Biology subjects

Zou, Q.

Publications and source records attributed to Zou, Q..

50 records · Page 3Linked to original sources

bioRxiv2021

Surface-based Single-subject Morphological Brain Networks: Effects of Morphological Index, Brain Parcellation and Similarity Measure, Sample Size-varying Stability and Test-retest Reliability

Morphological brain networks, in particular those at the individual level, have become an important approach for studying the human brain connectome; however, relevant methodology is far from being well-established in their formation, description and reproducibility. Here, we extended our previous study by constructing and characterizing single-subject morphological similarity networks from brain volume to surface space and systematically evaluated their reproducibility with respect to effects of different choices of morphological index, brain parcellation atlas and similarity measure, sample size-varying stability and test-retest reliability. Using the Human Connectome Project dataset, we found that surface-based single-subject morphological similarity networks shared common small-world organization, high parallel efficiency, modular architecture and bilaterally distributed hubs regardless of different analytical strategies. Nevertheless, quantitative values of all interregional similarities, global network measures and nodal centralities were significantly affected by choices of morphological index, brain parcellation atlas and similarity measure. Moreover, the morphological similarity networks varied along with the number of participants and approached stability until the sample size exceeded [~]70. Using an independent test-retest dataset, we found fair to good, even excellent, reliability for most interregional similarities and network measures, which were also modulated by different analytical strategies, in particular choices of morphological index. Specifically, fractal dimension and sulcal depth outperformed gyrification index and cortical thickness, higher-resolution atlases outperformed lower-resolution atlases, and Jensen-Shannon divergence-based similarity outperformed Kullback-Leibler divergence-based similarity. Altogether, our findings propose surface-based single-subject morphological similarity networks as a reliable method to characterize the human brain connectome and provide methodological recommendations and guidance for future research.

neuroscience↗

bioRxiv2020

u-track 3D: measuring and interrogating intracellular dynamics in three dimensions.

Particle tracking is a ubiquitous task in the study of dynamic molecular and cellular processes through microscopy. Light-sheet microscopy has opened a path to acquiring complete cell volumes for investigation in 3-dimensions (3D). However, hypothesis formulation and quantitative analysis have remained difficult due to fundamental challenges in the visualization and the verification of large and dense sets of 3D particle trajectories. Here we describe u-track 3D, a software package that addresses these two challenges. Building on the established framework of particle association in space and time implemented for 2D time-lapse sequences, we first report a complete and versatile pipeline for particle tracking in 3D. We then present the concept of dynamic region of interest (dynROI), which allows an experimenter to interact with dynamic 3D processes in 2D views amenable to visual inspection. Third, we present an estimator of trackability which defines a score for every trajectory, thereby overcoming the challenges of trajectory validation by visual inspection. With these combined strategies, u-track 3D provides a framework for the unbiased study of molecular processes in complex volumetric sequences.

cell biology↗

bioRxiv2020

A Probabilistic and Anisotropic Failure Metric for Ascending Thoracic Aortic Aneurysm Risk Stratification

Experimental studies have shown that aortic wall tensile strengths in circumferential and longitudinal directions are different (i.e., anisotropic), and vary significantly among patients with aortic aneurysm. To assess aneurysm rupture and dissection risk, material failure metric of the aortic wall needs to be accurately defined and determined. Previously such risk assessment methods have largely relied on deterministic or isotropic failure metric. In this study, we develop a novel probabilistic and anisotropic failure metric for risk stratification of ascending thoracic aortic aneurysm (ATAA). To this end, uniaxial tensile tests were performed using aortic tissue samples of 84 ATAA patients, from which a joint probability distribution of the anisotropic wall strengths was obtained. Next, the anisotropic failure probability (FP) based on the Tsai-Hill (TH) failure criterion was derived. The novel FP metric, which incorporates uncertainty in the anisotropic failure properties, can be evaluated after the aortic wall stresses are computed from patient-specific biomechanical analysis. For method validation, "ground-truth" risks of additional 41 ATAA patients were numerically-reconstructed using corresponding CT images and tissue testing data. Performance of different risk stratification methods (e.g., with and without patient-specific hyperelastic properties) was compared using p-value and receiver operating characteristic (ROC) curve. The results show that: (1) the probabilistic FP metric outperforms the deterministic TH metric; and (2) patient-specific hyperelastic properties can help to improve the performance of probabilistic FP metric in ATAA risk stratification.

