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Microbiota data from low biomass milk samples is markedly affected by laboratory and reagent contamination.
Dahlberg, Josef; Sun, Li; Persson Waller, Karin; Östensson, Karin; McGuire, Mark; Agenäs, Sigrid; Dicksved, Johan.
Afiliación
  • Dahlberg J; Department of Animal Nutrition and Management, Swedish University of Agricultural Sciences, Uppsala, Sweden.
  • Sun L; Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.
  • Persson Waller K; Department of Animal Health and Antimicrobial Strategies, National Veterinary Institute, Uppsala, Sweden.
  • Östensson K; Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.
  • McGuire M; Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.
  • Agenäs S; Department of Animal and Veterinary Science, University of Idaho, Moscow, United States of America.
  • Dicksved J; Department of Animal Nutrition and Management, Swedish University of Agricultural Sciences, Uppsala, Sweden.
PLoS One ; 14(6): e0218257, 2019.
Article en En | MEDLINE | ID: mdl-31194836
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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: ARN Ribosómico 16S / Leche / Microbiota Tipo de estudio: Prognostic_studies Límite: Animals Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2019 Tipo del documento: Article País de afiliación: Suecia Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: ARN Ribosómico 16S / Leche / Microbiota Tipo de estudio: Prognostic_studies Límite: Animals Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2019 Tipo del documento: Article País de afiliación: Suecia Pais de publicación: Estados Unidos