Biology subjects

Zhou, J.

Publications and source records attributed to Zhou, J..

At least 595 records · Page 33Linked to original sources

bioRxiv2020

Robustification of RosettaAntibody and Rosetta SnugDock

In recent years, the observed antibody sequence space has grown exponentially due to advances in high-throughput sequencing of immune receptors. The rise in sequences has not been mirrored by a rise in structures, as experimental structure determination techniques have remained low-throughput. Computational modeling, however, has the potential to close the sequence-structure gap. To achieve this goal, computational methods must be robust, fast, easy to use, and accurate. Here we report on the latest advances made in RosettaAntibody and Rosetta SnugDock--methods for antibody structure prediction and antibody-antigen docking. We simplified the user interface, expanded and automated the template database, generalized the kinematics of antibody-antigen docking (which enabled modeling of single-domain antibodies) and incorporated new loop modeling techniques. To evaluate the effects of our updates on modeling accuracy, we developed rigorous tests under a new scientific benchmarking framework within Rosetta. Benchmarking revealed that more structurally similar templates could be identified in the updated database and that SnugDock broadened its applicability without losing accuracy. However, there are further advances to be made, including increasing the accuracy and speed of CDR-H3 loop modeling, before computational approaches can accurately model any antibody.

biophysics↗

bioRxiv2020

Genetic Risk Scores for Cardiometabolic Traits in Sub-Saharan African Populations

There is growing support for the use of genetic risk scores (GRS) in routine clinical settings. Due to the limited diversity of current genomic discovery samples, there are concerns that the predictive power of GRS will be limited in non-European ancestry populations. Here, we evaluated the predictive utility of GRS for 12 cardiometabolic traits in sub-Saharan Africans (AF; n=5200), African Americans (AA; n=9139), and European Americans (EA; n=9594). GRS were constructed as weighted sums of the number of risk alleles. Predictive utility was assessed using the additional phenotypic variance explained and increase in discriminatory ability over traditional risk factors (age, sex and BMI), with adjustment for ancestry-derived principal components. Across all traits, GRS showed upto a 5-fold and 20-fold greater predictive utility in EA relative to AA and AF, respectively. Predictive utility was most consistent for lipid traits, with percent increase in explained variation attributable to GRS ranging from 10.6% to 127.1% among EA, 26.6% to 65.8% among AA, and 2.4% to 37.5% among AF. These differences were recapitulated in the discriminatory power, whereby the predictive utility of GRS was 4-fold greater in EA relative to AA and up to 44-fold greater in EA relative to AF. Obesity and blood pressure traits showed a similar pattern of greater predictive utility among EA. This work demonstrates the poorer performance of GRS in AF and highlights the need to improve representation of multiethnic populations in genomic studies to ensure equitable clinical translation of GRS. Key MessagesO_LIGenetic Risk Score (GRS) prediction is markedly poorer in sub-Saharan Africans compared with African Americans and European Americans C_LIO_LITo ensure equitable clinical translation of GRS, there is need need to improve representation of multiethnic populations in genomic studies C_LI

genomics↗

bioRxiv2020

COVIEdb: A database for potential immune epitopes of coronaviruses

2019 novel coronavirus (2019-nCoV) has caused large-scale pandemic COVID-19 all over the world. Its essential to find out which parts of the 2019-nCoV sequence are recognized by human immune system for vaccine development. And for the prevention of the potential outbreak of similar coronaviruses in the future, vaccines against immunogenic epitopes shared by different human coronaviruses are essential. Here we predict all the potential B/T-cell epitopes for SARS-CoV, MERS-CoV, 2019-nCoV and RaTG13-CoV based on the protein sequences. We found YFKYWDQTY in ORF1ab protein, VYDPLQPEL and TVYDPLQPEL in spike (S) protein might be pan-coronavirus targets for vaccine development. All the predicted results are stored in a database COVIEdb (http://biopharm.zju.edu.cn/coviedb/).

