The sirtuin family in health and disease

Signal Transduction and Targeted Therapy, Journal Year: 2022, Volume and Issue: 7(1)

Published: Dec. 29, 2022

Sirtuins (SIRTs) are nicotine adenine dinucleotide(+)-dependent histone deacetylases regulating critical signaling pathways in prokaryotes and eukaryotes, involved numerous biological processes. Currently, seven mammalian homologs of yeast Sir2 named SIRT1 to SIRT7 have been identified. Increasing evidence has suggested the vital roles members SIRT family health disease conditions. Notably, this protein plays a variety important cellular biology such as inflammation, metabolism, oxidative stress, apoptosis, etc., thus, it is considered potential therapeutic target for different kinds pathologies including cancer, cardiovascular disease, respiratory other Moreover, identification modulators exploring functions these prompted increased efforts discover new small molecules, which can modify activity. Furthermore, several randomized controlled trials indicated that interventions might affect expression human samples, supplementation diverse impact on physiological function participants. In review, we introduce history structure family, discuss molecular mechanisms elaborate regulatory SIRTs summarize inhibitors activators, review related clinical studies.

Language: Английский

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A foundation model for clinician-centered drug repurposing

Nature Medicine, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 25, 2024

Drug repurposing-identifying new therapeutic uses for approved drugs-is often a serendipitous and opportunistic endeavour to expand the use of drugs diseases. The clinical utility drug-repurposing artificial intelligence (AI) models remains limited because these focus narrowly on diseases which some already exist. Here we introduce TxGNN, graph foundation model zero-shot drug repurposing, identifying candidates even with treatment options or no existing drugs. Trained medical knowledge graph, TxGNN neural network metric learning module rank as potential indications contraindications 17,080 When benchmarked against 8 methods, improves prediction accuracy by 49.2% 35.1% under stringent evaluation. To facilitate interpretation, TxGNN's Explainer offers transparent insights into multi-hop paths that form predictive rationales. Human evaluation showed predictions explanations perform encouragingly multiple axes performance beyond accuracy. Many align well off-label prescriptions clinicians previously made in large healthcare system. are accurate, consistent use, can be investigated human experts through interpretable

Language: Английский

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Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer’s disease?

Frontiers in Aging Neuroscience, Journal Year: 2023, Volume and Issue: 15

Published: April 18, 2023

Alzheimer’s disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there no cure, detecting AD early important for the development of therapeutic plan care may preserve function prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission (PET), has served critical tool in establishing diagnostic indicators during preclinical stage. However, neuroimaging technology quickly advances, challenge analyzing interpreting vast amounts brain data. Given these limitations, great interest using artificial Intelligence (AI) to assist this process. AI introduces limitless possibilities future diagnosis AD, yet still resistance from healthcare community incorporate clinical setting. The goal review answer question whether should be used conjunction with AD. To question, possible benefits disadvantages are discussed. main advantages its potential improve accuracy, efficiency radiographic data, reduce physician burnout, advance precision medicine. include generalization data shortage, lack vivo gold standard, skepticism medical community, bias, concerns over patient information, privacy, safety. challenges present fundamental must addressed when time comes, it would unethical not use if can health outcome.

Language: Английский

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Testing the predictive power of reverse screening to infer drug targets, with the help of machine learning

Communications Chemistry, Journal Year: 2024, Volume and Issue: 7(1)

Published: May 9, 2024

Abstract Estimating protein targets of compounds based on the similarity principle —similar molecules are likely to show comparable bioactivity—is a long-standing strategy in drug research. Having previously quantified this principle, we present here large-scale evaluation its predictive power for inferring macromolecular by reverse screening an unprecedented vast external test set more than 300,000 active small against another bioactivity 500,000 compounds. We that machine-learning can predict correct targets, with highest probability among 2069 proteins, 51% molecules. The strong enrichment thus obtained demonstrates usefulness supporting phenotypic screens, polypharmacology, or repurposing. Moreover, impact knowledge available proteins terms number and diversity actives. Finally, advise developers such approaches follow application-oriented benchmarking use large, high-quality, non-overlapping datasets as provided here.

