Evidence map›Paper›PMID 38008808›Full record

ReviewCurrent atherosclerosis reports2023

Gene Regulatory Networks in Coronary Artery Disease.

Jenny Cheng, Michael Cheng, Aldons J Lusis, Xia Yang

Open access · greenAbstract readReview
In one paragraph

Review in Current atherosclerosis reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.5field-weighted citation impact, top 17% 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

5 citing papers in PubMed, 10 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
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 at 1 institution in 1 country.

Jenny Cheng *Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA, 90095, USA.
Michael Cheng *Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA, 90095, USA.
Aldons J LusisDepartment of Medicine, Division of Cardiology, University of California, Los Angeles, 650 Charles E Young Drive South, Los Angeles, CA, 90095, USA. jlusis@mednet.ucla.edu.
Xia YangDepartment of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA, 90095, USA. xyang123@ucla.edu.
University of California, Los Angeles · US

Funding

Sex Differences in the Metabolic SyndromeU54DK120342 · NIDDK · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI REUE, KAREN · 2018 to 2022
$7.6M
Systems Genetics Dissection of Non-alcoholic SteatohepatitisR01DK117850 · NIDDK · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Aldons Jake Lusis · 2019 to 2026
$4.6M
Systems genetics approach to inflammatory mechanisms in atherosclerosisR01HL147883 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LUSIS, ALDONS JAKE · 2019 to 2022
$3.0M
Precision Medicine Approach: Using genomic information to guide TBI treatmentR01NS111378 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI GOMEZ-PINILLA, FERNANDO, YANG, XIA · 2020 to 2024
$2.9M
Spatiotemporal Molecular Substrates of TBI at Single Cell ResolutionR01NS117148 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI GOMEZ-PINILLA, FERNANDO, WOLLMAN, ROY · 2020 to 2024
$2.8M
Establishing mechanistic links between the gut microbiome and atherosclerosisR01HL148577 · NHLBI · UNIVERSITY OF WISCONSIN-MADISON · PI LUSIS, ALDONS JAKE, REY, FEDERICO E · 2020 to 2023
$2.6M
NHLBI NIH HHS R01 HL147883NHLBI NIH HHS R01 HL148577NIDDK NIH HHS R01 DK117850NIDDK NIH HHS U54 DK120342NIH HHS DK120342, HL148577, and HL147883 (AJL). NS111378, NS117148, HL147883 (XY)NINDS NIH HHS R01 NS111378NINDS NIH HHS R01 NS117148
6 · The paper itself

Abstract

purpose of reviewCoronary artery disease is a complex disorder and the leading cause of mortality worldwide. As technologies for the generation of high-throughput multiomics data have advanced, gene regulatory network modeling has become an increasingly powerful tool in understanding coronary artery disease. This review summarizes recent and novel gene regulatory network tools for bulk tissue and single cell data, existing databases for network construction, and applications of gene regulatory networks in coronary artery disease. RECENT

findingsNew gene regulatory network tools can integrate multiomics data to elucidate complex disease mechanisms at unprecedented cellular and spatial resolutions. At the same time, updates to coronary artery disease expression data in existing databases have enabled researchers to build gene regulatory networks to study novel disease mechanisms. Gene regulatory networks have proven extremely useful in understanding CAD heritability beyond what is explained by GWAS loci and in identifying mechanisms and key driver genes underlying disease onset and progression. Gene regulatory networks can holistically and comprehensively address the complex nature of coronary artery disease. In this review, we discuss key algorithmic approaches to construct gene regulatory networks and highlight state-of-the-art methods that model specific modes of gene regulation. We also explore recent applications of these tools in coronary artery disease patient data repositories to understand disease heritability and shared and distinct disease mechanisms and key driver genes across tissues, between sexes, and between species.

Indexed as

Coronary Artery DiseaseGene Regulatory NetworksGene Expression RegulationHumansAtherosclerosisCoronary artery diseaseGene regulatory networksNetwork modelingRNA-sequencingSingle-cell RNA-sequencing

Identifiers

PMID38008808
PMCPMC11466510
OpenAlexW4389029579

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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 OpenQuestion graph.