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Abstract In microbiome fields of study, meta-analyses have proven to be a valuable tool for identifying the technical drivers of variation among studies and results of investigations in several diseases, such as those of the gut and sinuses. Meta-analyses also represent a powerful and efficient approach to leverage existing scientific data to both reaffirm existing findings and generate new hypotheses within the field. However, there are currently limited data in other fields, such as the paediatric respiratory tract, where extension of original data becomes even more critical due to samples often being difficult to obtain and process for a range of both technical and ethical reasons. Performing such analyses in an evolving field comes with challenges related to data accessibility and heterogeneity. This is particularly the case in paediatric respiratory microbiomics — a field in which best microbiome-related practices are not yet firmly established, clinical heterogeneity abounds and ethical challenges can complicate sharing of patient data. Having recently conducted a large-scale, individual participant data meta-analysis of the paediatric respiratory microbiota (n = 2624 children from 20 studies), we discuss here some of the unique barriers facing these studies and open and invite a dialogue towards future opportunities. Video Abstract

IntroductionNon-cystic fibrosis bronchiectasis is a respiratory health condition with many possible aetiologies, some of which are potentially reversible in childhood with early diagnosis and appropriate treatment. It is important to understand factors which contribute to progression or potential resolution of bronchiectasis. It is evident that respiratory exacerbations are a key feature of bronchiectasis disease progression. In this pilot study we document how the microbiota of the upper and lower airways presents during the course of an exacerbation and treatment.MethodsWe recruited children (aged 1-15) undergoing antibiotic treatment for bronchiectasis exacerbations at Starship Children’s Hospital and outpatient clinics. Sputum and nasal swabs were taken before and after antibiotic treatment. Sample DNA was extracted, then bacterial 16S rRNA genes amplified and sequenced via Illumina MiSeq.ResultsThirty patients were recruited into this study with 81 samples contributing to the final dataset, including 8 patients with complete sets of upper and lower airway samples at both (before and after antibiotics) timepoints. Changes in alpha-diversity over the course of an exacerbation and treatment were non-significant. However, sample composition did alter over the course of an exacerbation, with most notably a reduction in the relative abundance of amplicon sequence variants assigned to Haemophilus.DiscussionHaemophilus has been associated with more severe symptoms in respiratory infections and a reduction in its relative abundance may represent a positive shift in a patient’s microbiota. Current treatments for bronchiectasis may preserve bacterial diversity while altering microbiota composition.

Introduction: The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether particular bacteria are associated with a given disease, or if a more general, non-specific microbiota association with disease exists, as suggested for the gut. We investigated overarching patterns of bacterial association with acute and chronic paediatric respiratory disease in an individual participant data (IPD) meta-analysis of 16S rRNA gene sequences from published respiratory microbiota studies.Methods: We obtained raw microbiota data from public repositories or via communication with corresponding authors. Cross-sectional analyses of the paediatric (10 case subjects were included. Sequence data were processed using a uniform bioinformatics pipeline, removing a potentially substantial source of variation. Microbiota differences across diagnoses were assessed using alpha- and beta-diversity approaches, machine learning, and biomarker analyses.Results: We ultimately included 20 studies containing individual data from 2624 children. Disease was associated with lower bacterial diversity in nasal and lower airway samples and higher relative abundances of specific nasal taxa including Streptococcus and Haemophilus. Machine learning success in assigning samples to diagnostic groupings varied with anatomical site, with positive predictive value and sensitivity ranging from 43 to 100 and 8 to 99%, respectively.Conclusion: IPD meta-analysis of the respiratory microbiota across multiple diseases allowed identification of a non-specific disease association which cannot be recognised by studying a single disease. Whilst imperfect, machine learning offers promise as a potential additional tool to aid clinical diagnosis.