TY - JOUR
T1 - Machine learning algorithms reveal unique gene expression profiles in muscle biopsies from patients with different types of myositis
AU - Pinal-Fernandez, Iago
AU - Casal-Dominguez, Maria
AU - Derfoul, Assia
AU - Pak, Katherine
AU - Miller, Frederick W.
AU - Milisenda, Jose César
AU - Grau-Junyent, Josep Maria
AU - Selva-O'callaghan, Albert
AU - Carrion-Ribas, Carme
AU - Paik, Julie J.
AU - Albayda, Jemima
AU - Christopher-Stine, Lisa
AU - Lloyd, Thomas E.
AU - Corse, Andrea M.
AU - Mammen, Andrew L.
N1 - Funding Information:
Funding This research was supported in part by the intramural research Programme of the national institute of arthritis and Musculoskeletal and skin Diseases and the national institute of environmental Health sciences of the national institutes of Health. The Myositis research Database and Dr lc-s are supported by the Huayi and siuling Zhang Discovery Fund. iPF’s research was supported by a Fellowship from the Myositis association. The authors also thank Dr Peter Buck for support.
Publisher Copyright:
© 2020 Author(s) (or their employer(s)). No commercial re-use. See rights and permissions. Published by BMJ.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - 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.
AB - 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.
KW - autoantibodies
KW - autoimmune diseases
KW - autoimmunity
KW - dermatomyositis
KW - polymyositis
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U2 - 10.1136/annrheumdis-2019-216599
DO - 10.1136/annrheumdis-2019-216599
M3 - Article
C2 - 32546599
AN - SCOPUS:85087638928
SN - 0003-4967
VL - 79
SP - 1234
EP - 1242
JO - Annals of the rheumatic diseases
JF - Annals of the rheumatic diseases
IS - 9
ER -