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Modular networks and cumulative impact of lateral transfer in prokaryote genome evolution.

ABSTRACT: Lateral gene transfer is an important mechanism of natural variation among prokaryotes, but the significance of its quantitative contribution to genome evolution is debated. Here, we report networks that capture both vertical and lateral components of evolutionary history among 539,723 genes distributed across 181 sequenced prokaryotic genomes. Partitioning of these networks by an eigenspectrum analysis identifies community structure in prokaryotic gene-sharing networks, the modules of which do not correspond to a strictly hierarchical prokaryotic classification. Our results indicate that, on average, at least 81 +/- 15% of the genes in each genome studied were involved in lateral gene transfer at some point in their history, even though they can be vertically inherited after acquisition, uncovering a substantial cumulative effect of lateral gene transfer on longer evolutionary time scales.

SUBMITTER: Dagan T

PROVIDER: S-EPMC2474566 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature

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Modular networks and cumulative impact of lateral transfer in prokaryote genome evolution.

Dagan Tal T Artzy-Randrup Yael Y Martin William W

Proceedings of the National Academy of Sciences of the United States of America 20080716 29

Lateral gene transfer is an important mechanism of natural variation among prokaryotes, but the significance of its quantitative contribution to genome evolution is debated. Here, we report networks that capture both vertical and lateral components of evolutionary history among 539,723 genes distributed across 181 sequenced prokaryotic genomes. Partitioning of these networks by an eigenspectrum analysis identifies community structure in prokaryotic gene-sharing networks, the modules of which do ...[more]

PMID: 18632554

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Project description:BackgroundLarge scale genome arrangement, such as whole gene insertion/deletion, plays an important role in bacterial genome evolution. Various methods have been employed to study the dynamic process of gene insertions and deletions, such as parsimony methods and maximum likelihood methods. Previous maximum likelihood studies have assumed that the rate of gene insertions/deletions is constant over different genes. This assumption is unrealistic. For instance, it has been shown that informational genes are less likely to be laterally transferred than non-informational genes. However, how much of the variation in gene transfer rates is due to the difference between informational genes and non-informational genes is unclear. In this study, a Gamma-distribution was incorporated in the likelihood estimation by considering rate variation for gene insertions/deletions between genes. This makes it possible to address whether a difference between informational genes and non-informational genes is the main contributor to rate variation of lateral gene transfers.ResultsThe results show that models incorporating rate variation fit the data better than do constant rate models in many phylogenetic groups. Even though informational genes are less likely to be laterally transferred than non-informational genes, the degree of rate variation for insertions/deletions did not change dramatically and remained high even when informational genes were excluded from the study. This suggests that the variation in rate of insertions/deletions is not due mainly to the simple difference between informational genes and non-informational genes. Among genes that are not classified as informational and among the informational genes themselves, there are still large differences in the rates that these genes are inserted and deleted.ConclusionWhile the difference in informational gene rates contributes to rate variation, it is only a small fraction of the variation present; instead, a substantial amount of rate variation for insertions/deletions remains among both informational genes and among non-informational genes.

Project description:Neurobiological systems rely on hierarchical and modular architectures to carry out intricate computations using minimal resources. A prerequisite for such systems to operate adequately is the capability to reliably and efficiently transfer information across multiple modules. Here, we study the features enabling a robust transfer of stimulus representations in modular networks of spiking neurons, tuned to operate in a balanced regime. To capitalize on the complex, transient dynamics that such networks exhibit during active processing, we apply reservoir computing principles and probe the systems' computational efficacy with specific tasks. Focusing on the comparison of random feed-forward connectivity and biologically inspired topographic maps, we find that, in a sequential set-up, structured projections between the modules are strictly necessary for information to propagate accurately to deeper modules. Such mappings not only improve computational performance and efficiency, they also reduce response variability, increase robustness against interference effects, and boost memory capacity. We further investigate how information from two separate input streams is integrated and demonstrate that it is more advantageous to perform non-linear computations on the input locally, within a given module, and subsequently transfer the result downstream, rather than transferring intermediate information and performing the computation downstream. Depending on how information is integrated early on in the system, the networks achieve similar task-performance using different strategies, indicating that the dimensionality of the neural responses does not necessarily correlate with nonlinear integration, as predicted by previous studies. These findings highlight a key role of topographic maps in supporting fast, robust, and accurate neural communication over longer distances. Given the prevalence of such structural feature, particularly in the sensory systems, elucidating their functional purpose remains an important challenge toward which this work provides relevant, new insights. At the same time, these results shed new light on important requirements for designing functional hierarchical spiking networks.

Dataset Information

Modular networks and cumulative impact of lateral transfer in prokaryote genome evolution.

Publications

Modular networks and cumulative impact of lateral transfer in prokaryote genome evolution.

Similar Datasets

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