G C A T T A C G genes G C A T Article Intermediate-Salinity Systems at High Altitudes in the Peruvian Andes Unveil a High Diversity and Abundance of Bacteria and Viruses Hugo Gildardo Castelán-Sánchez 1 , Paola Elorrieta 2, Pedro Romoacca 3, Arturo Liñan-Torres 1, José Luis Sierra 4, Ingrid Vera 3, Ramón Alberto Batista-García 1 , Silvia Tenorio-Salgado 5, Gabriel Lizama-Uc 5, Ernesto Pérez-Rueda 6,7 , María Antonieta Quispe-Ricalde 2,* and Sonia Dávila-Ramos 1,* 1 Centro de Investigación en Dinámica Celular, Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos C.P. 62209, Mexico; hcastelans@gmail.com (H.G.C.-S.); arturo21lt@gmail.com (A.L.-T.); rabg@uaem.mx (R.A.B.-G.) 2 Departamento de Biología, Facultad de Ciencias, Universidad Nacional de San Antonio Abad del Cusco, Cusco C.P. 0800, Peru; pao91827364@gmail.com 3 Departamento de Farmacia y Bioquímica, Facultad de Ciencias de la Salud, Universidad Nacional de San Antonio Abad del Cusco, Cusco C.P. 0800, Peru; ordep.rh1211@gmail.com (P.R.); iveraf@yahoo.es (I.V.) 4 Escuela de Postgrado, Universidad Nacional de San Antonio Abad del Cusco, Cusco C.P. 0800, Peru; lsierrah77@gmail.com 5 Tecnológico Nacional de México, Instituto Tecnológico de Mérida, Mérida, Yucatán C.P. 97000, Mexico; s.tenorio.salgado@gmail.com (S.T.-S.); gabriel.lizama29@gmail.com (G.L.-U.) 6 Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Sede Mérida, Universidad Nacional Autónoma de México, Unidad Académica de Ciencias y Tecnología, Mérida, Yucatán C.P. 97302, Mexico; eprueda@gmail.com 7 Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Providencia, Santiago C.P. 7500000, Chile * Correspondence: antonieta.quispe@unsaac.edu.pe (M.A.Q.-R.); sonia.davila@uaem.mx (S.D.-R.); Tel.: +52-(777)-3297000 (ext. 3276) (S.D.-R.)  Received: 11 September 2019; Accepted: 26 October 2019; Published: 5 November 2019  Abstract: Intermediate-salinity environments are distributed around the world. Here, we present a snapshot characterization of two Peruvian thalassohaline environments at high altitude, Maras and Acos, which provide an excellent opportunity to increase our understanding of these ecosystems. The main goal of this study was to assess the structure and functional diversity of the communities of microorganisms in an intermediate-salinity environment, and we used a metagenomic shotgun approach for this analysis. These Andean hypersaline systems exhibited high bacterial diversity and abundance of the phyla Proteobacteria, Bacteroidetes, Balneolaeota, and Actinobacteria; in contrast, Archaea from the phyla Euryarchaeota, Thaumarchaeota, and Crenarchaeota were identified in low abundance. Acos harbored a more diverse prokaryotic community and a higher number of unique species compared with Maras. In addition, we obtained the draft genomes of two bacteria, Halomonas elongata and Idiomarina loihiensis, as well as the viral genomes of Enterobacteria lambda-like phage and Halomonas elongata-like phage and 27 partial novel viral halophilic genomes. The functional metagenome annotation showed a high abundance of sequences associated with detoxification, DNA repair, cell wall and capsule formation, and nucleotide metabolism; sequences for these functions were overexpressed mainly in bacteria and also in some archaea and viruses. Thus, their metabolic profiles afford a decrease in oxidative stress as well as the assimilation of nitrogen, a critical energy source for survival. Our work represents the first microbial characterization of a community structure in samples collected from Peruvian hypersaline systems. Genes 2019, 10, 891; doi:10.3390/genes10110891 www.mdpi.com/journal/genes Genes 2019, 10, 891 2 of 24 Keywords: Peruvian Andes; metagenomics; intermediate salinity; microbiome; virus 1. Introduction Millions of years ago (80–110 million years), the ocean covered the central region of Peru; during the formation of the Andes mountains, these marine waters remained inland and, by evaporation, formed deposits of salt in ponds. Different hypersaline water systems are distributed throughout Peru, such as the salterns of the Acos system and the brines from Maras, two thalassohaline environments located in the Andes mountains in southeast Peru. These two systems have not received much study. Acos is located in the district of Acomayo (southeast Peru) at an altitude of 2852 m above sea level, while Maras is located in the district of Urubamba at an altitude of 3380 m and is composed of 3000 small shallow ponds that form terraces on the slope of the mountain Qaqawiñay (a Quechua word meaning eternal rock) [1,2]. The hypersaline ecosystems are characterized by alkalinity and low oxygen concentrations [3–6]. Hypersaline aquatic environments are classified into two main categories: (1) thalassohaline environments, which result from the evaporation of seawater and contain a high concentration of NaCl, neutral or slightly alkaline pH, and a salinity exceeding that of seawater by a factor of 5–10; and (2) athalassohaline environments, which are not derived from seawater and contain high concentrations of ions such as Mg2+ or Ca2+ and a slightly acidic pH [3–6]. Aquatic hypersaline systems represent excellent models for the study of the ecology and diversity of microorganisms. Most saline systems are composed of ponds with different salinity gradients [7]. Microorganisms identified in hypersaline environments have been classified according to the concentration of salts in the environments they inhabit: weak halophiles (1–3% NaCl), moderate halophiles (3–15% NaCl), and extreme halophiles (more than 15% NaCl) [8]. In contrast, there is no generalized classification for saline environments, but they can be divided into low salinity (less than 10% NaCl), intermediate salinity (10–20% NaCl) [9], and high salinity (higher than 20% NaCl) [10]. Regarding microbial communities that live in these ecosystems, a great diversity of microorganisms has been reported, in particular of the Halobacteriaceae family within the Archaea domain. For bacteria, the Halorhodospira, Salinibacter, Halomonas, Chromohalobacter, and Salicola genera are abundant; and eukaryotic organisms such as Artemia salina, Colpodella edax, and Dunaliella salina have been identified in low proportions [5,11–13]. In addition, a high diversity of haloviruses has been identified, at concentrations of 7 ≥1 × 10 per mL in seawater, among which a few are cultivable [12]. In this work, the diversity of halophilic microorganisms and functional diversity were determined in two thalassohaline environments, Acos and Maras, that have physicochemical differences in salinity and pH. We expected that these intermediate-salinity environments would contain a greater microbial diversity than high-salinity environments and with a particular microbial community structure given the high altitude. Thus, we consider that this analysis opens diverse opportunities to describe the microbial diversity and functional profile within the Peruvian hypersaline systems and will contribute to knowledge in these environments. This is the first characterization of a microbial community structure of intermediate salinity in samples collected from Peruvian high-altitude salterns. 2. Materials and Methods 2.1. Sampling, DNA Extraction, and Sequencing Water (20 liters) was collected during the rainy season (January 2018) from two points where the water emerges in the mountain in two hypersaline systems located in Cusco, Peru. The first is in Maras (13◦57′59.3” S, 71◦05′65” W), and the second is in Acos (11◦16′25” S, 72◦9′15” W). The samples were obtained with sterilized tools and containers, and salinity and pH were measured in situ using a hand refractometer (Spectronic Instruments Inc., Rochester, NY, USA) and pH potentiometer (HANNA Genes 2019, 10, 891 3 of 24 Instruments, Portugal), respectively. All samples were transported to the laboratory under refrigerated conditions, where liters of water were filtered through 0.22-µm Millipore filters. The DNA was purified from the filters by using ZymoBIOMICS DNA kits (MoBio, West Carlsbad, CA, USA). The DNA concentration was determined using a NanoDrop 1000 spectrophotometer (Thermo Scientific), and fluorometry was measured using a Qubit 4 fluorometer (Invitrogen). The DNA was sequenced using the Illumina NextSeq 500 platform with the Nextera V2.0 kit (150 bp, 2 × 75 bases) at the Instituto de Biotecnología of Universidad Nacional Autónoma de México. 2.2. Quality Control and Assembly The quality control of sequences was performed by FASTQC v0.11.4 software [14], and duplicated sequences were removed using CD-HIT-DUP v4.7 [15] with a maximum mismatch number of 0.03. Reads were assembled in contigs using MEGAHIT v1.1.2 [16] under default parameters in paired-end mode, and contigs of a minimum length of 1000 bp were considered for further analysis. 2.3. Microbial Community Taxonomic Assignments Taxonomic assignments were performed with software Kaiju v16.0. In addition, we used MetaGenome Rapid Annotation Subsystems Technology (MG-RAST v4.03) [17], which compares the assembly sequences with a comprehensive non-redundant database sourced from the National Center for Biotechnology Information (NCBI) databases, and SEED, which categorizes gene function into five levels of resolution. An expected value (E) cutoff of 10−5 was employed for taxonomic classifications. Raw data of Metagenomes have been deposited in MG-RAST with accession numbers: mgm4810306.3, mgm4808260.3, and mgm4810472.3. For virus classification, the viral contigs were achieved with VirSorter v2 [18], and these contigs were classified with MEGAN v5.10.6. For fungi classification, the sequences were compared against a constructed database comprised of 35,296 complete and draft fungi genomes from NCBI. For both viruses and fungi, the best-scoring BLAST results with an E-value of 10−6 were parsed, and the taxonomic assignment was determined using MEGAN software [19]. The lowest common ancestor (LCA) method in MEGAN was used for taxonomic assignment, with the following parameters: minimum support of 2; minimum score of 50; top percent of 10. 2.4. Diversity Index The taxonomic profiles at the species level were used to calculate the diversity indices from all data, and different alpha diversity descriptors were obtained using the Phyloseq function in R v3.3.3 [20]. The beta diversity was determined by Bray-Curtis dissimilarity, and the sampling effort was evaluated through the rarefaction curves using a Vegan library implemented in R [21]. 