Abstract

BackgroundMicrobial communities in mining environments exhibit unique metabolic adaptations to extreme conditions, such as high metal concentrations and low pH. Their relatively low species complexity makes them an attractive model for fine-scale evolutionary analysis; nonetheless, genome-resolved metagenomic data from these environments are still scarce. Here, we employed genome-resolved metagenomics to analyze a high-quality Illumina-sequenced sample from the Cauquenes copper tailing in central Chile, one of the world's largest and oldest copper waste deposits. We aimed to uncover the taxonomic composition, metabolic potential, and evolutionary pressures shaping this extremophile community.ResultsWe reconstructed 44 medium- and high-quality metagenome-assembled genomes (MAGs), predominantly from the phyla Actinomycetota, Pseudomonadota, and Acidobacteriota. Taxonomic analysis revealed limited species-level classification, with only five MAGs assigned to known species, highlighting the challenges of characterizing extreme environments. Functional profiling identified enhanced metabolic capabilities in sulfur and copper pathways, critical for survival in mining ecosystems. Using evolutionary analysis on mining MAGs using dN/dS ratios, we uncoverd strong negative selection on genes involved in sulfur, copper, and iron metabolism, indicative of a conservative evolutionary state. In contrast, genes under positive selection were linked to motility, biofilm formation, and stress resistance, suggesting adaptive mechanisms for resource acquisition and survival.ConclusionsOur study provides a metagenome-wide evolutionary analysis of mining MAGs, demonstrating that microbial communities in copper tailings are highly specialized, with conserved metabolic pathways under strong purifying selection. At the same time, the recovery of previously unclassified species of extremophiles expands the known biodiversity of mining ecosystems. These findings emphasise the challenges of leveraging these communities for biotechnological applications, such as biomining, due to their evolutionary constraints.