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In bioproduction processes for pharmaceutical proteins, suspension cell lines enable large-scale cultivation in bioreactors, which is required in order to meet the demands for marketed drugs.
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However, the adaptation of cells from adherent to suspension growth and the differential cultivation procedures between adherent and suspension cells induces phenotypic changes to the cell lines.
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In order to develop a deeper understanding of such changes, we evaluated differences in gene expression levels between adherent and suspension progeny HEK293 cells.
[ { "end": 157, "label": "CellLine", "start": 151, "text": "HEK293" } ]
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Consensus differential expression results were found related to up-regulation of genes associated with cell component organization such as membrane, cytoskeleton and cell junction in suspension compared to adherent cells (Fig. 5a).
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Noteworthy, cell adhesion was found up-regulated in suspension compared to adherent cells.
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Amongst the most significant differentially expressed genes (adjusted p-value < 0.01) in the cell adhesion gene set (Supplementary Table S7), several members of the cadherin superfamily, including many different protocadherins (PCDH), desmoglein 2 (DSG2) and desmocollin 2 (DSC2) were found significantly up-regulated in suspension cells compared to adherent cells.
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This family of genes are involved in the formation of adherence junctions between cells.
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Notably, DSG2 and DSC2 are located in the region on chromosome 18 that had gained genomic copies in all progeny cell lines except 293E compared to the parental strain (Fig. 2a).
[ { "end": 134, "label": "CellLine", "start": 130, "text": "293E" } ]
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Moreover, four protocadherin members showed the highest fold-change of up-regulated genes in suspension cells (Supplementary Table S7).
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The higher expression of such cell adhesion molecules in suspension cell lines compared to adherent progeny HEK293 cells may be explained by the loss of culture dish support to grow on in case of suspension cells.
[ { "end": 114, "label": "CellLine", "start": 108, "text": "HEK293" } ]
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Upon disruption of adhesion interactions with other cells and extracellular matrix, a natural cellular response may be to increase or maintain the expression of adhesion molecules in an attempt to restore such connections.
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The ability of the suspension cell lines to form cell aggregates during suspension cultivation and the ease of the cells to attach to culture dish surfaces upon cultivation without shaking, can be speculated to support these findings.
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Such cell adhesion molecules found up-regulated in suspension cell lines may thus be appropriate cell line engineering targets for improved bioprocess performance of suspension cell lines.
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Further evaluation of the differentially expressed genes between adherent and suspension HEK293 progeny cell lines, based on metabolic gene set analysis, highlighted changes in biosynthesis of aromatic amino acids and pathways related to lipids and/or cholesterol metabolic processes (Fig. 4b).
[ { "end": 95, "label": "CellLine", "start": 89, "text": "HEK293" } ]
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These metabolic changes could be a result of different growth media compositions used for the cultivation of either adherent or suspension cells that may imply different concentrations of for instance amino acids, glucose or serum.
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When reducing the number of differentially expressed genes to those that consistently showed differential expression between adherent and suspension cells in pairwise comparisons of all cell lines, the cholesterol and sterol biosynthesis and metabolism pathway were found to be most significantly different between the cell types (Fig. 5b).
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Moreover, five of the consistently up-regulated genes in suspension HEK293 compared to adherent encode enzymes that have either direct roles in the cholesterol biosynthesis pathway (MSMO1, HMGCS1 and IDI1), or proteins that are associated with cholesterol metabolism by various processes (NPC1L1 and INSIG1).
[ { "end": 74, "label": "CellLine", "start": 68, "text": "HEK293" } ]
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As suspension cell lines are cultivated under serum free conditions, the increased expression of genes associated with for instance cholesterol in suspension cell lines may be a result of a lower cholesterol content in the medium.
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However, as cholesterol is a major component of the cell membrane and has an important function for membrane structure and cell signaling, the differential expression of genes associated with cholesterol synthesis and metabolism may also be of importance for the different morphologies between adherent and suspension HEK293 cells.
