Biology subjects

Zhou, X.

Publications and source records attributed to Zhou, X..

At least 685 records · Page 38Linked to original sources

bioRxiv2019

Droplet-digital Cas13a assay enables direct single-molecule microRNA quantification

The direct quantification of microRNA at the single-molecule level is still challenging. Herein, we developed a droplet-digital Cas13a assay (ddCA) as a general approach for the amplification-free and absolute quantification of single unlabeled miRNA molecules with single-nucleotide specificity. We demonstrate its simplicity, precise quantification capability, excellent specificity and broad applicability by analyzing microRNAs from synthetic and cell lines-derived materials.

biochemistry↗

bioRxiv2019

Aquila_stLFR: assembly based variant calling package for stLFR and hybrid assembly for linked-reads

MotivationIdentifying structural variants (SVs) is of critical importance in health and disease, however, detecting them remains a scientific and computing challenge. Several linked-read sequencing technologies, including 10X linked-read, TELL-Seq, and single tube long fragment read (stLFR), have been recently developed as cost-effective approaches to reconstruct multi-megabase haplotypes (phase blocks) from sequence data of a single sample. These technologies provide an optimal sequencing platform to characterize SVs, though few computational algorithms can utilize them. Thus, we developed Aquila_stLFR, an approach that resolves SVs through haplotype-based assembly of stLFR linked-reads. ResultsAquila_stLFR first partitions LFRs into two haplotype-specific blocks, by taking advantage of the potential phasing ability of the linked-read itself. Each haplotype is then assembled independently, to achieve a complete diploid assembly to finally reconstruct the genome-wide SVs. We benchmarked Aquila_stLFR on a well-studied sample, NA24385, and showed Aquila_stLFR can detect medium to large size (50bp - 10kb) deletions with a high sensitivity and insertions with a high specificity. AvailabilitySource code and documentation are available on https://github.com/maiziex/Aquila_stLFR. Contactmaizie.zhou@vanderbilt.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

Musculoskeletal fat imaging and quantification by high-resolution metabolite cycling magnetic resonance spectroscopic imaging at 3 T: A fast method to generate separate distribution maps of lipid components

PurposeTo provide a rapid, non-invasive fat quantification technique capable of producing separate lipid component maps.\n\nMethodsThe calf muscles in 5 healthy adolescents (age 12-16 years; BMI = 20 {+/-} 3 Kg/m2) were scanned by two different fat fraction (FF) quantification methods. A high-resolution, density-weighted concentric ring trajectory (DW-CRT) metabolite cycling (MC) magnetic resonance spectroscopic imaging (MRSI) technique was implemented to collect data with 0.25 mL resolution within 3 minutes and 16 seconds. For comparative purposes, the standard Dixon technique was performed. The two techniques were compared using structural similarity (SSIM) analysis. Additionally, the difference in the distribution of each lipid over the adolescent calf muscles was assessed based on the MRSI data.\n\nResultsThe proposed MRSI technique provided individual FF maps for eight musculoskeletal lipids identified by LCModel analysis (L09, L11, L13, L15, L21, L23, L53, and L55) with mean SSIM indices of 0.19, 0.04, 0.03, 0.50, 0.45, 0.04, 0.07, and 0.12, respectively compared to that of Dixons FF map. Further analysis of voxels with zero SSIM demonstrated an increased sensitivity of FF lipid maps from data acquired using this MRSI technique over the standard Dixon technique. The trend of lipid spatial distribution over calf muscles was consistent with previously published findings in adults.\n\nConclusionThe advantages of this MRSI technique make it a useful tool when individual lipid FF maps are desired within a short scanning time.

