Biology subjects

Xiao, G.

Publications and source records attributed to Xiao, G..

105 records · Page 6Linked to original sources

bioRxiv2020

Discovery of a novel coronavirus associated with the recent pneumonia outbreak in humans and its potential bat origin

Since the SARS outbreak 18 years ago, a large number of severe acute respiratory syndrome related coronaviruses (SARSr-CoV) have been discovered in their natural reservoir host, bats1-4. Previous studies indicated that some of those bat SARSr-CoVs have the potential to infect humans5-7. Here we report the identification and characterization of a novel coronavirus (nCoV-2019) which caused an epidemic of acute respiratory syndrome in humans, in Wuhan, China. The epidemic, started from December 12th, 2019, has caused 198 laboratory confirmed infections with three fatal cases by January 20th, 2020. Full-length genome sequences were obtained from five patients at the early stage of the outbreak. They are almost identical to each other and share 79.5% sequence identify to SARS-CoV. Furthermore, it was found that nCoV-2019 is 96% identical at the whole genome level to a bat coronavirus. The pairwise protein sequence analysis of seven conserved non-structural proteins show that this virus belongs to the species of SARSr-CoV. The nCoV-2019 virus was then isolated from the bronchoalveolar lavage fluid of a critically ill patient, which can be neutralized by sera from several patients. Importantly, we have confirmed that this novel CoV uses the same cell entry receptor, ACE2, as SARS-CoV.

microbiology↗

bioRxiv2020

Lipid-specific labeling of enveloped viruses with quantum dots for single-virus tracking

Quantum dots (QDs) possess optical properties of superbright fluorescence, excellent photostability, narrow emission spectra, and optional colors. Labeled with QDs, single molecules/viruses can be rapidly and continuously imaged for a long time, providing more detailed information than labeled with other fluorophores. While they are widely used to label proteins in single-molecule tracking studies, QDs have rarely been used to study virus infection, mainly due to lack of accepted labeling strategies. Here, we report a general method to mildly and readily label enveloped viruses with QDs. Lipid-biotin conjugates were used to recognize and mark viral lipid membranes, and streptavidin (SA)-QD conjugates were used to light them up. Such a method allowed enveloped viruses to be labeled in 2 hours with specificity and efficiency up to 99% and 98%. The intact morphology and the native infectivity of viruses could be furthest preserved. With the aid of this QD labeling method, we lit wild-type (WT) and mutant Japanese encephalitis virus (JEV) up, tracked their infection in living Vero cells, and found that H144A and Q258A substitutions in the envelope (E) protein didnt affect the virus intracellular trafficking. The lipid-specific QD labeling method described in this study provides a handy and practical tool to readily "see" the viruses and follow their infection, facilitating the widespread use of single-virus tracking and the uncovering of complex infection mechanisms. Author summaryVirus infection in host cells is a complex process comprising a large number of dynamic molecular events. Single-virus tracking is a versatile technique to study these events. To perform this technique, viruses must be fluorescently labeled to be visible to fluorescence microscopes. Quantum dot is a kind of fluorescent tags that has many unique optical properties. It has been widely used to label proteins in single-molecule tracking studies, but rarely used to study virus infection, mainly due to lack of accepted labeling method. In this study, we developed a lipid-specific method to readily, mildly, specifically, and efficiently label enveloped viruses with quantum dots by recognizing viral envelope lipids with lipid-biotin conjugates and recognizing these lipid-biotin conjugates with streptavidin-quantum dot conjugates. Such a method is superior to the commonly used DiD/DiO labeling and the other QD labeling methods. It is not only applicable to normal viruses, but also competent to label the key protein-mutated viruses and the inactivated high virulent viruses, providing a powerful tool for single-virus tracking.

biochemistry↗

bioRxiv2019

MB-GAN: Microbiome Simulation via Generative Adversarial Network

Simulation is a critical component of experimental design and evaluation of analysis methods in microbiome association studies. However, statistically modeling the microbiome data is challenging since that the complex structure in the real data is difficult to be fully represented by statistical models. To address this challenge, we designed a novel simulation framework for microbiome data using a generative adversarial network (GAN), called MB-GAN, by utilizing methodology advancements from the deep learning community. MB-GAN can automatically learn from a given dataset and compute simulated datasets that are indistinguishable from it. When MB-GAN was applied to a case-control microbiome study of 396 samples, we demonstrated that the simulated data and the original data had similar first-order and second-order properties, including sparsity, diversities, and taxa-taxa correlations. These advantages are suitable for further microbiome methodology development where high fidelity microbiome data are needed.

