Biology subjects

GUAN, Y.

Publications and source records attributed to GUAN, Y..

4 recordsLinked to original sources

bioRxiv2026

Vorinostat Can Reduce an Ongoing Fibrosis and Preserve Key Liver Metabolic Functions by Selectively Altering Mesenchymal Cell Differentiation

Liver fibrosis is a major global cause of mortality that is driven by the activation of hepatic stellate cells (HSCs) into extracellular matrix (ECM)-producing myofibroblasts (MyoFs). The lack of predictive human in vitro models has severely hampered the discovery of targeted anti-fibrotic therapies. Here, we describe a human iPSC-derived hepatic micro-organoid (microHO) model, which labels collagen producing cells, to study liver fibrosis pathophysiology and screen drug candidates. We discovered that while multiple drugs prevented fibrosis when co-administered prophylactically with TGF{beta}1, only the FDA-approved pan-HDAC inhibitor Vorinostat (SAHA) successfully reduced an established, ongoing fibrosis and preserved life-sustaining liver metabolic functions in a fibrotic microenvironment, including ammonia detoxification and CYP3A4-mediated drug metabolism. To uncover its mechanism of action, we utilized single-cell RNA sequencing and CRISPR-Cas12a lineage tracing. We found that Vorinostat actively redirects HSC differentiation away from pro-fibrotic MyoFs and toward a distinct, fibrosis-suppressing mesenchymal inhibitory (MesInh) cell. By reversing TGF{beta}1-induced epigenetic silencing, Vorinostat opens condensed chromatin to rescue metabolic gene expression and generates anti-fibrotic effectors that neutralize ongoing TGF{beta}1 signaling. Given its distinct capacity to remodel mesenchymal cell fate and reverse epigenetic repression of metabolic genes at clinically achievable concentrations, Vorinostat represents a highly promising drug for repurposing as a therapeutic for advanced human liver fibrosis.

pharmacology and toxicology↗

bioRxiv2025

AI-assisted Drug Re-purposing for Human Liver Fibrosis

Liver fibrosis is a severe disease with few treatment options due to the poor quality of the available animal and in vitro models. To address this, we investigated whether a hypothesis generating multi-agent AI system (AI co-scientist) could assist in re-purposing drugs for treatment of liver fibrosis and direct their experimental characterization. A multi-parameter image analysis workflow, which enabled anti-fibrotic efficacy and drug toxicity to be serially assessed in multi-lineage human hepatic organoids grown in microwells (i.e., microHOs), was used to assess the effects of 14 drugs. Remarkably, two of the three AI co-scientist-recommended drugs that targeted epigenomic modifiers exhibited significant anti-fibrotic activity. Analysis of the anti-fibrotic effects of five drugs indicated that two inhibited TGF{beta}-induced intracellular signaling and three drugs altered TGF{beta}-induced mesenchymal cell differentiation. Since all five of the anti-fibrotic drugs reduced TGF{beta}-induced chromatin structural changes, epigenomic changes play an important role in the pathogenesis of liver fibrosis. One AI co-scientist recommended drug is an FDA-approved anti-cancer treatment (Vorinostat) that reduced TGF{beta}-induced chromatin structural changes by 91% and promoted liver parenchymal cell regeneration in microHOs. Hence, the use of AI co-scientist and this microHO platform identified a potential new generation of liver fibrosis treatments that also promote liver regeneration.

bioinformatics↗

bioRxiv2020

A Human Multi-Lineage Hepatic Organoid Model for Liver Fibrosis

BackgroundTo characterize fibrogenic mechanisms, genome engineering and a human hepatic organoid system was used to produce an in vitro model for human liver fibrosis. Methods and resultsHuman hepatic organoids that were engineered to express the most common causative mutation for Autosomal Recessive Polycystic Kidney Disease (ARPKD) developed the key features of ARPKD liver pathology (abnormal bile ducts and hepatic fibrosis) in only 21 days. Second harmonic generation microscopy confirmed that the ARPKD mutation increased collagen abundance and thick collagen fiber production in hepatic organoids; and we demonstrated that these changes mirrored that occurring in ARPKD liver tissue. Transcriptomic and other analyses indicated that the ARPKD mutation generates cholangiocytes with increased TGF{beta}-associated pathway activation, which are actively involved in collagen fiber generation. The abnormal cholangiocytes promote the expansion of collagen-producing myofibroblasts with markedly increased PDGFR{beta} protein expression and an activated STAT3 signaling pathway. Moreover, the transcriptome of ARPKD organoid myofibroblasts resembled that of myofibroblasts in liver tissue obtained from patients with commonly occurring acquired forms of liver fibrosis. The involvement of the PDGFRB pathway was confirmed by the anti-fibrotic effect observed when ARPKD organoids were treated with PDGFRB inhibitors. ConclusionsBesides providing mechanistic insight into the pathogenesis of congenital (and possibly acquired) forms of liver fibrosis, ARPKD organoids could also be used to test the anti-fibrotic efficacy of potential anti-fibrotic therapies.

cell biology↗

bioRxiv2020

High Throughput Computational Mouse Genetic Analysis

BackgroundGenetic factors affecting multiple biomedical traits in mice have been identified when GWAS data that measured responses in panels of inbred mouse strains was analyzed using haplotype-based computational genetic mapping (HBCGM). Although this method was previously used to analyze one dataset at a time; but now, a vast amount of mouse phenotypic data is now publicly available, which could lead to many more genetic discoveries. ResultsHBCGM and a whole genome SNP map covering 53 inbred strains was used to analyze 8462 publicly available datasets of biomedical responses (1.52M individual datapoints) measured in panels of inbred mouse strains. As proof of concept, causative genetic factors affecting susceptibility for eye, metabolic and infectious diseases were identified when structured automated methods were used to analyze the output. One analysis identified a novel genetic effector mechanism; allelic differences within the mitochondrial targeting sequence affected the subcellular localization of a protein. We also found allelic differences within the mitochondrial targeting sequences of many murine and human proteins, and these could affect a wide range of biomedical phenotypes. ImplicationsThese initial results indicate that genetic factors affecting biomedical responses could be identified through analysis of very large datasets, and they provide an early indication of how this type of augmented intelligence can facilitate genetic discovery.

bioinformatics↗