biochemistry↗

bioRxiv2019

Assessment of human diploid genome assembly with 10x Linked-Reads data

BackgroundProducing cost-effective haplotype-resolved personal genomes remains challenging. 10x Linked-Read sequencing, with its high base quality and long-range information, has been demonstrated to facilitate de novo assembly of human genomes and variant detection. In this study, we investigate in depth how the parameter space of 10x library preparation and sequencing affects assembly quality, on the basis of both simulated and real libraries.\n\nFindingsWe prepared and sequenced eight 10x libraries with a diverse set of parameters from standard cell lines NA12878 and NA24385 and performed whole genome assembly on the data. We also developed the simulator LRTK-SIM to follow the workflow of 10x data generation and produce realistic simulated Linked-Read data sets. We found that assembly quality could be improved by increasing the total sequencing coverage (C) and keeping physical coverage of DNA fragments (CF) or read coverage per fragment (CR) within broad ranges. The optimal physical coverage was between 332X and 823X and assembly quality worsened if it increased to greater than 1,000X for a given C. Long DNA fragments could significantly extend phase blocks, but decreased contig contiguity. The optimal length-weighted fragment length (WFL) was around 50 - 150kb. When broadly optimal parameters were used for library preparation and sequencing, ca. 80% of the genome was assembled in a diploid state.\n\nConclusionThe Linked-Read libraries we generated and the parameter space we identified provide theoretical considerations and practical guidelines for personal genome assemblies based on 10x Linked-Read sequencing.

bioinformatics↗

bioRxiv2019

The glycine locating at random coil of picornaviruses VP3 enhances viral pathogenicity by targeting p53 to promote apoptosis and autophagy

Picornaviruses, comprising important and widespread pathogens of humans and animals, have evolved to control apoptosis and autophagy for their replication and spread. However, the underlying mechanism of the association between apoptosis/autophage and viral pathogenicity remains unclear. In the present study, VP3 of picornaviruses was demonstrated to induce apoptosis and autophagy. Foot-and-mouth disease virus (FMDV), which served as a research model here, can strongly induce both apoptosis and autophagy in the skin lesions. By directly interacting with p53, FMDV-VP3 facilitates its phosphorylation and translocation, resulting in Bcl-2 family-mediated apoptosis and LC3-dependent autophagy. The single residue Gly129 of FMDV-VP3 plays a crucial role in apoptosis and autophagy induction and the interaction with p53. Consistently, the comparison of rescued FMDV with mutated Gly129 and parental virus showed that the Gly129 is indispensable for viral replication and pathogenicity. More importantly, the Gly129 locates at a bend region of random coil structure, the mutation of Gly to Ala remarkably shrunk the volume of viral cavity. Coincidentally, the Gly is conserved in the similarly location of other picornaviruses, including poliovirus (PV), enterovirus 71 (EV71), coxsackievirus (CV) and seneca valley virus (SVA). This study demonstrates that picornaviruses induce apoptosis and autophagy to facilitate its pathogenicity and the Gly is functional site, providing novel insights into picornavirus biology.

microbiology↗

bioRxiv2019

CRMP2 mediates Sema3F-dependent axon pruning and dendritic spine remodeling

Regulation of axon guidance and pruning of inappropriate synapses by class 3 semaphorins is key to development of neural circuits. Collapsin response mediator protein 2 (CRMP2) has been shown to regulate axon guidance by mediating Semaphorin 3A (Sema3A) signaling and its dysfunction has been linked to schizophrenia, however, nothing is known about its role in the synapse pruning. Here, using newly generated crmp2-/- mice we demonstrate that while CRMP2 has only a moderate effect on Sema3A-dependent axon guidance in vivo, it is essential for Sema3F-dependent axon pruning and dendritic spine remodeling. We first demonstrate that CRMP2 deficiency interferes with Sema3A signaling in compartmentalized neuron cultures and leads to a mild defect in axon guidance in peripheral nerves and corpus callosum. Strikingly, we show that crmp2-/- mice display more prominent defects in dendritic spine pruning and stereotyped axon pruning in hippocampus and visual cortex consistent with impaired Sema3F signaling and with autism spectrum disorder (ASD)-rather than schizophrenia-like phenotype. Indeed, we demonstrate that CRMP2 mediates Sema3F-induced axon retraction in primary neurons and that crmp2-/- mice display early postnatal behavioral changes linked to ASD. In conclusion, we demonstrate that CRMP2 is an essential mediator of Sema3F-dependent synapse pruning and its dysfunction in early postnatal stages shares histological and behavioral features of ASD.