bioinformatics↗

bioRxiv2020

Structural basis of bile acid receptor activation and Gs coupling

G protein-coupled bile acid receptor (GPBAR) is a membrane receptor that senses bile acids to regulate diverse functions through Gs activation. Here, we report the cryo-EM structures of GPBAR-Gs complexes stabilized by either high-affinity P395 or the semisynthesized bile acid derivative INT-777 at 3-[A] resolution. These structures revealed a large oval-shaped ligand pocket with several sporadic polar groups to accommodate the amphipathic cholic core of bile acids. A fingerprint of key residues recognizing diverse bile acids in the orthosteric site, a putative second bile acid binding site with allosteric properties and structural features contributing to bias property were identified through structural analysis and mutagenesis studies. Moreover, structural comparison of GPBAR with other GPCRs uncovered an atypical mode of receptor activation and G-protein- coupling, featuring a different set of key residues connecting the ligand binding pocket to the Gs coupling site, and a specific interaction motif localized in intracellular loop 3. Overall, our study not only provides unique structural features of GPBAR in bile acid recognition, allosteric effects and biased signaling, but also suggests that distinct allosteric connecting mechanisms between the ligand binding pocket and the G protein binding site exist in the GPCR superfamily.

biochemistry↗

bioRxiv2020

Genome-wide landscape of RNA-binding protein dysregulation reveals a major impact on psychiatric disorder risk

Despite the strong genetic basis of psychiatric disorders, the molecular origins of these diseases are still largely unmapped. RNA-binding proteins (RBPs) are responsible for most post-transcriptional regulation, from splicing to translational to localization. RBPs thus act as key gatekeepers of cellular homeostasis, especially in the brain. Here, we leverage a deep learning approach to interrogate variant effects genome-wide, and discover that the dysregulation of RBP target sites is a principal contributor to psychiatric disorder risk. We show that specific modes of RBP regulation are genetically linked to the heritability of psychiatric disorders, and demonstrate that diverse RBP regulatory functions are reflected in distinct genome-wide negative selection signatures. Notably, RBP dysregulation has a stronger impact on psychiatric disorders than common coding region variants and explains heritability not currently captured by large-scale molecular QTL studies (expression QTLs and splicing QTLs). We share genome-wide profiles of RBP target site dysregulation, which we used to identify DDHD2 as a candidate schizophrenia risk gene, in a public web server. This resource provides a novel analytical framework to connect the full range of RNA regulation to complex disease.

genetics↗

bioRxiv2020

Deep profiling of protease substrate specificity enabled by dual random and scanned human proteome substrate phage libraries

Proteolysis is a major post-translational regulator of biology both inside and outside of cells. Broad identification of optimal cleavage sites and natural substrates of proteases is critical for drug discovery and to understand protease biology. Here we present a method that employs two genetically encoded substrate phage display libraries coupled with next generation sequencing (SPD-NGS) that allows up to 10,000-fold deeper sequence coverage of the typical 6 to 8 residue protease cleavage sites compared to state-of-the-art synthetic peptide libraries or proteomics. We applied SPD-NGS to two classes of proteases, the intracellular caspases 2, 3, 6, 7 and 8, and the ectodomains of the membrane sheddases, ADAMs 10 and 17. The first library (Lib 10AA) was used to determine substrate cleavage motifs. Lib 10AA contains a highly diverse randomized 10-mer substrate peptide sequences (109 unique members) that was displayed mono-valently on filamentous phage and bound to magnetic beads via an N-terminal biotin. The protease was allowed to cleave the SPD beads, and the released phage subjected to up to three total rounds of positive selection followed by next generation sequencing (NGS). This allowed us to identify from 104 to 105 unique cleavage sites over a 1000-fold dynamic range of NGS counts (ranging from 3-4000), and produced consensus and optimal cleavage motifs based positional sequencing scoring matrices that closely matched synthetic peptide data. A second SPD-NGS library (Lib hP) was constructed that allowed us to identify candidate human proteome sequences. Lib hP displayed virtually the entire human proteome tiled in contiguous 49AA sequences with 25AA overlaps (nearly 1 million members). After three rounds of positive selection we identified up to 104 natural linear cut sites depending on the protease and captured most of the examples previously identified by proteomics (ranging from 30 to 1500) and predicted 10 to 100-fold more. Structural bioinformatics was used to facilitate the identification of candidate natural protein substrates. SPD-NGS is rapid, reproducible, simple to perform and analyze, inexpensive, renewable, with unprecedented depth of coverage for substrate sequences. SPD-NGS is an important tool for protease biologists interested protease specificity for specific assays and inhibitors and to facilitate identification of natural protein substrates.

