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Yao M, He H, Wang B, Huang X, Zheng S, Wang J, Gao X, Huang T. Testing the Significance of Ranked Gene Sets in Genome-wide Transcriptome Profiling Data Using Weighted Rank Correlation Statistics. Curr Genomics 2024; 25:202-211. [PMID: 39086999 PMCID: PMC11288161 DOI: 10.2174/0113892029280470240306044159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 01/13/2024] [Accepted: 01/16/2024] [Indexed: 08/02/2024] Open
Abstract
Background Popular gene set enrichment analysis approaches assumed that genes in the gene set contributed to the statistics equally. However, the genes in the transcription factors (TFs) derived gene sets, or gene sets constructed by TF targets identified by the ChIP-Seq experiment, have a rank attribute, as each of these genes have been assigned with a p-value which indicates the true or false possibilities of the ownerships of the genes belong to the gene sets. Objectives Ignoring the rank information during the enrichment analysis will lead to improper statistical inference. We address this issue by developing of new method to test the significance of ranked gene sets in genome-wide transcriptome profiling data. Methods A method was proposed by first creating ranked gene sets and gene lists and then applying weighted Kendall's tau rank correlation statistics to the test. After introducing top-down weights to the genes in the gene set, a new software called "Flaver" was developed. Results Theoretical properties of the proposed method were established, and its differences over the GSEA approach were demonstrated when analyzing the transcriptome profiling data across 55 human tissues and 176 human cell-lines. The results indicated that the TFs identified by our method have higher tendency to be differentially expressed across the tissues analyzed than its competitors. It significantly outperforms the well-known gene set enrichment analyzing tools, GOStats (9%) and GSEA (17%), in analyzing well-documented human RNA transcriptome datasets. Conclusions The method is outstanding in detecting gene sets of which the gene ranks were correlated with the expression levels of the genes in the transcriptome data.
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Affiliation(s)
- Min Yao
- College of Animal Science, Yangtze University, Jingzhou, Hubei, 434025, China
| | - Hao He
- College of Animal Science, Yangtze University, Jingzhou, Hubei, 434025, China
| | - Binyu Wang
- College of Animal Science, Yangtze University, Jingzhou, Hubei, 434025, China
| | - Xinmiao Huang
- College of Animal Science, Yangtze University, Jingzhou, Hubei, 434025, China
| | - Sunli Zheng
- College of Animal Science, Yangtze University, Jingzhou, Hubei, 434025, China
| | - Jianwu Wang
- College of Agriculture, Yangtze University, Jingzhou, Hubei, 434025, China
| | - Xuejun Gao
- College of Animal Science, Yangtze University, Jingzhou, Hubei, 434025, China
| | - Tinghua Huang
- College of Animal Science, Yangtze University, Jingzhou, Hubei, 434025, China
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Castiglione F, Nardini C, Onofri E, Pedicini M, Tieri P. Explainable Drug Repurposing Approach From Biased Random Walks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1009-1019. [PMID: 35839194 DOI: 10.1109/tcbb.2022.3191392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Drug repurposing is a highly active research area, aiming at finding novel uses for drugs that have been previously developed for other therapeutic purposes. Despite the flourishing of methodologies, success is still partial, and different approaches offer, each, peculiar advantages. In this composite landscape, we present a novel methodology focusing on an efficient mathematical procedure based on gene similarity scores and biased random walks which rely on robust drug-gene-disease association data sets. The recommendation mechanism is further unveiled by means of the Markov chain underlying the random walk process, hence providing explainability about how findings are suggested. Performances evaluation and the analysis of a case study on rheumatoid arthritis show that our approach is accurate in providing useful recommendations and is computationally efficient, compared to the state of the art of drug repurposing approaches.