neuroscience↗

bioRxiv2019

Principles of Cell Circuits for Tissue Repair and Fibrosis

Tissue-repair is a protective response after injury, but repetitive or prolonged injury can lead to fibrosis, a pathological state of excessive scarring. To pinpoint the dynamic mechanisms underlying fibrosis, it is important to understand the principles of the cell circuits that carry out tissue-repair. In this study, we establish a cell-circuit framework for the myofibroblast-macrophage circuit in wound-healing, including the accumulation of scar-forming extracellular matrix. We find that fibrosis results from multistability between three outcomes, which we term hot fibrosis characterized by many macrophages, cold fibrosis lacking macrophages, and normal wound-healing. The cell-circuit framework clarifies several unexplained phenomena including the paradoxical effect of macrophage depletion, the limited time-window in which removing inflammation leads to healing, the effects of cellular senescence, and why scar maturation takes months. We define key parameters that control the transition from healing to fibrosis, which may serve as potential targets for therapeutic reduction of fibrosis.

systems biology↗

bioRxiv2019

Phylogenetic Tree-based Pipeline for Uncovering Mutational Patterns during Influenza Virus Evolution

Various computational and statistical approaches have been proposed to uncover the mutational patterns of rapidly evolving influenza viral genes. Nonetheless, the approaches mainly rely on sequence alignments which could potentially lead to spurious mutations obtained by comparing sequences from different clades that coexist during particular periods of time. To address this issue, we propose a phylogenetic tree-based pipeline that takes into account the evolutionary structure in the sequence data. Assuming that the sequences evolve progressively under a strict molecular clock, considering a competitive model that is based on a certain Markov model, and using a resampling approach to obtain robust estimates, we could capture statistically significant single-mutations and co-mutations during the sequence evolution. Moreover, by considering the results obtained from analyses that consider all paths and the longest path in the resampled trees, we can categorize the mutational sites and suggest their relevance. Here we applied the pipeline to investigate the 50 years of evolution of the HA sequences of influenza A/H3N2 viruses. In addition to confirming previous knowledge on the A/H3N2 HA evolution, we also demonstrate the use of the pipeline to classify mutational sites according to whether they are able to enhance antigenic drift, compensate other mutations that enhance antigenic drift, or both.

evolutionary biology↗

bioRxiv2019

Leveraging Gene Co-expression Patterns to Infer Trait-Relevant Tissues in Genome-wide Association Studies

Genome-wide association studies (GWASs) have identified many SNPs associated with various common diseases. Understanding the biological functions of these identified SNP associations requires identifying disease/trait relevant tissues or cell types. Here, we develop a network method, CoCoNet, to facilitate the identification of trait-relevant tissues or cell types. Different from existing approaches, CoCoNet incorporates tissue-specific gene co-expression networks constructed from either bulk or single cell RNA sequencing (RNAseq) studies with GWAS data for trait-tissue inference. In particular, CoCoNet relies on a covariance regression network model to express gene-level effect sizes for the given GWAS trait as a function of the tissue-specific co-expression adjacency matrix. With a composite likelihood-based inference algorithm, CoCoNet is scalable to tens of thousands of genes. We validate the performance of CoCoNet through extensive simulations. We apply CoCoNet for an in-depth analysis of four neurological disorders and four autoimmune diseases, where we integrate the corresponding GWASs with bulk RNAseq data from 38 tissues and single cell RNAseq data from 10 cell types. In the real data applications, we show how CoCoNet can help identify specific glial cell types relevant for neurological disorders and identify disease-targeted colon tissues as relevant for autoimmune diseases. Our results also provide empirical evidence supporting one hypothesis of the omnigenic model: that trait-relevant gene co-expression networks underlie disease etiology.