biochemistry↗

bioRxiv2020

Fusarium verticillioides peroxins associated with peroxisomal docking/translocation module serve important roles in fumonisin biosynthesis and virulence

Peroxisomes are ubiquitous organelles in eukaryotic cells that fulfill various important metabolic functions. In this study, we investigated the role of Docking/Translocation Module (DTM) peroxins, mainly FvPex8, FvPex13, FvPex14, and FvPex33, in Fusarium verticillioides virulence and fumonisin B1 (FB1) biosynthesis. Protein interaction experiments suggested that FvPex13 serves as the core subunit of F. verticillioides DTM. When we generated gene deletion mutants ({Delta}Fvpex8, {Delta}Fvpex13, {Delta}Fvpex14, {Delta}Fvpex33, {Delta}Fvpex33/14) and examined whether the expression of other peroxin genes were affected in the DTM mutants, {Delta}Fvpex8 strain showed most drastic changes to PEX gene expression profiles. Deletion mutants exhibited disparity in carbon source utilization and defect in cell wall integrity when stress agents were applied. Under nutrient starvation, mutants also showed higher levels of lipid droplet accumulation. Notably, {Delta}Fvpex8 mutant showed significant FB1 reduction and altered expression of FUM1 and FUM19 genes. However, FvPex13 was primarily responsible for virulence, while {Delta}Fvpex33/14 double mutant also showed virulence defect. In summary, our study suggests that FvPex13 is the core component of DTM, regulating peroxisome membrane biogenesis as well as PTS1- and PTS2-mediated transmembrane cargo transportation. Importantly, we predict FvPex8 as a key component in DTM that affects peroxisome function in FB1 biosynthesis in F. verticillioides.

microbiology↗

bioRxiv2020

Coupling metabolic addiction with negative autoregulation to improve strain stability and pathway yield

Metabolic addiction, an organism that is metabolically addicted with a compound to maintain its growth fitness, is an underexplored area in metabolic engineering. Microbes with heavily engineered pathways or genetic circuits tend to experience metabolic burden leading to degenerated or abortive production phenotype during long-term cultivation or scale-up. A promising solution to combat metabolic instability is to tie up the end-product with an intermediary metabolite that is essential to the growth of the producing host. Here we present a simple strategy to improve both metabolic stability and pathway yield by coupling chemical addiction with negative autoregulatory genetic circuits. Naringenin and lipids compete for the same precursor with inversed pathway yield in oleaginous yeast. Negative autoregulation of the lipogenic pathways, enabled by CRISPRi and fatty acid-inducible promoters, repartitioned malonyl-CoA to favor flavonoid synthesis and increased naringenin production by 74.8%. With flavonoid-sensing hybrid promoters to control leucine synthesis, this flavonoid addiction phenotype confers a selective growth advantage to the naringenin-producing cell. The engineered yeast persisted 90.9% of naringenin titer up to 324 generations. Cells without flavonoid addiction regained growth fitness but lost 94.5% of the naringenin titer after cell passage beyond 300 generations. Metabolic addiction and negative autoregulation may be generalized as basic tools to eliminate metabolic heterogeneity, improve strain stability and pathway yield.

synthetic biology↗

bioRxiv2020

The importomer is a peroxisomal membrane protein translocase

Three sites of membrane protein biogenesis (the endoplasmic reticulum, mitochondria and chloroplasts) receive unfolded substrates from organelle-specific protein targeting factors, then integrate them using separate translocation channels. Peroxisomes also receive membrane proteins from known targeting factors, but whether a separate translocase is needed for integration remains unknown. Here, using a novel genetic screening strategy, we reveal that the importomer-known for matrix protein import-integrates the peroxisomal membrane protein Pex14. In importomer mutants, Pex14 is arrested in a pre-integrated state on the peroxisome surface. To undergo integration, a Pex14 translocation signal binds the importomers substrate receptor Pex5 at a distinct site from matrix proteins. En bloc translocation of an engineered protein complex with Pex14s luminal region argues that integration occurs without substrate unfolding. Our work shows that the handling of membrane protein targeting and integration by discrete machineries is a fundamental principle shared by diverse membrane protein biogenesis pathways.

