Targeting HDAC6 to treat heart failure with preserved ejection fraction in mice.

Heart failure with preserved ejection fraction (HFpEF) poses therapeutic challenges due to the limited treatment options. Building upon our previous research that demonstrates the efficacy of histone deacetylase 6 (HDAC6) inhibition in a genetic cardiomyopathy model, we investigate HDAC6's role in HFpEF due to their shared mechanisms of inflammation and metabolism. Here, we show that inhibiting HDAC6 with TYA-018 effectively reverses established heart failure and its associated symptoms in male HFpEF mouse models. Additionally, in male mice lacking Hdac6 gene, HFpEF progression is delayed and they are resistant to TYA-018's effects. The efficacy of TYA-018 is comparable to a sodium-glucose cotransporter 2 (SGLT2) inhibitor, and the combination shows enhanced effects. Mechanistically, TYA-018 restores gene expression related to hypertrophy, fibrosis, and mitochondrial energy production in HFpEF heart tissues. Furthermore, TYA-018 also inhibits activation of human cardiac fibroblasts and enhances mitochondrial respiratory capacity in cardiomyocytes. In this work, our findings show that HDAC6 impacts on heart pathophysiology and is a promising target for HFpEF treatment.

Phenotypic screening with deep learning identifies HDAC6 inhibitors as cardioprotective in a BAG3 mouse model of dilated cardiomyopathy.

Dilated cardiomyopathy (DCM) is characterized by reduced cardiac output, as well as thinning and enlargement of left ventricular chambers. These characteristics eventually lead to heart failure. Current standards of care do not target the underlying molecular mechanisms associated with genetic forms of heart failure, driving a need to develop novel therapeutics for DCM. To identify candidate therapeutics, we developed an in vitro DCM model using induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) deficient in B-cell lymphoma 2 (BCL2)-associated athanogene 3 (BAG3). With these BAG3-deficient iPSC-CMs, we identified cardioprotective drugs using a phenotypic screen and deep learning. From a library of 5500 bioactive compounds and siRNA validation, we found that inhibiting histone deacetylase 6 (HDAC6) was cardioprotective at the sarcomere level. We translated this finding to a BAG3 cardiomyocyte-knockout (BAG3cKO) mouse model of DCM, showing that inhibiting HDAC6 with two isoform-selective inhibitors (tubastatin A and a novel inhibitor TYA-018) protected heart function. In BAG3cKO and BAG3E455K mice, HDAC6 inhibitors improved left ventricular ejection fraction and reduced left ventricular diameter at diastole and systole. In BAG3cKO mice, TYA-018 protected against sarcomere damage and reduced Nppb expression. Based on integrated transcriptomics and proteomics and mitochondrial function analysis, TYA-018 also enhanced energetics in these mice by increasing expression of targets associated with fatty acid metabolism, protein metabolism, and oxidative phosphorylation. Our results demonstrate the power of combining iPSC-CMs with phenotypic screening and deep learning to accelerate drug discovery, and they support developing novel therapies that address underlying mechanisms associated with heart disease.

Deep learning detects cardiotoxicity in a high-content screen with induced pluripotent stem cell-derived cardiomyocytes.

Drug-induced cardiotoxicity and hepatotoxicity are major causes of drug attrition. To decrease late-stage drug attrition, pharmaceutical and biotechnology industries need to establish biologically relevant models that use phenotypic screening to detect drug-induced toxicity in vitro. In this study, we sought to rapidly detect patterns of cardiotoxicity using high-content image analysis with deep learning and induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). We screened a library of 1280 bioactive compounds and identified those with potential cardiotoxic liabilities in iPSC-CMs using a single-parameter score based on deep learning. Compounds demonstrating cardiotoxicity in iPSC-CMs included DNA intercalators, ion channel blockers, epidermal growth factor receptor, cyclin-dependent kinase, and multi-kinase inhibitors. We also screened a diverse library of molecules with unknown targets and identified chemical frameworks that show cardiotoxic signal in iPSC-CMs. By using this screening approach during target discovery and lead optimization, we can de-risk early-stage drug discovery. We show that the broad applicability of combining deep learning with iPSC technology is an effective way to interrogate cellular phenotypes and identify drugs that may protect against diseased phenotypes and deleterious mutations.

Harnessing Colon Chip Technology to Identify Commensal Bacteria That Promote Host Tolerance to Infection.

Commensal bacteria within the gut microbiome contribute to development of host tolerance to infection, however, identifying specific microbes responsible for this response is difficult. Here we describe methods for developing microfluidic organ-on-a-chip models of small and large intestine lined with epithelial cells isolated from duodenal, jejunal, ileal, or colon organoids derived from wild type or transgenic mice. To focus on host-microbiome interactions, we carried out studies with the mouse Colon Chip and demonstrated that it can support co-culture with living gut microbiome and enable assessment of effects on epithelial adhesion, tight junctions, barrier function, mucus production, and cytokine release. Moreover, infection of the Colon Chips with the pathogenic bacterium, Salmonella typhimurium, resulted in epithelial detachment, decreased tight junction staining, and increased release of chemokines (CXCL1, CXCL2, and CCL20) that closely mimicked changes previously seen in mice. Symbiosis between microbiome bacteria and the intestinal epithelium was also recapitulated by populating Colon Chips with complex living mouse or human microbiome. By taking advantage of differences in the composition between complex microbiome samples cultured on each chip using 16s sequencing, we were able to identify Enterococcus faecium as a positive contributor to host tolerance, confirming past findings obtained in mouse experiments. Thus, mouse Intestine Chips may represent new experimental in vitro platforms for identifying particular bacterial strains that modulate host response to pathogens, as well as for investigating the cellular and molecular basis of host-microbe interactions.

Quantifying drug-induced structural toxicity in hepatocytes and cardiomyocytes derived from hiPSCs using a deep learning method.

Cardiac and hepatic toxicity result from induced disruption of the functioning of cardiomyocytes and hepatocytes, respectively, which is tightly related to the organization of their subcellular structures. Cellular structure can be analyzed from microscopy imaging data. However, subtle or complex structural changes that are not easily perceived may be missed by conventional image-analysis techniques. Here we report the evaluation of PhenoTox, an image-based deep-learning method of quantifying drug-induced structural changes using human hepatocytes and cardiomyocytes derived from human induced pluripotent stem cells. We assessed the ability of the deep learning method to detect variations in the organization of cellular structures from images of fixed or live cells. We also evaluated the power and sensitivity of the method for detecting toxic effects of drugs by conducting a set of experiments using known toxicants and other methods of screening for cytotoxic effects. Moreover, we used PhenoTox to characterize the effects of tamoxifen and doxorubicin-which cause liver toxicity-on hepatocytes. PhenoTox revealed differences related to loss of cytochrome P450 3A4 activity, for which it showed greater sensitivity than a caspase 3/7 assay. Finally, PhenoTox detected structural toxicity in cardiomyocytes, which was correlated with contractility defects induced by doxorubicin, erlotinib, and sorafenib. Taken together, the results demonstrated that PhenoTox can capture the subtle morphological changes that are early signs of toxicity in both hepatocytes and cardiomyocytes.