Comprehensive Analysis of Gene Expression Profiles and DNA Methylome reveals Oas1, Ppie, Polr2g as Pathogenic Target Genes of Gestational Diabetes Mellitus

Background Although the WW-domain-containing oxidoreductase (WWOX)/Hypoxia-inducible factor 1 (HIF1) pathway is a well-known regulator of cellular glucose and energy metabolism in pathophysiological processes, its role in gestational diabetes mellitus (GDM), remains elusive. We undertook this study to determine the effect of WWOX/HIF1A signaling on the expression of glucose metabolism genes in GDM patients. Methods Leukocytes were obtained from 135 pregnant women with (n = 98) or without (n = 37) GDM and, in turn, 3 months (n = 8) and 1 year (n = 12) postpartum. Quantitative RT-PCR was performed to determine gene expression profiles of the WWOX/HIF1A-related genes, including those involved in glucose transport (SLC2A1, SLC2A4), glycolytic pathway (HK2, PKM2, PFK, LDHA), Wnt pathway (DVL2, CTNNB1), and inflammatory response (NFKB1). Results GDM patients displayed a significant downregulation of WWOX with simultaneous upregulation of HIF1A which resulted in approximately six times reduction in WWOX/HIF1A ratio. As a consequence, HIF1A induced genes (SLC2A1, HK2, PFK, PKM) were found to be overexpressed in GDM compared to normal pregnancy and negative correlate with WWOX/HIF1A ratio. The postpartum WWOX expression was higher than during GDM, but its level was comparable to that observed in normal pregnancy. Conclusions The obtained results suggest a significant contribution of the WWOX gene to glucose metabolism in patients with gestational diabetes. Decreased WWOX expression in GDM compared to normal pregnancy, and in particular reduction of WWOX/HIF1A ratio, indicate that WWOX modulates HIF1α activity in normal tissues as described in the tumor. The effect of HIF1α excessive activation is to increase the expression of genes encoding proteins directly involved in the glycolysis which may lead to pathological changes in glucose metabolism observed in gestational diabetes.

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A Comprehensive Analysis of Human Gene Expression Profiles Identifies Stromal Immunoglobulin κ C as a Compatible Prognostic Marker in Human Solid Tumors

Clinical Cancer Research ◽

10.1158/1078-0432.ccr-11-2210 ◽

2012 ◽

Vol 18 (9) ◽

pp. 2695-2703 ◽

Cited By ~ 127

Author(s):

Marcus Schmidt ◽

Birte Hellwig ◽

Seddik Hammad ◽

Amnah Othman ◽

Miriam Lohr ◽

...

Keyword(s):

Gene Expression ◽

Solid Tumors ◽

Prognostic Marker ◽

Human Gene ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Comprehensive Analysis ◽

Human Gene Expression ◽

Human Solid Tumors

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Comprehensive analysis of the gene expression profiles in human gastric cancer cell lines

Oncogene ◽

10.1038/sj.onc.1205829 ◽

2002 ◽

Vol 21 (42) ◽

pp. 6549-6556 ◽

Cited By ~ 39

Author(s):

Jiafu Ji ◽

Xin Chen ◽

Suet Yi Leung ◽

Jen-Tsan A Chi ◽

Kent Man Chu ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Cell Lines ◽

Cancer Cell ◽

Gastric Cancer Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Comprehensive Analysis ◽

Human Gastric Cancer ◽

Gastric Cancer Cell Lines

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A comprehensive analysis of gene expression profiles in distal parts of the mouse renal tubule

Pflügers Archiv - European Journal of Physiology ◽

10.1007/s00424-010-0863-8 ◽

2010 ◽

Vol 460 (6) ◽

pp. 925-952 ◽

Cited By ~ 24

Author(s):

Sylvain Pradervand ◽

Annie Zuber Mercier ◽

Gabriel Centeno ◽

Olivier Bonny ◽

Dmitri Firsov

Keyword(s):

Gene Expression ◽

Renal Tubule ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Comprehensive Analysis

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Exploring the Mechanism of Skeletal Muscle in a Tacrolimus-Induced Posttransplantation Diabetes Mellitus Model on Gene Expression Profiles

Journal of Diabetes Research ◽

10.1155/2020/6542346 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Chenlei Zheng ◽

Cheng Wang ◽

Tan Zhang ◽

Ding Li ◽

Xiao-feng Ni ◽

...

Keyword(s):

Gene Expression ◽

Diabetes Mellitus ◽

Skeletal Muscle ◽

Muscle Development ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Quadriceps Muscle ◽

