and 9 ). Highly expressed genes In addition to searching for potential regulatory regions within sets of genes clustered by similarities of patterns of expression across sets of tissues and within regions immediately upstream of exon 1, we also sought to identify genes characterized by high expression in specific immune tissues. It is not known whether clustering by pattern of expression across tissues and/or grouping by high expression in specific tissues (or neither) will be a useful way to group genes for computational identification of regulatory elements and regulatory regions. It is clear, however, that although modules of cis- elements that regulate expression of genes in tissues can occur at many different locations relative to a gene's promoter, at least some regulatory elements are located within promoter regions and this is the region we have searched most intensively for conservation of known TF binding sites. For the purposes of this analysis, we defined genes that were highly expressed based on their normalized expression being at least 4 times higher in an individual immune tissue relative to their median signal across the entire database. High expression in a single tissue does not preclude significant expression in other tissues, so high expression is not synonymous with unique expression. We examined highly expressed mouse genes and their human orthologs for the presence of clusters of TF binding sites, with the additional constraint that at least one of the cis elements present in the cluster was a lymphoid element, as defined in Methods. Grouped by tissue, suitable paired mouse/human orthologs were: activated T-cells, 17 genes: Ctsz , Kpnb1 , Tnfrsf9 , Tnfrsf4 , Myc , Mcm2 , Mcm5 , Mcm6 , Mcm7 , Gzmb , Ncf4 , Gapd , Ccl4 , Pcna , Rpl13 , Cd86 , Icsbp1 ; thymus, 7 genes: Satb1 , Hdac7a , Sgpl1 , Abca1 , Prss16 , Abcg1 , C1qg ; stimulated lymph node, 4 genes, Stk10 , Irf5 , Cxcl9 , Tnfrsf1 . Identical analyses of 6 genes highly expressed in skeletal muscle ( Ckm , Myf6 , Aldo1 , Myog , Dmd , Chrm3 ) and 8 in liver ( G6pc , Cyp7a1 , Proc , Ttr , Aldo2 , Ins2 , Igf1 , Pah ) served as negative controls, i.e. not tissues that play a critical role in lymphocyte differentiation or the immune response. The MCM family and Myc are involved in replication of DNA and chromosomes. The TNF and TNFR families of genes encode receptors and ligands that couple directly to signaling pathways for cell proliferation, survival and differentiation [ 41 ]. Prss16 encodes a thymus specific protease which is specifically expressed by epithelial cells in the thymic cortex and plays a role in T-cell development and, perhaps, in susceptibility to autoimmunity [ 42 ]. Hdac7a encodes a histone deacetylase. Members of the Hdac family of genes modify histones and play a role in the regulation of expression of genes such as those functioning in the cell cycle, apoptosis, and transcription [ 43 ]. Cxcl9 is an inflammatory chemokine induced by interferon. Its promoter contains binding sites for CREB, STAT1, and NFKB [ 44 ]. The results of the above approach are shown in Table 3 , which lists examples of putative computationally identified regulatory modules of immune genes and the cis -elements that they contain. When modules of genes highly expressed in thymus, stimulated lymph nodes, or activated T-cells were compared with one another and to modules of genes expressed in muscle and liver, it is clear that the composition ( cis -elements) of modules are not unique to a specific gene. However there is some evidence of unique arrangements of elements within modules. There are also cis -elements that are not commonly shared. For example, the individual cis -elements HESF, HAML, MYT1 and P53F were not found in modules of genes other than those highly expressed in thymus. Likewise, E2FF was only present in modules of activated T-cells, but clearly does not play a unique role in the immune system. Members of the E2F family of TFs are key participants in cell proliferation, apoptosis, and differentiation [ 45 ]. E2FF is found in promoters of Mcm2 , Mcm5 , Mcm6 , Mcm7 , and Myc . These genes are highly expressed in proliferating cells generally, an example of which is the activated T-cell. Table 3 Examples of modules of shared cis-elements found in highly expressed genes in 3 immune tissues. Elements were clustered with at least 2 other cis-elements within a 200 bp window, indicating the presence of a putative regulatory module which contained at least 3 transcription factor binding sites, one of which was required to be a lymphoid element. All are located within 3 kb upstream and 100 bp downstream of the first bp of exon 1. The modules were present in the mouse and human orthologs of at least 2 genes from sets of genes that were highly expressed in thymus, stimulated lymph nodes, or activated T-cells. The number of genes for which orthologs were available: thymus, 7; lymph node, 4; activated T-cells, 17. Thymus Stimulated Lymph Node Activated T-cell MAZF SP1F ZBPF AP2F CDEF EGRF SP1F ZBPF ZF5F MAZF SP1F ZBPF EGRF MAZF SP1F ZBPF LHXF NKXH OCT1 RBIT CREB SP1F ZBPF ETSF SP1F ZBPF EGRF ETSF NFKB E2FF MAZF SP1F AP2F EGRF HESF MAZF SP1F ZBPF GATA HOXF NKXH ECAT PCAT SP1F ZBPF ETSF MAZF SP1F STAT ZBPF LHXF NKXH OCT1 ETSF MAZF MZF1 AP2F EGRF ETSF SP1F ZBPF NKXH OCT1 RBIT EGRF SP1F ZBPF EGRF MAZF P53F SP1F IKRS MAZF NFKB GATA HAML MYT1 E2FF EBOX ETSF MAZF SP1F ZF5F BCL6 CREB E2FF STAT HOXF LEFF LHXF OCT1 MAZF MZF1 NFKB PAX5 Regulatory modules, which have been proved biologically to regulate expression of genes, contain multiple TF binding sites, much as is shown in Figures 2 , 3 and 4 . Examples of modules of shared cis -elements (i.e., within a 200 bp window) in highly expressed genes are listed in Table 3 . For example, of the modules highly expressed in thymus SP1F MAZF ZBPF was present in paired orthologs of Abca1 , C1qg , Abcg1 and Sgpl1 ; module AP2F EGRF HESF MAZF SP1F ZBPF was present in Sgpl1 and Abcg1 . Of the modules highly expressed in stimulated lymph nodes, AP2F CDEF EGRF SP1F ZBPF ZF5F was present in Stk10 and Irf5 ; module GATA HOXF NKXH was present in Stk10 and Irf5 . Of the modules highly expressed in activated T-cells, E2FF EBOX ETSF MAZF SP1F ZF5F was present in Kpnb1 and Mcm6 ; module BCL6 CREB E2FF STAT was present in Icsbp1 and Tnfrsf4 . Discussion Individual differentiated biological states can be characterized by gene expression profiling. Large-scale comparisons of profiles of cells, tissues, and developmental stages have the potential to identify a wealth of coordinately regulated groups of genes that reflect the interplay of their functional relationships and transcriptional control mechanisms. We have built a database comprised of the mRNA expression profiles of 65 normal adult and fetal C57BL/6J mouse tissues using the Incyte Mouse GEM1, 8638 element, clone set. Using microarray analysis, 680 sequences were identified that were highly expressed in one or more of 6 immune tissues. Many were also expressed in certain other tissues. Some of these other tissues were organs such as heart, kidney, and brain which do not normally contain lymphocytes in large numbers and do not play a role in the immune response. Others, such as intestine and lung, interface with the external environment, contain significant numbers of lymphocytes, and can mount an immune response. The 680 expressed sequences were filtered to remove 320 that were expressed in "non-immune" brain or heart or kidney. This resulted in a list of 360 expressed sequences called "immune genes" that were less broadly expressed in tissues without immune function than were the 680. Mutations and polymorphisms in both the 680 expressed sequences and the 360 immune genes have a significant chance of specifically affecting immune function. We predict this will be more common with changes in the 360 immune genes. We tested this by comparing reports of disease causing mutations in the 360 immune genes with those reported for the 320 genes that were more broadly expressed (Online Mendelian Inheritance in Man). Of the 360 mouse immune genes, 32 had an ortholog with gene symbol in OMIM and17 had annotations that described a function clearly linked to development or function of the immune system. Mutations in 2 ( LCP2 and PARVG ) cause severe immunodeficiency disease. Examples of other diseases caused by mutations in these 32 genes were B- and T-cell malignancies, autoimmune disorders, and reduced viral or bacterial resistance. Of the 320 genes removed from the list of 680, 37 had orthologs with gene symbols listed in OMIM. 4 genes were expressed in lymphocytes and mutation in one, Bruton's tyrosine kinase, causes agammaglobulinemia. Mutations in other genes caused disorders of coagulation, red cells, or granulocytes, rather than the immune system. We conclude that the list of 360 immune genes includes a higher percentage of genes preferentially expressed in immunocompetent tissues and with more specific immune-related functions than does the full list of 680 sequences expressed in immune tissues, but also with expression in non-immune tissues. The 360 immune genes represent a portion of the complete set of genes that encode proteins and processes necessary for the differentiation, maintenance, and function of the immune system. These genes are functionally diverse and represent both ubiquitous and specialized cellular processes. 10 or more genes are in specific functional clusters that carry out general processes such as DNA and chromosomal replication, cell cycle regulation, transcription, and translation. Other genes are in functional clusters that carry out specialized functions, largely restricted to immune tissues. These include genes that encode proteins involved in antigen recognition and transport, chemokine synthesis, chemokine recognition, and the intracellular signaling cascade necessary to initiate transcription and new protein synthesis in lymphocytes, as part of the host response to antigen. Functional annotation of these genes is a work in progress. While probable functions have been assigned to most of the expressed sequences and the genes that encode them, using information shown in the Additional file 1 , there is much work to be done. Most functional annotations are based on the sharing of presently known protein domains and sequence homologies and provide general clues to the role a gene or protein may play in cells that participate in the immune response to antigen. A more precise understanding will come about as new laboratory data are correlated with studies of the expression of specific immune genes, their coordination with expression of other genes, and the structure and function of their products. For several reasons, the "immune genes" that we have identified are not all of the genes that are expressed in immune tissues: (1) the Incyte set of 8638 genes probably contains representative cDNAs from 25% or less of all mouse genes; (2) genes that are essential to immune function, but are expressed at similar levels in immune and other tissues will not be included in the immune set; and (3) a gene with a very low level of expression will be missed, if cDNA made from its RNA is not present in sufficient quantity to give a signal on the microarray. Genes may be included or excluded in error because of the large number of genes screened for expression with a limited number of replicates. Incyte cDNA microarrays are no longer manufactured and no Incyte arrays or public databases are available to check expression of our immune genes in other species. There are two relevant publicly available Novartis gene expression databases (Genomics Institute of the Novartis Research Foundation, [ 18 ]), which can be accessed. One uses Affymetrix chip U74Av2 and a set of 90 mouse tissues and cell lines and the second uses Affymetrix HG U133 and 158 human tissues and cells. Relating Affymetrix probes to Incyte cDNA probes is complex and the Novartis tissue sets do not contain the same tissues we have used. However, our immune genes, when expressed on the Novartis arrays, are generally clustered in tissues of the immunohematopoietic system, the gastrointestinal tract, and lung. These types of publicly available databases will permit identification and functional annotation of new immune genes with consequent availability of larger sets of coordinately regulated genes for searches of conserved regulatory modules. Using comparative genomics-based, cis -element analyses ( [ 15 ] and [ 46 ]), we identified compositionally similar clusters of cis -elements in upstream regions of mouse/human orthologs of several immune genes. There was an excellent agreement between the computationally predicted and experimentally determined arrangements of cis -elements in the promoters of the mouse H2-K and human HLA-A genes. Analyses of other immune genes identified a wealth of potential immune system-specific regulatory modules. For example (Table 2 ), Arid1a , Abcg1 , and Sgpl1 are members of a K-clustered set of immune genes and share a phylogenetically conserved module of 5 cis -elements: AP2F EGRF MAZF SP1F and ZBPF, all within a 200 bp interval. Other examples of clustered TF binding sites that could be within regulatory modules of genes highly expressed in specific tissues are given in RESULTS. Striking examples of putative modules include the 6 cis -element module AP2F EGRF HESF MAZF SP1F ZBPF in genes highly expressed in thymus; the 6 cis -element module E2FF EBOX ETSF MAZF SP1F ZFSF in genes highly expressed in activated T-cells; and the 6 element module AP2F CDEF EGRF SP1F ZBPF ZF5F in genes highly expressed in stimulated lymph nodes (Table 3 ). Putative regulatory modules are not distributed randomly across an entire segment of DNA, but are highly clustered within distinct short segments that are the computationally identified promoters and enhancers (Figure 3 ). Because of the nature of the scanning algorithm with its 200 bp window, variations of multiple modules may occur within one segment. These phenomena are more easily understood by examining Figure 3 . Our data support the hypothesis that (1) regulatory modules of genes are highly clustered in a few sites that can be computationally identified, (2) modules in different genes may share cis -elements that bind TFs, and (3) certain combinations of TF binding sites are phylogenetically conserved and appear to be reused across genes when specific patterns of expression are required. Cis -elements from the same family have a high probability of interacting with similar groups of transcription factors, although they will not necessarily be in the same position relative to the transcription start site. We have identified genes and putative regulatory modules that play a role in the differentiation, maintenance, and function of the immune system. These results serve to advance both our understanding of normal gene and immune system function and also to identify genes and regulatory regions whose mutation or polymorphic variation lead to immunologic disease. Methods C57BL/6J mice from The Jackson Laboratory were the source of normal adult and fetal tissues. The complete panel of tissues for microarray analyses by our group has been described [ 47 ]. Peripheral blood mononuclear cells were separated from whole blood on Ficoll/Hypaque gradients; unstimulated lymph nodes, spleen, and thymus were each collected from unimmunized mice and pooled separately; "stimulated" lymph nodes were collected from mice 10 days after they were immunized with hen egg-white lysozyme (HEL) in complete Freund's adjuvant; activated T cells were prepared by enriching T cells from peripheral blood and treating them with anti-CD3 and anti-CD28. Except for activated T-cells and pancreatic islet cells, all cells and tissues were collected in duplicate. 128 preparations of poly (A)-RNA were made from 65 different tissues, checked for quality, and quantified as previously described [ 19 , 47 ]. Microarray analyses were carried out using Incyte mouse GEM1 cDNA arrays (Incyte Genomics, Palo Alto, CA), as described previously for our group [ 19 , 47 ]. Relative abundance of probes was calculated as the ratio of the sample value against the value from the labeled whole mouse reference cDNA for each gene on each array. Data analyses were carried out with GeneSpring version 4.2.1 (Silicon Genetics) software, including filtering, K-means and hierarchical clustering. A list of all tissues in the full set of 65 normal adult and fetal tissues is provided in Additional file 12 . Our analyses focused on comparison gene expression in 18 tissues that were selected to represent a variety of adult and fetal tissues (Figure 1B ), most with immunological function. 6 of the 18 tissues were the "immune tissues" – unstimulated and stimulated lymph nodes, spleen, peripheral blood mononuclear cells, activated T-cells, and thymus. The remaining 12 tissues of the 18 tissue set were: fetal day 16.5 intestine and lung; adult duodenum, jejunum, ileum, proximal and distal colon; adult lung and liver, and joint synovium from normal adult mice and mice with acute and chronic arthritis. All pertinent microarray data are available through the Children's Hospital Research Foundation expression database web server within the ExpressionDB folders of the Incyte Mouse GEM1 chip genome. Genes on the Incyte array were identified by NCBI GenBank accession and systematic numbers and by gene symbol, where available. For those sequences that could not be assigned a gene symbol, sequence homologies to known mouse genes were sought using MouseBLAST [ 23 ], BLAT [ 12 ], MGD [ 23 ], and LocusLink [ 24 ]. BLAST comparisons of the human and mouse confirmed Ensembl predictions of human orthologs of mouse genes. Identity of genes was confirmed by BLAST comparison of the GenBank sequences from NCBI [ 48 ] with Ensembl [ 13 ] sequences. When downloading the genomic sequences with flanking sequences, it was important to have an mRNA that contained exon 1, so the site of initiation of transcription was correctly identified. Presence of an upstream exon 1 in an isoform would lead to re-defining of the promoter and intronic regions. Criteria for presence of exon 1 included: comparison of the number and location of exons in orthologous genes, alignment of transcripts of the gene as reported by different databases, and alignment of the 5' end of the transcript with the putative start site and signals in the gene. In cases where we encountered multiple high scoring transcript hits against the genome, we manually looked into the alignments to rule out the occurrence of pseudogenes that frequently lacked introns when compared to the "true" genes. Additional information about sequences of both the transcript and the gene was obtained from UCSC Golden Path [ 12 ]. Confirmation of the presence of exon 1 in orthologs was particularly important because of the need to locate the start site of transcription. Computational prediction of exons is error prone. DNA sequences of genes were downloaded to include at least 10,000 flanking base pairs upstream and downstream of the first and last exons respectively. The November 2002 and April 2003 assemblies of human and the February 2002 and February 2003 assemblies of mouse genome were used for this purpose depending upon their availability at the time of our analyses ( Additional files 10 and 11 list relevant FASTA sequences and genomic coordinates). The GO and MGI databases were searched for annotations of the immune genes, using Stanford SOURCE [ 40 ]. For genes not found or incompletely annotated, manual annotation was done using criteria similar to the Gene Ontology (GO) [ 49 ], Mouse Genome Informatics (MGI) [ 23 ], and LocusLink classifications [ 50 ]. A function was assigned if the encoded protein contained distinctive InterPro functional domains, or sequence similarity to paralogs previously annotated, or sequence similarity to functionally characterized SwissProt/TrEMBL proteins. Using the information about structure and function, the authors simplified annotations and grouped genes by major functions, such as antigen binding and processing (defense – immune function), transcription, protein synthesis, apoptosis, cell division. Highly detailed annotations are provided in the supplementary materials ( Additional file 1 ). To identify putative consensus cis -acting regulatory sequences in genes that were coordinately regulated, we first selected groups of genes based on their expression patterns in different immune tissues. The complete genomic sequences (with flanking upstream and downstream regions of 40 kb) of the selected genes and their orthologs were extracted from the Ensembl/UCSC human and mouse databases [ 12 , 13 ]. Where available the NCBI-RefSeq mRNAs were used as references for downloading the genomic sequences with upstream and downstream gene flanking regions of 40 kb. The transcription start site was thus at 40,000 in the downloaded sequences used in comparative genomic analysis for identification of potential regulatory clusters using Trafac server [ 15 ]. Repeat elements were masked using the RepeatMasker [ 51 ]. Conserved clusters of regulatory elements in the evolutionarily conserved non-coding regions of mouse and human orthologs were displayed using the TraFaC [ 15 ] or GenomeTraFaC [ 46 ] servers which integrate results from MatInspector Professional (Version 4.1, 2004; 356 individual matrices in 138 families) [ 52 ] and Advanced PipMaker (chaining option) [ 53 ] programs. We compared conserved putative cis -regulatory regions of each of the different groups of genes from mouse and human to identify known TF binding sites. The CisMols analyzer [ 22 ] permits selection of TFs that must be present in clusters of TFs that constitute a putative regulatory module. To convey specificity to the search for modules relevant to regulation of gene expression in immune tissues, we required the presence of one or more of the following TFs, which we call "lymphoid elements". They have been reported to play a role in some aspect of lymphoid biology (see for example, [ 1 - 3 ]: BCL6, CMYB, CREB, EGRF, ETSF, GATA, IKRS, IRFF, MZF1, NFAT, NFKB, OCT1 (site also binds OCT2), PAX5, SP1F, VMYB, and WHZF. ECAT and PCAT were also included because of their frequent occurrence in promoters at the start of transcription. The search was limited to a region 3 kb upstream and 100 bp downstream of the start site of exon 1 (based on the NCBI-RefSeq mRNA annotations). This is where the promoter and associated regulatory elements would be expected, given that additional regulatory elements (enhancers/silencers) are almost certain to be located elsewhere. Images of the CisMols analyses of genes to identify regulatory elements are also provided in supplementary materials ( Additional files 3 to 9 ). One example is shown in Figure 3 . Authors' contributions JJH and BJA were primarily responsible for the design, coordination and conduct of the study. AGJ and AG were responsible for regulatory region analyses and software development. AGJ was responsible for ortholog analysis and novel ortholog assignments. JJH, BJA and AGJ drafted the manuscript and figures and JJH, BJA and AGJ contributed editorial revisions. SK, SW and CE were responsible for generating, quality assurance, and initial assembly of the gene chip data. JDK provided purified lymphoid cells, read the manuscript and provided comments and discussion. All authors read and approved the final manuscript. Supplementary Material Additional File 1 List and annotation of 360 expressed sequences ("immune genes"). Click here for file Additional File 10 FASTA sequences and the corresponding coordinates on the human and mouse genome assemblies (May 2004) of the promoter regions used in the analysis and displayed in figures 2 and 4. Click here for file Additional File 2 Using K-means clustering in GeneSpring (Version 4.2.1), 160 annotated genes, were divided into distinct sets based on similarity of expression patterns across 15 tissues. Tissues were given equal weight, the number of clusters was set at 20, and similarity was measured by standard correlation. Click here for file Additional File 11 FASTA sequences and the corresponding coordinates on the human and mouse genome assemblies (May 2004) of the promoter regions used in the analysis and displayed in figures 2 and 4. Click here for file Additional File 3 CisMols display of location and composition of clusters of cis-elements that are putative regulatory modules for the genes in various groups (test and control). Each colored cube indicates a cluster of 3 or more cis-elements with at least one "lymphoid element". The region searched is upstream 3 kb and downstream 100 bp of transcription start site (as defined by the respective mRNAs from NCBI's RefSeq database). The legend in the lower left half of the figure indicates the composition of each of the modules and the genes that share them. Click here for file Additional File 4 CisMols display of location and composition of clusters of cis-elements that are putative regulatory modules for the genes in various groups (test and control). Each colored cube indicates a cluster of 3 or more cis-elements with at least one "lymphoid element". The region searched is upstream 3 kb and downstream 100 bp of transcription start site (as defined by the respective mRNAs from NCBI's RefSeq database). The legend in the lower left half of the figure indicates the composition of each of the modules and the genes that share them. Click here for file Additional File 5 CisMols display of location and composition of clusters of cis-elements that are putative regulatory modules for the genes in various groups (test and control). Each colored cube indicates a cluster of 3 or more cis-elements with at least one "lymphoid element". The region searched is upstream 3 kb and downstream 100 bp of transcription start site (as defined by the respective mRNAs from NCBI's RefSeq database). The legend in the lower left half of the figure indicates the composition of each of the modules and the genes that share them. Click here for file Additional File 6 CisMols display of location and composition of clusters of cis-elements that are putative regulatory modules for the genes in various groups (test and control). Each colored cube indicates a cluster of 3 or more cis-elements with at least one "lymphoid element". The region searched is upstream 3 kb and downstream 100 bp of transcription start site (as defined by the respective mRNAs from NCBI's RefSeq database). The legend in the lower left half of the figure indicates the composition of each of the modules and the genes that share them. Click here for file Additional File 7 CisMols display of location and composition of clusters of cis-elements that are putative regulatory modules for the genes in various groups (test and control). Each colored cube indicates a cluster of 3 or more cis-elements with at least one "lymphoid element". The region searched is upstream 3 kb and downstream 100 bp of transcription start site (as defined by the respective mRNAs from NCBI's RefSeq database). The legend in the lower left half of the figure indicates the composition of each of the modules and the genes that share them. Click here for file Additional File 8 CisMols display of location and composition of clusters of cis-elements that are putative regulatory modules for the genes in various groups (test and control). Each colored cube indicates a cluster of 3 or more cis-elements with at least one "lymphoid element". The region searched is upstream 3 kb and downstream 100 bp of transcription start site (as defined by the respective mRNAs from NCBI's RefSeq database). The legend in the lower left half of the figure indicates the composition of each of the modules and the genes that share them. Click here for file Additional File 9 CisMols display of location and composition of clusters of cis-elements that are putative regulatory modules for the genes in various groups (test and control). Each colored cube indicates a cluster of 3 or more cis-elements with at least one "lymphoid element". The region searched is upstream 3 kb and downstream 100 bp of transcription start site (as defined by the respective mRNAs from NCBI's RefSeq database). The legend in the lower left half of the figure indicates the composition of each of the modules and the genes that share them. Click here for file Additional File 12 Tissue lists used in the generation of microarray profile data. Click here for file | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC534115.xml |
535545 | Uterine extracellular matrix components are altered during defective decidualization in interleukin-11 receptor α deficient mice | Background Implantation of the embryo and successful pregnancy are dependent on the differentiation of endometrial stromal cells into decidual cells. Female interleukin-11 receptor α (IL-11Rα) deficient mice are infertile due to disrupted decidualization, suggesting a critical role for IL-11 and its target genes in implantation. The molecular targets of IL-11 in the uterus are unknown, but it is likely that IL-11 signaling modifies the expression of other genes important in decidualization. This study aimed to identify genes regulated by IL-11 during decidualization in mouse uterus, and to examine their expression and localization as an indication of functional significance during early pregnancy. Methods Decidualization was artificially induced in pseudopregnant wild type ( IL11Ra +/+ ) and IL-11Rα deficient ( IL11Ra -/- ) littermates by oil injection into the uterine lumen, and gene expression analyzed by NIA 15K cDNA microarray analysis at subsequent time points. Quantitative real-time RT-PCR was used as an alternative mRNA quantitation method and the expression and cellular localization of the protein products was examined by immunohistochemistry. Results Among 15,247 DNA probes, 13 showed increased and 4 decreased expression in IL11Ra -/- uterus at 48 h of decidualization. These included 4 genes encoding extracellular matrix proteins; collagen III α1, secreted acidic cysteine-rich glycoprotein (SPARC), biglycan and nidogen-1 (entactin). Immunohistochemistry confirmed increased collagen III and biglycan protein expression in IL11Ra -/- uterus at this time. In both IL11Ra -/- and wild type uterus, collagen III and biglycan were primarily localized to the outer connective tissue and smooth muscle cells of the myometrium, with diffuse staining in the cytoplasm of decidualized stromal cells. Conclusion These data suggest that IL-11 regulates changes in the uterine extracellular matrix that are necessary for decidualization. | Background Implantation of the embryo, formation of the placenta and successful pregnancy are dependent on the proliferation and differentiation of endometrial stromal cells into decidual cells. Decidualization occurs in response to endometrial and embryonic signals and is thought to involve complex interactions between ovarian steroid hormones, the uterine extracellular matrix (ECM), growth factors and cytokines (reviewed in [ 1 ]). When implantation is initiated at day 3.5 of pregnancy in the mouse (plug is day 0), decidualization begins adjacent to the embryo on the antimesometrial side of the uterus to form the primary decidual zone [ 2 ]. Decidual transformation then extends mesometrially to form the secondary decidual zone by day 6, accompanied by a dramatic increase in vascular permeability [ 3 ]. The endometrial response to implantation can be induced artificially by the application of oil into the lumen of the hormonally primed uterus, producing a deciduoma [ 4 ]. Natural and artificial decidualization share many of the same features, with distinct decidual zones [ 5 ], and the progression of each response can be monitored by an increase in uterine weight [ 6 ]. Female mice with a null mutation in the gene encoding interleukin-11 receptor α (IL-11Rα) are infertile due to disrupted decidualization [ 7 ], suggesting a critical role for IL-11 and its target genes in the decidual response. Despite normal estrous cycles and no detectable ovarian defects, female IL-11Rα deficient ( IL11Ra -/- ) mice are unable to support implantation of either IL11Ra -/- or wild type embryos. The failure of decidualization between days 4.5 and 10.5 in IL11Ra -/- females is characterized by greatly reduced vascular permeability at implantation sites, areas of hemorrhage, impaired secondary decidual zone formation, absence of mesometrial decidualization and aberrant infiltration of trophoblast giant cells [ 7 ]. Although morphologically similar to the decidua of pregnancy, a minority of artificially induced deciduomata in IL11Ra -/- mice show some mesometrial decidualization. Females homozygous for a hypomorphic IL-11Rα allele also show reduced decidualization, with decreased cell proliferation, progressive degeneration of the deciduae, infiltration of trophoblast giant cells and absence of placental formation [ 8 ]. Neither of these mutations have been found to cause hematopoietic defects [ 8 , 9 ]. Interleukin-11 is a multifunctional cytokine, initially described as a bone marrow stroma-derived hematopoietic growth factor [ 10 ]. IL-11 shares many functions with other members of the IL-6 family of cytokines, including the induction of acute phase proteins [ 11 ], inhibition of adipogenesis [ 12 ] and the regulation of bone ECM metabolism via induction of tissue inhibitor of metalloproteinases (TIMP)-1 [ 13 ]. Like IL-6, leukemia inhibitory factor (LIF), oncostatin M, ciliary neurotrophic factor and cardiotrophin-1, IL-11 exerts its biological effects via a multisubunit receptor complex involving the signal transducer gp130 [ 14 ]. Following the formation of its hexameric receptor composed of two molecules each of IL-11, the low-affinity ligand-binding IL-11Rα and gp130 [ 15 ], IL-11 is capable of activating a number of downstream signaling pathways. In most cell types, IL-11-activated gp130 mediates its effects through Janus tyrosine kinases (JAKs1-3 and Tyk2) and the signal transducers and activators of transcription (STATs1-6) (reviewed in [ 16 ]). The rate of transcription of target genes is then modified by binding of activated STAT dimers to a DNA element in the promoter region. IL-11 signaling can also control the initiation of translation via sequential activation of PI3-K, Pdk-1/Akt, p70 S6 kinase and ribosomal protein S6 [ 17 ]. Localization and expression of IL-11, IL-11Rα and gp130 in human endometrium across the menstrual cycle suggests a role for this cytokine in decidual transformation in preparation for pregnancy [ 18 - 20 ]. Levels of immunoreactive IL-11 are highest during the secretory phase of the cycle, when the endometrium is receptive to implantation, and IL-11 is produced by the decidualized stromal cells. Treatment of human endometrial stromal cells in culture with recombinant human IL-11 increases their secretion of the decidual markers prolactin and insulin-like growth factor binding protein (IGFBP)-1, and is associated with enhanced differentiation [ 21 ]. Plasma levels of IL-11 are decreased in women with first trimester spontaneous abortion [ 22 ], and there is decreased expression of IL-11 protein in chorionic villi and decidua from anembryonic compared to normal pregnancy [ 23 ]. The molecular targets of IL-11 in the uterus are unknown, but it is likely that IL-11 signaling modifies the expression of other genes important in decidualization. This study aimed to identify genes regulated by IL-11 during decidualization by cDNA microarray, and to examine their expression and localization by immunohistochemistry, as an indication of functional significance during early pregnancy. Methods Animals Mice deficient in IL-11 receptor α ( IL11Ra -/- ) had been previously generated by gene targeting, and serially crossed more than 10 generations onto a C57BL/6 background [ 9 ]. Heterozygotes were interbred to produce wild-type ( IL11Ra +/+ ) and IL-11Rα deficient ( IL11Ra -/- ) mice, which were identified by Southern blot analysis of genomic DNA obtained from tail biopsies [ 9 ]. All mice were housed in conventional conditions, fed and watered ad libitum and maintained in a 12-h light, 12-h dark cycle. All procedures were approved by the Monash Medical Centre (B) Animal Ethics Committee (AEC# MMCB 2001/04), and were carried out in compliance with the Helsinki Declaration. Artificial decidualization Surgery was performed under xylazine/ketamine-induced anesthesia, by intraperitoneal administration of 10 mg/kg xylazine hydrochloride (Ilium Xylazil-20, Troy Laboratories, Smithfield, NSW, Australia) and 80 mg/kg ketamine hydrochloride (Ketalar, Pfizer, West Ryde, NSW, Australia) in sterile phosphate-buffered saline (PBS). Anesthesia was reversed with 250 μg yohimbine hydrochloride and 400 μg 4-amino pyridine (Reverzine S.A., Parnell Laboratories, Alexandria, NSW, Australia) in sterile PBS. Female IL11Ra +/+ and IL11Ra -/- littermates at 8–12 weeks of age (n = 4 per genotype per time point) were mated with wild type vasectomized males to induce pseudopregnancy, with the day of plug detection designated day 0. At approximately 1400 h on day 3, decidualization was induced throughout both uterine horns of each animal by injection of 20 μl of sesame oil (Sigma Chemical Co., St Louis, MO) into the lumen of each uterine horn via a 26-gauge needle inserted just distal to the utero-tubal junction. Mice were necropsied prior to surgery (0 h) or at 18 h, 24 h or 48 h following artificial decidualization. These time points prior to the onset of secondary decidualization were chosen to ensure similar cellular composition of the IL11Ra +/+ and IL11Ra -/- uteri. Whole uteri were cleaned of fat and weighed. A section of each uterus was either fixed in 10% phosphate-buffered formalin overnight or Carnoy's fixative for 2 h and processed to paraffin wax, or snap frozen in liquid nitrogen for subsequent RNA isolation. Statistical analysis of uterine weight data The wet weights of whole uterus from IL11Ra +/+ (n = 4/time point) and IL11Ra -/- (n = 4/time point) mice were statistically analyzed using GB-Stat 6.5 (Dynamic Microsystems, Inc., Silver Spring, MD). Following Bartlett's test for homogeneity of variance, uterine weight was used as the dependent variable and genotype and time as the two independent variables in a two factor analysis of variance. Bonferroni multiple comparison testing was used to compare uterine weight across time in each genotype and between genotypes at each time point. A two-tailed p value of less than 0.05 was considered a significant difference. RNA preparation, cDNA microarray hybridization and data collection Total RNA was extracted from whole uterus by acid guanidinium thiocyanate-phenol-chloroform extraction [ 24 ], incorporating an additional chloroform purification step to remove contaminating phenol. RNA was then treated with ribonuclease (RNase)-free deoxyribonuclease (DNase; Ambion, Austin, TX) to remove genomic DNA. The concentration of RNA in the final preparation was determined spectrophotometrically, and RNA quality evaluated by gel electrophoresis (1.2% agarose; Roche Applied Science, Penzberg, Germany) and by the ratio of optical density (OD 260 :OD 280 = 1.8–2.0). Each artificially decidualized IL11Ra +/+ or IL11Ra -/- uterus was processed individually for microarray hybridization. A reference pool of RNA was prepared from wild type unstimulated uteri (n = 16). The experimental design used indirect comparisons between IL-11Ra +/+ or IL11Ra -/- and the reference pool. Total RNA (10 μg) served as a template for the synthesis of aminoallyl-cDNA, which was then coupled to a fluorescent dye ester (Cy3 or Cy5) as described by the manufacturers of the CyScribe cDNA Post Labelling Kit (Amersham Biosciences, Buckinghamshire, England). Microcon-30 size exclusion columns (Millipore, Billerica, MA) were used to initially concentrate RNA samples, and to purify the cDNA probes prior to and following the dye-coupling reaction. Slides were printed with sequence-verified duplicate spots of the NIA 15K cDNA clone set [ 25 ] at the Australian Genome Research Facility and prehybridized for 30 min at 42 C in 10 mg/ml bovine serum albumin (BSA, ICN Biomedicals, Aurora, OH), 25% formamide (BDH Laboratory Supplies, Poole, England), 5 × SSC (750 mM sodium chloride, 75 mM sodium citrate; BDH Chemicals/AnalaR, Kilsyth, Victoria, Australia) and 0.1% sodium dodecyl sulfate (SDS; BDH Chemicals/AnalaR). IL11Ra +/+ or IL11Ra -/- cDNA (Cy3) and reference cDNA (Cy5) were competitively hybridized to the microarrays in the presence of 1 mg/ml Cot1 DNA (Gibco-BRL, Life Technologies, Mount Waverley, Australia), 10 mg/ml salmon sperm DNA (Gibco-BRL) and 10 mg/ml polyadenylic acid (Sigma). Hybridizations were repeated using the alternate dye combinations to account for differential fluorescent dye incorporation. After washing, slides were scanned using an Axon GenePix 4000B microarray reader (Axon Instruments, Union City, CA) and GenePix Pro 4.0 software (Axon Instruments) to generate pairs of 16-bit tagged image file format (TIFF) files. Following manual quality control for hybridization artefacts, red (Cy5) and green (Cy3) mean foreground and median background fluorescence intensity measurements for each spotted DNA sequence were extracted for export to the statistical programming environment R 1.5.1. Microarray data analysis Differential gene expression between IL11Ra +/+ (WT) and IL11Ra -/- (KO) uterus at time points following artificial decidualization was determined using the normalization and analysis functions of the statistical language R [ 26 ] and the add-on package Statistics for Microarray Analysis [ 27 ]. For each time point, the red and green background-corrected intensities ( R and G ) from 4 slides were read into R using the read.genepix and init.data commands. The stat.ma function was used to calculate the log-ratios of expression ( M = log 2 R - log 2 G ) and average log-intensity (A = (log 2 R + log 2 G )/2) for each spot, and to normalize the red and green channels relative to one another using print-tip loess normalization [ 28 ]. Diagnostic MA plots of each slide were used to determine the effectiveness of this normalization method in adjusting for sources of variation arising from dye bias and print-tip effects. Given the indirect dye swap design [ 29 ] of these experiments, log-ratio values were reversed in the dye-swapped slides and the log-ratio for each gene was calculated as the difference of two independent log ratios from the equation log (KO/WT) = log (KO/reference) - log (WT/reference). Differentially expressed genes were identified by considering a univariate testing problem for each gene and then correcting for multiple testing using adjusted p-values [ 30 ]. The function stat.lm [ 31 ] was used to fit a linear model for each gene to the series of 4 arrays and estimate the average fold change and a standard deviation for each gene, taking into account the pattern of dye swaps and duplicate spots. The stat.bay.est command [ 31 ] then computed a moderated t -statistic and B -statistic for each gene, to give a log odds ratio of difference in mRNA expression between IL11Ra +/+ and IL11Ra -/- . Genes with a log odds score of greater than 3 (ie. adjusted p-value < 0.05) in both replicates were considered to be significantly up- or down-regulated in IL11Ra -/- uterus compared to wild type. Real-time RT-PCR Total RNA samples extracted from IL11Ra +/+ (n = 2) and IL11Ra -/- (n = 2) uterus at 48 h of decidualization and used for microarray analysis, and an additional two samples per genotype prepared in the same way were used for validation of the microarray data by real-time reverse transcription polymerase chain reaction (RT-PCR). All samples (n = 4/genotype) were further purified through an RNeasy Spin Column (Qiagen, Valencia, CA), and quantitated by RiboGreen Assay (Molecular Probes, Eugene, OR) according to the manufacturer's instructions, prior to triplicate reverse transcription reactions. Total RNA (1 μg) was reverse transcribed at 46 C for 1.5 h in 20 μl reaction mixture using 100 ng random hexanucleotide primers and 6 IU AMV reverse transcriptase (Roche, Castle Hill, Australia) in the presence of cDNA synthesis buffer (Roche), 1 mM dNTPs (Roche), 10 mM dithiothreitol (Roche) and 10 IU ribonuclease inhibitor (RNasin; Promega, Annandale, Australia). The resulting cDNA mixtures were heated at 95 C for 5 min before storage at -20 C in small volumes to avoid freeze-thawing. Negative controls were performed by omission of reverse transcriptase. For real-time quantification of selected mRNA transcript levels in IL11Ra +/+ and IL11Ra -/- uterus at 48 h of decidualization, PCR was carried out using a Roche LightCycler. Prior to LightCycler analysis, standard cDNA for each gene of interest was generated using a PCR Express block cycler (Thermo Hybaid Instruments, Franklin, MA). A 1 μl aliquot of RT product was amplified in a total volume of 40 μl using 4 μl of 10 × PCR Reaction Buffer (100 mM Tris-HCl, 15 mM MgCl 2 , 500 mM KCl, pH 8.3; Roche), 62.5 μM dNTPs (Gibco-BRL, Life Technologies), 10 pmol sense and antisense primers (Sigma Genosys, Castle Hill, NSW, Australia; Table 1 ) and 2.5 IU Taq DNA polymerase (Roche). The PCR amplification consisted of a hot start at 95 C for 5 min followed by 35 – 40 cycles of denaturation at 94 C for 50 sec, annealing at x C (see annealing temperature in Table 2 ) for 40 sec and extension at 72 C for 40 sec. The final extension was performed at 72 C for 10 min. Optimal annealing temperature (x) and cycle number were determined for each primer pair (Table 2 ). PCR products were electrophoresed on a 1.5% agarose gel containing 200 ng/ml ethidium bromide. Each single amplified product band was excised from the gel and purified using the UltraClean GelSpin DNA Extraction Kit (Mo Bio Laboratories, Solana Beach, CA). The cDNA concentration was measured using a spectrophotometer and each sequence identity confirmed at the Wellcome Trust Sequencing Centre, Monash Medical Centre. Standard curves for each transcript were generated using serial 1:10 dilutions of this standard cDNA using sterile water. Samples were diluted 1:10 – 1:40 prior to LightCycler analysis. Absolute concentrations of mRNA present in IL11Ra +/+ and IL11Ra -/- uterus at 48 h of decidualization were calculated relative to the standard curve, and adjusted for 18S rRNA expression levels. Table 1 Oligonucleotide primers used in real-time RT-PCR. Primer pairs previously published – COL3A1 [82], BGN [83], SPARC [84] and NID1 [85]. Target Sense primer sequence Antisense primer sequence COL3A1 5'-GGCTCCTGGTGAGCGAGGACG-3' 5'-CCCATTTGCACCAGGTTCTCC-3' BGN 5'-CGGGACCTTGCTGTCTTCTC-3' 5'-CCCGGCAAGAACCTGAAAG-3' SPARC 5'-AGAAGGCCTTTAGCCCTCTGC-3' 5'-ACTTTGCGGATACGGTTGTC-3' NID1 5'-AGCTTCTATGATCGTACGGACATCAC-3' 5'-GTAAAGAACTGTAGACCATCTTCAGG-3' 18S 5'-CGGCTACCACATCCAAGGAA-3' 5'-GCTGGAATTACCGCGGCT-3' Table 2 Real-time RT-PCR amplification conditions for each primer pair Primer pair PCR product size (bp) Annealing temp (C) No. of cycles [MgCl 2 ] (mmol/l) Melting temp of PCR product (C) COL3A1 522 64 40 3 91.2 BGN 131 58 40 5 86.3 SPARC 130 60 40 3 85.6 NID1 425 63 40 4 88.8 18S 187 60 35 4 86.8 The cDNA template (triplicate RT reactions for each of 4 animals per genotype; 4 μl) was added to sterile capillaries to a total volume of 20 μl containing SYBR Green I, dNTPs, Taq DNA polymerase and reaction buffer (LightCycler FastStart DNA Master SYBR Green I Kit; Roche), supplemented with 5 pmol of specific sense and antisense primers (Table 1 ) and an optimal concentration of MgCl 2 (Table 2 ). An initial denaturing step was performed for 10 min at 95 C, prior to 35 – 40 cycles of 95 C for 15 sec, x C for 5 sec and 72 C for 10 sec. Fluorescence was monitored continuously during cycling at the end of each elongation phase. At the end of each program, melting curve analysis confirmed the specificity of the reaction products. Statistical analysis of real-time RT-PCR data Triplicate RT reactions for each sample, the standard curve and a no RT negative control were analyzed in the same run, and each run was repeated once. A mean value for the initial target concentration of each sample was calculated using the fit points function of the LightCycler software. The mean concentration of 18S rRNA for each sample was used to control for RNA input, as it is considered a stable housekeeping gene and was not altered in expression between IL11Ra +/+ and IL11Ra -/- uterus. Following normalization, levels of RNA for COL3A1, BGN, SPARC and NID1 in IL11Ra -/- compared to wild type were statistically analysed using the paired t-test function of GraphPad Prism 3.0 (GraphPad Software, San Diego, CA). A two-tailed p value of less than 0.05 was considered a significant difference. Immunohistochemistry To confirm differential expression of collagen III, biglycan, SPARC and nidogen-1 at the protein level, immunohistochemistry was carried out on transverse sections of IL11Ra +/+ and IL11Ra -/- uterus collected at 48 h of decidualization, using specific antibodies (n = 5 mice per genotype, n = 3 sections per mouse). The morphology and cellular localization of artificial decidualization in IL11Ra +/+ and IL11Ra -/- uterus was also determined by immunostaining for the decidual marker desmin [ 32 ]. Primary antibodies used were rabbit anti-mouse collagen type III (Abcam #ab7778, Cambridge, UK) at 5 μg/ml, rabbit anti-mouse biglycan (LF-159 [ 33 ], gift from Dr. Larry Fisher, Matrix Biochemistry Unit, National Institutes of Health, Bethesda, MD) at 1:1000 dilution of whole serum, goat anti-mouse SPARC (Santa Cruz Biotechnology #sc13326, Santa Cruz, CA) at 5 μg/ml, rat anti-mouse entactin/nidogen-1 (Lab Vision-NeoMarkers #RT797P, Fremont, CA) at 15 μg/ml and goat anti-mouse desmin (Santa Cruz) at 200 μg/ml. Secondary antibodies were biotinylated swine anti-rabbit immunoglobulin G (IgG, DAKO, Glostrup, Denmark; 1:200), horse anti-goat IgG (Vector Laboratories, Burlingame, CA; 1:100) and rabbit anti-rat IgG (DAKO; 1:200). For collagen III, SPARC, nidogen-1 and desmin, negatives were performed using a matching concentration of non-immune IgG of the species in which the primary antibody was raised; rabbit IgG (DAKO), goat IgG (R&D Systems, Minneapolis, MN) or rat IgG (DAKO). For biglycan, negatives were performed using a matching dilution of normal rabbit serum (Sigma). Mouse lung (collagen III and biglycan) and kidney (nidogen-1 and SPARC) were used as positive control tissues, and a section from a single block was included in each staining run for quality control. For collagen III and SPARC immunolocalization, all dilutions and washes were carried out in high salt Tris-buffered saline (HS-TBS, 300 mM NaCl, 5 mM TrisHCl) with 0.6 % Tween 20. TBS (150 mM NaCl, 5 mM TrisHCl) with 0.6% Tween was used for nidogen-1, TBS/0.3% Tween for biglycan and HS-TBS/0.1% Tween for desmin. Five micron sections of Carnoy's-fixed (for collagen III and biglycan immunolocalization) or formalin-fixed (for SPARC, nidogen-1 and desmin) uterus were mounted on poly-L-lysine-coated glass slides, deparaffinized and rehydrated through a series of graded ethanols. In an humidified chamber at 25 C, sections were incubated for 10 min in 3% hydrogen peroxide to block endogenous peroxidase activity, then for 30 min (SPARC and desmin) or 1 h (collagen III, biglycan and nidogen-1) in 10% serum of the species in which the secondary antibody was raised (swine, horse or rabbit; Sigma) and 2% mouse serum in one of the above buffers. Sections were then incubated with primary antibody or negative at 4 C overnight (collagen III, biglycan, nidogen-1 and SPARC) or 30 min at 25 C (desmin), then washed in TBS/Tween prior to secondary antibody incubation for 30 min (SPARC and desmin) or 1 h (collagen III, biglycan and nidogen-1) at 25 C. Sections were again washed in TBS/Tween, then the secondary antibody detected using the Vectastain ABC Elite/HRP Kit (collagen III and nidogen-1; Vector Laboratories) or the StreptABComplex/HRP Kit (biglycan, SPARC and desmin; DAKO) according to the manufacturer's instructions. Protein localization was visualized using the Liquid DAB-Plus Substrate Chromogen System (DAKO), with Harris hematoxylin (Sigma) counterstain. Results Artificial decidualization of IL-11Rα deficient and wild type uterus Following the injection of oil into the wild type pseudopregnant uterus, a progressive increase in uterine weight was observed from 0 through to 48 h, reaching statistical significance ( p < 0.01) at the final time point (Fig. 1 ). In contrast, the weight of the artificially decidualized IL11Ra -/- uterus did not change significantly across consecutive time points. There was therefore a statistically significant difference ( p < 0.01) in uterine weight at 48 h of artificial decidualization between IL11Ra +/+ (96.9 +/- 9.8 mg) and IL11Ra -/- (30.4 +/- 7.7 mg). Figure 1 Uterine weight following artificial decidualization. Weight (mg +/- SEM) of uterine horns at times following artificial decidualization of IL11Ra +/+ (open bars) and IL11Ra -/- (shaded bars) littermates. ** p < 0.01. Differential gene expression following artificial decidualization Total RNA extracted from IL11Ra +/+ and IL11Ra -/- uterus artificially decidualized for 0, 18, 24 or 48 h (n = 2/genotype/time point) was used as a template for the hybridization of NIA 15K cDNA microarrays. Figure 2 shows the volcano style plots of the normalized data for all genes at each time point. Each plot summarizes the data for a series of 4 microarrays (two KO/REF and two WT/REF), with differentially expressed genes in each replicate represented by open circles above the horizontal line ( p = 0.05). At 0 h, prior to application of the decidualizing stimulus on day 3 of pseudopregnancy, there were no reproducible differentially expressed genes between IL11Ra +/+ and IL11Ra -/- (Fig. 2A,2B ). Following 18 h of decidualization (Fig 2C,2D ; Table 3 ), five expressed sequence tags (ESTs) were consistently upregulated 2 – 3-fold in IL11Ra -/- uterus compared to wild type. At 24 h of decidualization (Fig. 2E,2F ; Table 4 ), there was one EST upregulated 2.7-fold. Sequence information for these ESTs is available online [ 34 ], using the AGRF ID as a unique identifier. None of these ESTs are currently recognized as sharing strong homology to any known genes. At 48 h of decidualization, 13 cDNAs showed upregulation and 4 downregulation in IL-11Rα deficient uterus (Fig. 2G,2H ; Table 5 ). A number of these genes have previously described roles in the endometrium, but prior to this study, none have been shown to interact with IL-11. The ECM genes COL3A1, SPARC, BGN and NID1 were among those upregulated in IL11Ra -/- uterus compared to wild type. Transcripts representing COL3A1 and SPARC were present at two different locations on the array, and in each case, both sets of duplicate spots showed consistent upregulation in the absence of IL-11Rα. There were no genes or ESTs which were differentially expressed at more than one time point. Figure 2 Gene expression in IL11Ra +/+ and IL11Ra -/- uterus following artificial decidualization. Expression profiling of 15K genes between IL11Ra +/+ and IL11Ra -/- at 0 h (A, B), 18 h (C, D), 24 h (E, F) and 48 h (G, H) following the artificial induction of decidualization. Each volcano style plot shows the normalized log ratio (effect estimate) of IL11Ra -/- compared to wild type for each gene from a series of 4 microarrays, plotted against the log odds of differential expression. A, C, E, G represent the first replicates, and B, D, F, H the second dye-swapped replicates. Genes with log odds of differential expression greater than 3 (ie. adjusted p -value < 0.05, above horizontal line) are represented by open circles, and COL3A1, BGN, SPARC and NID1 are labeled in G and H. Only those genes with log odds of differential expression greater than 3 in both replicates were considered differentially expressed, as described in Methods . Table 3 Differentially expressed genes in IL11Ra -/- uterus compared to wild type at 18 h of decidualization AGRF ID GenBank Accession UniGene Cluster Gene Fold Change P Value H3084H06 BG070211 Mm.25335 EST: Cdc14b: CDC14 cell division cycle 14 homolog B (S. cerevisiae) + 3.1 0.044 H3137G08 BG074662 Mm.197274 EST: Moderately similar to GNMSLL retrovirus-related reverse transcriptase homolog – mouse retrotransposon (M. musculus) + 3.0 0.046 H3123E03 BG073512 Mm.197252 EST: Weakly similar to TLM MOUSE TLM PROTEIN (M. musculus) + 2.9 0.047 H3073G12 BG069200 Mm.328026 EST: Transcribed sequence with moderate similarity to protein ref:NP_083358.1 (M. musculus); RIKEN cDNA 5830411J07 + 2.5 0.038 H3082G05 AU014577 Not assigned EST + 2.1 0.032 Table 4 Differentially expressed genes in IL11Ra -/- uterus compared to wild type at 24 h of decidualization AGRF ID GenBank Accession Unigene Cluster Gene Fold Change P Value H3091F10 BG064439 Mm.182580 EST: Transcribed sequence with weak similarity to protein ref:NP_081764.1 (M. musculus); RIKEN cDNA 5730493B19 + 2.7 0.027 Table 5 Differentially expressed genes in IL11Ra -/- uterus compared to wild type at 48 h of decidualization AGRF ID GenBank Accession Unigene Cluster Gene Fold Change P Value H3012B10 BG063737 Mm.28870 45S pre rRNA gene + 4.2 0.002 H3133G03 BG074327 Mm.147387 Procollagen III alpha-1 (COL3A1) + 3.0 0.010 H3124H10 BG073709 Mm.147387 Procollagen III alpha-1 (COL3A1) + 2.7 0.011 H3116A04 BG072874 Mm.35439 Secreted acidic cysteine rich glycoprotein (SPARC/osteonectin/BM-40) + 2.4 0.010 H3152F04 BG075853 Mm.22699 Selenoprotein P plasma 1 (SEPP1) + 2.3 0.004 H3023A11 BG064718 Mm.21228 EST: RIKEN cDNA 2610101J03 gene; expressed sequence C79684 + 2.2 0.038 H3024A05 BG064802 Mm.35439 Secreted acidic cysteine rich glycoprotein (SPARC/osteonectin/BM-40) + 2.2 0.027 H3128D02 BG073888 Mm.77432 Thioredoxin interacting factor (VDUP1) + 2.2 0.003 Hs.ALAD BC000977 Hs.1227 Aminolevulinate delta dehydratase (ALAD) + 2.0 0.028 H3127D03 BG073809 Mm.2608 Biglycan (BGN/PGI) + 1.8 0.010 H3025E04 BG064933 Mm.4691 Nidogen-1 (NID1/entactin) + 1.8 0.007 H3129D02 BG073972 Mm.2137 Transcriptional regulator SIN3 yeast homolog B + 1.7 0.014 H3078E09 BG069642 Mm.27816 Hexosaminidase B (HEXB) + 1.7 0.013 H3101C10 BG071626 Mm.161419 Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) - 1.6 0.015 H3122C04 BG073406 Mm.68999 EST: RIKEN cDNA 9430015G10 gene - 1.6 0.036 H3116E07 BG072919 Mm.172198 EST: Weakly similar to GNMSLL retrovirus-related reverse transcriptase homolog – mouse retrotransposon (M. musculus) - 2.0 0.032 H3144C05 BG075183 Mm.337732 EST - 2.1 0.014 Validation of gene expression by real-time RT-PCR To confirm the altered mRNA expression of the ECM genes COL3A1, SPARC, BGN and NID1 at 48 h of decidualization, quantitative real-time RT-PCR was carried out using the same RNA samples used in the microarray analysis, plus two additional RNA samples of each genotype, collected in the same way. At a significance level of p < 0.05, there was no statistical difference in the abundance of 18S rRNA (data not shown), COL3A1 (Fig. 3A ), BGN (Fig. 3B ), SPARC (Fig. 3C ) or NID1 (Fig. 3D ) mRNA between IL11Ra +/+ and IL11Ra -/- uterus. When only the samples used in the microarray analysis were considered, the difference in NID1 abundance between IL11Ra +/+ (1.13 +/- 0.12 fg/μl) and IL11Ra -/- (1.66 +/- 0.09 fg/μl) uterus approached statistical significance at p = 0.0708. Figure 3 Quantitative real-time RT-PCR for extracellular matrix components. Quantitative real-time RT-PCR for (A) COL3A1, (B) BGN, (C) SPARC and (D) NID1. Circled data points indicate samples used in the cDNA microarray analysis, and horizontal lines the mean of each genotype. Absolute values for mRNA abundance were normalized to that of 18S rRNA. Validation of gene expression by immunohistochemistry Four genes found to be differentially expressed in IL11Ra -/- uterus compared to wild type at 48 h of decidualization were investigated at the protein level by immunohistochemistry using specific antibodies. Decidualizing and fully decidualized cells were identified in adjacent sections by immunostaining for the intermediate filament protein desmin, well characterized as a marker for decidual transformation [ 32 ]. Microarray data showing highly significant and reproducible increases in COL3A1 and BGN mRNA levels in IL11Ra -/- uterus were reflected in increased staining intensity for collagen III (Fig. 4A,4B,4C,4D ) and biglycan (Fig. 4E,4F,4G,4H ) in IL11Ra -/- uterus (Fig. 4B,4D,4F,4H ) compared to wild type (Fig. 4A,4C,4E,4G ). In both IL11Ra -/- and wild type uterus, collagen III and biglycan were primarily localized to the outer connective tissue and smooth muscle cells of the myometrium, with diffuse staining in the cytoplasm of decidualized stromal cells (Fig. 4D,4H inserts). Interstitial compartments underlying luminal and glandular epithelium and surrounding blood vessels also showed strong immunoreactivity for both proteins, while the epithelial cells were negative. In the absence of IL-11Rα, stronger staining for collagen III was particularly evident underlying luminal epithelium and in the ECM surrounding decidualizing stromal cells. There was a consistent absence of subluminal collagen III staining on the antimesometrial side of the uterus in wild type animals, an effect not seen in IL11Ra -/- littermates (Fig. 4C,4D ). There was also a clear difference in the localization of biglycan staining underlying luminal epithelium, with strong staining at the mesometrial pole of the uterus in wild type animals and no preferential localization to either pole in IL11Ra -/- animals (Fig 4E,4F,4G ). Biglycan staining surrounding glands was much more intense in IL11Ra -/- uterus (Fig. 4H ) compared to wild type (Fig. 4G insert). Figure 4 Immunohistochemistry for extracellular matrix components. Immunohistochemical staining of wild type (A, C, E, G, I, K, M, O, P, Q, S) and IL11Ra -/- (B, D, F, H, J, L, N, R, T) uterus at 48 h of decidualization using specific antibodies for collagen III (A, B, C, D), biglycan (E, F, G, H), nidogen-1 (I, J, K, L), SPARC (M, N, O, P) and desmin (Q, R, S, T). Negative controls using a matching concentration of non-immune IgG (collagen III, nidogen-1, SPARC and desmin) or normal serum (biglycan) in place of the primary antibody are inset in A, B, E, F, I, J, M, N, Q and R. Black squares on A and B indicate the antimesometrial pole magnified in C and D. Abbreviations: connective tissue (ct), myometrium (my), mesometrial pole (m), antimesometrial pole (am), luminal epithelium (le), glandular epithelium (ge), decidualized stromal cell (dsc), non-decidualized stromal cell (sc), blood vessel (bv), glycocalyx (gly). Scale bar = 50 μm (A, B, E, F, I, J, M, N, Q and R are at the same magnification; C, D, G and P are at the same magnification; H, K, L, O, S, T and inset in G are at the same magnification). While no detectable differences were observed in the overall intensity of immunostaining for nidogen-1 (Fig. 4I,4J,4K,4L ) or SPARC (Fig. 4M,4N,4O,4P ) in IL11Ra -/- uterus compared to wild type, the localization of these proteins has not previously been described in the deciduoma of wild type or IL11Ra -/- mice. In both genotypes, nidogen-1 was localized to the cytoplasm of decidual cells (Fig. 4K ) and glandular epithelial cells (Fig. 4L ), the basement membrane underlying luminal and glandular epithelium and surrounding blood vessels (Fig. 4L ). The cellular localization of SPARC was more similar to that of collagen III and biglycan, with strong staining in the outer connective tissue and myometrium (Fig. 4M,4N ). Strong SPARC staining was also detected in the cytoplasm of decidualized and non-decidualized stromal cells (Fig. 4O ), endothelial cells (Fig. 4P ), and at the glycocalyx of luminal and glandular epithelium (Fig. 4P ). Desmin immunostaining revealed a reduction in the overall extent of decidualization in IL11Ra -/- uteri at 48 h following the induction of deciduomata ( IL11Ra +/+ : Fig. 4Q,4S ; IL11Ra -/- : Fig. 4R,4T ), with an absence of secondary decidualization. Desmin-positive decidual cells were detected in all artificially decidualized uteri, indicating that the surgical induction of decidualization was successful in all cases. Discussion Interleukin-11 is one of only a few molecules known to be critical for decidualization in mice. This study has demonstrated for the first time that IL-11 regulates changes in the uterine extracellular matrix that are necessary for decidualization. The application of cDNA microarray analysis has revealed that lack of IL-11 signalling in IL11Ra -/- mice results in differences in mRNA expression compared to wild type during artificial decidualization. Each of the ECM molecules investigated further in this study, collagen III, biglycan, nidogen-1 and SPARC, showed protein expression patterns consistent with a role in decidualization, with immunostaining in endometrial stromal cells and their surrounding matrices. Collagen III and biglycan were more abundant during defective decidualization in IL11Ra -/- uterus, both at the mRNA and protein levels. This indicates that the cellular processes of decidualization including proliferation, differentiation, signal transduction and apoptosis may be facilitated by decreased expression of these matrix molecules. As well as providing a dynamic structural framework, the ECM of the endometrium interacts with its associated cells to mediate critical processes, including adhesion, migration and differentiation (reviewed in [ 35 ]). Growth factor availability can also be regulated by binding to ECM components [ 36 ]. Collagens, elastin, structural glycoproteins (eg. fibronectin, laminin, SPARC and nidogen), proteoglycans (eg. biglycan) and glycosaminoglycans are the major components of endometrial matrix, and can act as ligands for both cell-cell and cell-matrix interactions [ 37 ]. Decidualization of endometrial stromal cells is associated with dramatic changes in matrix composition, including the phagocytosis and digestion of collagen fibrils, an increase in collagen fibril diameter [ 38 ], deposition of basement membrane proteins [ 39 ], the synthesis and secretion of sulfated glycosaminoglycans into the extracellular space and a decrease in elastic fibrils surrounding mature decidual cells [ 1 ]. Disruptions to the composition of uterine ECM during decidualization may be responsible for the failure of implantation in IL11Ra -/- mice. While the microarray data showed consistent, reproducible upregulation of COL3A1, BGN, SPARC and NID1 in IL11Ra -/- compared to wild type uterus, this effect was not statistically significant when real-time RT-PCR was used as an alternative quantitation method. Many factors may contribute to discrepancies between cDNA microarray and real-time RT-PCR data. There are major differences in the approach to mRNA quantitation used by the two techniques. Using cDNA microarray, the mean fluorescence intensity ratio for each gene in IL11Ra -/- or IL11Ra +/+ uterus was calculated relative to a reference pool, and the ratio of IL11Ra -/- to IL11Ra +/+ determined by the use of computational algorithms. When quantitating the same mRNA species by real-time RT-PCR, a standard curve of known concentration was used to infer the absolute abundances of mRNA in the IL11Ra -/- and IL11Ra +/+ samples, which were then normalized for RNA input. Real-time RT-PCR was chosen for cDNA microarray validation in this study because it has higher sensitivity and lower RNA requirements than Northern blot, but the lack of agreement between the two methods is not unusual. It is well recognized that fold change values for a given gene may vary widely, even between two different microarray techniques [ 40 - 43 ]. In using real-time RT-PCR to evaluate microarray data, Rajeevan et al [ 44 ] found that the majority of the array data were qualitatively accurate, but it was not possible to consistently validate genes showing less than a 4-fold difference on the array. Each of the genes examined in this study showed less than a 3-fold difference. It is not known how well array data correlates overall with data from RT-PCR or any other mRNA quantitation method [ 45 ], further complicating the interpretation of conflicting results. There are a number of compelling arguments both for and against conducting corroborative studies for microarray data, and there is good evidence that the data is highly reliable when the experimental design and statistical analysis is sound [ 46 ]. In assessing the validity of the microarray data in this study, it is important to note that immunostaining for both collagen III and biglycan protein confirmed the differential expression seen by microarray analysis. This is striking, given that changes in protein expression detected by tissue microarray have been found to correlate with the mRNA change less than 50% of the time [ 45 ]. Given the cellular heterogeneity of the uterus, the localization of cell-specific expression is essential in extending microarray data on whole uterus to the investigation of decidualization. Neither SPARC nor nidogen-1 proteins were altered in expression by the absence of IL-11 signaling, but there may well be a delay between the mRNA and corresponding protein changes. This would not have been detected by using samples collected at the same time point for both mRNA and protein analyses. For each of the genes examined, upregulation during defective decidualization in IL11Ra -/- uterus is supported by existing data in the literature. Collagen III is a fibrillar collagen with known roles in differentiation and migration [ 47 ], and together with collagen I forms the structural support for the endometrium during the establishment of pregnancy [ 48 ]. Consistent with a role in decidualization, collagen III is both secreted and phagocytosed by mouse decidual cells [ 49 ], and secreted by first trimester decidual cells in the human [ 50 ]. The reduced number of collagen fibrils surrounding endometrial stromal cells in early pregnancy [ 51 ] may facilitate decidual transformation and blood vessel development [ 52 ]. This decrease has previously been detected primarily in the subepithelial endometrial stroma at day 4 [ 53 ]. In the rat, low levels of collagen III have been reported in the primary decidual zone, with much higher concentrations in the outer stroma and myometrium as decidualization progresses [ 54 ]. This is consistent with immunohistochemical data obtained for wild type mice in this study, showing very low intensity staining in the antimesometrial decidua, and higher intensity in the outer compartments of the uterus. During the human menstrual cycle, collagen III immunostaining is higher in the proliferative compared to the secretory phase [ 55 ], indicating that downregulation and/or metabolism and redistribution of collagen III occurs with the onset of endometrial receptivity. Compared to proliferative phase endometrium, the ratio of collagen III to collagen I is decreased in decidual cells [ 56 ]. Aplin et al [ 55 ] observed changes in collagen III distribution from dense fibrils in the proliferative phase to matrix channels between decidual cells in the secretory phase. This may be involved in maintaining tissue integrity as the level of hydration increases, and in supporting movement of leukocytes through the tissue [ 57 ]. Defects in any of these processes in IL11Ra -/- mice could contribute to impaired decidualization. Using microarray analysis, the mRNA encoding procollagen III α1 (COL3A1) has been previously shown to decrease in abundance in the mouse uterus at estrus [ 58 ], and between days 3.5 and 5.0 of gestation [ 59 ], and to increase following ovariectomy in the rat [ 60 ]. Together with data from this study showing increased COL3A1 mRNA and mature collagen III protein in IL11Ra -/- uterus at 48 h of decidualization, it appears that successful decidualization involves downregulation of COL3A1 transcription. Biglycan is a small leucine-rich proteoglycan, which binds to type I [ 61 ] and type V collagen fibrils, transforming growth factor-β [ 62 ] and tumour necrosis factor-α [ 63 ] in vitro. Its function in ECM has not been well defined, but biglycan is thought to be involved in the control of cell migration [ 64 ]. In the wild type mouse uterus, there is low endometrial biglycan expression post-implantation [ 65 ]. Biglycan mRNA expression has been shown by oligonucleotide microarray to be downregulated in the secretory compared to the proliferative phase of the menstrual cycle in human endometrium [ 66 , 67 ], coincident with the window of implantation. As defective decidualization in IL11Ra -/- mouse uterus was associated with the upregulation of biglycan mRNA, the activity of this proteoglycan in the ECM may inhibit the decidual response. Decidual cells are known to express nidogen-1 as part of the pericellular basement-membrane laid down during decidualization [ 39 ]. The main function of nidogen in the basement-membrane is to connect networks of collagen IV and laminin [ 68 ], but nidogen also binds perlecan [ 69 ], fibulins [ 70 ] and fibronectin [ 71 ]. Changes in nidogen mRNA levels have been reported during the establishment of the placenta in the mouse, with in situ hybridization revealing highly restricted expression in decidual and maternal endothelial cells [ 72 ]. This study has now shown much earlier nidogen-1 protein expression in the decidual cells, glandular epithelial cells and epithelial basement membrane of the artificially induced deciduoma, and indicated aberrant increased expression of the NID1 gene during defective decidualization. SPARC (osteonectin/BM-40) is described as a matricellular glycoprotein, in that it binds to both cells and ECM to regulate cell-matrix interactions [ 73 ]. Like other matricellular proteins, SPARC can bind and alter the activity of cytokines and induce the expression of proteinases and their inhibitors [ 74 ]. SPARC is often expressed in tissues undergoing cell proliferation, migration and ECM remodeling [ 75 ], so it is not surprising that substantial expression of SPARC has been observed in human decidua [ 76 ]. Differences in immunostaining intensity have been associated with the degree of decidualization, with the strongest staining seen in the cytoplasm of decidualizing cells, decreasing as decidualization progresses [ 76 ]. Fully decidualized cells were found to express SPARC pericellularly, indicating a role in mediating interactions of decidual cells with their surrounding matrix. Binding of SPARC to a number of ECM components, including collagen III and nidogen, may contribute to the structural integrity of the tissue [ 77 ]. It could therefore be hypothesized that during normal decidualization, SPARC, collagen III and nidogen-1 are coordinately downregulated to allow loosening of the tissue in preparation for trophoblast invasion. In both models of IL-11Rα deficiency [ 7 , 8 ], implantation sites have increased rather than decreased numbers of invading trophoblast giant cells. This pathological invasion is thought to occur subsequent to failure of decidualization, highlighting the importance of tight regulation of ECM components in normal decidual function. Using mRNA and protein expression studies alone, it is not possible to determine whether IL-11 affects ECM molecule expression directly or indirectly. The presence of STAT binding sites in the promoters for COL3A1, BGN, NID1 and SPARC would confirm that a direct interaction is possible, but would not establish that the interaction is occurring in the uterus during decidualization. Given that IL-11 is a secreted cytokine with autocrine and paracrine activity, a direct effect on ECM molecule transcription would be dependent on coincident expression patterns of the ECM molecules, IL-11 and its receptors. Maximal expression of IL-11 and IL-11Rα mRNA has been reported in the predecidual and decidual cells of the mouse implantation site by in situ hybridization [ 7 ]. Expression of gp130 mRNA is detectable in the glandular epithelium from days 3 – 7, and in decidual cells from day 5 [ 78 ]. Given that the ECM molecules investigated in this study are more widely expressed in the uterus, it is likely that regulation by IL-11 is indirect. Indirect effects of IL-11 on ECM composition could be mediated by matrix metalloproteinases (MMPs) and/or their inhibitors. Despite their presence on the NIA 15K microarray, differential expression was not observed for MMP-2, MMP-9, TIMP-2, or TIMP-3 in the IL11Ra -/- uterus compared to wild type. Previous in vitro studies have indicated that IL-11 inhibits MMP-1 and -3 protein in human synovium [ 79 ], and enhances the ability of mouse osteoblasts to synthesize MMPs responsible for the degradation of collagen I [ 80 ]. IL-11 does not influence the activity of stromelysin in human chondrocytes [ 13 ], or induce MMP-2, -7 or -9 in human endometrial epithelial or stromal cells [ 81 ], but tissue inhibitor of metalloproteinases (TIMP)-1 is known to be induced by IL-11 in vitro [ 13 ]. While not directly supporting a role in ECM degradation, these interactions suggest that IL-11 is involved in regulating the balance between MMP and TIMP activity in the tissue. Conclusions This investigation of the downstream targets of IL-11 during mouse decidualization has uncovered previously unknown interactions between IL-11 and uterine ECM composition. Dysregulation of collagen III, biglycan, nidogen-1 and SPARC in the absence of IL-11 signaling at the time of decidualization may indicate essential functions for these molecules during the implantation process in mice. Functional studies using mouse and human endometrium may further clarify the mechanisms of IL-11 action on the ECM during this critical time in embryo implantation. By elucidating the role of IL-11 regulated genes in decidualization, future work may identify potential new targets for the manipulation of human fertility. Authors' contributions CAW carried out all the experimental work and statistical analysis and drafted the manuscript. LR provided the heterozygote IL11Ra +/- breeding pairs. LAS conceived of the study, participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC535545.xml |
544394 | Nicotine signals through muscle-type and neuronal nicotinic acetylcholine receptors in both human bronchial epithelial cells and airway fibroblasts | Background Non-neuronal cells, including those derived from lung, are reported to express nicotinic acetylcholine receptors (nAChR). We examined nAChR subunit expression in short-term cultures of human airway cells derived from a series of never smokers, ex-smokers, and active smokers. Methods and Results At the mRNA level, human bronchial epithelial (HBE) cells and airway fibroblasts expressed a range of nAChR subunits. In multiple cultures of both cell types, mRNA was detected for subunits that constitute functional muscle-type and neuronal-type pentomeric receptors. Two immortalized cell lines derived from HBE cells also expressed muscle-type and neuronal-type nAChR subunits. Airway fibroblasts expressed mRNA for three muscle-type subunits (α1, δ, and ε) significantly more often than HBE cells. Immunoblotting of HBE cell and airway fibroblast extracts confirmed that mRNA for many nAChR subunits is translated into detectable levels of protein, and evidence of glycosylation of nAChRs was observed. Some minor differences in nAChR expression were found based on smoking status in fibroblasts or HBE cells. Nicotine triggered calcium influx in the immortalized HBE cell line BEAS2B, which was blocked by α-bungarotoxin and to a lesser extent by hexamethonium. Activation of PKC and MAPK p38, but not MAPK p42/44, was observed in BEAS2B cells exposed to nicotine. In contrast, nicotine could activate p42/44 in airway fibroblasts within five minutes of exposure. Conclusions These results suggest that muscle-type and neuronal-type nAChRs are functional in airway fibroblasts and HBE cells, that prior tobacco exposure does not appear to be an important variable in nAChR expression, and that distinct signaling pathways are observed in response to nicotine. | Background Nicotine, the addictive component of tobacco smoke, signals through its family of receptors, the nicotinic acetylcholine receptors (nAChR). Acetylcholine is the endogenous ligand for these receptors, and has been found in many tissues outside of the nervous system. Non-neuronal nAChR have also been identified in tissues such as the skin, vasculature, and nasal mucosa [ 1 ]. nAChR are pentamers that form ion channels permeable to either calcium or sodium. There are several types of nAChR, which are defined by the subunit composition of the receptor. Receptors contain all α-subunits, a combination of α and β subunits, or α, β, ε/γ, and δ subunits [ 2 ]. Heteropentamers have been classified as either muscle nAChR, which were first identified at the neuromuscular junction, or as neuronal type, which were discovered in the central nervous system. Homopentamers were also discovered in the CNS and are also considered to be neuronal-type receptors. The adult muscle type receptor contains the α1/β1/ε/δ subunits, with the β1 subunit occurring twice to make the pentamer. The neuronal heteropentamers occur with several different specific subunits, but always with three α and two β subunits, numbered α2 through α6 and β2 through β4. The homopentamer that has been characterized most completely is the α7 pentamer, although recently others have been identified (α8, α9, and α10). The α9 and α10 subunits are unique in that they can form functional homopentamers or can combine together to form a heteropentamer without a β subunit. They are also different from the other subunit combinations examined because nicotine acts as a competitive antagonist to receptors containing the α9 subunit [ 3 ]. The ionic permeability of nAChR is dependent upon the subunit composition of the receptor, with some receptors showing preference to either calcium or sodium [ 4 ]. However, regardless of initial preference, stimulation of all nAChR by agonist are thought to lead to a calcium influx, either directly through the nAChR channel or due to a change in membrane potential that leads to the opening of calcium L-channels [ 4 ]. Extended exposure of nAChR to agonist can lead to receptor inactivation [ 5 ]. Again, the degree and severity of inactivation depends upon the subunit composition of the receptor [ 5 ]. The primary route of exposure to nicotine is through inhalation, either by active smokers or non-smokers exposed to environmental tobacco smoke. Through inhalation, the lung, in particular, would be exposed to pharmacological doses of nicotine. In addition, receptor inactivation is likely to occur in sensitive receptors, due to the extended length of time that smokers use tobacco [ 2 , 5 ]. This could lead to changes in receptor expression over time; in the brain, it has been noted that the type of nAChR expressed is different in smokers than in never smokers. Using radiolabeled agonist, we have shown that saturable nicotine binding sites exist in the lung [ 6 ]. Other previous studies of the airways exposed to nicotine have shown changes in expression of collagen [ 7 ]. In vitro data has indicated that airway epithelial cells release GM-CSF upon exposure to nicotine, and activate Akt, a signaling molecular important in cell survival [ 8 , 9 ]. However, although a number of different nAChR subunits are reportedly expressed at the mRNA in human airway cells, [ 9 - 12 ] airway tissue has not been examined for the presence of neuronal-type nAChR at the protein level or for the muscle-type nAChR at the mRNA or protein levels. It is also not known if particular nAChR are more likely to signal through particular downstream pathways when more than one receptor type is present, if the nAChR present in the airway changes after long-term exposure to nicotine, or if calcium influx is responsible for downstream signaling. In this study, a series of 37 short-term human bronchial epithelial cultures, 25 airway fibroblast cultures, and 2 immortalized bronchial epithelial cell lines were examined by RT-PCR for nAChR expression. We also examined protein expression by immunoblot to determine which subunits are most highly expressed and to determine if appropriate combinations are present at the protein level to form functional receptors. We determined that the nAChR present are functional by examining calcium influx after agonist exposure, and blockade by antagonists. We also show that exposure of airway cells to nicotine leads to activation of downstream signaling pathways. Finally, we examined the nAChR present in HBE and airway fibroblasts derived from smokers, ex-smokers, and never smokers to determine if alterations in nAChRs based on tobacco exposure can be detected. Methods Primary Airway Cell Culture HBE cells are cultured from airway biopsies using standard methodology in serum-free medium [ 13 ]. Briefly, biopsies are taken from the carina in an area that is normal by appearance by white light bronchoscopy and confirmed using LIFE bronchoscopy. Biopsies are teased apart with forceps and HBE cells are cultured in BEGM (Cambrex Biosciences, Walkersville, MD) for a maximum of two passages on collagen IV coated flasks. This medium is selective for bronchial epithelial cells and at the first passage, cultures are examined and contain 95% or greater epithelial cells. Human airway fibroblasts are derived from bronchial tissue that is minced with scalpels and cultured in DMEM with 10% bovine serum. HBE cells cannot be propagated in this medium. Biopsies were obtained during surgical thoracic resection or bronchoscopy procedures from normal areas of the upper airway. All tissue donors gave informed consent under an approved IRB protocol and answered questionnaires regarding tobacco exposure. Airway Cell lines BEAS2B were all purchased from ATCC and cultured according to ATCC instructions. IB3-1 cells were derived from a cystic fibrosis patient and cultured in Hams medium with 10% serum [ 14 ]. Supplies All chemicals used were from Sigma (St. Louis, MO) and all supplies were from either Fisher Scientific (Pittsburgh, PA) or PGC Scientific (Frederick, MD), unless otherwise indicated. RNA and Protein Isolation RNA is isolated from cultures using standard guanidinium thiocyanate method [ 15 ]. RNA is quantitated using the absorbance at 260 nM and purity is determined using the A260/A280 ratio. After RNA is isolated from the aqueous phase of the solution, protein is extracted from the organic phase, using the method recommended by Invitrogen for the isolation of protein from Trizol. The protein pellet is resuspended in 1% SDS with protease and phosphatase inhibitors added and stored at -80°C. Alternatively, if RNA was not taken from the sample, protein was analyzed from whole cell lysates. Airway cells were scraped into RIPA buffer (150 mM NaCl, 50 mM Tris, pH 7.4, 5 mM EDTA, 1% igepal, 0.5% SDS, 1% deoxycholate) with protease inhibitors 10 μg/ml PMSF, 30 μl/ml aprotinin, and phosphatase inhibitor 1 mM sodium orthovanadate. Lysates were store at -80°C. All protein was quantitated using the BCA assay (Pierce, Rockford IL). RT-PCR of nAChR Primers were developed to unique regions of each subunit and were tested on human muscle and brain RNA purchased from Clontech (Palo Alto, CA). Optimized protocols were then used on RNA from airway cells. All primers span introns and do not amplify DNA. GAPDH or actin is always used as a positive control for RNA integrity. Oligo dT 12–18 (Invitrogen) was annealed to 1 μg total RNA and reverse transcribed with Superscript II (Invitrogen). The reaction contained 1 μg RNA, 500 ng Oligo dT 12–18 , 50 mM Tris-HCl, pH 8.3, 75 mM KCl, 3 mM MgCl2, 10 mM DTT, 1 mM each dNTP, 200 U superscript. Briefly, total RNA was incubated with oligo dT 12–18 at 70°C for 10 min. The cDNA produced was then used as a template for PCR using specific primers. Table 1 indicates the primer used and optimized conditions for each subunit. PCR amplification was performed in a 20 μl reaction containing 2 μl of the RT reaction, Taq DNA polymerase (Perkin Elmer), 1X PCR buffer, 1.5 mM MgCl2, 1 mM each dNTP and 1 μM primer. PCR was carried out in a Perkin-Elmer 9700 Thermocycler with 2 min, 95°C denaturation, followed by 30 cycles of 94°C for 30 s, 55°-62°C (see table 1 ) for 30 s and 72°C for 30 s. Final extension was at 72°C for 5 min. 10 μl of each reaction was run on a 1% TBE gel for analysis. β2 and δ subunits were detected using nested PCR. Primary PCR reactions were carried out as described above. 2 μl of the primary reaction was used as the template for the secondary PCR reaction/second round PCR. Thirty rounds of PCR were carried out at the temperatures listed in Table 1 . Table 1 RT-PCR Primers and Conditions Primer Optimal Annealing Temperature Product Size Sequence alpha 1* 55 580/505 CGT TCT GGT GGC AAA GCT CCG CTC TCC ATG AAG TT alpha 2* 55 466 CCG GTG GCT TCT GAT GA CAG ATC ATT CCA GCT AGG alpha 3 58 464 CTG GTG AAG GTG GAT GAA GT CTC GCA GCA GTT GTA CTT GA alpha 4 58 444 GGA TGA GAA GAA CCA GAT GA CTC GTA CTT CCT GGT GTT GT alpha 5* 55 525 GAT AAT GCA GAT GGA CGT TGA TGG TAT GAT CTC TTC alpha 6 58 372 GTG GCC TCT GGA CAA GAC AA CCT GCA GTT CCA AAT ACA CA alpha 7 58 375 GGA GCT GGT CAA GAA CTA CA CAG CGT ACA TCG ATG TAG CA beta 1 58 479 CTA CGA CAG CTC GGA GGT CA GCA GGT TGA GAA CCA CGA CA beta 2 62 453 CAA TGG CTC TGA GCT GGT GA CCA CTA GGT GTG AAG TCG TCC A 420 GGC TCT GAG CTG GTG ACA GTA CAC CTC ACT CTT CAG CAC CA beta 3 62 439 TGGAGA GTA CCT GCT GTT CA CGA GCC TGT TAC TGA CAC TA beta 4 58 524 GTG AAT GAG CGA GAG CAG AT GGG ATG ATG AGG TTG ATG GT delta 58 471 CAG ATC TCC TAC TCC TGC AA CCA CTG ATG TCT TCT CAC CA 426 CAA CGT GCT TGT CTA CCA CTA C GGT AGG TAG AAG ACC AGG TTG A gamma 546 CGC CTG CTC TAT CTC AGT CA GGA GAC ATT GAG CAC AAC CA epsilon 55 432 GTA ACC CTG ACG AAT CTC AT GTC GAT GTC GAT CTT GTT GA * From Maus et al. [12] Unique primers to each nAChR subunit were used in PCR reactions. All results were sequenced and compared to the known subunit sequences to confirm that the correct subunit was being amplified. Immunoblotting of nAChR 50 μg protein with loading buffer was denatured using 50 mM DTT and heated at 80°C for 15 min. Protein was loaded onto 10% Bis-Tris gels (Invitrogen). Brain lysate (US Biologicals) and lysates from myotubes cultures (gift, Z.Z. Wang, U. Pittsburgh) were used for positive control. Protein was transferred to PVDF (Biorad, Hercules, CA) or Multiblots (ISC Bioexpress, Kaysville, UT). PVDF membranes were blocked for 1 hr with 5% blocker (Biorad) in TBS-T (2.7 mM KCl, 138 mM NaCl, 20 mM Trizma, pH 7.4, 0.1% Tween-20 (Biorad)). Primary antibody was diluted in carrier protein, 5% blocker for PVDF or 0.5% casein (Pierce) for Multiblots and incubated at 4°C overnight, see Table 2 for details. After 4 washes with TBS-T, blots were incubated with appropriate HRP-linked secondary antibody (Santa Cruz, Santa Cruz, CA) at the dilution recommended by the manufacturer for 1 hr in carrier protein as previously. Following 4 more washes with TBS-T, ECL was then done (ECL kit, Amersham Biosciences). Table 2 nAChR Antibodies and Conditions Subunit Company Dilution alpha-1 Sigma 1:30,000 alpha-2 Santa Cruz 1:1000 alpha-3 Santa Cruz 1:2000 alpha-4 Santa Cruz 1:1000 alpha-5 Sigma 1:15,000 alpha-7 Sigma 1:30,000 beta-1 Sigma 1:10,000 beta-2 Santa Cruz 1:1,000 delta Wang et al. [30] 1:4000 Antibodies specific to particular nAChR subunits were purchased from either Santa Cruz or Sigma. Antibody specific to the δ subunit was a gift from Dr. Zhu-Zhong Wang, University of Pittsburgh. Calcium influx assay Airway cells were grown in 24-well dishes, with 10,000 cells per well. After 24 hours, medium was replaced with serum-free, prewarmed medium spiked with calcium-45, with a final specific activity of approximately 60 μCi/mM calcium. Drug was added to the wells as indicated, in triplicate. If nAChR antagonists or channel blockers were used, they were added 20 min before the addition of agonist. The calcium ionophore A23187 (Molecular Probes, Eugene OR) was used as a positive control for calcium influx. After incubation at 37°C, plates were put on ice and washed 3 times with ice-cold PBS. Lysis buffer (1% SDS, 0.3 N NaOH) was added. Lysates were transferred to scintillation vials, scintillation fluid added, and counted. Results are presented as percent control, with untreated control normalized to 100%. Phospho-protein Immunoblotting For phospho-PKC, phospho-p42/44, and phospho-p38, 10 minute exposure to 20 ng/ml EGF was used as the control. These conditions have been published as optimal for signaling by EGF in several published articles [ 16 , 17 ]. Immunoblotting for phospho-proteins was done by running 30 μg protein, reduced by heating to 80°C for 15 min in the presence of 50 mM DTT, on a 10% Tris-Bis gel (Invitrogen). Protein was transferred to PVDF then blocked with 5% blocker (Biorad) in TBS-T 1 hr. Antibody was diluted 1:1000 in 5% blocker as directed by Cell Signaling, Inc. and rocked overnight, 4°C. Membranes were then washed four times and probed with appropriate HRP-linked antibody (Santa Cruz) 1:5000 for 1 hr. After four TBS-T washes, ECL was done (Amersham). Results were normalized for loading differences by stripping with ImmunoPure IgG elution buffer (Pierce, Rockford IL) for 3 hours at 37°C, then probing for β-actin using an HRP-linked anti-β-actin antibody (Santa Cruz). Statistical Analysis Differences in expression frequency of nAChR subunits at either the RNA or protein level were analyzed by Fisher's Exact Test. In all other experiments, differences from control were determined using Student's T-test. All p-values reflect two-tailed tests. Results Neuronal and muscle-type nAChR are present on HBE cells and airway fibroblasts Using RT-PCR, we repeatedly detected mRNA for nAChR subunits in short-term cultures of human airway cells from bronchial biopsies. Figure 1 shows representative PCR products from two HBE cultures (Panel E and F), one airway fibroblast culture (Panel D), and immortalized HBE BEAS2B cells (Panel C). Brain mRNA was used as a positive control for neuronal-type nAChR (Figure 1 Panel A) and muscle mRNA as a positive control for muscle-type nAChR (Figure 1 Panel B), and GAPDH is included on each panel to indicate mRNA quality. A water-only sample was used as a negative control in each experiment. PCR products were found at the expected size for each subunit and were sequenced and compared to the known gene sequences, and confirmed to be the expected sequence for each subunit. HBE cells cultured from airway biopsies from a series of never smokers, ex-smokers, and active smokers were examined by RT-PCR and the results are summarized in Table 3 . Due to limited RNA yield, some samples were not examined for all possible subunits. We found that seven different nAChR subunits were expressed in over 50% of the sample examined, including α5, α6, α7, α9, β1, δ and ε. Three additional subunits, α1, α3, and β4 were expressed in at least 30% of cultures examined. The α2 and α4 subunits were also examined, but were never present (data not shown). Table 3 Expression of nAChR in HBE and Bronchial Epithelial Cells Lines as determined by RT-PCR HBE α1 α3 α5 α6 α7 α9 β1 β2 β3 β4 * δ ε Never smoker 2/9 (22%) 2/8 (25%) 6/9 (67%) 5/9 (56%) 7/9 (78%) 8/8 (100%) 9/9 (100%) 4/7 (57%) 0/8 (0%) 6/9 (67%) 7/8 (88%) 5/9 (56%) Active smoker 3/10 (30%) 0/3 (0%) 9/10 (90%) 3/4 (75%) 7/10 (70%) 3/3 (100%) 9/10 (90%) 5/9 (56%) 0/5 (0%) 1/10 (0%) 6/8 (75%) 7/10 (70%) Ex-smoker 7/18 (39%) 8/19 (42%) 18/19 (95%) 9/19 (47%) 11/18 (61%) 16/18 (89%) 17/19 (89%) 6/16 (38%) 1/19 (5%) 9/19 (47%) 11/18 (61%) 10/19 (53%) Total 12/37 (32%) 10/30 (33%) 33/38 (87%) 17/32 (53%) 25/37 (68%) 27/29 (93%) 35/38 (92%) 15/32 (47%) 1/32 (3%) 16/38 (42%) 24/34 (70%) 22/38 (58%) BEAS2B + - + + + + + + - + + + IB3-1 + - + - + + + + - + + + nAChR subunits present in HBE and bronchial epithelial cell lines as determined by RT-PCR. The table indicates the number of positive cultures out of the total number of cultures examined, as well as the percentage of positive cultures for each subunit. * indicates statistical significance when comparing cultures from never smokers to those of active or ex-smokers. Figure 1 Expression of nAChR subunits from cell types by RT-PCR. A) Brain; B) Muscle; C) BEAS2B cell line; D) normal airway fibroblasts; E) human bronchial epithelial cells; F) human bronchial epithelial cells. On each panel, the brightest band on the 100 bp ladder represents 600 bp. The subunits that are expressed by HBE cells could potentially combine to form muscle-type (α1/ β1/ δ /ε) heteropentamers, neuronal α7 or α9 homopentamers, and neuronal heteropentamer receptors α3/ α5/ β2 or β4 and α6/β2 or β4. By examining the pattern of mRNA expression in each individual culture, combinations were observed that would produce a functional muscle-type receptor in 7 of 33 (21%) of cultures, a functional α3-containing neuronal type receptor in 10 of 28 (35%) of cultures, α6-containing neuronal type receptor in 13 of 28 (46%), α7 homopentamer receptors in 25 of 37 (68%), and homopentamer α9 receptors in 27 of 29 cultures (93%). Only 2 of 35 (6%) HBE cultures did not express at least one functional nAChR subunit combination. Two immortalized airway epithelial cell lines also expressed mRNA for many of these nAChR subunits, including muscle-type subunits (Figure 1 , Table 3 ). BEAS2B, derived from normal HBE cells, and IB3-1, derived from the bronchial epithelial cells of a cystic fibrosis patient, were examined. Both immortalized epithelial cultures expressed the subunits required for a functional muscle-type nAChR (α1/β1/δ/ε) and the homopentamers α7 and α9, and BEAS2B cells express mRNA for subunits that may combine to form functional neuronal nAChRs (α6/ β2 or β4). Airway fibroblasts also expressed nAChR (Table 4 ). We found that mRNA for α1, α5, α6, α7, α9, β1, β2, δ and ε were all expressed more than 70% of the time. All other subunits were expressed less than one-third of the time. As for HBE cells, we examined the pattern of mRNA expression in each airway fibroblast culture. nAChR subunits could potentially combine to form muscle-type (α1/ β1/ δ / ε) receptors in 77% of fibroblast cultures and both neuronal α7 or α9 homopentamers in 100% of cultures. Neuronal heteropentameric nAChR containing the α6 subunit might be formed in 59% of cultures, and neuronal heteropentamers containing the α3 subunit, with or without the α5 subunit, could combine to form a functional receptor in 27% of cultures. Table 4 Expression of nAChR in Airway Fibroblasts as determined by RT-PCR NLFB α1 α3 α5 α6 α7 α9 β1 β2 β3 β4 δ ε Never smoker 5/5 (100%) 2/5 (40%) 5/5 (100%) 5/5 (100%) 5/5 (100%) 5/5 (100%) 5/5 (100%) 5/5 (100%) 2/5 (40%) 2/5 (40%) 5/5 (100%) 5/5 (100%) Active smoker 10/11 (91%) 3/11 (27%) 11/11 (100%) 8/11 (73%) 10/11 (91%) 11/11 (100%) 11/11 (100%) 8/11 (73%) 4/11 (36%) 2/11 (18%) 10/11 (91%) 10/11 (91%) Ex-smoker 9/9 (100%) 0/9 (0%) 8/9 (89%) 5/9 (56%) 5/9 (44%) 8/9 (89%) 6/9 (67%) 6/9 (67%) 2/9 (22%) 3/9 (33%) 9/9 (100%) 8/9 (89%) Total 24/25 (96%) 5/25 (20%) 24/25 (96%) 18/25 (72%) 20/25 (80%) 24/25 (96%) 22/25 (88%) 19/25 (76%) 8/25 (32%) 7/25 (28%) 24/25 (96%) 23/25 (92%) nAChR subunits present in airway fibroblasts as determined by RT-PCR. The table indicates the number of positive cultures out of the total number of cultures examined, as well as the percentage of positive cultures for each subunit. The mRNA expression of four nAChR subunits is significantly different when comparing airway fibroblasts and HBE cells. The muscle-type receptor subunits α1, δ, and ε are all expressed more frequently in airway fibroblasts (present in 96%, 96%, and 92%, respectively, of airway fibroblast cultures) than in HBE cells (α1 present in 32% of cultures, δ in 70% of cultures, and ε present in 58% of cultures, p < 0.02 for each subunit). In addition, the β3 subunit is also expressed more frequently in fibroblast cultures than in HBE cells (8/25 compared to 1/28, p < 0.01). The subunit combinations that could form functional receptors were also examined and compared between cell types. Consistent with the individual subunit data, the combination of all subunits for the muscle-type receptor is expressed significantly more frequently in human airway fibroblasts (74%) than in HBE cultures (21%)(p = 0.0001). The nAChR subunit α9 was frequently expressed by both HBE cells and airway fibroblasts. Although we find this nAChR frequently and it may have physiological significance, it is unlikely that signaling through this receptor would be responsible for the immediate downstream effects seen in our studies, which focus on the effects of nicotine, since nicotine does not act as an agonist for this receptor type. We next examined nAChR subunit protein expression using immunoblotting. All the cultures examined expressed protein for nAChR subunits, in general agreement with mRNA expression data (for representative results, see Figure 2 , Figure 3 ). In these experiments, whole brain and muscle tissue lysates as positive controls (Figure 2 , Figure 3 ). For each subunit, the culture was considered positive when a band matching the size of the positive control at the expected kilodalton size was found. A doublet band just above the expected size on the blots strongly suggests that both glycosylated and non-glycosylated forms of the protein are present, and that protein processing is in progress (Figures 2 , 3 ). Glycosylation is essential for nAChR folding and expression on the cell membrane, and the higher bands are expected. For the α3 subunit, bands below the expected size were observed which might represent cross-reactivity of the antibody with other nAChR subunits, or protein degradation products, but only those that matched the size of the brain control were considered a positive result. In HBE we found that α1, β2, and δ were usually present (Figure 2A,2B ). Although there are also lower cross-reacting bands for these nAChR subunits, a band of the correct size was also frequently seen, and upper bands suggest that the glycosylated form of the subunits are present (Figure 2A,2B ). Protein for the subunits α4 and α7 were not observed in HBE, and α3, α5, and β1 were sometimes present, although α5 was expressed at a barely detectable level compared to other subunits (Figure 2B ). Figure 2 HBE and airway fibroblasts immunoblotting for nAChR subunits. Whole brain tissue lysate was used as a positive control. A) nAChR in HBE cells from never smokers; B) nAChR in HBE cells from ex-smokers; C) nAChR in airway fibroblast cells from one ex-smoker (1089), two never smokers (1133,1190), and three active smokers (1215, 1225, 1233). For each subunit, the positive control band was seen at the following size: α1 55 kD, α3 60 kD, α4 70 kD, α5 53 kD, α7 56 kD, β1 59 kD, β2 57 kD, and δ 55 kD. Figure 3 Expression of nAChR subunit protein by BEAS2B and IB3-1. Immortalized bronchial epithelial cell lines BEAS2B and IB3-1 were immunoblotted for nAChR. For each subunit, the positive control band was seen at the following size: α1 55 kD, α2 60 kD, α3 60 kD, α4 70 kD, α5 53 kD, α7 56 kD, β1 59 kD, β2 57 kD, and δ55 kD. To examine these findings more generally, nAChR protein was examined in additional HBE cultures from never smokers, active smokers, and ex-smokers (Table 5 ). Only some of the cultures examined by immunoblot were derived from the same individual as those we had examined by RT-PCR, so a direct comparison with the mRNA expression results was not usually possible. Subunit protein expression is generally consistent with our previous results, showing that the muscle-type receptor subunits α1, β1, and δ are detected at the protein level and theoretically form a functional receptor together with ε. Although we have not found an antibody for ε that will detect this protein in our positive controls, RT-PCR results indicate that it is frequently expressed (Table 3 ). Table 5 Expression of nAChR in HBE and Bronchial Epithelial Cell Lines as determined by Immunoblot HBE α1 α3 α5* α7 β1 β2 δ Never smoker 5/8 (63%) 1/4 (25%) 0/4 (0%) 0/4 (0%) 1/4 (25%) 3/5 (60%) 3/4 (75%) Active 2/2 (100) n.d. 2 + /2 (100%) 0/2 (0%) 2/2 (100%) 2/2 (100%) n.d. Ex-smoker 5/7 (71%) 1/5 (20%) 5 + /7 (71%) 0/7 (0%) 4/7 (57%) 5/5 (100%) 5/5 (100%) Total 12/17 (71%) 2/9 (22%) 7/13 (54%) 0/13 (0%) 7/13 (54%) 10/12 (83%) 6/6 (100%) BEAS2B + - + - + - + IB3-1 + - + - + + + nAChR subunit protein present in HBE and bronchial epithelial cell lines as determined by immunoblot. The table indicates the number of positive cultures out of the total number of cultures examined, as well as the percentage of positive cultures for each subunit. * indicates statistical significance when comparing never smoking samples to combined active and ex-smoker samples. + The α5 subunit was present in these samples, but at low levels compared to other subunits and control. One difference between our RT-PCR results and our immunoblotting is in the frequency of α7 expression. α7 mRNA was expressed in 68% of our HBE cultures, but the protein for this subunit was never detected, although the protein was detected in positive controls and in airway fibroblasts. This subunit is either transcribed but not translated, or the protein may be expressed below the limit of detection for immunoblotting in HBE cells. We analyzed our protein data for combinations of subunits that would form functional receptors. We found that all three of the subunits needed for a functional muscle receptor were highly expressed at the protein level in 44% of the HBE cultures examined. The other functional combinations analyzed at the protein level are the neuronal α3-containing heteropentamers that were present in 33% of the cultures. We also determined that nAChR subunit protein is expressed in airway fibroblasts (Figure 2 ). We found that subunit expression had less variation among cultures from different individuals in fibroblasts than HBE cultures. The α1, α7, β2, and δ subunits were expressed in 100% of cultures. The α3, α4, and α5 were never expressed, and β1 was expressed in 83% of cultures. The frequency of expression of α7 was significantly higher in fibroblasts than HBE cultures (100% versus 0%, p < 0.0001), and α5 is more frequently expressed in HBE cultures (54% versus 0%, p < 0.05). We also found that nAChR subunit protein is expressed on two cell lines derived from normal bronchial epithelial cells described above (IB3-1, BEAS2B). Figure 3 is a representative immunoblot and Table 5 contains the complete table of results that have been repeated in independent experiments. We find that both cell lines express α1, β1, and δ protein. Therefore, the muscle-type (α1/ β1/ δ / ε) is most likely the major functional nAChR on BEAS2B and IB3-1. Neither cell line expresses protein for the α7 homopentamer. BEAS2B cells also express protein for α5 and β2 but do not appear to have another appropriate heterodimer partner, such as α3, to produce a functional neuronal nAChR. Based on the protein results, the only pentameric nAChR receptor detected in the BEAS2B and IB3-1 epithelial cell lines is the muscle-type heteropentamer. NAChR are functional in lung epithelial cells We examined functionality of the AChR on HBE cells by measuring calcium influx. After treatment with nicotine, extracellular radioactive calcium (Ca-45) is internalized by BEAS2B cells and short-term HBE cultures (Figure 4 ). BEAS2B cells were chosen for these experiments instead of IB3-1 due to the derivation of the cells. Since IB3-1 cells were derived from a cystic fibrosis patient with the classic sodium-channel defect, and nAChR can also act as sodium channels, their expression or function may be altered in this cell type due to abnormal ion levels. A derivative of the calcium ionophore myomycin was used as a positive control. Calcium-45 levels were 5 times over control levels using the positive control (data not shown). This high level of calcium influx is associated with loss of membrane integrity caused by ionophore. Figure 4 Calcium influx after exposure of HBE or BEAS2B cells to nicotine. Intracellular calcium-45 was measured after exposure to nicotine for 5 minutes. * statistically significant as compared to untreated control. The calcium influx seen in our experiments occurs within 5 minutes of treatment with nicotine and at concentrations from 1 μM to 100 μM in both cell types. Calcium influx was dose-dependent and reached 131% of control in BEAS2B cells and 137% of control in HBE cells. At all doses, influx of calcium-45 was statistically greater than control in BEAS2B cultures (p < 0.05). To test the specificity of this response, we used the nicotinic antagonists α-bungarotoxin and hexamethonium with BEAS2B cells and nicotine. α-Bungarotoxin will block muscle-type nAChR as well as α7 homopentamers and hexamethonium will block neuronal heteropentamer nAChR such as α3- and α6- containing receptors. We used ionophore alone and with the nicotinic antagonists as a control in these experiments. Antagonists had no effect on ionophore-induced calcium influx (data not shown). In this experiment we found that α-bungarotoxin could completely prevent the calcium-45 influx seen after nicotine treatment, while hexamethonium could only slightly inhibit the effect of nicotine in BEAS2B cells (Figure 5 ). Based on this data, the neuronal nAChR α3- and α6- containing receptors do not appear to play a significant role in controlling calcium influx after nicotine treatment since the inhibitor of this receptor type, hexamethonium, could not significantly inhibit influx. Consistent with our immunoblot data, the influx of calcium after exposure of BEAS2B cells to nicotine is likely mediated through muscle-type receptors (α1/β1/δ/ε), and can be blocked by the inhibitor specific to these receptor types, α-bungarotoxin. Figure 5 Calcium influx after exposure of BEAS2B cells in the presence of antagonist. BEAS2B cell were exposed to 1 μM nicotine in the presence or absence of nAChR antagonist. Intracellular calcium-45 was measured after exposure to nicotine for 5 minutes. * statistically significant as compared to untreated control. Signaling pathways are activated in response to nicotine We determined if protein kinase C (PKC) responds to nicotine because it is commonly activated by calcium influx. Calcium influx is an immediate effect of nicotine exposure to BEAS2B cells (Figures 4 and 5 ). The mitogen-activated protein kinase (MAPK) family members p38 and p42/44 were also examined because previous data showed that nAChR may be involved in regulation of apoptosis and growth [ 1 , 9 ]. In examination of PKC and MAPK, cells were treated with epidermal growth factor (EGF) as a positive control. We chose actin to correct for total protein for densitometry so that we could probe blots for a number of signaling pathways without compromising the protein on blots with unnecessary stripping procedures. Using phosphorylation as a marker of activation, we find that PKC and p38, but not p42/44 are activated after treatment with nicotine in BEAS2B (Figure 6 ). As shown in the time course in Figure 6 , phosphorylation of PKC is above control levels at 15 minutes of continuous treatment and stays above control through the longest time point, 60 minutes of continuous treatment. After correction for total protein using actin, densitometry shows that PKC levels are 120% of control after 1 minutes of exposure, and after 60 minutes are 221% of untreated control. In this experiment, a pan-phospho-PKC antibody was used that detects six isoforms of phosphorylated PKC between 78 and 85 KD. P38 was also phosphorylated immediately after nicotine treatment, with band intensity of 126% of control at the first timepoint examined, 5 minutes. Unlike PKC, phosphorylation of p38 was only briefly present, and phosphorylation drops to below control by 15 minutes of treatment. When probed for phospho-p42/44, phosphorylation state was never above control in BEAS 2B through 60 minutes of treatment (Figure 6 ). Figure 6 Phosphorylation of proteins after nicotine exposure in BEAS2B cells. Nicotine causes phosphorylation of PKC and p38, but not p42/44 in BEAS2B cells. Cells were exposed to 100 μM nicotine for the times indicated. Panel A) represents the effect of nicotine on phosphorylation of PKC; B) represents the effect of nicotine on phosphorylation of p42/44; C) represents the effect of nicotine on phosphorylation of p38 D) is densitometry for the immunoblots expressed as percent of untreated control after correction with actin. In contrast, signaling experiments done with short-term airway fibroblast cultures show that nicotine caused phosphorylation of p42/44 within 10 minutes of exposure (Figure 7 ). After densitometry and correction for loading differences, phosphorylation was increased to 198% of control at the 10 minute timepoint. Similarly to phosphorylation of p38 in BEAS2B cells, phosphorylation is tightly controlled, and returns to below control levels by 30 minutes. Use of α-bungarotoxin showed that phosphorylation of p42/44 can be blocked by this nAChR antagonist (data not shown), indicating that muscle-type and/or α7 receptors are involved in this response. Figure 7 Nicotine causes phosphorylation of p42/44 in airway fibroblasts. Cells were exposed to 100 μM nicotine for the times indicated. Panel A) represents the effect of nicotine on phosphorylation of p42/44; B) is densitometry for the immunoblots expressed as percent of untreated control after correction with actin. Together, these data indicate that the muscle-type (α1/β1/δ/ε) nAChR is consistently present on airway epithelial cells, while airway fibroblasts consistently demonstrate both a muscle-type and an α7 homomeric nAChR. Normal airway epithelial cells may also sometimes express the neuronal α3/α5/β2 or α6/β2 nAChR. The nicotinic receptors are functional, regulate calcium influx upon ligand binding, and lead to downstream activation of the signaling pathways MAPK or PKC when bound by nicotine. Downstream effects can be blocked by use of nicotinic antagonists. Long-term nicotine exposure and nAChR expression We examined the relationship of prior smoking to nAChR expression on airway cells. To do so, we compared subunits expressed at the mRNA and protein level in HBE cultures from never smokers, active smokers, and ex-smokers to determine if long-term exposure to nicotine was a factor in the type of nAChR expressed. As shown in Table 3 , comparison of HBE cultures from 9 never smokers and 10 active smokers indicates that active smoking was associated with a significant decrease in mRNA expression of the β4 subunit (6 of 9 never smokers expressed this subunit compared to 1 of 10 active smokers (p = 0.02). This difference was not significant when comparing active to ex-smokers. In addition, frequency of expression in HBE cells of other subunits that make up the neuronal nAChR was not significantly different between active and never smokers. At the protein level, our data show that α5 may be upregulated in HBE cells at the protein level in response to chronic nicotine exposure (Table 5 ). Fewer never-smokers express α5 protein than ever-smokers (p < 0.05), although at lower levels than its receptor partners. An available antibody to the β4 subunit was not found to be specific to that subunit in our controls, therefore we could not determine if the β4 protein is modulated by tobacco exposure, as observed at the mRNA level. Interestingly, in airway fibroblasts mRNA patterns for combinations of neuronal heteropentamers containing the α3 subunit were downregulated with smoking (Table 4 ). Cultures containing mRNA for all the subunits to form α3/ β2 or β4 receptors, with or without α5, were observed in 80% of never-smokers, but only 25% of active smokers and none of the ex-smokers examined. This difference was significant when comparing never smokers to ex-smokers or comparing never-smokers to ex- and active smokers together (p < 0.01). However, when analyzing protein results, no differences were found among active-, ex-, and non-smokers in the projected receptor type. Discussion Recent evidence suggest that endogenous acetylcholine is a local signaling molecule in non-neuronal tissue, and that nicotinic acetylcholine receptors are found outside the nervous system. Our data show that HBE cells can express mRNA for the neuronal α3/α5/β2 or β4, α6/β2 or β4, and α7 and α9 pentamers, as well as the muscle type α1/β1/δ/ε nAChR. Previous data in the human airway examined small numbers of HBE cultures for a limited number of nAChR subunits. Maus et al. [ 12 ] used RT-PCR and binding studies to show that the α3/α5/β2 nAChR were present on HBE cells. West et al. [ 9 ] also characterized nAChR on three non-immortalized bronchial epithelial cell lines and found the α3/α5/β2 nAChR subunits as well as α7, α9, and α10. However, neither investigation examined HBE cultures for the presence of the muscle-type nAChR. West et al. [ 9 ] examined α1 mRNA expression by RT-PCR, which was negative, consistent with our data that indicate that the mRNA for the α1 subunit is expressed in approximately one-third of HBE cultures from different individuals. In the present study, we have determined that several types of nAChR are consistently present on many primary cultures of HBE cells. Using a panel of 38 HBE cultures for RT-PCR analysis, we find that although not every nAChR subunit RNA is present in every culture, the mRNA for seven different nAChR subunits are consistently expressed in combinations that could combine to form both muscle and neuronal-type receptors. Immunoblotting confirmed that the nAChR mRNA for these subunits was translated into detectable protein. Not all subunits that are expressed at the mRNA level are highly expressed at the protein level, indicating that some regulation of nAChR expression may occur at the translational and post-translational levels. This has been previously shown in neuronal cells, where transcripts for the α7 subunit are present even on cells without functional α7 receptors [ 18 ]. Our data indicate that this also occurs in HBE cells, where α7 transcripts are frequently found, but protein of the correct size is not. Thus the conclusion in prior literature that various neuronal nAChR receptors, including α7, are responsible for actions of nicotine in HBE may not be definitive. Instead, our results suggest that the muscle-type nAChR present in HBE cells may have a functional role in HBE cells that has not previously been considered. The muscle-type receptor was more recently characterized than the neuronal type and previous literature never examined HBE cultures for the muscle-type receptor [ 9 , 10 , 12 ]. This finding was further supported by the observation that two immortalized cell lines derived from HBE also expressed protein for the muscle-type receptors, while lacking the α7 homopentamer protein. Similarly to HBE, mRNA and protein for nAChR subunits are commonly expressed by airway fibroblast cultures. Airway fibroblasts have never been examined for the presence of nicotinic receptors. Dermal fibroblasts have been shown to express mRNA for some receptors, although they were not examined for muscle-type receptors [ 19 ], and gingival fibroblasts respond to nicotine by decreasing expression of integrins and increasing expression of c-fos, but they have not been characterized for nAChR [ 20 - 22 ]. We find that airway fibroblasts express all the subunits required for muscle-type nAChR. In contrast to HBE, these cells also express readily detectable protein for the α7 receptor in all the cultures examined. Therefore, in these cells, the muscle-type receptor and the α7 neuronal homopentamer are the major functional nAChR that may be responsible for signaling initiated by nicotine. α7 receptors are less susceptible to inactivation in the long-term presence of agonist, while muscle-type receptors are more likely to undergo inactivation [ 23 ]. Therefore, in cells with these different receptors, there may be differences in the initiation of downstream signaling pathways especially in response to extended exposure to nicotine, such as occurs in a chronic smoker. Calcium influx is a hallmark of the opening of the nAChR ion channel. An increase in intracellular calcium from the extracellular milieu can occur either by a direct influx of calcium through the nAChR channel, as occurs in α7 receptors, or by an influx of sodium that leads to depolarization of the cell and the opening of L-channels, as occurs after agonist binding to heteropentamer nAChR [ 24 , 25 ]. We determined that nAChR are functional in both BEAS2B and HBE cells; treatment of cells with nicotine leads to an influx of extracellular calcium. The influx of calcium seen after nicotine treatment was similar in magnitude to calcium influx seen in neuronal cells after activation of L-channels (144%) [ 26 ]. Additionally, we differentiated between functional muscle-type receptors and neuronal heteropentamer receptors. In BEAS2B cells, calcium influx stimulated by nicotine was significantly inhibited by a muscle/ α7 antagonist, but only slightly inhibited by a neuronal heteropentamer antagonist. This confirms the observation that the neuronal heteropentamer nAChRs do not play a major functional role in the response of BEAS2B cells to nicotine in our hands. Since α7 protein was not detected in HBE or BEAS2B cells, the muscle-type nAChR is more likely the major functional receptor type. Nicotine has previously been shown to affect signaling in human airway cells, and acetylcholine, the endogenous ligand, causes proliferation of HBE cells [ 1 , 9 ]. In our experiments, nicotine initiated signaling pathways involved in cell growth and apoptosis in both HBE cells and airway fibroblasts. In BEAS2B cells, immediate phosphorylation of PKC is likely due to calcium influx. Calcium is a known cofactor for classical PKC activation and is required along with binding to diacylglyerol (DAG) for functional conformation. Phosphorylation of PKC, consistent with activation, increases over time. This may indicate that upregulation of other PKC cofactors, such as DAG, may occur as downstream events after calcium influx, leading to an enhanced PKC signal over time. Over the same time period, the MAPK family member p38 but not p42/44 is phosphorylated, a required step for activation. The activation of p38 is associated with regulation of apoptosis in response to cellular stress. The MAPK family kinases, such as p38, are not known to be directly activated by calcium; however there are several indirect pathways that lead to rapid phosphorylation of these pathways. These include signaling through the calcium/calmodulin-dependent protein kinases CaMKI, CaMKII, and CaMKIV as well as the calcium-activated signaling molecule PYK2. This finding is in contrast to signaling activated by nicotine in airway fibroblasts. These cells phosphorylate p42/44 immediately upon treatment with nicotine. Like p38, p42/44 is not directly activated by calcium, but could be phosphorylated by calcium-activated signaling molecules. However, the pathways that lead to phosphorylation of different MAPK family members in the two cell types in response to nicotine have not been elucidated. It is possible that the nAChR type differences are responsible for the differential MAPK effects. For example, the α7 receptor is highly expressed on airway fibroblasts but not on HBE cells. The additional nAChR type on these fibroblasts may change the signaling pathways activated by cells in response to nicotine. This is consistent with a previous study by Jull et al [ 27 ]. This study showed that ligand binding to the α7 nAChR in SCLC cells leads to activation of p42/44 through Raf [ 27 ]. A similar pathway may be activated by the binding of nicotine to the α7 nAChR in airway fibroblasts. Finally, we found that smoking had only modest effects on nAChR expression in the airway. Previous studies in the brain indicate that certain nAChR may be increased in frequency in smokers as compared to non-smokers [ 28 ], and a published experiment showed that there was an increase in the α3 nAChR expressed in respiratory epithelial cells in one smoker compared to one nonsmoker [ 10 ]. Due to the limited sample size in the study of lung cells, it is impossible to know if this difference was due to individual variation or was a smoking-induced change, especially when considering the variability of nAChR subunit expression seen among individuals in our study. In this study of HBE cultures using a panel of active smokers, never smokers, and ex-smokers, there were some statistical differences in the mRNA and protein expression of nAChR subunits in cultures derived from donors with different smoking histories, including the increased presence of protein for an α5 subunit in smokers that could combine with the α3 containing receptors and change the calcium permeability [ 29 ]. However, the α5 subunit was expressed at a much lower protein level than the α3/β2 subunits, and may only be present in some of the neuronal-type receptors. Other changes that were documented with smoking status did not occur in the major functional nAChR of the cell type. Corresponding changes also did not occur in the other subunit partners that are necessary for function, so the effect on overall functional receptor expression was probably unchanged with smoking status in HBE cells. In contrast, there was a significant decrease in the frequency of the expression of the functional combination of subunits for the α3-containing receptors in airway fibroblasts of smokers and ex-smokers compared to never-smokers. This nAChR type is a sodium channel that undergoes inactivation upon long-term exposure to agonist, and, in airway fibroblasts, appears to be downregulated with long-term exposure to nicotine. This change in receptor expression remains even after exposure to nicotine ceases, as evidenced by the reduced frequency of expression in ex-smokers. Conclusions We have shown that short-term cultures of normal airway fibroblasts as well as normal human bronchial epithelial cells from a number of different human donors consistently express functional nAChR and that these cell types differ in the type of nAChR they express. It is likely that the muscle-type nAChR plays a major role in the response of HBE cells to nicotine, and that the neuronal heteropentamers play a more minor role. Calcium influx as well as initiation of downstream signaling pathways indicate that receptors are functional and that both human bronchial epithelial cells and airway fibroblasts respond to nicotine, and those signaling responses may differ due to the difference nAChR present on the cell type. Together, these data suggest that exposure of the human airway to nicotine through tobacco smoke may have physiological consequences on airway homeostasis involving both the airway mucosa and the underlying submucosal mesenchymal cells. As such, nicotine may act to promote lung disease by acting to change cell growth and apoptosis. In airway fibroblasts this may leading to thickening of the airway wall seen in the pathogenesis of COPD. In the bronchial epithelium this may lead to preneoplasia or development of frank cancer. Abbreviations nAChR: nicotinic acetylcholine receptor HBE: human bronchial epithelial cell PKC: protein kinase C MAPK: mitogen activated protein kinase BEGM: bronchial epithelial growth medium EGF: epidermal growth factor P42/44: Extra-cellular signal regulated kinase isoforms 1 and 2 Authors' Contributions DLC designed and performed the majority of the experiments and data analysis, and wrote the manuscript. TMH designed and performed RT-PCR with the assistance of MJS. JDL and NAC contributed the tissues that were grown into primary cultures and provided information on smoking history. AGD cultured the primary cells. JMS conceived of the study and participated in its design and coordination. All authors read and approved the manuscript. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC544394.xml |
539357 | Homopolymer tract length dependent enrichments in functional regions of 27 eukaryotes and their novel dependence on the organism DNA (G+C)% composition | Background DNA homopolymer tracts, poly(dA).poly(dT) and poly(dG).poly(dC), are the simplest of simple sequence repeats. Homopolymer tracts have been systematically examined in the coding, intron and flanking regions of a limited number of eukaryotes. As the number of DNA sequences publicly available increases, the representation (over and under) of homopolymer tracts of different lengths in these regions of different genomes can be compared. Results We carried out a survey of the extent of homopolymer tract over-representation (enrichment) and over-proportional length distribution (above expected length) primarily in the single gene documents, but including some whole chromosomes of 27 eukaryotics across the (G+C)% composition range from 20 – 60%. A total of 5.2 × 10 7 bases from 15,560 cleaned (redundancy removed) sequence documents were analyzed. Calculated frequencies of non-overlapping long homopolymer tracts were found over-represented in non-coding sequences of eukaryotes. Long poly(dA).poly(dT) tracts demonstrated an exponential increase with tract length compared to predicted frequencies. A novel negative slope was observed for all eukaryotes between their (G+C)% composition and the threshold length N where poly(dA).poly(dT) tracts exhibited over-representation and a corresponding positive slope was observed for poly(dG).poly(dC) tracts. Tract size thresholds where over-representation of tracts in different eukaryotes began to occur was between 4 – 11 bp depending upon the organism (G+C)% composition. The higher the GC%, the lower the threshold N value was for poly(dA).poly(dT) tracts, meaning that the over-representation happens at relatively lower tract length in more GC-rich surrounding sequence. We also observed a novel relationship between the highest over-representations, as well as lengths of homopolymer tracts in excess of their random occurrence expected maximum lengths. Conclusions We discuss how our novel tract over-representation observations can be accounted for by a few models. A likely model for poly(dA).poly(dT) tract over-representation involves the known insertion into genomes of DNA synthesized from retroviral mRNAs containing 3' polyA tails. A proposed model that can account for a number of our observed results, concerns the origin of the isochore nature of eukaryotic genomes via a non-equilibrium GC% dependent mutation rate mechanism. Our data also suggest that tract lengthening via slip strand replication is not governed by a simple thermodynamic loop energy model. | Background DNA homopolymer tracts are the simplest of simple sequence repeats (SSRs); the two types being poly(dA).poly(dT) and poly(dG).poly(dC). They are present in all genomes, but in some eukaryotes they are found at high frequencies indicating that the tracts are highly enriched relative to their random occurrence within a random sequence DNA genome of similar base composition. Homopolymer tracts were previously examined systematically in the coding, intron and flanking regions of the slime mold D. discoideum [ 1 ]. Only long (N>10 bp) homopolymer tracts were observed at high frequencies in this AT-rich genome. The non-coding regions were found to be highly over-represented in the large poly(dA).poly(dT) tracts compared to random sequences of equivalent base composition containing tracts at frequencies expected for random occurrence. At shorter sequence lengths (2 bp<N<6 bp), poly(dG).poly(dC) tracts were over-represented somewhat more than poly(dA).poly(dT) tracts of comparable length. Although the elongation of SSR tracts may be due to more than one mechanism [ 2 ], most often the phenomenon has been attributed to slip-strand replication errors, which occur from the slippage and re-annealing of the nascent strand during DNA replication [ 3 - 5 ]. This is a type of mutation that can be affected by the proofreading function of DNA polymerases [ 6 - 8 ]. For example, it has been shown that the proofreading and repair function for DNA polymerase epsilon is efficient for short homopolymer tracts, but that only the mismatch repair system can prevent frameshift mutations in tracts of length 8 nucleotides or greater [ 8 ]. These slip-strand errors, which lead to the formation of longer homopolymer tracts, can have deleterious effects. In coding regions, the mutation can cause frame-shift errors, leading to transcription errors and aberrant protein translation. As would be expected from the triplet codon constraints, there appears to be selection against long homopolymer tracts in coding regions. Marx et al [ 1 ] demonstrated that long homopolymer tracts were not present at frequencies higher than expected in the coding regions of D. discoideum DNA. Compared to nonhomopolymer random B-DNA sequences, poly(dA).poly(dT) tracts have a shorter turn, a smaller axial rise, a narrower and deeper minor groove [ 9 ], a wider and shallower major groove, and are straighter and more rigid over longer lengths. These characteristics are due to the high propeller twist of the base pairs [ 10 ], the maximal overlap/stacking between bases on the same strand, and non-Watson-Crick cross-strand H-bonds between base pairs [ 9 - 13 ]. The result is that long tracts tend to be energetically excluded from nucleosomes [ 14 , 15 ]. In some definitive studies, it was first shown that tracts of critical and longer lengths are excluded from the reconstituted nucleosome [ 16 ]. Other investigators, using native nucleosomes derived from native chicken chromatin, demonstrated that long poly(dA).poly(dT) tracts are excluded from the central core regions [ 17 ]. This conclusion received confirmation from a study of the GenBank sequences of D. discoideum , where it was demonstrated that long poly(dA).poly(dT) tracts (N>10 bp) are preferentially spaced at sequence lengths corresponding to the average nucleosome DNA spacing in D. discoideum nucleosomes [ 18 ]. In this study, adjacent long poly(dA).poly(dT) tracts, combined with their short nonhomopolymer spacer sequences, exhibited average total lengths that correspond to D. discoideum nucleosomal linker lengths, suggesting their in vivo localization in these chromatin regions and avoidance of the nucleosomal core regions. Certain natural DNA sequences possess tertiary structures exhibiting a significant amount of curvature that is associated with short homopolymer lengths (4 – 6 bp) of poly(dA).poly(dT) [ 19 , 20 ]. Also, bending occurs at the junction of these and nonhomopolymer tracts [ 12 ]. When short bent poly(dA).poly(dT) tracts are distributed 10 bp apart, they produce additive long range in-plane bending in the axis of the DNA helix [ 20 , 21 ]. The exact molecular mechanism of this bending behavior is still the subject of considerable experimentation and speculation [ 22 ]. DNA bending patterns resulting from spaced poly(dA).poly(dT) tracts have been shown to occur in replication origins and in transcriptional regulatory regions, where a bent configuration is required for activity [ 23 - 25 ]. Poly(dG).poly(dC) tracts form an A-form double-helix. In contrast to poly(dA).poly(dT), the minor groove of these tracts is broad and shallow, while the major groove is deep. But, as with poly(dA).poly(dT) tracts, the tracts are rigid, which leads to the energetic exclusion of poly(dG).poly(dC) from nucleosomes [ 26 , 27 ]. These characteristics are due to the overlap of adjacent guanine bases and the invariant roll angle between them [ 28 ]. Beyond their structural properties, studies of homopolymer tracts have revealed some biological functions. The poly(dA).poly(dT) tracts can serve as protein binding sites [ 29 ], particularly as upstream promoter elements in the initiation of transcription [ 30 - 32 ] and in recombination [ 33 ]. And poly(dG).poly(dC) tracts have been found in certain eukaryotic promoter regions where they are postulated to form 4 stranded G-quartet structures [ 34 ]. As the number of DNA sequences publicly available increases, the representation (over and under) of homopolymer tracts in different genomes can be compared. Qualitative comparisons have been made between five eukaryotic and two prokaryotic genomes: P. falciparum (also very AT-rich), H. sapiens , S. cerevisiae , C. elegans , A. thaliana , E. coli and M. tuberculosis [ 35 ]. As with D. discoideum (1), it was shown in that study that homopolymer tracts occur in the non-coding regions at over-represented frequencies for poly(dA).poly(dT). Poly(dG).poly(dC) tracts were found to be over-represented in some but not all organisms at short lengths. However, over-representation was observed only in the eukaryotic genomes, not the prokaryotes. In the present study, we have carried out a broad survey of non-overlapping homopolymer tract frequencies in the genomic sequences of 27 eukaryotic organisms across the base composition range from 20–60% (G+C). Within the coding, intron and flanking DNA functional compartments of largely single copy genes from these organisms, we compared the observed poly(dA).poly(dT) and poly(dG).poly(dC) tract frequencies in two size ranges to the tract occurrence frequencies expected for random tract occurrence in DNA compartments of the same base compositions. A large fraction of the 27 eukaryotes exhibited significant over-representations (enrichment) of longer length (N ≥ 9 bp) poly(dA) and poly(dT) tracts in their intron and flanking sequences, but not their coding sequences. This occurred in a novel base composition dependent fashion. A much smaller number of the 27 organisms exhibited significant over-representations of longer length (N ≥ 9) poly(dG) and poly(dC) tracts. For P. falciparum and S. cerevisiae single gene containing sequences as well as whole chromosomes, all homopolymer tracts were found to have similar length dependent frequencies and therefore over-representation in their functional compartments. Results The purpose of this study was to reveal similarities and differences in the frequency of occurrence of homopolymer tracts of varying lengths in different eukaryotic sequences across the biological range of base compositions from 20 – 60% (G+C). Primarily single gene containing sequences of 27 organisms were investigated. This was done on purpose since most of the organisms had largely only single gene containing sequences available in the public databases. Restricting our comparative analysis in this fashion ensured that the results for all organisms could be easily compared. A total of 25,109 sequence documents were collected. As we mentioned earlier, there are often a significant level of redundancies in the sequences found in the public databases. CleanUP is a program we used to remove those redundancies, and following its application, 5.2 × 10 7 bases from 15,560 cleaned sequence documents were analyzed and compared [ 36 ]. As shown in Table 1 comparing columns before and after application of CleanUP, there are varying levels of redundancies in some of the original collected files for different organisms in the public database. In Table 1 , we list the complete name of each organism as well as an abbreviation that is used throughout the following discussion of our Results. Table 1 Summary of the sequence files of the 27 organisms studied ORGANISM ABBR. DNA type DOCUMENTS BEFORE CLEAN-UP DOCUMENTS AFTER CLEAN-UP TOTAL (bp) TOTAL GC% Dictyostelium discoideum Dd single genes 492 440 966261 25.70 Plasmodium falciparum Pf single genes 1652 790 1065133 27.11 chromosome II, III 2 2 2007209 19.82 Tetrahymena thermophila Tt single genes 109 97 214676 28.89 Candida albicans Ca single genes 439 378 959035 35.21 Manduca sexta Ms single genes 54 46 119657 35.62 Caenorhabditis elegans Ce single genes 234 221 1319875 36.92 Schizosaccharomyces pombe Spo single genes 813 720 1346439 37.54 Arabidopsis thaliana At single genes 1908 1520 3139637 38.09 Schistosoma mansoni Sm single genes 98 74 125605 38.37 Danio rerio Dr single genes 339 266 603534 38.60 Saccharomyces cerevisiae Sc single genes 2249 928 3815906 38.68 chromosome I-XVI 16 16 11426263 38.45 Drosophila melanogaster Dm single genes 1883 968 3459297 39.88 Strongylocentrotus purpuratus Spu single genes 153 113 116647 41.78 Xenopus laevis Xl single genes 568 411 805354 41.99 Oryza sativa Os single genes 605 507 1514656 44.61 Trypanosoma brucei Tb single genes 457 368 1039208 46.17 Fugu rubripes Fr single genes 216 181 1737132 46.30 Zea mays Zm single genes 629 480 1225027 46.92 Mus musculus Mm single genes 9288 5179 11191148 47.36 Anopheles gambiae Ag single genes 78 43 73592 48.20 Gallus gallus Gg single genes 1868 1061 1910331 50.00 Toxoplasma gondii Tg single genes 195 117 284187 50.70 Emericella nidulans En single genes 72 62 165648 51.07 Aspergillus niger An single genes 215 160 386447 52.65 Neurospora crassa Nc single genes 252 217 494046 53.37 Leishmania major Lm single genes 89 81 129155 59.11 Chlamydomonas reinhardtii Cr single genes 136 114 349624 61.84 The base compositions of the total sequence population from each organism ranges from a low of 25.70% (G+C) for D. discoideum (Dd) to a high of 61.84% for C. reinhardtii (Cr). From the standpoint of (G+C)%, the organisms we investigated are not evenly distributed. If we take as the midpoint, G. gallus (Gg), whose (G+C)% is exactly 50%, there are only 6 organisms over 50%, while the rest (19 of 27) are all below 50%. This is due to the fact that there are more available sequenced eukaryotic organisms that are AT-rich than GC-rich. In this study, we dissected DNA sequences into coding, intron and flanking functional compartments as shown in Table 2 . In every instance, the non-coding regions (intron and flanking) were found to be significantly more AT-rich than coding sequences. Table 2 The (G+C)% in different compartments of the 27 organisms ORGANISM ABBR. FLANK (bp) GC% INTRON (bp) GC% CODING (bp) GC% Dictyostelium discoideum Dd 207722 15.26 22407 11.14 621748 30.32 Plasmodium falciparum Pf 143401 16.27 18597 12.64 840733 29.41 Tetrahymena thermophila Tt 60095 20.99 8624 20.14 101889 34.37 Candida albicans Ca 302347 31.26 3035 33.05 631769 36.93 Manduca sexta Ms 43331 33.38 17299 30.87 21979 47.79 Caenorhabditis elegans Ce 737951 32.87 121316 33.07 384627 45.45 Schizosaccharomyces pombe Spo 432364 32.67 22638 30.44 813101 40.41 Arabidopsis thaliana At 1256492 33.20 270913 32.05 1015490 45.75 Schistosoma mansoni Sm 37773 36.12 11760 35.94 39913 40.05 Danio rerio Dr 232819 35.07 41465 34.21 147459 47.90 Saccharomyces cerevisiae Sc 1124634 36.06 17273 32.90 2356815 40.14 Drosophila melanogaster Dm 1238086 37.56 227141 39.54 747774 52.85 Strongylocentrotus purpuratus Spu 38732 36.85 11450 34.43 24405 53.22 Xenopus laevis Xl 266034 39.32 183822 38.36 164675 48.08 Oryza sativa Os 567768 40.35 133898 36.74 394283 55.89 Trypanosoma brucei Tb 120949 43.19 1621 44.97 415684 50.73 Fugu rubripes Fr 624550 43.85 230616 43.22 342435 54.13 Zea mays Zm 611822 43.30 114505 41.21 305091 55.88 Mus musculus Mm 4571933 46.05 1708894 46.75 1475920 53.15 Anopheles gambiae Ag 29527 42.93 8028 44.33 24151 56.93 Gallus gallus Gg 563319 50.08 457259 46.15 369315 55.13 Toxoplasma gondii Tg 100004 50.01 32167 49.49 94260 54.51 Emericella nidulans En 75257 48.72 2888 47.13 83665 53.12 Aspergillus niger An 136216 48.46 15832 45.95 188982 56.48 Neurospora crassa Nc 154422 50.44 18685 48.88 254177 56.07 Leishmania major Lm 34029 57.27 54 50.00 70014 60.71 Chlamydomonas reinhardtii Cr 163614 59.70 49607 62.03 106939 66.55 Long homopolymer tracts are over-represented in non-coding sequences of AT-rich eukaryotes The observed frequencies of non-overlapping base i tracts of length N, , in different DNA regions were analyzed as a function of tract length N in all 27 organisms. In Figure 1 , we present the results of analyzing all 4 base tracts from Pf, Dm and Nc sequences as representative examples. The total (G+C)% of the DNA analysed from these organisms is 27.11%, 39.88% and 53.37%, respectively, representing typical low, median and high base composition eukaryotes. Very long tracts (high N) are rare, leading to low counts and large fluctuations in log ( ). Therefore, for each organism, any tract count observed to be less than 4 for a given tract length N was excluded from the data analysis in order to eliminate noise in the data. Getting rid of the interference caused by noisy data enhanced and clarified the comparisons we made from slope determinations. Some of the points in Figure 1 are not connected because they did not present contiguous data along the x-axis (tract length N). Figure 1 Comparison of the observed length N dependent poly(dA), poly(dT), poly(dC) and poly(dG) tract frequencies found in sequences from different DNA functional regions from the organisms Pf, Dm and Nc A. flanking sequences; B. intron sequences; C. coding sequences From Figure 1A , it is clear that the frequency of poly(dA).poly(dT) tracts in the very AT-rich Pf flanking regions are much higher than Dm and Nc tract frequencies. This becomes more pronounced when the tract length N becomes larger (N ≥ 7 bp) and reaches a maximum at around N ≥ 10 bp. On the other hand, the frequencies of poly(dG) or poly(dC) tracts in Pf are significantly lower and no differential N dependence is observed. Meanwhile, no significant difference can be observed between Dm and Nc, except for the higher values at longer N in Nc sequences. Similar behavior is also observed in intron sequences in Figure 1B . The over-representation of poly(dA) or poly(dT) tracts in intron regions is also evident at higher N values. However, this behavior is almost non-existent in coding regions (Figure 1C ), except for Pf, which is the most AT-rich eukaryote in all the 27 of our survey. This organism exhibited over-representation of poly(dA) tracts in its coding region as well as poly(dT) tracts as we shall see below. It is also very clear that the longer poly(dA) and poly(dT) tracts, usually of length larger than 20 bp, are only detected in flanking regions. In all cases, the plotted curves exhibited a transition region of changing slope, points falling between tract lengths 6 bp and 9 bp. This behavior, as we have described previously in D. discoideum DNA, leads one to conclude that the long poly(dA) and poly(dT) tracts are over-represented relative to random tract occurrence in random DNA sequences of equivalent base composition [ 1 ]. This is a fact that we illustrate and quantitate later in this study. By contrast, the nearly linear relationship of points in Figure 1 , for all the organisms' tracts of all types at lengths N ≤ 6 bp indicates a similarity that differs for each organism only by the individual linear relationships being offset from each other. This is a trivial consequence of the different base compositions of the DNAs, giving rise to frequencies of any given tract at levels near those expected based on random occurrence in that base composition. Comparing tract frequencies from single genes with those from whole chromosomes In order to confirm that there is consistency in the homopolymer tract frequency levels between single gene data and whole chromosome data in any given organism, we collected single gene data and whole chromosome data separately for representative organisms – Pf and Sc, where fully annotated whole chromosome sequences were available. For Pf, we collected chromosomes II and III. For Sc, we collected sequences for all 16 chromosomes. The single gene data were compared to the chromosome data of each organism respectively. For both organisms, the comparison results are similar and, therefore, we only display in Figure 2 representative data here for Sc single gene data compared with Sc chromosome IV, the largest of the 16 chromosomes. Since the whole chromosome data is only annotated with coding and non-coding regions, we combined the results from single gene data for intron and flanking regions, which were previously separated in our Figure 1 analysis, in order to make a consistent comparison with the whole chromosome data. Aside from poly(dT) tracts in coding sequences, the analyses showed no consistent significant differences. Therefore, we judge that our conclusions using single gene data are representative of whole chromosome data for the 27 eukaryotes we analyzed in this survey. Figure 2 Comparison of the four tract frequencies from Sc chromosome 4 sequences and Sc single gene sequences as a function of N, the tract length, calculated from: A. coding sequences; B. non-coding sequences. In the legend, "sg" represents "single gene" and "chr" represents "chromosome". Quantitating the over-representation of tracts We next wished to quantitatively compare for all 27 eukaryotes, the differences between the length N dependent frequencies of short tracts (N ≤ 6 bp) and long tracts (N ≥ 9 bp) of the type that we presented in Figure 1 . We designed the data analysis method by separating the Figure 1 x-axis into two regions of different tract behavior: N ≤ 6 bp and N ≥ 9 bp. The data points in the short tract range and those in the long tract range were treated separately. For short and long tract point regions separately, the average frequency, f slope , of the tract base i in the particular genome compartment were calculated. The f slope parameter is the "effective" base i frequency for the sequences in that DNA compartment that would give rise to the observed log ( ) vs. N dependent tracts frequency behavior in that region based upon the eqn. [1b] random model. The f slope is obtained from the inverse of P', where the slope [-log(P')] is obtained from the log ( ) vs. N type plots in Figure 1 , fit by eqn. [1b], as we present in Methods and have previously described [ 1 ]. This is a model that assumes random occurrence of tracts. Although it is known that DNA sequences do not occur randomly and that 1 st Order Markov chain behavior can describe some of the behavior of eukaryotic sequences, we have chosen here to compare the occurrence of tracts in real sequences to that of tracts in random DNA of equivalent base composition because the comparison is intuitively easy to grasp. The results from all 27 organisms are presented here in Figures 3 , 4 and 5 for flanking, intron and coding sequences respectively. The data are plotted as frequency ratios ( f slope / f seq : where f seq is the actual base frequency tabulated from all bases comprising the sequences in the real sequence compartment) plotted versus the overall real (G+C)% of each individual DNA sequence compartment studied. The higher the frequency ratio in Figures 3 , 4 and 5 , the higher is the enrichment or over-representation of the tract. It is a common feature for all the organisms that when N ≤ 6 bp, the ratio is near 1. Therefore, in all the Figures 3 , 4 , 5 , the trend lines developed by the linear regression fit of only the N ≤ 6 bp data have slopes close to zero and intersect the y-axis at a ratio near 1 to 2. The regression lines extrapolating to a ratio near 1.0 (Figure 3A , Figure 4A and Figure 5A ) indicate that N ≤ 6 bp tracts occur at frequencies expected for the base compositions found in each of the organisms' sequence compartments. Interestingly, this behavior occurs for the poly(dA).poly(dT) tracts in all three functional compartments – coding, intron and flanking DNAs. On the other hand, for N ≤ 6 bp poly(dG).poly(dC) tracts of all organisms, the regression lines all have a slightly negative slope, with flanking and intron sequences (Figure 3B and Figure 4B , respectively) exhibiting an intercept ratio of 2 or greater. This clearly indicates a trend to greater over-representation of short poly(dG).poly(dC) tracts in organisms of higher (A+T)% base composition. Figure 3 Comparison of the frequency ratio, fslope/fseq, to the real (G+C)% of the particular organisms' flanking DNA. The fslope is calculated from the slopes of Figure 1 types of graphs for short (N ≤ 6 bp) and long (N ≥ 9 bp) tract data found in flanking sequences from 27 organisms. A. poly(dA).poly(dT) tracts. The straight solid lines are linear regression fits for short tracts (N ≤ 6 bp) of each type and the dashed line (R 2 = 0.5591) demonstrates the trend in long (N ≥ 9 bp) tracts; B. poly(dG).poly(dC) tracts. The straight lines are linear regression fits for short tracts of each type (N ≤ 6 bp). Figure 4 Comparison of the frequency ratio, f slope / f seq , to the real (G+C)% of the particular organisms' intron DNA. The fslope is calculated from the slopes of Figure 1 types of graphs for short (N ≤ 6 bp) and long (N ≥ 9 bp) tract data found in intron sequences from 27 organisms. A. poly(dA).poly(dT) tracts. The straight lines are linear regression fits for short tracts (N ≤ 6 bp) of each type and the dashed line (R 2 = 0.5474) demonstrates the trend in long (N ≥ 9 bp) tracts; B. poly(dG).poly(dC) tracts. The straight lines are linear regression fits for short tracts of each type (N ≤ 6 bp). Figure 5 Comparison of the frequency ratio, fslope/fseq to the real (G+C)% of the particular organisms' coding DNA. The fslope is calculated from the slopes of Figure 1 types of graphs for short (N ≤ 6 bp) and long (N ≥ 9 bp) tract data found in coding sequences from 27 organisms , A. poly(dA).poly(dT) tracts. The straight lines are linear regression fits for short tracts (N ≤ 6 bp) of each type; B. poly(dG).poly(dC) tracts. The straight lines are linear regression fits for short tracts (N ≤ 6 bp) of each type. For N ≥ 9 bp tracts in coding sequences (Figure 5A & 5B ), there were not enough tracts to allow calculation of f slope values from the Figure 1 type data. However, in flanking and intron sequences (Figure 3A and Figure 4A ), the ratio was determined and is much higher than 1 for all the organisms, indicating that the poly(dA).poly(dT) tracts are significantly over-represented. The behavior of poly(dA).poly(dT) tracts in both flanking and intron sequences are similar and demonstrate a novel and interesting dependence of over-representation on the base composition of the organism's DNA. Starting at 30 %(G+C) and increasing to 50 %(G+C) (note linear fit trend line), these tracts are increasingly over-represented as the ratio trends from around 1.5 up to 4.0. For lengths N ≥ 9 bp in Figures 3B and 4B , we observed similar behavior for poly(dG).poly(dC) tracts. In a few organisms between 35%–50% (G+C), there is a high ratio of frequencies indicating a high level of over-representation. However, longer poly(dG).poly(dC) tracts are not over-represented and do not occur at long lengths as we show later in nearly as many organisms as we observed for poly(dA).poly(dT) tracts. Therefore, even though there appears to be evidence for a trend in these figures, we have not indicated with a negative slope linear trend line, mirror-image trend behavior to that exhibited by the poly(dA).poly(dT) tracts. The over-representation of long poly(dA).poly(dT) tracts exhibit exponential frequency increases compared to predicted values In order to show more clearly the genomic over-representation of the long poly(dA) and poly(dT) tracts, we introduced a variable, , representing the predicted frequency of base i at length N based on random tract occurrence in DNA of equivalent base composition. Eqn. [ 2 ] is used to calculate . The ratio of equals R , the Threshold. In Figure 6 , we plot log R vs. N for only the poly(dA) tract data from Dd, Os, Cr, representing genomes of low, median and high (G+C)% base composition, respectively. Similar comparative data for all 27 organisms was determined but is not shown here. For comparison purposes, we include in Figure 6 the tract frequency results determined for a random sequence of 10 6 nucleotides of 50% (G+C) composition generated with a random number generator. Each base position in that random sequence was picked from all 4 bases having equal probability (0.25) of being selected. In the randomly generated sequence, there were no tracts longer than 9 bp. The small inset panel in the upper left corner of the figure presents an enlarged view of N from 0 to 10. In this panel, as expected, the random sequence exhibits points with values closely centered around 0 on the y-axis. The only exceptions are for the points N = 6 and higher that are noisy and exhibit fluctuations as high as 0.15, due to the low number of tracts occurring at those sizes. Thus, there are no significant differences between and and . In contrast, the results from real organisms show very different behaviors. There are two regions, a linear part with slope around 0 when N is relatively small and an exponentially increasing frequency ratio when N increases beyond a certain value. In Figure 6 , two of the three organisms exhibit this exponentially increasing ratio. Os is the first to go above the 0 line when N is around 3 bp. A similar change to an exponentially increasing ratio was observed for Dd at a different N value of about 7 bp. This is the same tract size where we previously observed that poly(dA) and poly(dT) tracts begin to exhibit over-representation [ 1 ]. Of all the organisms, the Dd data exhibited one of the most significant over-representation levels- a 10 13 -fold enrichment of these tracts occurring at lengths about 45 bp. Figure 6 Comparison of f obs vs . f exp calculated for eukaryotes Dd, Os, Cr and a randomly generated sequence as a function of the tract length N. The and values for D. discoideum are presented Due to the small differences exhibited between the organisms when N is small, we present the small inset figure for the region from N = 0–10 bp enlarged for clarity. Another significant feature of the Dd data, is the large difference between and . This indicates that a high over-representation of long poly(dA).poly(dT) tracts likely occurs in this organism, as we saw in Figure 2 . It also makes clear that this organism utilizes poly(dA).poly(dT) tracts to sizes at least 40 bp longer in length than would be expected, = 14 bp, based upon the random tract occurrence calculated from its base composition. It is also the highest over-proportional tract size we observed, as we present later. Poly(dA).poly(dT) tracts show inverse correlation between (G+C)% composition and threshold value The concept of a threshold value was introduced to provide a description of the N dependence of the observed frequency of the tracts, , as it begins to rise significantly above that of the calculated . The threshold is a particular value of the log( R ), where R is defined by eqn.3. In this study, we chose not to attempt a universal definition of the over-representation criterion. Rather, we decided to examine various thresholds that defined different over-representations beginning at 0.3, where the is 2 times greater than the , with increasing values up to 1.0, where the is exactly 10 times that of the . We present these data in Figure 7A for poly(dA) tracts, where the N values achieved at the different thresholds are plotted vs . the (G+C)% composition of the DNA. A negative slope between (G+C)% and N at threshold for poly(dA) tracts in flanking sequences was observed for all thresholds. Poly(dT) tracts displayed similar behavior (data not shown). The changing slopes of the linear correlation lines shown in Figure 7A exhibit a progression from highest negative slope at threshold 0.3 to lower negative slope at threshold of 1.0. Thus, the lower the (G+C)% base composition of the genome, the higher the N at which over-representation of poly(dA) tracts occurs. For poly(dC) and poly(dG) tracts in flanking sequences, a positive linear correlation between the (G+C)% base composition and N at threshold was observed (data not shown). Interestingly, the slopes of the correlation lines also resulted in a progression of slope values. Thus, for poly(dC) tracts in flanking sequences, the lower the genome (G+C)% base composition, the lower the N at which over-representation occurs. Figure 7 The (G+C)% dependence of a series of calculated threshold values for enrichment of each homopolymer tract type In panel A. data is presented for the length N observed at the given series of threshold values for poly(dA) tracts from all 27 organisms. Slopes determined for each threshold from the type of representative data presented in A. were then calculated from all 27 organism to provide the values for poly(dG).poly(dC) and poly(dA).poly(dT) tracts within: B. coding; C. intron; D. flanking regions. The legend in panel D applies as well to panels B and C. For homopolymer tracts of each type in coding, intron and flanking DNAs, data of the type shown for poly(dA) tracts in Figure 7A were calculated and the linear fit slopes are presented in Figure 7 panels B-D, respectively. For all the data, the poly(dA).poly(dT) tracts exhibit uniformly negative slopes between -4 and -10, while the poly(dG).poly(dC) tracts all exhibit positive slopes between 4.5 and 7. In coding sequences, poly(dA) tracts showed a sharp drop between 0.7 and 0.8 while poly(dT) tracts exhibited a slow increase. No poly(dG) or poly(dC) tracts of significant length occur in coding DNA, which did not allow over-representation to be exhibited at these threshold values. Therefore, no slope points are shown. For intron sequences in panel C, poly(dG).poly(dC) tracts exhibited no significant consistent slope trend. However, poly(dC) tracts have overall greater slopes than poly(dG) tracts. Likewise for poly(dA).poly(dT) tracts, no trend is evident but the latter possesses significantly greater slopes than the former. For flanking sequences, all four tract types exhibited increasing slopes as threshold values increased. As was true for intron sequences, poly(dC) again had overall somewhat greater slopes than poly(dG) tracts. Similar behavior was observed for poly(dA).poly(dT) tracts in both intron and flanking DNA sequence types, with poly(dA) again occurring at greater slopes than poly(dT). The highest over-representation and over-proportional length of homopolymer tracts appear in median GC% organisms We next used the proportion, P , eqn.5 for all the 4 homopolymer tracts to compare the maximum observed tract size with the maximum tract size expected for random tract occurrence within that (G+C)% base composition DNA. If the P quantity is greater than 1, tracts are over-proportional in length and if P is less than 1, tracts are under-proportional in length. We present P , / , for coding, intron and flanking DNAs from all 27 organisms in Figure 8 panels A-C, respectively. The values are calculated from eqn.4. Large differences are obvious between coding and non-coding sequences. It is clear that tracts in coding regions, being mostly less than 1, are under-proportional in length for all base types. However, poly(dA).poly(dT) tracts are slightly under-proportional in length in GC-rich organisms, a fact that agrees with our previous observation of over-representation in tract frequencies in Figure 5 . Figure 8 The relationship of the (G+C)% of the DNA analyzed to the calculated / (P) for all the sequences of 27 organisms A. coding; B. intron; C. flanking. By contrast, the average behavior of intron and flanking regions in Figure 8 is that tracts of all types, but especially poly(dA).poly(dT) tracts, occur at significantly over-proportional lengths. This fact is consistent with their significant over-representation levels that we previously presented in Figures 3 , 4 , 5 . Very long poly(dA).poly(dT) tracts are observed in non-coding regions of some organisms, at lengths greater than 20 bp in excess of the expected length. Interestingly, the highest over-representation levels of tracts are found in organisms between 30% – 50 % (G+C) base composition. The only exception to this was found in Dd, the most AT-rich organism we studied, where the longest poly(dA) tracts were 71 bp. Higher poly(dG).poly(dC) tract frequencies than expected for organisms greater than 40 % (G+C) base composition were observed in Figure 3B and Figure 4B . The same was true for the most AT-rich ones – Dd and Pf. Figure 8 panels B and C correspondingly exhibit significant levels of over-proportional lengths of poly(dG).poly(dC) tracts, consistent with over-representation, for organisms greater than 30% (G+C) base composition and exhibit moderate over-representation of poly(dG).poly(dC) tracts for Dd and Pf. Discussion As a result of recent progress in the rate of DNA sequencing, the amount of sequenced DNA from many organisms has grown significantly. This has allowed our systematic study of the behavior of non-overlapping homopolymer tract frequencies in the 27 eukaryotes in this study spanning the 20% – 60 % (G+C) base composition range. Pre-processing of each of the 27 eukaryotes' largely single gene containing sequence files eliminated sequence redundancies that would introduce bias into the frequency calculations [ 39 - 41 ], that would not be representative of the biological genomes. In most organisms, well over 10% of the documents obtained were judged to contain redundant sequences and were removed by the CleanUP program (Table 1 ). From our results in this study, it is clear that long homopolymer tracts are over-represented in non-coding sequences, but not coding sequences, within eukaryotic genomes of all base compositions. This is perhaps not surprising considering that the coding sequence populations must satisfy the constraints of the triplet genetic code. In addition, organisms might minimize the numbers of tracts in coding regions to avoid the severe, even fatal frame-shift mutations that might be introduced by slippage-replication events at tracts [ 3 - 5 , 42 ]. In nearly all the organisms we studied, poly(dA).poly(dT) tracts were very much over-represented, beginning to be significantly enriched at lengths around 4–10 bp. These tracts also occurred at over-proportional lengths. This was particularly the case for organisms between 30% – 50% (G+C) composition, where over-proportional lengths were pronounced. By contrast, poly(dG).poly(dC) tracts, somewhat over-represented, do not occur at over-proportional lengths. This extends the findings of our previous D. discoideum DNA study that first described the tract over-representation transition region occurring at around 8–10 bp for poly(dA).poly(dT) sequences and their high over-proportional lengths [ 1 ]. Somewhat similar observations were made in a subsequent study of five eukaryotic organisms [ 35 ]. In general studies of repetitive sequences, poly(dA).poly(dT) tracts have been observed to be over-represented within eukaryotic genomes while poly(dG).poly(dC) tracts are significantly rarer [ 2 , 43 ]. Specific human repetitive sequences, such as the Alu elements, have been shown to contain long poly(dA).poly(dT) tracts, representing a significant repetitive sequence location for some of the over-represented tracts we observed in this study [ 44 ]. It has been suggested in a previous study that the over-representation occurring around 7–10 bp represented the minimum thermodynamic length required for any simple sequence repeat such as homopolymer tracts to undergo expansion by slip strand replication [ 35 ]. However, in our current study of 27 eukaryotes of widely varying base composition, we present more extensive results, especially those in Figure 7 , that demonstrate this is not the case. Depending upon the threshold tract size value chosen to express over-representation of the tracts, the N value where over-representation occurs for A tracts can be seen in Figure 7A to range for all the organisms from as low as 4–6 bp for 0.3 threshold (2× enrichment) to 8–11 bp for 1.0 threshold (10× enrichment). Furthermore, for poly(dA).poly(dT) tracts, the (G+C)% base composition vs . N slopes are negative for all thresholds, while for poly(dG).poly(dC) tracts the slopes are positive for all thresholds. This means that the base composition of the organism is the most important determinant of the particular threshold N value where over-representation begins and argues against an absolute solely thermodynamic determinant to the N value where over-representation begins via slip strand replication. In fact, our observed negative slopes for the poly(dA) tracts in Figure 7A , means that in higher (G+C)% composition organisms, the poly(dA) tracts become enriched at shorter N values than in (G+C)% poor organisms. This result is counter-intuitive to a thermodynamic argument, since the high (G+C)% base composition in neighboring sequences around a short poly(dA) tract in a high (G+C)% organism would be expected to resist the tract looping out to allow for slip strand replication because of the higher level of base stacking stabilization energy in the (G+C)-rich neighboring sequences. We believe that these (G+C)% composition dependent variable threshold N values we observed here are describing a complex mechanism that determines successful tract lengthening, rather than a single thermodynamic criterion for successful DNA looping during slip strand replication. The reason why poly(dG).poly(dC) tracts occur only at short lengths in eukaryotes may have to do with some interesting structural and energetic polymorphisms of these sequences. Even short tracts of this type have the ability to rearrange from the right-handed double helix to form G-quadraplex structures. These structures have been implicated in biological function in systems as diverse as eukaryotic immunoglobulin switch regions [ 45 ], telomeric repeats on chromosome ends [ 46 ] and promoter regions [ 34 ]. Therefore, eukaryotes may select against these tracts at any significant length in order to minimize problems resulting from the significant structural plasticity of these tracts. Another potential problem with these tracts is the fact that they represent potential reservoirs of oxidative damage. Recently, long-range electron transfer has been demonstrated to occur through the delocalized molecular orbitals of the stacked bases in the DNA double helix [ 47 ]. The electron transfer energy in these studies is insensitive to distance along the helix, but is sensitive to the level of base stacking. Therefore, these electron transfer events ultimately cause oxidative damage at GG dinucleotides, a base pair doublet that has high stacking levels. Even greater intensities of photo-damage were observed for GGG triplets. Therefore, eukaryotic organisms have a second compelling reason to mostly avoid the use of these homopolymer tracts at any significant length-a fact reflected in the data we have presented here. It must be kept in mind in these discussions of homopolymer tract over-representation, that these tracts represent only a subset of the larger sequence class of polypurines and polypyrimidines that exist in and are over-represented within all eukaryotes. In a study of over 700 sequenced chromosomes or long sequences contained in plasmids [ 48 ], a bias toward longer polypurine and polypyrimidine tracts in eukaryotes was reported as a function of length N, similar to the homopolymer poly(dA).poly(dT) tract frequency behavior we have reported here. We have previously observed that the long (N>10 bp) poly(dA).poly(dT) tracts over-represented in D. discoideum DNA (1) were not randomly distributed within the sequences from that organism. In fact, they are arrayed with an average spacing that corresponds to the repeating nucleosome DNA length found experimentally in D. discoideum chromatin [ 18 ]. And in that study, adjacent long pairs of tracts plus the intervening non-tract DNA were found to occur within a length corresponding to the internucleosomal linker DNA size found in D. discoideum chromatin. These results suggest that the long tracts only occur in restricted locations in chromatin. This supposition is supported by more recent experimental studies in D. discoideum chromatin compared to calculations of poly(dA).poly(dT) tract spacings in D. discoideum DNA (Marx, K.A., Zhou, Y. and Kishawi, I. unpublished results). That long poly(dA).poly(dT) tracts avoid being located within nucleosome core regions was experimentally determined from sequencing studies of native chicken erythrocyte chromatin [ 17 ]. In agreement with this line of reasoning, recent studies have shown that the nucleosome structure readily incorporates DNA containing short tracts, such as the sequence A 5 TATA 4 , but longer tracts such as those found in the sequence A 15 TATA 16 , completely disrupt the phasing of nucleosomes [ 49 ]. Short tracts not only are incorporated into nucleosomes, but they actually represent more stable than average nucleosome positioning sequences when they occur in-phase with the helical turn at roughly every 10 bp [ 50 ]. In human NF1-the Alu repeat element is blocked in vitro from forming a nucleosome by the presence of a bipartite T 14 A 11 tract sequence [ 51 ]. Different investigators have postulated two additional functions for tracts. The first is their use as promoters. This function may be synergistic with the long poly(dA).poly(dT) tracts preventing the formation of nucleosome structures. The second is as DNA binding sequences for specific poly(dA).poly(dT) tract binding proteins that possess some as yet unknown function. There are a number of reports that poly(dA).poly(dT) tracts function in eukaryotes as promoter sequences. In D. discoideum DNA, the actin genes contain a remarkably long (45 bp) promoter upstream of the TATA box [ 52 ]. In this study, the length of the tract was shown to correlate with the transcriptional level of these genes. A number of studies have demonstrated similar long tract promoter activity in yeast promoter regions [ 32 , 53 , 54 ] and in various mammalian [ 55 ] and human promoters [ 56 ]. In many of these studies, it was demonstrated that the promoter activity of the long tracts was correlated with this sequence being nucleosome free or not complexed with a protein. In the case of potential tract function where proteins bind to long poly(dA).poly(dT) tracts, there are a few investigated examples. The small protein datin, 13 kD, has been isolated from S. cerevisiae cells [ 57 ]. It has a required tract-binding site that is 9–11 bp in length and its function upon tract binding is unknown. Two high affinity poly(dA).poly(dT) tract-binding proteins, 70 and 74 kD species of unknown function, have been identified in D. discoideum [ 58 ]. Another example of a tract binding protein has been discovered in D. discoideum , where some 200 copies of terminal repeat retrotransposons are under transcriptional control by a 134 bp DNA control element [ 59 ]. Within this control element, a nuclear protein called CMBF binds to two almost homopolymeric 24 bp poly(dA).poly(dT) sequences. This CMBF protein contains so-called 'A.T hook' regions that interact with a 5–6 contiguous A:T base pair tract. These 'A.T hooks' are found in a number of other (A+T)-rich sequence binding proteins such as HMG-I, DAT1 from yeast, D1 from D. melanogaster and human UBF. In summary, it is unclear what functions these various pure poly(dA).poly(dT) tract binding proteins serve, and how their binding occurs at specific tracts while other tracts remain free of protein. The one point that can be stated with certainty is the correlation between tract binding site size (8–11 bp) of the proteins and the upper threshold (8–11 bp) size where tracts become significantly over-represented or enriched in our study. We believe that this similarity is not coincidental and is a consequence of some functional linkage. A novel aspect of our study was that for both flanking and intron sequences the over-representation of the poly(dA) and poly(dT) tracts were actually more pronounced in less (A+T)-rich organisms as compared to the most (A+T)-rich, Dd and Pf. Also novel was that poly(dA) and poly(dT) tracts showed negative slopes between the organisms' DNA (G+C)% composition and the threshold value. Thus, the higher the (G+C) base composition, the lower the tract length at which over-representation occurs. In fact, the highest over-representations of homopolymer tracts were observed in median (G+C)% organisms from 30–50%. Also, the distribution was almost symmetric with respect to the different organisms' (G+C)%. We believe that these results could be explained as a result of the insertion of retrotransposon elements into DNA. Eukaryotic transposons are known to be a widely occurring class of repeated DNA sequences ranging in size from about 1 kb to 8 kb. They contain inverted sequence repeats at their termini. The most common transposon class is comprised of retrovirus-like transposons [ 60 ], thought to arise from the integration of retroviral RNA sequences into a given eukaryotic genome. The resulting retrotransposon elements do not represent infectious viral DNA and are not transcribed since they lack accompanying promoter sequences. These DNA sequences do possess a poly(dA).poly(dT) tract that resulted from the 3' poly A tail on the original viral mRNA that formed the retrotransposon. Typical retrovirus-like retrotransposons, such as copia in D. melanogaster and IAP in M. musculus , are known to occur in thousands of copies in their respective genomes [ 60 ]. Therefore, inserted retrotransposon elements represent the likely origin of the excess over-representation of poly(dA).poly(dT) sequences that we observed in the majority of eukaryotes in this study, irrespective of their overall base composition. Methods The single copy gene DNA sequences from 27 eukaryotic organisms were retrieved from GenBank, EMBL, and DDBJ, the members of the tripartite, international collaboration of sequence databases [ 61 ]. Every search excluded: ESTs (expressed sequence tag), STSs (sequence-tagged sites), and GSSs (genomic survey sequence), and were limited to organism and genomic DNA only. Moreover, sequences designated: "mitochondrion", "chloroplast", and "chromosome" were also excluded in the search query via these keywords using Boolean operators. In addition, the whole chromosome sequences from 2 of these organisms were also retrieved. The eukaryotic organisms covered are tabulated in Table 1 . The GenBank documents were processed by the program "CleanUP", kindly provided by the Department of Biochemistry and Molecular Biology, University of Bari, Italy [ 62 ]. Our purpose in using the program was to get rid of redundancy in our sequence collections so that no bias would be introduced into the homopolymer tract distributions we calculated [ 36 ]. The settings for the program are: precision factor (0), different adjacent nucleotides (2), threshold similarity percentage for searching (95.0), overlapping percent for searching (50.0), local similarity percent (70.0), percent ambiguous symbols (e.g. N) to skip matches (10), overlapping percent for cleaning (90.0), minimum length for overlapping (1), minimum length for overlapping segment (20), sequence minimum length so that a sequence is processed, otherwise is cleaned (30). This application of "CleanUP" results in eliminating all the sequences less than 30 bp in length, with more than 20 bp overlapping with the primary sequence (the sequence used use as a basis for comparison), and possessing over 90% similarity with the primary sequence. Then the redundancy cleaned sequence files were input into the "Compile" program. Compile is part of the "MeltSim" program for the Windows suite of applications [ 37 , 63 ]. This program was used to extract raw sequences from the GenBank-formatted documents. Sequences of the functional categories, coding, intron and flanking were extracted according to their location tags. Respectively, "CDS" is for coding sequences, "intron" is for intron sequences, and "5'UTR", "3'UTR" and any other sequences excluding "CDS" and "intron" are all included into flanking sequences. The sequences were then concatenated into ASCII text files, one each for coding, intron and flanking. The ends of the individual sequences, as they appeared in the individual GenBank-formatted documents, were tagged to prevent the artifactual joining of those sequences that could result in the creation of artifactual long tracts. The basic characteristics of the coding, intron and flanking files that we used as computational start points for homopolymer tract frequency determination are summarized in Table 2 . Each file was subsequently analyzed using the program "Poly" [ 38 , 64 ], which calculates parameters for non-overlapping homopolymer tracts, including the total base count for each file, GC composition, and the numbers and the frequencies of the homopolymer tracts of different lengths. Poly uses a moving window of 1 bp in length to differentiate tracts and spacers, taking into account the tags used to prevent the artifactual concatenation. These data and additional information are kept as data objects in the program and can be manipulated in various ways. Poly calculates the observed tract frequency of base i , , of length N by the formula: where is the number of observed tracts of base i at length N contained in each sequence and l seq is the total length of the sequence (total base count) in which those tracts were counted. Using the relationship: the of tracts of length N can be related to N and P'. The parameter P' is the inverse of the frequency, f slope , of the tract base i in the particular genome compartment and is determined from the slope [-log(P')] of an eqn. [1b] plot. The f slope quantity, which represents an effective base frequency for that DNA, can be determined for a given set of data, and then compared to the real frequency for that base occurring in the sequences being examined, as we have previously described [ 1 ]. The expected frequency, , of a homopolymer tract of length N randomly occurring is calculated by the formula: where base frequency is the fractional base composition of the tract base i within the DNA for that file, and N is the tract length. The level of tract representation for base i is then calculated as the ratio of observed to predicted frequencies, defined as "Representation"( R ): Values larger than 1 indicates base i tracts are "over-represented", while values less than 1 indicate tracts are "under-represented". The log( R ) verses N plots presented in Figure 6 were generated using the program Gnuplot [ 38 , 65 ]. The values of N at 0.3 to 1.0 of the log( R ) were found using linear interpolation of the data. We term these N values, thresholds , corresponding to particular enrichments of tract occurrence. The maximum expected length of a homopolymer tract of base i , , given the base composition of the entire sequence, , is calculated by the formula: The length of a given homopolymer tract can then be compared to its expected length by taking the ratio of the longest observed length, , to the longest expected length, . This parameter is defined as "Proportion ( P )". Thus, we have the formula: P values larger than 1 are called "over-proportional" and P values less than 1 are "under-proportional". P represents the tract length comparison on the x-axis of Figure 8 , which is complementary to the parameter R for tract frequency comparisons on y-axis. They are both important parameters for the evaluation of the frequency and length distributions of any tracts. Authors' contributions YZ carried out the sequence data collection, clean-up, and analysis, and drafted the major part of the manuscript. JWB contributed both major programs Meltsim and Poly in this study, and also participated in analyzing data and drafted part of the manuscript. KAM conceived the study, participated in its design and coordination and revised and finalized the manuscript in all its revisions. All authors read and approved the final manuscript. The authors acknowledge the contribution from a reviewer of the concepts of retroelement insertion and differential polymerase selectivity as potential origins for the higher A & T tract frequencies than G&C tract frequencies that we describe here. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC539357.xml |
534101 | Zinc/copper imbalance reflects immune dysfunction in human leishmaniasis: an ex vivo and in vitro study | Background The process of elimination of intracellular pathogens, such as Leishmania , requires a Th1 type immune response, whereas a dominant Th2 response leads to exacerbated disease. Experimental human zinc deficiency decreases Th1 but not Th2 immune response. We investigated if zinc and copper levels differ in different clinical forms of leishmaniasis, and if these trace metals might be involved in the immune response towards the parasite. Methods Blood was collected from 31 patients with either localized cutaneous (LCL), mucosal (ML) or visceral (VL) leishmaniasis, as well as from 25 controls from endemic and non-endemic areas. Anti- Leishmania humoral and cellular immune response were evaluated by quantifying specific plasma IgG, lymphoproliferation and cytokine production, respectively. Plasma levels of Cu and Zn were quantified by atomic absorption spectrophotometry. Results A significant decrease in plasma Zn was observed in all three patient groups (p < 0.01 for LCL and ML, p < 0.001 for VL), as compared to controls, but only VL (7/10) and ML (1/7) patients displayed overt Zn deficiency. Plasma Cu was increased in LCL and VL (p < 0.001) but not in ML, and was strongly correlated to anti- Leishmania IgG (Spearman r = 0.65, p = 0.0028). Cu/Zn ratios were highest in patients with deficient cellular (VL<<LCL<ML) and exacerbated humoral (VL>LCL>ML) immune response. Ex vivo production of parasite-induced IFN-γ was negatively correlated to plasma Cu levels in LCL (r = -0.57, p = 0.01). In vitro , increased Cu levels inhibited IFN-γ production. Conclusions 1. Zn deficiency in VL and ML indicate possible therapeutic administration of Zn in these severe forms of leishmaniasis. 2. Plasma Cu positively correlates to humoral immune response across patient groups. 3. Environmentally or genetically determined increases in Cu levels might augment susceptibility to infection with intracellular pathogens, by causing a decrease in IFN-γ production. | Background Leishmaniasis is endemic in several parts of the world, with a global prevalence of over 12 million cases and 1.5–2 million new cases emerging every year [ 1 ]. The infection is caused by protozoan parasites of the genus Leishmania , transmitted through the bite of the sand fly vector. Several Leishmania species are able to cause a wide spectrum of clinical manifestations, ranging from the mild cutaneous form, the disfiguring mucosal form and the life-threatening visceral form, also known as kala-azar. In Brazil, Leishmania (L.) braziliensis causes cutaneous and mucosal disease, L. amazonensis causes cutaneous and, sporadically, visceral disease, while L. chagasi is exclusively associated with visceral disease. The clinical outcome of infection thus not only depends on the species involved, but also on the patient's immunocompetence. In recent years, a protective immune response against intracellular pathogens, such as Leishmania, Listeria and mycobacteria, has been defined as type 1 (Th1), whereas protection against extracellular pathogens, such as helminths, requires a type 2 (Th2) response. The murine model of experimental leishmaniasis has been instrumental for the elaboration of the Th1/Th2 paradigm, inasmuch as the preferential action of Th1 (IFN-γ, IL-12, TNF-α) or Th2 cytokines (IL-4, IL-5, IL-10) results in cure or progression of the disease, respectively [ 2 , 3 ]. In human leishmaniasis, this Th1/Th2 dichotomy is much less explicit for in vitro or ex vivo cytokine production. However, striking differences in cellular (lymphoproliferation and IFN-γ production) and humoral (total and anti- Leishmania IgG) immune response can be observed in different clinical forms of the disease. Our group has recently shown that patients with localized cutaneous leishmaniasis (LCL) display a diminished Th1 response during the early phase of disease, which is reverted after treatment [ 4 ]. In mucosal leishmaniasis (ML), on the other hand, an exacerbated Th1 response with increased IFN-γ and TNF-α levels, is believed to provoke tissue destruction [ 5 ]. In patients with visceral leishmaniasis (VL), characterized by immunosuppression and absence of IFN-γ production [ 6 ], we were able to show the beneficial effect IFN-γ in vivo [ 7 ]. Although zinc deficiency has been shown to lead to a selective Th1 deficiency in human volunteers [ 8 ], only few data are available on the role of trace elements in human leishmaniasis, being restricted to Old World LCL, showing increased serum copper and decreased serum zinc in Turkish LCL patients infected by L. major [ 9 ]. In this study, we investigated if Zn and Cu levels differ in different clinical forms of the disease, and if these trace metals might be correlated to anti-parasite immune response. Methods Blood samples (10 ml heparinized tubes, Vacutainer) from 21 patients and 15 healthy controls (mostly patient's relatives) were obtained in an outpatient clinic in the district of Corte de Pedra, (Bahia state, Northeast of Brazil). This rural area has a low socio-economic status and a high incidence of infection with Leishmania braziliensis and, sporadically, Leishmania amazonensis . During a one year period, 14 patients with LCL (single lesion with less than 4 weeks of duration) were selected and treated (20 mg/kg of Sb (Glucantime) i.v. during 20 days). Of those, only 7 patients returned to draw blood after 3 months of treatment, but all patients cured during follow-up. Seven patients with ML were selected after several rounds of unsuccessful treatment and severe disease progression. Blood samples from 10 patients (at diagnosis, before treatment) with VL were obtained from two different urban areas (Salvador-Bahia and Teresina-Piaui). Ten healthy urban controls were recruited among students and laboratory staff (Salvador-Bahia). Diagnosis was confirmed by Montenegro skin test, serology, direct culture of parasites from lesions [ 4 ] or bone marrow aspiration for VL [ 7 ]. This study was approved by the Ethics Committee of the University Hospital Edgard Santos, Salvador. Cu and Zn were quantified by atomic absorption spectrophotometry (Varian 220) using an air/acetylene flame. One ml of plasma was diluted tenfold with 0.05 % Triton X-100, 1 % HNO3, sonicated for 10 min and analyzed in triplicate. All reagents used were analytical grade (Merck). Due precautions were taken to avoid external and internal (hemolysis) trace metal contamination. In order to investigate the influence of trace metals, antibodies and other endogenous plasma components, such as cytokines, on the ex vivo cellular immune response, we used a recently described model using whole blood and live Leishmania promastigotes [ 10 ], closely mimicking the early in vivo events following a sandfly bite. Whole blood was diluted tenfold in culture medium (RPMI supplemented with L-glutamine and gentamycin, all from Gibco-BRL) and stimulated with L. amazonensis promastigotes (10 5 /mL). Buffy coats from normal blood donors were used to obtain large quantities of cells required for in vitro experiments to examine the effect of exogenous trace metals upon cytokine production. Mononuclear cells were separated by density gradient centrifugation (Ficoll-Paque, Pharmacia, Uppsala, Sweden) and cultured in complete culture medium (supplemented with 10 % fetal calf serum, Gibco-BRL). Cu and Zn concentrations were below the detection limit in RPMI and less than 2 μM in complete culture medium. Supernatants were collected after 72 h of culture and frozen in aliquots for cytokine determination. Lymphoproliferation was quantified by measuring [H]-thymidine incorporation after 120 h of culture. IFN-γ, TGF-β1, TNF-α and IL-5 in plasma or culture supernatants were quantified using commercial ELISA kits (DuoSet, R&Dsystems). All results are expressed as mean ± SEM. Statistical evaluation of data was performed using GraphPad Prism software: Mann-Whitney test for comparing patients and control groups, Spearman rank test for correlation and Wilcoxon signed rank test for comparing in vitro treatments; a p-value <0.05 was considered significant. Results A significant decrease in plasma Zn was observed for both LCL and ML patients, as compared to controls from the same endemic area (0.80 ± 0.04 and 0.77 ± 0.05 vs. 1.01 ± 0.06 μg/mL, p < 0.01, Figure 1A ), and in VL patients, as compared to urban controls (0.55 ± 0.08 vs. 0.83+/-0.03 μg/mL, p < 0.001, Figure 1A ). Zn deficiency (plasma Zn <0.65 μg/mL), however, was observed only in VL (7/10) and ML (1/7) patients. As shown in Fig. 1B , plasma Cu was significantly increased in LCL (1.32 ± 0.10 vs. 1.01 ± 0.05 μg/mL, p < 0.001) and VL (1.42 ± 0.13 vs. 0.72 ± 0.06 μg/mL, p < 0.001), but not in ML (1.04 ± 0.05 vs. 1.01 ± 0.05 μg/ml). After three months of treatment, plasma Zn increased and Cu decreased in LCL patients, resulting in values indistinguishable from endemic controls. Although within normal physiological ranges [ 11 ], Cu and Zn levels were significantly increased in healthy controls from the endemic area, as compared to urban controls (p < 0.01, Fig, 1A and 1B ). Cu/Zn ratios, however, were similar in both control groups (p = 0.12), but significantly increased in all three patient groups (Fig. 1C ), reaching a three-fold molar excess of Cu to Zn in VL patients. To determine if these observations reflect possible changes in the patients' immune status, we quantified humoral and cellular anti- Leishmania immune response ex vivo and correlated them to trace element levels. When comparing patient groups, a stepwise increase in anti- Leishmania IgG and Cu/Zn ratio can be observed, with ML<LCL<VL (Table I and Fig. 1 ). In addition, a highly significant correlation between plasma Cu, but not Zn or Cu/Zn ratio, and anti- Leishmania IgG was observed across patient groups (Fig. 2 , Spearman r = 0.65, p = 0.0028). Since lymphoproliferation and IFN-γ production were virtually absent in VL, correlation with trace element levels across patient groups could not be calculated, but an inverse order was observed, with ML>LCL>>VL (Table I ) as compared to Cu/Zn ratio with ML<LCL<<VL (Fig. 1 ), confirming reciprocal regulation of Th1/cellular immune response and Th2/humoral immune response. However, we found a significant negative correlation between plasma Cu and ex vivo IFN-γ production (Spearman r = -0.86, p = 0.024) in untreated patients only, whereas no significant correlation was observed for Leishmania -or mitogen-induced lymphoproliferation, TGF-β1, TNF-α and IL-5 levels in plasma or culture supernatants (not shown). To verify the hypothesis that increased Cu levels might down-regulate IFN-γ production, we added Cu (10 μM, corresponding to the increase of plasma Cu observed ex vivo in LCL patients) to mitogen-or anti-CD3-stimulated in vitro cultures from healthy controls. As shown in Figure 3 , 10 μM of CuCl 2 significantly decreased anti-CD3-induced IFN-γ production (40.5 ± 9.3 % inhibition, p < 0.05). Interestingly, the addition of physiological concentrations of Zn (10–30 μM) to ex vivo or in vitro cultures did not revert the apparently inhibitory effect of endogenous or exogenous Cu on IFN-γ secretion. (not shown). Discussion Although plasma Zn was significantly decreased in all three patient groups, plain Zn deficiency was only observed in seven VL patients and one ML patient, being absent in LCL patient and in both control groups. In parallel, plasma Cu in VL patients increased to levels which have been shown to be toxic in vitro [ 12 ]. In addition, a highly significant positive correlation between plasma Cu and parasite-specific IgG across patient groups suggests that the trace element might interfere in anti- Leishmania immune response, e.g. by leading to a non-protective Th2/humoral immune response, known to be exacerbated in visceral leishmaniasis. Increased plasma Cu cannot be considered as a mere marker of inflammation, since it was not observed in ML, a chronic inflammatory condition characterized by high production of pro-inflammatory cytokines, such as TNF-α and IFN-γ [ 5 ]. Absence of correlation between TNF-α and trace metal levels underscores the specificity of the inhibitory effect of Cu upon IFN-γ production, which might in fact be the upstream event to an increased humoral anti- Leishmania response. Since our in vitro findings indicate Zn as a monocyte/macrophage activator [ 13 ], the significant decrease in Zn in ML patients might be responsible for the inability of the patients to clear the parasite, in spite of high IFN-γ levels and several rounds of treatment. LCL patients represent an intermediate group between ML and VL, displaying a detectable, but variable humoral and cellular immune response with production of both Th1 and Th2 cytokines, undergoing a shift towards the Th1/cellular pole after successful treatment [ 4 ]. We observed a reciprocal association between Cu/Zn levels and humoral and cellular immune response between the three patient groups (Table I and Fig. 1 ), as well as a complete reversal of increased Cu/Zn ratios after treatment in LCL patients. Taken together, these data indicate that Cu/Zn imbalance might serve as a marker for decreased Th1 response and immunodeficiency in leishmaniasis, being more pronounced in its most severe and possibly fatal visceral form. Unfortunately, no clinical follow-up was possible in VL patients, but mortality, mostly due to co-infections, and therapeutic failure occur in 6,1 % of the cases [ 14 , 15 ]. It is tempting to speculate that environmental exposure to copper might increase susceptibility to Leishmania and other intracellular pathogens, such as Listeria and mycobacteria, e.g. by directly interfering with cytokine production as previously shown [ 16 ]. Increased plasma Cu in endemic controls might reflect the use of Cu as a fungicide in cocoa plantations in the Corte de Pedra area. It should be stated that spouses and relatives were preferentially chosen as endemic controls, since environmental exposure and nutritional status are far more important determinants for Cu and Zn levels than sex or age [ 11 ]. In addition, (epi)genetic factors related to copper homeostasis might render normal individuals more susceptible to copper toxicity [ 17 ]. Thus, increased Cu levels and decreased Zn levels might be a cause, rather than a consequence of LCL. On the other hand, absence of Cu increase, linked to uncontrolled IFN-γ production, might underlie evolution towards ML. Long-term follow-up of treated patients and comparison with other endemic areas might learn if trace metal levels have predictive value for clinical evolution of and/or susceptibility to leishmaniasis. In addition, we propose that trace metal levels should be taken into account in vaccine strategies for leishmaniasis, because of the importance of Zn in a protective Th1 response [ 8 ] and because of the possibly deleterious effect of Cu described in this study. Two recent large-scale vaccination trials for cutaneous and visceral leishmaniasis [ 18 , 19 ] were carried out in regions where Zn deficiency is prevalent, namely Iran and Sudan, which might have contributed in part to the low protection rate observed in both trials. Administration of Zn in vivo has been shown to down-regulate increased Cu levels in patients with Wilson disease and to revert its toxicity [ 20 ], suggesting that systemic administration of Zn might be beneficial in addition to its direct immunostimulatory effect [ 8 ]. A recent report [ 21 ] demonstrated the safety and efficiency of oral Zn in Old World cutaneous leishmaniasis, a mild and self-healing form of the disease, with patients displaying normal Zn levels. Our results strongly suggest that zinc therapy should be considered in the mucosal and visceral forms of leishmaniasis, associated with high morbidity and mortality, as well as frequent failure of antimonial therapy. Conclusions Zn deficiency in visceral and mucosal leishmaniasis indicate possible therapeutic administration of Zn in these severe forms of leishmaniasis. Plasma Cu positively correlates to (non-protective) humoral immune response across patient groups, and reciprocally, increased Cu levels decreased in vitro (protective) IFN-γ production, implying that environmentally or genetically determined increases in Cu levels might augment susceptibility to infection with intracellular pathogens. Our data indicate that Cu/Zn imbalance can be a useful marker for immune dysfunction in leishmaniasis and suggest that trace metals are implicated in both humoral and cellular anti- Leishmania immune response, which should inspire future strategies for therapy and immunoprophylaxis of human leishmaniasis. Competing interests The author(s) declare that they have no competing interests. Authors' contributions JVW designed the scientific project, analyzed the data and wrote the paper. GS was responsible for sample processing and data collection. AD selected LCL and ML patients, as well as controls from the endemic area. CHC selected VL patients. AFS did the trace metal analysis. AB and EMC supervised the field work and reviewed the paper. MBN analyzed the data and reviewed the paper. Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC534101.xml |
526263 | Acetylcysteine for prevention of contrast-induced nephropathy after intravascular angiography: A systematic review and meta-analysis | Background Contrast-induced nephropathy is an important cause of acute renal failure. We assess the efficacy of acetylcysteine for prevention of contrast-induced nephropathy among patients undergoing intravascular angiography. Methods We conducted a systematic review and meta-analysis of randomized controlled trials comparing prophylactic acetylcysteine plus hydration versus hydration alone in patients undergoing intravascular angiography. Studies were identified by searching MEDLINE, EMBASE, and CENTRAL databases. Our main outcome measures were the risk of contrast-induced nephropathy and the difference in serum creatinine between acetylcysteine and control groups at 48 h. Results Fourteen studies involving 1261 patients were identified and included for analysis, and findings were heterogeneous across studies. Acetylcysteine was associated with a significantly reduced incidence of contrast-induced nephropathy in five studies, and no difference in the other nine (with a trend toward a higher incidence in six of the latter studies). The pooled odds ratio for contrast-induced nephropathy with acetylcysteine relative to control was 0.54 (95% CI, 0.32–0.91, p = 0.02) and the pooled estimate of difference in 48-h serum creatinine for acetylcysteine relative to control was -7.2 μmol/L (95% CI -19.7 to 5.3, p = 0.26). These pooled values need to be interpreted cautiously because of the heterogeneity across studies, and due to evidence of publication bias. Meta-regression suggested that the heterogeneity might be partially explained by whether the angiography was performed electively or as emergency. Conclusion These findings indicate that published studies of acetylcysteine for prevention of contrast-induced nephropathy yield inconsistent results. The efficacy of acetylcysteine will remain uncertain unless a large well-designed multi-center trial is performed. | Background Contrast-induced nephropathy is a leading cause for acquired acute reductions in kidney function [ 1 , 2 ]. Despite advances in supportive therapy, the incidence of contrast-induced nephropathy may continue to increase significantly with the broader utilization of radiocontrast media for diagnostic and interventional procedures.[ 3 ] Furthermore, contrast-induced nephropathy is associated with a greater risk of in-hospital morbidity, mortality, prolonged hospitalization, increased health care costs and potentially irreversible reduction in kidney function [ 4 - 8 ]. The pathophysiology of contrast-induced nephropathy remains incompletely understood. However, current evidence suggests that contrast media induce prolonged vasoconstriction and medullary ischemia coupled with generation of free radicals and oxidative injury to tubular cells [ 9 - 11 ]. Acetylcysteine, a thiol-containing anti-oxidant, has been hypothesized to prevent contrast-induced nephropathy. The potential benefit of acetylcysteine is believed to be mediated by its properties as a scavenger of free-radical species and by increasing the synthesis of nitric oxide, a potent vasodilator, in response to ischemic or other toxic injury in the kidney [ 12 ]. Given the recent publication of a series of randomized controlled trials assessing the efficacy of acetylcysteine in preventing the decline in kidney function following contrast exposure associated with intravascular angiography, we sought to conduct a systematic review and meta-analysis of these trials. The specific objectives of our meta-analysis were to assess the effect of acetycysteine on 1) the dichotomous endpoint of contrast-induced nephropathy (yes/no) and 2) serum creatinine levels following the administration of contrast media. We also conduct a meta-regression analysis to determine whether particular clinical or study quality factors influence the apparent effect of acetylcysteine on risk of contrast-induced nephropathy. Methods Search strategy We identified published randomized controlled trials of acetylcysteine for prevention of contrast-induced nephropathy during intravascular angiography using both electronic and manual search strategies. We supplemented this by scanning the reference lists of all identified articles, reviewing selected conference proceedings, and by contacting experts in the field. All languages and types of publications were considered eligible. The comprehensive literature search was initially performed in April 2003 and updated in June 2004 to identify any potential new studies that may have appeared. MEDLINE (1966 through April, 2003), EMBASE (1980 through April, 2003) and CENTRAL (Cochrane Controlled Clinical Trials Register 1996 through April, 2003) databases were searched via OVID using an approach recommended for systematic reviews of randomized trials [ 13 ]. PubMed was also searched [ 14 ]. We derived three comprehensive search themes that were then combined using the Boolean operator 'and'. The first theme used a recommended highly sensitive randomized controlled trial filter and systematic review filter method [ 15 ]. The second theme, contrast-induced nephropathy, was created by using the Boolean search term 'or' to search for the following terms appearing as both exploded medical subject headings (MeSH) or text words: 'contrast media' or 'radiocontrast' or 'kidney failure' or 'acute renal failure' or ' chronic renal failure' or 'contrast nephropathy' or 'dialysis'. The third theme, acetylcysteine, was created by a search using an exploded MeSH heading and textword search for: 'N-acetylcysteine' or 'NAC' or 'acetylcysteine' or 'Mucomyst'. Study selection criteria Two individuals (SMB and WAG) independently evaluated identified articles for eligibility on the basis of four inclusion criteria: 1) study design (randomized controlled trials), 2) target population (patients undergoing intravascular angiography), 3) intervention (trials of acetylcysteine plus hydration versus control) and 4) outcome (trials with explicit definition of contrast-induced nephropathy). Data extraction Two reviewers (SMB and WAG) independently extracted data from all primary studies fulfilling eligibility criteria. Any discrepancies in extracted data were resolved by consensus. Data extracted included identifying information, focus of the study, details of study protocol and demographic data. The primary outcome measures were the incidence of contrast-induced nephropathy and change in serum creatinine. The secondary outcome measure was requirement for renal replacement therapy. Authors of the studies were contacted for additional information when applicable. Assessment of methodological quality Two reviewers (SMB and WAG) independently assessed methodological quality of individual studies. Any disagreements were resolved by consensus. Items used to assess study quality were methods of randomization, any blinding, use of a placebo, reporting of losses to follow-up or missing outcome assessments, and evidence of important baseline differences between the groups [ 16 - 18 ]. An overall quality score was determined for each study as described by Jadad et al [ 16 ]. Prior hypotheses regarding sources of heterogeneity The presence of heterogeneity can compromise the interpretation and validity of meta-analyses and can result from significant differences in methodology, study populations, interventions, outcomes, or chance [ 19 ]. A priori consideration of potential factors contributing to heterogeneity for acetylcysteine in prevention of contrast-induced nephropathy included baseline serum creatinine levels, volume of contrast media, volume of hydration, age, diabetes mellitus, elective or emergency procedure, and a number of trial methodology factors. Statistical methods Data from all of the selected randomized controlled trials were combined to estimate the pooled odds ratio (OR) with 95% confidence intervals (CIs) using a random-effects model as described by Der Simonian and Laird [ 20 , 21 ]. The presence of heterogeneity across trials was evaluated using a chi-square test for homogeneity [ 22 ]. Meta-regression was performed to analyze for potential clinical and study quality factors that may influence treatment effects. We tested for potential publication bias using both a Begg's test for asymmetry and an Egger's test [ 23 , 24 ]. All statistical analyses were performed with Stata version 8.0 (StataCorp, College Station, TX). Results Identification of studies A total of 66 unique citations were identified by our initial search strategy (Figure 1 ). After the initial screen, 22 citations warranted further review. Among these, 15 citations were excluded: 8 were clinical reviews, 3 were prospective cohort studies, 2 were substudies of previously published randomized controlled trials, one did not include a control group, and one did not involve intravascular angiography. Therefore, we had identified 7 studies for inclusion. A repeat search of the literature conducted in June 2004 yielded seven additional eligible studies. Overall, 14 studies thus fulfilled our inclusion criteria [ 25 - 38 ]. All of these citations were identified by the electronic search strategy and are published in peer-reviewed journals [ 39 ]. Study characteristics All the randomized controlled trials were published in the years 2002 through 2004. Tables 1 and 2 present the characteristics of the 14 randomized controlled trials. A total of 1261 patients were studied in these 14 randomized controlled trials, among whom 631 received acetylcysteine and 630 were in control groups. There were 563 (44.6%) patients with diabetes mellitus, of whom 284 were assigned to receive acetylcysteine and 279 were assigned to a control group. The dosing and schedule of administration of acetylcysteine was variable across studies; however, in the majority of studies, acetylcysteine was initiated 12–24 h prior to angiography. In two trials, large doses of acetylcysteine were administered immediately prior to (within 1 h) and shortly following (within 3–4 h) angiography [ 26 , 29 ]. All patients were administered a hydration protocol around their procedure and all received low or iso-osmolar non-ionic contrast media. The definition of contrast-induced nephropathy was variable across studies. Four studies defined contrast-induced nephropathy as a > 44.2 μmol/L increase in serum creatinine from baseline [ 25 , 29 , 32 , 37 ], four used a > 25% increase in serum creatinine from baseline [ 26 , 30 , 33 , 35 ], four used either a > 44.2 μmol/L or a > 25% increase in serum creatinine from baseline [ 28 , 31 , 34 , 36 ], one used either a > 44.2 μmol/L or a > 33% increase in serum creatinine from baseline[ 38 ] and one study combined either a > 25% increase in serum creatinine from baseline or dialysis [ 27 ]. Generally, the time for ascertaining contrast-induced nephropathy for all studies was 48 h after the exposure to contrast media, with the exception of four studies, where presence or absence of contrast-induced nephropathy was determined at 24, 72 and 96 h [ 26 , 30 , 34 , 35 ]. Meta-analysis of incidence of contrast-induced nephropathy The reported incidence of contrast-induced nephropathy was variable across studies. Table 3 and Figure 2 present information on the incidence of contrast-induced nephropathy for all studies. Five studies provided evidence of a risk reduction for development of contrast-induced nephropathy with acetylcysteine [ 26 , 28 , 33 , 35 , 37 ], whereas nine studies reported no evidence of benefit [ 25 , 27 , 29 - 32 , 34 , 36 , 38 ]. Furthermore, six of the latter studies yielded an odds ratio > 1.0, suggesting a trend towards an increased risk of contrast-induced nephropathy [ 25 , 29 , 31 , 32 , 36 , 38 ]. The overall pooled odds ratio for development of contrast-induced nephropathy using a random-effects model was 0.54 (95% CI, 0.32–0.91, p = 0.022), suggesting a significant reduction in CIN with acetylcysteine (Figure 2 ). However, this pooled odds ratio should be interpreted with caution because the analysis comparing the occurrence of contrast-induced nephropathy across all studies revealed significant heterogeneity (chi-square = 23.96, p = 0.032). In total, six patients required dialysis, among whom two received acetylcysteine and two were in control groups. Group assignment was not reported for the other two patients who required dialysis. Meta-analysis of change in serum creatinine with acetylcysteine Table 3 shows a summary of the changes in serum creatinine across studies. The pooled estimate (using a random effects model) for the difference in 48 h serum creatinine between the acetylcysteine and control groups was -7.2 μmol/L (95% CI -19.7 to 5.3, p = 0.26) based on data available from eight studies [ 25 , 27 , 28 , 31 , 32 , 35 , 37 , 38 ]. This suggests no significant absolute change in serum creatinine with the administration of acetylcysteine (Figure 3 ). Again, this pooled estimate requires cautious interpretation owing to the availability of data from only eight studies and to the presence of significant heterogeneity across studies (Q = 50.9, p < 0.0005). The change in serum creatinine at 96 h was assessed in two studies as a primary outcome [ 26 , 30 ]. The pooled estimate for the difference in 96-h serum creatinine for these two studies was similarly non-significant [-1.8 μmol/L (95% CI -8.9 to 5.2, p = 0.61)]. Meta-regression Meta-regression was performed to assess a number of clinical and study quality factors that may have led to heterogeneity across studies. Interestingly, these analyses suggest that the heterogeneity may be partially explained by whether the angiography procedures were performed electively or as emergency, because studies where all enrolled patients were undergoing elective procedures had significantly lower odds ratios than did studies where emergency cases were included (coefficient for "elective-only" studies, -0.6, 95% CI, -1.24 to 0.03, p = 0.06). Other meta-regression analyses demonstrated that the heterogeneity could not be accounted for by differences in patient age (coefficient -0.04, 95% CI, -0.2 to 0.1, p = 0.6), baseline serum creatinine (coefficient -0.001, 95% CI, -0.01 to 0.01, p = 0.9), volume of contrast media (- 0.006, 95% CI, -0.02 to 0.07, p = 0.4) or diabetes mellitus (coefficient -0.01, 95% CI, -0.03 to 0.02, p = 0.6). Likewise, heterogeneity was not accounted for by differences in study quality including use of blinding (coefficient -0.6, 95% CI, -1.7 to 0.5, p = 0.3), concealment of randomization (coefficient -0.8, 95% CI, -3.8 to 2.1, p = 0.6), use of placebo (coefficient -0.6, 95% CI, -1.7 to 0.5, p = 0.30, consecutive patient enrollment (coefficient 0.5, 95% CI, -1.5 to 2.4, p = 0.6) or overall Jadad score (coefficient 0.05, 95% CI, -0.4 to 0.5, p = 0.8). There was some evidence to suggest possible publication bias according to Begg's test (p = 0.03, with continuity correction) and a trend with Egger's test (coefficient -3.03, 95% CI, -6.71 to 0.65, p = 0.09). Figure 4 demonstrates this graphically, as there is asymmetry in the funnel plot with a predominance of studies with large standard errors (i.e., usually small studies) showing benefit associated with acetylcysteine and a paucity of small negative studies. Discussion Our meta-analysis of 14 peer-reviewed studies of patients undergoing intravascular angiography may lead some to conclude that the administration of acetylcysteine causes a reduced incidence of contrast-induced nephropathy. However, such a conclusion may be premature based on data published to date because our systematic review reveals considerable heterogeneity of findings across trials. Furthermore, our meta-analysis of post-treatment creatinine values does not reveal any truly meaningful difference in serum creatinine levels at 48 h between the acetylcysteine and control groups. Finally, insufficient data are available to allow inferences to be drawn about the efficacy of acetylcysteine on clinically meaningful endpoints such as dialysis, length of hospitalization or mortality. This meta-analysis has several features that distinguish it from a similar meta-analysis by Birck et al that recently received considerable attention, and that rather firmly concluded that acetylcysteine is beneficial [ 40 ]. First, though our meta-analysis yielded a similar overall reduction in the incidence of contrast-induced nephropathy, we have included seven additional studies. Second, we have focused primarily on patients undergoing intravascular angiography. Third, we have used the pooled odds ratio across studies as a summary statistic because of its theoretical advantage to the use of relative risks in meta-analysis [ 21 ]. Fourth, we have included an analysis of differences in serum creatinine to complement the dichotomous endpoint of contrast-induced nephropathy. Fifth, we pointedly draw attention to the fact that there is some evidence to suggest publication bias, or at the very least funnel plot asymmetry. And finally, perhaps most importantly, we have explored the heterogeneity in results across studies in much greater detail than do Birck et al , and more directly address the relevance of this heterogeneity in the overall interpretation of study results. Two other meta-analyses have also recently been published, and similarly concluded that acetylcysteine is beneficial; however, these studies also failed to adequately address the issue of the considerable heterogeneity across studies [ 41 , 42 ]. Collectively, these three previously published meta-analyses unfortunately send a misleading bottom-line message to the medical community – that the evidence in favor of acetylcysteine is firm [ 43 ]. Many will correspondingly infer from these three 'positive' meta-analyses that there is no longer a need for primary research into the efficacy of acetylcysteine. Our global conclusion, meanwhile, is rather different, in that we more cautiously conclude that further data may be needed before a firm conclusion can be made regarding the efficacy of acetylcysteine. Two other very recent meta-analyses [ 44 , 45 ] make a similar conclusion to ours, though those meta-analyses do not include as many peer-reviewed and published studies as does our updated systematic review. The presence of heterogeneity and/or publication bias can compromise the interpretation of meta-analyses and result in erroneous and potentially misleading conclusions [ 19 , 43 ]. A striking example of early meta-analysis producing misleading results is that of intravenous magnesium in the treatment of acute myocardial infarction. The results of two meta-analyses of several small clinical trials on this treatment suggested a reduction in arrhythmias and mortality [ 46 , 47 ]. Furthermore, an argument was made at the time for the use of magnesium therapy because of ease of use, favorable side effect profile and low cost [ 47 , 48 ]. However, the subsequent publication of ISIS-4, a large multi-center trial involving over 58,000 patients, showed not only the absence of significant reduction in arrhythmias or mortality with magnesium, but in fact a trend towards an increased risk of heart failure [ 49 , 50 ], results that have since been further validated by publication of the MAGIC trial [ 51 ]. The early meta-analyses on intravenous magnesium were perhaps influenced by publication bias and the combination of data from several small randomized controlled trials [ 52 ]. There are many parallels between the intravenous magnesium story and our meta-analysis findings for acetylcysteine. The marked heterogeneity of findings across studies, and the finding of funnel plot asymmetry (indicating possible publication bias), ought to be viewed as strong cautionary points against making firm conclusions about the efficacy of acetylcysteine. And while it is true that acetylcysteine is inexpensive, easy to use and has a favorable side-effect profile, it is probably premature to conclude scientifically that it is definitely efficacious based on data published to date. Our firm conclusion based on this meta-analysis of published trails is that although the data seem quite promising, the efficacy of acetylcysteine has not been definitively proven. To isolate potential sources of heterogeneity we performed a meta-regression analysis exploring several clinical and study quality factors. There was no evidence of association between effect size and baseline serum creatinine, volume of contrast media, or diabetes mellitus, all independently identified risk factors for development of contrast-induced nephropathy [ 8 , 53 ]. However, whether the angiographic procedure was performed electively or as emergency showed a significant relation with the size of the acetylcysteine effect. The need to perform emergency cardiac angiography is common in patients presenting with suspected acute coronary syndromes. Patients undergoing emergency coronary angiography have been shown to have increased mortality and poor long-term survival, independent of the development of contrast-induced nephropathy [ 6 , 54 ]. Funnel plot asymmetry is often interpreted to indicate publication bias. However, it is important to consider that this asymmetry may also be due to other sources of bias that deserve further examination. In particular, fundamental disparities in study design, inconsistencies in methodological quality and differences in the definition of primary outcomes may have contributed to funnel plot asymmetry. Our meta-regression analysis explored the potential role of several study quality factors, and none were identified as statistically significant predictors of apparent acetylcysteine efficacy across trials. Nonetheless, it is quite possible that other unmeasured study quality factors may have contributed to biased results and accompanying funnel plot asymmetry. Contrast-induced nephropathy continues to be an active subject matter for clinical investigation [ 55 , 56 ]. A definitive randomized clinical trial comparing fenoldopam, a selective type 1 dopamine receptor agonist, with placebo recently demonstrated no significant difference in the incidence of contrast-induced nephropathy or any secondary outcomes including 30 day mortality, need for dialysis, or re-hospitalization rates [ 56 ]. Another, recent randomized trial of 192 patients undergoing intravascular angiography compared prophylactic acetylcysteine with fenoldopam [ 57 ]. The results demonstrated a 9.6% absolute risk reduction in patients randomized to acetylcysteine (4.1% vs 13.7%, respectively). Although the authors conclude that acetylcysteine is superior to fenoldopam for prevention of contrast-induced nephropathy, there was notably no significant difference in serum creatinine at 48 h. Of interest, in subgroup analysis, the authors speculate that patients with low ejection fractions (<40%) may attain additional benefit with acetylcysteine. Conclusion All of the above leads us to conclude that while acetylcysteine appears to be safe and inexpensive, its efficacy for the prevention of contrast-induced nephropathy remains unproven. The results of the trials that we reviewed to date should be viewed as early promising evidence of benefit, and suggest that it is now perhaps reasonable to use acetylcysteine in routine care because of its relative ease of use and safety. However, its true efficacy will remain uncertain unless a definitive well-designed multi-center trial is performed. Such a clinical trial will be most relevant if it addresses a priori clinically meaningful endpoints of renal insufficiency, rather than surrogate endpoints based on changes in creatinine levels alone, and further considers stratification on hypothesized important subgroups that may benefit such as those with a low ejection fraction [ 58 ]. Competing interests The authors declare that they have no competing interests. Authors' Contributions SMB developed the study protocol, conducted literature search, screened articles for eligibility, extracted data, analyzed data, interpreted results, wrote and revised the manuscript. WAG contributed to protocol development, screened articles for eligibility, extracted data, analyzed data, interpreted results, and provided critique of successive drafts of the manuscript. Both authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC526263.xml |
535569 | Regional Societies: Fostering Competitive Research Through Virtual Infrastructures | The MidSouth Computational Biology and Bioinformatics Society (MCBIOS) describes its efforts to provide local opportunities for researchers to learn and connect with colleagues | The rich get richer while the poor get poorer. This is as true for research in the life sciences as it is for society in general. Why is this? To put it simply: because existing resources can be leveraged to generate new ones. In life science research, these resources are often expressed in terms of people, equipment, and funding. Those who have more in these areas—large research organizations, support staff, extensive facilities, and pipelines of funding—can be more productive, and thereby more successful in maintaining and even expanding their infrastructure. In the United States, this “scientific wealth” is concentrated in certain geographic areas, particularly along the east and west coasts. While there are many highly competent individuals in other, less wealthy, regions, they often struggle to attract funding because they lack this infrastructure. Fortunately, scientific progress is also made through collaboration and cooperation—activities fostered through the exchange of viewpoints and the exploration of ideas. Given a framework in which this networking can occur, some of the problems that result from a lack of infrastructure can be overcome. By definition, collaboration involves the sharing of ideas, capabilities, and resources. While it may not be possible to access all necessary resources locally, finding partners—who have their own local capabilities and resources—can enhance one's own research environment. Through these kinds of efforts, “virtual infrastructures” grow. And through collaborative efforts, those of us in some of the “poorer” regions of the United States have opportunities to successfully compete for national sources of funding by becoming better able to address the “existing research environment” consideration on most grant applications. Building a virtual infrastructure is the primary purpose behind the formation, in the United States, of the MidSouth Computational Biology and Bioinformatics Society (MCBIOS; www.MCBIOS.org ), created to serve a geographical area that includes Arkansas, Louisiana, western Tennessee, Missouri, Mississippi, Oklahoma, and east Texas. By its very nature, bioinformatics involves people from many different backgrounds and is therefore ideal for collaborative efforts. Like many other regional societies, our primary goal is to provide a framework in which collaboration and cooperation can occur. By sponsoring regional activities (at the present time, primarily our annual conference) we hope to bring educators, researchers, and especially students together with others who have similar and/or complementary interests. Through the society, not only can medical scientists make contact with computational experts, but researchers in Arkansas can connect with researchers in Oklahoma, educators from Missouri can interact with educators from Louisiana, and students from Mississippi can find others from Tennessee who are working in the same area. Communication across specialties and areas of expertise is especially important for fostering interdisciplinary efforts and exposing students to a broader range of topics than might be available at their own institution. Nowhere is this truer than in the application of informatics to a variety of disciplines. Regional societies need not be in competition with national or international societies. In fact, many of these larger organizations are finding that it is to their advantage to encourage close relationships with their regional counterparts. The International Society of Computational Biology (ISCB; www.ISCB.org ), for example, has recognized MCBIOS under their “regional affiliate” program. Likewise, MCBIOS is encouraging its members to form “local chapters” (for example, the Oklahoma chapter of MCBIOS, www.OKBIOS.org ). These local chapters are eligible to host the annual MCBIOS conference and are able to support even closer interactions among their local participants. The hierarchy of affiliations provides an abundance of opportunities for members to participate based upon their interests, financial resources, and tolerance for travel. Regional events are by nature more inclusive, since smaller investments of time and money are required to participate. This, in turn, creates a more diverse group of attendees, since it enables those whose primary research or educational focus may be outside the subject matter to participate. While not everyone in our region may be able to attend the annual ISCB conference (in 2004 it was held in Glasgow, Scotland), we have deliberately chosen locations for the MCBIOS annual conference that would be within a day's drive for all members. At the inaugural 2003 MCBIOS conference, for example, with a theme of “Building Networks,” a number of computer scientists attended who were able to find out more about bioinformatics and their possible contribution to life science research; they would probably not have been able to make the investment to attend a national or international meeting on bioinformatics. While still maintaining high standards, regional events also increase the number of venues for the presentation of research results and creative efforts in the educational arena. While the ISCB program, which is already highly self-selecting, accepts less than 15% of their submissions, almost all submissions at the first annual MCBIOS conference could be accommodated, at least in poster form. These regional events can also attract well-respected keynote speakers and provide training opportunities that might not otherwise be available to the membership. For example, Dr. Alan Leshner, CEO of the American Association for the Advancement of Science, gave the keynote address of the 2004 MCBIOS conference. In 2003, we were also able to host a special training session by the National Center for Biotechnology Information on their GenBank and molecular biology tools. While MCBIOS is still new, we have plans to extend our activities—and thus our impact—beyond our annual conference. We are dedicated to supporting our local chapters, and have plans to develop a speakers' bureau to serve them. Plans to collaborate on regional technology efforts (e.g., a regional computing grid) and on multi-institutional educational programs are also in the works. Through the auspices of the society, we hope to increase regional credibility and attract national funding in support of these infrastructure improvements. In the long term—and in addition to supporting the intellectual efforts of our members through a vibrant organizational community—our goal is to increase extramural funding for our represented institutions. We hope to achieve this by fostering competitive research. And this competitive research will be possible on a larger scale through our development of a virtual infrastructure—one that comes about through regional collaboration and a pooling of resources. In conjunction with other infrastructure-building efforts (such as the Biomedical Research Infrastructure Network program sponsored by the National Institutes of Health's National Center for Research Resources), we hope to see externally funded research in the life sciences significantly increase within our region, the American “Midsouth.” (The 2003 and 2004 MCBIOS conferences have been supported in part by National Institutes of Health grant P20 RR-16460 from the Arkansas Biomedical Research Infrastructure Network Program [ http://BRIN.UAMS.edu ] of the National Center for Research Resources through a grant to underwrite student participation and awards at the conferences. The 2004 MCBIOS conference was also supported in part by a grant from the National Center for Toxicological Research [ www.FDA.gov/NCTR ].) | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC535569.xml |
