Biology subjects

Ma, J.

Publications and source records attributed to Ma, J..

At least 343 records · Page 19Linked to original sources

bioRxiv2021

REHE: Fast Variance Components Estimation for Linear Mixed Models

AO_SCPLOWBSTRACTC_SCPLOWLinear mixed models are widely used in ecological and biological applications, especially in genetic studies. Reliable estimation of variance components is crucial for using linear mixed models. However, standard methods, such as the restricted maximum likelihood (REML), are computationally inefficient and may be unstable with small samples. Other commonly used methods, such as the Haseman-Elston (HE) regression, may yield negative estimates of variances. Utilizing regularized estimation strategies, we propose the restricted Haseman-Elston (REHE) regression and REHE with resampling (reREHE) estimators, along with an inference framework for REHE, as fast and robust alternatives that provide non-negative estimates with comparable accuracy to REML. The merits of REHE are illustrated using real data and benchmark simulation studies.

genetics↗

bioRxiv2021

Analysis of Spleen Histopathology, Splenocyte composition and Hematological Parameters in Mice Infected with Plasmodium berghei K173

Malaria is a fatal disease that presents clinically as a continuum of symptoms and severity, which are determined by complex host-parasite interactions. Clearance of infection is believed to be accomplished by the spleen and mononuclear phagocytic system (MPS), both in the presence and absence of artemisinin treatment. The spleen filters infected RBCs from circulation through immune-mediated recognition of the infected RBCs followed by phagocytosis. Using different strains of mice infected with P. berghei K173 (PbK173), the mechanisms leading to splenomegaly, histopathology, splenocyte activation and proliferation, and their relationship to control of parasitemia and host mortality were examined. Survival time of mice infected with PbK173 varied, although the infection was uniformly lethal. Mice of the C57BL/6 strain were the most resistant, while mice of the strain ICR were the most susceptible. BALB/c and KM mice were intermediate. In the course of PbK173 infection, both strains of mice experienced significant splenomegaly. Parasites were observed in the red pulp at 3 days post infection in all animals. All spleens retained late trophozoite stages as well as a fraction of earlier ring-stage parasites. The percentages of macrophages in infected C57BL/6 and KM mice were higher than uninfected mice on 8 dpi. Spleens of infected ICR and KM mice exhibited structural disorganization and remodeling. Furthermore, parasitemia was significantly higher in KM versus C57BL/6 mice at 8 dpi. The percentages of macrophages in ICR infected mice were lower than uninfected mice, and the parasitemia was higher than other strains. The results presented here demonstrate the rate of splenic mechanical filtration and the splenic macrophages likely contribute to an individuals total parasite burden. This in turn can influence the pathogenesis of malaria. Finally, different genetic backgrounds of mice have different splenic mechanisms for controlling malaria infection.

microbiology↗

bioRxiv2021

Matriptase generates a tissue damage response via promoting Gq signalling, leading to RSK and DUOX activation

Tissues respond to damage by increasing inflammation and epithelial cell motility. How damage detection and responses are orchestrated is unclear. Overexpression of the membrane bound protease, Matriptase, or mutation of its inhibitor, Hai1, results in inflamed epithelia, in which cells have increased motility and are prone to carcinoma. How Matriptase leads to these cellular outcomes is unknown. We demonstrate that zebrafish hai1a mutants show increased H2O2, Nf{kappa}B signalling, and IP3R-mediated calcium flashes, and that these promote inflammation, but do not generate epithelial cell motility. In contrast, inhibition of the Gq subunit rescues both the hai1a inflammation and epithelial phenotypes, with the latter recapitulated by the DAG analogue, PMA. We demonstrate that hai1a has elevated pERK, inhibition of which rescues the epidermal defects. Finally, we identify RSK kinases as pERK targets disrupting adherens junctions in hai1a mutants. Our work maps novel signalling cascades mediating the potent effects of Matriptase on epithelia, with implications for tissue damage response and carcinoma progression.

