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. 2015 Aug 19;5:13258. doi: 10.1038/srep13258

Assessing the genetic diversity of Cu resistance in mine tailings through high-throughput recovery of full-length copA genes

Xiaofang Li 1, Yong-Guan Zhu 2,a, Babak Shaban 3, Timothy J C Bruxner 4, Philip L Bond 5, Longbin Huang 1
PMCID: PMC4541151  PMID: 26286020

Abstract

Characterizing the genetic diversity of microbial copper (Cu) resistance at the community level remains challenging, mainly due to the polymorphism of the core functional gene copA. In this study, a local BLASTN method using a copA database built in this study was developed to recover full-length putative copA sequences from an assembled tailings metagenome; these sequences were then screened for potentially functioning CopA using conserved metal-binding motifs, inferred by evolutionary trace analysis of CopA sequences from known Cu resistant microorganisms. In total, 99 putative copA sequences were recovered from the tailings metagenome, out of which 70 were found with high potential to be functioning in Cu resistance. Phylogenetic analysis of selected copA sequences detected in the tailings metagenome showed that topology of the copA phylogeny is largely congruent with that of the 16S-based phylogeny of the tailings microbial community obtained in our previous study, indicating that the development of copA diversity in the tailings might be mainly through vertical descent with few lateral gene transfer events. The method established here can be used to explore copA (and potentially other metal resistance genes) diversity in any metagenome and has the potential to exhaust the full-length gene sequences for downstream analyses.

Novel metal resistance genes in the environment, particularly mining-impacted soils, are valuable resources for industrial biomining1 and soil remediation2. copA is one of the core determinants for microbial resistance to Cu and its diversity has been examined in soils in a limited number of recent studies3,4,5,6,7. These studies are all amplicon-based and rely on the availability of degenerate primers to target conservative regions of copA. Unfortunately, available copA sequences in the literature are thought to be highly polymorphic and therefore available primers only cover a subset of copA sharing high similarity. This hinders the assessment of copA diversity and the discovery of novel copA in the environment. To overcome this difficulty, we report a method to recover full-length copA from a tailings metagenome by combining the methods of metagenome assembly, local BLASTN and evolutionary trace (ET) analysis. The metagenomic strategy has been successfully applied to annotate Cu resistance genes in an activated sludge metagenome8, and ET analysis can be used to check the reliability of candidate copA genes detected in a metagenome by screening for key conserved domains of their CopA proteins.

copA as a part of cop system and its homolog gene pcoA as a part of pco system were first identified in copper resistant strains of Pseudomonas syringae (PscopA) and Escherichia coli (EcpcoA), respectively. PscopA and EcpcoA were both plasmid-borne but many copA homologs were identified soon after, both chromosomal- (e.g. Enterococcus hirae) and plasmid-borne (e.g. Xanthomonas sp.). Interestingly, many chromosomal copA were found to be typical P-type ATPase9, while pcoA was then identified as multicopper oxidase10. Therefore, copA is thought to be highly polymorphic3,5 and the nature of copA has always been described as species-dependent in the literature11,12,13,14. This caused confusion when studying copA diversity in the environment. It is known that multicopper oxidase and ATPase are different protein families using different energy sources15,16. We thus hypothesize that all copA named based on sequence similarity in the literature are not homologs and can be divided into two groups encoding for multicopper oxidase and P-type ATPase, which are both highly conserved.

ET analysis is a method to extract functionally important residues from sequence conservation patterns in homologs, with the assumption that active site residues of a protein family are more conserved during its evolutionary history17. While complete or partial crystal structure of some CopA proteins has been available in the literature18,19,20,21, it is feasible now to identify the common active sites which may be highly conserved among CopA proteins. Therefore, ET analysis is possible to reveal the underlying root of the polymorphism of copA/CopA.

A metagenome obtained by high-throughput sequencing of environmental DNA conceptually provides all the gene resources in a given environment22. Metagenomic sequencing successfully overcomes the difficulties facing traditional isolation-based and conventional amplicon-based molecular methods in recovering genetic information from the environment. By means of bioinformatic tools (e.g. MIRA)23 which assemble the shorts reads in metagenomes into longer contigs or even nearly complete genomes, metagenomics allow us to explore a functional gene of interest in a high-throughput fashion24. BLAST is the most commonly used tool to find homologous sequences based on sequence similarity for functional attributes25. By building a local database of antibiotic resistance genes (ARGs), a local BLASTN procedure has been established to screen for the presence of ARGs in bacterial genomes26; yet this local-BLASTN method has not been applied to metal resistance genes or to explore metagenomes.

