Machine learning algorithms reveal unique gene expression profiles in muscle biopsies from patients with different types of myositis.
Adult
Animals
Apolipoproteins A
/ metabolism
Autoimmune Diseases
/ genetics
Biopsy
Calcium-Calmodulin-Dependent Protein Kinase Type 1
/ metabolism
Cell Adhesion Molecules
/ metabolism
Cell Culture Techniques
Dermatomyositis
/ genetics
Early Growth Response Transcription Factors
/ metabolism
Female
Humans
Hydroxymethylglutaryl CoA Reductases
/ metabolism
Interleukin-8
/ metabolism
Machine Learning
Male
Mice
Mucoproteins
/ metabolism
Muscle, Skeletal
/ metabolism
Muscular Diseases
/ genetics
Myositis
/ genetics
Myositis, Inclusion Body
/ genetics
Polymyositis
/ genetics
Transcriptome
autoantibodies
autoimmune diseases
autoimmunity
dermatomyositis
polymyositis
Journal
Annals of the rheumatic diseases
ISSN: 1468-2060
Titre abrégé: Ann Rheum Dis
Pays: England
ID NLM: 0372355
Informations de publication
Date de publication:
09 2020
09 2020
Historique:
received:
05
11
2019
revised:
27
04
2020
accepted:
14
05
2020
pubmed:
18
6
2020
medline:
2
10
2020
entrez:
18
6
2020
Statut:
ppublish
Résumé
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. 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. 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. 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.
Identifiants
pubmed: 32546599
pii: annrheumdis-2019-216599
doi: 10.1136/annrheumdis-2019-216599
pmc: PMC10461844
mid: NIHMS1919704
doi:
Substances chimiques
Apolipoproteins A
0
CXCL8 protein, human
0
Cell Adhesion Molecules
0
EGR4 protein, human
0
Early Growth Response Transcription Factors
0
Interleukin-8
0
MADCAM1 protein, human
0
Mucoproteins
0
apolipoprotein A-IV
0
HMGCR protein, human
EC 1.1.1.-
Hydroxymethylglutaryl CoA Reductases
EC 1.1.1.-
CAMK1G protein, human
EC 2.7.11.17
Calcium-Calmodulin-Dependent Protein Kinase Type 1
EC 2.7.11.17
Types de publication
Journal Article
Research Support, N.I.H., Intramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1234-1242Subventions
Organisme : Intramural NIH HHS
ID : Z01 ES101074
Pays : United States
Commentaires et corrections
Type : CommentIn
Type : CommentIn
Type : CommentIn
Informations de copyright
© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.
Déclaration de conflit d'intérêts
Competing interests: None declared.
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