Efficient PCR-Based Amplification of Diverse Alcohol Dehydrogenase Genes from Metagenomes for Improving Biocatalysis: Screening of Gene-Specific Amplicons from Metagenomes

Screening of gene-specific amplicons from metagenomes (S-GAM) has tremendous biotechnological potential. We used this approach to isolate alcohol dehydrogenase (adh) genes from metagenomes based on the Leifsonia species adh gene (lsadh), the enzyme product of which can produce various chiral alcohols. A primer combination was synthesized by reference to homologs of lsadh, and PCR was used to amplify nearly full-length adh genes from metagenomic DNAs. All adh preparations were fused with lsadh at the terminal region and used to construct Escherichia coli plasmid libraries. Of the approximately 2,000 colonies obtained, 1,200 clones were identified as adh positive (∼60%). Finally, 40 adh genes, Hladh-001 to Hladh-040 (for homologous Leifsonia adh), were identified from 223 clones with high efficiency, which were randomly sequenced from the 1,200 clones. The Hladh genes obtained via this approach encoded a wide variety of amino acid sequences (8 to 99%). After screening, the enzymes obtained (HLADH-012 and HLADH-021) were confirmed to be superior to LSADH in some respects for the production of anti-Prelog chiral alcohols.     

Metagenomes of the picoalga Bathycoccus from the Chile coastal upwelling

Among small photosynthetic eukaryotes that play a key role in oceanic food webs, picoplanktonic Mamiellophyceae such as Bathycoccus, Micromonas, and Ostreococcus are particularly important in coastal regions. By using a combination of cell sorting by flow cytometry, whole genome amplification (WGA), and 454 pyrosequencing, we obtained metagenomic data for two natural picophytoplankton populations from the coastal upwelling waters off central Chile. About 60% of the reads of each sample could be mapped to the genome of Bathycoccus strain from the Mediterranean Sea (RCC1105), representing a total of 9 Mbp (sample T142) and 13 Mbp (sample T149) of non-redundant Bathycoccus genome sequences. WGA did not amplify all regions uniformly, resulting in unequal coverage along a given chromosome and between chromosomes. The identity at the DNA level between the metagenomes and the cultured genome was very high (96.3% identical bases for the three larger chromosomes over a 360 kbp alignment). At least two to three different genotypes seemed to be present in each natural sample based on read mapping to Bathycoccus RCC1105 genome.     

Microbial Community Analysis with Ribosomal Gene Fragments from Shotgun Metagenomes

Shotgun metagenomic sequencing does not depend on gene-targeted primers or PCR amplification; thus, it is not affected by primer bias or chimeras. However, searching rRNA genes from large shotgun Illumina data sets is computationally expensive, and no approach exists for unsupervised community analysis of small-subunit (SSU) rRNA gene fragments retrieved from shotgun data. We present a pipeline, SSUsearch, to achieve the faster identification of short-subunit rRNA gene fragments and enabled unsupervised community analysis with shotgun data. It also includes classification and copy number correction, and the output can be used by traditional amplicon analysis platforms. Shotgun metagenome data using this pipeline yielded higher diversity estimates than amplicon data but retained the grouping of samples in ordination analyses. We applied this pipeline to soil samples with paired shotgun and amplicon data and confirmed bias against Verrucomicrobia in a commonly used V6-V8 primer set, as well as discovering likely bias against Actinobacteria and for Verrucomicrobia in a commonly used V4 primer set. This pipeline can utilize all variable regions in SSU rRNA and also can be applied to large-subunit (LSU) rRNA genes for confirmation of community structure. The pipeline can scale to handle large amounts of soil metagenomic data (5 Gb memory and 5 central processing unit hours to process 38 Gb [1 lane] of trimmed Illumina HiSeq2500 data) and is freely available at https://github.com/dib-lab/SSUsearch under a BSD license.     

Exploration of Noncoding Sequences in Metagenomes

Environment-dependent genomic features have been defined for different metagenomes, whose genes and their associated processes are related to specific environments. Identification of ORFs and their functional categories are the most common methods for association between functional and environmental features. However, this analysis based on finding ORFs misses noncoding sequences and, therefore, some metagenome regulatory or structural information could be discarded. In this work we analyzed 23 whole metagenomes, including coding and noncoding sequences using the following sequence patterns: (G+C) content, Codon Usage (Cd), Trinucleotide Usage (Tn), and functional assignments for ORF prediction. Herein, we present evidence of a high proportion of noncoding sequences discarded in common similarity-based methods in metagenomics, and the kind of relevant information present in those. We found a high density of trinucleotide repeat sequences (TRS) in noncoding sequences, with a regulatory and adaptive function for metagenome communities. We present associations between trinucleotide values and gene function, where metagenome clustering correlate with microorganism adaptations and kinds of metagenomes. We propose here that noncoding sequences have relevant information to describe metagenomes that could be considered in a whole metagenome analysis in order to improve their organization, classification protocols, and their relation with the environment.     