Therefore, this study aimed to develop a method for the recovery of full-length copA sequences in a tailings metagenome. A metagenomic library was obtained by assembling 7 individual tailings metagenomes using MIRA. The library was searched using the embedded BLASTN method in BioEdit against a local copA database built in this study, followed by recovering full-length copA by annotating the contigs containing putative copA. All identified putative copA were then examined for the presence of highly conserved metal-binding motifs found by ET analysis.

Results and Discussion

In generating the metagenomes, the MiSeq sequencing yielded >3.9 billion bp and >14.8 million reads after quality control for the 7 tailings samples. The 7 individual tailings metagenomes were pooled together for high-quality assembly and subsequent copA gene recalling. In total, 8,566,357 reads (greater than 300 bp) were used for metagenomic assembly which generated 82,334 contigs with an N50 of 1,700 bp and a longest contig of 123,516 bp. The data amount used for assembly in this study is much higher than earlier studies27,28,29 and comparable to recent studies30,31,32 where the characterized soil microbial communities have much higher complexity. This allows a deeper coverage of the functional gene diversity in the tailings microbial communities. Moreover, though metagenomes of microbial communities in acid mine drainage have been well studied33,34, this is, to our knowledge, the first report on microbial metagenomes from neutral mine tailings which more closely resemble natural soils than acidic tailings. While natural soil always harbors a microbial community with a complexity whose fine decoding is still beyond the capacity of current bioinformatics tools35, the tailings microbial metagenomes of lower diversity and community structure complexity characterized here may be a useful proxy for studying soil ecosystem functioning.

The Mt Isa Cu-Pb-Zn tailings are neutral and saline (EC > 2 mS/cm; in the dry season it is much higher as found in our previous study36) substrates and contain an average Cu concentration more than 16-fold greater than the background soil values37 (Table 1). In addition to the high levels of salinity and Cu, the tailings are also high in total Pb and Zn concentrations. The saline and metal stresses may have exerted a strong selective pressure on the microorganisms within the system, as indicated by the extremely low microbial biomass (Table 1) and the microbial diversity, which is dominated by either thermophiles or halophiles as found in our previous studies38,39. The dominance of extremophiles may explain the high GC contents (all > 60%) of the metagenome libraries40.

Table 1. General descriptors of the tailings samples and the metagenomes analysed in this study.

Sample pH EC (μS/cm) CEC (cmol+/kg) TOC (g/kg) MBC (mg/kg) Total Cu (mg/kg) Total Pb (mg/kg) Total Zn (mg/kg) Raw metagenome data size (Mb) Mean GC content after quality control (%) Total gene copy annotated Number of contigs after assembly Maximum contig size (bp) N50 of the contigs
Tailings_1 6.70 5,490 30.47 5.55 132.35 1316 5080 3914 706 63 ± 9 2,153,536      
Tailings_2 6.80 5,420 48.30 5.85 92.65 1294 5300 3234 707 62 ± 10 1,046,050      
Tailings_3 6.90 3,860 39.04 5.54 73.64 1285 4700 2620 551 60 ± 11 1,136,742      
Tailings_4 6.90 2,080 42.06 4.95 51.38 1247 5090 3164 551 64 ± 7 1,044,817 82,334 123,516 1,734
Tailings_5 6.70 3,120 15.63 4.43 38.49 1205 6350 2991 544 62 ± 9 1,115,745      
Tailings_6 6.80 3,040 12.19 3.77 28.04 1056 5860 3261 542 62 ± 9 1,265,601      
Tailings_7 6.80 3,390 20.51 3.54 27.78 1143 6140 3485 611 62 ± 9 1,130,746      
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Notes: EC, electrical conductivity; CEC, cation exchange capacity; TOC, total organic carbon; MBC, microbial biomass carbon.

