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Activation of immune responses leads to an enrichment of beneficial microbes in rice panicle. We therefore selected the enriched operational taxonomy units (OTUs) exhibiting direct suppression effects on fungal pathogens, and established a simplified synthetic community (SynCom) which consists of three beneficial microbes. Application of this SynCom exhibits protective effect against fungal pathogen infection in rice.

Breast milk naturally contains lactic acid bacteria, but their precise origin remains a subject of debate. In this study, we utilized a rat mastitis animal model to investigate the potential of a breast milk-derived probiotic strain, Lacticaseibacillus rhamnosus Probio-M9, in alleviating mastitis and enhancing the efficacy of antibiotic treatment. Through histopathological analysis of mammary tissue, we observed that Probio-M9 effectively relieved mastitis, mitigated inflammation, and improved the response to antibiotic treatment. Metagenomic analysis further revealed that Probio-M9 enhanced interactions among gut microbes, accompanied by an increase in the relative abundance of Ruminococcaceae and the regulation of specific genes and carbohydrate-active enzymes, subsequently impacting host immunity. Additionally, an intriguing finding was the translocation of live Probio-M9 from the gut to the mammary tissue only during bacterial mastitis and lactation, likely facilitated through lymphatic circulation. These findings advance our understanding of the intricate gut–mammary axis and provide valuable insights into the potential health benefits of probiotic interventions.

Functional cure for chronic hepatitis B (CHB) remains challenging due to the lack of direct intervention methods for hepatic inflammation. Multi-omics research offers a promising approach to understand hepatic inflammation mechanisms in CHB. A Bayesian linear model linked gene expression with clinical parameters, and population-specific expression analysis (PSEA) refined bulk gene expression into specific cell types across different clinical phases. These models were integrated into our analysis of key factors like inflammatory cells, immune activation, T cell exhaustion, chemokines, receptors, and interferon-stimulated genes (ISGs). Validation through multi-immune staining in liver specimens from CHB patients bolstered our findings. In CHB patients, increased gene expression related to immune cell activation and migration was noted. Marker genes of macrophages, T cells, immune-negative regulators, chemokines, and ISGs showed a positive correlation with serum alanine aminotransferase (ALT) levels but not hepatitis B virus DNA levels. The PSEA model confirmed T cells as the source of exhausted regulators, while macrophages primarily contributed to chemokine expression. Upregulated ISGs (ISG20, IFI16, TAP2, GBP1, PSMB9) in the hepatitis phase were associated with T cell and macrophage infiltration and positively correlated with ALT levels. Conversely, another set of ISGs (IFI44, ISG15, IFI44L, IFI6, MX1) mainly expressed by hepatocytes and B cells showed no correlation with ALT levels. Our study presents a multi-omics analysis integrating bulk transcriptomic, single-cell sequencing data, and clinical data from CHB patients to decipher the cause of intrahepatic inflammation in CHB. The findings confirm that macrophages secrete chemokines like CCL20, recruiting exhausted T cells into liver tissue; concurrently, hepatocyte innate immunity is suppressed, hindering the antiviral effects of ISGs.

Emerging evidence has demonstrated the profound impact of the gut microbiome on cardiovascular diseases through the production of diverse metabolites. Using an animal model of myocardial ischemia–reperfusion (I/R) injury, we found that the prophylactic administration of a well-known probiotic, Bifidobacterium infantis (B. infantis), exhibited cardioprotective effects in terms of preserving cardiac contractile function and preventing adverse cardiac remodeling following I/R and that these cardioprotective effects were recapitulated by its metabolite inosine. Transcriptomic analysis further revealed that inosine mitigated I/R-induced cardiac inflammation and cell death. Mechanistic investigations elucidated that inosine suppressed the production of pro-inflammatory cytokines and reduced the numbers of dendritic cells and natural killer cells, achieved through the activation of the adenosine A2A receptor (A2AR) that when inhibited abrogated the cardioprotective effects of inosine. Additionally, in vitro studies using C2C12 myoblasts revealed that inosine attenuated cell death by serving as an alternative carbon source for adenosine triphosphate (ATP) generation through the purine salvage pathway when subjected to oxygen-glucose deprivation/reoxygenation that simulated myocardial I/R injury. Likewise, inosine reversed the I/R-induced decrease in ATP levels in mouse hearts. Taken together, our findings indicate that B. infantis or its metabolite inosine exerts cardioprotective effects against I/R by suppressing cardiac inflammation and attenuating cardiac cell death, suggesting prophylactic therapeutic options for acute ischemic cardiac injury.

