Taxonomic and functional patterns across soil

Science of the Total Environment 609 (2017) 1064–1074

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Taxonomic and functional patterns across soil microbial communities of global biomes Melline Fontes Noronha a,b,⁎, Gileno Vieira Lacerda Júnior a,b, Jack A. Gilbert c,d, Valéria Maia de Oliveira a a

Microbial Resources Division, Multidisciplinary Center for Chemistry, Biology and Agriculture Research (CPQBA), Campinas University, Brazil Institute of Biology, Campinas University, Brazil The Microbiome Center, Department of Surgery, University of Chicago, Chicago, IL, USA d The Microbiome Center, Bioscience Division, Argonne National Laboratory, Lemont, IL, USA b c

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Structure of microbial communities differed clearly among the soil biomes. • Genes related to S, Fe and P metabolism were more abundant in non-desert soils. • CAZyme profiling showed enrichment of biomass-degrading genes in nondesert biomes. • Osmotic stress resistance genes were overrepresented in desert and semiarid soils. • Antibiotic resistance genes were enriched in forest and grassland soil biomes.

a r t i c l e

i n f o

Article history: Received 30 April 2017 Received in revised form 17 July 2017 Accepted 18 July 2017 Available online xxxx Editor: Elena Paoletti Keywords: Soil metagenomics Microbial community Biomes Carbohydrateactive enzymes (CAZymes) Antibiotics resistance genes

a b s t r a c t Soil microbial communities have been shown to vary across many spatial scales, yet while variability exists between samples closely located in the same soil, variation between soils of different ecosystems is larger, creating biogeographic trends. Herein, thirty publically-available metagenomes from 11 globally distributed ecosystems were selected. These metagenomes were clustered by biome (i.e. forest, grasslands, tundra, semiarid and desert) based on morphoclimatic features. Protein biosynthesis, central carbohydrate metabolism, and antibiotic resistance were the most statistically different SEED subsystems among biome groups. CAZy-based annotation revealed that genes related to biomass degradation, sucrose and starch metabolism, and cell wall biosynthesis were overrepresented in forest and grasslands soils. As expected, desiccation and other stress resistance genes were prevalent in desert and semiarid soils. Antibiotic Resistance Genes (ARGs) were more abundant in forest and grassland soils, and multidrug efflux pumps were the most abundant ARG class. Heat Shock Proteins (HSPs) were generally more abundant in tundra, semiarid and desert. However, HSP60 and HSP20, predominantly from Archaea, were enriched in the Saline Desert soils. These results suggest that while a core microbiome and functional potential exist in all studied soils, local environmental conditions select for enrichment of specific functions important for survival in a given ecosystem. © 2017 Elsevier B.V. All rights reserved.

⁎ Corresponding author at: Microbial Resources Division, Multidisciplinary Center for Chemistry, Biology and Agriculture Research (CPQBA), Institute of Biology, Campinas University, Av. Alexandre Cazellato, 999, Paulínia 13140-000, SP, Brazil. E-mail address: [email protected] (M.F. Noronha).

http://dx.doi.org/10.1016/j.scitotenv.2017.07.159 0048-9697/© 2017 Elsevier B.V. All rights reserved.

1. Introduction The classification of soils into global terrestrial ecoregions represents a biogeographic stratification based on decades of research and

M.F. Noronha et al. / Science of the Total Environment 609 (2017) 1064–1074

observation of climate, soil type, as well as animal and plant distributions (Olson et al., 2001). However, until recently, microbial communities were not included as a contributing factor in this classification. Soilassociated microorganisms are intrinsic to the productivity and activity of plants and the ecosystem as a whole, and it has been suggested that soil microbes can be classified into functional guilds based on plantassociations, and could be used to predict plant and animal abundance, diversity and composition (Classen et al., 2015). The biogeography of soil microbial communities can be related to edaphic properties such as soil pH, nutrient and water availability, and vegetation. Microbial diversity studies have identified soil pH as providing the greatest discriminatory power for bacterial distribution and specifically showed that neutral pH soil produced the greatest taxonomic diversity, which tapered in as conditions grew more acidic and alkaline (Fierer and Jackson, 2006; Lauber et al., 2009). Likewise, nitrogen (Fierer et al., 2012a) and water availability (Ma et al., 2016) were also suggested as significant factors affecting the structure of soil microbial communities. Although distinct physicochemical factors have been indicated as the major drivers of microbial community alterations, it is common sense in literature that abiotic factors greatly influence the microbial community structure of an ecosystem. Biogeographic studies of soil have helped us ascertain key ecological factors (selection, dispersal, speciation) defining microbial diversity and composition within each ecosystem (Pasternak et al., 2013; Ranjard et al., 2013; Shi et al., 2015). Such selection pressure can occur on a wide variety of scales, including sub-centimeter, leading to distinct shifts in the relative abundance of specific bacterial taxa in highly proximal samples (O'Brien et al., 2015). However, this apparently extreme spatial microbial diversity still allowed the classification of soils based on the microbial community composition, when profiles were compared across globally distributed soils. This suggests lower spatial resolution, selection and speciation (and possibly dispersal limitation) may act to form discrete microbial communities within habitats. Studies of global soil samples using shotgun metagenomics have demonstrated significant differences in taxonomic and functional profiles between microbial communities of desert and non-deserts soils (Fierer et al., 2012b). Desert soil-associated microbes were found to be enriched for genes encoding osmoregulation and dormancy properties. Meanwhile genes encoding antibiotic resistance and cell lysis were more abundant in non-desert soils, suggesting that forest, prairie and tundra soil microbial communities experienced much greater competition than desert soils (Fierer et al., 2012b). Also, a functional KEGGbased annotation revealed an enrichment of reductive pentose phosphate cycle module in desert soil samples. On the other hand, functional modules associated with plant-derived compound metabolism were significantly overrepresented in grassland and forest soils when compared to desert samples (Xu et al., 2014). Although previous studies have already discussed differences in microbial communities among biomes, some groups, such as semiarid and tundra, were poorly discussed or even not considered. In addition, in such studies the metagenome datasets were explored by using only general functional categories. In the present work, 30 publicallyavailable soil metagenomes were selected from 11 different terrestrial biomes, including biomes not yet described in global analyses, such as Cerrado (tropical savanna), and Mediterranean and Caatinga (semiarid regions). A detailed survey was performed to ascertain the functional features that allow microbial communities to survive and thrive among these biomes. For this, further analyses were focused on specific databases comprising carbohydrate-active enzymes, antibiotic resistance genes and heat shock proteins. We hypothesized that 1) the profile of genes related to carbohydrate metabolism will show specific differentiation based on vegetation and type of carbon source available in each biome, 2) the antibiotic resistome will be depend on the interaction of abiotic factors and environmental competitiveness, and 3) the heat shock protein profiles will differentiate based on ecologically relevant environmental stressors intrinsic to each biome.

