Biology subjects

Yu, B.

Publications and source records attributed to Yu, B..

At least 127 records · Page 7Linked to original sources

bioRxiv2021

B cell-specific XIST complex enforces X-inactivation and restrains atypical B cells

The long noncoding RNA (lncRNA) XIST establishes X chromosome inactivation (XCI) in female cells in early development and thereafter is thought to be largely dispensable. Here we show XIST is continually required in adult human B cells to silence a subset of X-linked immune genes such as TLR7. XIST-dependent genes lack promoter DNA methylation and require continual XIST-dependent histone deacetylation. XIST RNA-directed proteomics and CRISPRi screen reveal distinctive somatic cell-specific XIST complexes, and identify TRIM28 that mediates Pol II pausing at promoters of X-linked genes in B cells. XIST dysregylation, reflected by escape of XIST-dependent genes, occurs in CD11c+ atypical memory B cells across single-cell transcriptome data in patients with female-biased autoimmunity and COVID-19 infection. XIST inactivation with TLR7 agonism suffices to promote isotype-switched atypical B cells. These results suggest cell-type-specific diversification of lncRNA-protein complexes increase lncRNA functionalities, and expand roles for XIST in sex-differences in biology and medicine. HIGHLIGHTSO_LIXIST prevents escape of genes with DNA hypomethylated promoters in B cells. C_LIO_LIXIST maintains X-inactivation through continuous deacetylation of H3K27ac. C_LIO_LIXIST ChIRP-MS and allelic CRISPRi screen reveal a B cell-specific XIST cofactor TRIM28. C_LIO_LIXIST loss and TLR7 stimulation promotes CD11c+ atypical B cell formation. C_LI

genomics↗

bioRxiv2020

A nucleotide-sensing endonuclease from the Gabija bacterial defense system

The arms race between bacteria and phages has led to the development of exquisite bacterial defense systems including a number of uncharacterized systems distinct from the well-known Restriction-Modification and CRISPR/Cas systems. Here, we report functional analyses of the GajA protein from the newly predicted Gabija system. The GajA protein is revealed as an endonuclease unique in that: 1. It may function as a restriction enzyme or a site-specific nicking enzyme, depending on the arrangement of the recognition sequences; 2. Its activity is strictly regulated by nucleotides concentration. NTP and dNTP at physiological concentrations can fully inhibited the robust DNA cleavage activity of GajA. Interestingly, the nucleotide inhibition is mediated by an ATPase-like domain, which usually hydrolyzes ATP to stimulate the DNA cleavage when associated with other nucleases. These features suggested the mechanism of the Gabija defense in which an endonuclease activity was suppressed at normal condition, while activated by the depletion of NTP and dNTP upon the replication and transcription by invaded phages. This work highlights a concise strategy to utilize a single protein for phage resistance via nucleotide regulatory.

biochemistry↗

bioRxiv2020

Learning epistatic polygenic phenotypes with Boolean interactions

Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1, a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments.

bioinformatics↗

bioRxiv2020

Time-resolved single-cell RNAseq profiling identifies a novel Fabp5-expressing subpopulation of inflammatory myeloid cells in chronic spinal cord injury

Innate immune responses following spinal cord injury (SCI) participate in early secondary pathogenesis and wound healing events. Here, we used time-resolved scRNAseq to map transcriptional profiles of SC tissue-resident and infiltrating myeloid cells post-SCI. Our work identifies a novel subpopulation of Fabp5+ inflammatory myeloid cells, comprising both resident and infiltrating cells and displaying a delayed cytotoxic profile at the lesion epicentre, which may serve as a target for future therapeutics.

neuroscience↗

bioRxiv2020

In vitro transcription using psychrophilic phage VSW-3 RNA polymerase

RNA research and applications were underpinned by in vitro transcription (IVT), while the RNA impurity resulted from the enzymatic reagents severely impede downstream applications. To improve the stability and purity of synthesized RNA we had characterized a novel single-subunit RNA polymerase (RNAP) encoded by a psychrophilic phage VSW-3 from plateau lake to produce RNA at low temperature. The VSW-3 RNAP is capable of carrying out in vitro RNA synthesis at low temperature (4-25{degrees}C) to reduce RNA degradation and alleviate the need of costly RNase inhibitor. Compared to routinely used T7 RNAP, VSW-3 RNAP provides comparable yield of transcripts, but is insensitive to class II transcription terminators and synthesizes RNA without redundant 3 -cis extension. More importantly, through dot-blot detection with the J2 monoclonal antibody, we found that the RNA products synthesized by VSW-3 RNAP contain much lower amount of or virtually no double-stranded RNA (dsRNA) by-products, which are significant in most T7 RNAP products and may cause severe cellular immune response. Combining these advantages, the VSW-3 RNAP is an advantageous enzyme for IVT, especially to produce RNA for in vivo use.

