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A universal deep-learning model for zinc finger design enables transcription factor reprogramming.

ABSTRACT: Cys₂His₂ zinc finger (ZF) domains engineered to bind specific target sequences in the genome provide an effective strategy for programmable regulation of gene expression, with many potential therapeutic applications. However, the structurally intricate engagement of ZF domains with DNA has made their design challenging. Here we describe the screening of 49 billion protein-DNA interactions and the development of a deep-learning model, ZFDesign, that solves ZF design for any genomic target. ZFDesign is a modern machine learning method that models global and target-specific differences induced by a range of library environments and specifically takes into account compatibility of neighboring fingers using a novel hierarchical transformer architecture. We demonstrate the versatility of designed ZFs as nucleases as well as activators and repressors by seamless reprogramming of human transcription factors. These factors could be used to upregulate an allele of haploinsufficiency, downregulate a gain-of-function mutation or test the consequence of regulation of a single gene as opposed to the many genes that a transcription factor would normally influence.

SUBMITTER: Ichikawa DM

PROVIDER: S-EPMC10421740 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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A universal deep-learning model for zinc finger design enables transcription factor reprogramming.

Ichikawa David M DM Abdin Osama O Alerasool Nader N Kogenaru Manjunatha M Mueller April L AL Wen Han H Giganti David O DO Goldberg Gregory W GW Adams Samantha S Spencer Jeffrey M JM Razavi Rozita R Nim Satra S Zheng Hong H Gionco Courtney C Clark Finnegan T FT Strokach Alexey A Hughes Timothy R TR Lionnet Timothee T Taipale Mikko M Kim Philip M PM Noyes Marcus B MB

Nature biotechnology 20230126 8

Cys<sub>2</sub>His<sub>2</sub> zinc finger (ZF) domains engineered to bind specific target sequences in the genome provide an effective strategy for programmable regulation of gene expression, with many potential therapeutic applications. However, the structurally intricate engagement of ZF domains with DNA has made their design challenging. Here we describe the screening of 49 billion protein-DNA interactions and the development of a deep-learning model, ZFDesign, that solves ZF design for any ...[more]

PMID: 36702896

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Project description:Background & aimsNon-alcoholic fatty liver disease (NAFLD) contributes to the global epidemic of metabolic syndrome and is considered a prelude to end-stage liver diseases such as cirrhosis and hepatocellular carcinoma. During NAFLD pathogenesis, hepatic parenchymal cells (hepatocytes) undergo both morphological and functional changes owing to a rewired transcriptome. The underlying mechanism is not entirely clear. In the present study, we investigated the involvement of early growth response 1 (Egr1) in NAFLD.MethodsQuantitative PCR, Western blotting, and histochemical staining were used to assess gene expression levels. Chromatin immunoprecipitation was used to evaluate protein binding to DNA. NAFLD was evaluated in leptin receptor-deficient (db/db) mice.ResultsWe report here that Egr1 was upregulated by pro-NAFLD stimuli in vitro and in vivo. Further analysis revealed that serum response factor (SRF) was recruited to the Egr1 promoter and mediated Egr1 transactivation. Importantly, Egr1 depletion markedly mitigated NAFLD in db/db mice. RNA sequencing revealed that Egr1 knockdown in hepatocytes, on the one hand, boosted fatty acid oxidation (FAO) and, on the other hand, suppressed the synthesis of chemoattractants. Mechanistically, Egr1 interacted with peroxisome proliferator-activated receptor α (PPARα) to repress PPARα-dependent transcription of FAO genes by recruiting its co-repressor NGFI-A binding protein 1 (Nab1), which potentially led to promoter deacetylation of FAO genes.ConclusionsOur data identify Egr1 as a novel modulator of NAFLD and a potential target for NAFLD intervention.Impact and implicationsNon-alcoholic fatty liver disease (NAFLD) precedes cirrhosis and hepatocellular carcinoma. In this paper, we describe a novel mechanism whereby early growth response 1 (Egr1), a transcription factor, contributes to NAFLD pathogenesis by regulating fatty acid oxidation. Our data provide novel insights and translational potential for NAFLD intervention.

Dataset Information

A universal deep-learning model for zinc finger design enables transcription factor reprogramming.

Publications

A universal deep-learning model for zinc finger design enables transcription factor reprogramming.

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