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1.  Identification of novel growth phase- and media-dependent small non-coding RNAs in Streptococcus pyogenes M49 using intergenic tiling arrays 
BMC Genomics  2012;13:550.
Background
Small non-coding RNAs (sRNAs) have attracted attention as a new class of gene regulators in both eukaryotes and bacteria. Genome-wide screening methods have been successfully applied in Gram-negative bacteria to identify sRNA regulators. Many sRNAs are well characterized, including their target mRNAs and mode of action. In comparison, little is known about sRNAs in Gram-positive pathogens. In this study, we identified novel sRNAs in the exclusively human pathogen Streptococcus pyogenes M49 (Group A Streptococcus, GAS M49), employing a whole genome intergenic tiling array approach. GAS is an important pathogen that causes diseases ranging from mild superficial infections of the skin and mucous membranes of the naso-pharynx, to severe toxic and invasive diseases.
Results
We identified 55 putative sRNAs in GAS M49 that were expressed during growth. Of these, 42 were novel. Some of the newly-identified sRNAs belonged to one of the common non-coding RNA families described in the Rfam database. Comparison of the results of our screen with the outcome of two recently published bioinformatics tools showed a low level of overlap between putative sRNA genes. Previously, 40 potential sRNAs have been reported to be expressed in a GAS M1T1 serotype, as detected by a whole genome intergenic tiling array approach. Our screen detected 12 putative sRNA genes that were expressed in both strains. Twenty sRNA candidates appeared to be regulated in a medium-dependent fashion, while eight sRNA genes were regulated throughout growth in chemically defined medium. Expression of candidate genes was verified by reverse transcriptase-qPCR. For a subset of sRNAs, the transcriptional start was determined by 5′ rapid amplification of cDNA ends-PCR (RACE-PCR) analysis.
Conclusions
In accord with the results of previous studies, we found little overlap between different screening methods, which underlines the fact that a comprehensive analysis of sRNAs expressed by a given organism requires the complementary use of different methods and the investigation of several environmental conditions. Despite a high conservation of sRNA genes within streptococci, the expression of sRNAs appears to be strain specific.
doi:10.1186/1471-2164-13-550
PMCID: PMC3542284  PMID: 23062031
Streptococcus pyogenes; Small noncoding RNAs; Virulence; Transcriptional regulation; Pathogenesis
2.  sRNAMap: genomic maps for small non-coding RNAs, their regulators and their targets in microbial genomes 
Nucleic Acids Research  2008;37(Database issue):D150-D154.
Small non-coding RNAs (sRNAs) carry out a variety of biological functions and affect protein synthesis and protein activities in prokaryotes. Recently, numerous sRNAs and their targets were identified in Escherichia coli and in other bacteria. It is crucial to have a comprehensive resource concerning the annotation of small non-coding RNAs in microbial genomes. This work presents an integrated database, namely sRNAMap, to collect the sRNA genes, the transcriptional regulators of sRNAs and the sRNA target genes by integrating a variety of biological databases and by surveying literature. In this resource, we collected 397 sRNAs, 62 regulators/sRNAs and 60 sRNAs/targets in 70 microbial genomes. Additionally, more valuable information of the sRNAs, such as the secondary structure of sRNAs, the expressed conditions of sRNAs, the expression profiles of sRNAs, the transcriptional start sites of sRNAs and the cross-links to other biological databases, are provided for further investigation. Besides, various textual and graphical interfaces were designed and implemented to facilitate the data access in sRNAMap. sRNAMap is available at http://sRNAMap.mbc.nctu.edu.tw/.
doi:10.1093/nar/gkn852
PMCID: PMC2686527  PMID: 19015153
3.  sRNATarBase 3.0: an updated database for sRNA-target interactions in bacteria 
Nucleic Acids Research  2015;44(Database issue):D248-D253.
Bacterial sRNAs are a class of small regulatory RNAs of about 40–500 nt in length; they play multiple biological roles through binding to their target mRNAs or proteins. Therefore, elucidating sRNA targets is very important. However, only targets of a few sRNAs have been described. To facilitate sRNA functional studies such as developing sRNA target prediction models, we updated the sRNATarBase database, which was initially developed in 2010. The new version (recently moved to http://ccb1.bmi.ac.cn/srnatarbase/) contains 771 sRNA-target entries manually collected from 213 papers, and 23 290 and 11 750 predicted targets from sRNATarget and sTarPicker, respectively. Among the 771 entries, 475 and 17 were involved in validated sRNA–mRNA and sRNA–protein interactions, respectively, while 279 had no reported interactions. We also presented detailed information for 316 binding regions of sRNA-target mRNA interactions and related mutation experiments, as well as new features, including NCBI sequence viewer, sRNA regulatory network, target prediction-based GO and pathway annotations, and error report system. The new version provides a comprehensive annotation of validated sRNA-target interactions, and will be a useful resource for bacterial sRNA studies.
doi:10.1093/nar/gkv1127
PMCID: PMC4702819  PMID: 26503244
4.  5SRNAdb: an information resource for 5S ribosomal RNAs 
Nucleic Acids Research  2015;44(Database issue):D180-D183.
Ribosomal 5S RNA (5S rRNA) is the ubiquitous RNA component found in the large subunit of ribosomes in all known organisms. Due to its small size, abundance and evolutionary conservation 5S rRNA for many years now is used as a model molecule in studies on RNA structure, RNA–protein interactions and molecular phylogeny. 5SRNAdb (http://combio.pl/5srnadb/) is the first database that provides a high quality reference set of ribosomal 5S RNAs (5S rRNA) across three domains of life. Here, we give an overview of new developments in the database and associated web tools since 2002, including updates to database content, curation processes and user web interfaces.
doi:10.1093/nar/gkv1081
PMCID: PMC4702797  PMID: 26490961
5.  Determination of sRNA Expressions by RNA-seq in Yersinia pestis Grown In Vitro and during Infection 
PLoS ONE  2013;8(9):e74495.
