Nucleic acid detection with CRISPR-Cas13a/C2c2

Rapid, inexpensive, and sensitive nucleic acid detection may aid point-of-care pathogen detection, genotyping, and disease monitoring. The RNA-guided, RNA-targeting clustered regularly interspaced short palindromic repeats (CRISPR) effector Cas13a (previously known as C2c2) exhibits a "collateral effect" of promiscuous ribonuclease activity upon target recognition. We combine the collateral effect of Cas13a with isothermal amplification to establish a CRISPR-based diagnostic (CRISPR-Dx), providing rapid DNA or RNA detection with attomolar sensitivity and single-base mismatch specificity. We use this Cas13a-based molecular detection platform, termed Specific High-Sensitivity Enzymatic Reporter UnLOCKing (SHERLOCK), to detect specific strains of Zika and Dengue virus, distinguish pathogenic bacteria, genotype human DNA, and identify mutations in cell-free tumor DNA. Furthermore, SHERLOCK reaction reagents can be lyophilized for cold-chain independence and long-term storage and be readily reconstituted on paper for field applications.

Potential role of intratumor bacteria in mediating tumor resistance to the chemotherapeutic drug gemcitabine

Growing evidence suggests that microbes can influence the efficacy of cancer therapies. By studying colon cancer models, we found that bacteria can metabolize the chemotherapeutic drug gemcitabine (2',2'-difluorodeoxycytidine) into its inactive form, 2',2'-difluorodeoxyuridine. Metabolism was dependent on the expression of a long isoform of the bacterial enzyme cytidine deaminase (CDDL), seen primarily in Gammaproteobacteria. In a colon cancer mouse model, gemcitabine resistance was induced by intratumor Gammaproteobacteria, dependent on bacterial CDDL expression, and abrogated by cotreatment with the antibiotic ciprofloxacin. Gemcitabine is commonly used to treat pancreatic ductal adenocarcinoma (PDAC), and we hypothesized that intratumor bacteria might contribute to drug resistance of these tumors. Consistent with this possibility, we found that of the 113 human PDACs that were tested, 86 (76%) were positive for bacteria, mainly Gammaproteobacteria.

Simultaneous generation of many RNA-seq libraries in a single reaction

Although RNA-seq is a powerful tool, the considerable time and cost associated with library construction has limited its utilization for various applications. RNAtag-Seq, an approach to generate multiple RNA-seq libraries in a single reaction, lowers time and cost per sample, and it produces data on prokaryotic and eukaryotic samples that are comparable to those generated by traditional strand-specific RNA-seq approaches.

Distribution of centromere-like parS sites in bacteria: insights from comparative genomics

Partitioning of low-copy-number plasmids to daughter cells often depends on ParA and ParB proteins acting on centromere-like parS sites. Similar chromosome-encoded par loci likely also contribute to chromosome segregation. Here, we used bioinformatic approaches to search for chromosomal parS sites in 400 prokaryotic genomes. Although the consensus sequence matrix used to search for parS sites was derived from two gram-positive species, putative parS sites were identified on the chromosomes of 69% of strains from all branches of bacteria. Strains that were not found to contain parS sites clustered among relatively few branches of the prokaryotic evolutionary tree. In the vast majority of cases, parS sites were identified in origin-proximal regions of chromosomes. The widespread conservation of parS sites across diverse bacteria suggests that par loci evolved very early in the evolution of bacterial chromosomes and that the absence of parS, parA, and/or parB in certain strains likely reflects the loss of one of more of these loci much later in evolution. Moreover, the highly conserved origin-proximal position of parS suggests par loci are primarily devoted to regulating processes that involve the origin region of bacterial chromosomes. In species containing multiple chromosomes, the parS sites found on secondary chromosomes diverge significantly from those found on their primary chromosomes, suggesting that chromosome segregation of multipartite genomes requires distinct replicon-specific par loci. Furthermore, parS sites on secondary chromosomes are not well conserved among different species, suggesting that the evolutionary histories of secondary chromosomes are more diverse than those of primary chromosomes.

How deep is deep enough for RNA-Seq profiling of bacterial transcriptomes?

Background

High-throughput sequencing of cDNA libraries (RNA-Seq) has proven to be a highly effective approach for studying bacterial transcriptomes. A central challenge in designing RNA-Seq-based experiments is estimating a priori the number of reads per sample needed to detect and quantify thousands of individual transcripts with a large dynamic range of abundance.