cell biology↗

bioRxiv2020

DNA Methylation Atlas of the Mouse Brain at Single-Cell Resolution

Mammalian brain cells are remarkably diverse in gene expression, anatomy, and function, yet the regulatory DNA landscape underlying this extensive heterogeneity is poorly understood. We carried out a comprehensive assessment of the epigenomes of mouse brain cell types by applying single nucleus DNA methylation sequencing to profile 110,294 nuclei from 45 regions of the mouse cortex, hippocampus, striatum, pallidum, and olfactory areas. We identified 161 cell clusters with distinct spatial locations and projection targets. We constructed taxonomies of these epigenetic types, annotated with signature genes, regulatory elements, and transcription factors. These features indicate the potential regulatory landscape supporting the assignment of putative cell types, and reveal repetitive usage of regulators in excitatory and inhibitory cells for determining subtypes. The DNA methylation landscape of excitatory neurons in the cortex and hippocampus varied continuously along spatial gradients. Using this deep dataset, an artificial neural network model was constructed that precisely predicts single neuron cell-type identity and brain area spatial location. Integration of high-resolution DNA methylomes with single-nucleus chromatin accessibility data allowed prediction of high-confidence enhancer-gene interactions for all identified cell types, which were subsequently validated by cell-type-specific chromatin conformation capture experiments. By combining multi-omic datasets (DNA methylation, chromatin contacts, and open chromatin) from single nuclei and annotating the regulatory genome of hundreds of cell types in the mouse brain, our DNA methylation atlas establishes the epigenetic basis for neuronal diversity and spatial organization throughout the mouse brain.

genomics↗

bioRxiv2020

A C++ library for protein sub-structure search

SummaryMASTER is a previously published algorithm for protein sub-structure search. Given a database of protein structures and a query structural motif, composed of multiple disjoint segments, it finds all sub-structures from the database that align onto the query to within a pre-specified backbone root-mean-square deviation. Here, we present an improved version of the algorithm, MASTER v.2, in the form of an open-source C++ Application Program Interface library, thereby providing programmatic access to structure search functionality. An entirely reorganized approach to database representation now enables large structural databases to be stored in memory, further simplifying development of automated search-based methods. Given the increasingly important role of structure-based data mining, our improved implementation should find ample uses in structural biology applications. AvailabilityMASTER is available at https://grigoryanlab.org/master/master-v2.php. Contactgevorg.grigoryan@dartmouth.edu

bioinformatics↗

bioRxiv2020

Spatial Transcriptional Mapping of the Human Nephrogenic Program

Congenital abnormalities of the kidney and urinary tract are amongst the most common birth defects affecting 3% of newborns. The human kidney develops over a 30-week period in which a nephron progenitor pool gives rise to around a million nephrons. To establish a framework for human nephrogenesis, we spatially resolved a stereotypical process by which equipotent nephron progenitors generate a nephron anlagen, then applied data-driven approaches to construct three-dimensional protein maps on anatomical models of the nephrogenic program. Single cell RNA sequencing identified novel progenitor states which were spatially mapped to the nephron anatomy enabling the generation of functional gene-networks predicting interactions within and between nephron cell-types. Network mining identified known developmental disease genes and predicts new targets of interest. The spatially resolved nephrogenic program made available through the Human Nephrogenesis Atlas (https://sckidney.flatironinstitute.org/) will facilitate an understanding of kidney development and disease, and enhance efforts to generate new kidney structures.

cell biology↗

bioRxiv2020

scSTATseq: Diminishing Technical Dropout Enables Core Transcriptome Recovery and Comprehensive Single-cell Trajectory Mapping

