Mining-industry is one of the most important activities in the economic development of many countries and produces highly significant alterations on the environment, mainly due to the release of a strong acidic metal-rich wastewater called acid mine drainage (AMD). Consequently, the establishment of multiple wastewater treatment strategies remains as a fundamental challenge in AMD research. Bioremediation, as a constantly-evolving multidisciplinary endeavor had been complemented during the last decades by novel tools of increasingly higher resolution such as those based on omics approaches, which are providing detailed insights into the ecology, evolution and mechanisms of microbial communities acting in bioremediation processes. This review specifically addresses, reanalyzes and reexamines in a composite comparative manner, the available sequence information and associated metadata available in public databases about AMD impacted microbial communities; summarizing our understanding of its composition and functions, and proposing potential genetic enhancements for improved bioremediation strategies. 16 S rRNA gene-targeted sequencing data from 9 studies previously published including AMD systems reported and studied around the world, were collected and reanalyzed to compare and identify the core and most abundant genera in four distinct AMD ecosystems: surface biofilm, water, impacted soils/sediments and bioreactor microbiomes. We determined that the microbial communities of bioreactors were the most diverse in bacterial types detected. The metabolic pathways predicted strongly suggest the key role of syntrophic communities with denitrification, methanogenesis, manganese, sulfate and iron reduction. The perspectives to explore the dynamics of engineering systems by high-throughput sequencing and biochemical techniques are discussed and foreseen application of synthetic biology and omics exploration on improved AMD biotransformation are proposed.
Keywords: AMD bioremediation; AMD ecosystems; Bioreactor's microbiome; Microbial communities; Predicted functional profiles.
Copyright © 2019 Elsevier Ltd. All rights reserved.