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2. Material and methods 2.1. Selection of datasets Using Olson's terrestrial ecoregions of the world (Olson et al., 2001) as a guide, eleven biomes were identified including tropical and temperate forests, grasslands, and deserts. Shotgun metagenomic data from 30 soil samples collected from eleven of these globally distributed biomes were selected and downloaded from Metagenomics RAST server (MGRAST) (Meyer et al., 2008) and Metagenomic EBI (Mitchell et al., 2016). We selected three samples per biome for (1) tropical forests, (2) temperate steppes (temperate savanna), (3) tropical savannas, (4) tundra, (5) hot deserts, (5) cold deserts, (6) tropical dry forest/semiarid, and (7) saline deserts/flooded savannas. Only two metagenomes were available from (8) Mediterranean (chaparral) soils, and one metagenome was available from each (9) temperate coniferous, (10) temperate deciduous, (11) boreal forest and prairie (2 - temperate savanna). Boreal forest (Alaska), prairie (USA), temperate coniferous (USA), deciduous forest (USA), hot desert (USA) and Polar desert (Antarctica) datasets were used from a previous work on soil microbial community comparisons (Fierer et al., 2012b). Native soil samples were selected from Amazon forest (Brazil) dataset to represent the tropical forest biome (Mendes et al., 2015). Tundra biome samples (Alaska) were selected from Hultman and co-workers (Hultman et al., 2015) and temperate steppe (USA) from Cline and Zak (Cline and Zak, 2015). Samples from the Brazilian savanna, known as Cerrado, were selected to represent the tropical savanna biome (Souza et al., 2016). Mediterranean biome samples were chosen from Biomes of Australian Soil Environments database (Bissett et al., 2016). Moreover, two datasets were chosen: one from Caatinga and one from a seasonal saline desert. Caatinga biome is an exclusive Brazilian biome located in the northeast of Brazil known to be a semiarid. Soil samples from this biome were collected by our group at Embrapa Semiarid Experimental Station during dry season (unpublished data – datasets avaliable under MG-RAST project number mgp14747). The seasonal saline desert of Kutch Rann is located in India and is of interest because it is a salt desert at gets flooded during the monsoon seasons (Pandit et al., 2014). Supplementary information about our selected samples is available (Supplementary Table 3). 2.2. Data information and processing Raw data were submited to MG-RAST pipeline (Meyer et al., 2008) and downloaded after quality control (~135 Gb data, including all replicas described). Since sequencing depth and number of replicas differed among samples, all datasets were pruned to the same read depth of 1.6 million reads. Samples were normalized to 1,625,000 sequences for taxonomic and general functional annotation (Supplementary Table 1). Annotation of Carbohydrate enzymes, Antibiotics Resistance Genes and Heat Shock proteins were performed by clustering replicates into one dataset pruned to 3,750,000 reads (Supplementary Table 4). Datasets differed in read length, and since longer reads yielded a higher number of sequence assignments on databases, all reads were normalized to ~100 bp to reduce database assignment bias. Although lower coverage is described for AT-rich genomes when using Ion Torrent (Quail et al., 2012), our results suggested that read length normalization reduces bias on sample comparison within the same biome group from different sequencing technologies (Supplementary Table 4). Normalization was performed using in-house perl scripts (1) random subsampling of reads (duplication checked), and (2) random initial position on read length. 2.3. Taxonomic and functional annotation Normalized samples from each biome were annotated using the MG-RAST server pipeline (http://metagenomics.anl.gov). The SEED

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database was used for taxonomic assignment of sequences and functional annotation was done using SEED subsystems, COG (Clusters of Orthologous Groups), and KO (KEGG – Kyoto Encyclopedia of Genes and Genomes) databases. Both taxonomic and functional annotation was performed using the best BLASTX hit with 50 bp as minimum alignment length and E b 1 × 10−5 as E-value cut-off. The Statistical Analysis of Metagenomic Profiles (STAMP) software package (Parks et al., 2014) was used to perform taxonomic and functional comparison among biome samples. Spearman correlation analysis was performed by pairwise comparison between biome samples, further clustered into biome groups. Pairwise Spearman correlation, average and standard deviation were computed using the R environment.

Antibiotic resistance genes (ARGs) were assigned to predicted ORFs from normalized biome datasets using BLASTP (Altschul et al., 1990) against the Antibiotic Resistance Genes Database (ARDB) (Liu and Pop, 2009) using an e-value b10−5. Parameters were set as ≥80% read identity over an alignment length ≥ 25 amino acids to any resistance gene within the ARDB. ARG class annotation and formatting to network file were performed using in-house perl scripts. We built a network by correlating ARG hits to each biome dataset, in which edge width represents the hit abundance. MEGAN5 (Huson et al., 2007) with the LCA algorithm (maximum number of matches per read: 5, min support: 5, min score: 35) was used to analyze the taxonomic distribution of ARG predicted sequences based on results from BLASTX against the NCBI-NR protein database search.

1.60%). Nonetheless, reads annotated to Ascomycota were significantly more abundant in forest and grassland biome groups when compared to semiarid and desert soil samples. The Archaea domain showed lower abundance in forest and grassland soils when compared to semiarid soil samples (Welch's t-test; p b 0.05) (Supplementary Fig. 1). Taxonomic analyses showed that forest, grassland and Tundra soils had a significant higher proportion of Proteobacteria, Verrucomicrobia and Acidobacteria Phyla when compared to other soil samples. More specifically, in Proteobacteria, the orders Rhizobiales and Burkholderiales, which encompass many species related to plant growth and nitrogenfixation, were highly abundant. Also, Candidatus Solibacter usitatus, belonging to Acidobacteria phylum and related to nitrate and nitrite reduction and plant cell wall polysaccharide degradation, was found in high frequency (Kielak et al., 2016). The phyla Chloroflexi, particularly the thermophilic class Thermomicrobia, Firmicutes and Deinococcus-Thermus were significantly more abundant in semiarid and desert samples than in forest, grassland and tundra soil samples. Actinobacteria members were more abundant in semiarid soils, while members from Bacteroidetes and some thermophilic groups (Aquificae and Thermotogae) were slightly more abundant in desert soils when compared to other biome samples. Also, Cyanobacteria was three times more abundant in hot and cold deserts than in the forest biome group. This was expected due to their remarkable ability to resist desiccation stress and important role in oligotrophic arid environments, providing soil stability, moisture retention, fertility by N2 fixation and photosynthesis (Makhalanyane et al., 2015; Berrendero et al., 2016) (Supplementary Fig. 2; Supplementary Fig. 3). Principal component analysis (PCA) of taxonomical community composition was used to examine clustering between communities and showed high correlation between forest and grassland samples (Fig. 1a). Tundra samples were clustered with other grasslands, probably because the samples were collected during the summer, when tundra soil microbial communities are shaped by the summer monocotyledonous growth (Virtanen et al., 2015). Also, literature data have suggested a “steppe-tundra” gradient biome within Alaska (Lloyd et al., 1994), where samples were collected. The Caatinga and Mediterranean biomes clustered together, which is consistent with the fact that these biomes share similar characteristics such as xerophilic vegetation and climate seasons: dry summers and rainy winters.

