Environments shape the nucleotide composition of genomes

Foerstner, Konrad; Mering, Christian von; Hooper, Sean D.; Bork, Peer

doi:10.1038/sj.embor.7400538

Cited by 276 publications

(260 citation statements)

References 30 publications

Supporting

Mentioning

237

Contrasting

Unclassified

Order By: Relevance

“…This view was bolstered by sequence information showing that the base compositional differences among bacteria were most pronounced at synonymous and noncoding positions, sites that are thought to be under the least selective constraints (5). Furthermore, repeated attempts to link genomic base composition with an environmental factor or other selective processes have met with limited success (6)(7)(8)(9)(10).…”

mentioning

confidence: 99%

A selective force favoring increased G+C content in bacterial genes

Raghavan

Kelkar

Ochman

2012

Proc. Natl. Acad. Sci. U.S.A.

118

131

View full text Add to dashboard Cite

Bacteria display considerable variation in their overall base compositions, which range from 13% to over 75% G+C. This variation in genomic base compositions has long been considered to be a strictly neutral character, due solely to differences in the mutational process; however, recent sequence comparisons indicate that mutational input alone cannot produce the observed base compositions, implying a role for natural selection. Because bacterial genomes have high gene content, forces that operate on the base composition of individual genes could help shape the overall genomic base composition. To explore this possibility, we tested whether genes that encode the same protein but vary only in their base compositions at synonymous sites have effects on bacterial fitness. Escherichia coli strains harboring G+C-rich versions of genes display higher growth rates, indicating that despite a pervasive mutational bias toward A+T, a selective force, independent of adaptive codon use, is driving genes toward higher G+C contents.bacterial adaptation | genome evolution | mutational patterns

show abstract

mentioning

confidence: 99%

A selective force favoring increased G+C content in bacterial genes

Raghavan

Kelkar

Ochman

2012

Proc. Natl. Acad. Sci. U.S.A.

118

131

View full text Add to dashboard Cite

show abstract

“…Comparative metagenomics approaches have revealed significant variation in sequence composition (3), genome size (4), evolutionary rates (5), and metabolic capabilities (6)(7)(8) among qualitatively dissimilar environments (e.g., terrestrial vs. marine), providing evidence for genomic adaptations. Further, variation in specific community biological processes have been shown for different water column zones at a single geographic site (9), different climatic regions in the ocean (10), and, more recently, among 9 ecosystems (7).…”

mentioning

confidence: 99%

Quantifying environmental adaptation of metabolic pathways in metagenomics

Gianoulis

Raes²,

Patel³

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

189

161

View full text Add to dashboard Cite

Recently, approaches have been developed to sample the genetic content of heterogeneous environments (metagenomics). However, by what means these sequences link distinct environmental conditions with specific biological processes is not well understood. Thus, a major challenge is how the usage of particular pathways and subnetworks reflects the adaptation of microbial communities across environments and habitats-i.e., how network dynamics relates to environmental features. Previous research has treated environments as discrete, somewhat simplified classes (e.g., terrestrial vs. marine), and searched for obvious metabolic differences among them (i.e., treating the analysis as a typical classification problem). However, environmental differences result from combinations of many factors, which often vary only slightly. Therefore, we introduce an approach that employs correlation and regression to relate multiple, continuously varying factors defining an environment to the extent of particular microbial pathways present in a geographic site. Moreover, rather than looking only at individual correlations (one-to-one), we adapted canonical correlation analysis and related techniques to define an ensemble of weighted pathways that maximally covaries with a combination of environmental variables (many-to-many), which we term a metabolic footprint. Applied to available aquatic datasets, we identified footprints predictive of their environment that can potentially be used as biosensors. For example, we show a strong multivariate correlation between the energy-conversion strategies of a community and multiple environmental gradients (e.g., temperature). Moreover, we identified covariation in amino acid transport and cofactor synthesis, suggesting that limiting amounts of cofactor can (partially) explain increased import of amino acids in nutrientlimited conditions. environmental genomics ͉ network dynamics ͉ microbiology ͉ canonical correlation analysis

show abstract

“…Total of SNP from Pun1 gene found was 22 bp consisted of 10 bp in the exon-1 fragment, 6 bp in intron fragment, and 6 bp in the exon-2 fragment (Table 2). External factors such as light, water, soil, and pH could be the reason for nucleotide substitutions [13]. Protein structures analysis shows that the composition of secondary structure between this gene and references gene was slightly different (Table 3), likewise both protein structure (Fig.…”

Section: Resultsmentioning

confidence: 95%