2.5. Genome Reconstruction The reconstruction of the bacterial genome was directed to those species that had the highest abundance according to the taxonomic classification. The genomes were retrieved using the strategy fragment recruitments within Bowtie2 v2.2.6 [22]. The coverage was evaluated using BBmap v38.25 [23], and the consensus sequence was inferred using UGENE v1.31.1 [24]. For the reconstructed genome, the presence of contamination was evaluated using One Codex [25] and Genome Peek. Briefly, One Codex assigns an unknown nucleotide sequence for the identification of k-mers of fixed size k-31 in comparison with its own database. Genome Peek extracts the 16S gene and radA/recA, rpoB, and groEL, the principal molecular markers, from a genome for taxonomic identification. The annotation was achieved using Prokka v1.12 [26] and visualized with Genome Atlas. For viral sequences, identification was achieved by VirSorter [18] and was based on viral hallmark genes annotated as “major capsid protein,” “portal,” “terminase large subunit,” “spike,” “tail,” “virion formation”, and “coat,” among others. The entire contig was considered viral if more than 80% of predicted genes on a contig had a viral signal. This software finds new viruses at different confidence Genes 2019, 10, 891 4 of 24 levels, with scores of categories 1 to 4, with 4 being the highest confidence level. Viral sequences identified within category 1 by VirSorter were visualized with the easyfig v2.2.2 tool and also assessed with the PHAge search tool (PHAST) [27]. Finally, contigs with lengths of ≥10 kbp within category 2 (“quite sure”) in VirSorter were translated into protein sequences and classified taxonomically using the vConTACT v2 software [28] with default parameters (https://bitbucket.org/MAVERICLab/vcontact), with the aim of classifying these possible new viruses. 2.6. Binning for Putative Genomes Assembled contigs were clustered into bins or metagenome-assembled genomes (MAGs), using MaxBin v2.2.4 [29]. Briefly, MaxBin performs genome reconstruction from metagenomes based on two genomic characteristics, tetranucleotide frequencies and the level of bin coverage, using single-copy marker genes. The two metagenomes from Acos were used to recover the MAGs, which were later annotated with Prokka [26]. From the annotation of MAGs, the ribosomal sequences were extracted in single copy (L2, L3, L4, L5, L6, L14, L15, L16, L18, L22, L24, S3, S8, S10, S17, and S19), and then these sequences were aligned with those reported by Hung et al. [30] by using MAFFT v7.005 for taxonomic identification [31]. The phylogenetic analysis was performed using FastTree v2.1.7 [32], which considers an approximate maximum likelihood with 100 bootstrap replicates. Finally, the phylogenetic tree was displayed using ITOL [33]. 2.7. Functional Analysis and Biogeochemical Cycles Prodigal v2.6.3 [34] was used for predicting protein-coding genes in the assembled contigs by using the metagenomic mode, and the functional assignment was achieved using SUPERFOCUS [35], which contains the SEED database with an E-value of 10−5. From functional abundance tables, a heatmap using the ggplot2 library [36] and RColorBrewer library in R (www.ColorBrewer.org) was generated. Finally, microbial metabolic pathways involved in the biogeochemical cycles for carbon, sulfur, nitrogen, hydrogen, iron, and oxygen were identified using the Multigenomic Entropy-Based Score pipeline (MEBS v1) with a false-discovery rate of 0.0001 [37]. 3. Results and Discussion 3.1. Site Characterization and Field Sampling The water samples were collected from two locations in the district of Cusco, Peru. The first sample was collected from Maras; its pH was 7 and its salinity concentration was 23% NaCl (Figure 1). This concentration was slightly lower than previously reported (25% NaCl) in emergent water, whereas in the crystallizer ponds the concentration was higher (30% NaCl) [1]. The second and third samples were collected from Acos, with a pH of 7.9 and 19% salinity (Table 1). The salinities of the thalossohaline water samples from Maras and Acos [1,2] were similar to levels in other solar salterns with intermediate salinity, such as Marine Saltern in Santa Pola, Spain (13–19% NaCl) [7,38] and Saltern in Isla Cristina, Spain (21% NaCl) [38,39]. In this regard, salterns exhibiting an intermediate salinity have been found to contain a greater diversity of microorganisms than salterns with higher salt concentration [38]; the concentration of NaCl defines the diversity and structure of the microbiome in these environments [40]. Genes 2019, 10, 891 5 of 24 Genes 2019, 10, x FOR PEER REVIEW 5 of 25 FigFuigrue r1e. 1L.oLcaotciaotnio onf othf eth feiefilde lsditseist eins iCnuCsucosc PoePruer. u(a. )( aM) aMraasr awsiwthi t3h030000 s0hsahllaolwlo wpopnodnsd. (sb. )( bA)cAosc oast atht eth e oriogriing ionf othf eth we awteart.e r. The second anTda bthleir1d. sSaemqupelnecse wfeeartue rceoslolefchtyepde frrsoalmin eAmcoesta, gweintohm ae psHfro omf 7C.9u sacnod, P 1e9r%u. salinity (Table 1). The salinities of the thaNluomsbseorhofalinNeu wmbaetrer samples from Maras and Acos [1,2] were similar to levels Data in othSeert solSaarl isnaitylternpHs witPhai riendt-eEnrdmedofiCaotnet isgas linity, suSecqhu eansce Ms arine Saltern inT Saxaonnotma iPcaol Clala,s sSifipcaatiinon (13–19% Reads Assembled NaCl) [7,38] and Saltern in Isla Cristina, SpainCl a(s2si1fi%ed NaUCncll)a s[s3ifi8ed,39].B Iancte trihais reAgrcahradea, saltEeurknarsya exhiVbiirutisnesg an iAnctoesr1med1i9a%te sal7i.n9 ity 6h3,a38v7e,9 98been2 5f7o,3u14nd to 7c1o%ntain a 2g9r%eater di5v7%ersity o1f4 %microor2g%anisms 0t.2h%an Acos 2 19% 7.9 79,304,621 256,430 71% 29% 57% 16% 2% 0.2% salteMrnarsa s with23 %highe7r.0 salt5 6c,0o8n6,8c0e9ntrati2o65n0 [38]; th70e% concent3r0a%tion of 5N6%aCl def1i1n%es the 1d%iversity1 .3a2%nd structure of the microbiome in these environments [40]. 3.2. Community Structures of Intermediate Hypersaline Systems Table 1. Sequence features of hypersaline metagenomes from Cusco, Peru. In order to analyze the diversity, abundance, and genes involved in metabolic profiles of samples fDraotam Maras Number of Paired-End Number of Contigs Set Salinity anpHd Acos, shotgun metagenomic sequencing was performed. Ma Reads Assembled Sequences Traaxsonaomnidcal AClcasosisficsaatiolnte rns can be considered environments at high altitude with intermediate salinity (according to the determined pe rcentag e of s alt) (Table 1 ). However, salin ity is notCtlahsseifioedn lyUpncalarssaifmiede teBracttheraiat mAorcdhaiefia esEtuhkaeryaa buVnirdusaesn ce Aacnosd 1 div1e9%rs ity7o.9 f micro6o3,r3g87a,99n8i sms presen2t57i,n314t hese ecos71y%s tems; b29i%o geogr5a7p% hic p1a4%tt erns 2t%h at ma0.2y% also have a role include altitude, remoteness of these environments, oxygen availability, alkalinity, altitude, Acos 2 19% 7.9 79,304,621 256,430 71% 29% 57% 16% 2% 0.2% and UV irradiation [41–43]. Maras Wi2t3h% the 7m.0 etagen5o6,m086e,8s09o btained from265t0h e two loc7a0%ti ons, th3e0%g eneral56s%t ructu11r%e of th1e%m icro1.3b2i%o me was determined. To this end, the sequences were classified with Kaiju (Table 1), and the results showed 3.2a. hCiogmhmabuunnitdy aSntcreucotfubreasc otef rIinatleormrgeadniaistem Hsy(~pe5r7s%alionfe tShyestsemqus e nces), followed by Archaea (~16%). These results contrast with the abundance reported in crystallizer ponds in Maras, where the salinity of In order to analyze the diversity, abundance, and genes involved in metabolic profiles of >30% NaCl showed a microbiota dominated by Archaea (80–86% of total counts) with much lower samples from Maras and Acos, shotgun metagenomic sequencing was performed. Maras and Acos percentages of Bacteria (10–13%) [1]. salterns can be considered environments at high altitude with intermediate salinity (according to the The enrichment analysis of species and diversity in these sites, evaluated with Chao, Shannon, and determined percentage of salt) (Table 1). However, salinity is not the only parameter that modifies Simpson indexes, revealed that Acos samples had a greater richness than Maras samples (Supplementary the abundance and diversity of microorganisms present in these ecosystems; biogeographic patterns Table S1). These results correlated with the rarefaction curves, i.e., in Acos samples, the asymptotic that may also have a role include altitude, remoteness of these environments, oxygen availability, distribution was reached, which indicates a greater diversity showing correlation to the other diversity alkalinity, altitude, and UV irradiation [41–43]. indexes, whereas in the Maras sample the asymptote was not reached, since most of the contigs With the metagenomes obtained from the two locations, the general structure of the microbiome were assigned to Cutibacterium acnes, which is highly unlikely to reside in this environment and was was determined. To this end, the sequences were classified with Kaiju (Table 1), and the results considered a contaminant and was therefore eliminated from diversity curves and subsequent analyses. showed a high abundance of bacterial organisms (~57% of the sequences), followed by Archaea (~16%). These results contrast with the abundance reported in crystallizer ponds in Maras, where the Genes 2019, 10, 891 6 of 24 However, the remaining organisms present in this sample are halophilic, but as shown in the diversity curve it is necessary to perform new sampling to know the diversity in Maras (Supplementary Figure S1). In addition, the Bray-Curtis dissimilarity index was performed to evaluate the beta diversity, showing an index equal to 1, which indicates a different species composition between Maras and Acos. In contrast, the index value between the two samples from Acos was close to zero, suggesting that these samples contained the same species (Supplementary Figure S2). These results correlate with findings reported for other saltern ponds with intermediate salinity, such as those in Santa Pola, Spain, with 13–19% NaCl, where high abundance levels of bacteria (~73 and ~54%) and archaeal organisms (~27% and ~46%, respectively) were found [38,44]. The same was found when the Chao index was compared for these metagenomes [45]. In contrast, in the saltern pond located in Isla Cristina, Spain (21% NaCl), Archaea were predominant (~84%), followed by Bacteria (~16%) [38]; although the structure at the phylum level is equivalent, important differences at the genus level are attributed to particular local ecological conditions [38]. These results suggest that in environments with higher salt concentrations there is less diversity and species richness, probably because there is lower availability of nutrients and oxygen, in contrast to intermediate-salinity environments, where there is a greater availability of nutrients and oxygen. Therefore, salt concentration is an important factor that shapes the structure of the microbial community in hypersaline environments and determines its diversity and abundance. 