Dataset Information

Machine learning algorithms reveal unique gene expression profiles in muscle biopsies from patients with different types of myositis.

ABSTRACT:

Objectives

Myositis is a heterogeneous family of diseases that includes dermatomyositis (DM), antisynthetase syndrome (AS), immune-mediated necrotising myopathy (IMNM), inclusion body myositis (IBM), polymyositis and overlap myositis. Additional subtypes of myositis can be defined by the presence of myositis-specific autoantibodies (MSAs). The purpose of this study was to define unique gene expression profiles in muscle biopsies from patients with MSA-positive DM, AS and IMNM as well as IBM.

Methods

RNA-seq was performed on muscle biopsies from 119 myositis patients with IBM or defined MSAs and 20 controls. Machine learning algorithms were trained on transcriptomic data and recursive feature elimination was used to determine which genes were most useful for classifying muscle biopsies into each type and MSA-defined subtype of myositis.

Results

The support vector machine learning algorithm classified the muscle biopsies with >90% accuracy. Recursive feature elimination identified genes that are most useful to the machine learning algorithm and that are only overexpressed in one type of myositis. For example, CAMK1G (calcium/calmodulin-dependent protein kinase IG), EGR4 (early growth response protein 4) and CXCL8 (interleukin 8) are highly expressed in AS but not in DM or other types of myositis. Using the same computational approach, we also identified genes that are uniquely overexpressed in different MSA-defined subtypes. These included apolipoprotein A4 (APOA4), which is only expressed in anti-3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) myopathy, and MADCAM1 (mucosal vascular addressin cell adhesion molecule 1), which is only expressed in anti-Mi2-positive DM.

Conclusions

Unique gene expression profiles in muscle biopsies from patients with MSA-defined subtypes of myositis and IBM suggest that different pathological mechanisms underly muscle damage in each of these diseases.

SUBMITTER: Pinal-Fernandez I

PROVIDER: S-EPMC10461844 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Publications

Machine learning algorithms reveal unique gene expression profiles in muscle biopsies from patients with different types of myositis.

Pinal-Fernandez Iago I Casal-Dominguez Maria M Derfoul Assia A Pak Katherine K Miller Frederick W FW Milisenda Jose César JC Grau-Junyent Josep Maria JM Selva-O'Callaghan Albert A Carrion-Ribas Carme C Paik Julie J JJ Albayda Jemima J Christopher-Stine Lisa L Lloyd Thomas E TE Corse Andrea M AM Mammen Andrew L AL

Annals of the rheumatic diseases 20200616 9

<h4>Objectives</h4>Myositis is a heterogeneous family of diseases that includes dermatomyositis (DM), antisynthetase syndrome (AS), immune-mediated necrotising myopathy (IMNM), inclusion body myositis (IBM), polymyositis and overlap myositis. Additional subtypes of myositis can be defined by the presence of myositis-specific autoantibodies (MSAs). The purpose of this study was to define unique gene expression profiles in muscle biopsies from patients with MSA-positive DM, AS and IMNM as well as ...[more]

PMID: 32546599

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Project description:ObjectiveMuscle inflammation is a feature in myositis and Duchenne muscular dystrophy (DMD). Autoimmune mechanisms are thought to contribute to muscle weakness in patients with myositis. However, a lack of correlation between the extent of inflammatory cell infiltration and muscle weakness indicates that nonimmune pathologic mechanisms may play a role. The present study focused on 2 microRNA (miRNA) sets previously identified as being elevated in the muscle of patients with DMD-an "inflammatory" miRNA set that is dampened with glucocorticoids, and a "dystrophin-targeting" miRNA set that inhibits dystrophin translation-to test the hypothesis that these miRNAs are similarly dysregulated in the muscle of patients with myositis, and could contribute to muscle weakness and disease severity.MethodsA major histocompatibility complex class I-transgenic mouse model of myositis was utilized to study gene and miRNA expression and histologic features in the muscle tissue, with the findings validated in human muscle biopsy tissue from 6 patients with myositis. Mice were classified as having mild or severe myositis based on transgene expression, body weight, histologic disease severity, and muscle strength/weakness.ResultsIn mice with severe myositis, muscle tissue showed mononuclear cell infiltration along with elevated expression of type I interferon and NF-κB-regulated genes, including Tlr7 (3.8-fold increase, P < 0.05). Furthermore, mice with severe myositis showed elevated expression of inflammatory miRNAs (miR-146a, miR-142-3p, miR-142-5p, miR-455-3p, and miR-455-5p; ~3-40-fold increase, P < 0.05) and dystrophin-targeting miRNAs (miR-146a, miR-146b, miR-31, and miR-223; ~3-38-fold increase, P < 0.05). Bioinformatics analyses of chromatin immunoprecipitation sequencing (ChIP-seq) data identified at least one NF-κB consensus element within the promoter/enhancer regions of these miRNAs. Western blotting and immunofluorescence analyses of the muscle tissue from mice with severe myositis demonstrated reduced levels of dystrophin. In addition, elevated levels of NF-κB-regulated genes, TLR7, and miRNAs along with reduced dystrophin levels were observed in muscle biopsy tissue from patients with histologically severe myositis.ConclusionThese data demonstrate that an acquired dystrophin deficiency may occur through NF-κB-regulated miRNAs in myositis, thereby suggesting a unifying theme in which muscle injury, inflammation, and weakness are perpetuated both in myositis and in DMD.

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Dataset Information

Machine learning algorithms reveal unique gene expression profiles in muscle biopsies from patients with different types of myositis.

Objectives

Methods

Results

Conclusions

Publications

Machine learning algorithms reveal unique gene expression profiles in muscle biopsies from patients with different types of myositis.

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