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Tyagi N, Mehla K, Gupta D. Deciphering novel common gene signatures for rheumatoid arthritis and systemic lupus erythematosus by integrative analysis of transcriptomic profiles. PLoS One 2023; 18:e0281637. [PMID: 36928613 PMCID: PMC10019710 DOI: 10.1371/journal.pone.0281637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/24/2023] [Indexed: 03/18/2023] Open
Abstract
Rheumatoid Arthritis (RA) and Systemic Lupus Erythematosus (SLE) are the two highly prevalent debilitating and sometimes life-threatening systemic inflammatory autoimmune diseases. The etiology and pathogenesis of RA and SLE are interconnected in several ways, with limited knowledge about the underlying molecular mechanisms. With the motivation to better understand shared biological mechanisms and determine novel therapeutic targets, we explored common molecular disease signatures by performing a meta-analysis of publicly available microarray gene expression datasets of RA and SLE. We performed an integrated, multi-cohort analysis of 1088 transcriptomic profiles from 14 independent studies to identify common gene signatures. We identified sixty-two genes common among RA and SLE, out of which fifty-nine genes (21 upregulated and 38 downregulated) had similar expression profiles in the diseases. However, antagonistic expression profiles were observed for ACVR2A, FAM135A, and MAPRE1 genes. Thirty genes common between RA and SLE were proposed as robust gene signatures, with persistent expression in all the studies and cell types. These gene signatures were found to be involved in innate as well as adaptive immune responses, bone development and growth. In conclusion, our analysis of multicohort and multiple microarray datasets would provide the basis for understanding the common mechanisms of pathogenesis and exploring these gene signatures for their diagnostic and therapeutic potential.
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Affiliation(s)
- Neetu Tyagi
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India
- Regional Centre for Biotechnology, Faridabad, India
| | - Kusum Mehla
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India
| | - Dinesh Gupta
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India
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Keel BN, Lindholm-Perry AK. Recent developments and future directions in meta-analysis of differential gene expression in livestock RNA-Seq. Front Genet 2022; 13:983043. [PMID: 36199583 PMCID: PMC9527320 DOI: 10.3389/fgene.2022.983043] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 08/24/2022] [Indexed: 11/13/2022] Open
Abstract
Decreases in the costs of high-throughput sequencing technologies have led to continually increasing numbers of livestock RNA-Seq studies in the last decade. Although the number of studies has increased dramatically, most livestock RNA-Seq experiments are limited by cost to a small number of biological replicates. Meta-analysis procedures can be used to integrate and jointly analyze data from multiple independent studies. Meta-analyses increase the sample size, which in turn increase both statistical power and robustness of the results. In this work, we discuss cutting edge approaches to combining results from multiple independent RNA-Seq studies to improve livestock transcriptomics research. We review currently published RNA-Seq meta-analyses in livestock, describe many of the key issues specific to RNA-Seq meta-analysis in livestock species, and discuss future perspectives.
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Cervantes-Gracia K, Chahwan R, Husi H. Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach. Front Genet 2022; 13:828786. [PMID: 35186042 PMCID: PMC8855827 DOI: 10.3389/fgene.2022.828786] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 01/12/2022] [Indexed: 12/24/2022] Open
Abstract
The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to store and report raw datasets. The great variability among the techniques and the heterogeneous methodologies used to produce this data have placed meta-analysis methods as one of the approaches of choice to correlate the resultant large-scale datasets from different research groups. Through multi-study meta-analyses, it is possible to generate results with greater statistical power compared to individual analyses. Gene signatures, biomarkers and pathways that provide new insights of a phenotype of interest have been identified by the analysis of large-scale datasets in several fields of science. However, despite all the efforts, a standardized regulation to report large-scale data and to identify the molecular targets and signaling networks is still lacking. Integrative analyses have also been introduced as complementation and augmentation for meta-analysis methodologies to generate novel hypotheses. Currently, there is no universal method established and the different methods available follow different purposes. Herein we describe a new unifying, scalable and straightforward methodology to meta-analyze different omics outputs, but also to integrate the significant outcomes into novel pathways describing biological processes of interest. The significance of using proper molecular identifiers is highlighted as well as the potential to further correlate molecules from different regulatory levels. To show the methodology’s potential, a set of transcriptomic datasets are meta-analyzed as an example.
