Seed-based IntaRNA prediction combined with GFP-reporter system identifies mRNA targets of the small RNA Yfr1

Motivation: Prochlorococcus possesses the smallest genome of all sequenced photoautotrophs. Although the number of regulatory proteins in the genome is very small, the relative number of small regulatory RNAs is comparable with that of other bacteria. The compact genome size of Prochlorococcus offers an ideal system to search for targets of small RNAs (sRNAs) and to refine existing target prediction algorithms

sTarPicker: A Method for Efficient Prediction of Bacterial sRNA Targets Based on a Two-Step Model for Hybridization

Bacterial sRNAs are a class of small regulatory RNAs involved in regulation of expression of a variety of genes. Most sRNAs act in trans via base-pairing with target mRNAs, leading to repression or activation of translation or mRNA degradation. To date, more than 1,000 sRNAs have been identified. However, direct targets have been identified for only approximately 50 of these sRNAs. Computational predictions can provide candidates for target validation, thereby increasing the speed of sRNA target identification. Although several methods have been developed, target prediction for bacterial sRNAs remains challenging.Here, we propose a novel method for sRNA target prediction, termed sTarPicker, which was based on a two-step model for hybridization between an sRNA and an mRNA target. This method first selects stable duplexes after screening all possible duplexes between the sRNA and the potential mRNA target. Next, hybridization between the sRNA and the target is extended to span the entire binding site. Finally, quantitative predictions are produced with an ensemble classifier generated using machine-learning methods. In calculations to determine the hybridization energies of seed regions and binding regions, both thermodynamic stability and site accessibility of the sRNAs and targets were considered. Comparisons with the existing methods showed that sTarPicker performed best in both performance of target prediction and accuracy of the predicted binding sites.sTarPicker can predict bacterial sRNA targets with higher efficiency and determine the exact locations of the interactions with a higher accuracy than competing programs. sTarPicker is available at http://ccb.bmi.ac.cn/starpicker/

Transcriptome response of high- and low-light-adapted Prochlorococcus strains to changing iron availability

Prochlorococcus contributes significantly to ocean primary productivity. The link between primary productivity and iron in specific ocean regions is well established and iron-limitation of Prochlorococcus cell division rates in these regions has been demonstrated. However, the extent of ecotypic variation in iron metabolism among Prochlorococcus and the molecular basis for differences is not understood. Here, we examine the growth and transcriptional response of Prochlorococcus strains, MED4 and MIT9313, to changing iron concentrations. During steady-state, MIT9313 sustains growth at an order-of-magnitude lower iron concentration than MED4. To explore this difference, we measured the whole-genome transcriptional response of each strain to abrupt iron starvation and rescue. Only four of the 1159 orthologs of MED4 and MIT9313 were differentially-expressed in response to iron in both strains. However, in each strain, the expression of over a hundred additional genes changed, many of which are in labile genomic regions, suggesting a role for lateral gene transfer in establishing diversity of iron metabolism among Prochlorococcus. Furthermore, we found that MED4 lacks three genes near the iron-deficiency induced gene (idiA) that are present and induced by iron stress in MIT9313. These genes are interesting targets for studying the adaptation of natural Prochlorococcus assemblages to local iron conditions as they show more diversity than other genomic regions in environmental metagenomic databases.Gordon and Betty Moore FoundationNational Science Foundation (U.S.) (Biological Oceanography)United States. Office of Naval Research (ONR Young Investigator Award)National Science Foundation (U.S.) (Chemical Oceanography)National Science Foundation (U.S.) (Environmental Genomics grants