cell biology↗

bioRxiv2020

Sequencing of clinical samples reveals that adaptation keeps establishing during H7N9 virus infection in humans

The H7 subtype avian influenza viruses (AIV) have a much longer history and their adaptation through evolution pose continuous threat to humans 1. Since 2013 March, the novel reasserted H7N9 subtype have transmitted to humans through their repeated assertion in the poultry market. Through repeated transmission, H7N9 gradually became the second AIV subtype posing greater public health risk after H5N1 2,3. After infection, how the virus tunes its genome to adapt and evolve in humans remains unknown. Through direct amplification of H7N9 and high throughput (HT) sequencing of full genomes from the swabs and lower respiratory tract samples collected from infected patients in Shenzhen, China, we have analyzed the in vivo H7N9 mutations at the level of whole genomes and have compared with the genomes derived by in vitro cultures. These comparisons and frequency analysis against the H7N9 genomes in the public database, 40 amino acids were identified that play potential roles in virus adaptation during H7N9 infection in humans. Various synonymous mutations were also identified that might be crucial to H7N9 adaptation in humans. The mechanism of these mutations occurred in a single infection are discussed in this study.

microbiology↗

bioRxiv2020

Integrated bioinformatic analysis of miR-654-5p target genes and experimental validation of functional analyses

The pivotal roles of miRNAs in carcinogenesis, metastasis, and prognosis have been demonstrated recently in various cancers. This study intended to investigate the specific roles of hsa-miR-654-5p in lung cancer, which was poorly discussed. A series of closed-loop bioinformatic functional analyses were integrated with in vitro experimental validation to explore the overall biological functions and pan-cancer regulation pattern of miR-654-5p. We found that miR-654-5p abundance was significantly elevated in LUAD tissues and correlated with patients survival. 275 potential targets of miR-654-5p were then identified and the miR-654-5p-RNF8 regulation axis was validated in vitro as a proof of concept. Furthermore, we revealed the tumor-suppressing roles of miR-654-5p and demonstrated that miR-654-5p inhibited lung cancer cell epithelial-mesenchymal transition (EMT) process, cell proliferation, and migration using target-based, abundance-based and ssGSEA-based bioinformatic methods and in vitro validation. Following the construction of a protein-protein interaction network, 11 highly interconnected hub genes were identified and a five genes risk scoring model was developed to assess their potential prognostic ability. Our study will not only provide a basic miRNA-mRNA-phenotypes reference map for understanding the function of miR-654-5p in different cancers but also reveal the tumor-suppressing roles and prognostic values of miR-654-5p in lung cancer. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/423719v2_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@15da4caorg.highwire.dtl.DTLVardef@edfbb6org.highwire.dtl.DTLVardef@289028org.highwire.dtl.DTLVardef@1105a2e_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2020

Sterilizing immunity against SARS-CoV-2 in hamsters conferred by a novel recombinant subunit vaccine

A safe and effective SARS-CoV-2 vaccine is essential to avert the on-going COVID-19 pandemic. Here, we developed a subunit vaccine, which is comprised of CHO-expressed spike ectodomain protein (StriFK) and nitrogen bisphosphonates-modified zinc-aluminum hybrid adjuvant (FH002C). This vaccine candidate rapidly elicited the robust humoral response, Th1/Th2 balanced helper CD4 T cell and CD8 T cell immune response in animal models. In mice, hamsters, and non-human primates, 2-shot and 3-shot immunization of StriFK-FH002C generated 28- to 38-fold and 47- to 269-fold higher neutralizing antibody titers than the human COVID-19 convalescent plasmas, respectively. More importantly, the StriFK-FH002C immunization conferred sterilizing immunity to prevent SARS-CoV-2 infection and transmission, which also protected animals from virus-induced weight loss, COVID-19-like symptoms, and pneumonia in hamsters. Vaccine-induced neutralizing and cell-based receptor-blocking antibody titers correlated well with protective efficacy in hamsters, suggesting vaccine-elicited protection is immune-associated. The StriFK-FH002C provided a promising SARS-CoV-2 vaccine candidate for further clinical evaluation.

