The universality of the 16S rRNA genes makes them an ideal target for phylogenetic analysis and taxonomic classification (Olsen et al., 1986). Schmidt et al. (1991) were the pioneers in performing a community characterization based on metagenome amplified 16S rRNA genes. Since then, the diversity of other natural microbial communities started to be studied using this approach. Jim's Black Pool hot spring, in Yellowstone National Park (YNP), is reported to be the first metagenome-derived analysis of a high-temperature environment based on 16S rRNA gene profiling (Barns et al., 1994).

Initially, these studies required the amplification of the 16S rRNA genes followed by either denaturing gradient gel electrophoresis (DGGE, Muyzer et al., 1993) and sequencing or by cloning of the amplicons. In this case, the libraries obtained were screened using direct Sanger sequencing or restriction fragment length polymorphism (RFLP) analysis (Liu et al., 1997; Baker et al., 2001), to select and sequence those clones with unique patterns (Figure 1). As an example, the effect of pH, temperature, and sulfide in the hyperthermophilic microbial communities living in hot springs of northern Thailand was determined with the amplification of complete 16S rRNA genes followed by DGGE separation and sequencing (Purcell et al., 2007). In a different study, RFLP analysis and sequencing of clones with unique RFLP patterns was used to reveal the presence of abundant novel Bacteria and Archaea sequences in a 16S rRNA gene clone library prepared from the 55°C water and sediments of Boiling Spring Lake in California, USA (Wilson et al., 2008).

With the development of next generation sequencing (NGS) technologies, more samples can be analyzed at lower sequencing cost and time, improving the production of 16S rRNA gene-based biodiversity studies. Additionally, the use of NGS allows to recover more information about the taxonomy of the sample, as reflected by Song et al. (2013), who obtained greater detail in the community structures from 16 Yunnan and Tibetan hot springs with high throughput 454-pyrosequencing than previous studies using conventional clone library and DGGE (Song et al., 2010). These analyses often rely on a partial sequence of 16S rRNA genes, as the read length of most NGS platforms is relatively short. For this purpose, primers designed for amplification of variable regions of 16S rRNA, like the V4–V8 (Hedlund et al., 2013; Huang et al., 2013), or the V3–V4 (Chan et al., 2015) are used. In the last few years, a high amount of extreme temperature environments have been analyzed with this procedure, especially hot springs, some of which are summarized in Table 1. Thanks to this strategy, a large number of 16S rRNA sequences have been produced and deposited in public databases like the Ribosomal Database Project (RDP, Cole et al., 2014) or the SILVA database (Quast et al., 2013).

Even when the process of generating and sequencing the libraries is relatively fast, this PCR-based approach is biased due to limitations of primers, PCR artifacts like chimeras (Ashelford et al., 2005) and inhibitors that could be present in the sample hindering the amplification (Urbieta et al., 2015). Although there are some previous studies focused on primer design to acquire a high coverage rate (Wang and Qian, 2009), difficulties of the primers in recognizing all the 16S rRNA sequences have been described (Cai et al., 2013), leading to the unequal amplification of species 16S rRNA genes. Furthermore, analysis of 16S rRNA sequences can result in misidentification of the taxonomy, as closely related species may harbor nearly identical 16S rRNA genes. In addition, an overestimation of the community diversity could occur since sporadic cases of distant horizontal transfer of the 16S rRNA gene have been inferred from comparisons of these genes within and between individual genomes (Yap et al., 1999; Acinas et al., 2004).

The most used taxonomically informative genomic marker in targeted metagenomics is 16S rRNA, but there are other signature sequences that have been used to study the diversity of thermophiles such as internal transcribed spacer regions (ITS, Ferris et al., 2003) or 18S rRNA genes (Wilson et al., 2008), as well as different protein-coding genes such as aoxB gene fragment, which encodes the catalytic subunit of As(III) oxidase, employed by Sharma et al. (2015) in combination to 16S rRNA to assess the microbial diversity of the Soldhar hot spring in India.

Apart from the above mentioned amplicon-targeting strategy, in some studies a sequence capture technique coupled with NGS is driven to enrich the targeted sequences present in the metagenome. Captured metagenomics involves custom-designed hybridization-based oligonucleotide probes that hybridize with the metagenomic libraries followed by the sequencing of the probe-bound DNA fragments. Denonfoux et al. (2013) firstly used this procedure to explore the methanogen diversity in Lake Pavin (Frech Massif Central), showing that this GC-independent procedure is less biased and can detect broader diversity than traditional amplicon sequencing. The same approach has been used to enhance the capture of functional genes coding for carbohydrate-active enzymes and proteases in agricultural soils (Manoharan et al., 2015), and could also be an interesting tool to study thermophilic populations.