Background
Small non-coding RNAs (sRNAs) facilitate host-microbe interactions. They have a central function in the post-transcriptional regulation during pathogenic lifestyles. Hfq, an RNA-binding protein that many sRNAs act in conjunction with, is required for Y. pestis pathogenesis. However, information on how Yersinia pestis modulates the expression of sRNAs during infection is largely unknown.
Methodology and Principal Findings
We used RNA-seq technology to identify the sRNA candidates expressed from Y. pestis grown in vitro and in the infected lungs of mice. A total of 104 sRNAs were found, including 26 previously annotated sRNAs, by searching against the Rfam database with 78 novel sRNA candidates. Approximately 89% (93/104) of these sRNAs from Y. pestis are shared with its ancestor Y. pseudotuberculosis. Ninety-seven percent of these sRNAs (101/104) are shared among more than 80 sequenced genomes of 135 Y. pestis strains. These 78 novel sRNAs include 62 intergenic and 16 antisense sRNAs. Fourteen sRNAs were selected for verification by independent Northern blot analysis. Results showed that nine selected sRNA transcripts were Hfq-dependent. Interestingly, three novel sRNAs were identified as new members of the transcription factor CRP regulon. Semi-quantitative analysis revealed that Y. pestis from the infected lungs induced the expressions of six sRNAs including RyhB1, RyhB2, CyaR/RyeE, 6S RNA, RybB and sR039 and repressed the expressions of four sRNAs, including CsrB, CsrC, 4.5S RNA and sR027.
Conclusions and Significance
This study is the first attempt to subject RNA from Y. pestis-infected samples to direct high-throughput sequencing. Many novel sRNAs were identified and the expression patterns of relevant sRNAs in Y. pestis during in vitro growth and in vivo infection were revealed. The annotated sRNAs accounted for the most abundant sRNAs either expressed in bacteria grown in vitro or differentially expressed in the infected lungs. These findings suggested these sRNAs may have important functions in Y. pestis physiology or pathogenesis.
doi:10.1371/journal.pone.0074495
PMCID: PMC3770706  PMID: 24040259
6.  Comparative genome‐wide analysis of small RNAs of major Gram‐positive pathogens: from identification to application 
Microbial biotechnology  2010;3(6):658-676.
Summary
In the recent years, the number of drug‐ and multi‐drug‐resistant microbial strains has increased rapidly. Therefore, the need to identify innovative approaches for development of novel anti‐infectives and new therapeutic targets is of high priority in global health care. The detection of small RNAs (sRNAs) in bacteria has attracted considerable attention as an emerging class of new gene expression regulators. Several experimental technologies to predict sRNA have been established for the Gram‐negative model organism Escherichia coli. In many respects, sRNA screens in this model system have set a blueprint for the global and functional identification of sRNAs for Gram‐positive microbes, but the functional role of sRNAs in colonization and pathogenicity for Listeria monocytogenes, Staphylococcus aureus, Streptococcus pyogenes, Enterococcus faecalis and Clostridium difficile is almost completely unknown. Here, we report the current knowledge about the sRNAs of these socioeconomically relevant Gram‐positive pathogens, overview the state‐of‐the‐art high‐throughput sRNA screening methods and summarize bioinformatics approaches for genome‐wide sRNA identification and target prediction. Finally, we discuss the use of modified peptide nucleic acids (PNAs) as a novel tool to inactivate potential sRNA and their applications in rapid and specific detection of pathogenic bacteria.
doi:10.1111/j.1751-7915.2010.00171.x
PMCID: PMC3815340  PMID: 21255362
7.  BSRD: a repository for bacterial small regulatory RNA 
Nucleic Acids Research  2012;41(Database issue):D233-D238.
In bacteria, small regulatory non-coding RNAs (sRNAs) are the most abundant class of post-transcriptional regulators. They are involved in diverse processes including quorum sensing, stress response, virulence and carbon metabolism. Recent developments in high-throughput techniques, such as genomic tiling arrays and RNA-Seq, have allowed efficient detection and characterization of bacterial sRNAs. However, a comprehensive repository to host sRNAs and their annotations is not available. Existing databases suffer from a limited number of bacterial species or sRNAs included. In addition, these databases do not have tools to integrate or analyse high-throughput sequencing data. Here, we have developed BSRD (http://kwanlab.bio.cuhk.edu.hk/BSRD), a comprehensive bacterial sRNAs database, as a repository for published bacterial sRNA sequences with annotations and expression profiles. BSRD contains over nine times more experimentally validated sRNAs than any other available databases. BSRD also provides combinatorial regulatory networks of transcription factors and sRNAs with their common targets. We have built and implemented in BSRD a novel RNA-Seq analysis platform, sRNADeep, to characterize sRNAs in large-scale transcriptome sequencing projects. We will update BSRD regularly.
doi:10.1093/nar/gks1264
PMCID: PMC3531160  PMID: 23203879
8.  A genome-wide survey of sRNAs in the symbiotic nitrogen-fixing alpha-proteobacterium Sinorhizobium meliloti 
BMC Genomics  2010;11:245.
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 σ70 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.
doi:10.1186/1471-2164-11-245
PMCID: PMC2873474  PMID: 20398411
9.  sRNAscanner: A Computational Tool for Intergenic Small RNA Detection in Bacterial Genomes 
PLoS ONE  2010;5(8):e11970.
Background
Bacterial non-coding small RNAs (sRNAs) have attracted considerable attention due to their ubiquitous nature and contribution to numerous cellular processes including survival, adaptation and pathogenesis. Existing computational approaches for identifying bacterial sRNAs demonstrate varying levels of success and there remains considerable room for improvement.