molecular biology↗

bioRxiv2020

Polygenic Risk Scores for Kidney Function to the Circulating Proteome, and Incident Kidney Diseases: the Atherosclerosis Risk in Communities Study

Genome-wide association studies (GWAS) have revealed numerous loci for kidney function (estimated glomerular filtration rate, eGFR). The relationship of polygenic predictors of eGFR, risk of incident adverse kidney outcomes, and the plasma proteome is not known. We developed a genome-wide polygenic risk score (PRS) using a weighted average of 1.2 million SNPs for eGFR using the LDpred algorithm, summary statistics generated by a European-ancestry (EA) meta-analysis of the CKDGen Consortium (N=558,423) and UK Biobank GWAS for eGFR (90% of the cohort; N=289,432), followed by best parameter selection using data from the remaining 10% of the UK Biobank (N=32,159). We then tested the association of the PRS among 8,886 EA participants in the Atherosclerosis Risk in Communities (ARIC) study (mean age: 54{+/-}6 years, 53% female) with incident chronic kidney disease (CKD), end stage kidney disease (ESKD), kidney failure (KF), and acute kidney injury (AKI). We also examined 4,877 plasma proteins measured at two time points (visit 3 (1993-95) and visit 5 (2011-13)) in relation to the PRS and compared associations between the proteome and eGFR itself. All models were adjusted for age, sex, center, and the first 10 principal components of ancestry. The developed PRS had an R2 for eGFR of 0.07 in ARIC. Over 30 years of follow up, the number of incident CKD, ESKD, KF, and AKI were 2,959, 137, 470, and 1,723, respectively. The PRS showed significant associations with all outcomes: hazard ratios (95% CI) per 1 SD lower PRS were 1.33 (1.28, 1.39), 1.20 (1.00, 1.42), 1.17 (1.06, 1.28), and 1.07 (1.02, 1.12) for incident CKD, ESKD, KF, and AKI respectively. The PRS was significantly associated (Bonferroni threshold P<1.02 x 10-5) with 108 proteins at both time points. The strongest associations were with cystatin-C (a marker of kidney function used in clinical practice), collagen alpha-1 (XV) chain, and desmocollin-2. All significant correlations with the PRS were negative, except those of testican-2 and angiostatin. Correlations of proteins with eGFR were much stronger than those with the PRS. Overall, we demonstrated that the PRS for eGFR is now sufficiently strong to capture risk for a spectrum of incident kidney diseases as well as broadly influence the plasma proteome.

genetics↗

bioRxiv2020

StackPDB: predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

DNA binding proteins (DBPs) not only play an important role in all aspects of genetic activities such as DNA replication, recombination, repair, and modification but also are used as key components of antibiotics, steroids, and anticancer drugs in the field of drug discovery. Identifying DBPs becomes one of the most challenging problems in the domain of proteomics research. Considering the high-priced and inefficient of the experimental method, constructing a detailed DBPs prediction model becomes an urgent problem for researchers. In this paper, we propose a stacked ensemble classifier based method for predicting DBPs called StackPDB. Firstly, pseudo amino acid composition (PseAAC), pseudo position-specific scoring matrix (PsePSSM), position-specific scoring matrix-transition probability composition (PSSM-TPC), evolutionary distance transformation (EDT), and residue probing transformation (RPT) are applied to extract protein sequence features. Secondly, extreme gradient boosting-recursive feature elimination (XGB-RFE) is employed to gain an excellent feature subset. Finally, the best features are applied to the stacked ensemble classifier composed of XGBoost, LightGBM, and SVM to construct StackPDB. After applying leave-one-out cross-validation (LOOCV), StackPDB obtains high ACC and MCC on PDB1075, 93.44% and 0.8687, respectively. Besides, the ACC of the independent test datasets PDB186 and PDB180 are 84.41% and 90.00%, respectively. The MCC of the independent test datasets PDB186 and PDB180 are 0.6882 and 0.7997, respectively. The results on the training dataset and the independent test dataset show that StackPDB has a great predictive ability to predict DBPs.