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Gable AL, Szklarczyk D, Lyon D, Matias Rodrigues JF, von Mering C. Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments. Brief Bioinform 2022; 23:bbac355. [PMID: 36088548 PMCID: PMC9487593 DOI: 10.1093/bib/bbac355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 07/13/2022] [Accepted: 07/30/2022] [Indexed: 11/14/2022] Open
Abstract
A knowledge-based grouping of genes into pathways or functional units is essential for describing and understanding cellular complexity. However, it is not always clear a priori how and at what level of specificity functionally interconnected genes should be partitioned into pathways, for a given application. Here, we assess and compare nine existing and two conceptually novel functional classification systems, with respect to their discovery power and generality in gene set enrichment testing. We base our assessment on a collection of nearly 2000 functional genomics datasets provided by users of the STRING database. With these real-life and diverse queries, we assess which systems typically provide the most specific and complete enrichment results. We find many structural and performance differences between classification systems. Overall, the well-established, hierarchically organized pathway annotation systems yield the best enrichment performance, despite covering substantial parts of the human genome in general terms only. On the other hand, the more recent unsupervised annotation systems perform strongest in understudied areas and organisms, and in detecting more specific pathways, albeit with less informative labels.
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Affiliation(s)
- Annika L Gable
- Department of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland
| | - Damian Szklarczyk
- Department of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - David Lyon
- Department of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | | | - Christian von Mering
- Department of Molecular Life Sciences, University of Zurich, 8057 Zurich, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
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Application of text mining to develop AOP-based mucus hypersecretion genesets and confirmation with in vitro and clinical samples. Sci Rep 2021; 11:6091. [PMID: 33731770 PMCID: PMC7969622 DOI: 10.1038/s41598-021-85345-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 02/26/2021] [Indexed: 11/28/2022] Open
Abstract
Mucus hypersecretion contributes to lung function impairment observed in COPD (chronic obstructive pulmonary disease), a tobacco smoking-related disease. A detailed mucus hypersecretion adverse outcome pathway (AOP) has been constructed from literature reviews, experimental and clinical data, mapping key events (KEs) across biological organisational hierarchy leading to an adverse outcome. AOPs can guide the development of biomarkers that are potentially predictive of diseases and support the assessment frameworks of nicotine products including electronic cigarettes. Here, we describe a method employing manual literature curation supported by a focused automated text mining approach to identify genes involved in 5 KEs contributing to decreased lung function observed in tobacco-related COPD. KE genesets were subsequently confirmed by unsupervised clustering against 3 different transcriptomic datasets including (1) in vitro acute cigarette smoke and e-cigarette aerosol exposure, (2) in vitro repeated incubation with IL-13, and (3) lung biopsies from COPD and healthy patients. The 5 KE genesets were demonstrated to be predictive of cigarette smoke exposure and mucus hypersecretion in vitro, and less conclusively predict the COPD status of lung biopsies. In conclusion, using a focused automated text mining and curation approach with experimental and clinical data supports the development of risk assessment strategies utilising AOPs.
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Chaudhuri R, Krycer JR, Fazakerley DJ, Fisher-Wellman KH, Su Z, Hoehn KL, Yang JYH, Kuncic Z, Vafaee F, James DE. The transcriptional response to oxidative stress is part of, but not sufficient for, insulin resistance in adipocytes. Sci Rep 2018; 8:1774. [PMID: 29379070 PMCID: PMC5789081 DOI: 10.1038/s41598-018-20104-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Accepted: 01/12/2018] [Indexed: 02/06/2023] Open
Abstract
Insulin resistance is a major risk factor for metabolic diseases such as Type 2 diabetes. Although the underlying mechanisms of insulin resistance remain elusive, oxidative stress is a unifying driver by which numerous extrinsic signals and cellular stresses trigger insulin resistance. Consequently, we sought to understand the cellular response to oxidative stress and its role in insulin resistance. Using cultured 3T3-L1 adipocytes, we established a model of physiologically-derived oxidative stress by inhibiting the cycling of glutathione and thioredoxin, which induced insulin resistance as measured by impaired insulin-stimulated 2-deoxyglucose uptake. Using time-resolved transcriptomics, we found > 2000 genes differentially-expressed over 24 hours, with specific metabolic and signalling pathways enriched at different times. We explored this coordination using a knowledge-based hierarchical-clustering approach to generate a temporal transcriptional cascade and identify key transcription factors responding to oxidative stress. This response shared many similarities with changes observed in distinct insulin resistance models. However, an anti-oxidant reversed insulin resistance phenotypically but not transcriptionally, implying that the transcriptional response to oxidative stress is insufficient for insulin resistance. This suggests that the primary site by which oxidative stress impairs insulin action occurs post-transcriptionally, warranting a multi-level ‘trans-omic’ approach when studying time-resolved responses to cellular perturbations.