microbiology↗

bioRxiv2020

Predicting placenta transcriptional regulatory interactions based on immunohistochemistry images data and convolutional neural network

Aims: The dysfunction of placenta development is correlated to the defects of pregnancy and fetal growth. The detailed molecular mechanism of placenta development is not identified in human due to the lack of material in vivo. Image-based reconstructions of GRN are still very underdeveloped. Methods and Results: In this study, immunohistochemistry images of different TFs in chorionic villus were obtained by a high-resolution scanner. Next, we used a convolutional neural network and machine learning method to infer gene interaction networks of human placenta from these images based on the transfer learning technique. The experimental results show that deep learning models reveals regulatory roles that have not yet been fully recognized. The spatial expression data reveal new regulatory relationships that traditional experiments have failed to recognize, and has allowed the development of gene regulation networks based on the spatial distribution of gene expression. Conclusions: We demonstrate the effectiveness of this approach in building networks using high-resolution images of the human placenta. Our analysis is of certain significance for further exploration of the development of the placenta and the occurrence of pregnancy-related diseases in the future. The datasets and analysis provide a useful source for the researchers in the field of the maternal-fetal interface and the establishment of pregnancy.

bioinformatics↗

bioRxiv2020

Multiscale and integrative single-cell Hi-C analysis with Higashi

The advent of single-cell Hi-C (scHi-C) technologies offers an unprecedented opportunity to unveil cell-to-cell variability of 3D genome organization. However, the development of computational methods that can effectively enhance scHi-C data quality and extract 3D genome features in single cells remains a major challenge. Here, we report Higashi, a new algorithm that achieves state-of-the-art analysis of scHi-C data based on hypergraph representation learning. Extensive evaluations demonstrate that Higashi significantly outperforms existing methods for embedding and imputation of scHi-C data. Higashi is uniquely able to identify multiscale 3D genome features (such as compartmentalization and TAD-like domain boundaries) in single cells, allowing markedly refined delineation of cell-to-cell variability of 3D genome features. By applying to a scHi-C dataset from human prefrontal cortex, Higashi reveals complex cell types as well as new connections between 3D genome features and cell type-specific gene regulation. Higashi provides an end-to-end solution to scHi-C data analysis and is applicable to studying single-cell 3D genomes in a wide range of biological contexts.

bioinformatics↗

bioRxiv2020

SPICEMIX: Integrative single-cell spatial modeling for inferring cell identity

Spatial transcriptomics technologies promise to reveal spatial relationships of cell-type composition in complex tissues. However, the development of computational methods that can utilize the unique properties of spatial transcriptome data to unveil cell identities remains a challenge. Here, we introduce SO_SCPLOWPICEC_SCPLOWMO_SCPLOWIXC_SCPLOW, a new interpretable method based on probabilistic, latent variable modeling for effective joint analysis of spatial information and gene expression from spatial transcriptome data. Both simulation and real data evaluations demonstrate that SO_SCPLOWPICEC_SCPLOWMO_SCPLOWIXC_SCPLOW markedly improves upon the inference of cell types and their spatial patterns compared with existing approaches. By applying to spatial transcriptome data of brain regions in human and mouse acquired by seqFISH+, STARmap, and Visium, we show that SO_SCPLOWPICEC_SCPLOWMO_SCPLOWIXC_SCPLOW can enhance the inference of complex cell identities, reveal interpretable spatial metagenes, and uncover differentiation trajectories. SO_SCPLOWPICEC_SCPLOWMO_SCPLOWIXC_SCPLOW is a generalizable framework for analyzing spatial transcriptome data to provide critical insights into the cell type composition and spatial organization of cells in complex tissues.

bioinformatics↗

bioRxiv2020

Separase cleaves the kinetochore protein Meikin to direct the meiosis I/II transition

