A global RNA-seq-driven analysis of CHO host and production cell lines reveals distinct differential expression patterns of genes contributing to recombinant antibody glycosylation

Empirical Bayes is a choice framework for differential expression (DE) analysis for multi-group RNA-seq count data. Its characteristic ability to compute posterior probabilities for predefined expression patterns allows users to assign the pattern with the highest value to the gene under consideration. However, current Bayesian methods such as baySeq and EBSeq can be improved, especially with respect to normalization. Two R packages (baySeq and EBSeq) with their default normalization settings and with other normalization methods (MRN and TCC) were compared using three-group simulation data and real count data. Our findings were as follows: (1) the Bayesian methods coupled with TCC normalization performed comparably or better than those with the default normalization settings under various simulation scenarios, (2) default DE pipelines provided in TCC that implements a generalized linear model framework was still superior to the Bayesian methods with TCC normalization when overall degree of DE was evaluated, and (3) baySeq with TCC was robust against different choices of possible expression patterns. In practice, we recommend using the default DE pipeline provided in TCC for obtaining overall gene ranking and then using the baySeq with TCC normalization for assigning the most plausible expression patterns to individual genes.

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Selenoprotein Genes Exhibit Differential Expression Patterns Between Hepatoma HepG2 and Normal Hepatocytes LO2 Cell Lines

Biological Trace Element Research ◽

10.1007/s12011-015-0323-6 ◽

2015 ◽

Vol 167 (2) ◽

pp. 236-241 ◽

Cited By ~ 7

Author(s):

Hua Zhao ◽

Jiayong Tang ◽

Jingyang Xu ◽

Lei Cao ◽

Gang Jia ◽

...

Keyword(s):

Differential Expression ◽

Cell Lines ◽

Expression Patterns ◽

Selenoprotein Genes ◽

Hepatoma Hepg2

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Differential Expression Analysis Using A Model-Based Gene Clustering Algorithm for RNA-Seq Data

10.21203/rs.3.rs-86123/v1 ◽

2020 ◽

Author(s):

Takayuki Osabe ◽

Kentaro Shimizu ◽

Koji Kadota

Keyword(s):

Differential Expression ◽

Time Course ◽

Clustering Algorithm ◽

Expression Patterns ◽

Differential Expression Analysis ◽

Real Data ◽

Gene Clustering ◽

Rna Seq ◽

Model Based ◽

Group Data

Abstract Background RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering is used to classify DEGs with similar expression patterns for the subsequent analyses of data from experiments such as time-courses or multi-group comparisons. However, gene clustering has rarely been used for analyzing simple two-group data or differential expression (DE). In this study, we report a model-based clustering algorithm, MBCluster.Seq, that can be implemented using an R package for DE analysis.Results The input data originally used by MBCluster.Seq is DEGs, and the proposed method (called MBCdeg) uses all genes for the analysis. The method uses posterior probabilities of genes assigned to a cluster displaying non-DEG pattern for overall gene ranking. We compared the performance of MBCdeg with conventional R packages such as edgeR, DESeq2, and TCC that are specialized for DE analysis using simulated and real data. Our results showed that MBCdeg outperformed other methods when the proportion of DEG was less than 50%. However, the DEG identification using MBCdeg was less consistent than with conventional methods. We compared the effects of different normalization algorithms using MBCdeg, and performed an analysis using MBCdeg in combination with a robust normalization algorithm (called DEGES) that was not implemented in MBCluster.Seq. The new analysis method showed greater stability than using the original MBCdeg with the default normalization algorithm.Conclusions MBCdeg with DEGES normalization can be used in the identification of DEGs when the PDEG is relatively low. As the method is based on gene clustering, the DE result includes information on which expression pattern the gene belongs to. The new method may be useful for the analysis of time-course and multi-group data, where the classification of expression patterns is often required.

