Metagenomics and metatranscriptomics of prokaryotic and fungal microbiomes in produced water associated with petroleum degradation and pipeline corrosion from an oil terminal in Brazil
摘要
The prokaryotic microbial communities involved in hydrocarbon degradation and associated with oil pipeline corrosion have been extensively studied. Nonetheless, fungi can perform significant metabolic activities in these environments. Studies evaluating metabolically active microbial communities in oil reservoirs are limited. Our study investigated the total/DNA and active/RNA communities of Archaea, Bacteria, and Fungi in produced water samples from an onshore terminal in Brazil. DNA and RNA were sequenced using the Illumina HiSeq 2500 platform, and the meta-omics sequences were analyzed. Shannon alpha diversity (taxonomic and functional) revealed that total communities were more diverse than metabolically active ones, with Bacteria showing higher diversity than Archaea and Fungi. The bacterial genera Syntrophotalea (sulfur reducer) and Pseudodesulfovibrio (sulfate reducer) were most prominent in total communities, while Halanaerobium (acid producing) dominated active communities. These results confirm the presence of Microbially Influenced Corrosion (MIC); however, the aprAB and dsrABC genes showed very low expression. Methanogenic Archaea Methanocalculus, Methanoplanus, and Methanothrix were frequent in both total and active communities, and mcrABDG genes were significantly expressed in metatranscriptomic sequences. Fungal genera Absidia, Penicillium, and Rhizopus were dominant in DNA samples, whereas Saccharomycodes, Pichia, Coemansia, and Schizosaccharomyces dominated RNA samples. These fungi can remediate environments contaminated with recalcitrant hydrocarbons. Despite the limited information obtained from fungal functional profile, an in-depth investigation of their activities and interrelation with Archaea and Bacteria in oil reservoirs is crucial for monitoring and mitigating oil biodegradation and pipeline biocorrosion processes.
Graphical abstract