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Affiliation(s)
- Rima Chaudhuri
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - James R Krycer
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Daniel J Fazakerley
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | | | - Zhiduan Su
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Kyle L Hoehn
- School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney, NSW, 2052, Australia
| | - Jean Yee Hwa Yang
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.,School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Zdenka Kuncic
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.,School of Physics and Australian Institute for Nanoscale Science and Technology, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Fatemeh Vafaee
- School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney, NSW, 2052, Australia.
| | - David E James
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia. .,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia. .,Sydney Medical School, The University of Sydney, Sydney, NSW, 2006, Australia.
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Bonnet A, Servin B, Mulsant P, Mandon-Pepin B. Spatio-Temporal Gene Expression Profiling during In Vivo Early Ovarian Folliculogenesis: Integrated Transcriptomic Study and Molecular Signature of Early Follicular Growth. PLoS One 2015; 10:e0141482. [PMID: 26540452 PMCID: PMC4634757 DOI: 10.1371/journal.pone.0141482] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2015] [Accepted: 10/08/2015] [Indexed: 11/19/2022] Open
Abstract
Background The successful achievement of early ovarian folliculogenesis is important for fertility and reproductive life span. This complex biological process requires the appropriate expression of numerous genes at each developmental stage, in each follicular compartment. Relatively little is known at present about the molecular mechanisms that drive this process, and most gene expression studies have been performed in rodents and without considering the different follicular compartments. Results We used RNA-seq technology to explore the sheep transcriptome during early ovarian follicular development in the two main compartments: oocytes and granulosa cells. We documented the differential expression of 3,015 genes during this phase and described the gene expression dynamic specific to these compartments. We showed that important steps occurred during primary/secondary transition in sheep. We also described the in vivo molecular course of a number of pathways. In oocytes, these pathways documented the chronology of the acquisition of meiotic competence, migration and cellular organization, while in granulosa cells they concerned adhesion, the formation of cytoplasmic projections and steroid synthesis. This study proposes the involvement in this process of several members of the integrin and BMP families. The expression of genes such as Kruppel-like factor 9 (KLF9) and BMP binding endothelial regulator (BMPER) was highlighted for the first time during early follicular development, and their proteins were also predicted to be involved in gene regulation. Finally, we selected a data set of 24 biomarkers that enabled the discrimination of early follicular stages and thus offer a molecular signature of early follicular growth. This set of biomarkers includes known genes such as SPO11 meiotic protein covalently bound to DSB (SPO11), bone morphogenetic protein 15 (BMP15) and WEE1 homolog 2 (S. pombe)(WEE2) which play critical roles in follicular development but other biomarkers are also likely to play significant roles in this process. Conclusions To our knowledge, this is the first in vivo spatio-temporal exploration of transcriptomes derived from early follicles in sheep.
