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Comparative Genomic Analysis Reveals the Distribution,Organization, and Evolution of Metal Resistance Genes in theGenus Acidithiobacillus
Liangzhi Li,a,b Zhenghua Liu,a,b Delong Meng,a,b Xueduan Liu,a,b Xing Li,a,b Ming Zhang,a,b Jiemeng Tao,a,b Yabing Gu,a,b
Shuiping Zhong,c,d Huaqun Yina,b
aSchool of Minerals Processing and Bioengineering, Central South University, Changsha, ChinabKey Laboratory of Biometallurgy of Ministry of Education, Central South University, Changsha, ChinacCollege of Zijin Mining, Fuzhou University, Fuzhou, Fujian, ChinadNational Key Laboratory of Comprehensive Utilization of Low-Grade Refractory Gold Ores, Shanghang, China
ABSTRACT Members of the genus Acidithiobacillus, which can adapt to extremelyhigh concentrations of heavy metals, are universally found at acid mine drainage(AMD) sites. Here, we performed a comparative genomic analysis of 37 strains withinthe genus Acidithiobacillus to answer the untouched questions as to the mechanismsand the evolutionary history of metal resistance genes in Acidithiobacillus spp. Theresults showed that the evolutionary history of metal resistance genes in Acidithioba-cillus spp. involved a combination of gene gains and losses, horizontal gene transfer(HGT), and gene duplication. Phylogenetic analyses revealed that metal resistancegenes in Acidithiobacillus spp. were acquired by early HGT events from species thatshared habitats with Acidithiobacillus spp., such as Acidihalobacter, Thiobacillus, Acidi-ferrobacter, and Thiomonas species. Multicopper oxidase genes involved in copperdetoxification were lost in iron-oxidizing Acidithiobacillus ferridurans, Acidithiobacillusferrivorans, and Acidithiobacillus ferrooxidans and were replaced by rusticyanin genesduring evolution. In addition, widespread purifying selection and the predicted highexpression levels emphasized the indispensable roles of metal resistance genes inthe ability of Acidithiobacillus spp. to adapt to harsh environments. Altogether, theresults suggested that Acidithiobacillus spp. recruited and consolidated additionalnovel functionalities during the adaption to challenging environments via HGT,gene duplication, and purifying selection. This study sheds light on the distribution,organization, functionality, and complex evolutionary history of metal resistancegenes in Acidithiobacillus spp.
IMPORTANCE Horizontal gene transfer (HGT), natural selection, and gene duplica-tion are three main engines that drive the adaptive evolution of microbial genomes.Previous studies indicated that HGT was a main adaptive mechanism in acidophilesto cope with heavy-metal-rich environments. However, evidences of HGT in Acidi-thiobacillus species in response to challenging metal-rich environments and themechanisms addressing how metal resistance genes originated and evolved in Acidi-thiobacillus are still lacking. The findings of this study revealed a fascinating phe-nomenon of putative cross-phylum HGT, suggesting that Acidithiobacillus spp. re-cruited and consolidated additional novel functionalities during the adaption tochallenging environments via HGT, gene duplication, and purifying selection. Alto-gether, the insights gained in this study have improved our understanding of themetal resistance strategies of Acidithiobacillus spp.
KEYWORDS Acidithiobacillus spp., comparative genomics, evolution, horizontal genetransfer, metal resistance
Citation Li L, Liu Z, Meng D, Liu X, Li X, ZhangM, Tao J, Gu Y, Zhong S, Yin H. 2019.Comparative genomic analysis reveals thedistribution, organization, and evolution ofmetal resistance genes in the genusAcidithiobacillus. Appl Environ Microbiol85:e02153-18. https://doi.org/10.1128/AEM.02153-18.
Editor Shuang-Jiang Liu, Chinese Academy ofSciences
Copyright © 2019 American Society forMicrobiology. All Rights Reserved.
Address correspondence to Delong Meng,[email protected], or Huaqun Yin,[email protected].
Received 5 September 2018Accepted 19 October 2018
Accepted manuscript posted online 2November 2018Published
ENVIRONMENTAL MICROBIOLOGY
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The genus Acidithiobacillus is composed of a group of obligatory acidophilic chemo-lithotrophic Gram-negative bacteria that conduct energy conservation by oxidizing
reduced inorganic sulfur compounds (RISCs), such as thiosulfate (S2O32�), hydrogen
sulfide (H2S), and elemental sulfur (S0). This attribute has been exploited in thebioleaching of ores and has also been the main cause of the accelerating formation ofpolluting acid mine drainage (AMD) water, which brings disruptive consequences forterrestrial and aquatic ecosystems (1–5). Currently, 8 species have been reported in thegenus Acidithiobacillus, including Acidithiobacillus albertensis, Acidithiobacillus caldus,Acidithiobacillus cuprithermicus, Acidithiobacillus ferridurans, Acidithiobacillus ferriphilus,Acidithiobacillus ferrivorans, Acidithiobacillus ferrooxidans, and Acidithiobacillus thiooxi-dans. Acidithiobacillus species are universally found at AMD sites where highly toxicinorganic forms of heavy metals and metalloids such as arsenic, mercury, copper,cadmium, and cobalt often dominate (1, 6–9). As revealed in previous studies, strains ofA. ferrooxidans can adapt up to 800 mM Cu2�, 84 mM As3�, 1,071 mM Zn2�, 500 mMCd2�, 1,000 mM Ni2�, and 50 mM Hg2� (10, 11), while strains of A. thiooxidans and A.ferrooxidans are able to adapt up to 120 and 100 mM As5� (12). It is obvious thatAcidithiobacillus species surviving in acidic metal-rich leaching environments mustpossess certain highly efficient and advanced metal resistance mechanisms, renderingthem ideal research models to improve our understanding of metal resistance. Thereare four main strategies that acidophiles generally utilize to increase resistance tometals, including the chelation and precipitation of toxic metals, the efflux of the toxicmetals out of the cells by molecular pumps, the enzymatic conversion of metal ions toless toxic forms, and/or enhanced cell walls and membranes that are impermeable totoxic metal ions (13). These strategies are closely related to the genetic properties of thebacteria. The genes conferring toxic metal resistance are prone to clustering togetheras gene operons in Acidithiobacillus genomes, such as the mer operons that confermercury resistance (14). However, the mechanisms driving the clustering of genes intooperons are far from clear. For example, the mer operon generally contains merA, merD,merR, merP, merC, and merT, although their organization may be different. The reportedgenes related to mercury, arsenate, cadmium, zinc, cobalt, and copper resistance andtheir functions in bacteria are summarized in Table 1.
Horizontal gene transfer (HGT), natural selection, and gene duplication are threemain engines that drive the adaptive evolution of microbial genomes, whereas theirrelative importance is still ambiguous (15, 16). HGT events generally occur via mobilegenetic elements (MGEs) carrying functional genes that enhance the adaptability andversatility of the acceptors. Recent studies analyzing gene-transfer webs among a vastnumber of sequenced genomes revealed that more than 20% of the microbial genesmay be acquired through HGT (17, 18). MGEs such as plasmids, phages, genomic islands(GIs), transposons, and insertion sequences are indicative of HGT events. GIs, which arelarge chromosomal regions usually found adjacent to or integrated within tRNA genes,represent a versatile gene pool (19). Deviant G�C contents, as well as incongruencesbetween phylogenies of functional genes and species, can also be used as indicators ofHGT events (20–23). Some previous studies have described the overall metal resistancemechanisms in acidophiles and indicated that HGT was a main adaptive mechanism bywhich the acidophiles cope with heavy-metal-rich environments (13, 24–27); for exam-ple, it was reported that A. ferrooxidans ATCC 53993 harbored GIs, inserted after a tRNAmetabolism related gene (MiaB type), containing genes encoding mercury detoxifica-tion determinants (merA, merC, and merR) and copper translocating P-type ATPase (13).
However, direct evidence of HGT in other Acidithiobacillus species in response tochallenging metal-rich environments and the mechanisms addressing how metal re-sistance genes originated and evolved in Acidithiobacillus are still lacking. The rapiddevelopment of next-generation sequencing technologies has enriched the publicdatabase with numerous sequenced genomes. Exploiting these resources, large-scalecomparative genomic analyses are improving our understanding of extensive geneticdiversity and evolutionary history. To date, 8 species have been reported in genusAcidithiobacillus, and strains of 6 species (A. thiooxidans, A. ferrooxidans, A. caldus, A.
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ferrivorans, A. albertensis, and A. ferridurans) have been sequenced. To reveal the genetictraits and phylogenetic history of metal resistance in genus Acidithiobacillus, a com-parative genomic analysis of 37 strains within the genus Acidithiobacillus was carriedout. In addition, molecular phylogenetic analysis, gene contents, molecular conserva-tion, and linear analyses along with selective pressure and codon usage analyses wereused to explore the evolution of toxic metal resistance genes in the genus Acidithio-bacillus (28–31). Our results provide explicit evidence that HGT played a crucial role indriving the evolution of heavy metal resistance in Acidithiobacillus species survivingextreme environments. The insights gained in this study improve our understanding ofthe complex evolutionary history of toxic metal resistance genes in Acidithiobacillusspp. and its roles in the biogeochemical cycling of different metals.
RESULTSGenomic features. A summary of the features of 38 available sequenced genomes
of Acidithiobacillus spp. are listed in Table 2. The G�C contents of the 38 genomesrange from 52.6% to 61.5%. These genomes vary in size by approximately 2.48 Mb(ranging from 1.70 to 4.18 Mb) with coding sequence (CDS) numbers ranging from1,617 to 4,359, suggesting substantial strain-to-strain variations.
