Evidence map›Paper›PMID 39677760›Full record

ArticlebioRxiv : the preprint server for biology2025

Functional dissection of metabolic trait-associated gene regulation in steady state and stimulated human skeletal muscle cells.

Kirsten Nishino, Jacob O Kitzman, Stephen C J Parker, Adelaide Tovar

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Kirsten NishinoDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109.ORCID 0009-0005-3384-1249
Jacob O KitzmanDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109.ORCID 0000-0002-6145-882X
Stephen C J ParkerDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109.ORCID 0000-0001-8122-0117
Adelaide TovarDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109.ORCID 0000-0003-1699-7231

Funding

Bridging the gap between type 2 diabetes GWAS and therapeutic targetsUM1DK126185 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI CLAUSSNITZER, MELINA C, GLOYN, ANNA LOUISE · 2020 to 2024
$9.5M
Multidisciplinary Training Program in Basic Diabetes ResearchT32DK101357 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI PETER ARVAN, Ormond A MacDougald · 2014 to 2026
$4.1M
Context-specific and combinatorial genetic regulatory grammars in diabetesR01DK117960 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Stephen CJ Parker · 2018 to 2026
$2.8M
Expanding the Reach of Massively Parallel Variant Effect ScreensR35GM153286 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Jacob Otto Kitzman · 2024 to 2026
$1.5M
Using the continuum of genetic causality to investigate trans regulatory mechanisms.K99HG013676 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI TOVAR, ADELAIDE E · 2024 to 2025
$239k
NHGRI NIH HHS K99 HG013676NIDDK NIH HHS R01 DK117960NIDDK NIH HHS T32 DK101357NIDDK NIH HHS UM1 DK126185NIGMS NIH HHS R35 GM153286
6 · The paper itself

Abstract

Type 2 diabetes (T2D) is a common metabolic disorder characterized by dysregulation of glucose metabolism. Genome-wide association studies have defined hundreds of signals associated with T2D and related metabolic traits, predominantly in noncoding regions. While pancreatic islets have been a focal point given their central role in insulin production and glucose homeostasis, other metabolic tissues, including liver, adipose, and skeletal muscle, also contribute to T2D pathogenesis and risk. Here, we examined context-specific genetic regulation under basal and stimulated states. Using LHCN-M2 human skeletal muscle cells, we generated transcriptomic profiles and characterized regulatory activity of 327 metabolic trait-associated variants via a massively parallel reporter assay (MPRA). To identify condition-specific effects, we compared four different conditions: (1) undifferentiated, or (2) differentiated with basal media, (3) media supplemented with the AMP analog AICAR (to simulate exercise) or (4) media containing sodium palmitate (to induce insulin resistance). RNA-seq revealed these treatments extensively perturbed transcriptional regulation, with 498-3,686 genes showing significant differential expression between pairs of conditions. Among differentially expressed genes, we observed enrichment of relevant biological pathways including muscle differentiation (undifferentiated vs. differentiated), oxidoreductase activity (differentiated vs. AICAR), and glycogen binding (differentiated vs. palmitate). The results of our MPRA found broadly different levels of activity between all conditions. Our MPRA screen revealed a shared set of 7 variants with significant allelic activity across all conditions, along with a proportional number of variants showing condition-specific allelic bias and the total number of active oligos per condition. We found that a lead variant for serum triglyceride levels, rs490972, overlaps SP transcription factor motifs and has differential regulatory activity between conditions. Comparison of MPRA activity with paired gene expression data allowed us to predict that regulatory activity at this locus is mediated by SP1 transcription factor binding. While several of the MPRA variants have been previously characterized in other metabolic tissues, none have been studied in these stimulated states. Together, this work uncovers context-dependent transcriptomic and regulatory dynamics of T2D- and metabolic trait-associated variants in skeletal muscle cells, offering new insights into their functional roles in metabolic processes.

Indexed as

enhancersgenetic variationGWASmetabolic traitsskeletal muscletranscription factors

Identifiers

PMID39677760
PMCPMC11642805

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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 OpenQuestion graph.