Single-Cell-Genomics-Facilitated Read Binning of Candidate Phylum EM19 Genomes from Geothermal Spring Metagenomes

The vast majority of microbial life remains uncatalogued due to the inability to cultivate these organisms in the laboratory. This “microbial dark matter” represents a substantial portion of the tree of life and of the populations that contribute to chemical cycling in many ecosystems. In this work, we leveraged an existing single-cell genomic data set representing the candidate bacterial phylum “Calescamantes” (EM19) to calibrate machine learning algorithms and define metagenomic bins directly from pyrosequencing reads derived from Great Boiling Spring in the U.S. Great Basin. Compared to other assembly-based methods, taxonomic binning with a read-based machine learning approach yielded final assemblies with the highest predicted genome completeness of any method tested. Read-first binning subsequently was used to extract Calescamantes bins from all metagenomes with abundant Calescamantes populations, including metagenomes from Octopus Spring and Bison Pool in Yellowstone National Park and Gongxiaoshe Spring in Yunnan Province, China. Metabolic reconstruction suggests that Calescamantes are heterotrophic, facultative anaerobes, which can utilize oxidized nitrogen sources as terminal electron acceptors for respiration in the absence of oxygen and use proteins as their primary carbon source. Despite their phylogenetic divergence, the geographically separate Calescamantes populations were highly similar in their predicted metabolic capabilities and core gene content, respiring O₂, or oxidized nitrogen species for energy conservation in distant but chemically similar hot springs.     

CoMeta: Classification of Metagenomes Using k-mers

Nowadays, the study of environmental samples has been developing rapidly. Characterization of the environment composition broadens the knowledge about the relationship between species composition and environmental conditions. An important element of extracting the knowledge of the sample composition is to compare the extracted fragments of DNA with sequences derived from known organisms. In the presented paper, we introduce an algorithm called CoMeta (Classification of metagenomes), which assigns a query read (a DNA fragment) into one of the groups previously prepared by the user. Typically, this is one of the taxonomic rank (e.g., phylum, genus), however prepared groups may contain sequences having various functions. In CoMeta, we used the exact method for read classification using short subsequences (k-mers) and fast program for indexing large set of k-mers. In contrast to the most popular methods based on BLAST, where the query is compared with each reference sequence, we begin the classification from the top of the taxonomy tree to reduce the number of comparisons. The presented experimental study confirms that CoMeta outperforms other programs used in this context. CoMeta is available at https://github.com/jkawulok/cometa under a free GNU GPL 2 license.     

Identification and Characterization of a Novel Trehalose Synthase Gene Derived from Saline-Alkali Soil Metagenomes

A novel trehalose synthase (TreS) gene was identified from a metagenomic library of saline-alkali soil by a simple activity-based screening system. Sequence analysis revealed that TreS encodes a protein of 552 amino acids, with a deduced molecular weight of 63.3 kDa. After being overexpressed in Escherichia coli and purified, the enzymatic properties of TreS were investigated. The recombinant TreS displayed its optimal activity at pH 9.0 and 45 °C, and the addition of most common metal ions (1 or 30 mM) had no inhibition effect on the enzymatic activity evidently, except for the divalent metal ions Zn²⁺ and Hg²⁺. Kinetic analysis showed that the recombinant TreS had a 4.1-fold higher catalytic efficientcy (Kcat/K _m) for maltose than for trehalose. The maximum conversion rate of maltose into trehalose by the TreS was reached more than 78% at a relatively high maltose concentration (30%), making it a good candidate in the large-scale production of trehalsoe after further study. In addition, five amino acid residues, His172, Asp201, Glu251, His318 and Asp319, were shown to be conserved in the TreS, which were also important for glycosyl hydrolase family 13 enzyme catalysis.     

Metagenomes from High-Temperature Chemotrophic Systems Reveal Geochemical Controls on Microbial Community Structure and Function

