Metagenome expression libraries are constructed by inserting fragmented metagenomic DNA into expression vectors based on plasmids, cosmids, fosmids, or phages, after which gene expression is examined in a suitable host system. The advantage of directly screening for enzymatic activities from metagenome libraries is that researchers access previously unknown genes and their encoded enzymes. Furthermore, the sequences and enzyme activities are functionally guaranteed. However, some potential drawbacks compromise this approach. Thus, before a clone correctly expresses an active enzyme, several requirements must be met. First, when functional enzymatic activity depends on more than one genetic subunit, the clone must contain the complete gene sequence, or even a gene cluster. This problem can be resolved by selecting suitable vector systems. For small target genes, DNA fragment libraries with inserts between 2 and 10 kilobase (kb) are constructed in plasmids or Lambda expression vectors, and then screened for enzyme expression. Larger gene clusters, preferentially necessitate expression libraries with inserts between 20 and 40 kb in cosmids and fosmids, and up to 100 to 200 kb in bacterial artificial chromosome vectors. Although common E. coli host strains have relaxed requirements for promoter recognition and translation initiation, many genes from environmental samples may not be expressed efficiently in heterologous hosts due to differences in codon usage, transcription and/or translation initiation signals, protein-folding elements, post-translational modifications, such as glycosylation, or toxicity of the active enzyme. This obstacle is overcome partly by selecting suitable vector systems containing apposite transcription and translation-initiation sequences, and using suitable expression hosts, such as the E. coli Rosetta strains (Novagen, Madison, Wisconsin, USA) that contain the tRNA genes for rare amino acid codons 27, or co-expression of the chaperone proteins, such as GroES, GroEL, and heat-shock proteins 2829. Alternatively, host systems such as insect cells, the yeast Pichia pastoris, and bacterial hosts such as Pseudomonas putida, Streptomyces lividans, or Bacillus subtilis were suitably improved for heterologous gene expression 30. Furthermore, several modified function-based methods exist specifically for exploring metagenome libraries. Thus, Uchiyama and colleagues 31 developed substrate-induced gene-expression screening to rapidly identify clones that can be induced by a target substrate and display catabolic gene expression, while metabolite-regulated expression detects clones generating quorum-sensing gene-inducing compounds 32.

Function-based metagenome library screening has uncovered a wide range of biocatalysts. Here, we highlight several published results that screened for polysaccharide and plant cell wall biomass-degrading enzymes, most belonging to GHase families. In most cases, colorimetric-based analyses on agar plates employing dye-linked substrates or reaction products staining were used for preliminary screening. Candidate clones were then confirmed by enzyme activity assays.

Amylases attract much industrial interest and are the focus of many metagenome studies. Richardson et al. 33, Voget et al. 34, Yun et al. 35, and Lämmle et al. 36 detailed novel amylolytic enzyme activities from metagenome libraries; some of these enzymes were purified and characterized 3335. Cellulose is nature's most abundant biopolymer, and long has been recognized as a potential source of sugars for biofuel production. Voget and colleagues 37 obtained several cellulolytic clones by functionally screening a soil metagenome library from which they purified and characterized a cellulase. Rees et al. 38 screened a lake water metagenome library and retrieved four cellulolytic clones. From a metagenome library representing the microbial community present in the rabbit's cecum, several clones with cellulose activities were revealed 39. Functional screening of metagenome libraries from extreme (high salinity and alkalinity) environmental samples (soil from Soda Lake, California, and lake sediments from Africa and Egypt) also disclosed cellulolytic clones 19. Cellulolytic enzymes isolated from environments with extreme temperatures and pH values are receiving a lot of interest as these enzymes are expected to be better adapted to the conditions of industrial processes, such as the decomposition of recalcitrant plant cell wall biomass into fermentable sugars.

Chitin, a compound of the fungal cell wall, is the second most abundant natural biopolymer that is broken down by chitinases. Cottrell et al. 40 acquired clones with chitinase activities from metagenome libraries derived from marine samples (filtrated from coastal sea water and estuarine water near the Delaware Bay). Hemicellulose consists primarily of xylan and constitutes the second most abundant polymer in plant biomass. Xylanase activities were detected and expressed from metagenome libraries representing the microbial communities of an insect gut 41, and the waste water from a dairy farm 42.

The ester linkage between the 4-O-methyl-D-glucuronic acid of glucuronoxylan and lignin alcohols is one type of covalent linkage connecting lignin and hemicellulose in plant cell walls. Esterases, which belong to the group of carboxylester hydrolases, hydrolyze such linkages. Esterase activities were detected from metagenome libraries of soil 4344, lake water 38, drinking water 43, and the micro flora from bovine rumen 45. From these libraries, clones with endo-β-1,4-glucanase activity and a clone with cyclodextrinase activity were identified 45. Agarases are enzymes that liquefy agar by cleaving either the polymer's α-L-(1,3) linkage or its β-D-(1,4) linkage. Voget et al. 34 discovered six agarase genes in a soil metagenome library. The same library yielded two clones with pectate lyase activity, and one clone with 1,4-α-glucan branching enzyme activity 34. Table 1 summarizes the enzymes discovered via function-based screening, their metagenome origin, and the library types and sizes.