Another method for targeted metagenomics enrichment is stable isotope probing (SIP) in which the environmental microorganisms are grown in the presence of substrates labeled with isotopes. As a consequence of metabolic activity, the isotope (usually ¹³C or ¹⁵N) is incorporated into the nucleic acids of the microbes metabolizing the substrate, increasing the density of DNA or RNA that can be after separated from unlabelled ones (Coyotzi et al., 2016). The high-density community DNA is then used as template to amplify by PCR the 16S rRNA sequences (Brady et al., 2015) and/or some functional genes involved in the selected metabolic pathway, thus allowing the study of the microorganisms that are actively participating in the processes of interest. Gerbl et al. (2014) used this technique to assess the microbial populations implicated in the carbon cycle in the Franz Josef Quelle radioactive thermal spring (Austrian Central Alps).

Although the strategies of targeted metagenomics can be used to infer the taxonomic diversity of the community (16S rRNA gene profiling) or particular aspects of its functional diversity, a broader view of functional diversity, i.e., a more exhaustive answer to the question “what are they doing,” is provided by shotgun metagenomics (Figure 1).

Random sequencing of metagenomic DNA using high-throughput sequencing technology is becoming increasingly common. In this approach, DNA is extracted from the whole community and subsequently sheared into small fragments that are independently sequenced. At present, this is considered the most accurate method for assessing the structure of an environmental microbial community, since it does not comprise any selection and reduces technical biases, especially the ones introduced by amplification of the 16S rRNA gene (Lewin et al., 2013). Shah et al. (2011) compared bacterial communities analyzed with both 16S rRNA and whole shotgun metagenomics, revealing that the taxonomy derived from these two different approaches cannot be directly compared. This study also proposed that low abundance species are best identified through 16S rRNA gene sequencing. Therefore, some high-temperature studies use, in parallel, both techniques to assess the taxonomic composition of the microbial community (Dadheech et al., 2013; Klatt et al., 2013; Chan et al., 2015).

The biodiversity of several hot environments such as oil reservoirs (Kotlar et al., 2011), compost (Martins et al., 2013), or hot springs (Zamora et al., 2015; Mehetre et al., 2016), was studied using shotgun metagenomics sequencing. Some of them are summarized in Table 2.

Development of NGS has greatly enhanced this approach. The most widely used platforms for this kind of analysis in high temperature environments are Illumina and Roche 454 (Table 2). Illumina currently offers the highest throughput per run and the lowest cost per-base (Liu et al., 2012), generating read lengths up to 300 bp. On the other hand, Roche 454 gives longer reads (1 kb maximum), which are easier to map to a reference genome; however it is more expensive and has lower throughput (van Dijk et al., 2014). Even though they have substantial differences (Kumar et al., 2015), some studies have demonstrated that the information recovered from both sequencing platforms is comparable when analyzing the biodiversity of the same sample (Luo et al., 2012).

The main limitations of shotgun metagenome sequencing include its relatively expensive setup cost and the requirement of very high computing power for data storage, retrieval, and analysis. Another important drawback of this approach is that high quality whole community DNA is needed, which makes the extraction a critical step in the process of generating metagenomic data. Therefore, some studies have focused on the improvement of metagenomic DNA extraction from thermal environments (Mitchell and Takacs-Vesbach, 2008; Li et al., 2013a; Gupta et al., 2016). Nowadays the NGS platforms allow sequencing with low inputs of DNA, nevertheless in some cases it is necessary to amplify the metagenomic DNA to obtain enough quantity for preparing the sequencing libraries. As an example, Nakai et al. (2011) used multiple displacement amplification with Phi29 to sequence the metagenome of the hydrothermal fluid of the Mariana Trough, an active back-arc basin in the western Pacific Ocean. This amplification step is frequently required to generate viral metagenomic libraries, introducing a subsequent bias (Kim and Bae, 2011), as the extraction of enough high quality viral nucleic acids is a difficult process that usually relies on virus concentration methods.

To assess the taxonomic diversity with the short metagenomic reads obtained after sequencing, there are several non-exclusive approximations that can be done: analyzing taxonomically informative marker genes, grouping sequences into defined taxonomic groups (binning) or/and assembling sequences into definite genomes (Sharpton, 2014).

