Single cell genome sequencing of laboratory mouse microbiota improves taxonomic and functional resolution of this model microbial community.

Laboratory mice are widely studied as models of mammalian biology, including the microbiota. However, much of the taxonomic and functional diversity of the mouse gut microbiome is missed in current metagenomic studies, because genome databases have not achieved a balanced representation of the diverse members of this ecosystem. Towards solving this problem, we used flow cytometry and low-coverage sequencing to capture the genomes of 764 single cells from the stool of three laboratory mice. From these, we generated 298 high-coverage microbial genome assemblies, which we annotated for open reading frames and phylogenetic placement. These genomes increase the gene catalog and phylogenetic breadth of the mouse microbiota, adding 135 novel species with the greatest increase in diversity to the Muribaculaceae and Bacteroidaceae families. This new diversity also improves the read mapping rate, taxonomic classifier performance, and gene detection rate of mouse stool metagenomes. The novel microbial functions revealed through our single-cell genomes highlight previously invisible pathways that may be important for life in the murine gastrointestinal tract.

Penalized regression for left-truncated and right-censored survival data.

High-dimensional data are becoming increasingly common in the medical field as large volumes of patient information are collected and processed by high-throughput screening, electronic health records, and comprehensive genomic testing. Statistical models that attempt to study the effects of many predictors on survival typically implement feature selection or penalized methods to mitigate the undesirable consequences of overfitting. In some cases survival data are also left-truncated which can give rise to an immortal time bias, but penalized survival methods that adjust for left truncation are not commonly implemented. To address these challenges, we apply a penalized Cox proportional hazards model for left-truncated and right-censored survival data and assess implications of left truncation adjustment on bias and interpretation. We use simulation studies and a high-dimensional, real-world clinico-genomic database to highlight the pitfalls of failing to account for left truncation in survival modeling.

Caloric restriction disrupts the microbiota and colonization resistance.

Diet is a major factor that shapes the gut microbiome1, but the consequences of diet-induced changes in the microbiome for host pathophysiology remain poorly understood. We conducted a randomized human intervention study using a very-low-calorie diet (NCT01105143). Although metabolic health was improved, severe calorie restriction led to a decrease in bacterial abundance and restructuring of the gut microbiome. Transplantation of post-diet microbiota to mice decreased their body weight and adiposity relative to mice that received pre-diet microbiota. Weight loss was associated with impaired nutrient absorption and enrichment in Clostridioides difficile, which was consistent with a decrease in bile acids and was sufficient to replicate metabolic phenotypes in mice in a toxin-dependent manner. These results emphasize the importance of diet-microbiome interactions in modulating host energy balance and the need to understand the role of diet in the interplay between pathogenic and beneficial symbionts.

Gut microbiome stability and dynamics in healthy donors and patients with non-gastrointestinal cancers.

As microbial therapeutics are increasingly being tested in diverse patient populations, it is essential to understand the host and environmental factors influencing the microbiome. Through analysis of 1,359 gut microbiome samples from 946 healthy donors of the Milieu Intérieur cohort, we detail how microbiome composition is associated with host factors, lifestyle parameters, and disease states. Using a genome-based taxonomy, we found biological sex was the strongest driver of community composition. Additionally, bacterial populations shift across decades of life (age 20-69), with Bacteroidota species consistently increased with age while Actinobacteriota species, including Bifidobacterium, decreased. Longitudinal sampling revealed that short-term stability exceeds interindividual differences. By accounting for these factors, we defined global shifts in the microbiomes of patients with non-gastrointestinal tumors compared with healthy donors. Together, these results demonstrated that the microbiome displays predictable variations as a function of sex, age, and disease state. These variations must be considered when designing microbiome-targeted therapies or interpreting differences thought to be linked to pathophysiology or therapeutic response.

Meta-Analysis of Vaginal Microbiome Data Provides New Insights Into Preterm Birth.

Preterm birth (PTB) is defined as the birth of an infant before 37 weeks of gestational age. It is the leading cause of perinatal morbidity and mortality worldwide. In this study, we present a comprehensive meta-analysis of vaginal microbiome in PTB. We integrated raw longitudinal 16S rRNA vaginal microbiome data from five independent studies across 3,201 samples and were able to gain new insights into the vaginal microbiome state in women who deliver preterm in comparison to those who deliver at term. We found that women who deliver prematurely show higher within-sample variance in vaginal microbiome abundance, with the most significant difference observed during the first trimester. Modeling the data longitudinally revealed a number of microbial genera as associated with PTB, including several that were previously known and two newly identified by this meta-analysis: Olsenella and Clostridium sensu stricto. New hypotheses emerging from this integrative analysis can lead to novel diagnostics to identify women who are at higher risk for PTB and potentially inform new therapeutic interventions.

