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Introduction
The filoviruses (family Filoviridae ) from the genera Ebolavirus and Marburgvirus are etiologic agents of sporadic viral hemorrhagic fever outbreaks in humans with high mortality rates. An unprecedented outbreak of Ebola virus (EBOV; species Zaire ebolavirus ) disease that began in Guinea during December 2013 [ 1 ] subsequently spread into neighboring West African countries of Sierra Leone and Liberia, prompting the World Health Organization (WHO) to declare the epidemic a public health emergency of international concern ( http://www.who.int/mediacentre/news/statements/2014/ebola-20140808/en/ ). Phylogenetic analysis of viral isolates from this epidemic suggests a single transmission event introduced the virus, named the EBOV Makona variant [ 2 ], from an undetermined natural reservoir into humans in Guinea, followed by transmission between humans to spread the virus throughout Guinea and into Sierra Leone and Liberia [ 3 ]. Implementation of containment measures such as patient isolation and improved burial practices eventually controlled the epidemic, which resulted in 28,616 reported cases with a mortality rate of approximately 40% ( http://www.who.int/csr/disease/ebola/en/ ).
The severity of this epidemic and principle transmission from human to human underscored the need for efficacious vaccines (and therapeutics) against EBOV, accelerating the placement of candidate EBOV vaccines into clinical safety trials [ 4 – 6 ]. This need for safe and efficacious vaccines was again evident with the onset of the 10 th and largest outbreak in the Democratic Republic of the Congo (DRC) from 2018–2020. The 11 th outbreak of EVD continues in the Western DRC.
The characteristics of filovirus infection, where infected patients are contagious only after manifestation of symptoms, allows one to use a ring vaccination strategy for disease containment. Ring vaccination strategy relies on the combination of contact tracing for case identification and a rapid effective vaccine for use in contacts and contacts of contacts of infected patients. The application of this strategy led to the approval of rVSV-ZEBOV (ERVEBO ® ), a single dose vaccine, using the safety and efficacy data from the clinical trial during the 2014 outbreak in West Africa by the Food and Drug Administration in December 2019 ( https://www.fda.gov/news-events/press-announcements/first-fda-approved-vaccine-prevention-ebola-virus-disease-marking-critical-milestone-public-health ). The effectiveness of ERVEBO in a ring vaccination response provides an important countermeasure for public health but does not address all unresolved questions in filovirus vaccine utilization including duration of protection, alternate dosing regimens, and the effectiveness of filovirus vaccines based on other viral platforms or alternative strategies. The development of multiple countermeasures against a disease necessitates the use of a common assay based on a surrogate of protection which can be used to compare the elicited immune response between vaccines and provide valuable information as to the effectiveness and durability of protection. Ideally, this assay is not only informative but simple, reproducible, species independent, and transferrable between labs. For example, during the development of countermeasures against anthrax, a lethal toxin neutralization assay was developed and used by many laboratories [ 7 ].
The development of vaccine candidates for Ebola virus disease prophylaxis [ 8 ] continues today, including deployment of a heterologous prime boost vaccine with European Commission Market Authorization during the last outbreak. However, the demonstration of efficacy for new filovirus vaccines will be complicated in the absence of a large outbreak and may require evaluation under the FDA Animal Rule or via non-inferiority trials against ERVEBO. Regulatory evaluation using these approaches is only possible with a correlate of protection and a well-developed assay that can measure the response in well-characterized animal challenge models as well as in human clinical trials. The species-neutral ELISA is ideal for bridging data between humans and animal models. Also, since the assay likely will be utilized in multiple experiments at many sites, it is important to demonstrate that the assay is reproducible among different laboratories.
In order to facilitate the development of additional vaccine countermeasures and to address such questions as the durability of immunity, the FANG has supported the development of a human anti-EBOV GP IgG ELISA. This study describes the FANG efforts to determine if the performance of the human anti-EBOV GP IgG ELISA [ 9 ] is acceptable for sample evaluation across five participating laboratories. Each laboratory used an anti-EBOV GP IgG ELISA to measure levels of binding in human serum samples from a FANG designed human proficiency panel. The panel consisted of ten human serum samples created by the differential dilution of human serum lot number BMIZAIRE105 (pool of serum with an approximate anti-GP IgG concentration of 1,000 ELISA units/mL) with control human serum (BMI529) without antibody activity. The concentration of the proficiency panel samples ranged from 0.00 ELISA units/mL to approximately 800 ELISA units/mL.
Each participating laboratory received sufficient volume of the proficiency panel for initial testing plus repeats and used their own anti-EBOV GP IgG ELISA established assay. The assay was validated at some laboratories and qualified at others [ 9 ]. Data from the participating laboratories were compared by statistical analysis. Both intra-laboratory and inter-laboratory analyses were performed to evaluate repeatability, intermediate precision, dilutional linearity, and accuracy. This paper summarizes both the intra- and inter-laboratory analysis of the results generated in the five separate laboratories. Results from the laboratories are de-identified in the analysis and reported as Laboratory A through E. The repeatability estimate for Laboratory B was greater than the acceptance criteria as established in laboratories that validated the anti-EBOV GP IgG ELISA with human serum, and, as a result, the proficiency panel assay runs were repeated. Results from both the original and repeated runs were included in the analysis and labeled as being from Laboratory B1 and B2, respectively.
Assay method
A common assay method [ 9 ] was tech-transferred to the participating laboratories, but there were minor variations in equipment/materials/procedures between laboratories. The analysis of the proficiency panel in the ELISA was performed similarly at Labs A, B1, and B2. All three used two separate operators on separate days. Samples were analyzed using a starting dilution of 1:62.5 and followed the plate layout as illustrated in Table 1 . These plate layouts represent 15 plates with specific proficiency panel samples on each plate. All 15 plates were run twice for a total of 30 plates for each of Labs A, B1, and B2.
10.1371/journal.pone.0238196.t001
Table 1 Plate layout used at Laboratories A, B1, and B2.
Sample ID
Plate Number
1
2
3
4
5
6
7
8
BMI-ZPP-11
X (3)
X
X
X
BMI-ZPP-12
X
X
X
X
X (3)
BMI-ZPP-13
X (3)
X
X
X
X
X
BMI-ZPP-14
X
X
X
X
X
X
X (3)
BMI-ZPP-15
X
X (3)
X
X
BMI-ZPP-16
X
X
X
X
X
X
BMI-ZPP-17
X
X (3)
X
X
X
BMI-ZPP-18
X
X
X
X
X
BMI-ZPP-19
X
X (3)
X
X
BMI-ZPP-20
X
X
X
X
Sample ID
Plate Number
9
10
11
12
13
14
15
BMI-ZPP-11
X
X
X
X
X
X
BMI-ZPP-12
X
X
X
X
X
BMI-ZPP-13
X
X
X
X
BMI-ZPP-14
X
X
X
BMI-ZPP-15
X
X
X
X
X
X
BMI-ZPP-16
X (3)
X
X
X
BMI-ZPP-17
X
X
X
X
X
BMI-ZPP-18
X (3)
X
X
X
X
BMI-ZPP-19
X
X
X
X
X
X
BMI-ZPP-20
X
X (3)
X
X
X
X
An “X” indicates that sample was analyzed on the indicated plate.
An “X (3)” (shaded) indicates that sample was analyzed on the indicated plate three times.
The analysis of the proficiency panel in the ELISA was performed at Lab C by two separate operators over three days and at Lab D by two separate operators over five days. Samples were analyzed using a starting dilution of 1:50 and followed the plate layout as illustrated in Table 2 . These plate layouts represent 12 plates with specific proficiency panel samples on each plate. All 12 plates were run at least twice for a total of 24 plates for each of Labs C and D.
10.1371/journal.pone.0238196.t002
Table 2 Plate layout used at Laboratories C and D.
Sample ID
Plate Number
1
2
3
4
5
6
BMI-ZPP-11
X (3)
X
X
X
BMI-ZPP-12
X
X
X
X
X
X
BMI-ZPP-13
X (3)
X
X
X
BMI-ZPP-14
X
X
X
X
X
X
BMI-ZPP-15
X
X (3)
X
X
BMI-ZPP-16
X
X
X
X
X
X
BMI-ZPP-17
X
X
X (3)
X
BMI-ZPP-18
X
X
X
X
X
X
BMI-ZPP-19
X
X
X (3)
X
BMI-ZPP-20
X
X
X
X
X
X
Sample ID
Plate Number
7
8
9
10
11
12
BMI-ZPP-11
X
X
X
X
X
X
BMI-ZPP-12
X (3)
X
X
X
BMI-ZPP-13
X
X
X
X
X
X
BMI-ZPP-14
X (3)
X
X
X
BMI-ZPP-15
X
X
X
X
X
X
BMI-ZPP-16
X
X (3)
X
X
BMI-ZPP-17
X
X
X
X
X
X
BMI-ZPP-18
X
X
X (3)
X
BMI-ZPP-19
X
X
X
X
X
X
BMI-ZPP-20
X
X
X (3)
X
An “X” indicates that sample was analyzed on the indicated plate.
An “X (3)” (shaded) indicates that sample was analyzed on the indicated plate three times.
The analysis of the proficiency panel in the ELISA was performed at Lab E by two separate operators over four days. Samples were analyzed using a starting dilution of 1:50 and followed the plate layout as illustrated in Table 3 . This plate layout represents six plates with specific proficiency panel samples on each plate. The six plates were each run four times for a total of 24 plates. For all laboratories, some samples were analyzed three times on the same plate [denoted with “X (3)” in the plate layouts]. These contributed to assay repeatability.
10.1371/journal.pone.0238196.t003
Table 3 Plate layout used at Laboratory E.
Sample ID
Plate Number
1
2
3
4
5
6
BMI-ZPP-11
X
X
X
X
X
X
BMI-ZPP-12
X (3)
X
X
X
BMI-ZPP-13
X
X
X
X
X
X
BMI-ZPP-14
X (3)
X
X
X
BMI-ZPP-15
X
X
X
X
X
X
BMI-ZPP-16
X
X (3)
X
X
BMI-ZPP-17
X
X
X
X
X
X
BMI-ZPP-18
X
X
X (3)
X
BMI-ZPP-19
X
X
X
X
X
X
BMI-ZPP-20
X
X
X (3)
X
An “X” indicates that sample was analyzed on the indicated plate.
An “X (3)” (shaded) indicates that sample was analyzed on the indicated plate three times.
Samples on a given plate were excluded from analysis if the within-assay CV of at least three dilution-adjusted concentrations determined for that sample was greater than 20%. Samples were also excluded if the plate including that sample failed to meet system suitability criteria. Some samples and plates that failed to meet the sample suitability criteria or system suitability criteria were repeated on later days. The ELISA concentrations of each qualification test sample by laboratory are provided in the supplemental information ( S1 – S6 Tables).
This study, and specifically the use of human serum samples, was approved in writing by the Battelle Institutional Review Board in April of 2015 (approval number HSRE 0223–100062052). Human serum samples were collected from subjects by the sponsor (Crucell Holland) via written consent according to their IRB-approved protocol. These samples were not specifically collected for this interlaboratory study but rather for a different study. Battelle nor any authors were affiliated with this initial study. The sponsor subsequently provided Battelle volumes of these samples for the purposes of conducting the study described in this manuscript. Throughout its analysis of human biological materials and reporting, Battelle had no access to volunteer subjects’ identifiers nor any access to any code-key that would allow Battelle researchers to attribute any results of analysis to the original volunteer human research subjects.
Statistical methods
Inter-laboratory analysis was performed using the combined results across all laboratories. A mixed-effects analysis of variance (ANOVA) model was fitted to the base-10 log-transformed concentrations to evaluate both inter-laboratory precision (i.e., between lab precision) and intra-laboratory precision (i.e., within-laboratory precision). The model included a fixed effect for test sample and random effects for laboratory, test date nested within laboratory, and plate nested within day. Here, test operator was excluded as a random effect because this variable was indistinguishable from test day in most laboratories. Because of this confounding of effects, any variability attributable to test day may also be due to the different test operators.
Results were screened for outliers within each laboratory separately. Deleted studentized residuals were computed for each observation. If the absolute value of the deleted studentized residual was greater than four, then the observation was considered a statistical outlier and removed from the inter-laboratory analysis.
Variability associated with the random effects as well as intermediate precision, repeatability, and total assay variability were estimated separately for each lab using model-based percent coefficient of variation (CV). The percent CV for each source of variance was calculated using Tan’s [ 10 ] relative standard deviation as
100 × e ln ( 10 ) 2 × σ 2 − 1
where σ 2 is the model-estimated variance for the specific variance source. The percent CV associated with the residual variance served as an estimate for the assay repeatability. The percent CV associated with the test day and plate effects served as an estimate for the intermediate precision of the assay. Total assay variability was estimated using all variance components from the model (both inter- and intra-run variability).
The model intercept was obtained for each test sample from the mixed effects ANOVA model to serve as test sample consensus values across the laboratories. Agreement among laboratories was evaluated by comparing individual assay results from each laboratory to the consensus values. Boxplots were produced for each test sample to show the distribution of concentrations by laboratory in relation to the corresponding consensus value. The ratio of individual test results to consensus values was calculated by test sample to evaluate the level of agreement among laboratories based on two one-sided tests (TOST) of equivalence.
To assess dilutional linearity, a random coefficients linear regression model was fitted to the log-transformed observed concentrations versus the log-transformed target concentrations. The model included both a random intercept and slope effect for each laboratory, along with random effects for laboratory, test day nested within laboratory, and plate nested within laboratory. The random slope coefficients were modeled as laboratory-specific differences from the overall slope. The overall slope was used to assess the dilutional linearity based on a test of equivalence (TOST) and random slope coefficients were used to evaluate the level of agreement among the laboratories.
Results
Across all six laboratory runs, there were some false positive observations for Sample 18, a sample with a known negative concentration. All reportable values from Sample 18 were excluded from the statistical models. Table 4 lists five outliers that were removed from their respective intra-laboratory analyses that were also removed from this inter-laboratory analysis. One outlier each were removed from Laboratories B1 and B2. Three outliers were removed from Laboratory C. In the final analysis, Lab A contributed 204 reportable values, Lab B1 had 179 reportable values, Lab B2 had 214 reportable values, Lab C had 268 reportable values, Lab D had 216 reportable values, and Lab E had 218 reportable values.
10.1371/journal.pone.0238196.t004
Table 4 Statistical outliers identified during analysis of intra-laboratory data.
Laboratory
Test Sample
Observed Concentration (ELISA Units/mL)
Target Concentration (ELISA Units/mL)
Studentized Residual
B1
BMI-ZPP-17
4.28
200
-9.48
C
BMI-ZPP-13
896.47
300
5.34
C
BMI-ZPP-16
236.02
500
-4.74
B2
BMI-ZPP-19
51.20
100
-4.39
C
BMI-ZPP-14
1845.88
700
4.33
These observations were deleted from both intra- and inter-laboratory analyses.
Table 5 presents ANOVA variance estimates and %CV for each source of variability, intermediate precision, and total assay variability by laboratory. For Laboratory A, the %CV for test date and plate nested within test date were 0.0 and 9.8, respectively. For Laboratory B1, the %CV for test date and plate nested within test date were 10.8 and 15.3, respectively. For Laboratory B2, the %CV for test date and plate nested within test date were 4.5 and 8.9, respectively. For Laboratory C, the %CV for test date and plate nested within test date were 9.8 and 8.5, respectively. For Laboratory D, the %CV for test date and plate nested within test date were 18.9 and 10.5, respectively. Finally, for Laboratory E, the %CVs for test date and plate nested within test date were 7.3 and 5.0, respectively. Laboratory E had the lowest %CV for intermediate precision (8.9) while Laboratory A had the lowest %CV for repeatability (7.2) and total assay variability (12.2). Laboratory B1 had the highest repeatability and total assay variability (23.7%CV and 30.6%CV, respectively) while Laboratory D had the highest %CV for intermediate precision (21.7).
10.1371/journal.pone.0238196.t005
Table 5 Summary of variance components obtained from mixed ANOVA model fit to data from all laboratories (results shown by laboratory).
Laboratory A
Source of Variability
Variance
%CV
Test Date
0.0000
0.0
Plate Nested in Test Date
0.0018
9.8
Intermediate Precision 1
0.0018
9.8
Residual (Repeatability)
0.0010
7.2
Total Assay Variability 2
0.0028
12.2
Laboratory B1
Source of Variability
Variance
%CV
Test Date
0.0022
10.8
Plate Nested in Test Date
0.0044
15.3
Intermediate Precision 1
0.0065
18.8
Residual (Repeatability)
0.0103
23.7
Total Assay Variability 2
0.0169
30.6
Laboratory B2
Source of Variability
Variance
%CV
Test Date
0.0004
4.5
Plate Nested in Test Date
0.0015
8.9
Intermediate Precision 1
0.0019
9.9
Residual (Repeatability)
0.0033
13.3
Total Assay Variability 2
0.0052
16.7
Laboratory C
Source of Variability
Variance
%CV
Test Date
0.0018
9.8
Plate Nested in Test Date
0.0014
8.5
Intermediate Precision 1
0.0031
13.0
Residual (Repeatability)
0.0027
11.9
Total Assay Variability 2
0.0058
17.7
Laboratory D
Source of Variability
Variance
%CV
Test Date
0.0066
18.9
Plate Nested in Test Date
0.0021
10.5
Intermediate Precision 1
0.0087
21.7
Residual (Repeatability)
0.0023
11.2
Total Assay Variability 2
0.0110
24.6
Laboratory E
Source of Variability
Variance
%CV
Test Date
0.0010
7.3
Plate Nested in Test Date
0.0005
5.0
Intermediate Precision 1
0.0015
8.9
Residual (Repeatability)
0.0015
9.0
Total Assay Variability 2
0.0030
12.7
1 . Comprised of test date and plate nested within test date sources of variability.
2 . Comprised of repeatability and intermediate precision.
Table 6 shows the consensus values (geometric means) along with 95% confidence intervals for each test sample generated from the mixed model ANOVA fitted to the data. Boxplots by sample of the reportable values from each laboratory, with each plot including a horizontal line for the consensus value estimate for the given sample, are provided in the supplemental information ( S1 – S9 Figs).
10.1371/journal.pone.0238196.t006
Table 6 Consensus values by test sample generated from intercept of mixed ANOVA model fit to data from all laboratories.
Sample ID
Target Concentration
Consensus Value
95% CI Consensus Value
BMI-ZPP-11
600
695.93
(677.31, 715.06)
BMI-ZPP-12
400
475.20
(462.47, 488.27)
BMI-ZPP-13
300
325.47
(316.72, 334.46)
BMI-ZPP-14
700
844.08
(821.24, 867.55)
BMI-ZPP-15
800
871.34
(847.91, 895.41)
BMI-ZPP-16
500
561.81
(546.71, 577.33)
BMI-ZPP-17
200
226.25
(220.18, 232.49)
BMI-ZPP-19
100
110.63
(107.66, 113.68)
BMI-ZPP-20
50
70.81
(68.89, 72.79)
Table 7 shows the ratio of the mean concentration for each of the six individual laboratory runs to the consensus value for a given sample along with a 90% confidence interval for the ratio. Agreement among laboratories implies that these ratios should be close to one, indicating that the average concentrations are about the same as the consensus values. The ratios range from 0.95 to 1.08 for Laboratory A; from 0.96 to 1.19 for Laboratory B1; from 0.83 to 1.12 for Laboratory B2; from 0.96 to 1.16 for Laboratory C; from 0.71 to 0.97 for Laboratory D; and from 0.90 to 1.06 for Laboratory E. Fig 1 shows a graph of the mean ratio and 90% confidence interval for each test sample by laboratory.
10.1371/journal.pone.0238196.g001
Fig 1
Graph of ratio of laboratory mean concentration to consensus value with 90% confidence intervals for each test sample by laboratory.
Dotted lines show equivalence region (0.80 to 1.25) and perfect agreement with consensus value (1.00). All means and confidence bounds are entirely within equivalence region for Laboratories A, B2, C, and E.
10.1371/journal.pone.0238196.t007
Table 7 Ratio of laboratory mean concentration to overall consensus value with 90% confidence intervals for each test sample.
Sample ID
Laboratory A
Laboratory B1
Laboratory B2
Ratio
90% Confidence Interval
Ratio
90% Confidence Interval
Ratio
90% Confidence Interval
BMI-ZPP-11
1.08
(1.04, 1.13)
1.09
(0.97, 1.23)
0.83
(0.81, 0.85)
BMI-ZPP-12
1.02
(0.98, 1.08)
0.97
(0.87, 1.09)
1.12
(1.08, 1.16)
BMI-ZPP-13
1.02
(0.98, 1.06)
1.15
(1.02, 1.30) *
0.93
(0.89, 0.97)
BMI-ZPP-14
0.99
(0.96, 1.02)
0.96
(0.87, 1.05)
1.09
(1.04, 1.13)
BMI-ZPP-15
1.01
(0.97, 1.06)
1.08
(0.98, 1.18)
0.88
(0.84, 0.91)
BMI-ZPP-16
1.00
(0.96, 1.04)
1.07
(1.00, 1.14)
1.05
(1.00, 1.09)
BMI-ZPP-17
1.06
(1.01, 1.11)
1.01
(0.88, 1.15)
1.12
(1.06, 1.18)
BMI-ZPP-19
0.95
(0.91, 0.98)
0.98
(0.87, 1.09)
0.83
(0.79, 0.87) *
BMI-ZPP-20
0.98
(0.95, 1.02)
1.19
(1.02, 1.39) *
0.92
(0.88, 0.97)
Sample ID
Laboratory C
Laboratory D
Laboratory E
Ratio
90% Confidence Interval
Ratio
90% Confidence Interval
Ratio
90% Confidence Interval
BMI-ZPP-11
1.10
(1.05, 1.15)
0.85
(0.81, 0.88)
0.90
(0.87, 0.94)
BMI-ZPP-12
1.08
(1.03, 1.12)
0.74
(0.72, 0.77) *
0.96
(0.92, 0.99)
BMI-ZPP-13
1.09
(1.04, 1.14)
0.79
(0.75, 0.83) *
0.95
(0.92, 0.99)
BMI-ZPP-14
1.00
(0.96, 1.05)
0.75
(0.71, 0.80) *
1.06
(1.02, 1.10)
BMI-ZPP-15
1.10
(1.04, 1.15)
0.90
(0.84, 0.97)
0.98
(0.95, 1.01)
BMI-ZPP-16
1.06
(1.02, 1.11)
0.76
(0.73, 0.79) *
1.01
(0.97, 1.06)
BMI-ZPP-17
0.96
(0.93, 1.00)
0.71
(0.69, 0.74) *
1.02
(0.99, 1.05)
BMI-ZPP-19
1.16
(1.10, 1.22)
0.97
(0.94, 1.01)
0.92
(0.89, 0.96)
BMI-ZPP-20
1.10
(1.02, 1.19)
0.77
(0.73, 0.82) *
1.02
(0.98, 1.06)
* 90% confidence interval is outside the acceptance bounds of (0.80, 1.25). Therefore, the concentrations for this test sample are not equivalent to those of other laboratories.
An equivalence test was conducted to determine if the mean test sample concentrations for each laboratory were equivalent to the corresponding test sample consensus value. An equivalence interval of 0.80 to 1.25 (representing a difference of 20% on the log scale) for the ratio of laboratory mean concentration to consensus concentration was used. The mean laboratory concentration for a given test sample is said to be equivalent to the consensus value for that sample if the 90% confidence interval for the ratio of these two values falls completely within the interval (0.80, 1.25).
Following this equivalence criteria: two intervals from Laboratory B1 (corresponding to BMI-ZPP-13 and BMI-ZPP-20) had an upper bound greater than the upper acceptance limit of 1.25 (1.30 and 1.39); one interval from Laboratory B2 (corresponding to BMI-ZPP-19) had a lower bound less than the lower acceptance limit of 0.80 (0.79); and six intervals from Laboratory D (corresponding to BMI-ZPP-12, BMI-ZPP-13, BMI-ZPP-14, BMI-ZPP-16, BMI-ZPP-17, and BMI-ZPP-20) had a lower bound less than the lower acceptance limit of 0.80. Furthermore, three of the six intervals are entirely below the lower acceptance bound of 0.80. These findings indicate that mean concentrations observed at Laboratory D are not equivalent to the other laboratories for six of the nine test samples.
Table 8 presents the estimated slope across the five laboratories and the corresponding 90% confidence interval obtained from the random regression model fit to assess the relationship between log 10 (observed concentration) and log 10 (target concentration). The overall slope was estimated to be 0.95 with a 90% confidence interval of (0.93, 0.97). An equivalence test was conducted to determine if the overall slope was equivalent to 1.00 (perfect dilutional linearity). An equivalence interval of 0.80 to 1.25 for the overall slope was used. Because the 90% confidence interval for the overall slope was completely within the interval (0.80, 1.25), the concentrations were found to be dilutionally linear across the laboratories. The slope estimates specific to each laboratory ranged from 0.94 to 0.96 ( Table 8 ) and were consistent with the overall slope.
10.1371/journal.pone.0238196.t008
Table 8 Estimated slope and lower and upper 90% confidence interval bounds by laboratory from random coefficients regression model fit to all data.
Laboratory
Slope Estimate
90% Confidence Interval #
Overall (All Labs)
0.95
(0.93, 0.97)
A
0.96
(0.90, 1.02)
B1
0.94
(0.88, 1.01)
B2
0.96
(0.90, 1.02)
C
0.96
(0.90, 1.02)
D
0.95
(0.89, 1.00)
E
0.95
(0.90, 1.01)
# 90% confidence interval is within the acceptance bounds of (0.80, 1.25). Therefore, the concentrations were dilutionally linear across the laboratories.
Discussion
The value of an assay as a regulatory tool is dependent on its accuracy, consistency, simplicity, and reproducibility. An assay that is relevant, is species independent, and replicable among laboratories is a powerful tool for product development. The data from a number of clinical trials utilizing ERVEBO strongly suggest that the anti-EBOV GP IgG ELISA provides data that correlate with product efficacy against Ebola infection. The development of new vaccines, or the evaluation of durability or alternative dosing regimens will be based on interpretation of data using the human anti-EBOV GP IgG ELISA. Our ability to use, or trust the data generated from non-clinical studies in different laboratories and clinical trials carried out with sera evaluated at different sites will require an understanding regarding the consistency and reproducibility of the assay among laboratories. In particular, assays using material from animal studies may be performed in laboratories different from those where the assay was performed to evaluate clinical trials. If the assay performance is not consistent among species and across laboratories, then data interpretation will not be possible. This interlaboratory study provided a direct head-to-head comparison of the ELISA performance in five different laboratories. The results from this study confirm the assay can be a universal tool for Ebola virus vaccine evaluation since results were similar when using the assay at multiple labs. However, the small differences in assay performance reinforce that for regulatory purposes, it is still ideal to rely on only one test site where the assay is fully validated.
Intermediate precision for the six laboratory runs ranged from 8.9 to 21.7%CV and repeatability ranged from 7.2 to 23.7%CV. The total assay variability %CVs range from 12.2 to 30.6. As a point of reference, laboratories that validated the anti-EBOV GP IgG ELISA have used the following precision acceptance criteria: (1) The intermediate precision of the assay for samples within the analytic range of the assay must be no larger than 25% CV; and (2) the repeatability of the assay for samples within the analytic range of the assay must be no larger than 20% CV. The repeatability estimate for Laboratory B1 was greater than the upper acceptance bound as established in laboratories that validated the anti-EBOV GP IgG ELISA with human serum. However, a repeat of the proficiency panel run at this laboratory following additional training of laboratory staff resulted in a repeatability estimate less than the upper acceptance bound; thus, illustrating the importance of rigorous training of laboratory staff and the strict adherence to assay procedures to ensure consistent results between runs.
Similarly, laboratories that validated the anti-EBOV GP IgG ELISA have used the following dilutional linearity (relative accuracy) acceptance criteria: the 90% confidence interval for the slope from the random regression model fit to data between the limits of quantitation and relating log 10 (concentration) to log 10 (spike level) will be entirely within (-1.20, -0.80). The interlaboratory study models dilutional linearity as log 10 (observed concentration) to log 10 (target concentration) resulting in a positive relationship between the two variables. Therefore, to conclude that dilutional linearity is acceptable in relation to the validation in human serum, the 90% confidence interval for the slope should be positive and fall entirely between 0.80 and 1.20. The overall slope was 0.95 and has a 90% confidence interval estimate of (0.93, 0.97); thus, the dilutional linearity is within the acceptance criteria as established in the assay validation with human serum.
Agreement among laboratories implies that the ratios of the mean concentration for the five individual labs to the overall laboratory consensus value for a given sample should be close to one. The ratios range from 0.95 to 1.08 for Laboratory A; from 0.96 to 1.19 for Laboratory B1; from 0.83 to 1.12 for Laboratory B2; from 0.96 to 1.16 for Laboratory C; from 0.71 to 0.97 for Laboratory D; and from 0.90 to 1.06 for Laboratory E. Equivalence test results showed that the 90% confidence interval for the ratio were within the equivalence bounds of 0.80 to 1.25 for each laboratory except for samples BMI-ZPP-13 and BMI-ZPP-20 in Laboratory B1, BMI-ZPP-19 in Laboratory B2, and six samples in Laboratory D.
The assessment of between-laboratory performance revealed lower observed concentrations at Lab D and greater variability in assay results at Lab B1 relative to the other laboratories. The lower observed concentrations at Lab D illustrate the importance of monitoring assay performance and harmonizing across laboratories. Given the inherent differences from subject-to-subject in clinical trials and animal-to-animal in non-clinical studies, these differences observed at Lab D relative to the other laboratories are not likely to affect interpretation of study results. The variability in assay results at Lab B1 was mitigated by additional laboratory staff training.
The evaluation of the proficiency panel at these laboratories provides a limited assessment of assay precision (intermediate precision, repeatability, and total assay variability), dilutional linearity, and accuracy. This limited evaluation suggests that the within-laboratory performance of anti-EBOV GP IgG ELISA as implemented at the five laboratories is performing consistently with the intended use of the assay based on the acceptance criteria used by laboratories that have validated the assay.
Supporting information
S1 Fig
Observed concentration (ELISA Units/mL) by laboratory for sample BMI-ZPP-11.
(Consensus Concentration = 695.93). Center line in the box depicts the median concentration while the height of the box represents the 25 th and 75th percentile of the concentration distribution. Vertical lines extending above and below the box represent the maximum and minimum concentration values for the laboratory. Open circles show the observed concentrations.
(TIF)
S2 Fig
Observed concentration (ELISA Units/mL) by laboratory for sample BMI-ZPP-12.
(Consensus Concentration = 475.20). Center line in the box depicts the median concentration while the height of the box represents the 25 th and 75th percentile of the concentration distribution. Vertical lines extending above and below the box represent the maximum and minimum concentration values for the laboratory. Open circles show the observed concentrations.
(TIF)
S3 Fig
Observed concentration (ELISA Units/mL) by laboratory for sample BMI-ZPP-13.
(Consensus Concentration = 325.47). Center line in the box depicts the median concentration while the height of the box represents the 25 th and 75th percentile of the concentration distribution. Vertical lines extending above and below the box represent the maximum and minimum concentration values for the laboratory. Open circles show the observed concentrations.
(TIF)
S4 Fig
Observed concentration (ELISA Units/mL) by laboratory for sample BMI-ZPP-14.
(Consensus Concentration = 844.08). Center line in the box depicts the median concentration while the height of the box represents the 25 th and 75th percentile of the concentration distribution. Vertical lines extending above and below the box represent the maximum and minimum concentration values for the laboratory. Open circles show the observed concentrations.
(TIF)
S5 Fig
Observed concentration (ELISA Units/mL) by laboratory for sample BMI-ZPP-15.
(Consensus Concentration = 871.34). Center line in the box depicts the median concentration while the height of the box represents the 25 th and 75th percentile of the concentration distribution. Vertical lines extending above and below the box represent the maximum and minimum concentration values for the laboratory. Open circles show the observed concentrations.
(TIF)
S6 Fig
Observed concentration (ELISA Units/mL) by laboratory for sample BMI-ZPP-16.
(Consensus Concentration = 561.81). Center line in the box depicts the median concentration while the height of the box represents the 25 th and 75th percentile of the concentration distribution. Vertical lines extending above and below the box represent the maximum and minimum concentration values for the laboratory. Open circles show the observed concentrations.
(TIF)
S7 Fig
Observed concentration (ELISA Units/mL) by laboratory for sample BMI-ZPP-17.
(Consensus Concentration = 226.25). Center line in the box depicts the median concentration while the height of the box represents the 25 th and 75th percentile of the concentration distribution. Vertical lines extending above and below the box represent the maximum and minimum concentration values for the laboratory. Open circles show the observed concentrations.
(TIF)
S8 Fig
Observed concentration (ELISA Units/mL) by laboratory for sample BMI-ZPP-19.
(Consensus Concentration = 110.63). Center line in the box depicts the median concentration while the height of the box represents the 25 th and 75th percentile of the concentration distribution. Vertical lines extending above and below the box represent the maximum and minimum concentration values for the laboratory. Open circles show the observed concentrations.
(TIF)
S9 Fig
Observed concentration (ELISA Units/mL) by laboratory for sample BMI-ZPP-20.
(Consensus Concentration = 70.81). Center line in the box depicts the median concentration while the height of the box represents the 25 th and 75th percentile of the concentration distribution. Vertical lines extending above and below the box represent the maximum and minimum concentration values for the laboratory. Open circles show the observed concentration.
(TIF)
S1 Table
ELISA concentration of each test sample—Laboratory A.
(XLSX)
S2 Table
ELISA concentration of each test sample—Laboratory B1.
(XLSX)
S3 Table
ELISA concentration of each test sample—Laboratory B2.
(XLSX)
S4 Table
ELISA concentration of each test sample—Laboratory C.
(XLSX)
S5 Table
ELISA concentration of each test sample—Laboratory D.
(XLSX)
S6 Table
ELISA concentration of each test sample—Laboratory E.
(XLSX)
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Introduction
There is a clear link between the combined activity of neurons and specific neural computations [1] , [2] . A common observation from population recordings is that the correlation between the activities of pairs of neurons can be modulated – for instance, by the spatiotemporal structure of stimuli [3] , [4] , the perceptual state of the subject [5] , [6] , or the spatial focus of attention [7] – [9] . Theoretical work has focused on the cellular and circuit mechanisms that both determine and modulate correlation [10] – [20] . However, the general applicability of these theories is unclear [21] , and how neural populations modulate the correlation between their spiking activity remains an open question.
One complication is that spike train correlations reflect common activity that may be measured at different timescales, ranging from a few (synchrony) to hundreds of milliseconds (co-variation of firing rates). For example, pairs of neurons in visual cortex [22] , [23] , olfactory bulb [24] , and attention responsive cortical areas [7] – [9] show increases in spike time synchrony which accompany simultaneous decreases of rate co-variation. To indicate the complex temporal aspects of this modulation, we label a differential change in correlation over distinct timescales correlation shaping [19] , [24] . In this study, we use a combination of in vivo recordings and computational modeling of electrosensory neurons to study how the spatial structure of a stimulus shapes the correlation of primary sensory neurons.
Weakly electric fish detect perturbations of their self-generated electric field through an array of electroreceptor neurons scattered on their skin surface which synapse onto pyramidal neurons within the electrosensory lateral line lobe (ELL) [25] . Relevant stimuli can be broadly categorized as either local , stimulating only a small fraction of the skin, or global , projecting to a broad area of the animal's body. Local inputs are a reasonable approximation to the spatial scale of prey inputs, while global inputs mimic communication calls from conspecifics [26] . We recorded simultaneously from pairs of ELL pyramidal neurons and found that global inputs increased spike train correlations at short timescales while simultaneously decreasing correlations at long timescales, when compared to the spike train correlation induced by local inputs. While there is a general understanding about how local and global stimuli control single neuron responses [26] – [30] , the cellular and circuit mechanisms that allow the spatial extent of stimuli to shape correlated population activity in the electrosensory system are a new area of study.
Based on the well-characterized anatomy and physiology of electrosensory circuits [25] , we developed a spiking network model of ELL pyramidal neurons that captured the experimentally observed correlation shaping. Diffuse inhibitory feedback was activated preferentially by global stimuli and provided a decorrelating signal that reduced correlations at long timescales. Further, global stimuli recruited feedforward circuitry that increased correlations at short timescales which were immune to feedback decorrelation. For sufficiently weak stimuli, we use a linear response framework [28] , [31] to show how correlation shaping is consistent with a shaping of the single neuron stimulus-response gain function. We tested our model predictions experimentally by selectively blocking feedback input, causing spike train correlations at long timescales to increase, rather than decrease. This directly demonstrates how inhibition can be a source of decorrelation to pyramidal neurons, rather than a source of synchrony as described in many previous studies [10] , [11] , [32] – [35] . Finally, we used our understanding of the population's response properties to study how feedback selectively attenuates responses to distractor stimuli, improving the system's ability to represent relevant signals. In total, our results reveal novel principles by which feedforward and feedback neural circuits are differentially activated by stimuli to shape population spike train correlations.
Methods
Ethics Statement
Animals were obtained from local importers and were acclimated to the laboratory as per published guidelines [36] . All experimental procedures were approved by the McGill University Animal Care Committee and have been described in detail elsewhere [37] .
Electrophysiology
Briefly, dual extracellular recordings from the lateral and centrolateral ELL segments of Apteronotus leptorhynchus were made using metal-filled micropipettes [37] . Pyramidal cells within these segments can be distinguished from cells within the centromedial segment based on recording depth, the medio-lateral and rostro-caudal positions of the recording electrode with respect to surface landmarks such as the “T0” vein and its afferent veins [38] , and their responses to sensory input as previously described [39] . Superficial pyramidal cells were identified based on their low ( ) whereas deep cells were identified based on their high ( ) mean firing rates in the absence of EOD modulations [26] , [30] , [40] . All data was sampled at 10 kHz.
Random amplitude modulations of the animal's electric organ discharge (EOD) consisting of white noise low-pass filtered with a cutoff of 120 Hz (8th order Butterworth filter) were presented either globally via two electrodes positioned on either side of the animal or through a dipole located close to the skin surface [37] . The stimulus lasted and consisted of 6 concatenated segments of the same frozen noise epoch that lasted 20 s [37] .
Pharmacological blockade of the indirect feedback from EGp was performed by either applying the non-NMDA glutamate receptor antagonist CNQX within the ELL molecular layer [30] or by applying a 2% lidocaine solution to the praeminential-cerebellar tract (PECB) as done previously [41] . Since both manipulations gave rise to similar effects, the data was pooled.
Data Analysis
Spike train cross-covariance functions
The recorded signals from a pair of neurons in response to the stimulus were thresholded in order to obtain the spike times , where is the number of spikes from neuron ( ). The spike train from neuron is then given by: (1) Here is the discrete approximation of the Dirac delta function with if and is zero otherwise; throughout so that at most one spike was contained in any time window. We note that this is equivalent to discretizing time in bins of width ms and setting the content of bin to when there is a spike time such that and to otherwise, as was done previously [30] .
The firing rate for neuron is then estimated as: (2) where is the duration of a recording (typically 120 s). The spike train covariance at time lag between neurons and is defined as: (3) where the number of time bins in the discrete spike train is . We refer to as the auto-covariance, while for is called the cross-covariance.
Spike count correlations
We also considered the correlations between the spike counts of pairs of neurons. The spike count from neuron is simply defined as the number of spikes occurring in the time window . It is a random integer given by: (4) For a given window size , we computed a sequence of spike counts from neuron as , using overlapping windows to increase the number of estimates. We have that , where denotes the mean value of the sequence . We can also obtain second order statistics from including the spike count variance and co-variance, which are defined by: (5) (6)
From these one can define the correlation coefficient between the spike counts and over a time window : (7) We use to denote the average value of across all pairs and similarly for other pairwise statistics. For small , the correlation coefficient measures the degree of synchrony between the two trains, while, for large , measures the co-variation in the firing rates of a pair of neurons [12] , [13] .
The variance and covariance functions of the spike count and spike train are related by: (8) These equations are the well known relations between second order spike count and spike train statistics [42] , with resulting from the convolution of the windowing function that converts spike trains to spike counts.
Within-trial vs. across-trial covariance functions and correlation coefficients
We note that both the spike train covariance function and correlation coefficient are within-trial measures of co-variability, since they incorporate both signal induced as well as trial-to-trial variable (i.e noise) aspects of common input fluctuations. Since we presented the same (i.e frozen) realization of the signal six times in succession, we were able to compute the spike train covariance and spike count correlation that were due purely to the common signal by computing joint statistics from neuron pairs recorded in different trials (i.e. across-trial). Specifically, denote the spike train of neuron in response to the realization of the stimulus ( ) by . The across-trial spike train covariance between neurons and is then given by: (9) In Eq. (9) , . Eq. (9) measures the joint spike statistics from neuron pairs when the spike trains were not recorded simultaneously but were stimulated with the same signal. This is because the summation runs over all possibly non-repeating combinations ( ) of the responses of each neuron to the six presentations of the frozen stimulus.
Similarly, one can define the spike count sequence for neuron during stimulus realization as . The across-trial spike count correlation coefficient between neurons and is then given by: (10) where Cov with the sequence of spike counts from the realization of the stimulus.
Linear Response Approximation
We use linear response theory in order to derive an expression for the correlation coefficient in terms of the stimulus gain, as done in past studies [12] – [14] , [19] , [28] , [31] , [43] , [44] . We consider the Fourier transform of the spike train covariance function as the length of the trial becomes large and assuming the processes are stationary: (11) Throughout, we will refer to with as the cross spectrum and as the power spectrum. To relate spike count statistics to spike train statistics, we use the Wiener-Khinchin theorem to rewrite Eq. (8) (assuming is large): (12) (13) with . Note that approaches a -function centered at 0 as and a constant function on as . Therefore, for large , only the zero-frequency components of the spectra contribute to the integral, while for small , all frequencies contribute. A similar relation holds between and .
For a fixed stimulus , we assume that [13] , [28] , [31] , [43] : (14) where is the Fourier transform of the mean-subtracted spike train given a particular realization of , is the Fourier transform of the stimulus, and denotes an expectation over repeated presentations of the stimulus. is the single neuron stimulus-response gain of the neuron (which we refer to as the stimulus gain for brevity). It relates the amplitude of the response to that of a signal at a particular frequency. For both experimental data and numerical simulations, we compute as: (15) where is the cross spectrum between and and is the power spectrum of the signal.
Assuming that the spike trains are conditionally independent given the stimulus, we can write , where denotes an expectation over the random stimulus. Substituting Eq. (15) into Eq. (14) , (16) Finally, combining Eqs. (13) and (16) yields the following approximation: (17) Eq. (17) relates the joint spike count variability to the stimulus gain , and has been derived in several past studies [13] , [19] . We can then approximate the predicted across-trial correlation as: (18)
Modeling
ELL anatomy
The neuroanatomy and physiology of the electrosensory system have been extensively characterized [25] . Pyramidal neurons in the ELL are subdivided according to several criteria. Roughly half of all pyramidal neurons have a basilar dendritic tree (BP neurons) and receive direct electrosensory afferent input. The other half lack a basal dendrite (nBP neurons) and receive afferent input only indirectly via interneurons [45] . Both BP and nBP neurons have an apical dendritic arbor; however, the extent of the arbor is variable across neurons. Pyramidal neurons with small apical dendritic trees are called deep neurons and do not receive much feedback input [30] , [45] , [46] . In contrast, pyramidal neurons with large apical dendritic trees are called superficial neurons and receive large amounts of feedback [30] , [45] , [46] . It has been recently shown [45] that the spatial projection of electroreceptor input to individual pyramidal neurons establishes a putative column, composed of BP and nBP deep and superficial pyramidal neurons.
The afferent and efferent projections between the ELL and higher brain structures further distinguish ELL pyramidal neurons. Indeed, only deep pyramidal neurons project to the praeminentialis dorsalis (Pd) [46] , a second order isthmic structure that directly projects to the posterior eminentia granularis (EGp), which in turn projects back to the ELL along the dorsal molecular layer via parallel fibers [25] that make synaptic contact onto the large apical dendritic trees of superficial pyramidal neurons. Thus, the deep ELL EGp superficial ELL feedback pathway can be characterized as open-loop [46] . Electrophysiological studies suggests that EGp granule cells show temporal locking to electrosensory input [46] , [47] and that the indirect feedback input onto ELL pyramidal neurons is in the form of a negative image of the stimulus that is activated by spatially diffuse but not by spatial localized stimuli [30] , [46] .