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Affiliation(s)
- Agnes Bonnet
- INRA, UMR 1388 GenPhySE (Génétique, Physiologie et Systèmes d’Elevage), F-31326 Castanet-Tolosan, France
- Université de Toulouse, INP, ENSAT, GenPhySE (Génétique, Physiologie et Systèmes d’Elevage), F-31326 Castanet-Tolosan, France
- Université de Toulouse, INP, ENVT, GenPhySE (Génétique, Physiologie et Systèmes d’Elevage), F-31076 Toulouse, France
- * E-mail:
| | - Bertrand Servin
- INRA, UMR 1388 GenPhySE (Génétique, Physiologie et Systèmes d’Elevage), F-31326 Castanet-Tolosan, France
- Université de Toulouse, INP, ENSAT, GenPhySE (Génétique, Physiologie et Systèmes d’Elevage), F-31326 Castanet-Tolosan, France
- Université de Toulouse, INP, ENVT, GenPhySE (Génétique, Physiologie et Systèmes d’Elevage), F-31076 Toulouse, France
| | - Philippe Mulsant
- INRA, UMR 1388 GenPhySE (Génétique, Physiologie et Systèmes d’Elevage), F-31326 Castanet-Tolosan, France
- Université de Toulouse, INP, ENSAT, GenPhySE (Génétique, Physiologie et Systèmes d’Elevage), F-31326 Castanet-Tolosan, France
- Université de Toulouse, INP, ENVT, GenPhySE (Génétique, Physiologie et Systèmes d’Elevage), F-31076 Toulouse, France
| | - Beatrice Mandon-Pepin
- INRA, UMR1198 Biologie du Développement et de la Reproduction, F-78350 Jouy-en-Josas, France
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Jafari M, Mirzaie M, Sadeghi M. Interlog protein network: an evolutionary benchmark of protein interaction networks for the evaluation of clustering algorithms. BMC Bioinformatics 2015; 16:319. [PMID: 26437714 PMCID: PMC4595048 DOI: 10.1186/s12859-015-0755-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Accepted: 09/29/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In the field of network science, exploring principal and crucial modules or communities is critical in the deduction of relationships and organization of complex networks. This approach expands an arena, and thus allows further study of biological functions in the field of network biology. As the clustering algorithms that are currently employed in finding modules have innate uncertainties, external and internal validations are necessary. METHODS Sequence and network structure alignment, has been used to define the Interlog Protein Network (IPN). This network is an evolutionarily conserved network with communal nodes and less false-positive links. In the current study, the IPN is employed as an evolution-based benchmark in the validation of the module finding methods. The clustering results of five algorithms; Markov Clustering (MCL), Restricted Neighborhood Search Clustering (RNSC), Cartographic Representation (CR), Laplacian Dynamics (LD) and Genetic Algorithm; to find communities in Protein-Protein Interaction networks (GAPPI) are assessed by IPN in four distinct Protein-Protein Interaction Networks (PPINs). RESULTS The MCL shows a more accurate algorithm based on this evolutionary benchmarking approach. Also, the biological relevance of proteins in the IPN modules generated by MCL is compatible with biological standard databases such as Gene Ontology, KEGG and Reactome. CONCLUSION In this study, the IPN shows its potential for validation of clustering algorithms due to its biological logic and straightforward implementation.
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Affiliation(s)
- Mohieddin Jafari
- Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, 69 Pasteur St, PO Box 13164, Tehran, Iran.
- School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Shahid Lavasani St, PO Box 19395-5746, Tehran, Iran.
| | - Mehdi Mirzaie
- Department of Computational Biology, Faculty of High Technologies, Tarbiat Modares University, Jalal Ale Ahmad Highway, PO Box 14115-111, Tehran, Iran.
| | - Mehdi Sadeghi
- National Institute of Genetic Engineering and Biotechnology (NIGEB), Pajoohesh Blvd, 17 Km Tehran-Karaj Highway, PO Box 161-14965, Tehran, Iran.
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8
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Affiliation(s)
- Christine Nardini
- Lazzari Bologna, Italy ; Group of Clinical Genomic Networks, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences Shanghai, China
| | | | - Paolo Tieri
- Consiglio Nazionale delle Ricerche, Istituto per le Applicazioni del Calcolo Rome, Italy
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Chowdhury S, Sarkar RR. Comparison of human cell signaling pathway databases--evolution, drawbacks and challenges. Database (Oxford) 2015; 2015:bau126. [PMID: 25632107 PMCID: PMC4309023 DOI: 10.1093/database/bau126] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2014] [Revised: 11/27/2014] [Accepted: 12/18/2014] [Indexed: 12/14/2022]
Abstract
Elucidating the complexities of cell signaling pathways is of immense importance to gain understanding about various biological phenomenon, such as dynamics of gene/protein expression regulation, cell fate determination, embryogenesis and disease progression. The successful completion of human genome project has also helped experimental and theoretical biologists to analyze various important pathways. To advance this study, during the past two decades, systematic collections of pathway data from experimental studies have been compiled and distributed freely by several databases, which also integrate various computational tools for further analysis. Despite significant advancements, there exist several drawbacks and challenges, such as pathway data heterogeneity, annotation, regular update and automated image reconstructions, which motivated us to perform a thorough review on popular and actively functioning 24 cell signaling databases. Based on two major characteristics, pathway information and technical details, freely accessible data from commercial and academic databases are examined to understand their evolution and enrichment. This review not only helps to identify some novel and useful features, which are not yet included in any of the databases but also highlights their current limitations and subsequently propose the reasonable solutions for future database development, which could be useful to the whole scientific community.