[ { "end": 324, "label": "CellLine", "start": 318, "text": "HEK293" } ]
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Indeed, previous studies have shown that cholesterol plays a critical role in regulating the formation of cell-to-cell interactions in endothelial cells and that depletion of cholesterol reduces cell adhesion and increases endothelial cell stiffness.
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Increased cell surface stiffness has been reported for HEK293 cells in suspension compared to adherent state as a result of up-regulation and re-organization of the actin cytoskeleton.
[ { "end": 61, "label": "CellLine", "start": 55, "text": "HEK293" } ]
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This may partly be a result of altered cholesterol levels in the cell membrane since cholesterol is a regulator of the actin cytoskeleton and cholesterol depletion has been shown to induce actin polymerization.
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Interestingly, the Insulin-induced gene 1 protein (INSIG1), which was up-regulated in suspension compared to adherent HEK293, is a negative regulator of cholesterol synthesis and important for cholesterol homeostasis and knockout of INSIG1 has previously been shown to result in cholesterol accumulation.
[ { "end": 124, "label": "CellLine", "start": 118, "text": "HEK293" } ]
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Notably, a lower cholesterol biosynthesis in suspension cell lines compared to the original HEK293 strain was indeed predicted using IPA (Supplementary Fig. S7).
[ { "end": 98, "label": "CellLine", "start": 92, "text": "HEK293" } ]
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It should however be noted that this prediction does not take into consideration the effect of INSIG1, instead the predicted reduction in cholesterol biosynthesis in suspension cells compared to the HEK293 cell line is a result of down-regulation of SC5D (lathosterol oxidase).
[ { "end": 205, "label": "CellLine", "start": 199, "text": "HEK293" } ]
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From a bioprocess perspective, differences in intracellular cholesterol synthesis and metabolism may also be of interest with regards to the secretory capacity of a cell line since previous findings has shown that cholesterol is essential for ER to Golgi transport within the secretory pathway and that secreted productivity of CHO cells increases upon elevated intracellular cholesterol levels, through silencing of INSIG1, possibly due to increasing the volume of the Golgi compartment.
[ { "end": 331, "label": "CellLine", "start": 328, "text": "CHO" } ]
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It would therefore be of interest to gain further knowledge about the cholesterol content and distribution within HEK293 cell lines and potentially evaluate if this pathway can be targeted for enhanced bioproductivity without having a deleterious impact on suspension growth or cell morphology.
[ { "end": 120, "label": "CellLine", "start": 114, "text": "HEK293" } ]
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Four of the 38 genes (ID1, SMAD7, TXNIP and LOX) that were consistently differentially expressed between adherent and suspension HEK293 have previously been annotated to play a role in epithelial to mesenchymal transition (EMT), the event where stationary epithelial cells lose their cell–cell adhesion and change into motile and invasive mesenchymal cells.
[ { "end": 135, "label": "CellLine", "start": 129, "text": "HEK293" } ]
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However, when evaluating the expression of common markers for mesenchymal and endothelial phenotypes as well as predicting the outcome of the EMT pathway using IPA, the parental HEK293 strain showed the most mesenchymal-like phenotype whereas suspension cell lines were predicted to have reduced transition from epithelial to mesenchymal phenotype compared with HEK293 (Supplementary Fig. S8).
[ { "end": 184, "label": "CellLine", "start": 178, "text": "HEK293" }, { "end": 368, "label": "CellLine", "start": 362, "text": "HEK293" } ]
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These results indicate that the suspension adaptation of HEK293 lineages does not follow the EMT pathway.
[ { "end": 63, "label": "CellLine", "start": 57, "text": "HEK293" } ]
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Altogether nine of the 38 identified genes (LOX, ID1, ADAMTS1, ZIC1, KCNMA1, DHRS3, RARG, COL4A6 and ARRDC4), with differential expression between all adherent and suspension comparisons of HEK293 (Fig. 5a), were shown to have significantly different expression between adherent and suspension cells also in an extended validation of the genes in a set of 63 human cell lines from the HPA database (Fig. 6c).
