The rapid expansion of 16S rRNA gene sequencing in challenging clinical contexts has resulted in a growing body of literature of variable quality. To a large extent, this is due to a failure to address spurious signal that is characteristic of samples with low levels of bacteria and high levels of non-bacterial DNA. We have developed a workflow based on the paired-end read Illumina MiSeq-based approach, which enables significant improvement in data quality, post-sequencing. We demonstrate the efficacy of this methodology through its application to paediatric upper-respiratory samples from several anatomical sites. A workflow for processing sequence data was developed based on commonly available tools. Data generated from different sample types showed a marked variation in levels of non-bacterial signal and ‘contaminant’ bacterial reads. Significant differences in the ability of reference databases to accurately assign identity to operational taxonomic units (OTU) were observed. Three OTU-picking strategies were trialled as follows: de novo, open-reference and closed-reference, with open-reference performing substantially better. Relative abundance of OTUs identified as potential reagent contamination showed a strong inverse correlation with amplicon concentration allowing their objective removal. The removal of the spurious signal showed the greatest improvement in sample types typically containing low levels of bacteria and high levels of human DNA. A substantial impact of pre-filtering data and spurious signal removal was demonstrated by principal coordinate and co-occurrence analysis. For example, analysis of taxon co-occurrence in adenoid swab and middle ear fluid samples indicated that failure to remove the spurious signal resulted in the inclusion of six out of eleven bacterial genera that accounted for 80% of similarity between the sample types. The application of the presented workflow to a set of challenging clinical samples demonstrates its utility in removing the spurious signal from the dataset, allowing clinical insight to be derived from what would otherwise be highly misleading output. While other approaches could potentially achieve similar improvements, the methodology employed here represents an accessible means to exclude the signal from contamination and other artefacts.

Invasive methods requiring general anaesthesia are needed to sample the lung microbiota in young children who do not expectorate. This poses substantial challenges to longitudinal study of paediatric airway microbiota. Non-invasive upper airway sampling is an alternative method for monitoring airway microbiota; however, there are limited data describing the relationship of such results with lung microbiota in young children. In this study, we compared the upper and lower airway microbiota in young children to determine whether non-invasive upper airway sampling procedures provide a reliable measure of either lung microbiota or clinically defined differences. The microbiota in oropharyngeal (OP) swabs, nasopharyngeal (NP) swabs and bronchoalveolar lavage (BAL) from 78 children (median age 2.2 years) with and without lung disease were characterised using 16S rRNA gene sequencing. Permutational multivariate analysis of variance (PERMANOVA) detected significant differences between the microbiota in BAL and those in both OP swabs (p = 0.0001, Pseudo-F = 12.2, df = 1) and NP swabs (p = 0.0001; Pseudo-F = 21.9, df = 1) with the NP and BAL microbiota more different than the OP and BAL, as indicated by a higher Pseudo-F value. The microbiota in combined OP and NP data (upper airways) provided a more comprehensive representation of BAL microbiota, but significant differences between the upper airway and BAL microbiota remained, albeit with a considerably smaller Pseudo-F (PERMANOVA p = 0.0001; Pseudo-F = 4.9, df = 1). Despite this overall difference, paired BAL and upper airway (OP and NP) microbiota were >50 % similar among 69 % of children. Furthermore, canonical analysis of principal coordinates (CAP analysis) detected significant differences between the microbiota from clinically defined groups when analysing either BAL (eigenvalues >0.8; misclassification rate 26.5 %) or the combined OP and NP data (eigenvalues >0.8; misclassification rate 12.2 %). Upper airway sampling provided an imperfect, but reliable, representation of the BAL microbiota for most children in this study. We recommend inclusion of both OP and NP specimens when non-invasive upper airway sampling is needed to assess airway microbiota in young children who do not expectorate. The results of the CAP analysis suggest lower and upper airway microbiota profiles may differentiate children with chronic suppurative lung disease from those with persistent bacterial bronchitis; however, further research is needed to confirm this observation.

