SARS-CoV-2 Serosurveys: How Antigen, Isotype and Threshold Choices Affect the Outcome.

BACKGROUND: Evaluating the performance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serological assays and clearly articulating the utility of selected antigens, isotypes, and thresholds is crucial to understanding the prevalence of infection within selected communities. METHODS: This cross-sectional study, implemented in 2020, screened PCRconfirmed coronavirus disease 2019 patients (n 86), banked prepandemic and negative samples (n 96), healthcare workers and family members (n 552), and university employees (n 327) for antiSARS-CoV-2 receptor-binding domain, trimeric spike protein, and nucleocapsid protein immunoglobulin (Ig)G and IgA antibodies with a laboratory-developed enzyme-linked immunosorbent assay and tested how antigen, isotype and threshold choices affected the seroprevalence outcomes. The following threshold methods were evaluated: (i) mean 3 standard deviations of the negative controls; (ii) 100 specificity for each antigen-isotype combination; and (iii) the maximal Youden index. RESULTS: We found vastly different seroprevalence estimates depending on selected antigens and isotypes and the applied threshold method, ranging from 0.0 to 85.4. Subsequently, we maximized specificity and reported a seroprevalence, based on more than one antigen, ranging from 9.3 to 25.9. CONCLUSIONS: This study revealed the importance of evaluating serosurvey tools for antigen-, isotype-, and threshold-specific sensitivity and specificity, to interpret qualitative serosurvey outcomes reliably and consistently across studies.

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.

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.

The high prevalence of Clostridioides difficile among nursing home elders associates with a dysbiotic microbiome.

Clostridioides difficile disproportionally affects the elderly living in nursing homes (NHs). Our objective was to explore the prevalence of C. difficile in NH elders, over time and to determine whether the microbiome or other clinical factors are associated with C. difficile colonization.We collected serial stool samples from NH residents. C. difficile prevalence was determined by quantitative polymerase-chain reaction detection of Toxin genes tcdA and tcdB; microbiome composition was determined by shotgun metagenomic sequencing. We used mixed-effect random forest modeling machine to determine bacterial taxa whose abundance is associated with C. difficile prevalence while controlling for clinical covariates including demographics, medications, and past medical history.We enrolled 167 NH elders who contributed 506 stool samples. Of the 123 elders providing multiple samples, 30 (24.4%) elders yielded multiple samples in which C. difficile was detected and 78 (46.7%) had at least one C. difficile positive sample. Elders with C. difficile positive samples were characterized by increased abundances of pathogenic or inflammatory-associated bacterial taxa and by lower abundances of taxa with anti-inflammatory or symbiotic properties. Proton pump inhibitor (PPI) use is associated with lower prevalence of C. difficile (Odds Ratio 0.46; 95%CI, 0.22-0.99) and the abundance of bacterial species with known beneficial effects was higher in PPI users and markedly lower in elders with high C. difficile prevalence.C. difficile is prevalent among NH elders and a dysbiotic gut microbiome associates with C. difficile colonization status. Manipulating the gut microbiome may prove to be a key strategy in the reduction of C. difficile in the NH.

Alzheimer's Disease Microbiome Is Associated with Dysregulation of the Anti-Inflammatory P-Glycoprotein Pathway.

The microbiota-gut-brain axis is a bidirectional communication system that is poorly understood. Alzheimer's disease (AD), the most common cause of dementia, has long been associated with bacterial infections and inflammation-causing immunosenescence. Recent studies examining the intestinal microbiota of AD patients revealed that their microbiome differs from that of subjects without dementia. In this work, we prospectively enrolled 108 nursing home elders and followed each for up to 5 months, collecting longitudinal stool samples from which we performed metagenomic sequencing and in vitro T84 intestinal epithelial cell functional assays for P-glycoprotein (P-gp) expression, a critical mediator of intestinal homeostasis. Our analysis identified clinical parameters as well as numerous microbial taxa and functional genes that act as predictors of AD dementia in comparison to elders without dementia or with other dementia types. We further demonstrate that stool samples from elders with AD can induce lower P-gp expression levels in vitro those samples from elders without dementia or with other dementia types. We also paired functional studies with machine learning approaches to identify bacterial species differentiating the microbiome of AD elders from that of elders without dementia, which in turn are accurate predictors of the loss of dysregulation of the P-gp pathway. We observed that the microbiome of AD elders shows a lower proportion and prevalence of bacteria with the potential to synthesize butyrate, as well as higher abundances of taxa that are known to cause proinflammatory states. Therefore, a potential nexus between the intestinal microbiome and AD is the modulation of intestinal homeostasis by increases in inflammatory, and decreases in anti-inflammatory, microbial metabolism.IMPORTANCE Studies of the intestinal microbiome and AD have demonstrated associations with microbiome composition at the genus level among matched cohorts. We move this body of literature forward by more deeply investigating microbiome composition via metagenomics and by comparing AD patients against those without dementia and with other dementia types. We also exploit machine learning approaches that combine both metagenomic and clinical data. Finally, our functional studies using stool samples from elders demonstrate how the c microbiome of AD elders can affect intestinal health via dysregulation of the P-glycoprotein pathway. P-glycoprotein dysregulation contributes directly to inflammatory disorders of the intestine. Since AD has been long thought to be linked to chronic bacterial infections as a possible etiology, our findings therefore fill a gap in knowledge in the field of AD research by identifying a nexus between the microbiome, loss of intestinal homeostasis, and inflammation that may underlie this neurodegenerative disorder.

The nursing home elder microbiome stability and associations with age, frailty, nutrition and physical location.

PURPOSE: The microbiome from nursing home (NH) residents is marked by a loss in diversity that is associated with increased frailty. Our objective was to explore the associations of NH environment, frailty, nutritional status and residents' age to microbiome composition and potential metabolic function. METHODOLOGY: We conducted a prospective longitudinal cohort study of 23 residents, 65 years or older, from one NH that had four floors: two separate medical intensive floors and two floors with active elders. Residents were assessed using the mini nutritional assessment tool and clinical frailty scale. Bacterial composition and metabolic potential of residents' stool samples was determined by metagenomic sequencing. We performed traditional unsupervised correspondence analysis and linear mixed effect modelling regression to assess the bacteria and functional pathways significantly affected by these covariates.Results/Key findings. NH resident microbiomes demonstrated temporal stability (PERMANOVA P=0.001) and differing dysbiotic associations with increasing age, frailty and malnutrition scores. As residents aged, the abundance of microbiota-encoded genes and pathways related to essential amino acid, nitrogenous base and vitamin B production declined. With increasing frailty, residents had lower abundances of butyrate-producing organisms, which are associated with increased health and higher abundances of known dysbiotic species. As residents became malnourished, butyrate-producing organisms declined and dysbiotic bacterial species increased. Finally, the microbiome of residents living in proximity shared similar species and, as demonstrated for Escherichia coli, similar strains. CONCLUSION: These findings support the conclusion that a signature 'NH' microbiota may exist that is affected by the residents' age, frailty, nutritional status and physical location.