As mentioned before, the most frequently used taxonomically informative marker genes are rRNA genes or protein-coding genes that tend to be single copy and common to microbial genomes. In this approach, those reads that are homologs to the marker gene are identified in the sequences of the metagenome and annotated using sequence or phylogenetic similarity to the marker gene database sequences. Bioinformatics applications for this purpose include MetaPhyler (Liu et al., 2010), EMIRGE (Miller et al., 2011), and AMPHORA (Wu and Scott, 2012). Gladden et al. (2011) used EMIRGE to reconstruct near full-length small subunit (SSU) rRNA genes from metagenomic Illumina sequences to determine the taxonomy of compost-derived microbial consortia adapted to switchgrass at 60°C, finding a low-diversity community with predominance of Rhodothermus marinus and Thermus thermophilus. In another study, Klatt et al. (2011) used AMPHORA to identify the phylogenetic and functional marker genes in the assemblies of several hot springs cyanobacterial metagenomes from YNP. These studies allowed the discovery of novel chlorophototrophic bacteria belonging to uncharacterized lineages within the order Chlorobiales and within the Kingdom Chloroflexi. In a similar approach, Lin et al. (2015) and Colman et al. (2016) used a 16S rRNA gene-based diversity method blasting the metagenomic reads against the SILVA reference database to characterize bacterial populations in Shi-Huang-Ping acidic hot spring (Taiwan) and in two thermal springs in YNP, respectively.

Taxonomic binning is defined as the process of grouping reads or contigs and assigning them to operational taxonomic units, depending on information such as sequence similarity, sequence composition or read coverage (Dröge and McHardy, 2012). Metagenomic sequences can be binned based on their sequence similarity to a database of taxonomically annotated sequences using tools like MEGAN (Huson et al., 2011) or MG-RAST, a public resource for the automatic phylogenetic and functional analysis of metagenomes (Meyer et al., 2008). MEGAN bases its taxonomic classification on the NCBI taxonomy using BLAST. With this tool, Klatt et al. (2013) assessed the community structure of six phototrophic microbial mat communities in YNP and Badhai et al. (2015) revealed the dominance of Bacteria over Archaea in four geothermal springs in Odisha, India. Taxonomic binning can be done with assembled or unassembled reads, although assessing taxonomic abundance with assembled data can led to a miscalculation of the abundance of some taxa, as contigs are treated as a single sequence in most downstream analysis, hindering the analytical tools to accurately quantify the abundance of the taxon (Sharpton, 2014).

Assembly is described as the process of merging individual metagenomic reads into longer pieces of contiguous sequences (contigs) based on overlapping sequences and paired read information (Dröge and McHardy, 2012). Bioinformatic implements like MetaVelvet (Namiki et al., 2012) or IDBA-UD (Peng et al., 2012) have been used in the assembly of whole shotgun metagenome reads to study the taxonomical composition of different high-temperature environments. For example, MetaVelvet was applied in the study of eight globally distributed hot springs by Menzel et al. (2015) and IDBA-UD in the analysis of the community composition of Sungai Klah hot spring in Malaysia (Chan et al., 2015). This step can simplify bioinformatic analysis, but it may also produce chimeras, therefore researchers often bin reads and assemble each bin independently to decrease the probability of generating chimeras (Sharpton, 2014).

In recent studies, the integration of assembly and taxonomic binning by sequence composition allowed the reconstruction of several partial genomes from high-temperature environments such as the genome of a novel archaeal Rudivirus obtained from a Mexican hot spring, (Servín-Garcidueñas et al., 2013) or the draft genome sequence of Thermoanaerobacter sp. strain A7A, reconstructed from the metagenome of a 102°C hydrocarbon reservoir in the Bass Strait, Australia (Li et al., 2013b). Using a similar approach, Sangwan et al. (2015), reconstructed the genome of the bacterial predator Bdellovibrio ArHS, with the metagenomic assembly of the microbial mats of an arsenic rich hot spring in the Parvati river valley (Manikaran, India). Also, Sharma et al. (2016) combining genomic and metagenomic data, used two Cellulosimicrobium cellulans genomes derived from metagenomics, to study the evolution of pathogenicity across the species of C. cellulans.

