Biology subjects

Qiu, X.

Publications and source records attributed to Qiu, X..

At least 145 records · Page 8Linked to original sources

bioRxiv2020

Integrated profiling of single cell epigenomic and transcriptomic landscape of Parkinson's disease mouse brain

Parkinsons disease (PD) is a neurodegenerative disease leading to the impairment of execution of movement. PD pathogenesis has been largely investigated, but either restricted in bulk level or at certain cell types, which failed to capture cellular heterogeneity and intrinsic interplays among distinct cell types. To overcome this, we applied single-nucleus RNA-seq and single cell ATAC-seq on cerebellum, midbrain and striatum of PD mouse and matched control. With 74,493 cells in total, we comprehensively depicted the dysfunctions under PD pathology covering proteostasis, neuroinflammation, calcium homeostasis and extracellular neurotransmitter homeostasis. Besides, by multi-omics approach, we identified putative biomarkers for early stage of PD, based on the relationships between transcriptomic and epigenetic profiles. We located certain cell types that primarily contribute to PD early pathology, narrowing the gap between genotypes and phenotypes. Taken together, our study provides a valuable resource to dissect the molecular mechanism of PD pathogenesis at single cell level, which could facilitate the development of novel methods regarding diagnosis, monitoring and practical therapies against PD at early stage.

cell biology↗

bioRxiv2019

Homozygous STAT2 gain-of-function mutation by loss of USP18 activity in a patient with type I interferonopathy

Type I interferonopathies are monogenic disorders characterized by enhanced Type I interferon (IFN-I) activity. Inherited ISG15 and USP18 deficiencies underlie type I interferonopathies by preventing the regulation of late responses to IFN-I. Specifically, ISG15/USP18 are induced by IFN-I and sterically hinder JAK1 from binding to the IFNAR2 subunit of IFN-I receptor. We report an infant who died of autoinflammation due to a homozygous missense mutation (R148Q) in STAT2. The variant is gain-of-function (GOF) for ISGF3-dependent induction of late but not early response to IFN-I. Surprisingly, the mutation does not enhance the intrinsic transcriptional activity of ISGF3. Rather, the STAT2 R148Q variant is GOF because it fails to appropriately interact with and traffic USP18 to IFNAR2, preventing USP18 from negatively regulating responses to IFN-I. Overall, a STAT2 missense mutation that fails to facilitate USP18-mediated signal termination in the homozygous state underlies a novel genetic etiology of type I interferonopathy.

immunology↗

bioRxiv2019

Single cell atlas of domestic pig brain illuminates the conservation and divergence of cell types at spatial and species levels

Domestic pig (Sus scrofa domesticus) has drawn much attention from researchers worldwide due to its implications in evolutionary biology, regenerative medicine and agriculture. The brain atlas of Homo sapiens (primate), Mus musculus (rodent), Danio rerio (fish) and Drosophila melanogaster (insect) have been constructed at single cell resolution, however, the cellular compositions of pig brain remain largely unexplored. In this study, we investigated the single-cell transcriptomic profiles of five distinct regions of domestic pig brain, from which we identified 21 clusters corresponding to six major cell types, characterized by unique spectrum of gene expression. By spatial comparison, we identified cell types enriched or depleted in certain brain regions. Inter-species comparison revealed cell-type similarities and divergences in hypothalamus between mouse and pig, providing invaluable resources for the evolutionary exploration of brain functions at single cell level. Besides, our study revealed cell types and molecular pathways closely associated with several diseases (obesity, anorexia, bulimia, epilepsy, intellectual disability, and autism spectrum disorder), bridging the gap between gene mutations and pathological phenotypes, which might be of great use to the development precise therapies against neural system disorders. Taken together, we reported, so far as we know, the first single cell brain atlas of Sus scrofa domesticus, followed by comprehensive comparisons across brain region and species, which could throw light upon future evo-devo, regenerative medicine, and agricultural studies.

