Author Correction: A systems biology approach uncovers cell-specific gene regulatory effects of genetic associations in multiple sclerosis

Madireddy, Lohith; Patsopoulos, Nikolaos A.; Cotsapas, Chris; Bos, Steffan Daniël; Beecham, Ashley; McCauley, Jacob L.; Kim, Kicheol; Jia, Xiaoming; Santaniello, Adam; Caillier, Stacy J.; Andlauer, Till F. M.; Barcellos, Lisa F.; Berge, Tone; Bernardinelli, Luisa; Boneschi, Filippo Martinelli; Booth, David R.; Briggs, Farren; Celius, Elisabeth G; Comabella, Manuel; Comi, Giancarlo; Cree, Bruce A.C.; D’Alfonso, Sandra; Dedham, Katrina; Duquette, Pierre; Dardiotis, Efthimios; Esposito, Federica; Fontaine, Bertrand; Gasperi, Christiane; Dubois, Bénédicte; Gourraud, Pierre‐Antoine; Hadjigeorgiou, Georgios M.; Haines, Jonathan L.; Hawkins, Clive; Hemmer, Bernhard; Hintzen, Rogier Q.; Horáková, Dana; Isobe, Noriko; Kalra, Seema; Kira, Jun‐ichi; Khalil, Michael; Kockum, Ingrid; Lill, Christina M.; Lincoln, Matthew R.; Luessi, Felix; Martin, Roland; Oturai, Annette Bang; Palotie, Aarno; Kunkle, Brian W.; Henry, Roland G.; Saarela, Olli; Ivinson, Adrian J.; Olsson, Tomas; Taylor, Bruce; Stewart, Graeme; Hf, Harbo; Compston, Juliet; Hafler, David A.; Zipp, Frauke; Jager, Philip De; Sawcer, Stephen; Oksenberg, Jorge; Baranzini, Sergio E.

doi:10.1038/s41467-019-10951-1

Cited by 5 publications

(2 citation statements)

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“…They are located throughout the genome and include 32 independent variants in the HLA region and one on the X chromosome. The greatest MS association from the GWAS data is for the HLA-DRB1*15:01 risk allele ( International Multiple Sclerosis Genetics Consortium, 2019a ). Most of the genes involved in MS susceptibilities are expressed mainly in immune cells (B and T lymphocytes, natural killer [NK] cells, monocytes, macrophages) and microglia ( International Multiple Sclerosis Genetics Consortium, 2019b ); risk variants are often located on promoters and enhancers.…”

Section: Omics Approachesmentioning

confidence: 99%

Omics approaches to understanding the efficacy and safety of disease-modifying treatments in multiple sclerosis

et al. 2023

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From the perspective of precision medicine, the challenge for the future is to improve the accuracy of diagnosis, prognosis, and prediction of therapeutic responses through the identification of biomarkers. In this framework, the omics sciences (genomics, transcriptomics, proteomics, and metabolomics) and their combined use represent innovative approaches for the exploration of the complexity and heterogeneity of multiple sclerosis (MS). This review examines the evidence currently available on the application of omics sciences to MS, analyses the methods, their limitations, the samples used, and their characteristics, with a particular focus on biomarkers associated with the disease state, exposure to disease-modifying treatments (DMTs), and drug efficacies and safety profiles.

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Section: Omics Approachesmentioning

confidence: 99%

Omics approaches to understanding the efficacy and safety of disease-modifying treatments in multiple sclerosis

et al. 2023

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“…It is necessary to elucidate the role of monocytes in the progression of osteoporosis. Recent evidences have emerged that explorations of gene profile by bioinformatics analyses promote investigation of the molecular mechanisms for kinds of disease (15,16). However, the relationship between osteoporosis and monocytes subsets gene variation is rarely reported.…”

Section: Introductionmentioning

confidence: 99%

Integrative Analyses of Genes Associated With Osteoporosis in CD16+ Monocyte

Kong²,

Li³

et al. 2021

Front. Endocrinol.

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BackgroundOsteoporosis is a metabolic bone disease characterized by decreased bone mineral density and abnormal bone quality. Monocytes can secret cytokines for bone resorption, resulting in bone mass loss. However, the mechanism by which monocytes subpopulations lead to osteoporosis remains unclear. The aim of this study was to identify genes associated with osteoporosis in monocytes subsets.MethodsThree microarray datasets including GSE7158 (transcription of low/high-peak bone mass), GSE101489 (transcription of CD16+/CD16− monocyte) and GSE93883 (miRNA expression profile of primary osteoporosis) were derived from the Gene Expression Omnibus (GEO) database and analyzed with GEO2R tool to identify differentially expressed genes (DEGs). Functional enrichment was analyzed using Metascape database and GSEA software. STRING was utilized for the Protein–Protein Interaction Network construct. The hub genes were screened out using the Cytoscape software. Related miRNAs were predicted in miRWalk, miRDB, and TargetScan databases.ResultsTotal 368 DEGs from GSE7158 were screened out, which were mostly enriched in signaling, positive regulation of biological process and immune system process. The hub genes were clustered into two modules by PPI network analysis. We identified 15 overlapping DGEs between GSE101489 and GSE7158 microarray datasets. Moreover, all of them were up-regulated genes in both datasets. Then, nine key genes were screened out from above 15 overlapping DEGs using Cytoscape software. It is a remarkable fact that the nine genes were all in one hub gene module of GSE7158. Additionally, 183 target miRNAs were predicted according to the above nine DEGs. After cross-verification with miRNA express profile dataset for osteoporosis (GSE93883), 12 DEmiRNAs were selected. Finally, a miRNA-mRNA network was constructed with the nine key genes and 12 miRNAs, which were involved in osteoporosis.ConclusionOur analysis results constructed a gene expression framework in monocyte subsets for osteoporosis. This approach could provide a novel insight into osteoporosis.

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Genomic, proteomic, and systems biology approaches in biomarker discovery for multiple sclerosis

Huizar

Raphael

Forsthuber

2020

Cellular Immunology

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Author Correction: A systems biology approach uncovers cell-specific gene regulatory effects of genetic associations in multiple sclerosis

Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Cited by 5 publications

References 0 publications

Omics approaches to understanding the efficacy and safety of disease-modifying treatments in multiple sclerosis

Omics approaches to understanding the efficacy and safety of disease-modifying treatments in multiple sclerosis

Integrative Analyses of Genes Associated With Osteoporosis in CD16+ Monocyte

Genomic, proteomic, and systems biology approaches in biomarker discovery for multiple sclerosis

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