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Affiliation(s)
| | - Richard Chahwan
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
- *Correspondence: Richard Chahwan, ; Holger Husi,
| | - Holger Husi
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
- Division of Biomedical Sciences, Centre for Health Science, University of the Highlands and Islands, Inverness, United Kingdom
- *Correspondence: Richard Chahwan, ; Holger Husi,
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IFN- γ Mediates the Development of Systemic Lupus Erythematosus. BIOMED RESEARCH INTERNATIONAL 2020; 2020:7176515. [PMID: 33123584 PMCID: PMC7586164 DOI: 10.1155/2020/7176515] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 08/17/2020] [Accepted: 08/25/2020] [Indexed: 12/11/2022]
Abstract
Objective Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that can affect all organs in the body. It is characterized by overexpression of antibodies against autoantigen. Although previous bioinformatics analyses have identified several genetic factors underlying SLE, they did not discriminate between naive and individuals exposed to anti-SLE drugs. Here, we evaluated specific genes and pathways in active and recently diagnosed SLE population. Methods GSE46907 matrix downloaded from Gene Expression Omnibus (GEO) was analyzed using R, Metascape, STRING, and Cytoscape to identify differentially expressed genes (DEGs), enrichment pathways, protein-protein interaction (PPI), and hub genes between naive SLE individuals and healthy controls. Results A total of 134 DEGs were identified, in which 29 were downregulated, whereas 105 were upregulated in active and newly diagnosed SLE cases. GO term analysis revealed that transcriptional induction of the DEGs was particularly enhanced in response to secretion of interferon-γ and interferon-α and regulation of cytokine production innate immune responses among others. KEGG pathway analysis showed that the expression of DEGs was particularly enhanced in interferon signaling, IFN antiviral responses by activated genes, class I major histocompatibility complex (MHC-I) mediated antigen processing and presentation, and amyloid fiber formation. STAT1, IRF7, MX1, OASL, ISG15, IFIT3, IFIH1, IFIT1, OAS2, and GBP1 were the top 10 DEGs. Conclusions Our findings suggest that interferon-related gene expression and pathways are common features for SLE pathogenesis, and IFN-γ and IFN-γ-inducible GBP1 gene in naive SLE were emphasized. Together, the identified genes and cellular pathways have expanded our understanding on the mechanism underlying development of SLE. They have also opened a new frontier on potential biomarkers for diagnosis, biotherapy, and prognosis for SLE.
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Toro-Domínguez D, Villatoro-García JA, Martorell-Marugán J, Román-Montoya Y, Alarcón-Riquelme ME, Carmona-Sáez P. A survey of gene expression meta-analysis: methods and applications. Brief Bioinform 2020; 22:1694-1705. [PMID: 32095826 DOI: 10.1093/bib/bbaa019] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 01/09/2020] [Accepted: 02/04/2020] [Indexed: 02/07/2023] Open
Abstract
The increasing use of high-throughput gene expression quantification technologies over the last two decades and the fact that most of the published studies are stored in public databases has triggered an explosion of studies available through public repositories. All this information offers an invaluable resource for reuse to generate new knowledge and scientific findings. In this context, great interest has been focused on meta-analysis methods to integrate and jointly analyze different gene expression datasets. In this work, we describe the main steps in the gene expression meta-analysis, from data preparation to the state-of-the art statistical methods. We also analyze the main types of applications and problems that can be approached in gene expression meta-analysis studies and provide a comparative overview of the available software and bioinformatics tools. Moreover, a practical guide for choosing the most appropriate method in each case is also provided.
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Affiliation(s)
- Daniel Toro-Domínguez
- GENYO (Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada, Avenida de la Ilustración, 114, 18016 Granada, Spain
| | - Juan Antonio Villatoro-García
- GENYO (Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada, Avenida de la Ilustración, 114, 18016 Granada, Spain
| | - Jordi Martorell-Marugán
- GENYO (Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada, Avenida de la Ilustración, 114, 18016 Granada, Spain
| | - Yolanda Román-Montoya
- Department of Statistics and Operations Research, University of Granada, Granada, Spain
| | - Marta E Alarcón-Riquelme
- GENYO (Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada, Avenida de la Ilustración, 114, 18016 Granada, Spain.,Unit of Inflammatory Diseases, Department of Environmental Medicine, Karolinska Institute, 171 67, Solna, Sweden
| | - Pedro Carmona-Sáez
- GENYO (Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada, Avenida de la Ilustración, 114, 18016 Granada, Spain
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Zhang L, Xu P, Wang X, Zhang Z, Zhao W, Li Z, Yang G, Liu P. Identification of differentially expressed genes in primary Sjögren's syndrome. J Cell Biochem 2019; 120:17368-17377. [PMID: 31125139 DOI: 10.1002/jcb.29001] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 04/04/2019] [Accepted: 04/08/2019] [Indexed: 12/16/2022]
Abstract
Primary Sjögren's syndrome (pSS) is a chronic systemic autoimmune disease that affects exocrine glands. To study the molecular mechanism and identify crucial genes/pathways in pSS pathogenesis, the microarray-based whole-genome gene expression profiles from salivary glands of patients with pSS and non-sicca controls were retrieved. After normalization and subsequent batch effect adjustment, significance analysis of microarrays method was applied to five available datasets, and 379 differentially expressed genes (DEGs) were identified. The 300 upregulated DEGs were enriched in Gene Ontology terms of immune and inflammatory responses, including antigen processing and presentation, interferon-mediated signaling pathway, and chemotaxis. Previously reported pSS-associated genes, including HLA-DRA, TAP2, PRDM1, and IFI16, were found to be significantly upregulated. The downregulated DEGs were enriched in pathways of salivary secretion, carbohydrate digestion and absorption, and starch and sucrose metabolism, implying dysfunction of salivary glands during pathogenesis. Next, a protein-protein interaction network was constructed, and B2M, an upregulated DEG, was shown to be a hub, suggesting its potential involvement in pSS development. In summary, we found the activation of pSS-associated genes in pathogenesis, and provide clues for salivary glands dysfunction. Experimental investigation on the identified DEGs in this study will deepen our understanding on pSS.
