{"entity": "researcher", "timestamp": "2026-08-20T20:47:09.153Z", "family": "Carpenter", "given": "Anne E", "initials": "AE", "orcid": "0000-0003-1555-8261", "affiliations": ["Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02141, United States."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/9bda2a39a0ac4fd9a7d95298608afb74.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/9bda2a39a0ac4fd9a7d95298608afb74"}}, "publications": [{"entity": "publication", "iuid": "0824f2f7c1d844aca1638ef6b6910254", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/0824f2f7c1d844aca1638ef6b6910254.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/0824f2f7c1d844aca1638ef6b6910254"}}, "title": "Author Correction: Cell Painting: a decade of discovery and innovation in cellular imaging.", "authors": [{"family": "Seal", "given": "Srijit", "initials": "S", "orcid": "0000-0003-2790-8679", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cf360510777949acaa72a940f13e17bf.json"}}, {"family": "Trapotsi", "given": "Maria-Anna", "initials": "MA", "orcid": "0000-0002-9177-4241", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5f90a4eeefb14159b4397b84a116e152.json"}}, {"family": "Spjuth", "given": "Ola", "initials": "O", "orcid": "0000-0002-8083-2864", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2c192389f99d4801b91f3350e07dfb9e.json"}}, {"family": "Singh", "given": "Shantanu", "initials": "S", "orcid": "0000-0003-3150-3025", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7fab52cf5bb64d62878524fc2da36dd1.json"}}, {"family": "Carreras-Puigvert", "given": "Jordi", "initials": "J"}, {"family": "Greene", "given": "Nigel", "initials": "N"}, {"family": "Bender", "given": "Andreas", "initials": "A", "orcid": "0000-0002-6683-7546", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/fea25f8013a14bb587c85df858f74d55.json"}}, {"family": "Carpenter", "given": "Anne E", "initials": "AE", "orcid": "0000-0003-1555-8261", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9bda2a39a0ac4fd9a7d95298608afb74.json"}}], "type": "published erratum", "published": "2025-02-00", "journal": {"title": "Nat. Methods", "issn": "1548-7105", "volume": "22", "issue": "2", "pages": "447", "issn-l": "1548-7091"}, "abstract": null, "doi": "10.1038/s41592-024-02578-y", "pmid": "39668211", "labels": [], "xrefs": [{"db": "mid", "key": "NIHMS2077445"}, {"db": "pmc", "key": "PMC12221059"}, {"db": "pii", "key": "10.1038/s41592-024-02578-y"}], "notes": [], "created": "2026-08-20T09:03:21.194Z", "modified": "2026-08-20T09:03:21.283Z"}, {"entity": "publication", "iuid": "c24b1b477e1647c890209b9b74a6ebce", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/c24b1b477e1647c890209b9b74a6ebce.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/c24b1b477e1647c890209b9b74a6ebce"}}, "title": "Cell Painting: a decade of discovery and innovation in cellular imaging.", "authors": [{"family": "Seal", "given": "Srijit", "initials": "S", "orcid": "0000-0003-2790-8679", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cf360510777949acaa72a940f13e17bf.json"}}, {"family": "Trapotsi", "given": "Maria-Anna", "initials": "MA", "orcid": "0000-0002-9177-4241", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5f90a4eeefb14159b4397b84a116e152.json"}}, {"family": "Spjuth", "given": "Ola", "initials": "O", "orcid": "0000-0002-8083-2864", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2c192389f99d4801b91f3350e07dfb9e.json"}}, {"family": "Singh", "given": "Shantanu", "initials": "S", "orcid": "0000-0003-3150-3025", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7fab52cf5bb64d62878524fc2da36dd1.json"}}, {"family": "Carreras-Puigvert", "given": "Jordi", "initials": "J"}, {"family": "Greene", "given": "Nigel", "initials": "N"}, {"family": "Bender", "given": "Andreas", "initials": "A", "orcid": "0000-0002-6683-7546", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/fea25f8013a14bb587c85df858f74d55.json"}}, {"family": "Carpenter", "given": "Anne E", "initials": "AE", "orcid": "0000-0003-1555-8261", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9bda2a39a0ac4fd9a7d95298608afb74.json"}}], "type": "journal article", "published": "2025-02-00", "journal": {"title": "Nat. Methods", "issn": "1548-7105", "volume": "22", "issue": "2", "pages": "254-268", "issn-l": "1548-7091"}, "abstract": "Modern quantitative image analysis techniques have enabled high-throughput, high-content imaging experiments. Image-based profiling leverages the rich information in images to identify similarities or differences among biological samples, rather than measuring a few features, as in high-content screening. Here, we review a decade of advancements and applications of Cell Painting, a microscopy-based cell-labeling assay aiming to capture a cell's state, introduced in 2013 to optimize and standardize image-based profiling. Cell