Microbiota data from low biomass milk samples is markedly affected by laboratory and reagent contamination.
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Dahlberg J, Sun L, Persson Waller K, Östensson K, McGuire M, Agenäs S, Dicksved J
PLoS ONE 14 (6) e0218257 [2019-06-13; online 2019-06-13]
Discoveries of bacterial communities in environments that previously have been described as sterile have in recent years radically challenged the view of these environments. In this study we aimed to use 16S rRNA sequencing to describe the composition and temporal stability of the bacterial microbiota in bovine milk from healthy udder quarters, an environment previously believed to be sterile. Sequencing of the 16S rRNA gene is a technique commonly used to describe bacterial composition and diversity in various environments. With the increased use of 16S rRNA gene sequencing, awareness of methodological pitfalls such as biases and contamination has increased although not in equal amount. Evaluation of the composition and temporal stability of the microbiota in 288 milk samples was largely hampered by background contamination, despite careful and aseptic sample processing. Sequencing of no template control samples, positive control samples, with defined levels of bacteria, and 288 milk samples with various levels of bacterial growth, revealed that the data was influenced by contaminating taxa, primarily Methylobacterium. We observed an increasing impact of contamination with decreasing microbial biomass where the contaminating taxa became dominant in samples with less than 104 bacterial cells per mL. By applying a contamination filtration on the sequence data, the amount of sequences was substantially reduced but only a minor impact on number of identified taxa and by culture known endogenous taxa was observed. This suggests that data filtration can be useful for identifying biologically relevant associations in milk microbiota data.
Bioinformatics (NBIS) [Service]
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PubMed 31194836
DOI 10.1371/journal.pone.0218257
Crossref 10.1371/journal.pone.0218257
pii: PONE-D-18-29659
pmc: PMC6564671