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Integrative, normalization-insusceptible statistical analysis of RNA-Seq data, with improved differential expression and unbiased downstream functional analysis

Briefings in Bioinformatics ◽

10.1093/bib/bbaa156 ◽

2020 ◽

Author(s):

Dionysios Fanidis ◽

Panagiotis Moulos

Keyword(s):

Gene Expression ◽

Statistical Analysis ◽

Differential Expression ◽

Differential Gene Expression ◽

Expression Patterns ◽

Superior Performance ◽

P Value ◽

Rna Seq ◽

Daily Lives ◽

Differential Gene

Abstract The study of differential gene expression patterns through RNA-Seq comprises a routine task in the daily lives of molecular bioscientists, who produce vast amounts of data requiring proper management and analysis. Despite widespread use, there are still no widely accepted golden standards for the normalization and statistical analysis of RNA-Seq data, and critical biases, such as gene lengths and problems in the detection of certain types of molecules, remain largely unaddressed. Stimulated by these unmet needs and the lack of in-depth research into the potential of combinatorial methods to enhance the analysis of differential gene expression, we had previously introduced the PANDORA P-value combination algorithm while presenting evidence for PANDORA’s superior performance in optimizing the tradeoff between precision and sensitivity. In this article, we present the next generation of the algorithm along with a more in-depth investigation of its capabilities to effectively analyze RNA-Seq data. In particular, we show that PANDORA-reported lists of differentially expressed genes are unaffected by biases introduced by different normalization methods, while, at the same time, they comprise a reliable input option for downstream pathway analysis. Additionally, PANDORA outperforms other methods in detecting differential expression patterns in certain transcript types, including long non-coding RNAs.

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RNA-Seq reveals differential expression patterns of genes associated with carotenoid accumulation in loquat

Acta Physiologiae Plantarum ◽

10.1007/s11738-017-2463-0 ◽

2017 ◽

Vol 39 (8) ◽

Cited By ~ 1

Author(s):

Ting-ting Zheng ◽

Zhi-ke Zhang ◽

Muhammad Qasim Shahid ◽

Wei-ling Wei ◽

Faheem Shehzad Baloch ◽

...

Keyword(s):

Differential Expression ◽

Expression Patterns ◽

Rna Seq ◽

Carotenoid Accumulation

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Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data

BMC Bioinformatics ◽

10.1186/s12859-021-04438-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Takayuki Osabe ◽

Kentaro Shimizu ◽

Koji Kadota

Keyword(s):

Differential Expression ◽

Time Course ◽

Clustering Algorithm ◽

Expression Patterns ◽

Differential Expression Analysis ◽

Real Data ◽

Gene Clustering ◽

Rna Seq ◽

Model Based ◽

Group Data

Abstract Background RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering is used to classify DEGs with similar expression patterns for the subsequent analyses of data from experiments such as time-courses or multi-group comparisons. However, gene clustering has rarely been used for analyzing simple two-group data or differential expression (DE). In this study, we report that a model-based clustering algorithm implemented in an R package, MBCluster.Seq, can also be used for DE analysis. Results The input data originally used by MBCluster.Seq is DEGs, and the proposed method (called MBCdeg) uses all genes for the analysis. The method uses posterior probabilities of genes assigned to a cluster displaying non-DEG pattern for overall gene ranking. We compared the performance of MBCdeg with conventional R packages such as edgeR, DESeq2, and TCC that are specialized for DE analysis using simulated and real data. Our results showed that MBCdeg outperformed other methods when the proportion of DEG (PDEG) was less than 50%. However, the DEG identification using MBCdeg was less consistent than with conventional methods. We compared the effects of different normalization algorithms using MBCdeg, and performed an analysis using MBCdeg in combination with a robust normalization algorithm (called DEGES) that was not implemented in MBCluster.Seq. The new analysis method showed greater stability than using the original MBCdeg with the default normalization algorithm. Conclusions MBCdeg with DEGES normalization can be used in the identification of DEGs when the PDEG is relatively low. As the method is based on gene clustering, the DE result includes information on which expression pattern the gene belongs to. The new method may be useful for the analysis of time-course and multi-group data, where the classification of expression patterns is often required.

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IL-1-conferred gene expression pattern in ERα+ BCa and AR+ PCa cells is intrinsic to ERα- BCa and AR- PCa cells and promotes cell survival

10.21203/rs.2.14924/v4 ◽

2020 ◽

Author(s):

Afshan F. Nawas ◽

Mohammed Kanchwala ◽

Shayna E. Thomas-Jardin ◽

Haley Dahl ◽

Kelly Daescu ◽

...