Painting's ability to capture cellular responses to various perturbations has expanded owing to improvements in the protocol, adaptations for different perturbations, and enhanced methodologies for feature extraction, quality control, and batch-effect correction. Cell Painting is a versatile tool that has been used in various applications, alone or with other -omics data, to decipher the mechanism of action of a compound, its toxicity profile, and other biological effects. Future advances will likely involve computational and experimental techniques, new publicly available datasets, and integration with other high-content data types.", "doi": "10.1038/s41592-024-02528-8", "pmid": "39639168", "labels": [], "xrefs": [{"db": "mid", "key": "NIHMS2049550"}, {"db": "pmc", "key": "PMC11810604"}, {"db": "pii", "key": "10.1038/s41592-024-02528-8"}], "notes": [], "created": "2026-08-20T09:03:19.249Z", "modified": "2026-08-20T09:03:19.420Z"}, {"entity": "publication", "iuid": "2bcc555f37824dc2861f7e97514cb3a8", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/2bcc555f37824dc2861f7e97514cb3a8.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/2bcc555f37824dc2861f7e97514cb3a8"}}, "title": "Improved Detection of Drug-Induced Liver Injury by Integrating Predicted In Vivo and In Vitro Data.", "authors": [{"family": "Seal", "given": "Srijit", "initials": "S", "orcid": "0000-0003-2790-8679", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cf360510777949acaa72a940f13e17bf.json"}}, {"family": "Williams", "given": "Dominic", "initials": "D"}, {"family": "Hosseini-Gerami", "given": "Layla", "initials": "L"}, {"family": "Mahale", "given": "Manas", "initials": "M", "orcid": "0009-0007-3867-996X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/eee79bb6b5b9420582872573cb9bd5ac.json"}}, {"family": "Carpenter", "given": "Anne E", "initials": "AE", "orcid": "0000-0003-1555-8261", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9bda2a39a0ac4fd9a7d95298608afb74.json"}}, {"family": "Spjuth", "given": "Ola", "initials": "O"}, {"family": "Bender", "given": "Andreas", "initials": "A", "orcid": "0000-0002-6683-7546", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/fea25f8013a14bb587c85df858f74d55.json"}}], "type": "journal article", "published": "2024-08-19", "journal": {"title": "Chem. Res. Toxicol.", "issn": "1520-5010", "volume": "37", "issue": "8", "pages": "1290-1305", "issn-l": "0893-228X"}, "abstract": "Drug-induced liver injury (DILI) has been a significant challenge in drug discovery, often leading to clinical trial failures and necessitating drug withdrawals. Over the last decade, the existing suite of in vitro proxy-DILI assays has generally improved at identifying compounds with hepatotoxicity. However, there is considerable interest in enhancing the in silico prediction of DILI because it allows for evaluating large sets of compounds more quickly and cost-effectively, particularly in the early stages of projects. In this study, we aim to study ML models for DILI prediction that first predict nine proxy-DILI labels and then use them as features in addition to chemical structural features to predict DILI. The features include in vitro (e.g., mitochondrial toxicity, bile salt export pump inhibition) data, in vivo (e.g., preclinical rat hepatotoxicity studies) data, pharmacokinetic parameters of maximum concentration, structural fingerprints, and physicochemical parameters. We trained DILI-prediction models on 888 compounds from the DILI data set (composed of DILIst and DILIrank) and tested them on a held-out external test set of 223 compounds from the DILI data set. The best model, DILIPredictor, attained an AUC-PR of 0.79. This model enabled the detection of the top 25 toxic compounds (2.68 LR+, positive likelihood ratio) compared to models using only structural features (1.65 LR+ score). Using feature interpretation from DILIPredictor, we identified the chemical substructures causing DILI and differentiated cases of DILI caused by compounds in animals but not in humans. For example, DILIPredictor correctly recognized 2-butoxyethanol as nontoxic in humans despite its hepatotoxicity in mice models. Overall, the DILIPredictor model improves the detection of compounds causing DILI with an improved differentiation between animal and human sensitivity and the potential for mechanism evaluation. DILIPredictor required only chemical structures as input for prediction and is publicly available at https://broad.io/DILIPredictor for use via web interface and with all code available for download.", "doi": "10.1021/acs.chemrestox.4c00015", "pmid": "38981058", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC11337212"}], "notes": [], "created": "2026-08-20T08:09:13.843Z", "modified": "2026-08-20T08:09:14.053Z"}, {"entity": "publication", "iuid": "934f69a0412044b4b32374cc6cd1ae49", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/934f69a0412044b4b32374cc6cd1ae49.