Multi-omic analysis reveals VEGFR2, PI3K, and JNK mediate the small molecule induction of human iPSC-derived cardiomyocyte proliferation.

Mammalian hearts lose their regenerative potential shortly after birth. Stimulating the proliferation of preexisting cardiomyocytes is a potential therapeutic strategy for cardiac damage. In a previous study, we identified 30 compounds that induced the bona-fide proliferation of human iPSC-derived cardiomyocytes (hiPSC-CM). Here, we selected five active compounds with diverse targets, including ALK5 and CB1R, and performed multi-omic analyses to identify common mechanisms mediating the cell cycle progression of hiPSC-CM. Transcriptome profiling revealed the top enriched pathways for all compounds including cell cycle, DNA repair, and kinesin pathways. Functional proteomic arrays found that the compounds collectively activated multiple receptor tyrosine kinases including ErbB2, IGF1R, and VEGFR2. Network analysis integrating common transcriptomic and proteomic signatures predicted that MAPK/PI3K pathways mediated compound responses. Furthermore, VEGFR2 negatively regulated endoreplication, enabling the completion of cell division. Thus, in this study, we applied high-content imaging and molecular profiling to establish mechanisms linking pro-proliferative agents to mechanisms of cardiomyocyte cell cycling.

DeepGSEA: explainable deep gene set enrichment analysis for single-cell transcriptomic data.

MOTIVATION: Gene set enrichment (GSE) analysis allows for an interpretation of gene expression through pre-defined gene set databases and is a critical step in understanding different phenotypes. With the rapid development of single-cell RNA sequencing (scRNA-seq) technology, GSE analysis can be performed on fine-grained gene expression data to gain a nuanced understanding of phenotypes of interest. However, with the cellular heterogeneity in single-cell gene profiles, current statistical GSE analysis methods sometimes fail to identify enriched gene sets. Meanwhile, deep learning has gained traction in applications like clustering and trajectory inference in single-cell studies due to its prowess in capturing complex data patterns. However, its use in GSE analysis remains limited, due to interpretability challenges. RESULTS: In this paper, we present DeepGSEA, an explainable deep gene set enrichment analysis approach which leverages the expressiveness of interpretable, prototype-based neural networks to provide an in-depth analysis of GSE. DeepGSEA learns the ability to capture GSE information through our designed classification tasks, and significance tests can be performed on each gene set, enabling the identification of enriched sets. The underlying distribution of a gene set learned by DeepGSEA can be explicitly visualized using the encoded cell and cellular prototype embeddings. We demonstrate the performance of DeepGSEA over commonly used GSE analysis methods by examining their sensitivity and specificity with four simulation studies. In addition, we test our model on three real scRNA-seq datasets and illustrate the interpretability of DeepGSEA by showing how its results can be explained. AVAILABILITY AND IMPLEMENTATION: https://github.com/Teddy-XiongGZ/DeepGSEA.

Novel Mouse Model of Myocardial Infarction, Plaque Rupture, and Stroke Shows Improved Survival With Myeloperoxidase Inhibition.

BACKGROUND: Thromboembolic events, including myocardial infarction (MI) or stroke, caused by the rupture or erosion of unstable atherosclerotic plaques are the leading cause of death worldwide. Although most mouse models of atherosclerosis develop lesions in the aorta and carotid arteries, they do not develop advanced coronary artery lesions. Moreover, they do not undergo spontaneous plaque rupture with MI and stroke or do so at such a low frequency that they are not viable experimental models to study late-stage thrombotic events or to identify novel therapeutic approaches for treating atherosclerotic disease. This has stymied the development of more effective therapeutic approaches for reducing these events beyond what has been achieved with aggressive lipid lowering. Here, we describe a diet-inducible mouse model that develops widespread advanced atherosclerosis in coronary, brachiocephalic, and carotid arteries with plaque rupture, MI, and stroke. METHODS: We characterized a novel mouse model with a C-terminal mutation in the scavenger receptor class B, type 1 (SR-BI), combined with Ldlr knockout (designated SR-BI∆CT/∆CT/Ldlr-/-). Mice were fed Western diet (WD) for 26 weeks and analyzed for MI and stroke. Coronary, brachiocephalic, and carotid arteries were analyzed for atherosclerotic lesions and indices of plaque stability. To validate the utility of this model, SR-BI∆CT/∆CT/Ldlr-/- mice were treated with the drug candidate AZM198, which inhibits myeloperoxidase, an enzyme produced by activated neutrophils that predicts rupture of human atherosclerotic lesions. RESULTS: SR-BI∆CT/∆CT/Ldlr-/- mice show high (>80%) mortality rates after 26 weeks of WD feeding because of major adverse cardiovascular events, including spontaneous plaque rupture with MI and stroke. Moreover, WD-fed SR-BI∆CT/∆CT/Ldlr-/- mice displayed elevated circulating high-sensitivity cardiac troponin I and increased neutrophil extracellular trap formation within lesions compared with control mice. Treatment of WD-fed SR-BI∆CT/∆CT/Ldlr-/- mice with AZM198 showed remarkable benefits, including >90% improvement in survival and >60% decrease in the incidence of plaque rupture, MI, and stroke, in conjunction with decreased circulating high-sensitivity cardiac troponin I and reduced neutrophil extracellular trap formation within lesions. CONCLUSIONS: WD-fed SR-BI∆CT/∆CT/Ldlr-/- mice more closely replicate late-stage clinical events of advanced human atherosclerotic disease than previous models and can be used to identify and test potential new therapeutic agents to prevent major adverse cardiac events.

