Biology subjects

Ma, Q.

Publications and source records attributed to Ma, Q..

At least 163 records · Page 9Linked to original sources

bioRxiv2020

Large-scale single-cell analysis reveals critical immune characteristics of COVID-19 patients

Dysfunctional immune response in the COVID-19 patients is a recurrent theme impacting symptoms and mortality, yet the detailed understanding of pertinent immune cells is not complete. We applied single-cell RNA sequencing to 284 samples from 205 COVID-19 patients and controls to create a comprehensive immune landscape. Lymphopenia and active T and B cell responses were found to coexist and associated with age, sex and their interactions with COVID-19. Diverse epithelial and immune cell types were observed to be virus-positive and showed dramatic transcriptomic changes. Elevation of ANXA1 and S100A9 in virus-positive squamous epithelial cells may enable the initiation of neutrophil and macrophage responses via the ANXA1-FPR1 and S100A8/9-TLR4 axes. Systemic upregulation of S100A8/A9, mainly by megakaryocytes and monocytes in the peripheral blood, may contribute to the cytokine storms frequently observed in severe patients. Our data provide a rich resource for understanding the pathogenesis and designing effective therapeutic strategies for COVID-19. HIGHLIGHTSO_LILarge-scale scRNA-seq analysis depicts the immune landscape of COVID-19 C_LIO_LILymphopenia and active T and B cell responses coexist and are shaped by age and sex C_LIO_LISARS-CoV-2 infects diverse epithelial and immune cells, inducing distinct responses C_LIO_LICytokine storms with systemic S100A8/A9 are associated with COVID-19 severity C_LI

genomics↗

bioRxiv2020

Counterfactual Hypothesis Testing of Tumor Microenvironment Scenarios Through Semantic Image Synthesis

AO_SCPLOWBSTRACTC_SCPLOWRecent multiplexed protein imaging technologies characterize cells, their spatial organization, and interactions within microenvironments at an unprecedented resolution. Although observational data can reveal spatial associations, it does not allow users to infer salient biological relationships and cellular interactions. To address this challenge, we develop a generative model that allows users to test hypotheses about the effect of cell-cell interactions on protein expression through in silico perturbation. Our Cell-Cell Interaction GAN (CCIGAN) model employs a generative adversarial network (GAN) architecture to generate high fidelity synthetic multiplexed images from semantic cell segmentations. Our approach is unique in that it learns relationships between all imaging channels simultaneously and yields biological insights from multiple imaging technologies in silico, capturing known tumor-immune cell interactions missed by other state-of-the-art GAN models.

cancer biology↗

bioRxiv2020

Large-scale Gradient Dysfunction of the Functional Connectome in Major Depression

BackgroundPatients with major depressive disorder (MDD) exhibit concurrent deficits in sensory processing and high-order cognitive functions such as self-awareness and rumination. Connectome mapping studies have suggested a principal primary-to-transmodal gradient in functional brain networks, supporting the spectrum from sensation to cognition. However, whether this principal connectome gradient is disrupted in patients with MDD and how this disruption is associated with gene expression profiles remain unclear. MethodsUsing a large cohort of resting-state functional magnetic resonance imaging data from 2,234 participants (1,150 patients with MDD and 1,084 healthy controls) recruited at 10 sites, we investigated MDD-related alterations in the principal connectome gradient. We further used Neurosynth and postmortem gene expression data to assess the cognitive functions and transcriptional profiles related to the gradient alterations in MDD, respectively. ResultsRelative to controls, patients with MDD exhibited abnormal global topography of the principal primary-to-transmodal gradient, as indicated by reduced explanation ratio, gradient range, and gradient variation (Cohens d = -0.16[~]-0.21). Focal alterations of gradient scores were mostly in the primary systems involved in sensory processing and in the transmodal systems implicated in high-order cognition. The transcriptional profiles explained 53.9% of the spatial variance in the altered gradient patterns, with the most correlated genes enriched in transsynaptic signaling and calcium ion binding. ConclusionsThese results highlight the dysfunction of the core connectome hierarchy in MDD and its linkage with gene expression profiles, providing insights into the neurobiological and molecular genetic underpinnings of sensory-cognitive deficits in this disorder.

