Maturation of Polycistronic mRNAs by the Endoribonuclease RNase Y and Its Associated Y-Complex in Bacillus Subtilis

Endonucleolytic cleavage within polycistronic mRNAs can lead to differential stability, and thus discordant abundance, among cotranscribed genes. RNase Y, the major endonuclease for mRNA decay in Bacillus subtilis, was originally identified for its cleavage activity toward the cggR-gapA operon, an event that differentiates the synthesis of a glycolytic enzyme from its transcriptional regulator. A three-protein Y-complex (YlbF, YmcA, and YaaT) was recently identified as also being required for this cleavage in vivo, raising the possibility that it is an accessory factor acting to regulate RNase Y. However, whether the Y-complex is broadly required for RNase Y activity is unknown. Here, we used end-enrichment RNA sequencing (Rend-seq) to globally identify operon mRNAs that undergo maturation posttranscriptionally by RNase Y and the Y-complex. We found that the Y-complex is required for the majority of RNase Y-mediated mRNA maturation events and also affects riboswitch abundance in B. subtilis. In contrast, noncoding RNA maturation by RNase Y often does not require the Y-complex. Furthermore, deletion of RNase Y has more pleiotropic effects on the transcriptome and cell growth than deletions of the Y-complex. We propose that the Y-complex is a specificity factor for RNase Y, with evidence that its role is conserved in Staphylococcus aureus.National Institutes of Health (U.S.) (Grant R00GM105913)National Institutes of Health (U.S.) (Grant R35GM124732

A serine sensor for multicellularity in a bacterium

We report the discovery of a simple environmental sensing mechanism for biofilm formation in the bacterium Bacillus subtilis that operates without the involvement of a dedicated RNA or protein. Certain serine codons, the four TCN codons, in the gene for the biofilm repressor SinR caused a lowering of SinR levels under biofilm-inducing conditions. Synonymous substitutions of these TCN codons with AGC or AGT impaired biofilm formation and gene expression. Conversely, switching AGC or AGT to TCN codons upregulated biofilm formation. Genome-wide ribosome profiling showed that ribosome density was higher at UCN codons than at AGC or AGU during biofilm formation. Serine starvation recapitulated the effect of biofilm-inducing conditions on ribosome occupancy and SinR production. As serine is one of the first amino acids to be exhausted at the end of exponential phase growth, reduced translation speed at serine codons may be exploited by other microbes in adapting to stationary phase. DOI: http://dx.doi.org/10.7554/eLife.01501.00

Genome-wide screen for genes involved in eDNA release during biofilm formation by

Staphylococcus aureus is a leading cause of both nosocomial and community-acquired infection. Biofilm formation at the site of infection reduces antimicrobial susceptibility and can lead to chronic infection. During biofilm formation, a subset of cells liberate cytoplasmic proteins and DNA, which are repurposed to form the extracellular matrix that binds the remaining cells together in large clusters. Using a strain that forms robust biofilms in vitro during growth under glucose supplementation, we carried out a genome-wide screen for genes involved in the release of extracellular DNA (eDNA). A high-density transposon insertion library was grown under biofilm-inducing conditions, and the relative frequency of insertions was compared between genomic DNA (gDNA) collected from cells in the biofilm and eDNA from the matrix. Transposon insertions into genes encoding functions necessary for eDNA release were identified by reduced representation in the eDNA. On direct testing, mutants of some of these genes exhibited markedly reduced levels of eDNA and a concomitant reduction in cell clustering. Among the genes with robust mutant phenotypes were gdpP, which encodes a phosphodiesterase that degrades the second messenger cyclic-di-AMP, and xdrA, the gene for a transcription factor that, as revealed by RNA-sequencing analysis, influences the expression of multiple genes, including many involved in cell wall homeostasis. Finally, we report that growth in biofilm-inducing medium lowers cyclic-di-AMP levels and does so in a manner that depends on the gdpP phosphodiesterase gene. Keywords: Staphylococcus aureus; biofilm; eDNA; cyclic-di-AMPNational Institutes of Health (U.S.) (Grant P01-AI083214