535541 | Weather-based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia II. Weather-based prediction systems perform comparably to early detection systems in identifying times for interventions | Background Timely and accurate information about the onset of malaria epidemics is essential for effective control activities in epidemic-prone regions. Early warning methods that provide earlier alerts (usually by the use of weather variables) may permit control measures to interrupt transmission earlier in the epidemic, perhaps at the expense of some level of accuracy. Methods Expected case numbers were modeled using a Poisson regression with lagged weather factors in a 4 th -degree polynomial distributed lag model. For each week, the numbers of malaria cases were predicted using coefficients obtained using all years except that for which the prediction was being made. The effectiveness of alerts generated by the prediction system was compared against that of alerts based on observed cases. The usefulness of the prediction system was evaluated in cold and hot districts. Results The system predicts the overall pattern of cases well, yet underestimates the height of the largest peaks. Relative to alerts triggered by observed cases, the alerts triggered by the predicted number of cases performed slightly worse, within 5% of the detection system. The prediction-based alerts were able to prevent 10–25% more cases at a given sensitivity in cold districts than in hot ones. Conclusions The prediction of malaria cases using lagged weather performed well in identifying periods of increased malaria cases. Weather-derived predictions identified epidemics with reasonable accuracy and better timeliness than early detection systems; therefore, the prediction of malarial epidemics using weather is a plausible alternative to early detection systems. | Background Malaria epidemics are reported frequently and have caused high morbidity and mortality among all age groups in the African highlands [ 1 - 4 ]. Early detection and accurate forecasting of the time, place and intensity of these epidemics is important for emergency preparedness, planning and response [ 5 , 6 ]. Considerable efforts are being made to promote, develop and implement early warning systems for malaria epidemics in Africa [ 5 , 7 ]. Ideally, public health and vector control workers would have access to a system that alerts them when substantial numbers of excess cases are expected, and such alerts should be sensitive (so that alerts are reliably generated when excess cases are imminent), specific (so that there are few "false alarms") and timely (so that there is adequate lead time to act). Generally, each of these performance characteristics is enhanced at the expense of another. The value of interventions – such as larviciding, residual house spraying and mass drug administration – to control malaria epidemics has been documented [ 8 ]. However, due to the explosive nature of malaria epidemics, the usefulness of such interventions in epidemic settings depends on timely information about the onset of a severe epidemic. Early detection systems, which are used to detect epidemics once they have begun, can correctly identify periods that are defined by expert observers as "epidemic," albeit with varying specificity. A number of such systems have been proposed or implemented. For example, WHO has advocated the use of alerts when weekly cases exceed the 75 th percentile of cases from the same week in previous years [ 9 ] and other methods, based on smoothing or parametric assumptions, have also been considered [ 10 - 12 ]. However, an early detection system, which generates alerts once unusually high case numbers are already observable, will be useful for targeting interventions only if it identifies epidemics at an early phase, when there is still time to take effective action [ 13 ] and if the epidemics persist (and indeed grow) over time so that action taken after the warning can still have an effect. It was previously shown, using weekly case numbers from 10 districts in the Ethiopian highlands over approximately 10 years, that simple weekly percentile cutoffs used for early detection are capable of identifying periods with unusually high malaria incidence, and that interventions that take effect within two weeks of such alerts could have a substantial impact in reducing excess cases [ 14 ]. While early detection systems appear to provide timely information about the onset of severe epidemics, they intrinsically trigger alerts only when unusual transmission is already underway. Another approach, known as "early warning," attempts to predict epidemics before unusual transmission activity begins, usually by using weather variables that predict vector abundance and efficiency, and therefore, transmission potential [ 6 , 15 - 18 ]. The advance notice provided by an early warning system could allow action to be taken earlier in the course of the epidemic, or could increase the span of time available to undertake control measures before the predicted excess cases occur. A number of authors have used weather factors to attempt to predict malaria epidemics [ 19 - 24 ], and Teklehaimanot et al. [ 25 ] showed that polynomial distributed lag (PDL) models incorporating lagged effects of minimum temperature, maximum temperature and rainfall could mimic seasonal patterns of malaria incidence in the same ten sites for which early detection algorithms were evaluated. Because the significant weather predictors of malaria cases are lagged by four or more weeks, such prediction systems may, in principle, provide a means of anticipating unusual malaria incidence with more lead time than early detection methods. Here, an attempt to combine these avenues of previous work is described, using modified versions of previously described models based on weather factors to provide predictions of Plasmodium falciparum cases in these 10 districts of Ethiopia, and evaluating thresholds that trigger warnings. The hypothesis tested here is that the use of predicted cases (rather than actual cases, as in our previous work on early detection) would reduce the precision of the alert thresholds (resulting in alerts whose timing was less well matched to periods of excess cases than those generated by early detection), as the price of obtaining the alerts with greater advance notice. In fact, the early warning system based on predicted cases performed slightly worse in most cases than the early detection system, but the performance was rarely much worse and occasionally slightly better. These comparisons are described and their implications for the choice of malaria prediction/detection systems in epidemic-prone areas of Africa are discussed. Methods Study area and data Microscopically confirmed malaria cases were collected from a health facility in each of ten districts of Ethiopia over an average of 10 years; this data set has been previously described [ 14 ]. Each of these health facilities serves people living in the surrounding localities with few exceptions coming from other places. The data were extracted (by species) from records of outpatient consultations for the years 1990 through 2000. The analysis was restricted to P. falciparum . The original data collected on the basis of Ethiopian weeks (where the number of days in each week varies between 5 and 9) were normalized to obtain mean daily cases for each Ethiopian week [ 14 ]. Daily meteorological data (minimum and maximum temperatures and rainfall) recorded at the local weather stations nearest to the health facility were obtained from the National Meteorological Services Agency (NMSA) for the same period. These daily data were collapsed into weekly data to correspond with the weekly malaria cases. The weekly mean for minimum and maximum temperatures and the total weekly rainfall were calculated from the daily records. 1) Modeling the relationship between predictors and malaria cases The expected case numbers for a given week were modeled using a Poisson regression with lagged weather factors, an autoregressive term, a time trend and indicator variables for week of the year. Biological considerations about the interrelationship between weather, mosquito and malaria parasite suggest that malaria cases should follow periods of increased temperature and increased rainfall, at defined intervals [ 26 - 28 ]. Thus, lags of 4 – 12 weeks for rainfall, and 4 – 10 weeks for minimum and maximum temperatures were considered [ 25 ]. In addition week and time trend, as well as an autoregressive term (based on a moving average of the number of cases four, five and six weeks before) were included, which is intended to improve the prediction. Because of the Poisson regression context the autoregressive term enters logarithmically. A 4 th -degree polynomial distributed lag (PDL) model [ 29 ] was fitted to the data. This reduces the number of degrees of freedom for each weather factor from the number of lags considered and circumvents some of the difficulties associated with estimation of coefficients for multiple lags, including instability of estimates due to collinearity of the different lags of the same variable. The generalized form of the model is thus expressed as: where E ( Y st ) denotes expected value for the daily average number of malaria cases at site s on week t ; , , R t-i , and Y st-i are the weekly minimum and maximum temperatures, rainfall and autoregressive term i weeks previously; t s and W s designate time trend and week in a year at site s ; α s represent the intercept, at site s. 2) Epidemic Prediction Strategies For each week at each location in the data set, the number of cases was predicted using equation (1) and data available four weeks prior to the week for which the prediction is made. Coefficients of equation (1) were obtained using all years except that for which the prediction was being made, to avoid circularity. The prediction for week t was then made using this all-but-current-year model with weather and case data for the weeks up to week t-4. The predicted number of cases is thus estimated using the following model: where represent the predicted cases for year j; , , , , , and are parameter estimates (for intercept, minimum and maximum temperature, rainfall, time, week and autoregressive term respectively) from all years except j; , , , t j , and are minimum and maximum temperatures, rainfall, time, week and autoregressive term respectively from year j . 3) Evaluation of the prediction system Expected number of cases to be used as threshold levels In early detection algorithms, actual cases in a given time period are typically compared against some threshold level of cases to determine whether excess cases have been observed. Often, the threshold level represents an upper bound on "normal" case numbers from previous years. If this threshold level is crossed (perhaps, depending on the system, for several consecutive weeks), an alert is generated [ 14 ]. Such systems for early detection have been evaluated previously [ 14 ]. In this study of the usefulness of prediction systems for generating alerts, historically based thresholds were similarly used – weekly percentile (defined as a given percentile of the case numbers obtained in the same week) or weekly mean with standard deviation (defined as the weekly mean plus a defined number of standard deviations) algorithms as threshold levels [ 14 ] – but generated alerts when predicted cases for a week exceeded the threshold. The thresholds for each year were calculated on the basis of all other years in the data set for a given health facility, excluding the year under consideration. In each case, an alert was triggered if the defined threshold was exceeded by the predicted number of cases for two consecutive weeks (this choice is intended to improve the specificity of the alert system for any given threshold). If another alert was triggered within six months, it was ignored, on the assumption that intervention following the first alert would prevent another epidemic within the next six months. Algebraic descriptions of the thresholds are given below: 1. Weekly percentile Threshold is exceeded when , where T sij = Q psij , where Q psij represents the p th ( p = 70, 75, 80, 85, 90, or 95) percentile of observations from week i at facility s in years other than j . 2. Weekly mean with standard deviation. Threshold is exceeded when , where T sij = μ sij + βσ Ysij , where β = 0.5, 1.0, 1.5, 2.0, 2.5 or 3. Measure of performance of each alert The effectiveness of alerts generated by our four-week-ahead prediction system was compared against that of alerts based on a detection system using actual cases. Since the prediction system generates predicted numbers of cases four weeks ahead of time, this permits implementation of control measures four weeks earlier than under a detection system. On the other hand, one would expect that the accuracy of prediction might be less than that of detection. The comparisons were designed to assess this trade-off between the ability to act earlier in possible epidemics and the possible loss of accuracy. A method previously described [ 14 ] was used to compare different alert-generating procedures on a scale that reflects their operational uses. Briefly, this method quantifies the usefulness of a particular alert-generating system set to a given sensitivity by estimating how many malaria cases might be prevented by measures taken after each alert generated by the system, with defined assumptions about the lead time from alert to the effectiveness of such measures, and about the duration of effectiveness of these measures. Potentially prevented cases (PPC) for each alert are defined as a function of the number of cases in a window following the alert. To obtain the PPC, the following three assumptions were made. (a) It was assumed that four weeks elapse from the decision to make an intervention based on an alert until the interventions take effect. (b) From that time, the window of effectiveness is assumed to last either eight or 24 weeks (to account for control measures whose effects are of different durations). (c) Since no control measure would be expected to abrogate malaria cases completely, two possibilities were considered for the number of cases in each week of the window that could be prevented: 1) cases in excess of the seasonal mean (low effectiveness) and 2) cases in excess of the seasonal mean minus one standard deviation (high effectiveness). These different assumptions allowed testing the sensitivity of the performance of the prediction and detection systems to the length of the window of effectiveness and the choice of function to define potentially prevented cases. When the observed number of cases in a week is less than the seasonal mean or the seasonal mean minus the standard deviation, PPC is set to a minimum value of zero for that week. Methods of comparison For each value of each type of threshold (applied to either the predicted and observed number of cases) at each health facility, the number of PPC was transformed into a proportion (percentage), by adding the number of PPC for the alerts obtained and dividing this sum by the sum, over all weeks in the data set, of the number of potentially prevented cases. Proportion rather than actual cases were used because the numbers of malaria cases vary from district to district. To compare the performance of the predicted and observed cases on a single scale, a curve was plotted for each algorithm showing the mean percent of PPC (%PPC) over all districts versus the average number of alerts triggered per year, with each point representing a particular threshold value. Better methods of generating a warning were those that potentially prevent higher numbers of malaria cases using smaller numbers of alerts. Random and optimally timed alerts The performance of the alerts provided by both the predicted and observed cases was compared with random and optimally timed alerts. PPC was estimated for alerts chosen on random weeks during the sampling period. To estimate the performance of optimally-timed alerts (which could not have been implemented but is optimal in hindsight), the optimal timing of alerts were identified by retrospectively going through data if one had perfect predictive ability; the optimal week for one alert was chosen; then by going through the remaining weeks, the optimal week for a second alert was chosen, and so on. The optimal alert would serve as an upper bound curve for the best choice of alert times, given a defined alert frequency [ 14 ]. Cold versus hot districts The relative importance of weather factors in determining malaria transmission significantly depends on the climate of the area. It has recently been shown that although rainfall was significantly associated in cold and hot districts, minimum temperature contributed only in the cold districts of Ethiopia [ 25 ]. Furthermore, Zhou et al. [ 30 ] showed that there was high spatial variation in the sensitivity of malaria outpatient numbers to climate fluctuations in East African highlands. To determine the effect of the differential contribution of weather factors on the accuracy of predictions, the performance of predictions in the hot and cold environments were compared. Thus, districts with similar climatic characteristics (on the basis of altitude and temperature) were grouped, in order to produce more generalizable results within similar climatic conditions. The hot districts (altitude < 1700 mm above sea level) included Diredawa, Nazareth, Wolayita and Zeway; and the cold districts included Alaba, Awasa, Bahirdar, Debrezeit, Hosana and Jimma. Mean %PPC and the average number of alerts for the cold and hot districts were obtained and the same method was used to compare the performance of the prediction system in the hot and cold districts. Results The prediction algorithm indicates the overall pattern of cases well, yet underestimates the height of the largest peaks. Comparisons of the predicted and observed malaria cases, for each week in six of the ten districts, are shown in Figure 1 . The model predicted the actual cases well, although the agreement between the observed and predicted cases varied from district to district. However, the models were not able to differentiate clearly between years with very high and moderately high peaks. To explore whether the predicted number of malaria cases using weather factors can accurately identify time periods with increased number of malaria cases, the timing of alerts triggered, for example, by a mean plus 1.5 standard deviation threshold algorithm, is presented in the same figure. Despite the fact that the actual height of peaks in the highest-incidence periods is poorly predicted by the model, the model nonetheless often triggered alerts prior to these high-incidence periods. Figure 1 Observed and predicted number of malaria cases with alerts triggered by mean plus 1.5 SD using predicted cases. The solid lines for observed cases and the dotted lines for predicted cases. The red marks are the timing of alerts triggered using predicted cases; their position along the y-axis does not have a meaning. The prediction system generates alerts that could prevent nearly as many cases as alerts generated by a detection system. To obtain a quantitative estimate of the usefulness of the prediction algorithm as an early warning system, the %PPC obtained from alerts triggered by predicted cases were compared with %PPC obtained from alerts based on observed cases (Figure 2 ) under an early detection scheme similar to that previously analyzed [ 14 ]. Percentile and mean + standard deviation thresholds are shown, with each point representing a particular value of the threshold (e.g., 85 th percentile or mean + 1.50 standard deviations). The horizontal axis gives the number of alerts per year triggered by the particular threshold value, while the vertical axis shows the %PPC associated with that threshold value. Each point represents the mean across all 10 districts. Two different choices of the function for determining PPC (reducing cases to weekly mean: low-effectiveness, a and c, or weekly mean minus one s.d.: high-effectiveness, b and d) and the choice of window of effectiveness (eight weeks, a and b; 24 weeks, c and d) were considered. The performance of the predicted number of malaria cases using the mean plus (0.5, 1, and 1.5) standard deviation algorithm (for an eight-week window of low-effectiveness) reveals that it prevented 29%/0.9 alerts, 27.3%/0.6 alerts and 24.2%/0.43 alerts per year, which compares with 31.4%/0.85 alerts, 29.8%/0.65 alerts and 27.5%/0.52 alerts per year respectively when the observed cases are used to trigger alerts (Figure 2a ). In general, relative to alerts triggered by observed cases, the alerts triggered by the predicted number of malaria cases performed slightly worse, within 5% of the detection system. All alerts triggered by predicted and observed cases potentially prevented larger numbers of cases than random alerts. Relative to the optimally timed alerts, both systems performed well, within 10%–20% of the best achievable performance. On average, the number of alerts per year triggered by the prediction system is less than the number of alerts triggered by the observed cases for the corresponding level of alert threshold. Comparative performance of the detection and prediction methods was insensitive to the length of the window of effectiveness and the choice of function to define potentially prevented cases (Figure 2 ). Figure 2 Comparing performance of prediction and detection systems. Percent of PPC by number of alerts per year for different algorithms. (a) and (c) were obtained from cases in excess of the weekly mean (low effectiveness) with window of effectiveness of 8 and 24 weeks respectively. (b) and (d) were obtained from cases in excess of the weekly mean minus one standard deviation (high effectiveness) for windows of eight & 24 weeks, respectively. The solid lines are for detection (Obs) and the dotted lines for prediction (Pred). MeanSD and Percentile represent threshold algorithms based on mean plus standard deviation and percentile, respectively. Prediction-based systems perform much better in cold than in hot districts. To compare the relative importance of weather factors in cold and hot districts, the %PPC obtained from predicted cases in the cold and hot districts were evaluated separately. Figure 3 shows that alerts triggered by the predicted number of malaria cases in the cold districts perform much better than in the hot districts. Comparative performance in the cold and hot districts was insensitive to the length of the window of effectiveness and the choice of function to define potentially prevented cases. In all cases, the prediction-based alerts were able to prevent 10–25% more cases of malaria at a given sensitivity in cold districts than in hot ones. On the other hand, although, the performance of the detection algorithms in the cold and hot districts was similar with eight-week window of effectiveness, it performed better in the cold than in hot districts with 24-week effectiveness (not shown). Figure 3 Comparison of performance of prediction systems in cold and hot districts. Percent of PPC by number of alerts per year. PPC was obtained from cases in excess of the weekly mean (low effectiveness) with windows of effectiveness of eight weeks (a) and 24 weeks (b). The solid lines represent cold and the dotted lines hot districts. Discussion Timely and accurate information about the onset of P. falciparum epidemics is essential for effective control activities in epidemic-prone regions, especially those in which limited resources must be deployed to the areas of greatest need. In the Ethiopian highland fringe region, one such epidemic-prone area, early detection of epidemics based on simple algorithms for detecting excess cases had been shown to generate alerts that are well timed to precede periods of high incidence [ 14 ]. Early warning methods that provide earlier alerts may allow the interruption of transmission earlier in the epidemic, but perhaps at the expense of some level of accuracy. In this study, we have shown that predictions four weeks ahead, based on weather factors and past case numbers, can provide alerts that are of comparable value to those provided by an equivalent early detection system, based simply on observed cases. An interesting feature of the results was that the prediction system performed well in generating alerts for control measures, despite the fact that the model under-predicts high peaks. Correlation analyses (data not shown) indicate that for most (but not all) districts, the model performed well qualitatively, in the sense of predicting more cases than expected from the weekly mean when such excess cases occurred, and predicting fewer when in fact fewer cases than the weekly mean occurred. This finding focuses attention on the fact that a system can give timely and accurate alerts for epidemic control, even if it is unable to provide accurate predictions of case numbers (Figure 1 ). The initial hypothesis was that the improved timeliness of an early detection system comes at the expense of some accuracy. The overall results show that these two effects nearly balance each other, so that early warning systems based on our predictive model provide alerts whose value in terms of epidemic control is comparable to those provided by equivalent early detection systems. In a separate analysis (not shown), these two effects were separated out. If the alert system is based on prediction, but the alerts are timed such that their effects start eight weeks after the alert (i.e., four weeks after the week in which the predicted cases cross the alert threshold, equivalent to the timing for early detection), they identify 5% to 10% fewer PPC than the equivalent detection algorithm. The main analysis (Figure 2 ) showed that the additional four weeks of notice available by implementing control measures so that their effects begin by the week on which excess cases are predicted (four weeks earlier than if the detection algorithm were used) nearly makes up for this deficit. Studies have shown that temperature affects transmission in cold environments more than it does in hot environments [ 31 , 32 ]. Thus the addition of minimum and maximum temperature into the prediction model contributes less to predictions in the hot districts than it does in the cold districts. The study revealed this differential effect of weather on malaria transmission. The weather-based prediction system performed much better in the cold than the hot districts. Two mechanisms could have been responsible for this difference: epidemic alert algorithms in general could be less useful in hot districts, or weather-based algorithms, specifically, may be less useful in hot districts. Since simple detection-based alerts performed similarly in hot and cold districts (at least with an eight-week window of effectiveness), it appears that the problem in hot districts is with prediction-based methods. However, when 24 weeks were used as the window of effectiveness, the early detection system, like the prediction system, performed better in the cold than the hot districts. This may be because of the shorter transmission season in the hot than cold districts, due to evaporation and drying up of breeding sites in hot districts, such that rainfall's effects on transmission last for fewer weeks in hot areas [ 25 ]. In conclusion, an early warning system using weather and other predictor variables is more reliable in relatively cold than hot districts. Non-climatic factors such as population immunity, migration and drug resistance are believed to influence malaria transmission and have been cited as causes of malaria epidemics [ 33 - 36 ]. The variability in accuracy of prediction seen in the ten districts may have been due to such factors and others [ 37 - 41 ]. These findings are consistent with the findings of Zhou et al. which indicated that there was high spatial variation in the sensitivity of malaria outpatient number to climate fluctuations in East African highlands [ 30 ]. Determining the relative contribution of the non-climatic factors would be an important step in the development of an early warning system for malaria and a predictive model which incorporates such indicators would give more accurate predictions, but this is not feasible in practice at this moment due to the absence of quantitative data on these factors. The model chosen for the prediction of malaria cases was based loosely on a model previously evaluated for its ability to explain seasonal variation in malaria incidence in the same data set [ 25 ]. The former model, in turn, used polynomial distributed lags of weather factors based on biological considerations about the effects of these weather factors on malaria cases. To that model, additional terms – an autoregressive term and an indicator variable for the week of the year (on the Ethiopian calendar) were added – to improve predictive power. The usefulness of this predictive model has been shown, but modifications of the model have not been systematically explored which might improve its predictive ability still further. Further work should consider a range of prediction models for their ability to generate accurate and timely alerts. Conclusions This study showed that short-term (four-week-ahead) predictions of P. falciparum cases using lagged weather and case incidence data performed well in identifying periods of increased malaria cases. Furthermore, the prediction system allowed recognition of epidemic periods at an early stage, thereby facilitating interventions making epidemics preventable with adequate lead time. However, this study indicated that early warning system using weather and other predictor variables are more reliable in relatively cold than hot districts. In conclusion, it has been demonstrated that weather derived predictions identified epidemics with reasonable accuracy and better timeliness compared to early detection systems. Therefore, warning systems based on predictions derived from lagged weather variables may be a useful alternative to early detection systems for targeting resources against incipient falciparum malaria epidemics. Authors' contributions HDT, ML and JS conceived the study. HT and ML undertook statistical analysis. HT drafted the manuscript, which was revised by ML. JS participated in designing of the study and statistical analysis. AT initiated the study and made data available in collaboration with WHO and Ministry of Health of Ethiopia. All authors contributed to the writing of the manuscript and approved the submitted version of the manuscript. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC535541.xml |
517938 | Collecting duct carcinoma of the kidney: an immunohistochemical study of 11 cases | Background Collecting duct carcinoma (CDC) is a rare but very aggressive variant of kidney carcinoma that arises from the epithelium of Bellini's ducts, in the distal portion of the nephron. In order to gain an insight into the biology of this tumor we evaluated the expression of five genes involved in the development of renal cancer ( FEZ1/LZTS1 , FHIT , TP53 , P27 kip 1 , and BCL2 ). Methods We studied eleven patients who underwent radical nephrectomy for primary CDC. All patients had an adequate clinical follow-up and none of them received any systemic therapy before surgery. The expression of the five markers for tumor initiation and/or progression were assessed by immunohistochemistry and correlated to the clinicopathological parameters, and survival by univariate analysis. Results Results showed that Fez1 protein expression was undetectable or substantially reduced in 7 of the 11 (64%) cases. Fhit protein was absent in three cases (27%). The overexpression of p53 protein was predominantly nuclear and detected in 4 of 11 cases (36%). Immunostaining for p27 was absent in 5 of 11 cases (45.5%). Five of the six remaining cases (90%) showed exclusively cytoplasmic protein expression, where, in the last case, p27 protein was detected in both nucleus and cytoplasm. Bcl2 expression with 100% of the tumor cells positive was observed in 4 of 11 (36%) cases. Statistical analysis showed a statistical trend (P = 0.06) between loss and reduction of Fez1 and presence of lymph node metastases. Conclusions These findings suggest that Fez1 may represent not only a molecular diagnostic marker but also a prognostic marker in CDC. | Background Renal cancer accounts for 3% of adult malignancies. An estimated 35,710 new cases are expected to occur in the U.S. in the current year and 12,480 patients will die of this disease [ 1 ]. The majority of renal cancers arises from the proximal tubular epithelium, with a characteristic clear or granular cell appearance by light microscopy, and is referred to as renal cell carcinoma (RCC). Recent evidence suggests that solid renal parenchymal tumors arising in the distal portion of the nephron, such as oncocytomas, chromophobe renal cell carcinomas and collecting duct carcinomas represent an heterogeneous group of neoplasm from both clinical and biological perspectives [ 2 ]. Collecting duct carcinoma (CDC), also known as Bellini duct carcinoma, is a rare but highly aggressive renal neoplasm arising from the distal portion of the nephron, and represents approximately 2% of all the RCCs [ 3 , 4 ]. Clinically, CDC appears as a renal mass often accompanied by flank pain and hematuria and is frequently mistaken for RCC or transitional cell carcinoma of the renal pelvis [ 3 , 5 , 6 ]. CDC, however, can be identified based on gross, microscopic, histochemical, and immunohistochemical features. Macroscopically, CDCs are often located at the confluence of the medulla and renal pelvis, and show a characteristic gray-white-tan color, with absence of foci of necrosis and hemorrhage [ 7 ]. Histologically, CDC presents a tubulo-papillary morphology, often accompanied by desmoplasia, atypia in collecting ducts, and intratubular spread [ 7 ], features rarely seen in RCC. Histochemically, CDC cells contain intracytoplasmic mucicarminophilic material, where RCCs do not [ 7 ]. CDCs are also positive by immunohistochemical staining with high-molecular-weight keratin and lectin, proteins typically expressed in the epithelium of the distal tubules [ 8 , 9 ]. Conversely, almost all other renal carcinomas express antigens widely expressed in the cells of the proximal tubules, such as low molecular weight cytokeratins and vimentin [ 9 ]. Although little is known about the genetic profile of CDCs, a DNA flow cytometry study has demonstrated aneuploidy in 90% of these tumors [ 5 ], and cytogenetics has shown frequent monosomy of chromosomes 1, 6, 8, 14, 15, and 22 [ 10 , 11 ]. Loss of heterozygosity (LOH) analysis in six CDCs revealed allelic loss at chromosomes 8p and 13q in 50% of the tumors, and rarely at the short arm of chromosome 3 [ 12 , 13 ]. This data suggests that distinct genetic alterations from those observed in RCC, in which 3p LOH is common [ 14 ], occur in the development of this rare renal tumor. To improve our understanding of the biology of CDC and to explore the possibility that different genes may be involved in the etiology and prognosis of this neoplasm, we analyzed by immunohistochemistry eleven cases of CDC for the expression of five genes (Fez1, Fhit, p53, p27, and bcl2) often involved in the development of many common cancers. These findings were correlated with conventional clinical-pathological features including clinical outcome. Methods Patients Eleven patients with primary CDC (two women and nine men; age range 40 to 84 years, mean 62) underwent radical nephrectomy between 1983 and 2000 in the Department of Urology, University of Padua, Italy. Tissue specimens from these tumors were registered in the Department of Pathology at the same institution. All eleven patients had an adequate clinical follow up and were included in our study. Eight of the 11 patients had metastatic disease, five with lymph node metastasis and five with distant metastases at the time of surgery. None of these eleven patients received any systemic therapy before surgery. Samples were fixed in 10% buffered formalin for routine histological processing and were stained with hematoxylin and eosin. Pathological study Immunohistochemical reactions using anticytokeratin (Keratins 5, 8, 10, an 18) monoclonal antibodies, and Ulex Europaeus Lectin , polyclonal antibody were performed as previously described [ 15 ]. The tumors were classified histologically according to standardized criteria [ 3 , 5 - 7 ] and staged according to the guidelines of the tumor-node-metastasis (TNM) classification of malignant tumor [ 16 ]. Immunohistochemistry Paraffin sections containing non-neoplastic kidney as well as neoplastic areas were deparaffinized according to standard procedures followed by rehydration through graded ethanol series, and mounted on positively charged slides. Immunostaining was performed as previously described [ 17 ]. Briefly for Fez1, Fhit, p53, and p27 immunostaining, slides were immersed in citrate buffer [0.01 M sodium citrate (pH6.0)] and heated in a microwave oven at 600 W (three times for 5 min each) to enhance antigen retrieval. For retrieval of Bcl2 oncoprotein, we used the target retrieval solution, high pH from DAKO (Carpinteria, CA, USA) per manufacturers instructions. The primary antibodies used in this study were: anti-Fez1 rabbit polyclonal antibody [ 17 ], anti-Fhit rabbit polyclonal antibody (Zymed Laboratories, San Francisco, CA) at 1:1,000 dilution, anti-p53, anti-p27, anti-Bcl2 (DAKO, Carpinteria, CA, USA) at dilution of 1:25, 1:50, and 1:40 respectively, as specified by the manufacturers. The primary antibodies were omitted and replaced with pre-immune serum in the negative controls. Sections were reacted with biotinylated anti-rabbit or anti mouse antibodies and streptavidin-biotin-peroxidase (Histostain-SP Kit; Zymed laboratories, San Francisco, CA). Diaminobenzidine (DAB) was used as a chromogen substrate to visualize staining. Finally, sections were washed in distilled water and weakly counterstained with Harry's modified hematoxylin. The immunostaining was evaluated by two pathologists in a blinded fashion (A.V.; R.B.). For statistical analysis, cases were scored as positive if they had more than 20% (p53) or more than 40% (Fez1 and p27) of positive cells. Fhit and bcl2 cases showed either all positive or all negative cells and were scored accordingly. Statistical analysis We evaluated the association between each marker's expression (positive or negative) and each clinical-pathological outcome (metastasis and stage) with Fisher's exact test. For survival, we used the Kaplan-Meier method and log-rank test, as well as Cox proportional hazards regression. Results Pathological study All cases showed several common microscopic features. The main tumor consisted of a tubulo-papillary carcinoma showing pleomorphism and a high mitotic rate with several bizarre mitotic figures, and presence of small cystic spaces surrounded by a highly desmoplastic stroma. Two cases showed a predominant papillary pattern accompanied with dilated and atypical changes in the epithelium of the collecting ducts in the adjacent renal medulla, which was the most convincing feature supporting a collecting duct origin of these tumors. Metastases, when available for examination, were histologically similar to the infiltrating part of the primary tumors. Immunocytochemically, tumor cells from all cases examined did not express keratin 10, while strong positivity to anti-keratin 5, 8, and 18 antibodies, and anti-UEL antibody was observed. The positivity to high molecular weight keratin and UEL further substantiate the diagnosis of CDC. The clinical-pathological features and protein expression of the study's 11 cases are shown in Table 1 . Fez1 expression Our results showed that Fez1 protein expression was undetectable in six of the eleven (55%) cases. One case showed a substantial reduction of expression with 60% of the tumor cells negative, while the remaining four showed diffuse positivity for Fez1 immunostaining (Figures 1A and 1B ). Overall 64% of the cases showed absence or reduction of Fez1 expression. Loss and reduction of Fez1 were correlated with a higher prevalence of lymph node metastases (71% vs. 0%, p = 0.061), although the same was not true for distant metastases or tumor stage. Fez1-negative tumors also tended to have higher mortality than Fez1-positive tumors. Median survival time was estimated to be 17 months for the former group, while median survival was not reached for the latter group during the course of the study (i.e., fewer than half of the patients died) (p = 0.078; Figure 2A ). This corresponds to an estimated 5-fold increase in mortality risk for the Fez1-negative CDCs (hazard ratio of 5.5). Fhit expression Fhit protein was absent in three of eleven cases (27%), and the remaining eight showed diffuse immunoreactivity (Figure 1C ). Reduction of Fhit protein expression did not correlate with any of the clinicopathological features of the tumors. There was a tendency for Fhit-negative cases to have worse survival than Fhit-positive cases (median survival times of 17 vs. 35 months, respectively; hazard ratio of 2.7), although the difference was not statistically significant (p = 0.206: Figure 2B ). p53 expression p53 protein was found to be overexpressed mainly in the nucleus in four of eleven cases (36%) (Figure 1D ). One of the four positive cases showed overexpression in 85% of the cancer cells, whereas the remaining three showed 20%, 30% and 50% of positive cancer cells, respectively. p53 was not detectable in seven of eleven cases (63%) as well as in the normal renal epithelium adjacent to tumor. Overexpression of p53 was not related to histologic grade, tumor stage, lymph node status, and survival. p27 expression Immunoreactivity for p27 was observed in the nuclei of most glomerular and tubular cells in the normal kidney. p27 immunostaining in CDC showed high variability. Protein expression was absent in five of eleven cases (45.5%). All the remaining six carcinomas showed a high percentage (> 40%) of p27 positive cells. The expression of p27 protein was detected exclusively in the cytoplasm in five of the six positive cases (90%) while a mixture of nuclear and cytoplasmic protein staining was observed in the last case (Figure 1E ). Overall, we observed lack or subcellular compartmentalization of p27 in 90% of the cases. The status of p27 did not correlate to any of the clinical-pathological parameters tested. Bcl2 expression Bcl2 protein expression in 100% of the CDC tumor cells was observed in four of eleven cases (36%) (Figure 1F ), while the remaining seven (64%) were negative. Statistical analysis did not reveal any significant correlation between Bcl2 expression and other clinical-pathological parameters. Clinical-pathological features as well as immunohistochemistry results are listed in Table 1 . The proportion of marker expression ranged from 36% (Fez1 and Bcl2) to 73% (Fhit). Marker expression tended to be positively correlated, with the correlation between Fhit and p27 being the strongest (0.67), followed by that between Fhit and Fez1 and Fhit and Bcl2 (both 0.46). Discussion The application of the most recent molecular cytogenetic techniques revealed that renal parenchymal tumors can be classified into distinct subtypes based on the combination of specific genetic alterations [ 18 ]. The pathological and immunohistochemical description as well as the cytogenetic abnormalities support the hypothesis that CDC is more similar to urothelial carcinoma than to clear cell carcinoma of the kidney. Indeed, whereas allelic deletion of the short arm of chromosome 3 is considered a genetic hallmark of clear cell carcinoma, LOH at chromosomes 8p, 9p, and 17p has been frequently described in both transitional cell carcinoma and CDC. Here, we have reported the results of our immunohistochemical analysis of the expression of five genes ( FEZ1 , FHIT , P53 , P27 kip 1 , and BCL2 ) in a relatively large series of CDCs (eleven cases), considering the rarity of this tumor. FEZ1/LZTS1 (leucine zipper tumor suppressor 1) is a putative tumor suppressor gene located at 8p22 [ 19 ]. Studies have indicated this chromosomal region is the location of an important tumor suppressor gene (TSG) [ 20 ]. LOH at 8p has been described in 50% of the CDCs studied [ 12 ], suggesting that a TSG in this region may also play a role in the development of this rare tumor. In our study, we found loss of Fez1 expression in the majority of CDCs studied and a correlation with the presence of lymph node metastasis. Furthermore, lack of Fez1 protein correlated with a poorer prognosis in 90% of patients with median survival of 17 months. FEZ1 encodes a 67-kDa leucine-zipper protein with a region of similarity to cAMP-dependent activated protein [ 19 ]. Mutations of FEZ1 gene have been reported in several solid tumors, including prostate, breast, esophageal, and gastric carcinomas [ 17 , 19 ]. In addition, reduced Fez1 expression is associated with high-grade transitional cell carcinoma of the bladder [ 21 ]. Recent studies have shown that the introduction of FEZ1 into Fez1 null cancer cells reduced cell growth with the accumulation of cells at late S to G 2 /M phase of the cell cycle. Conversely, inhibition of Fez1 expression stimulated cells growth [ 22 ]. Furthermore, LOH at the chromosomal region where the FEZ1 gene lies (8p21-22) has been also associated with the invasive behavior of breast cancer [ 23 ] and with prostate cancer progression [ 24 ]. These data are consistent with an important role of FEZ1 in several human cancers including CDC. The tumor suppressor gene FHIT maps to the short arm of chromosome 3 (3p14.2), encompasses the common FRA3B fragile region, and encodes for a protein of 16.8 kDa, with diadenosine triphosphate hydrolase activity [ 25 ]. Reduction of Fhit protein expression as consequence of alteration of the FHIT gene has been observed by immunohistochemistry in many types of cancers [ 26 , 27 ]. Although Shoemberg et al. did not detect LOH involving chromosome 3p [ 12 ], Hadaczek et al. reported LOH at 3p in two cases of CDC [ 13 ]. The same authors described a correlation between reduced Fhit expression and 3p allelic loss in renal carcinomas, particularly in CDCs [ 28 ]. While in our study Fhit inactivation does not seem to be a common event in CDC, an involvement of the FHIT gene in tumorigenesis of this rare tumor cannot be excluded. The TP53 tumor suppressor gene maps to chromosome 17p13.1 and plays a major role in DNA transcription, cell growth, proliferation and apoptosis process [ 29 ]. In normal cells, expression of wild type p53 protein is generally below the detection level when studied by immunohistochemical method. However, p53 gene point mutations occur frequently (22–76%) in different solid neoplasms. Mutated p53 protein, being more resistant to degradation, accumulates in the cells and can be detected by immunohistochemistry. Although an association between p53 protein overexpression and tumor stage, grade and survival has been observed in RCC [ 30 ] our data suggest that involvement of p53 alterations does not occur with the same frequency in CDC. P27 is a member of the universal cyclin-dependent kinase inhibitor (CDKI) family. The expression of this important protein is regulated by cell to cell contact inhibition as well as by specific growth factors, such as transforming growth factor (TGF-β). In addition to its role as a CDKI, p27 is considered a putative tumor suppressor gene, a major regulator of drug resistance in solid tumors, and a promoter of apoptosis. p27 acts also as a safeguard against inflammatory injury and has a role in cell differentiation [ 31 ]. It has been suggested that loss of the p27 negative cell cycle regulation may contribute to oncogenesis and tumor progression in several tumor types. In renal cell carcinoma, Kamai et al. reported that low level of p27 protein was associated with tumor invasion and unfavorable prognosis, suggesting p27 as a powerful prognostic marker for survival in urinary tract cancer [ 32 ]. Masuda et al. indicated that p27 has an independent predictive prognostic value for transitional cell carcinoma of the renal pelvis [ 33 ]. Our results show that p27 loss or subcellular compartmentalization represents a frequent feature in CDCs. Previous studies have noted that cytoplasmic localization of p27 lead to an inactivation of its normal function as negative cell cycle regulator [ 34 ]. Nevertheless, we did not find statistical correlation to assess its involvement in CDC biology, possibly due to the limited number of tumors studied. The proto-oncogene BCL2 , implicated in the regulation of cell death by inhibiting apoptosis, seems to be vital in normal kidney morphogenesis. In fact, Bcl2 deficient mice develop polycystic kidneys characterized by dilated proximal and distal tubules [ 35 ]. High levels of Bcl2 protein expression have been found in many different types of cancer, suggesting a possible role for Bcl2 deregulation of apoptosis and malignant tissue transformation. Expression of Bcl2 has also been associated with poor prognosis in patients with various cancers including prostate cancer [ 36 ]. In the present study, Bcl2 expression was not associated with any clinical-pathological variables. Our results suggest a potential association between FEZ1 expression and CDC pathology and prognosis. No similar patterns were seen for any of the other markers studied. Even so, the statistical power of the study was limited and negative findings should not be construed as evidence that these markers are not important. Rather, a larger study would need to be carried out to further investigate their role in CDC. Conclusions Our results suggest that Fez1 expression may be associated to both clinical-pathological features and survival in patients with CDC. FEZ1 gene alterations may be linked to the high frequency of LOH found at 8p22, where FEZ1 resides. The lack of similar association for the other four genes studied may be due to the low statistical power of the study. Competing interests None declared. Abbreviations CDC, Collecting Duct Carcinoma; RCC, renal cell carcinoma; LOH, loss of heterozygosity Authors' contributions A.V. carried out the immunohistochemical analysis and reviewed the slides, he also contributed to the draft of the manuscript. T.P.G. was responsible for the clinical study. M.G. carried out the original histopathological diagnosis. H.I. participated in the immunohistochemical analysis and statistical analysis. E.G. participated in the immunohistochemical analysis and statistical analysis. F.P. was responsible for the clinical study. L.G.G. participated in designing the study and in drafting the manuscript. C.M.C. participated in designing the study. R.B. participated in the original design and coordination of the study, and in writing the manuscript Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC517938.xml |
534111 | Myasthenia gravis and pregnancy: clinical implications and neonatal outcome | Background The myasthenia gravis is twice as common in women as in men and frequently affects young women in the second and third decades of life, overlapping with the childbearing years. Generally, during pregnancy in one third of patients the disease exacerbates, whereas in two thirds it remains clinically unchanged. Complete remission can occur in some patients. Methods To describe the clinical course, delivery and neonatal outcome of 18 pregnant women with the diagnosis of myasthenia gravis. Retrospective chart review of pregnant patients with myasthenia gravis, followed at the National Institute of Perinatology in Mexico City over an 8-year period. Data was abstracted from the medical records on the clinical course during pregnancy, delivery and neonatal outcome. Results From January 1, 1996 to December 31, 2003 18 patients with myasthenia gravis were identified and included in the study. The mean ± SD maternal age was 27.4 ± 4.0 years. During pregnancy 2 women (11%) had an improvement in the clinical symptoms of myasthenia gravis, 7 women (39%) had clinical worsening of the condition of 9 other patients (50%) remained clinically unchanged. Nine patients delivered vaginally, 8 delivered by cesarean section and 1 pregnancy ended in fetal loss. Seventeen infants were born at mean ± SD gestational age of 37.5 ± 3.0 weeks and a mean birth weight of 2710 ± 73 g. Only one infant presented with transient neonatal myasthenia gravis. No congenital anomalies were identified in any of the newborns. Conclusions The clinical course of myasthenia gravis during pregnancy is variable, with a significant proportion of patients experiencing worsening of the clinical symptoms. However, neonatal transient myasthenia was uncommon in our patient population. | Background Myasthenia gravis (MG) is an acquired, neuromuscular, autoimmune disease that presents clinically with weakness and fatigue of the skeletal muscles. The disorder is characterized by a decrease of the number of acetylcholine receptors in the neuromuscular plates, due to an autoimmune process mediated by antibodies directed against the alpha-subunit of the nicotine receptor of the acetylcholine [ 1 ]. The disease is twice as common in women as in men and frequently affects young women in the second and third decades of life, overlapping with the childbearing years [ 2 , 3 ]. Generally, during pregnancy in one third of patients the disease exacerbates, whereas in two thirds it remains clinically unchanged [ 4 - 8 ]. Of the women who experience worsening, it usually occurs during the first trimester. Signs and symptoms of MG in pregnant women tend to improve during the second and third trimesters coinciding with the physiological immunosuppression which normally takes place in that period. Complete remission can occur in some patients [ 4 - 8 ]. Papazian [ 9 ] reported a 21% incidence of transient neonatal myasthenia gravis (TNMG) on infants born to mothers with MG. In this report 67% of infants developed TNMG within the first few hours after birth and within the first 24 hours of life in 78% of neonates [ 9 ]. Onset of TNMG beyond 3 days after birth has not been reported. Two clinical forms of TNMG have been described: typical (71%) and atypical (29%). Clinical features of the atypical form include the presence of arthrogriposis multiplex congenita (AMC) in the fetus or newborn [ 10 ]. The severity of AMC in the infant is variable and does not co-relate with neither the severity of maternal MG during pregnancy, or if it is the first or subsequent pregnancies [ 10 ]. In anti acetylcholine-receptor (anti-AchR) antibody-associated AMC, fetal or neonatal death is common. The possible mechanisms could be crossing of maternal antibodies through the placenta with consequent blockage of the function of the fetal isoform of the AchR leading to fetal paralysis causing AMC. In the typical form of TNMG the usual clinical findings include poor sucking and generalized hypotonia [ 11 ]. Other reported clinical manifestations are week cry (60% to 70%), facial diplegia or paresis (37 to 60%), swallowing and sucking difficulties (50 to 71%), and mild respiratory distress [ 9 - 11 ]. Ptosis (15%) and ophthalmoparesis (8%) are less common. Respiratory distress requiring assisted mechanical ventilation can occur in severe cases (29%) [ 9 , 10 , 12 ]. Response to an oral or parenteral anticholinesterase agent is usually very good. Complete recovery is expected in less than 2 months in 90% of patients and by 4 months of age in the remaining 10% [ 9 , 10 , 12 ]. It is not clear why only some babies develop TNMG, but the ability of the mother's serum antibodies to bind to the fetal isoform of the AchR in newborn may be a contributing factor [ 12 ]. The purpose of our present study was to report on the clinical course, delivery and neonatal outcome of pregnant women with the diagnosis of myasthenia gravis, followed in our perinatal center. Methods Patient population From January 1,1996 to December 31, 2003, 18 pregnant women with MG were treated during pregnancy and delivered at the National Institute of Perinatology, a tertiary referral center in Mexico City, Mexico. The clinical course of the disease during pregnancy, labor and post-partum period was reviewed, as well as the neonatal period in the 17 infants born to MG mothers. All clinical data was ascertained after reviewing and collecting data from the patient's medical records. The diagnosis of myasthenia gravis was made on clinical grounds and confirmed by positive edrophonium chloride and electromyography tests [ 13 , 14 ]. Transient neonatal myasthenia gravis was diagnosed on the bases of clinical signs of generalized hypotonia, sucking disturbances, weak cry and respiratory difficulties. Criteria for defining clinical improvement or deterioration After reviewing the medical records of patients the following criteria was used to define clinical change of MG during pregnancy: a) the first was the type and dosage of medications that the patient received before, during and after pregnancy. Data was collected on the type and doses of medications administered to the patient during the 3 periods, b) the second parameter was the stage of the disease according the Osserman's classification before, during and after pregnancy. The criteria for improvement, unchanged or worsening of MG during pregnancy were the following: 1) Remission: those patients that presented a total disappearance of the symptoms (Osserman's stage 0) and who did not require any specific medication, 2) Improvement: patients who had clinical improvement of the symptoms and decrease of the dosage of the medications that they received before pregnancy by 30% or more, 3) No change: patients with no clinical change in their symptoms (According to Osserman's classification) and same doses of medications compared with before pregnancy. 4) Deterioration: patients who had a deterioration of the disease (worsening of the Osserman's stage) and an increase in the dosages of medications compared with before the pregnancy, or the need for immunosuppressant drugs such as azathioprine and/or prednisone. The Osserman's classification used in this study was the one used by the Myasthenia Gravis Foundation of America: grade I: any ocular muscle weakness; grade II: mild weakness affecting other than ocular muscles; III: moderate weakness affecting other than ocular muscles; IV Severe weakness affecting other than ocular muscles; and grade V: Defined by tracheal intubation, with or without mechanical ventilation, except when employed in routine postoperative management [ 15 ]. Patient follow-up In the first two trimesters all patients were seen in the clinic once a month, every 15 days between 32 and 36 weeks, and weekly after 36 weeks of gestation. During every visit the dosage of the medications, and the Osserman's stage were reviewed. All patients were seen by a team of obstetricians and clinical neurologists. Statistical analysis Descriptive statistics was used to compute the results. Results During the study period 18 pregnant women with MG were seen at the hospital and had the medical records available for review. The mean ± SD maternal age was 27.4 ± 4.0 years. Before pregnancy 3 patients (17%) were in remission (Osserman's stage 0) and 15 patients (83%) were classified as Osserman's stage II. All the patients were clinically stable before pregnancy. Of the 15 patients with in stage II, 13 (86%) used pyridostigmine, one used pyridostigmine plus steroids (7%), and one used pyridostigmine, azathioprine and steroids. Thymectomy was performed in 17 patients (94%) before the pregnancy. The mean length of time from the start of symptoms to thymectomy was 24.0 months (range: 1–168 months). Other clinical conditions were also diagnosed in 5 patients (28%) before pregnancy: 3 (17%) had impaired glucose tolerance and 2 autoimmune thyroiditis (11%). Serum antibodies against the human acetylcholine receptor assayed by standard RIA were positive in 14 patients (77%). The patients became pregnant at a mean of 2 years post-thymectomy. In our center we prefer to do the thymectomy first and then when the patient is stable we recommend the pregnancy. This is not a generalized protocol in many centers but the majority of our patients were in good conditions before the pregnancy. During pregnancy 9 patients (50%) did not change the clinical status compared with before pregnancy, 2 (11%) had improvement and 7 (39%) had worsening of the MG. Of the seven patients who deteriorated, one did so in the first trimester and six in the second trimester. Only one patient experienced a myasthenic crisis during pregnancy. During pregnancy 11 patients (61%) received pyridostigmine, one patient (6%) received pyridostigmine plus steroids and another (6%) received pyridostigmine, steroids, and azathioprine. The pregnancy in two patients (11%) was complicated by eclampsy; one woman was diagnosed with chorioamnionitis and another with thrombocytopenia. Pregnancy duration was 37.5 ± 3.0 weeks (range 29–41 weeks). The clinical characteristics of patients is shown in Table 1 . Table 2 shows the delivery mode and neonatal outcome of our patients. Eight patients were delivered by caesarian section. The other ten were delivered by vaginal delivery (one forceps assisted), one of these products was a stillbirth. Seventeen infants were born at mean ± SD gestational age of 37.5 ± 3.0 weeks and a mean birth weight of 2710 ± 73 g. Only one infant presented with transient neonatal myasthenia gravis. This baby presented with sucking difficulties, which resolved spontaneously by day 7 and did not require any specific treatment. The patient with myasthenic crisis delivered by spontaneous vaginal delivery at 37 weeks. The weight of the newborn was 2800 g without complications. No congenital anomalies were identified in any of the newborns. Discussion Myasthenia gravis is not rare among women of reproductive age, the reported incidence ranges from 1:10,000 to 1:50,000 [ 3 ]. Literature describing the clinical course of pregnant myasthenic women mostly consists of single case reports and case series [ 6 - 8 ]. Generally it has been assumed that pregnancy is associated with physiological immunosuppression. There is evidence, as yet unexplained, that polymorphonuclear leukocyte chemotaxis and adherence functions are depressed beginning in the second trimester and continuing during the rest of the pregnancy. It is possible that these depressed leukocyte functions of pregnant women account in part for the improvement observed in some autoimmune diseases. It may also explain the increased susceptibility to certain infections [ 17 ]. On the other hand it is well known that some diseases could exacerbate during the pregnancy. This has been reported for example in patients with systemic lupus erythematosus and myasthenia gravis [ 16 , 17 ]. The clinical course of myasthenia gravis in pregnancy is considered to be unpredictable. It has been reported that: a) approximately one third of patients remain the same, one third improve, and the remaining one third worsens, b) the course in one pregnancy does not predict the course in subsequent pregnancies, c) exacerbations occur equally in all three trimesters and 4) therapeutic termination does not demonstrate a consistent benefit in cases of first trimester exacerbation [ 4 , 6 - 9 , 18 ]. Schlezinger [ 4 ] described the course of MG during pregnancy in 22 myasthenic women with a total of 33 pregnancies. He showed that in one third of the pregnant woman an exacerbation occurred, whereas two thirds showed no change or a remission occurred. In his series the exacerbation usually occurred during the first trimester, with minor clinical changes during the second and third trimesters [ 4 ]. Djelmis et al [ 8 ] reviewed their experience with 69 pregnancies among 65 women with MG managed over a 28 year-period. 24.6% of patients showed an improvement during the pregnancy, 44.9% did not change and 30.4% suffered exacerbations. In Djelmis et al [ 8 ] report the deterioration was observed in the last 4 weeks of pregnancy and in the puerperium. In another study Mitchell et al [ 6 ] reported the clinical course of 11 cases of pregnant myasthenia gravis patients. 27.2 % had improvement and 72.7% deteriorated during pregnancy. The deterioration was observed in the third trimester in all patients. One of their patients suffered respiratory failure. They concluded that there were no predictive factors to identify the mother at risk of exacerbation during pregnancy and the risk of neonatal myasthenia gravis. Batochi et al [ 7 ] evaluated the course of 47 women who became pregnant after the onset of MG. During pregnancy 42% had no change, 39% improved and 19% got worse. In the experience of Batochi et al [ 7 ] the clinical worsening was more frequent in the second trimester and two patients developed respiratory failure. He concluded that the course of the myasthenia gravis during gestation is highly variable and unpredictable and can change in subsequent pregnancies. Recently Picone at al [ 18 ] in a series of 12 patients showed worsening in 42% of patients during pregnancy. Our study showed a frequency of worsening of 33%, being an intermediate frequency compared with the reported frequencies of 15 to 55%. The majority of our patients showed that the worsening occurred in the second trimester as in the Batochi et al [ 7 ] series. In the series of Osserman [ 4 ] and Djlemis et al [ 8 ] the worsening was observed in the third trimester. It is clear from these reports that the clinical course of the disease during pregnancy is highly variable, and difficult to predict. In our study 8 patients had a cesarean section for delivery (47%) and 9 (53%) delivered vaginally (one by forceps extraction). In one patient the pregnancy ended in a stillbirth. Djelmis et al [ 8 ] reported vaginal deliver in 82%, Batochi et al [ 7 ] in 70%, Mitchel et al [ 6 ] in 90% and Picone at al [ 18 ] in 58%. Our study showed a rate of cesarean section of 47%, similar to the rate of 42% reported by Picone et al [ 18 ]. In a recent study Hoff et al [ 19 ] reported the results of a retrospective cohort in Norway. The study population consisted of 127 births to mothers with MG compared with a reference group of 1.9 million births to mothers without MG. They showed that women with MG had a higher rate of complications at delivery, and in particular the risk of preterm rupture of amniotic membranes was three times higher in the MG group compared with the reference group. The rate of interventions during birth was raised and cesarean sections doubled. They concluded that MG is associated with an increased risk of complications during delivery, leading to a higher need for surgical interventions. Regarding the newborns, our study showed that their weight is lower compared with other studies [ 6 , 7 ]. This may be explained by racial differences between our population and the population reported by others. The incidence of TNMG has been reported between 9 and 30% [ 6 , 7 ] Typical clinical findings in the typical form of TNMG are poor sucking and generalized hypotonia. Other manifestations are swallowing and sucking difficulties and mild respiratory distress. Response to oral or parenteral anticholinesterase agents is usually very good. Complete recovery is expected in less than 2 months in 90% of patients and in the remaining 10% by 4 months [ 9 , 10 ], [ 12 , 13 ]. Only 5% of our patients presented with TNMG, which is less than the rate reported by others. The reason for this lower rate is unexplained but it could be due to genetic variation as suggested by others [ 9 ]. In conclusion the present literature in pregnant patients with myasthenia gravis is somewhat limited. It consists mostly of case reports and case series. Our study adds to the body of literature showing that about third of our patients deteriorated during pregnancy, which was observed in the second trimester. Our cesarean section rate was high and the rate of TNMG was relatively low. Competing interests The author(s) declare that they have no competing interests. Authors' contributions JFTZ. Has made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data. LHR. Has made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data. VS. Has made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data. BE. Has been involved in drafting the article or revising it critically for important intellectual content ODS. Has been involved in drafting the article or revising it critically for important intellectual content All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC534111.xml |
539353 | GECKO: a complete large-scale gene expression analysis platform | Background Gecko (Gene Expression: Computation and Knowledge Organization) is a complete, high-capacity centralized gene expression analysis system, developed in response to the needs of a distributed user community. Results Based on a client-server architecture, with a centralized repository of typically many tens of thousands of Affymetrix scans, Gecko includes automatic processing pipelines for uploading data from remote sites, a data base, a computational engine implementing ~ 50 different analysis tools, and a client application. Among available analysis tools are clustering methods, principal component analysis, supervised classification including feature selection and cross-validation, multi-factorial ANOVA, statistical contrast calculations, and various post-processing tools for extracting data at given error rates or significance levels. On account of its open architecture, Gecko also allows for the integration of new algorithms. The Gecko framework is very general: non-Affymetrix and non-gene expression data can be analyzed as well. A unique feature of the Gecko architecture is the concept of the Analysis Tree (actually, a directed acyclic graph), in which all successive results in ongoing analyses are saved. This approach has proven invaluable in allowing a large (~ 100 users) and distributed community to share results, and to repeatedly return over a span of years to older and potentially very complex analyses of gene expression data. Conclusions The Gecko system is being made publicly available as free software . In totality or in parts, the Gecko framework should prove useful to users and system developers with a broad range of analysis needs. | Background In recent years, in response to the needs of our scientific community we have developed a comprehensive, company-wide gene expression data analysis platform based on a centralized client-server architecture (Figure 1 ). This platform, named Gecko (Gene Expression: Computation and Knowledge Organization) addresses the problems of analyzing large volumes of continuously generated data (thousands of Affymetrix scans per year), provides a broad spectrum of analysis tools, and creates a single, collaborative view of data for a large, decentralized community of users. Three organizing concepts have guided the construction of Gecko. The first is the use of the Analysis Tree (actually, a directed acyclic graph) which provides a complete historical and hierarchical display of all analyses conducted to date by the users. In particular, in support of this concept, Gecko permanently stores the results of all analyses performed. A second organizing concept is that of the agglomeration syntax, an "Erector Set" of operations for flexibly creating, combining and subsetting data matrices. The third organizing concept is the pervasive use of experimental designs , which are associated with each data matrix, and which enable the application of a wide range of statistical and pattern recognition tools. It is the aim of this paper to give an idea of the user's view of Gecko and how one conducts analyses using the system, as well as to provide a software-level overview of the Gecko system architecture. Indeed, we believe that Gecko presents a number of innovative features well-worth presenting, and in connection with this publication, we are making available a public release of the Gecko software[ 1 ]. In what follows, we first go "behind the scenes", and present the system architecture in some detail, including overall data organization, database structure, computational engines, statistical tools and models, and finally utility programs. We then present a focused discussion of a specific analysis example, so as to give the reader a more immediate impression of the Gecko system. Implementation The Gecko architecture Gecko is based on a client-server architecture, with a global structure shown in Figure 2 . The Gecko users have remote access to the system through a client application, currently designed for the Windows operating system and running on any desktop or laptop computer (a prototype Java-based client has also been developed, but is not yet in production use). Overall, the Gecko client is a "thin" client, focused on handling user requests and server responses, with most of the actual computation and data organizational tasks handled by the Gecko server. As indicated in the figure, the client not only manages interaction with the Gecko server, but also allows for local connection to applications such as Microsoft Excel or the Spotfire visualization tools[ 2 ], which can be invoked for additional data analysis after the data has been automatically streamed to these applications from the client. The exchanges between the client and the server occur through HTTP requests, which transit through a web server running on the server platform. A central aspect of the Gecko client is that it contains an embedded Internet Explorer browser, a feature which greatly simplifies the task of building user interfaces. Thus, forms for submitting parameters to the server are typically built in HTML dynamically generated by server-side Perl CGI or Java servlet programs, and displayed in the embedded browser. The Gecko server itself runs on a UNIX platform, and consists of the four main components indicated in Figure 2 : a database, that predominantly contains non-numerical, organizational data; a set of computational engines, written in C++, Java, or Perl; a set of request-handler programs (Perl CGI and Java servlet programs) that enable the client-server interaction; and a flat file repository, that contain files for both raw numerical expression data (scans) as well as for all derived data types (analyses). The Gecko database The set of tables in the Gecko database can be partitioned into three main groups, which we call the "Scan", "Chip" and "Analysis" groups in accordance to their functional roles. The Scan group of tables stores attributes of the individual scans of microarray data entered into the system. These attributes include a unique scan identifier (the scan name), as well as many parameters (project name, experiment name, sample name, compound(s) applied and treatment duration, hybridization protocols, etc), which record the nature of the biological sample used and how it was processed, and place the scan in a tree with experimental and biological context. While the Scan group of tables captures many items in common with the so-called MIAME (Minimum Information about a Microarray Experiment) annotation standards[ 3 ], it should be emphasized that its design antecedates the creation of the MIAME standards, and is neither as comprehensive, nor fully consistent with these standards. In current installations of Gecko, an independent laboratory information management system (LIMS), upstream of the Gecko analysis platform itself, provides considerably more detailed information about the samples. With our emphasis on Gecko as an analysis platform and not as a LIMS, we have so far deferred the question of how to best federate (under a MIAME-compliant heading) all of the experimental annotation information. The Scan group of tables also records numerical data in the form of summary statistics for each scan, including several measures of chip brightness and measures of noise and saturation. However, the bulk numerical data for each scan is stored as a file in the flat file repository, with only a file pointer stored in the database. The Chip group of tables stores the attributes of the Affymetrix chip designs currently known to the system. These include the names of the chip designs used, and for each chip design, all the qualifiers pertaining to it. The tables also store sequence annotation information on a qualifier-by-qualifier basis, including a short description line, as well as a URL that provides a link to more general annotation information for each qualifier. The annotation information is generated externally to the Gecko system, with periodic updates via flat files which can be automatically uploaded. The Analysis group of tables is central to all the analysis functions available in Gecko. Any analysis object generated in the system has a set of attributes which are saved under five categories of information: 1) its parent/child relations, 2) an internal pointer to the machine-generated file which contains the bulk numerical data, 3) the parameters for the operation which created the analysis object, 4) general parameters (name of the analysis object, data type, number of rows and columns in the data matrix, etc), and 5) experimental design parameters. Knowledge of the experimental design [[ 4 ], p.93] [[ 5 ], p.214] underlying a dataset is essential to many types of analyses (e.g. ANOVA, contrast calculations [[ 5 ], p.214], supervised classification). In Gecko, the experimental design parameters (factors and levels) of each data matrix are thus stored in the database, and can be accessed or modified by the user at any time. They are automatically retrieved and used whenever a relevant analysis is invoked. Finally we note that no access control is imposed in Gecko: any user can access any collection of scans, or visit any of the existing analyses created by other users. While this very open architecture has greatly fostered collaboration, it is conceivable that access control might eventually be required. To that end, limited architectural and programming modifications are needed. Modifications might consist of expansion of the current user tables, to include group definition and password fields, and addition of straightforward programming logic in both client and server, to mask access to data which is out of the scope of a given user. Computational engines Gecko incorporates a spectrum of computational tools, which enter into 5 major categories: 1) agglomeration, 2) statistical analysis, 3) clustering, 4) supervised classification and 5) transformation methods Agglomeration tools Data for a given experiment is typically distributed over many scans, numbering in some cases hundreds or even thousands. The ability to easily construct or modify the relevant data matrix, with appropriate normalization of scans with respect to each other, is thus critical to all downstream analysis, and this need is addressed by a suite of tools under the generic heading of agglomeration (Figure 3 ). For instance, the Gecko Concatenate tool enables assembly of large sets of scans (Figure 3a ) through the submission of a simple spreadsheet containing the list of scans in an ordered format. The spreadsheet data entry optionally includes specification of the experimental design (factors and levels), which can also be modified or created de novo at any later time. Once created, a data matrix then becomes accessible as a single object, to be used in higher-level agglomeration operations. For instance, Cat Ratio enables one to take ratios of two complete data matrices, on a element-by-element basis (Figure 3b ). To achieve this, the user needs only to specify the two relevant datasets by selecting the corresponding nodes in the Analysis Tree. All subsequent aspects of the computation (matching numerators and denominators pairwise, actual ratio calculations, on-the-fly normalizations, etc) are achieved automatically, and the resulting data matrix, now containing ratios, is registered in Gecko as a child of the two input datasets. The suite of agglomeration operations also includes concatenating data matrices to each other, merging replicates within an agglomerated dataset, subsetting on rows or columns, or performing join operations (Figs. 3c,3d,3e ), altogether approximating an "Erector Set" for building data matrices out of smaller or larger blocks. These operations are routinely performed on large datasets (currently up to ~ 50000 rows × 500 columns). To indicate processing times for these operations, we note that on a 400 MHz Sun Enterprise server, concatenation requires about 2 second per scan, while the more complex ratio calculations require about 10 seconds per scan pair. Thus concatenating, say, 1000 scans, will require about 30 minutes of processing time, while computing the ratio of 1000 scans to another 1000 scans simultaneously, will require about 3 hours of processing time. We finally note that users can bypass agglomeration of scan data altogether, and directly upload arbitrary data matrices into the system (see Data sources section below). This feature makes Gecko into a general analysis tool, for multivariate analysis in contexts quite different from that of gene expression. Statistical analysis tools The suite of statistical analysis tools includes application of both parametric and non-parametric tests to the agglomerated data matrices, on a qualifier-by-qualifier basis and using the associated experimental designs. Included are two-class comparison tests (Student t-tests, SAM[ 6 ], comparison of variances, Mann-Whitney [[ 5 ], p.265]), as well as multiple-class and multiple-factors tests (one and two-way ANOVA) and the ability to perform contrast calculations [[ 5 ], p. 241] of several different types. The parametric tests are available with a "renormalization" option which corrects P-values in accordance to an intra-class correlation (icc) model (JT, manuscript to be submitted for publication). For instance, when applied to a one-way ANOVA across several classes, the icc model folds part of the class-dependent effects into the null hypothesis, by mathematically assuming that they have a random component already explained by the null hypothesis, with variance proportional to the variance of the residuals within each class. The proportionality constant is then computed on-the-fly, by requiring that the resulting distribution of the F statistic over all genes is non-significant up to its median value. This renormalization suppresses weak or biologically unremarkable class-dependent effects, while preserving significant data in the upper tail of the observed F distribution. It typically avoids the conundrum of "all genes are significantly regulated" which very often occurs as the number of samples becomes large. Biased-variance versions of the parametric tests (where an additional, fixed variance term is introduced in the denominators of the t or F statistics so as to reduce noise) are also implemented, in a form where the icc model is combined with semi-parametric resampling to estimate accurate P-values. Alongside these statistical location tests, which depend on samples being assigned to different classes, one can compute class-independent statistics, such as χ 2 , grand means or standard deviations on a qualifier-by-qualifier basis across all samples. These tests are frequently useful in ranking expression profiles on the basis of one or several of these test statistics, typically for subsequent filtering-out of noisy profiles, or for overall statistical assessment of the dataset. Tests incorporating the calculation of the Pearson correlation coefficient are also implemented. These tests enable one to perform "nearest-neighbor" searches for the expression profiles most like those of single or multiple query profiles. As with the set of location tests, these correlation-based tests include options for renormalization, based on the icc model, and for biased-variance terms in the denominators of the equations for correlation coefficients. As an indication of typical execution times for statistical tests, we note that on a 400 MHz Sun Enterprise server, a two-way ANOVA with associated contrasts, applied to a ~ 22000 rows × 100 columns data matrix, requires about 120 seconds of processing time. In all cases, tests results are saved in the Gecko Analysis Tree and can be revisited a posteriori by use of the generic Get Stats tool, which internally computes receiver operating characteristics (ROCs) [[ 7 ], p. 48], generates graphics for the corresponding ROC plots, and allows for selection of qualifiers based on P-value or on false-discovery rate criteria[ 8 ]. Clustering and supervised classification tools The types of clustering tools implemented in Gecko include self-organized maps (SOM)[ 9 ], average linkage hierarchical clustering [[ 10 ], p. 318], principal component analysis (PCA) [[ 11 ], p. 23], multidimensional scaling (MDS) [[ 11 ], p. 107] and the ability to build and display correlation or distance matrices. Supervised classification tools include a gene expression k -nearest-neighbor classifier(GENNC)[ 12 ], in conjunction with fully self-consistent feature selection, based on a number of cross-validation methods (leave-one-out, leave-one-group-out, v-fold) [[ 13 ], p. 219]. Transformations Data transformations are frequently required in the course of analyses. Among those available in Gecko are point transformations, where each element of the data matrix is independently transformed (log-transformations, flooring of values to the noise standard deviation, and others), as well as more global transformations, including variance stabilization[ 14 ], standardization of rows and/or columns (by mean or median centering followed by division by the corresponding standard deviations) [[ 11 ], p. 8], and wholesale transposition of the data matrix. In Gecko, transformations usually appear as explicit steps in the Analysis Tree, rather than being "rolled into" other operations, such as clustering. Adding new analysis methods New analysis methods, if already available as executables or applications running from the UNIX command line (for instance, based on C++, Java, R, Matlab, or other languages), can be internally added to the Gecko system by straightforward programming steps. These steps include i) providing for a user interface, generated by server Perl CGI or Java servlet programs, and displayed as HTML in the client Browser window; and ii) constructing a server-based driver program, that will execute the UNIX command, using the parameters communicated by the user interface. We note that while an application programming interface (API) has not been formalized, a Gecko API is already well-approximated, by the existence of a modular set of methods for accessing the database, and for reading and writing to numerical flat files. For external analysis using other applications, direct streaming of all internal Gecko types is currently implemented for Spotfire[ 2 ] and Microsoft Excel. For saving data to local disk, generic data export in tab-separated values format is also possible. Furthermore, specially formatted types of data export to disk have also been implemented, in particular for the Cluster and TreeView[ 15 ] clustering and visualization programs. Extending the number of specially formatted export options to other analysis packages (for instance, to create R "data frames" to be used in BioConductor R packages[ 16 ]), should be a straightforward programming task, consisting of adding an appropriate formatting function to the existing Perl/CGI module. Data organization in Gecko: the Analysis Tree A central concern in the design of Gecko was to enable the user to perform and especially to later recall complex analysis work flows (such as the cell line data analysis, described in detail below). In general, graphs of analyses conducted in Gecko, with nodes corresponding to datasets and edges to operations on these datasets, result in directed acyclic graphs (DAGs). A DAG is unlike a tree, in that each of its nodes can have multiple parents, whereas in a tree each node has a unique parent; for simplicity however, we refer to the DAG generated by Gecko as the Analysis "Tree". Furthermore, in the Gecko client the DAG is actually displayed as a tree: the DAG topology is correctly maintained by replicating, for nodes with multiple parents, the corresponding subgraphs under each of the parent nodes. Once generated, the data file corresponding to a node in the Gecko Analysis Tree is permanently stored (unless the node is explicitly deleted by the user at some later time). This approach enables users to return at any time to potentially very large and complex panels of analysis results, without requiring them to regenerate all final and intermediate results on-the-fly, as might be required in an alternative real-time "dataflow" approach (in which only the sequence of operations is permanently stored, and in which data is recomputed every time a new session is started). We have found that the dataflow approach can entail a prohibitive computational cost and waiting time, whenever a large number of analyses are being simultaneously considered, as in the examples of Figure 4 (described in detail below). This situation is obviously exacerbated by the presence of individual lengthy computations, such as are required for instance for classifier cross-validation. The permanent storage of all analysis results might seem an extravagant use of computer resources, but experience shows that it results in reasonable use of server memory over time. For an expression analysis community of roughly 100 scientific users, over a span of 5 years memory use has been limited to about 150 GB (corresponding to the disk space available on a couple of current generation personal computers), reached with slow linear growth over time. Furthermore, should it be absolutely required, implementing a file archival and retrieval system for the oldest analyses would be a straightforward task. Noise model The Gecko noise model is based on the so-called PFOLD joint noise model and ratio estimation algorithm[ 17 ]. This model includes both additive (background, cross-hybridization) and multiplicative (coefficient-of-variation effects) noise terms, within a Bayesian estimation framework. Expression ratios and related P-values and confidence limits are computed on the basis of a posterior distribution of ratios conditional on measured intensities and noise terms. The rigorous mathematical derivation of the posterior distribution results in a formulation that seamlessly connects high and low signal-to-noise regimes, and allows estimation of ratios even when recorded intensities are zero or negative. Data sources While currently all scans uploaded into Gecko are generated by Affymetrix technology, in the past the system has also been used with other types of expression data, for instance generated by two-color hybridizations on spotted arrays. This has been possible at low programming cost, because the internal representation of scan data in Gecko is independent of microarray technology, with a generalized storage of intensity and noise information for each chip qualifier or microarray spot. Programming modifications needed for a new technology thus primarily occur in the design of the new raw-file parser (automatically invoked on entry by the scan processing pipeline). Note that for the two-color technologies mentioned above, data for each channel is entered as a separate intensity scan. Channel-to-channel ratios between matched scans are then computed downstream by the users, using the Cat Ratio Agglomeration tool mentioned above. An alternative and very flexible method for data entry into Gecko, which entirely bypasses scan entry, is to directly upload a tabular file through the Gecko client. In particular, this method enables one to upload gene expression data from the many public sources where it is provided only in spreadsheet format. Furthermore, as already stated, it also enables one to use the Gecko analysis tools in contexts unrelated to gene expression. Utilities: the Gecko scan processing pipeline As Gecko was designed as a centralized resource, but also for service of geographically remote sites (Figure 1 ), it was critical that the Affymetrix scan submission process be made as automatic and foolproof as possible. To that end, a two-step procedure was devised, described as follows. First, users register scans through an interface provided in the Gecko client, using an appropriate submission window. This registration step stores the scan attributes in the Gecko database (project name, experiment name, sample name, and so forth), but does not transfer the scan numerical data (intensity values) itself. In the second, independent step, the users send the scan numerical data, in the form of Affymetrix CEL files[ 18 ], to a specific incoming directory on the Gecko server, typically using the file transfer protocol (FTP) utility (Figure 1 ). The Gecko scan processing pipeline, run as a periodic "cron" job on the UNIX platform, automatically converts the Affymetrix CEL file data to Affymetrix MAS5[ 18 ] estimated values, using an emulator of the corresponding algorithm, and writes the results in a format specific to the Gecko system, finally setting a "processing pending" flag to off for each processed scan. Error statuses for files which exceptionally fail processing are written into the database and displayed in a client-based processing queue administration window. On a 400 MHz Sun Enterprise server, the processing time per scan is approximately 3 minutes, enabling upload of about 500 scans per 24 hour period. The processing pipeline has proven to be very robust, and can be readily modified to accept other sources of gene expression data, as already mentioned above. Thus, it has also been used to process cDNA microarray data[ 19 ] in the past. Results An analysis example As an example of an analysis workflow conducted in Gecko, we describe a study of a cancer cell line treated with a panel of compounds which are inhibitors of cell proliferation. Cultures of the A498 cell line (a cell line derived from kidney carcinoma and part of the NCI60 panel[ 20 ]) were treated with five different dimethyl sulfoxide(DMSO)-dissolved compounds (here named A1, A2, A3, B1 and B2) falling into two distinct classes (DNA replication inhibition or tubulin binding, A and B, respectively) depending on their mechanism of action. Control cell cultures, treated with the DMSO solvent alone, were also generated. Six biological replicates of the cell cultures were generated for each combination of compound and harvest time, with harvests occurring at 6 hours or 24 hours after the start of treatment. After processing of the cell extracts, the resulting cRNA samples were hybridized to HG_U133A Affymetrix chips[ 18 , 21 ], resulting in a total of 72 chip scans, which were submitted to the Gecko scan processing pipeline, and uploaded into the system. The Gecko client user interface Figure 4 shows the Gecko client as seen by the user. The client user interface consists of a list of menu items (top), with an associated list of icons (shortcuts to menu items, immediately below), under which are three large adjustable window panes, with content as follows. The left-hand window pane (Tree window) provides a tree representation of the data objects existing in Gecko; in the figure, it currently displays the Analysis Tree, which provides a full and permanent record of analysis operations and resulting datasets executed so far. This window can also display the Scan Tree, a hierarchical display of all scans in the system, by selection of the corresponding Scan Tree tab (upper left-hand corner). The right-hand window pane of the client (the Browser window) contains forms for submitting parameters to the analysis tools, and also displays analysis results. Currently selected is an input form for performing supervised classification of the compound-treated samples, using a k -nearest neighbor classifier[ 12 ]. The bottom window pane of the client (the Properties window) displays the properties of the object currently selected in the Analysis Tree. Here, the experimental design for the selected object, the data matrix compound-panel.AGG , is currently visible. The Analysis Tree contains nodes at three types of levels. Nodes at the highest, most general level are named Projects: in Figure 4 , the Analysis Tree is opened under the Project Oncology_compound_response . Nodes at the next, lower level, named Analyses, enable classification under more specific themes: in Figure 4 , the Analysis Tree is opened under the Analysis A498-series , which contains results specific to the A498 cell line assays. The nodes at all levels below Projects and Analyses contain the actual results of analysis operations, and are arranged in a recursive, parent-child hierarchy of arbitrary depth. Thus in Figure 4 , under A498-series , five generations of results are displayed. Note that each analysis result has a specific data type, indicated by the extension of its name and by a color-coded icon. A total of 33 data types are currently defined in Gecko. The analysis workflow for the A498 cell line data The analysis workflow of the A498 cell line data is indicated in an expanded view of the Analysis Tree (Figure 5 ). The analysis was started by creating a single data matrix out of the 72 independent scans which together constitute all data for the A498 series. The data matrix was created by a copy-and-paste submission of a spreadsheet containing the list of scans to the Concatenate tool, which then automatically assembled and normalized the relevant scan data. This operation resulted in two objects, a scan reference file, compound_panel.GPPL (grey square icon), containing the constitutive list of scans, and the data matrix itself, compound_panel.AGG (orange square icon). Note that these two objects were automatically inserted below the analysis node A498-series , with compound_panel.AGG inserted as a child of compound_panel.GPPL . It is important to emphasize that the data matrix compound-panel.AGG is physically stored on the server platform. This centrality insures that all users have simultaneous access to ongoing analyses, and if desired, that they can collaborate in real-time, even when working from very different geographical locations (Figure 1 ). The data matrix compound_panel.AGG has dimensions 22283 rows × 72 columns, with each row corresponding to a different Affymetrix qualifier on the HG_U133 chip (here the term "qualifier" is synonymous with Affymetrix "probe set"), and each column to a specific experimental sample. The associated experimental design [[ 4 ], p. 93] [[ 5 ], p. 219] of the A498 series, is also saved in the Gecko database in association with compound_panel.AGG , and is displayed in the Properties window (bottom window in Figure 4 ). The experimental design was originally specified in the spreadsheet submitted to the Concatenate tool, but can also be modified (or newly created) at any later time. It contains four factors, labeled dose , time_hr, compound and moa , corresponding to compound doseage, harvest time, compound name and compound mechanism of action, respectively. Based on a general experimental design, one can then automatically define in Gecko simpler two-factorial designs, by selection of the factors in the appropriate client interface. For instance, Figs. 6a and 6b display the two-factorial designs for compound_panel.AGG which result from the combinations ( compound × time_hr ) and ( moa × time_hr ), respectively. The number of replicates for every combination of levels is indicated in each cell of the tables. The factorial design ( compound × time_hr ) is of particular interest for finding genes with expression differentially regulated by the treatments with the different compounds, with or without concommittant time variation. In the A498 analysis workflow, this design was used to generate a two-way analysis of variance (ANOVA) [[ 5 ], p. 214] of compound_panel.AGG , resulting in the dataset compound-panel_compound_time_hr.ANOVA2 (Figure 5 , purple triangle icon), which was again automatically inserted as a child of its parent dataset. The two-way ANOVA is conducted on a qualifier-by-qualifier basis, and results in a file contains 22283 rows, each row consisting of the P-values (and associated statistics) for the compound, time_hr and compound × time_hr effects for the corresponding qualifier. Once created, compound-panel_compound_time_hr.ANOVA2 can be revisited for selection of statistically significant data using a generic utility called Get Stats . In particular, Get Stats internally computes receiver operating characteristics [[ 7 ], p. 48] for all the effects considered in the factorial design, and permits selection of significant qualifiers at a specified false-discovery rate (FDR)[ 8 ]. For instance, for a threshold FDR ≤ 0.05 used in conjunction with the compound effects, one finds that 517 qualifiers out of 22283 exhibit compound-related changes in expression. In the Analysis Tree, the data subset corresponding to these 517 qualifiers, compound-panel_compound_time_hr-517.ANOVA2 , is automatically inserted as a child of the parent file. The operation parameters (effect used for selection and threshold FDR) which generated the subset are also saved, and are displayed in the Properties window for reference. Following the ANOVA operations, the original data matrix, compound_panel.AGG , was then filtered to the rows corresponding to the 517 signifi-cant qualifiers contained in compound-panel_compound_time_hr-517.ANOVA2 , in preparation for down-stream clustering and supervised classification operations. This step, implemented by the subsetting tool Reduce on Qlist , results in the filtered data matrix compound_panel-517.AGG . Note that for all of the datasets discussed above, prior to each operation, a tentative output name was automatically created (typically by a concatenation of the input dataset name and of the name of the operation to be applied), and then presented to the user in a preview page. The tentative name can then be modified, if desired, before final submission. Clustering and supervised classification of the A498 cell line data Several additional analysis steps were performed on the A498 series data, illustrating the use of complementary unsupervised (clustering) methods, as well as a supervised classification approach. Starting from the filtered data matrix compound_panel-517.AGG (Figure 5 ), and after row standardization [[ 11 ], p. 8] ( compound_panel-517RmedNR.dat ), three clustering methods were first applied, resulting in i) a self-organized map[ 9 ] of the data with 1 × 64 cluster geometry ( compound_panel-517_1 × 64.SOM ), ii) a hierarchical clustering using average linkage [[ 10 ], p. 318] ( compound_panel-517.TREE ), and iii), a principal component analysis (PCA) [[ 11 ], p. 23] ( compound_panel-517.PCA ). Supervised classification of the samples was also performed, using the gene expression k -nearest-neighbor classifier[ 12 ] integrated into Gecko. The classification was done on the basis of mechanism of action of the compounds (excluding controls, and regrouping 6 hour and 24 hour samples), resulting in a two-class problem with class labels "DNA replication inhibition" and "tubulin binding". The Feature Selection tool was first used, to compute the misclassification error as a function of the number of features (qualifiers) retained in the dataset, using the Fisher interclass separation [[ 13 ], p. 135] as a feature selection criterion and with misclassification error computed using "leave-one-group-out" (LOGO) cross-validation [[ 13 ], p. 219]. In each step of the LOGO procedure, all instances corresponding to a given compound are simultaneously removed and cross-classified by the remaining instances in the training set. Applied to each compound in turn, this resulted in 5 separate cross-classifications, each applied to the 12 held-out samples, with a tally of all misclassifications errors applied at the very end. The results were saved in compound_panel-517RmedNR_feature_sel_SCAN.STAT . An explicit k -nearest-neighbor classification, using an optimal set of 60 qualifiers determined by the feature selection step was then performed. The final classification results, including an internally generated PCA representation of the data, were automatically saved in the data set compound_panel-517RmedNR_feature_sel_FILTER_60_moa.CVEC (pink circle icon with × pattern). Visualization of analysis results Gecko provides for flexible visualization of analysis results, with results either directly displayed in the client Browser window, or streamed to external visualization tools such as Spotfire[ 2 ]. In Figure 7 , the receiver operating characteristic for the distribution of P-values according to compound effects in the two-way ANOVA ( compound-panel_compound_time_hr.ANOVA2 ) is displayed in the Browser window. In Figure 8 , after streaming to Spotfire, a PCA representation of data for the supervised classification compound_panel-517RmedNR_feature_sel_FILTER_60_moa.CVEC is displayed as a three-dimensional scatter plot. Conclusions Constructed around the three organizing concepts of the Analysis Tree, the agglomeration syntax, and the pervasive use of experimental designs, Gecko has proven to be a robust analysis platform for a large and distributed scientific community. Gecko has allowed for flexible incorporation of new analysis methods over time, and has insured intelligible access to older, complex analyses, successfully answering the question of "where is my data?". It should be emphasized that the analysis framework afforded by Gecko is general and not limited to gene expression data. Data can be uploaded from many other sources, and the analysis methods relevant to the new data types can also be incorporated as needed. Thus, methodologies for the analysis of protein-protein interaction data[ 22 ], or for the analysis of Gene Ontology, categorical data[ 23 ] have been integrated into Gecko in the past. It is now hoped that with its public release, many other uses will be found for this general analysis platform. Availability and requirements All components of the Gecko software, including source code, are being made available as a package under SourceForge.net[ 1 ]. The Gecko project's home page will provide information regarding release schedules and availability. Interested parties may also directly contact the corresponding author (JT) for information. Installation of the complete platform will require manual intervention as well as execution of the automated builds provided in the package. Manual intervention is required for installation of the required external software libraries (Perl modules, GNU software, graphics software, etc) as well as for setting up the run-time Gecko infrastructure (web server, servlet engine, Oracle data base). The automated builds provide for compilation of the C++ and Java source code, for creation of required flat-file directories, and for the creation of the database tables. Existing installations of the Gecko platform are on Sun Enterprise UNIX servers running SunOS 2.8. Transposition to other operating systems, such as Linux, will thus require some additional "tuning" of components during installation. It should also be noted that the Gecko numerical analysis programs can be used in a standalone fashion (i.e. by execution from the command line), without requiring a complete installation of the platform. Authors' contributions JT designed and implemented the early browser-based version of Gecko; he then focused on algorithm and tool development and implementation, alongside giving overall scientific direction for the design of the production version. AU designed and implemented the database. AM designed and built the Java servlets which process user requests, as well as implement many analysis tools and utilities. JC designed and built the client application. DX designed and implemented several statistical analysis algorithms. RN and MH have been involved in running, packaging and in creating automated builds of the Gecko platform. CB had a central role in promoting the Gecko platform and in scientific design input. SB initiated and oversaw development of the production version of Gecko, and was closely involved with the design of the earlier browser-based versions. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC539353.xml |
509284 | A randomised controlled trial to measure the effect of chest pain unit care upon anxiety, depression, and health-related quality of life [ISRCTN85078221] | Background The chest pain unit (CPU) has been developed to provide a rapid and accurate diagnostic assessment for patients attending hospital with acute, undifferentiated chest pain. We aimed to measure the effect of CPU assessment upon psychological symptoms and health-related quality of life. Methods We undertook a single-centre, cluster-randomised controlled trial. Days (N = 442) were randomised in equal numbers to CPU or routine care. Patients with acute chest pain, undiagnosed by clinical assessment, ECG and chest radiograph, were recruited and followed up with self-completed questionnaires (SF-36 and HADS) at two days and one month after hospital attendance. Results Patients receiving CPU assessment had significantly higher scores on the physical functioning (difference 5.1 points; 95% CI 1.1 to 9.0), vitality (4.6; 1.3 to 8.0), and general health (5.7; 2.3 to 9.2) dimensions of the SF-36 at two days, and significantly higher scores on all except the emotional role dimension at one month. They also had significantly lower depression scores on the HADS depression scale at two days (0.93; 0.34 to 1.51) and one month (1.0; 0.36 to 1.66). However, initially lower anxiety scores at two days (0.89; 0.21 to 1.56) were not maintained at one month (0.48; -0.26 to 1.23). CPU assessment was associated with reduced prevalence (OR 0.71; 95% CI 0.52 to 0.97) and severity (6.5 mm on 100 m visual analogue scale; 95% CI 2.2 to 10.8) of chest pain at one month, but no significant difference in the proportion of patients taking time off work (OR 0.82; 95% CI 0.54 to 1.04). Conclusion CPU assessment is associated with improvements in nearly all dimensions of quality of life and with reduced symptoms of depression. | Background Acute chest pain is a common reason for emergency hospital attendance and admission. Patients with chest pain that remains undiagnosed after clinical assessment, ECG and chest radiograph pose a particular problem. They carry a low, but important risk of an acute coronary syndrome [ 1 ]. The potentially life-threatening nature of this diagnosis means that a cautious approach is often taken, with many patients being admitted to hospital for observation and investigation [ 2 ]. Yet most patients with undifferentiated chest pain do not have a coronary syndrome, whereas anxiety and psychological morbidity are common [ 3 - 5 ] and appear to be associated with impaired quality of life [ 6 ]. It is possible that anxiety could be influenced by the investigation and management of chest pain. If this is so, then decision analysis modelling suggests that the potential health gains that could be achieved by reducing anxiety and improving quality of life among the majority of patients who do not have an acute coronary syndrome substantially outweigh the potential health gains from detecting and treating acute coronary syndromes [ 7 ]. The chest pain unit (CPU) was developed to provide rapid and accurate diagnosis for patients presenting with acute undifferentiated chest pain [ 8 ]. Patients receive up to six hours of observation and biochemical testing followed by an exercise treadmill test. If these tests are positive then they are admitted to hospital with a clear diagnosis, if negative they are discharged home. Evaluation of CPU care has focussed upon cardiac events, process measures and economic measures [ 9 ]. There is some evidence that CPU care is associated with improved diagnostic certainty [ 10 ] and patient satisfaction [ 11 ], but no data to compare psychological morbidity and quality of life after CPU and routine care, despite substantial data to suggest that this is an important problem for patients [ 3 - 5 , 12 - 14 ]. The ESCAPE (effectiveness and safety of chest pain evaluation to prevent emergency admission) trial was a randomised controlled trial and economic evaluation of CPU versus routine care that showed that CPU care was associated with reduced hospital admission [ 15 ], improved health utility [ 15 ] and improved patient satisfaction [ 16 ], and was likely to be considered cost-effective [ 15 ]. This paper reports quality of life and psychological measures from the ESCAPE trial. We aimed to measure the effect of CPU care upon anxiety, depression, and health related quality of life, and to determine whether CPU care reduced subsequent symptoms of chest pain. Methods The Northern General Hospital Emergency Department provides adult emergency care to the 530,000 population of Sheffield, United Kingdom. In 1999 a CPU was established in the emergency department, staffed by three specialist chest pain nurses, and able to accommodate up to six patients with acute undifferentiated pain. Patients were selected using validated clinical predictors and received two to six hours of observation and biochemical cardiac testing, followed by, where appropriate, an exercise treadmill test. Full details of the CPU protocol have been published [ 17 ]. Routine care, prior to development of the CPU, consisted of assessment by a doctor who had access to biochemical cardiac tests, but not observation facilities or exercise treadmill testing. From 5 th February 2001 to 5 th May 2002 the CPU was subject to a cluster randomised controlled trial. Days of the week (N = 442) were randomised to CPU or routine care in equal numbers. All patients attending with acute chest pain were screened for eligibility in the trial. Patients were excluded if they had ECG changes diagnostic for an acute coronary syndrome, clinically obvious unstable angina, co-morbidity or alternative pathology requiring hospital admission (e.g. suspected pulmonary embolus), negligible risk of acute coronary syndrome (e.g. age less than 25 years), or if they were unable to consent to participation. Written, informed consent was requested and patients who agreed to participate were followed up in a review clinic at two days, and by postal questionnaire at one month. The study protocol was approved by the North Sheffield Research Ethics Committee. Full details of the ESCAPE trial have been published [ 15 ]. Health related quality of life was measured using the SF-36 questionnaire [ 18 ]. Anxiety and depression were measured using the Hospital Anxiety Depression Scale (HADS) [ 19 ]. Both are widely used, validated, self-completed questionnaires. Both were administered at two days and one month. At two days patients were handed the questionnaires in the review clinic and asked to complete it in their own time and return it to the Medical Care Research Unit. No reminder was sent to non-responders to this questionnaire. Further questionnaires were mailed at one month with one re-mailing for non-responders. A brief additional questionnaire was sent at one month that was designed specifically for the study. This predominantly asked questions about health service use for the economic evaluation, but also asked participants whether they had suffered any further chest pain. If they responded that they had, they were asked to score the severity of the chest pain on a 100 mm visual analogue scale. A further question asked whether the patient had taken time off work since their hospital attendance. The sample size estimate of 988 was based upon the primary outcome measure, the proportion of patients admitted to hospital. Assuming a response rate of 65% to the questionnaires, this sample size would provide 80% power to detect an effect size of 0.25 for these outcomes (alpha = 0.05). Using standard deviations derived from a two-week pilot study, this effect size equates to 1.1 points on the HADS anxiety or depression scores, 11.5 points of the SF-36 physical or emotional role dimensions, and 6 points on the other SF-36 dimensions. Data was analysed using Stata statistical software (version 8.0). Multi-level random effects modelling was used with day of week as a random effect to adjust for clustering by day of week. For the principal analysis no adjustment for confounding was made. For secondary analysis age, gender and past history of coronary heart disease were included as covariates (determined a priori to be important potential confounders), along with any variable that showed significant (p < 0.05) baseline imbalance between the study groups. Results During the 442-day study period there were 6957 attendances with chest pain or a related complaint. Of these, 764 (11.0%) had ECG changes diagnostic for an acute coronary syndrome, 2402 (34.5%) had clinically obvious unstable angina, 869 (12.5%) had co-morbidity or alternative pathology requiring hospital admission, 1291 (18.6%) had negligible risk of acute coronary syndrome, and 513 patients (7.4%) were unable to participate in the trial or provide consent. The remaining 1118 patients (16.1%) were asked to participate in the trial and 972 agreed (86.9%). Response rates were: 717 (73.8%) to the initial questionnaire and 679 (69.9%) to the one-month questionnaire. The CONSORT diagram and full details of exclusions have been published elsewhere [ 15 ]. Baseline characteristics of the study groups are shown in Table 1 . Source of referral, smoking status, and ECG at presentation showed significant baseline imbalance. Hence secondary analyses adjusted for these covariates, along with age, gender and past history of coronary heart disease. Table 1 Baseline characteristics of the study groups CPU care Routine care Age (years) 49.4 49.6 Male sex (%) 304 (63.5%) 318 (64.5%) Known CHD (%) 16 (3.3%) 27 (5.5%) Hypertension (%) 127 (26.5%) 120 (24.3%) Diabetes (%) 17 (3.5%) 29 (5.9%) Hyperlipidaemia (%) 58 (12.1%) 70 (14.2%) Smoker (%) 169 (35.3%) 143 (29.0%) Family history (%) 189 (39.5%) 200 (40.6%) Pain nature Indigestion / burning 60 (12.5%) 56 (11.4%) Stabbing / sharp 116 (24.2%) 113 (22.9%) Aching / dull / heavy 175 (36.5%) 181 (36.7%) Gripping / crushing 66 (13.8%) 59 (12.0%) Other 57 (11.9%) 71 (14.4%) Pain site Central 317 (66.2%) 335 (68.0%) Left chest 129 (26.9%) 125 (25.4%) Right chest 19 (4.0%) 16 (3.2%) Other 8 (1.7%) 8 (1.6%) Pain radiation None 183 (38.2%) 189 (38.3%) Left arm 118 (24.6%) 142 (28.8%) Right arm 31 (6.5%) 26 (5.3%) Neck 22 (4.6%) 22 (4.5%) Jaw 15 (3.1%) 13 (2.6%) Back 70 (14.6%) 53 (10.8%) Other 27 (5.6%) 30 (6.1%) Pain duration Continuous pain 312 (65.1%) 341 (69.2%) Intermittent pain 93 (19.4%) 95 (19.3%) Other symptoms Nausea 129 (26.9%) 161 (32.7%) Vomiting 25 (5.2%) 31 (6.3%) Dyspnoea 185 (38.6%) 202 (41.0%) Sweating 192 (40.1%) 210 (42.6%) ECG at presentation ECG normal (%) 412 (89.0%) 382 (82.2%) ECG non-specific (%) 38 (8.2%) 64 (13.8%) ECG old change (%) 13 (2.8%) 19 (4.1%) Source of referral GP referral 138 (28.8%) 116 (23.5%) Self referred 173 (36.1%) 155 (31.4%) 999 145 (30.3%) 189 (38.3%) Other 23 (4.8%) 33 (6.7%) Table 2 shows the final diagnosis recorded in the case notes, after hospital attendance and admission, of the most senior clinician to care for the patient. Those receiving routine care were more likely to have received a diagnosis of angina, whereas those receiving CPU care were more likely to have received a non-specific or non-cardiac diagnosis. Table 2 Diagnostic impression after initial hospital attendance Diagnosis CPU care Routine care Non-specific chest pain 144 (30.1%) 125 (25.4%) Anxiety 13 (2.7%) 21 (4.3%) Angina 63 (13.2%) 123 (24.9%) Myocardial infarction 28 (5.8%) 27 (5.5%) Gastro-oesophageal pain 74 (15.4%) 60 (12.2%) Musculo-skeletal pain 122 (25.5%) 106 (21.5%) Other diagnosis 26 (5.4%) 18 (3.7%) Not recorded 9 (1.9%) 13 (2.6%) P < 0.0001 for the difference in distribution across the categories Table 3 shows the mean SF-36 scores for both groups at two days, with the adjusted difference, 95% confidence interval, p-value and intraclass correlation coefficient. Table 4 shows these estimates at one month. At two days, CPU care was associated with significant improvements in physical functioning, vitality and general health. At one month, CPU care was associated with significant improvements in all dimensions of quality of life, except the emotional role dimension. Table 3 Mean SF-36 scores at two days N (% completed) CPU care Routine care Difference 95% CI P-value ρ Unadjusted Adjusted Physical functioning 694 (96.7%) 74.8 69.7 5.1 1.1 to 9.0 0.012 0.002 4.2 0.4 to 7.9 0.029 Social functioning 703 (98.0%) 72.2 69.8 2.4 -1.7 to 6.6 0.252 0 1.5 -2.7 to 5.6 0.49 Role-physical 684 (95.4%) 50.4 46.0 4.4 -2.2 to 11.0 0.191 0.028 3.3 -3.3 to 10.0 0.326 Role-emotional 685 (95.5%) 64.7 59.5 5.2 -1.2 to 11.6 0.113 0 5.1 -1.2 to 11.4 0.111 Mental health 700 (97.6%) 66.9 64.7 2.2 -0.9 to 5.3 0.158 0 2.3 -0.7 to 5.4 0.132 Vitality 697 (97.2%) 52.3 47.6 4.6 1.3 to 8.0 0.007 0 4.6 1.3 to 8.0 0.007 Pain index 701 (97.7%) 50.8 49.0 1.8 -1.9 to 5.5 0.351 0 2.0 -1.7 to 5.7 0.284 General health 688 (96.0%) 60.3 54.5 5.7 2.3 to 9.2 0.001 0 5.4 2.0 to 8.8 0.002 Upper row shows unadjusted analysis (primary analysis) Lower row shows adjusted analysis (secondary analysis) ρ = Intraclass correlation coefficient. This provides a measure of the amount of clustering of each outcome by the unit of randomisation (day). Table 4 Mean SF-36 scores at one month N (% completed) CPU care Routine care Difference 95% CI P-value ρ Unadjusted Adjusted Physical functioning 654 (96.3%) 74.1 66.2 7.8 3.8 to 11.9 <0.001 0.025 7.6 3.6 to 11.5 <0.001 Social functioning 654 (96.3%) 74.6 67.0 7.6 3.2 to 12.0 0.001 0 6.8 2.4 to 11.2 0.002 Role-physical 638 (94.0%) 54.1 46.0 8.2 1.3 to 15.0 0.02 0 7.0 0.4 to 13.6 0.039 Role-emotional 630 (92.8%) 63.9 60.2 3.7 -3.0 to 10.5 0.281 0 3.9 -2.8 to 10.5 0.256 Mental health 653 (96.2%) 69.1 64.4 4.7 1.3 to 8.2 0.007 0 5.2 1.9 to 8.6 0.002 Vitality 649 (95.6%) 52.6 47.1 5.5 1.8 to 9.2 0.003 0 5.8 2.2 to 9.3 0.002 Pain index 655 (96.5%) 66.4 62.0 4.4 0.2 to 8.5 0.04 0 4.3 0.2 to 8.3 0.041 General health 651 (95.9%) 59.7 51.7 8.0 4.6 to 11.5 <0.001 0 8.1 4.6 to 11.5 <0.001 Upper row shows unadjusted analysis (primary analysis) Lower row shows adjusted analysis (secondary analysis) ρ = Intraclass correlation coefficient Table 5 shows the summary HADS data at two days and one month. CPU care was associated with lower depression scores at both two days and one month. An early significant reduction in anxiety associated with CPU care was no longer significant at one month. HADS data is also summarised in the Figure 1 , categorised according to severity of anxiety and depression. Scores of zero to seven are normal, eight to ten are mild, eleven to fourteen are moderate, and fifteen to twenty-one are severe. Most participants had normal levels of depression, but only half reported normal levels of anxiety. CPU care was associated with increased prevalence of normal levels of anxiety at two days (53.4% vs 45.1%; p = 0.028) but not at one month (56.7% vs 50.8%; p = 0.129), and increased prevalence of normal levels of depression at two days (81.8% vs 72.9%; p = 0.005) and one month (80.4% vs 73.2%; p = 0.029). Table 5 Mean HADS scores at two days and one month N (% completed) CPU care Routine care Difference 95% CI P-value ρ Unadjusted Adjusted Anxiety- two days 702 (97.9%) 7.73 8.62 0.89 0.21 to 1.56 0.01 0 0.75 0.09 to 1.41 0.027 Depression-two days 701 (97.8%) 4.30 5.23 0.93 0.34 to 1.51 0.002 0 0.84 0.26 to 1.42 0.005 Anxiety-one month 645 (95.0%) 7.29 7.77 0.48 -0.26 to 1.23 0.203 0 0.58 -0.15 to 1.31 0.117 Depression-one month 644 (94.8%) 4.42 5.43 1.00 0.36 to 1.66 0.002 0 1.02 0.37 to 1.66 0.002 Upper row shows unadjusted analysis (primary analysis) Lower row shows adjusted analysis (secondary analysis) ρ = Intraclass correlation coefficient Figure 1 HADS scores categorised according to severity At one-month follow-up, 143 out of 318 participants (45.0%) receiving CPU care reported having further pain, compared to 168 out of 314 (53.5%) receiving routine care (unadjusted OR for further pain if receiving CPU care = 0.71, 95% CI 0.52 to 0.97, p = 0.032; adjusted OR = 0.65, 95% CI 0.10 to 0.76, p = 0.010). For those reporting further pain, the mean score on a 100 mm visual analogue pain score was 36.5 mm among those receiving CPU care and 43.0 mm among those receiving routine care (unadjusted difference = 6.5 mm, 95% CI 2.2 to 10.8, p = 0.003; adjusted difference= 6.8 mm, 95% CI 2.2 to 11.5, p = 0.004). Thus, at one month, CPU care was associated with a reduction in the incidence and severity of subsequent chest pain. One month after hospital attendance, 49 out of 315 participants receiving CPU care (15.6%) reported that they had taken time off work, compared to 58 out of 316 receiving routine care (18.4%). The unadjusted odds ratio for taking time off work after receiving CPU care was 0.82 (95% CI 0.54 to 1.24, p = 0.35; adjusted OR 0.79 (95% CI 0.59 to 1.22, p = 0.287). Discussion Main findings Patients with acute, undifferentiated chest pain who received CPU care had improved quality of life and reduced psychological symptoms. All dimensions of quality of life were improved at one month apart from the emotional role dimension. Anxiety was reduced two days after assessment, but there was no significant difference by one month, whereas reduced symptoms of depression at one month were still significant at one month. Patients receiving CPU care reported that subsequent symptoms of chest pain were less frequent and (if present) less severe. However, these reported differences in symptoms and quality of life were not associated with any significant difference in the need to take time off work. Comparison to other studies Previous studies of CPU care have focussed on cardiac events, process measures and economic measures [ 9 ]. One previous randomised trial found that CPU care was associated with greater diagnostic certainty [ 10 ] and improved patient satisfaction [ 11 ]. Our study suggests a more complicated picture, since more patients in the CPU group received a diagnosis of non-specific chest pain. CPU assessment may allow cardiac disease to be ruled out, but if an alternative diagnosis is not offered then this can hardly be said to increase diagnostic certainty, except in the somewhat convoluted sense that we may be more certain of what we know the cause is not. Nevertheless, CPU assessment was associated with reduced anxiety and improved quality of life. This is consistent with a previous study of diagnostic testing by Sox et al [ 20 ] that showed reduced anxiety among patients who were randomised to a more thorough outpatient diagnostic work-up for non-specific chest pain, but inconsistent with the findings of a study of exercise testing by Channer et al [ 21 ] that found no evidence of reassurance. Limitations of this study The main limitation of this study relates to our inability to blind participants to the intervention they received and to fact that they were involved in a trial of CPU care. Participants may have been influenced by this knowledge and improvements in psychological symptoms and quality of life may represent a positive response to receiving a novel form of care, rather than improvements specifically related to CPU care. The use of cluster randomisation has substantial advantages for pragmatic evaluation of changes in organisation, particularly if economic evaluation is undertaken [ 22 ]. However, the fact that randomisation occurs before recruitment means that there is the potential for selection bias. We attempted to reduce this risk by applying rigorous selection criteria and to address any potential bias by undertaking a secondary, adjusted analysis. Nevertheless it is possible that selection bias may have influenced the results. Although the measures used have been validated, they have not been widely used in the emergency setting. Changes in health status after an episode of chest pain may be very rapid, hence our need to measure outcomes only two days after intervention. Yet the HADS measures anxiety and depression over the previous week, while some SF-36 questions refer to the previous month. A recent episode of chest pain is likely to be an important determinant of reported health, but it may be that, if participants interpreted the questionnaires strictly, the initial questionnaire was recording health status before the intervention. Also, there may be doubts regarding what some of the outcomes are actually measuring. For example, some of the questions in the HADS measure symptoms that are useful markers for depression, such as levels of activity, which may also be changed by other health or social processes. Thus it may be that the reduced scores associated with CPU care measured on the depression scale relate to increased activity in response to the CPU exercise treadmill test, rather than reduced depression. Implications for practice and future research This study suggests that the assessment that patients receive when they present with acute chest pain can have an impact upon their subsequent health, even if this assessment does not, in most cases, provide a definitive diagnosis. It supports the findings of decision analysis modelling [ 7 ] that the potential health impact of chest pain assessment lies as much in addressing quality of life and psychological symptoms as in detecting and treating cardiac disease. The CPU assessment simply provides a rigorous and structured evaluation, yet this appears to have a significant effect upon anxiety (although this is not maintained), depression and quality of life. Yet it is not clear how this effect is achieved. It is possible that early, rigorous testing, particularly the exercise treadmill test, has a valuable effect in reassuring the patient that they are healthy and capable of normal physical functioning. Alternatively, it could be that consistent, reliable advice and attention from specialist chest pain nurses, rather than a variety of different doctors, is the key element. A third possibility, as previously discussed, is that bias plays an important role. Future research needs to determine which of these possibilities is the key factor. This is important for the specific issue of determining whether and how CPU care is effective, and thus what elements of CPU care are essential, and for the more general issue of exploring how diagnostic assessment effects subsequent well being. Conclusions CPU care for patients attending hospital with acute, undifferentiated chest pain is associated with reduced initial anxiety, reduced depression over the following month, and improvements in most dimensions of quality of life. Further research is required to establish how this effect is achieved. List of abbreviations OR: odds ratio CI: confidence interval ECG: electrocardiograph CPU: chest pain unit ESCAPE: effectiveness and safety of chest pain assessment to prevent emergency admission HADS: hospital anxiety depression scale Authors' contributions SG conceived and designed the study, analysed the data, drafted the paper, and participated in writing the final paper. JN assisted with study conception and design, supervised data analysis, and participated in writing the final paper. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC509284.xml |
526298 | Physical restraint use among nursing home residents: A comparison of two data collection methods | Background In view of the issues surrounding physical restraint use, it is important to have a method of measurement as valid and reliable as possible. We determined the sensitivity and specificity of physical restraint use a) reported by nursing staff and b) reviewed from medical and nursing records in nursing home settings, by comparing these methods with direct observation. Methods We sampled eight care units in skilled nursing homes, seven care units in nursing homes and one long-term care unit in a hospital, from eight facilities which included 28 nurses and 377 residents. Physical restraint use was assessed the day following three periods of direct observation by two different means: interview with one or several members of the regular nursing staff, and review of medical and nursing records. Sensitivity and specificity values were calculated according to 2-by-2 contingency tables. Differences between the methods were assessed using the phi coefficient. Other information collected included: demographic characteristics, disruptive behaviors, body alignment problems, cognitive and functional skills. Results Compared to direct observation (gold standard), reported restraint use by nursing staff yielded a sensitivity of 87.4% at a specificity of 93.7% (phi = 0.84). When data was reviewed from subjects' medical and nursing records, sensitivity was reduced to 74.8%, and specificity to 86.3% (phi = 0.54). Justifications for restraint use including risk for falls, agitation, body alignment problems and aggressiveness were associated with the use of physical restraints. Conclusions The interview of nursing staff and the review of medical and nursing records are both valid and reliable techniques for measuring physical restraint use among nursing home residents. Higher sensitivity and specificity values were achieved when nursing staff was interviewed as compared to reviewing medical records. This study suggests that the interview of nursing staff is a more reliable method of data collection. | Background Nursing homes have the mandate to offer care settings to frail dependent older individuals. However, a renewed emphasis has emerged over the past decades to become more than just a home for older people [ 1 ]. A growing number of facilities are actually striving to preserve residents' sense of control and dignity in order to achieve the highest level of well-being [ 2 , 3 ]. This new way of thinking is based upon values of respect of autonomy and freedom for older persons in various ways such as the resident's right to take risks or to make his/her own choices [ 4 ]. Although predominantly intended as protective devices, physical restraints in nursing homes are being denunciated as measures that go conversely with the aforementioned principles [ 5 ]. Justifications for controlling confusion, agitated and aggressive behaviors are being questioned [ 6 - 8 ] and beneficial effects of physical restraints on falls and injuries, incontinence, muscle atrophy and quality of life challenged [ 8 - 15 ]. Moreover, physical restraints have been associated with cognitive impairment, nosocomial infections, pressure sores and death [ 10 , 16 - 19 ]. According to the literature, the overall prevalence of restraint use in nursing homes ranges between 4 and 68% [ 5 , 20 ]. This wide variation may be explained by definitions of physical restraints used, study sample sizes, characteristics of care settings, and residents' characteristics and cognitive status. Another explanation could be the choice of techniques of data collection. Several methods have been used alone or in combination for the measurement of physical restraint use [ 20 - 22 ]: direct observation, survey or interview of nursing staff, review of medical and nursing records and, when the cognitive status allows it, interview with residents themselves. In view of the consequential issues surrounding the use of physical restraints, it is important to have a method of measurement as valid and reliable as possible. While direct observation is undoubtedly the most valid and reliable method of measurement, it is also the most expensive means to measure physical restraint use. On the other hand, abstracting data from medical records and interviewing nursing staff have the potential to reduce the cost associated with data collection, but their sensitivity and specificity values need to be demonstrated. In addition, apart from the USA, data sources such as Minimum Data Set (MDS) have not been widely implemented in nursing home facilities throughout the world. The objective of this study was to determine the sensitivity and specificity of the measurement of physical restraint use reported by members of the nursing staff and reviewed from medical and nursing records among nursing home residents, compared to direct observation. Since underreporting is much more susceptible to be problematic than overreporting, another objective of this study was to compare the sensitivity of the information reported by one nurse with that reported by two nurses or more questioned together. Our research hypothesis was that sensitivity of the interview is highest when the information is collected from more than one nurse. Methods The study was conducted in eight facilities representing a convenience sample of the long-term care facilities in the Quebec City area, Canada. These institutions were carefully selected in order to include a mix of characteristics in size (small and large), geographic location (urban and rural), university affiliation and vocation (units associated with psychiatric or rehabilitation team). Selection was made after discussion with nursing direction of each setting to gather units of different practice such as regular units and specialized units for residents with dementia or severe behavioral problems. Twenty-five subjects were randomly chosen from each unit; if an unit comprised less than 25 residents, all of its residents were included. This study was approved by the ethics committee at Laval University. Data collection took place between January and June 1992. Definition of physical restraint A physical restraint was defined as a mechanical means applied on a resident in order to interfere with his/her mobility, including: vest, waist, wrist or ankle restraints, geriatric chair or wheelchair with fixed tray table, or any other type of locally designed devices [ 23 ]. Restrictive siderails, defined as two raised full-length siderails [ 24 ], were considered as an intermediate measure and analyzed separately because they are frequently used to prevent bed-related falls during nighttime in long-term care settings [ 25 ]. Physical restraint measurements Physical restraint use was measured according to three methods: direct observation, interview with members of the nursing staff including licensed practical as well as registered nurses (one or more than one nurse, generally two, questioned together), and review of medical and nursing notes. Direct observation Direct observation of restraints on care units were made independently by two trained research assistants using a pre-tested questionnaire. For practical reasons, observations were made before the chart reviews and the nurses' interviews on three occasions (7h00 AM, 11h00 AM and 3h30 PM) on one day. These specific times were selected as being representative of periods of different nurse staffing, and of overloaded periods during morning and afternoon. Interview with nursing staff In order to reduce the occurrence of an information bias, the nursing staff was blinded to the main objective of the research project. Structured interviews were carried out the day following direct observation by one of the authors (PJD), who was unaware of the observations. Interviews with the nurse in charge of each unit were scheduled, although he/she had the liberty to be represented or assisted by other members of the nursing staff. Physical restraint use on each subject was identified for every hour during the last 24 hours, without knowledge of the times that direct observation was made, by means of a pre-tested questionnaire. The questionnaire covered questions about types of physical restraints (belt, vest, wrist, ankle, fixed tray table, siderails), reasons for use (risk of falls, agitation, wandering, aggressive behaviors, body alignment problems) and the duration including hours and minutes. Other information collected during the interview included: gross cognitive and functional information, risks for falls, history of falls during the last month, agitation, wandering, aggressive behavior and body alignment problems. Cognitive status was evaluated according to five items: recall, speech, and orientation to time, space and people. Three aspects of the functional status were assessed: urinary incontinence, fecal incontinence, and ability to transfer. Respondents could refer to subjects' clinical records at any time during the interview. Review of medical files Restraint use from subjects' medical charts and nursing orders for the last six months was reviewed with a pre-tested questionnaire by a research assistant who was blinded to the observations. Additional information taken into consideration comprised: demographic characteristics, prescriptions for restraints, methods of resident supervision, and psychotropic medications administered in the last 48 hours. Statistical analysis Sociodemographic characteristics of the study sample as well as physical restraint use by methods of data collection were examined using descriptive analysis. Interrater reliability between the two research assistants was tested using the kappa statistic. Direct observation served as the gold standard [ 26 ]. To be declared concordant, an observation had to agree with the nurses' interviews on the type of restraint, and on the time of use within one hour. This time frame was set to allow a margin of error of 30 minutes for a reported information and because assessment of restraint use per care unit took an average of another 30 minutes. Each observation was considered as an event independent from one another which may produce a slight overestimation of the precision but no bias. Sensitivity (probability that a person with restraints will be classified as such) and specificity (probability that a person without restraints will be classified as such) values were calculated according to 2-by-2 contingency tables. Differences between methods of measurement were assessed using the phi coefficient. The relationships between potential determinants of restraint use including residents' characteristics and other specific variables reported by nursing staff, and sensitivity values measured by comparing the use reported by nursing staff to direct observation, were examined using chi square tests. Stratification according to these variables allowed to identify specific reasons of underreporting restraint use in the context of a descriptive study. Results Data collection was carried out in 16 nursing units. Of these units, eight depicted skilled nursing home care units, seven nursing home care units, and one long-term care unit within a short-term care hospital. Information was collected for 377 residents with the help of 28 nurses. Residents' age ranged from 32 to 102 years, with a median of 80 years. The sample was 62% female, and median length of stay was 45 months (0 to 720 months). Benzodiazepines and neuroleptics were administered to 35% and 25% of the subjects, respectively. A total of 6,744 observations over a possibility of 6,786 were made (377 residents by three direct observations and six types of restraints). Prevalence results on physical restraint use according to direct observations (interrater reliability = 92.7%; kappa coefficient = 0.86 (95% confidence interval (CI): 0.73–0.97)), interviews with nursing staff and reviews of clinical records are summarized in Table 1 . Fixed tray tables were observed in 23.6% of residents, belts in 12.7% and vests in 4.0% whereas wrist, ankle or other restraints (including locally designed devices, straps or blankets) were used marginally. The nursing staff reported the use of lapboards, belts and vests in 27.6, 17.2 and 5.6% of residents, respectively. Medical and nursing records specified the use of lapboards in 17.2% of residents, the use of belts in 19.4% and the use of vests in 8%. Overall, one third (33.7%) of residents were observed restrained, 32.4% of residents were reported as such by members of the nursing staff, and 38.2% of residents in medical records. Siderails were observed in 62.9% of residents while they were reported by nursing staff in 63.7% of residents, and were mentioned in 72.1% of residents' clinical records. Table 1 Physical restraint use according to a) direct observation, b) interviews with the nursing staff, and c) reviews of medical and nursing records, among 377 nursing home residents Physical restraint use Direct observation Interview with nursing staff Review of clinical records Physical restraint N (%) N (%) N (%) Fixed tray table 89 (23.6) 104 (27.6) 65 (17.2) Belt 48 (12.7) 65 (17.2) 73 (19.4) Vest 15 (4.0) 21 (5.6) 30 (8.0) Wrist 2 (0.5) 1 (0.3) 2 (0.5) Ankle 0 (0) 1 (0.3) 0 (0) Others 3 (0.8) 5 (1.3) 14 (3.7) Any physical restraints 127 (33.7) 122 (32.4) 144 (38.2) Siderails 237 (62.9) 240 (63.7) 272 (72.1) The interview with nursing staff and the review of medical and nursing orders were both highly associated with the observation data (Table 2 ). The interview of nursing staff showed a somewhat stronger relationship with direct observation compared to the chart review (phi = 0.84 vs. 0.54). Sensitivity and specificity values of the information were highest when data was measured with the assistance of the nursing staff compared to chart reviews. Reported restraint use according to nursing staff (one nurse or more) gave a sensitivity value of 87.4% at a specificity of 93.7%. When data was reviewed from subjects' medical and nursing notes, sensitivity was reduced to 74.8%, and specificity to 86.3%. Restraint use was underreported in 12.6% (16/127) of interviews with nursing staff, and in 25.2% (32/127) of clinical records whereas it was over reported in 4.4% of interviews, and in 19.6% of clinical records. Table 2 Observed physical restraint use compared to restraint use reported a) by interview with the nursing staff, and b) by review of medical and nursing records, among 377 nursing home residents Direct observation Direct observation Yes No Total Yes No Total a) Interview with nursing staff* b) Review of medical and nursing records† Yes 111 11 122 Yes 95 49 144 No 16 239 255 No 32 201 233 Total 127 250 377 Total 127 250 377 * Sensitivity = 87.4%; specificity = 93.7%; phi = 0.84. † Sensitivity = 74.8%; specificity = 86.3%; phi = 0.54. Sensitivity values according to specific residents' characteristics and other reported variables are given in Table 3 . Increased sensitivity values by 10% or over were observed for perceived risk for falls, agitated behaviors, body alignment problems, aggressive behaviors, urinary incontinence, fecal incontinence, and incapacity to transfer. Sensitivity of the measurement was similar when two or more nurses were interviewed compared to one nurse, although a higher value was noticed when two nurses were questioned (94.1% vs. 85.1%). Significant relationships between perceived risk for falls ( p = 0.03), agitated behavior ( p = 0.04), body alignment problems ( p < 0.001) and aggressive behavior ( p = 0.01), and reported restraint use by nursing staff were observed. No association was observed for residents' age and sex, number of nurses interviewed, history of falls, wandering problem, disorientation to time, space or people, recall troubles, speech troubles, urinary and fecal incontinence, and ability to transfer. Table 3 Sensitivity values of specific variables reported by nursing staff, among 377 nursing home residents Variable N Sensitivity % Demographic Characteristic Sex Male 142 86.4 Female 235 88.0 Age (years) < 65 41 96.0 65 – 74 83 89.3 75 – 84 127 83.8 > 85 126 83.8 Reported restraint use Nurses interviewed One nurse 256 85.1 Two or more nurses 121 94.1 Justification for restraint use Perceived risk for falls Yes 249 91.3 No 127 77.1 History of falls Yes 47 92.9 No 327 86.6 Agitated behaviors Yes 92 95.6 No 285 82.9 Wandering Yes 53 81.8 No 324 87.9 Body alignment problems Yes 131 97.4 No 246 72.6 Aggressive behaviors Yes 123 97.8 No 254 81.7 Functional characteristics Disorientation to space Yes 202 89.9 No 169 82.2 Disorientation to time Yes 223 88.5 No 141 81.8 Disorientation to people Yes 166 91.1 No 208 82.5 Recall troubles Yes 220 88.9 No 144 81.6 Speech troubles Yes 184 89.0 No 192 84.1 Urinary incontinence Yes 249 88.8 No 128 72.7 Fecal incontinence Yes 226 89.2 No 151 75.0 Unable to transfer Yes 224 89.2 No 153 75.0 Discussion The measurement of physical restraint use according to interview with members of the nursing staff and review of medical charts and nursing orders both reflect accurately the reality observed in long-term care setting residents. Our study has also shown that sensitivity and specificity values of the reported measurement are higher than those calculated from medical charts and nursing orders. This phenomenon is not surprising considering that the keeping of medical and nursing orders in nursing homes isn't usually done on a daily basis [ 27 ], as opposed to acute care settings. The current investigation was carried out in units of diverse facilities. The selection of these facilities was intended to allow the participation of subjects and care units of various characteristics as compared to other studies usually designed [ 28 ]. The sample of nursing home residents included in this study corresponded well to the physically and cognitively impaired residents generally housing in long-term care institutions. Limitations of the current study must be taken into account when interpreting these findings. First, data were collected in 1992. Due to the implementation of the OBRA act, it is probable that the prevalence figures given in the current study are overestimations of those that would be observed in 2004. On the other hand, the province of Quebec just recently launched its first comprehensive policy on physical restraint use [ 23 ]. Furthermore, the purpose of this study was to compare the sensitivity values of two reporting techniques with direct observation. This comparison should not be affected by the prevalence of physical restraint use. In addition, although a higher proportion of restrained residents might seem more difficult for the nurses to remember as compared to a lower proportion, the nurses didn't show any hesitation when recalling the use of physical restraints as the majority of residents had been living there for a long period of time. Second, we used a convenience sample of long-term care facilities rather than one drawn randomly. We wanted to determine differences and similarities in various practice facilities regarding physical restrain use. The chosen sample provided a relatively broad range of clinical settings. Also, the assessment by nurses was performed the day after direct observation. This time period was chosen in order to reduce recall bias as much as possible, and therefore increase the sensitivity of the reporting technique although this may not be practical in many situations. Another limitation for the interpretation is the use of a descriptive study design. This design is useful to measure the frequency in which a situation occurs or collect data on possible risk factors, but does not allow to infer causal relationships. It is well known that the prevalence of residents with physical restraints is usually underreported since a social desirability bias tends to affect the validity of the information when the nursing staff has to declare the use of restraints [ 29 ]. Despite that restraints are generally applied for safety reasons, nurses nevertheless experience inner struggle when they have to apply them [ 11 , 22 ]. These feelings could influence the nurses' answer when they are interviewed individually and could introduce subsequently a differential misclassification error. In our study, although the sensitivity value improved when interviews were done with two nurses instead of one, the difference was not considered clinically significant. Reported use of physical restraints by two nurses reduced but did not eliminate the presence of an information bias, since the underreporting went down from 14.9 to 5.9%. On the other hand, we do not think that the resulting effect on the prevalence estimates is of consequence. This phenomenon is equally present but to a much lesser extent in the over reporting data since from 6.2% with one nurse, the prevalence of over reported restraint use was reduced to 1.1% when two nurses were interviewed. Even though other studies have observed an association between residents' characteristics and the risk of being restrained [ 8 , 10 , 17 , 30 , 31 ], these characteristics were not related to the use of physical restraints. Rather, justifications for the use of physical restraints such as disruptive behaviors (e.g. aggressiveness, agitation, and body alignment problems) were associated with their use. For example, it is noteworthy that the risk for falls as perceived by nursing staff was associated with restraint use whereas a history of falls was not. This means that physical restraints were used as preventive devices for a large proportion of subjects, in spite of numerous studies that do not support such practices [ 13 - 15 , 22 ]. Conclusions Compared to review of clinical records, reported physical restraint use by interviewing nursing staff is a simple, efficient, and valid technique of collection of data regarding their use in nursing homes. No severe information bias was observed even though the use of physical restraints may be associated with poor quality of care. This method of measurement appears to be reliable and valid for research purposes. Moreover, our study provides support to the American initiative in regard to the monitoring of several outcomes in nursing homes through nursing staff reports [ 32 - 35 ]. According to our results, interviewing nursing staff is a sensitive and specific method of eliciting information on physical restraint use. Finally, these results have implications for future research in the field. Interviewing nurses on different aspects of medical and nursing care seemed to be a reliable method. Competing interests The authors declare that they have no competing interests. Authors' contributions DL participated in the second line of statistical analyses, and drafted the manuscript. PV drafted parts of the document and contributed to the editing. RV contributed to the editing of the manuscript. PJD served as the Principal Investigator, designed the study, participated and oversaw field activity, revised and edited the manuscript. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC526298.xml |
545065 | Plasma prolactin in patients with colorectal cancer | Background Colorectal cancer is a common malignancy of the gastrointestinal tract. It is the second cancer cause of death in females and third in males. Production of prolactin has been reported with several tumours. However, elevated prolactin plasma levels in colorectal cancer patients remained unclear. Methods In this cross sectional study serum prolactin and carcinoembryonic antigen (CEA) concentrations were assayed using immunoradiometric assay kits, preoperatively in 47 patients, and the results were compared with 51 age and sex matched controls. Results Prolactin and CEA concentration in patients were significantly more as compared with controls. Hyperprolactinemia was found in 36 (76.6%) patients, while 28 (59.6%) had high level of CEA. Conclusions Prolactin may be a better tumour marker than CEA in patients with colorectal malignancy. | Background Colorectal cancer is the third highest cause of cancer mortality [ 1 ]. In Australia, the United Kingdom and the United States, it is the second common cancer for women after breast cancer (age-standardised incidence 22–33 per 100,000), and men after prostate or lung cancer (age-standardised incidence 31–47 per 100,000). About 148,300 new cases are reported each year in the USA and 56,600 Americans die annually from this cause [ 2 ]. Prognostic factors are very important for the evaluation, judgment and optimal treatment of patients with cancer. Carcinoembryonic antigen (CEA) has been recognized as a serum marker for colorectal cancer in the past three decades [ 3 ] and has an important role in the management of colorectal cancer [ 4 ]. Prolactin (PRL) is a hormone with multiple biological actions, synthesized by the anterior pituitary gland [ 5 ] and is best known for its roles in the mammary gland. However, it is now revealed that PRL is able to exert its effects on additional cells and tissues (decidual cells of the placenta, bone, brain, lymphocytes and breast epithelial cells) [ 6 , 7 ]. PRL is secreted not only by lactotrophic cells of the pituitary gland but also by a variety of other normal tissues and human tumours [ 8 ] including malignant tumours of the lung[ 9 ], kidney[ 10 ], uterine[ 11 ], ovary [ 12 ], and breast[ 13 ]. Increase of PRL in colorectal cancer is unclear. Bhatavdekar, et al reported a significantly higher preoperative prolactin levels in patients with colorectal carcinoma [ 14 ], While, Indinnimeo et al and Carlson et al could not confirm hyperprolactinemia in patients with colorectal cancer. [ 15 , 16 ] These conflicting results do not support the hypothesis of increasing level of PRL in colorectal carcinoma. The aim of this study was to see whether PRL levels are increased in colorectal cancer and to compare the preoperative serum levels of PRL and CEA concentrations in colorectal cancer patients; also we intended to find the possible correlation between hyperprolactinemia and the stage of colorectal cancer. Methods This cross-sectional study included 47 patients with colorectal cancer (18 women, 29 men; mean age 55.4 years, range 32–81) and 51 non-cancer patients (21 women, 30 men; mean age 58.5 years, range 20–81), who were candidates of colectomy. All patients were admitted in Shariati general hospital affiliated to Tehran University of Medical Sciences (TUMS) from April 2001 to January 2003. All women in the study were postmenopausal. None of the patients were on any drugs known to increase plasma prolactin levels in the last 6 months prior to the study and were not affected by endocrine, renal, and psychiatric disorders. There was no history of rectal bleeding and family history of colorectal cancer. Peripheral venous blood samples were daily collected from seven days before the operation to measure serum PRL and CEA levels. Blood samples were collected between 07:30 a.m. and 09:30 a.m. to avoid diurnal variation of PRL. Serum prolactin levels were measured with immunoradiometric assay (IRMA) kits produced by Kavoshian co (Tehran-Iran). Serum CEA levels were assayed by IRMA, who used kits from IMMUNOTECH co (Marseille-France). The PRL and CEA assays were carried out within 10 days of sampling. The cut off levels for PRL and CEA were 20 ng/ml and 5 ng/ml serum respectively [ 17 , 18 ]. Stages of cancer were determined by pathology assessment of neoplastic specimens. The study was approved by the Vice-Chancellor of ResearchEthic Committee of TUMS. The data evaluated by Chi-square test and T test using the Statistical Package of Social Science (SPSS Inc., Chicago, IL) for Windows version 11.5. A P-value of <0.05 was considered statistically significant. Results The number of male and female participants was 29 and 18 in case and 30 and 21 in control group. We compared serum PRL and CEA level of 47 patients with 51 healthy controls. There was no significant difference between the age and the sex distribution of the patients and control groups. Mean PRL serum level in colorectal cancer group was 21.6 ± 8.1versus 10.5 ± 4.6 in control group, which was significantly different (p < 0.0001). In other words 36 patients showed hyperprolctinemia (76.7%) while there was only one such patient in the control group (1.9%). CEA level in 59.6% of the cancer group was above the normal compared with 1.9% of controls (P < 0.0001). Out of 47 patients in the cancer group 9 were in stage I (19.1%), 19 in stage II (40.4%), 10 in stage III (21.3 %) and 9 in stage IV (19.1%). Hyperprolactinemia and high CEA concentrations were found in all stages (figure 1 ). Discussion Prognostic factors are crucial for the evaluation and optimal treatment of patients with cancer [ 17 ]. Therefore tumour markers have a greater role in the assessment of therapeutic response. Measurements of serum tumour markers may be helpful in detecting the metastatic process while still in the sub clinical phase [ 15 ]. The ectopic secretion of hormones like PRL by non-endocrine neoplasms is well recognized and used as therapeutic monitoring [ 15 ]. The main source of PRL is the pituitary gland, but in recent years, some studies have reported hyperprolactinemia in patients with breast [ 13 ], lung [ 9 ], prostate and ovary tumours [ 12 ]. Elevated circulatory levels of this hormone have also been detected in colorectal cancer. Bhatavdekar et al reported a high preoperative serum concentration of PRL in patients with colorectal cancer [ 14 ]. Ilan et al. reported elevated levels of PRL in 53% of the patients with colorectal malignancy [ 19 ]. Baert et al, in contrast didn't determine preoperative hyperprolactinemia in a series of 32 patients and their study didn't support the hypothesis of ectopic prolactin production by colorectal cancer [ 20 ]. Indinnimeo, et al found no hyperprolactinemia and prolactin positive immunostaining in colorectal cancer [ 15 ]. Previous studies, that reported normal levels of PRL [ 20 , 15 ], have suggested such factors as renal, endocrine and psychiatric disorders, medications and premenopausal situation may be the cause of hyperprolactinemia in patients with colorectal neoplasms. In the present study, patients with above factors were excluded and our results confirmed preoperative hyperprolactinemia in colorectal cancer. We found no correlation between the plasma PRL concentration and the stage of the tumour. CEA present in blood during fetal life, falls to very low levels in most adults, and circulates in high concentrations in patients with some cancers [ 21 ]. CEA is over expressed in most colorectal cancers and is an important tumour marker in the management of colorectal cancer. A preoperative high CEA value suggests advanced disease either locally or with a distant metastasis. The preoperative serum CEA level can be a useful predicting factor regarding the outcome of the surgical operation [ 4 ]. In the present study we found that serum PRL and CEA levels are increased in patients with colorectal cancer but the greater portion of the patients had an increased level of PRL compared with elevated level of CEA. Considering the fact that laboratory cost for detecting CEA is higher than PRL, We suggest PRL as a valuable tumour marker in colorectal cancer. There are studies that support the tumour marker value of PRL [ 18 , 22 ] and studies that do not [ 16 , 20 ]. Conclusions This study demonstrates that PRL, a pituitary gland hormone, is elevated in colorectal cancer. Moreover, our results show that PRL may be a better tumour marker than CEA in patients with colorectal malignancy. However, further prospective studies are warranted to better understand the evaluation of PRL as a prognostic factor, and to follow up patients after operation to determine the possible correlation between PRL levels, survival and response to therapy. Competing interests The author(s) declare that they have no competing interests. Authors' contributions Dr Ahmad Reza Soroush and Dr Hosein Mahmood zadeh participated in the design of study, acquisition of data and interpretation of data. Dr Mehrnush Moemeni participated in the design of study, acquisition of data, interpretation of data, and drafting the article. Behnam Shakiba participated in acquisition of data, interpretation of data, drafting the article and final approval of this version. Sara Elmi participated in analysis and interpretation of data, drafting the article and final approval of this version. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC545065.xml |
534105 | Low birth weight and longitudinal trends of cardiovascular risk factor variables from childhood to adolescence: the bogalusa heart study | Background Several studies have linked low birth weight to adverse levels of cardiovascular risk factors and related diseases. However, information is sparse at a community level in the U.S. general population regarding the effects of low birth weight on the longitudinal trends in cardiovascular risk factor variables measured concurrently from childhood to adolescence. Methods Longitudinal analysis was performed retrospectively on data collected from the Bogalusa Heart Study cohort (n = 1141; 57% white, 43% black) followed from childhood to adolescence by repeated surveys between 1973 and 1996. Subjects were categorized into low birth weight (below the race-specific 10 th percentile; n = 123) and control (between race-specific 50–75 th percentile; n = 296) groups. Results Low birth weight group vs control group had lower mean HDL cholesterol (p = 0.05) and higher LDL cholesterol (p = 0.05) during childhood (ages 4–11 years); higher glucose (p = 0.02) during adolescence. Yearly rates of change from childhood to adolescence in systolic blood pressure (p = 0.02), LDL cholesterol (p = 0.05), and glucose (p = 0.07) were faster, and body mass index (p = 0.03) slower among the low birth weight group. In a multivariate analysis, low birth weight was related independently and adversely to longitudinal trends in systolic blood pressure (p = 0.004), triglycerides (p = 0.03), and glucose (p = 0.07), regardless of race or gender. These adverse associations became amplified with age. Conclusions Low birth weight is characterized by adverse developmental trends in metabolic and hemodynamic variables during childhood and adolescence; and thus, it may be an early risk factor in this regard. | Background The growth of a fetus in an undernourished intrauterine environment is considered to result in adaptive fetal programming or metabolic imprinting with pathophysiologic consequences later in life [ 1 - 4 ]. It is contended that low birth weight at term (<2500 g), a surrogate for impaired gestational environment, is uncommon in industrialized societies, and deprivations that existed before the second world war no longer apply to pregnancies at present [ 5 , 6 ]. In reality, the United States birth data for year 2002 show a prevalence of 7.8% low birth weight, with blacks showing almost twice the rate of whites [ 7 ]. Studies world-wide, regardless of socio-economic background, have linked low birth weight to increased risk of insulin resistance, dyslipidemia, hypertension, coronary heart disease, and type 2 diabetes [ 8 - 13 ], although some studies have found a weak or no associations in this regard [ 5 , 14 - 16 ]. Several studies including our own have examined the association between low birth weight and selected cardiovascular risk factor variables in childhood and adolescence [ 17 - 27 ]. However, information is scant on data linking low birth weight to longitudinal changes of cardiovascular risk factor variables measured simultaneously and serially from childhood to adolescence. As part of the Bogalusa Heart Study, a biracial (black-white) community-based investigation of evolution of cardiovascular risk since childhood [ 28 ], the present analysis examines the relationship between low birth weight and the longitudinal trends of adiposity, blood pressure, lipids and lipoproteins, and measures of glucose homeostasis from childhood to adolescence. Methods Study cohorts Between 1973 and 1996, 7 cross-sectional surveys of children and adolescents were conducted in the community (65% white, 35% black) of Bogalusa, LA. This panel design, based on repeated cross-sectional examinations performed every 3 to 4 years, resulted in serial observations required for the longitudinal analysis. For the present report, two sets of data were merged as described previously [ 21 ]: 1) singleton new born cohort participants (n = 233) whose weights were measured at birth as part of the initial examination during 1973–1974; and 2) singletons (n = 1213) aged 7–11 years who participated in 1987–1988 cross-sectional survey and whose birth weight records were obtained from the Office of Vital Statistics in New Orleans in 1991. Of those with birth weight data (n = 1436), 1329 subjects participated in 2 to 7 surveys of children and adolescents. Exclusion of those with missing data (n = 170), congenital heart disease (n = 11), and diabetes (n = 7) resulted in 1141 eligible subjects (57% white, 47% female). Low birth weight and control groups were selected from the eligible cohort, according to birth weight percentile cut points [ 29 ]. Subjects (n = 123) who had birth weight below the race-specific 10 th percentile (whites: <2749 g; blacks: <2438 g) were categorized as low birth weight group; those (n = 296) in the upper normal range of 50–75 th percentile (whites: 3402 – 3770 g; blacks: 3133 g – 3487 g) as control group. Birth weights above the 75 th percentile were not included in the control group because of the u-shaped associations between birth weight and risk factors or disease [ 3 , 30 ]. Race-specific percentile, rather than World Health Organization (WHO) criterion for low birth weight (<2500 g) was used to define low birth weight because of black-white differences in birth weight distribution [ 31 , 32 ]. General examination Identical protocols were used by trained examiners across all surveys [ 33 ]. Subjects were instructed to fast for 12 hours prior to screening, and compliance was determined by interview on the morning of examination. Anthropometric and blood pressure measurements were made in replicate and mean values were used. Height and weight were measured 2 times; subscapular skinfold thickness 3 times. Body mass index (BMI) calculated as weight in kg divided by the square of the height in meters was used as a measure of overall adiposity; subscapular skinfold for truncal fatness. The reproducibility in terms of intraclass (intra-observer) correlation coefficient was greater than 0.99 for weight and height, and greater than 0.97 for subscapular skinfold. Blood pressure levels were measured in 6 replicates by 2 randomly assigned nurses on the right arm of subjects in a relaxed, sitting position. Systolic and diastolic blood pressures were recorded at the first, fourth, and fifth Korotkoff phases using mercury sphygmomanometer. For this analysis fourth phase was used for diastolic blood pressure because in our experience the fourth phase is more reliably measured in children and more predictive of adult hypertension [ 34 ]. Laboratory analyses From 1973 to 1986 cholesterol and triglyceride levels in serum were measured using chemical procedures on Technicon Autoanalyzer II (Technician Instrument Corp., Tarrytown, NY). Since then these measurements were done using enzymatic procedures on Abbott VP instrument (Abbott laboratories, North Chicago, IL). Serum lipoprotein cholesterols were analyzed by a combination of heparin-calcium precipitation and agar-agarose gel electrophoresis procedures [ 35 ]. Both chemical and enzymatic procedures met the performance requirement of the Lipid Standardization Program of the Centers for Disease Control and Prevention, Atlanta, GA. The laboratory has been monitored for precision and accuracy by the agency's surveillance program since 1973. For example, the average bias in levels of total cholesterol on CDC control samples ranged from -0.1 to -1.6 mg/dL between different cross-sectional surveys, with no consistent pattern over time within or between surveys. The intraclass correlation coefficients between the blind duplicate (10% random sample) values ranged from 0.87 to 0.99 for total cholesterol; 0.88 to 0.99 for triglycerides; 0.86 to 0.98 for LDL cholesterol; and 0.86 to 0.98 for HDL cholesterol. Plasma immunoreactive insulin levels were measured by a commercial radioimmunoassay kit (Phadebas, Pharmacia Diagnostics, Piscataway, NJ). Plasma glucose was measured by a glucose oxidase method either using a Beckman glucose analyzer (Beckman Instruments, Fullerton, CA) or as part of a multichemistry (SMA20) profile. The intraclass correlation coefficient between blind duplicate values ranged from 0.94 to 0.98 for insulin and 0.86 to 0.98 for glucose. An index of insulin resistance was calculated according to the homeostasis assessment model formula [ 36 ]: HOMA-IR=fasting insulin (μu/mL) × fasting glucose (mmol/L) ÷ 22.5. Statistical analyses For test of significance glucose and insulin were logarithmically transformed to approach normality. The average of multiple measurements for subjects within age groups 4 to 11 and 12 to 18 years corresponding to childhood and adolescence periods was used to calculate mean levels of risk variables by birth weight status and age groups. Mean levels of risk variables within each age group were compared between low birth weight and control group by a general linear model, adjusting for age, race, and gender. The longitudinal rates of change in risk variables was assessed by the generalized estimation equation (GEE) method [ 37 ] with age as predictor, adjusting for race and gender. Independent association of low birth weight with longitudinal trends of risk variables from childhood to adolescence was assessed by multivariate analysis (GEE). The model included birth weight (low vs control) and risk variables as applicable along with age, age 2 , race and gender and their interaction with birth weight (low vs control). A backward stepwise method was used to remove nonsignificant terms. Results Mean levels of cardiovascular risk variables during childhood (ages 4–11 years) and adolescence (ages 12–18 years) periods are shown in table 1 by birth weight groups. Of the risk variables adjusted for age, race, and gender, levels of HDL cholesterol were significantly lower and LDL cholesterol higher among low birth weight group vs control group during childhood. During adolescence, only glucose levels were significantly higher among low birth weight group vs control group. Table 1 Levels (mean ± SD) of risk variables during childhood and adolescence by birth weight. The Bogalusa Heart Study Variable Childhood (4–11 years) Adolescence (12–18 years) Low Birth Weight Control Low Birth Weight Control BMI (kg/m 2 ) 16.7 ± 2.6 17.5 ± 2.9 21.6 ± 5.2 22.7 ± 5.0 Subsc. Skinfold (mm) 8.2 ± 5.1 8.3 ± 6.4 15.9 ± 10.0 16.0 ± 10.7 Syst. BP (mm Hg) 96.4 ± 9.0 98.6 ± 8.1 108.8 ± 9.1 105.2 ± 8.7 Diast. BP (mm Hg) 57.2 ± 10.2 58.7 ± 7.9 66.1 ± 8.0 66.4 ± 7.4 Triglycerides (mg/dL) 62.6 ± 23.2 52.8 ± 20.3 87.1 ± 29.0 83.2 ± 35.3 HDL cholesterol (mg/dL) 44.3 ± 22.6 a 54.7 ± 17.4 49.9 ± 12.8 51.2 ± 11.5 LDL cholesterol (mg/dL) 76.0 ± 35.4 a 68.6 ± 37.6 99.5 ± 24.7 98.4 ± 24.3 Glucose (mg/dL) 79.7 ± 8.1 80.9 ± 9.7 85.4 ± 8.2 b 81.6 ± 7.4 Insulin (μu/mL) 8.5 ± 5.7 7.4 ± 4.6 14.8 ± 7.5 13.2 ± 8.6 HOMA-IR 1.7 ± 1.2 1.6 ± 1.0 3.0 ± 2.4 2.7 ± 2.0 Difference between groups (adjusted for age, race, and gender), a: p = 0.05; b: p = 0.02 HOMA-IR: homeostasis model assessment index of insulin resistance Longitudinal rates of change in cardiovascular risk variables from childhood to adolescence, adjusted for race and gender, are presented in table 2 by birth weight groups. The rate of increase in BMI was significantly lower in low birth weight group compared with control group, while the rate of increase in subscapular skinfold remained similar between the groups. With respect to blood pressure, the rate of increase in systolic blood pressure was significantly higher in low birth weight group than control group. Of the measures of glucose homeostasis, rate of increase of glucose was marginally significant in low birth weight group vs control group. Low birth weight was associated with significantly higher rate of increase in LDL cholesterol; and no significant trends in HDL cholesterol and triglycerides. Table 2 Rates of change in risk variables from childhood to adolescence by birth weight. The Bogalusa Heart Study Variable Low Birth Weight Control p-value BMI (kg/m 2 /y) 0.60 † 0.71 0.03 Subsc. Skinfold (mm/y) 0.91 1.12 0.27 Syst. BP (mmHg/y) 1.70 1.30 0.02 Diast. BP (mm Hg/y) 1.21 1.02 0.26 Triglycerides (mg/dL/y) 2.28 2.92 0.80 HDL cholesterol (mg/dL/y) -0.53 -0.91 0.24 LDL cholesterol (mg/dL/y) 0.80 0.64 0.05 Glucose (mg/dL/y) 0.50 0.11 0.07 Insulin (μu/mL/y) 0.79 0.67 0.70 HOMA-IR 0.18 0.14 0.44 † Regression slope with respect to age in years (y) adjusted for race and gender (generalized equation estimation method). HOMA-IR: homeostatis model assessment index of insulin resistance. In a multivariate analysis, low birth weight was retained as an independent predictor variable for adverse longitudinal trends in systolic blood pressure, triglycerides, and glucose (marginal) from childhood to adolescence, regardless of race or gender (table 3 ). Further, there was a significant interaction between low birth weight and age in this regard, denoting that these variables increased to a greater extent in the low birth weight group than in the control group as individuals became older. An analysis of this data set using the WHO criterion for low birth weight (<2500 g) showed that only 36 subjects were reclassified as having normal birth weight, and the results were essentially the same (data not shown). Table 3 Independent association of low birth weight with longitudinal trends of systolic blood pressure, triglycerides and glucose from childhood to adolescence Independent Variables Retained Syst. BP Triglycerides Glucose β † p-value β p-value β p-value Birth Weight (low vs control) 3.84 0.02 48.6 0.08 15.20 0.07 Gender (male vs female) -- -- -- -- 4.31 <0.001 Age 0.39 0.40 12.21 0.01 6.60 <0.001 Age 2 0.06 0.002 -0.44 0.04 -0.31 <0.001 Insulin 0.11 0.03 1.16 <0.001 0.22 0.02 BMI 0.34 <0.001 -- -- -- -- Birth weight × age 0.45 0.004 12.71 0.03 0.10 0.07 Birth weight × age 2 -- -- 0.55 0.02 2.65 0.10 † GEE regression coefficient. The model included birth weight (low vs control) along with age, age 2 , race, and gender, and their interaction with birth weight; and risk variable as applicable. Discussion Information is sparse at a community level in the U.S. general population regarding the effects of low birth weight on the longitudinal trends in C-V risk factor variables measured serially and concurrently from childhood to adolescence. The present community-based study demonstrates the adverse effects of low birth weight on the longitudinal (developmental) trends in systolic blood pressure, triglycerides, and glucose during childhood and adolescence, regardless of race or gender. These observations are in accord with the emerging evidence supporting the concept of intrauterine imprinting and its pathophysiologic consequences enunciated by the fetal origin or thrifty phenotype hypothesis [ 3 ]. Many, but not all, previous studies in children and adolescents have found adverse associations between birth weight and levels of cardiovascular risk factor variables [ 16 - 27 ]. In this study, the magnitude of differences in mean levels of cardiovascular risk factor variables between low birth weight and control groups during childhood and adolescence periods were small and nonsignificant for most of the study variables, except for the adverse levels HDL cholesterol and LDL cholesterol in childhood and glucose in adolescence among the low birth weight group. However, in a multivariate analysis of the serial data, the independent adverse effects of low birth weight on the longitudinal trends of systolic blood pressure, triglycerides, and glucose were discernable in the study cohort. Of note, the observed adverse trends associated with low birth weight vs control group were influenced by age in that the differences became greater in magnitude as the children got older. Earlier studies have reported that the inverse associations between birth weight and levels of cardiovascular risk factor variables became stronger with increasing age [ 26 , 38 ]. Whether the potentiating effect of increasing age on low birth weight – risk variable relationship reflects the interaction between fetal programming related to intrauterine malnutrition and the increasing burden with age of unhealthy life-style behaviors including overnutrition and sedentary life style is not clear. In this context, it should be noted that although the rate of yearly increase in BMI, which also includes muscle mass, was significantly lower in low birth weight group, the rate of increase in subscapular skinfold, a measure of truncal fat, remained similar to that of control group. This suggests a gaining of truncal fat, in relative term, over muscle mass in the low birth weight group. Although observational studies like the present one can not establish causality, several putative mechanisms link low birth weight to adverse trends in risk factor variables. It has been suggested that insulin resistance may be one mechanism by which intrauterine events may program disease risk [ 39 ]. Undernutrition in utero is known to cause permanent impairment in growth, structure and function of muscle [ 39 , 40 ], fat [ 41 , 42 ], endocrine pancreas [ 2 , 43 ], liver [ 30 ], renal nephrons [ 44 , 45 ] and vasculature [ 46 ] due to biologic programming, resulting in insulin resistance/glucose intolerance, hypertension, and dyslipidemia. Further, it has been suggested that intrauterine programming of the hypothalamic-pituitary-adrenal axis may be a functional mechanism underlying the link between low birth weight and above disorders [ 47 ], known as components of insulin resistance or metabolic syndrome [ 48 ]. This study has certain limitations. The lack of information on the duration of gestation precluded us from adjusting the birth weight for gestational age, a potential confounder. However, earlier studies have found that adverse effects of low birth weight on cardiovascular risk factor variables were independent of gestation period [ 10 , 17 , 49 ]. Further, it has been pointed out that inclusion of preterm births could actually underestimate these associations [ 28 ]. This study also lacks measurements of glucose tolerance and insulin action and secretion used in etiologic studies. Instead, we used the glucose homeostasis measures that are relatively easily measured and applicable at a population level. Conclusions Low birth is characterized by adverse developmental trends in metabolic and hemodynamic variables during childhood and adolescence, especially as the children get older. These observations in conjunction with earlier findings support the view that low birth weight, albeit a crude marker of prenatal growth and physiological environment, is a potential early risk factor for the emergence of metabolic and hemodynamic disorders and related diseases [ 1 , 50 ]. Abbreviations BMI, body mass index; LDL, low-density lipoprotein; HDL, high-density lipoprotein Competing interests The author(s) declare that they have no competing interests. Authors' contributions MGF participated in study design, data analysis and manuscript preparation. SRS and GSB contributed to study concept and design, data collection, acquisition of funding and manuscript preparation. JX was involved in measurements of laboratory variables. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC534105.xml |
520831 | Planned neck dissection following chemo-radiotherapy in advanced HNSCC | Background Neck dissection has traditionally played an important role in the management of patients with regionally advanced head and neck squamous cell carcinoma (HNSCC) treated with radical radiotherapy alone. However, with the incorporation of chemotherapy in the therapeutic strategy for advanced HNSCC and resultant improvement in outcome the routine use of post chemo-radiotherapy neck dissection is being questioned. Methods Published data for this review was identified by systematically searching MEDLINE, CANCERLIT & EMBASE databases from 1995 until date with restriction to the English language. Results There is lack of high quality evidence on the role of planned neck dissection in advanced HNSCC treated with chemo-radiotherapy. A systematic literature search could identify only one small randomized controlled trial (Level I evidence) addressing this issue, albeit with major limitations. Upfront neck dissection followed by chemo-radiotherapy resulted in better disease-specific survival as compared to chemoradiation only. Several single arm prospective and retrospective reports were also identified with significant heterogeneity and often-contradictory conclusions. Conclusions Planned neck dissection after radical chemo-radiotherapy achieves a high level of regional control, but its ultimate benefit is limited to a small subset of patients only. Unless there are better non-invasive ways to identify residual viable disease, the role of such neck dissection shall remain debatable. A large randomized controlled trial addressing this issue is needed to clarify its role and provide evidence-based answers. | Introduction The optimal management of the neck in loco-regionally advanced head & neck squamous cell carcinomas (HNSCC) following primary chemo-radiotherapy remains controversial [ 1 , 2 ]. Traditionally neck dissection (Fig 1 ) was thought to improve neck control in patients with regionally advanced disease (N2–N3 disease) treated with radical radiotherapy alone [ 3 , 4 ]. However, with the incorporation of chemotherapy in the therapeutic strategy for advanced HNSCC and resultant improvement in outcome [ 5 , 6 ], the routine use of post chemo-radiotherapy neck dissection is being questioned [ 7 , 8 ]. Some authors recommend neck dissection for bulky nodal disease after chemo-radiation as part of organ preservation protocol in an elective manner, regardless of the response in the neck provided the primary is controlled. Others argue that it is an ineffective procedure and should be abandoned. Nevertheless, most investigators agree that elective neck dissection be performed for patients with less than a complete response in the neck after combined modality therapy to optimize regional control. This review attempts to provide the discerning reader a bird's eye view of the available evidence on this controversial issue. Figure 1 Sketch anatomy of standard neck incision Methods Literature search strategy Published data for this review was identified by systematically searching the MEDLINE, CANCERLIT & EMBASE databases from 1995 until date with restriction to the English language. "Head & Neck cancer" OR "HNSCC" was combined with "chemo-radiotherapy" OR "chemo-radiation" as Medical Subject Heading (MeSH) terms and each of the following phrase used as text words: "adjuvant neck dissection"; "planned neck dissection"; and "neck management". Relevant cross-references were also considered. Results & Discussion The evidence There is only one small randomized control trial (American Society of Clinical Oncology [ 9 ] Level I evidence) evaluating the role of planned neck dissection in advanced HNSCC treated with primary chemo-radiotherapy. Carinci [ 10 ] et al randomly assigned patients with advanced unresectable HNSCC to either elective neck dissection followed by chemo-radiotherapy (Group I, n = 23) or chemo-radiotherapy alone (Group II, n = 31). The two groups were reasonably well balanced for known prognostic factors. The 2-and 5-year disease-specific survival rates significantly favored the surgical arm (52% and 26% for Group I versus 29% and 0% for Group II respectively). A Cox regression analysis adjusted for T-stage, N-stage, age and gender showed that only therapy (Group I versus II) reached a positive and significant odds ratio in association with the probability of death (p = 0.0366 in favor of neck dissection). This study however suffers from major limitations. Firstly, the trial methodology was not detailed adequately to assess the validity of the interpretations. The investigators neither specified the method of randomization (why was the distribution unequal in the two arms) nor about stratification on known prognostic factors. Secondly, the numbers of patients in each arm were too small to draw any definite conclusions without ruling out an element of bias. Thirdly, the radiotherapy delivery was suboptimal (only 60–65 Gy with conventional fractionation) for sterilizing advanced HNSCC, in which case the addition of neck dissection was expected to improve outcome. Finally, since neck dissection was done upfront rather than after chemo-radiotherapy, the results of this trial cannot be directly extrapolated to the issue under consideration. In absence of high quality evidence, the best available evidence tempered with clinical judgment often guides decision-making. Two of the recently published reports [ 11 , 12 ] somewhat at contradiction with each other are briefly discussed to illustrate the dilemma. Argiris [ 11 ] et al evaluated 131 patients with HNSCC having N2–N3 disease treated on concurrent chemo-radiotherapy protocols. Neck dissection was performed in 92 (70%) patients, either before (n = 31) or after chemo-radiotherapy (n = 61). With a median follow-up of 4.6 years, the 5-year loco-regional progression-free-survival (PFS) was significantly better in patients with planned neck dissection as compared to those without neck dissection (88% versus 74% respectively, p = 0.02) The addition of neck dissection to chemo-radiotherapy resulted in only one neck failure in 92 patients (neck PFS 99%) versus six neck failures in 39 patients (neck PFS 82%) not undergoing neck dissection (p = 0.0007). Neck dissection was however, not beneficial in patients with a complete clinical response (CCR). Of the 92 patients with a CCR, 62 underwent neck dissection, of which only 1 relapsed in the neck (neck PFS 98%). The neck PFS of 92% (2 neck failures) in the 30 patients in CCR who did not undergo neck dissection was not significantly different (p = 0.21). On subset analysis, in patients with N3 disease (n = 27), there was either a trend or a statistically significant advantage in all the survival parameters for the neck dissection arm. In contrast, in patients with N2 disease (n = 104), only the neck control improved with neck dissection. The local PFS, distant PFS, and the overall survival were similar irrespective of neck dissection. The authors concluded that in patients with N3 stage and less than CCR it was necessary to add neck dissection for optimal disease control, whereas in patients with N2 disease in CCR, neck dissection could safely be omitted without compromising outcome. Brizel [ 12 ] et al identified 108 patients with nodal disease from a cohort of 154 patients on concurrent chemo-radiation protocols. A modified neck dissection was performed in 65 (60%) of 108 patients. With a median follow up of 4 years for surviving patients, the neck control rate was 100% for N1 patients irrespective of neck dissection being performed or not. Their disease-free-survival (DFS) was 70% with no differences relative to neck dissection. In N2–N3 patients, a CCR was achieved in 43 (55%) patients. Ten patients with local progression or systemic dissemination were excluded from analysis. Of the 52 patients undergoing neck dissection in N2–N3 group, only 1 regional relapse was seen, in contrast to 3 neck failures out of 16 in those not undergoing dissection (p = 0.05). The 4-year DFS was 75% for N2–N3 patients with a CCR and neck dissection versus 53% for those with CCR but no neck dissection (p = 0.08). The 4-year overall survival was also better for the dissection arm (77% versus 50% respectively, p = 0.04). The authors concluded that the policy of neck dissection in patients with N2–N3 disease even in CCR is justified to optimize loco-regional control and survival. Apart from the afore-mentioned two reports, there are a few reasonably large studies (involving >50 patients: Table 1 ) and several smaller ones, both prospective and retrospective published in the last decade trying to define the benefit of such intervention with conflicting results [ 2 , 7 , 13 - 24 ]. However, significant heterogeneity in selection criteria as well as variable treatment schedules and response assessment methodology amongst these reports introduces a great deal of bias precluding any definitive conclusions. Table 1 Neck failure in selected series of chemo-radiotherapy for HNSCC treated with or without ND Author (year) No of pts (n) Pts in CCR Neck failures (overall) Neck failure (pts in CCR) Remark(s) ND done ND not done ND done ND not done McHam 2 (2003) N2–N3: 109 65 5/76 4/33 1/32 4/33 ND needed for all N2–N3 patients Grabenbauer 7 (2003) N0–N3: 142 97 Only patients with CCR offered ND 9/56 4/41 No clear benefit of ND after CCR Clayman 13 (2001) N2–N3: 66 29 5/18 6/48 0/4 0/25 ND not needed for patients in CCR Stenson 14 (2000) N2–N3: 69 30 1/69 NA 0/30 NA All 69 pts had ND; needed for N2–N3 Robbins 15 (1999) N2–N3: 52 (56 heminecks) 33 1/34 2/20 0/16 0/17 Good control with ND for N2–N3 Lavertu 16 (1999) N1–N3: 78 55 8/78 neck failures in all NK NK ND done for all pts with N2–N3 Lavertu 17 (1997) N0–N1: 47 43 0/6 4/38 0/5 4/38 ND needed for all N2–N3 patients even in CCR N2–N3: 53 30 1/35 3/12 0/18 3/12 ND = neck dissection; CCR = clinical complete response; pts = patients; NA = not applicable; NK = not known The potential benefit of planned neck dissection after a course of intensive chemo-radiotherapy in terms of improved regional control with or without an impact on survival needs to be weighed against the expected morbidity associated with the surgical procedure [ 12 , 14 , 25 ]. One argument put forward in favor of planned neck dissection even for patients in CCR is the high rate of pathological positivity (30%–50%) depending upon the meticulousness of sectioning by the pathologist [ 14 , 17 ]. However, a significant majority of them actually may represent microscopic non-viable residual disease only, as has been demonstrated by Strasser using Ki-67 proliferating index [ 26 ], unlikely to relapse later. Proponents of neck dissection also argue that the ultimate success rate of salvage neck dissection after a relapse in the neck treated with full dose chemo-radiotherapy is small, whereas the morbidity is high [ 10 , 25 ]. In contrast, the morbidity of a planned neck dissection is at best modest, when scheduled between 6–12 weeks from end of chemo-radiotherapy [ 7 , 14 , 17 ], which is supposed to be the time window between acute and chronic radiation injury. Conclusions & Recommendations Planned neck dissection after radical chemo-radiotherapy achieves a high level of regional control, but its ultimate benefit is limited to a small subset of patients only. The morbidity of such dissection is small, but significant. Its impact on survival is yet to be completely realized. In the majority of patients it is either unnecessary because there is no residual disease in the neck or futile because of unsalvageable primary recurrence or distant metastases. Nevertheless, it is recommended that planned neck dissection be performed for patients with less than a complete response in the neck after combined modality therapy to optimize regional control, provided the primary is controlled and there is no evidence of distant metastases. It should also be performed as part of salvage surgery for locally persistent or residual disease at primary site. The criterion for planned neck dissection for patients with advanced nodal disease with a CCR in the neck following chemo-radiotherapy should incorporate not only the nodal staging but also the actual size of the involved lymph nodes. Unless there are better non-invasive ways to identify residual viable disease, which could include functional imaging like Positron Emission Tomography and biological assays like hypoxia markers, the role of such neck dissection shall remain debatable. A large randomized controlled trial across several institutions addressing these issues is needed to clarify the role of planned neck dissection in advanced HNSCC treated with primary chemo-radiotherapy and provide evidence-based answers. Source of funding No source of funding involved in this review Competing interest or Conflict of interest None declared. Authors' Contributions Dr JP proposed the idea of systematic review on the issue Dr TG did the literature search & prepared the manuscript Dr JP critically reviewed and revised the manuscript | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC520831.xml |
535555 | Adaptive evolution of transcription factor binding sites | Background The regulation of a gene depends on the binding of transcription factors to specific sites located in the regulatory region of the gene. The generation of these binding sites and of cooperativity between them are essential building blocks in the evolution of complex regulatory networks. We study a theoretical model for the sequence evolution of binding sites by point mutations. The approach is based on biophysical models for the binding of transcription factors to DNA. Hence we derive empirically grounded fitness landscapes, which enter a population genetics model including mutations, genetic drift, and selection. Results We show that the selection for factor binding generically leads to specific correlations between nucleotide frequencies at different positions of a binding site. We demonstrate the possibility of rapid adaptive evolution generating a new binding site for a given transcription factor by point mutations. The evolutionary time required is estimated in terms of the neutral (background) mutation rate, the selection coefficient, and the effective population size. Conclusions The efficiency of binding site formation is seen to depend on two joint conditions: the binding site motif must be short enough and the promoter region must be long enough. These constraints on promoter architecture are indeed seen in eukaryotic systems. Furthermore, we analyse the adaptive evolution of genetic switches and of signal integration through binding cooperativity between different sites. Experimental tests of this picture involving the statistics of polymorphisms and phylogenies of sites are discussed. | Background The expression of a gene is controlled by other genes expressed at the same time and by external signals, a process called gene regulation [ 1 ]. Due to the combinatorial complexity of regulation, a large number of functional tasks can be performed by a limited number of genes. Differences in gene regulation are believed to be a major source of diversity in higher eukaryotes. To a large extent, gene regulation is the control of transcription. It is accomplished by a number of regulatory proteins called transcription factors that bind to specific sites on DNA. These binding sites contain about 10 – 15 base pairs relevant for binding and are mostly located in the cis-regulatory promoter region of a gene. A cis-regulatory region in E. coli is about 300 base pairs long and contains a few transcription factor binding sites [ 2 ]. There may be two or more sites for the same factor in one promoter region. At the same time, the sequences of binding sites are fuzzy , that is, different sites for the same factor differ by about 20 – 30 percent of the bases relevant for binding [ 2 ]. This makes the identification of sites a difficult bioinformatics problem [ 3 - 5 ]. Frequently, the simultaneous binding at two nearby sites is energetically favoured. This so-called binding cooperativity can be related to various functions. In a genetic switch such as the famous phage lambda switch in Escherichia coli [ 6 ], it produces a sharp increase of the expression level at a certain threshold concentration of a transcription factor. A pair of sites for two different kinds of factors with cooperative binding can be a simple module for signal integration , leading to the expression of the downstream gene only when both kinds of factors are present simultaneously [ 1 ]. These examples are discussed in more detail below. Regulation in higher eukaryotes shares these features but is vastly more complicated [ 7 ]. A promoter region is typically a few thousand base pairs long and contains many different binding sites with often complex interactions. At the same time, individual sites are shorter, with about 5–8 relevant base pairs. The sites are sometimes organized in modules interspersed between regions containing no sites. In many known cases, the expression of a gene depends on the simultaneous presence of several factors. Well-studied examples of regulatory networks in eukaryotes include the sea urchin Strongylocentrotus purpuratussea [ 8 ] and the early developmental genes in Drosophila [ 9 ]. The sequence statistics of binding sites has been addressed in two recent theoretical studies [ 10 , 11 ]. Based on a model incorporating the biophysics of sequence-factor interaction [ 12 , 13 ], a fitness landscape for binding site sequences is constructed (see the discussion in the next section). The resulting mutation-selection equilibrium is analysed using a mean-field quasispecies approach [ 14 ]. This approach, which neglects the effects of genetic drift, is applicable in very large populations. In both studies [ 10 , 11 ], fuzziness is attributed to mutational entropy as a possible reason: the single or few sequence states with optimal binding of the transcription factor can be outweighed by the vastly higher number of sub-optimal states at some mutational distance from the optimal binding sequence. This effect is similar to the fuzziness of amino acid sequences in proteins discussed in [ 15 ]. From an evolutionary perspective, explaining the molecular programming of regulatory networks presents a striking problem. The diversification of higher eukaryotes, in particular, requires the efficient generation and alteration of regulatory binding interactions. One likely mode of evolution is gene duplications with subsequent complementary losses of function in both copies [ 16 , 17 ]. However, the differentiation of regulation should also require complementary processes that generate new functions of genes as a response to specific demands. This task must be accomplished mainly by sequence evolution of regulatory DNA. There are examples of highly conserved regulatory sequences with a conserved function but binding sites can also appear, disappear, or alter their sequence even between relatively closely related species; see, e.g., refs. [ 18 - 22 ]. This turnover of binding sites has been argued to follow an approximate molecular clock in Drosophila [ 23 ]. The transcription factors themselves are known to remain more conserved, especially if they are involved in the regulation of more than one gene. The modes of regulatory sequence evolution and their relative importance remain largely to be explored. Contributions may arise from point mutations, slippage processes [ 24 ], and larger rearrangements of promoter regions [ 25 ]. The latter processes may lead to the shuffling of entire modules of binding sites between different genes. In this paper, we are more interested in the local sequence evolution within a module, which has been argued to contribute most of the promoter sequence difference between species [ 26 ]. It is also the most promising starting point for a quantitative analysis of binding site evolution. We study a theoretical model that takes into account point mutations, selection, and genetic drift. The form of selection is inferred from the biophysics of the binding interactions between transcription factors and DNA. We derive the stationary distribution of binding sites under selection, which shows specific correlations between nucleotide frequencies at different positions in a binding site. The non-stationary solutions of the model describe efficient adaptive pathways for the molecular evolution of regulatory networks by point mutations. This efficiency can be quantified in terms of the length of the binding motif, and the length of the promoter region, and the fitness landscape for factor binding, which is amenable to quite explicit modeling. With the parameters found in natural systems, our model predicts that a new binding site for a given transcription factor can be generated by a fast series of adaptive substitutions, even if the expression of the corresponding gene bears even a modest fitness advantage. The evolutionary time required for site formation in response to a newly arising selection pressure is estimated in terms of the characteristic time scales of mutation, selection, and drift. For Drosophila , it may be as short as 10 5 years even for moderate selection pressures. However, this pathway is found to depend crucially on the presence of selection. It would be too slow under neutral evolution, in contrast to the results of [ 7 ], see also the recent discussion in [ 27 ]. Cooperative interactions between binding sites can evolve adaptively on similar time scales, as we show for the two simple examples alluded to above, the genetic switch and the signal integration module. These results are discussed at the end of the paper with particular emphasis on possible experimental tests. Factor binding and selection The binding energy (measured in units of k B T ) between a transcription factor and its binding site is, to a good approximation, the sum of independent contributions from a small number of important positions of the binding site sequence, , with ≈ 10 - 15 [ 28 - 30 ]. The individual contributions ε i depend on the position i and on the nucleotide a i at that position. There is typically one particular nucleotide preferred for binding; the sequence is called the target sequence . The target sequence can be inferred as the consensus sequence of a sufficiently large number of equivalent sites. The so-called energy matrix ε i ( a ) has been determined experimentally for some factors from in vitro measurements of the binding affinity for each single-nucleotide mutant of the target sequence. Typical values for the loss in binding energy are 1–3 k B T per single-nucleotide mismatch away from the target sequence. In this paper, we use the further approximation ε i = ε if a i = and ε = 0 otherwise, the so-called two-state model [ 12 ]. The binding energy of any sequence is then, up to an irrelevant constant, simply given by its Hamming distance r to the target sequence: E / k B T = ε r . (The Hamming distance is defined as the number of positions with a mismatch a i ≠ .) It is important to note the status of this "minimal model" of binding energies for the discussion in this paper. Both approximations underlying the model can be violated. Even though typical mismatch energies are of the same order of magnitude, there can be considerable differences between different substitutions at one position and between different nucleotide positions. Moreover, deviations from the approximate additivity of binding energies for the single nucleotide positions have also been observed. However, these complications do not affect the order-of-magnitude estimates for adaptive sequence evolution. As it will become clear, the efficiency of binding site formation depends only on the qualitative shape of the fitness landscapes derived below. In these landscapes, the regime of weakly-binding sequences and of strongly-binding sequences are separated by only a few single nucleotide substitutions. The relative magnitude of the fitness increase of these substitutions does not matter in first approximation. Indeed, inhomogeneities in the values of the ε i ( a ) tend to reduce the number of crucial steps in the adaptive process and thereby to further increase its speed. Within the two-state model, the binding probability of the factor in thermodynamic equilibrium is Here ε is the binding energy per nucleotide mismatch and ε ρ is the chemical potential measuring the factor concentration. Both parameters are expressed in units of k B T and hence dimensionless. Appropriate values for typical binding sites have been discussed extensively in refs. [ 10 , 13 ]. It is found that ε should take values around 2, which is consistent with the measurements for known transcription factors mentioned above [ 28 - 30 ]. The chemical potential depends on the number of transcription factors present in the cell, on the binding probability to background sites elsewhere in the genome (which have a sequence similar to the target sequence by chance), and on the functional sites in the in the genome other than the binding site in question that may compete for the same protein. Binding to background sites does not significantly reduce the binding to a specific functional site [ 13 ]. This leads to values ρ ≈ (log n f )/ ε ≈ 2 - 4, given observed factor numbers n f of about 50 – 5000 [ 13 ]. Binding to other copies of the same functional sequence becomes only relevant at low factor concentrations and high number of copies, when sites compete for factors. A fitness landscape quantifies the fitness F of each sequence state at the binding site. Fitness differences arise due to different expression levels of the regulated gene, and these in turn depend on the binding of the transcription factors. It is only these fitness differences that enter the population dynamics of binding site sequences in the next section. Following the conceptual framework of ref. [ 10 ], we assume that the environment of the regulated gene can be described by a number of cellular states (labelled by the index α ) with different transcription factor concentrations, i.e., with different chemical potentials ρ α . These cellular states can be thought of as different stages within a cell cycle. In each state, the fitness depends on the expression level of the regulated gene in a specific way. This expression level is determined by the binding probability p α of the transcription factor. Assuming that both dependencies are linear (this is not crucial) and that the cellular states contribute additively to the overall fitness F , we obtain Here the selection coefficient s α is defined as the fitness difference (due to different expression of the downstream gene) between the cases of complete factor binding and no binding in the state α . Such fitness differences can now be measured directly in viral systems [ 31 ]. Inserting (1), the fitness becomes a function of the Hamming distance r only. We note that the fitness F is measured relative to that of a phenotype with zero binding probability in any state α . In a simple case, there are just two relevant cellular states. The on state favours expression of the gene, the off state disfavours it. It is then natural to assume selection coefficients of similar magnitude; here we take for simplicity s = s on = - s off > 0. We then obtain a crater landscape, with a high-fitness rim between ρ off and ρ on flanked by two sigmoid thresholds; see fig. 1(a) . The generic features of this fitness landscape are easy to interpret: the two-state selection assumed here favors intermediate binding strength (i.e., intermediate Hamming distances r ) where binding occurs and the gene is expressed in the on state but not in the off state. Sequences with large Hamming distance r > ρ on can bind the factor neither in the on nor in the off state, while sequences with r < ρ off lead to binding in the on and the off state. Both cases lead to misregulation of the downstream gene, and hence to a lower fitness. We note that the key feature of these fitness landscapes, the sigmoid thresholds, is independent of the particular choices of s on and s off . Figure 1 Fitness landscapes and adaptive evolution for a single binding site (a) Crater landscape (3) and (b) Mesa landscape (4), as a function of the Hamming distance r from the target sequence (within the approximation of the two-state model). r m gives the point where the binding probability reaches a maximum (crater landscape), or else values close to 1 (mesa landscape). r s approximately indicates the onset of selection, i.e. a binding probability appreciably different from zero. (c) Adaptive dynamics as a function of time t measured in units of 1/(2 s μ N ) in the crater landscape at strong selection ( sN = 100). Single history r ( t ) (dashed lines), ensemble average (thick solid lines) and width given by the standard deviation curves ± δ r ( t ) (thin solid lines), (d) Same as (c) in the mesa landscape at moderate ( sN = 6.8) selection, (e) Stationary ensembles P stat ( r ) of binding site sequences with in the crater landscape at strong selection (filled bars) and for neutral evolution (empty bars). (f) Same as (e) in the mesa landscape at moderate selection, together with the histogram of Hamming distances of CRP site sequences in E. coli from their consensus sequence (diamonds, from [10]). An even simpler fitness landscape is obtained if only the on state contributes significantly to selection, i.e., if s = s on > 0 and s off = 0. The crater landscape then reduces to the mesa landscape discussed in [ 10 , 32 ], which has a high-fitness plateau of radius ρ and one sigmoid threshold; see fig. 1(b) . In this case, all sequences with sufficiently small Hamming distance to the target sequence ( r < ρ on ) have a high fitness. In both cases, the parameters of the binding model have a simple geometric interpretation: ε gives the slope and the ρ α give the positions of the sigmoid thresholds in the fitness landscape. Eqs. (3) and (4) are again to be understood as minimal models of fitness landscapes for binding sites, representing target sequence selection for a given level of binding ( ρ off < r < ρ on ) and for sufficiently strong binding ( r < ρ on ), respectively. Despite its simplicity, this type of selection model based on biophysical binding affinities is nontrivial from a population-genetic viewpoint since it leads to generic correlations between frequencies of nucleotides a i and a j within a site, see the Results section below. We will also study generalized models with correlations between two sites generated by cooperative binding. On the other hand, these models neglect the context dependence of the binding process through cofactors and chromatin structure. However, they are a good starting point for order-of magnitude estimates of the adaptive evolution of binding sites. Mutation, selection, and genetic drift The rates of nucleotide point mutation show a great variation, ranging from μ ~ 10 -4 per site and generation for RNA viruses to values several orders of magnitude lower in eukaryotes, e.g. μ ≈ 2 × 10 -9 in Drosophila [ 33 ]. (Here we model mutation as a single-parameter Markov process; we do not distinguish between transitions and transversions.) The evolution of a sufficiently large population under mutation and selection can be described in terms of the average fraction of the population with a given binding sequence. This so-called mean-field approach neglects the fluctuations due to finite population size (genetic drift). It leads to the so-called quasispecies theory [ 14 ]. For a population of sequences at a single binding site, the quasispecies population equation can be written for the fraction n ( r , t ) of individuals at Hamming distance r from the target sequence at time t . Along with a generalisation for two binding sites, it has been analysed in detail in ref. [ 10 ]. For the mesa landscape, the stationary solution n stat ( r ) has been found exactly [ 32 ]. It depends only on the ratio s / μ and describes a stable polymorphic population, i.e., several sequence states coexist. The mean-field approach is valid as long as the stochastic reproductive fluctuations are leveled out by mutations. This requires absolute population numbers Nn stat ( r ) ≫ 1/ μ for all relevant r , a stringent condition on the total population size N . This paper is concerned with a different regime of population dynamics, as described by the Kimura-Ohta theory for finite populations evolving by stochastic fluctuations (genetic drift) and selection [ 34 - 36 ]. According to this theory, a new mutant with a fitness difference Δ F relative to the pre-existing allele could spread to fixation in the population. This is a stochastic process, whose rate constant is given by in a diffusion approximation valid for Δ F ≪ 1 [ 37 ]. Here N is the effective population size (with an additional factor 2 for diploid populations). Eq. (5) has three well-known regimes. For substantially deleterious mutations ( N Δ F ≲ - 1), substitutions are exponentially suppressed. Nearly neutral substitutions ( N |Δ F | ≪ 1) occur at a rate u ≈ μ approximately equal to the rate of mutations in an individual. For substantially beneficial mutations ( N Δ F ≳ 1), the substitution rate is enhanced, with u ≃ 2 μ N Δ F for N Δ F ≫ 1. In this picture, a population has a monomorphic majority for most of the time and occasional coexistence of two sequence states while a substitution is going on. The time of coexistence is T ~ N for nearly neutral and T ~ 1/Δ F for strongly beneficial substitutions. The picture is thus self-consistent for Tu ≪ 1, i.e., for μ N ≪ 1. Asymptotically, it describes monomorphic populations moving through sequence space with hopping rates u . Introducing an ensemble of independent populations, this stochastic evolution takes the form of a Master equation. For a single binding site, we obtain Here P ( r , t ) denotes the probability of finding a population at Hamming distance r from the target sequence, and u r , r' is given by (5) with Δ F = F ( r' ) - F ( r ). The combinatorial coefficients arise since a sequence at Hamming distance r can mutate in different ways that increase r , and in r ways that decrease r , where c = 4 is the number of different nucleotides. The stationary distribution is P stat ( r ) ~ exp[ S ( r ) + 2 NF ( r )]. (7) Here is the mutational entropy (the log fraction of sequence states with Hamming distance r ) [ 32 ] and we have used the exact result u r +1, r / u r , r +1 = e 2( N - 1)Δ F . To derive (7), we then simply approximated N - 1 by N . The form of P stat ( r ) reflects the selection pressure, i.e., the scale s of fitness differences in the landscape F ( r ). For neutral evolution (2 sN = 0), the stationary distribution is obtained from a flat distribution over all sequence states. For moderate selection (2 sN ~ 1), P stat ( r ) results from a nontrivial balance of stochasticity and selection. For strong selection (2 sN ≫ 1), P stat ( r ) takes appreciable values only at points of near-maximal fitness, where F ( r ) ≳ F m - 1/2 sN . In this regime, the dynamics of a population consists of beneficial mutations only, i.e., the system moves uphill on its fitness landscape. The Master equation (6) and the mean-field quasispecies equation thus describe opposite asymptotic regimes, μ N ≪ 1 and μ N ≫ 1, of the evolutionary dynamics. Effective population sizes show a large variation, from values of order 10 9 in viral systems to N ~ 10 6 in Drosophila and N ~ 10 4 - 10 5 in vertebrates. (These numbers bear some uncertainty; one reason is that TV varies across the genome [ 38 ].) We conclude that the mean-field quasispecies is well suited for viral systems, while eukaryotes clearly show a stochastic dynamics of substitutions. Results and discussion Stationary distributions and nucleotide frequency correlations In the previous sections, we have expressed the fitness landscape and the resulting population distributions as a function of the Hamming distance r because it is a convenient parameterization of the binding energy in the two-state model. In order to compare this approach to standard population genetics, it is useful to recast eq. (7) for the elementary sequence states ( a 1 ,..., a l ), where the sum runs over all sequence states at fixed r . At neutrality, the distribution over sequence states factorizes in the single nucleotide positions, In the specific case of the two-state model, ν 0 ( a i ) is simply a flat distribution over nucleotides but it is obvious how this form can be generalized to arbitrary nucleotide frequencies. According to eq. (7), the stationary distribution under selection takes the form The salient point is that F ( r ) is generically a strongly nonlinear function of r due to the sigmoid dependence of the binding probability on r . An analogous statement holds beyond the two-state approximation for the dependence of F on the binding energy E . Hence, even if ( a 1 ,..., a l ) factorizes in the single nucleotide positions, ( a 1 ,..., a l ) does not. The selection introduces specific correlations between the nucleotides: the fitness differences and, hence, the nucleotide frequencies at one position i depend on all other it l - 1 positions in the motif. Adaptive generation of a binding site We now apply the dynamics (6) to the problem of adaptively generating a binding site in response to a newly arising selection pressure. We study a case of strong selection ( sN = 100) in the crater fitness landscape (3) with parameters = 10, ε = 2, ρ on = 3, ρ off = 1 (implying that the factor concentrations differ by a factor of 50), and a case of moderate selection ( sN = 7) in the mesa landscape with parameters = 10, ε = 1, ρ = 3.6. (The mesa type may be most appropriate for factors with multiple binding sites such as the CRP repressor in E. coli , where binding to an individual site is negligible in the off state.) The fitness landscapes for both cases are shown in fig. 1(a),1(b) in units of the selection pressure s . Substantially beneficial mutations occur only on their sigmoid slopes, i.e., in narrow ranges of r . The upper boundary of this region is given by r s = ρ on + log[ sN ( e ε - 1)]/ ε which takes typical values r s = 5 - 7. In fig. 1(c),1(d) , we show a sample history of adaptive substitutions from r = 5 to lower values of r, which are close to the point r m of maximal fitness. The statistics of this adaptation is governed by the ensemble P ( r , t ); the average and the standard deviation δ r (t) appear also in fig. 1(c),1(d) . In the case of strong selection, the expected time of the adaptive process is readily estimated in terms of the uphill rates in (6), and takes values of a few times 1/ s μ N . We emphasize again that this simple form depends only on the qualitative form of the fitness landscape, namely, that weakly and strongly binding sequence states are separated only by few point mutations. The conclusions are thus largely independent of the details of the fitness landscape, which justifies using the two-state approximation. Can such a selective process actually happen? This depends on the initial state of the promoter region in question before the selection pressure for a new site sets in. The region is approximated as an ensemble of L 1 = L - + 1 candidate sites undergoing independent neutral evolution, i.e., the simultaneous updating of sites by one mutation is replaced by independent mutations. The length of the promoter region is denoted by L . At stationarity, the Hamming distance at a random site then follows the distribution P stat ( r ) ~ exp[ S ( r )] shown as empty bars in fig. 1(e),1(f) . The minimal distance r min in the entire region is given by the distribution , where is the cumulative distribution for a single site. is found to be strongly peaked, taking appreciable values only in the range around its average. We assume selective evolution sets in as soon as at least one site has a Hamming distance r ≤ r s . This is likely to happen spontaneously if , leading to a joint condition on , L , and r s . For , there is a neutral waiting time before the onset of adaptation. Its expectation value is calculated in the appendix. It is generically much larger than the adaptation time T s , rendering the effective generation of a new site less feasible. The stationary distribution P stat ( r ) under selection is given by (7) and shown as filled bars in fig. 1(e),1(f) . For strong selection, it is peaked at the point r m of maximal fitness. For moderate selection, it takes appreciable values for r = 0 - 4: the binding site sequences are fuzzy . Assuming that the CRP sites at different positions in the genome of E. coli have to a certain extent evolved independently, we can fit P stat ( r ) with their distance distribution (data taken from [ 10 ]). At the values of ε and ρ on chosen, the two distributions fit well, see fig. 1(f) . This finding is discussed in more detail below. Adaptation of binding cooperativity The cooperative binding of transcription factors involves protein-protein interactions which may be specific to the DNA substrate. These interactions often do not require conformational changes of either protein involved and depend only on few specific contact points. They result in a modest energy gain of order 3 – 4 k B T [ 1 ]. Hence, it is a reasonable simplification to study the adaptive adjustment of binding affinities using a simple generalisation of the two-state binding model. We define the energies E 1 / k B T = ε r 1 and E 2 / k B T = ε r 2 for the binding of a single factor and for the simultaneous binding of both factors. The cooperativity gain is assumed to result from mutations at positions in the DNA sequences of the factors, which encode the amino acids at the protein-protein contact points. These mutations define a Hamming distance from the target sequence for optimal protein-protein binding, and 2 γε / is the binding energy per nucleotide. Here we use the values ε = 2, = 6 and γ = 1 but the qualitative patterns shown below are rather robust. The resulting equilibrium probabilities for the four thermodynamic states (--) (both factors unbound), (+-) and (-+) (one factor bound), and (++) (both factors bound) are q-- , q +- = q -- exp[- ε ( r 1 - ρ 1 )], q +- = q -- exp[- ε ( r 2 - ρ 2 )], (14) q ++ = q -- exp[- ε ( r 1 + r 2 - ρ 1 - ρ 2 - 2 γ )], with the normalisation q -- + q +- + q -+ + q ++ = 1. The scaled chemical potentials ρ 1 and ρ 2 are independent variables if the two sites bind to different kinds of factors and are equal if they bind to the same kind. As before, the binding probabilities determine expression levels and, therefore, the fitness. Here we study only pairs of sites contributing additively to the expression level in each cellular state, where we have Other important cases include activator-repressor site pairs such as the famous lac operon [ 39 ], where the transcription-factor induced expression level is proportional to q +- . The stochastic dynamics of substitutions is straightforward to generalise; it leads to a Master equation like (6) for the joint distribution P ( r 1 , r 2 , , t ). This higher-dimensional equation can again be solved exactly for its steady state P stat ( r 1 , r 2 , ) ~ exp[ S ( r 1 ) + S ( r 2 ) + S ( ) + 2 NF ( r 1 , r 2 , )]. (16) Here we discuss two simple examples of fitness landscapes where binding cooperativity evolves by adaptation to specific functional demands. A genetic switch with a sharp expression threshold is favoured in a system with a single transcription factor having similar concentrations in its on and off cellular state. As can be seen from eq. (14), cooperative binding can sharpen the response of the binding probability to variations in factor concentration, q ++ ~ 1/[1 + exp(-2 ε ρ + ...)] versus p ~ 1/[1 + exp(- ε ρ + ...)] as given by (1) for individual binding. Figs. 2(a),2(c) show the fitness landscape F ( r 1 , r 2 , γ ) obtained from (14) and (15) for ρ on = 2.5, ρ off = 1.5, and s = s on = - s off . A simple signal integration module responds to two different factors in four different cellular states, ( on , on ), ( on , off ), ( off , on ), ( off , off ). Individually weak but cooperative binding leads to expression of the gene only if both factors are present simultaneously. This case is favoured by a fitness function of the form (15) with selection coefficients s = - s off,off = -s on,off = -s off,on = s on,on /2. The resulting fitness landscape F ( r 1 , r 2 , γ ) is shown in figs. 2(b),2(d) for chemical potentials ρ on = 3, ρ off = 1 (for each factor). Figure 2 Fitness landscapes and adaptive evolution for a pair of sites with cooperative binding. Genetic switch (left column), signal integration module (right column). (a,b) Fitness landscape F ( r 1 , r 2 ) without cooperativity ( γ = 0). (c,d) Fitness landscape. F ( r 1 , r 2 ) with cooperativity ( γ = 1). Next-nearest neighbour states ( r 1 , r 2 ) and of similar fitness are linked by compensatory mutations if the intermediate states ( r 1 , ) and ( , r 2 ) have lower fitness. (e,f) Adaptive dynamics: ensemble averages and (thick lines), ensemble width given by (same for r 2 and (thin lines); cf. fig. 1(e,f). In both cases, a pair of sites with weaker individual binding ( r 1 , r 2 = 3 - 4) and cooperativity ( γ = 1) is seen to have a higher fitness than an optimal pair ( r 1 = r 2 = 2) without cooperativity, as expected. Adaptive pathways and for strong selection ( sN = 100) are shown in fig. 2(e),2(f) . Typical adaptation times T s are again a few times 1/( s μ N ). A closer look reveals that this fast adaptation sometimes leads to a metastable local fitness maximum with some degree of cooperativity. Compensatory mutations (see below) are then required to reach the global maximum, a process that may be considerably slower. The fuzziness δ r 1,2 ( t ) and δγ ( t ) observed in fig. 2(e),2(f) decays on the larger time scale of compensatory mutations, reflecting the presence of such metastable states. Conclusions Transcription factors and their binding sites emerge as a suitable starting point for quantitative studies of gene regulation. Binding site sequences are short and their sequence space is simple. Moreover, the link between sequence, binding affinity, and fitness is experimentally accessible. For a single site, the simplest examples are of the mesa [ 10 ] or of the crater type, see fig. 1(a),1(b) . Landscapes for a pair of sites with cooperative binding interactions are of a similar kind as shown in fig. 2(a),2(b),2(c),2(d) . They can be used to predict the outcome of specific single-site mutation experiments to a certain extent. Fast adaptation may generate or eliminate a new binding site Despite this simplicity, the evolutionary dynamics of binding sites is far from trivial, since it is governed, in the generic case, by the interplay of three evolutionary forces: selection, mutation, and genetic drift. Here we have focused on the dynamical regime appropriate for eukaryotes, where the evolution can be approximated as a stochastic process of substitutions. We find the possibility of selective pathways generating a new site in response to a newly arising selection pressure, starting from a neutrally evolved initial state and progressing by point substitutions. Such a selective formation takes roughly T s ≈ Δ r /(2 s μ N ) generations, where Δ r is the number of adaptive substitutions required. This number is given by the Hamming distance between the onset of selection and the point of optimal fitness, Δ r = r s - r m , and takes values 2 – 3 for typical fitness landscapes; see fig. 1(a),1(b) . For Drosophila melanogaster , with μ ≈ 2 × 10 -9 [ 33 ] and N ≈ 10 6 , the resulting T s is of the order of 10 6 generations or 10 5 years even for sites with a relatively small selection coefficient s = 10 -3 . Such selective processes are faster than neutral evolution by a factor of about 1000 and would allow for independent generation of sites even after the split from its closest relative Drosophila simulans about 2.5 × 10 6 years ago. Notice that new sites are more readily generated in large populations. As discussed above, generating a new site may also require a neutral waiting time to T 0 until at least one candidate site in the promoter region of the gene in question reaches the threshold distance r s from the target sequence, where selection sets in. For site formation to be efficient, however, selection must be able to set in spontaneously, i.e., T 0 must not greatly exceed the adaptive time T s . This places a bound on the relevant length of the binding motif that can readily form in a promoter region of length L . Given L ≈ 300, for example, a motif with = 8 and r s = 3 could still allow for spontaneous adaptive site formation. (For longer motifs, corresponding to groups of sites with fixed relative distance, this pathway would require promoter regions of much larger L .) A more general case has recently been treated numerically in [ 27 ], where the dependence of the neutral waiting time on the G/C ratio of the initial sequence has been investigated. One may speculate that this adaptive dynamics is indeed one of the factors influencing the length of regulatory modules in higher eukaryotes. Clearly, the present model also allows for pathways of negative selection leading to the elimination of spurious binding sites in regulatory or non-regulatory DNA where the binding has an adverse fitness effect. This is important since under neutral evolution, candidate sites with a distance of at most r s from the target sequence occur frequently on a genome-wide scale. A recent study has indeed found evidence for such negative selection from the underrepresentation of binding site motifs over the entire genome [ 40 ]. Binding sites under selection have nucleotide frequency correlations We have shown that under stationary selection the frequencies of nucleotides at any two positions of the binding sequence are correlated. For the two-state model, the correlations are the same for any pair of positions i ≠ j and can be computed exactly from the joint distribution (11). We emphasize that these correlations refer to an ensemble of independently evolving (monomorphic) populations and are not to be confused with linkage disequilibria within one population. This finding limits the accuracy of bioinformatic weight matrices, which are often assumed to factorize in the nucleotide positions even in the presence of selection. Experimental tests: binding site polymorphisms and phylogenies The predictions of our model lend themselves to a number of experimental tests. In the dynamical regime appropriate for eukaryotes ( μ N ≪ 1), populations should be monomorphic at most positions of their binding site sequences and polymorphic at a few. On the other hand, the quasispecies model discussed in refs. [ 10 , 11 ] (which assumes μ N ≫ 1) may be most appropriate in viral systems. The intermediate regime μ N ~ 1 with frequent polymorphisms and genetic drift could be realized in some bacterial systems and presents a challenge for theory. Thus it would be very interesting to compare the statistics of single-nucleotide polymorphisms at binding sites in eukaryotes, bacteria, and viruses. Polymorphism data are expected to contain evidence for adaptive evolution. However, statistical tests of selection must be modified for promoter sequences [ 40 , 41 ]. A recent study uses data on binding sites in three yeast species and deduces the rates of sequence evolution [ 42 ]. A complementary source of information are phylogenies of binding sites. Trees with functional differences between branches contain information on the generation of new sites or of interactions between sites and on the time scales involved. In a tree for a conserved site or group of sites with sufficiently long branches, the fuzziness of the sequences observed on different branches is given by the ensemble P stat introduced above. For strong selection, P stat lives on the quasi-neutral network of sequence states with maximal fitness, where two neighbouring sequence states are linked by neutral mutations or by pairs of compensatory mutations at two different positions. In the crater landscape for a single site, this quasi-neutral network consists of all sequences with a fixed distance r = r max from the target sequence; see fig. 1(a) . Beyond the two-state approximation for binding energies, it will be smaller since only some of the positions are energetically equivalent. For a group of sites, however, quasi-neutral networks can be larger since compensatory mutations can also take place at positions on different sites as shown in fig. 2(d) for the example of a signal integration module. This is consistent with experimental evidence that the sequence divergence between Drosophila melanogaster and Drosophila pseudoobscura involves compensatory mutations and stabilising selection between different binding sites [ 43 ]. For weaker selection, site fuzziness increases further since P stat extends beyond the sequence states of maximal fitness and is influenced by mutational entropy. As shown in fig. 1(f) , one can explain in this way the observed fuzziness in CRP sites of E. coli . It would then reflect different evolutionary histories of independent populations, rather than sampling in one polymorphic population as in the quasispecies picture of refs. [ 10 , 11 ]. (In a mean-field quasispecies, appreciable fuzziness occurs only for selection coefficients s ~ μ , minute in other than viral systems.) However, the data are also compatible with strong selection if the selection coefficients s α , and hence the value of r m , vary between different genes. Clearly, comparing P stat with the distribution of sites in a single genome requires the assumption that the evolutionary histories of sites at different positions are at least to some extent independent. Future data of orthologous sites in a sufficient number of species will be more informative. Thus, further experimental evidence is needed to clarify the role of mutational entropy in the observed fuzziness. Evolvability of binding sites The present work was aimed at obtaining some insight into the molecular mechanisms and constraints underlying the dynamics of complex regulatory networks, thereby quantifying the notion of their evolvability . The programming of binding sites and of cooperative interactions between them is found to provide efficient modes of adaptive evolution whose tempo can be quantified for the case of point mutations. The formation of complicated signal integration patterns and of multi-factor interactions in higher eukaryotes, however, requires generalizing our arguments in two ways. There are further modes of sequence evolution such as slippage events, insertions and deletions, large scale relocation of promoter regions, and recombination. Our ongoing work is aimed at quantifying their relative importance in terms of substitution rates. Moreover, there are also more general fitness landscapes describing, e.g., binding sites interacting via the expression level of the regulated gene (such as activator-repressor site pairs) and the coupled evolution of binding sites in different genes. The rapid evolution of networks hinges upon the existence of adaptive pathways for these formative steps with a characteristic time scale T s ~ 1/( s μ N ) much smaller than T 0 ~ 1/ μ , the time scale of neutral evolution. The presence of these two time scales has a further interesting consequence. If the selection pressure on an existing site ceases, that site will disappear on the larger time scale T 0 . It is possible, therefore, that large existing networks have accumulated a considerable number of redundant regulatory interactions acquired by selection in their past. This may be one factor contributing to their robustness against perturbations. Methods – neutral evolution of binding sites To estimate the average neutral waiting time T 0 , we study the mutation dynamics in the restricted range r = r s + 1,..., , allowing mutations from r s + 1 to r s but suppressing mutations from r s back to r s + 1. We evaluate the time-dependent solution P ( r , t ) of the Master equation (6) with the initial condition P ( r , 0) = P stat ( r ), and the resulting cumulative probability . The current across the lower boundary, J ( t ) = μ ( r s + 1) P ( r s + 1, t ) = - dQ / dt , determines the waiting time for a single site, This is formally solved by expanding in eigenfunctions of the mutation operator. In the case relevant here, the system remains close to equilibrium since the boundary current is much smaller than typical currents for r ≥ r s . Hence, P ( r , t ) ≈ P stat ( r ) exp(- λ t ) with λ = J (0)/ Q (0) = μ ( r s + 1) P stat ( r s + 1)/ Q stat ( r s + 1). We conclude that the waiting time for a single site is positive with probability Q stat ( r s + 1), following a distribution ~exp(- λ t ), and 0 otherwise. The resulting expectation value is T 0 = Q stat ( r s + 1)/ λ . For L 1 independent sites, the distribution of positive waiting times is still exponential, and to is given by an expression of the form (17) with a total boundary current . This yields as given by (13). The average waiting time (in units of 1/ μ ) becomes large for values of r s in the tail of the distribution , where . This is the case for . Authors' contributions JB carried out analytical and numerical work, SW performed numerical work and data processing. ML conceived of the study, carried out analytical work, and coordinated the project. All authors read and approved the final manuscript. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC535555.xml |