ELL model description
Our model of the deep pyramidal neuron to superficial ELL feedback via the nP and EGp contained three distinct neural populations: a deep (Dp) ELL population that projected to a population of granule cells in the EGp, which in turn provided feedback to a population of ELL superficial (Sf) neurons. All cells were modeled with leaky integrate-and-fire (LIF) dynamics [48] . Numerical values of model parameters can be found in Table 1 , and a detailed model summary [49] can be found in Table S1 . The membrane potential obeyed linear subthreshold dynamics supplemented with a spike-reset rule so that implied that , and was marked as a spike time. The deep population consisted of neurons, and the membrane potential of the deep neuron obeyed: (19) The first two terms of the right hand side of Eq. (19) model a static rest state and an intrinsic leak process, respectively. The process models Gaussian stimulus locked electroceptor activity, while models stimulus independent activity afferent to neuron in population ( ). As in the experiments, we set , but the temporal structure of the processes was white with , , and for or . The electroreceptor input contrast was set by and the correlation of the stimulus locked component by .
10.1371/journal.pcbi.1002667.t001 Table 1
Parameter values used in numerical simulations.
Parameter
Description
Value
Number of deep neurons
800
Number of EGp neurons
200
Number of superficial neurons
2
Deep membrane time constant
10 ms
EGp membrane time constant
10 ms
Superficial membrane time constant
15 ms
Deep bias
−56 mV
EGp bias
−60 mV
Superficial bias
−56 mV
Threshold voltage
−55 mV
Reset voltage
−65 mV
Noise strength
1 mV
Deep to EGp synaptic strength
mV
EGp to Superficial synaptic strength
mV
EGp to Superficial synaptic time constant
5 ms
Local input correlation
0.1
Global input correlation
0.2
The EGp population consisted of neurons, and the membrane potential of the EGp granule cell followed: (20) Here is the spike train from the deep neuron, and is the strength of excitation from the Deep ELL EGp. The time constant was chosen as 10 ms, based on recent measurements of input resistance for these cells of approximately 2 G [47] and data from cerebellar granule cells indicating typical capacitance values of 3–5 pF [50] – [52] .
Finally, since we are only interested in the pairwise correlation between superficial neurons and because the feedback is open-loop, it is only necessary to consider a pair of superficial pyramidal neurons. As such, we set . The superficial pyramidal cell's membrane dynamics are given by: (21) Here where is the Heaviside function. The operation denotes convolution. The inhibitory coupling from EGp to the ELL was set by .
During local stimulation, a fraction of deep neurons received coherent, stimulus-locked electroreceptor input ( ), while all other deep neurons received uncorrelated input modeling spontaneous afferent activity. During global stimulation, all deep neurons ( ) received stimulus-locked input ( ). The increased value of reflects the fact that global stimuli will spatially saturate the receptive field center and will thus more effectively drive the afferent population [29] , [53] .
In our model, a pair of neurons in a given layer could receive correlated input from the previous layer in two ways. First, a neuron in the previous layer could project to both downstream neurons and thus correlate their input. Second, neurons in the previous layer could become locked to the stimulus and their pooled activity could correlate the downstream neurons, even if their projections did not overlap anatomically. In the linear model, we assumed that the first source of common input is negligible relative to common input from stimulus locked, pooled activity, as is often the case in feedforward networks [54] . Consequently, correlations between model neurons were due only to external signals that synchronously recruited electroreceptors. Therefore, for the model.
To evaluate for our model using the linear response approximation, we computed the superficial neuron stimulus gain . For numerical simulations, we estimated using Eq. (15) . However, following past work [28] , [31] , we derived a theoretical approach to compute . For global stimulation and assuming that both the input correlations and the effective coupling and are sufficiently small, we compute the feedback filter from the Deep ELL EGp Superficial ELL using the serial computation (22) where is the Fourier transform of the exponential synaptic kernel . This result follows simply from the linear convolution of Deep ELL activity to EGp and then from EGp activity to superficial ELL through . Here we have introduced , the single neuron cellular response function (which we refer to as the cellular response for brevity) that measures a neuron's response to an applied current, independent of network feedback. can be computed using standard techniques from statistical mechanics (see Text S1 ).
We note that can be calculated for mixed excitatory and inhibitory feedback to superficial neurons. In this case, the value of should be interpreted as the effective input strength from both excitatory and inhibitory populations. For example, if the fraction of excitatory synapses from EGp to superficial neurons is given by and the synaptic strength of excitation and inhibition are and , respectively, then we have . Previous studies have established that the stimulus-locked EGp feedback is net inhibitory [46] , and we therefore model the pathway as purely inhibitory for simplicity.
With , we calculate the stimulus gain of a superficial ELL neuron as given in Eq. (25) . Further, these techniques also permit a calculation for the power spectrum . With theoretical expressions for and , and assuming the signal is Gaussian white noise with unit variance, we use Eqs. (17) and (18) to obtain a theoretical prediction for the spike count correlation between the two superficial ELL neuron spike trains: (23) Here we have used the homogeneity of the spike trains to set and for all superficial neurons.
Results
Correlation Shaping with Global and Local Stimuli
We examined the response of ELL pyramidal neurons to time-varying electrosensory input. Broadband electrosensory stimuli (Gaussian, 0–120 Hz) were applied to awake, behaving weakly electric fish ( Apteronotus leptorhynchus ; see Methods). Throughout the study, we delivered stimuli in one of two spatial arrangements: a local or global configuration [26] , [27] , [29] . In the local configuration, stimuli were spatially compact, delivered through a small dipole (tip spacing of 2 mm), and excited only a small region of the skin surface ( Figure 1A , left, blue). Local inputs mimic prey stimuli which drive only a spatially localized portion of the receptive field of an ELL pyramidal neuron [55] . In the global configuration, stimuli were spatially broad, delivered through a pair of electrodes located on each side of the animal, and affected the entire surface of the animal ( Figure 1A , left, orange). Global inputs mimic stimuli caused by conspecifics which drive nearly the entire surface of one side of the animal, stimulating both the classical and non-classical receptive field of a target pyramidal neuron [29] , [56] . During both local and global stimulation, simultaneous extracellular recordings of ELL pyramidal neuron pairs were collected ( Figure 1A , right). There was an intentional selection bias for superficial basilar pyramidal (BP) neurons [25] , since these neurons are known to receive feedback projections that shape their responses to sensory input [30] , [45] , [46] . Superficial neuron firing rates in the local and global configurations were similar ( and respectively).
10.1371/journal.pcbi.1002667.g001
Figure 1
The spatial extent of electrosensory stimuli shapes the temporal correlation between the spike times from pairs of ELL pyramidal neurons.
A , Stimulus protocol for local and global stimulation. Left: Gaussian distributed electric field stimuli with broadband spectral content (uniform over 0–120 Hz) were applied in a spatially compact (local) or diffuse (global) manner. Right: Paired extracellular recordings of ELL pyramidal neurons were made during stimulation. B1 , Spike train cross-covariance function in the local and global stimulus configuration for pairs of simultaneously recorded superficial BP neurons (within-trial correlation). Correlation function is normalized by firing rate. B2 , Same as B1 except computed between spike trains recorded during distinct trials. C1 , Within-trial spike count correlation as a function of window length ( ) in the local and global stimulus configuration. C2 , Across-trial spike count correlation as a function of window length in the local and global stimulus configuration. D1 , Ratio of global and local within-trial spike count correlations shown in panel C1. D2 , Ratio of across-trial global and local spike count correlations shown in panel C2. The data set consists of n = 10 pairs of neurons, and all curves are population average quantities. In all panels, shaded regions denote standard error.
We used the simultaneous unit recordings to estimate the spike train cross-covariance function (see Methods Eq. 3 ) for neuron pairs in both the local and global stimulus configurations. Global stimulation set a narrow peak of the cross-covariance function with a high maximum at zero lag, while it was broad with a lower peak value for local stimulation ( Figure 1B1 ), consistent with previous reports [37] .
To quantify this shift in covariance at different timescales, we computed the correlation coeffcient between the spike counts of neuron pairs' outputs [22] , [42] . This provided a normalized measure of the similarity between the two spike trains as observed over windows over a particular length (see Methods Eq. 7 ). At small window sizes ( ), spike count correlation was larger during global stimulation than during local. For large window sizes ( ), this relationship was reversed ( Figure 1C1 ). Correlation is generally a rising function of window size [57] , since for small few spikes will occur in the same window. However, even small values of correlation (e.g. in magnitude) have substantial influence on the propagation of neural information [54] , [58] and neural coding [59] . To provide a relative measure of the shift in correlation between the two states, we considered the ratio of global correlation to local correlation. This was a decreasing function of window size which was substantially greater than 1 for small window sizes and lower than 1 for large window sizes ( Figure 1D1 ).
We performed statistical tests to confirm that the trends observed were significant. Nonparametric tests confirmed that the distributions for the local and global conditions were different ( , evaluated at , , two-sample Kolmogorov-Smirnov test). The trends with timescale were also significant ( , compared with , for local and for global stimulation, two-sample Kolmogorov-Smirnov tests). The means of the distributions were also different ( , evaluated at , , paired t-test). In summary, the spatial extent of the electrosensory signal shaped the timescales over which spike train pairs were correlated.
Shifts in Single-Neuron Response Gain Predict Correlation Shaping
In general, correlated neural activity can be decomposed into stimulus induced and non-stimulus induced components [21] , [60] . Stimulus induced correlations reflect the two neurons locking to a dynamic stimulus, while the non-stimulus induced correlations reflect the neurons sharing a portion of their trial-variable noise, presumably from a common pre-synaptic source. To uncover the cellular and circuit mechanisms underlying correlation shaping, we first determined whether the changes in correlation observed were present across trials and therefore related to how neurons responded to the repeated stimulus. Using spike trains across different trials of identical stimulus presentations, we computed the across-trial spike train cross-covariance functions and spike count correlations ( Figure 1B2,C2 ; see Methods Eqs. 9 , 10 ). The magnitude of these across-trial correlations was less than that of the within-trial correlations, indicating the presence of some trial-variable noise (compare Figure 1C1 and 1C2 ). Nevertheless, the differential shaping of correlations at short and long timescales was still present in the across-trial spike count correlation ( Figure 1C2,D2 ). This suggests that the way stimulus processing shifts between local and global conditions is related to the mechanisms responsible for correlation shaping.
To investigate this relationship, we considered the stimulus gain , which measures a neuron's response to an external electrosensory stimulus at frequency ( Figure 2A , see Methods Eq. 15 ). We computed the gain in the two stimulus conditions and found that during local stimulation, the gain function was low-pass, while during global stimulation, it was high-pass ( Figure 2B ), consistent with previous studies [29] , [30] . We then asked if the observed changes in correlation could be related to this shift in frequency selectivity.
10.1371/journal.pcbi.1002667.g002
Figure 2
Shifts in stimulus gain predict spike train correlation shaping.
A , Schematic illustration of stimulus gain. The gain is described as the ratio of the change in the output firing rate of a neuron that is evoked by an input sine wave stimulus of amplitude . B , Gain for neuron pairs during local and global stimulation. The signal was assumed to have unit amplitude. C , Across-trial spike count covariance (solid) and the prediction from a linear response theory (dashed, see Methods Eq. 17 ), in both global and local stimulus conditions. The data set consists of n = 10 pairs of neurons, and all curves are population averages. In all panels, shaded regions denote standard error.
Motivated by past studies [12] , [13] we assumed that the cross-spectrum between the spike trains was proportional to the product of their stimulus gain functions (see Methods Eq. 16 ). This amounts to assuming that the common stimulus is the only source of correlation in the neural responses. This theory predicts that the correlation for large window sizes is determined by stimulus gain at low frequencies. In contrast, correlation for small windows involves gain at high frequencies. The shift in from low frequency transfer for local inputs to high frequency transfer for global inputs therefore implies global stimulus correlation will be enhanced for small and attenuated for large , with the inverse true for local stimulation. We verified this hypothesis, obtaining a prediction of the spike count correlation in the two states that matched the experimental data (see Methods Eq. 18 ; Figure 2C , solid versus dashed curves). Thus, the shift in the frequency-selectivity of superficial neurons' stimulus gain between the local and global conditions indeed predicted the changes in correlation.
Modeling ELL Pyramidal Cell Responses
To understand mechanisms behind the shift in neuronal responses under the local and global stimulus conditions, we constructed a simplified population model of ELL pyramidal neurons based on known anatomical and functional data as well as our experimental results ( Figure 3A ; for a detailed discussion of the anatomy, see Methods). This model captured two generic circuit features that modulated population responses: feedforward sensory input and feedback inhibition. All pyramidal neurons received feedforward electrosensory input via electroreceptors, but were divided into two classes based on their feedback afferents: deep neurons did not receive feedback from higher regions, but superficial neurons did receive inhibitory feedback. This feedback arrived from the posterior eminentia granularis (EGp), which was in turn innervated by the deep neurons. In total, this structure formed an open-loop inhibitory feedback pathway, from deep neurons to EGp neurons to superficial neurons. Motivated by past studies, ELL pyramidal neurons were modeled as simple leaky integrate-and-fire units [27] , [28] , [46] . Consistent with experimental data [30] , superficial firing rates in the model were lower than deep firing rates (12 Hz and 36 Hz, respectively) in both local and global stimulation conditions.
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Figure 3
Open loop feedback inhibition in electrosensory neural circuitry.
A , Detailed schematic of peripheral neural circuitry in the electrosensory system. Basilar (BP) and non-basilar (nBP) pyramidal neurons in the electrosensory lateral line lobe (ELL) have their somata located in the Pyramidal cell layer (PCL). Deep pyramidal neurons (green) have small apical dendritic arbors, projecting only to the Ventral Molecular Layer (VML). In contrast, superficial pyramidal neurons (red) have large apical dendritic arbors, projecting to the Dorsal Molecular Layer (DML). Pyramidal neurons receive direct and/or indirect input from feedforward electroreceptor afferent input to the Deep Fiber Layer (DFL). Deep pyramidal neurons excite neurons in the praminentialis dorsalis (Pd), which in turn excite granule cells in the posterior eminentia granularis (EGp). The EGp projects parallel fiber feedback along the DML exclusively targeting ELL superficial pyramidal neurons. In total the deep ELL EGp superficial ELL pathway is an open loop feedback structure. Pyramidal neuron graphics were from example neurolucida traced neurons [46] . B , Stimulus correlation for pairs of experimentally recorded deep pyramidal neurons (n = 45 pairs; 10 neurons were used) that were driven by the stimulus in local and global (bottom). Little correlation shaping is present. For comparison purposes we show the stimulus correlation for pairs of superficial neurons (top, Figure 1C2 ). C , Simplified model of the ELL-EGp circuit. Individual neurons in the deep ELL, EGp, and superficial ELL were modeled with leaky integrate-and-fire neuron dynamics (example realizations on right). Electroreceptor input was modeled as white noise, with 5% of deep pyramidal neurons receiving a stimulus-locked component in local and 100% in global. We studied the spike responses the pair of superficial pyramidal neurons (labeled 1 and 2) that receive both afferent and EGp feedback inputs.
Previous studies have shown that EGp feedback modulates both the static [41] and dynamic [30] gain of single neuron responses. However, how it controls the ELL population response, and in particular correlations between pyramidal neurons, is unknown. To determine whether feedback is responsible for stimulus-dependent correlations, we recorded from deep pyramidal neurons receiving a frozen stimulus and computed stimulus correlations between the pairs of spike trains. Consistent with the lack of feedback projections to this subpopulation, these neurons did not show substantial shaping of correlations between the local and global conditions ( Figure 3B , bottom), in contrast with superficial pyramidal neurons ( Figure 3B , top). The small decrease in correlation for large time windows observed during global stimulation for deep neurons ( Figure 3B , bottom) is consistent with these neurons receiving little feedback input [40] .
Recruitment of Feedback in the Model During Local and Global Stimulation
We used our model to examine the stimulus dependence of EGp feedback. In our model, electrosensory stimulation caused the firing of deep pyramidal neurons to become stimulus-locked. When the stimulus was local, only a small fraction of this population was stimulus-locked, so that the average correlation across the deep population was low ( across the population, Figure 4B1 ). The weak stimulus correlation across the deep population failed to recruit coherent activity in the EGp granule cell population, resulting in a near tonic inhibitory feedback to the ELL ( Figure 4C1 ). In contrast, when the stimulus was global, the entire deep population was correlated by the stimulus ( , Figure 4B2 ). This led to a dynamic, stimulus locked EGp feedback to the superficial neuron pair ( Figure 4C2 ). Thus, our model captured a link between the temporal locking of EGp feedback and the spatial extent of the external stimulus, which has been suggested in past experiments [46] , [47] .
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Figure 4
Model EGp feedback is stimulus locked for global, but not local, stimulation.
A Low-pass (0–60 Hz) filtered version of the electrosensory stimulus. Filtering was done as a visual aid in relating the stimulus to the feedback in (C2). B1 , Raster plot of the deep neuron population during local stimulation. The signal weakly correlated only a small fraction of the population. B2 , Same as (b1), but during global stimulation. The spatially broad stimulus correlated the entire deep population. C1 , EGp feedback current during local stimulation, showing little stimulus locking. C2 , EGp feedback was stimulus-modulated by the global signal, due to recruitment of the deep population by the stimulus. The inhibitory feedback is a negative image of the stimulus (A2).
Having characterized the EGp feedback, we next determined how it shaped the responses of superficial neuron pairs. The total input to a model superficial pyramidal neuron, from both feedforward and feedback sources, is: (24) Here is the strength of the afferent activity to an ELL pyramidal neuron and and are Gaussian white noise processes modeling stimulus locked and unlocked (noise) afferent inputs, respectively. The parameter is the fraction of receptor afferents that are stimulus-locked, which determines the correlation between the electroreceptor input to neuron pairs. The function is the parallel fiber feedback kernel and involves compound processing of the stimulus by the population of deep ELL neurons, the EGp granule cells, and finally the inhibitory feedback pathway from the EGp to the ELL (see Methods Eq. 21 ). Assuming weak stimulus correlations (small ) and weak EGp feedback, we use linear response theory [28] , [31] , to obtain an expression for the stimulus gain of a superficial pyramidal neuron (see Methods): (25) Here is the Fourier transform of the feedback kernel (see Eq. 22 in Methods), and is the cellular response of a superficial neuron, which measures its response to a fluctuating current applied directly to the neuron (see Eq. 8 in Text S1 ). In contrast to the stimulus gain, the cellular response does not depend on network feedback. The parameter is the spatial extent of the stimulus ( ), with modeling the lack of stimulus-coherent EGp feedback for local stimulation, and the full recruitment of EGp feedback for global stimulation ( Figure 4 ). With this model of how shifts between local and global stimulus configurations, we next build a theory for the correlation shaping within the superficial ELL pyramidal neuron population.
Correlation Shaping in the ELL-EGp Network Model
We used our ELL-EGp network model to relate the spatial extent of an electrosensory stimulus and the timescale of the pairwise correlation between spike trains from superficial BP neurons. During local stimulation, pairs of nearby superficial neurons received correlated electroreceptor input ( Figure 3C ). The degree of correlation between the afferent input to the superficial pair was . The EGp feedback did not exhibit a substantial stimulus-locked component ( ) during local stimulation, and hence did not contribute to common fluctuations ( Figure 4C1 ). Thus, the stimulus gain in the local condition, denoted , reduced to: (26) Our theoretical (see Methods) quantitatively matched estimates from simulations of the ELL-EGp network of leaky integrate-and-fire neurons ( Figure 5a , blue curve and blue dots) and qualitatively matched the low-pass nature of obtained from experiments ( Figure 2B , blue). The calculation demonstrates that the gain to local stimuli of superficial pyramidal neurons is primarily determined by the cellular response , suggesting that feedback network dynamics can be ignored.
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Figure 5
Model ELL-EGp network captures correlation shaping between local and global stimulation.
A , Stimulus gain of superficial BP neurons in the model (compare to Figure 2B ). Our analytical theory (solid) matches the simulation results from the ELL-EGp network (dots). B , Correlation between superficial BP neuron pairs during local and global stimulation of the model (compare to Figure 1C ). Since our theory predicts a linear relationship between output correlation and input correlation, the output is shown in units of input correlation in the local state , which was 0.1 in simulations. C , Idealized schematic illustrating the effect of feedback on shared fluctuations. Left: local inputs fail to recruit EGp feedback via deep population (see Figure 4 ), so common input arises purely through feedforward stimulus drive. Center: Low frequency global input recruits a negative image of the stimulus, which cancels the common input to the pair of superficial pyramidal neurons. Right: The cancellation signal is weak for high frequency global inputs due to the low-pass nature of the feedback. Hence, the common fluctuations are not cancelled.
The lack of network activity for local stimulation ( ), was contrasted with the recruitment of EGp feedback for global stimulation ( ). During global stimulation, we also assumed that the receptive fields of neurons were fully saturated, rather than being partially driven due to the limited extent of the stimulus, as suggested by experimental estimates [53] . We therefore increased the correlation of electroreceptor afferents in the global state, so that . Combining these two model assumptions, we expressed the gain in the global configuration, , as: (27) If – that is, if the negative feedback were a perfect replica of the feedforward signal – the stimulus gain would be zero, indicating complete stimulus cancellation by the feedback pathway. However, since the negative feedback was low-pass due to neuronal integration and synaptic filtering along the feedback pathway, only the low frequency components of the gain were strongly attenuated. Consequently, for sufficiently low frequencies ( Figure 5A , compare orange and blue curves for ). However, for high frequencies ( Figure 5A , compare orange and blue curves for ), because of the increase in receptive field saturation ( ). Our theoretical matched simulations of the ELL-EGp network ( Figure 5A , orange curve and orange dots). Thus, the combination of feedback recruitment and feedforward saturation during global stimulation captured the experimentally determined shift in stimulus gain known to occur between local and global stimulation ( Figure 2B and see [29] , [30] ).
Next, we examined how this gain shift controlled correlations across the population of superficial pyramidal neurons. Using the linear response theory we used to predict signal correlations in the experimental data ( Figure 2 , see Methods Eq. 23 ), we calculated theoretically the correlations between model pyramidal neurons. Global stimulation simultaneously increased short correlation and decreased long correlation compared to local stimulation ( Figure 5B ). These findings matched the experimental results (compare Figures 1C and 5B ) and are the primary theoretical result of this study.
Our model provides clear intuition for how the combination of receptive field saturation and the recruitment of EGp feedback during global stimulation shapes the correlation of ELL pyramidal neuron activity ( Figure 5C ). During local stimulation, EGp feedback was not recruited and the feedback did not cancel the feedforward signal from the electroreceptors ( Figure 5C , left). This case is contrasted with global stimulation, in which a broad stimulus-induced synchronization of all of the deep ELL neurons recruited a stimulus-locked EGp feedback. This feedback was low-pass, and therefore canceled the low frequency components of the signal ( Figure 5C , middle), but not the high frequency components ( Figure 5C , right). Thus, correlations due to global stimulation were canceled only for sufficiently long timescales ( Figure 5B , ). Furthermore, the saturation of the receptive field input ( ) enhanced the correlation for small ( Figure 5B , ). In total, feedforward and feedback circuitry shaped depending on the spatial profile of the electrosensory signal.
Our ELL-EGp network model distills correlation shaping into two hypotheses that link the spatial properties of an electrosensory stimulus and the timescale of pairwise correlation between the spike responses of ELL superficial pyramidal neurons:
Receptive field saturation for spatially broad signals increases the short timescale correlation between the spike trains from superficial pyramidal neurons.
Recruitment of EGp feedback by spatially broad signals decreases the long timescale correlation between the spike trains from superficial pyramidal neurons.
To study these two components of correlation shaping in isolation from one another, we used a combination of analysis on a subclass of ELL pyramidal neurons and pharmacological blockade of EGp feedback.
Correlation Shaping of nBP Neuron Responses
We first tested how short timescale correlation was affected by receptive field saturation (Hypothesis 1). The ELL has two classes of pyramidal neuron: non-basilar pyramidal (nBP) and basilar pyramidal (BP) neurons, distinguished by the extent of their basilar dendritic arbor ( Figure 3A ). While BP neurons respond to positive deflections of the electric field, nBP neurons are oppositely tuned, due to their afferent inputs arriving solely via an inhibitory interneuron population [25] . This difference in the feedforward afferent architecture to nBP neurons compared to BP neurons produces nBP neuron classical receptive fields that are smaller than those of BP neurons [26] . Despite the difference in feedforward afferent input for BP and nBP neurons, both superficial BP and nBP neurons receive near equivalent feedback from EGp parallel fibers ( Figure 3A ). Thus, a comparison between BP and nBP neurons is sensitive to a difference in feedforward afferent drive, and not to EGp feedback. We hypothesized that global inputs would not drive nBP neurons as strongly as BP neurons because of their smaller classical receptive fields. Hence, short timescale correlation during global stimulation for nBP neurons should be less than for BP neurons.
We first calculated the stimulus gain for nBP neurons. The difference in gain between local and global stimuli for nBP neurons was different than that for BP neurons ( Figure 6A1 ; [30] ). In particular, while nBP and BP neurons both exhibited a reduction in low frequency gain during global stimulation, nBP neurons exhibited little enhancement of high frequency response. Our model network replicated this difference ( Figure 6A2 ) when we assumed that the nBP neurons integrate stimuli over smaller regions of space, such that local inputs saturate the receptive field ( ), in contrast to the BP neuron case ( ). The lack of high frequency shaping of gain for nBP neurons across local and global configurations predicts that the small correlations do not substantially increase in the global state, while EGp feedback still attenuates low frequency gain and hence large correlations. Measurements of for nBP neurons in both the ELL-EGp model ( Figure 6B2 ), as well as nBP neurons recorded in vivo ( Figure 6B1 ) supported this prediction. Thus, the known differences between the receptive field sizes of nBP and BP neurons, provide evidence for the link between the spatial extent of electrosensory stimuli and short timescale correlation observed for superficial BP neurons.
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Figure 6
Saturation of the receptive field for both local and global stimuli makes short timescale response insensitive to the spatial extent of electrosensory stimuli.
A1 , Experimental stimulus gain for nBP neurons (n = 14) in local and global stimulus configurations. The gain for BP neurons in the global configuration is shown for comparison (see Figure 2B ). A2 , Stimulus gain for model nBP neurons ( ) in local and global configurations, and the model BP neurons ( ) in global for comparison. B1 , Recorded spike count correlation over windows of length for pairs of nBP neurons. As with BP neuron pairs, firing rates in the local and global states were similar ( and , respectively). B2 , Spike count correlation for pairs of model nBP superficial neurons in the ELL-EGp network. For the model results (A2,B2) our analytical theory (solid) matches the simulation results from the ELL-EGp network (dots). Values are shown in units of input correlation in the local state .
Feedback Inhibition Cancels Long Timescale Correlations
We next tested how long timescale correlation is affected by recruitment of EGp feedback by global stimuli (Hypothesis 2). In our model, the EGp feedback was responsible for the decrease in low frequency stimulus gain and long timescale correlation in the global state. To experimentally confirm that this pathway was responsible for these effects, we pharmacologically blocked feedback from EGp to superficial neuron pairs (see Methods). We first tested whether attenuation of low frequency components of the stimulus gain was removed by the block. In experiments with global stimulation, we found that firing rates during the block were decreased significantly from the control condition (block: ; control: , , paired t-test). We remark that while the net impact of EGp feedback may be excitatory, the signal locked components of EGp feedback are thought to be inhibitory [46] , consistent with our model. To correct for the change in firing rates across control and block conditions, we normalized the gain by firing rate to show the relative modulation of firing rate by the stimulus. The normalized gain increased at low frequencies, yet remained unchanged at high frequencies ( Figure 7B1 , compare orange and gray curves), consistent with model predictions ( Figure 7B2 ). This effect was removed after a washout of the drug ( Figure 7B1 , compare orange and light orange curves).
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Figure 7
EGp feedback reduces correlations on long timescales when stimuli are global.
A Schematic indicating block of feedback with CNQX in the ELL circuit. B1 , Stimulus gain for individually recorded superficial BP neurons in control, block, and recovered conditions. Gain is normalized to output firing rate in the data. B2 , Stimulus gain for model superficial neurons for global stimuli when feedback was intact or absent. C1 , Left: Spike count correlation at and 200 ms for paired recordings of superficial BP neurons. Right: Spike count correlation as a function of for individual recordings with a frozen stimulus in control, block, and recovered conditions. The standard error bars overlap for both the pre-drug and recovery curves, while they do not overlap with those for the block. Differences between control and recovered conditions could be due to incomplete drug washout or the preparation being in different states before and after the application of the drug. C2 , Spike count correlation as a function of for model neuron pairs when feedback was intact or absent. Values are shown in units of input correlation in the local state .
The spike count correlations for simultaneously recorded superficial neurons in the global state with and without pharmacological block of feedback verified its role in shaping long timescale correlations. Specifically, the spike count correlations for showed a significant increase during the block ( , paired t-test), while correlations for were similar ( Figure 7C1 ; left). Due to the difficulty in obtaining paired recordings under pharmacological blockade, we further verified our theory with units recorded individually with frozen noise in the global state with and without pharmacological block of EGp feedback ( Figure 7C1 ; right). Correlations at long timescales were increased during the block compared to control ( Figure 7C1 ; left, compare orange and gray curves) and recovered to control values after drug washout ( Figure 7C1 ; left, compare orange and light orange curves), consistent with our model ( Figure 7C2 ). Thus, despite EGp feedback being a source of common synaptic input to a pair of superficial ELL pyramidal neurons, removing it during global stimulation increased the spike correlation between the neuron pair. In total, these data supported our second hypothesis: stimuli with large spatial extent recruit inhibitory feedback that cancels the input correlation expected from feedforward afferent projections.
Correlation Shaping and Population Coding of Natural Electrosensory Scenes
We have presented a general mechanism for how spike train correlations from pairs of ELL pyramidal neurons are shaped by the spatial extent of an electrosensory signal. We explored the mechanism with simple noise signals categorized into either spatially local or global inputs. However, natural electrosensory scenes are complex, with a broad range of spatial and temporal scales. In this section, we speculate on how correlation shaping influences the population representation of natural electrosensory scenes.
Sensory systems must produce high fidelity representations of biologically relevant signals, while ensuring that distractor inputs do not contaminate the neural code. The ELL pyramidal neuron population is responsible for coding two distinct electrosensory inputs. First, electric fish routinely perform prey detection, tracking, and capture, during which prey organisms produce electric images with low frequency components ( ) that stimulate a limited portion of the animal's electroreceptive field [55] . Second, electric fish use their electric organ to communicate with conspecifics, using signals that contain primarily high frequency components ( ) and drive a large region of the skin [56] , [61] . However, these two signals often coexist with distractor inputs that the electrosensory system must ignore. Natural distractors arise from the superposition of background electric fields from many neighboring fish [62] , or self generated signals from body and tail positioning [47] . These inputs consist of mostly low to mid range frequencies ( ) and drive a broad sensory area. A critical sensory computation in the ELL is the pyramidal neuron population faithfully locking to prey and communication signals, with minimal locking to distractor electrosensory inputs. The linear response analysis of the ELL-EGp network suggests that EGp feedback to the ELL plays an important role in this computation.
Using our linear theory, we calculated the response of a population of superficial BP neurons to mixed signal and distractor input, with and without EGp feedback. The signal was either a local 4 Hz sine wave ( Figure 8A1–D1 ), or a 50 Hz global sine wave ( Figure 8A2–D2 ). In both cases, the distractor input was 0–10 Hz broadband noise. The population response was modulated by the signal and the distractor, with relative strengths determined by the corresponding gain ( Figure 8D ). To test how EGp feedback affects the coding of relevant signals, we computed the signal to noise ratio (SNR) of this population response, defined as the ratio of the signal power integrated over all frequencies to the distractor power integrated over all frequencies. For both the 4 Hz local and 50 Hz global signals, the SNR was greater with feedback than without feedback ( Figure 8B,C . SNR decreased from 2.3 to 0.70 for the 4 Hz local signal and from 2.8 to 0.70 for the 50 Hz global signal when feedback was removed). This is because EGp feedback was recruited by distractor input, attenuating any distractor induced correlation (low gain for distractor inputs in Figure 8D1,D2 ). In contrast, prey inputs lacked sufficient spatial power to recruit EGp feedback, meaning an EGp cancellation signal was not passed and ELL population stimulus gain was high ( Figure 8D1 ). Communication calls have large spatial power, yet their high frequency power cannot be transmitted by the low pass parallel fiber pathway, again meaning ELL population stimulus gain was high ( Figure 8D2 ). The ELL-EGp network was therefore capable of removing spurious correlations due to distractors while still coding for relevant signals.
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Figure 8
EGp feedback cancels the ELL population response to global distractor inputs but not prey or communication signals.
A1 , Schematic of response to a prey signal, which occupies a limited spatial extent and contains power at low frequencies. A2 , Schematic of response to a communication call from a conspecific, which is a global, high frequency signal. B1 , Average population firing rate for ELL neurons responding to a local, 4 Hz signal (red) and the same signal with 0–10 Hz distractor noise (black). The SNR was 2.3. B2 , Same as B1, but with a global, 50 Hz signal. The SNR was 2.8. C1 , Same as B1, but without EGp feedback. The SNR was reduced to 0.70. C2 , Same as B2, but without EGp feedback. The SNR was reduced to 0.70. D1 , ELL pyramidal neuron stimulus gain for local inputs (which do not recruit feedback) and global inputs with and without feedback. The frequency of the signal is marked. Note that because the distractor is a global 0–10 Hz signal, its transfer will be enhanced by the removal of feedback, reducing SNR (compare gray and orange curves). D2 , Same as D1 but with a global, 50 Hz signal. Since the signal is high frequency, its stimulus gain is not substantially affected by feedback.
Discussion
Temporal shaping of correlated spiking activity has been observed in a variety of systems [7] , [9] , [19] , [22] – [24] . We have shown that the spatial extent of an electrosensory signal controls the timescale of correlation between the spiking outputs of principal neurons in the ELL of weakly electric fish. Specifically, an increase in the spatial extent of a signal increased pairwise spike time synchronization, while simultaneously decorrelating long timescale rate co-variations. Using a combination of computational modeling and targeted physiological analysis, we identified that correlation shaping in the ELL is mediated both by an increase in the strength of feedforward afferent drive and the recruitment of a feedback pathway for spatially broad signals. Electric fish offer a neuroethologically inspired functional context for correlation shaping, where it promotes an accurate population representation of relevant signals, even in the presence of distractor inputs. The generic circuit features that support correlation shaping and its use in feature selective population temporal codes suggest that the basic principles exposed here may be at play in other neural systems.
Correlation Shaping with Neural Architecture in the Electrosensory System
There has been extensive investigation of the gain shifts of single ELL pyramidal neurons between local and global stimulus configurations [26] – [30] , [46] . These studies have shown that both feedforward and feedback mechanisms mediated these shifts. Indeed, pharmacological manipulations of descending feedback to the ELL provided strong evidence for its role in controlling gain shifts of single unit response at low frequencies [27] , [29] , [30] , [46] . However, previous studies have shown that local stimuli only excited a fraction of the receptive field center [26] , [29] and that spatial saturation of the receptive field center mediated the gain shifts of single unit response at high frequencies only by recruiting a greater fraction of feedforward afferent input [29] , [30] . This importance of feedback activity prompted network models of the ELL and higher brain regions, and these models captured the sensitivity of single unit dynamics to the spatiotemporal structure of electrosensory stimuli [27] , . However, the models relied on heretofore untested assumptions about the population spike train statistics of ELL pyramidal neurons. In parallel to these single-unit studies, other work presented simultaneous recordings from pairs of ELL pyramidal neurons showing significant stimulus evoked correlation in spike activity [63] , and that the spike train correlation is sensitive to a stimulus' spatiotemporal structure [37] . However, these studies did not attempt to relate the dependence of pairwise statistics on stimulus structure to the extensive ELL single neuron experimental gain and network modeling literature. Our study merges the two avenues of research and shows that pairwise correlation shaping is related to gain shifts, as our linear response treatment of the ELL-EGp network model predicts. Thus, our analysis directly tests the proposed feedback mechanisms for single neuron response shifts [30] .
Previous studies of the ELL have focused on the generation of oscillations due to feedback from area nP to pyramidal neurons (the direct feedback pathway) [27] . Theoretical studies have demonstrated that such oscillations arise from a combination of spatially correlated noise and delayed inhibitory feedback [28] , [31] . Unlike neurons receiving closed-loop inhibitory feedback from nP, the superficial pyramidal neurons modeled in our study lack input from this direct pathway, and hence do not exhibit oscillations. Superficial neurons were excluded from the analysis in [27] and [28] , so that the results of our study concern a cell class that is distinct from these previous studies. This distinction emphasizes the qualitative differences in the dynamics induced by open- and closed-loop feedback pathways.
We used well-characterized anatomical data and pharmacological manipulation to study the network architecture that codes for time-varying electrosensory stimuli. This is in contrast to techniques such as the generalized linear model [64] that statistically determine the spike response and network filters that generate a response to a sensory signal with fixed statistics. Our approach allowed us to study the response of the system in distinct stimulus conditions, with varying input statistics. Further, network coupling suggested clear architectural predictions for the mechanisms behind correlation shaping (hypotheses 1 and 2). These predictions were validated with a combination of the known heterogeneity of ELL feedfoward architecture ( Figure 3 ), and a pharmacological blockade of feedback activity ( Figure 7 ). Organisms exist in environments with ever-changing sensory statistics yet must code these environments, often with a single neural population. Our study shows how neural architecture can help shift the response dynamics of neural populations as signals change to better meet this computational need.
Our results also highlight how architectural differences may lead to differential population activity in different layers. Recently, it has been shown that synchronization between neurons in visual cortex is layer-dependent [65] . Furthermore, the cognitive demands of a task may control the recruitment of feedback and influence spike train correlations [66] . Our results demonstrate that both layer-specific recruitment of feedback and connectivity profiles influence correlated population activity.
Finally, theoretical communities have recently made some progress in understanding how network architecture combines with cellular dynamics to determine the correlation between pairs of cells [44] , [67] , [68] . However, the work is general, and a clear neural motivation to base a concrete example upon is lacking. Our study demonstrates that the electrosensory system offers a prototypical system where cellular dynamics, a clear feedforward/feedback architecture, and a single stimulus feature (spatial extent) interact to shape the temporal structure of pairwise spike train correlation.
Decorrelating with Inhibition
The role of inhibition in neural circuits is a complex topic of study. Inhibition is linked to rhythmic, temporal locking between pairs of pyramidal neurons [32] . On fast timescales, inhibition is often thought to synchronize the activity of pairs of pyramidal neurons in both recurrent [10] , [27] , [33] – [35] and feedforward architectures [11] , [32] . However, on longer timescales, inhibition mediates competitive dynamics between populations of pyramidal neurons, and as such may be a source of anti-correlated activity [24] . Recently, studies of densely coupled cortical networks with balanced excitation and inhibition [17] , [18] and feedforward inhibitory cortical circuits [20] , [69] have provided new insights into the role of inhibitory dynamics. In these studies, fluctuations in correlated excitation to a pair of pyramidal neurons are cancelled by correlated inhibitory dynamics, yielding a roughly asynchronous cortical state. This cancellation of correlation is similar to the one explored in our study responsible for the reduction of correlation for global stimuli. However, our study was motivated by a primarily feedforward sensory architecture in which an external signal can drive correlated activity.
The strengths of the electrosensory preparation allowed us to extend the correlation cancellation mechanism along two important directions. First, the ease in controlling the spatiotemporal properties of external stimuli allowed an analysis of the limitations of correlation cancellation. The diffuse ELL EGp feedforward path restricts correlation cancellation to signals with broad spatial scale, while the slow filtering by the parallel fiber pathway can only cancel correlations of low frequency stimuli. Second, the well segregated parallel fibers that mediate EGp feedback to the ELL permitted a pharmacological blockade of inhibition, directly providing evidence for correlation cancellation. The parallel fibers are a source of common input to pyramidal neurons, and a naive analysis would predict that their removal would thus decrease pyramidal neuron spike train correlation. Nevertheless, the blockade of parallel fiber inputs increased the spike train correlation, suggesting that the common inhibition interacts with the common feedforward afferent input in a destructive, rather than cooperative, manner.
Studies of neural codes often investigate the distinction between signal evoked, across-trial correlations and additional ‘noise’ induced, within-trial correlations [60] . Across-trial correlations are attributable to a dynamic locking of the spike train pairs to the common signal. Within-trial correlations measure the trial-to-trial co-variability of a pair of spike trains and may be increased relative to across-trial correlations due to common synaptic input to the neuron pair. These common fluctuations are often deleterious to cortical population codes [60] , acting as a source of variability that cannot be removed through population averaging. The majority of our study presented simultaneously recorded spike train data which contains across-trial correlation as well as additional within-trial correlation. However, the shaping of correlation by the spatial profile of a stimulus was explained from knowledge of only of the across-trial correlation ( Figures 1 and 2 ), and thus our ELL-EGp network model ignored other sources of correlation entirely. Our analysis did study the effects of irrelevant distractor inputs which can act as a source of noise, though originating from external signals rather than internal circuit mechanisms. We found that low frequency distractors that drive a substantial portion of the network recruit a cancellation signal. We therefore predict that within-trial correlations may be cancelled by a similar mechanism if they drive a large number of neurons synchronously. This may be the case when within-trial correlations are driven by the local field potential, which is often low frequency and widespread to populations of neurons [70] , [71] .
In summary, we have identified the combination of feedforward and feedback architecture that allows the spatial extent of a stimulus to shape the temporal correlations between the spike trains of pairs of electrosensory principal cells. Furthermore, correlation shaping allows populations of neurons to respond to stimuli that match a specific spatiotemporal profile and ignore distractor inputs. The generic architectural features of our network and the fact that sensory systems must filter irrelevant signals suggest that our findings may generalize to other systems.
Supporting Information
Table S1
Model summary.
(PDF)
Text S1
Computation of cellular response function and power spectrum.
(PDF)
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Introduction
Foreseeably, past generations of patients have used their physicians as the key source of health related information, however, there is evidence that people are increasingly turning to the Internet to supplement their information needs [ 1 ]. For example, a Swedish study found that just over three-quarters (76.2%) of people diagnosed with cancer accessed the Internet for cancer-related information and more than one quarter used social media relating to their health [ 2 ]. Patients commonly report using webpages, blogs, interactive forums and social media to obtain information to help make informed decisions, find practical information or answers to health related questions, stay in touch with others, and share experiences [ 2 , 3 ].
Social media has become ubiquitous to our lives where we share, connect and communicate our experiences with friends, family, organisations and people otherwise unknown to us. Worldwide, approximately 2.5 billion people use social media and almost two-thirds of American adults use social networking sites: an almost ten-fold increase over the past decade [ 4 ]. The portability of these websites via mobile applications has no-doubt accelerated their uptake and allows for the capture of life’s most ephemeral events. The differences in user demographics that are seen between platforms (such as age, ethnicity, gender, education or income), lend themselves to being targeted for various health campaigns, health promotions or health research seeking to reach different audiences [ 5 , 6 ]. Social media data collection foreseeably provides large-scale and easily accessible data for patient reported information, particularly when compared with traditional patient-focused data collection methods [ 7 ].
One of the most popular social media platforms, Instagram, has almost one billion active monthly users [ 8 ]. Instagram is differentiated from other social media platforms by user-posts’ being dominated by a photo. Most users choose to add accompanying text to their photos as well as tags or labels, termed ‘hashtags’ denoted as # label , (e.g. #cancer). The accompanying hashtags provide a method of grouping photos to create virtual social communities of similarly themed content or purpose and allows users to easily connect and share content.
Acute myeloid leukaemia (AML) is a relatively rare and aggressive blood cancer that can occur at any age [ 9 , 10 ]. The standard treatment is immediate intensive chemotherapy, requiring lengthy hospital stays [ 11 ]. Additionally, research shows most patients have a reduced quality of life and persistent side effects or symptoms even after the completion of therapy or in remission [ 12 – 14 ].
AML makes up less than 1% of all cancer diagnoses per year, making research challenging to accrue participants, particularly in young adulthood where incidence is at its lowest [ 9 , 10 ]. However due to the popularity of Instagram, particularly in early adulthood [ 15 ] and the search functionality of hashtags, the Instagram platform presents an opportunity for proposing unique research questions, particularly those focused on rare-diseases (as with AML), or research with participants that are traditionally difficult to access. Despite the popularity of Instagram, and the unique participant group it can reach, little health research has been undertaking using this platform [ 1 ].
Given large numbers of people with cancer are accessing online health-related messages and the relative absence of Instagram research, this exploratory study will be the first to characterise AML-related content on Instagram; specifically who is posting AML-related content and what types of content are being posted. Characterising AML-related content on social media could be useful for targeting people most likely to benefit from health messages, interventions, or support. Using Instagram for this type of extant research has the potential to provide unique insights into the lived experience, as well as observing individuals providing or receiving support through virtual communities and the sharing of health-related information. Additionally we detail a method potentially of interest to other researchers.
Materials and methods
The methods outlined in this paper adhered to Instagram’s terms of service at the time of the research and all the content analysed in this study was publicly available on Instagram (available at www.instagram.com/instagram ). However the data is not owned by the authors and they do not have permission for reproduction of the data used in the analysis. Ethical approval for the study was granted from Monash University, Human and Research Ethics Committee (Project ID 18540) and included a waiver of consent (no individual consent was necessary from Instagram users). The ethics approval prohibits the publication of data that may inadvertently identify any individual.