The metagenomic reads obtained from shotgun sequencing of an environmental DNA can be annotated with functions to determine the functional diversity of the microbial community. This usually comprises two steps: identifying metagenomic reads that contain protein coding sequences (gene prediction), and comparing the coding sequences to a database of genes, proteins, protein families, or metabolic pathways (gene annotation) (Sharpton, 2014). Some frequently used databases for functional annotation are the SEED annotation system (Overbeek et al., 2014), the KEGG orthology (KO) database (Kanehisa et al., 2016) or the Pfam database, based on hidden Markov models (HMM) to classify in accordance with the protein domains (Finn et al., 2015). There are several robust web resources that can be easily used to perform gene prediction, database search, family classification, and annotation, including MG-RAST (Meyer et al., 2008), IMG/M (Markowitz et al., 2014), or SUPER-FOCUS (Silva et al., 2015). Considerable functional profiles of thermophilic populations have been based on these tools such as the study of the microbiota of Tuwa hot spring in India (Mangrola et al., 2015a) in which the functional annotation was performed using the MG-RAST pipeline. In this study, a high number of annotated features were classified as unknown function, suggesting the potential source of novel microbial species and their products. Similar results were found in the metagenome of Unkeshwar, another hot spring in India, where pathway annotation was done using KEGG (Mehetre et al., 2016). For each contig sequence, the assignment of KO numbers obtained from known reference hits was done, revealing up to 20% unclassified sequences. These results reflect a promising world of undiscovered proteins that could be explored to find new catalysts for biotechnological applications.

In this approach, it is important to consider that, despite the information given by functional annotation of the metagenomic sequences; the presence of a gene on a metagenome does not mean that it is expressed. Therefore, functional metagenomics, metatranscriptomics, and metaproteomics assays are necessary to assess the real community functional activity. To increase the probability of finding active functional genes involved in a substrate uptake and transformation, some studies use a substrate-induced enrichment of the community before the metagenomic DNA extraction. After, these genes can be detected either by sequence (Graham et al., 2011; Wang et al., 2016) or by functional metagenomics (Chow et al., 2012). Using this procedure, Graham et al. (2011) found and characterized an hyperthermophilic cellulase in an archaeal community, obtained by growth at 90°C of the sediment of a geothermal source enriched with crystalline cellulose.

Another important limitation of shotgun metagenomics is that the databases may be subjected to phylogenetic biases, as some communities are more accurately or more exhaustively annotated than others (Chistoserdova, 2010).

Function-based metagenomics relies on the construction of metagenomic libraries by cloning environmental DNA into expression vectors and propagating them in the appropriate hosts, followed by activity-based screening. After an active clone is identified, the sequence of the clone is determined, the gene of interest is amplified and cloned with the subsequent expression and characterization of the product to explore its biotechnological potential (Figure 2). This technique has the advantage of not requiring the cultivation of the native microorganisms or previous sequence information of known genes, thus representing a valuable approach for mining enzymes with new features.

The use of functional metagenomics allows the discovery of novel enzymes whose functions would not be predicted based on DNA sequence. This approach complements sequence-based metagenomics as the information from function-based analyses can be used to annotate genomes and metagenomes derived exclusively from sequence-based analyses (Lam et al., 2015). Therefore, several investigations in thermal environments combine sequencing methods (taxonomical and functional characterization) with functional screening of clones (Chen et al., 2007; Wemheuer et al., 2013; Leis et al., 2015; López-López et al., 2015b).

Depending on the size of the insert, functional metagenomics can be explored using fosmids (35–45 kb insert), BACs (~200 kb insert), cosmids (30–42 kb insert), or plasmids (<10 kb insert). Bigger inserts are more likely to contain complete genes and operons, allowing the expression of more enzymes. A great number of high temperature functional metagenomics studies use the commercial vector pCC1FOS (Table 3), which allows inserts up to 40 kb, and it is available in a toolkit to simplify the library construction. More information about this vector is compiled in Lam et al. (2015) review.

There are several technically challenging steps in library construction. Mainly, the high quality and length of the metagenomic DNA required for proceeding to the ligation into the vector and the need of obtaining a high proportion of clones in order to cover all the variability of the microbial community. This limitation is particularly important in soil studies, where it has been reported that contaminants like humic acids are present in metagenomic DNA extracts, interfering with the subsequent enzymatic reactions. Therefore, the widely extended method of soil DNA extraction established by Zhou et al. (1996), is usually accomplished with further purification of the sample that can lead to a loss of DNA yield. Some studies show that the humic acids can be easily removed by gel electrophoresis of the metagenomic DNA followed by gel extraction, as humic acids migrate faster than the large metagenomic DNA (Kwon et al., 2010). This simple approach was used to construct a Turpan Basin soil metagenomic library for a functional screening of thermostable beta-galactosidases (Wang et al., 2014). Alternatively, to avoid contaminating the circulating buffer, electrophoresis can be paused after humic acids have formed a front, excising the part of the gel containing the humic acids, and replacing it with fresh gel (Cheng et al., 2014).

