Biology subjects

Gao, X.

Publications and source records attributed to Gao, X..

At least 307 records · Page 17Linked to original sources

bioRxiv2020

Modeling the IL-2-Teff-Treg system in Systemic Lupus Erythematosus patients: possible mechanism and treatment strategies

Systemic lupus erythematosus (SLE) is a non-organ specific autoimmune disease, which the pathogenesis of development is still unclear. For revealing the underlying mechanism, we construct a mathematical model depicting the interactions among CD4+ regulatory T cells (Treg cells), CD4+ effect T cells (Teff cells) and IL-2 in SLE patients to simulate and reproduce the development of SLE. Through our analysis, one possible pathogenesis is that the activation point and deactivation point of Teff in SLE patients produced a forward shift compared to the normal Teff. According to our simulation, a therapeutic window existed in the treatment of lupus with exogenous IL-2, which means there is a strict limit to the dose of IL-2 in clinic. Finally, we study three different dosing strategies and reveal that specific dosing regimens can better exert the effects of IL-2 and relieve the possible side effect of high dose IL-2. It is possible to promote the study of autoimmune diseases through our efforts and assist the development of precision medical treatment.

systems biology↗

bioRxiv2020

Machine learning with biomedical ontologies

Ontologies have long been employed in the life sciences to formally represent and reason over domain knowledge, and they are employed in almost every major biological database. Recently, ontologies are increasingly being used to provide background knowledge in similarity-based analysis and machine learning models. The methods employed to combine ontologies and machine learning are still novel and actively being developed. We provide an overview over the methods that use ontologies to compute similarity and incorporate them in machine learning methods; in particular, we outline how semantic similarity measures and ontology embeddings can exploit the background knowledge in biomedical ontologies, and how ontologies can provide constraints that improve machine learning models. The methods and experiments we describe are available as a set of executable notebooks, and we also provide a set of slides and additional resources at https://github.com/bio-ontology-research-group/machine-learning-with-ontologies. Key pointsO_LIOntologies provide background knowledge that can be exploited in machine learning models. C_LIO_LIOntology embeddings are structure-preserving maps from ontologies into vector spaces and provide an important method for utilizing ontologies in machine learning. Embeddings can preserve different structures in ontologies, including their graph structures, syntactic regularities, or their model-theoretic semantics. C_LIO_LIAxioms in ontologies, in particular those involving negation, can be used as constraints in optimization and machine learning to reduce the search space. C_LI

bioinformatics↗

bioRxiv2020

Role of 1'-Ribose Cyano Substitution for Remdesivir to Effectively Inhibit both Nucleotide Addition and Proofreading in SARS-CoV-2 Viral RNA Replication

COVID-19 has recently caused a global health crisis and an effective interventional therapy is urgently needed. SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) is a promising but challenging drug target due to its intrinsic proofreading exoribonuclease (ExoN). Remdesivir targeting SARS-CoV-2 RdRp exerts high drug efficacy in vitro and in vivo. However, its underlying inhibitory mechanisms remain elusive. Here, we performed all-atom molecular dynamics simulations with an accumulated simulation time of 24 microseconds to elucidate the molecular mechanisms underlying the inhibitory effects of Remdesivir. We found that Remdesivirs 1-cyano group of possesses the dual role of inhibiting nucleotide addition and proofreading. The presence of its polar 1-cyano group at an upstream site in RdRp causes instability and hampers RdRp translocation. This leads to a delayed chain termination of RNA extension, which may also subsequently reduce the likelihood for Remdesivir to be cleaved by ExoN acting on the 3-terminal nucleotide. In addition, our simulations suggest that Remdesivirs 1-cyano group can also disrupt the cleavage active site of ExoN via steric interactions, leading to a further reduced cleavage efficiency. Our work provides plausible molecular mechanisms on how Remdesivir inhibits viral RNA replication and may guide rational design for new treatments of COVID-19 targeting viral replication.

biophysics↗

bioRxiv2020

Self-normalizing learning on biomedical ontologies using a deep Siamese neural network

