The microbiota confers colonization resistance, which blocks Salmonella gut colonization 1. As diet affects microbiota composition, we studied whether food-composition shifts enhance susceptibility to infection. Shifting mice to diets with reduced fiber or elevated fat contents for 24h boosted S. Typhimurium or E. coli gut colonization and plasmid transfer. Here, we studied the effect of dietary fat. Colonization resistance was restored within 48h of return to maintenance diet. Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

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Summary Immunology research in the last 50 years has made huge progress in understanding the mechanisms of anti‐bacterial defense of deep, normally sterile, tissues such as blood, spleen and peripheral lymph nodes. In the intestine, with its dense commensal microbiota, it seems rare that this knowledge can be simply translated. Here we put forward the idea that perhaps it is not always the theory of immunology that is lacking to explain mucosal immunity, but rather that we have overlooked crucial parts of the mucosal immunological language required for its translation: namely intestinal and bacterial physiology. We will try to explain this in the context of intestinal secretory antibodies (mainly secretory IgA), which have been described to prevent, to alter, to not affect, or to promote colonization of the intestine and gut‐draining lymphoid tissues, and where effector mechanisms have remained elusive. In fact, these apparently contradictory outcomes can be generated by combining the basic premises of bacterial agglutination with an understanding of bacterial growth (i.e. secretory IgA‐driven enchained growth), fluid handling and bacterial competition in the gut lumen.

The wide range of effector and memory T cells is instrumental for immune regulation and tailored mechanisms of protection against pathogens. Here, we will focus on human CD4 T cells and discuss T-cell plasticity and intraclonal diversification in the context of a progressive and selective model of CD4 T-cell differentiation.

Background Temperate phages influence the density, diversity and function of bacterial populations. Historically, they have been described as carriers of toxins. More recently, they have also been recognised as direct modulators of the gut microbiome, and indirectly of host health and disease. Despite recent advances in studying prophages using non-targeted sequencing approaches, methodological challenges in identifying inducible prophages in bacterial genomes and quantifying their activity have limited our understanding of prophage-host interactions. Results We present methods for using high-throughput sequencing data to locate inducible prophages, including those previously undiscovered, to quantify prophage activity and to investigate their replication. We first used the well-established Salmonella enterica serovar Typhimurium/p22 system to validate our methods for (i) quantifying phage-to-host ratios and (ii) accurately locating inducible prophages in the reference genome based on phage-to-host ratio differences and read alignment alterations between induced and non-induced prophages. Investigating prophages in bacterial strains from a murine gut model microbiota known as Oligo-MM12 or sDMDMm2, we located five novel inducible prophages in three strains, quantified their activity and showed signatures of lateral transduction potential for two of them. Furthermore, we show that the methods were also applicable to metagenomes of induced faecal samples from Oligo-MM12 mice, including for strains with a relative abundance below 1%, illustrating its potential for the discovery of inducible prophages also in more complex metagenomes. Finally, we show that predictions of prophage locations in reference genomes of the strains we studied were variable and inconsistent for four bioinformatic tools we tested, which highlights the importance of their experimental validation. Conclusions This study demonstrates that the integration of experimental induction and bioinformatic analysis presented here is a powerful approach to accurately locate inducible prophages using high-throughput sequencing data and to quantify their activity. The ability to generate such quantitative information will be critical in helping us to gain better insights into the factors that determine phage activity and how prophage-bacteria interactions influence our microbiome and impact human health.