Determining the metabolic footprints of hydrocarbon degradation using multivariate analysis

Renee J. Smith, Thomas C. Jeffries, Eric M. Adetutu, Peter G. Fairweather, James G. Mitchell

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
4 Downloads (Pure)

Abstract

The functional dynamics of microbial communities are largely responsible for the clean-up of hydrocarbons in the environment. However, knowledge of the distinguishing functional genes, known as the metabolic footprint, present in hydrocarbon-impacted sites is still scarcely understood. Here, we conducted several multivariate analyses to characterise the metabolic footprints present in a variety of hydrocarbon-impacted and non-impacted sediments. Non-metric multi-dimensional scaling (NMDS) and canonical analysis of principal coordinates (CAP) showed a clear distinction between the two groups. A high relative abundance of genes associated with cofactors, virulence, phages and fatty acids were present in the non-impacted sediments, accounting for 45.7% of the overall dissimilarity. In the hydrocarbon-impacted sites, a high relative abundance of genes associated with iron acquisition and metabolism, dormancy and sporulation, motility, metabolism of aromatic compounds and cell signalling were observed, accounting for 22.3% of the overall dissimilarity. These results suggest a major shift in functionality has occurred with pathways essential to the degradation of hydrocarbons becoming overrepresented at the expense of other, less essential metabolisms.
Original languageEnglish
Article numbere81910
Number of pages10
JournalPLoS One
Volume8
Issue number11
DOIs
Publication statusPublished - 2013

Open Access - Access Right Statement

Copyright: © 2013 Renee Smith. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Keywords

  • footprints
  • hydrocarbons
  • multivariate analysis

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