Emerging evidence suggests that microbial dysbiosis plays vital roles in many human cancers.However,knowledge of whether the microbial community in thyroid tumor is related to tumorigenesis remains elusive.In this stu...Emerging evidence suggests that microbial dysbiosis plays vital roles in many human cancers.However,knowledge of whether the microbial community in thyroid tumor is related to tumorigenesis remains elusive.In this study,we aimed to explore the microbial community in thyroid tissues and its contribution to papillary thyroid cancer(PTC).In parallel,we performed microbial profiling and transcriptome sequencing in the tumor and adjacent normal tissues of a large cohort of 340 PTC and benign thyroid nodule(BTN)patients.Distinct microbial signatures were identified in PTC,BTN,and their adjacent nontumor tissues.Intra-thyroid tissue bacteria were verified by means of bacteria staining,fluorescence in situ hybridization,and immunoelectron microscopy.We found that 17 bacterial taxa were differentially abundant in PTC compared with BTN,which included enrichment in PTC of the pathobionts Rhodococcus,Neisseria,Streptococcus,Halomonas,and Devosia,and depletion of the beneficial bacteria Amycolatopsis.These differentially abundant bacteria could differentiate PTC tumor tissues(PTC-T)from BTN tissues(BTN-T)with an area under the curve(AUC)of 81.66%.Microbial network analysis showed increased correlation strengths among the bacterial taxa in PTC-T in comparison with BTN-T.Immunefunction-corresponding bacteria(i.e.,Erwinia,Bacillus,and Acinetobacter)were found to be enriched in PTC with Hashimoto’s thyroiditis.Moreover,our integrative analysis revealed that the PTC-enriched bacteria had a positive association with key PTC-oncogenic pathway-related genes,including BRAF,KRAS,IRAK4,CTNNB1,PIK3CA,MAP3K7,and EGFR.In conclusion,our results suggest that intratumor bacteria dysbiosis is associated with the thyroid tumorigenesis and oncogenic signaling pathways of PTC.展开更多
The exponential growth of bioinformatics tools in recent years has posed challenges for scientists in selecting the most suitable one for their data analysis assignments.Therefore,to aid scientists in making informed ...The exponential growth of bioinformatics tools in recent years has posed challenges for scientists in selecting the most suitable one for their data analysis assignments.Therefore,to aid scientists in making informed choices,a community-based platform that indexes and rates bioinformatics tools is urgently needed.In this study,we introduce Bio Treasury(http://biotreasury.rjmart.cn),an integrated communitybased repository that provides an interactive platform for users and developers to share their experiences in various bioinformatics tools.Bio Treasury offers a comprehensive collection of well-indexed bioinformatics software,tools,and databases,totaling over 10,000 entries.In the past two years,we have continuously improved and maintained Bio Treasury,adding several exciting features,including creating structured homepages for every tool and user,a hierarchical category of bioinformatics tools and classifying tools using large language model(LLM).Bio Treasury streamlines the tool submission process with intelligent auto-completion.Additionally,Bio Treasury provides a wide range of social features,for example,enabling users to participate in interactive discussions,rate tools,build and share tool collections for the public.We believe Bio Treasury can be a valuable resource and knowledge-sharing platform for the biomedical community.It empowers researchers to effectively discover and evaluate bioinformatics tools,fostering collaboration and advancing bioinformatics research.展开更多
There are only eight approved small molecule antiviral drugs for treating COVID-19.Among them,four are nucleotide analogues(remdesivir,JT001,molnupiravir,and azvudine),while the other four are protease inhibitors(nirm...There are only eight approved small molecule antiviral drugs for treating COVID-19.Among them,four are nucleotide analogues(remdesivir,JT001,molnupiravir,and azvudine),while the other four are protease inhibitors(nirmatrelvir,ensitrelvir,leritrelvir,and simnotrelvir-ritonavir).Antiviral resistance,unfavourable drug‒drug interaction,and toxicity have been reported in previous studies.Thus there is a dearth of new treatment options for SARS-CoV-2.In this work,a three-tier cell-based screening was employed to identify novel compounds with anti-SARS-CoV-2 activity.One compound,designated 172,demonstrated broad-spectrum antiviral activity against multiple human pathogenic coronaviruses and different SARS-CoV-2 variants of concern.Mechanistic studies validated by reverse genetics showed that compound 172 inhibits the 3-chymotrypsin-like protease(3CLpro)by binding to an allosteric site and reduces 3CLpro dimerization.A drug synergistic checkerboard assay demonstrated that compound 172 can achieve drug synergy with nirmatrelvir in vitro.In vivo studies confirmed the antiviral activity of compound 172 in both Golden Syrian Hamsters and K18 humanized ACE2 mice.Overall,this study identified an alternative druggable site on the SARS-CoV-23CLpro,proposed a potential combination therapy with nirmatrelvir to reduce the risk of antiviral resistance and shed light on the development of allosteric protease inhibitors for treating a range of coronavirus diseases.展开更多
