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

Oropharyngeal Microbiome Analysis in Patients with Varying SARS-CoV-2 Infection Severity: A Prospective Cohort Study

Patients with COVID-19 infection have distinct oropharyngeal microbiota composition and diversity metrics according to disease severity. However, these findings are not consistent across the literature. We conducted a multicenter, prospective study in patients with COVID-19 requiring outpatient versus inpatient management to explore the microbial abundance of taxa at the phylum, family, genus, and species level, and we utilized alpha and beta diversity indices to further describe our findings. We collected oropharyngeal washing specimens at the time of study entry, which coincided with the COVID-19 diagnosis, to conduct all analyses. We included 43 patients in the study, of whom 16 were managed as outpatients and 27 required hospitalization. Proteobacteria, Actinobacteria, Bacteroidetes, Saccharibacteria TM7, Fusobacteria, and Spirochaetes were the most abundant phyla among patients, while 61 different families were detected, of which the Streptococcaceae and Staphylococcaceae families were the most predominant. A total of 132 microbial genera were detected, with Streptococcus being the predominant genus in outpatients, in contrast to hospitalized patients, in whom the Staphylococcus genus was predominant. LeFSe analysis identified 57 microbial species in the oropharyngeal washings of study participants that could discriminate the severity of symptoms of COVID-19 infections. Alpha diversity analysis did not reveal a difference in the abundance of bacterial species between the groups, but beta diversity analysis established distinct microbial communities between inpatients and outpatients. Our study provides information on the complex association between the oropharyngeal microbiota and SARS-CoV-2 infection. Although our study cannot establish causation, knowledge of specific taxonomic changes with increasing SARS-CoV-2 infection severity can provide us with novel clues for the prognostic classification of COVID-19 patients.

An explainable machine learning approach for Alzheimer's disease classification

The early diagnosis of Alzheimer's disease (AD) presents a significant challenge due to the subtle biomarker changes often overlooked. Machine learning (ML) models offer a promising tool for identifying individuals at risk of AD. However, current research tends to prioritize ML accuracy while neglecting the crucial aspect of model explainability. The diverse nature of AD data and the limited dataset size introduce additional challenges, primarily related to high dimensionality. In this study, we leveraged a dataset obtained from the National Alzheimer's Coordinating Center, comprising 169,408 records and 1024 features. After applying various steps to reduce the feature space. Notably, support vector machine (SVM) models trained on the selected features exhibited high performance when tested on an external dataset. SVM achieved a high F1 score of 98.9% for binary classification (distinguishing between NC and AD) and 90.7% for multiclass classification. Furthermore, SVM was able to predict AD progression over a 4-year period, with F1 scores reached 88% for binary task and 72.8% for multiclass task. To enhance model explainability, we employed two rule-extraction approaches: class rule mining and stable and interpretable rule set for classification model. These approaches generated human-understandable rules to assist domain experts in comprehending the key factors involved in AD development. We further validated these rules using SHAP and LIME models, underscoring the significance of factors such as MEMORY, JUDGMENT, COMMUN, and ORIENT in determining AD risk. Our experimental outcomes also shed light on the crucial role of the Clinical Dementia Rating tool in predicting AD.

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.

Saliva microbiome in relation to SARS-CoV-2 infection in a prospective cohort of healthy US adults

BACKGROUND

The clinical outcomes of SARS-CoV-2 infection vary in severity, potentially influenced by the resident human microbiota. There is limited consensus on conserved microbiome changes in response to SARS-CoV-2 infection, with many studies focusing on severely ill individuals. This study aimed to assess the variation in the upper respiratory tract microbiome using saliva specimens in a cohort of individuals with primarily mild to moderate disease.

METHODS

In early 2020, a cohort of 831 adults without known SARS-CoV-2 infection was followed over a six-month period to assess the occurrence and natural history of SARS-CoV-2 infection. From this cohort, 81 participants with a SARS-CoV-2 infection, along with 57 unexposed counterparts were selected with a total of 748 serial saliva samples were collected for analysis. Total bacterial abundance, composition, population structure, and gene function of the salivary microbiome were measured using 16S rRNA gene and shotgun metagenomic sequencing.

FINDINGS

The salivary microbiome remained stable in unexposed individuals over the six-month study period, as evidenced by all measured metrics. Similarly, participants with mild to moderate SARS-CoV-2 infection showed microbiome stability throughout and after their infection. However, there were significant reductions in microbiome diversity among SARS-CoV-2-positive participants with severe symptoms early after infection. Over time, the microbiome diversity in these participants showed signs of recovery.