bioinformatics↗

bioRxiv2019

A novel dynamic network imaging analysis method reveals aging-related fragmentation of cortical networks in mouse

Network analysis of large-scale neuroimaging data has proven to be a particularly challenging computational problem. In this study, we adapt a novel analytical tool, known as the community dynamic inference method (CommDy), which was inspired by social network theory, for the study of brain imaging data from an aging mouse model. CommDy has been successfully used in other domains in biology; this report represents its first use in neuroscience. We used CommDy to investigate aging-related changes in network parameters in the auditory and motor cortices using flavoprotein autofluorescence imaging in brain slices and in vivo. Analysis of spontaneous activations in the auditory cortex of slices taken from young and aged animals demonstrated that cortical networks in aged brains were highly fragmented compared to networks observed in young animals. Specifically, the degree of connectivity of each activated node in the aged brains was significantly lower than those seen in the young brain, and multivariate analyses of all derived network metrics showed distinct clusters of these metrics in young vs. aged brains. CommDy network metrics were then used to build a random-forests classifier based on NMDA-receptor blockade data, which successfully recapitulated the aging findings, suggesting that the excitatory synaptic substructure of the auditory cortex may be altered during aging. A similar aging-related decline in network connectivity was also observed in spontaneous activity obtained from the awake motor cortex, suggesting that the findings in the auditory cortex are reflections of general mechanisms that occur during aging. Therefore, CommDy therefore provides a new dynamic network analytical tool to study the brain and provides links between network-level and synaptic-level dysfunction in the aging brain.

neuroscience↗

bioRxiv2019

MetaPrism: A Toolkit for Joint Taxa/Gene Analysis of Metagenomic Sequencing Data

BackgroundIn microbiome research, metagenomic sequencing generates enormous amounts of data. These data are typically classified into taxa for taxonomy analysis, or into genes for functional analysis. However, a joint analysis where the reads are classified into taxa-specific genes is often overlooked. ResultTo enable the analysis of this biologically meaningful feature, we developed a novel bioinformatic toolkit, MetaPrism, which can analyze sequence reads for a set of joint taxa/gene analyses: 1) classify sequence reads and estimate the abundances for taxa-specific genes; 2) tabularize and visualize taxa-specific gene abundances; 3) compare the abundances between groups, and 4) build prediction models for clinical outcome. We illustrated these functions using a published microbiome metagenomics dataset from patients treated with immune checkpoint inhibitor therapy and showed the joint features can serve as potential biomarkers to predict therapeutic responses. ConclusionsMetaPrism is a toolkit for joint taxa and gene analysis. It offers biological insights on the taxa-specific genes on top of the taxa-alone or gene-alone analysis. MetaPrism is open source software and freely available at https://github.com/jiwoongbio/MetaPrism. The example script to reproduce the manuscript is also provided in the above code repository.

bioinformatics↗

bioRxiv2019

Endothelial CXCL5 negatively regulates myelination and repair after white matter stroke

Cerebral small vessel disease and resulting white matter pathologies are worsened by cardiovascular risk factors including obesity. The molecular changes in cerebral endothelial cells caused by chronic cerebrovascular risk factors remain unknown. We developed a novel approach for molecular profiling of chronically injured cerebral endothelial cells using cell-specific translating ribosome affinity purification (RiboTag) with RNA-seq in Tie2-Cre:RiboTag mice. We used this approach to identify the transcriptome of white matter endothelial cells after the onset of diet-induced obesity (DIO). DIO induces an IL-17B signaling pathway that acts on the cerebral endothelia through IL-17Rb to increase levels of both circulating CXCL5 and local endothelial expression of CXCL5 in both the DIO mouse model and in humans with imaging or pathologic evidence of cerebral small vessel disease. In the white matter, endothelial CXCL5 acts as a chemoattractant and promotes the association of oligodendrocyte progenitor cells (OPCs) with cerebral endothelia increasing vessel-associated OPC cell number and triggers OPC gene expression programs regulating migration and chemokine receptor activation. Targeted blockade of IL-17B with peripheral antibody administration reduced the population of vessel-associated OPCs by reducing endothelial CXCL5 expression. CXCL5-mediated sequestration of OPCs to white matter vasculature impairs OPC differentiation after a focal white matter ischemic lesion. DIO promotes a unique white matter endothelial-to-oligodendrocyte progenitor cell signaling pathway that compromises brain repair after stroke.