bioinformatics↗

bioRxiv2019

Can computers understand word meanings like the human brain does? Comparable semantic representation in neural and computer systems

Semantic representation has been studied independently in neuroscience and computer science. A deep understanding of human neural computations and the revolution to strong artificial intelligence appeal for a joint force in the language domain. We investigated comparable representational formats of lexical semantics between these two complex systems with fine temporal resolution neural recordings. We found semantic representations generated from computational models significantly correlated with EEG responses at an early stage of a typical semantic processing time window in a two-word semantic priming paradigm. Moreover, three representative computational models differentially predicted EEG responses along the dynamics of word processing. Our study provided a finer-grained understanding of the neural dynamics underlying semantic processing and developed an objective biomarker for assessing human-like computation in computational models. Our novel framework trailblazed a promising way to bridge across disciplines in the investigation of higher-order cognitive functions in human and artificial intelligence.

neuroscience↗

bioRxiv2019

Mitogen-independent cell cycle progression in B lymphocytes

The canonical view of the cell cycle posits that G1 progression signals are essential after each mitosis to enter S phase. A subset of tumor cells bypass this requirement and progress to the next cell division in the absence of continued signaling. B and T lymphocytes of the adaptive immune system undergo a proliferative burst, termed clonal expansion, to generate pools of antigen specific cells for effective immunity. There is evidence that rules for lymphocyte cell division digress from the canonical model. Here we show that B lymphocytes sustain several rounds of mitogen-independent cell division following the first mitosis. Such division is driven by unique characteristics of the post mitotic G1 phase and limited by extensive cell death that can be circumvented by appropriate anti-apoptotic signals. An essential component for continued cell division is Birc5 (survivin), a protein associated with chromosome segregation in G2/M. Our observation provides direct evidence for Pardees hypothesis that retention of features of G2M in post-mitotic cells could trigger further cell cycle progression. The partially active G1 phase and propensity for apoptosis that is inherited after each division may permit rapid burst of proliferation and cell death that are hallmarks of immune responses.

cell biology↗

bioRxiv2019

GRASP: a Bayesian network structure learning method using adaptive sequential Monte Carlo

Bayesian networks (BNs) provide a probabilistic, graphical framework for modeling high-dimensional joint distributions with complex dependence structures. BNs can be used to infer complex biological networks using heterogeneous data from different sources with missing values. Despite extensive studies in the past, network structure learning from data is still a challenging open question in BN research. In this study, we present a sequential Monte Carlo (SMC) based three-stage approach, GRowth-based Approach with Staged Pruning (GRASP). A double filtering strategy was first used for discovering the overall skeleton of the target BN. To search for the optimal network structures we designed an adaptive SMC (adSMC) algorithm to increase the diversity of sampled networks which were further improved by a new stage to reclaim edges missed in the skeleton discovery step. GRASP gave very satisfactory results when tested on benchmark networks. Finally, BN structure learning using multiple types of genomics data illustrates GRASPs potential in discovering novel biological relationships in integrative genomic studies.

bioinformatics↗

bioRxiv2019

Machine Learning for Prioritization of Thermostabilizing Mutations for G-protein Coupled Receptors

