Microbial Ecology

, Volume 68, Issue 3, pp 453–462 | Cite as

Insights into Biodegradation Through Depth-Resolved Microbial Community Functional and Structural Profiling of a Crude-Oil Contaminant Plume

  • Nicole FahrenfeldEmail author
  • Isabelle M. Cozzarelli
  • Zach Bailey
  • Amy Pruden
Environmental Microbiology


Small-scale geochemical gradients are a key feature of aquifer contaminant plumes, highlighting the need for functional and structural profiling of corresponding microbial communities on a similar scale. The purpose of this study was to characterize the microbial functional and structural diversity with depth across representative redox zones of a hydrocarbon plume and an adjacent wetland, at the Bemidji Oil Spill site. A combination of quantitative PCR, denaturing gradient gel electrophoresis, and pyrosequencing were applied to vertically sampled sediment cores. Levels of the methanogenic marker gene, methyl coenzyme-M reductase A (mcrA), increased with depth near the oil body center, but were variable with depth further downgradient. Benzoate degradation N (bzdN) hydrocarbon-degradation gene, common to facultatively anaerobic Azoarcus spp., was found at all locations, but was highest near the oil body center. Microbial community structural differences were observed across sediment cores, and bacterial classes containing known hydrocarbon degraders were found to be low in relative abundance. Depth-resolved functional and structural profiling revealed the strongest gradients in the iron-reducing zone, displaying the greatest variability with depth. This study provides important insight into biogeochemical characteristics in different regions of contaminant plumes, which will aid in improving models of contaminant fate and natural attenuation rates.


Much Probable Number Inductively Couple Plasma Atomic Emission Spectroscopy Contaminant Plume Azoarcus mcrA Gene 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was funded by the Virginia Tech Institute for Critical Technology and Applied Science and the NSF Research Experience for Undergraduates site award 1062860. This research was also supported by the USGS Toxic Substances Hydrology Program and the USGS National Research Program. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the author(s) and do not necessarily reflect the views of Virginia Tech or the NSF. Helpful review comments were provided by Denise Akob and Barbara Bekins, analytical support was provided by Jeanne Jaeschke, and GIS support provided by Melinda Erickson. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Supplementary material

248_2014_421_MOESM1_ESM.docx (29 kb)
Table S1 (DOCX 28 kb)


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Copyright information

© Springer Science+Business Media New York (outside the USA) 2014

Authors and Affiliations

  • Nicole Fahrenfeld
    • 1
    Email author
  • Isabelle M. Cozzarelli
    • 2
  • Zach Bailey
    • 3
  • Amy Pruden
    • 4
  1. 1.Civil and Environmental EngineeringRutgers, The State University of New JerseyPiscatawayUSA
  2. 2.U.S. Geological Survey National Research ProgramRestonUSA
  3. 3.Biomedical Engineering and SciencesVirginia TechBlacksburgUSA
  4. 4.Civil and Environmental EngineeringVirginia TechBlacksburgUSA

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