Pneumococcal RNase R globally impacts protein synthesis by regulating the amount of actively translating ribosomes

How hydrolytic exoribonucleases impact human disease: Two sides of the same story

RNAs are extremely important molecules inside the cell which perform many different functions. For example, messenger RNAs, transfer RNAs, and ribosomal RNAs are involved in protein synthesis, whereas non-coding RNAs have numerous regulatory roles. Ribonucleases are the enzymes responsible for the processing and degradation of all types of RNAs, having multiple roles in every aspect of RNA metabolism. However, the involvement of RNases in disease is still not well understood. This review focuses on the involvement of the RNase II/RNB family of 3'-5' exoribonucleases in human disease. This can be attributed to direct effects, whereby mutations in the eukaryotic enzymes of this family (Dis3 (or Rrp44), Dis3L1 (or Dis3L), and Dis3L2) are associated with a disease, or indirect effects, whereby mutations in the prokaryotic counterparts of RNase II/RNB family (RNase II and/or RNase R) affect the physiology and virulence of several human pathogens. In this review, we will compare the structural and biochemical characteristics of the members of the RNase II/RNB family of enzymes. The outcomes of mutations impacting enzymatic function will be revisited, in terms of both the direct and indirect effects on disease. Furthermore, we also describe the SARS-CoV-2 viral exoribonuclease and its importance to combat COVID-19 pandemic. As a result, RNases may be a good therapeutic target to reduce bacterial and viral pathogenicity. These are the two perspectives on RNase II/RNB family enzymes that will be presented in this review.

RNase R, a New Virulence Determinant of Streptococcus pneumoniae

Pneumococcal infections have increasingly high mortality rates despite the availability of vaccines and antibiotics. Therefore, the identification of new virulence determinants and the understanding of the molecular mechanisms behind pathogenesis have become of paramount importance in the search of new targets for drug development. The exoribonuclease RNase R has been involved in virulence in a growing number of pathogens. In this work, we used Galleria mellonella as an infection model to demonstrate that the presence of RNase R increases the pneumococcus virulence. Larvae infected with the RNase R mutant show an increased expression level of antimicrobial peptides. Furthermore, they have a lower bacterial load in the hemolymph in the later stages of infection, leading to a higher survival rate of the larvae. Interestingly, pneumococci expressing RNase R show a sudden drop in bacterial numbers immediately after infection, resembling the eclipse phase observed after intravenous inoculation in mice. Concomitantly, we observed a lower number of mutant bacteria inside larval hemocytes and a higher susceptibility to oxidative stress when compared to the wild type. Together, our results indicate that RNase R is involved in the ability of pneumococci to evade the host immune response, probably by interfering with internalization and/or replication inside the larval hemocytes.

Machine learning for identifying resistance features of Klebsiella pneumoniae using whole-genome sequence single nucleotide polymorphisms

Introduction. Klebsiella pneumoniae, a gram-negative bacterium, is a common pathogen causing nosocomial infection. The drug-resistance rate of K. pneumoniae is increasing year by year, posing a severe threat to public health worldwide. K. pneumoniae has been listed as one of the pathogens causing the global crisis of antimicrobial resistance in nosocomial infections. We need to explore the drug resistance of K. pneumoniae for clinical diagnosis. Single nucleotide polymorphisms (SNPs) are of high density and have rich genetic information in whole-genome sequencing (WGS), which can affect the structure or expression of proteins. SNPs can be used to explore mutation sites associated with bacterial resistance.Hypothesis/Gap Statement. Machine learning methods can detect genetic features associated with the drug resistance of K. pneumoniae from whole-genome SNP data.Aims. This work used Fast Feature Selection (FFS) and Codon Mutation Detection (CMD) machine learning methods to detect genetic features related to drug resistance of K. pneumoniae from whole-genome SNP data.Methods. WGS data on resistance of K. pneumoniae strains to four antibiotics (tetracycline, gentamicin, imipenem, amikacin) were downloaded from the European Nucleotide Archive (ENA). Sequence alignments were performed with MUMmer 3 to complete SNP calling using K. pneumoniae HS11286 chromosome as the reference genome. The FFS algorithm was applied to feature selection of the SNP dataset. The training set was constructed based on mutation sites with mutation frequency >0.995. Based on the original SNP training set, 70% of SNPs were randomly selected from each dataset as the test set to verify the accuracy of the training results. Finally, the resistance genes were obtained by the CMD algorithm and Venny.Results. The number of strains resistant to tetracycline, gentamicin, imipenem and amikacin was 931, 1048, 789 and 203, respectively. Machine learning algorithms were applied to the SNP training set and test set, and 28 and 23 resistance genes were predicted, respectively. The 28 resistance genes in the training set included 22 genes in the test set, which verified the accuracy of gene prediction. Among them, some genes (KPHS_35310, KPHS_18220, KPHS_35880, etc.) corresponded to known resistance genes (Eef2, lpxK, MdtC, etc). Logistic regression classifiers were established based on the identified SNPs in the training set. The area under the curves (AUCs) of the four antibiotics was 0.939, 0.950, 0.912 and 0.935, showing a strong ability to predict bacterial resistance.Conclusion. Machine learning methods can effectively be used to predict resistance genes and associated SNPs. The FFS and CMD algorithms have wide applicability. They can be used for the drug-resistance analysis of any microorganism with genomic variation and phenotypic data. This work lays a foundation for resistance research in clinical applications.

