Identification of key genes associated with multiple sclerosis based on gene expression data from peripheral blood mononuclear cells

Shang, Zhenwei; Sun, Wenjing; Zhang, Mingming; Xu, Lei; Jia, Xueyuan; Zhang, Ruijie; Fu, Songbin

doi:10.7717/peerj.8357

Cited by 13 publications

(8 citation statements)

References 46 publications

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“…These results were accompanied by enrichment pathways in cell type specific genes, indicating the different etiological mechanisms in PLS1 + and PLS1 − gene sets and the potential clinical applications of intersubject variability in WMFC. Considering multiple sclerosis as a typical disorder with abnormalities in WM, we also determined the relationships between WMFC intersubject variabilities and differentially expressed genes in multiple sclerosis 53 . We found that WMFC intersubject variability was correlated with dysregulated gene expression ( r = –0.29, P moran = 0.0002; Supplementary Result 7 and Supplementary Fig.…”

Section: Resultsmentioning

confidence: 99%

Transcriptomic and macroscopic architectures of intersubject functional variability in human brain white-matter

et al. 2021

Commun Biol

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Intersubject variability is a fundamental characteristic of brain organizations, and not just “noise”. Although intrinsic functional connectivity (FC) is unique to each individual and varies across brain gray-matter, the underlying mechanisms of intersubject functional variability in white-matter (WM) remain unknown. This study identified WMFC variabilities and determined the genetic basis and macroscale imaging in 45 healthy subjects. The functional localization pattern of intersubject variability across WM is heterogeneous, with most variability observed in the heteromodal cortex. The variabilities of heteromodal regions in expression profiles of genes are related to neuronal cells, involved in synapse-related and glutamic pathways, and associated with psychiatric disorders. In contrast, genes overexpressed in unimodal regions are mostly expressed in glial cells and were related to neurological diseases. Macroscopic variability recapitulates the functional and structural specializations and behavioral phenotypes. Together, our results provide clues to intersubject variabilities of the WMFC with convergent transcriptomic and cellular signatures, which relate to macroscale brain specialization.

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Section: Resultsmentioning

confidence: 99%

Transcriptomic and macroscopic architectures of intersubject functional variability in human brain white-matter

et al. 2021

Commun Biol

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“…For example, our analysis shows that hsa-miR-335-5p is the top ranked miRNA in the regulation of the black module. Since the black module consists mostly genes involved in inflammatory response, the upregulation of hsa-miR-335-5p is proposed to have inflammatory suppression effects [ 43 – 45 ]. More recently, hsa-miR-335-5p is shown to reduce inflammation via negative regulation of the TPX2-mediated AKT/GSK3β signaling pathway in a chronic rhinosinusitis mouse model [ 46 ].…”

Section: Discussionmentioning

confidence: 99%

Gene correlation network analysis to identify regulatory factors in sepsis

Zhang

Chen

et al. 2020

J Transl Med

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Background and objectives Sepsis is a leading cause of mortality and morbidity in the intensive care unit. Regulatory mechanisms underlying the disease progression and prognosis are largely unknown. The study aimed to identify master regulators of mortality-related modules, providing potential therapeutic target for further translational experiments. Methods The dataset GSE65682 from the Gene Expression Omnibus (GEO) database was utilized for bioinformatic analysis. Consensus weighted gene co-expression netwoek analysis (WGCNA) was performed to identify modules of sepsis. The module most significantly associated with mortality were further analyzed for the identification of master regulators of transcription factors and miRNA. Results A total number of 682 subjects with various causes of sepsis were included for consensus WGCNA analysis, which identified 27 modules. The network was well preserved among different causes of sepsis. Two modules designated as black and light yellow module were found to be associated with mortality outcome. Key regulators of the black and light yellow modules were the transcription factor CEBPB (normalized enrichment score = 5.53) and ETV6 (NES = 6), respectively. The top 5 miRNA regulated the most number of genes were hsa-miR-335-5p (n = 59), hsa-miR-26b-5p (n = 57), hsa-miR-16-5p (n = 44), hsa-miR-17-5p (n = 42), and hsa-miR-124-3p (n = 38). Clustering analysis in 2-dimension space derived from manifold learning identified two subclasses of sepsis, which showed significant association with survival in Cox proportional hazard model (p = 0.018). Conclusions The present study showed that the black and light-yellow modules were significantly associated with mortality outcome. Master regulators of the module included transcription factor CEBPB and ETV6. miRNA-target interactions identified significantly enriched miRNA.

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“…Specifically, through a systems biology approach, researchers can generate gene and protein networks and identify genetic variants and novel molecules that heavily contribute to the pathogenesis of MS (73,74). A previous study by Shang et al (75), as an example, made use of such techniques and identified differentially expressed genes in MS, while pinpointing a number of genes heavily associated with the disease, including transcription factors (FOS, TP53, JUN and ATF3) and genes whose function was associated with the immune system (OAS2) and inflammation (IL8).…”

Section: Multiple Sclerosis and Computational Biologymentioning

confidence: 99%

Multiple sclerosis and computational biology (Review)

et al. 2022

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Multiple sclerosis (MS) is an autoimmune neurodegenerative disease whose prevalence has increased worldwide. The resultant symptoms may be debilitating and can substantially reduce the of patients. Computational biology, which involves the use of computational tools to answer biomedical questions, may provide the basis for novel healthcare approaches in the context of MS. The rapid accumulation of health data, and the ever-increasing computational power and evolving technology have helped to modernize and refine MS research. From the discovery of novel biomarkers to the optimization of treatment and a number of quality-of-life enhancements for patients, computational biology methods and tools are shaping the field of MS diagnosis, management and treatment. The final goal in such a complex disease would be personalized medicine, i.e., providing healthcare services that are tailored to the individual patient, in accordance to the particular biology of their disease and the environmental factors to which they are subjected. The present review article summarizes the current knowledge on MS, modern computational biology and the impact of modern computational approaches of MS.

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Identification of key genes associated with multiple sclerosis based on gene expression data from peripheral blood mononuclear cells

Cited by 13 publications

References 46 publications

Transcriptomic and macroscopic architectures of intersubject functional variability in human brain white-matter

Transcriptomic and macroscopic architectures of intersubject functional variability in human brain white-matter

Gene correlation network analysis to identify regulatory factors in sepsis

Multiple sclerosis and computational biology (Review)

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