2.6. Heat shock proteins (HSPs)

3.2. Functional diversity of the soil microbial communities

HMM profiles were downloaded from Heat Shock Protein Information Resource (HSPIR) (Ratheesh Kumar et al., 2012) for 6 HSP families: Hsp100, 90, 70, 60, 40 and 20 (sHsp). Using HMMER3.1b2 software (Finn et al., 2011), proteins from all normalized datasets were predicted from HMM profiles using a cutoff of 1e-5. MEGAN5 and the LCA algorithm (maximum number of matches per read: 5, min support: 5, min score: 35) was used to analyze the taxonomic assignment of HSP20 and HSP40 predicted sequences from Saline desert based on results from BLASTX against the NCBI-NR protein database.

Principal component analysis (PCA) of SEED functional composition clustered the biome groups according to their metabolic potential, as demonstrated by SEED subsystems annotation (Fig. 1b). A Pairwise comparison using Spearman correlation was calculated to quantify similarity within and between biomes based on SEED, COG and KEGG database. However, it is worth to note that the Tundra biome was not included in pairwise Spearman correlation analysis, as well as in downstream post hoc plot analyses, due to the low representativeness of the dataset (only one sample with 3 biological replicates). This occurred due to the scarcity of WGS samples related to the Tundra biome in public databases. Soil functional profiles from the desert biome group were significantly less similar to each other than soil profiles within the forest, grassland and semiarid biomes (Fig. 1c). Therefore, while semiarid, grassland and forest soils with similar vegetation and climate are functionally similar even when located in very different geographic regions, desert soils buck the trend. Their inherently lower similarity may be associated with the variability in stress factors, such as hot and cold temperatures and hypersalinity. Three functional categories (SEED level 2) were responsible for differentiating the biomes: (i) resistance to antibiotics and toxic compounds, (ii) protein biosynthesis, and (iii) central carbohydrate metabolism

2.4. Carbohydrate-active enzymes overview Carbohydrate-active enzyme domains were assigned using the Carbohydrate-Active enZymes database (CAZy) (Lombard et al., 2014). FragGeneScan 1.30 (Rho et al., 2010) was used to predict Open Reading Frames (ORFs) and convert nucleotide sequences into amino acids from all normalized datasets. The HMMER3.1b2 (Finn et al., 2011) was used to predict proteins from HMM profiles available at the dbCAN website (Yin et al., 2012) using a full sequence cutoff of 1e-5. Profiles were downloaded and analyzed for each normalized biome dataset locally. ANOVA statistics and a post hoc test (Tukey-Kramer) were applied for multiple biome group comparisons (p b 0.05). 2.5. Antibiotic resistance genes

3. Results and discussion 3.1. Taxonomic diversity of the soil microbial communities Selected metagenomic datasets were grouped into five ecotypes named forest, grassland, semiarid, desert and tundra, for downstream analyses, based on vegetation features and supported by UPGMA clustering of functional profiles (data not shown). The relative abundance of fungi, bacteria and archaea were characterized for all samples. Although fungi and other eukaryotes represent a significant proportion of soil microbial communities, their representation in the datasets based on SEED taxonomic annotation was low (0.70%–


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Fig. 1. Principal coordinates analyses of taxonomical community (a) and functional composition (b) among forest (green color range), grassland (orange color range), tundra (cyan), semiarid (blue color range) and deserts soil samples (red color range). (c) Spearman correlation by pairwise comparison against COG, KEGG and SEED subsystems databases have shown a functional stronger correlation among forest, grassland and semiarid biomes and less correlation among desert group (bars represent standard deviation). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

(Supplementary Fig. 4). Sequences related to resistance to antibiotics and toxic compounds showed higher proportion in forest and grassland soils, corroborating the results observed in previous work when comparing non-desert and desert soil samples (Fierer et al., 2012b). This higher proportion was mainly due to cobalt zinc cadmium resistance and multidrug resistance efflux pump modules (SEED level 3). On the other hand, protein biosynthesis showed higher abundance in desert and semiarid biomes (Welch's test; two-sided, p-value b0.05). This subsystem was predominated by the universal GTPase module (level 3) that includes proteins involved in oxidative and gamma radiation induced stress (Basu and Apte, 2012; Wenk et al., 2012). These results corroborate previous stress responses frequently found in bacteria from dry-climate soils (Fredrickson et al., 2008; Slade and Radman, 2011). SEED-based annotation also revealed other genetic features related to nutrient metabolism in each ecotype group (SEED level 3). For example, forest, grassland and tundra soils showed a higher proportion of reads assigned to urea decomposition (especially enzymes that convert

urea into ammonia) and to ethanolamine utilization subsystems, suggesting that such compounds could act as a C and/or N source in these ecosystems (Garsin, 2010). Likewise, a higher proportion of sequences was assigned to anaerobic related metabolism such as the butyrate production by acetyl-CoA fermentation, and benzoate catabolism (Lovley and Klug, 1983; Kertesz, 2000). The higher abundance of sulfur metabolism (including alkane sulfonate assimilation and utilization; sulfur oxidation; taurine utilization) may disclose the importance of sulfur cycle in those environments. In addition, forest and grassland soils exhibited a higher proportion of sequences related to iron and phosphorus metabolisms. Grassland soils had a greater number of sequences related to chitin and L-fucose carbohydrate utilization. In addition, semiarid and grassland soil samples showed a higher abundance of sequences coding for dihydroxyacetone kinase and fructose utilization. On the other hand, desert soils showed the lowest proportion of crotonase and branched chain amino acid degradation regulons, which could be associated to

