Biology subjects

Cheng, H.

Publications and source records attributed to Cheng, H..

At least 163 records · Page 9Linked to original sources

bioRxiv2019

Efficient chromosome-scale haplotype-resolved assembly of human genomes

Haplotype-resolved or phased sequence assembly provides a complete picture of genomes and complex genetic variations. However, current phased assembly algorithms either fail to generate chromosome-scale phasing or require pedigree information, which limits their application. We present a method that leverages long accurate reads and long-range conformation data for single individuals to generate chromosome-scale phased assembly within a day. Applied to three public human genomes, PGP1, HG002 and NA12878, our method produced haplotype-resolved assemblies with contig NG50 up to 25 Mb and phased [~]99.5% of heterozygous sites to 98-99% accuracy, outperforming other approaches in terms of both contiguity and phasing completeness. We demonstrate the importance of chromosome-scale phased assemblies to discover structural variants (SVs), including thousands of new transposon insertions, and of highly polymorphic and medically important regions such as HLA and KIR. Our improved method will enable high-quality precision medicine and facilitate new studies of individual haplotype variation and population diversity.

bioinformatics↗

bioRxiv2019

Exact Distribution of Linkage Disequilibrium in the Presence of Mutation, Selection or Minor Allele Frequency Filtering

Linkage disequilibrium (LD), often expressed in terms of the squared correlation (r2) between allelic values at two loci, is an important concept in many branches of genetics and genomics. Genetic drift and recombination have opposite effects on LD, and thus r2 will keep changing until the effects of these two forces are counterbalanced. Several approximations have been used to determine the expected value of r2 at equilibrium in the presence or absence of mutation. In this paper, we propose a probability-based approach to compute the exact distribution of allele frequencies at two loci in a finite population at any generation t conditional on the distribution at generation t - 1. As r2 is a function of this distribution of allele frequencies, this approach can be used to examine the distribution of r2 over generations as it approaches equilibrium. The exact distribution of LD from our method is used to describe, quantify and compare LD at different equilibria, including equilibrium in the absence or presence of mutation, selection, and filtering by minor allele frequency. We also propose a deterministic formula for expected LD in the presence of mutation at equilibrium based on the exact distribution of LD.

genetics↗

bioRxiv2019

Trajectories from Snapshots: Integrated proteomic and metabolic single-cell assays reveal multiple independent adaptive responses to drug tolerance in a BRAF-mutant melanoma cell line

The determination of individual cell trajectories through a high-dimensional cell-state space is an outstanding challenge, with relevance towards understanding biological changes ranging from cellular differentiation to epigenetic (adaptive) responses of diseased cells to drugging. We report on a combined experimental and theoretic method for determining the trajectories that specific highly plastic BRAFV600E mutant patient-derived melanoma cancer cells take between drug-naive and drug-tolerant states. Recent studies have implicated non-genetic, fast-acting resistance mechanisms are activated in these cells following BRAF inhibition. While single-cell highly multiplex omics tools can yield snapshots of the cell state space landscape sampled at any given time point, individual cell trajectories must be inferred from a kinetic series of snapshots, and that inference can be confounded by stochastic cell state switching. Using a microfludic-based single-cell integrated proteomic and metabolic assay, we assayed for a panel of signaling, phenotypic, and metabolic regulators at four time points during the first five days of drug treatment. Dimensional reduction of the resultant data set, coupled with information theoretic analysis, uncovered a complex cell state landscape and identified two distinct paths connecting drug-naive and drug-tolerant states. Cells are shown to exclusively traverse one of the two pathways depending on the level of the lineage restricted transcription factor MITF in the drug-naive cells. The two trajectories are associated with distinct signaling and metabolic susceptibilities, and are independently druggable. Our results update the paradigm of adaptive resistance development in an isogenic cell population and offer insight into the design of more effective combination therapies.