The advent of single-cell RNA sequencing has provided illuminating information on complex systems. However, large numbers of genes tend to be scarcely detected in common scRNAseq approaches due to technical dropout. Although bioinformatics approaches have been developed to approximate true expression profiles, assess the dropout events on single-cell transcriptomes is still consequently challenging. In this report, we present a new plate-based method for scRNAseq that relies on Tn5 transposase to tagment cDNA following second strand synthesis. By utilizing pre-amplification tagmentation step, scSTATseq libraries are insulated against technical dropout, allowing for detailed analysis of gene-gene co-expression relationships and mapping of pathway trajectories. The entire scSTATseq library construction workflow can be completed in 7 hours, and recover transcriptome information on up to 8,000 protein-coding genes. Investigation of osteoclast differentiation using this workflow allowed us to identify novel markers of interest such as Rab15. Overall, scSTATseq is an efficient and economical method for scRNAseq that compares favorably with existing workflows.

genomics↗

bioRxiv2020

In-field bioreactors demonstrate dynamic shifts in microbial communities in response to geochemical perturbations.

Subsurface microbial communities mediate the transformation and fate of redox sensitive materials including organic matter, metals and radionuclides. Few studies have explored how changing geochemical conditions influence the composition of groundwater microbial communities over time. We temporally monitored alterations in abiotic forces on microbial community structure using 1L in-field bioreactors receiving background and contaminated groundwater at the Oak Ridge Reservation, TN. Planktonic and biofilm microbial communities were initialized with background water for 4 days to establish communities in triplicate control reactors and triplicate test reactors and then fed filtered water for 14 days. On day 18, three reactors were switched to receive filtered groundwater from a contaminated well, enriched in total dissolved solids relative to the background site, particularly chloride, nitrate, uranium, and sulfate. Biological and geochemical data were collected throughout the experiment, including planktonic and biofilm DNA for 16S rRNA amplicon sequencing, cell counts, total protein, anions, cations, trace metals, organic acids, bicarbonate, pH, Eh, DO, and conductivity. We observed significant shifts in both planktonic and biofilm microbial communities receiving contaminated water. This included a loss of rare taxa, especially amongst members of the Bacteroidetes, Acidobacteria, Chloroflexi, and Betaproteobacteria, but enrichment in the Fe- and nitrate-reducing Ferribacterium and parasitic Bdellovibrio. These shifted communities were more similar to the contaminated well community, suggesting that geochemical forces substantially influence microbial community diversity and structure. These influences can only be captured through such comprehensive temporal studies, which also enable more robust and accurate predictive models to be developed.

microbiology↗

bioRxiv2020

An analytical framework for interpretable and generalizable 'quasilinear' single-cell data analysis

Scaling single-cell data exploratory analysis with the rapidly growing diversity and quantity of single-cell omics datasets demands more interpretable and robust data representation that is generalizable across datasets. To address this challenge, here we developed a novel quasilinear framework that combines the interpretability and transferability of linear methods with the representational power of nonlinear methods. Within this framework, we introduce a data representation and visualization method, GraphDR, and a structure discovery method, StructDR, that unifies cluster, trajectory, and surface estimation and allows their confidence set inference. We applied both methods to diverse single-cell RNA-seq datasets from whole embryos and tissues. Unlike PCA and t-SNE, GraphDR and StructDR generated representations that both distinguished highly specific cell types and were comparable across datasets. In addition, GraphDR is at least an order of magnitude faster than commonly used nonlinear methods. Our visualizations of scRNA-seq data from developing zebrafish and Xenopus embryos revealed extruding branches of lineages from a continuum of cell states, suggesting that the current branch view of cell specification may be oversimplified. Moreover, StructDR identified a novel neuronal population using scRNA-seq data from mouse hippocampus. An open-source python library and a user-friendly graphical interface for 3D data visualization and analysis with these methods are available at https://github.com/jzthree/quasildr.