Methodology/Principal Findings
Here we have proposed a transcriptional signal-based computational method to identify intergenic sRNA transcriptional units (TUs) in completely sequenced bacterial genomes. Our sRNAscanner tool uses position weight matrices derived from experimentally defined E. coli K-12 MG1655 sRNA promoter and rho-independent terminator signals to identify intergenic sRNA TUs through sliding window based genome scans. Analysis of genomes representative of twelve species suggested that sRNAscanner demonstrated equivalent sensitivity to sRNAPredict2, the best performing bioinformatics tool available presently. However, each algorithm yielded substantial numbers of known and uncharacterized hits that were unique to one or the other tool only. sRNAscanner identified 118 novel putative intergenic sRNA genes in Salmonella enterica Typhimurium LT2, none of which were flagged by sRNAPredict2. Candidate sRNA locations were compared with available deep sequencing libraries derived from Hfq-co-immunoprecipitated RNA purified from a second Typhimurium strain (Sittka et al. (2008) PLoS Genetics 4: e1000163). Sixteen potential novel sRNAs computationally predicted and detected in deep sequencing libraries were selected for experimental validation by Northern analysis using total RNA isolated from bacteria grown under eleven different growth conditions. RNA bands of expected sizes were detected in Northern blots for six of the examined candidates. Furthermore, the 5′-ends of these six Northern-supported sRNA candidates were successfully mapped using 5′-RACE analysis.
Conclusions/Significance
We have developed, computationally examined and experimentally validated the sRNAscanner algorithm. Data derived from this study has successfully identified six novel S. Typhimurium sRNA genes. In addition, the computational specificity analysis we have undertaken suggests that ∼40% of sRNAscanner hits with high cumulative sum of scores represent genuine, undiscovered sRNA genes. Collectively, these data strongly support the utility of sRNAscanner and offer a glimpse of its potential to reveal large numbers of sRNA genes that have to date defied identification. sRNAscanner is available from: http://bicmku.in:8081/sRNAscanner or http://cluster.physics.iisc.ernet.in/sRNAscanner/.
doi:10.1371/journal.pone.0011970
PMCID: PMC2916834  PMID: 20700540
10.  Discovery and profiling of small RNAs responsive to stress conditions in the plant pathogen Pectobacterium atrosepticum 
BMC Genomics  2016;17:47.
Background
Small RNAs (sRNAs) have emerged as important regulatory molecules and have been studied in several bacteria. However, to date, there have been no whole-transcriptome studies on sRNAs in any of the Soft Rot Enterobacteriaceae (SRE) group of pathogens. Although the main ecological niches for these pathogens are plants, a significant part of their life cycle is undertaken outside their host within adverse soil environment. However, the mechanisms of SRE adaptation to this harsh nutrient-deficient environment are poorly understood.
Results
In the study reported herein, by using strand-specific RNA-seq analysis and in silico sRNA predictions, we describe the sRNA pool of Pectobacterium atrosepticum and reveal numerous sRNA candidates, including those that are induced during starvation-activated stress responses. Consequently, strand-specific RNA-seq enabled detection of 137 sRNAs and sRNA candidates under starvation conditions; 25 of these sRNAs were predicted for this bacterium in silico. Functional annotations were computationally assigned to 68 sRNAs. The expression of sRNAs in P. atrosepticum was compared under growth-promoting and starvation conditions: 68 sRNAs were differentially expressed with 47 sRNAs up-regulated under nutrient-deficient conditions. Conservation analysis using BLAST showed that most of the identified sRNAs are conserved within the SRE. Subsequently, we identified 9 novel sRNAs within the P. atrosepticum genome.
Conclusions
Since many of the identified sRNAs are starvation-induced, the results of our study suggests that sRNAs play key roles in bacterial adaptive response. Finally, this work provides a basis for future experimental characterization and validation of sRNAs in plant pathogens.
Electronic supplementary material
The online version of this article (doi:10.1186/s12864-016-2376-0) contains supplementary material, which is available to authorized users.
doi:10.1186/s12864-016-2376-0
PMCID: PMC4710047  PMID: 26753530
Small RNAs; Strand-specific RNA-seq; Pectobacterium atrosepticum; in silico prediction; Transcriptome; Riboswitches; 5′ UTR; 3′ UTR
11.  The Impact of 18 Ancestral and Horizontally-Acquired Regulatory Proteins upon the Transcriptome and sRNA Landscape of Salmonella enterica serovar Typhimurium 
PLoS Genetics  2016;12(8):e1006258.
We know a great deal about the genes used by the model pathogen Salmonella enterica serovar Typhimurium to cause disease, but less about global gene regulation. New tools for studying transcripts at the single nucleotide level now offer an unparalleled opportunity to understand the bacterial transcriptome, and expression of the small RNAs (sRNA) and coding genes responsible for the establishment of infection. Here, we define the transcriptomes of 18 mutants lacking virulence-related global regulatory systems that modulate the expression of the SPI1 and SPI2 Type 3 secretion systems of S. Typhimurium strain 4/74. Using infection-relevant growth conditions, we identified a total of 1257 coding genes that are controlled by one or more regulatory system, including a sub-class of genes that reflect a new level of cross-talk between SPI1 and SPI2. We directly compared the roles played by the major transcriptional regulators in the expression of sRNAs, and discovered that the RpoS (σ38) sigma factor modulates the expression of 23% of sRNAs, many more than other regulatory systems. The impact of the RNA chaperone Hfq upon the steady state levels of 280 sRNA transcripts is described, and we found 13 sRNAs that are co-regulated with SPI1 and SPI2 virulence genes. We report the first example of an sRNA, STnc1480, that is subject to silencing by H-NS and subsequent counter-silencing by PhoP and SlyA. The data for these 18 regulatory systems is now available to the bacterial research community in a user-friendly online resource, SalComRegulon.