bioengineering↗

bioRxiv2019

Kinetic Inference Resolves Epigenetic Mechanism of Drug Resistance in Melanoma

Drug-induced dedifferentiation towards a drug-tolerant persister state is a common mechanism cancer cells exploit to escape therapies, posing a significant obstacle to sustained therapeutic efficacy. The dynamic coordination of epigenomic and transcriptomic programs at the early-stage of drug exposure, which initiates and orchestrates these reversible dedifferentiation events, remains largely unexplored. Here we employ high-temporal-resolution multi-omics profiling, information-theoretic approaches, and dynamic system modeling to probe these processes in BRAF-mutant melanoma models and patient specimens. We uncover a hysteretic transition trajectory of melanoma cells in response to oncogene inhibition and subsequent release, driven by the sequential operation of two tightly coupled transcriptional waves, which orchestrate genome-scale chromatin state reconfiguration. Modeling of the transcriptional wave interactions predicts NF-{kappa}B/RelA-driven chromatin remodeling as the underlying mechanism of cell-state dedifferentiation, a finding we validate experimentally. Our results identify critical RelA-target genes that are epigenetically modulated to drive this process, establishing a quantitative epigenome gauge to measure cell-state plasticity in melanomas, which supports the potential use of drugs targeting epigenetic machineries to potentiate oncogene inhibition. Extending our investigation to other cancer models, we identify oxidative stress-mediated NF-{kappa}B/RelA activation as a common mechanism driving cellular transitions towards drug-tolerant persister states, revealing a novel and pivotal role for the NF-{kappa}B signaling axis in linking cellular oxidative stress to cancer progression.

cancer biology↗

bioRxiv2019

HeveaDB: a genetic resource database for rubber tree genomic study

With the proceedings in the genome study in rubber tree, more and more data were produced. However, these data are still deposited in public databases as raw format, and the utilization of these NGS data is hindered for the researchers in rubber tree community. HeveaDB was therefore constructed and maintained under the organization of International Rubber Research and Development Board. The HeveaDB 1.0 (http://hevea.catas.cn) mainly stores 4 versions of Hevea draft genome, 99 NGS transcriptomes, 18451annotated EST sequences, 12 curated gene families, 30200 gene annotations, 5049 wild germplasm phenotype data, and 18328 Wickham rubber clonal information. Several bioinformatic tools are integrated in the database to facilitate the utilization of the data for the users who are not skilled in bioinformatics. With the periodical update and functional improvement, the HeveaDB will serve as a potential platform for genomic studies and breeding technology in rubber tree.

bioinformatics↗

bioRxiv2019

Integration of Alzheimer’s disease genetics and myeloid cell genomics identifies novel causal variants, regulatory elements, genes and pathways

Genome-wide association studies (GWAS) have identified more than thirty loci associated with Alzheimers disease (AD), but the causal variants, regulatory elements, genes and pathways remain largely unknown thus impeding a mechanistic understanding of AD pathogenesis. Previously, we showed that AD risk alleles are enriched in myeloid-specific epigenomic annotations. Here, we show that they are specifically enriched in active enhancers of monocytes, macrophages and microglia. We integrated AD GWAS signals with myeloid epigenomic and transcriptomic datasets using novel analytical approaches to link myeloid enhancer activity to target gene expression regulation and AD risk modification. We nominate candidate AD risk enhancers and identify their target causal genes (including AP4E1, AP4M1, APBB3, BIN1, CD2AP, MS4A4A, MS4A6A, PILRA, RABEP1, SPI1, SPPL2A, TP53INP1, ZKSCAN1, and ZYX) in sixteen loci. Fine-mapping of these enhancers nominates candidate functional variants that likely modify disease susceptibility by regulating causal gene expression in myeloid cells. In the MS4A locus we identified a single candidate functional variant and validated it experimentally in human induced pluripotent stem cell (hiPSC)-derived microglia. Combined, these results strongly implicate dysfunction of the myeloid endolysosomal system in the etiology of AD.

genomics↗

bioRxiv2019

Domain-centric dissection and classification of prokaryotic poly(3-hydroxyalkanoate) synthases

