Oil type and temperature dependent biodegradation dynamics - Combining chemical and microbial community data through multivariate analysis

Abstract

BackgroundThis study investigates a comparative multivariate approach for studying the biodegradation of chemically dispersed oil. The rationale for this approach lies in the inherent complexity of the data and challenges associated with comparing multiple experiments with inconsistent sampling points, with respect to inferring correlations and visualizing multiple datasets with numerous variables. We aim to identify novel correlations among microbial community composition, the chemical change of individual petroleum hydrocarbons, oil type and temperature by creating modelled datasets from inconsistent sampling time points. Four different incubation experiments were conducted with freshly collected Norwegian seawater and either Grane and Troll oil dispersed with Corexit 9500. Incubations were conducted at two different temperatures (5 °C and 13 °C) over a period of 64 days.ResultsPCA analysis of modelled chemical datasets and calculated half-lives revealed differences in the biodegradation of individual hydrocarbons among temperatures and oil types. At 5 °C, most n-alkanes biodegraded faster in heavy Grane oil compared to light Troll oil. PCA analysis of modelled microbial community datasets reveal differences between temperature and oil type, especially at low temperature. For both oils, Colwelliaceae and Oceanospirillaceae were more prominent in the colder incubation (5 °C) than the warmer (13 °C). Overall, Colwelliaceae, Oceanospirillaceae, Flavobacteriaceae, Rhodobacteraceae, Alteromonadaceae and Piscirickettsiaceae consistently dominated the microbial community at both temperatures and in both oil types. Other families known to include oil-degrading bacteria were also identified, such as Alcanivoracaceae, Methylophilaceae, Sphingomonadaceae and Erythrobacteraceae, but they were all present in dispersed oil incubations at a low abundance (< 1%).ConclusionsIn the current study, our goal was to introduce a comparative multivariate approach for studying the biodegradation of dispersed oil, including curve-fitted models of datasets for a greater data resolution and comparability. By applying these approaches, we have shown how different temperatures and oil types influence the biodegradation of oil in incubations with inconsistent sampling points. Clustering analysis revealed further how temperature and oil type influence single compound depletion and microbial community composition. Finally, correlation analysis of degraders community, with single compound data, revealed complexity beneath usual abundance cut-offs used for microbial community data in biodegradation studies.Electronic supplementary materialThe online version of this article (10.1186/s12866-018-1221-9) contains supplementary material, which is available to authorized users.

The Biodegradation of Dispersed Oil Does Not Induce Toxicity at Environmentally-Relevant Concentrations

“…As expected [15] [17]- [26], biodegradation of oil under these conditions was rapid, and about half of the oil was biodegraded in the first four days. This rate is slightly faster than in most other measurements, most likely because the incubation temperature was somewhat warmer (26˚C) than the prevailing temperatures when the water was collected (19˚C).…”

A generic multivariate framework for the integration of microbiome longitudinal studies with other data types

“…A similar approach to ours was proposed by Ribicic et al (2018) who used linear regression models coupled with multivariate dimension reduction methods on 16S and chemical longitudinal data to study the effects of oil temperature and composition on the biodegradation of chemically dispersed oil. We have taken their approach one step further, with the appropriate handling of compositional data, a fully developed modelling framework, and the identification of key profiles assigned to different clusters using sparse multivariate integrative methods.…”

“…However, methods or frameworks to integrate multiple longitudinal datasets including microbiome data remain incomplete. To our knowledge, only one study (Ribicic et al, 2018) attempted to combine spline modelling (e.g. loess) with sparse Principal Component Analysis to explore the link between chemistry and microbial community data in the biodegradation of chemically dispersed oil, but their approach was not specifically looking for multi-omics signatures.…”

A Rotational Slurry Bioreactor Accelerates Biodegradation of A-Fuel in Oil-Contaminated Soil Even under Low Temperature Conditions

“…Bioaugmentation with oil-degrading bacteria has been used as an effective treatment to clean up the contaminated soil [3][4][5]. However, the certainty and efficiency of bioaugmentation is not necessarily stable, because various environmental conditions, including soil composition, water content, temperature, and indigenous microbes, influence the feasibility of this treatment [6][7][8]. Among these conditions, temperature was one of the most important factors, because the microbial process could be further slowed down in cooler or cold environments [7,8].…”

“…However, the certainty and efficiency of bioaugmentation is not necessarily stable, because various environmental conditions, including soil composition, water content, temperature, and indigenous microbes, influence the feasibility of this treatment [6][7][8]. Among these conditions, temperature was one of the most important factors, because the microbial process could be further slowed down in cooler or cold environments [7,8]. Therefore, the isolation and usage of microbial degraders adapted to cold environments have been performed in several reports [9][10][11][12][13][14][15].…”