The strong selective effects of metal stresses were also reflected by the significantly high abundance of resistance genes for heavy metals in the metagenomes, based on the annotations by MG-RAST pipeline. In comparison with a local soil close to the tailings site sampled, the tailings metagenomes contain average cop, czc (coding for multiple metal resistance) and ars (coding for arsenic resistance) gene abundances of 2.8, 2.5 and 1.7 folds, respectively, of those of the soil (unpublished results; the metagenome data will be released soon in MG-RAST). The heavy metals in the soil are close to background crustal values (Fig. 1). Increased levels of toxins can lead to the enrichment of relevant resistance genes, as reported for both antibiotic and heavy metal resistance genes41,42,43,44,45. Enrichment of copA has also been reported in various metal-contaminated environments such as paddy soil3, arable soil14 and sediment46. However, to our knowledge, statistical results from shotgun metagenomic sequencing for cop genes have not been reported until recently8. Considering the polymorphism of cop systems, the results in this study may cover a deeper diversity of Cu resistance genes than those which are amplicon-based and rely on the specificity of primers.

Figure 1.

Figure 1

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Total elemental concentrations of As, Cu, Pb and Zn in the tailings samples (n = 7) and a reference soil (n = 3) used in this study (B) and the corresponding abundances of resistance genes for these metals in the metagenomes from the tailings/soil samples (A). ars, resistance genes for As; cop, resistance genes for Cu; czc, resistance genes for multi-metals (e.g., Co, Pb, Zn).

A copA dataset containing 122 sequences annotated as copA obtained from full genomes available in Genbank has been built in this study. The abundance of copA selected from each phylum generally reflects the abundance of the available copA for that phylum in Genbank. Specification tests of the local BLASTN were done using copA sequences from the local database as well as full genomes containing putative copA. For the copA sequences from the database, the method was able to unambiguously identify all these genes with sensitivity and specificity at 100% and full coverage; at an elevated e-value, fragmented sequences of identity >80% can be found. At an e-value of 10−4, we were also able to locate copA-like genes in the three genomes of Acidithiobacillus ferrooxidans, Rubrobacter radiotolerans and Thioalkalivibrio sulfidophilus, who are phylogenetically close to those of abundant species in the tailings, with more than 10 hits with an identity >80% and an alignment length >40 bp (the cutoff used for subsequent contig annotation in this study). Additionally, 6 hits were returned with an identity >86% and alignment length >40 bp when the BLASTN was applied to the genome of Thermodesulfobacterium geofontis. These results demonstrate a good coverage of copA diversity in the database and the tool established here is theoretically able to cover the novel copA diversity in our tailings metagenome.

In total, 99 full-length putative copA genes were recovered from the tailings metagenome. All of these genes had high similarity (>70%) to copA sequences from the genomes in Genbank. As the local BLASTN method is similarity-based, the number of copA genes identified can vary largely upon how novel the subjected microbial gene pool is and the diversity of the copA database. The threshold of alignment length in local BLASTN results was arbitrarily set as 40 bp to limit the number of contigs subject for subsequent copA gene annotation. However, the number of copA sequences recovered can be increased if the threshold is reduced, since Gupta et al.26 found a minimum alignment length of 17 bp for putative new genes using a similar method for ARGs. Considering that the tailings microbial communities in this study are fairly low in species diversity, the local BLASTN method we have established has the potential to exhaust the copA diversity therein and can also be a novel tool to explore copA diversity in more complex soil environments.

