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Peltz, G.

Publications and source records attributed to Peltz, G..

15 recordsLinked to original sources

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↗

AI Analysis of a Copy Number Variant Database Identifies a Genetic Factor for a Murine Model of the Metabolic Syndrome

Copy number variants (CNVs) are a major source of genetic diversity and could contain some of the missing heritability for mouse models of human disease. However, mouse CNVs have not been comprehensively characterized because they are difficult to resolve in repeat-rich, segmentally duplicated or reference sequence-absent regions of the genome. Here we analyzed long range sequence (LRS) data for 40 inbred mouse strains and characterized CNVs using pangenome graph-based (and other) methods and a C57BL/6J telomere to telomere (T2T) genome reference sequence. We resolved 1,594 high-confidence CNVs that often overlap tandem repeats (60.3%), segmental duplications (44.8%) or pericentromeric regions (11.5%); and 131 CNVs were T2T sequence-specific. CNVs affected 384 protein-coding genes, which spanned a range of important functional classes. The 40-strain pangenome map expanded the genome sequence from 2.29 to 3.32 Gb, with the wild-derived strains accounting for the largest sequence increments. Two different AIs were sequentially used to analyze this database and identify a 29-kb deletion CNV within the Nlrp1b locus of KK mice that contributed to the metabolic syndrome they develop. Human NLRP1 alleles also were associated with metabolic syndrome features in human populations. Hence, AI analyses of this comprehensive T2T pangenome-based resource could uncover some of the missing heritability for mouse models of human diseases and biomedical traits.

genetics↗

A Tandem Repeat Atlas for the Genome of Inbred Mouse Strains: A Genetic Variation Resource

Tandem repeats (TRs) are a significant source of genetic variation in the human population; and TR alleles are responsible for over 60 human genetic diseases and for inter-individual differences in many biomedical traits. Therefore, we utilized long-read sequencing and state of the art computational programs to produce a database with 2,528,854 TRs covering 39 inbred mouse strains. As in humans, murine TRs are abundant and were primarily located in intergenic regions. However, there were important species differences: murine TRs did not have the extensive number of repeat expansions like those associated with human repeat expansion diseases and they were not associated with transposable elements. We demonstrate by analysis of two biomedical phenotypes, which were identified over 40 years ago, that this TR database can enhance our ability to characterize the genetic basis for trait differences among the inbred strains.

genetics↗

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↗

A Murine Database of Structural Variants Enables the Genetic Architecture of a Spontaneous Murine Lymphoma to be Characterized

A more complete map of the pattern of genetic variation among inbred mouse strains is essential for characterizing the genetic architecture of the many available mouse genetic models of important biomedical traits. Although structural variants (SVs) are a major component of genetic variation, they have not been adequately characterized among inbred strains due to methodological limitations. To address this, we generated high-quality long-read sequencing data for 40 inbred strains; and designed a pipeline to optimally identify and validate different types of SVs. This generated a database for 40 inbred strains with 573,191SVs, which included 10,815 duplications and 2,115 inversions, which also has 70 million SNPs and 7.5 million insertions/deletions. Analysis of this SV database identified an SV that is one component of a bi-genic model for lymphoma susceptibility in SJL mice, which provides mechanistic insight into the genetic basis for susceptibility to murine (and potentially human) lymphomas.

genetics↗

Genetic Discovery Enabled by A Large Language Model

Artificial intelligence (AI) has been used in many areas of medicine, and recently large language models (LLMs) have shown potential utility for clinical applications. However, since we do not know if the use of LLMs can accelerate the pace of genetic discovery, we used data generated from mouse genetic models to investigate this possibility. We examined whether a recently developed specialized LLM (Med-PaLM 2) could analyze sets of candidate genes generated from analysis of murine models of biomedical traits. In response to free-text input, Med-PaLM 2 correctly identified the murine genes that contained experimentally verified causative genetic factors for six biomedical traits, which included susceptibility to diabetes and cataracts. Med-PaLM 2 was also able to analyze a list of genes with high impact alleles, which were identified by comparative analysis of murine genomic sequence data, and it identified a causative murine genetic factor for spontaneous hearing loss. Based upon this Med-PaLM 2 finding, a novel bigenic model for susceptibility to spontaneous hearing loss was developed. These results demonstrate Med-PaLM 2 can analyze gene-phenotype relationships and generate novel hypotheses, which can facilitate genetic discovery.

genetics↗

GenomeMUSter mouse genetic variation service enables multi-trait, multi-population data integration and analyses

