A pipeline to analyse time-course gene expression data

The phenotypic diversity of cells is governed by a complex equilibrium between their genetic identity and their environmental interactions: Understanding the dynamics of gene expression is a fundamental question of biology. However, analysing time-course transcriptomic data raises unique challenging statistical and computational questions, requiring the development of novel methods and software. This workflow provides a step-by-step tutorial of the methodology used to analyse time-course data: (1) quality control and normalization of the dataset; (2) differential expression analysis using functional data analysis; (3) clustering of time-course data; (4) interpreting clusters with GO term and KEGG pathway enrichment analysis. As a case study, we apply this workflow to time-course transcriptomic data from mice exposed to four strains of influenza to showcase every step of the pipeline.

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Development and Maturation of the Immune System in Preterm Neonates: Results from a Whole Genome Expression Study

BioMed Research International ◽

10.1155/2014/498318 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 7

Author(s):

Magdalena Zasada ◽

Przemko Kwinta ◽

Wojciech Durlak ◽

Mirosław Bik-Multanowski ◽

Anna Madetko-Talowska ◽

...

Keyword(s):

Gene Expression ◽

Immune System ◽

Gestational Age ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Cell Receptor ◽

Preterm Neonates ◽

Pathway Enrichment Analysis ◽

Expression Of Genes ◽

Immune System Development

To expand the knowledge about the consecutive expression of genes involved in the immune system development in preterm neonates and to verify if the environment changes the gene expression after birth we conducted a prospective study that included three cohorts: (A) extremely (gestational age (GA): 23–26 weeks;n=41), (B) very (GA: 27–29 weeks;n=39), and (C) moderately preterm infants (GA: 30–32 weeks;n=33). Blood samples were drawn from the study participants on the 5th and 28th day of life (DOL). The mRNA samples were evaluated for gene expression with the use of GeneChip Human Gene 1.0ST microarrays. Differential expression analysis revealed small subsets of genes that presented positive or negative monotone trends in both the 5th (138 genes) and 28th DOL (308 genes) in the three subgroups of patients. Based on pathway enrichment analysis, we found that most of the pathways that revealed a positive monotone trend were involved in host immunity. The most significantly GA dependent pathways were T-cell receptor signaling pathway and intestinal immune network for IgA production. Overall 4431 genes were differentially expressed between the 5th and 28th DOL. Despite differences in gestational age, patients with the same postconceptional age have a very similar expression of genes.

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A Method of Pathway Enrichment Analysis Based Gene Expression Variability

ACTA AGRONOMICA SINICA ◽

10.3724/sp.j.1206.2012.00410 ◽

2013 ◽

Vol 40 (12) ◽

pp. 1256

Author(s):

XiaoDong JIA ◽

XiuJie CHEN ◽

Xin WU ◽

JianKai XU ◽

FuJian TAN ◽

...

Keyword(s):

Gene Expression ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Expression Variability ◽

Pathway Enrichment

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Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles

BMC Bioinformatics ◽

10.1186/s12859-021-04052-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Arika Fukushima ◽

Masahiro Sugimoto ◽

Satoru Hiwa ◽

Tomoyuki Hiroyasu

Keyword(s):

Gene Expression ◽

Time Course ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Response To Therapy ◽

Hcv Infection ◽

Entire Treatment Period ◽

Time Points ◽

Time Course Data ◽

Conventional Methods

Abstract Background Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.

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AB0005 IDENTIFICATION OF KEY GENES AND PATHWAYS FOR PSORIASIS BASED ON GEO DATABASES BY BIOINFORMATICS ANALYSIS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.1773 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 1037.2-1038

Author(s):

X. Sun ◽

S. X. Zhang ◽

S. Song ◽

T. Kong ◽

C. Zheng ◽

...