bioinformatics↗

bioRxiv2020

Comparison of SARS-CoV-2 infections among 3 species of non-human primates

COVID-19, caused by SARS-CoV-2 infection, has recently been announced as a pandemic all over the world. Plenty of diagnostic, preventive and therapeutic knowledges have been enriched from clinical studies since December 2019. However, animal models, particularly non-human primate models, are urgently needed for critical questions that could not be answered in clinical patients, evaluations of anti-viral drugs and vaccines. In this study, two families of non-human primates, Old world monkeys (12 Macaca mulatta, 6 Macaca fascicularis) and New world monkeys (6 Callithrix jacchus), were experimentally inoculated with SARS-CoV-2. Clinical signs were recorded. Samples were collected for analysis of viral shedding, viremia and histopathological examination. Increased body temperature was observed in 100% (12/12) M. mulatta, 33.3% (2/6) M. fascicularis and none (0/6) of C. jacchus post inoculation of SARS-CoV-2. All of M. mulatta and M. fascicularis showed chest radiographic abnormality. Viral genomes were detected in nasal swabs, throat swabs, anal swabs and blood from all 3 species of monkeys. Viral shedding from upper respiratory samples reached the peak between day 6 and day 8 post inoculation. From necropsied M. mulatta and M. fascicularis, the tissues showing virus positive were mainly lung, weasand, bronchus and spleen. No viral genome was seen in any of tissues from 2 necropsied C. jacchus. Severe gross lesions and histopathological changes were observed in lung, heart and stomach of SARS-CoV-2 infected animals. In summary, we have established a NHP model for COVID-19, which could be used to evaluate drugs and vaccines, and investigate viral pathogenesis. M. mulatta is the most susceptible to SARS-CoV-2 infection, followed by M. fascicularis and C. jacchus. One Sentence SummaryM. mulatta is the most susceptible to SARS-CoV-2 infection as compared to M. fascicularis and C. jacchus.

microbiology↗

bioRxiv2020

Hybrid origin of Populus tomentosa Carr. identified through genome sequencing and phylogenomic analysis

Populus tomentosa is widely distributed and cultivated in the Northern and Central China, where it is of great economic and ecological importance. However, the origin of P. tomentosa remains controversial. Here, we used a PacBio+Hi-C+Illumina strategy to sequence and assemble its 740.2 Mb (2n) genome. The assembly accounts for greater than 92.1% of the 800-megabase genome, comprises 38 chromosomes, and contains 59,124 annotated protein-coding genes. Phylogenomic analyses elucidated dynamic genome evolution events among its closely related white poplars, and revealed that tomentosa is comprised of two subgenomes, which we deomonstrate is likely to have resulted from hybridization between Populus adenopoda as the female, and Populus alba var. pyramidalis as the male, around 3.93 Mya. We also detected structural variations and allele-indels across genome. Our study presents a high quality and well assembled genome, unveils the origin of the widely distributed and planted P. tomentosa, and provides a powerful resource for comparative plant biology, breeding, and biotechnology.

genomics↗

bioRxiv2020

Epigenomic Diversity of Cortical Projection Neurons in the Mouse Brain

Neuronal cell types are classically defined by their molecular properties, anatomy, and functions. While recent advances in single-cell genomics have led to high-resolution molecular characterization of cell type diversity in the brain, neuronal cell types are often studied out of the context of their anatomical properties. To better understand the relationship between molecular and anatomical features defining cortical neurons, we combined retrograde labeling with single-nucleus DNA methylation sequencing to link epigenomic properties of cell types to neuronal projections. We examined 11,827 single neocortical neurons from 63 cortico-cortical (CC) and cortico-subcortical long-distance projections. Our results revealed unique epigenetic signatures of projection neurons that correspond to their laminar and regional location and projection patterns. Based on their epigenomes, intra-telencephalic (IT) cells projecting to different cortical targets could be further distinguished, and some layer 5 neurons projecting to extra-telencephalic targets (L5-ET) formed separate subclusters that aligned with their axonal projections. Such separation varied between cortical areas, suggesting area-specific differences in L5-ET subtypes, which were further validated by anatomical studies. Interestingly, a population of CC projection neurons clustered with L5-ET rather than IT neurons, suggesting a population of L5-ET cortical neurons projecting to both targets (L5-ET+CC). We verified the existence of these neurons by labeling the axon terminals of CC projection neurons and observed clear labeling in ET targets including thalamus, superior colliculus, and pons. These findings highlight the power of single-cell epigenomic approaches to connect the molecular properties of neurons with their anatomical and projection properties.

neuroscience↗