Author Summary
The transcriptional networks and the functions of small regulatory RNAs of Salmonella enterica serovar Typhimurium are being studied intensively. S. Typhimurium is becoming the ideal model pathogen for linking transcriptional and post-transcriptional gene regulation to bacterial virulence. Here, we systematically defined the regulatory factors responsible for controlling the expression of S. Typhimurium coding genes and sRNAs under infection-relevant growth conditions. As well as confirming published regulatory inputs for Salmonella pathogenicity islands, such as the positive role played by Fur in the expression of SPI1, we report, for the first time, the global impact of the FliZ, HilE and PhoB/R transcription factors and identify 124 sRNAs that belong to virulence-associated regulons. We found a subset of genes of known and unknown function that are regulated by both HilD and SsrB, highlighting the cross-talk mechanisms that control Salmonella virulence. An integrative analysis of the regulatory datasets revealed 5 coding genes of unknown function that may play novel roles in virulence. We hope that the SalComRegulon resource will be a dynamic database that will be constantly updated to inspire new hypothesis-driven experimentation, and will contribute to the construction of a comprehensive transcriptional network for S. Typhimurium.
doi:10.1371/journal.pgen.1006258
PMCID: PMC5001712  PMID: 27564394
12.  Genetic basis of sRNA quantitative variation analyzed using an experimental population derived from an elite rice hybrid 
eLife  null;4:e03913.
We performed a genetic analysis of sRNA abundance in flag leaf from an immortalized F2 (IMF2) population in rice. We identified 53,613,739 unique sRNAs and 165,797 sRNA expression traits (s-traits). A total of 66,649 s-traits mapped 40,049 local-sQTLs and 30,809 distant-sQTLs. By defining 80,362 sRNA clusters, 22,263 sRNA cluster QTLs (scQTLs) were recovered for 20,249 of all the 50,139 sRNA cluster expression traits (sc-traits). The expression levels for most of s-traits from the same genes or the same sRNA clusters were slightly positively correlated. While genetic co-regulation between sRNAs from the same mother genes and between sRNAs and their mother genes was observed for a portion of the sRNAs, most of the sRNAs and their mother genes showed little co-regulation. Some sRNA biogenesis genes were located in distant-sQTL hotspots and showed correspondence with specific length classes of sRNAs suggesting their important roles in the regulation and biogenesis of the sRNAs.
DOI: http://dx.doi.org/10.7554/eLife.03913.001
eLife digest
Genes within the DNA of a plant or animal contain instructions to make molecules called RNAs. Some RNA molecules can be decoded to make proteins, whereas others have different roles. A single gene often contains the instructions to make both protein-coding RNAs and non-coding RNAs.
Molecules called small RNAs (or sRNAs) do not code for proteins. Instead, sRNAs can control protein-coding RNA molecules or chemically alter the DNA itself; this allows them to perform many different roles in living organisms. In plants, for example, these molecules affect how the plant grows, the shapes and structures it forms, and how likely it is to survive challenges such as drought and diseases. Often different plants of the same species have different amounts of sRNAs, but the reasons for this remain unclear.
Now, Wang, Yao et al. have made use of a technique called ‘expression quantitative locus’ analysis to look at how sRNAs in rice plants are controlled by additional information encoded within DNA. The analysis identified over 53 million sRNA molecules from a population of rice plants. Many of these sRNAs varied in their abundance between different plants within the population. Wang, Yao et al. also found many thousands of individual instructions within the DNA of the rice that can either increase or reduce the abundance of their associated sRNA.
Some of the abundant sRNAs were influenced by instructions within their own genes; some were influenced by instructions from other genes; and some were influenced by both. Wang, Yao et al. also found that the control of protein-coding RNAs was not necessarily related to the control of sRNAs encoded by the same gene. Further work is now needed to identify which specific DNA sequences regulate the abundance of sRNA molecules in plants and other organisms.
DOI: http://dx.doi.org/10.7554/eLife.03913.002
doi:10.7554/eLife.03913
PMCID: PMC4415135  PMID: 25821986
rice; genetics; sRNA; other
13.  fRNAdb: a platform for mining/annotating functional RNA candidates from non-coding RNA sequences 
Nucleic Acids Research  2006;35(Database issue):D145-D148.
There are abundance of transcripts that code for no particular protein and that remain functionally uncharacterized. Some of these transcripts may have novel functions while others might be junk transcripts. Unfortunately, the experimental validation of such transcripts to find functional non-coding RNA candidates is very costly. Therefore, our primary interest is to computationally mine candidate functional transcripts from a pool of uncharacterized transcripts. We introduce fRNAdb: a novel database service that hosts a large collection of non-coding transcripts including annotated/non-annotated sequences from the H-inv database, NONCODE and RNAdb. A set of computational analyses have been performed on the included sequences. These analyses include RNA secondary structure motif discovery, EST support evaluation, cis-regulatory element search, protein homology search, etc. fRNAdb provides an efficient interface to help users filter out particular transcripts under their own criteria to sort out functional RNA candidates. fRNAdb is available at
doi:10.1093/nar/gkl837
PMCID: PMC1669753  PMID: 17099231
14.  Bacterial small RNAs in the Genus Rickettsia 
BMC Genomics  2015;16:1075.
Background
Rickettsia species are obligate intracellular Gram-negative pathogenic bacteria and the etiologic agents of diseases such as Rocky Mountain spotted fever (RMSF), Mediterranean spotted fever, epidemic typhus, and murine typhus. Genome sequencing revealed that R. prowazekii has ~25 % non-coding DNA, the majority of which is thought to be either “junk DNA” or pseudogenes resulting from genomic reduction. These characteristics also define other Rickettsia genomes. Bacterial small RNAs, whose biogenesis is predominantly attributed to either the intergenic regions (trans-acting) or to the antisense strand of an open reading frame (cis-acting), are now appreciated to be among the most important post-transcriptional regulators of bacterial virulence and growth. We hypothesize that intergenic regions in rickettsial species encode for small, non-coding RNAs (sRNAs) involved in the regulation of its transcriptome, leading to altered virulence and adaptation depending on the host niche.
