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. 2017 Jun 6;18(1):445.
doi: 10.1186/s12864-017-3828-x.

Genome analysis of the thermoacidophilic archaeon Acidianus copahuensis focusing on the metabolisms associated to biomining activities

María Sofía Urbieta  1   2 , Nicolás Rascovan  3 , Martín P Vázquez  3 , Edgardo Donati  4
Affiliations

Genome analysis of the thermoacidophilic archaeon Acidianus copahuensis focusing on the metabolisms associated to biomining activities

María Sofía Urbieta et al. BMC Genomics. .

Abstract

Background: Several archaeal species from the order Sulfolobales are interesting from the biotechnological point of view due to their biomining capacities. Within this group, the genus Acidianus contains four biomining species (from ten known Acidianus species), but none of these have their genome sequenced. To get insights into the genetic potential and metabolic pathways involved in the biomining activity of this group, we sequenced the genome of Acidianus copahuensis ALE1 strain, a novel thermoacidophilic crenarchaeon (optimum growth: 75 °C, pH 3) isolated from the volcanic geothermal area of Copahue at Neuquén province in Argentina. Previous experimental characterization of A. copahuensis revealed a high biomining potential, exhibited as high oxidation activity of sulfur and sulfur compounds, ferrous iron and sulfide minerals (e.g.: pyrite). This strain is also autotrophic and tolerant to heavy metals, thus, it can grow under adverse conditions for most forms of life with a low nutrient demand, conditions that are commonly found in mining environments.

Results: In this work we analyzed the genome of Acidianus copahuensis and describe the genetic pathways involved in biomining processes. We identified the enzymes that are most likely involved in growth on sulfur and ferrous iron oxidation as well as those involved in autotrophic carbon fixation. We also found that A. copahuensis genome gathers different features that are only present in particular lineages or species from the order Sulfolobales, some of which are involved in biomining. We found that although most of its genes (81%) were found in at least one other Sulfolobales species, it is not specifically closer to any particular species (60-70% of proteins shared with each of them). Although almost one fifth of A. copahuensis proteins are not found in any other Sulfolobales species, most of them corresponded to hypothetical proteins from uncharacterized metabolisms.

Conclusion: In this work we identified the genes responsible for the biomining metabolisms that we have previously observed experimentally. We provide a landscape of the metabolic potentials of this strain in the context of Sulfolobales and propose various pathways and cellular processes not yet fully understood that can use A. copahuensis as an experimental model to further understand the fascinating biology of thermoacidophilic biomining archaea.

Keywords: Acidianus copahuensis; Biomining genes; Thermoacidophilic archaea.

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Figures

Fig. 1
Fig. 1
Acidianus copahuensis proteins compared to all other Sulfolobales species. a Network analysis representing the results of an “all vs. all” BLASTP comparison of all Sulfolobales proteins from sequenced genomes. Only hits with more than 65% of both proteins aligned and E-value lower that 1E-20 were considered as a match between two proteins. The line width is proportional to the number of proteins shared by two species. b Heatmap analysis representing the Acidianus copahuensis proteins that were found in other Sulfolobales species based on the BLASTP comparison mentioned above. Proteins were classified depending on the number of genomes where each protein is present (see heatmap side bar and the upper-right bar chart as a reference for the categories used). Clustering was performed using the Euclidean distance
Fig. 2
Fig. 2
Phylogeny of Sulfolobales based on SOR proteins and 16S rRNA genes. Phylogenetic trees were obtained by the Maximum Likelihood method for all known SOR proteins found in NCBI database (a) and the corresponding 16S rRNA genes (b) in the same organisms. Bootstrap supports for nodes were obtained using 1000 repetitions and are expressed as the proportion of times (in decimals) that each node was supported. Archaea branches from the phylum Euryarchaeota are colored in blue and from the phylum Crenarchaeota in red while Bacteria branches are in green
Fig. 3
Fig. 3
Organization of dsrE-tusA-hdr-like gene clusters in Sulfolobales. Genomic organization of the cluster is almost identical in all Sulfolobales to that shown by Liu et al. 2014 for the M. cuprina genome [28]. Acidianus copahuensis proteins were used as references to calculate the percentage similarity to corresponding proteins from all other species. The homologous genes from different species are represented using the same color. Arrow orientation indicates the orientation of the ORF in the genome and lengths are proportional to the real length of the protein. “Cons. H.P.” is an acronym for conserved hypothetical protein. Double bar indicates that the gene is located in a different genomic region. All proteins from each species were concatenated and the resulting poly-proteins were used to build a Maximum Likelihood phylogenetic tree, placed on the left of the figure. Bootstrap supports for nodes were obtained using 1000 repetitions and are expressed as the proportion of times (in decimals) that each node was supported
Fig. 4
Fig. 4
Organization of fox genes clusters in Sulfolobales. Sulfolobus metallicus genes were used as reference because it is the model organism where this complex was first described (*). Similarity of the amino acid sequences of all Sulfolobales with fox genes to the S.metallicus references was estimated using a BLASTP analysis. The homologous genes from different species are represented using the same color, arrow orientation indicates the orientation of the ORF in the genome and lengths are proportional to the real length of the proteins. Homologous genes are linked by lines to track changes in genome organization among species. Double bar indicates that the gene is located in a different genomic region
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