Screening biomolecular markers from high-dimensional biological data is one of the long-standing tasks for biomedical translational research.With its advantages in both feature shrinkage and biological interpretabilit...Screening biomolecular markers from high-dimensional biological data is one of the long-standing tasks for biomedical translational research.With its advantages in both feature shrinkage and biological interpretability,Least Absolute Shrinkage and Selection Operator(LASSO)algorithm is one of the most popular methods for the scenarios of clinical biomarker development.However,in practice,applying LASSO on omics-based data with high dimensions and low-sample size may usually result in an excess number of predictive variables,leading to the overfitting of the model.Here,we present VSOLassoBag,a wrapped LASSO approach by integrating an ensemble learning strategy to help select efficient and stable variables with high confidence from omics-based data.Using a bagging strategy in combination with a parametric method or inflection point search method,VSOLassoBag can integrate and vote variables generated from multiple LASSO models to determine the optimal candidates.The application of VSOLassoBag on both simulation datasets and real-world datasets shows that the algorithm can effectively identify markers for either case-control binary classification or prognosis prediction.In addition,by comparing with multiple existing algorithms,VSOLassoBag shows a comparable performance under different scenarios while resulting in fewer features than others.In summary,VSOLassoBag,which is available at https://seqworld.com/VSOLassoBag/under the GPL v3 license,provides an alternative strategy for selecting reliable biomarkers from high-dimensional omics data.For user’s convenience,we implement VSOLassoBag as an R package that provides multithreading computing configurations.展开更多
Protein nitration and nitrosylation are essential post-translational modifications （PTMs）involved in many fundamental cellular processes. Recent studies have revealed that excessive levels of nitration and nitrosyla...Protein nitration and nitrosylation are essential post-translational modifications （PTMs）involved in many fundamental cellular processes. Recent studies have revealed that excessive levels of nitration and nitrosylation in some critical proteins are linked to numerous chronic diseases.Therefore, the identification of substrates that undergo such modifications in a site-specific manner is an important research topic in the community and will provide candidates for targeted therapy. In this study, we aimed to develop a computational tool for predicting nitration and nitrosylation sites in proteins. We first constructed four types of encoding features, including positional amino acid distributions, sequence contextual dependencies, physicochemical properties, and position-specific scoring features, to represent the modified residues. Based on these encoding features, we established a predictor called DeepNitro using deep learning methods for predicting protein nitration and nitrosylation. Using n-fold cross-validation, our evaluation shows great AUC values for DeepNitro, 0.65for tyrosine nitration, 0.80 for tryptophan nitration, and 0.70 for cysteine nitrosylation, respectively,demonstrating the robustness and reliability of our tool. Also, when tested in the independent dataset, DeepNitro is substantially superior to other similar tools with a 7%à42%improvement in the prediction performance. Taken together, the application of deep learning method and novel encoding schemes, especially the position-specific scoring feature, greatly improves the accuracy of nitration and nitrosylation site prediction and may facilitate the prediction of other PTM sites. DeepNitro is implemented in JAVA and PHP and is freely available for academic research at http：//deepnitro.renlab.org.展开更多
Influenza A virus,a highly virulent pathogen that has caused several pandemic events over the course of human history,still remains a major threat to human health at present.The most serious influenza pandemic in reco...Influenza A virus,a highly virulent pathogen that has caused several pandemic events over the course of human history,still remains a major threat to human health at present.The most serious influenza pandemic in recorded history was the 1918 Spanish flu outbreak,which killed about 20-100 million people worldwide（Murray et al.,2006）.Also,展开更多
基金supported by the National Natural Science Foundation of China(81772850 and 82273300)。
文摘Emerging evidence suggests that microbial dysbiosis plays vital roles in many human cancers.However,knowledge of whether the microbial community in thyroid tumor is related to tumorigenesis remains elusive.In this study,we aimed to explore the microbial community in thyroid tissues and its contribution to papillary thyroid cancer(PTC).In parallel,we performed microbial profiling and transcriptome sequencing in the tumor and adjacent normal tissues of a large cohort of 340 PTC and benign thyroid nodule(BTN)patients.Distinct microbial signatures were identified in PTC,BTN,and their adjacent nontumor tissues.Intra-thyroid tissue bacteria were verified by means of bacteria staining,fluorescence in situ hybridization,and immunoelectron microscopy.We found that 17 bacterial taxa were differentially abundant in PTC compared with BTN,which included enrichment in PTC of the pathobionts Rhodococcus,Neisseria,Streptococcus,Halomonas,and Devosia,and depletion of the beneficial bacteria Amycolatopsis.These differentially abundant bacteria could differentiate PTC tumor tissues(PTC-T)from BTN tissues(BTN-T)with an area under the curve(AUC)of 81.66%.Microbial network analysis showed increased correlation strengths among the bacterial taxa in PTC-T in comparison with BTN-T.Immunefunction-corresponding bacteria(i.e.,Erwinia,Bacillus,and Acinetobacter)were found to be enriched in PTC with Hashimoto’s thyroiditis.Moreover,our integrative analysis revealed that the PTC-enriched bacteria had a positive association with key PTC-oncogenic pathway-related genes,including BRAF,KRAS,IRAK4,CTNNB1,PIK3CA,MAP3K7,and EGFR.In conclusion,our results suggest that intratumor bacteria dysbiosis is associated with the thyroid tumorigenesis and oncogenic signaling pathways of PTC.