Results
We employed a combination of bioinformatics and in vitro approaches to explore the presence of sRNAs in a number of species within Genus Rickettsia. Using the sRNA Identification Protocol using High-throughput Technology (SIPHT) web interface, we predicted over 1,700 small RNAs present in the intergenic regions of 16 different strains representing 13 rickettsial species. We further characterized novel sRNAs from typhus (R. prowazekii and R. typhi) and spotted fever (R. rickettsii and R. conorii) groups for their promoters and Rho-independent terminators using Bacterial Promoter Prediction Program (BPROM) and TransTermHP prediction algorithms, respectively. Strong σ70 promoters were predicted upstream of all novel small RNAs, indicating the potential for transcriptional activity. Next, we infected human microvascular endothelial cells (HMECs) with R. prowazekii for 3 h and 24 h and performed Next Generation Sequencing to experimentally validate the expression of 26 sRNA candidates predicted in R. prowazekii. Reverse transcriptase PCR was also used to further verify the expression of six putative novel sRNA candidates in R. prowazekii.
Conclusions
Our results yield clear evidence for the expression of novel R. prowazekii sRNA candidates during infection of HMECs. This is the first description of novel small RNAs for a highly pathogenic species of Rickettsia, which should lead to new insights into rickettsial virulence and adaptation mechanisms.
Electronic supplementary material
The online version of this article (doi:10.1186/s12864-015-2293-7) contains supplementary material, which is available to authorized users.
doi:10.1186/s12864-015-2293-7
PMCID: PMC4683814  PMID: 26679185
Endothelial cells; Rickettsia; Small RNAs; Spotted Fever; Typhus; Deep-Sequencing; Bioinformatics; SIPHT/sRNAPredict3
15.  Genome-wide identification of Hfq-regulated small RNAs in the fire blight pathogen Erwinia amylovora discovered small RNAs with virulence regulatory function 
BMC Genomics  2014;15(1):414.
Background
Erwinia amylovora is a phytopathogenic bacterium and causal agent of fire blight disease in apples and pears. Although many virulence factors have been characterized, the coordination of expression of these virulence factors in E. amylovora is still not clear. Regulatory small RNAs (sRNAs) are important post-transcriptional regulatory components in bacteria. A large number of sRNAs require the RNA chaperone Hfq for both stability and functional activation. In E. amylovora, Hfq was identified as a major regulator of virulence and various virulence traits. However, information is still lacking about Hfq-dependent sRNAs on a genome scale, including the virulence regulatory functions of these sRNAs in E. amylovora.
Results
Using both an RNA-seq analysis and a Rho-independent terminator search, 40 candidate Hfq-dependent sRNAs were identified in E. amylovora. The expression and sizes of 12 sRNAs and the sequence boundaries of seven sRNAs were confirmed by Northern blot and 5’ RACE assay respectively. Sequence conservation analysis identified sRNAs conserved only in the Erwinia genus as well as E. amylovora species-specific sRNAs. In addition, a dynamic re-patterning of expression of Hfq-dependent sRNAs was observed at 6 and 12 hours after induction in Hrp-inducing minimal medium. Furthermore, sRNAs that control virulence traits were characterized, among which ArcZ positively controls the type III secretion system (T3SS), amylovoran exopolysaccahride production, biofilm formation, and motility, and negatively modulates attachment while RmaA (Hrs6) and OmrAB both negatively regulate amylovoran production and positively regulate motility.
Conclusions
This study has significantly enhanced our understanding of the Hfq-dependent sRNAs in E. amylovora at the genome level. The identification of multiple virulence-regulating sRNAs also suggests that post-transcriptional regulation by sRNAs may play a role in the deployment of virulence factors needed during varying stages of pathogenesis during host invasion by E. amylovora.
Electronic supplementary material
The online version of this article (doi: 10.1186/1471-2164-15-414) contains supplementary material, which is available to authorized users.
doi:10.1186/1471-2164-15-414
PMCID: PMC4070566  PMID: 24885615
RNA-seq; sRNA; Type III secretion system; Biofilm; Amylovoran; Motility; ArcZ
16.  Genome-wide analyses of small non-coding RNAs in streptococci 
Frontiers in Genetics  2015;6:189.
Streptococci represent a diverse group of Gram-positive bacteria, which colonize a wide range of hosts among animals and humans. Streptococcal species occur as commensal as well as pathogenic organisms. Many of the pathogenic species can cause severe, invasive infections in their hosts leading to a high morbidity and mortality. The consequence is a tremendous suffering on the part of men and livestock besides the significant financial burden in the agricultural and healthcare sectors. An environmentally stimulated and tightly controlled expression of virulence factor genes is of fundamental importance for streptococcal pathogenicity. Bacterial small non-coding RNAs (sRNAs) modulate the expression of genes involved in stress response, sugar metabolism, surface composition, and other properties that are related to bacterial virulence. Even though the regulatory character is shared by this class of RNAs, variation on the molecular level results in a high diversity of functional mechanisms. The knowledge about the role of sRNAs in streptococci is still limited, but in recent years, genome-wide screens for sRNAs have been conducted in an increasing number of species. Bioinformatics prediction approaches have been employed as well as expression analyses by classical array techniques or next generation sequencing. This review will give an overview of whole genome screens for sRNAs in streptococci with a focus on describing the different methods and comparing their outcome considering sRNA conservation among species, functional similarities, and relevance for streptococcal infection.
doi:10.3389/fgene.2015.00189
PMCID: PMC4438229  PMID: 26042151
sRNA; Streptococcus; array; RNAseq; NGS; virulence; gene regulation; transcriptome
17.  Small RNAs in the Genus Clostridium 
mBio  2011;2(1):e00340-10.
