Translational regulation of TFH cell differentiation and autoimmune pathogenesis.

Little is known regarding T cell translational regulation. We demonstrate that T follicular helper (TFH) cells use a previously unknown mechanism of selective messenger RNA (mRNA) translation for their differentiation, role in B cell maturation, and in autoimmune pathogenesis. We show that TFH cells have much higher levels of translation factor eIF4E than non-TFH CD4+ T cells, which is essential for translation of TFH cell fate-specification mRNAs. Genome-wide translation studies indicate that modest down-regulation of eIF4E activity by a small-molecule inhibitor or short hairpin RN impairs TFH cell development and function. In mice, down-regulation of eIF4E activity specifically reduces TFH cells among T helper subtypes, germinal centers, B cell recruitment, and antibody production. In experimental autoimmune encephalomyelitis, eIF4E activity down-regulation blocks TFH cell participation in disease pathogenesis while promoting rapid remission and spinal cord remyelination. TFH cell development and its role in autoimmune pathogenesis involve selective mRNA translation that is highly druggable.

Phenotype-Based Threat Assessment.

Bacterial pathogen identification, which is critical for human health, has historically relied on culturing organisms from clinical specimens. More recently, the application of machine learning (ML) to whole-genome sequences (WGSs) has facilitated pathogen identification. However, relying solely on genetic information to identify emerging or new pathogens is fundamentally constrained, especially if novel virulence factors exist. In addition, even WGSs with ML pipelines are unable to discern phenotypes associated with cryptic genetic loci linked to virulence. Here, we set out to determine if ML using phenotypic hallmarks of pathogenesis could assess potential pathogenic threat without using any sequence-based analysis. This approach successfully classified potential pathogenetic threat associated with previously machine-observed and unobserved bacteria with 99% and 85% accuracy, respectively. This work establishes a phenotype-based pipeline for potential pathogenic threat assessment, which we term PathEngine, and offers strategies for the identification of bacterial pathogens.

Effects of early-life penicillin exposure on the gut microbiome and frontal cortex and amygdala gene expression.

We have established experimental systems to assess the effects of early-life exposures to antibiotics on the intestinal microbiota and gene expression in the brain. This model system is highly relevant to human exposure and may be developed into a preclinical model of neurodevelopmental disorders in which the gut-brain axis is perturbed, leading to organizational effects that permanently alter the structure and function of the brain. Exposing newborn mice to low-dose penicillin led to substantial changes in intestinal microbiota population structure and composition. Transcriptomic alterations implicate pathways perturbed in neurodevelopmental and neuropsychiatric disorders. There also were substantial effects on frontal cortex and amygdala gene expression by bioinformatic interrogation, affecting multiple pathways underlying neurodevelopment. Informatic analyses established linkages between specific intestinal microbial populations and the early-life expression of particular affected genes. These studies provide translational models to explore intestinal microbiome roles in the normal and abnormal maturation of the vulnerable central nervous system.

Microbial genetic and transcriptional contributions to oxalate degradation by the gut microbiota in health and disease.

Over-accumulation of oxalate in humans may lead to nephrolithiasis and nephrocalcinosis. Humans lack endogenous oxalate degradation pathways (ODP), but intestinal microbes can degrade oxalate using multiple ODPs and protect against its absorption. The exact oxalate-degrading taxa in the human microbiota and their ODP have not been described. We leverage multi-omics data (>3000 samples from >1000 subjects) to show that the human microbiota primarily uses the type II ODP, rather than type I. Furthermore, among the diverse ODP-encoding microbes, an oxalate autotroph, Oxalobacter formigenes, dominates this function transcriptionally. Patients with inflammatory bowel disease (IBD) frequently suffer from disrupted oxalate homeostasis and calcium oxalate nephrolithiasis. We show that the enteric oxalate level is elevated in IBD patients, with highest levels in Crohn's disease (CD) patients with both ileal and colonic involvement consistent with known nephrolithiasis risk. We show that the microbiota ODP expression is reduced in IBD patients, which may contribute to the disrupted oxalate homeostasis. The specific changes in ODP expression by several important taxa suggest that they play distinct roles in IBD-induced nephrolithiasis risk. Lastly, we colonize mice that are maintained in the gnotobiotic facility with O. formigenes, using either a laboratory isolate or an isolate we cultured from human stools, and observed a significant reduction in host fecal and urine oxalate levels, supporting our in silico prediction of the importance of the microbiome, particularly O. formigenes in host oxalate homeostasis.