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/934f69a0412044b4b32374cc6cd1ae49"}}, "title": "A Decade in a Systematic Review: The Evolution and Impact of Cell Painting.", "authors": [{"family": "Seal", "given": "Srijit", "initials": "S", "orcid": "0000-0003-2790-8679", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cf360510777949acaa72a940f13e17bf.json"}}, {"family": "Trapotsi", "given": "Maria-Anna", "initials": "MA", "orcid": "0000-0002-9177-4241", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5f90a4eeefb14159b4397b84a116e152.json"}}, {"family": "Spjuth", "given": "Ola", "initials": "O", "orcid": "0000-0002-8083-2864", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2c192389f99d4801b91f3350e07dfb9e.json"}}, {"family": "Singh", "given": "Shantanu", "initials": "S", "orcid": "0000-0003-3150-3025", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7fab52cf5bb64d62878524fc2da36dd1.json"}}, {"family": "Carreras-Puigvert", "given": "Jordi", "initials": "J", "orcid": "0000-0002-7671-3707", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a9c300823ae241eea419a01e4397fcc2.json"}}, {"family": "Greene", "given": "Nigel", "initials": "N", "orcid": "0000-0003-0433-4596", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7e97367503f749bc934ddb3f10a84891.json"}}, {"family": "Bender", "given": "Andreas", "initials": "A", "orcid": "0000-0002-6683-7546", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/fea25f8013a14bb587c85df858f74d55.json"}}, {"family": "Carpenter", "given": "Anne E", "initials": "AE", "orcid": "0000-0003-1555-8261", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9bda2a39a0ac4fd9a7d95298608afb74.json"}}], "type": "preprint", "published": "2024-05-07", "journal": {"title": "bioRxiv", "issn": "2692-8205", "issn-l": null}, "abstract": "High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction. Moreover, machine learning methods recently surpassed classical approaches in their ability to extract biologically useful information from Cell Painting images. Cell Painting data have been used alone or in combination with other - omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. Overall, key methodological advances have expanded Cell Painting's ability to capture cellular responses to various perturbations. Future advances will likely lie in advancing computational and experimental techniques, developing new publicly available datasets, and integrating them with other high-content data types.", "doi": "10.1101/2024.05.04.592531", "pmid": "38766203", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC11100607"}, {"db": "pii", "key": "2024.05.04.592531"}], "notes": [], "created": "2026-08-20T10:54:50.205Z", "modified": "2026-08-20T10:54:50.323Z"}, {"entity": "publication", "iuid": "bc1a8965388d4dcab37b9050baee86f9", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/bc1a8965388d4dcab37b9050baee86f9.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/bc1a8965388d4dcab37b9050baee86f9"}}, "title": "From pixels to phenotypes: Integrating image-based profiling with cell health data as BioMorph features improves interpretability.", "authors": [{"family": "Seal", "given": "Srijit", "initials": "S", "orcid": "0000-0003-2790-8679", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cf360510777949acaa72a940f13e17bf.json"}}, {"family": "Carreras-Puigvert", "given": "Jordi", "initials": "J", "orcid": "0000-0002-7671-3707", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a9c300823ae241eea419a01e4397fcc2.json"}}, {"family": "Singh", "given": "Shantanu", "initials": "S", "orcid": "0000-0003-3150-3025", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7fab52cf5bb64d62878524fc2da36dd1.json"}}, {"family": "Carpenter", "given": "Anne E", "initials": "AE", "orcid": "0000-0003-1555-8261", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9bda2a39a0ac4fd9a7d95298608afb74.json"}}, {"family": "Spjuth", "given": "Ola", "initials": "O", "orcid": "0000-0002-8083-2864", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2c192389f99d4801b91f3350e07dfb9e.json"}}, {"family": "Bender", "given": "Andreas", "initials": "A", "orcid": "0000-0002-6683-7546", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/fea25f8013a14bb587c85df858f74d55.json"}}], "type": "journal article", "published": "2024-03-01", "journal": {"title": "Mol. Biol. Cell", "issn": "1939-4586", "volume": "35", "issue": "3", "pages": "mr2", "issn-l": "1059-1524"}, "abstract": "Cell Painting assays generate morphological profiles that are versatile descriptors of biological systems and have been used to predict in vitro and in vivo drug effects. However, Cell Painting features extracted from classical software such as CellProfiler are based on statistical calculations