MotivationOntologies are widely used in biomedicine for the annotation and standardization of data. One of the main roles of ontologies is to provide structured background knowledge within a domain as well as a set of labels, synonyms, and definitions for the classes within a domain. The two types of information provided by ontologies have been extensively exploited in natural language processing and machine learning applications. However, they are commonly used separately, and thus it is unknown if joining the two sources of information can further benefit data analysis tasks. ResultsWe developed a novel method that applies named entity recognition and normalization methods on texts to connect the structured information in biomedical ontologies with the information contained in natural language. We apply this normalization both to literature and to the natural language information contained within ontologies themselves. The normalized ontologies and text are then used to generate embeddings, and relations between entities are predicted using a deep Siamese neural network model that takes these embeddings as input. We demonstrate that our novel embedding and prediction method using self-normalized biomedical ontologies significantly outperforms the state-of-the-art methods in embedding ontologies on two benchmark tasks: prediction of interactions between proteins and prediction of gene-disease associations. Our method also allows us to apply ontology-based annotations and axioms to the prediction of toxicological effects of chemicals where our method shows superior performance. Our method is generic and can be applied in scenarios where ontologies consisting of both structured information and natural language labels or synonyms are used. Availabilityhttps://github.com/bio-ontology-research-group/Ontology-based-normalization Contactrobert.hoehndorf@kaust.edu.sa and xin.gao@kaust.edu.sa

bioinformatics↗

bioRxiv2020

Tocilizumab treatment in severe COVID-19 patients attenuates the inflammatory storm incited by monocyte centric immune interactions revealed by single-cell analysis

ABSTRACTDespite the current devastation of the COVID-19 pandemic, several recent studies have suggested that the immunosuppressive drug Tocilizumab can powerfully treating inflammatory responses that occur in this disease. Here, by employing single-cell analysis of the immune cell composition of severe-stage COVID-19 patients and these same patients in post Tocilizumab-treatment remission, we have identified a monocyte subpopulation specific to severe disease that contributes to inflammatory storms in COVID-19 patients. Although Tocilizumab treatment attenuated the strong inflammatory immune response, we found that immune cells including plasma B cells and CD8+ T cells still exhibited an intense humoral and cell-mediated anti-virus immune response in COVID-19 patients after Tocilizumab treatment. Thus, in addition to providing a rich, very high-resolution data resource about the immune cell distribution at multiple stages of the COVID-19 disease, our work both helps explain Tocilizumab’s powerful therapeutic effects and defines a large number of potential new drug targets related to inflammatory storms.Competing Interest StatementJingwen Fang is the executive officer of HanGen BiotechView Full Text

genomics↗

bioRxiv2020

Indomethacin has a potent antiviral activity against SARS CoV-2 in vitro and canine coronavirus in vivo

BackgroundThe outbreak of SARS CoV-2 has caused ever-increasing attention and public panic all over the world. Currently, there is no specific treatment against the SARS CoV-2. Therefore, identifying effective antiviral agents to combat the disease is urgently needed. Previous studies found that indomethacin has the ability to inhibit the replication of several unrelated DNA and RNA viruses, including SARS-CoV. MethodsSARS CoV-2 pseudovirus-infected African green monkey kidney VERO E6 cells treated with different concentrations of indomethacin or aspirin at 48 hours post infection (p.i). The level of cell infection was determined by luciferase activity. Anti-coronavirus efficacy in vivo was confirmed by evaluating the time of recovery in canine coronavirus (CCV) infected dogs treated orally with 1mg/kg body weight indomethacin. ResultsWe found that indomethacin has a directly and potently antiviral activity against the SARS CoV-2 pseudovirus (reduce relative light unit to zero). In CCV-infected dogs, recovery occurred significantly sooner with symptomatic treatment + oral indomethacin (1 mg/kg body weight) daily treatments than with symptomatic treatment + ribavirin (10-15 mg/kg body weight) daily treatments (P =0.0031), but was not significantly different from that with symptomatic treatment + anti-canine coronavirus serum + canine hemoglobin + canine blood immunoglobulin + interferon treatments (P =0.7784). ConclusionThe results identify indomethacin as a potent inhibitor of SARS CoV-2.