bioinformatics↗

bioRxiv2020

Fertility-LightGBM: A fertility-related protein prediction model by multi-information fusion and light gradient boosting machine

The identification of fertility-related proteins plays an essential part in understanding the embryogenesis of germ cell development. Since the traditional experimental methods are expensive and time-consuming to identify fertility-related proteins, the purposes of predicting protein functions from amino acid sequences appeared. In this paper, we propose a fertility-related protein prediction model. Firstly, the model combines protein physicochemical property information, evolutionary information and sequence information to construct the initial feature space ALL. Then, the least absolute shrinkage and selection operator (LASSO) is used to remove redundant features. Finally, light gradient boosting machine (LightGBM) is used as a classifier to predict. The 5-fold cross-validation accuracy of the training dataset is 88.5%, and the independent accuracy of the training dataset is 91.5%. The results show that our model is more competitive for the prediction of fertility-related proteins, which is helpful for the study of fertility diseases and related drug targets.

bioinformatics↗

bioRxiv2020

DNN-DTIs: improved drug-target interactions prediction using XGBoost feature selection and deep neural network

Research, analysis, and prediction of drug-target interactions (DTIs) play an important role in understanding drug mechanisms, drug repositioning and design. Machine learning (ML)-based methods for DTIs prediction can mitigate the shortcomings of time-consuming and labor-intensive experimental approaches, providing new ideas and insights for drug design. We propose a novel pipeline for predicting drug-target interactions, called DNN-DTIs. First, the target information is characterized by pseudo-amino acid composition, pseudo position-specific scoring matrix, conjoint triad, composition, transition and distribution, Moreau-Broto autocorrelation, and structure feature. Then, the drug compounds are encoded using substructure fingerprint. Next, we utilize XGBoost to determine nonredundant and important feature subset, then the optimized and balanced sample vectors could be obtained through SMOTE. Finally, a DTIs predictor, DNN-DTIs, is developed based on deep neural network (DNN) via layer-by-layer learning. Experimental results indicate that DNN-DTIs achieves outstanding performance than other predictors with the ACC values of 98.78%, 98.60%, 97.98%, 98.24% and 98.00% on Enzyme, Ion Channels (IC), GPCR, Nuclear Receptors (NR) and Kuang's dataset. Therefore, DNN-DTIs's accurate prediction performance on Network1 and Network2 make it logical choice for contributing to the study of DTIs, especially, the drug repositioning and new usage of old drugs.

bioinformatics↗

bioRxiv2020

Alternative polyadenylation is a determinant of oncogenic Ras function

Alternative polyadenylation of pre-mRNA has been recently shown to play important roles in development and cancer. Activating mutations in the Ras oncogene are common drivers of many human cancers but the mechanisms by which they cooperate with alternative polyadenylation are not known. By exploiting the genetics of C. elegans, we identified cfim-1/CFIm25, a subunit of the alternative polyadenylation machine, as a key determinant of hyperactive Ras function. Ablation of cfim-1 increased penetrance of multivulva phenotype in let-60/Ras gain-of-function (gf) mutant through shortening of transcripts at the 3 untranslated region, including p21 activated kinase pak-1/PAK1 and multidrug transporter mrp-5/ABCC1. Depletion of CFIm25 in human KRAS-driven cancer cells resulted in a similar shortening of 3 untranslated regions in the PAK1 and ABCC1 transcripts, which caused an epithelial-to-mesenchymal transition and increased cell migration. Exploiting the mechanisms by which alternative polyadenylation affects activated oncogene output could offer novel approaches for the treatment of Ras-driven tumors.

