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Biology subjects

Nguyen, V. B.

Publications and source records attributed to Nguyen, V. B..

3 recordsLinked to original sources

Regulation of the Balance between Concentric and Eccentric Cardiac Hypertrophy by a CDC14A-KMT5A Signaling Pathway

BackgroundDepending upon the type of pathological stress, the heart undergoes concentric or eccentric remodeling. This structural change is associated with diastolic and/or systolic ventricular dysfunction reflecting differentially altered cardiomyocyte morphology, ultrastructure, metabolism, contractility, and survival, as well as interstitial myocardial fibrosis. Despite an association of both concentric and eccentric remodeling with heart failure and sudden death, the molecular mechanisms resulting in abnormal cardiac geometry remain poorly understood. A better understanding of the basic mechanisms conferring these contrasting forms of remodeling should inform novel approaches to preserve normal cardiac structure and function in cardiovascular disease. The protein phosphatase Cell Division Cycle 14A (CDC14A) and its substrate the lysine methyltransferase KMT5A are identified herein as key regulators of the balance between concentric and eccentric pathological cardiac remodeling. MethodsThe regulation of adult rat ventricular myocyte morphology by CDC14A and KMT5A was studied in vitro following gain and loss of function by expression of wild-type and mutant proteins and RNA interference (RNAi). Epigenomic regulation by KMT5A was studied by mapping histone 4 lysine 20 mono-methylation (H4K20me1) modified chromatin sites and correlating them with gene transcription. Regulation of pathological cardiac remodeling in vivo was demonstrated by CDC14A and KMT5A RNAi using adeno-associated virus (AAV) mediated cardiomyocyte-specific small hairpin RNA (shRNA) expression in mice. ResultsCDC14A inhibited the growth in width of cultured adult myocytes stimulated by -adrenergic receptor activation or by serum response factor. KMT5A was downregulated by CDC14A in cardiomyocytes and was required for myocyte growth in width. -adrenergic stimulation of KMT5A-dependent H4K20 mono-methylation across transcription units correlated with regulation of gene transcription. Accordingly, AAV-expressed KMT5A shRNA induced eccentric remodeling and cardiac dysfunction in wild-type mice. Conversely, expression of Cdc14A shRNA improved systolic function and cardiac structure and inhibited pathological gene expression in the Tpm1 E54K mouse with Dilated Cardiomyopathy. ConclusionsCDC14A-KMT5A-dependent epigenomic regulation of gene transcription constitutes a molecular switch that determines concentric versus eccentric cardiac remodeling. These findings identify CDC14A as a potential therapeutic target for the treatment of dilated cardiomyopathy and other forms of heart failure with reduced ejection fraction. Clinical PerspectiveO_ST_ABSWhat is newC_ST_ABSO_LIA function is identified for the first time for the protein phosphatase CDC14A in the heart, regulation of cardiomyocyte morphology and overall cardiac geometry in pathological cardiac remodeling. C_LIO_LIThe lysine methyltransferase KMT5A is shown to mediate the effects of CDC14A in the adult cardiomyocyte by regulating H4K20 mono-methylation, such that reduced KMT5A expression promotes a phenotype resembling Dilated Cardiomyopathy. C_LIO_LIH4K20me1 epigenomic modification is identified as a regulator of cardiac structure and function. C_LI Clinical implicationsO_LICDC14A loss of function experimentation in vivo, resulting in improved cardiac structure and function in a mouse model of Dilated Cardiomyopathy, suggests that CDC14A is a novel therapeutic target for heart failure with reduced ejection fraction. C_LI

cell biology↗

An Experimental and Multiphysics Simulations Study of Clostridium carboxidivorans sp. 624 for Acid and Alcohol Production in CO2/H2

Biotechnological advances in CO2 utilization through gas fermentation offer a sustainable alternative to energy-intensive chemical processes. This study investigates and optimizes fermentation dynamics of Clostridium carboxidivorans sp. 624 for enhanced production of C2-C6 acids via combined experimental and computational methods. A systematic experimental evaluation of temperature and medium conditions, along with time-course analysis elucidates metabolic pathway regulation. Experimental optimisation, complemented by modelling, identified gas-liquid volumetric ratios VL/VG = 4 as optimal for maximizing longer-chain acids production, attributed to enhanced gas solubility and substrate bioavailability. Additionally, a hybrid model combining dynamic-kinetic model and computational fluid dynamics (CFD) successfully predict product formation. By capturing spatial inhomogeneities in gas-liquid interactions, the model also provides critical insights for optimizing fermentation performance and establishes a framework for improving process design parameters for CO2/H2 fermentation, paving the way for scalable and efficient CO2-based bioprocesses.

microbiology↗

Supervised non-negative matrix factorization on cell-free DNA fragmentomic features enhances early cancer detection

BackgroundCell-free circulating DNA (cfDNA) fragments exhibit non-random patterns in their length (FLEN), end-motif (EM), and distance to nucleosome position (ND). While these cfDNA features have shown promise as inputs for machine learning and deep learning models in early cancer detection, most studies utilize them as raw inputs, overlooking the potential benefits of pre-processing to extract cancer-specific features. This study aims to enhance cancer detection accuracy by developing a novel approach to feature extraction from cfDNA fragmentomics. MethodsWe implemented a supervised non-negative matrix factorization (SNMF) algorithm to generate embedding vectors capturing cancer-specific signals within cfDNA fragmentomic features. These embeddings served as input for a machine learning model to classify cancer patients from healthy individuals. ResultsWe validated our framework using two datasets: an in-house cohort of 431 cancer patients and 442 healthy individuals (dataset 1), and a published cohort comprising 90 hepatocellular carcinoma (HCC) patients and 103 individuals with cirrhosis or hepatitis B (dataset 2). In dataset 1, we achieved an AUC of 94% in pan-cancer detection. In dataset 2, our framework achieved an AUC of 100% for HCC vs healthy classification, 99% for HCC vs non-HCC patients classification, and 96% for identifying HCC patients among a mixed group of non-HCC patients and healthy donors. ConclusionThis study demonstrates the efficiency of SNMF-transformed features in improving both pan-cancer detection and specific HCC detection. Our approach offers a significant advancement in leveraging cfDNA fragmentomics for early cancer detection, potentially enhancing diagnostic accuracy in clinical settings.

bioinformatics↗