Phylogenic analyses and pangenome analyses. To associate the distributions ofmetal resistance genes in Acidithiobacillus spp. with their phylogenetic affiliations, weconstructed a phylogenetic tree of the Acidithiobacillus spp. and closely related species
TABLE 2 General features of bacterial genomes used in this study
Species Strain GenBank accession no. Level Size (Mb) GC% No. of genes No. of proteins
A. thiooxidans A01 GCA_000559045.1 Contig 3.8 53.1 4,131 3,719A. thiooxidans Licanantay GCA_000709715.1 Contig 3.9 52.8 4,359 3,774A. thiooxidans DMC GCA_001705625.1 Contig 3.9 53.1 4,196 3,772A. thiooxidans A02 GCA_001705645.1 Contig 3.7 53.0 4,034 3,639A. thiooxidans GD1-3 GCA_001705695.1 Contig 3.9 52.9 4,225 3,824A. thiooxidans BY-02 GCA_001705725.1 Contig 3.8 53.1 4,196 3,683A. thiooxidans JYC-17 GCA_001705755.1 Contig 3.8 53.1 4,176 3,737A. thiooxidans DXS-W GCA_001705805.1 Contig 3.9 52.9 4,229 3,833A. thiooxidans ZBY GCA_001756595.1 Contig 3.8 53.2 4,106 3,714A. thiooxidans CLST GCA_002079865.1 Scaffold 4.0 52.4 4,045 3,619A. thiooxidans ATCC 19377 GCA_000227215.2 Contig 3.0 53.2 3,106 2,847A. ferrooxidans BY0502 GCA_001652185.1 Contig 3.0 56.8 3,186 2,821A. ferrooxidans CCM 4253 GCA_003233765.1 Contig 3.2 58.6 3,278 3,049A. ferrooxidans IO-2C GCA_003309025.1 Contig 2.7 58.7 2,822 2,620A. ferrooxidans YQH-1 GCA_001418795.1 Scaffold 3.1 58.6 3,189 2,956A. ferrooxidans DLC-5 GCA_000732185.1 Contig 4.2 57.6 4,289 4,006A. ferrooxidans ATCC 23270 CP001219.1 Complete 3.0 58.8 3,087 2,909A. ferrooxidans ATCC 53993 CP001132.1 Complete 2.9 58.9 2,926 2,779A. ferrooxidans Hel18 GCA_001559335.1 Contig 3.1 58.6 3,179 2,955Acidithiobacillus sp. SH GCA_002847505.1 Contig 2.9 54.3 2,987 2,799Acidithiobacillus sp. NORP59 GCA_002733395.1 Contig 3.6 52.6 3,525 3,260Acidithiobacillus sp. GGI-221 GCA_000179815.2 Contig 3.2 58.6 4,079 4,007Acidithiobacillus sp. UBA2486 GCA_002341825.1 Contig 1.7 58.5 1,617 1,596A. albertensis DSM 14366 GCA_001931655.1 Contig 3.5 52.5 3,622 3,354A. ferrivorans SS3 CP002985.1 Complete 3.2 56.6 3,206 3,016A. ferrivorans PRJEB5721 GCA_900174455.1 Scaffold 3.5 56.5 3,555 3,296A. ferrivorans CF27 GCA_000750615.1 Contig 3.4 56.4 3,557 3,236A. ferrivorans YL15 GCA_001685225.1 Contig 3.0 56.6 3,087 2,778A. ferrivorans PQ33 GCA_001857665.2 Contig 3.3 56.6 3,382 3,077A. ferrivorans 21-59-9 GCA_002255305.1 Scaffold 2.7 59.0 3,208 2,909A. caldus ATCC 51756 GCA_000175575.2 Complete 3.0 61.4 3,005 2,849A. caldus SM-1 GCA_000221025.1 Complete 3.2 60.9 3,254 3,106A. caldus MTH-04 GCA_001650235.2 Complete 3.0 61.5 3,102 2,713A. caldus DX GCA_001756675.1 Contig 3.1 61.0 3,179 2,861A. caldus ZBY GCA_001756725.1 Contig 3.2 61.0 3,210 2,902A. caldus ZJ GCA_001756745.1 Contig 3.1 61.0 3,223 2,884A. caldus S1 GCA_001756775.1 Contig 2.8 60.9 2,906 2,396A. ferridurans JCM 18981 AP018795.1 Complete 2.9 58.4 3,043 2,802
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on the basis of 16S rRNA gene sequences using neighbor-joining (NJ) methods (see Fig.S1 in the supplemental material). The results showed that Acidithiobacillus species arephylogenetically close to Thermithiobacillus spp. Since no 16S rRNA gene sequencecould be retrieved from the genome of Acidithiobacillus sp. strain NORP59, we con-structed a phylogenetic tree of the 38 Acidithiobacillus genomes with their phyloge-netic affiliations on the basis of whole-genome sequences (see Fig. S2). The resultsshowed that Acidithiobacillus sp. NORP59 forms a clade with Thioglobus sp. strainMED-G25, distant from the standard strains of Acidithiobacillus spp. In addition, Acidi-thiobacillus sp. NORP59 showed low values of BLASTN-based average nucleotide iden-tity (ANI; average 75%) and tetranucleotide composition regression (Tetra; average0.758) in comparison with those of other available Acidithiobacillus strains (see TablesS1 and S2), indicating that strain NORP59 was not a member of genus Acidithiobacillus;thus, it was excluded from any further analyses.
To reveal the genomic features specific to each strain, we identified orthologousgroups among the 37 Acidithiobacillus genomes using the Bacterial Pan GenomeAnalysis (BPGA) pipeline (32). Our analysis of the 37 genomes revealed a pangenomecontaining 141,937 putative protein-coding genes in the genus Acidithiobacillus. Ofthese 141,937 genes, 16,317 (11.5%) were clustered into the core genome of Acidithio-bacillus spp., 95,704 (67.4%) were represented in the accessory genome, and 5,332(3.8%) were identified as strain-specific genes. The numbers of specific genes rangedfrom 0 to 811 (Fig. 1A). The results of BacMet database annotation revealed that theaccessory genome had a higher proportion (8.2% [7,846 genes]) of genes involved inheavy metal resistance activities (Fig. 1C) than the core genome (5.2% [851 genes]) anda higher proportion of unique genes (0.3% [16 genes]). We constructed a robust
FIG 1 Pangenome analysis of strains in the genus Acidithiobacillus. (A) Petal diagram of the pangenome. Each strain is represented by a colored oval. The centeris the number of orthologous coding sequences shared by all strains (i.e., the core genome). Numbers in nonoverlapping portions of each oval show thenumbers of CDSs unique to each strain. The total numbers of protein-coding genes within each genome are listed below the strain names. (B) Mathematicalmodeling of the pangenome and core genome of Acidithiobacillus. (C) Proportions of genes involved in heavy metal resistance in unique genes, accessorygenome, core genome, and pangenome according BacMet database annotation.
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phylogenetic tree of the 37 genomes on the basis of the concatenation of the 110single-copy core genes in each genome using the NJ method (see Fig. S3). Thephylogenetic trees, inferred using ML and UPGMA methods (see Fig. S4 and S5), werecongruent with the NJ phylogenetic tree, showing that the phylogenetic tree washighly reliable. A chronogram for NJ phylogenetic tree was constructed with a calibra-tion point to explore the divergence times of Acidithiobacillus (Fig. 2). The most recentcommon ancestor (MRCA) of Acidithiobacillus (i.e., the emergence time of A. caldus) wasestimated to have emerged around 800 million years ago (MYA) (Fig. 2), whereas theMRCA of A. thiooxidans, A. ferrooxidans, A. ferrivorans, and A. ferridurans was estimatedto have emerged around 439 MYA (Fig. 2). These well-supported core gene treesshowed that some strains in different reported species were grouped together, such asA. ferridurans JCM 18981 and A. ferrooxidans IO-2C, Acidithiobacillus sp. strain GGI-221and strains of A. ferrooxidans, and A. albertensis DSM 14366 and strains of A. thiooxidans.These results suggested that there were mistakes in the classification of Acidithiobacillusspp. The ANI approach was further applied to evaluate the genomic similarity ofmicroorganisms (33). The ANI results (Table S1) justified the conclusion from thephylogenetic analysis. All 37 strains could be classified into 9 species based on an ANIof �96%. A comparison of strains JCM 18981 and IO-2C resulted in a high ANI (98.9%),suggesting that they belong to the same species (A. ferridurans). A comparison between
FIG 2 Chronogram of the 37 Acidithiobacillus strains. The value near each internal branch is the estimated emerge timefor that branch. Nodes with fossil record corrections are indicated with a red star.
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strains A. albertensis DSM 14366 and A. thiooxidans spp. resulted in a high ANI (97.3%),suggesting that they belong to the same species (A. thiooxidans). A comparisonbetween strains GGI-221 and A. ferrooxidans spp. resulted in a high ANI (99.5%),suggesting that they belong to the same species (A. ferrooxidans). Thus, in this article,strain A. ferrooxidans IO-2C was renamed A. ferridurans IO-2C, strain A. albertensis DSM14366 was renamed A. thiooxidans DSM 14366, and Acidithiobacillus sp. strain GGI-221was renamed A. ferrooxidans GGI-221.
Model extrapolation of the pangenome of Acidithiobacillus. Vernikos et al. (34)stated that a mathematical extrapolation of the pangenome would be highly reliableprovided that sufficient genomes (more than five) are involved. As shown by thededuced power law regression function (Ps [n] � 4451.42n0.36465) (Fig. 1B), the pange-nome of Acidithiobacillus had a parameter (�) of 0.365, falling into the range of0 � � � 1, suggesting that the pangenome is open (35). The deduced exponentialregression (Fc [n] �2052.65e�0.0714494n) revealed that the extrapolated curve of thecore genome followed a steep slope, reaching a minimum of 441 gene families after the37th genome was added (Fig. 1B). The numbers of core genes were relatively constant,such that an extra genome added would not significantly affect the size of coregenome, which was consistent with the notion that the core genes were conservedgenes universally present in all strains (36).
Identification of putative mobile genetic elements. Mobile genetic elements(MGEs) are specific genome segments capable of intra- and extracellular movements,carrying gene(s) encoding putative functions, which are considered indicators of HGTevents (37). In this study, MGEs including transposases, integrases, genomic islands(GIs), and phage-associated genes were identified and compared in all genomes. EachAcidithiobacillus genome in this study contained a number of transposons (see TableS3). The number of transposon copies per genome ranged from 143 (A. thiooxidansstrain CLST) to 334 (A. caldus strain SM-1). Members of the IS3, IS5, IS21, and IS110families were the most common. The numbers of GI copies per genome ranged from25 (A. ferrivorans strain SS3) to 66 (A. thiooxidans CLST) (see Table S4). The numbers ofprophages and/or prophage remnants ranged from 0 to 6 (see Table S5). The BacMetannotation results revealed that the genomes of Acidithiobacillus spp. contained a vastrepertoire of metal resistance genes, with an average 160 BacMet hits in each genome.A visualization of the chromosomes of representative strains of different species ofAcidithiobacillus (see Fig. S6) revealed that many of these genes were distributed withingenomic island regions or flanking other mobile genetic elements such as transposons,indicating that putative HGT events contributed greatly to the adaptive survival ofAcidithiobacillus spp. in toxic metal-rich niches.
Estimation of gene family gains and losses. We further investigated how themetal-resistance-related gene families might have been gained and lost during theevolution of A. thiooxidans, A. ferrooxidans, A. ferrivorans, A. ferridurans, and A. caldus(see Fig. S7). Gene family gains were observed on the branches from A. ferridurans, A.caldus, and the most recent common ancestor (MRCA) of A. ferridurans and A. ferrooxi-dans. In all, new gene families arose over time probably via HGT, with only a few genefamilies being lost.
Case study of Hg-related genes. The overall distributions and organization of Hgresistance genes in 37 Acidithiobacillus strains are summarized in Fig. S8. Hg resistancegenes merA, merC, and merR were detected in most Acidithiobacillus genomes, whilemerD, merP, and merT were only detected in A. ferrooxidans, A. thiooxidans, and A.ferrivorans genomes. The gene merB that encodes organomercury lyase was missing inAcidithiobacillus genomes. The mer genes were prone to group together as mer clustersin Acidithiobacillus genomes, which could be categorized into six subgroups (mer-CAD, merRTPA, merTAR, merACR, merAR, and merA). Some strains in this studypossessed two different mer clusters in their genomes (Fig. S8). However, the merclusters were not detected in Acidithiobacillus sp. strain SH, possibly resulting fromincomplete sequencing.