neuroscience↗

bioRxiv2020

Time-course single-cell RNA sequencing reveals transcriptional dynamics and heterogeneity of limbal stem cells derived from human pluripotent stem cells

Human pluripotent stem cell-derived limbal stem cells (hPSC-derived LSCs) provide a promising cell source for corneal transplants and ocular surface reconstruction. Although recent efforts in the identification of LSC markers have increased our understanding of the biology of LSCs, the lack of knowledge of the developmental origin, cell fate determination, and identity of human LSCs hindered the establishment of differentiation protocols for hPSC-derived LSCs and hold back their clinical application. Here, we performed a time-course single-cell RNA-seq to investigate transcriptional heterogeneity and expression changes of LSCs derived from human embryonic stem cells. Based on current protocol, expression heterogeneity of reported LSC markers were identified in subpopulations of differentiated cells. EMT has been shown to occur during differentiation process, which could possibly result in generation of untargeted cells. Pseudotime trajectory analysis revealed transcriptional changes and signatures of commitment of hPSCs-derived LSCs and their progeny - the transit amplifying cells. Furthermore, several new makers of LSCs were identified, which could facilitate elucidating the identity and developmental origin of human LSCs in vivo.

developmental biology↗

bioRxiv2020

Inductive Inference of Gene Regulatory Network Using Supervised and Semi-supervised Graph Neural Networks

Discovering gene regulatory relationships and reconstructing gene regulatory networks (GRN) based on gene expression data is a classical, long-standing computational challenge in bioinformatics. Computationally inferring a possible regulatory relationship between two genes can be formulated as a link prediction problem between two nodes in a graph. Graph neural network (GNN) provides an opportunity to construct GRN by integrating topological neighbor propagation through the whole gene network. We propose an end-to-end gene regulatory graph neural network (GRGNN) approach to reconstruct GRNs from scratch utilizing the gene expression data, in both a supervised and a semi-supervised framework. To get better inductive generalization capability, GRN inference is formulated as a graph classification problem, to distinguish whether a subgraph centered at two nodes contains the link between the two nodes. A linked pair between a transcription factor (TF) and a target gene, and their neighbors are labeled as a positive subgraph, while an unlinked TF and target gene pair and their neighbors are labeled as a negative subgraph. A GNN model is constructed with node features from both explicit gene expression and graph embedding. We demonstrate a noisy starting graph structure built from partial information, such as Pearsons correlation coefficient and mutual information can help guide the GRN inference through an appropriate ensemble technique. Furthermore, a semi-supervised scheme is implemented to increase the quality of the classifier. When compared with established methods, GRGNN achieved state-of-the-art performance on the DREAM5 GRN inference benchmarks. GRGNN is publicly available at https://github.com/juexinwang/GRGNN. HighlightsO_LIWe present a novel formulation of graph classification in inferring gene regulatory relationships from gene expression and graph embedding. C_LIO_LIOur method leverages a powerful framework, gene regulatory graph neural network (GRGNN), which is flexible and powerful to ensemble statistical powers from a number of heuristic skeletons. C_LIO_LIOur results show GRGRNN outperforms previous supervised and unsupervised methods inductively on benchmarks. C_LIO_LIGRGNN can be interpreted and explained following the biological network motif hypothesis in gene regulatory networks. C_LI

bioinformatics↗

bioRxiv2020

MiREDiBase: a manually curated database of editing events in microRNAs

MicroRNAs (miRNAs) are regulatory small non-coding RNAs that function as translational repressors. MiRNAs are involved in most cellular processes, and their expression and function are presided by several factors. Amongst, miRNA editing is an epitranscriptional modification that alters the original nucleotide sequence of selected miRNAs, possibly influencing their biogenesis and target-binding ability. A-to-I and C-to-U RNA editing are recognized as the canonical types, with the A-to-I type being the predominant one. Albeit some bioinformatics resources have been implemented to collect RNA editing data, it still lacks a comprehensive resource explicitly dedicated to miRNA editing. Here, we present MiREDiBase, a manually curated catalog of editing events in miRNAs. The current version includes 3,059 unique validated and putative editing sites from 626 pre-miRNAs in humans and three primates. Editing events in mature human miRNAs are supplied with miRNA-target predictions and enrichment analysis, while minimum free energy structures are inferred for edited pre-miRNAs. MiREDiBase represents a valuable tool for cell biology and biomedical research and will be continuously updated and expanded at https://ncrnaome.osumc.edu/miredibase.