Keyword(s):

Gene Expression ◽

Signaling Pathways ◽

Differentially Expressed Genes ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Shanxi Province ◽

Receptor Interaction ◽

Term Therapy ◽

Pathway Enrichment Analysis ◽

Key Genes

Background:Psoriasis is an immune-mediated, genetic disease manifesting in the skin or joints or both, and also has a strong genetic predisposition and autoimmune pathogenic traits1. The hallmark of psoriasis is sustained inflammation that leads to uncontrolled keratinocyte proliferation and dysfunctional differentiation. And it’s also a chronic relapsing disease, which often necessitates a long-term therapy2.Objectives:To investigate the molecular mechanisms of psoriasis and find the potential gene targets for diagnosis and treating psoriasis.Methods:Total 334 gene expression data of patients with psoriasis research (GSE13355 GSE14905 and GSE30999) were obtained from the Gene Expression Omnibus database. After data preprocessing and screening of differentially expressed genes (DEGs) by R software. Online toll Metascape3 was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. Interactions of proteins encoded by DEGs were discovered by Protein-protein interaction network (PPI) using STRING online software. Cytoscape software was utilized to visualize PPI and the degree of each DEGs was obtained by analyzing the topological structure of the PPI network.Results:A total of 611 DEGs were found to be differentially expressed in psoriasis. GO analysis revealed that up-regulated DEGs were mostly associated with defense and response to external stimulus while down-regulated DEGs were mostly associated with metabolism and synthesis of lipids. KEGG enrichment analysis suggested they were mainly enriched in IL-17 signaling, Toll-like receptor signaling and PPAR signaling pathways, Cytokine-cytokine receptor interaction and lipid metabolism. In addition, top 9 key genes (CXCL10, OASL, IFIT1, IFIT3, RSAD2, MX1, OAS1, IFI44 and OAS2) were identified through Cytoscape.Conclusion:DEGs of psoriasis may play an essential role in disease development and may be potential pathogeneses of psoriasis.References:[1]Boehncke WH, Schon MP. Psoriasis. Lancet 2015;386(9997):983-94. doi: 10.1016/S0140-6736(14)61909-7 [published Online First: 2015/05/31].[2]Zhang YJ, Sun YZ, Gao XH, et al. Integrated bioinformatic analysis of differentially expressed genes and signaling pathways in plaque psoriasis. Mol Med Rep 2019;20(1):225-35. doi: 10.3892/mmr.2019.10241 [published Online First: 2019/05/23].[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

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Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data

Journal of Integrative Bioinformatics ◽

10.1515/jib-2017-0011 ◽

2017 ◽

Vol 14 (2) ◽

Author(s):

Qihua Tan ◽

Mads Thomassen ◽

Mark Burton ◽

Kristian Fredløv Mose ◽

Klaus Ejner Andersen ◽

...

Keyword(s):

Gene Expression ◽

Correlation Coefficient ◽

Parametric Analysis ◽

Time Course ◽

Expression Patterns ◽

Gene Expression Patterns ◽

Microarray Time Course ◽

Time Course Data ◽

Generalized Correlation ◽

Non Parametric

AbstractModeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

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Bioinformatics-Based Study to Investigate Potential Differentially Expressed Genes and miRNAs in Hashimoto’s Thyroiditis

10.21203/rs.3.rs-1023446/v1 ◽

2021 ◽

Author(s):

Li Guoquan ◽

Du Junwei ◽

He Qi ◽

Fu Xinghao ◽

Ji Feihong ◽

...

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Inflammatory Responses ◽

Expression Profiles ◽

Treatment Options ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Chronic Lymphocytic Thyroiditis ◽

Hub Genes

Abstract BackgroundHashimoto's thyroiditis (HT), also known as chronic lymphocytic thyroiditis, is a common autoimmune disease, which mainly occurs in women. The early manifestation was hyperthyroidism, however, hypothyroidism may occur if HT was not controlled for a long time. Numerous studies have shown that multiple factors, including genetic, environmental, and autoimmune factors, were involved in the pathogenesis of the disease, but the exact mechanisms were not yet clear. The aim of this study was to identify differentially expressed genes (DEGs) by comprehensive analysis and to provide specific insights into HT. MethodsTwo gene expression profiles (GSE6339, GSE138198) about HT were downloaded from the Gene Expression Omnibus (GEO) database. The DEGs were assessed between the HT and normal groups using the GEO2R. The DEGs were then sent to the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The hub genes were discovered using Cytoscape and CytoHubba. Finally, NetworkAnalyst was utilized to create the hub genes' targeted microRNAs (miRNAs). ResultsA total of 62 DEGs were discovered, including 60 up-regulated and 2 down-regulated DEGs. The signaling pathways were mainly engaged in cytokine interaction and cytotoxicity, and the DEGs were mostly enriched in immunological and inflammatory responses. IL2RA, CXCL9, IL10RA, CCL3, CCL4, CCL2, STAT1, CD4, CSF1R, and ITGAX were chosen as hub genes based on the results of the protein-protein interaction (PPI) network and CytoHubba. Five miRNAs, including mir-24-3p, mir-223-3p, mir-155-5p, mir-34a-5p, mir-26b-5p, and mir-6499-3p, were suggested as likely important miRNAs in HT. ConclusionsThese hub genes, pathways and miRNAs contribute to a better understanding of the pathophysiology of HT and offer potential treatment options for HT.