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the nutrient limiting under this soil condition. These results could elucidate different strategies of carbohydrates acquisition applied by microbial communities into each ecotype group. Interestingly, a higher proportion of sequences were assigned to Photosystem I subsystem in semiarid and desert soils. This function can be found in algae, plant and some bacterial genomes in different steps of photosynthesis, such as P700 reaction center coupled to iron sulfur complex (Webber and Lubitz, 2001), which may also explain the higher number of reads assigned for iron sulfur cluster assembly in desert soils. To provide better analytical resolution to ascertain the functional features that shaped community structure among these biomes, metagenomic reads were also annotated using the Antibiotic Resistance Genes DataBase (ARDB), the Heat Shock Protein Information Resource (HSPIR), and the Carbohydrate-Active enZYmes database (CAZy). 3.3. CAZyme analysis A total of 302,874 reads matched 315 CAZymes in all normalized metagenomes. The GH13 were the most abundant glycoside hydrolases (GHase) among biomes. This was somewhat expected, as this GHase family is seemingly one of the most abundant enzymes in many metagenomic soil studies (Cardenas et al., 2015; Manoharan et al., 2015). However, this CAZyme was more abundant in desert and semiarid biomes than in forest and grassland soils. Furthermore, GT4 enzymes were the most abundant glycosyltransferases (GT) in all biomes datasets (except in tropical forest) and it is also most abundant in desert and semiarid biomes among soil samples (Supplementary Fig. 6). This CAZyme accounted for 29% of all GT in desert and semiarid biomes and 16% in the other biomes (Supplementary Fig. 5). GH13 and GT4 are families which include a range of enzymes involved in trehalose uptake and utilization. The high abundance of these enzymes in such harsh environments could be explained by the fact that trehalose is a nonreducing disaccharide, stable in a wide variety of pH (3.5–10), known to play an important role in energy storage and cell protection under many stress conditions, including heating, freezing, drought and desiccation (Jiang et al., 2013; Walmagh et al., 2015). Comparison between global biomes revealed a higher abundance of enzymes related to cell wall heteropolymer in forest soils when compared to desert and semiarid soils. Two peptidoglycan lytic transglycosylases essential on cell walls heteropolymer biosynthesis and turnover (GH102 and GH103) were identified (Blackburn and Clarke, 2001). In addition, forest and grassland soils maintained a significantly greater relative abundance of lipid-A-disaccharide synthase (GT19) and a putative undecaprenyl phosphate-α-L-Ara4N (GT83) associated with the glycosyltransferase class. These CAZymes, and also a carbohydrate esterase CE11 (UDP-3-0-acyl N-acetylglucosamine), are related to lipid A biosynthesis, essential to gram-negative bacterial outer membrane synthesis, providing protection from environmental stressors such as antibiotics. These enzymes are also common targets for bacterial antibiotic production (Jackman et al., 2000; Metzger et al., 2012; Lee and Lee, 2013) (Fig. 2; Supplementary Fig. 7; Supplementary Fig. 8). Interestingly, many GHases related to plant biomass degradation, such as cellulases (GH44), hemicellulases like-laminarinase (GH17), pectinases (GH53), oligosaccharide-degrading enzymes (GH55 - βglucans) and accessory xylan-degrading enzymes (GH30) were overrepresented in forest and grassland soil groups in comparison to desert and semiarid groups. Additionally, grassland soils showed a higher abundance of GH54 (β-xylosidase) and GH67 (xylan α-1,2-glucuronidase) when compared to desert soils. These corroborate to the higher biomass density and total biomass of forest and grassland soils as compared to desert soils showed by Houghton et al. (2009) (Fig. 3; Supplementary Fig. 7; Supplementary Fig. 8). On the other hand, GH117 showed significantly greater abundance in desert soils when compared to forest and grassland soils (ANOVA statistics and a post hoc test Tukey-Kramer; p-value b 0.05) (Fig. 3). The

function of this hydrolase is still unclear, but it is known as a neoagarobiose hydrolase involved in the conversion of agar source into fermentable sugars (Lee et al., 2009). This ‘agar’ source could probably be provided by the cell wall of some algae living in desert soil crust, as reported in some previous works (Cameron, 1960; Lewis and Lewis, 2005; Cardon et al., 2008). Interestingly, sequences assigned to GH117 from desert soils were affiliated to Bacteroidetes phylum (by LCA algorithm). Genes related to starch and sucrose metabolism were also identified in the datasets. From these, GH133 (amylo-α-1,6-glucosidase), a possible starch glucosyltransferase (GT5) and a putative β-glucuronidase (GH79) were more abundant in forest soils than semiarid and desert soils. On the other hand, a putative endoglucanase (GH6) and two putative α-glucosidases (GH4 and GH63) exhibited greater abundance in semiarid soils in comparison to forest and grassland soils. In addition, trehalose metabolism related families (GT2, GT4, GT20 and GH13) were also more abundant in semiarid and desert soils, as well as the GT81, possibly involved in the glucosylglycerate biosynthesis (ANOVA; Tukey-Kramer test; p b 0.05). Both trehalose and glucosylglycerate osmolytes, widespread among halotolerant and thermotolerant bacteria, are involved in a greater resistance to osmotic and thermal stresses (Costa et al., 2007; Argandoña et al., 2010; Reina-Bueno et al., 2012) (Fig. 4; Supplementary Fig. 7; Supplementary Fig. 8). Glycosylation is a post-translational protein modification that plays an important role in glycoconjugate biosynthesis. This general mechanism is found in all domains of life, although N-glycosylation is rare in bacteria. The first report of N-glycosylation in bacteria was described in Campylobacter jejuni and it is currently limited to some species from Deltaproteobacteria and Epsilonproteobacteria classes, mainly pathogens. O-glycosylation was found in some bacterial species such as Neisseria spp. and Pseudomonas aeruginosa (Faridmoayer et al., 2007; Nothaft and Szymanski, 2010). Interestingly, post hoc tests (ANOVA, p-value b0.05) showed that semiarid and desert soils had a greater abundance of putative enzymes related to O-glycosylation (GT27 and GT39) and N-glycosylation (GT26, also named WecB) mechanisms in comparison to the other biome soils (Fig. 5) (Jarrell et al., 2014). In addition, desert soils also showed higher abundance α-Nacetylgalactosaminidase (GH109) compared to forest and grassland soils, and is possible involved in oligosaccharide degradation O-linked (Blackman et al., 2015). A higher expression level of such proteins was observed in Leptospirillum ssp. in a biofilm sample at higher temperatures (Mosier et al., 2014). Ligninolytic enzyme families, such as multicopper oxidases (AA1) and an iron reductase domain (AA8), showed higher abundance in desert and semiarid soils, respectively, when compared to other biomes. The alginate lyase family (PL5) was statistically significantly more abundant in forest and grassland soils when compared to other biome samples (ANOVA;Tukey-Kramer test; p b 0.05). This lyase plays important role in assimilation of alginate polysaccharide, a quite abundant carbon source in nature derived from algae and bacteria (Kim et al., 2011; Ertesvåg, 2015). Interestingly, alginate lyase sequences from forest and grassland soils were taxonomically affiliated to Acidobacteria class (LCA algorithm). 3.4. Antibiotic resistance genes distribution among soil biomes Soil bacteria have an extensive repertoire of antibiotic resistance genes (ARGs) (Lewis and Lewis, 2005; Nesme et al., 2014). Metagenomic sequencing has been used previously to determine ARG abundance and diversity in natural grassland soils (Delmont et al., 2012), glacier environments (Segawa et al., 2013), oak savannah soil (Riesenfeld et al., 2004) and remote Alaskan soils (Allen et al., 2009). A total of 2775 hits distributed into 52 ARG classes were identified among all biome samples, with only 16% showing sequence similarity higher or equal to 90%. This comprised between 0.002 and 0.00002% of metagenomic reads, which is similar to those ARGs abundances