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(i) Origin and evolution of mer clusters. The divergent distribution and organi-zation of mer genes in Acidithiobacillus raised a question as to their origin andevolution. The deviant G�C contents can be used to detect HGT (20, 21). Herein, wedetected the G�C contents of mer clusters and their corresponding genomes. Theresults showed that the G�C contents of the merCAD clusters were higher than thoseof the genomes in A. ferrooxidans strains (62.8% versus 57.6% to 61.0%, respectively);the G�C contents of respective merAPTR clusters were higher than those of thegenomes in A. thiooxidans strains (60.5% to 61.1% versus 52.4% to 53.2%), A. ferrooxi-dans strains (62.6% to 62% versus 58.6%), and A. ferridurans strains (63.7% to 64.1%versus 58.4% to 61.3%). The merTAR and merACR gene clusters also showed variationsof G�C contents between mer gene clusters and their corresponding genomes (see Fig.S9). To further elucidate the evolution of the mer gene clusters, we compared thechromosomal regions flanking the mer gene clusters among the 37 Acidithiobacillusstrains and found that the genes in the upstream and downstream regions wereconserved among strains of the same species except for merACR clusters (see Fig. S10).Several indicators of HGT, such as flanking tRNA genes and genes encoding mobileelement proteins, were found near the merRTPA and merACR gene clusters. The resultsshowed that strains of the same species generally shared identical insertion sites,whereas strains from different species had different insertion sites (Fig. S10), suggestingthat mer clusters may be acquired more than once in Acidithiobacillus species. merACRgene clusters were found in different insertion sites regardless of whether the specieswere different or the same, suggesting that merACR clusters may be acquired viadifferent HGT events (Fig. S10).
To elucidate the origin of mer genes clusters in Acidithiobacillus, a NJ phylogenetictree was constructed on the basis of the MerA protein sequences. As shown in Fig. 3,the strains were separated into three group: group I included merCAD and merRTPAclusters, group II represented merACR clusters, and group III contained merRTAC, merTA,and merAR clusters and stand-alone merA genes. The phylogenetic tree revealed thatthe group I and mer genes of Burkholderiales members were sister groups, while thosein group II were more similar to mer genes from Acidihalobacter spp., and those ingroup III were more similar to mer genes of Thiobacillus spp. and Acidiferrobacterthiooxydans, implying that the group I mer cluster may have been acquired via HGTfrom members of Burkholderiales, the group II mer cluster from Acidihalobacter spp., andthe group III mer cluster from Thiobacillus spp. or Acidiferrobacter spp. in early evolu-tionary history. In addition, A. thiooxidans Licanantay containing cluster merRTACbranches near the base of the merTA and merAR clade. We inferred that among groupIII, gene clusters merRTAC, merTA, and merAR likely originated from A. thiooxidans,followed by gene loss in A. thiooxidans and HGT to A. ferrivorans. Besides, onlystand-alone merA genes were detected in strains of A. caldus. The phylogenetic treerevealed that merA1 and merA2 genes of A. caldus form separate groups, in whichmerA2 genes of A. caldus were more similar to those from A. ferrivorans. The genesupstream and downstream were conserved for merA1 and merA2 genes, respectively(Fig. S10). Several genes encoding mobile element proteins which are indicators of HGTwere found near the merA2 genes. All these indicated that merA2 genes of A. caldusarose from early HGT events, most likely from members within genus Acidithiobacillus.
Case study of As-related genes. The overall distribution and organization of Asresistance genes in Acidithiobacillus spp. are summarized in Fig. S11. As resistancegenes (ars genes), including arsR, arsB, and arsC, were detected in all Acidithiobacillusgenomes, while arsA and arsD were only detected in A. ferrivorans genomes and somestrains from other species, including A. ferrooxidans strain DLC-5 and A. ferriduransIO-2C (A. ferrooxidans IO-2C), and arsH genes were only detected in genomes of A.ferrooxidans (within arsCRBH cluster) and A. thiooxidans (stand alone) (Fig. S11). Stand-alone arsM genes were also detected in all species. However, aio and arr, involved inarsenite oxidation and respiratory reduction of arsenate, were not found in Acidithio-bacillus genomes, suggesting that cytoplasmic As(V) reduction and As(III) extrusion are
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FIG 3 Neighbor-joining (NJ) phylogenetic tree of the MerA protein sequences derived from Acidithiobacillusspecies strains and other representative species. Bootstrap values indicated at each node are based on a totalof 1,000 bootstrap replicates. Branches representing group I mer clusters are in green, those representinggroup II mer clusters are in blue, and those representing group III mer clusters are in red and pink.
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the main As resistance strategies used in genus Acidithiobacillus spp. The ars genes inAcidithiobacillus genomes grouped together as ars clusters, including four subgroups,arsADCR, arsRB, arsBRC, and arsBRCH. The arsB gene of A. caldus strain MTH-04 wasintercepted by an exogenous insertion sequence, which may result in malfunction ofthis arsB gene, rendering A. caldus MTH-04 vulnerable to toxic arsenic ions (Fig. S11).
(i) Origin and evolution of ars clusters. The distribution and organization of arsgenes in Acidithiobacillus raise a question as to their evolution. The results showed thatthe G�C contents of the arsADCR clusters were higher than those of the genomes inA. ferrivorans strains (60.0% to 63.3% versus 56.4% to 58.6%, respectively); the G�Ccontents of respective arsBRC clusters were slightly higher than those of the genomesin A. caldus strains (62.3% to 63.1% versus 60.9% to 61.5%) and A. thiooxidans strains(53.1% to 55.3% versus 52.4% to 54.3%), showing the variation of G�C contentsbetween clusters and the corresponding genomes (see Fig. S12). A comparison of thechromosomal regions flanking the ars gene clusters among Acidithiobacillus strainsrevealed that the genes in the upstream and downstream regions were conservedamong strains of the same species (see Fig. S13). There were several genes encodingmobile element proteins (transposases or recombinases) located near the arsADCR andarsBRC clusters, and there were tRNA genes flanking the arsBRC clusters of A. caldus,indicating putative HGT events (Fig. S13). Strains of the same species share the sameinsertion sites, whereas strains of different species had different insertion sites, sug-gesting that ars clusters may have been acquired more than once via different HGTevents. The flanking regions of the arsBRC gene clusters in strain A. thiooxidans werehomologous to the corresponding regions of strains A. ferrivorans SS3 and A. ferrooxi-dans ATCC 23270 that have no arsBRC; the same phenomenon was found in arsBR geneclusters and arsH genes. These results suggested that ars genes may have beenacquired by different HGT events followed by gene loss in some strains (Fig. S13).
To gain insights into the origin of ars genes clusters, NJ phylogenetic trees wereconstructed on the basis of on the concatenated ArsBRC protein sequences and ArsADprotein sequences. As shown in Fig. 4, the phylogenetic analysis revealed that thestrains possessing arsBRC clusters formed a monophyletic clade. Acidithiobacillus spp.likely obtained the arsBRC cluster via HGT from Acidihalobacter, followed by rearrange-ment and gene loss, since ars gene clusters of Acidihalobacter prosperus branch near thebase of the clade of Acidithiobacillus. NJ phylogenetic trees based on concatenatedArsAD protein sequences showed that the ars clusters of Acidithiobacillus and arsclusters of Alcaligenes faecalis, Pseudomonas hunanensis, and Sulfuriferula spp. weresister groups (Fig. 5), implying that Alcaligenes faecalis, Pseudomonas hunanensis, andSulfuriferula spp. are likely donors of the arsADCR cluster.
Stand-alone arsM genes encoding As(III) S-adenosylmethyltransferase were foundlocated distant from canonical ars gene clusters. NJ phylogenetic trees constructed onthe basis of the ArsM protein sequences (see Fig. S14) revealed that the Acidithiobacillusstrains formed three separate subgroups, of which subgroup I comprises ArsMs from A.thiooxidans, A. ferrooxidans, A. ferridurans, A. ferrivorans, and Acidithiobacillus sp. strainSH, grouping with ArsM from Thiomonas. Subgroup II comprises two deviant ArsMsfrom A. ferrooxidans and A. thiooxidans (GenBank accession no. WP_113525444.1 andWP_080707752.1, respectively), grouping with ArsMs from Melaminivora and Pseu-domonas. Subgroup III comprises ArsMs from A. caldus, grouping with ArsMs frommembers of Rhodospirillales. Incongruences between the phylogenies of ArsMs andspecies imply that the subgroup I arsM genes may have been acquired via HGT frommembers of Thiomonas, subgroup II arsM genes from members of the genera Melamini-vora and Pseudomonas, and subgroup III arsM genes from members of Rhodospirillalesin early evolutionary history.
Case study of Cd-, Zn-, Co-, and Cu-related genes. Gene clusters encodingCzc/CusABC homologs, which are involved in the detoxification of divalent cations(cadmium and zinc and cobalt) (38, 39) and monovalent cations (copper and silver) (13,40, 41), were detected in all Acidithiobacillus genomes (see Fig. S15). In addition, genes
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encoding CzcD and genes encoding copper-translocating P-type ATPase (Cop) weredetected in all Acidithiobacillus genomes, most of which located next to czcABC geneclusters. However, genes that encode multicopper oxidase were only detected in strainsof A. thiooxidans and A. caldus. The above-mentioned genes represent the dominantgenetic traits that contribute resistance to Cd, Zn, Co, and Cu in Acidithiobacillusgenomes. Considerable numbers of additional copies of czc-like and cop-like geneswere also present. The czc clusters were divided into two subgroups, czcABC and czcCAB(Fig. S15). The gene clusters in the organization of czcABC were observed in strainsacross all species of Acidithiobacillus, of which strains of A. ferrooxidans and A. ferridu-
FIG 4 Neighbor-joining phylogenetic tree of concatenated ArsBRC protein sequences derived from Acidithiobacillus species strains and other representativespecies. Bootstrap values indicated at each node are based on a total of 1,000 bootstrap replicates. Branches representing ars clusters of Acidithiobacillus spp.are in blue.
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rans possess other copies of czcABC located on different sites, suggesting that czcclusters may be acquired more than once. There are also tRNA genes and genesencoding mobile element proteins flanking czcABC clusters (Fig. S15). Each A. caldusstrain possesses a czcCAB cluster in addition to an existing czcABC cluster (Fig. S15). Theczc gene clusters exhibited more than 90% identity within each subgroup and over 80%identity between two subgroups. Actually, the czc genes were detected in all strains ofAcidithiobacillus. The genes in the upstream and downstream regions of a czcABCcluster were conserved among strains of all species of Acidithiobacillus (Fig. S15A),which indicated that this czcABC gene cluster was acquired before the divergence ofgenus Acidithiobacillus. Notably, cop genes encoding copper-translocating P-typeATPase located upstream of the czcABC clusters are conserved across all species ofAcidithiobacillus (Fig. S15A).
(i) Origin and evolution of czc clusters. The G�C contents of the czcABC clustersare higher than those of the genomes in Acidithiobacillus spp. (62.2% to 64.2% versus
FIG 5 Neighbor-joining phylogenetic tree of concatenated ArsAD protein sequences derived from Acidithiobacillus species strains andother representative species. Bootstrap values indicated at each node are based on a total of 1,000 bootstrap replicates. Branchesrepresenting ars clusters of Acidithiobacillus spp. are marked in red.
