Abstract
Microbial remediation of oil polluted habitats remains one of the foremost methods for restoration of petroleum hydrocarbon contaminated environments. The development of effective bioremediation strategies however, require an extensive understanding of the resident microbiome of these habitats. Recent developments such as high-throughput sequencing has greatly facilitated the advancement of microbial ecological studies in oil polluted habitats. However, effective interpretation of biological characteristics from these large datasets remain a considerable challenge. In this study, we have implemented recently developed bioinformatic tools for analyzing 65 16S rRNA datasets from 12 diverse hydrocarbon polluted habitats to decipher metagenomic characteristics of the resident bacterial communities. Using metagenomes predicted from 16S rRNA gene sequences through PICRUSt, we have comprehensively described phylogenetic and functional compositions of these habitats and additionally inferred a multitude of metagenomic features including 255 taxa and 414 functional modules which can be used as biomarkers for effective distinction between the 12 oil polluted sites. Additionally, we show that significantly over-represented taxa often contribute to either or both, hydrocarbon degradation and additional important functions. Our findings reveal significant differences between hydrocarbon contaminated sites and establishes the importance of endemic factors in addition to petroleum hydrocarbons as driving factors for sculpting hydrocarbon contaminated bacteriomes.
Highlights
Microbial remediation of oil polluted habitats remains one of the foremost methods for restoration of petroleum hydrocarbon contaminated environments
Comprehensive characterization of bacterial community composition in hydrocarbon polluted environments was carried out using 61 publicly available and previously validated/published 16S rRNA amplicon sequencing datasets distributed over 11 different habitats (Table 1, Supplementary Table S1) along with 4 datasets generated in this study. mothur analysis of all datasets led to the identification of 18 phyla, orders and families at ≥2% average relative abundance in at least one habitat (Fig. 1A,B, Supplementary Tables S2 and S9)
Verrucomicrobia contribution in microbial community composition was higher in DWH, M and C, while mean relative abundances of Firmicutes and Cyanobacteria were elevated in OSTP and C samples respectively (Fig. 1A, Supplementary Table S2)
Summary
Microbial remediation of oil polluted habitats remains one of the foremost methods for restoration of petroleum hydrocarbon contaminated environments. The advent of high throughput massively-parallel sequencing methods has allowed us to investigate the entire complement of organisms inhabiting a certain environment These next-generation sequencing methods (NGS) include a variety of procedures to holistically study any biological system such as amplicon sequencing (for variant identification and phylogenetic surveys), whole genome shotgun sequencing (for single organism genome and metagenomes) and RNA-Seq (for transcriptomes, metatranscriptomes and identification of non-regular RNAs). These powerful methods have ushered in rapid advances in bioinformatic approaches leading to development of software capable of handling huge amounts of data and offering meaningful biological interpretations of the same. In general, most of these studies concentrated on interpretations from microbial community composition but inferred poorly regarding functional and metabolic properties of the same
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