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Deregulation of Trace Amine-Associated Receptors (TAAR) Expression and Signaling Mode in Melanoma

Biomolecules ◽

10.3390/biom12010114 ◽

2022 ◽

Vol 12 (1) ◽

pp. 114

Author(s):

Anastasia N. Vaganova ◽

Savelii R. Kuvarzin ◽

Anastasia M. Sycheva ◽

Raul R. Gainetdinov

Keyword(s):

Malignant Tumors ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Clinicopathological Characteristics ◽

Pathway Enrichment Analysis ◽

Pathway Enrichment ◽

Low Concentrations ◽

Trace Amine ◽

Specific Association ◽

Amine Compounds

Trace amine-associated receptors (TAARs) interact with amine compounds called “trace amines” which are present in tissues at low concentrations. Recently, TAARs expression in neoplastic tumors was reported. In this study, TAARs expression was analyzed in public RNAseq datasets in nevi and melanoma samples and compared to the expression of dopamine receptors (DRDs) that are known to be involved in melanoma pathogenesis. It was found that all DRDs and TAARs are expressed in nevi at comparable levels. Differential expression analysis demonstrated the drastic decrease of TAAR1, TAAR2, TAAR5, TAAR6, and TAAR8 expression in melanomas compared to benign nevi with only TAAR6, TAAR8, and TAAR9 remaining detectable in malignant tumors. No association of TAARs expression levels and melanoma clinicopathological characteristics was observed. TAARs co-expressed genes in melanoma and nevi were selected by correlation values for comparative pathway enrichment analysis between malignant and benign neoplasia. It was found that coexpression of TAARs with genes inquired in neurotransmitter signaling is lost in melanoma, and tumor-specific association of TAAR6 expression with the mTOR pathway and inflammatory signaling is observed. It is not excluded that TAARs may have certain functions in melanoma pathogenesis, the significance of which to tumor progression is yet to be understood.

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Comparative pathway enrichment analysis in gastrointestinal cell lines Caco-2, HT-29, HEPG2, and colon fibroblasts using a custom expression panel for tight-junction and cytoskeletal regulatory genes

10.1101/747105 ◽

2019 ◽

Cited By ~ 1

Author(s):

JM Robinson

Keyword(s):

Gene Expression ◽

Comparative Analysis ◽

Tight Junction ◽

Cell Lines ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Intestinal Epithelial ◽

Expression Data ◽

Pathway Enrichment ◽

Ht 29

AbstractThis brief report details results from a comparative analysis of Nanostring expression data between cell lines HEPG2, Caco-2, HT-29, and colon fibroblasts. Raw and normalized data are available publicly in the NCBI GEO/Bioproject databases. Results identify cell-line specific variations in gene expression relevant to intestinal epithelial function.

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Bioinformatic analysis identifies potential biomarkers and therapeutic targets of septic-shock-associated acute kidney injury

Hereditas ◽

10.1186/s41065-021-00176-y ◽

2021 ◽

Vol 158 (1) ◽

Author(s):

Yun Tang ◽

Xiaobo Yang ◽

Huaqing Shu ◽

Yuan Yu ◽

Shangwen Pan ◽

...