cancer biology↗

bioRxiv2020

Bacterial evolution in high osmolarity environments

Bacteria must maintain a cytosolic osmolarity higher than that of their environment in order to take up water. High osmolarity environments therefore present a formidable stress to bacteria. To explore the evolutionary mechanisms by which bacteria adapt to high osmolarity environments, we selected Escherichia coli in media with a variety of osmolytes and concentrations for 250 generations. Adaptation was osmolyte-dependent, with sorbitol stress generally resulting in increased fitness in conditions with higher osmolarity, while selection in high concentrations of proline resulted in increased fitness specifically on proline. Consistent with these phenotypes, sequencing of the evolved populations showed that passaging in proline resulted in specific mutations in an associated metabolic pathway that increases the ability to utilize proline for growth, while evolution in sorbitol resulted in mutations in many different genes that generally improve growth in high osmolarity conditions at the expense of growth at low osmolarity. High osmolarity decreased growth rate but increased mean cell volume compared with growth on proline as the sole carbon source, demonstrating that osmolarity-induced changes in growth rate and cell size follow an orthogonal relationship from the classical Growth Law relating cell size and nutrient quality. Isolates from a sorbitol-evolved population that capture the likely temporal sequence of mutations revealed by metagenomic sequencing demonstrate a tradeoff between growth at high and low osmolarity. Our study highlights the utility of experimental evolution for dissecting complex cellular networks and environmental interactions, particularly in the case of behaviors that can involve both specific and general metabolic stressors. ImportanceFor bacteria, maintaining higher internal solute concentrations than the environment allows cells to take up water. As a result, survival is challenging in high osmolarity environments. To investigate how bacteria adapt to high osmolarity environments, we evolved Escherichia coli in a variety of high osmolarity solutions for hundreds of generations. We found that evolved populations adopted different strategies to improve their growth depending on the osmotic passaging condition, either generally adapting to high osmolarity conditions or better metabolizing the osmolyte as carbon source. Single-cell imaging demonstrated that enhanced fitness was coupled to faster growth, and metagenomic sequencing revealed mutations that reflect growth tradeoffs across osmolarities. Our study demonstrates the utility of long-term evolution experiments for probing adaptation during environmental stress.

microbiology↗

bioRxiv2020

Multiethnic catalog of structural variants and their translational impact for disease phenotypes across 19,652 genomes

Genome sequencing at population scale provides unprecedented access to the genetic foundations of human phenotypic diversity, but genotype-phenotype association analyses limited to small variants have failed to comprehensively characterize the genetic architecture of human health and disease because they ignore structural variants (SVs) known to contribute to phenotypic variation and pathogenic conditions1-3. Here we demonstrate the significance of SVs when assessing genotype-phenotype associations and the importance of ethnic diversity in study design by analyzing SVs across 19,652 individuals and the translational impact on 4,156 aptamerbased proteomic measurements across 4,021 multi-ethnic samples. The majority of 304,533 SVs detected are rare, although we identified 2,336 protein-coding genes impacted by common SVs.\ We identified 64 significant SV-protein associations that comprise 36 cis- and 28 trans-acting relationships, and 21 distinct SV regions overlapped with genome-wide association study loci. These findings represent a more comprehensive mapping of regulatory and translational endophenotypes underlying health and disease.

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↗

bioRxiv2020

MpsLDA-ProSVM: predicting multi-label protein subcellular localization by wMLDAe dimensionality reduction and ProSVM classifier

Proteins play a significant part in life processes such as cell growth, development, and reproduction. Exploring protein subcellular localization (SCL) is a direct way to better understand the function of proteins in cells. Studies have found that more and more proteins belong to multiple subcellular locations, and these proteins are called multi-label proteins. They not only play a key role in cell life activities, but also play an indispensable role in medicine and drug development. This article first presents a new prediction model, MpsLDA-ProSVM, to predict the SCL of multi-label proteins. Firstly, the physical and chemical information, evolution information, sequence information and annotation information of protein sequences are fused. Then, for the first time, use a weighted multi-label linear discriminant analysis framework based on entropy weight form (wMLDAe) to refine and purify features, reduce the difficulty of learning. Finally, input the optimal feature subset into the multi-label learning with label-specific features (LIFT) and multi-label k-nearest neighbor (ML-KNN) algorithms to obtain a synthetic ranking of relevant labels, and then use Prediction and Relevance Ordering based SVM (ProSVM) classifier to predict the SCLs. This method can rank and classify related tags at the same time, which greatly improves the efficiency of the model. Tested by jackknife method, the overall actual accuracy (OAA) on virus, plant, Gram-positive bacteria and Gram-negative bacteria datasets are 98.06%, 98.97%, 99.81% and 98.49%, which are 0.56%-9.16%, 5.37%-30.87%, 3.51%-6.91% and 3.99%-8.59% higher than other advanced methods respectively. The source codes and datasets are available at https://github.com/QUST-AIBBDRC/MpsLDA-ProSVM/.

bioinformatics↗

bioRxiv2020

DMLDA-LocLIFT: Identification of multi-label protein subcellular localization using DMLDA dimensionality reduction and LIFT classifier

