Evidence map›Paper›PMID 39504244›Full record

ArticleCell reports2024

Uncovering cell-type-specific immunomodulatory variants and molecular phenotypes in COVID-19 using structurally resolved protein networks.

Prabal Chhibbar, Priyamvada Guha Roy, Munesh K Harioudh, Daniel J McGrail, Donghui Yang, Harinder Singh, Reinhard Hinterleitner, Yi-Nan Gong, S Stephen Yi, Nidhi Sahni and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

12 authors.

Prabal ChhibbarCenter for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Integrative Systems Biology PhD Program, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Priyamvada Guha RoyCenter for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Human Genetics PhD Program, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.
Munesh K HarioudhDepartment of Microbiology and Molecular Genetics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Daniel J McGrailCenter for Immunotherapy and Precision Immuno Oncology, Cleveland Clinic, Cleveland, OH, USA; Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
Donghui YangDepartment of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Harinder SinghCenter for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Reinhard HinterleitnerDepartment of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Yi-Nan GongDepartment of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
S Stephen YiLivestrong Cancer Institutes, Department of Oncology, Dell Medical School, The University of Texas at Austin, Austin, TX, USA; Department of Biomedical Engineering, Oden Institute for Computational Engineering and Sciences (ICES) and Interdisciplinary Life Sciences Graduate Programs, The University of Texas at Austin, Austin, TX, USA.
Nidhi SahniDepartment of Epigenetics and Molecular Carcinogenesis, MD Anderson Cancer Center, Houston, TX, USA; Program in Quantitative and Computational Biosciences (QCB), Baylor College of Medicine, Houston, TX, USA; Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Saumendra N SarkarDepartment of Microbiology and Molecular Genetics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA; Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Jishnu DasCenter for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. Electronic address: jishnu@pitt.edu.

Funding

Linking genome variation to transcriptional network dynamics in human B cellsU01HG012041 · NHGRI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Jishnu Das, HARINDER SINGH · 2021 to 2026
$6.1M
Network-based Framework to Decode Novel Gain-of-Function Mutations and their Mechanistic Roles in General Human DiseasesR35GM133658 · NIGMS · UNIVERSITY OF TEXAS AT AUSTIN · PI S. Stephen Yi · 2019 to 2026
$2.6M
Using three-dimensional protein networks to uncover immuno-modulatory molecular phenotypes in infectious diseaseDP2AI164325 · NIAID · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI DAS, JISHNU · 2021 to 2025
$2.4M
Deciphering Functional Consequences of Specific and Combinatorial Mutations in Protein Interaction NetworksR35GM137836 · NIGMS · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI SAHNI, NIDHI · 2020 to 2023
$1.8M
NHGRI NIH HHS U01 HG012041NIAID NIH HHS DP2 AI164325NIGMS NIH HHS R35 GM133658NIGMS NIH HHS R35 GM137836
6 · The paper itself

Abstract

Immunomodulatory variants that lead to the loss or gain of specific protein interactions often manifest only as organismal phenotypes in infectious disease. Here, we propose a network-based approach to integrate genetic variation with a structurally resolved human protein interactome network to prioritize immunomodulatory variants in COVID-19. We find that, in addition to variants that pass genome-wide significance thresholds, variants at the interface of specific protein-protein interactions, even though they do not meet genome-wide thresholds, are equally immunomodulatory. The integration of these variants with single-cell epigenomic and transcriptomic data prioritizes myeloid and T cell subsets as the most affected by these variants across both the peripheral blood and the lung compartments. Of particular interest is a common coding variant that disrupts the OAS1-PRMT6 interaction and affects downstream interferon signaling. Critically, our framework is generalizable across infectious disease contexts and can be used to implicate immunomodulatory variants that do not meet genome-wide significance thresholds.

Indexed as

COVID-19PhenotypeProtein Interaction MapsSARS-CoV-22',5'-Oligoadenylate SynthetaseHumansImmunomodulationSignal Transduction2',5'-Oligoadenylate SynthetaseCP: ImmunologyGWAShost-viral interactionsmachine learningprotein networkssystems immunology

Identifiers

PMID39504244
PMCPMC12824837

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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 OpenQuestion graph.