Integrative Analysis of the Genes Induced by the Intestine Microbiota of Infant Born to Term and Breastfed

Nowadays, the integration of biological data is a major challenge for bioinformatics. Many studies have examined gene expression in the epithelial tissue in the intestines of infants born to term and breastfed, generating a large amount of data. The integration of these data is important to understand the biological processes involved during bacterial colonization of the newborns intestine, particularly through breast milk. This work aims to exploit the bioinformatics approaches, to provide a new representation and interpretation of the interactions between differentially expressed genes in the host intestine induced by the microbiota.

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СONSECUTIVE INTEGRATION OF AVAILABLE MICROARRAY DATA FOR ANALYSIS OF DIFFERENTIAL GENE EXPRESSION IN HUMAN PLACENTA

Biotechnologia Acta ◽

10.15407/biotech14.01.38 ◽

2021 ◽

Vol 14 (1) ◽

pp. 38-45

Author(s):

O. Lykhenko ◽

Keyword(s):

Gene Expression ◽

Differential Gene Expression ◽

Differentially Expressed Genes ◽

Human Placenta ◽

Normal Pregnancy ◽

Integrative Analysis ◽

Differentially Expressed ◽

Second Trimester ◽

Placental Development ◽

Differential Gene

The purpose of the study was to provide the pipeline for processing of publicly available unprocessed data on gene expression via integration and differential gene expression analysis. Data collection from open gene expression databases, normalization and integration into a single expression matrix in accordance with metadata and determination of differentially expressed genes were fulfilled. To demonstrate all stages of data processing and integrative analysis, there were used the data from gene expression in the human placenta from the first and second trimesters of normal pregnancy. The source code for the integrative analysis was written in the R programming language and publicly available as a repository on GitHub. Four clusters of functionally enriched differentially expressed genes were identified for the human placenta in the interval between the first and second trimester of pregnancy. Immune processes, developmental processes, vasculogenesis and angiogenesis, signaling and the processes associated with zinc ions varied in the considered interval between the first and second trimester of placental development. The proposed sequence of actions for integrative analysis could be applied to any data obtained by microarray technology.

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Identification of aberrantly methylated differentially expressed genes in melanoma

10.21203/rs.2.24149/v1 ◽

2020 ◽

Author(s):

Wei Han ◽

Guo-liang Shen

Keyword(s):

Gene Expression ◽

Cell Proliferation ◽

Differentially Expressed Genes ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Regulation Of Transcription ◽

Biological Processes ◽

Hub Genes ◽

Positive Regulation ◽

Upregulated Genes

Abstract Background: Skin Cutaneous Melanoma (SKCM) is known as an aggressive malignant cancer, which could be directly derived from melanocytic nevi. However, the molecular mechanisms underlying malignant transformation of melanocytes and melanoma tumor progression still remain unclear. Increasing researches showed significant roles of epigenetic modifications, especially DNA methylation, in melanoma. This study focused on identification and analysis of methylation-regulated differentially expressed genes (MeDEGs) between melanocytic nevus and malignant melanoma in genome-wide profiles. Methods: The gene expression profiling datasets (GSE3189 and GSE114445) and gene methylation profiling datasets (GSE86355 and GSE120878) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) were identified via GEO2R. MeDEGs were obtained by integrating the DEGs and DMGs. Then, functional enrichment analysis of MeDEGs were performed. STRING and Cytoscape were used to describe protein-protein interaction(PPI) network. Furthermore, survival analysis was implemented to select the prognostic hub genes. Finally, we conducted gene set enrichment analysis (GSEA) of hub genes. Results: We identified 237 hypomethylated, upregulated genes and 182 hypermethylated, downregulated genes. Hypomethylation-upregulated genes were enriched in biological processes of the oxidation-reduction process, cell proliferation, cell division, phosphorylation, extracellular matrix disassembly and protein sumoylation. Pathway enrichment showed selenocompound metabolism, small cell lung cancer and lysosome. Hypermethylation-downregulated genes were enriched in biological processes of positive regulation of transcription from RNA polymerase II promoter, cell adhesion, cell proliferation, positive regulation of transcription, DNA-templated and angiogenesis. The most significantly enriched pathways involved the transcriptional misregulation in cancer, circadian rhythm, tight junction, protein digestion and absorption and Hippo signaling pathway. After PPI establishment and survival analysis, seven prognostic hub genes were CKS2, DTL, KIF2C, KPNA2, MYBL2, TPX2 and FBL. Moreover, the most involved hallmarks obtained by GSEA were E2F targets, G2M checkpoint and mitotic spindle. Conclusions: Our study identified potential aberrantly methylated-differentially expressed genes participating in the process of malignant transformation from nevus to melanoma tissues based on comprehensive genomic profiles. Transcription profiles of CKS2, DTL, KIF2C, KPNA2, MYBL2, TPX2 and FBL provided clues of aberrantly methylation-based biomarkers, which might improve the development of precise medicine.