Although the three-dimensional structures of G-protein-coupled receptors (GPCRs), the largest superfamily of drug targets, have enabled structure-based drug design, there are no structures available for 87% of GPCRs. This is due to the stiff challenge in purifying the inherently flexible GPCRs. Identifying thermostabilized mutant GPCRs via systematic alanine scanning mutations has been a successful strategy in stabilizing GPCRs, but it remains a daunting task for each GPCR. We developed a computational method that combines sequence, structure and dynamics based molecular properties of GPCRs that recapitulate GPCR stability, with four different machine learning methods to predict thermostable mutations ahead of experiments. This method has been trained on thermostability data for 1231 mutants, the largest publicly available dataset. A blind prediction for thermostable mutations of the Complement factor C5a Receptor retrieved 36% of the thermostable mutants in the top 50 prioritized mutants compared to 3% in the first 50 attempts using systematic alanine scanning.\n\nStatement Of SignifiganceG-protein-coupled receptors (GPCRs), the largest superfamily of membrane proteins play a vital role in cellular physiology and are targets to blockbuster drugs. Hence it is imperative to solve the three dimensional structures of GPCRs in various conformational states with different types of ligands bound. To reduce the experimental burden in identifying thermostable GPCR mutants, we report a computational framework using machine learning algorithms trained on thermostability data for 1231 mutants and features calculated from analysis of GPCR sequences, structure and dynamics to predict thermostable mutations ahead of experiments. This work represents a significant advancement in the development, validation and testing of a computational framework that can be extended to other class A GPCRs and helical membrane proteins.

biophysics↗

bioRxiv2019

Mapping Vector Field of Single Cells

Single-cell RNA-seq, together with RNA velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires dynamical models capable of predicting cell fate and unveiling the governing regulatory mechanisms. Here, we introduce dynamo, an analytical framework that reconciles intrinsic splicing and labeling kinetics to estimate absolute RNA velocities, reconstructs velocity vector fields that predict future cell fates, and finally employs differential geometry analyses to elucidate the underlying regulatory networks. We applied dynamo to a wide range of disparate biological processes including prediction of future states of differentiating hematopoietic stem cell lineages, deconvolution of glucocorticoid responses from orthogonal cell-cycle progression, characterization of regulatory networks driving zebrafish pigmentation, and identification of possible routes of resistance to SARS-CoV-2 infection. Our work thus represents an important step in going from qualitative, metaphorical conceptualizations of differentiation, as exemplified by Waddingtons epigenetic landscape, to quantitative and predictive theories.

systems biology↗

bioRxiv2019

Insufficiency in airway interferon activation defines clinical severity to infant RSV infection

Respiratory syncytial virus (RSV) is the leading cause of severe respiratory disease in infants. Other than age at the time of infection, the causes and correlates of severe illness in infants lacking known risk factors are poorly defined. We recruited a cohort of confirmed RSV-infected infants and simultaneously assayed the presence of resident airway microbiota and the molecular status of their airways using a novel method. Rigorous statistical analyses identified a molecular airway gene expression signature of severe illness dominated by excessive chemokine expression. Global 16S rRNA sequencing confirmed an association between H. influenzae and clinical severity. Interestingly, adjusting for H. influenzae in our gene expression analysis revealed an association between severity and airway lymphocyte accumulation. Exploring the relationship between airway gene expression and the time of onset of clinical symptoms revealed a robust, acute activation of interferon (IFN) signaling, which was absent in subjects with severe illness. Finally, we explored the relationship between IFN activity, airway gene expression and productive RSV infection using a novel in vitro model of bona fide pediatric human airway epithelial cells. Interestingly, blocking IFN signaling, but not IFN ligand production, in these cells leads to increased viral infection. Our data reveal that acute airway interferon responses are physiologically relevant in the context of infant RSV infection and may be a target for therapeutic intervention. Additionally, the airway gene expression signature we define may be useful as a biomarker for efficacy of intervention responses.