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Affiliation(s)
- Lei Zhang
- Department of Laboratory Medicine, Fuwai Central China Cardiovascular Hospital, Zhengzhou, Henan, PR China
| | - Poshi Xu
- Department of Laboratory Medicine, Fuwai Central China Cardiovascular Hospital, Zhengzhou, Henan, PR China
| | - Xiaoyu Wang
- Department of Laboratory Medicine, Fuwai Central China Cardiovascular Hospital, Zhengzhou, Henan, PR China
| | - Zongshan Zhang
- Department of Laboratory Medicine, Fuwai Central China Cardiovascular Hospital, Zhengzhou, Henan, PR China
| | - Wenxin Zhao
- Department of Laboratory Medicine, Fuwai Central China Cardiovascular Hospital, Zhengzhou, Henan, PR China
| | - Zhengmin Li
- Department of Laboratory Medicine, Fuwai Central China Cardiovascular Hospital, Zhengzhou, Henan, PR China
| | - Guangxia Yang
- Department of Laboratory Medicine, Fuwai Central China Cardiovascular Hospital, Zhengzhou, Henan, PR China
| | - Panpan Liu
- Department of Obstetrics and Gynecology, Henan Province People's Hospital, Zhengzhou, Henan, PR China
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Sen P, Kemppainen E, Orešič M. Perspectives on Systems Modeling of Human Peripheral Blood Mononuclear Cells. Front Mol Biosci 2018; 4:96. [PMID: 29376056 PMCID: PMC5767226 DOI: 10.3389/fmolb.2017.00096] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2017] [Accepted: 12/21/2017] [Indexed: 12/12/2022] Open
Abstract
Human peripheral blood mononuclear cells (PBMCs) are the key drivers of the immune responses. These cells undergo activation, proliferation and differentiation into various subsets. During these processes they initiate metabolic reprogramming, which is coordinated by specific gene and protein activities. PBMCs as a model system have been widely used to study metabolic and autoimmune diseases. Herein we review various omics and systems-based approaches such as transcriptomics, epigenomics, proteomics, and metabolomics as applied to PBMCs, particularly T helper subsets, that unveiled disease markers and the underlying mechanisms. We also discuss and emphasize several aspects of T cell metabolic modeling in healthy and disease states using genome-scale metabolic models.
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Affiliation(s)
- Partho Sen
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland
| | - Esko Kemppainen
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland
| | - Matej Orešič
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland.,School of Medical Sciences, Örebro University, Örebro, Sweden
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Nogueira Jorge NA, Wajnberg G, Ferreira CG, de Sa Carvalho B, Passetti F. snoRNA and piRNA expression levels modified by tobacco use in women with lung adenocarcinoma. PLoS One 2017; 12:e0183410. [PMID: 28817650 PMCID: PMC5560661 DOI: 10.1371/journal.pone.0183410] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Accepted: 08/03/2017] [Indexed: 12/22/2022] Open
Abstract
Lung cancer is one of the most frequent types of cancer worldwide. Most patients are diagnosed at advanced stage and thus have poor prognosis. Smoking is a risk factor for lung cancer, however most smokers do not develop lung cancer while 20% of women with lung adenocarcinoma are non-smokers. Therefore, it is possible that these two groups present differences besides the smoking status, including differences in their gene expression signature. The altered expression patterns of non-coding RNAs in complex diseases make them potential biomarkers for diagnosis and treatment. We analyzed data from differentially and constitutively expressed PIWI-interacting RNAs and small nucleolar RNAs from publicly available small RNA high-throughput sequencing data in search of an expression pattern of non-coding RNA that could differentiate these two groups. Here, we report two sets of differentially expressed small non-coding RNAs identified in normal and tumoral tissues of women with lung adenocarcinoma, that discriminate between smokers and non-smokers. Our findings may offer new insights on metabolic alterations caused by tobacco and may be used for early diagnosis of lung cancer.
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Affiliation(s)
- Natasha Andressa Nogueira Jorge
- Laboratory of Functional Genomics and Bioinformatics, Oswaldo Cruz Institute, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, Brazil
| | - Gabriel Wajnberg
- Laboratory of Functional Genomics and Bioinformatics, Oswaldo Cruz Institute, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, Brazil
| | | | | | - Fabio Passetti
- Laboratory of Functional Genomics and Bioinformatics, Oswaldo Cruz Institute, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, Brazil
- * E-mail:
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