iMeta最新文献

{"title":"Integrating genome- and transcriptome-wide association studies to uncover the host–microbiome interactions in bovine rumen methanogenesis","authors":"Wei Wang,&nbsp;Zhenyu Wei,&nbsp;Zhuohui Li,&nbsp;Jianrong Ren,&nbsp;Yanliang Song,&nbsp;Jingyi Xu,&nbsp;Anguo Liu,&nbsp;Xinmei Li,&nbsp;Manman Li,&nbsp;Huimei Fan,&nbsp;Liangliang Jin,&nbsp;Zhannur Niyazbekova,&nbsp;Wen Wang,&nbsp;Yuanpeng Gao,&nbsp;Yu Jiang,&nbsp;Junhu Yao,&nbsp;Fuyong Li,&nbsp;Shengru Wu,&nbsp;Yu Wang","doi":"10.1002/imt2.234","DOIUrl":"https://doi.org/10.1002/imt2.234","url":null,"abstract":"<p>The ruminal microbiota generates biogenic methane in ruminants. However, the role of host genetics in modifying ruminal microbiota-mediated methane emissions remains mysterious, which has severely hindered the emission control of this notorious greenhouse gas. Here, we uncover the host genetic basis of rumen microorganisms by genome- and transcriptome-wide association studies with matched genome, rumen transcriptome, and microbiome data from a cohort of 574 Holstein cattle. Heritability estimation revealed that approximately 70% of microbial taxa had significant heritability, but only 43 genetic variants with significant association with 22 microbial taxa were identified through a genome-wide association study (GWAS). In contrast, the transcriptome-wide association study (TWAS) of rumen microbiota detected 28,260 significant gene–microbe associations, involving 210 taxa and 4652 unique genes. On average, host genetic factors explained approximately 28% of the microbial abundance variance, while rumen gene expression explained 43%. In addition, we highlighted that TWAS exhibits a strong advantage in detecting gene expression and phenotypic trait associations in direct effector organs. For methanogenic archaea, only one significant signal was detected by GWAS, whereas the TWAS obtained 1703 significant associated host genes. By combining multiple correlation analyses based on these host TWAS genes, rumen microbiota, and volatile fatty acids, we observed that substrate hydrogen metabolism is an essential factor linking host–microbe interactions in methanogenesis. Overall, these findings provide valuable guidelines for mitigating methane emissions through genetic regulation and microbial management strategies in ruminants.</p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"3 5","pages":""},"PeriodicalIF":23.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.234","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sangerbox 2: Enhanced functionalities and update for a comprehensive clinical bioinformatics data analysis platform","authors":"Di Chen,&nbsp;Lixia Xu,&nbsp;Huiwu Xing,&nbsp;Weitao Shen,&nbsp;Ziguang Song,&nbsp;Hongjiang Li,&nbsp;Xuqiang Zhu,&nbsp;Xueyuan Li,&nbsp;Lixin Wu,&nbsp;Henan Jiao,&nbsp;Shuang Li,&nbsp;Jing Yan,&nbsp;Yuting He,&nbsp;Dongming Yan","doi":"10.1002/imt2.238","DOIUrl":"https://doi.org/10.1002/imt2.238","url":null,"abstract":"<p>In recent years, development in high-throughput sequencing technologies has experienced an increasing application of statistics, pattern recognition, and machine learning in bioinformatics analyses. SangeBox platform to meet different scientific demands. The new version of Sangs is a widely used tool among many researchers, which encourages us to continuously improve the plerBox 2 (http://vip.sangerbox.com) and extends and optimizes the functions of interactive graphics and analysis of clinical bioinformatics data. We introduced novel analytical tools such as random forests and support vector machines, as well as corresponding plotting functions. At the same time, we also optimized the performance of the platform and fixed known problems to allow users to perform data analyses more quickly and efficiently. SangerBox 2 improved the speed of analysis, reduced resource required for computer performance, and provided more analysis methods, greatly promoting the research efficiency.