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E.PAGE: A curated database and enrichment tool to predict modules associated with gene-environment interactions

10.1101/2022.01.03.474848 ◽

2022 ◽

Author(s):

Sachin Muralidharan ◽

Farah Zahir ◽

Ahmed M. Mehdi

Keyword(s):

Gene Expression ◽

Statistical Analysis ◽

Environmental Factors ◽

Differentially Expressed Genes ◽

R Package ◽

Differentially Expressed ◽

Toxic Chemicals ◽

Biological Processes ◽

Analysis Package ◽

Gene Modules

Aims/hypothesis: The purpose of this study is to manually and semi-automatically curate a database and develop an R package that will act as a comprehensive resource to understand how biological processes are dysregulated due to interactions with environmental factors. Methods: We followed a two-step process to achieve the objectives of this study. First, we conducted a systematic review of the existing gene expression datasets to identify the integrated genomic and environmental factors used in available studies. This enabled us to curate a comprehensive genomic-environmental database for four key environmental factors (smoking, diet, infections and toxic chemicals) associated with various autoimmune and chronic conditions. Second, we developed a statistical analysis package that allows users to understand the relationships between differentially expressed genes and environmental factors under different disease conditions. Results: The initial database search run on the Gene Expression Omnibus (GEO) and the Molecular Signature Database (MSigDB) retrieved a total of 90,018 articles. After title and abstract screening against pre-set criteria, a total of 186 studies were selected. From those, 243 individual sets of genes, or gene modules, were obtained. We then curated a database containing four environmental factors, namely cigarette smoking, diet, infections and toxic chemicals, along with a total of 25789 genes that had an association with one or more of these factors. In 6 case studies, the database and statistical analysis package were then tested with lists of differentially expressed genes obtained from the published literature related to type 1 diabetes, rheumatoid arthritis, small cell lung cancer, cobalt exposure, COVID-19 and smoking. On testing, we uncovered statistically enriched biological processes, which could help us understand the pathways associated with environmental factors and gene modules. Conclusions: A novel curated database and software tool is provided as an R Package. Users can enter a list of genes to discover associated environmental factors under various disease conditions.

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Investigation of Aberrantly Methylated Differentially Expressed Genes in Pancreatic Cancer by Integrated Bioinformatics Analysis

10.21203/rs.3.rs-132250/v1 ◽

2021 ◽

Author(s):

Cailin xue ◽

Peng gao ◽

Xudong zhang ◽

Xiaohan cui ◽

Lei jin ◽

...

Keyword(s):