Hundreds of inbred laboratory mouse strains and intercross populations have been used to functionalize genetic variants that contribute to disease. Thousands of disease relevant traits have been characterized in mice and made publicly available. New strains and populations including the Collaborative Cross, expanded BXD and inbred wild-derived strains add to set of complex disease mouse models, genetic mapping resources and sensitized backgrounds against which to evaluate engineered mutations. The genome sequences of many inbred strains, along with dense genotypes from others could allow integrated analysis of trait - variant associations across populations, but these analyses are not feasible due to the sparsity of genotypes available. Moreover, the data are not readily interoperable with other resources. To address these limitations, we created a uniformly dense data resource by harmonizing multiple variant datasets. Missing genotypes were imputed using the Viterbi algorithm with a data-driven technique that incorporates local phylogenetic information, an approach that is extensible to other model organism species. The result is a web- and programmatically-accessible data service called GenomeMUSter (https://muster.jax.org), comprising allelic data covering 657 strains at 106.8M segregating sites. Interoperation with phenotype databases, analytic tools and other resources enable a wealth of applications including multi-trait, multi-population meta-analysis. We demonstrate this in a cross-species comparison of the meta-analysis of Type 2 Diabetes and of substance use disorders, resulting in the more specific characterization of the role of human variant effects in light of mouse phenotype data. Other applications include refinement of mapped loci and prioritization of strain backgrounds for disease modeling to further unlock extant mouse diversity for genetic and genomic studies in health and disease.

genetics↗

Characterization of Pro-Fibrotic Signaling Pathways using Human Hepatic Organoids

Due to the limitations of available in vitro systems and animal models, we lack a detailed understanding of the pathogenetic mechanisms and have minimal treatment options for liver fibrosis. To overcome this barrier, we engineered a live cell imaging system that identifies collagen producing cells in a human multi-lineage hepatic organoid. This system was adapted for use as a microwell-based platform (i.e., microHOs) where exposure to PDGF or TGF{beta}1 induced the formation of thick collagen fibers. Transcriptomic analysis revealed that TGF{beta}1 exposure converted mesenchymal cells into myofibroblast-like cells that contribute to the development of liver fibrosis. When pro-fibrotic intracellular signaling pathways were examined using pharmacological probes, the anti-fibrotic effect of receptor-specific tyrosine kinase inhibitors was limited to the fibrosis induced by the corresponding growth factor, which indicates that their anti-fibrotic efficacy would be limited to fibrotic diseases that were solely mediated by that growth factor. Transcriptomic and transcription factor activation analyses were used to identify pathways that were jointly activated by PDGF and TGF{beta}1. GSK3{beta} or p38 MAPK inhibitors could prevent TGF{beta}1- or PDGF-induced fibrosis in microHOs because they block intracellular signaling pathways that are commonly utilized by the TGF{beta}1 and PDGF receptors. Hence, these studies identified GSK3{beta} and p38 MAPK inhibitors as potential new broad-spectrum therapies for liver fibrosis, and it is likely that other new therapies could subsequently be identified using this microHO system.

cell biology↗

Neuron Navigator 1 Regulates Learning, Memory, and the Response to Multiple Potentially Addictive Drugs

Genetic variation accounts for much of the risk for developing a substance use disorder (SUD). Inbred mouse strains exhibit substantial and heritable differences in the extent of voluntary cocaine intravenous self-administration (IVSA). Computational genetic analysis of IVSA data obtained from an inbred strain panel identified Nav1, a member of the neuron navigator family that regulates dendrite formation and axonal guidance, as a candidate gene. To test this hypothesis, we generated and characterized Nav1 knockout (KO) mice. Nav1 KO mice exhibited increased cocaine intake during IVSA testing. Surprisingly, Nav1 KO mice also displayed a reduced susceptibility to become opioid dependent or develop opioid-induced hyperalgesia after chronic morphine administration, and had impaired spatial learning/memory. Immunohistochemistry and electrophysiology studies revealed that inhibitory synapse density in the cortex of Nav1 KO mice was reduced, and excitatory synaptic transmission was increased in the Nav1 KO cortex and hippocampus. Transcriptomic analysis revealed that Nav1 KO mice had a marked increase in excitatory neurons in a deep cortical layer. Collectively, our results indicate that Nav1 regulates learning, memory, and the response to multiple addictive drugs, and that changes in the excitatory and inhibitory synaptic balance in the cortex and hippocampus could possibly mediate these phenotypic effects.

