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RNomics in Escherichia coli detects new sRNA species and indicates parallel transcriptional output in bacteria.

ABSTRACT: Recent bioinformatics-aided searches have identified many new small RNAs (sRNAs) in the intergenic regions of the bacterium Escherichia coli. Here, a shot-gun cloning approach (RNomics) was used to generate cDNA libraries of small sized RNAs. Besides many of the known sRNAs, we found new species that were not predicted previously. The present work brings the number of sRNAs in E.coli to 62. Experimental transcription start site mapping showed that some sRNAs were encoded from independent genes, while others were processed from mRNA leaders or trailers, indicative of a parallel transcriptional output generating sRNAs co-expressed with mRNAs. Two of these RNAs (SroA and SroG) consist of known (THI and RFN) riboswitch elements. We also show that two recently identified sRNAs (RyeB and SraC/RyeA) interact, resulting in RNase III-dependent cleavage. To the best of our knowledge, this represents the first case of two non-coding RNAs interacting by a putative antisense mechanism. In addition, intracellular metabolic stabilities of sRNAs were determined, including ones from previous screens. The wide range of half-lives (<2 to >32 min) indicates that sRNAs cannot generally be assumed to be metabolically stable. The experimental characterization of sRNAs analyzed here suggests that the definition of an sRNA is more complex than previously assumed.

SUBMITTER: Vogel J

PROVIDER: S-EPMC275561 | biostudies-literature | 2003 Nov

REPOSITORIES: biostudies-literature

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RNomics in Escherichia coli detects new sRNA species and indicates parallel transcriptional output in bacteria.

Vogel Jörg J Bartels Verena V Tang Thean Hock TH Churakov Gennady G Slagter-Jäger Jacoba G JG Hüttenhofer Alexander A Wagner E Gerhart H EG

Nucleic acids research 20031101 22

Recent bioinformatics-aided searches have identified many new small RNAs (sRNAs) in the intergenic regions of the bacterium Escherichia coli. Here, a shot-gun cloning approach (RNomics) was used to generate cDNA libraries of small sized RNAs. Besides many of the known sRNAs, we found new species that were not predicted previously. The present work brings the number of sRNAs in E.coli to 62. Experimental transcription start site mapping showed that some sRNAs were encoded from independent genes, ...[more]

PMID: 14602901

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Project description:BACKGROUND: sRNAs, which belong to the non-coding RNA family and range from approximately 50 to 400 nucleotides, serve various important gene regulatory roles. Most are believed to be trans-regulating and function by being complementary to their target mRNAs in order to inhibiting translation by ribosome occlusion. Despite this understanding of their functionality, the global properties associated with regulation by sRNAs are not yet understood. Here we use topological analysis of sRNA targets in terms of protein-protein interaction and transcription-regulatory networks in Escherichia coli to shed light on the global correlation between sRNA regulation and cellular control networks. RESULTS: The analysis of sRNA targets in terms of their networks showed that some specific network properties could be identified. In protein-protein interaction network, sRNA targets tend to occupy more central positions (higher closeness centrality, p-val = 0.022) and more cliquish (larger clustering coefficient, p-val = 0.037). The targets of the same sRNA tend to form a network module (shorter characteristic path length, p-val = 0.015; larger density, p-val = 0.019; higher in-degree ratio, p-val = 0.009). Using the transcription-regulatory network, sRNA targets tend to be under multiple regulation (higher indegree, p-val = 0.013) and the targets usually are important to the transfer of regulatory signals (higher betweenness, p-val = 0.012). As was found for the protein-protein interaction network, the targets that are regulated by the same sRNA also tend to be closely knit within the transcription-regulatory network (larger density, p-val = 0.036), and inward interactions between them are greater than the outward interactions (higher in-degree ratio, p-val = 0.023). However, after incorporating information on predicted sRNAs and down-stream targets, the results are not as clear-cut, but the overall network modularity is still evident. CONCLUSIONS: Our results indicate that sRNA targeting tends to show a clustering pattern that is similar to the human microRNA regulation associated with protein-protein interaction network that was observed in a previous study. Namely, the sRNA targets show close interaction and forms a closely knit network module for both the protein-protein interaction and the transcription-regulatory networks. Thus, targets of the same sRNA work in a concerted way toward a specific goal. In addition, in the transcription-regulatory network, sRNA targets act as "multiplexor", accepting regulatory control from multiple sources and acting accordingly. Our results indicate that sRNA targeting shows different properties when compared to the proteins that form cellular networks.

Project description:The causes and mechanisms of evolutionary diversification are central issues in biology. Geographic isolation is the traditional explanation for diversification, but recent theoretical and empirical studies have shown that frequency-dependent selection can drive diversification without isolation and that adaptive diversification occurring in sympatry may be an important source of biological diversity. However, there are no empirical examples in which sympatric lineage splits have been understood at the genetic level, and it is unknown how predictable this process is-that is, whether similar ecological settings lead to parallel evolutionary dynamics of diversification. We documented the genetic basis and the evolutionary dynamics of adaptive diversification in three replicate evolution experiments, in which competition for two carbon sources caused initially isogenic populations of the bacterium Escherichia coli to diversify into two coexisting ecotypes representing different physiological adaptations in the central carbohydrate metabolism. Whole-genome sequencing of clones of each ecotype from different populations revealed many parallel and some unique genetic changes underlying the derived phenotypes, including changes to the same genes and sometimes to the same nucleotide. Timelines of allele frequencies extracted from the frozen "fossil" record of the three evolving populations suggest parallel evolutionary dynamics driven at least in part by a co-evolutionary process in which mutations causing one type of physiology changed the ecological environment, allowing the invasion of mutations causing an alternate physiology. This process closely corresponds to the evolutionary dynamics seen in mathematical models of adaptive diversification due to frequency-dependent ecological interactions. The parallel genetic changes underlying similar phenotypes in independently evolved lineages provide empirical evidence of adaptive diversification as a predictable evolutionary process.

Dataset Information

RNomics in Escherichia coli detects new sRNA species and indicates parallel transcriptional output in bacteria.

Publications

RNomics in Escherichia coli detects new sRNA species and indicates parallel transcriptional output in bacteria.

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