Although many enzymes and multiple pathways involve in Polyhydroxyalkanoates (PHAs) synthesis, PHA synthases play a determinant role in the process, which include three subunits of PhaC, PhaE, and PhaR. Currently, PHA synthases are categorized into four classes according to its primary sequences, substrate specificity, and subunit composition. However, theoretical analysis of PHA synthases from the domain perspective has not been performed. In this study, we dissected PHA synthases thoroughly through analysis of domain organization. Both referenced bacterial and archaeal proteomes were then screened for the presence and absence of different PHA synthases along NCBI taxonomy ID-based phylogenetic tree. In addition, sequences annotated as bacterial and archaeal PhaCs in UniProt database were also analyzed for domain organizations and interactions. In sum, the in-silico study provided a better understanding of the domain features of PHA synthases in prokaryotes, which also assisted in the production of PHA polymers with optimized chemical properties.

genomics↗

bioRxiv2019

A missense variant in CREBRF is associated with taller stature in Samoans

ObjectivesStudies have demonstrated that rs373863828, a missense mutation in CREBRF, is associated with a number of anthropometric traits including body mass index (BMI), obesity, percent body fat, hip circumference, and abdominal circumference. Given the biological relationship between height and adiposity, we hypothesized that the effect of this variant on BMI might be due in part to a previously untested association of this variant with height.\n\nMethodsWe tested the hypothesis that minor allele of rs373863828 is associated with height in a Samoan population in two adult cohorts and in a separate cohort of children (age 5 - 18 years old) using linear mixed modeling.\n\nResultsWe found evidence of a strong relationship between rs373863828 and greater mean height in Samoan adults (0.77 cm greater average height for each copy of the minor allele) with the same direction of effect in Samoan children.\n\nConclusionsThese results suggest that the missense variant rs373863828 in CREBRF, first identified through an association with larger BMI, may be related to an underlying biological mechanism affecting overall body size including stature.

genetics↗

bioRxiv2019

Mesenchymal stem cells restore local microenvironment and systemically suppress leukemia via reprogramming macrophages

Bone marrow (BM) mesenchymal stem cells (MSCs) are critical components of the BM microenvironment and play an essential role in supporting hematopoiesis. Dysfunction of MSCs is associated with the impaired BM microenvironment that promotes leukemia development. However, whether and how restoration of the impaired BM microenvironment can inhibit leukemia development remain unknown. Using an established leukemia model and the RNA-seq analysis, we discovered functional degeneration of MSCs during leukemia progression. Importantly, intra-BM instead of systemic transfusion of donor healthy MSCs restored the BM microenvironment, thus systemically altering cytokine expression patterns, improving normal hematopoiesis, reducing tumor burden, and ultimately prolonging survival of the leukemia-bearing mice. Donor MSC treatment restored the function of host MSCs and reprogrammed host macrophages to fulfill tissue-repair function. Transfusion of MSC-reprogrammed macrophages largely recapitulated the therapeutic effects of MSCs. Further, we found that donor MSCs reprogrammed macrophages to reduce leukemia burden through autocrine of IL-6. Taken together, our study reveals that donor MSCs reprogram host macrophages to restore the BM microenvironment and inhibit leukemia development, thus offering rationales for local MSC administration as a potentially effective therapy for leukemia.\n\nKey PointsKey Point 1: Intra-BM transfusion of MSCs restores the BM microenvironment, improves thrombopoiesis, and suppresses MDS/MPN initiated by Nras mutation.\n\nKey Point 2: Donor MSCs reprogram macrophages to restore the BM microenvironment, improve thrombopoiesis, and suppress leukemia.

cancer biology↗

bioRxiv2019

Pitfalls and Remedies for Cross Validation with Multi-trait Genomic Prediction Methods

