The Bacterial Community Structure of Hydrocarbon-Polluted Marine Environments as the Basis for the Definition of an Ecological Index of Hydrocarbon Exposure

Abstract

The aim of this study was to design a molecular biological tool, using information provided by amplicon pyrosequencing of 16S rRNA genes, that could be suitable for environmental assessment and bioremediation in marine ecosystems. We selected 63 bacterial genera that were previously linked to hydrocarbon biodegradation, representing a minimum sample of the bacterial guild associated with this process. We defined an ecological indicator (ecological index of hydrocarbon exposure, EIHE) using the relative abundance values of these genera obtained by pyrotag analysis. This index reflects the proportion of the bacterial community that is potentially capable of biodegrading hydrocarbons. When the bacterial community structures of intertidal sediments from two sites with different pollution histories were analyzed, 16 of the selected genera (25%) were significantly overrepresented with respect to the pristine site, in at least one of the samples from the polluted site. Although the relative abundances of individual genera associated with hydrocarbon biodegradation were generally low in samples from the polluted site, EIHE values were 4 times higher than those in the pristine sample, with at least 5% of the bacterial community in the sediments being represented by the selected genera. EIHE values were also calculated in other oil-exposed marine sediments as well as in seawater using public datasets from experimental systems and field studies. In all cases, the EIHE was significantly higher in oiled than in unpolluted samples, suggesting that this tool could be used as an estimator of the hydrocarbon-degrading potential of microbial communities.

Bacterial Community Legacy Effects Following the Agia Zoni II Oil-Spill, Greece

“…The significance of model terms was assessed via likelihood ratio tests. The EIHE ( Lozada et al, 2014 ) was calculated using the script available at the ecolFudge GitHub page 1 ( Clark, 2019 ) and EIHE values modeled using Poisson GLMs. Correlations were performed using the Pearson’s correlation with an alpha of 0.05.…”

“…Five days after the oil spill in September there was a strong positive correlation (R 2 = 0.80, p < 0.01) between the relative abundance of 16S rRNA sequences assigned to hydrocarbon-degrading bacteria (all OTUs displayed in Figure 6) and the concentration of measured hydrocarbons. In addition, the EIHE, which quantifies the proportion of the bacterial community with hydrocarbon bioremediation potential on a scale of 0-1, whereby 1 represents 100% (Lozada et al, 2014), was calculated at 0.52 ± 0.14 in September. This EIHE result was significantly (p < 0.05) higher than the control site at 0.30 ± 0.11.…”

“…Using microbial ecology as a post-oil-spill monitoring tool is still in its infancy, however, models and indices to assist in this effort are available. Lozada et al (2014) published an "EIHE, " whereby hydrocarbon contamination was assumed based on the relative abundance of genera known to contain hydrocarbon-degrading bacteria. The simplicity of this model is both a strength and a weakness.…”

“…This phenomenon of prior exposure can be important in determining the rate at which any subsequent hydrocarbon inputs may be biodegraded (Leahy and Colwell, 1990). Such knowledge will also assist in designing or improving models such as the Ecological Index of Hydrocarbon Exposure (EIHE) (Lozada et al, 2014) and inform future oil-spill response, such as indicating the thoroughness of clean-up efforts and identifying when tourist/fishing activities can recommence.…”

Protein expression in the obligate hydrocarbon‐degrading psychrophile Oleispira antarctica RB‐8 during alkane degradation and cold tolerance

“…There is a growing interest in post‐spill monitoring of microbial communities (Kirby et al ., ), and an understanding of the key proteins involved in hydrocarbon degradation is important for designing gene primers that can be used in monitoring tools that are based on the abundance of specific functional genes. This can complement current tools based on community 16S rRNA genes, such as the Ecological Index of Hydrocarbon Exposure (Lozada et al ., ).…”