To generate haploid gametes, germ cells undergo two consecutive meiotic divisions requiring key changes to the cell division machinery. Here, we explore the regulatory mechanisms that differentially control meiotic events. We demonstrate that the protease Separase rewires key cell division processes at the meiosis I/II transition by cleaving the meiosis-specific protein Meikin. In contrast to cohesin, which is inactivated by Separase proteolysis, cleaved Meikin remains functional, but results in a distinct activity state. Full-length Meikin and the C-terminal Meikin Separase-cleavage product both localize to kinetochores, bind to Plk1 kinase, and promote Rec8 cleavage, but our results reveal distinct roles for these proteins in controlling meiosis. Mutations that prevent Meikin cleavage or that conditionally inactivate Meikin at anaphase I both result in defective meiosis II chromosome alignment. Thus, Separase cleavage of Meikin creates an irreversible molecular switch to rewire the cell division machinery at the meiosis I/II transition.

cell biology↗

bioRxiv2020

DANGO: Predicting higher-order genetic interactions

Higher-order genetic interactions, which have profound implications for understanding the molecular mechanisms of phenotypic variation, remain poorly characterized. Most studies to date have focused on pairwise interactions, as designing high-throughput experimental screenings for the vast combinatorial search space of higher-order molecular interactions is dauntingly challenging. Here, we develop DO_SCPLOWANGOC_SCPLOW, a computational method based on a self-attention hypergraph neural network, designed to effectively predict higher-order genetic interaction among groups of genes. As a proof-of-concept, we provide comprehensive predictions for over 400 million trigenic interactions in the yeast S. cerevisiae, significantly expanding the quantitative characterization of such interactions. Our results demonstrate that DO_SCPLOWANGOC_SCPLOW accurately predicts trigenic interactions, uncovering both known and novel biological functions related to cell growth. We further incorporate protein embeddings and model uncertainty scoring to enhance the biological relevance and interpretability of the predicted interactions. Moreover, the predicted interactions can serve as powerful genetic markers for growth response under diverse conditions. Together, DO_SCPLOWANGOC_SCPLOW enables a more complete map of complex genetic interactions that impinge upon phenotypic diversity.

bioinformatics↗

bioRxiv2020

O-GlcNAcAtlas: A Database of Experimentally Identified O-GlcNAc Sites and Proteins

O-linked {beta}-N-acetylglucosamine (O-GlcNAc) is a post-translational modification (i.e., O-GlcNAcylation) on serine/threonine residues of proteins. As a unique intracellular monosaccharide modification, protein O-GlcNAcylation plays important roles in almost all biochemical processes examined. Aberrant O-GlcNAcylation underlies the etiologies of a number of chronic diseases (including cancer, diabetes, and neurodegenerative disease). With the tremendous improvement of techniques, thousands of proteins along with their O-GlcNAc sites have been reported. However, until now there is no database dedicated to accommodate the rapid accumulation of such information. Thus, O-GlcNAcAtlas is created to integrate all experimentally identified O-GlcNAc sites and proteins from 1984 to Dec, 2019. O-GlcNAcAtlas consists of two datasets (Dataset-I and Dataset-II, for unambiguously identified sites and ambiguously identified sites, respectively), representing a total number of 4571 O-GlcNAc modified proteins. For each protein, comprehensive information (including gene name, organism, modification sites, site mapping methods and literature references) is provided. To solve the heterogeneity among the data collected from different sources, the sequence identity of these reported O-GlcNAc peptides are mapped to the UniProtKB protein entries. To our knowledge, O-GlcNAcAtlas is the comprehensive and curated database encapsulating all O-GlcNAc sites and proteins identified in the past 35 years. We expect that O-GlcNAcAtlas will be a useful resource which will facilitate site-specific O-GlcNAc functional studies and computational analyses of protein O-GlcNAcylation. The public version of the web interface to the O-GlcNAcAtlas can be found at https://oglcnac.org.

bioinformatics↗

bioRxiv2020

Centromere drive and suppression by parallel pathways for recruiting microtubule destabilizers

