The Urinary Microbiome of Older Adults Residing in a Nursing Home Varies with Duration of Residence and Shows Increases in Potential Pathogens

The community of bacteria that colonize the urinary tract, the urinary microbiome, is hypothesized to influence a wide variety of urinary tract conditions. Older adults that reside in nursing homes are frequently diagnosed and treated for urinary tract conditions such as urinary tract infection (UTI). We investigated the urinary microbiome of older adults residing in a nursing home to determine if there are features of the urinary microbiome that are associated specific conditions and exposure in this population. We were also interested in the stability of urinary microbiome over time and in similarities between the urinary and gastrointestinal microbiome. Urine samples were prospectively collected over a period of 10 months from a cohort of 26 older adults (age > 65 years) residing in single nursing home located in Central Massachusetts. Serial samples were obtained from 6 individuals over 10 months and 5 participants were concurrently enrolled in a study of the gastrointestinal microbiome. Information collected on participants included demographics, medical history, duration of residence in the nursing home, frailty, dementia symptoms, urinary symptoms, antibiotic treatment, urinary catherization, and hospitalizations over a 10-month period. Clean catch mid-stream urine samples were collected and stored at -80C. DNA was extracted and 16S rRNA gene sequencing performed. The length of stay in the nursing facility and the Clinical Frailty Scale correlated with significant changes in microbiome composition. An increase in the relative abundance of a putative urinary pathogen, Aerococcus urinae, was the largest factor influencing change that occurred over duration of residence.

Targeting DNA Methylation in the Adult Brain through Diet

Metabolism and nutrition have a significant role in epigenetic modifications such as DNA methylation, which can influence gene expression. Recently, it has been suggested that bioactive nutrients and gut microbiota can alter DNA methylation in the central nervous system (CNS) through the gut–brain axis, playing a crucial role in modulating CNS functions and, finally, behavior. Here, we will focus on the effect of metabolic signals in shaping brain DNA methylation during adulthood. We will provide an overview of potential interactions among diet, gastrointestinal microbiome and epigenetic alterations on brain methylation and behavior. In addition, the impact of different diet challenges on cytosine methylation dynamics in the adult brain will be discussed. Finally, we will explore new ways to modulate DNA hydroxymethylation, which is particularly abundant in neural tissue, through diet.

Gut Microbiome-Mediated Alteration of Immunity, Inflammation, and Metabolism Involved in the Regulation of Non-alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is one of the leading causes of end-stage liver disease, leading to a rapidly growing global public health burden. The term “gut microbiome (GM)” refers to the approximately 100 trillion microbial cells that inhabit the host’s gastrointestinal tract. There is increasing evidence that GM is involved in the pathogenesis of NAFLD and may be a potential target for intervention. To explore GM-based strategies for precise diagnosis and treatment of NAFLD, great efforts have been made to develop a comprehensive and in-depth understanding of the host–microbe interaction. This review evaluates this interaction critically, mainly considering the intricate regulation of the metabolism, immunity, and inflammatory status during the evolution of the disease pathogenesis, revealing roles for the GM in NAFLD by examining advances in potential mechanisms, diagnostics, and modulation strategies.Synopsis: Considering the intricate metabolic and immune/inflammatory homeostasis regulation, we evaluate the latest understanding of the host–microbe interaction and reveal roles for the gastrointestinal microbiome in NAFLD. Strategies targeting the gastrointestinal microbiome for the diagnosis and treatment of NAFLD are proposed.

Machine Learning Based Metagenomic Prediction of Inflammatory Bowel Disease

In this study, we investigate faecal microbiota composition, in an attempt to evaluate performance of classification algorithms in identifying Inflammatory Bowel Disease (IBD) and its two types: Crohn’s disease (CD) and ulcerative colitis (UC). From many investigated algorithms, a random forest (RF) classifier was selected for detailed evaluation in three-class (CD versus UC versus nonIBD) classification task and two binary (nonIBD versus IBD and CD versus UC) classification tasks. We dealt with class imbalance, performed extensive parameter search, dimensionality reduction and two-level classification. In three-class classification, our best model reaches F1 score of 91% in average, which confirms the strong connection of IBD and gastrointestinal microbiome. Among most important features in three-class classification are species Staphylococcus hominis, Porphyromonas endodontalis, Slackia piriformis and genus Bacteroidetes.

Microbiome-Specific Statistical Modeling Identifies Interplay Between Gastrointestinal Microbiome and Neurobehavioral Outcomes in Patients With Autism: A Case Control Study

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with unclear mechanisms of pathogenesis. Gastrointestinal microbiome alterations were found to correlate with ASD core symptoms, but its specific role in ASD pathogenesis has not been determined. In this study, we used a case-control strategy that simultaneously compared the ASD gastrointestinal microbiome with that from age-sex matched controls and first-degree relative controls, using a statistical framework accounting for confounders such as age. Enterobacteriaceae (including Escherichia/Shigella) and Phyllobacterium were significantly enriched in the ASD group, with their relative abundances all following a pattern of ASD > first degree relative control > healthy control, consistent with our hypothesis of living environment and shared microbial and immunological exposures as key drivers of ASD gastrointestinal microbiome dysbiosis. Using multivariable omnibus testing, we identified clinical factors including ADOS scores, dietary habits, and gastrointestinal symptoms that covary with overall microbiome structure within the ASD cohort. A microbiome-specific multivariate modeling approach (MaAsLin2) demonstrated microbial taxa, such as Lachnoclostridium and Tyzzerella, are significantly associated with ASD core symptoms measured by ADOS. Finally, we identified alterations in predicted biological functions, including tryptophan and tyrosine biosynthesis/metabolism potentially relevant to the pathophysiology of the gut-brain-axis. Overall, our results identified gastrointestinal microbiome signature changes in patients with ASD, highlighted associations between gastrointestinal microbiome and clinical characteristics related to the gut-brain axis and identified contributors to the heterogeneity of gastrointestinal microbiome within the ASD population.

Influenza A H1N1 Induced Disturbance of the Respiratory and Fecal Microbiome of German Landrace Pigs – a Multi-Omics Characterization

Here, we used swine as a biomedical model to elucidate the impact of influenza A H1N1 infection on structure and function of the respiratory and gastrointestinal tract microbiome by employing a multi-omics analytical approach. To our knowledge, this is the first study to investigate the temporal development of the porcine microbiome and to provide insights into the functional capacity of the gastrointestinal microbiome during influenza A virus infection.