Microbiota encoded fatty-acid metabolism expands tuft cells to protect tissues homeostasis during Clostridioides difficile infection in the large intestine.

Metabolic byproducts of the intestinal microbiota are crucial in maintaining host immune tone and shaping inter-species ecological dynamics. Among these metabolites, succinate is a driver of tuft cell (TC) differentiation and consequent type 2 immunity-dependent protection against invading parasites in the small intestine. Succinate is also a growth enhancer of the nosocomial pathogen Clostridioides difficile in the large intestine. To date, no research has shown the role of succinate in modulating TC dynamics in the large intestine, or the relevance of this immune pathway to C. difficile pathophysiology. Here we reveal the existence of a three-way circuit between commensal microbes, C. difficile and host epithelial cells which centers around succinate. Through selective microbiota depletion experiments we demonstrate higher levels of type 2 cytokines leading to expansion of TCs in the colon. We then demonstrate the causal role of the microbiome in modulating colonic TC abundance and subsequent type 2 cytokine induction using rational supplementation experiments with fecal transplants and microbial consortia of succinate-producing bacteria. We show that administration of a succinate-deficient Bacteroides thetaiotaomicron knockout (Δfrd) significantly reduces the enhanced type 2 immunity in mono-colonized mice. Finally, we demonstrate that mice prophylactically administered with the consortium of succinate-producing bacteria show reduced C. difficile-induced morbidity and mortality compared to mice administered with heat-killed bacteria or the vehicle. This effect is reduced in a partial tuft cell knockout mouse, Pou2f3+/-, and nullified in the tuft cell knockout mouse, Pou2f3-/-, confirming that the observed protection occurs via the TC pathway. Succinate is an intermediary metabolite of the production of short-chain fatty acids, and its concentration often increases during dysbiosis. The first barrier to enteric pathogens alike is the intestinal epithelial barrier, and host maintenance and strengthening of barrier integrity is vital to homeostasis. Considering our data, we propose that activation of TC by the microbiota-produced succinate in the colon is a mechanism evolved by the host to counterbalance microbiome-derived cues that facilitate invasion by intestinal pathogens.

Commensal antimicrobial resistance mediates microbiome resilience to antibiotic disruption.

Despite their therapeutic benefits, antibiotics exert collateral damage on the microbiome and promote antimicrobial resistance. However, the mechanisms governing microbiome recovery from antibiotics are poorly understood. Treatment of Mycobacterium tuberculosis, the world's most common infection, represents the longest antimicrobial exposure in humans. Here, we investigate gut microbiome dynamics over 20 months of multidrug-resistant tuberculosis (TB) and 6 months of drug-sensitive TB treatment in humans. We find that gut microbiome dynamics and TB clearance are shared predictive cofactors of the resolution of TB-driven inflammation. The initial severe taxonomic and functional microbiome disruption, pathobiont domination, and enhancement of antibiotic resistance that initially accompanied long-term antibiotics were countered by later recovery of commensals. This resilience was driven by the competing evolution of antimicrobial resistance mutations in pathobionts and commensals, with commensal strains with resistance mutations reestablishing dominance. Fecal-microbiota transplantation of the antibiotic-resistant commensal microbiome in mice recapitulated resistance to further antibiotic disruption. These findings demonstrate that antimicrobial resistance mutations in commensals can have paradoxically beneficial effects by promoting microbiome resilience to antimicrobials and identify microbiome dynamics as a predictor of disease resolution in antibiotic therapy of a chronic infection.

Association between microbiome and the development of adverse posttraumatic neuropsychiatric sequelae after traumatic stress exposure.