Keyword(s):

Gene Expression ◽

Expression Pattern ◽

Cell Lines ◽

Hormone Receptor ◽

Interleukin 1 ◽

Expression Patterns ◽

Gene Expression Pattern ◽

Treatment Resistance ◽

Rna Seq ◽

Data Set

Abstract Background: Breast (BCa) and prostate (PCa) cancers are hormone receptor (HR)-driven cancers. Thus, BCa and PCa patients are given therapies that reduce hormone levels or directly block HR activity; but most patients eventually develop treatment resistance. We have previously reported that interleukin-1 (IL-1) inflammatory cytokine downregulates ERα and AR mRNA in HR-positive (HR+) BCa and PCa cell lines, yet the cells can remain viable. Additionally, we identified pro-survival proteins and processes upregulated by IL-1 in HR+ BCa and PCa cells, that are basally high in HR- BCa and PCa cells. Therefore, we hypothesize that IL-1 confers a conserved gene expression pattern in HR+ BCa and PCa cells that mimics conserved basal gene expression patterns in HR- BCa and PCa cells to promote HR-independent survival and tumorigenicity. Methods: We performed RNA sequencing (RNA-seq) for HR+ BCa and PCa cell lines exposed to IL-1 and for untreated HR- BCa and PCa cell lines. We confirmed expression patterns of select genes by RT-qPCR and used siRNA and/or drug inhibition to silence select genes in the BCa and PCa cell lines. Finally, we performed Ingenuity Pathway Analysis (IPA) and used the gene ontology web-based tool, GOrilla, to identify signaling pathways encoded by our RNA-seq data set. Results: We identified 350 genes in common between BCa and PCa cells that are induced or repressed by IL-1 in HR+ cells that are, respectively, basally high or low in HR- cells. Among these genes, we identified Sequestome-1 (SQSTM1/p62) and SRY (Sex-Determining Region Y)-Box 9 (SOX9) to be essential for survival of HR- BCa and PCa cell lines. Analysis of publicly available data indicates that p62 and SOX9 expression are elevated in HR-independent BCa and PCa sublines generated in vitro, suggesting that p62 and SOX9 have a role in acquired hormone receptor independence and treatment resistance. We also assessed HR- cell line viability in response to the p62-targeting drug, verteporfin, and found that verteporfin is cytotoxic for HR- cell lines. Conclusions: Our 350 gene set can be used to identify novel therapeutic targets and/or biomarkers conserved among acquired (e.g. due to inflammation) or intrinsic HR-independent BCa and PCa.

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IL-1-conferred gene expression pattern in ERα+ BCa and AR+ PCa cells is intrinsic to ERα- BCa and AR- PCa cells and promotes cell survival

10.21203/rs.2.14924/v1 ◽

2019 ◽

Author(s):

Afshan F. Nawas ◽

Mohammed Kanchwala ◽

Shayna E. Thomas-Jardin ◽

Haley Dahl ◽

Kelly Daescu ◽

...

Keyword(s):

