encyclopedia of metagenomics || environmental shaping of codon usage and functional adaptation...

Environmental Shaping of Codon Usage and Functional AdaptationAcross Microbial Communities

Vedran Lucića, Masa Rollera, Istvan Nagyb and Kristian Vlahovičeka*aBioinformatics Group, Molecular Biology Department, Division of Biology, Faculty of Science, University of Zagreb,Zagreb, CroatiabInstitute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary

Definition

Whole microbial communities exhibit patterns similar to those of single microbial species in termsof synonymous codon usage, regardless of their phyletic composition. Therefore, methods applica-ble on single microbial genomes to predict for functionally important and lifestyle-relevant genesbased on translational optimization of synonymous codons can be applied to the study of the entiremetagenomes. Using these predictions opens up a possibility to discover new and functionallyunannotated genes relevant for the community metabolism and overall adaptation to a particularenvironment. This approach presents an integrated approach to the study of microbial communitygenomic information and provides an in silico functional metagenomic platform to complementmetaproteomic studies.

Introduction

Environmental diversity studies have bypassed the common problem where less than 1% ofmicrobes are amenable to cultivation in laboratory conditions (Staley and Konopka 1985) by insteadusing high-throughput sequencing to extract genomic information directly from the environmentalsample, without prior culturing. Various environments and geological sites have been sampled usingnew-generation sequencing, such as sea (Venter et al. 2004), soil (Tringe et al. 2005a), and variousextreme habitats (e.g., acid drainage from ametal mine (Tyson et al. 2004), as well as gastrointestinaltracts of diverse organisms – including human (Gill et al. 2006) and mouse (Turnbaugh et al. 2006)).Most of the analyss of the sampled environments were focused in two main directions. The first oneclassifies the functions of identified genes (open reading frames) according to annotation availablethrough orthology databases such as COG/KOG (Clusters of Orthologous Groups of genes)(Tatusov et al. 2003) or KEGG-KO (Kyoto Encyclopedia of Genes and Genomes – Orthology)(Kanehisa et al. 2006) and subsequently ranking the relative “importance” of a particular functionaccording to its abundance in the environment. The second direction focuses on estimating thephyletic distribution of microbial species represented in the environment, based on similaritysearches against known microbial species’ sequences (Huson et al. 2007).

For a thorough understanding of microbial communities at the systems level, it is necessary tocapture the interplay of community constituents and organizational complexity in the communitymetabolism. Microbes in the same environment live within the same physical and chemicalconstraints, such as temperature, pH, or ion concentration, probably causing the GC content to bemetagenome specific (Foerstner et al. 2005). Furthermore, communities of microbes have been

*Email: [email protected]

Encyclopedia of MetagenomicsDOI 10.1007/978-1-4614-6418-1_562-1# Springer Science+Business Media New York 2014

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shown to share tRNA pools to facilitate horizontal gene transfer (Tuller et al. 2011), which alsoimplies a limited choice of preferred cognate codons within the shared tRNA pool. It has also beenshown that fast growth rates introduce stronger bias in synonymous codon usage at the level ofwhole metagenomes (Vieira-Silva and Rocha 2010), much like the effect observed in singlemicrobial species (Rocha 2004; Sharp et al. 2005).

Microbial communities living under the same environmental constrains, at the level of genes, caneffectively be considered and studied as metagenomes, thereby using approaches and methodologyvalid for single microbial genome studies. One such approach is the functional characterization bytranslational optimization through synonymous codon usage bias.

The codon usage (CU) bias within a genome reflects the selection pressure for translationaloptimization of highly expressed genes – primarily the protein synthesis machinery such asribosomal genes and elongation factors, but also genes with environmental adaptation functions(Supek et al. 2010). At the level of a single microbial genome, the effect of CU bias is routinely usedto predict for functionally relevant and highly expressed genes (Sharp and Li 1987; Karlin andMrazek 2000; Plotkin and Kudla 2011). The choice of preferred codons in a single genome is mostclosely correlated with abundance of the cognate tRNA molecules (Ikemura 1985; Kanayaet al. 2001; Tuller et al. 2010) and further influenced by the genome’s GC content (Chen et al. 2004).

Eleven different microbial community sequencing samples (Table 1.) were used to demonstratethat microbes living in the same ecological niche, regardless of their phyletic diversity, sharea common preference for codon usage. CU bias is present at the community level and is alsodifferent between distinct communities. CU bias also varies within the community, with distribu-tions resembling that of single microbial species, i.e., the intercommunity CU bias can be observed.The effects of intercommunity CU bias and translational optimization concepts are utilized toidentify genes with CU close to that of the meta-ribosomal sample. These genes have high predictedexpression across the entire microbial community and define its “functional fingerprint.” Thisapproach establishes a functional metagenomic platform that enables functional studies at thelevel of the entire microbial community samples.