Hibernation-Promoting Factor Sequesters Staphylococcus aureus Ribosomes to Antagonize RNase R-Mediated Nucleolytic Degradation

Bacterial and eukaryotic hibernation factors prevent translation by physically blocking the decoding center of ribosomes, a phenomenon called ribosome hibernation that often occurs in response to nutrient deprivation. The human pathogen Staphylococcus aureus lacking the sole hibernation factor HPF undergoes massive ribosome degradation via an unknown pathway. Using genetic and biochemical approaches, we find that inactivating the 3′-to-5′ exonuclease RNase R suppresses ribosome degradation in the Δhpf mutant. In vitro cell-free degradation assays confirm that 30S and 70S ribosomes isolated from the Δhpf mutant are extremely susceptible to RNase R, in stark contrast to nucleolytic resistance of the HPF-bound 70S and 100S complexes isolated from the wild type. In the absence of HPF, specific S. aureus 16S rRNA helices are sensitive to nucleolytic cleavage. These RNase hot spots are distinct from that found in the Escherichia coli ribosomes. S. aureus RNase R is associated with ribosomes, but unlike the E. coli counterpart, it is not regulated by general stressors and acetylation. The results not only highlight key differences between the evolutionarily conserved RNase R homologs but also provide direct evidence that HPF preserves ribosome integrity beyond its role in translational avoidance, thereby poising the hibernating ribosomes for rapid resumption of translation.

Molecular mechanism of RNase R substrate sensitivity for RNA ribose methylation

RNA 2′-O-methylation is widely distributed and plays important roles in various cellular processes. Mycoplasma genitalium RNase R (MgR), a prokaryotic member of the RNase II/RNB family, is a 3′-5′ exoribonuclease and is particularly sensitive to RNA 2′-O-methylation. However, how RNase R interacts with various RNA species and exhibits remarkable sensitivity to substrate 2′-O-methyl modifications remains elusive. Here we report high-resolution crystal structures of MgR in apo form and in complex with various RNA substrates. The structural data together with extensive biochemical analysis quantitively illustrate MgR’s ribonuclease activity and significant sensitivity to RNA 2′-O-methylation. Comparison to its related homologs reveals an exquisite mechanism for the recognition and degradation of RNA substrates. Through structural and mutagenesis studies, we identified proline 277 to be responsible for the significant sensitivity of MgR to RNA 2′-O-methylation within the RNase II/RNB family. We also generated several MgR variants with modulated activities. Our work provides a mechanistic understanding of MgR activity that can be harnessed as a powerful RNA analytical tool that will open up a new venue for RNA 2′-O-methylations research in biological and clinical samples.

RNase R is associated in a functional complex with the RhpA DEAD-box RNA helicase in Helicobacter pylori

Ribonucleases are central players in post-transcriptional regulation, a major level of gene expression regulation in all cells. Here, we characterized the 3'-5' exoribonuclease RNase R from the bacterial pathogen Helicobacter pylori. The 'prototypical' Escherichia coli RNase R displays both exoribonuclease and helicase activities, but whether this latter RNA unwinding function is a general feature of bacterial RNase R had not been addressed. We observed that H. pylori HpRNase R protein does not carry the domains responsible for helicase activity and accordingly the purified protein is unable to degrade in vitro RNA molecules with secondary structures. The lack of RNase R helicase domains is widespread among the Campylobacterota, which include Helicobacter and Campylobacter genera, and this loss occurred gradually during their evolution. An in vivo interaction between HpRNase R and RhpA, the sole DEAD-box RNA helicase of H. pylori was discovered. Purified RhpA facilitates the degradation of double stranded RNA by HpRNase R, showing that this complex is functional. HpRNase R has a minor role in 5S rRNA maturation and few targets in H. pylori, all included in the RhpA regulon. We concluded that during evolution, HpRNase R has co-opted the RhpA helicase to compensate for its lack of helicase activity.

A Novel Cold-Adapted and Salt-Tolerant RNase R from Antarctic Sea-Ice Bacterium Psychrobacter sp. ANT206

A novel RNase R, psrnr, was cloned from the Antarctic bacterium Psychrobacter sp. ANT206 and expressed in Escherichia coli (E. coli). A bioinformatics analysis of the psrnr gene revealed that it contained an open reading frame of 2313 bp and encoded a protein (PsRNR) of 770 amino acids. Homology modeling indicated that PsRNR had reduced hydrogen bonds and salt bridges, which might be the main reason for the catalytic efficiency at low temperatures. A site directed mutation exhibited that His 667 in the active site was absolutely crucial for the enzyme catalysis. The recombinant PsRNR (rPsRNR) showed maximum activity at 30 °C and had thermal instability, suggesting that rPsRNR was a cold-adapted enzyme. Interestingly, rPsRNR displayed remarkable salt tolerance, remaining stable at 0.5-3.0 M NaCl. Furthermore, rPsRNR had a higher k_cat value, contributing to its efficient catalytic activity at a low temperature. Overall, cold-adapted RNase R in this study was an excellent candidate for antimicrobial treatment.

Bacterial ribonucleases and their roles in RNA metabolism

Ribonucleases (RNases) are mediators in most reactions of RNA metabolism. In recent years, there has been a surge of new information about RNases and the roles they play in cell physiology. In this review, a detailed description of bacterial RNases is presented, focusing primarily on those from Escherichia coli and Bacillus subtilis, the model Gram-negative and Gram-positive organisms, from which most of our current knowledge has been derived. Information from other organisms is also included, where relevant. In an extensive catalog of the known bacterial RNases, their structure, mechanism of action, physiological roles, genetics, and possible regulation are described. The RNase complement of E. coli and B. subtilis is compared, emphasizing the similarities, but especially the differences, between the two. Included are figures showing the three major RNA metabolic pathways in E. coli and B. subtilis and highlighting specific steps in each of the pathways catalyzed by the different RNases. This compilation of the currently available knowledge about bacterial RNases will be a useful tool for workers in the RNA field and for others interested in learning about this area.