The genus Clostridium includes major human pathogens and species important to cellulose degradation, the carbon cycle, and biotechnology. Small RNAs (sRNAs) are emerging as crucial regulatory molecules in all organisms, but they have not been investigated in clostridia. Research on sRNAs in clostridia is hindered by the absence of a systematic method to identify sRNA candidates, thus delegating clostridial sRNA research to a hit-and-miss process. Thus, we wanted to develop a method to identify potential sRNAs in the Clostridium genus to open up the field of sRNA research in clostridia. Using comparative genomics analyses combined with predictions of rho-independent terminators and promoters, we predicted sRNAs in 21 clostridial genomes: Clostridium acetobutylicum, C. beijerinckii, C. botulinum (eight strains), C. cellulolyticum, C. difficile, C. kluyveri (two strains), C. novyi, C. perfringens (three strains), C. phytofermentans, C. tetani, and C. thermocellum. Although more than one-third of predicted sRNAs have Shine-Dalgarno (SD) sequences, only one-sixth have a start codon downstream of SD sequences; thus, most of the predicted sRNAs are noncoding RNAs. Quantitative reverse transcription-PCR (Q-RT-PCR) and Northern analysis were employed to test the presence of a randomly chosen set of sRNAs in C. acetobutylicum and several C. botulinum strains, leading to the confirmation of a large fraction of the tested sRNAs. We identified a conserved, novel sRNA which, together with the downstream gene coding for an ATP-binding cassette (ABC) transporter gene, responds to the antibiotic clindamycin. The number of predicted sRNAs correlated with the physiological function of the species (high for pathogens, low for cellulolytic, and intermediate for solventogenic), but not with 16S rRNA-based phylogeny.
IMPORTANCE
Clostridia include major human pathogens and species important to human physiology, cellulose degradation, the carbon cycle, and biotechnology. Small RNAs (sRNAs) are increasingly recognized as crucial regulatory molecules in all organisms, but they remain virtually unexplored in clostridia. We provide the first comprehensive list of computationally identified and experimentally verified small RNAs in the genus Clostridium aiming to accelerate interest in and studies of small RNA molecules in a very important genus. The higher number of sRNAs found in clostridial pathogens suggests a good correlation between the physiological function or niche of the species and the number of predicted and conserved sRNAs. Our list of predicted sRNAs displays a strong enrichment of sRNAs upstream or downstream of ATP-binding cassette (ABC) transporter genes. This, combined with the identification of a conserved sRNA apparently involved in clindamycin resistance, provides a new perspective for future studies of possible regulation of antibiotic resistance genes by sRNAs in bacteria.
doi:10.1128/mBio.00340-10
PMCID: PMC3025663  PMID: 21264064
18.  Small RNA sX13: A Multifaceted Regulator of Virulence in the Plant Pathogen Xanthomonas 
PLoS Pathogens  2013;9(9):e1003626.
Small noncoding RNAs (sRNAs) are ubiquitous posttranscriptional regulators of gene expression. Using the model plant-pathogenic bacterium Xanthomonas campestris pv. vesicatoria (Xcv), we investigated the highly expressed and conserved sRNA sX13 in detail. Deletion of sX13 impinged on Xcv virulence and the expression of genes encoding components and substrates of the Hrp type III secretion (T3S) system. qRT-PCR analyses revealed that sX13 promotes mRNA accumulation of HrpX, a key regulator of the T3S system, whereas the mRNA level of the master regulator HrpG was unaffected. Complementation studies suggest that sX13 acts upstream of HrpG. Microarray analyses identified 63 sX13-regulated genes, which are involved in signal transduction, motility, transcriptional and posttranscriptional regulation and virulence. Structure analyses of in vitro transcribed sX13 revealed a structure with three stable stems and three apical C-rich loops. A computational search for putative regulatory motifs revealed that sX13-repressed mRNAs predominantly harbor G-rich motifs in proximity of translation start sites. Mutation of sX13 loops differentially affected Xcv virulence and the mRNA abundance of putative targets. Using a GFP-based reporter system, we demonstrated that sX13-mediated repression of protein synthesis requires both the C-rich motifs in sX13 and G-rich motifs in potential target mRNAs. Although the RNA-binding protein Hfq was dispensable for sX13 activity, the hfq mRNA and Hfq::GFP abundance were negatively regulated by sX13. In addition, we found that G-rich motifs in sX13-repressed mRNAs can serve as translational enhancers and are located at the ribosome-binding site in 5% of all protein-coding Xcv genes. Our study revealed that sX13 represents a novel class of virulence regulators and provides insights into sRNA-mediated modulation of adaptive processes in the plant pathogen Xanthomonas.
Author Summary
Since the discovery of the first regulatory RNA in 1981, hundreds of small RNAs (sRNAs) have been identified in bacteria. Although sRNA-mediated control of virulence was demonstrated for numerous animal- and human-pathogenic bacteria, sRNAs and their functions in plant-pathogenic bacteria have been enigmatic. We discovered that the sRNA sX13 is a novel virulence regulator of Xanthomonas campestris pv. vesicatoria (Xcv), which causes bacterial spot disease on pepper and tomato. sX13 contributes to the Xcv-plant interaction by promoting the synthesis of an essential pathogenicity factor of Xcv, i. e., the type III secretion system. Thus, in addition to transcriptional regulation, sRNA-mediated posttranscriptional regulation contributes to virulence of plant-pathogenic xanthomonads. To repress target mRNAs carrying G-rich motifs, sX13 employs C-rich loops. Hence, sX13 exhibits striking structural similarity to sRNAs in distantly related human pathogens, e. g., Staphylococcus aureus and Helicobacter pylori, suggesting that structure-driven target regulation via C-rich motifs represents a conserved feature of sRNA-mediated posttranscriptional regulation. Furthermore, sX13 is the first sRNA shown to control the mRNA level of hfq, which encodes a conserved RNA-binding protein required for sRNA activity and virulence in many enteric bacteria.
doi:10.1371/journal.ppat.1003626
PMCID: PMC3771888  PMID: 24068933
19.  Quantification of the gene silencing performances of rationally-designed synthetic small RNAs 
Systems and Synthetic Biology  2015;9(3):107-123.