509290 | Evidence for Widespread Convergent Evolution around Human Microsatellites | Microsatellites are a major component of the human genome, and their evolution has been much studied. However, the evolution of microsatellite flanking sequences has received less attention, with reports of both high and low mutation rates and of a tendency for microsatellites to cluster. From the human genome we generated a database of many thousands of (AC) n flanking sequences within which we searched for common characteristics. Sequences flanking microsatellites of similar length show remarkable levels of convergent evolution, indicating shared mutational biases. These biases extend 25–50 bases either side of the microsatellite and may therefore affect more than 30% of the entire genome. To explore the extent and absolute strength of these effects, we quantified the observed convergence. We also compared homologous human and chimpanzee loci to look for evidence of changes in mutation rate around microsatellites. Most models of DNA sequence evolution assume that mutations are independent and occur randomly. Allowances may be made for sites mutating at different rates and for general mutation biases such as the faster rate of transitions over transversions. Our analysis suggests that these models may be inadequate, in that proximity to even very short microsatellites may alter the rate and distribution of mutations that occur. The elevated local mutation rate combined with sequence convergence, both of which we find evidence for, also provide a possible resolution for the apparently contradictory inferences of mutation rates in microsatellite flanking sequences. | Introduction DNA base substitutions do not occur randomly ( Graur and Li 2000 ). Instead, they may be clustered in hotspots, for example around methylated CG dinucleotides, or subject to more general biases such as the excess of transitions relative to transversions. In addition, local structural context may be important, with neighbouring bases interacting to favour some changes over others ( Blake et al. 1992 ; Morton et al. 1997 ; Goodman and Fygenson 1998 ; Zavolan and Kepler 2001 ). However, many nonrandom patterns of sequence evolution remain unexplained. Here we explore how an abundant class of repetitive sequences, microsatellites, may influence the pattern of mutations in sequences that surround them. Microsatellites are sequences of repeated 1–6-bp motifs that mutate primarily through the gain and loss of repeat units, in a process thought to depend on DNA replication slippage ( Levinson and Gutman 1987 ; Tautz and Schlötterer 1994 ). Previous studies indicate that their flanking sequences evolve unusually and often contain mutated versions of microsatellites ( Matula and Kypr 1999 ). Estimates of flanking sequence mutation rates vary greatly. Very slow evolution is suggested by sequence comparisons between distantly related species, where divergence rates may be as low as 0.016% to 0.1% per million years ( Schlötterer et al. 1991 ; Rico et al. 1996 ; Zardoya et al. 1996 ). Elsewhere, pedigree studies suggest much higher rates and even hypermutability ( Stallings 1995 ). There is also disagreement about trends in mutation rate, some studies indicating an increase towards the microsatellite ( Blanquer-Maumont and Crouau-Roy 1995 ; Zardoya et al. 1996 ; Grimaldi and Crouau-Roy 1997 ; Brohede and Ellegren 1999 ) while others claim a more even distribution ( Karhu et al. 2000 ). To our knowledge, no one has yet conducted a systematic study of mutational biases operating around microsatellites. The direct study of naturally occurring mutations in flanking sequences is virtually prohibited by their slow rate of accumulation, and inferences based on comparisons between homologous microsatellite loci rely on small numbers of sequences. However, an indirect approach is possible, based on comparisons among very large numbers of microsatellite flanking sequences from the finished human genome. If microsatellites have little or variable influence on their flanking regions, among-locus similarities will be minimal or absent. Conversely, if microsatellites generate similar local mutation biases, nonhomologous loci should betray evidence of convergent evolution. With the publication of large blocks of sequence from the chimpanzee genome, one can extend this approach to ask questions about rate of divergence between homologous flanking sequences. Here we use a combination of these indirect approaches to show that microsatellites appear to create regions around them in which both the rate and spectrum of mutations are modified. Results We studied the most abundant class of human dinucleotide repeats, (AC) n , and for simplicity considered only ‘isolated’ repeats, defined as those at least 100 bp from the nearest AC repeat as small as two units in length. 47% of AC repeats on human Chromosome 1 match these criteria. From the human genomic sequence, maximum sample size was set at 5,000 randomly selected loci for length classes (AC) 2 to (AC) 5 . For longer microsatellites, of which fewer than 5,000 could be found, all sequences encountered were included. Figure 1 displays the length frequency distribution and sample sizes. Additionally, a control set of 5,000 randomly selected, non-microsatellite-associated sequences, each 50 bases long and containing no (AC) 2+ repeats, was generated from Chromosome 1. Figure 1 Frequency Distribution of Isolated, Pure (AC) n Microsatellite Lengths (in Repeat Units) in the Human Genome Black shading indicates numbers of microsatellites used for analyses in this study. Distribution of Cassette Type To distinguish between (AC) n and (CA) n repeats, (AC) n microsatellites were divided into subclasses, termed ‘cassettes’, according to their immediate 5′ and 3′ flanking bases such that 5′-X(AC) n Y-3′ is referred to as cassette X/Y. Hence, a (CA) 3 repeat would be classed as cassette C/A around (AC) 2 . Such a distinction may appear pedantic, but since both DNA replication fidelity and repair efficiency are known to be influenced by base order and local sequence context ( Goodman and Fygenson 1998 ; Marra and Schar 1999 ), it seems by no means certain that (AC) n and (CA) n are equivalent. This nomenclature also helps to resolve the problem of defining microsatellite length because (CA) 3 equals (AC) 2 . Figure 2 shows the frequency distribution of cassette types relative to expectations, calculated assuming that each cassette base forms a dinucleotide with the end base of the microsatellite it flanks. Thus, cassette X(AC) n Y is viewed as comprising two dinucleotides, XA and CY. The probability of observing XA and CY jointly is then calculated from the individual frequencies of XA and CY estimated using 1 Mb of randomly sampled sequence from Chromosome 1 containing no (AC) 2+ repeats. Figure 2 Frequency Distribution of the 16 Possible Microsatellite-Flanking 5′–3′ Base Combinations Relative to Random Expectation (A) Cassette frequencies around (AC) 2 microsatellites: black bars, observed; white bars, expected. Error bars show 95% confidence intervals and asterisks indicate significant difference ( χ 2 tests, 1 d.f. p < 0.05 with sequential Bonferroni corrections). (B) Deviation of cassette frequencies from random expectations around (AC) 2 , (AC) 5 , and (AC) 10 microsatellites: black, white, and hatched bars, respectively. (C) Sampled number (solid line) and proportion (dotted line) of microsatellites with cassette T/A as a function of microsatellite length. Around (AC) 2 microsatellites, the cassette frequencies are broadly similar to those expected from the frequencies of the component dinucleotides in unique sequence DNA, though eight cassettes show significant differences ( Figure 2 A). However, as AC repeat number increases, the relative frequencies of several cassettes begin to deviate more and more from expectations, either decreasing or increasing in frequency, summarised in Figure 2 B. Specifically, cassettes of the kind X/A, and particularly T/A are overrepresented, while cassettes X/C and X/T tend to be underrepresented. The total proportion of (AC) repeats with cassette T/A are shown as a function of repeat length in Figure 2 C. The observed pattern could arise either if cassette type influences the rate at which microsatellites change length, or if mutation biases generated by the microsatellite cause interconversion between cassette types. Flanking Sequence Base Composition We consider flanking sequences to extend 50 bases either side of a microsatellite. To compare flanking sequences, we first divided microsatellites according to (AC) repeat number and cassette type, and then calculated the frequency of each of the four nucleotides at each of the 100 possible positions. Any mutation biases present should be revealed by locally changed base composition, and this appears to be the case. For many microsatellite length–cassette combinations, the flanking sequences exhibit strong deviations from random. The observed patterns can be placed in six broad classes according to the strength of a two-base periodicity and the degree of 5′ to 3′ asymmetry. These patterns are illustrated in Figure 3 and summarised in Table 1 . Three further classes of less regular patterning can also be defined ( Figure S1 ). For illustration, we chose length (AC) 5 , since this exhibits the strong patterns while at the same time retaining sufficient sample sizes for analyses to be conducted on the rarer cassette types. Several features are apparent. First, levels of patterning can be remarkably strong, with the probability of observing a given base at a given site varying from less than half of that in unique sequence to more than double, often at adjacent sites. Second, many of the patterns show strong dinucleotide periodicities, presumably reflecting the dinucleotide structure of the microsatellite. Third, there are several examples of clear 5′ to 3′ asymmetry, indicating that mutational patterns on one side of a microsatellite may not be the same as those on the other. Figure 3 Flanking Sequence Frequency Distributions for Six Representative Nucleotide–Cassette Combinations for (AC) 5 Microsatellites In each panel, the microsatellite is centrally placed, represented as a gap at position zero, and the cassette type, base, and number of sequences considered (n) are given. Frequency distributions are plotted with separate 95% confidence intervals for odd- and even-numbered positions (shading). Horizontal lines indicate mean frequencies for the 3′ and 5′ flanking regions, calculated separately. (A–F) illustrate the six main classes of patterning where either dinucleotide periodicity or 5′–3′ asymmetry are present, summarised for all cassette–base combinations in Table 1 . Table 1 Summary of Patterns in Flanking Sequence Base Frequencies by Cassette As well as showing variation between cassettes, flanking sequence patterning also changes with microsatellite length. For those cassettes where sample size is sufficient to show a trend, that is, T/A and to some extent C/A and A/A, both amplitude of deviation from random and the breadth of patterning tend to increase with increasing AC repeat number. We do not see dramatic changes such as a reversal in 5′ to 3′ asymmetry or the emergence of new patterns. For cassette T/A, pattern strength increases up to (AC) 9 but then declines in longer microsatellites. Table 1 reveals a dominant role for nucleotides A and T, with excesses of base A tending to be complemented by deficits at the same positions of base T. In some cases, the excesses of A are interleaved with only weak deviations from base frequency expectation. In other cases, excesses of A are interleaved with excesses of base T. Dinucleotide Patterns In view of the strong two-base periodicities seen for some base–cassette combinations, we next examined the distribution of frequencies of all 16 possible dinucleotide motifs. As expected from the single nucleotide patterning, dinucleotides also tend to show periodic patterning ( Figures 4 and S2 ), and this is particularly pronounced for motif AT. As with the mononucleotide patterns, there is often marked 5′ to 3′ asymmetry (for example, cassette T/T, dinucleotide AT; Figure 4 B). Frequency plots for the 16 cassette types reveal patterns that fall into classes similar to those observed for the mononucleotides, summarised in Table 2 . Both the presence of patterning and the degree of 5′ to 3′ asymmetry show a strong dependence on cassette type. Figure 4 Flanking Sequence Frequency Distributions for Three Representative Dinucleotide Motif–Cassette Combinations for (AC) 5 Microsatellites (See Figure S4 for the four other patterns). In each panel, the microsatellite is centrally placed, represented as a gap at position zero, and the cassette type, dinucleotide motif, and number of sequences considered (n) are given. Frequency distributions are plotted with separate 95% confidence intervals for odd- and even-numbered positions (shading). Horizontal lines indicate mean frequencies for the 3′ and 5′ flanking regions, calculated separately. A summary of how all seven patterns are distributed among all dinucleotide motif–cassette combinations is given in Table 2 . Table 2 Summary of Patterns in Flanking Sequence Dinucleotide Frequencies by Cassette From Table 2 it seems that patterning occurs most commonly where the 5′ cassette base is T or the 3′ cassette base is A and least commonly where the 5′ cassette base is either G or A. In almost all cases where patterning is recorded, the dinucleotide involves one or both bases present in the cassette. However, it is unclear whether the flanking pattern is simply an extension of the cassette bases. For example, although 5′ AT or TA periodicity only occurs where the 5′ cassette base is T, CC deviations occur where the 5′ cassette base is A (cassette A/C). As with the mononucleotide patterning, the periodicity in dinucleotide frequencies changes in amplitude and width (5′ to 3′) as repeat number increases. For most cassettes, the paucity of long microsatellites precludes study of how the patterning changes with repeat number. However, cassette T/A is sufficiently abundant for the progression to be described, and cassettes A/A and C/A, although less common, nonetheless yield meaningful results. Figure 5 A– 5 F shows, for cassette T/A, how the patterning of motif AT first becomes detectable by eye around (AC) 3 , then increases towards a peak in strength at (AC) 9 before diminishing as AC repeat number increases further. Cassettes A/A and C/A show similar trends and together suggest that, where patterning occurs, it is apparent by (AC) 6 . When data were plotted without first categorizing them by cassette type, although a number of the dinucleotide patterns in Table 1 were seen, deviations from expectation were weaker (for example, about half as strong for dinucleotide AT). Figure 5 Dependence of Dinucleotide Flanking Sequence Patterning on AC Repeat Number Plots are as described in Figure 4 . The progression for dinucleotide AT is illustrated for the commonest cassette type, (T/A). (A–F) depict AT dinucleotide frequencies, where patterning is most extreme, and show how periodicity and amplitude increase towards a maximum at around (AC) 10 and decline thereafter. Other Repetitive Elements in the Genome An artefactual appearance of convergent evolution could arise if microsatellites within major classes of interspersed repetitive elements such as LINE and Alu repeats are treated as independent observations. Such loci will often share a common origin, and hence appear more similar to each other than expected. To address this problem, we used the program RepeatMasker ( Smit and Green 1996 ) to divide Chromosome 1 into sequences related or not related to known interspersed repeats. Just under half (45%) of all isolated microsatellites were found within interspersed repeats, but only a minority contained microsatellites as long or longer than (AC) 5 . Classifying loci by cassette type, length, and whether they occurred in LINE/L1, SINE/Alu, or unique sequence DNA yielded in most classes sample sizes too low to be of use. However, where sample sizes were adequate, that is, cassette T/A, dinucleotide AT, patterning in unique sequence loci was indistinguishable from that in LINE and SINE microsatellites ( Figure S3 ). If the among-locus similarities were due to shared evolutionary history, we would expect flanking sequences in the three classes to differ. That they do not, suggests an evolutionary process that depends little if at all on original context. Microsatellites in the Flanking Sequence The strongest two-base periodicities we find tend to involve motif AT. Such periodicity might arise in two main ways: either because AT motifs in phase with the microsatellite tend to expand through slippage to form (AT) n microsatellites, or because mutation biases favour the formation of AT motifs in phase with the AC tract. Consequently, we examined the extent to which patterning could be reduced by filtering the flanking sequences for the presence of AT motifs, focusing on cassette T/A and (AC) 5 to provide strong patterning and large sample sizes. The results of deleting all flanking sequences containing AT microsatellites with n or more repeats, where n = 2, 3, 4, and 5, are given in Figure 6 . This filtering effectively abolishes patterning in all but the region immediately adjacent to the AC repeat tract. Here, patterning extends as far as the maximum value of n allowed, suggesting that the dominant AT patterning results from (AT) n microsatellites developing immediately adjacent to the AC tract. Figure 6 Dependence of Dinucleotide Pattern Strength on the Presence of Repeat Clusters Beginning with the dataset from the scenario showing strong patterning and large sample size (cassette T/A, dinucleotide AT, (AC) 5 ; see Figure 5 C), flanking sequences containing (AT) x were excluded, where x equalled 2 or more (A), 3 or more (B), 4 or more (C), and 5 or more (D). Plotting conventions are the same as for Figure 4 . We next examined whether the phase of AT dinucleotides was determined solely by their tendency to form clusters next to (AC) n repeats. This is an important test given that our strict definition of a microsatellite restricts our analysis to pure AC repeats and allows the possibility that compound repeats, for example, (AT) n (AC) n (AT) n are included. To do this, we plotted the distribution of single AT motifs around (AC) 3+ microsatellites in the subset of flanking sequences with no (AT) n microsatellites, where n > 1. Summed over all AC microsatellite lengths, single AT dinucleotides are overrepresented in 5′ sequences at odd numbered positions and at even numbered positions in 3′ sequences ( Figure 7 ). Excesses (or deficits) in AT occur up to six bases away from the microsatellites, suggesting that the periodic patterns we see do indeed occur in the flanking sequence over and above the generation of AT microsatellites adjacent to the microsatellite itself. In other words, the patterning we see is generated beyond any tendency for our strict definition of a pure AC repeat to include compound repeats. Figure 7 Location of Single AT Dinucleotide Motifs Relative to the Central AC Microsatellite in Flanking Sequences Lacking (AT) 2+ Microsatellites Figure shows frequency of AT dinucleotides around all length classes of AC repeat microsatellites longer than (AC) 2 (5′ number of sequences, n = 2,924; 3′ number of sequences, n = 3,309), with significantly greater numbers at odd positions 5′ and even positions 3′. Data are for cassette T/A only. Error bars show upper 95% confidence limit. Convergence of Flanking Sequence Pattern To assess the level of any convergent evolution, we used a simple assignment test to determine how often individual sequences resemble others flanking unrelated microsatellites of similar length (see Materials and Methods ). Figure 8 summarises these results. If all sequences were evolving divergently, any given sequence would be assigned to each of the microsatellite length classes with equal probability of around 5%. Sequences not associated with (AC) 2 or longer were assigned back to their own class 57% of the time, showing that these sequences consistently differ from those near to AC repeat tracts. Remarkably, this figure falls to almost half (32%) for sequences flanking (AC) 2 , indicating that, even with just two repeats, similarities to other microsatellite flanking sequences already exist. The same pattern extends to other length microsatellites, with flanking sequences tending to be preferentially assigned back to their own or to an adjacent length class. When a flanking sequence is not assigned back to its own class, it is usually assigned to one of three other classes: the ‘1’ class of random sequences, the ‘9’ class where patterning is strongest, or to the longest class, class 21 (unpublished data). Figure 8 Cross-Locus Similarity among Sequences Flanking Microsatellites of Similar Length Length classes are as follows: class 1, randomly selected sequences not containing (AC) 2+ ; classes 2–20, (AC) 2 –(AC) 20 ; and class 21, (AC) 21–25 . Figure shows proportion of flanking sequences assigned on the basis of sequence similarity to their own AC repeat number class (dark grey), to the class above (grey), or to the class below (white). Expectation for assignment to self is shown by the horizontal line. Data are for cassette T/A only. Asterisks denote significant overassignment back to the same class or to an adjacent class, tested using χ 2 tests ( p < 0.05 using sequential Bonferroni corrections). These analyses reveal a tendency for microsatellite flanking sequences to be similar to each other, but fail to quantify the level of sequence change involved. To do this, we sought to estimate similarity among three classes of sequence: (1) blocks of 50 bp lying immediately adjacent to a microsatellite; (2) blocks of 50 bp chosen randomly to lie between 500 and 600 bases downstream from a microsatellite (the random selection aims to remove possible complications of exact position and phase with the microsatellite); and (3) blocks of 50 bp randomly selected from around the genome. Comparisons within class 3 define the average level chance similarity in the genome, here estimated at 12.77 ± 3.28 (sd) bases out of 50. Comparisons within class 1 estimate how much convergent evolution is apparent at any given repeat number, and reveal a profile that rises to a maximum of 14.31 at a length of seven repeats, followed by a gentle decline with increasing length thereafter ( Figure 9 A). Similarity is significantly above random for all but the very shortest microsatellites. As controls, we also made comparisons between class 1 and class 2 within a locus ( Figure 9 B), between class 1 and class 2 among loci ( Figure 9 C), and between class 1 and class 3 ( Figure 9 D). Each of these comparisons reveals above random similarity in a profile that approximates that of the class 1–class 1 comparisons but peaking at lower levels. Figure 9 Dependence of Sequence Similarity among Flanking Sequences on AC Repeat Number The average number of matches shown (± standard error) quantifies similarity among three classes of sequence: (1) blocks of 50 bp lying immediately adjacent to a microsatellite; (2) blocks of 50 bp chosen randomly to lie between 500 and 600 bases downstream from a microsatellite; and (3) randomly selected blocks of 50 bp from around the genome. Average level of chance similarity in the genome is shown by a black line in each plot (comparison among class 3). 5′ and 3′ sequences are shown separately. Comparisons among sequence classes are shown for class 1 to class 1 (A), class 1 to class 2 for sequences at the same locus (B), class 1 to class 2 for sequences at different loci (C), and class 1 to class 3 (D). Thus, in all cases, sequences immediately flanking a microsatellite show greater similarity to each other, to sequences nearby, and to sequences elsewhere in the genome than randomly selected sequences do to each other, a trend that is maximal for microsatellites around 7–10 repeats in length. We believe these similarities are generated primarily by the enhanced simplicity of microsatellite flanking regions and their tendency to gain AT motifs. Such characteristics allow unusually high matches when compared with random blocks of 50 bases that have high simplicity or contain polyA tracts. Over and above this background level of elevated similarity, proximity to any microsatellite appears to increase similarity, implying that microsatellites tend to lie more generally in regions of similar base composition. This might reflect either a tendency for microsatellites to arise preferentially in certain broad sequence contexts, or modification of the local base composition by the microsatellite itself. Moreover, similarity is further enhanced when a flanking sequence is compared with a neighbouring block. This suggests a local context effect such as might arise through the isochore structure of the genome, with neighbouring blocks being located in the same isochore and hence ‘coloured’ by the same nucleotide biases. How Big Is the Sphere of Influence of a Microsatellite? To define more precisely the regions where convergent evolution is occurring, we repeated the assignment test but instead of using the full 50 bases either side we now used a symmetric pair of moving 25-base windows placed either side of the AC microsatellite ( Figure 10 ). Close to the microsatellite, the assignment probability is similar to but a little greater than that observed for the full 50-base analysis. As expected, this value declines as the window is moved away from the microsatellite. However, overassignment of (AC) 2 flanking sequences to their own class is significant up to ten bases away from the microsatellite ( χ 2 = 9.7, d.f. = 1, p < 0.05 with sequential Bonferroni correction), and only when the window reaches 24 bases from the microsatellite does the assignment level fall to the value of 4.8% expected of random sequences. Figure 10 Relationship between the Probability of Assigning (AC) 2 Microsatellite Flanking Sequences to Self and Proximity to the AC Microsatellite Solid line shows the probability of assignment back to self. Analysis is restricted to (AC) 2 flanking sequences and is based on an assignment window 25 nucleotides wide on each side of the microsatellite. Dotted line indicates assignment probability expected of random DNA sequences. Do Microsatellites Increase the Local Rate of Evolution? So far we have considered only the nature of the mutations that affect flanking sequences, and not their rate. To examine whether mutation rates are affected by the presence of a microsatellite, we used Megablast (National Center for Biotechnology Information (NCBI); ftp://ftp.ncbi.nih.gov/blast/executables ) to compare microsatellites identified in completed sections of the chimpanzee genome against homologous loci identified in humans. If homologous loci are identified through comparisons among their immediate flanking sequences, an element of circularity will be introduced, since loci with lower rates of evolution will be identified preferentially. To circumvent this problem, we used a region 300 bases in length and 220 bases downstream of the microsatellite to conduct each Megablast search. A total of 8218 chimpanzee loci were identified, and they yielded 5537 unique human homologues. Since microsatellite flanking sequences may contain insertions or deletions, we adopted the following approach so as to minimise problems of alignment. The chimpanzee flanking sequence was divided into 20 contiguous blocks of 20 bases, ten on each side of the microsatellite. Each block was then compared against 1,000 bases of human sequence, downstream from the region identified by Megablast, to find the best possible match. To filter sequences with major rearrangement we required both that all 20 blocks match with at least 15/20 bases, and that matched blocks all lie in the same order as their homologues. Differences between pairs of homologous flanking sequences were then quantified, at each of the 20-block positions, as the proportion of perfectly matching blocks and the average number of matches within each block ( Figure S4 ). After the above filtering, our data base contained 5017 sequences (91% of the original number), and among these, approximately 77% of the blocks were identical. Around (AC) 2–3 there is little apparent variation in either measure of similarity with block position apart from a small tendency for 5′ blocks to show an increasing average percentage match closer to the microsatellite. However, although the trends are by no means strong, around longer microsatellites similarity of blocks near to the microsatellite is reduced, particularly when measured using average percentage matches ( Figure S4C and S4D ). Our analysis of dinucleotide frequencies around microsatellites shows patterns of similarity on a smaller scale than might be revealed with 20-base blocks of sequence. To investigate the number of changes in the immediate flanking bases, we also calculated the proportion of mismatches occurring at a given base for each 5′ and 3′ sequence in the immediate flanking blocks (blocks −1 and +1; Figure S5 ). The average proportion of mismatches is relatively constant along the flanking sequence around short microsatellites, with a possible rise immediately 3′ of the microsatellites. However, the clear overabundance of mutations in the immediate flanking region of long microsatellites, with a higher than average proportion of mismatches −9 bases 5′ and +4 bases 3′, indicates that the regions closest to the microsatellites are indeed experiencing elevated mutation rates. The dearth of mismatches further away from the microsatellites, and a similar overall mean proportion of mismatches to that around short microsatellites (0.012 versus 0.011, respectively), suggests that a higher mutation rate close to microsatellites is occurring at the expense of the number of mutations further from microsatellites. Discussion We have studied very large numbers of (AC) n microsatellite flanking regions culled from the human genome and asked questions about the extent that these evolve in any consistent and unusual manner. Patterning is present in the form of over- and underrepresentation of bases and dinucleotide motifs at odd and even positions either side of the microsatellite. Pattern strength is maximal around (AC) 9 , but appears present even around sequences as short as (AC) 2 and may extend as many as 50 bases either side. Some patterning is more or less symmetrical, but we also found several examples showing strong 5′ to 3′ asymmetry, implying that the two ends of a microsatellite are by no means equivalent. The net result is that sequences flanking microsatellites of a given length tend to be more similar to each other than to random sequences or to sequences flanking microsatellites of different lengths. Thus, there appears to be convergent evolution. Finally, we compared large numbers of homologous flanking sequences between humans and chimpanzees, and found evidence that mutation rates near microsatellites tend to be somewhat elevated. Sequences surrounding (AC) n tracts exhibit remarkable levels of patterning, with any given dinucleotide motif tending to be much more likely to occur at even numbered positions rather than odd, or vice versa. For several reasons, we believe that the patterning arises due to the structural properties of the microsatellite (see below), becoming more pronounced as repeat number increases. These reasons include the following: the consistently central placement of microsatellites within the patterning, the dependence of the strength of patterning on AC repeat number, the similarity between microsatellites in LINE and SINE elements and those elsewhere, the weakness of the patterning around (AC) 2 , and the strong influence of cassette type on the form of patterning. Unfortunately, it is surprisingly difficult to eliminate the alternative hypothesis, namely that the patterning arises due to some other force and that AC repeats then either form or expand more rapidly when placed centrally within the pattern. This ambiguity is particularly relevant to the question of cassette distribution, where it seems reasonable both that (AC) n tracts might cause biased interconversion between cassettes and that certain cassettes may allow slippage more than others. For example, while the structural properties of AC repeats are known to generate mutational biases in adjacent bases ( Timsit 1999 ) capable of changing cassette type, minisatellite mutation rate can depend critically on the presence of a particular base in the flanking sequence ( Monckton et al. 1994 ). The relationship between an AC microsatellite and its flanking sequences begins surprisingly early, with (AC) 2 already showing a small but significant bias in the distribution of cassette types and greater similarity to other sequences flanking AC microsatellites than to random sequences. In addition, the moving window assignment test indicates that significant convergence exists even when the ten bases closest to the microsatellite are excluded. Such a wide influence around such a common, short motif is remarkable and suggests that a high proportion of the genome may be affected by these and similar forces. To illustrate, (AC) 2 is expected to occur every 250 bases, as is (GT) 2 . Taking the sphere of influence on each side as ten bases plus half the 25-bp window yields a value of 45. This predicts that over 30% (approximately 45 bases of every 125) of the genome will be affected by (AC) 2 on one strand or the other, a figure that will only increase with inclusion of longer arrays and other microsatellite motifs. As AC repeat number increases, so does the strength of patterning, becoming pronounced by (AC) 5 and peaking in strength at (AC) 9 . Although patterning is seen in several different dinucleotide motifs, even in the human genome there are insufficient data to study any but the commonest cassette–motif combinations over a wide range of microsatellite lengths. Focusing on the motif AT, we found evidence that the strongest patterning was due to the development of AT microsatellites abutting AC tracts. However, this is not the only effect. After removal of all (AT) 2 or longer microsatellites, there remains a significant tendency for single AT motifs to appear in phase with AC tracts, suggesting that mutation bias as well as slippage is involved. Given the increase in strength of patterning between (AC) 2 and (AC) 9 , it might seem logical that the pattern would become stronger and stronger as repeat number increases further. Instead, (AC) 9 appears to be the peak strength, with longer microsatellites showing lower amplitude but a broader spread of patterning. It is interesting that this peak coincides with the length at which microsatellites begin to become polymorphic: a common rule of thumb for marker development in mammals is that primers are designed for loci carrying ten or more repeats ( Weber 1990 ). This may be mere coincidence or may reflect, for example, a change in mutation process associated with individuals who are heterozygous for alleles carrying different repeat numbers ( Rubinsztein et al. 1995 ; Amos et al. 1996 ; Amos and Harwood 1998 ). Again, there are parallels with minisatellites, where many mutations occur by the transfer of material from one homologous chromosome to the other ( Jeffreys et al. 1995 ). The rich patterning we find presumably arises through local mutation biases. Previous work on mutation biases has tended to reveal either generic effects such as isochors ( Bernardi 2000 ), where some bases are favoured over others in large regions of the genome, or specific but highly localised biases where one or two bases may influence what happens to their immediate neighbours ( Blake et al. 1992 ; Morton et al. 1997 ; Goodman and Fygenson 1998 ; Zavolan and Kepler 2001 ). The patterns we find suggest a somewhat intermediate process in which mutational dependency appears to extend over distances of 30 bases or more. At the same time, the patterning is position dependent, in that it involves not just, for example, a favouring of A over other bases, but, instead, a favouring of A over other bases at even numbered sites. The actual mechanism that causes patterning remains unclear, but our data suggest a model based on the structural properties of AC repeat tracts. Local variation in DNA structure is known to be associated with mutational biases ( Morton et al. 1997 ) and variation in mutation rate ( Petruska and Goodman 1985 ; Goodman and Fygenson 1998 ), as well as possibly influencing the mismatch repair process ( Werntges et al. 1986 ; Marra and Schar 1999 ). Tracts of repeating AC motifs tend to exhibit unusual structural properties with high propeller twist and shifted base pairing ( Timsit 1999 ), and hence may be considered prime candidates for sequences capable of influencing the evolution of their immediate surroundings. Indeed, crystallographic studies indicate that sequences like (AC) n and (A) n induce local mutation biases ( Timsit 1999 ). The unusual structure of microsatellite DNA may generate mutational biases in at least two ways. First, in AC repeat tracts, each base interacts unusually strongly with the neighbour of its complement base in a way that may lead to misincorporation of incoming nucleotides toward the ends of the microsatellite or in the immediate flanking region. Second, AC tract structure may influence the efficiency of the mismatch repair machinery in correcting either noncomplementary bases or loops resulting from slipped strand misalignment of repetitive DNA. Given that the mismatch repair system is strongly implicated in moderating the otherwise high rates of slippage mutation at microsatellite loci ( Levinson and Gutman 1987 ; Schlötterer 2000 ), it seems possible that even a small bias in the repair of loop structures might be responsible for the patterning we observe. However, although variation in mismatch repair efficiency may depend to some extent on DNA structure, the effect of sequence context on repair is not well understood ( Marra and Schar 1999 ). Unfortunately, with current understanding, none of these mechanisms would generate mutation biases that extend tens of bases away from the microsatellite, and hence this aspect must await further research. An alternative explanation for some of the patterning, for example, the tendency for single AT motifs to lie in phase with the microsatellite, could be that these elements represent the remnants of a longer and now eroded (AC) n repeat tract. Under this scenario, point mutations at specific positions along the microsatellite would presumably interrupt the repeats. Given a strong bias toward transition mutations, we can explain both the existence of strong AT pattern, with C to T transition mutations dominating over C to R (purine) or A to Y (pyrimidine) transversion mutations, and also the increase in pattern strength around longer microsatellites, with interruptions in longer arrays more likely to be internal to the repeat tract and hence be excluded from the analysis. However, we suggest that this model is unlikely for two reasons. First, such a model fails to accommodate the strong asymmetry in patterning that is observed for some dinucleotides and specific cassette bases around the (AC) n repeat tract. Polarity has been noted for minisatellite mutations, with mutational processes differing between the two ends of the repeat tract ( Armour et al. 1993 ; Jeffreys et al. 1994 ), but a microsatellite is much simpler in structure than a minisatellite and any polarity would have to affect some dinucleotides but not others. Second, the commonest and strongest patterning is observed for dinucleotide AT, and this would require high rates of C to T transitions but effectively no A to G transitions. More generally, the microsatellite erosion model predicts that flanking sequence patterning should be dominated by purine/pyrimidine, and this is not the case (see Table 2 ). The patterning we describe appears to represent an important component of the forces that shape genome evolution, both in terms of its ubiquity and the absolute strength of its effect. It follows that there are many possible practical and theoretical implications. For example, even very short microsatellites appear able to cause some level of convergent sequence evolution, and hence to confound phylogenetic analyses. Similarly, microsatellites near genes may increase local mutation rates and influence the spectrum of new mutations that arise. To explore the size of these effects we designed experiments both to measure absolute convergence and to ask about evidence for changes in mutation rate. To measure convergence, we made various comparisons between blocks of 50 bases chosen randomly, lying next to a microsatellite and lying near a microsatellite. We found an ordered progression of similarity from 12.77/50 bases for random–random through to a maximum of 14.31/50 bases between blocks adjacent to microsatellites 7–10 repeats long, an increase of 12% similarity. Although modest, trends are highly significant, with all comparisons showing a dependency on microsatellite length that peaks at around 7–10 repeats. The most parsimonious explanation for these similarities is that sequences flanking AC microsatellites tend to be AT-rich and to exhibit increased simplicity. Both these characteristics would increase the chance of flanking sequences being unusually similar both to each other and to random sequences that may contain polyA tails or other sources of simplicity. At the same time, the high scores gained by (AC) 7–10 both for assignment to their own class and for similarity to each other relative to random blocks provide a clear indication that convergent sequence evolution is occurring. Interestingly, any given flanking sequence tends to be more similar to a block 500 bases away than to a similarly placed block near a different microsatellite, suggesting longer range patterning such as might arise through placement within the same isochore ( Bernardi 2000 ). Furthermore, our attempts to measure variation in mutation rate indicate reduced similarity between homologous human and chimpanzee sequences, implying a higher rate of evolution, at least for a region in the order of ten bases around the microsatellite. On a scale of blocks of 20 bases the trends are less convincing. Having said this, it seems likely that any genuine variation in mutation rate would be to some extent masked by the convergent evolution, and hence that this aspect would benefit from further investigation. In conclusion, previous studies of microsatellite flanking sequences have identified several features, including a tendency to harbour other microsatellites, a locally increased mutation rate, and, conversely, conservation over unexpectedly large tracts of evolutionary time. Our analyses support all these trends and provide a possible resolution for the apparent contradiction between faster evolution but at the same time greater sequence conservation. Although there is evidence that mutation rates near microsatellites are elevated, we also find evidence of convergent evolution. Consequently, the increased rate of change may be to some extent neutralised and perhaps even reversed by the tendency for similar changes to occur in related lineages. Furthermore, the greatest changes appear to occur in flanking sequences around microsatellites that are below the length used as markers, at least in humans. Overall, therefore, we have been able to formalise previous anecdotal evidence and hence to document a remarkably widespread source of directional change and nonrandom evolution that undoubtedly plays an important role in shaping the make-up of our genomes. Materials and Methods Dataset. Our dataset of (AC) n dinucleotide repeats was extracted from the human genome (build 33, NCBI Reference Sequences; NCBI, Bethesda, Maryland, United States) using a custom macro written in Visual Basic. Only microsatellites separated by at least 100 bp from the nearest (AC) 2 or longer were included in the dataset. Thus (AC) 2 AT(AC) 10 would not be included in the dataset, whereas ACAT(AC) 10 would be included as (AC) 10 . Flanking sequences are here defined as the 50 bases lying either side of a microsatellite. No attempt was made to translate TG repeats with complementary AC repeats on the opposite strand. Consequently, all our microsatellites are 5′-(AC) n -3′. Flanking sequence base composition. For each frequency estimate, 95% confidence intervals were derived based on the binomial distribution ( n < 200 observations) or a normal approximation ( n ≥ 200 observations). Bases used to define cassette type were excluded from all calculations, and expected frequencies were taken as the average frequency across all positions. Convergence of flanking sequence pattern: assignment test. Microsatellites were divided into 21 classes according to repeat number. Class 1 was the control set, comprising 5,000 randomly selected, non-microsatellite-associated sequences from Chromosome 1. All other classes contained flanking sequences from single-length microsatellites, except class 21, which contained combined data from microsatellites 21–25 repeats long. Analysis was restricted to the most abundant cassette class, T/A, yielding sample sizes that peaked at 1,087 for (AC) 5 and declined to 175 for (AC) 20 (see Figure 2 ). As an index of similarity, we calculated the log likelihood of observing a given sequence based on its position-specific dinucleotide motif composition: where f ijk is the frequency of dinucleotide i at position j ( j ≠ −1 or 0, with position 0 including the microsatellite and its cassette bases) in flanking sequences of class k . To avoid bias, when a sequence was compared with its own class, its contribution to the dinucleotide frequencies was first removed. For each sequence in turn, A was calculated for every class and the sequence was then assigned to the class that yielded the highest index value. Under convergent evolution, we expect sequences to tend to be assigned to their own or similar length classes. Convergence of flanking sequence pattern: quantifying sequence change. Sequences were again divided into length classes 2 to 21, and each sequence contributed four 50-bp blocks of sequence, one from each side immediately adjacent to the microsatellite but excluding the cassette bases (class 1), and one from each side displaced by a randomly selected number 500–600 bases distal (class 2). In addition, we also generated a database of 5,000 non-microsatellite-associated sequences. When making comparisons within a class, nonindependence was avoided by randomising the sequence order and then comparing sequence 1 with sequence 2, 2 with 3, …, (n − 1) with n. Our index of similarity was simply a count of the number of matching bases. A few pairs of sequences (less than 0.1%) gave high similarity scores of over 30/50 matching bases, presumably because these loci have been duplicated or lie in repetitive elements. Such sequences were discarded. As with all other analyses, sequences containing base ambiguities (marked base N) were also discarded. Rate of evolution around microsatellites. (AC) n repeat microsatellites were extracted from the available chimpanzee finished-quality high-throughput genomic sequence (NCBI) as outlined above for humans. A 300-base region 220 bases upstream from each chimpanzee microsatellite was used by Megablast (Win32 version 2.2.6, NCBI) to identify homologous human loci in the finished genome sequence. Sequences with multiple high-scoring hits were discarded, as they presumably occur because a locus is found in repetitive elements or has been duplicated. Those nonoverlapping hits with at least 280/300 matching bases and an expectation (e-value) greater than five times that of any other hit to the same sequence were thus retained, giving a dataset of 5,537 sequences. Supporting Information Figure S1 Flanking Sequence Nucleotide Frequency Distributions Illustrating Three Classes of Patterning with Neither Strong Periodicity Nor Asymmetry From little structure of any kind (A) to complicated aperiodic clustering (C). Plots are as described in Figure 3 . (1.7 MB TIF). Click here for additional data file. Figure S2 Flanking Sequence Frequency Distributions for Four Dinucleotide Motif–Cassette Combinations Further to Those Shown in Figure 4 Plots are as described in Figure 4 . (3.0 MB TIF). Click here for additional data file. Figure S3 Dinucleotide Flanking Sequence Patterning in Interspersed Repeats and Unique Sequence DNA Figure depicts equivalent patterns of asymmetry in AT dinucleotide frequencies for the commonest cassette type, (T/A), around microsatellites in unique sequence DNA (A), LINE/L1 elements (B), and SINE/Alu elements (C). Plotting conventions are the same as for Figure 4 . (1.4 MB TIF). Click here for additional data file. Figure S4 Dependence of Differences among Homologous Loci on Location of Microsatellite Block position is relative to the central microsatellite (not shown). (A and B) Proportion of exact matches (with 95% binomial confidence intervals) and average number of matches, excluding exact matches (± standard error), with block position around (AC) 2–3 microsatellites ( n = 4,593). (C and D) As (A and B) but for (AC) 4+ microsatellites ( n = 356). Average proportion of exact matches and number of matches, calculated separately for 5′ and 3′ blocks around (AC) 2–3 microsatellites, are shown by a black line in (A) and (C), and (B) and (D), respectively. Average percentage match rather than average match is plotted in (B) and (D) because overlapping blocks were truncated to exclude overlapping regions from the analysis, with the result that not all blocks contained 20 bases. (2.9 MB TIF). Click here for additional data file. Figure S5 Mean Proportion of Mismatches along Homologous Flanking Sequences The proportion of mismatches occurring at a given base in a flanking sequence are averaged over (AC) 2–3 microsatellite loci (A) and over (AC) 4+ microsatellite loci (B). Shown ± standard error. The microsatellite at base position 0 is not shown. Expectation, calculated separately for 5′ and 3′ sequences around (AC) 2–3 microsatellites, is shown by a black line in both plots. (3.4 MB TIF). Click here for additional data file. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC509290.xml |
509280 | Emerging resistance among bacterial pathogens in the intensive care unit – a European and North American Surveillance study (2000–2002) | Background Globally ICUs are encountering emergence and spread of antibiotic-resistant pathogens and for some pathogens there are few therapeutic options available. Methods Antibiotic in vitro susceptibility data of predominant ICU pathogens during 2000–2 were analyzed using data from The Surveillance Network (TSN) Databases in Europe (France, Germany and Italy), Canada, and the United States (US). Results Oxacillin resistance rates among Staphylococcus aureus isolates ranged from 19.7% to 59.4%. Penicillin resistance rates among Streptococcus pneumoniae varied from 2.0% in Germany to as high as 20.2% in the US; however, ceftriaxone resistance rates were comparably lower, ranging from 0% in Germany to 3.4% in Italy. Vancomycin resistance rates among Enterococcus faecalis were ≤ 4.5%; however, among Enterococcus faecium vancomycin resistance rates were more frequent ranging from 0.8% in France to 76.3% in the United States. Putative rates of extended-spectrum β-lactamase (ESBL) production among Enterobacteriaceae were low, <6% among Escherichia coli in the five countries studied. Ceftriaxone resistance rates were generally lower than or similar to piperacillin-tazobactam for most of the Enterobacteriaceae species examined. Fluoroquinolone resistance rates were generally higher for E. coli (6.5% – 13.9%), Proteus mirabilis (0–34.7%), and Morganella morganii (1.6–20.7%) than other Enterobacteriaceae spp (1.5–21.3%). P. aeruginosa demonstrated marked variation in β-lactam resistance rates among countries. Imipenem was the most active compound tested against Acinetobacter spp., based on resistance rates. Conclusion There was a wide distribution in resistance patterns among the five countries. Compared with other countries, Italy showed the highest resistance rates to all the organisms with the exception of Enterococcus spp., which were highest in the US. This data highlights the differences in resistance encountered in intensive care units in Europe and North America and the need to determine current local resistance patterns by which to guide empiric antimicrobial therapy for intensive care infections. | Background Antimicrobial resistance has emerged as an important factor in predicting outcomes and overall resource use after infections in intensive care units (ICU) [ 1 ]. Globally ICUs are encountering emergence and spread of antibiotic-resistant pathogens. For some pathogens there are few therapeutic options available, e.g., vancomycin-resistant Enterococcus faecium . Awareness of these problems has been underscored with data from a number of surveillance studies aimed at improving the use of empiric therapy. In the United States there have been several national programs, which have focused on both the etiology of infections and resistance patterns of nosocomial or ICU infections including the National Nosocomial Infections Surveillance (NNIS) [ 2 ] and more recently an ICU-specific study examining the epidemiology of antimicrobial resistance, Project ICARE [ 3 , 4 ]. Stephen et al. collected strains from 28 ICUs from across the United States as part of the SENTRY Antimicrobial Surveillance Program in 2001 [ 5 ]. European data on the antimicrobial resistance of ICU pathogens has also been collected in several recent surveillance studies. A large prevalence survey of nosocomial infections in ICUs in 17 countries was published in 1995 [ 6 ], and more recently a number of nation-specific surveys were reported [ 7 - 9 ]. Several key points emerge: first, antimicrobial resistance among ICU pathogens is generally increasing, but variations do exist among different countries, probably due to individual antimicrobial use patterns; second, when new medical practices and alternative antimicrobials are introduced changes in the dominant microbial etiologies may emerge prompting novel empiric selections; and third, the standards of hygiene and infection control also vary across countries. Finally, appropriate therapy of ICU infections directed by local resistance data can have significant consequences for both patient and the healthcare system. It is against this background that local resistance surveillance programs are of most value in developing appropriate therapeutic guidelines for specific infections and patient types. For example, the recent modification to the American Thoracic Society guidelines for the treatment of hospital-acquired pneumonia [ 10 ] considered contemporary resistance data. Local surveillance data can be applied to other infections to assist in local formulary policy such as those governing treatment of nosocomial urinary tract infections [ 11 ]. This study using TSN program reports the antimicrobial resistance profiles of bacterial isolates from ICU patients in five countries during the period 2000–2002. The relevance of these recent nation-specific data will be discussed on a country-by-country basis, as part of improving and updating empiric therapeutic approaches to specific pathogens causing infections in the ICU setting according to each country. These surveillance programs help to maintain current knowledge of susceptibilities and relevant treatment options. Methods TSN Database – United States and Europe TSN is a queriable, real-time database that electronically assimilates daily antimicrobial susceptibility testing and patient demographic data from a network of geographically dispersed laboratories in the United States (283 hospital sites), France (63 hospital sites), Germany (169 hospital sites), Italy (48 hospital sites) and Canada (87 hospital sites) [ 12 ]. Laboratories included in TSN include those servicing university, community, and private hospitals with bed sizes ranging from 100 to >1000 beds. Routine diagnostic susceptibility testing results are collected daily from each participating laboratory. The methods used by these laboratories include VITEK (bioMérieux, St. Louis, MO), MicroScan (Dade-Microscan, Sacramento, CA), Sceptor and Pasco MIC/ID (Becton Dickinson, Sparks, MD) and Etest (AB Biodisk, Solna, Sweden), as well as manual broth microdilution MIC, disk diffusion and agar dilution. TSN reflects current testing in participant laboratories and represents the data reported to physicians from the respective laboratories [ 13 ]. Although some European countries have alternate breakpoints, all data forwarded to TSN Databases are derived from hospitals that utilized NCCLS standards and definitions (United States, Canada, Italy, and Germany) [ 14 ] or the Comité de L'Antibiogramme de La Societé Français de Microbiologie (France) [ 15 ] thus standardizing datasets. Results were interpreted as susceptible, intermediate (if available), or resistant in TSN, based upon the NCCLS interpretative guidelines in place during 2001 [ 16 ]. In addition, a series of quality-control filters (i.e., critical rule sets) were used in TSN to screen susceptibility test results for patterns indicative of testing error and suspect results were removed from analysis for laboratory confirmation. In TSN, any result from the same patient with the same organism identification and the same susceptibility pattern received within five days was considered a repeat culture and was counted only once in the database. Bacterial species and antimicrobials tested For this study, data from TSN results for each individual database from January 1, 2000 through to December 31, 2002 were included in the analysis to determine the proportion of species and their susceptibility to antimicrobial agents commonly tested in clinical laboratories throughout the participating regions. Only isolates derived from patients located in hospital ICUs were considered in the analysis. Gram-positive species included in the analysis were comprised of S. aureus , coagulase negative staphylococci, Enterococcus faecalis , Enterococcus faecium , Streptococcus pyogenes , Streptococcus pneumoniae and viridans group streptococci. Gram-negative species studied comprised the predominantly encountered enteric species ( Escherichia coli , Klebsiella oxytoca, Klebsiella pneumoniae, Proteus mirabilis, Morganella morganii and Serratia marcescens ), and Pseudomonas aeruginosa and Acinetobacter spp. The antibiotics studied are listed in Tables 2 , 3 , 4 , 5 . Among E. coli , putative ESBL production was defined as those isolates that were intermediate or resistant (non-susceptible) to ceftazidime [ 17 ]. Given the large number of isolate results included in the majority of analyses in this study, statistical analysis was not performed, as even subtle differences in percent resistance (<1%) to an antimicrobial agent for any time period or demographic parameters would be reported as highly significant ( P <0.001). Table 2 S. aureus , Coagulase-negative staphylococci, E. faecalis , and E. faecium isolated from ICU patients during 2000–2002 United States Canada Italy Germany France a Organism Agent Total n %S %R Total n %S %R Total n %S %R Total n %S %R Total n %S %R Staphylococcus aureus Ampicillin 19,703 6.7 93.3 3,792 12.6 87.4 1,665 5.6 94.4 2,867 16.2 83.8 15 6.7 93.3 Cefepime 1,260 52.9 46.9 NT b NT NT 304 15.8 84.2 483 80.5 17.0 <10 NA c> NA Cefotaxime 6,898 50.2 49.7 220 55.5 44.5 671 36.4 63.6 729 92.0 8.0 490 63.9 36.1 Ceftriaxone 5,914 45.6 54.3 153 69.3 30.7 1,048 28.1 71.8 220 88.6 11.4 23 73.9 26.1 Ciprofloxacin 24,350 47.4 51.0 5,958 74.5 24.1 4,600 39.7 58.6 5,243 73.4 26.1 316 57.0 40.5 Gentamicin 35,034 85.6 13.7 6,641 89.4 10.3 5,531 40.9 58.0 5,735 90.0 9.7 10,100 90.4 9.4 Oxacillin 44,939 47.7 52.3 10,105 80.3 19.7 6,147 40.6 59.4 6,475 79.0 21.0 10,512 59.4 40.6 Teicoplanin NT NT NT NT NT NT 5,868 100 0 4,632 99.8 0.2 8,232 100 0 Vancomycin 43,245 100 0 7,882 100 0 5,937 100 0 5,276 100 0 9,453 100 0 Staphylococcus aureus OSSA Ampicillin 9,047 14.5 85.5 3,055 15.7 84.3 741 12.6 87.4 2,414 19.3 80.7 10 0 100 Cefepime 672 99.1 0.4 NT NT NT 49 98.0 2.0 387 99.5 0.3 NT NT NT Cefotaxime 3,451 99.7 0.2 122 100 0 244 100 0 653 100 0 312 100 0 Ceftriaxone 2,707 99.5 0.2 106 100 0 295 99.0 0.3 194 100 0 16 100 0 Ciprofloxacin 11,827 91.2 6.5 4,692 93.5 4.8 1,902 91.4 4.9 4,171 91.4 8.0 188 90.4 6.4 Gentamicin 16,951 98.3 1.4 5,384 98.1 1.8 2,223 95.1 4.5 4,527 98.4 1.5 5,958 99.4 0.5 Oxacillin 21,416 100 0 8,110 100 0 2,495 100 0 5,115 100 0 6,244 100 0 Teicoplanin NT NT NT NT NT NT 2,402 100 0 3,593 99.9 0.1 5,018 100 0 Vancomycin 20,110 100 0 6,046 100 0 2,430 100 0 4,002 100 0 5,580 100 0 Staphylococcus aureus ORSA Ampicillin 10,656 0 100 737 0 100 924 0 100 453 0 100 <10 NA NA Cefepime 588 0 100 NT NT NT 255 0 100 96 4.2 84.4 <10 NA NA Cefotaxime 3,447 0.6 99.3 98 0 100 427 0 100 76 23.7 76.3 178 0.6 99.4 Ceftriaxone 3,207 0 100 47 0 100 753 0.3 99.7 26 3.8 96.2 <10 NA NA Ciprofloxacin 12,523 6.1 93.1 1,266 3.9 95.5 2,698 3.3 96.4 1,072 3.3 96.6 128 7.8 90.6 Gentamicin 18,083 73.7 25.2 1,257 52.0 46.8 3,308 4.5 94.0 1,208 58.7 40.5 4,142 77.5 22.2 Oxacillin 23,523 0 100 1,995 0.2 99.8 3,652 0 100 1,360 0 100 4,268 0 100 Teicoplanin NT NT NT NT NT NT 3,466 100 0 1,039 99.7 0.3 3,214 100 0 Vancomycin 23,135 100 0 1,836 100 0 3,507 100 0 1,274 100 0 3,873 100 0 Staphylcoccus species, coagulase-negative Ampicillin 16,288 5.7 94.3 3,533 6.3 93.7 2,142 10.6 89.4 4,075 8.1 91.9 <10 NA NA Cefepime 991 11.8 88.1 <10 NA NA 116 0 100 625 11.0 73.1 <10 NA NA Cefotaxime 5,538 17.7 82.3 240 17.9 82.1 335 16.7 83.3 625 37.4 62.4 174 28.7 69.0 Ceftriaxone 3,471 14.8 84.8 116 22.4 77.6 512 11.7 88.3 412 25.0 74.8 <10 NA NA Ciprofloxacin 18,829 40.2 58.3 5,366 44.4 54.7 5,102 42.7 54.0 6,197 29.5 67.6 198 44.4 53.0 Gentamicin 27,248 51.5 38.1 5,571 40.6 47.3 5,241 33.8 60.7 6,848 41.5 51.7 9,422 46.8 51.5 Oxacillin 35,135 15.8 84.2 9,172 20.6 79.4 5,961 15.2 84.8 7,442 18.6 81.4 9,884 30.1 69.9 Teicoplanin NT NT NT NT NT NT 5,797 93.7 2.4 5,096 95.6 0.7 7,670 84.6 3.1 Vancomycin 34,424 100 0 8,239 100 0 5,937 100 0 6,953 100 0 8,300 100 0 Staphylcoccus species, coagulase-negative Oxacillin susceptible Ampicillin 2,582 35.7 64.3 638 34.6 65.4 437 51.7 48.3 824 39.6 60.4 <10 NA NA Cefepime 117 100 0 NT NT NT NT NT NT <10 NA NA NT NT NT Cefotaxime 978 99.5 0.2 42 100 0 56 100 0 128 100 0 54 92.6 0 Ceftriaxone 523 98.3 0.4 26 100 0 59 100 0 103 100 0 <10 NA NA Ciprofloxacin 2,844 82.4 16.6 988 91.8 7.6 779 87.7 10.1 1,103 89.5 9.2 78 83.3 14.1 Gentamicin 4,424 93.5 4.2 1,068 91.9 5.3 698 94.3 5.3 1,263 96.5 2.7 2,822 93.9 5.4 Oxacillin 5,565 100 0 1,886 99.9 0.1 904 100 0 1,383 100 0 2,980 100 0 Teicoplanin NT NT NT NT NT NT 890 99.1 0.3 691 98.4 0.3 2,454 95.8 0.2 Vancomycin 5,240 100 0 1,587 100 0 897 100 0 981 100 0 2,467 100 0 Staphylcoccus species, coagulase-negative Oxacillin resistant Ampicillin 13,706 0.1 99.9 2,895 0 100 1,705 0 100 3,251 0.2 99.8 <10 NA NA Cefepime 874 0 99.9 <10 NA NA 116 0 100 624 10.9 73.2 <10 NA NA Cefotaxime 4,560 0.1 99.9 198 0.5 99.5 279 0 100 497 21.3 78.5 120 0 100 Ceftriaxone 2,948 0 99.8 90 0 100 453 0.2 99.8 309 0.0 99.7 <10 NA NA Ciprofloxacin 15,985 32.7 65.8 4,378 33.7 65.3 4,323 34.7 61.9 5,094 16.5 80.2 120 19.2 78.3 Gentamicin 22,824 43.3 44.7 4,503 28.5 57.3 4,543 24.5 69.2 5,585 29.1 62.8 6,600 26.6 71.3 Oxacillin 29,570 0 100 7,286 0 100 5,057 0 100 6,059 0 100 6,904 0 100 Teicoplanin NT NT NT NT NT NT 4,907 92.7 2.8 4,405 95.1 0.8 5,216 79.3 4.5 Vancomycin 29,184 100 0 6,652 100 0 5,040 100 0 5,972 100 0 5,833 100 0 Enterococcus faecalis Ampicillin 7,865 98.8 1.2 1,000 99.4 0.6 1,289 95.3 4.7 1,902 99.6 0.4 1,183 99.5 0.2 Ciprofloxacin 3,311 56.9 38.7 625 45.3 50.4 1,159 64.0 31.1 2,012 39.7 39.5 559 78.5 17.0 Gentamicin (HL Testing) 5,503 65.1 34.8 706 63.0 36.8 1,156 62.9 37.1 965 64.8 35.2 1,563 63.6 13.4 Teicoplanin NT NT NT <10 NA NA 1,248 97.1 2.4 1,294 99.7 0.2 1,747 99.9 0.1 Vancomycin 7,656 95.1 4.5 1,005 98.3 0.9 1,303 96.7 2.8 1,636 99.4 0.3 1,811 99.7 0.2 Enterococcus faecium Ampicillin 3,896 9.7 90.3 383 17.2 82.8 260 21.5 78.5 481 12.3 87.7 151 41.7 49.7 Ciprofloxacin 1,846 5.3 92.5 221 10.9 85.5 234 10.3 77.4 591 6.9 73.9 66 21.2 39.4 Gentamicin (HL Testing) 2,512 57.5 42.5 291 59.5 40.5 223 67.7 32.3 349 60.2 39.8 263 65.4 12.2 Teicoplanin 23 8.7 87.0 <10 NA NA 234 86.3 13.7 517 97.9 2.1 266 99.6 0.4 Vancomycin 4,066 23.2 76.3 415 85.1 14.5 264 75.4 24.2 628 93.9 4.8 247 98.4 0.8 a NCCLS breakpoints were used for all countries, except (CA-SFM) b Not tested c Not applicable if <10 isolates were tested Table 3 S. pneumoniae , S. pyogenes , S. agalactiae , and Viridans group streptococci isolated from ICU patients during 2000–2002 United States Canada Italy Germany France a Organism Agent Total n %S %R Total n %S %R Total n %S %R Total n %S %R Total n %S %R Streptococcus pneumoniae Amoxicillin 120 91.7 2.5 31 100 0 60 93.3 6.7 17 100 0 1,328 71.2 2.3 Cefepime 22 90.9 4.5 25 60.0 12.0 66 90.9 7.6 NT b NT NT <10 NA c NA Cefotaxime 1,571 82.2 6.3 145 93.8 0.7 108 93.5 4.6 63 100 0 1,181 77.1 0.8 Ceftriaxone 2,373 88.3 3.2 145 91.7 0.7 145 91.7 3.4 29 100 0 544 80.1 0.6 Clarithromycin 184 71.7 25.5 56 69.6 30.4 90 64.4 31.1 <10 NA NA NT NT NT Erythromycin 3,029 67.9 30.5 539 78.5 20.8 313 69.6 28.1 405 88.6 9.4 1,567 59.0 38.8 Levofloxacin 2,133 99.1 0.4 356 98.6 1.1 174 98.3 0.6 340 99.4 0.3 62 98.4 1.6 Penicillin 3,096 51.5 20.2 325 59.1 7.1 198 77.3 7.6 102 96.1 2.0 1,387 45.5 17.9 Vancomycin 2,865 100 - c 271 100 - 231 100 - 190 100 - 1,479 100 - Streptococcus pyogenes Amoxicillin NT NT NT NT NT NT NT NT NT NT NT NT 58 100 0 Cefepime <10 NA NA NT NT NT NT NT NT NT NT NT NT NT NT Cefotaxime 32 100 - 29 100 - <10 NA NA 11 100 - 30 100 - Ceftriaxone 75 100 - <10 NA NA <10 NA NA <10 NA NA <10 NA NA Clarithromycin 19 84.2 5.3 <10 NA NA 17 88.2 11.8 NT NT NT NT NT NT Erythromycin 118 92.4 6.8 102 81.4 11.8 59 74.6 23.7 63 84.1 11.1 170 82.9 14.7 Levofloxacin 71 97.2 1.4 <10 NA NA <10 NA NA 61 77.0 4.9 NT NT NT Penicillin 140 100 - 97 100 - 58 100 - 64 100 - 139 100 - Vancomycin 121 100 - 42 100 - 12 100 - 34 100 - 162 100 - Streptococcus agalactiae Amoxicillin NT NT NT NT NT NT NT NT NT NT NT NT 165 100 0 Cefepime 28 100 - NT NT NT <10 NA NA NT NT NT NT NT NT Cefotaxime 71 100 - 17 100 - 24 100 - 50 100 - 50 100 - Ceftriaxone 184 100 - <10 NA NA 38 100 - 37 100 - <10 NA NA Clarithromycin 21 81.0 9.5 <10 NA NA 21 71.4 28.6 NT NT NT <10 NA NA Erythromycin 489 76.3 21.7 222 82.9 14.9 121 77.7 18.2 192 83.9 10.9 588 79.9 16.2 Levofloxacin 333 97.9 1.2 <10 NA NA 51 98.0 0 180 91.1 1.7 173 99.4 0 Penicillin 518 100 - 226 100 - 145 100 - 184 100 - 369 100 - Vancomycin 463 100 - 179 100 - 143 100 - 65 100 - 526 100 - Streptococcus viridans group Amoxicillin NT NT NT NT NT NT NT NT NT NT NT NT 268 92.9 0.7 Cefepime 23 95.7 4.3 NT NT NT 12 66.7 33.3 NT NT NT NT NT NT Cefotaxime 434 83.6 11.1 101 92.1 4.0 31 90.3 9.7 75 97.3 2.7 56 94.6 0 Ceftriaxone 678 87.3 7.7 130 89.2 3.8 99 81.8 18.2 40 97.5 2.5 <10 NA NA Clarithromycin 34 52.9 38.2 21 76.2 19.0 21 71.4 23.8 <10 NA NA NT NT NT Erythromycin 959 57.2 37.7 289 71.6 23.2 192 64.6 32.8 796 88.1 9.2 626 59.9 31.6 Levofloxacin 331 96.1 2.7 <10 NA NA 16 87.5 0 93 89.2 4.3 <10 NA NA Penicillin 1,047 63.7 6.2 303 79.2 0 61 78.7 8.2 <10 NA NA 452 69.0 3.1 Vancomycin 1,095 100 - 276 100 - 180 100 - 277 100 - 580 100 - a NCCLS breakpoints were used for all countries, except France (CA-SFM) b Not tested c Breakpoints do not currently exist to interpret as S (susceptible) or R (resistant) Table 4 Enterobacteriaceae isolated from ICU patients during 2000–2002 United States Canada Italy Germany France a Organism Agent Total n %S %R Total n %S %R Total n %S %R Total n %S %R Total n %S %R Escherichia coli Cefepime 10,356 98.1 1.5 207 98.1 1.9 1,426 98.1 1.4 2,830 98.6 1.2 4,358 98.9 0.6 Cefotaxime 9,086 96.5 2.2 3,231 96.3 2.5 1,748 94.5 3.8 5,828 97.8 1.8 9,362 98.8 0.6 Ceftazidime 14,574 95.3 3.0 4,438 97.7 1.6 2,548 94.7 3.7 3,924 97.9 1.6 9,164 97.8 1.2 Ceftriaxone 15,897 97.4 1.7 3,829 96.8 2.2 1,423 94.4 4.2 534 99.8 0.2 834 98.6 1.0 Ciprofloxacin 17,294 89.0 10.7 5,028 90.3 9.5 2,616 87.0 12.7 4,615 86.7 12.4 8,577 93.1 6.5 Gentamicin 20,581 92.4 6.5 6,654 92.8 5.3 2,650 92.2 6.6 4,825 94.3 5.2 9,442 95.4 4.2 Imipenem 15,353 100 0 3,386 100 0 2,254 100 0 5,172 100 0 8,994 100 0 Levofloxacin 14,920 88.2 11.6 776 85.1 13.9 496 86.5 13.3 3,137 88.2 11.0 NT b NT NT Piperacillin-tazobactam 13,573 93.1 3.6 4,305 95.1 2.4 1,879 95.8 2.4 5,637 93.6 3.4 7,255 95.4 1.1 Trimethoprim-sulfamethoxazole 20,296 79.2 20.7 6,737 84.6 15.3 2,440 75.0 24.9 5,598 73.1 26.6 9,028 78.2 21.1 Klebsiella oxytoca Cefepime 1,476 96.2 3.3 19 100 0 255 99.6 0 566 96.8 2.7 478 97.1 0.4 Cefotaxime 1,324 92.7 4.7 486 94.2 4.5 230 96.5 1.7 1,117 93.8 4.4 865 96.3 0.8 Ceftazidime 1,909 91.7 7.0 661 94.9 4.1 361 83.4 15.2 749 95.3 4.5 870 98.3 0.5 Ceftriaxone 2,035 89.9 6.6 536 93.8 2.8 197 81.7 2.0 83 97.6 0 79 87.3 2.5 Ciprofloxacin 2,226 92.5 5.9 745 96.0 3.0 368 96.7 3.0 905 90.1 7.8 815 94.5 4.8 Gentamicin 2,569 89.9 8.3 857 95.0 4.9 366 89.6 3.0 1,016 98.2 1.2 865 97.1 2.4 Imipenem 2,061 100 0 516 100 0 337 100 0 1,062 100 0 845 100 0 Levofloxacin 1,754 93.3 3.4 159 96.9 1.3 133 97.0 3.0 560 94.6 3.2 NT NT NT Piperacillin-tazobactam 1,801 82.7 13.9 624 91.2 7.1 313 81.8 11.2 1,113 78.9 18.1 742 88.3 10.4 Trimethoprim-sulfamethoxazole 2,467 92.5 7.5 863 96.3 3.6 308 95.1 4.9 1,084 93.7 6.3 802 94.1 5.7 Klebsiella pneumoniae Cefepime 7,276 95.8 3.4 98 100 0 552 93.5 5.6 1,068 95.7 3.5 840 95.6 3.0 Cefotaxime 6,243 91.0 6.1 1,411 97.9 1.5 850 76.7 16.4 2,414 93.1 6.0 1,553 94.4 1.9 Ceftazidime 9,597 88.5 10.1 2,238 97.5 2.2 1,142 69.8 28.5 1,665 90.0 8.2 1,591 92.5 5.2 Ceftriaxone 10,337 92.7 4.7 1,736 97.9 1.1 816 75.2 15.0 166 98.8 0.6 112 86.6 5.4 Ciprofloxacin 11,089 89.9 8.4 2,484 91.8 7.2 1,190 88.2 9.9 2,128 85.4 9.4 1,473 89.5 8.7 Gentamicin 13,012 91.6 7.0 2,906 96.7 2.9 1,211 81.4 14.5 2,065 91.6 6.1 1,553 97.1 2.7 Imipenem 10,263 100 0 1,766 100 0 1,066 100 0 2,351 100 0 1,567 100 0 Levofloxacin 9,626 91.0 6.4 485 93.4 3.7 287 78.4 21.3 1,228 92.6 4.4 NT NT NT Piperacillin-tazobactam 9,359 85.9 7.4 2,160 91.5 2.7 746 82.2 14.6 2,408 84.9 8.3 1,286 89.4 5.1 Trimethoprim-sulfamethoxazole 12,641 88.6 11.1 2,924 92.8 7.1 1,103 82.0 18.0 2,324 82.2 17.2 1,443 88.2 10.9 Morganella morganii Cefepime 566 95.9 2.3 <10 NA NA 121 97.5 2.5 262 94.7 5.0 412 96.1 0.2 Cefotaxime 499 78.8 8.4 156 91.0 3.8 144 74.3 6.3 437 86.7 3.9 678 81.1 5.9 Ceftazidime 715 73.6 17.3 256 79.7 10.9 213 75.6 15.0 313 84.0 7.7 673 78.6 8.0 Ceftriaxone 806 91.1 2.2 219 96.3 1.4 125 91.2 3.2 22 86.4 0 57 84.2 5.3 Ciprofloxacin 841 78.1 20.7 292 94.2 4.5 220 87.3 9.5 344 97.7 2.0 634 88.6 8.5 Gentamicin 967 84.0 14.1 329 94.5 4.6 222 90.1 8.6 378 96.8 2.1 679 95.6 3.4 Imipenem 784 100 0 196 100 0 206 100 0 402 100 0 649 99.8 0 Levofloxacin 725 78.1 19.3 42 95.2 4.8 55 90.9 9.1 251 98.0 1.6 NT NT NT Piperacillin-tazobactam 725 91.2 5.1 254 97.2 1.6 150 94.0 3.3 430 94.2 3.5 564 91.0 4.6 Trimethoprim-sulfamethoxazole 936 75.1 24.7 329 91.8 8.2 193 79.8 20.2 435 93.1 6.9 627 83.9 14.2 Proteus mirabilis Cefepime 1,964 98.2 1.0 20 100 0 395 87.6 11.4 599 99.2 0.8 736 99.0 0.1 Cefotaxime 1,794 99.1 0.5 295 99.7 0 441 69.4 23.4 1,209 98.8 0.7 1,503 99.5 0.1 Ceftazidime 2,684 98.0 1.1 463 99.4 0.2 630 86.0 9.4 821 98.5 1.0 1,505 99.3 0.2 Ceftriaxone 3,034 99.4 0.3 392 99.5 0 385 80.5 13.8 77 98.7 0 72 100 0 Ciprofloxacin 3,169 85.2 12.7 504 95.2 4.6 657 70.6 22.7 980 92.9 5.1 1,424 90.9 6.8 Gentamicin 3,796 91.5 7.7 698 92.6 7.2 670 61.6 37.2 992 92.9 5.9 1,509 91.3 7.9 Imipenem 2,850 100 0 367 100 0 580 100 0 1,020 100 0 1,319 100 0 Levofloxacin 2,825 87.8 10.5 94 100 0 202 61.9 34.7 688 96.5 2.3 <10 NA c NA Piperacillin-tazobactam 2,715 97.7 0.8 449 98.2 0.2 465 95.7 2.8 1,201 98.6 0.8 1,231 99.3 0.2 Trimethoprim-sulfamethoxazole 3,706 85.2 14.7 708 89.4 10.6 615 61.6 38.0 1,159 80.8 19.1 1,411 79.7 18.6 Serratia marcescens Cefepime 3,653 96.7 2.3 52 96.2 1.9 497 96.8 2.2 546 94.1 3.5 509 98.6 0.2 Cefotaxime 3,134 87.0 5.7 670 92.8 2.7 470 79.6 9.8 951 84.0 7.5 809 81.5 3.3 Ceftazidime 4,718 89.7 7.9 1,113 95.2 3.0 738 81.4 13.3 851 89.7 7.5 812 94.7 3.0 Ceftriaxone 4,710 90.5 4.6 846 95.4 1.7 444 86.7 6.3 160 45.6 0 115 77.4 4.3 Ciprofloxacin 5,006 91.0 6.7 1,292 85.0 11.7 757 83.5 4.5 978 72.6 12.4 787 78.9 10.5 Gentamicin 5,905 92.9 5.9 1,313 94.6 5.2 758 97.4 2.1 665 92.9 6.3 808 91.6 6.6 Imipenem 4,960 100 0 880 100 0 727 100 0 1,018 100 0 805 100 0 Levofloxacin 4,356 94.3 4.2 264 92.4 4.2 266 95.5 1.5 595 87.6 6.6 <10 NA NA Piperacillin-tazobactam 4,337 88.1 5.1 1,155 91.6 3.3 547 92.7 3.8 1,053 77.6 3.1 749 82.6 2.4 Trimethoprim-sulfamethoxazole 5,697 95.9 3.9 1,325 94.9 5.1 646 81.4 18.6 908 88.1 10.9 699 84.1 13.6 a NCCLS breakpoints were used for all countries, except France (CA-SFM) b Not tested c Not applicable if <10 isolates were tested Table 5 P. aeruginosa and Acinetobacter spp isolated from ICU patients during 2000–2002 United States Canada Italy Germany France Organism Agent Total n %S %R Total n %S %R Total n %S %R Total n %S %R Total n %S %R Acinetobacter species Cefepime 5,162 43.8 40.2 97 67.0 23.7 475 17.9 73.7 623 74.2 10.8 857 28.0 40.3 Cefotaxime 3,830 23.3 49.9 705 36.7 34.9 555 11.0 78.7 1,254 34.9 24.6 671 15.4 38.7 Ceftazidime 5,954 42.2 40.8 1,162 70.8 22.9 692 25.6 68.5 988 66.7 14.5 1,106 34.9 35.5 Ceftriaxone 4,709 16.3 55.9 874 32.4 28.7 452 8.8 72.6 104 42.3 11.5 81 9.9 51.9 Ciprofloxacin 5,808 39.7 58.0 1,156 72.1 25.9 686 21.1 76.7 1,126 74.8 22.9 1,038 37.7 61.2 Gentamicin 6,618 47.2 47.2 1,185 72.8 22.8 768 23.3 72.4 979 82.0 14.1 936 49.3 43.5 Imipenem 6,006 87.0 7.5 918 95.8 1.9 569 77.9 19.0 1,253 96.2 3.4 1,088 93.8 3.8 Levofloxacin 5,099 43.8 52.2 489 61.1 25.6 295 13.9 75.3 840 82.0 10.5 NT b NT NT Meropenem 2,154 66.3 26.5 348 93.7 4.9 455 74.5 13.6 1,024 96.0 3.4 147 68.0 28.6 Piperacillin 4,658 35.4 45.9 959 66.5 19.5 635 19.5 69.9 1,171 59.7 12.9 805 35.0 50.3 Piperacillin-tazobactam 3,429 53.6 28.5 903 70.7 23.1 425 35.1 46.4 1,225 81.8 7.5 878 74.5 10.5 Trimethoprim-sulfamethoxazole 5,697 51.4 48.4 1,155 74.8 25.2 750 44.1 55.7 1,234 83.6 15.6 93 45.2 52.7 Pseudomonas aeruginosa Cefepime 20,220 72.5 12.4 371 73.3 12.4 5,056 58.9 28.9 3,483 80.3 7.8 7,967 52.6 16.2 Cefotaxime 11,283 9.2 50.4 1,836 13.3 47.5 4,181 6.0 70.7 2,689 7.7 52.2 NT NT NT Ceftazidime 26,353 71.2 17.4 6,036 73.7 13.4 7,640 56.7 31.3 5,141 76.2 14.9 8,547 70.2 14.9 Ceftriaxone 14,066 12.1 56.4 2,847 11.3 59.7 3,383 8.4 70.4 154 26.6 7.8 NT NT NT Ciprofloxacin 26,700 62.8 33.1 5,924 67.2 30.2 7,388 58.4 38.8 4,746 68.6 24.4 8,560 55.3 40.6 Gentamicin 29,268 69.4 21.5 5,951 72.2 15.9 7,522 52.2 41.7 3,913 74.0 14.3 7,327 44.0 46.1 Imipenem 26,076 73.5 22.1 3,775 77.9 18.2 7,057 59.7 27.8 4,412 70.5 19.0 8,575 69.5 21.4 Levofloxacin 21,059 62.7 31.7 713 56.8 33.5 2,427 44.9 51.0 2,953 68.0 23.9 NT NT NT Meropenem 7,540 76.0 18.2 1,266 80.3 14.5 4,082 57.3 32.7 4,351 77.8 13.8 1,818 81.1 6.4 Piperacillin 22,855 77.7 22.2 5,520 80.9 18.8 7,004 63.1 36.7 4,554 81.7 14.1 8,454 64.1 24.1 Piperacillin-tazobactam 21,848 85.5 14.4 4,190 91.0 9.0 5,252 77.7 22.0 4,746 85.8 10.7 8,256 69.6 15.9 Trimethoprim-sulfamethoxazole 15,618 3.6 96.4 4,283 4.0 96.0 7,054 4.1 95.8 3,375 4.2 95.8 NT NT NT a NCCLS breakpoints were used for all countries, except France (CA-SFM) b NT = not tested Results In vitro susceptibility data from over 220,000 isolates from ICUs in five countries over the period 2000–2002 were assimilated. The most frequent species isolated from infections in the ICU was S. aureus , being most common in three of the five countries (Table 1 ). The oxacillin resistance rates among S. aureus varied markedly across countries from 19.7% in Canada to 59.5% in Italy. E. coli (7.7%–15.5%) and P. aeruginosa (10.8%–22.3%) were the most frequent Gram-negative organisms encountered. The Gram-positive genus Enterococcus , either as E. faecalis, E. faecium or non-speciated isolates accounted for <10% of isolates in most countries with E. faecalis being the most common species <4.3%. Community-acquired respiratory pathogens such as Streptococcus pneumoniae and Haemophilus influenzae were relatively uncommon in all five countries. Table 1 Incidence of pathogens isolated from ICU patients by country (%) United States Canada Italy Germany France Organism Incidence (%) Organism Incidence (%) Organism Incidence (%) Organism Incidence (%) Organism Incidence (%) S. aureus a 20.2 S. aureus a 17.4 P. aeruginosa 22.3 CNS 16.4 S. aureus 1 17.2 CNS b 15.9 CNS 16.1 CNS 18.7 S. aureus a 13.6 CNS 16.7 P. aeruginosa 13.1 E. coli 12.6 S. aureus a 18.1 E. coli 12.3 E. coli 15.5 E. coli 9.2 P. aeruginosa 11.3 E. coli 7.7 P. aeruginosa 10.8 P. aeruginosa 13.8 K. pneumoniae 5.8 Enterococcus spp 7.6 E. faecalis 3.9 Enterococcus spp 7.4 S. pneumoniae 3.3 Enterococcus spp 5.4 K. pneumoniae 5.5 K. pneumoniae 3.5 K. pneumoniae 5.4 E. cloacae 3.3 E. cloacae 4.3 E. cloacae 4.2 Enterococcus spp 3.3 E. cloacae 4.7 E. faecalis 3.0 E. faecalis 3.7 S. marcenscens 2.5 E. cloacae 2.6 E. faecalis 4.3 K. pneumoniae 2.7 S. marcescens 2.7 H. influenzae 2.1 S. marcescens 2.2 P. mirabilis 2.6 P. mirabilis 2.5 A. baumanii 2.6 E. faecalis 2.1 P. mirabilis 1.9 K. oxytoca 2.4 Enterococcus spp 2.3 Enterobacteriaceae c (all species combined) 29.5 Enterobacteriaceae (all species combined) 33.0 Enterobacteriaceae (all species combined) 30.2 Enterobacteriaceae (all species combined) 36.0 Enterobacteriaceae (all species combined) 32.1 Total (n) 26,624 Total (n) 54,445 Total (n) 34,609 Total (n) 48,385 Total (n) 62,459 a Proportion of S. aureus testing as MRSA was USA (52.3%), Canada (19.7%), Italy (59.4%), Germany (21.0%), and France (40.6%) b CNS = Coagulase-negative staphylococci c Enterobacteriaceae includes all species of genera occurring at >0.1% Tables 2 , 3 , 4 , 5 show the antimicrobial susceptibility profiles of various Gram-positive and Gram-negative pathogens isolated from ICU patients against a range of relevant antimicrobials. Specifically notable susceptibility patterns include the vancomycin susceptibility of all strains of staphylococci. Generally, there was a low proportion of vancomycin resistant E. faecalis <5%, whereas vancomycin-resistant E. faecium was more prevalent ranging from 0.8% in France to 76.3% in the United States, with a wide inter-country variation (Table 2 ). Penicillin resistance rates varied among S. pneumoniae , from 2.0% in Germany to 20.2% in the US with concurrent ceftriaxone resistance rates of 0% in Germany to 3.4% in Italy (Table 3 ). β-lactam activity was assessed by comparing four different cephalosporins and a β-lactam/β-lactamase inhibitor combination, piperacillin-tazobactam. Overall, the putative production of ESBLs among E. coli was low, <6%, but ceftazidime resistance was reported at higher rates in K. pneumoniae and S. marcescens , with the highest rates seen in M. morganii , from 16.0% in Germany to 26.4% in the United States (Table 4 ). Among the gram-negative organisms tested, ceftriaxone resistance rates were usually lower than ceftazidime, with the exception among P. aeruginosa and Acinetobacter spp. Cefepime, a fourth generation cephalosporin with anti-pseudomonal activity was also more active than ceftazidime (Table 5 ). Against the Enterobacteriaceae , the β-lactam combination agent piperacillin-tazobactam was generally less active than ceftriaxone. These species showed a wide variation in fluoroquinolone susceptibility among both species and countries. Gentamicin resistance rates among the Enterobacteriaceae varied from 1.2% among K. oxytoca from Germany to 37.2% in P. mirabilis from Italy. Ciprofloxacin resistance rates among E. coli ranged from 6.5% in France to 12.7% in Italy. Variable fluoroquinolone resistance rates among S. marcescens were also demonstrated, with a range of resistance from 4.5% in Italy to 12.4% in Germany. Discussion Data derived from international surveillance studies, such as those presented here, can provide a unique contemporary perspective on the susceptibility of commonly encountered organisms to commonly used antibiotics. Such surveillance systems play a crucial role in detecting emerging trends in resistance. Comparisons of these with data of other recent surveillance programs show the wide variations in susceptibility profiles and the need for ongoing unit-specific surveys. In Germany the prevalence of resistance among gram-positive organisms remained comparatively low with an incidence of 21% MRSA. In 2000, Frank et al. reported that 96% of German isolates of S. marcescens and M. morganii were susceptible to ceftazidime, yet in this study we found 89.7% and 84.0%, respectively [ 9 ]. A similar decrease in activity was noted with E. coli and ciprofloxacin between the two studies, 91% in 1996–1997 compared with 86.7% in this study. Marked decreases in susceptibility of P. aeruginosa in Germany were also evident, with no agent showing >85.8% susceptibility (piperacillin-tazobactam) compared with most agents having 85%–94% susceptibility in 1996–1997. Changes of 15–20% have been reported with ceftazidime, imipenem, ciprofloxacin and meropenem, while piperacillin-tazobactam has shown the smallest decrease in susceptibility with <6% over the 4-year period. Piperacillin plus or minus tazobactam and cefepime were the most active agents, based on susceptibility, against P. aeruginosa in Germany. Conversely, ceftriaxone and imipenem were the most active agents, based on susceptibility, against Klebsiella spp., which account for almost 8% of ICU isolates. Staphylococcal species from French ICU isolates showed a high proportion of oxacillin resistance, 40.6% and 69. 9% of S. aureus and coagulase-negative staphylococci spp., respectively. S. pneumoniae showed penicillin resistance of 17.9%, higher than the other four countries, although the activity of third-generation cephalosporins, ceftriaxone and cefotaxime, showed only 0.6% and 0.8% resistance, respectively. Despite a lower ceftazidime susceptibility breakpoint compared to NCCLS standards (MIC 4 μg/ml instead of 8 μg/ml) putative ESBL expression were slightly lower in France than in Germany in 2000–2002. Ceftazidime non-susceptibility rates among E. coli , K. oxytoca , and P. mirabilis were ≤ 2.2%; however, ceftazidime non-susceptibility rates among K. pneumoniae , M. morganii and S. marcescens were 7.5%, 21.4%, and 5.3%, respectively. Imipenem was active against all Enterobacteriaceae . Against P. aeruginosa and Acinetobacter spp., imipenem resistance rates were 21.4% and 3.8%, respectively. Previously, a lower imipenem resistance of 24% among French isolates of P. aeruginosa was reported [ 7 ]. Among the Italian isolates of staphylococci, oxacillin resistance occurred in 59.4% of S. aureus and 84.8% of coagulase-negative isolates. This MRSA rate was similar to that reported by Frank et al. from bacteremic isolates in Italy; however, they reported an increase in MRSA from 25% to 55% over the period 1997 to 2001 [ 18 ]. Vancomycin resistance rates of 2.8% for E. faecalis and 24.2% for E. faecium are some of the highest rates recorded in Europe, although still modest compared to rates experienced in the United States; however, teicoplanin was more active with 2.4% and 13.7% of strains being resistant, respectively. Pneumococcal resistance to penicillin and erythromycin was 7.6% and 28.1%, respectively. The impact of alterations in penicillin-binding protein that reduce penicillin susceptibility have less effect on the activity of third-generation cephalosporins such as ceftriaxone with 3.4% and cefotaxime with 4.6% resistance, respectively. S. pyogenes was fully susceptible to penicillin; however, 11.8% of isolates were resistant to clarithromycin and 23.7% were resistant to erythromycin. The proportion of ESBLs was slightly higher in Italy with E. coli showing ceftazidime non-susceptibility of 5.3%, whereas K. pneumoniae and K. oxytoca demonstrated 30.2% and 16.6% ceftazidime non-susceptibility, respectively. Fluoroquinolone resistance rates among the Enterobacteriaceae , using ciprofloxacin as a marker, varied from 3.0% for K. oxytoca to 22.7% for P. mirabilis , and 12.7% for E. coli . Thus, among Enterobacteriaceae , ciprofloxacin was generally less active than the third-generation cephalosporin, ceftriaxone. P. aeruginosa and Acinetobacter spp. strains from Italian ICUs demonstrated significant resistance rates. Isolates of P. aeruginosa showed resistance rates of >28% for all agents tested except piperacillin-tazobactam. Thus empiric therapy for possible pseudomonal infections will require combination therapy. Acinetobacter spp. showed a similar lack of susceptibility except to imipenem and meropenem (19.0% and 13.6% resistant). An increase in fluoroquinolone resistance in E. coli and K. pneumoniae in bacteremic isolates from Italy was observed during 1997–2001, with rates of 26.7% and 24%, respectively [ 9 ]. An increase in ureidopenicillin resistance was noted in P. aeruginosa isolates in Italy from 30% to 37% in a 4-year period [ 9 ]. This study showed 22.0% piperacillin-tazobactam and 36.7% piperacillin resistance among ICU P. aeruginosa isolates. In Canada oxacillin-resistance among S. aureus was noted in 19.7% and coagulase-negative staphylococci in 79.4%. Vancomycin resistance was reported among 0.9% and 14.5% of E. faecalis and E. faecium , respectively. The lowest rate of penicillin resistance in S. pneumoniae in this study was noted from Canada at 7.1%; however, clarithromycin resistance was 30.4%. Ceftriaxone showed 0.7% resistance whereas cefepime exhibited 12.0% resistance among pneumococci from the ICU. Overall the susceptibility rates for Gram-negative isolates from Canadian ICUs were higher than those in the other four countries examined. A low rate of ESBLs was reported, but there was variable activity of piperacillin-tazobactam which showed >9% resistance among Klebsiella spp. and S. marcescens tested. The rate of fluoroquinolone resistance was similar to those of other countries with E. coli showing 13.9% levofloxacin resistance. Among Enterobacteriaceae , <10% of most species were resistant to third-generation cephalosporins tested with the exception of ceftazidime and M. morganii . Resistance among P. aeruginosa and Acinetobacter spp. was generally lower than in other countries apart from Germany. Only piperacillin-tazobactam showed reliable activity against P. aeruginosa (9% resistant), while resistance to all other agents was >19%. Acinetobacter spp. remained susceptible to only the carbapenems, imipenem and meropenem. Comparison of the data from Canadian isolates with those from the United States shows some significant differences. This demonstrates the limitations of pooling Canadian and United States data since the differences between the two regions, such as the rate of MRSA, may have some impact on empiric therapy. Data from the NNIS system has previously reported an increasing trend towards resistance within ICUs in the United States [ 19 ]. Oxacillin resistance among staphylococci from ICUs in the United States was 52.3% and 84.2% for S. aureus and coagulase-negative species, respectively. This value is identical to that of S. aureus and very similar to the CNS data reported by the 1999 NNIS system. The NNIS highlighted a 37% increase in MRSA over the period 1994–98 to 1999, but only a 2% increase among CNS strains [ 4 ]. Vancomycin resistance in the United States was observed in 4.5% of E. faecalis ; however, over 76% E. faecium were vancomycin non-susceptible. Although streptococci are uncommon ICU pathogens they can be rapidly invasive and possibly fatal unless adequate therapeutic approaches are adopted. S. pneumoniae in the United States has acquired a range of resistance mechanisms with resistance to penicillin and the macrolides, clarithromycin and erythromycin, being common, 20.2% and 25.5%–30.5% respectively. The newer generation cephalosporins, ceftriaxone, cefotaxime and cefepime showed good activity against pneumococci, 3.2%, 6.3% and 4.5% resistant, respectively. Less than 1.0% of isolates were resistant to levofloxacin. These data are similar to other recent reports [ 20 ]. For Enterobacteriaceae which account for approximately 30% of all isolates from ICU infections, the incidence of putative ESBLs was low in E. coli , 4.7% but ceftazidime non-susceptibility was higher in K. oxytoca 8.3%, K. pneumoniae 11.5%, S. marcescens 10.3% and M. morganii 26.4%. These data are consistent with other recent reports [ 21 ]. Fluoroquinolone resistance was observed in all Enterobacteriaceae tested, in the US for example, resistance rates were as follows, using ciprofloxacin as a marker: E. coli 10.7%, K. oxytoca 5.9%, K. pneumoniae 8.4%, M. morganii 20.7%, P. mirabilis 12.7% and S. marcescens 6.7%. These data show increased fluoroquinolone resistance compared with recent reports [ 21 ]. Jones et al. previously reported susceptibility data on ICU pathogens isolated over the period 1998–2001 [ 22 ]. Specifically, enteric bacteria showed changes over this time. Fluoroquinolone resistance doubled among E. coli isolates from 3.3–5.5% to 10.8–11.4% [ 22 ]. This study showed a generally higher level of activity among third-generation cephalosporins than other reports [ 23 ], with ceftriaxone showing <10% resistance rates against most species tested. Piperacillin-tazobactam showed less consistent activity with some species being >14% resistant, e.g. Klebsiella spp., P. aeruginosa , and Acinetobacter spp. present significant therapeutic challenges in ICUs in the United States. With the exception of cefepime, all other tested antimicrobials demonstrated >12% resistance to P. aeruginosa , many considerably higher. Piperacillin-tazobactam showed the next lowest resistance rate, 14.4%, with all other agents having rates of 17% or higher. Non-susceptibility to ciprofloxacin among P. aeruginosa was 37.2%, higher than in the Neuberger report. Sahm et al. reported a 10% increase in fluoroquinolone resistance among P. aeruginosa in the United States, whereas resistance emerged more slowly with the other classes of antimicrobials tested [ 12 ]. Acinetobacter infections continue to present significant therapeutic challenges due to the extensive resistance mechanisms demonstrated by the >25% resistance shown in Table 5 . Only imipenem has any reliable activity against Acinetobacter spp. with an 87% susceptibility rate. There are several implications of these data. It is essential that local surveillance programs be maintained in each country's ICU setting. The local data are vital to the formulary committees as they select appropriate agents to treat infections. There are clear differences among the five countries studied in this report. Although the predominant pathogens are similar, ongoing surveillance is essential to detect the emergence of resistant species. It is clear that certain classes of compounds are losing activity against the ICU pathogens tested. For example, the fluoroquinolones have reduced susceptibility among many Gram-negative species as well as staphylococci; however, the newer class members have enhanced activity against pneumococci. Advanced-generation cephalosporins have variable activity, with ceftriaxone showing consistently good activity against the Enterobacteriaceae and some staphylococci. Ceftazidime has lost potency due to the emergence of ESBL enzymes and also has diminished activity against P. aeruginosa . Piperacillin-tazobactam is generally active against P. aeruginosa in ICUs. The aminoglycoside, gentamicin has shown continued activity against most Enterobacteriaceae in all five countries, and modest activity against S. aureus but not against CNS strains. The gentamicin susceptibility of P. aeruginosa ranged from 44.0% in France to 74.0% in Germany, whereas Acinetobacter spp . showed more variable gentamicin susceptibility varying from 23.3% in Italy to 82.0% in Germany. These local data should be considered when treating infections in the ICU. Use of agents with anti-pseudomonal activity such as cefepime, piperacillin-tazobactam or the carbapenems should preferably be reserved for patient types or infections where this pathogen is present or risk factors exist, as per the ATS Community acquired-pneumonia guidelines [ 24 ]. A combination of a third-generation cephalosporin such as ceftriaxone with vancomycin may be appropriate for bloodstream infections based upon the NNIS etiology data from 1992–1999. Conclusions The current study confirmed the emergence of fluoroquinolone resistance among various Gram-negative species and staphylococci, which may be increasing due to the heightened use of these drugs; however the reported ESBL rates among Enterobacteriaceae was lower than noted in other studies and appeared to be stable. The prevalence of MRSA, perhaps the most significant resistant hospital pathogen, varied among the five countries and appeared to be increasing. Parenteral cephalosporins such as ceftriaxone and cefotaxime remained quite active against Enterobacteriaceae . Up-to-date susceptibility data should be made available as rapidly as possible to physicians so that appropriate targeted empirical therapy can be instituted, this approach can assist in maintaining the activity of the current antimicrobials. While local surveillance studies remain crucial, national surveillance studies such as this can provide an invaluable data source to provide guidance in formulary decision-making. Authors Contributions MJ conceived the study, provided data interpretation and drafted the manuscript. DD analyzed the study data; JK and DS provided expert microbiological analysis and interpretation of study data; RW provided clinical expertise in interpretation of data and drafting manuscript. All authors read and approved the final manuscript. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC509280.xml |
514558 | DLQI scores in vitiligo: reliability and validity of the Persian version | Background The objective of this study was to translate and to test the reliability and validity of the 10-item Dermatology Life Quality Index (DLQI) questionnaire in Iranian patients with vitiligo. Methods Using a standard "forward-backward" translation procedure, the English language version of the questionnaire was translated into Persian (the Iranian official language) by two bilinguals. Seventy patients with vitiligo attending the Department of Dermatology, Saadi Hospital, Shiraz, Iran, were enrolled in this study. The reliability and internal consistency of the questionnaire were assessed by Cronbach's alpha coefficient and Spearman's correlation, respectively. Validity was performed using convergent validity. Results In all, seventy people entered into the study. The mean age of respondents was 28.3 (SD = 11.09) years. Scores on the DLQI ranged from 0 to 24 (mean ± SD, 7.05 ± 5.13). Reliability analysis showed satisfactory result (Cronbach's α coefficient = 0.77). There were no statistically significant differences between daily activity (DA) and personal relationship (PR) scale mean scores in generalized versus focal-segmental involvement in sufferers (P = 0.056, P = 0.053, respectively). There were also strong differences between the mean scores of the PR (personal relationship) scale with the involvement of covered only and covered/uncovered areas (P= 0.016) that was statistically significant in the second group. Conclusions The study findings showed that the Persian version of the DLQI questionnaire has a good structural characteristic and is a reliable and valid instrument that can be used for measuring the effects of vitiligo on quality of life. | Background Vitiligo is an important skin disease having major impact on the quality of life of patients suffering from vitiligo. Appearance of skin can condition an individual self-image, and any pathological alteration can have psychological consequences [ 1 , 2 ]. Many vitiligo patients feel distressed and stigmatized by their condition. These patients often develop negative feeling about it, which are reinforced by their experiences over a number of years. Most patients of vitiligo report feelings of embarrassment, which can lead to a low self-esteem and social isolation [ 3 ]. The Dermatology Life Quality Index questionnaire is designed for use in adults, i.e. patients over the 16. It is self-explanatory and can be simply handed to the patient who is asked to fill it in without the need for detailed explanation. It is usually completed in one to two minutes. The questions were classified to 6 headings items: symptoms and feelings (questions 1 and 2), daily activities (questions 3 and 4), leisure (questions 5 and 6), and personal relationships (questions 8 and 9) each item with maximum score 6; work and school (question 7), and treatment (question10) each item with maximum score 3 [ 4 ]. The DLQI is calculated by summing the score of each question resulting in a maximum of 30 and a minimum of 0. The higher the score, the more quality of life is impaired. The DLQI can also be expressed as a percentage of the maximum possible score of 30. The scores for each of these sections can also be expressed as a percentage of either 6 or 3. Since the DLQI is a brief, simple, easy to complete, and its application in research settings as a screening tool is well documented, it was decided to translate the DLQI into Persian (the Iranian official language) and to examine reliability and validity of this questionnaire in an Iranian population with vitiligo. Methods The standard "forward-backward" procedure was applied to translate the questionnaire from English into Persian. Two independent bilinguals translated the items and two others translated the response categories and after cultural adaptation, a provisional version was provided. Subsequently, it was back translated into English and then the final version was provided. The cultural adaptation was done by the translation of the "partner" to the "spouse" in Persian language and adding of it in question 8 and 9, respectively. The final draft of the Persian version was administered to a sample of patients with vitiligo that referred to Department of Dermatology, Saadi Hospital, Shiraz, Iran. There were no restrictions on patient selection with regard to extension of lesions. The patients were introduced to the subject of this study and informed about the personal nature of the questionnaire, and all those who gave consent were given the DLQI questionnaire to complete. The questionnaires were completed by the patients whom were referred to our clinic for psoralen and UVA (PUVA) therapy. The patients were categorized by extension of lesions into covered only (vitiligo patches are covered by cloths) and covered/uncovered involvement and by severity of disease to focal-segmental (focal is defined as a single or a few depigmented macules that are located in a discrete area, segmental is the unilateral localization of one or more macules to one area of the body) [ 5 ], which in this study were settled in one category, and generalized involvement groups (widespread distribution of numerous macules over the integument in a random pattern) [ 6 ]. Age of patients, marital status and the number of treatment sessions were recorded. Responses on the DLQI were scored according to the guidelines of Finlay and Khan [ 4 ]. All statistical analyses were carried out using the Statistical Package for the Social Sciences (SPSS 11.0 for Windows). To test the reliability, the internal consistency of the questionnaire was assessed by Cronbach's alpha coefficient and alpha equal to or greater than 0.70 was considered satisfactory [ 7 ]. Validity was performed using convergent validity to demonstrate the extent to which the DLQI correlated with global quality of life. Construct validity was checked by factor analysis. Results Seventy patients aged 18 to 68 (mean ± SD, 28.3 ± 11.09) years completed the questionnaire. We did not have any incomplete questionnaires; therefore we included all questionnaires in our study. Scores on the DLQI ranged from 0 to 24(mean ± SD, 7.05 ± 5.13). The reliability of the questionnaire was obtained by Cronbach's alpha coefficient (α = 0.77). There were no statistically significant differences between the sex, item scores and mean DLQI score. Table 1 shows the results of item convergent validity tests. The scaling success rates were 100% for convergent validity of each scale. Table 1 Item scaling tests: convergent validity for DLQI scales Scale No. of items per scale Convergent validity (range of correlation) Scaling Success 1 Scaling Success Rate 2 Internal consistency (Cronbach's α) SF 2 0.69–0.78 2/2 100 0.70 DA 2 0.71–0.78 2/2 100 0.76 L 2 0.67–0.88 2/2 100 0.71 WS 1 1.00 1/1 100 1.00 PR 2 0.50–0.99 2/2 100 0.79 T 1 1.00 1/1 100 1.00 SF ( Symptoms and feelings ), DA ( Daily activities ), L ( Leisure ), WS ( Work and School ), PR ( Personal relationships) , T ( Treatment ) 1- Number of correlation between items and hypothesized scale corrected for overlap ≥ 0.4/ total number of convergent validity tests. 2- Scaling success rate is the previous column as a percentage. Cronbach's α coefficient by gender, marital status, severity, and extension of disease are shown in Table 2 . Table 2 Cronbach's coefficient by gender, marital status, severity, and extension of disease Variable Cronbach's coefficient (n 1 ) Gender Male 0.60 (27) Female 0.80 (43) Marital status Single 0.79 (42) Married 0.75 (28) Severity Focal/Segmental 0.58 (18) Generalized 0.79 (52) Extension Covered/Uncovered 0.78 (54) Covered 0.67 (16) 1 The number of patients in each category. There were no statistically significant differences between item and mean DLQI scores of males versus females and married versus single cases. Cronbach's α reliability coefficients ranged from 0.69 to 0.78 for symptoms and feelings (SF) scale, 0.71 to 0.78 for daily activities (DA) scale, 0.67 to 0.88 for leisure (L) scale, and 0.50 to 0.99 for personal relationships (PR) scale. The reliability coefficient for work and school (WS) scale was equal to treatment (T) scale that was 0.100. Table 2 shows comparison of Cronbach's α in each scale separately. The DA scale was found to have a strong association with gender (female scores were greater than male ones). The L scale was found to have significant relationship with the severity of the disease (generalized versus focal/segmental) (P value = 0.018). The DA and PR scales, also had no statistical association with severity factor (P = 0.056 and P = 0.053, respectively). The PR scale had strong correlation with the type of the extension of lesions (covered only versus covered/uncovered type) (P value = 0.016). There was no association between the numbers of treatment sessions with the type of disease (generalized versus focal/segmental). The number of treatment sessions and mean DLQI score was found to have a positive correlation coefficient (P value = 0.02, r = 0.28) but this correlation was statistically significant in the generalized type only (P value = 0.008, r = 0.37). Spearman's correlation coefficient of severity of the disease with questions 5, 6, and 8 were 0.25, 0.24, and 0.26 respectively. For question 8 and the extension of disease and also for question 1 and the stage of disease it was equal to 0.26. The result in question 4 and gender status was statistically significant (0.008) there was no statistically significance correlation between the age and each of questions. The result of question 9 and marital status was statistically significant (P = 0.002) and it was higher in married patients. The range of the paired correlations between the items was 0.17–0.68. Factor analysis is performed to determine the Persian version is a two-dimensional measure including social and psychological parameters (Table 3 ). Table 3 Factor loadings (rotated) 1 of two-factor solution DLQI items Social factor Psychological factor Q 1 .086 .485 Q 2 .545 .305 Q 3 .535 .374 Q 4 .470 .285 Q 5 .564 .473 Q 6 .190 .468 Q 7 .088 .619 Q 8 .813 .186 Q 9 .681 -.195 Q 10 .099 .500 1 Varimax Discussion The DLQI questionnaire is a well-known instrument for measuring dermatological distress and has been translated into a variety of languages [ 8 , 9 ] and [ 10 ]. The translation process set by the international quality of life assessment (IQOLA) project was built on lessons from cross-cultural psychology and other health survey projects to develop protocols for translating, validating, and norming health status questionnaires, entails forward translation by at least two translators who were native speakers of the target language, rating of translation equality by two other bilinguals, and back translation by two translators who were native speakers of American-English or British-English [ 11 ]. Because native English speakers were unavailable, we did not fully adhere to this strategy. Two independent Iranian health professionals translated the items and subsequently, it was back translated into English by two others and then the final version was provided. Vitiligo is an acquired depigmentation disorder of great cosmetic importance affecting 1–4% of the world's population. The disease has a major impact on quality of life of patients, many of whom feel stigmatized by their condition [ 12 ]. Porter et al. studied the effect of vitiligo on sexual relationships and found that embarrassment during sexual relationships was especially frequent for men with vitiligo [ 13 ]. Salzer and Schallreuter reported that 75% found their disfigurement moderately or severely intolerable [ 14 ]. Weiss et al compared the difficulties faced by patients with vitiligo with those with leprosy in India [ 15 ]. There may be a relationship between stress and the development of vitiligo. Al-Abadie et al. indicated that psychological stress increases levels of neuroendocrine hormones, affects the immune system and alters the level of neuropeptides, which may be the initial steps in pathogenesis of vitiligo [ 16 ]. In general, the finding of this study indicated that mental health in vitiligo patients is poor and it is strongly associated with their quality of life. Since the patients with higher DLQI scores responded less favorably to a given therapeutic modality [ 12 ], improving quality of life in this group becomes very important task.Severity (generalized versus focal/segmental) and extension of lesions on covered only or covered/uncovered areas has an effect on quality of life of patients. This study reports data from a validation study of the 10-item DLQI questionnaire in Iran. In general the findings showed promising results and were comparable with other research finding throughout the world [ 12 ]. The two-dimensional Persian version of DLQI questionnaire assessed the social and psychological difficulties as other studies [ 12 ]. There was no relationship of DLQI score with gender, which is consistent with the study of Parsad et al. [ 12 ]. The mean DLQI score in this study was 7.05 that is lower than that obtained by Finlay and Khan (mean 7.3) [ 4 ] and Parsad et al. (mean 10.67) [ 12 ], and it is higher than Kent and Al-Abadie's study (mean 4.82) [ 17 ]. Reliability was associated by internal consistency of the questionnaire reporting Cronbach's alpha coefficient and validity was examined by convergent validity showed satisfactory results (α = 0.77). Cronbach's α was < 0.7 for males, focal/segmental, and covered vitiligo that may be related to small sample size and cultural differences. The Persian version of the DLQI questionnaire proved to be acceptable to patients and it is worth nothing that occasionally the questionnaire was administered by a trained nurse in face-to-face interviews. However this was done in illiterate patients and some ones indicated that some questions were difficult to answer, especially question 8. Perhaps this was the reason why a weaker correlation was found for this item with its corresponding subscale. Conclusions The study finding showed that the Persian version of the DLQI questionnaire has a good structured characteristic and is a reliable and valid instrument that can be used for measuring the effects of the vitiligo on quality of life. Especially, the reliability of this questionnaire was high in females and patients with generalized involvement, because of the great cosmetic importance in these groups. Competing interests None declared. Abbreviations DLQI: Dermatology Life Quality Index SD: standard deviation IQOLA: international quality of life assessment Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC514558.xml |
517723 | ISSR and AFLP analysis of the temporal and spatial population structure of the post-fire annual, Nicotiana attenuata, in SW Utah | Background The native annual tobacco, Nicotiana attenuata , is found primarily in large ephemeral populations (typically for less than 3 growing seasons) after fires in sagebrush and pinyon-juniper ecosystems and in small persistent populations (for many growing seasons) in isolated washes typically along roadsides throughout the Great Basin Desert of the SW USA. This distribution pattern is due to its unusual germination behavior. Ephemeral populations are produced by the germination of dormant seeds from long-lived seed banks which are stimulated to germinate by a combination of unidentified positive cues found in wood smoke and the removal of inhibitors leached from the unburned litter of the dominant vegetation. Persistent populations may result where these inhibitors do not exist, as in washes or along disturbed roadsides. To determine if this germination behavior has influenced population structure, we conducted an AFLP (244 individuals), ISSR (175 individuals) and ISSR+ AFLP (175 individuals) analysis on plants originating from seed collected from populations growing in 11 wash and burns over 11 years from the SW USA. Results Genetic variance as measured by both ISSR and AFLP markers was low among sites and comparatively higher within populations. Cluster analysis of the Utah samples with samples collected from Arizona, California, and Oregon as out-groups also did not reveal patterns. AMOVA analysis of the combined AFLP and ISSR data sets yielded significantly low genetic differentiation among sites (Φct), moderate among populations within sites (Φsc) and higher genetic differentiation within populations (Φst). Conclusions We conclude that the seed dormancy of this post-fire annual and its resulting age structure in conjunction with natural selection processes are responsible for significantly low among sites and comparatively high within-population genetic variation observed in this species. | Background Nicotiana attenuata Torr. ex Watson (Solanaceae) (synonymous with N. torreyana Nelson and Macbr.) is an annual native to the Great Basin Desert of California, Nevada, Idaho, and Utah (USA) [ 1 - 3 ] and primarily occurs in large ephemeral populations (typically for less than 3 growing seasons) after fire in sagebrush and pinyon-juniper ecosystems, in small persistent (for >3 growing seasons) populations in isolated washes, and as a roadside weed after new construction in a previously undisturbed area [ 2 , 4 - 9 ]. Positive and negative control by environmental signals over germination from long-lived seed banks (estimated to be minimally 150 y [ 10 ] can account for its occurrence in these habitats. Specifically, dormant N. attenuata seeds are stimulated to germinate by unidentified factors in wood smoke [ 9 ] but are inhibited by factors, including ABA and 4 terpenes (bornane-2,5-dione, 1,8-cineole, β-thujaplicin and camphor [ 11 ] which leach from the litter of the dominant vegetation. Genotypes of N. attenuata produce seeds that vary in their genetically-determined primary dormancy [ 9 ]. Regardless of their degree of primary dormancy, seeds that are shed in unburned habitats with significant accumulations of litter develop strong secondary dormancy in response to the negative germination cues. If the seeds are shed into habitats without significant litter accumulations (e.g. in washes or roadside habitats), seeds without dormancy germinate. When fires pyrolyze the litter layer, removing the germination inhibitors and saturating the soils with smoke-derived germination stimulants, the seed bank responds with a dramatic, synchronized germination response the following growing season during favorable moisture and thermal regimes. This well-characterized germination behavior likely affects the genetic structure of this potential annual. Genetic structure of a population results from mutations, gene flow (as mediated by pollen and seed dispersal), drift, and selection, all acting in the context of an organism's life history traits [ 12 ]. Genetic differentiation may be more prevalent between primarily dormant and non-dormant populations, namely between plants found ephemerally (in burns) and those occurring more persistently (in washes). Within the ephemeral populations, the number of plants in the population will vary in relation to the size of the burn and the distribution of the seed bank. Because pollinators must locate these ephemeral populations in a landscape that may be largely composed of other plant associations, out-crossing may not be prevalent. Flowers of N. attenuata are self-compatible and outcrossing does not significantly affect seed production, seed mass or viability [ 13 ] indicating that this species relies on selfing as its primary form of reproduction. Selfing may keep genetic variation low, especially within populations. Persistent populations are more likely to experience outcrossing, owing to their predictability. These considerations in combination with the annual life cycle of plants in washes in contrast to the 7 – 150 year life cycle of plants growing in burns may increase the genetic differences among populations found in burns and washes. Here we examine the genetic structure of N. attenuata plants from wash and burn populations in the SW Utah (Fig. 1 ; Table 1 ) to determine if the particular germination behavior of this species has left signatures in the plant's population structure. We use an AFLP (amplified fragment length polymorphism) analysis, based on the selective polymerase chain reaction (PCR) amplification of restriction fragments from a total digest of genomic DNA [ 14 ] and an ISSR (inter-simple sequence repeats) analysis in which bands are generated by single primer PCR that amplifies products between two simple sequence repeats [ 15 ]. Both procedures produce reproducible markers useful for the quantification of genetic polymorphism within species [ 16 ]. Figure 1 Location of Nicotiana attenuata populations from which seeds were collected between 1988–1999 in Southwestern Utah . See Table 1 for number of plants grown for DNA extraction from each location for the AFLP and ISSR analysis. Locations labeled with circles represent single-site or -time collections, while squares signify multiple-site or -time collections Table 1 Number of individual-plant DNA samples harvested for AFLP and ISSR analysis of plants grown from seed collected from: A) six sites within Utah collected over multiple years; B) three burns and 4 roadside washes within Utah collected in 1999; C) 3 non-Utah collections.(Codes identify samples in Fig. 3 and Table 5 [see Additional file 1] Location Seed collection Year Codes SET I Used for only AFLP SET II Used for ISSR & AFLP Burn(B) or Wash (W) A. Time series from the following Utah sites:1 1. Motoqua roadside wash and burn (fire in 1994) Wash 1990 M1 4 W Lower Burn 1995 M2 3 B Middle burn 1995 M3 3 B Middle burn 1996 M4 8 4 B Lower Burn 1996 M5 3 3 B Wash 1999 M6 4 8 W 2. DI Ranch burns (yearly fires at garbage dump) 1988 D1 4 B 1990 D2 1 B 1992 D3 6 B 1993 D4 2 B 1995 D5 4 4 B 3. Goldstrike roadside washes #2 1990 2 W #5 1990 1 W #2 1993 G1 5 5 W #4 1993 G2 2 2 W #5 1993 G3 1 1 W 4. Shivwits Reservation roadside wash # 1 1988 1 W 1990 2 W 1992 1 W 1993 4 W 1995 1 W 1999 B7 8 8 W 5. Shivwits Reservation roadside wash # 2 1992 B1 1 W 1993 B2 3 W 1995 B3 4 W 1996 B4 5 W 1999 B5 9 8 W 1999 B6 1 W 6. Pahcoon Spring roadside wash 1990 1 W 1999 P9 9 8 W B) Burn and roadside wash populations collected in Utah 1999 1. Pahcoon Spring Burn (fire in 1998) Burn transect 1 1999 P1 9 6 B Burn transect 2 1999 P2 7 5 B Burn transect 3 1999 P3 9 8 B Burn transect 4 1999 P4 10 8 B Burn area 1 1999 P5 9 8 B Burn area 2 1999 P6 9 8 B Burn area 3 1999 P7 9 7 B Burn area 4 1999 P8 8 8 B 2. Rt 91 Burn (fire in 1998) Burn area 1 1999 R1 8 6 B Burn area 2 1999 R2 8 8 B 3. Single collection roadside washes and burns Shivwits Reservation 4 1999 B8 9 8 W Rt-91 1999 R3 9 7 W Lytle Ranch Preserve 1999 L1 10 10 W Jackson Spring 1999 J1 10 10 W Cedar Pockets 1999 C1 10 10 B C) Non-Utah collections Arizona 1996 3 3 Oregon 1994 2 2 California 1999 2 2 Total plants 244 175 Specifically, we compare plants growing from seeds collected from 11 large populations after fires, from small populations in 10 washes, from plants in transects across 5 large burns, and from plants growing in specific areas over 10 years during which a small wash population erupted into a large burn population as a result of a fire and returned to become a small wash population. By analyzing the genetic diversity across these N. attenuata populations, we aimed to answer the following questions: 1) Are plants growing in burn and wash populations genetically distinct? 2) Are plants growing in the same washes genetically similar through different years? 3) What is the genetic makeup of plants found growing across large burns and geographically adjacent populations? While genetic diversity among the various Nicotiana species has been studied with RAPD [ 17 ] and AFLP [ 18 ] markers, and with peroxidase isozymes [ 19 ], this is the first effort to study the spatial and temporal population structure of a native Nicotiana species. Results SET-I Set I (Table 1 ) consisting of 244 individuals, which was used only for AFLP analysis, produced a total of 207 loci (data not shown). This data was used for separate dendrogram and p rinciple c o- o rdinate (PCO) analyses. The Jaccard similarity index [ 20 ] based on u nweighted p air g roup m ethod a verage (UPGMA) dendrogram revealed a lack of distinct spatial or temporal structure and had brush- or star-structures with nodal bootstrap values of less than 60% (data not shown). The samples collected from the greatest spatial distances, namely California, Oregon and Arizona, did not form separate clusters from any of the Utah populations. A cluster analysis of 10 wash and 5 burn populations all grown from seed collected in 1999 (Table 1 ) revealed no clustering based either on type of population (wash or burn) or geographic location (data not shown).. Some structure was identified when each time series at particular locations (Motoqua, DI Ranch, Shivwits Reservation) were analyzed separately (Fig. 2A,2B,2C ), but the nodes did not correspond to a particular growing season. No genetic differentiation was observed from a cluster analysis of plants collected from Motoqua before (1990) during (1995–1996) and after (1999) a fire-associated population explosion (Fig. 2B ). A similar lack of structure was found in the time series analysis from the DI Ranch (Fig. 2A ) and Shivwits roadside washes (Fig. 2C ). Similarly, the PCO did not yield any apparent population structure or site- or population- specific grouping associations. Figure 2 UPGMA dendrograms based on the Jaccard similarity index calculated from an AFLP analysis of N. attenuata populations collected at three different sites. A: DI Ranch (17 individuals); B: Motoqua Burn and Wash (25 individuals); C: Shivwits Reservation (23 individuals) collected over a number of different years. Sample codes are given in Table 1. While substantial genetic variation was found, this variation was not organized in time. SET-II Set II (Table 1 ) is a subset of Set I (Table 1 ) and consists of 175 individuals analyzed by AFLP and ISSR (either combined or separate) and this dataset was used for dendrogram and PCO analyses. Combined (AFLP + ISSR) analysis revealed a total of 286 loci of which 268 were polymorphic (93.70%). Here, the AFLP analysis showed higher percent polymorphic loci than did the ISSR analysis (96.1% and 87.5%, respectively; Table 2 ). Interestingly in the AFLP analysis, the primer and the restriction enzyme combinations that produced the lowest number of loci also delivered the highest rate of polymorphism (Table 2 ). It produced an average of 68.7 loci per primer combination with a high percentage of unique bands (65 in the 0–10% frequency class; Fig. 3 ) and a high frequency of commonly shared loci (42 in the 91–100% frequency class; Fig. 3 ). The ISSR analysis, on the other hand, produced 16 loci per primer with a predominance of commonly shared loci (29 in the 91–100% frequency class as compared to 14 in the 0–10% frequency class; Fig. 3 ). Dendrograms and PCO produced from this data set had the same overall characteristics as those produced from showed same structure nature Set I. Table 2 AFLP and ISSR primers, total number of loci, polymorphic loci and percentage polymorphism. AFLP and ISSR Primer sequences (restriction enzymes) Total loci Nr. Polymorphic loci % Polymorphism AFLP Eco-AGC\Mse-CAG 66 61 88.4 Eco-AAC\Mse-CCG 59 59 100.0 Eco-ACC\Mse-CCT 81 78 96.3 Total 206 198 96.1 ISSR (AG) 8 T 16 13 81.3 (GA) 8 T 13 11 84.6 (CT) 8 A 16 12 75.0 (CT) 8 G 26 25 96.2 (CA) 8 G 9 9 100.0 Total 80 70 87.5 Total of ISSR and AFLP 286 268 93.7 Figure 3 Locus frequency class distribution of 206 AFLP-(open) and 80 ISSR-(solid) loci from 175 ecotypes of Nicotiana attenuata . Heterozygosity A Bayesian approach [ 21 ] was used for heterozygosity calculations. The total heterozygosity as measured from the combined AFLP and ISSR data set of Utah collections (SET-II, Table 1 : the 168 individuals from Utah without, Arizona California and Oregon) was 0.2771 ± 0.0018. The plants from Arizona California and Oregon were not included for heterozygosity and AMOVA analyses due to insufficient sampling of these populations. Different primer combinations produced different values; in particular, in the ISSR analysis, the (CA) 8 A primer produced comparatively high heterozygosity values. In contrast, the AFLP primers produced values that are more similar. Different regions had different measures of total heterozygosity with plants growing at the Lytle Ranch Preserve being the lowest (0.1881 ± 0.0052) and plants from Pahcoon, the highest (0.2043 ± 0.0027) (Table 3 ). Table 3 Heterozygosity estimated using Bayesian approach within N. attenuata populations from different AFLP / ISSR primer combinations, total AFLP/ISSR heterozygosity and θ B estimates. Pahcoon Rt 91 Motoqua Lytle Ranch Preserve Cedar Pockets Jackson Spring Shivwits Reserve DI Ranch Goldstrike Washes Total Fst (θ B ) AFLP E-AGC/M-CAG 0.1906 ± 0.0053 0.1975 ± 0.0079 0.2010 ± 0.0084 0.2135 ± 0.0091 0.2096 ± 0.0094 0.2233 ± 0.0090 0.1978 ± 0.0075 0.2055 ± 0.0103 0.2026 ± 0.0095 0.2152 ± 0.0044 0.0545 ± 0.0097 E-AAC/M-CCG 0.2224 ± 0.0059 0.2371 ± 0.0087 0.2334 ± 0.0094 0.2206 ± 0.0103 0.2363 ± 0.0103 0.2415 ± 0.0102 0.2176 ± 0.0087 0.2405 ± 0.0116 0.2269 ± 0.0109 0.2438 ± 0.0048 0.0598 ± 0.0095 E-ACC/M-CCT 0.2335 ± 0.0049 0.2370 ± 0.0071 0.2490 ± 0.0074 0.2531 ± 0.0083 0.2558 ± 0.0082 0.2453 ± 0.0082 0.2481 ± 0.0064 0.2530 ± 0.0092 0.2537 ± 0.0085 0.2606 ± 0.0041 0.0556 ± 0.0077 Total 0.2185 ± 0.0030 0.2260 ± 0.0044 0.2311 ± 0.0047 0.2332 ± 0.0052 0.2376 ± 0.0053 0.2387 ± 0.0053 0.2254 ± 0.0043 0.2357 ± 0.0058 0.2319 ± 0.0054 0.2432 ± 0.0024 0.0549 ± 0.0050 ISSR (AG) 8 GT 0.1969 ± 0.0139 0.1552 ± 0.0276 0.1644 ± 0.0277 0.1391 ± 0.0265 0.2211 ± 0.0237 0.2301 ± 0.0309 0.2384 ± 0.0156 0.1841 ± 0.0354 0.1252 ± 0.0301 0.2546 ± 0.0098 0.3250 ± 0.0591 (GA) 8 AT 0.1890 ± 0.0230 0.2007 ± 0.0285 0.1650 ± 0.0368 0.1554 ± 0.0372 0.1822 ± 0.0323 0.1597 ± 0.0389 0.2556 ± 0.0226 0.1669 ± 0.0421 0.1688 ± 0.0332 0.2517 ± 0.0179 0.3064 ± 0.0553 (CT) 8 A 0.3017 ± 0.0302 0.2816 ± 0.0345 0.3414 ± 0.0254 0.3659 ± 0.0258 0.3246 ± 0.0311 0.2701 ± 0.0346 0.2786 ± 0.0196 0.2850 ± 0.0393 0.2348 ± 0.0438 0.3600 ± 0.0167 0.1931 ± 0.0380 (CT) 8 G 0.2086 ± 0.0099 0.2172 ± 0.0139 0.2219 ± 0.0159 0.2135 ± 0.0160 0.2122 ± 0.0167 0.2220 ± 0.0157 0.2121 ± 0.0138 0.2235 ± 0.0181 0.2266 ± 0.0160 0.2284 ± 0.0099 0.0508 ± 0.0128 (CA) 8 G 0.2086 ± 0.0099 0.2172 ± 0.0139 0.2219 ± 0.0159 0.2135 ± 0.0160 0.2122 ± 0.0167 0.2220 ± 0.0157 0.2121 ± 0.0138 0.2235 ± 0.0181 0.2266 ± 0.0160 0.2284 ± 0.0099 0.1059 ± 0.0341 Total 0.2309 ± 0.0088 0.2190 ± 0.0100 0.2185 ± 0.0104 0.2145 ± 0.0109 0.2166 ± 0.0111 0.2188 ± 0.0118 0.2145 ± 0.0088 0.2190 ± 0.0131 0.2024 ± 0.0124 0.2452 ± 0.0056 0.1180 ± 0.0132 AFLP + ISSR Total 0.2043 ± 0.0027 0.1885 ± 0.0040 0.1916 ± 0.0044 0.1881 ± 0.0052 0.2001 ± 0.0053 0.2007 ± 0.0052 0.1919 ± 0.0036 0.1884 ± 0.0069 0.1844 ± 0.0056 0.2771 ± 0.0018 0.3305 ± 0.0088 AMOVA AMOVA analysis was performed separately for AFLP, ISSR and combined analysis of plants collected from Utah (168 individuals from SET-II, Tables 1 , 4 ). The combined data set was also used to partition variation between wash and burn populations and to examine the effects of the collection year. In separate analyses, ISSR revealed higher variance than did the AFLP in the among-sites, among-population, and within-site categories; whereas, variation in the within-population category from the AFLP analysis was higher than that from the ISSR analysis (Table 4 ). All values except the among-site category in the AFLP analysis ( p < 0.05) revealed highly significant differences at p < 0.001. AFLP and ISSR data was combined for an AMOVA analysis of all analyzed Utah populations. From this analysis, all three Φ categories were highly significant ( p < 0.001; Table 4 ) among sites (Φct), among populations within sites (Φsc) and within populations (Φst) values were 0.046, 0.116, and 0.156, respectively. Table 4 reveals low genetic differentiation among sites and a relatively high genetic differentiation within populations. Pair-wise genetic distances (pair-wise Φst) were calculated from the AMOVA. Of the 300 comparisons from the 25 populations, 220 showed highly significant differences and 29 were significant at the p = 0.05 level (Table 5) [see Additional file 1 ]. Very low among-site variation (0.18 %) was obtained when samples were compared as being derived from either burn or wash populations (Table 4 ). To determine the effect of collection year, all individuals were grouped according to their collection year; an AMOVA analysis revealed low (3.77 %) variance within years at p < 0.5 significance level (Table 4 ). Table 4 Summary of AMOVA analysis for 168 samples of Nicotiana attenuata individuals representing 25 populations from Utah region. Level of significance is based on 1000 iteration. Level of variation df Absolute Percent Φ values p Utah (AFLP+ISSR) Among sites 8 1.38 4.59 Φct = 0.046 <0.001 Among populations within sites 16 3.33 11.05 Φsc = 0.116 <0.001 Within populations 143 25.44 84.36 Φst = 0.156 <0.001 AFLP Among sites 8 0.59 2.72 Φct = 0.027 <0.05 Among populations within sites 16 1.89 8.72 Φsc = 0.090 <0.001 Within populations 143 19.17 88.55 Φst = 0.114 <0.001 ISSR Among sites 8 0.79 9.38 Φct = 0.093 <0.001 Among populations within sites 16 1.44 16.98 Φsc = 0.187 <0.001 Within populations 143 6.27 73.68 Φst = 0.263 <0.001 Burn and wash Among sites 1 0.05 0.18 Φct = 0.002 NS Among populations within sites 23 4.45 14.85 Φsc = 0.149 <0.001 Within populations 143 25.44 84.97 Φst = 0.150 <0.001 Time Among years 3 1.16 3.77 Φct = 0.038 <0.05 Among populations within years 21 4.70 13.56 Φsc = 0.141 <0.001 Within populations 143 25.44 82.66 Φst = 0.173 <0.001 Goldstrike among populations 2 0.06 0.23 Φst = 0.002 NS within populations 5 25.86 99.77 Motoqua among populations 2 4.81 16.48 Φst = 0.165 <0.001 within populations 12 24.39 83.52 Pahcoon among populations 8 3.19 12.06 Φst = 0.121 <0.001 within populations 57 23.25 87.94 Rt91 among populations 2 2.07 7.57 Φst = 0.076 <0.01 within populations 18 25.27 92.43 Shivwits among populations 2 5.42 18.80 Φst = 0.188 <0.001 within populations 21 23.39 81.20 NS = Not significant The AMOVA analysis had sufficient statistical power to detect small differences among populations, which accounted for 0.23 to 16.48 % of the variation, but this was dwarfed by the much larger genetic variation within populations, which ranged from 81.20 to 99.77 % (Table 4 ). This dramatic high degree of within-population variance was found in all populations. Again, populations from Goldstrike Canyon had the lowest among-population variance (0.23%) and highest within population variance (99.77%; Table 4 ). The Goldstrike populations were located in a narrow canyon produced by a stream and in this region seeds are likely transported among the populations during spring floods. The Φ-statistic is an analogue of the F-statistic [ 22 ]. This analysis revealed that the Φct (i.e. among site variation) values were 0.046, 0.027 and 0.093 for the AFLP + ISSR, AFLP and ISSR analyses, respectively (Table 4 ). When these sites were grouped by burn and wash populations, very little genetic differentiation was observed (Φct 0.002). Interestingly, Φst (within population variation) was always comparatively high in all analyses (Table 4 ). Substantially higher Fst values were estimated by the program Hickory (Ver 1.0) [ 23 ] as θ B for the combined AFLP + ISSR analysis (0.3305 ± 0.0088) (Table 3 ), but surprisingly, the individual data set θ B values were lower (AFLP: 0.0549 ± 0.0050; ISSR :0.1180 ± 0.0132; Table 3 ). Mantel test were conducted to analyze isolation by distance using pair-wise Φst values obtained by AMOVA (ver 1.55). The Φst values from the AFLP, ISSR and the combined data sets were separately correlated with geographical distance and all revealed non significant correlations (AFLP, r = 0.099, p = 0.81; ISSR, r = 0.122, p = 0.89 and AFLP + ISSR, r = 0.019, p = 0.63) Discussion The analysis revealed high levels of heterozygosity, with total heterozygosity from all populations (0.2771 ± 0.0018) being higher than that from comparable analyses of self-pollinating annual plants (0.131) [ 24 ]. The ISSR analysis (0.2452 ± 0.0056) yielded estimates of heterozygosity that were comparable with the AFLP analysis (0.2432 ± 0.0024) (Table 3 ) despite the basic difference in the logic of the two procedures. ISSRs are designed to span a repeat region of the genome whereas AFLP is designed to randomly sample the full genome [ 16 ] and most plant genomes are thought to evolve faster in the repeat regions [ 25 ]. However, despite the differences in absolute estimates of genetic variation, both procedures produced the same conclusion. The principle conclusion of this study is that large amount of genetic variation measured by AMOVA, within populations at a particular area significantly dwarfed that observed among sites, among populations growing in burns or washes, or collected during subsequent years growing at a given site. The conclusion that the genetic variation between neighbors is greater than that found between temporally- or spatially-separated populations, is dramatically reflected in the plants sampled along transects through the Pahcoon Springs burn. Only a small fraction (12.6%) of the total genetic variance is found among the 8 sub-samples from the extreme corners of this burn that covered more than 5000 hectares [ 26 ], while the majority (87.94 %) was found among plants growing within 10 m 2 of each other. Pahcoon, was the site from which highest number of populations were analyzed. (9 populations), whereas, from other sites smaller numbers of populations were analyzed. F-statistic was estimated by using Bayesian approach with an analogue of the F-statistic, the Φ-statistic. Low but significant genetic differentiation was estimated among sites (Φct) from AFLP (0.027), ISSR (0.093) and AFLP + ISSR (0.046) data sets, whereas within populations, the Φst values were very high (Table 3 ). In Platanthera leucophaea, Holsinger et. al [ 21 ] showed that θ B is substantially larger than the estimates from the AMOVA analyses (θ B = 0.392 Vs Φst = 0,252). In our study, AFLP + ISSR analysis also showed such difference (θ B = 0.3305 Vs Φst = 0.156) whereas, exactly opposite values were found separate analysis of AFLP and ISSR data sets (AFLP. θ B 0.0594 Vs Φst= 0.114; ISSR θ B = 0.1180 Vs Φst = 0.263). A number of factors, including N. attenuata 's unusual seed germination mechanisms and the irregular nature of fires, natural selection, gene flow mediated by pollination or the relocation of seeds via mammal-vectored transport could account for the lack of population structure and each deserve further discussion. Dormancy is a major adaptive response of native plants that allows them to cope with environmental variation and provide a means of habitat selection [ 27 - 29 ]. Moreover, dormancy is likely to influence the genetic structure of populations[ 30 , 31 ]. Seed banks serve as repositories of genetic diversity for most species. Many seeds use cues as general as temperature, photoperiod, moisture, or their own age to trigger germination and initiate vegetative growth [ 32 , 33 ]. To cope with the lack of reliability of these proximate signals and the unpredictability of the post-germination environment, some species may have evolved "bet-hedging" strategies, whereby only a certain fraction of the dormant seed bank germinates under favorable conditions. This has been experimentally shown by various researchers. In Plantago lanceolata [ 30 ], Calluna vulgaris [ 34 ], Clarkia springvillensis [ 35 ] and Lesquerella fendleri [ 36 ] it has been demonstrated that the seed banks have less genetic differentiation than do the adults of a given population. This strategy provides a statistical solution to the problem of cueing germination with unreliable signals [ 37 , 38 ]. Other species, however, use specific signals to time their germination with particular niches. Those species that specialize in the immediate post-fire habitat are a particular case in point [ 39 ]. Studies on another fire-dependant plant, Grevillea macleayana [ 40 ], which also has a long-lived seed bank, showed Fst (0.218) that were comparable to those measured in this study for N. attenuata , but had variable heterozygosity (H obs = 0.248 - 0.523). Another major difference from the current study was that G. macleayana showed significant isolation by distance. Seed dormancy increases the effective generation time of this annual plant and by doing so, prevents genetic decay and inhibits the formation of spatial structure between geographically distinct populations [ 12 ]. Additionally, a long-lived seed bank results in the overlap of generations [ 41 ], which has similar effects and additionally reduces the ability of genetic drift to drive unique alleles to fixation. Operating under the assumption that the synchronized germination response observed after fires represents a synchronized germination of cohorts from the seed bank, we examined populations that occurred over a 6–11 year interval at the same location (Fig. 2A,2B,2C ) to determine if temporally-defined genetic structure occurred in the populations, but none were found. This suggests that seed banks have a more complicated germination response whereby only fractions of a cohort may germinate at any particular interval and hence may represent a combination of "bet-hedging" [ 33 ] and the chemically-cued germination of the seed bank. N. attenuata has all the characteristics of species pollinated by moths at night (white fragrant flowers scenting and becoming receptive at night) but day-active humming birds ( Selaphorus sp.) and bumblebees ( Bombus sp.) are also known to visit the flowers [ 42 ]. Despite these traits that are thought to facilitate out-crossing, 16 years of field work with the Utah populations have revealed that the vast majority of seeds produced are the result of self-pollination. No evidence exists for inbreeding depression in plants self-pollinated for more than 20 generations (I. T. Baldwin, unpublished results). However, the plant species likely enjoys sporadic bursts of cross pollination during the rare outbreaks of hawk moths ( Hyles lineata and Manduca species(observed once in 16 years of observation at the study sites[ 43 ]. The amount and distance of gene flow that occurs during these rare events is not known. In the wind pollinated species such as Zea mays maximum distance of pollen dispersal was observed to be 18 m achieving outcrossing rate of not more than 1%; insect pollination does not substantially increase this rate [ 44 ]. Hence in comparison to seed dispersal these events are likely to have a minor effect on the homogeneity of populations [ 12 ]. Seeds of N. attenuata are small (160 μg) and could be dispersed by wind, water transport and animals, but none of these mechanisms are well documented. The seeds are eaten by various ground squirrels [ 45 ] but are not known to survive a transit through the digestive track. The greater heterogeneity within populations and low genetic differentiation among populations found along the stream in the Goldstrike canyon (Table 5) [see Additional file 1 ] suggests water transport may not be important. While seeds tend to be dispersed from the plant upon maturation of the seed capsules, the N. attenuata calyx is sticky and glandular and could be dispersed by adhering to animals. However, the plants ability to produce the defense secondary metabolite nicotine in substantial quantities in its calyx [ 9 ] may be a much more important determinant of its long distance transport. Native Americans are known to have smoked leaves and seed capsules for recreational and medicinal purposes, buried their dead with leather pouches containing N. attenuata seeds, burned the sagebrush to promote its growth and are likely to have transported seeds throughout its range in North America [ 46 ]. Hence, movement of N. attenuata genotypes across the landscape by humans who were smoking this plant may have contributed to the lack of correlation between geographical and pair wise Φst values, as reflected by Mantel test for isolation by distance. In summary, we conclude that the unusual nature of the N. attenuata populations from Utah revealed by AFLP and ISSR analysis is a likely result of combination of random dispersal by humans and its seed dormancy. Conclusions We conclude that the genetic structure of N. attenuata populations in Utah showed: 1) high similarity across collection sites; 2) small difference between populations growing in burns or washes; 3) small differences between growing seasons; and 4) large difference between individuals growing within populations. Methods Seed sources Seeds were from individual- and multiple-plant samples collected from 1988–1999 from the southwestern USA (Table 1 ; Fig. 1 ). A majority of the seed collections (244) originated from a 1500 km 2 region of the SW corner of Utah (T38S R10W-T43S R19WUSA). Collections from Arizona (Flagstaff), Oregon (Eugene) and California (Sequoia Natl. Park) served as out-groups. In Utah, seeds were collected from plants growing at 6 locations for a number of years and were used for a time series analysis (Table 1 ). One of these areas (Motoqua), the region surrounding a small wash population that had been sampled in 1990, was struck by lightening at the end of the growing season in 1994 (August) and 1163 hectares were burned. During the 1995–6 growing seasons, large populations of more than 100,000 plants were found, but by 1999 only a small population remained in the original wash. At this site, seeds were collected during the population explosion as well during the contraction of the population at this site (Table 1 ). A fire during the 1998 growing season at Pahcoon Springs created a large population (covering more than 5,000 hectares) in the 1999 growing season which was sampled in 8 locations: seeds from 10 individual plants growing along each of 4 line-transects with an inter-plant distance of 10 m and 10 plants growing within a 10 m 2 area at 4 locations were sampled to provide a small-scale spatial analysis of genetic variation for this population. Plant material Seeds (10 seeds per plant collected) were exposed for 1 h to 100 μL liquid smoke (House of Herbs INC., Passaic, NJ, USA): water (1:300, v/v) in 1-mL shell vials and 5 seedlings were planted in soil and grown to the rosette-stage in a glasshouse. Leaves from one plant randomly selected plant from each collection were harvested for DNA extraction. DNA extraction Leaves were flash-frozen in liquid nitrogen, ground to powder and suspended in 750 μL of 100 mM Tris/50 mM EDTA (pH 8.0), containing 250 μg/mL RNase A. Eight μL liquid laundry detergent (Ariel, Procter & Gamble, Schwalbach, Germany) were added. After 60 min incubation at 60°C and subsequent addition of 80 μL of 5 M NaCl, the suspension was centrifuged for 5 min at 16,000 × g. The supernatant was removed and extracted with phenol/chloroform. The DNA was precipitated with 600 μL isopropanol, pelleted by centrifugation at 16,000 × g for 5 min, washed with 200 μL 70% ethanol and dissolved in 50 μL of water. The purity and concentration of the extracted DNA were assessed by electrophoresis on a 1% agarose gel and optical density spectrometric measurements. Both AFLP and ISSR procedures were performed on the same DNA samples. AFLP procedure The four-step AFLP marker production procedure of Sharbel [ 47 ] was followed with minor modifications: (1) Restriction . The enzyme combination EcoRI/MseI was used to restrict 500 ng of genomic DNA per sample. A 10 μL digestion mix ( 2 μL NEB Buffer #2, 2 μL 10 × BSA, 1.25 μL Mse I, 0.25 μL Eco RI, 4.5 μL water ) was added to 10 μL preparation of genomic DNA and incubated at 37°C for 3 h. (2) Ligation of adaptators . The double stranded EcoRI and MseI adaptator sequences designed by Zabeau and Vos [ 48 ] were ligated to the restriction fragments. A 40 μL ligation mix {6 μL ligase buffer (10 × Buffer for T4 DNA ligase)}, 4 pmol "EcoRI-adaptor", 2 pmol "Mse-adaptor", 0.6 μL T4 DNA ligase, water) was added to the 20 μL ligation reaction and incubated 19°C for 12 to 16 h. (3) Pre-amplification . Primers designed by Vos [ 14 ] complementary to a strand of each of the two adaptors with an additional selective nucleotide extension ("EcoRI-A" and "Mse-C") were used for an initial PCR pre-amplification step. An aliquot (17.5 μL) of a pre-amplification mix (2 μL 10 × Taq Buffer, 0.5 μL dNTP's (10 mM), 50 ng primer Mse-C (1μg/μL solution), 50 ng primer Eco-A (1μg/μL solution), water (grade III), and 0.1 μL Taq Polymerase B (5 U/μL)) was added to 2.5 μL of the digestion-ligation reaction in 0.2-mL PCR tubes. The PCR cycles are described in Sharbel [ 47 ]. (4) Amplification . Three 6-selective nucleotide EcoRI and MseI primer combinations, also designed by Vos [ 14 ] and demonstrated to be useful in Nicotiana tabacum by Ren & Timco [ 18 ], were used in the subsequent PCR amplifications. The primer combinations EcoRI-AGC/MseI-CAG; EcoRI-AAC/Mse-CCG; EcoRI-ACC/MseI-CCT were chosen with the help of successful studies by Ren & Timko [ 18 ]. Each of the three Eco RI-NNN primer types manufactured by Perkin-Elmer was labeled with a distinct fluorescent dye (either JOE, NED or HEX) at the 3' end to a ratio of 1:5. The following procedure was used for each of the three PCR reactions: 18 μL of amplification mix [2 μL 10 × Taq Puffer, 0.4 μL dNTP's (10 mM), 30 ng primer "EcoRI-NNN", 30 ng primer "Mse-NNN," water, and 0.1 μL Taq Polymerase B (5 U/μL)] was added to 2μL of the pre-amplification PCR product. PCR cycles were the same as in Sharbel [ 47 ]. The separate PCR amplification products generated by each of the three primer combinations were loaded together with a ROX 500 GeneScan size standard onto the ABI Prism 310 automated genetic analysis system as described in the manufacturer's instructions. The samples were run with the following GeneScan settings: "GS STR POP 4 (1 mL) D" module; 150000 V run Voltage; 5 second sample injection; 60°C gel temperature, and 9 m Watt's laser power. The distinct emission spectra of the three fluorescently labeled Eco RI-primer types made it possible to distinguish the DNA fragments resulting from each of the different primer combinations separately while the samples were being separated in the same electrophoresis capillary. Collection of raw data and size alignment of the AFLP fragments was performed with ABI Prism GeneScan Analysis Software (Applied Biosystems) with the internal standard. Aligned data were subsequently imported into Genographer [ 49 ] for band calling. Each AFLP locus with an intensity ≥ 150 fluorescence units was scored with the 'thumbnail' option of genographer and converted into a 1/0 binary data matrix, which was used for further analysis. ISSR procedure The PCR reaction (25 μL) contained 20 ng genomic DNA, buffer (10 mM Tris-HCl pH 8.3, 50 mM KCl, 1.5 mM MgCl 2 ) 0.8 U Taq DNA polymerase (Eppendorf) 0.1 mM dNTPs, 0.3 μM primer. After 5 min initial denaturation at 94°C, 45 cycles of 1 min denaturation, 45 s annealing at 50°C, and 2 min for extension at 72°C, were followed by 5 min final extension in the PCR cycling program. A total of 55 primers were screened and 5 primers (Table 2 ) were selected because they reproducibly produced distinct banding patterns. The amplified products were separated on 2.0% agarose gel (28 samples plus 2-1 Kb ladder standards on each gel) in 0.5 × TAE buffer and bands were detected by ethidium bromide staining. The PCR reaction and separation of PCR products was carried out in duplicate for each DNA sample and only reproducible bands were scored manually as present (1) and absent (0). Data analysis Pair-wise genetic similarity was calculated with the Jaccard coefficient [ 20 ]. The resulting matrix was processed for dendrogram construction using the UPGMA ( u nweighted p air g roup m ethod a verage) clustering method and PCO ( p rinciple c o- o rdinate analysis) options of software MVSP (Multi-Variate Statistical Package ver 3.13: [ 50 ]) program. The entire AFLP (244 individuals. SET-I) and ISSR+AFLP (175 individuals, SET II) data sets were analyzed individually and the 175 individuals (Table 1 ) those were used in both procedures were combined for clustering analysis. Subsequently, the SET I data was analyzed for each time series separately (Fig. 2 ) Genetic diversity was estimated for SET II (without Oregon, Arizona and California individuals) (168 individuals) as heterozygosity using the Bayesian approach of Holsinger et. al[ 21 ]. For this analysis, the analysis program, Hickory (ver 1.0) [ 23 ], was used with the full model. Several runs were carried out with default sampling parameters (burn-in = 50,000, sample = 250,000 and thin 50) to ensure consistency of results. Since, dominant markers (AFLP and ISSR) are used in conjunction with a largely sefling species, we used an approach that does not assume Hardy Weinberg equilibrium (Holsinger [ 21 ]). The SET II was used to calculate molecular variance from the combined and separate AFLP and ISSR data sets (168 individuals) as partitioned into individual and population components with an AMOVA (ver1.55: [ 51 ]). We also calculated variation between different locations, burns and washes, by collection year and population, separately. Φ values generated by the AMOVA program were used to estimate pair-wise genetic diversity, which is an analogue of the F-statistic. The Mantel permutation test was used to correlate pair-wise Φst values obtained by separate analyses of the AFLP, ISSR and combined data sets with geographic distance. Authors' contributions RB carried out the entire ISSR analysis, the analysis of the AFLP data and contributed to writing the manuscript. DS grew the plants, extracted the DNA and conducted the AFLP analysis. CAP collected seeds, grew the plants and extracted the DNA. ITB was responsible for coordinating the study, collecting seeds for the analysis, and wrote the manuscript. Supplementary Material Additional File 1 Table 5 Pair-wise genetic difference (Φ st Lower diagonal of the matrix) among 25 populations of Nicotiana attenuata . Levels of significance are given in the upper diagonal of the matrix: * p < 0.05, ** p < 0.01, *** p < 0.001 and NS, Non Significant at p < 0.05. p -value Indicates the probability that a random genetic distance (Φst) is larger than observed distance and are based on 1000 iterations steps. Click here for file | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC517723.xml |
520825 | BRCA1-mediated repression of select X chromosome genes | Recently BRCA1 has been implicated in the regulation of gene expression from the X chromosome. In this study the influence of BRCA1 on expression of X chromosome genes was investigated. Complementary DNA microarrays were used to compare the expression levels of X chromosome genes in 18 BRCA1-associated ovarian cancers to those of the 13 "BRCA1-like" and 14 "BRCA2-like" sporadic tumors (as defined by previously reported expression profiling). Significance was determined using parametric statistics with P < 0.005 as a cutoff. Forty of 178 total X-chromosome transcripts were differentially expressed between the BRCA1-associated tumors and sporadic cancers with a BRCA2-like molecular profile. Thirty of these 40 genes showed higher mean expression in the BRCA1-associated samples including all 11 transcripts that mapped to Xp11. In contrast, four of 178 total X chromosome transcripts showed significant differential expression between BRCA1-associated and sporadic tumors with a BRCA1-like molecular profile. All four mapped to Xp11 and showed higher mean expression in BRCA1-associated tumors. Re-expression of BRCA1 in HCC1937 BRCA1-deficient breast cancer cell resulted in the repression of 21 transcripts. Eleven of the 21 (54.5%) transcripts mapped to Xp11. However, there was no significant overlap between these Xp11 genes and those found to be differentially expressed between BRCA1-associated and sporadic ovarian cancer samples. These results demonstrate that BRCA1 mediates the repression of several X chromosome genes, many of which map to the Xp11 locus. | Introduction The mechanisms by which mutations in BRCA1 and BRCA2 tumor suppressor genes lead to carcinogenesis are incompletely understood. It remains to be established whether pathways involved in BRCA1 and BRCA2-associated tumorigenesis are also altered in sporadic cancers. Two recent reports demonstrated that BRCA1 and BRCA2-associated tumors have distinct expression profiles in both breast [ 1 ] and ovarian [ 1 , 2 ] cancers. With respect to ovarian cancers, two additional novel patterns of gene expression were observed. First, the same set of genes that distinguished BRCA1 and BRCA2-associated tumors also segregated the sporadic (not BRCA1 or BRCA2-associated) ovarian cancers into 2 subgroups consisting of "BRCA1-like" and "BRCA2-like" gene expression profiles. This observation lends support to the hypothesis that the same or similar dichotomous molecular pathways are affected in major subgroups of both hereditary and sporadic ovarian tumors. Second, a disproportionate number of genes located on the Xp11 locus showed statistically significant higher expression in the BRCA1-associated tumors when compared to sporadic tumors. Related to this observation, Ganesan and colleagues recently demonstrated that BRCA1 colocalizes with XIST RNA covering the inactive X chromosome [ 3 ]. These investigators showed that repression of BRCA1 led to the increased expression of a green fluorescent protein (GFP) transgene targeted to the inactive X chromosome. However, it remains unknown whether BRCA1 mediates any changes in expression of normal X chromosome genes and whether any such changes are global (affecting the entire X chromosome) or specific to certain genes. The goal of this study was to investigate further the influence of BRCA1 on the expression of transcripts mapped to the X chromosome. For this purpose the BRCA-associated and sporadic ovarian cancer gene expression data set was analyzed with respect to the expression of 178 transcripts mapped to the X chromosome. Additional in vivo and in vitro experiments employing an X chromosome enriched cDNA microarray were also performed to further evaluate the expression patterns of X chromosome genes in a more comprehensive manner. Materials and Methods Comparison of gene expression between BRCA-linked and sporadic ovarian cancers The first part of this investigation consisted of a de novo analysis of the large publicly available data set generated by previous microarray experiments with respect to the 178 X chromosome specific genes [ 2 ]. Thus, the description of tumor samples used, BRCA1 and BRCA2 genotyping, tissue processing, and RNA extraction and amplification, and microarray technique were previously published [ 2 ]. In addition, detailed protocols describing RNA amplification and microarray hybridization methods are available at (under "Alternative Methods and Protocols"). Use of an X-chromosome enriched cDNA microarray for evaluating gene expression differences in BRCA1-associated and sporadic ovarian cancers For the second part of this investigation a recently developed cDNA microarray enriched in X chromosome transcripts was used. The developmental rationale and approach for this cDNA microarray are described in detail elsewhere [ 4 ]. The X-enriched microarray chip used in this investigation consisted of 5,296 features of which 2,879 mapped to the X chromosome. For the purposes of this investigation analysis was limited to only the X chromosome features. Since the cDNAs on this array had not been "sequence-verified" prior to spotting, the cDNA clones for all genes found to be differentially expressed between tumor samples and in cell line experiments were sequenced to ensure positive identification of the transcripts. Those transcripts for which PCR amplification did not result in a product or multiple bands were identified were eliminated from the final analysis. Adenovirus-Mediated BRCA1 expression in HCC1937 cells Tissue culture techniques and adenoviral infection of HCC1937 cells was performed as described previously [ 5 ]. Briefly, cells were plated 24 h before the infection at a density 7 x 10 5 cells per 100 mm plate. The cells were infected at 250 plaque-forming units per cell with adenovirus encoding full-length human BRCA1 or green fluorescent protein (GFP) cDNAs (the latter used as an irrelevant infection control). Twenty-four hours later cells were harvested and RNA was purified using Trizol Reagent (Life Technologies, Inc.) according to manufacturer's instruction. Experiments employing cDNA microarrays In studies evaluating gene expression differences between ovarian tumor samples using the X-enriched cDNA microarrays, combined RNA from 10 human cell lines (Stratagene, La Jolla, CA) was used as the reference RNA. In studies involving the HCC1937 cell line, gene expression in BRCA1 virally infected cells was directly compared to that of GFP infected controls. The logarithmic expression ratios for the spots on each array were normalized by subtracting the median logarithmic ratio for the same array. Data were filtered to exclude spots with a size of less than 25 μm, an intensity of less than two times background, or less than 300 units in both red and green channels and to exclude any poor quality or missing spots. In addition, any features found to be missing in greater than 20% of the arrays were not included in the analysis. Statistical comparison between tumor groups was performed with the "BRB Array Tools" software , consisting of a modified F test with P < 0.005 considered statistically significant. This stringent P value was selected in lieu of the Bonferroni correction for multiple comparisons, which was deemed excessively restrictive. For microarray experiments involving the HCC1937 cells infected with BRCA1 or GFP the geometric mean of BRCA1:GFP expression ratio from two separate microarray experiments was used. Genes exhibiting a mean expression ratio change of two-fold or greater were considered significant. Quantitative RT-PCR 1 μg of total RNA was reverse transcribed in 50 μl reaction and 5 μl of cDNA was then used for PCR reaction according to Applied Biosystems technical manual. Separate reaction of the same samples with β-actin was performed for normalization purposes. The difference in threshold number of cycles between the ARAF1 and β-actin was then calculated and converted into real fold difference. All measurements were done in triplicates and the results were averaged. Probes for ARAF1 and β-actin were purchased from Applied Biosystems Inc. Results It was previously shown that when compared to sporadic cancers BRCA1-associated ovarian tumors were characterized by higher mean expression levels of genes mapped to Xp11 [ 2 ]. This observation was confirmed when considering all genes mapped to the X chromosome (Fig. 1A ). Eleven of the 178 X chromosome mapped transcripts were differentially expressed between the BRCA1-associated and sporadic cancers (P < 0.005). Of these 11 transcripts, six (55%) were located on Xp11. Because the sporadic tumors could be divided into subgroups with "BRCA1- and BRCA2-like" expression profiles [ 2 ], one may anticipate differences in the expression levels of genes mapped to Xp11 when comparing each of these sporadic cancer subgroups to the BRCA1-associated tumors. The expression levels of X chromosome genes in the 18 BRCA1-associated cancers was compared to those of the 13 BRCA1-like and 14 BRCA2-like sporadic tumors (Fig. 1A,1B,1C,1D ). Only 4 genes showed significant differential expression between the BRCA1-associated and the sporadic tumors with a BRCA1-like molecular profile (Fig. 1B ). All 4 mapped to Xp11 and showed higher mean expression in BRCA1-associated tumors. In contrast, 40 of the 178 X-chromosome transcripts were differentially expressed between the BRCA1-associated tumors and sporadic cancers with a BRCA2-like molecular profile (Fig. 1C top panel). Thirty of the 40 transcripts showed higher mean expression in the BRCA1-associated samples including all 11 genes that mapped to Xp11 (Fig. 1C top panel). These data suggest that BRCA1 may be involved in the regulation of gene expression from the X-chromosome. Furthermore, there appears to be a role for BRCA1 in suppressing the expression of several genes mapped to the Xp11 locus that were all higher expressed in BRCA1-associated tumors. This pattern of expression was observed when the BRCA1-associated samples were compared to all sporadic cancers regardless of their expression profile characterization as BRCA1- or BRCA2-like. Figure 1 X chromosome gene expression differences BRCA1-associated (B followed by a number) and sporadic (C followed by a number) ovarian cancers (P < 0.005). Genes are shown as hierarchical clusters (using centered correlation and average linkage), samples were not clustered. The red and green color intensities represent expression levels shown as standard normal deviation (Z score) values from each gene's mean expression level (represented as black) across all compared tumor samples. The symbol for each gene is followed by the I.M.A.G.E. clone number of the corresponding cDNA spotted on the array. A. Genes differentially expressed between BRCA1-associated tumors and all sporadic samples. B. Genes differentially expressed between BRCA1-associated cancers and sporadic tumors characterized as "BRCA1-like" based on gene expression profile as described in reference 2. C. Genes differentially expressed between BRCA1-associated tumors and sporadic cancers characterized as "BRCA2-like" based on gene expression profile as described in reference 2. D. An X chromosome enriched cDNA microarray was used to further investigate gene expression differences among a subset of BRCA1-associated and BRCA2-like sporadic tumors. The results of these experiments confirmed the findings observed above in (C). The significance of this differential pattern of gene expression between the BRCA1-associated and sporadic cancers is unclear at this time. However, higher expression from Xp11 may be related to the earlier age of presentation of epithelial ovarian cancers in BRCA1 mutation carriers compared to tumors in BRCA2 mutation carriers and patients with sporadic ovarian cancer [ 6 ]. This observation cannot be solely explained by an earlier occurrence of a "second hit" as modeled by Knudson's two-hit hypothesis [ 7 ] because the age of presentation of ovarian cancer in BRCA2 tumors is no different than that observed in patients with sporadic epithelial ovarian cancers [ 2 , 6 ], thus indicating a BRCA1-specific effect on the age of presentation. In order to confirm and expand on the above noted differences in gene expression between BRCA1-associated and sporadic tumors, an X-chromosome enriched cDNA microarray was used [ 4 ]. Due to the limited availability of resources this cDNA microarray was used to evaluate gene expression in a representative subset of 9 BRCA1-associated and 8 sporadic tumors. The sporadic tumors were selected from the subgroup with a "BRCA2-like profile" as these samples showed more robust differences in X chromosome gene expression in the above noted experiments using our conventional cDNA microarray (Fig. 1B and 1C ). Twenty-one transcripts showed significant differential expression between the BRCA1-associated and sporadic tumors with a BRCA2-like profile (P < 0.005). Consistent with our earlier findings and despite the smaller number of samples used for this comparison, the majority of transcripts exhibited higher mean expression in BRCA1-associated samples including all but one of the transcripts located on Xp11 (Fig. 1D ). This pattern of expression was confirmed using quantitative RT-PCR of the ARAF1 gene in a representative sample of BRCA1-associated and sporadic ovarian cancer samples (Fig. 2 ). Figure 2 Quantitative RT-PCR evaluation of ARAF1 expression confirms cDNA microarray data. Average of three RT-PCR repeats for each sample is shown on top left panel. ARAF1 expression levels derived by cDNA microarray relative to universal reference RNA is shown in the bottom left panel. Mean expression levels are graphed to the right; error bars represent standard error of mean. We next sought to determine if differences in X chromosome gene expression between BRCA1-associated and sporadic tumors were directly mediated by BRCA1 as oppose to other, possibly confounding, features of these tumors. The HCC1937 breast cancer cells that are either homozygous or hemizygous for the BRCA15382insC mutation were used as a model. Gene expression in HCC1937 cells following virally mediated expression of wild-type BRCA1 was compared to gene expression following viral infection of GFP which was employed as an irrelevant infection control. Prior to using the X-chromosome enriched array, the validity of this approach was tested using a 7.5 K microarray whose features included BRCA1. This preliminary experiment demonstrated that viral infection did in fact result in a 3.4 fold higher BRCA1 expression compared to the GFP control (data not shown). BRCA1 expression was also demonstrated using Western blotting which confirmed BRCA1 protein expression following viral infection (Fig. 3 ). Figure 3 Adenovirus-mediated BRCA1 expression in HCC1937 cells. Twenty-one X-chromosome transcripts demonstrated at least a two-fold mean expression change across two independent experiments following BRCA1 infection. It is notable that all 21 transcripts (representing 16 unigene clusters) showed decreased expression following BRCA1 transfection with a median repression of 2.4 fold. This uniform repression is particularly significant because median based normalization of logarithmic ratios was employed in microarray data analysis. Thus, the median log expression ratio for all 5,296 features on the array was adjusted to zero (corresponding to an expression ratio of 1.0). Following this median based normalization, the median log expression ratio of the transcripts mapped to the X chromosome (2,879 of the total 5,296 features on the array) was also very close to zero (median log 2 ratio = 0.016 corresponding to an expression ratio of 1.01). Thus, the observed down regulation of genes does not appear to be a result of the X-enriched composition of the cDNA microarray used in these experiments nor can it be explained in terms of a global down regulation of all X chromosome transcripts. Rather, the observed repression appears to be a specific effect of BRCA1 expression on these 21 transcripts. Eleven of the 21 (52.4%) transcripts map to Xp11 and represent 8 unigene clusters (Table 1 ). The most highly repressed (11.6-fold) transcript was that of LOC139135 (Hs.160594) whose protein product contains a PAS domain and is weakly similar to the circadian locomoter output cycles kaput (CLOCK) protein of human and several other organisms. The PAS domain is a highly conserved motif essential for sensing changes in a variety of different environmental conditions including light, oxygen tension and redox potential [ 8 , 9 ]. HIF-1 alpha and EPAS-1, two other PAS containing proteins, are upregulated in a number of human tumors and play an important role in angiogenesis by stimulating VEGF expression [ 9 - 11 ]. LOC139135 deserves additional investigation of its role as a possible protooncogene subject to regulation by BRCA1. Table 1 X chromosome genes showing two-fold or greater change following BRCA1 expression in HCC1937 BRCA1-null cells. Name Clone ID* Locus Unigene Fold repression Comments LOC139135 C03503 Xq28 Hs.160594 11.6 Similar to CLOCK protein EST 32930 Xp11 Hs.99070 8.9 EST 34280 Xp11.2 Hs.99070 8.5 CSTF2 1705354 Xq22.1 Hs.693 5.5 EST 1535341 Xq13 Hs.444962 5.4 EST 1614299 Xq13 Hs.197801 4.3 JM11 1913391 Xp11.23 Hs.417068 3.5 ZNF6 1564783 Xq13 Hs.326801 3.0 ZNF6 5201496 Xq13 Hs.326801 2.4 LOC158572 2070337 Xp11.23 Hs.408191 2.8 LOC158572 1880263 Xp11.2 Hs.408191 2.4 LOC158572 470925 Xp11.23 Hs.408191 2.1 EST 4402168 Xq26 Hs.175894 2.6 KLF8 2148451 Xp11.21 Hs.411296 2.5 KLF8 2148451 Xp11.21 Hs.411296 2.3 EST 2516780 Xp11.23 Hs.293317 2.4 Moderately similar to PAGE-5 protein EST 2659258 Xq28 Hs.312560 2.2 ED1 2030638 Xq12-q13 Hs.105407 2.2 Weakly similar to PAGE-5 protein TIMP1 172210 Xp11.23 Hs.446641 2.1 EST 2111889 Xp11.23 Hs.163473 2.0 Weakly similar to XAGE-5 protein FLJ23614 1938584 Xq26 Hs.28780 1.9 * I ntegrated M olecular A nalysis of G enomes and their E xpression (I.M.A.G.E.) Consortium cDNA clone identification. Two transcripts representing the same unigene cluster, Hs. 99070, showed greater than 8 fold repression following BRCA1 expression in HCC1937 cells (Table 1 ). In addition, an EST (clone ID 32930) belonging to this same unigene cluster was found to be significantly higher expressed in BRCA1-associated ovarian cancers compared to sporadic tumors (Fig. 1D ). As such this gene may be one potentially important target of BRCA1 regulation of gene expression from the X chromosome. There was no other overlap between the list of genes differentially expressed following BRCA1 expression in HCC1937 cells and the list of genes differentially expressed between BRCA1-associated and sporadic ovarian cancers. The lack of a broader overlap between the list of genes repressed following BRCA1 expression in the HCC1937 and those differentially expressed between BRCA1-associated and sporadic ovarian cancers is notable. This signifies that BRCA1's influence over transcription is unlikely to be gene specific and rather may involve more global influences over transcription such chromatin remodeling and changes in methylation states. BRCA1 expression led to the down regulation of several ESTs homologous to PAGE-5, a member of the cancer-testis antigen group of genes (MAGE, GAGE, PAGE, etc.). These ESTs are likely to represent as yet undiscovered members of this family of genes that are known for their characteristic pattern of expression, usually limited to the testes and tumors [ 12 , 13 ]. Intriguing parallels exist between expression characteristics of cancer testis antigens and expression changes mediated by BRCA1. The vast majority of cancer-testis antigen genes are located within discrete loci on the X chromosome [ 13 ]. Our results demonstrate that BRCA1 represses the expression of clusters of genes on Xp11, Xp21-p22, Xq13, and Xq26-q28 (Table 1 ), which correspond to the genomic location of several major cancer testis antigen gene clusters [ 12 , 13 ]. Furthermore, high expression of BRCA1 in pachytene spermatids [ 14 , 15 ] correlates with a significant down regulation of at least one cancer testis antigen, MAGE-B4 [ 16 ]. Finally, recent reports have documented the aberrant expression of several cancer-testis antigens in a significant portion of ovarian cancers and linked their expression to drug-resistance [ 17 , 18 ]. Although not completely understood, the expression of cancer testis antigens is thought to be, at least partially regulated by DNA methylation [ 12 ]. These data point to changes in DNA methylation as another possible mechanism involved in the BRCA1-mediated repression of gene clusters within the X chromosome. Further investigation of the sequence and genomic organization of genes in these loci will be useful for elucidating features responsible for the co-regulation of these genes. Discussion Whether any of these BRCA1-regulated X chromosome genes are involved in ovarian carcinogenesis and / or tumor progression remains to be determined. However, several lines of evidence support a possible connection between the X chromosome and ovarian neoplasia. First, the loss of a number of regions in the X-chromosome has been associated with ovarian agenesis or premature ovarian failure, which are commonly observed in Turner syndrome and related disorders [ 19 , 20 ]. Thus, the X chromosome is likely to contain genes involved in ovarian maintenance [ 19 ]. Aberrant overexpression of such potential ovarian survival/growth regulators on the X chromosome through a mechanism involving the loss of BRCA1 may be involved in ovarian carcinogenesis and/or tumor progression. Chromosome X alterations have been reported in sporadic ovarian carcinomas and borderline tumors [ 21 , 22 ]. Non-random X inactivation has been reported in populations of hereditary ovarian and breast cancer syndrome patients including BRCA1 mutation carriers [ 23 , 24 ]. Finally, in a comparison of gene expression between matched primary and recurrent chemoresistant ovarian cancer samples from the same patient XIST was the most differentially expressed gene and its expression was negatively correlated with response to paclitaxel chemotherapy [ 25 ]. Until recently, a mechanistic explanation for how BRCA1 may affect the expression of multiple genes on the X chromosome was lacking. Evidence for the existence of one such mechanism has been provided by Ganesan and colleagues who have demonstrated the co-localization and interaction between XIST and BRCA1 [ 3 ]. This interaction was shown to be sufficient and necessary to repress the expression of a green fluorescent protein transgene introduced into the inactive X chromosome. Our investigation shows that genes endogenous to the X chromosome are also repressed by BRCA1 and that genes on certain loci are preferentially affected. The exact nature of this interaction and possible differential effects on gene expression from various regions of the X chromosome remain to be determined. It is unclear whether BRCA1's effect involves changes in XIST RNA expression. Ganesan et al. did not observe such an effect, but other investigators have reported a two-fold increase in XIST RNA levels following BRCA1 expression [ 26 ]. Using an X chromosome enriched microarray that has previously been shown to be able to detect changes in XIST expression associated in X chromosome polysomies [ 4 ], no increase in XIST RNA was observed following BRCA1 expression. BRCA1 may target XIST in such a way as to bring about changes in the expression of various loci on the X chromosome. It is also possible that BRCA1 may be acting independent of XIST through a different mechanism such regulation of DNA methylation. Alterations in DNA methylation play an integral role in the normal process of X chromosome inactivation and are also involved in the characteristic expression of cancer-testis antigens most of which reside on the X chromosome as discussed above. One of the unexplained features of germ-line BRCA1 mutations is the overwhelmingly disproportionate risk of cancer in female carriers. One hypothesis put forth to explain this observation is that estrogen is the inciting event by leading to deregulated proliferation and carcinogenesis in hormonally responsive tissues [ 27 ]. An alternative, non-mutually exclusive, hypothesis is that the deregulated expression of an X-linked gene normally under BRCA1 control may play a role in predisposing women to carcinogenesis. This a plausible scenario if BRCA1 proves to be involved in the process of X-chromosome inactivation and /or gene dosage regulation for those genes on the X chromosome that do not undergo inactivation. Accordingly, the lack of a need for X chromosome inactivation and X-linked gene dose adjustment in men may explain why male BRCA1 mutation carriers do not have the same increased risk for cancers. Future studies will be aimed at testing these hypotheses. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC520825.xml |
533859 | Melatonin promoted chemotaxins expression in lung epithelial cell stimulated with TNF-α | Background Patients with asthma demonstrate circadian variations in the airway inflammation and lung function. Pinealectomy reduces the total inflammatory cell number in the asthmatic rat lung. We hypothesize that melatonin, a circadian rhythm regulator, may modulate the circadian inflammatory variations in asthma by stimulating the chemotaxins expression in the lung epithelial cell. Methods Lung epithelial cells (A549) were stimulated with melatonin in the presence or absence of TNF-α(100 ng/ml). RANTES ( R egulated on A ctivation N ormal T -cells E xpressed and S ecreted) and eotaxin expression were measured using ELISA and real-time RT-PCR, eosinophil chemotactic activity (ECA) released by A549 was measured by eosinophil chemotaxis assay. Results TNF-α increased the expression of RANTES (307.84 ± 33.56 versus 207.64 ± 31.27 pg/ml of control, p = 0.025) and eotaxin (108.97 ± 10.87 versus 54.00 ± 5.29 pg/ml of control, p = 0.041). Melatonin(10 -10 to 10 -6 M) alone didn't change the expression of RNATES (204.97 ± 32.56 pg/ml) and eotaxin (55.28 ± 6.71 pg/ml). However, In the presence of TNF-α (100 ng/ml), melatonin promoted RANTES (410.88 ± 52.03, 483.60 ± 55.37, 559.92 ± 75.70, 688.42 ± 95.32, 766.39 ± 101.53 pg/ml, treated with 10 -10 , 10 -9 , 10 -8 , 10 -7 ,10 -6 M melatonin, respectively) and eotaxin (151.95 ± 13.88, 238.79 ± 16.81, 361.62 ± 36.91, 393.66 ± 44.89, 494.34 ± 100.95 pg/ml, treated with 10 -10 , 10 -9 , 10 -8 , 10 -7 , 10 -6 M melatonin, respectively) expression in a dose dependent manner in A549 cells (compared with TNF-α alone, P < 0.05). The increased release of RANTES and eotaxin in A549 cells by above treatment were further confirmed by both real-time RT-PCR and the ECA assay. Conclusion Taken together, our results suggested that melatonin might synergize with pro-inflammatory cytokines to modulate the asthma airway inflammation through promoting the expression of chemotaxins in lung epithelial cell. | Backgound Eosinophils are known to be the important effector cells in asthmatic airway inflammations[ 1 ]. Previous studies have demonstrated that eosinophils are accumulated in the peripheral blood, the bronchoalveolar lavage fluid, and the airway of the asthmatic patients or the allergen-sensitized animals[ 2 ]. Eosinophil trafficking is regulated by a wide variety of chemotactic factors[ 3 ]. Eotaxin and RANTES ( R egulated on A ctivation N ormal T -cells E xpressed and S ecreted) are C-C chemotaxins that can recruit eosinophils to the airway in asthma[ 4 ]. A variety of tissues and cell types, including lung epithelial cell, produce eotaxin and RANTES which play an important role in airway[ 5 ]. Pro-inflammatory cytokines such as tumor necrosis factor (TNF) and interleukin (IL)-1 are released in the early stage of allergic inflammation. In endothelial and epithelial cells, TNF-α induces an influx of eosinophils into tissues through the increased expression of adhesion molecules[ 6 , 7 ]. Although eotaxin and RANTES tend to be expressed constitutively in several cell types, their expression may also be regulated in response to TNF-α in other cell lines[ 8 ]. Melatonin(N-acetyl-5-methoxytryptamine) is a key regulator of circadian rhythm homeostasis in humans[ 9 , 10 ]. It also appears to have an important immunomodulatory effect in allergic diseases[ 11 , 12 ]. Melatonin promotes the cytokine production in the peripheral blood mononuclear cell. Pinealectomized rats sensitized to ovalbumin demonstrated that pinealectomy significantly reduces the inflammatory cell counts in the bronchoalveolar lavage fluid after ovalbumin challenge, and that melatonin administration to pinealectomized rats restores the ability of inflammatory cells to migrate to the bronchoalveolar fluid. Those results suggest that melatonin may modulate the expression of chemotaxins in airway epithelial or endothelial cells[ 13 ]. The circadian variations of lung function in nocturnal asthma are associated with the increased airway inflammation during night. As a key regulator in human circadian rhythm homeostasis as well as an immunomodulator in allergic diseases, melatonin may regulate the circadian airway inflammation in asthma through modulating the expression of chemotaxins in the airway epithelial cells. In order to test this hypothesis, we conducted the present study to answer two questions. First, whether melatonin is able to up-regulate RANTES and eotaxin expression in the lung epithelia cell line-A549. Second, what is the combinatory effect of melatonin and TNF-α on RANTES and eotaxin expression and whether this effect increases the eosinophils chemotactic activity (ECA) released in A549. The answers to these questions might provide new insights into the pathophysiology of asthma. Methods This study was approved by the medical ethics committee of the West China Hospital of Sichuan University. Informed consents were obtained from all subjects in the study. Cell Culture A549 cells, human type II-like epithelial lung cells, were obtained from ATCC (Manassas, VA, USA). The cells were cultured in tissue flasks incubated in 100% humidity and 5% CO 2 at 37°C in DMEM medium (GIBCO BRL, Grand Island, NY) supplemented with 10% heat-inactived fetal bovine serum (GIBCO BRL) and penicillin-streptomycin (50 μg/ml, GIBCO BRL), at 1 × 10 6 cells/ml. A549 cells were then plated onto 6-well, flat-bottom tissue culture plates (Becton Dickinson and Co., NJ, USA) at a density of 1 × 10 6 cells/ well in DMEM medium. The medium was changed every 2 d until the cells became confluent and then the cells were used for the experiments. Cytokine Assays As IL-1β and TNF-α have similar effect on the expression of many chemotaxins[ 14 , 15 ], we chose TNF-α as the representative pro-inflammatory cytokines in the asthmatic lung in this study. After the cells became confluent, the medium was changed to serum-free DMEM medium for 12 h. A549 cells were then exposed to increasing concentrations of melatonin (10 -10 , 10 -9 , 10 -8 , 10 -7 , 10 -6 M, the physiology concentration are 10 -9 to 10 -7 M during day and night[ 16 ]) (Sigma, St. Louis, MO, USA) and TNF-α (100 ng/ml) (Sigma), for 12 h. The cells were also stimulated with a combination of melatonin (10 -10 , 10 -9 , 10 -8 , 10 -7 , 10 -6 M) and TNF-α (100 ng/ml). The epithelial cell layers were then washed three times with Hanks' balanced salt solution (GIBCO BRL) and incubated for 48 h. Cell-free culture supernatants were collected. RANTES and eotaxin were assayed using enzyme-linked immunosorbent assay (ELISA) kits according to the instructions of the manufacturers. Assay kits for RANTES and eotaxin were purchased from R&D Systems (Minneapolis, MN, USA), and the minimum detectable concentration of RANTES and eotaxin was 5 pg/ml. Experiments were performed at least three times with the similar results. RNA extraction and real-time PCR RNA extraction and real-time PCR were performed as previously described[ 17 , 18 ]. After the cells became confluent, the medium was changed to fetal bovine serum free DMEM medium for 12 h. A549 cells were then exposed to different concentrations of melatonin, together with or without TNF-α (100 ng/ml) (Sigma) for 12 h. Total cellular RNA was extracted using an acid guanidinium-phenol-chloroform method (Trizol; GIBCO BRL). RNA integrity was confirmed by electrophoresis on 1% agarose gels and ethidium bromide staining. Total cellular RNA, 1 μg, was reverse transcribed at 37°C for 70 min in 20 μl containing 2.5 U Superscript-II reverse transcriptase (GIBCO BRL); 10 mM dithiothreitol, 1 mM each of deoxyadenosine triphosphate (dATP), deoxythymidine triphosphate (dTTP), deoxycytidine triphosphate (dCTP), and deoxyguanidine triphosphate (dGTP); and 5 μg/ml oligo-dT primer (Pharmacia, Piscataway, NJ). Reactions were stopped by heat inactivation for 10 min at 85°C. Primers for human eotaxin, RANTES and β-actin were synthesized, HPLC purified as GIBCO BRL Custom Primers (Hong Kong, China). Primer sequences were as follows: Eotaxin: upstream primer: 5'- ACA TGA AGG TCT CCG CAG CAC TTC -3', downstream primer: 5'- TTG GCC AGG TTA AAG CAG CAG GTG -3'. RANTES upstream primer: 5'- GGC ACG CCT CGC TGT CAT CCT CA-3'; downstream primer: 5'- CTT GAT GTG GGC ACG GGG CAG TG-3'. β-actin upstream primer: 5'- AAG AGA GGC ATC CTC ACC CT -3',downstream primer 5'- TAC ATG GCT GGG GTG TTG AA -3'. Real-time PCR was performed on the ABI Prism 7700 sequence detection system (PE Applied Biosystems) by using SYBR green (Roche Diagnostics, Somerville, NJ) as a dsDNA-specific binding dye. The PCR were cycled 40 times after initial denaturation (95°C, 2 min) with the following parameters: denaturation, 95°C, 15s; and annealing and extension, 60°C, 1 min. The threshold cycle (CT) was recorded for each sample to reflect the mRNA expression levels. The fold changes of eotaxin or RANTES gene expression were calculated as previously described[ 18 ]. Eosinophil Chemotaxis Assay Eosinophil chemotaxis assay was performed as described previously[ 19 ]. Briefly, eosinophils were isolated from the peripheral blood of three healthy donors by negatively selected with immunomagnetic beads. Erythrocytes in venous peripheral blood were removed by hypotonic lysis. Neutrophils and mononuclear cells were depleted with anti-CD16 and anti-CD3 immunomagnetic beads (Miltenyi Biotec GmbH, Bergisch Gladbach, Germany). Eosinophils were stained with Randolph's stain and counted in a hemocytometer. Cytospins of each preparation were stained with Diff-Quik (International Reagent Corp., Green Cross, Osaka, Japan). The mean percentage of the eosinophil purity was 98.0 ± 0.3%. The viability measured by trypan blue exclusion was consistently greater than 95.0%. Eosinophil chemotaxis assay was measured by the Boyden's blind-well chamber technique using a 48-well, multiwell chamber (NeuroProbe Inc., Bethesda, MD). The bottom wells of the chamber were filled with 26.5 μl of the A549 cell supernatant stimulated by various chemicals, as described previously, in triplicate. A polycarbonate filter with a pore size of 5 μm (Nucleopore, Pleasanton, CA) was placed over the bottom wells, and isolated eosinophils were placed into each of the top wells. The chambers were then incubated at 37°C, 5% CO 2 for 90 min. After incubation, eosinophils in the top wells were removed by scraping. The filter was then stained with Diff-Quik. Eosinophil chemotactic activity (ECA) is shown as the total number of migrated eosinophils counted in 10 high-power fields under a light microscope (Olympus, Lake Success, NY) at × 400 magnification. Data analysis Data were expressed as means ± SD. Differences between groups were assessed by one-way ANOVA followed by the LDS significant difference test. A value of p < 0.05 was considered statistically significant. Results Effect of TNF-α and melatonlin on RANTES and eotaxin released from A549 cells RANTES released from A549 cells increased significantly when the cells incubated with TNF-α(100 ng/ml). Melatonin alone didn't have this effect on A549 in dose from10 -10 to 10 -6 M. However, TNF-α induced RANTES release in A549 increased significantly by incubation with melatonin (from10 -10 to 10 -6 M). Similarly, eotaxin released from A549 cells also increased significantly when the cells incubated with TNF-α; Melatonin alone had no effect on eotaxin released from A549 at dose range from10 -10 to 10 -6 M. However, eotaxin released from A549 increased significantly when the cells incubated with melatonin and TNF-α (Figure 1 ). Figure 1 RANTES and eotaxin released from A549 cells. Melatonin(10 -6 M) alone did not change RANTES and eotaxin released from A549 cells. However, it (from10 -10 to 10 -6 M) promoted RANTES and eotaxin released from A549 cells in a dose dependent manner when co-stimulated with TNF-α (100 ng/ml). * and **, p < 0.05 and 0.01, compared with control and melatonin alone (pg/ml, n = 3). $ and #, p < 0.05 and 0.01, compared with TNF-α alone (pg/ml, n = 3). Effect of TNF-α and melatonlin on the expression of RANTES and eotaxin in A549 cells To determine whether the production of RANTES and eotaxin is accompanied by the transcription of the corresponding genes, we used real-time RT-PCR to examine RANTES and eotaxin mRNA expression in A549 cells. A549 were stimulated with melatonin (10 -10 , 10 -9 , 10 -8 , 10 -7 , 10 -6 M) and TNF-α (100 ng/ml). Melatonin alone did not change the RANTES and eotaxin mRNA expression in A549. TNF-α can promote the RANTES and eotaxin expression in A549 cells. When stimulated with TNF-α, melatonin synergistically increased the RANTES and eotaxin expression in a dose dependent manner (Fig 2 ). Figure 2 RANTES and eotaxin mRNA expression in A549 cells. Melatonin(10 -6 M) alone did not change the RANTES and eotaxin mRNA expression in A549 cells. TNF-α (100 ng/ml) could promote the RANTES and eotaxin expression in A549 cells. Melatonin (from10 -10 to 10 -6 M) increased the RANTES expression of A549 cell in a dose dependent manner when co-stimulated with TNF-α(100 ng/ml). **, p < 0.01, compared with control and melatonin alone (n = 3). #, p < 0.01, compared with TNF-α alone (n = 3). Effect of TNF-α and melatonlin on eosinophil chemotactic activity (ECA) released by A549 Cells When stimulated with TNF-α(100 ng/ml), ECA released by A549 cells increased significantly. Melatonin (from10 -10 to 10 -6 M) alone didn't have this effect. When stimulated with TNF-α (100 ng/ml) and melatonin, ECA released increased in A549 cells in a dose dependent manner (Fig 3 ). Figure 3 Eosinophil chemotactic activity (ECA) released from A549 cells. Melatonin (10 -6 M) alone did not change the ECA released from A549 cells. TNF-α (100 ng/ml) could increase the ECA released from A549 cells. Melatonin (from10 -10 to 10 -6 M) increased the ECA released from A549 cell in a dose dependent manner when co-stimulated with TNF-α(100 ng/ml). **, p < 0.01, compared with control and melatonin alone (n = 3). #, p < 0.01, compared with TNF-α alone (n = 3). Discussion In this study, we examined the RANTES and eotaxin protein level and the gene expression in A549 in response to TNF-α and melatonin stimulation using ELISA and real-time RT-PCR. We also measured the ECA released by A549 in response to TNF-α and melatonin stimulation. Unexpected, we found that the eotaxin and RANTES protein level and gene expression in A549 cells were unchanged when treated with melatonin alone, and the ECA released by A549 remained unchanged too. However, when A549 cells co-stimulated with melatonin and TNF-α, eotaxin and RANTES released from the cells increased in a melatonin dose dependent manner. The gene expression of eotaxin and RANTES, and the ECA also increased at the same time. This result support our hypothesis that melatonin play an important role in airway inflammation through up-regulation of the eotaxin and RANTES expression in lung epithelial cell when the cells stimulated with pro-inflammatory cytokines. The pro-inflammatory characteristics of TNF-α have been documented extensively. Numerous studies have demonstrated that these attributes contribute to the inflammatory conditions present in airways of asthmatic subjects. TNF-α has been shown to activate the inflammatory cells, up-regulate the adhesion molecules on endothelium and circulating leukocytes, increase the production of chemotaxins[ 20 ], the bronchial responsiveness. TNF-α is expressed primarily by the alveolar cells and tissue macrophages, mast cells, and bronchial epithelial cells. Additionally, in most other airway cell systems studied, conditions simulating an inflammatory state result in expression of TNF-α. Thus, it is not surprising that TNF-α concentration is higher in the bronchoalveolar lavage fluid from symptomatic asthmatics compared with normal control subjects[ 21 ]. In this study, we found that TNF-α could promote the RANTES and eotaxin production in A549 and melatonin further exaggerated this effect of TNF-α. Lung function in a healthy individual varies in a circadian rhythm, with the peak lung function occurring near 4:00 PM (1600 hours) and the minimal lung function occurring near 4:00 AM (0400 hours). An episode of nocturnal asthma is characterized by an exaggeration in this normal variation in lung function from daytime to nighttime, with diurnal changes in the pulmonary function generally of > 15%. A recent study showed that the circadian variability in pulmonary function in asthma was related to changes in the airway eosinophils recruitment and activation[ 22 ]. Although the molecular mechanism responsible for the selective infiltration of eosinophils into the inflamed tissue in asthma has not been elucidated, chemotaxin may play an important role in this process. Eotaxin is a chemotaxin that binds with high affinity and specificity to the chemotaxin receptor CCR3 and plays an important role in the pathogenesis of allergic disease. RANTES, a C-C chemotaxin, was initially shown to be chemoattractant for T cells and monocytes but has subsequently been shown to be a potent eosinophil chemoattractant[ 23 , 24 ]. In other studies, an up-regulation of RANTES message was observed in the airways of asthmatic patients[ 25 ], and increased levels of RANTES have been detected in the nasal aspirates of children with the viral exacerbation of asthma[ 26 ], suggesting an important role for RANTES in this process. From the result of our study, together with the studies above, we can infer that melatonin, the most important circadian rhythm regulator, may also regulate the asthma airway inflammation by up-regulating the expression of eotaxin and RANTES in the airway epithelium in inflammatory status of asthma. RANTES and eotaxin expression are regulated by two important transcriptional factors: active protein-1 (AP-1) and nuclear factor kappa B(NFκB). Benis et al[ 27 ] found that melatonin could suppress the activation of NFκB and AP-1. Although NFκB and AP-1 could up-regulate the expression of many pro-inflammatory cytokines and chemotaxins, other transcriptional factors also could be involved in the regulation of RANTES and eotaxin. Further studies are needed to elucidate the mechanism of how melatonin regulates the transcription of these chemotaxins. The role of melatonin as an immunomodulator is poorly understood and, in some cases, contradictory results have been reported. For example, Shafer's study showed that melatonin has no effect on the activity of stimulated macrophages[ 28 ]. However, pinealectomy of rats significantly reduces airway inflammation after ovalbumin inhalational challenge, and melatonin administration to the pinealectomized rats seems to restore the airway inflammation, which further supports the pro-inflammatory effect of melatonin. In addition, up-regulation of the gene expression of transforming growth factor-β(TGF-β), macrophage-colony stimulating factor (M-CSF), TNF-α and stem cell factor (SCF) in peritoneal exudate cells, and up-regulation of the gene expression of IL-1β, M-CSF, TNF-α, interferon-γ (IFN-γ) and SCF in splenocytes, were observed in male C57 mice received 10 consecutive daily intraperitoneal injections of melatonin[ 12 ]. Further research should be directed at evaluating the mechanism of melatonin regulating the transcription of those kinds of cytokines. Conclusion Melatonin alone did not change eotaxin and RANTES protein level and gene expression in A549 cells, and had no effect on ECA released by A549 cells. However, when A549 cells were stimulated with melatonin, together with TNF-α, the mRNA expression and protein release of eotaxin and RANTES increased significantly. This result suggested that combined with pro-inflammatory cytokines, melatonin may play a role in the airway inflammation through up-regulation of the eotaxin and RANTES expression in the lung epithelial cells. Authors' contributions FML conceived of the experiment, carried out all experiments and prepared the manuscript. XJL conceived of the experiment and performed RNA extraction and real-time RT-PCR. SQL conceived of the experiment and assisted in collection and analysis of ELISA samples. CTL performed cell culture and provided expert advice and interpretation of the study's results. WZL participated in the study's design, coordination and final revisions of the manuscript. All authors read and approved the final manuscript. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC533859.xml |
545059 | Recurrent cardiac events in patients with idiopathic ventricular fibrillation, excluding patients with the Brugada syndrome | Background The recurrence of cardiac events in patients with idiopathic ventricular fibrillation (VF) excluding patients with the Brugada syndrome is unclear since this entity remains present in previous studies. Methods Since 1992, 18 patients (72% male) with idiopathic VF out of 455 ICD implants were treated with an implantable cardioverter defibrillator (ICD). The mean age at first ICD implantation was 42 ± 14 years. Brugada syndrome, as well as other primary electrical diseases (e.g. long QT), were systematically excluded in all patients by the absence of the typical electrocardiogram (ST elevation in the right precordial leads) at rest and/or after pharmacological tests (ajmaline, flecainide, or procainamide). Recurrence of cardiac events was prospectively assessed. Results During a mean follow-up period of 41 ± 27 months, VF recurrence with appropriate shock occurred in 7 patients (39%) covering a total of 27 shocks. The median time to first appropriate shock was 12 ± 9 months. There were no deaths. In the electrophysiological study, 39% of patients were inducible, but inducibility failed to predict subsequent arrhythmic events. Forty-four percent of patients suffered 21 inappropriate shocks, which were caused by sinus tachycardia, atrial arrhythmias or lead malfunction. Conclusion Idiopathic ventricular fibrillation patients have a high recurrence rate of potentially fatal ventricular arrhythmias, excluding patients with the Brugada syndrome or other known causes. ICD prevents sudden cardiac death but inappropriate shocks remained a major issue in this young and active population. | Background Idiopathic ventricular fibrillation (VF) is defined as spontaneous ventricular fibrillation in the absence of any structural heart disease, including coronary artery disease, valvular heart disease, myocarditis, cardiomyopathy or electrophysiological diseases, with a well-defined cause, such as the long QT syndrome, the Brugada syndrome, ventricular pre-excitation (WPW), and drug intoxication [ 1 ]. The consensus statement of the joint steering committees of the UCARE registry of Europe and of the idiopathic VF registry of US reported in 1997 that patients with the Brugada syndrome should be considered a variant of idiopathic VF [ 1 ]. Brugada syndrome is characterized by a unique electrocardiographic (ECG) pattern of right bundle branch block with ST elevation in the right precordial leads. Transient forms of the disease in which the ECG normalizes for a period of time have been described. Administration of sodium channel blockers will unmask the abnormal ECG in patients with transient normalized ECGs [ 2 , 3 ]. The American Heart Association has recently proposed diagnostic criteria for the Brugada syndrome [ 4 ]. Moreover, the incidence of Brugada syndrome among patients with idiopathic VF remains unclear. The prevalence of a Brugada type ECG pattern was reported in 21% [ 5 ] and 24% [ 6 ] of idiopathic VF populations. However, on the basis of pharmacological tests, it has been speculated that up to 40% – 60% of patients diagnosed with idiopathic VF might actually suffer from the Brugada syndrome [ 7 ]. Since this is a new clinical entity, older publications probably classified some patients with Brugada syndrome as idiopathic VF, since intravenous administration of a sodium channel blocker to unmask a concealed form of the disease was not systematically performed. Our report describes the clinical and electrophysiological characteristics of consecutive sudden cardiac arrest survivors from idiopathic VF who received an ICD, and in whom electrical and structural heart diseases including Brugada syndrome, long QT, arrhythmogenic right ventricular dysplasia, and hypertrophic cardiomyopathy characterized by a high recurrence rate, were systematically excluded. Methods Patient population The total population under investigation included 455 consecutive patients who received a third generation ICD, with stored electrograms capabilities for hemodynamically poorly tolerated ventricular tachyarrhythmias, from 1992 to 2000. Of these, 29 were initially diagnosed as idiopathic VF associated with a structurally normal heart, normal left and right ventricular ejection fraction and normal coronary arteries. During the follow-up, 11 patients developed a new diagnosis potentially explaining VF, and were excluded from the final analysis. Four had right ventricular dysplasia, 5 had Brugada syndrome with the typical ECG manifestations after a drug challenge, and 2 patients had long QT syndrome. A final diagnosis of idiopathic VF was made in the remaining 18 patients. All patients had survived either an episode of cardiac arrest due to VF (12 patients) or a syncopal episode associated with documented self-terminating polymorphic ventricular tachycardia or fibrillation (6 patients). In 15 patients, the documented VF was seen during daily activity (one episode during sustained effort) and in 3 patients, during night-time. Out of the 18 patients, 10 had a previous history of unexplained syncope. Definitions and investigation Idiopathic VF was defined as VF in the absence of demonstrable cardiac abnormalities as previously reported [ 1 ]. Thus, we excluded other known causes of VT/VF such as WPW syndrome, congenital long QT syndrome, short-coupled torsade de pointes , catecholamine-induced polymorphic VT, and Brugada syndrome. No patient had a past history of ischemic heart disease, congestive heart failure or family history of unexpected sudden cardiac death. All patients had a normal physical examination, blood testing and exercise test. Structural heart disease was excluded in all by echocardiography, cardiac catheterization including right and left ventricular angiography and coronary angiography. Patients on anti-arhythmic therapy before their cardiac arrest were excluded from the study, as well as those with electrolyte disturbances, history of significant alcohol or drug abuse or prolonged QT c . Of the 18 patients, 10 patients had normal right ventricular endomyocardial biopsy and 8 patients had a normal cardiac magnetic resonance imaging. Moreover, in 5 patients, an ergonovine provocation test was performed to exclude coronary artery spasm. Brugada syndrome was systematically excluded in all patients by negative serial ECG recordings with the typical coved or saddle shaped-type ST-segment elevation in the right precordial leads. All patients received iv administration of a sodium channel blocker to unmask a concealed form of the disease and all tests were negative. Flecainide (2 mg/kg body weight) was used in 9 patients, procainamide (10 mg/kg body weight) in 4 patients, and ajmaline (0.7 mg/kg body weight) in 7 patients. Electrophysiological study Programmed electrical stimulation was performed in all patients using up to 3 extra-stimuli at 3 different drive cycle lengths (600, 500, and 430 ms) delivered to the right ventricular apex. The coupling interval of the first 2 extra-stimuli was not shorter than 180 ms and not shorter than 200 ms for the third extra-stimuli. In case of non-inducibility, programmed stimulation was also performed from the right ventricular outflow tract. Sustained VF was defined as VF at a cycle length ≤ 200 ms and lasting ≥ 30 seconds or requiring immediate defibrillation. Sustained ventricular tachycardia (VT) was defined as VT lasting ≥ 30 seconds or requiring termination secondary to hemodynamic instability. Non-sustained VT was defined as >6 consecutive ventricular complexes or VT lasting <30 seconds. ICD implantation All patients received a transvenous ICD with stored electrograms. Recorded episodes were reviewed and adjudicated as appropriate or inappropriate therapies. VF was defined at follow-up as consecutive ventricular beats recorded from the device at a cycle length of 240 msec or less. In all patients, the ICD was programmed with only 1 ventricular fibrillation detection zone at 180 bpm (333 msec). Only shock therapy was programmed. All patients were seen at follow-up in our centre. Statistical analysis All data are expressed as mean ± standard deviation. Kaplan-Meier analysis was used to analyze time intervals until the first appropriate shock. Results The study cohort consisted of 13 men and 5 women (Table 1 ), with a mean age at the first ICD implantation of 42 ± 14 years (range 20 to 70 years). Baseline electrocardiograms were normal in 16 patients. One patient had a left bundle branch block and 1 had a right bundle branch block. The mean QT c for the entire population was 410 ± 20 msec. All patients had a normal left ventricular ejection fraction (≥ 50%) without regional wall motion abnormalities. Mean left ventricular ejection fraction (EF) was 69 ± 8% (range 52 to 81%). No patient had significant coronary artery disease (defined as ≥ 50% stenosis). Microscopic examination of right ventricular biopsy specimens in 10 patients showed no evidence of a viral, infiltrative or dysplasic process in the myocardium. Baseline electrophysiological studies were performed in all patients and showed normal sinus node, atrioventricular node and His-Purkinje function in all. Inducible sustained ventricular arrhythmias occurred in 7 patients (39%). From these, 4 patients had sustained VF and 3 patients had sustained inducible monomorphic VT or ventricular flutter (cycle length 230 ± 20 ms). Two extra-stimuli were required in 5 patients and 3 extra-stimuli in 2 patients. Non-sustained VT-VF was induced in 3 patients. No arrhythmia could be induced in 8 patients. Table 1 Clinical characteristics and outcome of 18 patients with idiopathic VF Baseline Characteristics ICD Therapy Patient Age (years) sex CE Drug test to exclude Brugada PES AS NSVT IS Ind S Time to 1 st AS (months) Follow-up (months) AAD during follow-up 1 47,M Sy-VF F SMVT 6 7 - - 4 17 sotalol 2 51,F CA F NI 7 21 - - 1 19 sotalol 3 66,M CA AJ VF - - - - - 5 - 4 70,F CA P NI - - 6(af) - - 19 amiodarone 5 46,M CA F NI - 1 1(af) - - 23 sotalol 6 20,M Sy-VF A VF - - 3(st-lp) - - 92 b-blocker 7 46,F CA P VF - 120 - - - 27 sotalol 8 40,M CA A NSVT 1 - - - 13 75 - 9 33,M Sy-VF F NSVT - 1 - - - 19 - 10 26,M CA F NI 1 - 3(st-lp) - 7 9 sotalol 11 33,F CA F NI - - - - - 18 - 12 50,F CA F SMVT 6 - 1(st) - 13 86 flecainide 13 25,M CA AJ + P NSVT - 1 - - - 62 - 14 36,M CA AJ NI 2 - 2(af) 4 27 55 sotalol 15 58,M Sy-VF AJ NI - - 3(af) 2 - 57 sotalol 16 44,M CA F + P NI - - - - - 48 - 17 28,M Sy-VF F VF 4 - 2(st) - 18 55 sotalol 18 35,M Sy-VF A SMVT - - - - - 59 - CE: clinical events; CA: cardiac arrest; Sy-VF: syncope with documented self-terminating polymorphic VT or VF PES: programmed electrical stimulation; M: male; F: female SM: sustained monomorphic; VT: ventricular tachycardia; VF: ventricular fibrillation NS: non sustained; NI: non inducibility; af: atrial fibrillation; st: sinus tachycardia; lp: lead problem AS: appropriate shock; IS: inappropriate shock; Ind S: indeterminate cause for shocks; AAD: antiarrhythmic drug; F: flécaïnide; AJ: ajmaline; P: procainamide Long-term outcome After a mean follow-up of 41 ± 27 months (ranging from 5 to 92 months), 7 of the 18 patients (39%) with idiopathic VF had VF or sustained polymorphic VT recurrence and received appropriate shocks (Table 1 ). These patients experienced a total of 27 shocks; 5 of them had more than 1 episode (2 to 7). Mean time to first appropriate shock was 12 ± 9 months (ranging from 0.4 to 27 months). No arrhythmic storm was observed at follow-up (defined as ≥ 3 separate VT/VF episodes within 24 h). With the use of stored electrograms, 6 patients had documented non-sustained polymorphic VT or VF for a total of 149 episodes (range 1 to 120). Four of them have not received any appropriate shock. These episodes were detected by the ICD but shock delivery was appropriately aborted by the non-committed function of the device. There was no relationship between the initial clinical and arrhythmic presentation and subsequent arrhythmic events recorded from the ICD at follow-up (Table 1 ). From the intracardiac electrograms, the arrhythmia initiation was always associated with PVCs with a mean coupling interval of 300 ± 35 ms. A long-short initiating sequence of VF was never observed. In 5 patients, multiple VF episodes were recorded by the ICD, all of which for a single patient were associated with the same PVC coupling interval. Since the stored electrograms in the present study were obtained from endocardial sites and were single-channel recordings, we could not assess the origin of the PVCs. The mean QTc at the time of VF recurrence was normal in all patients (mean 418 ± 22 msec). The ICD effectively recognized and promptly treated all the polymorphic VT or VF recurrences and prevented the possible occurrence of sudden cardiac death. No death was reported during follow-up. Non-invasive follow-up examinations failed to detect any new structural heart disease or primary electrical disease such as long QT or Brugada syndrome. Value of electrophysiological Testing Programmed electrical stimulation failed to predict subsequent cardiac events. The sensitivity and specificity were 43 and 64%, respectively. The positive and negative predictive values were also 43 and 64%, respectively, that is not considered clinically useful. Causes and incidence of inappropriate shocks In this population, we observed a high incidence of inappropriate shocks. Eight of the 18 patients (44%) received an inappropriate shock for a total of 21 discharges. Atrial fibrillation was responsible for 57% of them (12 episodes in 4 patients). These were older patients with a mean age of 53 ± 14 years. Six inappropriate shocks (4 patients) were triggered by sinus tachycardia. These patients were younger with a mean age of 31 ± 13 years. Lead malfunction caused 3 inappropriate shocks in 2 patients. Six shocks in 2 patients were classified as of unknown cause, even after careful examination of the intracardiac electrograms. These shocks were probably inappropriate since no clinical symptoms occurred during these episodes and these 2 patients already experienced inappropriate shocks for atrial fibrillation. Device follow-up During the follow-up period, first-time generator replacement was performed in 9 patients after a mean of 43 ± 11 months after initial implantation (range 21 to 58). End of life battery was the indication for replacement in 8 patients, and 1 had an ICD component failure. A second replacement was done in 2 patients. Lead replacement was also indicated in 2 patients for lead insulation fracture. Adjunctive anti-arrhythmic drugs were required in 11 patients after ICD implantation in order to control for the occurrence of appropriate or inappropriate shocks (Table 1 ). Discussion To our knowledge, this report is the first study to describe the clinical outcome of ICD patients with a diagnosis of idiopathic VF in whom the Brugada syndrome, characterized by a high recurrence rate, was systematically excluded. Since there is a wide variability in the ECG expression in individual patients with the Brugada syndrome, we performed iv administration of sodium channel blockers to unmask the ECG features of the syndrome in all our idiopathic VF patients. Two studies reported the long-term outcome of patients with idiopathic VF without the Brugada syndrome [ 5 , 6 ]. Viskin et al. [ 5 ] performed iv administration of a sodium channel blocker to unmask a concealed form of Brugada syndrome in only 15 % of their idiopathic VF population, whereas Remme et al. [ 6 ] tested the effect of an iv sodium channel blocker on ECG morphology in only 30% of the patients. The unique finding of the present report is the high recurrence rate of sustained ventricular arrhythmias in this well-characterized idiopathic VF group, even after a careful systematic evaluation to exclude the presence of Brugada syndrome. After a mean follow-up of 41 ± 27 months, 39% of the patients received an appropriate ICD discharge for VF or polymorphic VT. In accordance with references [ 8 , 9 ], ICD therapy offered good protection against fatal outcome due to recurrent ventricular arrhythmias, with no mortality during the follow-up. The high recurrence rate is consistent with the relatively high recurrence rate of arrhythmic events or sudden death from the UCARE registry [ 10 ], as well as the high frequency of electrical discharges from ICDs reported in a large series of patients with idiopathic VF [ 6 , 8 , 9 ]. In comparison, appropriate ICD discharges have been reported in 48% of arrhythmogenic right ventricular dysplasia population [ 11 ], 40 to 56% of inducible population receiving an ICD [ 12 , 13 ] and 23% (7%/year) in the hypertrophic cardiomyopathy population [ 14 ]. However, some authors have reported a more benign clinical course of their idiopathic VF population with a lower recurrence rate [ 15 - 17 ]. Our high ICD discharge rate might also be explained by the rapid ICD intervention for fast ventricular arrhythmia that could have been self-terminated. The definition of idiopathic VF remains problematic, since numerous studies including idiopathic VF population are heterogeneous. Indeed, 61% (11/18 patients) of our cohort remained free of sustained arrhythmia recurrence during follow-up despite the same initial VF or syncopal presentation. Of note, idiopathic VF is always a diagnosis of exclusion. The patients in our cohort, as well as in other reported series, had normal hearts, as defined by clinical, non-invasive and invasive testing. Even after a careful investigation, a transient phenomenon such as a reversible localized myocardial disease, silent myocardial ischemia due to coronary artery spasm or sudden manifestation of an unknown primary electrophysiological disease could easily be missed by the clinician, and be responsible for the VF episode. Idiopathic VF is in fact an amalgam of different diseases in which the first clinical manifestation is VF. The ultimate answer probably lies in a more comprehensive approach and a more precise understanding of the molecular genetics and associated electrophysiological abnormalities in finding a specific treatment avoiding ICD implantation with its associated complications. Anti-arrhythmic drugs have also been used in this clinical condition. Belhassen et al. [ 18 ] reported excellent results in their idiopathic VF population with EP-guided therapy using Class 1A drugs, primarily quinidine; no death occurred during a mean follow-up of >9 years. Recent evidence also suggests that frequent premature ventricular contractions arising from the Purkinje system are responsible for initiation of ventricular fibrillation, and can be mapped and ablated in selected patients [ 19 ]. Until another proven therapy has been assessed prospectively, the possibility of VF recurrence mandates ICD implantation as currently proposed in ICD guidelines [ 20 ]. Electrophysiological findings The inducibility rate (39%) of sustained VT/VF observed in our study is also in accordance with the results of the UCARE registry [ 10 ]. Of importance, programmed electrical stimulation was of limited value with a poor sensitivity and specificity. Moreover, inducibility failed to predict subsequent arrhythmic events. Our findings differ from the observed 79% average inducibility rate reported by Belhassen et al. [ 17 , 18 ]. This may be due to different patient characteristics and stimulation protocol. Our study excluded patients with Brugada syndrome known to have a high inducibility rate [ 21 ]. Compared to our stimulation protocol, Belhassen et al. [ 22 ] used up to 3 extra-stimuli, 2 basic cycle lengths, 2 RV sites (first RVA, then RVOT), and repetition of extra-stimulation ( n = 10 for double, and n = 5 for triple) at the shortest coupling intervals that resulted in ventricular capture. Three patients had a fast monomorphic VT inducible even if the clinical presentation was aborted sudden cardiac death. This may suggest an underlying structural heart disease undetected by the current investigation. Although general cardiac function can be normal, patients might have discrete abnormalities, which are currently unidentifiable. Better risk stratification is required to identify patients who will experience recurrent VF over time. Therefore, defibrillator implantation could be seen as the primary therapy in patients with idiopathic VF, since no stratification has yet identified patients at risk of arrhythmia recurrence. ICD limitations The impact of multiple battery replacements over time in this young population with idiopathic VF needs to be emphasized. It is noteworthy that half the population (9/18 patients) had an ICD replacement and 2 other patients had their transvenous lead replaced during follow-up. We also observed a high incidence of inappropriate shocks (44%), mainly caused by atrial fibrillation and sinus tachycardia. This is well in accordance with other studies where up to 40% inappropriate discharge rates were observed even with fourth generation ICDs [ 23 ]. The addition of an anti-arrhythmic drug to decrease the incidence of inappropriate shocks was required in this group. Four patients experienced atrial fibrillation, which might indicate the presence of an associated primary electrophysiological disease also affecting the atrium. In patients with idiopathic VF, the characteristics, is the recurrence of VF and not monomorphic VT compared to other ventricular arrhythmias. To decrease the number of inappropriate shocks, one should program a higher VF zone around 200–220 bpm and a lower monitoring zone to detect atrial arrhythmia. Third-generation ICD still have limitations and complications over time, with a significant proportion of patients having hardware-related complications or inappropriate shocks [ 24 ]. Future developments in ICD technology is needed, which will hopefully address these issues. Conclusions Idiopathic VF patients have a high recurrence rate of ventricular arrhythmias, even after the systematic exclusion of patients with the Brugada syndrome or other known electrical diseases from the analysis. ICD prevents sudden cardiac death in this population, but treat only the final manifestation of an unknown disease. Inappropriate shocks remain a major concern in this young population. Competing interests The author(s) declare that they have no competing interests. Author's contributions JC participated in the hypothesis generation and drafted the manuscript. PG and PB were project managers. FP participated in the final writing of the manuscript. All authors have read and approved the final version of the manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC545059.xml |