Loss of TET2 increases B-1 cell number and IgM production while limiting CDR3 diversity.

Recent studies have demonstrated a role for Ten-Eleven Translocation-2 (TET2), an epigenetic modulator, in regulating germinal center formation and plasma cell differentiation in B-2 cells, yet the role of TET2 in regulating B-1 cells is largely unknown. Here, B-1 cell subset numbers, IgM production, and gene expression were analyzed in mice with global knockout of TET2 compared to wildtype (WT) controls. Results revealed that TET2-KO mice had elevated numbers of B-1a and B-1b cells in their primary niche, the peritoneal cavity, as well as in the bone marrow (B-1a) and spleen (B-1b). Consistent with this finding, circulating IgM, but not IgG, was elevated in TET2-KO mice compared to WT. Analysis of bulk RNASeq of sort purified peritoneal B-1a and B-1b cells revealed reduced expression of heavy and light chain immunoglobulin genes, predominantly in B-1a cells from TET2-KO mice compared to WT controls. As expected, the expression of IgM transcripts was the most abundant isotype in B-1 cells. Yet, only in B-1a cells there was a significant increase in the proportion of IgM transcripts in TET2-KO mice compared to WT. Analysis of the CDR3 of the BCR revealed an increased abundance of replicated CDR3 sequences in B-1 cells from TET2-KO mice, which was more clearly pronounced in B-1a compared to B-1b cells. V-D-J usage and circos plot analysis of V-J combinations showed enhanced usage of VH11 and VH12 pairings. Taken together, our study is the first to demonstrate that global loss of TET2 increases B-1 cell number and IgM production and reduces CDR3 diversity, which could impact many biological processes and disease states that are regulated by IgM.

Genome-scale chromatin binding dynamics of RNA Polymerase II general transcription machinery components.

A great deal of work has revealed, in structural detail, the components of the preinitiation complex (PIC) machinery required for initiation of mRNA gene transcription by RNA polymerase II (Pol II). However, less-well understood are the in vivo PIC assembly pathways and their kinetics, an understanding of which is vital for determining how rates of in vivo RNA synthesis are established. We used competition ChIP in budding yeast to obtain genome-scale estimates of the residence times for five general transcription factors (GTFs): TBP, TFIIA, TFIIB, TFIIE and TFIIF. While many GTF-chromatin interactions were short-lived ( < 1 min), there were numerous interactions with residence times in the range of several minutes. Sets of genes with a shared function also shared similar patterns of GTF kinetic behavior. TFIIE, a GTF that enters the PIC late in the assembly process, had residence times correlated with RNA synthesis rates. The datasets and results reported here provide kinetic information for most of the Pol II-driven genes in this organism, offering a rich resource for exploring the mechanistic relationships between PIC assembly, gene regulation, and transcription.

DARDN: A Deep-Learning Approach for CTCF Binding Sequence Classification and Oncogenic Regulatory Feature Discovery.

Characterization of gene regulatory mechanisms in cancer is a key task in cancer genomics. CCCTC-binding factor (CTCF), a DNA binding protein, exhibits specific binding patterns in the genome of cancer cells and has a non-canonical function to facilitate oncogenic transcription programs by cooperating with transcription factors bound at flanking distal regions. Identification of DNA sequence features from a broad genomic region that distinguish cancer-specific CTCF binding sites from regular CTCF binding sites can help find oncogenic transcription factors in a cancer type. However, the presence of long DNA sequences without localization information makes it difficult to perform conventional motif analysis. Here, we present DNAResDualNet (DARDN), a computational method that utilizes convolutional neural networks (CNNs) for predicting cancer-specific CTCF binding sites from long DNA sequences and employs DeepLIFT, a method for interpretability of deep learning models that explains the model's output in terms of the contributions of its input features. The method is used for identifying DNA sequence features associated with cancer-specific CTCF binding. Evaluation on DNA sequences associated with CTCF binding sites in T-cell acute lymphoblastic leukemia (T-ALL) and other cancer types demonstrates DARDN's ability in classifying DNA sequences surrounding cancer-specific CTCF binding from control constitutive CTCF binding and identifying sequence motifs for transcription factors potentially active in each specific cancer type. We identify potential oncogenic transcription factors in T-ALL, acute myeloid leukemia (AML), breast cancer (BRCA), colorectal cancer (CRC), lung adenocarcinoma (LUAD), and prostate cancer (PRAD). Our work demonstrates the power of advanced machine learning and feature discovery approach in finding biologically meaningful information from complex high-throughput sequencing data.