bioengineering↗

bioRxiv2020

An in silico approach to identification, categorization and prediction of nucleic acid binding proteins

The interaction between proteins and nucleic acid plays an important role in many processes, such as transcription, translation and DNA repair. The mechanisms of related biological events can be understood by exploring the function of proteins in these interactions. The number of known protein sequences has increased rapidly in recent years, but the databases for describing the structure and function of protein have unfortunately grown quite slowly. Thus, improving such databases is meaningful for predicting protein-nucleic acid interactions. Furthermore, the mechanism of related biological events, such as viral infection or designing novel drug targets, can be further understood by understanding the function of proteins in these interactions. The information for each sequence, including its function and interaction sites, were collected and identified, and a database called PNIDB was built. The proteins in PNIDB were grouped into 27 classes, such as transcription, immune system, and structural protein, etc. The function of each protein was then predicted using a machine learning method. Using our method, the predictor was trained on labeled sequences, and then the function of a protein was predicted based on the trained classifier. The prediction accuracy achieved a score of 77.43% by 10-fold cross validation. Availability and ImplementationPNIDB is now fully working and can be freely accessed at: http://server.malab.cn/PNIDB/index.html. All the data are publicly available for non-commercial use, distribution, and reproduction in any medium. Contactzouquan@nclab.net

bioinformatics↗

bioRxiv2020

Potential host range of multiple SARS-like coronaviruses and an improved ACE2-Fc variant that is potent against both SARS-CoV-2 and SARS-CoV-1

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a currently uncontrolled pandemic and the etiological agent of coronavirus disease 2019 (COVID-19). It is important to study the host range of SARS-CoV-2 because some domestic species might harbor the virus and transmit it back to humans. In addition, insight into the ability of SARS-CoV-2 and SARS-like viruses to utilize animal orthologs of the SARS-CoV-2 receptor ACE2 might provide structural insight into improving ACE2-based viral entry inhibitors. Here we show that ACE2 orthologs of a wide range of domestic and wild animals support entry of SARS-CoV-2, as well as that of SARS-CoV-1, bat coronavirus RaTG13, and a coronavirus isolated from pangolins. Some of these species, including camels, cattle, horses, goats, sheep, pigs, cats, and rabbits may serve as potential intermediate hosts for new human transmission, and rabbits in particular may serve as a useful experimental model of COVID-19. We show that SARS-CoV-2 and SARS-CoV-1 entry could be potently blocked by recombinant IgG Fc-fusion proteins of viral spike protein receptor-binding domains (RBD-Fc) and soluble ACE2 (ACE2-Fc). Moreover, an ACE2-Fc variant, which carries a D30E mutation and has ACE2 truncated at its residue 740 but not 615, outperforms all the other ACE2-Fc variants on blocking entry of both viruses. Our data suggest that RBD-Fc and ACE2-Fc could be used to treat and prevent infection of SARS-CoV-2 and any new viral variants that emerge over the course of the pandemic.

microbiology↗

bioRxiv2020

VPTMdb: a viral post-translational modification database

In viruses, post-translational modifications (PTMs) are essential for their life cycle. Recognizing viral PTMs is very important for better understanding the mechanism of viral infections and finding potential drug targets. However, few studies have investigated the roles of viral PTMs in virus-human interactions using comprehensive viral PTM datasets. To fill this gap, firstly, we developed a viral post-translational modification database (VPTMdb) for collecting systematic information of viral PTM data. The VPTMdb contains 912 PTM sites that integrate 414 experimental-confirmed PTM sites with 98 proteins in 45 human viruses manually extracted from 162 publications and 498 PTMs extracted from UniProtKB/Swiss-Prot. Secondly, we investigated the viral PTM sequence motifs, the function of target human proteins, and characteristics of PTM protein domains. The results showed that (i) viral PTMs have the consensus motifs with human proteins in phosphorylation, SUMOylation and N-glycosylation. (ii) The function of human proteins that targeted by viral PTM proteins are related to protein targeting, translation, and localization. (iii) Viral PTMs are more likely to be enriched in protein domains. The findings should make an important contribution to the field of virus-human interaction. Moreover, we created a novel sequence-based classifier named VPTMpre to help users predict viral protein phosphorylation sites. Finally, an online web server was implemented for users to download viral protein PTM data and predict phosphorylation sites of interest. Author summaryPost-translational modifications (PTMs) plays an important role in the regulation of viral proteins; However, due to the limitation of data sets, there has been no detailed investigation of viral protein PTMs characteristics. In this manuscript, we collected experimentally verified viral protein post-translational modification sites and analysed viral PTMs data from a bioinformatics perspective. Besides, we constructed a novel feature-based machine learning model for predicting phosphorylation site. This is the first study to explore the roles of viral protein modification in virus infection using computational methods. The valuable viral protein PTM data resource will provide new insights into virus-host interaction.