Results

We have conducted a systematic examination of how changes in the number of RNA-Seq reads per sample influences both profiling of a single bacterial transcriptome and the comparison of gene expression among samples. Our findings suggest that the number of reads typically produced in a single lane of the Illumina HiSeq sequencer far exceeds the number needed to saturate the annotated transcriptomes of diverse bacteria growing in monoculture. Moreover, as sequencing depth increases, so too does the detection of cDNAs that likely correspond to spurious transcripts or genomic DNA contamination. Finally, even when dozens of barcoded individual cDNA libraries are sequenced in a single lane, the vast majority of transcripts in each sample can be detected and numerous genes differentially expressed between samples can be identified.

Conclusions

Our analysis provides a guide for the many researchers seeking to determine the appropriate sequencing depth for RNA-Seq-based studies of diverse bacterial species.

RNA-Seq-based monitoring of infection-linked changes in Vibrio cholerae gene expression

Pathogens adapt to the host environment by altering their patterns of gene expression. Microarray-based and genetic techniques used to characterize bacterial gene expression during infection are limited in their ability to comprehensively and simultaneously monitor genome-wide transcription. We used massively parallel cDNA sequencing (RNA-seq) techniques to quantitatively catalog the transcriptome of the cholera pathogen, Vibrio cholerae, derived from two animal models of infection. Transcripts elevated in infected rabbits and mice relative to laboratory media derive from the major known V. cholerae virulence factors and also from genes and small RNAs not previously linked to virulence. The RNA-seq data was coupled with metabolite analysis of cecal fluid from infected rabbits to yield insights into the host environment encountered by the pathogen and the mechanisms controlling pathogen gene expression. RNA-seq-based transcriptome analysis of pathogens during infection produces a robust, sensitive, and accessible data set for evaluation of regulatory responses driving pathogenesis.

Bacteriophage control of Shiga toxin 1 production and release by Escherichia coli

The stx genes of many Shiga toxin-encoding Escherichia coli (STEC) strains are encoded by prophages of the lambda bacteriophage family. In the genome of the Stx1-encoding phage H-19B, the stx(1)AB genes are found approximately 1 kb downstream of the late phage promoter, p(R)', but are known to be regulated by the associated iron-regulated promoter, p(Stx1). Growth of H-19B lysogens in low iron concentrations or in conditions that induce the prophage results in increased Stx1 production. Although the mechanism by which low iron concentration induces Stx1 production is well understood, the mechanisms by which phage induction enhances toxin production have not been extensively characterized. The studies reported here identify the factors that contribute to Stx1 production after induction of the H-19B prophage. We found that replication of the phage genome, with the associated increase in stx(1)AB copy number, is the most quantitatively important mechanism by which H-19B induction increases Stx1 production. Three promoters are shown to be involved in stx(1)AB transcription after phage induction, the iron-regulated p(Stx1) and the phage-regulated p(R) and p(R)' promoters, the relative importance of which varies with environmental conditions. Late phage transcription initiating at the p(R)' promoter, contrary to previous findings in the related Stx2-encoding phage phi 361, was found to be unnecessary for high-level Stx1 production after phage induction. Finally, we present evidence that phage-mediated lysis regulates the quantity of Stx1 produced by determining the duration of Stx1 accumulation and provides a mechanism for Stx1 release. By amplifying stx(1)AB copy number, regulating stx(1)AB transcription and allowing for Stx1 release, the biology of the Stx-encoding phages contributes greatly to the production of Stx, the principal virulence factor of STEC.

Efficient and robust RNA-seq process for cultured bacteria and complex community transcriptomes

We have developed a process for transcriptome analysis of bacterial communities that accommodates both intact and fragmented starting RNA and combines efficient rRNA removal with strand-specific RNA-seq. We applied this approach to an RNA mixture derived from three diverse cultured bacterial species and to RNA isolated from clinical stool samples. The resulting expression profiles were highly reproducible, enriched up to 40-fold for non-rRNA transcripts, and correlated well with profiles representing undepleted total RNA.

High-throughput, kingdom-wide prediction and annotation of bacterial non-coding RNAs

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.

Experimental discovery of sRNAs in Vibrio cholerae by direct cloning, 5S/tRNA depletion and parallel sequencing

Direct cloning and parallel sequencing, an extremely powerful method for microRNA (miRNA) discovery, has not yet been applied to bacterial transcriptomes. Here we present sRNA-Seq, an unbiased method that allows for interrogation of the entire small, non-coding RNA (sRNA) repertoire in any prokaryotic or eukaryotic organism. This method includes a novel treatment that depletes total RNA fractions of highly abundant tRNAs and small subunit rRNA, thereby enriching the starting pool for sRNA transcripts with novel functionality. As a proof-of-principle, we applied sRNA-Seq to the human pathogen Vibrio cholerae. Our results provide information, at unprecedented depth, on the complexity of the sRNA component of a bacterial transcriptome. From 407 039 sequence reads, all 20 known V. cholerae sRNAs, 500 new, putative intergenic sRNAs and 127 putative antisense sRNAs were identified in a limited number of growth conditions examined. In addition, characterization of a subset of the newly identified transcripts led to the identification of a novel sRNA regulator of carbon metabolism. Collectively, these results strongly suggest that the number of sRNAs in bacteria has been greatly underestimated and that future efforts to analyze bacterial transcriptomes will benefit from direct cloning and parallel sequencing experiments aided by 5S/tRNA depletion.