</p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"3 5","pages":""},"PeriodicalIF":23.7,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.238","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"USEARCH 12: Open-source software for sequencing analysis in bioinformatics and microbiome","authors":"Yuanping Zhou,&nbsp;Yong-Xin Liu,&nbsp;Xuemeng Li","doi":"10.1002/imt2.236","DOIUrl":"https://doi.org/10.1002/imt2.236","url":null,"abstract":"<p>The well-known bioinformatic software USEARCH v12 was open sourced. Its meaning encourages the microbiome research community to constantly develop excellent bioinformatic software based on the codes. The open source and popularization of artificial intelligence (AI) will make a better infrastructure for microbiome research.\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"3 5","pages":""},"PeriodicalIF":23.7,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.236","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TCellSI: A novel method for T cell state assessment and its applications in immune environment prediction","authors":"Jing-Min Yang,&nbsp;Nan Zhang,&nbsp;Tao Luo,&nbsp;Mei Yang,&nbsp;Wen-Kang Shen,&nbsp;Zhen-Lin Tan,&nbsp;Yun Xia,&nbsp;Libin Zhang,&nbsp;Xiaobo Zhou,&nbsp;Qian Lei,&nbsp;An-Yuan Guo","doi":"10.1002/imt2.231","DOIUrl":"https://doi.org/10.1002/imt2.231","url":null,"abstract":"<p>T cell is an indispensable component of the immune system and its multifaceted functions are shaped by the distinct T cell types and their various states. Although multiple computational models exist for predicting the abundance of diverse T cell types, tools for assessing their states to characterize their degree of resting, activation, and suppression are lacking. To address this gap, a robust and nuanced scoring tool called T cell state identifier (TCellSI) leveraging Mann–Whitney <i>U</i> statistics is established. The TCellSI methodology enables the evaluation of eight distinct T cell states—Quiescence, Regulating, Proliferation, Helper, Cytotoxicity, Progenitor exhaustion, Terminal exhaustion, and Senescence—from transcriptome data, providing T cell state scores (TCSS) for samples through specific marker gene sets and a compiled reference spectrum. Validated against sizeable pseudo-bulk and actual bulk RNA-seq data across a range of T cell types, TCellSI not only accurately characterizes T cell states but also surpasses existing well-discovered signatures in reflecting the nature of T cells. Significantly, the tool demonstrates predictive value in the immune environment, correlating T cell states with patient prognosis and responses to immunotherapy. For better utilization, the TCellSI is readily accessible through user-friendly R package and web server (https://guolab.wchscu.cn/TCellSI/). By offering insights into personalized cancer therapies, TCellSI has the potential to improve treatment outcomes and efficacy.</p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"3 5","pages":""},"PeriodicalIF":23.7,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.231","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Role of respiratory system microbiota in development of lung cancer and clinical application","authors":"Bowen Li,&nbsp;Daoyun Wang,&nbsp;Chengye Zhang,&nbsp;Yadong Wang,&nbsp;Zhicheng Huang,&nbsp;Libing Yang,&nbsp;Huaxia Yang,&nbsp;Naixin Liang,&nbsp;Shanqing Li,&nbsp;Zhihua Liu","doi":"10.1002/imt2.232","DOIUrl":"https://doi.org/10.1002/imt2.232","url":null,"abstract":"<p>Microbes play a significant role in human tumor development and profoundly impact treatment efficacy, particularly in immunotherapy. The respiratory tract extensively interacts with the external environment and possesses a mucosal immune system. This prompts consideration of the relationship between respiratory microbiota and lung cancer. Advancements in culture-independent techniques have revealed unique communities within the lower respiratory tract. Here, we provide an overview of the respiratory microbiota composition, dysbiosis characteristics in lung cancer patients, and microbiota profiles within lung cancer. We delve into how the lung microbiota contributes to lung cancer onset and progression through direct functions, sustained immune activation, and immunosuppressive mechanisms. Furthermore, we emphasize the clinical utility of respiratory microbiota in prognosis and treatment optimization for lung cancer.