The Yellowstone caldera contains the most numerous and diverse geothermal systems on Earth, yielding an extensive array of unique high-temperature environments that host a variety of deeply-rooted and understudied Archaea, Bacteria and Eukarya. The combination of extreme temperature and chemical conditions encountered in geothermal environments often results in considerably less microbial diversity than other terrestrial habitats and offers a tremendous opportunity for studying the structure and function of indigenous microbial communities and for establishing linkages between putative metabolisms and element cycling. Metagenome sequence (14–15,000 Sanger reads per site) was obtained for five high-temperature (>65°C) chemotrophic microbial communities sampled from geothermal springs (or pools) in Yellowstone National Park (YNP) that exhibit a wide range in geochemistry including pH, dissolved sulfide, dissolved oxygen and ferrous iron. Metagenome data revealed significant differences in the predominant phyla associated with each of these geochemical environments. Novel members of the Sulfolobales are dominant in low pH environments, while other Crenarchaeota including distantly-related Thermoproteales and Desulfurococcales populations dominate in suboxic sulfidic sediments. Several novel archaeal groups are well represented in an acidic (pH 3) Fe-oxyhydroxide mat, where a higher O₂ influx is accompanied with an increase in archaeal diversity. The presence or absence of genes and pathways important in S oxidation-reduction, H₂-oxidation, and aerobic respiration (terminal oxidation) provide insight regarding the metabolic strategies of indigenous organisms present in geothermal systems. Multiple-pathway and protein-specific functional analysis of metagenome sequence data corroborated results from phylogenetic analyses and clearly demonstrate major differences in metabolic potential across sites. The distribution of functional genes involved in electron transport is consistent with the hypothesis that geochemical parameters (e.g., pH, sulfide, Fe, O₂) control microbial community structure and function in YNP geothermal springs.     

Analysis of Hydrocarbon-Contaminated Groundwater Metagenomes as Revealed by High-Throughput Sequencing

The tendency for chlorinated aliphatics and aromatic hydrocarbons to accumulate in environments such as groundwater and sediments poses a serious environmental threat. In this study, the metabolic capacity of hydrocarbon (aromatics and chlorinated aliphatics)-contaminated groundwater in the KwaZulu-Natal province of South Africa has been elucidated for the first time by analysis of pyrosequencing data. The taxonomic data revealed that the metagenomes were dominated by the phylum Proteobacteria (mainly Betaproteobacteria). In addition, Flavobacteriales, Sphingobacteria, Burkholderiales, and Rhodocyclales were the predominant orders present in the individual metagenomes. These orders included microorganisms (Flavobacteria, Dechloromonas aromatica RCB, and Azoarcus) involved in the degradation of aromatic compounds and various other hydrocarbons that were present in the groundwater. Although the metabolic reconstruction of the metagenome represented composite cell networks, the information obtained was sufficient to address questions regarding the metabolic potential of the microbial communities and to correlate the data to the contamination profile of the groundwater. Genes involved in the degradation of benzene and benzoate, heavy metal-resistance mechanisms appeared to provide a survival strategy used by the microbial communities. Analysis of the pyrosequencing-derived data revealed that the metagenomes represent complex microbial communities that have adapted to the geochemical conditions of the groundwater as evidenced by the presence of key enzymes/genes conferring resistance to specific contaminants. Thus, pyrosequencing analysis of the metagenomes provided insights into the microbial activities in hydrocarbon-contaminated habitats.     

Metagenomes of complex microbial consortia derived from different soils as sources for novel genes conferring formation of carbonyls from short-chain polyols on Escherichia coli

Metagenomic DNA libraries from three different soil samples (meadow, sugar beet field, cropland) were constructed. The three unamplified libraries comprised approximately 1267000 independent clones and harbored approximately 4.05 Gbp of environmental DNA. Approximately 300000 recombinant Escherichia coli strains of each library per test substrate were screened for the production of carbonyls from short-chain (C2 to C4) polyols such as 1,2-ethanediol, 2,3-butanediol, and a mixture of glycerol and 1,2-propanediol on indicator agar. Twenty-four positive E. COLI clones were obtained during the initial screen. Fifteen of them contained recombinant plasmids, designated pAK201-215, which conferred a stable carbonyl-forming phenotype on E. coli Sequencing revealed that the inserts of pAK201-215 encoded 26 complete and 14 incomplete predicted protein-encoding genes. Most of these genes were similar to genes with unknown functions from other microorganisms or unrelated to any other known gene. The further analysis was focused on the 7 plasmids (pAK204, pAK206, pAK208, and pAK210-213) recovered from the positive clones, which exhibited an NAD(H)-dependent alcohol oxidoreductase activity with polyols or the correlating carbonyls as substrates in crude extracts. Three genes (ORF6, ORF24, and ORF25) conferring this activity were identified during subcloning of the inserts of pAK204, pAK211, and pAK212. The sequences of the three deduced gene products revealed no significant similarities to known alcohol oxidoreductases, but contained putative glycine-rich regions, which are characteristic for binding of nicotinamide cofactors.     