neuroscience↗

bioRxiv2019

Computational staining of pathology images to study tumor microenvironment in lung cancer

The spatial organization of different types of cells in tumor tissues reveals important information about the tumor microenvironment (TME). In order to facilitate the study of cellular spatial organization and interactions, we developed a comprehensive nuclei segmentation and classification tool to characterize the TME from standard Hematoxylin and Eosin (H&E)-stained pathology images. This tool can computationally \"stain\" different types of cell nuclei in H&E pathology images to facilitate pathologists in analyzing the TME.\n\nA Mask Regional-Convolutional Neural Network (Mask-RCNN) model was developed to segment the nuclei of tumor, stromal, lymphocyte, macrophage, karyorrhexis and red blood cells in lung adenocarcinoma (ADC). Using this tool, we identified and classified cell nuclei and extracted 48 cell spatial organization-related features that characterize the TME. Using these features, we developed a prognostic model from the National Lung Screening Trial dataset, and independently validated the model in The Cancer Genome Atlas (TCGA) lung ADC dataset, in which the predicted high-risk group showed significantly worse survival than the low-risk group (pv= 0.001), with a hazard ratio of 2.23 [1.37-3.65] after adjusting for clinical variables. Furthermore, the image-derived TME features were significantly correlated with the gene expression of biological pathways. For example, transcription activation of both the T-cell receptor (TCR) and Programmed cell death protein 1 (PD1) pathways was positively correlated with the density of detected lymphocytes in tumor tissues, while expression of the extracellular matrix organization pathway was positively correlated with the density of stromal cells.\n\nThis study developed a deep learning-based analysis tool to dissect the TME from tumor tissue images. Using this tool, we demonstrated that the spatial organization of different cell types is predictive of patient survival and associated with the gene expression of biological pathways. Although developed from the pathology images of lung ADC, this model can be adapted into other types of cancers.

bioinformatics↗

bioRxiv2019

METABOLIC DIVERSITY IN HUMAN NON-SMALL CELL LUNG CANCER CELLS

Intermediary metabolism in cancer cells is regulated by diverse cell-autonomous processes including signal transduction and gene expression patterns arising from specific oncogenotypes and cell lineages. Although it is well established that metabolic reprogramming is a hallmark of cancer, we lack a full view of the diversity of metabolic programs in cancer cells and an unbiased assessment of the associations between metabolic pathway preferences and other cell-autonomous processes. Here we quantified over 100 metabolic features, mostly from 13C enrichment of molecules from central carbon metabolism, in over 80 non-small cell lung cancer (NSCLC) cell lines cultured under identical conditions. Because these cell lines were extensively annotated for oncogenotype, gene expression, protein expression and therapeutic sensitivity, the resulting database enables the user to uncover new relationships between metabolism and these orthogonal processes.

cancer biology↗

bioRxiv2019

VAMPr: VAriant Mapping and Prediction of antibiotic resistance via explainable features and machine learning

Antimicrobial resistance (AMR) is an increasing threat to public health. Current methods of determining AMR rely on inefficient phenotypic approaches, and there remains incomplete understanding of AMR mechanisms for many pathogen-antimicrobial combinations. Given the rapid, ongoing increase in availability of high density genomic data for a diverse array of bacteria, development of algorithms that could utilize genomic information to predict phenotype could both be useful clinically and assist with discovery of heretofore unrecognized AMR pathways. To facilitate understanding of the connections between DNA variation and phenotypic AMR, we developed a new bioinformatics tool, variant mapping and prediction of antibiotic resistance (VAMPr), to (1) derive gene ortholog-based sequence features for variants; (2) interrogate these explainable gene-level variants for their known or novel associations with AMR; and (3) build accurate models to predict AMR based on whole genome sequencing data. Following the Clinical & Laboratory Standards Institute (CLSI) guidelines, we curated the publicly available sequencing data for 3,393 bacterial isolates from 9 species along with AMR phenotypes for 29 antibiotics. We detected 14,615 variant genotypes and built 93 association and prediction models. The association models confirmed known genetic antibiotic resistance mechanisms, such as blaKPC and carbapenem resistance consistent with the accurate nature of our approach. The prediction models achieved high accuracies (mean accuracy of 91.1% for all antibiotic-pathogen combinations) internally through nested cross validation and were also validated using external clinical datasets. The VAMPr variant detection method, association and prediction models will be valuable tools for AMR research for basic scientists with potential for clinical applicability.