Patients exposed to trauma often experience high rates of adverse post-traumatic neuropsychiatric sequelae (APNS). The biological mechanisms promoting APNS are currently unknown, but the microbiota-gut-brain axis offers an avenue to understanding mechanisms as well as possibilities for intervention. Microbiome composition after trauma exposure has been poorly examined regarding neuropsychiatric outcomes. We aimed to determine whether the gut microbiomes of trauma-exposed emergency department patients who develop APNS have dysfunctional gut microbiome profiles and discover potential associated mechanisms. We performed metagenomic analysis on stool samples (n = 51) from a subset of adults enrolled in the Advancing Understanding of RecOvery afteR traumA (AURORA) study. Two-, eight- and twelve-week post-trauma outcomes for post-traumatic stress disorder (PTSD) (PTSD checklist for DSM-5), normalized depression scores (PROMIS Depression Short Form 8b) and somatic symptom counts were collected. Generalized linear models were created for each outcome using microbial abundances and relevant demographics. Mixed-effect random forest machine learning models were used to identify associations between APNS outcomes and microbial features and encoded metabolic pathways from stool metagenomics. Microbial species, including Flavonifractor plautii, Ruminococcus gnavus and, Bifidobacterium species, which are prevalent commensal gut microbes, were found to be important in predicting worse APNS outcomes from microbial abundance data. Notably, through APNS outcome modeling using microbial metabolic pathways, worse APNS outcomes were highly predicted by decreased L-arginine related pathway genes and increased citrulline and ornithine pathways. Common commensal microbial species are enriched in individuals who develop APNS. More notably, we identified a biological mechanism through which the gut microbiome reduces global arginine bioavailability, a metabolic change that has also been demonstrated in the plasma of patients with PTSD.

The intestinal microbiota predicts COVID-19 severity and fatality regardless of hospital feeding method.

SARS-CoV-2-positive patients exhibit gut and oral microbiome dysbiosis, which is associated with various aspects of COVID-19 disease (1-4). Here, we aim to identify gut and oral microbiome markers that predict COVID-19 severity in hospitalized patients, specifically severely ill patients compared to moderately ill ones. Moreover, we investigate whether hospital feeding (solid versus enteral), an important cofounder, influences the microbial composition of hospitalized COVID-19 patients. We used random forest classification machine learning models with interpretable secondary analyses. The gut, but not the oral microbiota, was a robust predictor of both COVID-19-related fatality and severity of hospitalized patients, with a higher predictive value than most clinical variables. In addition, perturbations of the gut microbiota due to enteral feeding did not associate with species that were predictive of COVID-19 severity. IMPORTANCE SARS-CoV-2 infection leads to wide-ranging, systemic symptoms with sometimes unpredictable morbidity and mortality. It is increasingly clear that the human microbiome plays an important role in how individuals respond to viral infections. Our study adds to important literature about the associations of gut microbiota and severe COVID-19 illness during the early phase of the pandemic before the availability of vaccines. Increased understanding of the interplay between microbiota and SARS-CoV-2 may lead to innovations in diagnostics, therapies, and clinical predictions.

Oropharyngeal microbiome profiled at admission is predictive of the need for respiratory support among COVID-19 patients.

The oropharyngeal microbiome, the collective genomes of the community of microorganisms that colonizes the upper respiratory tract, is thought to influence the clinical course of infection by respiratory viruses, including Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the causative agent of Coronavirus Infectious Disease 2019 (COVID-19). In this study, we examined the oropharyngeal microbiome of suspected COVID-19 patients presenting to the Emergency Department and an inpatient COVID-19 unit with symptoms of acute COVID-19. Of 115 initially enrolled patients, 50 had positive molecular testing for COVID-19+ and had symptom duration of 14 days or less. These patients were analyzed further as progression of disease could most likely be attributed to acute COVID-19 and less likely a secondary process. Of these, 38 (76%) went on to require some form of supplemental oxygen support. To identify functional patterns associated with respiratory illness requiring respiratory support, we applied an interpretable random forest classification machine learning pipeline to shotgun metagenomic sequencing data and select clinical covariates. When combined with clinical factors, both species and metabolic pathways abundance-based models were found to be highly predictive of the need for respiratory support (F1-score 0.857 for microbes and 0.821 for functional pathways). To determine biologically meaningful and highly predictive signals in the microbiome, we applied the Stable and Interpretable RUle Set to the output of the models. This analysis revealed that low abundance of two commensal organisms, Prevotella salivae or Veillonella infantium (< 4.2 and 1.7% respectively), and a low abundance of a pathway associated with LPS biosynthesis (< 0.1%) were highly predictive of developing the need for acute respiratory support (82 and 91.4% respectively). These findings suggest that the composition of the oropharyngeal microbiome in COVID-19 patients may play a role in determining who will suffer from severe disease manifestations.