Gene Expression ◽

Pancreatic Cancer ◽

Signaling Pathways ◽

Differentially Expressed Genes ◽

Dna Sequences ◽

Differentially Expressed ◽

Receptor Interaction ◽

Biological Processes ◽

Ppi Network ◽

Hub Genes

Abstract Background: Abnormal methylation of DNA sequences plays an important role in the development and progression of pancreatic cancer (PC). The purpose of this study was to identify abnormal methylation genes and related signaling pathways in PC by comprehensive bioinformatic analysis of three datasets in the Gene Expression Omnibus (GEO). Methods: Datasets of gene expression microarrays (GSE91035, GSE15471) and gene methylation microarrays (GSE37480) were downloaded from the GEO database. Aberrantly methylated-differentially expressed genes (DEGs) were analysis by GEO2R software. GO and KEGG enrichment analyses of selected genes were performed using DAVID database. A protein–protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. Core module analysis was performed by Mcode in Cytoscape. Hub genes were obtained by CytoHubba app. in Cytoscape software. Results: A total of 267 hypomethylation-high expression genes, which were enriched in biological processes of cell adhesion, biological adhesion and regulation of signaling were obtained. KEGG pathway enrichment showed ECM-receptor interaction, Focal adhesion and PI3K-Akt signaling pathway. The top 5 hub genes of PPI network were EZH2, CCNA2, CDC20, KIF11, UBE2C. As for hypermethylation-low expression genes, 202 genes were identified, which were enriched in biological processes of cellular amino acid biosynthesis process and positive regulation of PI3K activity, etc. The pathways enriched were the pancreatic secretion and biosynthesis of amino acids pathways, etc. The five significant hub genes were DLG3, GPT2, PLCB1, CXCL12 and GNG7. In addition, five genes, including CCNA2, KIF11, UBE2C, PLCB1 and GNG7, significantly associated with patient's prognosis were also identified. Conclusion: Novel genes with abnormal expression were identified, which will help us further understand the molecular mechanism and related signaling pathways of PC, and these aberrant genes could possibly serve as biomarkers for precise diagnosis and treatment of PC.

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Microarray Analysis of Gene Expression Profiles of Rat Small Intestine in Response to Heat Stress

CrossRef Listing of Deleted DOIs ◽

10.1177/1087057111403928 ◽

2011 ◽

Vol 16 (6) ◽

pp. 655-667 ◽

Cited By ~ 10

Author(s):

An Lu ◽

Huichuan Wang ◽

Xiaolin Hou ◽

Huanrong Li ◽

Guilin Cheng ◽

...

Keyword(s):

Gene Expression ◽

Heat Treatment ◽

Small Intestine ◽

Heat Stress ◽

Differentially Expressed Genes ◽

Microarray Analysis ◽

Expression Profiles ◽

Differentially Expressed ◽

Epithelial Tissue ◽

Rat Small Intestine

Ambient temperature is a critical factor that affects biological organisms in many ways. In this study, the authors investigated gene expression changes in rat small intestine in response to heat stress. Male Sprague-Dawley rats were randomly divided into control and heat-stressed groups. Both groups were housed at 25 °C, although the heat-stressed group was also subjected to 40 °C for 2 h each day for 10 successive days. Rats were sacrificed 1, 3, 6, and 10 days after heat treatment, and sections of their small intestine epithelial tissue were excised for morphological examination and microarray analyses. The rat rectal and body surface temperatures and serum cortisol levels were all significantly increased after heat treatment (p < 0.05). The jejuna were significantly damaged by 3 days after heat treatment began. Microarray analysis showed that 422 genes were differentially expressed, of which 290 genes were significantly upregulated and 132 genes were significantly downregulated. Subsequent bioinformatics analyses revealed that the differentially expressed genes were mainly related to stress, immune regulation, and metabolism processes. The bioinformatics analysis of the differentially expressed genes should be beneficial to further investigations on the underlying mechanisms involved in heat stress–induced damage in the small intestine.

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Effects of threshold choice on the results of gene expression profiling, using microarray analysis, in a model feeding experiment with rats

Archives Animal Breeding ◽

10.5194/aab-52-65-2009 ◽

2009 ◽

Vol 52 (1) ◽

pp. 65-78

Author(s):

A. Hartmann ◽

G. Nuernberg ◽

D. Repsilber ◽

P. Janczyk ◽

C. Walz ◽

...