Small RNAs (sRNAs) are genetic tools for the efficient and specific tuning of target genes expression in bacteria. Inspired by naturally occurring sRNAs, recent works proposed the use of artificial sRNAs in synthetic biology for predictable repression of the desired genes. Their potential was demonstrated in several application fields, such as metabolic engineering and bacterial physiology studies. Guidelines for the rational design of novel sRNAs have been recently proposed. According to these guidelines, in this work synthetic sRNAs were designed, constructed and quantitatively characterized in Escherichia coli. An sRNA targeting the reporter gene RFP was tested by measuring the specific gene silencing when RFP was expressed at different transcription levels, under the control of different promoters, in different strains, and in single-gene or operon architecture. The sRNA level was tuned by using plasmids maintained at different copy numbers. Results demonstrated that RFP silencing worked as expected in an sRNA and mRNA expression-dependent fashion. A mathematical model was used to support sRNA characterization and to estimate an efficiency-related parameter that can be used to compare the performance of the designed sRNA. Gene silencing was also successful when RFP was placed in a two-gene synthetic operon, while the non-target gene (GFP) in the operon was not considerably affected. Finally, silencing was evaluated for another designed sRNA targeting the endogenous lactate dehydrogenase gene. The quantitative study performed in this work elucidated interesting performance-related and context-dependent features of synthetic sRNAs that will strongly support predictable gene silencing in disparate basic or applied research studies.
Electronic supplementary material
The online version of this article (doi:10.1007/s11693-015-9177-7) contains supplementary material, which is available to authorized users.
doi:10.1007/s11693-015-9177-7
PMCID: PMC4531877  PMID: 26279705
Small RNA; Synthetic biology; Quantitative characterization; Mathematical modelling; Operon; Lactate dehydrogenase
20.  Identification of 17 Pseudomonas aeruginosa sRNAs and prediction of sRNA-encoding genes in 10 diverse pathogens using the bioinformatic tool sRNAPredict2 
Nucleic Acids Research  2006;34(12):3484-3493.
sRNAs are small, non-coding RNA species that control numerous cellular processes. Although it iswidely accepted that sRNAs are encoded by most if not all bacteria, genome-wide annotations for sRNA-encoding genes have been conducted in only a few of the nearly 300 bacterial species sequenced to date. To facilitate the efficient annotation of bacterial genomes for sRNA-encoding genes, we developed a program, sRNAPredict2, that identifies putative sRNAs by searching for co-localization of genetic features commonly associated with sRNA-encoding genes. Using sRNAPredict2, we conducted genome-wide annotations for putative sRNA-encoding genes in the intergenic regions of 11 diverse pathogens. In total, 2759 previously unannotated candidate sRNA loci were predicted. There was considerable range in the number of sRNAs predicted in the different pathogens analyzed, raising the possibility that there are species-specific differences in the reliance on sRNA-mediated regulation. Of 34 previously unannotated sRNAs predicted in the opportunistic pathogen Pseudomonas aeruginosa, 31 were experimentally tested and 17 were found to encode sRNA transcripts. Our findings suggest that numerous genes have been missed in the current annotations of bacterial genomes and that, by using improved bioinformatic approaches and tools, much remains to be discovered in ‘intergenic’ sequences.
doi:10.1093/nar/gkl453
PMCID: PMC1524904  PMID: 16870723
21.  Identification of four novel small non-coding RNAs from Xanthomonas campestris pathovar campestris 
BMC Genomics  2010;11:316.
Background
In bacteria, small non-coding RNAs (sRNAs) have been recognized as important regulators of various cellular processes. Approximately 200 bacterial sRNAs in total have been reported. However, very few sRNAs have been identified from phytopathogenic bacteria.
Results
Xanthomons campestris pathovar campestris (Xcc) is the causal agent of black rot disease of cruciferous crops. In this study, a cDNA library was constructed from the low-molecular weight RNA isolated from the Xcc strain 8004 grown to exponential phase in the minimal medium XVM2. Seven sRNA candidates were obtained by sequencing screen of 2,500 clones from the library and four of them were confirmed to be sRNAs by Northern hybridization, which were named sRNA-Xcc1, sRNA-Xcc2, sRNA-Xcc3, and sRNA-Xcc4. The transcription start and stop sites of these sRNAs were further determined. BLAST analysis revealed that the four sRNAs are novel. Bioinformatics prediction showed that a large number of genes with various known or unknown functions in Xcc 8004 are potential targets of sRNA-Xcc1, sRNA-Xcc3 and sRNA-Xcc4. In contrast, only a few genes were predicted to be potential targets of sRNA-Xcc2.
Conclusion
We have identified four novel sRNAs from Xcc by a large-scale screen. Bioinformatics analysis suggests that they may perform various functions. This work provides the first step toward understanding the role of sRNAs in the molecular mechanisms of Xanthomonas campestris pathogenesis.
doi:10.1186/1471-2164-11-316
PMCID: PMC2996969  PMID: 20482898
22.  High-Throughput, Kingdom-Wide Prediction and Annotation of Bacterial Non-Coding RNAs 
PLoS ONE  2008;3(9):e3197.