BackgroundMulti-label proteins occur in two or more subcellular locations, which play a vital part in cell development and metabolism. Prediction and analysis of multi-label subcellular localization (SCL) can present new angle with drug target identification and new drug design. However, the prediction of multi-label protein SCL using biological experiments is expensive and labor-intensive. Therefore, predicting large-scale SCL with machine learning methods has turned into a hot study topic in bioinformatics. MethodsIn this study, a novel multi-label learning means for protein SCL prediction, called DMLDA-LocLIFT, is proposed. Firstly, the dipeptide composition, encoding based on grouped weight, pseudo amino acid composition, gene ontology and pseudo position specific scoring matrix are employed to encode subcellular protein sequences. Then, direct multi-label linear discriminant analysis (DMLDA) is used to reduce the dimension of the fused feature vector. Lastly, the optimal feature vectors are input into the multi-label learning with Label-specIfic FeaTures (LIFT) classifier to predict the location of multi-label proteins. ResultsThe jackknife test showed that the overall actual accuracy on Gram-negative bacteria, Gram-positive bacteria, and plant datasets are 98.60%, 99.60%, and 97.90% respectively, which are obviously better than other state-of-the-art prediction methods. ConclusionThe proposed model can effectively predict SCL of multi-label proteins and provide references for experimental identification of SCL. The source codes and data are publicly available at https://github.com/QUST-AIBBDRC/DMLDA-LocLIFT/.

bioinformatics↗

bioRxiv2020

Prediction of Protein-Protein Interactions Based on L1-Regularized Logistic Regression and Gradient Tree Boosting

Protein-protein interactions (PPIs) are of great importance to understand genetic mechanisms, disease pathogenesis, and guide drug design. With the increase of PPIs sequence data and development of machine learning, the prediction and identification of PPIs have become a research hotspot in proteomics. In this paper, we propose a new prediction pipeline for PPIs based on gradient tree boosting (GTB). First, the initial feature vector is extracted by fusing pseudo amino acid composition (PseAAC), pseudo-position-specific scoring matrix (PsePSSM), reduced sequence and index-vectors (RSIV) and autocorrelation descriptor (AD). Second, to remove redundancy and noise, we employ L1-regularized logistic regression to select an optimal feature subset. Finally, GTB-PPI model based on GTB is constructed. Five-fold cross-validation showed GTB-PPI achieved the accuracies of 95.15% and 90.47% on Saccharomyces cerevisiae and Helicobacter pylori, respectively. In addition, GTB-PPI could be applied to predict Caenorhabditis elegans, Escherichia coli, Homo sapiens, and Mus musculus independent test sets, the one-core PPIs network for CD9, and the crossover PPIs network. The results show that GTB-PPI can significantly improve prediction accuracy of PPIs. The code and datasets of GTB-PPI can be downloaded from https://github.com/QUST-AIBBDRC/GTB-PPI/.

bioinformatics↗

bioRxiv2020

Complete genome characterisation of a novel coronavirus associated with severe human respiratory disease in Wuhan, China

Emerging and re-emerging infectious diseases, such as SARS, MERS, Zika and highly pathogenic influenza present a major threat to public health1-3. Despite intense research effort, how, when and where novel diseases appear are still the source of considerable uncertainly. A severe respiratory disease was recently reported in the city of Wuhan, Hubei province, China. At the time of writing, at least 62 suspected cases have been reported since the first patient was hospitalized on December 12nd 2019. Epidemiological investigation by the local Center for Disease Control and Prevention (CDC) suggested that the outbreak was associated with a sea food market in Wuhan. We studied seven patients who were workers at the market, and collected bronchoalveolar lavage fluid (BALF) from one patient who exhibited a severe respiratory syndrome including fever, dizziness and cough, and who was admitted to Wuhan Central Hospital on December 26th 2019. Next generation metagenomic RNA sequencing4 identified a novel RNA virus from the family Coronaviridae designed WH-Human-1 coronavirus (WHCV). Phylogenetic analysis of the complete viral genome (29,903 nucleotides) revealed that WHCV was most closely related (89.1% nucleotide similarity similarity) to a group of Severe Acute Respiratory Syndrome (SARS)-like coronaviruses (genus Betacoronavirus, subgenus Sarbecovirus) previously sampled from bats in China and that have a history of genomic recombination. This outbreak highlights the ongoing capacity of viral spill-over from animals to cause severe disease in humans.

pathology↗