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Microarray Data ◽

Expression Profiling ◽

High Reliability ◽

Food Additives ◽

Fold Change ◽

Differentially Expressed ◽

Biological Processes ◽

Feeding Experiment

Abstract. Global gene expression studies using microarray technology are widely employed to identify biological processes which are influenced by a treatment e.g. a specific diet. Affected processes are characterized by a significant enrichment of differentially expressed genes (functional annotation analysis). However, different choices of statistical thresholds to select candidates for differential expression will alter the resulting candidates list. This study was conducted to investigate the effect of applying a False Discovery Rate (FDR) correction and different fold change thresholds in statistical analysis of microarray data on diet-affected biological processes based on a significantly increased proportion of differentially expressed genes. In a model feeding experiment with rats fed genetically modified food additives, animals received a supplement of either lyophilized inactivated recombinant VP60 baculovirus (rBV-VP60) or lyophilized inactivated wild type baculovirus (wtBV). Comparative expression profiling was done in spleen, liver and small intestine mucosa. We demonstrated the extent to which threshold choice can affect the biological processes identified as significantly regulated and thus the conclusion drawn from the microarray data. In our study, the combined application of a moderate fold change threshold (FC≥1.5) and a stringent FDR threshold (q≤0.05) exhibited high reliability of biological processes identified as differentially regulated. The application of a stringent FDR threshold of q≤0.05 seems to be an essential prerequisite to reduce considerably the number of false positives. Microarray results of selected differentially expressed molecules were validated successfully by using real-time RT-PCR.

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Association of Wolbachia with gene expression in Drosophila testes

10.21203/rs.3.rs-188597/v1 ◽

2021 ◽

Author(s):

Weihao Dou ◽

Yunheng Miao ◽

Jinhua Xiao ◽

Dawei Huang

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Differentially Expressed ◽

Symbiotic Bacteria ◽

Biological Processes ◽

Lysosomal Degradation ◽

Additional Information ◽

The Relationship ◽

Sperm Dysfunction ◽

Compare Gene Expression

Abstract Wolbachia is a genus of intracellular symbiotic bacteria that are widely distributed in arthropods and nematodes. These maternally inherited bacteria regulate host reproductive systems in various ways to facilitate their vertical transmission. Since the identification of Wolbachia in many insects, the relationship between Wolbachia and the host has attracted great interest. Numerous studies have indicated that Wolbachia modifies a variety of biological processes in the host. Previous studies in Drosophila melanogaster (D. mel) have demonstrated that Wolbachia can affect spermatid differentiation, chromosome deposition, and sperm activity in the early stages of spermatogenesis, leading to sperm dysfunction. Here, we explored the putative effect of Wolbachia in sperm maturation using transcriptomic approaches to compare gene expression in Wolbachia-infected and Wolbachia-free D. mel adult testes. Our findings show that Wolbachia affects many biological processes in D. mel adult testes, and most of the differentially expressed genes involved in carbohydrate metabolism, lysosomal degradation, proteolysis, lipid metabolism, and immune response were upregulated in the presence of Wolbachia. In contrast, some genes that are putatively associated with cutin and wax biosynthesis and peroxisome pathways were downregulated. We did not find any differentially expressed genes that are predicted to be related to spermatogenesis in the datasets. This work provides additional information for understanding the Wolbachia-host intracellular relationships.

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Correlation Between Gene Expression Levels and Limitations of the Empirical Bayes Methodology for Finding Differentially Expressed Genes

Statistical Applications in Genetics and Molecular Biology ◽

10.2202/1544-6115.1157 ◽

2005 ◽

Vol 4 (1) ◽

Cited By ~ 23

Author(s):

Xing Qiu ◽

Lev Klebanov ◽

Andrei Yakovlev

Keyword(s):

Gene Expression ◽

Statistical Inference ◽

Differentially Expressed Genes ◽

Empirical Bayes ◽

Biological Data ◽

Differentially Expressed ◽

Test Statistics ◽

Gene Expressions ◽

Expression Levels ◽

Gene Expression Levels

Stochastic dependence between gene expression levels in microarray data is of critical importance for the methods of statistical inference that resort to pooling test statistics across genes. The empirical Bayes methodology in the nonparametric and parametric formulations, as well as closely related methods employing a two-component mixture model, represent typical examples. It is frequently assumed that dependence between gene expressions (or associated test statistics) is sufficiently weak to justify the application of such methods for selecting differentially expressed genes. By applying resampling techniques to simulated and real biological data sets, we have studied a potential impact of the correlation between gene expression levels on the statistical inference based on the empirical Bayes methodology. We report evidence from these analyses that this impact may be quite strong, leading to a high variance of the number of differentially expressed genes. This study also pinpoints specific components of the empirical Bayes method where the reported effect manifests itself.