genomics↗

bioRxiv2019

Genotype-dependent and non-gradient patterns of RSV gene expression

Respiratory syncytial virus (RSV) is a nonsegmented negative-strand (NNS) RNA virus and a leading cause of severe lower respiratory tract illness in infants and the elderly. Transcription of the ten RSV genes proceeds sequentially from the 3 promoter and requires conserved gene start (GS) and gene end (GE) signals. Previous studies using the prototypical GA1 genotype Long and A2 strains have indicated a gradient of gene transcription. However, recent reports show data that appear inconsistent with a gradient. To better understand RSV transcriptional regulation, mRNA abundances from five RSV genes were measured by quantitative real-time PCR (qPCR) in three cell lines and cotton rats infected with virus isolates belonging to four different genotypes (GA1, ON, GB1, BA). Relative mRNA levels reached steady-state between four and 24 hours post-infection. Steady-state patterns were genotype-specific and non-gradient, where mRNA levels from the G (attachment) gene exceeded those from the more promoter-proximal N (nucleocapsid) gene across isolates. Transcript stabilities could not account for the non-gradient patterns observed, indicating that relative mRNA levels more strongly reflect transcription than decay. While the GS signal sequences were highly conserved, their alignment with N protein in the helical ribonucleocapsid, i.e., N-phase, was variable, suggesting polymerase recognition of GS signal conformation affects transcription initiation. The effect of GS N-phase on transcription efficiency was tested using dicistronic minigenomes. Ratios of minigenome gene expression showed a switch-like dependence on N-phase with a period of seven nucleotides. Our results indicate that RSV gene expression is in part sculpted by polymerases that initiate transcription with a probability dependent on GS signal N-phase.\n\nAuthor SummaryRSV is a major viral pathogen that causes significant morbidity and mortality, especially in young children. Shortly after RSV enters a host cell, transcription from its nonsegmented negative-strand (NNS) RNA genome starts at the 3 promoter and proceeds sequentially. Transcriptional attenuation is thought to occur at each gene junction, resulting in a gradient of gene expression. However, recent studies showing non-gradient levels of RSV mRNA suggest that transcriptional regulation may have additional mechanisms. We show using RSV isolates belonging to four different genotypes that gene expression is genotype-dependent and one gene (the G or attachment gene) is consistently more highly expressed than an upstream neighbor. We hypothesize that variable alignment of highly conserved gene start (GS) signals with nucleoprotein (i.e., variable GS N-phase) can affect transcription and give rise to non-gradient patterns of gene expression. We show using dicistronic RSV minigenomes wherein the reporter genes differ only in the N-phase of one GS signal that GS N-phase affects gene expression. Our results suggest the existence of a novel mechanism of transcriptional regulation that might play a role in other NNS RNA viruses.

molecular biology↗

bioRxiv2019

Airway Gene-Expression Classifiers for Respiratory Syncytial Virus (RSV) Disease Severity in Infants

BackgroundA substantial number of infants infected with RSV develop severe symptoms requiring hospitalization. We currently lack accurate biomarkers that are associated with severe illness. MethodWe defined airway gene expression profiles based on RNA sequencing from nasal brush samples from 106 full-tem previously healthy RSV infected subjects during acute infection (day 1-10 of illness) and convalescence stage (day 28 of illness). All subjects were assigned a clinical illness severity score (GRSS). Using AIC-based model selection, we built a sparse linear correlate of GRSS based on 41 genes (NGSS1). We also built an alternate model based upon 13 genes associated with severe infection acutely but displaying stable expression over time (NGSS2). ResultsNGSS1 is strongly correlated with the disease severity, demonstrating a naive correlation ({rho}) of {rho}=0.935 and cross-validated correlation of 0.813. As a binary classifier (mild versus severe), NGSS1 correctly classifies disease severity in 89.6% of the subjects following cross-validation. NGSS2 has slightly less, but comparable, accuracy with a cross-validated correlation of 0.741 and classification accuracy of 84.0%. ConclusionAirway gene expression patterns, obtained following a minimally-invasive procedure, have potential utility for development of clinically useful biomarkers that correlate with disease severity in primary RSV infection.

immunology↗

bioRxiv2019

Germline-encoded TCR-MHC contacts promote TCR V gene bias in umbilical cord blood T cell repertoire