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52.9% to 61.5%, respectively) except in A. caldus, in which the G�C contents of theclusters are lower than of the A. caldus genomes (59.4% to 59.5% versus 60.9% to61.4%, respectively); the G�C contents of respective czcBAC clusters are higher thanthose of the genomes in A. caldus strains (64.9% to 66.0% versus 60.9% to 61.5%,respectively), presenting variations of G�C contents between clusters and the corre-sponding genomes (see Fig. S16). To gain insights into the origin of czc genes clustersin Acidithiobacillus, a NJ phylogenetic tree was constructed on the basis of concate-nated CzcABC protein sequences. As shown in Fig. 6, the strains possessing czcABC andczcBAC clusters formed separate groups. Notably, the phylogeny reveals that theczcABC of Acidithiobacillus and czc clusters of Thiomonas spp. and Acidihalobacter spp.are sister groups, whereas czcBAC of Acidithiobacillus and czc clusters of another clade
FIG 6 Neighbor-joining phylogenetic tree of concatenated CzcA, CzcB, and CzcC protein sequences derived from Acidithiobacillus species strains and otherrepresentative species. Bootstrap values indicated at each node are based on a total of 1,000 bootstrap replicates. Branches representing czcABC clusters arein green; czcBAC clusters formed in another group are in blue.
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of Thiomonas spp. are sister groups. These results implied that the czcABC cluster mayhave been acquired via HGT from members of Acidihalobacter and Thiomonas, whileczcBAC may have been acquired from members of Thiomonas.
(ii) mco genes in Acidithiobacillus spp. Stand-alone mco genes that encodemulticopper oxidase, which oxidizes Cu� to the less toxic Cu2� form, were found in allstrains of A. thiooxidans and A. caldus in this study. An NJ phylogenetic tree based onthe multicopper oxidase protein sequences revealed that the multicopper oxidases ofAcidithiobacillus were adjacent to those of Acidihalobacter spp., implying that Acidithio-bacillus and Acidihalobacter have a common multicopper oxidase ancestor (see Fig.S17). Synteny of the chromosomal regions flanking the mco genes showed thatmulticopper oxidase was probably lost in strains of A. ferrivorans, A. ferridurans, and A.ferrooxidans (see Fig. S18).
(iii) czc-like and cop-like genes in Acidithiobacillus. Our studies revealed thatthere are multiple czc-like genes and cop-like genes present in the 37 genomes,including 1 to 5 pairs of czcA-like genes, 1 to 5 pairs of czcB-like genes, 1 to 4 pairs ofczcC-like genes, 1 to 4 pairs of czcD-like genes, and 1 to 3 pairs of cop-like genes (seeFig. S20, S21, S22, S23, and S24). An alignment of residues of CzcA and CzcA-likeproteins revealed that the “DDE” motifs essential for CzcA function (42) are highlyconserved (see Fig. S25). Alignments of residues of Cop and Cop-like proteins revealedthat the metal binding domains (CASC . . . . CASC), translocation domains (CPCAMGLA),and phosphorylation domains (FDKTGTLT) (43) are conserved, indicating that thesegenes may be functional (see Fig. S26). A phylogenetic analysis revealed that czc-likeand cop-like genes from the same species clustered together and that there wereincongruences between the phylogenies of functional genes and species, which sug-gested that multiple czc-like and cop-like genes may have arisen from gene duplicationand HGT (Fig. S20, S21, S22, S23, and S24).
Assessment of functionality of metal resistance genes. We also assessed indi-rectly the functionality of above-mentioned metal-resistance-related genes by assess-ing the strength of natural selection acting on these genes, as well as the codonadaption indexes (CAIs) of these genes. The results showed that 83.3% of the above-mentioned metal resistance genes had a rate of nonsynonymous substitutions that waslower than the rate of synonymous substitutions (dN/dS � 1), suggesting purifyingselection on these genes (Fig. 7 and Table S6). Only a merA gene from Acidithiobacillusthiooxidans strain A02 (dN/dS � 1.51), a cop gene from Acidithiobacillus ferrooxidansstrain CCM 4253 (dN/dS � 1.39), and an arsH gene from Acidithiobacillus ferridurans JCM18981 (dN/dS � 1.82) showed dN/dS ratios of �1, indicating that they might be under
FIG 7 Distributions and ranges of selection pressures on different metal resistance genes of Acidithio-bacillus spp.
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positive selection. The lowest dN/dS ratio was observed for arsB genes (averagedN/dS � 0.06) and arsM genes (average dN/dS � 0.04), showing strong purifyingselection. All these metal resistance genes with dN/dS ratios of �1 were supposed tobe most essential for the growth of Acidithiobacillus spp. However, merA genes from A.ferrooxidans, arsR genes from A. thiooxidans, merC genes from A. caldus, merP genesfrom A. thiooxidans and A. ferridurans, merC genes from A. caldus, mco genes from A.thiooxidans, and some cop-like and czc-like genes showed dN/dS ratios of �1, indicatingthat the selection force had relaxed on them (Fig. 7 and Table S6). Highly expressedgenes in bacteria often have a stronger codon bias than genes expressed at lowerlevels, due to translational selection (44–46). In this study, the expression levels ofmetal-resistance-related genes in the Acidithiobacillus spp. were predicted using theCAI as a numerical estimator (47, 48). Approximately 53.2% of the metal resistancegenes were predicted to be highly expressed genes using CAI cutoff values of greaterthan 0.73 in A. thiooxidans, greater than 0.75 in A. caldus, and greater than 0.76 in A.ferrooxidans, A. ferrivorans, and A. ferridurans (Fig. 8 and see Table S7). The cutoff valueswere indicated with average CAI values of Tu genes in each species.
DISCUSSION
In the present work, we characterized the genomic features of Acidithiobacillus spp.,focusing on the distribution, organization, complex evolutionary history, and function-ality of metal resistance genes. With the rapid advances in genomic analysis technol-ogy, new measurements such as ANI are being developed to evaluate the genomicsimilarity between bacteria (33). In this study, we redetermined the phylogenetic statusof 38 Acidithiobacillus spp. by using ANI and phylogenetic trees. Our results showedthat strains DSM 14366, GGI-221, and IO-2C were misnamed. We therefore reclassifiedstrain A. ferrooxidans IO-2C as A. ferridurans IO-2C, A. albertensis DSM 14366 as A.thiooxidans DSM 14366, and Acidithiobacillus sp. strain GGI-221 as A. ferrooxidansGGI-221. Low ANI values for Acidithiobacillus sp. strain NORP59 in comparison to thoseof other Acidithiobacillus strains and a distant phylogenetic relation indicated that strainNORP59 is not a member of genus Acidithiobacillus. Our study revealed the complextaxonomy of genus Acidithiobacillus and demonstrated the usefulness and accuracy ofadvanced bioinformatics measurements such as ANI in rectifying inaccurate identifica-tions from previous studies.
There is generally a positive correlation between the abundance of mobile geneticelements and the frequency of HGT (49). We found that Acidithiobacillus spp. contain 0to 6 prophages and/or prophage remnants, 143 to 334 insertion elements (IS), and 25
FIG 8 Ranges of CAI values of different metal resistance genes of Acidithiobacillus spp. with Tu gene asa reference.
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to 66 GIs per genome, suggesting that Acidithiobacillus spp. are rich in mobile geneticelements. The existence of transposable elements and prophages near the heavy metalresistance genes and gene clusters suggested that they may be involved in HGT ofheavy metal resistance genes, which is consistent with previous studies illustrating thatHGT was a major mechanism that confers upon bacteria crucial traits such as metalresistance for adaptation to toxic metal-containing habitats (15, 50–52). In addition,BacMet database annotation revealed that the accessory genome had a higher pro-portion (8.2% [7,846 genes]) of genes related to heavy metal resistance activity (Fig. 1C),and models of extrapolation revealed that the pangenome of 37 Acidithiobacillus spp.is “open,” which is in line with the results showing that metal-resistance-associatedgene family gain events overcame loss events. We inferred that during evolutionaryniche adaption, Acidithiobacillus spp. accessorized their genome armaments with ge-netic elements transferred from members outside their species or genus, with the helpof MGEs. The “accessory genome” was therefore considered a massive resource thatprovided Acidithiobacillus spp. with unprecedented elasticity to improve their fitnesswhile adapting to harsh conditions (53–55). Our results also showed that the transfor-mation of heavy metals to less toxic forms followed by their efflux out of cells was anoften-used metal resistance strategy of Acidithiobacillus spp., which is also a processcontributing to the biogeochemical cycling of metals.
We implemented a combination of several approaches to discover putative HGTevents, including deviant G�C content detection, phylogenetic analysis, mobile ge-netic element detection, and chromosomal synteny analysis, since it was difficult toidentify HGT events via deviant G�C contents alone if HGT happened to occur betweenorganisms with similar G�C contents (56). The different gene contents and organiza-tion of metal resistance genes suggested a complicated evolutionary history of thesegenes and also suggested different genetic requirements for effective cellular metaldetoxification and regulation of metal resistance operons. We supposed Thiomonasspp. to be the donors of the arsM gene and czcABC and czcBAC gene clusters inAcidithiobacillus spp., which was reasonable since members of Thiomonas spp. areubiquitous in heavy-metal-contaminated environments, especially in AMD sites, thesame habitats as Acidithiobacillus species, and usually contained a vast flexible genepool. For instance, the genome of Thiomonas arsenitoxydans contained a circularchromosome and a plasmid (pTHI) where more than 20 GIs (ThGEI-A to ThGEI-S)resided, which were associated with heavy metal resistance and conjugation (57–59). Inaddition, the median G�C content of Thiomonas species genomes was 64.0%, whichwas close to those of czcABC (64.2%) and czcBAC (64.9%) gene clusters in Acidithioba-cillus spp. All these indicate that Thiomonas spp. are the most possible sources ofmultiple heavy metal resistance operons that were acquired by Acidithiobacillus spp. viacross-phylum HGT. Similar traits were also shown in case of Acidihalobacter spp., theputative cross-phylum donors of merACR, arsBRC, and the multicopper oxidase-encoding gene, which are a group of halophilic, mesophilic, heavy-metal-tolerantextreme acidophiles that share the same habitats with Acidithiobacillus spp., with anaverage genome G�C content (61.9%) similar to those of merACR (60.0%) and arsBRC(61.7%) gene clusters in Acidithiobacillus spp. (60, 61). This could also be applied toAcidiferrobacter spp. that inhabit AMD sites, which were predicted to be the cross-phylum donors of the merAPTR gene cluster and rusticyanin-encoding gene (62).Multiple czc-like genes and cop-like genes were present in the genomes of Acidithio-bacillus spp., which may arise from gene duplication. Metal resistance proteins encodedby genes such as czc and cop are in high demand for Acidithiobacillus spp. for copingwith harsh metal-rich environments. The presence of duplicate metal resistance genesmay be beneficial by enhancing the regulatory elasticity of Acidithiobacillus spp., simplybecause extra amounts of protein or RNA products are generated (63).
The gene contexts of metal resistance genes in different Acidithiobacillus specieswere not conserved, which indicated that these genes may be acquired more than oncevia different HGT events. Synteny of the chromosomal regions flanking the mco genesshowed that multicopper oxidases were possibly lost in strains of A. ferridurans, A.