and often not readily biologically interpretable. In this study, we propose a new feature space, which we call BioMorph, that maps these Cell Painting features with readouts from comprehensive Cell Health assays. We validated that the resulting BioMorph space effectively connected compounds not only with the morphological features associated with their bioactivity but with deeper insights into phenotypic characteristics and cellular processes associated with the given bioactivity. The BioMorph space revealed the mechanism of action for individual compounds, including dual-acting compounds such as emetine, an inhibitor of both protein synthesis and DNA replication. Overall, BioMorph space offers a biologically relevant way to interpret the cell morphological features derived using software such as CellProfiler and to generate hypotheses for experimental validation.", "doi": "10.1091/mbc.E23-08-0298", "pmid": "38170589", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC10916876"}], "notes": [], "created": "2026-08-20T09:38:45.160Z", "modified": "2026-08-20T09:38:45.255Z"}, {"entity": "publication", "iuid": "eeaa6e472c3a4ff88c013a1d34b4e740", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/eeaa6e472c3a4ff88c013a1d34b4e740.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/eeaa6e472c3a4ff88c013a1d34b4e740"}}, "title": "Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA Drug-Induced Cardiotoxicity Rank.", "authors": [{"family": "Seal", "given": "Srijit", "initials": "S", "orcid": "0000-0003-2790-8679", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cf360510777949acaa72a940f13e17bf.json"}}, {"family": "Spjuth", "given": "Ola", "initials": "O"}, {"family": "Hosseini-Gerami", "given": "Layla", "initials": "L"}, {"family": "Garc\u00eda-Orteg\u00f3n", "given": "Miguel", "initials": "M", "orcid": "0000-0003-4372-4706", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/edbfcd5c5cd64e06adffa372ab46519f.json"}}, {"family": "Singh", "given": "Shantanu", "initials": "S"}, {"family": "Bender", "given": "Andreas", "initials": "A", "orcid": "0000-0002-6683-7546", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/fea25f8013a14bb587c85df858f74d55.json"}}, {"family": "Carpenter", "given": "Anne E", "initials": "AE", "orcid": "0000-0003-1555-8261", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9bda2a39a0ac4fd9a7d95298608afb74.json"}}], "type": "journal article", "published": "2024-02-26", "journal": {"title": "J Chem Inf Model", "issn": "1549-960X", "volume": "64", "issue": "4", "pages": "1172-1186", "issn-l": "1549-9596"}, "abstract": "Drug-induced cardiotoxicity (DICT) is a major concern in drug development, accounting for 10-14% of postmarket withdrawals. In this study, we explored the capabilities of chemical and biological data to predict cardiotoxicity, using the recently released DICTrank data set from the United States FDA. We found that such data, including protein targets, especially those related to ion channels (e.g., hERG), physicochemical properties (e.g., electrotopological state), and peak concentration in plasma offer strong predictive ability for DICT. Compounds annotated with mechanisms of action such as cyclooxygenase inhibition could distinguish between most-concern and no-concern DICT. Cell Painting features for ER stress discerned most-concern cardiotoxic from nontoxic compounds. Models based on physicochemical properties provided substantial predictive accuracy (AUCPR = 0.93). With the availability of omics data in the future, using biological data promises enhanced predictability and deeper mechanistic insights, paving the way for safer drug development. All models from this study are available at https://broad.io/DICTrank_Predictor.", "doi": "10.1021/acs.jcim.3c01834", "pmid": "38300851", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC10900289"}, {"db": "figshare", "key": "10.6084/m9.figshare.24312274"}], "notes": [], "created": "2026-08-20T08:10:28.376Z", "modified": "2026-08-20T08:10:28.514Z"}, {"entity": "publication", "iuid": "d231ebae020f4ea2a3a8b143ad8583c1", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/d231ebae020f4ea2a3a8b143ad8583c1.