Language: Английский

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Logic-based mechanistic machine learning on high-content images reveals how drugs differentially regulate cardiac fibroblasts

Proceedings of the National Academy of Sciences, Journal Year: 2024, Volume and Issue: 121(5)

Published: Jan. 24, 2024

Fibroblasts are essential regulators of extracellular matrix deposition following cardiac injury. These cells exhibit highly plastic responses in phenotype during fibrosis response to environmental stimuli. Here, we test whether and how candidate anti-fibrotic drugs differentially regulate measures fibroblast phenotype, which may help identify treatments for fibrosis. We conducted a high-content microscopy screen human fibroblasts treated with 13 clinically relevant the context TGFβ and/or IL-1β, measuring across 137 single-cell features. used phenotypic data from our imaging train logic-based mechanistic machine learning model (LogiMML) signaling. The predicted pirfenidone Src inhibitor WH-4-023 reduce actin filament assembly actin–myosin stress fiber formation, respectively. Validating LogiMML prediction that PI3K partially mediates effects inhibition, found inhibition reduces formation procollagen I production fibroblasts. In this study, establish modeling approach combining strengths network models regularized regression models. apply predict mechanisms mediate differential on fibroblasts, revealing acting via as potential therapy

Language: Английский

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Lipid nanoparticle (LNP) mediated mRNA delivery in cardiovascular diseases: Advances in genome editing and CAR T cell therapy

Journal of Controlled Release, Journal Year: 2024, Volume and Issue: 372, P. 113 - 140

Published: June 15, 2024

Language: Английский

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Gradient coating of extracellular matrix derived from endothelial cells on aligned PCL nanofibers for rapid endothelialization

Frontiers in Bioengineering and Biotechnology, Journal Year: 2025, Volume and Issue: 12

Published: Jan. 8, 2025

Artificial vascular scaffolds can mimic the structure of natural blood vessels and replace damaged by implanting them at injury site to perform corresponding functions. Electrospinning technology perfectly combine biological signals topographical cues synergistically induce directed cell migration growth. In this study, poly (caprolactone) (PCL) nanofibers, PCL nanofibers uniformly coated with extracellular matrix derived from endothelial cells (ECd), bi-directional linear gradient ECd-coated were prepared electrospinning electrospray techniques evaluate their effects on proliferation Human umbilical vein (HUVECs) rapid endothelialization. The results showed that HUVECs could successfully adhere surface these three maintain high viability. indicated bidirectional coating accelerate endothelialization process. On basis, types bionic scaffolds, including scaffold, uniform further prepared. topology signal scaffold promoted more effectively. This provides a new way clinically promote structural functional recovery develop personalized or universal artificial which is great importance in cardiovascular regenerative medicine.

Language: Английский

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Trends in Drug Repurposing: Advancing Cardiovascular Disease Management in Geriatric Populations

Current Research in Translational Medicine, Journal Year: 2025, Volume and Issue: 73(2), P. 103496 - 103496

Published: Jan. 18, 2025

Language: Английский

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Logic-based machine learning predicts how escitalopram attenuates cardiomyocyte hypertrophy

Proceedings of the National Academy of Sciences, Journal Year: 2025, Volume and Issue: 122(10)

Published: March 4, 2025

Cardiomyocyte hypertrophy is a key clinical predictor of heart failure. High-throughput and AI-driven screens have the potential to identify drugs downstream pathways that modulate cardiomyocyte hypertrophy. Here, we developed LogiRx, logic-based mechanistic machine learning method predicts drug-induced pathways. We applied LogiRx discover how discovered in previous compound screen attenuate experimentally validated predictions neonatal cardiomyocytes, adult mice, two patient databases. Using predicted antihypertrophic for seven currently used treat noncardiac disease. escitalopram (Lexapro) mifepristone inhibit cultured cardiomyocytes contexts. The model prevents through an “off-target” serotonin receptor/PI3Kγ pathway, mechanistically using additional investigational drugs. Further, reduced mouse fibrosis. Finally, mining both FDA University Virginia databases showed patients with depression on lower incidence cardiac than those prescribed other reuptake inhibitors do not target receptor. Mechanistic by discovers drug perturb cell states, which may enable repurposing limit remodeling off-target

Language: Английский

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Systems immunology-based drug repurposing framework to target inflammation in atherosclerosis

Nature Cardiovascular Research, Journal Year: 2023, Volume and Issue: 2(6), P. 550 - 571

Published: June 8, 2023

Abstract The development of new immunotherapies to treat the inflammatory mechanisms that sustain atherosclerotic cardiovascular disease (ASCVD) is urgently needed. Herein, we present a path drug repurposing identify for ASCVD. integration time-of-flight mass cytometry and RNA sequencing identified unique signatures in peripheral blood mononuclear cells stimulated with ASCVD plasma. By comparing these large-scale gene expression data from LINCS L1000 dataset, drugs could reverse this response. Ex vivo screens, using human samples, showed saracatinib—a phase 2a-ready SRC ABL inhibitor—reversed responses induced by In Apoe −/− mice, saracatinib reduced atherosclerosis progression reprogramming reparative macrophages. rabbit model advanced atherosclerosis, plaque inflammation measured [ 18 F]fluorodeoxyglucose positron emission tomography–magnetic resonance imaging. Here show systems immunology-driven preclinical validation strategy aid immunotherapies.

Language: Английский

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