Table 1 Metagenomes used to demonstrate the concept of environmental shaping of codon usage

MetagenomeNCBI ProjectID Reference

Global Ocean Sampling Expedition Metagenome,the Sargasso Sea version 1

13694 (Venter et al. 2004)

Waseca County farm soil metagenome 13699 (Tringe et al. 2005b)

Whale fall metagenomes 13700

5-way (CG) acid mine drainage biofilm metagenome 13696 (Tyson et al. 2004)

Human distal gut biome 16729 (Gill et al. 2006)

Lean mouse 1 gut metagenome 17391 (Turnbaughet al. 2006)

Obese mouse 1 gut metagenome 17397

US EBPR sludge metagenome 17657 (Martin et al. 2006)

OZ EBPR sludge metagenome 17659


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Description

Microbes living in the same ecological niche share a bias in CU. When comparing the distance ofeach gene’s CU in a metagenome from overall metagenome CU in the metagenome of origin with allother metagenomes, genes originating from one metagenome form a distinct cluster (as shown inFig. 1a) and have CU predominantly closer to that of metagenome overall CU than genes from othermetagenomes. If the amino acid sequence of each gene is kept constant but the codons randomlychosen (Fig. 1b), the genes’ CU becomes equidistant to both metagenomes (i.e., occupy the sameportion of the plot) regardless of their metagenome of origin.

The Variability of Single Species’ Codon Usage Across MetagenomesWhen comparing CU of species present in two distinct metagenomes, they can be compared in termsof CU distance with (i) their respective metagenome overall CU and (ii) CU of genes from the samespecies in a different metagenome. The resulting distance distributions, quantified with the intraclasscorrelation coefficient measure (ICC), show a statistically significant difference in CU patterns ofcompared phylogenies – the within-species’CU pattern is more variable betweenmetagenomes thanin different species within the same metagenome (Fig. 2).

Comparison of CU variability of independently sequenced strains of microbes living in distinctniches is used to demonstrate that CU is a dynamic property that changes with different environ-mental constraints at the level of single bacterial species. Comparison between 12 strains ofPropionibacterium acnes (Bruggemann et al. 2004; Hunyadkurti et al. 2011), commensal gram-positive bacteria that live in consistent environmental conditions, with 6 strains of cosmopolitanbacterium Rhodopseudomonas palustris (Larimer et al. 2004; Oda et al. 2008), shows that there isless variation in CU per orthologous group in P. acnes strains than in the R. palustris strains (Fig. 3).

Fig. 1 Codon usage is metagenome specific. Soil versus human gut metagenome codon usage (CU) frequencies. (a)The distance (MILC) of each gene’s CU frequency to overall CU frequencies of two microbial communities. Genes (redin human gut (N ¼ 33,422) and blue in Waseca soil (N ¼ 88,696) metagenome) are predominantly closer to theirrespective metagenome of origin therefore forming two distinct groups (the distribution of log2 ratio of the two distancesfor each gene is shown in the inset). If the amino acid composition of metagenomes is kept constant and the codons arerandomly chosen, CU bias of each metagenome would be eliminated resulting in uniform distribution of CU distancesand overlap of two samples, as shown in b)


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Despite the fact that the sampling includes more than twice as many strains from constrainedenvironmental conditions (P. acnes) than variable conditions (R. palustris), the variability in CUis smaller in the constrained environmental conditions. R. palustris samples show on overall highervariability in CU, suggesting plasticity of codon usage that reflects on translational optimization andadopts to each specific environment. Even though the R. palustris strains generally show more

Fig. 2 Codon usage variability between same species in different metagenomes is larger than within a metagenome.ORFs from each identified species (using MEGAN) were compared against their originating metagenome (orange, totalcomparisons N¼ 2,058) and against same-species ORFs in a different metagenome (green, total comparisons N¼ 1,029comparisons). ICC measures were calculated, representing how “close” the CU profiles match, with ICC ¼1 denotingthe perfect match. The orange distribution shows less variability and is shifted toward higher ICC values, denoting thecloser overall match of species’ CU to their metagenome of origin

Fig. 3 Environmental variability of codon usage. Variability of codon usage per COG category in 6 strains ofRhodopseudomonas palustris and in 12 strains of Propionibacterium acnes. The codon usage variability (calculatedas median CU distance from the ribosomal set within an orthologous group to its centroid CU) for the strains of P. acnes(N ¼ 15,436), living in consistent environmental conditions, is shifted to the left, i.e., it shows smaller variation andhigher bias than for the R. palustris strains (N ¼ 24,071) living in diverse environmental conditions


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variation in CU (Fig. 3), both species, regardless of environmental constraints, show the leastrelative variation of CU within the COG categories (i.e., orthologous genes) for housekeeping,including ribosomal protein genes.

The Variability of Codon Usage in Metagenomes upon Removal of Dominant PhylaCommunity-level codon usage bias is not an effect caused by the most abundant species. CUfrequencies of the Sargasso Sea metagenome, the largest dataset in this study, were compared toother investigated metagenomes and to itself but with dominant phyla removed. The comparisonsbetween Sargasso Sea CU frequencies and other metagenomes all show ICC< 0.75, while the sameSargasso sample with dominant phyla of the Alphaproteobacteria class removed (�36% of thewhole set) and the Alphaproteobacteria class itself show virtually no deviation (ICC > 0.98 and0.95, respectively) from the original metagenome CU.