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Affiliation(s)
- Saikat Chowdhury
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Dr. Homi Bhaba Road, Pune, Maharashtra 411008, India and Academy of Scientific & Innovative Research (AcSIR), New Delhi 110 001, India Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Dr. Homi Bhaba Road, Pune, Maharashtra 411008, India and Academy of Scientific & Innovative Research (AcSIR), New Delhi 110 001, India
| | - Ram Rup Sarkar
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Dr. Homi Bhaba Road, Pune, Maharashtra 411008, India and Academy of Scientific & Innovative Research (AcSIR), New Delhi 110 001, India Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Dr. Homi Bhaba Road, Pune, Maharashtra 411008, India and Academy of Scientific & Innovative Research (AcSIR), New Delhi 110 001, India
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Tieri P, Zhou X, Zhu L, Nardini C. Multi-omic landscape of rheumatoid arthritis: re-evaluation of drug adverse effects. Front Cell Dev Biol 2014; 2:59. [PMID: 25414848 PMCID: PMC4220167 DOI: 10.3389/fcell.2014.00059] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2014] [Accepted: 09/26/2014] [Indexed: 12/19/2022] Open
Abstract
Objective: To provide a frame to estimate the systemic impact (side/adverse events) of (novel) therapeutic targets by taking into consideration drugs potential on the numerous districts involved in rheumatoid arthritis (RA) from the inflammatory and immune response to the gut-intestinal (GI) microbiome. Methods: We curated the collection of molecules from high-throughput screens of diverse (multi-omic) biochemical origin, experimentally associated to RA. Starting from such collection we generated RA-related protein-protein interaction (PPI) networks (interactomes) based on experimental PPI data. Pharmacological treatment simulation, topological and functional analyses were further run to gain insight into the proteins most affected by therapy and by multi-omic modeling. Results: Simulation on the administration of MTX results in the activation of expected (apoptosis) and adverse (nitrogenous metabolism alteration) effects. Growth factor receptor-bound protein 2 (GRB2) and Interleukin-1 Receptor Associated Kinase-4 (IRAK4, already an RA target) emerge as relevant nodes. The former controls the activation of inflammatory, proliferative and degenerative pathways in host and pathogens. The latter controls immune alterations and blocks innate response to pathogens. Conclusions: This multi-omic map properly recollects in a single analytical picture known, yet complex, information like the adverse/side effects of MTX, and provides a reliable platform for in silico hypothesis testing or recommendation on novel therapies. These results can support the development of RA translational research in the design of validation experiments and clinical trials, as such we identify GRB2 as a robust potential new target for RA for its ability to control both synovial degeneracy and dysbiosis, and, conversely, warn on the usage of IRAK4-inhibitors recently promoted, as this involves potential adverse effects in the form of impaired innate response to pathogens.
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Affiliation(s)
- Paolo Tieri
- IAC - Istituto per le Applicazioni del Calcolo "Mauro Picone," CNR - Consiglio Nazionale delle Ricerche Rome, Italy ; Group of Clinical Genomic Networks, Key Laboratory of Computational Biology, Chinese Academy of Sciences - Max Planck Society Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences Shanghai, China
| | - XiaoYuan Zhou
- Group of Clinical Genomic Networks, Key Laboratory of Computational Biology, Chinese Academy of Sciences - Max Planck Society Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences Shanghai, China
| | - Lisha Zhu
- Group of Clinical Genomic Networks, Key Laboratory of Computational Biology, Chinese Academy of Sciences - Max Planck Society Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences Shanghai, China
| | - Christine Nardini
- Group of Clinical Genomic Networks, Key Laboratory of Computational Biology, Chinese Academy of Sciences - Max Planck Society Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences Shanghai, China
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