bioinformatics↗

bioRxiv2019

OsCYP71D8L as a key regulator involved in growth and stress response by mediating gibberellins and cytokinins homeostasis in rice

Phytohormones are pivotal in the regulation of plant growth and development, and acclimation to adverse environments. Multiple cytochrome P450 monooxygenases (CYP450s) are involved in the biosynthesis and catabolism of phytohormones. Here, we reported that a CYP450 member of CYP71 clan, OsCYP71D8L, participated in the control of multiple agronomic traits and abiotic stress responses by affecting gibberellin (GA) and cytokinin (CK) homeostasis in rice. We found that its gain-of-function mutant (cyp71d8l) and transgenic plants overexpressing OsCYP71D8L (OsCYP71D8L-OE) displayed similar phenotypes such as dwarfed plant, reduced panicle length and grain number per panicle. In OsCYP71D8L-OE seedlings, endogenous GAs and CKs was notably decreased and increased as compared with wild type (WT), respectively. Correspondingly, the dwarfed plant and less developed root of cyp71d8l and OsCYP71D8L-OE seedlings could be rescued by exogenous GA3, but more exacerbated by exogenous 6-BA. Importantly, cyp71d8l and OsCYP71D8L-OE seedlings maintained high chlorophyll contents and low reactive oxygen species level, and showed significantly enhanced tolerances to drought and high salt compared with WT. Thus, our results suggest that OsCYP71D8L plays roles in regulating rice growth and stress responses by coordinating GAs and CKs homeostasis, and it is useful to engineer stress-tolerant rice.

plant biology↗

bioRxiv2018

Screening of FDA-approved Drugs and Identification of Novel Lassa Virus Entry Inhibitors

Lassa virus (LASV) belongs to the Mammarenavirus genus (family Arenaviridae) and causes severe hemorrhagic fever in humans. At present, there are no Food and Drug Administration (FDA)-approved drugs or vaccines specific for LASV. Herein, high-throughput screening of an FDA-approved drug library was performed against LASV entry using a pseudo-type virus enveloping LASV glycoproteins. Two hit drugs, lacidipine and phenothrin, were identified as LASV entry inhibitors in the micromolar range. A mechanistic study revealed that both drugs inhibited LASV entry by blocking low-pH-induced membrane fusion. Moreover, lacidipine irreversibly bound to the LASV glycoprotein complex (GPC), resulting in virucidal activity. Adaptive mutant analyses demonstrated that replacement of T40, located in the ectodomain of the stable-signal peptide (SSP), with lysine (K) conferred LASV resistance to lacidipine without apparent loss of the viral growth profile. Furthermore, lacidipine showed antiviral activity and specificity against both LASV and the Guanarito virus (GTOV), which is also a category A new world arenavirus. Drug-resistant variants indicate that the V36M in ectodomain of SSP mutant and V436A in the transmembrane domain of GP2 mutant conferred GTOV resistance to lacidipine, suggesting that lacidipine might act via a novel mechanism other than calcium inhibition. This study shows that both lacidipine and phenothrin are candidates for LASV therapy, and the membrane-proximal external region of the GPC might provide an entry-targeted platform for inhibitors.

microbiology↗

bioRxiv2018

Obesity blocks oligodendrocyte precursor cell differentiation and impedes repair after white matter stroke

Obesity is a growing public health problem that increases rates of white matter atrophy and increases the likelihood of ischemic lesions within white matter. However, the cellular and molecular mechanisms that regulate these changes are unknown. We hypothesized that obesity may alter oligodendrocytes and myelin priming white matter for worsening injury and repair responses after ischemia. C57Bl/6 mice fed a high fat diet (60% kcal from fat) show increased numbers of oligodendrocyte precursor cells (OPCs), decreased myelin thickness with elevated g-ratios, and shorter paranodal axonal segments, indicating accelerated myelin turnover. Fate mapping of OPCs in PDGFR-CreERT;Rpl22tm1.1Psam mice demonstrated that OPC differentiation rates are enhanced by obesity. Gene expression analyses using a novel oligodendrocyte staging assay demonstrated OPC differentiation is blocked by obesity in between the pre-myelinating and myelinating stage. Using a model of subcortical white matter stroke, the number of stroke-responsive OPCs in obese mice was increased after stroke. At early time points after ischemic white matter stroke, spatial mapping of stroke-responsive OPCs indicates that obesity leads to increased OPCs at the edge of ischemic white matter lesions. At later time points, obesity results in increased OPCs within the ischemic lesion while reducing the number of GST-{pi}-positive mature oligodendrocytes in the lesion core. These data indicate that obesity disrupts normal white matter biology by blocking oligodendrocyte differentiation, leads to an exaggerated response of OPCs to white matter ischemia, and limits remyelination after stroke.\n\nSignificance StatementObesity is a growing public health crisis that specifically increases the development of white matter lesions and silent brain infarcts. This relationship implies a direct cellular effect on white matter yet direct evidence is limited. We modeled diet-induced obesity in mice and studied the effect on oligodendrocyte biology and the response to focal white matter stroke. Adult-onset obesity results in thinner myelin, compromised axonal microdomain structure, and blocks the differentiation of OPCs, leaving the white matter with increased numbers of OPCs. After focal white matter stroke in obese mice, the early OPC response to stroke is exaggerated while late reparative OPC differentiation is blocked. These results suggest that obesity specifically blocks OPC differentiation with consequence on brain repair after stroke.