Another important drawback that compromises the functional metagenomics approach is the selection of the expression host. Although the commonly used E. coli strains have relaxed requirements for promoter recognition and translation initiation, some genes from environmental samples may not be efficiently expressed due to differences in codon usage, transcription and/or translation initiation signals, protein-folding elements, post-translational modifications, or toxicity of the active enzyme (Uchiyama and Miyazaki, 2009). This problem could be even worse when the proteins expressed need special conditions to be active, such as high temperatures, considering that mesophiles, like E. coli, do not survive at these high temperature conditions. Accordingly, an alternative expression host may be required to overcome the heterologous expression of some genes derived from hot environments and thus, identify a broader range of enzymes. The thermophilic bacterium T. thermophilus has been proposed as a good candidate for function-based detection of thermozymes. In a recent functional screening to detect esterases, Leis et al. (2015) constructed two large insert fosmid metagenomic libraries of compost and hot spring water using pCT3FK, a pCC1FOS derived T. thermophilus/E. coli shuttle fosmid (Angelov et al., 2009), in T. thermophilus and compared them to the same libraries expressed in E. coli. Only two esterases were found at 60°C in the libraries generated in E. coli while 5 different esterases were discovered in the same libraries expressed in T. thermophilus. Therefore, this could be a suitable system to improve the detection of metagenome-derived thermozymes. The main restriction of this approach is that pCT3FK integrates into T. thermophilus chromosomal DNA. In fact, the genomes of the positive clones isolated by Leis et al. (2015) were completely sequenced before proceeding with the PCR amplification and cloning of the candidate genes, with the consequent cost of time and money. Other versatile broad-host-range cosmids that have been used in a soil study (pJC8 and pJC24) allow the phenotypic screening of the library in bacteria such as Bacillus and in the yeast Saccharomyces (Cheng et al., 2014). The selection of the appropriate substrate for the functional screening is also a crucial step in this approach, as the substrate may cause biases in the selection of the activities of interest. Recent studies suggest that the initial selection of active clones with general substrates should be followed by a more specific one to improve the effectiveness of the detection (Ferrer et al., 2016). Other biases and limitations of functional metagenomics and strategies for its improvement have been previously reviewed by Ferrer et al. (2005) and Ekkers et al. (2012).

Some hot environments where function-based screening of microbial communities have been done include hot springs (López-López et al., 2015b), deserts (Neveu et al., 2011), petroleum reservoirs (de Vasconcellos et al., 2010), or human-made environments like a biogas plant (Ilmberger et al., 2012), demonstrating the potential of functional metagenomics as a very important source of new thermozymes.

Lipolytic enzymes, comprising esterases (EC 3.1.1.1) and lipases (EC 3.1.1.3), are extensively distributed in microorganisms, plants, and animals. They catalyze the hydrolysis, synthesis, or transesterification of ester bonds. At present, these enzymes represent about 20% of commercialized enzymes for industrial use (López-López et al., 2015a), as they have great potential in several industrial processes such as production of biodegradable polymers, detergents, food flavoring, oil biodegradation, or waste treatment, among others (Anobom et al., 2014). Therefore, a considerable number of functional metagenomics studies are focused on mining thermal environments in search for these enzymes (Table 3).