genetics↗

bioRxiv2019

Enhancing single-cell cellular state inference by incorporating molecular network features

In biological systems, genes function in conjunction rather than in isolation. However, traditional single-cell RNA-seq (scRNA-seq) analyses heavily rely on the transcriptional similarity of individual genes, ignoring the inherent gene-gene interactions. Here, we present SCORE, a network-based method, which incorporates the validated molecular network features to infer cellular states. Using real scRNA-seq datasets, SCORE outperforms existing methods in accuracy, robustness, scalability, data integration and removal of batch effect. When applying SCORE to a newly generated human ileal scRNA-seq dataset, we identified several novel stem/progenitor clusters, including a Cripto-1+ cluster. Moreover, two distinct groups of goblet cells were identified and only one of them tended to secrete mucus. Besides, we found that the recently identified BEST4+OTOP2+ microfold cells also highly expressed CFTR, which is different from their colonic counterparts. In summary, SCORE enhances cellular state inference by simulating the dynamic changes of molecular networks, providing more biological insights beyond statistical interpretations.

bioinformatics↗

bioRxiv2019

A Whole-Genome Approach Discovers Novel Genetic and Non-Genetic Variance Components Modulated by Lifestyle for Cardiovascular Health

Both genetic and non-genetic factors can predispose individuals to cardiovascular risk. Finding ways to alter these predispositions is important for cardiovascular disease (CVD) prevention. Here, we use a novel whole-genome framework to estimate genetic and non-genetic effects on--hence their predispositions to--cardiovascular risk and determine whether they vary with respect to lifestyle factors. We performed analyses on the Atherosclerosis Risk in Communities Study (ARIC, N=6,896-7,180) and validated findings using the UK Biobank (UKBB, N=14,076-34,538). Cardiovascular risk was measured using 23 traits in the ARIC and eight traits in the UKBB, such as body mass index (BMI), resting heart rate, white blood cell count and blood pressure; and lifestyle factors included information on physical activity, smoking, alcohol consumption and dietary intake. Physical activity altered both genetic and non-genetic effects on heart rate and BMI, genetic effects on HDL cholesterol level, and non-genetic effects on waist-to-hip ratio. Alcohol consumption altered both genetic and non-genetic effects on BMI, while smoking altered non-genetic effects on heart rate, pulse pressure, and white blood cell count. In addition, saturated fat intake modified genetic effects on BMI, and total daily energy intake modified non-genetic effects on waist-to-hip ratio. These results highlight the relevance of lifestyle changes for CVD prevention. We also stratified individuals according to their genetic predispositions and showed notable differences in the effects of lifestyle on cardiovascular risk across stratified groups, implying the need for individualizing lifestyle changes for CVD prevention. Finally, we showed that neglecting lifestyle modulation of genetic and non-genetic effects will on average reduce SNP heritability estimates of cardiovascular traits by a small yet significant amount, primarily owing to overestimation of residual variance. Thus, current SNP heritability estimates for cardiovascular traits, which commonly do not consider modulating effects of lifestyle covariates, are likely underestimated.

genetics↗

bioRxiv2019

CASLFA: CRISPR/Cas9-mediated lateral flow nucleic acid assay

The lateral flow assay is one of the oldest and most convenient analytical techniques for analyzing the immune response, but its applicability to precise genetic analyses is limited by the tedious and inefficient hybridization steps. Here, we have introduced a new version of the lateral flow assay, termed Cas9-mediated lateral flow nucleic acids assay (CASLFA), to address such issues. In this study, CASLFA is utilized to identify Listeria monocytogenes, genetically modified organisms (GMOs), and African swine fever virus (ASFV) at a sensitivity of hundreds of copies of genome samples with high specificity within 1 h. CASLFA satisfies some of the characteristics of a next-generation molecular diagnostics tool due to its rapidity and accuracy, allowing for point-of-care use without the need for technical expertise and complex ancillary equipment. This method has great potential for analyzing genes in resource-poor or nonlaboratory environments.