Identification of 17 Pseudomonas aeruginosa sRNAs and prediction of sRNA-encoding genes in 10 diverse pathogens using the bioinformatic tool sRNAPredict2

sRNAs are small, non-coding RNA species that control numerous cellular processes. Although it is widely 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.

TargetRNA2: identifying targets of small regulatory RNAs in bacteria

Many small, noncoding RNAs (sRNAs) in bacteria act as posttranscriptional regulators of messenger RNAs. TargetRNA2 is a web server that identifies mRNA targets of sRNA regulatory action in bacteria. As input, TargetRNA2 takes the sequence of an sRNA and the name of a sequenced bacterial replicon. When searching for targets of RNA regulation, TargetRNA2 uses a variety of features, including conservation of the sRNA in other bacteria, the secondary structure of the sRNA, the secondary structure of each candidate mRNA target and the hybridization energy between the sRNA and each candidate mRNA target. TargetRNA2 outputs a ranked list of likely regulatory targets for the input sRNA. When evaluated on a comprehensive set of sRNA-target interactions, TargetRNA2 was found to be both accurate and efficient in identifying targets of sRNA regulatory action. Furthermore, TargetRNA2 has the ability to integrate RNA-seq data, if available. If an sRNA is differentially expressed in two or more RNA-seq experiments, TargetRNA2 considers co-differential gene expression when searching for regulatory targets, significantly improving the accuracy of target identifications. The TargetRNA2 web server is freely available for use at http://cs.wellesley.edu/∼btjaden/TargetRNA2.

Identification of small RNAs in diverse bacterial species

Small, non-coding bacterial RNAs (sRNAs) have been shown to regulate a plethora of biological processes. Up until recently, most sRNAs had been identified and characterized in E. coli. However, in the past few years, dozens of sRNAs have been discovered in a wide variety of bacterial species. Whereas numerous sRNAs have been isolated or detected through experimental approaches, most have been identified in predictive bioinformatic searches. Recently developed computational tools have greatly facilitated the efficient prediction of sRNAs in diverse species. Although the number of known sRNAs has dramatically increased in recent years, many challenges in the identification and characterization of sRNAs lie ahead.

Characterizing spontaneous induction of Stx encoding phages using a selectable reporter system

Shiga toxin (Stx) genes in Stx producing Escherichia coli (STEC) are encoded in prophages of the lambda family, such as H-19B. The subpopulation of STEC lysogens with induced prophages has been postulated to contribute significantly to Stx production and release. To study induced STEC, we developed a selectable in vivo expression technology, SIVET, a reporter system adapted from the RIVET system. The SIVET lysogen has a defective H-19B prophage encoding the TnpR resolvase gene downstream of the phage PR promoter and a cat gene with an inserted tet gene flanked by targets for the TnpR resolvase. Expression of resolvase results in excision of tet, restoring a functional cat gene; induced lysogens survive and are chloramphenicol resistant. Using SIVET we show that: (i) approximately 0.005% of the H-19B lysogens are spontaneously induced per generation during growth in LB. (ii) Variations in cellular physiology (e.g. RecA protein) rather than in levels of expressed repressor explain why members of a lysogen population are spontaneously induced. (iii) A greater fraction of lysogens with stx encoding prophages are induced compared to lysogens with non-Stx encoding prophages, suggesting increased sensitivity to inducing signal(s) has been selected in Stx encoding prophages. (iv) Only a small fraction of the lysogens in a culture spontaneously induce and when the lysogen carries two lambdoid prophages with different repressor/operators, 933W and H-19B, usually both prophages in the same cell are induced.