Annotation of new genes is mostly sequence-similarity-based47, including the early studies detecting copA homologs. While both multicopper oxidase-like and P-type ATPase-like copA have been referred to in the early literature9,48, more recent studies state these as two “types” of copA14. However, we propose that the so-called two types of copA are not homologs and are incorrectly associated due to the limited number of available sequences used for similarity comparison in early studies. A wide range of protein sequences was used for phylogenetic analysis in this study. These were sequences that were verified experimentally or annotated as CopA and CopA-like Cu translocating proteins obtained from reported Cu resistant microorganisms. The sequences clearly separated into two distinct groups, one containing typical multicopper oxidases CueO and the other containing the typical P-type ATPase CtpA and ZosA (Fig. 2; Table 2). The former group includes the protein products of copA genes detected in early studies from the plasmids of P. syringae and E. coli., and the latter group includes P-type ATPase Cu translocating proteins from A. fulgidus, E. hirae and L. pneumophila whose crystal structure has been resolved. These two groups of CopA may have distinct roles in Cu resistance, as implied by the experimental assays in vitro of typical CopA. Members of multicopper oxidase group CopA have been reported to be able to oxidize substrates of laccase, like phenol10,49, while members of ATPase CopA have been found to bind and transport Cu(I)19,50,51. Meanwhile, in the E. coli genome, the model microorganism for cop system studies, both groups of copA are present and their roles are found to be different. Plasmid-borne pcoA functions synergistically with chromosomal copA, and the protein PcoA can oxidise Cu(I) carried by PcoC and substitute the role of chromosomal CueO10,52,53. Our analysis here indicated that many model species for copA studies contain both groups of copA, such as E. hirae and P. syringae (Fig. 2). The different roles of the two groups of copA may also be implied by their different sequence and tertiary structure. Multicopper oxidase CopA has no crystal structure resolved so far but prediction by SWISS-MODEL54 showed that EcPcoA and PsCopA have similar crystal structure with laccase, which is distinct from the transmembrane figuration of the ATPase CopA detected in A. fulgidus and L. pneumophila16,19,50,55. ET analysis indicated that the two groups of CopA have different metal binding motifs (Supplementary Figure 1). The ATPase group typically uses Cys as a metal binding residue in the motifs of CXXC (with HXXH as a variant) and CXC (the typical transmembrane metal binding motif), while the multicopper group uses His for metal binding in the form of HXH which is highly conservative within the group. Taken together, we suggest that CopA homologous to PcoA should be named as PcoA which is a multicopper oxidase, to differentiate it from ATPase CopA. Consequently, the metal binding motifs detected here can be used to screen for copA candidates with a high potential of functioning in Cu resistance.

Figure 2. A phylogenetic tree based on the alignment of the CopA and reference protein sequences used for ET analysis in this study.

Figure 2

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Microbial species containing CopA sequences from both the P-type ATPase and Multicopper oxidase groups are highlighted in colour. When the Cu resistance function has been verified this is mentioned for those taxa.

Table 2. Selected microbial species used for evolutionary trace analysis in this study.

Species MIC (mM)a
Acidithiobacillus caldus DSM 9239 241
Acidithiobacillus ferrooxidans isolate N39-30-03 8001
Agrobacterium tumefaciens CCNWRS33-2 >577
Archaeoglobus fulgidus Protein functioning verifiedb50
Arthrobacter arilaitensis 11J 3.178
Azotobacter vinelandii GZC24 4.779
Bacillus cereus BC21 3.979
Bacillus subtilis Protein functioning verified80
Bacillus thuringiensis N2 >577
Cyanobacterium Synechococcus Unknownc81,82
Enterobacter aerogenes NTG-01 283
Enterococcus hirae Protein functioning verified17
Escherichia coli RJ92 40084
2085  
Protein functioning verified18  
Methylobacterium extorquens Unknown86
Pseudomonas fluorestens 09906 1.687
Pseudomonas putida CZ1 >577
Pseudomonas putida S4 >188
Pseudomonas syringue 1.0–3.289
1.2–2.090  
Protein functioning verified  
Rahnella aquatilis MT7 3.979
Staphylococcus pasteuri N2 >577
Stenotrophomonas maltophilia AAP56 >0.491
Synechococcus elongatus Unknown92
Xanthomonas campestris BrC2 0.393
Protein functioning verified49  
Haloferax volcanii LccA Protein functioning verified72
Escherichia coli CueO Protein functioning verified73
Mycobacterium smegmatis CtpA Protein functioning verified74
Bacillus subtilis ZosA Protein functioning verified75
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The MIC of Cu, and whether the protein function has been determined is included. Genomes of these species all harbour copA or copA-like copper translocating genes.

aminimum inhibitory concentration as of the strains of the species studied and under the specific test conditions in the corresponding references; bfunctions of copA genes have been experimentally verified or the crystal structure of CopA proteins has been resolved; creferred as Cu resistant but MIC is unknown or not provided.