Data collection
Instagram is primarily a mobile application but has a desktop website with limited functionality. Only the website accessible version of Instagram was used in this study, to ensure complete separation between the researchers and their private accounts. This also ensured that only publicly available posts were being accessed (no Instagram account or login required). One hundred posts were chosen as a convenient number for a time-consuming manual exploratory method. The posts were found by using Instagram’s search bar at the top of the webpage using hashtags that had been previously scoped as being used by people with AML: #acutemyeloidleukemia, #acutemyeloidleukaemia and #amlsurvivor. Each hashtag was searched for separately.
Posts were excluded from the study if they were videos (we were unable to extract these using our context extraction method), had non-English accompanying text or the subject matter was focused on children’s cancer. Children’s cancer was able to be determined by accompanying hashtag, such as #childhoodcancersucks or though examining the post photo and/or the accompanying text.
This study was undertaken after Instagram removed the automatic application program interface (API), which allowed for automation in downloads and much of the accompanying meta-data. Therefore, we detail a manual method of data extraction that may be of use to other researchers. This manual method allowed for retrospective capture for all eligible posts made over seven consecutive days in February 2019; eight consecutive days in April 2019 and twelve consecutive days in May 2019, to obtain a consecutive sample of 100 posts during the collection periods. Posts in chronological order (as opposed to most popular) can be found by scrolling past the initial “top posts” to the “most recent”. It is was these most recent posts that were accessed taking note of the date of the post to ensure it complied with our sampling time-frame. This sampling method was used to avoid awareness campaigns or trending content, (which may generate atypical Instagram posts and traffic), cultural and ethnic influences between users’ geographical location and for researcher convenience. One hundred posts was deemed to be sufficient given practicality of methods employed and the rarity of AML for an initial exploratory study.
As a user can modify or delete the content or their Instagram account, a screenshot was taken of the post and the user profile, thereby creating a ‘post-record’, which became the main unit of analysis. The post-record was made using Microsoft Word. We analysed the content of a post to include both the photo and the accompanying text and hashtags but excluded subsequent comments (and hashtags) that were made by either the ‘post-owner’ or other users.
For each post, basic data points were gathered about the user and the post: age and gender of the user (self-reported in the user profile), and country of origin data by using the location specified as part of the post or contained in the user profile, as well as post-specific information (description of the photo/s, accompanying text and hashtags and the number of likes and comments etc.). We also captured the username, but as duplicates became apparent, we adopted our identification system for each post to be able to distinguish between different posts from the same users.
Whilst the Instagram posts are publically available the data cannot be reproduced to comply with the Instagram terms of service, comply with the ethical approval of this study and to protect the privacy of the individuals posting on Instagram.
Data analysis
We used an adapted mixed-method social network model to frame our analysis [ 16 ]. The model describes sourcing data (Instagram), constructing the data (organising and preparing for analysis) and analysing the data (using network analysis or linear modelling). The framework was appropriate as the study was exploratory and observational and employed a content analysis, however it was modified as we did not employ the network analysis or linear modelling. Fig 1 demonstrates our approach.
10.1371/journal.pone.0250641.g001
Fig 1
Method process, adapted from a mixed methods social network analysis framework [ 16 ].
The content analysis is ideal for exploratory research, as this method seeks to unobtrusively explore the explicit description of the communication and the trends, patterns and frequency of this communication found within data [ 17 , 18 ]. No a priori coding was developed owing to an absence of literature relating to the content of AML on Instagram. An inductive approach was employed to identify frequently occurring content categories and themes [ 18 ].
In brief the process included open coding, creating higher headings and then categories. After reviewing the post records multiple times, open codes were developed in a consultative and iterative process of reviewing the first ten post-records, at which time an open coding scheme was generated, that we thought could be applied to the whole data set [ 19 ]. A further ten posts were classified according to our coding scheme and codes were refined as necessary. The first initial ten posts were re-coded as per this scheme. This process was repeated twice, until we had a open coding scheme (after coding 40 posts) that could be applied to the entire data set. Higher order headings were then able to be developed from the open codes using researcher interpretation as to which codes belong in each higher order heading and then into categories ( Fig 2 ) [ 20 ]. The process was undertaken by two reviewers and any discordance in coding between reviewers was discussed to reach consensus [ 18 ].
10.1371/journal.pone.0250641.g002
Fig 2
Process of generating categories.
After the process was finished and the researchers were reflecting on the findings, we went back and coded for a single theme: ‘hope and/or gratitude’, as the researchers felt that even though this was outside the content analysis it was an interesting finding relevant to the research. This theme was based on the researchers’ interpretation of the image and accompanying text.
Most data were expressed as both means with standard deviations, medians with interquartile ranges (IQRs), as well as frequency and range because the content and distribution varied considerably. Microsoft Excel was used for these descriptive statistics.
Results
During the search period, almost all posts were found using #acutemyeloidleukemia (94%). During the window of analysis, 51 unique users posted content and 16 of these posted more than once resulting in the analysis of 100 posts, consisting of 138 photos—one post can contain up to 10 photos.
Age and gender were mostly unavailable. Only two profiles stated age but we have chosen to conceal this for re-identification protection. Gender was rarely specified in the user profile, and we deemed it unreliable to discern gender either, from self-description (e.g. mom, wife etc.), appearance or socially gender-normative names, so this has not been reported. As shown in Table 1 , we were mostly unable to determine the country of the post origin for most users (34/51).
10.1371/journal.pone.0250641.t001
Table 1 Country of post origin of the post or user account (n = 51).
Country
Frequency n (%)
United States
11 (22)
Canada
1 (2)
United Kingdom
3 (6)
Hong Kong
1 (2)
Malaysia
1 (2)
Unknown
34 (66)
We identified three user categories from the data: patient personal stories, personal support networks and professional organisations ( Table 2 ). The most frequent users were patients themselves (66% of the posts), followed by personal support networks that we interpreted as family and friends (24% of the posts) and lastly professional organisations (10% of the posts).
10.1371/journal.pone.0250641.t002
Table 2 Frequency of posts and photos in each user category.
User categories
Number of posts (n = 100) n (%)
Number of photos (n = 138) n (%)
Patient personal stories
66 (66)
99 (72)
Personal support networks
24 (24)
26 (19)
Professional organisations
10 (10)
13 (9)
As shown in Table 3 , the most frequent content posted in the analysis was patients communicating their health update (31% of the whole sample). The majority of posts made by personal support networks was a health update on behalf of a patient (50% of the personal support networks user category). Professional organisations only accounted for 10% of the total sample and the majority of the content was either patient information provision (40% of the posts) or raising disease awareness (50% of the posts).
10.1371/journal.pone.0250641.t003
Table 3 The content classification frequency by user category and content classification.
User category
Content classification
Frequency of posts for each user category n (%)
Frequency of content classification for whole sample (n = 100) %
Patient personal stories (n = 66)
Personal health
31 (47)
31
Reflection
24 (36)
24
Self-care
11 (17)
11
Personal support networks (n = 24)
Someone else’s health
12 (50)
12
Remembrance
4 (17)
4
Raising awareness
8 (33)
8
Professional organisations (n = 10)
Information provision for patients
4 (40)
4
Raising awareness
5 (50)
5
Other
1 (10)
1
The 10 organisational posts comprised of seven users and thirteen photos. Five of the seven users had an unknown country of origin, while one was based in the United States and the other in the United Kingdom as discerned from their profile or dot-org websites.
One-quarter of all posts detailed symptoms that were being experienced by patients and 19/25 posts containing symptoms came from patients with the remaining posts being made by personal support networks. Please note to the protect privacy of individuals (for example via reverse identification), the quotes chosen below have been altered to encompass the overall sentiment of the quote [ 21 ].
“#selfie #nofilter long term chemotherapy effects have mostly subsided. Still can’t shake that #red eye…” (picture of a person smiling into the camera). Patient personal story .
“…Hubby had platelets to fix his bleeding gums…” (picture of a person sitting upright in bed, surrounded by medical equipment) Personal support networks .
Likes and comments were used as a proxy measure for engagement ( Table 4 ). Overall there was between three and 394 likes and between zero and 54 comments. There was little engagement with organisational posts as measured by ‘likes’ and comments. There were between eleven and 41 likes on the posts and five posts had no comments.
10.1371/journal.pone.0250641.t004
Table 4 Engagement with posts by user category as measured by likes and comments.
User category
Likes
Comments
Mean (SD)
Median (IQR)
Range
Mean (SD)
Median (IQR)
Range
Patient personal stories
67.41 (68.61)
36.5 (51.5)
251
7.47 (10.16)
4 (8.75)
54
Personal support networks
79.71 (41)
91.87 (52.75)
391
5.58 (7.9)
3 (4.75)
31
Professional organisations
28.9 (8.64)
31 (12.75)
30
1 (1)
1 (1)
11
All posts
66.51 (73.59)
35 (50)
391
6.43 (9.35)
3(6)
54
Additionally, throughout the analysis, we noticed a prominent theme of hope often accompanied by gratitude, in the posts, either implicitly but commonly through the use of the accompanying text or hashtags (e.g. #gratitude or #grateful or #thankyou or #hopeful). Almost half (49%) of all the posts demonstrated this theme hope and/or gratitude. Thirty-four of these were made by the user category of patient personal stories, eleven by personal support networks and four by professional organisations.
“…Each day has something good in it, even on the toughest of days…” (Image of a motivational meme) Patient personal story .
Discussion
While much of the social media cancer communication research has focused on Facebook and Twitter, very few studies have focused on Instagram, particularly with a focus on such an emotionally and physically burdensome cancer like AML. Instagram differs from Facebook and Twitter by incorporating visual cancer communication and to our knowledge this is the first study to describe the content of Instagram communication concerning AML, thereby addressing this research gap. The novel method we have outlined is most useful for other investigators looking to utilise social media in the their research and our findings should be considered in the context of the limitations of our methods.
Our exploratory descriptive research showed in our sample, that people with AML communicating personal health updates, was the most common content being posted about AML on Instagram. Personal story sharing related to AML was also prominent by the personal support networks user category of people with AML. This finding was congruent with other Instagram disease-related research [ 3 , 22 , 23 ].
Why people tell such personal stories through Instagram may be explained by social media use being linked with patient empowerment through improved self-management and enhanced psychological and subjective well-being [ 1 , 3 ]. These benefits may be obtained through real or perceived social connectedness of users of Instagram where they feel a sense of intimacy through sharing or social support, through community [ 24 – 26 ]. By posting intimate stories, users may also provide and receive social and emotional support through these virtual online communities [ 23 ]. This is further supported by the high prevalence of hope and/or gratitude in our data, where Steffen et al found in a study of advanced lung cancer patients, that hope may be important in providing support to social and role functioning, irrespective of physical symptom severity [ 27 ]. In sentiment analysis, Cho and colleagues also found hope was the most commonly expressed emotion in their melanoma study [ 23 ]. Whether hope is a common finding on social media contained to people with a malignant disease remains unknown.
In contrast to a Facebook content analysis including breast, prostate and other reproductive cancers, Instagram users concerned with AML do not appear to be information seeking, which may be due to the inherent functionalities of the platform [ 28 ]. This means that health professionals, researchers and professional organisations should endeavour to tailor their communication respectively to the most appropriate platform. However, if users are predominately seeking or providing support through personal storytelling, Instagram presents an opportunity for health providers and other organisations tasked with awareness-raising or support and wellbeing. Furthermore, it is likely patients and their friends and family are highly motivated to sustain the engagement with cancer communication initiated by reputable professional organisations [ 28 , 29 ]. It is worth noting, we were unable to identify any health providers (individually or part of a health facility) posting during our data extraction period. The content of what patients communicate via social media outside the immediate doctor-patient consult provides an unique viewpoint unhindered by bustling waiting rooms or the interpretation of clinicians, to contextualise patient experience and decision making [ 7 ]. In our study, only about 10% of posts were organisational suggesting that Instagram may represent an untapped resource for cancer support communities and awareness campaigns. This suggestion possibly holds relevance for all cancer types. Furthermore, public awareness is particularly relevant for malignant haematological diseases where up to 70% of patients need to seek bone marrow transplant donors outside of their family and only 7% of the American population are registered bone marrow donors [ 30 ]. Increasing public awareness through emotional appeal and capitalising on hope as a concept, may increase the number of registered donors to ensure sufficient diversity in the donor pool to meet the patient demand for bone marrow transplant [ 31 , 32 ].
Social media research can complement other research methods: Crawford et al used YouTube to complement a literature review about the patient experience of haematological malignancies and found that YouTube provided supplementary information that highlighted the multifactorial experiences of patients that may not have been otherwise apparent through traditional research methods [ 7 ].
Certainly some individual healthcare professionals can and are using social media. A recent Italian study of neurologists showed that 56% of the sample used social media to have direct contact with patients and most of these health professionals were in favour of this communication method [ 33 ]. Instagram may provide an opportunity for clinican-led content that is trustworthy and appeals to patients, yet clinician-led social media posts are lacking, yet [ 34 ]. Moorhead et al. [ 35 ] suggests that both health professionals and their patients may need training to maximise the use of social media in their healthcare interaction. However, as yet it remains unknown how effective social media can be in its’ perceived role in healthcare [ 35 ] and how this applies to inherently passive platforms such as Instagram where interactivity between user and viewer is limited.
Given the popularity of Instagram and the potential reach of posts, further research is warranted to understand the implications of online visual communication and how this information can be harnessed to improve health communication, patient experience and the experience of healthcare and balancing this with minimising the perpetuation of misinformation to vulnerable individuals.
Our study is not without limitations: critics rightfully observe that Instagram is often curated and may not reflect real life—experiences are complex and Instagram is a snapshot in time. Additionally, our sample may not reflect the breadth of posts due to our sample size, which was limited by the practicality of employing a manual method and resourcing. The manual method we employed and limitations in the search function also meant the study was unable to capture videos and Instagram stories (which are only available for 24 hours from posting). The sample used in this study had many users posting multiple times, potentially meaning our results may be less diverse and biased towards fewer individual experiences of AML. The retrospective nature of this study only allowed for the capture of data about age, gender and location that the user chose to share and it is therefore unknown whether there are dominating age groups, gender or country of origin in our analysis.
The strengths of this study are that we have demonstrated a unique and innovative way to potentially reach and/or observe hard to reach populations or people suffering rare conditions. Additionally, photos are a unique and expressive medium not conventionally used in cancer support services so other researchers with appropriate research question could also choose to employ an interactive image-based study design.
Conclusion
This exploratory study, presents a novel method whereby we have characterised AML-related Instagram content that contributes to the understanding of how social media fits into the lives of people affected by AML. Our results suggest that social media may have a role to play particularly for social connectedness and support and that there is a potential role to play for health professionals and health organisations.
Further research should focus on exploring the feasibility and effectiveness of targeted awareness campaigns, as well as deploying support networks or health interventions to aid people by providing or seeking support.
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Introduction
The c-MYC proto-oncogene encodes a transcription factor that plays a central role in cell proliferation, differentiation, apoptosis, metabolism, and survival [ 1 , 2 ]. It can promote tumorigenesis in a variety of human malignancies [ 3 , 4 ]. c-MYC alteration occurs through various mechanisms, including chromosomal translocation, gene amplification, and perturbation of upstream signaling pathways [ 5 , 6 ]. Gene copy-number (GCN) gain or amplification is the most common c-MYC alteration in solid tumors [ 7 ].
Nevertheless, few studies have examined the clinicopathological implications of c-MYC status in colorectal cancer (CRC). Previous reports have shown that c-MYC GCN gain in CRC is found in approximately 10% of patients [ 8 ]. A recent study reported that several significant amplifications were focused on chromosome 8, including the 8q24 region which contains c-MYC , and suggested that c-MYC was a new marker for aggressive disease in CRC [ 9 ]. However, more recently, Christopher et al . reported data obtained by immunohistochemistry (IHC), indicating that c-MYC protein overexpression was significantly associated with improved prognosis in CRC patients [ 10 ]. Consequently, the prognostic value of c-MYC alterations in CRC is controversial.
Recently, the range of options for systemic chemotherapy has expanded and targeted therapy has been used in advanced CRC patients, increasing patient survival [ 11 ]. However, some CRC patients respond poorly to targeted therapy despite showing positive results in targeted therapy-specific mutation studies [ 12 ]. Tumor heterogeneity is a potential cause for failure of targeted therapy and several studies have reported that CRC possess a heterogenic genotype including KRAS , p53 , and BRAF [ 13 – 15 ]. Therefore, genetic variation between the primary tumor and corresponding metastatic sites needs to be clarified to improve the management of CRC patients with metastatic disease.
The heterogeneity of c-MYC and its prognostic implications have not been systematically studied in primary CRC patients. The aim of this study was to evaluate c-MYC gene status and its clinical significance in CRC. We also analyzed the heterogeneity of c-MYC in the primary tumor and distant metastasis.
Materials and Methods
Patients and samples
A total of 519 CRC patients treated with radical surgery at Seoul National University Bundang Hospital were enrolled in this retrospective study. First, to evaluate the clinicopathologic significance of c-MYC gene status, 367 consecutive CRC patients treated between January 2005 and December 2006 were enrolled (cohort 1). Second, to investigate the discordance between the primary and metastatic tumors, 152 advanced CRC patients with synchronous or metachronous metastasis who had undergone surgical resection for primary CRC between May 2003 and December 2009, were enrolled (cohort 2). All the cases were reviewed by two pathologists (K. S. L. and H. S. L.). The clinicopathological characteristics were obtained from the patients’ medical records and pathology reports. Follow-up information including patient outcome and the interval between the date of surgical resection and death was collected. Data from patients lost to follow-up or those who had died from causes other than CRC were censored.
Ethical statement
All samples were obtained from surgically resected tumors examined pathologically at the Department of Pathology, Seoul National University Bundang Hospital. All samples and medical record data were anonymized before use in this study and the participants did not provide written informed consent. The use of medical record data and tissue samples for this study was approved by the Institutional Review Board of Seoul National University Bundang Hospital (reference: B-1210/174-301).
Tissue array method
Surgically resected primary CRC specimens were formalin-fixed and paraffin-embedded (FFPE). For each case, representative areas of the donor blocks were obtained and rearranged into new recipient blocks (Superbiochips Laboratories, Seoul, South Korea) [ 16 ]. A single core was 2 mm in diameter and those containing > 20% tumor cells were considered valid cores.
Dual-color silver in situ hybridization
The c-MYC gene was visualized by using a blue-staining system (ultraView silver in situ hybridization [SISH] dinitrophenol [DNP] detection kit and c-MYC DNP probe, Ventana Medical Systems, Tucson, AZ, USA). The centromere of chromosome 8 (CEP8) was visualized by using a red-staining system (ultraView red ISH digoxigenin [DIG] detection kit and chromosome 8 DIG probe, Ventana Medical Systems). Positive signals were visualized at 60 × magnification and counted in 50 non-overlapping tumor cell nuclei for each case ( Fig 1 ) [ 17 ]. Small and large clusters were scored as 6 and 12 signals, respectively.
10.1371/journal.pone.0139727.g001
Fig 1
Representative figures of c-MYC status detected by dual-color silver in situ hybridization (A and B) in colorectal cancer patients.
(A) c-MYC gene copy number gain (60 × magnification); (B) c-MYC gene disomy (60 × magnification).
Immunohistochemistry
IHC analysis of c-MYC was carried out using a commercially available rabbit anti-c-MYC antibody (clone Y69, catalog ab32072, Abcam, Burlingame, CA, USA). The staining procedures were carried out using the ultraView Universal DAB kit (Ventana Medical Systems) and an automated stainer (BenchMark®XT, Ventana Medical Systems), according to the manufacturer’s instructions. Nuclear immunostaining of c-MYC was negative in normal mucosa. For statistical analysis, c-MYC nuclear staining of any intensity in greater than 10% of neoplastic cells was scored as positive ( S2 Fig ) [ 10 ].
Microsatellite instability
Microsatellite instability (MSI) was assessed in CRC cases with available tissue. MSI results were generated by comparing the allelic profiles of 5 microsatellite markers (BAT-26, BAT-25, D5S346, D17S250, and S2S123) in the tumor and corresponding normal samples. Polymerase chain reaction (PCR) products from the FFPE tissues were analyzed using an automated DNA sequencer (ABI 3731 Genetic Analyser, Applied Bio systems, Foster City, CA, USA) according to the protocol described previously [ 18 ].
KRAS mutation analysis
KRAS mutation detection was achieved by melting curve analysis using the cobas 4800 System (Roche, Branchburg, NJ, USA) with automated result interpretation software. This is a TaqMelt-based real-time PCR assay designed to detect the presence of 21 KRAS mutations in codons 12, 13, and 61. The workflow and testing process have been described previously [ 19 ].
Statistical analyses
The association between the clinicopathological features and c-MYC status was analyzed using the chi-square or Fisher’s exact test, as appropriate. The correlation between the detection methods was examined using the Pearson correlation coefficient. The patients’ survival was analyzed by using the Kaplan-Meier method and the log-rank test was used to determine if there were any significant differences between the survival curves. Univariate and multivariate regression analysis were performed by using Cox’s proportional hazards model to determine the hazard ratio and 95% confidence intervals for each factor. A P- value < 0.05 was accepted as statistically significant. All statistical analyses were performed using the SPSS statistics 21 software (IBM, Armonk, NY, USA).
Results
c-MYC gene status and clinical implications for consecutive primary CRC patients
In consecutive primary CRC cases (cohort 1), the median c-MYC :CEP8 ratio was 1.29 (range, 0.58–5.17). c-MYC gene amplification, defined by a c-MYC :CEP8 ratio ≥ 2.0 and similar to that established for HER2 [ 20 ], was detected in 31 (8.4%) of 367 patients. The mean c-MYC GCN was 2.88 (range, 1.22–13.12). In the present study, we defined the GCN gain as ≥ 4.0 c-MYC copies/nucleus [ 21 ], and this was detected in 63 (17.2%) of 367 CRC patients. All c-MYC amplification was included in c-MYC GCN gain. A c-MYC GCN gain ≥ 4 had the lowest P -value ( P = 0.015) and thus, was observed to be the most predictive cut-off point for patient prognosis ( Fig 2 ); ≥ 5.0 c-MYC copies/nucleus also influenced patient prognosis ( P = 0.026). There was no significant association between patient prognosis and either c-MYC amplification ( P = 0.149) or > 2, ≥ 3, and ≥ 6 c-MYC copies/nucleus ( P = 0.752, P = 0.175, and P = 0.122, respectively).
10.1371/journal.pone.0139727.g002
Fig 2
Kaplan-Meier survival curves illustrating the prognostic effect of c-MYC status in colorectal cancer (cohort 1).
(A) c-MYC gene copy number (GCN) gain; (B) c-MYC GCN gain in the stage II-III subgroup; (C) c-MYC amplification.
Table 1 shows the relationships between c-MYC status and the clinicopathological parameters in consecutive primary CRCs (cohort 1). Amplification of c-MYC correlated with early-stage disease ( P = 0.039). c-MYC GCN gain was frequently observed in sigmoid colon and rectum tumors ( P = 0.034), small tumors ( P = 0.041), and those classified as microsatellite stable or MSI-low ( P = 0.029).
10.1371/journal.pone.0139727.t001
Table 1 The association between clinicopathological parameters and c-MYC status in 367 CRC patients (cohort1).
Total
c-Myc
P -Value
c-Myc
P -Value
c-Myc IHC
P -Value
4 > GCN
4 ≦ GCN
Non-amplification
Amplification
Negative
Positive
Age
0.983
0.383
0.537
mean
64.2
64.2
64.2
64.1
66.0
64.6
63.9
Sex
0.740
0.619
0.431
male
205
171 (83.4%)
34 (16.6%)
189 (92.2%)
16 (7.8%)
89 (43.4%)
116 (56.6%)
female
162
133 (82.1%)
29 (17.9%)
147 (90.7%)
15 (9.3%)
77 (47.5%)
85 (52.5%)
Location
0.034
0.437
< 0.001
Rectum/sigmoid
237
189 (79.7%)
48 (20.3%)
121 (93.1%)
9 (6.9%)
90 (38.0%)
147 (62.0%)
others
130
115 (88.5%)
15 (11.5%)
215 (90.7%)
22 (9.3%)
76 (58.5%)
54 (41.5%)
pT stage
0.692
0.571
< 0.001
0–2
58
47 (81.0%)
11 (19.0%)
52 (89.7%)
6 (10.3%)
14 (24.1%)
44 (75.9%)
3–4
309
257 (83.2%)
52 (16.8%)
284 (91.9%)
25 (8.1%)
152 (49.2%)
157 (50.8%)
Differentiation
0.139
0.055
0.007
LG
331
271 (81.9%)
60 (18.1%)
300 (90.6%)
31 (9.4%)
142 (42.9%)
189 (57.1%)
HG
36
33 (91.7%)
3 (8.3%)
36 (100.0%)
0 (0.0%)
24 (66.7%)
12 (33.3%)
LN metastasis
0.609
0.070
0.058
absent
168
141 (83.9%)
27 (16.1%)
149 (88.7%)
19 (11.3%)
67 (39.9%)
101 (60.1%)
present
199
163 (81.9%)
36 (18.1%)
187 (94.0%)
12 (6.0%)
99 (49.7%)
100 (50.3%)
Lymphatic invasion
0.152
0.896
0.073
absent
158
136 (86.1%)
22 (13.9%)
145 (91.8%)
13 (8.2%)
63 (39.9%)
95 (60.1%)
present
209
168 (80.4%)
41 (19.6%)
191 (91.4%)
18 (8.6%)
103 (49.3%)
106 (50.7%)
Perineural invasion
0.631
0.530
0.025
absent
154
212 (83.5%)
42 (16.5%)
231 (90.9%)
23 (9.1%)
49 (58.7%)
105 (41.3%)
present
113
92 (81.4%)
21 (18.6%)
105 (92.9%)
8 (7.1%)
61 (54.0%)
52 (46.0%)
Venous invasion
0.776
0.999
0.814
absent
296
246 (83.1%)
50 (16.9%)
271 (91.6%)
25 (8.4%)
133 (44.9%)
163 (55.1%)
present
71
58 (81.7%)
13 (18.3%)
65 (91.5%)
6 (8.5%)
33 (46.5%)
38 (53.5%)
Tumor border
0.524
0.327
0.544
expanding
60
48 (80.0%)
12 (20.0%)
53 (88.3%)
7 (11.7%)
25 (41.7%)
35 (58.3%)
infiltrative
307
256 (83.4%)
51 (16.6%)
283 (92.2%)
24 (7.8%)
141 (45.9)
166 (54.1%)
Size (cm)
0.041
0.061
< 0.001
mean
5.3
5.4
4.7
5.3
4.5
5.8
4.8
Distant metastasis
0.123
0.544
0.252
absent
299
252 (84.3%)
47 (15.7%)
275 (92.0%)
24 (8.0%)
131 (43.8%)
168 (56.2%)
present
68
52 (76.5%)
16 (23.5%)
61 (89.7%)
7 (10.3%)
35 (51.5%)
33 (48.5%)
pTNM stage
0.822
0.039
0.050
I, II
162
135 (83.0%)
27 (17.0%)
140 (88.1%)
19 (11.9%)
64 (39.5%)
98 (60.5%)
III, IV
205
169 (82.4%)
36 (17.6%)
193 (94.1%)
12 (5.9%)
102 (49.8%)
103 (50.2%)
MSI status
0.029
0.256
0.490
MSS/MSI-L
323
264 (81.7%)
59 (18.3%)
294 (91.0%)
29 (9.0%)
141 (38.4%)
182 (49.6%)
MSI-H
32
31 (96.9%)
1 (3.1%)
31 (96.9%)
1 (3.1%)
16 (1.6%)
16 (4.4%)
Abbreviations: CRC, colorectal cancer; T, tumor; LG, low grade; HG, high grade; LN, lymph node; MSS, microsatellite stable; MSI-L, microsatellite instability-low; MSI-H, microsatellite instability-high; GCN, gene copy number; IHC, immunohistochemistry
P -values are calculated by using χ 2 -test or Fisher’s exact test
Prognostic significance of c-MYC gene status in CRC patients
All CRC patients were successfully followed up for inclusion in the survival analysis ( Fig 2 ). In cohort 1, the mean follow-up period was 55 months (range, 1–85 months) and 101 (27.5%) patients died during the follow-up period. Kaplan-Meier analysis showed that c-MYC GCN gain was significantly associated with poor survival in CRC patients ( P = 0.015), but c-MYC amplification was not ( P = 0.149). In the stage II-III subgroup, c-MYC -GCN gain also predicted poor prognosis ( P = 0.034). Multivariate analysis of c-MYC status is summarized in Table 2 , and showed that c-MYC -GCN gain independently predicted poor prognosis in the consecutive cohort ( P < 0.001) and in the subgroup of patients with stage II-III CRC ( P = 0.040).
10.1371/journal.pone.0139727.t002
Table 2 Multivariate Cox proportional hazard models for the predictors of overall survival (cohort 1).
Univariate survival analysis
Multivariate survival analysis
Factors
HR (95% CI)
P value
HR (95% CI)
P value
c-MYC GCN SISH (4≦ vs. 4>)
1.756 (1.117–2.763)
0.015
2.350 (1.453–3.801)
<0.001
Age
1.026 (1.008–1.045)
0.005
1.025 (1.007–1.043)
0.006
Size
1.244 (1.059–1.244)
0.001
1.099 (0.995–1.214)
NS (0.062)
Histologic grade (high vs. low)
3.143 (1.904–5.188)
<0.001
2.844 (1.625–4.977)
<0.001
Stage (3/4 vs. 1/2)
6.151 (3.494–10.829)
<0.001
3.069 (1.603–5.878)
0.001
Lymphatic invasion
3.661 (2.242–5.980)
<0.001
1.251 (0.709–2.205)
NS (0.439)
Perineural invasion
3.942 (2.648–5.870)
<0.001
2.325 (1.487–3.636)
<0.001
Venous invasion
3.985 (2.671–5.946)
<0.001
2.304 (1.490–3.676)
<0.001
c-MYC GCN SISH (4≦ vs. 4>) in a subgroup of stage II/III
2.057 (1.039–4.073)
0.038
2.058 (1.032–4.105)
0.040
Age
1.037 (1.009–1.067)
0.010
1.036 (1.007–1.066)
0.014
Stage (3 vs. 2)
2.955 (1.493–5.850)
0.002
1.705 (0.802–3.623)
NS (0.165)
Lymphatic invasion
2.882 (1.456–5.703)
0.002
1.846 (0.887–3.845)
NS (0.101)
Perineural invasion
3.536 (1.952–6.405)
0.001
2.921 (1.558–5.476)
<0.001
Abbreviations: SISH, silver in-situ hybridization; GCN, gene copy number; HR, hazard ratio
P -values are calculated by using χ 2 -test or Fisher’s exact test
Correlation between the c-MYC GCN gain and protein overexpression
Overexpression of c-MYC protein was detected in 201 (54.8%) of 367 CRC patients (cohort 1) and was associated with early pT stage ( P < 0.001), low grade of histologic differentiation ( P = 0.007), absence of perineural invasion ( P = 0.025) and smaller size ( P < 0.001) ( Table 1 ). Overexpression of c-MYC protein was associated with GCN gain (ρ, 0.211; P < 0.001), which was categorized as weakly correlation according to Dancey and Reidy’s categorization (2004) [ 22 ]. Furthermore, only 46 (22.9%) of 201 patients with c-MYC overexpression showed a GCN gain.
c-MYC status and heterogeneity according to tumor location in advanced CRC patients
To evaluate the regional heterogeneity of c-MYC status, we examined tissue from 3 sites including the primary cancer, distant metastasis, and lymph-node metastasis for each patient with advanced CRC (cohort 2). In the primary tumors of cohort 2, the median c-MYC :CEP8 ratio was 1.14 (range, 0.57–2.97). c-MYC gene amplification was detected in 8 (5.3%) of 152 patients. The mean c-MYC GCN was 2.97 (range, 1.40–9.94). c-MYC GCN gain was detected in 48 (31.6%) of 152 CRC patients. In addition, c-MYC GCN gain was found in 33 (21.7%) patients with distant metastatic tumors. Lymph-node metastasis was observed in 79 of 152 advanced CRC patients and c-MYC GCN gain was observed in 18 (22.8%) of these cases. The heterogeneity of c-MYC GCN gain according to tumor location is shown in Table 3 . Of 152 cases, discordance between c-MYC GCN gain in the primary tumor and distant metastasis was noted in 39 (25.7%) cases. Discordance between c-MYC GCN gain in the primary tumor and lymph-node metastasis was detected in 24/79 (30.4%) cases. Thus, regional heterogeneity of c-MYC GCN gain was quite common in advanced CRC. c-MYC GCN heterogeneity was not correlated with clinicopathological factors and prognosis ( P > 0.05; data not shown).
10.1371/journal.pone.0139727.t003
Table 3 Heterogeneity of c-MYC GCN gain with respect to tumor location in advanced CRC (cohort 2).
c-MYC GCN gain (%)
Primary
negative
positive
total
Distant metastasis
negative
92 (60.5)
27 (17.8)
152 (100)
positive
12 (7.9)
21 (13.8)
LN metastasis
negative
44 (55.7)
17 (21.5)
79 (100)
positive
7 (8.9)
11 (13.9)
Abbreviations: GCN, gene copy number; LN: lymph node
P -values are calculated by using χ 2 -test or Fisher’s exact test
There was no statistically significant correlation between the clinicopathological factors and c-MYC GCN gain in primary, distant metastatic, and lymph-node metastatic tumors from cohort 2 CRC patients ( P > 0.05; data not shown). The mean follow-up time was 42 months (range, 1–105 months) and 67 patients (44.1%) died from cancer during the follow-up period. Kaplan-Meier analysis showed that patients with c-MYC GCN gain in the primary tumor had a poor outcome than those without, but this result was not statistically significant ( P = 0.499). However, ≥ 3.0 c-MYC copies/nucleus in the primary tumor was significantly associated with a poor prognosis ( P = 0.044; S1 Fig ). There was no significant correlation between the patients’ prognosis and c-MYC GCN gain in distant or lymph-node metastases ( P = 0.981 and P = 0.417, respectively; data not shown).
KRAS mutations in advanced CRC
The cobas KRAS test was performed on 152 primary tumors from advanced CRC cases (cohort 2). KRAS gene mutations were observed in 84 (55.3%) cases and were associated with tumors located in the right colon ( P = 0.019), but were not correlated with other clinicopathological factors ( P > 0.05; data not shown). Additionally, there was no statistical difference between the survival of CRC patients with mutated or wild-type KRAS ( P = 0.688; data not shown). Of 68 cases with wild-type KRAS , c-MYC amplification was noted in 4 (5.9%) and a c-MYC GCN gain in 28 (41.2%). Of 84 cases with mutated KRAS , 4 showed c-MYC amplification (4.8%) and 20 (23.8%) revealed a c-MYC GCN gain. c-MYC GCN alterations occurred in patients with both wild-type and mutated KRAS. Therefore, c-MYC GCN alterations and KRAS mutations were not mutually exclusive.
Discussion
Although there have been several reports on c-MYC status in human cancers, there are no established criteria for GCN gain. Cancers with a c-MYC GCN gain are often associated with a poor prognosis. A previous study of mucinous gastric carcinoma showed that c-MYC amplification, defined as a c-MYC :CEP8 ratio > 2.0, was strongly correlated with the advanced stages of cancer [ 23 ]. Another report found an association between c-MYC amplification (> 4 copies/cell in a minimum of 10% of tumor cells) and the advanced stages of ovarian cancer [ 21 ]. In a study of prostate cancer, the c-MYC GCN gain included the criterion of a c-MYC/ CEP8 ratio > 1.5, and a poor prognosis was observed for patients in this category [ 24 ]. In recent research on adenocarcinoma of the lung, patients with > 2 c-MYC copies/nucleus were classified as having an increased c-MYC GCN, which was found to be an independent poor prognostic factor [ 25 ]. In CRC patients, it was reported that c-MYC amplification, defined as a c-MYC/ CEP8 ratio > 2, was frequently detected by using fluorescent in situ hybridization (9.0–14.2%), but was unrelated to clinical outcome and pathological data [ 26 ]. Therefore, we have applied diverse criteria to determine c-MYC amplification or GCN gain in this study, and have defined the c-MYC GCN gain as ≥ 4 copies/nucleus, because it had the lowest P -value for disease prognosis ( Fig 2 ). In cohort 1, the large consecutive cohort, CRC patients with a c-MYC GCN gain had a poor survival than those without ( P = 0.015). Furthermore, in multivariate analysis, c-MYC GCN gain was a significant CRC prognostic factor, both in the consecutive cohort and for those with stage II-III disease. The predictive value of the c-MYC GCN was found to be independent of known prognostic factors. The c-MYC GCN gain criteria used in the present study, together with the SISH method, may be useful in assessing CRC patients because it clearly identified patients expected to have poor survival, regardless of the c-MYC :CEP8 ratio.
In cohort 2, we showed that there was c-MYC GCN regional heterogeneity between the primary site and its related metastases. A c-MYC GCN gain (c-MYC GCN ≥ 4.0) in the primary cancer was not significantly associated with poor survival ( P = 0.499; S1 Fig ), which might be because all of cohort 2 consisted of advanced CRC patients with synchronous and metachronous metastasis and cohort 2 was largely comprised of stage IV CRC (98 cases; 64.5%). They received a variety of personalized treatment respectively and these might reflect the statistical insignificance. Interestingly, we applied slightly non-restrictive criteria of GCN gain ( c-MYC GCN ≥ 3.0) and its prognosis was changed to statistically significant ( P = 0.044; S1 Fig ). In a broad sense, c- MYC GCN gain of primary cancer tends to correlated with poor survival in advanced CRC. On the other hand, c-MYC status in distant and lymph-node metastatic lesion was not related to patient prognosis although we tried every possible GCN criteria. Even though, c-MYC heterogeneity was observed frequently in advanced CRC, a c-MYC GCN gain in the primary cancer was often associated with poor survival. Consequently, the c-MYC GCN in the non-metastatic lesion should be used when evaluating prognosis.
In a previous study, overexpression of c-MYC mRNA in CRC was found to be associated with a better prognosis [ 27 ], but this result was contradicted by another study [ 28 ]. Christopher et al . recently demonstrated that c-MYC overexpression determined by IHC alone, was significantly associated with a better survival in CRC patients when assessed by univariate analysis, but not by multivariate analysis [ 10 ]. Interestingly, we found conflicting results in a previous c-MYC overexpression study; presumably, because c-MYC expression is controlled by a complex regulatory pathway involving multiple interactions with other molecules, rather than just simple GCN gain [ 29 ]. Furthermore, we found a weak correlation between c-MYC protein overexpression and GCN gain in CRC patients. c-MYC GCN gain was not observed in most c-MYC protein overexpression cases. Unlike the c-MYC GCN gain, overexpression of c-MYC protein was correlated with less aggressive features ( Table 1 ). These results suggest that c-MYC GCN gain is probably only partly responsible for protein overexpression. As overexpression of c-MYC is not the same as a c-MYC GCN gain, further research is needed to explain the difference of c-MYC overexpression and GCN gain in CRC tumorigenesis.
Mutations in KRAS are evident in 30–40% of colorectal tumors [ 30 – 32 ]. Indeed, previous studies reported that a KRAS mutation was associated with resistance to anti-epidermal growth factor receptor (EGFR) monoclonal therapy and a poor survival [ 33 – 35 ]. In our study, KRAS mutations were present in 55.3% of advanced CRC patients (cohort 2) and were not associated with prognosis. It may be because we investigated KRAS mutation status in advanced CRC patients. Phipps et al. also reported that KRAS -mutation status was not associated with poor disease specific survival in cases who presented with distant-stage CRC [ 33 ]. c-MYC amplification was observed in 5.9% of wild-type KRAS and 4.8% of mutated KRAS CRCs. A c-MYC GCN gain was observed in 41.2% of wild-type KRAS and 23.8% of mutated KRAS CRCs. It is noteworthy that these 2 genetic events were not mutually exclusive. Further studies are required to investigate the possibility of using c-MYC genetic alterations as therapeutic targets in advanced CRC patients with primary and secondary resistance to anti-EGFR therapies.
In summary, we comprehensively analyzed the c-MYC gene status of CRC patients by using SISH. c-MYC GCN gain and amplification were observed in 17.2% and 8.4% of consecutive CRC patients, respectively. The c-MYC GCN gain was an independent poor prognostic factor, both in the consecutive cohort and in the subgroup of patients with stage II-III disease. These findings show that c-MYC status can be used to predict the prognosis of CRC patients, and may inform future studies on the pathogenesis and mechanisms involved in the progression of CRC.
Supporting Information
S1 Fig
Kaplan-Meier survival curves illustrating the prognostic effect of c-MYC status in primary lesions of colorectal cancer (cohort 2).
(A) c-MYC gene copy number (GCN) ≥ 3.0; (B) c-MYC GCN ≥ 4.0.
(TIF)
S2 Fig
Representative figures of c-MYC overexpression by immunohistochemistry (A and B) in colorectal cancer patients.
(A) c-MYC overexpression (40 × magnification); (B) No c-MYC expression (40 × magnification);
(TIF)
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Introduction
Hantaan (HTNV) and Seoul viruses (SEOV), etiologic agents of hemorrhagic fever with renal syndrome (HFRS) in the Old World, pose serious health threats to military and civilian personnel residing, working, or conducting routine military operations in rodent-infested environments. HTNV, transmitted through the inhalation of dusts containing HTNV-infected Apodemus agrarius excreta, poses the greatest threat due to its extended incubation period (up to 50 days), mean duration of illness from onset of symptoms to complete recovery, and overall morbidity and mortality rate of 9.46% reported for US military personnel in the presence of quality medical management [ 1 – 4 ]. The morbidity of SEOV ranges from mild to moderate and mortality rate <1%, is similarly transmitted by inhalation of dusts containing SEOV-infected Rattus norvegicus excreta, and while much less severe than HTNV infections, poses a threat for personnel occupying or conducting operations in rat infested urban environments [ 5 ].
In 2000, the 18 th Medical Command launched a comprehensive rodent-borne disease surveillance aimed primarily to assess HFRS risks based on activities of military personnel, habitat characteristics, relative rodent population densities, and HTNV IgG antibody-positive (Ab+) at United States (US) and Republic of Korean (ROK) operated military training sites [ 4 , 6 – 10 ]. Rodent-borne disease surveillance at training sites near the demilitarized zone (DMZ) showed that A . agrarius populations, based on trap rates, remained relatively stable and that high gravid rates were observed in August-September, which correlated with increased numbers of HFRS cases from late September-December [ 4 , 7 , 9 , 10 ]. The proposed consolidation of US military forces to Camp (Cp) Humphreys resulted in the purchase of adjoining farmlands (rice paddies and associated irrigation and drainage systems). By 2008, the area immediately adjacent to the preexisting Cp Humphreys boundary was under development (land graded and paddies filled) to make way for the construction of roads, drainage systems, housing, and other structures that support military operations and other related activities. From 1986–2016, when detailed epidemiological data were available, there were no reported HFRS cases attributed to exposure at Cp Humphreys and nearby local training areas. However, due to landscape modifications and construction activities that may alter HFRS risks, a comprehensive rodent-borne disease surveillance program was conducted at the Cp Humphreys and the new expansion site. HFRS risk factors were estimated based on associated human activities that considered environmental, biological, and hantavirus prevalence associated with rodents and soricomorphs for both the established cantonment area of Cp Humphreys and the fallow farmlands that were being graded/filled that may alter HFRS risks. This report focuses on epidemiological data that identifies environmental factors, seasonal rodent population densities, biological factors, and HTNV serological and molecular prevalence related to HTNV transmission risks for Cp Humphreys and the expansion site, Pyeongtaek. The whole genomic sequences of HTNV newly obtained for Cp Humphreys and the expansion site, Pyeongtaek, were phylogenetically characterized demonstrating a greater diversity of rodent-borne hantaviruses.
These data are useful for developing local HFRS disease threat awareness, analysis, and risk reduction strategies for civilians and military personnel in southern Gyeonggi province, ROK.
Materials and methods
Ethics statement
All trapping of small mammals was approved by US Forces Korea (USFK) in accordance with USFK Regulation 40–1 “Prevention, Surveillance, and Treatment of Hemorrhagic Fever with Renal Syndrome” at US Military Installations and US and ROK Operated Military Training Sites [ 11 ]. Standard procedures were followed for the collection and transportation of specimens to minimize hazards from potentially infected animals as previously described and all personnel processing animals at the Korea University laboratory were vaccinated using a Korean-approved Hantavirus vaccine (Hantavax ® ) [ 12 , 13 ]. Small mammals were euthanized by cardiac puncture under isoflurane anesthesia in strict accordance with the Korea University Institutional Animal Care and Use Committee (KUIACUC, #2010–212) protocol approved for this study. All efforts were made to minimize suffering.