[ { "end": 196, "label": "CellLine", "start": 190, "text": "HEK293" } ]
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Five of these genes (LOX, ID1, ZIC1, DHRS3 and RARG) showed a consistent expression profile (same direction of up- or downregulation) between adherent and suspension cells compared to the results presented in Fig. 5b, suggesting a key role of these genes in the morphologies of adherent and suspension human cell lines.
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In support of this hypothesis, up-regulation of ID1, as found in adherent cells compared to suspension cell lines, has been associated with the mesenchymal-to-epithelial transition.
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Moreover, ID1 silencing has also been shown to significantly reduce adhesion of neural stem cells and conversely, increased ID1 expression in epithelial cells has been related to increased adhesion.
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In addition, lysyl oxidase (LOX), an enzyme responsible for the covalent cross-linking between elastin and collagen in the extracellular matrix, has been shown to be important for cell–matrix adhesion formation, supporting the adherent phenotype of adherent cells but is also associated with cell invasion and induction of EMT.
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Besides the EMT-related genes, the additional three genes (RARG, ZIC1 and DHRS3), consistently up-regulated in adherent cells compared to suspension cell lines, have previously been associated with increased cell adhesion through the retinoic acid signaling pathway.
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In line with this, retinol metabolism was found to be down-regulated in suspension cells in the metabolic gene set analysis (Fig. 4b).
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Our study has outlined the genomic and transcriptomic variations between six industrially relevant HEK293 cell lines, in an attempt to improve the understanding of their respective differences in phenotype.
[ { "end": 105, "label": "CellLine", "start": 99, "text": "HEK293" } ]
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We report a selective pressure to develop certain expression profiles during the evolution and continuous cultivation, evidenced by the numerous genes and pathways detailed here.
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The key common changes between HEK293 and its progeny cell lines involve in particular cell membrane proteins and processes related to cell adhesion, motility and the organization of various cellular components such as the cytoskeleton and extracellular matrix.
[ { "end": 37, "label": "CellLine", "start": 31, "text": "HEK293" } ]
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In addition, changes associated with differences between adherent and suspension cell growth in particularly involve changes in cell adhesion protein expression, cholesterol metabolism and a set of six key genes (RARG, ID1, ZIC1, LOX and DHRS3) with potentially key roles in the differentiation between the two groups.
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These results could be of importance when pursuing further cell line engineering or bioprocess optimization of these and other human cell lines.
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The adherent cell lines HEK293 (ATCC-CRL-1573), HEK293T (ATCC-CRL-3216) and 293E (ATCC-CRL-10852) were obtained from ATCC and propagated in DMEM (D6429) supplemented with 10% FBS at 37 °C in a humidified incubator with 5% CO2 in air.
[ { "end": 30, "label": "CellLine", "start": 24, "text": "HEK293" }, { "end": 45, "label": "CellLine", "start": 32, "text": "ATCC-CRL-1573" }, { "end": 55, "label": "CellLine", "start": 48, "text": "HEK293T" }, { "end": 70, "label": "CellLine", "start": 57, "text": "ATCC-CRL-3216" }, { "end": 80, "label": "CellLine", "start": 76, "text": "293E" }, { "end": 96, "label": "CellLine", "start": 82, "text": "ATCC-CRL-10852" } ]
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Suspension cell lines 293-F, 293-H and Freestyle 293-F (Gibco) were obtained from Thermo Fisher Scientific and cultivated in 293 SFM II medium (Gibco) supplemented with Glutamax at a final concentration of 4 mM (Gibco).
[ { "end": 27, "label": "CellLine", "start": 22, "text": "293-F" }, { "end": 34, "label": "CellLine", "start": 29, "text": "293-H" } ]
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Suspension cells were cultivated in 125-ml Erlenmeyer shake flasks (Corning) at 37 °C and 120 rpm in a humidified incubator with 8% CO2 in air.
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All cells were propagated from frozen stocks for no longer than 20 passages.
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Adherent cells were detached by trypsinization and both adherent and suspension cells were harvested by centrifugation.