bioinformatics↗

bioRxiv2020

A Novel Non-hypermucoviscous ST11 Hypervirulent Carbapenem-resistant Klebsiella pneumoniae Lacking Classical Virulence Factors

BackgroundHypervirulent Klebsiella pneumoniae lacking classical virulence factors is uncommon, and the virulence mechanisms of this organism are not understood. MethodsFollowing a retrospective study of carbapenem-resistant K. pneumoniae based on core genome multilocus sequence typing (cgMLST), isolates that caused high mortality were investigated with a genome-wide association study (GWAS), proteome analysis and an animal model. ResultsThe sublineage of sequence type 11 (ST11) K. pneumoniae, which belongs to complex type 3176 (CT3176) and K-locus 47 (KL47), was highlighted due to the high mortality of infected patients. GWAS analysis showed that ampR was associated with the CT3176 isolates. In a mouse model, the mortality of ampR-carrying isolates was comparable to that of the typical hypervirulent isolate GM2. Even during the first 24 hours of infection, the bacterial load and pathological changes of the ampR-carrying isolates in the lungs were more severe than those of GM2. The ampR complement mutant was able to enhance the virulence of the KL47 isolate but not the virulence of KL1. Proteome analysis showed that the expression of WcaJ in the ampR+ isolates was significantly higher than that in the ampR- isolates, and this result was also confirmed by transcription tests and capsule staining. It is suggested that the enhancement of the initial stage of capsule synthesis may be the cause of the high virulence of these non-hypermucoviscous ST11 carbapenem-resistant K. pneumoniae isolates. ConclusionsNon-hypermucoviscous ST11 hypervirulent carbapenem-resistant K. pneumoniae warrants continued surveillance and investigation.

microbiology↗

bioRxiv2020

mAML: an automated machine learning pipeline with a microbiome repository for human disease classification

Due to the concerted efforts to utilize the microbial features to improve disease prediction capabilities, automated machine learning (AutoML) systems designed to get rid of the tediousness in manually performing ML tasks are in great demand. Here we developed mAML, an ML model-building pipeline, which can automatically and rapidly generate optimized and interpretable models for personalized microbial classification tasks in a reproducible way. The pipeline is deployed on a web-based platform and the server is user-friendly, flexible, and has been designed to be scalable according to the specific requirements. This pipeline exhibits high performance for 13 benchmark datasets including both binary and multi-class classification tasks. In addition, to facilitate the application of mAML and expand the human disease-related microbiome learning repository, we developed GMrepo ML repository (GMrepo Microbiome Learning repository) from the GMrepo database. The repository involves 120 microbial classification tasks for 85 human-disease phenotypes referring to 12,429 metagenomic samples and 38,643 amplicon samples. The mAML pipeline and the GMrepo ML repository are expected to be important resources for researches in microbiology and algorithm developments. Database URLhttp://39.100.246.211:8050/Home

bioinformatics↗

bioRxiv2020

Therapeutic Drugs Targeting 2019-nCoV Main Protease by High-Throughput Screening

2019 Novel Coronavirus (2019-nCoV) is a virus identified as the cause of the outbreak of pneumonia first detected in Wuhan, China. Investigations on the transmissibility, severity, and other features associated with this virus are ongoing. Currently, there is no vaccine or therapeutic antibody to prevent the infection, and more time is required to develop an effective immune strategy against the pathogen. In contrast, specific inhibitors targeting the key protease involved in replication and proliferation of the virus are the most effective means to alleviate the epidemic. The main protease of SARS-CoV is essential for the life cycle of the virus, which showed 96.1% of similarity with the main proteaseof 2019-nCoV, is considered to be an attractive target for drug development. In this study, we have identified 4 small molecular drugs with high binding capacity with SARS-CoV main protease by high-throughput screening based on the 8,000 clinical drug libraries, all these drugs have been widely used in clinical applications with guaranteed safety, which may serve as promising candidates to treat the infection of 2019-nCoV.