Haemophilus influenzae is an opportunistic bacterial pathogen that exclusively colonises humans and is associated with both acute and chronic disease. Despite its clinical significance, accurate identification of H. influenzae is a non-trivial endeavour. H. haemolyticus can be misidentified as H. influenzae from clinical specimens using selective culturing methods, reflecting both the shared environmental niche and phenotypic similarities of these species. On the molecular level, frequent genetic exchange amongst Haemophilus spp. has confounded accurate identification of H. influenzae, leading to both false-positive and false-negative results with existing speciation assays. Whole-genome single-nucleotide polymorphism data from 246 closely related global Haemophilus isolates, including 107 Australian isolate genomes generated in this study, were used to construct a whole-genome phylogeny. Based on this phylogeny, H. influenzae could be differentiated from closely related species. Next, a H. influenzae-specific locus, fucP, was identified, and a novel TaqMan real-time PCR assay targeting fucP was designed. PCR specificity screening across a panel of clinically relevant species, coupled with in silico analysis of all species within the order Pasteurellales, demonstrated that the fucP assay was 100 % specific for H. influenzae; all other examined species failed to amplify. This study is the first of its kind to use large-scale comparative genomic analysis of Haemophilus spp. to accurately delineate H. influenzae and to identify a species-specific molecular signature for this species. The fucP assay outperforms existing H. influenzae targets, most of which were identified prior to the next-generation genomics era and thus lack validation across a large number of Haemophilus spp. We recommend use of the fucP assay in clinical and research laboratories for the most accurate detection and diagnosis of H. influenzae infection and colonisation.

Otitis media (OM) imposes a great burden of disease in indigenous populations around the world, despite a variety of treatment and prevention programs. Improved understanding of the pathogenesis of OM in indigenous populations is required to advance treatment and reduce prevalence. We conducted a systematic review of the literature exploring the upper airway and middle ear microbiota in relation to OM in indigenous children. Papers targeting microbiota in relation to OM in children < 18 years indigenous to Australia, New Zealand, North America, and Greenland were sought. MEDLINE, CINAHL, EMBASE, Cochrane Library, and Informit databases were searched using key words. Two independent reviewers screened titles, abstracts, and then full-text papers against inclusion criteria according to PRISMA guidelines. Twenty-five papers considering indigenous Australian, Alaskan, and Greenlandic children were included. There were high rates of nasopharyngeal colonization with the three main otopathogens (Haemophilus influenzae, Streptococcus pneumoniae, and Moraxella catarrhalis) in indigenous children with OM. Middle ear samples had lower rates of otopathogen detection, although detection rates increased when molecular methods were used. Pseudomonas aeruginosa and Staphylococcus aureus were commonly detected in middle ear discharge of children with chronic suppurative OM. There was a significant heterogeneity between studies, particularly in microbiological methods, which were largely limited to culture-based detection of the main otopathogens. There are high rates of otopathogen colonization in indigenous children with OM. Chronic suppurative OM appears to be associated with a different microbial profile. Beyond the main otopathogens, the data are limited. Further research is required to explore the entire upper respiratory tract/middle ear microbiota in relation to OM, with the inclusion of healthy indigenous peers as controls.

Culture-independent sequencing methods are increasingly used to investigate the microbiota associated with human mucosal surfaces, including sites that have low bacterial load in healthy individuals (e.g. the lungs). Standard microbiota methods developed for analysis of high bacterial load specimens (e.g. stool) may require modification when bacterial load is low, as background contamination derived from sterile laboratory reagents and kits can dominate sequence data when few bacteria are present. Bacterial load in respiratory specimens may vary depending on the specimen type, specimen volume, the anatomic site sampled and clinical parameters. This review discusses methodological issues inherent to analysis of low bacterial load specimens and recommends strategies for successful respiratory microbiota studies. The range of methods currently used to process DNA from low bacterial load specimens, and the strategies used to identify and exclude background contamination are also discussed. Microbiota studies that include low bacterial load specimens require additional tests to ensure that background contamination does not bias the results or interpretation. Several methods are currently used to analyse the microbiota in low bacterial load respiratory specimens; however, there is scant literature comparing the effectiveness and biases of different methods. Further research is needed to define optimal methods for analysing the microbiota in low bacterial load specimens.