Evidence map›Paper›PMID 34499219›Full record

ArticleActa neuropathologica2021

A network of core and subtype-specific gene expression programs in myositis.

David R Amici, Iago Pinal-Fernandez, Lisa Christopher-Stine, Andrew L Mammen, Marc L Mendillo

Open access · greenAbstract read
In one paragraph

Article in Acta neuropathologica, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
3.0field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed, 40 citations in OpenAlex.

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  8. A neuromuscular clinician's primer on machine learning.Journal of neuromuscular diseases · 2026
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 2 countries.

David R AmiciDepartment of Biochemistry and Molecular Genetics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Iago Pinal-FernandezMuscle Disease Unit, Laboratory of Muscle Stem Cells and Gene Regulation, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA.
Lisa Christopher-StineDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Andrew L MammenMuscle Disease Unit, Laboratory of Muscle Stem Cells and Gene Regulation, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA. andrew.mammen@nih.gov.
Marc L MendilloDepartment of Biochemistry and Molecular Genetics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. mendillo@northwestern.edu.
National Institute of Arthritis and Musculoskeletal and Skin Diseases · USNorthwestern University · USJohns Hopkins University · US

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM008152 · NIGMS · NORTHWESTERN UNIVERSITY AT CHICAGO · PI ARDEHALI, HOSSEIN · 1987 to 2021
$24.0M
Elucidating the stress response regulatory networks that enable malignancyR00CA175293 · NCI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI MENDILLO, MARC · 2016 to 2018
$747k
Elucidating the Role of C16orf72 in the Cellular Stress Response NetworkF30CA264513 · NCI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI AMICI, DAVID RUSSELL · 2021 to 2025
$222k
HSRD VA PPO 16-135NCI NIH HHS F30 CA264513NCI NIH HHS R00 CA175293NIGMS NIH HHS T32 GM008152
6 · The paper itself

Abstract

Myositis comprises a heterogeneous group of skeletal muscle disorders which converge on chronic muscle inflammation and weakness. Our understanding of myositis pathogenesis is limited, and many myositis patients lack effective therapies. Using muscle biopsy transcriptome profiles from 119 myositis patients (spanning major clinical and serological disease subtypes) and 20 normal controls, we generated a co-expression network of 8101 dynamically regulated transcripts. This network organized the myositis transcriptome into a map of gene expression modules representing interrelated biological processes and disease signatures. Universally myositis-upregulated network modules included muscle regeneration, specific cytokine signatures, the acute phase response, and neutrophil degranulation. Universally myositis-suppressed pathways included a specific subset of myofilaments, the mitochondrial envelope, and nuclear isoforms of the anti-apoptotic humanin protein. Myositis subtype-specific modules included type 1 interferon signaling and titin (dermatomyositis), RNA processing (antisynthetase syndrome), and vasculogenesis (inclusion body myositis). Importantly, therapies exist to target influential proteins in many myositis-dysregulated modules, and nearly all modules contained understudied proteins and non-coding RNAs - many of which were extraordinarily dysregulated in myositis and may represent novel therapeutic targets. Finally, we apply our network to patient classification, finding that a deep learning algorithm trained on patient-level network "images" successfully assigned patients to clinical groups and further into molecular subclusters. Altogether, we provide a global resource to probe and contextualize differential gene expression in myositis.

Indexed as

Deep LearningTranscriptomeGene Regulatory NetworksHumansMyositisSequence Analysis, RNACo-expressionDeep learningMyopathyMyositisNetworkTranscriptome

Identifiers

PMID34499219
PMCPMC8555743
OpenAlexW3197400544

What Socratic holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.