Site description
Cp Humphreys, located near Pyeongtaek in southern Gyeonggi Province, was designated as a US military hub incorporating US military installations near the DMZ and elsewhere ( Fig 1 ) . The areas surveyed included: (1) Cp Humphreys (hereafter referred to as “Cp Humphreys”) and (2) the new expansion site (referred to hereafter as “expansion site”) for the construction of structures for military operations and housing and development of roads, drainage systems, and outdoor recreational areas.
10.1371/journal.pone.0176514.g001
Fig 1
Location of Camp Humphreys, Gyeonggi province, Republic of Korea where rodent-borne disease surveillance was conducted from 2008–2010 and 2012.
Cp Humphreys is bounded by a small town (Anjeong-ri, Paengseong-eup), farmland, and the expansion site. It consists of an airstrip, asphalt roads, and structures for military operations and housing, streams, man-made water impoundments, and drainage ditches. Most areas bordering structures and roadside ditches were well maintained, while unmanaged grasses/herbaceous vegetation and shrubs border ponds, streams, and some drainage ditches, providing limited space, cover, and food for small mammals.
The expansion site under development is bounded by Cp Humphreys and the Anseongcheon River. During 2008, areas immediately adjacent to Cp Humphreys were under development for the construction of structures for military operations and housing, roads, drainage systems, and recreation areas. A large area not under development consisted of low lying hills of grasses/herbaceous vegetation, small groves of trees, major irrigation/drainage systems to reduce flooding, rice paddies, and limited dry-land farmland that lay fallow with unmanaged grasses/herbaceous vegetation. The major irrigation/drainage systems were bordered by moderate/tall grasses along the banks extending to roadways that harbored relatively high small mammal populations. Grading and filling of the unmanaged fallow lands continued and by 2010 much of the expansion site had been graded and cleared of grasses/herbaceous vegetation in preparation for land fill and construction. Habitats of small mammals (e.g., rodents and soricomorphs) and predators (e.g., weasels, raccoon dogs, and feral cats) were destroyed as lands were sequentially graded and filled, leaving these areas devoid of vegetation except for limited areas of grasses/herbaceous vegetation bordering ditches, major drainage systems for flood control, and temporary roads that provided habitat for small mammals.
Small mammal trapping
Trapping was conducted monthly from January 2008-December 2009, quarterly during 2010, and semiannually in 2012. Areas surveyed included: limited tall grasses/herbaceous vegetation that bordered streams, retaining ponds, fence lines, park perimeters, and open fields at Cp Humphreys and expansive tall grasses/herbaceous vegetation bordering dirt and paved roads, streams, ditches, water impoundments, flood control drainage systems, spillways, and the Anseongcheon River at the expansion site, which did not interfere with military or construction activities. Collapsible live-capture Sherman® traps (7.7x9x23 cm; H.B. Sherman, Tallahassee, FL) were set in grasses/herbaceous vegetation (providing shade) at 4–5 m intervals (25–50 traps/trap line) in the late afternoon over a 2–3 day period and picked up early the following morning as previously described [ 4 , 6 – 10 ]. Traps positive for small mammals were sequentially numbered, placed in a secure container, and transported to Korea University where they were euthanized, identified to species using morphological techniques, sexed, weighed and then, tissues (lung, liver, kidney, and spleen) collected and stored at -80°C until used [ 14 ].
Serological and molecular screening for hantaviruses
Small mammal sera were diluted 1:16 in phosphate buffered saline and examined for IgG antibodies against HTNV, SEOV, Prospect Hill virus, and Imjin virus (MJNV) by indirect immunofluorescent antibody test (IFAT) [ 15 – 18 ]. Lung tissues of hantavirus Ab+ rodents and soricomorphs were used for the identification of the hantavirus gene by RT-PCR that amplified a portion of the G C glycoprotein-encoding M segment. Total RNA, extracted from lung tissues of the seropositive animals using the RNA-Bee Kit (TEL-TEST Inc., Friendswood, TX), was reverse-transcribed using Superscript® II RNase H-reverse transcriptase kit (Invitrogen, Carlsbad, CA) according to the manufacturer’s instructions. Nested-PCR using the primers (outer primer set, 5’-TGGGCTGCAAGTGC-3’ , 5’-ACATGCTGTACAGCCTGTGCC-3’ ; inner primer set, 5’-TGGGCTGCAAGTGCATCAGAG-3’ , 5’-ATGGATTACAACCCCAGCTCG-3’ ) was performed to recover a 373-nucleotide (nt) region of the hantavirus M segment [ 19 – 21 ]. Amplified products were size-fractionated by electrophoresis on 1.5% agarose gels containing ethidium bromide (0.5 mg/mL). The PCR products were cloned using the pST Blue-1 vector (Novagen, Dormstadt, Germany) and plasmid DNA purified using the QIAprepSpinMiniprep kit (QIAGEN Inc., Chatsworth, CA). DNA sequencing was performed in both directions from at least three clones of each PCR product using the Big-Dye® Terminator v3.1 cycle sequencing kit (Applied Biosystems, Foster City, CA) on an automated sequencer (Model 3730, Applied Biosystems).
Real-time quantitative PCR (RT-qPCR)
cDNA was synthesized using a High Capacity RNA-to-cDNA kit (Applied Biosystems) by adding 1 μg of total RNA from lung tissues of HTNV-positive A . agrarius . RT-qPCR was conducted using SYBR Green PCR Master mix (Applied Biosystems) on a StepOne Real-Time PCR System (Applied Biosystems). The primer sequences included a forward primer; 5’-TTATTGTGCTCTTCATGGTTGC-3’ and a reverse primer; 5’-CATCCCCTAAGTGGAAGTTGTC-3’ for HTNV S segment [ 22 ]. The cycling program was a cycle of 95°C for 10 min, followed by 40 cycles of 15 s at 95°C and 1 min at 60°C.
Whole genome sequencing of HTNV
Total RNA was isolated from lung tissues of HTNV-positive A . agrarius using a Hybrid R Kit (GeneAll Biotechnology, Seoul, ROK). cDNA was synthesized using High Capacity RNA-to-cDNA kit (Applied Biosystems) with random hexamer or 5′-TAGTAGTAGACTCC-3′ . Using Ex Taq DNA polymerase (TaKaRa BIO Inc., Shiga, Japan), first and second RT-PCR were performed at 95°C for 4 min, followed by 6 cycles of denaturation at 94°C for 30 sec, annealing at 37°C for 30 sec, elongation at 72°C for 1 min 30 sec, and then 32 cycles of denaturation at 94°C for 30 sec, annealing at 42°C for 30 sec, and elongation at 72°C for 1 min 30 sec, and 72°C for 5 min (ProFlex PCR System, Life Technology, CA, USA). To complete whole genome sequencing of HTNV, rapid amplification of cDNA ends (RACE) for 3’ and 5’ termini were performed using 3’ and 5’-Full RACE Core Sets (Takara, Shiga, Japan) according to manufacturer specifications.
Genetic and phylogenetic analyses
Alignments of whole genome sequences of HTNV L, M, and S segments were facilitated using the Clustal W method (Lasergene program version 5, DNASTAR Inc. Madison, WI). The phylogenetic tree was generated by neighbor joining (NJ) and maximum likelihood (ML) methods (Molecular Evolutionary Genetics Analysis, 6.0). Genetic distances were computed, and topologies evaluated by bootstrap analysis of 1,000 iterations [ 23 ].
Results
Capture rates for species
A total of 2,364 (capture rate = 20.92%) small mammals over 11,300 trap nights, belonging to the Orders Rodentia [Families Muridae (3 species) and Cricetidae (3 species)] and Soricomorpha [Family Soricidae (1 species)], were captured monthly from 2008–2009, quarterly during 2010, and biannually during 2012 at Cp Humphreys (11.91%) and the expansion site (28.88%) ( Table 1 ) .
10.1371/journal.pone.0176514.t001
Table 1 Number, capture rate, and percent of small mammals captured, by species, at both Camp Humphreys and the expansion site (under development), monthly 2008–2009, quarterly 2010, and biannually 2012.
Location a (# Traps) b
Species
Apodemus agrarius
Microtus fortis
Micromys minutus
Mus musculus
Myodes regulus
Rattus norvegicus
Crocidura lasiura
Tscherskia triton
Total
Camp Humphreys (5,300)
Number Captured
510
34
12
25
1
0
49
0
631
Capture Rate
9.62
0.64
0.23
0.47
0.02
0.00
0.92
0.00
11.91
Percent Captured
80.82
5.39
1.90
3.96
0.16
0.00
7.77
0.00
Expansion Site (6,000)
Number Captured
1,302
220
29
42
1
3
135
1
1,733
Capture Rate
21.70
3.67
0.48
0.70
0.02
0.05
2.25
0.02
28.88
Percent Captured
75.13
12.69
1.67
2.42
0.06
0.17
7.79
0.06
Total (11,300)
Number Captured
1,812
254
41
67
2
3
184
1
2,364
Capture Rate
16.04
2.25
0.36
0.59
0.02
0.03
1.63
0.01
20.92
Percent Captured
76.65
10.74
1.73
2.83
0.08
0.13
7.78
0.04
a Preexisting Camp Humphreys installation and the projected expansion site under construction (grading and land fill of rice paddies).
b Number of traps set at Camp Humphreys and the expansion site.
Overall, A . agrarius (striped field mouse) accounted for 76.65% (1,812) of all small mammals captured, followed by Microtus fortis (reed vole) (254, 10.74%), Crocidura lasiura (Ussuri white-toothed shrew) (184, 7.78%), Mus musculus (house mouse) (67, 2.83%), Micromys minutus (harvest mouse) (41, 1.73%), Rattus norvegicus (brown rat) (3, 0.13%), Myodes regulus (Royal or Korean red-backed vole) (2, 0.08%), and Tscherskia triton (greater long-tailed hamster) (1, 0.04%). The overall capture rate of A . agrarius for unmanaged tall grass habitats at the expansion site were higher (21.70%) than for limited tall grass habitats at Cp Humphreys (9.62%) ( S1 Fig ) . A . agrarius accounted for 80.82% and 75.13% of all small mammals at Cp Humphreys and the expansion site, respectively. The lower proportion of A . agrarius captured at the expansion site was due to large numbers of canals and flooded fallow rice paddies that are primary habitats for M . fortis .
Capture rates for years and months
Annual capture rates for A . agrarius for both Cp Humphreys and the expansion site for years 2008–2009 were more than 4-fold and 3-fold higher, respectively, than for years 2010 and 2012, when a large portion of unmanaged vegetation was cleared at the expansion site ( Table 2 , S2 Fig ) .
10.1371/journal.pone.0176514.t002
Table 2 Annual capture rates for small mammals, by species, at Camp Humphreys and the expansion site, captured monthly during 2008–2009, quarterly 2010, and biannually 2012.
Collection Site
Year (No. Traps)
Apodemus agrarius
Microtus fortis
Micromys minutus
Mus musculus
Myodes regulus
Rattus norvegicus
Crocidura lasiura
Tscherskia triton
Total
Camp Humphrey a
2008 (1,600)
14.31
0.75
0.44
0.00
0.00
0.00
1.81
0.00
17.31
2009 (2,400)
9.17
0.75
0.21
0.58
0.00
0.00
0.58
0.00
11.29
2010 (1,000)
4.90
0.40
0.00
0.00
0.00
0.00
0.60
0.00
5.90
2012 (300)
2.00
0.00
0.00
1.83
0.17
0.00
0.00
0.00
4.00
Expansion Site b
2008 (2,050)
29.66
0.93
0.73
0.24
0.05
0.00
1.41
0.00
33.02
2009 (2,475)
25.01
7.84
0.36
1.29
0.00
0.12
4.28
0.00
38.91
2010 (1,175)
4.68
5.61
0.50
0.26
0.00
0.00
0.00
0.00
5.87
2012 (300)
6.67
0.33
0.00
0.67
0.00
0.00
0.00
0.33
8.00
Overall Mean
2008 (3,650)
22.93
0.85
0.60
0.14
0.03
0.00
1.59
0.00
26.14
2009 (4,875)
17.21
4.35
0.29
0.94
0.00
0.06
2.46
0.00
25.31
2010 (2,175)
4.78
0.46
0.23
0.14
0.00
0.00
0.28
0.00
5.89
2012 (600)
5.33
0.17
0.00
2.17
0.17
0.00
0.00
0.17
8.00
a Preexisting Camp Humphreys installation where uncut tall grasses/ herbaceous vegetation and shrubs predominated along ditches, streams, and water impoundment areas.
b Expansion site under development (clearing vegetation, grading and land fill) and construction of structures for housing and military operations, roads, drainage systems, and recreational areas.
Monthly capture rates for A . agrarius varied for both Cp Humphreys (range 3.50–17.00%) and the expansion site (range 9.63–55.79%) ( Fig 2 ) .
10.1371/journal.pone.0176514.g002
Fig 2
Monthly capture rates of Apodemus agrarius collected at Camp Humphreys and the new expansion site from 2008–2010 and 2012.
Capture rates for sexes
Apodemus agrarius capture rates for males (46.69%, range 33.33–55.56%) and females (53.31%, range 44.44–66.67%), were not significantly different ( p = 0.062) ( Table 3 ) . Gravid females were only observed from April-November, with the highest gravid rates observed during June (40.00%), August (41.67%), and September (45.16%) and ranged from 9.43–16.21 for the other months ( Table 3 ).
10.1371/journal.pone.0176514.t003
Table 3 The total number and percentage of Apodemus agrarius males and females captured and number females (%) gravid at Camp Humphreys and the new expansion site, monthly from 2008–2009, quarterly 2010, and biannually 2012.
Month
No. Males
(%)
No. Females
(%)
No. Gravid
(%)
January
41
40.59
60
59.40
0
0
February
98
50.00
98
50.00
0
0
March
74
46.83
84
53.16
0
0
April
51
40.80
74
59.20
12
16.21
May
61
42.96
81
57.04
13
16.05
June
48
51.61
45
48.39
18
40.00
July
164
54.85
135
45.15
21
15.56
August
60
55.56
48
44.44
20
41.67
September
77
45.29
93
54.71
42
45.16
October
53
33.33
106
66.67
10
9.43
November
17
50.00
17
50.00
2
11.76
December
102
44.93
125
55.07
0
0
TOTAL
846
46.69
966
53.31
138
14.29
Capture rates for weights
Few A . agrarius weighed ≤10 g (0.61%) or >40 g (2.37%), with most weighing 10–20 g (39.96%), 20–30 g (37.97%) and 30–40 g (19.09%) ( Table 4 ) .
10.1371/journal.pone.0176514.t004
Table 4 The total number and percentage of male and female A . agrarius captured, by weight category, mean weights for each category, and differences of mean male weights—Mean female weights for each weight category during 2008–2010 and 2012.
Categories
≤10 g
10–20 g
20–30 g
30–40 g
>40 g
Total
Number (%) a ♂
6 (54.54)
244 (33.70)
315 (45.78)
242 (69.94)
38 (88.37)
845 (46.63)
Number (%) a ♀
5 (45.45)
480 (66.30)
373 (54.21)
104 (30.06)
5 (11.63)
967 (53.37)
Total (♂,♀) b
11 (0.61)
724 (39.96)
688 (37.97)
346 (19.09)
43 (2.37)
1,812
Mean Weight c ♂
9.3
17.0
24.7
34.2
43.7
25.5
Mean Weight ♀
8.8
16.3
25.3
33.5
42.9
23.2
Difference (♂-♀)
+0.5
+0.7
-0.6
+0.7
+0.8
+2.3
a Percent males or females = number males or females captured for each weight category/total number of males or females collected for each weight category, respectively.
b Percent = total number of males and females captured by weight category/total number captured.
c Mean weights = total weight of all specimens, by weight category/total number of specimens for each category.
Males accounted for 69.94% and 88.37% of those weighing 30–40 g and >40 g (old rodents), while females accounted for 66.30% and 54.21% of those weighing 10–20 g and 20–30 g (young rodents), respectively. On the average, male A . agrarius weighed 0.4–0.8 g more than females for each weight category, except for those weighing 20–30 g where females was heavier by 0.6 g more than the males.
Seasonal variations in population weights coincided with reproductive activity, e.g., increased proportions of lower weight individuals following high gravid rates ( Table 5 ) .
10.1371/journal.pone.0176514.t005
Table 5 Quarterly total number and percentage (%) of A . agrarius captured, by weight category, at Camp Humphreys and the expansion site from 2008–2010 and 2012.
Trapping Season
Weight Class (g)
Total (%)
≤ 10
10–20
20–30
30–40
>40
Winter (Jan-Mar)
0
274 (60.21)
159 (34.95)
21 (4.61)
1 (0.22)
455 (25.11)
Spring (Apr-Jun)
4 (1.11)
92 (25.56)
202 (56.11)
57 (15.83)
5 (1.38)
360 (19.86)
Summer (Jul-Sep)
5 (0.87)
86 (14.90)
224 (38.82)
230 (39.86)
32 (5.55)
577 (31.84)
Fall (Oct-Dec)
2 (0.48)
272 (64.76)
103 (24.52)
38 (9.05)
5 (1.19)
420 (23.18)
TOTAL (%)
11 (0.61)
724 (39.96)
688 (37.97)
346 (19.09)
43 (2.37)
1,812
The proportion of A . agrarius weighing ≤20 g declined from a high of 82.18% in January to a low of 10.37% by July before increasing to a high of 71.37% by December ( Fig 3 ) . In contrast, the proportion of A . agrarius weighing >30 g increased from a low of 1.98% in January to a high of 51.76% by September as a result of a maturing populations, gravid females, and abundant food supply, but rapidly declined to 1.76% by December that followed high numbers of gravid females during August (41.67%) and September (45.16%).
10.1371/journal.pone.0176514.g003
Fig 3
Overall percentage of Apodemus agrarius captured monthly, by weight, at Camp Humphreys and the new expansion site from 2008–2010 and 2012.
Serological prevalence of HTNV at Cp Humphreys and the expansion site, Pyeongtaek
Among 1,812 A . agrarius captured at Cp Humphreys and the expansion site, IFAT showed that 39 (2.15%) rodents were positive for anti-HTNV IgG. HTNV Ab+ rates among A . agrarius were significantly higher for Cp Humphreys (2.41%, range 0.0–7.89%) than for the expansion site (2.11%, range 0.0–3.51%) (χ 2 = 17.279, df = 1, P<0.001) ( Fig 4 ) . However, based on trap rates (Cp Humphreys, 9.62; expansion site, 21.70), the number of HTNV Ab+ A . agrarius captured/100 traps was 2-fold greater at the expansion site (0.46) compared to Cp Humphreys (0.23). The quarterly seasonal proportions of HTNV Ab+ A . agrarius for all weight categories at Cp Humphreys and the expansion site varied from 0.95% (Oct-Dec) to 2.86% (Jan-Mar) ( Table 6 ) .
10.1371/journal.pone.0176514.g004
Fig 4
Overall percentage of Apodemus agrarius captured monthly at Camp Humphreys and the new expansion site that were antibody-positive for Hantaan virus.
10.1371/journal.pone.0176514.t006
Table 6 Quarterly number (%) of A . agrarius (total number HTNV Ab+/total number captured, by weight category, at Camp Humphreys and the expansion site, Pyeongtaek, Gyeonggi Province, 2008–2010 and 2012.
Trapping Season
Weight Class (g)
Total (%)
≤ 10
10–20
20–30
30–40
>40
Winter (Jan-Mar)
0
8/274 (2.92)
5/159 (3.14)
0/21 (0.0)
0/1 (0.0)
13/455 (2.86)
Spring (Apr-Jun)
0/4 (0.0)
0/92 (0.0)
5/202 (2.48)
1/57 (1.75)
0/5 (0.0)
6/360 (1.67)
Summer (Jul-Sep)
0/5 (0.0)
2/86 (2.33)
4/224 (1.79)
6/230 (2.61)
4/32 (12.50)
16/577 (2.77)
Fall (Oct-Dec)
0/2 (0.0)
1/272 (0.37)
2/103 (1.94)
1/38 (2.63)
0/5 (0.0)
4/420 (0.95)
TOTAL (%)
0/11 (0.0)
11/724 (1.52)
16/688 (2.33)
8/346 (2.31)
4/43 (9.30)
39/1,812 (2.15)
Overall, monthly HTNV Ab+ rates for males (2.25%) and females (2.07%) were similar, but varied monthly from 0.0–4.08% for males and 0.0–5.95% for females ( Fig 5 ) . The proportion of A . agrarius males and females that were serologically positive for HTNV for weight categories 10–20 g (1.52%), 20–30 g (2.33%), and 30–40 g (2.31%) were similar ( Fig 6 ) . However, the serological positivity (9.30%) of HTNV Ab for A . agrarius weighting > 40 g (oldest rodents) was only observed in males and was significantly higher than the other weighted groups (One-way ANOVA test, p<0.0001).
10.1371/journal.pone.0176514.g005
Fig 5
Overall monthly percentage of male and female Apodemus agrarius captured at Camp Humphreys and the new expansion site that were antibody-positive for Hantaan virus, 2008–2010 and 2012.
10.1371/journal.pone.0176514.g006
Fig 6
Overall percent of Apodemus agrarius , by sex, captured at Camp Humphreys and the new expansion site that were HTNV Ab+ for each weight category (lines) and percent HTNV Ab+ within each weight category (bars).
Molecular prevalence of HTNV at Cp Humphreys and the expansion site, Pyeongtaek
To identify HTNV RNA in the seropositive A . agrarius , RT-PCR was performed by targeting the G C glycoprotein-encoding M segment (373 bps). A total of 11 (28.21%) A . agrarius harbored HTNV RNA in 39 HTNV Ab+ rodents. The molecular prevalence of HTNV for males and females was 26.32% (5/19) and 30.00% (6/20), respectively ( Table 7 ) .
10.1371/journal.pone.0176514.t007
Table 7 Serological and molecular prevalence of HTNV from A . agrarius captured, by sex, weight, season category, at Camp Humphreys and the expansion site from 2008–2010 and 2012.
Categories
Seropositive rate for anti-HTNV IgG antibody (%)
HTNV RNA positivity (%)
Sex (n = 1,812)
Males
19/845 (2.25)
5/19 (26.32)
Females
20/967 (2.07)
6/20 (30.00)
Weight (n = 1,812)
≤10 g
0/11
0
10–20 g
11/724 (1.52)
2/11 (18.18)
20–30 g
16/688 (2.33)
6/16 (37.50)
30–40 g
9/346 (2.60)
2/9 (22.22)
>40 g
3/43 (6.98)
1/3 (33.33)
Season (n = 1,812)
Winter (Jan-Mar)
7/455 (1.54)
4/7 (57.14)
Spring (Apr-Jun)
20/360 (5.56)
3/20 (15.00)
Summer (Jul-Sep)
8/577 (1.39)
2/8 (25.00)
Fall (Oct-Dec)
4/420 (0.95)
2/4 (50.00)
The proportion of HTNV-positive A . agrarius for weight categories 10–20 g (18.18%), 20–30 g (37.50%), 30–40 g (22.22%), and 40 g (33.33%) was similar. Seasonal positivity of HTNV in A . agrarius showed 57.1% (Jan-Mar), 15% (Apr-Jun), 25% (Jul-Sep), and 50% (Oct-Dec).
The partial sequences of HTNV M segment (328nt length) were trimmed and used for analysis. All HTNV strain sequences were submitted to GenBank (Accession numbers; KX119152-119162). The partial M segment sequences (coordinates 1,994 to 2,321) of 11 HTNV strains from Cp Humphreys and the expansion site were phylogenetically compared to HTNV strains previously identified in military training sites, northern Gyeonggi province ( Fig 7 ) . The nucleotide and amino acid homologies of the 11 HTNV strains from Cp Humphreys and the expansion site varied between 0–3.1% and 0–2.8%, respectively.
10.1371/journal.pone.0176514.g007
Fig 7
Phylogenetic analysis of Hantaan virus strains identified in Cp Humphreys and the new expansion site, Pyeongtaek, based on a 328-nt region of the G C glycoprotein-encoding M segment.
The phylogenetic tree was generated by Neighbor-joining (NJ) method. Branch lengths are proportional to the number of nucleotide substitutions, while vertical distances are for clarity. The numbers at each node are bootstrap probabilities (expressed as percentages), as determined for 1000 iterations (GenBank accession numbers; KX119152-119162).
Quantitative RT-PCR, whole-genome sequencing, and phylogenetic analyses
To obtain whole genome sequences of HTNV in serological and molecular positive A . agrarius from Cp Humphreys and the extension region, Pyeongtaek, HTNV RNA copies were quantified in the lung tissue by RT-qPCR. The threshold of Cycle (Ct) value are shown in the Fig 8 . Aa09-198 demonstrated the lowest Ct value (highest viral loads), followed by Aa08-1111 and Aa09-189. The whole genome sequences of the three HTNV strains were recovered by conventional RT-PCR and RACE PCR for both 3’ and 5’ end sequences. The whole genome sequences of the HTNV strains deposited in GenBank (Accession numbers; KY594712- KY594720).
10.1371/journal.pone.0176514.g008
Fig 8
Determination of threshold cycle (Ct) values of Hantaan virus RNA in HTNV-positive Apodemus agrarius collected at Camp Humphreys and the new expansion site.
HTNV RNA was examined for the HTNV S segment in anti-HTNV IgG seropositive and HTNV RNA positive rodents (n = 11). The vertical axis represents the Ct value of HTNV S segment RNA.
The genetic diversity and phylogenetic relationship of HTNV in Cp Humphreys and the extension region were determined in comparison to strains obtained from lung tissue of seropositive rodents previously captured at a variety of HFRS-endemic areas, e.g. Twin Bridge Training Area (TBTA) North, TBTA South, and Dagmar North in Paju, Yeoncheon, and Pocheon ( Fig 9 ) . The L segment of the HTNV formed an independent outgroup of all of HTNV in Gyeonggi province. The phylogenetic analysis of the M segment showed a well-supported genetic lineage with HTNV 76–118. The S segment formed a geographic-specific group within HTNV strains, including HTNV 76–118, in Gyeonggi province.
10.1371/journal.pone.0176514.g009
Fig 9
Phylogenetic analyses of the whole genome sequences of Hantaan virus L, M, and S segments identified at Camp Humphreys and the new expansion site, Pyeongtaek, Gyeonggi province.
The phylogenetic tree was generated by Maximum-likelihood (ML) method. The phylogeny of the L segment (a), M segment (b), and S segment (c) is described. Branch lengths are proportional to the number of nucleotide substitutions, while vertical distances are for clarity. The numbers at each node are bootstrap probabilities (expressed as percentages), as determined for 1000 iterations. The phylogenetic positions of HTNV strains are shown in relationship to representative Aa10-434 (L segment, KT934970; M segment, KT935004; S segment, KT935038), Aa10-518 (L segment, KT934971; M segment, KT935005; S segment, KT935039), Aa14-204 (L segment, KT934977; M segment, KT935011; S segment, KT935045), Aa14-207 (L segment, KT934978; M segment, KT935012; S segment, KT935046), Aa05-331 (L segment, KT934962; M segment, KT934996; S segment, KT935030), Aa05-771 (L segment, KT934963; M segment, KT934997; S segment, KT935031), Aa09-410 (L segment, KU207177; M segment, KU207185; S segment, KU207193), Aa09-948 (L segment, KT934966; M segment, KT935000; S segment, KT935034), Aa14-172 (L segment, KT934974; M segment, KT935008; S segment, KT935042), Aa14-188 (L segment, KT934975; M segment, KT935009; S segment, KT935043), HTNV 76–118 (L segment, X55901; M segment, M14627; S segment, M14626) and HV004 (L segment, JQ083393; M segment, JQ083394; S segment, JQ083395).
Discussion
Cp Humphreys was designated a major US military hub with an estimated final US military and civilian population of >20,000 personnel. To accommodate for the increased population and expansion for military operations and outdoor recreational areas, adjacent lands that consisted mostly of low-lying rice paddies were purchased. The resulting environmental modifications of purchased properties included sequential grading of terrain and filling of low-lying fallow rice paddies for the construction of roads, ditches, major drainage system/recreational areas, military housing, schools, hospital and medical clinics, and other structures designed for military operations
Apodemus agrarius is associated with unmanaged lands characterized by abundant grasses/herbaceous vegetation in rural areas, including military training sites [ 24 – 27 ]. Similar to this survey and other annual and multi-year surveys, A . agrarius was the most commonly collected small mammal at US and ROK operated military training sites and installation field environments [ 4 , 6 – 10 ]. Compared to the expansion site of unmanaged grasses during 2008–2009, rodent populations were much higher than for Cp Humphreys where habitat was often limited to narrow strips of unmanaged vegetation along drainage systems/holding ponds. While capture rates of A . agrarius associated with the expansion site were high and movement of large trucks that created dusts on dirt, gravel, and hardened roads, the transmission risks of HTNV were reduced by very low HTNV Ab+ rates. Although no cases were reported among US military and civilian personnel, there may have been cases among local contractors and truck drivers that we were not aware of since these cases were not reported through the military medical system.
The Korea Centers for Control and Prevention (KCDC) [ 28 ] reports approximately 400–500 cases of HFRS annually, which are mainly caused by HTNV and SEOV. HTNV is the most common causative agent of HFRS in rural areas of the ROK and is characterized by severe medical manifestations and high mortality rate (9.46%) among US military personnel in Korea with good quality medical care from 1986–2014 [ 2 , 4 – 15 ]. In Korea, human infections of HTNV among military members are usually associated with high populations of A . agrarius in field environments or mice-infested vacant buildings in combination with “dust-creating” activities (e.g., back-blast from artillery, convoy operations, and track and wheeled vehicle maneuvers/operations in field environments), while SEOV infections are usually associated with urban environments activities (e.g., dry sweeping or vacuuming rodent infested buildings) where R . norvegicus predominates [ 3 , 5 ]. While HFRS caused by HTNV infections poses a serious health threat in Korea, it is classified by the US National Medical Intelligence Center (NCMI) as a rare disease, frequently occurring is small clusters. The most recent cluster among US military personnel deployed to the ROK was observed in 2005, when three US soldiers acquired HFRS at TBTA associated with exposure of contaminated dusts in wheeled vehicle cabs (cavalry unit) [ 3 ]. During the same year, another HFRS case was acquired at Firing Point 60, Yeoncheon, associated with the back-blast of artillery. More recently (Nov., 2014) a single case was reported for a US soldier conducting convoy and driver’s training at Dagmar North when HTNV seropositive rates in A . agrarius were 19.3% (considered a HFRS high-risk area), 27–30 days post-exposure that preceded infection [ 29 ]. The epidemiology of these cases was only accomplished through comparative analysis of the HTNV RNA from HFRS patients and associated rodents where the soldiers had trained, as the HTNV varies geographically [ 3 ]. Since 1986, only one case of SEOV has been reported from a US Airman that was vacuuming a rat-infested building and who had a relatively mild case of HFRS [ 5 ]. R . norvegicus , the primary reservoir for SEOV, is routinely captured by the Department of Public Works near housing and other facilities at Cp Humphreys. These resources would provide risk analyses for SEOV risks among US populations residing or working in buildings infested with rats.
Similar to rodent-borne disease surveillance conducted at training sites near the DMZ and other US military installations, A . agrarius was the most frequently collected small mammal [ 4 , 6 – 10 ]. Low to moderate A . agrarius capture rates were reported for limited tall grass habitats at Cp Humphreys and were similar to capture rates observed at other installations, e.g., Osan, Gunsan, and Gwangju Air Bases (unpublished data). During 2008–2009, high capture rates were observed for expansive tall grasses/herbaceous vegetation habitats at the expansion site and were similar to capture rates observed for expansive tall grass habitats at US and ROK operated training sites near the DMZ [ 4 , 6 – 10 ]. However, trap rates were significantly lower during 2010 and 2012 following grading and removal of much of the vegetation from the landscape that provided food and harborage for small mammals. Additionally, in part, the decline may have been due to over predation as the predator populations (e.g., raccoon dogs, feral cats, weasels, and predatory birds) were pushed into space-limited habitats surrounded by farming activities and urban environments. Over time, predator populations will likely stabilize based on available food sources and small mammal populations may rebound to near previous levels for undisturbed areas.
HFRS risks are associated with a combination of factors, including: environmental, reservoir host bionomics, and types of human exposure. Overall, HTNV Ab+ rates for Cp Humphreys/expansion site and Osan Air Base (50 km south of Seoul and 20 km north of Pyeongtaek), are very low, usually ≤6%, when compared to US and ROK operated training areas near the DMZ where seasonal HTNV Ab+ rates varied up to 60% during monthly surveys and overall annual rates varied from 15% to 25% [ 4 , 7 , 9 , 10 ]. Limited surveys at Gunsan and Gwangju Air Bases, near the southern tip of the Korean Peninsula, were indicative of low small mammal populations, as well as none of the A . agrarius were HTNV Ab+ (TA Klein, personal communication). The reason for high HTNV Ab+ rates near the DMZ that decrease over distance to the tip of the peninsula is not understood, but may be related to reproductive behaviors. For training areas near the DMZ, there were observed low reproductive periods during the winter (0–0.3%), followed by a late spring increase in reproduction (4.2–24.6%) (Apr-May), low reproduction during the summer (0–1.3%), and very high reproduction in the late summer/early fall (27.3–70.0%) (Aug-Sep). A large influx of HTNV naïve mice observed at training areas near the DMZ during the fall/early winter periods when temperatures become cooler and habitat is shrinking as vegetation dies likely results in increased territorial disputes, wounding, and higher rates of HTNV transmission [ 30 ]. At Cp Humphreys and the expansion site, peak numbers of gravid females were observed earlier (June, 40.0%) and similarly in August and September (42.5 and 25.2%, respectively), while moderate numbers of gravid females were observed during the early spring (April/May, 16.0–16.2%), July (15.6%), and early winter (October/November, 9.4–11.8%) [ 4 , 6 – 10 ]. In the southern area, young naïve A . agrarius broods throughout the summer may reduce territorial disputes in the fall due to relatively sufficient habitat and food. This proposed decreased movement and competition of naïve young rodents at Cp Humphreys and the expansion site may impact negatively on rodent-to-rodent HTNV transmission and result in lower HTNV Ab+ rates than those observed at the military training areas located near the DMZ, northern Gyeonggi province [ 31 ]. Additionally, the greatly reduced numbers of A . agrarius during 2010 likely reduced the potential for acute infections and corresponding viral shedding during the late fall/early winter when the majority of HFRS cases are reported. Although gravid females were observed throughout the early spring and summer, similar to training areas near the DMZ, the overall age (based on weight) of the population increased through September before rapidly declining as a result of the influx of young naïve rodents during the late fall reproductive cycle. By January, much of the population (based on weight) was replaced by young mice born during the late fall, indicating that the life span of A . agrarius live is approximately one year [ 24 ].
The overall HTNV seropositive rates of Cp Humphreys were higher than observed for the extensive tall grass habitats for undisturbed fallow rice paddies of the expansion area. However, the numbers of HTNV seropositive mice/100 traps were nearly 2-fold greater for the expansion site compared to Cp Humphreys, thereby increasing HFRS risks associated with less disturbed and unmanaged lands. The movement of potentially contaminated soil and vegetation and soil covered concrete roads created the potential for contaminated dusts and HTNV infections, especially for truck drivers and construction site monitors and workers. HTNV risks, while present at Cp Humphreys, are very low as a result of hard surface roads and recreation sites with short-cut grasses in the center, greatly reducing HTNV reservoir host habitats.
In this study, 39 (2.15%) of 1,812 A . agrarius were HTNV seropositive. The partial genome sequence of HTNV M segment was identified from 11 (28.21%) rodent lung tissues of HTNV Ab+ samples. RT-qPCR results showed varied viral loads in both sero- and molecular positive samples. The whole genome sequences of HTNV tripartite RNA were obtained from Aa08-1111, Aa09-189, and Aa09-198 that contained higher number of HTNV RNA copies. The termini of 3’ and 5’ sequences were determined by RACE PCR. Both end sequences of HTNV L, M, and S segments contained a mismatch at 9 th and the noncanonical U-G pair at 10 th nucleotides, suggesting the incomplete complementarity as previously described [ 32 ]. The total length of HTNV L segment for Cp Humphreys and the expansion site, Pyeongtaek, was three nucleotides shorter (6,530nt) than that of HTNV 76–118, demonstrating the deletion of 5’-AUC-3’ at the 5’ end of the L segment. U at the 12 th nucleotide on the M segment was defined compared to that on the HTNV 76–118 M segment.
The phylogenetic analyses of HTNV strains from Cp Humphreys and the extension site demonstrated a greater diversity of the rodent-borne hantavirus; the L segment showed distinct outgroup from entire HTNV strains, previously described in Gyeonggi province. The M segment formed a genetic cluster with HTNV 76–118, while the S segment was a geographic lineage within HTNV strains in Gyeonggi province. The natural reassortment and recombination of HTNV tripartite RNA genomes were observed near DMZ areas, northern Gyeonggi province [ 29 , 33 ]. Thus, the phylogenetic position and characterization of HTNV in Pyeongtaek will be clarified when additional genomic sequences of HTNV are acquired in southern areas of Korean peninsula.
A total of 10 (5.43%) C . lasiura were positive for MJNV, which was identified from shrews distributed in ROK and China, and the sera do not cross react with other rodent-borne hantaviruses [ 17 ]. Recently, there was a report that African shrew-borne hantaviruses were likely to infect humans [ 34 ]. Whether MJNV in C . lasiura poses a human health threat remains to be investigated. A total of 2/254 (0.79%) M . fortis and 1/41 (2.44%) M . minutus were serologically positive for hantaviruses, which was likely the result of interspecies transmission of HTNV since tissues were negative for hantaviruses by RT-PCR.
In summary, the characterization of US military installations undergoing expansion, in combination with small mammal surveillance, provides epidemiological information for the relative abundance of reservoir populations, hantavirus Ab+ rates, and other bionomic and environmental factors that are necessary to identify potential HTNV transmission risks. These transmission risks combined with human activities and exposure, which can be applied for disease risk analyses, are essential to the process of developing strategies for disease prevention. Comprehensive and long term rodent-borne disease surveillance should be the goal of US military preventive medicine to not only identify changes in HFRS disease risks due to modification of feral lands, but subsequently to better understand HFRS disease risks to soldiers, civilians, and family members residing and/or working on the installation. The whole genome sequences of HTNV at Cp Humphreys and the extension site show a greater diversity of rodent-borne hantaviruses in the ROK. Taken together, these data provide the robust impact to increase our knowledge of military activities, environmental conditions, and the genetic diversity of HTNV that can be applied to strategies to improve land management, disease risk mitigation, and the understanding of hantavirology.
Supporting information
S1 Fig
Overall capture rates (%) for rodents and soricomorphs captured at Camp Humphreys and the new expansion site from 2008–2010 and 2012.
(TIF)
S2 Fig
Annual capture rates and overall mean capture rates for years 2008–2010 and 2012 (bars) for rodents and soricomorphs captured at both Camp Humphreys and the new expansion site.
Numbers are the overall mean capture rates, by species, for all years (2008–2010 and 2012).
(TIF)
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Introduction
Cutaneous leishmaniasis (CL) is an important neglected tropical skin disease (skin NTD) of public health importance and is the commonest form of leishmaniasis, characterized by skin lesions which may result in ulcers, scars, disability and stigma [ 1 , 2 ]. Globally, it is estimated that between 0.7 to 1.3 million new cases of CL are reported annually [ 3 ].
A localized outbreak of skin ulcers suspected to be cases of CL was first reported in Ghana from the Ho municipality of the Volta Region in 1999 based on the identification of Leishmania amastigotes in some skin lesion biopsies [ 4 ]. Subsequent studies have identified L . major , uncharacterized Leishmania species, and recently, members of the Leishmania enriettii complex from suspected CL cases in the Ho municipality, suggesting a complex epidemiology of CL in the region [ 5 – 7 ].
Although the Oti region has been part of the Volta region until the year 2019, no previous confirmation of CL cases had been made there. This study was therefore initiated following reports of skin ulcers which were suggestive of CL in some communities of the Oti region, after leishmanin skin test (LST) had been conducted to establish Leishmania infection (reported elsewhere).
Materials and methods
Ethics statement
Ethical approval to conduct this study was obtained from the ethics review committee of the Ghana Health Service (GHS-ERC006/08/18). Written informed consent was obtained from all study participants. For participants under 18 years, written consent was obtained from a parent or guardian.
Study design
This study was based on a cross-sectional study design approach. The study was conducted from October to December 2018 in three communities of the Oti region of Ghana having at least three suspected cases of active CL (ACL). A suspected ACL lesion was defined clinically as any open ulcer with diameter bigger than 5mm. Prevalence of CL among study participants with skin ulcers was investigated. Demographic and epidemiological data were obtained by a structured interviewer administered questionnaire.
Study area
This study was conducted in the following three communities of the Oti region of Ghana: Ashiabre, Keri, and Sibi Hilltop. Ashiabre is in the Tutukpene sub-district of the Nkwanta South municipality while Keri is in the Keri sub-district of the municipality. Sibi Hilltop is in the Sibi sub-district of the Nkwanta North district of the region.
The population of Nkwanta South municipality is estimated to be 117,878 with males constituting 49.6% of the population. Covering a land area of approximately 2733 km 2 , the Nkwanta South municipality is located between latitudes 7 o 30’ and 8 o 45’ North and longitude 0 o 10’ and 0 o 45’East [ 8 ].
The population of the Nkwanta North district is estimated to be 64,553 with males constituting 50.2% of the population. The district is located between Latitude 7°30’N and 8°45’N and Longitude 0°10’W and 045’E. It shares boundaries with Nkwanta South municipality to the south, Nanumba South to the north, Republic of Togo to the east, and Kpandai District to the west [ 9 ].
Inclusion criteria
Eligible study participants were residents in the study community for ≥ 12 months, aged between 2 to 65 years (inclusive).
Sample size considerations
For active case detection, assuming a current CL prevalence (P) of 22.1% [ 4 , 10 ], Z 2 = (1.96) 2 for 95% confidence interval D 2 = maximum 0.05, a minimum sample size (N) of 265 individuals was required for screening for active case detection using the formula:
N = ((Z) 2 P/D 2 ) *(1-P)
Selection of households for study inclusion
Using a sorted list of households, 200 households (with an average of 5–7 persons per household) were selected for study inclusion in each study community using a systematic sampling approach. For this study, a household was defined as a person or a group of persons, who live together in the same house or compound and share the same house-keeping arrangements. The head of each household was defined as a male or female member of the household recognised as such by the other household members. The head of a particular household is generally the person with economic and social responsibility for the household. As a result, household relationships were defined with reference the household head [ 11 ]. The community household list was obtained for each study community based on a household census. The number of households per study community determined by household census was 945, 795, and 1184 in Ashiabre, Keri, and Sibi Hilltop respectively.
A sampling interval I was determined, where I = N/n with N being the sum of individual households in the study community while n was the number of households to be selected. The I was rounded to 2 decimal places.
Using Microsoft excel, the RANDBETWEEN command was used to generate a random decimal integer R between 0 and 1 rounded up to two decimal points. The sequence of households that were selected in each study community were R*I, R*I + I, R*I +2*I, R*I +3*I,….R*I + (n -1 )*I, each rounded up to the next highest whole number [ 12 ]. With the assistance of community-based volunteers, the selected households were identified after which all members of the selected households aged 2 to 65 years were invited to participate in the study, using a door-to-door invitation approach. Because the invitation to participate in the study was extended to households, a household was not included in the study if the household head declined to allow his or her household to participate in the study. However, the agreement of the household head did not make it compulsory for every household member of age 2 to 65 years to participate in the study. Each household member was given the opportunity to go through the informed consent process to decide whether they wish to participate or not.
Sampling of suspected active cutaneous leishmaniasis (ACL) lesions (ulcers)
Each study participant was asked to disclose the occurrence of any skin ulcer(s) on their body. Interviewers also examined the exposed parts of participants body such as legs, arms, neck, and face to identify any suspected active CL (ACL) lesion(s). The location, size, and duration of each suspected ACL was documented. For each suspected ACL lesion, a non-invasive diagnostic sampling technique using sequential tape strips with a diameter of 22 mm (D-Squame, CuDerm Corporation, Texas, USA) was used to obtain samples for subsequent DNA isolation [ 13 ].
For the non-invasive skin sampling, one tape disc was placed on each suspected skin lesion after which even pressure was applied to the disc on the lesion using a plunger which was gently held on the disc and pressed for approximately 20 seconds. The tape disc was then detached and transferred into a sterile 1.5ml Eppendorf vial and stored at 4°C for transportation to the laboratory for further analysis ( Fig 1 ). Participants received standard wound care after sample collection.
10.1371/journal.pntd.0009416.g001
Fig 1
Non-invasive sampling of skin lesions.