bioinformatics↗

bioRxiv2020

scREAD: A single-cell RNA-Seq database for Alzheimer's Disease

SummaryAlzheimers disease (AD) is a progressive neurodegenerative disorder of the brain and the most common form of dementia among the elderly. The single-cell RNA-sequencing (scRNA-Seq) and single-nucleus RNA-sequencing (snRNA-Seq) techniques are extremely useful for dissecting the function/dysfunction of highly heterogeneous cells in the brain at the single-cell level, and the corresponding data analyses can significantly improve our understanding of why particular cells are vulnerable in AD. We developed an integrated database named scREAD (single-cell RNA-Seq database for Alzheimers Disease), which is the first database dedicated to the management of all the existing scRNA-Seq and snRNA-Seq datasets from human postmortem brain tissue with AD and mouse models with AD pathology. scREAD provides comprehensive analysis results for 55 datasets from eight brain regions, including control atlas construction, cell type prediction, identification of differentially expressed genes, and identification of cell-type-specific regulons. Availability and ImplementationscREAD is a one-stop and user-friendly interface and freely available at https://bmbls.bmi.osumc.edu/scread/. The backend workflow can be downloaded from https://github.com/OSU-BMBL/scread/tree/master/script, to enable more discovery-driven analyses. Contactqin.ma@osumc.edu or hongjun.fu@osumc.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

scGNN: a novel graph neural network framework for single-cell RNA-Seq analyses

Single-cell RNA-sequencing (scRNA-Seq) is widely used to reveal the heterogeneity and dynamics of tissues, organisms, and complex diseases, but its analyses still suffer from multiple grand challenges, including the sequencing sparsity and complex differential patterns in gene expression. We introduce the scGNN (single-cell graph neural network) to provide a hypothesis-free deep learning framework for scRNA-Seq analyses. This framework formulates and aggregates cell-cell relationships with graph neural networks and models heterogeneous gene expression patterns using a left-truncated mixture Gaussian model. scGNN integrates three iterative multi-modal autoencoders and outperforms existing tools for gene imputation and cell clustering on four benchmark scRNA-Seq datasets. In an Alzheimers disease study with 13,214 single nuclei from postmortem brain tissues, scGNN successfully illustrated disease-related neural development and the differential mechanism. scGNN provides an effective representation of gene expression and cell-cell relationships. It is also a novel and powerful framework that can be applied to scRNA-Seq analyses.

bioinformatics↗

bioRxiv2020

A single-cell transcriptome atlas of the aging human and macaque retina

The human retina is a complex neural tissue that detects light and sends visual information to the brain. However, the molecular and cellular processes that underlie aging primate retina remain unclear. Here, we provide a comprehensive transcriptomic atlas based on 119,520 single cells of the foveal and peripheral retina of humans and macaques covering different ages. The molecular features of retinal cells differed between the two species, suggesting the distinct regional and species specializations of the human and macaque retinae. In addition, human retinal aging occurred in a region- and cell-type- specific manner. Aging of human retina exhibited a foveal to peripheral gradient. MYO9A- rods and a horizontal cell subtype were greatly reduced in aging retina, indicating their vulnerability to aging. Moreover, we generated a dataset showing the cell-type- and region- specific gene expression associated with 55 types of human retinal disease, which provides a foundation to understand the molecular and cellular mechanisms underlying human retinal diseases. Together, these datasets are valuable for understanding the molecular characteristics of primate retina, as well as the molecular regulation of aging progression and related diseases.

neuroscience↗

bioRxiv2020

GsERF enhances aluminum tolerance through an ethylene-mediated pathway in Arabidopsis thaliana