T cells recognize antigens as peptides bound to major histocompatibility complex (MHC) proteins through T cell receptors (TCRs) on their surface. To recognize a wide range of pathogens, each individual possesses a substantial number of TCRs with an extremely high degree of variability. It remains controversial whether germline-encoded TCR repertoire is shaped by MHC polymorphism and, if so, what is the preference between MHC genetic variants and TCR V gene compatibility. To investigate the \"net\" genetic association between MHC variations and TRBV genes, we applied quantitative trait locus (QTL) mapping to test the associations between MHC polymorphism and TCR {beta} chain V (TRBV) genes usage using umbilical cord blood (UCB) samples of 201 Chinese newborns. We found TRBV gene and MHC loci that are predisposed to interact with one another differ from previous conclusions. The majority of MHC amino acid residues associated with the TRBV gene usage show spatial proximities in known structures of TCR-pMHC complexes. These results show for the first time that MHC variants bias TRBV gene usage in UCB of Chinese ancestry and indicate that germline-encoded contacts influence TCR-MHC interactions in intact T cell repertoires.

immunology↗

bioRxiv2019

Structural basis for the transport mechanism of the human glutamine transporter SLC1A5 (ASCT2)

Alanine-serine-cysteine transporter 2 (ASCT2, SLC1A5) is the primary transporter of glutamine in cancer cells and regulates the mTORC1 signaling pathway. The SLC1A5 function involves finely tuned orchestration of two domain movements that include the substrate-binding transport domain and the scaffold domain. Here, we present cryo-EM structures of human SLC1A5 and its complex with the substrate, L-glutamine in an outward-facing conformation. These structures reveal insights into the conformation of the critical ECL2a loop which connects the two domains, thus allowing rigid body movement of the transport domain throughout the transport cycle. Furthermore, the structures provide new insights into substrate recognition, which involves conformational changes in the HP2 loop. A putative cholesterol binding site was observed near the domain interface in the outward-facing state. Comparison with the previously determined inward-facing structure of SCL1A5 provides a basis for a more integrated understanding of substrate recognition and transport mechanism in the SLC1 family. Our structures are likely to aid the development of potent and selective SLC1A5 inhibitors for the treatment of cancer and autoimmune disorders.

biophysics↗

bioRxiv2019

A multi-omic cohort as a reference point for promoting a healthy human gut microbiome

More than a decade of gut microbiome studies have a common goal for human health. As most of the disease studies sample the elderly or the middle-aged, a reference cohort for young individuals has been lacking. It is also not clear what other omics data need to be measured to better understand the gut microbiome. Here we present high-depth metagenomic shotgun sequencing data for the fecal microbiome together with other omics data in a cohort of 2,183 adults, and observe a number of vitamins, hormones, amino acids and trace elements to correlate with the gut microbiome and cluster with T cell receptors. Associations with physical fitness, sleeping habits and dairy consumption are identified in this large multi-omic cohort. Many of the associations are validated in an additional cohort of 1,404 individuals. Our comprehensive data are poised to advise future study designs to better understand and manage our gut microbiome both in population and in mechanistic investigations.

microbiology↗

bioRxiv2019

Integrating Genome-Wide Association and eQTLs Studies Identifies the Genes Associated with Age at Menarche and Age at Natural Menopause

ObjectiveAn early onset of menarche and, later, menopause are well-established risk factors for the development of breast cancer and endometrial cancer. Although the largest GWASs have identified 389 independent signals for age at menarche (AAM) and 44 regions for age at menopause (ANM), GWAS can only identify the associations between variants and traits. The aim of this study was to identify genes whose expression levels were associated with AAM or ANM due to pleiotropy or causality by integrating GWAS data with genome-wide expression quantitative trait loci (eQTLs) data. We also aimed to identify the pleiotropic genes that influenced two phenotypes.\n\nMethodWe employed GWAS data of AAM and ANM and Genome-wide eQTL data from whole blood. The summary data-based Mendelian randomization (SMR) method was used to prioritize the associated genes for further study. The colocalization analysis was used to identify the pleiotropic genes.\n\nResultsWe identified 31 genes whose expression was associated with AAM and 24 genes whose expression was associated with ANM due to pleiotropy or causality. Two pleiotropic genes were identified to be associated with two phenotypes.\n\nConclusionThe results point out the most possible genes which were responsible for the association. Our study prioritizes the associated genes for further functional mechanistic study of AAM and ANM and illustrates the benefit of integrating different omics of data into the study of complex traits.

