Abstract
Petroleum pollution of coastal areas such as Nigeria’s Niger Delta is increasingly alarming. After an oil spill, aquatic organisms, including indigenous microbial communities are affected, leading to an increased abundance of oil degraders in the affected environment. During the last decade, microbial ecology research has shifted from cultivation and characterization of individual environmental isolates to metagenomics studies that are focused on the characterization of community dynamics and composition. In this study, we determined the indigenous bacterial community in water bodies polluted with crude oil in Delta State, Nigeria by Next Generation Sequencing (NGS). Oil hydrocarbon composition was determined by Gas Chromatography–Mass Spectrometry analysis (GC–MS) and the physical-chemical dynamics were assessed using standard procedures. Results revealed Proteobacteria (76.9%), Bacteroidetes (8.5%) and Firmicutes (1.1%) as the dominant phyla. GC–MS data revealed different petroleum hydrocarbon concentrations across the seasons and locations sampled. The indigenous bacterial community included the prevalence of oil degrades with Methylotenera as the major genus. This study suggests that Methylotenera could be a useful agent for bioremediation in the Niger Delta.
Acknowledgments
We do acknowledge the Microbiome Core (Center for Gastrointestinal Biology and Disease CGIBD P30 DK034987, the UNC Nutrition Obesity Research Center NORC P30 DK056350) and in particular Dr. M Andre Azcárate-Peril for her patience and expertise all through the period of this research. We also appreciate Professor Mosaad Abdel-Wahhab for his timely edits.
Consent for publication
All authors in this manuscript have consented to the submission of this manuscript.
Author’s contributions
All authors contributed immensely in the course of this research.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The genomic raw data has been submitted to the National Center for Biotechnology Information’s Sequence Read Archive (NCBI-SRA) under accession number PRJNA525410. The data is accessible at https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA525410