544353 | An unusual pathological finding of chronic lymphocitic leukemia and adenocarcinoma of the prostate after transurethral resection for complete urinary retention: case report | Background We describe a patient who underwent transurethral resection of the prostate for urinary obstructive symptoms and had histological findings of adenocarcinoma of the prostate with prostatic localization of chronic lymphocitic leukemia (CLL).The contemporary presence of CLL, adenocarcinoma of the prostate and residual prostatic gland after transurethral resection has never been reported before and the authors illustrate how they managed this unusual patient. Case presentation A 79-years-old white man, presented with acute urinary retention, had a peripheral blood count with an elevated lymphocytosis (21.250/mL) with a differential of 65.3% lymphocytes and the prostate-specific antigen (PSA) value was 3.38 ng/mL with a percent free PSA of 8.28%. The transrectal ultrasound (TRUS) indicated an isoechonic and homogenic enlarged prostate of 42 cm 3 and the abdomen ultrasound found a modest splenomegaly and no peripheral lymphadenophaty. The patient underwent transurethral resection of the prostate and had a pathological finding of adenocarcinoma in the prostate with a Gleason Score 4 (2+2) of less than 5% of the material (clinical stage T1a), associated with a diffused infiltration of chronic lymphocitic leukemia elements. Conclusions The incidental finding of a prostatic localization of a low-grade non-Hodgkin's lymphoma does not modify eventually further treatments for neither prostate cancer nor lymphoma. The presence of a low-grade and low-stage lymphoma, confirmed by a hematological evaluation, and the simultaneous evidence of an adenocarcinoma after transurethral resection of the prostate for acute urinary retention do not require any immediate treatment due to its long-term survival rate and the follow-up remains based on periodical PSA evaluation and complete blood count. | Background The incidence of non-Hodgkin's lymphoma among prostate cancer patients is extremely low (0.2%), and, even more the leukemic infiltration of the prostate [ 1 , 2 ]. It has been reported that a chronic lymphocitic leukemia (CLL) may have its first clinical manifestation with acute urinary retention [ 3 ]. We describe a patient who underwent transurethral resection of the prostate for urinary obstructive symptoms and had histological findings of adenocarcinoma of the prostate with prostatic localization of CLL. The contemporary presence of CLL, adenocarcinoma of the prostate and residual prostatic gland after transurethral resection has never been reported before and the authors illustrate how they managed this unusual patient. Case presentation A 79-year-old white man, presented with acute urinary retention, was initially treated by indwelling catheter and was referred for complete urological evaluation. The digital rectal examination revealed a benign feeling prostate of 40 g, the physical examination was negative and the International Prostatic Symptoms Score (I-PSS) was 21. The transrectal ultrasound (TRUS) indicated an isoechonic and homogenic enlarged prostate of 42 cm 3 and the abdomen ultrasound found a modest splenomegaly and no peripheral lymphadenophaty. The peripheral blood count showed an elevated lymphocytosis (21.250/mL) with a differential of 65.3% lymphocytes, while other parameters as platelet count, number of erythrocytes and lactate dehydrogenase hormone level were all normal. The prostate-specific antigen (PSA) value was 3.38 ng/mL with a percent free PSA of 8.28%. Due to his age and symptoms, the patient underwent transurethral resection of the prostate. The pathological examination of the tissue revealed an adenocarcinoma in the prostate with a Gleason Score 4 (2+2) of less than 5% of the material (clinical stage T1a), associated with a diffused infiltration of chronic lymphocitic leukemia elements (see Figure 1 ), CD5 and CD20 positive (see Figure 2 ). The patient underwent a computed tomography of the abdomen, confirming only a moderate splenic enlargement but not lymphadenomegaly (stage 2 of Rai) and was then referred for a haematological evaluation. No other investigations or treatments were required due to the age, the low grade and stage of the disease in this patient. At a follow-up of 24 months the patient is alive, with a PSA value of 0.65 ng/ml, a stable haematological condition and the International Prostatic Symptoms Score (I-PSS) is 6. Conclusions The prostatic involvement by non-Hodgkin's lymphomas is an unusual finding. The simultaneous presence in the prostate of CLL and adenocarcinoma has been reported in a percentage variable from 0% (4319 patients underwent radical prostatectomy in Eisemberger et al. ,) to 0.8% (1092 patients underwent radical prostatectomy in Terris et al. ) [ 1 , 2 ]. It has been demonstrated that the presence of malignant lymphocitic infiltrate in the prostate cannot be detected on TRUS, and the ultrasound-guided prostate needle biopsies exhibit a lack of diagnostic accuracy with a sensitivity of only 22% for detecting leukemic infiltration of the prostate [ 4 ]. In our case, the ultrasound images showed only a modest splenomegaly, with no other specific finding, and the low value of PSA with the age of the patient did not indicate for prostate biopsies and eventually consequent radical prostatectomy. The patient underwent transurethral resection on the basis of his voiding symptoms. The presence of a low-grade and low-stage lymphoma, confirmed by a haematological evaluation, and the simultaneous evidence of an adenocarcinoma after transurethral resection of the prostate for acute urinary retention, do not require any immediate treatment due to their long-term survival rate and the follow-up should be based on periodical PSA evaluation and complete blood count [ 5 ]. The incidental finding of a prostatic localization of a low-grade non-Hodgkin's lymphoma, even in presence of a residual prostatic gland, does not modify eventually further treatments for neither prostate cancer nor lymphoma. Competing interests The author(s) declare that they have no competing interests. Author's contributions RB and PB managed the patient, edited the manuscript and coordinated the submission. LR carried out the literature search, SC performed the surgical procedure and MZ performed the pathological examination and realised the figures. WA revised the manuscript for scientific content. All authors contributed to the preparation of the manuscript. All the authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC544353.xml |
520819 | A preliminary report of an educational intervention in practice management | Background Practice management education continues to evolve, and little information exists regarding its curriculum design and effectiveness for resident education. We report the results of an exploratory study of a practice management curriculum for primary care residents. Methods After performing a needs assessment with a group of primary care residents at Wright State University, we designed a monthly seminar series covering twelve practice management topics. The curriculum consisted of interactive lectures and practice-based application, whenever possible. We descriptively evaluated two cognitive components (practice management knowledge and skills) and the residents' evaluation of the curriculum. Results The mean correct on the knowledge test for this group of residents was 74% (n = 12) and 91% (n = 12) before and after the curriculum, respectively. The mean scores for the practice management skill assessments were 2.62 before (n = 12), and 3.65 after (n = 12) the curriculum (modified Likert, 1 = strongly disagree, 5 = strongly agree). The residents rated the curriculum consistently high. Conclusions This exploratory study suggests that this curriculum may be useful in developing knowledge and skills in practice management for primary care residents. This study suggests further research into evaluation of this curriculum may be informative for practice-based education. | Background Practice management education for residents has traditionally included training physicians in management issues related to the practice environment, including fiscal management, leadership skills, business and management skills, and managed care concepts [ 1 ]. Managed care concepts include ethics, communication skills, payment systems, population medicine, informatics and disease prevention. Although in existence since the 1970's, most practice management curricula have focused on managed care concepts, with little attention to the other skills [ 2 - 7 ]. In 2001, educators from Tuft's University wrote a report for curriculum development in the evolving practice environment [ 8 ]. This report, which was synthesized from nine component reports of national medical educational organizations, recommended future curriculum development beyond the traditional scope of managed care curriculum. It recommended redefining practice management as a curricular domain of fiscal, business, and practice system management skills distinct from traditional managed care topics [ 8 ]. The ACGME has recognized the need for residency training in the evolving practice environment, and has recommended training to include practice-based learning and improvement and systems-based practice [ 9 ]. The regression of traditional third-party managed care plans also implies an increased value and need for practice management skills [ 10 ]. Given this broad support of practice-based learning, physicians will need ongoing practice management and health systems education for the foreseeable future. A few studies on practice management curricula exist for resident education, but much more information is needed on successful curriculum design and evaluation [ 11 - 13 ]. We describe, in detail, a pilot practice management curriculum design using the evolving curricular theme for a group of primary care residents. We also report its initial analysis on improving resident knowledge and skills, and describe the residents' evaluation of the curriculum. Methods Educational setting We developed the curriculum for the University Medicine/Pediatrics Practice (UMP). This practice is a primary care-oriented, faculty-resident practice on the campus of Wright State University. Thirteen internal medicine/pediatrics residents, five general internist faculty, two internal medicine/pediatrics faculty, and one pediatrics faculty practice here. The practice is managed by Premier Healthnet, a 100 physician multi-site primary care group. Although UMP's mission includes addressing the needs of indigent patients in the Dayton area, it is modeled after a community-based, teaching practice model. Therefore, faculty and residents are expected to use effective practice management skills in their individual practices. A typical resident from our program enters a small (1–5 physician) community practice upon graduation and practices both internal medicine and pediatrics. Needs assessment In 2001, two faculty members (GEC and RJS) at the Wright State University Departments of Medicine and Community Health were identified as lead faculty for this curricular project. Primary care faculty in the Departments of Pediatrics and Medicine tasked these two lead faculty members to development a practice management curriculum to reflect the evolving practice theme. By "evolving practice theme," we mean teaching practice management topics similar to the Tuft's curricular theme. One lead faculty member (GEC) performed a needs assessment on the Internal Medicine/Pediatrics residents at UMP in the spring of 2001. The assessment used qualitative analysis via informal interviews of two senior residents, one of whom was chief resident. The interviews included open-ended questions on the need for practice management knowledge (example question: "What do you need to learn this year to help prepare you for running a community practice?"). The needs assessment also included an open feedback session with the residents after discussion of potential topics at the monthly resident education meeting (majority of residents present). The results of the need assessment were uniform; the residents felt inadequately trained in practice management. The lead faculty concluded that these residents had some training in a few specific content areas (i.e., coding), but lacked an overall basic practice management knowledge or skill. Curriculum design The lead faculty met again in mid-2001 to design the curriculum. The goals of the curriculum were to give the residents a basic understanding of practice management concepts and skills in the evolving practice environment. The lead faculty were free to select the most effective methods to meet their goals. They did face some challenges. They were given only 30-minute time slots each month and had 12 months to accomplish these goals. They also had to show some objective evidence of its effectiveness and have support of the residents at the end of the 12 months to continue the curricular project. The design process resulted in a series of seminars covering 12 topics, listed in Table 1 , with objectives. The seminars began in July 2001, and concluded in June 2002. The lead faculty assigned teachers to each seminar who were content experts, and included a medical biller, a nurse manager, a health systems researcher, two local HMO medical directors, a financial advisor, a risk manager, and a WSU junior faculty member. The assignment of seminar teachers is listed in Table 1 , and one lead faculty member (RJS) led two sessions (referred to as the health systems researcher listed under Revenue Management and Accounts Payable Management in Table 1 ). Although the seminar teachers were free to utilize any method and media to meet their session objectives, they were encouraged to use as much interactive teaching approach as possible. The sessions were primarily in the form of teacher-centered discussions augmented primarily with handouts, overheads, and slides. The seminar teachers often supplied references and reference materials as tools for the residents in their daily practices. We encouraged ambulatory practice faculty throughout the year to discuss with residents, during resident ambulatory practices, application of principles learned in the seminar series. Table 1 Schedule of topics (in bold), teacher assignments, and objectives for the practice management seminar series Topic, teacher Objectives Basic Coding , Medical Biller Introduction to the Fee Ticket E/M and PT Basics ICD-9 Basics Revenue Management , Health Systems Researcher Health System Overview Payment Systems How Physicians Get Paid Optimizing Coding to Enhance Reimbursement , Medical Biller Reimbursable Diagnoses in Primary Care Using Modifiers Procedures and Medication Coding Physician Personal Finance , Financial Advisor Financial Goals Financial Planning Insurance Systems and Payment Mechanisms , HMO Director #1 Insurance Contracts IPAs and Collective Bargaining Dynamics of Group Practice , HMO Director #2 Partnerships Structures: Solo, Small Group, Multi-specialty Practices Physician Leadership and Consensus Building Getting a Good Job , WSU Faculty Member Finding Positions and Writing CVs The Interview Process Contract Negotiations Accounts Receivable Management , Medical Biller The A/R Sheet Fiscal Targets Collections Management Accounts Payable Management , Health Systems Researcher Minimizing Expenses in Primary Care Economics of Running a Primary Care Practice Human Resources , Nurse Manager Staffing Needs Assessment Hiring/Firing/EEO Payroll & Benefits Conflict Resolution Risk Management , Risk Manager Minimizing Medico-legal Risk in Practice Regulatory Restrictions in Practice , Nurse Manager Understanding CLEA, OSHA, and HIPPA Curriculum evaluation The respondents were a convenience sample of Internal Medicine/Pediatrics residents from all four years of training. We used a pre-experimental (one-group pretest/posttest) design for this exploratory study. We descriptively evaluated the curriculum on two cognitive components: practice management knowledge and skills. We also assessed the residents' evaluation of the curriculum. To evaluate practice management knowledge, we used a knowledge test consisting of identical 12 item (true/false statements), and each question covered one objective from each topical area from Table 1 . One example of a test item in the content area of coding is: "An established patient who has an expanded problem focused history and exam may be billed at a 99215 level." We administered the 0-month test to the entire group immediately before the first seminar session. We administered the 12-month test to the entire group immediately after the last seminar session. To evaluate practice management skills, we devised a survey of self-assessed practice management skills. The survey consisted of 12 statements, and each statement queried the residents to respond on their assessment of their own practice management skills. Each statement consisted of one specific skill from an objective from each topical area listed in Table 1 . An example of one survey item in the content area of coding is: "I understand how to use modifiers with E/M (evaluation and management) coding." We based the responses to the statements on a modified Likert scale, with 1 being strongly disagree, and 5 being strongly agree. We administered the 0-month self-assessed skill survey to the entire group immediately before the first seminar session. We administered the 12-month self-assessed skill survey to the entire group immediately after the last seminar session. To explore the residents' evaluation of the curriculum, we devised another survey. This survey consisted of four statements querying the residents on their overall assessment of this curriculum and practice management education in general. The statements from the survey are given in Table 2B . The responses were based on the same Likert scale described above. This survey was administered to the entire group immediately after the last session. Table 2 Resident self-assessed practice management skills (A) and curriculum evaluation (B) (modified Likert scale: 1 = strongly disagree and, 5 = strongly agree) Evaluation component 0-month (n = 12) 12-month (n = 12) Mean (95%CI) Mean (95%CI) A: Self-assessed practice management skills: Results from 12 item survey 2.62 (2.27 – 2.97) 3.65 (3.41–4.08) B: Evaluation of practice management curriculum: Mean (1 SD) Mean (1 SD) Practice management series was effective in teaching me basic practice management knowledge NA 4.13 (0.61) I feel more confident in my own practice skills because of this curriculum NA 3.96 (0.45) I feel practice management curriculum should be incorporated into primary care curriculum NA 4.67 (0.65) I would be interested in expanding my primary care curriculum to include more practice management education NA 4.67 (0.49) The process of test instrument development was the same for both the knowledge test and the self-assessed skills survey. One lead faculty member (GEC) would generate a list of candidate items based on each objective in Table 1 . The second lead faculty member (RJS) would review the list and select and/or modify items to match the item content to the objectives listed. Thus, both instruments possessed good face validity. Reliability testing was not performed due to the small sample size. Post-hoc item analysis on the 0-month knowledge test showed that only 2 items were answered 100% correct and the lowest item scored was 33% correct for this group. This suggests minimal floor and ceiling effects in the item mix. All other items ranged from 52% to 92% correct. Results The participants were the 13 Internal Medicine/Pediatrics residents, and represented all four years of training (2, 4 th -year; 3, 3rd-year; 4, 2 nd year; and 4, 1 st -year residents). A third year resident failed to complete the 0-month tests and surveys, and a first year resident failed to complete the 12-month tests and surveys. This left 12 responses for both sets (0- and 12-month) tests and surveys. The average attendance for the sessions was 12, with a range of 10–13 attendees. The results from the knowledge test are given in Figure 1 . As a group, the residents' mean score was 74% (95% CI, 68%–80%) for the 0-month survey and 91% (95% CI, 85–96%) for the 12-month survey. These confidence intervals do not overlap. This suggests that, if hypothesis testing were done, the results would probably reach statistical significance for the knowledge test. Figure 1 Practice knowledge test results (mean and 95% CI): before (0-month) and after (12-month) the course On follow-up, we performed two post-hoc analyses. First, we were interested if these knowledge scores would decline over time. Therefore, we compared the knowledge test scores on the first six months topics to the scores on the last six months topics. Both sets of scores were derived from the 12-month knowledge test. We found that the mean scores appeared similar (first 6 months mean scores: 92% correct; the last 6 months mean scores: 90% correct). Second, we were interested if the missing data on the 0-month and 12-month data could have impacted the results. Since one third-year resident completed the 12-month but not the 0-month test, we were interested in exploring if his responses on the 12-month test could have caused a larger difference between these two tests. After censoring his data, there appeared to be little impact on the 12-month results (censored mean score = 0.91, censored 95% CI, 85–96%). Additionally, the first year resident who failed to complete the 12-month test may have also impacted the results. Due to loss of identity links, we could identify her data to censor from the 0-month test. However, we censored the lowest score on the 0-month test as representing hers (this assumes that her score lowered the 0-month data the most, and, therefore, had the largest impact on 0-month mean score by skewing it away from the 12-month mean score). After censoring this data, we found no significant change in the 0-month results (censored mean score 0.75, censored 95% CI, 70–80%). The results for the self-assessed skill survey are given in Table 2A . The mean scores on the 12-month survey (3.65) were higher than in the mean scores for the 0-month survey (2.62). The confidence intervals from this data do not overlap. This suggests that, if hypothesis testing were done, the results would probably reach statistical significance for the self-assessed practice management skills survey. The results of the curriculum evaluation survey are given in Table 2B . All statements had a mean rating of greater than 3.90. The two statements assessing the residents' views towards practice management education in general (value of practice management education and the need to expand their education) both had mean scores of 4.67. Discussion The practice environment continues to evolve [ 8 ]. Although a traditional term for educating physicians in the practice environment, "managed care curriculum" is a vague terminology and lacks comprehensiveness [ 8 ]. The Tufts' report did not use this term for specific curricular terminology, and this may parallel the purported demise of the term for the traditional payer system [ 10 ]. The Tufts' report included a comprehensive list of 10 curriculum domains in the evolving practice environment [ 8 ]. This report gave the practice management domain, which had lacked emphasis in half of its nine component reports, equal emphasis as the traditional managed care curricular domains [ 8 ]. The practice management domain included training on topics such as basic business skills, management skills, financial risk, payment systems, process improvement, and practice systems [ 8 ]. With respect to the evolving practice environment, the challenge for educators is devising practice management curricula that cover these topics adequately and relating them to other curricular domains (i.e., health systems, quality improvement, etc.). We were interested in whether a curriculum design with this evolving theme may be useful in primary care education. We describe, in detail, a curriculum design similar to the evolving theme designed for a small group of primary care residents. The advantage of such a program as ours is its detailed design based on general and specific needs assessments and a description of evaluation methodologies. Our data suggests that this intervention may have had an impact on resident knowledge scores and self-assessed skills. Additionally, the residents appeared remarkably positive towards this practice management curriculum and practice management education in general. A few studies have been published on practice management curricular design and evaluation for primary care residents. In a response to the growing need physician-managers, both Zoorob and Taylor and Johnson described curricular designs they proposed would fill this need [ 12 , 14 ]. Lynch and Johnson published a report on the evaluation of business management skills in primary care residents, and found no improvement with a short educational intervention (two day seminar) [ 11 ]. Werblun et al. described a proposed curriculum design and evaluation that would meet the needs for business management skills, and like our curriculum, recommended implementation over the course of the term of residency. Our study does have some limitations. Because our small sample size, formal hypothesis testing was not possible and our data remains descriptive only. Stronger conclusions of these results would need to be re-evaluated with more subjects using formal hypothesis testing methods. Our experience suggests that internal motivation was probably one key factor to acceptance and apparent acceptance of this curriculum; the request for developing the curriculum came from our residents themselves. Also, the UMP faculty is uniformly positive towards developing these skills in themselves and in the residents, and this probably influenced residents' motivation to learn the subject matter. Since our faculty-resident practice is based on a primary care, community model, it may be difficult to generalize it to hospital-based practices or specialty residency training. Conclusions We conclude that an extended curriculum in practice management with an evolving practice theme may be useful in primary care education. We also believe that attention to instructional design, including performing a needs assessments, using many teaching methods, and applying the concepts learned in learners' practices, may contribute to its acceptance and success. Future educational designs for this curriculum include its continued expansion, exploring more educational opportunities for implementation, and addressing specific characteristics of success and failure. Future educational research in this area would require a more formal research design to derive stronger conclusions regarding its effectiveness. Competing interests None declared. Author contributions GEC participated in the curricular needs assessments, curricular design, curriculum implementation, and drafting of the manuscript. RJS participated in the curricular design, curriculum implementation, and drafting of the manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC520819.xml |
546236 | Prolonged treatment of genetically obese mice with conjugated linoleic acid improves glucose tolerance and lowers plasma insulin concentration: possible involvement of PPAR activation | Background Studies in rodents and some studies in humans have shown that conjugated linoleic acid (CLA), especially its trans -10, cis -12 isomer, reduces body fat content. However, some but not all studies in mice and humans (though none in rats) have found that CLA promotes insulin resistance. The molecular mechanisms responsible for these effects are unclear, and there are conflicting reports on the effects of CLA on peroxisomal proliferator-activated receptor-γ (PPARγ) activation and expression. We have conducted three experiments with CLA in obese mice over three weeks, and one over eleven weeks. We have also investigated the effects of CLA isomers in PPARγ and PPARα reporter gene assays. Results Inclusion of CLA or CLA enriched with its trans -10, cis -12 isomer in the diet of female genetically obese ( lep ob / lep ob ) mice for up to eleven weeks reduced body weight gain and white fat pad weight. After two weeks, in contrast to beneficial effects obtained with the PPARγ agonist rosiglitazone, CLA or CLA enriched with its trans -10, cis -12 isomer raised fasting blood glucose and plasma insulin concentrations, and exacerbated glucose tolerance. After 10 weeks, however, CLA had beneficial effects on glucose and insulin concentrations. At this time, CLA had no effect on the plasma TNFα concentration, but it markedly reduced the plasma adiponectin concentration. CLA and CLA enriched with either isomer raised the plasma triglyceride concentration during the first three weeks, but not subsequently. CLA enriched with its trans -10, cis -12 isomer, but not with its cis -9, trans -11 isomer, stimulated PPARγ-mediated reporter gene activity; both isomers stimulated PPARα-mediated reporter gene activity. Conclusions CLA initially decreased but subsequently increased insulin sensitivity in lep ob / lep ob mice. Activation of both PPARγ and PPARα may contribute to the improvement in insulin sensitivity. In the short term, however, another mechanism, activated primarily by trans -10, cis -12-CLA, which probably leads to reduced adipocyte number and consequently reduced plasma adiponectin concentration, may decrease insulin sensitivity. | Background The term conjugated linoleic acid (CLA) refers to a mixture of positional and geometric isomers of linoleic acid ( cis -9, cis -12-octadienoic acid). The major components of CLA, are the cis -9, trans -11 (c9, t11) and the trans -10, cis -12 (t10, c12) isomers, both of which have biological activities. The t10, c12-isomer is the one primarily responsible for the effects of CLA on weight gain and insulin sensitivity. The CLA used in the present study contains these isomers in roughly equal proportions. CLA may be of benefit in cancer, atherosclerosis and possibly some disorders of the immune system. In addition, it reduces weight gain and fat accretion in rats and mice. Some studies have found that CLA causes fat loss in humans [ 1 - 4 ], and two studies have shown weight loss[ 2 , 5 ], but another did not find any significant effect of CLA on body composition or body weight [ 6 ]. More marked effects of CLA on body weight and body composition have been obtained in rats and especially mice, possibly because the rate of energy expenditure relative to energy stores is much higher in rodents than in humans. In previous studies, CLA or t10, c12-CLA has exacerbated glucose tolerance and raised plasma insulin levels in normal and genetically obese ( lep ob / lep ob ) mice, despite causing weight loss [ 7 - 10 ]. Both t10, c12-CLA and c9, t11-CLA have also been reported to cause insulin resistance in humans [ 11 - 13 ], although in other studies neither CLA nor its major isomers affected insulin resistance significantly [ 14 - 17 ]. By contrast, CLA or t10, c12-CLA improved glucose tolerance in Zucker fatty lep fa / lep fa and Zucker diabetic fatty lep fa / lep fa rats [ 18 - 21 ]. One difference between the rodent species is that peroxisomal proliferator-activated receptor (PPAR)α-regulated genes are more responsive to CLA in mice than in rats [ 22 ], but this would be expected to correlate with improved insulin sensitivity in mice rather than rats [ 23 ]. By contrast with this result for PPARα knockout mice, at least one response to CLA depends on expression of PPARγ: the beneficial effect of CLA in a mouse model of colitis was absent in mice that lacked PPARγ in the colon [ 24 ]. In view of the variety of reports of CLA's effects on PPARγ activity and expression, it is possible that PPARγ responses may vary between species; the different proportions of isomers in the CLA used in different studies may also be important [ 25 ]. Some reports describe activation of PPARγ by CLA or t10, c12-CLA [ 18 , 33 ]; others describe little or no agonist activity [ 10 ], but antagonism of rosiglitazone [ 25 , 27 ]. One report describes increased expression of PPARγ mRNA in white adipose tissue of rats [ 28 ] and another describes increased expression in liver of mice [ 10 ], but studies in isolated adipocytes or of adipose tissue from treated mice show decreased expression [ 25 , 27 , 29 , 30 ]. Since PPARγ agonists increase insulin sensitivity but promote adipogenesis, decreased activity of PPARγ in adipose tissue could explain why CLA reduces obesity but increases insulin resistance. Various other mechanisms have also been suggested to explain why CLA exacerbates insulin sensitivity despite causing loss of fat. One of these is increased expression of tumour necrosis factor (TNF)α, since TNFα is associated with apoptosis of adipocytes but causes insulin resistance. TNFα mRNA levels were markedly increased in isolated adipocytes from normal mice that had been fed on CLA for as little as four days [ 8 ]. Surprisingly however, serum TNFα levels were reduced by CLA in both normal rats [ 31 ] and mice [ 32 ]. Moreover CLA reduced the expression of TNFα in mouse macrophages [ 33 ]. Although most studies in mice have found that CLA exacerbates glucose tolerance and raises the plasma insulin concentration, in one study treatment of lep db / lep db mice with CLA for eleven weeks improved glucose tolerance and reduced plasma insulin concentration during the glucose tolerance test, indicating improved insulin sensitivity. Treatment for five weeks also improved glucose tolerance but plasma insulin was raised [ 34 ]. It is sometimes difficult to interpret studies in lep db / lep db mice because β-cell failure as well as insulin resistance affects glucose homeostasis, and glycosuria can cause weight loss. Therefore, to investigate the effect of CLA on glucose homeostasis further, we have conducted four experiments in lep ob / lep ob mice. We have included rosiglitazone as a comparator in one experiment and compared the effects of t10, c12- and c9, t11-enriched CLA with CLA in another experiment. The first three experiments were over 22 days, whereas the fourth was extended to eleven weeks and included measurements of plasma TNFα and adiponectin. An intriguing finding is that whereas CLA initially exacerbated glucose tolerance and raised the plasma insulin level, after ten weeks it began to improve glucose tolerance and lower the plasma insulin levels. We also describe activation of PPARγ by t10, c12 but not by c9, t11-CLA, whereas PPARα was activated by both isomers. Results Weight gain Inclusion of CLA in the diet of genetically obese ( lep ob / lep ob ) mice reduced their body weight, or weight gain compared to mice fed on a diet containing a similar amount of sunflower oil (Figure 1 ). Mice fed on chow supplemented with sunflower oil gained weight at the same rate as mice fed on chow alone (Figure 1b ). The effect of CLA on weight gain appears to be primarily due to t10, c12-CLA, since CLA enriched to 90% with this isomer had more effect on weight gain than CLA containing 50% t10, c12-CLA and 50% c9, t11-CLA (Figure 1c ). CLA that was enriched to 90% with the c9, t11 isomer had only a small effect on weight gain, and this may have been due to the 10% t10, c12-CLA. Food intake was measured over two days in experiment 4 and was not reduced by CLA (food intake per group: control, 33.1 and 37.5 g; CLA 10 g/kg diet, 35.1, 43.4 g; CLA 25 g/kg diet, 39.9, 39.6). Rosiglitazone (10 mg/kg diet) reduced body weight (Figure 1a ) but to a lesser extent than CLA (15 g/kg diet). Figure 1 Weight gain in mice fed on diets that contained (doses per kg diet) (a) experiment 1: sunflower oil (●, 15 g), rosiglitazone (◇,10 mg), CLA (▲, 15 g); (b) experiment 2: chow only (○), sunflower oil (●, 25 g), CLA (▲, 25 g); (c) experiment 3: sunflower oil (●, 25 g), CLA (▲, 25 g), t10, c12-CLA (△, 25 g), c9, t11-CLA (▽, 25 g); (d) experiment 4: sunflower oil (●, 25 g), CLA (10 g) plus sunflower oil (15 g) (■), CLA (▲, 25 g). * P < 0.05; ** P < 0.01: *** P < 0.001 compared to sunflower oil group. Tissue weights and body length The weight of the parametrial white fat pads was reduced by CLA and by CLA enriched with t10, c12- but not c9, t11-CLA (Tables 1 and 2 ). The weight of the interscapular brown fat pad was reduced by CLA in experiment 4 (Table 2 ) but not in experiment 3 (Table 1 ). This may be because experiment 4 lasted for 11 weeks, whereas experiment 3 lasted for only 3 weeks and the control brown fat pad was more than three times heavier at the end of experiment 4 than at the end of experiment 3, presumably due primarily to its lipid content. Table 1 Terminal tissue weights and NEFA levels in experiment 3. NEFA levels were measured in 5h-fasted mice on day 13. Termination was on day 22. Supplements were included in the diet at concentrations of 25 g/kg. Sunflower oil CLA t10, c12-CLA c9, t11-CLA White adipose wt (g) 0.95 ± 0.12 0.49 ± 0.05 + 0.39 ± 0.06 + 0.82 ± 0.04 Brown adipose wt (g) 0.21 ± 0.03 0.23 ± 0.02 0.27 ± 0.11 0.18 ± 0.03 Liver wt (g) 2.02 ± 0.15 3.17 ± 0.05 + 3.25 ± 0.10 + 2.76 ± 0.18 + Pancreas wt (g) 0.12 ± 0.02 0.12 ± 0.01 0.11 ± 0.02 0.20 ± 0.02 NEFA (mM) 1.09 ± 0.09 1.13 ± 0.14 0.90 ± 0.07 0.82 ± 0.11 + P < 0.001; n = 6 Table 2 Terminal tissue weights and plasma hormones and NEFA levels in experiment 4. NEFA levels were determined in 5h-fasted mice on the days shown. Other measurements were in fed mice on day 77. Sunflower oil 1% Clarinol A80 2.5% Clarinol A80 Body length (mm) 95.0 ± 1.0 85.7 ± 0.3 + 86.4 ± 1.4 + White adipose wt (g) 2.11 ± 0.15 0.63 ± 0.07 + 0.19 ± 0.02 + Brown adipose wt (g) 0.74 ± 0.07 0.33 ± 0.04 + 0.007 ± 0.005 + Liver wt (g) 4.96 ± 0.17 6.27 ± 0.58 6.77 ± 0.36* Pancreas wt (g) 0.18 ± 0.01 0.20 ± 0.01 0.16 ± 0.02 NEFA (mM) Day 14 3.20 ± 0.17 3.58 ± 0.07 3.27 ± 0.13 Day 35 3.46 ± 0.42 3.93 ± 0.22 2.82 ± 0.40 Day 70 2.90 ± 0.19 3.44 ± 0.13* 1.92 ± 0.09 + Plasma adiponectin (ng/ml) 35.6 ± 7.0 8.2 ± 1.2 + 0.89 ± 0.13 + Plasma TNFα (pg/ml) 34.0 ± 5.7 33.0 ± 2.4 42.3 ± 7.1 * P < 0.05; + P < 0.001; n = 6 Liver weight was increased by CLA and by CLA enriched with either isomer, c9, t11-enriched CLA having the smallest effect. The weight of the pancreas was unaffected by all treatments (Tables 1 and 2 ). Others have reported that t10, c12- and c9, t11-CLA increase liver weight in lean mice, the effect of the t10, c12 isomer being associated with increased liver lipid [ 35 ]. Body length was reduced by CLA (Table 2 ). Glucose tolerance Rosiglitazone lowered fasting blood glucose, improved glucose tolerance (Figure 2a ) and reduced the area under the glucose tolerance curve (Figure 3a ). Two weeks treatment with CLA or with CLA enriched with t10, c12-CLA raised fasting blood glucose, exacerbated glucose tolerance (Figures 2b to 2d ) and increased the area under the glucose tolerance curve (Figures 3b to 3d ) compared to the sunflower oil and chow alone diets. CLA-enriched with c9, t11-CLA had no effect (Figures 2c and 3c ). Figure 2 Oral glucose tolerance. Symbols are the same as in Figure 1 and again (a) is experiment 1, (b) is experiment 2 and (c) is experiment 3. (d), (e) and (f) are oral glucose tolerance curve on days 14, 35 and 70 for experiment 4. Figure 3 Areas under the glucose tolerance curves shown in Figure 2. Panel (d) shows areas from Figure 2 (d), (e) and (f). Doses of CLA are shown as g/kg diet. * P < 0.05; ** P < 0.01: *** P < 0.001 compared to sunflower oil group. After five weeks of treatment the higher dose of CLA raised glucose levels less than it had after two weeks (Figures 2d, e ; 3d, e ), and after 10 weeks this dose actually improved glucose tolerance (Figure 2f ) and reduced the area under the glucose tolerance curve (Figure 3d ). Its effect was similar to that of rosiglitazone over two weeks in experiment 1 (Figures 2a and 3a ). The lower dose reduced the peak glucose level after 10 weeks, but it did not reduce the area under the glucose tolerance curve significantly. Insulin The improvement in glucose tolerance in response to rosiglitazone was associated with a reduction in the fasting plasma insulin level, but rosiglitazone did not reduce the terminal fasting insulin level (Figure 4a ). The exacerbations of glucose tolerance in response to two weeks treatment with CLA and CLA enriched with t10, c12-CLA were associated with increases in the fasting plasma insulin level. CLA enriched with c9, t11-CLA did not alter the fasting insulin level on the day that it showed no effect on glucose tolerance. However, c9, t11-enriched CLA increased the plasma insulin level in fed mice on day 21 (Figure 4c ). Figure 4 Plasma insulin values from (a) experiment 1, (b) experiment 2, (c) experiment 3, and (d) experiment 4. Doses are described in Methods and the legend to Figure 1. In panel (d) the does of CLA are in g/kg diet. Day 13 or 14, day 35 and day 70 values are following a 4.5 h fast; day 22 values are following a 16 h fast; and day 21 values are for fed animals. Doses of CLA are shown as g/kg diet. * P < 0.05; ** P < 0.01: *** P < 0.001 compared to sunflower oil group. After five weeks both doses of CLA still raised the fasting plasma insulin level, but after ten weeks, both doses reduced the plasma insulin level compared to the control group (Figure 4d ). Insulin resistance A useful indicator of insulin resistance can be obtained by multiplying values for fasting blood glucose and plasma insulin concentrations [ 36 ]. This is analogous to homeostatic model assessment (HOMA), which has been developed for studies in humans, although the full version, which originally involved dividing the product of glucose (mM) and insulin (mU/ml) concentrations by 22.5 is not suitable for studies in rodents [ 37 ]. By multiplying fasting blood glucose (mM) by plasma insulin (nM) we illustrate in Figure 5 how CLA initially exacerbated but subsequently improved insulin resistance. Figure 5 Effect of CLA on insulin sensitivity in experiment 4. The insulin sensitivity index was determined by multiplying the fasting blood glucose concentration (Figure 2d, e, f, 0 min) by the fasting plasma insulin concentration (Figure 4d). Triglycerides and fatty acids CLA raised the fasting triglyceride level in experiments 1 to 3 (Figures 6a to 6c ), but in experiment 4 the only statistically significant effect was an increase elicited by the lower dose after two weeks (Figure 6d ). CLA enriched with t10, c12-CLA had a similar or greater effect than CLA. Surprisingly on day 13 c9, t11-CLA, but not CLA or t10, c12-CLA caused a significant increase in the plasma triglyceride level, although c9, t11-CLA did not have a significant effect compared to CLA (Figure 6c ). Figure 6 Plasma triglyceride values from (a) experiment 1, (b) experiment 2, (c) experiment 3, and (d) experiment 4. Doses are described in Methods and the legend to Figure 1. In panel (d) the does of CLA are in g/kg diet. Day 13 or 14, day 35 and day 70 values are following a 4.5 h fast; day 22 values are following a 16 h fast; and day 21 values are for fed animals. Doses of CLA are shown as g/kg diet. * P < 0.05; ** P < 0.01: *** P < 0.001 compared to sunflower oil group. CLA and isomer-enriched CLAs had no effect on the fasting plasma non-esterified fatty acid (NEFA) concentration in experiment 3 (Table 1 ), and CLA had no effect after two and five weeks in experiment 4 (Table 2 ). After ten weeks, however, the lower dose of CLA raised the plasma concentration of NEFA, whereas the higher dose lowered it (Table 2 ). TNFα and adiponectin After ten weeks CLA had no effect on the plasma TNFα concentration. The plasma adiponectin concentration, by contrast, was markedly decreased (Table 2 ). PPAR activation CLA enriched with t10, c12-CLA (50 and 100 μM), but not with c9, t11-CLA, stimulated PPARγ-mediated reporter gene activity (Figure 7a ). In contrast, both CLA isomers (100 μM) elicited a significant increase in PPARα-mediated reporter gene expression, and c9, t11-CLA was effective at 10 μM (Figure 7b ). Figure 7 Effect of the c9, t11 and t10, c12-enriched CLA on ( a ) PPARγ- and ( b ) PPARα mediated gene expression. Cos-7 cells were transiently transfected with the plasmids pPPRE3TK-luc, pRLTK, pRSV/hRXRα and pcDNA3/hPPARγ 1 ( a ) or pcDNA3/hPPARα ( b ). Transfected cells were treated for 46 ( a ) or 24 ( b ) hours. Cell extracts were assayed for firefly and renilla luciferase activity. Reporter gene activity was determined by normalising firefly luciferase activity against renilla luciferase activity. The CLA isomers were prepared in 0.1% DMSO so that each of the stated isomers was at the given concentration. * P < 0.05 and ** P < 0.01; n = 6. Discussion Physiology A number of studies have found that CLA reduces weight gain and fat accretion in both rats and mice. Those that have failed to show such effects are generally those that have used low levels of CLA or CLA that contained low concentrations of t10, c12-CLA [ 6 ]. Studies disagree, however, as to whether CLA improves or exacerbates glucose tolerance and insulin resistance in these species: studies in rats show improvements [ 18 - 21 ], whereas studies in mice, with the exception of one study in lep db / lep db mice [ 34 ], show exacerbations [ 7 - 10 ]. In humans, several studies have shown a loss of fat [ 1 - 4 ], but doubts have been raised about the use of CLA for the treatment of obesity by reports that both t10, c12-CLA and c9, t11-CLA exacerbated insulin resistance in abdominally obese men [ 11 - 13 ], and that t10, c12- (but not c9, t11-CLA) reduced the HDL cholesterol concentration or the HDL:LDL cholesterol ratio [ 11 , 12 , 14 ]. These are by no means consistent findings, however [ 13 - 17 ]. Our study raises the possibility that an initial exacerbation of glucose tolerance, apparently due to insulin resistance, might, after prolonged treatment with a high dose of CLA, be followed by improved insulin sensitivity and glucose tolerance. We used genetically obese ( lep ob / lep ob ) mice as our model of insulin resistance. There is only one previous report of the effect of CLA in lep ob / lep ob mice [ 9 ]. In agreement with other studies in rodents, we found that CLA reduced weight gain and perigenital fat pad weight, and that this effect appeared to be produced by the t10, c12- rather than the c9, t11-isomer. We also found, like other studies in mice, that glucose tolerance was initially exacerbated and the plasma insulin concentration was raised. Again, these effects were primarily due to the t10, c12-isomer. However, in our last experiment we found that after ten weeks glucose tolerance improved and the fasting plasma insulin concentration was reduced by treatment of lep ob / lep ob mice with CLA. These benefits were achieved faster with the higher (25 g/kg diet, including 40% t10, c12-CLA) than the lower dose (10 g/kg diet) of CLA. Our results are similar to those of Hamura et al [ 34 ], insofar as they found that treatment of lep db / lep db mice with CLA for eleven weeks improved glucose tolerance and lowered insulin levels during the glucose tolerance test. Hamura et al . found that glucose tolerance was also improved in lep db / lep db mice after five weeks of treatment, but this was apparently due to increased insulin secretion rather than improved insulin sensitivity. It is not clear why others have not also found improved glucose tolerance and reduced insulin levels following prolonged treatment of mice with CLA. In the one previous study in lep ob / lep ob mice, the dose of t10, c12-CLA was only about 5.9 g/kg diet and treatment was for only four weeks [ 9 ]. A study in lean C57Bl/6J mice similarly used a dose of 4 g/kg diet of t10, c12-CLA for four weeks [ 10 ]. Other studies lasted for twelve weeks and eight months, however. The dose of CLA was only 10 g/kg diet in both studies and neither was conducted in exceptionally obese or insulin resistant mice [ 7 , 8 ]. We therefore suggest that improved insulin sensitivity is most likely to be found when mice are initially markedly obese and insulin resistant, and when treatment is prolonged and results in a major loss of adipose tissue. The relevance of our findings to humans is unclear, but it is interesting that in one 12 month study plasma glucose was raised after two weeks treatment with CLA, but not at any subsequent time [ 16 ]. After ten weeks the higher dose of CLA lowered the plasma fasting non-esterified fatty acid concentration, which is consistent with improved insulin sensitivity (Table 2 ). The lower dose raised the NEFA concentration at this time, despite apparently improving insulin sensitivity, even though it did not raise the NEFA concentration at earlier times when glucose tolerance was reduced and plasma insulin levels were raised. This paradox seems to be largely a consequence of the low control NEFA concentration after ten weeks: the highest NEFA level in the low dose CLA group was after five weeks of treatment and it seems possible that the NEFA concentration in the low dose group would have fallen with further treatment, just as it was falling in the high dose group (Table 2 ). Hamura et al . [ 34 ] found that after twelve weeks CLA reduced the plasma NEFA concentration in lep db / lep db mice, suggesting that insulin sensitivity was improved. After six weeks, however, CLA raised the NEFA concentration, suggesting that CLA exacerbated insulin sensitivity at this time. The elevated plasma NEFA concentration may have been partly responsible for the elevated plasma insulin and improved glucose tolerance after six weeks in their study. CLA and its major isomers have little or no effect on plasma NEFA levels in humans [ 11 , 12 , 14 ]. Mechanism No single mechanism has been identified that can account for the various effects of CLA on lipid and carbohydrate metabolism, let alone its anticarcinogenic and immunomodulatory activities. In part this is because CLA is a mixture of isomers, each with its own balance of activities. The activities of even the most active isomer, t10, c12-CLA, cannot, however, be pinned down to a single mechanism. We can first rule out any possibility that responses to CLA in our experiments were mediated by a fall in leptin levels as has been suggested by others [ 8 ], because our study was conducted in lep ob / lep ob mice. In any event, when fat loss is achieved by reducing energy intake, insulin action and glucose tolerance improve despite a reduction in the plasma leptin concentration. Antagonism of PPARγ by t10, c12-CLA has been suggested to contribute to both decreased adipogenesis and insulin sensitivity [ 38 ]. A recent report that c9, t11-CLA exacerbates insulin sensitivity [ 12 ] is consistent with a report that it too antagonises PPARγ, albeit a little less effectively than t10, c12-CLA [ 25 ]. We found little evidence that c9, t11-CLA exacerbates insulin sensitivity in lep ob / lep ob mice, however (Figures 3c and 4c ), and others have reported no effect [ 9 ]. Some workers favour the hypothesis that t10, c12-CLA decreases adipogenesis and insulin sensitivity by a mechanism that involves decreased expression of PPARγ in adipose tissue [ 38 ]. Since PPARγ agonists increase PPARγ expression in some situations [ 39 , 40 ], antagonism of PPARγ might decrease PPARγ expression. By contrast with mice, however, in rats CLA increases both PPARγ mRNA expression and insulin sensitivity [ 28 ]. Thus it is possible that CLA activates one mechanism that decreases and another that increases insulin sensitivity, and that PPARγ expression reflects the balance of these forces, rather than having a causal role. In any event, our results do not support antagonism of PPARγ as a mechanism of action of CLA. In agreement with some other workers [ 18 , 33 ], we find that t10, c12-CLA, but not c9, t11-CLA activates PPARγ. Activation of PPARγ might contribute to the improvement in insulin sensitivity in lep ob / lep ob mice following prolonged treatment with CLA. Activation of PPARα might also contribute to improved insulin sensitivity. t10, c12-CLA was more potent as an activator of PPARα than of PPARγ in the particular assays that we used, although it is inappropriate to make precise comparisons of potency in view of the different plasmids, their levels and other differences between the two assays. In contrast to some previous reports [ 10 , 41 ], t10, c12-CLA was, if anything, more potent than c9, t11-CLA as an activator of PPARα. Nevertheless, c9, t11-CLA was sufficiently effective to raise the possibility that it contributed to the improvement in insulin sensitivity due to the prolonged treatment with CLA. Activation of PPARα and peroxisomal proliferation might contribute to the increased liver weight in c9, t11-CLA-treated mice, steatosis also playing a role, at least in the case of t10, c12-CLA [ 10 ]. Activation of PPARα does not, however, appear to have a major role in the anti-obesity effect of CLA, because CLA reduced body fat content in PPARα knockout mice [ 42 ]. Moreover, some investigators have found that c9, t11-CLA is more effective than t10, c12-CLA as an activator of PPARα [ 10 , 41 ]. Other workers have shown that CLA also activates PPARβ/δ [ 10 , 41 ], but the effect was small in one these studies and c9, t11-CLA was more potent than t10, c12-CLA [ 10 ]. We measured plasma TNFα and adiponectin concentrations following prolonged treatment with CLA. Neither hormone contributed to the improved insulin sensitivity at this time: the plasma TNFα concentration was unchanged by CLA, and the plasma adiponectin concentration was reduced rather than increased. Other investigators have reported that t10, c12-CLA but not c9, t11-CLA reduces the level of adiponectin mRNA in white adipose tissue of lean mice [ 35 ]. Since adiponectin is released primarily from adipocytes but plasma levels are low in obesity and increased by weight loss [ 43 ], the marked reduction in plasma adiponectin in the CLA-treated mice is consistent with their having less, rather than smaller, adipocytes. This is in turn consistent with the view that the anti-obesity effect of CLA and the initial decrease in insulin sensitivity is due to apoptosis of adipocytes. Decreased fat cell number may also account for the tendency of CLA to increase plasma triglyceride levels in lep ob /lep ob mice, the remaining fat cells being too full to accommodate the triglyceride released by cells that have undergone apoptosis. Other workers have reported that CLA reduces plasma triglyceride levels; but in some cases this reduction was in lean mice with less replete adipocytes [ 42 , 26 ], and in the one other study in lep ob / lep ob mice, it was an effect of c9, t11-CLA, which presumably causes little apoptosis of adipocytes [ 9 ]. Interestingly, CLA has been reported to increase plasma adiponectin levels in Zucker diabetic fatty rats, at the same time decreasing plasma triglycerides and improving insulin sensitivity [ 21 ]. We therefore suggest that the main effect of CLA in Zucker diabetic fatty rats is to reduce fat cell size, rather than to promote apoptosis and reduce fat cell number. Conclusions Treatment with CLA initially decreased but subsequently increased insulin sensitivity in lep ob / lep ob mice. Activation of both PPARγ and PPARα may contribute to the improvement in insulin sensitivity. In the short term, however, another mechanism, activated by t10, c12-CLA but not c9, t11-CLA, which probably leads to reduced adipocyte number and consequently plasma adiponectin concentration, may decrease insulin sensitivity. Methods Animals Female C57Bl/6 lep ob / lep ob mice were obtained from Harlan Olac (Bicester, UK) and maintained at 23 ± 1°C with lights on from 07:00 to 19:00 h. They were housed in plastic cages with bedding and fed 'rat and mouse standard diet' (Beekay Feed, B & K Universal Ltd., Hull, UK). Six days before the start of the studies they were allocated to treatment groups (6 mice per group), such that each group had a similar mean bodyweight. The CLA and other treatments were mixed with powdered diet and given ad libitum . The mice were killed by cervical dislocation in experiments 1 to 3; in experiment 4 they were anaesthetised with sodium pentobarbitone (Sagatal; 80 mg/kg, i.p.) and exsanguinated through an aortic catheter. All procedures were conducted in accordance with our Home Office, UK project licence under the Animals (Scientific Procedures) Act and as agreed by the University of Buckingham Ethical Review Board. Materials added to diets CLA (in acid form), including isomer-enriched CLA, and sunflower oil were provided by Loders Croklaan, Wormerveer, The Netherlands. The CLA used in experiments 1, 2 and 3 was Clarinol™ A-60, containing 30% c9, t11-CLA and 31% t10, c12-CLA and 25% oleic acid. The CLA used in experiment 4 was Clarinol™ A-80, containing 40% c9, t11-CLA and 40% t10, c12-CLA. Rosiglitazone was synthesised by Dextra Laboratories, Reading, Berks, UK. Experiment 1 At the start of treatment the mice weighed 34.5 ± 4.3 g (mean ± S.D.). The treatment groups were high oleic (83.5%) sunflower oil (control; 15 g/kg diet); rosiglitazone (10 mg/kg diet) plus high oleic sunflower oil (15 g/kg diet); CLA (Clarinol™ A-60: 15 g/kg diet). An oral glucose tolerance test was conducted after 14 days as described below. 30 min prior to giving glucose, when the mice had been fasted for 4.5 hours, blood (100 μl) was taken for the measurement of plasma insulin and triglycerides. Plasma insulin and triglycerides were also measured after 21 days when the mice were feeding ad libitum , and after 22 days when they had been fasted for 16 hours, immediately before termination of the experiment. Experiment 2 At the start of the treatment the mice weighed 44.0 ± 3.2 g (mean ± S.D.). A group fed on powdered chow alone was included. The doses of CLA (Clarinol™ A-60) and sunflower oil in the other two groups were increased to 25 g/kg diet. The oral glucose tolerance test and other measurements were carried out as described for experiment 1, except that the glucose tolerance test was one day earlier (i.e. after 13 days). Experiment 3 At the start of treatment the mice weighed 34.1 ± 2.1 g (mean ± S.D.). The treatment groups were high oleic sunflower oil (25 g/kg diet); CLA (Clarinol ™ A-60: 25 g/kg diet); CLA with the CLA component of Clarinol enriched to 90% with t10, c12-CLA (25 g/kg diet); similarly, CLA enriched to 90% with c9, t11-CLA (25 g/kg diet). The oral glucose tolerance test and other measurements were carried out as described for experiment 2. In addition, the plasma non-esterified fatty acid concentration was measured in the blood taken prior to the glucose tolerance test, and the liver, pancreas, parametrial white adipose tissue depot and interscapular brown adipose tissue depot were weighed at termination. Experiment 4 At the start of treatment the mice weighed 31.6 ± 3.8 g (mean ± S.D.). The treatment groups were high oleate (as glyceride) sunflower oil (25 g/kg diet); CLA (Clarinol A80: 10 g/kg diet) plus high oleic sunflower oil (15g/kg diet); CLA (Clarinol A80: 25 g/kg diet). Oral glucose tolerance tests preceded by blood sampling for the measurement of triglycerides, insulin and non-esterified fatty acids were taken after 14, 35 and 70 days of treatment. After 77 days of treatment the animals were anaesthetised in the fed state and blood was taken from the thoracic aorta for the measurement of plasma TNFα and adiponectin. Tissues were weighed as in experiment 3. Body length was measured from the tip of the nose to the anus. Oral glucose tolerance tests In each experiment oral glucose tolerance was measured after 13 or 14 days. In experiment 4 it was also measured after five and ten weeks. The mice were fasted for five hours before being dosed with glucose (3 g/kg, p.o. in experiments 1 and 2; 2 g/kg, p.o. in experiments 3 and 4). Blood samples (20 μl) were taken from the tip of the tail after applying a local anaesthetic (lignocaine) and immediately before and 30, 60, 90, 120 and 180 min after dosing the glucose. They were mixed with haemolysis reagent and blood glucose was measured in duplicate using the Sigma Enzymatic (Trinder) colorimetric method and a SpectraMax 250 (Molecular Devices Corporation, Sunnyvale, CA, USA). Areas under the glucose tolerance curve (0–180 min) and other manipulations and analysis of the data were carried out using Prism software, version 3.0 (GraphPad Software Inc., San Diego, CA, USA). Other plasma analytes Blood was collected into EDTA-coated microcuvettes (Sarstedt microcuvette, Aktiengsellschaft & Co., Nämbrecht, Germany) for the measurement of plasma insulin, non-esterified fatty acids and triglycerides. Plasma was stored at -80°C. 5 μl plasma samples were assayed using kits for triglyceride (Sigma enzymatic colorimetric method), non-esterified fatty acids (Wako Chemicals, Neuss, Germany) and insulin (mouse standard; Crystal Chemistry Incorporated, Downers Grove, IL, USA). For the measurement of plasma TNFα (Linco; St Charles, MI, USA) and adiponectin (Biosource UK, Nivelles, Belgium), blood was taken into tubes containing 200 units of heparin and plasma was stored at -80°C. Plasmids for reporter gene assays pRSV/hRXRα and pcDNA3/hPPARγ1 were both obtained from Professor VKK Chatterjee (Addenbrooke's Hospital, Cambridge, UK). PPPRE3TK-luc was prepared by replacing the NF-kB enhancer element of pNF-κB-luc (Clontech) with a cassette of 3 PPAR Response Elements (PPREs). A double-stranded PPRE cassette was prepared using the 'Klenow fill-in' technique. An 113 bp oligonucleotide (PPRE3) 5'- GCATTCACGCGTCAAATATAGGCCATAGGTCATTCTCGAGCAAATATAGGCCATAGGTCATTCTCGAGCAAATATAGGCCATAGGTCAGATTCGATCAATATAGGCCATAGGTCACTCGAGGCAACAGATCTTACGCATG -3' containing a triplet of PPREs and appropriate restriction endonuclease sites was used as a template for synthesis of a second DNA strand. This was primed by PPRE3R, 5'-CATGCGTAAGATCTGTTGCC-3', which is complementary to the 3' region of PPRE3. 20 μl annealed PPRE3 and PPRE3R, 1.5 μl 2 mM dNTPs, 1 × Klenow Buffer and 5 units DNA Polymerase I (Klenow fragment), were incubated for 1 hour at 37°C and then 10 minutes at 75°C, purified by ethanol precipitation and resuspended in sterile water. The double-stranded PPRE cassette was digested with Mlu I and Bgl II and ligated into pNF-κ B-luc that had been cleaved with the same restriction endonucleases. Ligated DNA was transformed into competent JM109, E. coli cells (Promega). pcDNA3/PPARα was prepared by removing the human PPARα cDNA insert from pUC18/hPPARα as a Nru I/ Bam HI fragment and ligating it into Eco RV/ Bam HI cleaved pcDNA3.1(-) (Invitrogen). Ligated DNA was transformed into competent JM109, E. coli cells (Promega). PPARγ and PPARα reporter gene assays Cos-7 cells (ECACC No. 87021302) were routinely cultured in DMEM containing 10% FCS, 2 mM L-Glutamine, 100 iu/ml penicillin and 100 μg/ml streptomycin at 37°C/5% CO 2 . Transient transfections were performed using LipofectAMINE as directed by the manufacturers (GibcoBRL). For the PPARγ assay Cos-7 cells were plated in 24-well plates at a density of 0.375 × 10 5 cells/well. Cells were transfected in serum-free medium (DMEM containing 2 mM L-glutamine) with pPPRE3TK-luc, pRLTK, pRSV/hRXRα and pcDNA3/hPPARγ 1 at concentrations of 0.4, 0.03, 0.02 and 0.02 μg/well, respectively. Five hours after transfection cells were fed with 250 μl/well of serum-free medium containing various concentrations (0–100 μM) of CLA (either the c9, t11 isomer or t10, c12 isomer prepared in 0.1% DMSO). Cell lysates were prepared after 46 hours using 100 μl 1 × passive lysis buffer (Promega) per well. Firefly and renilla luciferase activities were measured using a Dual Luciferase Assay kit (Promega), as described by the manufacturers. Measurements were performed on an MLX microtitre plate luminometer (Dynex). For the PPARα assay Cos-7 cells were plated in 24-well plates at a density of 0.5 × 10 5 cells/well. Cells were transfected in serum-free medium with pPPRE3TK-luc, pRLTK, pRSV/hRXRα and pcDNA3/hPPARα at concentrations of 0.4, 0.04, 0.03 and 0.03 μg/well, respectively. Five hours after transfection DMEM supplemented with 2 mM L-glutamine and 20% SBCS (charcoal-stripped bovine calf serum, Sigma) was added to the cells. Following 18 hours incubation at 37°C/5% CO 2 the medium was removed and replaced with medium (DMEM supplemented with 2 mM L-glutamine and 10% SBCS) containing various concentrations (0-100 μM) of CLA enriched to 85% with the c9, t11 isomer or 81% with the t10, c12 isomer prepared in 0.1% DMSO. After 24 hours of treatment cell lysates were prepared and luciferase activity measured as described above. Statistics Data were analysed by one-way analysis of variance followed by LSD test with the sunflower oil treatment as the control. Means are of 6 values with SEM. Authors' contributions MC, LB, IM and MS devised the experiments. EW, MS and CS, supported by JO and SW conducted the in vivo experiments and analysed materials from these. AM conducted the in vitro experiments. MS and MC conducted the initial analysis and interpretation of the data. JA reanalysed some of the data and wrote the manuscript with input from the other authors, and in particular from AE and IM with respect to interpretation and perspectives. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC546236.xml |
533865 | d-matrix – database exploration, visualization and analysis | Background Motivated by a biomedical database set up by our group, we aimed to develop a generic database front-end with embedded knowledge discovery and analysis features. A major focus was the human-oriented representation of the data and the enabling of a closed circle of data query, exploration, visualization and analysis. Results We introduce a non-task-specific database front-end with a new visualization strategy and built-in analysis features, so called d-matrix. d-matrix is web-based and compatible with a broad range of database management systems. The graphical outcome consists of boxes whose colors show the quality of the underlying information and, as the name suggests, they are arranged in matrices. The granularity of the data display allows consequent drill-down. Furthermore, d-matrix offers context-sensitive categorization, hierarchical sorting and statistical analysis. Conclusions d-matrix enables data mining, with a high level of interactivity between humans and computer as a primary factor. We believe that the presented strategy can be very effective in general and especially useful for the integration of distinct data types such as phenotypical and molecular data. | Background d-matrix, originally designed with cardiovascular clinical and molecular genetic data in mind, is a generic database front-end that can be used to explore, visualize and analyze different typologies of datasets. Both the generation and the analysis of genome, transcriptome and proteome data are becoming increasingly widespread, and these data must be merged to generate a molecular phenotype. Moreover, the correlation between molecular and phenotypical data requires acquiring both with comparable profoundness leading to the development of large and small scale databases holding both information [ 1 - 3 ]. In the same line, we developed a CardioVascular Genetic database (CVGdb), storing the detailed clinical phenotype of patients with congenital heart diseases as well as molecular data such as gene expression analysis results [ 4 ] and genotypes. However, querying and analyzing the stored data to uncover the valuable information hidden in the databases are difficult tasks. With some exceptions, these are approached by a two-step procedure, in which a database specific front-end serves the query and extraction of data, which are subsequently imported in stand-alone analysis tools for visualization, mining and statistics [ 5 - 11 ]. Moreover, the visualization and mining tools frequently focus on presenting overall views of data sets for a specific task and seldom permit single-case addressability or have drill-down capability. In today's systems, the perceptual abilities of human users are only used to a limited extend. We believe that it is essential to make users part of the overall process through computer support of their intelligence, creativity and perceptual abilities. Hence, a major research challenge is to find human-oriented forms of representing information and enabling rapid interaction between humans and computers in the query, visualization and analysis process [ 12 ]. It is not the purpose of this paper to survey the various solutions available to query, visualize and mine data, but rather to illustrate how such concepts could be combined usefully within one software tool. Here, the layout should not only preserve the structure of the information, it should also convey the quality of the distribution of the values contained in the database. The features of the display should then be designed to highlight those regularities, patterns or dependencies that are not easily detectable with an ordinary front-end. One visual representation, which motivated the graphical display of the tool we describe here, is the data matrices handled in microarray studies, in which rows in the matrices typically represent genes and columns individual samples [ 13 ]. Rather than showing a numerical 'spreadsheet', it is convenient to display microarray data in such matrices, which indicate varying expression levels in a grid of varying colors. With d-matrix we propose a generic front-end solution capable of extracting, exploring, visualizing and analyzing complex data. The software can be interfaced with the most common relational database management systems without any intervention on the schema or pre-processing phase. As the name suggests, the visual model proposed has the form of a matrix. Its elements are boxes whose colors show the quality of the underlying information. The granularity of the data display allows consequent drill-down, i.e. the user is able to focus the observation on a single data point. In addition, value frequency bars are available to present compact overviews. It also offers the possibility to define categories using context-sensitive rules and to assign colors to classes. The direct implementation of a broad range of descriptive and advanced statistics together with a hierarchical sorting feature permits user-defined exploration of the data. Implementation Data Model The process of developing a uniform web interface for disparate data sources is a complex task because of the variability in the data models that underlie each source. To enable an effective two-dimensional display, the d-matrix model consists of a three-level tree. For the representation of a large database schema requiring a higher number of levels, several d-matrix instances can be built on the same database. Within the proposed model, the main table addressing the objects of a study is considered as the root, the first level of the tree. The second level consists of tables that are joined with the root by means of its primary key and the third level consists of tables that are further joined with the ones at level two. In particular, the dependency of the root table with the second level tables can be either one-to-many or one-to-one, while the dependency of the second level table with the third level ones can be either many-to-one or one-to-one. To apply different query and visualization rules each branch of this tree is defined as a data group characterized by the same storing strategy. In cases where Entity-Attribute-Value (EAV) tables are interfaced, the Entity must correspond to the main ID. As an example, we can refer to the CardioVascular Genetics database (CVGdb) schema set up by our group (Figure 1 ). Here, we selected the table Patients as the root of the tree, so that the CVGdb instance main ID is the Patients primary key. This selection is arbitrary and one could also choose Clones or Hybridizations, thereby focusing on different aspects of the overall dataset. In Figure 2 , data groups and tree levels are represented. The data groups 2 to 4 address the EAV tables Invasive_Treatments, Medications and Samples; the groups 5 to 6 contain the same table Clones joined with different tables containing gene expression analysis results [ 4 ]; whereas the last group is built by two tables describing sequence variations (SV). Data selection and query The schema is presented to the user in a structure recalling a file system selector (Figure 3A ). Nodes represent attributes or value attributes that can be optionally divided further into more folders without any depth limitation. Collecting nodes in visually distinct entities becomes a necessity when coping with a large number of attributes. To obtain a quantitative measure of the information that is contained within groups of nodes, a summary node can be included in the query form. For each value of the x-axis, the values of the summary nodes are computed by counting the nonempty nodes in the respective folders. The query process consists of two steps. First, the users select all nodes they want to be included in the query (Figure 3A ); second, these are listed in a query form where conditions and analysis features can be specified (Figure 3B ). To visually distinguish between nodes referring to data belonging to single-table data group and two-table data groups, single-table group nodes are represented as sheet-like-icons, whereas two-tables data group nodes are represented by double-arrow-like icons: diagonally oriented for the nodes that belong to the second level tables, and vertically oriented for the nodes that belong to the third level tables (Figure 3A ). The attribute on which the query display shall be focused can be selected by means of the three-banded icons placed on the right side of the nodes. For each of the nodes, the query form permits the definition of sorting order and direction (ascendant/descendent), values and operators for query conditions, display order and parameters for statistical evaluations. The value cell is not shown if the node itself is an attribute value. Alternatively to the matrix view of the query result, the user can optionally export the resulting dataset in form of text or XML (Figure 3B ). Data visualization The graphical output of d-matrix consists of two-dimensional matrices, whose colored boxes code the meaning of the underlying information, the description of the chosen nodes and a prospect of statistical evaluations (Figure 4 ). The display of the data is determined by the data dimensionality. The main ID corresponds always to the x-axis of the matrix. To permit the display of single and multiple dependencies with the main ID, the y-axis shows either node descriptions or node values. In cases of single dependency each data point is represented by one box of the matrix. If there is a multiple dependency (two-table data groups), subsequently more rows for each value of a single node are displayed. EAV data groups can lead to both single or multiple dependency; in the second case the entries are aggregated in one matrix box. In Figure 4 the tuples of the data group "PHENOTYPES" addressing the table Patients are displayed in the first matrix. Each tuple corresponds to a column whereas row headers are node descriptions. The tuples of the data group "SEQUENCE VARIATIONS" addressing the tables SV_Genotypes and SV_Loci are aggregated column-wise and grouped by the main ID. Here, there is more than one tuple for each column whereas row headers are values of the node Locus ID. Hence, each column of boxes on the matrix display represents an aggregation of more than one tuple of the query result. Following data mining terminology, we can say that in d-matrix cases (and aggregations of them) are represented column-wise. When the matrix oversize the available space, the use of two distinct scrollbars lets the user move the data matrix horizontally and vertically. The general overview is given together with the advantage of single-case addressability, i.e. each case (tuple) representation is entirely visible and its components clearly distinguishable. The display is obtained as a group of images (generated using the Perl GD module and stored as temporary files), each in a separate HTML DIV container, which can be moved independently. Drill-down The matrix display represents a summarized view of the query. Each box holds three levels of detail: first, the coordinates that uniquely identify the box position and represent two units of information; second, the color that corresponds to either a single value or a category; third, the hidden content of the box obtained by drill-down, which gives all remaining information for that box. In the d-matrix display the drill-down can be obtained for each box in form of a pop-up window (Figure 4 ). The content structure of this new window varies according to the data group to which the box belongs, although it always contains the value that is substituted by its color code together with the underlying node description. Further supplementary data can be included from attributes of the same data group. It is possible to add further detail by the mean of hyperlinks to grant access to remote databases, external analysis results and multimedia documents (Figure 4 ), or even to trigger further analysis processes. Schema interface and configuration The software requires four configuration files: the data definitions file that is needed to connect d-matrix with the relational schema, a database settings file storing the information to access the database, a color file for the definition of the colors used in the matrix and a general server settings file. Every configuration file is maintained as plain text to permit easy access and modification. The structure of the data definitions file must reflect the hierarchy in which the metadata (relational schema definition) have to be organized on the screen, while its textual content depicts a level of abstraction ( definitional abstraction ) [ 14 ] between the database physical representation and the human-comprehensible view of the data. Therefore, the data definitions file reflects the subdivision of the database schema in data groups. For each group the table attributes, information about identifiers, joining conditions as well as aggregation (where needed), display settings and the content of the pop-up window have to be defined. User-defined human-intelligible terms can be assigned for any term used in the database. Besides the attributes' names, types and descriptions, it is possible to define categories, orderings and associations with colors. It is important to notice that the rules that define categories can even involve other attributes of the same data group. This context-sensitive categorization, intended as a qualitative abstraction [ 14 ], allows the concurrent representation of two layers of information. For each attribute value, value range or defined category, rules can be given to assign its respective color. This leads to a common method to visualize both discrete and continuous variables. In addition, categorized numeric values can be treated as categorical in specific contexts like sorting and statistics. Furthermore, colored boxes can be composed by combining the values of two nodes, which enables, for example, the visualization of both Alleles within horizontally split boxes for sequence variations (Figure 4 ). Several data definitions files (each defining a separate d-matrix instance) can independently coexist on the same server for the same or different database systems and schemata. Visual data mining and statistical analysis d-matrix permits consecutive data-filtering operations that – as a whole – can be seen as a single user-driven data mining session. A compact and information-dense graphical outcome, context-sensitive categorization, hierarchical sorting and drill-down enable this mining process. Frequency bars give an overview of the overall queried dataset whereas box plots improve the visual perception of the data distribution. A key feature within the mining process is the opportunity to obtain different views of a single data set rapidly in parallel using different browser windows. Here, the interactivity becomes a primary factor and is supported by the human-oriented representation. A wide range of descriptive statistics and statistical tests is directly accessible. This permits statistical evaluation of the correlation between attributes and determination whether it is reasonable or not to assume that a sample fits to a specific distribution. For numerical values it is possible to perform up to ten different statistical tests, while for non-numerical entities (Boolean and categorical data) the Chi-square and Fisher exact tests are available. The user interface automatically performs a selection of attributes and tests according to their respective compatibility. In addition to directly implemented tests, external data analysis environments like R [ 15 ] or user defined routines can be easily interfaced. The results of the tests, together with the descriptive statistics, are displayed at the side of the matrix and colors of the boxes reflect the results (e.g. significance) of the tests. CardioVascular Genetics database (CVGdb) For interfacing d-matrix with the CVGdb, we assigned categories if appropriate and colors to more than 700 nodes. Figure 5 shows an example of a single user-driven data mining session, which was initiated with the aim to discover cardiac phenotype features associated with shunts abroad the interventricular septum (IVS shunt). Therefore, the only query condition specified is that "IVS shunt" is not "NULL". This condition is fulfilled by 211 out of 560 IDs stored to date. In addition, a subset of nodes referring to phenotype descriptions physically surrounding the interventricular septum has been chosen to be displayed. To structure the display, hierarchical sorting has been applied to the 'IVS shunt' and an arbitrary selection of other nodes. Viewing the entrance matrix (Figure 5A ), one could easily recognize data clusters such as the relation of the category 'bidirectional' of the 'IVS shunt' (blue boxes) to categories of interatrial septum shunts (IAS shunt) and right ventricular systolic pressure 'RV sys pressure'. Almost all patients with a bidirectional 'IAS shunt' are also characterized by a bidirectional 'IVS shunt'. Furthermore, the majority of bidirectional 'IVS shunt' is associated with severe 'RV sys pressure', whereas the non-sorted nodes pulmonary valve morphology (PV morphology), pulmonary valve systolic pressure gradient (PV Psys gradient) and right ventricular anatomy (RV anatomy) are distributed in a questionable co-occurrence to each other in this first matrix. For further evaluation, we focus on the 'RV anatomy' or the 'PV morphology' chosen as the first sorted nodes in the second and third matrix (Figure 5B,5C ), respectively. By using the tree save/reload option to retrieve these new matrices, only the sorting criteria needed to be modified to obtain different views on the same data set in parallel using three browser windows. Hence, the frequency bars remain the same in all visualization sessions. Now it becomes clear that more than half of the patients with infundibular stenosis (RV anatomy) show a stenotic 'PV morphology', which by itself is highly associated with an extreme 'PV Psys gradient'. Applying the correlation analysis implemented in d-matrix, the significance of the correlation of the 'PV Psys gradient' with the 'RV sys pressure' could be verified (Figure 5D ). The described data are available for the exploration using d-matrix at the web supplement. Finally, the session explained is just one out of several examples in which d-matrix proved to be highly effective for the visualization of regularities and dependencies within the CVGdb data. Moreover, based on the general visualization concept, d-matrix provides an integration between clinical and genetic information that is crucial for the correlation of phenotypical and molecular data (Figure 4 ). Other applications With respect to an ongoing project on gene regulation, we found it very convenient to visualize potential transcription factor binding sites (TFBS) in promotor sequences by interfacing d-matrix [ 16 ]. Here, the nucleotides are used as the main ID (x-axis) and the TFBS are consequently displayed at the y-axis. This allows a much higher level of interactivity than a usual figure output. One could easily have different views of the data set by sorting or parallel display of different information, like color coded core or matrix match similarities. To demonstrate the versatility of the software, we further interfaced d-matrix with a database that represents the periodic table of the elements [ 16 ]. Although we did not expect unusual or unexpected regularities in such a simple case, it was easy to obtain a matrix that shows the well-known dependency between Atomic Number, Atomic Mass and Energy Levels and the obvious lack of available information about elements with seven energy levels, which are the most unstable and rare. The interfacing with both dataset required only one working day for each. Results and discussion We have presented d-matrix, a non-task-specific database front-end with a new visualization strategy with embedded analysis features. The graphical outcome of d-matrix consists of colored boxes arranged in matrices; it permits single-case addressability with further drill-down capability. Together with the hierarchical sorting and statistical feedbacks, d-matrix enables consecutive data-filtering operations that – as a whole – can be considered as a single data mining session. Also, the result of such a session can be exported for further study. For a qualitative evaluation of d-matrix, one should not only focus the attention on the final display, which only represents the end product of a sequence of user-driven data exploration sessions. The high level of interactivity that our approach offers is indeed a primary factor; with d-matrix, the communication between human and computer is a rapid interaction. The future development of d-matrix will focus on the implementation of clustering algorithms to be executed before display. Furthermore, we envisage the design of instruments to inquire metadata to maximize the quantity of information that will be eventually displayed and analyzed [ 17 ]. In addition, a user-friendly way to interact with configuration files will be granted by specific CGI scripts leading to a further reduction of the time to interface d-matrix with relational schemata. An inquiry of the solutions reported to date for data exploration, visualization and analysis resulted in an approximate distinction between reports about efforts for database development with their task specific front-end solutions and stand-alone data analysis, visualization and mining tools. In our view, d-matrix stands in between those two groups and aims to combine features of both efforts, which we believe can be very effective and useful in general and especially for the association of distinct data types such as phenotypical and molecular data. As a front-end, it does not require complex installation processes or maintenance, and it is suitable for multi-user remote access. As a visual data mining tool, it gives an effective display that allows the detection of exceptions, trends, regularities, clusters and dependencies, as well as incomplete or erroneous data. Availability and requirements Project name: d-matrix Project home page: Operating system(s): Platform independent Programming language: Perl Other requirements: d-matrix was successfully interfaced to Oracle 8i, MySQL, Microsoft Access and text-based databases and is compatible with recent JavaScript-enabled browsers. License: d-matrix is available on request from the author. To academic institutions d-matrix is available for a fee of 250 Euro that is intended to cover our costs of distribution and maintenance. Authors' contributions DS developed the first generation of d-matrix and carried out the main programming work. RG is the current maintainer and carried out the main implementation. SM and HPS participated in the design, testing and quality control. HH participated in the conceptual design. SS conceived the development of d-matrix, managed and participated in its design and implementation. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC533865.xml |
514564 | Explaining computation of predictive values: 2 × 2 table versus frequency tree. A randomized controlled trial [ISRCTN74278823] | Background Involving patients in decision making on diagnostic procedures requires a basic level of statistical thinking. However, innumeracy is prevalent even among physicians. In medical teaching the 2 × 2 table is widely used as a visual help for computations whereas in psychology the frequency tree is favoured. We assumed that the 2 × 2 table is more suitable to support computations of predictive values. Methods 184 students without prior statistical training were randomised either to a step-by-step self-learning tutorial using the 2 × 2 table (n = 94) or the frequency tree (n = 90). During the training session students were instructed by two sample tasks and a total of five positive predictive values had to be computed. During a follow-up session 4 weeks later participants had to compute 5 different tasks of comparable degree of difficulty without having the tutorial instructions at their disposal. The primary outcome was the correct solution of the tasks. Results There were no statistically significant differences between the two groups. About 58% achieved correct solutions in 4–5 tasks following the training session and 26% in the follow-up examination. Conclusions These findings do not support the hypothesis that the 2 × 2 table is more valuable to facilitate the calculation of positive predictive values than the frequency tree. | Background Diagnostic procedures are increasingly expected by consumers to ensure their health; "certainty" has become a product [ 1 ]. Assuming that test results are certain, only a minority is aware about false positive and false negative alarms. Previous research has shown that even physicians have great difficulties in estimating the positive predictive values of diagnostic tests [ 2 - 4 ]. One study reported that 95 out of 100 physicians estimated the positive predictive value of screening mammography to be between 70–80% rather than 7.8% [ 2 ]. Similar results were reported for AIDS counselors for low-risk clients. The majority of counselors assured that false positives would never occur and half of the counselors incorrectly assured that if a low-risk person tests positive, it is absolutely certain (100%) that he or she is infected with the virus [ 5 ]. An incorrect probability judgment may result in unnecessary tests or pseudo certainty. Therefore, the understanding, presentation and communication of test quality are a challenge for both: lay people and professionals. Involving lay people in decision making on diagnostic procedures requires a basic level of statistical thinking. Help for computing Bayesian inference is needed. Statistical thinking can be enhanced by representing statistical information in terms of natural frequencies rather than probabilities [ 6 , 7 ]. This is explained by the evolution of the human reasoning system. Gigerenzer proposed that human reasoning is algorithms designed for information that comes in a format that was present in the "environment of evolutionary adaptiveness" [ 8 ]. Human reasoning processes are adapted to natural frequencies. Also Bayesian computations are easier when the information is communicated this way. In cognitive psychology the frequency tree is used as visual help for the representation of frequencies, a variant of a tree structure often used in decision analysis to teach computing the positive predictive value the simple way (Figure 1 ) [ 4 ]. This format allows a multistage presentation of the numerical information and demonstrates the reasoning process. Figure 1 Frequency tree. In contrast, in medical science the 2 × 2 table is the standard method to teach computing predictive values (Figure 2 ) [ 9 , 10 ]. In addition, the 2 × 2 table is used for other calculations, e.g. odds ratios or relative risks [ 9 ]. Figure 2 2 × 2 table. In the present study, we compare the two visual helps in non-medical students. We hypothesized that the 2 × 2 table is more eligible than the frequency tree to facilitate correct answers in tasks of calculations of positive predictive values 4 weeks after an initial training-session. We also describe students' ability to calculate positive predictive values, analyzing the transfer of the numerical information into the visual help and the correct computation. Methods Participants We approached 238 students without prior statistical training to recruit the necessary 184 students who agreed to participate. (See power calculation below) Students attending the University of Hamburg (health sciences, biology and sports), a vocational college (health and nursing) or taking part in an in-service training (nursing and public health) were informed about the timing and content procedure of the study during their courses. Procedure The study was carried out between October 2000 and July 2001 and consisted of two supervised sessions lasting about 1 h each. The recruited 184 students were randomly assigned either to the frequency tree group (n = 94) or to the 2 × 2 table group (n = 90) using blocked randomization in blocks of 10. Concealed allocation based on computer-generated random numbers was done by an external person. In addition, the external person prepared sealed envelopes for both sessions including the tutorial with the tasks and a questionnaire for survey of age, gender, years of school, mark in mathematics and social state. The training consisted of a written step-by-step self-learning tutorial ( Additional file 1 , 2 , 3 ). The participants had to compute 5 positive predictive values in each session. The tutorial and tasks followed the recommendations for the presentation of numerical information [ 4 ]. Participants were asked to reveal how they achieved their solutions. Participants were allowed to use a pocket calculator. Correct results were presented and discussed after each session. In the follow-up examination participants were again asked to solve 5 different diagnostic problems of similar level of difficulty but without having the tutorial instructions at their disposal ( Additional file 4 , 5 , 6 ). Participants who missed the date were repeatedly contacted by letter, phone or e-mail. Efforts were discontinued after 4 weeks. Assessing performance Correct solution of the tasks A solution was classified correct, when the documented positive predictive value was equivalent to the correct solution rounding up or down to the next full percentage point. If a participant used the correct computation (correct positives divided by all positives) but made a calculation error either in the transfer of the numerical information into the visual help or within the division, we ignored calculation errors. Whenever a different computation such as rule of three – a mechanical method for solving proportions – was used or the calculation protocol was missing the rounded solutions were classified likewise as correct by congruence. If the protocol indeed showed that a correct rounded solution resulted from an incorrect computation such as positive predictive value = correct positives / false positives the answer was classified as incorrect. Tasks that had not been worked on were also classified incorrect. Correct transfer To evaluate the usefulness of the different visual helps, we evaluated the ability of correct transfer of the numerical information into the charts. A transfer was classified as correct, when the numerical information of the problems was inserted into the gaps provided. It was sufficient to insert the relevant values for the computation, calculation errors were ignored. Correct computation The computation was classified as correct Bayesian approach when the following computation was used: positive predictive value = correct positives / (correct positives + false positives) or positive predictive value = correct positives / all positives. The computation was classified as Non-Bayesian approach when the computation was used with false values. Other computations were classified as other strategies. Statistical power and analyses Table 1 shows the hypothesized distribution of correct answers within the different categories as primary outcome measure between the two study groups (Table 1 ). By using the Wilcoxon (Mann-Whitney) rank-sum Test in a sample of 92 persons in each group (84 + 10% drop-out) the hypothesized differences are detected with a power of 80% at a 2- tailed α of 0.05. For our one-sided hypothesis that the 2 × 2 table is superior to the frequency tree the power is 88% at sample size of n1 = n2 = 80. Table 1 Hypothesized distribution of correct answers after 4 weeks between the two study groups Categories* (numbers of correct answers) Frequency tree 2 × 2 table 0 0.40 0.30 1 0.15 0.05 2 0.15 0.05 3 0.10 0.20 4 0.10 0.20 5 0.10 0.20 * category 0 = 0 answers correct category 1–5 = 1–5 answers correct Analysis is based on the intention-to-participate principle that includes all randomised participants as randomised. Drop outs were considered as having solved none of the positive predictive values correctly. Results Figure 3 shows the flow of participants through the trial (Figure 3 ). There were 18% drop outs in the frequency tree group and 20% in the 2 × 2 table group resulting in a power of 78% for the two-sided and 86% for the one-sided hypothesis. For grouping into three categories as used for analyses the power is 81% for the two-sided and 89% for the one-sided hypothesis. Figure 3 Flow of participants. The groups were similar regarding demographic variables (Table 2 ). Table 2 Baseline characteristics* Frequency tree (n = 94) 2 × 2 table (n = 90) Age Median (range) 29 (20–54) 26 (19–51) Missing values 3 (3) 2 (2) Gender Male 15 (16) 20 (22) Female 77 (82) 67 (75) Missing values 2 (3) 3 (3) Years of school < 10 years 1 (1) 1 (1) 10–12 years 22 (23) 19 (21) > 12 years 68 (72) 67 (75) Missing values 3 (3) 3 (3) Mark in mathematics 1 (highest level) 6 (6) 6 (7) 2 20 (21) 18 (20) 3 35 (37) 32 (36) 4 14 (15) 17 (19) 5 (lowest level) 5 (5) 9 (10) Missing values 14 (15) 8 (9) Group University of Hamburg 59 (63) 55 (61) Vocational College 14 (15) 15 (17) Non-academic students 21 (22) 20 (22) *Values are numbers (percentages) of participants unless stated otherwise Correct solutions of the tasks Table 3 shows the solutions of both sessions with regard to the primary outcome. Within the training session 20% of participants in both groups calculated only 0–1 answers correctly; 58% (95% CI, 47%–68%) (2 × 2 table) and 59% (95% CI, 48%–69%) (frequency tree), respectively, solved 4 or 5 tasks correctly. In the follow-up examination most participants could not solve more than 0–1 tasks correctly (72% frequency tree and 67% 2 × 2 table). Table 3 Numbers of correct solutions of positive predictive values* Category Training session Follow-up examination Frequency tree (n = 94) 2 × 2 table (n = 90) Frequency tree (n = 74) 2 × 2 table (n = 75) 0–1 (0–1 answer correct) 19(20) 18(20) 53 (72) 50 (67) 2–3 (2–3 answers correct) 20 (21) 20 (22) 2 (3) 5 (7) 4–5 (4–5 answers correct) 55 (59) 52(58) 19 (26) 20(27) * Values are numbers (percentages) of participants Within the category 4–5 correct answers 27% of participants (95% CI, 17%–38%) (2 × 2 table) and 26% (95% CI, 16%–37%) (frequency tree) had correct solutions. The differences between the two study groups were not statistically significant neither in the training session (p = 0.95 {0.49 one-sided}) nor in the follow-up examination (p = 0.48 {0.24} for the analysis on intention-to-participate and p = 0.61 {0.31} for the analysis on-participation (Table 3 ). In addition, we analyzed every single task in terms of correct solution. In the training session 66% of all questions [(n = 309/470 (frequency tree); n = 297/450 (2 × 2 table)] were solved correctly in both groups. The amount of correct solutions decreased to 26% (n = 98/370) and 31% (n = 115/375), respectively, in the follow-up examination. Differences between groups were not statistically significant (Table 4 ). Table 4 Analysis of each task regarding correct solutions, transfer of numerical information and Bayesian computations* Correct solution Training session Follow-up examination frequency tree (n = 94) 2 × 2 table (n = 90) frequency tree (n = 74) 2 × 2 table (n = 75) Task A 67 (71) 66 (73) 18 (24) 25 (33) Task B 63 (67) 64 (71) 22 (30) 24 (32) Task C 69 (73) 63 (70) 19 (26) 23 (31) Task D 67 (71) 54 (60) 21 (28) 23 (31) Task E 43 (46) 50 (56) 18 (24) 20 (27) Correct transfer Training session Follow-up examination frequency tree (n = 94) 2 × 2 table (n = 90) frequency tree (n = 74) 2 × 2 table (n = 75) Task A 84 (89) 79 (88) 53 (72) 57 (76) Task B 83 (88) 78 (87) 52 (70) 57 (76) Task C 71 (76) 65 (72) 45 (61) 53 (71) Task D 73 (78) 67 (74) 42 (57) 49 (65) Task E 54 (57) 53 (59) 42 (57) 48 (64) Correct Bayesian Computation Training session Follow-up examination frequency tree (n = 94) 2 × 2 table (n = 90) frequency tree (n = 74) 2 × 2 table (n = 75) Task A 62 (66) 59 (66) 13 (18) 18 (24) Task B 60 (64) 58 (64) 17 (23) 18 (24) Task C 70 (75) 60 (67) 15 (20) 15 (20) Task D 69 (73) 52 (58) 16 (22) 17 (23) Task E 46 (49) 44 (49) 15 (20) 15 (20) * Values are numbers (percentages) of tasks Correct transfer Transfer of the numerical information into the visual help in the training session could be managed in 78% (n = 365/470 frequency tree) and 76% (n = 342/450 2 × 2 table) of the tasks. In the follow-up examination in 63% (n = 234/370) and 70% (n = 264/375), respectively, the information was correctly transferred into the visual helps (Table 4 ). Correct computation The application of the Bayesian computation in the training session was correctly used in 65% (n = 307/470 frequency tree) and in 61% (n = 273/450 2 × 2 table). In the follow-up examination 21% (n = 76/370) and 22% (n = 83/375), respectively, used correct Bayesian computation (Table 4 ). Incorrect Bayesian approaches Table 5 shows the commonly used incorrect Bayesian approaches which lead to incorrect solutions of the tasks (Table 5 ). Table 5 The commonly used incorrect Bayesian approaches* Training session Follow-up examination total Frequency tree 2 × 2 table total Frequency tree 2 × 2 table correct positive rate/ false positive rate 41 26 (63) 15 (37) 16 11 (69) 5 (31) disease yes / all positives 14 7 (50) 7 (50) 37 20 (54) 17 (46) correct positives / disease yes 11 6 (55) 5 (45) 22 11 (50) 11 (50) all positives / total 4 4 (100) 0 (0) 14 6 (43) 8 (57) all positives / 100 0 0 (0) 0 (0) 6 6 (100) 0 (0) disease yes / correct positives 4 1 (25) 3 (75) 1 1 (100) 0 (0) all positives/ correct positives 4 0 (0) 4 (100) 5 5 (100) 0 (0) not identified 23 13 (57) 10 (43) 29 14 (48) 15 (52) total 101 57 (56) 44 (44) 130 74 (57) 56 (43) * Values are numbers (percentages) of incorrect Bayesian approaches. Discussion Differences between the 2 × 2 table and the frequency tree groups were neither meaningful nor statistically significant with regard to the primary outcome measure of correct calculation of the positive predicted values. In the training session the majority of participants were able to calculate the positive predictive value of all tasks correctly. In the reexamination after 4 weeks the proportion of participants with solutions of all tasks decreased to 26% in both groups. The transfer of the numerical information into the visual helps was comparable between the two sessions. However, participants had major difficulties in applying the correct computation as a precondition of a correct solution. In all our tasks we have used frequency formats following the recommendation of Gigerenzer & Hoffrage [ 4 ]. In those earlier studies the frequency tree without caption has been used and we adopted this format of the frequency tree in our study. However, in more recent studies a captioned frequency tree has been used [ 11 ]. Therefore, we cannot exclude that when comparing the 2 × 2 table with a captioned frequency tree the results might be different. Our study is the first that has compared the two visual helps 2 × 2 table and frequency tree. Previous studies have concentrated on teaching methods using either one of the visual helps or both in combination [ 4 , 12 ]. These previous studies addressed different target groups, mainly medical students and physicians and focused different questions. In contrast, we addressed non-medical students without prior statistical knowledge as a first approach to lay people. Therefore, the overall results of our study are difficult to compare to previous publications. The primary aim of our study was not to investigate different teaching methods for computing predictive values. We have tried to apply the most appropriate method according to actual research at the initiation of the study. However, overall performance of our students was poor. In the training session 58% of participants were able to calculate the positive predictive value of 4 or 5 tasks correctly. In the follow-up examination after 4 weeks the proportion of correct solutions in 4 or 5 tasks decreased to 26%. In addition, after 4 weeks participants had major difficulties in applying the correct computation as a precondition of a correct solution whereas there was only a minor deterioration with respect to the transfer of the numerical information into the visual helps. A recent study used a computerized tutorial programme to teach Bayesian inference [ 11 ]. Within the study carried out in a rather small sample of mostly medical students, the role of the graphical aids captioned frequency tree presenting data as natural frequencies versus probability tree presenting data as probabilities in teaching Bayesian inference was explored. After 3 month participants who used the frequency tree reached 100% Bayesian solutions compared with 57% of participants using the probability tree. The authors hypothesized that it is much more important whether the proper representation is used than which graphical aid is applied [ 11 ]. Kurzenhauser & Hoffrage studied the effects of a classroom tutorial using both visual helps to teach Bayesian reasoning [ 12 ]. They achieved 47% correct answers after 2 months. Participants of the study were medical students in their second and third semester. Generalisability of the results with respect to the overall correct solutions of our study may be limited by the prevalent innumeracy that has lately been ascertained for Germany within the OECD Programme for international student assessment (PISA). Mathematics literacy was stated to be poor in Germany especially in girls [ 13 ]. A high percentage of participants in our study were women which corresponds to the distribution of students. Transferring the self-learning tutorial to people without general qualification for university entrance would probably result in an even lower amount of correct solutions. Conclusions In conclusion, our findings do not support the hypothesis that the 2 × 2 table is more valuable to facilitate the calculation of positive predictive values than the frequency tree. Regardless which visual help is used there is a need for improvement of teaching methods to approach lay people who want to participate in medical decision making. Competing interests None declared. Authors' contributions AS as the principal investigator planned and performed the study analysed the data and wrote the paper. AB contributed to planning and performance of the study. JB calculated the power of the study and carried out the statistical analysis of data. IM contributed to all parts of the study. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: Supplementary Material Additional File 1 Original questionnaire used in the 2 × 2 table group in the 1. session in German language. Click here for file Additional File 2 Original questionnaire used in the frequency tree group in the 1. session in German language. Click here for file Additional File 3 Tasks used in the questionnaires of the training session in English language. Click here for file Additional File 4 Original questionnaire used in the 2 × 2 table group in the 2. session in German language. Click here for file Additional File 5 Original questionnaire used in the frequency tree group in the 2. session in German language. Click here for file Additional File 6 Tasks used in the questionnaires of the follow-up examination in English language. Click here for file | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC514564.xml |
544347 | Rooting a phylogenetic tree with nonreversible substitution models | Background We compared two methods of rooting a phylogenetic tree: the stationary and the nonstationary substitution processes. These methods do not require an outgroup. Methods Given a multiple alignment and an unrooted tree, the maximum likelihood estimates of branch lengths and substitution parameters for each associated rooted tree are found; rooted trees are compared using their likelihood values. Site variation in substitution rates is handled by assigning sites into several classes before the analysis. Results In three test datasets where the trees are small and the roots are assumed known, the nonstationary process gets the correct estimate significantly more often, and fits data much better, than the stationary process. Both processes give biologically plausible root placements in a set of nine primate mitochondrial DNA sequences. Conclusions The nonstationary process is simple to use and is much better than the stationary process at inferring the root. It could be useful for situations where an outgroup is unavailable. | Background Several approaches for inferring a phylogenetic tree from the substitution patterns in multiply aligned sequences are available; they include maximum parsimony, distance-based, maximum likelihood and Bayesian methods [ 1 ]. Typically, the inferred tree is unrooted, because the explicit or implicit substitution process used is usually time-reversible. An effective way to put the root on the unrooted tree is to perform a phylogenetic analysis on the sequences of interest together with an outgroup, which is a set of distantly related sequences [ 2 , 3 ]. If the ingroup is monophyletic in the combined phylogenetic tree, then the point where the outgroup touches the ingroup tree is the estimated root. The practical challenge is to find suitable outgroups, and if no such outgroup is available, then one is forced to root the tree using just the ingroup. Several such methods include the molecular clock and nonreversible substitution processes. It seems clear that compared to the outgroup method, the success of these methods is more dependent on the extent to which the accompanying assumptions about the substitution process are satisfied in the data. For example, the molecular clock method should work well if the lineages indeed evolved more or less at the same rate. Likewise, as shown by Huelsenbeck et al . [ 4 ], a nonreversible process is more likely to succeed the less reversible the real substitution process is. The nonreversible substitution process, introduced by Yang [ 5 ], is stationary , i.e., the sequence composition is unchanged in time, and is equal to the equilibrium distribution of the rate matrix Q . The consensus is that it does not have enough power to discriminate among the candidate rooted trees. In this paper, we investigate a slightly more general, nonstationary process: in which the initial distribution π may not be the equilibrium distribution of the rate matrix Q . A priori, giving up stationarity is expected to produce a much better fit to data, since sequence composition is known to evolve, and should be accounted for. Indeed, substitution models where each branch has its own rate matrices had been used to resolve deep splittings in certain phylogenetic trees; see Yang and Roberts, and Galtier and Gouy [ 6 , 7 ]. Our process, which to our knowledge has not been investigated in this context, may be viewed as the simplest case of such nonstationary processes, with many fewer parameters. Thus, it can be used to decide whether the substitution processes on certain branches should be modeled differently. The input to our procedure is a multiple alignment and the topology of an unrooted binary tree. For each rooted tree associated with the given unrooted tree, we seek the maximum likelihood (ML) estimates of the branch lengths, π and Q . The rooted trees are then ranked in descending order of likelihoods. We model systematic variation in substitution rates among sites by assigning sites into several classes, and the relative rate for each class is estimated by ML; this is equivalent to the combined analysis framework of Yang [ 8 ]. We compared the ability of the stationary and nonstationary processes to place the root in three groups of species where the answer is considered well-known: (1) human, chimpanzee and gorilla, (2) human, chimpanzee, gorilla and orangutan, (3) human, mouse, chicken and frog ( xenopus laevis ). The analyses were based on all available mitochondrial protein-coding genes, as well as two nuclear protein-coding genes. Next, we applied the methods to a set of primate mitochondrial DNA sequences. Results Verification studies We fitted the nonstationary (NONSTA), stationary (STA) and reversible (REV) substitution models to all available mitochondrial protein-coding genes, as well as the nuclear genes albumin and c-myc , for three groups of organisms: (1) human, chimpanzee and gorilla, (2) human, chimpanzee, gorilla and orangutan, and (3) human, mouse, chicken and frog ( xenopus laevis ). The sequences were downloaded from Genbank and aligned using the CLUSTALW alignment of the amino acid sequences. Most alignments looked quite solid [see Additional files]. The beginning of the alignments for the genes COX1 , CYTB , ND1 and ND6 were slightly adjusted. The root positions are assumed to be on the (1) gorilla, (2) orangutan, and (3) frog branch, respectively. The branches on a tree are referred to by the organism names, except for the case of four taxa, where there is an internal branch (Figure 1 ). For groups (2) and (3), it was assumed that human was most closely related to chimpanzee and mouse respectively; thus the unrooted tree is determined. In group 1, the NONSTA and STA processes correctly placed the root in 8 and 6 genes respectively, out of 13 genes (Table 1 ). In group 2, NONSTA correctly placed the root in 9 genes out of 13 genes, compared to 2 genes for STA (Table 2 ). In group 3, NONSTA correctly placed the root in 11 genes out of 15 genes, compared to 7 genes for STA (Table 3 ). Furthermore, NONSTA gives stronger signal, or has better discriminative power: the highest-scoring rooted tree often has noticeably higher log likelihoods than competing rooted trees; this is not so with STA. Thus, NONSTA is much better than STA in placing the root at the individual gene level. Combining the log likelihoods across genes yields overall evidence for the root placements. Table 4 shows that NONSTA is unambiguously correct in all three analyses, while STA only gets the root correctly in group 3, and the signal is weak. Figure 1 Unrooted tree with four taxa The four branches adjacent to leaf nodes will be referred to by the corresponding taxon names. Table 1 Human, chimpanzee and gorilla Log-likelihoods (rounded to closest integer) of the MLEs for three rooted trees under the nonstationary (NONSTA), stationary (STA) and reversible (REV) models. If NONSTA or STA places the root correctly, the corresponding log likelihood appears in bold. gene root placement NONSTA STA REV human -1320 -1324 -1324 ATP6 chimp -1318 -1323 -1324 gorilla -1318 -1322 -1324 human -384 -389 -392 ATP8 chimp -384 -389 -392 gorilla -384 -389 -392 human -2842 -2876 -2877 COX1 chimp -2846 -2874 -2876 gorilla -2834 -2875 -2876 human -1285 -1293 -1295 COX2 chimp -1286 -1294 -1295 gorilla -1281 -1292 -1295 human -1477 -1493 -1496 COX3 chimp -1476 -1493 -1496 gorilla -1472 -1493 -1496 human -2205 -2236 -2236 CYTB chimp -2208 -2235 -2236 gorilla -2203 -2235 -2236 human -1787 -1804 -1805 ND1 chimp -1783 -1804 -1805 gorilla -1776 -1802 -1805 human -1949 -1974 -1975 ND2 chimp -1950 -1974 -1975 gorilla -1941 -1974 -1975 human -663 -679 -680 ND3 chimp -666 -679 -680 gorilla -665 -679 -680 human -2593 -2612 -2613 ND4 chimp -2589 -2612 -2613 gorilla -2579 -2612 -2613 human -519 -525 -525.8 ND4L chimp -523 -525 -525.8 gorilla -520 -526 -525.8 human -3600 -3624 -3629 ND5 chimp -3611 -3628 -3629 gorilla -3583 -3628 -3629 human -913 -917 -918 ND6 chimp -912 -917 -918 gorilla -913 -917 -918 Table 2 Human, chimpanzee, gorilla and orangutan Log-likelihoods (rounded to closest integer) of the MLEs for five rooted trees under the nonstationary (NONSTA), stationary (STA) and reversible (REV) models. If NONSTA or STA places the root correctly, the corresponding log likelihood appears in bold. gene root placement NONSTA STA REV human -1649 -1654 -1655 chimp -1647 -1654 -1655 ATP6 gorilla -1647 -1654 -1655 orangutan -1642 -1654 -1655 interior -1647 -1654 -1655 human -510 -514 -517 chimp -510 -515 -517 ATP8 gorilla -509 -515 -517 orangutan -509 -515 -517 interior -509 -515 -517 human -3456 -3465 -3467 chimp -3450 -3464 -3467 COX1 gorilla -3448 -3465 -3467 orangutan -3437 -3465 -3467 interior -3453 -3465 -3467 human -1485 -1496 -1497 chimp -1485 -1496 -1497 COX2 gorilla -1481 -1492 -1497 orangutan -1479 -1492 -1497 interior -1480 -1492 -1497 human -1769 -1791 -1796 chimp -1780 -1791 -1796 COX3 gorilla -1781 -1794 -1796 orangutan -1772 -1794 -1796 interior -1780 -1791 -1796 human -2593 -2673 -2674 chimp -2594 -2673 -2674 CYTB gorilla -2590 -2672 -2674 orangutan -2581 -2672 -2674 interior -2588 -2672 -2674 human -2214 -2234 -2236 chimp -2210 -2235 -2236 ND1 gorilla -2205 -2234 -2236 orangutan -2191 -2233 -2236 interior -2209 -2235 -2236 human -2441 -2469 -2470 chimp -2443 -2469 -2470 ND2 gorilla -2437 -2469 -2470 orangutan -2423 -2469 -2470 interior -2437 -2469 -2470 human -837 -855 -856 chimp -840 -855 -856 ND3 gorilla -838 -856 -856 orangutan -834 -855 -856 interior -838 -855 -856 human -3151 -3206 -3209 chimp -3149 -3205 -3209 ND4 gorilla -3141 -3205 -3209 orangutan -3169 -3207 -3209 interior -3145 -3206 -3209 human -623 -631 -631 chimp -622 -631 -631 ND4L gorilla -620 -631 -631 orangutan -619 -631 -631 interior -621 -631 -631 human -4469 -4501 -4503 chimp -4474 -4502 -4503 ND5 gorilla -4453 -4502 -4503 orangutan -4448 -4503 -4503 interior -4466 -4502 -4503 human -1069 -1076 -1078 chimp -1067 -1076 -1078 ND6 gorilla -1070 -1077 -1078 orangutan -1068 -1077 -1078 interior -1069 -1076 -1078 Table 3 Human, mouse, chicken and frog Log-likelihoods (rounded to closest integer) of the MLEs for five rooted trees under the nonstationary (NONSTA), stationary (STA) and reversible (REV) models. If NONSTA or STA places the root correctly, the corresponding log likelihood appears in bold. gene root placement NONSTA STA REV human -7722 -7728 -7731 mouse -7708 -7728 -7731 Albumin chicken -7723 -7731 -7731 frog -7705 -7728 -7731 interior -7723 -7728 -7731 human -2608 -2619 -2620 mouse -2607 -2619 -2620 ATP6 chicken -2590 -2619 -2620 frog -2585 -2618 -2620 interior -2585 -2618 -2620 human -679 -680 -682 mouse -677 -681 -682 ATP8 chicken -675 -679 -682 frog -678 -680 -682 interior -675 -680 -682 human -3872 -3885 -3887 mouse -3869 -3885 -3887 Cmyc chicken -3854 -3883 -3887 frog -3814 -3882 -3887 interior -3853 -3883 -3887 human -4704 -4792 -4794 mouse -4709 -4791 -4794 COX1 chicken -4700 -4794 -4794 frog -4679 -4791 -4794 interior -4698 -4792 -4794 human -2382 -2399 -2400 mouse -2382 -2399 -2400 COX2 chicken -2377 -2398 -2400 frog -2375 -2398 -2400 interior -2376 -2399 -2400 human -2502 -2537 -2542 mouse -2503 -2540 -2542 COX3 chicken -2483 -2538 -2542 frog -2485 -2539 -2542 interior -2486 -2540 -2542 human -3782 -3833 -3836 mouse -3783 -3832 -3836 CYTB chicken -3760 -3832 -3836 frog -3747 -3832 -3836 interior -3760 -3833 -3836 human -3457 -3483 -3486 mouse -3443 -3483 -3486 ND1 chicken -3435 -3484 -3486 frog -3434 -3482 -3486 interior -3442 -3482 -3486 human -4275 -4298 -4300 mouse -4275 -4298 -4300 ND2 chicken -4258 -4298 -4300 frog -4253 -4296 -4300 interior -4255 -4299 -4300 human -1348 -1353 -1355 mouse -1347 -1351 -1355 ND3 chicken -1337 -1353 -1355 frog -1335 -1352 -1355 interior -1335 -1353 -1355 human -5382 -5406 -5406 mouse -5380 -5406 -5406 ND4 chicken -5366 -5404 -5406 frog -5345 -5405 -5406 interior -5365 -5405 -5406 human -1259 -1261 -1265 mouse -1259 -1264 -1265 ND4L chicken -1254 -1262 -1265 frog -1245 -1263 -1265 interior -1254 -1263 -1265 human -7053 -7089 -7094 mouse -7053 -7091 -7094 ND5 chicken -7034 -7093 -7094 frog -7006 -7090 -7094 interior -7029 -7091 -7094 human -2022 -2025 -2028 mouse -2020 -2025 -2028 ND6 chicken -1995 -2023 -2028 frog -1998 -2025 -2028 interior -1998 -2025 -2028 Table 4 Combined analysis Combined log likelihoods over all genes under the nonstationary (NONSTA), stationary (STA), and reversible (REV) models. If NONSTA or STA places the root correctly, the corresponding log likelihood appears in bold. group root placement NONSTA STA REV human -21536 -21743 -21765 1 chimp -21551 -21746 -21765 gorilla -21470 -21744 -21765 human -26266 -26566 -26589 2 chimp -26270 -26567 -26589 gorilla -26223 -26566 -26589 orangutan -26172 -26566 -26589 interior -26241 -26563 -26589 human -53049 -53387 -53427 mouse -53029 -53393 -53427 3 chicken -52848 -53388 -53427 frog -52682 -53382 -53427 interior -52833 -53391 -53427 The nuclear genes albumin and c-myc and three mitochondrial genes, COX1 , COX2 and ATP6 from group 3 (with some mouse genes replaced with rat genes) were studied by Huelsenbeck et al . [ 4 ]. For these five genes, NONSTA and STA performed equally, getting all the correct root placements, except for ATP6 , with NONSTA again noticeably more discriminative. Primate mitochondrial DNA Brown et al . and Yang [ 5 , 9 ] studied a set of mitochondrial DNA (mtDNA) sequences from human, chimpanzee, gorilla, orangutan, gibbon, crab-eating monkey, squirrel monkey, tarsier and lemur. The topology of Yang's unrooted tree and the branch labels are shown in Figure 2 . The mtDNA sequences consist of two protein-coding fragments, separated by three RNA genes. Thus, four site classes are required. Analysis with NONSTA shows that the root is most likely on the tarsier branch, followed closely by the lemur and "f" branches, and the corresponding log likelihoods are quite different from the others (see Table 5 ). Under STA, the most likely root placements are on the squirrel monkey and lemur branches. Thus, both processes give predictions that are consistent (NONSTA more than STA) with the idea that the root should be somewhere near tarsier and lemur. However, as observed before, NONSTA has much greater discriminative power, and fits the data much better, than STA. Figure 2 Unrooted tree for nine primate mtDNA sequences The assumed unrooted tree is that presented in Yang [5]. The branches adjacent to leaf nodes are referred to by the corresponding organisms, while the interior branches are labelled a through f as indicated. Table 5 Nine primates Log-likelihoods (rounded to closest integer) of the MLEs for 15 rooted trees under the nonstationary (NONSTA), stationary (STA) and reversible (REV) models. root placement NONSTA STA REV human -4960 -4965 -4965 chimp -4959 -4965 -4965 gorilla -4961 -4965 -4965 orangutan -4961 -4965 -4965 gibbon -4962 -4964 -4965 crab-eating macaque -4955 -4963 -4965 squirrel monkey -4941 -4961 -4965 tarsier -4932 -4963 -4965 lemur -4935 -4961 -4965 a -4962 -4965 -4965 b -4961 -4965 -4965 c -4961 -4964 -4965 d -4957 -4964 -4965 e -4948 -4963 -4965 f -4936 -4963 -4965 Discussion Our results confirmed earlier findings that the stationary process (STA) is not very good at discriminating among rooted trees corresponding to the same unrooted tree. In contrast, the nonstationary (NONSTA) process seems much more effective, with individual genes, and with combined genes. It is quite clear that the difference in log likelihoods between fitting STA and the reversible process (REV) is often small, and statistically insignificant, based on the likelihood ratio test, while those between NONSTA and STA, and between NONSTA and REV, are often large, and statistically very significant. Though the chi-square distribution may be inappropriate [ 10 ], it seems to be satisfatory in practice [ 11 ]. This indicates that NONSTA fits the data much better than STA and REV. Thus it appears that allowing an initial distribution that is uncoupled with the rate matrix gives a better description of the data, and that the greater capacity of NONSTA over STA at estimating the root placement may stem from the ability of NONSTA to allow for some amount of evolution in base composition. Although Huelsenbeck et al .'s analysis using STA failed to place the root correctly in any of the genes albumin , c-myc , COX1 , COX2 and ATP6 , there are some differences between the analyses. The raw data were different: the rat albumin and c-myc genes were used by Huelsenbeck et al .; since mouse and rat are very similar, this is not likely to matter much. Secondly, the alignments were probably different, though since the sequences are quite similar, this should not be too important. It is plausible that most of the discrepancies between the results is due to the difference in the estimation procedure (maximum likelihood vs. Bayesian) and to the fact that in Huelsenbeck et al ., site variation was modeled by the gamma distribution [ 12 ], whereas here we only accounted for the codon position effect. Estimates of the relative rates are quite independent of the model used, and their relative magnitudes are largely within expectations. In particular, for group 3, the relative rates for codon positions 1, 2, and 3 fall between .2 and 1.1, .1 and .6, and 1.5 and 2.7 respectively. For all genes, the third codon position evolved the fastest, followed by the first and second positions. To gauge the contribution from the third codon position, we left out the corresponding bases in group 3 and reran the analysis with NONSTA. This gave the correct root placement in only three genes: albumin , c-myc and ND2 , showing the usefulness of the third codon position in this dataset, despite its markedly higher substitution rates. We also found that the pairwise identity at the third codon positions for all genes in groups 3 ranges from 34% to 61%. Base composition being generally nonuniform, the expected pairwise identity at saturation (i.e., infinite evolutionary distance) is lower than 25%. This seems to indicate that the third codon position is not saturated, and hence the phylogenetic information from this position is not just the base composition at each taxon. In addition, the base composition at the third codon position for some genes is quite different from the other positions. Our model does not fit these genes as well as a model where separate processes are associated with the codon positions. Such a model will be investigated in future. The NONSTA process is only slightly more complicated to apply, compared to the STA and REV processes. The fact that it works quite well in the verification studies and predicts biologically plausible roots for the nine-primate data demonstrates its utility and perhaps argues for its use in routine phylogenetic analysis. In any case, if no suitable outgroup is available, it could be worthwhile to try it. Though the NONSTA process is the most general time-homogeneous Markov process, it is still simplistic and imposes a severe constraint on the evolution of base composition: if two leaf nodes are at the same distance from the root, then the process stipulates that the corresponding sequences must have the same composition. This is patently unrealistic: once lineages split, they should evolve quite independently, and may explain the failure of the process at estimating the root placement for some genes. However, it is still valuable even if it does not always work, in that it can serve as a base from which exploration of richer models can be launched. For instance, one could identify lineages where the evolution significantly deviates from expectations, and then allow these lineages to have different rate matrices, which brings us closer to the very rich models of [ 6 , 7 , 13 , 14 ]. Conclusions The nonstationary substitution process is simple to use, has much greater power at estimating the root compared to the stationary process, and also fits data much better than the stationary and reversible processes. It seems feasible to use this process in analyses where a suitable outgroup is not easily available. It is also a good starting point for conducting more sophisticated phylogenetic analysis with richer models. Methods Substitutions in DNA sequences are assumed to occur independently at each site according to a Markov process, i.e., given the present base, future substitutions are independent of past substitutions. Furthermore, it is assumed that the process is time-homogeneous, i.e., substitution rates stay constant in time. As usual, the substitution rate from base a to b is the ( a , b )-entry in a 4 × 4 rate matrix Q ; the diagonal entries are such that each row sums to 0. For any t > 0, the transition probability P ( t ) is given by P ( t ) = exp( Qt ). Let π be a probability distribution on the DNA bases. The pair ( π , Q ) defines a substitution process on a rooted tree, as follows: pick a base at the root according to π , then run the substitution process according to Q down the tree, splitting into independent copies whenever a branching is encountered. The joint probability of the observed bases at the leaf nodes can be computed using almost exactly the same algorithm by [ 15 ]. There are two important special cases of the time-homogeneous process ( π , Q ). Associated with the rate matrix Q is a unique distribution π Q , called the equilibrium distribution of Q , such that the matrix product π Q × Q is the zero vector. The process ( π Q , Q ) is stationary , i.e., the sequence composition remains unchanged through time, and is described by π Q . Q is said to be reversible if it satisfies the detailed balance condition: Π Q Q = Q 'Π Q where Π Q is the diagonal form of π Q and Q ' is the transpose of Q . The process ( π Q , Q ) is then reversible , i.e., statistically the process looks the same in forward and backward time. In particular, as shown in [ 15 ], the joint distribution of the leaf bases is the same regardless of where the root is placed on the tree. The reversible process is known as the REV or time-reversible process in the molecular evolution literature [ 5 , 16 , 17 ]. Special cases of the REV process include those by Jukes and Cantor, Kimura, Felsenstein (two processes), Hasegawa, Kishino and Yano, and Tamura and Nei [ 15 , 18 - 22 ]. The nonreversible stationary process was first explored by Yang [ 5 ], and subsequently by Huelsenbeck et al . [ 4 ]. Yang referred to this process as "unrestricted", but we use the abbreviation STA here. We shall refer to the nonstationary process as NONSTA. The numbers of free parameters in the NONSTA, STA and REV processes are respectively 15 (3 in π and 12 off-diagonal entries in Q ), 12 (off-diagonal entries in Q ) and 9 (3 in π Q and 12 off-diagonal entries in Q , minus 6 detailed balance constraints). Since the models are nested, the likelihood ratio test can be used to assess the relative goodness-of-fit of the MLEs. It is standard practice to allow only calibrated rate matrices, i.e., Q satisfies so that a branch length is the average number of substitution events per site. We adopt this practice, and remark that for the nonstationary process ( π , Q ), with calibrated Q , since in general π ≠ π Q , it is not true that the expected number of substitutions in 1 time unit is 1, but the difference gets arbitrarily small as time goes to infinity. The sites in a DNA sequences can have very different substitution rates, the most well-known example being coding sequences, where the third codon positions evolved much faster than the others because of the degeneracy of the genetic code. In cases where the assignment of sites into several classes is known in advance, such as a coding sequence, the easiest way to deal with it is to associate to class i an unknown positive number r i , with the constraint that where n i is the number of sites in class i . The relative rate r i either expands or shrinks the tree depending on whether it is more or less than 1. The constraint gives a new interpretation of a branch length: it is now the average over all sites of their expected number of substitutions. Thus, this approach is similar to [ 8 ]: effectively, the classes are treated as separate datasets. In this study, coding sequences are divided into three classes by codon position. In the last dataset consisting of nine primate mitochondrial sequences, an additional class is created to account for the RNA-coding bases. Another source of site variation is related to the three-dimensional structure of the protein. For example, hydrophilic residues are usually exposed, hence tend to evolve faster than hydrophobic residues which are deeply buried. Our present approach does not model this and other less obvious sources of site variation. Possible remedies include using the gamma distribution [ 12 ] or the hidden Markov model [ 23 ]. Given a rooted tree relating aligned coding sequences, we seek the ML estimates of the branch lengths, the substitution parameters, and the relative rates. For other sequences, the relative rates are not estimated. Gradient-based methods are perhaps the most efficient at finding the maximum. The EM algorithm [ 24 ] is another possibility. We implemented the simplex method [ 25 ], which is slower but is less likely to be misled to local maxima than gradient-based methods. To further reduce the chance of being fooled by local maxima, different initial estimates were used, and the final estimates with the highest likelihood was picked. The initial estimates were obtained by first deriving a reversible rate matrix from a pairwise comparison of two sequences, then using the associated REV process to find the most likely branch lengths and relative rates; all pairwise comparisons were used in this study, so that, for example, four taxa give six initial estimates. The estimation procedure was implemented in C, and the source code can be requested from the first author. Authors' contributions The idea was conceived by the first author and was inspired and refined by the second author. The first author composed the code and performed the data analysis. Supplementary Material Additional File 1 A text file containing the amino acid sequence alignments for group 1. Click here for file Additional File 2 A text file containing the amino acid sequence alignments for group 2. Click here for file Additional File 3 A text file containing the amino acid sequence alignments for group 3. Click here for file | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC544347.xml |
514570 | Endotoxin leads to rapid subcellular re-localization of hepatic RXRα: A novel mechanism for reduced hepatic gene expression in inflammation | Background Lipopolysaccharide (LPS) treatment of animals down-regulates the expression of hepatic genes involved in a broad variety of physiological processes, collectively known as the negative hepatic acute phase response (APR). Retinoid X receptor α (RXRα), the most highly expressed RXR isoform in liver, plays a central role in regulating bile acid, cholesterol, fatty acid, steroid and xenobiotic metabolism and homeostasis. Many of the genes regulated by RXRα are repressed during the negative hepatic APR, although the underlying mechanism is not known. We hypothesized that inflammation-induced alteration of the subcellular location of RXRα was a common mechanism underlying the negative hepatic APR. Results Nuclear RXRα protein levels were significantly reduced (~50%) within 1–2 hours after low-dose LPS treatment and remained so for at least 16 hours. RXRα was never detected in cytosolic extracts from saline-treated mice, yet was rapidly and profoundly detectable in the cytosol from 1 hour, to at least 4 hours, after LPS administration. These effects were specific, since the subcellular localization of the RXRα partner, the retinoic acid receptor (RARα), was unaffected by LPS. A potential cell-signaling modulator of RXRα activity, c-Jun-N-terminal kinase (JNK) was maximally activated at 1–2 hours, coincident with maximal levels of cytoplasmic RXRα. RNA levels of RXRα were unchanged, while expression of 6 sentinel hepatic genes regulated by RXRα were all markedly repressed after LPS treatment. This is likely due to reduced nuclear binding activities of regulatory RXRα-containing heterodimer pairs. Conclusion The subcellular localization of native RXRα rapidly changes in response to LPS administration, correlating with induction of cell signaling pathways. This provides a novel and broad-ranging molecular mechanism for the suppression of RXRα-regulated genes in inflammation. | Background LPS, a major constituent of the outer membrane of gram-negative bacteria, potently stimulates host innate immune response [ 1 ]. LPS-induced activation of monocytes/macrophages leads to the release of proinflammatory cytokines such as interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNFα) in addition to other mediators such as cysteinyl leukotrienes [ 2 ]. LPS and LPS-induced cytokines have been implicated in the pathogenesis and progression of a variety of liver diseases, including cholestasis, as well as being principal mediators of the negative hepatic APR [ 3 ]. The cholestatic effect of LPS is primarily due to cytokine-mediated inhibition of the function and expression of hepatic genes encoding critical proteins involved in bile formation and transport (reviewed in [ 4 ]). These hepatocellular transporters include the basolateral sodium/taurocholate cotransporter ( Ntcp/Slc10a1 ) and organic anion transporting proteins ( Oatp1/Slc21a1 ), as well as the canalicular multispecific organic anion exporter ( Mrp2/Abcc2 ) and the bile salt export protein ( Bsep/Abcb11 ). Transcriptional down-regulation of the principal hepatic bile acid importer, Ntcp contributes to the reduction in bile acid uptake by hepatocytes in inflammation, whereas reduced Mrp2 expression leads to impaired excretion of conjugated bilirubin, glutathione and other organic anions into bile [ 5 , 6 ]. Recent reports have provided insights into the link between inflammation-mediated cell signaling and regulation of bile acid homeostasis in the liver. Geier et al . showed that LPS-mediated suppression of Ntcp RNA was almost completely blocked by pre-treatment with anti-IL-1β specific antibodies, indicating that the cholestatic effects of LPS on the expression of this gene may be primarily mediated by the cell signaling pathways initiated by this one cytokine [ 7 ]. We have shown that IL-1β treatment of HepG2 cells, or primary rat hepatocytes, leads to JNK-dependent repression of nuclear binding activity of the Ntcp transactivator, RXRα:RARα, with consequent down-regulation of Ntcp promoter activity [ 8 ]. Finally, LPS, cytokines, and activated JNK have been linked to reduced expression of the rate-limiting enzyme in the bile acid biosynthetic pathway, cholesterol 7α-hydroxylase (CYP7A1), thus linking inflammatory signaling in the liver to the known and coordinated suppression of both bile acid import and synthesis [ 9 - 11 ]. How activated JNK leads to reduced RXRα function is not known, but is likely to involve direct phosphorylation of RXRα [ 8 ]. Phosphorylation of nuclear receptors (NRs) is a rapid and potentially powerful means of regulating NR activity, that, depending upon the NR, can affect transcriptional activity, protein stability, sub-cellular localization, protein-protein interactions or DNA binding activity [ 12 , 13 ]. Phosphorylation of transfected RXRα was reported to alter its transactivation properties in vitro, however, a definite functional role for native RXRα phosphorylation remains controversial. Both enhanced and reduced proteasome-mediated RXRα degradation have been associated with RXRα phosphorylation [ 14 , 15 ]. Hyperphosphorylation of RXRα by JNK was reported by Adam-Stitah et al [ 16 ] and the phosphorylation sites were mapped to several residues (serines 61 and 75 and threonine 87) in the N-terminal region and serine 265 in the ligand binding domain of mouse RXRα. However, JNK-mediated hyperphosphorylation of RXRα did not affect the transactivation properties of transfected RXRα homodimers or RXRα:RARα heterodimers in cultured cells [ 16 ]. In contrast, we and others have demonstrated that phosphorylation of RXRα by JNK signaling pathways is associated with reduced RXRα-dependent promoter activity [ 8 , 17 ]. Clearly, the consequences of RXRα phosphorylation are complex and poorly understood. NR ligands and extracellular signal-mediated pathways can alter subcellular NR localization, some of which involves phosphorylation-dependent mechanisms [ 12 , 13 , 18 - 20 ]. The xenobiotic receptors, CAR (NR1I3) [ 19 ] and PXR (NR1I2) [ 20 ] are localized in the cytoplasm of mouse hepatocytes and translocated into the nucleus after administration of their respective ligands. GR and VDR are well-known to undergo ligand-dependent nuclear import in transfected cells [ 12 , 13 , 18 , 21 ]. In contrast to the well-described events leading to NR nuclear import, little is known about NR nuclear export, including RXRα. Perhaps the best understood example of cell signaling targeting of NR nuclear export is JNK-mediated phosphorylation of GR, as a means of terminating GR-mediated transcription [ 18 ]. Such a mechanism for RXRα has never been shown, although a reduction in nuclear RXRα protein levels has been demonstrated in an animal model of obstructive cholestasis induced by bile duct ligation, raising the possibility of nuclear export [ 22 ]. In these studies, we sought to determine whether alterations in RXRα-dependent hepatic gene expression seen in inflammation may be related to nucleo-cytoplasmic re-distribution of RXRα. LPS treatment resulted in the activation of hepatic JNK coinciding with marked reduction in nuclear RXRα levels, and the rapid appearance of RXRα in the cytosol. RNA levels of RXRα and six of its heterodimeric partners highly expressed in liver were analyzed after LPS treatment: RXRα , RARα , FXR (farnesoid X receptor) and PPARα (peroxisome proliferator-activated receptor) RNA levels were stable, whereas CAR (constitutive androstane receptor) and PXR (pregnane X receptor) RNA levels were markedly suppressed and LXR (liver X receptor) RNA was elevated. Hepatic RNA levels of multiple RXRα target genes whose expression depends upon adequate nuclear levels of RXRα were significantly reduced by LPS. This is likely to be a consequence of reduced nuclear binding activity of RXRα heterodimer pairs. Notably, the reduction in RXRα nuclear protein levels (~50%) quantitatively correlated with the reduction in RNA levels of RXRα target genes. Taken together, these studies indicate that post-translational modification and cellular re-distribution of RXRα coinciding with induction of cell signaling is a novel, broad-ranging, and rapid mechanism contributing to the negative hepatic APR phenotype in the inflamed liver. Results LPS activates hepatic JNK In order to determine the in vivo role for LPS-induced activation of hepatic JNK, we first established a time course for JNK activation, by measuring phospho-JNK and phospho-c-Jun levels. Liver whole cell extracts were prepared at various time points from 1–16 hours after injection with either LPS (2 μg/g bw) or vehicle (0.9% saline) (Fig. 1A ). Phosphorylated JNK levels were maximal at 1–2 hours, and significantly higher than saline-injected animals at all time points studied. Total JNK levels did not vary between vehicle and LPS-treated samples. c-Jun is a direct substrate for phospho-JNK, and phospho-c-Jun levels are a well-described indicator of JNK activity [ 23 ]. LPS treatment led to maximal c-Jun phosphorylation at 1 to 2 hours, with a slight reduction at 4 and 6 hours, and was undetectable by 16 hours. Thus, in an animal model, LPS administration activates JNK signaling in the liver as early as 1 hour, with evidence for prolonged JNK activity lasting at least 6 hours. Figure 1 LPS activates JNK and leads to rapid nuclear export of RXRα. C57BL/6 male mice were injected IP with 0.9% saline (Sal) or 2 μg/g bw of Salmonella LPS. Livers were isolated at the indicated time-points and whole cell extracts were prepared. A . Phosphorylation of c-JUN (P-cJUN) and JNK (P-JNK) was determined by immunoblotting cell lysates with phospho-c-JUN and phospho-JNK antibodies respectively. Total JNK levels in the liver tissue ( JNK ) are shown in the lower panels . This data is representative of three animals per treatment group. B. Nuclear (Nuc) and cytosolic (Cyt) extracts were analyzed by immunoblotting with antibodies to RXRα and RARα to determine subcellular localization of RXRα. The extracts from 4 animals were combined to account for inter-animal variability. Note the high molecular weight smear in LPS-treated extractions (most evident at 1 h). Data quantified and normalized to saline-injected samples (set at 1.0). C. Immunofluorescent analysis of formalin-fixed mouse liver tissues after 1 h of saline or LPS treatment. The blue color indicates DAPI staining of the nuclei, the green color indicates RXRα detected with FITC-labeled secondary antibody, DAPI/FITC are the merged images. The saline and LPS-treated samples are represented in the left and right panels, respectively. LPS treatment leads to the rapid reduction of nuclear RXRα protein levels concomitant with the appearance of RXRα in the cytoplasm Treatment of HepG2 cells or primary rat hepatocytes with IL-1β leads to JNK-dependent repression of RXRα :RARα nuclear binding activity, with the consequent down-regulation of target gene expression [ 8 ]. However, whether such changes are observed in RXRα activity after LPS challenge in an animal model is unknown. As early as one hour after LPS treatment, the maximal point of JNK activation, nuclear RXRα levels were significantly reduced compared to control, and remained so for at least 16 hours after LPS treatment (Fig. 1B ). RXRα was not present in the cytoplasmic fraction at any time point after saline treatment, yet was robustly evident within one to two hours after LPS treatment, and decreased thereafter. Interestingly, immunoblot analysis of nuclear RXRα revealed a slower migrating species after LPS treatment (most evident in the 1 hour LPS sample), suggestive of LPS-induced post-translational modification. Notably, hepatic RARα levels in both nuclear and cytoplasmic compartments were unchanged by LPS. To confirm the intracellular localization of RXRα in liver, immunofluorescence staining was carried out in formalin-fixed liver tissues prepared from mice 1 h after saline or LPS injection (Fig. 1C ). In LPS-treated mouse livers, RXRα was clearly observed in both the nucleus and cytoplasm of hepatocytes, whereas it remained exclusively nuclear in saline-treated controls. Thus, there is a rapid, dramatic, and specific subcellular re-distribution of hepatic RXRα in response to LPS-administration, coinciding with induction of cell signaling. Effect of LPS on steady-state mRNA levels of RXRα and its partners in the liver One possible explanation for reduced nuclear RXRα levels could be suppression of hepatic RXRα RNA expression, as seen in response to higher doses of LPS [ 24 ]. We investigated whether low dose LPS administration had an effect on the RNA levels of RXRα , six of its heterodimeric partners and SHP–all known to be involved in hepatic gene expression [ 25 ] (Fig. 2A ). At 16 h after LPS administration, RNA levels for RXRα, RAR , FXR and PPARα were unchanged, whereas PXR and CAR RNA levels were reduced and LXRα RNA levels were increased (Fig. 2A ). LPS-mediated down-regulation of PXR and CAR RNA levels in mice have been reported by Beigneux et al [ 26 ], however our results do not support the reduction in RNA levels of RXRα , FXR and LXR and PPARα seen by others, perhaps due to differences in the experimental model, LPS dose or mouse strain [ 24 , 27 ]. Figure 2 Effects of LPS on RNA levels of NRs and RXRα target genes. C57BL/6 mice were injected with 0.9% saline ( white bars ) or 2 μg/g bw of Salmonella LPS ( black bars ) for 16 hours ( n = 6 per group). RNA was prepared from the livers and analyzed for A. NRs and B. RXRα target genes by real-time PCR. All data were presented as mean ± SD and standardized for GAPDH RNA levels. Expression in the saline-treated control animals was set to 1. The asterisk s indicate significant difference (p < 0.05). See supplemental information for primers and probes. Hepatocyte-selective RXRα-null mice have impaired metabolic function, with reductions in CAR, FXR, LXRα, PPARα, and PXR target gene expression [ 28 ]. As examples of genes regulated by RXRα and its partners, we studied RNA expression of six sentinel hepatic genes regulated by various RXRα heterodimer pairs: Ntcp (RARα), Bsep (FXR), Mrp2 (CAR, FXR, PXR), Cyp3A11 (CAR, PXR), Abcg5 (LXRα) and Lfabp (PPARα) (Fig. 2B ). RNA levels of all of these hepatic RXRα-regulated genes were significantly reduced by LPS treatment. Ntcp , Bsep and Abcg5 RNA levels decreased by 50–60%, Cyp3A11 RNA by 80%, while Lfabp and Mrp2 expression were each reduced approximately 60–70% after LPS treatment. The comparatively greater reduction in Cyp3A11 gene expression can be attributed to the combined effects of diminished PXR and CAR RNA expression along with reduced nuclear RXRα protein levels; both PXR & CAR activate Cyp3A11 gene expression (reviewed in [ 29 ]). The orphan nuclear receptor SHP (small heterodimer protein, NR0B2) is known to repress the activities of RXRα and other NRs [ 11 , 29 ]. One possibility is that LPS-mediated suppression of hepatic genes could be mediated by the activation of the repressor, SHP [ 9 ]. However, this is unlikely, since LPS treatment dramatically reduced SHP RNA levels (Fig. 2A ). Taken together, these studies indicate that the effect of LPS on hepatic RXRα-dependent gene expression is not due to reduced RXRα RNA levels or increased SHP–rather it appears to be a consequence of post-translational modification and rapid LPS-induced subcellular re-distribution of RXRα protein. This is in agreement that SHP-1 is a FXR/RAR target gene [ 11 ]. Effect of LPS on DNA binding activity of Type II nuclear receptor pairs in liver In order to determine if reduced nuclear RXRα protein levels leads to impaired DNA binding activity of RXRα and its partners, electrophoretic mobility shift analyses were performed. Nuclear extracts were prepared from livers of saline or LPS-treated mice and incubated with oligonucleotides containing canonical DNA elements scanning Type II NR binding sites–direct repeats of the hexad AGGTCA, separated by 1 to 5 nucleotides (DR1-5), or an inverted repeat separated by 1 nucleotide (IR1) [ 30 , 31 ]. As Type II NRs, RXRα partners with either RARα, PPARα, PXR, CAR, LXR or FXR to bind to one or more of these sites (reviewed in [ 25 ]). At 16 h after LPS treatment, binding to all 6 RXRα-containing canonical sequences was significantly reduced in hepatic nuclear extracts from LPS-treated animals (~45–70% reduction) (Fig. 3 ), consistent with a diminished nuclear RXRα. Since the expression of PXR and CAR was reduced upon LPS administration (Fig. 2A ), there was a more dramatic decrease in binding to their recognition elements, DR3 and DR4 (~70% reduction). Binding to the consensus AP1 DNA sequence was increased (~70%) upon administration of LPS; this serves as a positive control for JNK-mediated activation of hepatic inflammation as well as an indication of specificity of suppression of RXRα-heterodimer pair DNA binding (Fig. 3 ). Figure 3 LPS reduces binding activities of RXRα-containing heterodimer pairs to canonical DNA elements. Electrophoretic mobility shift assay analysis of hepatic nuclear extracts prepared from C57BL/6 mice injected with control saline or 2 μg/g bw LPS for 16 h. Radiolabeled double-stranded DR1, DR2, DR3, DR4 and DR5 elements or a consensus AP1 element were employed (see Materials and Methods). The samples were electrophoresed through a 6% non-denaturing polyacrylamide gel, dried and analyzed by autoradiography. Discussion The negative hepatic APR is characterized by suppression of hepatic gene expression in response to inflammation and is well-modeled by LPS administration [ 32 , 33 ]. We hypothesized that reduced nuclear levels of RXRα after LPS administration would be manifested by broad alterations in RXRα-dependent gene expression across diverse physiological processes [ 28 ]. Our results demonstrate that LPS signaling induces rapid and profound reduction of hepatic nuclear RXRα protein levels, concomitant with appearance of RXRα in the cytoplasm, leading to subsequent reduction in the expression of RXRα-dependent hepatic genes. Recent studies have led to a broader understanding of the molecular basis for the role of LPS in intracellular signaling and hepatic function [ 24 , 26 , 34 ]. Activation of monocytes/macrophages by LPS leads to the secretion of a number of proinflammatory cytokines such as TNFα, IL-1β, and IL-6 [ 2 ]. LPS-induced activation of Kupffer cells, the resident hepatic macrophages, triggers several crucial intracellular signaling pathways in hepatocytes, including stress-activated mitogen-activated protein kinases, extracellular signal-regulated kinase (ERK), JNK and p38 mitogen-activated protein kinase (p38 MAPK) [ 35 ]. Stress-activated protein kinases, mitogen-activated protein kinase kinase-4 (MKK4/SEK1) and its downstream mediator JNK was shown to directly phosphorylate RXRα[ 8 , 17 ]. Previous studies by our group[ 8 ] demonstrated that inhibition of the JNK signaling pathway completely blocked IL-1β-mediated suppression of RXRα-dependent Ntcp gene expression, thus implicating JNK to be a central player in inflammation-induced cholestasis. Most evident in the 1 hour sample are high molecular weight forms of RXRα, consistent with covalent post-translational modification (Fig. 1B ), although the actual nature of this high molecular weight species is currently unknown and under investigation. One plausible interpretation of these data is that LPS-induced activation of JNK leading to phosphorylation and likely further modification of RXRα, triggering its transport from nucleus to cytoplasm, where it may be targeted for degradation. Phosphorylation has been shown to be involved in the degradation of RXRα :RARα heterodimers by proteasomes, thus providing a mechanism for JNK-mediated inhibition of RXRα-dependent target gene transactivation[ 14 , 15 ] RNA levels of RXRα were not affected by LPS, further supporting nuclear export of RXRα as a primary mechanism of suppression of hepatic genes during negative hepatic APR. The interrelationship and roles played by JNK and phospho-RXRα are neither readily nor definitively explored in an in vivo model, especially using such a broadly-acting inflammatory agent like LPS. Hepatocytes and liver-derived HepG2 cells in culture respond to LPS-induced cytokines like TNFα and IL-1β by suppressing the expression of negative hepatic APR genes [ 8 , 24 , 26 , 36 ]. Recent work in our laboratory indicates that treatment of HepG2 cells with IL-1β leads to RXRα nuclear export, dependent upon JNK-mediated phosphorylation of select residues in RXRα (Zimmerman et al ., manuscript in preparation). In transfected cells, nerve growth factor (NGF)-induced phosphorylation of the orphan nuclear receptor NGFI-B (Nur77) resulted in the translocation of RXR-NGFI-B complex out of the nucleus, indicating that distribution of RXR in these cells was regulated by NGFI-B [ 37 ]. The data presented here are the first to indicate that inflammation-mediated cell signaling leads to rapid subcellular redistribution of native RXRα, changing the previous impression of RXRα as a stable nuclear resident [ 38 ]. Finally, these findings indicate significant cross-talk between JNK-signaling and NR-mediated gene expression. Conclusions Overall, we conclude that RXRα is rapidly exported out of the nucleus in response to LPS. RXRα, as an obligate heterodimer with other class II NRs, regulates the expression of a broad array of genes involved in critical metabolic pathways in the liver, many of which are impaired during the negative hepatic APR. This helps explain how inflammation-induced signaling can lead to rapid, diverse and multiple alterations in hepatic gene expression, which has implications for future therapeutic targets of both acute and chronic liver diseases. Materials and methods Materials LPS ( Salmonella typhimurium ) was purchased from Sigma Chemical Co. (St. Louis, MO) and freshly diluted to the desired concentration in pyrogen-free 0.9% saline before injection. Anti-JNK (#9252), phospho-JNK (#9251) and phospho-cJUN antibodies (Ser 63) (#9261) (Cell Signaling, Beverly, MA); anti-RXRα (D-20) (#sc-553) and anti-RARα antibodies (#sc-551) (Santa Cruz Biotechnology, Santa Cruz, CA) were used according to manufacturer's instructions. [γ- 32 P]ATP was obtained from PerkinElmer Life Sciences (Boston, MA). Oligonucleotides were obtained from Sigma Genosys and Synthegen, Houston, TX. All reagents for real-time PCR were purchased from Applied Biosystems (Foster City, CA). Animals Adult male (8–10 weeks) C57BL/6 mice (20–25 g) were purchased from Charles River Laboratories, (Wilmington, MA). The animals were maintained in a temperature- and humidity-controlled environment and were provided with water and rodent chow ad lib. Mice were given intraperitoneal injection with 2 μg/g body wt LPS ( Salmonella typhimurium ; Sigma Chemical Co., St. Louis, MO) in saline or saline alone. LPS in this dose range has been shown previously to induce cholestasis, maximally inhibit bile acid uptake, and significantly reduce Ntcp mRNA from 12 to 16 hours after injection, while not inducing hepatic damage [ 6 , 39 ]. Livers were removed at the time indicated in the figure legends (1 to 16 hours) after treatment. All animal protocols were approved by the Baylor College of Medicine Institutional Animal Care and Use Committee. Experiments were performed in triplicate and repeated three to four times. Preparation and analysis of nuclear and cytoplasmic and whole cell extracts Nuclear and cytoplasmic extracts were prepared according to Itoh et al [ 18 ] Whole cell extracts were prepared according to Li et al [ 8 ]. Protein concentration was determined by BCA assay according to the manufacturer's protocol (Pierce, Rockford, IL). These fractions were analyzed by immunoblotting. Signals were developed by a standard enhanced chemiluminescence method following the manufacturer's protocol (Perkin Elmer Life Sciences, Boston, MA) and quantified by a densitometer using ImageQuant software. Immunofluorescent analysis Livers were isolated from saline and LPS injected mice after 1 hour of treatment, fixed in 10% buffered neutral formalin overnight at 4°C and then stored in 70% ethanol. Fluorescent detection was performed by using anti-RXRα (D-20) antibody and fluorescein isothiocyanate (FITC)-labeled secondary antibody and nuclei was stained with 4'-6-diamidino-2-phenylindole (DAPI). Visualization was performed with a Deltavision Spectris Deconvolution Microscope System (Applied Precision, Inc.). Electrophoretic gel mobility shift assays Nuclear extracts were prepared according to Timchenko et al. [ 40 ] with some modifications. Double-stranded oligonucleotide probes were end-labeled and purified according to standard procedures [ 41 ]. 10 μg of nuclear extracts were incubated on ice for 30 min with 32 P end-labeled oligonucleotide as described previously [ 41 ]. The oligonucleotide sequences are provided in Table 1. After binding, the samples were electrophoresed through a non-denaturing 6% polyacrylamide gel, dried and exposed to x-ray film. In addition, gels were exposed to a PhosphorImager screen and quantified using a PhosphorImager and ImageQuant software. Real time quantitative PCR analysis Total RNA was isolated from mouse liver tissues using the RNaesy kit from Qiagen. cDNA was synthesized from 7.5 μg of total RNA using the ProSTAR™ First-Strand RT-PCR Kit (Stratagene, La Jolla, CA). Real time quantitative PCR (RTQ-PCR) was performed using an ABI PRISM 7700 Sequence Detection System instrument and software (Applied Biosystems, Inc., Foster City, CA). Briefly, each amplification reaction (50 μl) contained 40–200 ng of cDNA, 300 nM of forward primer, 300 nM of reverse primer, 200 nM of fluorogenic probe and 25 μl of TaqMan ® Universal PCR master mix. PCR thermocycling parameters were 50°C for 2 min, 95°C for 10 min and 40 cycles of 95°C for 15 s, and 60°C for 1 min. Quantitative expression values were extrapolated from standard curves and were normalized to GAPDH. The sequences of the primers and probes were obtained from the literature [ 42 ] or purchased from Applied Biosystems, and are listed in Table 2. Abbreviations The abbreviations used are: RXR, retinoid X receptor; RAR, retinoic acid receptor; FXR, farnesoid X receptor; PPAR, peroxisome proliferator-activated receptor; PXR, pregnane X receptor; CAR, constitutive androstane receptor; LXR, liver X receptor; SHP, small heterodimer partner; NR, nuclear receptor; GR, glucocorticoid receptor; PR, progesterone receptor; VDR, vitamin D receptor; JNK, c-Jun N-terminal kinase; AP-1, activator protein-1; Ntcp, sodium/taurocholate cotransporting polypeptide; Bsep, Bile salt export pump; Mrp2, multidrug resistance associated protein 2; Lfabp, liver fatty acid binding protein; Cyp3A11, cytochrome P450 3A11; PCR, polymerase chain reaction; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; APR, acute phase response; DR, Direct Repeat; IR, Inverted Repeat; FITC, fluorescein isothiocyanate; DAPI, 4'-6-diamidino-2-phenylindole. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC514570.xml |
548958 | Correction: Cro-Magnons Conquered Europe, but Left Neanderthals Alone | null | Published February 15, 2005 In PLoS Biology , volume 2, issue 12. 10.1371/journal.pbio.0020449 The art credits were missing from the image accompanying this synopsis. The image caption should read as follows: Reconstruction of Neanderthal woman (Photo: Bacon Cph; makeup: Morton Jacobsen) | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC548958.xml |
547896 | Public appraisal of government efforts and participation intent in medico-ethical policymaking in Japan: a large scale national survey concerning brain death and organ transplant | Background Public satisfaction with policy process influences the legitimacy and acceptance of policies, and conditions the future political process, especially when contending ethical value judgments are involved. On the other hand, public involvement is required if effective policy is to be developed and accepted. Methods Using the data from a large-scale national opinion survey, this study evaluates public appraisal of past government efforts to legalize organ transplant from brain-dead bodies in Japan, and examines the public's intent to participate in future policy. Results A relatively large percentage of people became aware of the issue when government actions were initiated, and many increasingly formed their own opinions on the policy in question. However, a significant number (43.3%) remained unaware of any legislative efforts, and only 26.3% of those who were aware provided positive appraisals of the policymaking process. Furthermore, a majority of respondents (61.8%) indicated unwillingness to participate in future policy discussions of bioethical issues. Multivariate analysis revealed the following factors are associated with positive appraisals of policy development: greater age; earlier opinion formation; and familiarity with donor cards. Factors associated with likelihood of future participation in policy discussion include younger age, earlier attention to the issue, and knowledge of past government efforts. Those unwilling to participate cited as their reasons that experts are more knowledgeable and that the issues are too complex. Conclusions Results of an opinion survey in Japan were presented, and a set of factors statistically associated with them were discussed. Further efforts to improve policy making process on bioethical issues are desirable. | Background In Japan, it was not until 1997 that a law was finally enacted to legalize organ transplant from a brain-dead body. Since 1968, when the first heart transplantation from a person declared brain dead was performed, there have been long-standing struggles in Japan for and against this procedure. In addition to many non-governmental institutions and individuals, the Japanese government – both the legislature and administrative bodies – engaged in a variety of efforts for this enactment. A number of factors have been suggested for the prolonged lack of policy in this area: deep public mistrust of the medical profession caused by the 1968 heart transplant; the Japanese culture which still holds traditional Japanese view of death and the body; and the lack of the broad public consensus required as a precondition for a policy [ 1 ]. As elsewhere, public policy to resolve these social disputes was pursued [ 2 ]. Observers of the past policy process toward enactment offer contradictory evaluations: those for the speedy introduction of new medical technologies, for example, complain that possible organ recipients have suffered from the prolonged, impractical, and fruitless policy disputes, while those against hasty use of immature and controversial technologies have a good appraisal of the care exercised in past debates. Some argue that even now there are many unsettled issues. Previously, there has been no systematic study assessing past policy processes, factors that affect public appraisal of government efforts, and what future agenda items should include to ensure successful policy enactment. In the policy process, which entails the introduction and implementation of a policy, public opinion is generally considered an important factor affecting its fate. Without favorable public opinion, a policy cannot be introduced and implemented effectively [ 3 ]. Public opinion regarding the process of policymaking is another important indicator of how well the government functions. This public appraisal essentially measures the degree of congruence between the public's expectations of government actions and perceived fulfillment of these ideals. When contending ethical value judgments are involved, satisfaction with the process affects the legitimacy of political institutions and processes. This, in turn, could influence the fate of proposed policies and condition future policy making [ 4 ]. Public opinion thus constitutes valuable information for determining how government bodies can and should proceed. An important aspect in policy making is the degree of public participation, which is defined as a set of measures to consult with, involve, and inform the public to encourage participation in policy development [ 5 ]. Since experts, policymakers, and citizens all are limited in some aspects of their knowledge, public involvement is expected to improve the substantive quality of decisions, by incorporating public values, assumptions, and preferences. Public involvement also works toward educating the public, fostering trust in institutions, and reducing conflicts [ 6 ]. Citizen involvement legitimizes government efforts at policymaking, by lending credibility and thus increasing public trust in the political process. This study examines public appraisal of past efforts of the Japanese government to legalize organ transplant from brain-dead bodies. Other goals are to quantify the public's intent to participate in future policy discussions and to identify possible factors affecting both appraisals and intentions to participate. A brief chronology of Japan's efforts since the 1960's toward legalization of organ transplant upon brain death is also presented. Implications of the study findings are discussed, as are suggestions for future research. Methods Questionnaire and subjects A questionnaire includes sections on demographic characteristics (age, sex, education, occupation), health conditions (hospitalization in the past five years, current health condition), period of first issue attention (when did you first hear of the issue on brain death and organ transplant?), period of opinion formation (when did you arrive at the opinion you have now on this issue?), knowledge about donor card (are you familiar with the donor card, which indicates a personal directive to donate organs when determined to be brain-dead?), knowledge of past government efforts at informing the public and inviting their opinions (asking number of measures employed by the government), appraisal of past government efforts (How do you rate past government efforts around the issue of brain death and organ transplant?), and intention to participate in future (bioethics-related) policy discussions. Those who indicated unwillingness to participate were asked to provide reasons for that decision. The final questions concerned important agenda items for future policy processes. Questionnaires were sent to 3000 people, selected from 15 cities and towns nationwide by the stratified random sampling method. They were requested to answer the questions on the sheets, and send them back by mail in a postage prepaid envelope. The study was conducted in January 2002, and the overall response rate was 34.5%. Statistical analysis First, association of the questionnaire items both with a) appraisal of past government efforts and b) participation intent was examined using Mann-whitney tests (between two groups), Kruskal-Wallis tests (among multiple groups), and/or Spearman's rank correlations. Next, a multiple logistic regression model was applied to identify possible explanatory variables (entered and removed at the significance level of p = 0.05) to determine a set of variables that best predicts the dependent variables. Also, stepwise logistic regression analysis was conducted to determine factors affecting people's selection of important future agenda items. Additional materials Extant opinion polls regarding public perceptions of brain death and organ transplants from brain-dead patients were studied to identify possible trends. When more than one poll was conducted in a year, results were averaged to avoid possible biases deriving from different survey designs. The following national polls were used to plot the trends in opinions: Yomiuri Shimbun (1982, 1984–95, 1997–99); Asahi Shumbun (1985, 1988, 1992, 1996–98); Mainichi Shimbun (1985, 1990–91, 1997); Office of the Prime Minister (1987, 1991, 1998); Nippon Hoso Kyokai (1991–92, 1996); and Jiji Tsushin (1992, 1994) [ 7 ]. Results Descriptive statistics indicating basic attributes of study participants, as well as all other survey results, are shown in Table 1 . A relatively large percentage of people responded that they became aware of the issue either at the point when the Ministry of Health and Welfare (MHW) drew up the diagnostic criteria for brain death (23.7%), or when the Organ Transplant Act was adopted (33.6%). Most people (97.2%) were aware of the issue. The majority of respondents (69.9%) crystallized their opinions either when the Act was first adopted or around the time when the first organ transplant was conducted from a brain-dead donor. Close to half of the respondents (43.3%) were completely unaware of any governmental efforts regarding the issue, while the remainder were divided in their appraisal of those efforts (26.3% positive, 30.6% negative ratings). A majority (61.8%) responded that they would not participate in future policy discussions on bioethical issues. Table 1 Description of subjects Attribute Category: number (frequency %); and/or average (sd) Age 20s: 81 (8.1), 30s: 130 (13.1), 40s: 186 (18.7), 50s: 231 (23.2), 60s: 218 (21.9), over 70: 149 (14.9) Sex male: 491 (49.7), female: 498 (50.4) Education junior high: 170 (17.2), senior high: 459 (46.4), vocational: 87 (8.8), vocational high: 10 (1.0), community college: 72 (7.3), college: 182 (18.4), grad school: 10 (1.0) Occupation private enterprise: 268 (27.3), civil service: 58 (5.9), self-employed: 153 (15.6), part-time: 100 (10.2), housekeeping: 240 (24.4), student: 18 (1.8), others: 147 (14.9) Hospitalization (past 5 years) none: 756 (76.0), once: 181 (18.2), repeated for a disease: 33 (3.3), several times for multiple reasons: 25 (2.5) CurrentHealthCondition healthy: 359 (36.2), relatively healthy: 545 (55.0), unhealthy: 87 (8.8) FirstAttentionPeriod Per1 : 230 (23.7), Per2: 114 (11.8), Per3: 138 (14.2), Per4: 326 (33.6), Per5: 135 (13.9), Per7: 27 (2.8) OpinionFormationPeriod Per2: 97 (11.0), Per3: 73 (8.3), Per4: 257 (29.2), Per5: 188 (21.4), Per7: 265 (30.1) KnowDonorCard yes: 894 (91.0), no: 89 (9.1) KnowGovtEfforts (score range: 0–7) 0: 92 (9.2), 1: 295 (29.5), 2: 233 (23.3), 3: 168 (16.8), 4: 129 (12.9), 5: 60 (6.0), 6:22 (2.2); average: 2.22 (1.48); Cronbach's alpha: 0.603 Appraisal of past governmental efforts (score range: 1–5) sufficient: 35 (3.6), relatively sufficient: 222 (22.7), relatively insufficient: 178 (18.2), insufficient: 121 (12.4), do not know any efforts: 424 (43.3) Average:3.69 (1.32) Participation intent(score range: 1–4) yes: 35 (3.5), relatively yes: 344 (34.7), relatively no: 531 (53.5), no: 82 (8.3) Note: Notation of Periods; Per1:1985 (MHW Brain death standard), Per2:1989 (Brain death ad hoc council set), Per3:1992 (Brain death ad hoc council rep), Per4:1997 (Law adopted), Per5:1999 (TPBD first conducted), Per6:2002 (15 cases done), Per7:(not yet accepted, not yet decided, not interested). KnowGovtEfforts: Score of the knowledge about the past government efforts, calculated as the number of items known. Items comprise Opinion polls & hearings, Expert councils, Public statements and announcements, Referendum, Town meeting & roundtables, Public comments, and Others. Figure 1 shows the general trends in public opinion concerning brain death and organ transplant from brain-dead bodies, the cumulative proportion of people who attended to the issue, and the cumulative proportion of people who initially formed their personal opinions at the point of study. Since the early 1980, Japanese acceptance of the concept of brain death steadily increased, while disapproval minimally declined. As public acceptance of organ transplants from brain-dead bodies increased, so did public opposition, meaning that more people were forming opinions than in the past. This is further indicated in the trend study: The proportion of people attending to the issue consistently increased over time, as did solidification of personal opinions, although on a smaller scale. Figure 1 Trend of public opinion on brain death and organ transplant in Japan Table 2 presents the correlation of factors associated with appraisal of past government efforts or personal intent to participate in future policy discussion. Sex, time period of first attention to the issue, period of opinion formation, knowledge of donor card, and knowledge of past government efforts were significantly associated with appraisal. On the other hand, age, education, period of first attention to the issue, period of opinion formation, and knowledge of past government efforts were associated with participation intent. For both appraisal and participation, there were similar tendencies observed. Respondents indicate more positive appraisals or greater participation intent when they are older, male, attended to or formed opinion of the issue earlier, are aware of the donor card, and more knowledgeable about past governmental efforts. Table 2 Determinants of public appraisal towards governmental efforts, and of public intention to participate in policy discussion (Bivariate analysis) Appraisal score [best = 1, worst = 5] Participation score [most = 1, least = 4] Category: average appraisal score + sd, and/or Spearman's correlation coefficient (rho) p-value Category: average appraisal score + sd, and/or Spearman's correlation coefficient (rho) p-value Age rho = -0.062 0.053 (3) rho = -0.085 0.009(3) male (3.58 + 0.06), female (3.80 + 0.06) 0.002 (1) male (2.64 + 0.71), female (2.71 + 0.03) 0.086 (1) Education junior high (3.73 + 1.39), senior high (3.76 + 1.37), vocational (3.1 + 1.20), vocational high (3.69 + 1.31), community college (3.73 + 1.28), college (3.83 + 1.47), grad school (3.17 + 1.47) 0.545 (2) junior high (2.73 + 0.75), senior high (2.73 + 0.61), vocational (2.60 + 0.67), vocational high (2.70 + 0.48), community college (2.69 + 0.75), college (2.49 + 0.73), grad school (2.36 + 0.67) 0.004 (2) Occupation 1 (3.74 + 1.35), 2 (3.80 + 1.27),3(3.79 + 1.36), 4 (3.92 + 1.30), 5 (3.79 + 1.41), 6 (3.42 + 1.30), 7 (3.60 + 1.38) 0.746 (2) 1(2.63 + 0.66), 2(2.60 + 0.72),3(2.64 + 0.67), 4(2.70 + 0.69), 5(2.70 + 0.66), 6(2.32 + 0.75), 7(2.71 + 0.69) 0.287 (2) Hospitalization 5 yrs rho = -0.021 0.510 (3) rho = 0.009 0.614 (2) 0.777 (3) CurrentHealthCondition rho = 0.013 0.689 (3) rho = 0.042 0.343 (2) 0.200(3) FirstAttentionPeriod Per1 (3.52 + 1.37), Per2 (3.75 + 1.33), Per3 (3.50 + 1.34), Per4 (3.73 + 1.37), Per5 (4.30 + 1.18), Per7 (4.31 + 1.46); rho = 0.210 0.000 (2) 0.000 (3) Per1 (3.52 + 1.37), Per2 (3.75 + 1.33), Per3 (3.50 + 1.34), Per4 (3.73 + 1.37), Per5 (4.30 + 1.18), Per7 (4.31 + 1.46); rho = 0.181 0.000(2) 0.000(3) OpinionFormationPeriod Per2 (3.40 + 1.30), Per3 (3.32 + 1.39), Per4 (3.45 + 1.34), Per5 (3.68 + 1.32), Per7 (4.27 + 1.24), others(3.98 + 1.38); rho = 0.269 0.000 (2) 0.000 (3) Per2 (3.40 + 1.30), Per3 (3.32 + 1.39), Per4 (3.45 + 1.34), Per5 (3.68 + 1.32), Per7 (4.27 + 1.24); rho = 0.204 0.000(2) 0.000 (3) KnowDonorCard yes (3.62 + 1.32), no (4.31 + 1.23) 0.000 (1) yes (2.66 + 0.67), no(2.80 + 0.70) 0.084(1) KnowGovtEfforts rho = -0.083 0.009 (2) rho = -0.175 0.000(3) Participation intent rho = 0.061 0.056 (3) Appraisal score rho = 0.061 0.056(3) Note: Significance in appraisal score across groups were tested by (1) Mann-whitney tests (between two groups), (2) Kruskal-Wallis tests (among multiple groups), and/or (3) Spearman's rank correlations. Moreover, when people are older, they are more likely to have recognized the issue and formed their opinions earlier, but know less about donor cards and past governmental efforts. Knowledge of donor cards and of government efforts were positively correlated, as were period of first attention to the issue and period of opinion formation. There was no significant correlation between appraisal and participation intent. Table 3 shows the results of multiple regression analyses, with the appraisal score and the participation intent score as dependent variables. A combination of age, period of opinion formation, and knowledge of the donor card best predicts public appraisal of the past governmental efforts, while a combination of age, period of first attention to the issue, period of opinion formation, and knowledge of governmental efforts best predicts individual intention to participate in future policy discussions. Individuals had more favorable appraisals of government actions when they were older, formed their opinions earlier, and knew about donor cards. On the other hand, individuals indicated greater intention to participate in future policy discussions when they were younger, paid attention to the issue earlier, formed their opinions earlier, and had more knowledge about past government efforts. Table 3 Predictors of appraisal/participation (stepwise multiple regression analysis) Selected independent variables (coeff + sd, p-value, 95% CI) Model adjusted-R2 (p-value) Appraisal score as a dependent variable [best = 1, worst = 5] Age (-0.059 + 0.030, 0.049, -0.117 - -0.000)Opinion Formation Period (0.196 + 0.027, 0.000, 0.144 - 0.248) Know Donor Card (0.583 + 0.168, 0.001, 0.253 - 0.913) 0.085 (0.000) Participation intent as a dependent variable [most = 1, least = 4] Age (0.043 + 0.016, 0.008, 0.011 - 0.075) First Attention Period (0.053 + 0.017, 0.002, 0.019 - 0.087) Opinion Formation Period (0.064 + 0.015, 0.000, 0.034 - 0.093) Know Govt Efforts (-0.048 + 0.016, 0.003, -0.080 - -0.017) 0.070 (0.000) Note: Age (20s = 1, 30s = 2, 40s = 3, 50s = 4, 60s = 5, over70 = 6), Know Donor Card (know = 0, do not know = 1), Notation of other variables explained in Table 1 (Note). As shown in Table 4 , respondents (42.3–69.7%) indicated that the opinions of patients, experts, and citizens should be more respected in policymaking, and also that more information disclosure is desirable. About 20% of people thought that the time spent for policymaking was not appropriate, either too long or too short. Very few people believed that government opinion should be weighted more, or that the process timeline is just right (status quo). Bivariate analyses disclosed no significant relationship of the selection of particular future agenda items either with the appraisal of government efforts or with participation intent. Results of multiple logistic regression analysis indicated that knowledge of past government efforts is related to all the future agenda items, except for one (more government opinion). Among those items associated, all but status quo were more likely to be chosen as knowledge of past government efforts increased. Those who were unwilling to participate in future policy discussion cited several reasons for their decision: Experts know better (50.1%); Issue is difficult (44.9%); Participation is ineffective (21.6%); Too busy (14.1%); and Not interested (7.9%). Table 4 Important future agenda and their predictors Agenda Yes/No: numbers (%) Predictors of respondents who selected the items: selected independent variables (odds ratio + sd, p-value, 95% CI) Model peudo-R2 (p-value) More disclosure Yes: 695 (69.6) No: 304 (30.4) Education (1.178 + 0.060, 0.001, 1.065 - 1.302) KnowGovtEfforts (1.629 + 0.112, 0.000, 1.424 - 1.865) FirstAttentionPeriod (0.873 + 0.049, 0.016, 0.782 - 0.975) 0.104 (0.000) More citizen opinion Yes: 423 (42.3) No: 576 (57.7) KnowGovtEfforts (1.267 + 0.061, 0.000, 1.153 - 1.393) 0.021 (0.000) More patients' opinion Yes: 696 (69.7) No: 303 (30.3) Age (0.853 + 0.048, 0.005, 0.764 - 0.952) Sex (1.593 + 0.257, 0.004, 1.161 - 2.186) KnowGovtEfforts (1.443 + 0.088, 0.000, 1.280 - 1.627) 0.064 (0.000) More experts' opinion Yes: 565 (56.6) No: 434 (43.4) Age (1.122 + 0.056, 0.020, 1.018 - 1.236) KnowGovtEfforts (1.392 + 0.073, 0.000, 1.255 - 1.543) 0.038 (0.000) More gov't opinion Yes: 9 (0.9) No: 990 (99.1) Age (2.337 + 0.836, 0.018, 1.160 - 4.712) 0.098 (0.006) More time Yes: 211 (21.1) No: 788 (78.9) Age (1.214 + 0.077, 0.002, 1.071 - 1.375) Health condition (1.686 + 0.247, 0.000, 1.265 - 2.246) KnowGovtEfforts (1.255 + 0.073, 0.000, 1.119 - 1.407) 0.043 (0.000) More Speedy process Yes: 205 (20.5) No: 794 (79.5) Age (0.880 + 0.052, 0.030, 0.784 - 0.988) OpinionFormationPeriod (0.875 + 0.046, 0.011, 0.789 - 0.969) KnowGovtEfforts (1.243 + 0.071, 0.000, 1.112 - 1.391) 0.034 (0.000) Status quo Yes: 12 (1.2) No: 987 (98.8) KnowGovtEfforts (0.182 + 0.093, 0.001, 0.067 - 0.494) 0.199 (0.000) Possible explanatory (independent) variables for stepwise logistic regression analysis: KnowGovtEfforts, Age, Sex, Education, Occupation, Hospitalization, CurrentHealthCondition, FirstAttentionPeriod, OpinionFormationPeriod, KnowDonorCard, Appraisal score, and Participation intent. Discussion Using data obtained in an opinion survey, this study seeks to evaluate public appraisal of past government efforts to legalize organ transplant from brain-dead bodies in Japan. Even though public opinion is relatively unstable and sometimes an irrational response to surrounding symbols, it continues to be an important factor in policy advocacy. Public acceptance of a policy is considered important not only for the practical reason that adopted policy cannot be implemented effectively and efficiently without public consent, but also for the ideological democratic point of view that policy is to be based on the judgment of a rational, informed and willing public, or at least on conscious delegation of individual autonomy to expert authorities [ 8 ]. The degree of public approval also conditions the future course of events. In this context, efforts to keep relevant public informed are quite important, as are those toward the accomplishment of public policymaking. Policy advocates should generate and meet public expectations of responsible and reliable government actions, either by assuming a leadership role to generate public expectations or by taking a conforming posture to help meet those expectations. Chronology of the act on organ transplant in Japan The chronology of the major events leading to enactment of the Organ Transplant Act and the successful implementation of the first several operations in Japan has been described elsewhere in detail [ 9 ]. Here we present a succinct summary. An act which enabled cornea transplant operations with the consent of the family was passed in 1957. In the same time period, kidney (1956-) and liver (1964-) transplants also commenced. The first heart transplant in Japan was performed at Sapporo Medical College in 1968. Since brain death had not been officially established, there was concern about a possible conflict of interest, and some believed that the extraction of a heart was murder. Extended efforts have taken place since then, to develop criteria for death, specifically medical and biological definition and diagnosis, their social use, and how to determine a human/ individual death in relation to the criteria. In the medical field, a committee of the EEG Society published standards for diagnosing brain death in 1974. The donor card system was sanctioned by the government shortly thereafter in 1977, without defining the criteria for death. In the meantime, organ transplant was sporadically performed with the consent of families, but without any official regulation of the process. Consequently, the transplant of cadaveric organs spread only gradually, since harvesting organs from brain-dead bodies continued to invoke public dispute and sometimes resulted in lawsuits. In 1984, for example, when the first multiple transplant (a combined kidney/pancreas transplant) was performed from a brain-dead body at Tsukuba University, the Patients' Rights Conference (PRC) soon filed a charge of murder in the case. The medical community began to advocate legislation governing brain death and organ transplantation more openly. In 1985, the MHW announced its diagnostic criteria for brain death, though it stated that a patient's death couldn't be judged by brain death. [Period 1]. In 1986, the Japan Medical Association formed the Bioethics Discussion Group, a study group of interdisciplinary nature, and in 1988 issued its Final Report, which encouraged brain death legislation to facilitate organ transplantation. With the goal of shaping public opinion, the Japan Organ Transplantation Society sponsored a series of open symposia in 1989. [Period 2]. A group of politicians from the major party started investigating the current situation in other countries, considering possible legislation. Activities of patients' groups reportedly helped shift public attention away from the brain-dead potential donor to the seriously ill person who needs a transplant [ 10 , 11 ]. At the same time, opposition was increasing, especially from the PRC, the Japanese Society of Psychiatry and Neurology, and the Japan Federation of Bar Associations. These groups called for a (social) consensus, unitary and conclusive definition of death [ 12 ]. Due to these public debates and advocacy efforts, media coverage on brain death and organ transplant increased dramatically. Finally, in early 1990, more than 30 years after the first transplant, the office of the Prime Minister established a special commission, Provisional Commission for the Study of Brain Death and Organ Transplantation. To encourage public involvement, the Commission held a series of public hearings and town meetings, and issued newsletters. Its 1991 interim and 1992 final reports presented both a majority view and a minority view. The former stated that a social consensus on brain death had already been achieved, and the latter argued that such a consensus had not yet been achieved. Both groups approved organ transplant when the consent of the donor was definitely obtained. A number of scholars argued that it should be a personal decision whether or not one's death is to be determined by brain death criteria, making individual consent the basis of both brain death and organ donation [ 13 ]. [Period 3]. A bill to legalize the transplantation of organs was presented to the Diet first in 1994. After several years of discussion, the modified bill finally became law in October 1997. Organ transplantation was thereby legalized where the donor has given written consent both to transplant and to the determination of brain death. Brain death was accepted only in such a case to enable organ transplants. In practice, the patient's family can still override the prior consent decision. [Period 4]. In 1999, two years after the law was passed, the first heart transplantation was successfully conducted, at Osaka University Hospital [ 14 ]. That same year, the second and third cases were successfully operated at the National Cardiovascular Center. [Period 5]. Though several issues, such as privacy protection and information disclosure, coordination of donors and recipients, as well as medical expense coverage, were raised during this series of successful operations, strong opposition to the law was no longer voiced. By 2002, 15 organ transplants had been conducted from brain-dead bodies. [Period 6]. Issue attention and opinion formation As was indicated in Figure 1 , though it is difficult to make qualitative judgments, the extended debates and struggles helped increase public awareness and knowledge of the issue, leading many to form policy preferences. More than anything else, the official diagnostic guidelines for brain death (1985) and the legislation (1997) increased public attention to the issue, and facilitated opinion formation (Proportion of people varied across time periods significantly at p = 0.05, by chi-squared tests). These official actions, accompanied by wide media coverage, mobilized a previously inattentive public through their social conspicuousness. Our results also indicate that as the number of people approving organ transplants from brain-dead bodies increased, the number of opponents increased in parallel, although in smaller numbers. This increase in political awareness or in political knowledge, as suggested elsewhere [ 15 ], led to increased polarization of attitude reports, resulting in the wider division between policy opinions. It should also be noted, however, that significant numbers of people had not formed their opinions until the first case of organ transplant was (successfully) achieved, and that about 30% of people remain undecided. The former appear to have waited to see what were the real consequences of the technology, i.e., its success or failure, as well as the social reaction. The latter are either watching for future developments or uninterested. As in other countries, the public debates on brain death and organ transplant, both in the private sphere and in the public sphere, were new attempts at governance of socio-technical innovation in the field of biomedicine. If social mobilization is fueled by the inability of the institutional system to respond adequately to public concerns, the issue status in Japan in the 1980s, when many individuals and institutions started to pay attention and get involved in the debate, might indicate insufficient mediation of the actors for conflict resolution (by the government) before and during that period. Generally in post-war Japan, a relatively small number of political and administrative elites have left the handling of many social conflicts to the workings of traditional social relations [ 16 ]. Similarly, the government, for a long time, largely left issue of brain death to medical communities and to a set of mobilized individuals and groups. The sporadic but recurrent implementation of transplant operations under no official rules made latent value conflicts manifest, randomly shaping the political landscape. Although lack of government action exacerbated social disputes, which in turn inhibited government intervention, it helped increase public awareness of the issue. Official actions were thus preceded by these social disputes. In the early 1990s when the Ad-hoc Council was set up, the Japanese government introduced a variety of measures to resolve social disputes by inviting the public into policy discussions. Our finding that many people recognized the issue and formed their opinions at times other than this period, however, suggests that these tactics were not very effective in terms of raising public awareness. In European countries, it was reported, public involvement measures served well as focusing devices, which helped attract attention and facilitate discussion among the various public [ 17 ]. The difference between Japan and European countries might be attributed to the difference in their participatory nature, as expected and instilled by these measures. At face value, the Japanese measures are designed to increase public involvement and active mobilization, but instead they function more as mechanisms for public consultation. A widespread norm of situational decision making, perceiving events and making judgments while experiencing them, could also induce people to reserve their judgments and opinion formation until implementation [ 18 ] Public appraisal of the past government efforts It is remarkable that more than 40% of respondents were unaware of any past government policy, despite longstanding struggles around the issue and much media coverage. Of those who were aware, about half of them had favorable opinions of government efforts, while a slightly larger percentage had negative views. The fact that only 30% of respondents reported satisfaction indicates that there is much room to improve public awareness, acceptance, and appraisal in the policy process on bioethical issues. Multiple regression analysis disclosed that age, period of opinion formation, and knowledge of donor cards are independent factors affecting public appraisal of past governmental efforts. Individuals tended to give higher appraisal points when they were older. Indeed, age seems to be a major factor in opinion formation on several policy issues [ 19 ]. Perhaps older people are more concerned with the issue of death and how it is defined because of its imminence and also because older people are more inclined to the traditional and community-oriented viewpoints. This in turn makes them more aware of and inclined to accept and praise a careful policy deliberation process. Younger people are, on the other hand, more free from traditional values and more concerned about individual rights and liberty, as was suggested by our finding that younger people were more likely to indicate that greater respect for patients' opinions and a speedier process are important items for future agendas. They could therefore have been frustrated by the time-consuming search for a social and unified definition of death. The finding that a higher appraisal was given by those people who made up their minds by the early debates and events, and by those who were aware of the donor card, might suggest that these people again value the freewheeling but deliberate process without any dictatorship. A national effort to incorporate ethical considerations into policy rests on an academic reservoir of technical experts, legal scholars and humanists, and on the public understanding of science and its social implications, as well [ 20 ]. The prolonged absence of direct leadership or clear policy provided society with ample time and opportunity for public debate, which is a collective learning process through a set of exchanges of viewpoints and/or social confrontations. After the early struggles which searched for unified value judgments, the policy discussions gradually shifted more to the social rules allowing adversarial opinions. Through these deliberations, many people have come to realize that organ transplant can be an acceptable and promising medical therapy so long as the donor's human rights are protected, and that the policy can protect the common good by tolerating divergent values and allowing individual choice of death criteria at the time of organ donation. This long social debate, which bore fruit in the enactment of a law, was considered an acceptable and even necessary step, by attentive members of the public, even though the debates were not necessarily strategically planned. According to Taylor and Fiske [ 21 ], people react critically to the arguments they encounter only to the extent that they are knowledgeable about political affairs. Hill [ 22 ] argues that ordinary citizens are rational only to a limited extent, but capable of good judgment when they have access to reliable facts and interpretations prepared by experts. In light of these arguments, the finding that the more attentive members of the public provided a better appraisal is promising, especially if those appraisals are more informed and rational than those rendered by the less attentive. If measures are taken to better inform the public of past policy discussions and of current policy as well, the public could be more content with government activities. In this context, the expert role of informing the public of past policy discussions would be critical to nurturing opinions [ 23 ]. In relation to this point, it should be noted that some changes in the policymaking process were regarded as important. A majority of respondents suggested that better information disclosure and more respect for both patients' and experts' opinions are desirable in the policy process. It follows that more effective involvement of the public, especially those stakeholders, in policymaking is warranted. Although an empirical assessment is not available, some procedures used in France and Germany might merit attention in modeling future policymaking for other countries. The National Consultative Bioethics Committee of France holds an annual public conference where, in addition to an activity report from the Committee, many ethical topics are discussed, with the participation of both experts and laypeople [ 24 ]. The German Reference Center for Ethics in the Life Sciences functions as a clearinghouse and library, open both to researchers, policymakers, and the public, while the German National Ethics Council holds conferences and issues newsletters both of which are open to the public [ 25 ]. Regular activities of this kind, targeting and involving the public, could be expected to increase the base of attentive, informed, and rational citizenry. Public involvement in the future In many countries, participation has gained momentum in a variety of policy domains [ 26 ]. In health care decisions, public participation, public involvement, or public consultation is considered desirable and even necessary by both policymakers and members of the public [ 27 ]. The participation process is used to obtain information from, and to provide information to, the community, which helps ensure fair, transparent and legitimate decision-making and garner support for the outcomes of the process [ 28 , 29 ]. In our study, a majority of people (61.8%) responded that they would not participate in future policy discussions, while the rest (38.2%) responded that they would. The absence of association between government appraisal and participation intent indicates that the latter is determined by factors other than the former. Multivariate analysis showed that younger age, earlier period of first attention, earlier period of opinion formation, and more knowledge of past governmental efforts are positively associated with the intent to participate in future policy discussions. This indicates that the more attentive members of the public, namely those long-term observers with their own opinions and knowledge of current policy, have more interest and consequently a greater intention to participate in policy discussions. This finding is consistent with past studies indicating that participation is facilitated by policy knowledge and/or political sophistication [ 30 ]. Positive association of knowledge of past governmental efforts with participation intent might, more specifically, suggest that a variety of measures newly employed by the government to incorporate public opinion, such as public hearings and comments, town meetings, and expert councils, were welcomed by the public, and somehow inspired their participation in the policymaking process. Reasons cited for being unwilling to participate in the process indicate that many feel unqualified or unknowledgeable but not necessarily too busy or uninterested. Further analysis revealed that older people are more likely to cite "Experts better" and "Ineffective", and are less likely to choose "Busy"; that females are more likely to cite "Difficult", and that people tend to cite "Ineffective" when they are more knowledgeable about past government efforts, while choose "Busy" or "Uninterested" when they are less knowledgeable. These findings suggest that despite their latent interest in the issue, people are unwilling to commit themselves to policy discussions because of their perception of inadequacy stemming both from their lack of knowledge and sense of inefficacy, as judged from past experiences. The absence of an association between appraisal and participation suggests that people might be uncertain about their own competence and efficacy in policymaking. It can be inferred that people hope for a means of understanding the issue, so as to formulate their opinions for themselves. Political participation is facilitated by having a personal stake and perceived self-efficacy in policymaking. Conversely, it could be hampered by both indifference to the issue and a sense of powerlessness [ 31 ]. More specifically, the factors influencing participation encompass the structural and social context of the population as predisposing factors (e.g., income and education), the institutional context for decision-making as an enabling factor (e.g., the activities of media, governments and other institutions), and the role of interests and interest groups as precipitating factors. Different degrees of public participation and different kinds of public involvement measures could be potent enabling factors affecting participation intent [ 32 ]. People are more willing to be involved in decision-making when there is a guarantee that their contributions will be heard and that decisions taken following consultation will be explained [ 33 ]. For the public to be effectively mobilized into policy debates, they must feel assured they are sufficiently informed and can assume an important role in policymaking [ 34 ]. Essentially, people will become involved if they believe they have the proper tools and their efforts will make a difference. In the context of Japan, it is important to remember that public involvement measures thus far used were not very effective in raising public awareness and that people consider some changes desirable in the policymaking process. It is possible that more people could be inspired to participate by changing the design of public involvement measures, from a consultation type of involvement to a partnership model [ 35 ]. Efforts to keep people informed, to help them understand the issues, to generate spheres for public deliberation, while at the same time creating mechanisms to ensure their voices will be heard in the policy process, could help mobilize the public and facilitate a discursive formation of opinion among them [ 36 ]. Both the public and the policymakers should acknowledge the important role citizens can play in policy discussions around biomedical ethics [ 37 ]. More innovative methods of public participation show promise and deserve consideration in improving policy process on medico-ethical issues and increasing public satisfaction with policy and politics. These include consensus conferences, citizens' juries, scenario workshops, deliberative opinion polls, among others [ 38 ]. Distinct from traditional opinion surveys, these methodologies seem intended to redress the deficiencies in citizen ability, such as limited expertise and attention to the issue among laypeople, and seek to elicit informed and rational choices. In some cases, debates at conferences are publicized through mass media, to raise public awareness of the issue and invite further social discussion [ 39 , 40 ]. Though overall satisfaction with and acceptance of these measures by the public has not been fully documented, the innovative methods reportedly had considerable success in increasing public awareness of an issue and facilitating logical and comprehensive discussion, which served as the basis for subsequent legislation [ 41 ]. Study limitations and future research agenda As is always the case with mass opinion surveys, this study cannot escape the possible bias introduced by the low response rates of polls [ 42 ]. A sampling with a disproportionately large number of the attentive public, omitting those with moderate positions, may result in opinion polarization, exaggerating the true conditions, while missing attentive part of the public can cause opinion neutralization, overlooking some important traits. These issues should be addressed in future research, hopefully also validating findings through the comparison of different studies. As was noted above, public participation in policy-making is a trend in many countries. The generalizability of our findings should be carefully tested by empirical studies. Among many topics to be considered for future research is the function of (mass) media vis-à-vis public opinion formation. The media should be examined critically as they influence both experts, policymakers, and the public. Mass media have a dual function in these processes: as a conduit of debates and negotiations as well as a source of influence [ 43 ]. Also on public side, the possibility of an active role for audiences in meaning creation should be explored. This study, without directly asking individual policy preferences, fell short of proposing or validating any theoretical model of opinion formation. The accumulation of knowledge by experimental and innovative public participation measures, such as deliberative polls, might answer some of these as yet unanswered questions. Conclusions Government decisions and their outcomes, namely the enactment and subsequent implementation of organ transplants, attracted public attention and helped formulate public opinion on the issue, more than did the processes leading to enactment. In the case of the concept of brain death and the legalization of organ transplant in Japan, many people still were unaware of past government efforts in policymaking, including the measures used for public involvement, despite past longstanding social debates. Only a small percentage of the public indicated satisfaction with the process. However, those who were attentive to the issue, knowledgeable of the past policy process as well as of the current policy, tended to rate the policy process more positively. Although people do not always manifest their intent to participate in future policy discussions, they might maintain sufficient interest in biomedical issues and have a latent wish to get involved in the policy process. Competing interests The author(s) declare that they have no competing interests. Authors' contributions All the authors (HS, AA, IK) fully participated in the planning, designing, and carrying out of the surveys for this study. HS performed the statistical analysis and drafted the article. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC547896.xml |
368179 | A Mechanism for Amphetamine-Induced Dopamine Overload | xx | The notion of a neurally encoded “reward system” that reinforces pleasure-seeking behaviors first emerged fifty years ago. Psychologists James Olds and Peter Milner discovered this phenomenon when their “lack of aim” landed an electrode outside their target while studying the behavioral responses of rats given electrical stimulation to a particular brain region. It was known that stimulation of certain brain regions would induce an animal to avoid the behavior that produced the stimulus. But in the rat with the “misplaced” electrode, stimulation of this new region caused the rat to repeat the behavior that caused the stimulus. Stimulation of certain brain regions provides a very strong incentive to restimulate, creating a feedback loop that reinforces both the behavior and the neural response to it. When gentle shocks were delivered to the rat hypothalamus, for example, the animals would “self-stimulate” 2,000 times per hour by pushing a lever. The neurotransmitter dopamine, it was later discovered, plays an important role in the brain's reward system—and in laying the biochemical foundation of drug addiction. Measuring changes in dopamine transport Essential for normal central nervous system function, dopamine signaling mediates physiological functions as diverse as movement and lactation. The dopamine transporter (DAT) is involved in terminating dopamine signaling by removing the dopamine chemical messenger molecules from nerve synapses and returning them into the releasing neurons (a process called reuptake). DAT can also bind amphetamine, cocaine, and other psychostimulants, which inhibit dopamine reuptake, and, in the case of amphetamine, also stimulate the release of dopamine through DAT. It's thought that abnormal concentrations of dopamine in synapses initiate a series of events that cause the behavioral effects of these drugs. The biochemical steps underlying amphetamine-induced dopamine release, however, are not well characterized. Now, a team led by Jonathan Javitch and Aurelio Galli has identified a chemical modification of DAT that is essential for DAT-mediated dopamine release in the presence of amphetamine. Since this modification does not inhibit the ability of DAT to accumulate dopamine, it may suggest a molecular target for treating drug addiction. Embedded in the membrane of nerve cells, the dopamine transporter has a “tail,” called the N-terminal domain, that protrudes into the cell interior and consists of a stretch of about 60 amino acids. Many of these amino acids are potential sites of phosphorylation, a chemical reaction in which a phosphate group is added through the action of enzymes called kinases. Amphetamine has been shown to increase kinase activity and Margaret Gnegy, a coauthor of the current research article, showed previously that inhibiting protein kinase C activity blocks amphetamine's ability to release dopamine. Therefore, Javitch, Galli, and Gnegy hypothesized that N-terminal phosphorylation of DAT might play a critical role in the dopamine overload caused by amphetamine. The researchers found that amphetamine-induced dopamine release was reduced by 80% in cells expressing a mutant dopamine transporter in which the first 22 amino acids of the N-terminal domain had been removed (del-22). Surprisingly, this truncated transporter displayed normal dopamine uptake. In a full-length DAT, mutation of the five N-terminal serine amino acids to alanine amino acids, which prevented phosphorylation, produced an effect similar to removing the 22 amino acids. In contrast, replacing these five serine residues with aspartate residues to mimic phosphorylation led to normal dopamine release as well as normal dopamine uptake. These findings suggest that phosphorylation of one or more of these serine residues is necessary for amphetamine to flood the synapses with dopamine. While phosphorylation is a normal mechanism for regulating protein activity in a cell—and DAT is “significantly phosphorylated” under normal conditions—amphetamine could increase the level of DAT phosphorylation. Elucidating the mechanisms through which phosphorylation of DAT's N-terminus facilitates dopamine overload could lead to the development of drugs that block the “rewarding” effects of amphetamines and other addictive psychostimulants without interfering with normal dopamine clearance. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC368179.xml |
548794 | Early Detection of Disease Outbreaks | null | For disease outbreak detection, the public-health community has historically relied on the watchful eyes of doctors and other health-care workers, who have reported individual cases or clusters of cases of particular diseases to health-care and other authorities. The increased availability of electronic health-care data, however, raises the possibility of more automated and earlier outbreak detection and subsequent intervention. Besides diagnoses of known diseases, pre-diagnostic syndromic indicators—such as the primary complaints of patients coming to the emergency room or calling a nurse hotline—are being collected in electronic formats and could be analyzed if suitable methods existed. Martin Kulldorff and colleagues have been developing such methods, and now report a new and very flexible approach for prospective infectious disease outbreak surveillance. Their method, which they call the “space–time permutation scan statistic,” is an extension of a method called scan statistic. All previously developed scan statistics require either (i) a uniform population at risk (with the same number of expected disease cases in every square kilometer), (ii) a control group (such as emergency visits not due to the disease of interest), or (iii) other data that provide information about the geographical and temporal distribution of the underlying population at risk, such as census numbers. The new method, because of a different probability model, can be used for the early detection of disease outbreaks when only the number of cases is available. It also corrects for missing data and makes minimal assumptions about the spatiotemporal characteristics of an outbreak. To make it widely accessible, the method has been implemented as a feature of the freely available SaTScan software. Disease surveillance in New York City In their article, Kulldorff and colleagues illustrate the utility of the new method by applying it to data collected from hospital emergency departments in New York City. The researchers analyzed diarrhea records from 2002, and did both a “residential analysis” (based on the home address of the patients) and a “hospital analysis” (based on hospital locations). The former has more detailed geographical information, the latter maybe be better able to detect outbreaks not primarily related to place of residence but, for example, school or workplace. They found four highly unusual clusters of diarrhea cases, three of which heralded citywide gastrointestinal outbreaks due to rotavirus and norovirus. Since November 2003, the space–time permutation scan statistic has been used daily to analyze emergency department data in New York City in parallel with other methods, and it seems to perform well. As the authors discuss, as any other surveillance method, theirs has limitations. Because it adjusts for purely temporal clusters, the method can only detect outbreaks if they start locally (not simultaneously across the entire surveillance area). The less geographically compact an outbreak is, the less power there is to detect it. And some outbreaks, for example, those caused by exposure to an infectious agent in the subway, will be hard to cluster by place of residence or choice of emergency department. In the present study, Kulldorff and colleagues have applied their method to infectious disease surveillance in a metropolitan area in the United States. As they state, however, “the ability to perform disease surveillance without population-at-risk data is especially important in developing countries, where these data may be hard to obtain.” | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC548794.xml |
538273 | Open Access to essential health care information | Open Access publishing is a valuable resource for the synthesis and distribution of essential health care information. This article discusses the potential benefits of Open Access, specifically in terms of Low and Middle Income (LAMI) countries in which there is currently a lack of informed health care providers – mainly a consequence of poor availability to information. We propose that without copyright restrictions, Open Access facilitates distribution of the most relevant research and health care information. Furthermore, we suggest that the technology and infrastructure that has been put in place for Open Access could be used to publish download-able manuals, guides or basic handbooks created by healthcare providers in LAMI countries. | 'Essential healthcare information' is the basic information required by primary health care workers to perform their role within the community. This basic information would be most useful if it is informed by relevant research, produced locally, and made available in the local language. The potential benefits of Open Access in terms of access to the research literature in general, and to research from low- and middle-income (LAMI) countries in particular, have been well described elsewhere. We would like to introduce a new dimension into this debate: Open Access has an untapped potential to enhance the synthesis and distribution of essential healthcare knowledge. Open Access, as opposed to free access, allows readers the right to use the article without restriction. Local publishers can therefore filter, reproduce and distribute the most relevant research and healthcare information from any and all Open Access journals. In essence, they can create journals focused on local issues based on content from a variety of journals. These "local journals" can be circulated in print – a medium that remains essential in countries with limited computer and Internet access. To our knowledge, this has yet to be done, although we are hoping someone will exploit this opportunity soon. In the future, we imagine the technology and infrastructure that has been developed for Open Access could be used to publish download-able manuals, guides or basic handbooks created by healthcare providers in these countries. These free resources could then be accessed worldwide and, where necessary, reproduced within local communities in the optimal medium. In an "author-pays" Open Access model the charges would be standard and could be covered by a national government organization or charity. Open Access will increase the availability of research and, in doing so, stimulate researchers in LAMI countries to develop their own research and practices. With research published in the Open Access medium it also becomes possible for producers of healthcare materials to optimize the use of research produced from their own and other countries. Thus, Open Access will optimize the distribution of local healthcare information, with potential benefits worldwide. Competing Interests CELS is an employee of BioMed Central, an Open Access publisher that is funded through article-processing charges levied on accepted manuscripts. CELS receives a fixed salary, which is unaffected by the amount of money received by BioMed Central from article-processing charges. MP is the Editor-in-Chief of World Journal of Surgical Oncology , an Independent journal published by BioMed Central. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC538273.xml |
