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.

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 U 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.

Tumor-associated macrophages (TAMs) greatly contribute to immune checkpoint inhibitor (ICI) resistance of cancer. However, its underlying mechanisms and whether TAMs can be promising targets to overcome ICI resistance remain to be unveiled. Through integrative analysis of immune multiomics data and single-cell RNA-seq data (iMOS) in lung adenocarcinoma (LUAD), lymphotoxin β receptor (LTBR) is identified as a potential immune checkpoint of TAMs, whose high expression, duplication, and low methylation are correlated with unfavorable prognosis. Immunofluorescence staining shows that the infiltration of LTBR⁺ TAMs is associated with LUAD stages, immunotherapy failure, and poor prognosis. Mechanistically, LTΒR maintains immunosuppressive activity and M2 phenotype of TAMs by noncanonical nuclear factor kappa B and Wnt/β-catenin signaling pathways. Macrophage-specific knockout of LTBR hinders tumor growth and prolongs survival time via blocking TAM immunosuppressive activity and M2 phenotype. Moreover, TAM-targeted delivery of LTΒR small interfering RNA improves the therapeutic effect of ICI via reversing TAM-mediated immunosuppression, such as boosting cytotoxic CD8⁺ T cells and inhibiting granulocytic myeloid-derived suppressor cells infiltration. Taken together, we bring forth an immune checkpoint discovery pipeline iMOS, identify LTBR as a novel immune checkpoint of TAMs, and propose a new immunotherapy strategy by targeting LTBR⁺ TAMs.

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.

With the widespread adoption of metagenomic sequencing, new perspectives have emerged for studying microbial ecological networks, yielding metabolic evidence of interspecies interactions that traditional co-occurrence networks cannot infer. This protocol introduces the integrated Network Analysis Pipeline 2.0 (iNAP 2.0), which features an innovative metabolic complementarity network for microbial studies from metagenomics sequencing data. iNAP 2.0 sets up a four-module process for metabolic interaction analysis, namely: (I) Prepare genome-scale metabolic models; (II) Infer pairwise interactions of genome-scale metabolic models; (III) Construct metabolic interaction networks; and (IV) Analyze metabolic interaction networks. Starting from metagenome-assembled or complete genomes, iNAP 2.0 offers a variety of methods to quantify the potential and trends of metabolic complementarity between models, including the PhyloMint pipeline based on phylogenetic distance-adjusted metabolic complementarity, the SMETANA (species metabolic interaction analysis) approach based on cross-feeding substrate exchange prediction, and metabolic distance calculation based on parsimonious flux balance analysis (pFBA). Notably, iNAP 2.0 integrates the random matrix theory (RMT) approach to find the suitable threshold for metabolic interaction network construction. Finally, the metabolic interaction networks can proceed to analysis using topological feature analysis such as hub node determination. In addition, a key feature of iNAP 2.0 is the identification of potentially transferable metabolites between species, presented as intermediate nodes that connect microbial nodes in the metabolic complementarity network. To illustrate these new features, we use a set of metagenome-assembled genomes as an example to comprehensively document the usage of the tools. iNAP 2.0 is available at https://inap.denglab.org.cn for all users to register and use for free.

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.

ImageGP is an extensively utilized, open-access platform for online data visualization and analysis. Over the past 7 years, it has catered to more than 700,000 usages globally, garnering substantial user feedback. The updated version, ImageGP 2 (available at https://www.bic.ac.cn/BIC), introduces a redesigned interface leveraging cutting-edge web technologies to enhance functionality and user interaction. Key enhancements include the following: (i) Addition of modules for data format transformation, facilitating operations such as matrix merging, subsetting, and transformation between long and wide formats. (ii) Streamlined workflows with features like preparameter selection data validation and grouping of parameters with similar attributes. (iii) Expanded repertoire of visualization functions and analysis tools, including Weighted Gene Co-Expression Network Analysis, differential gene expression analysis, and FASTA sequence processing. (iv) Personalized user space for uploading large data sets, tracking analysis history, and sharing reproducible analysis data, scripts, and results. (v) Enhanced user support through a simplified error debugging feature accessible with a single click. (vi) Introduction of an R package, ImageGP, enabling local data visualization and analysis. These updates position ImageGP 2 as a versatile tool serving both wet-lab and dry-lab researchers with expanded capabilities.

Gut microbiota is an intricate microbial community containing bacteria, fungi, viruses, archaea, and protozoa, and each of them contributes to diverse aspects of host health. Nevertheless, the influence of interaction among gut microbiota on host health remains uncovered. Here, we showed that the interaction between intestinal fungi and bacteria shaped lung inflammation during infection. Specifically, antifungal drug-induced dysbiosis of gut mycobiota enhanced lung inflammation during infection. Dysbiosis of gut mycobiota led to gut Escherichia coli (E. coli) overgrowth and translocation to the lung during infection, which induced lung accumulation of the CD45⁺F4/80⁺Ly6G⁻Ly6C⁻CD11b⁺CD11c⁺ macrophages. Clearance of macrophages or deletion of TLR4 (Toll-like receptor 4, recognition of LPS) rather than Dectin-1 (recognition of beta-1,3/1,6 glucans on fungi) blocked the antifungal drug-induced aggravation of lung inflammation during infection. These findings suggest that the interaction between intestinal mycobiota and commensal bacteria affects host health through the gut–lung axis, offering a potential therapeutic target for ameliorating lung inflammation during infection.

Gut microbiota dysbiosis has been implicated in rheumatoid arthritis (RA) and influences disease progression. Although molecular and culture-independent studies revealed RA patients harbored a core microbiome and had characteristic bacterial species, the lack of cultured bacterial strains had limited investigations on their functions. This study aimed to establish an RA-originated gut microbial biobank (RAGMB) that covers and further to correlates and validates core microbial species on clinically used and diagnostic inflammation and immune indices. We obtained 3200 bacterial isolates from fecal samples of 20 RA patients with seven improved and 11 traditional bacterial cultivation methods. These isolates were phylogenetically identified and selected for RAGMB. The RAGMB harbored 601 bacterial strains that represented 280 species (including 43 novel species) of seven bacterial phyla. The RAGMB covered 93.2% at species level of medium- and high-abundant (relative abundances ≥0.2%) RA gut microbes, and included four rare species of the phylum Synergistota. The RA core gut microbiome was defined and composed of 20 bacterial species. Among these, Mediterraneibacter tenuis and Eubacterium rectale were two species that statistically and significantly correlated with clinically used diagnostic indices such as erythrocyte sedimentation rate (ESR) and IL-10. Thus, M. tenuis and E. rectale were selected for experimental validation using DSS-treated and not DSS-treated mice model. Results demonstrated both M. tenuis and E. rectale exacerbated host inflammatory responses, including shortened colon length and increased spleen weight, decreased IL-10 and increased IL-17A levels in plasma. Overall, we established the RAGMB, defined the RA core microbiome, correlated and demonstrated core microbial species effected on host inflammatory and immune responses. This work provides diverse gut microbial resources for future studies on RA etiology and potential new targets for new biomedical practices.