Incorporating measurements on correlated traits into genomic prediction models can increase prediction accuracy and selection gain. However, multi-trait genomic prediction models are complex and prone to overfitting which may result in a loss of prediction accuracy relative to single-trait genomic prediction. Cross-validation is considered the gold standard method for selecting and tuning models for genomic prediction in both plant and animal breeding. When used appropriately, cross-validation gives an accurate estimate of the prediction accuracy of a genomic prediction model, and can effectively choose among disparate models based on their expected performance in real data. However, we show that a naive cross-validation strategy applied to the multi-trait prediction problem can be severely biased and lead to sub-optimal choices between single and multi-trait models when secondary traits are used to aid in the prediction of focal traits and these secondary traits are measured on the individuals to be tested. We use simulations to demonstrate the extent of the problem and propose three partial solutions: 1) a parametric solution from selection index theory, 2) a semi-parametric method for correcting the cross-validation estimates of prediction accuracy, and 3) a fully non-parametric method which we call CV2*: validating model predictions against focal trait measurements from genetically related individuals. The current excitement over high-throughput phenotyping suggests that more comprehensive phenotype measurements will be useful for accelerating breeding programs. Using an appropriate cross-validation strategy should more reliably determine if and when combining information across multiple traits is useful.

genetics↗

bioRxiv2019

A Sequence Learning Model for Decision Making in the Brain

The brain makes flexible and adaptive responses in the complicated and ever-changing environment for the organisms survival. To achieve this, the brain needs to choose appropriate actions flexibly in response to sensory inputs. Moreover, the brain also has to understand how its actions affect future sensory inputs and what reward outcomes should be expected, and adapts its behavior based on the actual outcomes. A modeling approach that takes into account of the combined contingencies between sensory inputs, actions, and reward outcomes may be the key to understanding the underlying neural computation. Here, we train a recurrent neural network model based on sequence learning to predict future events based on the past event sequences that combine sensory, action, and reward events. We use four exemplary tasks that have been used in previous animal and human experiments to study different aspects of decision making and learning. We first show that the model reproduces the animals choice and reaction time pattern in a probabilistic reasoning task, and its units activities mimics the classical findings of the ramping pattern of the parietal neurons that reflects the evidence accumulation process during decision making. We further demonstrate that the model carries out Bayesian inference and may support meta-cognition such as confidence with additional tasks. Finally, we show how the network model achieves adaptive behavior with an approach distinct from reinforcement learning. Our work pieces together many experimental findings in decision making and reinforcement learning and provides a unified framework for the flexible and adaptive behavior of the brain.

neuroscience↗

bioRxiv2019

The coiled coil domain of DEF6 facilitates formation of large vesicle-like, cytoplasmic aggregates that trap the P-body marker DCP1 and exhibit prion-like features

DEF6, also known as SLAT and IBP, is critical for the development of autoimmune disease and cancer. In T cells, DEF6 participates in TCR-mediated signalling determining T helper cell-mediated immune responses. In addition, DEF6 acts as a guanine nucleotide exchange factor for Rho GTPases facilitating F-actin assembly and stabilisation of the immunological synapse. However, DEF6 is also a component of mRNA processing bodies (P-bodies) linking it to mRNA metabolism. DEF6 can adopt multiple conformations that result in different cellular localisations and functions. Post translational modifications such as phosphorylation result in conformational change liberating functional domains that are masked in the native stage of DEF6. ITK phosphorylation of Try210/222 liberates the N-terminal end and to a certain extend also the C-terminal coiled coil domain of DEF6 resulting in P-body colocalisation. In fact, the N-terminal 45 amino acids of DEF6 that encode a Ca2+-binding EF hand are sufficient to target P-bodies. Mutant proteins that unleashed the C-terminal coiled coil domain of DEF6 spontaneously aggregated forming large vesicle-like, cytoplasmic structures. These aggregates trapped proteins such as the P-body component DCP1 altering its cytoplasmic localisation. However, cellular stress reversed aggregate formation in mutant DEF6 proteins that contained ITAM and PH domain in conjunction with the coiled coil domain resulting in colocalisation with DCP1. Furthermore, coiled coil-mediated aggregates appeared to function like prions enforcing conformational change onto wild type DEF6 protein.

molecular biology↗

bioRxiv2019

N-terminal 45 amino acids of DEF6 are necessary and sufficient to spontaneously colocalise with DCP1 in P-bodies