Gene Expression ◽

Expression Pattern ◽

Cell Lines ◽

Interleukin 1 ◽

Expression Patterns ◽

Gene Expression Pattern ◽

Treatment Resistance ◽

Rna Seq ◽

Drug Inhibition

Abstract Background: Breast (BCa) and prostate (PCa) cancers are hormone receptor (HR)-driven cancers. Thus, BCa and PCa patients are given therapies that reduce hormone levels or directly blocks HR activity; but most patients eventually develop treatment resistance. We have previously reported that interleukin-1 (IL-1) inflammatory cytokine downregulates ER𝛼 and AR mRNA in HR-positive (HR+) BCa and PCa cell lines, yet the cells can remain viable. Additionally, we identified pro-survival proteins and processes upregulated by IL-1 in HR+ BCa and PCa cells, that are basally high in HR- BCa and PCa cells. Therefore, we hypothesize that IL-1 confers a conserved gene expression pattern in HR+ BCa and PCa cells that mimics conserved basal gene expression patterns in HR- BCa and PCa cells to promote HR-independent survival and tumorigenicity.Methods: We performed RNA sequencing (RNA-seq) for HR+ BCa and PCa cell lines exposed to IL-1 and for untreated HR- BCa and PCa cell lines. We confirmed expression patterns of select genes by RT-qPCR and used siRNA and/or drug inhibition to silence select genes in HR- BCa cell lines. Finally, we performed Ingenuity Pathway Analysis (IPA) to identify signaling pathways encoded by our RNA-seq data set.Results: We identified 350 genes in common between BCa and PCa cells that are induced or repressed by IL-1 in HR+ cells that are, respectively, basally high or low in HR- cells. Among these genes, we identified Sequestome-1 (SQSTM1/p62) and SRY (Sex-Determining Region Y)-Box 9 (SOX9) to be essential for survival of HR- BCa and PCa cell lines. Analysis of publicly available data indicates that p62 and SOX9 expression are elevated in HR-independent BCa and PCa sublines generated in vitro, suggesting that p62 and SOX9 have a role in acquired treatment resistance. We also assessed HR- cell line viability in response to the p62-targeting drug, verteporfin, and found that verteporfin is cytotoxic for HR- cell lines. Conclusions: Our 350 gene set can be used to identify novel therapeutic targets and/or biomarkers conserved among acquired (e.g. due to inflammation) or intrinsic HR-independent BCa and PCa.

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IL-1-conferred gene expression pattern in ERα+ BCa and AR+ PCa cells is intrinsic to ERα− BCa and AR− PCa cells and promotes cell survival

10.1101/773978 ◽

2019 ◽

Author(s):

Afshan F. Nawas ◽

Mohammed Kanchwala ◽

Shayna E. Thomas-Jardin ◽

Haley Dahl ◽

Kelly Daescu ◽

...

Keyword(s):

Gene Expression ◽

Expression Pattern ◽

Cell Lines ◽

Interleukin 1 ◽

Expression Patterns ◽

Gene Expression Pattern ◽

Treatment Resistance ◽

Rna Seq ◽

Data Set

ABSTRACTBackgroundBreast (BCa) and prostate (PCa) cancers are hormone receptor (HR)-driven cancers. Thus, BCa and PCa patients are given therapies that reduce hormone levels or directly blocks HR activity; but most patients eventually develop treatment resistance. We have previously reported that interleukin-1 (IL-1) inflammatory cytokine downregulates ERα and AR mRNA in HR-positive (HR+) BCa and PCa cell lines, yet the cells can remain viable. Additionally, we identified pro-survival proteins and processes upregulated by IL-1 in HR+ BCa and PCa cells, that are basally high in HR− BCa and PCa cells. Therefore, we hypothesize that IL-1 confers a conserved gene expression pattern in HR+ BCa and PCa cells that mimics conserved basal gene expression patterns in HR− BCa and PCa cells to promote HR-independent survival and tumorigenicity.MethodsWe performed RNA sequencing (RNA-seq) for HR+ BCa and PCa cell lines exposed to IL-1 and for untreated HR− BCa and PCa cell lines. We confirmed expression patterns of select genes by RT-qPCR and used siRNA and/or drug inhibition to silence select genes in HR− BCa cell lines. Finally, we performed Ingenuity Pathway Analysis (IPA) to identify signaling pathways encoded by our RNA-seq data set.ResultsWe identified 350 genes in common between BCa and PCa cells that are induced or repressed by IL-1 in HR+ cells that are, respectively, basally high or low in HR− cells. Among these genes, we identified Sequestome-1 (SQSTM1/p62) and SRY (Sex-Determining Region Y)-Box 9 (SOX9) to be essential for survival of HR− BCa and PCa cell lines. Analysis of publicly available data indicates that p62 and SOX9 expression are elevated in HR-independent BCa and PCa sublines generated in vitro, suggesting that p62 and SOX9 have a role in acquired treatment resistance. We also assessed HR− cell line viability in response to the p62-targeting drug, verteporfin, and found that verteporfin is cytotoxic for HR− cell lines.ConclusionsOur 350 gene set can be used to identify novel therapeutic targets and/or biomarkers conserved among acquired (e.g. due to inflammation) or intrinsic HR-independent BCa and PCa.

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