509247 | Chronic pain self-management for older adults: a randomized controlled trial [ISRCTN11899548] | Background Chronic pain is a common and frequently disabling problem in older adults. Clinical guidelines emphasize the need to use multimodal therapies to manage persistent pain in this population. Pain self-management training is a multimodal therapy that has been found to be effective in young to middle-aged adult samples. This training includes education about pain as well as instruction and practice in several management techniques, including relaxation, physical exercise, modification of negative thoughts, and goal setting. Few studies have examined the effectiveness of this therapy in older adult samples. Methods/Design This is a randomized, controlled trial to assess the effectiveness of a pain self-management training group intervention, as compared with an education-only control condition. Participants are recruited from retirement communities in the Pacific Northwest of the United States and must be 65 years or older and experience persistent, noncancer pain that limits their activities. The primary outcome is physical disability, as measured by the Roland-Morris Disability Questionnaire. Secondary outcomes are depression (Geriatric Depression Scale), pain intensity (Brief Pain Inventory), and pain-related interference with activities (Brief Pain Inventory). Randomization occurs by facility to minimize cross-contamination between groups. The target sample size is 273 enrolled, which assuming a 20% attrition rate at 12 months, will provide us with 84% power to detect a moderate effect size of .50 for the primary outcome. Discussion Few studies have investigated the effects of multimodal pain self-management training among older adults. This randomized controlled trial is designed to assess the efficacy of a pain self-management program that incorporates physical and psychosocial pain coping skills among adults in the mid-old to old-old range. | Background The problem of chronic pain in the elderly Chronic pain is a common problem in the elderly, and is often associated with significant physical disability and psychosocial problems [ 1 ]. Estimates of the prevalence of chronic pain problems among community-dwelling older adults range from 58–70% [ 1 ]. The most common painful conditions among older adults are musculoskeletal conditions such as osteoarthritis, low back pain, and previous fracture sites [ 2 ]. Chronic pain often results in depression, sleep disturbance, decreased mobility, increased health care utilization, and physical and social role dysfunction [ 1 ]. Despite its high prevalence, pain in the elderly often is inadequately assessed and treated [ 1 ]. As the United States population grows older, the public health problem of chronic pain and its sequelae will worsen. Projections show dramatic increases in this age group; approximately 25% of the population will be age 65 years or older in 2050. Moreover, by 2030 there will be an estimated 8 million people who are 85 years or older [ 3 ]. Thus, there is an urgent and growing need for interventions that are effective in decreasing pain, suffering, and pain-related disability in this group. The role of self-management in the treatment of chronic painful conditions There is substantial empirical evidence that attention to cognitive and behavioral factors, in addition to physiological factors, is necessary for the successful treatment of chronic nonmalignant pain [ 4 , 5 ]. Empirically supported multimodal therapies that incorporate cognitive and behavioral strategies now exist for many chronic pain conditions, including rheumatoid arthritis, osteoarthritis, fibromyalgia, and low back pain [ 6 - 10 ]. These therapies aim to enhance the ability of patients to successfully self-manage their pain, using a variety of techniques. Related approaches and strategies are described under the rubrics "cognitive-behavioral therapy" (CBT), "psycho-educational" or "educational," and "self-management" or "self-help." Although there are variations among these approaches, they share some or all of the following components: education about pain, instruction in the identification and modification of negative thoughts, exercise, communication skills, relaxation training, and physical therapies. The goal of the therapies is to enhance function, improve mood, and decrease pain intensity by changing the emotional, cognitive, and behavioral responses to pain. Despite their documented efficacy in young to middle-aged samples [ 9 - 12 ], cognitive-behavioral and self-management pain therapies have been little-studied in elderly populations. In one of the first examinations of CBT for elderly patients with pain, 69 outpatients with chronic pain were randomly assigned either to immediate treatment or delayed (wait list) treatment [ 13 ]. Approximately half of the sample was over 60 years of age, and age was unrelated to outcome. The intervention resulted in significant decreases in pain interference with daily activities and increases in participants' self-reported ability to cope with pain. Limitations of this study included the fairly small sample size and the lack of intent-to-treat analysis. Keefe and colleagues [ 14 ] evaluated the efficacy of a pain coping skills training (CST) intervention as compared with arthritis education and standard care in decreasing pain and physical and psychological disability among 99 middle-aged to older outpatients with osteoarthritic knee pain. The CST consisted of 10 weekly group sessions focusing on identifying and reducing irrational thoughts, diverting attention away from the pain, and changing activity patterns to manage pain. The CST group showed significantly less pain and psychological disability following treatment as compared with the other two groups. At 6-month follow-up, the CST group showed significantly less physical and psychological disability as compared with the education group and marginally less psychological disability as compared with the standard care group [ 15 ]. Although this study provides evidence for the benefits of cognitive-behavioral therapy for older adults, it focused on arthritis patients and not older adults per se. Moreover, the average subject age was 64 years. It is not clear whether these findings would generalize to mid-old (i.e., 75–85 years) and old-old (85 years and older) adults. These groups have been shown to differ from their younger counterparts (i.e., those 65–74 years) in several dimensions, including pain prevalence, physical and cognitive function, involvement in recreational and social activities, and social support [ 16 - 19 ], that potentially could affect the pain experience and response to pain therapies. One study that examined a cognitive-behavioral therapy in old-old adults (mean age 77.2 years) evaluated the efficacy of a 10-week CBT intervention (n = 11) versus an attention/support (AS) condition (n = 10) for nursing home residents [ 20 ]. The CBT condition incorporated pain education, progressive relaxation, imagery, coping skills training, cognitive restructuring, and attention diversion. CBT participants reported significantly less pain and pain-related disability following the intervention, as compared to the AS group. These significant differences were maintained at the 4-month follow-up. This study provides important evidence that CBT can be successfully applied to old-old adults; however, the results need to be replicated in other, larger samples, including non-institutionalized elderly. Retirement communities as a study setting As the U.S. population continues to age, retirement communities have gained popularity. A retirement community allows older adults with varying lifestyles and physical abilities to live in an environment that encourages independence while providing needed access to health and social resources [ 21 ]. Although most residents of these communities live independently (some facilities also include assisted living apartments and skilled nursing facilities), the retirement community, on average, represents a mid-old to old-old population that is vulnerable to physical disability, health problems, and social isolation [ 22 ]. The growing population in retirement communities, then, is one in which self-management group therapies for chronic pain may hold great promise. Adoption of regular wellness-oriented pain management strategies may contribute to enhanced functioning and prolonged independence. Study purpose and specific aims The primary goal of this study is to evaluate the efficacy of a pain self-management group intervention (SMG), as compared with a control condition (BOOK), in decreasing physical disability, pain, pain-related interference with activities, and depression in older retirement community residents with chronic pain. In addition, we wish to determine the extent to which SMG participation is associated with changes in specific pain-related beliefs and coping strategies, and the extent to which changes in these process variables are associated with changes in outcomes (physical disability, pain intensity, pain-related interference with activities, and depression). We plan to test the following hypotheses: 1. At post-treatment and each follow-up, participants assigned to SMG, as compared with participants assigned to BOOK, will report less physical disability (primary outcome), and lower pain intensity, pain-related interference with activities, and depressive symptom severity (secondary outcomes). 2. Participants assigned to SMG, as compared with participants assigned to BOOK, will show greater pre- to post-treatment increases in self-efficacy and use of adaptive pain coping strategies and greater decreases in catastrophizing. Significant differences between SMG and BOOK groups in pain-related beliefs and coping strategies will be maintained at 6-month and 1-year follow-ups. 3. Pre- to post-treatment changes in specific pain-related beliefs (catastrophizing, self-efficacy) and coping strategies (Chronic Pain Coping Inventory subscales) will be associated significantly with changes in physical and social functioning, pain intensity, and depression over the same period among SMG participants. These changes in beliefs and coping strategies will be maintained at 6-month and 1-year follow-ups. Figure 1 depicts the hypothesized relationships among study variables. Figure 1 Hypothesized relationships among study variables Methods/Design Design This is a currently ongoing randomized controlled trial. The study procedures and measures have been approved by the Swedish Medical Center institutional review board. Figure 2 outlines study procedures and follow-up. Figure 2 Study flowchart Participants Participants (targeted enrollment n = 273) are recruited from residents living in one of the 34 participating retirement communities in Seattle, Washington and the surrounding area. Study inclusion criteria are: (1) 65 years of age or older, (2) pain > 3 months duration that interferes with regular activities; and (3) ability to read and complete study questionnaires in English. Exclusion criteria are: (1) current, active cancer and (2) surgery within the past 6 months or surgery planned in the next 6 months. Recruitment and randomization procedures Participants are recruited using newsletter announcements, flyers, brochures, and informational talks given at each facility. Retirement community residents who are interested in the study are screened to assess eligibility. Eligible residents who provide written informed consent are then asked to complete the baseline measures and to provide the name of their primary care provider (PCP), as well as permission to contact the PCP. PCPs of SMG group participants are sent a letter about the study and asked to contact the research nurse if there is any medical reason to restrict the resident's participation in the exercise portion of the study. After all participants from a facility have completed the baseline questionnaires, the facility is randomized to receive either the BOOK or the SMG. Randomization is done by facility, rather than by individual participant, for several reasons. First, it expands the number of participating facilities by making feasible recruitment from smaller facilities. If approximately 5% of residents were recruited from any one facility, then it would not be scientifically or financially sound to involve facilities with fewer than 200 residents in independent or assisted living. A pilot study indicated that the ideal self-management group size is 5–12 participants. If we randomized within facilities, at least 10 participants would need to be recruited from each facility to allow 5 SMG participants. However, if all participants within a facility are randomized to the same condition, then smaller facilities can participate. If more than 12 residents in a facility randomized to the SMG condition enroll in the study, more than one group is scheduled. A second advantage of randomization by facility is that there is little risk of participants in one condition talking with participants randomized to another condition about their experiences in the study. Thus, there is less treatment contamination and likelihood that participants from different conditions will compare treatments, a situation that can provoke dissatisfaction among participants who do not receive the treatment of their choice. Pain self-management group (SMG) The SMG intervention, which consists of seven weekly 90-minute group sessions, includes the major components of empirically-supported self-management interventions [ 23 , 24 ], refined for use with the elderly (see Table 1 ). For example, we include a discussion of myths about pain in older adults (e.g., pain is an inevitable part of aging) and focus on exercises that are effective and safe for older adults with musculoskeletal pain. The intervention is designed to decrease participants' physical disability and pain intensity; increase participation in home, social, and recreational activities; and enhance participants' self-efficacy for managing chronic pain. To accomplish these objectives, the intervention provides basic information about pain management, teaches problem-solving and relaxation skills, and provides practice with a variety of pain management techniques. Participants receive a class syllabus, relaxation tape, Theraband ® tubing for the performance of selected exercises, and two hot/cold gel packs. Table 1 Summary of the self-management group intervention SESSION NUMBER : TOPICS MAJOR CONTENT AND ACTIVITIES Session 1 : Introduction; Basic principles of pain Review purpose of the program/study. Review definition, types, & mechanisms of pain. Discuss myths about pain in older adults. Emphasize goals of chronic pain management. Discuss signs/symptoms that require medical attention. Introduce problem-solving techniques for pain management. Session 2 : Role of exercise & physical activity in pain management Discuss exercise in pain management: problem of de-conditioning, types of exercise, tips for starting exercise program. Demonstrate & practice specific exercises. Introduce relaxation and breathing techniques as effective pain management strategies. Practice progressive muscle relaxation & abdominal breathing. Session 3 : Engaging in pleasant, meaningful activities; pacing activities Discuss ways in which chronic pain may be limiting participation in enjoyable or meaningful activities Use problem solving to develop individualized plans for increasing these activities. Discuss strategies for activity pacing and rationale for avoiding guarding and inactivity. Practice relaxation. Session 4 : Challenging negative thoughts; Dealing with pain flare-ups and setbacks Discuss critical role of thoughts and appraisals about pain in determining affective and behavioral responses to pain. Help participants to identify negative thoughts that they may have in response to pain. Practice challenging negative thoughts with positive thoughts about effective ways to manage pain. Discuss strategies for dealing with setbacks and pain-flare-ups. Practice relaxation. Session 5 : Non-drug pain therapies; Heat & cold; Dealing with pain flare-ups and setbacks (continued) Describe rationale for using nondrug pain therapies. Describe and practice application of heat and cold; review precautions in using heat and cold for pain Continue discussion about coping with pain flare-ups & setbacks in pain management. Practice relaxation. Session 6 : Pain medications & complementary therapies Describe the role of medications for pain management. Discuss the major types of pain medications. Describe the use of complementary therapies in pain management. Discuss steps in making informed decisions about all pain therapies. Session 7 : Pain management plan; Wrap-up Discuss maintenance of gains made through the program. Review coping with set backs & pain flare-ups. Revise written individualized maintenance plans for each participant. A key component of this self-management group is the development of personalized pain management plans. Participants begin developing a plan during the first class and revise it each week as they learn and practice additional pain management skills. With the assistance of the facilitator and, at times, other group members, participants review pain control strategies that they have learned and practiced and choose one or several strategies that best meet their individual needs and interests. Participants identify specifically what they will do (in measurable terms) and define the parameters (e.g., how many times per week, how far they will walk, how many repetitions of each exercise they will do). Although each person develops his or her own plan, the plans incorporate the same repertoire of activities that are taught in the class. These plans are monitored weekly during the classes and during follow-up phone calls (described below). Educational book control condition (BOOK) Participants who are assigned to the BOOK condition receive a copy of The Chronic Pain Workbook, 2 nd Edition [ 25 ]. Facilitators telephone participants 1 and 4 weeks after participants receive the workbook. The BOOK condition was designed to control for attention and information. In these calls, facilitators inquire about participants' current pain and functioning, and ask about use of pain therapies and self-management techniques. There is no specific therapeutic component in the phone calls and facilitators do not help BOOK participants identify goals or develop a pain management plan. Booster and follow-up phone calls The SMG group facilitator telephones each participant at 12, 16, 22, and 30 weeks after the final group session. During the booster phone calls, facilitators inquire about pain and functioning, current pain management plans, and successes and obstacles in meeting pain management goals, as well as provide encouragement and assistance in problem-solving obstacles encountered in pain management. BOOK participants receive follow-up phone calls at the same intervals to control for attention. Steps taken to ensure and monitor group facilitator adherence Group facilitators are nurses and psychologists with expertise in geriatrics and/or pain management and experience in facilitating therapeutic groups. All are specifically trained according to the treatment protocol. We monitor group facilitator adherence to the self-management group protocol, as recommended by Waltz et al. [ 26 ]. All facilitators receive and review a facilitator's syllabus that contains a detailed protocol describing the goals, contents, and activities for each of the 7 sessions. Group facilitators have met 3 times to discuss protocol and treatment integrity issues. Finally, each session for each treatment group is audiotaped. Twenty percent of the audiotapes are randomly chosen and reviewed by a trained research nurse who is not involved in any other aspects of the study. The research nurse listens to the tapes and evaluates the degree to which the group sessions are conducted according to the protocol using a checklist developed for this purpose. Measures Study measures were chosen based on psychometric properties, including sensitivity to change; brevity; and appropriateness for use with community-dwelling, older adults with chronic pain. They are described below and summarized in Table 2 . Table 2 Measures and assessment times CONSTRUCT/MEASURE SCREENING/BASELINE POST-INTERVENTION 6-MONTH FOLLOW-UP 12-MONTH FOLLOW-UP Physical Functioning √ √ √ √ Roland-Morris Disability Questionnaire Brief Pain Inventory (BPI) – pain interference subscale Pain Intensity √ √ √ √ Brief Pain Inventory – pain intensity subscale Mood Disturbance/ Social functioning √ √ √ Geriatric Depression Scale Pain Beliefs and Coping √ √ √ Chronic Pain Coping Inventory – ( includes pain medication use) Coping Strategies Questionnaire – catastrophizing, praying/hoping subscales Self-efficacy Scale Pain Knowledge √ √ Demographics, Medical Conditions, Medications √ Screening & Intake Questionnaire Adapted Charlson Index Cognitive Functioning √ Folstein Mini-Mental State Examination Pretreatment Expectations √ Adherence to Treatment √ √ Attendance at classes Completion of reading assignments Attainment of goals (Personal Pain Management Plan) Descriptive measures The following measures are administered at baseline to describe the sample and to explore whether these variables are associated with treatment response. We will compare the two study groups on these measures to determine whether they are comparable at baseline. Screening and intake interview schedule – demographic information and pain history During the screening process and baseline assessment, participants are asked a series of questions to elicit demographic and pain history variables, including age, race, ethnicity, gender, marital status, education level, sites and duration of pain, and prior and current pain treatments. Folstein mini-mental state examination (MMSE) [ 27 ] The MMSE is a measure that is widely used to assess cognitive function, particularly in older adults. It consists of 30 items, and requires 5–10 minutes to administer. Items assess orientation, memory, attention, and calculation. The MMSE has been demonstrated to be valid and to have good test-retest reliability [ 28 ]. Charlson index of comorbidity (CI) The CI is an extensively used, valid, and reliable measure of comorbid medical conditions [ 29 ]. The CI uses 19 categories of comorbidity; each category is weighted and scored according to an algorithm [ 29 ]. Higher scores indicate greater health burden from comorbid causes. In this study, we are using a self-report version of the CI demonstrated to be reliable and valid in a group of older adults [ 30 ]. Because comorbid conditions may be associated with pain appraisal, coping, and outcomes [ 31 ], we will examine the association between comorbid conditions and response to therapy. Process measures Self-efficacy scale (SES) Participants complete the 8-item version of Lorig et al.'s Self- Efficacy Scale [ 32 ], which assesses confidence in ability to manage pain and associated problems such as fatigue and negative mood [ 33 , 34 ]. Previous studies have supported the reliability and validity of this measure [ 32 , 34 , 35 ]. The SES has been tested and used in studies of older adults [ 36 ]. Coping strategies questionnaire (CSQ) [ 37 ] The CSQ is one of the most widely used measures of pain coping and catastrophizing [ 38 , 39 ]. Measures derived from the CSQ have been shown to be associated with various measures of functioning among patients with different pain conditions [ 38 , 40 - 43 ]. The CSQ has demonstrated reliability and validity in several samples of older adults, including those who are older than 75 years [ 44 ]. For this study, only the catastrophizing and praying/hoping subscales are used. Catastrophizing is included because prior studies have shown that this variable is associated with pain intensity, depression, and disability [ 45 ]. The praying/hoping subscale was included because this coping strategy has been found to be associated with the pain experience of older persons[ 46 ]. Chronic pain coping inventory (CPCI) The CPCI measures cognitive and behavioral coping strategies used by people to manage chronic pain. It contains 9 subscales: guarding, resting, asking for assistance, relaxation, task persistence, exercise/stretch, seeking support, coping self-statements, and medication use [ 47 ]. The CPCI scales have been shown to have acceptable internal consistency and test-retest reliability, and to be associated significantly with physical disability and depression [ 47 - 49 ]. Additional development and psychometric testing have supported the reliability and validity of an additional activity pacing subscale [ 49 ]. Pretreatment expectations Prior to learning the study condition to which they are randomized, participants are asked the degree to which they believe that each study condition will be helpful to them. They respond using a 0 to 10 scale, with 0 indicating "not helpful at all" and 10 indicating "extremely helpful." Treatment adherence 1. Class attendance. Group leaders document weekly class attendance. Total attendance is assessed as a percentage of classes the participant attended (out of 7). 2. Reading log/usefulness. Both BOOK and SMG participants complete a form in which they report the amount read on each topic using a 0–5 scale ("I did not look at the section at all" to "I read the section thoroughly"). They also rate the usefulness of each section using a 0–5 scale ("Not at all useful" to "Very useful"). 3. Goal attainment. Attainment of SMG participants' pain management goals is assessed using the Personal Pain Management Plan (PPMP). Each week, participants in the SMG group are asked to document the type and frequency of each activity they have chosen to utilize in the management of their chronic pain. They monitor and document the pain management activities that they actually performed over the week. Participants also document obstacles that they have encountered in trying to meet their goals and the solutions they have identified to overcome those obstacles. This form is printed on 2-page paper. The top copy is turned in each week and participants keep the bottom copy for their own records. The PPMP serves several purposes: 1) to assist participants to identify and follow through on their personalized goals; 2) to assess treatment adherence; and 3) to cross-validate data that are collected using the Chronic Pain Coping Inventory. Outcome measures Primary outcome Roland-Morris disability questionnaire (RMDQ): The RMDQ [ 50 ] is widely used to assess physical disability associated with low back pain. The RMDQ has been demonstrated to be valid, reliable, and responsive to change [ 50 - 55 ]. Although developed as a measure of physical disability related to back pain, the RMDQ, re-worded without reference to the back, has been found to be a reliable and valid measure of physical disability for patients with other chronic pain problems as well [ 52 ]. The RMDQ is scored from 0–24, with higher scores indicating more severe physical disability. Physical disability, as measured by the RMDQ, is the primary study outcome. Secondary outcomes Brief pain inventory (BPI): The BPI is a widely-used, reliable, valid instrument that assesses pain history, location, intensity, and activity interference [ 56 , 57 ]. For this study, pain intensity is measured by calculating the mean of four items in which respondents are asked to rate their average, current, least, and worst pain during the past week, using a scale of 0 ("No pain") to 10 ("Pain as bad as you can imagine"). [ 58 ]. Pain-related interference is a composite measure of the degree to which pain limits a person's general function [ 57 ]. This variable is calculated as the mean of ratings of pain interference with general activity, mood, walking, work (including housework), relations with others, sleep, and enjoyment of life. Each item is rated on a scale of 0 ("Does not interfere") to 10 ("Completely interferes"). Geriatric depression scale (GDS): The GDS [ 59 ] is a 30-item self-report measure specifically designed to assess depressive symptoms in older persons. Scores of 11 or higher are considered indicative of depression in older adults. Good sensitivity and specificity for detecting depression in geriatric psychiatric and medical outpatients has been demonstrated (84–100% sensitivity; 73–96% specificity) [ 60 , 61 ]. The GDS was selected over other available depression measures because of its screening efficiency with geriatric outpatient populations, its focus on affective rather than physical symptoms, and its true/false scoring format, which studies have found to be simpler for older adults to complete [ 61 ]. Sample size calculations and statistical analyses Power analysis/sample size calculations A mixed effects model will be used to analyze data using the participant as the unit of analysis and controlling for baseline value of the outcome as a covariate. A reasonably accurate approximation to this analysis could be obtained by the following procedure: first compute change scores (pre to post) for each person, then collapse to get the mean change score within each site, then do t-tests on these means. This simpler model was used for power calculations, since it allows standard software to be used. The plan to randomize by site, rather than by individual participant, required additional considerations in calculating statistical power. With this group-randomized design, power depends on the correlation of people within sites, or the intra-class correlation. Effect size is defined as the mean change score of all individuals in the intervention group minus the mean change score of all individuals in the control group, divided by the standard deviation of change score within groups. Power calculations for the proposed study are based on estimates of 34 sites (17 intervention and 17 control), 6.4 participants per site (N = 218) providing data at 12 months (20% attrition rate). Table 3 shows how power (the probability of detecting a difference) varies with the correlation of individuals within site and the effect size. The second column of this table shows the "effective sample size," meaning that the study would have the same power as a study with this sample size and no clustering. If the correlation is zero, the effective sample size is 272, the actual sample size. A correlation of 1 would indicate that all individuals in each site have exactly the same outcome (i.e., no different from having one person per site), so the effective sample size would be 27. Analysis of data from a pilot study showed an intra-class correlation (ICC) of .07 [ 62 ]. Although this estimate should be interpreted cautiously because of the limited number of sites in the pilot study, it indicates that the intra-class correlation will probably be fairly small, perhaps 0.05 to 0.1. Our target sample size of 273 enrolled and 20% attrition at 12-month follow-up (yielding a final sample size of 218), assuming ICC=.1, will result in 84% power for detecting an effect size of .5, which Cohen [ 63 ] refers to as a "moderate" effect size. Table 3 Power for detecting a difference between the intervention and control group, depending on effect size and intra-class correlation (34 sites, average 6.4 subjects per site) Intra-class correlation Effective sample size Effect size (the difference in means between the two groups, divided by the within-group standard deviation) .40 .50 .60 .70 0.00 218 84% 96% 99% 100% 0.05 171 74% 90% 97% 100% 0.10 141 65% 84% 94% 98% 0.20 105 52% 71% 86% 94% 0.30 83 43% 61% 77% 88% 0.50 59 32% 46% 61% 75% 1.00 34 20% 29% 40% 51% Statistical analysis The test of hypothesis 1 compares the SMG and BOOK participants on the primary outcome (physical disability) and secondary outcomes (pain intensity, pain-related interference with activities, and depressive symptom severity) at each of the 3 follow-up assessments. The analytic method that we will use to evaluate this hypothesis is the mixed effects analysis of covariance (ANCOVA), as proposed by Laird and Ware [ 64 ] and implemented in the SAS PROC MIXED procedure [ 65 , 66 ]. This model will have two random effects, site and person nested within site . Group (i.e., treatment or control) and Time will be fixed effects. The repeated measurements of physical disability at post-intervention, 6 months, and 1 year will be the outcome measure. The baseline value of physical disability will be included in the model as a covariate. Any baseline variables that are correlated with the outcome variable and/or differ between the two treatment groups (e.g., gender, age, comorbidity) will also be included as covariates in the analysis. If the main effect for group is significant, contrasts within this model will be used to test for treatment effect separately at each of the three outcome times. Secondary analyses will be similar, fitting a mixed effects model that uses one of the secondary outcomes (e.g., pain intensity, pain-related interference, and depression) in place of physical disability. The analysis of Hypothesis 1 will be by intent-to-treat. Hypothesis 2 involves comparing the SMG and BOOK groups on changes in process variables (pain-related beliefs and coping strategies). A mixed effects model, as described under hypothesis 1, will be used for these analyses. As for hypothesis 1, baseline variables that are predictive of outcome and/or differ between groups will be included as covariates in the analyses for hypothesis 2. Hypothesis 3 involves the correlation of changes in beliefs and coping to changes in the outcome variables. For each assessment time, change from baseline will be computed and scatter plots will be used to describe relationships, with Pearson and/or Spearman correlation coefficients used to summarize the strength of the association. Although we hypothesize that significant associations in changes will occur only in the SMG group, we also will perform exploratory analyses in the BOOK group to assess for these associations. In addition to performing major analyses to test study hypotheses, we will also perform exploratory analyses to examine whether there are subgroups of participants in whom the intervention had a particularly strong or a particularly weak effect. For example, we will explore whether: (1) there is a difference in response to therapy based on age group (young-old, mid-old, old-old); (2) men respond differently to therapy than women; and (3) pain severity at baseline is related to strength of treatment effect. These analyses will be performed using the ANCOVA described above, augmented by adding, for example, an indicator for female gender and the interaction term between gender and treatment group. Discussion Persistent pain is a common problem in older adults that can be debilitating. Self-management strategies that incorporate physical and psychosocial pain coping skills are effective in decreasing pain and improving function and mood in younger adults. Little is known, however, about the efficacy of this therapy for older adults, especially those in the mid-old to old-old range. Our randomized controlled trial assesses the efficacy of such a treatment program, as compared with a control condition, in decreasing pain and improving physical and psychosocial functioning in elderly retirement community residents with chronic pain. Competing interests None declared. Authors' contributions ME and JAT developed the intervention and conducted the pilot test of the self-management groups. KCC developed the analysis plan, conducted the power calculations, and wrote the related sections of the paper. CAK assisted in refining the intervention. ME and JAT wrote the initial description of the intervention and this article. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC509247.xml |
529424 | Doing New Research? Don't Forget the Old | Nobody should do a new research study, says Clarke, without first systematically reviewing the literature. And journal editors should insist that all research papers are accompanied by an up-to-date systematic review | On May 2, 1898, George Gould used his address to the founding meeting of the Association of Medical Librarians in Philadelphia to present a vision of the future of health information. ‘I look forward,’ he said, ‘to such an organisation of the literary records of medicine that a puzzled worker in any part of the civilised world shall in an hour be able to gain a knowledge pertaining to a subject of the experience of every other man in the world’ [ 1 ]. Has his vision been realised? Information Overload In these early years of the 21st century, with tens of thousands, if not hundreds of thousands, of new research articles being published every year, people who need to make decisions about health care are much more overwhelmed with information than they were in 1898. Some of this information is of good quality, but some of it is not. Thus, anyone wishing to use the health literature to make well-informed decisions must both identify the relevant research from amidst this vast amount of information and then appraise it. This is an impossible task for many. Even though making access to the literature easier and cheaper will increase the ability of people to find research, it will also reveal just how much information there is out there and how daunting is the task of making sense of it. You can get a good idea of the size of the task—and of how electronic publishing and the Internet have transformed the situation—by imagining the following four steps. How long would it take to find articles in your area of interest by paging through back copies of a relevant journal? What about by using its index? Now imagine finding articles of interest to you by going to the Internet and searching PubMed ( www.ncbi.nlm.nih.gov/PubMed ). What about if you searched the whole of the Internet with one or more search engines? Almost certainly, as the speed of the search increased through these four approaches, so would the number of articles retrieved—and also the time that it would take to read through them, appraise them, and decide if they were relevant to whatever decision you were trying to make. Doctors are overloaded with information, much of it irrelevant to their practice (Illustration: Rusty Howson, sososo design) Should you just take a shortcut by relying on a single study in a high-profile journal? No. Sampling in this way might lead you to research whose findings are the most striking or atypical, but you would miss similar research that was ‘less fortunate’ with its results. However good the conduct of a piece of research, chance effects mean that some studies will produce an overestimate and some an underestimate of the true effects, but the easy-to-find literature is likely to be dominated by the former [ 2 ]. In addition, using a sample rather than the whole body of relevant research will have less statistical and evidential power to answer the question of interest. The Value of Systematic Reviews Systematic reviews provide a means to minimise these problems. In systematic reviews, the methods to be followed are stated and an attempt is made to identify, appraise, and where appropriate, statistically combine, all relevant research. In fact, a decade before George Gould's address, Lord Rayleigh, at the meeting of the British Association for the Advancement of Science in Montreal, had described such a process for scientific research in general. ‘If, as is sometimes supposed,’ he said, ‘science consisted in nothing but the laborious accumulation of facts, it would soon come to a standstill, crushed, as it were, under its own weight. The suggestion of a new idea, or the detection of a law, supersedes much that has previously been a burden on the memory, and by introducing order and coherence facilitates the retention of the remainder in an available form. Two processes are thus at work side by side, the reception of new material and the digestion and assimilation of the old. One remark, however, should be made. The work which deserves, but I am afraid does not always receive, the most credit is that in which discovery and explanation go hand in hand, in which not only are new facts presented, but their relation to old ones is pointed out’ [ 3 ]. Relating the New to the Old If today's health researchers discussed their findings in the context of relevant, already-existing research, many of the problems of information overload would be eased. You would only need to find the most recent report of a relevant study; its discussion section would place that study within the context of an updated systematic review. This was suggested by the original CONSORT statement on the reporting of randomised trials in 1996 [ 4 ]. However, studies of five of the major general medical journals ( Annals of Internal Medicine, BMJ, Lancet, JAMA, and the New England Journal of Medicine ) in 1997 and 2001 found that this was not the case, at least for these journals. Only two of more than 50 reports of randomised trials in these journals in May of those two years included an updated systematic review [ 5 , 6 ]. Including an updated systematic review along with a report of a randomised trial (or any other piece of research) might seem too much to expect of researchers, who might not feel able or willing to do the additional work required. However, the absence of a review should raise the question: on what did the researchers base the design of their new study? To embark on a new study without first systematically reviewing what has been done before is to risk doing research for which the answer is already known. It would also mean that the researchers had denied themselves the opportunity to learn from the successes and failures of others when designing their own study. In addition, researchers have a responsibility to the participants in their research to make sure that the study is of the most appropriate design possible. To help make sure that this is the case, when designing a new study researchers should ensure that they have been adequately informed about what research has been done previously. Box 1 lists practical suggestions to researchers for making sure that their new study builds on prior knowledge. Box 1. Practical Suggestions for Researchers Conduct a systematic review of your research question before embarking on a new study, or identify a relevant review done by someone else. Design your study to take account of the relevant successes and failures of the prior studies, and of the evidence within them. Discuss the findings of your study in the context of an updated systematic review of relevant research. Publish the systematic review within, alongside, or shortly after the report of your study. Provide information from your study to others doing systematic reviews of similar topics. It might even be the case that the researchers are able to draw on the work of others, who already have done a systematic review of the relevant topic. Over the last decade, The Cochrane Collaboration ( Box 2 ) has produced more than 2,000 Cochrane systematic reviews ( www.cochrane.org ) [ 7 ]. There are thousands more reviews scattered throughout the literature ( www.york.ac.uk/inst/crd/darefaq.htm ). And with the ability to publish longer versions of articles on the Internet than are practical in print, concerns about article length should no longer be a barrier to the inclusion of a systematic review. Box 2. The Cochrane Collaboration The Cochrane Collaboration ( www.cochrane.org ) is an international, nonprofit, and independent organisation dedicated to helping people make well-informed decisions about health care by preparing, maintaining, and promoting the accessibility of systematic reviews. These reviews are published electronically in The Cochrane Library, which is available on the Internet and CD-ROM. The Cochrane Collaboration was established in 1993 and is named after the British epidemiologist, Archie Cochrane. Updating Gould's Vision As we progress through the 21st century, and health care information continues to become ever more plentiful, there are tremendous opportunities to make knowledge about health care more accessible. However, for this to happen without overwhelming the people who are trying to make health care decisions—for themselves or for someone else—the need for new research to be designed and reported using systematic reviews becomes ever more pressing. Returning to George Gould's vision, but bringing it into the modern era, I hope for a system in which everyone making a decision about health care in any part of the world would be able, in 15 minutes, to obtain up-to-date, reliable evidence of the effects of interventions they might choose, based on all the relevant research. Journals, especially new ones such as PLoS Medicine , will help achieve this by only publishing a report of a new research study under the following conditions. First, the researchers must justify their study on the basis of a previous systematic review. Second, the journal should publish an updated systematic review (which incorporates the new study) within the new study, alongside it, or shortly thereafter. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC529424.xml |
406389 | Organizing the Vertebrate Embryo—A Balance of Induction and Competence | The current understanding of organizer formation and neural induction in vertebrate embryos is discussed | In what is usually referred to as the most famous experiment in embryology, Hans Spemann and Hilde Mangold (1924) showed that a specific region in early frog embryos called the blastopore lip can induce a second complete embryonic axis, including the head, when transplanted to a host embryo. Most of the axis, including the nervous system, was derived from the host, whose cells were induced to form an axis by the graft, therefore named the organizer. Induction refers to the change in fate of a group of cells in response to signals from other cells. The signal-receiving cells must be capable of responding, a property termed competence. The Spemann–Mangold organizer. which—as the transplantation experiment shows—is able to turn cells whose original fate would be gut or ventral epidermis into brain or somites, is the prototypical inducing tissue. And neural induction has for a long time been regarded as a process by which organizer signals, in their normal context, redirect ectodermal cells from an epidermal towards a neural fate. The nature of the neural inducer or inducers and the mechanism of neural induction have been and remain hot topics in developmental biology. For half a century after Spemann and Mangold, studies on amphibians monopolized the subject, and even more recently, a large part of the progress in analyzing organizer formation and function and neural induction was based on amphibians, mostly the model species Xenopus laevis . In the past few years, however, work in other vertebrate and nonvertebrate chordate systems has come to play an important role in the field and has shed light on generalities and differences among chordates. If the present primer uses Xenopus to illustrate the process, it is because it accompanies an article in this issue of PLoS Biology dealing with neural development in this species ( Kuroda et al. 2004 ) and, of course, because of the experience of this author. Here I shall outline the understanding of organizer formation and neural induction as it has evolved over recent times and attempt to integrate recent results from different species into a common pattern. Cortical Rotation and Nuclear Localization of β-Catenin The frog egg is radially symmetrical around the animal–vegetal axis that has been established during oogenesis. Fertilization triggers a rotation of the cortex relative to the cytoplasm that is associated with the movement of dorsal determinants from the vegetal pole to the future dorsal region of the embryo ( Gerhart et al. 1989 ). (A brief parenthetical point is in order here. Conventionally, the side of the amphibian and fish embryo where the organizer forms has been called dorsal, with the opposite side labeled as ventral. This axis assignment does not project unambiguously onto the clearly defined dorsal–ventral polarity of the larva, as pointed out forcefully in recent publications [ Lane and Smith 1999 ; Lane and Sheets 2000 , 2002 ]. In these papers, a new proposal is made for polarity assignments in the gastrula that, I believe, has some merit, but also presents some difficulties. As the conventional approach of equating organizer side with dorsal seems to remain in wide use at present, I shall apply this convention, albeit with the reservation above.) While the nature of the dorsal determinants is still in dispute, it is clear that the consequence of their translocation is the nuclear localization of β-catenin in a wide arc at the future organizer side ( Figure 1 ) ( Schneider et al. 1996 ; Schohl and Fagotto 2002 ). Nuclear localization of β-catenin appears to be the first event that determines dorsal/ventral polarity in the Xenopus and zebrafish embryos ( Hibi et al. 2002 ). No comparable early event appears to be involved in amniote (e.g., chick and mouse) embryos. Figure 1 Early Development in X. laevis After fertilization, dorsal determinants are transported from the vegetal pole to one side of the embryo, where β-catenin will achieve nuclear localization. By 32 cells, the row of cells labeled 1 is specified as dorsal. Movements towards the vegetal pole (arrow) start at early cleavage stages. The organizer forms from C1 and B1 progenitors, the dorsal ectoderm or BCNE mostly from A1 progenitors (see Figure 2 ). The organizer is indicated in the gastrula embryo. See the text for further explanation. Induction by the Organizer: Antagonizing Bone Morphogenetic Protein As gastrulation starts, the Spemann–Mangold organizer, which includes mostly axial mesodermal precursors, was classically believed to instruct naïve ectoderm to convert to neural tissue. In transplant or explant studies, animal ectoderm that forms epidermis, when undisturbed, is susceptible to neural induction by the organizer. This fact prompted a search for neural inducers that eventually led to the identification of several substances with the expected properties—organizer products that can neuralize ectoderm. Their molecular properties were at first surprising: they proved to be antagonists of other signaling factors, mostly of bone morphogenetic proteins (BMPs) and also of WNT (a secreted protein homologous to the Drosophila Wingless protein) and Nodal factors ( Sasai and De Robertis 1997 ; Hibi et al. 2002 ). These observations led to the formulation of a “default” model of neural induction ( Weinstein and Hemmati-Brivanlou 1997 ), which states that ectodermal cells will differentiate along a neural pathway unless induced to a different fate. The heuristic simplicity and logical cogency of this model facilitated its wide acceptance, although it did not explain the processes that set the “default.” Some of these processes have been the subject of subsequent studies that were conducted in several different species, and this has led to a more refined (and probably more accurate) picture. The Role of Fibroblast Growth Factor For example, additional signaling pathways are now known to operate. Recent work on neural induction comes to two major conclusions: (i) the fibroblast growth factor (FGF) signaling pathway plays a major role in this process, and (ii) neural specification starts well before gastrulation and thus before the formation and function of the organizer. Studies on the role of FGF in early Xenopus development initially discovered its role in mesoderm induction and the formation of posterior tissues ( Kimelman et al. 1992 ). And while the involvement of FGF in neuralization was observed early in this system ( Lamb and Harland 1995 ; Launay et al. 1996 ; Hongo et al. 1999 ; Hardcastle et al. 2000 ), in view of the impressive effects seen with Chordin and other BMP pathway antagonists, the relevance of FGF in neural specification in amphibians and fish was slow to be recognized. It took elegant studies, mostly in chick embryos ( Streit et al. 2000 ), and their eloquent exposition ( Streit and Stern 1999 ; Wilson and Edlund 2001 ; Stern 2002 ) to turn the tide, but there is now no doubt that the FGF signaling pathway plays a major role in the specification and early development of the neural ectoderm in chordates. FGF does not seem to behave as a classical organizer-derived neural inducer, however. Maternal FGF mRNA and protein appear to be widely distributed in the early embryo, and at least one FGF family member is expressed primarily in the animal, pre-ectodermal region during blastula stages ( Song and Slack 1996 ). A detailed study of the regions where different signaling pathways are active during embryogenesis ( Schohl and Fagotto 2002 ) showed that the entire ectoderm is probably exposed to FGF signals at or prior to the time of neural induction, with the more vegetal, mesoderm-proximal region of the ectoderm being exposed to higher levels. Thus, exposure to FGF is required to endow the ectoderm with the competence to respond to additional signals that will act later on its way towards neural specification. Such a process was deduced from experiments in the chick, where an FGF signal must be followed by exposure to organizer signals to sensitize the tissue to BMP antagonists that ultimately stabilize the neural fate ( Stern 2002 ). An exciting recent study shows that exposure of the epiblast (ectoderm) to FGF induces, after a time delay, a transcription factor named Churchill. Churchill expression inhibits cell ingression leading to mesoderm formation; the cells remaining in the epiblast assume a neural fate ( Sheng et al. 2003 ). The time delay in Churchill induction appears to be the key in explaining how one signal, FGF, can be involved in mesodermal and neural development at the same time in cells that are in close proximity. The question how FGF signaling can lead to different outcomes was also addressed in a study on neural specification in ascidians ( Bertrand et al. 2003 ). Here, the FGF signal leads to neural induction through the coordinated activation of two transcription factors, Ets1/2 and GATAa, whereas FGF does not activate GATAa during its function in mesoderm formation. Thus, similar input leads to distinct output as a result of different responses by target tissues, stressing the importance of competence in this inductive process. Molecular Predisposition Not surprisingly, then, attention has turned to the target tissues and to the prepatterns that might already exist. In Xenopus , it was long known that the animal region or pre-ectoderm is not uniform or naïve, in that the dorsal, organizer-proximal region is predisposed towards a neural fate ( Sharpe et al. 1987 ). The paper by Kuroda et al. (2004) adds much information about neural specification before gastrulation in Xenopus and the factors involved in this process. The authors identify a region in the dorsal ectoderm of the blastula that they name the “blastula Chordin- and Nogginexpressing” (or BCNE) region ( Figure 2 ). They show that this region, which I prefer to simply call dorsal ectoderm, expresses siamois , chordin , and Xnr3 , another β-catenin target. The dorsal ectoderm or BCNE is fully specified as anterior neural ectoderm, as excision of this region led to headless embryos, and explants differentiated into neural tissue in culture, even when the formation of any mesodermal cells was blocked by interference with nodal signaling ( Kuroda et al. 2004 ). Figure 2 Expression Patterns in Dorsal Ectoderm Expression patterns of selected genes in the late blastula of Xenopus , based on the work of Kuroda et al. (2004) . See the text for further explanation. Kuroda et al. (2004) further show that induction of anterior neural tissue initiated by β-catenin requires Chordin, whereas formation of posterior neural tissue does not. This latter point concerns an issue not yet mentioned here, namely anterior–posterior patterning of the neural ectoderm, a process that occurs in concert with neural induction per se. This patterning appears to involve the interaction of various signaling factors, including FGF, BMP, WNT, and retinoic acid, all of which act as posteriorizing factors ( Kudoh et al. 2002 ). Suppression of BMP signaling by expression of its antagonists is the condition that specifies the dorsal ectoderm or BCNE as future anterior neural ectoderm; in contrast, posterior neural ectoderm may form under the influence of FGF even in the presence of BMP signaling. The work by Kuroda et al. (2004) thus shows that initial specification of anterior neural ectoderm in Xenopus , as in other vertebrates, takes place before gastrulation and does not require organizer signals; this is not to say that full differentiation and patterning of the nervous system could be achieved without organizer participation. Induction and Competence The formation of the vertebrate nervous system thus depends on multiple signaling pathways, such as the FGF, BMP, and WNT signaling cascades, that interact in complex ways (e.g., Pera et al. 2003 ). In contrast to the classical view, neural induction is not exclusively promoted by organizer-derived signals, in that earlier signals and intrinsic processes that determine ectodermal competence are prominently involved. Whether inductive signals or competence of responding tissue is more important in embryology has been debated, much like the nature–nurture controversy in the behavioral arena. Current work has given some boost to the competence side of the argument, but, as in behavior, the truth lies somewhere in between, though not necessarily at the halfway mark. Studies such as those discussed here bring us closer to finding the answer to this question. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC406389.xml |
533871 | A genomic timescale of prokaryote evolution: insights into the origin of methanogenesis, phototrophy, and the colonization of land | Background The timescale of prokaryote evolution has been difficult to reconstruct because of a limited fossil record and complexities associated with molecular clocks and deep divergences. However, the relatively large number of genome sequences currently available has provided a better opportunity to control for potential biases such as horizontal gene transfer and rate differences among lineages. We assembled a data set of sequences from 32 proteins (~7600 amino acids) common to 72 species and estimated phylogenetic relationships and divergence times with a local clock method. Results Our phylogenetic results support most of the currently recognized higher-level groupings of prokaryotes. Of particular interest is a well-supported group of three major lineages of eubacteria (Actinobacteria, Deinococcus , and Cyanobacteria) that we call Terrabacteria and associate with an early colonization of land. Divergence time estimates for the major groups of eubacteria are between 2.5–3.2 billion years ago (Ga) while those for archaebacteria are mostly between 3.1–4.1 Ga. The time estimates suggest a Hadean origin of life (prior to 4.1 Ga), an early origin of methanogenesis (3.8–4.1 Ga), an origin of anaerobic methanotrophy after 3.1 Ga, an origin of phototrophy prior to 3.2 Ga, an early colonization of land 2.8–3.1 Ga, and an origin of aerobic methanotrophy 2.5–2.8 Ga. Conclusions Our early time estimates for methanogenesis support the consideration of methane, in addition to carbon dioxide, as a greenhouse gas responsible for the early warming of the Earths' surface. Our divergence times for the origin of anaerobic methanotrophy are compatible with highly depleted carbon isotopic values found in rocks dated 2.8–2.6 Ga. An early origin of phototrophy is consistent with the earliest bacterial mats and structures identified as stromatolites, but a 2.6 Ga origin of cyanobacteria suggests that those Archean structures, if biologically produced, were made by anoxygenic photosynthesizers. The resistance to desiccation of Terrabacteria and their elaboration of photoprotective compounds suggests that the common ancestor of this group inhabited land. If true, then oxygenic photosynthesis may owe its origin to terrestrial adaptations. | Background The evolutionary history of prokaryotes includes both horizontal and vertical inheritance of genes [ 1 - 3 ]. Horizontal gene transfer (HGT) events are of great interest in themselves, for their roles in creating functionally new combinations of genes [ 4 ], but they pose problems for investigating the phylogenetic history and divergence times of organisms. The existence of a core of genes that has not been transferred is still under debate as HGTs have been detected in genes previously considered to be immune to these events [ 2 , 5 - 11 ]. Although a complete absence of HGT appears to be unlikely, genes belonging to different functional categories seem to be horizontally transferred with different frequencies [ 11 - 13 ]. Genes forming complex interactions with other cellular components (e.g. translational proteins) have a lower frequency of HGT and are generally more conserved among organisms. Recent studies based on analyses of these genes have obtained similar phylogenies suggesting an underlying phylogenetic signal [ 3 , 14 - 17 ]. If we accept the use of core genes for phylogeny reconstruction then they should also be of use for time estimation with molecular clocks. Moreover the increasing number of prokaryotic genomes available has facilitated the detection of HGT through more accurate detection of orthology, paralogy, and monophyletic groups, and the concatenation of gene and protein sequences has helped increase the confidence of nodes and decrease the variance of time estimates [ 14 , 16 , 18 , 19 ]. Temporal information concerning prokaryote evolution has come from diverse sources. For eukaryotes, the fossil record provides an abundant source of such data, but this has not been true for prokaryotes, which are difficult to identify as fossils [ 20 , 21 ]. Limited information on specific groups or metabolites has been obtained from analyses of isotopic concentrations [ 22 ] and detection of biomarkers [ 23 , 24 ]. By making some simple assumptions – e.g., that aerobic organisms evolved after oxygen became available [ 25 ]- it is possible to constrain some nodes in the prokaryote timescale, but only in a coarse sense. However, most information on the timescale of prokaryote evolution has come from analysis of DNA and amino acid sequence data with molecular clocks [ 26 - 30 ]. The detection of evolutionary patterns in metabolic innovations, as a consequence of a phylogeny not dominated by HGT events, allows more detailed constraints on a prokaryote timescale. In contrast to conventional interpretations of cyanobacteria as being among the most ancient of life forms on Earth [ 31 ], these studies have consistently found a late origin of cyanobacteria [ 28 , 30 ], nearly contemporaneous with the major Proterozoic rise in oxygen at 2.3 billion years ago (Ga), termed the Great Oxidation Event (GOE) [ 32 ]. In this study we have assembled a data set of amino acid sequences from 32 proteins common to 72 species of prokaryotes and eukaryotes and estimated phylogenetic relationships and divergence times with a local clock method. These results in turn have been used to investigate the origin of metabolic pathways of importance in evolution of the biosphere. Results Data set The majority (81%) of the 32 proteins that were used are classified in the "information storage and processes" functional category of the COG. The other categories represented are "cellular processes" (10%), "metabolism" (3%), and "information storage and processing" + "metabolism" (proteins with combined functions; 6%). Other studies that have analyzed prokaryote genome sequence data for phylogeny have found a similar high proportion of proteins in the "information storage and processes" functional category, presumably because HGT is more difficult with such genes that are vital for the survival of the cell [ 3 , 18 , 33 , 34 ]. The concatenated and aligned data set of 32 proteins contained 27,205 amino acid sites (including insertions and deletions). With alignment gaps removed, the two data sets analyzed were 7,338 amino acid sites (Archaebacteria) and 7,597 amino acid sites (Eubacteria). The data sets were complete in the sense that sequences of all taxa were present for all proteins. Phylogeny The phylogeny of eubacteria (Fig. 1 ) shows significant bootstrap support for most of the major groups and subgroups. All proteobacteria form a monophyletic group (support values 95/47/99 for ME, ML and Bayesian respectively) with the following relationships of the subgroups: (epsilon (alpha (beta, gamma))). There has been debate about the effect of base composition and substitution rate on the phylogenetic position of the endosymbiont Buchnera among γ-proteobacteria [ 35 , 36 ]. Its position here (Fig. 1 ) differs slightly from both studies; accordingly, any conclusions concerning its divergence time should be treated with caution. Spirochaetes cluster with Chlamydiae, Actinobacteria with Cyanobacteria and Deinococcus (support values for Cyanobacteria + Deinococcus are 92/80/99) and the hyperthermophiles ( Thermotoga , Aquifex ) branch basally in the tree. These groups and relationships are similar to those found previously with analyses of prokaryote genome sequences [ 3 , 18 , 33 , 34 ]. Figure 1 Phylogenetic tree (ME; α = 0.94) of eubacteria rooted with archaebacteria, using sequences of 32 proteins (7,597 amino acids). Bootstrap values are shown on nodes; asterisks indicate support values > 95%. For major groups, support values from three phylogenetic methods (ME/ML/Bayesian) are indicated in italics (dash indicates a group was not present). The phylogeny of archaebacteria (Fig. 2 ) agrees with some but not all aspects of previous phylogenetic analyses of prokaryote genomes using sequence data [ 3 , 14 , 18 , 30 , 37 , 38 ] and the presence and absence of genes [ 37 , 39 - 41 ]. For example, each of the two major clades of Archaebacteria (excluding Korarchaeota, which was not represented) is monophyletic. This is consistent with some analyses [ 14 , 18 ] but not others [ 3 ]. Also, the position of Crenarchaeota as closest relatives of eukaryotes (Fig. 2 ), instead of Euryarchaeota, has been debated [ 14 , 18 , 30 , 42 , 43 ]. The faster rate of evolution in eukaryotes (Fig. 2 ), as noted elsewhere [ 30 , 44 ], requires some caution in drawing conclusions regarding their phylogenetic position. Methanogens were found to be monophyletic in some previous analyses [ 3 , 41 ] but were paraphyletic in other analyses [ 38 , 45 , 46 ] and in our analysis (Fig. 2 ). The phylogenetic position of one species of methanogen in particular, Methanopyrus kandleri , has differed among previous studies [ 47 - 49 ]. However, it is difficult to make direct comparisons among various studies because they have included different sets of taxa. Figure 2 Phylogenetic tree (ME; α = 1.20) of archaebacteria rooted with eubacteria, using sequences of 32 proteins (7,338 amino acids). Bootstrap values are shown on nodes; asterisks indicate support values > 95%. For major groups, support values from three phylogenetic methods (ME/ML/Bayesian) are indicated in italics. Time estimation Times of divergence were estimated for all nodes in the phylogenies of eubacteria (Fig. 1 ) and archaebacteria (Fig. 2 ) using the alternative constraints (calibrations) described in the Methods. The eubacteria time estimates show an average 7% increase from the molecular to the geologic (2.3 Ga minimum) calibration point. Two other additional geologic calibration points were used in the analyses (see Methods), 2.3 Ga fixed and 2.7 Ga minimum, which showed respectively 10% younger and 11% older time estimates compared with the 2.3 Ga minimum calibration point. The times estimated with the fossil calibration point in the archaebacteria data set were on average only 10% younger than the ones estimated with the molecular calibration. Moreover there was even a smaller effect on the time estimates of the deepest nodes, which were the ones of interest in this study (node M 3.2%, node N 2.1%, node O 1.8% and node P 1.3%). This variation is due not only to the different calibration times but also to the type of constraints used (i.e. minimum boundaries only vs. minimum and maximum bounds). A single timetree (Fig. 3 ) was constructed from the phylogenetic and divergence time data. The time estimates summarized in that tree derive only from the best-justified calibrations. For eubacteria, the 2.3 Ga minimum calibration (constraint), from the geologic record, was chosen because it encompasses all of the hypothesized time estimates for the origin of cyanobacteria. For archaebacteria, the 1.2 Ga calibration (minimum 1.174 Ga, maximum 1.222 Ga), from the red algae fossil record, was selected because it provides a conservative constraint on the divergence of plants and animals. Time estimates and 95% credibility intervals for all nodes under all calibrations are presented elsewhere [see Additional file 1 , Additional file 2 , and Additional file 3 ], and those data are summarized for selected nodes and calibrations for eubacteria and archaebacteria (Table 1 ). Although some undetected HGT could be a source of bias in the time estimates, the direction of the bias (raising or lowering the estimate) would depend on the specific node and groups involved, and it is unlikely to have had a major affect on the results, even if present. Figure 3 A timescale of prokaryote evolution. Letters indicate nodes discussed in the text. The last common ancestor was arbitrarily placed at 4.25 Ga in the tree, although this placement was not part of the analyses. The grey rectangle shows the time prior to the initial rise in oxygen (presumably anaerobic conditions). Mtb: Methanothermobacter , Tab: Thermoanaerobacter , Tsc: Thermosynechococcus . Table 1 Time estimates for selected nodes in the tree of eubacteria (A-K) and archaebacteria (L-P). Letters refer to Fig. 3. Time (Ma) a CI b Node A 102 57–176 Node B 2508 2154–2928 Node C 2800 2452–3223 Node D 1039 702–1408 Node E 2558 2310–2969 Node F 2784 2490–3203 Node G 2923 2587–3352 Node H 3054 2697–3490 Node I 3186 2801–3634 Node J 3644 3172–4130 Node K 3977 3434–4464 Node L 233 118–386 Node M 3085 2469–3514 Node N 3566 2876–3948 Node O 3781 3047–4163 Node P 4112 3314–4486 a Averages of the divergence times estimated using the 2.3 Ga minimum constraint and the five ingroup root constraints (nodes A-K) and using the 1.198 ± 0.022 Ga constraint and the five ingroup root constraints (nodes L-P). b Credibility interval (minimum and maximum averages of the analyses under the five ingroup root constraints) Divergence times within eubacteria (Fig. 3 , Table 1 , nodes A-K) show a pattern seen previously [ 30 ] whereby most major groups diverge from one another (nodes B-I excluding node D) in a relatively limited time interval, approximately between 2.5–3.2 Ga. The position of the hyperthermophiles has been debated, with some studies showing them in a basal position whereas others place them more derived. The high G-C composition of these taxa is believed to be responsible for this difficulty in phylogenetic placement. Here, they branch basally (node J, 3.17–4.13 Ga and node K, 3.43–4.46 Ga), but this should be interpreted with caution for this reason. The divergence of Escherichia coli from Salmonella typhimurium (Fig. 3 , Table 1 , node A; 0.06–0.18 Ga) is consistent with the time estimated previously from consideration of mammalian host evolution (0.12–0.16 Ga) [ 26 ]. On the other hand an inconsistency with the fossil record is represented by the divergence of unicellular ( Thermosynechococcus elongatus ) and heterocyst-forming ( Nostoc sp. ) cyanobacteria. Our time estimate for this divergence is 0.70–1.41 Ga (Fig. 3 , Table 1 , node D) while microfossils of both groups have been identified in Mesoproterozoic (1.5–1.3 Ga) and Paleoproterozoic (2.12–2.02 Ga) rocks [ 50 - 52 ]. However the identification of these latter fossils has been debated [ 51 ]. Branch lengths of cyanobacteria in our protein tree and in 16S ribosomal RNA trees [ 34 ] do not suggest obvious substitutional biases or rate changes as they are neither unusually long nor unusually short. The reason for the discrepancy between the molecular and fossil times remains unclear but a possible misinterpretation of the fossil record cannot be dismissed. Divergence times of most internal nodes among archaebacteria (Fig. 3 , Table 1 , nodes L-P) are closely spaced in time and relatively ancient, approximately between 3.1–4.1 Ga, regardless of the initial setting (prior) for the ingroup root. Node P is the earliest divergence, separating Euryarchaeota from Crenarchaeota+eukaryotes. Node O represents the common ancestor of the methanogens in our analysis ( Methanopyrus kandleri , Methanothermobacter thermoautotrophicus , Methanococcus jannaschii , Archaeoglobus fulgidus , Methanosarcina mazei and M. acetivorans ). Therefore, methanogenesis presumably arose between nodes P and O, or between 4.11 Ga (3.31–4.49 Ga) and 3.78 Ga (3.05–4.16 Ga) (Fig. 3 , Table 1 ). If the position of Methanopyrus kandleri is not considered, in lieu of the current debate concerning its relationships (noted above), node N (Fig. 3 , Table 1 ), the minimum time for the origin of methanogenesis drops only slightly, from 3.78 Ga (3.05–4.16 Ga) to 3.57 Ga (2.88–3.95 Ga). Discussion Origin of life on Earth Neither the time for the origin of life, nor the divergence of archaebacteria and eubacteria, was estimated directly in this study. Nonetheless, one divergence within archaebacteria was estimated to be as old as 4.11 Ga (Node P), suggesting even earlier dates for the last common ancestor of living organisms and the origin of life. This is in agreement with previous molecular clock analyses using mostly different data sets and methodology [ 28 , 30 ]. A Hadean (4.5–4.0 Ga) origin for life on Earth is also consistent with the early establishment of a hydrosphere [ 31 , 53 ]. Nevertheless, the earliest geologic and fossil evidence for life has been debated [ 21 , 54 - 59 ] leaving no direct support for such old time estimates. Methanogenesis The lower luminosity of the sun during the Hadean and Archean predicts that surface water would have been frozen during that time. Instead there is evidence of liquid water and moderate to high surface temperatures [ 60 , 61 ]. The long term carbon cycle (carbonate-silicate cycle), which acts as a temperature buffer, combined with greenhouse gases, probably explain this "Faint Young Sun Paradox" [ 61 ]. Arguments have been made in support of either methane [ 62 - 64 ] or carbon dioxide [ 65 ] as the major greenhouse gas involved. If methane was important, it would have necessarily come from organisms (methanogens), given the volume required. Archaebacteria are the only prokaryotes known to produce methane. Our time estimate of between 4.11 Ga (3.31–4.49 Ga) and 3.78 Ga (3.05–4.16 Ga) for the origin of methanogenesis suggests that methanogens were present on Earth during the Archean, consistent with the methane greenhouse theory [ 64 ]. Nonetheless, this does not rule out the alternative (carbon dioxide) explanation [ 65 ]. Anaerobic methanotrophy Anaerobic methanotrophy, or anaerobic oxidation of methane (AOM), is a metabolism associated with anoxic marine sediments rich in methane. This metabolism is characterized by the coupling of two reactions, oxidation of methane and sulfate reduction. The methane oxidizers are represented by archaebacteria phylogenetically related to the Methanosarcinales, while the sulfate reducers, when present, are eubacterial members of the δ-proteobacteria division [ 66 ]. These two groups of prokaryotes have been found associated in syntrophies, thus suggesting the coupling of these two pathways [ 66 - 69 ]. Archaebacteria have been found also isolated in monospecific clusters, oxidizing methane through an unknown reaction. It has been suggested that they may use elements of both the methanogenesis and sulfate-reducing pathways [ 70 ]. An example of coexistence of genes from both of these pathways is Archaeoglobus fulgidus . The particular condition of this archaebacterium has been explained with an ancient horizontal gene transfer from an eubacterial lineage, most likely a δ-proteobacterium [ 71 , 72 ]. The phylogenetic position of the anaerobic methanotrophs with the Methanosarcinales places the maximum date for the origin of this metabolism at 3.09 (2.47–3.51) Ga (Fig. 3 , Table 1 , node M). The minimum time estimate of 0.23 Ga (0.12–0.39 Ga) (Fig. 3 , Table 1 , node L), probably a substantial underestimate of the true time, results from the limited phylogenetic sampling available for this group. Aerobic methanotrophy Aerobic methanotrophs are represented in the α and γ divisions of the proteobacteria. This suggests an origin for this metabolism between node C (2.80 Ga; 2.45–3.22 Ga) and node B (2.51 Ga; 2.15–2.93 Ga) (Fig. 3 , Table 1 ). Shared genes from this pathway and from methanogenesis also have been found in the Planctomycetales [ 73 ]. This has suggested a revision of the direction of the HGT, usually considered from archaebacteria to eubacteria [ 1 ], that presumably has spread these genes in the two domains. However the absence of Planctomycetales from our dataset and its controversial phylogenetic position [ 74 ] does not allow us to discriminate among these possibilities. Both anaerobic and aerobic methanotrophy have been used to explain the highly depleted carbon isotopic values found in 2.8–2.6 Ga geologic formations [ 22 , 75 ]. Our time estimates for these two metabolisms are both compatible with the isotopic record. Molecular clock methods have estimated the origin of cyanobacteria at 2.56 Ga (2.04–3.08 Ga) [ 30 ]. Because oxygenic photosynthesis would have been necessary for aerobic methanotrophy [ 75 ], an anaerobic metabolism seems more likely to explain the isotopic record. Phototrophy The ability to utilize light as an energy source (phototrophy, photosynthesis) is restricted to eubacteria among prokaryotes. Phototrophic eubacteria are found in five major phyla (groups), including proteobacteria, green sulfur bacteria, green filamentous bacteria, gram positive heliobacteria, and cyanobacteria [ 4 , 76 ]. Only cyanobacteria produce oxygen. There are three explanations for this broad taxonomic distribution of phototrophic metabolism; it evolved in one lineage of eubacteria and spread at a later time to other lineages by horizontal transfer, the common ancestor of these groups possessed this metabolism and genetic machinery, or there was a combination of horizontal transfer and vertical inheritance [ 4 ]. Because two of the three explanations require a phototrophic common ancestor, and because some features of the Archean geologic record require this metabolism if biologically produced [ 77 ], we have assumed here that the common ancestor (Node I) was phototrophic. Therefore, we estimate that phototrophy evolved prior to 3.19 (2.80–3.63) Ga (Fig. 3 , Table 1 , node I). Because the hyperthermophiles Aquifex and Thermotoga are not phototrophic and branch more basally, 3.64 (3.17–4.13) Ga (Node J) can be considered a maximum date for phototrophy. However, if those hyperthermophiles instead occupy a more derived position on the tree, as some analyses have indicated [ 33 ], then the maximum date is no longer constrained in this analysis. The colonization of land The evolution of phototrophy was most likely linked to the evolution of other features essential to survival in stressful environments. Considerable biological damage can occur from exposure to ultraviolet radiation, especially prior to the GOE and later formation of the protective ozone layer [ 78 ]. The synthesis of pigments such as carotenoids, which function as photoprotective compounds against the reactive oxygen species created by UV radiation [ 79 ], is an ability present in all the photosynthetic eubacteria and in groups that are partly or mostly associated with terrestrial habitats such as the actinobacteria, cyanobacteria, and Deinococcus - Thermus . Pigmentation was probably a fundamental step in the colonization of surface environments [ 80 ]. Besides the sharing of photoprotective compounds, these three groups (cyanobacteria, actinobacteria, and Deinococcus ) also share a high resistance to dehydration [ 81 - 84 ], which further suggests that their common ancestor was adapted to land environments. Therefore we propose the name Terrabacteria (L. terra , land or earth) for the group that includes the bacterial phyla Actinobacteria, Cyanobacteria, and Deinococcus-Thermus . An early colonization of land is inferred to have occurred after the divergence of this terrestrial lineage with Firmicutes (Fig. 3 , Table 1 , node H), 3.05 (2.70–3.49) Ga, and prior to the divergence of Actinobacteria with Cyanobacteria + Deinococcus (Fig. 3 , Table 1 , node F), 2.78 (2.49–3.20) Ga. These molecular time estimates are compatible with time estimates (2.6–2.7 Ga) based on geological evidence for the earliest colonization of land by organisms (prokaryotes) [ 85 ]. Many groups of prokaryotes currently inhabit terrestrial environments, indicating that land has been colonized multiple times in different lineages. Oxygenic photosynthesis From the above analyses and discussion, some of the early steps leading to oxygenic photosynthesis apparently were acquisition of protective pigments, phototrophy, and the colonization of land. Currently, hundreds of terrestrial species of cyanobacteria are known, broadly distributed among the orders, with species occurring in some of the driest environments on Earth. It is possible that a terrestrial ancestry of cyanobacteria, where stresses resulting from desiccation and solar radiation were severe, may have played a part in the evolution of oxygenic photosynthesis. Nonetheless, there is ample evidence that horizontal gene transfer also has played an important role in the assembly of photosynthetic machinery [ 4 ]. Although we have used the origin of cyanobacteria as a calibration (2.3 Ga, geologic time based on GOE), such minimum constraints permit the estimated time to be much older in a Bayesian analysis. However, in this case, the time estimated for node E (2.56 Ga; 2.31–2.97 Ga; Fig. 3 , Table 1 ) was not much older than the constraint itself. It also agrees with an earlier molecular time estimate (2.56 Ga; 2.04–3.08 Ga) based on a largely different data set and methods [ 30 ]. When we used the older minimum constraint of 2.7 Ga, corresponding to 2α-methyl-hopane evidence considered to represent a biomarker of cyanobacteria [ 86 ], the estimated time was likewise only slightly older [see Additional file 1 ]. The oldest time estimates for oxygenic photosynthesis that we obtained are still considerable younger than has been assumed – generally – in the geologic literature [ 31 , 32 , 87 ]. This suggests that carbon isotope excursions, microfossils, microbial mats, stromatolites, and other pre-3 Ga evidence ascribed to cyanobacteria should be re-evaluated. Conclusions The analyses presented here are based on the assumption, still under debate, that historical information (phylogenies and divergence times) can be retrieved from genes in the prokaryote genome that have not been affected by horizontal gene transfer. Our prokaryotic timeline shows deep divergences within both the eubacterial and archaebacterial domains indicating a long evolutionary history. The early evolution of life (>4.1 Ga) and early origin of several important metabolic pathways (phototrophy, methanogenesis; but not oxygenic photosynthesis) suggests that organisms have influenced the Earth's environment since early in the history of the planet (Fig. 4 ). An inferred early presence of methanogens (3.8–4.1 Ga) is consistent with models suggesting that methane was important in keeping the Earth's surface warm in the Archean but does not rule out the possibility that carbon dioxide may have been equally (or more) important. In contrast to many classical interpretations of the early evolution of life, we find no compelling evidence for a pre-3 Ga evolution of cyanobacteria and oxygenic photosynthesis. This unique metabolism apparently evolved relatively late in the radiation of eubacterial clades, shortly before the Great Oxidation event (~2.3 Ga). The evolution of oxygenic photosynthesis may have involved a combination of adaptations to stressful terrestrial environments as well as acquisition of genes through horizontal transfer. Figure 4 A time line of metabolic innovations and events on Earth. The minimum time for oxygenic photosynthesis is constrained by the Great Oxidation Event (2.3 Ga) whereas the maximum time for the origin of life is constrained by the origin of Earth (4.5 Ga). Horizontal lines indicate credibility intervals, white boxes indicate minimum and maximum time constraints on the origin of a metabolism or event, and colored boxes indicate the presence of the metabolism or event. Methods Data assembly We assembled a dataset that maximized the number of taxa and proteins from available organisms with complete genome sequences of prokaryotes and selected eukaryotes. In doing so, we omitted a few taxa (e.g., Agrobacterium tumefaciens Cereon str C58 and Halobacterium sp. NRC-1 ) whose addition to the data set would have resulted in a substantial reduction in the total number of proteins. Data assembly began with the Clusters of Orthologous Groups of Proteins (COG) [ 88 ], which consisted of 84 proteins common to 43 species. With that initial dataset we added other species from among completed microbial genomes (NCBI; National Center for Biotechnology Information), assisted by BLAST and PSI-BLAST [ 89 ]. In total 72 species were included in the study (54 eubacteria, 15 archaebacteria and three eukaryotes). The species of Archaebacteria and their accession numbers are: Aeropyrum pernix K1 (NC_000854), Archaeoglobus fulgidus (NC_000917), Methanothermobacter thermoautotrophicus str. Delta H (NC_000916), Methanococcus jannaschii (NC_000909), Methanopyrus kandleri AV19 (NC_003551), Methanosarcina acetivorans str. C2A (NC_003552), Methanosarcina mazei Goe1 (NC_003901), Pyrobaculum aerophilum (NC_003364), Pyrococcus abyssi ( NC_000868), Pyrococcus furiosus DSM 3638 (NC_003413), Pyrococcus horikoshii (NC_000961), Sulfolobus solfataricus (NC_002754), Sulfolobus tokodaii (NC_003106), Thermoplasma acidophilum (NC_002578), Thermoplasma volcanium (NC_002689). The species of Eubacteria are: Aquifex aeolicus (NC_000918), Bacilllus halodurans (NC_002570), Bacillus subtilis (NC_000964), Borrelia burgodorferi (NC_001318), Brucella melitensis (NC_003317, NC_003318), Buchnera aphidicola str. APS (Acyrthosiphon pisum) (NC_002528), Campylobacter jejuni (NC_002163), Caulobacter crescentus CB15 (NC_002696), Chlamydia muridarum (NC_002620), Chlamydia trachomatis (NC_000117), Chlamydophila pneumoniae CWL029 ( NC_000922), Chlorobium tepidum str. TLS (NC_002932), Clostridium acetobutylicum (NC_003030), Clostridium perfringens (NC_003366), Corynebacterium glutamicum ATCC 13032 (NC_003450), Deinococcus radiodurans (NC_001263, NC_001264), Escherichia coli O157:H7 EDL933 (NC_002655), Fusobacterium nucleatum subsp. nucleatum ATCC 25586 (NC_003454) , Haemophilus influenzae Rd (NC_000907), Helicobacter pylori 26695 (NC_000915), Lactococcus lactis subsp. lactis (NC_002662), Listeria innocua (NC_003212), Listeria monocytogenes EGD-e (NC_003210) , Mesorhizobium loti (NC_002678), Mycobacterium leprae (NC_002677), Mycobacterium tuberculosis H37Rv (NC_000962), Mycoplasma genitalium G-37 (NC_000908), Mycoplasma pneumoniae (NC_000912), Mycoplasma pulmonis (NC_002771), Neisseria meningitidis MC58 (NC_003112), Nostoc sp. PCC7120 (NC_003272), Pasteurella multocida (NC_002663), Pseudomonas aeruginosa PA01 (NC_002516), Ralstonia solanacearum (NC_003295), Rickettsia conorii (NC_003103), Rickettsia prowazekii (NC_000963), Salmonella enterica subsp. enterica serovar Typhi (NC_003198), Salmonella typhimurium LT2 (NC_003197), Sinorhizobium meliloti (NC_003047), Staphylococcus aureus Mu50 (NC_002758), Streptococcus pneumoniae TIGR4 (NC_003028), Streptococcus pyogenes M1 GAS (NC_002737), Streptomyces coelicolor A3(2) (NC_003888), Synechocystis PCC6803 (NC_000911), Thermoanaerobacter tengcongensis (NC_003869), Thermosynechococcus elongatus BP-1 (NC_004113), Thermotoga maritima (NC_000853), Treponema pallidum subsp. pallidum str. Nichols (NC_000919), Ureaplasma parvum serovar 3 str. ATCC 700970 (NC_002162), Vibrio cholerae O1 biovar eltor str. N16961 (NC_002505, NC_002506), Xanthomonas campestris pv. campestris str. ATCC 33913 (NC_003902), Xanthomonas axonopodis pv. citri str. 306 (NC_003919), Xylella fastidiosa 9a5c (NC_002488) , Yersinia pestis (NC_003143). The eukaryotes were Arabidopsis thaliana , Drosophila melanogaster , Homo sapiens . Accession numbers for eukaryote proteins are presented elsewhere [ 90 ]. This dataset consisted of 60 proteins that were individually analysed as a step in orthology determination. The proteins were aligned with CLUSTALW [ 91 ]. Then phylogenetic trees of each protein were built and visually inspected. Initial trees were constructed using Minimum Evolution (ME), with MEGA version 2.1 [ 92 ]. The major criterion that we used in determining which genes to include or exclude was the monophyly of domains. We rejected genes with domains (archaebacteria and eubacteria) that were non-monophyletic, as these would be the best examples of HGT; this amounted to 61% of the genes rejected. Some other genes were omitted if there were detectable cases of HGT within a domain, such as the deep nesting of a species from one Phylum within a clade of another Phylum. Otherwise we did not eliminate genes that had a different branching order of phyla within a domain or different relationships of groups of lower taxonomic categories. Admittedly, ancient cases of HGT might be an explanation for some of those topological differences, but they are not detectable. However, we further tested the effectiveness of our criteria by examining the stability of individual protein trees, using different gamma values (α = 1, 0.5 and 0.3). We kept only the genes that were stable to such perturbations (in terms of remaining in that category of non-HGT genes). The position of eukaryotes, which varies depending on the gene, was not considered in assessing monophyly of eubacteria and archaebacteria. The 32 remaining proteins were concatenated for analysis. The α parameters used during the tree building process were estimated with the program PamL (JTT+gamma model) [ 93 ]. From the concatenation, trees were constructed with ME, Maximum Likelihood (ML) [ 94 ] and Bayesian [ 95 ] methods. The phylogenies obtained with ME, ML and Bayesian were similar, differing only at non-significant nodes assessed by the bootstrap method [ 96 ], with one only significant exception on the position of M. kandleri in the Bayesian phylogeny. The sequence alignments and other supplementary data are presented elsewhere [ 90 ]. Time estimation Time estimation was conducted separately within each domain (Archaebacteria and Eubacteria) using reciprocal rooting and several calibration points. All time estimates were calculated with a Bayesian local clock approach [ 97 ] utilizing concatenated data sets of multiple proteins and a JTT+gamma model of substitution [ 19 , 98 , 99 ]. The following settings were used: numsamp (10,000), burnin (100,000), and sampfreq (100). This method permitted rates to vary on different branches, which was necessary given the known rate variation among prokaryote and eukaryote nuclear protein sequences [ 30 , 44 ]. Calibration of rate in this method was implemented by assigning constraints to nodes in the phylogeny. Five different initial settings (prior distributions) were used in each domain [see Additional file 4 ]. These were chosen at intervals of 0.5 Ga starting from 4.5 Ga, which is approximately the age of the Earth and Solar System, to 2.5 Ga, which is slightly before the major rise in oxygen (Great Oxidation Event; GOE) as recorded in the geologic record [ 32 ] and related to the presence of oxygenic cyanobacteria. Those constraints pertained to the ingroup root, or deepest divergence in the tree excluding the outgroup. Because of the relatively small number of duplicate genes available for rooting the tree of life, we were unable to estimate the time of the last common ancestor (the divergence of eubacteria and archaebacteria). For the archaebacterial data set, we included eukaryotes for calibration purposes because reliable calibration points were unavailable among those prokaryotes. In doing so, only proteins in which eukaryotes clustered with archaebacteria were included [ 30 ]. An outgroup was used that consisted of representatives of the major groups of eubacteria [ 90 ]. We used the fossil and molecular times (separately) of the plant-animal divergence as calibration points, for comparison. The fossil calibration was the first appearance of a representative of the plant lineage (red algae) at 1.198 ± 0.022 Ga [ 100 ]. The molecular time estimate for this divergence was 1.609 ± 0.060 Ga from a study of 143 rate-constant proteins [ 98 ]. We used the minimum and maximum bounds for these calibration times as constraints in the Bayesian analysis. Although the results of these two different calibrations are provided for comparison, our preferred calibration is the 1.2 Ga fossil calibration because it has the best justification (supporting evidence). Therefore, our summary time estimates for archaebacteria, presented in the timetree (Fig. 3 ), use only this fossil calibration. For the eubacterial data set, we used four internal time constraints in separate analyses, all involving the origin of cyanobacteria. The first and most conservative constraint was a fixed origin (minimum and maximum bounds) at 2.3 Ga, which corresponds to the GOE. For the second constraint we used 2.3 Ga as a minimum bound, with no maximum bound. For the third constraint we used a previous molecular time estimate (2.56 Ga) for the divergence of cyanobacteria from closest living relatives among eubacteria, and fixed the minimum (2.04 Ga) and maximum (3.08 Ga) values to the 95% confidence limits of that time estimate [ 30 ]. The fourth constraint for the origin of cyanobacteria was set at 2.7 Ga (minimum constraint) based on biomarker evidence for the presence of 2α-methylhopanes [ 86 ]. We did not consider the fossil record of cyanobacteria because the earliest indisputable fossils [ 52 ] are younger (2000 Ma) than the indirect evidence (GOE) for the presence of these oxygen-producing organisms. Older fossils of cyanobacteria are known but are disputed [ 52 , 101 ]. The use of these four alternative constraints for the origin of cyanobacteria considers most of the widely discussed hypotheses but does not rule out an origin prior to 2.7 Ga. Although the results of the four different calibrations are provided for comparison, our preferred calibration is the 2.3 (minimum) geologic calibration because it has the best justification (supporting evidence). Therefore, our summary time estimates for eubacteria, presented in the timetree (Fig. 3 ), use only this geologic calibration. For each of these calibration points, all five initial settings were applied, resulting in 15 and 20 analyses for the Archaebacteria and Eubacteria (respectively). The effects of the different initial settings on the analyses were found to be minimal. A 44% difference in the priors, in fact, generated a maximum 2.7% (average of all significant nodes) difference in the time estimates (fossil calibration point) in the archaebacteria and a maximum 3.5% (average of all significant nodes) difference in the eubacteria (molecular calibration point) [see Additional file 5 ]. Authors' contributions AF assembled and aligned the dataset and conducted initial analyses. FUB conducted phylogenetic and molecular clock analyses and co-drafted the manuscript. SBH directed the research and co-drafting the manuscript. Supplementary Material Additional File 1 Complete time estimation analyses. Estimated times for each node and calibration for Eubacteria and Archaebacteria. The node numbers refer to additional files 1 (eubacteria) and 2 (archaebacteria). Click here for file Additional File 2 Eubacteria tree. Phylogenetic tree of eubacteria (ME; α = 0.94). Node numbers assigned during the time estimation analyses are represented in italics. Click here for file Additional File 3 Archaebacteria tree. Phylogenetic tree of archaebacteria (ME; α = 1.20). Node numbers assigned during the time estimation analyses are represented in italics. Click here for file Additional File 4 Prior distribution values. Mean of the prior distribution for the rate of molecular evolution of the ingroup root node (rtrate) in Eubacteria and Archaebacteria. Click here for file Additional File 5 Percentage difference. Divergence time estimates and percentage difference due to different ingroup root constraints used under each calibration point. Node numbers refer to additional file 2 (eubacteria) and additional file 3 (archaebacteria). Click here for file | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC533871.xml |
545071 | Chromosomal duplications and cointegrates generated by the bacteriophage lamdba Red system in Escherichia coli K-12 | Background An Escherichia coli strain in which RecBCD has been genetically replaced by the bacteriophage λ Red system engages in efficient recombination between its chromosome and linear double-stranded DNA species sharing sequences with the chromosome. Previous studies of this experimental system have focused on a gene replacement-type event, in which a 3.5 kbp dsDNA consisting of the cat gene and flanking lac operon sequences recombines with the E. coli chromosome to generate a chloramphenicol-resistant Lac- recombinant. The dsDNA was delivered into the cell as part of the chromosome of a non-replicating λ vector, from which it was released by the action of a restriction endonuclease in the infected cell. This study characterizes the genetic requirements and outcomes of a variety of additional Red-promoted homologous recombination events producing Lac+ recombinants. Results A number of observations concerning recombination events between the chromosome and linear DNAs were made: (1) Formation of Lac+ and Lac- recombinants depended upon the same recombination functions. (2) High multiplicity and high chromosome copy number favored Lac+ recombinant formation. (3) The Lac+ recombinants were unstable, segregating Lac- progeny. (4) A tetracycline-resistance marker in a site of the phage chromosome distant from cat was not frequently co-inherited with cat . (5) Recombination between phage sequences in the linear DNA and cryptic prophages in the chromosome was responsible for most of the observed Lac+ recombinants. In addition, observations were made concerning recombination events between the chromosome and circular DNAs: (6) Formation of recombinants depended upon both RecA and, to a lesser extent, Red. (7) The linked tetracycline-resistance marker was frequently co-inherited in this case. Conclusions The Lac+ recombinants arise from events in which homologous recombination between the incoming linear DNA and both lac and cryptic prophage sequences in the chromosome generates a partial duplication of the bacterial chromosome. When the incoming DNA species is circular rather than linear, cointegrates are the most frequent type of recombinant. | Background The Red recombination system of bacteriophage λ promotes efficient double strand break repair/recombination. An Escherichia coli strain in which RecBCD has been genetically replaced by Red exhibits greatly elevated levels of recombination between its chromosome and short linear double-stranded DNA species sharing sequences with the chromosome [ 1 ]. In previous studies, we have characterized a recombination event, pictured in Figure 1A , in which a 3.5 kbp dsDNA consisting of the cat gene and flanking lac operon sequences recombines with the E. coli chromosome to generate a chloramphenicol-resistant Lac- recombinant [ 2 - 4 ]. The dsDNA was delivered into the cell as part of the chromosome of a non-replicating λ vector, from which it was released by the action of the PaeR7 restriction endonuclease in the infected cell. Formation of Lac- recombinants was found to depend upon red and the bacterial recombination genes recA , recF , recO , recR , recQ , ruvAB , and ruvC . Large numbers of chloramphenicol-resistant Lac+ recombinants were generated in these crosses as well. In this study, we characterize the Lac+ recombinants and the processes which generate them. Most appear to arise from events in which homologous recombination between the incoming DNA and the chromosome generates a partial duplication of the bacterial chromosome. Figure 1 λ lac :: cat variants A. Recombination between a 3.6 kbp linear dsDNA fragment and lac genes in the bacterial chromosome. A non-functional lacZ :: cat allele is generated by the recombination event pictured. The linear DNA is released from the chromosome of a non-replicating λ phage, by the action of PaeR7 restriction endonuclease in the infected cell. The lac genes are shown as oriented in the conventional E. coli map. Their transcription is from right to left; replication forks travel through them from left to right. B. Structures of the cat substitutions in the λ phages used in this study. White bars represent cat and adjacent sequences from Tn9 (1 kbp). Colored bars represent lac sequences (1.3 kbp or 40 bp). PaeR7 sites are represented by P. All the substitutions replace the same λ sequences, bp 23,135–33,498, with the indicated sequences and an additional 1.9 kbp of sequence from phage P22 gene 9 (not shown in the diagrams). λ sequences replaced by the substitutions include the attachment site, int , xis , exo , bet , gam, and cIII . The substitutions result in net deletions of 5.0–7.6 kbp from the λ chromosome. Results and discussion Genetic requirements for Lac+ recombinant formation Data presented in Table 1 show that formation of Lac+ recombinants depends upon the same recombination functions as the Lac- recombinants: recA , recF , recO , recR , recQ , ruvAB , and ruvC . Deletion of recG increased the production of both Lac+ and Lac- recombinants. Lac+ recombinants accounted for 10–60% of the total chloramphenicol-resistant offspring of the crosses. These results strongly suggest that formation of the Lac+ recombinants takes place via homologous recombination, though they do not rule out the possibility that non-homologous end joining, or other varieties of "illegitimate" recombination, might be involved as well. Table 1 Genetic requirements of Lac+ recombinant formation Strain Relevant genotype Recombination % Lac+ recG+ background 507 wild type 1.00 ± 0.16 41 527 recA 0.01 ± 0.01 42 638 recQ 0.05 ± 0.02 37 540 ruvAB 0.34 ± 0.06 30 523 ruvC 0.22 ± 0.08 29 606 sulA 1.00 ± 0.31 40 608 sulA lexA 0.51 ± 0.01 44 615 sulA recF 0.12 ± 0.09 51 614 sulA recO 0.06 ± 0.00 42 625 sulA recR 0.03 ± 0.02 61 recGΔ background 554 wild type 1.00 ± 0.13 28 532 recA 0.01 ± 0.01 37 639 recQ 0.05 ± 0.03 46 559 ruvAB 0.34 ± 0.25 41 555 ruvC 0.08 ± 0.02 10 607 sulA 1.00 ± 0.14 37 609 sulA lexA 0.29 ± 0.12 22 628 sulA recF 0.03 ± 0.03 58 626 sulA recO 0.01 ± 0.01 21 627 sulA recR 0.02 ± 0.01 34 Bacterial strains were grown to log phase and infected at a multiplicity of 10 with λ lac :: cat819 nin5 . Infected cells were aerated for 1 hour at 37°C, then plated on LB agar and LB agar supplemented with chloramphenicol, isopropylthiogalactopyranoside, and X-Gal. Recombination frequencies represent the ratio of chloramphenicol-resistant recombinants to total cell titer, normalized to the ratio of the strain at the top of each group of strains. Means and standard errors for at least three measurements are shown. Actual frequencies were 507, 2.49%; 606, 2.50%, 554, 7.76%; 607, 7.81%. The frequencies of Lac- recombinants in these crosses were published previously (Poteete and Fenton, 2000). The reason for considering mechanisms other than homologous recombination in the formation of the Lac+ recombinants is because of the expectation that some of the phage-borne lac :: cat sequences would be attached to phage sequences. Heitman et al. [ 5 ] showed that double strand breaks generated by a restriction endonuclease in vivo are rapidly repaired by DNA ligase. The results of previous physical studies with other substituted λ phages cut in vivo by PaeR7 lead to the expectation that the λ lac :: cat819 chromosome would be uncut, or only singly cut, much of the time in the infected cell [ 2 , 6 ]. Preliminary characterization of the Lac+ recombinants formed in cells which were wild-type for all the recombination functions revealed that most, possibly all, were unstable. When streaked on plates containing chloramphenicol and X-Gal, they segregated Lac- (colorless) progeny at variable frequencies. Southern gel analysis of chromosomal DNA from unstable Lac+ recombinants revealed that some of them were multiploid for lacZ (not shown). When the lac :: cat dsDNA was delivered into the cell by a λ vector bearing a tetracycline resistance determinant (Δ nin :: tet859 – described below) neither the Lac+ nor Lac- recombinants acquired tetracycline resistance. These findings suggest that the process which formed the recombinants did not involve the entire phage chromosome. A baseline level of Lac+ recombinants was to be expected in these crosses. The recombination event pictured in Figure 1A , occurring in a cell with a pre-existing duplication of the lac locus, will produce a Lac+, chloramphenicol-resistant recombinant. Data presented below, in which simpler lac :: cat recombining substrates generate Lac+ recombinants at a frequency approximately 1000-fold lower than Lac-, suggest the frequency of spontaneous lac duplications in Red+ but otherwise wild type E. coli is approximately 10 -3 , consistent with estimates of spontaneous duplication frequency in Salmonella made by Roth et al. [ 7 ]. The Lac+ recombinants formed in the crosses summarized in Table 1 occurred at a much higher frequency – approaching 5% of the infected cells – suggesting that pre-existing chromosomal duplications were not involved in the generation of the majority of the Lac+ recombinants. Apparently, normal Red-mediated homologous recombination between the phage and bacterial chromosomes duplicated or amplified sequences in the bacterial chromosome. Multiplicity effects The crosses described above were done by infecting log phase host cells grown in rich medium. Such cells contain multiple copies of their chromosome. Complications might arise if the lac :: cat segment were to recombine with more than one chromosome at a time. Further complicating interpretation of the experiment, the cells were infected at a multiplicity of 10 phages per cell. To reduce the complexity of the system, we switched to a cross procedure involving low multiplicity infection (0.1 phage per cell) of stationary phase cells. Presumably, under these conditions, most of the events producing chloramphenicol-resistant recombinants take place between single copies of the lac :: cat segment and single copies of the bacterial chromosome. As shown in Table 2 , the low copy infections produced substantial numbers of Lac+ recombinants, though fewer than the high copy infections (4–7% of the chloramphenicol-resistant progeny versus 28–40%). The Lac+ colonies produced in this way were still unstable, segregating Lac- progeny (colorless colonies) when restreaked, or suspended and plated, on medium containing chloramphenicol and X-Gal (not shown). Table 2 Recombinant formation by λ lac::cat variants phage description host genotype a % recombinant % Lac+ 181 λ lac :: cat819 cut both sides 507 wild 0.54 ± 0.06 7 197 λ lac :: cat930 cut right 507 wild 0.92 ± 0.04 10 198 λ lac :: cat931 cut left 507 wild 2.8 ± 0.07 0.2 196 λ lac :: cat929 cut neither side 507 wild 0.055 ± 0.001 48 196 λ lac :: cat929 cut neither side 839 hsdR 0.039 ± 0.002 29 181 λ lac :: cat819 cut both sides 554 recG 0.93 ± 0.08 7 197 λ lac :: cat930 cut right 554 recG 3.7 ± 0.04 13 198 λ lac :: cat931 cut left 554 recG 4.6 ± 0.2 0.2 196 λ lac :: cat929 cut neither side 554 recG 0.10 ± 0.03 47 186 λ lac :: cat819 nintet 507 wild 0.28 ± 0.04 4 186 λ lac :: cat819 nintet 849 pae 0.024 ± 0.003 45 186 λ lac :: cat819 nintet 842 pae hsdR 0.027 ± 0.005 49 186 λ lac :: cat819 nintet 850 pae recA 0.001 ± 0.002 95 186 λ lac :: cat819 nintet 856 pae red 0.008 ± 0.008 52 208 λ cat988 nintet right flank only 849 pae 0.008 ± 0.003 58 209 λ cat989 nintet left flank only 849 pae 0.003 ± 0.0002 100 211 λ cat995 nintet no flank 849 pae 0.004 ± 0.0002 100 195 λ lac :: cat921 short flank 507 wild 0.002 ± 0.001 30 195 λ lac :: cat921 short flank 554 recG 0.002 ± 0.0002 50 Bacterial strains were grown to stationary phase without active aeration, and infected at a multiplicity of 0.1 with the indicated phages. Recombination frequencies represent the percentages of infected cells which became chloramphenicol-resistant recombinants; % Lac+ refers to the percentage of chloramphenicol-resistant recombinants which were Lac+. Means and standard errors for at least three measurements are shown. a. All strains have the Δ (recC - ptr - recB - recD) :: P tac - gam - bet - exo - pae - cI822 substitution, except as noted. Recombinant formation by single-cut variants of λ lac :: cat To examine the dependence of Lac- and Lac+ recombinant formation on the structure of the DNA substrate, we constructed variants of λ lac :: cat , which are diagrammed in Figure 1 . λ lac :: cat819 , the progenitor of this series, has two PaeR7 sites. Upon infecting a host cell bearing the Δ recBCD :: Ptac - gam - bet - exo - pae - cI substitution, it injects its chromosome, which circularizes. Expression of its lytic genes, including those necessary for phage DNA replication, is blocked by the action of cI repressor. Cutting of its chromosome by the PaeR7 restriction endonuclease in the infected cell releases a 3.5 kbp linear dsDNA, consisting of the cat gene and 1.3 kbp flanks of lac sequences on the right and left. (In this and subsequent descriptions, "right" refers to the upstream, or 5' end of the lac operon). The ends of the flanking lac sequences match precisely their counterparts in the chromosome. λ lac :: cat930 and λ lac :: cat931 have only single PaeR7 sites, on the right and left, respectively. Cutting by PaeR7 in the infected cell should produce large linear dsDNAs related to the lac :: cat819 fragment, but with long tails of non-homologous DNA extending from the left and right sides, respectively. Interestingly, both of these single-cut phages produce recombinants more efficiently than the double-cut λ lac :: cat819 , in both recG+ and Δ recG backgrounds (Table 2 ). The right-cut λ lac :: cat930 produces relatively more Lac+ recombinants, whereas the left-cut λ lac :: cat931 produces fewer. The frequency of Lac+ λ lac :: cat931 recombinants, 0.2%, is roughly consistent with the expected frequency of pre-existing lac duplications in the population of infected cells, suggesting that λ lac :: cat931 does not generate duplications, but λ lac :: cat930 does. We cannot rule out the possibility that some of the 0.2% Lac+ recombinants made by λ lac :: cat931 are cointegrates. Cointegrate formation is discussed further in the next section. Recombinant formation by circular λ lac :: cat λ lac :: cat929 has no PaeR7 site. Its chromosome should stay circular in the infected Δ recBCD :: Ptac - gam - bet - exo - pae - cI cell. Surprisingly, it produces chloramphenicol-resistant recombinants only 10-fold less efficiently than λ lac :: cat819 . Unlike the PaeR7-cuttable λ lac :: cat variants, λ lac :: cat929 also efficiently produces weakly chloramphenicol-resistant microcolonies (not counted in the data presented in Table 2 ). The provenance of the microcolonies is readily understandable. The uncuttable λ lac :: cat929 chromosome cannot replicate; it also is not destroyed, and should produce chloramphenicol acetyl transferase in the cell in which it resides. The infected cell, and perhaps its descendants for one or two generations, might be able to divide in the presence of low-concentration chloramphenicol. Approximately half of the strongly chloramphenicol-resistant recombinants generated by the uncuttable phage λ lac :: cat929 are Lac- (Table 2 ). This observation suggests that they are "legitimate" recombinants, in which the single chromosomal copy of lacZ is replaced by lacZ :: cat . These recombinants were unexpected. All Red-mediated recombination events are thought to involve at least one linear partner [ 6 ]. How, then, can circular λ lac :: cat929 recombine with the circular E. coli chromosome? Three possible explanations were considered. (1) Some of the λ lac :: cat929 chromosomes might be cut by the EcoK restriction endonuclease. The infected cells contained a functional EcoK restriction-modification system. λ lac :: cat929 was grown in a similarly EcoK+ host, and therefore was presumably EcoK-modified. However, if the modification were not complete, there might be residual EcoK restriction activity, resulting in 10% of the phage chromosomes being cut. (The phage chromosome contains five EcoK sites. If each site were 98% methylated, then approximately one in ten phage chromosomes would have a single unmethylated site). (2) The production of the recombinants might not be Red-mediated. (3) The linear partner might be a broken bacterial chromosome. To test the first explanation, we replaced the gene encoding the EcoK restriction endonuclease, hsdR , with a tetracycline resistance determinant. This replacement had little effect on the formation of recombinants by λ lac :: cat929 (Table 2 ). To explore further the origins of the recombinants formed between circular phage chromosomes and the bacterial chromosome, we constructed a series of bacterial strains bearing variants of the Δ recBCD :: Ptac - gam - bet - exo - pae - cI substitution lacking pae . In these hosts, no cutting takes place at PaeR7 sites, and so all of the lac :: cat -substituted phages should remain circular. The results of crosses in these strains are shown in Table 2 . As in the Pae+ crosses, elimination of hsdR in the Pae- background did not reduce the yield of recombinants. In the Pae- background, elimination of recA reduced the yield of recombinants twenty-fold. The great majority of the residual recombinants were Lac+, suggesting that their formation was by a process not necessarily involving homologous recombination. Elimination of red reduced the yield of recombinants three-fold, showing that most, but not all of the recombination events were Red-mediated. As in the Red+ cross, approximately half of the recombinants were Lac-, indicating that formation of both Lac- and Lac+ recombinants was Red-mediated in approximately the same proportions. Thus, the second hypothesis mentioned above, that the recombinants are formed by a Red-independent process, is not supported. The third hypothesis, that the linear partner might be a broken bacterial chromosome, is thus favored. It additionally seems reasonable, given that spontaneous double-strand breaks are frequent in E. coli [ 8 ]. We sought to detemine what proportion of the circular phage-by-chromosome recombinants were cointegrates, by testing for co-inheritance of cat and a tetracycline resistance-conferring element present at a remote location in the phage chromosome. Among the Lac+ recombinants produced in the Pae+ host TP507 (Table 2 ), only 2 out of 39 tested were found to be tetracycline-resistant; none of the 40 Lac- recombinants we tested was tetracycline-resistant. This result was consistent with our earlier finding that the great majority of recombinants formed in the high mulitiplicity infections of log phase cells acquired only the homology-flanked cat segment of the infecting λ chromosome. In contrast, in the Pae- host, 27 of 55 Lac- recombinants, and 23 of 25 Lac+ recombinants, were additionally tetracycline-resistant. These observations indicate that the most frequent Red-generated product of recombination between the uncut phage chromosome and the bacterial chromosome is a cointegrate. Cointegrates could in principle be formed by four different single reciprocal recombination events between the circular λ lac :: cat and bacterial chromosomes. These events could involve the right side lac sequences or the left side lac sequences, as diagrammed in Figure 2 . A third event, not shown, involves recombination between the cI genes borne by both the phage and bacterial chromosomes (the latter at the substituted recBCD locus). A fourth event, also not shown, involves recombination between λ genes and homologues in cryptic prophages in the E. coli chromosome (discussed below). As suggested in the figure, cointegrates formed by recombination in the right-side lac flank are phenotypically Lac-, while recombination in the left-side lac flank (or other loci) forms Lac+ recombinants. To test this idea, we constructed variants of λ lac :: cat missing either or both lac flanks; they are diagrammed in Figure 1 . As shown in Table 2 , in the non-cutting Pae- host, only the right flank-containing phage chromosome forms Lac- recombinants. The left-flank and no-flank phages form only Lac+ recombinants. Figure 2 Cointegrates formed by recombination between circular λ cat and the bacterial chromosome. A. The cointegrate formed by recombination between the chromosome and λ cat989 , which bears only the left-side lac flank, leaves the chromosomal lacZ gene intact. B. The cointegrate formed by recombination between the chromosome and λ cat988 , which bears only the right-side lac flank, disrupts the chromosomal lacZ gene. Short-flank recombination The ability of the λ Red system to promote recombination events involving short sequence homologies makes it particularly useful for genetic engineering. Yu et al. [ 9 ] reported that a linear cat cassette with 1000 bp homologous flanks was only 10-fold more efficient than one with 40 bp flanks. Their experiments were done with cells which had been subjected to heat shock and electroporation. To test the efficiency of short-flank recombination under less extreme conditions, we constructed a short-flank λ lac :: cat variant. The fragment released by PaeR7 from the chromosome of λ lac :: cat921 consists of the cat gene flanked by 40 bp sequences corresponding exactly to the terminal 40 bp at each end of the 1.3 kbp flanks of λ lac :: cat819 . As shown in Table 2 , λ lac :: cat921 exhibits a several hundred-fold lower efficiency of recombinant formation than its long-flank counterpart, in both recG+ and Δ recG backgrounds. λ lac :: cat921 was similarly inefficient in log phase cells (data not shown). These observations suggest that short-flank recombination may not be a significant activity of the Red system in nature. However, they do not speak to the question of whether long flanks work better because they provide a larger homology target for synapsis, or because they provide non sequence-specific protection, perhaps delaying exonucleolytic degradation of the recombining sequences long enough to permit recombination to take place. Recombination with inverted partners The observation that Lac+ recombinants are formed efficiently by the right side-cut λ lac :: cat930 , but not by the left side-cut λ lac :: cat931 (Table 2 , discussed above) raised questions concerning the sequence determinants of this directionality. To explore these questions, we constructed locally inverted variants of the bacterial chromosome and the phages, as diagrammed in Figure 3 . Figure 3 Construction of inversions. A. A plasmid containing the lac operon and some flanking sequences was constructed. The SphI site was converted to a BsrG1 site, the large BsrG1 lac segment was inverted, and the inverted operon was crossed into the bacterial chromosome. Sequence segments between certain boundaries – the inversion endpoints, the ends of the lac flanks, and the 12 bp of lacZ replaced by the cat gene – are given letter designations to simplify representations of parent and recombinant chromosomes in crosses described below. B. The structure of λ lac :: cat930 is represented as a linear map. T designates the left side flanking λ sequences shared by plasmids and phages, consisting of λ bp 22346–23134. 9 designates phage P22 bp 17725–15955. R is the right side λ flank, bp 33502–34504. P is the rest of the phage λ chromosome. The lac :: cat segment was inverted relative to the flanking sequences in plasmids pTP930 and 931 (generating pTP1032 and 1033), and the inverted elements were crossed into λ. The structure of λ lac :: cat1033 is illustrated. The strategy for inverting the chromosomal lac operon consisted of five steps (detailed in the Methods section and Table 4 ): (1) deletion of the entire operon from the chromosome, replacing it with the cat gene; (2) cloning the cat gene from the deletion mutant, along with flanking chromosomal sequences; (3) using the cloned, plasmid-borne flanking sequences, and Red-mediated gap repair, to clone the lac operon in a plasmid; (4) inverting the lac operon, relative to its flanking sequences, in the plasmid; (5) replacing the Δ lac :: cat chromosomal allele with the plasmid-borne lac - inv allele. Table 4 Bacterial strains used in this study Strain Relevant Genotype Source, reference, or construction AB1157 Background a KM22 Δ( recC-ptr-recB-recD ):: P lac -gam-bet-exo-kan Murphy, 1998 KM32 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-cat Poteete et al., 1999 TP507 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 " TP523 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 ruvC53 eda ::Tn 10 Poteete and Fenton, 2000 TP527 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 Δ( srl-recA ) 306 :: Tn10 " TP532 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 recG258 :: kan Δ( srl-recA ) 306 ::Tn 10 " TP540 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 Δ ruvAB6203 :: tet " TP554 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 Δ recG6202 " TP555 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 Δ recG6202 ruvC53 eda :: Tn10 " TP559 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 Δ recG6202 Δ ruvAB6203 :: tet " TP606 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 Δ sulA6209 :: tet " TP607 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 Δ recG6202 Δ sulA6209 :: tet " TP608 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 Δ sulA6209 :: tet lexA71 :: Tn5 " TP609 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 Δ recG6202 Δ sulA6209 :: tet lexA71 :: Tn5 " TP614 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 Δ sulA6209 :: tet recO1504 :: Tn5 " TP615 Δ( recC-ptr-recB-recD) :: P tac -gam-bet-exo-pae-cI822 Δ sulA6209 :: tet recF400 :: Tn5 " TP625 Δ (recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 Δ sulA6209 :: tet recR252 :: Tn10–9kan " TP626 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 Δ recG6202 Δ sulA6209 :: tet recO1504 :: Tn5 " TP627 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 Δ recG6202 Δ sulA6209 :: tet recR252 :: Tn10–9kan " TP628 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 Δ recG6202 Δ sulA6209 :: tet recF400 :: Tn5 " TP638 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 Δ recQ6216 :: tet " TP639 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 Δ recG6202 Δ recQ6216 :: tet " TP839 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 Δ hsdR :: tet TP507 × PCR product b TP842 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-cI978 Δ hsdR :: tet TP849 × P1(TP839) TP849 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-cI978 KM22 × pTP978 linear b TP850 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-cI978 Δ recA6207 :: tet TP849 × P1(TP796) TP856 Δ( recC-ptr-recB-recD ):: P tac -gam-cI996 KM32 × pTP996 linear b MG1655 Background TP798 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-cat MG1655 × P1(KM32) TP829 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 TP798 × pTP822 linear b TP832 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-bla979 TP798 × pTP979 linear b TP872 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-bla979 Δ lac :: cat TP832 × PCR product b TP890 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 Δ lac :: cat TP829 × P1(TP872) TP894 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 lac-inv TP890 × pTP1034 linear b TP896 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-bla979 lac-inv TP872 × pTP1034 linear b MDS12 Background c TP750 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 Poteete, 2004 TP796 Δ( recC-ptr-recB-recD ):: P tac -gam-bet-exo-pae-cI822 Δ recA6207 :: tet " a. These strains are presumed to bear all the other genetic markers of AB1157 (Bachmann BJ: Derivations and genotypes of some mutant derivatives of Escherichia coli K-12. In: Escherichia coli and Salmonella: cellular and molecular biology . Edited by Neidhardt FC, III RC, Ingraham JL, Lin ECC, Low KB, Magasanik B, Reznikoff WS, Riley M, Schaechter M, Umbarger HE. Washington, D.C.: ASM Press; 1996: 2460–2488). b. Plasmids and PCR products used in the construction of some of the strains are described in the Methods section. c. MDS12 is a derivative of wild type E. coli strain MG1655 bearing 12 large deletions (Kolisnychenko et al., 2002). The single-cut lac :: cat930 and lac :: cat931 alleles were constructed by replacing the left-side and right-side PaeR7 sites, respectively, of pTP819, with XbaI sites. To produce inverted derivatives of these two alleles, the plasmids pTP930 and pTP931 were digested with XbaI and XhoI (a PaeR7 isoschizomer); lac :: cat inserts and bla - ori backbone fragments from the two plasmids were exchanged. The inverted alleles were then crossed into phage λ, as described in the Methods section. In constructing the chromosomal inversion, we switched genetic backgrounds, from the AB1157-derived strains with which most recombination studies have been done, to MG1655, the sequenced wild type E. coli K-12 [ 10 ]. In the MG1655 background, the same directionality of Lac+ recombinant formation was observed. The efficiencies of recombination of the MG1655 derivatives with both λ lac :: cat930 and λ lac :: cat931 , and the efficiency of Lac+ recombinant formation by λ lac :: cat930 , were slightly elevated relative to those in the corresponding AB1157-derived strain. The eight combinations of normal and inverted phages and bacteria (left- and right-cut phages, and their inverted counterparts, in normal and lac -inverted E. coli ) were tested for Lac+ recombinant formation. The results are shown in Figure 4 . Only two of the eight combinations produced large numbers of Lac+ recombinants: lac :: cat930 (right-cut, normal orientation) in lac -wild type, and lac :: cat1033 (left-cut, inverted orientation) in lac - inv . Figure 4 Normal and inverted phage-by-chromosome crosses. Phages bearing the indicated lac :: cat alleles were crossed with wild type (TP829) and lac - inv (TP894) bacteria. Sequence segments are designated as shown in Figure 3. Bacterial sequences are colored blue, phage sequences yellow, and the cat gene white. The percentages of Lac+ bacteria among the chloramphenicol-resistant recombinant progeny are indicated, as the means and standard errors from three measurements. Crosses were done by low multiplicity infection of stationary phase cells. The total yields of chloramphenicol-resistant recombinants ranged from .02 to .04 per infected cell. Recombination with electroporated linear DNA species The linear DNA produced by cutting of the λ lac :: cat phages at a single site is large and complex relative to the lac :: cat segment itself. To reduce the complexity, we generated linear DNA species which correspond to shortened versions of the single-cut phage chromosomes. The DNA species were generated by transferring, into a conditionally-replicating vector, parts of the plasmids previously used to introduce the lac :: cat substitutions into λ. The vector can replicate only in a host which supplies the plasmid R6K Pir protein [ 11 ]. Details of the plasmid constructions are given in the Methods section. Plasmids bearing the cloned lac :: cat and flanking sequences from λ were digested with restriction enzymes, and the DNA fragments were introduced into bacteria by electroporation. The results of some of these crosses are shown in Figure 5 . The electroporated DNAs faithfully mimicked their single-cut phage counterparts: only the two crosses corresponding to the high Lac+ producer crosses of Figure 4 generated high proportions of Lac+ recombinants. Figure 5 Normal and inverted linear DNA-by-chromosome crosses. Plasmids bearing the indicated lac :: cat alleles (corresponding to the phages in Figure 4; the numbers of the plasmids bearing these alleles are given in the Methods section) were digested with BamHI and XhoI (PaeR7 isoschizomer), and electroporated into wild type and lac - inv bacteria (strains TP832 and TP896). Sequence segments are labeled as in Figure 3. The percentages of Lac+ bacteria among the chloramphenicol-resistant recombinant progeny are indicated, as the means and standard errors from two measurements. The total yields of chloramphenicol-resistant recombinants ranged from 20,000 to 200,000 per pmol. The observation that only DNAs with sequences designated "T-9" on the left side generated many Lac+ recombinants (Figure 5 ) led us to try a linear species in which T was detached by digestion with a restriction enzyme (KpnI). Removal of the T segment greatly reduced the ability of the linear DNA to generate Lac+ recombinants (Table 3 ). A BLAST search of the E. coli genome for sequences related to T revealed four loci in MG1655 with close matches to T's leftmost 200 bp, which constitute the C-terminal third of the λ tfa gene. The four bacterial sequence segments are located in cryptic prophages: ybcX and an unnamed gene fragment in prophage DLP12, tfaR in prophage Rac, and tfaQ in prophage Qin. The locations and orientations of the first three of these are such as to permit a linear DNA species to recombine both with them and with the lac locus. Such an event is diagrammed in Figure 6 , in which the three recombining bacterial loci are designated I, II, and III; closely related events have been extensively documented in other studies [ 7 ]. In the pictured cross, recombination between the linear DNA and the bacterial chromosome at lac and I generates a recombinant bearing intact lac , lac :: cat , a duplication of all sequences between lac and I, a smaller duplication of the left lac flank, and an insertion of λ sequences unrelated to the cryptic prophages (see Figure 6c ). Table 3 Recombinant formation by variant linear DNA species Plasmid Restriction digest Recombining linear species % Lac+ 1019 Bam + Xho T9ACB 27 ± 2 1019 Bam + Xho + Kpn 9ACB 0.6 ± 0.2 1047 Bam + Xho T9ACB(Δχ) 27 ± 2 1052 Bam + Xho TACB 14 ± 0.1 1055 Sph + Xho GCB 95 ± 3 1056 BsrG + Xho ACF 99 ± 0.5 1057 Bam + Xho TCB 96 ± 2 1018 Bam + Xho ACB 0.2 ± 0.04 The indicated DNAs were introduced into TP832 by electroporation. DNA segments are labeled as in Figures 3–5. B(Δχ) is a variant of the 1.3 kbp right-side lac flank from which the 396 bp immediately adjacent to the cat segment (C) have been deleted, eliminating a Chi site. Figures indicate the means and standard errors from two measurements. Numbers in the first row are from Figure 5, top left, and are repeated here for comparison. Figure 6 Model for Lac+ recombinant formation in linear DNA-by-chromosome crosses. (a) Recombination between the B sequence segments generates two broken chromosome arms. In the diagram, which is not to scale, I, II, and III designate the cryptic prophage tfa homologues ybcX in the DLP12 prophage remnant at 0.58 Mbp in the MG1655 chromosome, a closely linked unnamed pseudogene in the same element, and tfaR in the Rac prophage at 1.43 Mbp, respectively. The lac operon is at 0.36 Mbp. (b) The T segment-ended arm can recombine with any of the three T segments in an unbroken copy of the chromosome, generating a Lac+ chloramphenicol-resistant recombinant. Short arrows indicate the primers used to demonstrate the unique junction formed in the type I recombinant. (c) The recombinant contains two repeated sequence segments, a short one consisting only of the A segment (1.3 kbp), and a long one (0.2 or 1.1 Mbp) consisting of bacterial sequences from B through T. The hypothesis that the high-frequency Lac+ chloramphenicol-resistant recombinants are generated by homologous recombination with cryptic prophage sequences in the chromosome predicts that such recombinants would not be generated in an E. coli strain in which the cryptic prophages are deleted. To test this prediction, we electroporated linear lac :: cat930 DNA into TP750, a Red+ derivative of MDS12, the reduced-genome E. coli strain constructed by Kolisnychenko et al. [ 12 ]. In this cross, Lac+ recombinants constituted only 0.06% (average of 6 measurements) of the total chloramphenicol-resistant progeny, a frequency no higher than expected for spontaneous pre-existing duplications in the bacterial chromosome. The hypothetical Lac+ chloramphenicol-resistant recombinant pictured in Figure 6 has some predicted properties which were confirmed experimentally. First, it is predicted to segregate Lac- recombinants at low frequency, and chloramphenicol-sensitive recombinants at high frequency, the results of recombination between the short and long duplicated segments, respectively. Overnight cultures of three Lac+ recombinants grown in the absence of selection were found to include Lac- chloramphenicol-resistant clones at an average frequency of 0.03%, and Lac+ chloramphenicol-sensitives at 30%. Second, it should be possible to demonstrate specific duplication junctions in each of the three types of recombinants by the use of PCR (see Figure 6c ). Primers were designed to this end, and employed in colony PCR with a collection of 12 Lac+ recombinants. Each of the 12 was found to have one of the three predicted junctions: seven were type I, two were type II, and three were type III. Examples are shown in Figure 8 . Figure 8 PCR products indicating specific duplication junctions. Colonies of Lac+ recombinants were tested by PCR as described in the Methods section. Lanes labeled S are standards (1 kb ladder, Invitrogen). Lanes 1, 2, and 3 are type I, II, and III recombinants, respectively, formed by electroporation of cells with the lac :: cat930 DNA species diagrammed in Figure 5. The sizes of the expected products are: type I-1272 bp, II-1026 bp, III-1872 bp. Lane 4 is a recombinant formed by electroporation of cells with the GCB DNA species diagrammed in Figure 7. The size of the expected product is 3032 bp. Lane 5 is a recombinant formed by electroporation of cells with the ACF DNA species diagrammed in Figure 7. The size of the expected product is 2765 bp. Lane 6 is a recombinant formed by infection of cells with λ lac :: cat819 . The expected size of the product is 3653 bp. Lanes 7–10 are wild type cells used as template in control PCRs with the primers used in lanes 1–3, 4, 5, and 6, respectively. We constructed several other linear DNA substrates to test the generality of the model in Figure 6 . The abilities of these substrates to generate Lac+ recombinants are shown in Table 3 . The first of these was a derivative of lac :: cat930 lacking its χ site. The χ site is located in the right lac flank, which is labeled "B" in Figures 3–6, close to the cat gene. The orientation of this χ site is such that it would be expected to interact productively with RecBCD enzyme approaching from the PaeR7- or Xho-generated right end of lac :: cat930 . While no activity of χ in these crosses was expected, as the bacteria lack RecBCD, the directionality of Lac+ recombinant formation was reminiscent of the directionality of χ-RecBCD interaction (see [ 13 ] for a review). The Δχ substrate was just as active as the χ + version, showing that χ does not contribute significantly to Lac+ recombinant formation. Similarly, a lac :: cat930 derivative (pTP1052) lacking its sequences derived from phage P22 was equally proficient at generating Lac+ recombinants. A derivative missing the left lac flank made Lac+ recombinants almost exclusively; the small number of Lac- recombinants in this case probably represent cointegrates made by uncut plasmid DNA in the fragment preparation. Plasmids pTP1055 and 1056 were constructed to test whether duplications of the type pictured in Figure 6 could be generated by Red-mediated recombination involving arbitrarily chosen sequences on either side of lac in the chromosome. As indicated in Table 3 , linear DNAs from these plasmids also generated Lac+ recombinants almost exclusively. The structures of the plasmids, chromosome, and expected recombinants are diagrammed in Figure 7 . That the expected duplications were in fact generated was demonstrated by the production of duplication-spanning products in PCR using the primers Ddi and Edi, described in the Methods section and represented schematically in Figure 7 . Examples are shown in Figure 8 . Figure 7 Red-generated duplications to the left and right of lac. Recombination between electroporated linear dsDNA species ACF (left) and GCB (right) generate duplications of chromosomal sequences to the left and right of lac , respectively, through a sequence of recombination events as pictured in Figure 6. (a) In the diagram, the first events are crossovers between the A or B lac segments, with crossovers between the associated F or G flanks pictured as occurring second (b). The same recombinants would also be formed if the orders were reversed, for example, if F recombined before A (not shown). Short arrows indicate the primers initiating DNA synthesis divergently from the D and E segments. (c) In the recombinants, the primers generate PCR products spanning the duplications, of 2765 and 3032 bp, respectively. Structures of the λ lac :: cat Lac+ recombinants The major class of Lac+ chloramphenicol-resistant recombinants formed in crosses involving infection by λ lac :: cat819 (cut left and right) and λ lac :: cat930 (cut right) behave like their counterparts generated by electroporation of linear dsDNAs into Red+ cells: they segregate Lac- chloramphenicol-resistant clones at low frequency, and Lac+ chloramphenicol-sensitives at high frequency (data not shown). In addition, their formation also depends upon the presence of cryptic prophages in the chromosome: λ lac :: cat819 was found to produce Lac+ chloramphenicol-resistant recombinants as only 0.13% (average of six measurements) of the total chloramphenicol-resistant progeny in crosses with TP750. These properties suggested both kinds of recombinant might have the same structures as well, but PCR tests of 50 of the phage-generated recombinants with the primers used to demonstrate type I, II, and III recombinants (Figure 8 ) were negative (data not shown). Further computer analysis revealed a cryptic prophage sequence which could recombine with the phages, but not with the shorter, plasmid-derived linear DNAs, producing recombinants by the mechanism drawn in Figure 6 : a 3.5 kbp patch of DLP12 closely matching sequences in the vicinity of the λ cos site. This site is located immediately to the left of the tfaD locus in the chromosome. Recombinants formed by crossing over at this site and at the right lac flank would be expected to contain duplications of bacterial sequences identical to those of type I recombinants (pictured in Figure 6 ), but to contain a larger part of the phage chromosome as well. PCR tests of six λ lac :: cat819 and six λ lac :: cat930 recombinants with primers designed to demonstrate cos recombinant junctions showed that all twelve had them. An example is shown in Figure 8 . The predominance of cos recombinants over tfa recombinants among the phage-generated Lac+ recombinants is to be expected. First, λ Exo, traveling in from the end of the long left-side non-homologous tail of λ lac :: cat930 encounters cos before tfa ; cos therefore presumably has a kinetic advantage. Second, the bacterial cos homology patch is significantly larger than the tfa homologies. A third possible advantage of cos is that, at least in some parts of the phage lytic cycle, it is the site of a double-strand break for the DNA encapsulation step of phage assembly. We expected that cos would be unbroken almost all the time in our crosses. The λ chromosome is linear at the time of injection, but is rapidly circularized by annealing and ligation [ 14 ]. The other parts of the phage lytic cycle are inhibited by the presence of cI repressor in the infected cells. Even so, the hypothesis that Red sometimes manages to gain access to cos ends in these crosses remains plausible; particularly so because Red is present in the cell at all times, whereas, in a normal infection, Red is not present until its genes are expressed from the phage chromosome. An unexpectedly significant presence of cos ends in the infected cells could also help explain the otherwise surprisingly high frequency of chloramphenicol-resistant recombinants seen in crosses involving phage chromosomes not cut by restriction endonucleases, described above. One aspect of Lac+ recombinant formation by λ lac :: cat819 is not explained by the model of Figure 6 . The λ lac :: cat819 contains two PaeR7 sites. Cutting by PaeR7 should release a linear dsDNA with lac flanks and no attached non- lac DNA. This DNA species did not generate Lac+ recombinants (above the background of pre-existing duplications) when electroporated directly into cells (Table 3 ). How does it apparently do so in the infected cells? As discussed above, it is expected that the λ lac :: cat819 chromosome would be uncut, or only singly cut, much of the time in the infected cell. If it recombined at a time at which it was cut only on the right, the type of recombinant pictured in Figure 6 could be formed. In this recombinant, an uncut and unmodified PaeR7 site would sit in the chromosome. Presumably, the presence of this site would make the recombinant unstable, initially; but the PaeR7 site might eventually become modified, that is, escape restriction. The pae -expressing strains employed in these experiments restrict plaque formation by single PaeR7 site-bearing λ phages only approximately 5-fold (unpublished data). Conclusions The bacteriophage λ Red recombination system is of general interest for two main reasons. First, it is an intensively characterized and relatively simple system, which serves as a model for studies of homologous recombination [ 15 - 17 ]. Second, it has emerged as a powerful tool for genetic engineering in gram-negative enteric bacteria [ 1 , 9 , 18 - 24 ]. It was therefore of interest to elucidate the structures of the previously observed but unexplained Lac+ recombinants formed by recombination between lac :: cat -bearing λ phages and the bacterial chromosome, as well as the mechanism of their formation. Our studies uncovered a diversity of recombinant structures, including complex duplications and cointegrates. Recombination between λ and cryptic prophage sequences in the bacterial chromosome was found to be the most significant mechanism generating Lac+ recombinants. All of the Lac+ recombinants could be generated by homologous recombination events of types which have been previously described. In particular, there was no evidence for end-joining or other non-homologous recombination events. Perhaps the most surprising finding was the high frequency with which large duplications in the bacterial chromosome – up to 1 Mbp – could be generated by the Red system. Methods Bacteria E. coli strain DH5α (λ pir) [ 11 ] was used for propagation of plasmids bearing the R6K origin. Other strains used in this study are described in Table 4 . The Δ hsdR :: tet allele in TP842 was constructed by using a Tn 10 -containing E. coli strain as template in PCR with primers hsdRut (5'-TTGGACAGGCCCGCACAGCAATGGATTAATAACAATGATGCTCGACATCTTGGTTACCGT-3') and hsdRdt (5'-GCTGAATTTGCCCAGCAGGGTATCGAGATTATCGTCAAAGCGCGGAATAACATCATTTGG-3'). The Δ lac :: cat allele in TP872 was constructed by using a Tn 9 -containing E. coli strain as template in PCR with primers cat15 (5'-TCTGGTGGCCGGAAGGCGAAGCGGCATGCATTTACGTTGAATGAGACGTTGATCGGCACG-3') and cat16 (5'-AGAGTACATCTCGCCGTTTTTTCTCAATTCATGGTGTACAATTCAGGCGTAGCACCAGGC-3'). Plasmids Δ nin::tet An EcoR1 fragment of λ cI857 Sam7 containing the nin region genes was cloned into the EcoR1 site of pBR322. In the resulting plasmid, pnin, the nin genes are read clockwise in the conventional map of pBR322. pTP772 was constructed by deleting sequences between the two HindIII sites of pnin. pTP859 was constructed by cutting pTP772 with SacII and ClaI, blunting the ends with T4 DNA polymerase, ligating with NotI linkers (5'-AGCGGCCGCT-3'), cutting with NotI, and ligating with a NotI fragment of pTP857 [ 25 ] containing the tetR and tetA genes of transposon Tn10. lac :: cat PaeR7 site variants pTP819, in which the cat gene is flanked on both sides by lac sequences, PaeR7 sites, and λ sequences (for crossing into the phage), has been described [ 2 ]. pTP828 and pTP829 were described previously (without names) as intermediate plasmids in the construction of pTP819 [ 2 ]; each bears the cat gene with a single lac flank and PaeR7 site. pTP922 was constructed by deleting the lac and cat sequences between the two PaeR7 sites in pTP819. (In this and other plasmid constructions, PaeR7 sites were cut with XhoI, an isoschizomer). pTP926, pTP927, and pTP928 were constructed by ligating the oligonucleotide 5'-TCGACAGTCTAGACTG-3' into the PaeR7 sites of pTP828, pTP829, and pTP922, respectively, eliminating the PaeR7 sites and replacing them with XbaI sites. pTP929 was constructed by ligating the XbaI-NcoI fragment of pTP926 containing the N-terminal coding sequences of cat and the NcoI-XbaI fragment of pTP927 containing the C-terminal coding sequence of cat into the XbaI site of pTP928. The orientation of the reconstructed cat gene is the same as in pTP819. pTP930 was constructed by ligating together the large ApaI-SacII fragment of pTP929 and the small ApaI-SacII fragment of pTP819. pTP931 was constructed by ligating together the small ApaI-SacII fragment of pTP929 and the large ApaI-SacII fragment of pTP819. pTP921 (short-flank lac :: cat ) pTP921 was constructed by ligating together two XhoI-digested DNAs: pTP922 and a PCR product made by amplifying the cat gene from a Tn9-containing E. coli strain with primers 5'-GACGCACTCGAGGCGTTAACCGTCACGAGCATCATCCTCTGCATGGTCAGGCCGGCCACTGGAGCACCTCAAAAACACCA-3' and 5'-GACGCACTCGAGGCACACAGCGCCCAGCCAACACAGCCAAACATCCGCGCGGGCCCGACCGGGTCGAATTTGCTTTCGAA-3'. The presence of the expected sequences from the synthetic oligonucleotides in pTP921 DNA was verified by automated sequencing (data not shown). lac flank variants pTP988 (left lac flank only) was constructed by replacing the XbaI-ApaI lacZY segment of pTP930 with a mixture of two oligonucleotides, 5'-CTAGTTGCAAGCTTGGGCC-3' and 5'-CAAGCTTGCAA-3'. pTP989 (right lac flank only) was constructed by replacing the NgoMI-XhoI lacZ segment of pTP930 with a mixture of two oligonucleotides, 5'-CCGGCAAGCTTGCTGGTGGGCAA-3' and 5'-TCGATTGCCACCAGCAAGCTTG-3'. pTP995 (no lac flank) was constructed by ligating together the small NcoI fragment of pTP988 and the large, ori-containing NcoI fragment of pTP989. Inverted lac :: cat pTP1032 was constructed by ligating together the XbaI- and PaeR7-ended cat -containing fragment of pTP930 and the XbaI- and PaeR7-ended ori -containing fragment of pTP931. pTP1033 was constructed by ligating together the XbaI- and PaeR7-ended cat -containing fragment of pTP931 and the XbaI- and PaeR7-ended ori -containing fragment of pTP930. Inverted lac pTP1016 was constructed by ligating into the NotI site of pTP809 [ 25 ] the NotI-digested PCR product made by amplifying the cat gene and flanking sequences from strain TP872 (Δ lac :: cat ) with primers 5'-CATCATCACGCGGCCGCGACGTTTGCCGCTTCTGAA-3' and 5'-ATCATCCACGCGGCCGCTGCGTTTTGCACCAGTACG-3'. In pTP1016, the cat gene is flanked closely by unique SphI and BsrGI sites. pTP1027 was constructed by electroporating SphI- and BsrGI-digested pTP1016 into strain TP829 ( lac+ ); a plasmid formed by gap repair was isolated. pTP1028 was constructed by ligating SphI-digested pTP1027 with the oligonucleotide 5'-GTTGTACAACCATG-3', converting the SphI site into a BsrGI site. pTP1034 was constructed by cutting pTP1028 with BsrGI, ligating the two fragments back together, and screening for plasmids in which the lac genes were inverted relative to their flanking sequences. R6K oriγ lac::cat plasmids pTP1029, a tetracycline resistance bearing vector capable of replicating only in cells expressing R6K pir function, was constructed by ligating together two AatII- and Bam-digested DNA species: a PCR product made by amplifying a Tn10-containing E. coli strain with primers 5'-TCAACGTAAATGCATGGACGTCCTCGACATCTTGGTTACCGT-3' and 5'-TGTACACCATGAATTGGATCCCGCGGAATAACATCATTTGG-3'; and the ori -containing fragment of plasmid pLD54 [ 26 ]. pTP1018, 1019, 1020, 1044, and 1045 were constructed by ligating the BamHI lac :: cat -containing fragments of pTP819, 930, 931, 1032, and 1033, respectively, into the BamHI site of pTP1029. Rearranged lac :: cat variants (Table 3 ) pTP1047 was constructed by ligating the complementary oligonucleotides 5'-TCGTCTAGAGT-3' and 5'-CCGGACTCTAGACGAAGCT-3' between the SacI and NgoMIV sites of pTP1019. pTP1051 was constructed by ligating the complementary oligonucleotides 5'-CAGCATGCAT-3' and 5'-CTAGATGCATGCTGGTAC-3' between the KpnI and XbaI sites of pTP930. pTP1052 was constructed by ligating the complementary oligonucleotides 5'-CAGCATGCAGGGCC-3' and 5'-CTGCATGCTGGTAC-3' between the KpnI and ApaI sites of pTP1019. pTP1053 was constructed by ligating the complementary oligonucleotides 5'-GGCATGCAGGTTCTTTGAGTCCTTTGGGCGGCCGCGGGCC-3' and 5'-CGCGGCCGCCCAAAGGACTCAAAGAACCTGCATGCCGC-3' between the SacII and ApaI sites of pTP1019. pTP1054 was constructed by ligating the complementary oligonucleotides 5'-CCGGCGCGGCCGCAGGTTCTTTGAGTCCTTTGGTGTACAGAGCT-3' and 5'-CTGTACACCAAAGGACTCAAAGAACCTGCGGCCGCG-3' between the NgoMIV and SacI sites of pTP1020. pTP1055 was constructed by ligating the small SphI-NotI fragment of pTP1016 between the SphI and NotI sites of pTP1053. pTP1056 was constructed by ligating the small NotI-BsrGI fragment of pTP1016 between the NotI and BsrGI sites of pTP1054. pTP1057 was constructed by ligating the BamHI cat -containing fragment of pTP1051 into the BamHI site of pTP1029. Δ recBCD :: Ptac-gam-bet-exo-pae-cI variants pTP822, which bears a synthetic Ptac-gam-bet-exo-pae-cI operon flanked by sequences upstream from recC on one side and sequences internal to recD on the other, has been described [ 2 ]. pTP978 was constructed by ligating together two NcoI- and XbaI-digested DNA species: pTP822, and a PCR product made by amplifying the cI gene from E. coli strain TP507 with the same primers used in the construction of pTPP822. This construction has the effect of simply deleting the pae genes from pTP822. pTP996 was constructed by deleting the C-terminal coding sequences of bet and the N-terminal coding sequences of exo between the two HpaI sites of pTP978. pTP979 was constructed by ligating together two NcoI- and XbaI-digested DNAs: pTP822 and a PCR product made by amplifying the bla gene from pBR322 with primers 5'-CCACCAATCATCCATGGCGCGGAACCCCTATTTGTTT-3' and 5'-TTGTTGGACGATCTAGAGGTCTGACAGTTACCAATGC-3'. This construction has the effect of replacing pae and cI with bla . Phages λ lac :: cat819 and λ lac :: cat819 nin5 have been described [ 2 ]. λΔ nin :: tet859 was made by crossing λ wild type with plasmid pTP859, infecting a tetracycline-sensitive strain with the resulting lysate, and selecting a tetracycline-resistant lysogen. The Δ nin :: tet859 substitution replaces λ bp 40388–43825 with the tetR and tetA genes of transposon Tn 10 . The lac :: cat921 , 929 , 930 , 931 , 988 , 989 , 995, 1032 , and 1033 substitutions were crossed into λ wild type and/or λ Δ nin :: tet859 . The parent phages were crossed with the cat substitution-bearing plasmids. Phages which had acquired the substitution were either selected by plating on a strain lysogenic for phage P2 (Spi- phenotype), or identified by their clear-plaque morphologies (all the cat substitutions replace the cIII gene). The structures of the substituted phages were all verified by their ability to generate specific products when used as templates in PCR (not shown). Crosses Phages were crossed with plasmids by spotting enough phage to make a confluent zone of lysis on a lawn of a sensitive bacterial strain bearing the parent plasmid. After overnight incubation at 37°C, material from the zone was collected in TM (10 mM Tris-HCl pH 7.5, 10 mM MgSO 4 ), and shaken with chloroform. Two methods for crosses monitoring recombination between phage-injected DNA and the bacterial chromosome, both previously described, were employed: high-multiplicity infection of log phase cultures [ 27 ], and low-multiplicity infection of cells grown to stationary phase by standing overnight incubation [ 4 ]. Crosses involving recombination between bacterial chromosomes and electroporated DNA fragments were carried out as described by Murphy and Campellone [ 23 ]. The DNA fragments were generated by digesting various plasmids with restriction endonucleases. Amounts of DNA used varied among experiments, corresponding to 250–500 ng of the 3595-bp lac :: cat Xho fragment; within an experiment, equimolar amounts of different recombining linear species were used. DNA was quantitated by running samples in an agarose gel, staining with ethidium bromide, and measuring fluorescence of the bands by the use of a Kodak Gel-Logic 200 system with 1-D software. PCR The structures of various Red-generated duplications in the E. coli were verified by PCR. Colonies were picked and added to 40 μl mixtures containing 1 unit Taq polymerase, 63 μM dNTPs, 1.88 mM MgCl 2 , 5% (v/v) dimethylsulfoxide, 20 mM Tris-HCl pH 8.4, 50 mM KCl, and primers at 0.7 μM. Samples were heated to 95C for 5 min, then put through 30 cycles of 94C for 1 min, 55C for 1 min, 72C for 2–4 min, depending upon the length of the expected products. Primers for demonstrating the type I, II, and III tfa junctions were combined in a single mixture of 4 oligonucleotides: Jsp1 (GTTGAATGGGCGGATGCTAA), Jsp2 (TCTTCCACCAGAAAGCTACC), JncA (TGCCGTGTGAACGGTTTAC), and Jnc2 (TTCTAGCCCCATCATCTGTG). Primers for spanning the ACF-generated duplication (see Figure 7 ) were Ddi1 (CTCTTTCCGTTACGGGACAC) and Ddi2 (TGGTGAACATGATGCCGACA). Primers for spanning the GCB-generated duplication were Edi1 (CGCCGAAATCCCGAATCTCT) and Edi2 (ACCGGCATACTCTGCGACAT). Primers for demonstrating the DLP12 cos junction were Csj1 (GATTGAGCGTGAAGTCTGTTTGTG) and Csj2 (CGAATAGTCGGCTCAACGTGGGTT). Abbreviations PCR: polymerase chain reaction. X-Gal: 5-bromo-4-chloro-3-indolyl-β-D-galactopyranoside. IPTG: isopropyl β-D-thiogalactopyranoside. bp: base pairs. Authors' contributions ACF carried out the experiments summarized in Table 1 . AN and ARP carried out the experiments summarized in Table 2 . ARP carried out the other experiments and wrote the paper. ARP, ACF, and AN constructed the plasmids, phages, and bacterial strains. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC545071.xml |
555538 | Human desmoid fibroblasts: matrix metalloproteinases, their inhibitors and modulation by Toremifene | Background Desmoid tumour is a benign, non metastasising neoplasm characterised by an elevated deposition of organic macromolecules in the extracellular matrix (ECM). The matrix metalloproteinases (MMPs) are a family of zinc-dependent proteinases involved in the degradation of ECM macromolecules. The MMPs and their natural inhibitors (TIMPs) have been implicated in tumour growth, invasion and metastasis. In this study we provide evidence that the in vitro cultured cell line from desmoid tumour accumulates more collagen fibres in the ECM than healthy fibroblasts. Methods We investigated collagen accumulation by 3 H-thymidine incorporation, MMP expression by substrate gel zymography and TIMP expression by Western blot analysis. Results Desmoid fibroblasts showed a reduction in MMP activity and an increase of type I and III collagen and TIMPs compared to normal fibroblasts. Conclusion The increase in collagen in desmoid fibroblasts was due to inhibited collagen degradation (reduction of MMP activity) rather than to increased collagen synthesis. Adding toremifene, an anti-estrogen triphenylethylene derivate, to desmoid fibroblasts reduced collagen accumulation by decreasing mRNA expression and increasing collagen degradation. | Background Desmoid tumours, which are frequently observed in Gardner's syndrome, are rare, slow-growing, histologically benign tumours caused by autosomal dominant gene mutation [ 1 , 2 ]. They are, however, locally aggressive, compress surrounding structures and show frequent recurrences after surgical removal. Desmoid cells are characterized by abundant deposition of organic macromolecules in the extracellular matrix (ECM), by enhanced transforming growth factor β 1 (TGFβ 1 ) gene expression and increased protein secretion [ 3 ]. Cell proliferation, angiogenesis and the accumulation of ECM macromolecules are all facilitated by tumour cell production of TGFβ 1 [ 3 - 6 ]. All components of ECM are degraded by matrix metalloproteinases (MMPs), a family of zinc-dependent neutral endopeptidases [ 7 ]. Two types of MMPs are required for dissolution of interstitial collagen: collagenases and gelatinases [ 8 ]. Collagenase-1 (MMP-1), collagenase-2 (MMP-8) and collagenase-3 (MMP-13) are the principal secreted neutral proteinases that initiate degradation of native fibrillar collagens of type I, II, III and V. They all cleave fibrillar collagens at a specific site, resulting in the generation of N-terminal 3/4 and C-terminal 1/4 fragments, which are further degraded by gelatinases [ 7 , 9 , 10 ]. Gelatinase-A (MMP-2) is expressed by several types of cells, especially fibroblasts, whereas gelatinase-B (MMP-9) is restricted to epithelial cells. MMP-2 and MMP-9 are thought to play major roles in the final degradation of fibrillar collagens after first cleavage by collagenases and denaturation [ 11 ]. MMP-2 also cleaves native type I collagen to N-terminal 3/4 and C-terminal 1/4 fragments which are identical to those generated by collagenases [ 12 ]. Several different tissue inhibitors of matrix metalloproteinases (TIMPs; TIMP-1 to TIMP-4) have been identified as the major natural inhibitors of MMPs [ 13 ]. TIMP-1 and TIMP-2 inhibit the activity of most MMPs [ 11 ]. Expression of TIMP-1 is up-regulated at the transcription level by various growth factors such as TGFβ 1 , whereas TIMP-2 is largely expressed constitutively by cultured cells [ 14 ]. Our previous studies showed desmoid fibroblasts enhanced deposition of organic macromolecules in the ECM and TGFβ 1 secretion [ 3 ]. Even if desmoid cells do not have estrogen receptors [ 3 ], adding toremifene, an antiestrogenic triphenylethylene derivate, decreased TGFβ 1 production and ECM macromolecule accumulation through a mechanism of action that still remains unclear [ 3 , 6 , 15 , 16 ]. The present study investigates the rule of MMPs and TIMPs in the desmoid tumour and describes, for the first time, the effects of toremifene on MMPs and TIMPs. The results provided evidence that toremifene reduced ECM accumulation by decreasing collagen synthesis and increasing collagen degradation. Methods Antiestrogen Toremifene (4-chloro-1,2-diphenyl-1-{4-[2-(N,N-dimethylamino) ethoxy] phenyl}-1-butene) citrate was purchased from Farmos (Farmos Group Ltd, Finland). Cell cultures Fibroblast cell lines were obtained from patients with Gardner's syndrome and were provided by NIGMS (Camden, N.J.). The GMO 6965 cell line was obtained from phenotypically healthy fibroblasts, and the GMO 6888 cell line was obtained from desmoid fibroblasts. All cell lines were cultured in Eagle's minimal essential medium (MEM) (Sigma, St. Louis, MO) supplemented with 20% fetal bovine serum (FBS) (GIBCO-Invitrogen, Basel, Switzerland), 2% non-essential amino acids (GIBCO), 2 mM L-glutamine, 100 U/ml penicillin and 100 U/ml streptomycin in a humidified 5% CO 2 atmosphere at 37°C. Confluent cultures were obtained after 48 h of in vitro maintenance. The cells were cultured for 12 h in MEM. The medium was then discarded to avoid serum factor contamination. Toremifene was dissolved in ethanol and all the cultures were maintained in MEM containing ethanol or MEM containing toremifne in ethanol and treated as described below. Cell viability Normal (control) and desmoid fibroblasts were cultured for 24 h in MEM and ethanol or MEM containing 1 μM toremifene in ethanol (final concentration 0.1% v/v). Then 50 μl of sterile 0.4% trypan blue solution (final concentration 0.05%) was added to each culture well; cultures were incubated at 37°C for 15 min. Viable cells (trypan blue negative) and dead cells (trypan blue positive) were counted by a Burker chamber. Collagen synthesis Confluent cultures of normal (GMO 6965) and desmoid fibroblasts (GMO 6888) were cultured for 3, 24 and 48 h in MEM without serum supplemented L-ascorbic acid (50 μg/ml), β-aminopropionitrile fumarate (50 μg/ml), 8 μCi/ml of 3 H-proline (specific activity 35 Ci/mmole, Amersham, Freiburg, Germany) in the presence or absence of 1 μM toremifene. In a second set of experiments desmoid fibroblasts were cultured in MEM supplemented with L-ascorbic acid (50 μg/ml), β-aminopropionitrile fumarate (50 μg/ml) for 48 h with or without toremifene. 3 H-proline was added for 48 h, for the last 24 and for the last 3 h. At the end of the labelling period collagen was extracted using the method of Webster and Harvey [ 17 ]. Samples were digested with pepsin (1 mg/ml) in mild agitation overnight at 4°C. Collagen was precipitated and redissolved in 500 μl cold acetic acid 0.5 M. Total radioactivity was counted in a liquid scintillation counter and expressed as cpm/μg protein. Northern blot analysis of procollagen α 1 (I) Total RNA was isolated from confluent cultures of normal and desmoid fibroblasts maintained for 48 h in MEM alone or supplemented with 1 μM toremifene using the method of Chomczynski and Sacchi [ 18 ]. For Northern blot analysis equal amounts of total RNA (20 μg) were electrophoresed on 1% agarose gel containing 0,66 M formaldehyde and transferred on to nylon filters (Hybond N, Amersham). Before blotting, the gel was rinsed in water for 15 min at room temperature and then in 20X SSC (1 X SSC is 0.15 M sodium chloride, 0.015 M sodium citrate, pH 7) for 10 min. Blots were pre-hybridised in 20 ml of a cocktail containing 1 mM EDTA pH 8, 0.25 M Na 2 HPO 4 pH 7.2 and 7% sodium dodecyl sulfate (SDS) for 4 h at 65°C. Probes were labelled with [α- 32 P] dCTP (3000 Ci/mM) by random priming (Amersham RPN 1601). Hybridisation was performed at 65°C overnight using 10 6 cpm/ml probe in the same buffer used for pre-hybridisation. After hybridisation, the nylon membrane was washed twice in 1 mM EDTA pH 8, 20 mM Na 2 HPO 4 pH 7.2 and 5% SDS at 65°C (5 min each) and then washed twice with 1 mM EDTA pH 8, 20 mM Na 2 HPO 4 pH 7.2 and 1% SDS at 65°C (5 min each). The filters were stripped and re-hybridised with a GAPDH probe to assess blot loading. For autoradiography the membranes were exposed to Kodak X-Omat film (Rochester, NY) at -80°C for 1 day. Autoradiographies were analysed by computerised scanning densitometry. Results are expressed as the ratio of procollagen α1(I)/control GAPDH densitometry signals. cDNA probes A 670 bp Eco RI-Hind III cDNA fragment from human pro-collagen α1(I) and a 1.3 kb PstI cDNA fragment from rat glyceraldehyde-3-phosphate dehydrogenase were used as probes in hybridisation [ 18 ]. Collagenase activity Collagenase activity was determined using the method of Khorramizadeh et al., [ 19 ]. Confluent normal and desmoid fibroblasts were washed with MEM and cultured for 48 h in MEM or in MEM containing 1 μM toremifene. Proteins in the medium were precipitated by ammonium sulphate 65% w/v; precipitates were collected by centrifugation, dissolved in assay buffer (0.05 M Tris-HCl, 0.2 M NaCl, 5 mM CaCl 2 , 0.02% sodium azide, pH 7.4) and then dialysed overnight against the same buffer. The latent procollagenase was activated with trypsin (10 μg/ml) and then the trypsin was inactivated with soybean trypsin inhibitor (100 μg/ml). Acetic acid soluble type I collagen (25 μl of a solution 2 mg/ml) from bovine skin was incubated with the activated collagenase solution for 24 h. The products of the collagen digestion were separated by electrophoresis using 6% acrylamide gel containing SDS. The gels were stained with 0.25% Coomassie brilliant blue G-250 (50% methanol, 10% acetic acid), destained appropriately (40% methanol, 10% acetic acid) and fixed (5% methanol, 7.5% acetic acid). The digestion products were quantified with a computerised scanner. Preparation of conditioned media (CM) Confluent normal (GMO 6965) and desmoid (GMO 6888) fibroblasts were washed with 0.9% NaCl and cultured for 12 h in serum-free MEM. This medium was discarded to avoid contamination by serum factors and cells were cultured for the next 24 h in MEM and ethanol (control) or in MEM containing 1 μM toremifene in ethanol (final ethanol concentration 0.1% v/v). Conditioned media (CM) were collected and centrifuged for 10 min at 350 g to remove cell debris, dialysed against bidistilled water for 24 h, lyophilised and used for zymography and Western blot analysis as described below. Collagen and gelatin zymography CM were analysed for gelatinases and collagenases by zymography. Samples were separated under non reducing condition on 6% polyacrylamide gels containing 1 mg/ml of gelatin (Sigma Chemical, St Louis; MO, USA) or 1 mg/ml collagen (Sigma) [ 20 ]. In one set of samples the proenzymatic forms were activated using 2 mM aminophenylmercuric acetate (APMA) for 1 h at 37°C. Samples were lyophilised and resuspended in Tris-HCl 0.4 M pH 6.8, SDS 5%, 20% glycerol and 0.03% bromophenol blue. Gels were loaded with 8 μg protein per sample or with 2 μg trypsin and run under Laemmli conditions [ 21 ]. After electrophoresis, gels were washed twice in 200 ml of 2.5% Triton X-100 (30 min each) under constant mechanical stirring and incubated in 50 mM Tris-HCl pH 7.5, 5 mM CaCl 2 , 0.02% Brij-35 and 200 mM NaCl at 37°C for 24 h. Gels were stained with Coomassie brilliant blue G-250. Proteinase activity, observed as cleared (unstained) regions, was converted to dark regions to better observation of bands. Western-blot analysis CM were analysed for type I and type III collagen, MMP-1, MMP-2, MMP-9, TIMP-1 and TIMP-2 by Western blotting using specific monoclonal antibodies. Aliquots of CM, containing 50 μg of proteins, were separated on SDS-10% polyacrylamide gels under reducing conditions and transferred on to a nitrocellulose membrane. The membrane was blocked with blocking solution (5% w/v dried skimmed-milk powder in TBS 1X, 2 h at room temperature) and incubated with the specific monoclonal antibody in antibody solution (1% w/v dried skimmed-milk powder in TBS 1X, 2 h at room temperature). Bound antibody was detected with a sheep anti-mouse peroxidase-conjugated antibody in antibody solution. Western analysis was performed using chemiluminescence reagents from Amersham Pharmacia Biotech. Protein determination Protein concentrations were determined by the Lowry assay [ 22 ] of aliquots of cell lysate. Statistical analysis In some experiments, statistical analysis was performed using Student's t -test. Data are expressed as the means ± SD of four determinations. In other experiments, the results are reported as means ± SD of three separate experiments, each performed in quadruplicate. Statistical analysis was performed by Student's two-tailed t -test and by analysis of variance (ANOVA) followed by Sheffe F-test. Results Cell viability The amount of dead cells and viable cells in normal fibroblasts, desmoid fibroblasts and desmoid fibroblasts plus toremifene was evaluated after 24 h of in vitro maintenance in the presence of trypan blue (Table 1 ). Granted that the number of intact viable cells was high in all the experimental conditions, desmoid fibroblasts had the highest number of cells/culture and the lowest percentage of dead cells (0.0014%). Treatment of desmoid fibroblasts with toremifene enhanced the percentage of dead cells (0.011%) which, nevertheless, remained lower than in normal fibroblasts (0.025%.). Effects of toremifene on collagen synthesis Collagen synthesis was evaluated after 3, 24 and 48 h of in vitro maintenance in the presence of 3 H-proline (Table 2 ). No significant difference was observed after 3 hours culture. After 24 and 48 h culture collagen production was significantly higher in desmoid than in normal fibroblasts, in both the cellular and extracellular compartments. The increase was 1.4 fold in the cells and 1.8 fold in the medium after 24 h; 1.3 fold in the cells and 1.8 fold in the medium after 48 h. Adding toremifene significantly decreased collagen synthesis at 24 and at 48 h. The reduction was greater after 48 h (42% in the cells and 38% in the medium). In a second set of experiments desmoid fibroblasts were cultured for 48 h with or without toremifene. The radiolabelled precursor was added for 48 h, in the last 24 h and in the last 3 h (Table 3 ). Treatment with toremifene had an inhibitory effect at all times. The decrease in total collagen (cells + media) in desmoid fibroblasts treated with toremifene was 28% in the presence of 3 H-proline for 48 h, 46% and 52% respectively in the presence of 3 H-proline in the last 24 or 3 h of in vitro maintenance (Table 3 ). Procollagen α 1 (I) mRNA expression Northern blots were performed to analyse procollagen α 1 (I) mRNA level in normal and desmoid fibroblasts (Fig. 1 ). Relative densitometric units were normalised to GAPDH mRNA levels. Normal and desmoid fibroblasts exhibited no significant differences in the steady-state mRNA levels for procollagen α 1 (I). Toremifene down regulated procollagen mRNA expression by 58% in desmoid cells. Western-blot analysis of type I and III collagen Media from normal and desmoid fibroblasts with or without toremifene were analysed by Western blotting to evaluate the presence of type I and III collagen using specific monoclonal antibodies (Fig. 2 ). Densitometric tracing of the autoradiograms quantified collagen secretion. Desmoid fibroblasts secreted much more type I (1.6 fold) and III (2.2 fold) collagen than normal cells. Toremifene reduced type I and III collagen by 31% and 18% respectively in desmoid fibroblasts. Collagenase activity Collagenases, from ammonium sulphate-precipitated proteins of media of normal fibroblasts, desmoid fibroblasts and desmoid fibroblasts treated with toremifene, were incubated with soluble collagen and the digested products were evaluated by gel electrophoresis. Collagenases in the medium of normal fibroblasts digested more α 1 and α 2 chains of type I collagen into their corresponding 3/4 and 1/4 fragments than the collagenase in desmoid fibroblasts (Fig. 3 ). When band staining intensity was quantified by densitometry, the abundance of the 3/4 and 1/4 products of collagenase digestion was significantly greater in normal than in desmoid fibroblasts. Adding toremifene to desmoid fibroblasts markedly increased collagenase activity as shown by the increased amount of 3/4 and 1/4 fragments of α 1 and α 2 chains (Fig. 3 ). Collagen and gelatin zymography Collagen and gelatin zymograms dosed the enzymatic activity of collagenases and gelatinases. Collagen zymogram, reported in Fig. 4 (panel A and B), showed the samples produced a band of 52 kDa corresponding to MMP-1. Densitometric analysis of the counts, assuming the value of normal fibroblasts as 100%, demonstrated 2.3 fold increase in the 52 kDa collagenase activity in desmoid fibroblasts. When desmoid fibroblasts were treated with toremifene, the level of collagenase activity in the media was only minimally affected. No bands were present in trypsin (Fig. 4 , panel A, line C), which can degrade gelatin but not collagen, confirming that collagen has been degraded in panel A. The gelatin zymogram (Fig. 4 , panel C and D) showed two bands, one of 92 kDa corresponding to MMP-9, the other of 66 kDa corresponding to MMP-2. Desmoid fibroblasts produced the same amount of MMP-9, and larger (about 2 fold) amounts of MMP-2, than normal fibroblasts Adding toremifene to desmoid fibroblasts increased only MMP-2 activity by about 1.32 fold. To verify whether the bands detected in the collagen and gelatin zymography were due only to MMPs, two control gels were washed and incubated in buffers containing 10 mM EDTA. No bands were detected after this treatment, which indicated that the bands obtained in collagen and gelatin zymographies were entirely due to MMP activity. Toremifene addition to desmoid cells was accompanied by no changes in gelatinase activity. One set of samples in collagen and gelatin zymograms was treated with APMA to activate the proenzymes. Activation of the proenzymatic form had no significant effects on collagenase activity (Fig. 4 , panel B), but enhanced gelatinase activity in desmoid fibroblasts (Fig. 4 , panel D). Western-blot analysis of MMP-1, MMP-2, MMP-9 The presence of MMP-1, MMP-2, MMP-9 in the media of normal fibroblasts, desmoid fibroblasts and desmoid fibroblasts plus toremifene was evaluated by Western-blot analysis using specific monoclonal antibodies (Fig. 5 ). Western blot analysis of MMP-1 (Fig. 5 , panel A) showed that the amount of the protein was higher in desmoid (2 fold) and in desmoid than in normal fibroblasts (2 fold). Toremifene exhibited no significant increase of MMP-1 in desmoid cells (about 2.2 fold). MMP-2 (Fig. 5 , panel B) showed two bands, the first due to the proenzymatic form (72 kDa) and the second to the active form (66 kDa). MMP-2 was significantly increased in desmoid fibroblasts (2.2 fold) and even more in desmoid fibroblasts plus toremifene (3.2 fold) compared with normal fibroblasts. No significant differences emerged in the production of MMP-9 (Fig. 5 , panel C). Western-blot analysis of TIMP-1 and TIMP-2 Western blot analysis showed that desmoid fibroblasts produced about 7.2 and 3.4 fold TIMP-1 (Fig. 6 , panel A) and TIMP-2 respectively (Fig. 6 , panel B) than normal fibroblasts. Adding toremifene to desmoid fibroblasts decreased TIMP-1 by 18%, but had no effect on TIMP-2. Discussion Desmoid tumour is a benign non-invasive and non-metastasising neoplasm with an abnormal ECM macromolecule deposition which is stimulated by TGFβ 1 [ 3 , 23 , 24 ]. The regulation of extracellular matrix dynamics is clearly complicated, involving a balance between the deposition of structural components such as collagen and their degradation by MMPs, i.e. collagenases and gelatinases. MMP activity is itself regulated by a variety of mechanisms, including a requirement for enzyme modification to elicit maximal enzymatic activity and the activity of specific TIMPs [ 25 ]. There is now evidence that desmoid cells undergo dramatic clinical response to toremifene, implying the drug has a direct effect upon fibroblasts. Our previous studies showed that toremifene significantly inhibited TGFβ 1 activity which was six fold higher in desmoid than in normal fibroblasts [ 3 ]. As desmoid tumour is also associated with abnormal collagen production [ 26 ], in the present study we examined the rate of collagen synthesis and degradation in the presence or absence of toremifene. In our experimental conditions, type I and III collagen accumulation in the intra- and extra-cellular compartments showed no differences after 3 h of in vitro maintenance, but increased significantly more after 24 and 48 h in desmoid fibroblasts than in normal fibroblasts. No increase in collagen after 3 hours suggests its accumulation in desmoid fibroblasts is due to inhibition of degradation rather than to increased synthesis. The results are confirmed by procollagen α 1 (I) gene expression, which showed mRNA levels were only lower in desmoid cells treated with toremifene. Normal and desmoid fibroblasts expressed different amounts of MMPs. Several studies suggest that MMPs are over-expressed in malignant tumour progression and facilitate both local tumour invasion and metastasis [ 27 , 28 ]. Different MMPs may play distinct roles at different stages of tumour development [ 29 ]. They may form a network, in which a single MMP is crucial for the cleavage of certain native or partially degraded matrix components and for the activation of other latent MMPs. MMP-1 plays a pivotal role in cancer progression and poor prognosis in colon-rectal, oesophageal and gastric cancer has been correlated with high MMP-1 expression [ 25 , 30 ]. Nishiota [ 31 ] showed MMP-1 is expressed more strongly in the cancer front of invasion. MMP-2 is increased in cancer tissue and its over-expression is correlated with tumour-related basement membrane degradation and vascular invasion [ 32 , 33 ]. Therefore inhibition of the expression or activity of only one MMP could potentially reduce peritumoural proteolytic activity and tumour invasion [ 34 ]. In this study we investigated the metalloproteinases most involved in type I collagen degradation, i.e. MMP-1 (collagenase-1), MMP-2 (gelatinase-A) and MMP-9 (gelatinase-B) and their natural inhibitors TIMP-1 and TIMP-2 [ 11 ]. Moreover TIMP-2 is 10-fold more potent than TIMP-1 against MMP-2 [ 11 ] which is involved either in the final degradation of native collagen or in the initial degradation cleaving native type I collagen to 3/4 and 1/4 fragments identical to those generated by MMP-1 [ 12 ]. Using Western blot we showed no differences in MMP-9 production, while MMP-1 and MMP-2 were higher in desmoid than in normal fibroblasts. Collagen and gelatin zymograms, in which the proteolytic enzymes were separated from TIMPs before the assay, proved the activities of collagenase MMP-1 and gelatinase MMP-2, as dosed in conditioned media, were higher in desmoid than in normal fibroblasts. However, collagenase activity, in the presence of TIMPs, was reduced in desmoid compared to normal fibroblasts as shown by the lower amount of 3/4 and 1/4 fragments of fibrillar collagen in desmoid cells. Together these results indicated the higher MMP-1 and MMP-2 activity in desmoid cells was masked by a 7-fold increase in TIMP-1 and a 3-fold increase in TIMP-2. TIMP-1 is a potent inhibitor of apoptosis in many cells types, its up-regulation protects the cells against apoptotic stimuli [ 35 ]; hence, greater number of viable cells in desmoid tumour. Upregulation in both inhibitors of MMPs may explain why the Desmoid tumour is characterised by an abundant deposition of ECM macromolecules and is neither malignant nor invasive. Toremifene addition to desmoid fibroblasts reduced the accumulation of collagen fibres but its mechanism of action remains unclear. Toremifene increased MMP-1 and MMP-2 activity by 8% and 25% respectively and decreased TIMP-1 by 18%. Despite these modest effects type I collagen degradation in 3/4 and 1/4 fragments increased almost 4-fold. Conclusion Our previous studies showed that TGFβ 1 was 6-fold higher in desmoid than in normal fibroblasts and that toremifene significantly reduced TGFβ 1 activity and TGFβ 1 membrane-receptors [ 3 ]. So the effects of toremifene on MMPs and TIMPs could be linked to its effects on TGFβ 1 because the growth factor enhances organic macromolecule accumulation in the ECM via a reduction in MMP-1 and MMP-2 [ 36 ] and an increase in TIMP-1 [ 37 ], so favouring tumour mass growth through an inhibition of ECM macromolecule degradation. In the light of these data the reduction of organic macromolecules in the ECM in the presence of toremifene can be ascribed to its inhibition not only of collagen synthesis, but also of TGFβ 1 activity. Further studies on the regulation of MMP activities may clarify the role of toremifene on ECM degradation and provide important clues about pathogenesis of desmoid tumour. Competing interests The author(s) declare that they have no competing interests. Authors' contributions CB carried out collagen synthesis, collagenase activity and drafted the manuscript. CL and GB participated in the design of the study and carried out Northern blot analysis. LM and GG carried out RT-PCR, zimography and oestrogen receptor assay. AB and LC carried out Western blot analysis and performed the statistical analysis. PL conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC555538.xml |
406404 | Neural Induction without Mesoderm in Xenopus | xx | Formation of the central nervous system has long been thought to result from an induction process, whereby signals emanating from a portion of the dorsal endomesoderm (the inner middle layer of the developing embryo), known as the Spemann–Mangold organizer, instruct cells of the overlying dorsal ectoderm (outer layer) to become neural instead of epidermal. The Spemann–Mangold organizer was itself defined in Spemann and Mangold's seminal 1924 publication as a portion of the dorsal “vegetal” half (also known as the endodermal, or inner, layer) of a gastrulating Xenopus frog embryo that could induce the differentiation of a whole new axis, including a new central nervous system, when grafted into an abnormal location. (Gastrulation is the process that establishes the basic body plan of the organism as cells arrange themselves into three embryonic germ layers: the endoderm, mesoderm, and ectoderm.) From these and later experiments, the notion emerged that neural induction in Xenopus takes place at gastrulation and requires signals from the mesoderm. (The Spemann–Mangold organizer is itself derived from the endomesoderm.) Blastula cells that give rise to the brain Now Hiroki Kuroda, Oliver Wessely, and Edward De Robertis challenge this model by demonstrating that a group of cells in the dorsal region of the prospective ectoderm is fated to become neuronal as early as the blastula stage (which precedes gastrulation) and that these cells can express their neural character in the absence of any mesodermal influence. The authors call this group of cells the BCNE (blastula Chordin- and Noggin-expressing) center, based on their previous observation that this center expresses the proteins Chordin and Noggin at the blastula stage. Chordin and Noggin are also expressed later in the Spemann–Mangold organizer and are among the key signals that mediate neural induction by the organizer. The presence of the neural inducers in blastula ectodermal precursor cells prompted the authors to test these cells' neural potential. They first demonstrated that BCNE cells normally give rise to the anterior portion of the brain, which confirms these cells' neural fate. Moreover, when cultured in vitro, BCNE cells taken from tissue begin to express neural protein markers, even when extra care is taken to prevent any contact with mesodermal precursors. It therefore appears that BCNE cells are already specified to become neural by the blastula stage, before the Spemann–Mangold organizer forms. To further demonstrate BCNE cells' independence from mesodermal signals, the authors generate embryos without a mesoderm. Having previously observed that such embryos do develop a central nervous system, Kuroda et al. now demonstrate that this intrinsic neuronal potential depends on Chordin and Noggin expression in BCNE cells. The model that emerges from these experiments suggests that neural induction begins at the blastula stage, with Chordin and Noggin signaling within the BCNE center and may later be consolidated or modulated by signals emanating from the organizer. What of the endodermal portion of the Spemann–Mangold organizer? It expresses a secreted protein called Cerberus that is involved in development of the head. The authors show that abolishing Cerberus function in the prospective endoderm results in headless embryos. Complete brain removal can also be achieved by partially inhibiting Cerberus function, so long as Chordin is simultaneously inhibited in the dorsal ectoderm. It is therefore likely that while BCNE cells harbor an intrinsic neural potential, neural induction in a living organism occurs via cooperation between the germ layers. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC406404.xml |
546222 | Single nucleotide polymorphisms (SNPs) are inherited from parents and they measure heritable events | Single nucleotide polymorphisms (SNPs) are extensively used in case-control studies of practically all cancer types. They are used for the identification of inherited cancer susceptibility genes and those that may interact with environmental factors. However, being genetic markers, they are applicable only on heritable conditions, which is often a neglected fact. Based on the data in the nationwide Swedish Family-Cancer Database, we review familial risks for all main cancers and discuss the evidence for a heritable component in cancer. The available evidence is not conclusive but it is consistent in pointing to a minor heritable etiology in cancer, which will hamper the success of SNP-based association studies. Empirical familial risks should be used as guidance for the planning of SNP studies. We provide calculations for the assessment of familial risks for assumed allele frequencies and gene effects (odds ratios) for different modes of inheritance. Based on these data, we discuss the gene effects that could account for the unexplained proportion of familial breast and lung cancer. As a conclusion, we are concerned about the indiscriminate use of a genetic tool to cancers, which are mainly environmental in origin. We consider the likelihood of a successful application of SNPs in gene-environment studies small, unless established environmental risk factors are tested on proven candidate genes. | Introduction Genetic association studies on complex diseases have become very popular and most of them are case-control studies using single nucleotide polymorphisms (SNPs) as markers. There has been concern about the poor reproducibility of the results and the reasons for such discrepancies have been discussed [ 1 - 5 ]. However, the theoretical underpinnings of such studies have attracted less attention, apart from the use of SNPs as mapping tools, an application which we will not discuss in the present article [ 6 - 8 ]. Heritable etiology in many common diseases may not be overwhelming, and the use of genetic tools to dissect disease causation may thus be questionable. For cancer, all useful etiological measures, such as incidence changes upon time and migration, and aggregation of cancer among twins and families, point to a predominant environmental contribution to cancer causation [ 9 - 15 ]. However, in these contexts, the environment is anything that is not inherited, including variables that can be measured in epidemiological studies, in addition to un-measurable and random, stochastic events. The fact that only a minority of smokers are diagnosed with lung cancer is often cited as evidence for inherited differences in susceptibility to tobacco carcinogenesis. There are also other possible reasons, such as time-dependent stochastic effects, which in inbred animals are the likely reason that only some animals develop cancer when exposed to a constant level of a carcinogen. How feasible is it then to carry out SNP studies on cancer, particularly when the subjects are overwhelmingly unselected cases, among whom familial cases are rare. It is worrisome that genotyping is now almost a required standard component in epidemiological studies, without consideration of the expected heritable influence. Formal sample size and power calculations are irrelevant if there are no data on the assumed heritable component in the causation of a particular cancer. It is also worrisome that the fundamental differences between purely genetic and gene-environment studies regarding control populations and multiple testing problems are not appreciated. In this contribution, we first review study designs and aims of SNP studies, then we give data on familial risks for the main types of cancer, to give an idea of the upper bounds for the risks that can be expected in genotyping studies. Finally, we will calculate familial risks resulting from variants of assumed genotype relative risk and allele frequency. Such data are useful in the assessment of study designs and in the evaluation of obtained results. We use the term 'familial' to denote cancers in two or more first-degree relatives and 'heritable' when an inherited gene defect is known or inferred due to a high risk [ 16 ]. Instead of 'genotype relative risk', we refer to 'odds ratio' (OR), consistent with terminology of most association studies. Study designs and aims In the simplest form, a SNP with a known or assumed function is selected, and the genotypes are determined in cases and controls to test for association. Most studies published on cancer are of this kind, purely "genetic studies" testing the effect of the genotypes on the risk of cancer, without considering any other variables [ 17 , 18 ]. In addition to genotype effects, some studies have incorporated the effects of haplotypes and data on functional effects of the studied SNPs [ 19 - 21 ]. Population stratification has been an issue in association studies and it is important that the control subjects are drawn from the same ethnic and geographic population [ 2 , 5 ]. However, there is no need for individually based matching (age, gender etc.), typical of epidemiological case-control studies, as long as the genotypes of the control population follow the Hardy-Weinberg equilibrium [ 22 , 23 ]. Multiple testing is an issue in "genetic studies" but solutions are available, for example using the Bonferroni adjustment [ 2 , 5 ]. Most studies have selected SNPs with assumed functional effects or they test the effects of haplotypes. For some genes, null or truncating alleles exist and homozygotes would then lack a functional protein. However, for missense types of SNPs the functional effects may be small or nil when tested in in vivo systems [ 21 ]. Unfortunately, for many genes an in vivo functional test cannot be easily devised. Drug metabolism genes are a fortunate exception in this regard, because aberrant responses in humans have lead to the characterization of the underlying gene variants. One common feature of almost all the published studies is that patients have been collected without regard of a family history, thus sacrificing statistical power but attempting to compensate with a large sample size [ 24 , 25 ]. Using familial cases would be advantageous statistically, and some effects, such as that of CHEK2*1100delC, have only been detected among familial cases [ 26 ]. A variant of the candidate gene approach is the "gene-environment study", which is founded on the assumption that in complex diseases environmental factors interact with heritable factors, and a strong effect can be detected when both are present [ 27 - 29 ]. In epidemiology, interactions (also called effect modifications) are best described for multiple exposures, which may be additive, multiplicative or mixed [ 30 ]; these probably also apply to gene-environment interactions, but there are few bonefied examples on quantified gene-environment interactions [ 29 ]. It is conceptually appealing to assume that environmental factors interact with the genetic make-up to cause a differential susceptibility to cancer. However, examples are needed in order to verify this concept and its magnitude in cancer causation. The SNP component has become a favored adjunct to epidemiological studies, promoted with the hypothesis that the small, perhaps insignificant effects noted between exposure and cancer can be salvaged by incorporation genetic host factors into the study. These studies always include multiple comparisons, firstly, including the epidemiological variables in various classes, which may add up to thousands of cells (a small study with 5 variables in 5 strata each results in 5 5 = 3125 unique cells), and, secondly, the genes that are selected for analysis, are taken from a pool of tens or hundreds of potential candidate genes. No solutions have been found for this "two-dimensional" multiple testing problem. However, important for the present discussion, gene-environment interactions only exist if there is a heritable component in the particular cancer, and the likelihood of observing an effect is larger if the heritable component is large. In populations of random mating, it may be plausible that gene-environment interactions of epidemiologically measurable magnitude exit in the absence of a measurable familial risk. In the case of many exposures, some with harmful and others with protective effects, interacting with many genes, it may be possible that the familial risks are missed in spite of true gene-environment effects. Similarly, non-conventional dose-effect relationships, such as those suggested for blood vitamin D levels and prostate cancer [ 31 ], would be difficult to reconcile in terms of any genetic models. According to the complex disease paradigm, many relatively common alleles, interacting with environmental factors, cause susceptibility to common diseases [ 28 , 32 ]. Such a "non-Mendelian" inheritance may not cause an appreciable familial risk because the penetrance is so low that the likelihood of several family members being affected would be small. Twin studies should be able to assess the contribution of polygenic heritability [ 33 ], and the heritability estimates derived for colorectal (35% heritability), breast (27%) and prostate (42%) cancers, the only significant ones among site-specific cancers, encompass the total (broad) heritability, as definable using the twin model [ 11 ]. The much smaller heritability estimates, generated from family studies, were thought to result in part because of inability to consider such polygenic effects [ 13 ]. Familial risks and proportions Familial risk of a disease is a measure of its clustering in family members. Commonly, familial risk is defined between those who have a relative (e.g., parent or sibling) with cancer compared to those whose relatives are free from cancer, given as a familial relative risk or familial standardized incidence ratio (SIR). The SIRs shown below have been obtained from the Swedish Family-Cancer Database, the largest dataset of the kind in the world [ 34 ]. Familial SIRs have been adjusted for age, socio-economic status, period and region, and for women, for reproductive parameters. Table 1 shows familial SIRs for 0 to 68 year old offspring whose parents had the same cancer [ 35 ]. Table 1 also shows the number of observed cases, 95% confidence intervals (95%CIs) for the SIRs and the familial proportions, i.e., the percentage of all affected offspring who have an affected parent. A total of 4938 concordant familial cancers were found, with an overall SIR of 2.02. All the site-specific familial risks were significantly increased, except those for connective tissue. Hodgkin's disease showed the highest SIRs of 4.91, followed by testicular (4.35) and non-medullary thyroid cancers (3.26). Esophageal and ovarian cancers and multiple myeloma had SIRs in excess of 3.00. Among common cancers, the SIRs were increased for female breast (1.84), prostate (2.45) and colorectal adenocarcinoma (1.86), and the number of familial pairs ranged between 681 and 1779 for these common cancers. Apart from colorectal, breast and ovarian cancer, hardly anything is known about the genetic bases of these neoplasms, which should be good news to those who want to test the effects of candidate genes [ 36 , 37 ]. Table 1 SIR for cancer in offspring by parental concordant cancer Cancer site O SIR 95%CI Familial proportion, % Breast 1779 1.84 1.76 1.93 8.5 Prostate 922 2.45 2.29 2.61 15.4 Colorectum (adenocarcinoma) 681 1.86 1.73 2.01 10.1 Lung 365 2.09 1.88 2.32 6.6 Melanoma 166 2.62 2.23 3.05 2.5 Urinary bladder 117 1.75 1.45 2.10 3.9 Nervous system 112 1.75 1.44 2.11 1.8 Ovary 97 3.15 2.56 3.85 3.3 Endometrium 83 2.47 1.97 3.07 2.9 Stomach 82 2.17 1.72 2.69 5.5 Skin, squamous cell 77 2.52 1.99 3.15 4.1 Non-Hodgkin's lymphoma 74 1.82 1.43 2.29 2.1 Kidney 64 1.87 1.44 2.39 2.8 Leukemia 55 1.88 1.42 2.45 1.7 Pancreas 46 1.87 1.37 2.49 3.0 Upper aerodigestive tract 39 1.71 1.22 2.35 1.9 Cervix 39 1.82 1.29 2.49 1.7 Endocrine glands 38 2.22 1.57 3.06 1.5 Liver 37 1.66 1.17 2.28 2.6 Myeloma 23 3.32 2.10 5.00 2.6 Thyroid gland, nonmedullary 12 3.26 1.68 5.71 1.0 Testis 10 4.35 2.07 8.04 0.5 Esophagus 8 3.14 1.34 6.22 1.3 Hodgkin's disease 8 4.91 2.10 9.73 0.7 Connective tissue 4 1.87 0.49 4.84 0.5 All 4938 2.02 1.97 2.08 5.5 Bold type: 95%CI does not include 1.00. Familial proportion: % of affected offspring with affected parent Familial cases constituted 15.4% of all prostate cancers, which was the largest proportion by far. The proportion was 10.1% for colorectal adenocarcinoma and 8.5% for breast cancer, but only 0.5% for connective tissue and testicular tumors. The proportions are generally higher for common cancers. Overall familial cancers (same cancer in offspring and parents) constituted 5.5% of all cancers. We have tried to estimate the degree of environmental contribution to the familial risk by comparing cancer risks betweens spouses. Spouse concordance, which does not generally exceed an SIR of 1.3, can be noted only for cancers with known strong environmental risk factors: lung and genital cancers and early onset gastric and pancreatic cancers and melanoma [ 38 , 39 ]. Spouse correlation does not consider environmental sharing early in the life; this has been estimated by comparing cancer risks between siblings with a small or large age difference, respectively [ 40 ]. For most sites, including the breast and the colorectum, heritability is likely to be the main contributor to familial cancer [ 41 , 42 ]. Environmental factors are probably a large contributor to the familial aggregation of cervical, lung and upper aerodigestive tract cancers, and a minor contributor to familial risks for melanoma and squamous cell skin cancer [ 43 , 44 ]. If environmental causes of familial clustering have been quantified or excluded, familial SIRs and proportions give estimates on the heritable effects for cancer at the level of nuclear families (here between parents and offspring). Because of low penetrance, familial proportions underestimate true heritable effects. On the other hand, the twin model assumes that the shared environmental effects of monozygotic and dizygotic twins are identical, which may not be true. If monozygotic twins share more than dizygotic twins, the estimated heritability is exaggerated. Thus, the heritability estimates for cancer are still unreliable, and, due to possible interactions, a dichotomous classification into heritable and environmental components is conceptually inaccurate [ 45 ]. Moreover, the current models for twin studies do not allow the existence of interactions, a condition probably violated for many cancers. Nevertheless, the available data suggest that the heritability is low for most cancers, and even for prostate, breast and colorectal cancer it contributes a small etiological proportion. Familial risks from snps Results from a successful SNP study can imply that the particular variant contributes to a familial risk of the particular cancer. The resulting familial risk depends on the allele frequency of the SNP, observed OR and the mode of inheritance, i.e., on the relative risks of heterozygotes compared to homozygotes. In the dominant model, the risk of heterozygotes equals that of the variant homozygotes; in the recessive model, the risk of heterozygotes equals that of the wild type homozygotes. In the additive model, the risk of heterozygotes is the mean of the two homozygotes; in the multiplicative model, the risks between the genotypes differ by a constant multiplier. The methods for the calculation of familial risks to offspring of affected parents (comparable to SIRs of Table 1 ), based on allele frequency and OR of the genotype are presented elsewhere [ 46 ]. According to Table 2 , the calculated familial risk is negligible at very low and very high allele frequencies when ORs are below 10, and at any allele frequency when OR is 2 or less. Most SNP studies are carried out on variants with frequencies at 5% or higher, and then substantial familial risks may be caused by a single gene with a high OR. Familial risk of breast cancer was 1.84 in Table 1 ; however, because the known genes, including BRCA1/2 , ATM , p53 and CHEK2 , explain about 25% of the risk [ 47 ], the unexplained familial risk is about 1.6. In Table 2 we have fold-faced SIRs that are incompatible with the empirical data for breast cancer (risk 1.60 or more), i.e., the resulting familial SIRs would be too high. If a single dominant gene would explain all the remaining familial risk of breast cancer, the allele frequency should be 0.2 and OR about 15; with allele frequency of 0.01, OR should be about 10. In the more likely scenario, many genes contribute to the familial risk, but their joint effect cannot exceed the above values. Table 2 Familial risk to offspring of affected parents assuming define allele frequencies and their effects according various genetic models OR Allele frequency 1.5 2 5 10 20 Dominant model 0.001 1.00 1.00 1.02 1.08 1.33 0.01 1.00 1.01 1.13 1.57 2.84 0.05 1.01 1.04 1.36 1.99 2.90 0.1 1.02 1.05 1.38 1.80 2.24 0.2 1.02 1.06 1.28 1.46 1.60 0.3 1.02 1.05 1.18 1.27 1.33 0.4 1.01 1.03 1.11 1.15 1.18 0.5 1.01 1.02 1.06 1.08 1.10 Additive model 0.001 1.00 1.00 1.00 1.02 1.09 0.01 1.00 1.00 1.04 1.17 1.63 0.05 1.00 1.01 1.13 1.46 2.13 0.1 1.01 1.02 1.18 1.50 1.97 0.2 1.01 1.03 1.20 1.41 1.63 0.3 1.01 1.03 1.17 1.31 1.42 0.4 1.01 1.03 1.14 1.23 1.29 0.5 1.01 1.03 1.11 1.17 1.20 Recessive model 0.001 1.00 1.00 1.00 1.00 1.00 0.01 1.00 1.00 1.00 1.00 1.00 0.05 1.00 1.00 1.00 1.01 1.04 0.1 1.00 1.00 1.01 1.06 1.23 0.2 1.00 1.01 1.08 1.28 1.75 0.3 1.00 1.02 1.16 1.47 1.93 0.4 1.01 1.03 1.23 1.52 1.85 0.5 1.01 1.04 1.25 1.48 1.68 Multiplicative model 0.001 1.00 1.00 1.00 1.00 1.01 0.01 1.00 1.00 1.01 1.04 1.11 0.05 1.00 1.01 1.06 1.18 1.42 0.1 1.00 1.01 1.11 1.28 1.60 0.2 1.01 1.02 1.16 1.36 1.67 0.3 1.01 1.03 1.17 1.36 1.61 0.4 1.01 1.03 1.16 1.32 1.51 0.5 1.01 1.03 1.15 1.27 1.40 Because the prevalence has no effect on the calculated familial risks, Table 2 can be used for any cancers of variable prevalences. The familial SIR for lung cancer was 2.09 (Table 1 ). However, judged from the spouse correlation, probably a large but undefined proportion of familial risk for lung cancer can be explained by environmental factors, and the unexplained heritable component may be not very different from breast cancer. For upper aerodigestive tract cancers, the familial SIR was 1.71, but tobacco smoking and other environmental factors probably contribute to familial clustering and the heritable component is likely to be relatively small in this cancer. It is of interest to examine the magnitude of familial risks which would be predicted from the published ORs for candidate genes. In a review of 34 polymorphisms in 18 different genes tested for breast cancer, a large proportion of the associations were not significant and the ORs were below 2.0 [ 17 ]. Even many significant ORs were below 2.0 and the resulting familial risk is negligible. However, there were some exceptions; in one study, TNF-alpha with an allele frequency of 0.2 showed an additive risk of about 10 (homozygote/homozygote). According to Table 2 , the resulting familial risk would be about 1.4, i.e., if the effect were true, this gene would explain half of all familial risk for breast cancer; in that respect it would be two times more important than BRCA1 and BRCA2 combined. The effects of metabolic polymorphisms on various cancers have been reviewed in an IARC publication [ 48 ]. Among many genes, CYP2D6 has been analyzed in many studies as a risk factor for lung cancer, although it is not expressed in lung tissue [ 49 , 50 ]. Some genotyping studies have reported ORs between 5 and 15 for poor metabolizer genotypes. Assuming an allele frequency of 0.02 and a dominant OR of 10, Table 2 gives a familial risk of about 1.8, which, if true, would account for all familial risk of lung cancer not explainable by environmental factors. Conclusions The poor reproducibility of candidate gene studies has most commonly been associated to small sample size, population stratification and low prior probability, i.e., poor selection of genes or SNPs; a SNP with small functional effect would also imply a low prior probability for an effect [ 5 , 32 ]. We agree that the low prior probability is an important factor but we would like to widen the scope of the query from the right gene to the right tool: is the genomic tool generally applicable to a disease that is mainly environmental? It is likely that some successes will continue to come in associating new genes with cancers of a reasonable heritable component, such as that of CHEK2*1100delC in breast cancer, and populations of familial cancers will be important either in finding the initial association or in confirming the effect. In spite of the unsolved multiple testing problems, we consider plausible that gene-environment interactions will be established between demonstrated risk factors and proven candidate genes, for which tobacco-induced lung cancer would appear an obvious choice; however, even the candidate genes for lung cancer are still being searched. Testing of unproven genes and/or unproven environmental factors for gene-environment interactions is the high-risk design for a multiple testing outcome. It is worrisome to the field of gene-environment interactions that no such proof-of-principle has yet been demonstrated. It is commendable that all available molecular and environmental data are being used in attempts to understand the mechanisms of human cancer [ 51 - 53 ]. With increasing understanding of the cellular mechanisms more useful tools will become available. Even though these cellular systems are governed by heritable genes, variants in these genes may not have an impact large enough to predispose to heritable cancer. With current technological resources there is a growing danger that technology rather than biology is becoming the driving force in population studies [ 3 ]. Although the new technologies will allow benefits for the analysis of multiple SNPs and haplotypes in genetic pathways rather than in individual genes, they will not change the fact that cancer is mainly an environmental disease, expressed as somatic alterations on a heritable background. Forcing unproven genetic paradigms into all epidemiological studies is risky, and the likelihood of contradictory results may increase, leading to a gradual erosion of credibility. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC546222.xml |
523848 | Fever, Headache, and Visual Blurring in a 17-Year-Old Woman | A fascinating case, with much to learn about diagnosis and treatment of patients with abnormal CSF results. After learning from the case, take our online quiz | DESCRIPTION of CASE A 17-year-old woman, who was born in Bangladesh, presented to an accident and emergency department in the United Kingdom with a history of being unwell for 24 hours. She had a headache and fever, and was vomiting. On questioning, there was no photophobia or neck stiffness. She lives in the United Kingdom and had been for a holiday to Bangladesh 4 months previously. The family members were all well, and none had similar symptoms. The woman had no previous medical history and was on no medication. On examination, she looked unwell, with a temperature of 38.5 °C, pulse 96 beats per minute, blood pressure 112/52 mm Hg, and a respiratory rate of 16 breaths per minute. She had a Glasgow Coma Score of 14, there was no neck stiffness, and her ocular fundi were said to be normal. There were no other significant findings on examination. Immediate investigations showed a normal blood count apart from a raised white blood cell (WBC) count of 11.1 × 10 9 per millilitre (normal range, 4.3–10.8 × 10 9 , per millilitre), with 84% neutrophils. Her erythrocyte sedimentation rate was 15 mm in the first hour (normal range, 1–12 mm), and her C-reactive protein was less than 5 mg/l (normal range, less than 10 mg/l). A malaria screen was negative, and renal and liver function tests were normal. What Clinical Diagnoses Were Being Considered? A clinical diagnosis of probable viral meningitis was made pending the results of further investigations. Chest X ray was normal, as was an unenhanced CT brain scan. Lumbar puncture showed a normal cerebrospinal fluid (CSF) opening pressure, and the CSF was not blood stained. Laboratory analysis showed 1,606 WBC/ml (normally there are less than 5 cells/ml), of which 60% were lymphocytes with protein of 1.08 g/l (normally less than 0.6 g/l) and glucose of 2.9 mmol/l (normally greater than 50% of plasma glucose), with a corresponding plasma glucose of 5.7 mmol/l (normal range, 4–9 mmol/l). No organisms were seen on Gram stain. The CSF was negative for pneumococcal, meningococcal, and Haemophilus antigens, and bacterial culture was subsequently negative. The patient was given 2 g of ceftriaxone intravenously immediately, and ceftriaxone treatment was continued following the CSF results. Aciclovir IV, at a dose of 10 mg/kg every 8 hours, was added to cover the possibility of herpes encephalitis. What Was the Subsequent Differential Diagnosis? The differential diagnosis was now viral, bacterial, tuberculous (TB), fungal, or malignant meningitis, or sarcoidosis. Additional investigations were requested, including polymerase chain reaction (PCR) on CSF for viruses and tuberculosis. There were no risk factors for HIV infection (HIV seroconversion can present with meningitis). Mollaret’s meningitis (recurrent aseptic meningitis associated with herpes simplex virus) was a possibility [ 1 ], though this condition characteristically presents as recurrent episodes of apparent aseptic meningitis. The following morning the patient’s temperature returned to normal at 37 °C, and she felt better. Referral was made to the Infectious Disease Service. Twenty-four hours after admission, she was afebrile but clinically worse, with marked headache and vomiting. A day later she spiked a fever of 39 °C. No organisms were cultured from the CSF, urine, or blood. CSF bacterial antigens, cryptococcal antigen, and CSF auramine stain (for mycobacteria) were also all negative. The following day the patient complained of further headache, nausea, blurred vision, and photophobia. In addition, she was noted to have bilateral large pupils, which did not react to light, and very pink optic nerves. Papilloedema was thought likely, and no other neurological signs were detected. The original CSF sample was negative on PCR for tuberculosis and herpes simplex virus, C-reactive protein remained at less than 5 mg/l, and a further head CT scan with contrast was normal. The diagnosis was revised to meningoencephalitis. Other agents—such as listeria, tuberculosis (a systematic review found that PCR has a sensitivity of only 56% [95% CI, 46%–66%] for detecting TB meningitis [ 2 ]), and viruses such as others in the herpes group, mumps, and West Nile virus—were considered. The aciclovir was stopped, and quadruple tuberculosis therapy was started. What Did the Eye Signs Mean? The fixed, dilated pupils were of major concern, and urgent ophthalmic review was requested. Examination by the ophthalmologist showed reduced vision at 6/60 right eye and 6/36 left eye. On testing with Ishihara charts, the patient had severely reduced colour vision. Her pupils were large and non-reactive to light or accommodation in both eyes. The eyes were inflamed, with cells present in the aqueous and vitreous humours. The optic nerves were swollen and very pink ( Figure 1 ), but there was normal spontaneous venous pulsation present—demonstrating that this was not papilloedema (see Video 1 ). Bilateral choroidal infiltrates with overlying serous retinal detachments were also present. Figure 1 Fundal Appearance of the Patient's Eye The large arrow indicates the pink optic nerves; the star shows localised retinal detachment; and the small arrow pointing down shows small, white choroidal granulomas. Video 1 Spontaneous Venous Pulsation of the Veins at the Optic Nerve Head What Was the Final Diagnosis and Treatment? The combination of the clinical symptoms, signs, and ocular features was characteristic of Vogt-Koyanagi-Harada (VKH) syndrome [ 3 ]. All antibiotics were stopped, and high-dose corticosteroids were started at 100 mg prednisolone daily. At one week there was no significant ocular improvement, although the patient's headache and vomiting were now gone, and she felt much better. Additional immunosuppressive therapy was initiated with cyclosporin and mycophenolate, and within a further week the patient's vision started to improve, with settling of the ocular signs. Oral corticosteroids were tapered, as was the cyclosporin, and by one month the patient's vision had returned to normal, and the ocular signs continued to settle. By three months the cyclosporin was discontinued, the steroid dose was reduced to 5 mg daily, and the mycophenolate dose was tapered. By six months all therapy was discontinued. At review six months later, the patient remained well, with normal vision and normal optic nerves. DISCUSSION This young patient presented acutely with a fever and some signs suggestive of meningitis. She was initially treated as having viral meningitis, but the CSF findings indicated that other aetiologies needed to be considered. In particular, in a woman who previously lived in and recently visited Bangladesh, with a lymphocytic meningitis and borderline CSF glucose, tuberculosis had to be considered. Initially the ocular symptoms and signs were not a prominent feature, but the signs were likely to have been present when the patient was first seen. The typical CSF changes associated with meningitis of different aetiologies are shown in Table 1 . In this case the mixed lymphocytes and neutrophil leucocytosis with a borderline CSF glucose on the patient's initial CSF sample were consistent with bacterial or TB meningitis. Viral infection was far less likely, as only mumps is consistently associated with reduced CSF glucose. Table 1 CSF Changes in the Most Commonly Encountered Types of Meningitis Infectious Causes of Meningitis There is a wide range of infectious causes of meningitis worldwide. The likely infecting organism will be determined by the age and immune status of the patient plus the situation in which the infection was contracted. Thus, in an immunocompetent adult in the UK, enteroviruses are the commonest cause of viral meningitis, with meningococcus and pneumococcus the commonest bacterial agents. Tuberculosis is more common in people who have lived in a highly endemic area. In other parts of the world, the differential diagnosis may include viral infections such as West Nile virus in the continental United States and Japanese B encephalitis in Asia, or other pathogens such as rickettsiae, borrelia (Lyme disease), and protozoa. In the immunocompromised host, listeria must be considered, and there is an increased risk of fungal infection and tuberculosis. Finally, it is important to consider sexual exposure, as both secondary syphilis and HIV seroconversion may present with meningitis. It is important, therefore, in the evaluation and management of patients presenting with a meningoencephalitis that the differential diagnosis be continually reviewed if the patient is not responding to therapy ( Table 2 ). When appropriate investigations have been performed and are negative and symptoms persist, non-infective causes of CSF inflammation must be considered—as turned out to be the case here ( Table 3 ). Table 2 What to Do When the Patient Is Not Getting Better Table 3 Non-Infectious Causes of Abnormal CSF VKH Syndrome VKH syndrome [ 4 , 5 , 6 ] is a systemic disease involving various melanocyte-containing organs. It is rare in white Northern Europeans and white Americans but much more common in people with darker, pigmented skin. For example, among patients presenting with uveitis, about one in ten in Japan and one in 50 in India have VKH syndrome [ 6 , 7 ]. It presents acutely with varying symptoms and signs, which include meningoencephalitis, visual blurring, and deafness. The eye signs are very characteristic and can help to make the diagnosis. The most prominent ocular finding is intensely pink optic nerves (see Figure 1 ), with severe visual reduction and loss of function, which accounts for the absent pupillary responses. VKH syndrome is usually bilateral, but occasionally the eyes can be affected asymmetrically so that one is very mildly involved. The syndrome is accompanied by marked intraocular inflammation, and there are choroidal infiltrates ( Figure 2 ) associated with serous retinal detachments, which may be localised ( Figure 3 ) or affect the whole retina ( Figure 4 ). It is likely that these detachments are due to the retinal pigment epithelium (RPE) being affected by the underlying inflammatory choroidal granulomas (which heal leaving scars; see Figure 5 ), and fluid accumulates underneath the retina because of reduced function of the RPE when it becomes inflamed. Figure 2 White Choroidal Infiltrates (Arrow) Seen in VKH Syndrome with Very Pink Optic Nerve Head Figure 3 Localised Retinal Detachment Figure 4 Total Retinal Detachment, Where Whole Retina is Grey in Colour Figure 5 Scarring When Choroidal Granulomas Subside The disorder is caused by an immune response to melanin and affects parts of the body where melanin is found. The initiating stimulus for this response is unknown, but T-cells sensitised to melanin-associated antigens are found in the peripheral blood. In the ear, the melanocytes of the inner ear are the target, and the inflammatory response here results in hearing loss and balance problems. In longstanding untreated cases, depigmentation may occur in other sites such as skin (vitiligo; Figure 6 ) and eyelashes (poliosis; Figure 7 ), but these are uncommon when corticosteroids and other immunosuppressive agents are used in treatment. Depigmentation of the RPE can also occur, giving a ‘sunset’ appearance to the dark fundus. Figure 6 Vitiligo on Skin of Forearm Figure 7 Poliosis Note white eyelashes on child. Treatment with high-dose corticosteroids is essential [ 3 ] and should be initially 1–2 mg/kg/day. This treatment can be given orally or intravenously, depending on how unwell the patient is. However, patients commonly need other immunosuppressive agents as well, so as to allow the dose of steroids to be reduced more quickly. Both cyclosporin and mycophenolate are very useful as steroid-sparing agents, with cyclosporin having the advantage of a variable-dose regimen for more rapid onset of action. On the down side, cyclosporin can cause hirsutism, especially in combination with corticosteroids (which can also cause this side effect). Unfortunately, the costs of cyclosporin and mycophenolate may preclude their use in resource-poor settings, with the result that patients may require high-dose corticosteroids for much longer, with all the concomitant side effects. Inadequate initial treatment may increase the risk of recurrence and long-term complications. Treatment is required until the disease goes into remission. The meningoence-phalitic signs and retinal and choroidal signs settle quickly, often within a week or so, whereas the optic nerve inflammation may take longer to settle. The visual prognosis depends on the degree of permanent damage to the optic nerve and the macula area, which often shows considerable pigment clumping as a result of the damage to the RPE. Relapse affecting the optic nerve, choroids, and retina is uncommon, provided that treatment has been given for long enough. However, recurrent anterior uveitis requiring steroid drops is common. This is not a threat to sight if adequately controlled. As with any other cause of intraocular inflammation particularly associated with choroidal involvement, VKH syndrome can lead to reduced vision via cataracts, glaucoma damaging the optic nerve, and new vessels growing into the retina through the damaged RPE (choroidal neovascular membrane). Key Learning Points Consider meningitis in the differential diagnosis of a patient presenting with fever and headache. CSF analysis is essential to confirm meningitis and as part of establishing the cause. Consider non-infectious causes when a patient does not respond rapidly to therapy. Blurring of vision must be investigated and may help in determining the underlying diagnosis or the presence of papilloedema. Suggested Reading Sutlas PN Unal A Forta H Senol S Kirbas D 2003 Tuberculous meningitis in adults: Review of 61 cases Infection 31 387 391 Early suspicion and appropriate long-term anti-tuberculosis therapy together with corticosteroids may reduce mortality and morbidity in patients with TB meningitis 14735380 Redington JJ Tyler KL 2002 Viral infections of the nervous system Arch Neurol 59 712 718 This review is an update on diagnosis and treatment 12020250 Thomson RB Jr Bertram H 2001 Laboratory diagnosis of central nervous system infections Infect Dis Clin North Am 15 1047 1071 This paper discusses conventional tests, such as culture, and others such as antigen testing and PCR 11780267 Rotbart HA 2000 Viral meningitis Semin Neurol 20 277 292 The virology, pathogenesis, epidemiology, clinical manifestations, diagnostic studies, and established and potential antiviral therapies for viral meningitis are discussed. A differential diagnosis of the aseptic meningitis syndrome is provided 11051293 Negrini B Kelleher KJ Wald ER 2000 Cerebrospinal fluid findings in aseptic versus bacterial meningitis Pediatrics 105 316 319 Polymorphonuclear cell predominance in the CSF does not discriminate between aseptic and bacterial meningitis 10654948 Kamondi A Szegedi A Papp A Seres A Szirmai I 2000 Vogt-Koyanagi-Harada disease presenting initially as aseptic meningoencephalitis Eur J Neurol 7 719 722 This paper describes the neurological and eye signs in VKH syndrome 11136362 Seehusen DA Reeves MM Fomin DA 2003 Cerebrospinal fluid analysis Am Fam Physician 68 1103 1108 Lumbar puncture is frequently performed in primary care, and this review outlines the interpretation of the clinical and laboratory findings 14524396 Shah KH Edlow JA 2002 Distinguishing traumatic lumbar puncture from true subarachnoid hemorrhage J Emerg Med 23 67 74 The purpose of this article is to assist emergency physicians in distinguishing traumatic lumbar punctures from subarachnoid hemorrhage 12217474 | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC523848.xml |
546008 | Hematopoietic chimerism after allogeneic stem cell transplantation: a comparison of quantitative analysis by automated DNA sizing and fluorescent in situ hybridization | Background Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is performed mainly in patients with high-risk or advanced hematologic malignancies and congenital or acquired aplastic anemias. In the context of the significant risk of graft failure after allo-HSCT from alternative donors and the risk of relapse in recipients transplanted for malignancy, the precise monitoring of posttransplant hematopoietic chimerism is of utmost interest. Useful molecular methods for chimerism quantification after allogeneic transplantation, aimed at distinguishing precisely between donor's and recipient's cells, are PCR-based analyses of polymorphic DNA markers. Such analyses can be performed regardless of donor's and recipient's sex. Additionally, in patients after sex-mismatched allo-HSCT, fluorescent in situ hybridization (FISH) can be applied. Methods We compared different techniques for analysis of posttransplant chimerism, namely FISH and PCR-based molecular methods with automated detection of fluorescent products in an ALFExpress DNA Sequencer (Pharmacia) or ABI 310 Genetic Analyzer (PE). We used Spearman correlation test. Results We have found high correlation between results obtained from the PCR/ALF Express and PCR/ABI 310 Genetic Analyzer. Lower, but still positive correlations were found between results of FISH technique and results obtained using automated DNA sizing technology. Conclusions All the methods applied enable a rapid and accurate detection of post-HSCT chimerism. | Background Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is performed mainly in patients with high-risk or advanced hematologic malignancies and aplastic anemias, and for some of them it is the only curative treatment. After allo-HSCT, incomplete engraftment and appearance of recipient's hematopoietic cells can lead to a coexistence of donor and host hematopoiesis – a situation known as mixed chimerism. Complete recovery of hematopoiesis of the donor origin is referred to as complete chimerism. Widely accepted molecular methods for analysis of chimerism after allo-HSCT, aimed at distinguishing precisely between donor's and recipient's cells, are PCR-based analyses of polymorphic DNA markers, such as variable number of tandem repeats (VNTR) or short tandem repeats (STR) [ 1 ]. Such analyses can be performed regardless of donor's and recipient's sex [ 2 ]. Additionally, in patients after sex-mismatched allo-HSCT, fluorescent in situ hybridization (FISH) can be applied. This technique is based on identification of Y-chromosome-specific sequences in the posttransplant sample examined. Variants of these techniques can be used for precise, quantitative assessment of the amount of donor's cells in recipient's peripheral blood and/or bone marrow after transplantation, in the long run giving a picture of the dynamics of changes in chimeric status within a hematopoietic compartment. Chimerism also reflects response to treatment [ 3 ], since it correlates with the risk of malignancy relapse. Relapse is the most frequent cause of treatment failure in recipients transplanted for hematologic malignancies, but it is still controversial if patients with mixed chimerism have an increased risk of developing relapse or graft failure. Most probably only a progressive mixed chimerism (a dynamic rise in the number of recipient's cells over time) seems to reflect a relapse or rejection. Successful outcome has been associated with a state of stable complete chimerism [ 4 ]. Here we report results of a comparison of different techniques for analysis of posttransplant chimerism: fluorescent in situ hybridization (FISH) and PCR-based molecular methods with fluorescent products detected in an ALF Express DNA Sequencer (Pharmacia) or ABI 310 Genetic Analyzer (PE). Methods Patients Investigation of hematopoietic chimerism was performed in ten children (eight girls, two boys) aged 6–16 years. They were diagnosed with acute myelogenous leukemia (n = 4), acute lymphoblastic leukemia (n = 2), chronic myelogenous leukemia (n = 1), myelodysplastic syndrome (n = 1), or Fanconi Anemia (n = 2). All recipients received hematopoietic stem cells from HLA-matched sibling donors, and sex-mismatch between donor and recipient was present in all cases. The material (peripheral blood) for hematopoietic chimerism quantification was collected in different periods after transplantation. In all 10 children reconstitution of hematopoiesis was observed. Out of 8 children transplanted for hematologic malignancies, 4 are well and alive, in complete continuous remission (CCR), while in the other 4, leukemia relapse occurred 4–23 months after transplantation. DNA isolation High-molecular-weight DNA was extracted from frozen whole blood (approximately 5 ml) or bone marrow (approximately 3–5 ml) by the standard treatment with sodium dodecyl sulfate (SDS) and proteinase K, and the salting-out method. DNA was isolated from the donors' and patients' blood samples collected before and after transplantation at various intervals in order to determine the chimeric status. Analysis of PCR products by an ABI 310 Genetic Analyzer The PCR protocol optimized for the Qiagen polymerase and the PE 9700 thermocycler was performed as described previously [ 5 ]. For fragment analysis (after capillary electrophoresis), an ABI 310 Genetic Analyzer (PE) was used [ 5 ]. All analyses were performed in the University Children's Hospital, Tuebingen. Analysis of PCR products by an ALF Express DNA Sequencer The PCR protocol optimized for the Thermal Controller MJ Research (Watertown, MA) model PTC-100™ was applied. PCR was performed in a volume of 10 μl; the PCR reaction mixture contained: 2.5 pM of each forward and reverse primer, 200 μl of each dNTP, 0.4 U Taq polymerase (Qiagen, Chatsworth, CA), 1x PCR buffer (Qiagen, Chatsworth, CA), and 40 ng of genomic DNA. Conditions for PCR were as follows: 5 min at 94°C for the first denaturation; 26 cycles of amplification with a temperature profile of 45 sec at 94°C, 1 min at 55°C, 1 min at 72°C; with additional 5 min at 72°C in the last cycle. STR loci were amplified with fluorescent PCR primers described previously [ 6 ]. Primers for microsatellite markers were labeled with Cy5 dye (TIB MOLBIOL). A 1.5-μl aliquot of PCR reaction was resuspended in 7 μl of loading solution (formamide, bromophenol blue) containing 100 bp and 300 bp internal markers. All samples, after denaturation at 95°C for 5 min, were analyzed on 6% denaturing polyacrylamide gel with 7 M urea in the sequencer. A 50–500 sizer labeled with Cy5 dye was used as an external marker (for calculation of allele sizes). Electrophoresis was carried out in 0.6xTBE buffer at 1500 V/min. The helium-neon laser was operated at a wavelength of approximately 700 nm and laser power value of 2.5 mW. Allele sizes and peak areas of fluorescent products were analyzed and calculated with the use of Fragment Manager software (Pharmacia). PCR and analysis of PCR products by the ALF Express DNA Sequencer was performed at the Institute of Human Genetics, Poznan. FISH The experiments were performed on interphase nuclei obtained by standard short culturing of fresh whole blood samples, with probes specific for chromosomes X (locus DXZ1 ) and Y (locus DYZ1 ). The FISH procedure according to Cytocell Ltd. was used [ 7 ]. The number of scored nuclei was 250 to 550, with a median of 300. FISH experiments were performed at the Institute of Human Genetics, Poznan. Since the material for FISH analysis was not collected in all designated periods after transplantation in some patients, FISH experiments were not performed then. Quantification of chimerism After electrophoresis in the ABI 310 Genetic Analyzer (PE), all obtained data were analyzed by GeneScan 3.1 software and then transferred to Genotyper 2.5 software [ 5 ]. All data obtained after electrophoresis of fluorescent products in the ALFExpress DNA Sequencer were transferred to Fragment Manager™ software (Pharmacia). For both, calculation of the amount of recipient's DNA was performed using the formula: % of recipient's DNA = (R1 + R2)/(D1+ D2 + R1 + R2) × 100, where: R1, R2 = peak areas of recipient's alleles; and D1, D2 = peak areas of donor's alleles. Only informative markers were used for the analysis. If donor and recipient were heterozygous but shared one allele, only the area of the non-shared alleles was considered for the analysis [ 8 ]. To make sure that quantification is accurate, we performed serial dilution experiments, where standardized mixed chimeric samples were created by mixing donor's and pretransplant recipient's DNA in a range between 0 and 100 percent. The sensitivity strongly depends on the size of alleles, the detection level was around 3–5% of patient cells. The results of chimerism detection by different methods were compared by the Spearman correlation test. Results The results of chimerism quantification with the use of an ALF Express DNA Sequencer, ABI 310 Genetic Analyzer, and FISH are compared in Table 1 . In three patients (no. 5, 6, 7) only donor's cells were detected in all post-HSCT samples and in one patient (no. 1) only recipient's cells were present in all samples examined. These results were confirmed with the use of all three methods. Three other patients (no. 2, 3, 4) exhibited mixed posttransplant chimerism according to PCR and/or FISH. In the last three patients (no.8, 9, 10), complete chimerism was detected by PCR and automated DNA sizing, but in some of their samples, low numbers of recipients' cells were detected by FISH. Table 1 Comparison of results using different DNA sizing technologies and FISH No. UPN Sex F/M Diagnosis Material examined HSCT date Days after HSCT PCR/ALF Express* PCR/ ABI 310 Genetic Analyser* FISH with probes specific to X, Y chro-mosomes* 1. 67 F AML PB 04.09.1998 542 100% 100% 100% 556 100% 100% 100% 574 100% 100% 100% 590 100% 100% 100% 639 100% 100% 100% 675 100% 100% 100% 697 100% 100% 100% 721 100% 100% 100% 750 100% 100% 100% 2. 102 M AML PB 12.10.2000 14 84% 86% 89% 19 49% 47% 51% 28 40% 40% 35% 35 47% 42% 12% 57 46% 35% 6% 3. 106 M FA PB 21.12.2000 7 100% 100% 92% 48 26% 27% 32% 91 13% 14% 9% 4. 87 F FA PB 27.12.1999 21 10% 28% ND 38 93% 94% ND 285 15% 18% ND 313 17% 19% ND 357 0% 0% ND 5. 45 F CML PB 15.11.1996 953 0% 0% ND 1030 0% 0% ND 1216 0% 0% ND 1284 0% 0% 0% 1374 0% 0% 0% 1459 0% 0% 0% 1492 0% 0% ND 6. 93 F AML PB 20.04.2000 28 0% 0% 0% 158 0% 0% 0% 277 0% 0% 0% 7. 109 F MDS PB 26.01.2001 14 0% 0% 0% 21 0% 0% ND 34 0% 0% ND 42 0% 0% ND 8. 108 F ALL PB 19.01.2001 17 0% 0% ND 21 0% 0% ND 26 0% 0% 2% 39 0% 0% ND 52 0% 0% ND 60 0% 0% ND 9. 88 F AML PB 21.01.2000 29 0% 0% ND 87 0% 0% 0% 178 0% 0% ND 267 0% 0% ND 365 0% 0% 2% 10. 95 F ALL PB 16.06.2000 27 0% 0% ND 83 0% 0% ND 200 0% 0% ND 218 0% 0% 1% ALL = acute lymphoblastic leukemia; AML = acute myelogenous leukemia; CML = chronic myelogenous leukemia; FA = Fanconi Anemia; FISH = fluorescent in situ hybridization; HSCT = hematopoietic stem cell transplantation; MDS = myelodysplastic syndrome; ND = no data; PB = peripheral blood; UPN = unique patient's number * results expressed as % of recipient's cells Coefficients of Spearman rank correlation between results of chimerism quantification by the three different methods are shown in Table 2 . All coefficients were statistically significant (p < 0.001). The correlation between PCR/ALF Express and PCR/ABI 310 was stronger than between FISH and both PCR methods. Table 2 Coefficients of Spearman rank correlation between results of chimerism quantification by different methods (all coefficients significant, P < 0.001). PCR/ABI 310 Genetic Analyzer FISH PCR/ALF Express 0.987 0.801 PCR/ABI 310 Genetic Analyzer 0.825 Comparison of methods used One of the advantages of application of automated DNA sizing techniques for detection of posttransplant chimerism is that the use of radioactivity is not necessary. It is arelatively simple and rapid method, consisting of two steps: polymerase chain reaction with fluorescent primers and automated detection of fluorescently labeled PCR products, separated by electrophoresis. The analysis of fluorescently labeled PCR products provides better accuracy and precision of measurement than traditional electrophoretic methods. The most time-consuming step, which might prolong the examination process, is the search for informative markers. Analysis of the chimerism status by amplification of STR loci can be performed regardless of donor's and recipient's sex. The most advantageous is the high sensitivity of the detection system used, so that only small amounts of DNA are needed. Fragment analysis in the ABI 310 Genetic Analyzer (PE) is performed after capillary electrophoresis. The examination of one sample requires about 6 hrs; 144 samples can be analyzed per day if three colors are simultaneously analyzed [ 5 ]. The advantage of this method is that thanks to the combination of three-color detection, multiple sets of samples or multiple loci for a single sample can be analyzed on one gel. It is possible to analyze more than one marker at a time after multiplex amplification of informative loci. In the ALFExpress DNA Analyzer, electrophoresis is carried out in an off-vertical gel cassette specially designed for easy and safe gel casting. The number of samples to be loaded on a gel is limited to 40 per run. The ALFwin Fragment Analyzer is used afterwards for fragment analysis. It is provided with versatile application software for the control of DNA fragment separation runs and subsequent analysis of the data. Collected data are used to accurately size PCR product peaks on the basis of external and internal standards. One analysis of 40 samples takes 7 hrs, including PCR, electrophoresis in the gel cassette, and paperwork. The appropriate assembling and cleaning of the gel cassette is critical and time consuming. It is well known that FISH is a good quantitative method of fluorescent signal detection, but requires lots of technical experience and expertise. Fluorescent in situ hybridization for one patient sample lasts at least 5 hrs, including preparation of interphase nuclei, hybridization with specific probes (X, Y dual-color FISH), and analysis. The high cost of the procedure is definitely a disadvantage. Discussion PCR-based techniques allow the relative proportions of recipient's and donor's cells in the post-HSCT period to be identified and quantified and is not only limited to sex-mismatched transplants. Although when using chimerism analysis one cannot assess whether or not the population of recipient's nucleated cells contains leukemic cells, samples taken at various intervals can show if the expansion rate of the particular population is consistent with hematologic and clinical symptoms of the disease. When it is not possible to find an informative marker for PCR amplification, only FISH analysis enables assessing the chimerism status. However, cytogenetic Y chromosome probing by FISH is limited exclusively to sex-mismatched transplantations. Results obtained with the use of ALFExpress DNA Sequencer and ABI 310 Genetic Analyzer are identical or very similar. We showed that appropriate quantitative assessment of chimerism after HSCT by using microsatellite genotyping and automated DNA sizing does not depend on the sequencer model used. The high correlation between results from the PCR/ALFExpress and PCR/ABI 310 Genetic Analyzer indicate that these two methods can be used interchangeably. The superiority of the ABI 310 Genetic Analyzer is limited to the possibility of analysis of three samples at the same time in one reaction tube and the technical ease of capillary electrophoresis with no need for the time-consuming and cumbersome use of glass plates. Lower, but still positive correlations were found between results of FISH analysis and these two methodological variants of PCR. However, in some samples analyzed with PCR, no recipient's signals were found, attesting to full donor chimerism, while at the same time residual host cells turned out to be detectable by FISH. We suggest that these results are within the range of error of the method applied. Conclusions Finally, we conclude that all the methods applied enable a rapid and accurate detection of post-HSCT chimerism and with due caution can be used interchangeably. Competing interests The author(s) declare that they have no competing interests. Authors' contributions J.J. carried out the molecular chimerism studies and drafted the manuscript. T.S. performed the statistical analysis. A.P and D.B. supplied clinical data. P.B. initiated quantitative analysis. J.W. supervised clinical part and final writing M.W. supervised laboratory part and final writing Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC546008.xml |