The ethylene response factor (ERF) transcription factor is a subfamily of AP2/ERF superfamily in plants, which plays multiple roles in plant growth and development as well as stress response. In this study, we found that the GsERF gene from BW69 line of wild soybean held a constitutive expression pattern and induced by aluminum stress with more transcripts in soybean root. The putative GsERF protein containing an AP2 domain was in the nucleus and transactivation activity. In addition, the overexpression of the GsERF gene enhanced root relative length rate in Arabidopsis and shallow staining by hematoxylin under the treatments of AlCl3. The ethylene synthesis related genes such as ACS4, ACS5 and ACS6 are upregulated in the GsERF overexpressed plants than those in wild type plants under the treatment of AlCl3. Furthermore, expression levels of stress/ABA-responsive marker genes, including ABI1, ABI2, ABI4, ABI5, RD29B and RD22 in transgenic lines compared with those in wild type Arabidopsis were affected by AlCl3 treatments. Taken together, the results indicate that overexpression of GsERF may enhance aluminum tolerance through an ethylene-mediated pathway and/or ABA signaling pathway in Arabidopsis thaliana.View Full Text

molecular biology↗

bioRxiv2020

Regulation of meiotic progression by Sertoli-cell androgen signaling

Androgen receptor (AR) signaling in Sertoli cells is known to be important for germ-cell progression through meiosis, but the extent to which androgens indirectly regulates specific meiosis stages is not known. Here, we combine synchronization of spermatogenesis, cytological analyses and single-cell RNAseq (scRNAseq) in the Sertoli cell androgen receptor knockout (SCARKO) mutant and control mice, and demonstrate that SCARKO mutant spermatocytes exhibited normal expression and localization of key protein markers of meiotic prophase events, indicating that initiation of meiotic prophase is not androgen dependent. However, spermatocytes from SCARKO testes failed to acquire competence for the meiotic division phase. ScRNAseq analysis of wild type and SCARKO mutant testes revealed a molecular transcriptomic block in an early meiotic prophase state (leptotene/zygotene) in mutant germ cells, and identified several misregulated genes in SCARKO Sertoli cells, many of which have been previously implicated in male infertility. Together, our coordinated cytological and single-cell RNAseq analyses identified germ-cell intrinsic and extrinsic genes responsive to Sertoli-cell androgen signaling that promotes cellular states permissive for the meiotic division phase.

genetics↗

bioRxiv2020

Annexin-A1 tripeptide attenuates surgery-induced neuroinflammation and memory deficits through regulation of the NLRP3 inflammasome

Neuroinflammation is a growing hallmark of perioperative neurocognitive disorders (PNDs), including delirium and longer-lasting cognitive deficits. We have developed a clinically-relevant orthopedic mouse model to study the impact of a common surgical procedure on the vulnerable brain. The mechanism underlying PNDs remain unknown. Here we evaluated the impact of surgical trauma on the NLRP3 inflammasome signaling, including the expression of apoptosis-associated speck-like protein containing a CARD (ASC), caspase-1, and IL-1{beta} in the hippocampus of C57BL6/J male mice, adult (3-months) and aged (>18-months). Surgery triggered ASC specks formation in CA1 hippocampal microglia, but without inducing significant morphological changes in NLRP3 and ASC knockout mice. Since no therapies are currently available to treat PNDs, we assessed the neuroprotective effects of a biomimetic peptide derived from the endogenous inflammation-ending molecule, Annexin-A1 (ANXA1). We tested the hypothesis that this peptide (ANXA1sp) inhibits NLRP3 inflammasome activation, thus preventing microglial activation and hippocampal-dependent memory deficits. Together these results uncover a previously underrecognized role of the NLRP3 inflammasome in triggering postoperative neuroinflammation and offer a new target for advancing treatment of PNDs through resolution of inflammation.

neuroscience↗

bioRxiv2020

Genomic analysis of worldwide sheep breeds reveals PDGFD a major target of fat-tail selection in sheep