Lipases are generally defined as carboxylesterases hydrolyzing water-insoluble (acyl chain length >10) triglycerides, with trioleoylglycerol as the standard substrate. In contrast, esterases catalyze the hydrolysis of short-chain esters (acyl chain length <10) with tributylglycerols (tributyrin) as the standard substrate, although lipases are also capable of hydrolyzing esterase substrates (Rhee et al., 2005). At least 200 different substrates have been successfully applied in assays for functional selection of esterase/lipase biocatalysts in metagenomic clone libraries (Ferrer et al., 2016), including the widely used tributyrin (Rhee et al., 2005; Meilleur et al., 2009; López-López et al., 2015b), and p-nitrophenyl (NP) acetate (Wang et al., 2013). Meilleur et al. (2009) isolated a new alkali-thermostable lipase with an optimal activity at 60°C and pH 10.5 by functional screening of a metagenomic cosmid library from the biomass produced in a gelatin enriched fed-batch reactor. Another gene coding for a thermostable esterase was detected by functional screening of fosmid environmental DNA libraries constructed with metagenomes from thermal environmental samples of Indonesia (Rhee et al., 2005). The recombinant esterase was active from 30 up to 95°C with an optimal pH of approximately 6.0. Mayumi et al. (2008), generated a metagenomic library with the community DNA extracted from biodegradable polyester poly(lactic acid) (PLA) disks buried in compost and found a PLA depolymerase that had an esterase domain. Purified enzyme showed the highest activity at 70°C and degraded not only PLA, but also various aliphatic polyesters, tributyrin, and p-NP esters. As mentioned before, those enzymes able of retaining activity even in the presence of organic solvents are considered very interesting for industrial applications. A new thermophilic organic solvent-tolerant and halotolerant esterase with an optimum pH and temperature of 7.0 and 50°C, respectively, was found in the functional screening of a soil metagenomic library with 48,000 clones (Wang et al., 2013).

Apart from these above cited sources, metagenomic esterases, and lipases have been isolated by functional screening of other hot environments like deep-sea hydrothermal vents (Zhu et al., 2013) and hot springs (López-López et al., 2015b) as shown in Table 3. A more extensive review of metagenome derived extremophilic lipolytic enzymes can be found in López-López et al. (2014).

The enzymes that hydrolyze glycosidic bonds between two or more sugars or a sugar and a nonsugar moiety within carbohydrates or oligosaccharides are known as glycosyl hydrolases (GHs) or glycosidases (Sathya and Khan, 2014). There are 115 GH families, collected in the Carbohydrate Active enZyme database (CAZy; http://www.cazy.org) (Lombard et al., 2014), including a broad number of enzymes like cellulases, β-galactosidases, amylases, and pectinases.

Cellulases encompass a group of complex enzymes conformed by endo-β-1,4 glucanases, cellobiohydrolases, cellodextrinases, and β-glucosidases. These enzymes work together to degrade cellulose into simple sugars and their thermostable representatives could be used in biofuel production from lignocellulosic biomass (Bhalla et al., 2013). Several substrates can be employed in plate-based screens for the functional detection of clones harboring cellulase activity, such as carboxymethyl-cellulose in combination with trypan blue, Gram's iodine, or Congo Red. Meddeb-Mouelhi et al. (2014), found that Gram's iodine may lead to the identification of false positives, making Congo Red a more suitable dye for this approach. Using Congo Red dye as a colorimetric substrate, Ilmberger et al. (2012) obtained two fosmid clones derived from a carboxymethyl-cellulose (CMC)-enriched library from a biogas plant. These two fosmids were designated as pFosCelA2 and pFosCelA3, encoding two thermostable cellulases with significant activities in the presence of 30% (v/v) ionic liquids (ILs). This is an interesting property for the cellulose degradation, as cellulose could increase its solubility in the ILs.

From the group of cellulases, β-glucosidases have attracted considerable attention in recent years due to their important roles in various biotechnological processes such as hydrolysis of isoflavone glucosides or the production of fuel ethanol from agricultural residues (Singhania et al., 2013). Other uses of β-glucosidases include the cleavage of phenolic and phytoestrogen glucosides from fruits and vegetables for medical applications or to enhance the quality of beverages. An archaeal β-glucosidase (Bgl1) showing activity toward cellobiose, cellotriose, and lactose was isolated from a metagenome from a hydrothermal spring in the island of Sγo Miguel (Azores, Portugal) (Schröder et al., 2014).

β-Galactosidases (EC 3.2.1.23), which hydrolyze lactose to glucose and galactose, have two main applications in the food industry: the production of low-lactose milk and dairy products for lactose intolerant people and the generation of galactooligosaccharides from lactose by the transgalactosylation reaction. These enzymes can be also used in the revalorisation of cheese whey (Becerra et al., 2015), a by-product of the dairy industry with a high organic load that can be considered a pollutant.