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ferrivorans, and A. ferrooxidans during evolution, since mco genes in A. thiooxidansexperienced relaxed selection pressure (dN/dS � 1) and A. thiooxidans arose before thespeciation of A. ferridurans, A. ferrivorans, and A. ferrooxidans in evolutionary history (seeFig. S7 in the supplemental material). We supposed that the function of this enzymewas replaced in A. ferridurans, A. ferrivorans, and A. ferrooxidans by rusticyanin, aperiplasmic protein with an affinity for Cu�, forming part of an iron-oxidizing super-complex and supposed to confer a higher level of copper resistance (64, 65). Rusticya-nin was absent in species incapable of oxidizing iron, such as A. thiooxidans and A.caldus, and was acquired via HGT from Acidiferrobacter spp. to A. ferridurans, A.ferrivorans, and A. ferrooxidans, as revealed by a phylogenetic analysis (see Fig. S19).Most nonsynonymous substitutions are deleterious, especially for functionally impor-tant genes, since the functionality of important proteins might be substantially affectedeven if small changes happens in its sequences, resulting in a dN/dS if �1 for thesegenes considering that mutants of these genes are prone to be eliminated by purifying(negative) selection. On the contrary, when new mutations are beneficial, conferringadaptive advantages, these genes might be under diversifying (directional or positive)selection, reflected by a dN/dS of �1, namely, there are more nonsynonymous thansynonymous nucleotide substitutions. Nonsynonymous nucleotide substitutions andsynonymous substitutions will accumulate at similar rates (dN/dS � 1) if genes becomenonfunctional in the population, since there is no selection pressure acting to maintainthe adaptive variant (neutral evolution), and these genes are more prone to loss duringevolution (66). The results showed that 83.3% of metal resistance genes in Acidithio-bacillus species had a lower rate of nonsynonymous substitution than of synonymoussubstitution (dN/dS � 1), which is in consistent with the assumption of predominantpurifying selection on functionally important metal resistance genes. Besides, approx-imately 53.2% of the metal resistance genes were predicted to be highly expressed viathe CAI (codon adaption index). There is a close correlation between codon usage biasand gene expressivity, which results from the selection for efficient and accuratetranslation of highly expressed genes (67, 68). The CAI was used to measure thesynonymous codon usage bias for a given DNA sequence with an ever-improvingalgorithm by comparing the similarity between the synonymous codon usage of a geneand the synonymous codon frequency of a reference data set of more than 30 highlyexpressed genes derived from prokaryotes and lower eukaryotes, which can serve as areliable indicator of codon usage bias as well as a numerical estimator of geneexpressivity for genes of prokaryotes and lower eukaryotes (65, 69). Widespread lowdN/dS ratios and relative high CAI values across metal-resistance-related genes empha-sized their indispensable functions in supporting the growth of these extremophiles inthe face of harsh environments. Altogether, our research revealed that Acidithiobacillusspp. recruited and consolidated additional novel resistant functionalities during adap-tion to challenging metal-rich environments via HGT, gene duplication, and purifyingselection. However, further experimental confirmations of the functionalities and ex-pression levels of metal-resistance-related genes in Acidithiobacillus spp. are still nec-essary for an advanced understanding of these genes.
Concluding remarks. We performed a comparative genomic analysis of 37 strainswithin the genus Acidithiobacillus to explore the distribution, organization, functional-ity, and complex evolutionary history of metal resistance genes in Acidithiobacillus spp.The results indicated that the evolutionary history of toxic metal resistance genes inAcidithiobacillus spp. involved a combination of gene gain and loss, HGT, gene dupli-cation, and gene family expansion. Gene clusters involved in toxic metal resistance,such as mer, ars, and czc-cus, were found to be acquired by early horizontal genetransfer (HGT) events from sources related to Burkholderiales, Rhodospirillales, Acidiha-lobacter, Thiobacillus, Acidiferrobacter, Alcaligenes, Pseudomonas, Sulfuriferula, Melamini-vora, and Thiomonas, many of which share habitats with Acidithiobacillus spp., whereasthe multicopper oxidase genes of iron-oxidizing A. ferridurans, A. ferrivorans, and A.ferrooxidans involved in copper detoxification were lost and replaced by rusticyanin
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genes during evolution. An assessment of the functionality of metal resistance genesshowed that 83.3% of metal resistance genes in Acidithiobacillus spp. were underpurifying selection (dN/dS � 1) and 53.2% of them were highly expressed. The insightsgained in this study have greatly improved our understanding of the metal resistancestrategies of Acidithiobacillus spp.
MATERIALS AND METHODSA total of 38 sequenced strains belonging to genus Acidithiobacillus were collected from the NCBI
database, containing one to three sequenced standard strains in each species and four unidentifiedstrains. The general features of the bacterial genomes used in this study are summarized in Table 2.
Average nucleotide identity. Comparisons of average nucleotide identities (ANIs) based on a BLASTalgorithm (70) and tetranucleotide frequency correlation coefficient (Tetra) (71) were conducted usingthe web server JSpeciesWS (33, 72) with default parameters.
Phylogenic analyses. To associate the distribution of metal resistance genes in Acidithiobacillus spp.with their phylogenetic affiliations, we constructed the phylogenetic tree of the Acidithiobacillus spp.with closely related species on the basis of 16S rRNA gene sequences using the neighbor-joining methodin MEGA v7.0 (73) and with rRNA genes predicted using the RNAmmer v1.2 (74). Since no 16S rRNA genesequence could be retrieved from the genome of Acidithiobacillus sp. strain NORP59, we also constructeda phylogenetic tree of the 38 Acidithiobacillus species genomes with their phylogenetic affiliations on thebasis of whole-genome sequences with CVTree3 (75). A phylogenetic tree of the 37 Acidithiobacillusspecies genomes on the basis of the concatenation of the 110 single-copy core genes in a genome wasconstructed with the maximum likelihood (ML) method using MEGA v7.0 (73) with Jones-Taylor-Thornton (JTT) model (76) and 1,000 bootstraps, rooted by Escherichia coli and Salmonella enterica. Theambiguous areas of alignment were removed by Gblocks 0.91 b (77) before constructing the tree. Aneighbor-joining tree and UPGMA tree were also constructed with MEGA v7.0 (73), both under ap-distance model with 1,000 bootstraps of the above-mentioned core genes. A chronogram for the NJphylogenetic tree of the 37 Acidithiobacillus species with branch lengths reflecting divergence times wasinferred using the divergence time between Escherichia coli and Salmonella enterica (140 MYA [millionsof years ago]) (78) as calibration points with Timetree Wizard, a built-in program of MEGA v7.0 (73) withthe JTT matrix-based model (76) and the real-time method (79).
Genes for toxic metal resistance. Genes involved in heavy metal resistance in Acidithiobacillusgenomes were identified by performing BLASTP searches against the BacMet database (80), whichcontains genes with experimentally confirmed metal resistance functions. Genes meeting the followingcutoffs were considered reliable hits: E value, �1e�50; sequence similarity, �35%. The evolutionaryhistory of genes for toxic metal resistance was inferred using MEGA v7.0 (73) with the neighbor-joiningmethod and 1,000 bootstraps.
Pangenome analysis and model extrapolations of Acidithiobacillus. The Bacterial Pan GenomeAnalysis (BPGA) pipeline (32) was used to identify orthologous groups among Acidithiobacillus testinggenomes and to extrapolate the pangenome models of Acidithiobacillus spp. by applying defaultparameters. The set of genes shared among all testing strains was defined as their core genome, the setof genes shared with more than two but not all testing strains was defined as their accessory genome,the set of genes not shared with other strains in each testing strain was defined as the unique genes, andthe set of nonhomologous genes with all testing genomes was defined as the pangenome. The detailsof all testing strains used are listed in Table 2. Genes involved in heavy metal resistance in thepangenome, core genome, and accessory genome and unique genes were identified by performingBLASTP searches against the BacMet database (80) with cutoffs of an E value of �1e�50 and a sequencesimilarity of �35%.
In this study, the size of the Acidithiobacillus pangenome was extrapolated by implementing anpower law regression function, Ps � �n�, using a built-in program of the BPGA pipeline (32), in which Ps
represents the total number of nonorthologous gene families within its pangenome, n represents thenumber of tested strains, and both � and � are free parameters (35). An exponent � of �0 suggests thepangenome is “closed,” where the size of the pangenome reaches a constant value as extra genomes areadded. Conversely, the species is predicted to harbor an open pangenome for � values between 0 and1. In addition, the size of the core genome was extrapolated by fitting into an exponential decay function,Fc � �cexp(-n/�c) � �, with a built-in program of the BPGA pipeline (32), where Fc is the number of coregene families, and �c, �c, and � are free parameters (81).
Estimation of gene family gains and losses. BadiRate (82) was used with the “BDI-FR-CWP” methodto estimate the number of metal-resistance-related gene family gains and losses during the evolution ofAcidithiobacillus. The analysis was conducted only on 5 representative Acidithiobacillus strains, includingA. thiooxidans CLST, A. ferrooxidans ATCC 53993, A. ferrivorans PQ33, A. ferridurans JCM 18981, and A.caldus SM-1, because the estimation required complete sets of testing gene families, which are onlyavailable in species with considerable phylogenetic distance and have high-quality (complete or nearlycomplete) genome databases. The orthologous groups of metal resistance genes based on reciprocalbest hits within each Acidithiobacillus species was inferred using the CD-HIT software (global sequenceidentity cutoff was 0.9 and bandwidth of alignment cutoff was 20 amino acids [aa]) (83). These data wereused to construct a gene family size file (a matrix [column, species; row, number of orthologous groups]).The topology of the core-gene tree was used as the reference tree. The number of gene families relatedto metal resistance in each internal node was inferred using those numbers in branch species and thephylogenetic branch lengths. Gene family gains and losses in each phylogenetic branch were then
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estimated using the Wagner parsimony algorithm under the BDI stochastic model and free rates (FR)model, assuming that each branch has its own turnover rates.
Prediction of mobile genetic elements. The ISFinder platform (84) was used to predict and classifyinsertion sequences (IS) and transposases distributed over Acidithiobacillus genomes, with default criteria.The IslandViewer 4 platform (85), which integrates two prediction methods that make use of sequencecomposition, i.e., IslandPath-DIMOB (86) and SIGI-HMM (87), and a comparative genomic islands predic-tion method IslandPick (88), were used to identify putative genomic islands (GIs). PHASTER (Phage SearchTool Enhanced Release) (89) was used for the identification and annotation of prophage sequenceswithin Acidithiobacillus genomes. In addition, tRNA genes were predicted using the tRNAscan-SE v2.0(90). The genes involved in heavy metal resistance were visualized, along with the tRNAs, insertionsequences, prophage sequences, and putative genomic islands, as well as the core genes, accessorygenes, and unique genes, using BRIG-0.95 (91).
Selective pressure analysis and expressivity prediction. The strength of natural selection wasassessed by estimating the relative rates of nonsynonymous (dN) to synonymous (dS) nucleotidesubstitutions of orthologous metal resistance genes by using the HyPhy package (92) with the adaptivebranch-site random effects likelihood (aBSREL) method (93) via the datamonkey web server (94). A genein the node or tip of a given tree was considered under positive (diversifying) selection (dN/dS � 1) ornegative (purifying) selection (dN/dS � 1) if the P value was �0.05 using the likelihood ratio test aftercorrecting for multiple testing. The CAI (codon adaption index) was used as a numerical estimator ofgene expression level (47, 48), a method previously described by Wu et al. (95). CAIcal (96) was used tocalculate the CAI values of the above-mentioned metal resistance genes. CAI values range from 0 to 1.0,with a higher CAI value indicating a higher expression level. Putative highly expressed (PHX) genesrelated to toxic metal resistance in the Acidithiobacillus spp. were inferred, with Tu genes as thereferences, which encode elongation factors that are supposed to be highly expressed across mostorganisms (97).
