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.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the pandemic Coronavirus Disease 2019 (COVID-19) and now many face the burden of prolonged symptoms-long-lasting COVID-19 symptoms or "long-COVID". Long-COVID is thought to be linked to immune dysregulation due to harmful inflammation, with the exact causes being unknown. Given the role of the microbiome in mediating inflammation, we aimed to examine the relationship between the oral microbiome and the duration of long-COVID symptoms. Tongue swabs were collected from patients presenting with symptoms concerning for COVID-19. Confirmed infections were followed until resolution of all symptoms. Bacterial composition was determined by metagenomic sequencing. We used random forest modeling to identify microbiota and clinical covariates that associated with long-COVID symptoms. Of the patients followed, 63% (17/27) developed ongoing symptomatic COVID-19 and 37% (10/27) went on to long-COVID. Patients with prolonged symptoms had significantly higher abundances of microbiota that induce inflammation, such as members of the genera Prevotella and Veillonella. Of note are species that produce lipopolysaccharides and the similarity of long-COVID patients' oral microbiome to those of patients with chronic fatigue syndrome. All together, we our findings suggest an association with the oral microbiome and long-COVID revealing the possibility that dysfunction of the oral microbiome may contribute to this draining disease.

Older adults in nursing homes (NHs) have increased frailty, medication, and antimicrobial exposures, all factors that are known to affect the composition of gut microbiota. Our objective was to define which factors have the greatest association with the NH resident gut microbiota, explore patterns of dysbiosis and compositional changes in gut microbiota over time in this environment. We collected serial stool samples from NH residents. Residents were assessed using the Mini Nutritional Assessment tool and Clinical Frailty Scale. Bacterial composition of resident stool samples was determined by metagenomic sequencing. We used mixed-effect random forest modeling to identify clinical covariates that associate with microbiota. We enrolled and followed 166 residents from 5 NHs collecting 512 stool samples and following 15 residents for > 1 year. Medications, particularly psychoactive and anti-hypertensive medications, had the greatest effect on the microbiota. Age and frailty also contributed, and were associated with increased and decreased diversity, respectively. The microbiota of residents who had lived in the NH for > 1 year were enriched in inflammatory and pathogenic species and reduced in anti-inflammatory and symbiotic species. We observed intra-individual stability of the microbiome among older adults who had lived in the NH already for > 1 year followed with sample collections 1 year apart. Older adult NH gut microbiome is heavily influenced by medications, age, and frailty. This microbiome is influenced by length of NH residence with dysbiosis becoming evident at 12 months, however after this point there is demonstrated relative stability over time.

The reason for the striking differences in clinical outcomes of SARS-CoV-2 infected patients is still poorly understood. While most recover, a subset of people become critically ill and succumb to the disease. Thus, identification of biomarkers that can predict the clinical outcomes of COVID-19 disease is key to help prioritize patients needing urgent treatment. Given that an unbalanced gut microbiome is a reflection of poor health, we aim to identify indicator species that could predict COVID-19 disease clinical outcomes. Here, for the first time and with the largest COVID-19 patient cohort reported for microbiome studies, we demonstrated that the intestinal and oral microbiome make-up predicts respectively with 92% and 84% accuracy (Area Under the Curve or AUC) severe COVID-19 respiratory symptoms that lead to death. The accuracy of the microbiome prediction of COVID-19 severity was found to be far superior to that from training similar models using information from comorbidities often adopted to triage patients in the clinic (77% AUC). Additionally, by combining symptoms, comorbidities, and the intestinal microbiota the model reached the highest AUC at 96%. Remarkably the model training on the stool microbiome found enrichment of Enterococcus faecalis, a known pathobiont, as the top predictor of COVID-19 disease severity. Enterococcus faecalis is already easily cultivable in clinical laboratories, as such we urge the medical community to include this bacterium as a robust predictor of COVID-19 severity when assessing risk stratification of patients in the clinic.