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Fig. 2. Tukey-Kramer post-hoc plots comparing Global biomes groups of enzymes related to cell wall biosynthesis (ANOVA statistical with Benjamini-Hochberg FDR correction using pvalue cutoff b0.05 and 0.95 confidence).

reported for paddy soils (0.0007–0.0010%; Xiao et al., 2016). Forest and grassland soils exhibited a greater abundance of ARG-like sequences. This mirrors the CAZy trend, which demonstrated a significantly greater abundance of enzymes related to antibiotic resistance in forests and grasslands. Efflux pump was the predominant resistance mechanism among all samples, and MexEF (22%), MacAB (14%) and Ceo (8%) were the top three most abundant ARDB classes. About 6% of reads were annotated as tetracycline, 6% to bacitracin, and 1.4% to vancomycin resistance. Multidrug resistance (MDR) efflux pumps (belonging MexHI, MexVW, MexEF and MacAB classes), tetracycline (tet_RPP) and bacitracin classes (BacA and bcr) were widely distributed among all biomes (Fig. 6; Supplementary Table 4). MDR efflux pump is a mechanism common to all microorganisms and works efficiently to reduce intracellular antibiotic concentration. It is also involved in the detoxification of intracellular metabolites, virulence, and signal trafficking (Martínez, 2008; Xiao et al., 2016). A network correlating the ARG-like classes to the biomes and their abundance revealed a higher number of ARGs matches to forest and grassland soil samples (Fig. 6; Supplementary Fig. 9a). Caatinga and hot desert showed high abundance of MacAB and MexEF resistance genes. Tundra showed high number of hits to tet_RPP and MacAB (tetracycline and MDR macrolide resistance). Some ARGs classes matched to a unique biome, such as KsgA (Kasugamycin resistance) to tropical forest, Ble (Bleomycin resistance) to temperate coniferous, catB and cml (Chloramphenicol resistance) to saline and hot desert, respectively, fos (fosfomycin resistance) to Caatinga and tsnr (thiostrepton) to Mediterranean soil. Thiostrepton is derived from several strains of Streptomyces and are potent antibacterial agents against Gram-positive pathogens such as methicillin-resistant (Staphylococcus aureus) and vancomycinresistant (enterococci) bacteria (Kelly et al., 2009).

Polymyxin resistance was significantly more abundant in forest, grassland and tundra biomes. Polymyxin is produced by Grampositive bacteria and is selectively toxic to many Gram-negative bacteria (Velkov et al., 2010). However, some Gram-negative bacteria carries ARGs encoding polymyxin resistance protein (arnA) involved in the Lipid A biosynthesis, which was more abundant in forest soils (CAZy annotation). A high number of tetracycline resistance genes was found in the tundra biome (17% of tundra ARG-like sequences). Although tetracycline resistance genes are normally associated with human and animal feces (Wang et al., 2014b; Zhu et al., 2013), some studies have reported their occurrence under the permafrost and active layer from extremely cold remote regions, mainly in northern Canada and Alaskan soils (Allen et al., 2009; Schloss et al., 2010; Perron et al., 2015). From a total of 2775 ARG-like annotated reads, only 1094 sequences were taxonomically identified by LCA algorithm. Gammaproteobacteria (34%), Alphaproteobacteria (27%) and Betaproteobacteria (19%) were the most abundant taxonomic classes harboring ARG-like sequences overall. Forest and grassland samples were dominated by Gammaproteobacteria, Alphaproteobacteria, Betaproteobacteria and Actinobacteria. Sequences assigned to Actinobacteria dominated the hot and cold desert and semiarid soil samples. Planctomycetia and Acidobacteria-related sequences were only present in boreal soil samples (associated to MDR efflux pump and bacitracin resistance). Interestingly, Caatinga soil was the only sample with ARG-like sequences assigned to Bacilli, which contained a vast range of antibiotic resistance genes, including vancomycin, bacitracin, fosfomycin, Bleomycin, β-lactams and mdr (Supplementary Fig. 9a). Additionally, co-occurrence patterns among ARG classes and taxonomic assignments reveals Gammaproteobacteria, Betaproteobacteria, Verrucomicrobia, Acidobacteria and Bacilli co-occurring with MDR classes: MexVW, MexEF, MexAB, Ceo, RosAB, aph, tcmA, Amr, SmeABC and MacAB. Also, Acidobacteria members harboring bcr (bacitracin

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Fig. 3. Tukey-Kramer post-hoc plots comparing Global biomes groups of enzymes related to biomass degradation and neogarabiose (GH117) applying ANOVA statistical with BenjaminiHochberg FDR correction using p-value cutoff b0.05 and 0.95 confidence.

resistance) and Mfpa (fluoroquinolone resistance) classes (Supplementary Fig. 10a). Hierarchical clustering plot was performed to better understand ARG patterns among the biome group datasets. (Supplementary Fig. 10b). A close correlation was observed among ARGs and in forest and

Grasslands samples, but a lower similarity was found among semiarid and desert soils. Although functional annotation may suggest a pattern among them, desert samples differ from each other on average temperature, salinity and many other parameters, and that may influence on ARG types associated to this ecosystem environment.

Fig. 4. Tukey-Kramer post-hoc plots comparing Global biomes groups of enzymes related to starch and sucrose metabolism (ANOVA statistical with Benjamini-Hochberg FDR correction using p-value cutoff b0.05 and 0.95 confidence).


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Fig. 5. Tukey-Kramer post-hoc plots comparing Global biomes groups of enzymes related to glycosylation pathway (ANOVA statistical with Benjamini-Hochberg FDR correction using pvalue cutoff b0.05 and 0.95 confidence).

