Prediction of Sinorhizobium meliloti sRNA genes and experimental detection in strain 2011

Valverde C, Livny J, Schlüter J-P, Reinkensmeier J, Becker A, Parisi G. Prediction of Sinorhizobium meliloti sRNA genes and experimental detection in strain 2011. BMC Genomics. 2008;9(1): 416.Background: Small non-coding RNAs (sRNAs) have emerged as ubiquitous regulatory elements in bacteria and other life domains. However, few sRNAs have been identified outside several well-studied species of gamma-proteobacteria and thus relatively little is known about the role of RNA-mediated regulation in most other bacterial genera. Here we have conducted a computational prediction of putative sRNA genes in intergenic regions (IgRs) of the symbiotic alpha-proteobacterium S. meliloti 1021 and experimentally confirmed the expression of dozens of these candidate loci in the closely related strain S. meliloti 2011. Results: Our first sRNA candidate compilation was based mainly on the output of the sRNAPredictHT algorithm. A thorough manual sequence analysis of the curated list rendered an initial set of 18 IgRs of interest, from which 14 candidates were detected in strain 2011 by Northern blot and/or microarray analysis. Interestingly, the intracellular transcript levels varied in response to various stress conditions. We developed an alternative computational method to more sensitively predict sRNA-encoding genes and score these predicted genes based on several features to allow identification of the strongest candidates. With this novel strategy, we predicted 60 chromosomal independent transcriptional units that, according to our annotation, represent strong candidates for sRNA-encoding genes, including most of the sRNAs experimentally verified in this work and in two other contemporary studies. Additionally, we predicted numerous candidate sRNA genes encoded in megaplasmids pSymA and pSymB. A significant proportion of the chromosomal- and megaplasmid- borne putative sRNA genes were validated by microarray analysis in strain 2011. Conclusion: Our data extend the number of experimentally detected S. meliloti sRNAs and significantly expand the list of putative sRNA-encoding IgRs in this and closely related alpha-proteobacteria. In addition, we have developed a computational method that proved useful to predict sRNA-encoding genes in S. meliloti. We anticipate that this predictive approach can be flexibly implemented in many other bacterial species

A genome-wide survey of sRNAs in the symbiotic nitrogen-fixing alpha-proteobacterium Sinorhizobium meliloti

Schlüter J-P, Reinkensmeier J, Daschkey S, et al. A genome-wide survey of sRNAs in the symbiotic nitrogen-fixing alpha-proteobacterium Sinorhizobium meliloti. BMC Genomics. 2010;11(1): 245.BACKGROUND: Small untranslated RNAs (sRNAs) are widespread regulators of gene expression in bacteria. This study reports on a comprehensive screen for sRNAs in the symbiotic nitrogen-fixing alpha-proteobacterium Sinorhizobium meliloti applying deep sequencing of cDNAs and microarray hybridizations. RESULTS: A total of 1,125 sRNA candidates that were classified as trans-encoded sRNAs (173), cis-encoded antisense sRNAs (117), mRNA leader transcripts (379), and sense sRNAs overlapping coding regions (456) were identified in a size range of 50 to 348 nucleotides. Among these were transcripts corresponding to 82 previously reported sRNA candidates. Enrichment for RNAs with primary 5'-ends prior to sequencing of cDNAs suggested transcriptional start sites corresponding to 466 predicted sRNA regions. The consensus sigma70 promoter motif CTTGAC-N17-CTATAT was found upstream of 101 sRNA candidates. Expression patterns derived from microarray hybridizations provided further information on conditions of expression of a number of sRNA candidates. Furthermore, GenBank, EMBL, DDBJ, PDB, and Rfam databases were searched for homologs of the sRNA candidates identified in this study. Searching Rfam family models with over 1,000 sRNA candidates, re-discovered only those sequences from S. meliloti already known and stored in Rfam, whereas BLAST searches suggested a number of homologs in related alpha-proteobacteria. CONCLUSIONS: The screening data suggests that in S. meliloti about 3% of the genes encode trans-encoded sRNAs and about 2% antisense transcripts. Thus, this first comprehensive screen for sRNAs applying deep sequencing in an alpha-proteobacterium shows that sRNAs also occur in high number in this group of bacteria

