Genome-wide identification and characterization of small RNAs in Rhodobacter capsulatus and identification of small RNAs affected by loss of the response regulator CtrA

The Calvin Benson cycle in bacteria: New insights from systems biology

The Calvin Benson cycle in phototrophic and chemolithoautotrophic bacteria has ecological and biotechnological importance, which has motivated study of its regulation. I review recent advances in our understanding of how the Calvin Benson cycle is regulated in bacteria and the technologies used to elucidate regulation and modify it, and highlight differences between and photoautotrophic and chemolithoautotrophic models. Systems biology studies have shown that in oxygenic phototrophic bacteria, Calvin Benson cycle enzymes are extensively regulated at post-transcriptional and post-translational levels, with multiple enzyme activities connected to cellular redox status through thioredoxin. In chemolithoautotrophic bacteria, regulation is primarily at the transcriptional level, with effector metabolites transducing cell status, though new methods should now allow facile, proteome-wide exploration of biochemical regulation in these models. A biotechnological objective is to enhance CO2 fixation in the cycle and partition that carbon to a product of interest. Flux control of CO2 fixation is distributed over multiple enzymes, and attempts to modulate gene Calvin cycle gene expression show a robust homeostatic regulation of growth rate, though the synthesis rates of products can be significantly increased. Therefore, de-regulation of cycle enzymes through protein engineering may be necessary to increase fluxes. Non-canonical Calvin Benson cycles, if implemented with synthetic biology, could have reduced energy demand and enzyme loading, thus increasing the attractiveness of these bacteria for industrial applications.

Bioinformatic and experimental methods to identify and validate bacterial RNA-human RNA interactions

Ample evidence supports the importance of the microbiota on human health and disease. Recent studies suggest that extracellular vesicles are an important means of bacterial-host communication, in part via the transport of small RNAs (sRNAs). Bacterial sRNAs have been shown to co-precipitate with human and mouse RNA-induced silencing complex, hinting that some may regulate gene expression as eukaryotic microRNAs do. Bioinformatic tools, including those that can incorporate an sRNA's secondary structure, can be used to predict interactions between bacterial sRNAs and human messenger RNAs (mRNAs). Validation of these potential interactions using reproducible experimental methods is essential to move the field forward. This review will cover the evidence of interspecies communication via sRNAs, bioinformatic tools currently available to identify potential bacterial sRNA-host (specifically, human) mRNA interactions, and experimental methods to identify and validate those interactions.

Selective extracellular secretion of small double-stranded RNA by Tetragenococcus halophilus

Small double-stranded RNAs (dsRNAs) abundantly produced by lactic acid bacteria demonstrate immunomodulatory activity and antiviral protective immunity. However, the extracellular secretion of dsRNA from lactic acid bacteria and their compositional and functional differences compared to the intracellular dsRNA is unknown. In this study, we compared the intracellular and secreted extracellular dsRNA of the lactic acid bacteria, Tetragenococcus halophilus, commonly present in fermented foods, by growing in RNA-free and RNase-free media. We used RNA deep sequencing and in-silico analysis to annotate potential regulatory functions for the comparison. A time series sampling of T. halophilus culture demonstrated growth phase-dependent dynamics in extracellular dsRNA secretion with no major change in the intracellular dsRNA profile. The RNA deep sequencing resulted in thousands of diverse dsRNA fragments with 14-21 nucleotides in size from T. halophilus culture. Over 70% of the secreted extracellular dsRNAs were unique in their sequences compared to the intracellular dsRNAs. Furthermore, the extracellular dsRNA abundantly contains sequences that are not T. halophilus genome encoded, not detected intracellularly and showed higher hits on human transcriptome during in-silico analysis, which suggests the presence of extrachromosomal mobile regulatory elements. Further analysis showed significant enrichment of dsRNA target genes of human transcriptome on cancer pathways and transcription process, indicating the extracellular dsRNA of T. halophilus is different not only at the sequence level but also in function. Studying the bacterial extracellular dsRNA is a promising area of future research, particularly for developing postbiotic fermented functional foods and understanding the impact of commensal gut bacteria on human health.