SUPPLEMENTAL MATERIALSupplemental material for this article may be found at https://doi.org/10.1128/AEM
.02153-18.SUPPLEMENTAL FILE 1, PDF file, 7.8 MB.SUPPLEMENTAL FILE 2, XLSX file, 0.02 MB.SUPPLEMENTAL FILE 3, XLSX file, 0.02 MB.SUPPLEMENTAL FILE 4, XLSX file, 0.1 MB.SUPPLEMENTAL FILE 5, XLSX file, 0.3 MB.SUPPLEMENTAL FILE 6, XLSX file, 0.03 MB.SUPPLEMENTAL FILE 7, XLSX file, 0.02 MB.SUPPLEMENTAL FILE 8, XLSX file, 0.02 MB.
ACKNOWLEDGMENTSWe thank the NCBI for providing the genome sequences of Acidithiobacillus species
strains.This work was funded by the National Natural Science Foundation of China (NSFC;
grants 31570113, 41807332, 41877345, 91851206 [Major Research Plan of MechanismUnderlying Elemental Cycling on the Earth by Microorganisms in Hydrosphere
launched by NSFC], and 41573072) and open funding of the State Key Laboratory ofComprehensive Utilization of Low-Grade Refractory Gold Ores (Zijin Mining Group Co.,Ltd.; no. 738010212).
We also thank the Hunan International Scientific and Technological CooperationBase of Environmental Microbiology and Application, China. We declare no competinginterests.
REFERENCES1. Chen LX, Hu M, Huang LN, Hua ZS, Kuang JL, Li SJ, Shu WS. 2015.
Comparative metagenomic and metatranscriptomic analyses of micro-bial communities in acid mine drainage. ISME J 9:1579. https://doi.org/10.1038/ismej.2014.245.
2. Marrero J, Coto O, Goldmann S, Graupner T, Schippers A. 2015.Recovery of nickel and cobalt from laterite tailings by reductivedissolution under aerobic conditions using Acidithiobacillus species.Environ Sci Technol 49:6674 – 6682. https://doi.org/10.1021/acs.est.5b00944.
3. Xia L, Liu X, Zeng J, Yin C, Gao J, Liu J, Qiu G. 2008. Mechanism ofenhanced bioleaching efficiency of Acidithiobacillus ferrooxidans after
adaptation with chalcopyrite. Hydrometallurgy 92:95–101. https://doi.org/10.1016/j.hydromet.2008.01.002.
4. Valdés J, Pedroso I, Quatrini R, Dodson RJ, Tettelin H, Blake R, Eisen JA,Holmes DS. 2008. Acidithiobacillus ferrooxidans metabolism: from ge-nome sequence to industrial applications. BMC Genomics 9:597.https://doi.org/10.1186/1471-2164-9-597.
5. Xiao Y, Liu X, Fang J, Liang Y, Zhang X, Meng D, Yin H. 2017. Responsesof zinc recovery to temperature and mineral composition during sphal-erite bioleaching process. AMB Express 7:190. https://doi.org/10.1186/s13568-017-0491-1.
6. Farasin J, Andres J, Casiot C, Barbe V, Faerber J, Halter D, Heintz D,
Comparative Genomics of Acidithiobacillus Applied and Environmental Microbiology
January 2019 Volume 85 Issue 2 e02153-18 aem.asm.org 19
on Septem
ber 15, 2020 by guesthttp://aem
.asm.org/
Dow
nloaded from
Koechler S, Lièvremont D, Lugan R, Marchal M, Plewniak F, Seby Y,Bertin PN, Arsène-Ploetze1 F. 2015. Thiomonas sp. CB2 is able todegrade urea and promote toxic metal precipitation in acid minedrainage waters supplemented with urea. Front Microbiol 6:993.https://doi.org/10.3389/fmicb.2015.00993.
7. Lièvremont D, Bertin PN, Lett MC. 2009. Arsenic in contaminatedwaters: biogeochemical cycle, microbial metabolism and biotreat-ment processes. Biochimie 91:1229 –1237. https://doi.org/10.1016/j.biochi.2009.06.016.
8. Oremland RS, Stolz JF. 2005. Arsenic, microbes and contaminatedaquifers. Trends Microbiol 13:45– 49. https://doi.org/10.1016/j.tim.2004.12.002.
9. Cheng H, Hu Y, Luo J, Xu B, Zhao J. 2009. Geochemical processescontrolling fate and transport of arsenic in acid mine drainage (AMD)and natural systems. J Hazard Mater 165:13–26. https://doi.org/10.1016/j.jhazmat.2008.10.070.
10. Sugio T, Komoda T, Okazaki Y, Takeda Y, Nakamura S, Takeuchi F. 2010.Volatilization of metal mercury from organomercurials by highlymercury-resistant Acidithiobacillus ferrooxidans MON-1. Biosci Biotech-nol Biochem 74:1007–1012. https://doi.org/10.1271/bbb.90888.
11. Dopson M, Bakeraustin C, Koppineedi PR, Bond PL. 2003. Growth insulfidic mineral environments: metal resistance mechanisms in acido-philic micro-organisms. Microbiology 149:1959 –1970. https://doi.org/10.1099/mic.0.26296-0.
12. Leng F, Li K, Zhang X, Li Y, Zhu Y, Lu J, Li H. 2009. Comparative studyof inorganic arsenic resistance of several strains of Acidithiobacillusthiooxidans and Acidithiobacillus ferrooxidans. Hydrometallurgy 98:235–240. https://doi.org/10.1016/j.hydromet.2009.05.004.
13. Navarro CA, Diego VB, Jerez CA. 2013. Heavy metal resistancestrategies of acidophilic bacteria and their acquisition: importancefor biomining and bioremediation. Biol Res 46:363–371. https://doi.org/10.4067/S0716-97602013000400008.
14. Zhang X, She S, Dong W, Niu J, Xiao Y, Liang Y, Liu X, Zhang X, Fan F,Yin H. 2016. Comparative genomics unravels metabolic differences atthe species and/or strain level and extremely acidic environmentaladaptation of ten bacteria belonging to the genus Acidithiobacillus.Syst Appl Microbiol 39:493–502. https://doi.org/10.1016/j.syapm.2016.08.007.
15. Hemme CL, Green SJ, Rishishwar L, Prakesh O, Pattenato A, ChakrabortyR, Deutschbauer AM, Van Nostrand JD, Wu L, He Z, Jordan IK, Hazen TC,Arkin AP, Kostka JE, Zhou J. 2016. Lateral gene transfer in a heavymetal-contaminated-groundwater microbial community. mBio7:e02234-15. https://doi.org/10.1128/mBio.02234-15.
16. Zhang X, Liu X, Liang Y, Guo X, Xiao Y, Ma L, Miao B, Liu H, Peng D,Huang W, Zhang Y, Yin H. 2017. Adaptive evolution of extreme acido-phile Sulfobacillus thermosulfidooxidans potentially driven by horizontalgene transfer and gene loss. Appl Environ Microbiol 83:e03098-16.https://doi.org/10.1128/AEM.03098-16.
17. Popa O, Hazkani-Covo E, Landan G, Martin W, Dagan T. 2011. Directednetworks reveal genomic barriers and DNA repair bypasses to lateralgene transfer among prokaryotes. Genome Res 21:599 – 609. https://doi.org/10.1101/gr.115592.110.
18. Polz MF, Alm EJ, Hanage WP. 2013. Horizontal gene transfer and theevolution of bacterial and archaeal population structure. Trends Genet29:170 –175. https://doi.org/10.1016/j.tig.2012.12.006.
19. Sullivan JT, Ronson CW. 1998. Evolution of rhizobia by acquisition of a500-kb symbiosis island that integrates into a phe-tRNA gene. Proc NatlAcad Sci U S A 95:5145–5149. https://doi.org/10.1073/pnas.95.9.5145.
20. Ochman H, Lawrence JG, Groisman EA. 2000. Lateral gene transfer andthe nature of bacterial innovation. Nature 405:299 –304. https://doi.org/10.1038/35012500.
21. Xie J-B, Du Z, Bai L, Tian C, Zhang Y, Xie J-Y, Wang T, Liu X, Chen X,Cheng Q, Chen S, Li J. 2014. Comparative genomic analysis of N2-fixingand non-N2-fixing Paenibacillus spp.: organization, evolution and ex-pression of the nitrogen fixation genes. PLoS Genet 10:e1004231.https://doi.org/10.1371/journal.pgen.1004231.
22. Bolhuis H, Severin I, Confurius-Guns V, Wollenzien UI, Stal LJ. 2010.Horizontal transfer of the nitrogen fixation gene cluster in the cyano-bacterium Microcoleus chthonoplastes. ISME J 4:121–130. https://doi.org/10.1038/ismej.2009.99.
23. Khaldi N, Collemare J, Lebrun MH, Wolfe KH. 2008. Evidence for hori-zontal transfer of a secondary metabolite gene cluster between fungi.Genome Biol 9:R18. https://doi.org/10.1186/gb-2008-9-1-r18.
24. Zhang X, Niu J, Liang Y, Liu X, Yin H. 2016. Metagenome-scale analysis
yields insights into the structure and function of microbial communi-ties in a copper bioleaching heap. BMC Genet 17:21. https://doi.org/10.1186/s12863-016-0330-4.
25. Dopson M, Holmes DS. 2014. Metal resistance in acidophilic microor-ganisms and its significance for biotechnologies. Appl Microbiol Bio-technol 98:8133– 8144. https://doi.org/10.1007/s00253-014-5982-2.
26. Nies DH. 2003. Efflux-mediated heavy metal resistance in pro-karyotes. FEMS Microbiol Rev 27:313–339. https://doi.org/10.1016/S0168-6445(03)00048-2.
27. Hemme CL, Deng Y, Gentry TJ, Fields MW, Wu L, Barua S, Barry K, TringeSG, Watson DB, He Z, Hazen TC, Tiedje JM, Rubin EM, Zhou J. 2010.Metagenomic insights into evolution of a heavy metal-contaminatedgroundwater microbial community. ISME J 4:660 – 672. https://doi.org/10.1038/ismej.2009.154.
28. Nelson KE, Clayton RA, Gill SR, Gwinn ML, Dodson RJ, Haft DH, HickeyEK, Peterson JD, Nelson WC, Ketchum KA. 2015. Evidence for lateralgene transfer between archaea and bacteria from genome sequence ofThermotoga maritima. Nature 399:323–329. https://doi.org/10.1038/20601.
29. Dagan T, Artzy-Randrup Y, Martin W. 2008. Modular networks andcumulative impact of lateral transfer in prokaryote genome evolution.Proc Natl Acad Sci U S A 105:10039 –10044. https://doi.org/10.1073/pnas.0800679105.
30. Brochier-Armanet C, Forterre P. 2007. Widespread distribution of ar-chaeal reverse gyrase in thermophilic bacteria suggests a complexhistory of vertical inheritance and lateral gene transfers. Archaea-anInternational Microbiological J 2:83–93. https://doi.org/10.1155/2006/582916.