Bacterial virulence phenotypes of Escherichia coli and host susceptibility determine risk for urinary tract infections

Urinary tract infections (UTIs) are caused by uropathogenic Escherichia coli (UPEC) strains. In contrast to many enteric E. coli pathogroups, no genetic signature has been identified for UPEC strains. We conducted a high-resolution comparative genomic study using E. coli isolates collected from the urine of women suffering from frequent recurrent UTIs. These isolates were genetically diverse and varied in their urovirulence, that is, their ability to infect the bladder in a mouse model of cystitis. We found no set of genes, including previously defined putative urovirulence factors (PUFs), that were predictive of urovirulence. In addition, in some patients, the E. coli strain causing a recurrent UTI had fewer PUFs than the supplanted strain. In competitive experimental infections in mice, the supplanting strain was more efficient at colonizing the mouse bladder than the supplanted strain. Despite the lack of a clear genomic signature for urovirulence, comparative transcriptomic and phenotypic analyses revealed that the expression of key conserved functions during culture, such as motility and metabolism, could be used to predict subsequent colonization of the mouse bladder. Together, our findings suggest that UTI risk and outcome may be determined by complex interactions between host susceptibility and the urovirulence potential of diverse bacterial strains.

High-resolution definition of the Vibrio cholerae essential gene set with hidden Markov model-based analyses of transposon-insertion sequencing data

The coupling of high-density transposon mutagenesis to high-throughput DNA sequencing (transposon-insertion sequencing) enables simultaneous and genome-wide assessment of the contributions of individual loci to bacterial growth and survival. We have refined analysis of transposon-insertion sequencing data by normalizing for the effect of DNA replication on sequencing output and using a hidden Markov model (HMM)-based filter to exploit heretofore unappreciated information inherent in all transposon-insertion sequencing data sets. The HMM can smooth variations in read abundance and thereby reduce the effects of read noise, as well as permit fine scale mapping that is independent of genomic annotation and enable classification of loci into several functional categories (e.g. essential, domain essential or ‘sick’). We generated a high-resolution map of genomic loci (encompassing both intra- and intergenic sequences) that are required or beneficial for in vitro growth of the cholera pathogen, Vibrio cholerae. This work uncovered new metabolic and physiologic requirements for V. cholerae survival, and by combining transposon-insertion sequencing and transcriptomic data sets, we also identified several novel noncoding RNA species that contribute to V. cholerae growth. Our findings suggest that HMM-based approaches will enhance extraction of biological meaning from transposon-insertion sequencing genomic data.

NanoRNAs prime transcription initiation in vivo

It is often presumed that, in vivo, the initiation of RNA synthesis by DNA-dependent RNA polymerases occurs using NTPs alone. Here, using the model Gram-negative bacterium Pseudomonas aeruginosa, we demonstrate that depletion of the small-RNA-specific exonuclease, Oligoribonuclease, causes the accumulation of oligoribonucleotides 2 to ∼4 nt in length, "nanoRNAs," which serve as primers for transcription initiation at a significant fraction of promoters. Widespread use of nanoRNAs to prime transcription initiation is coupled with global alterations in gene expression. Our results, obtained under conditions in which the concentration of nanoRNAs is artificially elevated, establish that small RNAs can be used to initiate transcription in vivo, challenging the idea that all cellular transcription occurs using only NTPs. Our findings further suggest that nanoRNAs could represent a distinct class of functional small RNAs that can affect gene expression through direct incorporation into a target RNA transcript rather than through a traditional antisense-based mechanism.

Multiple small RNAs identified in Mycobacterium bovis BCG are also expressed in Mycobacterium tuberculosis and Mycobacterium smegmatis

Tuberculosis (TB) is a major global health problem, infecting millions of people each year. The causative agent of TB, Mycobacterium tuberculosis, is one of the world’s most ancient and successful pathogens. However, until recently, no work on small regulatory RNAs had been performed in this organism. Regulatory RNAs are found in all three domains of life, and have already been shown to regulate virulence in well-known pathogens, such as Staphylococcus aureus and Vibrio cholera. Here we report the discovery of 34 novel small RNAs (sRNAs) in the TB-complex M. bovis BCG, using a combination of experimental and computational approaches. Putative homologues of many of these sRNAs were also identified in M. tuberculosis and/or M. smegmatis. Those sRNAs that are also expressed in the non-pathogenic M. smegmatis could be functioning to regulate conserved cellular functions. In contrast, those sRNAs identified specifically in M. tuberculosis could be functioning in mediation of virulence, thus rendering them potential targets for novel antimycobacterials. Various features and regulatory aspects of some of these sRNAs are discussed.