Selfish centromere DNA sequences bias their transmission to the egg in female meiosis. Evolutionary theory suggests that centromere proteins evolve to suppress costs of this "centromere drive". In hybrid mouse models with genetically different maternal and paternal centromeres, selfish centromere DNA exploits a kinetochore pathway to recruit microtubule-destabilizing proteins that act as drive effectors. We show that such functional differences are suppressed by a parallel pathway for effector recruitment by heterochromatin, which is similar between centromeres in this system. Disrupting heterochromatin by CENP-B deletion amplifies functional differences between centromeres, whereas disrupting the kinetochore pathway with a divergent allele of CENP-C reduces the differences. Molecular evolution analyses using newly sequenced Murinae genomes identify adaptive evolution in proteins in both pathways. We propose that centromere proteins have recurrently evolved to minimize the kinetochore pathway, which is exploited by selfish DNA, relative to the heterochromatin pathway that equalizes centromeres, while maintaining essential functions.

evolutionary biology↗

bioRxiv2020

Double Lock of a Potent Human Monoclonal Antibody against SARS-CoV-2

Receptor recognition and subsequent membrane fusion are essential for the establishment of successful infection by SARS-CoV-2. Halting these steps can cure COVID-19. Here we have identified and characterized a potent human monoclonal antibody, HB27, that blocks SARS-CoV-2 attachment to its cellular receptor at sub-nM concentrations. Remarkably, HB27 can also prevent SARS-CoV-2 membrane fusion. Consequently, a single dose of HB27 conferred effective protection against SARS-CoV-2 in two established mouse models. Rhesus macaques showed no obvious adverse events when administrated with 10-fold of effective dose of HB27. Cryo-EM studies on complex of SARS-CoV-2 trimeric S with HB27 Fab reveal that three Fab fragments work synergistically to occlude SARS-CoV-2 from binding to ACE2 receptor. Binding of the antibody also restrains any further conformational changes of the RBD, possibly interfering with progression from the prefusion to the postfusion stage. These results suggest that HB27 is a promising candidate for immuno-therapies against COVID-19. HighlightsO_LISARS-CoV-2 specific antibody, HB27, blocks viral receptor binding and membrane fusion C_LIO_LIHB27 confers prophylactic and therapeutic protection against SARS-CoV-2 in mice models C_LIO_LIRhesus macaques showed no adverse side effects when administered with HB27 C_LIO_LICryo-EM studies suggest that HB27 sterically occludes SARS-CoV-2 from its receptor C_LI

biophysics↗

bioRxiv2020

cancerAlign: Stratifying tumors by unsupervised alignment across cancer types

Tumor stratification, which aims at clustering tumors into biologically meaningful subtypes, is the key step towards personalized treatment. Large-scale profiled cancer genomics data enables us to develop computational methods for tumor stratification. However, most of the existing approaches only considered tumors from an individual cancer type during clustering, leading to the overlook of common patterns across cancer types and the vulnerability to the noise within that cancer type. To address these challenges, we proposed cancerAlign to map tumors of the target cancer type into latent spaces of other source cancer types. These tumors were then clustered in each latent space rather than the original space in order to exploit shared patterns across cancer types. Due to the lack of aligned tumor samples across cancer types, cancerAlign used adversarial learning to learn the mapping at the population level. It then used consensus clustering to integrate cluster labels from different source cancer types. We evaluated cancerAlign on 7,134 tumors spanning 24 cancer types from TCGA and observed substantial improvement on tumor stratification and cancer gene prioritization. We further revealed the transferability across cancer types, which reflected the similarity among them based on the somatic mutation profile. cancerAlign is an unsupervised approach that provides deeper insights into the heterogeneous and rapidly accumulating somatic mutation profile and can be also applied to other genome-scale molecular information. Availabilityhttps://github.com/bowen-gao/cancerAlign