31. Rice SA, Lampson BC. 1995. Phylogenetic comparison of retron ele-ments among the myxobacteria: evidence for vertical inheritance. JBacteriol 177:37– 45. https://doi.org/10.1128/jb.177.1.37-45.1995.
32. Chaudhari NM, Gupta VK, Dutta C. 2016. BPGA- an ultra-fast pan-genome analysis pipeline. Sci Rep 6:24373. https://doi.org/10.1038/srep24373.
33. Richter M, Rosselló-Móra R. 2009. Shifting the genomic gold standardfor the prokaryotic species definition. Proc Natl Acad Sci U S A 106:19126 –19131. https://doi.org/10.1073/pnas.0906412106.
34. Vernikos G, Medini D, Riley DR, Tettelin H. 2015. Ten years of pan-genome analyses. Curr Opin Microbiol 23:148 –154. https://doi.org/10.1016/j.mib.2014.11.016.
35. Tettelin H, Riley D, Cattuto C, Medini D. 2008. Comparative genomics:the bacterial pan-genome. Curr Opin Microbiol 11:472– 477. https://doi.org/10.1016/j.mib.2008.09.006.
36. Zhang Y, Sievert SM. 2014. Pan-genome analyses identify lineage- andniche-specific markers of evolution and adaptation in Epsilonproteobac-teria. Front Microbiol 5:110. https://doi.org/10.3389/fmicb.2014.00110.
37. Ullrich SR, González C, Poehlein A, Tischler JS, Daniel R, SchlömannM, Holmes DS, Mühling M. 2016. Gene loss and horizontal genetransfer contributed to the genome evolution of the extreme aci-dophile “Ferrovum”. Front Microbiol 7:797. https://doi.org/10.3389/fmicb.2016.00797.
38. Chakraborty J, Das S. 2014. Characterization and cadmium-resistantgene expression of biofilm-forming marine bacterium Pseudomonasaeruginosa JP-11. Environ Sci Pollut Res 21:14188 –14201. https://doi.org/10.1007/s11356-014-3308-7.
39. Nies DH. 1995. The cobalt, zinc, and cadmium efflux system CzcABCfrom Alcaligenes eutrophus functions as a cation-proton antiporter inEscherichia coli. J Bacteriol 177:2707–2712. https://doi.org/10.1128/jb.177.10.2707-2712.1995.
40. Colombi E, Straub C, Künzel S, Templeton MD, Mccann HC, Rainey PB.2016. Evolution of copper resistance in the kiwifruit pathogen Pseu-domonas syringae pv. actinidiae through acquisition of integrative con-jugative elements and plasmids. Environ Microbiol 19:819 – 832. https://doi.org/10.1111/1462-2920.13662.
41. Masami N, Masao G, Katsumi A, Tadaaki H. 2004. Nucleotide sequenceand organization of copper resistance genes from Pseudomonas syrin-gae pv. actinidiae. Eur J Plant Pathol 110:223–226. https://doi.org/10.1023/B:EJPP.0000015360.88352.a9.
42. Goldberg M, Pribyl T, Juhnke S, Nies DH. 1999. Energetics and topologyof CzcA, a cation/proton antiporter of the resistance-nodulation-celldivision protein family. J Biol Chem 274:26065–26070. https://doi.org/10.1074/jbc.274.37.26065.
43. Orell A, Navarro CA, Arancibia R, Mobarec JC, Jerez CA. 2010. Life inblue: copper resistance mechanisms of bacteria and Archaea used in
Li et al. Applied and Environmental Microbiology
January 2019 Volume 85 Issue 2 e02153-18 aem.asm.org 20
on Septem
ber 15, 2020 by guesthttp://aem
.asm.org/
Dow
nloaded from
industrial biomining of minerals. Biotechnol Adv 28:839. https://doi.org/10.1016/j.biotechadv.2010.07.003.
44. Zhao F, Yu C, Liu Y. 2017. Codon usage regulates protein structure andfunction by affecting translation elongation speed in Drosophila cells.Nucleic Acids Res 45:8484 – 8492. https://doi.org/10.1093/nar/gkx501.
45. Brandis G, Hughes D. 2016. The selective advantage of synonymouscodon usage bias in Salmonella. PLoS Genet 12:e1005926. https://doi.org/10.1371/journal.pgen.1005926.
46. Yu CH, Dang Y, Zhou Z, Wu C, Zhao F, Sachs MS, Liu Y. 2015. Codonusage influences the local rate of translation elongation to regulateco-translational protein folding. Mol Cell 59:744 –754. https://doi.org/10.1016/j.molcel.2015.07.018.
47. Carbone A, Zinovyev A, Képès F. 2003. Codon adaptation index as ameasure of dominating codon bias. Bioinformatics 19:2005–2015.https://doi.org/10.1093/bioinformatics/btg272.
48. Sharp PM, Li WH. 1987. The codon adaptation index–a measure ofdirectional synonymous codon usage bias, and its potential applica-tions. Nucleic Acids Res 15:1281–1295. https://doi.org/10.1093/nar/15.3.1281.
49. Springael D, Top EM. 2004. Horizontal gene transfer and microbialadaptation to xenobiotics: new types of mobile genetic elements andlessons from ecological studies. Trends Microbiol 12:53–58. https://doi.org/10.1016/j.tim.2003.12.010.
50. Li LG, Xia Y, Zhang T. 2017. Co-occurrence of antibiotic and metalresistance genes revealed in complete genome collection. ISME J 11:651– 662. https://doi.org/10.1038/ismej.2016.155.
51. Villegas-Torres MF, Bedoya-Reina OC, Salazar C, Vives-Florez MJ, DussanJ. 2011. Horizontal gene transfer among microorganisms isolated fromarsenic polluted soil. Int Biodeterior Biodegradation 65:147–152.https://doi.org/10.1016/j.ibiod.2010.10.007.
52. Cai L, Liu G, Rensing C, Wang G. 2009. Genes involved in arsenictransformation and resistance associated with different levels ofarsenic-contaminated soils. BMC Microbiol 9:4. https://doi.org/10.1186/1471-2180-9-4.
53. Sugawara M, Epstein B, Badgley BD, Unno T, Xu L, Reese J, GyaneshwarP, Denny R, Mudge J, Bharti AK, Farmer AD, May GD, Woodward JE,Médigue C, Vallenet D, Lajus A, Rouy Z, Martinez-Vaz B, Tiffin P, YoungND, Sadowsky MJ. 2013. Comparative genomics of the core and acces-sory genomes of 48 Sinorhizobium strains comprising five genospecies.Genome Biol 14:R17. https://doi.org/10.1186/gb-2013-14-2-r17.
54. Croll D, McDonald BA. 2012. The accessory genome as a cradle foradaptive evolution in pathogens. PLoS Pathog 8:e1002608. https://doi.org/10.1371/journal.ppat.1002608.
55. Kung VL, Ozer EA, Hauser AR. 2010. The accessory genome of Pseu-domonas aeruginosa. Microbiol Mol Biol Rev 74:621– 641. https://doi.org/10.1128/MMBR.00027-10.
56. Hirsch AM, Mckhann HI, Reddy A, Liao J, Fang Y, Marshall CR. 1995.Assessing horizontal transfer of nifHDK genes in eubacteria: nucleotidesequence of nifK from Frankia strain HFPCcI3. Mol Biol Evol 12:16 –27.https://doi.org/10.1093/oxfordjournals.molbev.a040184.
57. Slyemi D, Moinier D, Brochier-Armanet C, Bonnefoy V, Johnson DB.2011. Characteristics of a phylogenetically ambiguous, arsenic-oxidizing Thiomonas sp., Thiomonas arsenitoxydans strain 3AsT sp.nov. Arch Microbiol 193:439 – 449. https://doi.org/10.1007/s00203-011-0684-y.
58. Arsène-Ploetze F, Koechler S, Marchal M, Coppée JY, Chandler M,Bonnefoy V, Brochier-Armanet C, Barakat M, Barbe V, Battaglia-Brunet F,Bruneel O, Bryan CG, Cleiss-Arnold J, Cruveiller S, Erhardt M, Heinrich-Salmeron A, Hommais F, Joulian C, Krin E, Lieutaud A, Lièvremont D,Michel C, Muller D, Ortet P, Proux C, Siguier P, Roche D, Rouy Z,Salvignol G, Slyemi D, Talla E, Weiss S, Weissenbach J, Médigue C, BertinPN. 2010. Structure, function, and evolution of the Thiomonas spp.genome. PLoS Genet 6:e1000859. https://doi.org/10.1371/journal.pgen.1000859.
59. Bryan CG, Marchal M, Battaglia-Brunet F, Kugler V, Lemaitre-GuillierC, Lièvremont D, Bertin PN, Arsène-Ploetze F. 2009. Carbon andarsenic metabolism in Thiomonas strains: differences revealed di-verse adaptation processes. BMC Microbiol 9:127. https://doi.org/10.1186/1471-2180-9-127.
60. Khaleque HN, Corbett MK, Ramsay JP, Kaksonen AH, Boxall NJ, WatkinELJ. 2017. Complete genome sequence of Acidihalobacter prosperusstrain F5, an extremely acidophilic, iron- and sulfur-oxidizing halophilewith potential industrial applicability in saline water bioleaching of
chalcopyrite. J Biotechnol 262:56 –59. https://doi.org/10.1016/j.jbiotec.2017.10.001.
61. Dopson M, Holmes DS, Lazcano M, McCredden TJ, Bryan CG, MulroneyKT, Steuart R, Jackaman C, Watkin ELJ. 2016. Multiple osmotic stressresponses in Acidihalobacter prosperus result in tolerance to chlorideions. Front Microbiol 7:2132. https://doi.org/10.3389/fmicb.2016.02132.
62. Hallberg KB, Hedrich S, Johnson DB. 2011. Acidiferrobacter thiooxydans,gen. nov. sp. nov.; an acidophilic, thermo-tolerant, facultatively anaer-obic iron- and sulfur-oxidizer of the family Ectothiorhodospiraceae.Extremophiles 15:271–279. https://doi.org/10.1007/s00792-011-0359-2.
63. Zhang J. 2003. Evolution by gene duplication: an update. Trends EcolEvol 18:292–298. https://doi.org/10.1016/S0169-5347(03)00033-8.
64. Navarro CA, von Bernath D, Martínez-Bussenius C, Castillo RA, JerezCA. 2016. Cytoplasmic CopZ-like, periplasmic rusticyanin and AcoPproteins are possible new copper resistance determinants in Acidi-thiobacillus ferrooxidans ATCC 23270. Appl Environ Microbiol 82:1015–1022. https://doi.org/10.1128/AEM.02810-15.
65. Walter RL, Ealick SE, Friedman AM, Blake Ii RC, Proctor P, Shoham M.1996. Multiple wavelength anomalous diffraction (MAD) crystal struc-ture of rusticyanin: a highly oxidizing cupredoxin with extreme acidstability. J Mol Biol 263:730. https://doi.org/10.1006/jmbi.1996.0612.
66. Nielsen R. 2005. Molecular signatures of natural selection. Annu Rev Genet39:197–218. https://doi.org/10.1146/annurev.genet.39.073003.112420.
67. Zhou M, Guo J, Cha J, Chae M, Chen S, Barral JM, Sachs MS, Liu Y.2013. Non-optimal codon usage affects expression, structure andfunction of clock protein FRQ. Nature 495:111–115. https://doi.org/10.1038/nature11833.