Simultaneous detection of genotype and phenotype enables rapid and accurate antibiotic susceptibility determination

Multidrug resistant organisms (MDROs) are a serious threat to human health^1,2. Fast, accurate antibiotic susceptibility testing (AST) is a critical need in addressing escalating antibiotic resistance, since delays in identifying MDROs increase mortality^3,4 and use of broad-spectrum antibiotics, further selecting for resistant organisms. Yet current growth-based AST assays, such as broth microdilution⁵, require several days before informing key clinical decisions. Rapid AST would transform the care of infected patients while ensuring that our antibiotic arsenal is deployed as efficiently as possible. Growth-based assays are fundamentally constrained in speed by doubling time of the pathogen, and genotypic assays are limited by the ever-growing diversity and complexity of bacterial antibiotic resistance mechanisms. Here, we describe a rapid assay for combined Genotypic and Phenotypic AST through RNA detection, GoPhAST-R, that classifies strains with 94–99% accuracy by coupling machine learning analysis of early antibiotic-induced transcriptional changes with simultaneous detection of key genetic resistance determinants to increase accuracy of resistance detection, facilitate molecular epidemiology, and enable early detection of emerging resistance mechanisms. This two-pronged approach provides phenotypic AST 24–36 hours faster than standard workflows, with <4 hour assay time on a pilot instrument for hybridization-based multiplexed RNA detection implemented directly from positive blood cultures.

sRNAPredict: an integrative computational approach to identify sRNAs in bacterial genomes

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.

A spectrum of CodY activities drives metabolic reorganization and virulence gene expression in Staphylococcus aureus

The global regulator CodY controls the expression of dozens of metabolism and virulence genes in the opportunistic pathogen Staphylococcus aureus in response to the availability of isoleucine, leucine and valine (ILV), and GTP. Using RNA-Seq transcriptional profiling and partial activity variants, we reveal that S. aureus CodY activity grades metabolic and virulence gene expression as a function of ILV availability, mediating metabolic reorganization and controlling virulence factor production in vitro. Strains lacking CodY regulatory activity produce a PIA-dependent biofilm, but development is restricted under conditions that confer partial CodY activity. CodY regulates the expression of thermonuclease (nuc) via the Sae two-component system, revealing cascading virulence regulation and factor production as CodY activity is reduced. Proteins that mediate the host-pathogen interaction and subvert the immune response are shut off at intermediate levels of CodY activity, while genes coding for enzymes and proteins that extract nutrients from tissue, that kill host cells, and that synthesize amino acids are among the last genes to be derepressed. We conclude that S. aureus uses CodY to limit host damage to only the most severe starvation conditions, providing insight into one potential mechanism by which S. aureus transitions from a commensal bacterium to an invasive pathogen.

A mucosal imprint left by prior Escherichia coli bladder infection sensitizes to recurrent disease

Recurrent bacterial infections are a significant burden worldwide, and prior history of infection is often a significant risk factor for developing new infections. For urinary tract infection (UTI), a history of two or more episodes is an independent risk factor for acute infection. However, mechanistic knowledge of UTI pathogenesis has come almost exclusively from studies in naive mice. Here we show that, in mice, an initial Escherichia coli UTI, whether chronic or self-limiting, leaves a long-lasting molecular imprint on the bladder tissue that alters the pathophysiology of subsequent infections, affecting host susceptibility and disease outcome. In bladders of previously infected versus non-infected, antibiotic-treated mice, we found (1) an altered transcriptome and defects in cell maturation, (2) a remodelled epithelium that confers resistance to intracellular bacterial colonization, and (3) changes to cyclooxygenase-2-dependent inflammation. Furthermore, in mice with a history of chronic UTI, cyclooxygenase-2-dependent inflammation allowed a variety of clinical E. coli isolates to circumvent intracellular colonization resistance and cause severe recurrent UTI, which could be prevented by cyclooxygenase-2 inhibition or vaccination. This work provides mechanistic insight into how a history of infection can impact the risk for developing recurrent infection and has implications for the development of therapeutics for recurrent UTI.

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

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.

Best practices on the differential expression analysis of multi-species RNA-seq

Advances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

Depleting Mycobacterium tuberculosis of the transcription termination factor Rho causes pervasive transcription and rapid death

Rifampicin, which inhibits bacterial RNA polymerase, provides one of the most effective treatments for tuberculosis. Inhibition of the transcription termination factor Rho is used to treat some bacterial infections, but its importance varies across bacteria. Here we show that Rho of Mycobacterium tuberculosis functions to both define the 3' ends of mRNAs and silence substantial fragments of the genome. Brief inactivation of Rho affects over 500 transcripts enriched for genes of foreign DNA elements and bacterial virulence factors. Prolonged inactivation of Rho causes extensive pervasive transcription, a genome-wide increase in antisense transcripts, and a rapid loss of viability of replicating and non-replicating M. tuberculosis in vitro and during acute and chronic infection in mice. Collectively, these data suggest that inhibition of Rho may provide an alternative strategy to treat tuberculosis with an efficacy similar to inhibition of RNA polymerase.