Cooking shapes the structure and function of the gut microbiome.

Diet is a critical determinant of variation in gut microbial structure and function, outweighing even host genetics1-3. Numerous microbiome studies have compared diets with divergent ingredients1-5, but the everyday practice of cooking remains understudied. Here, we show that a plant diet served raw versus cooked reshapes the murine gut microbiome, with effects attributable to improvements in starch digestibility and degradation of plant-derived compounds. Shifts in the gut microbiota modulated host energy status, applied across multiple starch-rich plants, and were detectable in humans. Thus, diet-driven host-microbial interactions depend on the food as well as its form. Because cooking is human-specific, ubiquitous and ancient6,7, our results prompt the hypothesis that humans and our microbiomes co-evolved under unique cooking-related pressures.

AlleleAnalyzer: a tool for personalized and allele-specific sgRNA design.

The CRISPR/Cas system is a highly specific genome editing tool capable of distinguishing alleles differing by even a single base pair. Target sites might carry genetic variations that are not distinguishable by sgRNA designing tools based on one reference genome. AlleleAnalyzer is an open-source software that incorporates single-nucleotide variants and short insertions and deletions to design sgRNAs for precisely editing 1 or multiple haplotypes of a sequenced genome, currently supporting 11 Cas proteins. It also leverages patterns of shared genetic variation to optimize sgRNA design for different human populations. AlleleAnalyzer is available at https://github.com/keoughkath/AlleleAnalyzer .

Topical silver diamine fluoride for dental caries arrest in preschool children: A randomized controlled trial and microbiological analysis of caries associated microbes and resistance gene expression.

OBJECTIVES: The Stopping Cavities Trial investigated effectiveness and safety of 38% silver diamine fluoride in arresting caries lesions. MATERIALS AND METHODS: The study was a double-blind randomized placebo-controlled superiority trial with 2 parallel groups. The sites were Oregon preschools. Sixty-six preschool children with ≥1 lesion were enrolled. Silver diamine fluoride (38%) or placebo (blue-tinted water), applied topically to the lesion. The primary endpoint was caries arrest (lesion inactivity, Nyvad criteria) 14-21days post intervention. Dental plaque was collected from all children, and microbial composition was assessed by RNA sequencing from 2 lesions and 1 unaffected surface before treatment and at follow-up for 3 children from each group. RESULTS AND CONCLUSION: Average proportion of arrested caries lesions in the silver diamine fluoride group was higher (0.72; 95% CI; 0.55, 0.84) than in the placebo group (0.05; 95% CI; 0.00, 0.16). Confirmatory analysis using generalized estimating equation log-linear regression, based on the number of arrested lesions and accounting for the number of treated surfaces and length of follow-up, indicates the risk of arrested caries was significantly higher in the treatment group (relative risk, 17.3; 95% CI: 4.3 to 69.4). No harms were observed. RNA sequencing analysis identified no consistent changes in relative abundance of caries-associated microbes, nor emergence of antibiotic or metal resistance gene expression. Topical 38% silver diamine fluoride is effective and safe in arresting cavities in preschool children. CLINICAL SIGNIFICANCE: The treatment is applicable to primary care practice and may reduce the burden of untreated tooth decay in the population.

Development of Inflammatory Bowel Disease Is Linked to a Longitudinal Restructuring of the Gut Metagenome in Mice.

The gut microbiome is linked to inflammatory bowel disease (IBD) severity and altered in late-stage disease. However, it is unclear how gut microbial communities change over the course of IBD development, especially in regard to function. To investigate microbiome-mediated disease mechanisms and discover early biomarkers of IBD, we conducted a longitudinal metagenomic investigation in an established mouse model of IBD, where damped transforming growth factor ß (TGF-ß) signaling in T cells leads to peripheral immune activation, weight loss, and severe colitis. IBD development is associated with abnormal gut microbiome temporal dynamics, including damped acquisition of functional diversity and significant differences in abundance trajectories for KEGG modules such as glycosaminoglycan degradation, cellular chemotaxis, and type III and IV secretion systems. Most differences between sick and control mice emerge when mice begin to lose weight and heightened T cell activation is detected in peripheral blood. However, levels of lipooligosaccharide transporter abundance diverge prior to immune activation, indicating that it could be a predisease indicator or microbiome-mediated disease mechanism. Taxonomic structure of the gut microbiome also significantly changes in association with IBD development, and the abundances of particular taxa, including several species of Bacteroides, correlate with immune activation. These discoveries were enabled by our use of generalized linear mixed-effects models to test for differences in longitudinal profiles between healthy and diseased mice while accounting for the distributions of taxon and gene counts in metagenomic data. These findings demonstrate that longitudinal metagenomics is useful for discovering the potential mechanisms through which the gut microbiome becomes altered in IBD. IMPORTANCE IBD patients harbor distinct microbial communities with functional capabilities different from those seen with healthy people. But is this cause or effect? Answering this question requires data on changes in gut microbial communities leading to disease onset. By performing weekly metagenomic sequencing and mixed-effects modeling on an established mouse model of IBD, we identified several functional pathways encoded by the gut microbiome that covary with host immune status. These pathways are novel early biomarkers that may either enable microbes to live inside an inflamed gut or contribute to immune activation in IBD mice. Future work will validate the potential roles of these microbial pathways in host-microbe interactions and human disease. This study was novel in its longitudinal design and focus on microbial pathways, which provided new mechanistic insights into the role of gut microbes in IBD development.

