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).

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.

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.

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).

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.

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.

Genome-scale chromatin interaction dynamic measurements for key components of the RNA Pol II general transcription machinery.

Background: A great deal of work has revealed in structural detail the components of the machinery responsible for mRNA gene transcription initiation. These include the general transcription factors (GTFs), which assemble at promoters along with RNA Polymerase II (Pol II) to form a preinitiation complex (PIC) aided by the activities of cofactors and site-specific transcription factors (TFs). However, less well understood are the PIC assembly pathways and their kinetics, an understanding of which is vital for determining on a mechanistic level how rates of RNA synthesis are established and how cofactors and TFs impact them.

ProtoCell4P: an explainable prototype-based neural network for patient classification using single-cell RNA-seq.

Motivation: The rapid advance in single-cell RNA sequencing (scRNA-seq) technology over the past decade has provided a rich resource of gene expression profiles of single cells measured on patients, facilitating the study of many biological questions at the single-cell level. One intriguing research is to study the single cells which play critical roles in the phenotypes of patients, which has the potential to identify those cells and genes driving the disease phenotypes. To this end, deep learning models are expected to well encode the single-cell information and achieve precise prediction of patients' phenotypes using scRNA-seq data.

Interpretable meta-learning of multi-omics data for survival analysis and pathway enrichment.

Motivation: Despite the success of recent machine learning algorithms' applications to survival analysis, their black-box nature hinders interpretability, which is arguably the most important aspect. Similarly, multi-omics data integration for survival analysis is often constrained by the underlying relationships and correlations that are rarely well understood. The goal of this work is to alleviate the interpretability problem in machine learning approaches for survival analysis and also demonstrate how multi-omics data integration improves survival analysis and pathway enrichment.

A Step Toward Personalized Surgical Decision Making: Machine Learning Predicts 1 Versus Numerous Melanoma Lymph Node Metastases Using RNA-sequencing.

Objective: Develop a predictive model to identify patients with 1 pathologic lymph node (pLN) versus >1 pLN using machine learning applied to gene expression profiles and clinical data as input variables.

Background: Standard management for clinically detected melanoma lymph node metastases is complete therapeutic LN dissection (TLND). However, >40% of patients with a clinically detected melanoma lymph node will only have 1 pLN on final review.

Generalization Performance of Quantum Metric Learning Classifiers.

Quantum computing holds great promise for a number of fields including biology and medicine. A major application in which quantum computers could yield advantage is machine learning, especially kernel-based approaches. A recent method termed quantum metric learning, in which a quantum embedding which maximally separates data into classes is learned, was able to perfectly separate ant and bee image training data.

Inhibition of DYRK1a Enhances Cardiomyocyte Cycling After Myocardial Infarction.

Background: DYRK1a (dual-specificity tyrosine phosphorylation-regulated kinase 1a) contributes to the control of cycling cells, including cardiomyocytes. However, the effects of inhibition of DYRK1a on cardiac function and cycling cardiomyocytes after myocardial infarction (MI) remain unknown.

Methods: We investigated the impacts of pharmacological inhibition and conditional genetic ablation of DYRK1a on endogenous cardiomyocyte cycling and left ventricular systolic function in ischemia-reperfusion (I/R) MI using () (denoted ) and mice.

Associations of immune cell homing gene signatures and infiltrates of lymphocyte subsets in human melanomas: discordance with CD163 myeloid cell infiltrates.

Background: Immune cells in the tumor microenvironment have prognostic value. In preclinical models, recruitment and infiltration of these cells depends on immune cell homing (ICH) genes such as chemokines, cell adhesion molecules, and integrins. We hypothesized ICH ligands CXCL9-11 and CCL2-5 would be associated with intratumoral T-cells, while CXCL13 would be more associated with B-cell infiltrates.

IGF1R and Src inhibition induce synergistic cytotoxicity in HNSCC through inhibition of FAK.

Head and neck cancer is the sixth most common cancer worldwide with a 5-year survival of only 65%. Targeting compensatory signaling pathways may improve therapeutic responses and combat resistance. Utilizing reverse phase protein arrays (RPPA) to assess the proteome and explore mechanisms of synergistic growth inhibition in HNSCC cell lines treated with IGF1R and Src inhibitors, BMS754807 and dasatinib, respectively, we identified focal adhesion signaling as a critical node.

Quantum computing at the frontiers of biological sciences.

Computing plays a critical role in the biological sciences but faces increasing challenges of scale and complexity. Quantum computing, a computational paradigm exploiting the unique properties of quantum mechanical analogs of classical bits, seeks to address many of these challenges. We discuss the potential for quantum computing to aid in the merging of insights across different areas of biological sciences.