Post-Transcriptional Control of Multicellularity in Bacillus subtilis

Bacteria are capable of sensing changes in their environment and responding to those changes by altering patterns of gene expression. There are many mechanisms by which bacteria can sense internal and external signals. The most well studied systems for this, two-component systems, are composed of a histidine kinase that can act as a detector and a response regulator that when activated by the histidine kinase can carry out a function. In the case of regulating gene expression, once a signal is detected, the response regulator can directly act as a transcription factor or control the activity of other transcription factors to determine the genes that are transcribed and the genes that are not. Historically, much of gene regulation has been thought to be regulated at the level of transcription. There are, however, several processes that take place after a gene is transcribed that ultimately determine the level of a gene product These include mRNA stability, translation initiation, translation efficiency, and protein degradation, all of which the cell could potentially target to fine-tune protein levels. Many such post-transcriptional mechanisms of regulation have been identified, such as controlled proteolysis and the regulation of translation by riboswitches. Here I report on mechanisms operating at the level of translational efficiency and mRNA stability that control entry into a multicellular state in the bacterium Bacillus subtilis. Like many bacteria, B. subtilis can form architecturally complex communities known as biofilms. Biofilm formation by B. subtilis is largely governed by a dedicated repressor for matrix production, SinR. At the transcriptional level, biofilm formation is regulated by the response regulator Spo0A, which turns on the gene for the anti-repressor SinI. SinI, in turn, binds to and inactivates SinR. The work that I have been a part of and that is presented here has revealed two additional mechanisms in which SinR is regulated post-transcriptionally. The first is a simple mechanism in which B. subtilis can sense changes in nutrient availability without a dedicated protein or RNA. Instead certain synonymous serine codons cause pausing when serine levels are reduced, and enrichment for these codons in sinR decreases its rate of translation under biofilm-inducing conditions, contributing to derepression of matrix genes. The second, which is the main focus of this thesis, acts at the level of the stability of the mRNA for sinR. This mechanism was unexpectedly uncovered while determining the function of three mysterious proteins, YlbF, YmcA, and YaaT, which are required for biofilm formation. I have found that YlbF, YmcA, and YaaT form a three-protein complex that interacts with and is required for the full activity of the ribonuclease RNase Y. This complex is required for biofilm formation because it destabilizes the mRNA for sinR. In addition RNA-sequencing experiments reveal a global role for the complex in RNA turnover. Turnover of many known targets of RNase Y, including mRNA and non-coding RNAs, are dependent on the complex. In light of these findings, the decision to form a multicellular community can be seen as the convergence of three regulatory pathways operating at three different levels of the expression of the gene for the master regulatory protein for biofilm formation SinR.Biology, Molecular and Cellula

Transcriptional regulation and mechanism of sigN (ZpdN), a pBS32-encoded sigma factor in bacillus subtilis

Laboratory strains of Bacillus subtilis encode many alternative sigma factors, each dedicated to expressing a unique regulon such as those involved in stress resistance, sporulation, and motility. The ancestral strain of B. subtilis also encodes an additional sigma factor homolog, ZpdN, not found in lab strains due to being encoded on the large, low-copy-number plasmid pBS32, which was lost during domestication. DNA damage triggers pBS32 hyperreplication and cell death in a manner that depends on ZpdN, but how ZpdN mediates these effects is unknown. Here, we show that ZpdN is a bona fide sigma factor that can direct RNA polymerase to transcribe ZpdN-dependent genes, and we rename ZpdN SigN accordingly. Rend-seq (end-enriched transcriptome sequencing) analysis was used to determine the SigN regulon on pBS32, and the 5= ends of transcripts were used to predict the SigN consensus sequence. Finally, we characterize the regulation of SigN itself and show that it is transcribed by at least three promoters: PsigN1, a strong SigA-dependent LexArepressed promoter; PsigN2, a weak SigA-dependent constitutive promoter; and PsigN3, a SigN-dependent promoter. Thus, in response to DNA damage SigN is derepressed and then experiences positive feedback. How cells die in a pBS32-dependent manner remains unknown, but we predict that death is the product of expressing one or more genes in the SigN regulon. IMPORTANCE Sigma factors are utilized by bacteria to control and regulate gene expression. Some sigma factors are activated during times of stress to ensure the survival of the bacterium. Here, we report the presence of a sigma factor that is encoded on a plasmid that leads to cellular death after DNA damage.National Institutes of Health (U.S.) (Grant 35GM124732

Transcriptional Regulation and Mechanism of SigN (ZpdN), a pBS32-Encoded Sigma Factor in Bacillus subtilis

Sigma factors are utilized by bacteria to control and regulate gene expression. Some sigma factors are activated during times of stress to ensure the survival of the bacterium. Here, we report the presence of a sigma factor that is encoded on a plasmid that leads to cellular death after DNA damage.Laboratory strains of Bacillus subtilis encode many alternative sigma factors, each dedicated to expressing a unique regulon such as those involved in stress resistance, sporulation, and motility. The ancestral strain of B. subtilis also encodes an additional sigma factor homolog, ZpdN, not found in lab strains due to being encoded on the large, low-copy-number plasmid pBS32, which was lost during domestication. DNA damage triggers pBS32 hyperreplication and cell death in a manner that depends on ZpdN, but how ZpdN mediates these effects is unknown. Here, we show that ZpdN is a bona fide sigma factor that can direct RNA polymerase to transcribe ZpdN-dependent genes, and we rename ZpdN SigN accordingly. Rend-seq (end-enriched transcriptome sequencing) analysis was used to determine the SigN regulon on pBS32, and the 5′ ends of transcripts were used to predict the SigN consensus sequence. Finally, we characterize the regulation of SigN itself and show that it is transcribed by at least three promoters: PsigN1, a strong SigA-dependent LexA-repressed promoter; PsigN2, a weak SigA-dependent constitutive promoter; and PsigN3, a SigN-dependent promoter. Thus, in response to DNA damage SigN is derepressed and then experiences positive feedback. How cells die in a pBS32-dependent manner remains unknown, but we predict that death is the product of expressing one or more genes in the SigN regulon

