The small DUF1127 protein CcaF1 from Rhodobacter sphaeroides is an RNA-binding protein involved in sRNA maturation and RNA turnover

Many different protein domains are conserved among numerous species, but their function remains obscure. Proteins with DUF1127 domains number >17 000 in current databases, but a biological function has not yet been assigned to any of them. They are mostly found in alpha- and gammaproteobacteria, some of them plant and animal pathogens, symbionts or species used in industrial applications. Bioinformatic analyses revealed similarity of the DUF1127 domain of bacterial proteins to the RNA binding domain of eukaryotic Smaug proteins that are involved in RNA turnover and have a role in development from Drosophila to mammals. This study demonstrates that the 71 amino acid DUF1127 protein CcaF1 from the alphaproteobacterium Rhodobacter sphaeroides participates in maturation of the CcsR sRNAs that are processed from the 3′ UTR of the ccaF mRNA and have a role in the oxidative stress defense. CcaF1 binds to many cellular RNAs of different type, several mRNAs with a function in cysteine / methionine / sulfur metabolism. It affects the stability of the CcsR RNAs and other non-coding RNAs and mRNAs. Thus, the widely distributed DUF1127 domain can mediate RNA-binding, affect stability of its binding partners and consequently modulate the bacterial transcriptome, thereby influencing different physiological processes.

sRNARFTarget: A fast machine-learning-based approach for transcriptome-wide sRNA Target Prediction

Bacterial small regulatory RNAs (sRNAs) are key regulators of gene expression in many processes related to adaptive responses. A multitude of sRNAs have been identified in many bacterial species; however, their function has yet to be elucidated. A key step to understand sRNAs function is to identify the mRNAs these sRNAs bind to. There are several computational methods for sRNA target prediction, and the most accurate one is CopraRNA which is based on comparative-genomics. However, species-specific sRNAs are quite common and CopraRNA cannot be used for these sRNAs. The most commonly used transcriptome-wide sRNA target prediction method and second-most-accurate method is IntaRNA. However, IntaRNA can take hours to run on a bacterial transcriptome. Here we present sRNARFTarget, a machine-learning-based method for transcriptome-wide sRNA target prediction applicable to any sRNA. We comparatively assessed the performance of sRNARFTarget, CopraRNA and IntaRNA in three bacterial species. Our results show that sRNARFTarget outperforms IntaRNA in terms of accuracy, ranking of true interacting pairs, and running time. However, CopraRNA substantially outperforms the other two programs in terms of accuracy. Thus, we suggest using CopraRNA when homolog sequences of the sRNA are available, and sRNARFTarget for transcriptome-wide prediction or for species-specific sRNAs. sRNARFTarget is available at https://github.com/BioinformaticsLabAtMUN/sRNARFTarget.

Mechanism-of-Action Classification of Antibiotics by Global Transcriptome Profiling

ABSTRACT Antimicrobial resistance (AMR) is an ever-growing public health problem worldwide. The low rate of antibiotic discovery coupled with the rapid spread of drug-resistant bacterial pathogens is causing a global health crisis. To facilitate the drug discovery processes, we present a large-scale study of reference antibiotic challenge bacterial transcriptome profiles, which included 37 antibiotics across 6 mechanisms of actions (MOAs) and provide an economical approach to aid in antimicrobial dereplication in the discovery process. We demonstrate that classical MOAs can be sorted based upon the magnitude of gene expression profiles despite some overlap in the secondary effects of antibiotic exposures across MOAs. Additionally, using gene subsets, we were able to subdivide broad MOA classes into subMOAs. Furthermore, we provide a biomarker gene set that can be used to classify most antimicrobial challenges according to their canonical MOA. We also demonstrate the ability of this rapid MOA diagnostic tool to predict and classify the expression profiles of pure compounds and crude extracts to their expression profile-associated MOA class.

Defining the impact of exoribonucleases in the shift between exponential and stationary phases

The transition between exponential and stationary phase is a natural phenomenon for all bacteria and requires a massive readjustment of the bacterial transcriptome. Exoribonucleases are key enzymes in the transition between the two growth phases. PNPase, RNase R and RNase II are the major degradative exoribonucleases in Escherichia coli. We analysed the whole transcriptome of exponential and stationary phases from the WT and mutants lacking these exoribonucleases (Δpnp, Δrnr, Δrnb, and ΔrnbΔrnr). When comparing the cells from exponential phase with the cells from stationary phase more than 1000 transcripts were differentially expressed, but only 491 core transcripts were common to all strains. There were some differences in the number and transcripts affected depending on the strain, suggesting that exoribonucleases influence the transition between these two growth phases differently. Interestingly, we found that the double mutant RNase II/RNase R is similar to the RNase R single mutant in exponential phase while in stationary phase it seems to be closer to the RNase II single mutant. This is the first global transcriptomic work comparing the roles of exoribonucleases in the transition between exponential and stationary phase.

Spatial organization shapes the turnover of a bacterial transcriptome

Spatial organization of the transcriptome has emerged as a powerful means for regulating the post-transcriptional fate of RNA in eukaryotes; however, whether prokaryotes use RNA spatial organization as a mechanism for post-transcriptional regulation remains unclear. Here we used super-resolution microscopy to image the E. coli transcriptome and observed a genome-wide spatial organization of RNA: mRNAs encoding inner-membrane proteins are enriched at the membrane, whereas mRNAs encoding outer-membrane, cytoplasmic and periplasmic proteins are distributed throughout the cytoplasm. Membrane enrichment is caused by co-translational insertion of signal peptides recognized by the signal-recognition particle. Time-resolved RNA-sequencing revealed that degradation rates of inner-membrane-protein mRNAs are on average greater that those of the other mRNAs and that this selective destabilization of inner-membrane-protein mRNAs is abolished by dissociating the RNA degradosome from the membrane. Together, these results demonstrate that the bacterial transcriptome is spatially organized and suggest that this organization shapes the post-transcriptional dynamics of mRNAs.