Predictive Metagenomic Analysis of Autoimmune Disease Identifies Robust Autoimmunity and Disease Specific Microbial Signatures.

Within the last decade, numerous studies have demonstrated changes in the gut microbiome associated with specific autoimmune diseases. Due to differences in study design, data quality control, analysis and statistical methods, many results of these studies are inconsistent and incomparable. To better understand the relationship between the intestinal microbiome and autoimmunity, we have completed a comprehensive re-analysis of 42 studies focusing on the gut microbiome in 12 autoimmune diseases to identify a microbial signature predictive of multiple sclerosis (MS), inflammatory bowel disease (IBD), rheumatoid arthritis (RA) and general autoimmune disease using both 16S rRNA sequencing data and shotgun metagenomics data. To do this, we used four machine learning algorithms, random forest, eXtreme Gradient Boosting (XGBoost), ridge regression, and support vector machine with radial kernel and recursive feature elimination to rank disease predictive taxa comparing disease vs. healthy participants and pairwise comparisons of each disease. Comparing the performance of these models, we found the two tree-based methods, XGBoost and random forest, most capable of handling sparse multidimensional data, to consistently produce the best results. Through this modeling, we identified a number of taxa consistently identified as dysregulated in a general autoimmune disease model including Odoribacter, Lachnospiraceae Clostridium, and Mogibacteriaceae implicating all as potential factors connecting the gut microbiome to autoimmune response. Further, we computed pairwise comparison models to identify disease specific taxa signatures highlighting a role for Peptostreptococcaceae and Ruminococcaceae Gemmiger in IBD and Akkermansia, Butyricicoccus, and Mogibacteriaceae in MS. We then connected a subset of these taxa with potential metabolic alterations based on metagenomic/metabolomic correlation analysis, identifying 215 metabolites associated with autoimmunity-predictive taxa.

Changes in the Gut Microbiota of Urban Subjects during an Immersion in the Traditional Diet and Lifestyle of a Rainforest Village.

People living traditional lifestyles have higher gut microbiota diversity than urban subjects. We hypothesized that shifting lifestyles from an urban environment to a traditional rainforest village would lead to changes in the microbiota of visitors, which would become more similar to the microbiota of villagers. Here, we characterized at different time points the microbiota of 7 urban visitors (5 adults and 2 children) staying in a rainforest Amerindian village for 16 days and compared them with a reference collection of samples from age-matched local villagers. We performed a 16S rRNA gene survey of samples from multiple body sites (including fecal, oral, nasal, and skin samples) using Illumina MiSeq sequencing. The main factor segregating the microbiotas of each body site was the human group (i.e., visitors versus villagers), with the visitor microbiota tending to have lower alpha diversity; the lowered alpha diversity was statistically significant in the microbiota of skin and in the children's fecal and oral microbiota. During the rainforest period, all visitors experienced microbiota changes within their personal cloud of variation. For all body sites, the microbiota conformations in the visitor children better matched the microbiota conformations in villagers of the same age than did those of the visitor adults, which showed a lower "microbiota age" than the microbiota of the villagers. The results suggest higher stability in the adult microbiota, with the less resilient children's microbiota responding more to dietary changes.IMPORTANCE Despite the limitations of a small study, our results evidence higher resilience of the gut microbiota with respect to dietary manipulation in adults than in children and urge further studies to understand the extent of microbiota plasticity in response to dietary changes and the mechanisms underlying microbiota resilience. These studies are relevant to the potential of future human pre- and probiotics in preventing or curing microbiota-associated diseases.