The most widely used substrate for the β-galactosidase screening, 5-bromo-4-chloro-3-indolyl-β-D-galactopyranoside (X-gal), is the substrate providing, in some cases, the lowest number of positive hits in relation to the total number of clones screened (Ferrer et al., 2016). Usually, the positive clones capable of hydrolyzing the X-gal are further tested against ortho-NP-β-galactoside (ONPG) and lactose (Wierzbicka-Woś et al., 2013). Mayor drawbacks for the use of β-galactosidase in industrial processes is the inhibition by the reaction products, leading to a decrease in the reaction rates or even to stop the enzymatic reaction completely. The thermostable β-galactosidase (Gal308) discovered by Zhang et al. (2013) exhibited high tolerance to galactose and glucose with the highest activity at 78°C, an optimum pH of 6.8 and high enzyme activity with lactose as substrate. The authors suggest that these properties would make it a good candidate for the production of low-lactose milk and dairy products. Another novel and thermostable alkalophilic β-D-galactosidase with an optimum temperature at 65°C and with high transglycosylation activity was identified through functional screening of a metagenomic library from a hot spring in northern Himalayan region of India (Gupta et al., 2012).

Xylans, made of β-1,4 linked xylopyranoses as a linear backbone with branches, constitute the second most significant group of polysaccharides in plant cell walls and are degraded by xylanases (Sathya and Khan, 2014). These hemicellulolytic enzymes are mostly used as biobleaching agents in the paper and pulp industry. The discovery of thermostable and alkali-stable xylanases has become an important goal in this field since this process requires high temperatures and alkali media, but this is not the only application of thermostable xylanases (Kumar et al., 2016). Functional metagenomics of hot environments represents an interesting source of xylanases. As an example, a novel alkali-stable and thermostable GH-11 endoxylanase encoding gene (Mxyl), was isolated by functional screening of a compost-soil metagenome (Verma et al., 2013). The thermostability of this enzyme was subsequently engineered by directed site mutagenesis (Verma and Satyanarayana, 2013).

Amylases are known as enzymes that catalyze the hydrolysis of starch into sugars (Sundarram et al., 2014). A novel and thermostable amylase with the highest activity at 90°C was retrieved from a black smoker chimney by combining fosmid library construction with pyrosequencing (Wang et al., 2011). Another α-amylase was isolated in the functional screening of a metagenomic library of Western Ghats soil constructed in pCC1FOS. This amylase retained 30% activity after incubation for 60 min at 80°C and had an optimal pH of 5.0 and could be potentially used in some industrial processes like liquefaction and saccharification of starch in food industry, or formulation of enzymatic detergents and removing starch from textiles (Vidya et al., 2011).

Proteases are protein-hydrolyzing enzymes classified into acidic, neutral, or alkaline groups, based on their optimum pH. They can also be classified into aspartic, cysteine, glutamic, metallo, serine, and threonine protease types based on the amino acids present in their active sites (Singh et al., 2015). These enzymes are widely used in various industries such as detergent, food, and leather (Haddar et al., 2009; George et al., 2014). A thermotolerant, alkali-stable and oxidation resistant protease (CHpro1) was found by functional screening of a metagenomic library constructed from sediments of hot springs in Chumathang area of Ladakh, India (Singh et al., 2015). This enzyme, that showed optimum activity at pH 11 and stability in high alkaline range, could be especially interesting for the detergent industry, as the pH of laundry detergents is generally in the range of 9.0–12.0. This property, in addition to the resistance in the presence of detergent compounds, like oxidizing agents, and the possibility of working at high wash temperatures (optimum activity at 80°C), makes it a very suitable detergent protease.

These enzymes catalyze oxidation-reduction reactions, in which hydrogen or oxygen atoms or electrons are transferred between molecules and are important biocatalysts for several industrial processes. From a pharmaceutical point of view, oxidoreductases can act like quorum-quenching enzymes, degrading signal molecules to block quorum-sensing-dependent infection, as reported by Bijtenhoorn et al. (2011), who found a soil-derived dehydrogenase/reductase implicated in the decreasing of Pseudomonas aeruginosa biofilm formation and virulence of Caenorhabditis elegans. These enzymes can also be used in food industry as they catalyze oxidation- reduction reactions that can play an important role in taste, flavor and nutritional value of aliments such as virgin olive oil (Peres et al., 2015). Another relevant application of oxidoreductases is their role in decomposing specific recalcitrant contaminants by precipitation or by transforming them to other products, leading to a better final treatment of the waste. Some oxidoreductases that can be used for this purpose include peroxidases, polyphenol oxidases, and estradiol dioxygenases (EDOs) (Durán and Esposito, 2000). Suenaga et al. (2007) constructed a metagenomic library from activated sludge used to treat coke plant wastewater containing various organic pollutants like phenol, mono- and polycyclic nitrogen-containing aromatics or aromatic hydrocarbons, among others. The library was screened for EDOs, using catechol as a substrate, yielding 91 EDO-positive clones, 38 of them were sequenced in order to conduct similarity searches using BLASTX. A polyphenol oxidase enzyme, with alkaline laccase activity and highly soluble expression, showing the optimum activity of 55°C, was isolated from a functional screening of DNA from mangrove soil (Ye et al., 2010).