Oropharyngeal Microbiome Profiled at Admission is Predictive of the Need for Respiratory Support Among COVID-19 Patients.

The clinical course of infection due to respiratory viruses such as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2), the causative agent of Coronavirus Disease 2019 (COVID-19) is thought to be influenced by the community of organisms that colonizes the upper respiratory tract, the oropharyngeal microbiome. In this study, we examined the oropharyngeal microbiome of suspected COVID-19 patients presenting to the Emergency Department and an inpatient COVID-19 unit with symptoms of acute COVID-19. Of 115 enrolled patients, 74 were confirmed COVID-19+ and 50 had symptom duration of 14 days or less; 38 acute COVID-19+ patients (76%) went on to require respiratory support. Although no microbiome features were found to be significantly different between COVID-19+ and COVID-19-patients, when we conducted random forest classification modeling (RFC) to predict the need of respiratory support for the COVID-19+ patients our analysis identified a subset of organisms and metabolic pathways whose relative abundance, when combined with clinical factors (such as age and Body Mass Index), was highly predictive of the need for respiratory support (F1 score 0.857). Microbiome Multivariable Association with Linear Models (MaAsLin2) analysis was then applied to the features identified as predicative of the need for respiratory support by the RFC. This analysis revealed reduced abundance of Prevotella salivae and metabolic pathways associated with lipopolysaccharide and mycolic acid biosynthesis to be the strongest predictors of patients requiring respiratory support. These findings suggest that composition of the oropharyngeal microbiome in COVID-19 may play a role in determining who will suffer from severe disease manifestations. Importance: The microbial community that colonizes the upper airway, the oropharyngeal microbiome, has the potential to affect how patients respond to respiratory viruses such as SARS-CoV2, the causative agent of COVID-19. In this study, we investigated the oropharyngeal microbiome of COVID-19 patients using high throughput DNA sequencing performed on oral swabs. We combined patient characteristics available at intake such as medical comorbidities and age, with measured abundance of bacterial species and metabolic pathways and then trained a machine learning model to determine what features are predicative of patients needing respiratory support in the form of supplemental oxygen or mechanical ventilation. We found that decreased abundance of some bacterial species and increased abundance of pathways associated bacterial products biosynthesis was highly predictive of needing respiratory support. This suggests that the oropharyngeal microbiome affects disease course in COVID-19 and could be targeted for diagnostic purposes to determine who may need oxygen, or therapeutic purposes such as probiotics to prevent severe COVID-19 disease manifestations.

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 who reside in nursing homes are frequently diagnosed and treated for urinary tract conditions such as urinary tract infection. 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 with 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 (aged >65 years) residing in a 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 catheterization, and hospitalizations over a 10-month period. Clean catch, midstream urine samples were collected and stored at -80°C. DNA was extracted and 16S rRNA gene sequencing was 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 the duration of residence.

Inflammation-type dysbiosis of the oral microbiome associates with the duration of COVID-19 symptoms and long COVID.

In the COVID-19 pandemic, caused by SARS-CoV-2, many individuals experience prolonged symptoms, termed long-lasting COVID-19 symptoms (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 COVID-19 symptoms. 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 are associated with long COVID symptoms. Of the patients followed, 63% developed ongoing symptomatic COVID-19 and 37% went on to long COVID. Patients with prolonged symptoms had significantly higher abundances of microbiota that induced inflammation, such as members of the genera Prevotella and Veillonella, which, of note, are species that produce LPS. The oral microbiome of patients with long COVID was similar to that of patients with chronic fatigue syndrome. Altogether, our findings suggest an association with the oral microbiome and long COVID, revealing the possibility that dysfunction of the oral microbiome may have contributed to this draining disease.

The Nursing Home Older Adult Gut Microbiome Composition Shows Time-dependent Dysbiosis and Is Influenced by Medication Exposures, Age, Environment, and Frailty.

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 antihypertensive 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 intraindividual 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 the length of NH residency with dysbiosis becoming evident at 12 months, however, after this point there is demonstrated relative stability over time.