Codon Usage in Metagenomes Follows Similar Patterns as in Single MicrobialGenomesAs has been established at the level of single microbial genomes (Ikemura 1985; Kanaya et al. 2001),the distance of each gene’s CU frequency to the overall CU of the whole genome and to that ofa “reference set” of highly expressed genes (ribosomal protein genes) gives a characteristic crescent-shaped plot (Fig. 4a, introduced by (Karlin and Mrazek 2000)). Metagenomes exhibit similar CUdistance distributions to those observed in single bacterial genomes, despite the fact that theycomprise of genes that originate from diverse phylogenies (i.e., Santa Cruz whale carcass bone inFig. 4b). If the amino acid composition of genes in a metagenome is kept constant but the codons arerandomly chosen, the crescent plot shape analogous to single bacterial genomes and CU bias is lost.

Fig. 4 Metagenomes show codon usage distribution similar to single genomes. The distance of each gene’s codon usage(CU) frequency forms the overall CU of the (meta)genome and ribosomal reference set, displayed as a Karlin B-plot for(a) a single microbial genome (Escherichia coli, N ¼ 4,358) and (b) a metagenome (whale carcass, N ¼ 33,422). Themetagenome shows the same characteristic distribution as the genome with ribosomal genes closer to the CU of theribosomal set than the overall CU of the whole (meta)genome


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PredictingMetagenomic Expression and Functional Profiles Through SynonymousCodon UsageUnder different environmental constraints, CU varies in single bacterial species, and metagenomesshare synchronized CU as do single bacterial species. CU bias in metagenomes can be used topredict the expression levels of genes in the same manner as is routinely used to predict genesoptimized for high levels of expression in single microbial genomes (Sharp and Li 1987; Karlin andMrazek 2000; Supek and Vlahovicek 2005). Figure 5 depicts the resulting predictions at the level ofwhole metagenomes using the meta-ribosomal protein reference set. The most significantly enrichedfunctions in the high expression level sets are (i) amino acid transport and metabolism (COGsupercategory E) for Sargasso Sea, (ii) energy production and conservation (COG supercategoryC) for the Whale fall metagenomes, and (iii) inorganic ion transport and metabolism (COGsupercategory P) for the acid mine biofilm metagenome. The most striking difference betweenmetagenomes was lack of enrichment in energy production and carbohydrate metabolism (COGsupercategories C and G) in the obese mice microbiota sample, in contrast to both lean human andmouse microbiota samples, indicating high metabolic activity of lean gut bacteria.

Artificial metagenomes, constructed from randomly selected genes of whole genome bacterialsequences from the NCBI with the same COG composition as their corresponding microbialsamples, show loss of environment-specific enrichment of optimization in their expression profiles.

Validation with Metaproteomic DataPredictions of gene expression for Sargasso Sea metagenome were compared to the Sargasso Seametaproteomic study (Sowell et al. 2008) and a functionally (COG) classified subset of the humangut metaproteomic study (Verberkmoes et al. 2009). Predicted expression values based on CUoptimization positively correlate with abundance in metaproteomic studies, both for the comparisonof each gene with the protein most similar in sequence (Sargasso Sea rho¼0.34) and when medianvalues per gene and protein COG are compared (human gut rho¼0.34). This opens up for an in silicoprediction of overall metagenomic proteome status.

Fig. 5 Enrichment of functions within highly expressed genes in metagenomes. Enrichment or depletion of functionalannotations in the 3% genes with highest predicted expression (highest MELP measure) relative to the abundance ofeach COG supercategory in the whole metagenome for the OZ EBPR sludge (N ¼ 29,754), Waseca farm soil (N ¼88,696), acid mine biofilm (N¼ 79,257), Sargasso Sea (N¼ 688,539), US EBPR sludge (N¼ 20,175), Whale fall SantaCruz microbial mat (N ¼ 40,916), Whale fall Antarctic bone (N ¼ 30,503), Whale fall Santa Cruz bone (N ¼ 33,422),obese mouse gut (N ¼ 4,058), lean mouse gut (N ¼ 4,955), human gut (N ¼ 47,765), Santa Cruz whale fall bone (N ¼33,422), and acid mine (N ¼ 79,257). Metagenomes show different functional enrichment patterns that are consistentwith environmental requirements (e.g., metabolite transport functions [E] in the Sargasso Sea or energy conversion [C]in the whale carcass metagenome). Letters at the bottom represent COG supercategories


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Summary

Analysis of eleven distinct metagenomes shows that microbial communities exhibit codon usagebias similar to that already described for single microbial species. Microbial communities sharing anenvironment are likely to have similar synonymous codon usage-based translational optimizationfor expression of environment-specific genes. This effect can be used to identify genes withunknown function and “optimal” codon encoding, indicating their potential for high expressionand therefore high relative importance in the community metabolism and lifestyle.

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