Biology subjects

Cheng, H.

Publications and source records attributed to Cheng, H..

167 records · Page 10Linked to original sources

bioRxiv2018

NDUFAB1 Protects Heart by Coordinating Mitochondrial Respiratory Complex and Supercomplex Assembly

The impairment of mitochondrial bioenergetics, often coupled with exaggerated reactive oxygen species (ROS) production, is emerging as a common mechanism in diseases of organs with a high demand for energy, such as the heart. Building a more robust cellular powerhouse holds promise for protecting these organs in stressful conditions. Here, we demonstrate that NDUFAB1 (NADH:ubiquinone oxidoreductase subunit AB1), acts as a powerful cardio-protector by enhancing mitochondrial energy biogenesis. In particular, NDUFAB1 coordinates the assembly of respiratory complexes I, II, and III and supercomplexes, conferring greater capacity and efficiency of mitochondrial energy metabolism. Cardiac-specific deletion of Ndufab1 in mice caused progressive dilated cardiomyopathy associated with defective bioenergetics and elevated ROS levels, leading to heart failure and sudden death. In contrast, transgenic overexpression of Ndufab1 effectively enhanced mitochondrial bioenergetics and protected the heart against ischemia-reperfusion injury. Our findings identify NDUFAB1 as a central endogenous regulator of mitochondrial energy and ROS metabolism and thus provide a potential therapeutic target for the treatment of heart failure and other mitochondrial bioenergetics-centered diseases.

cell biology↗

bioRxiv2018

Loss of SDHB reprograms energy metabolisms and inhibits high fat diet induced metabolic syndromes

Mitochondrial respiratory complex II utilizes succinate, key substrate of the Krebs cycle, for oxidative phosphorylation, which is essential for glucose metabolism. Mutations of complex II cause cancers and mitochondrial diseases, raising a critical question of the (patho-)physiological functions. To address the fundamental role of complex II in systemic energy metabolism, we specifically knockout SDHB in mice liver, a key complex II subunit that tethers the catalytic SDHA subunit and transfers the electrons to ubiquinone, and found that SHDB deficiency abolishes the assembly of complex II without affecting other respiration complexes while largely retaining SDHA stability. SHDB ablation reprograms energy metabolism and hyperactivates the glycolysis, Krebs cycle and {beta}-oxidation pathways, leading to catastrophic energy deficit and early death. Strikingly, sucrose supplementation or high fat diet resumes both glucose and lipid metabolism and prevent early death. Also, SDHB deficient mice are completely resistant to high fat diet induced obesity. Our findings reveal that the unanticipated role of complex II orchestrating both lipid and glucose metabolisms, and suggest that SDHB is an ideal therapeutic target for combating obesity.

molecular biology↗

bioRxiv2017

An evidence-based approach to globally assess the covariate-dependent effect of MTHFR SNP rs1801133 on plasma homocysteine: a systematic review and meta-analysis

BackgroundThe single nucleotide polymorphism (SNP) of the gene Methylenetetrahydrofolate Reductase (MTHFR) C677T (or rs1801133) is the most established genetic factor that increases plasma total homocysteine (tHcy) and consequently results in hyperhomocysteinemia. Yet given the limited penetrance of this genetic variant, it is necessary to individually predict the risk of hyperhomocysteinemia for a rs1801133 carrier.\n\nObjectiveWe hypothesized that variability of this genetic risk is largely due to the presence of factors (covariates) that serve as effect modifiers and/or confounders, such as folic acid (FA) intake, and aimed to assess this risk in the complex context of these covariates.\n\nDesignWe systematically extracted from published studies the data of tHcy, rs1801133, and any previously reported rs1801133 covariates. The resulting meta-dataset was first used to analyze the covariates modifying effect by meta regression and other statistical means. Subsequently, we stratified tHcy data by the rs1801133 genotypes and analyzed under each genotype the variability of the risk resulted from the covariates confounding.\n\nResultsThe dataset contains data of 36 rs1801133 covariates that were collected from 114,448 subjects and 249 qualified studies, among which 6 covariates (sex, age, race, FA intake, smoking, and alcohol consumption) are the most frequently informed and therefore included for statistical analysis. The effect of rs1801133 on tHcy exhibits significant variability that can be attributed to effect modification and, to a larger degree, confounding by these covariates. Via statistical modeling, we predicted the covariate-dependent risk of tHcy elevation and hyperhomocysteinemia in a systematic manner.\n\nConclusionswe demonstrated an evidence-based approach that globally assesses the covariate-dependent effect of rs1801133 on tHcy. The results should assist clinicians in interpreting the rs1801133 data from genetic testing for their patients. Such information is also important for the public that increasingly receives genetic data from commercial services without interpretation of its clinical relevance.

genetics↗

bioRxiv2017

Parallel Computing to Speed up Whole-Genome Bayesian Regression Analyses Using Orthogonal Data Augmentation

1 AbstractBayesian multiple regression methods are widely used in whole-genome analyses to solve the problem that the number p of marker covariates is usually larger than the number n of observations. Inferences from most Bayesian methods are based on Markov chain Monte Carlo methods, where statistics are computed from a Markov chain constructed to have a stationary distribution equal to the posterior distribution of the unknown parameters. In practice, chains of about fifty thousand steps are typically used in whole-genome Bayesian regression analyses, which is computationally intensive. In this paper, we have shown how the sampling of marker effects can be made independent within each step of the chain. This is done by augmenting the marker covariate matrix by adding p new rows to it such that columns of the augmented marker covariate matrix are orthogonal. The phenotypes corresponding to the augmented rows of marker covariate matrix are considered missing. Ideally, the computations at each step of the MCMC chain, can be speeded up by the number k of computer processors up to the number p of markers. Addressing the heavy computational burden associated with Bayesian methods by parallel computing will lead to greater use of these methods.

genetics↗

bioRxiv2017

Untangling The Gene-Epigenome Networks: Timing Of Epigenetic Regulation Of Gene Expression In Acquired Cetuximab Resistance

BACKGROUNDTargeted therapies specifically act by blocking the activity of proteins that are encoded by genes critical for tumorigenesis. However, most cancers acquire resistance and long-term disease remission is rarely observed. Understanding the time course of molecular changes responsible for the development of acquired resistance could enable optimization of patients treatment options. Clinically, acquired therapeutic resistance can only be studied at a single time point in resistant tumors. To determine the dynamics of these molecular changes, we obtained high throughput omics data weekly during the development of cetuximab resistance in a head and neck cancer in vitro model.\n\nRESULTSAn unsupervised algorithm, CoGAPS, was used to quantify the evolving transcriptional and epigenetic changes. Applying a PatternMarker statistic to the results from CoGAPS enabled novel heatmap-based visualization of the dynamics in these time course omics data. We demonstrate that transcriptional changes result from immediate therapeutic response or resistance, whereas epigenetic alterations only occur with resistance. Integrated analysis demonstrates delayed onset of changes in DNA methylation relative to transcription, suggesting that resistance is stabilized epigenetically.\n\nCONCLUSIONSGenes with epigenetic alterations associated with resistance that have concordant expression changes are hypothesized to stabilize resistance. These genes include FGFR1, which was associated with EGFR inhibitor resistance previously. Thus, integrated omics analysis distinguishes the timing of molecular drivers of resistance. Our findings provide a relevant towards better understanding of the time course progression of changes resulting in acquired resistance to targeted therapies. This is an important contribution to the development of alternative treatment strategies that would introduce new drugs before the resistant phenotype develops.

cancer biology↗