DNA isolation from tape strip disc and PCR amplification of Leishmania species
DNA extraction was performed using SpeedTools Tissue DNA Extraction Kit (Biotools, Inc).
A nested polymerase chain reaction (Ln-PCR) approach was used to amplify DNA of Leishmania species from the human skin lesions following an adaptation of the protocol by Cruz et al., 2002 [ 14 ], with the target being the small subunit ribosomal ribonucleic acid (SSU rRNA) gene. Positive control used was Leishmania infantum (JPC strain) with distilled water as negative control.
Data management
Data was managed using Microsoft Access software version 2013 and analyzed using STATA software version 14. Association between nominal variables was assessed using Pearson’s chi square test of association and Fishers exact test. All statistical tests were performed at a 95% confidence level.
Results
Of 600 households (200 in each study community) invited to participate in this study, a total of 587 households comprising 189 (32.2%), 200 (34.1%), and 198 (33.7%) from Ashiabre, Keri and Sibi Hilltop respectively, were included in this study. The study households had a total of 3718 members out of which 3,440 (92.5%) consisting of 1,194, 941, 1305 from Ashiabre, Keri, and Sibi Hilltop respectively were enrolled in the study.
The average household size was 6.3 with a range of 1 to 18 household members. Ashiabre and Sibi Hilltop had an average household size of 7 while Keri had an average household size of 5.
Out of 3440 persons physically examined for ulcers, a total of 595 skin ulcers were observed on 426 (12.4%) ( Table 1 ). Of the 426 persons, 314 (73.7%) were within the age group 5–15 years while those under five constituted 13.6%. The number of skin ulcers observed on the participants ranged from 1 to 7 with those having one ulcer (47.1%) and two ulcers (27.6%) being the majority. Although skin ulcers were observed on various parts of the participants’ body, majority occurred on the lower legs (71.3%) and feet (17.1%). In Ashiabre, Keri, and Sibi Hilltop, 65.2%, 70.1%, and 74.3% of persons with skin ulcers had the ulcer on their lower legs respectively ( Table 1 ).
10.1371/journal.pntd.0009416.t001
Table 1 Individuals with skin ulcers, ulcers sampled and result of Leishmania PCR test.
Characteristic
Category
Ashiabre
Keri
Sibi Hilltop
Total
n
%
n
%
n
%
n
%
P value
Age of individuals with skin ulcers
<5 years
12
21.4
22
11.7
24
13.2
58
13.6
0.141
5–15 years
35
62.5
145
77.1
134
73.6
314
73.7
16–45 years
9
16.1
19
10.1
18
9.9
46
10.8
>45 years
0
0
2
1.1
6
3.3
8
1.9
Total
56
100
188
100
182
100
426
100
Sex of individuals with skin ulcers
Male
36
64.3
109
58
110
60.4
255
59.9
0.684
Female
20
35.7
79
42
72
39.6
171
40.1
Total
56
100
188
100
182
100
426
100
Number of Skin ulcers tested
1
31
44.9
116
41.3
133
54.3
280
47.1
<0.001
2
28
40.6
84
29.9
52
21.2
164
27.6
3
8
11.6
46
16.4
37
15.1
91
15.3
4
0
0
30
10.7
11
4.5
41
6.9
5
2
2.9
5
1.8
0
0
7
1.2
6
0
0
0
0
5
2
5
0.8
7
0
0
0
0
7
2.9
7
1.2
Total
69
100
281
100
245
100
595
100
Skin ulcer locations
Face/Head
3
4.3
5
1.8
6
2.4
14
2.4
0.029
Upper arm
0
0
2
0.7
0
0
2
0.3
Lower arm
1
1.4
13
4.6
11
4.5
25
4.2
Palm/Back of palm
0
0
2
0.7
3
1.2
5
0.8
Chest
0
0
1
0.4
0
0
1
0.2
Back (upper part below neck))
0
0
0
0
2
0.8
2
0.3
Stomach
2
2.9
0
0
0
0
2
0.3
Buttocks
1
1.4
0
0
2
0.8
3
0.5
Thighs
1
1.4
8
2.8
6
2.4
15
2.5
Lower legs(crus/cnemis)
45
65.2
197
70.1
182
74.3
424
71.3
Feet
16
23.2
53
18.9
33
13.5
102
17.1
Total
69
100
281
100
245
100
595
100
Leishmania pcr result
Negative
55
79.7
219
77.9
171
69.8
445
74.8
0.061
Positive
14
20.3
62
22.1
74
30.2
150
25.2
Total
69
100
281
100
245
100
595
100
PCR test of the 595 ulcer samples indicated that 150 (25.2%) of them were Leishmania positive. In the study communities, 14 (20.3%), 62 (22.1%), and 74 (30.2%) of skin ulcers tested from Ashiabre, Keri, and Sibi Hilltop respectively were positive for Leishmania ( Table 1 ).
Of the 595 ulcer samples tested, 365 (61.3%) were obtained from males while 90 (60.0%) of the 150 Leishmania positive samples were also obtained from males. Also, 437 (73.4%) of the ulcer samples tested as well as 112 (74.7%) of the Leishmania positive ulcer samples were obtained from people within the age group 5–15 years ( Table 2 ).
10.1371/journal.pntd.0009416.t002
Table 2 Skin ulcers tested for Leishmania parasite using PCR by age and sex.
Sex
Age
Number of skin
Leishmania positive ulcers
ulcers tested
n (%)
Males
< 5 years
48
8 (16.7)
5–15 years
276
70 (25.4)
16–45 years
36
10 (27.8)
>45 years
5
2 (40.0)
Subtotal
365
90 (24.7)
Females
< 5 years
45
13 (28.9)
5–15 years
161
42 (26.1)
16–45 years
19
4 (21.1)
>45 years
5
1 (20.0)
Subtotal
230
60 (26.1)
Total
< 5 years
93
21 (22.6)
5–15 years
437
112 (25.6)
16–45 years
55
14 (25.5)
>45 years
10
3 (30.0)
Total
595
150 (25.2)
The 150 Leishmania positive ulcer samples were obtained from 136 study participants of which 123 (90.4%) had single Leishmania positive skin ulcer, 12 (8.8%) had two Leishmania positive skin ulcers and 1 person had three Leishmania positive skin ulcers ( Table 3 ). Majority of individuals with Leishmania positive ulcers were within the age group of 5–15 years (73.5%) followed by children under five (14.0%) and persons aged 16–45 years (10.3%). Across the study sites and among males and females respectively, majority of persons with Leishmania positive skin ulcer(s) were within the age group 5–15 years ( Table 3 ).
10.1371/journal.pntd.0009416.t003
Table 3 Distribution of individuals with Leishmania positive skin ulcers by age, sex, and community of residence.
Characteristic
Category
Ashiabre
Keri
Sibi Hilltop
Total
Male (%)
Female (%)
Male
Female
Male
Female
Male
Female
Total
Individuals with one Leishmania positive skin ulcer
<5 years
1 (25.0)
1 (12.5)
3 (9.4)
5 (27.8)
4 (9.8)
3 (15.0)
8 (10.4)
9 (19.6)
17 (13.8)
5–15 years
3 (75.0)
6 (75.0)
23 (71.9)
11 (61.1)
31 (75.6)
15 (75.0)
57 (74.0)
32 (69.6)
89 (72.4)
16–45 years
0
1 (12.5)
6 (18.8)
1 (5.6)
4 (9.8)
2 (10.0)
10 (13.0)
4 (8.7)
14 (11.4)
>45 years
0
0
0 (0)
1 (5.6)
2 (4.9)
0
2 (2.6)
1 (2.2)
3 (2.4)
Sub total
4 (100)
8 (100)
32 (100)
18 (100)
41 (100)
20 (100)
77 (100)
46 (100)
123 (100)
Individuals with two Leishmania positive skin ulcers
<5 years
0
0
0
1 (33.3)
0
1 (25.0)
0
2 (28.6)
2 (16.7)
5–15 years
1 (100)
0
3 (100)
2 (66.7)
1 (100)
3 (75.0)
5 (100)
5 (71.4)
10 (83.3)
16–45 years
0
0
0
0
0
0
0
0
0
>45 years
0
0
0
0
0
0
0
0
0
Sub total
1 (100)
0
3 (100)
3 (100)
1 (100)
4 (100)
5 (100)
7 (100)
12 (100)
Individuals with three Leishmania positive skin ulcers
<5 years
0
0
0
0
0
0
0
0
0
5–15 years
0
0
0
0
1 (100.0)
0
1 (100.0)
0
1 (100.0)
16–45 years
0
0
0
0
0
0
0
0
0
>45 years
0
0
0
0
0
0
0
0
0
Sub total
0
0
0
0
1 (100)
0
1 (100)
0
1 (100)
Individuals with Leishmania positive skin ulcer(s)
<5 years
1 (20.0)
1 (12.5)
3 (8.6)
6 (28.6)
4 (9.3)
4 (16.7)
8 (9.6)
11 (20.8)
19 (14.0)
5–15 years
4 (80.0)
6 (75.0)
26 (74.3)
13 (61.9)
33 (76.7)
18 (75.0)
63 (75.9)
37 (69.8)
100 (73.5)
16–45 years
0
1 (12.5)
6 (17.1)
1 (4.8)
4 (9.3)
2 (8.3)
10 (12.0)
4 (7.5)
14 (10.3)
>45 years
0
0
0 (0)
1 (4.8)
2 (4.7)
0
2 (2.4)
1 (1.9)
3 (2.2)
Sub total
5 (100.0)
8 (100.0)
35 (100)
21 (100)
43 (100)
24 (100)
83 (100)
53 (100)
136 (100)
The overall prevalence of cutaneous leishmaniasis ( Leishmania infection observed among those with skin ulcers) was 31.9% (136/426) with prevalence of 23.2% (13/56), 29.8% (56/188), and 36.8% (67/182) observed in Ashiabre, Keri and Sibi Hilltop respectively.
The average size of the skin ulcers observed was 10.2mm by 10.3mm with 573 (96.3%) of them reported to have started in the year 2018. Among the ulcers which started in the year 2018, 17 (3.0%) started between January to July 2018 while 13 (2.3%), 70 (12.2%), 346 (60.4%), 127 (22.2%) of them started in August, September, October and November of the year 2018 respectively. Examples of Leishmania positive skin ulcers observed is captured as Fig 2 .
10.1371/journal.pntd.0009416.g002
Fig 2
Examples of skin ulcers which tested positive for Leishmania parasite.
A. Location: Left lower leg; dimension:10.1mm by 5.9mm. B. Location: Left lower arm; dimension:17.0mm by 15.1mm. C. Location: Left lower arm; dimension:17.6mm by 11.0mm. D. Location: Left lower leg; dimension:14.4mm by 5.2mm.
Of the 426 individuals with skin ulcers, 419 (98.4%) indicated that they applied some form of treatment. Majority of them (67.5%) used herbs while 35.3%, and 14.2% of them used hot stone and hot water respectively as treatment of their skin ulcers ( Table 4 ).
10.1371/journal.pntd.0009416.t004
Table 4 Summary of ulcer treatment methods reported by study participants.
Treatment
Ashiabre
Keri
Sibi Hilltop
Total
method
No.
%
No.
%
No.
%
No.
%
Herbs
21
40.4
117
62.6
145
80.6
283
67.5
Hot stone
3
5.8
72
38.5
73
40.6
148
35.3
Dermacot
7
13.5
31
16.6
3
1.7
41
9.8
Penicillin
7
13.5
14
7.5
8
4.4
29
8.1
Amoxycillin
5
9.6
10
5.3
2
1.1
17
4.7
Hotwater
5
9.6
19
10.2
27
15
51
14.2
Other treatment
5
9.6
4
2.1
6
3.3
15
4.2
Total
52
100
187
100
180
100
419
100
Discussion
Cutaneous leishmaniasis among study participants
The control of CL requires an understanding of the disease epidemiology [ 15 ]. This study confirmed cutaneous leishmaniasis in the study communities by detecting Leishmania infection in 150 (25.2%) out of 595 ulcer biopsies tested by PCR. The overall prevalence of cutaneous leishmaniasis among persons with skin ulcers was 31.9% (136/426) with prevalence of 23.2% (13/56), 29.8% (56/188), and 36.8% (67/182) observed in Ashiabre, Keri and Sibi Hilltop respectively. In Mali, a systematic review reported a prevalence of 40.3% for cutaneous leishmaniasis among suspected CL cases[ 10 ].
Majority of the persons with CL in this study (73.5%) were in the age group of 5–15 years, with males in this age group constituting majority of those infected among persons with skin ulcers. A study in Mali which screened study participants with skin lesions for CL using PCR, confirmed Leishmania infection in samples from 8 persons who were all under 18 years [ 16 ].
A review of literature on CL suggests that although Leishmania infection and subsequent leishmaniasis disease generally tends to be influenced by factors associated with the host, the parasite, as well as the disease vectors, the prevalence of CL usually increases with age till about 15 years [ 17 ]. It is assumed that the prevalence of CL levels of at about 15 years because persons exposed early on in life to Leishmania infection may have acquired some level of immunity to the infection by then [ 17 ]. Observation of the highest prevalence of CL in 5–15 years age group in this study suggest a need to prioritize this group in future CL control planning in the study area.
Treatment of persons with cutaneous leishmaniasis
An important aspect of disease control is treatment of affected people. The data on treatment of skin lesions by study participants indicate that majority of them use herbs (67.5%) followed by those who use hot stone (33.5%) and hot water (14.2%) respectively.
In the case of cutaneous leishmaniasis, the first choice of treatment is pentavalent antimonials with its attendant cost and possible adverse effects [ 18 – 22 ]. However, the evidence for what can be described as optimal treatment for CL has been described as patchy and generally weak. There is therefore a need for the development of improved guidelines for management of CL in addition to the conduct of more robust studies to improve the existing body of evidence for treatment of CL [ 18 , 23 – 25 ].
Furthermore, although efforts are ongoing to develop a vaccine against leishmaniasis, there is currently no vaccine licensed for use against leishmaniasis [ 26 , 27 ]. Given the gaps in the treatment of leishmaniasis and ongoing global efforts to develop vaccines, there is a need to develop measures in the local Ghanaian context, to protect people who are affected by leishmaniasis while research continues to provide data on critical aspects of the disease such as the vectors and reservoirs.
Need for investigation of skin ulcers which were negative for Leishmania infection
Given that not all skin ulcers observed in the study communities were infected with Leishmania parasites, there is a need for continuous diagnoses of skin ulcers observed in the study communities in order to identify the ulcers infected by Leishmania parasite for the appropriate treatment to be applied [ 28 – 30 ].
Some studies have reported occurrence of other skin ulcers such as buruli ulcer, and yaws in Ghana [ 31 – 34 ]. A pilot study aimed at using azithromycin as treatment for yaws in some communities of the West Akim district of Ghana for instance, used sero-positivity based on a point of care dual treponemal and non-treponemal test as the primary outcome in addition to presentation with clinically active yaws like lesions (as secondary outcome) to select yaws cases [ 35 ].
As a result, future studies aimed at screening a larger sample of persons in the study area for yaws and other skin ulcer causing diseases such as buruli ulcer, incorporating more sophisticated laboratory diagnostic approaches may help to better characterize the causes of skin ulcers in the study area.
Conclusions
Out of 426 individuals observed with various numbers of skin ulcers in the study communities, 136 (31.9%) individuals had various numbers of confirmed Leishmania positive skin ulcers. The observation of skin ulcers which tested negative to Leishmania infection suggests a need to test for additional causes of skin ulcers such as Treponema pallidum pertenue and Mycobacterium ulcerans in the study area.
Limitations of the study
Molecular characterization of the ulcer samples for agents of other skin ulcer causing diseases reported in Ghana such as yaws, and buruli ulcer would have enriched the data.
Inclusion of a household in the study depended on the consent of the household head. This may have led to the exclusion of a few households, given that 587 households were included out of 600 households invited.
Supporting information
S1 STROBE checklist
Checklist according to The Strengthening the Reporting of Observational studies in Epidemiology (STROBE) guidelines.
(DOCX)
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Introduction
Cyclodextrins are water-soluble cyclic oligosaccharides with hydrophilic outer surface and hydrophobic inner cavity. Their chemical structure enables them to form inclusion complexes with lipophilic molecules in aqueous solutions leading to the increment of aqueous solubility of guest molecules. The complex formation ability of cyclodextrins is utilized mainly in pharmaceutical industry for the formulation of water insoluble or poorly soluble drugs of Class II and Class IV of the Biopharmaceutics Classification System (BCS). Solubility- and absorption-enhancing effects of cyclodextrins lead to higher bioavailability of intestinal formulations, and complex formation can increase the stability of active substances [1] [2] . Several cyclodextrin derivatives were synthesized to improve the complexation efficacy and decrease toxicity. Lipophilic cyclodextrins such as methylated cyclodextrins (e.g. randomly methylated β-cyclodextrin) and hydrophilic cyclodextrins like hydroxypropyl derivatives (e.g. 2-hydroxypropyl-β-cyclodextrin) are distinguished, even if their solubility in water is high [3] . Besides the pharmaceutical applications, β-cyclodextrins are also used in cell biology research for the removal of cholesterol from cell membrane [4] and to study the role of cholesterol on cellular functions. In the case of β-cyclodextrins a relationship could be identified among the substituents of the cyclodextrin ring, cholesterol solubilization, hemolytic activity and cytotoxicity [5] . Membrane cholesterol extraction can induce several cellular effects. The activity of membrane transporters, such as P-glycoprotein is sensitive to the presence of cholesterol [6] , [7] , [8] . The disruption of cholesterol rich membrane rafts alters the integrity of tight junctions and barrier functions of cell layers [9] , [10] . These effects can also increase the permeability and absorption of drug molecules from the intestine. On the other hand membrane cholesterol depletion with high cyclodextrin concentration inhibits endocytotic processes [11] , [12] and increases exocytosis [13] .
The chemical structure, number of hydrogen donors and acceptors, relatively high molecular weight (>1000 Da) and the hydrophilicity of cyclodextrins predict that these molecules are not able to permeate biological membranes and have poor absorption [14] ; only lipophilic cyclodextrins are considered to be absorbed from the gastrointestinal tract to some extent [3] . In general, only the free form of drug, which dissociates from the cyclodextrin complex, is thought to be absorbed. According to this mechanism cyclodextrin delivers the drug to the surface of cell membrane, the drug molecule penetrates into the lipophilic membrane, but after delivery the cyclodextrin remains extracellular [3] . Interestingly in vivo studies showed that relatively high amount of hydroxypropyl-β-cyclodextrin and dimethyl-β-cyclodextrin were absorbed via rectum of rats and excreted into the urine, suggesting that not only the free form of drugs, but also cyclodextrin complexes may be absorbable through the rectal mucosa [15] .
Although cyclodextrins most likely cannot permeate the cell membrane by diffusion, recent findings revealed that they are able to enter cells. Methyl-β-cyclodextrin-dextran conjugates and hydroxypropyl-β-cyclodextrin were found to enter cells by endocytosis, as they reduced intracellular cholesterol accumulation in Niemann-Pick type C mutant cells acting at the level of endocytotic organelles inside the cells [16] . Intracellular accumulation of the fluorescent mono-4-(N-6-deoxy-6-amino-β-cyclodextrin)-7-nitrobenzofuran (NBD-β-CD) was also detected in HepG2 and SK-MEL-24 cells, and endocytosis as a possible mechanism for the transmembrane passage of NBD-β-CD was suggested [17] . Macropinocytosis of amphiphilic cationic cyclodextrin transfection complexes were also observed in Caco-2 intestinal epithelial cells [12] , and clathrin-dependent endocytosis of a fluorescent methyl-β-cyclodextrin by HeLa cells was demonstrated [18] .
These results raise the possibility that cyclodextrin molecules not only increase the solubility of poorly soluble drugs and act as permeation enhancers in the intestine, but are able to enter intestinal cells by the endocytotic pathway. This mechanism, the intracellular route and fate of cyclodextrins have not been investigated on intestinal epithelial cells yet, although transcytosis is known in the case of intestinal epithelial Caco-2 cells [19] . There is also limited information about the permeability of cyclodextrins on Caco-2 monolayers.
In the present study our aim was to examine the interaction of the fluorescently labeled randomly methylated β-cyclodextrin (FITC-RAMEB) with Caco-2 colon cell layer and examine the cellular uptake of cyclodextrins on intestinal epithelial cells.
Materials and Methods
Randomly-methylated β-cyclodextrin (RAMEB) was purchased from Wacker Chemie (Munich, Germany). 6-monodeoxy-6-mono[(5/6)-fluoresceinylthioureido]-RAMEB (FITC-RAMEB) (DS = 1 for FITC, DS ∼ 12 for methyl) was the product of CycloLab Ltd(Budapest, Hungary). FITC-RAMEB was prepared by reacting methylated amino-cyclodextrin with fluorescein-5(6)-isothiocyanate as described elsewhere [18] . By this reaction FITC was covalently coupled to RAMEB. CellMask Deep Red plasma membrane stain and CellLight ® Early Endosomes-RFP *BacMam 2.0* was from Invitrogen (Budapest, Hungary). All other reagents were purchased from Sigma-Aldrich (Budapest, Hungary).
Caco-2 Cell Culture
Caco-2 cell line originates from the European Collection of Cell Cultures (ECACC UK). Caco-2 cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% heat-inactivated foetal bovine serum, 1% non essential amino acid and 1% penicillin-streptomycin solution at 37°C in an incubator containing 5% CO 2 . The passage number of the cells was between 25 and 40.
For permeability experiments and release studies, Caco-2 cells were seeded at density of 200,000 cells/well on Transwell® (Corning Costar, USA) polycarbonate filters (pore size 0.4 µm, surface area 1.12 cm 2 ). Culture medium was replaced with fresh medium every two or three days in the inserts. Monolayers were used for the experiments between 20 and 35 days after seeding. The formation of functional epithelial layers was monitored by the development of transepithelial electrical resistance (TEER) and measured with a Millicell–ERS voltohmmeter (Millipore, USA). In permeability experiments TEER values were also measured at the beginning and at the end of sampling to check monolayer integrity and follow the effects of cyclodextrin treatments.
Transepithelial RAMEB Permeability Measurements
In permeability measurements two different cyclodextrin solutions were used for the treatments: 0.05 mM FITC-RAMEB (FR) solution and 5.0 mM RAMEB solution containing 0.05 mM FITC-RAMEB (FRR). The solvent was Hank’s Balanced Salt Solution (HBSS). Caco-2 monolayers were washed twice and pre-incubated with HBSS for 20 minutes at 37°C and then incubated apically with FR or FRR solutions for 2 hours at 37°C. Samples were collected from the basolateral side at 60 and 120 minutes and the volume was replenished with HBSS. The monolayers were washed five times with HBSS and cells were lysed with 1% Triton X-100 (TX-100) (Roche Diagnostics GmbH (Mannheim, Germany). The permeated amount and the FITC-RAMEB content of the cell lysates were determined by FLUOstar Optima microplate reader (BMG LABTECH, Offenburg, Germany) at 492 nm excitation and 520 nm emission wavelength.
FITC-RAMEB permeation rates across the monolayers were determined from the concentration values. With the formula below the apparent permeability coefficients were calculated:
P app : apparent permeability coefficient (cm/s)
dQ/dt: permeability rate of substances (mol/s)
C 0 : initial concentration of the substances in the apical chamber (mol/ml)
A: surface area of the membrane (cm 2 ).
FITC-RAMEB Uptake Studies of Caco-2 Monolayers
In this experiment Caco-2 cells were seeded in black 96 well plates at the density of 10 4 cells/well. After 7 days the cells were washed twice with HBSS and incubated with FR or FRR solutions at 37°C for 5-, 10-, 30-, 60- or 120 minutes. After the treatment cells were washed four times with ice cold HBSS, kept on ice and fixed with 3% paraformaldehyde solution (37°C, 15 min). Fluorescence intensities of the samples were measured with FLUOstar Optima microplate reader. After the measurement 4′,6-diamidino-2-phenylindole dihydrochloride (DAPI), at 300 nM final concentration was added to each well and incubated for further 15 minutes. DAPI was measured at 355 nm excitation and 485 nm emission wavelengths. This dye was used to normalize FITC-RAMEB for DAPI fluorescence intensities.
FITC-RAMEB Release Studies on Caco-2 Monolayers
The first part of this experiment was the same like permeability measurements, except that the apical chamber contained 0.5 mM FITC-RAMEB solution. At the end of the 120 minutes long incubation, inserts were washed five times with ice cold HBSS and divided into two groups. The monolayers of the control group were lysed with 1% Triton X-100 solution and FITC-RAMEB contents were determined with FLUOstar Optima microplate reader. The other group of the inserts was incubated in HBSS at 37°C for another 120 minutes. During the second incubation samples were collected from apical and basolateral chambers at 10-, 30-, 60- and 120 minutes and the released amount of FITC-RAMEB was measured. After incubation these monolayers were also washed twice with HBSS, lysed and the FITC-RAMEB content of the cell layers was determined. The rate of the release was expressed as the percentage of FITC-RAMEB content of the monolayers of control group.
Confocal Microscopy
For microscopic investigations 80,000 cells/well were seeded on round glass cover-slips in 12-well plates. 24 hours later cells were treated with CellLight ® Early Endosomes-RFP *BacMam 2.0* at density of 30 particles per cell and incubated for further 48 hours in cell culture medium. Then samples were washed twice with HBSS and treated with FR or FRR solutions for 30 minutes at 37°C. To completely remove FITC-RAMEB, cells were washed eight times with ice cold HBSS and samples were stained with 1 µg/ml solution of CellMask Deep Red plasma membrane stain for 5 minutes at 37°C. After washing cells twice with HBSS and fixing them with 3% paraformaldehyde solution, cell nuclei were stained with DAPI (300 nM). In some experiments Caco-2 monolayers were treated applying the same protocol for plasma membrane and cell nucleus staining but in these samples Early Endosomes-RFP was not used. In experiments performed in Transwell®, the insert membranes were excised and placed on slides. Confocal microscopy measurements and analyses were carried out by a Zeiss LSM 510 META (Jena, Germany) confocal microscope. To eliminate spectral cross talk samples were illuminated with three different excitations subsequently using multi-track mode (UV lines: 351.1 nm and 363.8 nm of an Ar-ion laser, these two lines were used simultaneously; blue line: 488 nm of another Ar-ion laser; and red line: 633 nm of a He-Ne laser). Emissions above 420 nm, above 505 nm and above 650 nm were detected subsequently in three channels with the META detector, respectively. For confocal imaging pinhole size was set to 1 Airy unit.
Flow Cytometry
Flow cytometric experiments were used to verify endocytosis of FITC-RAMEB by Caco-2 cells. For these experiments cells were trypsinized, washed twice with HBSS and resuspended at 1×10 6 cells/ml concentration. Cells were incubated with FITC-RAMEB, Lucifer Yellow (LY) or calcein AM solutions in different concentrations for 30 minutes at 37°C or at 0°C. Dyes were used in the following concentration ranges: FITC-RAMEB from 0 to 500 µM, calcein AM from 0 to 1 µM, and LY from 0 to 960 µM. At the end of the treatments cells were washed three times with ice cold HBSS and kept on ice until measurements. Propidium-iodide was added to the cells at the concentration of 2 µg/ml to recognize dead cells. In uptake inhibition experiments cells were pre-incubated with 10 µM rottlerin for 45 minutes before adding FITC-RAMEB or LY. Cells were analyzed by five-laser BD FACSaria II flow cytometer (BD Biosciences, San Jose, CA). In the case of FITC-RAMEB and calcein AM staining, cells were illuminated with 488 nm laser line, while for LY staining with the more optimal 445 nm. In all previous cases fluorescence emission was detected via 502 nm long pass dichroic mirror and 530/30 nm band pass filter. Single cell events were recognized using both the area and width of the forward-scattered light and side-scattered light signals. Viable cells were gated in according to their low intensity propidium iodine fluorescence excited at 561 nm and detected via 590 nm long pass filter.
Statistical Analysis
For statistical analysis SigmaStat softver (version 3.1; SPSS Inc.) was used. Data are presented as means ± SD. Comparison of two groups was performed by unpaired or paired t-test, while comparison of more than two groups was performed using ANOVA. Differences were considered significant at p<0.05.
Results
Transepithelial FITC-RAMEB Permeability in Caco-2 Cell Monolayers
In order to investigate the permeability of the fluorescent derivative of RAMEB through the intestinal epithelial barrier we applied Caco-2 monolayers. Two cyclodextrin solutions, 0.05 mM FITC-RAMEB (FR) and 5.0 mM RAMEB solution containing 0.05 mM FITC-RAMEB (FRR) were used. The permeability of FITC-RAMEB was determined in both cases and the results were expressed in apparent permeability values (P app ). The apparent permeability of FITC-RAMEB was very low both in FR and FRR treatments, 3.35±1.29×10 −8 and 4.23±1.46×10 −8 cm/s, respectively. There was no significant difference between these two average permeability values (n = 9 for FR and n = 6 for FRR treatments, p>0.05), indicating that 5 mM RAMEB co-treatment had no effect on the permeability of FITC-RAMEB and the integrity of the monolayer.
The integrity of monolayers was tested by measuring transepithelial electrical resistance (TEER). The TEER values did not decrease significantly after the cyclodextrin treatments (p>0.05) ( Fig. 1 ).
10.1371/journal.pone.0084856.g001 Figure 1
Transepithelial electric resistance (TEER) of Caco-2 monolayers before and after 120 minutes permeability experiments.
Cell layers were treated with 0.05-RAMEB (FR) alone or in the presence of 5 mM RAMEB (FRR). Untreated monolayers were kept in HBSS. Values are expressed as means ± SD, n = 9 for FR, n = 6 for FRR treatment and n = 6 for untreated samples. There were no significant differences between TEER values before and after the treatments (p>0.05) and among the groups (p>0.05).
At the end of the permeability measurements cell layers were washed thoroughly with HBSS and lysed with 1% TX-100. The fluorescence of cell lysates were measured with FLUOstar Optima microplate reader. The fluorescence of FR and FRR treated samples were significantly higher than the untreated monolayers (p<0.001), indicating, that Caco-2 cell layers accumulated fluorescently-labeled RAMEB ( Fig. 2 ). FRR, containing 5 mM RAMEB did not change the accumulation of FITC-RAMEB in the cell layers (p>0.05).
10.1371/journal.pone.0084856.g002 Figure 2
Accumulation of FITC-RAMEB in Caco-2 monolayers after 120 minutes permeability experiments.
Caco-2 monolayers were treated with 0.05 mM FITC-RAMEB (FR) alone or in combination with 5 mM RAMEB (FRR) for 120 minutes. Monolayers were washed and the fluorescence intensity of the accumulated FITC-RAMEB was determined with FLUOstar Optima microplate reader. Presented values are means ± SD, n = 7 for FR, n = 4 for FRR treatment and n = 5 for untreated samples. FR and FRR treatments increased significantly the fluorescence of monolayers compared to the untreated control (p<0.001).
FITC-RAMEB Accumulation in Caco-2 Monolayers
The time dependence of FITC-RAMEB accumulation was measured in 96-well plates with a microplate reader. Caco-2 cells were treated with FR and FRR solutions for 5-, 10-, 30-, 60- or 120 minutes. No difference could be seen between FR and FRR treatment up to 120 minutes. The accumulated amount of FITC-RAMEB increased during the 120 minutes of the experiment. The rate of uptake was fast during the first 5–10 minutes and slower in the remaining period ( Fig. 3 ).
10.1371/journal.pone.0084856.g003 Figure 3
Kinetics of FITC-RAMEB uptake in Caco-2 monolayers.
Cell monolayers were treated with 0.05-RAMEB (FR) alone or in combination with 5 mM RAMEB (FRR) and in different time points the incubation was stopped. After washing cells were fixed with 3% paraformaldehyde solution and the accumulated FITC-RAMEB was determined by microplate reader. Cell nuclei were labeled with DAPI and fluorescence intensities of FITC-RAMEB were normalized for DAPI fluorescence intensities. Values are expressed as means ± SD, n = 3 for FR and FRR treatments.
Release of Accumulated FITC-RAMEB from Caco-2 Monolayers
In FITC-RAMEB release studies Caco-2 monolayers were treated with 0.5 mM FITC-RAMEB solution for 120 minutes, washed five times with ice-cold HBSS and the fluorescence of control group of monolayers was determined and considered as 100%. The second group of monolayers was kept for further 120 minutes in fresh HBSS at 37°C and the released amount of FITC-RAMEB in apical and basal chambers was determined as a function of time. FITC-RAMEB appeared rapidly in both chambers. About 85% of initial fluorescence was released into the apical chamber within the first hour, while only about 7.4% of the accumulated FITC-RAMEB was released into the basal chamber from monolayers after 2 hours of incubation ( Fig. 4 ). At the same time the fluorescence of the monolayers decreased drastically, from 100±8.8% to 8.9±1.5%.
10.1371/journal.pone.0084856.g004 Figure 4
Release of FITC-RAMEB from Caco-2 monolayers.
Treatment with 0.5-RAMEB was carried out on Transwell® inserts for 120 minutes, then the monolayers were washed and FITC-RAMEB release was followed in the apical and basolateral chambers during the next 120 minutes. FITC fluorescence intensities were determined in the control group of the samples after the first 120 minutes and considered as 100% accumulation. The rate of release in the second group was compared to this value. Values are means ± SD, n = 4.
FITC-RAMEB Internalization in Undifferentiated and Differentiated Caco-2 Cells and Colocalization with Rab5a Early Endosome Marker
The accumulation of FITC-RAMEB in Caco-2 cells and monolayers was visualized by confocal laser scanning microscopy. In undifferentiated Caco-2 cells, FITC-RAMEB could be detected on CLSM images as small bright particles, located in the cytoplasm ( Fig. 5 ). In the mid-sections the cyclodextrin-loaded granules were found under the cell membrane and near the cell nuclei.
10.1371/journal.pone.0084856.g005 Figure 5
Confocal images of undifferentiated Caco-2 cells.
Cells were treated with the solution of 0.05-RAMEB and 5 mM RAMEB (FRR). FITC-RAMEB (green) is localized in small vesicles (white arrows) under the CellMask labeled cell membrane (red) or in larger vesicles near the DAPI stained cell nucleus (light blue). Aggregated particles of FITC-RAMEB can be also seen outside the cell membrane (A). Nine consecutive confocal sections of a cluster of cells were recorded (B). Each section is one and half micrometer thick. FITC-RAMEB (green) is located in cytoplasmic granules. The granular bright particles are observed inside the cell membrane and outside of the cell nuclei.
To examine the FITC-RAMEB uptake of differentiated Caco-2 cells, monolayers grown on Transwell® inserts were used. Fig. 6 shows that FITC-RAMEB is able to enter the Caco-2 monolayer and it is localized in granules within the cytoplasm. There was no difference between cellular uptakes of FITC-RAMEB after FR or FRR treatments on the confocal images.
10.1371/journal.pone.0084856.g006 Figure 6
Cyclodextrin enters differentiated Caco-2 cells of a high resistance Caco-2 cell layer.
A confluent layer was treated with 0.05-RAMEB and imaged by confocal microscope in twelve two-micrometer thick sections, of which six are demonstrated in the panels on the left side (A–F). On the right, one middle section of the image shows the top view of the cell layer (H) at the level indicated by blue lines in side sections. Upper (G) and right (I) side images are appropriate sections from perpendicular directions at green and at red lines. Crosshair (green and red lines at the long white arrow) set to an intense FITC-RAMEB (green) granule (indicated by arrows), which is located at the nuclear (light blue DAPI stain) level of cells. Several smaller FITC-RAMEB green granules can be seen below the cell membrane marked by CellMask (dark blue).
These observations suggested that cyclodextrin molecules enter the cells by endocytosis. To confirm this hypothesis, we investigated the colocalization of FITC-RAMEB with the small GTPase Rab5, which is a key determinant of early endosomes [20] . Caco-2 cells were transiently transfected with a plasmid coding for a red fluorescent protein tagged Rab5a GTPase fusion protein (RFP-Rab5a) that was strongly expressed in the cell membrane. As Fig. 7 shows, FITC-RAMEB colocalizes with RFP-Rab5a. Colocalization is marked by yellow pixels. Pearson’s correlation coefficients were calculated and they were between 0.55 and 0.78 after 30 minutes incubation, indicating that the entry of RAMEB into the cytoplasm and the formation of early endosomes are associated. High degree of colocalization could be observed after 2 minutes of incubation, and after 30 minutes colocalization could be still detected indicating, that endocytosis functioned continously (see also Figure S1 and S2 .).
10.1371/journal.pone.0084856.g007 Figure 7
FITC-RAMEB colocalized with Rab5 proteins.
RFP-Rab5a transfected Caco-2 cells were treated with 0.05 mM FITC-RAMEB (green) for 30 minutes. Colocalization is indicated by yellow pixels in the confocal microscopic images on the sections marked by the 18-micrometer and 21-micrometer label. Figure shows nine subsequent, three-micrometer thick confocal sections giving altogether a twenty-four-micrometer cross layer of a Caco-2 cell. CellMask (dark blue) was used for labeling membrane at the cell surface and nucleus was stained by DAPI (light blue). Rab5 proteins (red) are visualized by transient transfection of a plasmid coding Red Fluorescent Protein (RFP) tagged Rab5.
Internalization of FITC-RAMEB and its Inhibition by the Fluid Phase Endocytosis Inhibitor Rottlerin
Internalization of FITC-RAMEB was also investigated by flow cytometry. Caco-2 cell suspensions were treated by FITC-RAMEB, the macropinocytosis marker Lucifer Yellow and the lipophilic membrane permeability marker calcein-AM, both at 37°C and 0°C. Major differences could be seen between the uptake of hydrophilic and lipophilic molecules ( Fig. 8 ). Intracellular accumulation of calcein was dependent on dye concentration, but was independent of temperature. At the same time, both FITC-RAMEB and Lucifer Yellow uptake increased as a function of the dye concentration, but it was inhibited at 0°C.
10.1371/journal.pone.0084856.g008 Figure 8
Cellular uptake of calcein-AM (A), FITC-RAMEB (B) and Lucifer Yellow (C) as a function of ligand concentration.
Cells were treated at 37°C and 0°C and the cellular fluorescence was determined by flow cytometry, after excluding dead cells with propidium iodide.(Graphs show results of a representative experiment).
Rottlerin, a macropinocytosis inhibitor decreased significantly both FITC-RAMEB and Lucifer Yellow internalization in Caco-2 cells. Although the inhibition was not complete, the extent of inhibition of FITC-RAMEB uptake was similar to that of Lucifer Yellow ( Fig. 9 ).
10.1371/journal.pone.0084856.g009 Figure 9
Effect of 10 µM rottlerin on the cellular uptake of FITC-RAMEB and Lucifer Yellow.
Caco-2 cells were pre-incubated for 45 minutes with rottlerin and the internalization of the fluorescent molecules was detected by flow cytometer. (n = 3, p<0.01).
Discussion
In the present study we investigated the permeability and cellular uptake of the fluorescent methyl-β-cyclodextrin in intestinal Caco-2 cells. The available data regarding the absorption and oral bioavailability of cyclodextrins is very limited and there are no Caco-2 permeability values in the literature. In early studies of intestinal absorption of 14 C-labelled β-cyclodextrin in rats only 5% of the administered activity could be detected in the blood. It was concluded that β-cyclodextrin was not absorbed from the stomach and the small intestine, and the low absorption was explained with the amylase action: only the open-chain dextrins and the glucose formed from cyclodextrins were absorbed [21] . According to recent publications the oral bioavailability of HPBCD is less, than 0.03% and it is approximately 0.3% for β-cyclodextrin [22] , while RAMEB has an oral bioavailability of about 12% in rats, [23] .
Our permeability results with FITC-RAMEB are in accordance with the low intestinal absorption of cyclodextrins, as the P app values were 3.35±1.29×10 −8 and 4.23±1.46×10 −8 cm/s for FR and FRR treatments, respectively. These data are also in agreement with the permeability results of natural cyclodextrins (α-, β-, and γ-cyclodextrin) on pulmonary Calu-3 cell layers, which were in the same order of magnitude [24] .
Methylated cyclodextrins are also used for membrane cholesterol depletion in 5–10 mM concentration [4] , [25] and can enhance the penetration of drugs [10] . 0.05 mM cyclodextrin presumably does not affect membrane cholesterol significantly, since it is 1/100 of the usually applied concentration. At 5 mM RAMEB concentration we did not observe cytotoxicity on Caco-2 cells previously [5] , therefore we investigated the effect of 5 mM RAMEB on FITC-RAMEB permeability and the TEER of the monolayers. No significant difference could be observed on permeability and resistance values of FR and FRR treatments (p>0.05), and 5 mM RAMEB had no effect on the permeability of the monolayer.
Examining the fluorescence of the cells at the end of the permeability experiments we found that a significant amount of FITC-RAMEB accumulated in the cell layers. These cyclodextrins could not be removed by extensive washing. We investigated the time-dependence of FITC-RAMEB accumulation in the monolayers and as Fig. 3 shows Caco-2 cells successively accumulated both FR and FRR up to 120 min of the experiment. To reveal the fate of the accumulated FITC-RAMEB we loaded the cells with 0.5 mM FITC-RAMEB solution, using 10 time higher concentration than in permeability studies. This resulted in 10 time higher accumulation, but the permeability of FITC-RAMEB did not increase (2.28±0.34×10 −8 cm/s). After 120 minutes, the release of the accumulated cyclodextrins was followed both in apical and basolateral directions. Interestingly FITC-RAMEB appeared in both the apical and basal chambers, but the majority of the accumulated cyclodextrin was released to the apical direction. Only 7.4% of the accumulated FITC-RAMEB reached the basal chamber.
These results indicated that although the intestinal Caco-2 monolayer is an almost impermeable barrier for the cyclodextrin molecules, the cells are able to take up cyclodextrins from solutions with a mechanism different from simple diffusion. Studies on Calu-3 monolayers suggested that cyclodextrins traverse these monolayers by paracellular route, although transcytosis could not be excluded [24] . Recent publications revealed that certain cell types are able to internalize cyclodextrins by endocytosis [16] , [17] , [18] ; therefore we investigated the intracellular localization of FITC-RAMEB by confocal microscopy. Fig. 5 and 6 show that FITC-RAMEB is able to enter into the cytoplasm of both undifferentiated and differentiated Caco-2 cells. Since the labeled cyclodextrin was localized in vesicles in the cytoplasm, the possibility of endocytosis was investigated hereafter. In the cell membrane RFP-Rab5a fusion protein and FITC-RAMEB showed colocalization. Rab5 is a key organizer of early endosomes, but it cannot be detected in late endosomes [20] . In our confocal microscopy images Rab5 and FITC-RAMEB did not exhibit colocalization in vesicles in deeper layers. The colocalization of RFP-Rab5a and FITC-RAMEB suggests that endosome formation is involved in the initiation of cyclodextrin internalization.
Endocytosis has two major routes, phagocytosis and pinocytosis or fluid-phase uptake. Fluid-phase endocytosis, which requires the cargo molecules to be dissolved, can be subdivided into macropinocytosis, clathrin-mediated, caveolin-mediated and clathrin- and caveolin-independent endocytosis [26] . The widely used marker of macropinocytosis is Lucifer Yellow [27] , [28] , [29] . Flow cytometry analyses revealed that in Caco-2 cells Lucifer Yellow was internalized in a concentration dependent manner and its uptake could be inhibited at 0°C [28] , [29] . FITC-RAMEB showed similar cellular uptake: at 37°C accumulated in the cells as a function of concentration, while at 0°C FITC-RAMEB uptake was diminished. On the other hand lipophilic calcein AM showed the same cellular accumulation at 0°C and 37°C, as it rapidly permeated the lipid membrane [30] , and the intracellular accumulation was not inhibited by cooling. The macropinocytosis inhibitor rottlerin [27] had similar inhibitory effect on FITC-RAMEB and LY accumulation. These results indicate, that in Caco-2 cells macropinocytosis is involved in the entry of FITC-RAMEB. It also explains why the majority of the accumulated FITC-RAMEB was released to the apical direction. It was demonstrated in human epidermoid A431 cells, that macropinosomes recycle their content to the cell surface [31] . It seems that the same mechanism could be observed in Caco-2 cells, as the mechanism of internalization was macropinocytosis, the majority of accumulated cyclodextrin was guided to the apical cell surface. Nevertheless, the total recycling of the internalized cyclodextrin molecules took at least one hour, which means that this process prolongs the contact between cylodextrins or cyclodextrin-drug complexes and the membrane of macropinosomes. It is important to note, that other endocytotic mechanisms should be also taken into consideration. Previous studies implicated fluid-phase endocytosis and clathrin-dependent endocytosis [16] , [17] , [18] for the mechanism of cyclodextrin internalization. Nevertheless, phagocytosis could be also a possibility for cyclodextrin internalization in concentrated cyclodextrin solutions. It is reported that at high concentrations, natural β-cyclodextrin [32] and the fluorescent tetraamino rhodaminyl hydroxypropyl-β-cyclodextrin [33] form large, nano-sized aggregates in water. However, the substitution of OH groups with methyl groups on the cyclodextrin ring inhibits the aggregation of RAMEB and at 12 mM no aggregation was observed [32] . In this study 0.05 mM FITC-RAMEB was applied alone or in combination with 5 mM RAMEB, which is 40–240 times lower cyclodextrin concentration than what was found to form aggregates above, thus phagocytosis can be excluded from among the possible mechanisms of cyclodextrin uptake.