3.3. Bacterial and Archaeal Community Composition Previous studies have shown that the halophilic world is highly diverse, but this diversity is reduced with increasing salt concentrations [46]. In the case of intermediate-salinity environments, several moderately halophilic bacteria have been reported, including Halomonas, Salinivibrio, Halobacillus, Thalassobacillus, Bacillus, Salinicoccus, Idiomarina, Chromohalobacter, and Salinicoccus [7,38,47–49]. In the metagenomic samples from Maras, bacteria from the phylum Proteobacteria (38%) were the most abundant, followed by Actinobacteria (11.58%), Firmicutes (2.68%), Cyanobacteria (0.40%), Bacteroidetes (0.40%), Deinococcus-Thermus (0.26%), and Verrucomicrobia (0.26%) (Figure 2). At the species level in Maras salterns, it was interesting that the most abundant bacterium was Thiohalorhabdus denitrificans (11.51%), which is an extremely halophilic species [50], followed by Thiohalospira halophila (0.87%) [51]. Both of these species are chemolithoautotrophic sulfur-oxidizing bacteria which use thiosulfate as the electron donor [50,51], and neither has been reported previously in intermediate-salinity settings. Other halophilic bacteria, such as Pseudomonas (2.15%) and Halomonas (0.94%), were identified in lower proportions than previously reported [1,44,47]. Even the main bacteria described in hypersaline systems, such as Salinibacter ruber [52,53] and Rhodovibrio salinarum [1], were found in low abundance (~0.07%, each species) in our study, probably because the altitudes of these sites affect bacterial structures, as we have shown. In addition, predominant non-halophilic bacteria found included Lawsonella clevelandensis (7.06%), Escherichia coli (2.08%), Clostridium difficile (1%), Cutibacterium acnes (0.9%), and Ralstonia solanacearum (0.8%). The presence of non-halophilic bacteria in hypersaline environments has been previously described in the Santa Pola saltern (19% NaCl), and some of these organisms have developed adaptation mechanisms, such as a strong GC bias, as has been identified in halophilic organisms as a strategy to avoid UV-induced thymidine dimer formation [44,45,54,55]. The two samples from Acos exhibited similar compositions of microorganisms: Proteobacteria corresponded to ~59% of identified sequences, followed by Bacteroidetes (11%), Balneolaeota (6%), Firmicutes (5%), and Actinobacteria (2%). Both Acos metagenomes had the same composition as environments of intermediate salinity previously reported, showing a high diversity and abundance of bacteria [7,38]. Interestingly, in Acos salterns members of the Balneolaeota phylum were identified, including moderate halophiles (5–10% NaCl) abundant in sediments, saline soils, and marine habitats [55,56] (Figure 2b,c). Genes 2019, 10, 891 7 of 24 a) b) 22 7 Candidatus Nanohaloarchaeota Candidatus Haloredivivus sp. G17 2.94k 1.30k Haloarculaceae Haloferax 8 Nanohaloarchaea archaeon SG9 5 295 Halococcus salifodinae 3 10 3.18k Candidatus Woesearchaeota Halococcus Halobacteriaceae 8.77k 8.76k 5 Halorhabdus tiamatea 31.1k Halohasta Halohasta litchfieldiae 35.3k Archaea 4 8 Crenarchaeota 31 Halanaeroarchaeum sulfurireducens Haloarculaceae 39 4.89k Archaea Euryarchaeota 253 Halodesulfurarchaeum formicicum Haloferacaceae 66 39 1.66k Halobacteriaceae Halodesulfurarchaeum Halorubrum Euryarchaeota 5 Haloplanus natans 12.0k 39 Halorubraceae Haloferacaceae 16 Halorubraceae 4.09k 33 Natrialbaceae Natrialbaceae 11 1.18k Corynebacterium Salinibacter ruber 2.32k 105 105 Thaumarchaeota 1.41k Lawsonella Lawsonella clevelandensis Nitrosopumilaceae 6.62k 1.18k 19 Salinibacter Aliifodinibius roseus Mycobacterium 5.50k 307 19 1.17k Flavobacteriaceae Mycobacteriaceae Acidobacteria 6.62k 17 14 Cutibacterium acnes 2.14k Cutibacterium 1.02k Rhodothermaceae Aliifodinibius Actinobacteria 3.74k Gracilimonas tropica 66 6 Actinobacteria 774 Propionibacterium sp. KPL1844 Rhodohalobacter halophilus Propionibacteriaceae 15 Clostridioides 15 9.36k Clostridioides difficile 16.8k Balneolaceae 2.91k 1.10k 16 Halanaerobium kushneri Peptostreptococcaceae Bacteroidetes Halanaerobium 1.49k 6 Bacteroidetes 11 Staphylococcus 6 9.36k 3 Chloroflexi Streptococcus pneumoniae 1.86k Balneolaeota Gemmatimonas 6 305 305 2.31k Cyanobacteria 1.03k Marinimicrobium agarilyticum 4 Deinococcus-Thermus Chloroflexi 2.94k 5.40k 1.08k 71 2.70k Halanaerobiaceae Marinobacter Marinimicrobium sp. LS-A18 Thiohalorhabdus Thiohalorhabdus denitrificans Cyanobacteria 2.04k Firmicutes Gemmatimonadaceae 365 4 146k Deinococcus-Thermus 1.14k 4.14k Planctomycetes 7.42k Bacteriovorax Arhodomonas aquaeolei 1.01k 14 20 13 Ralstonia Burkholderiaceae Ralstonia solanacearum Firmicutes 15 5.84k Chromatiaceae 3.25k Alteromonadaceae 3.45k 16 Gemmatimonadetes Marinimicrobium 2.02k Comamonadaceae 29 Bacteria 340 4.14k Halofilum ochraceum 83 Thioalkalivibrio Ignavibacteriae 4.14k Arhodomonas 23 876 Cellvibrionaceae 952 Ectothiorhodospirace2a3e 2.02k Chromohalobacter japonicus Thiohalospira Thiohalospira halophila Nitrospinae Halofilum 1.20k 4.27k 121 Nitrospirae 1.20k 33 Chromohalobacter Halomonas elongata Enterobacteriaceae Bacteria Escherichia 31 827 Proteobacteria Planctomycetes 8.94k 9.71k 2.38k Escherichia coli 86.8k Halomonas utahensis Ectothiorhodospiraceae Halomonas 18 Halomonadaceae 7 878 Kluyvera intestini 11.2k Henriciella aquimarina 1.81k 14 16 Halomonas Henriciella Halomonadaceae 907 Moraxellaceae 4.50k Idiomarina loihiensis 13 2.28k Idiomarina Acinetobacter Hyphomonadaceae 1.08k 933 Proteobacteria Idiomarina sp. 28-8 4.52k Nitrosomonas 39 32 Idiomarinaceae 1.12k Pseudomonadaceae Pseudomonas Pseudomonas 5.14k 949 4 Spirochaetes Rhodobacteraceae Thiohalorhabdus denitrificans 2 15 11 Thermotogae Rhizobiaceae Rhizobium 4 981 Verrucomicrobia 4.52k Wenzhouxiangella marina 36 382 Rhodospirillaceae Rhodobacteraceae Spirochaetes 2.18k Rhodovibrio 440 2.18k Verrucomicrobia Rhodovibrio salinarum D K P F G S D K P F G S 8 3 19 19 19 1.04k Halococcus 1.31k 8.78k c) Haloferax Halohasta litchfieldiae 2.92k Haloarculaceae 35.0k 30.9k 3.11k Euryarchaeota Halobacteriaceae 8.79k Archaea Halohasta 4.90k Haloferacaceae 1.66k 12.0k Halorubrum Halorubraceae 1.12k Salinibacter ruber 2.27k Thaumarchaeota 4.03k Natrialbaceae 1.38k Nitrosopumilaceae 6.59k 1.12k Aliifodinibius roseus Salinibacter 5.47k 1.15k Flavobacteriaceae 6.59k Acidobacteria 2.06k Aliifodinibius 778 Rhodohalobacter halophilus 3.78k Rhodothermaceae Actinobacteria 9.28k 986 16.7k Balneolaceae Gracilimonas tropica Bacteroidetes 2.92k 1.07k Halanaerobium Halanaerobium kushneri 9.28k Balneolaeota 1.82k 1.01k Chloroflexi 2.95k Gemmatimonas Halanaerobiaceae 2.55k 2.32k Cyanobacteria Marinimicrobium agarilyticum 364 145k Deinococcus-Thermus 5.46k 1.11k 7.39k 1.99k Marinobacter Marinimicrobium sp. LS-A18 Firmicutes Gemmatimonadaceae 4.13k 3.20k 1.19k Arhodomonas aquaeolei Gemmatimonadetes Bacteriovorax 3.49k 331 2.02k Ignavibacteriae Marinimicrobium Halofilum ochraceum 5.89k 839 4.13k Nitrospinae Alteromonadaceae Arhodomonas 908 Chromohalobacter japonicus 1.16k Nitrospirae 4.18k 2.02k Cellvibrionaceae 4.31k Bacteria Halofilum 825 Halomonas elongata Planctomycetes 8.91k 1.14k Chromohalobacter 2.39k 86.7k Ectothiorhodospiraceae 9.75k Halomonas utahensis 11.2k Halomonas 903 Henriciella aquimarina Halomonadaceae 1.82k Henriciella 952 2.29k Idiomarina loihiensis Hyphomonadaceae 4.61k Proteobacteria Idiomarina 946 4.63k Idiomarina sp. 28-8 Idiomarinaceae 1.13k Pseudomonas 5.09k Rhodobacteraceae 4.47k Rhodospirillaceae 946 Thiohalorhabdus denitrificans 968 2.19k Wenzhouxiangella marina 390 Spirochaetes Rhodovibrio 436 2.19k Verrucomicrobia Rhodovibrio salinarum D K P F G S Figure 2. Taxonomic profile in hypersaline metagenomes from Cusco, Perú. (a) Maras; (b) Acos 1; (c) Acos 2. On the x-axis are the taxonomic levels: D, domain; P, phylum; C, class; O, order; F, family; G, genus; S, species. Numbers correspond to the assigned contigs. At the level of genus, Halomonas was the most abundant (8.4%), with more than 70 different species identified in Acos; Halomonas elongata (2.8%) was the most abundant, followed by Halomonas utehensis (1.6%). In this regard, organisms of the Halomonas genus are aerobic heterotrophic, halo-alkaliphilic, and sulfur-oxidizing bacteria and are commonly found in intermediate-salinity, high-altitude environments [57,58]; they are also a source for the production of bioplastic polyhydroxyalkanoates [59]. In contrast, at the species level, the most abundant bacteria were Aliifodinibius roseus (~5%) within the phylum Balneolaeota; this species is considered moderately halophilic (6–10% NaCl for optimal growth). Also abundant were two species, Halomonas elongata (2.93%) and Arhodomonas aquaeolei (2.84%), an obligately halophilic bacterium with optimal growth at 15% NaCl; both of these species have been shown to degrade phenol [60]. To our knowledge, only a few reports have described these bacteria in a metagenome from an intermediate-salinity environment. Marinimicrobium agarilyticum, Rhodovibrio salinarum (1.50%), Salinibacter ruber (0.80%), and Idiomarina sp. (0.64%) were in low abundance. Idiomarina loihiensis is a bacterium identified in environments a wide range of temperatures (from 4 ◦C to 46 ◦C) and salinities (from 5% to 21%) that presents polyextremophile behavior [61]. In both the Maras and Acos sites, the low abundance of S. ruber is understandable, since this bacterium prefers environments with higher salinity. Genes 2019, 10, 891 8 of 24 Therefore, different species of moderately halophilic bacteria were found in Acos, with Proteobacteria the most abundant. These results correlate with findings from