Maturation of polycistronic mRNAs by the endoribonuclease RNase Y and its associated Y-complex in Bacillus subtilis

Endonucleolytic cleavage within polycistronic mRNAs can lead to differential stability, and thus discordant abundance, among cotranscribed genes. RNase Y, the major endonuclease for mRNA decay in Bacillus subtilis, was originally identified for its cleavage activity toward the cggR-gapA operon, an event that differentiates the synthesis of a glycolytic enzyme from its transcriptional regulator. A three-protein Y-complex (YlbF, YmcA, and YaaT) was recently identified as also being required for this cleavage in vivo, raising the possibility that it is an accessory factor acting to regulate RNase Y. However, whether the Y-complex is broadly required for RNase Y activity is unknown. Here, we used end-enrichment RNA sequencing (Rend-seq) to globally identify operon mRNAs that undergo maturation posttranscriptionally by RNase Y and the Y-complex. We found that the Y-complex is required for the majority of RNase Y-mediated mRNA maturation events and also affects riboswitch abundance in B. subtilis. In contrast, noncoding RNA maturation by RNase Y often does not require the Y-complex. Furthermore, deletion of RNase Y has more pleiotropic effects on the transcriptome and cell growth than deletions of the Y-complex. We propose that the Y-complex is a specificity factor for RNase Y, with evidence that its role is conserved in Staphylococcus aureus.National Institutes of Health (U.S.) (Grant R00GM105913)National Institutes of Health (U.S.) (Grant R35GM124732

Correction: Detecting rare diseases in electronic health records using machine learning and knowledge engineering: Case study of acute hepatic porphyria.

[This corrects the article DOI: 10.1371/journal.pone.0235574.]

Detecting rare diseases in electronic health records using machine learning and knowledge engineering: Case study of acute hepatic porphyria.

BACKGROUND:With the growing adoption of the electronic health record (EHR) worldwide over the last decade, new opportunities exist for leveraging EHR data for detection of rare diseases. Rare diseases are often not diagnosed or delayed in diagnosis by clinicians who encounter them infrequently. One such rare disease that may be amenable to EHR-based detection is acute hepatic porphyria (AHP). AHP consists of a family of rare, metabolic diseases characterized by potentially life-threatening acute attacks and chronic debilitating symptoms. The goal of this study was to apply machine learning and knowledge engineering to a large extract of EHR data to determine whether they could be effective in identifying patients not previously tested for AHP who should receive a proper diagnostic workup for AHP. METHODS AND FINDINGS:We used an extract of the complete EHR data of 200,000 patients from an academic medical center and enriched it with records from an additional 5,571 patients containing any mention of porphyria in the record. After manually reviewing the records of all 47 unique patients with the ICD-10-CM code E80.21 (Acute intermittent [hepatic] porphyria), we identified 30 patients who were positive cases for our machine learning models, with the rest of the patients used as negative cases. We parsed the record into features, which were scored by frequency of appearance and filtered using univariate feature analysis. We manually choose features not directly tied to provider attributes or suspicion of the patient having AHP. We trained on the full dataset, with the best cross-validation performance coming from support vector machine (SVM) algorithm using a radial basis function (RBF) kernel. The trained model was applied back to the full data set and patients were ranked by margin distance. The top 100 ranked negative cases were manually reviewed for symptom complexes similar to AHP, finding four patients where AHP diagnostic testing was likely indicated and 18 patients where AHP diagnostic testing was possibly indicated. From the top 100 ranked cases of patients with mention of porphyria in their record, we identified four patients for whom AHP diagnostic testing was possibly indicated and had not been previously performed. Based solely on the reported prevalence of AHP, we would have expected only 0.002 cases out of the 200 patients manually reviewed. CONCLUSIONS:The application of machine learning and knowledge engineering to EHR data may facilitate the diagnosis of rare diseases such as AHP. Further work will recommend clinical investigation to identified patients' clinicians, evaluate more patients, assess additional feature selection and machine learning algorithms, and apply this methodology to other rare diseases. This work provides strong evidence that population-level informatics can be applied to rare diseases, greatly improving our ability to identify undiagnosed patients, and in the future improve the care of these patients and our ability study these diseases. The next step is to learn how best to apply these EHR-based machine learning approaches to benefit individual patients with a clinical study that provides diagnostic testing and clinical follow up for those identified as possibly having undiagnosed AHP