Gene context conservation of a higher order than operons

Lathe, Warren C.; Snel, Berend; Bork, Peer

doi:10.1016/s0968-0004(00)01663-7

Cited by 191 publications

(185 citation statements)

References 22 publications

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“…That one could obtain all of the proteins involved in a biological process by an iterative search for conserved gene order has been suggested before (6,14,20). Actually, it is not so straightforward, as such an iterative search tends to connect ''everything with everything.''…”

Section: Discussionmentioning

confidence: 99%

“…In this network the nodes are orthologous groups of genes, and the edges are the genomic associations between these groups. It has been suggested before, that by such iterative approaches, one would be able to obtain all of the proteins involved in a biological process (6,14,20). All of the proteins from a pathway such as the purine biosynthesis could thus be extracted with only one potential ''false positive,'' a hypothetical protein (6).…”

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confidence: 99%

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The identification of functional modules from the genomic association of genes

Snel

Bork

Huynen

2002

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

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229

149

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By combining the pairwise interactions between proteins, as predicted by the conserved co-occurrence of their genes in operons, we obtain protein interaction networks. Here we study the properties of such networks to identify functional modules: sets of proteins that together are involved in a biological process. The complete network contains 3,033 orthologous groups of proteins in 38 genomes. It consists of one giant component, containing 1,611 orthologous groups, and of 516 small disjointed clusters that, on average, contain only 2.7 orthologous groups. These small clusters have a homogeneous functional composition and thus represent functional modules in themselves. Analysis of the giant component reveals that it is a scale-free, small-world network with a high degree of local clustering (C ‫؍‬ 0.6). It consists of locally highly connected subclusters that are connected to each other by linker proteins. The linker proteins tend to have multiple functions, or are involved in multiple processes and have an above average probability of being essential. By splitting up the giant component at these linker proteins, we identify 265 subclusters that tend to have a homogeneous functional composition. The rare functional inhomogeneities in our subclusters reflect the mixing of different types of (molecular) functions in a single cellular process, exemplified by subclusters containing both metabolic enzymes as well as the transcription factors that regulate them. Comparative genome analysis, thus, allows identification of a level of functional interaction between that of pairwise interactions, and of the complete genome.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

The identification of functional modules from the genomic association of genes

Snel

Bork

Huynen

2002

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

Self Cite

229

149

View full text Add to dashboard Cite

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“…Nevertheless, the evolutionary basis that leads to gene clustering is not totally understood. Phylogenetic distance is a good parameter to measure the evolutionary distance between organisms and a phylogenetic distance matrix can be constructed from a set of DNA or protein sequences of homologous genes from such organisms (Tamames et al, 1997;Lathe et al, 2000;Huynen and Bork, 1998;Aravind, 2000;Tamames, 2001;Rogozin et al, 2004;Dandekar et al, 1998). Here, a phylogenetic analysis of 129 bacterial species using their TS and DHFR sequences was presented.…”

Section: Resultsmentioning

confidence: 99%

“…In prokaryotes, conservation of gene order follows a common trend in all the species; gene order is generally well preserved at close phylogenetic distances (Tamames et al, 1997). However, it is even possible to observe a lack of gene order conservation as phylogenetic distance increases, mainly because of cluster of genes that remain well conserved during bacterial evolution (Lathe et al, 2000;Huynen and Bork, 1998). Information about co-localized prokaryotic genes can be used to derive functional inferences; for instance, if the function of one gene in a conserved gene cluster is known, the function of a neighboring gene can be inferred (Aravind, 2000).…”

Section: Introductionmentioning

confidence: 99%

Genomic Distance between Thymidylate Synthase and Dihydrofolate Reductase Genes Does Not Correlate With Phylogenetic Evolution in Bacteria