The 99 full-length putative copA sequences were translated and aligned with the known CopA used for ET analysis. The presence of metal binding motifs abovementioned was screened in the putative CopA; For ATPase like CopA, the ATP binding motifs (GDGIN) were also used for screening. In total, 70 putative CopA were thought to have a high chance of functioning in Cu resistance.

Phylogenetic analysis was performed on the CopA sequences affiliated with the dominant species detected in the tailings (Fig. 3). The topology of the tree is largely consistent with that of the 16S rRNA gene based phylogeny. This indicates that the copA gene diversity was mainly controlled by vertical descent in the history of tailings community evolution, at least among the dominant species. Similar conclusions have been drawn for merA genes responsible for mercury resistance2. P-type ATPase metal homeostasis genes are probably ancient genes and have been essential for microbial survival, and thus lateral gene transfer plays a minor role in their evolution56.

Figure 3. A phylogenetic tree showing the representative copA sequences affiliated with Proteobacteria and Deinococcus-Thermus recovered from the tailings metagenome after assembly.

Figure 3

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It is worth noting that the 29 putative copA sequences with no metal binding or phosphatase binding domains found may also function for Cu-resistance. For one thing, it is possible the combined presence of all the metal binding motifs found here are not essential for Cu resistance. Studies on EcCopA have found that the metal binding domain C479PC481 motif is essential for Cu resistance but the other two C14XXC17 and C110XXC113 are not57,58. Meanwhile, some Cu resistance ability may have evolved with sequences containing alternative metal binding domains. For example, in the alignment of ATPase group CopA, about half of the members lack the C84XXC87 motif; and CopA of Sulfolobus solfataricus, in which the Cu resistance function has been experimentally verified, uses YPC instead of CPC as a transmembrane metal binding domain (Supplementary Figure 1). Furthermore, it must be highlighted that the CopA proteins are relatively divergent in terms of both sequence composition and length. Inaccurate alignments can be made and lead to failure for detecting motifs when the sequences are too divergent59. Therefore, the accuracy of alignment methods may largely determine the ability to detect the metal binding motifs of the subject sequences. For example, the alignment method used in this study failed to align CueO of E. coli with the others, and manual adjustments were required to locate the three experimentally-verified metal binding domains60.

Conclusions

A local BLASTN method was established in this study to recover full-length copA genes in an assembled tailings metagenome. The detected copA genes were further screened for potentially functioning copA by screening active sites in their encoding proteins, which were inferred by ET analysis using known CopA. The method established here can be used to recover full-length copA (and potentially other resistance genes) in any assembled metagenomes.

Materials and Methods

Sampling and DNA extraction

Tailings samples were sampled in June in 2013 from a field trial site located at Mount Isa (Mt Isa) tailings impoundment. Mt Isa (20.73 °S, 139.5 °E) is located in northwest Queensland, Australia. Mt Isa has a semi-arid climate with an annual pan evaporation of 2800 mm and an average rainfall of 400 mm, with the vast majority of the rain falling during the wet season (November to February). The tailings used for field trial were highly weathered and collected from a tailings storage facility (TD5) that contained mixed streams of Cu and Pb-Zn tailings and was decommissioned about 40 years ago, which had received streams of Cu and Pb-Zn tailings for decades. The details of tailings properties and treatment setup can be found in our previous studies38,39. Briefly, the tailings mainly consist of quartz, dolomite, pyrite, gypsum and kaolinite, and contain 0.13 ± 0.03% of total Cu, 0.18 ± 0.02% of total Pb, and 0.29 ± 0.01% of total Zn. A field revegetation trial was established in 2010, and within 3 years the woodchips amendment and/or revegetation treatments did not substantially change the tailings physiochemical properties and the dominant microbial species. Dominant microbial species were affiliated with Rubrobacter spp. of Actinobacteria, Truepera spp. of Deinococcus-Thermus and Thioalkalivibrio spp. and Thiobacillus spp. of Proteobacteria39,61.

Tailings sample were stored in an iced container and shipped to the laboratory within 24 hours for DNA extraction. For physiochemical analyses part of the tailings were oven-dried at 40 °C, sieved through a 2 mm screen and mixed thoroughly before use. Methods for physiochemical analyses, such as electrical conductivity (EC), cation exchange capacity (CEC), total organic carbon (TOC), microbial biomass carbon (MBC) and total elemental concentrations (Table 1), can be found in our previous studies39,61.

