Characterizing biomarkers based on microbiome profiles has great potential for translational medicine and precision medicine. Here, we present microbiomeMarker, an R/Bioconductor package implementing commonly used normalization and differential analysis (DA) methods, and three supervised learning models to identify microbiome markers. microbiomeMarker also allows comparison of different methods of DA and confounder analysis. It uses standardized input and output formats, which renders it highly scalable and extensible, and allows it to seamlessly interface with other microbiome packages and tools. In addition, the package provides a set of functions to visualize and interpret the identified microbiome markers. microbiomeMarker is freely available from Bioconductor (https://www.bioconductor.org/packages/microbiomeMarker). Source code is available and maintained at GitHub (https://github.com/yiluheihei/microbiomeMarker). Supplementary data are available at Bioinformatics online.

Food safety has become an important public health issue worldwide. However, conventional methods for detection of food-borne pathogens are complicated, and labor-intensive. Moreover, the sensitivity is often low, and it is difficult to achieve high-throughput detection. This study developed a TaqMan real-time polymerase chain reaction (PCR) assay for the simultaneous detection and quantification of 12 common pathogens in a single reaction, including Escherichia coli O157:H7, Listeria monocytogenes/ivanovii, Salmonella enterica, Vibrio parahaemolyticus, β-streptococcus hemolyticus, Yersinia enterocolitica, Enterococcus faecalis, Shigella spp., Proteus mirabilis, Vibrio fluvialis, Staphylococcus aureus, and Campylobacter jejuni in food and drinking water. Based on published sequence data, specific primers, and fluorescently-labeled hybridization probes were designed targeting based on the virulence genes of the 12 pathogens, and these primers and probes were optimized to achieve consistent reaction conditions. The assay was evaluated using 106 pure bacterial culture strains. There was no cross-reaction among the different pathogens. The analytical sensitivity was 1 copy/μL for E. coli O157:H7, L. monocytogenes/ivanovii, β-streptococcus hemolyticus, Shigella spp., P. mirabilis, and V. fluvialis, 10 copies/μL for S. enterica, V. parahaemolyticus, Y. enterocolitica, E. faecalis, S. aureus, and C. jejuni, respectively. The limit of detection (LOD) was 296, 500, 177, 56, 960, 830, 625, 520, 573, 161, 875, and 495 CFU/mL for E. coli O157:H7, L. monocytogenes/ivanovii, S. enterica, V. parahaemolyticus, β-streptococcus hemolyticus, Y. enterocolitica, E. faecalis, Shigella spp., P. mirabilis, V. fluvialis, S. aureus, and C. jejuni, respectively. The limit of detection for the assay in meat samples was 10³ CFU/g for V. parahaemolyticus and 10⁴ CFU/g for other 11 strains. Together, these results indicate that the optimized TaqMan real-time PCR assay will be useful for routine detection of pathogenic bacteria due to its rapid analysis, low cost, high-throughput, high specificity, and sensitivity.

Summary: Heat acclimation (HA) is the best strategy to improve heat stress tolerance by inducing positive physiological adaptations. Evidence indicates that the gut microbiome plays a fundamental role in the development of HA, and modulation of gut microbiota can improve tolerance to heat exposure and decrease the risks of heat illness. In this study, for the first time, we applied 16S rRNA gene sequencing and untargeted liquid chromatography–mass spectrometry (LC‐MS) metabolomics to explore variations in the gut microbiome and faecal metabolic profiles in rats after HA. The gut microbiota of HA subjects exhibited higher diversity and richer microbes. HA altered the gut microbiota composition with significant increases in the genera Lactobacillus (a major probiotic) and Oscillospira alongside significant decreases in the genera Blautia and Allobaculum . The faecal metabolome was also significantly changed after HA, and among the 13 perturbed metabolites, (S)‐AL 8810 and celastrol were increased. Moreover, the two increased genera were positively correlated with the two upregulated metabolites and negatively correlated with the other 11 downregulated metabolites, while the correlations between the two decreased genera and the upregulated/downregulated metabolites were completely contrary. In summary, both the structure of the gut microbiome community and the faecal metabolome were improved after 28 days of HA. These findings provide novel insights regarding the improvement of the gut microbiome and its functions as a potential mechanism by which HA confers protection against heat stress.

Trained immunity is a newly emerging concept that defines the ability of the innate immune system to form immune memory and provide long-lasting protection against previously encountered antigens. Accumulating evidence reveals that trained immunity not only has broad benefits to host defense but is also harmful to the host in chronic inflammatory diseases. However, all trained immunity-related information is scattered in the literature and thus is difficult to access. Here, we describe Trained Immunity DataBase (TIDB), a comprehensive database that provides well-studied trained immunity-related genes from human, rat and mouse as well as the related literature evidence. Moreover, TIDB also provides three modules to analyze the function of the trained-immunity-related genes of interest, including Reactome pathway over-representation analysis, Gene Ontology enrichment analysis and protein–protein interaction subnetwork reconstruction. We believe TIDB will help developing valuable strategies for vaccine design and immune-mediated disease therapy. Database URL:http://www.ieom-tm.com/tidb

Campylobacter jejuni (C. jejuni), a foodborne pathogenic bacterium, is among the most prevalent causes of human gastroenteritis globally. We developed and evaluated a loop‐mediated isothermal amplification (LAMP) method to detect C. jejuni. Outer primers and inner primers were designed based on the hipO gene. The ratio between the concentrations of the inner and outer primers and the reaction temperature were then optimized to achieve optimal assay conditions. The analytical specificity tests showed that, among 12 genera of 74 pure bacterial culture strains, only four C. jejuni isolates could be detected, whereas no amplification was observed in C. coli, C. lari, and the other 11 genera of foodborne pathogens (n = 70). Moreover, the LAMP assay showed a higher analytical sensitivity (34.2 fg μL^-1) than the conventional PCR method (342 fg μL^-1). The limit of detection of C. jejuni based on the LAMP assay was 10³ CFU g^-1 in the artificially spiked samples of chicken meat. In conclusion, the developed LAMP assay will be a powerful and practical tool for the fast, specific, and sensitive detection of C. jejuni.