DEF6 (Differentially Expressed in FDCP 6, also known as IBP and/or SLAT) is critical for the development of autoimmune disease and cancer. In T cells, DEF6 participates in TCR-mediated signalling determining T helper cell-mediated immune responses. In addition, DEF6 acts as a guanine nucleotide exchange factor (GEF) for Rho GTPases facilitating F-actin assembly and stabilisation of the immunological synapse (IS). However, DEF6 is also a component of mRNA processing bodies (P-bodies) linking it to mRNA metabolism. Including DEF6, more than 34 proteins have been shown to localise in P-bodies many of which contain a coiled coil domain, a super-secondary structure likely to facilitate interaction between these proteins. Accordingly, we suggested that the coiled coil domain in the C-terminal end of DEF6 was mediating P-body localisation of DEF6 under cellular stress conditions. However, a comprehensive analysis of wild type and mutant DEF6 proteins expressed in COS7 cells revealed that the coiled coil domain is dispensable for P-body colocalisation. Instead, we show here that the N-terminal 45 amino acids of DEF6 that contain one Ca2+-binding EF hand motif are sufficient to target DEF6 to P-bodies whereas the N-terminal 30 amino acids containing a disrupted EF hand motif are insufficient.

molecular biology↗

bioRxiv2019

Feature Design for Protein Interface hotspots using KFC2 and Rosetta

Protein-protein interactions regulate many essential biological processes and play an important role in health and disease. The process of experimentally charac-terizing protein residues that contribute the most to protein-protein interaction affin-ity and specificity is laborious. Thus, developing models that accurately characterize hotspots at protein-protein interfaces provides important information about how to inhibit therapeutically relevant protein-protein interactions. During the course of the ICERM WiSDM workshop 2017, we combined the KFC2a protein-protein interaction hotspot prediction features with Rosetta scoring function terms and interface filter metrics. A 2-way and 3-way forward selection strategy was employed to train support vector machine classifiers, as was a reverse feature elimination strategy. From these results, we identified subsets of KFC2a and Rosetta combined features that show improved performance over KFC2a features alone.

bioinformatics↗

bioRxiv2018

BitMapperBS: a fast and accurate read aligner for whole-genome bisulfite sequencing

As a gold-standard technique for DNA methylation analysis, whole-genome bisulfite sequencing (WGBS) helps researchers to study the genome-wide DNA methylation at single-base resolution. However, aligning WGBS reads to the large reference genome is a major computational bottleneck in DNA methylation analysis projects. Although several WGBS aligners have been developed in recent years, it is difficult for them to efficiently process the ever-increasing bisulfite sequencing data. Here we propose BitMapperBS, an ultrafast and memory-efficient aligner that is designed for WGBS reads. To improve the performance of BitMapperBS, we propose various strategies specifically for the challenges that are unique to the WGBS aligners, which are ignored in most existing methods. Our experiments on real and simulated datasets show that BitMapperBS is one order of magnitude faster than the state-of-the-art WGBS aligners, while achieves similar or better sensitivity and precision. BitMapperBS is freely available at https://github.com/chhylp123/BitMapperBS.

bioinformatics↗

bioRxiv2018

Gata4 drives Hh-signaling for second heart field migration and outflow tract development