68. Hiraoka Y, Kawamata K, Haraguchi T, Chikashige Y. 2010. Codon usagebias is correlated with gene expression levels in the fission yeastSchizosaccharomyces pombe. Genes Cells 14:499 –509. https://doi.org/10.1111/j.1365-2443.2009.01284.x.
69. Kalhoefer D, Thole S, Voget S, Lehmann R, Liesegang H, Wollher A,Daniel R, Simon M, Brinkhoff T. 2011. Comparative genome analysisand genome-guided physiological analysis of Roseobacter litoralis. BMCGenomics 12:324 –324. https://doi.org/10.1186/1471-2164-12-324.
70. Goris J, Konstantinidis KT, Klappenbach JA, Coenye T, Vandamme P,Tiedje JM. 2007. DNA-DNA hybridization values and their relationshipto whole-genome sequence similarities. Int J Syst Evol Microbiol 57:81–91. https://doi.org/10.1099/ijs.0.64483-0.
71. Teeling H, Meyerdierks A, Bauer M, Amann R, Glöckner FO. 2004.Application of tetranucleotide frequencies for the assignment ofgenomic fragments. Environ Microbiol 6:938 –947. https://doi.org/10.1111/j.1462-2920.2004.00624.x.
72. Richter M, Rosselló-Móra R, Oliver Glöckner F, Peplies J. 2015.JSpeciesWS: a web server for prokaryotic species circumscription basedon pairwise genome comparison. Bioinformatics 32:929. https://doi.org/10.1093/bioinformatics/btv681.
73. Kumar S, Stecher G, Tamura K. 2016. MEGA7: Molecular EvolutionaryGenetics Analysis version 7.0 for bigger datasets. Mol Biol Evol 33:1870.https://doi.org/10.1093/molbev/msw054.
74. Lagesen K, Hallin P, Rødland EA, Stærfeldt HH, Rognes T, Ussery DW.2007. RNAmmer: consistent and rapid annotation of ribosomal RNAgenes. Nucleic Acids Res 35:3100 –3108. https://doi.org/10.1093/nar/gkm160.
75. Zuo G, Hao B. 2015. CVTree3 web server for whole-genome-based andalignment-free prokaryotic phylogeny and taxonomy. Genomics Pro-teomics Bioinformatics 13:321–331. https://doi.org/10.1016/j.gpb.2015.08.004.
76. Jones DT, Taylor WR, Thornton JM. 1992. The rapid generation ofmutation data matrices from protein sequences. Comput Appl Biosci8:275–282. https://doi.org/10.1093/bioinformatics/8.3.275.
77. Castresana J. 2000. GBLOCLKS: selection of conserved blocks frommultiple alignments for their use in phylogenetic analysis. Mol Biol Evol17:540 –552. https://doi.org/10.1093/oxfordjournals.molbev.a026334.
78. Bergthorsson U, Ochman H. 1998. Distribution of chromosome lengthvariation in natural isolates of Escherichia coli. Mol Biol Evol 15:6.https://doi.org/10.1093/oxfordjournals.molbev.a025847.
79. Tamura K, Battistuzzi FU, Billing-Ross P, Murillo O, Filipski A, Kumar S.2012. Estimating divergence times in large molecular phylogenies. ProcNatl Acad Sci U S A 109:19333–19338. https://doi.org/10.1073/pnas.1213199109.
80. Pal C, Bengtsson-Palme J, Rensing C, Kristiansson E, Larsson DGJ. 2014.BacMet: antibacterial biocide and metal resistance genes database.Nucleic Acids Res 42:D737–D743. https://doi.org/10.1093/nar/gkt1252.
Comparative Genomics of Acidithiobacillus Applied and Environmental Microbiology
January 2019 Volume 85 Issue 2 e02153-18 aem.asm.org 21
on Septem
ber 15, 2020 by guesthttp://aem
.asm.org/
Dow
nloaded from
81. Tettelin H, Masignani V, Cieslewicz MJ, Donati C, Medini D, Ward NL,Angiuoli SV, Crabtree J, Jones AL, Durkin AS, Deboy RT, Davidsen TM,Mora M, Scarselli M, Margarit y Ros I, Peterson JD, Hauser CR, SundaramJP, Nelson WC, Madupu R, Brinkac LM, Dodson RJ, Rosovitz MJ, SullivanSA, Daugherty SC, Haft DH, Selengut J, Gwinn ML, Zhou L, Zafar N,Khouri H, Radune D, Dimitrov G, Watkins K, O’Connor KJ, Smith S,Utterback TR, White O, Rubens CE, Grandi G, Madoff LC, Kasper DL,Telford JL, Wessels MR, Rappuoli R, Fraser CM. 2005. Genome analysisof multiple pathogenic isolates of Streptococcus agalactiae: implica-tions for the microbial “pan-genome”. Proc Natl Acad Sci U S A 102:13950 –13955. https://doi.org/10.1073/pnas.0506758102.
82. Librado P, Vieira FG, Rozas J. 2012. BadiRate: estimating family turnoverrates by likelihood-based methods. Bioinformatics 28:279 –281. https://doi.org/10.1093/bioinformatics/btr623.
83. Li W, Godzik A. 2006. Cd-Hit: a fast program for clustering and com-paring large sets of protein or nucleotide sequences. Bioinformatics22:1658. https://doi.org/10.1093/bioinformatics/btl158.
84. Siguier P, Perochon J, Lestrade L, Mahillon J, Chandler M. 2006. ISfinder:the reference centre for bacterial insertion sequences. Nucleic AcidsRes 34:D32–D36. https://doi.org/10.1093/nar/gkj014.
85. Bertelli C, Laird MR, Williams KP, Lau BY, Hoad G, Winsor GL, BrinkmanFS. 2017. IslandViewer 4: expanded prediction of genomic islands forlarger-scale datasets. Nucleic Acids Res 45:W30 –W35. https://doi.org/10.1093/nar/gkx343.
86. Hsiao W, Wan I, Jones SJ, Brinkman FS. 2003. IslandPath: aiding detec-tion of genomic islands in prokaryotes. Bioinformatics 19:418 – 420.https://doi.org/10.1093/bioinformatics/btg004.
87. Waack S, Keller O, Asper R, Brodag T, Damm C, Fricke W, Surovcik K,Meinicke P, Merkl R. 2006. Score-based prediction of genomic islands inprokaryotic genomes using hidden Markov models. BMC Bioinformat-ics 7:142. https://doi.org/10.1186/1471-2105-7-142.
88. Langille MG, Hsiao WW, Brinkman FS. 2008. Evaluation of genomicisland predictors using a comparative genomics approach. BMC Bioin-formatics 9:329. https://doi.org/10.1186/1471-2105-9-329.
89. Arndt D, Grant JR, Marcu A, Sajed T, Pon A, Liang Y, Wishart DS. 2016.PHASTER: a better, faster version of the PHAST phage search tool.Nucleic Acids Res 44:W16 –W21. https://doi.org/10.1093/nar/gkw387.
90. Lowe TM, Eddy SR. 1997. tRNAscan-SE: a program for improved detec-tion of transfer RNA genes in genomic sequence. Nucleic Acids Res25:955–964. https://doi.org/10.1093/nar/25.5.955.
91. Alikhan NF, Petty NK, Zakour NLB, Beatson SA. 2011. BLAST Ring ImageGenerator (BRIG): simple prokaryote genome comparisons. BMCGenomics 12:402. https://doi.org/10.1186/1471-2164-12-402.
92. Pond SL, Frost SD, Muse SV. 2005. HyPhy: hypothesis testing usingphylogenies. Bioinformatics 21:676 – 679. https://doi.org/10.1093/bioinformatics/bti079.
93. Smith MD, Wertheim JO, Weaver S, Murrell B, Scheffler K, Kosakovsky
Pond SL. 2015. Less is more: an adaptive branch-site random effectsmodel for efficient detection of episodic diversifying selection. Mol BiolEvol 32:1342–1353. https://doi.org/10.1093/molbev/msv022.
94. Weaver S, Shank SD, Spielman SJ, Li M, Muse SV, Kosakovsky Pond SL.2 January 2018. Datamonkey 2.0: a modern web application for char-acterizing selective and other evolutionary processes. Molecular Biol-ogy & Evolution https://doi.org/10.1093/molbev/msx335.
95. Wu G, Culley DE, Zhang W. 2005. Predicted highly expressed genes inthe genomes of Streptomyces coelicolor and Streptomyces avermitilisand the implications for their metabolism. Microbiology 151:2175–2187. https://doi.org/10.1099/mic.0.27833-0.
96. Puigbò P, Bravo IG, Garcia-Vallve S. 2008. CAIcal: a combined set oftools to assess codon usage adaptation. Biol Direct 3:38. https://doi.org/10.1186/1745-6150-3-38.
97. Puigbo P, Romeu A, Garcia-Vallve S. 2008. HEG-DB: a database ofpredicted highly expressed genes in prokaryotic complete genomesunder translational selection. Nucleic Acids Res 36:D524 –D527. https://doi.org/10.1093/nar/gkm831.
98. Boyd ES, Barkay T. 2012. The mercury resistance operon: from an originin a geothermal environment to an efficient detoxification machine.Front Microbiol 3:349. https://doi.org/10.3389/fmicb.2012.00349.
99. Yuan C, Lu X, Qin J, Rosen BP, Le XC. 2008. Volatile arsenic species releasedfrom Escherichia coli expressing the AsIII S-adenosylmethionine methyl-transferase gene. Environ Sci Technol 42:3201–3206. https://doi.org/10.1021/es702910g.
100. Qin J, Rosen BP, Zhang Y, Wang G, Franke S, Rensing C. 2006. Arsenicdetoxification and evolution of trimethylarsine gas by a microbialarsenite S-adenosylmethionine methyltransferase. Proc Natl Acad SciU S A 103:2075–2080. https://doi.org/10.1073/pnas.0506836103.
101. Chang JS, Yoon IH, Kim KW. 2018. Arsenic biotransformation potentialof microbial arsH responses in the biogeochemical cycling of arsenic-contaminated groundwater. Chemosphere 191:729 –737. https://doi.org/10.1016/j.chemosphere.2017.10.044.
102. Anton A, Große C, Reißmann J, Pribyl T, Nies DH. 1999. CzcD Is a heavymetal ion transporter involved in regulation of heavy metal resistancein Ralstonia sp. strain CH34. J Bacteriol 181:6876 – 6881.
103. Williams CL, Neu HM, Gilbreath JJ, Michel SLJ, Zurawski DV, Merrell DS.2016. Copper resistance of the emerging pathogen Acinetobacter bau-mannii. Appl Environ Microbiol 82:6174 – 6188. https://doi.org/10.1128/AEM.01813-16.
104. Grass G, Rensing C. 2001. CueO is a multi-copper oxidase that conferscopper tolerance in Escherichia coli. Biochem Biophys Res Commun286:902–908. https://doi.org/10.1006/bbrc.2001.5474.
105. Rensing C, Fan B, Sharma R, Mitra B, Rosen BP. 2000. CopA: an Esche-richia coli Cu(I)-translocating P-type ATPase. Proc Natl Acad Sci U S A97:652– 656. https://doi.org/10.1073/pnas.97.2.652.
Li et al. Applied and Environmental Microbiology
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