基金supported by the National Key Research and Development Program of China(2021YFA1302100)the National Natural Science Foundation of China(82172861,32200542)+2 种基金the Young Elite Scientists Sponsorship Program by Guangzhou Association for Science and Technology(QT-2023-045)the Youth Talent Support Program of Guangdong Provincial Association for Science and Technology(SKXRC202313)the Young Talents Program of Sun Yat-sen University Cancer Center(YTP-SYSUCC-0033)。
文摘The exponential growth of bioinformatics tools in recent years has posed challenges for scientists in selecting the most suitable one for their data analysis assignments.Therefore,to aid scientists in making informed choices,a community-based platform that indexes and rates bioinformatics tools is urgently needed.In this study,we introduce Bio Treasury(http://biotreasury.rjmart.cn),an integrated communitybased repository that provides an interactive platform for users and developers to share their experiences in various bioinformatics tools.Bio Treasury offers a comprehensive collection of well-indexed bioinformatics software,tools,and databases,totaling over 10,000 entries.In the past two years,we have continuously improved and maintained Bio Treasury,adding several exciting features,including creating structured homepages for every tool and user,a hierarchical category of bioinformatics tools and classifying tools using large language model(LLM).Bio Treasury streamlines the tool submission process with intelligent auto-completion.Additionally,Bio Treasury provides a wide range of social features,for example,enabling users to participate in interactive discussions,rate tools,build and share tool collections for the public.We believe Bio Treasury can be a valuable resource and knowledge-sharing platform for the biomedical community.It empowers researchers to effectively discover and evaluate bioinformatics tools,fostering collaboration and advancing bioinformatics research.
基金National Natural Science Foundation of China(NSFC)/Research Grants Council(RGC)Joint Research Scheme(N_HKU767/22 and 82261160398)Health and Medical Research Fund(COVID190121)+13 种基金the Food and Health Bureau,The Government of the Hong Kong Special Administrative Regionthe National Natural Science Foundation of China(32322087,32300134,and 82272337)Guangdong Natural Science Foundation(2023A1515012907)Health@-InnoHK,Innovation and Technology Commission,the Government of the Hong Kong Special Administrative Regionthe Collaborative Research Fund(C7060-21G and C7002-23Y)and Theme-Based Research Scheme(T11-709/21-N)of the Research Grants Council,The Government of the Hong Kong Special Administrative RegionPartnership Programme of Enhancing Laboratory Surveillance and Investigation of Emerging Infectious Diseases and Antimicrobial Resistance for the Department of Health of the Hong Kong Special Administrative Region GovernmentSanming Project of Medicine in Shenzhen,China(SZSM201911014)the High Level-Hospital Program,Health Commission of Guangdong Province,Chinathe research project of Hainan Academician Innovation Platform(YSPTZX202004)Emergency Collaborative Project of Guangzhou Laboratory(EKPG22-01)and the National Key R&D Program of China(projects 2021YFC0866100 and 2023YFC3041600)The University of Hong Kong Seed Fund for Collaborative Research(2207101537)and Hunan University(521119400156)donations of Providence Foundation Limited(in memory of the late Lui Hac Minh).
文摘There are only eight approved small molecule antiviral drugs for treating COVID-19.Among them,four are nucleotide analogues(remdesivir,JT001,molnupiravir,and azvudine),while the other four are protease inhibitors(nirmatrelvir,ensitrelvir,leritrelvir,and simnotrelvir-ritonavir).Antiviral resistance,unfavourable drug‒drug interaction,and toxicity have been reported in previous studies.Thus there is a dearth of new treatment options for SARS-CoV-2.In this work,a three-tier cell-based screening was employed to identify novel compounds with anti-SARS-CoV-2 activity.One compound,designated 172,demonstrated broad-spectrum antiviral activity against multiple human pathogenic coronaviruses and different SARS-CoV-2 variants of concern.Mechanistic studies validated by reverse genetics showed that compound 172 inhibits the 3-chymotrypsin-like protease(3CLpro)by binding to an allosteric site and reduces 3CLpro dimerization.A drug synergistic checkerboard assay demonstrated that compound 172 can achieve drug synergy with nirmatrelvir in vitro.In vivo studies confirmed the antiviral activity of compound 172 in both Golden Syrian Hamsters and K18 humanized ACE2 mice.Overall,this study identified an alternative druggable site on the SARS-CoV-23CLpro,proposed a potential combination therapy with nirmatrelvir to reduce the risk of antiviral resistance and shed light on the development of allosteric protease inhibitors for treating a range of coronavirus diseases.