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Genomic DNA was extracted using the Blood and Cell Culture DNA Mini Kit (Qiagen) according to the manufacturer’s guidelines and concentrations were determined by using a NanoDrop ND-1000 spectrophotometer (Thermo Scientific).
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Genome sequencing was performed at the National Genomics Infrastructure (Scilifelab, Solna, Sweden) using the Illumina HiSeq X platform.
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For RNA extraction, cells grown in log phase from three biological replicates were collected (derived from successive propagations).
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Cell pellets were resuspended in RNAlater Stabilization Solution (Invitrogen) according to the manufacturer’s recommendations until RNA extraction.
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Total RNA was extracted from three replicates of each cell line using Qiagen’s RNeasy plus Mini Kit according to the manufacturer’s instructions.
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Concentrations were determined with a NanoDrop ND-1000 spectrophotometer and RNA quality was assessed on a 2100 Bioanalyzer (Agilent Technologies) using RNA 6000 Nano chips (Agilent Technologies).
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All samples had an RNA integrity number of at least 9.9.
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RNA sequencing was performed at GATC (Konstanz, Germany) using the Inview Transcriptome Advance service and an Illumina HiSeq instrument.
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Genome sequencing reads were aligned to the reference (human_g1k_v37.fasta) using bwa (0.7.12).
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The raw alignments were then deduplicated, recalibrated and cleaned using GATK (version 3.3–0-geee94ec, gatk-bundle/2.8).
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Quality control information was gathered using Qualimap (v2.2).
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SNVs and indels have been called using the GATK HaplotypeCaller.
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These variants were then functionally annotated using snpEff (4.1) and snpEff reference GRCh37.75.
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The Piper pipeline from the National Genomics Infrastructure was used.
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The correlation between BAM files was assessed using multibamsummary and its plotCorrelation function from deepTools2.
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Spearman was used to calculate correlation coefficients between samples, and the clusters are joined with the nearest neighbor.
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The R package seqCAT was used to compare SNVs between samples, its compare_profiles function mode parameter was set to the default value “intersection”.
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The heatmap in Supplementary Fig. S1 was based on the similarity scores between the cell lines and Euclidean distances.
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To compare the Large T antigen sequences of 293T and 293E, unmapped reads were extracted to new bam files using SAMtools, converted to fastq with BEDTools, and de novo assembled with MEGAHIT.
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NCBI BLAST was used to identify the Large T antigen in the assembled contigs.
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To evaluate and visualize copy number variations, CNVkit was used with its whole-genome sequencing method, cbs segmentation and the HEK293 alignment as reference.
[ { "end": 138, "label": "CellLine", "start": 132, "text": "HEK293" } ]
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GO enrichment analysis of genes with high or moderate impact SNPs was performed using PANTHER classification system.
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Kallisto was used to quantify transcripts by pseudo-alignment based on human genome assembly version GrCh37.
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Log transformed normalized data by DESeq2 was used for cell line clustering and calculation of Euclidean distances of samples.
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The expression comparison of the viral elements was based on normalized counts from DESeq2.
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Significant testing of differential mRNA expression of E1A/B elements was done by Welch two sample t-test.
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For differential expression analysis, raw count data from Kallisto was imported using the tximport package and analyzed with DESeq2.
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Wald tests were used to calculate p-values, and the BH method was used for multiple testing correction.
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Throughout the article a gene is considered differentially expressed if log2- fold change > ± 1 and FDR < 0.05.
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In the differential expression analysis between adherent and suspension cells, all suspension cell-lines were compared to all adherent cell-lines, and additionally, all pairwise combinations between suspension and adherent cell-lines were evaluated.
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For evaluating differential expression of 38 common differentially expressed genes between adherent and suspension HEK293 cell lines in a set of 63 human cell lines, RNA-seq data from each cell line deposited in the HPA database was used.
[ { "end": 121, "label": "CellLine", "start": 115, "text": "HEK293" } ]
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Based on the growth characteristics, cells were divided into two groups of adherent and suspension cells.
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A Mann–Whitney U-test was used to compare normalized counts based on library size between the two groups for each of the 38 differentially expressed genes.