A Parsimony Approach to Biological Pathway Reconstruction/Inference for Genomes and Metagenomes

A common biological pathway reconstruction approach—as implemented by many automatic biological pathway services (such as the KAAS and RAST servers) and the functional annotation of metagenomic sequences—starts with the identification of protein functions or families (e.g., KO families for the KEGG database and the FIG families for the SEED database) in the query sequences, followed by a direct mapping of the identified protein families onto pathways. Given a predicted patchwork of individual biochemical steps, some metric must be applied in deciding what pathways actually exist in the genome or metagenome represented by the sequences. Commonly, and straightforwardly, a complete biological pathway can be identified in a dataset if at least one of the steps associated with the pathway is found. We report, however, that this naïve mapping approach leads to an inflated estimate of biological pathways, and thus overestimates the functional diversity of the sample from which the DNA sequences are derived. We developed a parsimony approach, called MinPath (Minimal set of Pathways), for biological pathway reconstructions using protein family predictions, which yields a more conservative, yet more faithful, estimation of the biological pathways for a query dataset. MinPath identified far fewer pathways for the genomes collected in the KEGG database—as compared to the naïve mapping approach—eliminating some obviously spurious pathway annotations. Results from applying MinPath to several metagenomes indicate that the common methods used for metagenome annotation may significantly overestimate the biological pathways encoded by microbial communities.     

Identification of Cellulase Genes from the Metagenomes of Compost Soils and Functional Characterization of One Novel Endoglucanase

Metagenomics, a new research field developed over the past decade, aims to identify potential enzymes from nonculturable microbes. In this study, genes encoding three glycoside hydrolase family (GHF) 9 endoglucanases and one GHF 5 endoglucanase were cloned and identified from the metagenome of the compost soils. The shared identities between the predicted amino acid sequences of these genes and their closest homologues in the database were less than 70%. One GHF 9 endoglucanase, Umcel9B, was further characterized. The recombinant protein, Umcel9B, showed activity against carboxymethyl cellulose, indicating that Umcel9B is an endoactive enzyme. Enzymatic activity occurs optimally at a pH of 7.0 and a temperature of 25°C. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Metagenomes of tropical soil-derived anaerobic switchgrass-adapted consortia with and without iron

Tropical forest soils decompose litter rapidly with frequent episodes of anoxia, making it likely that bacteria using alternate terminal electron acceptors (TEAs) such as iron play a large role in supporting decomposition under these conditions. The prevalence of many types of metabolism in litter deconstruction makes these soils useful templates for improving biofuel production. To investigate how iron availability affects decomposition, we cultivated feedstock-adapted consortia (FACs) derived from iron-rich tropical forest soils accustomed to experiencing frequent episodes of anaerobic conditions and frequently fluctuating redox. One consortium was propagated under fermenting conditions, with switchgrass as the sole carbon source in minimal media (SG only FACs), and the other consortium was treated the same way but received poorly crystalline iron as an additional terminal electron acceptor (SG + Fe FACs). We sequenced the metagenomes of both consortia to a depth of about 150 Mb each, resulting in a coverage of 26× for the more diverse SG + Fe FACs, and 81× for the relatively less diverse SG only FACs. Both consortia were able to quickly grow on switchgrass, and the iron-amended consortium exhibited significantly higher microbial diversity than the unamended consortium. We found evidence of higher stress in the unamended FACs and increased sugar transport and utilization in the iron-amended FACs. This work provides metagenomic evidence that supplementation of alternative TEAs may improve feedstock deconstruction in biofuel production.     

Ecogenomic Perspectives on Domains of Unknown Function: Correlation-Based Exploration of Marine Metagenomes

Background

The proportion of conserved DNA sequences with no clear function is steadily growing in bioinformatics databases. Studies of sequence and structural homology have indicated that many uncharacterized protein domain sequences are variants of functionally described domains. If these variants promote an organism''s ecological fitness, they are likely to be conserved in the genome of its progeny and the population at large. The genetic composition of microbial communities in their native ecosystems is accessible through metagenomics. We hypothesize the co-variation of protein domain sequences across metagenomes from similar ecosystems will provide insights into their potential roles and aid further investigation.

Methodology/Principal findings

We calculated the correlation of Pfam protein domain sequences across the Global Ocean Sampling metagenome collection, employing conservative detection and correlation thresholds to limit results to well-supported hits and associations. We then examined intercorrelations between domains of unknown function (DUFs) and domains involved in known metabolic pathways using network visualization and cluster-detection tools. We used a cautious “guilty-by-association” approach, referencing knowledge-level resources to identify and discuss associations that offer insight into DUF function. We observed numerous DUFs associated to photobiologically active domains and prevalent in the Cyanobacteria. Other clusters included DUFs associated with DNA maintenance and repair, inorganic nutrient metabolism, and sodium-translocating transport domains. We also observed a number of clusters reflecting known metabolic associations and cases that predicted functional reclassification of DUFs.

Conclusion/Significance

Critically examining domain covariation across metagenomic datasets can grant new perspectives on the roles and associations of DUFs in an ecological setting. Targeted attempts at DUF characterization in the laboratory or in silico may draw from these insights and opportunities to discover new associations and corroborate existing ones will arise as more large-scale metagenomic datasets emerge.