another high-altitude saltern located in Atacama, Chile, at 2,700 m above sea level, where halophilic bacteria able to grow at intermediate salinity were isolated [62]. In general, the moderately halophilic bacteria are aerobic or facultative anaerobic microorganisms that belong to different genera, as part of a physiologically heterogeneous group of bacteria [47]. In intermediate-salinity salterns, such as the Peruvian hypersaline systems, the abundance of archaeal organisms is low, as found in the Maras samples, where Euryarchaeota organisms were found to be highly abundant, followed by “Candidatus Nanohaloarchaeota,” and “Candidatus Woesearchaeota.” In both samples from Acos, Euryarchaeota organisms were the most abundant, followed by Thaumarchaeota, Crenarchaeota, and “Candidatus Bathyarchaeota.” Within the Euryarchaeota phylum, the Halobacterium family was found to be predominant, similar to findings from other salterns and salty lakes [6,45,63,64]. In Maras, Halodesulfurarchaeum formicicum was the most abundant species. Halodesulfurarchaeum is a novel anaerobic genus that was discovered in a deep-sea salt-saturated anoxic environment and in sediments from hypersaline lakes [65]. In Acos, the most abundant archaeon was Halohasta litchfieldiae (~3.5%), a chemoorganotrophic aerobic that can grow in salt concentrations around 12–28%, presenting adaptation to low temperatures [66–68] as occurs in the area of the Peruvian Andes where minimum temperatures reach between −7 ◦C and 4.4 ◦− C. The taxonomic assignment analysis was also carried out with MG-RAST; the abundance of archaea was low, in accordance with the results of Kaiju. From the class Halobacteria, 14 different genera were identified, with Haloarcula genus the most predominant in the three samples (Figure 3). The presence of this genus is interesting because it has been reported to be involved in recombination processes. This process could be occurring between bacteria and archaea, among their sharing genes, for example, rhodopsin family genes which are common and have different functions such ion pumps, channels, enzymes, photGoesnees n20s1o9, r10y, xr FeOcRe PpEEtRo RrEsVtIhEWa t could favor the adaptation [69,70]. 9 of 25 Figure 3. Taxon omic classification of archaea according to MetaGenome Rapid Annotation Subsystems Technology (MG-RAST). (a) Composition of the archaeal community at the phylum level, where Euryarchaeota pFrigeuvrae il3.. (Tbax)oDnoimviecr scilatyssifwicaittihonin otfh aercchlaaesas aHccaorldobinagc tteor iaM.etTaGheenogmeen uRaspHid aAlonanroctuatliaonp revails in Subsystems Technology (MG-RAST). (a) Composition of the archaeal community at the phylum level, all samples. where Euryarchaeota prevail. (b) Diversity within the class Halobacteria. The genus Haloarcula prevails in all samples. 3.4. Composition of the Viral Community Although viruses are sources of genetic variation, as they can modify a genome’s plasticity and alter the structure of populations and also biogeochemical cycles, few reports have described the structure of virus communities in hypersaline environments [71–74]. In this work, the taxonomic assignment performed with Kaiju revealed that 0.2% to 1.32% of the reads were associated with viruses (Table 1). This was probably because we did not perform a viral enrichment with our samples; however, it was possible to find viruses, because they would be included within the host cells or in the form of proviruses [75]. Because of the low percentage of detected viruses in the samples, we used Virsorter, which detects the viral signal in metagenomic datasets [18]. From the assembly of reads, we identified the viral contigs according to VirSorter, and they were subsequently classified taxonomically with MEGAN (See Materials and Methods). The results identified the order Caudovirales, specifically, the Siphoviridae, Podoviridae, and Myoviridae families, in the samples; indeed, these families seem to be ubiquitous in marine environments [76]. Interestingly, in Maras eukaryotic viruses such as Adenovirus and Herpesvirus were identified, probably as a consequence of the composition of eukaryotic organisms in the samples, as also reported for Red Sea brines [77]. Additional double-stranded DNA (dsDNA) viruses associated with eukaryotes were also found in the Acos samples, mainly viruses from the Phycodnaviridae, Poxviridae, Mimiviridae, and Pandoravidae families (Figure 4); all of these are Megavirales, which are Genes 2019, 10, 891 9 of 24 3.4. Composition of the Viral Community Although viruses are sources of genetic variation, as they can modify a genome’s plasticity and alter the structure of populations and also biogeochemical cycles, few reports have described the structure of virus communities in hypersaline environments [71–74]. In this work, the taxonomic assignment performed with Kaiju revealed that 0.2% to 1.32% of the reads were associated with viruses (Table 1). This was probably because we did not perform a viral enrichment with our samples; however, it was possible to find viruses, because they would be included within the host cells or in the form of proviruses [75]. Because of the low percentage of detected viruses in the samples, we used Virsorter, which detects the viral signal in metagenomic datasets [18]. From the assembly of reads, we identified the viral contigs according to VirSorter, and they were subsequently classified taxonomically with MEGAN (See Materials and Methods). The results identified the order Caudovirales, specifically, the Siphoviridae, Podoviridae, and Myoviridae families, in the samples; indeed, these families seem to be ubiquitous in marine environments [76]. Interestingly, in Maras eukaryotic viruses such as Adenovirus and Herpesvirus were identified, Genes 2019, 10, x FOR PEER REVIEW 10 of 25 probably as a consequence of the composition of eukaryotic organisms in the samples, as also reported nfourcRleeodcySetoapblraisnmesic[ 7l7a]r.gAe dDdNitAio nvairludso (uNbCleL-sDtrVans)d. eNdCDLNDAV(sd isnDfeNcAt a)nviimruasless aansdso ucinaitceedllwuliatrh eeuukkaarryyootteess w[7e8r]e foaulsnodf oinu notdhienr thhyepAercsoaslisnaem epnlveisr,omnmaiennlytsv, isruucshe sasfr tohme SthaletoPnh ySceoad inna vthireid Uaen,iPteodx vSitraidtease ,aMndim Oivrigraidnaiec, LanadkeP iann tdhoer aAvindtaaerfcatimc i[l7ie9s]. ( Figure 4); all of these are Megavirales, which are nucleocytoplasmic large DNAAvniortuhser( NimCpLoDrVtasn)t. gNroCuLpD oVf vs iirnufseecst faonuinmda ilns Aancdosu wnaicse allnu luanr celuasksaifriyeodt easrc[h7a8e]aflo dusnDdNiAn ovtihruesr (hFyipguerrsea 4li)n; tehiesn vviirruosn hmaes nbtese,ns ruecphoratsedth ien hSiagltho anbuSenadainncteh ien UhynpiteerdsaSlitnaete esnvanirdonOmregnatnsi,c wLitahk sepiinndtlhee- Ashnatpaercdt icm[o79rp].hologies of Haloarchaea viruses, but this happens when salt concentration reaches saturAatnioonth, ewrhiemrep oinr tgaenntegrraol uAprcohfaveair aursee ps rfeoduonmdiinnaAntc [o1s2w,72a,s80a]n. unclassified archaeal dsDNA virus (FiguIrne s4u)m; mthaisryv, iwrues idheanstibfeieedn sriexp voirrtuesd fainmihliiegsh aassboucniadtaendc we iitnh heuykpaerrysaoltiinc eceelnlsv airnodn mfiveen tfsa,mwiliitehs tshpaint dinlef-escht aBpaecdtemriao rapnhdo lAorgciheaseoaf. HThaliosa lracshta egarovuirpu swesa,sb tuhtet hmisohsta papbuennsdwanhte, nacscaoltrdcoinngce tnot rtahteio mn irceraocbhieasl csaotmurpaotsioitnio, nw ihne trheiisn egnevnireoranlmAerncht.a ea are predominant [12,72,80]. FFiigguurree 44.. CCoomppoossiittiioonnss ooff tthhee vviirraall ccoommuunniittiieess aatt tthhee ssppeecciieess lleevveell iinn AAccooss aanndd Maarraass.. 3.5. Composition of Fungal Communities The diversity of microorganisms in intermediate-salinity systems is not restricted to prokaryotes. Approximately 2% of the reads corresponded to eukaryotes. According to the Megan classification system, two phyla of fungi were found, Ascomycota (with 85%) and Basidiomycota (with 10%), as has been reported for other hypersaline environments [81]. At the family level, the most abundant were Arpergillacea, followed by Sordariaceae, Sporidiobolaceae, and Chaetomiaceae (Figure 5). Aspergillus has been reported to be dominant in salterns of Slovenia, along with Cladosporium and Penicillium [82]. These filamentous fungi are ubiquitous and have been isolated with high frequency in hypersaline environments [83]. Some species in the Sordariaceae family have also been isolated from hypersaline environments. The Sporidiobolaceae family is within the Basidiomycota phylum, which has been recovered from sea water, glacier ice, and extremophile environments. Rhodotorula was recovered from hypersaline ponds in Israel [84]. The Chaetomiaceae family was recovered together with 19 inhabiting hyphomycetes fungi in soils from the hypersaline Urmia Lake [85]. Thus, a high diversity of fungi has been found in hypersaline environments, where the most abundant are melanized Aspergillus, which is a ubiquitous genus used in biotechnology applications for its production of citric acid and enzymes, and non-melanized Rhodotorula, which comprises several species that can be used in bioremediation [86]. Genes 2019, 10, 891 10 of 24 In summary, we identified six virus families associated with eukaryotic cells and five families that infect Bacteria and Archaea. This last group was the most abundant, according to the microbial composition in this environment. 3.5. Composition of Fungal Communities The diversity of microorganisms in intermediate-salinity systems is not restricted to prokaryotes. Approximately 2% of the reads corresponded to eukaryotes. According to the Megan classification system, two phyla of fungi were found, Ascomycota (with 85%) and Basidiomycota (with 10%), as has been reported for other hypersaline environments [81]. At the family level, the most abundant were Arpergillacea, followed by Sordariaceae, Sporidiobolaceae, and Chaetomiaceae (Figure 5). Aspergillus has been reported to be dominant in salterns of Slovenia, along with Cladosporium and Penicillium [82]. These filamentous fungi are ubiquitous and have been isolated with high frequency in hypersaline environments [83]. Some species in the Sordariaceae family have also been isolated from hypersaline environments. The Sporidiobolaceae family is within the Basidiomycota phylum, which has been recovered from sea water, glacier ice, and extremophile environments. Rhodotorula was recovered from hypersaline ponds in Israel [84]. The Chaetomiaceae family was recovered together with 19 inhabiting hyphomycetes fungi in soils from the hypersaline Urmia Lake [85]. Thus, a high diversity of fungi has Genes 2019, 10, x FOR PEER REVIEW 11 of 25 been found in hypersaline environments, where the most abundant are melanized Aspergillus, which is a ubiquiItno ussugmenmusaruys,e dthine brioetseuclhtsn oslhogoywa papnl iciamtipoonrstfaonrti tsdpivroerdsuitcyti oonf offucintrgiic awciidthainnd tehnez yhmyepse, rasnadline none-nmvierloannmizeedntRs;h hoodowtoervuelra,, twhehiirc hfucnocmtiopnriss easres esvtiellr aulnscpleeacire. s that can be used in bioremediation [86]. FigFuirgeu5re. 5D. Divievresristiytyt taaxxoonnoomyy ooff fufunnggi.i T. hTeh AespAesrgpielrlagciellaaec feaame iflaym wialys twhea smtohset ambuonstdaanbtu innd Aacnots ianndA Mcoasras. and Maras. 