In vitro versus in vivo models of kidney fibrosis: Time-course experimental design is crucial to avoid misinterpretations of gene expression data

Previous studies have shown that abnormal methylation is an early key event in the pathogenesis of most human cancers, contributing to the development of tumors. However, little attention has been given to the potential of DNA methylation patterns as markers for Helicobacter pylori- (H. pylori-) associated gastric cancer (GC). In this study, an integrated analysis of DNA methylation and gene expression was conducted to identify some potential key epigenetic markers in H. pylori-associated GC. DNA methylation data of 28 H. pylori-positive and 168 H. pylori-negative GC samples were compared and analyzed. We also analyzed the gene expression data of 18 H. pylori-positive and 145 H. pylori-negative GC cases. Finally, the results were verified by in vitro and in vivo experiments. A total of 5609 differentially methylated regions associated with 2454 differentially methylated genes were identified. A total of 228 differentially expressed genes were identified from the gene expression data of H. pylori-positive and H. pylori-negative GC cases. The screened genes were analyzed for functional enrichment. Subsequently, we obtained 28 genes regulated by methylation through a Venn diagram, and we identified five genes (GSTO2, HUS1, INTS1, TMEM184A, and TMEM190) downregulated by hypermethylation. HUS1, GSTO2, and TMEM190 were expressed at lower levels in GC than in adjacent samples ( P < 0.05 ). Moreover, H. pylori infection decreased HUS1, GSTO2, and TMEM190 expression in vitro and in vivo. Our study identified HUS1, GSTO2, and TMEM190 as novel methylation markers for H. pylori-associated GC.

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Assessing Chemical-Induced Liver Injury In Vivo From In Vitro Gene Expression Data in the Rat: The Case of Thioacetamide Toxicity

Frontiers in Genetics ◽

10.3389/fgene.2019.01233 ◽

2019 ◽

Vol 10 ◽

Cited By ~ 3

Author(s):

Patric Schyman ◽

Richard L. Printz ◽

Shanea K. Estes ◽

Tracy P. O’Brien ◽

Masakazu Shiota ◽

...

Keyword(s):

Gene Expression ◽

Liver Injury ◽

Gene Expression Data ◽

Expression Data

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Computationally predicted gene regulatory networks in molluscan biomineralization identify extracellular matrix production and ion transportation pathways

Bioinformatics ◽

10.1093/bioinformatics/btz754 ◽

2019 ◽

Cited By ~ 2

Author(s):

Victoria A Sleight ◽

Philipp Antczak ◽

Francesco Falciani ◽

Melody S Clark

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Time Course ◽

Shell Growth ◽

Extracellular Proteins ◽

Supplementary Information ◽

Gene Interactions ◽

Expression Data ◽

Gene Regulatory

Abstract Motivation The molecular processes regulating molluscan shell production remain relatively uncharacterized, despite the clear evolutionary and societal importance of biomineralization. Results Here we built the first computationally predicted gene regulatory network (GRN) for molluscan biomineralization using Antarctic clam (Laternula elliptica) mantle gene expression data produced over an age-categorized shell damage-repair time-course. We used previously published in vivo in situ hybridization expression data to ground truth gene interactions predicted by the GRN and show that candidate biomineralization genes from different shell layers, and hence microstructures, were connected in unique modules. We characterized two biomineralization modules of the GRN and hypothesize that one module is responsible for translating the extracellular proteins required for growing, repairing or remodelling the nacreous shell layer, whereas the second module orchestrates the transport of both ions and proteins to the shell secretion site, which are required during normal shell growth, and repair. Our findings demonstrate that unbiased computational methods are particularly valuable for studying fundamental biological processes and gene interactions in non-model species where rich sources of gene expression data exist, but annotation rates are poor and the ability to carry out true functional tests are still lacking. Availability and implementation The raw RNA-Seq data is freely available for download from NCBI SRA (Accession: PRJNA398984), the assembled and annotated transcriptome can be viewed and downloaded from molluscDB (ensembl.molluscdb.org) and in addition, the assembled transcripts, reconstructed GRN, modules and detailed annotations are all available as Supplementary Files. Supplementary information Supplementary data are available at Bioinformatics online.

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Clinical Drug Response Prediction by Using a Lq Penalized Network-Constrained Logistic Regression Method

Cellular Physiology and Biochemistry ◽

10.1159/000495826 ◽

2018 ◽

Vol 51 (5) ◽

pp. 2073-2084 ◽

Cited By ~ 5

Author(s):

Hai-Hui Huang ◽

Jing-Guo Dai ◽

Yong Liang

Keyword(s):

Gene Expression ◽

Logistic Regression ◽

Personalized Medicine ◽

Gene Expression Data ◽

Drug Response ◽

Prediction Models ◽

Response Prediction ◽

Expression Data

Background/Aims: One of the most important impacts of personalized medicine is the connection between patients’ genotypes and their drug responses. Despite a series of studies exploring this relationship, the predictive ability of such analyses still needs to be strengthened. Methods: Here we present the Lq penalized network-constrained logistic regression (Lq-NLR) method to meet this need, in which the predictors are integrated into the gene expression data and biological network knowledge and are combined with a more aggressive penalty function. Response prediction models for two cancer targeting drugs (erlotinib and sorafenib) were developed from gene expression data and IC50 values from a large panel of cancer cell lines by utilizing the proposed approach. Then the drug responders were tested with the baseline tumor gene expression data, yielding an in vivo drug sensitivity prediction. Results: These results demonstrated the high effectiveness of this approach. One of the best results achieved by our method was a correlation of 0.841 between the cell line in vitro drug response and patient’s in vivo drug response. We then applied these two drug prediction models to develop a personalized medicine approach in which the subsequent treatment depends on each patient’s gene-expression profile. Conclusion: The proposed method is much better than the existing approach and can capture a more accurate reflection of the relationship between genotypes and phenotypes.

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