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Deriving accurate microbiota profiles from human samples with low bacterial content through post-sequencing processing of Illumina MiSeq data
Open accessAbstract
BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.
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“Additional putative external contamination was identified on the basis that sequences from contaminants generally have frequencies that are inversely correlated with sample analyte concentration16,38.”
From RETRACTED ARTICLE: Microbiome analyses of blood and tissues suggest cancer diagnostic approach (2020) · cites this paper as [38]
In the citing paper
“As an additional quality control step, all OTUs were tested for correlations between the proportional abundance of the OTU and the post-PCR amplicon concentration of a sample using the method developed by Jervis-Brady et al. [90] as implemented in the contam_test program for R (https://github.com/eclarke/eclectic).”
From Population structure of human gut bacteria in a diverse cohort from rural Tanzania and Botswana (2019) · cites this paper as [90]
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“Inverse correlation of a taxon relative abundance with bacterial load as an indicator of a possible reagent contaminant was initially described for bacterial genera in a mock community [4] and subsequently confirmed at the OTU level in cultures and ‘real’ microbiota samples [2, 3, 5, 11].”
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“The inclusion of controls and accurate post-processing of sequences is thus highly recommended (Jervis-Bardy et al. 2015) in order to identify and filter these background bacterial contaminants from datasets, but this has been rarely applied to date (but see Meadow, Altrichter and Green 2014;…”
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“First, the bacterial biomass in the tumor specimen was low; therefore, the bacterial genomic content was likely overwhelmed by the host/tumor genomic content, resulting in low PCR efficiency and inaccurate results [176].”
The microbiome of otitis media with effusion in Indigenous Australian children
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Laboratory contamination in airway microbiome studies
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“[56] excluded OTUs detected in negative controls or clinical specimens when a strong negative correlation was observed between the relative abundance and the bacterial load.”
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BackgroundDuodenal Dysbiosis and Relation to the Efficacy of Proton Pump Inhibitors in Functional Dyspepsia
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“Although it is common to exclude bacteria containing a relative prevalence less than 1% of a microbial community (Jervis-Bardy et al., 2015), organisms at very low prevalence are capable of causing chronic disease (Silva et al., 2015) and it may be erroneous to perform such arbitrary exclusions.”
BackgroundMarsh, R. L., Aho, C., Beissbarth, J., Bialasiewicz, S., Binks, M., Cervin, A., Kirkham, L. S., Lemon, K. P., Slack, M., & Smith‐Vaughan, H. (2019) · International Journal of Pediatric Otorhinolaryngology · doi:10.1016/j.ijporl.2019.109836 · Cited by 37
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“Another interesting approach was recently reported by Jervis-Bardy and colleagues who applied a statistical method to distinguish true hits from environmental contaminations based on a correlation between the relative abundance across samples and sequencing depths [68].”
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“We further excluded the species whose relative abundance was greater than 0.05% in at least one negative blank control and the species whose relative abundance showed a significant inverse correlation with DNA concentrations (ρ < −0.2, p < 0.05, Spearman’s correlation) (Jervis-Bardy et al., 2015).”
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“Still, others maintain that any DNA detected using DNA-based methods may represent contaminants.(12) Whether infants are colonized prior to birth or not remains unclear, most agree that mode of birth, vaginal or Cesarean section (C-section), impacts infant gut microbial communities.”
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Bacterial communities of Aedes aegypti mosquitoes differ between crop and midgut tissues
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Examining the Evidence for an Adult Healthy Middle Ear Microbiome
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Self-citationThe Influence of DNA Extraction and Lipid Removal on Human Milk Bacterial Profiles
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“Additionally, a high host to bacterial DNA ratio, as found in our samples, potentially derived from the patient, hospital and/or lab personnel can lead to PCR biases, thus reducing amplification efficiency and validity of results [51].”
BackgroundNext Generation Sequencing and the Microbiome of Chronic Rhinosinusitis
Jervis-Bardy, J., & Psaltis, A. J. (2016) · Annals of Otology Rhinology & Laryngology · doi:10.1177/0003489416641429 · Cited by 30
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The bovine milk microbiome – an evolving science
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Challenges for case-control studies with microbiome data
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“The exception is Pseudomonas, which is not commonly found in the nasopharynx, but has been identified as a potential contaminant in several studies [51, 54].”
BackgroundTotal bacterial load, inflammation, and structural lung disease in paediatric cystic fibrosis
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Topical cream with live lactobacilli modulates the skin microbiome and reduce acne symptoms
Lebeer, S., Oerlemans, E., Claes, I., Wuyts, S., Henkens, T., Spacova, I., van den Broek, M., Tuyaerts, I., Wittouck, S., De Boeck, I., et al. (2018) · doi:10.1101/463307 · Cited by 29
Metadata
- Authors
- Jervis-Bardy, J., Leong, L. E. X., Marri, S., Smith, R. J., Choo, J. M., Smith-Vaughan, H. C., Nosworthy, E., Morris, P. S., O’Leary, S., Rogers, G. B., & Marsh, R. L.
- Affiliations
- Flinders University (AU)
- South Australian Health and Medical Research Institute (AU)
- Charles Darwin University (AU)
- The University of Melbourne (AU)
- Corresponding author
- Jervis-Bardy, Jake
- ORCID
- Venue
- Microbiome
- Published
- 2015-05-05
- Volume
- 3
- Issue
- 1
- Pages
- 19-19
- ISSN
- 2049-2618 (ISSN-L)
- PMC
- PMC4428251
- OpenAlex
- W2063276097
- Cited by
- 192
- References
- 25
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 10 · 2023: 20 · 2022: 14 · 2021: 23 · 2020: 25 · 2019: 27 · 2018: 23 · 2017: 20 · 2016: 18 · 2015: 3
- FWCI
- 9.27
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Microbiome (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion, cc-by)
- Griffith Research Online (submittedVersion, cc-by)
- Funders
- Medical Research Council
- National Health and Medical Research Council (1007641)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Keywords
- 16S rRNA gene sequencing, microbiota profiling, reagent contamination, operational taxonomic units, paired-end reads, middle ear fluid, reference database
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