Fat tail is a special trait in sheep acquired during sheep domestication. Several genomic analyses have been conducted in sheep breeds from limited geographic origins to identify the genetic factors underlying this trait. Nevertheless, these studies obtained different candidates. The results of these regional studies were easily biased by the breed structures. To minimize the bias and distinguish the true candidates, we used an extended data set of 968 sheep representing 18 fat-tailed breeds and 14 thin-tailed breeds from around the world, and integrated two statistic tests to detect selection signatures, including Genetic Fixation Index (FST) and difference of derived allele frequency ({Delta}DAF). The results showed that platelet derived growth factor D (PDGFD) exhibited the highest genetic differentiation between fat- and thin-tailed sheep breeds. Further analysis of sequence variation identified that a 6.8-kb region within the first intron of PDGFD is likely the target of positive selection and contains regulatory mutation(s) in fat-tailed sheep. Histological analysis and gene expression analysis demonstrated that PDGFD expression is associated with maturation and hemostasis of adipocytes. Luciferase reporter assays showed that a segment of conserved sequence surrounding the orthologous site of one sheep mutation is functional in regulating PDGFD expression in human. These results reveal that PDGFD is the predominant factor for the fat tail phenotype in sheep by contributing to adiopogenesis and maintaining the hemostasis of mature adipocytes. This study provides insights into the evolution of fat-tailed sheep and has important application to animal breeding, as well as obesity-related human diseases.

genomics↗

bioRxiv2020

Insights into substrate-mediated assembly of the chloroplast TAT receptor complex

The Twin Arginine Transport (TAT) system translocates fully folded proteins across the thylakoid membrane in the chloroplast (cp) and the cytoplasmic membrane of bacteria. In chloroplasts, cpTAT transport is achieved by three components: Tha4, Hcf106, and cpTatC. Hcf106 and cpTatC function as the substrate recognition/binding complex while Tha4 is thought to play a significant role in forming the translocation pore. Recent studies challenged this idea by suggesting that cpTatC-Hcf106-Tha4 function together in the active translocase. Here, we have mapped the inter-subunit contacts of cpTatC-Hcf106 during the resting state and built a cpTatC-Hcf106 structural model based on our crosslinking data. In addition, we have identified a substrate-mediated reorganization of cpTatC-Hcf106 contact sites during active substrate translocation. The proximity of Tha4 to the cpTatC-Hcf106 complex was also identified. Our data suggest a model for cpTAT function in which the transmembrane helices of Hcf106 and Tha4 may each contact the fifth transmembrane helix of cpTatC while the insertion of the substrate signal peptide may rearrange the cpTatC-Hcf106-Tha4 complex and initiate the translocation event. One sentence summaryProtein subunits of the thylakoidal twin arginine transport complex function together during substrate recognition and translocase assembly.

plant biology↗

bioRxiv2020

Tcf21+ mesenchymal cells contribute to testis somatic cell development, homeostasis, and regeneration

Testicular development and function relies on interactions between somatic cells and the germline, but similar to other organs, regenerative capacity decline in aging and disease. Whether the adult testis maintains a reserve progenitor population with repair or regenerative capacity remains uncertain. Here, we characterized a recently identified mouse testis interstitial population expressing the transcription factor Tcf21. We found that Tcf21+ cells are bipotential somatic progenitors present in fetal testis and ovary, maintain adult testis homeostasis during aging, and act as reserve somatic progenitors following injury. In vitro, Tcf21+ cells are multipotent mesenchymal progenitors which form multiple somatic lineages including Leydig and myoid cells. Additionally, Tcf21+ cells resemble resident fibroblast populations reported in other organs having roles in tissue homeostasis, fibrosis, and regeneration. Our findings reveal that the testis, like other organs, maintains multipotent mesenchymal progenitors that can be leveraged in development of future therapies for hypoandrogenism and/or infertility. HighlightsO_LIMultipotent Tcf21+ MPs can differentiate into somatic testis cell types C_LIO_LITcf21+ cells contribute to testis and ovary somatic cells during gonadal development C_LIO_LITcf21+ cells replenish somatic cells of the aging testis and in response to tissue injury C_LIO_LITestis Tcf21 cells resemble resident fibroblast populations in multiple organs C_LI

developmental biology↗

bioRxiv2020

An integrated mosquito small RNA genomics resource reveals dynamic evolution and host responses to viruses and transposons.

