The relationship between gut and nasopharyngeal microbiome composition can predict the severity of COVID-19

eLife, Journal Year: 2024, Volume and Issue: unknown

Published: March 26, 2024

Coronavirus disease 2019 (COVID-19) is a respiratory illness caused by severe acute syndrome coronavirus 2 (SARS-CoV-2) that displays great variability in clinical phenotype. Many factors have been described to be correlated with its severity, and microbiota could play key role the infection, progression, outcome of disease. SARS-CoV-2 infection has associated nasopharyngeal gut dysbiosis higher abundance opportunistic pathogens. To identify new prognostic markers for disease, multicentre prospective observational cohort study was carried out COVID-19 patients divided into three cohorts based on symptomatology: mild (n = 24), moderate 51), severe/critical 31). Faecal samples were taken, analysed. Linear discriminant analysis identified Mycoplasma salivarium, Prevotella dentalis, Haemophilus parainfluenzae as biomarkers microbiota, while bivia timonensis defined faecal microbiota. Additionally, connection between identified, significant ratio P. (faeces) dentalis M. salivarium (nasopharyngeal) abundances found critically ill patients. This serve novel tool identifying cases.

Language: Английский

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Characterization of the oral mycobiome of Portuguese with allergic rhinitis and asthma

Current Research in Microbial Sciences, Journal Year: 2024, Volume and Issue: 7, P. 100300 - 100300

Published: Jan. 1, 2024

Language: Английский

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The relationship between gut and nasopharyngeal microbiome composition can predict the severity of COVID-19

Published: Dec. 4, 2024

Coronavirus disease 2019 (COVID-19) is a respiratory illness caused by severe acute syndrome coronavirus 2 (SARS-CoV-2) that displays great variability in clinical phenotype. Many factors have been described to be correlated with its severity, and microbiota could play key role the infection, progression, outcome of disease. SARS-CoV-2 infection has associated nasopharyngeal gut dysbiosis higher abundance opportunistic pathogens. To identify new prognostic markers for disease, multicenter prospective observational cohort study was carried out COVID-19 patients divided into three cohorts based on symptomatology: mild (n=24), moderate (n=51), severe/critical (n=31). Faecal samples were taken, analyzed. Linear discriminant analysis identified M. salivarium , P. dentalis H. parainfluenzae as biomarkers microbiota, while bivia timonensis defined faecal microbiota. Additionally, connection between identified, significant ratio (faeces) (nasopharyngeal) abundances found critically ill patients. This serve novel tool identifying cases.

Language: Английский

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The nasal mycobiome of individuals with allergic rhinitis and asthma differs from that of healthy controls in composition, structure and function

Frontiers in Microbiology, Journal Year: 2024, Volume and Issue: 15

Published: Dec. 17, 2024

Allergic rhinitis (AR) and asthma (AS) are two of the most common chronic respiratory diseases a major public health concern. Multiple studies have demonstrated role nasal bacteriome in AR AS, but little is known about airway mycobiome its potential association to inflammatory diseases. Here we used internal transcriber spacers (ITS) 1 2 high-throughput sequencing characterize 339 individuals with AR, (ARAS), AS healthy controls (CT). Seven ten 14 abundant fungal genera ( Malassezia, Alternaria, Cladosporium, Penicillium, Wallemia, Rhodotorula, Sporobolomyces, Naganishia, Vishniacozyma , nd Filobasidium ) cavity differed significantly p ≤ 0.049) between or ARAS, CT. However, none same varied three disease groups. The mycobiomes ARAS patients showed highest intra-group diversity, while CT lowest. Alpha-diversity indices microbial richness evenness only 0.024) CT, all groups significant differences 0.0004) structure (i.e., beta-diversity indices) when compared samples. Thirty metabolic pathways (PICRUSt2) were differentially (Wald’s test) patients, them associated 5-aminoimidazole ribonucleotide (AIR) biosynthesis over (log2 Fold Change >0.75) group. AIR has been pathogenesis plants. Spiec-Easi networks among groups, more similar interactions their members than those mycobiome; this suggests allergic may disrupt connectivity cavity. This study contributes valuable data results understand relationships allergy-related conditions. It demonstrates for first time that mycobiota varies during (with without comorbid asthma) reveals specific taxa, relate disease.

Language: Английский

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Methodology for Biomarker Discovery with Reproducibility in Microbiome Data using Machine Learning

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Dec. 6, 2023

Abstract Background: In recent years, human microbiome studies have receivedincreasing attention as this field is considered a potential source for clinicalapplications. With the advancements in omics technologies and AI, researchfocused on discovery biomarkers microbime usingmachine learning tools has produced positive outcomes. Despite promisingresults, several issues can still be found these such datasets withsmall number of samples, inconsistent results, lack uniform processing andmethodologies, other additional factors lead to reproducibility inbiomedical research. work, we propose methodology that combines theDADA2 pipeline 16s rRNA sequences Recursive EnsembleFeature Selection (REFS) multiple increase andobtain robust reliable results biomedical Results: Three experiments were performed analysing data frompatients/cases Inflammatory Bowel Disease (IBD), Autism Spectrum Disorder(ASD), Type 2 Diabetes (T2D). each experiment, biomarkersignature one dataset applied further validation. Theeffectiveness proposed was compared with featureselection methods K-Best F-score random selection baseline. The Area Under Curve (AUC) employed measure diagnosticaccuracy used metric comparing proposedmethodology feature methods. Conclusions: We developed reproducible biomarker discoveryfor sequence analysis, addressing related withdata dimensionality, validation across independentdatasets. findings from three experiments, 9 different datasets,show achieved higher accuracy toother This first approach increasereproducibility, provide results.

Language: Английский

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