Whole-Genome Sequencing of Drug-Resistant Salmonella enterica Isolates from Dairy Cattle and Humans in New York and Washington States Reveals Source and Geographic Associations.

Multidrug-resistant (MDR) Salmonella enterica can be spread from cattle to humans through direct contact with animals shedding Salmonella as well as through the food chain, making MDR Salmonella a serious threat to human health. The objective of this study was to use whole-genome sequencing to compare antimicrobial-resistant (AMR) Salmonella enterica serovars Typhimurium, Newport, and Dublin isolated from dairy cattle and humans in Washington State and New York State at the genotypic and phenotypic levels. A total of 90 isolates were selected for the study (37 S Typhimurium, 32 S Newport, and 21 S Dublin isolates). All isolates were tested for phenotypic antibiotic resistance to 12 drugs using Kirby-Bauer disk diffusion. AMR genes were detected in the assembled genome of each isolate using nucleotide BLAST and ARG-ANNOT. Genotypic prediction of phenotypic resistance resulted in a mean sensitivity of 97.2 and specificity of 85.2. Sulfamethoxazole-trimethoprim resistance was observed only in human isolates (P < 0.05), while resistance to quinolones and fluoroquinolones was observed only in 6 S Typhimurium isolates from humans in Washington State. S Newport isolates showed a high degree of AMR profile similarity, regardless of source. S Dublin isolates from New York State differed from those from Washington State based on the presence/absence of plasmid replicons, as well as phenotypic AMR susceptibility/nonsusceptibility (P < 0.05). The results of this study suggest that distinct factors may contribute to the emergence and dispersal of AMR S. enterica in humans and farm animals in different regions.IMPORTANCE The use of antibiotics in food-producing animals has been hypothesized to select for AMR Salmonella enterica and associated AMR determinants, which can be transferred to humans through different routes. Previous studies have sought to assess the degree to which AMR livestock- and human-associated Salmonella strains overlap, as well as the spatial distribution of Salmonella's associated AMR determinants, but have often been limited by the degree of resolution at which isolates can be compared. Here, a comparative genomics study of livestock- and human-associated Salmonella strains from different regions of the United States shows that while many AMR genes and phenotypes were confined to human isolates, overlaps between the resistomes of bovine and human-associated Salmonella isolates were observed on numerous occasions, particularly for S Newport. We have also shown that whole-genome sequencing can be used to reliably predict phenotypic resistance across Salmonella isolated from bovine sources.

Identifying phenotypic signatures of neuropsychiatric disorders from electronic medical records.

OBJECTIVE: Mental illness is the leading cause of disability in the USA, but boundaries between different mental illnesses are notoriously difficult to define. Electronic medical records (EMRs) have recently emerged as a powerful new source of information for defining the phenotypic signatures of specific diseases. We investigated how EMR-based text mining and statistical analysis could elucidate the phenotypic boundaries of three important neuropsychiatric illnesses-autism, bipolar disorder, and schizophrenia. METHODS: We analyzed the medical records of over 7000 patients at two facilities using an automated text-processing pipeline to annotate the clinical notes with Unified Medical Language System codes and then searching for enriched codes, and associations among codes, that were representative of the three disorders. We used dimensionality-reduction techniques on individual patient records to understand individual-level phenotypic variation within each disorder, as well as the degree of overlap among disorders. RESULTS: We demonstrate that automated EMR mining can be used to extract relevant drugs and phenotypes associated with neuropsychiatric disorders and characteristic patterns of associations among them. Patient-level analyses suggest a clear separation between autism and the other disorders, while revealing significant overlap between schizophrenia and bipolar disorder. They also enable localization of individual patients within the phenotypic 'landscape' of each disorder. CONCLUSIONS: Because EMRs reflect the realities of patient care rather than idealized conceptualizations of disease states, we argue that automated EMR mining can help define the boundaries between different mental illnesses, facilitate cohort building for clinical and genomic studies, and reveal how clear expert-defined disease boundaries are in practice.