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To discover significant alterations of gene sets and metabolic pathways between HEK293 cell lines, the Piano package in R was used.
[ { "end": 86, "label": "CellLine", "start": 80, "text": "HEK293" } ]
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The adjusted p-values and fold changes from the differential expression was used in combination with a gene set collection based on “goslim_generic Biological Process”.
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The heatmap for the progeny cells lines vs. HEK293 was based on the consensus score calculated based on GO term rank aggregation in Piano for each directionality from all pairwise gene set statistics calculations with Wilcoxon rank-sum test.
[ { "end": 50, "label": "CellLine", "start": 44, "text": "HEK293" } ]
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The heatmap for suspension cells (293-H, 293-F) vs. adherent cells (293E, 293T) was based on the consensus score from gene set statistics calculations with mean, median, sum, Stouffer and tailStrength tests and was calculated with Piano’s consensusHeatmap function.
[ { "end": 39, "label": "CellLine", "start": 34, "text": "293-H" }, { "end": 46, "label": "CellLine", "start": 41, "text": "293-F" }, { "end": 72, "label": "CellLine", "start": 68, "text": "293E" }, { "end": 78, "label": "CellLine", "start": 74, "text": "293T" } ]
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To produce the network plot, gene sets were exported from HMR2.
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For finding differentially expressed pathways of genes between adherent and suspension cell lines, we used the Wilcoxon statistical test and filtered gene sets with adjusted p-value lower than 0.05 as significantly changed.
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In addition, for gene set analysis of 38 common DE genes between adherent and suspension cell lines we used EnrichR and GO biological process as gene set collection.
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In order to predict the pathway changes between cell lines based on differentially expressed genes from pairwise comparisons, ingenuity pathway analysis (IPA, QIAGEN Inc.,) was performed.
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To consider a gene as differentially expressed we used log2 fold change > 1 or < 1 and adjusted p-value < 0.05.
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For filtering results of gene set analysis by IPA we used Benjamini–Hochberg multiple testing corrected p-values lower than 0.05 to find gene sets with a different expression pattern.
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The Caco-2 cell line derived from human colon carcinoma is commonly used to assess the permeability of compounds in in vitro conditions.
[ { "end": 10, "label": "CellLine", "start": 4, "text": "Caco-2" } ]
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Due to the significant increase in permeability studies using the Caco-2 cell line in recent years, the need to standardize this biological model seems necessary.
[ { "end": 72, "label": "CellLine", "start": 66, "text": "Caco-2" } ]
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The pharmaceutical requirements define only the acceptance criteria for the validation of the Caco-2 cell line and do not specify the protocol for its implementation.
[ { "end": 100, "label": "CellLine", "start": 94, "text": "Caco-2" } ]
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Therefore, the aim of this study is to review the conditions for permeability studies across the Caco-2 monolayer reported in the available literature concerning validation guidelines.
[ { "end": 103, "label": "CellLine", "start": 97, "text": "Caco-2" } ]
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We summarized the main aspects affecting the validation process of the Caco-2 cell line, including the culture conditions, cytotoxicity, cell differentiation process, and monolayer transport conditions, and the main conclusions may be useful in developing individual methods for preparing the cell line for validation purposes and further permeability research.
[ { "end": 77, "label": "CellLine", "start": 71, "text": "Caco-2" } ]
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Oral administration of drugs is a common administration route due to its safety, ease of ingestion, and versatility to accommodate various types of drugs .
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However, not all drugs can be administered orally due to difficulties in obtaining therapeutic concentrations, e.g., due to the first-pass effect .
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Therefore, to determine the route of administration, in vitro systems are widely used to predict drug bioavailability (BA).
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The BA of a drug after oral administration is largely determined by the drug’s dissolution rate, its solubility characteristics in gastrointestinal fluids, and its permeability across biological membranes (intestinal permeability) .
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The Biopharmaceutics Classification System (BCS), as defined by Amidon et al., is a widely used tool for predicting the in vivo BA of a drug substance in in vitro conditions .
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