Network-based screen in iPSC-derived cells reveals therapeutic candidate for heart valve disease
Open accessAbstract
Machine learning for medicine: Small-molecule screens aimed at identifying therapeutic candidates traditionally search for molecules that affect one to several outputs at most, limiting discovery of true disease-modifying drugs. Theodoris et al. developed a machine-learning approach to identify small molecules that broadly correct gene networks dysregulated in a human induced pluripotent stem cell disease model of a common form of heart disease involving the aortic valve. Gene network correction by the most efficacious therapeutic candidate generalized to primary aortic valve cells derived from more than 20 patients with sporadic aortic valve disease and prevented aortic valve disease in vivo in a mouse model. Science , this issue p. eabd0724
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In NOTCH1-deficient CAVD, a small-molecule screen plus a machine-learning classifier identified target molecules in patient-derived iPS cells.
“a screen of small molecules that correct dysregulated gene networks in NOTCH1-deficient calcific aortic valve disease (CAVD) was combined with a machine learning algorithm that was trained to classify gene expression levels as wild type or diseased, allowing the detection of target molecules in patient-derived iPS cells108.”
From Human disease models in drug development (2023) · cites this paper as [108]
Targeted RNA sequencing of nearly 1,600 molecules found an oestrogen-related receptor-α inverse agonist that corrected CAVD gene dysregulation in patient valve endothelial cells and modified mice.
“Almost 1,600 molecules were screened using targeted RNA sequencing, resulting in the identification of an inverse agonist of oestrogen-related receptor-α as the lead compound, which proved effective in correcting dysregulated CAVD-relevant genes in primary aortic valve endothelial cells from individuals with CAVD and in genetically modified mice.”
From Human disease models in drug development (2023) · cites this paper as [108]
Mapping the gene regulatory networks that drive disease progression enables screening for molecules that correct the network by normalizing core regulatory elements, rather than targeting peripheral downstream effectors that may not be disease modifying.
“Mapping the gene regulatory networks that drive disease progression enables screening for molecules that correct the network by normalizing core regulatory elements, rather than targeting peripheral downstream effectors that may not be disease modifying4,5.”
From Transfer learning enables predictions in network biology (2023) · cites this paper as [5]
The authors previously mapped the NOTCH1-dependent gene network governing cardiac valve disease and identified central regulatory nodes whose correction had broad restorative impact on the network at large.
“We previously mapped the NOTCH1 (N1)-dependent gene network governing cardiac valve disease and identified central regulatory nodes whose correction had broad restorative impact on the network at large4,5.”
From Transfer learning enables predictions in network biology (2023) · cites this paper as [5]
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- Authors
- Theodoris, C. V., Zhou, P., Liu, L., Zhang, Y., Nishino, T., Huang, Y., Kostina, A., Ranade, S. S., Gifford, C. A., Uspenskiy, V., Malashicheva, A., Ding, S., & Srivastava, D.
- Affiliations
- Gladstone Institutes (US)
- Russian Academy of Sciences (RU)
- Federal Almazov North-West Medical Research Centre (RU)
- Corresponding author
- Srivastava, Deepak
- ORCID
- Venue
- Science
- Published
- 2021-02-12
- Volume
- 371
- Issue
- 6530
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC7880903
- OpenAlex
- W3112652901
- Cited by
- 105
- References
- 35
- Citations by year
- 2026: 8 · 2025: 20 · 2024: 23 · 2023: 15 · 2022: 18 · 2021: 19 · 2019: 1
- FWCI
- 6.45
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health (U01 HL098179, U01-HL100406, T32 GM007618, T32 HL007544, T32 HD007470, C06-RR018928)
- Damon Runyon Cancer Research Foundation (DRG-2206-14)
- California Institute for Regenerative Medicine (DISC2-09098)
- Russian Science Foundation (18-14-00152)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Pluripotent Stem Cells Research
- Keywords
- valvular heart disease, aortic valve disease, gene regulatory networks, iPSC-derived disease models, small molecule screening, network-based drug discovery, machine learning, disease-modifying therapy, mouse model
- MeSH terms
- Machine Learning, RNA-Seq, Aortic Valve Disease, Algorithms, Animals, Aortic Valve, Aortic Valve Stenosis, Calcinosis, Disease Models, Animal, Drug Evaluation, Preclinical, Gene Expression Regulation, Humans, Mice, Inbred C57BL, Nitriles, Thiazoles, Mice, Receptor, Notch1, Gene Regulatory Networks, Small Molecule Libraries, Drug Discovery, Induced Pluripotent Stem Cells, Haploinsufficiency
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