neuroscience↗

bioRxiv2018

Development and Validation of a Nomogram Prognostic Model for Small-Cell Lung Cancer Patients

BackgroundSmall-cell lung cancer (SCLC) accounts for almost 15% of lung cancer cases in the United States. Nomogram prognostic models could greatly facilitate risk stratification and treatment planning, as well as more refined enrollment criteria for clinical trials. We developed and validated a new nomogram prognostic model for SCLC patients using a large SCLC patient cohort from the National Cancer Database (NCDB).\n\nMethodsClinical data of 24,680 SCLC patients diagnosed from 2004 to 2011 were used to develop the nomogram prognostic model. The model was then validated using an independent cohort of 9,700 SCLC patients diagnosed from 2012 to 2013. The prognostic performance was evaluated using p value, concordance index and integrated Area Under the (time dependent Receiver Operating Characteristic) Curve.\n\nResultsThe following variables were contained in the final prognostic model: age, gender, race, ethnicity, Charlson/Deyo Score, TNM Stage (assigned according to the AJCC 8th edition), treatment type (combination of surgery, radiation therapy and chemotherapy), and laterality. The model was validated in an independent testing group with a concordance index of 0.722 {+/-} 0.004 and an integrated AUC of 0.79. The nomogram model has a significantly higher prognostic accuracy than previously developed models, including the AJCC 8th edition TNM-staging system. We implemented the proposed nomogram and two previously published nomograms in an online webserver.\n\nConclusionsWe developed a nomogram prognostic model for SCLC patients, and validated the model using an independent patient cohort. The nomogram performs better than earlier models, including AJCC staging.

cancer biology↗

bioRxiv2018

Comprehensive analysis of lung cancer pathology images to discover tumor shape features that predict survival outcome

Pathology slide images capture tumor histomorphological details in high resolution. However, manual detection and characterization of tumor regions in pathology slides is labor intensive and subjective. Using a deep convolutional neural network (CNN), we developed an automated tumor region recognition system for lung cancer pathology slides. From the identified regions, we extracted 22 well-defined tumor shape features and found that 15 of them were significantly associated with patient survival outcome in lung adenocarcinoma patients from the National Lung Screening Trial. A tumor shape-based prognostic model was developed and validated in an independent patient cohort (n=389). The predicted high-risk group had significantly worse survival than the low-risk group (p value = 0.0029). Predicted risk group serves as an independent prognostic factor (high-risk vs. low-risk, hazard ratio = 2.25, 95% CI 1.34-3.77, p value = 0.0022) after adjusting for age, gender, smoking status, and stage. This study provides new insights into the relationship between tumor shape and patient prognosis.

cancer biology↗

bioRxiv2018

Lung Cancer Explorer (LCE): an open web portal to explore gene expression and clinical associations in lung cancer

We constructed a lung cancer-specific database housing expression data and clinical data from over 6,700 patients in 56 studies. Expression data from 23 \"whole-genome\" based platforms were carefully processed and quality controlled, whereas clinical data were standardized and rigorously curated. Empowered by this lung cancer database, we created an open access web resource - the Lung Cancer Explorer (LCE), which enables researchers and clinicians to explore these data and perform analyses. Users can perform meta-analyses on LCE to gain a quick overview of the results on tumor vs normal differential gene expression and expression-survival association. Individual dataset-based survival analysis, comparative analysis, and correlation analysis are also provided with flexible options to allow for customized analyses from the user.

cancer biology↗