RNAs não codificadores regulatórios em bactérias do gênero Aeromonas

Orientadora : Profa. Dra. Maria Berenice R.SteffensCoorientador : Prof. Dr. Alexandre Rossi Paschoal, Profa. Dra. Cyntia Maria T. Fadel-PichethDissertação (mestrado) - Universidade Federal do Paraná, Setor de Educação Profissional e Tecnológica, Programa de Pós-Graduação em Bioinformática. Defesa: Curitiba,18/04/2017Inclui referências : f. 66-75Resumo: Bactérias do gênero Aeromonas são Gram-negativas com forma de bastonetes e que não formam esporos. São anaeróbias facultativas e quimiorganotróficas. Desde 1992 formam a nova família Aeromonadaceae, ordem Aeromonadales, classe Proteobacteria e subclasse gammaProteobacteria. São ubíquas de ambientes aquáticos e apresentam alto grau de patogenicidade, causando infecções oportunistas. Produzem fatores de virulência que constituem foco do interesse clínico. Em bactérias, os RNAs não codificantes com função regulatória (ncRNAs) podem modular respostas fisiológicas, principalmente através da interação ncRNAmRNA. A predição de ncRNAs em bactérias do gênero Aeromonas pode ampliar o conhecimento sobre a regulação dos mecanismos de patogenicidade. Neste trabalho utilizamos a abordagem bioinformática para obter sequências candidatas a ncRNAs e seus prováveis alvos nas espécies A. hydrophila, A. caviae, A. sobria, A. trota e A. veronii. Para isso foram utilizadas as ferramentas Infernal versão 1.1.1. (inference of RNA alignments) e TargetRNA2, respectivamente. Segundo o banco de famílias de RNA Rfam, dentre os 231 candidatos preditos, 50 deles foram classificados como ncRNAs com propriedades regulatória. Foram também encontrados 175 RNAs curtos (smallRNAs) e 6 riboswitches. Identificamos ncRNAs reguladores que atuam in cis e ncRNAs que atuam in trans, sendo estes últimos frequentemente dependentes da proteína Hfq, uma chaperona de RNA. Dentre os eventos regulados estão virulência, formação de biofilmes e sobrevivência celular. Em A. veronii bv sobria 312M, a expressão de alguns ncRNAs foi confirmada por sequenciamento de RNA (RNA-Seq) extraído de células cultivadas na presença ou ausência de desoxicolato de sódio, que mimetiza a exposição da bactéria a um sal biliar secundário. Foi verificado o aumento da expressão do RNA curto 6S (200%) e a diminuição da expressão dos ncRNAs CsrB1(81%), CsrB2 (56%) e PrrB_RsmZ (23%). O 6S RNA é comumente expresso na fase estacionária e regula a atividade da holoenzima RNA Polimerase. Os ncCsrB fazem parte do sistema regulatório CsrA/CsrB que tem um efeito regulador negativo sobre a síntese de glicogênio, gliconeogênese e catabolismo de glicogênio e um efeito regulador positivo na glicólise. E, aparentemente, o ncPrrB_RsmZ poderia formar um complexo regulatório ncPrrB_RsmZ/mRNAcsrA. Também foi investigada a existência de ilhas genômicas em A. veronii B565. Foram empregados as ferramentas GIPSy, ZIsland e IslandView. Destaca-se a presença do gene nudF, que é alvo do ncRNA CsrB, em uma das ilhas genômicas preditas pelo ZIsland. A proteína NudF está envolvida na regulação da síntese do glicogênio. Utilizando o IslandView foi possível visualizar os genes lysR, CheW, bvgS que também são alvos de ncRNAs. Finalmente, os resultados alcançados apontam uma nova linha de investigação para a compreensão do metabolismo e da patogenicidade de Aeromonas spp. Palavras-chave: Aeromonas, RNA não codificador, virulência, bactérias.Abstract: Bacteria of the genus Aeromonas are gram-negative rod-shaped and do not form spores. They are facultative anaerobes and chemororganotrophic. Since 1992 they form the new family Aeromonadaceae, order Aeromonadales, class Proteobacteria and subclass gama-Proteobacteria. They are ubiquitous in aquatic environments and present a high degree of pathogenicity, causing opportunistic infections. Produce virulence factors are the focus of clinical interest. In bacteria, non-coding RNAs with regulatory function (ncRNAs) may modulate physiological responses, primarily through the ncRNA-mRNA interaction. The prediction of ncRNAs in bacteria of the genus Aeromonas can increase the knowledge about the regulation of pathogenicity mechanisms. In this work we used the bioinformatic approach to obtain candidate sequences for ncRNAs and their probable targets in the species A. hydrophila, A. caviae, A. sobria, A. trota and A. veronii. For this, the Infernal 1.1.1 tools were used. (Inference of RNA alignments) and TargetRNA2, respectively. According to the RNA Rfam family bank, among the 231 candidates predicted, 50 of them were classified as ncRNAs with regulatory properties. There were also 175 small RNAs and 6 riboswitches. We identified regulatory ncRNAs that act in cis and ncRNAs that act in trans, the latter often being dependent on the Hfq protein, an RNA chaperone. Among the regulated events are virulence, formation of biofilms and cell survival. In A. veronii bv sobria 312M, the expression of some ncRNAs was confirmed by RNA sequencing (RNA seq) extracted from cells cultured in the presence or absence of sodium deoxycholate, which mimics the exposure of the bacterium to a secondary bile salt. Were observed increased expression of small 6S RNA (200%) and decreased expression of ncRNAs CsrB1 (81%), CsrB2 (56%) and PrrB_RsmZ (23%). 6S RNA is commonly expressed in the stationary phase and regulates the activity of the holoenzyme RNA polymerase. The ncCsrB make the regulatory system CsrA/CsrB that has a negative regulatory effect on glycogen synthesis, gluconeogenesis and glycogen catabolism and a positive regulatory effect on glycolysis. And, apparently, ncPrrB_RsmZ could form a regulatory complex ncPrrB_RsmZ/mRNAcsrA. We also investigated the existence of genomic islands in A. veronii B565. The GIPSy, ZIsland and IslandView tools were used. We highlight the presence of the nudF gene, which is the target of ncRNA CsrB, in one of the genomic islands predicted by ZIsland. The NudF protein is involved in the regulation of glycogen synthesis. Using IslandView it was possible to visualize the lysR, CheW, bvgS genes that are also targets of ncRNAs. Finally, the results show a new line of research to understand the metabolism and pathogenicity of Aeromonas spp. Key-words: Aeromonas, non-coding RNA, virulence, bacterium