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/d231ebae020f4ea2a3a8b143ad8583c1"}}, "title": "From Pixels to Phenotypes: Integrating Image-Based Profiling with Cell Health Data Improves Interpretability", "authors": [{"family": "Seal", "given": "Srijit", "initials": "S", "orcid": "0000-0003-2790-8679", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cf360510777949acaa72a940f13e17bf.json"}}, {"family": "Carreras-Puigvert", "given": "Jordi", "initials": "J", "orcid": "0000-0002-7671-3707", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a9c300823ae241eea419a01e4397fcc2.json"}}, {"family": "Carpenter", "given": "Anne E", "initials": "AE", "orcid": "0000-0003-1555-8261", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9bda2a39a0ac4fd9a7d95298608afb74.json"}}, {"family": "Spjuth", "given": "Ola", "initials": "O", "orcid": "0000-0002-8083-2864", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2c192389f99d4801b91f3350e07dfb9e.json"}}, {"family": "Bender", "given": "Andreas", "initials": "A", "orcid": "0000-0002-6683-7546", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/fea25f8013a14bb587c85df858f74d55.json"}}], "type": "posted-content", "published": "2023-07-16", "journal": {"issn-l": null}, "abstract": null, "doi": "10.1101/2023.07.14.549031", "pmid": null, "labels": [], "xrefs": [], "notes": [], "created": "2026-08-20T10:47:10.362Z", "modified": "2026-08-20T10:47:10.444Z"}, {"entity": "publication", "iuid": "fc0e165f6b7d4b9799aade30ff640b29", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/fc0e165f6b7d4b9799aade30ff640b29.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/fc0e165f6b7d4b9799aade30ff640b29"}}, "title": "Genes in human obesity loci are causal obesity genes in C. elegans.", "authors": [{"family": "Ke", "given": "Wenfan", "initials": "W", "orcid": "0000-0002-7047-5445", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/0da39fa46a2e476d9df0cab381100a4d.json"}}, {"family": "Reed", "given": "Jordan N", "initials": "JN", "orcid": "0000-0002-6190-9037", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1e1ade34f91f43a2b9ac4b991dcefa8c.json"}}, {"family": "Yang", "given": "Chenyu", "initials": "C", "orcid": "0000-0001-6319-965X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/772ae0c299284421a4cce172b594005b.json"}}, {"family": "Higgason", "given": "Noel", "initials": "N"}, {"family": "Rayyan", "given": "Leila", "initials": "L", "orcid": "0000-0002-2560-2900", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cdb542e6098c45998df30f124304a7e3.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/833afe3444d84c24be12ea1468563bea.json"}}, {"family": "Carpenter", "given": "Anne E", "initials": "AE", "orcid": "0000-0003-1555-8261", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9bda2a39a0ac4fd9a7d95298608afb74.json"}}, {"family": "Civelek", "given": "Mete", "initials": "M", "orcid": "0000-0002-8141-0284", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e58560d32be6472291f574de934c94f3.json"}}, {"family": "O'Rourke", "given": "Eyleen J", "initials": "EJ", "orcid": "0000-0003-0503-4181", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ee2c1b0186c74385a2fd2374c004fc10.json"}}], "type": "journal article", "published": "2021-09-00", "journal": {"title": "PLoS Genet", "issn": "1553-7404", "volume": "17", "issue": "9", "pages": "e1009736", "issn-l": "1553-7390"}, "abstract": "Obesity and its associated metabolic syndrome are a leading cause of morbidity and mortality. Given the disease's heavy burden on patients and the healthcare system, there has been increased interest in identifying pharmacological targets for the treatment and prevention of obesity. Towards this end, genome-wide association studies (GWAS) have identified hundreds of human genetic variants associated with obesity. The next challenge is to experimentally define which of these variants are causally linked to obesity, and could therefore become targets for the treatment or prevention of obesity. Here we employ high-throughput in vivo RNAi screening to test for causality 293 C. elegans orthologs of human obesity-candidate genes reported in GWAS. We RNAi screened these 293 genes in C. elegans subject to two different feeding regimens: (1) regular diet, and (2) high-fructose diet, which we developed and present here as an invertebrate model of diet-induced obesity (DIO). We report 14 genes that promote obesity and 3 genes that prevent DIO when silenced in C. elegans. Further, we show that knock-down of the 3 DIO genes not only prevents excessive fat accumulation in primary and ectopic fat depots but also improves the health and extends the lifespan of C. elegans overconsuming fructose. Importantly, the direction of the association between expression variants in these loci and obesity in mice and humans matches the phenotypic outcome of the loss-of-function of the C. elegans ortholog genes, supporting the notion that some of these genes would be causally linked to obesity across phylogeny. Therefore, in addition to defining causality for several genes so far merely correlated with obesity, this study demonstrates the value of model systems compatible with in vivo high-throughput genetic screening to causally link GWAS gene candidates to human diseases.", "doi": "10.1371/journal.pgen.1009736", "pmid": "34492009", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC8462697"}, {"db": "pii", "key": "PGENETICS-D-21-00734"}], "notes": [], "created": "2026-08-20T12:43:46.066Z", "modified": "2026-08-20T12:43:46.338Z"}]}