基金supported by National Key R&D Program of China(2021YFA1302100 to Q.Z)the National Natural Science Foundation of China(82172861 to Q.Z)+1 种基金Guangdong Basic and Applied Basic Research Foundation(2021A1515011743 to Q.Z)National Key Clinical Discipline(to D.Z)。
文摘Screening biomolecular markers from high-dimensional biological data is one of the long-standing tasks for biomedical translational research.With its advantages in both feature shrinkage and biological interpretability,Least Absolute Shrinkage and Selection Operator(LASSO)algorithm is one of the most popular methods for the scenarios of clinical biomarker development.However,in practice,applying LASSO on omics-based data with high dimensions and low-sample size may usually result in an excess number of predictive variables,leading to the overfitting of the model.Here,we present VSOLassoBag,a wrapped LASSO approach by integrating an ensemble learning strategy to help select efficient and stable variables with high confidence from omics-based data.Using a bagging strategy in combination with a parametric method or inflection point search method,VSOLassoBag can integrate and vote variables generated from multiple LASSO models to determine the optimal candidates.The application of VSOLassoBag on both simulation datasets and real-world datasets shows that the algorithm can effectively identify markers for either case-control binary classification or prognosis prediction.In addition,by comparing with multiple existing algorithms,VSOLassoBag shows a comparable performance under different scenarios while resulting in fewer features than others.In summary,VSOLassoBag,which is available at https://seqworld.com/VSOLassoBag/under the GPL v3 license,provides an alternative strategy for selecting reliable biomarkers from high-dimensional omics data.For user’s convenience,we implement VSOLassoBag as an R package that provides multithreading computing configurations.
基金supported by grants from the National Natural Science Foundation of China (Grant Nos. 91753137, 31471252, 31771462, 81772614, and U1611261)National Key Research and Development Program of China (Grant No. 2017YFA0106700)+2 种基金Guangdong Natural Science Foundation (Grant Nos. 2014TQ01R387 and 2014A030313181)Science and Technology Program of Guangzhou, China (Grant Nos. 201604020003 and 201604046001)China Postdoctoral Science Foundation (Grant No. 2017M622864)
文摘Protein nitration and nitrosylation are essential post-translational modifications （PTMs）involved in many fundamental cellular processes. Recent studies have revealed that excessive levels of nitration and nitrosylation in some critical proteins are linked to numerous chronic diseases.Therefore, the identification of substrates that undergo such modifications in a site-specific manner is an important research topic in the community and will provide candidates for targeted therapy. In this study, we aimed to develop a computational tool for predicting nitration and nitrosylation sites in proteins. We first constructed four types of encoding features, including positional amino acid distributions, sequence contextual dependencies, physicochemical properties, and position-specific scoring features, to represent the modified residues. Based on these encoding features, we established a predictor called DeepNitro using deep learning methods for predicting protein nitration and nitrosylation. Using n-fold cross-validation, our evaluation shows great AUC values for DeepNitro, 0.65for tyrosine nitration, 0.80 for tryptophan nitration, and 0.70 for cysteine nitrosylation, respectively,demonstrating the robustness and reliability of our tool. Also, when tested in the independent dataset, DeepNitro is substantially superior to other similar tools with a 7%à42%improvement in the prediction performance. Taken together, the application of deep learning method and novel encoding schemes, especially the position-specific scoring feature, greatly improves the accuracy of nitration and nitrosylation site prediction and may facilitate the prediction of other PTM sites. DeepNitro is implemented in JAVA and PHP and is freely available for academic research at http：//deepnitro.renlab.org.
基金supported by grants from the National Basic Research Program(973 project,No.2013CB933900)the National Natural Science Foundation of China(Nos.31471252,31500813 and U1611261)+2 种基金the Guangdong Natural Science Foundation(Nos.2014TQ01R387 and 2014A030313181)the Science and Technology Program of Guangzhou,China(No.201604020003)the Fundamental Research Funds for the Central Universities(No.141gjcl4)
文摘Influenza A virus,a highly virulent pathogen that has caused several pandemic events over the course of human history,still remains a major threat to human health at present.The most serious influenza pandemic in recorded history was the 1918 Spanish flu outbreak,which killed about 20-100 million people worldwide（Murray et al.,2006）.Also,