406410 | Correction | null | In PLoS Biology, volume 2, issue 3: Table of Contents Page iii This photograph was used on the March 2004 Table of Contents, where Adam Lazarus, who generously supplied the image, should have been acknowledged. We apologize for this omission. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC406410.xml |
521078 | A comparative ultrastructural and molecular biological study on Chlamydia psittaci infection in alpha-1 antitrypsin deficiency and non-alpha-1 antitrypsin deficiency emphysema versus lung tissue of patients with hamartochondroma | Background Chlamydiales are familiar causes of acute and chronic infections in humans and animals. Human pulmonary emphysema is a component of chronic obstructive pulmonary disease (COPD) and a condition in which chronic inflammation manifested as bronchiolitis and intra-alveolar accumulation of macrophages is common. It is generally presumed to be of infectious origin. Previous investigations based on serology and immunohistochemistry indicated Chlamydophila pneumoniae infection in cases of COPD. Furthermore, immunofluorescence with genus-specific antibodies and electron microscopy suggested involvement of chlamydial infection in most cases of pulmonary emphysema, but these findings could not be verified by PCR. Therefore, we examined the possibility of other chlamydial species being present in these patients. Methods Tissue samples from patients having undergone lung volume reduction surgery for advanced alpha-1 antitrypsin deficiency (AATD, n = 6) or non-alpha-1 antitrypsin deficiency emphysema (n = 34) or wedge resection for hamartochondroma (n = 14) were examined by transmission electron microscopy and PCR. Results In all cases of AATD and 79.4% of non-AATD, persistent chlamydial infection was detected by ultrastructural examination. Intra-alveolar accumulation of macrophages and acute as well as chronic bronchiolitis were seen in all positive cases. The presence of Chlamydia psittaci was demonstrated by PCR in lung tissue of 66.7% AATD vs. 29.0% non-AATD emphysema patients. Partial DNA sequencing of four positive samples confirmed the identity of the agent as Chlamydophila psittaci . In contrast, Chlamydophila pneumoniae was detected only in one AATD patient. Lung tissue of the control group of non-smokers with hamartochondroma was completely negative for chlamydial bodies by TEM or chlamydial DNA by PCR. Conclusions These data indicate a role of Chlamydophila psittaci in pulmonary emphysema by linking this chronic inflammatory process to a chronic infectious condition. This raises interesting questions on pathogenesis and source of infection. | Background Several species of the family Chlamydiaceae are well-known etiological agents of acute and chronic infections in humans and animals [ 1 , 2 ]. The first description of chlamydial respiratory disease in humans referred to psittacosis, also known as ornithosis, and dates back to 1879 [ 3 ]. Chlamydia (C.) psittaci , the agent responsible for this disease, has had several different names and, according to a recent proposal, should now be called Chlamydophila (Cp.) psittaci [ 4 ]. A century later, in 1986, Grayston et al. discovered another chlamydial respiratory agent, strain TWAR, which was later assigned to the species C. pneumoniae [ 5 , 6 ] currently reclassified as Chlamydophila pneumoniae [ 4 ]. Meanwhile, a variety of respiratory conditions in humans has been shown to be associated with this agent. Evidence of Cp. pneumoniae infection based on serology was reported in severe cases of chronic obstructive pulmonary disease (COPD), in which emphysema is dominant [ 7 , 8 ], as well as in exacerbations of COPD [ 9 ] and in persistent infections of the respiratory tract [ 10 , 11 ]. The detection rate of Cp. pneumoniae by immunohistochemical staining was elevated in lung tissue from subjects with COPD, but controls were not completely negative [ 12 ]. Initially Cp. pneumoniae was thought to be virulent for humans only, but recent descriptions of isolates from horse, koala, frog and reptiles suggest a wider host spectrum and even the possibility of zoonotic transmission [ 13 - 15 ]. Our previous investigations by means of immunofluorescence using a genus-specific antiserum against chlamydial LPS and scanning as well as transmission electron microscopy showed infection of the alveolar parenchyma and the bronchioles by Chlamydia spp. in patients having undergone lung volume reduction surgery for advanced pulmonary emphysema [ 16 , 17 ]. Accumulation of alveolar macrophages as well as different forms of bronchiolitis and focal pneumonia accompanying emphysematic changes were found regularly [ 18 ]. In preliminary examinations using an established nested PCR with DNA hybridization [ 19 ], DNA specific of Cp. pneumoniae was detected in two out of ten cases [ 20 ]. But this detection rate was far lower than that in electron microscopy or immunofluorescence using genus-specific antibodies, which showed Chlamydia spp. in over 80% [ 17 ]. Because of this fact PCR was extended to other Chlamydiaceae . Here we report the results of a more detailed study involving a larger number of cases and samples including controls. Methods Samples Lung tissue of adequate quality from patients with advanced emphysema undergoing lung volume reduction surgery was used for the present study [ 18 , 21 ]. Samples examined by transmission electron microscopy (TEM) included five specimens from alpha-1 antitrypsin deficiency (AATD) and 34 from non-AATD patients. PCR examinations were conducted on six AATD and 31 non-AATD specimens. History showed a status of cigarette smoking with over ten packyears in most of these patients (91.7%). There were two non-smokers among nine patients with AATD. Furthermore patients with hamartochondroma undergoing wedge resection were reviewed for clinical data (A.M.) and histology. Normal lung tissue of 14 non-smokers taken with resection of hamartochondroma was selected as a control group. Statistical analysis was done using SPSS, version 11.5 (SPSS Inc., Chicago, USA) on a PC running Windows XP Professional (Microsoft, Redmond, USA) as operating system. A test value below 0.05 was considered to be statistically significant. Light Microscopy and Transmission Electron Microscopy (TEM) Formalin-fixed lung tissue was embedded in paraffin wax (Tissuewax™; Medite GmbH, Burgdorf, Germany), slides of 3–7 μm thickness were cut using a rotatory microtome (Microm GmbH, Walldorf, Germany) and stained by hematoxylin and eosin. For TEM, tissue was fixed in 2.5% buffered glutaraldehyde or 3.5% formaldehyde and embedded in epon after postfixation with osmium tetroxide and block contrastation with uranyl acetate. In the cases of the hamartochrondroma control group, cores with diameters of 0.60 cm and 0.24 cm were obtained from paraffin blocks using a prototypical self-made manual tissue puncher (a device for tissue microarray construction developed by M.W.). These cores were used to select an area well defined by light microscopy for PCR (0.60 cm cores) and TEM (0.24 cm cores). For TEM, tissue was dewaxed with xylene and processed as described above. Semithin sections (prepared on a Reichert Om U3 ultramicrotome; Reichert, Vienna, Austria) were stained with basic fuchsin and methylene blue to define blocks of adequate quality. Ultrathin sections from two to five blocks were stained with lead citrate and examined using a Zeiss EM 900 transmission electron microscope (Zeiss, Oberkochen, Germany). Polymerase Chain Reaction (PCR) Tissue from two different resources was used. Firstly, frozen tissue was collected immediately after resection and stored at -80°C (n = 31). Secondly, paraffin-embedded tissue (PET) containing histologically discernible inflammation sites was used, and sections or 0.60 cm cores were dewaxed using xylene (n = 12 and 14 controls). In five cases, material was available as both frozen and PET. DNA was isolated from lung tissue using the High Pure PCR Template Preparation Kit (Roche Diagnostics, Mannheim, Germany) according to the instructions of the manufacturer. Five μl of the DNA extract were used as template in PCR. Samples were tested for C. psittaci and Cp. pneumoniae by a modified version of the nested PCR procedure described by Kaltenböck et al. [ 22 ], which targets the omp A gene. The first step was genus-specific amplification using primers 191CHOMP (5'-GCI YTI TGG GAR TGY GGI TGY GCI AC-3') and CHOMP371 (5'-TTA GAA ICK GAA TTG IGC RTT IAY GTG IGC IGC-3'). For the second amplification, we used 1 μl of the genus-specific product and primer combination 218PSITT (5'-GTA ATT TCI AGC CCA GCA CAA TTY GTG-3') / CHOMP336s (5'-CCR CAA GMT TTT CTR GAY TTC AWY TTG TTR AT-3') for C. psittaci , or 201CHOMP (5'-GGI GCW GMI TTC CAA TAY GCI CAR TC-3') / PNEUM268 (5'-GTA CTC CAA TGT ATG GCA CTA AAG A-3'), for Cp. pneumoniae , respectively. The sizes of specific amplicons are: 576–597 bp (genus-specific product), and 389–404 bp for C. psittaci or 244 bp for Cp. pneumoniae after nested PCR. A detailed protocol of the procedure is contained in [ 23 ]. DNA sequencing In order to discriminate the different members of the C. psittaci -group [ 4 ], five μl of the final DNA extract served as template for PCR amplification of the 16S rRNA signature region. Amplicon bands were cut out of agarose gels, extracted using the QIAquick Gel Extraction Kit (QIAGEN, Hilden, Germany), and subjected to cycle sequencing using the BigDye™ Terminator Cycle Sequencing Ready Reaction Kit (Applied Biosystems, Darmstadt, Germany). The oligonucleotides 16SIGF (5'-CCG CGT GGA TGA GGC AT-3') and 16SIGR (5'-TCA GTC CCA GTG TTG GC-3') were used as amplification and sequencing primers [ 4 ]. Nucleotide sequences were determined on an ABI Prism 310 Genetic Analyzer (Applied Biosystems). Results Light microscopy The median value of age was 46.5 in AATD, 58.0 in non-AATD emphysema and 64.5 in hamartochondroma. The rate of females varied between 33.3% in AATD, 37.5% in non-AATD and 42.9% in hamartochondroma. Histology revealed destruction of the alveoli, intra-alveolar accumulation of macrophages, and acute alongside chronic bronchiolitis in all cases of AATD (Fig. 1A,1B ) and non-AATD emphysema (Fig. 2A,2B ) consistent with previous examinations. In cases of hamartochondroma only some macrophages and mucus in the bronchioli could be detected (Fig. 3 ). Figure 1 Histology of alpha-1 antitrypsin deficiency emphysema. In alpha-1 antitrypsin deficiency, advanced panacinar destruction of the lung parenchyma and accumulation of macrophages (A, hematoxylin eosin, original magnification ×40), as well as severe acute and chronic bronchiolitis are seen (B, periodic acid Schiff's reaction, original magnification ×100). Figure 2 Histology of non-alpha-1 antitrypsin deficiency emphysema. In non-alpha-1 antitrypsin deficiency emphysema, chronic respiratory bronchiolitis, destruction of the alveolar architecture, prominent accumulation of macrophages (A, hematoxylin eosin, original magnification ×40) and marked bronchiolitis of the terminal bronchioles is found (B, hematoxylin eosin, original magnification ×100). Figure 3 Histology of normal lung tissue in patients with hamartochondroma. In cases of hamartochondroma, only some macrophages and mucus can be detected in the bronchioli (hematoxylin eosin, original magnification ×15). No signs for emphysema or bronchiolitis could be detected. Transmission electron microscopy TEM images illustrate that the cell and tissue morphology appeared severely destroyed in emphysema samples. Chlamydial elementary and reticular bodies of 0.2 to 0.8 μm diameter were found on the surface of alveolar or bronchiolar epithelium and showed adherence to microvilli as previously described [ 16 , 17 ]. Reticular and some elementary bodies were seen in AATD (Fig. 4 ) and non-AATD emphysema (Fig. 5A,5B,5C ), they were scattered within the interstitium (Fig. 5A ) and also assembled in groups (Fig. 5B ). Perinuclear inclusions could be detected in fibroblasts (Fig. 5C ). Altogether, in 32 cases (82%) typical morphological structures indicating persistent chlamydial infection were present. In seven cases of emphysema, chlamydial bodies could not be detected or the findings were ambiguous. Detection rates were higher in AATD emphysema (5/5 = 100%) than in non-AATD emphysema (27/34 = 79.4%, Table 1 ). The control group of patients with hamartochondroma showed no signs of chlamydial infection in TEM. Figure 4 Transmission electron microscopy of alpha-1 antitrypsin deficiency emphysema. Chlamydial bodies (arrows) and destruction of the interstitial connective tissue are seen in alpha-1 antitrypsin deficiency. Ultrastructure is less well preserved after fixation in formaldehyde. Figure 5 Transmission electron microscopy of non-alpha-1 antitrypsin deficiency emphysema. In non-alpha-1 antitrypsin deficiency destruction of the connective tissue and chlamydial bodies are detected (A-C). Higher magnification reveals different developmental stages of chlamydial bodies within a lytic area (B) and perinuclear inclusions (arrows) (C). Table 1 Detection of Chlamydia spp. in emphysema by TEM and PCR Groups Cases C. psittaci C. pneumoniae Fisher Yates test vs. controls AATD emphysema TEM 5 5 (100%) 0.00009 PCR 6 4 (66.7%) 1 (16.7%) 0.00310* Non AATD emphysema TEM 34 27 (79.4%) 0.00000 PCR 31 9 (29.0%) 0 (0%) 0.03989 Controls TEM 14 0 (0%) PCR 14 0 (0%) 0 (0%) AATD = alpha-1 antitrypsin deficiency, * = for C. psittaci only Polymerase Chain Reaction (PCR) Examination by PCR revealed the presence of C. psittaci -specific DNA in four (66.7%) specimens with AATD and nine (29%) with non-AATD emphysema (Fig. 6 , Table 1 ). In one case of AATD, the amplicon was identified as Cp. pneumoniae . PCR was negative in all cases with hamartochondroma. The detection rate for C. psittaci in emphysematic tissue was higher from PET than from frozen material (50% vs. 21.9%, Fisher Yates test n. s.). In five cases, where both PET and frozen tissue were examined, one patient was positive in TEM and another one in PCR. Figure 6 Detection of chlamydiae by nested omp A-PCR from frozen lung tissue of patients with advanced emphysema (samples N1 to N43). DNA was extracted from tissue samples, subjected to nested amplification, and PCR products were electrophoresed on 2% agarose gels. The amplicon of approximately 400 bp is specific for Chlamydia psittaci . Strain DC 5 of Chlamydophila psittaci was used as positive amplification control, nc1 and nc2 are negative (reagent) controls. Lane M shows the 100-bp ladder (Invitrogen, Karlsruhe, Germany). DNA sequencing To confirm the identity of the chlamydial species, DNA from four of the positive samples, i.e. N16, N25, N26, and N33, was sequenced in the 16S rRNA signature region (approximately 300 bp, Fig. 7 ). A BLAST search of these sequences revealed close to 100 % homology to the species Cp. psittaci . Figure 7 Sequence alignment of four tissue samples and reference strains (16S signature region). The samples N16, N25, N33, and N26 were sequenced in the 16S signature region. A BLAST search confirmed the species as Cp. psittaci . Discussion Strains of Cp. psittaci are known to cause infections in over 130 avian species and 32 other domestic and wild animals. Classical psittacosis represents a systemic disease in psittacine birds of acute, protracted, chronic or subclinical manifestation. Avian strains of the agent are known to be pathogenic to humans, the symptoms being mainly non-specific and influenza-like, but severe pneumonia, endocarditis and encephalitis are not uncommon [ 24 , 25 ]. The possibility of persistent infection in man was first described in the 1950s [ 26 ]. In the present study and in previous investigations, transmission electron microscopy revealed elementary bodies as well as typical and aberrant reticular bodies, thus indicating active infection alongside persistent infection [ 10 , 16 , 17 ]. Chlamydiae could not be detected in each case, but the rate of positive findings in TEM and PCR (Table 1 ) was comparable to that of Cp. pneumoniae in atherosclerosis [ 27 , 28 ]. Pear-shaped elementary bodies as typically found in Cp. pneumoniae infection [ 29 ] were not observed. While the findings of TEM are more indicative of Cp. psittaci infection [ 30 ], it must be noted that this method provides no clear-cut differentiation among Chlamydiaceae species, for even strains of the same species exhibit different morphology at the various developmental stages. Rather unexpectedly, Cp. pneumoniae was detected only in one sample by PCR, not indicating an important role of this agent in the cases examined here. The fact that, apart from psittacosis, Cp. psittaci has not been associated with human respiratory disorders in recent decades may be a question of sensitivity and specificity of detection. Particularly PCR with its capability to specifically identify all individual species of Chlamydiaceae at a detection limit of less than one inclusion-forming unit has opened up new possibilities in this respect. Higher detection rates of Cp. psittaci in tissue with histological evidence of inflammation in comparison to unselected frozen tissue (6/12 = 50% vs. 7/32 = 21.9%, Fisher Yates test n.s.) indicate an association with regional activity of infection and inflammation. Besides psittacosis, Cp. psittaci has been recently associated with chronic inflammation in patients with ocular adnexal lymphomas [ 31 ]. In COPD, activated macrophages and neutrophils produce matrix metalloproteinases which are relevant in the development of emphysema [ 32 , 33 ]. The release of matrix metalloproteinases was shown to be stimulated by cytokines produced in the course of chlamydial infection [ 34 ] and by chlamydial heat shock protein 60 as well [ 35 ]. These findings represent a link to the established pathogenetic concepts in pulmonary emphysema. Cp. psittaci was found at comparable and statistically significant rates in AATD and non AATD emphysema. Conclusions The fact that the chlamydial agent present in the emphysema tissue was identified by DNA sequencing as an avian serovar of Cp. psittaci provides an important indication on the source of infection. It is conceivable that the patients were infected through contact with birds, although this could not be verified for lack of relevant data on history. The detection rate of chlamydiae in cases of AATD emphysema vs. non-AATD emphysema was clearly higher, thus indicating a relevant role of Cp. psittaci infection in this disorder, or at least a higher susceptibility of AATD patients for an infection of their lungs with Cp. psittaci . Further investigations concerning smokers and non-smokers, pathogenetic relevance and zoonotic implications are required. In any circumstances, Cp. psittaci has to be considered an underestimated pathogen with considerable importance in public health, although the various facets of its specific impact have yet to be evaluated. Competing interests None declared. Authors' contributions Dirk Theegarten has designed and organized this study, done light microscopy, written most of the manuscript, participitated in its statistical analysis and reviewed results of transmission electron microscopy. Olaf Anhenn participitated in designing the study, collected the data, performed statistical analysis and reviewed the manuscript. Helmut Hotzel carried out PCR analysis and DNA sequencing. Konrad Sachse has done the sequence alignments and written the parts of the manuscript concerning molecular biology and veterinary aspects, and also participated in PCR analysis. Mathias Wagner developed and used the tissue puncher for this study. Georgios Stamatis has done lung volume reduction surgery. Alessandro Marra and Georgios Stamatis have done the clinical parts. Alessandro Marra has reviewed the history of the patients. Grigori Mogilevski performed transmission electron microscopy. All authors have discussed and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC521078.xml |
523860 | Idiopathic isolated clitoromegaly: A report of two cases | Background Clitoromegaly is a frequent congenital malformation, but acquired clitoral enlargement is relatively rare. Methods Two acquired clitoromegaly cases treated in Atatürk Training Hospital, Izmir, Turkey are presented. Results History from both patients revealed clitoromegaly over the last three years. Neither gynecological nor systemic abnormalities were detected in either patient. Karyotype analyses and hormonal tests were normal. Abdominal and gynaecological ultrasound did not show any cystic lesion or other abnormal finding. Computerized tomography scan of the adrenal glands was normal. Clitoroplasty with preservation of neurovascular pedicles was performed for the treatment of clitoromegaly. Conclusion The patients were diagnosed as "idiopathic isolated" clitoromegaly. To the best of our knowledge, there has been no detailed report about idiopathic clitoromegaly in the literature. | Case reports Two cases with clitoromegaly were treated in Atatürk Training Hospital, Izmir, Turkey. A 22-year-old gravida 0 (case 1) and 19-year-old gravida 0 (case 2) presented with acquired clitoromegaly, leading to psychological distress. Histories taken from both patients revealed a gradually growing clitoris in the last three years, no history of drug abuse or family history of clitoromegaly and no clitoral irritation secondary to masturbation or other sexual functions. Case one had a phallus 20 mm in length that increased to 30 mm on arousal (Figure 1 ) and case two had a phallus 30 mm in length, which increased to 40 mm with arousal (Figure 2 ). Secondary sexual features were normal in both cases. Sexual hair was normal and there were no signs of hirsutism. The patients were not obese, weighing 65 and 68 kg, respectively. Neither patient had any sign of polycystic ovaries. No gynaecological or systemic abnormalities were detected in either patient. The only clinical finding was 'isolated clitoromegaly' on physical examination. Figure 1 View of the Case 1. Figure 2 View of the Case 2. Karyotype analysis was done in both cases and reported as 46, XX. Results of routine laboratory tests were normal. In addition, serum electrolytes, oestradiol, sex hormone binding globulin (SHBG), testosterone, androstenedione, dehydroepiandrosterone sulphate (DHEA-S), follicule stimulating hormone (FSH), luteinizing hormone (LH), 17 hydroxy progesterone (17-OH-P), prolactin, adrenocorticotropic hormone (ACTH), cortisol, placental lactogen (PL), deoxycorticosterone, deoxycortisol 11, triiodothyronine (T3), thyroxine (T4), thyroid stimulating hormone (TSH), beta-human chorionic gonadotrophin (β-hCG), carcinoembryonic antigen (CEA) were measured before the operations and the results were normal. 17-ketosteroid output in 24-hour-urine specimen was normal in both patients. Abdominal and gynecological ultrasound did not show any cystic lesion or abnormal finding. Computed tomography scan of the adrenal glands was normal. No abnormality suggestive of a possible relation to clitoromegaly was found in all laboratory and radiological tests. Both patients underwent clitoroplasty with preservation of the neurovascular pedicles under general anesthesia. A traction suture of 3/0 nylon was placed in the glans of clitoris (Figure 3 ). An incision was made on the lateral phallus perpendicular to the axis of the clitoral shaft, and carried through a 270 degree semicircular arc to the base of the glans as described by Papageorgoiou et al [ 1 ]. Two longitudinal incisions were made lateral to the dorsal neurovascular bundle. Two crura were identified, clamped and the mid-body of the clitoris was resected. The base of the glans was sutured to the divided corpora with 4/0 vicryl, and proximal and distal ends of the corpora were closed with 4/0 vicryl. The skin was closed with 4/0 vicryl sutures as well. Histopathological examinations of the resected specimens showed "normal corporal tissue". There was no abnormal finding on microscopic examination of the specimen obtained from clitoral and submucosal tissue. Figure 3 Traction of the clitoris per-operatively. Patients were followed up for one year after the operation. There was no early or late post-operative complication. Sensation was normal and patients were satisfied with the aesthetical and functional results. Discussion Clitoromegaly is a frequently seen congenital malformation, but acquired clitoral enlargement is rarely detected [ 2 ]. A detailed history and physical examination are required for the evaluation of clitoral enlargement because clitoromegaly may result from a variety of conditions [ 3 ]. The causes of clitoromegaly can be classified into four groups; hormonal conditions, non-hormonal conditions, pseudoclitoromegaly and idiopathic clitoromegaly (Table 1 ). Table 1 Classification of the clitoromegaly based on causative factors Causative factors of clitoromegaly A. Hormonal conditions 1. Endocrinopathies 2. Masculinizing tumors 3. Exposure to the androgens 4. Syndromes B. Non-Hormonal conditions 1. Neurofibromatosis 2. Epidermoid cysts 3. Syndromes 4. Nevus C. Pseudoclitoromegaly D. Idiopathic Endocrinopathies, masculinizing tumors, exposure to the androgens and various syndromes are the main hormonal causes of clitoromegaly. The most common cause is female pseudohermaphroditism secondary to congenital adrenal hyperplasia (CAH) or adrenogenital syndrome, caused by an enzyme defect in the normal pathway of steroid biosynthesis [ 4 ]. Virilization of the external genitalia may cause profound clitoromegaly but rarely causes formation of a true penile urethra. However, clitoromegaly may be accompanied by fusion of the labioscrotal folds and perineoscrotal hypospadias, and a persistence of the urogenital sinus closing the external opening of the vagina [ 5 ]. Bilateral hilus cell tumors of the ovary, steroid producing gonadal tumors, adrenal androgen-secreting carcinoma, Leydig cell tumor of the ovaries and metastatic carcinosarcoma of the urinary bladder have been reported to cause clitoromegaly [ 6 - 9 ]. Fetal exposure to danazol has been described as cause for clitoromegaly [ 10 ]. An interesting case was reported by Akcam and Topaloglu of clitoromegaly possibly following blood transfusion from an adult in a premature infant [ 11 ]. Among the non-hormonal conditions are neurofibromatosis (NF)[ 12 ], epidermoid cysts[ 3 ], various syndromes and nevus lipomatous cutaneous superficialis. The majority of clitoromegaly cases related to NF are congenital. Clitoral cysts arise from epidermis displaced into the dermis or the subcutaneous tissue either during the prenatal period or after a trauma. Various syndromes resulting from non-hormonal conditions may cause clitoromegaly. Kazlauskaite et al reported a case diagnosed as congenital generalized lipodystrophy (CGL) presenting with generalized body-fat loss, prominent musculature, hepatomegaly, clitoromegaly and mild hirsutism [ 13 ]. CGL is an autosomal recessive disorder characterized by severe metabolic derangement associated with the absence of subcutaneous adipose tissue and clitoromegaly. Turner syndrome (TS) is a chromosomal disorder in females and results from a partial or complete loss of an X chromosome. Abnormalities include short stature and gonadal dysgenesis. Haddad et al presented a case of clitoromegaly and TS [ 15 ]. Fraser syndrome is another rare cause of clitoromegaly [ 14 ]. Androgen insensitivity syndrome is a heterogeneous disorder with a wide spectrum of phenotypic abnormalities, ranging from a complete female phenotype to ambiguous forms that more closely resemble males. The primary abnormality is a defective androgen receptor protein due to mutation of the androgen receptor gene. Nevus lipomatous cutaneous superficialis (NLCS) is a relatively rare condition characterized by groups of ectopic fat cells dispersed in various parts of the body [ 16 ] that may cause clitoromegaly when located on the clitoris. Pseudohypertrophy of the clitoris has been reported in small girls due to masturbation: manipulations of the skin of prepuce leads to repeated mechanical trauma, which expands the prepuce and labia minora, thus imitating true clitoral enlargement [ 2 ]. The objectives of clitoroplasty are preservation of sexual arousal function and sensation, and cosmetic. Historically, until 1960s, clitoral hypertrophy was treated surgically by amputation (clitoridectomy)[ 4 ]. Surgical methods for correction of clitoral hypertrophy were first described in 1934 by Young, who performed an operation for clitoral reduction in a child with CAH [ 17 ]. Several clitoroplasty methods have been reported, but few describe preservation of dorsal and ventral neurovascular bundles in sexually mature women. Clitoroplasty with preservation of the neurovascular pedicle may be the optimal operative technique for the treatment of clitoromegaly. Competing interests The authors declare that they have no competing interests. Authors' contributions EC conceived the study and prepared the manuscript draft for submission. AA, NS, OC and YO did the literature search and participated in the preparation of the manuscript. All authors read and approved the final manuscript. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC523860.xml |
538267 | Mercury exposure, malaria, and serum antinuclear/antinucleolar antibodies in amazon populations in Brazil: a cross-sectional study | Background Mercury is an immunotoxic metal that induces autoimmune disease in rodents. Highly susceptible mouse strains such as SJL/N, A.SW, B10.S (H-2 s ) develop multiple autoimmune manifestations after exposure to inorganic mercury, including lymphoproliferation, elevated levels of autoantibodies, overproduction of IgG and IgE, and circulating immune complexes in kidney and vasculature. A few studies have examined relationships between mercury exposures and adverse immunological reactions in humans, but there is little evidence of mercury-associated autoimmunity in humans. Methods To test the immunotoxic effects of mercury in humans, we studied communities in Amazonian Brazil with well-characterized exposures to mercury. Information was collected on diet, mercury exposures, demographic data, and medical history. Antinuclear and antinucleolar autoantibodies (ANA and ANoA) were measured by indirect immunofluorescence. Anti-fibrillarin autoantibodies (AFA) were measured by immunoblotting. Results In a gold mining site, there was a high prevalence of ANA and ANoA: 40.8% with detectable ANoA at ≥1:10 serum dilution, and 54.1% with detectable ANA (of which 15% had also detectable ANoA). In a riverine town, where the population is exposed to methylmercury by fish consumption, both prevalence and levels of autoantibodies were lower: 18% with detectable ANoA and 10.7% with detectable ANA. In a reference site with lower mercury exposures, both prevalence and levels of autoantibodies were much lower: only 2.0% detectable ANoA, and only 7.1% with detectable ANA. In the gold mining population, we also examined serum for AFA in those subjects with detectable ANoA (≥1:10). There was no evidence for mercury induction of this autoantibody. Conclusions This is the first study to report immunologic changes, indicative of autoimmune dysfunction in persons exposed to mercury, which may also reflect interactions with infectious disease and other factors. | Background Mercury has been recognized as a significant environmental and public health problem for more than 40 years, primarily for its effects on the developing nervous system, as expressed in tragic episodes of human poisoning in Japan and Iraq [ 1 ]. Awareness of the effects of mercury on the immune system has increased in the last decade [ 2 , 3 ]. In rodent models exposure to inorganic and organic mercury has a range of immunotoxic effects, functionally associated with decreased cell-mediated immunity and the induction of autoimmunity [ 4 ]. These effects vary with strain [ 5 - 7 ]. Both inorganic and organic forms of mercury are immunotoxic, although they differ quantitatively and qualitatively in their effects on the immune system; methylmercury may require metabolism into inorganic species to induce immunotoxic effects, such that the effects of methylmercury are delayed and reduced in appearance [ 6 ]. Ethylmercury (C 2 H 5 Hg + ), the active compound in thimerosal and other medical compounds, induces in a dose-dependent pattern all the features of systemic autoimmunity that have been described after exposure to mercuric chloride (HgCl 2 ) [ 8 ]. Mercury can enter the body through inhalation, as elemental mercury (Hg 0 ), through dermal or eye contact, as ethylmercury, and by absorption through the gastrointestinal track, primarily as methylmercury (CH 3 Hg + ) through ingestion of contaminated fish [ 1 ]. Inhaled Hg 0 vapor easily crosses the pulmonary alveolar membranes to enter the circulatory system, where it is primarily bound to red blood cells, and is rapidly distributed to the central nervous system, and the kidneys [ 9 ]. Mercury absorbed through skin contact is oxidized in the liver to Hg 2+ by glutathione [ 10 ]. After entering the blood stream, mercury is distributed to all tissues, including the brain, kidney, lungs, hair, nails, liver, fetus, milk, etc [ 1 , 10 ]. In the literature, no cases of frank autoimmune disease have been reported in persons exposed to mercury, occupationally or environmentally [ 3 ]. A few studies have examined relationships between mercury exposures and adverse immunological reactions, particularly in connection with mercury amalgam, but these are controversial [ 1 ]. At relatively high levels of occupational exposure, changes in immunoglobulins have been reported, but not consistently [ 3 , 11 - 13 ]. Nephropathy described in workers with either acute or chronic exposures to Hg 0 vapor may involve deposition of autoantibodies to basement membrane proteins in the glomerulus [ 3 , 14 ]. In a study of chloralkali workers, circulating anti-laminin antibodies were found in some workers as well as autoantibodies against glomerular basement membrane and circulating immune complexes, but no significant increases in antinuclear autoantibodies (ANA) were found [ 12 ]. No studies of immune parameters have been conducted in the large longitudinal studies of children exposed to methylmercury via fish consumption in the Seychelles or in the Faeroe Islands [ 1 , 15 , 16 ]. In a cross-sectional study of a maritime population of children with exposure to polychlorinated biphenyls and methylmercury via seafood consumption, numbers of naïve T-cell subsets (CD4 + CD45RA), T-cell proliferation, and plasma IgM were decreased, while IgG levels were increased, relative to controls [ 17 ]. The goal of this study was to test the hypothesis that exposures to methylmercury and/or inorganic mercury may have effects on specific markers of mercury-induced autoimmunity, that is, ANA and antinucleolar (ANoA) autoantibodies, and in a subset of subjects anti-fibrillarin (AFA) autoantibodies. ANoA autoantibodies, a marker found in some human autoimmune diseases [ 18 ], have been reported to be elevated by mercury in mice [ 19 ]. More recently, Pollard et al. have proposed that ANoA antibodies targeting the nucleolar 34-KDa protein fibrillarin may be specific biomarkers of mercury-induced immunotoxicity [ 20 , 21 ]. Mercury-induced ANoA in mice reacts with a conserved epitope of fibrillarin [ 20 , 21 ], which is indistinguishable from the AFA response seen in scleroderma. A recent case-control study reported that severely affected scleroderma patients with AFA were more likely to have higher levels of mercury in urine, as compared either to less severely affected cases without AFA, or controls, suggesting an etiologic role for mercury in this autoimmune disease [ 22 ]. However, the sample size was small and levels of mercury were low in all subjects. We were able to conduct this study in collaboration with an ongoing epidemiological surveillance of mercury exposures in Amazonian Brazil, where populations are exposed to both inorganic and organic mercury associated with gold mining activities [ 23 , 24 ]. In Amazonian Brazil, as in many other regions of the world, elemental mercury is used in liquid form for amalgamation of gold particles in placer deposits [ 23 , 25 ]. The gold miners are directly exposed to inorganic mercury and residents of downstream communities are exposed to methylmercury via consumption of fish. Extensive work has been done on many of these populations, documenting a range of exposures among miners and fish consumers [ 24 - 26 ], many well above the levels found in populations in North America and Europe, and well in excess of the levels found in the Seychelles and Faeroes cohorts [ 1 ], although lower than those reported in Minamata [ 27 ]. In this study we analyzed autoantibodies and mercury exposures in three populations from the state of Pará, Brazil. These groups were exposed to different types of mercury in different settings, with exposure to other risk factors not all of which were determined. Therefore, these may contribute to the observed differences among communities, in addition to mercury exposures. We report here that exposures to mercury are associated with significant increases in the prevalence of elevated serum ANoA. Methods To test our hypothesis we examined three separate populations, selected from ongoing studies of mercury exposures and health status being conducted by FUNASA (Fundaçao Nacional de Saúde), under the leadership of Dr Santos of the Evandro Chagas Institute. The communities in our study were chosen from this surveillance database on the basis of differences in exposures to mercury and other risk factors in Pará, Brazil. At Rio-Rato , a garimpo or gold mine site, most of the population was directly involved in gold extraction and refining, resulting in relatively high but often episodic exposures to inorganic mercury, similar to those described by us and others [ 25 , 26 ]. This site is in the lower Tapajós River watershed, an area of high malaria transmission [ 28 ]. At Jacareacanga , a riverine community on the Tapajós River several hundred km downstream from the region of active gold mining in Pará, the inhabitants consume fish known to be contaminated with methylmercury [ 24 , 29 , 30 ]. There is little autochthonous malaria in this town but many people have histories of malaria because of contact with the nearby region [ 31 ]. Finally, at the village of Tabatinga , located on the lower Amazon River east of the Tapajós, the population has no direct or indirect contact with gold mining, and fish collected in this village have levels of methylmercury [ 24 ] within the guidelines for safe consumption recommended by the WHO and the US FDA [ 1 ]. Tabatinga has had no prevalent malaria over the past ten years, according to data from FUNASA (personal communication JM Souza). Study design The overall design of the mercury surveillance studies conducted by Dr Santos is a community based, cross sectional survey of Brazilian populations in Amazonia, focused on the states of Pará, Amazonas, Acre, and Rondônia, including gold mining sites, riverine communities, and populations without exposure to mercury. The study design is described in detail by Santos and colleagues [ 29 , 32 ]. In all studies, a census was first conducted at each site to determine sampling strategy. Subjects were then contacted by house-to-house survey and enrolled in proportion to the population in terms of age and gender. Overall, between 80 and 90% of contacted persons consented to participate at each site. Information was collected by interview, administered in Portuguese by trained personnel, to provide information on demographics (age, gender, educational attainment), diet (with particular emphasis on fish), birthplace, current/previous occupation (including use of mercury), income, health status, reproductive history (women), drug and alcohol use, past/current malaria, number of people per household, time residing at the site, and medical history. A short clinical examination was conducted, and samples of hair, blood, urine, and stool were taken for laboratory analyses, including mercury levels in hair and urine. Malaria was assessed by questionnaire to determine past history of malaria (self reported), as well as by thick smears taken to determine prevalent malaria. All smears were read by trained technicians. Data on past malaria were stratified using Baird as reference [ 33 ], in which he determined the minimum number (4) of prior malaria infections associated with acquisition of functional immunity (i.e., no disease and/or parasitemia after biting). The study was approved by the institutional review board of the IEC and FUNASA. The University of Maryland Medical School and the Johns Hopkins Medical Institutions Institutional Review Boards also approved the analyses conducted in this study. Mercury exposure Subjects' exposure to mercury was determined in two ways. First, information was gathered by questionnaire on occupational history (contact with and use of mercury in gold mining), and/or fish consumption (by weekly frequency and predominant types of fish consumed). Second, mercury concentrations were measured in biologic compartments. For persons in Tabatinga and Jacareacanga with chronic exposure via fish consumption, hair mercury (μg Hg/g of hair) was used as the exposure biomarker as recommended [ 1 ]. Hair samples were collected in 2 cm lengths (from the scalp) and analyzed using standard methods of atomic absorption spectrophotometry by cold vapor technique in the laboratory of Dr Santos, which participates in the international QA/QC program with the Université de Quebec [ 34 ]. For persons with occupational exposures to inorganic mercury, in Rio-Rato, urine mercury was used as the biomarker (μg Hg/L of urine, no data on creatinine was available). This is the standard method utilized by Santos and others for assessing occupational exposures to inorganic mercury and generally reflects relatively recent exposures [ 1 , 35 ]. Immunologic outcomes Blood samples were collected by venipuncture and sera were separated on site by centrifuge, aliquoted and immediately frozen on liquid nitrogen for transport by air to the IEC in Belem (Pará). Aliquots of frozen serum were stored at -80°C and then transferred on dry ice to Baltimore via air transport accompanied by Dr Silbergeld. All analyses were done under blinded conditions. Detection of ANA/ANoA The serum samples were stored at -80°C until analysis. Each aliquot was thawed and 10 μL taken for analysis by indirect immunofluorescence (IIF) microscopy using commercially available slides prepared from human epithelial cells (HEp-2) as substrate (INOVA Diagnostics) following the methods of Burek and Rose [ 36 ]. The slides were stored in the dark at 4°C until they were analyzed by a blinded reader (IAS or AG). Randomly selected slides were re-checked by an experienced immunologist (CLB). Detection of AFA The antigen proteins were obtained from rat liver nuclei [ 20 ], and the proteins were separated by 15% SDS-PAGE. Preparations were first fractionated by SDS-PAGE and subsequently transferred to nitrocellulose and immunoblotted. Briefly, nitrocellulose was blocked in PBS/0.1% Tween-20/5% dry milk for 2 h at room temperature. Incubation with primary antibody (serum samples) (1/50 in blocking solution) was performed at room temperature for 1 h, followed by 3 washing steps of 10 min each in PBS/0.1% Tween-20. Secondary antibody (horseradish peroxidase-conjugated goat anti-human IgG) (Caltag Lab, CA) was used at a dilution of 1/2000 in blocking solution for 1 h at room temperature followed by 3 washes of 10 min each in PBS/0.1% Tween-20. Bound antibody was detected using chemiluminescence. The 34 KDa protein was detected by molecular weight using serum of scleroderma (SC) patients as a positive standard. The SC serum revealed one band at the expected molecular weight of 34 KDa. Data analyses The concentration of serum autoantibodies is expressed in terms of the dilution factor at which fluorescence could still be detected. Detection of autoantibodies at a serum dilution of ≥1:40 is considered "positive" for most clinical uses [ 37 ]. However, detectability at dilutions between 1:10 and 1:40 can also have health implications [ 37 ] and may be relevant as biomarkers of mercury exposure. Since we are studying the autoantibodies as biomarkers of immunotoxicity rather than as indicators of disease, we present our findings at both dilutions, ≥1:40 and ≥1:10. Statistical analysis Means for continuous variables (median for variables with skewed distributions) and percents for categorical variables were computed for the descriptive analysis in our data. Chi Square test was used to compare categorical variables and Student's t-test was used for continuous variables. In Jacareacanga we stratified hair mercury levels based on World Health Organization guidance (≤8 or ≥8 μg/g hair). In Tabatinga we stratified exposure by the observed median level (5.57 μg/g) since most hair mercury concentrations were below 8 μg/g. In Rio-Rato we used urine mercury levels based on WHO guidance (≤5 or ≥5 μg/L). We used the mean age for each population to stratify by age. Logistic regression modeling was used to evaluate the effect of mercury exposures on prevalence of ANA and ANoA (for 1:10 and 1:40 cutoffs), while controlling for age, sex, prevalent malaria, past history of malaria, and occupation. All data were analyzed using the SAS v.8.1 statistical package. Results Because of substantial differences among the populations and sites, we present the results for each site separately. Tabatinga Tabatinga is a typical riverine community in the lower Amazon. The community sample consisted of 98 adults, with 73% females, and a mean age of 44 years, (Table 1 ). This community has no occupational exposures to inorganic mercury, and the fish consumed have relatively low methylmercury contamination. The distribution of hair mercury is shown in Figure 1A ; the majority of the persons had hair mercury levels below 8 μg/g. The median hair mercury concentration of 5.57 μg/g is higher than that reported in European and North America populations, which may reflect the very high frequency of fish consumption rather than excessive fish contamination [ 38 ]. No present malaria cases were found, and only 10% reported any past malaria (Table 2 ). Otherwise, the population was in good health. Table 1 Demographic characterization of the 3 populations Current Occupation (%) Prev. Occupation Populations N Age [Mean] Sex (%) F/M Gold Mine Fisherman Others Students Gold Miner (%) Tabatinga-adults 98 44 73/27 0 1.1 98.9 0 0 Jacareacanga 140 25 54/46 0 2.2 72.4 25.4 9.4 Rio-Rato 98 30 35/65 54 0 46 0 N/A N/A = data not available from original survey. Figure 1 Distribution of mercury levels Population distributions are shown for (A) Tabatinga and (B) Jacareacanga in μg Hg/g hair and (C) Rio-Rato in μg Hg/L urine. Table 2 Malaria and Hg data from the 3 populations Malaria status (prevalent and reported past infections) and mercury exposures in the three populations. Malaria Hg (median) Hg Populations Prevalent (%) History (%) Urine (microgram/L) Hair (microgram/g) range values Tabatinga-adults N = 98 0 10.1 ND 6.4 1.19–16.96 Jacareacanga N = 140 0 69.6 ND 8 0.29–58.47 Rio-Rato N = 98 93.9 N/A 4 ND 0.01–81.37 N/A = data not available from original survey. ND = analysis not completed in original survey. The prevalence of detectable ANA and ANoA in the Tabatinga samples was very low (Table 3 ; Figure 2 ). Most measurements (90%) were not detectable even at the lowest (1:10) dilution. These data are similar to those recently reported for a referent population in Sao Paulo [ 39 ]. In the few subjects with ANA or ANoA detectable at ≥1:10, there was no relationship between ANA or ANoA for any of the variables studied. Table 3 Percentages of detectable ANA and ANoA in serum from the 3 populations ANA (%) ANoA (%) ANA + ANoA (%) Populations <det ≥1:10 ≥1:40 <det ≥1:10 ≥1:40 <det ≥1:10 ≥1:40 Tabatinga-adults N = 98 92.9 7.1 2.0 97.9 2.1 0 100 0 0 Jacareacanga N = 140 89.3 10.7 3.6 82.0 18.0 13.0 100 0 0 Rio-Rato N = 98 45.9 54.1 51.0 59.2 40.8 36.7 89.0 11.0 10.0 <det = below detection level at lowest dilution. ANA ≥1:10 or ANoA ≥1:10 percentages include ANA 1 1:40 or ANoA ≥1:40 percentages. Figure 2 Detectable levels of serum autoantibodies Population distributions of (A, C, E) ANA and (B, D, F) ANoA are shown for (A&B) Rio-Rato, (C&D) Jacareacanga and (E&F) Tabatinga, at varying serum dilutions. Jacareacanga Jacareacanga is a riverine settlement of approximately 500 persons, located on the mid-Tapajós River. The 140 subjects consisted of 54% women and had a mean age of 25 years (Table 1 ). Fish are the primary protein source and piscivorous species sold at local markets have reported to have elevated concentrations of methylmercury [ 29 , 30 ]. No persons reported current employment in gold mining or refining, but some persons reported a history of such activities. The distribution of hair mercury is shown in Figure 1B . Median hair mercury levels were 8 μg/g (Table 2 ), substantially higher than that found in unexposed populations [ 1 ]. Fish consumption was the major predictor of hair mercury; previous occupation as a gold miner was also related to higher hair mercury concentrations. No subjects were positive for malaria by blood smear at the time of survey (Table 2 ). However, a majority reported a history of past malaria (Table 2 ). Among these subjects, 50% reported 2 or fewer infections, while the maximum number of past infections reported was 6. As shown in Table 3 , nearly 11% of the population had detectable ANA ≥1:10, and nearly 20% had detectable ANoA ≥1:10. In those subjects where ANA was detectable, most (96.4%) presented at low concentrations, while 13% had ANoA detectable at 1:40 (Figure 2 ). No subjects were positive for both autoantibodies. A significantly higher percentage of subjects with detectable ANoA (33%) had hair mercury levels greater than the median value of 8 μg/g. In the logistical model only mercury, from all the variables studied, was significantly correlated with the presence of ANoA (≥1:10) (Table 4 ). Individuals with higher hair mercury levels, who reported any past malaria, were more likely to have detectable concentrations of ANoA (40%) as compared to those with low mercury levels. In persons reporting fewer than 4 past malaria infections, hair mercury was positively correlated with the presence of detectable ANA (≥1:10; ≥1:40) and ANoA (≥1:10) (Table 5 ). In persons with low hair mercury, there was a positive correlation of number of past malaria infection with detectable ANA at either ≥1:10 or ≥1:40. Table 4 Jacareacanga-odds ratio between risk factors and prevalence of ANoA ≥1:10 Logistical model for odds of detectable ANoA (≥1:10) and mercury exposure, gender, age, occupation, and malaria history, p < 0.05*. Variable Odds ratio 95% Confidence interval p-value Hg 3.27 1.28 – 8.37 0.014* Gender 1.16 0.44 – 3.02 0.769 Age 0.93 0.36 – 2.39 0.871 Past-malaria 1.28 0.43 – 3.83 0.663 N past-malaria infections 1.18 0.39 – 3.55 0.772 Other occupations: gold miner 0.74 0.14 – 3.75 0.711 Table 5 Serum ANA and ANoA (Jacareacanga) stratified for mercury and past malaria infections P values obtained comparing Hg <8 with >8 μg/g hair (* p < 0.05) and number of malaria infections <4 with ≥4 ( § p < 0.05). # malaria infections <4 (%) # malaria infections ≥4 (%) Hg >8: ANA 1:10 3.61 17.65 § ANA 1:40 0 11.76 § ANoA 1:10 12.05 17.65 ANoA 1:40 8.43 17.65 Hg ≥8: ANA 1:10 14.81 * 10.00 ANA 1:40 11.11 * 0 ANoA 1:10 33.33 * 30.00 ANoA 1:40 22.22 20.00 Rio-Rato Rio-Rato is a gold mining community, where a small settlement has grown up around a still active mining site in the mid-Tapajós watershed. Approximately 2/3 of the population was male with a mean age of 30 years (Table 1 ). Educational and socioeconomic variables were low. Urine mercury levels (4 μg/L) were lower than those found in other mining populations in Amazonia [ 25 , 26 , 40 ] (Figure 1C ). Only 6 had levels ≥25 μg/L, the median value found by us in another gold mine population [ 40 ]. This may have been due to the timing of our visit, during the dry season, when gold amalgamation activities were reduced. A high degree of variability in urine mercury levels among gold miners has been reported by others [ 26 , 41 ]. Because of this, we used exposure history to characterize mercury exposures in this population. This region has a high rate of malaria transmission [ 28 , 31 ]. Over 90% of the Rio-Rato population had prevalent malaria, detected by thick film slide at the time of the survey (Table 2 ). No data on past malaria episodes were collected. Over half of the population had ANA detectable at ≥1:10, and nearly half had ANoA detectable at ≥1:10 (Table 3 ; Figure 2 ). At or above 1:40, the Rio-Rato population still presented with a high prevalence of elevated ANA (51%) and ANoA (36.7%) (Table 3 ; Figure 2 ). In 10% of the population, levels of both autoantibodies were detectable at 1:40. About a quarter had concentrations up to a dilution 1:160 and some persons in this sample had very high concentrations of autoantibodies, detectable up to a dilution of 1:320. The likelihood of ANoA detectable at 1:40 was significantly higher in those individuals with a longer history of work in gold mining (≥7 years compared to <4 years). The presence of autoantibodies against the 34 KDa protein fibrillarin was determined by immunoblotting in those serum samples from Rio-Rato with ANoA detectable ≥1:10. Of 40 subjects, 3 had serum with detectable AFA, as shown in Figure 3 . Figure 3 AFA in Rio-Rato serum samples Photograph of denaturing gel electrophoresis of 3 AFA positive samples from 41 ANoA positive serum samples previously determined by IIF. Fibrillarin = 34 Kda protein. Discussion In this paper we report the first data on specific biomarkers of autoimmune dysfunction in persons exposed to inorganic mercury or methylmercury. One earlier study reported elevations in anti-laminin autoantibodies in workers exposed to mercury [ 12 ]; however, no correlation with mercury exposure was observed. While our data are limited by sample size, and are likely influenced by other variables in addition to mercury, the results are consistent with the experimental literature indicating that mercury can alter immune function and increase circulating levels of autoantibodies, including ANA and ANoA [ 5 , 6 , 42 ]. There was an overall qualitative correlation between mercury exposures and levels of ANA or ANoA, both by study site and within study sites. Our ability to compare these populations more directly was limited by differences in the original study design with respect to mercury exposure assessment (hair at Jacareacanga and Tabatinga, urine at Rio-Rato). Persons from Tabatinga, with the lowest range of mercury exposures, had lower prevalence of detectable ANA or ANoA, and in those few persons with detectable autoantibodies, the concentrations were low. Nonetheless, Tabatinga subjects had significantly elevated mercury levels, as compared to North America populations [ 1 , 38 ], which is probably attributable to their very high intake of locally caught fish, such that even though these fish had methylmercury levels below the US FDA or WHO guidance, these consumption rates resulted in elevated body burdens, as compared to North Americans eating fish much less frequently [ 43 ]. Persons from Jacareacanga were exposed to methylmercury from fish consumption. The median hair mercury levels in Tabatinga and Jacareacanga are relatively close, but the distribution of hair mercury in Jacareacanga shows that there are many persons with exposures well above those obtained in Tabatinga. In Jacareacanga subjects, higher prevalence of detectable ANoA was observed, mostly at low concentrations (1:10 or 1:40), but several had levels measurable in dilutions as high as 1:160. In Rio-Rato persons were highly exposed to inorganic mercury from gold mining activities, as well as methylmercury via fish consumption. Detectable levels of ANA and/or ANoA were prevalent and detectable at high concentrations (1:320). It is possible that exposure to inorganic mercury may be more "autoimmunogenic" than exposure to methylmercury, as shown in mice models [ 6 ]. In contrast to studies in mice [ 20 , 21 ], we found little evidence that AFA, levels are specifically affected by mercury. This may indicate a difference between humans and mice. However, as shown in Figure 3 , there appear to be many unidentified nuclear antigens observed in serum samples from this population, which were not observed in either the SC serum, or in studies of US control subjects (data not shown). It would be very pertinent to analyze sera from all these three populations, using a range of other nuclear antigens known to be targeted in autoimmune diseases [ 19 , 37 ]. We examined the serum ANA and ANoA results at both 1:10 and 1:40 dilutions. The results in Jacareacanga and Rio-Rato subjects are clearly different from studies of healthy individuals in the US and Brazil [ 39 ]. Interpretation must be cautious. Tan et al. [ 37 ] showed that many "healthy individuals" (31.7%) show detectable ANA at dilutions <1:40. This suggests that such a cutoff point for serum dilution may have relatively little diagnostic value. However, the purpose of this study was not to detect persons with latent autoimmune disease, but rather to use these antibody measurements as biomarkers to test the hypothesis that mercury exposures might induce autoimmune dysfunction. A recent publication on the prevalence of ANA in serum of normal blood donors in Brazil found no age-related differences in prevalence of detectable ANA among adults, and that very few subjects had detectable ANA at dilutions >1:40 [ 39 ]. None of the subjects in these populations were reported to have autoimmune disease or overt clinical disease of any type, except malaria, but only routine clinical assessments were done. In Rio-Rato and Jacareacanga subjects, other risk factors were related to elevations in ANoA and ANA. In Rio-Rato, time spent at the site and in gold mining was positively correlated with likelihood of elevated ANoA. This variable, time spent at the site, may represent length of exposure to both mercury and malaria infection. In Jacareacanga, there was a positive relationship between malaria (any past reported cases) and likelihood of elevated ANoA. We examined these potential biological interactions among mercury, malaria, and autoimmune biomarkers further, because of studies demonstrating that repeated malaria infections are associated with increased levels of autoantibodies, including ANA, presumably due to cytotoxic damage and exposure of intracellular epitopes [ 44 , 45 ]. Other studies have shown that autoantibodies are produced in mice infected with malaria, which react with several nuclear antigens, namely RNA, soluble nuclear material and DNA [ 46 ]. Our data indicate that in persons with lower mercury exposures (less than the median of 8 μg/g hair), increasing number of past malaria infections (≥4) were associated with increased likelihood of ANA, but not ANoA. In persons with higher mercury exposure, increased malaria exposure did not further increase ANA. We do not, at present, have an explanation for these observations, except to speculate that higher mercury exposures may induce a strong autoimmune dysfunction, such that additional effects of malaria are not significant. It is difficult to draw any firm conclusions from these analyses, since the malaria data in Jacareacanga were based upon unconfirmed self-reports. We could not test this hypothesis in the Rio-Rato group, since almost all subjects had prevalent malaria and extensive histories of past infection. We have reported a suggested correlation between mercury exposures and number of past malaria infections among gold miners in another gold mine settlement, in Brazil, at Piranha [ 40 ]. We have also reported that exposure of mice to low levels of mercury both decreases host resistance to murine malaria ( Plasmodium yoelii ) and impairs acquisition of immunity to murine malaria in the Nussenzweig model [ 47 ]. Mercury may reset immunologic responses to malaria, to increase expression of autoantibodies through its documented effects to up regulate Th2 mediated immune responses [ 48 , 49 ]. Finally, we may speculate as to why we have been able to observe associations between mercury exposures and these biomarkers of autoimmune dysfunction in these populations, while most clinical or epidemiological studies of mercury and immunotoxicity have been negative or only weakly positive [ 11 - 13 ]. In these other studies, the cohorts were relatively small (between 44–70), and they were exposed only to elemental or inorganic mercury through working in mercury or chloralkali plants [ 11 , 13 ]. No reports of exposure to other risk factors, such as infectious diseases, were reported. In our study, some of the exposures were chronic in nature and included exposures to methylmercury (certainly for fish consumption in Amazonian populations, where fish form the major portion of the protein consumed) [ 30 , 50 ]. In contrast, the gold miners were likely to have relatively variable exposures to inorganic mercury, with episodes of very high inhalation exposure [ 25 , 26 , 41 ]. The role of genotype may also be important. In rodents, genotype clearly plays an important role in both modulating the immune response to inorganic mercury as well as toxicokinetics [ 5 - 7 , 51 ]. In susceptible mice, induction of genetically restrictive ANoA by mercury are linked to mouse MHC (H-2) haplotype s and q [ 7 ], while most other haplotypes confer relative resistance [ 52 ]. Non-MHC genes decide the strength of ANoA response in susceptible mice exposed to mercury [ 6 , 7 ]. In addition, mercury toxicokinetics differ among inbred mouse strains. As Nielson and Hultman [ 52 ] demonstrated, there is a correlation between mercury toxicokinetics and AFA production in mice. In this study, the populations in these communities represent a wide range of ethnicities, including Europeans, Africans, and indigenous groups of Amazonia. Their immunogenetics may include persons with increased susceptibility to mercury-induced autoimmune dysfunction. Conclusions Our study is the first to report a correlation between biomarkers (ANA and ANoA) and mercury exposure in humans. In addition, co-exposures to mercury and infectious diseases, including malaria, may set the stage for eliciting discernible alterations in immune function. Whether such co-exposures increase the risks of autoimmune disease will require further studies, which are underway. List of abbreviations ANA – antinuclear autoantibodies ANoA – antinucleolar autoantibodies AFA – anti-fibrillarin autoantibodies IIF – indirect immunofluorescence Competing interests The authors declare that they have no competing interests. Authors' contributions IA Silva carried out the analysis of autoantibodies, participated in the statistical analysis, and drafted the manuscript. JF Nyland helped in autoantibody analysis and technical editing of the manuscript; A Gorman helped in autoantibody analysis; AM Ventura, JM de Sousa, and ECO Santos carried out the field studies, malaria assessments, and mercury analysis. CL Burek and NR Rose guided the antibody analysis. EK Silbergeld participated in the design and coordinated the study; she also participated in the Jacareacanga field study, and in the draft of the manuscript. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC538267.xml |
423135 | Absence of the TAP2 Human Recombination Hotspot in Chimpanzees | Recent experiments using sperm typing have demonstrated that, in several regions of the human genome, recombination does not occur uniformly but instead is concentrated in “hotspots” of 1–2 kb. Moreover, the crossover asymmetry observed in a subset of these has led to the suggestion that hotspots may be short-lived on an evolutionary time scale. To test this possibility, we focused on a region known to contain a recombination hotspot in humans, TAP2, and asked whether chimpanzees, the closest living evolutionary relatives of humans, harbor a hotspot in a similar location. Specifically, we used a new statistical approach to estimate recombination rate variation from patterns of linkage disequilibrium in a sample of 24 western chimpanzees (Pan troglodytes verus). This method has been shown to produce reliable results on simulated data and on human data from the TAP2 region. Strikingly, however, it finds very little support for recombination rate variation at TAP2 in the western chimpanzee data. Moreover, simulations suggest that there should be stronger support if there were a hotspot similar to the one characterized in humans. Thus, it appears that the human TAP2 recombination hotspot is not shared by western chimpanzees. These findings demonstrate that fine-scale recombination rates can change between very closely related species and raise the possibility that rates differ among human populations, with important implications for linkage-disequilibrium based association studies. | Introduction Recombination is a fundamental biological feature about which we still know remarkably little, especially in mammals. Understanding recombination is also of practical importance for evolutionary inference and human genetics ( Nachman 2002 ; Arnheim et al. 2003 ). Unfortunately, the process is difficult to study, because recombination events occur extremely rarely (e.g., with a probability of ∼10 −8 per bp per generation in a typical region of the human or Drosophila melanogaster genome; Ashburner 1989 ; Kong et al. 2002 ). Thus, direct measurements for closely linked sites often require the examination of a prohibitive number of individuals. As a result, our knowledge of recombination rates stems primarily from estimates for markers that are megabases apart, obtained from crosses or, for humans, obtained from pedigrees (e.g., Kong et al. 2002 ). One way to learn about finer-scale recombination rates in males is sperm typing ( Li et al. 1988 ; Hubert et al. 1994 ; Jeffreys et al. 2001 ). In this approach (reviewed by Arnheim et al. 2003 ), genetic markers are amplified and typed from a large number of sperm in order to estimate the fraction of recombinant sperm and hence the recombination rate. Fine-scale rates can also be measured indirectly from patterns of allelic associations, or linkage disequilibrium (LD), observed in samples from natural populations ( Hudson 1987 ; Pritchard and Przeworski 2001 ). In humans, both direct estimates of the recombination rate using sperm typing techniques and indirect approaches based on analyses of LD have suggested the existence of substantial heterogeneity in rates of recombination at small scales ( Daly et al. 2001 ; Jeffreys et al. 2001 ; Gabriel et al. 2002 ; Schneider et al. 2002 ; Wall and Pritchard 2003 ). In particular, sperm typing experiments have demonstrated that, in several regions of the human genome, crossover resolutions are not uniformly distributed but instead tend to cluster within narrow regions of 1–2 kb termed “recombination hotspots” ( de Massy 2003 and references therein). While there has been recent progress characterizing the extent of spatial variation in recombination rates, the time scale over which recombination rates change remains an open question. It has been known for decades that natural populations harbor genetic variation for recombination rates ( Brooks 1988 and references therein). In humans, in particular, there are significant differences in recombination rates among females ( Kong et al. 2002 ) as well as among males ( Cullen et al. 2002 ). Thus, there is a clear potential for the evolution of recombination rates. However, there are only a couple of demonstrated cases that help to delimit the time scale on which this might occur: at the megabase scale, the best example is probably D. melanogaster and D. simulans, two sibling species that differ in their recombination landscape ( True et al. 1996 ). Among primates, the genetic map of humans is approximately 28% longer than that of an Old World monkey, the baboon ( Papio hamadryas; Rogers et al. 2000 ), suggesting that—if physical maps are roughly similar—recombination rates in humans may be higher overall. These instances demonstrate that large-scale recombination rates can change between species that differ on average at roughly 6% to 10% of nucleotide positions ( Betancourt and Presgraves 2002 ; Thomas et al. 2003 ). At a finer scale, the only evidence stems from a recent study of the β-globin gene, where a hotspot had been characterized by sperm typing in humans. Wall et al. (2003) found no evidence of rate variation in LD data collected from the rhesus macaque (Macaca mulatta), another Old World monkey. For more closely related species, nothing is known. However, observations in yeast (e.g., Petes 2001 ; Steiner et al. 2002 ) and mammals ( Jeffreys and Neumann 2002 ; Yauk et al. 2003 ) raise the possibility that local recombination rates could change rapidly. Indeed, at the MS32 and DNA2 hotspots in humans ( Jeffreys et al. 1998 ; Jeffreys and Neumann 2002 ) as well as at the E β hotspot in mice ( Mus sp.; Yauk et al. 2003 ), some haplotypes were found to lead to higher rates of initiation of crossover events. Such haplotypes tended to be undertransmitted in crossover products ( Jeffreys and Neumann 2002 ), an asymmetry that favors the loss of recombination hotspots ( Boulton et al. 1997 ). If this is a common phenomenon, it may lead hotspots to be short-lived on an evolutionary time scale ( Jeffreys and Neumann 2002 ). To evaluate whether fine-scale recombination rates can change rapidly, we were interested in comparing rates in humans with those in their closest evolutionary relative, the chimpanzee (Pan troglodytes). The two species are thought to have had a common ancestor five to six million years ago and differ at approximately 1.2% of base pairs on average ( Ebersberger et al. 2002 ). Since it is difficult to use sperm typing techniques in chimpanzees, not least of all because of the need for chimpanzee sperm, we took an indirect approach and estimated the extent of recombination rate variation from patterns of LD in a population sample. To do so, we modified a recently developed statistical approach ( Li and Stephens 2003 ). The method estimates recombination rates by exploiting the fact that patterns of LD reflect the rate and distribution of recombination events in the ancestors of the sample (see Materials and Methods for more details). Although it is based on simplistic assumptions about population demography, it has been shown to produce reliable estimates of recombination rates for data sets simulated under a range of demographic assumptions ( Li and Stephens 2003 ; D. C. Crawford, T. Bhangale, N. Li, G. Hellenthal, M. J. Rieder, et al., unpublished data). We focused on the TAP2 genic region, where a sperm typing study of humans characterized a ∼1.2 kb recombination hotspot in one of the introns ( Jeffreys et al. 2000 ). Application of the statistical method to polymorphism data collected for this region ( Jeffreys et al. 2000 ) led to estimates similar to those obtained by sperm typing, providing further evidence for its reliability ( Li and Stephens 2003 ). Samples that include individuals from diverged populations are expected to harbor high levels of LD that may lead to incorrect estimates of recombination rate variation ( Pritchard and Przeworski 2001 ). This is of particular concern in chimpanzees, for which previous studies have reported high levels of genetic differentiation between subspecies ( Morin et al. 1994 ; Stone et al. 2002 ; Fischer et al. 2004 ). In addition, there appears to be a high proportion of less informative, rare alleles in samples from central (P. t. troglodytes) but not western (P. t. verus) chimpanzees ( Gilad et al. 2003 ; Fischer et al. 2004 ). We therefore collected polymorphism data from a sample of 24 chimpanzees that were all known to be from the western subspecies. Strikingly, we found no evidence for recombination rate variation at TAP2 in these data. Results In humans, LD data for the TAP2 region were previously collected by Jeffreys et al. (2000) , who resequenced ∼9.7 kb in a sample of eight individuals from the United Kingdom (UK) and found 46 single nucleotide polymorphisms (SNPs), excluding insertion-deletions. The SNPs were then typed in a sample of 30 individuals from the UK, in whom haplotypes were determined experimentally (by allele-specific PCR). We collected genotype data for the same region in western chimpanzees by resequencing 24 individuals (see Materials and Methods for details). This led to the discovery of 57 SNPs. When differences in study design are taken into account, diversity levels in western chimpanzees are very similar to those observed in samples of humans from the UK ( θ W = 0.145% versus θ W = 0.144% per bp, respectively), consistent with previous findings (e.g., Gilad et al. 2003 ; Fischer et al. 2004 ). The LD data are summarized in Figure 1 ; overall, there is much less LD in humans than in chimpanzees. In particular, in humans, strong allelic associations are only seen between pairs of sites in close physical proximity, while in chimpanzees, such associations are also found among more distant pairs. Whether this reflects differences in the underlying recombination landscape or chance variation is unclear from visual inspection of these plots alone. We therefore used a statistical approach to assess the evidence for recombination rate variation in the two species. Specifically, we assumed that there is (at most) one hotspot in the region and, as a first step, specified its location according to the results of the sperm typing study in humans. We then applied our modification of the method of Li and Stephens (2003) to estimate a background population recombination rate, ρ, and the relative intensity of recombination in the hotspot segment, λ (see Materials and Methods ). Within this model, a λ value of 1 corresponds to an absence of recombination rate variation, while values of λ greater than 1 indicate a hotspot. The approach taken here is Bayesian (see Materials and Methods ) so, as a measure of support for a hotspot in the LD data, we report estimates for the probabilities Pr( λ > 1) and Pr( λ > 10); these are the posterior probabilities of a hotspot of any kind and of a hotspot of intensity at least ten times the background rate, respectively. Figure 1 Patterns of Pairwise LD in Humans and Chimpanzees Only SNPs with minor allele frequencies above 0.1 are included. The rows correspond to the consecutive SNPs in the region, as do the columns. Each cell indicates the extent of LD between a pair of sites, as measured by | D ′| (estimated using the Expectation Maximization algorithm, as implemented by Arlequin: http://lgb.unige.ch/arlequin/ ). Application of this method to the human haplotype data led to extremely strong support for rate variation: we estimated Pr( λ > 1) = 1 and Pr( λ > 10) = 0.982. When the same method was applied to the human genotype data (i.e., ignoring the information about the phase of multiple heterozygotes), we estimated Pr( λ > 1) = 1 and Pr( λ > 10) = 0.992. The results are almost identical, suggesting minimal loss of information with the use of genotypes. Interestingly, the point estimate of λ using either haplotypes (28.4) or genotypes (32.1) is higher than the corresponding estimate from sperm typing (11). This difference may reflect error in the estimates; alternatively, it may point to a more intense hotspot in females than in males ( Jeffreys et al. 2000 ). Next, we applied the same method to the genotype data collected from western chimpanzees. The estimate of the background rate of recombination, ρ^ , is 5.0 × 10 −4 per base pair, which is very similar to the estimate from the human genotype data ( Figure 2 ). However, in contrast to what is found in humans, there is no evidence for recombination rate variation: our estimate of λ is 1, suggesting a uniform rate of recombination throughout the region, and our estimates of Pr( λ > 1) = 0.200 and Pr( λ > 10) = 0.006, reflecting tepid support for a hotspot of any kind and almost no support for a hotspot similar to the one observed in humans. Indeed, the latter figure represents very strong evidence against a hotspot of moderate intensity and rules out the possibility that the chimpanzee polymorphism data are simply uninformative, because of, for example, insufficient sample size or diversity. Figure 2 Estimates of the Recombination Hotspot Intensity, λ, Based on Genotype Data We assumed that, if the hotspot is present, it is in the same location as estimated by sperm typing in humans (see Materials and Methods ). A λ value of one corresponds to the absence of recombination rate variation, while values of λ greater than one indicate a hotspot. The estimates for humans from the UK are shown in blue and those for western chimpanzees in orange. To assess how likely we would be to obtain such weak support if there were in fact a hotspot in western chimpanzees similar to the one in humans, we generated 200 simulated genotype data sets under a model with a hotspot of intensity λ = 11 and then tabulated the proportion with posterior probability estimates as low or lower than that observed (see Materials and Methods ). We took the λ value estimated from sperm typing because it is the lowest of the various estimates for humans and hence its use was conservative for our purposes. With the ρ value estimated from the data (5.0 × 10 −4 per bp), the probability of obtaining Pr( λ > 1) ≤ 0.200 is p = 0.010 and the probability of obtaining Pr( λ > 10) ≤ 0.006 is p = 0.005. With a lower ρ value (2.7 × 10 −4 per bp; see Materials and Methods ), the probability of obtaining Pr( λ > 1) ≤ 0.200 is p = 0.020. In other words, we can reject the null hypothesis that there is a hotspot in western chimpanzees similar to the one in humans, because we would expect to see more support for a hotspot in these data if one were there. It appears that western chimpanzees do not harbor a hotspot in the same location as humans. The possibility remains, however, that there is a hotspot in a slightly different position in chimpanzees. To evaluate this, we used a more general model in which there is at most one hotspot in the region, but where the location is unknown and estimated together with ρ and λ (see Materials and Methods ). Again, we found very little evidence for recombination rate variation: across all pairs of consecutive segregating sites, the largest posterior probability of elevated recombination is estimated to be < 0.060 ( Figure 3 ). Thus, the hotspot appears to be entirely absent from the ∼9.4 kb surveyed in western chimpanzees. Figure 3 Estimates of Recombination Rate Variation in Humans and Western Chimpanzees In this model, there is at most one hotspot in the region, the location and width of which are unknown and estimated along with λ and ρ. On the y -axis is an estimate of the posterior probability of elevated recombination, Pr( λ > 1), between each pair of consecutive SNPs (plotted at the midpoint position). Discussion These estimates of recombination rate parameters are based on assumptions of neutrality, constant population size, and random mating, raising the concern that the hotspot is not absent but instead masked by departures from model assumptions. However, we chose to focus on western chimpanzees precisely because previous studies reported allele frequencies in rough accordance with the assumptions of our model. Consistent with these studies ( Gilad et al. 2003 ; Fischer et al. 2004 ), the allele frequencies at TAP2 are not significantly different from the expectations of the standard neutral model (as assessed by Tajima's D = 0.848, p = 0.237; see Materials and Methods ). Moreover, simulations suggest that the power to detect a hotspot is not strongly affected by population history ( Li and Stephens 2003 ). To some extent, this is expected, as population history tends to affect LD in the entire region, not only in the hotspot, so that estimates of the relative rates of recombination are unlikely to be substantially altered. In summary, there is no evidence for a marked departure from model assumptions in the allele frequencies, and the method is expected to be robust to small departures. Consistent with this, in humans, the approach yields similar results to sperm typing experiments that do not rely on the same assumptions. On this basis, it seems that the hotspot is truly absent from the homologous region in western chimpanzees. This finding implies that the hotspot was lost in chimpanzees or gained in humans, or that it moved in one of the species (over a larger distance than we surveyed). This in turn raises a number of more general questions. Are hotspots frequently born de novo or do they tend to migrate within circumscribed regions of the genome? Are particular sequence motifs sufficient to produce recombination hotspots, or are larger-scale requirements, such as chromatin accessibility, required for their formation ( Petes 2001 )? The systematic comparison between closely related species with different recombination landscapes may be helpful in addressing these problems. As an illustration, in these data, we found two motifs that were previously implicated in the formation of recombination hotspots ( Smith et al. 1998 ; Badge et al. 2000 and references therein) and that varied between the two species: a Pur binding motif that is present in humans but absent in chimpanzees (because of a single base pair difference) and two scaffold attachment sites that are in different positions in the two species. The significance of these differences cannot be determined on the basis of a single example; however, once a larger sample of hotspot regions has been surveyed, one can begin to test for an association between particular sequence motifs or features and the presence of hotspots. Comparative studies of hotspot regions will also increase our understanding of the determinants of mutation rates. As noted by Jeffreys et al. (2000) , there is a significant excess of diversity within the hotspot region in humans from the UK ( Figure 4 ): when the hotspot region is compared to the 8,735 other windows of the same size, only 0.3% have as many or more SNPs. In contrast, in western chimpanzees, levels of diversity are not higher than elsewhere in the region ( Figure 4 ): 17.0% of comparable windows harbor at least as many SNPs as the hotspot. Nor are levels of human–chimpanzee divergence unusual in the hotspot region: 67.3% of windows show the same or higher numbers of fixed differences between species ( Figure 4 ). Given the evidence for a recombination hotspot in humans but not in chimpanzees, these observations are consistent with an association between recombination and mutation in primates ( Hellmann et al. 2003 ) and, in particular, with a mutagenic effect of recombination ( Rattray et al. 2002 ). If indeed recombination events introduce mutations, the lack of a peak of human–chimpanzee divergence in the hotspot region ( Jeffreys et al. 2000 ; Figure 4 ) would suggest that the hotspot arose fairly recently in human evolution. Figure 4 Distribution of Variable Sites in the Genomic Region The positions of sites that differ between humans and chimpanzees are shown on the first line, while the positions of sites polymorphic in humans from the UK or in western chimpanzees are shown on the next two lines. The human hotspot region is underlined. The dashed lines indicate regions not surveyed for variation in western chimpanzees (see Materials and Methods ). In conclusion, these analyses demonstrate that fine-scale recombination rates can change between closely related species. Together with the observations that crossover frequencies can depend on specific haplotypes ( Jeffreys and Neumann 2002 ) and that large-scale recombination rates differ among individuals ( Cullen et al. 2002 ; Kong et al. 2002 ), this finding raises the possibility that local rates can vary among human groups that differ in their allele frequencies. Unfortunately, demonstrating compelling evidence for variation among human populations on the basis of LD data alone promises to be substantially harder than demonstrating such differences between chimpanzees and humans. In particular, human populations share most of their evolutionary history, making differences between extant populations, if they exist, more difficult to detect. Nevertheless, LD studies should be helpful in identifying interesting regions for further study via sperm typing. The extent to which local recombination rates vary among human populations influences the degree of similarity of LD patterns among them, with important consequences for the design of efficient LD-based association studies (including, for example, the choice of appropriate “haplotype tagging SNPs” [ Johnson et al. 2001 ] in different human populations) and for the relevance of data generated by the current human HapMap project to populations not currently represented in that study ( International Hapmap Consortium 2003 ). Perhaps most importantly, if local recombination rates do vary among groups, then the study of regions with the most pronounced differences should lead to further insights into the underlying biological processes that cause fine-scale variation in recombination rates. Materials and Methods Samples We used DNA from 24 western chimpanzees (Pan troglodytes verus) that were wild caught or known to be unrelated based on recent pedigrees. Twelve samples (Annaclara, Frits, Hilko, Liesbeth, Louise, Marco, Oscar, Regina, Socrates, Sonja, Yoran, and Yvonne) are from the collection stored at the Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany, while 12 other samples (NDH0311G1, NDH0312G1, NDH0313G1, NDH0314G1, NDH0317G1, NDH0320G1, NDH0321G1, NDH0322G1, NDH0325G1, NDH0326G1, NDH0328G1, and NDH0329G1) were kindly provided by P. Morin and the Primate Foundation of Arizona. Primer design We amplified 9,491 bp from the TAP2 region, corresponding to base pairs 113102–122585 of the sequence from Beck et al. (1996) (see Supporting Information ); the slight discrepancy in the number of base pairs is due to indels. To minimize the chance of allelic dropout, we designed the PCR primers such that most of the sequence would be amplified by two independent sets of primers. The 20 overlapping primer sequences are listed in Protocol S1 . PCR and DNA sequencing DNA amplification reactions contained 250 μM of each dNTP, 1–2 mM MgCl 2 , PCR buffer (10 mM Tris-HCl, 50 mM KCl; pH 8.3), 0.5 U of Taq DNA polymerase (all reagents from Roche, Basel, Switzerland), and 10 pmol of each primer. We used 50–100 ng of DNA in each 30 μl PCR. Amplification conditions for all regions were the following: incubation for 3 min at 94 °C, 35 cycles (45 s at 94 °C, 1 min at 45–62 °C, and 1 min at 72 °C) and a final elongation of 5 min at 72 °C. A nested PCR was performed to obtain regions 6 and 7 by using the product of the primers Tap2 5 5′ and Tap2 8 3′ as a template. PCR products were separated from primers and unincorporated dNTPs by treatment with a solution of 10% PEG 8000/1.25 M NaCl followed by centrifugation. PCR products were then air dried and resuspended in 10–15 μl of H 2 O. Sequencing reactions consisted of 1 μl of ABI Prism BigDyeTM Terminators version 2.0 (Perkin Elmer Biosystems, Torrance, California, United States), 8–10 ng of purified PCR product, and 1 μl of 2.5 μM primer (the same primers used for PCR) in a volume of 7 μl. Cycling conditions were 96 °C for 2 min and then 35 cycles of 96 °C (20 s), annealing temperature (30 s), and 60 °C (4 min). Isopropanol-precipitated cycle sequencing products were run on an ABI 3730 DNA analyzer. Base calling was done with ABI Prism DNA Sequencing Analysis version 5.0 and ABI Basecaller. BioEdit version 5.0.6 was used for sequence analysis and alignment. In total, 2-fold coverage of a 9,370 bp sequence was obtained for each individual; these are available from GenBank (see Supporting Information ). Most of the region was sequenced from both DNA strands. However, due to the presence of insertions, deletions, and T or A stretches, this was not possible for a subset of segments; for these, 2-fold coverage was achieved by sequencing the same strand. For segment 6, we did not obtain reliable sequence data for all individuals (due to suspected allelic dropout); we therefore excluded this region of 487 bp. Otherwise, there are no missing data. SNPs were identified by visualization of the chromatograms using BioEdit version 5.0.6. The polymorphism data used for the analyses are available in Protocol S1 . Data analysis We estimated the population mutation rate, θ = 4 N e μ ( N e is the diploid effective population size and μ is the mutation rate per generation), using Watterson's estimator, θ W ( Watterson 1975 ), based on the number of segregating sites in the sample. We also calculated a commonly used summary of the allele frequency spectrum, Tajima's D ( Tajima 1989 ); both D and θ W were calculated with DNAsp ( Rozas and Rozas 1999 ). We used the D statistic to test the fit of the standard neutral model (of a random mating population of constant size) to allele frequencies in western chimpanzees. Specifically, we ran 10 4 coalescent simulations of the standard neutral model with the same number of chromosomes and base pairs as in the actual data, with θ equal to θ W, and with the population recombination rate equal to the estimated value (see below). We then tabulated the proportion of simulated runs with a Tajima's D value as or more extreme than that observed. We calculated the GC content of the region and searched for sequence motifs previously associated with recombination hotspots ( Badge et al. 2000 ; Petes 2001 ; Wall et al. 2003 ) using the program “scan_for_matches” available from http://bioweb.pasteur.fr/seqanal/interfaces/scan_for_matches.html . The list of motifs found in the human and chimpanzee sequences is given in Protocol S1 . Analyses of LD To assess the support in the polymorphism data for a recombination hotspot, we used the Product of Approximate Conditionals (PAC) model of Li and Stephens (2003) . Assuming haplotypes are known, the method considers each one in turn and attempts to represent it as a mosaic of the previously considered haplotypes. Qualitatively, the larger the regions over which haplotypes tend to resemble one another, the fewer the pieces required in each mosaic, and the lower the estimates of the recombination rates. The method uses simplistic assumptions about population demography to quantify this qualitative relationship and hence to estimate recombination rates across the region. More formally, the model of Li and Stephens (2003) defines the probability of observing haplotypes H given the underlying recombination parameters α (which in our case may include the background recombination rate and the hotspot location and intensity; see below). This can be used directly to estimate α from H in situations where haplotypes have been experimentally determined (e.g., Li and Stephens 2003 ). However, in our case the chimpanzee haplotypes are not known. Rather, we have genotype data G and we wish to estimate α from G. A simple approximate solution to this would be first to use a statistical method (e.g., that of Stephens et al. 2001 ) to obtain an estimate H^ for the haplotypes H from the genotypes G , and then to estimate α from H^. However, a risk of this approach is that overconfident conclusions will be drawn by ignoring uncertainty in the estimated haplotypes. A better solution, and the approach we take here, is to jointly estimate H and α from G , or, more specifically, to obtain a sample from the joint posterior distribution, Pr( H , α | G ). To do so, we start with an initial guess for the haplotypes, and iterate the following steps: (i) estimate a new value for α , using the current estimate of H and (ii) estimate a new value for H , using the genotypes G and the current value for α . Step (i) is performed using the PAC-B model of Li and Stephens (2003) and the priors on α described below. Step (ii) is performed by using the method for haplotype inference described in Stephens and Donnelly (2003) , but replacing the conditional distribution that they use (which ignores recombination) with the conditional distribution of Fearnhead and Donnelly (2001) (which takes into account recombination) computed using two quadrature points. (Actually, we modified the Fearnhead and Donnelly conditional distribution slightly, replacing the equation q i = z i ρ/(j+ z i ρ) in their Appendix A with q i =1−exp(−z i ρ/j) .) Both the PAC-B model and the Fearnhead and Donnelly conditional require the specification of a mutation parameter, θ , and a mutation process. In each case, we used the value of θ given in Li and Stephens (2003) and a mutation process whereby each mutation event at a biallelic site results in a change from one allele to the other. This iterative scheme defines a Markov chain whose stationary distribution is the distribution Pr( H , α | G ) from which we wish to sample. Provided that the algorithm is run for sufficiently long, the estimates of α obtained each iteration provide a sample from the distribution Pr( α | G ), and thus allow α (i.e., the underlying recombination process) to be estimated directly from G, taking full account of the fact that the actual underlying haplotypes are not known. The algorithm is implemented within the software package PHASE version 2.1, which is available online at http://www.stat.washington.edu/stephens/software.html . We considered two versions of the simple hotspot model of Li and Stephens (2003) . In this model, there is a single hotspot of constant intensity λ . Crossovers occur as a Poisson process (i.e., there is no interference) of constant rate r (per base pair) outside the hotspot and of constant rate λr inside the hotspot; gene conversion is not explicitly modeled. In the first version, we assumed that, if present, the hotspot is at the same location as estimated by sperm typing in humans (4180–5417). (This location is not precisely the same as the one used by Li and Stephens [2003] , which is why our estimates differ from theirs.) There are two parameters to be estimated: the background population recombination parameter ρ (= 4 N e r , where N e is the effective population size) and λ . We assumed a priori that a hotspot exists with probability 0.5 and that, if the hotspot exists, λ is between one and 100. Specifically, we assumed that λ = 1 with probability 0.5 and otherwise that log 10 (λ) is uniformly distributed on (0, 2). The prior on ρ is uniform on log 10 (ρ) in the range (−8, 3), which covers all plausible values. In the second version, we assumed that the location and width of the hotspot are unknown and to be estimated along with λ and ρ . In this case, we assumed a priori that the hotspot exists with probability 0.18 (corresponding to an assumption that a hotspot occurs roughly once per 50 kb of sequence), that the center of the hotspot is equally likely to be anywhere along the length of the sequence, and that the width of the hotspot is between 200 and ∼4,000 bp (specifically, we assumed that the width had a normal distribution, with a mean of 0 bp and a standard deviation of 2,000 bp, truncated to lie above 200 bp). Priors on ρ and on λ (conditional on there being a hotspot) are as in the first version. To allow for potential problems with convergence of this Markov chain Monte Carlo algorithm, we ran the algorithm ten times for each analysis, using different seeds for the pseudorandom number generator. For each run, we obtained a point estimate of the parameters (using sample posterior medians) and posterior probabilities. The reported estimates are the median of the estimates obtained from the ten runs. To test how likely we would be to obtain such weak support for a hotspot in the LD data if there were in fact a hotspot similar to the one in humans, we ran 200 coalescent simulations of the standard neutral model ( Hudson 1990 ) with the same number of base pairs and sample size as the actual data (48 chromosomes), a hotspot of intensity λ = 11, and θ = θ W . Haplotypes were randomly paired to form genotypes and phase information was ignored. The data were masked to mimic the actual data structure, i.e., they included a gap of 487 bp in the same position. We then counted the proportion of simulated data sets for which our estimate of the posterior probability was as low as observed or lower (using the first version of the Li and Stephens [2003] model). Since we obtained estimates for the simulated data in the same way as for the actual data, significance values obtained from this analysis are valid independent of the convergence, or even the correctness, of the Markov chain Monte Carlo scheme. In the first set of 200 simulations, we used ρ = ρ^ , the background rate that we estimated from the western chimpanzee data. In the second set of simulations, we used ρ = 4N^ e r^ = 2.7 × 10 −4 per bp, where N^ e = 17,100 is an estimate of the effective population size of western chimpanzees (based on Fischer et al. 2004 ) and r^ = 0.4 cM/Mb is the rough estimate of the background recombination rate reported in Jeffreys et al. (2000) . Supporting Information Protocol S1 Supplementary Materials (91 KB DOC). Click here for additional data file. Accession Numbers The GenBank ( http://www.ncbi.nlm.nih.gov/ ) accession number for the human TAP2 region of Beck et al. (1996) is X87344. The numbers for the 9,370-bp sequences obtained from the 24 western chimpanzees are AY559252–AY559299. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC423135.xml |
529430 | An Inflammatory Cascade Leading to Hyperresistinemia in Humans | Background Obesity, the most common cause of insulin resistance, is increasingly recognized as a low-grade inflammatory state. Adipocyte-derived resistin is a circulating protein implicated in insulin resistance in rodents, but the role of human resistin is uncertain because it is produced largely by macrophages. Methods and Findings The effect of endotoxin and cytokines on resistin gene and protein expression was studied in human primary blood monocytes differentiated into macrophages and in healthy human participants. Inflammatory endotoxin induced resistin in primary human macrophages via a cascade involving the secretion of inflammatory cytokines that circulate at increased levels in individuals with obesity. Induction of resistin was attenuated by drugs with dual insulin-sensitizing and anti-inflammatory properties that converge on NF-κB. In human study participants, experimental endotoxemia, which produces an insulin-resistant state, causes a dramatic rise in circulating resistin levels. Moreover, in patients with type 2 diabetes, serum resistin levels are correlated with levels of soluble tumor necrosis factor α receptor, an inflammatory marker linked to obesity, insulin resistance, and atherosclerosis. Conclusions Inflammation is a hyperresistinemic state in humans, and cytokine induction of resistin may contribute to insulin resistance in endotoxemia, obesity, and other inflammatory states. | Introduction Dietary and lifestyle changes during the last century have entailed an unprecedented epidemic of obesity and associated metabolic diseases, including type 2 diabetes and atherosclerosis [ 1 ]. Many individuals suffer simultaneously from more than one of these conditions, and epidemiological studies in humans, as well as studies in animal models, suggest that obesity-related insulin resistance is a common pathogenic feature [ 2 ]. Indeed, insulin resistance is the keystone of the “metabolic syndrome,” a major cardiovascular risk factor even in the absence of demonstrable glucose intolerance or diabetes [ 3 ]. Obesity and insulin resistance are strongly associated with systemic markers of inflammation, and, indeed, inflammation may contribute to insulin resistance [ 4 ]. Similarities and overlap between obesity and inflammatory states are emerging. Inflammatory cytokines such as tumor necrosis factor α (TNF α) and interleukin (IL)-6 are produced by adipocytes as well as by monocytes and macrophages, and they circulate at increased levels in individuals with obesity [ 5 , 6 ]. Moreover, bone-marrow-derived macrophages home in on adipose tissue in individuals with obesity [ 7 , 8 ], and adipocytes and macrophages may even be interconvertible [ 9 ]. Furthermore, inflammation is increasingly recognized as a major component and predictor of atherosclerotic vascular disease, a major clinical consequence of insulin resistance [ 10 ]. Hence, the interrelationships between obesity, insulin resistance, and atherosclerosis are of great scientific and clinical interest. We originally identified and characterized resistin as a circulating mouse adipocyte gene product that is regulated by antidiabetic drugs [ 11 ]. In rodents, resistin is derived exclusively from adipocytes [ 11 , 12 ], circulates at increased levels in obese animals [ 11 ], and causes dysregulated hepatic glucose production, leading to insulin resistance [ 13 , 14 ]. A syntenic gene exists in humans, but is expressed at higher levels in monocytes and macrophages than in adipocytes [ 15 , 16 ], raising questions about the relationship between resistin and human metabolic disease. Recently, several studies have suggested that metabolic abnormalities are associated with polymorphisms in the human resistin gene [ 17 , 18 ]. Furthermore, several studies, though not all, have reported increased serum resistin levels in patients with obesity, insulin resistance, and/or type 2 diabetes [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. However, the mechanism and importance of increased resistin levels in human metabolic disease are not known. Here we show that the endotoxin lipopolysaccharide (LPS), a potent inflammatory stimulant, dramatically increases resistin production by inducing secretion of inflammatory cytokines such as TNFα. This increase in resistin production is blocked by both aspirin and rosiglitazone, drugs that have dual anti-inflammatory and insulin-sensitizing actions and have been shown to antagonize NF-κB. Indeed, activation of NF-κB is sufficient to induce resistin expression, and loss of NF-κB function abolishes LPS induction of resistin. Resistin serum levels are increased dramatically by endotoxemia in humans, and correlate with a marker of inflammation in patients with type 2 diabetes. Thus, systemic inflammation leads to increased resistin production and circulating levels in humans. The increased level of resistin in humans with obesity is likely an indirect result of elevated levels of inflammatory cytokines characteristic of states of increased adiposity. Hence, obesity and acute inflammation are both hyperresistinemic states associated with insulin resistance. Methods Differentiation of Primary Human Macrophages Peripheral blood mononuclear cells were isolated from whole blood of healthy donors following apheresis and elutriation. Greater than 90% of these monocytes expressed CD14 and HLA-DR. Cells were plated in 24-well plates at a density of 10 6 cells per well, allowed to adhere for 4 h, then washed with Dulbecco's Modified Eagles Medium and further cultured in 10% FBS in Dulbecco's Modified Eagles Medium supplemented with 5 ng/ml GM-CSF (Sigma, St. Louis, Missouri, United States) to promote macrophage differentiation. All experiments were performed after overnight equilibration with macrophage serum-free medium (GIBCO, San Diego, California, United States; Invitrogen, Carlsbad, California, United States) supplemented with 5 ng/ml GM-CSF. Cells were treated with LPS (Sigma), aspirin (Sigma), SN50, and/or control peptide (Biomol, Plymouth Meeting, Pennsylvania, United States), MG132, PD98059, SB20358 (Calbiochem, San Diego, California, United States), and TNFα (R&D Systems, Minneapolis, Minnesota, United States). Neutralizing antibodies to TNFα, IL-6, and anti-IL-1β, as well as control IgG, were obtained from R&D Systems. Adenovirus expressing activated IKK in pAD easy with GFP and control vector was a generous gift from Steven Shoelson. RNA Isolation and Quantification RNA was isolated using RNeasy Mini Kit (Qiagen, Valencia, California, United States), then subjected to DNase digestion followed by reverse transcription (Invitrogen). mRNA transcripts were quantified by the dual-labeled fluorogenic probe method for real-time PCR, using a Prism 7900 thermal cycler and sequence detector (Applied Biosystems, Foster City, California, United States). Real-time PCR was performed using Taqman Universal Polymerase Master Mix (Applied Biosystems). The primers and probes used in the real-time PCR were the following: Sense-Resistin, 5′-
AGCCATCAATGATAGGATCCA-3′; Antisense-Resistin, 5′-
TCCAGGCCAATGCTGCTTAT-3′; Resistin Probe, 5′-Fam-
AGGTCGCCGGCTCCCTAATATTTAGGG-TAMRA-3′; Sense human 36B4 sense, 5′-
TCGTGGAAGTGACATCGTCTTT-3′; Antisense 36B4, 5′-
CTGTCTTCCCTGGGCATCA-3′; and 36B4 Probe, 5′-FAM-
TGGCAATCCCTGACGCACCG-TAMRA-3′. Primer and probe for TNFα were obtained from Applied Biosystems. The cycle number at which the transcripts of the gene of interest were detectable (CT) was normalized to the cycle number of 36B4 detection, referred to as deltaCT. The fold change in expression of the gene of interest in the compound-treated group relative to that in the vehicle-treated group was expressed as 2 −deltadeltaCT , in which deltadeltaCT equals the deltaCT of the compound-treated group minus the deltaCT of the chosen control group, which was normalized to 1. ELISA Resistin concentrations, in cell media and human plasma, were assessed with a commercially available ELISA (Linco Research, St. Charles, Missouri, United States) and normalized to cell protein. The average correlation coefficient for standards using a four-parameter fit was 0.99. Intra-assay and inter-assay coefficients of variance were 4.7% and 9.1%, respectively. Direct comparison of standard curves generated by the Linco kit with those yielded by another commercially available resistin ELISA (Biovendor Laboratory Medicine, Brno, Czech Republic) yielded high correlation (rho = 0.99, p < 0.001), except that the Biovendor values were approximately 30% lower than those determined with the Linco assay. This appeared to be related to the standards used for calibration. Discrepant absolute values among different assays, including the Biovendor assay, were recently described by others [ 22 ]. Resistin levels in 40 plasma samples were measured using both Linco and Biovendor ELISA kits, with moderate correlation (rho = 0.66). Levels of soluble TNFα receptor 2 (sTNFR2) were measured using a commercially available immunoassay (R&D Systems). Intra-assay and inter-assay coefficients of variance were 5.1% and 9.8%, respectively. Human Endotoxemia Study Healthy volunteers ( n = 6, three male and three female), aged 18–45 y with BMI between 20 and 30 and on no medications, were studied. The University of Pennsylvania Institutional Review Board approved the study protocol, and all participants gave written informed consent. Following screening and exclusion of individuals with any clinical or laboratory abnormalities, participants were admitted to the General Clinical Research Center at the University of Pennsylvania for a 60 h stay. Serial blood samples were collected during the 24 h prior to and 24 h following the intravenous administration of human-research-grade endotoxin (obtained from National Institutes of Health Clinical Center, reference endotoxin [CCRE] [lots 1 and 2; National Institutes of Health Clinical Center PDS #67801]) at a dose of 3 ng/kg given at 6 AM. Plasma and whole blood RNA (PAX tube isolators, Qiagen) samples were isolated from blood, and stored under appropriate conditions for subsequent assays. Type 2 Diabetes Study Participants with type 2 diabetes ( n = 215, 167 male and 48 female), aged 35–75 y and free from clinical cardiovascular diseases, were recruited through the diabetes clinics at the University of Pennsylvania Medical Center and the Veterans Affairs Medical Center, Philadelphia, Pennsylvania, to an ongoing study of cardiovascular risk factors in type 2 diabetes. The sample was composed of 59% Caucasians and 35% African-Americans. All participants were evaluated at the University of Pennsylvania General Clinical Research Center in a fasting state at 8 AM. The University of Pennsylvania Institutional Review Board approved the study protocol, and all participants gave written informed consent. The patient population is described in more detail elsewhere [ 27 ]. Statistical Methods Data are reported as mean and standard error of the mean (SEM) for continuous variables. Because of baseline variation in cell populations between batches of primary human monocytes isolated from multiple donors, cell culture experiments were performed in triplicate and data from representative experiments are presented. For cell culture experiments with multiple treatments, analysis of variance (ANOVA) was used to test for differences in means across treatment groups. When significant global differences were found, post hoc t -tests were used to compare specific treatment groups to the control. Data from the human endotoxemia experiment were analyzed by repeated measures ANOVA. In the type 2 diabetes study, Spearman correlations of plasma levels of resistin with plasma sTNFR2 levels are presented. Results Induction of Resistin Gene and Protein Expression by Endotoxin Treatment of Human Macrophages The regulation of resistin expression was studied in primary cultures of human monocytic cells. Immediately upon plating of elutriated primary human monocytes, resistin gene expression was detectable but highly variable from experiment to experiment (data not shown). One day after plating, resistin gene expression remained detectable at low levels ( Figure 1 A). Subjection of the cells to a protocol leading to differentiation along the macrophage lineage led to a modest, time-dependent enhancement of resistin gene expression ( Figure 1 A). In agreement with a previous report [ 28 ], treatment of primary macrophages with the endotoxin LPS led to a dramatic, dose-responsive increase in resistin gene expression ( Figure 1 B). We also determined that this effect of LPS was paralleled by an increase in resistin protein secretion into the medium ( Figure 1 C). Of note, activated mouse peritoneal macrophages harvested after thioglycolate treatment did not express detectable levels of mouse resistin, even after treatment with LPS (data not shown). Figure 1 Induction of Resistin in Human Macrophages (A) Induction of resistin during human macrophage differentiation ex vivo. Expression of resistin on days 1, 3, and 7 following isolation and culture of human peripheral blood monocytes under macrophage differentiation conditions. Results shown are the mean (± SEM) of three separate experiments with triplicate samples. The ANOVA F statistic for change of resistin mRNA expression during differentiation was 7.06 ( p < 0.01). *, p < 0.01 for post hoc t -tests. (B) Resistin mRNA is induced by endotoxin in primary human macrophage cultures. The ANOVA F statistic for change of resistin mRNA expression in response to increasing concentration of LPS (24 h treatment) was 423.57 ( p < 0.001). *, p < 0.001 for post hoc t -tests. (C) Resistin protein secretion by human macrophages is induced by endotoxin. The ANOVA F statistic for change of resistin protein secretion in response to increasing concentration of LPS (24 h treatment) was 35.36 ( p < 0.001). *, p < 0.001 for post hoc t -tests. For LPS dose response studies, shown in (B) and (C), results (mean ± SEM) of representative experiments, with triplicate samples, are presented. Similar results were obtained in two independent experiments. Endotoxin Induction of Resistin Is Delayed with Respect to TNFα Induction of resistin gene expression by LPS exposure of human macrophages began between 6 and 24 h after treatment, with peak expression at 24 h ( Figure 2 A). This time course of resistin induction was delayed relative to induction of TNFα gene expression, which was detectable at 2 h and peaked 6 h after LPS exposure ( Figure 2 B). The secretion of TNFα followed a similar time course ( Figure 2 C). By contrast, secretion of resistin did not increase until much later, more closely following the pattern of the appearance of sTNFR2, a marker of TNFα action ( Figure 2 C) [ 29 ]. Figure 2 Endotoxin Induction of Resistin Occurs after Induction of TNFα Primary cultures of human macrophages were treated with LPS (1 μg/ml) for various times. (A) Time course of induction of resistin mRNA. The ANOVA F statistic for the change in resistin mRNA over time was 105.45 ( p < 0.001). (B) Time course of induction of TNFα mRNA. The ANOVA F statistic was 34.57 ( p < 0.001). (C) Time course of secretion of resistin, TNFα, and sTNFR2 into medium. ANOVA F statistics for the effect of LPS on resistin (66.51, p < 0.001), sTNFR2 (12.86, p < 0.001), and TNFα (20.48, p < 0.001) were highly significant. Maximal secreted protein levels were as follows: resistin, 21.9 ng/ml/mg; TNFα, 207.2 ng/ml/mg; and sTNFR2, 39.3 ng/ml/mg. Results of representative experiments with triplicate samples are expressed as mean (± SEM). Similar results were obtained in three independent experiments. Endotoxin Induction of Resistin Is Blocked by Immunoneutralization of Multiple Cytokines Resistin gene expression was also induced by TNFα treatment of primary human macrophages ( Figure 3 A) [ 28 ], and resistin secretion increased in parallel ( Figure 3 B). Since LPS induction of TNFα preceded the increase in resistin (see Figure 2 C), we hypothesized that TNFα, or a similar cytokine produced early after LPS exposure, was responsible for the later induction of resistin. Indeed, neutralizing antibodies to TNFα markedly attenuated the increase in resistin gene expression ( Figure 3 C). LPS treatment also induces other cytokines, including IL-6 and IL-1β [ 30 ], and IL-6 induces resistin modestly (data not shown) [ 28 ]. Antibodies to IL-6 and IL-1β individually had minor effects on LPS stimulation of resistin ( Figure 3 C). However, the combination of antibodies to TNFα, IL-6, and IL-1β markedly attenuated LPS induction of resistin ( Figure 3 C). These data clearly show that resistin induction by endotoxin is mediated by a cascade in which the primary event is secretion of inflammatory cytokines that, in turn, induce resistin. Figure 3 Endotoxin-Induced Cytokines Regulate Resistin Induction (A) TNFα induces production of resistin mRNA by primary human macrophages. The ANOVA F statistic for the effect of increasing TNFα concentrations on resistin was 23.81 ( p < 0.001). *, p < 0.001 for post hoc t- tests. (B) TNFα induces resistin protein secretion by primary human macrophages. ANOVA F statistic for the effect of TNFα on resistin was 79.85 ( p < 0.001). *, p < 0.005 for post hoc t -tests. Results of representative experiments with triplicate samples are expressed as the mean (± SEM). Similar results were obtained in two independent experiments. (C) LPS (1 μg/ml) induction of resistin is abrogated by antibody neutralization of cytokines (7.5 μg/ml per antibody). ANOVA F statistic for the effect of neutralizing antibodies on resistin was 3.08 ( p < 0.05). p -Values for post hoc t -tests versus IgG: *, p < 0.05; **, p < 0.001. Results are expressed as the mean (± SEM) of three separate experiments with triplicate samples. Induction of Resistin Is Blocked by Anti-Inflammatory Insulin-Sensitizing Drugs That Target NF-κB Mouse resistin, produced exclusively by adipocytes, is down-regulated by antidiabetic thiazolidinediones, including rosiglitazone [ 11 ]. Consistent with an earlier report [ 16 ], rosiglitazone down-regulated resistin gene expression ( Figure 4 A) in LPS-stimulated human macrophages. Resistin protein secretion was also significantly reduced by rosiglitazone ( Figure 4 B). Hence, macrophage expression of resistin and its induction by LPS is species-specific, but down-regulation of resistin by thiazolidinedione occurs both in rodents and humans. Rosiglitazone has marked anti-inflammatory effects on macrophages [ 31 ]. This led us to examine the effect of aspirin, an anti-inflammatory compound that targets IκB kinase and has insulin-sensitizing effects [ 32 ]. Remarkably, aspirin dramatically decreased endotoxin-induced resistin expression in a dose-dependent manner ( Figure 4 C). Both aspirin (via IκB kinase) and rosiglitazone (via PPARγ) inhibit NF-κB [ 31 , 32 ], which is activated by LPS. Indeed, treatment of the macrophages with the proteasome inhibitor MG132, which prevents NF-κB activation [ 33 ], abrogated endotoxin-induced activation of resistin expression (data not shown). Moreover, treatment of the macrophages with SN50, a cell-permeable peptide that specifically prevents activation of NF-κB by inhibiting its nuclear translocation [ 34 ], nearly abolished endotoxin-induced activation of resistin expression ( Figure 4 D). Thus, activation of NF-κB is required for LPS induction of resistin in human macrophages. Furthermore, constitutive activation of NF-κB by adenoviral expression of activated IκB kinase was sufficient to induce resistin in primary human macrophages ( Figure 4 E). The magnitude of this activation was less than that caused by LPS, which is known to also activate MAP-kinase (MAPK). Indeed, inhibition of either p42 MAPK by PD98059, or p38 MAPK (using SB20358) partially blocked the induction of resistin by LPS ( Figure 4 F). Together these results show that NF-κB activation is necessary and sufficient for resistin induction by LPS, with MAPK activation increasing the magnitude of the response. Figure 4 Inhibition of Resistin Induction by Anti-Inflammatory Insulin Sensitizers (A) Down-regulation of resistin mRNA by rosiglitazone. ANOVA F statistic for the effect Rosiglitazone on resistin expression was 62.52 ( p < 0.001). p value for post hoc t-tests, is depicted in the Figure. * p < 0.005 versus control for post hoc t -tests. (B) Down-regulation of resistin protein secretion by human macrophages treated with rosiglitazone. The ANOVA F statistic for the effect of rosiglitazone on resistin protein secretion was 29.44 ( p < 0.001). p -Values for post hoc t -tests versus control: *, p < 0.05; **, p < 0.001. Cells were pre-treated with rosiglitazone for 24 h and with LPS (1 μg/ml) and rosiglitazone for an additional 24 h. Results of representative experiments with triplicate samples are expressed as mean (± SEM). Similar results were obtained in three independent experiments. (C) Down-regulation of resistin gene expression by aspirin. The ANOVA F statistic for the effect of aspirin on resistin expression was 61.33 ( p < 0.001). p -Values for post hoc t -tests versus no aspirin: *, p < 0.01; **, p < 0.001; ***, p < 0.0001. Cells were pre-treated with aspirin for 2 h and with LPS (1 μ g/ml) and aspirin for an additional 24 h. Results of representative experiments with triplicate samples are expressed as mean (± SEM). Similar results were obtained in two independent experiments. (D) Down-regulation of resistin gene expression by NF-κB inhibitor SN50. *, p < 0.001 versus control peptide by t -test. Cells were pre-treated with SN50 or control peptide at 100 ug/ml for 2 h, and with LPS (1 μg/ml) and SN50 or control peptide for an additional 24 h. Results are the expressed as the mean (± SEM) of two independent experiments performed in triplicate. (E) Induction of resistin by activation of NF-κB. *, p < 0.05 versus control virus by t -test. Cells were infected with adenovirus expressing activated IKK or control virus for 24 h. Results of representative experiments with triplicate samples are expressed as mean (± SEM). Similar results were obtained in two independent experiments. (F) Down-regulation of resistin gene expression by inhibitors of p38 and p42 MAPK. The ANOVA F statistic for the effect of the MAPK inhibitor on resistin expression was 11.54 ( p < 0.005). *, p < 0.005 versus control for post hoc t -tests. Cells were pretreated with 50 μM PD98059 or 2.5 μM SB20358 for 2 h and with LPS (1 μg/ml) and PD98059 or SB20358 for an additional 24 h. Results are expressed as the mean (± SEM) of two independent experiments performed in triplicate. LPS Robustly Increases Circulating Resistin Levels in Healthy Humans Next, we asked whether our findings from ex vivo studies of human macrophages would translate into in vivo observations in humans. Six healthy volunteers were injected with LPS, using a protocol similar to that shown to produce insulin resistance [ 35 ]. Baseline circulating resistin levels were approximately 4 ng/ml, and remained relatively constant for several hours prior to LPS infusion ( Figure 5 A). Remarkably, resistin levels rose dramatically because of endotoxemia, peaking 8–16 h after LPS administration ( Figure 5 A). The time course of hyperresistinemia paralleled the increase in circulating levels of sTNFR2, although the increase in resistin levels was more marked and sustained ( Figure 5 A). The increase in resistin protein levels correlated with increased resistin gene expression in peripheral blood mononuclear cells following systemic endotoxemia ( Figure 5 B). Figure 5 Endotoxin Dramatically Induces Plasma Resistin in Humans (A) Plasma resistin and sTNFR2 levels were measured serially in six healthy volunteers for 24 h before and after intravenous LPS (3 ng/kg) administration. The repeated measures ANOVA F statistics for the effect of LPS on plasma resistin (9.25, p < 0.001) and sTNFR2 (23.65, p < 0.001) were highly significant . (B) Mean resistin RNA expression in whole blood cells of healthy volunteers ( n = 2) before and after treatment with LPS (3 ng/kg). Circulating Resistin Levels Correlate with the Inflammatory Marker sTNFR2 in Patients with Type 2 Diabetes Patients with type 2 diabetes and insulin resistance, many of whom are obese, have elevated levels of several inflammatory markers, including IL-6, TNFα, and sTNFR2 [ 36 ]. LPS administration has been shown to induce acute insulin resistance in humans [ 37 ]. Given that LPS infusion increased resistin levels, we measured resistin in a cohort of 215 patients with type 2 diabetes. Circulating resistin levels were significantly correlated with levels of sTNFR ( Figure 6 A). Thus, there is an association between resistin levels and systemic inflammation in patients with type 2 diabetes. Figure 6 Plasma Resistin Levels Correlate with sTNFR2 Levels in Humans with Type 2 Diabetes (A) The correlation (Spearman coefficient rho = 0.38, p < 0.001) of plasma resistin and sTNFR2 levels in 215 humans with type 2 diabetes is presented. The line represents the linear regression fit between log-transformed plasma levels of resistin and sTNFR2. (B) Model to explain hyperresistinemia in mice and humans with obesity despite the species differences in the source of plasma resistin. Circulating inflammatory cytokines TNFα and IL-6 are depicted because of their role in resistin induction in human macrophages and their implied role in insulin resistance. Other cytokines and inflammatory markers may also contribute to insulin resistance and/or resistin induction. Discussion We have demonstrated that, in human macrophages, an inflammatory cascade with secretion of cytokines, including TNFα and IL-6, is sufficient and necessary for the induction of resistin. Insulin sensitizers that have anti-inflammatory properties, including a synthetic PPARγ agonist as well as aspirin, suppress macrophage resistin expression, as does direct inhibition of NF-κB. Experimental endotoxemia in healthy volunteers, based on the well-established gram-negative bacterial inflammatory response in humans [ 38 , 39 , 40 ], induces a dramatic elevation of circulating resistin levels. Hence, resistin gene and protein expression are increased by inflammatory stimuli both ex vivo and in vivo. In rodents, resistin is produced exclusively by adipocytes, regulates normal glucose homeostasis, and causes insulin resistance at high circulating levels [ 11 , 13 ]. Translation of resistin's metabolic effects from rodents to humans has been problematic because peripheral blood mononuclear cells and macrophages appear to be a primary source of resistin in humans [ 15 , 16 ]. This species difference in primary locus of expression is yet another example of the close and functionally overlapping relationship between adipocytes and macrophages [ 41 ]. Numerous studies have reported that circulating resistin levels are increased in human obesity [ 20 , 25 , 26 , 41 ] and diabetes [ 19 , 20 , 23 , 42 , 43 ]. Our data suggest that, whereas hyperresistinemia in obese rodents derives directly from adipocytes, human resistin is indirectly regulated by the inflammatory internal milieu of obesity ( Figure 6 B). Indeed, obesity is associated with elevated levels of cytokines whose systemic administration leads to impaired glucose homeostasis [ 36 , 44 , 45 ], such as TNFα and IL-6, which we show here to mediate the inflammatory induction of human resistin. Thus, in both species, adipose tissue is an endocrine organ containing adipocytes as well as macrophages that regulates energy metabolism and glucose homeostasis through secretion of multiple factors, including inflammatory cytokines [ 46 ]. Clearly the relationship between obesity, inflammation, and resistin expression is complex, and needs to be systematically studied in larger and varied patient populations. Intriguingly, we found a strong correlation between plasma levels of resistin and sTNFR2, the soluble cleavage product of the activated TNFα receptor, in diabetic patients. A comparable correlation between resistin and sTNFR2 ( R = 0.31, p < 0.001) was found in a cohort of 879 non-diabetic individuals, in whom resistin levels independently correlated with coronary atherosclerotic disease (M. P. Reilly, M. Lehrke, M. L. Wolfe, A. Rohatgi, M. A. Lazar, and D. J. Rader, unpublished data). LPS binds to pathogen-associated-molecular-pattern innate immune receptors, such as CD14 and Toll-like receptor 4, activating signal cascades involving NF-κB and MAPK [ 47 ] and thereby inducing the transcription and secretion of early cytokines, including TNFα and IL-1 [ 48 ]. We have shown here that these early cytokines are responsible for secondary induction or enhancement of resistin expression in macrophages. Hyperresistinemia impairs glucose homeostasis in rodents [ 49 , 50 ], and inflammatory states are associated with insulin resistance [ 36 ], which may serve as a physiological attempt to increase the provision of glucose to the brain under stress conditions. Indeed, induction of acute inflammation by administration of LPS causes insulin resistance in humans [ 37 ], and here we have demonstrated the concomitant induction of resistin. Interestingly, the peak in TNFα and IL-6 levels after LPS administration to humans precedes a phase of prolonged insulin resistance that begins approximately 6 h after LPS administration [ 37 ], closely approximating the time course of resistin induction. Hence resistin is a potential mediator of insulin resistance in humans with acute inflammation. Moreover, obesity is associated with activation of innate immunity [ 6 ], including the inflammatory mediators that induce resistin. In this context it is intriguing that resistin levels are increased in obesity [ 25 , 26 ] and that insulin-sensitizing agents such as aspirin and rosiglitazone, with disparate primary molecular targets, antagonize resistin induction. Indeed, thiazolidinedione suppression of resistin levels has recently been correlated with hepatic insulin sensitization [ 43 ]. Future work will be needed to better understand the relationship between circulating resistin levels and the insulin resistance characteristic of inflammatory states, including obesity. Patient Summary Why Was This Study Done? There is a very close connection between obesity and diabetes: diabetes is more common among obese people, and people with type 2 diabetes know that weight control is an essential part of their diabetes treatment. But the link between extra body fat and diabetes remains a puzzle. Recent experiments in mice suggested that a hormone called resistin could be the missing link. One reason is that resistin levels respond to a particular class of diabetes drugs called thiazolidinediones. But studies in humans found that mice and humans are quite different when it comes to resistin. One difference is that in mice resistin is produced by fat cells, but in humans it is produced by special immune cells called macrophages that are involved inflammation. Researchers are now studying what role—if any—resistin might have in humans with obesity and diabetes and are studying the similarities in the ways in which the body reacts to obesity and inflammation. What Did the Researchers Do? The researchers examined what happens to resistin levels when human macrophages or human patients are exposed to substances that trigger inflammation. What Did They Find? The substances that trigger inflammation caused higher resistin levels, but resistin levels were lowered again by thiazolidinediones. What Does This Mean? Because in mice higher resistin levels (produced by fat cells) are linked to diabetes, one possibility is that obesity in humans, by being similar to inflammation, causes immune cells to make lots of resistin and hence promotes diabetes that way. What Next? More research is necessary to confirm these findings and to find out how important resistin is as a link between obesity and diabetes, and how resistin promotes diabetes. Additional Information United States National Institute of Diabetes, Digestive, and Kidney Diseases (NIDDK) information on obesity: http://www.niddk.nih.gov/health/nutrit/pubs/unders.htm NIDDK information on diabetes: http://diabetes.niddk.nih.gov/ International Diabetes Federation: http://www.idf.org/ | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC529430.xml |
544838 | Soluble expression of recombinant proteins in the cytoplasm of Escherichia coli | Pure, soluble and functional proteins are of high demand in modern biotechnology. Natural protein sources rarely meet the requirements for quantity, ease of isolation or price and hence recombinant technology is often the method of choice. Recombinant cell factories are constantly employed for the production of protein preparations bound for downstream purification and processing. Eschericia coli is a frequently used host, since it facilitates protein expression by its relative simplicity, its inexpensive and fast high density cultivation, the well known genetics and the large number of compatible molecular tools available. In spite of all these qualities, expression of recombinant proteins with E. coli as the host often results in insoluble and/or nonfunctional proteins. Here we review new approaches to overcome these obstacles by strategies that focus on either controlled expression of target protein in an unmodified form or by applying modifications using expressivity and solubility tags. | Introduction Microorganisms like the enterobacterium Escherichia coli are outstanding factories for recombinant expression of proteins. An expression system for the production of recombinant proteins in E. coli usually involves a combination of a plasmid and a strain of E. coli [ 1 ]. The main purpose of recombinant protein expression is often to obtain a high degree of accumulation of soluble product in the bacterial cell. This strategy is not always accepted by the metabolic system of the host and in some situations a cellular stress response is encountered. Another response encountered in recombinant systems is the accumulation of target proteins into insoluble aggregates known as inclusion bodies. These aggregated proteins are in general misfolded and thus biologically inactive [ 2 ]. Under normal cellular conditions a subset of cytoplasmic proteins are able to fold spontaneously [ 3 ] while aggregation prone proteins require the existence of a number of molecular chaperones that interact reversibly with nascent polypeptide chains to prevent aggregation during the folding process [ 4 ]. Aggregation of recombinant proteins overexpressed in bacterial cells could therefore result either from accumulation of high concentrations of folding intermediates or from inefficient processing by molecular chaperones. No universal approach has been established for the efficient folding of aggregation prone recombinant proteins [ 1 ]. The literature describes a number of methods for the redirection of proteins from inclusion bodies into the soluble cytoplasmic fraction (Figure 1 ). Overall they can be divided into procedures where protein is refolded from inclusion bodies [ 5 ] and procedures where the expression strategy is modified to obtain soluble expression. In this review we focus on methods developed for soluble expression in the E. coli cytoplasm. Refolding from inclusion bodies is in many cases considered undesireable, but is however sometimes the method of choice. The major obstacles are the poor recovery yields, the requirement for optimization of refolding conditions for each target protein and the possibility that the resolubilization procedures could affect the integrity of refolded proteins. In addition, the purification of highly expressed soluble protein is less expensive and time consuming than refolding and purification from inclusion bodies. Maximizing the production of recombinant proteins in a soluble form is therefore an attractive alternative to in vitro refolding procedures. The methods used to mediate soluble expression can be divided into procedures where target modification is avoided and procedures where the target sequence is engineered (Figure 1 ). Strategies where target modification is avoided Some proteins directly influence the cellular metabolism of the host by their catalytic properties, but in general expression of recombinant proteins induces a "metabolic burden". The metabolic burden is defined as the amount of resources (raw material and energy), which are withdrawn from the host metabolism for maintenance and expression of the foreign DNA [ 6 ]. The formation of inclusion bodies occurs as a response to the accumulation of denatured protein. The metabolic burden and inclusion body formation are not directly linked but are both among the main factors to determine the ability of cells to produce soluble recombinant protein. Since the accumulation of denatured protein and the metabolic burden can be controlled by a number of environmental factors, we are partially able to control the formation of soluble protein in vivo . Protein expression at reduced temperatures A well known technique to limit the in vivo aggregation of recombinant proteins consists of cultivation at reduced temperatures [ 7 ]. This strategy has proven effective in improving the solubility of a number of difficult proteins including human interferon α-2, subtilisin E, ricin A chain, bacterial luciferase, Fab fragments, β-lactamase, rice lipoxygenase L-2, soybean lypoxygenase L-1, kanamycin nuclotidyltransferase and rabbit muscle glycogen phosphorylase (see [ 8 ] and references cited therein). The aggregation reaction is in general favored at higher temperatures due to the strong temperature dependence of hydrophobic interactions that determine the aggregation reaction [ 9 ]. A direct consequence of temperature reduction is the partial elimination of heat shock proteases that are induced under overexpression conditions [ 10 ]. Furthermore, the activity and expression of a number of E. coli chaperones are increased at temperatures around 30°C [ 11 , 12 ]. The increased stability and potential for correct folding at low temperatures are partially explained by these factors. However, a sudden decrease in cultivation temperature inhibits replication, transcription and translation [ 13 ]. Traditional promoters used in vectors for recombinant protein expression are also strongly affected in terms of efficiency [ 14 ]. A similar transcriptional effect is achieved when a moderately strong or weak promoter is used or when a strong promoter is partially induced. Low induction levels have been found to result in higher amounts of soluble protein [ 15 ]. This is a result of the reduction in cellular protein concentration which favors folding. However, bacterial growth is decreased, thus resulting in a decreased amount of biomass. Different strategies aimed at optimizing the expression of recombinant proteins at low temperature are as follows. A system based on the cspA promoter was developed for the expression of proteins at low temperature [ 16 ]. The cspA promoter is highly induced at low temperature and is well repressed at and above 37°C. A sequence encoding the TolAI-β-lactamase fusion protein which is toxic to E. coli and rapidly degraded at 37°C was placed under the control of the cspA promoter. Temperature downshift to 15 or 23°C abolished degradation of the fusion protein and the toxic phenotype associated with expression at 37°C was suppressed. It was suggested that this system is a valuable tool for the production of proteins containing membrane-spanning domains or otherwise unstable gene products in E. coli . A principle that allows for protein expression and folding at 4°C was presented recently [ 17 ]. This principle is based on co-expression of the target protein with chaperones from a psychrophilic bacterium. The two chaperones (Cpn60 and Cpn10 from Oleispira antarctica RB8 T ) allow E. coli to grow at high rates at 4°C [ 12 ]. An esterase from O. antarctica RB8 T was co-expressed with Cpn60 and Cpn10 in E. coli at 4°C. This procedure increased the specific activity of the purified esterase 180 fold as compared to enzyme prepared from cultivations at 37°C. It was concluded that the low temperature was beneficial to folding and the system was suggested as a tool for expression and correct folding of recombinant proteins in the cytoplasm of E. coli . E. coli strains used to improve soluble expression Numerous specialized host strains have been developed to overcome the metabolic burden related to high level protein expression. Two E. coli mutant strains have contributed significantly to the soluble expression of difficult recombinant proteins. C41(DE3) and C43(DE3) are mutants that allow over-expression of some globular and membrane proteins unable to be expressed at high-levels in the parent strain BL21(DE3) [ 18 ]. Expression of the F 1 F o ATP synthase subunit b membrane protein in these strains, in particular C43(DE3), is accompanied by the proliferation of intracellular membranes and inclusion bodies are absent [ 19 ]. These strains are now commercialized by Avidis and a high number of reports on their use in expression of difficult proteins have been published [ 20 - 23 ]. A recent work reports that the stability of plasmids encoding toxic proteins is increased in C41(DE3) and especially in C43(DE3) [ 24 ]. Cysteines in the E. coli cytoplasm are actively kept reduced by pathways involving thioredoxin reductase and glutaredoxin. The disulfide bond dependent folding of heterologous proteins is improved in the Origami strains from Novagen. Disruption of the trxB and gor genes encoding the two reductases, allow the formation of disulfide bonds in the E. coli cytoplasm. The trxB (Novagen AD494) and trxB/gor (Novagen Origami) negative strains of E. coli have been selected in several expression situations [ 25 - 27 ]. Folding and disulfide bond formation in the target protein, is enhanced by fusion to thioredoxin in strains lacking thioredoxin reductase ( trxB ) [ 28 ]. Overexpression of the periplasmic foldase DsbC in the cytoplasm stimulates disulfide bond formation further [ 27 ]. Modification of cultivation strategies to obtain soluble protein The simplest way to produce a recombinant protein is by batch cultivation. Here all nutrients required for growth are supplied from the beginning and there is a limited control of the growth during the process. This limitation often leads to changes in the growth medium such as changes in pH and concentration of dissolved oxygen as well as substrate depletion. Furthermore inhibitory products of various metabolic pathways accumulate. Cell densities and production levels are only moderate in batch cultivations. In fed batch cultivations, the concentration of energy sources can be adjusted according to the rate of consumption. Several other factors can also be regulated in order to obtain the maximal production level in terms of target protein per biomass. The formation of inclusion bodies can be followed in fed batch cultivations by monitoring changes in intrinsic light scattering by flow cytometry [ 29 ]. This allows for real time optimization of growth conditions as soon as inclusion bodies are detected even at low levels and inclusion body formation can potentially be avoided [ 30 ]. Folding of some proteins require the existence of a specific cofactor. Addition of such cofactors or binding partners to the cultivation media may increase the yield of soluble protein dramatically. This was demonstrated for a recombinant mutant of hemoglobin for which the accumulation of soluble product was improved when heme was in excess [ 31 ]. Similarly, a 50% increase in solubility was observed for gloshedobin when E. coli recombinants were cultivated in the presence of 0.1 mM Mg 2+ [ 32 ]. An important factor in soluble expression of recombinant proteins is media composition and optimization. Although this is attained mostly by trial and error, it nevertheless may be beneficial. Molecular chaperones drive folding of recombinant proteins A possible strategy for the prevention of inclusion body formation is the co-overexpression of molecular chaperones. This strategy is attractive but there is no guarantee that chaperones improve recombinant protein solubility. E. coli encode chaperones, some of which drive folding attempts, whereas others prevent protein aggregation [ 4 , 11 , 33 ]. As soon as newly synthesized proteins leave the exit tunnel of the E. coli ribosome they associate with the trigger factor chaperone [ 34 ]. Exposed hydrophobic patches on newly synthesized proteins are protected by association with trigger factor from unintended inter- or intramolecular interactions thus preventing premature folding. Proteins can start or continue their folding into the native state after release from trigger factor. Proteins trapped in non-native and aggregation prone conformations, are substrates for DnaK and GroEL. DnaK (Hsp70 chaperone family) prevents the formation of inclusion bodies by reducing aggregation and promoting proteolysis of misfolded proteins [ 11 ]. A bi-chaperone system involving DnaK and ClpB (Hsp100 chaperone family) mediates the solubilization or disaggregation of proteins [ 35 ]. GroEL (Hsp60 chaperone family) operates the protein transit between soluble and insoluble protein fractions and participates positively in disaggregation and inclusion body formation. Small heat shock proteins lbpA and lbpB protect heat denatured proteins from irreversible aggregation and have been found associated with inclusion bodies [ 36 , 37 ]. Simultaneous over-expression of chaperone encoding genes and recombinant target proteins proved effective in several instances. Co-overexpression of trigger factor in recombinants prevented the aggregation of mouse endostatin, human oxygen-regulated protein ORP150, human lysozyme and guinea pig liver transglutaminase [ 38 , 39 ]. Soluble expression was further stimulated by the co-overexpression of the GroEL-GroES and DnaK-DnaJ-GrpE chaperone systems along with trigger factor [ 39 ]. The chaperone systems are cooperative and the most favorable strategies involve co-expression of combinations of chaperones belonging to the GroEL, DnaK, ClpB and ribosome associated trigger factor families of chaperones [ 40 - 42 ]. Interaction partners and protein folding Protein insolubility in the E. coli cytoplasm is partially related to the distribution of hydrophobic residues on the surface of the protein. The soluble expression of subunits of hetero multimeric proteins therefore sometimes suffers from inclusion body formation in the absence of an appropriate binding partner. Soluble expression in E. coli of the bacteriophage T4 gene 23 product (major capsid protein) required the co-expression of gene product 31 (phage co-chaperonin gp31) [ 43 ]. Expression of the correct interaction partner enabled gp23 to fold correctly and form long regular structures in the cytoplasm of E. coli . Another study reports the purification of a heterodimeric complex by expression of each subunit (pheromaxein A and C) as a fusion to thioredoxin [ 44 ]. Each subunit remained soluble in solution, when thioredoxin was proteolytically removed, only in the presence of the other. Conclusively, interaction partners potentially favour in vivo solubility of target proteins. New systems for co-expression of multiple proteins involved in complex structures enable such strategies [ 1 ]. Strategies involving engineered target protein Target proteins are not always expressed in a soluble form by the strategies described above. The last part of this review discusses how misfolded proteins can be engineered or pushed to evolve and selected to gain soluble expression. Fusion protein technology The use of affinity tags in recombinant protein purification has a long tradition. Not only have they been exploited for the development of generic purification strategies. Affinity tags have been observed to improve protein yield, to prevent proteolysis and to increase solubility in vivo [ 1 , 45 ]. Among the most potent solubility enhancing proteins characterized to date are the E. coli maltose binding protein (MBP) and the E. coli N-utilizing substance A (NusA). MBP (40 kDa) and NusA (54.8 kDa) act as solubility enhancing partners and are especially suited for the expression of proteins prone to form inclusion bodies. Although many proteins are highly soluble, they are not all effective as solubility enhancers. E. coli MBP proved to be a much more effective solubility partner than the highly soluble GST and thioredoxin proteins in a comparison of solubility enhancing properties [ 46 ]. Solubility enhancement is a common trait of maltodextrin-binding proteins (MBPs) from a number of organisms and some of them are even more effective than E. coli MBP [ 47 ]. A precise mechanism for the solubility enhancement of MBP has not been found. However, MBP might act as a chaperone by interactions through a solvent exposed "hot spot" on its surface which stabilizes the otherwise insoluble passenger protein [ 48 , 49 ]. The ability of MBP to promote the solubility of fusion partners can be improved by addition of supplemental tags. Different configurations for MBP fusion proteins have been suggested for high-throughput protein expression and purification [ 50 ]. Wilkinson and Harrison proposed a model for the theoretical calculation of solubility percentages of recombinant proteins expressed in the E. coli cytoplasm [ 51 ]. A webserver for the calculation of this index is found at . The Wilkinson-Harrison model along with experimental data identified NusA as a highly favorable solubility partner [ 52 ]. The major advantage of NusA, in addition to the good solubility characteristics, is its high expressivity. Both MBP and NusA have been used for the solubilization of highly insoluble ScFv antibodies in the cytoplasm of E. coli [ 48 , 53 ]. Numerous examples of MBP and NusA as functional solubility enhancers are found in the literature [ 54 - 57 ]. Natural molecular chaperones that have been used as solubility enhancers include prolyl cis trans isomerases (PPIases) [ 58 ], thioredoxin [ 59 ] and dsbA [ 60 ]. Fusion partners such as MBP and NusA are relatively large proteins. We recently suggested the use of a highly soluble N-terminal fragment of translation initiation factor IF2 (17.4 kDa) as a solubility partner [ 61 ]. The use of a small partner reduces the amount of energy required to obtain a certain number of molecules, diminishes steric hindrance and simplify downstream applications such as NMR. Another relatively small protein, barnase was suggested to exert chaperone like functions both in vivo and in vitro when fused to the C-terminus of the light chain variable domain of an IgG [ 62 ]. In a recent study it was shown that a 17 residue C-terminal extension of Pfg27 resulted in several fold enhancement of soluble expression [ 63 ]. Several studies have shown that the nature of terminal residues in proteins can play a role in recognition and subsequent action by proteases [ 64 , 65 ]. The terminal extension of proteins might therefore indirectly protect them from the denaturaturation/misfolding associated to partial proteolytic degradation. It has also been suggested that large net charges of peptide extensions increases electrostatic repulsion between nascent polypeptides and therefore enhances their correct folding [ 66 ]. Screening strategies have been employed to select for favorable fusion partners in a high throughput manner. In such a system more than 80% of the proteins tested showed high levels of expression of soluble products with at least one of eight fusion partners including NusA, intein, thioredoxin, His-tag, MBP, calmodulin binding protein and glutathione-S-transferase [ 67 ]. These results were supported by another similar study [ 68 ]. Screening for and selection of soluble variants Structural and functional genomics and proteomics are important elements in the evaluation of gene function. The expression and purification of properly folded proteins in a high throughput manner are key elements in these studies. A number of different approaches to the high throughput screening of soluble expression products have been described recently. The intrinsic folding yield, stability and solubility of target proteins can be improved by engineering the target protein. When structural information is available, the solubility of the expressed protein has been improved by rational site directed mutagenesis [ 69 ]. A more general approach is to find more soluble variants by directed evolution. Libraries generated in this context include random point mutants, deletions and fragments [ 70 ]. The generated mutants are screened for solubility either by the function of the protein of interest or by more general screens. A screen based on biological activity implies that a new assay has to be developed for every new protein studied. Moreover, in many cases the protein or protein domain studied does not display any known activity at all. The general screens include fusion reporter methods, stress reporter methods and direct methods and are therefore usually preferred for high-throughput approaches. Fluorescence of E. coli cells expressing target genes fused to the GFP-gene is related to the solubility of the target gene expressed alone [ 71 ]. Hence, protein folding in E. coli can be improved by directed evolution approaches for a certain target protein by screening for fluorescing mutants. This approach evolved three insoluble proteins including Pyrobaculum aerophilum methyl transferase, tartrate dehydratase β-subunit and nucleoside diphosphate kinase to be 50%, 95% and 90% soluble respectively [ 72 ]. The GFP reporter system was further used to screen for solubilizing interaction partners to insoluble targets. Fusion of integration host factor β upstream to GFP resulted in aggregation, whereas co-expression of the binding partner (integration host factor α) increased fluorescence dramatically [ 73 ]. A similar approach is the use of selective pressure. By fusing target proteins with chloramphenicol acetyl transferase (CAT) more soluble fusion protein mutants were selected on media containing progressively higher levels of chloramphenicol [ 74 ]. Furthermore, selective pressure (fusion to kanamycin phosphotransferase) was used in a system aiming at the obtainment of soluble proteins encoded by cDNA fragments in a high throughput approach [ 75 ]. Another fusion reporter method use the β-galactosidase α peptide as fusion partner in a screen for lacZα complementation in a system where inactive lacZΩ is supplied in trans . Active β-galactosidase can be detected when the α peptide becomes soluble and restore enzyme activity by binding to lacZΩ [ 76 ]. An innate host cell response is induced when recombinantly expressed proteins are misfolded. This response can be monitored by the transcription from E. coli promoters that are up-regulated when misfolded proteins are expressed. It was found that the promoter for the small heat shock protein ibpA could be fused to lacZ and used as a reporter for misfolded protein [ 77 ]. This reporter could discriminate soluble, partially soluble and insoluble recombinant proteins. Genetic screens and directed evolution is further reviewed elsewhere [ 78 ]. Soluble fusion proteins are not necessarily biologically active and properly folded. Several reports have demonstrated that soluble preparations of fusion proteins have low biological activity as compared to the non-fused protein [ 79 ]. It was shown that a fusion of HPV oncoprotein E6 to MBP formed soluble multimeric aggregates composed of folded MBP and misfolded E6. These "soluble inclusion bodies" could be avoided by optimization of the expression conditions by screening for monodispersity [ 79 ]. Alternative strategies A few strategies that are radically different from the conventional fusion partner and selection approaches have been developed for the potential rescuing of recombinant proteins from misfolding in the E. coli cytoplasm. A system based on artificial oil bodies was developed and illustrated by a fusion protein composed of oleosin and GFP [ 80 ]. The expressed fusion protein was found in the insoluble cellular fraction but could be reconstituted as oil-bodies by addition of triacylglycerol and phospholipids to the purified inclusion bodies. GFP could subsequently be separated from the oil bodies using an engineered factor Xa cleavage site and centrifugation. An in vivo rescuing system based on the E. coli ribosome was recently presented [ 81 ]. Target proteins are rescued from in vivo aggregation by fusing them to ribosomal protein L23. The fusion protein is expressed in a strain of E. coli deficient in the essential L23 ribosomal protein. This allows for the covalent coupling of target proteins to the highly soluble ribosomal particles. Ribosomes with coupled target protein can subsequently be isolated by centrifugation methods and the target protein released in a highly enriched form by site specific protease cleavage. Conclusions We have reviewed the most recent improvements in obtaining soluble and functional protein preparations from E. coli recombinants. A subset of the methods focus on relieving the cellular stress that is a response to the extreme metabolic situation experienced by the host cell during the process of hyperexpression of a single or a few proteins. A second subset of methods focus on improving the solubility and structural stability of the expressed protein, by the combination of the target protein with specific peptide tags. A common trait in modern expression strategies is the skillful combination of the utensils in the genetic toolbox, but also a constant reconsideration of the accepted paradigms in trade of protein expression. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC544838.xml |
340959 | A New Breast Cancer Model | null | Thanks to the tools of molecular biology, our understanding of the 100-plus diseases known collectively as cancer has increased dramatically over the past decades. While each of these cancers exhibits unique characteristics reflecting the particular cell or tissue it springs from, the disease follows a similar arc in nearly all its forms. Cancer is a multistep disease that begins when genetic damage—initiated by a multitude of agents—unleashes a single cell from the normal constraints on cellular proliferation. This single transformed cell generates a colony of similarly abnormal progeny that can take decades to develop into malignancies. While events that stimulate uncontrolled cell division can promote cancer, mutations in tumor suppressor genes figure prominently in tumor progression. Disruptions in the pRb (retinoblastoma 1) tumor suppressor, for example, are often seen early in cancer development, sensitizing cells to tumorigenesis. pRb, along with other “pocket proteins”—so-called because they share an amino acid domain called the Rb pocket—regulate cell cycle progression, apoptosis (programmed cell death), and cellular differentiation. Some tumor suppressors, such as p53, can trigger apoptosis, ultimately sacrificing cells that have sustained DNA damage or other types of cellular stress. Mutations in both the pRb and p53 tumor suppressor pathways are commonly seen in human cancers, though their interactions appear to vary depending on the tissue. In mouse brain epithelial cells, for example, loss of p53 function coupled with loss of pRb results in reduced apoptosis and increased tumor growth, while p53 loss in mouse brain astrocytes (cells that support neurons) does not affect tumor growth. Building on this work, Terry Van Dyke and colleagues report that loss of the pRb tumor suppressor in mammary tissue has the same effect—predisposition to tumor formation—seen in these other cell types. Despite the different environment inherent in each cell type, the initial events following loss of the pRb pathway were the same: increased proliferation and apoptosis, followed by tumorigenesis. But, surprisingly, pRb and p53 interactions varied in different cell types. Like most cancers, mammary gland cancer has a long latency period, prompting the researchers to ask what events engineer tumor progression. To investigate the relative contribution of pRb and p53 in tumorigenesis, the researchers generated a novel mouse model with a dysfunctional pRb pathway and various levels of p53 function in several cell types. This is a significant achievement in itself, as many agents that inactivate the pRb pathway also disrupt the p53 pathway. pRb inactivation, they show, causes abnormalities in mammary cell proliferation, apoptosis, and tissue morphology. In these mammary-specific pRb-deficient mice, p53 was responsible for most of the apoptotic response—decreased levels of p53 resulted in reduced apoptosis and accelerated tumorigenesis, but had no effect on proliferation. Interestingly, in other mouse models where aberrant proliferation is caused by disabling other pathways, loss of p53 was associated with increased proliferation—rather than reduced apoptosis—and early tumor formation. And while p53 is the main effector of apoptosis in brain and mammary epithelial cells, this is not the case in all tissues: in astrocytes, for example, the tumor suppressor Pten regulates apoptosis in response to pRb inactivation. Together these results indicate that specific cellular responses to a cancer-causing stimulus vary depending on the nature of the initial genetic injury and the cell type and that pRb and p53 interact in different ways in different tissues. And p53, it appears, contributes to tumor suppression—and thus progression—through multiple mechanisms. By creating a mouse model that disentangles the pRb and p53 pathways, Van Dyke and colleagues have added a valuable resource for studying breast cancer. This model, they propose, will facilitate further investigations into the relative contributions of these overlapping pathways to cancer progression. What's more, the model offers a vehicle for examining how pRb interacts with other breast cancer mutations, like the inherited mutations in the human BRCA1 and BRCA2 genes, to shed light on the complex series of events that ultimately cause breast cancer. Transgene expression is associated with increased cell proliferation and cell death (apoptosis) | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC340959.xml |
544192 | Immunodominant PstS1 antigen of mycobacterium tuberculosis is a potent biological response modifier for the treatment of bladder cancer | Background Bacillus Calmette Guérin (BCG)-immunotherapy has a well-documented and successful clinical history in the treatment of bladder cancer. However, regularly observed side effects, a certain degree of nonresponders and restriction to superficial cancers remain a major obstacle. Therefore, alternative treatment strategies are intensively being explored. We report a novel approach of using a well defined immunostimulatory component of Mycobacterium tuberculosis for the treatment of bladder cancer. The phosphate transport protein PstS1 which represents the phosphate binding component of a mycobacterial phosphate uptake system is known to be a potent immunostimulatory antigen of M. tuberculosis . This preclinical study was designed to test the potential of recombinant PstS1 to serve as a non-viable and defined immunotherapeutic agent for intravesical bladder cancer therapy. Methods Mononuclear cells (PBMCs) were isolated from human peripheral blood and stimulated with PstS1 for seven days. The activation of PBMCs was determined by chromium release assay, IFN-γ ELISA and measurement of lymphocyte proliferation. The potential of PstS1 to activate monocyte-derived human dendritic cells (DC) was determined by flow cytometric analysis of the marker molecules CD83 and CD86 as well as the release of the cytokines TNF-α and IL-12. Survival of presensitized and intravesically treated, tumor-bearing mice was analyzed by Kaplan-Meier curve and log rank test. Local and systemic immune response in PstS1-immunotherapy was investigated by anti-PstS1-specific ELISA, splenocyte proliferation assay and immunohistochemistry. Results Our in vitro experiments showed that PstS1 is able to stimulate cytotoxicity, IFN-γ release and proliferation of PBMCs. Further investigations showed the potential of PstS1 to activate monocyte-derived human dendritic cells (DC). In vivo studies in an orthotopic murine bladder cancer model demonstrated the therapeutic potential of intravesically applied PstS1. Immunohistochemical analysis and splenocyte restimulation assay revealed that local and systemic immune responses were triggered by intravesical PstS1-immunotherapy. Conclusion Our results demonstrate profound in vitro activation of human immune cells by recombinant PstS1. In addition, intravesical PstS1 immunotherapy induced strong local and systemic immune responses together with substantial anti-tumor activity in a preclinical mouse model. Thus, we have identified recombinant PstS1 antigen as a potent immunotherapeutic drug for cancer therapy. | Background Urothelial carcinoma of the bladder accounts for about 4% of all cancer related death in man. The large majority of tumors (70–80%) is superficial at diagnosis and has a high rate of local recurrence (70%) and progression (30%) after local surgical therapy. Therefore, patients require lifelong medical follow-up examinations and effective prophylactic treatment to prevent recurrences and progression of the tumor. In this type of cancer, the immunotherapeutic use of Mycobacteria – specifically Bacillus Calmette-Guerin (BCG), a non-pathogenic strain of Mycobacterium bovis – has a well-documented and successful clinical history. Immunotherapy with BCG is performed by six weekly instillations of viable Mycobacteria (for induction course) into the bladder of patients after initial transurethral resection of the tumor. Until now various clinical trials have shown that this type of therapy is superior to topical chemotherapy and transurethral resection of the tumor alone to prevent recurrences and local progression especially in patients with high risk tumors [ 1 - 3 ]. Despite several clear advantages of BCG immunotherapy for the treatment of bladder cancer, several problems and limitations compromise its use. Although BCG is the most effective agent against superficial transitional cell carcinoma (TCC), currently there are still 30 to 40% of patients not responding to the therapy [ 4 ]. Furthermore, in the case of muscle invasive bladder cancer, BCG has not been shown to be effective [ 5 ]. BCG's activity appears to be strictly localized and as a living organism, BCG poses unique toxicity problems associated with its use. Although only 5% of these problems are severe, most if not all patients experience some irritable bladder symptoms (cystitis) during BCG therapy [ 6 ]. Roughly 40% develop hematuria and 30% experience flu-like symptoms including fever, malaise and nausea or vomiting. Actual BCG sepsis is a rare event and has been reported in only 0.4% of all cases. In addition, some of these cases have been fatal [ 7 , 8 ]. Thus, the reported side effects limit the clinical applicability and acceptance of this effective immunotherapy and underscore the need for alternative forms of treatment. In order to limit toxicity recent endeavors are therefore focused on the development of alternative non-viable products and some of those have already been tested in preclinical and clinical studies [ 9 - 12 ]. The mycobacterial antigen PstS1 is known as a highly immunogenic and immunostimulatory component of the mycobacterial cell membrane [ 13 ]. PstS1 is the phosphate binding subunit of the inorganic phosphate uptake system from M. tuberculosis belonging to the family of ABC (ATP-binding cassette) transporters [ 14 , 15 ]. It is a glycosylated lipoprotein which can be found both, intracellularly and secreted into the extracellular culture supernatant [ 16 , 17 ]. Moreover PstS1 represents one of the most immunogenic antigens in active multibacillary tuberculosis [ 18 ]. We hypothesized that this highly immunogenic protein antigen could function as an effective biological response modifier in immunotherapy of bladder cancer. To test the tumortherapeutic potential of recombinantly expressed PstS1 [ 19 ] we performed a detailed analysis of the immunostimulatory capacity of this antigen in a well-defined human in vitro system and in a previously described murine model of experimental bladder cancer therapy [ 20 ]. Because the role of prior exposure of bladder cancer patients to mycobacterial antigens for the effectiveness of BCG therapy is controversely discussed [ 21 - 23 ], we performed intravesical PstS1-immunotherapy with and without prior sensitization of mice. The data reported herein demonstrate that PstS1 is a potent activator of human tumor-cytotoxic MNCs, induces maturation and activation of human dendritic cells and most importantly is very effective in the treatment of experimental orthotopic bladder cancer. While local and systemic immune responses were observed in sensitized and non-sensitized mice, immunotherapy of cancer was only successful in non-sensitized animals. Methods Cell culture The human bladder tumor cell line T-24 was cultured at 37°C and 5% CO 2 in RPMI 1640 (PAA Laboratories, Linz/Austria) containing 10% FCS (Linaris, Bettingen/Germany), 1% glutamine, 100 U/ml penicillin and 100 μg/ml streptomycin. The murine bladder tumor cell line MB-49 was cultured at 37°C and 5% CO 2 in DMEM (PAA Laboratories, Linz/Austria) containing 10% FCS (Linaris, Bettingen/Germany), 1% L-glutamine, 100 U/ml penicillin and 100 μg/ml streptomycin. Stimulation of human PBMCs MNCs from heparinized blood of healthy human donors were obtained by discontinuous gradient centrifugation using Biocoll Separating Solution (Biochrom, Berlin/Germany) and adjusted to a concentration of 1 × 10 6 /ml in RPMI-1640 medium containing 5% human serum, 100 U/ml penicillin and 100 μg/ml streptomycin. Recombinant PstS1 in concentrations from 0.1 μg/ml – 100 μg/ml, BCG (Connaught substrain, Immucyst, 4 × 10 4 cfu/ml), or PBS (100 μl) was added and the cells were cultured for 7 days in 6-well microtiter plates at 37°C and 5% CO 2 . Chromium release assay Cytotoxicity was determined in a standard 4-hour chromium release assay. Target cells were labeled with Na 2 51 CrO 4 (ICN, Irvine/USA) for 1 h at 37°C, washed and resuspended at 5 × 10 4 cells/ml. Effector cells were added to a total of 100 μl of target cells at an effector:target ratio of 40:1. The radioactive content of the supernatant was measured in a gamma-counter (Berthold, Wildbad/Germany). The specific lysis was determined according to the formula: spec. lysis (%) = 100 × (Exp – Spo)/ (Max – Spo) where Exp is the experimental release, Spo the spontaneous release and Max the maximum release. Human IFN-γ ELISA Supernatants of PstS1, BCG and PBS stimulated PBMCs were recovered at days 2, 5 or 7 and examined for the presence of IFN-γ. Each experiment was carried out several times with different donors. Detection was performed with an anti human IFN-γ ELISA-Kit according to the manufacturers' instructions (eBioscience, San Diego/USA). Proliferation of human PBMC PBMCs were stimulated for 2 to 7 days with PstS1, BCG, PBS or PHA (Sigma). 2 × 10 5 cells / well of the stimulated PBMCs were cultured in a microwell plate and 1 μCi 3 H-Thymidine (five wells per sample) (Amersham, Freiburg/Germany) was added during the last 18 h of stimulation. DNA was harvested on filter membranes and thymidine incorporation was measured by liquid scintillation counting (counter: LKB Wallace 1205 beta-plate, Turku/Finland). Generation and stimulation of human monocyte derived dendritic cells After separating peripheral blood mononuclear cells (PBMCs) from heparinized blood of healthy donors by Ficoll-Paque centrifugation, monocytes were elutriated by counterflow centrifugation. For generation of immature DCs, 2 × 10 6 monocytes were cultured for seven days with 2 ml RPMI 1640, 10% FCS (Linaris, Bettingen/Germany) 1% penicillin/streptomycin and IL-4/GM-CSF (500 U/ml each) (TEBU/PeproTech, Offenbach/Germany) in 24-well cell culture plates (Nunc, Wiesbaden/Germany). Exchange of medium was carried out at day three and day five. This procedure resulted in full differentiation of monocytes with no undifferentiated monocytes present in the cultures after seven days. Immature DCs were stimulated for three days with 10 μg/ml PstS1 or BCG (MOI = 0.01) and subjected to flow cytometry. Debris was excluded from the analysis according to FSC/SSC gating. Flow cytometry Expression of cell surface molecules was analyzed by flow cytometry (FACSCalibur, Becton Dickinson, Franklin Lakes/USA) using phycoerythrin (PE) conjugated monoclonal antibodies (mAbs): anti-CD1a (Biosource, Camarillo/USA), anti-CD83 (Pharmingen, San Diego/USA), anti-CD14 and anti-CD86 (Dianova, Hamburg/Germany). 1 × 10 5 DCs suspended in PBS containing 5% human serum and 0.1 % sodium acide were incubated with mAbs for 30 minutes on ice. Then cells were washed with PBS and resuspended in 400 μl 1.5 % paraformaldehyde in PBS. human TNF-α and IL-12 ELISA Supernatants of PstS1, BCG and PBS stimulated DCs were recovered at day 3 after stimulation and examined for the presence of TNF-α and IL-12. Each experiment was carried out several times with different donors. TNF-α detection was performed with a quantitative ELISA, provided by Dr. H. Gallati (Intex, Muttenz/Switzerland). IL-12 detection was carried out with an ELISA-Kit from eBioscience (San Diego/USA). PLG-Particle preparation and protein loading PLG-particles (10 mg/ml) (Lionex, Braunschweig/Germany) were diluted 4:1 with PBS and the pH was adjusted to 7.0 with NaOH. PstS1 was recombinantly expressed in E. coli and purified by standard chromatography. 250 μg of PLG-particles were loaded with PstS1 or BSA by coincubation with 60 μg of the respective protein for 15 h at RT on a shaker (overall volume 100 μl). Afterwards the particles were spun down and washed for 5 min with 100 μl PBS. The supernatant was removed and the particles were diluted in 100 μl of PBS. 10 μl of loaded particle solution were centrifuged and the pellet was treated for 30 min at RT with 0.1 M NaOH/ 10% SDS. After centrifugation in a minifuge, supernatant was removed and analyzed on a 10% SDS page. Using recombinant PstS1 as a calibration standard it was determined that 250 μg PLG-particles bound approximately 50 μg of PstS1 protein. PLG-particle vaccination and immunotherapy of experimental bladder cancer Female C57BL/6 mice were purchased from Charles-River Laboratories (Sülzfeld/ Germany) at the age of 6–8 weeks. To test efficacy of PstS1 therapy in C57/BL6 mice a published syngeneic, orthotopic bladder cancer model was used [ 10 ]. Trial I: 15 animals per group were subcutaneously injected with 250 μg PLG-particles coated with 50 μg PstS1 or 250μg PLG-particles alone. Ten days later the mice were anesthetized by intraperitoneal treatment with Pentobarbital (0.067 mg/g body weight). After insertion of a 24-gauge teflon intravenous catheter (Insyte-W, Becton Dickinson, Franklin Lakes/USA) transurethrally into the bladder, electrocoagulation was performed with a guide wire. Thereafter 6 × 10 4 MB-49 cells were instilled into the bladder. Intravesical immunotherapy with 100 μg PstS1 in 100 μl PBS was performed on days 1, 8, 15 and 22 after tumor implantation. Control groups were instilled with PBS (100 μl) alone. The viability status of the mice was checked daily. Surviving mice were sacrificed on day 70. Survival of mice was compared using Kaplan-Meier analysis and log-rank test. Trial II: Performance similar to trial I with the following differences. 11 animals per group were subcutaneously injected with a) 250 μg empty PLG-particles b) 250 μg PLG-particles coated with 50 μg BSA c) 100μl of PBS. Intravesical treatments were performed with 100 μg PstS1 or PBS only. Immunohistochemistry of murine bladders C57/BL6 mice (5 per group) were treated as described in table I . One day after the fourth instillation animals were sacrificed. Bladders were dissected immediately, shock-frozen in liquid nitrogen and stored at -80°C. The immunohistochemistry and analysis of cellular influx of different leukocyte subsets was performed on 5μm frozen sections as described previously [ 24 ]. anti-PstS1-IgG ELISA from murine blood Murine blood was obtained immediately after sacrificing mice by heart punctation and centrifuged in a microcentrifuge to obtain the serum for antibody analysis which was performed with the PstS1-ELISA-kit (Lionex GmbH, Braunschweig/Germany) according to the manufacturer's instructions. Splenocyte restimulation assay Splenocytes were isolated, washed with PBS and erythrocytes were lysed with H 2 O. Afterwards 2 × 10 5 cells / well were cultured in a microwell plate and stimulated with 10 μg/ml PstS1 for a period of five days (five wells per sample). Then 1μCi 3 H-Thymidine (Amersham, Freiburg/Germany) was added and cells were incubated for 15 h at 37°C and 5% CO 2 . Finally the DNA was harvested on filter membranes and 3 H-Thymidine incorporation was measured by liquid scintillation counting (counter: LKB Wallace 1205 beta-plate, Turku/Finland). Results PstS1 activates human PBMCs To assess the immunostimulatory properties of our PstS1-preparation we analyzed several human PBMC in vitro systems. As in these systems the immunostimulatory mechanisms of whole BCG bacteria have been thoroughly studied in the past [ 9 , 25 - 27 ] we used BCG as a positive control and reference stimulus. In a first set of experiments the optimal concentration of PstS1 to stimulate human PBMC cytotoxicity (Fig. 1A ), IFN-γ release (Fig. 1B ) and proliferation (Fig. 1C ) was determined. Using a concentration range from 0.1 μg/ml to 100 μg/ml PstS1 showed a typical bell-shaped dose response curve. A concentration of 10 μg/ml was found to be optimal for stimulation of human PBMC in all three readout systems. In order to analyze the time course of human PBMC activation, a time kinetic study of PBMC stimulation was performed. BCG, which has been previously described to induce potent anti-tumor cytotoxicity in human PBMCs [ 27 ] was used as a reference stimulus and positive control. As depicted in figure 2A,2B,2C activation of human PBMC by PstS1 was time-dependent with the strongest activation on day 7 and only marginal activation at early time points (day 2). Overall, in this study we have tested the activation of PBMC of ten different human donors in response to PStS1 stimulation. As expected, we observed a certain degree of donor variability in this assays with e.g. IFN-γ induction ranging from 500 pg/ml to 5 ng/ml. Comparison with BCG consistently indicates a similar time-kinetic of PBMC activation between these two biological response modifiers albeit with higher absolute levels of activation induced by BCG. This kinetic of activation is contrasted by mitogenic stimulation with PHA (figure 2c ) or ConA (not shown) which show an expected peak of PBMC stimulation at early time points with a subsequent dramatic decrease. Dendritic cell activation by PstS1 Dendritic cells are central cellular mediators for the induction of anti-tumor immune responses. We tested the potential of PstS1 to induce activation and maturation of human monocyte-derived DCs. After 7 days of differentiation human monocyte-derived dendritic cells showed the typical immature phenotype with low or absent expression of the monocyte marker CD14 and the maturation markers CD83 and CD86 (Fig. 3A ). At the same time immature DCs expressed high amounts of CD1a. Stimulation of dendritic cells with either PstS1 or BCG induced strong upregulation of CD83 and CD86 indicating phenotypical maturation of DC after challenge with PstS1. In parallel, the cytokine response of DC was assessed and PstS1 was found to induce substantial amounts of TNF-α and IL-12 p70, two key cytokines in dendritic cell biology. Interestingly, PstS1 reproduceably induces IL-12p70 in the ng range, while BCG only induced relatively low levels of IL-12p70 (50–500 pg/ml) (Fig. 3B ). Intravesical immunotherapy with PstS1 in sensitized and non-sensitized mice After we have shown profound immunostimulatory properties of PstS1 in vitro we conducted a series of studies to test its immunotherapeutic potential in vivo . Prompted by the controversy about the role of prior exposure to mycobacterial antigens in BCG immunotherapy we wanted to assess the in vivo activity of PstS1 in sensitized and non-sensitized mice. For this purpose two independent in vivo experiments were carried out. PLG-particles have previously been described as useful agents for sensitization of mice to mycobacterial antigens and were used as such in our study [ 13 ]. In a first series of experiments we analyzed mice which had been s.c. sensitized with empty control particles or with particles loaded with PstS1 antigen. Ten days later mice received inoculation of tumor cells and subsequent intravesical treatment with either PstS1 or PBS control solution (four weekly instillations). Mice which received s.c. PBS followed by intravesical PBS served as negative controls. Using this experimental set up we could show that pre-vaccination with empty particles and intravesical treatment with PstS1 protein significantly prolonged survival of mice and thus provided a clear therapeutic benefit in this model of experimental bladder cancer (Fig. 4a ). When mice received s.c. injections of PLG-particles followed by intravesical control PBS, survival of mice was marginally increased suggesting a possible minor non-specific effect of the sensitization procedure (Fig. 4b ). Unexpectedly, antigen-specific sensitization with PstS1-loaded particles prior to intravesical PstS1 inoculation completely abrogated the therapeutic effect (Fig. 4c ). To further substantiate the findings of trial one a second trial with a modified setup was conducted. In this second trial we compared the effect of s.c. PBS injections, injections of empty PLG-particles and injections of PLG-particles loaded with an irrelevant antigen (BSA) on the effect of intravesical PstS1 treatment. This experimental set up revealed a modest therapeutic benefit of intravesical PstS1 in the absence of prior sensitization (Fig. 5a ). The number of mice in this experiment was, however, too small to achieve statistical significance using a stringent log-rank test (p = 0.1052). When intravesical PstS1 instillations were combined with pre-vaccination with empty PLG-particles, the result from the previously described trial was confirmed and again a clearly prolonged survival of mice became evident (Fig. 5b ). Based on the hypothesis that the sensitization by itself, irrespective of the antigen used for sensitization, might have impeded the effect of intravesical PstS1 in trial one, we tested the effect of particles loaded with irrelevant BSA protein. However, as shown in Fig. 5c , the therapeutic effect of PstS1 remained virtually unchanged suggesting that the inhibition was not due to the sensitization by itself but rather PstS1 specific. Local and systemic immune response in PstS1-immunotherapy After we had demonstrated successful immunotherapy of bladder cancer by local instillation of PstS1 we next analyzed the local and systemic immune response of the various treatment groups to obtain initial insight into the immunological basis of this novel immunotherapy. To achieve this we analyzed the systemic antibody response by a semiquantitative anti-PstS1 IgG ELISA, the systemic lymphocyte response by splenocyte restimulation assays and the local immune response in the bladder by immunohistology. As expected no anti-PstS1 IgG response was detected in the two groups which neither had been sensitized by PstS1 antigen s.c. nor received intravesical inoculations of PstS1. On the other hand, both s.c. and intravesical challenge with PstS1 resulted in positive antibody responses in every animal of the respective groups. This is an interesting finding as it indicates that systemic anti-PstS1 immune responses cannot only be induced by the well established s.c. route but also by intravesical instillations of antigen into the murine bladder (Table 1 ). Positive antibody responses were also observed in the group which received a combination of s.c. PstS1-loaded particles and intravesical PstS1 and as such failed therapy. A similar picture was observed with regard to the response of restimulated splenocytes (Fig. 6 ). The two groups of mice with no prior contact to PstS1 only minimally responded to in vitro stimulation with PstS1. In contrast, the four groups of mice which had been exposed to PstS1 either s.c. or intravesically or via both routes strongly responded to specific restimulation of their splenocytes. Again these data indicate that systemic immune responses to PstS1 can be achieved by both s.c. and intravesical challenge. While a s.c. sensitization of mice with PstS1 abrogated the anti-tumor effect of intravesical PstS1 (see Fig. 4 ) the systemic immune response was not negatively affected but even enhanced in some animals (see Fig. 6 ). After the analysis of the systemic immune response to PstS1 we continued our experiments with an immunohistological study of the local immune response. To this end we specifically looked at the influx of lymphocytes, dendritic cells, macrophages and granulocytes (table 2 ). As expected, bladders of control mice with no PstS1 injections (groups 1–2) were only minimally infiltrated by granulocytes (Gr-1 antigen) or CD11b-positive cells (activation marker for granulocytes and macrophages). On the other hand, after local instillation of PstS1 a distinct influx of granulocytes and macrophages was observed (groups 4–5, table 2 ). This cellular infiltration of the bladder with granulocytes and macrophages was not substantially altered by additional sensitization of the mice with PstS1-loaded particles (group 6, table 2 ). In contrast to what is known about classical immunotherapy with BCG [ 24 ], challenge of mice with PstS1 only induced a moderate infiltration of the bladder with CD4-positive lymphocytes (groups 4–5, table 2 ). A slight increase in the number of CD8-positive cells was noted in mice which received s.c. PstS1 sensitization in addition to intravesical PstS1 (group 6, table 2 ). Dendritic cells (CD11c) were a rare cell population in control mice without PstS1 injections (groups 1–2). A slightly increased number of dendritic cells was noted in the bladders of mice after intravesical instillation of PstS1. Nonetheless, the overall local immune response in the bladder during PstS1 immunotherapy is clearly dominated by the influx of granulocytes and macrophages. A certain induction of local bladder cellularity was also noted in mice which received s.c. injections of PstS1 but no intravesical instillation of the antigen (group 3). The reason for this effect of s.c. PstS1 injection on bladder cellularity is unclear. Discussion BCG therapy is a clinically successful therapy in the treatment of bladder cancer but its acceptance is hampered by hazards and side effects related to the use of viable mycobacteria. The aim of this study was to evaluate the anti-tumor potential of the well-defined, non-viable mycobacterial antigen PstS1. To this end we first tested the potential of recombinant PstS1 to activate human PBMC cultures. In this series of experiments PstS1 induced strong cytotoxicity, proliferation and IFN-γ production in human PBMC. Ten μg/ml PstS1 were found to be optimal for activation of human PBMC. We used BCG lyophilisate as a reference stimulus in these experiments as we have previously described BCG as a potent activator of human PBMC functions [ 27 ]. PstS1 followed a similar time kinetic as BCG in the stimulation of human PBMC, albeit activation of PBMC functions was somewhat lower with PstS1 compared with BCG. PstS1 was previously shown to induce profound cellular immunity in different vaccination studies [ 28 - 30 ]. Therefore, our results further confirm the potential of PstS1 to function as a potent inducer of T-helper 1 and cell-mediated immune responses. While IFN-γ is a key cytokine in TH1 immune responses produced by activated lymphocytes, IL-12 is mainly produced by monocytes/macrophages as well as dendritic cells and important in the early phase of cellular immunity [ 31 ]. IL-12 is a potent inducer of anti-tumor immunity [ 32 ] and acts together with IFN-γ and TNF-α in a positive feedback loop [ 25 , 33 , 34 ]. Although BCG mycobacteria are relatively weak inducers of bioactive IL-12p70 in human dendritic cells in the absence of additional costimulation like CD154 or IFN-γ (figure 3b ), IL-12 has been shown to be essential for therapeutic efficacy in experimental BCG immunotherapy of bladder cancer [ 24 ]. Given the importance of dendritic cells for the initiation of anti-tumor immune responses [ 35 ] and as a source of IL-12 and TNF-α we tested the capacity of recombinant PstS1 to activate human monocyte-derived dendritic cells. Our results clearly identified PstS1 as a potent stimulator of human DCs as it provoked upregulation of CD83 and CD86 surface expression as well as induction of cytokines. Of interest is the expression of high amounts of bioactive IL-12 after stimulation of DCs with PstS1 as only small amounts of this cytokine are produced after challenge with BCG or other mycobacterial antigens (figure 3 and unpublished observations). The encouraging in vitro experiments then prompted us to evaluate the immunotherapeutic potential of PstS1 in a well-established murine model of experimental bladder cancer [ 20 ]. The effect of prior sensitization or responsiveness to mycobacterial antigens for the effectiveness and outcome of subsequent immunotherapy with BCG is still a matter of intensive debate [ 21 - 23 ]. Taking this into account we took advantage of the fact that we had in hand a well-defined, recombinant antigen already evaluated in vaccination studies against mycobacterial infections and included a prime-boost treatment regimen into our experimental immunotherapy protocol. The main objective of our series of in vivo experiments was to determine the anti-tumor potential of local instillations of recombinant PstS1 and to compare the effect of intravesical PstS1 in sensitized and non-sensitized mice. Because adsorption of PstS1 to L-particles has previously been described as very efficient in inducing specific cellular immunity to this antigen [ 29 ], we adopted this method of sensitization for our treatment protocol. Using a protocol of four weekly instillations of PstS1 into the bladder (adopted from the treatment schedule in use for immunotherapy with BCG) we observed a strong therapeutic effect of PstS1 instillations. Intravesical PstS1 significantly prolonged survival of mice and induced systemic immune responses and cellular infiltration of the bladder with different subpopulations of leukocytes. The anti-tumor effect of PstS1 was already evident after intravesical instillation of PstS1 only but was further enhanced after s.c. injection of empty L-particles (Fig. 5 ). Unexpectedly, sensitization of mice with PstS1-loaded particles almost completely abrogated the therapeutic effect of intravesical PstS1 (Fig. 4 ). In order to test whether the negative effect of prior sensitization was specific for PstS1 or could also be induced by s.c. injection of L-particles loaded with an irrelevant antigen, we compared the therapeutic effect of intravesical PstS1 combined with a) s.c. sensitization with BSA-loaded L-particles, b) s.c. injection of empty L-particles and c) s.c. injection of PBS only (Fig. 5 ). Clearly, the injection of BSA-loaded particles did not influence the therapeutic effect of intravesical PstS1 indicating that only PstS1-specific previous sensitization is detrimental (Fig. 5a and 5c ). We analyzed the systemic serum antibody response, activation of splenocytes and the local cellular infiltration of the bladder wall in sensitized and non-sensitized mice (Fig. 6 ) in order to understand and explain the remarkable therapeutic potential of intravesical PstS1 as well as the negative effect of prior specific sensitization. Interestingly, even without sensitization, instillation of PstS1 into the bladder provoked a systemic anti-PstS1 response visualized by murine anti-PstS1 serum antibodies and splenocyte proliferation after rechallenge in vitro . In addition to this systemic immune activation, intravesical PstS1 also induced the local influx of lymphocytes, macrophages and granulocytes into the bladder. While only a minimal influx of CD8-cells could be observed after instillation of PstS1, a considerable infiltration of the bladder with granulocytes, macrophages and CD4-cells was noted. Although prior specific sensitization with PstS1 completely abrogated the anti-tumor effect of intravesical PstS1, the cellular infiltration of the bladder wall remained essentially unchanged when comparing sensitized and non-sensitized animals. In the splenocyte restimulation assay an enhanced response of two out of four mice was noted in the group of mice which received s.c. PstS1 followed by intravesical PstS1 compared with the two groups which received either treatment alone (Fig. 6 ). This indicates that the combination of s.c. sensitization and intravesical treatment indeed augmented the systemic immune response at least in some animals. Surprisingly, this enhanced systemic immune response coincided with an abrogation of tumor-therapeutic efficacy. A possible explanation for this phenomenon could be that the anti-PstS1 antibodies, which were induced after s.c. priming would bind to the recombinant PstS1 shortly after instillation into the bladder and thereby neutralize its function and anti-tumor effect. However, as mentioned earlier, even in the PstS1-specific prime-boost treatment regimen an unchanged cellular infiltration of the bladder wall was noted. Alternatively, the sensitization might render the subsequent local immunotherapeutic immune response insufficient, because the host immune system is still actively responding to the priming at the onset of immunotherapy. This situation might be called "immune exhaustion" making the host unable to mount a sufficient local anti-tumor immune response while still responding to the specific priming. Further experiments are needed to clarify whether a modification of the time schedule of such a "prime-immunotherapy" protocol could prevent the negative effects observed in this study and might even enhance immunotherapy with PstS1. Nonetheless, our combined in vitro and in vivo analyses clearly identified PstS1 from M. tuberculosis as a potent immunostimulant and a potential immunotherapeutic anti-cancer agent for topical treatment strategies. Our data do not show and do not imply that PstS1 is the major or only immunostimulatory component of whole BCG mycobacteria in BCG immunotherapy of bladder cancer. Conclusions We have identified the immunodominant mycobacterial PstS1 antigen as a potent biological response modifier for tumor immunotherapy. Using a human in vitro system of PBMC activation and a murine model of experimental bladder cancer immunotherapy we could show strong immunostimulatory capacity of PstS1 as well as significant anti-tumor activity. In a model of prime-boost immunotherapy we observed that antigen-specific sensitization might jeopardize the positive effects of topical immunotherapy and therefore has to be considered and evaluated with caution. GMP-production of PstS1 and a clinical trial in humans is currently being established and might open the door for an efficient and safe alternative in the field of bladder cancer immunotherapy. Competing interests The author(s) declare that they have no competing interests. Authors' contributions CS carried out most of the experiments, provided experimental protocols and prepared the initial version of the manuscript. ABu contributed to figure 2 and worked on the manuscript. GB performed the immunohistochemistry and cell culture. RS and FJ performed the α-PstS1 IgG ELISA and provided the recombinant PstS1. MS, ABö and SB jointly participated in fund raising and coordination. SB is the principal investigator, edited the manuscript and advised CS on experimental design and study concept. Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC544192.xml |
516788 | Recurrence of primary extramedullary plasmacytoma in breast both simulating primary breast carcinoma | Background Extramedullary myelomas (plasmacytoma) are malignant proliferations of plasma cells in the absence of bone involvement. When they occur in the soft tissue they usually involve the upper respiratory tract and oral cavity. Extramedullary plasmacytomas of breast are uncommon. Case presentation A 70 year-old woman with bilateral breast masses underwent excisional biopsy for suspected primary carcinoma that subsequently proved to be a recurrence from extramedullary plasmacytoma of the mediastinum. This was diagnosed and treated 5-years prior to appearance of breast lumps. Conclusion Though uncommon, considering the possibility of metastatic carcinoma and primary, secondary or recurrent lymphoproliferative disease presenting as a breast mass may avoid unnecessary surgeries. | Background Extramedullary myelomas (plasmacytoma) are malignant proliferations of plasma cells in the absence of bone involvement. When occur in the soft tissue, it usually involve the upper respiratory tract and oral cavity [ 1 ]. Plasmacytomas of breast are very rare. This report describes a patient with bilateral breast masses who underwent excision biopsy for suspected primary carcinoma that subsequently proved to be a recurrence from extramedullary plasmacytoma of mediastinum treated 5 years ago. To the best of our knowledge, this is the first case report of bilateral recurrence of a primary extramedullary plasmacytoma in breast tissues after a long disease-free interval. Case presentation A 70 year-old woman with a one-month history of bilateral breast masses was referred to our cancer center for surgical evaluation. There was no associated breast pain, skin change or nipple discharge. There was no history of bone pain, weight loss, fatigue, fever or other systemic complaints and no family history of breast cancer. Significant past medical history included treatment for an extramedullary retrosternal plasmacytoma 5-years prior this admission. At the time of the initial work-up for the retrosternal mass, immunoelectrophoresis showed no evidence for hyperproteinemia or paraproteinemia. Whole body bone scan was negative and a bone marrow biopsy revealed less than 5% of plasma cells. Therefore, multiple myeloma was excluded by nuclear medicine, laboratory and histology studies. The patient underwent radiation therapy (40 Gy with fraction size of 200 cGy delivered over 4 weeks) followed by chemotherapy with cyclophosphamide, cisplatin and prednisolone. The patient was followed by laboratory tests, chest roentgenography, and computed tomography annually. A bone scintigraphy was carried out after 2 years and showed no uptake patient was thereafter lost to follow-up. Five years after initial diagnosis of extramedullary plasmacytoma the patient presented with bilateral breast masses. Physical examination revealed a 3.5 cm × 2.5 cm, mass in the upper inner quadrant of the right breast and a similar 5.0 cm × 4.5 cm mass in the lower inner quadrant of the left breast. No asymmetry, skin dimpling or signs of inflammation were present. There was no axillary or supraclavicular lymphadenopathy. Mammography confirmed a well-defined 3.2 cm oval-shaped mass in the upper inner quadrant of the right breast, and a lobulated 5.5 cm density in lower inner quadrant of the left breast without any tissue distortion, inflammation and fibrotic reaction.(Figure 1 ) There were no microcalcification and satellite lesions. These masses were solid and hypoechoeic with multiple septations in sonography. Figure 1 Mammography of the patients' breasts (A: mediolateral oblique, view B: craniocaudal view) Excisional biopsy of the masses revealed a 5.0 (left) and 3.0 (right) well-defined, capsulated gritty mass surrounded by normal breast tissue. There was no extension from the capsulated masses to pectoral muscles or chest wall. Histopathological examination showed high-grade tumors composed of immature and mature plasma cells. Mitosis, necrosis, nuclear pleomorphism and binucleated and multinucleated plasma cells were seen. (Figure 2 ) Additional studies such as serum protein electrophoresis and immunoelectrophoresis were normal. No Bence Jones or other M components were detected in the urine. Skeletal surveys (Tc 99 bone scan and skull and pelvic X-rays) did not show any pathological changes. There was no evidence of anemia, hypercalcemia or renal insufficiency. However, the patient refused a second bone marrow biopsy. Figure 2 Photomicrograph showing nuclear pleomorphism, binucleated and multinucleated plasma cells with enlarged nucleoli (Hematoxylin & Eosin). Immunohistochemical studies were performed on the paraffin embedded tissues to determine if the infiltrate had monoclonal character. The tumor cells were diffusely and strongly positive for lambda chains but negative for kappa chains. (Figure 3 ) Figure 3 Lambda and kappa immunohistochemical stain showing strong and diffuse positivity for lambda (a) and negativity for kappa (b). The tumor cells were weakly positive for monoclonal mouse anti human placental V538C, and plasma cell markers (CD138). Nuclear prognostic marker (Ki67) showed 50% to 80% nuclear expression indicative of high proliferative activity and suggesting a plasmacytic tumor with anaplastic components (Figure 4 ). Other immunohistochemical stains including CD21, cytokeratin, S100, and HMB45 were negative. Figure 4 Ki67 immunohistochemical stain showing 50–80% nuclear positivity A retrospective microscopic review of the mediastinal mass showed similar morphology to the breast tumor. Hence, the histological diagnosis of recurrent plasmacytoma was made. The patient was treated with oral Melfalan and Prednisone. The patient has been disease free for twenty months after treatment and has showed no evidence of recurrence in the mediastinum, breast or any other region. Discussion Primary soft tissue extramedullary plasmacytoma (SEP) is uncommon and is defined as a malignant tumor of plasma cells arising in the soft tissue in the absence of bone involvement. It can occur in any organ as a solitary form of plasma cell neoplasm [ 2 ]. Although SEP can arise throughout the body, almost 90% of the cases arise in the head and neck areas, most commonly in the upper respiratory tract including the nasal cavity, para nasal sinuses, oropharynx, salivary glands and larynx [ 3 - 7 ]. Several other sites can rarely be involved, including testis, bladder, urethra, breast, ovary, lung, pleura, thyroid, orbit, brain and skin tissues (1, 8–17). Approximately forty-five cases of breast plasmacytoma have been reported in published literature since 1928 (1, 2, 18–21). More than half of the lesions were unilateral (66%), with the majority of the cases occurring in the setting of multiple myeloma (77%) (1, 2, 18–20, 22). When plasmacytoma originates from soft tissues, like the case presented here, the disease is usually associated with a relatively mild clinical behavior and long survival, suggesting that it is a truly different disease entity compared to other plasma cell tumors [ 23 ]. Dimopoulos et al (1999) reported that solitary extramedullary (soft tissue) plasmacytomas (SEP) are less common than solitary bone plasmocytoma (SBP), and have a better prognosis as the majority can be cured by local radiotherapy [ 24 ]. Liebross et al (1999) reported local recurrence rates of less than 5% after radiotherapy [ 6 ]. Mayr et al (1990) noted that the risk of distant relapse is more than 30%, which is significantly less than that seen with SBP [ 25 ]. Progressive disease may accrue as multiple myeloma, SBP or involvement of lymph nodes, skin or subcutaneous tissues. Its recurrence, if any, tends to be within 2–3 years of initial diagnosis. The reported ten year survival rate is at least 66% [ 3 , 7 ]. Involvement of the breast with SEP is uncommon and may occur either as a solitary primary tumor or as evidence of disseminated multiple myeloma [ 19 ]. Our patient was referred with bilateral breast masses. In an approach to a patient with bilateral breast masses, differential diagnosis includes: fibroadenomas, complex cysts, metastasis, lymphoma, synchronous breast cancer, focal fibrosis, fat necrosis, abscess, and phyllodes tumor [ 20 ]. Metastatic lesions of the breast from extramammary neoplasms are rare and in larger studies have been reported to constitute 0.4 to 2% of all breast malignancies. The most common are lymphomas and other tumors of hematological origin [ 26 ]. The striking feature in this case was the recurrence of an isolated plasmacytoma (which was treated successfully five year prior) in both breasts. As approximately 20% of patients who present with isolated extramedullary plasmacytoma will eventually develop multiple myeloma, close follow-up is strongly recommended [ 1 , 22 ]. Our patient did not have any prior breast aspiration cytology. There are many tumors, which may present with plasmacytoid appearance on aspiration cytology. Tumors such as small ductal carcinoma and lobular carcinoma of breast, metastatic carcinoid, metastatic melanoma and some lymphomas may represent with uncohesive group of cells with eccentric nuclei resembling plasma cells. Hence, a plasmacytoma (with anaplastic plasma cells) may be readily mistaken for carcinoma (or other undifferentiated neoplasm) not only clinically, but also on cytological examination. This would justify excision biopsy and the use of an extended immunohistochemical panel to include such markers as cytokeratin and S-100 in the assessments. To help the cytopathologist avoid misinterpretation, clinical history and presentation are extremely helpful. Most of the errors in histopathology and cytopathology diagnosis occur when pathologist is not aware of medical history of the patient and unusual clinical presentation. This case emphasizes the importance of distinguishing a plasmacytoma of the breast from primary mammary carcinomas and other benign lesions to avoid unnecessary surgery and provide the appropriate treatment and adjuvant therapy. Authors' Contributions AK carried out excision biopsies and drafted the manuscript. MJZ did the histopathological examination and contributed to the pathological content of the manuscript. SKR is the pathologist who confirmed the diagnosis and prepared the immunohistochemical stains and illustrations. He also contributed to pathological content of the manuscript MN followed-up the patient and contributed to the manuscript preparation All authors read and approved the final manuscript. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC516788.xml |
544964 | Australian health system restructuring – what problem is being solved? | Background In recent years, Australian state and territory governments have reviewed and restructured the health systems they lead and regulate. This paper examines the outcomes of the most recent official published reviews of systems and structures; identifies the common themes; and addresses two questions: what problems are being addressed? And how would we know if the changes were successful? Results In all the broad, systemic reviews, the main health system problems identified were money, hospital utilisation and a weak primary health care system. The solutions are various, but there is a common trend towards centralisation of governance, often at state health authority level, and stronger accountability measures. Other common themes are hospital substitution (services to avoid the need for admission); calls for cooperation across the Commonwealth:state divide, or for its abolition; and the expected range of current efficiency and effectiveness measures (eg amalgamate pathology and support services) and ideas in good currency (eg call centres). The top-down nature of the public review process is noted, along with the political nature of the immediate catalysts for calling on a review. Conclusion The long-standing tension between the pull to centralisation of authority and the need for innovation in care models is heightened by recent changes, which may be counterproductive in an era dominated by the burden of chronic disease. I argue that the current reforms will not succeed in achieving the stated goals unless they make a difference for people with chronic illness. And if this is correct, the most useful focus for evaluation of the success of the reforms may be their impact on the system's ability to develop and deliver better models of care for this growing group of patients. | Background In recent years, there has been a rolling (and sometimes repetitive) tide of structural change in the way state and territory governments organise to lead and/or provide health care within their jurisdictions, with every state and territory of Australia involved at least once in the last 10 years. This paper examines the outcomes of the most recent official published reviews of systems and structures; identifies the common themes; and addresses two questions: what problems are being addressed? And how would we know if the changes were successful? This analysis focuses on those reviews which are 'systemic' in the sense that they examine broadly the structure and performance of a state/territory health system, and/or address governance of the system. The NSW restructure has been included, although it differs from the others in the absence of an independent review process and in the related lack of detailed documentation of the rationale for change. Review of reviews The most recent wave of systemic reviews in the Australian public health system saw New South Wales [ 1 ], South Australia [ 2 ], the Northern Territory [ 3 ], Western Australia [ 4 ] and the Australian Capital Territory [ 5 ] go in for restructuring. Victoria reviewed metropolitan health system governance [ 6 ], but pulled back from major structural change, having had a round of it in 2000 [ 7 ]. NSW has relinquished its status as an island of relative stability, which had been maintained since 1986 in spite of several reviews, penultimately by IPART [ 8 ]. In the aftermath of a scandal at MacArthur Health Service [ 9 ], the Minister announced the abolition of all Area Health Service boards, and is restructuring the health services into 8 'super-regions' with CEO's who report directly to the head of the Department [ 1 ]. Clinicians and the community will be represented on advisory structures, and a new agency will take over support functions. Queensland stays with central control (virtually no boards of governance to dilute the Department's authority) while Tasmania is reviewing hospital services only [ 10 ], having restructured in 1991 (from 'atomised' to regionalised) and 1997 (from regionalised to centralised) [ 11 ]. Results The pattern of systemic reviews over the last 10 years is summarised in Table 1 . One notable trend is that the decision to review is often no longer presented publicly as a matter solely for the health minister. The premier or a financial/regulatory arm of government (mostly in concert with the health minister) commissioned the most recent reviews in Western Australia, South Australia, and the Northern Territory. The NSW restructure arises from a different process than the others undergoing structural change, with a brief booklet announcing and explaining the decision [ 1 ], rather than extended public review processes with opportunities for community and health service provider input. Table 1 Review Dateline Year States Year States 2004 WA, NSW, Tasmania (hospitals only) 2000 Victoria 2003 NSW, Victoria#, SA, NT 1996 Tasmania, Qland, ACT 2002 ACT, 1995 Victoria, SA 2001 WA Notes: #No major structural changes recommended – focus on governance The trend: centralisation of governance In what has emerged as a strong centralising tendency, 6 of 8 jurisdictions have centralised governance authority for public sector health care agencies at the level of the state or territory health authority. Victoria and South Australia are mixed, with regionalised or 'networked' structures predominating in the capital city; and several different approaches to both regional and institutional governance elsewhere. As Somgen points out, Victoria and South Australia were the states most strongly influenced by the 1990's trend to privatisation, outsourcing and output-based funding [ 12 ], with less focus on structures and central planning. Table 2 summarises the current arrangements by state, in order of population size, with the population shown in brackets in the left-hand column (M = million). Table 2 Governance of public health care agencies in Australian states/territories State Current Status Recent changes NSW 6.64M Centralising by 1 January 2005; regionalised since 1986. Moving from 17 Area Health Services with separate governance authority to 8 Area Health Services within Departmental governance. Victoria 4.87M Rurals partly regionalised for many years; Melbourne 'networked' since 1995. Melbourne networks restructured from 7 to 12 and names changed in 2000. Rural structures mix of regionalised and atomised. Q'land 3.71M Centralised at state level since 1996 after 5 years of regionalisation. Long history of centralisation with advisory hospital boards; Regional Health Authorities 1991–1996. WA 1.93M Centralised at state level in 2001/02. Moved from 'atomised' in Perth to one board in 1997, governance centralised in 2001; state now centralised. SA 1.52M Regionalised in rural areas since 1995; Adelaide partly regionalising. Moved from atomised to regionalised, with 2 regional and 1 specialised health services in the capital as of July 2004. Tasmania 0.47M Centralised at state level Moved from atomised to regionalised in 1991; centralised at state level in 1997. ACT 0.32M Centralised (single city system) Single board for Canberra established in 1996; abolished in 2002. NT 0.2M Centralised at territory Level Never devolved. Some autonomous Aboriginal Health Services. The recent NSW decision means that there is now a strong predominance of governance at state health authority level, with two-thirds of the Australian population living in areas served by centralised health services. The second notable trend is the virtual end of 'atomised' structures – stand-alone, single-service agencies (ie, hospitals, community health, or mental health services) in the public sector. There are of course exceptions (women's and children's hospitals may be the last ones standing in a few years), and the picture is different for non-government organisations (like district nursing) which are less amenable to restructuring. Common Themes The most recent reviews in WA and SA are characterised by claims to radical change, based on both financial and health goals: '...incremental reform is no longer the pathway to a financially sustainable vision for WA. A fundamental re-prioritisation of the public health system is needed, and should be carried out over the next decade in a systematic and integrated way' [ 4 ], p v ). 'The people of South Australia have a decision to make on what type of health system they need now and for the future generation...there needs to be a significant shift from a system focused on illness to a health system reoriented towards health promotion, illness prevention and early intervention' [ 2 ] p xiii . Western Australia is taking on the tertiary hospitals, and reducing the number of tertiary sites from 5 to 2 (with the women's and children's hospital group to be collocated but organisationally separate). All state-run health services are to report through three metropolitan regions (north, south and Women's and Children's) and one rural region, with the CEO's reporting directly to the Department – there are to be no boards of governance. South Australia has succeeded in amalgamating most of Adelaide's hospital and community health boards to form 2 regional health services and 1 child, youth and women's health service (incorporating the women's and children's hospital). This is a notable achievement for a minority government, after at least five separate attempts in the last 20 years to rebalance power and responsibility had largely failed [ 13 - 17 ]. Not all of the systemic reviews claim to set a bold new vision, but there are strong common themes. The reports tell a familiar story of the need to bring increases in state health spending to sustainable levels, set against the trend of increasing costs due to increasing incidence of chronic disease, and more technologies for intervention, in an ageing population. They all focus on the need to improve quality and safety for patients. The reviews also find that the health system is too fragmented to meet the needs of patients with long-term complex conditions well. This is seen to be partly because the system was designed for acute illness, with the current funding mechanisms also designed primarily on the pattern of acute interventions. The reviews call for better integration of services, so that navigating the system is easier for patients, their carers and care providers. The reviews consistently argue that in order to achieve this, the primary care system needs to be more effective in managing or coordinating patients' needs for several different kinds of services when and where they are needed. The inevitable corollary is that inpatient care and hospitals have to become less central in the organisation and funding of the system. What can be done elsewhere should be; and the primary care level must have more of the action and more of the pulling power. In turn, this will require different facilities for different modes of service delivery; different funding allocations and methods of allocation; and a solution to the atomisation of primary care caused by the Commonwealth/state split and the current model of fee-for-service medicine. The need for changes in the private sector is noted in the reviews, but proposals are not developed, because the states have such a limited role here. Much is also made of the need for providers to be more accountable to government and the community, and/or better governed and managed. While the reports mostly call for less micro-managing from the head offices of health authorities, and a better separation between the roles of central policy-makers and peripheral service providers (or regional CEO's), there is also a countervailing tendency to recommend tighter engagement and control. For example, the Kibble review of governance in Victoria (2003) notes confusion about relative roles and responsibilities and calls for the Department of Human Services to reduce 'attention to the day-to-day operations of Health Services and monitoring of detailed activities' (p 27) but later recommends 'a standardised reporting template' for internal reports to boards across the system (p 35), along with stronger accountability for the CEOs to the Secretary of the Department. More public reporting of service outcomes and activity levels is a related common theme, intended to inform the public and to underpin attention to safety and quality. The final major common theme in the reviews is the inclusion of an opportunistic range of technical efficiency and effectiveness measures, picking up ideas in good currency or known productivity opportunities. For example, almost everyone recommends a call centre; a web-based method of sharing innovations; amalgamation of support services where relevant; and improvements in the effectiveness of information systems and the use of information. There are two other commonalities worth noting. Firstly, it is a fact of organisational and political life that official reviews are a top-down affair, commissioned by one level of the system to examine a lower level. Thus it is not surprising that there are no published official reviews of the roles and responsibilities of the Commonwealth health authority in the last twenty years. When the published reviews do address the roles and responsibilities of state health authorities, it is either because they are the providers (NT, WA, ACT) or because intended changes to the service provider level of the system require changes to the roles of central health authorities. This is an important limitation in the current environment, when some of the key barriers to improving the effectiveness of health care delivery lie in the system's seldom seen upper reaches. As the reviews note, the way that the Commonwealth/state split of responsibility for health is enacted and managed is probably the single most significant problem in health system design. But it is not the only one. I refer not so much to important policy settings (like funding allocation models and public health priorities) which are studied and articulated in publicly-available documents, but rather to the influence of administrative decisions (like who gets special grants and who doesn't) and the effectiveness of relationships with health care provider organisations (as judged from the bottom up as well as the top down). The administrative actions of health authorities seem to go largely unexamined. Secondly, while the underlying problems the reviews set out to address are all about money, hospital utilisation and a weak primary health care system, the immediate context is often the election of a new government (Victoria, SA, NT), the appointment of a new minister or health authority CEO (WA), media unrest about health in a state with a looming election (ACT) or scandal (NSW). This observation may simply be another way of saying that the health portfolio is highly politically sensitive as well as complex, and so risky that reference to independent expertise is seen as essential. Discussion The main line of logic running through the recent reviews seems sound. The primary care system needs strengthening; what can be done outside hospitals should be; and a continuing focus on safety, performance and accountability is necessary. The reports also make it clear that this is all about responding to the major challenge for the system: to improve its capacity to prevent, intervene early in, and manage chronic disease, the main driver of increased demand. Such a focus is clearly justified. Chronic disease is responsible for approximately 80% of the total burden of disease, with an estimated three million Australians suffering from one or more chronic illnesses [ 18 ]. About 40% of total health expenditure, or $12.6 billion, was spent on chronic illness in 1993/94, just less than half of it in hospitals [ 19 ]. The system must be able to deliver the kind of care needed by people with (or at risk of) chronic disease, including older people and Indigenous people, and thereby enhance the system's effectiveness and perhaps even reduce the slope of the increasing cost curve. If this is the imperative, the trend away from atomised governance structures, and towards bringing multiple agencies which serve (at least some) common patients together, seems like the right direction. But there is another important requirement which may not be served by these moves – that is, a stronger focus on innovation in care models. While recommendations abound, we don't yet really know what will work best for the new pattern of illness – how do you best coordinate care around the needs of the chronicly ill? Uncertainty about care models, and the institutional and policy arrangements needed to support them, can only be resolved through the continued development and testing of innovative approaches, on the ground in health care delivery, as happened most notably with the Coordinated Care Trials [ 20 ]. As many of the reviews argue, the engagement of clinicians is critical to this endeavour. This reality implies that there is a secondary criterion by which the effectiveness of health system structural changes might be judged: do the changes enhance or inhibit the system's ability to innovate? The requirement for innovation and experimentation may not sit comfortably with government requirements for standardisation of known good practice. However in an area where best practice is not known, innovation is critical, and must be supported. Unfortunately, the Commonwealth:state responsibility split, the one structural barrier most central to the systemic weakness of Australian primary care (and therefore most important for the capacity to develop and support new models of care for chronic diseases), is one that a state can't address, at least not alone. The Productivity Commission's recent call for an independent public review of the whole health system [ 21 ], focused on overlapping roles and responsibilities for funding, offers grounds to hope for movement in this otherwise intractable problem. The other pessimistic sign is the trend to more direct control of health care provision by state governments, related no doubt to the twin problems of increasing demand (and therefore cost) and increasing disclosure of safety and quality problems, both of which can only politicise the system more. Research on innovation, in relation to quality and safety as well as other performance measures, indicates that micro-management from above is not helpful [ 22 , 23 ]. Local evidence to support this view is scant. While the effectiveness of the new arrangements during Queensland's brief period of devolved governance was judged harshly when it came time to re-centralise, some commentators suggest that this period also allowed Queensland to catch up to other states in areas like accreditation, casemix, IT and 'attention to performance management and outcomes' [ 24 ]. This may be a critical problem. While recognising that perspectives on this question are highly related to one's place in the structure, I would suggest that real innovation in public hospitals and health services is less likely to be driven by clinicians who are more tightly controlled, staff who have learnt to be risk-averse, or managers who are increasingly frightened of tomorrow's headlines, and whose planning horizon extends to next month's financial and activity data. This problem is only compounded while hospitals and community health services on the one hand, and GPs on the other, continue to work with so little in the way of common incentives. Conclusion The recent reviews were established largely to address financial imperatives in an environment of upward pressure on demand for services, and accountability concerns (in relation to quality and safety, and general good governance), mostly in a highly political context. The reviewers rightly sought to take a longer-term strategic perspective. They attempted (with varying degrees of success) to focus on good system design and capacity to meet the broad and complex purposes of public health systems, recognizing the growing challenges the systems face. Structural reform is hardly ever evaluated, other than when its weaknesses are articulated by those proposing the next round of changes, as part of the rationale for their efforts. There are many reasons for this failure, some of them political. One pertinent reason is that outcomes like containing the pressure of future growth in demand, or improving health outcomes for the population, cannot be judged within a realistic time frame. However, in the current environment, with strong convergence in the themes addressed by a fairly comprehensive round of reviews of Australian health systems, an argument can be made for evaluation 'at the pointy end' of the changes. Given the challenges the reviews were intended to address, there are grounds to suggest that the current reforms will not succeed in achieving the stated (shorter- and longer-term) goals unless they make a difference for people with chronic illness. And if this is correct, the most useful immediate focus for evaluation of the success of the reforms may be their impact on the system's ability to develop and deliver better models of care for this growing group of patients. Methods The 'data' for this project were the published reports of systemic reviews of the health systems, and related material published on departmental websites and in the professional and academic literature. These sources were analysed to generate an understanding of the recommended governance authority structures; and the common themes emerging from the reasoning on which the recommendations were based. The themes underpinning the recommendations were then assessed in the light of the overarching goals of reform. Competing Interests I was a member of the South Australian 'Generational Health Review' Steering Committee, chaired its Governance and Funding Task Force and continue to consult to the SA Department of Human Services. I worked for twenty years in health care agencies, and only one in a central health authority. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC544964.xml |
