Clostridioides difficile exploits toxin-mediated inflammation to alter the host nutritional landscape and exclude competitors from the gut microbiota.

Clostridioides difficile is a bacterial pathogen that causes a range of clinical disease from mild to moderate diarrhea, pseudomembranous colitis, and toxic megacolon. Typically, C. difficile infections (CDIs) occur after antibiotic treatment, which alters the gut microbiota, decreasing colonization resistance against C. difficile. Disease is mediated by two large toxins and the expression of their genes is induced upon nutrient depletion via the alternative sigma factor TcdR. Here, we use tcdR mutants in two strains of C. difficile and omics to investigate how toxin-induced inflammation alters C. difficile metabolism, tissue gene expression and the gut microbiota, and to determine how inflammation by the host may be beneficial to C. difficile. We show that C. difficile metabolism is significantly different in the face of inflammation, with changes in many carbohydrate and amino acid uptake and utilization pathways. Host gene expression signatures suggest that degradation of collagen and other components of the extracellular matrix by matrix metalloproteinases is a major source of peptides and amino acids that supports C. difficile growth in vivo. Lastly, the inflammation induced by C. difficile toxin activity alters the gut microbiota, excluding members from the genus Bacteroides that are able to utilize the same essential nutrients released from collagen degradation.

Evaluating Use Cases for Human Challenge Trials in Accelerating SARS-CoV-2 Vaccine Development.

Human challenge trials (HCTs) have been proposed as a means to accelerate SARS-CoV-2 vaccine development. We identify and discuss 3 potential use cases of HCTs in the current pandemic: evaluating efficacy, converging on correlates of protection, and improving understanding of pathogenesis and the human immune response. We outline the limitations of HCTs and find that HCTs are likely to be most useful for vaccine candidates currently in preclinical stages of development. We conclude that, while currently limited in their application, there are scenarios in which HCTs would be extremely beneficial. Therefore, the option of conducting HCTs to accelerate SARS-CoV-2 vaccine development should be preserved. As HCTs require many months of preparation, we recommend an immediate effort to (1) establish guidelines for HCTs for COVID-19; (2) take the first steps toward HCTs, including preparing challenge virus and making preliminary logistical arrangements; and (3) commit to periodically re-evaluating the utility of HCTs.

Pathogen resistance may be the principal evolutionary advantage provided by the microbiome.

To survive, plants and animals must continually defend against pathogenic microbes that would invade and disrupt their tissues. Yet they do not attempt to extirpate all microbes. Instead, they tolerate and even encourage the growth of commensal microbes, which compete with pathogens for resources and via direct inhibition. We argue that hosts have evolved to cooperate with commensals in order to enhance the pathogen resistance this competition provides. We briefly describe competition between commensals and pathogens within the host, consider how natural selection might favour hosts that tilt this competition in favour of commensals, and describe examples of extant host traits that may serve this purpose. Finally, we consider ways that this cooperative immunity may have facilitated the adaptive evolution of non-pathogen-related host traits. On the basis of these observations, we argue that pathogen resistance vies with other commensal-provided benefits for being the principal evolutionary advantage provided by the microbiome to host lineages across the tree of life. This article is part of the theme issue 'The role of the microbiome in host evolution'.

Current State of and Future Opportunities for Prediction in Microbiome Research: Report from the Mid-Atlantic Microbiome Meet-up in Baltimore on 9 January 2019.

Accurate predictions across multiple fields of microbiome research have far-reaching benefits to society, but there are few widely accepted quantitative tools to make accurate predictions about microbial communities and their functions. More discussion is needed about the current state of microbiome analysis and the tools required to overcome the hurdles preventing development and implementation of predictive analyses. We summarize the ideas generated by participants of the Mid-Atlantic Microbiome Meet-up in January 2019. While it was clear from the presentations that most fields have advanced beyond simple associative and descriptive analyses, most fields lack essential elements needed for the development and application of accurate microbiome predictions. Participants stressed the need for standardization, reproducibility, and accessibility of quantitative tools as key to advancing predictions in microbiome analysis. We highlight hurdles that participants identified and propose directions for future efforts that will advance the use of prediction in microbiome research.

Consistent and correctable bias in metagenomic sequencing experiments.

Marker-gene and metagenomic sequencing have profoundly expanded our ability to measure biological communities. But the measurements they provide differ from the truth, often dramatically, because these experiments are biased toward detecting some taxa over others. This experimental bias makes the taxon or gene abundances measured by different protocols quantitatively incomparable and can lead to spurious biological conclusions. We propose a mathematical model for how bias distorts community measurements based on the properties of real experiments. We validate this model with 16S rRNA gene and shotgun metagenomics data from defined bacterial communities. Our model better fits the experimental data despite being simpler than previous models. We illustrate how our model can be used to evaluate protocols, to understand the effect of bias on downstream statistical analyses, and to measure and correct bias given suitable calibration controls. These results illuminate new avenues toward truly quantitative and reproducible metagenomics measurements.

In Nature, There Is Only Diversity.

Microbial ecology has been transformed by the advent of high-throughput marker gene and metagenomic sequencing methods. These tools provide expansive descriptions of microbial communities, but the descriptions are framed in terms of molecular objects, such as 97% ribosomal operational taxonomic units (OTUs), rather than biological objects, such as species. A recent study by A. B. Chase and colleagues (mBio 8:e01809-17, 2017, https://doi.org/10.1128/mBio.01809-17) explores the so-called microdiversity within the Curtobacterium OTU, the most abundant OTU in a leaf litter community. Perhaps unsurprisingly, they find that some important ecologic traits, such as drought response, are coherent within the OTU, but that others vary significantly. Here we discuss their findings in relation to the more general issue of how molecular tools can be effectively used to study microbial ecology. We specifically note the need for investigators to choose the right molecular methods for their biological problem, as nature does not respect the limitations and conventions associated with our methods.