microbiology↗

bioRxiv2020

SARS-CoV-2 neutralizing serum antibodies in cats: a serological investigation

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in Wuhan, China, and rapidly spread worldwide. Previous studies suggested cat could be a potential susceptible animal of SARS-CoV-2. Here, we investigated the infection of SARS-CoV-2 in cats by detecting specific serum antibodies. A cohort of serum samples were collected from cats in Wuhan, including 102 sampled after COVID-19 outbreak, and 39 prior to the outbreak. 15 of 102 (14.7%) cat sera collected after the outbreak were positive for the receptor binding domain (RBD) of SARS-CoV-2 by indirect enzyme linked immunosorbent assay (ELISA). Among the positive samples, 11 had SARS-CoV-2 neutralizing antibodies with a titer ranging from 1/20 to 1/1080. No serological cross-reactivity was detected between the SARS-CoV-2 and type I or II feline infectious peritonitis virus (FIPV). Our data demonstrates that SARS-CoV-2 has infected cat population in Wuhan during the outbreak.

microbiology↗

bioRxiv2020

Automatic identification of small molecules that promote cell conversion and reprogramming

Controlling cell fate has great potential for regenerative medicine, drug discovery, and basic research. Although numerous transcription factors have been discovered that are able to promote cell reprogramming and trans-differentiation, methods based on their up-regulation tend to show low efficiency. The identification of small molecules that can facilitate conversion between cell types can ameliorate this problem working through safe, rapid, and reversible mechanisms. Here we present DECCODE, an unbiased computational method for the identification of such molecules solely based on transcriptional data. DECCODE matches the largest available collection of drug-induced profiles (the LINCS database) for drug treatments against the largest publicly available dataset of primary cell transcriptional profiles (FANTOM5), to identify drugs that either alone or in combination enhance cell reprogramming and cell conversion. Extensive in silico and in vitro validation of DECCODE in the context of human induced pluripotent stem cells (hIPSCs) generation shows that the method is able to prioritize drugs enhancing cell reprogramming. We also generated predictions for cell conversion with single drugs and drug combinations for 145 different cell types and made them available for further studies.

bioinformatics↗

bioRxiv2020

DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning

Alternative polyadenylation (APA) is a crucial step in post-transcriptional regulation. Previous bioinformatic works have mainly focused on the recognition of polyadenylation sites (PAS) in a given genomic sequence, which is a binary classification problem. Recently, computational methods for predicting the usage level of alternative PAS in a same gene have been proposed. However, all of them cast the problem as a non-quantitative pairwise comparison task and do not take the competition among multiple PAS into account. To address this, here we propose a deep learning architecture, DeeReCT-APA, to quantitatively predict the usage of all alternative PAS of a given gene. To accommodate different genes with potentially different numbers of PAS, DeeReCT-APA treats the problem as a regression task with a variable-length target. Based on a CNN-LSTM architecture, DeeReCT-APA extracts sequence features with CNN layers, uses bidirectional LSTM to explicitly model the interactions among competing PAS, and outputs percentage scores representing the usage levels of all PAS of a gene. In addition to the fact that only our method can predict quantitatively the usage of all the PAS within a gene, we show that our method consistently outperforms other existing methods on three different tasks for which they are trained: pairwise comparison task, highest usage prediction task and ranking task. Finally, we demonstrate that our method can be used to predict the effect of genetic variations on APA patterns and shed light on future mechanistic understanding in APA regulation. Our code and data are available at https://github.com/lzx325/DeeReCT-APA-repo.

bioinformatics↗

bioRxiv2020

Screening for functional circular RNAs using the CRISPR-Cas13 system

Circular RNAs (circRNAs) produced from back-spliced exons are widely expressed, but individual circRNA functions remain poorly understood due to inadequate methods, such as RNAi and genome engineering, in distinguishing overlapped exons in circRNAs from those in linear cognate mRNAs1,2. Here we report that the programable RNA-guided, RNA-targeting CRISPR-Cas13, RfxCas13d, effectively and specifically discriminates circRNAs from mRNAs, using guide (g)RNAs targeting sequences spanning the back-splicing junction (BSJ) sites featured in RNA circles. Using a lentiviral library that targets sequences across BSJ sites of highly expressed human circRNAs, we show that a group of circRNAs are important for cell growth mostly in a cell-type specific manner and that a common oncogenic circRNA, circFAM120A, promotes cell proliferation in vitro and in vivo by preventing FAM120A mRNA from binding the translation inhibitor IGF2BP2 for efficient translation. Application of RfxCas13d/BSJ-gRNA screening has also uncovered circMan1a2 with regulatory potential in mouse embryo preimplantation development. Together, these results establish CRISPR-RfxCas13d as a useful tool for the discovery and functional study of circRNAs at both individual and large-scale levels.