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Transcriptomics reveal stretched human pluripotent stem cell-derived cardiomyocytes as an advantageous hypertrophy model

10.1101/2021.12.13.472451 ◽

2021 ◽

Author(s):

Lotta Pohjolainen ◽

Heikki Ruskoaho ◽

Virpi Talman

Keyword(s):

Gene Expression ◽

Stem Cell ◽

Differentially Expressed Genes ◽

Pluripotent Stem Cell ◽

Left Ventricular ◽

Differentially Expressed ◽

Neonatal Rat ◽

Cardiomyocyte Hypertrophy ◽

Published Data ◽

Biological Processes

Left ventricular hypertrophy, characterized by hypertrophy of individual cardiomyocytes, is an adaptive response to an increased cardiac workload that eventually leads to heart failure. Previous studies using neonatal rat ventricular myocytes (NRVMs) and animal models have revealed several hypertrophy- and mechanical load-associated genes and signaling pathways. However, these models are not directly applicable to humans. Here, we studied the effect of cyclic mechanical stretch on gene expression of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) using RNA sequencing. HiPSC-CMs showed distinct hypertrophic changes in gene expression at the level of individual genes and in biological processes. We also identified several differentially expressed genes that have not been previously associated with cardiomyocyte hypertrophy and thus serve as attractive targets for future studies. When compared to previously published data attained from stretched NRVMs and human embryonic stem cell-derived cardiomyocytes, hiPSC-CMs displayed a smaller number of changes in gene expression, but the differentially expressed genes revealed more pronounced enrichment of hypertrophy-related biological processes and pathways. Overall, these results establish hiPSC-CMs as a valuable in vitro model for studying human cardiomyocyte hypertrophy.

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Differential gene expression analysis of ankylosing spondylitis shows deregulation of the HLA-DRB, HLA-DQB, ITM2A, and CTLA4 genes

Egyptian Journal of Medical Human Genetics ◽

10.1186/s43042-021-00161-0 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Rowan AlEjielat ◽

Anas Khaleel ◽

Amneh H. Tarkhan

Keyword(s):

Gene Expression ◽

Ankylosing Spondylitis ◽

Differentially Expressed Genes ◽

Gene Network ◽

Disease Diagnosis ◽

Receptor Activation ◽

Functional Enrichment ◽

Differentially Expressed ◽

Inflammatory Disorder ◽

Data Set

Abstract Background Ankylosing spondylitis (AS) is a rare inflammatory disorder affecting the spinal joints. Although we know some of the genetic factors that are associated with the disease, the molecular basis of this illness has not yet been fully elucidated, and the genes involved in AS pathogenesis have not been entirely identified. The current study aimed at constructing a gene network that may serve as an AS gene signature and biomarker, both of which will help in disease diagnosis and the identification of therapeutic targets. Previously published gene expression profiles of 16 AS patients and 16 gender- and age-matched controls that were profiled on the Illumina HumanHT-12 V3.0 Expression BeadChip platform were mined. Patients were Portuguese, 21 to 64 years old, were diagnosed based on the modified New York criteria, and had Bath Ankylosing Spondylitis Disease Activity Index scores > 4 and Bath Ankylosing Spondylitis Functional Index scores > 4. All patients were receiving only NSAIDs and/or sulphasalazine. Functional enrichment and pathway analysis were performed to create an interaction network of differentially expressed genes. Results ITM2A, ICOS, VSIG10L, CD59, TRAC, and CTLA-4 were among the significantly differentially expressed genes in AS, but the most significantly downregulated genes were the HLA-DRB6, HLA-DRB5, HLA-DRB4, HLA-DRB3, HLA-DRB1, HLA-DQB1, ITM2A, and CTLA-4 genes. The genes in this study were mostly associated with the regulation of the immune system processes, parts of cell membrane, and signaling related to T cell receptor and antigen receptor, in addition to some overlaps related to the IL2 STAT signaling, as well as the androgen response. The most significantly over-represented pathways in the data set were associated with the “RUNX1 and FOXP3 which control the development of regulatory T lymphocytes (Tregs)” and the “GABA receptor activation” pathways. Conclusions Comprehensive gene analysis of differentially expressed genes in AS reveals a significant gene network that is involved in a multitude of important immune and inflammatory pathways. These pathways and networks might serve as biomarkers for AS and can potentially help in diagnosing the disease and identifying future targets for treatment.

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