Conservation and Occurrence of Trans-Encoded sRNAs in the Rhizobiales

Post-transcriptional regulation by trans-encoded sRNAs, for example via base-pairing with target mRNAs, is a common feature in bacteria and influences various cell processes, e.g., response to stress factors. Several studies based on computational and RNA-seq approaches identified approximately 180 trans-encoded sRNAs in Sinorhizobium meliloti. The initial point of this report is a set of 52 trans-encoded sRNAs derived from the former studies. Sequence homology combined with structural conservation analyses were applied to elucidate the occurrence and distribution of conserved trans-encoded sRNAs in the order of Rhizobiales. This approach resulted in 39 RNA family models (RFMs) which showed various taxonomic distribution patterns. Whereas the majority of RFMs was restricted to Sinorhizobium species or the Rhizobiaceae, members of a few RFMs were more widely distributed in the Rhizobiales. Access to this data is provided via the RhizoGATE portal [1,2]

Bioinformatics Methods for the Identification and Characterization of non­‐coding RNAs in Prokaryotes

Reinkensmeier J. Bioinformatics Methods for the Identification and Characterization of non­‐coding RNAs in Prokaryotes. Bielefeld; 2016.The advent of next-generation sequencing technologies and their adoption to RNA sequencing (RNA-seq) has revolutionized the field of transcriptomics. RNA-seq approaches revealed an unexpected complexity of prokaryotic transcriptomes and in particular led to the discovery of a wealth of previously unknown non-coding RNAs (ncRNAs) in diverse bacterial species. Primary applications of RNA-seq in the context of ncRNAs are a) discovery of novel and (re-)annotation of known ncRNAs, b) determination of transcription start sites (TSS), and c) identification of transcripts that are associated with RNA binding proteins, such as the RNA chaperone Hfq. Given the immense amounts of data obtained from RNA-seq approaches bioinformatics methods are crucial for their analysis and interpretation. Moreover, the constant change of design and scope as well as of protocols and applications of RNA-seq experiments requires the development and adaption of computational methods.The present thesis focuses on the development of biocomputational methods that enable the discovery and characterization of ncRNAs in prokaryotes. These methods include: First, methods for processing differential RNA sequencing data that aid in the reconstruction and classification of ncRNA transcripts. Second, methods that allow for the precise determination of transcription start sites in sequencing data obtained from RNA-seq approaches enriched with primary transcripts. Besides, methods were developed, which exploit TSS information for the prediction of promoter sequences. Third, methods for the identification of Hfq-bound transcripts in sequencing data generated by Hfq co-immunoprecipitation experiments. Fourth, methods for building RNA families. RNA-seq approaches predict hundreds of unannotated ncRNAs but do not provide much information about their biological function. Studying conservation patterns and phylogenetic distribution of ncRNAs by means of RNA family models aids in the functional characterization of ncRNAs. Building RNA family models is neither standardized nor automated. In this work a systematic construction strategy starting from single ncRNAs was designed and implemented for covariance models, the de facto standard for modeling structural RNA families. Fifth, an integrative model of the structurally varying "cuckoo" RNA family by means of thermodynamic matchers was devised and the model was used for systematic homology search in a wide spectrum of bacterial species.The computational methods that were developed as part of this thesis were applied in several studies on the transcriptome of the nitrogen fixing alphaproteobacterium *Sinorhizobium meliloti*. This resulted in the unprecedented characterization of the transcriptomic landscape of *S. meliloti* and provided deep insights into the presence and organization of ncRNAs

Thermodynamic matchers for the construction of the cuckoo RNA family

<div><p>RNA family models describe classes of functionally related, non-coding RNAs based on sequence and structure conservation. The most important method for modeling RNA families is the use of covariance models, which are stochastic models that serve in the discovery of yet unknown, homologous RNAs. However, the performance of covariance models in finding remote homologs is poor for RNA families with high sequence conservation, while for families with high structure but low sequence conservation, these models are difficult to built in the first place. A complementary approach to RNA family modeling involves the use of thermodynamic matchers. Thermodynamic matchers are RNA folding programs, based on the established thermodynamic model, but tailored to a specific structural motif. As thermodynamic matchers focus on structure and folding energy, they unfold their potential in discovering homologs, when high structure conservation is paired with low sequence conservation. In contrast to covariance models, construction of thermodynamic matchers does not require an input alignment, but requires human design decisions and experimentation, and hence, model construction is more laborious. Here we report a case study on an RNA family that was constructed by means of thermodynamic matchers. It starts from a set of known but structurally different members of the same RNA family. The consensus secondary structure of this family consists of 2 to 4 adjacent hairpins. Each hairpin loop carries the same motif, CCUCCUCCC, while the stems show high variability in their nucleotide content. The present study describes (1) a novel approach for the integration of the structurally varying family into a single RNA family model by means of the thermodynamic matcher methodology, and (2) provides the results of homology searches that were conducted with this model in a wide spectrum of bacterial species.</p></div