3.5. Heat shock proteins Heat Shock proteins (HSPs) are a highly conserved family of molecular chaperons induced by a wide variety of stresses, including exposure to cold, UV light or biotic stresses (Park and Seo, 2015). These proteins are named by their molecular weight expressed in kilodaltons (kDa), as HSP20, HSP40, HSP60, HSP70, HSP90 and HSP100 families. A total of 39,012 HSPs were identified within all soil samples. Saline desert soils showed the highest abundance of HSPs; and tundra, semiarid and desert biomes had a greater abundance of HSPs than forests and grasslands (Fig. 7). HSP100 and HSP70 families were the most abundant ones within all samples. HSP100, an ATP-binding protein family, acts on protein aggregation and then solubilized proteins could be refolded with the assistance of the HSP70 family system. HSP70 proteins were reported as hubs in the cellular network of molecular chaperones playing an essential role in normal cell function (Parsell et al., 1994; Wang et al., 2014a). These findings could explain the high abundance of those HSP families widely distributed across all biomes. Furthermore, ANOVA statistics and post hoc test Tukey-Kramer (p-value b 0.05) reveals a higher abundance of HSP90 in forest and grassland soils when compared to semiarid and desert soils. Proteins of this HSP family is highly conserved and involved in signal transduction and chromosome maintenance (Ratheesh Kumar et al., 2012). Saline desert soil showed higher abundance of HSP20 and HSP60 compared to the other biomes. The HSP20 family prevents protein denaturation, keeping proteins in a folding-competent state, so that they can be involved in ATP-dependent disaggregation through the HSP70/90 chaperone system (Park and Seo, 2015). HSP60 is a mitochondrial heat shock protein involved in properly folding of proteins. In saline desert samples, 36% of the HSP20 and 70% of HSP60 assigned sequences were affiliated with Archaea, which are known mainly for their extremophilic

properties. Gamma-, Delta- and Alphaproteobacteria were the main bacterial groups harboring HSP20 and HSP60 sequences. These HSP families are very important for keeping bacterial energy metabolism alive in high stress settings. Lower survival rates were detected in HSP60 mutants in Caulobacter crescentus during oxidative, saline, and osmotic stresses (Susin et al., 2006). Overexpression of HSPs, as HSP20, were observed in Halobacterium during high temperature treatment (Shukla, 2006).

4. Conclusions To provide a high-resolution analysis of the microbial taxonomic and functional profiles of global soils, 30 metagenomes representing 11 biomes were selected from publicly available studies. Soil microbial community structure was clearly differentiated among forest, grassland, semiarid and desert biomes. Correlation analysis based on functional profiles revealed a lower similarity among desert soils in comparison with the samples from other ecotype groups, as they differ from each other in terms of temperature, salinity and other parameters. Further, specific databases were used for unveiling the most striking functional differences between biomes. Profiling of carbohydrate-active enzymes revealed enrichment of biomassdegrading genes in forest and grassland biomes, while desert and semiarid soils were overrepresented by CAZymes related to thermo- and osmoprotection as well as other environmental stress resistances. Multidrug efflux pumps (MDR) were the most abundant ARG class in all soil datasets, especially in forest and grassland soils. Furthermore, desert and semiarid soils also showed a profuse arsenal of HSPs (especially in the saline desert), a group of chaperones essential for keeping bacterial energy metabolism alive in high stress settings in harsh environments.

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Fig. 6. Antibiotic resistance genes network among biome soil samples (similarity level greater or equal to 80% and length greater or equal to 25 aa were used). Yellow dots represent biome samples and white dots represent antibiotic resistance classes. Edges show a correlation between biomes and antibiotics, where (a) green edges represent forest samples, (b) orange edges represent grassland samples, (c) dark blue edges represent semiarid regions, (d) light blue represents Tundra samples, and (e) red edges represent desert samples. Edge width represents the number of hits. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Finally, this study corroborated previous work, and provided deeper insights for understanding global soil microbial distribution and their functional potential for survival in a given ecosystem driven by environmental conditions.

Conflict of interest statement The authors confirm that this article content has no conflicts of interest.

Fig. 7. Heat Shock Proteins distribution among biomes and HSPs families showing a higher number of HSPs in the tundra, semiarid and desert, mainly in Saline desert.


Acknowledgements We would like to thank Dawn Field for her extensive review of this material and to Center for Computational Engineering and Sciences FAPESP/Cepid (2013/08293-7). Also, São Paulo State Research Foundation (FAPESP; process no. 2013/09386-9) for grants supporting GVLJ and the US National Science Foundation for supporting JAG through award DEB1442309. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2017.07.159. References Allen, H.K., Moe, L. a, Rodbumrer, J., et al., 2009. Functional metagenomics reveals diverse beta-lactamases in a remote Alaskan soil. ISME J 3, 243–251. Altschul, S.F., Gish, W., Miller, W., et al., 1990. Basic local alignment search tool. J. Mol. Biol. 215, 403–410. Argandoña, M., Nieto, J.J., Iglesias-Guerra, F., et al., 2010. Interplay between iron homeostasis and the osmotic stress response in the halophilic bacterium Chromohalobacter salexigens. Appl. Environ. Microbiol. 76, 3575–3589. Basu, B., Apte, S.K., 2012. Gamma radiation-induced proteome of Deinococcus radiodurans primarily targets DNA repair and oxidative stress alleviation. Mol. Cell. Proteomics 11, M111.011734. Berrendero, E., Valiente, E.F., Perona, E., et al., 2016. Nitrogen fixation in a nonheterocystous cyanobacterial mat from a mountain river. Sci Rep 6, 30920. Bissett, A., Fitzgerald, A., Meintjes, T., et al., 2016. Introducing BASE: the Biomes of Australian Soil Environments soil microbial diversity database. Gigascience 5. http://dx.doi.org/10.1186/s13742-016-0126-5. Blackburn, N.T., Clarke, A.J., 2001. Identification of four families of peptidoglycan lytic transglycosylases. J. Mol. Evol. 52, 78–84. Blackman, L.M., Cullerne, D.P., Torreña, P., et al., 2015. RNA-Seq analysis of the expression of genes encoding cell wall degrading enzymes during infection of lupin (Lupinus angustifolius) by Phytophthora parasitica. PLoS One 10, 1–30. Cameron, R.E., 1960. Arizona-Nevada Academy of science communities of soil algae occurring in the Sonoran Desert in Arizona. J. Ariz. Acad. Sci. 1, 85–88. Cardenas, E., Kranabetter, J.M., Hope, G., et al., 2015. Forest harvesting reduces the soil metagenomic potential for biomass decomposition. ISME J 9, 1–12. Cardon, Z.G., Gray, D.W., Lewis, L.A., 2008. The green algal underground: evolutionary secrets of desert cells. Bioscience 58, 114. Classen, A.T., Sundqvist, M.K., Henning, J.A., et al., 2015. Direct and indirect effects of climate change on soil microbial and soil microbial-plant interactions: What lies ahead? Ecosphere 6, art130. Cline, L.C., Zak, D.R., 2015. Soil microbial communities are shaped by plant-driven changes in resource availability during secondary succession. Ecol. Soc. Am. 96, 3374–3385. Costa, J., Empadinhas, N., Da Costa, M.S., 2007. Glucosylglycerate biosynthesis in the deepest lineage of the bacteria: characterization of the thermophilic proteins GpgS and GpgP from Persephonella marina. J. Bacteriol. 189, 1648–1654. Delmont, T.O., Prestat, E., Keegan, K.P., et al., 2012. Structure, fluctuation and magnitude of a natural grassland soil metagenome. ISME J 6, 1677–1687. Ertesvåg, H., 2015. Alginate-modifying enzymes: biological roles and biotechnological uses. Front. Microbiol. 6, 1–10. Faridmoayer, A., Fentabil, M.A., Mills, D.C., et al., 2007. Functional characterization of bacterial oligosaccharyltransferases involved in O-linked protein glycosylation. J. Bacteriol. 189, 8088–8098. Fierer, N., Jackson, R.B., 2006. The Diversity and Biogeography of Soil Bacterial Communities. 103 pp. 626–631. Fierer, N., Lauber, C.L., Ramirez, K.S., et al., 2012a. Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. ISME J 6, 1007–1017. Fierer, N., Leff, J.W., Adams, B.J., et al., 2012b. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc. Natl. Acad. Sci. 109, 21390–21395. Finn, R.D., Clements, J., Eddy, S.R., 2011. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 39, 29–37. Fredrickson, J.K., Li, S.W., Gaidamakova, E.K., et al., 2008. Protein oxidation: key to bacterial desiccation resistance? ISME J 2, 393–403. Garsin, D., 2010. Ethanolamine utilisation in bacterial pathogens: roles and regulation. Nat. Rev. Microbiol. 8, 290–295. Houghton, R.A., Hall, F., Goetz, S.J., 2009. Importance of biomass in the global carbon cycle. J. Geophys. Res. Biogeosci. 114, 1–13. Hultman, J., Waldrop, M.P., Mackelprang, R., et al., 2015. Multi-omics of permafrost, active layer and thermokarst bog soil microbiomes. Nature 521, 208–212. Huson, D.H., Auch, A., Qi, J., Schuster, S.C., 2007. MEGAN Analysis of Metagenomic Data. Genome Res. 17, 377–386. Jackman, J.E., Fierke, C. a, Tumey, L.N., et al., 2000. Antibacterial agents that target lipid a biosynthesis in gram-negative bacteria. J. Biol. Chem. 275, 11002–11009. Jarrell, K.F., Ding, Y., Meyer, B.H., et al., 2014. N-linked glycosylation in Archaea: a structural, functional, and genetic analysis. Microbiol. Mol. Biol. Rev. 78, 304–341.