In summary, our results on Caco-2 cells are in accordance with earlier findings, the cellular internalization of water soluble FITC-RAMEB is governed by fluid phase endocytosis in intestinal Caco-2 cells. It is hard to predict the quantitative importance of this mechanism. Even if permeability data are suitable to value the extent of absorption of cyclodextrins, it is difficult to quantify the amount of continuously internalized and released cyclodextrins with this setup of the model. The intestinal absorptive surface area relative to the volume of the gut is much bigger than the surface area of Caco-2 monolayers and on the other hand the peristaltic movement should be also considered. Thus the extent of internalization can be much higher in vivo, even if cyclodextrins are released back to the lumen of the gut and as the process can be continuous along the small intestine its efficiency can be much higher.
Conclusions
Cyclodextrins are used to increase solubility, bioavailability and stability of poorly water-soluble drugs. Our results demonstrate for the first time that randomly methylated-β-cyclodextrins can enter into intestinal epithelial cells by endocytosis. This process can contribute to the enhancement of the intestinal delivery and bioavailability of drugs by cyclodextrins in several ways. It can help to overcome the intestinal membrane barrier, the endosome formation increases the contact surface area between the cyclodextrin-drug complexes and the cell membrane and prolongs the retention time of cyclodextrins in the epithelial cells. Since this study has demonstrated the role of macropinocytosis in the uptake of methylated-β-cyclodextrin in intestinal cells, this mechanism merits further investigations in connection with drug absorption mediated by cyclodextrins.
Supporting Information
Figure S1
Colocalization of RFP-Rab5a with FITC-RAMEB, cell nucleus and cell membrane. RAMEB shows high degree of colocalization with Rab5a immediately below the cell surface membrane of two connected cells at 2 minutes incubation (R = 0.93). Caco2 cells attached to surface of coverslip were transiently transfected by Rab5a (red) tagged by red fluorescent protein (RFP), treated by Fitc-RAMEB (green) for 2 minutes, and fixed. Before imaging surface membrane and nuclei were labeled by CellMask (dark blue) and DAPI (cyan), respectively, for 5 minutes. Panel A shows colocalization of Fitc-RAMEB and RFP-Rab5a; panel B indicates colocalization of DAPI and RFP-Rab5a as negative control (R = −0.39) and panel C specifies colocalization of surface membrane (CellMask) and RFP-Rab5a as positive control (R = 0.95) at a confocal image section crossing RAMEB granules (white areas in panel A right side, section thickness is 1.5 micrometer). Right panels show two channel images of signals tested for colocalization (bar is 10 micrometer), while corresponding left panels show two parameter histograms of signals of the two channels. White areas in right side images were chosen by setting channel signals above thresholds indicated by red signs on scales of corresponding left side two parameter histograms. R indicates Pearson correlation coefficients calculated in images at location of the white areas. In two parameter histograms the highest colocalization between tested channels would be indicated by a 45° diagonal line corresponding to R = 1 (in left panels of A and C R is close to this value), while a 135° diagonal line would indicate a negative correlation (left panel of B).
(TIF)
Figure S2
Colocalization of FITC-RAMEB with RFP-Rab5a in the function of the time. Colocalization of RAMEB and Rab5a was monitored in time during the endocytosis process. The highest average colocalization (0.76±0.01) was measured at 2 minutes after initiation of the endocytosis at 37°C. In later time points, at 5, 10, 20 and 30 minutes R was dropped to a lower but still significant value (R = 0.5–0.6). R, Pearson correlation coefficient was measured in region of interests (ROI) set to those locations where RAMEB granules were observed in confocal sections (one section was 1.5 micrometer thick). Pattern of the intracellular localization of colocalized molecules also changed in time. At 2 minutes colocalization was either dispersed in the surface membrane of cell or in the cytoplasm close to surface membrane. At later time points RAMEB granules moved closer to cell nuclei with lower, but still significant R for Rab5a colocalization (means±SD).
(TIF)
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Introduction
To colonize the host, bacterial pathogens and commensals are limited to metabolites present in tissues to fulfill nutritional requirements. While strategies employed by bacteria to acquire nutrient transition metals such as iron have been an intense area of research, studies defining mechanisms of nutrient sulfur procurement from host environments are garnering heightened interest [ 1 – 6 ]. Sulfur is essential due to its capacity to fluctuate between redox states and therefore catalyze numerous cellular reactions [ 7 , 8 ]. Ultimately, cells require sulfur to synthesize cysteine (Cys) as it is the fulcrum of sulfur metabolism by serving as an intermediate for methionine (Met) and sulfur-containing cofactors such as Fe-S clusters [ 8 – 11 ]. In host cells and some bacterial species, Cys is also required to generate the low molecular weight thiol glutathione (GSH) [ 12 ].
GSH concentrations range between 0.5 and 10 mM in mammalian tissues, making it a relatively abundant source of nutrient sulfur for invading pathogens [ 3 , 12 , 13 ]. In addition to Cys, GSH consists of glutamate and glycine. A unique γ-peptide bond links the glutamate γ-carboxyl to the Cys amine. To maintain Cys reservoirs, organisms rely on GSH catabolism via the γ-glutamyl cycle [ 3 , 12 ]. Liberation of Cys from GSH is a two-step process that requires γ-glutamyl transpeptidase (Ggt), a specialized protease conserved in all domains of life due to its role in the γ-glutamyl cycle [ 12 , 14 , 15 ]. Ggt is localized within the Gram-negative periplasm or the periphery of eukaryotic cells where it degrades endogenous GSH [ 16 – 18 ], but it has also been shown to fulfill the nutritional sulfur requirement of Francisella tularensis [ 3 , 19 ].
Staphylococcus aureus is the leading cause of superficial and invasive bacterial diseases in the United States and Europe [ 20 , 21 ]. Strategies S . aureus employs to obtain nutrient sulfur during pathogenesis are largely unknown. Previous work demonstrated that reduced cysteine (Cys), oxidized cysteine or cystine (CSSC), sodium sulfide, thiosulfate, or GSH stimulated in vitro proliferation of S . aureus [ 22 ]. Further investigation revealed that the TcyP and TcyABC transporters support Cys and CSSC utilization; however, mechanisms of GSH acquisition have not been defined [ 22 , 23 ]. S . aureus does not synthesize GSH but encodes a putative Ggt. This fact supports the hypothesis that staphylococcal Ggt catabolizes exogenous host GSH, liberating Cys as a means to satisfy the nutrient sulfur requirement [ 24 ]. Here we demonstrate that S . aureus utilizes oxidized GSH (GSSG) as a nutrient sulfur source and isolate mutants that display significantly decreased proliferation in a medium supplemented with GSSG or GSH as the sole source of nutrient sulfur. These mutants harbor transposon (Tn) insertions within a five-gene locus, SAUSA300_0200 – 0204 , that encodes a predicted ATP-binding-cassette (ABC) transporter ( SAUSA300_0200–0203 ) and putative Ggt ( SAUSA300_0204 ). Based on the mutant proliferation defects observed in GSH- or GSSG-supplemented media, we name this transporter the G lutathione i mport s ystem (GisABCD). We determine that S . aureus Ggt is localized within the cytoplasm and that the recombinant enzyme cleaves both GSH and GSSG. A search for GisABCD-Ggt across Firmicutes revealed that only a select clade within the Staphylococcus genus, one that excludes S . epidermidis , encodes homologues of the system. Consistent with this finding, S . aureus outcompetes S . epidermidis in GSSG- or GSH-supplemented conditions in a GisABCD-Ggt-dependent manner. Therefore, this newly described nutrient sulfur acquisition system provides a competitive advantage for S . aureus over other staphylococci associated with the human microbiota.
Results
S. aureus proliferates in medium supplemented with GSSG as the sole source of nutrient sulfur
A previous study qualitatively reported that S . aureus proliferates on a chemically defined agar medium supplemented with GSH as the sole sulfur source, indicating that the abundant host metabolite is a viable source of nutrient sulfur [ 22 ]. However, S . aureus likely encounters both reduced and oxidized GSH (GSSG) as the pathogen induces a potent oxidative burst during infection [ 25 ]. Therefore, we hypothesized that S . aureus also utilizes GSSG as a source of nutrient sulfur. To quantitatively assess GSH and GSSG utilization as sulfur sources by S . aureus , a chemically defined medium, referred to as PN, was employed [ 26 ]. PN contains sulfate (MgSO 4 ) and methionine (Met), but S . aureus is not capable of assimilating sulfate or utilizing Met to generate Cys due to an apparent lack of a methionine S -methyltransferase homologue [ 9 ]; thus, oxidized cysteine or cystine (CSSC) is typically added as the source of nutrient sulfur [ 22 , 27 ]. In keeping with this, a USA300 LAC strain of S . aureus (JE2) exhibits substantially decreased proliferation in PN that lacks CSSC ( Fig 1A ). Notably, replacing CSSC with either 50 μM GSH or 25 μM GSSG stimulates robust S . aureus proliferation ( Fig 1A ). To determine whether utilization of GSSG is conserved throughout the species, we examined proliferation of clinical isolates in GSSG-supplemented medium. Growth of methicillin-susceptible and methicillin-resistant clinical isolates was quantified in PN supplemented with GSSG as the sole sulfur source. Compared to PN lacking a viable sulfur source, GSSG supplementation stimulates proliferation ( Figs 1B and S1A ). GSSG supplementation also promotes growth of other S . aureus strains ( Figs 1C and S1B ). S . aureus utilization of GSSG expands the number of sulfur-containing metabolites present in host tissues that are capable of supporting its proliferation.
10.1371/journal.pgen.1010834.g001
Fig 1
Supplementation of GSSG as the sole source of nutrient sulfur supports proliferation of S . aureus .
A. S . aureus JE2 was cultured in PN medium lacking a viable sulfur source (-S) or supplemented with CSSC (25 μM), GSSG (25 μM), or GSH (50 μM). B. JE2 and either MSSA or MRSA clinical isolates were grown in PN supplemented without a viable sulfur source (-S) or 25 μM GSSG for 19 hrs. C. S . aureus strains were cultured in PN medium supplemented without sulfur (-S) or 25 μM GSSG and cultured for 25 h. Bars depict the mean terminal optical density at 600 nm (OD 600 ), and circles represent individual replicate terminal OD 600 . The mean of at least three independent trials and error bars representing ± 1 standard error of the mean are presented.
The SAUSA300_0200–0204 locus supports S . aureus utilization of GSH and GSSG as sulfur sources
To determine genetic factors required for S . aureus utilization of GSSG as a sulfur source, we screened the Nebraska Transposon Mutant Library for mutants that exhibit decreased proliferation in PN medium supplemented with 25 μM GSSG as the sole source of sulfur [ 28 ]. Five GSSG proliferation-impaired mutants were identified in the screen, each harboring an independent Tn insertion in one of five genes present in the SAUSA300_0200-ggt locus ( Fig 2A ). The ggt gene ( SAUSA300_0204 ) encodes an annotated γ-glutamyl transpeptidase, an enzyme that catabolizes GSH in other organisms [ 12 , 14 , 15 ]. Notably, SAUSA300_0200–0203 encodes a putative nickel-peptide ABC transporter. Backcrossing these Tn-inactivated genes into an otherwise wild type (WT), JE2 strain significantly decreased proliferation in medium supplemented with 25 μM GSSG ( Fig 2B ). The Tn mutants also displayed decreased proliferation in PN supplemented with 50 μM GSH as the sole sulfur source ( Fig 2C ). However, the mutant strains demonstrated WT-like growth in medium supplemented with 25 μM CSSC or in a rich medium, indicating the proliferation defect is specific to GSH and GSSG ( Fig 2C inset ). To address complications associated with auto-oxidation of GSH to GSSG in aerobic environments, we tested anaerobic proliferation in media supplemented with GSH or GSSG. Mutant strains harboring a Tn in SAUSA300_0201 or an in-frame deletion of all five genes were used in this assessment. In these conditions, the mutant strains proliferate to WT levels in PN supplemented with CSSC but display diminished growth upon GSH or GSSG supplementation ( S2 Fig ). This finding confirms that SAUSA300_0200 – ggt supports S . aureus utilization of both GSH and GSSG as distinct sources of nutrient sulfur. Complementation experiments tested whether proliferation of a ggt mutant cultured in PN medium supplemented with GSH or GSSG could be restored by providing WT or a C-terminal His-tagged ggt encoded on a plasmid. ggt mutant strains harboring either plasmid display WT-like growth, confirming that decreased proliferation in GSSG- or GSH-supplemented medium is due to genetic inactivation of ggt ( S3 Fig ).
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Fig 2
SAUSA300_0200- ggt supports S . aureus utilization of GSSG and GSH as sources of nutrient sulfur.
A. Orientation of SAUSA300_0200- ggt encoded within the S . aureus genome. B and C. Strains cultured in PN supplemented with 25 μM GSSG (B), 50 μM GSH (C), or 25 μM CSSC (C-inset). The mean of at least three independent trials and error bars representing ± 1 standard error of the mean are presented.
Given the decreased proliferation exhibited by the mutants in GSH- or GSSG-supplemented PN media and the fact that SAUSA300_0200 – SAUSA300_0204 encodes a putative ABC-transporter with a predicted γ-glutamyl transpeptidase (Ggt, SAUSA300_0204), we propose to rename SAUSA300_0200 – SAUSA300_0203 the g lutathione i mport s ystem ( gisABCD ) ( Fig 2A ). Domain architecture analysis of the GisABCD-Ggt system reveals that GisA contains ATP-binding cassette domains ( S4 Fig ). Consistent with the prediction, recombinant GisA purified from Escherichia coli exhibits ATP hydrolysis activity ( S5 Fig ). GisB and GisC contain nine predicted transmembrane regions, consistent with their membrane-bound annotation, while GisD contains a signal peptide with a putative lipid attachment site ( S4 Fig ). Finally, our domain architecture analysis predicts that ggt encodes a γ-glutamyl transpeptidase domain spanning most of the protein ( S4 Fig ).
We next sought to determine whether GisABCD promotes S . aureus GSSG acquisition. To test this, liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to quantify GSSG in WT, a mutant strain harboring an inframe deletion of gisABCD-ggt (Δ gis ), and the previously described ggt transposon mutant. Cells were cultured to mid-exponential phase using 25 μM CSSC as the sulfur source, washed, and then incubated in the absence or presence of 25 μM GSSG for 5 min. In keeping with the fact that S . aureus does not synthesize GSH and therefore is incapable of using it as its low molecular weight thiol, we were unable to detect GSSG in the absence of supplementation across the three strains ( Fig 3 ) [ 29 ]. Importantly, GSSG levels in supplemented WT were significantly increased compared to the supplemented Δ gis mutant ( Fig 3 ). There was no significant difference between GSSG levels in the transporter encoding ggt mutant compared to WT upon exposure ( Fig 3 ). Taken together with the proliferation phenotypes of the gis mutants and the bioinformatic evidence, these data support the conclusion that GisABCD functions as an importer.
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Fig 3
GisABCD promotes acquisition of GSSG.
Levels of GSSG in WT, Δ gis , or ggt mutant cells after a 5 min exposure measured by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Samples were normalized by OD 600 (ODU). The mean and standard deviation of three biological replicates are presented. * P -value < 0.05 as determined by two-way ANOVA with Dunnett’s multiple comparisons test.
Ggt hydrolyzes GSH and GSSG γ-peptide bonds and localizes to the staphylococcal cytoplasm
A hallmark of γ-glutamyl transpeptidases is their capacity to cleave the GSH γ–peptide bond, liberating glutamate. To validate the domain prediction, we sought to determine whether S . aureus Ggt cleaves the γ–peptide bond linking glutamate to Cys in GSH and GSSG [ 30 ]. C-terminal His-tagged recombinant Ggt (rGgt) was expressed and purified from E . coli ( S6 Fig ). In other species, Ggt is translated as an inactive polypeptide that is auto-catalytically cleaved to generate approximate 40 kDa and 35 kDa subunits [ 30 , 31 ]. In keeping with this, a tripartite banding pattern consisting of full-length pro-Ggt (75 kDa) and smaller, mature enzyme subunits (40 kDa and 35 kDa) are observed ( S6 Fig ). Mature Ggt cleaves GSH by attacking the glutamyl residue, transferring it to the enzyme. Ultimately, water hydrolyzes the γ-peptide bond, liberating glutamate [ 32 ]. Therefore, to quantify S . aureus Ggt γ-glutamyl transpeptidase activity, rGgt was incubated with increasing concentrations of GSH or GSSG and glutamate release was measured via mass spectrometry. Glutamate was detected in reactions containing rGgt incubated in the presence of either GSSG or GSH ( Fig 4A and 4B ). Importantly, glutamate was not detected in reactions lacking substrate or rGgt, indicating glutamate release resulted from enzymatic activity. K m values of Ggt for GSSG and GSH were determined to be 38.6 μM and 58.5 μM, respectively. These values are similar to previously reported Ggt homologues expressed in other organisms (e.g. the K m of E . coli Ggt for GSH is 29 μM [ 33 ]). S . aureus rGgt V max for GSSG and GSH are 1.1 μmoles min -1 and 1.0 μmoles min -1 , respectively. These data support in silico predictions and provide a molecular explanation for the ggt mutant proliferation defect in medium supplemented with GSH or GSSG as sources of nutrient sulfur— ggt mutant cells fail to initiate Cys liberation due to an inability to hydrolyze the GSH or GSSG γ-peptide bond. Next, we sought to determine whether hydrolysis of GSH and GSSG occurs intracellularly or extracellularly by probing Ggt localization in S . aureus subcellular fractions.
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Fig 4
S . aureus Ggt liberates glutamate from GSH and GSSG in the cytoplasm.
A and B. rGgt was incubated with indicated concentrations of GSSG (A) or GSH (B). Mean glutamate release per minute was measured using four independent rGgt protein preparations. Glutamate released per minute was calculated and data were fit with the Michaelis Menten equation using GraphPad Prism. Error bars represent ± standard error of the mean. C. Supernatant or whole cell lysate (WCL) fractions of mid-exponential S . aureus ggt ::Tn harboring pOS1 P lgt :: ggt (His -) or pOS1 P lgt :: ggt -His (His +) probed with an anti-His tag antibody (αHis) or anti-Hla antibody (αHla). His-tagged recombinant Ggt (rGgt) was used as a positive control. D. Cell wall and protoplast lysate fractions generated from strains cultured to mid-exponential phase expressing His-tagged (+) or untagged (-) ggt were probed with αHis antibodies or anti-protein A antibodies (αSpa). E. Cell wall or intact protoplasts derived from the indicated cells were incubated in the presence (Prot K+) or absence (Prot K -) of proteinase K (Prot K). To monitor fractionation and Prot K activity, samples were probed with αSpa. F. Bioinformatic predictions and experimental evidence support the presented model of S . aureus import and catabolism of exogenous GSH and GSSG. The predicted substrate-binding protein, GisD, binds GSH or GSSG in the extracellular milieu, which are transported into the cytoplasm by the transmembrane permease complex, GisBC. GisA hydrolysis of ATP provides energy needed for import. Finally, GSH and GSSG are cleaved in the cytoplasm by Ggt, generating glutamate and cysteinyl-glycine or cysteinyl-glycine disulfide, depending on the substrate. The model illustration was created using BioRender.
Bacillus spp. secrete Ggt [ 19 ]; however, structural and cellular localization predictions of S . aureus Ggt did not detect canonical secretion signal sequences within the primary sequence ( S4 Fig ). For example, SignalP predictions detected considerably low likelihoods of signal peptide, twin-arginine translocation (TAT) signal peptide, or lipoprotein signal peptide sequences (0.007, 0.003, 0.008, respectively). TatP 1.0 also failed to predict a signal peptide [ 34 , 35 ]. Ggt localization varies across organisms, but in this case, localization has implications for the substrate of GisABCD. Extracellular Ggt supports a model whereby GisABCD imports Ggt products, whereas intracellular Ggt suggests GisABCD imports GSH and GSSG intact. Evidence for the latter is supported by the presence of GSSG in GSSG-exposed WT but not Δ gis mutant cells ( Fig 3 ). We used the previously described His-tagged Ggt expression vector (Ggt-His) that functionally complements the ggt ::Tn mutant ( S3 Fig ) to determine subcellular localization of the enzyme. S . aureus cells expressing native or Ggt-His were cultured in PN supplemented with 25 μM GSSG, collected at mid-exponential phase, and fractionated into supernatant and whole cell lysate (WCL). An αHis tag antibody (αHis) was used to monitor Ggt-His within each fraction. rGgt served as a size comparison control. Bands corresponding to Ggt-His were not detected in the supernatant fractions; however, a band at ~35kDa was observed in both the Ggt-His WCL and rGgt samples. This band is specific to Ggt-His as it was not observed in WCL generated from cells expressing Ggt lacking the His-tag and corresponds to the rGgt subunit containing the His-tag. Presence of Ggt-His signal within the WCL fraction supports the conclusion that Ggt is cell-associated ( Fig 4C ). To further resolve Ggt localization, cells were fractionated into peptidoglycan cell wall and protoplast fractions. Protoplasts were lysed, generating protoplast lysate that contained the cytoplasm and membrane. A His-dependent ~35 kDa signal was increasingly apparent in the protoplast lysate fraction compared to the cell wall fraction when equivalent levels of total protein are assessed ( Fig 4D ). Protein A (Spa), a protein covalently linked to the peptidoglycan cell wall, was used as a fractionation control. To rule out peripheral, external association of Ggt with the outer leaflet of the plasma membrane, intact protoplasts were isolated and sensitivity to proteinase K (Prot K) was monitored. Spa was used to control for fractionation and Prot K activity. As expected, Spa predominantly localizes to the cell wall and was sensitive to Prot K ( Fig 4E ). On the other hand, a His-dependent band corresponding to Ggt was present in the protoplast fraction in samples treated with or without Prot K ( Fig 4E ). This finding indicates that the membrane shields Ggt from Prot K, supporting the conclusion that the protein resides within the cytoplasm. Taken together, the lack of a secretion signal sequence, cell lysate association, and Prot K protection support a model whereby GSH and GSSG are imported intact and catabolized in cytoplasm, liberating Cys to satisfy the nutrient sulfur requirement ( Fig 4F ). Cytoplasmic localization of Ggt has been reported in only one other bacterial pathogen, Neisseria meningitidis [ 18 ].
GisABCD-Ggt is not required for systemic infection of S . aureus
Given its critical role in nutrient sulfur acquisition in vitro and the abundance of GSH in host tissues, we next tested the potential role of GisABCD-Ggt in S . aureus pathogenesis using a systemic murine model of infection. Contrary to our expectations, we found that mice infected with a gisB ::Tn mutant contained equivalent bacterial burdens compared to WT-infected animals ( S7 Fig ). This result indicates that GisABCD-Ggt is dispensable for S . aureus pathogenesis during systemic infection. There are at least two possibilities for the WT-like virulence exhibited by the gisB ::Tn mutant. First, acquisition of other sulfur sources, such as Cys or CSSC, is sufficient to fulfill nutrient sulfur acquisition in the absence of GSH or GSSG scavenging. In keeping with this, a previous study showed that the TcyP and TcyABC cysteine transporters support S . aureus fitness during heart and liver colonization [ 4 ]. The second possible explanation is that S . aureus encodes multiple GSH and GSSG transporters. In fact, while GisABCD-Ggt supports proliferation of S . aureus in micromolar concentrations of GSSG or GSH, increasing GSH concentrations to levels present in host tissues restores Δ gis mutant proliferation in PN medium ( S8 Fig ). However, amplifying GSSG concentrations did not stimulate Δ gis mutant proliferation. These results support the conclusion that GisABCD-Ggt is an absolute requirement for GSSG utilization but that another, potentially low-affinity, GSH transporter is also active in this pathogen.
GisABCD-Ggt is conserved in select Firmicutes
To define a function for GisABCD-Ggt beyond systemic host colonization, we traced the conservation and evolution of the system throughout Firmicutes using a molecular evolution and phylogenetic approach [ 36 ]. Due to the ubiquity of ABC-transporters across bacterial genera, we first focused on potential Ggt homologues. We found that many genera encode Ggt homologues, including Bacillus , Gracilibacillus , Lysinibacillus , and Brevibacterium ; however, subsequent identification of potential GisABCD homologues was limited to a small subset ( S9 Fig ). Overall distribution of GisABCD-Ggt homologues across Firmicutes revealed six distinct clusters. Cluster 3 is the least populated, containing species encoding only Ggt homologues (e.g., Clostridium tetanomorphum ). Firmicutes in clusters 2 and 4 encode Ggt and either a GisB or a GisC homologue, respectively (e.g., Gracibacillus thailandensis ). Genomes in Cluster 5 contain GisB, GisC, and Ggt homologues (e.g., Bacillus licheniformis ; S9 Fig ). Cluster 6 includes a few bacilli species that encode nearly the complete S . aureus GisABCD-Ggt system (e.g., B . subtilis ), but Cluster 1 stands apart as these genomes encode the full GisABCD-Ggt system similar to the S . aureus query sequences (>80% similarity; e.g., Staphylococcus simiae ). Notably, this cluster is restricted to members of the Staphylococcus genus ( S9 Fig ).
Homologues of a complete GisABCD-Ggt system were exclusively identified in the S . aureus -related cluster complex (Cluster 6)—which includes S . argenteus , S . schweitzeri , and S . simiae ( Fig 5A ) [ 37 , 38 ]. Conservation rapidly diverged with increasing 16S rRNA phylogenetic distance as the next closest related species, S . epidermidis , lacks apparent GisB and GisD homologues ( Fig 5B ). Furthermore, the GisA, GisC, and Ggt homologues exhibit exceedingly low percent similarities ( Fig 5A ). This finding suggests that S . epidermidis is incapable of utilizing low concentrations of GSH or GSSG as sources of nutrient sulfur.
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Fig 5
GisABCD is conserved in exclusive staphylococci and promotes competition in GSH- or GSSG-supplemented media.
A. Heatmap depicting percent similarity of GisA, GisB, GisC, GisD, and Ggt proteins across select staphylococcal species ( S . aureus USA300_FPR3757 GisA, GisB, GisC, GisD, and Ggt amino acid sequences were used as the starting point for the homology search). Conservation cross Firmicutes is depicted in S9 Fig . B. Phylogenic relationship between Staphylococcus species based on 16S rRNA sequences. C. In vitro competition experiments between S . epidermidis strain RP62a and WT (closed circles) or Δ gisABCD-ggt (Δ gis ) S . aureus (open squares). The mean and standard deviation are presented. *** indicates P -value = 0.0003, **** indicates P -value <0.0001 as determined by one-way ANOVA with a Sidak multiple test correction.
GisABCD-Ggt promotes interspecies Staphylococcus competition in a GSH-specific manner
To quantify S . epidermidis organic sulfur source-dependent proliferation, we first needed to define whether this species is capable of utilizing Met, a component of PN medium, as a source of nutrient sulfur. Additionally, S . epidermidis encodes predicted sulfate assimilation enzymes; thus, sulfate (MgSO 4 ) was removed from PN medium [ 39 , 40 ]. Supplementation of sulfate depleted PN with Met as the sole source of nutrient sulfur stimulates proliferation of S . epidermidis , but not S . aureus ( S10A and S10B Fig ). Therefore, Met was also removed and the resulting medium, PN mod , was used to assess GSH- and GSSG-dependent proliferation of S . epidermidis . Compared to S . aureus , S . epidermidis growth exhibited considerable lag phases in media supplementation with either 25 μM GSSG or 50 μM GSH ( S10C and S10E Fig ). Increasing GSH concentrations to 750 μM resulted in comparable proliferation between S . epidermidis and S . aureus ( S10F Fig ). Increasing GSSG concentrations from 25 μM to 375 μM slightly decreased the S . epidermidis lag phase ( S10D Fig ). While GSH and GSSG eventually promote S . epidermidis growth, clearly this species struggles to proliferate when GSSG and GSH are added as sulfur sources compared to S . aureus unless a physiologically relevant concentration of GSH is supplied. These results are consistent with the conclusion that GisABCD-Ggt facilitates efficient S . aureus GSSG and GSH utilization and suggest that low affinity GSH acquisition might be conserved between the two species.
Next, the capacity of GisABCD-Ggt to provide a competitive advantage to S . aureus over S . epidermidis was determined. To evaluate this, competitive indices between S . aureus and S . epidermidis were quantified after a 24 h co-culture in PN mod supplemented with different sulfur sources. As expected, S . aureus outcompeted both a S . epidermidis clinical isolate and the laboratory RP62a strain in PN mod supplemented with 25 μM GSSG or 50 μM GSH ( Figs 5C and S10G ). Conversely, S . epidermidis strains outcompeted S . aureus in medium with 50 μM Met. S . aureus exhibited a competitive advantage over S . epidermidis in 750 μM GSH, despite equivalent S . epidermidis monoculture proliferation ( Figs 5C and S10G ). Both S . epidermidis strains outcompeted S . aureus Δ gis in 25 μM GSSG and 50 μM GSH, underscoring the importance of GisABCD-Ggt in promoting S . aureus competition over S . epidermidis in environments containing GSH or GSSG. Equivalent quantities of S . epidermidis and S . aureus Δ gis were recovered in medium supplemented with 750 μM GSH ( Figs 5C and S10G ). These findings support the conclusion that Gis-independent GSH acquisition is conserved between S . aureus and S . epidermidis , while GisABCD-Ggt promotes S . aureus competition over S . epidermidis .
Discussion
This study increases our knowledge of S . aureus nutrient sulfur acquisition strategies by expanding the host-derived metabolites capable of satisfying the sulfur requirement to include GSSG and identifying proteins that play a pivotal role in the use of GSSG and GSH. The proteins include an ABC transporter, which we named GisABCD, and the γ-glutamyl transpeptidase, Ggt. Lithgow et al . previously showed that chemically defined agar medium supplemented with GSH, CSSC, Cys, sulfide, or thiosulfate stimulated proliferation of S . aureus while sulfate and methionine failed to promote appreciable growth [ 22 ]. Previous investigations into the sulfur acquisition pathways used by S . aureus revealed strategic employment of redundancy. In fact, S . aureus encodes two transporters, TcyP and TcyABC, to acquire Cys and CSSC while the current study highlights multiple mechanisms of GSH acquisition and catabolism [ 4 ]. This is exemplified by the finding that Δ gis mutant proliferation defects in medium supplemented with 50 μM GSH can be suppressed by increasing GSH quantities to physiologically relevant concentrations. A multifaceted strategy to acquire GSH makes implicit sense given the relative abundance of GSH in host cells and because the host invests considerable energy maintaining GSH in the reduced form [ 12 , 41 ]. S . aureus likely encounters an oxidized environment as a consequence of the host innate immune response; therefore, targeting GSSG increases the number of sulfur sources available within the overall host sulfur metabolite reservoir, potentially extending the dynamic range of tissue environments and conditions conducive to staphylococcal growth. Whether the abundance GSH and GSSG change relative to each other as a result of staphylococcal infection is a focus of ongoing investigations, though a recent report showed that GSH is the fourth most abundant metabolite increased in bronchoalveolar lavage as a consequence of S . aureus infection [ 42 ].
While this is the first study to identify a transporter involved in S . aureus GSH and GSSG utilization, GSH transporters have been established in other bacteria. The first bacterial GSH transporter to be discovered was the E . coli ABC transporter GsiABCD (formerly yliABCD ) [ 43 ]. Though GisABCD and GsiABCD are both members of the ABC transporter family, there are several noteworthy distinctions. First, gsiACBD is entirely operonic, while gisA is presumed to be divergently transcribed from gisBCD-ggt . Second, while gisBCD is operonic with ggt , GsiABCD lacks ggt , but encodes an ORF of unknown function called iaaA [ 43 , 44 ]. In addition, the amino acid sequences of the GisD and GsiB substrate binding lipoproteins indicate different mechanisms of GSH and GSSG recognition [ 45 ]. Together, these observations support a distinct nomenclature. Other mechanisms of GSH import have been reported in Gram-positive pathogens. For example, in Streptococcus mutans the substrate binding lipoprotein, GshT, promotes GSH import [ 46 , 47 ]. Subsequent studies revealed that GshT works in concert with TcyBC to acquire GSH and satisfy the sulfur requirement [ 48 ]. GshT homologues are encoded in other streptococci including S . pyogenes and S . pneumoniae . In S . pneumoniae , a Δ gshT mutant was shown to be more sensitive to superoxide, copper, cadmium, zinc, and innate-derived hypothiocyanous acid [ 13 , 49 , 50 ]. The Δ gshT mutant exhibited decreased burdens compared to WT in the nasal cavity and blood [ 13 ]. S . pyogenes Δ gshT mutants were more sensitive to H 2 O 2 and challenge with human neutrophils compared to WT [ 51 ]. Whether S . aureus scavenges GSH as a mechanism to protect against reactive oxygen species, in addition to satisfying nutritional requirements, is a focus of ongoing study.
GSH acquisition has also been explored in Gram-negative pathogens. For instance, Francisella tularensis is capable of replicating within macrophages and independent transposon mutagenesis screens identified Ggt as an important intracellular growth factor [ 3 , 5 ]. Additionally, genes harboring Tn insertions in the Cys-Gly transporter, DptA, and an alternative GSH peptidase, ChaC, also exhibited decreased intracellular proliferation within murine macrophages [ 5 ]. Together, these data support a model whereby GSH is catabolized in the F . tularensis periplasm by either ChaC or Ggt, generating Cys-Gly which is transported into the cytoplasm via DptA, fulfilling the sulfur requirement. The finding that Ggt and ChaC both participate in GSH catabolism reveal that multiple GSH degradation pathways can be active in a bacterial cell. Evidence of redundant mechanisms of GSH catabolism are also presented here as S . aureus proliferation in medium supplied with 750 uM GSH is independent of Ggt. These results suggest S . aureus encodes another peptidase capable of cleaving the GSH γ-peptide bond. Identification of this hypothetical peptidase is a focus of active investigation.
Ggt is conserved throughout all three kingdoms and is typically expressed extra-cytoplasmically. In humans, GGT is peripherally associated with the outer leaflet of the plasma membrane where it plays important roles in sulfur, Cys and redox homeostasis [ 52 ]. In the Gram-positive bacterial species B . subtilis and B . licheniformis , Ggt is secreted [ 17 , 19 ]. In Gram-negative bacteria, like E . coli , Proteus mirabilis , and Helicobacter pylori , Ggt is secreted to the periplasm where it partakes in GSH catabolism [ 16 , 53 – 55 ]. A notable exception is N . meningitidis in which Ggt resides in the cytoplasm [ 18 ]. S . aureus produces the low molecular weight thiol bacillithiol in lieu of GSH suggesting that cytoplasmic Ggt expression primarily functions in nutrient sulfur acquisition rather than GSH or Cys homeostasis [ 56 ]. However, N . meningitidis synthesizes GSH despite expressing Ggt within its cytoplasm [ 57 ]. These observations demonstrate that the relationship between Ggt function and localization is increasingly complex and will need to be resolved by additional analyses of Ggt localization in other bacterial species. Nonetheless, Ggt is an established virulence factor for F . tularensis , N . meningitidis , H . pylori , Acinetobacter baumanii , Campylobacter jejuni , and Bacillus anthracis [ 3 , 5 , 55 , 58 – 61 ]. Therefore, understanding whether the enzyme functions in nutrient sulfur acquisition, Cys and redox homeostasis, or both could provide a framework for designing toxic analogues that synergize with the host oxidative burst. While a role for Ggt in staphylococcal systemic infection can be ruled out, the enzyme may prove to be important in other models of pathogenesis. Alternatively, identification of the secondary GSH peptidase will help determine whether GSH catabolism is important for S . aureus blood stream infections.
Tracing conservation of GisABCD-Ggt across Firmicutes revealed that the system is encoded by an exclusive clade of Staphylococcus species which includes S . argenteus , S . schweitzeri , and S . simiae , but excludes S . epidermidis [ 38 ]. S . epidermidis proliferates poorly in a GSSG-supplemented medium and GisABCD-Ggt promotes S . aureus competition over S . epidermidis in GSH- and GSSG-limiting environments. These results further underscore the importance of GisABCD-Ggt to acquisition of GSSG and GSH as sources of nutrient sulfur. Both S . aureus and S . epidermidis are common residents of the human skin microflora and S . argenteus causes skin and soft tissue infections and sepsis in humans [ 62 – 64 ]. Therefore, the skin represents a dynamic host niche that could be further explored to determine whether GisABCD-Ggt provides a competitive advantage to S . aureus and S . argenteus over S . epidermidis . S . epidermidis , S . aureus , and S . schweitzeri also colonize nasal passages of humans or closely related primates [ 37 , 65 ]. However, the lack of GisABCD-Ggt conservation in S . epidermidis suggests that the system is not essential for nasal colonization. Interestingly, de novo Met synthesis is a critical pathway for S . aureus nasal colonization with Cys presumably serving as the precursor [ 66 ]. Previous reports indicate negative associations between S . aureus and S . epidermidis in the nasal cavity, but accounts of co-colonized individuals have also been established [ 67 , 68 ]. The presence of sulfate in nasal secretions seems to favor S . epidermidis [ 66 ], and yet roughly 30% of the population is colonized by S . aureus [ 69 ]. Given that the nares represent a potential environment where nutrient sulfur competition between the species could promote niche expansion of the winner, it is tempting to speculate that the distinct nutrient sulfur sources targeted by S . epidermidis and S . aureus allow them to gain a foothold, compete, and or coexist within the complex nasal environment.
Materials and methods
Ethics statement
This study was conducted in meticulous accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The approved protocol, PROTO201800068, was reviewed by the Animal Care and Use Committee at Michigan State University.
Bacterial strains used in this study
The WT S . aureus strain used in these studies was JE2, a laboratory derivative from the community-acquired, methicillin-resistant USA300 LAC [ 28 ]. SAUSA300_0200 -ggt mutant strains were generated via transduction of the transposon-inactivated gene from the Nebraska Transposon Mutant Library strain into JE2 using previously described techniques [ 28 , 70 ]. Bacterial strains used in this study are presented in S1 Table .
A strain harboring an in-frame deletion of gisABCD - ggt (Δ gis ) was constructed using a previously described allelic exchange methodology for S . aureus [ 71 ]. One kb upstream of SAUSA300_0200 and one kb downstream of ggt were amplified using primers listed in S2 Table and cloned into pKOR1-mcs. pKOR1-mcs was confirmed to have correct 1kb homology sequences by Sanger sequencing. The deletion strain was screened for hemolysis on blood agar plates and displayed WT-like hemolysis.
Isolation of clinical isolates
Four clinical isolates of S . aureus were obtained from de-identified specimens at a regional hospital clinical laboratory. Three abscess isolates were confirmed to be methicillin-resistant (strains 1055–1057) and the other was a methicillin-susceptible bone isolate (strain 1059). Identification and minimum inhibitory concentration assays were performed following Clinical and Laboratory Standards Institute approved methods. After initial isolation, subcultures were grown on tryptic soy agar (TSA, Remel) overnight.
Sulfur source-dependent proliferation analysis
Chemically defined (PN) medium was prepared as previously described [ 4 , 27 ]. PN medium was supplemented with 5 mg mL -1 glucose for this work. Prior to inoculation in PN S . aureus was cultured in tryptic soy broth (TSB, BD Bacto) overnight, washed with phosphate-buffered saline (PBS), and resuspended in PN medium to an OD 600 equal to 1. Round bottom 96-well plates containing PN supplemented with 5 mg mL -1 glucose and the indicated sulfur sources were inoculated with S . aureus strains at an initial inoculum of OD 600 0.01. Growth analysis was carried out in a Biotek Epoch 2 plate reader set to 37° C with continuous shaking for the indicated time. PN was modified to test sulfur source-dependent proliferation of S . epidermidis and S . aureus by replacing MgSO 4 with MgCl 2 and omitting Met, resulting in PN mod . S . aureus and S . epidermidis growth curves were performed as described above in PN mod supplemented with the indicated sulfur sources for 25 h. Sulfur sources were purchased from Millipore Sigma and GSH solutions were freshly prepared prior to each trial to limit oxidation. Alternatively, to ensure CSSC, GSH, and GSSG were maintained in their respective reduced or oxidized forms, stock solutions were prepared by weighing the appropriate amount of the chemical aerobically and immediately transferring it to an anaerobic chamber (Coy) with a 95%:5% nitrogen:hydrogen atmosphere. Sulfur sources were then resuspended in either anaerobically acclimated water (GSH and GSSG) or anaerobically acclimated 1 N HCl (CSSC). Anaerobic proliferation was monitored within the Coy chamber using a Biotek Epoch 2 plate reader.
Isolation of GSSG-proliferation impaired transposon mutants
Mutant strains that comprise the Nebraska transposon mutant library (NTML) were transferred from flat bottom 96-well plates archived at -80° C into flat bottom 96-well plates containing TSB supplemented with 10 μg mL -1 erythromycin using a 96-pronged replica plater (Millipore Sigma). Mutant cells were cultured overnight at 37° C and subcultured using a 1:150 dilution into a fresh round bottom 96-well plate containing 150 μL PN media supplemented with 25 μM GSSG instead of 25 μM CSSC as the primary sulfur source. Proliferation of each mutant within the 96-well plate was assessed by monitoring OD 600 over 22 h using a H1 Biotek plate reader set to 37° C with continuous shaking. This process was repeated for all twenty 96-well plates that comprise the NTML. To account for plate-to-plate variation, proliferation was averaged across all 96 transposon (Tn) mutant strains within a given 96-well plate. Tn mutants that exhibited decreased proliferation equal to at least two standard deviations below the 96-well plate average across mid-exponential and stationary phase were isolated by streak plating onto TSA supplemented with 10 μg mL -1 erythromycin. Isolated Tn mutants were validated in a secondary screen that compared proliferation in PN media supplemented with 25 μM GSSG to WT JE2 in technical and biological triplicate using round bottom 96-well plates and monitoring OD 600 over 22 h using a H1 Biotek plate reader set to 37° C with continuous shaking. Growth in TSB was used as a positive proliferation control. The following Tn mutants displayed significantly reduced growth compared to WT JE2 in 25 μM GSSG supplemented PN but not TSB: NE392 ( SAUSA300_0200 ::Tn), NE541 ( SAUSA300_0201 ::Tn), NE457 ( SAUSA300_0202 ::Tn), NE215 ( SAUSA300_0203 ::Tn), NE254 ( ggt ::Tn) ( S1 Table ). These mutants were selected for two additional steps of identification and validation. First, the location of the bursa aurealis Tn insertion for each mutant strain was verified using a previously described inverse PCR method followed by Sanger sequencing [ 28 ]. Second, the Tn mutations were backcrossed into the WT JE2 parental strain using a previously described phage transduction method [ 28 , 70 ] and the resulting backcrossed mutants were analyzed for proliferation as mentioned above in PN supplemented with 25 μM GSSG or the indicated sulfur source ( S1 Table ).
Liquid Chromatography-Tandem Mass spectrometry quantification of S . aureus -associated GSSG
WT JE2, Δ gis and ggt ::Tn strains were cultured in a 1 L Erlenmeyer flask containing 250 mL PN supplemented with 25 μM CSSC (4:1 flask-to-volume ratio). Cultures were incubated at 37° C with 225 rpm to an OD 600 of 0.4–0.5. At this time the three cultures (WT JE2, Δ gis and ggt ::Tn) were split into two 50 mL conical tubes each containing 50 mL aliquots of culture, the cells washed once in PBS, and resuspended in 50 mL of prewarmed PN media supplemented with either 0 μM or 25 μM GSSG. The six 50 mL conical tubes were incubated at 37° C without shaking for 5 min. Cells were centrifuged, washed twice with 10 mL PBS, and resuspended in 500 μL HPLC grade methanol containing [ 2 H 10 ] GSSG (Toronto Research Chemicals, Canada) as the internal standard. Cells were lysed via bead beating using 0.1 μm zirconia beads (BioSpec, Bartlesville, OK) in a Bullet Blender Storm 24 (Next Advanced, Troy, NY) at maximum speed for 2 min. The lysate was collected and centrifuged at maximum speed for 10 min to remove insoluble debris. Metabolite-containing supernatants for each of the six samples were dried using a Savant DNA 120 SpeedVac concentrator (Thermo Scientific) and were stored overnight at -20° C. Samples were reconstituted 500 μL of 0.1% formic acid in preparation for mass spectrometry.
