Evidence map›Paper›PMID 40972584›Full record

ArticleCell genomics2025

Network-based drug repurposing for psychiatric disorders using single-cell genomics.

Chirag Gupta, Noah Cohen Kalafut, Declan Clarke, Jerome J Choi, Kalpana Hanthanan Arachchilage, Saniya Khullar, Yan Xia, Xiao Zhou, Cagatay Dursun, Mark Gerstein and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Review
  6. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Chirag GuptaWaisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53076, USA.
Noah Cohen KalafutWaisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA; Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53076, USA.
Declan ClarkeProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA.
Jerome J ChoiWaisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA; Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI 53726, USA.
Kalpana Hanthanan ArachchilageWaisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53076, USA.
Saniya KhullarWaisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53076, USA.
Yan XiaProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA.
Xiao ZhouProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA.
Cagatay DursunProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA.
Mark GersteinProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA; Department of Computer Science, Yale University, New Haven, CT 06520, USA; Department of Statistics & Data Science, Yale University, New Haven, CT 06520, USA; Department of Biomedical Informatics & Data Science, Yale University, New Haven, CT 06520, USA. Electronic address: mark@gersteinlab.org.
Daifeng WangWaisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53076, USA; Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53076, USA. Electronic address: daifeng.wang@wisc.edu.

Funding

Waisman Center Intellectual and Developmental Disabilities Research CenterP50HD105353 · NICHD · UNIVERSITY OF WISCONSIN-MADISON · PI Qiang Chang · 2021 to 2026
$8.5M
Discovery and validation of neuronal enhancers as development of psychiatric disorders supplementU01MH116492 · NIMH · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI GERSTEIN, MARK BENDER, WENG, ZHIPING · 2018 to 2023
$6.2M
PsychENCODE Data Analysis and Coordination CenterU24MH136793 · NIMH · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI Mark Bender Gerstein, Zhiping Weng · 2024 to 2026
$2.5M
Machine learning analyses of single-cell multi-modal data for understanding cell-type functional genomics and gene regulationRF1MH128695 · NIMH · UNIVERSITY OF WISCONSIN-MADISON · PI WANG, DAIFENG · 2022 to 2022
$1.2M
Creation of a Schwann Cell Gene Regulatory NetworkR21NS127432 · NINDS · UNIVERSITY OF WISCONSIN-MADISON · PI SVAREN, JOHN P, WANG, DAIFENG · 2022 to 2023
$401k
Prediction and Validation of Oligodendrocyte Gene Regulatory Network from Multi-OmicsR21NS128761 · NINDS · UNIVERSITY OF WISCONSIN-MADISON · PI SVAREN, JOHN P, WANG, DAIFENG · 2022 to 2022
$401k
NICHD NIH HHS P50 HD105353NIMH NIH HHS RF1 MH128695NIMH NIH HHS U01 MH116492NIMH NIH HHS U24 MH136793NINDS NIH HHS R21 NS127432NINDS NIH HHS R21 NS128761
6 · The paper itself

Abstract

Neuropsychiatric disorders lack effective treatments due to a limited understanding of the underlying cellular and molecular mechanisms. To address this, we integrated population-scale single-cell genomics data and analyzed 23 cell-type-level gene regulatory networks across schizophrenia, bipolar disorder, and autism. Our analysis revealed potential druggable transcription factors co-regulating known risk genes that converge into cell-type-specific co-regulated modules. We applied graph neural networks on those modules to prioritize novel risk genes and leveraged them in a network-based drug repurposing framework to identify 220 drug molecules with the potential for targeting specific cell types. We found evidence for 37 of these drugs in reversing disorder-associated transcriptional phenotypes. Additionally, we discovered 335 drug-cell quantitative trait loci (eQTLs), revealing genetic variation's influence on drug target expression at the cell-type level. Our results provide a single-cell network medicine resource that provides potential mechanistic insights for advancing treatment options for neuropsychiatric disorders.

Indexed as

Drug RepositioningGene Regulatory NetworksGenomicsMental DisordersSingle-Cell AnalysisBipolar DisorderHumansQuantitative Trait LociSchizophreniacell-type-disorder genesdrug repurposingpsychiatric disorderssingle-cell network medicine

Identifiers

PMID40972584
PMCPMC12648111

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.