genetics↗

Analysis of Inbred Mouse strains' High-Impact Genotype-phenotype Hypotheses (AIMHIGH) reveals novel disease-causing candidate genes

Inbred mouse strains reveal the molecular basis of mammalian traits and diseases, particularly recessive ones. We utilized mouse community curated resources to set up an automated screen to discover novel testable gene function hypotheses. Using 11,832 community contributed strain-differentiating experiments and trait presence/absence scoring, we searched for all experiments where strains can be split by their phenotypic values (e.g., high vs. low responders). Then, using 48 sequenced strains, we found one or more candidate gene for each experiment where homozygous high-impact variants (such as stopgain, frameshifts) segregate strains into these same binary grouping. Our approach rediscovered 212 known gene-phenotype relationships, almost always highlighting potentially novel causal variants, as well as thousands of gene function hypotheses. To help find the most exciting hypotheses, we improved the state of the art in machine learning driven literature-based discovery (LBD). Reading on our top 3 ranked candidate genes per experiment reveals 80% of rediscovered relationships, compared to 5% reading at random. We proposed 1,842 novel gene-phenotype testable hypotheses using our approach. We built a web portal at aimhigh.stanford.edu to allow researchers to view all our testable hypotheses in detail. Our open-source code can be rerun as more sequenced strains and phenotyping experiments become available.

genomics↗

Micropatterned Organoids Enable Modeling of the Earliest Stages of Human Cardiac Vascularization

Although model organisms have provided insight into the earliest stages of cardiac vascularization, we know very little about this process in humans. Here we show that spatially micropatterned human pluripotent stem cells (hPSCs) enable in vitro modeling of this process, corresponding to the first three weeks of in vivo human development. Using four hPSC fluorescent reporter lines, we create cardiac vascular organoids (cVOs) by identifying conditions that simultaneously give rise to spatially organized and branched vascular networks within endocardial, myocardial, and epicardial cells. Using single-cell transcriptomics, we show that the cellular composition of cVOs resembles that of a 6.5 post-conception week (PCW) human heart. We find that NOTCH and BMP pathways are upregulated in cVOs, and their inhibition disrupts vascularization. Finally, using the same vascular-inducing factors to create cVOs, we produce hepatic vascular organoids (hVOs). This suggests there is a conserved developmental program for creating vasculature within different organ systems. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=159 HEIGHT=200 SRC="FIGDIR/small/499233v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@17b265org.highwire.dtl.DTLVardef@1c1113eorg.highwire.dtl.DTLVardef@531c18org.highwire.dtl.DTLVardef@596bd2_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

Analysis of Structural Variation Among Inbred Mouse Strains Identifies Genetic Factors for Autism-Related Traits

The genomes of six inbred strains were analyzed using long read (LR) sequencing. The results revealed that structural variants (SV) were very abundant within the genome of inbred mouse strains (4.8 per gene), which indicates that they could impact genetic traits. Analysis of the relationship between SNP and SV alleles across 53 inbred strains indicated that we have a very limited ability to infer whether SV are present using short read sequence data, even when nearby SNP alleles are known. The benefit of having a more complete map of the pattern of genetic variation was demonstrated by identifying at least three genetic factors that could underlie the unique neuroanatomic and behavioral features of BTBR mice that resemble human Autism Spectrum Disorder (ASD). Similar to the genetic findings in human ASD cohorts, the identified BTBR-unique alleles are very rare, and they cause high impact changes in genes that play a role in neurodevelopment and brain function.

genomics↗

The Effect of Population Structure on Murine Genome-Wide Association Studies

Population structure (PS) has been shown to cause false positive signals in genome-wide association studies (GWAS). Since PS correction is routinely used in human GWAS, it was assumed that it also should be utilized for murine GWAS using inbred strains. Nevertheless, there are fundamental differences between murine and human GWAS, and the impact of PS on murine GWAS results has not been thoroughly investigated. To assess the impact of PS on murine GWAS, we examined 8223 datasets that characterized biomedical responses in panels of inbred mouse strains. Surprisingly, we found that the PS had a minimal impact on datasets characterizing responses in [≤]20 strains; and relatively little impact on the majority of datasets characterizing >20 strains. Moreover, there were examples where association signals within known causative genes could be rejected if PS correction methods were utilized. PS assessment should be carefully used and should be considered in conjunction with other criteria when assessing the candidate genes identified in GWAS using inbred mouse strains.

genetics↗

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↗

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↗