pharmacology and toxicology↗

bioRxiv2019

Studying the drug treatment pattern based on the action of drug and multi-layer network model

ObjectivesA drug can treat multiple diseases, indicating that the treatment of the drug has certain patterns. In this paper, we studied the treatment pattern of drugs from a new perspective based on the action of drug and multi-layer network model (STAM). Diseases affect the gene expression in related tissues and each disease corresponds to a tissue-specific protein-protein interaction (TSPPI) network. Therefore, a drug is associated with a multi-layer TSPPI network associated with diseases it treats. Single tissue-specific PPI network cannot consider all disease-related information, leading to find the potential treatment pattern of drugs difficultly. Research on multi-layer networks can effectively solve this disadvantage. Furthermore, proteins usually interact with other proteins in PPI to achieve specific functions, such as causing disease. Hence, studying the drug treatment patterns is equivalent to study common module structures in the multi-layer TSPPI network corresponding to drug-related diseases. Knowing the treatment patterns of the drug can help to understand the action mechanisms of the drug and to identify new indications of the drug.\n\nMethodsIn this paper, we were based on the action of drug and multi-layer network model to study the treatment patterns of drugs. We named our method as STAM. As a case of our proposed method STAM, we focused on a study to trichostatin A (TSA) and three diseases it treats: leukemia, breast cancer, and prostate cancer. Based on the therapeutic effects of TSA on various diseases, we constructed a tissue-specific protein-protein interaction (TSPPI) network and applied a multi-layer network module mining algorithm to obtain candidate drug-target modules. Then, using the genes affected by TSA and related to the three diseases, we employed Gene Ontology (GO), the modules significance, co-expression network and literatures to filter and analyze the identified drug-target modules. Finally, two modules (named as M17 and M18) were preserved as the potential treatment patterns of TSA.\n\nResultsThe processed results based on the above framework STAM demonstrated that M17 and M18 had strong potential to be the treatment patterns of TSA. Through the analysis of the significance, composition and functions of the selected drug-target modules, we validated the feasibility and rationality of our proposed method STAM for identifying the drug treatment pattern.\n\nConclusionThis paper studied the drug treatment pattern from a new perspective. The new method STAM used a multi-layer network model, which overcame the shortcomings of the single-layer network, and combined the action of drug. Research on drug treatment model provides new research ideas for disease treatment.

bioinformatics↗

bioRxiv2019

Lyb-2-4, a unique genomic region of hypervirulent carbapenem-resistant Acinetobacter baumannii

Acinetobacter baumannii is an important human pathogen due to its multi-drug resistance, but is usually with low-grade virulence. Although a mouse model revealed different virulence grades of clinical carbapenem-resistant A. baumannii (CRAB) strains, the genetic basis remains unknown. We collected 61 CRAB isolates from intensive care unit of Shenzhen Peoples Hospital (Shenzhen, China), and analyzed them used whole genome sequencing (WGS), multilocus sequence typing (MLST) and core genome MLST (cgMLST), transmission chain reconstruction and Comparative genomic tools. A mouse pneumonia model was used to confirm the hypervirulent phenotype. Eleven complex types (CT) were identified based on core genome multilocus sequence typing scheme. CT512 showed higher transmissibility and bloodstream infection rates than other CTs. A genomic region Lyb-2-4 was shared by CT512 and CT2092 but not CT2085. The mortality rates of patient infected with CRAB harboring Lyb-2-4 was significantly higher than those infected with CRAB isolates without Lyb-2-4 (77.8% vs 24.5%, p < 0.01). In the mouse model, the survival rates of strains containing the Lyb-2-4 region (LAC-4, 5122 and 2092) were significantly lower than for strains without Lyb-2-4 (7152, 71517, 20859 and ATCC17978). One open reading frame (ORF) was a marker for the presence of Lyb-2-4, and PCR of a segment of this ORF, designated as hvcT, served as a tag for hypervirulent CRAB. Our study should be very useful in advising the clinician to implement medical intervention earlier, and also making the worldwide surveillance of these hypervirulent CRAB strains easier.\n\nIMPORTANCEHypervirulent CRAB strains are expected to pose a threat to human health because infection of these strains is associated with high mortality and multidrug resistance. The rapid hypervirulent CRAB identification assay will facilitate prompt medical intervention. Our findings should provoke surveillance for hypervirulent CRAB strains harboring Lyb-2-4 in other countries. Further research should focus on the mechanism of hypervirulence, the acquisition of this genomic region and the development of control measures to prevent further dissemination.

microbiology↗

bioRxiv2019

Does monetary reward increase visual working memory performance?