bioinformatics↗

bioRxiv2020

scPretrain: Multi-task self-supervised learning for cell type classification

Rapidly generated scRNA-seq datasets enable us to understand cellular differences and the function of each individual cell at single-cell resolution. Cell type classification, which aims at characterizing and labeling groups of cells according to their gene expression, is one of the most important steps for single-cell analysis. To facilitate the manual curation process, supervised learning methods have been used to automatically classify cells. Most of the existing supervised learning approaches only utilize annotated cells in the training step while ignoring the more abundant unannotated cells. In this paper, we proposed scPretrain, a multi-task self-supervised learning approach that jointly considers annotated and unannotated cells for cell type classification. scPretrain consists of a pre-training step and a fine-tuning step. In the pre-training step, scPretrain uses a multi-task learning framework to train a feature extraction encoder based on each datasets pseudo-labels, where only unannotated cells are used. In the fine-tuning step, scPretrain fine-tunes this feature extraction encoder using the limited annotated cells in a new dataset. We evaluated scPretrain on 60 diverse datasets from different technologies, species and organs, and obtained a significant improvement on both cell type classification and cell clustering. Moreover, the representations obtained by scPretrain in the pre-training step also enhanced the performance of conventional classifiers such as random forest, logistic regression and support vector machines. scPretrain is able to effectively utilize the massive amount of unlabelled data and be applied to annotating increasingly generated scRNA-seq datasets. Availabilityhttps://github.com/ruiyi-zhang/scPretrain\

bioinformatics↗

bioRxiv2020

Bat influenza vectored NS1-truncated live vaccine protects pigs against heterologous virus challenge

Swine influenza is an important disease for the swine industry. Currently used whole inactivated virus (WIV) vaccines can induce vaccine-associated enhanced respiratory disease (VAERD) in pigs when the vaccine strains mismatch with the infected viruses. Live attenuated influenza virus vaccine (LAIV) is effective to protect pigs against homologous and heterologous swine influenza virus infections without inducing VAERD, but has safety concerns due to potential reassortment with circulating viruses. Herein, we used a chimeric bat influenza Bat09:mH3mN2 virus, which contains both surface HA and NA gene open reading frames of the A/swine/Texas/4199-2/1998 (H3N2) and six internal genes from the novel bat H17N10 virus, to develop modified live-attenuated viruses (MLVs) as vaccine candidates which cannot reassort with canonical influenza A viruses by co-infection. Two attenuated MLV vaccine candidates including the virus that expresses a truncated NS1 (Bat09:mH3mN2-NS1-128, MLV1) or expresses both a truncated NS1 and the swine IL-18 (Bat09:mH3mN2-NS1-128-IL-18, MLV2) were generated and evaluated in pigs against a heterologous H3N2 virus using the WIV vaccineb as a control. Compared to the WIV vaccine, both MLV vaccines were able to reduce lesions and virus replication in lungs and limit nasal virus shedding without VAERD, also induced significantly higher levels of mucosal IgA response in lungs and significantly increased numbers of antigen-specific IFN-{gamma} secreting cells against the challenge virus. However, no significant difference was observed in efficacy between the MLV1 and MLV2. These results indicate that bat influenza vectored MLV vaccines can be used as a safe live vaccine to prevent swine influenza.

microbiology↗

bioRxiv2020

Characterization of LC-MS based urine metabolomics in healthy subjects across the life span

Previous studies reported that gender and age could influence urine metabolomics, which should be considered in biomarker discovery. As a consequence, for the baseline of urine metabolomics characteristics, it becomes critical to avoid confounding effects in clinical cohort studies. In this study, we provided a comprehensive lifespan characterization of urine metabolomics in a cohort of 348 healthy children and 315 adults aged 1 to 70 years using liquid chromatography coupled with high resolution mass spectrometry. Our results suggested that gender-dependent urine metabolites are much greater in adults than in children. The pantothenate and CoA biosynthesis and alanine metabolism pathways were enriched in early life. Androgen and estrogen metabolism showed high activity during adolescence and youth stages. Pyrimidine metabolism was enriched in the old stage. Based on the above analysis, metabolomic characteristics of each age stage were provided. This work could help us understand the baseline of urine metabolism characteristics and contribute to further studies of clinical disease biomarker discovery.

biochemistry↗