Background
Diverse bacterial genomes encode numerous small non-coding RNAs (sRNAs) that regulate myriad biological processes. While bioinformatic algorithms have proven effective in identifying sRNA-encoding loci, the lack of tools and infrastructure with which to execute these computationally demanding algorithms has limited their utilization. Genome-wide predictions of sRNA-encoding genes have been conducted in less than 3% of all sequenced bacterial strains, leading to critical gaps in current annotations. The relative paucity of genome-wide sRNA prediction represents a critical gap in current annotations of bacterial genomes and has limited examination of larger issues in sRNA biology, such as sRNA evolution.
Methodology/Principal Findings
We have developed and deployed SIPHT, a high throughput computational tool that utilizes workflow management and distributed computing to effectively conduct kingdom-wide predictions and annotations of intergenic sRNA-encoding genes. Candidate sRNA-encoding loci are identified based on the presence of putative Rho-independent terminators downstream of conserved intergenic sequences, and each locus is annotated for several features, including conservation in other species, association with one of several transcription factor binding sites and homology to any of over 300 previously identified sRNAs and cis-regulatory RNA elements. Using SIPHT, we conducted searches for putative sRNA-encoding genes in all 932 bacterial replicons in the NCBI database. These searches yielded nearly 60% of previously confirmed sRNAs, hundreds of previously annotated cis-encoded regulatory RNA elements such as riboswitches, and over 45,000 novel candidate intergenic loci.
Conclusions/Significance
Candidate loci were identified across all branches of the bacterial evolutionary tree, suggesting a central and ubiquitous role for RNA-mediated regulation among bacterial species. Annotation of candidate loci by SIPHT provides clues into the potential biological function of thousands of previously confirmed and candidate regulatory RNAs and affords new insights into the evolution of bacterial riboregulation.
doi:10.1371/journal.pone.0003197
PMCID: PMC2527527  PMID: 18787707
23.  An in silico model for identification of small RNAs in whole bacterial genomes: characterization of antisense RNAs in pathogenic Escherichia coli and Streptococcus agalactiae strains 
Nucleic Acids Research  2011;40(7):2846-2861.
Characterization of small non-coding ribonucleic acids (sRNA) among the large volume of data generated by high-throughput RNA-seq or tiling microarray analyses remains a challenge. Thus, there is still a need for accurate in silico prediction methods to identify sRNAs within a given bacterial species. After years of effort, dedicated software were developed based on comparative genomic analyses or mathematical/statistical models. Although these genomic analyses enabled sRNAs in intergenic regions to be efficiently identified, they all failed to predict antisense sRNA genes (asRNA), i.e. RNA genes located on the DNA strand complementary to that which encodes the protein. The statistical models enabled any genomic region to be analyzed theorically but not efficiently. We present a new model for in silico identification of sRNA and asRNA candidates within an entire bacterial genome. This model was successfully used to analyze the Gram-negative Escherichia coli and Gram-positive Streptococcus agalactiae. In both bacteria, numerous asRNAs are transcribed from the complementary strand of genes located in pathogenicity islands, strongly suggesting that these asRNAs are regulators of the virulence expression. In particular, we characterized an asRNA that acted as an enhancer-like regulator of the type 1 fimbriae production involved in the virulence of extra-intestinal pathogenic E. coli.
doi:10.1093/nar/gkr1141
PMCID: PMC3326304  PMID: 22139924
24.  sRNAPredict: an integrative computational approach to identify sRNAs in bacterial genomes 
Nucleic Acids Research  2005;33(13):4096-4105.
Small non-coding bacterial RNAs (sRNAs) play important regulatory roles in a variety of cellular processes. Nearly all known sRNAs have been identified in Escherichia coli and most of these are not conserved in the majority of other bacterial species. Many of the E.coli sRNAs were initially predicted through bioinformatic approaches based on their common features, namely that they are encoded between annotated open reading frames and are flanked by predictable transcription signals. Because promoter consensus sequences are undetermined for most species, the successful use of bioinformatics to identify sRNAs in bacteria other than E.coli has been limited. We have created a program, sRNAPredict, which uses coordinate-based algorithms to integrate the respective positions of individual predictive features of sRNAs and rapidly identify putative intergenic sRNAs. Relying only on sequence conservation and predicted Rho-independent terminators, sRNAPredict was used to search for sRNAs in Vibrio cholerae. This search identified 9 of the 10 known or putative V.cholerae sRNAs and 32 candidates for novel sRNAs. Small transcripts for 6 out of 9 candidate sRNAs were observed by Northern analysis. Our findings suggest that sRNAPredict can be used to efficiently identify novel sRNAs even in bacteria for which promoter consensus sequences are not available.
doi:10.1093/nar/gki715
PMCID: PMC1180744  PMID: 16049021
25.  The Functional RNA Database 3.0: databases to support mining and annotation of functional RNAs 
Nucleic Acids Research  2008;37(Database issue):D89-D92.
We developed a pair of databases that support two important tasks: annotation of anonymous RNA transcripts and discovery of novel non-coding RNAs. The database combo is called the Functional RNA Database and consists of two databases: a rewrite of the original version of the Functional RNA Database (fRNAdb) and the latest version of the UCSC GenomeBrowser for Functional RNA. The former is a sequence database equipped with a powerful search function and hosts a large collection of known/predicted non-coding RNA sequences acquired from existing databases as well as novel/predicted sequences reported by researchers of the Functional RNA Project. The latter is a UCSC Genome Browser mirror with large additional custom tracks specifically associated with non-coding elements. It also includes several functional enhancements such as a presentation of a common secondary structure prediction at any given genomic window ⩽500 bp. Our GenomeBrowser supports user authentication and user-specific tracks. The current version of the fRNAdb is a complete rewrite of the former version, hosting a larger number of sequences and with a much friendlier interface. The current version of UCSC GenomeBrowser for Functional RNA features a larger number of tracks and richer features than the former version. The databases are available at http://www.ncrna.org/.
doi:10.1093/nar/gkn805
PMCID: PMC2686472  PMID: 18948287

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