bioengineering↗

bioRxiv2019

Neck Musculoskeletal Model Generation through Anthropometric Scaling

A new methodology was developed to quickly generate whole body models with detailed neck musculoskeletal architecture that are properly scaled in terms of anthropometry and muscle strength. This method was implemented in an anthropometry model generation software that allows users to interactively generate any new male or female musculoskeletal models with adjustment of anthropometric parameters (such as height, weight, neck circumference, and neck length) without the need of subject-specific motion capture or medical images. 50th percentile male and female models were developed based on the 2012 US Army Anthropometric Survey (ANSUR II) database and optimized with a novel bilevel optimization method to have strengths comparable to experimentally measured values in the literature. Other percentile models (ranging from the 1st to 99th percentile) were generated based on anthropometric scaling of the 50th percentile models and compared. The resultant models are reasonably accurate in terms of both musculoskeletal geometry and neck strength, demonstrating the effectiveness of the developed methodology for interactive neck model generation with anthropometric scaling.

bioengineering↗

bioRxiv2019

Testing and controlling for horizontal pleiotropy with the probabilistic Mendelian randomization in transcriptome-wide association studies

Integrating association results from both genome-wide association studies (GWASs) and expression quantitative trait locus (eQTL) mapping studies has the potential to shed light on the molecular mechanisms underlying disease etiology. Several statistical methods have been recently developed to integrate GWASs with eQTL studies in the form of transcriptome-wide association studies (TWASs). These existing methods can all be viewed as a form of two sample Mendelian randomization (MR) analysis, which has been widely applied in various GWASs for inferring the causal relationship among complex traits. Unfortunately, most existing TWAS and MR methods make an unrealistic modeling assumption and assume that instrumental variables do not exhibit horizontal pleiotropic effects. However, horizontal pleiotropic effects have been recently discovered to be wide spread across complex traits, and, as we will show here, are also wide spread across gene expression traits. Therefore, not allowing for horizontal pleiotropic effects can be overly restrictive, and, as we will be show here, can lead to a substantial inflation of test statistics and subsequently false discoveries in TWAS applications. Here, we present a probabilistic MR method, which we refer to as PMR-Egger, for testing and controlling for horizontal pleiotropic effects in TWAS applications. PMR-Egger relies on an MR likelihood framework that unifies many existing TWAS and MR methods, accommodates multiple correlated instruments, tests the causal effect of gene on trait in the presence of horizontal pleiotropy, and, with a newly developed parameter expansion version of the expectation maximization algorithm, is scalable to hundreds of thousands of individuals. With extensive simulations, we show that PMR-Egger provides calibrated type I error control for causal effect testing in the presence of horizontal pleiotropic effects, is reasonably robust for various types of horizontal pleiotropic effect mis-specifications, is more powerful than existing MR approaches, and, as a by-product, can directly test for horizontal pleiotropy. We illustrate the benefits of PMR-Egger in applications to 39 diseases and complex traits obtained from three GWASs including the UK Biobank. In these applications, we show how PMR-Egger can lead to new biological discoveries through integrative analysis.

genetics↗

bioRxiv2019

Guilty by association: How group-based (collective) guilt arises in the brain

People do not only feel guilty for transgressions of social norms/expectations that they are causally responsible for, but they also feel guilty for transgressions committed by those they identify as in-group (i.e., collective or group-based guilt). However, the neurocognitive basis of group-based guilt and its relation to personal guilt are unknown. To address these questions, we combined functional MRI with an interaction-based minimal group paradigm in which participants either directly caused harm to victims (i.e., personal guilt), or observed in-group members cause harm to the victims (i.e., group-based guilt). In three experiments (N = 90), we demonstrated that perceived shared responsibility with in-group members in the transgression predicted behavioral and neural manifestations of group-based guilt. Multivariate pattern analysis of the functional MRI data showed that group-based guilt recruited a similar brain representation in anterior middle cingulate cortex as personal guilt. These results have broaden our understanding of how group membership is integrated into social emotions.