Target regulation and prioritization by the small RNA SgrS in Escherichia coli

Regulation of gene expression by small non-coding RNAs is ubiquitous in all domains of life. In bacteria, small RNAs are known regulators of various stress responses. Diverse mechanisms employed by small RNAs demonstrate multi-faceted nature of gene regulation, tailored to respond to stress with optimal efficiency. Likewise, the Escherichia coli small RNA SgrS controls a response to metabolic stress that occurs upon cytoplasmic accumulation of glucose-phosphates due to mutations in glycolysis (e.g. in pgi) or when cells take up glucose-analogs α-methyl-D-glucoside (αMG) and 2-deoxyglucoside (2DG). SgrS base pairs with and represses translation of ptsG and manXYZ mRNAs, which encode sugar transporters, and activates translation of yigL mRNA, encoding a sugar phosphatase. In this study, transcriptomic analyses along with genetics and biochemistry defined four new direct targets of E. coli SgrS. These new target mRNAs, asd, adiY, folE and purR, encode transcription factors or enzymes of diverse metabolic pathways, including aspartate semialdehyde dehydrogenase, arginine decarboxylase gene activator, GTP cyclohydrolase I and a repressor of purine biosynthesis, respectively. SgrS represses translation of each of the four target mRNAs via distinct mechanisms. SgrS binding sites overlapping the Shine-Dalgarno sequences of adiY and folE mRNAs suggest that SgrS pairing with these targets directly occludes ribosome binding and prevents translation initiation. SgrS binding within the purR coding sequence recruits the RNA chaperone Hfq to directly repress purR translation. Two separate SgrS binding sites were found on asd mRNA, and both are required for full translational repression. Ectopic overexpression of asd, adiY and folE is specifically detrimental to cells experiencing glucose-phosphate stress, suggesting that SgrS-dependent repression of the metabolic functions encoded by these targets promotes recovery from glucose-phosphate stress. Further studies determined that SgrS regulates its targets with different efficiencies. We showed that SgrS establishes a hierarchy of targets by prioritizing regulation of targets in the following order: 1/2) ptsG and yigL 3) asd 4) manX, 5) purR. However, SgrS binding strength to the target mRNAs is not the sole determinant of regulatory efficiency or prioritization. Looking more carefully at what determines efficiency of SgrS regulation of asd mRNA, we discovered that SgrS binds cooperatively at the two stem structures within asd mRNA. SgrS binding at both sites is not only required for optimal repression of asd translation, but also changes its priority within the regulatory hierarchy. Besides SgrS regulatory mechanisms, this study provides additional insights into the nature of glucose-phosphate stress. Growth experiments in the minimal media demonstrate some differences in toxicity of αMG and 2DG. Importantly, the simultaneous presence of both glucose analogs results in a synthetic phenotype, highly indicative of αMG and 2DG affecting different pathways