Dominant mutations of Gata4, an essential cardiogenic transcription factor (TF), cause outflow tract (OFT) defects in both human and mouse. We investigated the molecular mechanism underlying this requirement. Gata4 happloinsufficiency in mice caused OFT defects including double outlet right ventricle (DORV) and conal ventricular septum defects (VSDs). We found that Gata4 is required within Hedgehog (Hh)-receiving second heart field (SHF) progenitors for normal OFT alignment. Increased Pten-mediated cell-cycle transition, rescued atrial septal defects but not OFT defects in Gata4 heterozygotes. SHF Hh-receiving cells failed to migrate properly into the proximal OFT cushion in Gata4 heterozygote embryos. We find that Hh signaling and Gata4 genetically interact for OFT development. Gata4 and Smo double heterozygotes displayed more severe OFT abnormalities including persistent truncus arteriosus (PTA) whereas restoration of Hedgehog signaling rescued OFT defects in Gata4-mutant mice. In addition, enhanced expression of the Gata6 was observed in the SHF of the Gata4 heterozygotes. These results suggested a SHF regulatory network comprising of Gata4, Gata6 and Hh-signaling for OFT development. This study indicates that Gata4 potentiation of Hh signaling is a general feature of Gata4-mediated cardiac morphogenesis and provides a model for the molecular basis of CHD caused by dominant transcription factor mutations.\n\nAuthor SummaryGata4 is an important protein that controls the development of the heart. Human who possess a single copy of Gata4 mutation display congenital heart defects (CHD), including the double outlet right ventricle (DORV). DORV is an alignment problem in which both the Aorta and Pulmonary Artery originate from the right ventricle, instead of originating from the left and the right ventricles, respectively. To study how Gata4 mutation causes DORV, we used a Gata4 mutant mouse model, which displays DORV. We showed that Gata4 is required in the cardiac precursor cells for the normal alignment of the great arteries. Although Gata4 mutation inhibits the rapid increase in number of the cardiac precursor cells, rescuing this defects does not recover the normal alignment of the great arteries. In addition, there is a movement problem of the cardiac precursor cells when migrating toward the great arteries during development. We further showed that a specific molecular signaling, Hh-signaling, is responsible to the Gata4 action in the cardiac precursor cells. Importantly, over-activating the Hh-signaling rescues the DORV in the Gata4 mutant embryos. This study provides an explanation for the ontogeny of CHD.

developmental biology↗

bioRxiv2018

EnsembleCNV: An ensemble machine learning algorithm to identify and genotype copy number variation using SNP array data

The associations between diseases/traits and copy number variants (CNVs) have not been systematically investigated in genome-wide association studies (GWASs), primarily due to a lack of robust and accurate tools for CNV genotyping. Herein, we propose a novel ensemble learning framework, ensembleCNV, to detect and genotype CNVs using single nucleotide polymorphism (SNP) array data. EnsembleCNV a) identifies and eliminates batch effects at raw data level; b) assembles individual CNV calls into CNV regions (CNVRs) from multiple existing callers with complementary strengths by a heuristic algorithm; c) re-genotypes each CNVR with local likelihood model adjusted by global information across multiple CNVRs; d) refines CNVR boundaries by local correlation structure in copy number intensities; e) provides direct CNV genotyping accompanied with confidence score, directly accessible for downstream quality control and association analysis. Benchmarked on two large datasets, ensembleCNV outperformed competing methods and achieved a high call rate (93.3%) and reproducibility (98.6%), while concurrently achieving high sensitivity by capturing 85% of common CNVs documented in the 1000 Genomes Project. Given this CNV call rate and accuracy, which are comparable to SNP genotyping, we suggest ensembleCNV holds significant promise for performing genome-wide CNV association studies and investigating how CNVs predispose to human diseases.

bioinformatics↗

bioRxiv2018

Crystal structure of the MBD domain of MBD3 in complex with methylated CG DNA

MBD3 is a core subunit of the Mi-2/NuRD complex, and has been previously reported to lack methyl-CpG binding ability. However, recent reports show that MBD3 recognizes both mCG and hmCG DNA with a preference for hmCG, and is required for the normal expression of hmCG marked genes in ES cells. Nevertheless, it is not clear how MBD3 recognizes the methylated DNA. In this study, we carried out structural analysis coupled with isothermal titration calorimetry (ITC) binding assay and mutagenesis studies to address the structural basis for the mCG DNA binding ability of the MBD3 MBD domain. We found that the MBD3 MBD domain prefers binding mCG over hmCG through the conserved arginine fingers, and this MBD domain as well as other mCG binding MBD domains can recognize the mCG duplex without orientation selectivity. Furthermore, we found that the tyrosine-to-phenylalanine substitution at Phe34 of MBD3 is responsible for its weaker mCG DNA binding ability compared to other mCG binding MBD domains. In summary, our study demonstrates that the MBD3 MBD domain is a mCG binder, and also illustrates its binding mechanism to the methylated CG DNA.

biochemistry↗