Biology subjects

Zheng, W.

Publications and source records attributed to Zheng, W..

241 records · Page 14Linked to original sources

bioRxiv2018

Identification of Novel Common Breast Cancer Risk Variants in Latinas at the 6q25 Locus

Background: Breast cancer is a partially heritable trait and over 180 common genetic variants have been associated with breast cancer in genome wide association studies (GWAS). We have previously performed breast cancer GWAS in Latinas and identified a strongly protective single nucleotide polymorphism (SNP) at 6q25 with the protective minor allele originating from Indigenous American ancestry. Here we report on additional GWAS and replication in Latinas.\n\nMethods: We performed GWAS in 2385 cases and 7342 controls who were either U.S. Latinas or Mexican women. We replicated 2412 cases and 1620 controls of U.S Latina, Mexican, and Colombian women. In addition, we replicated the top novel variants in study of African American and African women and in one study of Chinese women. In each dataset we used logistic regression models to test the association between SNPs and breast cancer risk and corrected for genetic ancestry using either principal components or genetic ancestry inferred from ancestry informative markers using a model based approach.\n\nResults: We identified 3 SNPs (p=1.9x10-8 - 2.8x10-8) at 6q25 locus not in linkage disequilibrium (LD) with variants previously reported at this locus. These SNPs were in high LD with each other, with the top SNP, rs3778609, associated with breast cancer with an odds ratio (OR) and 95% confidence interval (95% CI) of 0.75 (0.68-0.83). In a replication in women of Latin American origin, we also observed a consistent effect (OR: 0.88; 95% CI: 0.78-0.99; p=0.037). Since the minor allele was common in East Asians and African American but not European ancestry populations, we replicated in a meta-analysis of those populations and also observed a consistent effect (OR 0.94; 95% CI: 0.91 - 0.97; p=0.013).\n\nConclusion: The effect size of this variant is relatively large compared to other common variants associated with breast cancer and adds to evidence about the importance of the 6q25 locus for breast cancer susceptibility. Our finding also highlights the utility of performing additional searches for genetic variants for breast cancer in non-European populations.

genetics↗

bioRxiv2018

Structure simulation of thrombopoietin receptor C-MPL with missense SNPs and its geographical distribution

Mpl is a key gene controlling the process of proliferation and differentiation of megakaryocyte and several studies reported that the mutation of mpl will even cause accurate megakaryocyte leukemia. By overlapping the missense mutation in NCBI and recorded SNP in 1000 genome database, we sorted out 4 SNPs(rs190983971, rs563996763, rs546510242, rs373621350) to find out its conformational changes. With Phyre v2.0, we simulated the secondary and tertiary structure change caused by the SNP and compared it with the original structure. The significant changes indicated its potential in leading to diseases associated with platelet reproduction. Finally, according to 1000 genome database, we constructed the geographically distribution of different populations and its SNPs carrying rate.

bioinformatics↗

bioRxiv2017

Sequence determinants of protein phase behavior from a coarse-grained model

Membraneless organelles important to intracellular compartmentalization have recently been shown to comprise assemblies of proteins which undergo liquid-liquid phase separation (LLPS). However, many proteins involved in this phase separation are at least partially disordered. The molecular mechanism and the sequence determinants of this process are challenging to determine experimentally owing to the disordered nature of the assemblies, motivating the use of theoretical and simulation methods. This work advances a computational framework for conducting simulations of LLPS with residue-level detail, and allows for the determination of phase diagrams and coexistence densities of proteins in the two phases. The model includes a short-range contact potential as well as a simplified treatment of electrostatic energy. Interaction parameters are optimized against experimentally determined radius of gyration data for multiple unfolded or intrinsically disordered proteins (IDPs). These models are applied to two systems which undergo LLPS: the low complexity domain of the RNA-binding protein FUS and the DEAD-box helicase protein LAF-1. We develop a novel simulation method to determine thermodynamic phase diagrams as a function of the total protein concentration and temperature. We show that the model is capable of capturing qualitative changes in the phase diagram due to phosphomimetic mutations of FUS and to the presence or absence of the large folded domain in LAF-1. We also explore the effects of chain-length, or multivalency, on the phase diagram, and obtain results consistent with Flory-Huggins theory for polymers. Most importantly, the methodology presented here is flexible so that it can be easily extended to other pair potentials, be used with other enhanced sampling methods, and may incorporate additional features for biological systems of interest.\n\nAuthor summaryLiquid liquid phase separation (LLPS) of low-complexity protein sequences has emerged as an important research topic due to its relevance to membraneless organelles and intracellular compartmentalization. However a molecular level understanding of LLPS cannot be easily obtained by experimental methods due to difficulty of determining structural properties of phase separated protein assemblies, and of choosing appropriate mutations. Here we advance a coarse-grained computational framework for accessing the long time scale phase separation process and for obtaining molecular details of LLPS, in conjunction with state of the art enhanced sampling methods. We are able to capture qualitatively the changes of phase diagram due to specific mutations, inclusion of a folded domain, and to variation of chain length. The model is flexible and can be used with different knowledge-based potential energy functions, as we demonstrate. We expect a wide application of the presented framework for advancing our understanding of the formation of liquid-like protein assemblies.

biophysics↗

bioRxiv2017

Assessing the genetic effect mediated through gene expression from summary eQTL and GWAS data