\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"3 5","pages":""},"PeriodicalIF":23.7,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.232","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dietary therapies interlinking with gut microbes toward human health: Past, present, and future","authors":"Jiali Chen,&nbsp;Jiaqiang Luo,&nbsp;Sjaak Pouwels,&nbsp;Beijinni Li,&nbsp;Bian Wu,&nbsp;Tamer N. Abdelbaki,&nbsp;Jayashree Arcot,&nbsp;Wah Yang","doi":"10.1002/imt2.230","DOIUrl":"https://doi.org/10.1002/imt2.230","url":null,"abstract":"<p>Overview of personalized dietary therapies. This flow chart exhibits the future prospect for integrating human microbiome and bio-medical research to revolutionize the precise personalized dietary therapies. With the development of artificial intelligence (AI), incorporating database may achieve personalized dietary therapies with high precision.\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"3 5","pages":""},"PeriodicalIF":23.7,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.230","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"tigeR: Tumor immunotherapy gene expression data analysis R package","authors":"Yihao Chen,&nbsp;Li-Na He,&nbsp;Yuanzhe Zhang,&nbsp;Jingru Gong,&nbsp;Shuangbin Xu,&nbsp;Yuelong Shu,&nbsp;Di Zhang,&nbsp;Guangchuang Yu,&nbsp;Zhixiang Zuo","doi":"10.1002/imt2.229","DOIUrl":"https://doi.org/10.1002/imt2.229","url":null,"abstract":"<p>Immunotherapy shows great promise for treating advanced cancers, but its effectiveness varies widely among different patients and cancer types. Identifying biomarkers and developing robust predictive models to discern which patients are most likely to benefit from immunotherapy is of great importance. In this context, we have developed the tumor immunotherapy gene expression R package (tigeR 1.0) to address the increasing need for effective tools to explore biomarkers and construct predictive models. tigeR encompasses four distinct yet closely interconnected modules. The Biomarker Evaluation module enables researchers to evaluate whether the biomarkers of interest are associated with immunotherapy response via built-in or custom immunotherapy gene expression data. The Tumor Microenvironment Deconvolution module integrates 10 open-source algorithms to obtain the proportions of different cell types within the tumor microenvironment, facilitating the investigation of the association between immune cell populations and immunotherapy response. The Prediction Model Construction module equips users with the ability to construct sophisticated prediction models using a range of built-in machine-learning algorithms. The Response Prediction module predicts the immunotherapy response for the patients from gene expression data using our pretrained machine learning models or public gene expression signatures. By providing these diverse functionalities, tigeR aims to simplify the process of analyzing immunotherapy gene expression data, thus making it accessible to researchers without advanced programming skills. The source code and example for the tigeR project can be accessed at http://github.com/YuLab-SMU/tigeR.</p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"3 5","pages":""},"PeriodicalIF":23.7,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.229","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OmicShare tools: A zero-code interactive online platform for biological data analysis and visualization","authors":"Hongyan Mu,&nbsp;Jianzhou Chen,&nbsp;Wenjie Huang,&nbsp;Gui Huang,&nbsp;Meiying Deng,&nbsp;Shimiao Hong,&nbsp;Peng Ai,&nbsp;Chuan Gao,&nbsp;Huangkai Zhou","doi":"10.1002/imt2.228","DOIUrl":"https://doi.org/10.1002/imt2.228","url":null,"abstract":"<p>The OmicShare tools platform is a user-friendly online resource for data analysis and visualization, encompassing 161 bioinformatic tools. Users can easily track the progress of projects in real-time through an overview interface. The platform has a powerful interactive graphics engine that allows for the custom-tailored modification of charts generated from analyses. The visually appealing charts produced by OmicShare improve data interpretability and meet the requirements for publication. It has been acknowledged in over 4000 publications and is available in https://www.omicshare.com/tools/.