Moreau¹

2010

American Journal of Biochemistry and Biotechnology

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Problem statement: Dihydrofolate Reductase (DHFR) and Thymidylate Synthase (TS) exist as bifunctional enzymes coded into unique polypeptide chain in protozoans. Bifunctional DHFR-TS is associated with an increase in the enzymatic activity by channeling the substrate between the active sites. In some bacteria, DHFR and TS genes are neighbors in the genome, whereas in others, they are located millions of base pairs apart. Gene neighboring gained importance in evolution because it was found to promote the interaction between expressed proteins in gene clusters. Co-expression of neighboring genes might favor protein associations, increasing the enzymatic efficiency. The basis of genomic evolution that leads to gene ordering is not totally understood; however, one could suppose that increasing the efficiency of metabolic pathways could work as an evolutionary pressure to get genes together in the genome of an organism. Approach: In this study, phylogenetic analysis of DHFR and TS sequences and the genomic distance between these genes in bacteria were measured. Results: No significant correlation was found between genomic distances, in base pairs, of DHFR and TS genes and phylogenetic distance among the studied bacteria. Conclusion/Recommendations: This suggested that DHFR and TS enzymes clusters, even if they are coexpressed, might not exert a pivotal role in natural selection of bacteria.

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“…If regulons are not available, we simply set the corresponding weight factors to a constant that then will have no effect on the final prediction. We have found that one good way to get regulon information is through the prediction of über operons (46) or gene neighborhoods (47), which could be predicted through sequence-based methods. Basically, our framework is general enough to incorporate all such information as constraints of orthologous gene mapping if it is available.…”

Section: Prediction Of Operons: the Prerequisite For Application Of Pmentioning

confidence: 99%

Mapping of orthologous genes in the context of biological pathways: An application of integer programming

Mao¹,

Su²,

Olman³

et al. 2005

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

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Mapping biological pathways across microbial genomes is a highly important technique in functional studies of biological systems. Existing methods mainly rely on sequence-based orthologous gene mapping, which often leads to suboptimal mapping results because sequence-similarity information alone does not contain sufficient information for accurate identification of orthology relationship. Here we present an algorithm for pathway mapping across microbial genomes. The algorithm takes into account both sequence similarity and genomic structure information such as operons and regulons. One basic premise of our approach is that a microbial pathway could generally be decomposed into a few operons or regulons. We formulated the pathway-mapping problem to map genes across genomes to maximize their sequence similarity under the constraint that the mapped genes be grouped into a few operons, preferably coregulated in the target genome. We have developed an integer-programming algorithm for solving this constrained optimization problem and implemented the algorithm as a computer software program, P-MAP. We have tested P-MAP on a number of known homologous pathways. We conclude that using genomic structure information as constraints could greatly improve the pathway-mapping accuracy over methods that use sequence-similarity information alone.genomic structure ͉ operon ͉ regulon ͉ ortholog ͉ pathway mapping C omparative genome analysis represents a powerful technique for functional inference of genes. Its foundation is the ability to identify homologous (or more specifically, orthologous) genes. Here orthologous genes refer to isofunctional and heterospecic genes (1-3) for practical purposes. Several methods have been developed for mapping orthologous genes through sequence comparison. A popular approach is based on reciprocal BLAST searches, the so-called bidirectional best-hit (BDBH) approach (4). Based on this strategy, Wall et al. (5) recently designed a more sophisticated scheme for finding orthologous genes through BLAST searching followed by a more accurate sequence-alignment scheme (e.g., CLUSTALW and PAML). Koonin and co-workers (6) developed a popular method for orthologous gene identification based on the idea of clusters of orthologs groups (COG). Whereas all these approaches have provided useful practical tools for prediction of orthologous genes, their prediction accuracy has been less than optimal, as discussed below. One of the key issues with all these methods is their underlying assumption that sequence similarity alone contains sufficient information for prediction of orthologous gene relationship, which is probably far from being true.One important piece of information for orthologous gene identification across microbial genomes could come from the genomic structures such as operons (7) and regulons (8). Using such information has proven to be very helpful in orthology mapping in our previous studies (9, 10). Based on this observation, we have developed an algorithm, called P-MAP, for mapping orthologous...

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Gene context conservation of a higher order than operons

Cited by 191 publications

References 22 publications

The identification of functional modules from the genomic association of genes

The identification of functional modules from the genomic association of genes

Genomic Distance between Thymidylate Synthase and Dihydrofolate Reductase Genes Does Not Correlate With Phylogenetic Evolution in Bacteria

Mapping of orthologous genes in the context of biological pathways: An application of integer programming

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