Integrating genome-wide association (GWAS) and expression quantitative trait locus (eQTL) data into transcriptome-wide association studies (TWAS) based on predicted expression can boost power to detect novel disease loci or pinpoint the susceptibility gene at a known disease locus. However, it is often the case that multiple eQTL genes colocalize at disease loci, making the identification of the true susceptibility gene challenging, due to confounding through linkage disequilibrium (LD). To distinguish between true susceptibility genes (where the genetic effect on phenotype is mediated through expression) and colocalization due to LD, we examine an extension of the Mendelian Randomization Egger regression method that allows for LD while only requiring summary association data for both GWAS and eQTL. We derive the standard TWAS approach in the context of Mendelian Randomization and show in simulations that the standard TWAS does not control Type I error for causal gene identification when eQTLs have pleiotropic or LD-confounded effects on disease. In contrast, LD Aware MR-Egger regression can control Type I error in this case while attaining similar power as other methods in situations where these provide valid tests. However, when the direct effects of genetic variants on traits are correlated with the eQTL associations, all of the methods we examined including LD Aware MR-Egger regression can have inflated Type I error. We illustrate these methods by integrating gene expression within a recent large-scale breast cancer GWAS to provide guidance on susceptibility gene identification.

epidemiology↗

bioRxiv2017

Drosophila Kruppel homolog 1 represses lipolysis through interaction with dFOXO

Transcriptional coordination is a vital process contributing to metabolic homeostasis. As one of the key nodes in the metabolic network, the forkhead transcription factor FOXO has been shown to interact with diverse transcription co-factors and integrate signals from multiple pathways to control metabolism, oxidative stress response, and cell cycle. Recently, insulin/FOXO signaling has been implicated in the regulation of insect development via the interaction with insect hormones, such as ecdysone and juvenile hormone. In this study, we identified an interaction between dFOXO and the zinc finger transcription factor Kruppel homolog 1 (Kr-h1), one of the key players in juvenile hormone signaling in Drosophila. We found that Kr-h1 mutants have reduced triglyceride storage, decreased insulin signaling and delayed larval development. Notably, Kr-h1 physically and genetically interacts with dFOXO in vitro and in vivo to regulate the transcriptional activation of adipose lipase brummer (bmm). The transcriptional co-regulation by Kr-h1 and dFOXO may represent a broad mechanism by which Kruppel-like factors integrate with insulin signaling to maintain metabolic homeostasis and coordinate organism growth.

genetics↗

bioRxiv2017

DNA 5-Hydroxymethylcytosines from Cell-free Circulating DNA as Diagnostic Biomarkers for Human Cancers

DNA modifications such as 5-methylcytosines (5mC) and 5-hydroxymethylcytosines (5hmC) are epigenetic marks known to affect global gene expression in mammals(1, 2). Given their prevalence in the human genome, close correlation with gene expression, and high chemical stability, these DNA epigenetic marks could serve as ideal biomarkers for cancer diagnosis. Taking advantage of a highly sensitive and selective chemical labeling technology(3), we report here genome-wide 5hmC profiling in circulating cell-free DNA (cfDNA) and in genomic DNA of paired tumor/adjacent tissues collected from a cohort of 90 healthy individuals and 260 patients recently diagnosed with colorectal, gastric, pancreatic, liver, or thyroid cancer. 5hmC was mainly distributed in transcriptionally active regions coincident with open chromatin and permissive histone modifications. Robust cancer-associated 5hmC signatures in cfDNA were identified with specificity for different cancers. 5hmC-based biomarkers of circulating cfDNA demonstrated highly accurate predictive value for patients with colorectal and gastric cancers versus healthy controls, superior to conventional biomarkers, and comparable to 5hmC biomarkers from tissue biopsies. This new strategy could lead to the development of effective blood-based, minimally-invasive cancer diagnosis and prognosis approaches.

cancer biology↗

bioRxiv2017

The AWSEM-Amylometer: Predicting Amyloid Propensity And Fibril Topology Using An Optimized Folding Landscape Model

Amyloids are fibrillar protein aggregates with simple repeated structural motifs in their cores, usually {beta}-strands but sometimes -helices. Identifying the amyloid-prone regions within protein sequences is important both for understanding the mechanisms of amyloid-associated diseases and for understanding functional amyloids. Based on the crystal structures of seven cross-{beta} amyloidogenic peptides with different topologies and one recently solved cross- fiber structure, we have developed a computational approach for identifying amyloidogenic segments in protein sequences using the Associative memory, Water mediated, Structure and Energy Model. The AWSEM-Amylometer performs favorably in comparison with other predictors in predicting aggregation-prone sequences in multiple datasets. The method also predicts the specific topologies (the relative arrangement of {beta}-strands in the core) of the amyloid fibrils well. An important advantage of the AWSEM-Amylometer over other existing methods is its direct connection with an efficient, optimized protein folding simulation model, AWSEM. This connection allows one to combine efficient and accurate search of protein sequences for amyloidogenic segments with the detailed study of the thermodynamic and kinetic roles that these segments play in folding and aggregation in the context of the entire protein sequence. We present new simulation results that highlight the free energy landscapes of peptides that can take on multiple fibril topologies. We also demonstrate how the Amylometer methodology can be straightforwardly extended to the study of functional amyloids that have the recently discovered cross- fibril architecture.

biophysics↗