Although mosquitoes are major transmission vectors for pathogenic arboviruses, viral infection has little impact on mosquito health. This immunity is due in part to mosquito RNA interference (RNAi) pathways that generate antiviral small interfering RNAs (siRNAs) and Piwi-interacting RNAs (piRNAs). RNAi also maintains genome integrity by potently repressing mosquito transposon activity in the germline and soma. However, viral and transposon small RNA regulatory pathways have not been systematically examined together in mosquitoes. Therefore, we developed an integrated Mosquito Small RNA Genomics (MSRG) resource that analyzes the transposon and virus small RNA profiles in mosquito cell cultures and somatic and gonadal tissues across four medically important mosquito species. Our resource captures both somatic and gonadal small RNA expression profiles within mosquito cell cultures, and we report the evolutionary dynamics of a novel Mosquito-Conserved piRNA Cluster Locus (MCpiRCL) composed of satellite DNA repeats. In the larger culicine mosquito genomes we detected highly regular periodicity in piRNA biogenesis patterns coinciding with the expansion of Piwi pathway genes. Finally, our resource enables detection of crosstalk between piRNA and siRNA populations in mosquito cells during a response to virus infection. The MSRG resource will aid efforts to dissect and combat the capacity of mosquitoes to tolerate and spread arboviruses.

genomics↗

bioRxiv2020

Prediction of protein-protein interactions based on elastic net and deep forest

Prediction of protein-protein interactions (PPIs) helps to grasp molecular roots of disease. However, web-lab experiments to predict PPIs are limited and costly. Using machine-learning-based frameworks can not only automatically identify PPIs, but also provide new ideas for drug research and development from a promising alternative. We present a novel deep-forest-based method for PPIs prediction. First, pseudo amino acid composition (PAAC), autocorrelation descriptor (Auto), multivariate mutual information (MMI), composition-transition-distribution (CTD), and amino acid composition PSSM (AAC-PSSM), and dipeptide composition PSSM (DPC-PSSM) are adopted to extract and construct the pattern of PPIs. Secondly, elastic net is utilized to optimize the initial feature vectors and boost the predictive performance. Finally, GcForest-PPI model based on deep forest is built up. Benchmark experiments reveal that the accuracy values of Saccharomyces cerevisiae and Helicobacter pylori are 95.44% and 89.26%. We also apply GcForest-PPI on independent test sets and CD9-core network, crossover network, and cancer-specific network. The evaluation shows that GcForest-PPI can boost the prediction accuracy, complement experiments and improve drug discovery. The datasets and code of GcForest-PPI could be downloaded at https://github.com/QUST-AIBBDRC/GcForest-PPI/.

bioinformatics↗

bioRxiv2020

StackRAM: a cross-species method for identifying RNA N6-methyladenosine sites based on stacked ensemble

N6-methyladenosine is a prevalent RNA methylation modification, which plays an important role in various biological processes. Accurate identification of the m6A sites is fundamental to deeply understand the biological functions and mechanisms of the modification. However, the experimental methods for detecting m6A sites are usually time-consuming and expensive, and various computational methods have been developed to identify m6A sites in RNA. This paper proposes a novel cross-species computational method StackRAM using machine learning algorithms to identify the m6A sites in S. cerevisiaeH. sapiens and A. thaliana. First, the RNA sequences features are extracted through binary encoding, chemical property, nucleotide frequency, k-mer nucleotide frequency, pseudo dinucleotide composition, and position-specific trinucleotide propensity, and the initial feature set is obtained by feature fusion. Secondly, the Elastic Net is used for the first time to filter redundant and noisy information and retain important features for m6A sites classification. Finally, the base-classifiers output probabilities are combined with the optimal feature subset corresponding to the Elastic Net, and the combination feature input the second-stage meta-classifier SVM. The jackknife test on training dataset S. cerevisiae indicates that the prediction performance of StackRAM is superior to the current state-of-the-art methods. StackRAM prediction accuracy for independent test datasets H. sapiens and A. thaliana reach 92.30% and 87.06%, respectively. Therefore, StackRAM has development potential in cross-species prediction and can be a useful method for identifying m6A sites. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/StackRAM/.

bioinformatics↗