3.6. Genome Reconstruction In summary, the results show an important diversity of fungi within the hypersaline environments; how3.e6v.1e.r B, tahceteirrifauln Gcteinoonms aer Re esctiollnustnrculcetairo.n in A Hypersaline Environment One of the aims in this work was the reconstruction of complete genomes that could provide information about the main metabolic pathway associated with hypersaline metabolism. To this end, the genomes were retrieved using a fragment recruitment strategy with the reads aligned against the available reference genome [87], and their integrity was assessed with Genome Peek [88] and One codex [25] (see Materials and Methods). According to the abundance levels found with our taxonomic assignments, the most abundant genomes were Halomonas elongate-like and Idiomarina loihiensis-like and these were retrieved from the metagenome. The complete reference genomes reported in NCBI for these bacteria were used for fragment recruitment. First, the Halomonas elongate-like genome was reconstructed, comparing the sequences against the reference genome MAJD01000001.1. Both samples of Acos showed 93% breadth coverage and a deep coverage of 8.3x The circular chromosome of ~3.7 Mb is graphically represented in Figure 6; it has a GC content of 64%, similar to Halomonas elongata isolated from Huanoquite at Peru. The average nucleotide identities (ANIs) between the reference genome (MAJD01000001.1) and the genomes from Acos 1 and Acos 2 were 98.04% and 98.02%, respectively (Table 2). A comparison between Halomonas elongata strain HEK1 (MAJD01000001.1), H. elongata strain MH25661 (QJUB00000000.1), and the two genomes recovered from this work revealed that all strains share a core of 2,984 genes. Indeed, both genomes recovered from Acos share 916 genes, a significant number of genes in comparison to the other genomes. In addition, Idiomarina loihiensis-like, reported as a predominant genome in a saline environment, was also assembled. The coverage was 74%-78% with a reference genome (I. loihiensis L2TR GCA_000008465.1), and the deep coverage was 5-6x for Acos 1 and Acos 2. Both strains share 93.0% of identity according to their ANIs. Finally, the two genomes share 2151 common genes (Supplementary Figure S3). Genes 2019, 10, 891 11 of 24 3.6. Genome Reconstruction 3.6.1. Bacterial Genome Reconstruction in A Hypersaline Environment One of the aims in this work was the reconstruction of complete genomes that could provide information about the main metabolic pathway associated with hypersaline metabolism. To this end, the genomes were retrieved using a fragment recruitment strategy with the reads aligned against the available reference genome [87], and their integrity was assessed with Genome Peek [88] and One codex [25] (see Materials and Methods). According to the abundance levels found with our taxonomic assignments, the most abundant genomes were Halomonas elongate-like and Idiomarina loihiensis-like and these were retrieved from the metagenome. The complete reference genomes reported in NCBI for these bacteria were used for fragment recruitment. First, the Halomonas elongate-like genome was reconstructed, comparing the sequences against the reference genome MAJD01000001.1. Both samples of Acos showed 93% breadth coverage and a deep coverage of 8.3x The circular chromosome of ~3.7 Mb is graphically represented in Figure 6; it has a GC content of 64%, similar to Halomonas elongata isolated from Huanoquite at Peru. The average nucleotide identities (ANIs) between the reference genome (MAJD01000001.1) and the genomes from Acos 1Geannesd 20A19c, o10s, x2 FwORe PreEE9R8 R.0E4V%IEWa nd 98.02%, respectively (Table 2). 12 of 25 FigureFi6g.uDrer a6f. tDgreanfto gmeneocmome cpoamripsaornisoofn Hofa lHomaloomnaosnaeslo enlognagtaataa nadndI dIidoimomaarrininee llooiihhiieennssiiss.. FFrroomm ththe eoouutstisdied e to centerto fcetnhteerg oefn tohme geesnomf Hesa olofm Hoanloamsoenlaosn eglaonteg-altiek-elikfero frmomA Acocoss2 2( (rreed) and AAccoos s1 1(b(lbuelu),e t)h,et hineneinrmneorsmt ost rings srhinogws psheorcwe npteArcTe,nGt CAsTk, eGwC, G sCkecwo,n tGenCt ,cgolnotbenalt,i ngvloebrtael dinrveperetaetds ,rCepDeSa-t,s,C CDDS+S-,, pCoDsiSti+o, npporseitfieorne nce, stackinpgreefenreerngcye,, asntadckiinntgr inesniecrgcyu,r vaantdu rien,trwinhsiicc hcaurrevactoulroer, ewdhinicha garrea dcioelnotr,eadn idn tha egirnatdeirepnrte, taantido nthoef the referenincteesrptorectaotlioorni osfi nthteh reefifegreunrceelse tgoe ncodl.orT ihse inId tihoem fairgiunreel oleihgieennds.i sT-lhike eIdgioemnaorminee lforiohmienAsisc-oliske2 g(gerneoemne) and Acos 1fr(obmlu eA)caons d2 t(hgereiennn) earnmdo Astcorisn 1g s(bslhuoew) atnhde tshaem ineninertmeropsrte rtiantgios nshoofwg etnhoe msaemHe. ienltoenrgparteata. tion of genome H. elongata. Table 2. Features of Halomonas elongata and Idiomarina loihiens is genomes with number of transfer RNA (tRNAT)a, btrlaen 2s.f eFre-amtuersesse onfg HeralRomNoAnas( temloRngNatAa )aanndd Idriiobmoasroinma alol iRhiNenAsis( rgRenNoAm)e.s with number of transfer RNA (tRNA), transfer-messenger RNA (tmRNA) and ribosomal RNA (rRNA). Features Halomonas elongata Halomonas elongata Idiomarina loihiensis Idiomarina loihiensis (HAaclosm1o)nas H(aAlcoomso2n)as Idi(oAmcaorsin1)a Idiom(Aarcionsa2 ) Length sFizeeat(ubrpe)s 3,7e6lo8n,1g2a7ta 3e,7lo6n3g,7a7t0a lo2i,h1i1e1n,s1i7s5 loih2ie,2n2s7i,s0 77 (Acos 1) (Acos 2) (Acos 1) (Acos 2) % GC 64 64 47.2 47.3 CLDenSgth size 4564 4678 4060 3871 3,768,127 3,763,770 2,111,175 2,227,077 tRNA(sbp) 65 69 55 56 tmRN%A GC 164 164 47.12 47.3 1 rRNAs 12 12 12 13 CDS 4564 4678 4060 3871 tRNAs 65 69 55 56 tmRNA 1 1 1 1 rRNAs 12 12 12 13 Interestingly, the reconstructed genomes of Halomonas elongata and Idiomarina loihiensis show different strategies for maintaining osmotic equilibrium, according to the annotation; de novo synthesis of the ectoine pathway is complete in H. elongata. Ectoine is a compatible solute of low molecular weight of aspartate metabolism, which is produced when there are increased K+-glutamate levels [89]. In contrast, in Idiomarina loihiensis, de novo synthesis of ectoine was absent; however, we identified genes encoding ABC transporters such as the ATP-dependent Na+ exporter natAB, in addition to other iron transporters, which promote detoxification in hypersaline environments. These findings show different adaptation strategies of bacteria in hypersaline environments. The annotation of the genes exclusively shared between two Halomonas genomes from Acos revealed that most of them were related to nitrogen metabolism, chemical reactions, and pathways Genes 2019, 10, 891 12 of 24 A comparison between Halomonas elongata strain HEK1 (MAJD01000001.1), H. elongata strain MH25661 (QJUB00000000.1), and the two genomes recovered from this work revealed that all strains share a core of 2,984 genes. Indeed, both genomes recovered from Acos share 916 genes, a significant number of genes in comparison to the other genomes. In addition, Idiomarina loihiensis-like, reported as a predominant genome in a saline environment, was also assembled. The coverage was 74–78% with a reference genome (I. loihiensis L2TR GCA_000008465.1), and the deep coverage was 5-6x for Acos 1 and Acos 2. Both strains share 93.0% of identity according to their ANIs. Finally, the two genomes share 2151 common genes (Supplementary Figure S3). Interestingly, the reconstructed genomes of Halomonas elongata and Idiomarina loihiensis show different strategies for maintaining osmotic equilibrium, according to the annotation; de novo synthesis of the ectoine pathway is complete in H. elongata. Ectoine is a compatible solute of low molecular weight of aspartate metabolism, which is produced when there are increased K+-glutamate levels [89]. In contrast, in Idiomarina loihiensis, de novo synthesis of ectoine was absent; however, we identified genes encoding ABC transporters such as the ATP-dependent Na+ exporter natAB, in addition to other iron transporters, which promote detoxification in hypersaline environments. These findings show different adaptation strategies of bacteria in hypersaline environments. The annotation of the genes exclusively shared between two Halomonas genomes from Acos revealed that most of them were related to nitrogen metabolism, chemical reactions, and pathways involving organic acids. Regarding the genes related to the metabolism of nitrogen, genes encoding a nitrate/nitrite sensor protein, nitrate reductase, and ammonia monooxygenase were found. This is interesting since Halomonas use nitrogen as the last acceptor of electrons even in conditions of low oxygen, as is the case in hypersaline environments [90]. In general, these Proteobacteria play an important role in the nitrogen cycle, through recycling of nitrogen by assimilation of gaseous nitrogen from the atmosphere and decomposition of organic matter, causing nitrogen to be constantly available [90]. 