1073

Jiang, L., Lin, M., Zhang, Y., et al., 2013. Identification and characterization of a novel trehalose synthase gene derived from saline-alkali soil metagenomes. PLoS One 8, 1–11. Kelly, W.L., Pan, L., Li, C., 2009. Thiostrepton biosynthesis: prototype for a new family of bacteriocins. J. Am. Chem. Soc. 131, 4327–4334. Kertesz, M.A., 2000. Riding the sulfur cycle - metabolism of sulfonates and sulfate esters in gram-negative bacteria. FEMS Microbiol. Rev. 24, 135–175. Kielak, A.M., Barreto, C.C., Kowalchuk, G.A., et al., 2016. The ecology of Acidobacteria: moving beyond genes and genomes. Front. Microbiol. 7, 1–16. Kim, H.S., Lee, C., Lee, E.Y., 2011. Alginate lyase: structure, property, and application. Biotechnol. Bioprocess Eng. 851, 843–851. Lauber, C.L., Hamady, M., Knight, R., et al., 2009. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl. Environ. Microbiol. 75, 5111–5120. Lee, C., Lee, J.-H., 2013. Lipid a biosynthesis of multidrug-resistant pathogens-a novel drug target. Curr. Pharm. Des. 1–17. Lee, S., Lee, J.Y., Ha, S.C., et al., 2009. Crystallization and preliminary X-ray analysis of neoagarobiose hydrolase from Saccharophagus degradans 2–40. Acta Crystallogr. Sect. F Struct. Biol. Cryst. Commun. 65, 1299–1301. Lewis, L.A., Lewis, P.O., 2005. Unearthing the molecular phylodiversity of desert soil green algae (Chlorophyta). Syst. Biol. 54, 936–947. Liu, B., Pop, M., 2009. ARDB - antibiotic resistance genes database. Nucleic Acids Res. 37, 443–447. Lloyd, A.H., Armbruster, W.S., Edwards, M.E., 1994. Ecology of a steppe-tundra gradient in interior Alaska. J. Veg. Sci. 5, 897–912. Lombard, V., Golaconda Ramulu, H., Drula, E., et al., 2014. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 42, 490–495. Lovley, D.R., Klug, M.J., 1983. Sulfate reducers can outcompete methanogens at freshwater sulfate concentrations. Appl. Environ. Microbiol. 45, 187–192. Ma, B., Wang, H., Dsouza, M., et al., 2016. Geographic patterns of co-occurrence network topological features for soil microbiota at continental scale in eastern China. ISME J 1–11. Makhalanyane, T.P., Valverde, A., Gunnigle, E., et al., 2015. Microbial ecology of hot desert edaphic systems. FEMS Microbiol. Rev. 39, 203–221. Manoharan, L., Kushwaha, S.K., Hedlund, K., et al., 2015. Captured metagenomics: largescale targeting of genes based on “sequence capture” reveals functional diversity in soils. DNA Res. 22, 451–460. Martínez, J.L., 2008. Antibiotics and antibiotic resistance genes in natural environments. Science 80 (321), 365–367. Mendes, L.W., Tsai, S.M., Navarrete, A.A., et al., 2015. Soil-borne microbiome: linking diversity to function. Microb. Ecol. http://dx.doi.org/10.1007/s00248-014-0559-2. Metzger, L.E., Lee, J.K., Finer-Moore, J.S., et al., 2012. LpxI structures reveal how a lipid a precursor is synthesized. Nat. Struct. Mol. Biol. 19, 1132–1138. Meyer, F., Paarmann, D., D'Souza, M., et al., 2008. The metagenomics RAST server—a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinf. 9, 386. Mitchell, A., Bucchini, F., Cochrane, G., et al., 2016. EBI metagenomics in 2016 - an expanding and evolving resource for the analysis and archiving of metagenomic data. Nucleic Acids Res. 44, D595–603. Mosier, A.C., Li, Z., Thomas, B.C., et al., 2014. Elevated temperature alters proteomic responses of individual organisms within a biofilm community. ISME J 9, 1–15. Nesme, J., Cècillon, S., Delmont, T.O., et al., 2014. Large-scale metagenomic-based study of antibiotic resistance in the environment. Curr. Biol. 24, 1096–1100. Nothaft, H., Szymanski, C.M., 2010. Protein glycosylation in bacteria: sweeter than ever. Nat. Rev. Microbiol. 8, 765–778. O'Brien, S.L., Gibbons, S.M., Owens, S.M., et al., 2015. Spatial scale drives patterns in soil bacterial diversity. Environ. Microbiol. http://dx.doi.org/10.1111/1462-2920.13231 (In review). Olson, D.M., Dinerstein, E., Wikramanayake, E.D., et al., 2001. Terrestrial ecoregions of the world: a new map of life on earth. Bioscience 51, 933. Pandit, A.S., Joshi, M.N., Bhargava, P., et al., 2014. Metagenomes from the Saline Desert of Kutch. Genome Announc. 2, e00439-14. Park, C., Seo, Y., 2015. Heat Shock Proteins: A Review of the Molecular Chaperones for Plant Immunity. 31 pp. 323–333. Parks, D.H., Tyson, G.W., Hugenholtz, P., et al., 2014. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30, 3123–3124. Parsell, D.A., Kowal, A.S., Singer, M.A., et al., 1994. Protein disaggregation mediated by heat-shock protein Hsp104. Nature 372, 475–478. Pasternak, Z., Al-Ashhab, A., Gatica, J., et al., 2013. Spatial and temporal biogeography of soil microbial communities in arid and semiarid regions. PLoS One 8. http:// dx.doi.org/10.1371/journal.pone.0069705. Perron, G.G., Whyte, L., Turnbaugh, P.J., et al., 2015. Functional characterization of bacteria isolated from ancient arctic soil exposes diverse resistance mechanisms to modern antibiotics. PLoS One 10, 1–19. Quail, M.M., Smith, M.E., Coupland, P., et al., 2012. A tale of three next generation sequencing platforms: comparison of ion torrent, pacific biosciences and illumina MiSeq sequencers. BMC Genomics 13, 1. Ranjard, L., Dequiedt, S., Chemidlin Prévost-Bouré, N., et al., 2013. Turnover of soil bacterial diversity driven by wide-scale environmental heterogeneity. Nat. Commun. 4, 1434. Ratheesh Kumar, R., Nagarajan, N.S., Arunraj, S.P., et al., 2012. HSPIR: a manually annotated heat shock protein information resource. Bioinformatics 28, 2853–2855. Reina-Bueno, M., Argandoña, M., Salvador, M., et al., 2012. Role of trehalose in salinity and temperature tolerance in the model halophilic bacterium Chromohalobacter salexigens. PLoS One 7. http://dx.doi.org/10.1371/journal.pone.0033587. Rho, M., Tang, H., Ye, Y., 2010. FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res. 38, 1–12.