Metabolites from a 10 μL aliquot from each sample were separated in reversed-phase mode using an Acquity HSS T3 column (1.8 μm 100 X 2.1 mm 2 ; Waters, Milfor, MA). Solvent A was 0.1% formic acid in water and solvent B was methanol. The mobile phase gradient was as follows: 0 min−100% A, 1 min − 100% A, 5 min − 60% A, 7 min − 1% A, 8 min − 1% A; 8.01 min − 100% A, and 10 min − 100% A with a flow rate of 0.3 mL/min. Mass spectrometry detection was performed using a Xevo G2-XS quadrupole time-of-flight (QTof) by positive ion electrospray ionization. GSSG detection was based on the retention time of 2.96 min to 3.00 min and mass accuracy using MassLynx Version 4.2 (Waters). GSSG calibration curves were generated with a six-point curve of serially diluted unlabeled GSSG standards with the corresponding concentration of [ 2 H 10 ] GSSG.
Expression and purification of Ggt and GisA
Open reading frames corresponding to the ggt or gisA genes were PCR amplified from the S . aureus JE2 genome with primers listed in S2 Table . Subsequently, the genes were cloned into the pET28b expression vector using NEB HiFi Gibson assembly kit (NEB, New England, MA) after the plasmid has been linearized with NcoI-HF and XhoI-HF. The assembly mixture was transformed into E . coli , cells were recovered in lysogeny broth (LB), and plated onto LB agar (Fisher) containing 50 μg mL -1 kanamycin and 5 mg mL -1 glucose. Plasmids were confirmed using Sanger sequencing and transformed into an E . coli NEB strain 3016 slyD mutant [ 72 ]. Transformed E . coli were cultured in LB with 50 μg mL -1 kanamycin overnight at 37° C with shaking at 225 rpm, sub-cultured 1:50 into 500 mL LB with 50 μg mL -1 kanamycin in a 2 L flask and grown to an OD 600 of 0.4–0.7. Ggt or GisA protein expression was induced by addition of 200 μM isopropyl-1-thio-β-D-galactopyranoside (IPTG) and the culture was separated into five 500 mL flasks containing 100 mL of culture and incubated for 4 h at 27° C and 225 rpm shaking. After induction, cells were centrifuged at 10,000 x g for 10 min at 4° C and washed with PBS. Resulting GisA and Ggt induction pellets were resuspended in 40 mL of buffer containing 50 mM tris, 200 mM KCl, 20 mM imidazole at pH 8, or 40-mL buffer containing 50 mM tris, 500 mM NaCl, 20 mM imidazole at pH 8, respectively. Cells were lysed via five consecutive cycles through a fluidizer set to 20,000 psi. Lysates were then centrifuged at 15,000 x g for 15 min to remove intact cells and the resulting supernatant was retained. To purify the target proteins, Ni-NTA chromatography was used. Purification was performed by incubating the cleared lysate with 1 mL Ni-NTA resin (Qiagen, Hilden, Germany) on a rotating platform at 4° C for 2 h. Protein was eluted with 50 mM tris 400 mM imidazole. Buffers used to purify GisA contained 200 mM KCl while buffers used to purify Ggt contained 500 mM NaCl. Each buffer contained 1x protease inhibitor cocktail (Millipore-Sigma). The GisA elutant was dialyzed using 10 mM tris, 200 mM KCl at pH 7.5 as the dialysis buffer for 18 h. The Ggt elutant was dialyzed using 10 mM tris, 150 mM at pH 7.0 as the dialysis buffer for 18 h. Both elutions were concentrated using a 10 kDa molecular weight cutoff protein concentrators. Purification was confirmed via electrophoresis using 12% SDS-PAGE gels. Protein concentrations were determined with the bicinchoninic acid (BCA) protein kit (Pierce ThermoFisher).
Quantitation of Ggt enzyme kinetics
γ-glutamyl transpeptidase reactions contained 5 μg recombinant Ggt, reaction buffer (10 mM tris with 150 mM NaCl), and the indicated concentrations of GSH and GSSG dissolved in reaction buffer. Reactions proceeded for 30 min at 37° C after which samples were incubated at 80° C for 5 min to stop the reaction. Samples were dried using a roto-vac speed vacuum and stored at -80° C until they were hydrated via resuspension in water, derivatized with carboxybenzyl (CBZ), and applied to a Waters Xevo TQ-S triple quadrupole mass spectrometer as previously described [ 73 ]. Peak processing was performed by MAVEN, and the signal was normalized to a 13 C-glutamine internal standard [ 74 ]. An external glutamate standard curve was generated using the same chromatographic conditions, and the signal was normalized to a 13 C-glutamine internal standard. A fit equation to the standard curve was employed to quantify glutamate within the samples. Glutamate released per min was calculated and data were fit to the Michaelis-Menten equation using GraphPad Prism. Data represent the average of glutamate quantified from four independent protein purifications.
Fractionation and western blot analysis of His-tagged S . aureus Ggt
The His-tagged ggt ORF was amplified from pET28b:: ggt and cloned into pOS1 P lgt digested with NdeI and HindIII using Gibson assembly to generate Ggt-His (His +). The ggt ORF was amplified from JE2 genomic DNA and pOS1 P lgt digested with NdeI and HindIII using Gibson assembly to generate the untagged Ggt (His -). Plasmids were confirmed by Sanger sequencing and transformed from E . coli DH5α into S . aureus RN4220 via electroporation. Plasmids were purified from RN4220 and transformed into JE2 ggt ::Tn. An empty vector control strain was generated by transforming JE2 and ggt ::Tn with pOS1 P lgt . To assess Ggt localization, S . aureus ggt ::Tn pOS1 P lgt :: ggt (His -) and ggt ::Tn pOS1 P lgt :: ggt -His (His +) cultures were prepared as previously described in the proliferation analysis section by sub-culturing into three, 250 mL flasks each containing 100 mL PN supplemented 25 μM GSSG and 10 μg mL -1 chloramphenicol at a starting OD 600 equal to 0.1. Cells were cultured for 4 h at 37° C and 225 rpm shaking to mid-exponential phase. At this time cells were collected via centrifugation, the supernatant recovered, and the cell pellet washed with PBS. A total of 50 mL of supernatant was precipitated with trichloracetic acid (TCA, final percent of 10% v/v), incubated overnight at 4° C, pelleted, and the resulting pellet washed twice with 95% ethanol. Whole cell lysates (WCL) were prepared by resuspending the washed cell pellet in membrane buffer (50 mM Tris-HCl pH 7.0, 10 mM MgCl 2 , 60 mM KCl) and transferring the suspension to a 2 mL bead beating tube containing 500 μL volume of 0.1 mm zirconia/silica beads (BioSpec Bartlesville, OK). Cells were bead beaten using a Mini-beadbeater 16 (BioSpec) thrice for 1 min and centrifuged 4,000 x g for 10 min to remove cells that were not lysed. Cell wall and lysed protoplast fractions were generated from whole cells cultured as described above. Staphylococci were pelleted via centrifugation, resuspended in TSM (100 mM Tris-HCl pH 7.0, 500 mM sucrose, 10 mM MgCl 2 ), and incubated with 100 μg lysostaphin for 1 h at 37° C. Protoplasts were recovered by centrifugation at 13,000 x g for 15 min. The supernatant containing the cell wall fraction was collected and the protoplasts were washed twice with TSM prior to resuspension in membrane buffer and lysed via bead beating as previously described. Intact protoplasts were removed via centrifugation at 4,000 x g for 10 min, generating the protoplast lysate. Protein concentrations of the whole cell lysate (WCL), cell wall, and protoplast lysate were determined using the BCA method (Pierce ThermoFisher). The cell wall fraction was further concentrated using the TCA precipitation method previously described for the supernatant. WCL and lysed protoplast fractions were mixed 1:1 by volume with 2x Laemmli buffer, boiled for 10 min, and an equivalent of 40 μg was loaded onto a 12% SDS-PAGE gel. Concentrated cell wall pellet was resuspended in 1x Laemmli buffer, boiled for 10 min, and 40 μg was loaded onto a 12% SDS-PAGE gel. PAGE was performed using Tris-glycine running buffer and samples were transferred at 65 volts for 1 h to a PVDF membrane (GVS North America) at 4° C. Membranes were incubated overnight in phosphate buffered saline tween-20 (PBST) with 3% bovine serum albumin (BSA) at 4° C with agitation. An αHis mouse antibody (Millipore) was used as the primary antibody at a 1:4,000 dilution in PBST supplemented with 5% BSA and incubated for 1 h with shaking. The membrane was washed thrice with PBST. An α-mouse IgG conjugated to horseradish peroxidase (HRP) was used as the secondary antibody at a dilution of 1:4,000 (Sigma-Aldrich). To assess supernatant and WCL fraction a rabbit anti-α-hemolysin (αHla) primary antibody (Sigma-Aldrich) was used at a 1:8,000 dilution. To control for proper cell wall fractionation a mouse anti-protein A (αSpa) primary antibody (Sigma-Aldrich) was used at 1:6,000 dilution. Membranes were washed thrice in PBST after incubation with either a 1:10,000 diluted horseradish peroxidase (HRP)- linked goat anti-rabbit IgG (Sigma) or 1:5,000 diluted HRP-linked goat anti-mouse IgG (Millipore) secondary antibodies. Membranes were developed using the ECL Prime kit (Cytiva, Marlborough, MA) and imaged using Amersham Imager 600 (GE Healthcare, Amersham, Buckinghamshire, UK). Fractionation was repeated with three independent sets of cultures and one set of immunoblots is presented.
Proteinase K protection of S . aureus Ggt
S . aureus ggt ::Tn pOS1 P lgt :: ggt and ggt ::Tn pOS1 P lgt :: ggt -His were cultured as described in the western blot analysis section. Cells were collected via centrifugation after normalizing OD 600 . The resulting pellet was washed with PBS, resuspended in TSM, and incubated with 100 μg lysostaphin for 1 h at 37° C prior to treatment with 10 μg mL -1 proteinase K (Thermo-Scientific) for 30 min at 37° C. At this time 5 mM phenylmethylsulfonyl fluoride was added to halt Proteinase K activity. Protoplasts were isolated from the cell wall fraction via centrifugation at 13,000 x g at 4° C for 15 min and washed twice with TSM. The cell wall fraction protein concentration was determined using BCA and was TCA precipitated as previously described. Protoplasts were lysed, and protein concentrated using the Wizard Genomic DNA Purification Kit (Promega) nuclei lysis buffer and protein precipitation solution, respectively. Cell wall and protoplast protein pellets were reconstituted in Laemmli buffer and immunoblotted as described above.
Identification of GisABCD-Ggt homologues across bacteria
The USA300_FPR3757 (assembly GCF_000013465.1) Ggt protein sequence (ABD22038.1) was used as the query protein for homology searches with MolEvolvR using DELTA-BLAST and the NCBI RefSeq database [ 36 , 75 – 77 ]. Data were filtered to include only Firmicutes that encoded Ggt homologues containing a glutamyl transpeptidase domain. Only genomes harboring Ggt homologues were used to query USA300_FPR3757 GisABCD. Percent similarities to the S . aureus GisABCD-Ggt protein amino acid sequences were used to generate a heatmap. The heatmap and hierarchical clustering of similar protein profiles were generated using the R package, pheatmap.
16S rRNA phylogenic analysis of Staphylococcal species
16S ribosomal RNA DNA sequences were retrieved from NCBI for the following strains: S . aureus USA300 FPR3757, S . epidermidis RP62s, S . simiae NCTC 13838, S . schweitzeri NCTC13712, and S . argenteus 58113. Parsimonious reconstruction was conducted using kSNP4 using default parameters and selecting Macrococcus caseolyticus FDAARGOS_868 as the outgroup [ 78 , 79 ].
Quantitation of S . aureus and S . epidermidis competition
S . aureus , S . aureus Δgis , and S . epidermidis were cultured overnight in TSB, pelleted, washed in PBS, and normalized to the same OD 600 in PN mod . Strains were mixed in a 1:1 ratio (v/v) and inoculated into 5 mL PN mod . PN mod was supplemented with 25 μM GSSG, 50 μM GSH, 750 μM GSH, or 50 μM Met. Dilution plating of the freshly mixed co-culture was plated onto mannitol salt agar (MSA) to quantify initial counts of each organism. Cultures were incubated for 24 h at 37° C with 225 rpm shaking after which the cultures were dilution plated onto MSA and allowed to grow for 48 h at 35° C. S . aureus ferments mannitol and appears yellow on MSA, while S . epidermidis does not and maintains a pink color; consequently, yellow and pink colored colonies were enumerated to assess quantities of each organism. Competitive indices (CI) were calculated by dividing the S . aureus to S . epidermidis output ratio by the S . aureus to S . epidermidis input ratio. A CI greater than one indicates more S . aureus than S . epidermidis while a CI less than one signifies greater quantities of S . epidermidis compared to S . aureus .
Supporting information
S1 Text
Supporting Materials and Methods.
(DOCX)
S1 Table
Staphylococcus strains used in this study.
(DOCX)
S2 Table
Primers used in this study.
(DOCX)
S1 Fig
GSSG supplementation as the sole source of nutrient sulfur stimulates proliferation of S . aureus .
(DOCX)
S2 Fig
GisABCD-Ggt promotes anaerobic proliferation in PN medium supplemented with GSSG or GSH.
(DOCX)
S3 Fig
Ectopic expression of native or His-tagged Ggt complements ggt mutant proliferation in medium supplemented with reduced or oxidized GSH.
(DOCX)
S4 Fig
Domain architectures and secondary structure predictions for the S . aureus GisABCD-Ggt system.
(DOCX)
S5 Fig
GisA encodes ATPase domain signatures and demonstrates ATP hydrolysis activity.
(DOCX)
S6 Fig
Heterologous expression and purification of S . aureus Ggt from Escherichia coli .
(DOCX)
S7 Fig
Virulence of a gisB ::Tn mutant strain mimics wild type.
(DOCX)
S8 Fig
S . aureus acquires GSH independent of GisABCD-Ggt in physiologically relevant concentrations of GSH.
(DOCX)
S9 Fig
Conservation of Ggt and GisABCD across Firmicutes.
(DOCX)
S10 Fig
S . epidermidis and S . aureus nutrient sulfur source utilization is distinct and promotes interspecies competition.
(DOCX)
S1 Data Table
Raw data that support the graphs in the figures.
(XLSX)
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Introduction
Bacterial pathogens that are resistant to conventional antibiotics are becoming more difficult to treat. Natural products are a valuable and fundamental source for new drug discovery. Among the potential candidates, antimicrobial peptides (AMPs) have recently drawn increasing interest and have been proposed to be a promising alternative to conventional antibiotics [1] , [2] . AMPs exist in various species, including animals, plants, fungi and bacteria, and act in the innate defense of the organisms [3] . Investigating the antimicrobial mechanisms of the AMPs is important for their application.
Among the various AMPs, the fungi-derived cationic peptaibols are an important group that constitute a large family of approximately 850 members and could be a potential source for new antimicrobial drugs [4] . Peptaibols are characterized as linear peptides of 5–20 residues that contain a C-terminal amino alcohol, an acylated N-terminus, and a high proportion of non-standard amino acid residues, including α-amino isobutyric acid (Aib), isovaleric acid (Iva) and imino acid hydroxyproline (Hyp).
Peptaibols have been isolated from at least 23 fungal genera, and the genus Trichoderma and related genera are the most abundant sources of peptaibols [5] , [6] . While most conventional antibiotics do not act against fungi, and fungi-killing drugs are not active against bacteria [7] , peptaibols and other AMPs have excellent activity against both bacterial and fungal pathogens [7] . Moreover, earlier research has shown that peptaibols can also function as suppressors of tumor cells by inducing apoptosis and autophagy in hepatocellular carcinoma cells while causing no obvious harm to normal liver cells [8] .
Alterations in the morphological and nanomechanical properties of bacteria induced by AMPs are directly related to the mechanisms of antimicrobial action of the peptaibol agents. Therefore, atomic force microscopy (AFM), which is advantageous for investigating the ultrastructural and nanomechanical properties of bacteria [9] , [10] , [11] , is useful in researching the mechanisms of AMPs against microorganisms. However, currently published AFM studies on the antimicrobial effects of AMPs have mostly focused on Gram-negative bacteria, such as E. coli and Pseudomonas aeruginosa [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , and studies using AFM to observe the morphological and nanomechanical properties of Gram-positive bacteria treated with AMPs are rare.
Here, we report the antimicrobial effects of trichokonin VI, a peptaibol produced by Trichoderma pseudokoningii, on the Gram-positive bacterium B. subtilis . Changes to the morphological and nanomechanical properties were monitored and probed by AFM in combination with other experimental methods. The mechanism of the action of trichokonin VI on bacterial membranes is also discussed.
Materials and Methods
Preparation of Trichokonin VI
Trichokonin VI was prepared from T. pseudokoningii SMF2 using solid-state fermentation following previously described methods [21] . The purity of the prepared Trichokonin VI was confirmed by HPLC (data not shown). The purified trichokonin VI (10 mg) was first dissolved in methanol (0.1 ml) and diluted with Milli-Q water (4.9 ml) to a final concentration of 2 mg/ml as a stock solution. The stock solution of trichokonin VI was stored at 4°C.
Preparation of Bacterial Cells
B. subtilis from a single colony was grown at 37°C in Mueller Hinton Broth (MHB; Hangzhou Microbial Reagent Co., Ltd.; China) overnight to a final concentration of 10 8 ∼10 9 CFU/ml. The bacterial culture was diluted with fresh MHB to a concentration of 10 6 CFU/ml for the susceptibility test and time-killing test.
Susceptibility Test
The minimal inhibitory concentration (MIC) was determined for trichokonin VI according to a modified microtiter-broth dilution method [22] . Trichokonin VI stock solution was diluted with Milli-Q water to a concentration of 200, 100, 50, 25, 12.5 6.25, 3.12, 1.56, 0.78 and 0.39 µM. Each dilution (100 µl) was transferred to a microtiter plate well, and 100 µl of bacterial suspension (at a concentration of 10 6 CFU/ml) was added to each cell. The final concentration of trichokonin VI was 100, 50, 25, 12.5 6.25, 3.12, 1.56, 0.78, 0.39 and 0.2 µM per well. The well that contained only growth medium was set as sterility control. The well that contained only bacterial suspension was set as growth control. Three rows were used for replicates on each microtiter plate for every concentration, and three microtiter plates were used for parallel experiment. The plates were covered with a plastic lid to avoid contamination and incubated at 37°C for 16–20 h without shaking. The MIC was defined as the lowest concentration that inhibited the visible growth of bacteria compared with the control sample. The experiment was repeated three times.
Time-kill Curves
The stock solution of trichokonin VI was diluted with Milli-Q water to a concentration of 1, 2 and 4 × MIC (corresponding to 25 µM, 50 µM and 100 µM), and 1 ml of the trichokonin VI solution was mixed with 1 ml of bacterial suspension in a test tube, which resulted in final trichokonin VI concentrations of 0.5, 1 and 2 × MIC (corresponding to 12.5 µM, 25 µM and 50 µM). The mixture of 1 ml bacterial suspension and 1 ml Milli-Q water was used as a control. The bacteria were incubated at 37°C with agitation. Samples were collected at predetermined time points (0, 2, 4, 6, 8, 10, 12 and 20 h). The samples were serially diluted with Milli-Q water and spread on Luria-Bertani (LB) broth agar plates, with three replicates used for every dilution of each time point. The number of viable colonies was counted after the plates were incubated at 37°C for 18–24 h.
AFM Imaging
The B. subtilis cells were incubated at 37°C with trichokonin VI at 0.5, 1 and 2 × MIC (corresponding to 12.5 µM, 25 µM and 50 µM). Control samples were not treated with trichokonin VI. Sample preparation process was the same with that described in Time-kill curves section. Samples were collected at 0.5, 1, 2, and 5 h time points and were centrifuged at 7,000 g for 10 min. The B. subtilis cells were suspended in Milli-Q water. A drop (2.5 µl) of B. subtilis was spread onto freshly cleaved mica and air-dried at room temperature before imaging. AFM images were obtained using a Multimode Nanoscope V (Bruker AXS; German) in tapping mode, and a probe (NSC11, MikroMasch) with a cantilever length of 90 µm was used.
Surface Roughness Analysis
The AFM data of the B. subtilis cells treated with trichokonin VI at the MIC (25 µM) for different time periods were flattened and used to calculate surface roughness. The surface roughness of a selected area was calculated using the NanoScope Analysis AFM software. Root mean square average roughness ( Rq ) was calculated with Equations 1 . (1) Z i is the height value at the i th point, and N is the number of points within the selected area. For each sample, roughness was measured near the center area on a bacterial cell with fixed sizes of 400 × 400 nm 2 . At least 5 different areas from different bacterial cells were measured.
Force-curve Measurements
B. subtilis cells treated with trichokonin VI at the MIC for different time periods were selected for the force-curve measurements. The spring constant k c of the probe cantilever (NSC11, MikroMasch) with a length of 200 µm was determined in air using the thermal noise method and Nanoscope software. Samples were first imaged in tapping mode to identify bacterial cells. At least 10 force curves per sample were collected in contact mode near the center of the selected bacterial cell. The spring constant of the cantilever used in the force experiments was approximately 2.0 N/m, which is smaller than that used to measure the mechanical properties of bacteria in air in some other works [23] , [24] . The spring constant of the cantilever was larger than the spring constant used in liquid [25] , [26] , [27] , due to the higher rigidity of the cells in air.
Mechanical Property Analysis
From the approaching branch of the force-distance curves, the stiffness of the bacteria could be determined. When force was applied on a softer sample, the slope of the linear portion of the approaching branch in the contact region was lower. The bacteria and cantilever could be modeled as two connected springs, and the spring constant of the bacteria could be determined by the slope ( s ) of the linear portion of the force curve [28] . The spring constant of the bacteria can be calculated according to Equation 2 . (2)
In Equation 3 , k b is the spring constant of the bacteria, and k c is the spring constant of the cantilever.
The jump-off contact point of the retract branch from the force-distance curve reflects the tip-sample adhesion interaction. This adhesive force may be due to biomacromolecules and thin water films on the cellular surface. The adhesion force between the probe tip and bacterial surface could be calculated according to Equation 3 . (3)
In Equation 4, F is the adhesion force and d is the deflection of the cantilever that resulted from the adhesive interaction between the tip and bacterial surface.
As the area in the retract branch from the force-distance curve below the zero force line represents the work performed by the adhesion force, the adhesion energy, which is also an indicator of bacterial surface properties, was subsequently calculated.
Leakage of Cellular UV-absorbing Materials
To analyze the damage of the bacterial cytoplasmic membrane caused by trichokonin VI, leakage of UV-absorbing cellular substances following sample treatment was monitored. Bacterial cell suspensions were mixed with trichokonin VI to a final concentration of 0.5, 1, 2 and 4 × MIC (corresponding to 12.5 µM, 25 µM, 50 µM and 100 µM), and the controls were cells without trichokonin VI treatment. Samples were collected at different time points. The samples were centrifuged at 7,000 g for 10 min to remove the bacterial cells. The supernatants were diluted, and their absorbance at 210 nm, 260 nm and 280 nm was recorded. The absorbance of trichokonin VI at concentrations of 0.5, 1, 2 and 4 × MIC (corresponding to 12.5 µM, 25 µM, 50 µM and 100 µM) were measured to exclude its absorption. Absorbance was measured at room temperature using a UV/VIS-550 spectrophotometer (Jasco; Japan).
Membrane Permeabilization Test
Trichokonin VI was added to B. subtilis cultures to a final concentration of 0.5, 1 and 2 × MIC (corresponding to 12.5 µM, 25 µM and 50 µM). Bacterial samples without treatment were used as a control. After treatment for 60 min, samples were centrifuged at 7,000 g for 10 min and washed twice in phosphate buffer. The bacterial cells were suspended in phosphate buffer to a concentration of 10 5 –10 6 CFU/ml and incubated with SYTOX Green (to a final concentration of 2 µM) for 10 min. Fluorescence of SYTOX Green was examined by flow cytometry (FACSCalibur, Becton-Dickinson).
Results
Antimicrobial Susceptibility Tests
The antimicrobial activity of trichokonin VI against B. subtilis was determined using the broth-dilution method. Gram-positive B. subtilis showed susceptibility to trichokonin VI in the test. In the wells on the microtiter plates with the concentration of trichokonin VI at 12.5 µM or lower, the deposits or turbidity which indicated the growth of bacteria was visible. However, in the wells with the concentration of trichokonin VI at 25 µM or higher, the liquids in the wells were clean, and neither turbidity nor deposits could be visualized. By carefully examining all parallel experiments for the antimicrobial susceptibility tests, the MIC value of trichokonin VI to B. subtilis was determined to be 25±0 µM, which is smaller than those of the AMPs such as LEAP-2 [29] and Cn-AMP1 [30] , similar to that of Magainin2 [29] and higher than those of LL-37 [29] and MSI-594 [31] . The error in the MIC could not be determined as it was less than the dilution factor used.
Time-kill Curves
The time-kill curves of trichokonin VI against B. subtilis are shown in Fig. 1 . Trichokonin VI exhibited a concentration-dependent antimicrobial activity in the bacterial viability test. Trichokonin VI at a concentration of 0.5 × MIC reduced cell growth in the first few hours when compared with the control; however, the growth curves were similar after 10 h of incubation. Trichokonin VI at the MIC inhibited the growth of B. subtilis ; however, the bacteria number increased slightly after treatment for 20 h. At a higher concentration (2 × MIC), trichokonin VI led to a progressive decrease in number of bacterial colony forming units, and no living bacteria were detected after 20 h.
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Figure 1
Time-kill curves of trichokonin VI against B. subtilis .
The concentrations of trichokonin VI used were 0 µM, 12.5 µM, 25 µM and 50 µM, which corresponded to 0, 0.5, 1 and 2 × MIC, respectively.
AFM Images of B. subtilis
Images of the B. subtilis cells freshly collected from the bacterial culture were acquired ( Fig. 2 ). The surfaces of cells are reasonably smooth with a bacillary shape. No visible pores or ruptures could be observed in all examined cells. Cross sections of the bacterial cells were acquired. The measured length, width and height, shown in Fig. 2A , were 4.4, 1.3, and 0.44 µm, respectively, which are comparable to the reported dimensions [24] . However, the bacteria were observed to have a variation in length, and this variation may be a result of detecting living bacteria at different growth stages. Trace amount of methanol in trichokonin VI solution had no obvious effect on the morphology of the B. subtilis cells ( Fig. S1 ). Suspending bacteria in deionized water would bring about the hypo-osmotic shock to the bacterial cells. However, the sample preparation step in this experiment was short and no obvious random damage to the bacteria cells of B. subtilis was observed ( Fig. S2 ).
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Figure 2
Morphology and section analysis of B. subtilis .
A and B are 3-dimensional AFM height images of B. subtilis cells, and B shows a dividing cell. C and F show the 2-dimensional height data from A and B, respectively, indicating the cross section position. D and E are the cross sections of the image indicated in C; and G and H are the cross sections of the image indicated in F. Scale bar is 2 µm.
AFM Images of B. subtilis Cells Treated with Trichokonin VI
The antimicrobial effect of trichokonin VI on B. subtilis at different treatment times was monitored. For each sample, randomly selected cells were examined and analyzed. B. subtilis was treated with trichokonin VI at the MIC for a period of 0.5, 1, 2 and 5 h, and the treated cells were collected and imaged. After treatment, the bacterial cells retained their rod-like form ( Fig. 3A–D ). However, minor corrugations to the bacterial surface could be distinguished after a 0.5-h incubation. The corrugation was more evident as the incubation time was increased. Treatment for 2 h or longer induced greater disruption in the cell morphology and collapse of the cell wall was observed. After incubation, the height of the B. subtilis cells declined from approximately 500 nm to less than 400 nm ( Fig. S3 ), which suggests a dramatic decrease in cell volume after trichokonin VI treatment.
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Figure 3
The 3-dimensional height images of B. subtilis treated with trichokonin VI.
B. subtilis were treated with trichokonin VI at the MIC (25 µM, top row), 0.5 × MIC (12.5 µM, middle row) and 2 × MIC (50 µM, bottom row) for 0.5 h (first column), 1 h (second column), 2 h (third column) and 5 h (fourth column). Scale bar is 2 µm.
Changes to the surface characteristics of B. subtilis as a result of trichokonin VI treatment are obvious. Treatment at 0.5 × MIC resulted in minor surface perturbations after 2 h, and cells with more obvious changes could be detected after 5 h ( Fig. 3E–H and Fig. S4 ). Upon treatment at 2 × MIC, a more pronounced collapse of the cell wall could be detected ( Fig. 3I–L ), and collapse of the apical end of the cell was evident. Surface characteristics changed after the first 0.5 h of incubation. After 5 h of incubation, the height of the treated bacterial cells declined to less than 300 nm ( Fig. S5 ), suggesting a greater reduction in cell volume. The formation of granules on the B. subtilis cells after treatment with trichokonin VI was observed, and the granules are most likely formed from the condensation of cells because of the leakage of cellular materials.
Bacterial cells diluted only with growth broth and incubated for different time periods were set as control. These bacterial cells were also imaged ( Fig. S6 ). The morphologies of the bacteria were comparable to those freshly collected from bacteria culture. No obvious morphological collapse was observed. Thus, the results indicated that the alteration in bacterial morphological properties was induced by trichokonin VI.
Surface Roughness Analysis
B. subtilis cells treated with trichokonin VI showed alterations in surface characteristics compared with untreated cells. Images were analyzed by calculating the roughness of the bacterial cell surface. Surface roughness increased as the treatment time increased ( Fig. 4 ). The surface roughness, Rq , of the untreated cells over an area of 400 × 400 nm was calculated to be 9.9±0.9 nm. After incubation with trichokonin VI for 0.5 h, Rq slightly increased (10.2±1.4 nm) compared to the untreated cells. After 1 h of treatment, Rq increased to11.5±4.1 nm and it increased to 15.6±4.0 nm after 5 h of incubation. The results showed that the antimicrobial effects of trichokonin VI on B. subtilis are time dependent.
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Figure 4
Surface roughness of B. subtilis treated with trichokonin VI at the MIC for different periods of time.
Rq was calculated in fixed-square size with a side length of 400 nm.
Mechanical Properties of the B. subtilis Cells
For force-distance measurements, the force curves were collected near the center of the bacterial cells. The force measurements were performed on the B. subtilis cells treated with trichokonin VI at the MIC for different incubation times. The spring constants of the B. subtilis cells were first calculated. For the untreated cells, the spring constant was 12.1±4.1 N/m ( Table 1 ). The measured value was larger than the values obtained from bacteria in liquid [27] . The influence of trichokonin VI to the nanomechanical properties of B. subtilis incubated with trichokonin VI for 0.5, 1 and 2 h was analyzed. It was observed from the force-distance curves that the slopes of the curves on the bacteria are all less steep than on the slopes on mica, and an increased incubation time led to shallower slopes ( Fig. 5 ). Statistical analysis revealed that the spring constant of the cells decreased with an increase in the incubation time. The spring constants of the B. subtilis cells treated with trichokonin VI are summarized in Table 1 . After a 0.5-h treatment, the spring constant decreased to 9.8±3.8 N/m and further dropped to 7.3±4.1 N/m after a 1-h incubation. When treated with trichokonin VI for 2 h, the spring constant of the B. subtilis cells decreased to 6.2±3.1 N/m, or approximately half the spring constant of untreated cells.
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Figure 5
Representative approaching branch of force-distance curves on mica and B. subtilis .
Curves were collected on mica and B. subtilis incubated with trichokonin VI at the MIC (25 µM) for 0 min (A), 30 min (B), 60 min (C), and 120 min (D).
10.1371/journal.pone.0045818.t001 Table 1
Nanomechanical properties of B. subtilis incubated with trichokonin VI at MIC for different time periods.
Control
30 min
60 min
120 min
spring constant (N/m)
12.1±4.1
9.8±3.8
7.3±4.1
6.2±3.1
adhesive force (nN)
26.4±3.3
23.2±4.4
25.9±8.5
44.5±11.9
adhesive energy (×10 −18 J)
406±61
339±127
427±183
753±332
The retract branch of the force curves between the AFM tip and B. subtilis cells treated with trichokonin VI for different time periods showed that the adhesion force had only minor changes compared with the untreated cells in the first 1 h. However, the force dramatically increased after 2 h of incubation with trichokonin VI ( Fig. S7 and Table 1 ), which suggests marked changes to the bacterial surface properties. Work performed by adhesion forces, such as adhesion energy, was also calculated ( Table 1 ), and these forces exhibited similar trends as the adhesion force upon trichokonin VI treatment.
Leakage of Cellular UV-absorbing Materials
Leakage of cellular UV-absorbing substances is an indicator of changes in membrane permeability. Thus, we monitored the UV-absorption of the supernatant after B. subtilis cells were treated with trichokonin VI. Low doses of trichokonin VI had little effect on the leakage of cellular UV-absorbing substances ( Fig. 6 ). An increase in absorption at 260 and 280 nm could only be observed when B. subtilis cells were treated with high concentrations of trichokonin VI for a relatively long time. However, the absorption at 210 nm increased after incubation for only 20 min at high trichokonin VI concentrations. For treatment with low trichokonin VI concentrations, variations in the 210-nm absorption values after long incubation times were observed.
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Figure 6
Release of UV-absorbing substances from B. subtilis treated with trichokonin VI at different concentrations.
B. subtilis were treated with trichokonin VI at 0.5 × MIC (12.5 µM), MIC (25 µM), 2 × MIC (50 µM) and 4 × MIC (100 µM). Absorbance was measured at 280 nm (A), 260 nm (B) and 210 nm (C).
Membrane Permeabilization Test
SYTOX Green is a high affinity nucleic acid dye that becomes more fluorescent when bound to DNA [32] , [33] . SYTOX Green does not cross the membranes of live cells but can enter into cells with a compromised membrane [32] , [33] . When SYTOX Green was applied to the healthy bacterial cells which were untreated with trichokonin VI, a weak but detectable fluorescence emission could be noticed ( Fig. 7A ). This weak fluorescence resulted from the binding of dye to bacterial surfaces, which is consistent with previous report [34] . Fluorescence intensity from B. subtilis cells treated with trichokonin VI at low concentrations appeared similar to the untreated cells. However, when the bacterial cells were treated with trichokonin VI at MIC or higher, an enhancement of SYTOX Green fluorescence could be observed ( Fig. 7B ), suggesting that the membranes of B. subtilis cells have been disrupted by the membrane-active compound trichokonin VI at these concentrations.
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Figure 7
Membrane permeabilization effects of trichokonin VI on B. subtilis cells.
The B. subtilis cells were stained with SYTOX Green and analyzed with flow cytometry. The bacteria were untreated (A), treated with trichokonin VI at a concentration of 0.5 × MIC (B), MIC (C) or 2 × MIC (D) for 1 h.
Discussion
Trichokonin VI was determined to be effective against Gram-positive B. subtilis with a MIC of 25 µM. The MIC of trichokonin VI against B. subtilis was comparable to some AMPs, such as Magainin2 [29] , and higher than other AMPs, such as LL-37 [29] and MSI-594 [31] . Here, we combined AFM and other methods to investigate the antimicrobial effects of trichokonin VI on B. subtilis and determined that the peptaibol induced changes to the morphological and mechanical properties of the bacterium as well as permeabilized the bacterial membrane. Our results suggest a concentration- and time-dependent mode of action in modifying the morphological and nanomechanical properties of B. subtilis cells.
Microscopic methods are important tools for studying the interactions between AMPs and microorganisms. AFM is among the most powerful of the microscopic tools. Bacterial ultrastructures can be investigated with a high resolution, and their mechanical properties can be monitored by AFM. Height and surface roughness of the cells can be analyzed from the AFM images. Cellular morphological and mechanical properties are directly related to the mode of action of the antimicrobial agents; therefore, these advantages, in combination with other tools, may help us better understand the antimicrobial mechanisms of AMPs. Efforts have been made to evaluate the morphological changes of the bacteria treated with AMPs. Most of this work has focused on Gram-negative bacteria, which has an outer membrane that surrounds the peptidoglycan layer. However, there has been little work on the application of AFM to monitor the antimicrobial effects of AMPs on Gram-positive bacteria.
Imaging with a 3-dimensional scale using AFM allows for analysis of the surface roughness of bacterial cells. Roughness analysis of the cell surface can help us to understand and quantify the cellular alteration process, especially the detection of subtle changes caused by AMPs after treatment. It is widely accepted that AMPs interact with membranes, and the cell membrane of Gram-positive bacteria is surrounded by a thick layer of peptidoglycan. Therefore, changes in the roughness properties might be due to changes beneath the peptidoglycan layer. Along with the increased treatment time, surface roughness of B. subtilis cells gradually increased. Alteration to the surface roughness was a time-dependent process, which is in agreement with the morphological observations.
The results show that the mechanical strength of B. subtilis was altered under treatment with trichokonin VI. The mechanical properties of bacteria usually originate from the cell wall and turgor pressure of the cell [35] . Maintaining the bacterial structure necessarily involves the mechanical strength of the cell, which needs the cell wall to constrain the cellular contents under turgor pressure and defines the cell shape. Turgor pressure is generated from the cellular contents in the cytoplasm, which push against the cytoplasmic membrane and peptidoglycan wall. The permeabilization experiments indicated that the membranes are the target of trichokonin VI. With an increase in incubation time, it was observed that the spring constant of the B. subtilis cells gradually decreased. Thus, alterations in the mechanical properties are most likely due to the leakage of the intracellular materials, which led to changes in the turgor pressure, and resulted in a reduction in cellular elasticity as well as a collapse of the cell structure. From the retract part of the force-distance curve, adhesion forces between the AFM tip and bacterial surface can be measured. The adhesion force may originate from the interactions between the probe tip and molecules on the bacterial cell wall, including peptidoglycans, leaked biomacromolecules and water layers. A change in the adhesion force is an indicator of a change in the cell surface characteristics.
Research on morphological alterations has clearly exhibited a time- and concentration-dependent process for the antimicrobial effects of trichokonin VI on B. subtilis . Structural collapse usually results from leakage of intracellular materials or starvation. Structural shriveling of the bacterial cells could be observed in nutrient-free buffer [13] . In our experiments, the collapse was not a result of starvation ( Fig. S6 ). Alves et al. discovered that the AMPs induced a collapse in the septional region of the E. coli envelopes [13] . However, in the case of B. subtilis , it is interesting that the polar region of the B. subtilis cells appears to be sensitive to attack and is dependent of the trichokonin VI concentrations used in the experiments.
At 0.5 × MIC, the effects of trichokonin VI on B. subtilis cells are less clear. Minor morphological alterations could be observed after 2 h of treatment in the AFM images. After longer treatment times, the cell surface became rougher and section analysis showed a minor reduction in height, which indicated a loss of cellular volume. The minor morphological alterations in the B. subtilis cells suggest that trichokonin VI only has a slight effect on the bacteria at this concentration. The growth of B. subtilis could not be completely inhibited, which was determined using the time-killing assay.
At higher trichokonin VI concentrations (MIC), the antimicrobial effects on B. subtilis cells are much more obvious in the AFM experiments. After the first 0.5 h, only a slight collapse could be detected. After 1 h of treatment, granules were detected. A reduction in cell volume suggests that leakage of the intracellular material was occurring, and the changes in the mechanical properties suggest a decrease in bacterial turgor pressure, which was also a potential result from the leakage of the intracellular materials. Small molecules were allowed to cross the cell membrane, which indicates that the membranes have been permeabilized. Permeabilization of the membrane led to a reduction in cell volume, which caused alterations in morphology, surface roughness, and resulted in cell death.
When the concentration of trichokonin VI was increased to 2 × MIC or higher, more profound changes were detected. The AFM images show that the collapse of the cell structure was more obvious and led to the appearance of more granules on cellular surfaces. Considering the profound reduction in cell height, which was determined using section analysis, the granules are most likely are result of the collapse of the cell structure. Detection of leakage of cellular materials showed that trichokonin VI severely permeabilized the membrane at high concentrations, which caused a much quicker reduction in cellular volume and more profound changes in cell morphology.
In our leakage and membrane permeabilization experiments, the results indicated that the membrane did not allow small organic molecules to cross at low concentrations. At higher concentrations, the membranes were permeabilized and small molecules were allowed to cross the membrane. Trichokonin VI has a helical structure, which is a requirement for channel forming peptides, and the helical structure of trichokonin VI was determined using circular dichroism [36] . Furthermore, trichokonin VI has a length of 20 amino acid residues. Works on other helical peptaibols indicated that the long chain is also a requirement for the peptaibols to be able to penetrate the lipid bilayers and form channels [37] , [38] .Thus, based on our results and previous studies, it is possible that the target of trichokonin VI is the membrane of bacterial cells, and leakage of intracellular materials induced by trichokonin VI appears to be the reason for the changes in the morphological and nanomechanical properties probed by AFM.
In summary, we have demonstrated that trichokonin VI, a peptaibol isolated from T. pseudokoningii , is effective against the Gram-positive bacterium B. subtilis . Morphological and mechanical studies observed a concentration- and time-dependent effect against the B. subtilis cells. Nanoindentation experiments revealed a progressive decrease in the stiffness of the cells. Furthermore, we monitored the membrane permeabilization effect and suggest that leakage of intracellular materials is a potential mechanism of action of trichokonin VI on B. subtilis .
Supporting Information
Figure S1
Effect of methanol on B. subtilis cells. When preparing trichokonin VI stock solutions, 10 mg of trichokonin VI was dissolved in methanol (0.1 ml), and the solution was diluted with Milli-Q water (4.9 ml) to a concentration of 2 mg/ml as a stock solution. When trichokonin VI stock solutions were diluted to the MIC, the solution contained methanol at a concentration of 0.05% (v/v). Thus, it is necessary to examine whether trace amounts of methanol had any effect on B. subtilis . We incubated B. subtilis cells (10 6 CFU/ml) with methanol at a concentration of 0.05% (A) and 0.2% (B) (v/v), and these concentrations correspond to methanol content at the MIC and 4 × MIC. Samples were collected after 5 h of treatment. Cells were centrifuged at 7,000 g for 10 min and suspended in Milli-Q water. The cells were imaged by AFM, and the representative results are shown. As shown in the figures, B. subtilis cells retained their smooth surfaces and rod shape, and the sizes of the treated cells are comparable to that of the untreated cells ( Fig. 2 ), which suggests that methanol at the concentrations used in our experiments had no visible influence on the B. subtilis cells.
(TIF)
Figure S2
Effect of water on B. subtilis cells during sample preparation. Suspending the bacteria in deionized water would bring about the hypo-osmotic shock to the bacteria cells. To check the osmotic effect of incubation with water on the morphology of B. subtilis , we incubated B. subtilis in Milli-Q water for 0.5 h (A) and 1 h (B). These bacterial cells appeared intact with no visible holes, granules, or breakages in the cell envelop, and the morphologies of the bacteria are distinctly different from those treated by antimicrobial peptide. Thus we consider that this sample preparation step did not leave to random damage to the bacteria cells.
(TIF)
Figure S3
Section analysis of B. subtilis treated with trichokonin VI at the MIC for different incubation times. B. subtilis were treated with trichokonin VI at the MIC (25 µM) for 0.5 h (A, B), 1 h (C, D), 2 h (E, F) and 5 h (G, H). A and B are cross sections of the image in Fig. 3A ; C and D are cross sections of the image in Fig. 3B ; E and F are cross sections of the image in Fig. 3C ; and G and H are cross sections of the image in Fig. 3D . A, C, E and G are section profiles along the short axis of the bacterial cells. B, D, F and H are section profiles along the long axis of the bacterial cells.
(TIF)
Figure S4
Section analysis of B. subtilis treated with trichokonin VI at 0.5 × MIC for different times. B. subtilis were treated with trichokonin VI at 0.5 × MIC (12.5 µM) for 0.5 h (A, B), 1 h (C, D), 2 h (E, F) and 5 h (G, H). A and B are cross sections of the image in Fig. 3E ; C and D are cross sections of the image in Fig. 3F ; E and F are cross sections of the image in Fig. 3G ; G and H are cross sections of the image in Fig. 3H . A, C, E and G are section profiles along the short axis of the bacterial cells. B, D, F and H are section profiles along the long axis of the bacterial cells.
(TIF)
Figure S5
Section analysis of B. subtilis treated with trichokonin VI at 2 × MIC for different times. B. subtilis were treated with trichokonin VI at 2 × MIC (50 µM) for 0.5 h (A, B), 1 h (C, D), 2 h (E, F) and 5 h (G, H). A and B are cross sections of the image in Fig. 3I ; C and D are cross sections of the image in Fig. 3J ; E and F are cross sections of the image in Fig. 3K ; and G and H are cross sections of the image in Fig. 3L . A, C, E and G are section profiles along the short axis of the bacterial cells. B, D, F and H are section profiles along the long axis of the bacterial cells.