3.6.2. Reconstruction of Viral Genomes Traditional techniques limited us in obtaining viral genomes, but through metagenomics it was possible to reconstruct these genomes, allowing us to expand knowledge about the influence of viruses in this particular environment. In this regard, the viral contigs identified correspond to bacteriophages, as expected, since bacteria were more abundant in our metagenomes. In the Maras sample, a genome with 97% similarity with the lambda phage of Enterobacteria (Siphoviridae family) was found. This phage infects Escherichia coli, a non-halophilic bacterium that was abundant in this sample (Figure 7a). In Acos samples, around 100 different contigs with viral signals were identified; because many of these could be fragments of viral sequences, different criteria were used, such as the presence of inverted terminal repeats in the case of circular genomes and similarities in size lengths with a reference genome (no more than 10% of size length) [18]. In the Acos samples, two phages of Halomonas elongata were recovered. This finding was somewhat expected, since H. elongata is abundant in these intermediate-salinity environments, but this is the first time that bacteriophages have been reported in this bacterium. The two recovered phages have a size length of approximately 28 Kbp, and a comparative analysis with two ΦHAP-1 reference genomes revealed that they have the same pattern of synteny and a protein identity greater than 65%. (Figure 7b). The phage used for comparison was Halomonas phage ΦHAP-1. This is a Hapunavirus belonging to the family Myoviridae and was isolated from Halomonas aquamarina. The GC content in ΦHAP-1 is 59%, which is slightly lower than other phages such as ΦHAP-1, found in Acos with a 64% GC content, and similar to the GC content of the host genome (H. elongata) [91]. The ΦHAP-1-type phages from Acos have 40 putative open reading frames (ORFs) with 6 genes fewer than the reference genome. Genes coding for proteins such as the RepA replication protein, the prophage repressor, the prophage antirepressor, and the protelomerase were not identified; the latter is necessary for the maintenance of the linear state of the prophage within the host genome [92]. Genes 2019, 10, x FOR PEER REVIEW 13 of 25 involving organic acids. Regarding the genes related to the metabolism of nitrogen, genes encoding a nitrate/nitrite sensor protein, nitrate reductase, and ammonia monooxygenase were found. This is interesting since Halomonas use nitrogen as the last acceptor of electrons even in conditions of low oxygen, as is the case in hypersaline environments [90]. In general, these Proteobacteria play an important role in the nitrogen cycle, through recycling of nitrogen by assimilation of gaseous nitrogen from the atmosphere and decomposition of organic matter, causing nitrogen to be constantly available [90]. 3.6.2. Reconstruction of Viral Genomes GenesT2r0a19d, i1t0i,o8n9a1l techniques limited us in obtaining viral genomes, but through metagenomics i1t3 wofa2s4 possible to reconstruct these genomes, allowing us to expand knowledge about the influence of vIniruasdedsi tiino nt,hiinsv epratretdicruelpaer aetsnvwierorenmfouenntd. aInt ptohsiist iorengsa2r8d,,4 2th8–e2 v8,i4r5a2l tcoon2t8i,g4s5 5i–d2e8n,4ti7f9iewd itchorarelesnpgotnhds itzoe boafc2t5erbioppahnadgeasn, iadse enxtiptyecotefd1,0 0si%n,cesu bgagcetsetriinag wthearet tmheorgee naobmunediasnint ianc oirucru lmareftoagrmen,obmeceasu. sIen tthhise kMinadraosf sianmveprltee,d a rgeepneoamtede wseiqthu e9n7c%e issimusiluaarliltyy fwouitnhd thine lraemgibodnas pprhoacgees soefd Ebnyteprorboatcetleormia e(rSaispehsovainriddaoer ifgaimnailtye)s wfraosm fotuhnedre. lTehaisse pohfapghea ignefewctisth Ecsochvearliechnital ycocllio, sae ndoenn-dhsa.loAplhl iolifc tbhaecsteefirinudmin tghsats uwgagse asbt uthnadtatnhte ipnh tahgise scaomulpdleb (eFiinguthree i7raf)r.e e form. a) b) FFiigguurree 77. . GeGneonmomese os f onfonvoevl eblabctaecrtieorpiohpagheasg.e s(a. ) E(an)teEronbtaecrotebraicat eprhiaagpeh alagme dlaam-lidkae- lfirkoemf rMomaraMs;a (rba)s ; H(ba)loHmaolnomaso pnhasagpeh-aligkee- l(ipkhei(HphAiPH-A1)P f-r1o)mfr oAmcoAs.c os. The rest of the viral sequences obtained in the metagenomes through BLAST analysis had very poor identity with sequences in the NCBI database, and they were used for taxonomic or functional allocation. Thus, the contigs of >10 kb was clustered using the Viral RefSeq database and vConTACT2; this tool allows classification of viral sequences with protein comparisons. In Figure 8, two examples of viral assignation taxonomy are presented. In Figure 8 a viral sequence with ~27 Kpbs shares identity with proteins from Cellulophaga phage, which infects algae typically found in marine environments. In Figure 8 are four viruses with size lengths of about ~11 to 30 Kpbs that shared identities with different enterophages, showing a mosaicism as a reflection of horizontal gene transfer. In total, 27 sequences could have a taxonomic assignment as new viruses with this strategy (Supplementary Figure S4). Genes 2019, 10, x FOR PEER REVIEW 14 of 25 In Acos samples, around 100 different contigs with viral signals were identified; because many of these could be fragments of viral sequences, different criteria were used, such as the presence of inverted terminal repeats in the case of circular genomes and similarities in size lengths with a reference genome (no more than 10% of size length) [18]. In the Acos samples, two phages of Halomonas elongata were recovered. This finding was somewhat expected, since H. elongata is abundant in these intermediate-salinity environments, but this is the first time that bacteriophages have been reported in this bacterium. The two recovered phages have a size length of approximately 28 Kbp, and a comparative analysis with two ΦHAP-1 reference genomes revealed that they have the same pattern of synteny and a protein identity greater than 65%. (Figure 7b). The phage used for comparison was Halomonas phage ΦHAP-1. This is a Hapunavirus belonging to the family Myoviridae and was isolated from Halomonas aquamarina. The GC content in ΦHAP-1 is 59%, which is slightly lower than other phages such as ΦHAP-1, found in Acos with a 64% GC content, and similar to the GC content of the host genome (H. elongata) [91]. The ΦHAP-1-type phages from Acos have 40 putative open reading frames (ORFs) with 6 genes fewer than the reference genome. Genes coding for proteins such as the RepA replication protein, the prophage repressor, the prophage antirepressor, and the protelomerase were not identified; the latter is necessary for the maintenance of the linear state of the prophage within the host genome [92]. In addition, inverted repeats were found at positions 28,428–28,452 to 28,455–28,479 with a length size of 25 bp and an identity of 100%, suggesting that the genome is in a circular form, because this kind of inverted repeated sequence is usually found in regions processed by protelomerases and originates from the release of phage with covalently closed ends. All of these findings suggest that the phage could be in their free form. The rest of the viral sequences obtained in the metagenomes through BLAST analysis had very poor identity with sequences in the NCBI database, and they were used for taxonomic or functional allocation. Thus, the contigs of >10 kb was clustered using the Viral RefSeq database and vConTACT2; this tool allows classification of viral sequences with protein comparisons. In Figure 8, two examples of viral assignation taxonomy are presented. In Figure 8 a viral sequence with ~27 Kpbs shares identity with proteins from Cellulophaga phage, which infects algae typically found in marine environments. In Figure 8 are four viruses with size lengths of about ~11 to 30 Kpbs that shared identities with different enterophages, showing a mosaicism as a reflection of horizontal gene Genes 2019, 10, 89t1ransfer. In total, 27 sequences could have a taxonomic assignment as new viruses with this strategy 14 of 24 (Supplementary Figure S4). Genes 2019, 10, x FOR PEER REVIEW 15 of 25 metagenome-assembled genomes (MAGs). The binning methods can also d escribe novel species in Figureth8es.e PenrovFtiiregoiunnrem- s8eh. naPtrrsoi.n tTegihn-ens hbeaitrnwingoi nrngke towofo fmrkge eotafn goemnoemeoi cfo fsC eCeqellulluuelnloopchpeahsg aaw gpaahsap gphee. arYfgoeelrlm.oweY dlei nlolenos lwyin dfloiicrna tAee scsotirsno,n dbgie ccaatueses trong similiatat rleitays,t tawsniomd silabitmaluripteyle, lsai nwdei stbhlui tneh dlein isceasam tineed oiwcraietgea iwnk eaasrkiem snimielcailearsrisittayr. yT Thtouh seu, ntshr,iec tvhhi rteuhsev odifr aluetans.g Ftohrf o2lm7e,3n 5th8g itbshp, ac2 ot7ou,tl3da 5lb o8ef ab4 2p bcinosu ld be a novewlevrier uass.nemovbell evdir uisn. annotated draft genomes, and their ribosomal genes were extracted. However, some of this process resulted in a low degree of completeness. Therefore, we performed a 3.6.3. MAGphsyl3o.g6.e3n. eMtiAc Gans a lysis that revealed that 31 MAGs were classified within a specific domain and seven of them wAenroet hcelor ssetrlyat ereglya ttoe dre ttori ethvee nHeawlo gbeanctoemriae sc flraosms ( mBient a5g, eBnionm 1i4c, s Beqinu e1n5c, eBsi wn i3th1, l iBttilne 3o0r ,n Boi ind e3n4t,i tayn d AnothBienr ws4t1itr)ha st(eFqgiguyuenrtecoe s9r a)e ltrerwaieidtvhy ierne npotehrwtee dEg iuse rbnyyoa rbmcihneanesiontfgar, oinpm hwyhmluicmeht ,ga egwneohmnicoehsm airisec aspsesreeqmduobemlendinc waensitth woiunit hah ryleipfteterlresnaocliern en o identity with sequenvciesreosqnaumelrneenceat.s d.T yher beipnnoirntge dmeitshobdy hbasi nthnei naigm, tion cwlashsiifcyh cogneting osmequeesncaerse ina sas sepmecibfilce tdaxwoni,t chaolleudt a reference sequence. ThFeoburi nMnAinGgs wmereet chloosdelyh raeslattehde waiithm Atlpohacplraostesoibfayctecroian utnigclassesiqfiuede n(Bcine s10i,n Bian 1s2p, eBcinifi 1c6, tBainx on, called metageno1m7)e, -aansds e14m MbAleGds gweenreo cmloesesly(M reAlatGeds )t.o TGhamembaipnrontienobgacmtereiat.h Inoddesedc,a mnoastl sof the found bacteria corresponded to Proteobacteria, in accordance with the bacteria found in our metoagdeneosmcreisb (Beinn o1,v Beinl species in these envi3ro, Bnimn 8e, nBtins. 