1074


Riesenfeld, C.S., Goodman, R.M., Handelsman, J., 2004. Uncultured soil bacteria are a reservoir of new antibiotic resistance genes. Environ. Microbiol. 6, 981–989. Schloss, P.D., Allen, H.K., Klimowicz, A.K., et al., 2010. Psychrotrophic strain of Janthinobacterium lividum from a cold Alaskan soil produces prodigiosin. DNA Cell Biol. 29, 533–541. Segawa, T., Takeuchi, N., Rivera, A., et al., 2013. Distribution of antibiotic resistance genes in glacier environments. Environ. Microbiol. Rep. 5, 127–134. Shi, Y., Grogan, P., Sun, H., et al., 2015. Multi-scale variability analysis reveals the importance of spatial distance in shaping Arctic soil microbial functional communities. Soil Biol. Biochem. 86, 126–134. Shukla, H.D., 2006. Proteomic analysis of acidic chaperones, and stress proteins in extreme halophile Halobacterium NRC-1: a comparative proteomic approach to study heat shock response. Proteome Sci. 4, 6. Slade, D., Radman, M., 2011. Oxidative Stress Resistance in Deinococcus Radiodurans. Souza, R.C., Mendes, I.C., Reis-Junior, F.B., et al., 2016. Shifts in taxonomic and functional microbial diversity with agriculture: how fragile is the Brazilian Cerrado? BMC Microbiol. 16, 42. Susin, M.F., Baldini, R.L., Gueiros-Filho, F., et al., 2006. GroES/GroEL and DnaK/DnaJ have distinct roles in stress responses and during cell cycle progression in Caulobacter crescentus. J. Bacteriol. 188, 8044–8053. Velkov, T., Thompson, P.E., Nation, R.L., et al., 2010. Structure-activity relationships of polymyxins antibiotics. J. Med. Chem. 53, 1898–1916. Virtanen, R., Oksanen, L., Oksanen, T., et al., 2015. Where do the treeless tundra areas of northern highlands fit in the global biome system: toward an ecologically natural subdivision of the tundra biome. Ecol. Evol. 143–158.

Walmagh, M., Zhao, R., Desmet, T., 2015. Trehalose analogues: latest insights in properties and biocatalytic production. J Mol Sci]–>Int. J. Mol. Sci. 16, 13729–13745. Wang, W., Guo, Q., Xu, X., et al., 2014a. High-level tetracycline resistance mediated by efflux pumps Tet(A) and Tet(A)-1 with two start codons. J. Med. Microbiol. 63, 1454–1459. Wang, K., Zhang, X., Goatley, M., et al., 2014b. Heat shock proteins in relation to heat stress tolerance of creeping bentgrass at different N levels. PLoS One 9, 1–10. Webber, A.N., Lubitz, W., 2001. P700: the primary electron donor of photosystem I. Biochim. Biophys. Acta Bioenerg. 1507, 61–79. Wenk, M., Ba, Q., Erichsen, V., et al., 2012. A universally conserved ATPase regulates the oxidative stress response in Escherichia coli. J. Biol. Chem. 287, 43585–43598. Xiao, K.-Q., Li, B., Ma, L., et al., 2016. Metagenomic profiles of antibiotic resistance genes (ARGs) in paddy soils from South China. FEMS Microbiol. Ecol. http://dx.doi.org/ 10.1093/femsec/fiw023. Xu, Z., Hansen, M.A., Hansen, L.H., et al., 2014. Bioinformatic approaches reveal metagenomic characterization of soil microbial community. PLoS One 9. http:// dx.doi.org/10.1371/journal.pone.0093445. Yin, Y., Mao, X., Yang, J., et al., 2012. DbCAN: a web resource for automated carbohydrateactive enzyme annotation. Nucleic Acids Res. 40, 445–451. Zhu, Y.-G., Johnson, T.A., Su, J.-Q., et al., 2013. Diverse and abundant antibiotic resistance genes in Chinese swine farms. U S A]–>Proc. Natl. Acad. Sci. U. S. A. 110, 3435–3440.