Right ventricular hypertrophy (RVH) occurs in high pressure afterload, e.g., tetralogy of Fallot/pulmonary stenosis (TOF/PS). Such RVH is associated with alterations in energy metabolism, neurohormonal and epigenetic dysregulation (e.g., microRNA), and fetal gene reprogramming in animal models. However, comprehensive expression profiling of competing endogenous RNA in human RVH has not been performed. Here, we unravel several previously unknown circular, long non-coding, and microRNAs, predicted to regulate expression of genes specific to human RVH in the non-failing heart (TOF/PS). These genes are significantly overrepresented in pathways related to regulation of glucose and lipid metabolism (SIK1, FABP4), cell surface interactions (THBS2, FN1), apoptosis (PIK3IP1, SIK1), extracellular matrix composition (CTGF, IGF1), and other biological events. This is the first unbiased RNA sequencing study of human compensated RVH encompassing coding and non-coding RNA expression and predicted sponging of miRNAs by non-coding RNAs. These findings advance our understanding of adaptive RVH and highlight future therapeutic targets.
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http://dx.doi.org/10.1016/j.isci.2021.102232 | DOI Listing |
Annu Rev Biophys
January 2025
1CREST Center for Cellular and Biomolecular Machines, University of California, Merced, California, USA; email:
Like their prokaryotic counterparts, eukaryotic transcription factors must recognize specific DNA sites, search for them efficiently, and bind to them to help recruit or block the transcription machinery. For eukaryotic factors, however, the genetic signals are extremely complex and scattered over vast, multichromosome genomes, while the DNA interplay occurs in a varying landscape defined by chromatin remodeling events and epigenetic modifications. Eukaryotic factors are rich in intrinsically disordered regions and are also distinct in their recognition of short DNA motifs and utilization of open DNA interaction interfaces as ways to gain access to DNA on nucleosomes.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, United States.
Pathway analysis plays a critical role in bioinformatics, enabling researchers to identify biological pathways associated with various conditions by analyzing gene expression data. However, the rise of large, multi-center datasets has highlighted limitations in traditional methods like Over-Representation Analysis (ORA) and Functional Class Scoring (FCS), which struggle with low signal-to-noise ratios (SNR) and large sample sizes. To tackle these challenges, we use a deep learning-based classification method, Gene PointNet, and a novel $P$-value computation approach leveraging the confusion matrix to address pathway analysis tasks.
View Article and Find Full Text PDFSTAR Protoc
January 2025
Department of Statistics, University of Georgia, 310 Herty Drive, Athens, GA 30602, USA. Electronic address:
Spatial transcriptomics enhances our understanding of cellular organization by mapping gene expression data to precise tissue locations. Here, we present a protocol for using weighted ensemble method for spatial transcriptomics (WEST), which uses ensemble techniques to boost the robustness and accuracy of existing algorithms. We describe steps for preprocessing data, obtaining embeddings from individual algorithms, and ensemble integrating all embeddings as a similarity matrix.
View Article and Find Full Text PDFSci Transl Med
January 2025
Graduate Program in Human Genetics, University of Miami Miller School of Medicine, 1501 NW 10th Avenue (M-860), Miami, FL 33136, USA.
Primary mitochondrial disorders are most often caused by deleterious mutations in the mitochondrial DNA (mtDNA). Here, we used a mitochondrial DddA-derived cytosine base editor (DdCBE) to introduce a compensatory edit in a mouse model that carries the pathological mutation in the mitochondrial transfer RNA (tRNA) alanine (mt-tRNA) gene. Because the original m.
View Article and Find Full Text PDFSci Transl Med
January 2025
Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
At this stage in the COVID-19 pandemic, most infections are "breakthrough" infections that occur in individuals with prior severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure. To refine long-term vaccine strategies against emerging variants, we examined both innate and adaptive immunity in breakthrough infections. We performed single-cell transcriptomic, proteomic, and functional profiling of primary and breakthrough infections to compare immune responses from unvaccinated and vaccinated individuals during the SARS-CoV-2 Delta wave.
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