9,T Bhine 1b1i,n Bnini n19g, Boifnm 23e, tBaign e2n4,o Bmini 2c5s, eBqinu 2e6n, Bciens 2w8, aBsinp 2e9r, fBoinrm32,e Bdino 4n2l)y. TfhoerreAfocreo,s i,nb ecause at least two sthaims apnlaelsyswis ithe tPhreotesoabmacteeroiar ipghiynluamr epnreevcaeils soavreyr ttohee Enurriycahrcthhaeeotda apthay.luFmro imn Athcois ,samtoptleasl, of 42 bins were assemindbilceatdinign thaant snaolitnaittye pdladyrs aafnt imgepnorotamnte rso,lea innd thteh setriurcrtuibreo osfo tmhe aclomgemnuensityw oef rmeicerxootrragacnteisdm.s However, some of thtihsapt irnohcaebsitss rthis ecosystem. The strateegsuielst efodr itnhea rleocownsdtreugctrieoen ooff cgoenmomplees,t esnucehs sa.s Tfrhaegrmeefnotr ere,cwrueitmpeernfto arnmd eMdaaxBpinh, ylogenetic analysis thoaffterreedv edaiflfeerdentth raetsu3l1ts,M sinAcGe tshew feirrset sctlraatsesgiyfi iesd a wtarigthetiend asesaprcehc iafincd dfoor mthae isnecaonndd ssteravteegny othfet hem were closely relasetaerdcht ostathrtes fHroamlo sbcarcatechri ianc olbatsasin(iBngin d5ra,fBt ginen1o4m,eBs.i nAn1o5t,hBeri nim3p1o,rBtainnt d3i0ff,eBreince3 4is, tahnatd thBei nlat4te1r) (Figure 9) within theaEssuemrybalirecsh, acoeomtiangp fhroymlu mmo,rew thhainc honies mperteadgeonmominea, cnotuildn bhuyilpd echrsimaleirnice geennovmireos.n Hmoewnevtse.r, with the two strategies, genomes of Gammaproteobacteria similar to Halomonas could be reconstructed. Figure 9. Phylogenetic tree, including the binned sequences associated with different taxa clades. Branc hes in orange correspond to Eukaryota; branches in blue correspond to Archaea; branches in green correspond to Bacteria; Branches in red correspond to bins. Bootstrap levels are noted. Genes 2019, 10, 891 15 of 24 Four MAGs were closely related with Alphaproteobacteria unclassified (Bin 10, Bin 12, Bin 16, Bin 17), and 14 MAGs were closely related to Gammaproteobacteria. Indeed, most of the found bacteria corresponded to Proteobacteria, in accordance with the bacteria found in our metagenomes (Bin 1, Bin 3, Bin 8, Bin 9, Bin 11, Bin 19, Bin 23, Bin 24, Bin 25, Bin 26, Bin 28, Bin 29, Bin32, Bin 42). Therefore, in this analysis the Proteobacteria phylum prevails over the Euryarchaeota phylum in Acos samples, indicating that salinity plays an important role in the structure of the community of microorganisms that inhabits this ecosystem. The strategies for the reconstruction of genomes, such as fragment recruitment and MaxBin, offered different results, since the first strategy is a targeted search and for the second strategy the search starts from scratch in obtaining draft genomes. Another important difference is that the latter assemblies, coming from more than one metagenome, could build chimeric genomes. However, with the two strategies, genomes of Gammaproteobacteria similar to Halomonas could be reconstructed. 3.7. Functional Community Composition The strategies that halophilic organisms use to survive in hypersaline environments are diverse and include thickening of the cell wall, increase in pigmentation, production of compatible solutes, solute transport mechanisms, and production of antibiotic proteins to limit the growth of other populations [93]. Therefore, we analyzed the functional composition of microorganisms in intermediate-salinity environments in order to determine how these mechanisms are potentially used by microorganisms in these environments. Thus, the contigs from hypersaline metagenomes were annotated using SEED subsystems, and these results revealed that 11–13% of coding sequences from Acos and 14% of those from Maras were related to metabolism of carbohydrates (central carbohydrate metabolism, synthesis of monosaccharides and polysaccharides) (Figure 10). The genes classified into the category of amino acids and derivatives functions were present in ~8% to 12% in Acos and ~11% in Maras. Overall, in the three metagenomes the synthesis of lysine, threonine, methionine, and cysteine were the more abundant categories. This correlated with the fact that in some halophilic bacteria there is a preferential use of codons to encode these amino acids [94]. In this regard, most of these amino acids are hydrophobic, found on the inside of proteins, especially in hypersaline environments, which strengthen the hydrophobic interactions [92]. Other categories overrepresented, with ~6.7% to ~9.77% abundance in samples, were respiration, functions related to donating/accepting electrons, and ATP synthases. All of these participate in the transfer of electrons to obtain energy. Interestingly, the category related to pigment functions was found in 8% to 10% Acos sequences and 7% of Maras sequences. The class Halobacteriaceae is mainly responsible for α-bacterioruberin pigment, a pink-red product in hypersaline environments. In addition, Salinibacter ruber is responsible for producing salinixanthin carotenoid, a C-40 acyl glycoside carotenoid that also contributes to the coloration of salterns. This bacteria and these pigments are important in hypersaline environments as they reduce the UV irradiation that damages DNA, which tends to be high in these saline environments [9]. The category of membrane transport was present in ~3% to ~5% abundance; in particular, the membrane proteins in Gram-negative bacteria were more abundant than in Gram-positive bacteria, including the YrbG Na+/Ca2+ cation antiporter, a very important protein in this kind of saline environment. This system has been reported in Haloarchaea, which have a wide variety of ion transporters, to have a role in regulating fluctuating salinity levels and avoiding osmotic shock [95]. In other salterns with intermediate salinity, such as Santa Pola (13% NaCl), genes related to this function have been reported to be overrepresented [45]. The function of resistance to antibiotics and toxic compounds was also found to be abundant in these metagenomes, including pathways involved in sulfur heavy metal cycling, cobalt–zinc–cadmium resistance, and also copper homeostasis and resistance to arsenic. Since heavy metals such as arsenic Genes 2019, 10, x FOR PEER REVIEW 16 of 25 Figure 9. Phylogenetic tree, including the binned sequences associated with different taxa clades. Branches in orange correspond to Eukaryota; branches in blue correspond to Archaea; branches in green correspond to Bacteria; Branches in red correspond to bins. Bootstrap levels are noted. 3.7. Functional Community Composition The strategies that halophilic organisms use to survive in hypersaline environments are diverse and include thickening of the cell wall, increase in pigmentation, production of compatible solutes, solute transport mechanisms, and production of antibiotic proteins to limit the growth of other populations [93]. Therefore, we analyzed the functional composition of microorganisms in intermediate-salinity environments in order to determine how these mechanisms are potentially used byG meneisc2r0o1o9r, g10a,n8i9s1ms in these environments. 16 of 24 Thus, the contigs from hypersaline metagenomes were annotated using SEED subsystems, and thdesoen roestuhlatsv reebvieoalloegdi ctahlart o1l1e–s1, 3lo%w ocf ocnodceinngtr saetiqounesnacrees tforoxmic tAoctohse acnedll 1, 4a%nd otfh tehroesfeo rfreommi cMroaorargs awneisrme s rehlaatveed mteoc hamniestmabsofloisrmre doufc tiocnar.bMohayndyrAatrecsh ae(acehnatvrael dicaerrbeonhtyhderaavtye mmeteatlatbraonlisspmo,r tesrysn[t9h6e].sis of ff monosaccharides and polysaccharides) (Figure 10). FigFuigrue r1e01. 0H. eHatematampa opf othf eth reelraetliavteiv aebaubnudnadnacne coef opfrporteoitnesin bsabsaedse odno SnESEEDE Dclacslasisfsiicfiactiaotniosn. s. ThIne gaedndeist icolnas, stihfieds tirnetsos thcaet ceagtoergyorwy aosf ammoirneoa abcuidnsd andt idnerMivartaivses(~ f1u4n%ct)iotnhsa nweinre Aprceossen(~t 6in% ). ~8T%h itso c1a2t%eg oinry Aicnocsl uadneds ~p1r1e%do imn iMn anrat sf.u Oncvteioranlsl, siun cthea sthorxeied mateivtaegsetnreosms,eos stmheo tsiycnstthresiss, ohfe alytssitnre,s s, thdreotonxinifiec, amtieotnh,iocnolidnes,h aoncdk ,cyanstdeipner wipelares mthiec mstroerses .abTuhnedseantyt pceasteogfofruiensc.t Tiohnis cwoerrelpatredv awleintht tinheM facrta s, thant din aslotmhoeu hgahlotphheilcioc nbcaecntetriaat itohneroef iss al pt rwefaesrehnitgihale ur sien oMf caordaosntsh aton eincoAdceo tsh, easeg aremaitneor apcriedsse n[9c4e].o f Inn tohnis- hreagloaprdh,i lmicoosrtg oafn tihsemses wamasiniod eancitdifise adrein hMydarroapsh. oItbiisc,w foeullnkdn onw tnhteh iantsoidreg aonf ipsmrosteginrosw, eisnpgeciniahlliyg h inc hoynpcenrstraalitnioen esnovfirsoanltmacecnutms, uwlahtiechst sretrsesnmgtohlecnu tlhees, hsyudchroapshroebacicti ivneteorxaycgtieonnsp [e9c2i]e. s, and the organisms muOstththeerr ceaftoergeohraievse omvercrheapnriessmenstfeodr, twheitihr d~6e.t7o%xi fitoc a~t9i.o7n7%[9 7a]b.undance in samples, were respiration, functioIns trheelastaemd etow daoyn, astaimngp/alecscefrpotimngA ecloecstprornese, natnedd AabTuPn sdyanntht aosxeisd. aAtilvl eofs ttrheessef upanrcttiicoinpsa.teR iena tchtiev e traonxsyfgeer nofs eplecitersonins thoy opbetrasianl ienneeregnyv.i ronments are common, thus organisms in these environments have detoxification mechanisms. In particular, in microaerophilic and anaerobic metagenomes, oxygen-detoxifying enzymes have been identified, such as superoxide dismutases, catalases, peroxidases, and glutathione peroxidase [98]. In the Acos metagenome, we identified enzymes involved in the response to oxidative stress, such as 5-oxoprolinase, and enzymes responsible for maintaining the reducing environment, such as glutathione reductase, glutathione hydrolase (involved in reduction of glutathione disulfide), and hydroperoxide resistance (responsible for detoxification of organic hydroperoxides). Since the microorganisms are under oxidative stress, it is common to identify redundant enzymes responsible for DNA repair [99]. However, reactive oxygen species are not the only compounds that modify the genetic material; other agents that produce data in the genetic material include UV light Genes 2019, 10, 891 17 of 24 exposure and desiccation, and so, as expected, the functions of DNA synthesis and DNA repair are the most represented in proteins found in the Acos samples. According to our analysis with MG-RAST, detoxification enzymes were identified in Archaea, within the classification “housecleaning nucleoside triphosphate pyrophosphatases”; all of these belonged to class Nudix hydrolases, including nucleoside 5-triphosphatase and 5-nucleotidase SurE. In Bacteria, these enzymes were found in high abundance, as was the dimeric dUTPase enzyme. Interestingly, viruses also possess detoxification enzymes of this category, in particular the enzyme deoxyuridine 5′-triphosphate nucleotidohydrolase, that decreases the intracellular concentration of dUTP so that uracil cannot be incorporated into viral progeny DNA. All of the above enzymes are responsible for the elimination of damaged nucleotides caused by reactive oxygen species. For viruses, the incorporation of damaged nucleotides in nucleic acids is detrimental to replication of viral progeny. In this way, the