Sequence-based screening methods rely on known conserved sequences, and cannot uncover non-homologous enzymes. Therefore, the drawback of this 'closed approach' is its failure to detect fundamentally different 'new' genes. However, unlike function-based methods, it can disclose target genes, regardless of gene expression and protein folding in the host, and irrespective of the completeness of the target gene's sequence. The success of this approach rests on meeting several conditions:

(1) The more complex the community, the larger must be the sequencing effort. Here, the development of new sequencing technology, such as the next-generation 454-pyrosequencing, has changed the outcome. For instance, one of the first metagenome projects was the exploration of microbial communities in the drainage from acid mines 12, wherein only three bacterial and three archaeal lineages were detected. Nowadays, metagenome projects using new sequencing technologies not only generate greater total base pair reads but also have more even coverage of species within the community 17.

(2) While the metagenomic approach captures representative DNA samples from diverse organisms, many sequence reads remain unassembled due to the variety of sizes of the environmental genomes, and their abundance. Therefore, a shift in focus emerged, from complete metagenome sequencing to bulk sequencing of as many possible genes and/or functions. In this latter approach, where there is less need to assemble sequences into contigs, the limiting factor becomes the lengths of the fragments that can be obtained for high-throughput screening and cloning. Ideally, the fragments must be long enough to contain the full open reading frame for the functions of interest. Accordingly, optimized 454 sequencing (approximately 450 nucleotide (nt) sequence length) looks more promising than extremely high-volume short-run (25 nt) sequencing 4647, but still has its limitations for downstream cloning and expression of genes like GHase that vary in length from less than 1 kb to more than 20 kb. Gene-finding tools, such as MetaGene, were demonstrated to predict 90% of shotgun sequences 48.

(3) New bioinformatics tools are needed for data mining, based not only on primary sequence homology but also able to predict protein structures, putative catalytic sites, and activities. With the betterment of protein classification tools, models might be designed to correlate enzyme mechanisms and protein folding. Based on this folding and the creation of putative active sites, gene function can be predicted 495051525354. We anticipate that soon sequence-based metagenome databases searches combined with bioinformatics tools will have a greater influence on mining novel biocatalyst genes than function-based methods.

Several publications describe searching metagenome sequence databases in prospecting for genes and their enzymes that will be useful in biofuel production. For example, in sequencing a metagenome library of hindgut microbiota from the largest family of wood-feeding termites (Termitidae), Warnecke and colleagues 55 generated 71 million base pairs of sequence data. By detecting complete domains using global alignment, they identified more than 700 domains homologous to glycoside-hydrolase catalytic corresponding to 45 different carbohydrate-active enzymes (CAZy) families 56, including a rich diversity of putative cellulases and hemicellulases. Schlüter and colleagues 5758 sequenced, using 454-pyrosequencing technology, a metagenome library of the microbial community from the biogas fermenter of an agricultural biogas plant. From among the 141 million base pair sequences generated, bacteria that played dominant roles in methanogenesis and gene-encoding cellulolytic functions were identified from among the Clostridia spp. 5758. In the near future, we anticipate more publications on mining novel biocatalysts using sequence-based metagenome searches.

Although the new ultra-fast sequencing technologies quickly generate a remarkable number of target gene candidates, functional assays are still needed to confirm them. Assays for protein function represent one of the most reliable and irreplaceable tools for mining target genes, and, therefore, developing high through-put functional screening methods is a priority for reducing the time exhausted in primary screening. Furthermore, such future screening methods might valuably be combined with other technologies, such as micro-arrays, biosensors, or proteomics tools to accelerate the discovery of new biocatalyst genes.

The metagenomics approach provided valuable insight into a full range of microbial diversity in the environment, regardless of their cultivability. However, the complexity of microbial species, together with the limitations of the technology to cover fully whole genome sequences of every species present still pose a great challenge for metagenome research. A few bioinformatics programs are established for assembling and binning metagenome sequences, for gene prediction and annotation, estimating community composition, and data management (see Kunin et al. 60 for review). In addition, the European Union-funded 'MetaFunctions' project 61 also covers the development of 'metagenomes Mapserver', a data-mining system that correlates genetic patterns in genomes and metagenomes with contextual environmental data. Nevertheless, more innovative and sophisticated bioinformatics tools must be devised to assure continued valuable progress in the field of metagenomics.