DNA was extracted from 24 tailings samples (8 plots with 3 replicates each plot). DNA extraction was done using commercial kits after cell enrichment by sucrose density centrifugation; detailed methods can be found in our previous studies39,61. For MiSeq shotgun sequencing, replicates were combined to obtain enough DNA, and one sample of pure tailings failed the quality test for sequencing. Therefore, 7 independent tailings DNA samples representing the gene pool in the tailings landscape were sequenced using Illumina MiSeq platform in this study. The DNA concentrations and quality were measured using the Qubit® 2.0 Fluorometer (Thermo Fisher Scientific). DNA concentrations of the 7 samples ranged from 4.5 to 25.2 ng/μl, giving between 0.23 to 1.51 μg of DNA to use for the sequencing.

MiSeq sequencing

DNA sequencing libraries were prepared using the Illumina TruSeq DNA LT Sample Prep Kit (Illumina), following the standard manufacturer’s protocol (Part #15026486 Rev. C July 2012) with modifications as described below. Genomic DNA was fragmented by sonication (Covaris S2) using the “Whole-genome Resequencing” settings in the protocol, after which the DNA was purified using AMPure XP beads. The purified, fragmented DNA was end repaired and again purified using AMPure XP beads. The purified, end repaired DNA was size-selected using a double-SPRI method to obtain insert sizes of approximately 600 bp (actual average insert size ranges from 456–667 bp). The size-selected DNA was A-tailed and then had adapters ligated, followed by an AMPure XP purification. This ligated product was amplified by PCR to produce the final library. The final individual libraries were visualized and quantified on the Agilent BioAnalyzer 2100 using the High Sensitivity DNA Kit. The libraries were then pooled in an equimolar ratio, and the pool was quantified by qPCR using the KAPA Illumina Library Quantification Kit (KAPA Biosystems). The library pool was sequenced on the Illumina MiSeq (MiSeq control software v2.0.5/Real Time Analysis 1.18), using the MiSeq Reagent Kit v3 (600-cycle) with paired-end 300 bp reads.

Shotgun metagenomic analyses

For functional annotations, the paired-end raw data was uploaded to MG-RAST server (Rapid Annotation using Subsystems Technologies for Metagenomes)62. Sequences were annotated to functional categories against M5NR database using BLASTX at an e-value cutoff of 1 × 10−5. Results of general descriptors and annotations were downloaded from MR-RAST server for download analyses. Gene abundance was counted manually for cop, czc and ars genes, which are responsible for Cu, Pb/Zn and As resistance, respectively. For metagenomic assembly, the raw reads were trimmed using Skewer63 to have a Q > 25 and a minimum length of 200 bp. Quality trimmed reads were used for de-novo assembly using the open source software package Mira (4.0rc5)23 on a 24 core, 192 GB server with multithreading enabled. The seven tailings libraries were combined to improve the quality of assembly64. The contig dataset was then subjected to local BLASTN for searching of copA-like genes.

Local BLASTN

A local BLASTN method, similar to ARGAnnot by Gupta et al.26, was established to recall copA-like genes in the assembled metagenomic database. Briefly, nucleotide sequences annotated as copA in Genbank were retrieved manually and formed a local copA database (Supplementary Material 1). Then the assembled metagenomic dataset was aligned against the copA database using the embedded BLASTN method in BioEdit65. Then the results were sorted based on aligned length and a threshold of 40 bp was used for screening copA-like genes in the metagenomic dataset. All the contigs containing candidate copA were picked out manually and then subjected to gene finding using Glimmer66. For copA-like gene finding, the selected contigs were aligned against the Genbank database using BLASTN one-by-one and the sequence region annotated as copA or heavy metal resistance genes were compared with the open reader framework (ORF) found by Glimmer. The candidate ORF was then manually curated again against the Genbank database. The closest copA sequences were also retrieved for phylogenetic analysis. Maximum-likelihood phylogenetic trees were constructed for all the copA sequences in the database and selected found copA sequences from the tailings metagenome, using the method described below.

Local BLASTN against the copA database was done on a Window 7 computer equipped with dual-core 2.8 GHz CPU and 8 G RAM. The assembled dataset was cut into 6 sub-datasets and BLASTN for each dataset used up to 6 computing hours.