Previous work has shown that humans distribute their visual working memory (VWM) resources flexibly across items: the higher the importance of an item, the better it is remembered. A related, but much less studied question is whether people also have control over the total amount of VWM resource allocated to a task. Here, we approach this question by testing whether increasing monetary incentives results in better overall VWM performance. In three experiments, subjects performed a delayed-estimation task on the Amazon Turk platform. In the first two experiments, four groups of subjects received a bonus payment based on their performance, with the maximum bonus ranging from $0 to $10 between groups. We found no effect of the amount of bonus on intrinsic motivation or on VWM performance in either experiment. In the third experiment, reward was manipulated on a trial-by-trial basis using a within-subjects design. Again, no evidence was found that VWM performance depended on the magnitude of potential reward. These results suggest that encoding quality in visual working memory is insensitive to monetary reward, which has implications for resource-rational theories of VWM.

animal behavior and cognition↗

bioRxiv2017

Prediction of potential disease-associated microRNAs using structural perturbation method

MotivationThe identification of disease-related microRNAs(miRNAs) is an essential but challenging task in bioinformatics research. Similarity-based link prediction methods are often used to predict potential associations between miRNAs and diseases. In these methods, all unobserved associations are ranked by their similarity scores. Higher score indicates higher probability of existence. However, most previous studies mainly focus on designing advanced methods to improve the prediction accuracy while neglect to investigate the link predictability of the networks that present the miRNAs and diseases associations. In this work, we construct a bilayer network by integrating the miRNA-disease network, the miRNA similarity network and the disease similarity network. We use structural consistency as an indicator to estimate the link predictability of the related networks. On the basis of the indicator, a derivative algorithm, called structural perturbation method (SPM), is applied to predict potential associations between miRNAs and diseases.\n\nResultsThe link predictability of bilayer network is higher than that of miRNA-disease network, indicating that the prediction of potential miRNAs-diseases associations on bilayer network can achieve higher accuracy than based merely on the miRNA-disease network. A comparison between the SPM and other algorithms reveals the reliable performance of SPM which performed well in a 5-fold cross-validation. We test fifteen networks. The AUC values of SPM are higher than some well-known methods, indicating that SPM could serve as a useful computational method for improving the identification accuracy of miRNA-disease associations. Moreover, in a case study on breast neoplasm, 80% of the top-20 predicted miRNAs have been manually confirmed by previous experimental studies.\n\nAvailability and Implementationhttps://github.com/lecea/SPM-code.git\n\nContactlinyuan.lv@gmail.com, zouquan@nclab.net.\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2016

Tumor Origin Detection with Tissue-Specific miRNA and DNA methylation Markers

MotivationCancer of unknown primary origin constitutes 3-5% of all human malignancies. Patients with these carcinomas present with metastases without an established primary site, which may not be found even by thorough histological search methods. Patients with cancer of unknown primary origin always have poor prognosis and hardly have efficient treatment since most cancers respond well to specific chemotherapy or hormone drugs. Many studies have proposed classifiers based on miRNAs or mRNAs to predict the tumor origins, but few study focus on high-dimensional DNA methylation profiles.\n\nResultsWe introduced three classifiers with novel feature selection algorithm combined with random forest to effectively identify highly tissue-specific epigenetics biomarkers such as microRNAs and CpG sites, which can help us predict the origin site of tumors. This algorithm, incorporating differential analysis and descending dimension algorithm, was applied on 14 histological tissues and over 5000 samples based on miRNA expression and DNA methylation profiles to assign given primary tumor to its origin tissue. Our study shows all of these three classifiers have an overall accuracy of 87.78% (72.55%-97.54%) based on miRNA datasets and an accuracy of 96.43% (MRMD: 87.85%-99.76%) or 97.06% (PCA: 92.44%-100%) based on DNA methylation datasets on predicting the origin of tumors and suggests that the biomarkers we selected can efficiently predict the origin of tumors and allow the clinicians to avoid adjuvant systemic therapy or to choose less aggressive therapeutic options. We also developed a user-friendly webserver which enables users to predict the origin site of tumors by uploading the miRNAs expression or DNA methylation profiles of those cancers.\n\nAvailabilityThe webserver, data, and code are accessible free of charge at http://server.malab.cn/MMCOP/\n\nContactzouquan@nclab.net\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