The MASS cohort comprises 2000 ICU patients with severe pneumonia, covering community-acquired pneumonia, hospital-acquired pneumonia, and ventilator-associated pneumonia, sourced from 19 hospitals across 10 cities in three provinces. A wide array of samples including bronchoalveolar lavage fluid, sputum, feces, and whole blood are longitudinally collected throughout patients' ICU stays. The cohort study seeks to uncover the dynamics of lung and gut microbiomes and their associations with severe pneumonia and host susceptibility, integrating deep metagenomics and transcriptomics with detailed clinical data.

Majorbio Cloud (https://cloud.majorbio.com/) is a one-stop online analytic platform aiming at promoting the development of bioinformatics services, narrowing the gap between wet and dry experiments, and accelerating the discoveries for the life sciences community. In 2024, three single-omics workflows, two multiomics workflows, and extensions were newly released to facilitate omics data mining and interpretation.

Body size is a key ecological trait of soil microorganisms related to their adaptation to environmental changes. In this study, we reveal that the smaller microorganisms show stronger community resistance than larger organisms in both maize and rice soil. Compared with larger organisms, smaller microorganisms have higher diversity and broader niche breadth to deploy survival strategies, because of which they are less affected by environmental selection and thus survive in complex and various kinds of environments. In addition, the strong correlation between smaller microorganisms and ecosystem functions reflects their greater metabolic flexibility and illustrates their significant roles in adaptation to continuously changing environments. This research highlights the importance of body size in maintaining stability of the soil microbiome and forecasting agroecosystem dynamics under environmental disturbances.

Hundreds of microbiota gene expressions are significantly different between healthy and diseased humans. The “bottleneck” preventing a mechanistic dissection of how they affect host biology/disease is that many genes are encoded by nonmodel gut commensals and not genetically manipulatable. Approaches to efficiently identify their gene transfer methodologies and build their gene manipulation tools would enable mechanistic dissections of their impact on host physiology. This paper will introduce a step-by-step protocol to identify gene transfer conditions and build the gene manipulation tools for nonmodel gut microbes, focusing on Gram-negative Bacteroidia and Gram-positive Clostridia organisms. This protocol enables us to identify gene transfer methods and develop gene manipulation tools without prior knowledge of their genome sequences, by targeting bacterial 16s ribosomal RNAs or expanding their compatible replication origins combined with clustered regularly interspaced short palindromic repeats machinery. Such an efficient and generalizable approach will facilitate functional studies that causally connect gut microbiota genes to host diseases.

BioLadder (https://www.bioladder.cn/) is an online data analysis platform designed for proteomics research, which includes three classes of experimental data analysis modules and four classes of common data analysis modules. It allows for a variety of proteomics analyses to be conducted easily and efficiently. Additionally, most modules can also be utilized for the analysis of other omics data. To facilitate user experience, we have carefully designed four different kinds of functions for customers to quickly and accurately utilize the relevant analysis modules.

Rapid and accurate diagnostic tests are fundamental for improving patient outcomes and combating infectious diseases. The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) Cas12a-based detection system has emerged as a promising solution for on-site nucleic acid testing. Nonetheless, the effective design of CRISPR RNA (crRNA) for Cas12a-based detection remains challenging and time-consuming. In this study, we propose an enhanced crRNA design system with deep learning for Cas12a-mediated diagnostics, referred to as EasyDesign. This system employs an optimized convolutional neural network (CNN) prediction model, trained on a comprehensive data set comprising 11,496 experimentally validated Cas12a-based detection cases, encompassing a wide spectrum of prevalent pathogens, achieving Spearman's ρ = 0.812. We further assessed the model performance in crRNA design for four pathogens not included in the training data: Monkeypox Virus, Enterovirus 71, Coxsackievirus A16, and Listeria monocytogenes. The results demonstrated superior prediction performance compared to the traditional experiment screening. Furthermore, we have developed an interactive web server (https://crispr.zhejianglab.com/) that integrates EasyDesign with recombinase polymerase amplification (RPA) primer design, enhancing user accessibility. Through this web-based platform, we successfully designed optimal Cas12a crRNAs for six human papillomavirus (HPV) subtypes. Remarkably, all the top five predicted crRNAs for each HPV subtype exhibited robust fluorescent signals in CRISPR assays, thereby suggesting that the platform could effectively facilitate clinical sample testing. In conclusion, EasyDesign offers a rapid and reliable solution for crRNA design in Cas12a-based detection, which could serve as a valuable tool for clinical diagnostics and research applications.