Specificity tests were done for the local BLASTN method using 1) 4 copA sequences included in the database (gi_294009986_Sphingobium japonicum; gi_258541105_Acetobacter pasteurianus; gi_347756788_Micavibrio aeruginosavorus; gi_162145846_Gluconacetobacter diazotrophicus), 2) three complete genomes (gi_198282148_A. ferrooxidans, gi_627776062_R. radiotolerans, and gi_220933193_T. sulfidophilus who are phylogenetically close to those of abundant species in the tailings) containing copA novel but with homologs in the copA database and 3) a complete genome (gb_CP002829.1_T. geofontis) belonging to phylum Thermodesulfobacteria which is not included in the database and probably containing novel copA.

Evolutionary trace analysis

Evolutionary trace was done following the method by Wilkins et al.67. Since microorganisms harbouring copA homologs can also be Cu-sensitive68, gene sequences for ET analysis in this study were selected based on three criteria (Table 2): 1) the microorganisms must be reported as Cu-resistant; 2) the full genome and/or complete sequence of plasmid(s) must be available in the Genbank; and 3) the gene must be annotated as copA or copA-like translocating genes in the full genome. In addition, five CopA protein sequences whose gene functions have been verified experimentally through mutagenesis or whose crystal structure has been resolved were also obtained from UniProt69. Information on active sites of these verified CopA was gathered from the literature10,16,17,18,19,50. Information on active sites of plasmid-encoded PsCopA was obtained from the predictions of UniPro since no crystal structure has been resolved so far for this group of CopA, and crystal structure of EcCueO and other multicopper oxidases were used as references. Furthermore, two multicopper oxidases, LccA (laccase) of Haloferax volcanii and CueO of E. coli for Cu homeostasis, and two P-type ATPases, CtpA of Mycobacterium smegmatis for cadmium transport and ZosA of Bacillus subtilis for Zn transport, 38 CopA sequences of known or highly possible to have Cu-resistance functions were aligned using the method of MUSCLE70 embedded within MEGA 671. The crystal structures of reference proteins, LccA, CueO, CtpA and ZosA, have also been resolved72,73,74,75. For active sites comparison, the Cu-binding motifs were searched manually based on the available information of verified proteins said above. For phylogenetic analysis, the alignment in FASTA format was used to determine the conserved regions using Gblocks 0.91 b online76. Two highly conserved regions, one at the C-terminal and one at the N-terminal, were found, and the variable beginning and tail parts were trimmed in MEGA 6. The aligned regions of the alignment was then used for the construction of a Maximum-Likelihood tree using default values within MEGA.

Screening of CopA containing active sites found in ET analysis

All the CopA sequences detected from the tailings metagenome were aligned with the reference CopA (those with verified functions) using the alignment method of MUSCLE, as described above. The active site regions were checked manually based on the alignment for screening of CopA, gaps were manually adjusted to refine the alignment, and those containing the conserved motifs were determined as highly possible to be functioning as P-type ATPase Cu translocating proteins.

Additional Information

How to cite this article: Li, X. et al. Assessing the genetic diversity of Cu resistance in mine tailings through high-throughput recovery of full-length copA genes. Sci. Rep. 5, 13258; doi: 10.1038/srep13258 (2015).

Supplementary Material

Supplementary Information
srep13258-s1.pdf (237.1KB, pdf)
Supplementary Information
srep13258-s2.pdf (597.7KB, pdf)

Acknowledgments

This project is financially supported by UQ Postdoctoral Fund, UQ Early Career Researcher Grants (ID ECR122) and Xstrata Copper Ltd. We thank Dr Gregory Baillie of the Institute for Molecular Bioscience within UQ for his help in shotgun sequencing.

Footnotes

Author Contributions All authors commented on the manuscript and helped with interpretation of the data. X.L. and Y.Z. initiated the concepts. X.L. and L.H. designed the experiments. X.L. performed the experiment, did the downstream bioinformatics analyses and wrote the draft of the paper. L.H. did all of the sampling. T.B. and X.L. did the MiSeq metagenomics sequencing. B.S. processed the raw Illumina sequencing data.

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