INTERPRETATION

These findings demonstrate the resilience of the salivary microbiome in relation to SARS-CoV-2 infection. Mild to moderate infections did not significantly disrupt the stability of the salivary microbiome, suggesting its ability to maintain its composition and function. However, severe SARS-CoV-2 infection was associated with temporary reductions in microbiome diversity, indicating the limits of microbiome resilience in the face of severe infection.

FUNDING

This project was supported in part by Danone North America and grants from the National Institutes of Health, United States.

Nanopore sequencing technology and its applications

Since the development of Sanger sequencing in 1977, sequencing technology has played a pivotal role in molecular biology research by enabling the interpretation of biological genetic codes. Today, nanopore sequencing is one of the leading third-generation sequencing technologies. With its long reads, portability, and low cost, nanopore sequencing is widely used in various scientific fields including epidemic prevention and control, disease diagnosis, and animal and plant breeding. Despite initial concerns about high error rates, continuous innovation in sequencing platforms and algorithm analysis technology has effectively addressed its accuracy. During the coronavirus disease (COVID-19) pandemic, nanopore sequencing played a critical role in detecting the severe acute respiratory syndrome coronavirus-2 virus genome and containing the pandemic. However, a lack of understanding of this technology may limit its popularization and application. Nanopore sequencing is poised to become the mainstream choice for preventing and controlling COVID-19 and future epidemics while creating value in other fields such as oncology and botany. This work introduces the contributions of nanopore sequencing during the COVID-19 pandemic to promote public understanding and its use in emerging outbreaks worldwide. We discuss its application in microbial detection, cancer genomes, and plant genomes and summarize strategies to improve its accuracy.

Characterization of upper airway microbiome across severity of COVID-19 during hospitalization and treatment

Longitudinal studies on upper respiratory tract microbiome in coronavirus disease 2019 (COVID-19) without potential confounders such as antimicrobial therapy are limited. The objective of this study is to assess for longitudinal changes in the upper respiratory microbiome, its association with disease severity, and potential confounders in adult hospitalized patients with COVID-19. Serial nasopharyngeal and throat swabs (NPSTSs) were taken for 16S rRNA gene amplicon sequencing from adults hospitalized for COVID-19. Alpha and beta diversity was assessed between different groups. Principal coordinate analysis was used to assess beta diversity between groups. Linear discriminant analysis was used to identify discriminative bacterial taxa in NPSTS taken early during hospitalization on need for intensive care unit (ICU) admission. A total of 314 NPSTS samples from 197 subjects (asymptomatic = 14, mild/moderate = 106, and severe/critical = 51 patients with COVID-19; non-COVID-19 mechanically ventilated ICU patients = 11; and healthy volunteers = 15) were sequenced. Among all covariates, antibiotic treatment had the largest effect on upper airway microbiota. When samples taken after antibiotics were excluded, alpha diversity (Shannon, Simpson, richness, and evenness) was similar across severity of COVID-19, whereas beta diversity (weighted GUniFrac and Bray-Curtis distance) remained different. Thirteen bacterial genera from NPSTS taken within the first week of hospitalization were associated with a need for ICU admission (area under the receiver operating characteristic curve, 0.96; 95% CI, 0.91-0.99). Longitudinal analysis showed that the upper respiratory microbiota alpha and beta diversity was unchanged during hospitalization in the absence of antimicrobial therapy.

Nutrients, herbal bioactive derivatives and commensal microbiota as tools to lower the risk of SARS-CoV-2 infection

The SARS-CoV-2 outbreak has infected a vast population across the world, causing more than 664 million cases and 6.7 million deaths by January 2023. Vaccination has been effective in reducing the most critical aftermath of this infection, but some issues are still present regarding re-infection prevention, effectiveness against variants, vaccine hesitancy and worldwide accessibility. Moreover, although several old and new antiviral drugs have been tested, we still lack robust and specific treatment modalities. It appears of utmost importance, facing this continuously growing pandemic, to focus on alternative practices grounded on firm scientific bases. In this article, we aim to outline a rigorous scientific background and propose complementary nutritional tools useful toward containment, and ultimately control, of SARS-CoV-2 infection. In particular, we review the mechanisms of viral entry and discuss the role of polyunsaturated fatty acids derived from α-linolenic acid and other nutrients in preventing the interaction of SARS-CoV-2 with its entry gateways. In a similar way, we analyze in detail the role of herbal-derived pharmacological compounds and specific microbial strains or microbial-derived polypeptides in the prevention of SARS-CoV-2 entry. In addition, we highlight the role of probiotics, nutrients and herbal-derived compounds in stimulating the immunity response.