518969 | High-dose ibuprofen therapy associated with esophageal ulceration after pneumonectomy in a patient with cystic fibrosis: a case report | Background Lung disease in patients with cystic fibrosis is thought to develop as a result of airway inflammation, infection, and obstruction. Pulmonary therapies for cystic fibrosis that reduce airway inflammation include corticosteroids, rhDNase, antibiotics, and high-dose ibuprofen. Despite evidence that high-dose ibuprofen slows the progression of lung disease in patients with cystic fibrosis, many clinicians have chosen not to use this therapy because of concerns regarding potential side effects, especially gastrointestinal bleeding. However, studies have shown a low incidence of gastrointestinal ulceration and bleeding in patients with cystic fibrosis who have been treated with high-dose ibuprofen. Case presentation The described case illustrates a life-threatening upper gastrointestinal bleed that may have resulted from high-dose ibuprofen therapy in a patient with CF who had undergone a pneumonectomy. Mediastinal shift post-pneumonectomy distorted the patient's esophageal anatomy and may have caused decreased esophageal motility, which led to prolonged contact of the ibuprofen with the esophagus. The concentrated effect of the ibuprofen, as well as its systemic effects, probably contributed to the occurrence of the bleed in this patient. Conclusions This report demonstrates that gastrointestinal tract anatomical abnormalities or dysmotility may be contraindications for therapy with high-dose ibuprofen in patients with cystic fibrosis. | Background Airway inflammation is thought to cause much of the lung damage in patients with cystic fibrosis (CF) [ 1 , 2 ]. Such inflammation has been found in infants with CF, even in the absence of bacterial infections or symptomatic lung disease [ 3 ]. Recent therapies for CF lung disease have been shown to preserve lung function by decreasing airway exposure to inflammation. For example, airway clearance, antibiotics (inhaled tobramycin and oral azithromycin), rhDNase, and oral corticosteroids have been used to help decrease airway inflammation [ 4 - 6 ]. High-dose ibuprofen therapy also has been shown to be effective in decreasing inflammation, probably by decreasing polymorphonuclear cell influx into the lungs [ 7 ]. We present a patient with CF who developed a rare complication of high-dose ibuprofen therapy related to his pneumonectomy. Case presentation The patient was a teenager with CF who suffered for several years from right lower lobe consolidation, as a result of recurrent Pseudomonas aeruginosa pneumonia. The consolidation was unresponsive to intensive therapy including intravenous (IV) antibiotics, chest physiotherapy, systemic steroids, and rhDNase. The patient developed recurrent severe pulmonary exacerbations necessitating IV antibiotic therapy 4 times per year between the ages of 14 and 15 years. A computerized tomography scan of the chest when the patient was 15-years-old revealed complete collapse of his right lung. A ventilation/perfusion scan revealed only 8% of his ventilation and perfusion in his right chest, while the remaining 92% was in his left chest. In addition, the left lung had begun to herniate into his right chest. It was felt that the right lung was a recurrent source of infection, which was causing serious morbidity. Therefore, the patient underwent a total right pneumonectomy that he tolerated well. Approximately one year later, in preparation for initiating high-dose ibuprofen therapy, the patient's stool was checked for occult blood. He was found to have occasional guaiac positive stools. An upper gastrointestinal endoscopy revealed chronic esophagitis, esophageal ulcerations, and Barrett's esophagus thought to be attributable to gastroesophageal reflux. A 24-hour pH probe revealed significant gastroesophageal reflux despite anti-reflux therapy. Therefore, a fundoplication was performed in order to prevent further esophageal damage. Subsequently, it was believed that his esophageal ulcerations resolved as multiple stools were documented to be guaiac negative. High-dose ibuprofen therapy was initiated when the patient was 17-years-old, based on published recommended dosage and pharmacokinetic protocols [ 8 ]. The patient's dose was determined by a pharmacokinetic analysis [ 8 ] that documented a peak ibuprofen plasma concentration of 69 mcg/ml, following a test dose of 1,000 mg (22 mg/kg). A week after initiation of ibuprofen at a dose of 1,000 mg b.i.d. the patient developed severe abdominal pain, hematemesis and bright red blood per rectum. He became hemodynamically compromised and an emergency endoscopy revealed bleeding esophageal ulcerations in the distal 12 cm of his esophagus. After stabilization and observation without further bleeding, a barium swallow demonstrated that his esophagus was deviated towards the right side and the lower segment of the esophagus was relatively horizontal proximal to the gastro-esophageal junction (Figure 1 ). In addition, a pancreatic enzyme capsule emptied of the enzymes and filled with barium was retained within that esophageal segment for several minutes (Figure 1 ). Figure 1 Barium swallow in a patient with cystic fibrosis following right pneumonectomy. A – The study demonstrates deviation of esophagus to the right side, and a relatively horizontal lower esophageal segment proximal to the gastro-esophageal junction. B – A pancreatic enzyme capsule emptied of the enzymes and filled with barium (indicated by the arrow) is retained within the lower esophagus for several minutes. Following this episode the patient's ibuprofen was discontinued, and his oral medications (e.g., antibiotics and vitamins) were switched to liquid form, whenever possible. His pancreatic enzymes were administered after removing the enzyme microspheres from their capsule and mixing them in applesauce. Reported complications of pneumonectomy include mediastinal shift with herniation of the remaining lung, cardiac herniation, cardiac arrhythmias, bronchopleural fistula, esophageal motility disorders, and development of scoliosis [ 9 - 11 ]. It is thought that pediatric patients have more mediastinal shift following pneumonectomy than adults because of increased elasticity and compliance of the lung and mediastinum that allow for more severe anatomical derangements [ 9 ]. A study of 17 post-pneumonectomy pediatric patients revealed that all of the patients had marked herniation of the remaining lung with a mediastinal shift to the opposite side as evident on chest radiographs or computerized tomography scans [ 11 ]. One patient with a right pneumonectomy displayed excessive shift of the esophagus as well, but did not have any associated dysphagia or reflux [ 11 ]. In another study, esophageal motility was measured in 13 patients before and after pneumonectomy [ 9 ]. Patients post-pneumonectomy were shown to have esophageal dysmotility even without reporting dysphagia [ 9 ]. The dysmotility was thought to be attributable to the mediastinal shift [ 9 ]. Thus, for the patient in the present report, it is likely that the deleterious effects of gastroesophageal reflux may have been increased because of esophageal dysmotility after pneumonectomy. In our patient, esophageal dysmotility along with the distortion of esophageal anatomy probably combined to slow the esophageal transit time of the ingested ibuprofen, which may have led to development of ulcerations as a result of prolonged concentrated contact within the esophagus. Also, the ibuprofen could have contributed to the development of ulcerations by inhibiting cyclooxygenase systemically, which decreased prostaglandin E production [ 12 ]. In turn, less prostaglandin E was available to promote bicarbonate and mucus secretion, which are protective of the gastrointestinal mucosa [ 12 ]. Delayed esophageal transit may have occurred with the patient's other oral medications prior initiation of ibuprofen. For example, his pancreatic enzymes likely were retained in the same esophageal segment, which placed him at risk of developing esophageal damage akin to the development of fibrosing colonopathy and strictures described in patients with CF who received high-dose pancreatic enzymes [ 13 ]. The enzymes may have remained inactive because the environment was not alkaline enough for their activation [ 14 ], which may be the reason that the patient did not demonstrate esophageal strictures. According to the 2002 CF Foundation registry, only 3.8% of patients in North America with CF were treated with high-dose ibuprofen [ 15 ]. In 1999, a survey of 67 CF center physicians revealed that safety issues were a major reason that they did not prescribe this therapy [ 16 ]. Gastrointestinal bleeding, a known adverse effect of non-steroidal anti-inflammatory agents, was the safety issue of most concern to these physicians [ 16 ]. Other known side-effects of high-dose ibuprofen include renal failure (often transient), and epistaxis [ 3 , 17 ]. The initial randomized, double-blind, placebo control study of high-dose ibuprofen in CF did not demonstrate serious side effects during 4 years of therapy with ibuprofen sufficient to achieve peak plasma concentrations of 50–100 mcg/L [ 8 ]. Seven of 85 study patients developed abdominal pain, while only 2 of these 7 were on ibuprofen. One patient in the placebo group developed esophagitis. Of note, abdominal pain, which is very common in patients with CF, actually improved in many patients. In another randomized, double-blind, placebo controlled study, involving 19 children with cystic fibrosis; 13 children received sufficient ibuprofen for 26 months to maximum concentrations 48 +/- 17 mcg/ml, but no adverse effects could be attributed to the ibuprofen [ 18 ]. The incidence of gastrointestinal disease in patients reported to the CF Foundation registry from 1996–2000, was compared between patients who were and were not taking ibuprofen [ 19 ]. Peptic ulcer disease was reported in 0.32% of 1,186 CF patients taking high-dose ibuprofen, as compared to an incidence of 0.22% in 18,587 patients not taking ibuprofen. Gastrointestinal bleeding was reported in 0.49% of patients taking ibuprofen, as compared to 0.23% of the others (p = .0004). In the first year after initiation of high-dose ibuprofen therapy for 91 patients at the Texas Children's Hospital CF Center, one patient developed upper gastrointestinal bleeding, and one developed gastritis [ 20 ]. In a published case report, a 12-year-old patient with CF on high-dose ibuprofen developed emesis and feeding intolerance [ 21 ]. She was found to have pyloric channel stricture as a result of healing antral and pyloric ulcers, which may have been caused by ibuprofen. Conclusions The risk of developing gastrointestinal side-effects from high-dose ibuprofen therapy is low for patients with CF. However, ibuprofen may be contraindicated for those who are at increased risk because of gastroesophageal reflux, history of gastrointestinal ulcerations, or abnormal gastrointestinal motility or anatomy. Competing interests None declared. Authors' contributions JM wrote the case report. RA treated the reported patient, and edited the report. Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC518969.xml |
517509 | Diurnal difference in CAR mRNA expression | Background The constitutive androstane receptor (CAR, NR1I3) plays a key role in the transcriptional activation of genes that encode xenobiotic/steroid and drug metabolizing enzymes. Results The expression of CAR mRNA throughout the circadian rhythm is reported for the first time in phase with the clock gene Bmal1 and in antiphase with the clock-controlled gene Rev-erbα mRNAs, with a peak at Zeitgeber time (ZT) 20 and a trough at ZT8, and a peak/trough ratio of 2.0. The diurnal difference in CAR mRNA expression might underlie the 1.7-fold difference in the magnitude of the PB-dependent induction of CYP2B1/2 mRNA. Conclusion The circadian oscillation of xenosensor gene CAR mRNA expression is partially responsible for chronopharmacokinetics and chronopharmacology in disease. | Background The superfamily of nuclear hormone receptor comprises a group of transcription factors that play significant roles in response to a number of biological regulators. In addition to the pregnane X receptor (PXR, NR1I2), the constitutive androstane receptor [ 1 ] (CAR, NR1I3) plays a role in the transcriptional activation of genes that encode xenobiotic/steroid and drug metabolizing enzymes, such as cytochrome P450 (CYP) 2Bs, 2C19, 3As, multidrug resistance-associated protein 2 (MRP2), UDP-glucuronosyltransferase (UGT1A1), and 5-aminolevlinic acid synthase 1 (ALAS1) [ 2 - 8 ]. In response to xenobiotic PB, and other PB-like ligands such as 1,4-bis [2-(3,5-dichlorpyridyloxy)]benzene (TCPOBOP) in rodents [ 9 ] and 6-(4-Chlorpphenyl)imidazo [2,1-b][ 1 , 3 ]thiazole-5-carbaldehyde O -(3,4-dichlorobenzyl)oxime (CITCO) in humans [ 10 ], high doses of acetaminophen [ 11 ], and bilirubin [ 12 ], CAR forms a heterodimer with retinoid X receptor alpha (RXRα) and subsequently binds to the direct repeat (DR-4) motifs in such as the phenobarbital (PB)-responsive enhancer module (PBREM) in the far upstream promoter regions of mouse, rat and human CYP2B genes. In contrast, androstanol and androstenol were initially identified as inverse agonists [ 13 ] that reverse the constitutive transactivating potency of CAR. Recently, the mRNA expression of nuclear receptors, such as peroxisome proliferater-activated receptor alpha (PPARα), retinoic acid receptor (RAR)-related orphan receptor (ROR) and RER-ERBα, have been reported to show circadian rhythms in the liver [ 14 - 17 ]. Hepatic PPARα mRNA and protein levels follow a diurnal rhythm which parallels that of circulating corticosterone. In addition, REV-ERBα expression is regulated by a circadian positive feedback loop attributable to the function of BMAL1/CLOCK heterodimers, and is negatively controlled by circadian negative lobe PER/CRY heterodimers [ 18 , 19 ]. Circadian variations in the chronopharmacokinetics and chronopharmacology of various drugs such as theophylline and propranolol have been recently reported [ 20 ]. Furthermore, daily fluctuations in hepatic P450 monooxygenase activities responsible for the first phase of metabolism of various xenobiotics are well known. For example, Cyp2a4, Cyp2a5, CYP7, and CYP3A are among those that show circadian rhythmicities [ 21 - 23 ] that result from the preceding rhythmic oscillations of transcription factors including nuclear receptors. We previously determined the transcriptional start site of the rat Car gene (Kanno et al., 2003), resulting in the discovery of the putative REV-ERBα/ROR responsive element (RORE) at around -1.2 kb on the basis of published genomic sequence (Mazny et al., accession number AC099236). Thus, expression of the Car gene is expected to occur in antiphase to that of the Per1 gene and in phase with the Bmal1 gene. In the present study, the expression profile of the Car gene in rat liver was studied in comparison with those of the clock gene Bmal1 , clock-controlled gene Rev-erbα and CAR-dependent PB-inducible CYP2B1/2 gene. Results Rat hepatic expression of nuclear receptor CAR mRNA follows a circadian rhythm Apart from the clock gene BMAL1 and clock-directed gene REV-ERBα, a time-dependent profile of CAR mRNA expression was observed for the rat liver. CAR mRNA levels oscillated during the day in phase with BMAL1 and in antiphase with REV-ERBα mRNAs, with a peak at ZT20 and a trough at ZT8, and a peak/trough ratio of 2.0 (Figs. 1 , 2A ). The CYP2B mRNA expression profile was resembled to the circadian oscillation of CAR mRNA but in a much more blunted manner (Fig. 2B ). Figure 1 Diurnal variations in CAR mRNA in the rat liver. Animals were sacrificed every 4 hours at Zeitgeber times (ZT) 4, 8, 12, 16, 20 and 24/0. mRNA levels of CAR, CYP2B1/2, BMAL1, REV-ERBα and GAPDH were amplified by semi-quantitative RT-PCR. After oligo(dT)-primed cDNA was synthesized from rat liver total RNA, PCR was conducted with an initial enzyme activation step at 95°C for 5 min followed by divergent cycles of denaturation at 95°C for 15 sec, annealing at 60°C for 30 sec and extension at 72°C for 60 sec; CAR (27 cycles), CYP2B1/2, REV-ERBα and BMAL1(30 cycles), and GAPDH (24 cycles). The reaction products were separated by agarose gel electrophoresis and stained with ethidium bromide. Figure 2 Diurnal difference in CAR and CYP2B mRNA levels in the rat liver. Animals were sacrificed at ZT8 and ZT20 (n = 3–4), and CAR ( A ) and CYP2B ( B ) mRNA levels were measured by semi-quantitative RT-PCR as described in the legend to Fig. 1. The results were normalized against those for GAPDH. The columns and bars represent the means ± SD with a significant difference at *: p < 0.01 Diurnal difference in the induction of CYP2B by phenobarbital Since CAR is associated with the induction of metabolic enzymes such as CYP2B, CYP3A, and UGT1A1, the circadian rhythmicity of CAR mRNA expression may be reflected in the diurnal-difference of PB-induction of CYP2B1/2 mRNA. Therefore, we investigated the time-dependent difference of the effect of PB-treatment on the induction of CYP2B1/2 mRNA. CYP2B1/2 mRNA expression was comparatively evaluated at ZT13 and ZT1 after 5-hours of PB treatment during ZT8 to ZT13 (the minimum zone of CAR mRNA expression) and ZT20 to ZT1 (during which the expression of CAR mRNA was maximal), respectively. Hepatic CYP2B1/2 mRNA was induced 2.2-fold over the control level in the rats treated with PB between ZT8 and ZT13 [daytime treatment]. In contrast, it was increased by 3.8-fold of the control level when the rats were treated from ZT20 to ZT1 [nighttime treatment] (Fig. 3 ). These data suggest that the diurnal-difference in CYP2B1/2-induction might be affected by the circadian rhythm of CAR mRNA expression. Figure 3 Diurnal difference of CYP2B induction. Animals were sacrificed at ZT13 and ZT25/1 5-hour after the injection of PB (gray columns) or veihcle (black columns) during ZT8-13 [Day] and ZT20-1 [Night], respectively. Oligo(dT)-primed cDNA was synthesized from rat liver total RNA from each animal, and CYP2B mRNA levels were measured by STBR Green real-time RT-PCR. The results were normalized against those of GAPDH. The columns and bars represent the means ± SD with significant differences compared to the individual controls at *, # : p < 0.05 Discussion We previously reported that the induction of rat CYP2B1/2 by PB is absent in the lung in contrast to the marked response in the liver due to the improper splicing of CAR mRNA during its maturation. [ 24 , 25 ]. The longitudinal expression of CAR mRNA along the gastrointestinal tract increases from the duodenum to the terminal jejunum and then decreases toward the distal ileum while only marginal expression can be observed in the stomach and colon, implying a role for endogenous ligands such as bilirubin glucuronides secreted in the duodenum [ 26 ]. A single transcriptional start site was determined by comparison between the full-length mRNA and genomic sequences. In the present study, we investigated whether the expression of hepatic CAR mRNA shows circadian rhythmicity, because clock-controlled regulation is expected due to the presence of putative RORE in the promoter region and electrophoretic gel mobility-shift assay showed a slowly migrating band binding to the RORE probe using nuclear proteins (data not shown). The CAR mRNA level was found oscillation daily with a peak at ZT20 and a nadir at ZT8. In contrast, BMAL1 mRNA peaked at ZT24/0 and hit the bottom at ZT12 with a 4-hours retardation, and REV-ERBα mRNA showed a peak at ZT8 and a trough at ZT20 exactly in antiphase with CAR mRNA (Fig. 1 ). In contrast to the self-sustained central clock present in the brain, peripheral circadian clocks are retrained by humonal factors such as glucocorticoid hormones [ 27 , 28 ], as reflected in the diurnal rhythms observed for PPARα and REV-ERBα. Glucocorticoids are also responsible for the induction of human CAR mRNA and protein via a distal glucocorticoid response element in the 5'-franking region of the gene [ 29 ], and the same might be true for its rat counterpart, which was inducible by dexamethasone (data not shown). PPARα mRNA levels followed a similar diurnal rhythm to that of the plasma level of corticosterone, which is low in the morning (around ZT2), and increases in the afternoon to reach a peak 2–3 hours before the lights out (ZT9.5). Therefore, CAR mRNA oscillation might not be retrained by the physiological diurnal variation of glucocorticoids in rats. Recently, bilirubin was reported to be an endogenous activator of the CAR gene, which is in turn associated with the induction of bilirubin metabolising proteins, such as organic anion transporter SLC21A6, glutathione-S-transferase (GST), UGT1A1 and MRP2. Blood-bilirubin level reaches a minimum at the end of the light period and a maximum at the end of the dark period [ 30 ]. It is probable that blood bilirubin may contribute to the retraining of CAR expression to optimise bilirubin clearance. Hepatic CYP2B1/2 mRNA level was found to be synchronized with the CAR mRNA oscillation (Fig. 1 ). In the clock-controlled gene cascade or network, the circadian rhythm of CYP2B1/2 mRNA expression might be partially, if not fully, explained by the hepatic CAR level. Furukawa et al. showed that hepatic P450-dependent monooxygenase activities measured by the O -dealkylation of 7-alkoxycoumarin fluctuate daily in F344 rats with high values during the dark period [ 31 ]. In addition, these fluctuations are regulated by a central clock present in the suprachiasmatic nucleus [ 32 ]. Further, cholesterol 7-α hydroxylase (CYP7), coumarin 7-α hydroxylase (Cyp2a4) and coumarin 15-α hydroxylase (Cyp2a5) exhibit circadian rhythmicities. These enzymes are transcriptionally regulated by albumin D-site-binding protein (DBP), which is another primary clock-controlled gene expressed according to a robust daily rhythm in the SCN and several peripheral tissues. Besides DBP, REV-ERBα is transactivated by the binding of the BMAL1-CLOCK heterodimer to the E-box motif in its enhancer region [ 33 ], and is down-regulated by the clock gene PER-CRY heterodimer. Neuronal PAS domain protein 2 (NPAS2) is highly related in primary amino acid sequence to CLOCK, being able to dimerize with BMAL1 as in the case of CLOCK. Furthermore, BMAL1-NPAS2 heterodimer was found to transactivate the same target genes as those of BMAL1-CLOCK such as Per1 , Per2, Cry1 and Rev-erb α. Recently, the transcription of Alas1 gene encoding for the aminolevulinate synthase 1 ( Alas1 ) that is rate-limitting enzyme in a heme biosynthesis was reported to be controlled in the circadian clock mechanism. Although Alas1 is regulated transcriptionally by CAR-modulators having DR4 motifs in the promoter region as well as CYP2B1/2 , BMAL1-NPAS2 and BMAL1-CLOCK heterodimers would be responsible for the daily physiological fluctuation in phase with Rev-erbα [ 34 ]. The circadian transcription of CAR and CYP2B1/2 is likely directly, indirectly or in combination dominated by these peripheral clocks and clock-controlled genes. The direct role of RevErb in the regulation of CAR will have to be established in further studies. For example ChIP analysis would be required to show diurnal occupancy of the putative RORE in the CAR promoter, and it has not yet been shown that Rev Erb α can modulate the transcription of the CAR promoter. We were also interested in whether the PB-dependent induction of CYP2B1/2 mRNA is affected by the diurnal rhythm of CAR. As shown in Fig. 3 , PB-treatment at night [ZT20-1] was 1.7-fold more effective than treatment during the daytime [ZT8-13] in terms of the induction of CYP2B1/2 mRNA. Although the timing of the injection of PB and monitoring of CYP2B1/2 mRNA levels adopted in this work might not have been optimal, the results obtained suggested that the diurnal difference in the expression of xenosensor genes may underlie chronopharmacokinetics and chronopharmacology in a clinical setting. Conclusions Nuclear receptor CAR mRNA expression oscillates during the day with a peak at ZT20 and trough at ZT8 in antiphase with REV-ERBα, as expected due to the presence of putative ROREs in the promoter region. Since the magnitude of PB-induction of CYP2B1/2 mRNA showed at least a 1.7-fold difference during the day, the diurnal-difference of CYP2B-induction by PB might be controlled by the circadian rhythm of CAR mRNA expression. Methods Animals and treatments Eight week-old male Wistar rats (Clea) were kept under a 12-hours light-dark (LD12:12) cycle and provided food and water ad libitum . After more than 2 weeks of housing, the rats were killed at Zeitgeber times (ZT) 0, 4, 8, 12, 16, 20 and 24: ZT0 was lights-on and ZT12 is lights-out. For the PB-induction of CYP2B, the rats were i.p. injected with PB at ZT8 and ZT20 and sacrificed at ZT13 and ZT1, respectively. The livers were then dissected and used for the isolation of total RNA. RNA analysis by RT-PCR and Real-Time RT-PCR Total RNA was extracted from rat liver homogenate using an RNeasy Kit (QIAGEN, Hilden, Germany). After incubation at 65°C for 10 min, the extracts were quickly placed in an ice-cold water bath. Oligo-dT primed cDNA was synthesized from 1 μg of total RNA using RTG You-Prime First-Strand Beads (Amersham Biosciences, NJ), and left at room temperature for 1 min. Reverse transcription was then performed at 37°C for 1 hour to obtain cDNA. PCR was next performed in a total reaction mixture (25 μl) containing 1 μl each of RT-reaction mixture, Ex Taq DNA polymerase (Takara, Japan) and each of primer pair. cDNA was amplified for 24 (GAPDH), 27 (CAR) or 30 (BMAL1, REV-ERVα, CYP2B) cycles of denaturation at 95°C for 15 sec, annealing at 60°C for 30 sec, and extension at 72°C for 1 min in a thermal cycler. The reaction products were separated by agarose gel electrophoresis and analyzed by a Flour Imager (Amersham Biosciences) after staining with ethidium bromide. Real-time PCR was carried out for the quantitation of each transcript in a reaction mixture consisting of 2 μl of the cDNA, 1 μl each pair of primers, 21 μl of water and 25 μl of iQ SYBER™ Green Supermix (BIO-RAD, CA). PCR was performed with an initial enzyme activation step at 95°C for 5 min followed by 50 cycles of denaturation at 95°C for 30 sec, annealing at 56°C for 30 sec and extension at 72°C for 45 sec in a real-time DNA thermal cycler (iCycler™, BIO-RAD). The following oligonucleotides were used as forward and reverse primers, respectively: 5'-ACCAGTTTGTGCAGTTCAGG-3' and 5'-CTTGAGAAGGGAGATCTGGT-3' for CAR, 5'-GAGTTCTTCTCTGGGTTGCTG-3' and 5'-ACTGTGGGTCATGGAGAGCTG-3' for CYP2B1/2, 5'-AACATGGCACTGAGCAGGTCTCC-3' and 5'-GGCATGTCCTATGAACATGTACC-3' for REV-ERBα, 5'-GCAAACTACAAGCCAACATTTCTAT-3' and 5'-CTTAACTTTGGCAATATCTTTTGGA-3' for BMAL1, and 5'-ACCACAGTCCATGCCATCAC-3' and 5'-TCCACCACCCTGTTGCTGTA-3' for glyceraldehyde-3-phosphate dehydrogenase (GAPDH). The amplified cDNA was quantitated by the number of cycles (or cross point) at which the fluorescence signal was greater than a defined threshold during the logarithmic phase of amplification. The results were shown relatively to the control level after normalization to that of GAPDH. Competing interests None declared. Authors' contributions K.Y. conceived of the study, carried out all experiments and drafted the manuscript. S.O. and T.H. contributed to the experiment, and N.T. participated in the design of the study and its coordination. Y.I. participated in the design of the study and drafted the manuscript in collaboration with K.Y. All authors read and approved the final manuscript. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC517509.xml |
546197 | Clinical and echocardiographic features of aorto-atrial fistulas | Aorto-atrial fistulas (AAF) are rare but important pathophysiologic conditions of the aorta and have varied presentations such as acute pulmonary edema, chronic heart failure and incidental detection of the fistula. A variety of mechanisms such as aortic dissection, endocarditis with pseudoaneurysm formation, post surgical scenarios or trauma may precipitate the fistula formation. With increasing survival of patients, particularly following complex aortic reconstructive surgeries and redo valve surgeries, recognition of this complication, its clinical features and echocardiographic diagnosis is important. Since physical exam in this condition may be misleading, echocardiography serves as the cornerstone for diagnosis. The case below illustrates aorto-left atrial fistula formation following redo aortic valve surgery with slowly progressive symptoms of heart failure. A brief review of the existing literature of this entity is presented including emphasis on echocardiographic diagnosis and treatment. | Case A 66 year old male with history of rheumatic heart disease and aortic valve replacement (AVR) (twice for severe native and subsequent prosthetic valve regurgitation) presented with progressive worsening fatigue, exertional dyspnea and paroxysmal nocturnal dyspnea. His other medical problems included poorly controlled hypertension and hyperlipidemia. Following his second AVR 4 years prior to this presentation, a routine follow-up 2-dimensional echocardiogram (TTE) had shown preserved left ventricular and prosthetic valve function and a aorto-atrial fistula with color flow between the aorta-and the left atrium. Notably, only a soft and short ejection murmur across the mechanical prosthesis was appreciated and no continuous murmurs were heard. This was felt to be a possible postoperative complication currently not of clinical significance given his asymptomatic status and he was treated medically and did well for the last 3–4 years. Physical examination during this visit revealed an afebrile patient with a blood pressure of 138/84 mm Hg, regular pulse of 84/minute. An ejection systolic murmur (2/6 in intensity) was heard all over the precordium likely from the flow across his prosthesis. No continuous murmurs were heard. No evidence for clinical heart failure, anemia, jaundice or infection was noted. Laboratory tests revealed no leukocytosis and blood cultures were negative. Given prior echo documentation of fistula and new symptomatology suggestive of heart failure, a transesophageal echocardiography (TEE) was requested for more detailed assessment of prosthesis and AAF. TEE revealed normal left ventricular function, normal aortic prosthesis function with trivial aortic regurgitation. An echolucent area above the mechanical prosthesis, close to the left atrium near the orifice of the left coronary artery was noted. There appeared to be expansion of a portion of this lucency into the left atrium during systole suggesting communication with the aorta (Fig 1 ) with turbulent color flow from the aorta into the left atrium (suggestive of AAF) throughout the cardiac cycle but mainly in systole as shown by color and continuous wave doppler (Figs 2 and 3 ). Compared to the prior 2-D echo there appeared to be mild left atrial dilation, mild left ventricular hypertrophy and significantly more prominent fistula flow suggesting either progressive shunting and enlargement of the fistula over time or underestimation by the prior 2-D echo. Although the echocardiographic findings mimicked changes which could also be related to endocarditis (abscess around prosthesis with pseudoaneurysm formation), the absence of any obvious vegetations or prosthetic malfunction combined with lack of clinical and laboratory evidence of endocarditis favoured a more slowly progressive postoperative complication rather than an infectious process . Based on his heart failure symptoms and progressive increase in AAF size and flow, surgical correction was recommended. He underwent uncomplicated surgical repair of the AAF which was found during surgery to be inferior to the left coronary ostium. No evidence of abscess or infection was found and the prosthesis appeared intact and healthy. The echo lucent area represented a postoperative weakening of the aortic wall adjacent to the left atrium, predisposing to the fistula formation and was also repaired. Intra-operative TEE showed no residual fistula by color flow at the site of repair. Figure 1 Off axis 2-D short axis TEE view demonstrates the left atrium (LA) the prosthetic aortic valve (AV). An echolucent area (EL) around the aortic valve protruding into LA is seen with focal outpouching into the LA. This represents weakening of the wall of the aorta near the posterior aspect of the LA Figure 2 Off axis 2-D short axis TEE view with the probe advanced further into mid-esophagus: demonstrates turbulent color flow entering the LA from the EL region of the aortic valve illustrated in Fig 1 Figure 3 Continuous wave Doppler signal across turbulent jet showing high velocity throughout cardiac cycle but predominantly in systole consistent with aorto-left atrial fistula Discussion In a large collection of about 4000 cases of thoracic aortic aneurysms, Boyd first reported AAF as an incidental finding on autopsy back in 1924 [ 1 ]. Most of common etiologies of AAF are related to its occurrence as a result of bacterial endocarditis, paravalvular abscess, ruptured sinus of Valsalva, aortic dissection and possibly of congenital etiology [ 2 - 6 ]. Clinical presentation of AAF could vary from an acute presentation with acute chest pain syndrome due to rupture in the setting of dissection [ 5 ] or a refractory heart failure picture in the setting of endocarditis [ 7 ] and aortic dissection [ 8 ]. In a previous report we have highlighted the fulminant course of prosthetic valve endocarditis due to Proteus mirabilis leading to aorto-right atrial fistula from rupture of a pseudoaneurysm secondary to prosthetic valve endocarditis [ 9 ]. Isolated case reports of AAF as an immediate postoperative complication has also been reported [ 10 ]. Role of Echocardiography TTE and TEE form an integral part of assessment of patients presenting with chest pain and heart failure symptoms particularly if audible murmurs or valvular pathologies are suspected. TTE is the intial test of choice in routine prosthetic aortic valve assessment and gradients estimation. Nevertheless TEE is superior to TTE in real time assessment of prosthetic valve function and morphology and for better delineation of intracardiac pathology such as complications of endocarditis namely root abscess and fistulas [ 7 , 11 , 12 ]. TEE has better signal to noise ratio and proximity of transducer to the heart leading to higher quality images with lesser attenuation. Furthermore since aorto-left atrial fistulas usually occur from the posterior aspect of the aorta, this area is better delineated with TEE than TTE. Turbulent flow of AAF can be mistaken on TTE particularly if near the prosthetic valve for prosthetic malfunction in the setting of endocarditis or heart failure. Inter-chamber communications and the fistulous tracts that are particularly small are best tracked by multiplane TEE. The exact origin, chamber communications and even the size of the fistulous opening can be well assessed by TEE. Furthermore, coexistent complications with AAF in the setting of aortic endocarditis such as presence of annular abscess, extension to the upper interventricular septum or the subaortic area and pseudoaneurysm formation are best seen by TEE. Nevertheless cases of underestimation of cardiac involvement also been reported with TEE [ 9 ]. This reveals the limitations of viewing a three dimensional structure such as the heart in a two dimensional fashion, a void which may be filled by 3-dimensional (3-D) echocardiography. This case highlights the importance of intraoperative TEE in guiding valvular surgery identifying potential intraoperative cardiac complications, which can be corrected in the same setting. Since repeat sternotomy and cardiac surgery by itself carries a higher risk of perioperative complications, intraoperative pre and post pump TEE play an integral role in guiding the surgeons as to any new complications which may have risen during surgery. This is more so in valve surgeries or aortic reconstruction surgery where a real time 2-D TEE with color assessment pre and post pump provides important information regarding success of surgical intervention and new complications. Since clinical diagnosis of AAF is difficult, definitive diagnosis is by a thorough echocardiographic evaluation (TTE and TEE). Apart from antibiotic treatment for endocarditis, definitive treatment revolves around surgical correction. Since many of these patients may have had some form of surgical intervention in the past [ 5 ], reoperation is challenging. Mortality is high in patients who are continued on medical therapy particularly in the setting of aortic dissection [ 5 , 7 ]. Surgical intervention consists of repairing the affected aortic segment, replacing prosthesis if the valve is destroyed, annular debridement in the setting of abscess and suture of the fistula. Conclusion Aorto atrial fistulas are rare but important complications of many disease processes of the aorta and aortic valve. Classical clinical signs of continuous murmurs may not be present and echocardiography forms the cornerstone of diagnosis. AAF should be suspected in patients with poorly controlled heart failure and prior aortic surgery. Prompt surgical repair is usually helpful in relieving symptoms and decreasing mortality. Competing interests The author declares that he has no competing interests in preparation of this mansucript and has fully contributed to preparation of this manuscript. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC546197.xml |
503391 | Shoulder posture and median nerve sliding | Background Patients with upper limb pain often have a slumped sitting position and poor shoulder posture. Pain could be due to poor posture causing mechanical changes (stretch; local pressure) that in turn affect the function of major limb nerves (e.g. median nerve). This study examines (1) whether the individual components of slumped sitting (forward head position, trunk flexion and shoulder protraction) cause median nerve stretch and (2) whether shoulder protraction restricts normal nerve movements. Methods Longitudinal nerve movement was measured using frame-by-frame cross-correlation analysis from high frequency ultrasound images during individual components of slumped sitting. The effects of protraction on nerve movement through the shoulder region were investigated by examining nerve movement in the arm in response to contralateral neck side flexion. Results Neither moving the head forward or trunk flexion caused significant movement of the median nerve. In contrast, 4.3 mm of movement, adding 0.7% strain, occurred in the forearm during shoulder protraction. A delay in movement at the start of protraction and straightening of the nerve trunk provided evidence of unloading with the shoulder flexed and elbow extended and the scapulothoracic joint in neutral. There was a 60% reduction in nerve movement in the arm during contralateral neck side flexion when the shoulder was protracted compared to scapulothoracic neutral. Conclusion Slumped sitting is unlikely to increase nerve strain sufficient to cause changes to nerve function. However, shoulder protraction may place the median nerve at risk of injury, since nerve movement is reduced through the shoulder region when the shoulder is protracted and other joints are moved. Both altered nerve dynamics in response to moving other joints and local changes to blood supply may adversely affect nerve function and increase the risk of developing upper quadrant pain. | Background Non-specific arm pain (NSAP), often called repetitive strain injury, describes the common problem of upper limb pain and functional impairment without objective physical findings. The contributing factors to the development of NSAP are not fully understood but ergonomic guidelines commonly suggest that good upper body posture protects against NSAP (e.g. [ 1 ]). In a study of 485 NSAP patients, shoulder protraction and forward head position were reported in a majority of patients (78% and 71% respectively) [ 2 ]. Poor upper body posture (e.g. rounded shoulders, head forward) has also been reported to increase the incidence of neck and shoulder pain [ 3 ]. The possible mechanisms leading to pain in patients with postural malalignment have not been examined in any detail. The painful symptoms often associated with NSAP suggest a minor neuropathy involving at least in part the median nerve [ 4 ]. Together, shoulder protraction, forward head position and flexion of the trunk form the main components of slumped sitting. The present study examines the effects of each of these components on longitudinal sliding of the median nerve using high frequency ultrasound imaging in asymptomatic normal subjects. In addition, the effects of sustained protraction on nerve sliding through the shoulder region are examined. Methods Ultrasound imaging Longitudinal nerve movement was measured using high frequency ultrasound imaging, as previously described by Dilley et al. [ 5 , 6 ]. A Diasus ultrasound system (Dynamic Imaging, Livingston, Scotland, UK) was used to collect sequences of ultrasound images at 10 frames/second for 50 to 70 seconds, running at 10–22 MHz and using a 26 mm linear transducer. A cross-correlation algorithm was used to determine relative movement between adjacent frames in sequences of images [ 5 ]. The maximum correlation coefficient (r) was calculated for each pixel shift determining the relative movement between frames. To account for probe movement the same method was employed on deep stationary structures (eg. bone or interosseous membrane) and the result subtracted from the nerve excursion values. Subject details Fourteen healthy subjects, 5 male and 9 female, aged 25–38 years (mean = 32 years) were screened to exclude upper limb or cervical spine pathologies, rheumatological or neurological conditions. In each subject the nerve bed length was estimated, from the C6 spinous process to the tip of the index finger (mean = 97.0 cm (SD, 5.9)), and used to normalise the ultrasound transducer position between individuals. All measurements were taken from the right upper limb only. Set-up and procedure The median nerve was imaged in longitudinal section in the forearm during forward head position, trunk flexion and protraction and in the forearm and upper arm during contralateral neck side flexion (CNSF). Each movement was repeated three times, including some reverse trials. Forward head position Each subject (n = 8) was imaged in the proximal forearm whilst positioned upright on a chair fitted with a back and head support, hips and knees at 90° flexion, and the trunk fixed with Velcro strapping. The right upper limb was strapped to a Perspex plate in 90° flexion and 20° abduction at the glenohumeral joint, with the elbow fully extended, 45° forearm supination, and the wrist, hand and fingers in neutral. An active forward head position movement was performed, which included lower cervical spine flexion and upper cervical spine extension. Trunk flexion Each subject (n = 8) was imaged in the proximal forearm whilst positioned upright on a chair, with hips and knees at 90 degrees flexion. The right upper limb was positioned as for the forward head position trials. The subject was taught to actively flex their trunk whilst posteriorly tilting their pelvis. Protraction Each subject (n = 13) was imaged at two locations in the forearm and positioned as for the forward head position. The distal upper arm was imaged in three of the 13 subjects. For each trial the shoulder girdle was passively protracted from neutral (i.e. the scapulothoracic joint in neutral) by sliding the Perspex plate supporting the arm on an adjustable table. In three of these subjects, additional data was also obtained during ultrasound imaging. A potentiometer attached using strong thread to the acromion process allowed measurement of the amount of protraction. Protraction data was captured on to a PC and synchronised offline to the recorded ultrasound sequence. In four subjects, good quality images of the median nerve within the upper arm could be obtained. In the majority of subjects, it was difficult to acquire good quality images because of dense tissue overlying the nerve that reduced the image quality. From these images, nerve trunk bowing was measured in the distal upper arm with the shoulder girdle in the neutral and protracted positions. The maximum deviation of the nerve from a straight line across single ultrasound frames was measured offline in both positions and the difference used as a measure of additional bowing. Repeat trials were averaged. Contralateral neck side flexion Each subject (n = 11) was imaged in the distal forearm and distal upper arm, whilst lying supine with the right upper limb abducted to 90° at the glenohumeral joint. Ninety degree abduction at the glenohumeral joint rather than 90° glenohumeral flexion was used, so that the present data could be related to previous work [ 6 ] which has shown that median nerve movements can be reliably measured with the glenohumeral joint abducted to 90°. The examined limb was fixed to a Perspex plate using Velcro strapping with the elbow extended, forearm supinated and the wrist, hand and fingers in neutral. The head was supported on a movable plate, with the centre of rotation positioned at the C7 spinous process. In each subject, the neck was passively moved to 35° CNSF with (a) the scapulothoracic joint in neutral (i.e. relaxed lying in supine) and (b) in full protraction. This movement was repeated several times. In four subjects, a potentiometer attached to the plate allowed continuous measurement of the angle of CNSF. Joint angle data was captured on to a PC and synchronised offline to the recorded ultrasound sequence. Subject movement measurements For each procedure the range of movement was determined from pictures obtained using a digital camera. Changes in joint angle and distance for each movement were determined from skin surface markers and measured using either CorelDraw (Kodak Digital Science, USA) or "tpsDig" (F. James Rohlf, Department of Ecology and Evolution, State University of New York). Measurements for the individual components of slumped sitting are summarised in figure 1 . The posterior-anterior shift of the acromion was used as a measure of protraction during CNSF. Strain calculations Strain is defined by the difference in the amount of elongation that occurs at two points along a nerve divided by the distance between these two points. In practice strain was determined by using regression lines fitted to plots of nerve movement against the distance along the arm. Note that the strain estimates represent the additional strain produced by the movement rather than the total nerve strain. Statistical analysis Comparisons of nerve movement and strain in scapulothoracic neutral and protraction during CNSF were performed using paired t-tests. Results Median nerve movements in the arm in response to components of slumped posture Forward head position Moving the head forward while maintaining the shoulder and trunk position was tested in 8 subjects. This movement produced no detectable median nerve excursion in the forearm, the average trend being a movement of 0.1 mm (SEM, 0.02) occurring in a proximal direction. The repeat measure variability within subjects was very low, with a standard deviation ranging from 0–0.2 mm (mean = 0.1 mm). The mean change in the angle of lower cervical spine flexion and upper cervical spine extension was 23.6° (SD, 2.8) and 2.9° (SD, 1.9) respectively. Trunk flexion Trunk flexion also produced minimal median nerve excursion with a mean over 8 subjects of 0.1 mm (SEM, 0.1) proximal movement. The mean change in the angle of trunk flexion was 19.7° (SD, 4.7). Shoulder protraction In 13 subjects the median nerve moved in a proximal direction during shoulder protraction with more movement at proximal locations (mean in forearm = 3.5 mm (SEM, 0.3), mean in upper arm = 5.9 mm (SEM, 0.6)) (figure 2 [see additional file 1 for ultrasound sequence of median nerve sliding in the forearm]). The mean extent of scapular anterior translation was 38.3 mm (SD, 13). The additional strain on the median nerve was 0.7% (SEM, 0.3), given by the slope of the regression of nerve movement against distance along the arm (Figure 2 ). Median nerve excursion was measured in 3 subjects with simultaneous measurement of protraction. The results revealed an initial delay of 6.5–33.0 mm (mean = 17.0 mm; equivalent to 15.8–34.0% (mean = 23.7%) of the total protraction) before significant nerve movement occurred (see figure 3 ). After the initial delay, nerve movement was proportional to the extent of protraction. In three of four subjects, nerve bowing was observed in the upper arm with the shoulder girdle in the neutral test position. The maximum nerve course deviation from a straight line with the shoulder girdle in neutral compared to protraction was approximately 0.5 mm in all three subjects over the length of the ultrasound transducer, (26 mm). The nerve straightened during protraction (figure 4 ). Median nerve movement in the arm in response to contralateral neck side flexion (CNSF) with or without shoulder protraction In 11 subjects the median nerve moved in a proximal direction during 35° CNSF when the scapulothoracic joint was in neutral, as reported previously [ 6 ]. The movement increased at the more proximal location (scapulathoracic neutral, mean in upper arm = 2.3 mm (SEM, 0.2) and forearm = 1.5 mm (SEM, 0.2)). With the shoulder protracted, there was a 60% reduction in nerve movement in both upper arm and forearm locations (p < 0.05 for both locations) (mean in upper arm = 0.9 mm (SEM, 0.2) and forearm = 0.6 mm (SEM, 0.1)) (figure 5 ). The mean extent of protraction was 48.0 mm (SEM, 4.3). The additional strain on the median nerve was 0.3% (SEM, 0.1) in scapulothoracic neutral. There was a significant reduction in strain in protraction (0.1% (SEM, 0.1); p < 0.05, paired t-test). Median nerve excursion was measured in 4 subjects with simultaneous measurement of CNSF. The results revealed no obvious delay in the onset of nerve movement in protraction compared to scapulothoracic neutral. Despite less movement in protraction, the pattern of nerve movement mimicked that observed in scapulothoracic neutral (figure 6 ). Nine of 11 subjects reported paraesthesia in the distribution of the median nerve dermatone once the shoulder girdle was sustained in protraction. The onset of symptoms ranged from 1 to 4 minutes. Symptoms disappeared when the shoulder was repositioned in scapulothoracic neutral. Discussion Direct effects of the components of slumped sitting on median nerve movement Nerves are designed to slide and stretch to accommodate joint movement. Using the method of Dilley et al. [ 5 , 6 ], median nerve sliding was examined during the individual components of slumped sitting. Both forward head position and trunk flexion produced only minimal nerve movement in the forearm. The only examined component to produce substantial nerve movement was shoulder protraction. The median nerve strain in the forearm with protraction was 0.7%, which was well below the limits that cause changes to nerve function (reviewed in Grewel et al. [ 7 ]). As the shoulder girdle is protracted there is a delay in nerve movement, which is followed by a steady increase in nerve excursion. During this initial toe region the median nerve appears bowed in the upper arm. The nerve trunk appears to straighten as the range of protraction progresses. It therefore seems that with the upper limb in scapulothoracic neutral and the glenohumeral joint in 90° flexion, the median nerve is unloaded. If this is the case, the strain value of 0.7% will represent the total strain. Effects of protraction on the transmission of median nerve movement through the shoulder region The results for CNSF provide evidence for a possible restriction within the shoulder region during shoulder protraction. With the shoulder protracted there was a 60% reduction in the transmission of nerve movement through the upper limb. Consistent with a reduction in movement, there was also significantly less strain in the forearm. The possibility that the nerve becomes unloaded when the shoulder is protracted is unlikely since it had been found that protraction itself causes some median nerve stretch. In addition, there was no obvious delay in nerve movement in response to CNSF (Figure 6 ). The evidence for a restriction is consistent with previous suggestions that shoulder protraction may cause a neurovascular impingement within the shoulder region resulting in pain [ 2 , 8 ]. This suggestion was further supported by the experience of paraesthesia within the median nerve distribution during sustained protraction in 82% of subjects. These symptoms indicate the presence of a vascular restriction, which in turn affects neural function. Scapular protraction is a complicated movement, often resulting in the combined movement of numerous other structures within the shoulder girdle, including anterior displacement of the head of the humerus. It is therefore difficult to establish the precise cause of a neurovascular entrapment. Shortening of pectoralis minor and the downward displacement of the coracoid process might affect sliding of the cords of the brachial plexus. Alternatively, elevation of the first rib during full protraction (due to its soft tissue attachments with surrounding structures) might reduce the space between the clavicle and the first rib, restricting nerve sliding. Clinical significance The components of slumped sitting (i.e. forward head position, trunk flexion and protraction) are associated with poor posture [ 2 , 9 - 11 ], and are often adopted by office workers. Shoulder protraction is the only component of this posture to tension the median nerve, although the level of nerve strain in the forearm with the shoulder at 90° flexion and elbow extension, is not sufficiently high to result in direct neural injury. Problems are more likely to result from local effects of shoulder protraction on the chords of the brachial plexus. The present study shows that protraction restricts nerve sliding through the shoulder region. Most subjects also experienced paraesthesia when maintaining shoulder protraction plus elbow extension and shoulder abduction. Therefore, sustained shoulder protraction may place the median nerve at enhanced risk of injury and possibly cause a vascular compromise. This may in turn explain for the trend that a high number of NSAP patients have poor shoulder posture. (e.g. [ 2 ]). Conclusions The direct effects of slumped sitting on median nerve strain are not sufficient to alter nerve function. However, shoulder protraction does appear to restrict nerve sliding, and prolonged protraction leads to pareasthesias. Competing interests None declared. Authors' contributions AJ and RL participated in the study design, data collection, analysis and manuscript preparation. AD participated in the study design, analysis and manuscript preparation. BL participated in the study design and manuscript preparation. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: Supplementary Material Additional File 1 Median nerve sliding in the forearm during protraction. Ultrasound sequence of median nerve sliding in the distal forearm during protraction. The subject was imaged with the limb in 90° flexion and 20° abduction at the glenohumeral joint and elbow neutral. The median nerve can be seen to slide in a proximal direction as the shoulder is protracted. In a repeat of the sequence the nerve movement is tracked using cross-correlation analysis (yellow plus sign). The total nerve movement was 4.70 mm. Click here for file | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC503391.xml |
550656 | Ontological visualization of protein-protein interactions | Background Cellular processes require the interaction of many proteins across several cellular compartments. Determining the collective network of such interactions is an important aspect of understanding the role and regulation of individual proteins. The Gene Ontology (GO) is used by model organism databases and other bioinformatics resources to provide functional annotation of proteins. The annotation process provides a mechanism to document the binding of one protein with another. We have constructed protein interaction networks for mouse proteins utilizing the information encoded in the GO annotations. The work reported here presents a methodology for integrating and visualizing information on protein-protein interactions. Results GO annotation at Mouse Genome Informatics (MGI) captures 1318 curated, documented interactions. These include 129 binary interactions and 125 interaction involving three or more gene products. Three networks involve over 30 partners, the largest involving 109 proteins. Several tools are available at MGI to visualize and analyze these data. Conclusions Curators at the MGI database annotate protein-protein interaction data from experimental reports from the literature. Integration of these data with the other types of data curated at MGI places protein binding data into the larger context of mouse biology and facilitates the generation of new biological hypotheses based on physical interactions among gene products. | Background Protein networks Cellular processes require the interaction of many proteins across several cellular compartments. Interactions can range in stability from persistent, such as between members of a stable complex, to transient, such as binding while being phosphorylated. Determining the collective network of such interactions should provide insight into which processes the individual members participate, and how they may be regulated. Understanding protein interaction networks requires two steps. First, the interacting proteins must be identified, usually through some experimental methods. Secondly, the significance of the interaction networks needs to be assessed. Recently, there has been a focus on devising large scale screening methods to collect data on interacting proteins [ 1 - 3 ]. Additionally, several strategies have been used to predict networks based on small peptide interaction [ 4 ], analysis of co-evolution of protein families [ 5 ], analysis of orthology [ 6 ], and co-inheritance [ 7 ]. However, many of these types of studies are hindered by their inability to place the significance of the interaction networks in the broader biological context. In addition to the large screening efforts, a significant amount of specific protein-protein interaction data has been reported in the literature over the years. Quite often, these studies report on only a few interacting proteins. It is difficult to place these isolated, yet specific reports in the larger biological context and interconnect them with other data. Recently, there have been efforts to extract such literature-based interaction information using text mining [ 8 ], or combinations of text mining and other predictive methods [ 9 ]. These then can be integrated into larger protein-protein interaction datasets. The work reported here presents a methodology for integrating and exploring information on protein-protein interactions. Model organism databases Model Organism Databases (MODs) have been collecting diverse types of data about the genes and proteins from their respective organisms since the early 1990s ( e.g . [ 10 - 13 ]). The goal of these databases is to integrate information about these organisms, placing experimental data in the context of the biology of the organism as a whole. Biological information on gene sequence, function, tissue-specific and developmental expression, as well as associated genetic and mutant phenotype data is incorporated into these systems. The documentation of protein-protein interactions and the integration with other data types allows potential for determining the significance of the interactions and placing these molecular interactions into greater biological context. The Mouse Genome Informatics system (MGI) is the MOD for the laboratory mouse [ 14 ]. MGI integrates not only data used for GO annotation, but also data on a variety of aspects of mouse biology including gene sequence, orthologs, embryonic gene expression, alleles and their phenotypes, strains, and chromosome feature maps [ 15 , 16 ]. MGI provides highly curated information to the research community and to other bioinformatics resources [ 17 ]. GO annotation The Gene Ontology Consortium provides the biological community a structured vocabulary with which to enable consistent functional annotation of genes and gene products. [ 18 ]. Guidelines for the use of the GO vocabulary are provided by the Consortium [ 19 ]. Users of the GO are required to submit their annotations in a specified format, which is then made available to the public via the GO database [ 20 ]. Each annotation row lists the object being annotated, the GO term that is being assigned, an evidence code specifying the type of evidence that was used to make the assignment, and a reference. The format of the annotation includes the use of "modifier" fields which can be used either to modify the use of the term, or the use of the evidence code. One important modifier field is the "with" field. This field can be used to specify an external database link and provides the ability to qualify or support a given evidence code with a specific gene, nucleic acid sequence, protein sequence, or allele. In the course of over six years, curators at MGI have made 79690 annotations to 15231 gene products using 3742 GO terms (All database statistics used in this paper are from the MGI release as of 7/30/04). The curation policy focuses on experiments in which the murine protein gene product is investigated. Many of the detailed annotations have been added on a paper-by-paper basis using the MGI literature collection that contains primary experimental information about mouse genes from over 90,000 references. The accumulation and use of these papers in annotation has been, for the most part, undirected. However, the structure of the GO and the relationships among terms allow grouping of the gene products that share common annotations. Such strategies may reveal hitherto unsuspected relationships between these proteins. Annotation with "protein binding" "Protein binding" (GO:0005515), as used by the GO in the Molecular Function ontology, is defined as "interacting selectively with any protein or protein complex" [ 21 ]. This term has 70 sub-terms. A gene product can be annotated to "protein binding" using the IPI (inferred from physical interaction) evidence code and the "with" or "inferred from" field when the protein that it binds to has been specifically identified. In the case of the IPI evidence code, the "with" field requires a protein identifier, such as a SwissProt/Trembl ID (now UniProt). MGI curators use this evidence code to curate experimental evidence that demonstrates protein interactions An example of GO annotation that includes "protein-binding" is shown for the gene product of Ager . In the case of Ager (advanced glycosylation end product-specific receptor, Figure 1 ), Takaki et al . [ 22 ] have demonstrated that the murine AGER protein binds to SPTR:Q8BQ02, the protein encoded by Hmgb1 (high mobility group box 1). A curator at MGI has captured this information in an MGI GO annotation for Ager . For completeness, a curator also annotated the gene product of Hmgb1 with "protein binding" with an IPI to SPTR:Q62151, the protein product of Ager , using the same reference. In this case, these are the only "protein binding" annotations for either of these proteins. These annotations represent an experimentally tested interaction of two proteins. Beyond this specific reference, either of these two proteins could have further annotations from separate experiments reported in other references reporting binding to other proteins, which in turn have been annotated to binding to still others, thereby outlining a network of protein interactions. An example of a simple network is shown in Figure 2 . The protein product of Hcph (hemopoietic cell phosphatase), has been shown to bind both the protein product of Jak2 (Janus kinase 2) ([ 23 ]) and Klrb1b (killer cell lectin-like receptor subfamily B member 1B) ([ 24 ]). JAK2 not only binds HCPH ([ 23 ]), but also SOCS1 (suppressor of cytokine signaling 1) [ 25 ], which in turn has been shown to bind PIM2 (proviral integration site 2) ([ 26 ]). KLRB1B has been demonstrated to bind OCIL (osteoclast inhibitory lectin) ([ 24 ]), which binds KLRB1D (killer cell lectin-like receptor Subfamily B member 1D) [ 27 , 24 ]. Thus, a seven member "network" has been described by integrating the data several independent investigations. MGI has presently 1851 genes annotated to the term GO:0005515, "protein binding", or its sub-terms. These genes have 2247 annotations to this term, indicating that some of the gene products must bind more than one protein. These annotations were made independently over the years as curators entered data reference by reference. By collecting all of these annotation pairs, and identifying shared partners, it is possible to search for the presence of more complex networks that were not necessarily identified in each original piece of research literature. Results & discussion Discovery by inference Figure 3 shows all 1318 annotated interactions captured by GO annotation. These include 129 binary interactions, and 125 interaction sets of three or greater. Figure 4 displays some of the associations in more detail. Figure 4A displays three sets of heterodimers. Figure 4B shows interactions among three proteins. Note the loop-back in the case of TIMELESS. This indicates that the protein forms a homodimer. Many of the annotation networks depict interactions among the subunits of protein and or riboprotein complexes. For example, Figure 4C shows the interactions of Cops (constitutive photomorphogenic) proteins homologs. These have been shown to assemble into a "signalosome complex" (GO:0008180) [ 28 ]. Thus, the GO data implicitly reveals connections among the many separate annotations to "protein-binding" made over the course of collecting data at MGI. Utilization of the interaction web to infer biological process information for experimentally uncharacterized genes (guilt by association) There are instances in the annotations where a protein product has been shown to be able to bind another protein, but otherwise, nothing is known about the biological role of the protein. In these cases, MGI curators make an annotation to "protein binding", but also use a special annotation to indicate that nothing is known about the cellular location (GO:0008372, "cellular_component unknown") of the gene product or the process it is involved in (GO:0000004, "biological_process unknown"). A simple example is seen in the case of TIPIN (timeless interacting protein) (Figure 3B ). It has been shown to bind the protein product of Timeless , a homolog of the Drosophila gene [ 29 ]. However, GO annotation of Timeless indicates that it is involved in biological processes of lung development and branching morphogenesis [ 30 ], and thus we would predict that Tipin , which is currently annotated to "biological_process unknown" might also play a role in these processes. Additionally, the Gene Expression index in MGI indicates that the Tipin is expressed in similar spatial and temporal patterns as Timeless , supporting the hypothesis that Tipin may be involved in similar processes. that the interaction may be significant [ 29 ]. These inferences can form the basis for directed experiments, such studying the effects of antisense RNA inhibition, as has been done for Timeless [ 30 ]. Cellular location may also be inferred from protein interactions. SOCS1 (suppressor of cytokine signaling 1) has "kinase inhibitor activity" (GO:0019210) and has been implemented in the "cytokine and chemokine mediated signaling pathway" (GO:0019221), and the JAK-STAT cascade (GO:0007259). However, its cellular location has not been documented in the available mouse literature. Analysis of the SOCS1 protein using predictive software such as Psort [ 31 ]) and SubLoc [ 32 ] predict that SOCS1 is a nuclear protein. However, there is as yet no direct evidence that this is so. The murine SOCS1 binds to JAK2 (Figure 3D [ 26 ]) which has been reported to be localized to the cytoplasm [ 33 ]. Therefore, we might expect that SOCS1 may also be localized to the cytoplasm. So, algorithmic evidence predicts that SOCS1 may also be localized to the nucleus and to the cytoplasm. These two independent predictions could stimulate investigations by direct experimentation. Although these types of analyses can be repeated for several proteins, their utility becomes unwieldy when analyzing networks larger than a few components. Analysis of larger interaction sets Three networks involve over 30 partners, the largest involving 109 proteins (Figure 5 ). Can we draw any inferences from these networks? Do they have anything in common? Several tools are available for using the GO in analysis and visualization of groupings of genes with respect to additional parameters after they have been selected by an experiment method, such as a microarray analysis, etc. In this case, our "method' is the mining of documented measurements of protein binding. These tools include GO_Term_Finder and GO_Slim Chart Tool) [ 34 ] Figure 6 ). The GO_Slim Chart Tool bins sets of genes based on shared annotations to specific predefined GO subtrees. It therefore reveals to a User the annotations that their genes have in common. The GO_Slim used for this study is summarized at the following site [ 35 ]. For the set of 109 proteins shown in figure 5A fifty-one of the gene products have annotations that fall into the "signal transduction" bin (Figure 6A ). A number of the gene products in Figure 5B have been annotated to processes involved in proliferation (twenty proteins) and protein metabolism (seventeen), and twenty-two are nuclear (Figure 6B and 6C ). Finally, fifteen of the gene products in the third largest set are involved in transport (Figure 6D ). In all of these cases, one might begin to develop hypotheses to test whether the unannotated members of the networks may be involved in these processes. Tools such as GO_Term_Finder [ 36 ] and its graphical counterpart Vlad [ 37 ] can be useful in finding commonality as well suggesting additional information about the roles of proteins in the cell which could be then tested experimentally. GO_Term finder computes the significance of the annotations for a selected set of genes within an annotation set compared to all the annotations of the entire set using a hypergeometric distribution algorithm. In this study, the entire set is the set of all genes in MGI with GO annotation. For example, for the 109 gene products shown in Figure 5A , thirty-two have process annotations for signal transduction or one of its subterms (p < 1.0E-23), suggesting that the interaction of the proteins may depict a large signal transduction network. Thirty-six of 109 gene products currently have either no annotation to the process ontology, or are annotated to "biological_process_unknown". These proteins may also be involved in the process of signal transduction. Seventeen the proteins depicted in the 40-member network (Figure 5B ) have been annotated to "regulation of the cell cycle" (GO:0000074, p < 1.0E-26). Therefore 1190002H23Rik is likely involved in regulation of the cell cycle. Further support for this is that this protein has been annotated to be involved in the "cell cycle" based on sequence similarity to human RGC32 [ 38 ]. Finally, twelve of the proteins displayed in Figure 5C have annotations to exocytosis or its children in common (GO:0006887, p < 1.0E-23). The networks suggested by the collection of annotations to this GO term involve interactions that are more or less stable under experimental conditions. A gene product is shown to have protein binding activity by a variety of direct assays such as yeast two-hybrid screening [ 39 ], co-immunoprecipitation and other immunoaffinity methods [ 40 ], GST-or other tag pull-down assays [ 41 ], fluorescence resonance transfer [ 42 ], or other direct measurements [ 43 ]. Due to the nature of some of the assays, caution must be taken when attributing significance. For example, false positives may obtained from yeast two-hybrid assays for a variety of reasons [ 44 ]. Therefore, confirmation by other methods, such as co-immunoprecipitation, may strengthen the likelihood of the implied interaction. Currently, the GO annotation does not allow for the capture of any distinction among these assays, with the result that they are all included together. Despite these serious considerations, large data sets can be effectively examined using these procedures and the results can provide a basis for directed hypotheses and experimentation. Integration with MGI The Mouse Genome Informatics system integrates not only data used for GO annotation, but also data on a variety of aspects of mouse biology including embryonic gene expression, alleles and their phenotypes, and chromosome location. The integration of these datasets allows for complex queries, such as "list all genes expressed in the liver at Tyler Stage 15, located on chromosome 12, annotated to "protein binding" AND "nucleus". The integration of protein-protein network visualization into such queries can aide in determining the significance of more complex interaction networks. By combining the above query with our graphical tools, it is possible to get a graphical view of all protein interaction networks in the nucleus of a 9.5 dpc mouse embryo. As annotation progresses and becomes more complete, these types of queries will become more and more informative. During the generation of the interaction sets, it was found that programs such as Graphviz, could easily visualize missing annotations based on the interaction of two proteins. When information about a protein comes from different sources, a curator that is curating a single reference may not necessarily record all of the information implied by a physical interaction, such as cellular location in the example above. Views such as Graphviz can help curators to spot missing data and they may at some point be useful in themselves to display annotations. MGI curators aggressively adopted the use of the "with" field when annotating to "protein binding" during the early stages of annotation efforts at the database. Similar networks may also be mined from the GO data sets available from the other model organism databases participating in the GO. Recently, Lehner and Fraser used GO annotation to analyze a human interaction set predicted from orthology to yeast, Drosophila , and C. elegans interaction sets [ 45 ]. The GO is used by many species-specific organism databases to annotate gene products. The use of these annotation sets to construct species-specific interaction will compliment curated interaction resources such as BIND [ 46 ] and HPRD [ 47 ] to guide hypothesis generation in suggesting specific experimental investigations. Conclusions We have demonstrated that functional annotations curated via GO hierarchies can be used to obtain a summary set from independent annotations to "protein-binding" to form protein-protein interaction networks. The members of these protein-protein interaction sets can be further examined for additional shared GO annotations. Integration of these data with the other types of data curated at MGI places protein binding data into the larger context of mouse biology and will aid in the discovery of new biological knowledge based on physical interactions among gene products. Methods Gene annotations for protein binding interactions are made by manual inspection of published literature. In every case, experimental evidence is supplied in the manuscript to support the interaction that is reported. Annotation of genes to other GO terms is made by a variety of methods including the conservative translation of functional information contained in SwissProt protein records, conservative inference from InterPro domains, and manual curation of the published literature. Data was obtained from the Mouse Genome Informatics system by use of custom SQL queries to collect all markers that had been annotated to "protein binding" or its children using the IPI evidence code. The protein sequence identifier in the "inferred from field" was matched to the appropriate gene in the database. The final output consisted of a two-column file with column 1 being the first protein, and column 2 the protein it binds. This formed the basic data set that was passed to Graphviz [ 48 ] for display. Additional Perl scripts were used to separate out each individual network. The two column lists were also used as the basis for data files listing all unique genes in each network. These were then used for input files for GO_Slim Tool [ 34 ] and GO_Term finder [ 36 ]. These files are available on the MGI ftp site . GraphViz on the Macintosh OS X platform is a product of Pixelglow [ 49 ]. GraphViz is an open source program made available by ATT [ 50 ]. Authors' contributions HJD conceived of and designed this study and implemented the graphical displays using Graphviz and analyzed the significance of the results. He has also been involved in contributing to the GO annotations. CH devised the Perl scripts used to parse out individual interaction sets and removal of redundancies. DPH helped to draft the manuscript and contributed to the annotations. JAB has been involved in the overall design of the GO project at MGI and of gene annotations in MGI overall, and has added critical revisions for important intellectual content to this paper. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC550656.xml |
517496 | Coactivators p300 and PCAF physically and functionally interact with the foamy viral trans-activator | Background Foamy virus Bel1/Tas trans -activators act as key regulators of gene expression and directly bind to Bel1 response elements (BRE) in both the internal and the 5'LTR promoters leading to strong transcriptional trans -activation. Cellular coactivators interacting with Bel1/Tas are unknown to date. Results Transient expression assays, co-immunoprecipitation experiments, pull-down assays, and Western blot analysis were used to demonstrate that the coactivator p300 and histone acetyltransferase PCAF specifically interact with the retroviral trans -activator Bel1/Tas in vivo . Here we show that the Bel1/Tas-mediated trans -activation was enhanced by the coactivator p300, histone acetyltransferases PCAF and SRC-1 based on the crucial internal promoter BRE. The Bel1/Tas-interacting region was mapped to the C/H1 domain of p300 by co-immunoprecipitation and pull-down assays. In contrast, coactivator SRC-1 previously reported to bind to the C-terminal domain of p300 did not directly interact with the Bel1 protein but nevertheless enhanced Bel1/Tas-mediated trans -activation. Cotransfection of Bel1/Tas and p300C with an expression plasmid containing the C/H1domain partially inhibited the p300C-driven trans -activation. Conclusions Our data identify p300 and PCAF as functional partner molecules that directly interact with Bel1/Tas. Since the acetylation activities of the three coactivators reside in or bind to the C-terminal regions of p300, a C/H1 expression plasmid was used as inhibitor. This is the first report of a C/H1 domain-interacting retroviral trans -activator capable of partially blocking the strong Bel1/Tas-mediated activation of the C-terminal region of coactivator p300. The potential mechanisms and functional roles of the three histone and factor acetyltransferases p300, PCAF, and SRC-1 in Bel1/Tas-mediated trans -activation are discussed. | Background In the sequential model of transcriptional regulation including viral trans -activation, coactivators CBP/p300 require concerted action of multiple protein factors provided the nucleosomal structures allow access to the DNA template [ 1 - 3 ]. The factors that interact with coactivators encompass sequence-specific DNA binding activators, non-DNA binding coactivators, and essential components of the basal transcriptional machinery. Two large, closely related human proteins, p300 and CBP, were identified and shown to function as versatile signal integrators of many transcription factors to facilitate transcriptional activation or repression, and, in addition, as connectors of multiple transduction pathways. Both proteins contain several conserved domains that include three Cys/His-rich (C/H1, C/H2, and C/H3) domains, the histone acetyltransferase (HAT), KIX, and Gln-rich (Q) domains among others (Fig. 1 ) [ 2 , 4 , 5 ]. It is mainly due to these domains that a plethora of transcriptional activators interact with p300/CBP. Thus, p300/CBP coactivators act as a physical and functional scaffold or bridge between various cellular or viral trans -activators and the basal transcriptional machinery. Both proteins function by mediating positive or negative cross talk between different signaling pathways and participate in fundamental cellular processes that include embryonic development, cell growth, differentiation, and apoptosis. In addition, they can act as tumor suppressors and, last but not least, directly interact with diverse viral trans -activators to facilitate virus replication or viral activator-mediated transformation [ 6 , 7 ]. Figure 1 Coactivator p300 enhances Bel1/Tas-mediated trans -activation. Simplified schematic drawing of p300 domains and structure of deletion mutants (not drawn to scale). Characterized domains of p300 [2, 5] relevant for this report are highlighted by differently marked boxes: the FLAG epitope upstream of the N-terminus of p300 is marked by shaded boxes in constructs no. 1 through 8, the C/H1 and KIX domains by black and striped boxes, respectively. Numbers above the rectangles indicate the number of amino acids of p300 and its derivatives. The intrinsic HAT domain of p300 is depicted between the C/H2 and C/H3 domains; additional HATs PCAF and SRC-1 reported to associate with the distinct p300 regions [8, 24, 25] are indicated as rectangles above p300. The domain marked "Q" is the glutamine-rich domain of p300. Construct no. 5 has a deletion between the C/H1 and KIX domain (broken line). Constructs no. 9 and 10 contain the GST-C/H1 and GST-ΔC/H1-KIX fusion proteins; partial shadings mark GST regions. Several models have been proposed to explain transcriptional activation. Coordinated recruitment of coactivators by diverse transcriptional activators to specific promoter target sites has been shown by a collective effort of many groups [ 8 ]. According to the models, different coactivators either modify chromatin structure by altering the nucleosomal DNA thereby affecting its accessibility to DNA-binding proteins or, alternatively, form complexes with HAT activities that by acetylation of specific Lysines in histone N-terminal tails weaken interactions between DNA and the histone octamer. Moreover, the HAT activities of some coactivators acetylate non-histone substrates such as viral and cellular trans -activators, for instance p53 [ 9 ]. Diverse viral trans -activator proteins were found to interact with distinct domains of p300/CBP and PCAF. Prominent among them are the early adenoviral E1A antigen [ 10 , 11 ], Epstein-Barr virus protein EBNA-2 [ 12 ], and human T-cell leukemia virus oncoprotein Tax [ 13 , 14 ]. Several other modifications including methylation, phosphorylation, and ubiquitination lead to either diminished or increased DNA binding of the activators that, in turn, will result in either a repression or activation of gene expression. In addition, both coactivators were reported to interact with additional HAT enzymes, namely PCAF and SRC-1 [ 15 , 16 ]. The apparently nonpathogenic primate foamy viruses (PFV) show a wide host range and tissue tropism and have been developed into vectors that efficiently transduce SCID-repopulating cells [ 17 ]. The PFV Bel1/Tas protein has been characterized as a transcriptional trans -activator of the acidic class and is known to directly interact with its responsive elements (BRE) [ 18 , 19 ]. Bel1/Tas is a nuclear protein and acts as the key regulator absolutely required for virus replication. The minimal Bel1-specific DNA target site is 27 base-pairs long and located within the internal promoter (IP.BRE) upstream of the second cap site that is part and parcel of the second PFV transcription unit [ 20 ]. Additional Bel1/Tas DNA target sites in the LTR region of the PFV DNA genome were not analyzed in this study. The acidic trans -activation domain (TAD) was mapped to the C-terminus of Bel1 with little if any protein homology to other FV Bel1/Tas proteins from different species. Previously, we identified the nuclear factor 1 (NF1) as a repressor of Bel1/Tas-mediated trans -activation [ 21 ]. This repression was due to the fact that the specific family members NF1-C and -X interacted with parts of the IP.BRE and its flanking sequences. Since the NF1-mediated repression of the promoter of mouse mammary tumor virus was abrogated by distinct coactivators [ 22 ], we investigated which of the known coactivators and HAT proteins were capable of interacting with the PFV Bel1/Tas activator in the context of the IP.BRE promoter that is absolutely required for virus replication [ 20 ]. Here we report that the Bel1/Tas DNA binding protein functionally interacted with p300 and with the well-known HAT factor PCAF. In addition, SRC-1 enhanced Bel1/Tas trans -activation. This is the first time that these cellular coactivators have been shown to interact with the Bel/Tas1 trans-activator protein. Furthermore, Bel1/Tas binding to the C/H1 domain of p300 and coactivator-driven trans -activation seem to follow a unique pathway. Results Coactivator p300 enhances Bel1/Tas-mediated activation To examine whether p300 (Fig. 1 , first line) enhanced the ability of Bel1 to trans -activate the Bel1 internal promoter (IP.BRE), transient reporter gene assays were performed. The IP.BRE that extends from -1 to -192 of the second cap site of the PFV genome was cloned into the pGL3-pro-luc reporter plasmid [ 21 ]. The results of the luc assays showed that full-length p300FL enhanced Bel1/Tas-mediated activation in an apparently nonlinear fashion (Fig. 2 , upper left panel). To monitor the expression level of the p300 protein, Western blot analysis was carried out in parallel with increasing concentrations of the coactivator at fixed concentrations of the pbel1s expression plasmid that carries the retroviral trans -activator under the control of the CMV-IE promoter. The results of immunoblotting shown in the lower left panel of Figure 2 revealed that Bel1/Tas was expressed at similar levels, as expected, and p300FL expression levels proportional to the input. In parallel experiments, truncated p300 forms, p300N and p300M, were also assayed and yielded moderate levels of enhancement of Bel1/Tas-mediated activation lower than those of p300FL (Fig. 3 ). Unexpectedly, the most extensive level of enhancement of Bel1/Tas-induced activation was reached with the C-terminal region p300C (Fig. 2 , upper right hand panel). Again, Western blot analysis of p300C showed that expression levels of p300C protein increased proportionately to the transfected plasmid DNA while Bel1/Tas expression levels were unchanged (Fig. 2 , lower right panel). The precise boundaries of the three p300 versions used are shown in Figure 1 . The p300 bands marked by arrows are likely due to modified p300 proteins that are known to be modified by phosphorylation, acetylation and sumoylation (2, 5, 26). We next sought to determine whether the enhancing effect of p300 on Bel1/Tas-mediated activation was due to a physical interaction with the Bel1/Tas protein. Figure 2 Coactivator p300 enhances Bel1/Tas-mediated activation . Transient expression assays were performed with pGL3-luc plasmids containing the internal PFV BRE promoter (-1 to -192) after transfection of the pCMV-bel1s expression plasmid alone or separate cotransfection with p300FL and p300C expression plasmids [4]. Normalized luc activities are shown as fold activation in upper panels (for details, see Methods). Expression levels of Bel1/Tas and p300 proteins after cotransfection of 293T cells with increasing p300FL and p300C DNA concentrations and pbel1s DNA of 1.0 μg. Aliquots of the cellular lysates used for luc assays shown in upper panel were in parallel subjected to immunoblot analysis with monoclonal antibody against the FLAG epitope of the p300FL protein (lower left panel), and against the p300C protein (lower right hand panel). Polyclonal antibody against Bel1 was used for Bel1/Tas expression (bottom lanes in lower panels). Figure 3 Reporter gene assays of shortened p300 expression plasmids . Transient expression assays were done as described under Fig. 2 except for that cotransfections were carried out with p300N and p300M plasmids; for boundaries of p300, see Fig. 1. Bel1/Tas interacts with p300 in vivo To examine whether the coactivator p300 interacts with the retroviral activator Bel1/Tas, binding of p300FL to the Bel1/Tas protein was analyzed by immune precipitation. 293T cells were cotransfected with the full-length p300FL and pbel1s expression plasmids and metabolically labeled with [ 35 S]-Methionine and -Cysteine. Cellular lysates were precleared and subjected to immunoprecipitation with an antibody directed against the Bel1/Tas protein except for that in lane 1 (Fig. 4 ). After separation by SDS-PAGE and exposure, the resulting autoradiogram showed that the Bel1-specific antibody had effectively precipitated the p300FL-Bel1/Tas protein complex at both p300FL DNA concentrations of 10 μg (Fig. 4 , lane 3) and 20 μg (lane 6). In the control where pbel1s was omitted the immune precipitation did not reveal any band comparable in size to p300 (lane 4). In the immunoprecipitation shown in lane 1 instead of an antibody against Bel1/Tas, an antibody against p300 was used and showed that the bands marked in Fig. 4 was p300 (lane 1). Figure 4 Direct physical interaction of Bel1/Tas with p300 determined by co-immunoprecipitation . Bel1/Tas binds to p300 in vitro and in vivo . After transfection of 293T cells with expression plasmids pbel1s and p300FL, cells were labeled with [ 35 S]-Methionine plus [ 35 S]-Cysteine. Cellular extracts were precipitated, separated by SDS-PAGE and exposed. Protein p300FL is marked by bold arrowhead (lanes 1, 3, and 6). In the control, pbel1s was omitted (lane 4). Arrowheads indicate the following protein size markers (M, lanes 2 and 5): myosin of apparent molecular mass of 236, phosphorylase b of 97, and BSA of 66 kDa. To check the data obtained, co-immunoprecipitations were performed with non-labeled 293T cells followed by Western blot analysis. Cellular lysates were prepared from 293T cells separately cotransfected with pbel1s and each one of the three truncated p300 expression plasmids, p300N, p300M, or p300C and analyzed as described above. A polyclonal antibody directed against the Bel1/Tas protein was used in the co-immunoprecipitation followed by immunoblotting with a monoclonal antibody directed against the FLAG epitope fused in-frame with the N-terminus of the three truncated p300 versions. The results shown in Figure 5 indicate that p300N protein specifically interacted with Bel1/Tas (lane 1) whereas the middle and C-terminal regions, p300M and p300C, respectively, do not seem to bind to Bel1/Tas under the conditions used (Fig. 5 , lanes 3 and 5). This experiment was repeated several times and yielded the same result. In the controls, pbel1s was omitted in the cotransfections (lanes 2, 4, and 6). Figure 5 Analysis of physical interaction of Bel1/Tas with truncated forms of p300. After cotransfection of 293T cells with 2 μg pbel1s, and, separately with 2 μg each of p300N, p300M, and p300C expression plasmids, cellular extracts were prepared in parallel, divided into equal parts, and analyzed by co-immunoprecipitation and Western blotting. Lysates were precipitated with anti Bel1/Tas antibody and the Western blot was developed with a monoclonal anti-FLAG antiserum. The three arrows in the middle panel mark the correct sizes of the p300N, p300M, and p300C proteins (two blots pasted together). Controls for each p300 plasmid without pbel1s are in lanes 2, 4, and 6. Lower part confirms that the p300N protein specifically binds the Bel1/Tas protein (lane1, marked by arrow); two blots pasted together. Mapping of the p300-Bel1/Tas interaction domain We next determined which N-terminal p300 domain was responsible for the specific interaction with the Bel1/Tas protein. Different truncated versions of p300N (Fig. 1 ) were prepared, cloned, and subjected to separate immunoprecipitations and Western blot analyses as described above for p300N. In addition, two different GST fusion proteins that contained either the C/H1 or KIX domain were bacterially expressed, purified, and analyzed (Fig. 1 ). Two different expression plasmids p300N-C/H1-ΔKIX that lacked the KIX but still expressed the C/H1 domain were capable of binding Bel1/Tas (Fig. 6 , right panel, lanes 2 and 3). In contrast, the results shown in Fig. 6 , lanes 4 and 5, revealed that the plasmid p300-ΔC/H1-ΔKIX that lacks both the C/H1 and KIX domains but retains the short N-terminal region of 196 amino acids did not bind Bel1/Tas. The expression levels of the five constructs were monitored by immunoblot analysis and exhibited the expected bands of p300-derived proteins (Fig. 6 , left panel). Figure 6 Mapping of the p300 Bel1/Tas-interaction domain . The p300 expression plasmids, constructs no. 2, and 5 to 8 shown in Fig. 1 were analyzed by immunoprecipitation as described above for p300N. The right hand panel shows the results of immunoblotting obtained after reaction with the monoclonal antibody against the FLAG epitope; arrows and asterisks mark the Bel1/Tas-interacting protein bands from cellular extracts of pbel1s-cotransfected cells (lanes 1–3); intentionally overexposed to visualize the marked bands in lanes 1 and 2. The left panel presents the Western blots of the five recombinant p300N plasmids used for separate co-immunoprecipitations. Numbers in brackets refer to Fig 1. To unambiguously demonstrate that the C/H1 domain of p300 is the Bel1/Tas-interacting region, we performed pull-down assays with two pGST-C/H1 and pGST-ΔC/H1-KIX fusion proteins (Fig. 1 , constructs no. 9 and 10). The results revealed that purified pGST-C/H1 clearly interacted with Bel1/Tas as shown in Fig. 7 , lane 2 whereas the pGST-ΔC/H1-KIX and a control GST plasmid did not (lanes 1 and 3). Figure 7 Bel1/Tas interacts with GST-C/H1 fusion proteins detected by GST pull-down assays . The recombinant GST-C/H1 and GST-ΔC/H1-KIX proteins were expressed in E. coli BL21 cells and purified by binding to glutathione Sepharose 4B. Each GST-fusion protein bound to glutathione Sepharose 4B was separately mixed with lysates obtained from pbel1s-transfected 293T cells. After incubation and extensive washing with the binding buffer, bound proteins were eluted, separated by SDS-PAGE, and visualized by staining (upper panel); immunoblotting was carried out with an antibody against Bel1/Tas protein (lower panel). To summarize this part, our data show that p300 physically interacted with Bel1/Tas in vivo , and that the C/H1 domain of p300 was responsible for this interaction at least in vitro . Effect of the Bel1-C/H1 domain on Bel1/Tas-mediated activation by p300C To gain more insight into the mechanism of the C/H1-Bel1 complex that affects3 p300C-mediated activation, cotransfections of pbel1s and p300C with expression plasmid p300N-C/H1-ΔKIX were carried out (Fig. 1 , construct 6). Cellular lysates of 293T cells were prepared and luciferase assays performed. Cotransfections of fixed concentrations of the pbel1s, 0.5 μg, with the pC/H1-ΔKIX expression plasmid did not enhance p300C-mediated trans -activation (Fig. 8 , lanes 5, 7, 9, 11, and 13). In contrast, the expression of the C/H1 domain resulted in a partial suppression of the p300C-driven activation at higher C/H1 domain concentrations (Fig. 8 ). Similar degrees of inhibition were obtained when lower Bel1/Tas concentrations were used. Western blot analysis was carried out in parallel with increasing concentrations of the coactivator to ascertain Bel1/Tas expression (data not shown). The inhibition by the C/H1 domain explains why the level of trans -activation of p300N and p300FL did not reach the full extent of p300C-driven activation. Figure 8 Effect of p300-C/H1 domain expression on enhancement of p300C-mediated trans -activation. Transient expression assays with pGL3-luc containing the internal BRE promoter after cotransfection of the p300C and pC/H1 expression plasmids with pbel1s. PCAF interacts with Bel1/Tas We next analyzed whether different HAT-expressing genes such as GCN5, PCAF, and SRC-1 were able to enhance and interact with Bel1/Tas. Transient luciferase gene assays with pGCN5 expression plasmids did not affect Bel1/Tas-mediated activation (data not shown). In contrast, when the HAT PCAF expression plasmid was used for co-expression, an enhancement of Bel1/Tas-induced activation was detectable at a concentration of 0.02 μg PCAF DNA and 0.5 μg pbel1s (Fig. 9 , upper panel). At higher PCAF DNA concentrations, repression of Bel1/Tas-mediated activation was observed. When the levels of PCAF and Bel1/Tas protein expression was determined by Western blot analysis, a decreased level of the Bel1/Tas expression was detected that was likely due to degradation (Fig. 9 , lower panel). The band of the PCAF protein corresponded to the correct size of about 95 kDa (marked by arrow). Figure 9 Functional and physical interaction of Bel1/Tas with PCAF . A , Transient expression assays with pGL3-luc containing the internal BRE promoter after cotransfection of the pCI-FLAG-PCAF with 0.5 μg pbel1s expression plasmid (upper panel). Aliquots were subjected to Western blot analysis (lower panel) We next analyzed the potential interaction between the Bel1/Tas and PCAF proteins by carrying out co-immunoprecipitation with 293T cellular lysates that had been cotransfected with 2.0 μg pbel1s and pCI-PCAF expression plasmids, followed by immunoreaction with an antibody against Bel1 and subsequent immunoblotting with an anti-FLAG antibody to detect PCAF. The results showed that PCAF did indeed interact with the PFV Bel1/Tas activator (Fig. 10 , lane 2). As control, an immunoprecipitation and Western blot analysis was performed with the PCAF plasmid in the absence of pbel1s (Fig. 10 , lane 1). The result showed that a PCAF band was not detectable under the conditions used. Figure 10 Physical interaction of PCAF with Bel1/Tas. Co-immunoprecipitation and immunoblot analysis of pCI-FLAG-PCAF-cotransfected 293T cells with pbel1s DNA (lane 2). The cellular lysates were incubated with an anti Bel1/Tas antibody and treated as described in the legend to Fig. 5 except for that an antibody directed against the FLAG epitope of PCAF was used for immunoblotting. In the control, cotransfection was done without pbel1s (lane 1). To determine if an additional HAT protein, SRC-1, affected Bel1/Tas-mediated activation, transient reporter gene expression assays were carried out after cotransfection of 293T cells with the coactivator SRC-1a and 0.5 μg pbel1s. Surprisingly, a relatively strong enhancement of Bel1/Tas-mediated activation was detected (Fig. 11 , upper panel). The level of expression of both SRC-1a and Bel1/Tas proteins was determined and found to be approximately proportional to the input (Fig. 11 , lower panel). Figure 11 Coactivator SRC-1 enhances Bel1/Tas-mediated trans -activation . Reporter luc gene expression assays with pGL3-luc plasmids containing the internal PFV BRE promoter after cotransfection of pCR3.1-FLAG-SRC-1a with the pbel1s expression plasmid (upper panel). Immunoblotting of 293T cellular extracts cotransfected with the SRC-1a and pbel1s expression plasmids (lower panel). To assess whether SRC-1a was able to physically interact with the Bel1/Tas protein, immunoprecipitations and Western blot analysis were carried out under different conditions. The specificity of SRC-1a was ascertained by using monoclonal antibody directed against SRC-1a in control reaction. However, evidence for an interaction between the SRC-1a protein and Bel1/Tas was not obtained. Discussion It was previously reported that Bel1/Tas is capable of inducing the expression of many cellular genes [ 23 ]. While it is known that Bel1/Tas binds directly and to a large number of DNA target sites [ 18 - 21 ], the mechanism of activation and the identity of the cellular partner molecules of Bel1/Tas remained unknown. As a first step, we have sought to identify the cellular proteins that interact with the PFV retroviral trans -activator and mediate its activating potential. The data presented here show that the coactivators p300 and HAT PCAF physically bound Bel1/Tas in vitro and both enhanced Bel1/Tas-mediated activation whereas SRC-1 enhanced with Bel1/Tas activation without direct binding. According to our data, Bel1/Tas specifically interacted with the C/H1 domain of p300, although we cannot rule out binding to other p300 domains with much lower affinity not detectable under the rather harsh conditions of co-immunoprecipitation used here. When the levels of the relative luciferase activity of p300FL and its three shortened versions are compared, it is noteworthy that p300C reached the highest level of enhancement of Bel1/Tas-mediated activation (Fig. 2 ). Besides its intrinsic HAT activity, p300C contains both the intact C/H3 and Q domains that interact with the HAT enzymes PCAF and SRC-1, respectively [ 10 , 24 , 25 ]. These three HAT enzymes are likely responsible for the large enhancement of the observed trans -activation either directly by acetylation of Bel1/Tas or indirectly by histone acetylation, or both. In contrast to p300C, p300N and p300M do not possess any HAT activities nor do they bind to HAT-containing interaction partners. On the other hand, it is well known that p300 and its three subregions bind a plethora of various partner molecules leading to either activation or repression of transcription. Since the high level of p300C-mediated activation was partially inhibited in cotransfections with C/H1 and pbel1s, competition for Bel1/Tas between the C/H1 and the p300C-terminal interaction partners PCAF and SRC-1 cannot be ruled out so that both direct and indirect mechanisms might be responsible for the relative increase in Bel1/Tas-mediated activation by p300C. When Bel1/Tas binds to the C/H1 domain, the degree of Bel1/Tas acetylation may be much lower, since the protein surface of Bel1/Tas may be occluded and, hence less accessible. Some residual Bel1 acetylation might still occur by endogenous p300 and PCAF. Other factors might play additional roles. The ability of full-length p300 to trans -activate Bel1/Tas was relatively low for two reasons. First, the transfection efficiency of the full-length p300 is very low because of its large plasmid size, and the concentration of endogenous p300 is limiting. Secondly, the activation loop of p300 HAT is not fully activated by auto-acetylation as required for full trans -activation [ 26 ]. Since the relative activation by SRC-1 was not as high as that of p300C, we consider the HAT activity of PCAF as one of the major players of Bel1/Tas-mediated trans -activation. This result is supported by the observed enhancement of Bel1/Tas activation after cotransfection with PCAF that resulted in higher levels of Bel1 acetylation thereby leading to increased binding to the IP.BRE [J. Bodem, personal communication]. Thus, the observed high level of enhancement of p300C might be due to the synergistic effects brought about by formation of ternary p300C-PCAF-Bel1 and binary p300C-SRC complexes, respectively. In these multimeric protein complexes, Bel1/Tas binds p300C indirectly through PCAF. Consistent with the HAT activities of PCAF and p300, we detected acetylated Bel1/Tas in pbel1s-transfected 293T cells using monoclonal antibody against acetyl-Lysine after immunoblotting (our unpublished data). It is intriguing that the distribution of the closely spaced Lysines of Bel1/Tas apparently mimics the correspondingly spaced Lysines in histones. This observation is further complicated by our observation that cotransfection with higher levels of PCAF led to a reduced stability of Bel1/Tas (Fig. 9 ). We assume that PCAF acetylates or even hyper-acetylates the Bel1/Tas protein at closely spaced Lysines in analogy to other activators reported previously [ 9 ]. The decreased stability of acetylated Bel1/Tas might indicate that modified Bel1/Tas is less stable than the unmodified form. This observation adds an additional layer of combinatorial regulation to Bel1/Tas-mediated trans -activation. It is intriguing that some viral trans -activators interact with more than a single p300 domain [ 10 , 14 ]. However, Bel1/Tas might recruit a second interacting region of p300 through binding PCAF (Fig. 10 ) that is known to interact with a p300 domain different from the C/H1 domain (Fig. 1 ) [ 10 ]. The complex nature of p300-Bel1/Tas interactions reported here might serve to strengthen the overall binding affinity between Bel1/Tas and the PCAF interaction domain of p300 within a larger transcriptional complex [ 27 ]. PCAF is known to specifically acetylate distinct Lysine residues of a subset of core histones and thereby regulate the transcriptional activity of many genes depending on the genetic context. It is well documented that acetylation, methylation and other covalent histone modifications are essential signals for the regulation of transcription [ 28 ]. It remains to be seen whether the stronger level of enhancement of p300C is a special if not unique feature of Bel1/Tas activation and due to over-expression or to repressive effects of other p300-interacting protein factors that cannot bind to the truncated p300 protein. Alternatively, many other factors were reported to bind to the C-terminal domains of p300 that also encompass general transcription factors TBP and TFIIB proteins that might also be responsible for the enhancement observed here [ 1 - 3 ]. In search of viral and cellular activators that are comparable with the ability of Bel1/Tas to interact with the C/H1 domain of p300, we found one case. A report indicates that EBNA-2 protein shares many features with Bel1/Tas that include the C-terminal acidic activation domain as well as the abilities to bind both the C/H1 domain and PCAF [ 12 ]. There remain two differences, however. First, EBNA-2 binds to both the C/H1 and the C/H3 domain, and, secondly, PCAF does not coactivate EBNA-2 in strong contrast to Bel1/Tas [ 12 ]. Of note, an additional HAT enzyme, GCN5, did not interact with Bel1/Tas when tested in reporter genes assays indicating that only distinct HAT sets such as those identified in this report specifically interact with Bel1/Tas in trans -activation. The precise roles of the HAT activities of PCAF, p300, and SRC-1 during Bel1/Tas-mediated trans -activation remain to be addressed in future studies. Conclusions Coactivators PCAF and p300 were identified to physically and functionally interact with the spumaviral Bel1/Tas trans -activator. Coactivator SRC-1 was found to strongly enhance Bel1/Tas-mediated trans -activation. The C/H1 domain of p300 was responsible for binding the retroviral activator and found to partially inhibit the p300-driven trans -activation. Methods Antibodies Mouse monoclonal antibodies directed against the FLAG epitopes of p300FL, p300N, p300M, p300C, and PCAF were purchased from Sigma, rabbit polyclonal antibodies against the N-terminal p300FL, the C-terminal p300C, and SRC-1a from Santa Cruz Biotechnology. The polyclonal serum direct against Bel1/Tas was used as described previously [ 23 ]. Typically, 5 μl of each antiserum was used for each immunoprecipitation. Monoclonal antibody against acetyl-Lysine was purchased from Sigma. Plasmids, cells, transfections, and metabolic labeling Plasmids pUC18, pCMVβ-gal, pbel1s [ 29 ], pGL3-pro-IP. BRE (-1 to -192), pCI-FLAG-p300FL, pCI-FLAG-p300N, pCI-FLAG-p300M, pCI-FLAG-p300C [ 4 ], and pCI-FLAG-PCAF [ 29 ] were separately, or in the combinations indicated, transfected into 293T cells using Lipofectamine 2000 (Invitrogen). In general, unless otherwise indicated, 0.1 – 10 μg plasmid DNA were transfected into 293T cells grown in Petri dishes with a diameter of six cm. Full-length pCI-FLAG-300FL plasmids and three different shortened versions (Fig. 1 , constructs no. 1 to 4) were grown in E. coli , DH5α cells [ 4 ]. The PFV internal promoter was constructed by PCR-mediated amplification of the defined promoter fragments as reported previously [ 19 , 21 ]. Recombinant clone p300N-C/H1-ΔKIX* (construct no. 5, Fig. 1 ), was constructed by first generating two PCR products with pCI-p300FL as template using the sense (s) and antisense (as) primers s1: 5'-CTTATGGTTCACCATATACTCAGAATCC-3', as1: AAACTGGAACCATGCCTGCATTTCTCTTATCACC-3', s2: 5'-GAAATGCAGGCATGGTTCCAGTTTCCAT-3', and as2: 5'-GGAAGGAACTGGCCCTGGTTGGAAGGCTGTTG-3' to amplify the sequence from nucleotide 755 to 1275 and nucleotides 1984 to 2297 fused in-frame. The resulting DNA product of 833 nucleotides was cloned into the pCR2.1 Topo vector and designated as pCR2.1-ΔKIX. An SphI-NotI DNA fragment obtained from pCR2.1-ΔKIX and was inserted into pCI-p300N that had been predigested with Sph I and NotI . The borders of construct no. 5 are shown in Fig. 1 and the expressed recombinant protein had the expected size. p300-C/H1-ΔKIX (no. 6) was constructed by inserting the SphI / NotI DNA fragment from pGEX-C/H1 (35) into pCI-p300N digested with SphI / NotI for expression of residues 1 to 424 of p300. Construct no. 7 (Fig. 1 ) was constructed by digesting pCI-p300N with SphI and re-ligating the larger fragment for the expression of residues 1 through 347 of p300. Finally, p300ΔN-ΔC/H1-ΔKIX was constructed from pCI-p300N by digesting with MunI and re-ligated to express residues 1 to 196 of p300. Bacterial plasmids coding for glutathion-S-transferase (GST) fusion proteins pGST-C/H1 (328–424) and pGST-KIX (436–661) (Fig. 1 ) were constructed from pGEX-6p-2GST-p300 [ 30 ] and grown in E. coli , BL21 cells. pCR3.1-FLAG-SRC-1a was grown in E. coli , JM109 cells [ 24 ]. Human 293T or HeLa cells were cultivated in DMEM medium supplemented with 1% penicillin and streptomycin, 1% Glutamine and 10 % fetal calf serum. Plasmid p300FL was transfected into 293T cells and metabolically labeled with L-[ 35 S]-Methionine plus L-[ 35 S]-Cysteine (spec. act. of 37 TBq/mM, PRO-MIX, Amersham) for 6 hr. Cells were harvested and used for immunoprecipitation as described above. The precipitates were analyzed by SDS-PAGE on 12% gels, dried, and exposed on KODAK Biomax MR1 films. Luc reporter gene expression assays Plasmid pCMV-βgal directing β-galactosidase expression from the CMV-IE promoter was used for normalization of transfection efficiency. Luc reporter gene assays were performed and quantified as described [ 31 ] using a Luminoskan TL Plus luminometer (Labsystems, Frankfurt, FRG). pUC18 vector plasmid DNA was used as carrier DNA to equalize the DNA concentration of each transfection. Cells were harvested 18 h after transfection. The results of luc assays were based upon at least triplicate experiments on multiple independent occasions. Expression levels were monitored by Western blot analysis. Co-immunoprecipitation Immunoprecipitation was performed as described previously with minor modifications [ 32 ]. Lysates of subconfluent cotransfected layer of 293T cells were prepared by first washing the cells with PBS, and subsequently lysed in lysis buffer (150 mM NaCl, 20 mM Tris-HCl [pH 7.5], 1 mM phenylmethylsulfonyl-fluoride containing 1% (v/v) Triton X-100. To inhibit unspecific protease activity, protease inhibitors (Biomol) were added to the lysis buffer. Lysates were precleared with protein A-SepharoseCL-4B (Amersham Bioscience AB, Uppsala). Co-immunoprecipitation of p300FL, p300N, p300M, p300C, SRC-1, and PCAF were performed with rabbit anti Bel1 antiserum [ 23 ]. The immune precipitates were retrieved with protein A-SepharoseCL-4B (Pharmacia) and eluted by boiling. To detect the specific precipitate, immunoblotting was performed with specific antibodies against expressed p300, shortened p300 versions, and SRC-1. For the detection of expressed PCAF, Western blot analysis with monoclonal anti-FLAG antibody was carried out. The immunoprecipitates were washed three times with the lysis buffer and analyzed by immunoblotting on 12% gels for expressed p300FL and on 14% gels for shortened p300 forms, PCAF, and SRC-1a. The experiments were repeated at least three times, specially the immunoprecipitations of lysates of p300C-transfected cells. GST pull-down assays The recombinant GST-C/H1 and GST-KIX proteins were expressed in E. coli BL21/DE3 cells after transformation with the corresponding plasmids. Expressed proteins were purified by binding to glutathione Sepharose 4B resin. Each of the GST-fusion proteins bound to glutathione Sepharose 4B were mixed with lysates obtained from pbel1s-transfected 293T cells. After incubation at 4°C overnight in binding buffer [ 30 ] and extensive washing with the binding buffer, bound proteins were eluted, separated by SDS-PAGE, and visualized by staining with Coomassie Brilliant Blue according to Ariumi [ 30 ] or detected by Western blot analysis. Immunoblotting Cells were harvested two days after transfection by lysis in 1% SDS and the protein concentration was determined using the DC protein assay (BioRad). Identical amounts of proteins were separated by SDS-PAGE on 12% gels, blotted, reacted with monoclonal serum directed against the FLAG epitope of the four different FLAG-tagged p300 (Sigma), or polyclonal serum direct against Bel1 [ 23 ], and detected by enhanced chemoluminescence. List of abbreviations used CBP, CREB-binding protein; C/H1, 2, and 3, Cys/His-rich domains of p300; FL, full-length; HAT, histone acetyltransferase; LTR, long terminal repeat; luc, luciferase; PCAF, p300/CBP-associated factor; Q, Glutamine-rich domain of p300; SRC, steroid receptor coactivator; p300N, p300M, and p300C, amino-, middle and C-terminal regions of p300; PFV, primate foamy virus; IP.BRE, internal promoter Bel1 response element; TAD, trans -activation domain; NF1, nuclear factor 1; CMV, cytomegalovirus; GST, glutathione-S-transferase. Authors' contributions HB and WM contributed equally to the manuscript. VO carried out the molecular cloning of p300 derivatives. YN participated in the design of the study. All authors read and approved the final manuscript. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC517496.xml |
512288 | Candidate high myopia loci on chromosomes 18p and 12q do not play a major role in susceptibility to common myopia | Background To determine whether previously reported loci predisposing to nonsyndromic high myopia show linkage to common myopia in pedigrees from two ethnic groups: Ashkenazi Jewish and Amish. We hypothesized that these high myopia loci might exhibit allelic heterogeneity and be responsible for moderate /mild or common myopia. Methods Cycloplegic and manifest refraction were performed on 38 Jewish and 40 Amish families. Individuals with at least -1.00 D in each meridian of both eyes were classified as myopic. Genomic DNA was genotyped with 12 markers on chromosomes 12q21-23 and 18p11.3. Parametric and nonparametric linkage analyses were conducted to determine whether susceptibility alleles at these loci are important in families with less severe, clinical forms of myopia. Results There was no strong evidence of linkage of common myopia to these candidate regions: all two-point and multipoint heterogeneity LOD scores were < 1.0 and non-parametric linkage p-values were > 0.01. However, one Amish family showed slight evidence of linkage (LOD>1.0) on 12q; another 3 Amish families each gave LOD >1.0 on 18p; and 3 Jewish families each gave LOD >1.0 on 12q. Conclusions Significant evidence of linkage (LOD> 3) of myopia was not found on chromosome 18p or 12q loci in these families. These results suggest that these loci do not play a major role in the causation of common myopia in our families studied. | Background Myopia is one of the leading causes of vision loss around the world[ 1 ]. In the United States, myopia affects approximately 25% of adult Americans[ 2 ]. Ethnic diversity appears to distinguish different groups with regard to prevalence. Caucasians have a higher prevalence than African Americans[ 3 ]. Asian populations have the highest prevalence rates with reports ranging from 50–90%[ 1 , 4 , 5 ]. Jewish Caucasians, one of the target populations of the present study, have consistently demonstrated a higher myopia prevalence than the general Caucasian population in both U.S. and European population surveys; Orthodox Jewish males in particular show increased susceptibility[ 6 , 7 ]. Despite many decades of research, little is known about the precise molecular defects and abnormal biochemical pathways that result in myopia. Compelling data from familial aggregation and twin studies indicate that susceptibility to myopia is inherited. Several familial aggregation studies have reported a greater prevalence of myopia in children of myopic parents compared to children of nonmyopic parents [ 8 - 12 ]. Several twin studies have demonstrated a very high heritability (estimates ranging from 60 to 90%) for myopia [ 13 - 15 ]. Other recent genetic studies of families with -6.00 D or more of myopia (termed high or pathological myopia) have reported significant linkage to regions on chromosome 18p11.31, 12q21-23, 17q21-22 and 7q36 [ 16 - 19 ]. The 18p candidate region has been confirmed in an independent study of high myopia [ 20 ]. Mutti et al.[ 21 ] examined the hypothesis that families with milder, juvenile onset myopia might show linkage to these same candidate regions. They found no evidence to support such a role in this more common form of myopia but their study was not highly powered in the presence of heterogeneity. Evidence also exists that myopia may be under environmental influences. The rapid increase in the prevalence of myopia over the last several decades suggests that environmental factors are important [ 22 , 23 ]. Furthermore, studies have shown a positive correlation of specific environmental factors, such as nearwork, with myopia [ 24 , 25 ]. It has been postulated that myopia develops in a person who engages in significant periods of sustained nearwork as an adaptive response to achieve better focus for near images[ 26 ]. Interestingly, Cordain et al.[ 27 ] suggest a positive correlation for myopia with increased consumption of carbohydrates, hyperinsulinemia and type II diabetes. Finally, experimental findings from animal studies show that the refractive state of young chicks will adapt to compensate for refractive errors induced by spectacle lenses[ 28 ]. The combination of genetic and environmental influences on the development of myopia suggests that myopia is a complex disorder and should not be classified as a simple Mendelian trait. Further evidence is shown by studies that have reported correlation coefficients for myopia between offspring and parents and between pairs of siblings to lie between 0.07–0.36 [ 29 - 32 ]. Due to the possible complexity of myopia, population isolates offer many advantages for genome-wide mapping studies[ 33 ]. First, they have reduced genetic complexity. Second, the people in most isolates share a common environment and culture. Differences in diet, exercise, sanitary conditions, and exposure to infectious diseases are minimized. A common language and religion usually promote social cohesion. Therefore, some of the environmental noise surrounding complex diseases that are determined by a combination of nature and nurture may be avoided. To avoid some of the complexity in mapping genes for myopia we have collected refractive measurements and DNA samples from Amish and orthodox Jewish families with myopia. The Old Order Amish are mostly rural farmers and craftsmen. They lead a culturally and technologically distinct lifestyle. They are a genetically well-defined founder population with large families and well-documented genealogies [ 34 , 35 ]. Family history records of the Amish in Lancaster County, Pennsylvania, beginning from 1727 are highly preserved[ 36 ]. Other features of this population include a relatively high standard of living, low migratory tendencies, and no practice of birth control, which facilitate the recruitment of large and extended families. The orthodox Jewish families in this study are all of Ashkenazi descent, a population with known founder effects in other common diseases[ 37 ]. This population also has somewhat larger family sizes than average in the US. In this initial report, we describe the design of our study and show that two regions (18p and 12q) previously reported to be linked to high myopia cannot explain the familial aggregation in these families with mostly moderate to milder forms of myopia. We had hypothesized that allelic heterogeneity might exist at these candidate loci such that in addition to highly penetrant alleles for extreme high myopia, there might also exist other susceptibility alleles of (possibly) lower penetrance that produce milder phenotypic forms of myopia. However, we found no strong evidence in support of this hypothesis. Methods Family screening The study protocol adhered to the tenet of the Declaration of Helsinki and was approved by the University of Pennsylvania and the National Human Genome Research Institute, National Institutes of Health institutional review boards. Informed consent was obtained from the subjects after explanation of the nature and possible consequences of the study. The collection of orthodox Jewish individuals was begun by a mass mailing of 3900 letters to all the known orthodox Jewish families living in Lakewood, New Jersey. Questionnaires were sent with letters explaining the study. If willing to participate, individuals completed and returned questionnaires that included their contact and physician information. Second and third mailings went out to individuals who did not respond – either positively or negatively – to the first mailing. The total number of questionnaires returned was 1,310. All Jewish individuals included in the study were of Ashkenazi heritage. Collection of Amish families was done by an advertisement in an Amish newspaper, referrals from local eye doctors in the Lancaster County community and word of mouth. Criteria for entry into the study included the following: 1) Negative history of systemic or ocular disease which may predispose to myopia, 2) negative history of a premature birth, 3) proband must be affected and must have a family history of myopia in either their parents or children, 4) only one parent (as opposed to both parents) of the proband can be myopic. For the Orthodox Jewish population, an individual's myopic status was obtained from the most recent (within 2 years) measurement of refractive error. If not recent, an individual was given a repeat exam by their local eye doctor or one of the study investigators (D.S.). For the Amish subjects, all participants were examined by a study investigator (D.S.) at the Amish Eye Clinic in Strasburg, PA. Amish participants were brought to the study site because they do not have phone access making it difficult to obtain a past history and records. Cycloplegic refractions were done on all individuals less than 40 years of age with one drop each of 1% cyclogyl, 1% mydriacyl and 2.5% phenylephrine. A manifest refraction was performed if an individual was older than 40 years of age. Classification as myopic required at least -1.00D in each meridian of both eyes. Individuals were classified as nonmyopes if they were over 21 years of age and did not meet the above criteria for myopia. Other individuals were classified as nonmyopic if they were 5–10 years old and had ≥ +3.00D in each meridian, 10–18 years old with ≥ +2.00D in each meridian or 18–21 years old with ≥ +0.50D in each meridian. All other individuals were classified as unknown for the trait. This ascertainment protocol resulted in the collection of 40 Amish families and 38 orthodox Jewish families. Of the 40 Amish families, phenotype data were available on 340 persons (170 individuals were affected and 170 were unaffected) but only 323 DNA samples were available to be genotyped. In the 38 Jewish families, phenotype data were available for 313 persons (177 affected, 122 unaffected and 14 of indeterminate phenotype) and DNA samples were available and genotyped for 290 of these family members. DNA extraction and genotyping Peripheral blood was collected from family members. High molecular weight genomic DNA extraction from the blood samples was performed with a kit (Puregene; Gentra Systems, Inc.; Minneapolis, MN, USA). Polymerase chain reactions were performed in a 17.05 ul volume containing 12–320 ng/ul of DNA; 880 uM each of dATP, dCTP, dGTP, and dTTP; 3 mM MgCl 2 ; 10 mM Tris/HCl (pH8.3); 50 mM KCl; 0.6 uM of each primer; and 7.6 units/ul of Taq polymerase. Standard thermocycling was as follows: 94°C for 30 sec., 55°C for 30 sec. and 72°C extension time for 30 sec. Markers used included D12S85, D12S1706, D12S346, D12S78, D12S79, D12S86, D18S59, D18S481, D18S63, D18S452, D18S53, and D18S474 located in the 18p and 12q regions implicated in high myopia [ 16 , 17 ]. Power studies A simulation study was conducted on the first 44 Ashkenazi Jewish families collected in this study, using the computer program SIMLINK [ 38 , 39 ], to compare the projected power from alternative parametric trait models (five of these families contributed no information about linkage and so were not genotyped and the sixth family was dropped after genotyping because of sample problems that resulted in inadequate linkage information). It was assumed that the myopia trait is controlled by an autosomal dominant bi-allelic locus and the frequency of the high risk allele was varied in different simulations, using both 0.05 and 0.01. The actual observed pedigree structures, trait phenotypes and DNA sample availability were used to simulate the trait locus genotypes and linked and unlinked marker loci were also simulated. A highly polymorphic marker locus (9 equally frequent alleles) was assumed. The power available from these families to detect linkage was evaluated using different models for penetrance and sporadic rates. For each of the 12 models tested, simulations were performed assuming that the underlying proportion of families linked to the same marker locus (α) was 25%, 50%, 75% and 100%. Furthermore, for each model at each specified level of α, simulations were performed for six recombination distances (θ) between the disease and the marker loci (i.e., θ = 0.01, 0.05, 0.1, 0.2, and 0.5); for three maximum penetrances (0.6, 0.7 and 0.8) for gene carriers; and for two phenocopy rates (0.08 and 0.15). LOD scores assuming homogeneity were calculated for each of 100 replicates. The average LOD score over all replicates (ELod) and its standard deviation were calculated for each model simulated. The power of these families to detect a linkage (i.e., to obtain a LOD score ≥ 3.0) was tabulated for the linked marker and the probability of obtaining a LOD score greater than 1.0 when no linkage exists (Type I error) was tabulated for the unlinked marker in all simulations. Linkage analysis The data on 40 Amish and 38 Ashkenazi Jewish families were checked for misspecification of family structures, data entry errors and genotyping errors using the program SIBPAIR[ 40 ]. This program was also used to estimate allele frequencies at marker loci from the unrelated founder individuals in the families. Parametric two-point linkage analysis was performed with the MLINK program of the FASTLINK package [ 41 , 42 ] and the utility programs MAKEPED, Linkage Control Program, and Linkage Report Program from LINKAGE 5.1 [ 43 - 45 ]. Intermarker distances (Kosambi cM) of the microsatellite markers were obtained from the Marshfield database : D12S85-42.78-D12S1706-0.53-D12S346-7.22-D12S78-13.44-D12S79-9.23-D12S86; D18S59-6.94-D18S481-1.36-D18S63-10.4-D18S452-22.54-D18S53-30.08-D18S474. To carefully explore the possibility of linkage of common myopia to these high myopia candidate regions, we utilized 12 different parametric models (Table 1 ). Analyses were performed assuming all combinations of three different frequencies for the myopia susceptibility allele (0.0133, 0.5 and 0.10) and four different sets of genotypic penetrances for the gene carriers and non-gene carriers, respectively: 0.90 and 0.0; 0.80 and 0.0; 0.80 and 0.05; and 0.60 and 0.15. Models 1–4 (Table 1 ) assume an allele frequency for the putative myopia susceptibility allele of 0.0133, which is the same value used by Young et al. in their linkage studies of high myopia [ 16 , 17 ] and close to the value of 0.01 that showed good power in our power simulation (note that a more frequent allele frequency of 0.05 resulted in similar but always lower predicted power in our simulations than the power obtained when an allele frequency value of 0.01 was used; note also that this allele frequency applies only to the linked trait locus, so that if there are multiple loci and environmental factors involved in causing myopia under a heterogeneity model, any single locus might only account for a small proportion of all myopia cases). No sex difference was assumed in any of these models. All persons younger than age 5 were coded as unknown for the trait. This analysis assumed autosomal dominant inheritance of a myopia susceptibility allele. Recombination fractions were assumed to be equal in men and women. The program HOMOG[ 46 ] was used to test for evidence of heterogeneity in the presence of linkage in the two-point parametric linkage analyses. The heterogeneity testing was performed separately in the Jewish and Amish families and also in a joint analysis of LOD scores from the two datasets combined. Multipoint parametric and nonparametric linkage analyses were performed with the GENEHUNTER program[ 47 ]. Because of program memory constraints, one large Amish pedigree was split into three small ones for the GENEHUNTER analysis. The parametric analyses in GENEHUNTER used the same models described above, while allowing for locus heterogeneity. The nonparametric statistic NPL all , which estimates the statistical significance of alleles shared IBD between all affected family members, was calculated also, together with an estimated P value for the Amish and Jewish datasets separately. A nonparametric analysis combining the Amish and Jewish families was then performed by calculating the sum of NPL scores for each family (obtained in the separate Amish and Jewish analyses just described) divided by the square root of the total number of families (N = 78)[ 48 ] to obtain an overall combined NPL score. Table 1 Different parametric models utilized for the linkage analysis Model Allele Frequency Penetrance in DD:Dd susceptibility allele carriers Penetrance in dd normal homozygotes 1 0.0133 0.90 0.00 2 0.0133 0.80 0.00 3 0.0133 0.80 0.05 4 0.0133 0.60 0.15 5 0.05 0.90 0.00 6 0.05 0.80 0.00 7 0.05 0.80 0.05 8 0.05 0.60 0.15 9 0.10 0.90 0.00 10 0.10 0.80 0.00 11 0.10 0.80 0.05 12 0.10 0.60 0.15 Results Power simulation As expected, the estimated average maximum LOD score decreases with the distance between the linked marker locus and the trait locus, and with increasing heterogeneity. However, only minimal changes in projected power for our Ashkenazi families were observed as penetrance, phenocopy rate and disease allele were varied. Projected power was always higher when an allele frequency of 0.01 was used for the susceptibility allele at the trait locus than when an allele frequency of 0.05 was used; however, these differences in power were very small. Table 2 shows a representative sample of the predicted power results for detecting linkage to a marker 5 cM from the trait locus (the average maximum distance that a trait locus would be from our genotyped markers if it fell within the confines of either of these two candidate regions on 18p and 12q) assuming an autosomal dominant susceptibility allele frequency of 0.01. If all families were linked to one locus, these families were predicted to have 100% power to detect linkage to a marker 5 cM away from the trait locus with a LOD of 3 or more (the ELods were all ≥ 14). As less families were linked to the marker locus (i.e., as genetic heterogeneity increased) the power decreased but was still good (≥ 67%) if 50% or more of the families were linked. Even when only 25% of families were linked to the same locus, the expected LOD score was over 1.0 for all models. Of course, these LOD scores were calculated assuming homogeneity, and it is well known that power can be substantially increased when heterogeneity exists if LOD scores are calculated assuming heterogeneity (HLODs) as we have done in this study. So we would expect our actual power to detect linkage to be even higher than our simulations of heterogeneity predict. Observed Type I error rates were compatible with the nominal Type I error levels for all models. Between 0 and 1% of replicates produced a LOD score > 1 at any test map distance for unlinked markers. Table 2 Power and ELods from 100 replicates of simulated data, dominant susceptibility allele frequency of 0.01 PENETRANCE IN DD:Dd SUSC. ALLELE CARRIERS PENETRANCE IN dd NORMAL HOMOZYGOTE % FAMILIES LINKED 100% 75% 50% 25% Power 1 ELod ± s.d. 2 Power 1 ELod ± s.d. 2 Power 1 ELod ± s.d. 2 Power 1 ELod ± s.d. 2 0.8 0.08 100 14.0 ± 0.3 99 8.9 ± 0.3 82 4.7 ± 0.2 18 1.7 ± 0.1 0.8 0.15 100 14.7 ± 0.3 100 9.1 ± 0.3 73 4.8 ± 0.2 14 1.6 ± 0.1 0.7 0.08 100 14.8 ± 0.3 99 8.5 ± 0.3 77 4.8 ± 0.2 17 1.8 ± 0.15 0.7 0.15 100 15.2 ± 0.3 99 8.8 ± 0.3 70 4.5 ± 0.2 14 1.6 ± 0.1 0.6 0.08 100 15.0 ± 0.3 98 8.6 ± 0.25 67 4.3 ± 0.2 14 1.7 ± 0.1 0.6 0.15 100 14.2 ± 0.3 100 8.4 ± 0.3 73 4.3 ± 0.2 5 1.2 ± 0.1 1 Power = 100 X Proportion of replicate samples that yielded a homogeneity LOD score ≥ 3.0 2 ELod = average homogeneity LOD score over all replicates ± its standard deviation Linkage Parametric and nonparametric LOD scores were calculated for 40 Amish pedigrees and 38 Jewish pedigrees. The six markers on chromosome 12q21-q23 spanned 73 cM, and the six markers on chromosome 18p11.2-p11.32 spanned 70 cM. Markers D12S1706, D12S346, D18S59, D18S481 and D18S63 were previously reported by Young et al. [ 16 , 17 ] as showing evidence of linkage to autosomal dominant high myopia. Under all 12 parametric models, the evidence in favor of linkage to these candidate regions was minimal and this evidence varied only slightly as the assumptions of the trait model were changed across the models. Therefore, only the results from model 1 are presented here. Two-point linkage analyses in the Amish and Jewish populations Results of two-point parametric linkage analysis of myopia assuming linkage heterogeneity to the chromosome 12 and 18 markers in 40 Amish families are presented in Table 3 . Statistically significant or suggestive linkage under locus homogeneity was not observed for either chromosome 12 or chromosome 18. Only one marker, D18S474 showed a two-point LOD ≥ 1.0 (LOD = 1.39 at θ = 0.3). Testing for linkage heterogeneity using HLODs in HOMOG did not significantly improve the evidence for linkage to any of these markers. Table 3 Two-point parametric LOD scores for myopia (Model 1) in 40 Amish Families RECOMBINATION FRACTION, θ MARKER 0.0 0.01 0.05 0.1 0.2 0.3 0.4 D12S85 -19.55 -12.97 -9.02 -6.27 -2.92 -1.15 -0.31 D12S1706 -39.91 -29.33 -17.82 -10.79 -3.85 -1.06 -0.18 D12S346 -36.18 -24.54 -13.68 -7.57 -1.99 -0.14 0.08 D12S78 -40.39 -28.61 -17.98 -11.46 -4.57 -1.5 -0.33 D12S79 -49.21 -32.42 -19.78 -12.36 -4.82 -1.5 -0.2 D12S86 -40.95 -32.39 -20.01 -12.52 -5.14 -1.85 -0.43 D18S59 -26.98 -20.07 -11.74 -6.77 -2.2 -0.48 -0.02 D18S481 -29.61 -19.93 -11.02 -5.99 -1.42 -0.04 0.16 D18S63 -32.32 -23.1 -12.11 -5.98 -0.87 0.5 0.35 D18S452 -38.37 -27.85 -16.16 -9.31 -2.76 -0.35 0.15 D18S53 -35.69 -25.42 -15.08 -9.07 -3.22 -0.77 -0.01 D18S474 -26.64 -14.04 -5.89 -2.01 0.94 1.39 0.7 The same markers on chromosome 12q21-q23 and chromosome 18p11.2-p11.32 were analyzed using 38 Ashkenazi Jewish families (Table 4 ). Statistically significant or suggestive linkage was not observed on either chromosome, no homogeneity LOD scores ≥ 1.0 were observed, and testing for linkage in the presence of heterogeneity (HLODs in HOMOG) did not alter this result. Table 4 Two-point parametric LOD scores for myopia (Model 1) in 38 Ashkenazi Jewish families RECOMBINATION FRACTION, θ MARKER 0 0.01 0.05 0.1 0.2 0.3 0.4 D12S85 -7.5 -6.71 -4.62 -2.56 -0.44 0.19 0.17 D12S1706 -36.21 -28.66 -17.67 -10.77 -3.85 -1.04 -0.15 D12S346 -32.76 -24.46 -13.34 -7.11 -1.68 -0.04 0.11 D12S78 -27.99 -20.59 -10.1 -4.65 -0.46 0.43 0.24 D12S79 -38.53 -30.26 -18.47 -11.21 -4.12 -1.24 -0.26 D12S86 -39.59 -31.68 -19.83 -12.58 -5.12 -1.8 -0.45 D18S59 -35.34 -27.25 -16.82 -10.69 -4.4 -1.51 -0.26 D18S481 -32.69 -24 -14.58 -9.19 -3.73 -1.26 -0.24 D18S63 -36.39 -28.55 -17.92 -11.36 -4.65 -1.63 -0.35 D18S452 -34.97 -25.21 -15.47 -9.72 -3.78 -1.22 -0.23 D18S53 -38.03 -26.36 -14.65 -8.62 -3.15 -0.94 -0.14 D18S474 -29.88 -22.79 -14.41 -9.29 -3.98 -1.45 -0.31 Heterogeneity testing using HOMOG in the combined Jewish and Amish families also did not yield any significant evidence of linkage in these two regions, with the maximum HLOD's being 0.39 and 0.95 on chromosomes 12 and 18 respectively. Furthermore, nonparametric two-point NPL scores did not show any significant evidence for linkage in either the Amish or Jewish populations. The observed combined NPL score of 1.37 for D12S1706 approached nominal significance at p = 0.09 but was not close to the significance level of at least p = 0.01 needed to provide confirmation of a prior linkage[ 49 ]. Multipoint linkage analyses in the Amish and Jewish populations Multipoint parametric linkage analyses assuming homogeneity were consistently negative in both the Amish and Jewish datasets. A maximum multipoint parametric HLOD of 0.92 was observed at D18S474 in the Amish population. However, multipoint parametric HLOD scores were essentially zero for the chromosome 12 region in the Amish and for both the chromosome 12 and 18 regions in the Jewish families. The multipoint nonparametric analyses did not show statistically significant evidence for linkage of myopia to either candidate region in the Amish (Figures 1 and 2 ) or the Jewish (Figures 3 and 4 ) families. Only very mild evidence for linkage of myopia in the Amish was observed between markers D18S59 and D18S481 (NPL= 1.54, p = 0.05) (Figure 2 ). Figure 1 Multipoint nonparametric linkage analysis of myopia to chromosome 12q in 40 Amish families Figure 2 Multipoint nonparametric linkage analysis of myopia to chromosome 18p in 40 Amish families Figure 3 Multipoint nonparametric linkage analysis of myopia to chromosome 12q in 38 Ashkenazi Jewish families Figure 4 Multipoint nonparametric linkage analysis of myopia to chromosome 18p in 38 Ashkenazi Jewish families Individual families showing linkage Only one Amish family (3061) showed marginal evidence for linkage (LOD > 1.0) to the region previously reported on chromosome 12 (D12S1706 and D12S346) for both two-point and multipoint parametric analyses (Table 5 ). Three Amish families gave LOD >1.0 at 2 markers on chromosome 18p. In both two-point and multipoint parametric analyses, family 3064 showed mild evidence of linkage (LOD = 1.30) to marker D18S63, and families 3049 and 3053 showed slight evidence of linkage to marker D18S474 (two-point LOD= 1.14 and 1.30, respectively). Simulations using SIMLINK (model 1) showed that for these individual families, the maximum two-point LOD score obtained when a linked marker was simulated at a recombination fraction of 0.0 ranged from 0.99 to 1.4, and the probability of obtaining a LOD over 1.0 for an unlinked marker ranged from <0.01 to 0.036. The nominal significance level corresponding to a LOD score of 1.0 is approximately 0.01. A total of three Jewish families showed marginal evidence for linkage to chromosome 12q markers for both two-point and multipoint analyses, with two-point and multipoint parametric LOD > 1.0. Simulations using SIMLINK (model 1) showed that for these individual families the maximum two-point LOD score obtained when a linked marker was simulated at a recombination fraction of 0.0 ranged from 0.93 to 1.06, and the probability of obtaining a LOD over 1.0 for an unlinked marker ranged from <0.001 to 0.05. Table 5 Families showing slight evidence for linkage of myopia (Model 1) to chromosome 12q or 18p AMISH FAMILIES FAMILY ID TWO-POINT LOD MULTIPOINT Marker Zmax LOD NPL P value 3061 D12S1706 1.04 1.04 * N/A D12S346 1.04 1.04 * N/A D12S78 1.04 1.04 * N/A 3049 D18S474 1.14 1.27 2.19 0.02 3053 D18S474 1.30 1.30 -* N/A 3064 D18S63 1.30 1.30 -* N/A JEWISH FAMILIES FAMILY ID TWO-POINT LOD MULTIPOINT Marker Zmax LOD NPL P value 20 D12S79 1.12 1.12 3.45 0.02 D12S86 1.12 1.12 3.45 0.02 58 D12S1706 1.16 1.16 3.01 0.06 D12S346 1.16 1.16 3.01 0.06 78 D12S85 1.07 1.07 3.38 0.01 * Only single affected parent-offspring pairs were genotyped in these families so the NPL analysis was uninformative in these families. However, the parametric LOD score analysis that uses both affected and unaffected family members was informative for linkage. Discussion The overall results of these preliminary studies do not indicate any strong evidence for linkage of myopia in these families to the candidate regions on chromosomes 12 or 18. Although some families show marginal evidence of linkage to one of these regions, the results could be due to chance. Our negative results for these candidate regions have several possible explanations. First, the diagnostic criteria used in the previous studies [ 9 , 10 ] that implicated these candidate regions were based on limiting the affection status to the sphere component of a plus cylinder refraction. An individual was considered affected with high myopia if the sphere was equal to or greater than -6D regardless of the astigmatic error. Our study required an individual to have -1D in each meridian to be considered affected. Therefore, the criterion for being affected was quite different between the two studies. Second, the study population in our study included moderate and low myopes in addition to a small number of high myopes. None of our families showed strong aggregation of high myopia. Therefore, there were no families in our study recruited exclusively for high myopia and no families that would have been highly powerful for the detection of linkage to a high myopia trait. We utilized this study design to search for allelic heterogeneity with regard to the 18p and 12q loci thinking that one or both loci may predispose to moderate/mild forms of myopia. Thus, the current linkage analysis was done to test the hypothesis that other alleles at the candidate high myopia loci on chromosomes 18 and 12 might contribute to the etiology of moderate/mild myopia. The mild evidence of linkage in a few families indicates that this hypothesis cannot be fully ruled out for a very small proportion of families with mild forms of myopia. However, there is no strong evidence in favor of this hypothesis and strong negative evidence against linkage in most of the families in this study. Previous studies attempting to confirm the high myopia loci on chromosomes 18 and 12 have yielded inconsistent results. Naiglin et al.[ 19 ]collected 23 French families with high myopia (spherical equivalent ≥ -6D) and performed a genome scan with 400 markers. Significant linkage was not found on 18p and 12q. Lam et al.[ 20 ] mapped 15 families with high myopia, ≥ -6.0D, using only 18p markers. Statistically significant (LOD > 3) linkage was not demonstrated although a multipoint LOD over 2.0 was observed, thus giving evidence of confirmation of the 18p candidate region. Mutti et al.[ 21 ] collected 53 families with varying degrees of myopia (affected ≥ -0.75D in each meridian) and genotyped the family members with 18p and 12q loci markers implicated in high myopia. No evidence of linkage to milder forms of myopia was found to the chromosome 18p and 12q loci previously associated with high myopia. Our study, although consistent with the results of Mutti et al.[ 21 ] was significantly different in design. First, Mutti et al. used a heterogeneous population that could decrease the chances of obtaining significant linkage for a minor gene effect from 18p or 12q if substantial ethnic heterogeneity exists. Both the Amish and Ashkenazi populations used in our study are more homogeneous and each sample was analyzed using marker allele frequencies estimated from the sample. Second, their study utilized 221 samples while we genotyped 613 individuals. Our power simulations predicted higher power in the presence of heterogeneity in our Ashkenazi families than was predicted for the Mutti et al. study. Our Amish families were of similar size and structure and so should have similar predicted power as the Ashkenazi families, and our combined analyses of the two data sets should provide much more power than that predicted by the simulations of the Ashkenazi families alone. The combination of the Mutti et al. study with the results presented here strongly suggest that these two candidate regions do not play a large role in the causation of moderate/mild myopia in several populations examined. These studies suggest that myopia is complex and probably caused by the interaction of multiple genes with the environment. Therefore, to understand myopia it is necessary to apply the equation: Genes + Environment=Outcome. The difficulty here is the uncertainty surrounding both terms in the equation; ideally, one set of genetic factors will interact with one set of environmental influences to produce identical outcomes, but it is unknown whether this is always going to be the case. Therefore, to lessen the problem of multiple gene interaction as well as gene-environment interaction confounding the results, strategies to limit this problem should be utilized in the genetic mapping of myopia. The use of isolated populations is one approach to limiting the heterogeneity across populations and is the approach we are using for a genome wide scan in these families. Furthermore, the definition of myopia needs to be standardized so comparisons across studies can be made accurately. Previous studies have utilized different requirements with regard to affection status making cross comparisons difficult. In conclusion, we find little evidence implicating previously described susceptibility loci for high myopia on chromosomes 12 and 18 as being important in the etiology of common, moderate/mild myopia in our two population samples. Competing interests None declared. Authors' contributions Dwight Stambolian, Lauren Reider, Debra Dana, Robert Owens, and Elise Ciner recruited patients for the study. Melissa Schlifka carried out the genotyping in chromosomes 18 and 12. Dwight Stambolian and Joan E. Bailey-Wilson performed the study design and wrote part of the manuscript. Joan Bailey-Wilson oversaw all statistical analyses; Grace Ibay performed statistical analyses and wrote part of the manuscript; Taura Holmes, Betty Doan and Jennifer O'Neill assisted with analyses of the data. All authors read and approved of the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC512288.xml |
314475 | PLoS Biology in Action | Some examples of how the PLoS Biology content is used and a request for feedback from creative users | In the first month after our launch, the PDF of the “monkey-robot” article by Miguel Nicolelis received tens of thousands of downloads. We are not sure who downloaded the paper because we do not ask people to register at our site. We suspect, however, that its popularity is in part due to the widespread media coverage of the article (from Time magazine to Al-Jazeera and Die Zeit ), which demonstrates a thirst for the original scientific paper and makes a strong argument for open access. We were even more surprised by the popularity of the “issue PDF,” a whopping 72 MB file that contains the entire journal, cover to cover. The inaugural issue PDF was downloaded thousands of times within the first few weeks, even though we had printed 30,000 hard copies and distributed them widely. 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503385 | Identification and characterization of a nontypeable Haemophilus influenzae putative toxin-antitoxin locus | Background Certain strains of an obligate parasite of the human upper respiratory tract, nontypeable Haemophilus influenzae (NTHi), can cause invasive diseases such as septicemia and meningitis, as well as chronic mucosal infections such as otitis media. To do this, the organism must invade and survive within both epithelial and endothelial cells. We have identified a facilitator of NTHi survival inside human cells, v irulence- a ssociated protein D ( vapD Hi , encoded by gene HI0450). Both vapD Hi and a flanking gene, HI0451, exhibit the genetic and physical characteristics of a toxin/antitoxin (TA) locus, with VapD Hi serving as the toxin moiety and HI0451 as the antitoxin. We propose the name VapX Hi for the HI0451 antitoxin protein. Originally identified on plasmids, TA loci have been found on the chromosomes of a number of bacterial pathogens, and have been implicated in the control of translation during stressful conditions. Translation arrest would enhance survival within human cells and facilitate persistent or chronic mucosal infections. Results Isogenic mutants in vapD Hi were attenuated for survival inside human respiratory epithelial cells (NCI-H292) and human brain microvascular endothelial cells (HBMEC), the in vitro models of mucosal infection and the blood-brain barrier, respectively. Transcomplementation with a vapD Hi allele restored wild-type NTHi survival within both cell lines. A PCR survey of 59 H. influenzae strains isolated from various anatomical sites determined the presence of a vapD Hi allele in 100% of strains. Two isoforms of the gene were identified in this population; one that was 91 residues in length, and another that was truncated to 45 amino acids due to an in-frame deletion. The truncated allele failed to transcomplement the NTHi vapD Hi survival defect in HBMEC. Subunits of full-length VapD Hi homodimerized, but subunits of the truncated protein did not. However, truncated protein subunits did interact with full-length subunits, and this interaction resulted in a dominant-negative phenotype. Although Escherichia coli does not contain a homologue of either vapD Hi or vapX Hi , overexpression of the VapD Hi toxin in trans resulted in E. coli cell growth arrest. This arrest could be rescued by providing the VapX Hi antitoxin on a compatible plasmid. Conclusion We conclude that vapD Hi and vapX Hi may constitute a H. influenzae TA locus that functions to enhance NTHi survival within human epithelial and endothelial cells. | Background Culturable Haemophilus influenzae are acquired in the nasopharynx shortly after birth, and are thought to persist throughout life. H. influenzae adheres to and penetrates into and between cultured human respiratory epithelial cells, a mechanism that may contribute to its persistence in chronic obstructive pulmonary disease (COPD) and cystic fibrosis (CF) patients [ 1 , 2 ]. H. influenzae can be found in the respiratory tracts of these patients even after they have undergone antibiotic treatment [ 3 ]. As well, COPD sputum cultures can be sterile, while H. influenzae can still be isolated from the subepithelial matrix [ 4 ]. Finally, we have found in a recent in vivo study that H. influenzae can persist in a human bronchiolar xenograft for at least three weeks [ 5 ]. This suggests that the organism can survive and persist in protected biological compartment(s). The ability of H. influenzae to survive antibiotic treatment and reappear when growth is favorable may be responsible for the reseeding of the middle ear observed in chronic otitis media. Often, middle ear fluid from children presenting with otitis media with effusion are sterile when cultured, but PCR analysis of the fluid has determined the presence of H. influenzae [ 6 ]. Further, RT-PCR studies of this sterile fluid have shown the presence of bacterial mRNA, confirming that the bacteria are alive and persisting in a viable but nonreplicative state [ 7 ]. Persistence was investigated in vitro using a NTHi strain that was susceptible to β-lactam antibiotics. This strain was allowed to invade a human respiratory epithelial cell monolayer for 24 hours, which was subsequently treated with a 4 hour incubation in 10 × MIC concentrations of the β-lactam antibiotics ampicillin, imipenem, cefuroxim, amoxycillin/clavulanic acid, or cephalothin. The antibiotics killed all the extracellular bacteria, but none of the intra- or paracellular bacteria, suggesting that the organism was not replicating inside or between the epithelial cells [ 8 ]. Non-replicating bacteria are not susceptible to the cidal action of β-lactam and aminoglycoside antibiotics. During a study aimed at identifying genes associated with virulence in pathogenic strains of the Gram-negative, strict anaerobe Dichelobacter nodosus , the causative agent of ovine footrot, Katz et al . [ 9 ] reported the discovery of a novel area of the chromosome that hybridized to all virulent strains tested, but to only 23% of the avirulent strains studied. They designated the four genes found on this fragment as vapA - D , for v irulence- a ssociated p roteins. Homologues of these genes appear on the chromosomes and plasmids of a number of pathogenic microorganisms, including Neisseria gonorrhoeae , Helicobacter pylori, Reimerella anatipestifer and Actinobacillus actinomycetemcomitans . The chromosome of H. influenzae strain Rd KW20 (hereinafter referred to as strain Rd) contains vapA , vapBC , and vapD homologues, with one pair, vapBC , in duplicate. The genome organization of the vap genes in H. influenzae differs from that of D. nodosus , in that vapA Hi (HI1250) is preceded by a conserved hypothetical protein, HI1251, and both genes are likely transcribed as an operon. As well, vapD Hi is flanked by a gene encoding another conserved hypothetical protein, HI0451 (which we have named vapX Hi ), again in an apparent operon configuration (8 nucleotides separate HI0450 and HI0451). To determine if the vap homologues played a role in the persistence of NTHi, we chose vapD Hi (HI0450) for further study, since this protein was found in a proteomic survey to be expressed in the soluble fraction of strain Rd [ 10 ]. VapD Hi is 40% identical and 67% similar to the Dichelobacter VapD and belongs to the Cluster of Orthologous Groups (COG) 3309 and Pfam 04605, termed the "N-terminal conserved domain of VapD". Results Mutation of vapD Hi results in attenuated survival in human endothelial cells When the Rd vapD Hi mutant strain was used to invade the in vitro model of the blood-brain barrier, human brain microvascular endothelial cells (HBMEC) in 12-well plates, the amount of gentamicin-resistant bacteria recovered from the monolayer after three hours declined to approximately 60% of wild type levels, an average of 2.2 × 10 3 CFU/ml for the wild-type strain Rd versus 1.3 × 10 3 CFU/ml for the Rd vapD Hi mutant (n = 3 (number of independent assays performed in at least duplicate); P < 0.05, Student's t test). No significant difference was observed between the wild type and vapD Hi mutant in adherence to the human cell monolayers: the average number of cell-associated bacteria (both adherent and invaded) recovered for strain Rd was 1.5 × 10 5 CFU/ml and 1.8 × 10 5 CFU/ml for the Rd vapD Hi mutant (n = 3; P > 0.05). To determine if the phenotype of attenuated survival observed in the Rd vapD Hi mutant was a general phenomenon and not restricted to strain Rd, another isogenic pair was constructed and analyzed using a different strain, R3001. R3001 is a bronchoalveolar lavage isolate from a pediatric cystic fibrosis patient, and is considered invasive since it came from a normally sterile site [ 5 ]. The average number of gentamicin-resistant bacteria recovered from HBMEC monolayers was 1.2 × 10 5 CFU/ml for the parent strain R3001 versus 7.1 × 10 4 CFU/ml for the R3001 vapD Hi mutant (n = 3; P < 0.05). Although the absolute numbers of bacteria recovered were higher with strain R3001 than with Rd (as is often observed with invasive isolates), the attenuation of survival inside the HBMEC monolayer of ≤ 60% observed in the strain with a vapD Hi mutation was maintained. There was no significant difference between the wild-type R3001 and the R3001 vapD Hi mutant in adherence to the monolayer: the average numbers of cell-associated bacteria recovered for strain R3001 were 1.7 × 10 7 CFU/ml versus 1.3 × 10 7 CFU/ml for R3001 vapD Hi (n = 4; P > 0.05). Unlike Rd, strain R3001 carries the high molecular weight (HMW) adhesins, which may account for its more efficient adherence to the HBMEC monolayer [ 11 ]. No significant difference in the growth rates of either of the vapD Hi mutants versus their cognate parent strains were observed, whether grown in bacteriological media (sBHI broth) or on HBMEC or NCI-H292 monolayers (data not shown). Mutation of vapD Hi results in diminished long-term survival inside human respiratory epithelial cells To determine if the vapD Hi mutation would affect the ability of H. influenzae to survive inside human respiratory epithelial cells over a longer period of time, 18-hour invasion assays were performed using NCI-H292 cells. The number of gentamicin-resistant bacteria recovered from the NCI-H292 monolayer after 18 hours for the parent strain Rd was an average of 6.4 × 10 4 CFU/ml versus 3.2 × 10 4 CFU/ml for the Rd vapD Hi mutant (n = 3; P < 0.05). This represents a 50% reduction in survival of the vapD Hi mutant within epithelial cells as compared to the parent strain, more attenuation than was seen for the three hour assays. Transcomplementation of Rd vapD Hi The vapD Hi locus from strain R3001 was cloned into the mobilizable broad host range plasmid pDD515, creating pDD564, and conjugally transferred into the Rd vapD Hi mutant (Table 1 ). The plasmid pDD515 is a derivative of the IncQ plasmid RSF1010 and has an approximate copy number of 12 per chromosome in H. influenzae [ 12 ]. The survival inside HBMEC of strain Rd (pDD515) was within 5% of strain Rd without the vector in identical assays (data not shown). Carrying a vapD Hi locus in trans restored wild-type survival of Rd vapD Hi (pDD564) within HBMEC monolayers. The amount of gentamicin-resistant bacteria recovered from the endothelial cell monolayer after a three hour invasion assay was an average of 8.0 × 10 2 CFU/ml for Rd (pDD515), the vector control, and 7.6 × 10 2 CFU/ml for the mutant strain that carried the wild-type vapD Hi allele in trans , Rd vapD Hi (pDD564) (n = 3; P > 0.05), indicating that there was no significant difference in the survival inside HBMEC monolayers of the wild type strain versus the transcomplemented strain. These data confirm that the phenotype of attenuated survival was due to the interruption in vapD Hi and not to polar effects. The R3001 vapD Hi mutant mirrored the survival defect seen with the Rd vapD Hi mutant, at approximately 60% of wild-type R3001 levels. However, attempts to conjugate the mobilizable broad host range plasmid carrying the vapD Hi locus, pDD564, into strain R3001 for transcomplementation studies failed repeatedly. This clinical isolate likely has a plasmid or an origin of replication of the same incompatibility group incorporated into its chromosome and therefore will not maintain the broad host range plasmid for transcomplementation [ 13 ]. Reverse-transcriptase PCR of bacteria from human cell monolayers To determine if the vapD Hi locus was transcribed during contact with a human cell monolayer, total RNA was isolated from the wild-type strain Rd recovered after 18 hours on HBMEC or NCI-H292 monolayers and was used as the template for RT-PCR. Figure 1 shows the 153 bp band amplified with the vapD Hi -specific primers, with a molecular weight marker in lane A. The template for lane B is Rd RNA from HBMEC endothelial cell monolayers, lane C is its cognate negative control, with no reverse transcriptase added to the RNA prior to PCR amplification. Lane D shows the results using Rd RNA from NCI-H292 epithelial cell monolayers; lane E is its cognate negative control. The vapD Hi locus is transcribed when strain Rd is in contact with either human epithelial or endothelial cell monolayers. PCR survey of vapD Hi In order to estimate the carriage rate of vapD Hi among the highly heterologous NTHi strains, a PCR survey of 59 commensal and disease-associated strains was undertaken (Table 2 ). The vap HI primer set was used. In Rd, these primers amplify a 769 bp PCR product that includes the full-length vapD Hi gene. Purified chromosomal DNA preparations from 53 randomly-chosen NTHi strains and one each of the six capsular serotypes of H. influenzae (types a through f) from the American Type Culture Collection (ATCC) reference strains described in Table 2 were subjected to PCR with the vap HI forward and reverse primers. The NTHi strains included nasopharyngeal, blood, CSF, middle ear, tracheal aspirate, and sputum isolates. A PCR product was amplified in 100% of the strains. All of the ATCC encapsulated reference strains and Rd displayed a full-length vapD Hi allele. Ninety-three percent of the nasopharyngeal strains carried a full-length allele, as did 71% of the blood and CSF isolates, and 50% of the middle ear, tracheal aspirate, and sputum isolates. Overall, only ten strains of the 59 included in the study displayed a truncated gene. Sequencing of the truncated vapD Hi allele To study the truncated vapD Hi in more detail, five out of the ten alleles that represented the smaller isoform of vapD Hi from the PCR survey were sequenced on both strands. It was found that, in all cases, the gene had undergone a deletion event that had left the protein in frame, but missing 46 amino acids from the interior of the protein, resulting in a 45 amino acid protein rather than the full-length 91 amino acid protein (Figure 2 ). This corresponds to the loss of Rd genome coordinates 473123 to 473263. In addition, all of the smaller alleles had an aspartate residue inserted at position #7 as compared to Rd, which has a leucine at that position. The significance of this is unclear, as the full-length R3001 vapD Hi allele, which did transcomplement the Rd vapD Hi mutant, also has an aspartic acid inserted at the same position, resulting in a 91 amino acid protein. Interestingly, the VapD homologues from N. gonorrhoeae , H. pylori and A. actinomycetemcomitans , as well as VapD in D. nodosus , all have an aspartate at that position. Rd appears to be the only H. influenzae strain studied which lacks that particular residue. Full-length VapD Hi homodimerizes Using an E. coli -based protein-protein interaction system that is dependent upon the DNA-binding domain (DBD) of LexA, homodimerization of identical protein subunits can be quantitated [ 14 , 15 ]. In this system, protein-protein interactions result in a LexA dimer that is active as a repressor, and consequently, the beta-galactosidase activity of the reporter strain (SU101) diminishes. Full-length VapD Hi from strain R3001 was ligated to the DBD of LexA in plasmid pDD559 and the clones were analyzed on MacConkey agar with lactose. If there was no homodimerization of the LexA::VapD Hi fusion protein, the colonies appeared red on MacConkey agar, as the native level of beta-galactosidase expression in the reporter strain was not inhibited. If the subunits interacted, the colonies appeared pale on MacConkey, as the engineered LexA operator controlling the lacZ reporter gene had been repressed by a homodimer of the LexA fusion protein. This repression was quantitated by beta-galactosidase activity assays. Each measurement is the mean of at least three experiments performed in triplicate. It was found that VapD Hi interacted strongly with itself. The beta-galactosidase activity of the reporter strain SU101 carrying the vector control (pSR658) was an average of 975 (± 29) Miller units, and the activity of SU101 with the LexA::VapD Hi fusion (pDD559) was an average of 16 (± 1) Miller units, indicating strong interaction. Full-length VapD Hi forms homodimers in vivo . This protein may also form higher-order multimers, since this would result in a number of dimeric forms being available to act as a repressor of lacZ transcription in the reporter strain. Truncated VapD Hi does not homodimerize, but interacts with full-length VapD Hi Homodimerization assays with the small allele revealed that the subunits of the truncated VapD Hi did not interact efficiently. The vector control for the homodimerization assays SU101 (pSR658) yielded 1490 (± 31) Miller units, and SU101 carrying the LexA fusion to the truncated VapD Hi protein from strain R2866 (pDD577) displayed 1357 (± 54) Miller units of beta-galactosidase activity, showing little repression in this system. Since the wild-type VapD Hi subunits homodimerized strongly but the truncated subunits did not, the truncated subunit and the full-length subunit were examined for interaction in heterodimerization assays. In the reporter strain SU202, a LexA operator with a mutated half-site was engineered upstream of the lacZ gene. This strain was then transformed with two compatible plasmids, one that carried a fusion of the truncated VapD Hi with a wild-type LexA DBD, and one that carried the full-length VapD Hi fusion to a mutated LexA DBD that only recognized the mutated LexA operator half-site in SU202 [ 15 ]. If a heterodimer of a truncated subunit and a full-length subunit was formed, a functional LexA repressor could recognize the hybrid operator and repress transcription of lacZ . It was determined that the truncated and full-length subunits could interact with each other. The vector control for the heterodimerization assays (SU202 with pSR658 and pSR659) yielded 1855 ± 196 Miller units, and SU202 carrying the LexA fusion to the truncated VapD Hi protein from strain R2866, pDD577, coupled with the mutated LexA DBD fusion to the full-length VapD Hi protein from strain R3001, pDD561, resulted in 792 ± 19 Miller units of beta-galactosidase activity, showing that the truncated subunit did heterodimerize with the full-length VapD Hi subunit. Truncated VapD Hi does not transcomplement the mutant and has a dominant-negative effect in the wild-type strain To investigate whether the truncated VapD Hi protein could transcomplement a mutation in the full-length gene, the truncated locus from strain R2866 was cloned into the mobilizable broad host range vector pDD515, creating pDD594, and conjugally transferred into the Rd vapD Hi mutant strain. Three-hour survival assays using HBMEC monolayers were performed, and the number of gentamicin-resistant bacteria recovered from the monolayer was an average of 5.6 × 10 2 CFU/ml for Rd (pDD515), the vector control, versus 1.9 × 10 2 CFU/ml for Rd vapD Hi (pDD594) (n = 3; P < 0.005). Survival in HBMEC by the mutant strain did not increase to wild-type levels, as was the case with the Rd vapD Hi mutant transcomplemented with a full-length vapD Hi allele on pDD564. Since subunits of the full-length VapD Hi and the truncated protein interacted, the plasmid pDD594 carrying the truncated allele was conjugally transferred into wild-type strain Rd to determine whether expression of the small protein would interfere with the function of the wild-type VapD Hi protein. The strain was used in three-hour assays of HBMEC monolayers, and it was found that Rd (pDD594) was attenuated in human cell survival as compared to Rd (pDD515), the wild-type strain carrying the vector without an insert. The average number of gentamicin-resistant bacteria recovered from the monolayer for Rd (pDD515), the vector control, was 5.7 × 10 2 CFU/ml versus 1.8 × 10 2 CFU/ml for Rd (pDD594) (n = 3; P < 0.005). The in trans expression of a truncated vapD Hi allele in the wild-type strain Rd resulted in a dominant-negative effect on survival within HBMEC monolayers. Expression of vapD Hi in Escherichia coli DH5α results in cell growth arrest To test the hypothesis that VapD Hi constituted the toxin, and that VapX Hi encoded the antitoxin portion of a TA locus, both proteins were expressed in an E. coli background. E. coli does not contain a homologue of either protein. Initially, the vapD Hi gene, HI0450, was cloned into the pTrcHisA vector (Invitrogen, Carlsbad, CA). This resulted in vapD Hi being under the control of the strong P trc promoter, which is repressed for the most part until induced by IPTG. This plasmid was designated pDD560. Both the vector control, DH5α (pTrcHisA), and DH5α (pDD560) were grown to mid-log phase in LB broth with 100 μg/ml ampicillin and aliquots were spread on LB agar plates with 100 μg/ml ampicillin and 0.1 mM IPTG. Strain DH5α (pTrcHisA) grew on the plates, but DH5α (pDD560) did not, indicating that induction and overexpression of vapD Hi was toxic to E. coli . The putative antitoxin VapX Hi , was then cloned into the pTrcHisA vector. No growth disruption occurred in DH5α with the overexpression of VapX Hi alone. The vapX Hi gene plus the lacI q gene were then subcloned into the broad host range mobilizable plasmid, pDD515, resulting in pDD672. This strategy allowed the DH5α test strain to carry two compatible plasmids, one which encoded the vapD Hi gene (pDD560) and the other expressing the vapX Hi antitoxin gene (pDD672). Both genes were under the control of a P trc promoter and were therefore both repressed until induced by IPTG. When both genes were induced with 0.1 mM IPTG on LB agar plates that contained 100 μg/ml ampicillin and 10 μg/ml chloramphenicol in strain DH5α (pDD560, pDD672), growth was restored. This indicated that the concurrent expression of the vapX Hi antitoxin with the vapD Hi toxin ameliorated the cell growth arrest observed with expression of vapD Hi alone, and that vapX Hi was necessary for this rescue. Discussion Mutation of the vapD Hi allele in strains Rd and R3001 resulted in attenuation of survival within both HBMEC and NCI-H292 monolayers, suggesting that in H. influenzae , the presence of a functional VapD Hi facilitates persistence within epithelial and endothelial cells. Mutants invaded and survived in human cells at ≤ 60% of wild-type levels. Although relatively modest, this level of attenuation has been observed during the mutational analysis of other Haemophilus virulence factors, such as opacity-associated protein A as well as the high molecular weight (HMW) proteins [ 11 , 16 ]. H. influenzae survival within human cells is multifactorial, and our data indicate that VapD Hi contributes to this process. However, strain Rd contains three other vap genes ( vapA Hi , vapB Hi , and vapC Hi ), and it is possible that these Haemophilus vap genes act synergistically, such that multiple mutations may result in a more attenuated survival phenotype. Indeed, a recent study has determined that a chromosomally-located homologue of the VapBC locus acts as a toxin-antitoxin module in the spirochete Leptospira interrogans [ 17 ]. It would be interesting to characterize a Haemophilus strain with mutations in all the vap genes. Neither of the vapD Hi mutants displayed differences in adherence to the monolayers compared to the parent strain, so the defect occurred after binding and affected the organism's ability to persist inside or between cells. Interestingly, the vapD Hi mutants were not attenuated in growth rate when compared to the parent strains, either in bacteriological media or on the surface of human cell monolayers. The observed survival attenuation of the mutants could be transcomplemented with a full-length allele from a clinical isolate, R3001, demonstrating that the phenotype was due to the mutation in vapD Hi and not polar effects. A truncated allele from another clinical isolate, R2866, did not transcomplement the Rd vapD Hi strain, indicating that the truncated protein was not functional in vivo . RT-PCR analysis confirmed that the full-length vapD Hi locus in Rd was transcribed during contact with both epithelial and endothelial cells. VapD Hi has also been identified in the soluble fraction of strain Rd grown in bacteriological media [ 10 ]. It remains to be seen if the transcription of this locus increases upon contact with human cells. Results of a PCR survey on 59 randomly-chosen strains showed that nearly all of the genetically highly heterologous NTHi commensal isolates surveyed (93%) carried a full-length vapD Hi allele on their chromosomes, suggesting that maintenance of a functional vapD Hi gene was beneficial to survival in this niche (Table 2 ). Of the invasive strains isolated from the blood or cerebrospinal fluid, 71% retained a full-length allele. Fifty percent of the isolates from sputum, tracheal aspirates, and the middle ear carried the full-length allele. Finally, all the encapsulated strains tested contained a full-length allele. It must be noted, however, that this analysis was not an exhaustive study, since a limited number of strains were included. Many clinical NTHi strains have previously been shown to express various virulence factors that enhance adherence and invasion into human cells which are not found in the sequenced Rd strain [ 11 , 18 - 20 ]. The strains identified in this PCR study that lack a functional vapD Hi allele probably compensate for its loss with genes that are not found in Rd, and these "extra genes" may well include other TA loci. The calculated molecular mass of the VapD Hi protein in Rd is approximately 10 kilodaltons, and small bacterial proteins often form multimers. Full-length VapD Hi subunits exhibited strong homodimerization in a LexA-based protein-protein interaction system, and this may indicate that the subunits form higher-order multimers such as homotrimers or homotetramers in vivo . However, the protein encoded by the truncated allele did not homodimerize in the same system, further evidence of its loss of function. Interestingly, the truncated subunit did interact with full-length subunits in heterodimerization assays. Further evidence of this heterodimerization in vivo was that the expression of the truncated subunit in the wild-type strain resulted in a dominant-negative effect on survival within HBMEC monolayers, the levels of which mimicked the attenuation observed with the vapD Hi mutation. This was likely due to truncated subunits forming hybrid complexes with full-length subunits and interfering with their structure and/or function, resulting in the observed dominant-negative phenotype. The activities of only a few toxins encoded by TA loci have been elucidated thus far. Two specific targets of plasmid-encoded toxins have been identified: CcdB of the F plasmid and ParE of plasmid RK2 inhibit DNA gyrase, and Kid of plasmid R1 was previously thought to interact with DnaB helicase, but has recently been shown to cleave cellular mRNA [ 21 - 23 ]. The target and function of a toxin from a chromosomally-encoded TA locus ( relBE ) was determined to be cleavage of mRNA in the ribosomal A site [ 24 ]. Strain Rd contains relBE homologues (HI0710 and HI0711) as well as a homologue of higA ( h ost i nhibition of g rowth antidote protein) from plasmid Rts1. The higBA TA locus is unusual in that the toxin gene ( higB ) exists upstream of the antidote protein ( higA ). Interestingly, VapA Hi of strain Rd is 29% identical and 53% similar to HigA. While the data acquired in this study suggests that VapD Hi and VapX Hi form a toxin-antitoxin pair, it is unusual to find homologues of VapX Hi only in H. influenzae and the gonococcal plasmid. H. influenzae strains R2846 and R2866 both have a truncated vapD Hi toxin gene, and possess a vapX Hi which is 100% identical to that gene in strain Rd. Their respective genomes can be searched at . Interestingly, there are complete genome sequences available for two isolates of H. pylori, N. meningitidis and Haemophilus somnus , but only one strain of each has a homologue of vapD Hi . Thus, several features of the vapD Hi / vapX Hi gene pair are unusual for a toxin-antitoxin locus. Conclusions Persistence of NTHi is important in the progression of disease caused by this organism. Many investigators have previously reported the discovery of a number of virulence factors associated with adherence, invasion and survival of NTHi inside human cells [ 1 , 3 , 4 , 16 , 18 , 25 ]. Here we report a locus that is also involved in the pathogenesis of nontypeable H. influenzae . Further studies are required to fully characterize the mechanism of VapD Hi function and to define its role in the modulation of NTHi persistence in human cells. Methods Bacterial strains, media and reagents All H. influenzae strains used are listed in Table 2 . H. influenzae was grown on chocolate agar (36 g Difco GC medium, 10 g hemoglobin, 10 ml Difco Supplement B (Becton Dickinson, Sparks, MD), 5,000 Units bacitracin per liter) or supplemented BHI (sBHI) broth or agar (37 g brain heart infusion media ± 15 g Bacto agar per liter (Remel, Lenexa, KS) with 10 μg/ml β-NAD, 10 μg/ml heme-histidine, and 5 Units/ml bacitracin). Strains containing the TSTE cassette [ 26 ] were grown on media with 15 μg/ml ribostamycin sulfate (CalBioChem, San Diego, CA). Bacteria were diluted for plating with PBS-G (phosphate-buffered saline (pH 7.0) with 0.1% gelatin). Escherichia coli strains used were DH5α, to clone fragments of NTHi DNA; DD12, as the host strain in conjugations; and SU101 or SU202 as the reporter strains for the homodimerization and heterodimerization assays, respectively [ 14 , 15 ]. T4 bacteriophage was obtained from the American Type Culture Collection (ATCC #11303). Antibiotics and other chemicals were from Sigma-Aldrich (St. Louis, MO). Restriction enzymes, deoxyribonucleotides, T4 DNA polymerase, and T4 DNA ligase were from Promega (Madison, WI). Enzymes and other reagents for PCR were from Eppendorf Scientific (Westbury, NY) and Bioline (Canton, MA). Enzymes and reagents for reverse transcriptase PCR (MasterAmp RT-PCR Kit) were from Epicentre Technologies (Madison, WI). Oligonucleotide primers were synthesized by Integrated DNA Technologies (Coralville, IA). DNA sequencing was performed by the University of Missouri DNA Core Facility (Columbia, MO), Davis Sequencing, LLC (Davis, CA), and the DNA Core Facility at Seattle Biomedical Research Institute (Seattle, WA). Plasmids were isolated using the Wizard SV Plus Plasmid Miniprep kit, PCR products and restriction digests were purified using the Wizard PCR Prep kit, and total bacterial RNA was isolated using the SV Total RNA Isolation System (Promega, Madison, WI). Plasmids and conjugations For transcomplementation, the 276 bp R3001 vapD Hi allele, along with 269 bp upstream and 227 bp downstream, was PCR-amplified using primers vap HI forward 5'-TATG TCTAGA CAGTCGCTTCATAAGC-3' and vap HI reverse 5'-CCAT TCTAGA TTTGAGGTTAAATATGG-3'. Both primers included a XbaI site (underlined) and amplified Rd genome coordinates 472803 to 473572. The product was sequenced and cloned both into the XbaI site of pBluescript SK + (creating plasmid pDD562) and into the NheI site of pDD515, a mobilizable broad-host range vector that could be conjugally transferred into and stably maintained in H. influenzae [ 12 ], creating plasmid pDD564 (Table 1 ). Plasmid pDD564 was used for transcomplementation of Rd vapD Hi . The same primers were used to PCR amplify the 135 bp truncated vapD Hi allele from strain R2866, the product of which was cloned into the NheI site of pDD515, creating pDD594. The insert was sequenced, then the plasmid was conjugally transferred into both Rd vapD Hi and wild-type Rd. Conjugations were carried out as previously described [ 12 ]. For allelic exchange, the plasmid pDD563 was constructed, which consisted of pDD562 with an interruption of vapD Hi by an aminoglycoside phosphotransferase gene ( aph (3')II ). Specifically, the 2184 bp BamHI fragment from pTSTE [ 26 ], which had been rendered blunt-ended with mung bean nuclease, was inserted into the BsaBI site of vapD Hi . For the homodimerization assays, the plasmids pDD559 and pDD577 were derived from pSR658 and carried the NTHi strain R3001 vapD Hi allele or the NTHi strain R2866 vapD Hi allele fused in-frame to the wild-type LexA DNA-binding domain, respectively [ 27 ]. For the heterodimerization assays, the plasmid pDD561 derived from pSR659 was constructed, which consisted of the R3001 vapD Hi allele fused in-frame to the mutated LexA DNA-binding domain. Mutation of vapD Hi The vapD Hi genes in Rd and strain R3001 were disrupted by allelic exchange. Briefly, strains Rd and R3001 were made competent using the M-IV media technique [ 28 ] and pDD563 linearized with XmnI was used to transform each strain. Transformants were selected on chocolate agar supplemented with 15 μg/ml ribostamycin sulfate (CalBioChem, San Diego, CA). The insertion in vapD Hi was confirmed by Southern blotting using a digoxygenin-labeled denatured PCR fragment of vapD Hi as the probe. The orientation of the aminoglycoside phosphotransferase cassette was determined by PCR using a primer that originated inside the aph (3')II gene and another that flanked vapD Hi . The resistance gene was found to be transcribed in the opposite orientation of vapD Hi in both strains. Cell culture Human brain microvascular endothelial cells (HBMECs) were a gift from K. S. Kim [ 29 ]. Cells were passaged in collagen-1 coated T-25 flasks and monolayers for invasion assays were grown in 12-well collagen-1 coated BioCoat plates (Becton Dickinson, Bedford, MA). HBMEC media contained 760 ml RPMI 1640 with 25 mM HEPES and 2 mM L-glutamine, 100 ml heat-inactivated fetal calf serum, 10 ml each of 200 mM L-glutamine, 100× MEM non-essential amino acid solution, 100× MEM vitamin solution, 100 mM MEM sodium pyruvate solution (Gibco, Grand Island, NY), and 100 ml heat-inactivated NuSerum V (Becton Dickinson, Bedford, MA) per liter. Media was changed every two days and cells were passaged every 3–5 days. Monolayers were seeded at a density of ~2.0 × 10 5 cells per well and used 48 to 72 hours after seeding. NCI-H292 human respiratory epithelial cells (ATCC catalogue # CRL-1848) were passaged in collagen-1 coated T-25 flasks and monolayers for invasion assays were grown to confluency in 12-well collagen-1 coated BioCoat plates (Becton Dickinson, Bedford, MA). NCI-H292 media consisted of 870 ml RPMI 1640 medium with 25 mM HEPES and 2 mM L-glutamine, 10 ml of 100 mM MEM sodium pyruvate solution, 10 ml of 7.5% w/v sodium bicarbonate solution (Gibco, Grand Island, NY), 10 ml of 450 mg/ml filter-sterilized glucose solution, and 100 ml heat-inactivated fetal calf serum per liter. As above, media was changed every two days and cells were passaged every 3–5 days. Monolayers were seeded at a density of ~2.5 × 10 5 cells per well and used 72 to 96 hours after seeding. Invasion and survival assays Gentamicin-resistance invasion and survival assays were performed on HBMEC and NCI-H292 monolayers as previously described [ 5 ]. Briefly, the inoculum used was 1.0 – 5.0 × 10 6 CFU of H. influenzae in a volume of 1 ml per well of a 12-well plate (an MOI of ≤ 10:1). After a 3 or 18 hour incubation in an atmosphere of 5% CO 2 at 37°C, each monolayer was extensively washed with Dulbecco's PBS and 1.5 ml of media containing 100 μg/ml gentamicin was added to each well. Following a subsequent one hour incubation in the antibiotic, the wells were again washed extensively, harvested with 1% saponin, diluted in PBS-G and plated on chocolate or sBHI agar for viable intracellular CFU/ml. To quantitate total cell-associated bacteria (both intracellular and adherent), wells were also harvested and plated after the first wash and prior to gentamicin addition. Methods for statistical analysis Statistical analyses were performed using the statistical analysis functions of Microsoft Excel (Microsoft Office 1997). For most comparisons of data, the Student's t -test was used and P -values of <0.05 were considered to indicate statistically significant differences. Reverse transcriptase PCR (RT-PCR) Total RNA was isolated using the SV Total RNA isolation system (Promega, Madison, WI) from the wild type strain Rd recovered from the media of 18-hour invasions of either HBMEC (endothelial) or NCI-H292 (epithelial) monolayers. Standard procedures were used, with the modification that two separate DNAseI incubations were performed instead of the single one recommended with the kit. RT-PCR using the MasterAmp RT-PCR kit was then performed as per the manufacturer's instructions (Epicentre Technologies, Madison, WI). Negative controls of no reverse transcriptase added to the RNA followed by traditional PCR using Biolase DNA polymerase (Bioline, Madison, WI) were used to ensure that both RNA preparations were free of contaminating DNA. The primers for RT-PCR, 450 RT for (5'-CAGGCTTATACAGACATTGG-3') and 450 RT rev (5'-TCGTACCGACTGAGAAATCC-3') amplified a 153 bp internal portion of the vapD Hi cDNA. Protein-protein interaction assays The vapD Hi alleles from strains R3001 (full-length) and R2866 (truncated) were amplified by PCR and fused in-frame to the LexA DNA-binding domain (DBD) in pSR658, resulting in pDD559 and pDD577, respectively, and used to transform the reporter strain SU101 for homodimerization assays [ 27 ]. Briefly, strain SU101 carries a lacZ gene controlled by a wild-type LexA operator site [ 14 ]. If a homodimer of two LexA DBD fusions was formed, the complex could bind to the LexA operator region and shut down transcription of lacZ , resulting in diminished levels of beta-galactosidase. The vapD Hi allele from strain R3001 was also fused in-frame to the mutated LexA DNA-binding domain in pSR659, creating pDD561. The compatible plasmids pDD561 (full length vapD Hi ) and pDD577 (truncated vapD Hi ) were both used to transform the reporter strain SU202 for heterodimerization assays. Strain SU202 [ 14 ] also has a lacZ gene controlled by a LexA operator, but this operator site is engineered such that only a mutated LexA DBD subunit (coded on pSR659) can bind to one half-site, while a wild-type LexA DBD subunit (coded on pSR658) can bind to the other half-site. Consequently, only a heterodimer composed of one mutated LexA DBD fusion subunit and one wild-type LexA DBD fusion subunit could bind to this engineered site and decrease transcription of lacZ in SU202. Three independent beta-galactosidase assays were carried out in triplicate as previously described [ 27 ]. Author's contributions DAD conceived of the study, carried out the protein-protein interaction and molecular genetics work, and drafted the manuscript. JJ carried out the intracellular survival experiments. ALS supported the study and participated in its design and coordination. All authors read and approved the final manuscript. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC503385.xml |
515295 | Phosphorylated guanine nucleotide exchange factor C3G, induced by pervanadate and Src family kinases localizes to the Golgi and subcortical actin cytoskeleton | Background The guanine nucleotide exchange factor C3G (RapGEF1) along with its effector proteins participates in signaling pathways that regulate eukaryotic cell proliferation, adhesion, apoptosis and embryonic development. It activates Rap1, Rap2 and R-Ras members of the Ras family of GTPases. C3G is activated upon phosphorylation at tyrosine 504 and therefore, determining the localization of phosphorylated C3G would provide an insight into its site of action in the cellular context. Results C3G is phosphorylated in vivo on Y504 upon coexpression with Src or Hck, two members of the Src family tyrosine kinases. Here we have determined the subcellular localization of this protein using antibodies specific to C3G and Tyr 504 phosphorylated C3G (pY504 C3G). While exogenously expressed C3G was present mostly in the cytosol, pY504 C3G formed upon Hck or Src coexpression localized predominantly at the cell membrane and the Golgi complex. Tyrosine 504-phosphorylated C3G showed colocalization with Hck and Src. Treatment of Hck and C3G transfected cells with pervanadate showed an increase in the cytosolic staining of pY504 C3G suggesting that tyrosine phosphatases may be involved in dephosphorylating cytosolic phospho-C3G. Expression of Src family kinases or treatment of cells with pervanadate resulted in an increase in endogenous pY504 C3G, which was localized predominantly at the Golgi and the cell periphery. Endogenous pY504 C3G at the cell periphery colocalized with F-actin suggesting its presence at the subcortical actin cytoskeleton. Disruption of actin cytoskeleton by cytochalasin D abolished phospho-C3G staining at the periphery of the cell without affecting its Golgi localization. Conclusions These findings show that tyrosine kinases involved in phosphorylation of C3G are responsible for regulation of its localization in a cellular context. We have demonstrated the localization of endogenous C3G modified by tyrosine phosphorylation to defined subcellular domains where it may be responsible for restricted activation of signaling pathways. | Background Guanine nucleotide exchange factors (GNEFs) are components of signaling pathways that link transmembrane receptors to intracellular GTPase family members regulating a wide variety of cellular functions such as proliferation, differentiation, adhesion and apoptosis. C3G (RapGEF1) is an ubiquitously expressed GNEF for Ras family proteins that particularly targets Rap1, Rap2 and R-Ras [ 1 - 4 ]. It has been shown to mediate signals received from B and T cell receptor activation, growth factors, cytokines, G protein coupled receptors and also adhesion [ 5 - 15 ]. C3G is present in the cytoplasm in a complex with members of the Crk family of small adapter molecules. In response to stimuli, this complex is recruited to the cell membrane involving association of Crk with phosphotyrosine containing proteins like receptor tyrosine kinases, p130 Cas, IRS-1 and paxillin [ 16 - 18 ]. Following translocation from cytosol to cell membrane, C3G activates downstream signaling. Its activation has been shown to lead to an activation of mitogen activated protein kinase and Jun N-terminal kinase [ 9 , 12 , 19 - 21 ]. Studies involving overexpression of membrane targeted C3G or dominant negative forms have shown that C3G is involved in both growth suppression as well as transformation [ 22 - 24 ]. C3G appears to play an important role in mammalian development because C3G-/- mice die before embryonic day 7.5. These studies have shown that C3G is required for vascular myogenesis and for cell adhesion and spreading [ 25 , 26 ]. The C-terminus of C3G, which shows homology to CDC25, harbors the catalytic domain. The central region of C3G, which spans about 300 residues, has polyproline tracts with the ability to bind to SH3 domains of various proteins like Crk, p130 Cas, Grb2 and Hck [ 1 , 2 , 9 , 18 , 27 ]. No function has particularly been attributed to the N-terminal sequences, which do not show homology to any defined protein sequences. The non-catalytic domain of C3G has been shown to negatively regulate its catalytic activity. Deletion of the N-terminal sequences or its association through its proline sequences to Crk leads to its activation [ 16 ]. Integrin mediated cell adhesion causes tyrosine phosphorylation of C3G [ 28 ]. It has been shown that overexpression of c-Crk1 or stimulation of cells with growth hormone leads to specific phosphorylation of Y504 [ 21 , 29 ]. This modification results in an increase in C3G catalytic activity towards Rap1. Src and JAK have been implicated in Y504 phosphorylation of C3G. More recently we have used site – specific antibodies to show that the activation of Src family kinase Hck, leads to C3G phosphorylation on Y504 suggesting that Src family kinases can directly regulate C3G activity and function [ 27 ]. The effectiveness and precision of intracellular signal transduction depends on protein-protein interactions that regulate enzyme activity as well as subcellular localization. Cell surface receptor activation leads to assembly of adaptor protein complexes at the plasma membrane, which serve to localize guanine nucleotide exchange proteins. Earlier, both endogenous as well as exogenously expressed C3G has been shown to localize to the cytoplasm and not to associate with plasma membrane [ 22 , 30 ]. Since activation of C3G occurs primarily through phosphorylation at Tyr 504 and membrane recruitment, we undertook a detailed study of the subcellular localization of both exogenously expressed and endogenous Y504 phosphorylated C3G (pY504 C3G). Expression of Src family kinases or pervanadate treatment of cells, which mimics stimulation by growth factors, resulted in marked tyrosine phosphorylation of C3G at Y504. Conventional as well as optical sectioning microscopy revealed that pY504 C3G was predominantly located at the Golgi complex and the subcortical actin cytoskeleton unlike non-phosphorylated C3G, which was largely cytosolic. Results Colocalization of C3G with Hck We have recently shown that Hck interacts with and phosphorylates C3G in vivo and we wished to determine if their interaction leads to changes in the subcellular distribution of C3G and whether pY504 C3G locates to specific subcellular domains. Cos-1 cells were transfected with C3G in the presence or absence of Hck and immunostained using anti C3G antibodies. As shown in Fig. 1A in a majority of cells, exogenously expressed C3G showed diffuse cytoplasmic staining that extended up to the plasma membrane. Variation in the level of C3G was observed among the transfected cells with weakly expressing cells showing a more prominent juxtanuclear staining. When cotransfected with Hck, most cells showed prominent staining of C3G at the plasma membrane and a juxtanuclear organelle in addition to the diffuse cytoplasmic staining. This pattern appeared similar to that seen for exogenously expressed Hck and therefore we performed colocalization studies to confirm their distribution. When coexpressed, Hck and C3G are targeted predominantly to the plasma membrane and other intracellular membranous structures (Fig. 1B ). Merged images show that these two proteins colocalize in the subcellular context. Similar patterns of colocalization were observed when C3G and Hck were expressed in HeLa cells (data not shown). Figure 1 Subcellular localization of C3G. (A) Cos-1 cells grown on coverslip were either transfected with C3G or cotransfected with Hck and indirect immunofluorescence staining performed using anti-C3G antibodies and Cy3 conjugated anti rabbit secondaries. (B) Cells transfected with Hck and C3G were stained for both the antigens as described in Materials and Methods. Hck was visualized using FITC conjugated secondaries and C3G by Cy3 conjugated secondaries. The dual panel shows the merged image of an optical section taken using the confocal microscope where the yellow signal generated shows colocalization of the two proteins. Src family kinases phosphorylate C3G and phospho-C3G localizes to the Golgi and plasma membrane To determine the subcellular distribution of phospho-C3G, which is known to be the activated form, specificity of a rabbit polyclonal phosphorylation site-specific antibody (pY504-C3G) was verified by examining its reactivity using cell lysates expressing C3G or Y504F-C3G alone or with Hck. As shown in Fig. 2A , pY504-C3G antibody recognizes only C3G when coexpressed with Hck. Neither the C3G protein expressed in itself nor the Y504F mutant coexpressed with Hck show any reactivity with this antibody suggesting that it reacts only with Y504 phosphorylated C3G. Phosphotyrosine blotting showed that a large number of cellular polypeptides are phosphorylated on tyrosine upon Hck expression, (Fig. 2A , right panel) but except for C3G none of the others show any reactivity with pY504 antibody. Y504F mutant of C3G, which shows low level of phosphorylation on other tyrosine residues is not detected by this antibody indicating its specificity towards Y504 phosphorylated C3G. Unlike Hck, whose expression is restricted to a subclass of hematopoietic cells, C3G is ubiquitously expressed and we wished to determine if other Src family kinases could phosphorylate C3G. We coexpressed C3G with an expression construct for the fusion protein c-Src-GFP and western blotting was performed using pY504-C3G antibody. As shown in Fig. 2B , in vivo, c-Src was also able to induce Y504 phosphorylation of C3G, but not that of the Y504F C3G mutant. Figure 2 Specificity of phosphospecific antibody, and phosphorylation of C3G on Y504 upon coexpression with Src family kinases. Cos-1 cells were transfected with the expression constructs for Hck (A) or c-Src (B) along with C3G as indicated and western blotting of whole cell lysates was performed using the phosphospecific antibody pY504. The blots were reprobed with C3G, Hck and anti pTyr (Panel A) or Src (Panel B) to show their expression in the lysates. pY504 C3G and pTyr was detected by ECL and C3G, Hck and Src by alkaline phosphatase dependent color development. UT indicates untransfected cell lysates. Y504F is a mutant of C3G in which tyrosine 504 is replaced by phenylalanine. We wished to determine whether C3G that colocalizes with Hck was the phosphorylated component and therefore used pY504-C3G antibodies to determine the localization of pY504 C3G in cells expressing Hck and C3G. As shown in Fig. 3A , pY504 C3G showed a staining pattern that exactly matched that of Hck with prominent staining of the plasma membrane, a juxtanuclear organelle and other intracellular membranes (3A). The prominent staining appeared to correspond with the Golgi structure and Hck has earlier been shown to localize to the Golgi [ 31 ]. In cells transfected with C3G and Hck, the pattern of pY504 C3G staining was also compared with that of total C3G as detected by the Flag tag antibody. As shown in Fig. 3B , it was observed by confocal analysis that the tag antibody detects the presence of C3G spread throughout the cytoplasm with some prominence in the juxtanuclear region and plasma membrane. The pattern suggests that the majority of the protein is cytosolic. In contrast staining for phospho-C3G was non-uniform and was particularly prominent at the Golgi and cell membrane. Colocalization of C3G with that of phospho-C3G is seen at the plasma membrane and in the juxtanuclear region. This also suggests that only a proportion of the expressed C3G is phosphorylated at Tyr504. To confirm the presence of pY504 C3G in the Golgi, we coexpressed the viral protein, VSVG-GFP known to localize to the Golgi with Hck and C3G and observed the staining pattern of pY504 C3G and that of GFP. VSVG-GFP locates predominantly at the Golgi, trans-Golgi network and also the endoplasmic reticulum and plasma membrane in a temperature-dependent manner [ 32 ]. As shown in Fig. 3C , the yellow signal generated in the dual image showed colocalization of pY504 C3G with VSVG-GFP suggesting that pY504 C3G was predominantly targeted to the Golgi complex. Unlike C3G, pY504 C3G appeared to be restricted to the plasma membrane and other intracellular membranes with particular concentration in the Golgi. When overexpressed, a large amount of C3G was present in the cytosol and we wished to determine whether cytosolic C3G does not get phosphorylated upon Hck coexpression or whether pY504 C3G in the cytosol is transient due to the action of tyrosine phosphatases. Cos-1 and HeLa cells transfected with Hck and C3G were either left untreated, or, subjected to pervanadate treatment for 10 minutes prior to fixation and stained for pY504 C3G. Pervanadate is a strong inhibitor of tyrosine phosphatases; therefore treatment of cells with pervanadate results in dramatic augmentation of tyrosine phosphorylation on cellular proteins [ 33 ]. As shown in Fig. 3D , pervanadate-treated cells showed an increase in the pY504 C3G staining in the cytoplasm suggesting that it was dephosphorylated by cytosolic tyrosine phosphatases. Figure 3 pY504-C3G colocalizes with Hck and shows predominant Golgi and membrane localization. (A) pY504 C3G colocalizes with Hck. Cos-1 cells transfected with Hck and C3G were stained for pY504 C3G (Cy3) and Hck (FITC) and examined using a confocal microscope. Figure shows an optical section for the individual stains as well as that of the merged (Dual) image. (B) Cos-1 cells transfected with Hck and C3G were dual labeled to detect phospho-C3G (Cy3 staining) and C3G using the Flag tag antibody (FITC staining). Panels show optical sections taken using the confocal microscope. (C) pY504 C3G is localized to the Golgi apparatus. Cos-1 cells were transfected with Hck, C3G and VSVG-GFP expression constructs and stained using pY504 primary antibody and Cy3 conjugated secondary. An optical section taken using the apotome is represented. (D) HeLa or Cos-1 cells transfected with Hck and C3G were left untreated (control) or treated with pervanadate (PV) prior to fixation and stained for pY504. Counter staining with Dapi shows cell nuclei. Phosphorylation of endogenous C3G and its localization to the Golgi and subcortical actin cytoskeleton In the above experiments phosphorylation and localization of C3G was studied using exogenously expressed protein and we wished to determine whether endogenous C3G could be phosphorylated and similarly targeted. Towards this end we checked the phosphorylation of endogenous C3G under conditions of Src and Hck overexpression or upon activation of cellular tyrosine kinases by pervanadate treatment. C3G protein is expressed as a doublet of about 140–150 kDa, which are products of two differentially spliced mRNAs [ 34 ]. Whole cell lysates were prepared from Cos-1 cells and those transfected with Hck or Src and western blotting performed using pY504 antibodies. As shown in Fig. 4A , overexpression of Src or Hck induces tyrosine 504 phosphorylation of endogenous C3G. The same blot was reprobed with C3G, Src and Hck antibodies to show their presence in the lysates. We examined the localization of endogenous phosphorylated C3G after c-Src expression and found that similar to the phosphorylated form of the exogenously expressed C3G, endogenous pY504 C3G was present predominantly at sites of c-Src localization. Intense staining of the Golgi and cell membranes was evident and merged images show colocalization of the two proteins (4B). Cells that did not express Src, did not show any phosphorylated C3G. Figure 4 Phosphorylation of endogenous C3G upon overexpression of Hck and its localization to the Golgi. (A) Cos-1 cells were transfected with expression constructs as indicated and whole cell lysates used in western blotting for pY504-C3G. ECL was used for detection. Blots were reprobed to show expression of C3G and the kinases. (B) Endogenous pY504 C3G colocalizes with c-Src. Cos-1 cells were transfected with the c-Src GFP fusion protein vector and cells stained for pY504-C3G expression (Cy3). c-Src expression was visualized as GFP fluorescence. Images shown are optical sections taken using the apotome. (C) Endogenous pY504-C3G localizes to the Golgi. Cos-1 cells were transfected with Hck along with VSVG-GFP and stained for pY504 C3G. Cells were left untreated (control) or treated with nocodazole (Noc) prior to fixation as described in Methods. Panels show optical section for pY504 by Cy3 and the VSVG-GFP by GFP fluorescence. The localization of endogenous pY504 C3G to the Golgi was examined in Cos-1 cells transfected with Hck and VSVG-GFP. Immunostaining for pY504 C3G was seen predominantly at the cell periphery and the Golgi (Fig. 4C ), which was confirmed by colocalization with VSVG-GFP. The effect of Golgi perturbing drugs on the localization of pY504 C3G was examined by treatment of cells with nocadazole for depolymerization of microtubules and concomitant Golgi fragmentation. Under these conditions pY504 C3G was detected as dispersed vesicles scattered in the cytoplasm and remained colocalized with VSVG-GFP (4C) confirming that endogenous pY504 C3G localized to the Golgi complex. The localization of pY504 C3G formed by the activation of endogenous tyrosine kinases was determined in cells treated with pervanadate, which is known to activate Src family kinases, in addition to inhibiting tyrosine phosphatases [ 35 - 37 ]. Pervanadate treatment results in the dramatic augmentation of phosphorylation of a large number of cellular proteins on tyrosine and therefore mimics activation of signaling pathways by growth factors [ 38 ]. While normal HeLa cells do not show any pY504 C3G, cells treated with pervanadate showed distinct presence of pY504 C3G in whole cell lysates as seen by western blotting (5A). The large number of other cellular proteins phosphorylated on tyrosine (seen upon blotting with antiphosphotyrosine antibodies), as a consequence of pervanadate treatment, do not show reactivity with pY504 antibody. In order to confirm that the signal observed upon pervanadate treatment was specific to phospho Y504-C3G, Cos-1 cells were transfected with either C3G or Y504F mutant of C3G. They were left untreated, or treated for 10 minutes with pervanadate and indirect immunofluorescence performed to observe expression of the wild type or mutant proteins as well as that of phospho-C3G. C3G, and Y504FC3G expression was monitored by staining for Flag and His tags respectively. As observed in Fig. 5B only cells expressing C3G showed intense staining for phosphoC3G while Y504F expressing cells showed no enhanced signal above that of the other non-expressing cells in the field. These results reaffirmed the specificity of the phospho-C3G antibody in detecting only Y504 phosphorylated C3G. Phosphorylated endogenous C3G staining was seen weakly in the non-expressing cells upon pervanadate treatment. Figure 5 Phosphorylation of endogenous C3G upon activation of endogenous tyrosine kinases. (A) Cells were either left untreated (UT) or treated with pervanadate (PV) and western blotting was performed using pY504 antibody. The same blot was reprobed with C3G to show the presence of endogenous C3G in these cells. (B) Cos-1 cells on coverslips were transfected with either C3G or Y504F mutant of C3G and fixed without any treatment (cont.) or after pervanadate treatment (PV). Dual labeling was performed using the tag antibodies (stained with FITC) and pY504 antibody (stained with Cy3). Panels show optical sections obtained by confocal microscopy. (C) Cos-1 and HeLa cells grown on coverslips and transfected with VSVG-GFP were fixed without any treatment (control) or after treatment with pervanadate and stained for pY504 expression. GFP fluorescence was used to visualize the staining pattern of VSVG-GFP protein. Optical sections taken using the apotome are shown. Areas of colocalization are seen from the yellow color generated in the merged images. Indirect immunoflourescence was performed to determine the localization of endogenous pY504 C3G formed by the activation of intracellular tyrosine kinases. Cos-1 and HeLa cells were stimulated by pervanadate and as seen in Fig. 5C , pY504 C3G staining, which is evident only in the treated cells, localized at the cell periphery and the Golgi. Colocalization with VSVG confirmed its localization to the Golgi complex. The staining at the cell periphery appeared to match that of the subcortical actin cytoskeleton. In order to confirm this, we dual stained the cells treated with pervanadate for F-actin and found that the pY504 C3G seen at the cell periphery colocalizes with F-actin suggesting that pY504 C3G is targeted to the subcortical actin cytoskeleton upon activation of endogenous tyrosine kinases by pervanadate (Fig. 6A ). It was also observed that pY504 C3G staining at the cell periphery was particularly prominent in confluent cells compared to cells that were sparsely growing in isolation suggesting that pY504 C3G is particularly enriched along cell-cell junctions. Phospho C3G also shows partial colocalization with filamentous actin known to be associated with the Golgi complex [ 39 ]. Figure 6 pY504C3G localizes to the subcortical actin cytoskeleton . (A) HeLa cells grown on coverslips were left untreated or treated with pervanadate and stained for pY504 expression using Cy3 secondaries. The coverslips were then stained with Oregon-green phalloidin to detect F-actin. (B) C3G phosphorylation requires the activity of Src family kinases and the presence of an intact cytoskeleton. HeLa cells were pretreated with PP2 or cytochalasin D as described in methods prior to pervanadate treatment. Images show the localization of pY504 C3G labeled with Cy3 and F-actin stained with oregon green. Images shown are a single optical section visualized using the apotome. We have observed that pervanadate treatment increased tyrosine phosphorylation of endogenous C3G. Since overexpression of Src as well as Hck results in phosphorylation of C3G, it was of interest to determine whether pervanadate induced tyrosine phosphorylation of C3G was mediated by Src family kinases within the cell. Cells were treated with PP2, a specific SFK inhibitor prior to PV treatment [ 40 ]. As shown in Fig. 6B , pY504 C3G staining of cells stimulated with pervanadate was considerably reduced, but not totally abolished when they were pretreated with PP2 suggesting the possibility of other tyrosine kinase family members activated by pervanadate contributing to C3G phosphorylation. To determine whether the increase in pY504 C3G staining was dependent on the presence of an intact cytoskeleton, we observed its localization in cells treated with cytochalasin D, a reagent that effectively disrupts the actin cytoskeletal network. Under these conditions there is a collapse of cell morphology and F-actin staining shows an irregular distribution at the cell cortex. As observed in Fig. 6B , the staining for pY504 C3G in the subcortical cytoskeleton was largely absent under conditions of moderate disruption of actin organization. Under these conditions, pY504 C3G staining at the Golgi complex, which shows a more dispersed morphology appeared not to be affected. Discussion C3G is involved in a variety of signaling pathways and therefore its dynamic localization under normal and activated situations may be physiologically relevant. In this study we demonstrate the limited subcellular distribution of Y504 phosphorylated C3G, which is predominantly targeted to the Golgi apparatus and the subcortical actin cytoskeleton. This localization has been substantiated by colocalization with a Golgi marker protein and F-actin respectively. Rap1, the substrate of C3G has been localized to the Golgi, lysosomal vesicles and cortical actin cytoskeleton [ 41 ]. But, of the at least eight known exchange factors for Rap1, C3G is the only one that has been linked definitively to the tyrosine kinase signaling pathway. Src family members like Src and Hck have been shown to localize to the plasma membrane and other intracellular membranes with particular concentration in the Golgi [ 31 , 42 ]. When endogenous C3G was phosphorylated by overexpressed Hck or Src, the localization of pY504 C3G matched that of the kinases suggesting that they may be part of the same molecular complexes. This is also evident from the staining pattern of pY504 C3G when C3G is expressed along with Hck, which is distinctly seen in the Golgi and plasma membrane. Since exogenously expressed C3G is predominantly cytosolic, it implies that, at any given time, only a small fraction of it is phosphorylated at Y504 at the plasma membrane and the Golgi. We observed more exogenously expressed pY504 C3G in the cytoplasmic compartment under conditions of inhibition of tyrosine phosphatases suggesting that pY504 C3G may be targeted by cytosolic tyrosine phosphatases. This regulation may help in restricting the activity of C3G to specific compartments. We observe very little endogenous pY504 C3G in the cytosol when HeLa or Cos-1 cells are treated with pervanadate, which not only inactivates tyrosine phosphatases, but also activates tyrosine kinases. It is possible that upon PV treatment endogenous C3G present in the cells is phosphorylated at the sites of location of the activated kinases. Pervanadate treatment has been shown to increase phosphotyrosine staining at the cell periphery indicating activation of kinases present in this subcellular domain [ 43 ]. Recently c-Src and Jak2 have been implicated in the phosphorylation of C3G in response to growth hormone stimulation of NIH 3T3 cells because dominant negative mutants of these kinases inhibit C3G phosphorylation [ 21 ]. It was suggested that this phosphorylation of endogenous C3G by c-Src occurs at Y504 because exogenously expressed Y504F mutant of C3G was not phosphorylated. Using a phosphospecific antibody we have directly shown the phosphorylation of endogenous C3G at Y504 upon overexpression of Hck [ 27 ] and c-Src (this report). c-Src is present in Cos-1 and HeLa cells which lack Hck. c-Src localizes to the cell membrane, focal adhesions and also to the Golgi [ 42 , 44 ]. Since pervanadate is a good activator of Src (36,37), it is possible that pY504 C3G seen in pervanadate treated cells is because of C3G being a Src substrate. Adhesion dependent Src activation leads to Rap-1 activation mediated by Crk and C3G [ 14 ]. Fibroblasts lacking C3G are essentially compromised in adhesion-mediated responses [ 25 , 26 ]. The localization of endogenous pY504 C3G at the subcortical actin cytoskeleton therefore suggests that this may be the site of action of C3G in mediating responses to cell adhesion. Modification of C3G by phosphorylation at defined subcellular domains may be important for restricted activation of C3G mediated signaling functions in the cells. Close structural and functional relationship is known to exist between the structural elements at the cell periphery and the signal transduction machinery. Several tyrosine kinases are known to be located in adherence junctions and the kinetics of phosphorylation and dephosphorylation appears to be controlled by structural molecules at the junctions. Our observation that disruption of actin cytoskeleton results in a loss of pY504 C3G staining at the cell periphery, but not at the Golgi complex reveals an important role for cytoskeletal network in the regulation of C3G. Conclusions The activity of guanine nucleotide exchange factor C3G is known to be regulated by tyrosine phosphorylation and membrane targeting. Using phospho-specific antibodies, we directly demonstrate that expression of Src family kinases or pervanadate treatment of cells induces phosphorylation of C3G on Y504. Unlike C3G, which is mostly cytosolic, pY504C3G locates to the Golgi and subcortical actin cytoskeleton. Demonstration of the localization of the active component of C3G to the Golgi and subcortical cytoskeleton provides evidence for a possible function for C3G at these cellular compartments. Methods Cell culture and treatment of cells HeLa and Cos-1 cells were cultured in DMEM supplemented with 10% FCS. Transfections were performed on cells grown as a monolayer in either 35 mm dishes or glass coverslips using the cationic lipid DHDEAB as described [ 45 ]. Briefly, 1 μl lipid diluted in 50 μl serum free DMEM was mixed with 1 μg DNA in 50 μl serum free DMEM. The mix was kept at room temperature for 30 min to allow complex formation before adding to the cell monolayer. Cells were fed with serum 5 hrs later and harvested 24–30 hrs after transfection. Cells were subjected to pervanadate treatment by the addition of a freshly prepared solution of pervanadate at 50 μM conc. for 10 min prior to harvesting. Pervanadate stock solution (50 mM) was prepared by mixing equal volumes of 100 mM solution of H 2 O 2 , with 100 mM solution of sodium orthovanadate. It was added to the cells within 5 mins of preparation. Golgi disruption was performed by treating the cells with 5 μg/ml of nocadazole for 30 min prior to fixation. To disrupt actin cytoskeleton, cells were treated with 1 μg/ml cytochalasin D for 20 mins. PP2 was added to cells 2 hrs before pervanadate treatment at a concentration of 10 μM to inhibit Src family kinases. Expression constructs Full length human C3G cloned in pcDNA3-FLAG was kindly provided by Dr S Tanaka. Y504F mutant of C3G in which tyrosine 504 is mutated to phenylalanine cloned in a His tagged expression vector was provided by Dr M Matsuda. The wild type rat p59 Hck cDNA was cloned in the pCI plasmid (Promega) and has been described earlier [ 27 ]. Expression plasmid for vesicular stomatitis virus glycoprotein as a GFP fusion protein (VSVG-GFP) was a kind gift from Jennifer Lippincott-Schwartz [ 32 ]. c-Src-GFP expression vector expressing wild type c-Src fused to GFP at C-terminal was from Dr D L Anders [ 46 ]. Wild type human Hck cDNA cloned into pCDNA6 expression vector was a kind gift of Dr Todd Miller [ 47 ]. Western blotting Whole cell lysates were prepared by lysing cells directly in Laemli's sample buffer and subjected to SDS-polyacrylamide gel electrophoresis. After transfer onto nitrocellulose membranes, they were processed for western blotting using the required primary antibodies. Detection was based on either color development using alkaline phosphatase conjugated secondary antibodies or on chemiluminescence using horse radish peroxidase conjugated secondaries. Indirect immunoflourescence and microscopy Cells were processed for immunoflourescence staining as described earlier [ 27 ]. The primary antibodies used were rabbit polyclonal anti-C3G (Santa Cruz Biotechnology), rabbit polyclonal anti pY504-C3G (SC-12926 R from Santa Cruz) and anti-Hck (3E9 monoclonal) made in our laboratory [ 48 ]. Dual labeling for Hck and C3G was performed by incubating the cells serially with C3G antibody, anti rabbit Cy3, monoclonal anti-Hck, and anti-mouse FITC. Cells were incubated with Oregon-green phalloidin after staining for pY504 with Cy3 to visualize F-actin. Cells transfected with vectors encoding GFP fusion proteins (GFP-Src or VSVG) were observed directly by fluorescent microscopy. Dual labeling for the C3G constructs and phospho-C3G was performed using the corresponding monoclonal tag antibodies (detected by FITC) and pY504 antibody (detected by Cy3). C3G was detected using Flag tag antibody (from Sigma) and Y504FC3G by His tag antibody (from Qiagen). Cells were examined using an Olympus microscope equipped with a cool SNAP color CCD camera. Images were captured using Image Pro Plus software. Immunoflurescence staining and colocalization was also observed using a Zeiss Axioplan 2 microscope fitted with an Apotome. The apotome (from Carl Zeiss Microimaging) is a new 3D imaging system for contrast enhancement in fluorescence microscopy. It uses structured illumination to reject signals belonging to regions of the sample that are outside the best focus position of the microscope. Images were captured using the Axiocam (Zeiss) CCD camera and processed using the Axiovision 4 software. Colocalization was also determined by observing the staining patterns using the LSM 510 Meta confocal microscope from Carl Zeiss. Abbreviations GNEF – guanine nucleotide exchange factor SFK – Src family kinase PV – Pervanadate pY504 C3G – Tyrosine 504 phosphorylated C3G VSVG – Vesicular stomatitis virus glycoprotein DMEM – Dulbecco's modified Eagle's medium FITC – Fluorescein isothiocyanate Authors contributions VR designed and carried out the experiments, analysed the data and drafted the manuscript. GS helped with designing the experiments, analyzing the data and writing the manuscript. AR provided technical help for western blotting and indirect immunoflourescence experiments. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC515295.xml |