A Machine Learning Model Utilizing a Novel SNP Shows Enhanced Prediction of Coronary Artery Disease Severity.

Machine learning (ML) has emerged as a powerful approach for predicting outcomes based on patterns and inferences. Improving prediction of severe coronary artery disease (CAD) has the potential for personalizing prevention and treatment strategies and for identifying individuals that may benefit from cardiac catheterization. We developed a novel ML approach combining traditional cardiac risk factors (CRF) with a single nucleotide polymorphism (SNP) in a gene associated with human CAD ( rs11574) to enhance prediction of CAD severity; ML models incorporating CRF along with genotype at rs11574 were evaluated.

COCOA: coordinate covariation analysis of epigenetic heterogeneity.

A key challenge in epigenetics is to determine the biological significance of epigenetic variation among individuals. We present Coordinate Covariation Analysis (COCOA), a computational framework that uses covariation of epigenetic signals across individuals and a database of region sets to annotate epigenetic heterogeneity. COCOA is the first such tool for DNA methylation data and can also analyze any epigenetic signal with genomic coordinates.

Implementation of a Hamming distance-like genomic quantum classifier using inner products on ibmqx2 and ibmq_16_melbourne.

Motivated by the problem of classifying individuals with a disease versus controls using a functional genomic attribute as input, we present relatively efficient general purpose inner product-based kernel classifiers to classify the test as a normal or disease sample. We encode each training sample as a string of 1 s (presence) and 0 s (absence) representing the attribute's existence across ordered physical blocks of the subdivided genome. Having binary-valued features allows for highly efficient data encoding in the computational basis for classifiers relying on binary operations.

Upregulation of human endogenous retrovirus-K (HML-2) mRNAs in hepatoblastoma: Identification of potential new immunotherapeutic targets and biomarkers.

Purpose: Hepatoblastoma is the most common liver malignancy in children. In order to advance therapy against hepatoblastoma, novel immunologic targets and biomarkers are needed. Our purpose in this investigation is to examine hepatoblastoma transcriptomes for the expression of a class of genomic elements known as Human Endogenous Retrovirus (HERVs).

Stem Cell Pluripotency Genes Klf4 and Oct4 Regulate Complex SMC Phenotypic Changes Critical in Late-Stage Atherosclerotic Lesion Pathogenesis.

Background: Rupture and erosion of advanced atherosclerotic lesions with a resultant myocardial infarction or stroke are the leading worldwide cause of death. However, we have a limited understanding of the identity, origin, and function of many cells that make up late-stage atherosclerotic lesions, as well as the mechanisms by which they control plaque stability.

Methods: We conducted a comprehensive single-cell RNA sequencing of advanced human carotid endarterectomy samples and compared these with single-cell RNA sequencing from murine microdissected advanced atherosclerotic lesions with smooth muscle cell (SMC) and endothelial lineage tracing to survey all plaque cell types and rigorously determine their origin.

Human endogenous retrovirus-K mRNA expression and genomic alignment data in hepatoblastoma.

Human Endogenous Retroviruses are a class of genomic elements that are the result of ancient retroviral infection of the human germline. Many are biologically active elements that have been implicated in multiple diseases including cancer. The most recent class to invade the human genome is the HERV-K(HML-2) (HERV-K) family.

Pausing sites of RNA polymerase II on actively transcribed genes are enriched in DNA double-stranded breaks.

DNA double-stranded breaks (DSBs) are strongly associated with active transcription, and promoter-proximal pausing of RNA polymerase II (Pol II) is a critical step in transcriptional regulation. Mapping the distribution of DSBs along actively expressed genes and identifying the location of DSBs relative to pausing sites can provide mechanistic insights into transcriptional regulation. Using genome-wide DNA break mapping/sequencing techniques at single-nucleotide resolution in human cells, we found that DSBs are preferentially located around transcription start sites of highly transcribed and paused genes and that Pol II promoter-proximal pausing sites are enriched in DSBs.

HIV-1 Rev interacts with HERV-K RcREs present in the human genome and promotes export of unspliced HERV-K proviral RNA.

Background: The HERV-K (HML-2) viruses are the youngest of the human endogenous retroviruses. They are present as several almost complete proviral copies and numerous fragments in the human genome. Many HERV-K proviruses express a regulatory protein Rec, which binds to an element present in HERV-K mRNAs called the RcRE.

Systems analysis of barrier molecule and ARNT-related gene expression regulation in melanoma.

: We have identified, in melanomas, a set of genes encoding proteins that mediate mechanical barrier function in normal skin (barrier molecule genes, BMGs) and whose overexpression is associated with decreased immune signatures and shorter patient survival. The most overexpressed of these, filaggrin (FLG), is expressed on chromosome 1q21.3, which also encodes genes of the epidermal differentiation complex (EDC).