molecular biology↗

bioRxiv2020

Long-read Individual-molecule Sequencing Reveals CRISPR-induced Genetic Heterogeneity in Human ESCs

Accurately quantifying the genetic heterogeneity of a cell population is essential to understanding of biological systems. We develop a universal method to label individual DNA molecules for analyzing diverse types of rare genetic variants, with frequency as low as 4x10-5, using short- or long-read sequencing. It enables base-resolution haplotype-resolved quantitative characterization of rare variants. It provides the first quantitative evidence of persistent nonrandom large deletions and insertions following DNA repair of double-strand breaks induced by CRISPR-Cas9 in human pluripotent stem cells.

molecular biology↗

bioRxiv2020

The hidden cost of receiving favors: A theory of indebtedness

Receiving help or a favor from another person can sometimes have a hidden cost for the beneficiary. In this study, we explore these hidden costs by developing and validating a conceptual model of indebtedness across three studies that combine a large scale online questionnaire, an interpersonal game, computational modeling, and neuroimaging. Our model captures how individuals perceive the altruistic and strategic intentions of the benefactor. These inferences produce distinct feelings of guilt and obligation that together comprise indebtedness and motivate reciprocity. Perceived altruistic intentions convey care and concern and are associated with activity in insula, ventromedial prefrontal cortex and dorsolateral prefrontal cortex, while inferred strategic intentions convey expectations of future reciprocity and are associated with activation in temporal parietal junction and dorsomedial prefrontal cortex. We further develop a neural utility model of indebtedness using multivariate patterns of brain activity that captures the tradeoff between these feelings and reliably predicts reciprocity behavior.

neuroscience↗

bioRxiv2020

Structural insights into the insecticidal Vip3A toxin of Bacillus thuringiensis

The vegetative insecticidal proteins (Vips) secreted by Bacillus thuringiensis are regarded as the new generation of insecticidal toxins because they have different insecticidal properties compared with commonly applied insecticidal crystal proteins (Cry toxins). Vip3A toxin, representing the vast majority of Vips, has been used commercially in transgenic crops and bio-insecticides. However, the lack of both structural information of Vip3A and a clear understanding of its insecticidal mechanism at the molecular level, limits its further development and broader application. Here we present the first crystal structure of the Vip3A toxin in an activated form. Since all members of this insecticidal protein family are highly conserved, the structure of Vip3A provides unique insight into the general domain architecture and protein fold of the Vip3 family of insecticidal toxins. Our structural analysis reveals a four-domain organization, featuring a potential membrane insertion region, a receptor binding domain, and two glycan binding domains of activated Vip3A. We further identify the specific glycan moieties recognized by Vip3A through a glycan array screen. Taken together, these findings provide insights into the mode of action of Vip3 family of insecticidal toxins, and will boost the development of Vip3 into more efficient bio-insecticides.

microbiology↗

bioRxiv2020

Avid binding by B cells to the Plasmodium circumsporozoite protein repeat suppresses responses to protective subdominant epitopes

Antibodies targeting the NANP/NVDP repeat domain of the Plasmodium falciparum circumsporozoite protein (CSPRepeat) can confer protection against malaria. However, it has also been suggested that this repeat domain exists as a decoy that distracts the immune system from mounting protective responses targeting other domains of CSP. Here we show that B cell responses to the repeat domain are indeed [~]10 fold higher than responses to the N- and C-terminal regions of CSP after sporozoite immunization. We investigated the role of the number of CSPRepeat-specific naive precursor B cells and high avidity binding by B cells in driving the immunodominance of the CSPRepeat. Using adoptive transfer of germline precursors specific for the CSPRepeat, we found that increasing precursor number did indeed increase the responses to the repeat region, but not to the detriment of responses to other epitopes. To investigate the role of avid binding by B cells to the CSPRepeat in driving immunodominance we generated CSP9: a truncated CSP molecule with just 9 NANP repeats. Compared to near full length CSP molecules, CSP9 induced lower BCR signalling in CSPRepeat-specific cells and induced stronger responses to non-repeat epitopes. Finally, we found mice immunized with CSP9 molecules were strongly protected against mosquito bite challenge. Collectively these data demonstrate that the CSPRepeat does function as an immunodominant decoy and that truncated CSP molecules may be a promising avenue for future malaria vaccines. Significance StatementMalaria kills approximately 420,000 individuals each year(1). Our best vaccine, RTS,S/AS01 is based on the circumsporozoite protein that coasts the surface of the parasite. However, this vaccine is only partially protective. Here we show that responses to a repeat region in the circumsporozoite dominate the immune response. However, immunizing with a circumsporozoite protein with a shortened repeat region induces a more diverse immune response, which could be an avenue to make better malaria vaccines.

