Nasal Bacteriomes of Patients with Asthma and Allergic Rhinitis Show Unique Composition, Structure, Function and Interactions

Pérez‐Losada, Marcos; Castro-Nallar, Eduardo; Boechat, José Laerte; Delgado, Luís; Rama, Tiago Azenha; Berrios-Farías, Valentín; Oliveira, Manuela

doi:10.3390/microorganisms11030683

Cited by 14 publications

(10 citation statements)

References 124 publications

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“…Supporting this, characterization of bacterial microbiota composition at phyla and genera levels for nasopharyngeal and stool samples indicated a more evident association between changes on them and the severity of the disease. Specifically, in nasopharyngeal swabs, the presence of Bacteroidota and Actinobacteriota has been previously linked to a better prognosis of SARS-CoV-2 infection, since these bacteria have been proposed to exert beneficial effects by preventing respiratory diseases, including COVID-19 [54][55][56][57][58]. Moreover, in nasopharyngeal samples, the abundance of Bacillota and Pesudomonadota was increased in patients with severe symptomatology, thus supporting previous studies in which higher counts of Bacillota (Staphylococcus sp.…”

Section: Discussionsupporting

confidence: 79%

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

Martin-Castaño,

Diez-Echave,

García-García

et al. 2024

Preprint

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Background: Coronavirus disease 2019 (COVID-19) is a respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that displays great variability in clinical phenotype. Many factors have been described to be correlated with its severity but no specific determinants of infection outcome have been identified yet, maybe due the complex pathogenic mechanisms. The microbiota could play a key role in the infection and in the progression and outcome of the disease. Hence, SARS-CoV-2 infection has been associated with nasopharyngeal and gut dysbiosis and higher abundance of opportunistic pathogens. Methods: To identify new prognostic markers for the disease, a multicenter prospective observational cohort study was carried out in COVID-19 patients that were divided in three cohorts according to their symptomatology: mild (n=24), moderate (n=51) and severe/critical (n=31). Faecal and nasopharyngeal samples were taken and the microbiota was analysed. Results: Microbiota composition could be associated with the severity of the symptoms and the linear discriminant analysis identified the generaMycoplasmaandPrevotellaas severity biomarkers in nasopharyngeal samples, andAllistipes,EnterococcusandEscherichiain faecal samples. Moreover,M. salivariumwas defined as a unique microorganism in COVID-19 patients' nasopharyngeal microbiota whileP. biviaandP. timonensiswere defined in faecal microbiota. A connection between faecal and nasopharyngeal microbiota in COVID-19 patients was also identified as a strong positive correlation betweenP. timonensis(faeces) towardsP. dentalisandM. salivarium(nasopharyngeal) was found in critically ill patients. Conclusions: This ratio could be used as a novel prognostic biomarker for severe COVID-19 patients.

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Section: Discussionsupporting

confidence: 79%

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

Martin-Castaño,

Diez-Echave,

García-García

et al. 2024

Preprint

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“…No other studies so far have applied this approach to predict metabolic functions in the oral microbiome of asthmatic or rhinitic patients. However, previous research in other sections of the airways (Pérez-Losada et al, 2015;Li et al, 2019;Al Bataineh et al, 2020;Chiu et al, 2020;Hu et al, 2020;Samra et al, 2021;Chiang et al, 2022;Pérez-Losada et al, 2023) has suggested that some of the microbial metabolic pathways predicted here involved in amino acid (e.g., pyrimidine, leucine and arginine) and carbohydrate (e.g., peptidoglycan complexes and mannan) biosynthesis and degradation and metabolism (e.g., glycolysis and TCA cycle) may be associated with allergic sensitization, IgE sensitivity and inflammation of the airways and host immune response (e.g., superpathway of UDP-Nacetylglucosamine-derived O-antigen building blocks biosynthesis; Caspi et al, 2020;Chiu et al, 2021). Thus, our study suggests that oral dysbacteriosis may alter bacterial community functionality, thereby affecting the occurrence of allergic rhinitis or asthma.…”

Section: Discussionmentioning

confidence: 98%

The oral bacteriomes of patients with allergic rhinitis and asthma differ from that of healthy controls

et al. 2023

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Allergic rhinitis and asthma are two of the most common chronic respiratory diseases in developed countries and have become a major public health concern. Substantial evidence has suggested a strong link between respiratory allergy and upper airway dysbacteriosis, but the role of the oral bacteriota is still poorly understood. Here we used 16S rRNA massive parallel sequencing to characterize the oral bacteriome of 344 individuals with allergic rhinitis (AR), allergic rhinitis with asthma (ARAS), asthma (AS) and healthy controls (CT). Four of the most abundant (>2%) phyla (Actinobacteriota, Firmicutes, Fusobacteriota, and Proteobacteria) and 10 of the dominant genera (Actinomyces, Fusobacterium, Gemella, Haemophilus, Leptotrichia, Neisseria, Porphyromonas, Prevotella, Streptococcus, and Veillonella) in the oral cavity differed significantly (p ≤ 0.03) between AR, ARAS or AS and CT groups. The oral bacteriome of ARAS patients showed the highest intra-group diversity, while CT showed the lowest. All alpha-diversity indices of microbial richness and evenness varied significantly (p ≤ 0.022) in ARAS vs. CT and ARAS vs. AR, but they were not significantly different in AR vs. CT. All beta-diversity indices of microbial structure (Unifrac, Bray-Curtis, and Jaccard distances) differed significantly (p ≤ 0.049) between each respiratory disease group and controls. Bacteriomes of AR and ARAS patients showed 15 and 28 upregulated metabolic pathways (PICRUSt2) mainly related to degradation and biosynthesis (p < 0.05). A network analysis (SPIEC-EASI) of AR and ARAS bacteriomes depicted simpler webs of interactions among their members than those observed in the bacteriome of CT, suggesting chronic respiratory allergic diseases may disrupt bacterial connectivity in the oral cavity. This study, therefore, expands our understanding of the relationships between the oral bacteriome and allergy-related conditions. It demonstrates for the first time that the mouth harbors distinct bacteriotas during health and allergic rhinitis (with and without comorbid asthma) and identifies potential taxonomic and functional microbial biomarkers of chronic airway disease.

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“…Despite the promising results and findings, more research and experimentation should be done with microbiome sequencing because counterexamples can be found that make this methodology ineffective. Such is the case with datasets related to asthma: PRJEB44044 [51], PRJNA601757 [52], and PRJNA913468 [53], where the feature selection and testing phase were inefficient. This was due to the lack of datasets with samples from the same source, the quality of the sequences, the lack of documentation, variations in the technical sequencing equipment used, also known as the batch effect [54,55].…”

Section: Discussionmentioning

confidence: 99%

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

Rojas-Velazquez,

Kidwai,

Kraneveld

et al. 2023

Preprint

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

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Nasal Bacteriomes of Patients with Asthma and Allergic Rhinitis Show Unique Composition, Structure, Function and Interactions

Cited by 14 publications

References 124 publications

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

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

The oral bacteriomes of patients with allergic rhinitis and asthma differ from that of healthy controls

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

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