Towards the biogeography of prokaryotic genes.
Ontology highlight
ABSTRACT:
SUBMITTER: Coelho LP
PROVIDER: S-EPMC7613196 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundPhotosynthetic light-harvesting proteins are the mechanism by which energy enters the marine ecosystem. The dominant prokaryotic photoautotrophs are the cyanobacterial genera Prochlorococcus and Synechococcus that are defined by two distinct light-harvesting systems, chlorophyll-bound protein complexes or phycobilin-bound protein complexes, respectively. Here, we use the Global Ocean Sampling (GOS) Project as a unique and powerful tool to analyze the environmental diversity of photosynthetic light-harvesting genes in relation to available metadata including geographical location and physical and chemical environmental parameters.MethodsAll light-harvesting gene fragments and their metadata were obtained from the GOS database, aligned using ClustalX and classified phylogenetically. Each sequence has a name indicative of its geographic location; subsequent biogeographical analysis was performed by correlating light-harvesting gene budgets for each GOS station with surface chlorophyll concentration.Conclusion/significanceUsing the GOS data, we have mapped the biogeography of light-harvesting genes in marine cyanobacteria on ocean-basin scales and show that an environmental gradient exists in which chlorophyll concentration is correlated to diversity of light-harvesting systems. Three functionally distinct types of light-harvesting genes are defined: (1) the phycobilisome (PBS) genes of Synechococcus; (2) the pcb genes of Prochlorococcus; and (3) the iron-stress-induced (isiA) genes present in some marine Synechococcus. At low chlorophyll concentrations, where nutrients are limited, the Pcb-type light-harvesting system shows greater genetic diversity; whereas at high chlorophyll concentrations, where nutrients are abundant, the PBS-type light-harvesting system shows higher genetic diversity. We interpret this as an environmental selection of specific photosynthetic strategy. Importantly, the unique light-harvesting system isiA is found in the iron-limited, high-nutrient low-chlorophyll region of the equatorial Pacific. This observation demonstrates the ecological importance of isiA genes in enabling marine Synechococcus to acclimate to iron limitation and suggests that the presence of this gene can be a natural biomarker for iron limitation in oceanic environments.
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Project description:BackgroundProtein-coding gene detection in prokaryotic genomes is considered a much simpler problem than in intron-containing eukaryotic genomes. However there have been reports that prokaryotic gene finder programs have problems with small genes (either over-predicting or under-predicting). Therefore the question arises as to whether current genome annotations have systematically missing, small genes.ResultsWe have developed a high-performance computing methodology to investigate this problem. In this methodology we compare all ORFs larger than or equal to 33 aa from all fully-sequenced prokaryotic replicons. Based on that comparison, and using conservative criteria requiring a minimum taxonomic diversity between conserved ORFs in different genomes, we have discovered 1,153 candidate genes that are missing from current genome annotations. These missing genes are similar only to each other and do not have any strong similarity to gene sequences in public databases, with the implication that these ORFs belong to missing gene families. We also uncovered 38,895 intergenic ORFs, readily identified as putative genes by similarity to currently annotated genes (we call these absent annotations). The vast majority of the missing genes found are small (less than 100 aa). A comparison of select examples with GeneMark, EasyGene and Glimmer predictions yields evidence that some of these genes are escaping detection by these programs.ConclusionsProkaryotic gene finders and prokaryotic genome annotations require improvement for accurate prediction of small genes. The number of missing gene families found is likely a lower bound on the actual number, due to the conservative criteria used to determine whether an ORF corresponds to a real gene.
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Project description:luxR genes encode transcriptional regulators that control acyl homoserine lactone-based quorum sensing (AHL QS) in Gram negative bacteria. On the bacterial chromosome, luxR genes are usually found next or near to a luxI gene encoding the AHL signal synthase. Recently, a number of luxR genes were described that have no luxI genes in their vicinity on the chromosome. These so-called solo luxR genes may either respond to internal AHL signals produced by a non-adjacent luxI in the chromosome, or can respond to exogenous signals. Here we present a survey of solo luxR genes found in complete and draft bacterial genomes in the NCBI databases using HMMs. We found that 2698 of the 3550 luxR genes found are solos, which is an unexpectedly high number even if some of the hits may be false positives. We also found that solo LuxR sequences form distinct clusters that are different from the clusters of LuxR sequences that are part of the known luxR-luxI topological arrangements. We also found a number of cases that we termed twin luxR topologies, in which two adjacent luxR genes were in tandem or divergent orientation. Many of the luxR solo clusters were devoid of the sequence motifs characteristic of AHL binding LuxR proteins so there is room to speculate that the solos may be involved in sensing hitherto unknown signals. It was noted that only some of the LuxR clades are rich in conserved cysteine residues. Molecular modeling suggests that some of the cysteines may be involved in disulfide formation, which makes us speculate that some LuxR proteins, including some of the solos may be involved in redox regulation.
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Project description:MotivationCorrelated events of gains and losses enable inference of co-evolution relations. The reconstruction of the co-evolutionary interactions network in prokaryotic species may elucidate functional associations among genes.ResultsWe developed a novel probabilistic methodology for the detection of co-evolutionary interactions between pairs of genes. Using this method we inferred the co-evolutionary network among 4593 Clusters of Orthologous Genes (COGs). The number of co-evolutionary interactions substantially differed among COGs. Over 40% were found to co-evolve with at least one partner. We partitioned the network of co-evolutionary relations into clusters and uncovered multiple modular assemblies of genes with clearly defined functions. Finally, we measured the extent to which co-evolutionary relations coincide with other cellular relations such as genomic proximity, gene fusion propensity, co-expression, protein-protein interactions and metabolic connections. Our results show that co-evolutionary relations only partially overlap with these other types of networks. Our results suggest that the inferred co-evolutionary network in prokaryotes is highly informative towards revealing functional relations among genes, often showing signals that cannot be extracted from other network types.Availability and implementationAvailable under GPL license as open source.Contacttalp@post.tau.ac.il.Supplementary informationSupplementary data are available at Bioinformatics online.
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