Control Group ◽

Bioinformatics Analyses

Objective. Posttransplantation diabetes mellitus (PTDM) is a known complication of transplantation that affects the prognosis. Tacrolimus (Tac or FK506) is a widely used immunosuppressant that has been reported to be a risk factor for PTDM and to further induce complications in heart and skeletal muscles, but the mechanism is still largely unknown. In our preliminary experiments, we found that after Tac treatment, blood glucose increased, and the weight of skeletal muscle declined. Here, we hypothesize that tacrolimus can induce PTDM and influence the atrophy of skeletal muscle. Methods. We designed preliminary experiments to establish a tacrolimus-induced PTDM model. Gene expression profiles in quadriceps muscle from this rat model were characterized by oligonucleotide microarrays. Then, differences in gene expression profiles in muscle from PTDM rats that received tacrolimus and control subjects were analyzed by using GeneSpring GX 11.0 software (Agilent). Functional annotation and enrichment analysis of differentially expressed genes (DEGs) helped us identify clues for the side effects of tacrolimus. Results. Our experiments found that the quadriceps in tacrolimus-induced PTDM group were smaller than those in the control group. The study identified 275 DEGs that may be responsible for insulin resistance and the progression of PTDM, including 86 upregulated genes and 199 downregulated genes. GO and KEGG functional analysis of the DEGs showed a significant correlation between PTDM and muscle development. PPI network analysis screened eight hub genes and found that they were related to troponin and tropomyosin. Conclusions. This study explored the molecular mechanism of muscle atrophy in a tacrolimus-induced PTDM model by bioinformatics analyses. We identified 275 DEGs and identified significant biomarkers for predicting the development and progression of tacrolimus-induced PTDM.

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RNAseq Studies Reveal Distinct Transcriptional Response to Vitamin a (VA) Deficiency in Small Intestine (SI) versus Colon, Discovering Novel VA-regulated Genes (P15-002-19)

Current Developments in Nutrition ◽

10.1093/cdn/nzz037.p15-002-19 ◽

2019 ◽

Vol 3 (Supplement_1) ◽

Author(s):

Zhi Chai ◽

Yafei Lyu ◽

Qiuyan Chen ◽

Cheng-Hsin Wei ◽

Lindsay Snyder ◽

...

Keyword(s):

Gene Expression ◽

Small Intestine ◽

Vitamin A ◽

Target Genes ◽

Expression Profiles ◽

Expression Patterns ◽

Differentially Expressed Gene ◽

Gene Expression Profiles ◽

Illumina Hiseq ◽

The Impact

Abstract Objectives To characterize and compare the impact of vitamin A (VA) deficiency on gene expression patterns in the small intestine (SI) and the colon, and to discover novel target genes in VA-related biological pathways. Methods vitamin A deficient (VAD) mice were generated by feeding VAD diet to pregnant C57/BL6 dams and their post-weaning offspring. Total mRNA extracted from SI and colon were sequenced using Illumina HiSeq 2500 platform. Differentially Expressed Gene (DEG), Gene Ontology (GO) enrichment, and Weighted Gene Co-expression Network Analysis (WGCNA) were performed to characterize expression patterns and co-expression patterns. Results The comparison between vitamin A sufficient (VAS) and VAD groups detected 49 and 94 DEGs in SI and colon, respectively. According to GO information, DEGs in the SI demonstrated significant enrichment in categories relevant to retinoid metabolic process, molecule binding, and immune function. Immunity related pathways, such as “humoral immune response” and “complement activation,” were positively associated with VA in SI. On the contrary, in colon, “cell division” was the only enriched category and was negatively associated with VA. WGCNA identified modules significantly correlated with VA status in SI and in colon. One of those modules contained five known retinoic acid targets. Therefore we have prioritized the other module members (e.g., Mbl2, Mmp9, Mmp13, Cxcl14 and Pkd1l2) to be investigated as candidate genes regulated by VA. Comparison of co-expression modules between SI and colon indicated distinct VA effects on these two organs. Conclusions The results show that VA deficiency alters the gene expression profiles in SI and colon quite differently. Some immune-related genes (Mbl2, Mmp9, Mmp13, Cxcl14 and Pkd1l2) may be novel targets under the control of VA in SI. Funding Sources NIH training grant and NIH research grant. Supporting Tables, Images and/or Graphs

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PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients

Scientific Reports ◽

10.1038/s41598-021-95698-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Kota Fujisawa ◽

Mamoru Shimo ◽

Y.-H. Taguchi ◽

Shinya Ikematsu ◽

Ryota Miyata

Keyword(s):

Gene Expression ◽

Feature Extraction ◽

Target Genes ◽

Gene Selection ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Principal Component ◽

Data Set ◽

Immune Related Genes ◽

Unsupervised Feature Extraction

AbstractCoronavirus disease 2019 (COVID-19) is raging worldwide. This potentially fatal infectious disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the complete mechanism of COVID-19 is not well understood. Therefore, we analyzed gene expression profiles of COVID-19 patients to identify disease-related genes through an innovative machine learning method that enables a data-driven strategy for gene selection from a data set with a small number of samples and many candidates. Principal-component-analysis-based unsupervised feature extraction (PCAUFE) was applied to the RNA expression profiles of 16 COVID-19 patients and 18 healthy control subjects. The results identified 123 genes as critical for COVID-19 progression from 60,683 candidate probes, including immune-related genes. The 123 genes were enriched in binding sites for transcription factors NFKB1 and RELA, which are involved in various biological phenomena such as immune response and cell survival: the primary mediator of canonical nuclear factor-kappa B (NF-κB) activity is the heterodimer RelA-p50. The genes were also enriched in histone modification H3K36me3, and they largely overlapped the target genes of NFKB1 and RELA. We found that the overlapping genes were downregulated in COVID-19 patients. These results suggest that canonical NF-κB activity was suppressed by H3K36me3 in COVID-19 patient blood.

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