544958 | Are zinc-bound metallothionein isoforms (I+II and III) involved in impaired thymulin production and thymic involution during ageing? | Background With advancing age, thymic efficiency shows progressive decline due to thymic involution allowing impaired cell-mediated immunity and the appearance of age-related diseases. The intrinsic cause of thymic involution is still undefined. Chronic inflammation and high glucocorticoids (GCs) may be involved. However, transgenic mice, with increased GC sensitivity and over expression of GC receptors, display delayed age-associated thymic involution. This fact suggests that other substances may affect thymic involution. Among them, both isoforms of metallothioneins (MTs) I+II and III are the major candidates because their increments leads to organ atrophy in constant stress and are induced by IL-6, which increases in ageing. Enhanced MTs in ageing allows constant sequester of zinc ions and no subsequent zinc release leading to low zinc ion bioavailability for thymic efficiency. This sequester is very limited in very old age. Thus, we have investigated the MTmRNA (I+II and III) in the thymus from young, old and very old mice. Methods MTmRNA and IL-6mRNA (RT-PCR) in the thymus from different donors were tested. Concomitantly, TECs proliferation, zinc ion bioavailability (ratio total thymulin/active thymulin), thymulin activity and corticosterone were tested from different donors. Results Both isoforms of MTmRNA and IL-6mRNA increase in old thymus coupled with low zinc ion bioavailability, reduced TECs proliferation, impaired thymulin activity and enhanced plasma corticosterone in comparison with young. Conversely, although the thymus is involuted in very old mice because of no changes in thymus weight in comparison to old mice, reduced MTmRNA, especially MT-I+II isoforms, and low IL6mRNA occur. Concomitantly, good zinc ion bioavailability, maintained TECs proliferation, satisfactory thymulin activity and reduced corticosterone are observed in very old mice. Conclusions The concomitant increments by high IL-6 of both MT isoforms in the thymus from old mice may be involved in thymic involution because provoking low zinc ion bioavailability, which is relevant for thymic efficiency. By contrast, the limited increments of MTs by low IL-6 induce good zinc ion bioavailability and satisfactory thymic efficiency in very old mice. Therefore, abnormal increased MTs may provoke complete thymic involution during ageing and the possible appearance of age-related diseases. If their increments are instead limited by low inflammation, healthy ageing and longevity may be reached. | Introduction The thymus gland is a central lymphoid organ in which bone marrow-derived T cell precursors undergo a complex process of maturation and differentiation leading to migration of positively selected thymocytes to the T cell-dependent peripheral areas [ 1 ]. Although thymocytes proliferation and differentiation persist throughout life, they diminish with ageing. Older thymuses are significantly atrophied and have fewer thymocytes than younger ones. Therefore, the thymus undergoes an age-dependent degenerative process, which allows a progressive loss of thymocytes as well as thymic lymphoid tissue becoming involuted, atrophic and full of fat [ 2 ]. Thymic involution is particularly important in relation to immunosenescence because leading to an impaired T cell-mediated immunity with the subsequent appearance of some age-related diseases [ 3 ]. The loss of thymocytes in ageing is also due, other than to diminished size of thymic cortex, to decreased production of thymic hormonal factors, which are important for thymocytes maturation, differentiation and proliferation [ 4 ]. Thymic hormonal factors, such as thymulin, thymopentin and thymosines, are produced by the Thymic Epithelial Cells (TECs), which number and proliferation decrease in ageing together with thymocytes [ 5 ]. The following one another of thymic negative events during ageing have been attributed to concomitant increments of glucocorticoids (GCs). Specific GCs receptors are present both on thymocytes and TECs leading the thymic cells to undergo apoptosis via Fas [ 6 ]. However, it has been recently reported in transgenic mice with increased GC sensitivity and over expression of GC receptors, a delayed age-associated thymic involution when compared with wild-type mice. These mice display a higher number of thymocytes and, surprisingly, thymic apoptosis is unaffected [ 7 ]. These data suggest that endogenous GCs may not be directly involved in thymic atrophy in ageing or, at least, they may act concomitantly or synergistically with other substances. In this context, some proteins, such as zinc-bound metallothioneins (MT) (isoforms I+II and III), may be involved in age-related thymic involution for the following reasons. Firstly, MT induction is controlled by GCs and pro-inflammatory cytokines (IL-6) [ 8 ], which increase in ageing and inflammation [ 9 ]; and also high IL-6 is involved in thymic dysregulation [ 10 ]. Second, MT increases in ageing and strictly related to high IL-6 and GCs [ 11 ]. Third, high MT are harmful in immunosenescence because they sequester zinc and are unable, within old lymphocytes, in the zinc release [ 11 ], which is in turn pivotal for immune efficiency and in conferring biological activity to thymulin [ 12 ], and thymulin activity, immune efficiency and free zinc ion bioavailability decrease in ageing [ 13 ]. Fourth, concomitant increments of MT-I+II and III during persistent stress like-conditions, as it occurs in ageing [ 14 ], lead to pancreas atrophy in stressed mice [ 15 ]. Although, MT-III isoform may be only present in the brain [ 16 ], its existence also in peripheral organs has been reported [ 17 ]. Following these considerations, we have investigated the presence of MT I+II and III gene expression and zinc content in the thymus from young, old and very old mice. Concomitantly, the IL-6 gene expression and TECs number and proliferation in the thymus from different donors have been evaluated as well as thymulin activity, corticosterone and zinc plasma levels. We have chosen very old mice because the MT (I+II) gene expression is low, like in younger, allowing satisfactory peripheral immune response [ 11 ]. Materials and Methods Mice Balb/c male inbred mice were used at the age of 2–3 months (young = n.10 mice), at the age of 20 months (old = n.10 mice) and at the age of 28–30 months (very old = n.10 mice). Although the maximum thymus sizes as well as peaks in thymocytes maturation and differentiation occur at 2–4 weeks of age in mice [ 18 ], no differences in thymulin activity and TECs number exist among 1 and 2–3 months of age [ 19 ]. Therefore, the choice of young mice at 2–3 months of age for the present study is appropriate. Mice were housed in plastic non-galvanized cages (5–6 mice for cage) and fed with standard pellet food (Nossan, Italy) and tap water ad libitum. Under our housing condition, the life span of Balb/c mice was of 30 months [ 13 ]. Since about 50% of survival occurred at 20 months of age, mice at this age were considered old [ 13 ]. Mice were maintained on a 12-h light/12-h dark cycle from 7:00 a.m. to 7:00 p.m. at constant temperature (20 ± 1°C) and humidity (50 ± 5%). Mice were sacrificed under ether anaesthesia. Heparinized blood samples were collected by cardiac puncture for plasma determinations of corticosterone, thymulin and zinc. Freshly thymuses were frozen in liquid nitrogen for MT-I+II, MT-III and IL-6 mRNA expressions and for testing zinc content. RNA isolation and RT-PCR analysis Total RNA was extracted from frozen thymus using Tri-Reagent according manufacture s protocol (Sigma, USA). 3 μg of RNA sample were reverse transcribed adding Olio d(T) and kept at 70°C for 10 min. dent, Raise inhibitor and MMLV reverse transcriptase were subsequently added and incubated at 37°C for 1 h. Samples were heated at 95°C to inactivate enzymes and stored at 20°C. PCRs were performed using sense and antisense primers as follows: MT-I: 5'-ATGGACCCCAACTGCTCCTGCTCCACC-3', 5'-GGGTGGAACTGTATAGGAAGACGCTGG-3' (259 bp) MT-III:5'-ATGGACCCTGAGACCTGCCCCTGTCCT-3', 5'-GGCCTCTGCCTTGGCCCCCTCTTCACC-3',(183 bp); β-actin: 5'-GGACTCCTATGTGGGTGACGAGG-3', 5'-GGGAGAGCATAGCCCTCGTAGAT-3' (366 bp); IL-6: 5'-ATGAAGTTCCTCTCTGCAAGAGACT-3', 5'-CACTAGGTTTGCCGAGTAGATCTC-3' (615 bp). Conditions for amplification were as follows: for MT-I each cycle consisted of 94°C 0.30 min, 50°C 0.30 min, 72°C 0.30 min with 30 cycles; for MT-III each cycle consisted 94°C 45 sec., 55°C 30 sec., 72°C 1.5 min with 30 cycles; for β-actin each cycle consisted 94°C 1 min, 61°C 1 min, 72°C 1 min with 24 cycles; for IL-6 each cycle consisted 94°C 1 min, 65°C 2 min, 72°C 3 min with 40 cycles. The products of amplification were size-fractionated by 2% agarose gel electrophoresis and visualized by staining with ethidium bromide. Semi-quantitative analysis of the amplified products was performed with an image analyser (Gel-doc 2000 instrument, Bio-Rad, USA). The results were evaluated as a relative unit determined by normalisation of the density of each band to that of the β-actin one. This method reflects MT protein production tested with Ag + saturation method [ 11 ]. Plasma Active Thymulin (AT) and total thymulin (TT) Plasma active zinc-bound thymulin (AT), as extensively described elsewhere [ 19 ], was measured using a bioassay based on the ability to restore the inhibitory effect of azathioprine on rosette formation in spleen cells from young Tx mice. Results were expressed as log -2 of the maximal dilution of tested plasma able to induce this phenomenon [ 19 ]. In order to avoid interference due to zinc, zinc sulphate at final concentration of 200 nM was added up to plasma samples. This fact shows the total amount of thymulin produced (active thymulin+ inactive thymulin) (TT) [ 19 ]. The ratio TT/AT is an index of zinc ion bioavailability because of strict inverse correlation between ratio TT/AT and plasma zinc levels. In particular, ratio >2 = low zinc ion bioavailability; ratio <2 = mild zinc ion bioavailability; ratio = 1 normal zinc ion bioavailability [ 19 ]. Plasma zinc and thymus zinc content Plasma and tissue zinc content were determined in Atomic Absorption Spectrophotometer (AAS) against zinc reference standards (Sigma USA). Plasma zinc was determined after plasma dilution 1:5. Thymus tissue (1 gr) was put in muffle furnace at 550°C overnight. The ash obtained was diluted with 3 ml of 3 N HCl and transferred to a 25 ml volumetric flask and further diluted with 3 ml of 0.36 N HCl. The determination of zinc was then performed at AAS. Plasma Corticosterone Plasma corticosterone level (ng/ml) was determined by RIA rat-corticosterone- 3 H kit (ICN Biomedicals, CA, USA) and referred against a standard curve. The percentage of cross-reaction with other steroid was <0.01. The sensitivity was of 0.05 ng/ml of corticosterone. Immunocytochemistry studies a) TECs characterization Anti pan-cytokeratin IgG1/FITC MoAb (Sigma, USA) diluted 1/25 and anti-keratin MoAb (Sigma, USA) diluted 1/20 were used. For this latter, guinea pig IgG/FITC (Sigma, USA) diluted 1/60 was used as second antibody. These MoAbs are specific to detect TECs (cortical and medullary) [ 20 ]. b) TEC separation and percentage TECs were separated with method described by Kurz et al. [ 20 ]. Briefly, the thymus from young, old and very old mice after 6 h of culture was minced into small fragments and incubated with collagenase (1 mg/ml, Sigma, USA) in PBS for 1 hr at 37°C (1 ml of collagenase solution/thymus). The choice of 6 h of culture is because the maximum thymulin production and TECs number and proliferation occurred at this time of culture in experiments of thymulin kinetic (from 1 h to 12 hrs) from young thymic cultures [ 21 , 22 ]. The suspension was then centrifuged (2 min, 400 g) and the pellet suspended in 1 ml of Dulbecco s modified Eagle medium/Ham s F12 medium (1:1) (DMEM/F12, Gibco, Germany). The cells were subjected to two-steps trypsin (0.1 and 0.25%, respectively) and 0.001% DNase treatment in order to avoid fibroblasts [ 19 ]. After three washes in PBS, the cells were dissociated by cautious triturating through Eppendorf tips and incubated in 3 ml of DMEM/F12 medium for 2–3 h at 37°C in humidified 5% CO 2 -atmosphere in order to make to adhere the cells. The supernatant containing unattached TEC was seeded into another plastic flask containing DMEM/F12 medium supplemented with 10% horse serum and put in culture in humidified 5% CO 2 -atmosphere. The cultures were inspected for morphologically visible fibroblasts (spindle shaped cells). In cases of significant contamination, the cells were washed with PBS and underwent again to trypsinization [ 20 ]. Separated TECs were washed three times in PBS. An aliquota (10 3 ) was resuspended in 1 ml of medium and underwent to TEC percentage analysis. Percentages of separated TECs were counted in 1.000 cells at fluorescence microscope [ 22 ]. Tests were performed after pre-fixation with cold methanol in the slides. Controls were performed without the primary antibodies. c) TECs proliferation After TECs separation, another aliquota (40 × 10 3 ) was resuspended in 4 ml of DMEM/F12 medium for TEC proliferation analysis, which was approached using [ 3 H] thymidine incorporation using 96 microtiter plates (Nunc, Denmark). 40 × 10 3 TECs were put in 40 wells (100 μl/well = 103 TECs/well). 10 wells were used as young; 10 wells as old; 10 wells as very old. Concomitantly, 1 μCi [ 3 H]-thymidine/well (Amersham, UK) was added. The plates were incubated in humidified 5%-CO2 atmosphere for 6 hrs. Automatic harvester collected the samples and the amount of incorporated radioactivity was determined in a liquid scintillation beta-counter (Perkin-Elmer, USA). Statistical analysis Two-tailed Student s t test, and ANOVA test (one-way) evaluated differences between means. Correlations were determined by linear regression analysis by the least square method. Differences were evaluated by analysis of covariance. Differences were significant when p < 0.05. Results MT-(I+II and III) and IL-6 mRNAs and zinc content in the thymus from young, old and very old mice Table 1 shows that MT-I+II and MT-III increase in old mice in comparison with young (p < 0.001). The same increment is also observed in very old mice as compared to young ones (p < 0.01), but at lower levels than old especially for MT-I+II. The increments of both isoforms of MT in old mice are correlated with high gene expression of IL-6 when compared to young mice (p < 0.01). The increments of IL-6 from the thymus of very old mice are lower, but still significant when compared to young (p < 0.05). Conversely, the zinc content within the thymus is very high in old mice as compared to young and very old mice (p < 0.01). Since AAS tests zinc-bound and zinc unbound [ 12 ], this last finding is not so surprising because it suggests that a large amount of zinc ions are bound to MT in the thymus from old mice. As a consequence, free zinc ions are not available for thymic efficiency in old age. Significant positive correlation exists between zinc content and MT-I+IImRNA from the thymus of young, old and very old mice (r = 0.83, p < 0.01). The thymus weight from old and very old mice is strongly reduced in comparison to young mice (p < 0.001), but with no changes between old and very old mice (Table 1 ). Table 1 MT-I+II, MT-III, IL-6 mRNAs and zinc content in the thymus from young, old and very old mice. Mice MT-I+II (MT-I/βactin) IL-6 (IL-6/βactin) MT-III (MT-III/βactin) Zinc content (μg/gr.) Absolute thymus weight (mg) Young 0.18 ± 0.02 0.14 ± 0.03 0.48 ± 0.02 62.3 ± 11.2 30.6 ± 5.0 Old 3.52 ± 0.3* 0.23 ± 0.02 § 1.65 ± 0.03* 107.4 ± 27.5** 13.6 ± 2.0 § Very old 1.29 ± 0.6 + 0.18 ± 0.04+ 1.63 ± 0.02* 77.4 ± 8.7 15.4 ± 2.3 § *p < 0.001 when compared to young mice; + p < 0.01 when compared to old mice; **p < 0.01 when compared to young and very old mice; § p < 0.01 when compared to young mice; ++ p < 0.05 when compared to old mice Thymic efficiency, plasma zinc and Corticosterone in young, old and very old mice Table 2 shows that thymulin activity is strongly reduced in old mice in comparison with young (p < 0.001). Thymulin activity is instead satisfactory in very old mice when compared to old ones (p < 0.05), even if its plasma value does not reach to that observed in young mice (Table 2 ). These data reflect the number (in percent) and the proliferation of TECs. Both TECs number and proliferation are reduced in old mice when compared to young and very old mice (p < 0.01), even if the TECs proliferation is lower in very old mice than in young ones, but still significant in comparison with old mice (p < 0.05) (Table 2 ). The proliferation data in young mice agree with testing TECs proliferation in pure murine TECs cell line (IT-45RI), as previously shown [ 21 ]. Table 2 Zinc ion bioavailability, corticosterone, thymulin and TECs number and proliferation in young, old and very old mice Mice Thymulin activity (log -2 ) AT/TT (log -2 (zinc ion bioavailability) Corticosterone (ng/ml) Plasma zinc (μg/dl) % TECs TECs proliferation (cpm) Young 5.5 ± 0.5 1.1 ± 0.3 153 ± 18.3 110 ± 11 53 ± 11 750 ± 25 Old 1.07 ± 0.3* 3.0 ± 0.3** 265 ± 16.6** 80 ± 5.7 ++ 21 ± 7** 227 ± 34** Very old 2.5 ± 0.3 + 1.0 ± 0.3 180 ± 12.4 + 87 ± 43 ++ 40 ± 12 + 450 ± 39 + *p < 0.001 when compared to young mice; + p < 0.05 when compared to old mice; **p < 0.01 when compared to young and very old mice; ++ p < 0.01 when compared to young mice. The ratio total thymulin (TT)/active thymulin (AT) represents the zinc ion bioavailability. More high is the ratio (≥ 2) less zinc ion bioavailability is present, whereas ratio < 2 or equal to 1 means satisfactory or good zinc ion bioavailability, respectively [ 19 ]. The ratio TT/AT is higher in old mice in comparison with young and very old mice (p < 0.01) (Table 2 ). This fact means that a good zinc ion bioavailability exists in very old mice, as in younger ones, despite plasma zinc levels are lower in very old mice than in young ones (p < 0.01) and, at the same time, not different to those observed in old mice (Table 2 ). With regard to plasma Corticosterone, higher values are observed in old mice when compared to young and very old mice (p < 0.01) (Table 2 ). Significant inverse correlation exists between zinc and Corticosterone (r = -0.71, p < 0.01), whereas significant positive correlation exists between thymulin activity and TECs number and proliferation (r = 0.81, p < 0.01; r = 0.79, p < 0.01, respectively) from young, old and very old mice. Discussion Although MT-III isoform may be present exclusively within the brain [ 16 ], some peripheral organs (testis, prostate, epididymis, tongue, ovary, uterus, stomach, heart, pancreas and seminal vesicles) may also express MT-III isoform together with MT-I+II [ 17 ]. We herein present for the first time the concomitant gene expression of MT-I+II and MT-III also in the thymus. Both isoforms of zinc-bound MT (I+II and III)mRNA increase within the thymus of old mice, but with a minor extent of MT-I+II isoform in the thymus from very old mice. Concomitantly, the gene expression of IL-6 is higher in old mice than in young and very old ones. The zinc content within the thymus is enhanced in old mice respect to young and very old mice, whereas some thymic functions (thymulin activity and TECs number and proliferation) are impaired in old mice and preserved in very old mice. These last findings regarding to zinc content and thymic efficiency seems contradictory between old and very old mice. Really, they are not contradictory. Since AAS tests zinc-bound and zinc-unbound [ 13 ], the higher zinc content in the old thymus is largely due to high zinc-bound MTs, which recall zinc from the periphery, via zinc transporters ZnT1–4 [ 23 ], and sequester a lot amount of zinc ions [ 22 ]. The inflammation provokes zinc loss with subsequent impairment of immune response [ 24 ]. Thus, such a recall and sequester by MT are due to the great inflammation by high IL-6 and GCs because zinc ions have not to be lost. But, free zinc ions are subsequently not available for thymic efficiency due to inability of MT in zinc release in constant inflammation [ 14 ]. Conversely, limited recall of zinc ions occurs in very old mice because the inflammation is less deep. As a consequence, the zinc content in the thymus from very old mice is lower and, at the same time, more free zinc ions are available for thymic efficiency by low zinc-bound MT. Anyway, the present data show that abnormal increments of both isoforms of MT are present in the atrophic thymus from old and very old mice, but with less extent of MT-I+II in very old mice. MT-I+II and III are expressed in the brain with a balance between the two isoforms (25). When one isoform increases, the other decreases due to a possible genetic control of MRE region on the chromosome 8, which brings the two isoforms [ 26 ]. Concomitant increments of the two isoforms within the hippocampus from old rats leads to impaired number and functions of synapses coupled with low zinc ion bioavailability [ 27 ]; and synaptic function is zinc-dependent [ 28 ]. The same phenomena occur in age-related neurodegenerative diseases [ 29 ], suggesting a possible role of increased MT-I+II and III in neurodegeneration [ 27 ]. Moreover, the concomitant presence of MT isoforms provokes the atrophy of the pancreas in stressed mice [ 15 ]. Therefore, enhanced MT I+II and III in the old thymus may lead to the thymic involution and atrophy because of an unbalance between the two MT isoforms. The mechanism may be largely due to the constant sequester of zinc ions by MT (I+II and III) with no subsequent zinc release leading to low zinc ion bioavailability for thymic endocrine activity and TECs proliferation [ 13 ]. In this context, IL-6 and glucocorticoids (GCs) may play key roles because IL-6 and GCs affect MTmRNA [ 8 ] and, in turn, abnormal high GC levels are involved in thymic atrophy through the activation of GC receptors on TECs and thymocytes [ 6 ]. A lack of free zinc ions also provokes thymic atrophy [ 30 ]. We have found enhanced IL-6mRNA within the thymus from old mice. Concomitantly, strong increments of plasma corticosterone are observed. Such increments are strictly related to high MTmRNA and low zinc ion bioavailability. These findings suggest that the chronic inflammation, via IL-6 and GCs, allows high MTs induction, low zinc ion bioavailability and subsequent thymic atrophy in old mice. The thymus is obviously atrophic in very old mice. But, the MT-I+IImRNA and IL-6mRNA are lower than old mice as well as reduced corticosterone. High corticosterone provokes zinc loss by urine and faeces [ 31 ]. Corticosterone is low in very old mice (Table 2 ). Thus, very old mice display more free zinc ion bioavailability with subsequent more thymic efficiency and preserved TECs number and proliferation. TECs produce thymulin, a zinc-dependent thymic hormone [ 12 ]. Zinc-bound MTs transfer zinc to thymulin in TECs [ 32 ]. The less MT-I+IImRNA in very old mice may thus allow less sequester of zinc ions. Alternatively, an easier release of zinc by MT for thymic efficiency might occur due to reduced inflammation by low IL-6mRNA and corticosterone. Anyway, the thymus from very old mice is still efficient despite it is involuted because the thymus weight between old and very old mice does not change (Table 1 ). This means that the thymic reconstitution in old age might not be necessary because the age-related loss of thymic efficiency appears to be only quantitative and not qualitative [ 33 ]. Thus, it might be sufficient to maintain inflammatory status and MTs homeostasis below a critical threshold in order to preserve thymic efficiency. Further experiments in thymic output from very old mice and in genes involved in thymocytes maturation and differentiation (Rag 1 and Rag 2) [ 2 ] are in progress in our lab. On the other hand, MT-ImRNA is lower in lymphocytes from human nonagenarians coupled with satisfactory thymic and peripheral immune efficiency and good zinc ion bioavailability [ 11 ]. Moreover, very old mice display still efficient thymic functions during liver regeneration after partial hepatectomy (model of acute and constant inflammation) [ 34 ]. In addition, the presence of involuted thymus in stressed MT transgenic mice [ 22 ] further supports the involvement of high MT in thymic involution during ageing. However, the thymic involution is not irreversible phenomena because zinc treatment in old mice restores thymic efficiency with a re-growth of thymic cortex [ 19 ]. This finding suggests that in ageing the thymus is in quiescent phase, which is less deep in very old age due probably to more zinc ion bioavailability, via MTs homeostasis, and less inflammation. Indeed, zinc also affects cell cycle and, therefore, cellular proliferation [ 35 ]. Thus, the thymus from very old mice is still active and not quiescent. The satisfactory zinc ion bioavailability coupled with the maintenance of TECs proliferation in the thymus from very old mice is in line with this interpretation. This means that in vivo condition TECs from very old mice are still capable to proliferate at the occurrence, for example in presence of external noxae in order to have a sufficient thymulin production for a prompt immune response, as occurring in human centenarians, who are high responder individuals showing a great capacity in remodelling thymulin activity [ 36 ]. In conclusion, concomitant increments of zinc-bound MT-I+II and III within the thymus may lead to thymic involution in ageing because they sequester zinc and are unable in the subsequent zinc release, which is indispensable for thymic efficiency. The cause may be related to chronic inflammation by high IL-6 and GCs. Without excluding a direct role of GCs in thymic involution [ 6 ], GCs may synergistically act with MT isoforms because GCs also affect MTmRNA [ 8 ]. Less inflammation by low IL-6 and GCs in very old mice allows reduced MTmRNA with subsequent satisfactory zinc ion bioavailability and, therefore, preserved thymic efficiency. However, if the thymic involution may be a necessary event in order to avoid autoimmune phenomena in ageing is an intriguing point to be investigated. Indeed, high IL-6 provokes an enlargement of the thymus with the appearance of autoimmune phenomena [ 10 ]. Thus, if on one hand high MTs may be of protection inducing thymic involution in order to escape autoimmune phenomena by high IL-6, on the other hand high MT are harmful because leading to low zinc ion bioavailability for thymic efficiency. However, in this context, the involvement of zinc and MT in the efficiency of extrathymic T-cell pathway [ 13 ] has to be also considered, because this pathway is prominent in ageing and autoimmunity in order to compensate thymic failure [ 37 ]. Very old mice display satisfactory thymic efficiency (present study) and good extrathymic T-cell functions [ 38 ]. Moreover, thyroid autoantibodies are rare in healthy centenarians [ 39 ]. Therefore, the thymic involution, via MTs homeostasis, has to be limited or controlled concomitantly with the appearance of efficient extrathymic T-cell functions in order to reach healthy ageing and longevity. In other words, a correct balance between thymic involution and extrathymic T-cell functions has to exist in ageing. Otherwise, a complete thymic atrophy by abnormal high MT-I+II and III, via high IL-6 may provoke continuous immune dysfunctions (thymic and extrathymic). Altered genetic controls between the two MT isoforms on MRE region of chromosome 8 may be involved, representing an interesting field of investigation in immunosenescence. Works are in progress in our lab. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC544958.xml |
535810 | Thromboembolic events and haematological diseases: a case of stroke as clinical onset of a paroxysmal nocturnal haemoglobinuria | Some haematological diseases are associated to an increased risk of thromboembolic events. We report a case of paroxysmal nocturnal haemoglobinuria (PNH) in which a cerebrovascular event represented the first clinical manifestation of disease. PNH is associated to thromboembolic events, generally of venous districts often involving unusual locations such as mesenteric vessels, sagittal veins, inferior vena cava and renal veins. To our knowledge arterial thrombotic episodes are rare and the involvement of arterial cerebral vessels is exceptional. Then, our case points out the importance of investigating about haematological disorders in all patients presenting with a stroke, in which the common predisposing conditions are excluded. | Background Paroxysmal nocturnal haemoglobinuria (PNH) is an acquired clonal disorder of haematopoietic stem cells clinically characterized by acute intravascular haemolytic crisis, in particular nocturnal, often overlapped to chronic haemolysis, and by thrombotic events and bone marrow failure. It is associated with a somatic mutation in the phosphatidylinositol glycan complementation class A (PIG-A) gene, mapped to the X chromosome; the subsequent deficiency of glycosylphosphatidylinositol (GPI) and of GPI-anchored molecules, as the decay accelerating factor (DAF or CD55) and the membrane inhibitor of reactive lysis (MIRL or CD59), causes an increased susceptibility to complement-mediated lysis of erythrocytes, leukocytes and platelets [ 1 ]. The association between PNH and thromboembolic accidents, generally manifesting as thrombotic events of venous vessels sometimes complicated by pulmonary embolism, is well established. Arterial thrombotic episodes, particularly of cerebral vessels are enough rare [ 2 ]. We report a case of PNH presenting with thromboembolic events, both venous (proximal deep venous thrombosis of lower limbs) and arterial (stroke). Case history Clinical summary A 56-year-old woman, with history of peptic ulcer and family history for cerebrovascular disease was referred to our Division of Internal Medicine with asthenia and generalized discomfort. She reported a cerebrovascular accident manifesting as a right brachial and crural hyposthenia ten month ago, almost completely receded at observation time; she also referred recurrent episodes of proximal deep venous thrombosis (DVT) of lower limbs in the last seven months. Pathological findings In order to identify any hypercoagulable state (i.e. inherited or acquired thrombophilia), in view of her personal and familiar history, we tested prothrombin time, as INR, activated partial thromboplastin time, as ratio, fibrinogen, protein C and S, antithrombin III, activated protein C resistance, anti-cardiolipin antibodies IgG and IgM, lupus anticoagulant, plasminogen activator inhibitor type 1, d-dimer, gene polimorphism of clotting factor II and V, gene polimorphism C9774T and G3775A of apolipoprotein B and gene polimorphism C3932T and C4070T of apolipoprotein E resulted all in normal range; while gene polimorphism of tetrahydrofolate reductase and angiotensin converting enzyme revealed heterozigosity for both. Subsequently, homocysteinemia test revealed mild hyperhomocysteinemia. All thrombophilic tests are summarised in table 1 . Table 1 Thrombophilic tests Thrombophilic tests (units of measurement) Results Normal range Protein C (antigen) (%) 99% 60–125 Protein S (antigen) (%) 102% 60–125 Antithrombin (activity) (%) 105% 80–120 Activated protein C resistance (Bertina) 0,90 >0,77 Anti-cardiolipin antibodies IgG (U/GPL) 4 <7 Anti-cardiolipin antibodies IgM (U/MPL) 2 <4 Lupus anticoagulant absent absent Plasminogen activator inhibitor type 1 (ng/dl) 30 4–44 PTHRA20210 gene polimorphism wild type wild type Factor V Leiden gene polimorphism wild type wild type Apolipoprotein B gene polimorphism C9774T and G3775A wild type wild type Apolipoprotein E gene polimorphism C3932T and C4070T wild type wild type Methylene-tetrahydrofolate C677T gene polimorphism heterozigosity wild type Angiotensin converting enzyme deletion gene polimorphism insertion/deletion insertion/insertion Homocysteinemia (μM) 22 5–15 Prothrombin time (INR) 0.95 0.8–1.2 Activated partial thromboplastin time (ratio) 0.92 0.8–1.2 Fibrinogen (mg/dl) 305 220–400 D-dimer (ug/l) 188 0–198 A magnetic resonance imaging scan showed little and multiple ischemic lesions in particular in left cerebral peduncle (fig 1A ), semioval centres (fig 1B ), left pons and midbrain. Moreover, a vascular ultrasound examination ruled out the presence of significant stenosis of arterial cerebral vessels and confirmed proximal DVT and post-thrombotic syndrome of lower limbs. Figure 1 Magnetic resonance imaging scan showing multiple ischemic lesions in left cerebral peduncle (1A) and semioval centres (1B). Other available data showed: red blood cells 2.470.000/mm 3 , hemoglobin 7.9 g/dl, hematocrit 25%, mean corpuscular volume 99,6 fl, mean corpuscular hemoglobin 32 pg, mean corpuscular hemoglobin concentration 32 gr/dl, white blood cells 4,040/mm 3 , platelets 93.000/mm 3 , reticulocytes 5,4%, serum iron 76 μg/dl, erytro-sedimentation rate 1° hour 40 mm, lactate dehydrogenase 944 UI/l, total bilirubin 0,72 mg/dl, indirect bilirubin 0,36 mg/dl, and presence of hemoglobinuria. Coombs' test, cold agglutinins, antinuclear antibodies, anti-extractable nuclear antigens antibodies, anti-mithocondrial antibodies, anti-smooth muscle antibodies were negative. Laboratory data and their range are summarised in table 2 . Table 2 Other laboratory findings Laboratory data (units of measurement) Results Normal range Erytro-sedimentation rate 1° hour (mm) 40 <10 lactate dehydrogenase (UI/l) 944 100–190 total bilirubin (mg/dl) 0,72 0–1 indirect bilirubin (mg/dl) 0,36 0–0,5 antinuclear antibodies absent absent anti-extractable nuclear antigens antibodies absent absent anti-mithocondrial antibodies absent absent anti-smooth muscle antibodies absent absent An abdominal ultrasonography excluded a hypersplenism and/or Kasabath-Merritt syndrome. A peripheral blood smear did not show any finding suggestive for haematological disorders. A bone marrow biopsy showed a slight hyperplasia of erythrocytic bone marrow cell line. These laboratory and morphological findings suggested a non-immune haemolytic anemia. In particular, due to the exclusion of other non-immune haemolityc disorders by means of age and clinical history together with the presence of hemoglobinuria and pancytopenia, we hypothesized paroxysmal nocturnal hemoglobinuria. This diagnosis was confirmed by an immunophenotypic profile of peripheral blood cells, showing a 15% of deficient CD59 erythrocytes, and by the presence of hemosiderinuria. Haematological findings are summarised in table 3 . Table 3 Haematological data Laboratory data (units of measurement) Results Normal range red blood cells (cells/mm 3 ) 2.470.000 4.200.000 – 5.400.000 hemoglobin (g/dl) 7,9 12–16 hematocrit (%) 25 37–45 mean corpuscolar volume (fl) 99,6 81–99 mean corpuscolar hemoglobin (pg) 32 27–31 mean corpuscolar hemoglobin concentration (g/dl) 32 32–36 white blood cells (cells/mm 3 ) 4.040 4.800 – 10.800 Platelets (cells/mm 3 ) 93.000 130.000 – 400.000 Reticulocytes (%) 5,4 <2 haemoblobinuria traces absent Hemosiderinuria present absent Coombs'test negative negative cold agglutinins negative negative Peripheral blood smear Normal Bone marrow biopsy slight hyperplasia of erythrocytic cell line Immunophenotypic profile of peripheral blood cells 15% of deficient CD59 erythrocytes During her hospitalization two haemotrasfusions were necessary in occasion of two concurrent haemolytic crises. Following dismission, in order to prevent further thromboembolic events, the patient began oral anticoagulation therapy with warfarin according with INR value in range of 2–2,5. Moreover she was treated with B12 vitamin and folate supplementation. Discussion The association between haematological diseases and thromboembolic events is well established. In particular high thrombotic risk is recognized in patients with essential thrombocythemia, polycythemia vera, PNH and drepanocytosis [ 3 ]. PNH is associated to venous thrombosis in approximately one third of cases. The most frequently reported locations are unusual such as mesenteric vessels, sagittal veins, inferior vena cava and renal veins. When thrombosis occurs in the pre-hepatic or hepatic veins, the patient develops a Budd-Chiari syndrome [ 4 ]. Arterial thrombosis is rare, even if few cases of cerebral arterial thrombosis [ 5 ] and acute myocardial infarction [ 6 ] are described in the literature. The mechanism whereby PNH causes an hypercoagulable state is not clear. PNH platelets lack the GPI-linked proteins CD55 and CD59, and respond to the deposition of terminal complement components by vesiculations of portions of their plasma membrane, resulting an increased procoagulant property. PNH cells also lack the receptor of the GPI-linked urokinase plasminogen activator, which may result in impaired fibrinolysis [ 4 ]. Also an increase of membrane-derived procoagulant microparticles (phosphatidylserin) stemming from the platelets of PNH patients has been described [ 3 ]. In our case, thrombotic events represented the clinical onset of PNH and involved both venous (DVT) and arterial (stroke) vessels. Neurological manifestations in PNH patients are generally due to cerebral venous thrombosis [ 7 , 8 ], even if a few cases of cerebral arterial episodes, involving large vessels, are described. However, usually cerebral ischaemia in PNH did not occur as presenting sign of the disease nor affect small and middle cerebrovascular arteries [ 5 ]. In our patient the relationship between PNH and thrombotic events is strongly suggested, especially after excluding inherited or acquired thrombophilia and atherosclerotic risk factors. Heterozigosities for gene polimorphism of tetrahydrofolate reductase and angiotensin converting enzyme, detected in our patient, are not associated to an increased risk of stroke, while acquired or inherited hyperhomocysteinemia may be involved [ 9 - 11 ]. Also haemotological findings agree with PNH diagnosis because of the association of thrombosis, anemia and thrombocytopenia. We excluded further causes of non-immune haemolityc anemia (i.e. spherocytosis, enzymatic disorders, microangiopathic anemia) and thrombocytopenia (i.e. disseminated intravascular coagulation, haematological malignancies, systemic erythematosus lupus, primary or secondary antiphospholipid syndrome, hypersplenism). In conclusion, PNH is associated to thromboembolic events, especially in the venous district and should be considered as a possible cause of an hypercoagulable state, in particular when unusual vascular locations are involved. Our case indicates the possibility of arterial thrombotic episodes in a patient with PNH and suggests a thorough evaluation of any haematological disorders in patients presenting with stroke or myocardial infarction, especially in the absence of atherosclerosis risk factors and/or a thrombophilic state. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC535810.xml |
549529 | Perils of paradigm: Complexity, policy design, and the Endocrine Disruptor Screening Program | The Endocrine Disruptor Screening Program (EDSP), mandated by the United States Congress in the Food Quality Protection Act of 1996, attempts to protect public health from adverse endocrine effects of synthetic chemical compounds by establishing a new testing regime. But the complexities and uncertainties of endocrine disruption and its broader regulatory and social context all but ensure the failure of this policy. This article addresses the issues facing EDSP comprehensively and in detail, in order to move beyond the current regulatory paradigm and foster discourse on a positive role for scientists in support of EDSP's end goal: to protect public health. | Introduction The United States Environmental Protection Agency (EPA) created the Endocrine Disruptor Screening Program (EDSP) to regulate endocrine-disrupting chemicals (EDCs) as mandated in the Food Quality Protection Act of 1996 (FQPA) and the Safe Drinking Water Amendments Act of 1996 (SDWAA). Unlike the more easily appreciated effects of toxic chemicals, EDCs interact with the human body indirectly by mimicking, blocking, or otherwise disrupting the normal function of hormones. The prolific study of endocrine disruption has since uncovered many unconventional and worrisome mechanisms, exposures, and effects [ 1 ]. The goal of EDSP is to: [D]evelop a screening program, using appropriate validated test systems and other scientifically relevant information, to determine whether certain substances may have an effect in humans that is similar to an effect produced by a naturally occurring estrogen, or such other endocrine effects as the [EPA] Administrator may designate [ 2 ]. If such an effect is discovered, "the [EPA] Administrator shall, as appropriate, take action under such statutory authority as is available...as is necessary to ensure the protection of public health" [ 2 ]. Unfortunately, due to four complicating factors, EDSP cannot protect public health. The first complication is practical considerations. EPA estimates the universe of potential EDCs numbers more than 87,000 items. Testing this many chemicals would take an unreasonable investment of time and resources, but even scientifically prioritizing among them is highly problematic. The second complication is hazard complexity. Establishing relationships between EDCs and health hazards proves very difficult if not impossible. Endocrine-disrupting action breaks all the rules and assumptions that have guided toxicology through the era of modern chemical regulation. Without these simplifying assumptions, science cannot establish causation efficiently or with sufficient certainty for regulation. The third complication is exposure complexity. Determining exposure levels becomes more important and more difficult because the low-dose effects of many EDCs means that low-dose and transient exposure can be just as or more dangerous than high-dose and prolonged exposure. Assessments typically discount these ill-defined exposures, but we can no longer assume them insignificant. The final complication is regulatory deficiencies. Although FQPA and SDWAA provided new authority to test for endocrine disruption, they provided no new authority for the regulation of EDCs. As a result, multiple government agencies must manage future test-positive EDCs under their jurisdiction using fragmentary and incomplete statutory authorities and different regulatory standards. This introduces significant confusion to the institutional and decision-making aspects of the EDSP regulatory framework. The EDSP policy design represents revision at the margins of U.S. chemical regulatory policy, not a radical revision. EDSP employs the same basic strategy used to regulate carcinogenic pesticides or toxic industrial chemicals – scientifically proving harm prior to regulating a chemical. Two important aspects of this strategy include an epistemological assumption that science has the capacity to 'prove' harm under the relevant scientific and legal standards, and an ethical position that prioritizes profit over human health by placing the burden of proof on public and environmental health advocates. These assumptions remain all but unchallenged in the U.S. context, and thus comprise a paradigm. While this paradigm has faced some critique in the context of carcinogenic pesticides and toxic industrial chemicals, questions of its efficacy remain unresolved. Because EDCs present new and fundamental difficulties for the science underlying the regulatory paradigm, a critical analysis of EDSP provides a more compelling case that the current chemical regulatory paradigm is in need of radical revision. This study investigates the policy design of EDSP and its broader context. The above four complications play varying roles in each stage of the EDSP policy design as discussed below. See figure 1 for a diagram roughly depicting the relationship between the four complications and the policy stages of EDSP. After considering the complications of each policy stage, this study briefly discusses the role of politics in regulation before considering the implications for the conventional chemical regulatory paradigm and a positive role for scientists in support of EDSP's end goal to protect public health. Figure 1 EDSP complications and policy stages relationship The following discussion provides a comprehensive empirical basis for considering alternatives to the status quo. This study aims to integrate the many factors conditioning the failure of EDSP for the purpose of fostering constructive discussion on U.S. regulatory policy concerning EDCs and chemicals more generally. The author does not possess a unique answer to the many and complicated issues surrounding endocrine disruption and the U.S. chemical regulatory paradigm. Given that no simple, well-developed alternatives exist that merit immediate consideration by decision-makers, it stands to reason that more creative and open discussions of EDCs, the chemical regulatory paradigm, and possible roles for the scientific community may provide long-term payoffs in public and environmental health protection well worth our attentions today. Discussion The design of EDSP consists of three main stages: priority setting, screening and testing, and a risk analysis leading to potential regulation. The complications shown in figure 1 and detailed throughout this study undermine each of these stages. Before dealing explicitly with these policy stages, however, some practical considerations deserve note. EPA estimates 87,000 chemicals require testing as potential EDCs, including pesticide chemicals, non-pesticide commercial chemicals, cosmetic ingredients, food additives, nutritional supplements, mixtures, and environmental contaminants [ 3 , 4 ]. This sets a daunting task; no U.S. chemical regulatory program has ever successfully tested so many chemicals. A quote from U.S. Congressman Mike Synar (D-OK), during a committee hearing on the safety of pesticides in foods states the problem dramatically: "Almost 20,000 pesticide products have been under review since 1972 and only 31 have been re-registered. At this rate it will take us to the year 15,520 A.D. to complete. I believe in good science. What I don't believe in is geologic time" [ 5 ]. Other researchers and watchdogs note the failure of other U.S. chemical regulatory programs to effectively gather information or protect public health (e.g. TSCA [ 6 , 7 ], and FQPA [ 8 ]; for a broader critique [ 9 , 10 ]). By applying Congressman Synar's analysis to endocrine-disrupting chemicals, we can expect characterization of all potential EDCs to take 59,000 years. EPA stated: "Testing of all of these chemicals cannot be supported at the same time because, even if EPA and industry had the resources to do so, there are not enough laboratories or other facilities capable of conducting the testing" [ 11 ]. In other words, there is reason and precedent to doubt our ability to accomplish this feat. More importantly, despite our best efforts to mobilize science in support of this difficult task, policy mechanisms allow chemical use and abuse to go forward regardless of scientific results or lack thereof. "Pesticides are registered for use while important health and safety data are still being generated; they may continue to be used after evidence of their hazards is given to EPA; they may be registered through alternative processes that bypass important tests; and they may never be required to be tested for certain kinds of hazards" ([ 9 ], also see [ 12 ]). Additionally, politics often plays a greater role in the decision to regulate than science (see Politics section below). Politics and policy design play significant roles in the modern chemical regulatory regime. Hence, a comprehensive analysis of EDC regulation must take politics and policy design as well as science into account. The appropriate standard by which to judge these disparate policy elements is Congress's mandated end goal: to protect public health. We will return to this issue. Priority setting After EPA sorts chemicals according to statutory considerations, data availability, and qualitative judgment, EPA decides which of the estimated 87,000 chemicals merit consideration first through 'priority setting' [ 13 ]. The sorting of chemicals into the four categories in figure 2 and the setting of priorities within 'Category 2' require functionally equivalent information (Note figure 2 disaggregates the policy stages from the right hand column of figure 1 ). So priority setting, as used in this article, applies to both EDSP activities described as sorting and priority setting (i.e. everything above the dashed line in figure 2 ). The Endocrine Disruptor Screening and Testing Advisory Committee (EDSTAC) defined 'priority' as 'of greatest concern' in their final report, presumably as determined by science [ 14 ]. EPA, however, added statutory criteria to the scientific considerations by necessity: "EPA plans to use three main categories of information to set priorities: exposure-related information, effects-related information, and statutory criteria" [ 15 ]. Setting aside the chemicals Congress mandated EPA test (the statutory criteria), how well can EPA scientifically set priorities among potential EDCs? Figure 2 EDSP policy design EPA simply cannot set priorities based on science alone. Almost no data on potential endocrine disruption exists for the 87,000 prospective EDCs, creating a catch-22 of needing unavailable information to decide how to gather information. EPA wants to prioritize which chemicals to develop data on by examining hazard and exposure data on those chemicals. In the information poor environment of endocrine disruption, EPA has no basis to commence setting priorities. Two methodologies, high-throughput pre-screening (HTPS) and quantitative structure activity relationships (QSAR) both discussed below, have attracted attention and resources due to a general recognition of this problem. But more important than our current lack of data, EDCs operate with a high degree of complexity. Because of system complexity, some uncertainty about endocrine disruption probably cannot be resolved – resulting in some abiding doubt about the significance of a chemical as a potential endocrine disruptor. The scientific community and EPA seem quite cognizant of this complexity, but its relevance for policy and for the end goal of protecting public health deserves careful attention. EPA has not realized their ideal of priority setting based on hazard and exposure information because of the catch-22 mentioned above. As a result, "EPA's proposed approach focuses on human exposure-related factors rather than on a combination of exposure- and [hazard]-related factors" [ 16 ]. While this statement acknowledges some of the difficulty using scientific information for priority setting, it is misleading. EPA's current stated policy prioritizes only pesticide active ingredients and high production volume (HPV) pesticidal inerts not because of scientific criteria (hazard- or exposure-related), but for statutory reasons; Congress specifically mandated testing of these compounds [ 2 , 17 ]. In fact, the concept of setting priorities for potential EDCs based only on exposure-related factors is fundamentally flawed. To set priorities based on exposure factors alone, one must assume greater exposure to a chemical implies greater potential hazard (or some other arbitrary assumption). For EDCs, this assumption is scientifically insupportable. The complexity of low-dose effects (discussed in more detail in the Screening and testing section below) implies that exposure to some EDCs at extremely dilute doses may have a greater effect than massive exposure to that same chemical. Transient or low-concentration EDCs may also pose a greater risk than other high-exposure chemicals. Low-dose effects and other exposure complexities make exposure alone a poor proxy for setting priorities. Since different vulnerabilities and sometimes different health effects manifest at different developmental stages, any exposure-only judgment will run into significant difficulties defining spatial and temporal boundaries for exposure determinations. Some short-lived chemicals may have important endocrine- disrupting effects, but may not show up in EPA's most robust exposure data sources: biological sampling and environmental monitoring [ 15 ]. Further complexities undermine scientific determinations of exposure. Maternal metabolism of fat stores containing bioaccumulated EDCs may lead to practically unidentifiable fetal exposure. Some poorly understood exposure sources, such as flame retardants in clothing and furniture or phthalates leaching from plastics, would be extremely difficult to determine because of the complex social, cultural, and ecological conditions that affect chemical release and exposure. Even conventional exposure determinations, such as ingested pesticides, are fundamentally dependent upon patterns of food consumption. Averaging exposure may obscure vulnerabilities brought on by complicated cultural, social, and economic patterns of food consumption and other subpopulation attributes or behaviors. For example, research has shown significant differences in the exposure of adults and children to certain pesticides via residual contamination of fresh and processed foods [ 18 , 19 ]. FQPA may further obscure exposure determinations through mandates requiring EPA to assess cumulative exposure, including all exposure routes and sources, all chemicals with similar modes of action, and other mixtures of multiple chemicals. The complexity of endocrine disruption undermines old assumptions about the relevance of exposure and prevents scientifically meaningful prioritization on the basis of exposure data alone. Understanding this limitation to some degree, EPA continues to develop and evaluate two methodologies to include health-effects criteria in the prioritization process. The first method is high-throughput pre-screening, or HTPS. This method allows for fast, large-scale testing of chemicals for interactions with estrogen, androgen, and now thyroid receptors. HTPS, unfortunately, has flaws as a means of detecting potential hazard for priority setting. Most basically, HTPS only tests for hormone receptor interactions. The possibility of this leading to a systematic bias against consideration of non-receptor mediated endocrine disruption is significant. Receptor interaction is only one means by which a chemical can disrupt the endocrine system. Interaction with the hormone molecules themselves, stimulation or suppression of hormone production, and disruption of old hormone metabolism can all lead to endocrine-disrupting effects as well. HTPS cannot test for these effects. Other hazard-related shortcomings relevant to HTPS are discussed more thoroughly in the Screening and testing section of this study. An EPA feasibility study cited some of the same issues raised here in declaring HTPS insufficient for regulatory purposes [ 20 ]. A second methodology under development is a computer modeling technique called quantitative structure activity relationships, or QSAR. QSAR simulates the behavior of a chemical based on its structure. EPA would use QSAR to predict chemical binding with estrogen, androgen, and thyroid receptors. The dominant criticism of HTPS applies to QSAR as well – it tests for receptor binding only. However, the use of computer models will incorporate new uncertainties via the selection of system boundaries and functional relationships that may preclude mechanisms and variables relevant to some endocrine-disrupting action. While a modeling effort may yield useful knowledge, as a decision-making tool QSAR is wanting. The inevitable and likely widespread false positive and false negative results will demand a parallel testing procedure to establish QSAR's utility for priority setting. But the drive to develop QSAR derives from an inability to devise an efficient and reliable testing procedure (like HTPS) in the first place. While these limitations may or may not be overcome in time, at present the methodology is not useful for setting priorities. It is instructive, however, to consider the justifications for the development of QSAR. "Systematic toxicity testing, using conventional toxicology methodologies, of single chemicals and chemical mixtures is highly impractical because of the immense numbers of chemicals and chemical mixtures involved and the limited scientific resources" [ 21 ]. QSAR was developed as an attempt to solve the very problems cited in the Policy Design section above. Although models can provide much useful information, they are unlikely to help prioritize EDCs anytime soon. Screening and testing To amass the evidence necessary for regulation, EPA designed two tiers of scientific assays. Tier 1 screening involves short-term assays to detect potential chemical interaction with the endocrine system. Tier 2 testing involves long-term assays to establish such interactions, explore more complicated endpoints, and establish dose-response relationships. If enough data exists, a chemical can go straight to Tier 2 testing. Otherwise chemicals are assigned to Tier 1, where chemicals are prioritized and screened, with all positive results forwarded for Tier 2 testing (see figure 2 for the policy design). "The Tier 2 tests are longer in duration than Tier 1 tests and are designed to encompass critical life stages and processes as well as a broad range of doses, and are intended to be administrated by a relevant route of exposure" [ 16 ]. Although screening and testing are separate EDSP regulatory stages, their vulnerabilities to complexity are similar enough to group them together for purposes of this discussion. Both screening and testing focus on identifying hazard, leaving exposure considerations for the final risk assessment. As such, this discussion addresses only hazard-related complexities and uncertainties. The toxicology of endocrine disruption is inherently complex in the sense that scientists must abandon the simplifying assumptions of standard toxicology. Most notably, we must abandon the assumption of monotonic dose-response relationships, which assume an increased exposure to a substance always leads to an increase in effect. Increasing exposure to some EDCs swamps the endocrine system and prevents or reduces dysfunction (i.e. an inverted U dose-response, e.g. [ 22 , 23 ]), while other EDCs exhibit effects at both high- and low-doses, but not in between (i.e. a U- or J-shaped dose-response, e.g. [ 24 ]); still others may exhibit hormesis, whereby a small dose has a beneficial effect [ 25 , 26 ]. The monotonic assumption allows for statistically significant results using smaller sample sizes exposed to higher doses for shorter periods of time. Linearly scaling these results down to typical exposure levels presumably yields approximate quantitative rates of, for example, disease or cancer. Abandoning this assumption decreases testing efficiency and multiplies the time and other resources necessary to understand the potential hazard posed by a chemical. A quote from University of Washington, Seattle toxicologist David Eaton, states the issue simply: "It's just too expensive ... you'll never be able to characterize [a low-dose effect] to the point where people think it's real" [ 25 ]. Non-monotonic dose-responses may also indicate some unresolvable system complexity. Other standard toxicological assumptions suffer the same fate as monotonic dose-response, for example: the threshold assumption and the assumption that a chemical has a uniform effect. Other factors complicate a scientific determination of hazard. Two chemicals can interact in ways that alter their effects. Some chemicals together inhibit their individual effects, reducing or preventing an adverse effect where one is expected. Others simply add their effects together, and yet others interact synergistically, magnifying the effect either or both would normally have. "Synergistic interactions are the most problematic, because they indicate that the effects of multiple chemicals together can be significantly more powerful than might be predicted simply by adding up their effects one at a time. Regulatory science rarely incorporates any interactions; it is incapable, at present, of coping with synergies" [ 27 ]. Regardless of this incapacity, EPA seems determined to try and deal with this complexity: "EPA recognizes that the science of evaluating mixtures remains complex and unclear, but believes that it should begin to confront the issues raised by them" [ 28 ]. Additionally, scientists have evidenced possible synergism between EDCs and infectious disease agents [ 29 ]. Synergies with nutrients or poor nutrient levels might also prove significant (e.g. lead, [ 30 ]). These interaction effects further aggravate the difficulty of determining hazard. Several studies of the body burden of chemicals in humans evidence high and diverse concentrations of synthetic chemicals, indicating the importance and likelihood of chemical interactions [ 31 - 33 ]. Another serious complication involves the selection of testing endpoints, or dysfunctions possibly caused by EDCs. Some of the dysfunctions already identified through animal studies include cancer susceptibility and birth defects, but also more subtle endpoints like immunological dysfunction, suppression of secondary sex characteristics, decreased fertility, increased aggression, decreased mental capacity and focus, disrupted brain development, etc [ 34 , 35 ]. While scientists can examine some of these endpoints relatively easily in human populations (e.g. cancer incidence), others would be incredibly difficult to observe, measure, or prove with sufficient statistical certainty (e.g. feminization of boys or masculinization of girls). The difficulty of isolating and measuring these more subtle effects makes them impractical as regulatory endpoints. The inability of scientific testing to measure such endpoints, however, does not justify their exclusion from regulatory consideration. Such a policy would (and in fact does) bias the regulation of chemicals by exempting the most complicated chemicals and the most complex health effects from regulatory consideration. Risk analysis and regulation A risk analysis concludes the EDSP policy design. EPA claims it will use its standard human health risk assessment process for EDCs [ 36 ]. Simply put, EPA considers hazard and exposure data and uncertainties to make regulatory decisions (see bottom figure 2 to right of dashed line). For example, an extremely hazardous chemical associated with insignificant exposure probably would not require regulation while a mildly hazardous chemical with widespread and pervasive exposure probably would. Safety factors are built into this process to protect public health. The standard safety factor for pesticides is 100× to compensate for uncertainties such as response differences between humans and the animals studied. FQPA added an additional 10× safety factor to protect children, but EPA's uses this additional safety factor inconsistently [ 8 ]. Because EPA's risk analysis demands an explicit integration of hazard and exposure data, the risk assessment itself is vulnerable to the exposure complexities treated in the Priority setting section and the hazard complexities treated in the Screening and testing section. These complexities include: HAZARD – low-dose effects, mixtures and synergies, and uncertain endpoints; and EXPOSURE – transient and low-concentration exposure to EDCs, maternal metabolism of bioaccumulated EDCs, varying vulnerability and response by developmental stage, poorly understood exposure sources, vulnerable subpopulations, and cultural, social, and economic patterns and spatial and temporal bounds of chemical release and exposure. More science cannot resolve all of these complexities and uncertainties. But these complexities translate into an even more pernicious difficulty, as decisions assumed answerable by science must be made under conditions of scientific ambiguity. Ambiguity is related to complexity as follows: Complexity refers to properties of the system under study, not the study itself. Such properties include interactive effects (like synergies), feedback loops, temporal delays between cause and effect, chaotic or stochastic system behavior, large numbers of intervening variables, and inter-individual variations. Uncertainty refers to limitations of the human analysis of a complex system. While science is often employed 'to reduce uncertainty,' the complexity of a system sets bounds on the prospective certainty of informed scientific judgment. For example, further science can never remove the chaos from a chaotic system or the randomness from a stochastic one. And to illustrate the point, exposure of a fetus to environmental EDC contamination in utero might easily be a chaotic or stochastic system. Ambiguity refers to a situation in which existing scientific data support equally valid but competing interpretations of risk (see [ 37 ] for a discussion of concepts). Ambiguity is the single greatest limitation to the use of endocrine disruption science in policy. While an oft-cited truism holds that decision-makers can make better decisions with reliable information at hand, this hardly tells the whole story. Other considerations play into decision-making, including value tradeoffs, time and resource limitations, and informational constraints, including scientific ambiguity. A National Academy of Sciences report on endocrine disruption substantiates the ambiguity in this field: [I]t became clear as the work of the committee progressed that the same data could be approached from different viewpoints. Those different views led to different judgments among the committee members about the significance of the threat posed by [EDCs]. In [some] cases, the differences do not reflect the need for research but reflect differing judgments about the significance of information. The differences are not confined to the members of this committee but are also reflected in the scientific community at large and in the comments received during review [ 35 ]. The recommendations of this committee amounted to suggestions for more research. While more research might be a good thing, such a suggestion provides no guidance on how to address the more policy-relevant question of how to make decisions with ambiguous scientific information. In a nutshell, more science is not a panacea for all the problems of risk analysis or decision-making. This brings us to making decisions about regulation. If a reasonably certain determination of harm about an EDC could somehow be made, how would one regulate that chemical? This discussion of regulation is only tentative since EPA's Regulatory Activities Workgroup continues to study the issue. Since EPA has not yet validated any screening or testing procedures [ 38 ], regulation under EDSP has so far received little attention. Under section 408p of FQPA, EPA must use "such statutory authority as is available " to protect public health [ 2 ] (emphasis added). In other words, the statutes requiring testing for endocrine disruption provide no new process by which to regulate those chemicals – the standards for regulation remain those of previous regulatory laws. Unfortunately, this complicates enforcement authority for EDCs. Regulation must be authorized under one of four laws: the Toxic Substances Control Act (TSCA), the Federal Food, Drug, and Cosmetic Act (FFDCA), the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA), or the Safe Drinking Water Act (SDWA). The agencies with regulatory jurisdiction for EPA's list of 87,000 chemicals are: the Environmental Protection Agency (EPA), the Food and Drug Administration (FDA) in the U.S. Department of Health and Human Services, and the Food Safety and Inspection Service (FSIS) in the U.S. Department of Agriculture. EDSTAC recommended and EPA adopted the following list of chemicals for endocrine disruptor testing: 75,500 commercial chemicals listed under TSCA, 900 pesticide active ingredients, 2,500 pesticide inert ingredients, 5,000 cosmetic ingredients, 3,000 food additives, an unspecified number of nutritional substances, and an unspecified number of natural hormonally active plant residues [ 14 ]. Testing and enforcement authority for this universe of chemicals is fragmentary. For example, EPA has authority under FIFRA and FFDCA to set tolerances for pesticides on food, but enforcement authority falls to FSIS for meat and poultry products, and FDA for other food items. The authority is also incomplete. For example, FDA has authority over the estimated 5,000 cosmetic chemicals, but FDA has no authority to require any information from the manufacturer or to mandate product safety testing. FDA's regulatory authority over cosmetics begins only after a product (possibly without any safety information) enters the market. Additionally, the standards by which to regulate differ. Under TSCA and SDWA the economic costs of regulation must be balanced against the public health threat, but under FIFRA and FFDCA, economics can be considered in only narrowly crafted situations – the standard is largely health-only based. FFDCA and FIFRA as amended by FQPA use the "reasonable certainty of no harm" standard. This standard translates into a 95% certainty that fewer than one in a million additional cancer deaths will occur due to the expected exposure over a lifetime. No translation specific to EDCs of this standard is yet available. TSCA, on the other hand, must prevent "unreasonable risk" of injury to health or the environment. A risk is "unreasonable" if the risks exceed the benefits associated with that activity, including economic benefits. SDWA explicitly requires a consideration of the cost of compliance to state, local, and other water systems when setting safety standards. The estimated 2,500 pesticide inert ingredients may defy regulation due to trade secret norms, poor EPA data quality, and historic government neglect [ 39 , 40 ]. Additionally, no federal statute delegates any authority at all for the testing or regulation of nutritional supplements. Presumably, endocrine-disrupting nutritional supplements could be regulated only voluntarily, but the onus of testing would fall completely upon the executive agency that volunteers to expand its responsibilities. Significant difficulties involving confidential business information, including proprietary chemicals and chemical mixtures may further compromise enforcement capability. The complexity of the institutional and legal landscape (multiple interacting agencies with multiple overlapping mandates and authority) creates substantial regulatory confusion. In this situation, the powers and responsibilities of different government agencies might be interpreted differently by other agencies or by affected parties. Such confusion leaves room for interpretation that may require long delays and intensive court battles to resolve. The history of TSCA indicates that such confusion (for TSCA, 'balancing economic cost' with regulations to protect public health) as well as the menace of legal action can lead to crippling regulatory inaction [ 41 ]. By requiring enforcement under existing statutory authority, FQPA leaves the regulation of the already complicated universe of EDCs to a complicated web of regulatory regimes of questionable efficacy. Politics Finally, the role of politics in regulatory decision-making deserves note. The conventional ideal of regulation under the current paradigm is that good science leads to an informed decision-maker who can then remove or limit a proven hazardous chemical from commerce (or, rarely, prevent its introduction). The complexities of endocrine disruption science, practical considerations, and the regulatory deficiencies discussed above impose limitations on this conventional ideal. Neglecting the role of politics in regulatory decision-making, however, is perhaps this ideal's most significant omission. A variety of academic and government research as well as environmental and public interest group analysis points to the failure of testing regimes to produce significant regulation or protect public health (e.g. [ 7 , 8 , 10 , 12 , 41 , 42 ]). In fact, past regulation often addressed specific chemicals by legislative mandate (e.g. the mandated ban of PCBs in TSCA) or due to media-promoted public awareness and its resultant outcry (e.g. the January 1971 court order essentially forcing EPA Administrator Ruckelshaus to ban DDT). In other words, politics often leads to regulation regardless of scientific considerations. The Alar 'scare' of 1989, when the public and EPA reacted to evidence that contamination of apples might endanger child health, provides a visible recent example of this dynamic. A 60 Minutes show aired on February 26, 1989 [ 43 ] dedicated to the findings of a Natural Resources Defense Council study titled 'Intolerable Risk: Pesticides in our Children's Food' [ 18 ]. The public outcry about Alar (a.k.a. daminozide) led to a drop in apple sales and pushed EPA and Alar's manufacturer, Uniroyal Chemical Company, Inc., to take action [ 44 ]. After announcing the safety of Alar in March 1989, and an intention to take no action before July 1990 [ 45 ], EPA announced a preliminary determination to eventually cancel all registrations of daminozide used on foods in May 1989 [ 46 ]. A little over a week later, Uniroyal announced a voluntary recall of all remaining stocks of Alar, and EPA approved a voluntary cancellation of all Uniroyal's daminozide registrations that November [ 47 ]. In an attempt to avoid the still bitter battle between Alar critics and advocates, the relevant point is not whether Alar is or is not a health hazard, but that politics played a major if not a dominant role in its regulation. The very fact that a bitter argument about the actual risk posed by Alar persists indicates that science does not always play a definitive role in regulatory decision-making [ 44 , 48 , 49 ]. But if politics significantly affects decision-making, what is the role of science? To understand the interplay between science and regulation, we must critically consider the conventional assumptions of the modern U.S. chemical regulatory paradigm. Conclusions The conventional paradigm underlying EDSP and most other U.S. chemical regulation amounts to 'science leads to regulation;' it assumes a scientific determination of harm must and, in fact, does precede regulatory action. In this context, Congress mandated EPA protect public health from EDCs, but only after "develop[ing] a screening program, using appropriate validated test systems and other scientifically relevant information, to determine [harm]" [ 2 ]. Real progress on protecting public health waits on the development of a scientific testing regime, on faith that scientific testing is both necessary and sufficient to protect public health. Practical considerations, hazard complexity, exposure complexity, and regulatory deficiencies all challenge the naivety of this assumption. Rational analysis of these factors leads to the inescapable conclusion that science has limitations within the existing regulatory regime and that other important factors are disregarded by the current paradigm. This criticism does not discount the contributions of science, although it seriously questions the assumption that simply doing more science will protect public health. Additionally, this argument does not promote unconsidered action, although it does stress the need to make decisions in the face of ambiguous information. The paradigm itself, though invisible to most adherents, is quite real. The words and actions of industry and environmental groups, government agency personnel, members of Congress, and other concerned interests, regardless of their side of the debate, indicate near universal buy-in to the 'science leads to regulation' paradigm (see [ 10 ] for discussion). But the complexity, uncertainty, and ambiguity of endocrine disruption and its broader context undermine this paradigm's simple logic. The internal validity of 'science leads to regulation' presumes the capacity of science to prove harm with sufficient certainty to regulate. Exposure complexities, including transient and low-concentration exposure to EDCs, maternal metabolism of bioaccumulated EDCs, varying vulnerability and response by developmental stage, poorly understood exposure sources, vulnerable subpopulations, and cultural, social, and economic patterns and spatial and temporal bounds of chemical release and exposure, place the ability of science to make solid exposure determinations in significant doubt (see Priority setting section). Hazard complexities, including low-dose effects, mixtures and synergies, and uncertain endpoints, contribute further obstacles to an unambiguous scientific determination of harm (see Screening and testing section). Under current policy mechanisms, these complications of endocrine-disruption science will prevent any meaningful regulatory action. Essentially, endocrine disruption is too complex and our science too uncertain; most scientific information regarding EDCs will remain ambiguous, with the available information supporting quite different judgments of risk. Recall this observation by the National Research Council: "In [some] cases, the differences [in scientific judgments of EDC significance] do not reflect the need for research but reflect differing judgments about the significance of information" [ 35 ]. The National Research Council has also recognized the more general prejudice that hinders regulation: "The assumption of the null hypothesis as used in risk analysis [as in the case of regulating chemicals] contains an implicit bias because it places a greater burden of proof on those who would restrict than those who would pursue a hazardous activity, presuming these activities are safe until proven otherwise" [ 50 ]. In other words, the paradigm is biased against regulation, and the complexity and uncertainty of endocrine disruption will further undermine attempts to regulate. The external validity of 'science leads to regulation' must take into account the broader context of potential endocrine disruptor regulation. The regulatory deficiencies addressed in the Risk analysis and regulation section, the practical considerations addressed in the Policy Design section, and the significant role of politics in regulation discussed in the Politics section challenge the arbitrary constraints the paradigm places on non-scientific factors. The roles of actors besides the scientific community and agency scientists can make or break regulation. Furthermore, the legal ambiguity of regulating different chemicals under four statutes with different regulatory standards and fragmentary, incomplete statutory authority guarantees further difficulty with regulation, even if the science could meet the near-impossible burden of proof. The most basic practical considerations, including other U.S. regulatory precedents, policy mechanisms to avoid regulation, and most importantly, the sheer number of potential EDCs, each bring the conventional paradigm into doubt. 'Science leads to regulation' simply leaves too much of the decision-making context out of the picture. Yet discussions of improving chemical regulation primarily deal with the minutiae of scientific testing regimes. For example, an Environmental Defense Fund analysis exposing the utter futility of chemical regulation under TSCA (based on the U.S. Government's own damning analysis) came to the conclusion that the failure of testing in the past means we need to test better in the future [ 7 , 41 ]. Though better testing might improve things, such a suggestion still ignores the hard reality of decision-making. Scientific information often remains ambiguous and consistent with quite different action alternatives. Furthermore, scientific information forever remains only one consideration of the decision maker – economic impact, resource tradeoffs, political considerations, constituent needs, and agency funding are other obvious and equally relevant considerations. The relevance of science for regulatory decision-making lies predominantly outside the current trend of increasingly detailed mechanistic investigation of endocrine disruption. While endocrine disruption science can contribute much to a decision-maker, it cannot provide unambiguous information that 'objectively' determines the correct decision. But science could help guide decision-making under conditions of ambiguity; uncertainty does not entail 'anything goes.' On the contrary, the uncertainty itself might guide decision-making better than any other factual information. The 2001 Intergovernmental Panel on Climate Change reports provide a precedent in the explicit treatment of uncertainty – the reports indicate the relevance of scientific information for decision makers by ranking scientific conclusions by Bayesian confidence estimates (e.g. virtually certain means greater than 99% confidence, very likely 90–99%, etc) [ 51 ]. This empowers decision makers and the public by contextualizing expert knowledge and frees scientists to provide relevant information that has not yet met the rigorous standards of scientific proof and peer review. Some excellent work extrapolating from this precedent and considering its ramifications already exists [ 52 , 53 ]. A franker treatment of uncertainty can improve the relevance of science to decision-making and promote realistic expectations of science and the scientific community. Such progress might turn the spotlight on more significant impediments to regulatory decision-making under conditions of ambiguity, such as the difficulty of testing 87,000 chemicals and the role of politics and values in making regulatory decisions. Scientists can take action within their own communities to support the policy goal of protecting public health by exploiting and improving the role scientific information plays in chemical regulation. The possibility that scientists can empower decision makers and the public by developing community standards and norms explicitly addressing uncertainty is one creative idea, but more are needed. The key to progress is taking an active and creative role in support of the protection of public health. Waiting for a solution in the form of national legislation has failed to inspire significant change over the last several decades. But explicit action within the scientific community that discourages unrealistic expectations of science will support long-term progress on chemical regulatory policy and the protection of public health. List of abbreviations DDT – dichlorodiphenyltrichloroethane EDC(s) – endocrine disrupting chemical(s) EDSP – (U.S.) Endocrine Disruptor Screening Program EDSTAC – Endocrine Disruptor Screening and Testing Advisory Committee EPA – (U.S.) Environmental Protection Agency FDA – (U.S.) Food and Drug Administration FFDCA – (U.S.) Federal Food, Drug, and Cosmetic Act FIFRA – (U.S.) Federal Insecticide, Fungicide, and Rodenticide Act FSIS – (U.S.) Food Safety and Inspection Service FQPA – (U.S.) Food Quality Protection Act HPV – high production volume HTPS – high-throughput pre-screening PCBs – polychlorinated biphenyls QSAR – quantitative structure activity relationships SDWA – (U.S.) Safe Drinking Water Act SDWAA – (U.S.) Safe Drinking Water Amendments Act TSCA – (U.S.) Toxic Substances Control Act Competing interests The author(s) declare that they have no competing interests. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC549529.xml |
548151 | Results of paclitaxel (day 1 and 8) and carboplatin given on every three weeks in advanced (stage III-IV) non-small cell lung cancer | Background Both paclitaxel (P) and carboplatin (C) have significant activity in non-small cell lung cancer (NSCLC). The weekly administration of P is active, dose intense, and has a favorable toxicity profile. We retrospectively reviewed the data of 51 consecutive patients receiving C and day 1 and 8 P chemotherapy (CT) regimen in advanced stage NSCLC to evaluate the efficacy and toxicity. Methods Patients treated in our institutions having pathologically proven NSCLC, no CNS metastases, adequate organ function and performance status (PS) ECOG 0–2 were given P 112.5 mg/m 2 intravenously (IV) over 1 hour on day 1 and 8, followed by C AUC 5 IV over 1 hour, repeated in every three weeks. PC was given for maximum of 6 cycles. Results Median age was 58 (age range 39–77) and 41 patients (80%) were male. PS was 0/1/2 in 29/17/5 patients and stage was IIIA/IIIB/IV in 3/14/34 patients respectively. The median number of cycles administered was 3 (1–6). Seven patients (14%) did not complete the first 3 cycles either due to death, progression, grade 3 hypersensitivity reactions to P or lost to follow up. Best evaluable response was partial response (PR) in 45% and stable disease (SD) in 18%. Twelve patients (24%) received local RT. Thirteen patients (25%) received 2nd line CT at progression. At a median follow-up of 7 months (range, 1–20), 25 (49%) patients died and 35 patients (69%) progressed. Median overall survival (OS) was 11 ± 2 months (95% CI; 6 to 16), 1-year OS ratio was 44%. Median time to progression (TTP) was 6 ± 1 months (95% CI; 4 to 8), 1-year progression free survival (PFS) ratio was 20%. We observed following grade 3 toxicities: asthenia (10%), neuropathy (4%), anorexia (4%), anemia (4%), hypersensitivity to P (2%), nausea/vomiting (2%), diarrhea (2%) and neutropenia (2%). Two patients (4%) died of febrile neutropenia. Doses of CT were reduced or delayed in 12 patients (24%). Conclusions P on day 1 and 8 and C every three weeks is practical and fairly well tolerated outpatient regimen. This regimen seems to be comparably active to regimens given once in every three weeks. | Background Lung cancer is the leading cause of cancer related deaths all around the world. About 80% of all lung cancers are non-small cell lung cancer (NSCLC) and more than 50% of these patients present with locally advanced or metastatic disease. Meta-analysis of several randomized trials have demonstrated a modest survival advantage for treatment with cisplatin-based regimens in patients with advanced stages of NSCLC [ 1 , 2 ]. Furthermore, chemotherapy (CT) also has been shown to ameliorate symptoms and increase quality of life [ 3 ]. Addition of second generation CT regimens with cisplatin or carboplatin plus newer agents, such as taxanes (paclitaxel and docetaxel), gemcitabine, vinorelbine have shown increased response rates and 1-year survival ratios, but overall survivals have not been altered [ 4 - 6 ]. Being the first of the taxane antimicrotubule agents, paclitaxel (P) demonstrated overall response rates of 21–24% and 1-year survival rates of 37–42% in the phase II trials where it was used as a single agent [ 7 , 8 ]. Antiangiogenic effect of P has also been reported (9). Carboplatin (C) has also demonstrated comparable activity but better toxicity profile than cisplatin in the treatment of advanced NSCLC [ 10 , 11 ]. P and C used in combined chemotherapy regimens have significant activity in NSCLC. PC given every three weeks is considered to be one of the standard regimens being used worldwide [ 10 ]. The weekly administration of P is active, dose intense, and has a favorable toxicity profile. To evaluate the efficacy and toxicity of C and day 1 and 8 P in advanced stage NSCLC, we retrospectively reviewed 51 consecutive patients receiving this CT regimen. Methods All patients with stage III or IV NSCLC treated at Medical Oncology Units of Marmara University Hospital, Dr. Lutfi Kirdar Research and Training Hospital, SSK Sureyyapasa Chest and Cardiovascular Diseases Hospital and Gulhane Military Medical Academy Hospital within July 2002 and August 2003 were considered for this protocol. Eligible patients were required to have pathologically proven NSCLC, stage III or IV disease at presentation or progressed after surgery, performance status (PS) ECOG 0–2, objective measurable disease, adequate bone marrow functions (white blood cell count ≥ 3500/mm 3 , hemoglobin ≥ 9 g/dl, and platelet count ≥ 100000/mm 3 ), and adequate liver functions (bilirubin ≤ 1.5 mg/dl and alanine aminotransferase ≤ 2 times upper limit of normal and ≤ 5 times upper limit of normal for the patients with liver metastases) and kidney functions (creatinine ≤ 1.5 mg/dl). No prior chemotherapy or radiotherapy (except to bone metastases for palliation) was allowed. Patients presenting with known central nervous system (CNS) disease and uncontrolled cardiac arrhythmia were excluded from this study and they were treated with other chemotherapy regimens (single agent or combination of platinum and vinorelbine, docetaxel or gemcitabine). Patients were treated with P (112.5 mg/m 2 /day) on days 1 and 8, followed by C (AUC 5/6) on day 1, repeated in every three weeks. Both drugs were diluted in 250 ml of normal saline and given intravenously (IV) over 1 hour. No growth factors were administered. Anti-allergic premedication included IV diphenhydramine 50 mg, IV ranitidine 50 mg, and IV dexamethasone 16 mg 1 hour prior to P administration. Toxicity evaluation and routine physical examination were performed in every 3 weeks. Complete blood count (CBC) was done on days 1 and 8 of each cycle, liver and kidney function tests on every 2 cycles. Cranial computed tomography scans (CT), magnetic resonance imaging (MRI) and bone scans were performed as clinically indicated. Side effects of the treatment were graded according to the National Cancer Institute Common Toxicity Criteria (CTC), version 2.0 [ 12 ]. Colony stimulating factors were not used. Response was evaluated with CT of chest and/or abdomen on every 3 rd cycle and standard World Health Organization (WHO) criteria were used to determine response [ 13 ]. Independent of the stage at presentation, patients having partial response (PR), stable disease (SD) or progressive disease (PD) during CT were consulted for radiotherapy (RT) for either primary treatment or palliation. The treatment was stopped for patients with PD. Patients with CR, PR or SD after 3 cycles continued their treatment. PC was given for maximum of 6 courses to the patients having PR or SD. Patients were irradiated with CT based treatment planning and multiple fields arrangements with custom blocking to all fields and involved hilar and mediastinal lymph nodes up to 40–41.4 Gy. Boost was given to the primary tumor. Total dose of 60–61.2 Gy was administered in 1.8–2 Gy daily fractions for 5 days a week and completed in 6 weeks. Statistical analysis Overall survival (OS) and time to progression (TTP) were assessed from the date of diagnosis to the date of death (any cause) and the date of objective disease progression (death was considered a progression event in patients who died before disease progression), respectively. Survival rates were calculated by using the Kaplan-Meier method [ 14 ]. The pre-specified prognostic value of age (< 60 years vs. ≥ 60 years), gender, PS (0 vs. 1–2), histology (adenocarcinoma vs. squamous cell vs. NSCLC), stage (III vs. IV), smoking history (present vs. absent), and response after third cycle of CT (PR vs. other) were evaluated in univariate and multivariate analyses. Log rank test was used for univariate survival analysis [ 15 ]. The multivariate Cox proportional hazard model was applied to identify the variables that can independently influence survival. Results The data of 51 patients receiving PC treatment were collected retrospectively between July 2002 and November 2003. Median follow-up time was 7 months (range, 1–20). Median age was 58 years (range 39–77) and 45% of patients were 60 year-old or above. Eighty percent were male. PS was 0 in 57% of patients and 67% had presented with stage IV disease. Most frequent metastatic sites were the other lung (17), adrenal (10), liver (7) and bone (7). Eighty-two percent of the patients had smoking history, median of which was 40 pack-years (range, 0–135). Patients' baseline characteristics are presented in Table 1 . The median number of cycles administered was 3 (range, 1–6). Seven patients (14%) did not complete the first 3 cycles either due to death (2), progression (3), grade 3 hypersensitivity reaction to P (1) or lost to follow up (1). Best evaluable response was PR in 45% and SD in 18%. Only 22 (43%) patients continued the treatment after the 3rd course. At the end of treatment of these 22 patients 10 (46%) had PR and 6 (27%) had SD, but the other 6 patients (27%) had PD. No complete remission was seen. Twelve patients (24%) received local RT and 4 of these patients were given low dose gemcitabine (75 mg/m2/week × 5–6 weeks) as radiosensitizing agent. Of these 12 patients 3 presented with stage IIIA and all had PR to PC therapy. But of the 5 patients with IIIB disease who were irradiated only one patient had PR, 3 had SD and another one had PD after the 3 cycles of CT. Four patients with stage IV were offered RT for palliation. Thirteen patients (25%) received 2nd line CT at progression and of those only one patient had PR and another SD to this treatment. For 2nd line CT gemcitabine ± cisplatin or C was used in 10 patients (78%) and the rest received other agents like vinorelbine, C or docetaxel. Details of this data can be seen in Table 2 . At a median follow-up of 7 months 25 (49%) patients died and 35 patients (69%) progressed. Median OS time was 11 ± 2 months (95% CI; 6 to16), 1-year OS ratio was 44% (Figure 1 ). Median TTP was 6 ± 1 months (95% CI; 4 to 8), 1-year progression free survival (PFS) ratio was 20% (Figure 2 ). The most frequent toxicity related symptoms were asthenia (61%), neuropathy (42%) and anorexia (35%). We observed the following grade 3 toxicities: asthenia (10%), neuropathy (4%), anorexia (4%), anemia (4%), hypersensitivity to P (2%), nausea/vomiting (2%), diarrhea (2%) and neutropenia (2%). Two patients (4%) died of febrile neutropenia due to a three day delay in referral to hospital after the onset of fever > 38°, although they were warned about the side effects of the therapy. Doses of CT was reduced or delayed in 12 patients (24%) (Table 3 ). Univariate analysis showed that patients presenting with PS of 0, stage III disease and having PR after the 3rd cycle of PC have statistically higher OS (p = 0.015, p = 0.018 and p = 0.047, respectively)(Table 4 ). PS and stage of the disease at presentation and response to the CT after the 3rd cycle were also statistically significant independent prognostic factors influencing the OS in multivariate Cox regression analysis (p = 0.034, p = 0.049 and 0.021, respectively). Discussion Paclitaxel and carboplatin have been shown to be an effective and well tolerated CT regimen in advanced stage NSCLC [ 10 ]. PC given once in every three weeks is one of the most widely used standard schedules worldwide based on the spectrum of activity and the ease of administration. This regimen results in an objective response rate of 17–25% with a median survival time of 8 months in stage IIIB and IV NSCLC patients. The major toxicities of this regimen are neuropathy and neutropenia [ 10 , 16 ]. Weekly P is a relatively new strategy for lowering toxicity and increasing dose-intensity and possibly efficacy. Alvarez et al. have used weekly P on patients who progressed or remained stable on P administered in every three weeks and reported that it can induce response in 62.5% of patients with low toxicity [ 17 ]. Akerley has also studied weekly P administration on phase I and phase II settings [ 18 - 20 ]. They started with a P dose of 175 mg/m 2 /week × 6 every 8 weeks in the phase II trial, but they had to reduce the dose up to 50% due to primarily neutropenia and neuropathy with extended therapy. Therefore, they recommended 150 mg/m 2 as the weekly dose of P [ 20 ]. Weekly dose of P in combination with cisplatin or C had been administered in NSCLC patients by Belani et al. [ 21 , 22 ]. They used this combination in a multicenter three arm trial in 401 patients with stage IIIB and IV disease [ 21 ]. In that trial P was given 100 mg/m 2 /week for 3 weeks out of 4 week cycles in arms I and II, with C either AUC of 6 on day 1 or AUC of 2 on days 1, 8 and 15 of each of four 4-week cycles. Arm III of this trial consisted of P (150 mg/m 2 ) and C (AUC = 2) given weekly for 6 out of 8 weeks for a total of two cycles. Greater percentage of the patients on arm I received intended CT (30% of P and 55% of C) compared with the other arms (28–29% of P and 21–22% of C). Patients on arm I received more than half of the planned C dose. The main reasons for discontinuation of therapy were progression of disease (31%) and adverse events (15%). Median time to progression and median survival time were significantly higher for arm I than arm II for patients with stage IIIB disease. Performance status of the patients was also statistically related to the survival times. Patients with PS-0/1 had longer median PFS with treatment arm I than arm II and patients with PS-2 had higher median OS with arm I than arm II. Although arm I was the most easily tolerable schedule between the three arms, grade 3 or 4 neutropenia was observed in 22% of the patients included. In this trial treatment arm I had a response rate of 32%, median TTP of 6.9 months, median OS time of 11.3 months and 1-year survival rate of 47%. In our study, response rate was 45%, median TTP was 6 months, median OS time was 11 months and 1-year survival rate was 44%. The majority of our patients comprised of stage IIIB and IV disease, similar to the patient group in Belani's study resulting in similar response rates and survival data [ 21 ]. These results also seem more effective than the regimen given once in every three weeks of the same drugs [ 10 , 16 ]. We used the standard dose of P (225 mg/m 2 ) given in every three weeks and divided into two consecutive weeks. C dose was calculated according to Calvert formulation with an AUC of 5. This is a lower dose than the dose of C being used in other phase III trials in the literature. In our study only 4 patients (8%) had dose reduction of 10% and 16% of patients had treatment delays of 1 week because of side effects. According to this data, 76% of patients have received the total planned doses of the drugs on scheduled date. Two patients (4%) died of febrile neutropenia due to a three day delay in referral to hospital after the onset of fever > 38°, although they were warned about the side effects of the therapy. It is worth mentioning that none of our patients received any colony stimulating factors. We have shown that the response to treatment after the third cycles of CT was one of the independent prognostic factors influencing OS. It has already been reported by Socinski et al. that 4 cycles of CT give the maximum benefit which could be obtained from CT in patients with stage IIIB and IV NSCLC [ 23 ]. Smith et al. also studied 3 cycles versus 6 cycles of CT in the same group of patients and failed to show any survival advantage for longer treatment durations [ 24 ]. In addition, there was an increase in the side effects such as fatigue, nausea and vomiting in the patients receiving six courses. It has been shown that PC combination has relatively mild toxicity profile. Belani et al. observed in their phase I trial that patients who received the PC combination in every three weeks experienced less severe thrombocytopenia than would be expected from C alone. In the view of this finding they suggested that there was a platelet-sparing effect of P on the dose-limiting thrombocytopenia side effect of C [ 25 ]. This phenomenon was also shown by Akerley [ 18 ] and Kearns [ 26 ]. Akerley reported that platelet counts rose by 17000/mL/week with weekly P administration [ 18 ]. Belani also speculated on the mechanism for this platelet protective effect and said that it may involve some alteration of megakaryocytopoiesis or thrombocytopoiesis, which could result in increased levels of endogenous thrombopoietin or other cytokines [ 27 ]. Kearns et al. suggested that prior exposure to P may suppress the inhibition of platelet formation, which is associated with C [ 26 ]. None of our patients experienced thrombocytopenia during our CT treatment with day 1 and 8 P with day 1 C on every three weeks. One of the most frequent side effects during our treatment was neuropathy, but it was usually mild (Grade 1 or 2), only 4% of our patients experiencing grade 3 sensory neuropathy. Grade 3 or 4 neuropathy has been reported to be 10–20% in schedules given every three weeks [ 10 , 16 ]. Belani reported 3–13% of grade 3 or 4 neuropathy, but the incidence was lower for arms 1 (P given weekly and C every four weeks) and 2 (P and C both given weekly), at only 5% and 3%, respectively [ 21 ]. This result for arm 1 is similar to the neuropathy rate in our study. Besides the reduced toxicity, weekly administration of P also increases the drugs' anti-angiogenic and apoptotic effects. The metronomic schedule of P has been studied widely during the last few years. P had been shown to inhibit endothelial cell proliferation, motility, invasiveness, and cord formation both in vitro and in vivo Matrigel assays in a dose dependent manner [ 9 ]. Belani et al. randomized the patients having objective response to weekly P and C regimens into two arms (maintenance and observation arms). Patients were either treated with weekly P (70 mg/m 2 /week × 3 weeks out of four weeks cycles) in maintenance arm or observed until disease progression has occurred. They reported that the maintenance arm was compared to the observation arm and had a median PFS of 38 weeks vs. 29 weeks, median OS of 75 weeks vs. 60 weeks, respectively [ 21 ]. Although there was not a statistically significant difference between the two arms, authors concluded that this might be a result of low number of patients enrolled in the study (only 65 patients in each arm). It is not yet known whether these responses have an anti-angiogenic basis, or whether such responses will translate into a significant prolongation of survival. Although our study is a retrospective analysis, it is one of the few manuscripts on this PC scheduling in NSCLC in the literature. Conclusions Paclitaxel on day 1 and 8 and carboplatin every three weeks is a practical and fairly well tolerated outpatient regimen. This regimen seems to be comparably active to regimens given every three weeks. This schedule needs to be further evaluated by well planned randomized phase III trials where it could be compared to the standard regimens in patients with advanced stage NSCLC. Competing interests The author(s) declare that they have no competing interests. Authors' contributions PFY designed the study, followed the patients, collected the data, performed the statistical analysis and drafted the manuscript. NST followed the patients and helped with the manuscript. MG followed the patients and helped with statistical analysis. NFH, OT, AO, TS, MA followed the patients. RA confirmed the diagnosis. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC548151.xml |
521681 | Normal histone modifications on the inactive X chromosome in ICF and Rett syndrome cells: implications for methyl-CpG binding proteins | Background In mammals, there is evidence suggesting that methyl-CpG binding proteins may play a significant role in histone modification through their association with modification complexes that can deacetylate and/or methylate nucleosomes in the proximity of methylated DNA. We examined this idea for the X chromosome by studying histone modifications on the X chromosome in normal cells and in cells from patients with ICF syndrome ( I mmune deficiency, C entromeric region instability, and F acial anomalies syndrome). In normal cells the inactive X has characteristic silencing type histone modification patterns and the CpG islands of genes subject to X inactivation are hypermethylated. In ICF cells, however, genes subject to X inactivation are hypomethylated on the inactive X due to mutations in the DNA methyltransferase (DNMT3B) genes. Therefore, if DNA methylation is upstream of histone modification, the histones on the inactive X in ICF cells should not be modified to a silent form. In addition, we determined whether a specific methyl-CpG binding protein, MeCP2, is necessary for the inactive X histone modification pattern by studying Rett syndrome cells which are deficient in MeCP2 function. Results We show here that the inactive X in ICF cells, which appears to be hypomethylated at all CpG islands, exhibits normal histone modification patterns. In addition, in Rett cells with no functional MeCP2 methyl-CpG binding protein, the inactive X also exhibits normal histone modification patterns. Conclusions These data suggest that DNA methylation and the associated methyl-DNA binding proteins may not play a critical role in determining histone modification patterns on the mammalian inactive X chromosome at the sites analyzed. | Background Although it has been known for some time that histone modifications play a role in gene expression [ 1 ], it is only in the last several years that the details of these modifications have been more fully described. Acetylation and methylation of histone tails, for example, exhibit characteristic patterns for expressed and repressed genes in all eukaryotes studied [ 2 ]. This generality of histone modification and gene expression holds for eukaryotes with and without DNA methylation, indicating that DNA methylation is not required for histone modification. In organisms with DNA methylation, however, interactions between histone modification and DNA methylation do appear to exist. In Neurospora , histone methylation appears to determine DNA methylation patterns [ 3 , 4 ]. In Arabidopsis , non-CpG DNA methylation also appears to be determined by histone methyltransferases, whereas CpG methylation does not [ 5 , 6 ]. In mammals, there is considerable evidence suggesting that methyl-CpG binding proteins may play a significant role in histone modification through their association with histone deacetylases [ 7 - 11 ]. Mutations in the MeCP2 methyl-DNA binding protein, which are the cause of most Rett syndrome cases [ 12 ], support this model, because human male and female cells with MECP2 mutations exhibit histone hyperacetylation [ 10 ]. Histone hyperacetylation was also observed in mice with Mecp2 mutations [ 13 ]. Thus, DNA methylation is upstream of histone modification in this model of methyl-DNA binding proteins and histone modification. Another possibility is that DNA methyltransferases themselves may target histone deacetylases through a noncatalytic domain, leading to histone modifications that are independent of other methyl-DNA binding proteins [ 14 ]. We are especially interested in the X chromosome with respect to the question of the relationship between DNA methylation and histone modification. The mammalian X chromosome is unusual in that about a thousand gene-associated CpG islands are hypermethylated on the inactive X and hypomethylated on the active X. Except for imprinted loci, methylation patterns at most other regions of the genome are similar between homologs. Histone modification differences known to be associated with either silent or expressed chromatin also distinguish the active and inactive X chromosomes [ 15 - 19 ]. Thus, the mammalian X chromosome inactivation system would appear ideal for testing whether or not a methyl DNA binding protein – histone modification pathway exists for the inactive X chromosome. To examine more fully the possible relationships between DNA methylation and histone modification, we have utilized cell cultures from individuals with a human hypomethylation disease called the ICF syndrome. This disease is clinically characterized by " I mmune deficiency, C entromeric region instability, and F acial anomalies". In most cases, the molecular defects result from mutations in the DNMT3B methyltransferase gene [ 20 - 22 ]. Certain heterochromatic regions are markedly hypomethylated as a result of these mutations, including the CpG islands on the inactive X chromosome that are associated with genes [ 23 ] and LINE-1 elements [ 24 ]. If DNA methylation is upstream of histone modification, the histones on the inactive X should not be modified to a silent form in ICF cells. Our results indicate, however, that these histones do have modifications typical of silenced genes, suggesting that methyl-DNA binding proteins may not be critical with respect to histone modification on the inactive X chromosome. In addition, we examined clonal primary fibroblast cultures from two individuals with Rett syndrome and found that the histone modification pattern of the inactive X is not affected by mutations in MECP2 . This suggests that this specific methyl-DNA binding protein does not have a major role in silencing the inactive X through histone modification. Results Cytological analysis of histone modification DNMT3B mutant cells (ICF syndrome) We examined histone modifications known to be associated with the inactive X chromosome in two ICF cell lines and normal control cells. Specific histone modifications including histone H3 and H4 acetylation, and histone H3 methylation at K4 and K9, were examined using antibodies to stain nuclei and metaphases [ 15 - 17 ]. We also examined histone macroH2A1 staining, which is known to be concentrated on the inactive X at interphase [ 25 ]. One hundred or more interphase nuclei that had an obvious sex chromatin body by DAPI staining were scored for histone modification. For acetylated histone (H3 and H4) and K4-methylated histone H3, the majority (>60%) of nuclei showed a characteristic hole at the sex chromatin body in both normal and ICF cells (Fig. 1A ). For histone H3 methylation at K9, the frequency of positive cells (Fig. 1B ) was lower (about 50%). We often noted a positive signal for methylated K4 histone H3 in an otherwise negative-staining sex chromatin region in both normal and ICF cells (Fig. 1A ). This signal appears to derive from the DXZ4 locus that was previously reported by Chadwick and Willard [ 19 ] as having active-type histone modifications. DXZ4 is a megabase-sized region known to be hypermethylated on the active X and hypomethylated on the inactive X in normal cells [ 26 ]; this locus appears to be modified normally in ICF cells. Surprisingly, we did not observe this signal on metaphase spreads, suggesting that our resolution on these preparations may be much lower than on interphase chromatin. For metaphase spreads (acetylated histone H3 and H4 and histone H3 methylated at K4) we analyzed 50 cells per line, and in the great majority of analyzable metaphases (>80%) a single lightly-labeled chromosome was detected (Fig. 2 ). In some of these cells, the tip of the short arm was labeled, as would be expected for the pseudoautosomal region (Fig. 2 ). The expected patterns of macroH2A1 histone concentration and histone modification on the inactive X were found in cells derived from ICF individuals and in control normal cells (Figs. 1 and 2 and Table 1 ). At the cytological level, therefore, no difference could be found between normal and ICF cells with respect to the histone modifications on the inactive X. MECP2 mutant cells (Rett syndrome) We examined histone modifications and macrohistone association in clones from two individuals heterozygous for a mutation in MECP2 , an X-linked gene that is subject to X inactivation [ 12 ]. MECP2 mutations lead to Rett syndrome, and the protein product codes for a methyl-CpG binding protein known to recruit a transcriptional silencing complex that deacetylates histones. In Rett syndrome and in mice with mutant Mecp2 , histones exhibit hyperacetylation [ 10 , 13 ], as would be expected if this methyl-DNA binding protein functions upstream of histone modification. In one case, we had complementary clones with either the mutant (1786YY) or wild type MECP2 allele (1786QQ) on the inactive X, and in the second case, we had one clone with the wild type MECP2 on the inactive X (1789V) [ 27 ]. For all the histone modifications we examined (H3 and H4 acetylation and H3 K4 and K9 methylation), the cytological patterns on the active and inactive X chromosomes in mutant MeCP2-expressing cells were indistinguishable from those in clones expressing the wild type allele or in other control cultures (Fig. 1 ). These results suggest that MeCP2 does not function in determining these histone modification patterns on the inactive X chromosome. ChIP analysis To verify our cytological histone modification results at the gene level, we searched for promoter polymorphisms at seven X-linked loci ( G6PD , NEMO , MECP2 , SYBL1 , AR , FMR1 , and PGK1 ) in ICF cells so that we could employ allele-specific chromatin immunoprecipitation (ChIP) analysis. We restricted our search for polymorphisms to the promoter region, as several reports have indicated that marked differences in histone modifications between active and inactive alleles are seldom detected at other regions [ 28 - 30 ]. We found useful promoter polymorphisms at two loci, SYBL1 (synaptobrevin-like gene in the pseudoautosomal portion of Xq28) and AR (androgen receptor in Xq12). Previously, one of us (RSH) has reported on a ChIP study at the SYBL1 locus in male ICF cells where the inactive Y allele had reactivated and the histone modification pattern was that of an active gene [ 31 ]. Here we report on ChIP studies at the SYBL1 and AR loci in ICF female cultures using antibodies to histone H3 dimethylated at K4 and to acetylated histone H3. Both loci are subject to X inactivation, and the inactive X alleles remain inactive in ICF cells even though the 5' CpG islands are hypomethylated [ 23 ]. In the case of the SYBL1 inactive X allele, the methylation level is reduced by over 90% with most chromosomes exhibiting no methylation. An XhoI restriction site polymorphism in the untranslated exon 1 of SYBL1 permitted separation of the active and inactive alleles in cloned cells. A CAG repeat number polymorphism in the 5' coding region of the AR gene (1.3 kb downstream of the transcription start site according to reference sequence NM000044) was informative in one ICF sample (PT 4) and in several controls, thus permitting separation of the active and inactive alleles in cloned cells, and in cultures with highly skewed X inactivation. The antibodies were highly specific under the amplification conditions chosen, so that a strong signal was seen for the pull-down experiment with antibody and little or no signal for the "no antibody" control (Fig. 3A ). The fluorescent amplification products from the AR gene were then separated on an automated sequencer according to CAG repeat number. Two major peaks are detected in the input control DNA, corresponding to the active (A) and inactive (I) X alleles, differing in CAG repeat number (Fig. 3B ). An allele was determined to be from the active X by RT-PCR analysis (data not shown). The lesser "shadow band" peaks, labeled S, probably derive from PCR errors. In the methylated K4 H3 and acetylated H3 antibody ChIPs, a single peak predominates in both normal and ICF cells (Figs. 3B and 3C ) that corresponds to the active X allele. Our ChIP analysis of the inactive X at the SYBL1 locus in an ICF female (PT3) also showed normal histone H3 hypoacetylation and K4 H3 hypomethylation in spite of the very low levels of DNA methylation in this CpG island region (Fig. 4 ). These data, therefore, agree with our cytological observations in that only the active X alleles are positive for the histone modifications known to be associated with active genes, though a minor portion of the inactive X allele was found to precipitate with the acetylated H3 antibody in both normal and ICF cells (Fig. 3C and data not shown). Discussion The major observation reported here is that ICF cells, despite being hypomethylated at gene- and L1-associated CpG islands on the inactive X chromosome, exhibit the same histone modification patterns as inactive Xs in normal cells. In addition, we show that cells mutant for MeCP2, a methyl DNA binding protein, also exhibit normal histone modification patterns on the inactive X. These results imply that DNA methylation and/or this methyl DNA binding protein are not critical for determining histone modification patterns on the inactive X chromosomes. Two major questions can be raised about our results: (1) is the sensitivity of the cytological histone modification assay too low to detect active-type histone modifications on the ICF inactive X? and (2) is the extent of methylation on the ICF inactive X greater than is suggested by CpG island and LINE-1 methylation patterns? The cytological results imply that most of the genes on the inactive X in ICF cells are subject to inactivation, a conclusion supported by our allele-specific expression analyses of individual genes, such as AR , in ICF cells ([ 23 ] and data not shown). For genes subject to X inactivation in ICF cells, we expect histone modifications at the gene level to be similar to those detected cytologically at the chromosome level, and this is what we have shown here for the AR gene. For genes that escape X inactivation in ICF cells, we expect their histone modification patterns to be those of expressed genes, and one of us (RSH) has previously reported this to be the case for the SYBL1 gene ([ 31 ] and data not shown). We did not detect these active patterns cytologically, suggesting that there are no large blocks of genes escaping inactivation in ICF cells except at the Xp pseudoautosomal region, which normally contains escaping genes (Fig. 2 ). Methylation levels at inactive X-linked CpG islands in ICF cells are decreased by an average of 89% from normal as determined by bisulfite analyses at the G6PD , FMR1 , and SYBL1 loci, and many of the cloned alleles analyzed were completely unmethylated like active X alleles [ 23 ]. It is possible that DNA methylation at other CpG-rich regions (e.g., Alu and LINE-1 elements) on the X chromosome might be differentially methylated and play a role in the X chromosome histone modification pathway. One of us (RSH) has recently shown that LINE-1 elements are hypermethylated on both active and inactive X chromosomes in normal cells but, surprisingly, they are hypomethylated on the inactive X and hypermethylated on the active X in ICF cells [ 24 ]. These results argue against a role for LINE-1 methylation in histone modification on the inactive X chromosome. A more complete DNA methylation analysis of the ICF and normal inactive Xs needs to be done, however, because other widespread sequences may be hypermethylated on the ICF inactive X that could direct histone modifications. Because we know that promoter methylation is important in gene expression, it seems reasonable that if DNA methylation were directly involved in the histone modification pathway, CpG island methylation would play a critical role. Further support for this idea comes from the fact that histone modifications distinguishing active and inactive X-linked genes are concentrated in promoter regions [ 28 , 32 ]. In fact, Rougeulle et al. [ 32 ] propose that the promoter-restricted histone modification seen at X-linked loci may be unique to monoallelically-expressed genes and provide them with an epigenetic mark. That DNA methylation is not critical to the developmental appearance of histone modifications is further supported by recent murine studies showing that differential histone modification of the Xs in early development precede differential developmental appearance of DNA methylation [ 33 , 34 ]. The fact that DNA methylation does not appear to be critical to the development of histone modifications in X-linked gene expression should not be confused with a more important role for DNA methylation in maintaining repression of X-linked genes. Some years ago we showed that the earliest events in reactivating inactive X-linked genes were hemidemethylation followed by chromatin hypersensitivity, and then transcription factor binding and transcription [ 35 , 36 ]. More extensive studies have recently pointed to a similar conclusion [ 11 , 37 , 38 ]. Thus, DNA methylation appears to play a dominant role in maintaining repression, even though it is a late event in establishing silent chromatin. We can also consider the implication of this work for the proposed role of methyl-CpG binding proteins in the histone modification pathway. Our ICF cell data and the results from murine developmental studies, showing that histone modification of X-linked genes precedes DNA methylation, argue against such a role for the X chromosome. A role for methyl-DNA binding proteins in the histone modification pathway is supported by studies with Rett syndrome cells where a methyl-DNA binding protein, MeCP2, is mutated. In both humans and mice with Rett syndrome mutations, general hyperacetylation of histones was reported, albeit at different sites. In human cell lines, H4 was hyperacetylated preferentially at K16 [ 10 ], while in mouse mutant tissues hyperacetylation was reported specifically at H3K9 [ 10 , 13 ]. In our work, however, we saw no major effect of two different MECP2 mutations on inactive X histone modification. The recent discovery that LINE-1 elements on the inactive X are methylated by a methyltransferase distinct from the one that carries out the same modification on the active X raises the possibility that the inactive X could have its own modification rules [ 24 ]. We must consider, therefore, the possibility that the inactive X chromosome does not utilize methyl-DNA binding proteins in the histone modification pathway. Such a possibility would fit with the failure to detect protein footprints at promoters on the inactive X chromosome, whereas they are readily detectable on the active X [ 36 , 39 - 42 ]. It should be noted that only a small fraction of possible histone modifications have been elucidated at this time, and it is possible that histone modification on the inactive X that depends on methyl-DNA binding protein(s) will be found in the future. Finally, we would like to comment on the implication of this study regarding the inactive X silencing complex. Systems controlling gene expression tend to be multilayered and the X inactivation system is no exception. We know that silencing on the inactive X involves XIST RNA, DNA methylation, histone modification patterns, chromatin sensitivity, and delayed replication. It is our opinion that these factors tend to act in a more or less independent manner, as we have suggested several times in the past [ 23 , 43 - 46 ]. For example, promoter demethylation of inactive X-linked genes, as occurs in ICF cases, does not necessarily lead to reactivation; markedly advanced replication time must also be present for reactivation to take place [ 23 ]. The present study would appear to add further support to this idea. Conclusions The inactive X chromosome in mammalian cells is characterized by a particular set of histone modifications. It has been suggested that methyl-DNA binding proteins may be involved in these modifications through their interactions with histone deacetylases. We have investigated this idea by studying histone modification patterns on the inactive X in ICF and Rett syndrome cells. ICF cells are hypomethylated on the inactive X, in contrast to normal cells, and the Rett cells we studied were deficient in MeCP2, a specific X-linked methyl-DNA binding protein. We found that the histone modification patterns on the inactive X in these mutant cells were indistinguishable from those in normal cells. We conclude that DNA methylation and the associated methyl-DNA binding proteins do not appear to play a critical role in determining histone modification patterns on the mammalian inactive X chromosome, either globally or at the level of the promoter. Methods Cells and cell culture Mutant fibroblast cell cultures included two from female ICF individuals, whose DNMT3B mutations have been previously described [ 20 ], and complementary clonal cultures from an individual (Rett 1) heterozygous for a mutation (1155del132) in the MECP2 gene [ 27 ]. In one clone (1786YY), the mutant gene is on the active X, leading to a culture with nonfunctional MeCP2 protein; the complementary control clone (1786QQ) has functional MeCP2 because the mutant gene is on the inactive X. In another clone (1789V), derived from a Rett individual with the mutation R106W (Rett 2), the active X carried the mutant allele. Normal fibroblast cultures were obtained from commercial sources. For chromatin precipitation studies, the ICF fibroblast clones were immortalized with hTERT, as previously described [ 47 ]. Cells were grown in AmnioMax-C100 (Gibco Invitrogen Corp.; Carlsbad, CA) and harvested in trypsin:EDTA (Gibco Invitrogen Corp.) under standard conditions [ 46 , 48 ]. Cytology For analysis of interphase stages, cells were plated on alcohol-washed 22 mm square cover slips in 35 × 10 mm Petri dishes. On the following day the medium was removed and the cells were washed once with PBS followed by fixation in 95% ethanol:5% acetic acid for 1 min at room temperature. The rest of the procedure followed the "Immunocytochemistry Protocol" of Upstate (Lake Placid, NY). DAPI-stained slides mounted in antifade were examined with a Nikon Microphot FXA microscope and images were captured with a Nikon Coolpix 995 digital camera. The inactive X was recognized under DAPI staining as sex chromatin. Absence of a particular histone modification on the inactive X was seen as a hole or gap at the sex chromatin location. For analysis of metaphase cells, we plated cells in 150 × 25 mm tissue culture dishes and added colcemid (Gibco Invitrogen Corp.) 48 h later (0.1 μg/ml for 2 h). The medium was removed and the cells were washed once with Hanks' balanced salt solution, followed by trypsinization (Gibco Invitrogen Corp.) with slight agitation to collect metaphase cells. Serum was added to stop tryptic action and the cells were recovered by centrifugation, then placed in hypotonic solution (3 mg/ml KCl and 1 mg/ml sodium citrate) at 37°C for 15 min. Cells were collected on to slides using a Cytospin centrifuge and then fixed in 95% ethanol:5% acetic acid for 1 min. The rest of the procedure followed the Upstate protocol mentioned above, followed by DAPI staining, mounting in antifade, and examination with a Nikon Microphot FXA microscope. Antibodies used to detect histone modifications were obtained from Upstate and included: "Anti-acetyl-Histone H4," recognizing acetylated lysines 5, 8, 12, and 16, "Anti-acetyl-Histone H3 (Lys 9)," "Anti-dimethyl-Histone H3 (Lys 9)," "Anti-dimethyl-Histone H3 (Lys 4)," and "Anti-Histone macroH2A1." ChIP studies Chromatin immunoprecipitation was performed using the protocol of Upstate with slight modifications. For each experiment, a near-confluent 75 cm 2 tissue culture flask (about 3 × 10 6 cells) was washed with PBS and treated with 4% formaldehyde (pH > 7) for 10 min at 37°C. Protease inhibitor cocktail (Complete) from Roche Diagnostics (Indianapolis, IN) was used in place of individual inhibitors, and Protein A-Separose 4B (Zymed Laboratories Inc.; South San Francisco, CA) was used to collect immune complexes. After elution of immune complexes, they were heated at 65°C for 4 h to reverse crosslinks, and the DNA was recovered with a "QIAquick" PCR purification kit from Qiagen Inc. (Valencia, CA). Primary amplification of AR DNA was across the polymorphic 5' CAG repeat region as previously described [ 23 ], using 27–35 cycles of PCR amplification with 10% of the immunoprecipitated material or 50 ng of input DNA in a 50 μl reaction volume. Allele-specific analysis was performed by amplifying the primary product with a 5'-FAM-labeled nested primer as previously described [ 23 ], using 6–15 cycles of PCR amplification with 2–10 μl primary product in a 50 μl reaction volume. All amplification conditions were chosen so as to produce visible products by ethidium staining only for antibody-precipitated material, and not for "no antibody" controls. The fluorescent products were then run on an ABI PRISM 310 capillary sequencer (Applied Biosystems; Foster City, CA) to separate alleles differing in CAG repeat number and analyzed using GeneScan software (Applied Biosystems). Allele-specific expression analysis by RT-PCR was performed on DNaseI-treated RNA using a similar procedure [ 23 ]. Analysis of the 5' region of the SYBL1 gene was performed similarly to that of AR , except the allele-specific reaction entailed XhoI digestion of the primary amplification product followed by these products being separated by agarose gel electrophoresis. Conditions for PCR amplification and XhoI digestion were as previously described [ 31 ]. List of abbreviations ac = acetylated ChIP = chromatin immune precipitation DAPI = 4,6-diamidino-2-phenylidole FB = fibroblast FAM = 5-carboxyfluorescein FITC = fluorescein-isothiocyanate ICF = immune deficiency, centromeric region instability, facial anomalies H3 = histone 3 H4 = histone 4 K4 = lysine 4 K9 = lysine 9 LINE-1 = long interspersed nuclear element 1 MeCP2 = methyl-CpG binding protein WT = wild type Xa = active X chromosome Xi = inactive X chromosome Authors' contributions SMG and RSH conceived the study design, supervised and coordinated its progress, and drafted and prepared the final manuscript. KRV and PL carried out the cell culture and cytological studies. TKC carried out the ChIP analyses. JT and UF developed the cloned Rett cell cultures. All authors read and approved the manuscript. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC521681.xml |
535804 | Testicular seminoma after the complete remission of extragonadal yolk sac tumor : a case report | Background Between 2% and 5% of malignant germ-cell tumors in men arise at extragonadal sites. Of extragonadal germ cell tumors, testicular carcinoma in situ (CIS) are present in 31–42% of cases, and CIS are reported to have low sensitivity to chemotherapy in spite of the various morphology and to have a high likelihood of developing into testicular tumors. A testicular biopsy may thus be highly advisable when evaluating an extragonadal germ cell tumor. Case presentation A 36-year-old man was diagnosed as having an extragonadal non-seminomatous germ cell tumor, that was treated by cisplatin-based chemotherapy, leading to a complete remission. In the meantime, testicular tumors were not detected by means of ultrasonography. About 4 years later, a right testicular tumor was found, and orchiectomy was carried out. Microscopically, the tumor was composed of seminoma. Conclusions We herein report a case of metachronous occurrence of an extragonadal and gonadal germ cell tumor. In the evaluation of an extragonadal germ cell tumor, a histological examination should be included since ultrasonography is not sufficient to detect CIS or minute lesions of the testis. | Background Between 2% and 5% of malignant germ-cell tumors in men arise at extragonadal sites [ 1 ]. Cytogenetically most extragonadal germ-cell tumors (EGGCTs) i.e., the seminomas and non-seminomas, are similar to their testicular counterparts [ 2 , 3 ]. But there are CIS in 31–42% of EGGCT patients' testes [ 4 ]. Ultrastructural studies indicate that CIS originate from rather primitive cells and can develop into different categories of germ cell carcinomas. Furthermore, since CIS are reported to respond poorly to chemotherapies, a metachronous development of testicular cancer will possibly occur, in spite of the various morphologies of testicular cancer [ 5 , 6 ]. In the present case, the etiology of a metachronous appearance of EGGCT and testicular cancer is discussed. Case presentation A 36-year-old man was admitted to our hospital with the chief complaint of right-sided scrotal enlargement. He had previously received treatment for an extragonadal germ cell tumor. At the age of 32, he presented with lumbago. CT showed a retroperitoneal tumor (Figure 1 ), and a transabdominal needle biopsy was carried out. Microscopically, the tumor was composed of a yolk sac tumor (Figure 2A,2B ). We performed two courses of systemic chemotherapy using bleomycin, etoposide, and cisplatin, leading to a partial response. As the tumor size was not seemed to decrease after the two courses of the chemotherapy, retroperitoneal lymph node dissection was performed, but failed to show any residual viable cells. An ultrasonic study did not reveal any testicular tumors. About 4 years after the previous treatment, he presented with scrotal enlargement and tumor markers such as AFP and HCG β were within normal limit. A right orchiectomy was performed on 23 rd July. Pathology showed the resected tumor was a seminoma with CIS (Figure 3A,3B ). No recurrence has been seen since the surgery (Figure 1B ). Conclusions There are reports that approximately 4% of patients with EGGCT develop a metachronous testicular cancer despite the use of cisplatin-based chemotherapy [ 7 ], and the cumulative risk of developing a metachronous testicular cancer 10 years after a diagnosis of EGGCT is 10.3% [ 8 ]. However, there is disagreement over whether EGGCT is a primary disease or metastatic from the burned-out primary testicular lesion. Actually, burned-out tumors have been detected in 76% of cases of EGGCT [ 9 ]. CIS is also found via biopsy in 31–42% cases [ 4 , 10 ]. Testicular CIS is thought to have resistance to systemic chemotherapies and to develop later to metachronous testicular cancer. In the present case, the EGGCT was a non-seminomatous germ cell tumor including a yolk sac component, whereas the testicular cancer was a seminoma. We believe CIS was present at the time of the treatment of EGGCT and testicular CIS is so primitive that it could differentiate into any type of germ cells. It is also possible that these metachronously developing germ cell tumors developed independently. A testicular biopsy could clarify the relationship between these tumor cells and the expansion of the disease. In the present case no biopsy was done, but an ultrasonic examination ruled out the possibility of testicular CIS. Giwercman et al. [ 11 ] emphasized the necessity of histological examinations of the testis upon an evaluation of EGGCT [ 12 - 14 ] and also urged a careful follow-up for patients with EGGCT who do not have simultaneous testicular cancer. On the other hand, there is an opinion that any patients with retroperitoneal masses should undergo scrotal ultrasound. Comiter et al. [ 15 ] showed definite pathological evidence of a burned-out testicular carcinoma in 5 of 6 patients (83%) with presumed extragonadal germ cell tumors and concluded that scrotal ultrasound studies are useful for the evaluation of the palpably normal testes [ 15 ]. Kitahara et al. reviewed the incidence of scrotal echogenic leisions with testicular cancers or burned-out tumors of 22 EGGCT patients and found echogenic changes in 17 patients (77.3%) [ 16 ]. This means that disease was overlooked by ultrasonic examinations in 22.7% of cases. In our case, it is possibile that metachronous testicular cancer oriented in testicular CIS, grew from a burned-out tumor, or was independent of the EGGCT. We should have performed testicular biopsies at the time of the diagnosis of EGGCT and reflect the strategies of treatments of EGGCT. Now we propose a surveillance protocol of EGGCT as Table 1 , concerning with following four points. 1. As we mentioned, the overall risk of development a testicular tumor is not so high(4–10.3%). 2. The side effect of CIS therapy (whether irradaition, orchiectomy or chemotherapy) are significant, especially concerning fertility and androgen production. 3. Testicular tumors early detected by adequate surveillance respond well to treatments. 4. Testicular biopsy is not entitled to detect all the CIS. Competing interests The author(s) declare that they have no competing interests. Authors' contribution IK, MU, HY, KN, TT and ND carried out clinical treatments. TM carried out histopathological studies. Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC535804.xml |
516039 | Topical NSAIDs for chronic musculoskeletal pain: systematic review and meta-analysis | A previous systematic review reported that topical NSAIDs were effective in relieving pain in chronic conditions like osteoarthritis and tendinitis. More trials, a better understanding of trial quality and bias, and a reclassification of certain drugs necessitate a new review. Studies were identified by searching electronic databases, and writing to manufacturers. We identified randomised, double blind trials comparing topical NSAID with either placebo or another active treatment, in adults with chronic pain. The primary outcome was a reduction in pain of approximately 50% at two weeks, and secondary outcomes were local and systemic adverse events and adverse event-related withdrawals. Relative benefit and number-needed-to-treat (NNT), and relative harm and number-needed-to-harm (NNH) were calculated, and the effects of trial quality, validity and size, outcome reported, and condition treated, were examined by sensitivity analyses. Twelve new trials were added to 13 trials from a previous review. Fourteen double blind placebo-controlled trials had information from almost 1,500 patients. Topical NSAID was significantly better than placebo with relative benefit 1.9 (95% confidence interval 1.7 to 2.2), NNT 4.6 (95% confidence interval 3.8 to 5.9). Results were not affected by trial quality, validity or size, outcome reported, or condition treated. Three trials with 764 patients comparing a topical with an oral NSAID found no difference in efficacy. Local adverse events (6%), systemic adverse events (3%), or the numbers withdrawing due to an adverse event were the same for topical NSAID and placebo. Topical NSAIDs were effective and safe in treating chronic musculoskeletal conditions for two weeks. Larger and longer trials are necessary to fully elucidate the place of topical NSAIDs in clinical practice. | Background A systematic review of topical NSAIDs reported that they were effective for relieving pain in both acute and chronic conditions [ 1 ]. Number-needed-to-treat (NNT), the number of patients that need to be treated for one to benefit from a particular drug, who would not have benefited from placebo, was used to estimate efficacy. In chronic conditions, NNT for topical NSAIDs at two weeks was 3.1 (2.7 to 3.8). There are three reasons why an updated review of topical NSAIDs in chronic pain is needed. First, we have a better appreciation of factors that can introduce bias [ 2 - 4 ], and would not now accept trials that were not double blind, or were very small. Second, topical salicylate and benzydamine are no longer classed as topical NSAIDs [ 5 ]. Thirdly, there are now more trials. We believed that updating the review would improve efficacy estimates for topical NSAIDs, with a prior intent to determine efficacy for individual drugs. Methods Searching Relevant studies were sought regardless of publication language, type, date or status. Studies included in the previous review were considered for inclusion, and the Cochrane Library, MEDLINE and PreMedline, EMBASE and PubMed, were searched for relevant studies published since the last review, for the years 1996 to April 2003. The search strategy included "application: topical" together with "cream", "gel" etc, together with generic names of NSAIDs, and proprietary preparations of topical treatment in which the principal active ingredient was an NSAID [ 6 , 7 ] ( additional file 1 :search strategy). Reference lists of retrieved articles were also searched. We wrote to 20 pharmaceutical companies in the UK, 66 in continental Europe, and two in North America, known to manufacture topical NSAIDs, asking if they could supply papers. Selection We identified reports of randomised, double blind, active or placebo-controlled trials in which treatments were given to adult patients with moderate to severe chronic pain resulting from musculoskeletal or other painful disorders. We excluded treatments for mouth or eye diseases. At least ten patients had to be randomised to a treatment group and application of treatment had to be at least once daily. Outcomes closest to two weeks (but at least seven days) were extracted. Longer outcomes were also accepted when available. Quality and validity assessment Trial quality was assessed using a validated three-item scale with a maximum quality score of five [ 8 ]. Included studies had to score at least two points, one for randomisation and one for blinding. A sixteen-point scale was used to assess trial validity [ 9 ]. Quality and validity assessments were made independently by at least two reviewers and verified by one other reviewer. Disputes were settled by discussion between all reviewers. Outcomes We defined our own outcome of clinical success, representing approximately a 50% reduction in pain [ 1 ]. This was either the number of patients with a "good" or "excellent" global assessment of treatment, or "none" or "slight" pain on rest or movement (or comparable wording) measured on a categorical scale. A hierarchy of outcomes was used to extract efficacy information [ 1 ], shown below in order of preference: 1) number of patients with a 50% or more reduction in pain 2) patient reported global assessment of treatment 3) pain on movement 4) pain on rest or spontaneous pain 5) physician or investigator global assessment of treatment In addition, the number of patients showing undefined "improvement" was also accepted. All of these outcomes were grouped together as a "success", and categories 1–4 were used as preferred outcomes in the sensitivity analysis. Secondary outcomes were extracted from included papers that reported them. These were the number of patients (i) reporting one or more local adverse event (itching, stinging, rash), (ii) reporting one or more systemic adverse event (iii) withdrawing from trials due to adverse events. Quantitative data synthesis The number of patients randomised into each treatment group (intention to treat) was used in the efficacy analysis. Information was pooled for the number of patients in each trial approximating at least 50% pain relief, or similar measure, for both topical NSAID and control. These were used to calculate NNT with a 95% confidence interval (CI) [ 10 ]. Relative benefit and relative risk estimates with 95% CIs were calculated using the fixed effects model [ 11 ]. A statistically significant benefit of topical NSAID over control was assumed when the lower limit of the 95% confidence interval (CI) of the relative benefit was greater than one. A statistically significant benefit of control over active treatment was assumed when the upper limit of the 95% CI was less than one. Homogeneity of trials was assessed visually [ 12 - 14 ]. Number-needed-to-harm (NNH) and relative risk were calculated in the same way as for NNTs and relative benefit. All calculations were performed using Microsoft Excel X for the Macintosh and RevMan 4.2. In sensitivity analyses the z test was used [ 15 ]. QUOROM guidelines were followed [ 16 ]. Sensitivity analysis Our prior intention was to perform sensitivity analyses on pooled outcomes using the z test [ 15 ] for quality score (2 versus 3 or more), validity score (8 or less versus 9 or more), trial size (less than 40 patients per group versus more than 40 patients per group), reported outcome (higher versus lower preference), drug, and condition treated (knee osteoarthritis versus other musculoskeletal). At least three studies had to be available in each category before information was pooled. Results Study characteristics Ten out of the 20 UK companies, and two out of the 66 continental European companies replied to our request for studies. Only three companies supplied useful material, either published studies or bibliographies. None provided unpublished material. Searches identified 60 target papers, but 35 were excluded; 23 studies failed to meet the inclusion criteria and 12 had no useable data. Twenty-four of these 60 target papers were included in the previous review. We included 13 of those in this review, and excluded 11; seven were not double blind, two compared a salicylate with placebo or oral analgesics, one did not have daily application, and one had insufficient data ( additional file 2 : excluded studies, additional file 3 : QUOROM flow diagram). Twenty-five trials therefore met the selection criteria, 12 of which were additional trials. Fifteen trials had only placebo controls [ 17 - 31 ], seven only active controls [ 32 - 38 ], and three had both placebo and active controls [ 39 - 41 ]. Of the 10 active controlled trials, four compared a topical NSAID with a different topical NSAID, three compared a topical NSAID with a different oral NSAID, and one each compared a topical NSAID with a homeopathic gel, a topical rubefacient, and topical trinitroglycerin (GTN). Details of all included studies with outcomes and quality and validity scores are in additional files 4 (Outcome details of placebo-controlled trials) and additional files 5 (Outcome details of active-controlled trials). Patients were generally over 40 years old, predominantly with musculoskeletal disorders, and with baseline pain of moderate to severe intensity. Fourteen studies examined general musculoskeletal conditions, and eleven examined osteoarthritis (9 studies of the knee, one of finger joints, and one of mixed sites). Five studies in osteoarthritis specified use of a standard scale (ACR, Kellgren and Lawrence, ISK) to assess the severity of disease, four specified that the disease was radiologically confirmed, one specified that patients had "well documented mild osteoarthritis", and one made no statement. Quality scores were high, with 16/18 placebo controlled and 9/10 active controlled trials scoring 3 or more points out of a maximum of 5. Validity scores were also high, with 14/18 placebo controlled and 8/10 active controlled trials scoring 9 or more out of a maximum of 16 ( additional files 4 and 5 ). Placebo controlled trials Dichotomous information was available to pool from 14 placebo controlled trials for efficacy, from 16 placebo controlled trials for local adverse events, 17 placebo controlled trials for systemic adverse events, and from 11 placebo controlled trials for adverse event related withdrawals. Efficacy Fourteen trials (1,502 patients) provided data on efficacy. Topical NSAIDs were significantly better than placebo (Table 1 ). The mean placebo response rate was 26% ranging from 7% to 78%. The mean treatment response rate was 48% ranging from 2% to 90% (Figure 1 ). The NNT was 4.6 (95% CI 3.8 to 5.9) for one patient to experience improvement in chronic musculoskeletal pain at two weeks with topical NSAIDs, compared with placebo. Sensitivity analyses (Table 1 ) showed no significantly greater effect for low quality trials (quality score 2/5) compared with higher quality trials (quality score 3–5/5) (z = 1.69, p = 0.091). There was no significant difference for smaller versus larger trials using 50 patients per group (median group size for topical NSAID was 49) as a cut off (z = 0.40, p = 0.69), for preferred outcomes versus lower preference outcomes (physician determined or general improvement) (z = 1.56, p = 0.12), or for patients with knee osteoarthritis compared with other musculoskeletal conditions (z = 0.99, p = 0.32) (Figure 2 ). The 10 trials with both a quality score of 3/5 or greater and a validity score of 9/16 or greater had an NNT of 4.4 (95% CI 3.6 to 5.6). There were insufficient data to allow comparisons of efficacy between different NSAIDs. Harm All 18 placebo controlled trials (2,032 patients) provided some information on adverse events (Table 2 ). There was no statistically significant difference between topical NSAID and topical placebo for the number of patients experiencing local adverse events (6%), systemic adverse events (3%), or the number withdrawing due to an adverse event (1%). With topical NSAID or topical placebo, local adverse events were usually described as rash, itching or stinging, and were predominantly mild. Active controlled trials Efficacy There was sufficient information to pool results only from the three trials comparing a topical NSAID with an oral NSAID in patients with osteoarthritis of the knee or finger joints. One trial [ 34 ] compared piroxicam 0.5% gel with oral ibuprofen 1200 mg daily, another [ 38 ] compared diclofenac 1% gel with oral ibuprofen 1200 mg daily, and the third [ 41 ] compared eltenac 1% gel with oral diclofenac 100 mg daily. In these trials, with 764 patients, 37% had a successful outcome both with topical NSAID and oral NSAID. There was no statistically significant difference (relative risk 1.1; 95% CI 0.9 to 1.3). The other seven studies used different topical preparations and different comparators in small trials ( additional file 5 : Outcome details of active-controlled trials). Harm Eight of the active controlled trials (1,461 patients) provided some information on adverse events (Table 2 ). In two active controlled trials comparing topical with oral NSAID, local adverse events occurred more frequently (8%) with topical than with oral NSAID (3%). Systemic adverse events and adverse event withdrawals did not differ between topical and oral NSAID. No study documented specific instances of upper gastrointestinal bleeding or symptomatic ulcers. Discussion Patients in these trials all had moderate to severe baseline pain, and for those with osteoarthritis, disease severity was generally mild to moderate. Patients with most severe disease were specifically excluded in several trials because authors regarded topical NSAID to be inappropriate for their treatment. Both the original and this updated review concluded that topical NSAIDs were effective in chronic conditions. However, removing trials of lower quality, and topical agents that are not now regarded as topical NSAIDs, increased (worsened) the NNT from 3.1 (95% CI 2.7 to 3.8) to 4.7 (95% CI 3.8 to 5.9) for the outcome of at least half pain relief at two weeks for all topical NSAIDs compared to placebo. For every four or five patients with chronic pain treated with topical NSAID, one would benefit who would not have done with placebo. Three trials comparing topical with oral NSAID found no difference in efficacy. There are a number of aspects of this review that might question this demonstration of efficacy. The trials spanned several decades and retrospective examination finds fault with them in several respects. Many trials were small, and small size can allow chance effects to influence treatment and placebo event rates [ 4 ]. Different preparations were used, with different formulations, concentrations of active ingredient, and application schedules. Reported outcomes were not consistent, and a hierarchy of outcomes had to be constructed. It was inevitable that there would be some clinical heterogeneity, even when similar patients were treated, and when trials were both randomised and double blind, and of appropriate duration. We addressed these limitations with pre-planned sensitivity analyses. Using studies with higher quality and validity scores, larger size, or higher rather than lower preference outcomes made no difference. Patients treated for knee osteoarthritis derived the same degree of pain relief as those treated for general musculoskeletal conditions. The evidence was that topical NSAIDs were effective whatever strategy was used for sensitivity analysis, improving the robustness of the overall result. A possible criticism might be that there has been selective publication of trials showing topical NSAIDs to be effective, and suppression of trials where there was no difference between topical NSAID and placebo. Funnel plots do not reliably detect publication bias [ 13 , 14 ], so we did not use them or make any adjustment for possible publication bias [ 42 ]. We did approach every company in the world that we could identify as being involved with topical NSAID manufacture or sale for any additional unpublished trials, but no more unpublished material was identified. When unpublished material is found, it often does not change the relevance of a result [ 43 - 45 ]. It is important to emphasise that both active and placebo treatments were rubbed on, making any effect of rubbing equal in both groups. The mean placebo response in the included trials was 26%, compared with the mean response of 48% with topical NSAID. The response with placebo is consistent with that found in acute and chronic pain with a variety of conditions and endpoints [ 46 ]. Local adverse events were reported with equal frequency for topical NSAID and topical placebo in placebo-controlled trials, but more often for topical NSAID than oral NSAID in active controlled trials. There were no differences between topical NSAID and topical placebo, or topical NSAID and oral NSAID, for systemic adverse events, or withdrawals due to adverse events. Studies of short duration will not capture important long-term safety information, and this may be important for ongoing applications of gels, creams or sprays in chronic conditions. There is, however, information that indicates that topical NSAIDs do not cause the gastrointestinal harm found with oral NSAIDs [ 47 ], nor are they associated with increased renal failure [ 48 ]. Clearly there is a body of evidence to support the efficacy of topical NSAIDs in chronic painful musculoskeletal conditions. Despite removing smaller studies that were not double blind, and substituting newer, larger trials of higher quality, topical NSAIDs remained effective, though the NNT was higher (worse) than originally estimated [ 1 ]. More information of high quality is required, to compare the relative efficacy of topical and oral NSAIDs, and between different topical NSAIDs. We are able to compare the evidence for different topical analgesics in chronic musculoskeletal pain (Table 3 ). Systematic reviews of topical salicylate [ 49 ] and capsaicin [ 50 ], tell us what is known about those treatments. As Table 3 shows, topical NSAIDs have been tested in many more studies, and in four times as many patients as these other topical analgesics, and have the lowest (best) NNT. The limitation of this comparison is essentially the same limitation as with all these reviews, that the included trials were too short and too small to be sure of the result. Topical NSAIDs have the best evidence for chronic musculoskeletal pain, supporting the excellent evidence available in acute painful conditions [ 51 ]. Authors' contributions LM was involved with planning the study, searching, data extraction, analysis, and preparing a manuscript; RAM with planning, data extraction, analysis and writing the manuscript; JE with searching, data extraction, and writing; SD with data extraction, analysis, and writing; HJM with planning, analysis and writing. All authors read and approved the final manuscript. Competing interests RAM & HJM have consulted for various pharmaceutical companies. RAM, HJM & JE have received lecture fees from pharmaceutical companies related to analgesics and other healthcare interventions. All authors have received research support from charities, government and industry sources at various times, but no such support was received for this work. No author has any direct stock holding in any pharmaceutical company. Pre-publication history The pre-publication history for this paper can be accessed here: Supplementary Material Additional File 1 Search strategy for RCTs of topical NSAIDs in chronic pain Click here for file Additional File 2 Excluded studies Click here for file Additional File 3 QUOROM flow diagram Click here for file Additional File 4 Outcome details of placebo-controlled trials Click here for file Additional File 5 Outcome details of active-controlled trials Click here for file | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC516039.xml |
553968 | Determining the quality of educational climate across multiple undergraduate teaching sites using the DREEM inventory | Background Our obstetrics and gynaecology undergraduate teaching module allocates 40–50 final year medical students to eight teaching hospital sites in the West Midlands region. Based on student feedback and concerns relating to the impact of new curriculum changes, we wished to objectively assess whether the educational environment perceived by students varied at different teaching hospital centres, and whether the environment was at an acceptable standard. Methods A Dundee Ready Education Environment (DREEM) Questionnaire, a measure of educational environment, was administered to 206 students immediately following completion of the teaching module. Results The overall mean DREEM score was 139/200 (70%). There were no differences in the education climate between the teaching centres. Conclusion Further research on the use of DREEM inventory, with follow up surveys, may be useful for educators to ensure and maintain high quality educational environments despite students being placed at different teaching centres. | Background The undergraduate curriculum at our medical school was redesigned in 1998/99 to bring it in line with recommendations suggested by the General Medical Council (GMC) in Tomorrow's Doctors [ 1 ]. Obstetrics and Gynaecology is taught as a final year module. Around 20–30 students, of a total year group of around 200 students, are allocated to eight teaching hospital sites in the West Midlands region, and remain their for the length of the module (eight weeks). Throughout the placement, all formal lectures take place at the principal Teaching Hospital (Birmingham Women's Hospital). A comprehensive course handbook and web-based multiple choice formative assessment accompany the module, and detail the teaching, practical and assessment objectives for students and clinicians. We have aimed to ensure there are no significant differences in the way the curriculum is delivered between centres. All 200 students sit the final exam in Obstetrics and Gynaecology straight after completing the 8-week course module. Based on previous student feedback reporting differences in educational experiences, together with our concerns relating to the impact of new curriculum changes, we wished to objectively assess whether the educational environment perceived by students varied at different teaching hospital centres, and whether the environment was at an acceptable standard. In particular, was there any potential loss of teaching experience when students were placed away from the principal Teaching Hospital. Thus, the null hypothesis we wished to test was that there was no difference in the learning environment between centres. Several questionnaire-based educational tools are available that set out to 'quantify' the educational environment [ 2 - 4 ]. However, we chose to use the Dundee Ready Education Environment Measure (DREEM) inventory, as more studies had evaluated and validated this method [ 5 , 6 ]. The DREEM inventory consists of 50 questions, each scoring 4, giving a total maximum individual DREEM score of 200. The five domains that comprise the DREEM are depicted in Table 1 . Table 1 Domains of DREEM questionnaire TOPIC Number of questions Maximum DREEM Score Students' Perception of Learning 12 48 Students' Perception of Teachers 11 44 Students' Academic Self-Perceptions 8 32 Students' Perception of Atmosphere 12 48 Students' Social Self-Perceptions 7 28 Total 50 200 Methods The DREEM questionnaire, based on a Likert scale, was administered to the full class of 206 final-year Birmingham University medical students undertaking the exam module in Obstetrics and Gynaecology in 2000. All questionnaires were distributed and returned the same day of the exam, which allowed us to achieve a 100% response rate. Students were told to only comment on their recent 8-weeks experience of Obstetrics and Gynaecology. Statistical analysis was performed using Microsoft Excel and Arcus Quickstat Biomedical Statistical software, and utilised single-sample T test and One-way analysis of variance (ANOVA). Results The year group comprised 42% male and 58% female. The overall mean DREEM score for the study group was 139/200 (95% CL 136.1 to 141.9), or expressed as percentage of the maximal score, 70% (95% CL 68% to 71%). There was no statistically significant difference between the mean scores for the contributory DREEM domains, which were as follows: perception of learning, 34.52/48 (72%); perception of teaching, 32.05/44 (73%); academic self-perception, 19.46/32 (61%); perception of atmosphere, 34.07/48 (71%), and for social self perceptions, 18.90/28 (68%). The DREEM scores for each hospital, with comparison of all contributory elements of the DREEM inventory, are depicted in Table 2 and Figure 1 . Table 2 The DREEM domains and overall score for each hospital HOSPITAL Number of Students LEARNING Mean Score/48 TEACHERS Mean Score/44 ACADEMIC SELF-PERCEPTION Mean Score/32 ATMOSPHERE Mean Score/48 SOCIAL Mean Score/28 OVERALL DREEM Score/200 DREEM percentage for each hospital (total of 206) Percentage of maximum score Percentage of maximum score Percentage of maximum score Percentage of maximum score Percentage of maximum score Percentage of maximum score BWH 53 33.77 31.89 19.77 33.40 19.32 138.15 69% Good Hope 20 33.30 30.10 18.15 33.40 18.20 133.15 67% B'ham Heartlands 26 34.15 32.73 18.92 32.77 19.58 138.15 69% Walsall Manor 20 34.10 34.35 19.90 34.30 19.05 141.70 71% City 32 35.13 28.31 19.41 34.97 17.75 135.56 68% Wolverhampton 22 35.77 32.59 20.41 33.64 18.86 141.27 71% Shrewsbury 13 34.77 32.85 18.69 35.00 19.15 140.46 70% Wordsley 20 35.15 33.60 20.40 35.10 19.30 143.55 72% Mean overall 34.52 72% 32.05 73% 19.46 61% 34.07 71% 18.90 68% 139.00 70% Lower 95% CL 33.83 70% 30.41 69% 18.77 59% 33.33 69% 18.38 66% 136.13 68% Upper 95% CL 35.21 73% 33.69 77% 20.14 63% 34.82 73% 19.42 69% 141.88 71% CL Confidence Limit Figure 1 Graphical representation of the contribution of each DREEM domain to the overall mean DREEM score When converting the raw DREEM score to percentages, two-sided P-value single-sample Student's T test showed no statistically significant difference between hospitals by each DREEM domain, or between each DREEM domain within the same hospital. Greatest variation between hospitals occurred in the Students' Perception of Atmosphere domain, where there were four hospitals beyond the 95% Confidence Limits; this compared to three hospitals beyond 95% Confidence Limits in all other DREEM domains (Table s 2 ). One-Way analysis of variance (ANOVA) yielded F (variance ratio) = 0.5222, P = 0.8111, which indicated no statistically significant differences between hospitals, DREEM domains, or overall DREEM scores (Table 3 ). Table 3 ANOVA analysis between different hospitals for the differing DREEM domains Percentage of maximum score for each DREEM component for each hospital Birmingham Women's Good Hope Birmingham Heartlands Walsall City Wolver-hampton Shrewsbury Wordsley Learning 70% 69% 71% 71% 73% 75% 72% 73% Teachers 72% 68% 74% 78% 64% 74% 75% 76% Academic 62% 57% 59% 62% 61% 64% 58% 64% Atmosphere 70% 70% 68% 71% 73% 70% 73% 73% Social 69% 65% 70% 68% 63% 67% 68% 69% One-way analysis of variance (ANOVA) yielded F (variance ratio) = 0.5222, P = 0.8111. Discussion We have used the Dundee Ready Education Environment Measure (DREEM) in 'diagnosing' the educational environment of eight different teaching centres and making comparative analysis between these centres. The overall mean DREEM score was 139/200, or expressed as a percentage, 70% (95% CL 68–71%). The educational learning environment did not vary between centres. The two lowest scoring contributory domains, academic self-perception (61%) and social self-perceptions (68%), were not statistically significantly different from the other three DREEM domains or overall mean DREEM score. This study has benefited by using an established educational measure and obtaining a 100% response rate. No students had been previously taught at the principal teaching hospital as this was solely used for Obstetrics and Gynaecology teaching. However, some of the students (surveyed to be 16/206, 8%) had previously attended the other seven teaching hospital centres due to prior clinical teaching attachments. Thus, previous experiences may have biased the teaching assessment completed by some students. Furthermore, the DREEM questions are of such a nature that it is likely that the environment of the entire curriculum was being assessed. However, by performing the DREEM survey immediately at the end of the obstetrics and gynaecology module, and emphasising reporting only the last eight weeks experience, we believe this maximised the chance that the DREEM measure assessed only the recent hospital teaching site and minimised any recall bias. Other groups [ 7 ] have highlighted the potential flaws in using means and parametric statistical tests on ordinal data from Likert scales. As there is no firmly established consensus, we adopted to use the Student's T test and ANOVA calculation to fulfil best statistical methodology. The DREEM domains are unlikely to be independent variables, and may be less of an environment test but more of a measure of the overall motivation and learning attitude of the individual. The Course Valuing Inventory (CVI) score is made up of five domains: worthiness of learning experience, emotional awareness, personal development, cognitive enhancement and task drive. A recent study of first year medical students showed a correlation between higher Course Valuing Inventory (CVI) scores, female gender, stronger self-confidence as a learner, greater motivation to learn and higher DREEM scores [ 8 ]. There is no accepted agreement on what is an acceptable DREEM inventory score from published literature. Nevertheless, our DREEM score of 139/200 was higher than other reports. A study of final year medical students in Trinidad reported an overall mean DREEM of 109.9/200 [ 5 ]. A larger scale study, involving students from both final and earlier undergraduate training years, showed a DREEM score of 118/200 in a Nigerian medical school, and 130/200 in a Nepalese medical school [ 9 ]. Our higher score is reassuring, and is perhaps an indicator of better hospital teaching environment, the positive value of using a comprehensive course handbook, and the encouragement of formative self-assessment as guided by the course handbook and web-based package. The non-significant differences between the DREEM domains and between hospitals were significant findings. This was conveyed to our tutors based at the various teaching centres as a positive and encouraging result. In practical terms, this meant that regardless of hospital capacity or student group size, their education delivery and environment was no different to other centres in the student's curriculum. The DREEM inventory may thus be a useful tool for educators to ensure and maintain high quality educational environments and uniformity in educational delivery despite students being placed at different teaching centres. Competing interests The author(s) declare they have no competing interests. Authors' contributions RV and ET carried out the statisitcal analysis, data interpretation, and drafted the manuscript. JKG conceived and coordinated the study, acquired the results, and made revisions of the manuscript. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC553968.xml |
521695 | Chromosome loops arising from intrachromosomal tethering of telomeres occur at high frequency in G1 (non-cycling) mitotic cells: Implications for telomere capture | Background To investigate potential mechanisms for telomere capture the spatial arrangement of telomeres and chromosomes was examined in G1 (non-cycling) mitotic cells with diploid or triploid genomes. This was examined firstly by directly labelling the respective short arm (p) and long arm subtelomeres (q) with different fluorophores and probing cell preparations using a number of subtelomere probe pairs, those for chromosomes 1, 3, 4, 5, 6, 7, 9, 10, 12, 17, 18, and 20. In addition some interstitial probes (CEN15, PML and SNRPN on chromosome 15) and whole chromosome paint probes (e.g. WCP12) were jointly hybridised to investigate the co-localization of interphase chromosome domains and tethered subtelomeres. Cells were prepared by omitting exposure to colcemid and hypotonic treatments. Results In these cells a specific interphase chromosome topology was detected. It was shown that the p and q telomeres of the each chromosome associate frequently (80% pairing) in an intrachromosomal manner, i.e. looped chromosomes with homologues usually widely spaced within the nucleus. This p-q tethering of the telomeres from the one chromosome was observed with large (chromosomes 3, 4, 5), medium sized (6, 7, 9, 10, 12), or small chromosomes (17, 18, 20). When triploid nuclei were probed there were three tetherings of p-q subtelomere signals representing the three widely separated looped chromosome homologues. The separate subtelomere pairings were shown to coincide with separate chromosome domains as defined by the WCP and interstitial probes. The 20% of apparently unpaired subtelomeric signals in diploid nuclei were partially documented to be pairings with the telomeres of other chromosomes. Conclusions A topology for telomeres was detected where looped chromosome homologues were present at G1 interphase. These homologues were spatially arranged with respect to one-another independently of other chromosomes, i.e. there was no chromosome order on different sides of the cell nuclei and no segregation into haploid sets was detected. The normal function of this high frequency of intrachromosomal loops is unknown but a potential role is likely in the genesis of telomere captures whether of the intrachromosomal type or between non-homologues. This intrachromosomal tethering of telomeres cannot be related to telomeric or subtelomeric sequences since these are shared in varying degree with other chromosomes. In our view, these intrachromosomal telomeric tetherings with the resulting looped chromosomes arranged in a regular topology must be important to normal cell function since non-cycling cells in G1 are far from quiescent, are in fact metabolically active, and these cells represent the majority status since only a small proportion of cells are normally dividing. | Background In both plants and animals, during early meiosis in normal cells there is a clustering of all or most of the telomeres of the entire chromosome set to a single region on the nuclear membrane [ 1 - 3 ]. This meiotic looping of chromosomes with clustered ends has been termed the bouquet arrangement which appears synchronously with synapsis of bivalents. The reason why the telomeres attach to the nuclear membrane in meiosis is not dependent on the presence of normal numbers of TTAGGG repeats and, in fact, still occurs in late generation Terc-/- mice without detectable pantelomere repeats [ 5 ]. In plants the meiotic telomere clustering can be inhibited by colchicine [ 6 ] but a polarization still remains within the nuclei such that microtubules and nuclear pores are still arranged in a region that normally would face the telomere cluster on the opposite side of the nuclear membrane [ 3 ]. In mammals the bouquet arrangement seen at early meiosis occurs with some minor differences between males and females [ 4 ] but has disappeared in both by diplotene/dictyotene. For mitosis there is much less data on the position or possible associations of telomeres and subtelomeres. However, the spatial arrangement of chromosomes at mitotic interphase has been studied intensively [ 7 , 8 ] but there are few studies with data on the principles that dictate nuclear organization. Nagele et al. [ 9 , 10 ] using whole chromosome paints on fixed normal diploid human cells described a radial array (rosette) of prometaphase chromosomes where the chromosomes were apparently arranged in two tandemly linked haploid sets. That interphase chromatin formed ring-like shapes was already known [ 11 , 12 ] but Nagele et al. [ 9 , 10 ] proposed that there was a chromosome order in each of the haploid sets in diploid cells during mitosis which was thought to be reversed with respect to one-another. A chromosome order was also described as being present at interphase in non-cycling cells [ 13 ] where the nuclear organization seems to be fundamentally different from that in dividing cells [ 14 , 15 ]. From observations in triploid cells, Nagele et al. [ 10 ] proposed that the three haploid sets were spatially arranged, two with the chromosomes arranged in tandem and the third with a reversed chromosome order. The relationship of subtelomeric regions to these concepts of a chromosome order within a radial chromosome array is less clear. Stout et al . [ 16 ] studied subtelomeric chromosome regions at interphase and showed that compared to interstitial chromosome sites, subtelomeres showed an increased number of somatic pairings. By FISH within living cells, Molenaar et al. [ 17 ] were able to demonstrate that these telomeric associations are dynamic. The rate of telomeric associations apparently depends on the stage of the cell cycle. Nagele et al. [ 18 ] utilising a telomere-specific peptide nucleic acid probe has demonstrated that the prevalence of such telomeric associations is far higher at interphase in non-cycling cells than in their cycling counterparts. In the present study we examine the telomere associations in mitotic interphase in human non-cycling cells of diploid or triploid karyotype. The cell types used were from skin, fetal cartilage, and long-term culture of chorionic villi but colchicine and hyptotonic treatments were avoided during cell harvest because of the potential effect of disrupting any topology present [ 6 , 19 ]. We report a new finding, the detection of looped chromosomes in mitotic G1 by the intrachromosomal tethering of short-arm (p) and long-arm (q) telomeres. This new finding has implications for the understanding of the normal dynamics of chromosome behaviour at interphase but also for the processes involved in telomere capture. Results Fluorescence in situ hybridization (FISH) performed with the various subtelomere probes (Table 1 ) gave discrete signals in all experiments attempted. Figure 1 shows FISH of cells probed with the p-subtelomeres labeled red and q-subtelomeres labeled green for chromosomes 4, 5, 7, 10, 17, and 20, arranged respectively in figure 1A,1B,1C,1D,1E,1F (diploid cell line CG04-0743BBRS) and for chromosomes 18, 12 and 6 in figure 1G,1H,1I (triploid cell line CG01-2042YA). The proportion of p-q associated signals is shown in Table 2 . The frequency of p-q subtelomere tethering ranged from 76–85% in the diploid cells but was a little less in the triploid cells (58–94%). Not all signal pairs were tethered. The percentage of diploid cells with all p-q signals tethered was 46–72% as compared to 33–58% in the triploid cells. This would be expected with more opportunity for interhomologous tethering in the triploid nuclei with an extra chromosome. Table 1 Origin and derivation of the telomere clones used in the study. Clone Chrom Supplier 1186B18 3p Flint 196F04 3q Incyte 36P21 4p Incyte 963K6 4q Flint 189N21 5p Incyte 240G13 5q Incyte 62I11 6p Incyte 57H24 6q Incyte 164D18 7p Incyte 3K23 7q Incyte 43N06 9p Incyte 112N13 9q Incyte 306F07 10p Incyte 137E24 10q Incyte 496A11 12p Flint 221K18 12q Incyte 2111b1 17p ATCC 362K4 17q Flint 52M11 18p ATCC 964M9 18q Flint 1061L1 20p Flint 81F12 20q Incyte Note: The host strain was E. coli DH10B with kanamycin resistance in all cases except the clone 2111b1 (17p probe) which was ampicillin resistant. Table 2 Rate of tethering in non-cycling cells at G1 interphase of p (short arm) to q (long arm) subtelomeric signals in single homologues. Chromosome Genome of cells Numbers and [Percentage] of p-q signal pairs** tethered (95% confidence limits) Numbers and [Percentage] of cells with all p-q signals tethered# (95% confidence limits). 4 Diploid 50/60 [83] (71–91%) 10/14 [72] (42–92%) 5 Diploid 123/148 [83] (76–89%) 26/41 [63] (47–78%) 7 Diploid 86/113 [76] (67–84%) 26/45 [58] (42–72%) 9 Diploid ND* ND* 10 Diploid 92/108 [85] (77–91%) 24/35 [69] (51–83%) 17 Diploid 62/76 [82] (71–90%) 13/28 [46] (28–66%) 20 Diploid 72/90 [80] (70–88%) 14/24 [58] (37–78%) 3 Triploid 25/30 [83] (65–94%) 5/10 [50] (19–81%) 6 Triploid 49/61 [80] (68–89%) 12/22 [55] (32–76%) 12 Triploid 57/82 [70] (58–79%) 8/24 [33] (16–55%) 18 Triploid 65/85 [77] (66–85%) 12/28 [43] (25–63%) ND-not determined. * Cross-hybridization between 9q and 18p prevented analysis. ** p-q subtelomere signal pairs scored, irrespective of cell numbers. #Separate scoring of discrete cells only; i.e. diploid cells with clearcut tethering of both p (short-arm) to q (long-arm) subtelomere signal pairs and triploid cells with all three p-q subtelomere signal pairs tethered. Figure 1 A-I Intrachromosomal tethering of the subtelomeres of each single homologue in diploid and triploid non-cycling interphase nuclei at G1. FISH of diploid (A-F) or triploid interphase nuclei (G-I) from the following cell lines: CG04-0743BBRS (diploid) derived from skin and CG01-2042YA (triploid) derived from CVS. These non-cycling cells were probed with p-subtelomeric probe (labelled with spectrum orange) and q-subtelomeric probe (spectrum green) for (A) chromosome 4; (B) chromosome 5; (C) chromosome 7, (D) chromosome 10, (E) chromosome 17, (F) chromosome 20, (G) chromosome 18, (H) chromosome 12, (I) chromosome 6. The proportion of p-q tethered signals is shown in Table 2. In each case the majority of cells (76%–85%) showed pairing of short-arm and long-arm subtelomeres from single homologues often arranged on opposite sides of the interphase nucleus. Note also that an interphase topology is exhibited such that oval rosettes of chromatin can be seen in the present study in figs 1H, 1I, an elongated rosette in fig 1C, and off-centre rosettes in figs 1A, 1B. The triploid cells were used to test the likelihood that intrachromosomal pairing of subtelomeric signals was occurring rather than the pairing of p and q signals with the q and p signals of the other homologue(s). As can be seen in figure 1G,1H,1I , there were three p-q tethered signal pairs in the triploid interphase nuclei. Figure 2 shows single arm subtelomeric probes from three different chromosomes demonstrating that there is no linkage of positioning (chromosome order) between non-homologues. This is evidence challenging the claims of haploid groups being present at the interphase of non-cycling cells. Figure 2 A-F Chromosome homologues at G1 in nuclei of non-cycling cells are spatially arranged without respect to non-homologues. Same cell line and same cell harvest as the triploid cells probed in Fig 1. Two combinations of three subtelomeric probes (see Table 1 for clones) are shown hybridized to triploid cells. In fig 2A-C the nuclei are probed with three single subtelomere probes from 4p (spectrum orange); 18q (spectrum green) and 6p (both spectrum orange and spectrum green labels, i.e. yellow signal). In figs 2D-F, the nuclei are probed with three subtelomere probes labelled 5p (spectrum orange), 12q (spectrum green), and 20p (spectrum orange and spectrum green, i.e. yellow signal). Note: There was no segregation into haploids sets of chromosomes at G1 interphase. Homologues were regularly arranged without any defined relationship to non-homologous signal groups; i.e. haploid sets of interphase chromosomes distributed to separate nuclear regions do not appear to exist. Note also there is a low frequency of isolated non-homologous associations: between 4p and 6p (Fig 2A); between 6p and 18q (Fig 2C), and between 5p and 20p (Fig 2D). Further confirmation that the p-q tetherings in figure 1 were from single chromosomes is shown in figure 3 . Figure 3A,3B,3C , shows chromosome 15 interstitial loci (diploid cell line CG04-0743BBRS) probed together with the 15 alpha centromeric probe, and a 15q subtelomeric locus. Separate chromosome domains surround the subtelomeric signals (Fig 3A,3B,3C ). Similarly for chromosome 12 (Fig 3D,3E,3F ), using the same diploid cells (CG04-0743BBRS), subtelomeric probe pairs are defined to occur within the two separate chromosome domains by jointly using WCP12. Figure 3 A-F Looped chromosomes in G1 arrested cells: the distribution of tethered subtelomeric signals coincides with chromosome domains. Diploid non-cycling cells harvested after confluence arrest. The diploid cells are from the same cell line as in Fig 1 (i.e. CG04-0743BBRS). Fig 3A-3C shows diploid cells probed for chromosome 15 with CEN15 (larger signal spectrum green); SNRPN at 15q12 (spectrum orange); PML at 15q22 (spectrum orange); and subtelomeric 15q probe (smaller signal spectrum green). Note: The two chromosome 15 domains coincide with and envelop the 15q subtelomeric signals (there is no currently recognized specific 15p subtelomeric sequence and hence no 15p subtelomeric probe). Fig 3D-3F shows diploid cells probed for chromosome 12 with the subtelomeric probes for 12p (labeled with spectrum orange) and 12q (labeled with spectrum green) and the WCP chromosome 12 (the spectrum green smear). Note: The three chromosome 12 domains as defined by the (directly labeled) WCP12 envelop the three tethered subtelomeric probe pairs. This confirms that the telomeric tethering represents looped chromosomes. Discussion Evidence for short-arm and long-arm subtelomeres of the one homologue associating This study shows that the pairings of red/green signals from the subtelomeres of the short-arm and long-arm respectively occur at high frequency in these non-cycling diploid nuclei. In many cases the association is so close that the subtelomeric signals are superimposed (e.g. figure 1A,1F ). The pairs of red/green, p/q signals are from a single chromosome with the two diploid homologues arranged on different sides of the nucleus. This has been shown in this study in several ways. Firstly, it is highly likely that separate looped chromosomes are involved since the paired subtelomeric signals occur with small chromosomes (chromosome 17, 18, 20), intermediate chromosomes (7, 9, 10, 12) or large chromosomes (3, 4, 5) and are observed in triploid as well as diploid cells. Indeed the wide separation of the two subtelomeric signals from pairs of homologues (e.g. fig 1B,1D ) supports the present interpretation that the telomeric tetherings of p-q signal pairs are intrachromosomal and not between homologues. Secondly, when interstitially located probes are used, for example on chromosome 15 (Fig 3A,3B,3C ) in diploid cells, two distinct chromosome domains are seen. Thirdly, when subtelomeric probe pairs are used with a WCP probe for example on chromosome 12 (Fig 3D,3E,3F ) in diploid cells, two distinct chromosome domains are seen that envelop the two tethered pairs of subtelomeric regions. In diploid nuclei the pairs of tethered subtelomeric signals are distributed to two areas and in triploid nuclei (fig. 1G,1H,1I ), the tethered signals are distributed to three areas. If the signal pairings were between the short-arm from one homologue with the long-arm of another it is especially unlikely in the triploid cells that the chromosomes could span the diameter of the nucleus consistently. This is especially unlikely in light of the finding by Nagele et al. [ 10 ] that the nucleus normally exhibits a rosette of (chromosome rich) chromatin with a less dense central core (doughnut shape). If inter-homologous telomeric associations were the explanation for the regular p-q signal pairings then, especially in triploid cells, chromosomal threads would have to be arranged in very complex formations across the chromatin poor cores of rosettes. Finally, there is separate evidence that there are small non-overlapping chromosome territories at interphase in mammalian cells [ 20 , 21 ] where the chromosomes are extended but not entwined. In the present study we have also been able to show the presence of these interphase chromosome domains both with the use of several probes spanning the length of chromosomes (e.g. Fig 3A,3B,3C ) or with chromosome paints (e.g. Fig 3D,3E,3F ). Nagele et al. [ 18 ] showed that there were very few coincident telomeric associations (TA's) in rapidly cycling mitotic cells. However, these authors showed [ 18 ] that in non-cycling cells there was a high rate of double associations, and a lesser frequency of triple and quadruple associations or unassociated telomeres. These authors [ 18 ] concluded that the replicative status of the cells was the prime determinant in the level of telomere associations. The finding of a high intrachromosomal p-q telomere association rate in the present study probably explains the underlying high telomere association rate described by Nagele et al. [ 18 ]. In that study [ 18 ], a universal telomere probe was used so the specificity of the associations, if present, was unrecognisable. In the present study, there was a high (~80% but not saturated) rate of intrachromosomal pairing with only ~20% of telomeres unpaired with their homologous subtelomere. These two studies can be reconciled if the apparently (~20%) unpaired subtelomeres (present study) were actually associated with non-homologous subtelomeres. Fig 2 shows the presence of an underlying low rate of non-homologous telomere tetherings in these G1 arrested cells. Regulation of telomere associations In early meiotic cells the presence of the normal numbers of universal TTAGGG sequences is not required for massed telomere clustering [ 5 ]. A complementary finding was reported by Nagele et al . [ 18 ] who showed that in late passage mitotic cells the number of telomere associations (TA's) did not increase during progression to late passage crisis. This indicates that telomere shortening did not increase the number of TA's. Since the pantelomeric repeats occur at all telomeres, the specific intrachromosomal association presently observed also cannot be due to their presence. Neither can the mechanism of tethering be related to chromosome specific subtelomeric sequences since the two homologues with identical sequences remain separated (Fig 1 ). There clearly are similarities between the looping of chromosomes seen in the present non-cycling mitotic cells and in the chromosome bouquets of early meiosis [ 2 , 3 ]. These two apparently disparate phenomena may be related. If the synapsis of bivalents, unnecessary in mitotic cells, was removed from the meiotic bouquet arrangement mechanism, the intrachromosomal tethering of separated homologues as presently observed is what may be left. This mitotic looping may have been originally present since meiosis is believed to have evolved from mitosis. Chromosome topology at interphase The global organisation of the interphase nucleus has provoked the interests of cell biologists for several decades but detecting the presence of any macromolecular domains has been challenging [ 8 ]. Nagele et al. [ 9 , 10 ] was able to confirm with Feulgen staining and FISH that the chromosomes were arranged in rosettes, a ring of chromatin with partly-condensed chromosomes, which persisted through mitosis and was even maintained in the daughter cells at telophase. Oval rosettes can be seen in the present study in figs 1H,1H , and 3D ; an elongated rosette in fig 1C , and off-centre rosettes in figs 1A,1B , and 2B . Through the use of FISH with chromosome specific alphoid probes and whole chromosome paints, Nagele et al. [ 10 , 13 ] attempted to show that chromosomes in the rosettes appeared to be in an orderly arrangement in both diploid and triploid cells. These authors interpreted this order as specifically positioned haploid sets [ 9 , 10 , 13 ]. The pairing of subtelomere signals in non-cycling cells at interphase, as in the present study, is in some aspects consistent with these prior observations though we do not accept that haploid sets are spatially segregated and we found no evidence for an interphase chromosome order in the non-cycling cells of our cell lines. We have repeated this work with centromeric probes (not shown) and again there was no evidence of haploid groups or of a regular chromosome order though widely spaced homologous centromeric signals are usually observed (with respect to each chromosome considered separately). With respect to telomeric tethering in cycling cells (at G2) no such p-q telomeric tethering pattern is present in our observations of lymphocytes (not shown) and the only associations are of sister chromatids. That most lymphocytes are at G2 can be observed by the doubled signals representing sister chromatids (not shown) which is in contrast to the single (chromatid) signals in the unreplicated G1 nuclei (see fig 1 ). With centromeric and painting probes, Nagele et al. [ 9 , 10 , 13 ] detected the presence of what they believed to be haploid sets of chromosomes in both diploid and triploid cells with the sets on opposite sides of the nucleus. In some cell shapes (e.g. elongated, polymorphic, or lenticular shaped cells) this regular order was obscured but in spherical nuclei it was mostly evident. Whereas there is often a spatial separation of the telomeric signals from the various homologues of the diploid or triploid G1-arrested cells in the present data (see fig 1 ) there was no evidence for a chromosome order or haploid groups in the cell nuclei (fig 2 ). In the explanation of Nagele et al. [ 13 ] the haploid sets these authors proposed represented maternal and paternal chromosome contributions. In the present data each set of identical homologues (two in diploid or three in triploid cells) appear to be arranged without respect to those of other chromosomes (fig 2 ), i.e. the spatial arrangement is not an interchromosomal phenomenon. This means the theoretical haploid sets of chromosomes described by Nagele et al. [ 9 , 10 , 13 ] probably do not exist. Figure 2 illustrates the two experiments performed in the current study to address the possible existence of haploid sets. These comprised examining the chromosome order for the single telomeres 4p (labelled with spectrum orange – Vysis, Downers Grove, Illinois), 18q (spectrum green), and 6p (spectrum green and spectrum orange, i.e. yellow signal) jointly hybridised to the same confluence arrested cells, and in a second experiment: 5p (spectrum orange label), 12q (spectrum green), and 20p (spectrum green and spectrum orange) hybridised to a second slide of triploid cell nuclei. These cells are from the same harvest as those shown to display the interphase topology of p-q intrachromosomal subtelomere tethering. In these latter results, homologous subtelomeres were regularly arranged without any defined relationship to non-homologous signal groups. This demonstrates that there is no interchromosomal order transferable between nuclei and challenges the concept of the presence of haploid sets within these non-cycling cells. In the view of Nagele et al. [ 10 ] the dual odd topology that he observed with (i) homologues arranged on opposite sides of the nuclei (diploid cells) or regularly arranged around the nucleus (triploid cells), and (ii) a chromosome order possibly manifesting as "haploid sets" may just be a relic of fertilization. Whereas, in our view, these intrachromosomal telomeric tetherings with the resulting looped chromatids must be important to normal cell function. Possible relationship of telomere tetherings to telomere captures As reviewed by Ballif et al. [ 23 ] there are two general pathways whereby chromosomes can acquire a new telomere and thus become stabilised. Firstly, by "telomere healing", i.e. the direct addition of telomeric repeats by: (i) telomerase [ 24 ] or by (ii) telomerase-independent recombination-based mechanisms [reviewed in [ 25 ]]. The second pathway is by "telomere capture" in which a chromosome acquires a telomere from another chromosome or chromosome end [reviewed in [ 23 ]]. Telomere captures are observed in two forms, those that are within the one homologue or intrachromosomal telomeric captures or transpositions [ 22 , 23 ], and those between non-homologues [ 26 ]. Ballif et al. [ 23 ] considered four different models for telomeric captures involving the p and q arms of a single homologue (intrachromosomal captures). These telomeric captures where the telomere from one chromosome arm is deleted and replaced by a telomere from the other arm of the homologous chromosome were termed intrachromosomal transpositions of telomeres [ 22 ] because of the uncertainty that simple reciprocal translocation was involved in this type of telomere capture. Ballif et al . [ 23 ] suggested that breakage induced replication (BIR), reviewed in Kolodner et al. [ 28 ], was the most likely model for these intrachromosomal captures based on their observation that there was observed heterozygosity between the duplicated ends on the one chromosome. This mechanism was initially described by Reddel et al. [ 27 ] under the unwieldy name "alternative lengthening of telomeres mechanism". Ballif et al. [ 23 ] suggested that BIR simply copied the sequence from the other end of the same homologue. Furthermore, that obligatory crossing-over during meiosis would mean that heterozygosity between duplicated ends would occur in many cases. The detection in the present study for the first time that in non-cycling mitotic cells in G1 most short-arm and long-arm telomeres from the one chromosome are tethered together is a likely staging point for mitotic events as per the fourth model of telomere capture reviewed in Ballif et al. [ 23 ]. This fourth model is that of the present authors in a prior study [ 22 ]. In the explanation of that fourth model by Ballif et al . [ 23 ], telomere capture was believed to occur by a pre-meiotic interhomologous exchange. The imbalanced chromosome was then generated through normal meiotic recombination. This (model) thus resulted firstly in a balanced translocation, termed telomere transposition by Daniel et al. [ 22 ] since reciprocal translocation was unproven. This translocation relocated the telomeres to the opposite chromosome arm and then by recombination the result was a duplication of one telomere on both chromosome ends and a deletion of the other. For this model to be correct a high frequency of balanced telomeric translocations would have to occur. These would be observed as large pericentric inversions and are rarely reported – see review in Daniel, 1988 [ 30 ]. However, the transposition of telomeres to opposite chromosome ends resulting in large pericentric inversions would not be easily noticed during FISH in many cases. This is in contrast to translocations between non-homologues which are very obvious to an observer in a FISH study. In this connection, for telomere translocations between non-homologous the rate of clinically ascertained balanced translocations has been reported as very high. Flint and Knight [ 26 ] record that for the several types of (non-homologous) telomeric rearrangements: unbalanced translocations account for 54% of cases; deletions for 39%; and duplications for 6%. According to Flint and Knight [ 26 ] in almost all cases unbalanced translocations occur because a parent carries the balanced form. When the data used to draw this conclusion are scrutinised, see De Vries et al. [ 29 ] it includes many rearrangements that are microscopically detectable, i.e. essentially regular reciprocal translocations. Such latter rearrangements are not really "telomere captures", are often familial, and would be expected to be associated with a high rate of balanced carriers. In our experience (Greg Peters and Luke St Heaps – CHW Telomere Study Group) we have not detected a balanced carrier of a telomere capture of either the intrachromosomal type or the interchromosomal type. In our view the issue of the frequency of balanced telomere rarrangements needs to be revisited since telomere captures are technically sub-microscopic telomere rearrangements. This data impinges on the likelihood that BIR is the preferred method of telomere capture [see that view expressed in ref [ 23 ]]. Since with the BIR model immediate recombinants are formed with no balanced carriers, if balanced (telomere capture) carriers are frequently reported, then BIR is ruled out as the common mechanism of telomere capture. This judgement currently cannot be performed without a more rigorous approach to the whole data set and additional assessment of the de novo or alternative origin of telomere rearrangements. Conclusions A topology for telomeres was detected where looped chromosomes were present at G1 interphase in confluence arrested cells. It was shown that the p and q telomeres of each chromosome in G1 cells associate frequently (80% pairing) in an intrachromosomal manner which was confirmed by studying chromosome domains with interstitial probes (chromosome arms) and WCP probes. It was found that homologues were regularly arranged without any defined relationship to non-homologous signal groups; i.e. there was no apparent chromosome order on different sides of the nuclei and no segregation into haploid chromosome sets was detected. The normal function of this high frequency of intrachromosomal telomeric pairings is unknown but a potential role is likely in the genesis of telomere captures whether of the intrachromosomal type or between non-homologues. In our view, these intrachromosomal telomeric tetherings with the resulting looped chromosomes arranged in a regular topology must be important to normal cell function since non-cycling cells in G1 are far from quiescent, are in fact metabolically active, and these cells represent the majority status since only a small proportion of cells are normally dividing. Materials and methods Cell culture Cell lines were retrieved from liquid nitrogen, washed in Dulbecco's phosphate buffer (DPB) and reconstituted in Hams F10 medium. The following lines were used: a diploid skin fibroblast line CG04-0743BBRS with karyotype 46,XX derived from fetal cartilage and a triploid 69,XXX cell line CG01-2042YA of diandric origin derived from mesodermal cells of a chorionic villus biopsy. These were cultured until they reached confluence via contact inhibition. At this stage the cells exhibit a number of swirls of closely packed cells in parallel. They were then severally prepared for trypsin harvest usually 48 hours after the last media change without colcemid/colchicine treatment and without the usual 0.075 M KCl hypotonic treatment. The cells were trypsinised off and fixed three times in 3:1 methanol to glacial acetic acid and were stored at room temperature (R.T.) in fixative for 1–3 days. This period allowed some mild acidic digestion of the chromatin and spreading of the nuclei when slides were prepared. At the end of the storage period, cells were rewashed once with fresh fixative and dropped onto glass slides as per routine techniques, and stored on trays in a low humidity cabinet until used for FISH. Choice of probe and probe label The identity of the probes used in the study is shown in Table 1 . The clones containing the DNA for the subtelomere probes were obtained from three sources: Incyte Genomics (Fremont, California); Dr Jonathan Flint (John Radcliffe Hospital, Oxford, U.K.), via Dr David Mowat, or the ATCC, (Manassas, Virginia). All were grown in Luria Broth (LB) with kanamycin by standard techniques unless specified otherwise (Table 1 ). Plasmid DNA was extracted with QIAGEN midi kits as per the manufacturer's instructions except that DNA elution was achieved at 60°C overnight. Probes were all labelled by nick translation (using VYSIS kit and the fluorophores spectrum orange and spectrum green, Vysis, Downers Grove, Illinois) as per the manufacturer's instructions. Fluorescence in situ hybridization (FISH) Slides were pretreated with a combined Pepsin/Rnase step. This was performed by prewarming RNAse and pepsin to 37°C, 200 μl of RNAse (0.1 mg in saline/sodium citrate – 2xSSC) was dispensed onto each slide, coverslipped and incubated at 37°C for 40 minutes in a humidified chamber. Coverslips were removed and slides washed twice for 5 minutes in 2xSSC at room temperature (RT). Slides were briefly drained and 200 μl of pepsin (0.2% in 0.01 M HCl) was placed on the slides, coverslipped and incubated at 37°C for 30 seconds. Coverslips were removed and slides were washed twice for five minutes in phosphate buffered saline (PBS) at RT. Fixation was with 6% paraformaldehyde in PBS, by dispensing 200 μl/slide, and adding a coverslip for 2 minutes at RT. Slides were then washed twice for five minutes in PBS at RT, dehydrated through 70, 90 and 100% ethanol for 3 minutes/wash at RT, and air dried. Probes in hybridisation mix were stored at -20°C, removed and thawed for 30 minutes; dispensed onto slides, covered with 15 mm diameter coverslips, and sealed with liquid rubber – art cement. Joint denaturation was achieved at 75°C for 5 minutes on a Omnigene hot plate, transferred to a humidified hybridization chamber at 37°C and hybridised overnight. After this the coverslips were removed. Post-hybridization washes were 0.4 SSC/0.3% NP40 at 73°C for 2 minutes then quickly transferred to 2xSSC/0.1% NP40 at RT for 1 minute. Slides were counterstained in DAPI and then rinsed and air dried. When ready, slides were mounted in antifade (2.3% DABCO in 40% glycerol/0.02 M TRIS-HCl) and covered until fluorescence examination. Slides were examined on a Zeiss Axioscop 20 fitted with a Zeiss fluoarc light source and images captured on an Applied Imaging Cytovision station using the false colours that are attributed by the software. Scoring of signal pairings to detect telomere tethering Initially, the subtelomere probes were labelled in Spectrum Orange for all short arms and Spectrum Green for all chromosome long arms. Cells were separately probed with the two subtelomeric probes for a single chromosome at the one time. Probe pairs were used for the subtelomeres of chromosomes 1, 3, 4, 5, 6, 7, 9, 10, 12, 17, 18, and 20. Images were captured for a large number of cell groups for each chromosome and pairings were scored on the captured images. Signals were interpreted as paired if the distance between signals was 10% or less of the greatest diameter of the nucleus (many cells were oval in shape). In addition to the above subtelomeric probe pairs, other probes were used to investigate the frequency of non-homologous tetherings (subtelomeric probes 1p and 9q, see Table 3 ) and the coincidence of the subtelomere tetherings and interphase chromosome domains (Fig 3 ). For the latter experiment the following Vysis probes were used: PML (promyelocytic leukemia locus) mapping to 15q22, SNRPN (small nuclear ribosomal protein locus) mapping to 15q12 – both labelled with spectrum orange; CEN15 (a probe for alpha centromeric sequences specific to chromosome 15) labelled with spectrum green. In addition, the chromosome 12 subtelomeric probes, i.e. 12p (labelled with spectrum orange) and 12q (spectrum green), and the WCP (Vysis whole chromosome painting probe) for chromosome 12 (spectrum green). Table 3 Rate of subtelomeric tetherings of non-homologues in G1 of non-cycling cells. Pairs of telomeres tested for tethering No (%) of signal pairs tethered 95% confidence limits 1p telomere; 9q telomere* 7/109 (6.4) 2.6–12.8% Note: This is a control for chromosome specific p-q subtelomere signal pair tethering (Fig 2, Table 2). For this experiment the 1p subtelomere was labelled with spectrum orange and the 9q subtelomere with spectrum green. Subtelomere tethering between these non-homologues was not increased over chance expectation. The telomere pair* 1p and 9q were chosen because they represent one of the most frequent telomere translocations reported (Lisa Shaffer, personal communication 2004). Up to 10% of signal associations in a two colour matrix can be regarded as random (VYSIS guidelines for interphase FISH scoring). There was no departure from randomness for tethering with respect to these two pairs of non-homologous subtelomeres. Additional experiments to detect a regular chromosome order reflecting the possible existence of haploid sets regularly arranged around the nuclei Two such experiments were performed in the current study (see fig 2 ). These comprised examining the chromosome order for the single subtelomeres (see Table 1 for clones) 4p (labelled in spectrum orange), 18q (spectrum green), and 6p (spectrum green and spectrum orange, i.e. yellow signal) jointly hybridised to the same triploid cells. In a second experiment subtelomeres were labelled as follows: 5p (spectrum orange), 12q (spectrum green), and 20p (spectrum green and spectrum orange, i.e. yellow signal) hybridised to a second slide of diploid/triploid cell nuclei. Authors' contributions AD designed the study, captured and analysed all FISH signals, and drafted the manuscript. LH performed all growing of probes, labelling of probes, and most probe hybridizations. | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC521695.xml |
514612 | Investigating the utility of combining Φ29 whole genome amplification and highly multiplexed single nucleotide polymorphism BeadArray™ genotyping | Background Sustainable DNA resources and reliable high-throughput genotyping methods are required for large-scale, long-term genetic association studies. In the genetic dissection of common disease it is now recognised that thousands of samples and hundreds of thousands of markers, mostly single nucleotide polymorphisms (SNPs), will have to be analysed. In order to achieve these aims, both an ability to boost quantities of archived DNA and to genotype at low costs are highly desirable. We have investigated Φ29 polymerase Multiple Displacement Amplification (MDA)-generated DNA product (MDA product), in combination with highly multiplexed BeadArray™ genotyping technology. As part of a large-scale BeadArray genotyping experiment we made a direct comparison of genotyping data generated from MDA product with that from genomic DNA (gDNA) templates. Results Eighty-six MDA product and the corresponding 86 gDNA samples were genotyped at 345 SNPs and a concordance rate of 98.8% was achieved. The BeadArray sample exclusion rate, blind to sample type, was 10.5% for MDA product compared to 5.8% for gDNA. Conclusions We conclude that the BeadArray technology successfully produces high quality genotyping data from MDA product. The combination of these technologies improves the feasibility and efficiency of mapping common disease susceptibility genes despite limited stocks of gDNA samples. | Background In order to locate the disease variants involved in complex common disease it is now generally accepted that very large sample numbers will be required [ 1 - 4 ]. Not only do the sample collections need to provide high quality gDNA, for the purpose of accurate genotyping, they also need to be sustainable. If, for example, one million SNPs were to be genotyped in a whole genome association scan and only 1 ng were required per SNP genotype, 1 mg of DNA would be required from each clinical sample. Given that the gDNA yield from a typical blood sample of 8 ml is approximately 200 μg, and that the typical yield from a mouth-swab is just 10 μg, there is clearly a short-fall in available quantities unless other means are employed to amplify the DNA resource. Moreover, many existing and effectively irreplaceable DNA sample collections, which have been used in previous studies and are now depleted, may consist of only nanogram quantities of gDNA. At present, the gold standard method for generating gDNA from whole blood samples is through the process of immortalisation by transformation of the peripheral blood lymphocytes with Epstein-Barr Virus (EBV) [ 5 ]. Although this method of transfecting EBV creates an unlimited resource of gDNA, the procedure is costly, lengthy and not applicable to existing collections for which the gDNA has already been extracted. If there was a reliable method to enzymatically amplify the whole genome from nanogram-levels of gDNA and directly from clinical samples to microgram amounts then this would enable the use of archived gDNA in future studies, as well as providing an accelerated route to full use of newly collected clinical samples for high-throughput genotyping. Molecular Staging, Inc. (MSI) (New Haven, CT, USA) have developed a method for whole genome amplification by Φ29 polymerase Multiple Displacement Amplification (MDA). It has been reported by the company that this method can reliably amplify the whole genome from gDNA, whole blood and other clinical samples [ 6 - 8 ]. Each DNA sample should give similar yields of product in all reactions with little dependency on the quantity of starting template [ 6 , 7 ]. Moreover, the MDA reaction should give complete coverage of the genome with little regional bias [ 6 ], which is critical when the product is to be used for high-throughput SNP genotyping. We set up a series of experiments with MSI in order to validate their claims that MDA product from gDNA is a viable alternative template to un-manipulated gDNA in SNP genotyping. Recent studies have been conducted using MDA product from Amersham [ 9 , 10 ], and report the high level of accuracy achieved when these products are genotyped using TaqMan or multiplex, four-colour fluorescent minisequencing with six and 45 SNPs, respectively. However, without DNA resource limitations, a genotyping bottleneck exists mostly as a result of time- and assay set-up costs and hence, in order to achieve large-scale genotyping, highly multiplexed assays are required. In such multiplexed assays, there is greater potential for erosion of genotyping quality, due to reduced substrate integrity. The validation of the use of amplified DNA resources with such highly multiplexed methods is, therefore, essential. The BeadArray genotyping platform of Illumina™ Inc. (San Diego, CA, USA) offers a high-throughput, highly multiplexed and highly automated genotyping service facility [ 11 ]. The BeadArray platform is highly miniaturised, using fibre optic bundles as a substrate for a high-density microarray [ 12 ]. It is the combination of this miniaturisation with an ability to multiplex up to 1,536 SNP assays [ 11 ] that makes BeadArray an attractive potential solution to the genotyping bottleneck. A recent study by Barker and colleagues, with 2,320 SNPs and five samples, found 99.86% concordance between MSI MDA product and gDNA [ 13 ]. However, since only five samples were studied it was not possible to evaluate accurately the efficacy of BeadArray on MDA product template, including estimation of sample exclusion and failure rates. In the present report we have, therefore, studied 86 MDA product samples and 384 SNPs using BeadArray, allowing comparison with the single-plex methods TaqMan ® (Assays-by-Design SM , Applied Biosystems, Foster City, CA, USA) and Invader ® (Third Wave Technologies, Inc., Madison, WI, USA) with gDNA. Results MDA yield We selected and sent to MSI 20 ng of 88 gDNA samples for amplification, from which an average of 200 μg of MDA product was yielded in 100 μl reactions. The yields ranged from 85 μg to 280 μg, with 61% of samples yielding between 100 μg and 200 μg. The HLA-DRB1 genotype of each MDA sample was entirely concordant with the corresponding gDNA template, verifying the identity of each MDA sample and ruling out the possibility of contamination. When 100 ng of 448 gDNA samples were amplified using reagents supplied by MSI in kit form and the amplification carried out in-house, an average of 155 μg of MDA product was yielded in 150 μl reactions. The yields ranged from 31 μg to 260 μg, with 80% of samples yielding between 100 μg and 200 μg. Compatibility of MDA product with TaqMan and Invader Of 88 MDA products and their corresponding gDNAs tested at 95 SNPs using the TaqMan method of genotyping there were no samples that consistently failed to produce any data. This confirmed that, for all samples, amplification had been sufficiently successful for the TaqMan chemistry to perform at most SNPs. Genotype concordance rates between MDA product and gDNA and genotype failure rates are given in Table 1 . These results demonstrate that the use of MDA product as a template for the TaqMan assay produces accurate data comparable to that from gDNA. We observed that, for the majority of TaqMan assays, the clustering of data points was less distinct when MDA product was used as a template, compared to gDNA. Example data from an assay in which deterioration in clustering was observed are shown in Figure 1 . For a single SNP of the 95 tested, the insulin gene ( INS ) -23 Hph I (rs689), an allelic bias was observed in the MDA process, which resulted in the merging of the heterozygote cluster with the homozygote cluster of the major allele, making the correct assignments of genotype impossible, shown in Figure 2 [see additional file 1 ]. MDA product used as a template for the Invader genotyping method at this SNP produced similarly un-useable data. Using gDNA template at this SNP, however, both the TaqMan and Invader methods produced acceptable results, shown in Figure 2 [see additional file 1 ], indicating that the allelic bias was occurring at the MDA stage and not during subsequent genotyping. Interestingly, allelic bias has previously been observed at two other SNPs at INS in PCR reactions designed for the Pyrosequencing method (Pyrosequencing AB, Uppsala, Sweden) [ 14 ]. These results may indicate that INS may be situated in a sequence region that is predisposed to such allelic bias and the INS variable number of tandem repeats polymorphism, only 580 bp 5' to the -23 Hph I SNP, is a candidate for such an effect. For 13 additional SNPs for which Invader genotyping was performed, comparison of genotypes generated from MDA product with those from gDNA are shown in Table 1 . Two SNPs were genotyped by TaqMan on the 448 samples amplified using reagents supplied by MSI in kit form (and the amplification carried out in-house), and on the corresponding gDNAs. The gDNA samples used for amplification had been extracted from whole blood. Genotype concordance rates between MDA product and gDNA and genotype failure rates are given in Table 1 . Validation of BeadArray genotyping technology with gDNA template We commissioned Illumina to conduct a large-scale project using BeadArray genotyping technology involving 3,036 samples (2,950 gDNA samples and 86 MDA products) and 384 SNPs i.e. >1.1 million genotypes. In the first instance, 757 SNP sequences were sent to Illumina for in silico assay design. These SNPs were selected for their relevance to a range of ongoing projects in our laboratory, located at genes of strong functional candidacy and within regions of linkage to type 1 diabetes e.g. the putative IDDM10 locus on chromosome 10p14-11. All SNPs were validated, having been identified either from empirically confirmed SNPs in dbSNP or from our own re-sequencing efforts. Based on ranking from the in silico design criteria [ 15 ], 404 SNPs from 757 (53%) were suggested as most suitable for assay development, from which 384 were chosen. Thirty-nine of these failed to be converted into a viable assay (10.2%), leaving a total of 345 working assays. As well as excluding SNPs that fail to produce robust genotypes, the Illumina protocol excludes samples that do not consistently perform. Of the total number of samples 10.5% were excluded and as a consequence very few data points were missing from the data set, resulting in an apparently low genotype failure rate (Table 2 ). Within the 2,781 successfully genotyped gDNA sample set, 26 were duplicate samples. Of these 52 samples at 345 SNPs, the genotypes of 23 duplicates did not match each other and 19 data points were missing, giving a discordance rate (error rate) of 0.26% (23 of 8,951 data points). As our samples were family-based, a quality control check of misinheritance rates was possible using PedCheck [ 16 ]. Of the 345 SNPs, 20 displayed ≥ 10 misinheritances in the 742 families genotyped. For ten of these SNPs, TaqMan genotyping was attempted in the same samples in order to verify the results. It was possible to design TaqMan assays to only seven of the ten SNPs and, of these, only three produced interpretable data. At these three SNPs the numbers of misinheritances were 41, four and eight, respectively, by TaqMan, compared to 17, 22 and 14, respectively, by BeadArray. The number of SNPs with <10 or <5 misinheritances from the BeadArray experiment is shown in Table 3 , along with our previous year's TaqMan results considering SNPs with allele frequencies >1%. The poor performance of both Illumina and TaqMan at the ten SNPs compared in detail, as well as the lab misinheritance rate for TaqMan (Table 3 ), indicates that the high misinheritance rates observed for some SNPs in the Illumina experiment is not a technology-specific failing. Within the panel of 384 SNPs attempted by Illumina, 17 were controls for which we had already produced genotyping data by either TaqMan or Invader methods, enabling an evaluation of the BeadArray data for concordance. Two of these 17 control SNPs failed to be converted to a working assay, giving 15 SNPs and a maximum of 2,503 samples that were genotyped in common. Excluding failed duplicates noted above, comparison of BeadArray genotypes with existing data revealed a concordance rate of 99.6% (129 discordant in 34,219 comparisons), indicating the compatibility of the non-excluded gDNA samples with BeadArray, and the quality of existing data. Of the 15 control SNPs, 11 had been genotyped using TaqMan and four using Invader. The concordance rates for each platform were 99.7% using TaqMan (104 discordant in 25,203 comparisons) and 99.6% using Invader (25 discordant in 9,016 comparisons) when compared with BeadArray data, showing no significant difference between the two platforms. Compatibility of BeadArray with MDA product Within the BeadArray experiment described above were 86 MDA products and their corresponding gDNA samples. These data were directly compared for sample failure rate and genotype failure rate as shown in Table 2 . BeadArray genotype concordance rate between MDA product and gDNA are given in Table 1 . These results provide evidence for the compatibility of the non-excluded MDA products with BeadArray technology. Evaluation of the Illumina's quality scores revealed no significant difference between the MDA and gDNA samples for any SNP ( t -test P- value >0.05 for every SNP). Discussion In this study we have evaluated the Φ29 polymerase MDA whole genome amplification method from MSI by assessing the compatibility of its product with the established TaqMan and Invader genotyping chemistries and with the highly multiplexed BeadArray genotyping platform. We have also evaluated Illumina's BeadArray genotyping platform for a large-scale experiment using gDNA. At 95 SNPs, comparison of TaqMan genotypes generated from MDA product and gDNA templates revealed a very good concordance rate but a higher failure rate for MDA product compared to gDNA. This would need be estimated in a sample size larger than the current n = 88 in order to be confirmed. This result is comparable to the smaller study by Tranah et al . [ 9 ], in which six SNPs were genotyped by TaqMan on 172 samples, resulting in 100% concordance of pre- and post-MDA DNA. In the present study, the MDA product genotypes were slightly more difficult to assign, owing to more dispersed clusters. This was not observed by Lovmar et al . with fluorescent minisequencing on Amersham MDA products compared to gDNA [ 10 ]. One marker in our study, which may be unusually prone to allelic bias, was impossible to score using MDA product but was acceptable when using gDNA as a template ( INS -23 Hph I, rs689). Compared to the yields indicated in Dean et al. [ 7 ], our average yield from in-house amplification using the reagents in kit form were in the order of five- to six-fold higher. This was probably due to differences in the two protocols: for example, our protocol used an increased reaction volume compared to the protocol used in Dean et al. [ 7 ]. Furthermore, the Dean et al. [ 7 ] protocol omitted the denaturation step, which is now standard practice. One other potential explanation for this variation is possible differences between laboratories in the quantitation of DNA using PicoGreen, the application of which requires a standard reference data set. We cannot at present fully resolve the differences in yields between studies but we can conclude that very large amounts of DNA are synthesized during the Φ29 reaction and that this is an excellent template for genotyping. MDA product should, therefore, be quantified and its concentration on completion of the MDA reaction not assumed to be consistent. Genotype failure rate, concordance rates with gDNA and the nature of genotype clustering showed similar patterns to service-generated MDA. However, a larger number of SNP markers would need to be genotyped on the MDA product using purchased kit reagents in order to verify these figures for in-house amplifications. In the evaluation of MDA product in conjunction with BeadArray technology, the high concordance rate between genotypes obtained from MDA product and gDNA templates is encouraging. A concordance rate of 99.86% has been reported by Barker et al . using 2,320 SNPs and five samples [ 13 ]. However, as our study used 86 samples, we were able to observe differences in genotype failure rate between the different templates, not noted in the previous study [ 11 ]. As with the TaqMan evaluation, BeadArray had a higher genotype failure rate for MDA product compared to gDNA (0.2% for MDA versus 0.06% for gDNA). We did not find any evidence for allele drop-out with MDA compared to gDNA. BeadArray genotyping excluded more MDA samples than gDNA samples (10.5% for MDA versus 5.7% for gDNA) indicating that gDNA is a superior genotyping template for BeadArray technology. This 2-fold exclusion rate for MDA is consistent with the approximately 2 to 3-fold genotype failure rate of MDA typically observed with TaqMan and Invader, compared to gDNA (unpublished data). The performance of MDA product is continuously being monitored in our laboratory. In a study blinded to all genotypers and database administrators, 288 family-based gDNA samples (prepared by the salting out method), were replaced with MDA product and left in continual use in our genotyping pipeline for 12 months. The change went undetected by all users. The failure rate for MDA was 3.34% for 15,921 genotypes, compared to 2.39% for 19,272 gDNA genotypes. Therefore, this improvement in the MDA performance for TaqMan is likely to be applicable to BeadArray, which improves the feasibility of mapping susceptibility loci in complex traits. When using a highly multiplexed, highly automated genotyping platform, slight reductions in the quality of template material are likely to have a greater adverse effect on data than in scenarios in which markers are assessed individually and manual scoring is undertaken. Our results indicate that MDA is an adequate solution for the vast majority of SNP markers, even in this highly multiplexed allelic assay platform. It is noted that 5.8% of markers that passed the Illumina acceptable scoring threshold were in fact showing high misinheritance rates in our family samples. This problem was at the same magnitude as TaqMan for individually genotyped markers. This highlights the importance of checking potential positive results with a second genotyping technology. MDA should allow the continuation of genetic analysis on archived DNA in researchers' freezers worldwide, providing the very necessary increases in sample sizes so urgently required [ 1 , 2 , 17 ]. Conclusions The combination of BeadArray high throughput, multiplex genotyping and amplified DNA (MDA product) successfully produced high quality genotype data thereby improving the feasibility and efficiency of mapping common disease susceptibility genes despite limited stocks of gDNA samples. Methods MDA product preparation For both the validation experiments (MDA product as a template for TaqMan and Invader genotyping) and for the combined experiment (MDA product as a template for BeadArray genotyping) the same MDA samples were tested. We sent to MSI 20 ng (5 μl at 4 ng/μl) of 88 gDNA samples for amplification, which was performed as a service according to the protocol for human gDNA with the omission of the denaturing step [ 7 ]. These gDNA samples had been extracted from cell pellets of EBV derived cell lines using a standard chloroform protocol that produces very high quality and stable gDNA [ 18 ]. The MDA product returned to us was quantified using PicoGreen dsDNA quantitation reagent (Molecular Probes Europe B.V., Leiden, the Netherlands). In order to verify the identity of each MDA-produced sample, genotyping was performed at HLA-DRB1 and comparison made with data generated from the corresponding gDNA. HLA-DRB1 genotyping was performed using the Dynal Auto RELI™ SSO HLA-DRB Test system (Dynal ® Biotech, Wirrel, UK) for each gDNA sample and their MDA products. Although, these samples were amplified by MSI as a service, the reagents are also available from MSI in a kit form for amplification in-house. Following the amplification by MSI we have amplified 448 DNA samples by using the reagents in kit form and 100 ng (25 μl at 4 ng/μl) gDNA template in 100 μl reactions. In the interim, one major change to the MDA protocol had taken place, the inclusion of a denaturation of the DNA template prior to amplification. Previously no denaturation step took place. Two TaqMan markers were tested on these 448 samples and the genotype failure rate calculated. Evaluation of MDA product as a template for TaqMan and Invader genotyping SNP TaqMan assays were carried out for allelic discrimination, 8 ng of DNA (2 μl at 4 ng/μl) used in a 5 μl total reaction volume. TaqMan genotypes from the 88 MDA samples, described above, were compared with TaqMan genotypes generated from their corresponding gDNAs, at 95 SNPs, with a broad range of allele frequencies. The Invader method was used to genotype 13 additional SNPs on the same samples. Comparison was made between data generated from both templates by the measurement of genotype failure and genotype concordance rates. Evaluation of BeadArray genotyping technology and its compatibility with MDA product Of the 384 SNPs selected for genotyping 3,036 samples, 17 were control SNPs for which we had existing genotype data, generated by either TaqMan or Invader methods, with which comparison of genotype failure and genotype concordance rates were made. These 384 SNPs covered a broad range of allele frequencies. Incorporated into this experiment was an assessment of suitability and compatibility of the BeadArray genotyping method with MDA product. This involved 86 of the 88 amplified samples, described above, for which genotyping was attempted at all 384 SNPS. Concordance between genotypes generated from MDA product and gDNA templates, together with the genotype and sample failure rates of each template type were measured. In our laboratory we store genotyping data in a MySQL database on a Sun server. The volume of data expected from Illumina was the equivalent of 6 months' in-house genotyping. We separated phenotypic and pedigree information, which is associated with a sample, from genotype data, which is associated with a DNA plate and well position, with a link table to relate the two. Sample aliases are also supported, so that no recoding of identifiers is required, either to export or import Illumina data [ 19 ]. Authors' contributions RP participated in the design of the study, performed in-house genotyping experiments, and assisted in preparing the manuscript. HER prepared DNA and MDA amplified samples. BJB participated in the design of the study and assisted in the preparation of the manuscript. SN prepared DNA and MDA amplified samples. DS performed in-house genotyping experiments. MS prepared DNA and MDA amplified samples. RCJT participated in the design of the study and assisted in the preparation of the manuscript. AS participated in the design of the study. ACL and LJS performed bioinformatics. NMW managed the data and participated in its interpretation. JAT participated in the design of the study and assisted in the preparation of the manuscript. All authors read and approved the final version of the manuscript. Supplementary Material Additional File 1 Figure 2 TaqMan and Invader fluorescence data plotted for the INS -23 HphI SNP. Both the MDA product plots could not be scored. All plots represent the same individual samples with gDNA plots containing 8 additional samples. Click here for file | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC514612.xml |
547909 | A7DB: a relational database for mutational, physiological and pharmacological data related to the α7 nicotinic acetylcholine receptor | Background Nicotinic acetylcholine receptors (nAChRs) are pentameric proteins that are important drug targets for a variety of diseases including Alzheimer's, schizophrenia and various forms of epilepsy. One of the most intensively studied nAChR subunits in recent years has been α7. This subunit can form functional homomeric pentamers (α7) 5 , which can make interpretation of physiological and structural data much simpler. The growing amount of structural, pharmacological and physiological data for these receptors indicates the need for a dedicated and accurate database to provide a means to access this information in a coherent manner. Description A7DB is a new relational database of manually curated experimental physiological data associated with the α7 nAChR. It aims to store as much of the pharmacology, physiology and structural data pertaining to the α7 nAChR. The data is accessed via web interface that allows a user to search the data in multiple ways: 1) a simple text query 2) an incremental query builder 3) an interactive query builder and 4) a file-based uploadable query. It currently holds more than 460 separately reported experiments on over 85 mutations. Conclusions A7DB will be a useful tool to molecular biologists and bioinformaticians not only working on the α7 receptor family of proteins but also in the more general context of nicotinic receptor modelling. Furthermore it sets a precedent for expansion with the inclusion of all nicotinic receptor families and eventually all cys-loop receptor families. | Background Nicotinic acetylcholine receptors (nAChRs) are the most studied members of the cys-loop family of ligand-gated ion channels (LGICs) which also contains γ-aminobutyric acid (GABA) receptors, glycine receptors and 5-HT 3 receptors [ 1 ]. Distinct subtypes of nAChRs mediate, for example, fast synaptic transmission in the brain and at neuromuscular junctions [ 2 ]. All are believed to be pentameric assemblies of various combinations of different subunits (α, β, γ/ε, δ), some of which exist as multiple isoforms [ 3 ]. nAChRs are important targets for novel analgesics as well as new drugs being devised for Alzheimer's disease and schizophrenia [ 4 , 5 ]. Mutations in nAChRs are associated with certain forms of epilepsy [ 6 , 7 ] and several congenital myasthenias [ 8 ]. There is a substantial and growing body of physiological, pharmacological, genomic structural and modeling data on receptors formed from these subunits. The volume and diversity of these data present severe challenges for their efficient storage and interpretation. Here we describe a relational database, initially relating to the α7 subunit, whose aim is to provide an easy to use, extensible web-based interface to access functional data and relate it back, wherever possible, to structural data. Construction and content The database is currently limited to nAChR α7 subunits. This makes the initial task of populating the database manageable. To allow 3 rd level normalization, the principal information prototype stored in the database is referred to as an "experiment event". An experiment event is a collection of simultaneous measurements and their associated measurement conditions. For example, two drugs tested against the wild type α7 and a mutant under the same experimental conditions would be described by 4 (2 drugs × 2 sequences) experiment events. If the identical measurements were repeated in, say, calcium-free saline, this would yield 8 experiment events. Using this prototype a unique set of experimental conditions is associated with a single set of data. Therefore, the database tables (see Figure 1 ) have been designed around the central experiment table [see Additional file 1 ]. Quite often, several real experiments are performed within any one publication. We wanted to capture all of that data. Thus, all the data for one experiment that was reported have been stored (non-redundantly) as a separate entity. The database is managed by the MySQL relational database management system. PHP scripts query the MySQL system and transform the data into HTML pages for serving to the client. The database currently aims to store molecular information rather than whole-organism genetic information which can be sourced from elsewhere, see for example [ 9 , 10 ]. A typical set of experiments reported in one paper might concern the properties of one mutation at a particular position and its associated changes in physiology and/or pharmacology. All of this data is held in the database. By use of appropriate alignments (either stored within the server or uploaded) the positions of these data can be highlighted on a homology model based on the acetylcholine binding protein AChBP [ 11 ]. The model is viewable by either using the Chime plugin, or by setting the browser to use Rasmol as a helper application. User instructions for configuring different browsers are provided on the server, as well as the list of combinations of operating system and browser that we have tested to date. For any database, data integrity will ultimately determine its usefulness. The initial population of the database has been carried out by the authors, but it is desirable and practical in the long term to have a procedure whereby any laboratory can submit data. To help ensure reliable deposition procedure, each depositor will have a unique identifier (thus making the data accountable). A depositor will submit their data which is then tabulated and presented as a form which the depositor is then asked to confirm as being correct. Only when this confirmation is received is the data committed to the database. Although this will reduce some entry-based mistakes, the database will nonetheless need to be curated to maintain integrity. The database does not attempt to duplicate the functionality of other databases. For example we store a local alignment of α7 subunits taken directly from pfam [ 12 ], but we do not perform any alignments ourselves. Such tools are already available on numerous web-servers and in any case the user may wish to upload their own alignment. Wherever possible, we link to a well-established resource, as is the practice in for example, SWISS-PROT [ 13 ]. Utility and discussion Access to the database is through various routes. The first route is by a very simple search string (e.g. citation = Smith*). The user is then presented with a page informing him of the number of hits and asking what information should be displayed from those hits. The second is a simple incremental search in which the user applies criteria sequentially until he chooses to examine the results. As the query is built, the number of hits returned by the query in its current state is shown. The third search method is also incremental but via the use of a set of tabbed pages which divide the information into intuitive categories such as pharmacology and physiology. Additionally, the user can make choices about alignments used and even upload their own. As the query is built, summary information of its status is also displayed. The fourth method to access the data is provided by a 'fast lane' route which provides more experienced users with a quicker and more direct route to the data of interest. The user formulates and constructs a query offline and then uploads it as a simple ASCII file. A sample query form and details of the format are presented in the supplementary information. The result of a query is presented in tabular form. Only the information requested is actually presented with the option for further or refined searches. In addition to this, the results of any position matches can be displayed via the homologous 3-dimensional structure of the AChBP. This is automatically available if the user selects the alignments available from the server. If the user uploads their own alignment the model will only be produced if AChBP was included. The server streams a Rasmol script or spawns a Chime window depending on browser configuration. In addition to these query routes, the user is also able to browse the contents of the database. The current database has been designed such that it could easily be extended to include other nAChR subunits and then on to other members of the cys-loop ligand-gated ion channel family. We are currently pursuing this aspect. Such expansion will allow trends that link structure to function in this receptor family to be more readily identified. The database is very complementary to some existing resources, in particular the LGICdb [ 14 , 15 ]. This database contains sequence, phylogenetic data and sets of coordinates, but does not attempt to store all aspects of individual experiments as we report here. However, this could be used in conjunction with our database to for example explore equivalent positions in related receptors using sequence and phylogenetic information stored therein. Our approach is somewhat similar to that employed by the ProTherm database [ 16 ] which aims to store thermodynamic data for mutant and wildtype proteins [ 17 ]. Although currently there are no entries common to both databases, it is envisaged that thermodynamic data reported for either the α7 nAChR receptor or related channels will appear in the ProTherm database and can thus be used in conjunction with the A7DB to help illustrate for example how loss of function might be related to protein stability. Furthermore, the similarity in design of these databases should make cross-interrogation possible in the future. We should state that the main difference between A7DB and ProTherm is that instead of thermodynamic data we store pharmacological and physiological data and in that sense it is closer to the voltage-gated potassium channel database [ 18 , 19 ]. Finally, the A7DB is also complementary to the Protein Mutant Database [ 20 ] where mutations across many proteins are stored but the searching and data tabulation is primarily text-based. This might be particularly useful if another mutation had been reported in a different protein that bound a similar ligand, for example in acetylcholine also binds to acetylcholinesterase, which we do not store data for. Conclusions We have shown here how the collation and careful storage of experimental data pertaining to one sub-family (the α7 nAChR sub-family) of the ligand-gated ion channels can be assembled into a useful resource. However, the real power of the database will be when it is combined with machine-learning technologies to explore complex relationships between sequence, structure and function [ 21 ]. We believe that this resource will grow in usefulness as the amount of data increases. For example, during the construction of this database, atomic coordinates were released for the transmembrane region of the nAChR from Torpedo marmorata [ 22 ]. Its development is particularly timely given these recent structural developments which allow three-dimensional information to be correlated with function derived from an ever-increasing body of experimental data. Availability and requirements The database is freely available at: Authors' contributions The project was conceived and technical aspects managed by PCB. The coding and design was done by SB. MSPS and DBS provided critical evaluation of the design and construction process. DBS, LP, AKJ and LB populated the database. All authors have approved the manuscript. Supplementary Material Additional File 1 Supplementary information. This doc file provides a complete description of the table entries and also provides a description of the uploadable query file. Further information about the online help is also provided. Click here for file | /Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC547909.xml |
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