virus could contribute to the adaptation of the host to its environment. Regarding DNA repair, we found bacterial systems that contribute to this function, among which were base excision repair (BER), repair of DNA double-strand breaks (DSBs) (RecBCD pathway and RecFOR pathway), nucleotide excision repair (NER), and DNA mismatch repair (MutL-MutS system). However, the mechanisms of nucleotide excision repair (NER) and DNA mismatch repair (MutL-MutS system) were more abundant in Bacteria. The function related to nucleotide excision repair has also been reported to be overrepresented in hypersaline environments [100]. In addition, we identified eight proteins related to DNA DSB repair in the annotations for viral sequences; this is one of the most common damaging events [101]. However, bacteriophages and some NCLDV possess homologous proteins, such as Rad50/SbcC, which is probably involved in the processing of dsDNA ends for processing during recombination [102]. These proteins were also identified in circular genomes of bacteriophages, such as Vibrio parahaemolyticus bacteriophage [103], which could indicate that these proteins are also propagated in this type of virus and could have implications in the repair of genetic material in stress environments. Other genes for methyltransferase enzymes, which are ubiquitous in the prokaryotic world and are associated with host protection of DNA damage, were also identified in our viral sequences. Other functionally important genes found in viral sequences were auxiliary metabolic genes (AMGs) originally from the genome host. The AMGs found were ribonucleotide reductases and phoH, among others. The ribonucleotide reductases are associated with lytic rather than temperate viruses, and the phoH gene plays a role in the transport of phosphate in conditions of starvation. Synechococcus and Prochlorococcus (cyanophages) carry AMGs; however, in this study we found these families were in low abundance, as they are predominantly found in marine environments, so it correlates with the abundance of these families reported above [104]. 3.8. Metabolic Pathway Involved in Biogeochemical Cycles In order to evaluate the contribution of different metabolic pathways in the biogeochemical cycles associated the metagenomes, MEBS software was used to analyze the three samples. From this analysis, only two complete pathways of the carbon cycle were identified (Figure 11), while the nitrogen and sulfur cycles in the samples were more highly represented (Figure 11). In the case of nitrogen, the pathways of denitrification and the reduction of nitrate by assimilation were found to be more prevalent, since that nitrite is generally produced under anoxic conditions such as in hypersaline environments [6]. On the other hand, the reduction of dissimilatory nitrate (nitrite-ammonia) involving the proteins encoded by the genes nirB, nirD, nrfA, and nrfH is generally more highly expressed in Proteobacteria, Bacteroidetes, Euryarchaeota, and Verrucomicrobia [90]. Those were found as complete in our metagenomes, which correlates with the great abundance of Proteobacteria in the metagenomes. Genes 2019, 10, x FOR PEER REVIEW 19 of 25 Genes 2019, 10, 891 18 of 24 Figure 11. Completeness pathways of biogeochemical cycles. Mainly the pathways of nitrogen and sulfur cycles are complete within the hypersaline samples. Figure 11. Completeness pathways of biogeochemical cycles. Mainly the pathways of nitrogen and Bseuclafurs ecyocxleysg aerne ciosmlipmleittee dw,itdhein itthreifi hcyapteiorsnal(ineit sraamtep-nleist.r ite) is another pathway that contributes to the nitrogen cycle. In addition, species in the environment use nitrogen as a source of growth [105,106]. TheseBpeactahuwsea yosxywgeenre isa llismo itfeodu,n ddenaistrcifoicmatpiolent e(niintrathtee-nmiterittaeg) eisn oanmoetsheorf pAatchows,awy thhiacth cionndtricibautetesst thoe imthpeo rntiatnrocgeeonf cnyitcrloeg. eInn iandhdyitpioenrs, aslpineecieens vinir otnhme eenntvsi.roInnmtheinstp uasthe wnaityr,otgheenp aros tae insosuerncceo dofe dgrboywtthhe ge[1n0e5s,1n0a6rG]. HThIJe,snea ppAatBh,wnairyKsS w, neroer BaCls,oa fnodunndos aZs acroeminpclleuted eind ;ththe emseetgaegneensoamreese xopf rAecsosesd, wbhyicBha citnedroicidaetteess , Euthrye airmchpaoeorttaa,nacne dofP nroitteroobgaecnte rinia .hIynpaedrsdailtiinoen ,etnhveirnoanrmL genentse. iInn tthheisv pirauths wcoamy,p tehnes aptreostefoinrst heencmoedteadb oblyic paththe wgaeynseso fnathrGeHmIJi,c rnoaoprAgBan, insmirKsSf,o rnonriBtrCo, gaenndm neotsaZb oalrisem in[c9l0u,d1e0d7]; . thFeinsea lglye,niens tahree Mexapraressssaedm pblye , paBratcitaelrloyidceotmes,p Eleutreyadrecnhiateroitfiac, aatniodn Pproattehowbaactyesriwa. eIrne afdoduintidon(a, t4h0e- 6n0a%rL ogferneep irne sthene tvaitriuosn )c,oimndpiecnastiantegst fhoart mtihcer omoergtaabnoislimc psactahnwcaoynst roifb tuhtee mtoictrhoeorregdanuicstmiosn foofr nniittrroatgeenan mdentaitbroitleisfmor [9th0e,1p07ro].d Fuinctailolyn, oinf tNhe. Maras samSpolme, epoarrgtaianlilsym c 2 so, msupclhetaes Pdreontietorbifaicctaetriioana npdatThhwauamysa rcwheareeot af,oaurnedr es(ap o4n0s-i6b0le%f oorfp rreopdruecsienngtantiitorna)t,e indicating that microorganisms can contribute to the reduction of nitrate and nitrite for the by nitrification at high salt concentrations [93,108], as well as the route of nitrogen fixation; however, production of N2. they were partially complete, despite nitrite being an important energy source (Figure 11). In this regard, Some organisms, such as Proteobacteria and Thaumarchaeota, are responsible for producing nitrate nibtyri tneitirsifnicoatttihoen oant lhyigshou sraclet coofnecneenrtgryatiinonths i[s9e3n,1v0i8r]o,n ams wenetl,l saisn ctheem roanuyteA orfc nhiatreoagaennd fisxoamtioenb;a hcotewrieavuers,e suthlfeuyr cwoemrep opuanrtdiaslalys dcoonmoprsleotre,e ldeecstrpointea ncciterpitteo rbsefionrge naner gimypporortdaunctt ieonne[r1g0y9 ]s.oIunrtchei s(Fciagsuer, eth 1e1p).a tIhnw thaiyss rerleagteadrdt,o nsiturlifitet eiso nxoidt athtieo no,nolyx isdoautirocen ooff esnuelfrugry DinM thS,isa nendvoirxoindmateionnt, osfindciem metahnyyl sAurlcfohnaeioap arnodp isoonmatee (DbMacStePr)iaw uesree fsouulfnudr tcoombepcooumndpsl eatse .dMonaoinrsly o, rD eMleScPtrohna sabcceeepntroerps ofortre denienrgayb upnrdoadnucceti,ownh [i1c0h9]in. Idni ctahtiess thcaatseD, MthSeP pisatahnwiamysp orretlaantetds otuor cseuolffitcea robxoindaatinodn,e noexrigdyati[o1n10 ]o.f Tshuelfruerfo rDeM, BSa, ctaenrdia oaxniddaAtirocnh aoefa codnimtriebtuhtyelstuolftohneiooxpirdoaptiioonnaotef D(DMMSSPPa) swaenree nfoerugnyds toou brcee c, oamt dpilffeetere. nMtapirnolyp,o DrtMionSsP. has been reported 4.inC oanbculnudsaionnces, which indicates that DMSP is an important source of carbon and energy [110]. Therefore, Bacteria and Archaea contribute to the oxidation of DMSP as an energy source, at different proIpnortthioisnsst. udy, we present a snapshot of microbial and functional diversity of two intermediate hypersaline environments in the Peruvian Andes, based on a metagenomics shotgun approach. Th4.e Cinotnecrlmuseidoinast e salinity environments show a great diversity and abundance of bacteria, more so than the archaea in the samples. At the level of phylum, Proteobacteria are the most abundant and predominated over other bacteria and archaea. However, the Balneolaeota phylum was found only in Genes 2019, 10, 891 19 of 24 Acos in great abundance, but was not diverse. In addition, we reconstructed the draft genomes of H. elongata and I. loihiensis, which have different mechanisms of adaptation to hypersaline environments, via de novo synthesis of ectoine and natAB transporters, respectively. Also, we obtained whole genomes from bacteriophages. Functional analysis indicated that microorganism in hypersaline environments contribute to the biogeochemical cycles involving carbon and nitrogen as the source of energy. We also found genes related to oxidative stress and DNA repair. Interestingly, viruses also had such repair protein genes, which are otherwise exclusive to eukaryotes and bacteria. This study contributes to the current knowledge of intermediate-salinity environments at high altitudes. Supplementary Materials: The following are available online at http://www.mdpi.com/2073-4425/10/11/891/s1, Figure S1: Rarefaction curves based at level species diversity, Figure S2: Dendrogram of all samples. Analysis of beta-diversity was carried out at species level using hclust and Bray-Curtis dissimilarity, Figure S3: Pangenome of draft genome (a) Halomonas elongata (b) Idiomarina loihienis, Figure S4: Protein-sharing viral network of virus from samples of Acos, Table S1: Indexes of diversity. Author Contributions: Conceptualization, H.G.C.-S., M.A.Q.-R., and S.D.-R.; Data curation, R.A.B.-G.; Formal analysis, H.G.C.-S. and E.P.-R.; Funding acquisition, M.A.Q.-R. and S.D.-R.; Investigation, H.G.C.-S. and S.D.-R.; Methodology, H.G.C.-S., P.E., P.R., A.L.-T., J.L.S., R.A.B.-G., G.L.-U., E.P.-R., and M.A.Q.-R.; Resources, I.V., E.P.-R., M.A.Q.-R., and S.D.-R.; Software, H.G.C.-S., E.P.-R., and S.D.-R.; Supervision, S.D.-R.; Writing—original draft, H.G.C.-S., R.A.B.-G., S.T.-S., and E.P.-R.; Writing—review and editing, M.A.Q.-R. and S.D.-R. Funding: This research was partially funded by contract number 227-2015-FONDECYT approved by resolution CU-005-2016_UNSAAC and contract number 23-2018-UNSAAC approved by resolution R-392-2018-UNSAAC to M.A.Q.R.; and by the Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México, PAPIIT IN-201117 to E.P.R. H.G.C.S. is a scholarship recipient of Mexican Consejo Nacional de Ciencia y Tecnología (CONACYT Number 227229) program fellowship. Acknowledgments: We thank the Unidad de Secuenciación Masiva y Bioinformática of Instituto de Biotecnología-UNAM for giving us access to its computer cluster and the Centro de Investigación en Dinámica Celular for giving us access to its server. Conflicts of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References 1. Maturrano, L.; Santos, F.; Rosselló-Mora, R.; Antón, J. Microbial diversity in Maras salterns, a hypersaline environment in the Peruvian Andes. Appl. Environ. Microbiol. 2006, 72, 3887–3895. [CrossRef] [PubMed] 2. Hernández, L.M. Caracterización de la Microbiota de las Salinas de Maras, un Ambiente Hipersalino de los Andes de Perú. 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