neuroscience↗

bioRxiv2019

Aquila: diploid personal genome assembly and comprehensive variant detection based on linked reads

Variant discovery in personal, whole genome sequence data is critical for uncovering the genetic contributions to health and disease. We introduce a new approach, Aquila, that uses linked-read data for generating a high quality diploid genome assembly, from which it then comprehensively detects and phases personal genetic variation. Assemblies cover >95% of the human reference genome, with over 98% in a diploid state. Thus, the assemblies support detection and accurate genotyping of the most prevalent types of human genetic variation, including single nucleotide polymorphisms (SNPs), small insertions and deletions (small indels), and structural variants (SVs), in all but the most difficult regions. All heterozygous variants are phased in blocks that can approach arm-level length. The final output of Aquila is a diploid and phased personal genome sequence, and a phased VCF file that also contains homozygous and a few unphased heterozygous variants. Aquila represents a cost-effective evolution of whole-genome reconstruction that can be applied to cohorts for variation discovery or association studies, or to single individuals with rare phenotypes that could be caused by SVs or compound heterozygosity.

genomics↗

bioRxiv2019

CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies

MotivationAlthough genome-wide association studies (GWAS) have deepened our understanding of the genetic architecture of complex traits, the mechanistic links that underlie how genetic variants cause complex traits remains elusive. To advance our understanding of the underlying mechanistic links, various consortia have collected a vast volume of genomic data that enable us to investigate the role that genetic variants play in gene expression regulation. Recently, a collaborative mixed model (CoMM) [42] was proposed to jointly interrogate genome on complex traits by integrating both the GWAS dataset and the expression quantitative trait loci (eQTL) dataset. Although CoMM is a powerful approach that leverages regulatory information while accounting for the uncertainty in using an eQTL dataset, it requires individual-level GWAS data and cannot fully make use of widely available GWAS summary statistics. Therefore, statistically efficient methods that leverages transcriptome information using only summary statistics information from GWAS data are required.\n\nResultsIn this study, we propose a novel probabilistic model, CoMM-S2, to examine the mechanistic role that genetic variants play, by using only GWAS summary statistics instead of individual-level GWAS data. Similar to CoMM which uses individual-level GWAS data, CoMM-S2 combines two models: the first model examines the relationship between gene expression and genotype, while the second model examines the relationship between the phenotype and the predicted gene expression from the first model. Distinct from CoMM, CoMM-S2 requires only GWAS summary statistics. Using both simulation studies and real data analysis, we demonstrate that even though CoMM-S2 utilizes GWAS summary statistics, it has comparable performance as CoMM, which uses individual-level GWAS data.\n\nContactjin.liu@duke-nus.edu.sg\n\nAvailability and implementationThe implement of CoMM-S2 is included in the CoMM package that can be downloaded from https://github.com/gordonliu810822/CoMM.\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

Age-dependent sexual dimorphism in the adult human gut microbiota

A decade of studies has established the importance of the gut microbiome in human health. In spite of sex differences in the physiology, lifespan, and prevalence of many age-associated diseases, sex and age disparities in the gut microbiota have been little studied. Here we show age-related sex differences in the adult gut microbial composition and functionality in two community-based cohorts from Northern China and the Netherlands. Consistently, women harbour a more diverse and stable microbial community across broad age ranges, whereas men exhibit a more variable gut microbiota strongly correlated with age. Reflecting the sex-biased age-gut microbiota interaction patterns, sex differences observed in younger adults are considerably reduced in the elderly population. Our findings highlight the age- and sex-biased differences in the adult gut microbiota across two ethnic population and emphasize the need for considering age and sex in studies of the human gut microbiota.

genomics↗