Keyword(s):

Gene Expression ◽

Septic Shock ◽

Acute Kidney Injury ◽

Molecular Mechanisms ◽

Therapeutic Targets ◽

Kidney Injury ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Blood Samples ◽

Potential Biomarkers

Abstract Background Sepsis and septic shock are life-threatening diseases with high mortality rate in intensive care unit (ICU). Acute kidney injury (AKI) is a common complication of sepsis, and its occurrence is a poor prognostic sign to septic patients. We analyzed co-differentially expressed genes (co-DEGs) to explore relationships between septic shock and AKI and reveal potential biomarkers and therapeutic targets of septic-shock-associated AKI (SSAKI). Methods Two gene expression datasets (GSE30718 and GSE57065) were downloaded from the Gene Expression Omnibus (GEO). The GSE57065 dataset included 28 septic shock patients and 25 healthy volunteers and blood samples were collected within 0.5, 24 and 48 h after shock. Specimens of GSE30718 were collected from 26 patients with AKI and 11 control patents. AKI-DEGs and septic-shock-DEGs were identified using the two datasets. Subsequently, Gene Ontology (GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis were performed to elucidate molecular mechanisms of DEGs. We also evaluated co-DEGs and corresponding predicted miRNAs involved in septic shock and AKI. Results We identified 62 DEGs in AKI specimens and 888, 870, and 717 DEGs in septic shock blood samples within 0.5, 24 and 48 h, respectively. The hub genes of EGF and OLFM4 may be involved in AKI and QPCT, CKAP4, PRKCQ, PLAC8, PRC1, BCL9L, ATP11B, KLHL2, LDLRAP1, NDUFAF1, IFIT2, CSF1R, HGF, NRN1, GZMB, and STAT4 may be associated with septic shock. Besides, co-DEGs of VMP1, SLPI, PTX3, TIMP1, OLFM4, LCN2, and S100A9 coupled with corresponding predicted miRNAs, especially miR-29b-3p, miR-152-3p, and miR-223-3p may be regarded as promising targets for the diagnosis and treatment of SSAKI in the future. Conclusions Septic shock and AKI are related and VMP1, SLPI, PTX3, TIMP1, OLFM4, LCN2, and S100A9 genes are significantly associated with novel biomarkers involved in the occurrence and development of SSAKI.

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Identification of Key Genes in ‘Luang Pratahn’, Thai Salt-Tolerant Rice, Based on Time-Course Data and Weighted Co-expression Networks

Frontiers in Plant Science ◽

10.3389/fpls.2021.744654 ◽

2021 ◽

Vol 12 ◽

Author(s):

Pajaree Sonsungsan ◽

Pheerawat Chantanakool ◽

Apichat Suratanee ◽

Teerapong Buaboocha ◽

Luca Comai ◽

...

Keyword(s):

Salt Stress ◽

Salt Tolerance ◽

Stress Responses ◽

Time Course ◽

Rice Variety ◽

Enrichment Analysis ◽

Normal Growth ◽

Growth Conditions ◽

Pathway Enrichment Analysis ◽

Key Genes

Salinity is an important environmental factor causing a negative effect on rice production. To prevent salinity effects on rice yields, genetic diversity concerning salt tolerance must be evaluated. In this study, we investigated the salinity responses of rice (Oryza sativa) to determine the critical genes. The transcriptomes of ‘Luang Pratahn’ rice, a local Thai rice variety with high salt tolerance, were used as a model for analyzing and identifying the key genes responsible for salt-stress tolerance. Based on 3' Tag-Seq data from the time course of salt-stress treatment, weighted gene co-expression network analysis was used to identify key genes in gene modules. We obtained 1,386 significantly differentially expressed genes in eight modules. Among them, six modules indicated a significant correlation within 6, 12, or 48h after salt stress. Functional and pathway enrichment analysis was performed on the co-expressed genes of interesting modules to reveal which genes were mainly enriched within important functions for salt-stress responses. To identify the key genes in salt-stress responses, we considered the two-state co-expression networks, normal growth conditions, and salt stress to investigate which genes were less important in a normal situation but gained more impact under stress. We identified key genes for the response to biotic and abiotic stimuli and tolerance to salt stress. Thus, these novel genes may play important roles in salinity tolerance and serve as potential biomarkers to improve salt tolerance cultivars.

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