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"3 5","pages":""},"PeriodicalIF":23.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.228","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-cell landscape revealed immune characteristics associated with disease phases in brucellosis patients","authors":"Yi Wang,&nbsp;Siyuan Yang,&nbsp;Bing Han,&nbsp;Xiufang Du,&nbsp;Huali Sun,&nbsp;Yufeng Du,&nbsp;Yinli Liu,&nbsp;Panpan Lu,&nbsp;Jinyu Di,&nbsp;Laurence Don Wai Luu,&nbsp;Xiao Lv,&nbsp;Songnian Hu,&nbsp;Linghang Wang,&nbsp;Rongmeng Jiang","doi":"10.1002/imt2.226","DOIUrl":"10.1002/imt2.226","url":null,"abstract":"<p>A comprehensive immune landscape for <i>Brucella</i> infection is crucial for developing new treatments for brucellosis. Here, we utilized single-cell RNA sequencing (scRNA-seq) of 290,369 cells from 35 individuals, including 29 brucellosis patients from acute (<i>n</i> = 10), sub-acute (<i>n</i> = 9), and chronic (<i>n</i> = 10) phases as well as six healthy donors. Enzyme-linked immunosorbent assays were applied for validation within this cohort. <i>Brucella</i> infection caused a significant change in the composition of peripheral immune cells and inflammation was a key feature of brucellosis. Acute patients are characterized by potential cytokine storms resulting from systemic upregulation of <i>S100A8</i>/<i>A9</i>, primarily due to classical monocytes. Cytokine storm may be mediated by activating S100A8/A9-TLR4-MyD88 signaling pathway. Moreover, monocytic myeloid-derived suppressor cells were the probable contributors to immune paralysis in acute patients. Chronic patients are characterized by a dysregulated Th1 response, marked by reduced expression of IFN-γ and Th1 signatures as well as a high exhausted state. Additionally, <i>Brucella</i> infection can suppress apoptosis in myeloid cells (e.g., mDCs, classical monocytes), inhibit antigen presentation in professional antigen-presenting cells (APCs; e.g., mDC) and nonprofessional APCs (e.g., monocytes), and induce exhaustion in CD8⁺ T/NK cells, potentially resulting in the establishment of chronic infection. Overall, our study systemically deciphered the coordinated immune responses of <i>Brucella</i> at different phases of the infection, which facilitated a full understanding of the immunopathogenesis of brucellosis and may aid the development of new effective therapeutic strategies, especially for those with chronic infection.</p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"3 4","pages":""},"PeriodicalIF":23.7,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.226","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141812274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient and easy-to-use capturing three-dimensional metagenome interactions with GutHi-C","authors":"Yu-Xi Lu,&nbsp;Jin-Bao Yang,&nbsp;Chen-Ying Li,&nbsp;Yun-Han Tian,&nbsp;Rong-Rong Chang,&nbsp;Da-Shuai Kong,&nbsp;Shu-Lin Yang,&nbsp;Yan-Fang Wang,&nbsp;Yu-Bo Zhang,&nbsp;Xiu-Sheng Zhu,&nbsp;Wei-Hua Pan,&nbsp;Si-Yuan Kong","doi":"10.1002/imt2.227","DOIUrl":"10.1002/imt2.227","url":null,"abstract":"<p>Hi-C can obtain three-dimensional chromatin structure information and is widely used for genome assembly. We constructed the GutHi-C technology. As shown in the graphical abstract, it is a highly efficient and quick-to-operate method and can be widely used for human, livestock, and poultry gut microorganisms. It provides a reference for the Hi-C methodology of the microbial metagenome. DPBS, Dulbecco's phosphate-buffered saline; Hi-C, high-through chromatin conformation capture; LB, Luria-Bertani; NGS, next-generation sequencing; PCR, polymerase chain reaction; QC, quality control.\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"3 5","pages":""},"PeriodicalIF":23.7,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.227","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141815430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}