Conservation and Occurrence of Trans-Encoded sRNAs in the Rhizobiales

Reinkensmeier J, Schlüter J-P, Giegerich R, Becker A. Conservation and Occurrence of Trans-Encoded sRNAs in the Rhizobiales. GENES. 2011;2(4):925-956

Riboregulation in plant-associated alpha-proteobacteria

Becker A, Overloeper A, Schlueter J-P, et al. Riboregulation in plant-associated alpha-proteobacteria. RNA Biology. 2014;11(5):550-562.The symbiotic alpha-rhizobia Sinorhizobium meliloti, Bradyrhizobium japonicum, Rhizobium etli and the related plant pathogen Agrobacterium tumefaciens are important model organisms for studying plant-microbe interactions. These metabolically versatile soil bacteria are characterized by complex lifestyles and large genomes. Here we summarize the recent knowledge on their small non-coding RNAs (sRNAs) including conservation, function, and interaction of the sRNAs with the RNA chaperone Hfq. In each of these organisms, an inventory of hundreds of cis- and trans-encoded sRNAs with regulatory potential was uncovered by high-throughput approaches and used for the construction of 39 sRNA family models. Genome-wide analyses of hfq mutants and co-immunoprecipitation with tagged Hfq revealed a major impact of the RNA chaperone on the physiology of plant-associated alpha-proteobacteria including symbiosis and virulence. Highly conserved members of the SmelC411 family are the AbcR sRNAs, which predominantly regulate ABC transport systems. AbcR1 of A. tumefaciens controls the uptake of the plant-generated signaling molecule GABA and is a central regulator of nutrient uptake systems. It has similar functions in S. meliloti and the human pathogen Brucella abortus. As RNA degradation is an important process in RNA-based gene regulation, a short overview on ribonucleases in plant-associated alpha-proteobacteria concludes this review

Global mapping of transcription start sites and promoter motifs in the symbiotic a-proteobacterium Sinorhizobium meliloti 1021

Schlueter J-P, Reinkensmeier J, Barnett MJ, et al. Global mapping of transcription start sites and promoter motifs in the symbiotic a-proteobacterium Sinorhizobium meliloti 1021. Bmc Genomics. 2013;14(1): 156.Background: Sinorhizobium meliloti is a soil-dwelling a-proteobacterium that possesses a large, tripartite genome and engages in a nitrogen fixing symbiosis with its plant hosts. Although much is known about this important model organism, global characterization of genetic regulatory circuits has been hampered by a lack of information about transcription and promoters. Results: Using an RNAseq approach and RNA populations representing 16 different growth and stress conditions, we comprehensively mapped S. meliloti transcription start sites (TSS). Our work identified 17,001 TSS that we grouped into six categories based on the genomic context of their transcripts: mRNA (4,430 TSS assigned to 2,657 protein-coding genes), leaderless mRNAs (171), putative mRNAs (425), internal sense transcripts (7,650), antisense RNA (3,720), and trans-encoded sRNAs (605). We used this TSS information to identify transcription factor binding sites and putative promoter sequences recognized by seven of the 15 known S. meliloti sigma factors sigma(70), sigma(54), sigma(H1), sigma(H2), sigma(E1), sigma(E2), and sigma(E9)). Altogether, we predicted 2,770 new promoter sequences, including 1,302 located upstream of protein coding genes and 722 located upstream of antisense RNA or trans-encoded sRNA genes. To validate promoter predictions for targets of the general stress response s factor, RpoE2 (sigma(E2)), we identified rpoE2-dependent genes using microarrays and confirmed TSS for a subset of these by 5' RACE mapping. Conclusions: By identifying TSS and promoters on a global scale, our work provides a firm foundation for the continued study of S. meliloti gene expression with relation to gene organization, s factors and other transcription factors, and regulatory RNAs