Although the new ultra-fast sequencing technologies quickly generate a remarkable number of target gene candidates, functional assays are still needed to confirm them. These assays for protein function represent one of the most reliable and invaluable tools for mining target genes. Thus, developing of high-throughput screening (HTS) methods and improved chromogenic substrates for the detection of thermozymes (Kračun et al., 2015) is a priority for reducing the time invested in primary screening. HTS techniques increase the success of function-based metagenomic screens since they compensate for the often low hit rates in such screens (Ekkers et al., 2012). Apart from conventional high throughput screens, which use microtiter plate wells to store a large number of clones (Ko et al., 2013), microarray-based technologies coupled with microfluidic devices, cell compartmentalization, flow cytometry, and cell sorting are arising as promising new technologies for this purpose (Najah et al., 2014; Meier et al., 2015; Vidal-Melgosa et al., 2015). Microfluidic technologies are of undeniable interest when it comes to reaching screening rates of a million clones per day (Ufarté et al., 2015). This screening method generally uses fluorogenic substrates (Najah et al., 2013) and it is based on the encapsulation of single clones of the metagenomic library in droplets, followed by the substrate induced gene-expression screening and the fluorescence-activated cell sorting to isolate plasmidic clones containing the genes of interest (Colin et al., 2015; Hosokawa et al., 2015). The main advantages of this ultra-fast screening method are the small volume required (usually picoliters to femtoliters) and the capability of detecting intracellular, extracellular, and membrane proteins. This approach could be used for the screening of thermozymes, as droplets can be incubated at high temperature before proceeding to the screening and fluorescence sorting. In this regard, there is an ongoing FP7 Marie Curie Action named HOTDROPS that involves four companies and four academic partners (including the authors' group) aimed to develop a microfluidics-based ultra-high-throughput platform for the selection of thermozymes from metagenomics and directed evolution libraries.

Until recently, most of the sequences collected in reference databases were related to humans and their pathogens. Currently, the advances in sequencing technologies have enabled the generation of considerable amounts of longer reads in less time. This fact, in addition to the lower per base cost and the development of metagenomics, has produced a relevant increase in the number of genomes sequenced and annotated deposited in databases like GenBank, thus covering a high range of microorganisms from a wide variety of habitats, including high temperature environments. Therefore, the bias in the databases toward microorganisms with clinical or pathogenic interest is decreasing, allowing a better analysis of the populations with metagenomics. Furthermore, metagenomics is becoming a tool in reach of many laboratories with the recent release of new cheaper and smaller devices such as the Oxford Nanopore MinION, a USB flash drive-size sequencer that measures deviations in electrical current as a single DNA strand passes through a protein nanopore (Bayley, 2015). However, this technology presents high error rates compared to the others (Goodwin et al., 2015) and still has to be improved.

Altogether, these breakthrough developments make metagenomics a more affordable and robust tool to explore the taxonomy and the functional diversity of microbial communities. Nevertheless, the complexity of microbial species, together with the limitations of the technology to cover fully whole genome sequences, still pose a great challenge for metagenome research. NGS technologies have limitations and remain at least an order of magnitude more expensive than other conventional microbiological assays, thus samples often must be individually barcoded and pooled into single runs to decrease costs. All these deficiencies will probably disappear as technologies continue developing like they did in the last years, from the end of the human genome sequencing project in 2003 (Collins et al., 2003), up to now.

Due to the massive amount of metagenome data generated in the last 10 years, infrastructural developments associated with managing and serving sequence data are needed. Additionally, the fast growth in the size of data complicates its storage, organization, and distribution. As the volume of metagenomics data keeps growing, new assemblers have been developed, namely MEGAHIT that can assemble large and complex metagenomics data in a time and cost-efficient way, especially on a single-node server (Li et al., 2015).

New bioinformatic pipelines designed to support researchers involved in functional and taxonomic studies of environmental microbial communities have been released like BioMaS (Fosso et al., 2015), DUDes (Piro et al., 2016), or MOCAT2 (Kultima et al., 2016), among others.

Since there is an increasing number of complex communities sequenced, improved statistical methodology is needed, especially to enhance comparative studies where a large number of covariates (e.g., environmental or host physiological parameters) are collected for each sample.

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.