immunology↗

bioRxiv2019

Identifying Novel Targets by using Drug-binding Site Signature: A Case Study of Kinase Inhibitors

Current FDA-approved kinase inhibitors cause diverse adverse effects, some of which are due to the mechanism-independent effects of these drugs. Identifying these mechanism-independent interactions could improve drug safety and support drug repurposing. We have developed "iDTPnd", a computational approach for large-scale discovery of novel targets for known drugs. For a given drug, we construct a positive and a negative structural signature that captures the weakly conserved structural features of drug binding sites. To facilitate assessment of unintended targets iDTPnd also provides a docking-based interaction score and its statistical significance. We were able to confirm the interaction of sorafenib, imatinib, dasatinib, sunitinib, and pazopanib with their known targets at a sensitivity and specificity of 52% and 55% respectively. We have validated 10 predicted novel targets, using in vitro experiments. Our results suggest that proteins other than kinases, such as nuclear receptors, cytochrome P450 or MHC Class I molecules can also be physiologically relevant targets of kinase inhibitors. Our method is general and broadly applicable for the identification of protein-small molecule interactions, when sufficient drug-target 3D data are available.

bioinformatics↗

bioRxiv2019

Analysis of the effects of related fingerprints on molecular similarity using an eigenvalue entropy approach

Two-dimensional (2D) chemical fingerprints are widely used as numerical features for the quantification of structural similarity of chemical compounds, which is an important step in similarity-based virtual screening (VS). Here, using an eigenvalue-based entropy approach, we sought to identify 2D fingerprints with little to no contribution to shaping the eigenvalue distribution of the feature matrix as related fingerprints and examined the degree to which these related 2D fingerprints influence molecular similarity scores via Tanimoto coefficient. We found that there are many related fingerprints in publicly available fingerprint schemes and that their presence in the feature set tends to decrease the similarity scores. Our results have implication in the optimal selection of 2D fingerprints and the identification of potential hits for compounds with target biological activity in VS.

bioinformatics↗

bioRxiv2019

A resource of targeted mutant mouse lines for 5,061 genes

The International Mouse Phenotyping Consortium reports the generation of new mouse mutant strains for over 5,000 genes from targeted embryonic stem cells on the C57BL/6N genetic background. This includes 2,850 null alleles for which no equivalent mutant mouse line exists, 2,987 novel conditional-ready alleles, and 4,433 novel reporter alleles. This nearly triples the number of genes with reporter alleles and almost doubles the number of conditional alleles available to the scientific community. When combined with more than 30 years of community effort, the total mutant allele mouse resource covers more than half of the genome. The extensively validated collection is archived and distributed through public repositories, facilitating availability to the worldwide biomedical research community, and expanding our understanding of gene function and human disease.

genetics↗

bioRxiv2019

PointSite: a point cloud segmentation tool for identication of protein ligand binding atoms

Accurate identifications of ligand binding sites (LBS) on protein structure is critical for understanding protein function and designing structure-based drug. As the previous pocket-centric methods are usually based on the investigation of pseudo surface points (PSPs) outside the protein structure, thus inherently cannot incorporate the local connectivity and global 3D geometrical information of the protein structure. In this paper, we propose a novel point clouds segmentation method, PointSite, for accurate identification of protein ligand binding atoms, which performs protein LBS identification at the atom-level in a protein-centric manner. Specifically, we first transfer the original 3D protein structure to point clouds and then conduct segmentation through Submanifold Sparse Convolution (SSC) based U-Net. With the fine-grained atom-level binding atoms representation and enhanced feature learning, PointSite can outperform previous methods in atom-IoU by a large margin. Furthermore, our segmented binding atoms can work as a filter on predictions achieved by previous pocket-centric approaches, which significantly decreases the false-positive of LBS candidates. Through cascaded filter and re-ranking aided by the segmented atoms, state-of-the-art performance can be achieved over various canonical benchmarks and CAMEO hard targets in terms of the commonly used DCA criteria. Our code is publicly available through https://github.com/PointSite.

bioinformatics↗