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Figure S6
Representative images of the control B. subtilis cells which were not treated with trichokonin VI. The B. subtilis cells which were not treated with trichokonin VI were set as control samples to those which were treated with trichokonin VI. The morphologies of the control bacterial cells incubated for different time periods were monitored. The morphological properties of the bacterial cells incubated for 2 h (A) or 5 h (B) was comparable to that freshly collected from broth medium ( Fig. 2 ). Scale bar, 2 µm.
(TIF)
Figure S7
Representative retract branches of force-distance curves on mica and B. subtilis. Curves were collected on mica and B. subtilis incubated with trichokonin VI at the MIC (25 µM) for 0 h (A), 0.5 h (B), 1 h (C) and 2 h (D). The jump-off contact point represents the adhesion force between the probe tip and bacterial surface.
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Introduction
Colorectal cancer (CRC) is one of the most common malignancies in the Western societies. Long-term survival of CRC-diagnosed patients is correlated with disease stage at diagnosis. In early stages as well as in selected patients with advanced disease, surgery is the main modality of treatment [1] . At least 40% of patients with CRC will develop either synchronous or metachronous distant metastases, most of them will succumb to their disease and die [2] . Some of characteristics of the malignant phenotype of CRC are correlated with overexpression and hyper-activation of receptor tyrosine kinases such as epidermal growth factor receptor (EGFR), which make these receptors attractive targets for cancer treatment [3] . In most CRC patients, the progression from normal colonic mucosa to cancer involves a defined cascade of molecular changes, that spreads over years [4] . Endoscopic polypectomy was shown to reduce CRC-related mortality [5] . This procedure requires fibro-optic colonoscopy visualization of the CRC tissue followed by histological evaluation. In addition the CRC tissues are often evaluated by RT-PCR [6] , immunohistochemistry [7] and in situ hybridization [8] techniques, which showed a much higher degree of discordance between primaries and related CRC metastases [9] .
EGFR is frequently overexpressed in a variety of solid tumor, of the brain, breast, lung, ovary and pancreas, and is associated with increased metastatic potential and poor prognosis of CRC [10] . Biological agents that inhibit EGFR have demonstrated clinical activity as single agents or in combination with chemotherapy, the most promising of these agents being cetuximab and panitumumab. Unfortunately, these antibodies are clinically effective in only a minority of patients with CRC [11] . The clinical success of these monoclonal antibody therapies is uniformly limited by the development of acquired resistance to EGFR blockade [12] . One mechanism to resistance was recently elucidated: cetuximab resistant cells contain an EGFR mutation in the extracellular domain (S492R) that impairs cetuximab, but not epidermal growth factor (EGF) binding [12] . Therefore, since the response to therapy require the EGFR target to be present, the development of BOI methods for quantitative detection of EGFR protein levels in CRC primary and secondary tumor tissues is necessary, in order to guide the treatment of individual selected for EGFR targeted antibody treatment and in particular those who relapse while on EGFR targeting therapies [13] . The advent of EGFR-targeted antibodies, cetuximab and panitumumab has paved the way to individualized medicine of mCRC. Current data suggests that the evaluation of KRAS and bRAF mutation and PI3K/PTEN alteration could be useful for selecting patients who are unlikely to respond to anti-EGFR-targeted antibodies. It was found that responsive CRC tumors carry wild type KRAS/bRAF and tend to have a modest, increase copy number of the EGFR gene, which is translated into a modest increase in EGFR level. Therefore, the EGFR gene copy number detection and quantitative evaluation of EGFR protein level will likely improve tailoring of cetuximab and panitumumab therapies for mCRC patients [12] . However, the technical difficulties of the immunohistochemistry technique, which is used to assess the expression of the EGFR in fixed tissues, may have limited the detection of small EGFR protein level increase so far [11] . Therefore, novel sensitive BOI methods of EGFR protein level in mCRC are needed. EGFR scintigraphy, represents such a method which is based on the binding, internalization, and retention of the radiolabeled EGFR-targeted agents in intracellular compartments and has been demonstrated with radiolabeled EGF and with radiolabeled monoclonal antibody directed against EGFR [14] . However, the disadvantage of these methods is the use of radioactive materials.
Near infrared (NIR) optical BOI offers unique advantages for diagnostics of mCRC: it offers high sensitivity, it can be used with different NIR tags and it can provide dynamic, real time in vitro and in vivo images by non radioactive means [15] . NIR light (700–1000 nm wavelength) can penetrate into tissue, and offers a potentially safe, noninvasive method of characterizing tumors [16] . In most applications, NIR BOI is used for assisting targeted fluorescent contrast agents that not only provide enhanced contrast, but also, more importantly, reveal specific molecular events associated with CRC tumor initiation and progression [17] . Recent studies have established the use of Affibody-mediated targeting of NIR excitable fluorescent contrast agents for the detection of malignant cells and tumors [18] .
Therefore, the aims of the present study were to develop and characterize novel in vitro CRC models that resembled CRC heterogeneity and to assess whether it is possible to quantify the level of EGFR in ex vivo fresh CRC tissue samples, orthotopic tumor in mice and newly developed cell lines models by using EGF conjugated with IRDye 800CW (EGF-NIR) probe. We found optimal conditions for BOI of EGFR using EGF-NIR probe in these models, applicable for endoscopic and Odyssey Infrared Imager analyses. Furthermore, by using image processing analysis and western blotting we confirmed that the intensity of EGF-NIR signal to background ratio reflects EGFR protein level in the in vitro CRC models and in situ human CRC tissues investigated.
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Figure 1
Synthesis, purification, spectrum, electrophoresis properties and signaling of EGF-NIR.
(A) Reaction scheme for the synthesis of EGF-NIR conjugate, the first amino acid asparagine at the amino terminal is indicated as Asn1. (B) Separation of synthesis reaction mixture on gel permeation chromatography and of EGF-NIR sample from gel permeation on (C) anion exchange chromatography; EGF-NIR-full line (800 nm); gradient of NaCl-broken line; unconjugated EGF-dotted line. (D) HPLC separation of EGF-NIR purified from anion exchanger chromatography. Full line represents absorbance at 226 nm and dotted line indicates the gradient. Insert-12% SDS-PAGE analysis of 10 µg of EGF-NIR scanned with Odyssey and unmodified EGF stained with coomassie blue. (E) NIR spectrum of EGF-NIR [excitation (gray line) and emission (black line)]; Insert-IRDye 800CW NHS ester; (F) EGF-NIR induced Erk phosphorylation.
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Figure 2
The specificity and selectivity of EGF-NIR probe binding measured by IC-NIR imaging.
HT-29 cells were incubated for 15 minutes at 37°C (A) and 4°C (B) with 7 nM EGF-NIR in the presence or absence of 100 nM EGF. Competition experiments with 500 nM of cetuximab, TGF-α or NRG1 were also conducted. In control experiments the cultures were incubated with 7 nM NIR-Dye to evaluate nonspecific labeling of the cells. The NIR intensity at 800 nm was estimated under identical conditions for all cultures and the mean ± SD (n = 9) is presented. Upper inserts: NIR scans; lower inserts: phase-contrast photomicrographs of the cultures,* p<0.05 vs. NIR-Dye; ** p<0.05 vs. EGF-NIR.
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Figure 3
siRNA-induced knock down of EGFR evaluated by IC-NIR imaging.
HT-29 cells were transfected for 2 days with 5 nM anti-EGFR Silencer select siRNA or scrambled RNA or left untreated (control). Evaluation of EGFR expression was performed by In Cell NIR imaging using 7 nM EGF-NIR (black bars) and western blotting (gray bars). The values are mean ± SD (n = 3). * p<0.05 vs. scrambled or control.
Materials and Methods
Cell Culture
Human colon carcinoma cell lines HT-29, SW620, COLO205, A431 human epithelial squamous carcinoma cells and rat small intestine epithelial cell clone IEC 6 were purchased from American Type Culture Collection (ATCC, Manassas, VA) and adjusted for growth in Dulbecco’s Modified Eagle’s Medium (DMEM) containing 10% fetal bovine serum, 2 mM L-glutamine and 10000 U/ml penicillin and 100 µg/ml streptomycin. The cells were grown at 37°C, 6% CO 2 in a humidified incubator. All experiments were carried out under GLP conditions using a clean room according to ISO7 requirements (10,000 particles/m 3 ).
Preparation, Purification and Characterization of EGF-NIR
EGF-NIR was synthesized according to LI-COR Biosciences (Lincoln, NE, USA) instructions using IRDye 800CW NHS ester (2-(3-{5-[7-(5-amino-1-carboxy-pentylcarbamoyl)-heptanoylamino]-1-carboxy-pentyl}ureido)-pentanedioic acid) for conjugation to human recombinant EGF (Peprotech, Asia, Rehovot, Israel). Briefly, EGF (32 nmole) was incubated with 5 equivalents of IRDye 800CW NHS ester in 1 M K2HPO4, pH 9.0, for 2 hours in darkness at 20°C, with stirring. Following conjugation, the coupling mixture of free reagents and EGF-NIR was applied to Hiprep 26/10 (Fine Sephadex G-25, particles size 90 µm) desalting column (GE Healthcare, Life sciences, Buckinghamshire, UK) of a volume of 55 ml (Vo = 15 ml). This column was equilibrated and eluted at 10 ml/min with distilled water using FPLC AKTA P900 instrument (GE-Healthcare Life Sciences, Buckinghamshire, UK). This was followed by collecting the excluded peak and adjusting it to pH 8.0 by addition of 1 ml of 0.02 M Tris HCl buffer (pH 8.0). The solution was applied for anion exchange chromatography on Hitrap DEAE FF (DEAE Sepharose High Flow, GE Healthcare, Life sciences, Buckinghamshire, UK) equilibrated with 0.02 M Tris HCl buffer (pH 8.0) at a flow rate of 1 ml/min and EGF-NIR was eluted from the column using a gradient of 1–100% NaCl in the equilibration buffer. The purified EGF-NIR was dialyzed in 3000 cut off dialysis bags (Thomas Scientific, Swedesboro, NJ, USA) for 14 hours in dark at 4°C against distilled water. The distilled solution was lyophilized, and 0.1 mg of dry samples of EGF-NIR dissolved in 1% triflouroacetic acid (TFA) were finally separated by HPLC using a Sperisorb DDS2 column (LKB instruments, Gaithersburg MD) using two linear gradients: the first gradient from 10–35%, followed by a second gradient of 35–85% acetonitrile in 1% TFA. The purification was performed at a flow rate of 4.7 ml/min (100 bar pressure). The full run continued for 45 min and the EGF-NIR peak was estimated by the optical absorbance at 226 and 700 nm. Molar concentrations of dyes and EGF were calculated using molar extinction coefficients of 270,000 M −1 cm −1 for IRDye 800CW at 780 nm, and 18,000 M −1 cm −1 for EGF. Absorbance at 280 nm was used to calculate EGF protein concentration based on its molar extinction coefficient. Dual wavelength absorbance was used to determine dye: protein ratio. EGF-NIR emission was measured in PBS using a Fluoro-Max 4 spectrofluorimeter (JY Horiba, Edison, NJ, USA) with a Xenon arc lamp as excitation source of 774 nm in 1 cm cuvette, and at a scanning rate of 80 nm/sec. For validation, EGF-NIR from LI-COR Biosciences (Lincoln, NE, USA) was also used. EGF-NIR was submitted for analysis on 12% SDS-PAGE by comparison with native, unlabeled EGF. Samples of 10 µg proteins were separated and visualized by commassie blue staining and the NIR emission was measured by positioning and scanning the gel in the Odyssey® Infrared Imager (LI-COR Biosciences, Lincoln, NE, USA).
Western Blotting of EGFR and Erk Phosphorylation
The levels of EGFR in cell lines and CRC tissue and Erk phosphorylation, were estimated upon extraction with cell lysis buffer (Cell Signaling Technology, Inc. Danvers, MA, USA). The ability of EGF-NIR to stimulate ERK phosphorylation in A431 cells was compared to that of unlabeled EGF. 2×10 6 cells at 90% confluence in a 6 well plate were serum starved for 2 hours. Starvation media was replaced with regular medium containing 7 nM EGF-NIR. Cells were incubated for 15 min at room temperature and harvested. 50 µg protein lysates were separated by 10% polyacrylamide SDS-PAGE and transferred on ice to nitrocellulose membranes (90 V for 1.5 hours; Whatman, Dassel, Germany). Non-specific binding was blocked by incubation of the membranes for 2 hours at room temperature (RT) with 5% non-fat powdered milk (Bio-Rad, Hercules, CA, USA) in Tris buffered saline containing 0.1% Tween-20. Immunodetection was performed with monoclonal anti-EGFR antibody (Cell Signaling Technology, Inc. Danvers, MA, USA) or primary antibodies (1∶1,000) against phospho-or pan-Erk1/2 (Cell Signaling Technology, Inc. Danvers, MA, USA), followed by horseradish peroxidase (Jackson ImmunoResearch, West Grove, PA, USA) and developed with ECL (Pierce, Rockford, IL, USA).
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Figure 4
Saturation, kinetics and sensitivity of EGF-NIR binding using IC-NIR imaging of CRC cultures.
(A) Left: a scheme of CRC polyp with high spots of transformed CRC cells (focal area) and heterogeneous area including both normal and transformed CRC cells; Middle: generation of a heterogeneous mixture at different ratios (%) between HT-29 and SW 620; 0 – no cells; +/++ -presence of different cell concentrations; Right: focal plating (in a ring) of HT-29 and A431 monolayer surrounded by SW620 monolayer; insert-the level of EGFR (170 kD) and non-mature EGFR (150 kD) in the cells; (B) The relationship between the NIR intensity of 15 min binding with 7 nM EGF-NIR (mean ± SD, n = 9) and the percentage of SW620 in the cell mixture with either HT-29 (closed circles) or HCT116 (open circles) * p<0.05 vs. 100% SW620; (C) The relationship between SBR (mean ± SD, n = 9) and EGF-NIR concentration; A431 (open circles); HT-29 (closed circles); binding was performed for 15 minutes. Insert: NIR scans; * p<0.05 vs. 0.01 nM; (D) The kinetics of 7 nM EGF-NIR binding (mean ± SD, n = 9) to focal cultures of A431 (open circles) or HT-29 (closed circles); Insert: NIR scans; * p<0.05 vs. 0 min.
Preparation of in vitro CRC Models and in Cell NIR Imaging (IC-NIR)
Homogenous monolayer of an individual cell line
CRC cells were plated at a density of 150,000 cells/well in 12 wells tissue culture plates (Nunc, Rochester, NY, USA) two days before the experiments generating homogenous cell monolayer. Thereafter, the culture medium was replaced with fresh medium containing 7 nM EGF-NIR, for 15 min at 4°C and 37°C, to measure total binding. At the end of the experiment the cultures were washed three times with 1 ml PBS and cell associated NIR intensity was estimated. To evaluate the non specific binding, sister cultures were incubated with the same concentration of EGF-NIR, at the same conditions, in the presence of excess of 100 nM EGF. Specific binding by EGF-NIR imaging is defined as the difference between the NIR intensity of total binding and NIR intensity of non specific binding. Competition experiments with 500 nM of either cetuximab, transforming growth factor α (TGF-α) or neuregulin 1 (NRG1) were performed by concomitant incubation of the competitor with EGF-NIR. The results are presented as the mean ± SD of at least three independent experiments (n = 9). The NIR imaging was estimated using Odyssey Infrared Imager at the following conditions range: resolution: 170–340 microns; pixel area: 0.03 mm 2 (approximately 15–20 cells); quality: medium-low; focus offset: 1–3; channels: 800 nm; intensity: 1–3.
Heterogeneous focal monolayer of CRC cells
To mimic high spots of transformed CRC cells in the polyp [19] and to enable direct measurements of signal to background ratio in the same experiment, a focal cell culture approach was used. Different colorectal cancer cell lines (with different levels of EGFR) or 15×10 3 A431 (high levels of EGFR) were plated to confluence inside a 4 mm inner diameter cloning ring (Sigma-Aldrich, St Louis, MO, USA) placed at the center of a well. Cells were left to adhere for 2 hours in an incubator. Thereafter, 15×10 3 SW620 cells (lacking EGFR) were plated in the cell-free area surrounding the cloning ring and left to adhere for 2 hours at the same conditions. Two types of such experiments were performed: a. one focus. b. two foci. At the end of cell adherence step, the cloning ring was removed and the cells were washed with culture medium. Two days after generating the model, the cultures were subjected to binding and imaging experiments as previously described. Signal/Background ratio (SBR) values indicate the ratio of NIR fluorescent signal of the central circle (focal area of CRC or A431 cells) to NIR fluorescent signal of outside area (SW620).
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Figure 5
IC-NIR BOI of EGFR levels in CRC clones, in relation to the level of expression of CEA.
Different CRC cells were focally plated on a background of IEC6 enterocyte monolayer. The cultures were incubated for 15 min at 37°C with 7 nM EGF-NIR in the presence (nonspecific binding – white bars) or absence (total binding – grey bars) of 100 nM unmodified EGF. The signal (CRC cell line)/ background (IEC6) ratio was estimated at identical conditions for all cultures and is presented as the mean ± SD (n = 9). Significance: * p<0.05 compared to IEC6 values; ** p<0.05 compared to total binding of the respective group p< 0.05 compared to A431 and & p<0.05 compared to COLO 205; Lower inserts: NIR scans; Upper inserts: left-EGFR protein expression by western blotting; arrow indicate the position of mature EGFR 170 kD protein and non-glycosylated 150 kD protein; right-mRNA expression of CEA and β-actin in cell cultures.
Heterogeneous suspensions of CRC cells
To mimic heterogeneous distribution of transformed CRC cells in the polyp [20] and to enable evaluation of sensitivity of detection of the minimal amount of EGFR expressing cells among control normal enterocytes, heterogeneously mixed cultures of HT-29 and SW620 cells were prepared. Suspension cultures of HT-29 were mixed with suspension cultures of SW620 to generate different percentage of the individual cell culture in the same volume. SW620 cells could be distinguished from HT-29 cells by their elongated morphology. At conditions designated as “0%” the suspension contains 0% HT29 cells and 100% SW620 cells, and at “100%”, the suspension contains 100% HT-29 cells and 0% SW620 cells. In the other cases the percentages indicate the ratio between percentage HT-29 cells and percentage of SW620 cells. The binding experimental conditions and measurements of NIR fluorescent intensity (arbitrary units/mm 2 ) of the different heterogeneous cultures was performed as above.
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Figure 6
Whole body in vivo and isolated tissue ex vivo EGF-NIR BOI of mice with HT-29 orthotopic tumors.
(A) Photograph of an orthotopic tumor and EGFR protein expression in the tumors; (B) Time course of EGF-NIR accumulation in tissues of tumor-bearing mice. The mice were injected i.v. with 1 nmol of EGF-NIR in untreated mice (upper row, n = 6) or mice pre-injected with 1 µg/ml cetuximab (lower row, n = 4); high resolution BOI of a mouse injected with EGF-NIR and circles indicate ROI measurements (C) Time course of tissue accumulation of EGF-NIR at 48 hours from mice presented in B; Signal intensity at 800 nm were normalized to background fluorescence using an arbitrary tumor circle (10–20 ROIs/mouse) compared to an identical area on the flank (adjacent muscle); * p<0.05 compared to EGF-NIR 4 hours; ** p<0.05 compared to mice injected with EGF-NIR; (D) EGF-NIR signal/background ratio in isolated tissue from the tumor-bearing mice 48 hours after injection. * p<0.05 compared to muscle, ** p<0.05 compared to liver; Insert: Upper-photographs of tissues in the dish; Middle-NIR images; Lower-spectral intensity maps; Intensity scale-red-brown (5) high expression; blue (3) very low expression.
RT-PCR and EGFR siRNA Silencing
Total RNA was isolated and genomic DNA was degraded from the RNA preparations, using the SV total RNA isolation system (Qiagen GmbH, Hilden, Germany). 1 µg of total RNA was reverse transcribed (Promega, Madison, WI, USA), according to the manufacturer’s instructions. PCR was performed in a final volume of 50 µl containing 5 µg cDNA, 50 pmol of each upstream sense and downstream sense primers of CEA or EGFR [21] , [22] , and 25 µl of GoTaq® Green Master Mix (Promega, Madison, WI). PCR experiments were conducted for 35 cycles. To generate various cDNA fragments, a Mastercycler gradient (Eppendorf, Hamburg, Germany) was programmed as follows: denaturation at 95°C for 1 min, annealing at 61°C and elongation at 72°C for 1 min. To knock down EGFR the standard amine transfection agent protocol of Ambion (Applied Biosystem, Austin, TX, USA) was followed. Briefly, 5 nM of 21 mer anti-EGFR Silencer select small interference RNA (siRNA) and scrambled RNA were reverse transfected into A431 cell cultures using siPORTNeoFX transfection agent according to manufacturer protocol. The cells at a density of 80,000 cells/ml were applied on 12 well plates and 2 days after transfections were analyzed. Knock down of EGFR mRNA was confirmed by Western blotting. The following carcino embryonic antigen (CEA) and EGFR primers, prepared by SyntezzaBioScience Ltd., Jerusalem, Israel, were used:
Human CEA CAM5: Sense: 5′-CGCATACAGTGGTCGAGAGA-3′ ; Antisense: 5′-ATTGCTGGAAAGTCCCATTG-3′
Rat CEA1: Sense: 5′-CTACAGGCTGAGGGATGCTC-3′ Antisense: 5′-GGTCCCGTCACAGTTACGTT-3′
Human EGFR: Sense: 5′-CGAGGGCAAATACAGCTT-3′ Antisense: AAATTCACCAATACCTATT-3′
Human EGFR siRNA: Sense: 5′-CCAUAAAUGCUACGAAUAUtt-3′ Antisense: 5′-AUAUUCGUAGCAUUUAUGGag-3′
Scrambled siRNA: Sense: 5′-UAACGACGCGACGACGUAATT-3′ Antisense: 5′-UUACGUCGUCGCGUCGUUATT-3′
Preparation and NIR Imaging of CRC Orthotopic Tumors in Mice
This study using Male Balb/c nude (Harlan, Israel) mice was approved, performed and supervised by the guidelines of The Chaim Sheba Medical Center Animal Care and Use Committee. HT-29 cells were trypsinized, washed and resuspended at concentration of 1×10 7 cells/ml in PBS. For tumor implantation, the mice were anesthetized by intra-peritoneal injection of a mixture of ketamine (100 mg/kg) and xylazine (20 mg/kg). Trans-anal injection of 1×10 6 HT-29 cells was performed under microscope magnification (X40) using a 27 g needle. The injection was directed submucosally into the distal, posterior rectum, approximately 2–3 mm beyond the anal canal and into the rectal mucosa [23] . Mice were monitored two times weekly for tumor initiation and progression. Tumors reached ∼ 0.75 cm in size at 3–4 weeks. In vivo imaging of EGF-NIR fluorescence in mice was performed with a LI-COR Biosciences small-animal imager Odyssey MousePOD ® . To visualize the tumors, 1 nmol of EGF-NIR in 100 µl saline was injected via the tail vein into tumor-positive mice in the presence (n = 4) or absence (n = 6) of cetuximab (1 µg/ml) and evaluated for systemic clearance by NIR imaging at intervals of 1–8 hours over a period of three days, after which time >95% of the signal had cleared. The mice were imaged up to two days post injection and euthanized. Statistical analysis of the images for each mouse was normalized using the same intensity scales, under the conditions previously described. SBR was calculated as follow: mean NIR intensity of the tumor divided by mean NIR intensity of the background of the adjacent muscle. Regions of interest (ROI) with identical areas were used for both tumor and background. The standard deviation of mean backgrounds was calculated using 10–20 ROIs. Due to tumor size differences between the animals receiving EGF-NIR, tumor signal divided by the background signal of similar size ROI corrected for area (pixels), provided the tumor two dimensional (2D) total labeling. Tumors, skeletal muscle and liver tissue of euthanized mice were dissected and their urine samples were collected. The tissues were weighted and introduced into plastic tubes dishes. The dishes were scanned on Odyssey Infrared Imager. NIR intensity of ROI of the tumor was compared to adjacent muscle to generate SBR. Isolated tissue analyses were performed by scanning at 800 nm channel, for the tissue accumulated EGF-NIR fluorescence signal and the SBR was calculated. The NIR imaging was estimated at the following conditions: resolution: 170–340 microns; pixel area: 0.03 mm 2 ; quality: medium-low; focus offset: 1–3; channels: 800 nm; intensity: 1–3.
Patients and CRC Tissue Specimen Collection
18 patients over the age of 18 years with histologically confirmed primary adenocarcinoma of the colon were offered participation in the study. 5 patients who received prior radiation or chemotherapy were ineligible for the study. The study protocol was approved by the Institutional Review Board (IRB, Helsinki Committee) of Hadassah-Hebrew University Medical Center. All samples were obtained from consenting study subjects undergoing surgical tumor resection who signed a written informed consent. All specimens underwent routine macroscopic and microscopic analysis by a board certified pathologist according to the College of American Pathologists (CAP) guidelines of histopathology reporting ( www.cap.org ). Tissues identified by the study pathologist as colonic adenocarcinoma and adjacent tissues [24] were then used for IC-NIR imaging.
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Figure 7
BOI of EGF-NIR binding in human CRC tissues.
36 Slices of CRC tissues and 19 slices of adjacent colon tissue (n = 10–15 ROI in each slice) were submitted for ex vivo binding assay for 45 min at 37°C with 70 nM EGF-NIR in the presence (non specific) or absence (total binding) of 1 µM unlabeled EGF. The NIR intensity was estimated at identical conditions for all slices (n = 12). Significance: * p<0.01 compared to respective group in adjacent colon EGFR-, ** p<0.05 compared to respective group in CRC tissue EGFR-; Insert: typical western blotting for EGFR of the slices investigated.
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Figure 8
Spectral intensity maps of BOI images of specific EGF-NIR binding in human CRC tissues.
Images of five typical slices, specifically labeled with EGF-NIR, as described in legend for Fig. 7. The 800 nm Odyssey Infrared Imager acquired images were processed using applied spectral imaging software, Spectral View. Intensity scale-red-brown (5) high expression of EGF-NIR binding; green (4) intermediate expression; blue (3) very low expression.
NIR BOI of CRC Tissues
Fresh tumor tissue or adjacent colon tissues were divided in horizontal slices, 230 µm thick, which were prepared using a vibratome VT1000S (Leica, Nussloch, Germany) and incubated in ice-cold DMEM binding solution. The slices were transferred to 24 well tissue culture plates filled with DMEM saturated with 95% O 2 and 5% CO 2 similar to conditions enabling rectal organ cultures [25] . The plates were maintained on ice for 45 min duration in DMEM and the binding experiment was performed by addition of 70 nM EGF-NIR in the presence (non specific binding) or absence (total binding) of 1 µM of unmodified EGF. From each tissue, triplicate slices were incubated with 70 nM NIR-Dye (IRDye800CW) to evaluate the non specific binding of the dye. The binding experiment was terminated by washing the tissue three times with cold PBS. The wet slices were transferred to new 24-well plates in 1 ml/well PBS and scanned for NIR imaging intensity using Odyssey Infrared Imager, under the conditions described above. Serially, over a two years period, 43 CRC and 23 adjacent colon tissue samples were evaluated. ROIs with identical areas were used for slices from the different experimental groups. The means and standard deviations of NIR intensity (arbitrary fluorescent units/mm 2 area) were calculated using 10–15 ROIs in each individual slice. Each slice submitted for the EGF-NIR binding experiment was also evaluated after the imaging for EGFR expression by western blotting. The data achieved was categorized according to EGFR positive CRC tissues, EGFR negative CRC tissues and adjacent colon tissue which in a majority were EGFR negative. The lack of EGFR was proved by Western blotting. 85% of the slices were included in the statistical analyses according to the following pharmacological criteria: a. total binding; b. specific binding were higher than the value of non specific absorption of NIR-Dye. The slices images were processed by high resolution imaging [26] using applied spectral imaging software, Spectral View TM (ASI, Migdal Ha’Emek, Israel).
Statistical Analysis
All results are presented as the mean ± SD of large series of independent experiments using different batches of EGF, cells, CRC and mice tumor tissues. All data were evaluated using the InStat statistics program (GraphPad, La Jolla, CA, USA). Statistically significant differences between experimental groups were determined by analysis of variance (ANOVA) with Bonferroni post-hoc test and considered significant when p<0.05.
Results
Preparation, Characterization and Validation of EGF-NIR Probe
The reaction scheme for synthesis of EGF-NIR is shown in Figure 1A: EGF molecule has one free amino group available for conjugation which is located at the amino-terminal of the protein and can be conjugated with IRDye800CW [27] , [28] . After reaction with these reagents the EGF-NIR was purified on size exclusion ( Fig. 1B ) followed by anion exchange chromatography ( Fig. 1C ). Final purification of the EGF conjugate was achieved by high pressure liquid chromatography ( Fig. 1D ) resulting with a single peak found very homogenous upon SDS-PAGE electrophoresis and NIR visualization ( Fig. 1D -insert). EGF absorbance and excitation spectra clearly indicates the presence of IRDye800CW ( Fig. 1E ) in the conjugate. Compared to the unmodified EGF, EGF-NIR stimulated EGFR resulting with increased phosphorylation of Erk ( Fig. 1F ). These findings indicate that the modified EGF preserved bioactivity, as evident from the ability to stimulate EGFR-induced Erk signaling pathway. Validation of EGF-NIR binding properties on CRC cell line was performed using HT-29 cultures expressing relative high level of EGFR. Comparison of NIR intensity signals of homogeneous monolayer cultures of HT-29 incubated with EGF-NIR in the presence or absence of different ligands at 4°C ( Fig. 2B ) and 37°C ( Fig. 2A ) conditions, reflects EGF-NIR cell surface binding and ligand-induced receptor internalization, respectively. Furthermore, the results clearly indicate specific and selective binding of EGF-NIR probe, similar to unmodified EGF, as concluded from lack of competition with NRG1 and partial competition with EGF, TGF-α and cetuximab.To demonstrate a direct relationship between EGFR level and binding of the EGF-NIR, we used siRNA anti-EGFR to knockdown the receptor. The efficiency of siRNA was validated using RT-PCR and indicated a knock down of ∼ 50% of EGFR mRNA. EGFR protein level, as evident from the western blotting and EGF-NIR binding, was reduced by 65% and 35% (p < 0.05), respectively, as compared to untreated controls or cultures transfected with scrambled RNA ( Fig. 3 ), as previously documented with other cells [22] . Therefore, we conclude that the decrease NIR intensity (decreased specific binding of EGF-NIR) reflects the reduced expression of EGFR protein level.
Development of Novel CRC in vitro Models for BOI
To mimic high spots of transformed CRC cells in the tumor, a focal cell culture approach ( Fig. 4A -focal plating) was developed using HT-29 cells expressing high level of EGFR and SW620 lacking EGFR ( Fig. 4A -insert) and for comparative purposes A431 overexpressing EGFR. In another approach to mimic clinical presentation of diffused tumor cells, a suspension of both types of above cells, at different ratios was prepared and plated ( Fig. 4A -heterogeneous plating). Heterogeneous mixed cultures of HT-29 or HCT116 and SW620, at different ratios were incubated with 7 nM EGF-NIR for 15 min ( Fig. 4B ). It is evident that the presence of 15–30% HT-29 or HCT116 cells in a heterogeneous mixture with SW620 cells, represented the threshold of detection of the minimal amount of EGFR expressing cells among EGFR negative cells, providing a significant NIR intensity signal ( Fig. 4B ). Using the focal culture model, saturation ( Fig. 4C ) and time-course ( Fig. 4D ) binding experiments were preformed using IC-NIR imaging of the CRC HT-29 cell line (EGFR++) compared to A431. As found for A431, 7 nM EGF-NIR induced maximal SBR, however the maximal binding was optimal between 1 and 5 min and thereafter decreased due to increased nonspecific binding to SW620. The lower SBR in CRC HT29 compared to A431 in the different experiments is in direct correlation with the lower level of EGFR in the cells. Based on these experiments, the optimal conditions for IC-NIR imaging with the focal and heterogeneous cultures models using either A431 or HT-29 cells were setup on 7 nM EGF-NIR and 15 min of incubation since at higher concentrations of EGF-NIR or time of incubation the SBR decreased due to the increased background (increased nonspecific binding of EGF-NIR).
Since EGFR-targeting therapies are currently in use for the treatment of metastatic CRC [29] the above in vitro CRC models may be considered for BOI. To further evaluate the relationship between the EGFR level and SBR using EGF-NIR we took advantage of a panel of human CRC cell lines with different expression levels of EGFR and CEA ( Fig. 5 , top). Using the focal model, we performed IC-NIR BOI with these cell lines which also exhibit highly variable growth and metastatic capacities [30] . For comparison purposes COLO 205 (EGFR++) and A431 (EGFR+++) or HT 29 cells (EGFR++) were plated in different rings surrounded by small intestine epithelial IEC6 cells (EGFR -). The binding experiment was performed in the absence and presence of EGF to measure total and non specific binding, respectively ( Fig. 5 ). It was found that the SBR of EGF-NIR binding to COLO 205 is significantly higher than that of HT 29, and lower than that of A431, in accordance to EGFR 170 kDa protein isoform expression level ( Fig. 5 -insert). Although the SBR of total binding of CRC clones was between 3–6 (about 3 fold lower than A431), the non specific values of SBR between 1–2 allowed calculations of specific binding (NIR intensity) values of 3–4, which are in the sensitivity range of NIR detection systems [31] , [32] .
BOI of Tumors Positive for EGFR in Orthotopic Mice Model
To confirm the suitability of EGF-NIR for in vivo BOI, HT-29 CRC orthotopic tumors in nude mice were generated ( Fig. 6A ). Western blotting analysis of dissected tumors confirmed the expression of EGFR in the tumors ( Fig. 6A ). Figs. 6B presents typical NIR fluorescence images of mice bearing EGFR positive tumors 4, 24 and 48 hours after i.v. injection of 1 nmol EGF-NIR, an optimal dose which affords the highest SBR, clearance and imaging results [28] . The control groups were i.v. injected first with 1 µg/ml cetuximab to block EGFR in orthotopic tumor and tissues [28] and after 5 hours injected with 1 nmol of EGF-NIR. In the first four hours the animal injected with the imaging agent show a very strong whole body fluorescence signal. After 24 hours, about 80% of the fluorescence signal cleared, and after 48 hours the signal intensity fell back to background level, in contrast to EGF-NIR labeling in tissues expressing EGFR: tumor, bladder and liver ( Fig. 6B right image, taken at high resolution). Quantitative 2D surface measurements of the SBRs 4, 24 and 48 hours after injection, at each pixel (mm 2 ), on ROI taken from the tumor and liver region, compared to an identical area on the flank (adjacent muscle) region are presented in Fig. 6C . It is evident that already after 24 hours EGF-NIR is specifically (competitive with cetuximab) and significantly accumulating in the tumor, providing an SBR value around 4.2 ± 0.6. This value is further increased at 48 hours. In the liver, a tissue highly abundant in EGFR [16] , a significant accumulation or slowly clearance was observed after 48 hours and EGF-NIR binding was fully competitive with i.v. injected cetuximab. We assume that the fast accumulation at 24 hours of EGF-NIR reflects the very high level of EGFR in the tumors responsible for its uptake. Macroscopic 3D NIR estimations of harvested organs and urine were performed 48 hours after injection ( Fig. 6D ). The urine was strongly fluorescent, since EGF is known to be excreted unmodified into the urine [33] , providing the highest SBR NIR fluorescence compared to that of the muscle. The liver/muscle SBR NIR fluorescence strongly exceeded compared to tumor ( Fig. 6D ) since the NIR intensity of the isolated tissue is higher than its value upon scanning the whole animal. Tumor/ muscle SBR NIR fluorescence was 11.3 ± 1.7 while the SBR 2D measurements of the whole animals was 6.6 ± 1.1. NIR imaging of isolated organs, in close proximity with the laser instrument, is more sensitive than the imaging of the organs in the animals, due to the lack of absorbance and scattering of the NIR fluorescence by the animal tissues [28] .
Specificity and Heterogeneity of CRC Tissue Upon ex vivo NIR Imaging
Figs 7 and 8 present ex vivo NIR imaging of human CRC tissues performed with EGF-NIR and processed by high resolution imaging to emphasize the distribution of EGFR. The binding experiments were conducted with fresh slices from CRC tissues identified by western blotting as either EGFR positive or EGFR negative ( Fig. 7 -insert). We also assessed slices from adjacent, colonic tissue close to the tumor, identified as EGFR negative in the same way previously performed for detection of CRC associated transcript-1 biomarker in malignant and pre-malignant CRC human tissues [24] . The binding was performed for 45 min to allow optimal diffusion of EGF-NIR into the slice. Significant total and specific binding of EGFNIR imaging agent was detected only in the CRC tissues positive for EGFR ( Fig. 7 ). The ratios between specific binding of EGF-NIR to “CRC tissue EGFR+/adjacent colon EGFR-” or “CRC tissue EGFR+/CRC tissue EGFR-” were 46 and 16 (p < 0.05), respectively. These high values are easily detected by NIR instruments scanners. Fig. 8 presents 5 slices randomly chosen from the different tissues and their NIR imaging was processed by high resolution analyses. Spectral intensity maps of the slices indicate distinct focal areas of high level of expression of EGFR in CRC tissues EGFR+, with very high specific NIR labeling, few high spots of EGFR in adjacent colon tissue EGFR-with practically no specific NIR labeling, and very low level of EGFR in CRC tissue EGFR-with extremely low specific NIR labeling. These findings indicate a direct relationship between EGFR expression and EGF-NIR BOI and further indicate heterogeneity of CRC tissues in EGFR expression analyzed by spectral imaging software.
Discussion
A new platform of NIR reagents based on IRDye 800CW was developed and used for preparation of IRDye 800CW conjugated EGF [27] , [28] , [34] which was accepted by NIH database as a molecular imaging and contrast agent for optical visualization of prostate carcinomas in vitro and in mice. We took advantage of this progress to prepare an EGF-NIR bio-imaging agent according to this procedure, and evaluated its pharmacological properties for recognition of EGFR in novel CRC models in vitro , resembling tumor heterogeneity, orthotopic CRC tumors in mice and CRC tissue slices ex vivo , thus translating results from CRC cells to human tissue specimens. EGF-NIR was homogeneous and pharmacologically active, as evident from binding experiments using CRC models. It specifically and selectively binds EGFR but not NRG 1 and its binding at 37°C was significantly higher than at 4°C, indicating that it is internalized at 37°C as expected from native growth factor. The binding of the probe measured by SBR directly reflected the level of EGFR, as evident from binding experiments with cells expressing lower EGFR levels, due to siRNA knockdown of EGFR and experiments with CRC cultures expressing different levels of EGFR. The EGF-NIR, rapidly saturated the EGFR in a focal CRC set-up, and was able to detect with high sensitivity 15–30% of EGFR expressing cells in a heterogeneous mixture of carcinoma/enterocyte cells in vitro . Consistent with the in vitro findings, specific targeting of EGF-NIR to EGFR was demonstrated by analyzing the NIR images of mice bearing EGFR positive CRC orthotopic tumors. Selective accumulation of EGF-NIR as evident from in vivo competition with cetuximab, was clearly seen after 24 and 48 hours in the tumor and EGFR positive organs such as the liver. The measurements of EGF-NIR in the tumors normalized to skeletal muscle and compared to the liver, indicated a faster accumulation of the agent in the tumor, compared to the liver, supporting the possibility of a specific EGFR- mediated process. These findings are reminiscent of accumulation studies using EGF- conjugated to Cy5.5 fluorophore in mice with breast cancer xenografts [16] and experiments of confocal endomicroscopy targeting EGFR with fluorescently-labeled antibodies [35] . We found relatively high SBRs for EGF-NIR binding to the whole animal and isolated tumors, supporting the notion that EGF-NIR is a suitable imaging agent for CRC tumor visualization using NIR endoscopy. This conclusion is also in line with studies in which dual labeling of a peptide with IRDye800CW and In 111 , generated satisfactory SBRs of 1.6 for NIR and 1.7 for nuclear imaging providing high quality images of the tumors in mice with human melanoma xenografts [36] . As documented by Goetz et al. [35] , which measured a factor of 10 fold signal distinction of neoplastic from non-neoplastic CRC tissue using confocal laser endomicroscopy, we would like to stress that SBR value of 10 for isolated organs, scanned with the Odyssey Infrared Imager, represent a much higher value than the sensitivity threshold required by a NIR endoscope. SBR is an important parameter influencing the sensitivity of detection. Since EGFR is overexpressed in CRC tissue, it enables upon binding EGF-NIR, a relatively high SBR, facilitating detection with NIR scanners.
Following satisfactory results obtained in mice, we further characterized the applicability of EGF-NIR for ex vivo NIR imaging of human CRC tissues. Mouse and human EGF show more than 70% homology [37] , therefore the EGF-NIR probe efficiently cross reacts with both mouse and human EGFR, enabling experiments with both mouse and human CRC tissues. We proved its specific binding to CRC tissue-EGFR positive, but not CRC tissue-EGFR negative and adjacent colon tissue-EGFR negative ( Fig. 7 and 8 ), as previously demonstrated using FITC-labeled anti-EGFR antibody imaging of CRC with confocal endomicroscopy [35] . The ratios between specific binding of EGF-NIR to “CRC tissue-EGFR positive/CRC tissue-EGFR negative” or “CRC tissue-EGFR positive/adjacent colon tissue-EGFR negative” were 46 and 16 (p < 0.05), respectively. These high values prove that it is possible to generate a strong NIR fluorescence signal to detect fresh, unfixed CRC tissue-EGFR positive, using NIR scanners, as previously documented with an anti CEA antibody labeled with indocyanin green (ICG) NIR-Dye [31] . The heterogeneity of EGFR distribution among different slices and in the same slice of CRC tissue as observed in the present study may explain previous reports on the lack of correlation between clinical response to EGFR protein expression on immunohistochemical analyses of patients with refractory metastatic CRC [38] , [39] . Since in the clinic, during the tissue processing, CRC EGFR may lose affinity upon handling and fixation, and antibodies used for diagnosis recognize epitopes different then EGF binding domain, we would like to propose that EGFNIR imaging of CRC fresh tissue slices may complement the above method [40] . This possibility needs to be further addressed using a larger cohort of human CRC tissues, in parallel with immunohistochemical analyses.
On one hand, EGF-NIR is significantly smaller then antibodies, it has higher tissue permeability, and its clearance as unbound agent can be achieved with simple washing steps, compared to antibodies labeled with NIR probes. Also its use can be more accurate in measuring EGFR expression than cetuximab in CRC tissue with variable affinity of EGFR for cetuximab [41] . On another hand, the risk in using such an agent is its ability to activate EGFR mediated downstream signaling such as RAS-Erk required for tumor proliferation. This problem can be solved by future designing of biomimetic peptides with reduced proliferative activity compared with native EGF or using EGFR targeting nanoparticles. Present findings provide EGF-NIR as an enabling platform technology for possible implementation of the binding protocol to visualize ex vivo EGFR in human CRC tissue, complementary to the gold standard technique of RT-PCR for EGFR mRNA quantification [6] , in situ hybridization [8] and EGFR immunohistochemistry [7] .
Fluorescence optical imaging has become a valuable tool as an adjunct to fiber-optic endoscopy due to its low cost and its ability to track multiple probes in a real time manner. Compared with the visible spectrum, the NIR dyes overcome the endogenous autofluorescence, maximize tissue penetration and are suitable for non-invasive whole body and organs imaging in small animals [17] . Over the last decade, NIR endoscopes were developed for high resolution imaging in small animals [42] and in identification of hyperplastic and adenomatous polyps in the colon [43] . The instruments currently available and/or under development, for clinical human imaging, such as Zeiss Pentero, with the microendoscope consisting of 20-gauge fiber optic catheter and dichroic beam splitters that simultaneously display visible light and 700 nm and 800 nm NIR fluorescent are used for intra-operative vascular flow and vascular surgery [44] . This instrument follow up the endogenous NIR autofluorescence of hemoglobin in the blood or of the FDA approved fluorescent ICG dye, either as a free molecule injected i.v. or conjugated to a relevant antibody such as anti-CEA and anti-mucin antibodies in local application [45] . Therefore, it is anticipated that present results, achieved with Odyssey Infrared Imager, will be reproduced with fiber-optic NIR endoscopes.
In conclusion, our study underscores the potential benefits of NIR optical imaging for BOI of EGFR in CRC tissues as evident from cell lines models in vitro , orthotopic tumors in mice at low dose (1 nmol/mouse) and in situ human tissues. The results suggest that BOI of EGFR using EGF-NIR probe may provide a sensitive, highly selective, non invasive tool for the detection and characterization of EGFR expressing CRC tissues without the need of radioactive imaging. This technology may complement immunohistochemical assessment of EGFR protein expression in CRC tissue which might contribute to future standardization methods measuring EGFR protein level, to improve the identification of patients who will benefit from anti-EGFR monoclonal antibody therapy.
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