genetics↗

bioRxiv2019

Streamlined low-input transcriptomics through EASY-RNAseq

High-throughput sequencing for transcriptome profiling is an increasingly accessible and important tool for biological research. However, accurate profiling of small cell populations remains challenging due to issues with gene detection sensitivity and experimental complexity. Here we describe a streamlined RNAseq protocol (EASY RNAseq) for sensitive transcriptome assessment starting from low amount of input materials. EASY RNAseq is technically robust enough for sequencing small pools of homogenous and heterogeneous cells, recovering higher numbers of genes and with a more even expression distribution pattern than other commonly used methods. Application of EASY RNAseq to single human embryos at the 8-cell stage was able to achieve detection of 70% protein-coding genes. This workflow may thus serve as a useful tool for sensitive interrogation of rare cell populations.

genomics↗

bioRxiv2019

Highly Efficient Hypothesis Testing Methods for Regression-type Tests with Correlated Observations and Heterogeneous Variance Structure

BackgroundFor many practical hypothesis testing (H-T) applications, the data are correlated and/or with heterogeneous variance structure. The regression t-test for weighted linear mixed-effects regression (LMER) is a legitimate choice because it accounts for complex covariance structure; however, high computational costs and occasional convergence issues make it impractical for analyzing high-throughput data. In this paper, we propose computationally efficient parametric and semiparametric tests based on a set of specialized matrix techniques dubbed as the PB-transformation. The PB-transformation has two advantages: 1. The PB-transformed data will have a scalar variance-covariance matrix. 2. The original H-T problem will be reduced to an equivalent one-sample H-T problem. The transformed problem can then be approached by either the one-sample Students t-test or Wilcoxon signed rank test. ResultsIn simulation studies, the proposed methods outperform commonly used alternative methods under both normal and double exponential distributions. In particular, the PB-transformed t-test produces notably better results than the weighted LMER test, especially in the high correlation case, using only a small fraction of computational cost (3 versus 933 seconds). We apply these two methods to a set of RNA-seq gene expression data collected in a breast cancer study. Pathway analyses show that the PB-transformed t-test reveals more biologically relevant findings in relation to breast cancer than the weighted LMER test.. ConclusionsAs fast and numerically stable replacements for the weighted LMER test, the PB-transformed tests are especially suitable for "messy" high-throughput data that include both independent and matched/repeated samples. By using our method, the practitioners no longer have to choose between using partial data (applying paired tests to only the matched samples) or ignoring the correlation in the data (applying two sample tests to data with some correlated samples).

bioinformatics↗

bioRxiv2019

The vagino-cervical microbiome as a woman's life history

The gut microbiome has been the center of attention for human commensal microbiome studies. The vaginal microbiome is also densely populated with bacteria, viruses and fungi, and the presence of microorganisms beyond the cervix is increasingly reported in non-infectious conditions1-3. Due to the over 90% of human sequences in female reproductive tract samples3,4, metagenomic information has been very limited. 16S rRNA gene amplicon sequencing studies have identified community types in the vaginal microbiota, and observed its dynamic changes due to menstrual cycles and sexual behaviors in small cohorts5,6. Here we perform metagenomic shotgun sequencing on cervical samples from 516 women of reproductive age (more than 10-fold of the Human Microbiome Project (HMP)4), and dissect major factors, especially pregnancy and delivery histories and contraception methods on the microbiome composition. Features of other body sites, such as mood fluctuations and facial speckles could potentially be deduced from the vagino-cervical microbiome. Our results offer an unprecedented glimpse into the microbiota in the female reproductive tract and imply disease susceptibilities that may be relieved by behavioural changes.

microbiology↗