Prediction of Bacterial sRNAs Using Sequence-Derived Features and Machine Learning

Small ribonucleic acid (sRNA) sequences are 50–500 nucleotide long, noncoding RNA (ncRNA) sequences that play an important role in regulating transcription and translation within a bacterial cell. As such, identifying sRNA sequences within an organism’s genome is essential to understand the impact of the RNA molecules on cellular processes. Recently, numerous machine learning models have been applied to predict sRNAs within bacterial genomes. In this study, we considered the sRNA prediction as an imbalanced binary classification problem to distinguish minor positive sRNAs from major negative ones within imbalanced data and then performed a comparative study with six learning algorithms and seven assessment metrics. First, we collected numerical feature groups extracted from known sRNAs previously identified in Salmonella typhimurium LT2 (SLT2) and Escherichia coli K12 (E. coli K12) genomes. Second, as a preliminary study, we characterized the sRNA-size distribution with the conformity test for Benford’s law. Third, we applied six traditional classification algorithms to sRNA features and assessed classification performance with seven metrics, varying positive-to-negative instance ratios, and utilizing stratified 10-fold cross-validation. We revisited important individual features and feature groups and found that classification with combined features perform better than with either an individual feature or a single feature group in terms of Area Under Precision-Recall curve (AUPR). We reconfirmed that AUPR properly measures classification performance on imbalanced data with varying imbalance ratios, which is consistent with previous studies on classification metrics for imbalanced data. Overall, eXtreme Gradient Boosting (XGBoost), even without exploiting optimal hyperparameter values, performed better than the other five algorithms with specific optimal parameter settings. As a future work, we plan to extend XGBoost further to a large amount of published sRNAs in bacterial genomes and compare its classification performance with recent machine learning models’ performance.

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 programsin 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.

Small regulatory RNAs of oral streptococci and periodontal bacteria

Small regulatory RNAs (sRNAs) belong to a family of non-coding RNAs, and many of which regulate expression of genes via interaction with mRNA. The recent popularity of high-throughput next generation sequencers have presented abundant sRNA-related data, including sRNAs of several different oral bacterial species. Some sRNA candidates have been validated in terms of their expression and interaction with target mRNAs. Since the oral cavity is an environment constantly exposed to various stimuli, such as fluctuations in temperature and pH, and osmotic pressure, as well as changes in nutrient availability, oral bacteria require rapid control of gene expression for adaptation to such diverse conditions, while regulation via interactions of sRNAs with mRNA provides advantages for rapid adaptation. This review summarizes methods effective for identification and validation of sRNAs, as well as sRNAs identified to be associated with oral bacterial species, including cariogenic and periodontal pathogens, together with their confirmed and putative target genes.

Potential Non-coding RNAs from Microorganisms and their Therapeutic Use in the Treatment of Different Human Cancers

Cancer therapy describes the treatment of cancer, often with surgery, chemotherapy, and radiotherapy. Additionally, RNA interference (RNAi) is likely to be considered a new emerging, alternative therapeutic approach for silencing/targeting cancer-related genes. RNAi can exert antiproliferative and proapoptotic effects by targeting functional carcinogenic molecules or knocking down gene products of cancer-related genes. However, in contrast to conventional cancer therapies, RNAi based therapy seems to have fewer side effects. Transcription signal sequence and conserved sequence analysis-showed that microorganisms could be a potent source of non-coding RNAs. This review concluded that mapping of RNAi mechanism and RNAi based drug delivery approaches is expected to lead a better prospective of cancer therapy.

Navigation through the twists and turns of RNA sequencing technologies: Application to bacterial regulatory RNAs

Discovered in the 1980s, small regulatory RNAs (sRNAs) are now considered key actors in virtually all aspects of bacterial physiology and virulence. Together with transcriptional and translational regulatory proteins, they integrate and often are hubs of complex regulatory networks, responsible for bacterial response/adaptation to various perceived stimuli. The recent development of powerful RNA sequencing technologies has facilitated the identification and characterization of sRNAs (length, structure and expression conditions) and their RNA targets in several bacteria. Nevertheless, it could be very difficult for non-experts to understand the advantages and drawbacks related to each offered option and, consequently, to make an informed choice. Therefore, the main goal of this review is to provide a guide to navigate through the twists and turns of high-throughput RNA sequencing technologies, with a specific focus on those applied to the study of sRNAs. This article is part of a Special Issue entitled: RNA and gene control in bacteria edited by Dr. M. Guillier and F. Repoila.

Prioritizing bona fide bacterial small RNAs with machine learning classifiers

Bacterial small (sRNAs) are involved in the control of several cellular processes. Hundreds of putative sRNAs have been identified in many bacterial species through RNA sequencing. The existence of putative sRNAs is usually validated by Northern blot analysis. However, the large amount of novel putative sRNAs reported in the literature makes it impractical to validate each of them in the wet lab. In this work, we applied five machine learning approaches to construct twenty models to discriminate bona fide sRNAs from random genomic sequences in five bacterial species. Sequences were represented using seven features including free energy of their predicted secondary structure, their distances to the closest predicted promoter site and Rho-independent terminator, and their distance to the closest open reading frames (ORFs). To automatically calculate these features, we developed an sRNA Characterization Pipeline (sRNACharP). All seven features used in the classification task contributed positively to the performance of the predictive models. The best performing model obtained a median precision of 100% at 10% recall and of 64% at 40% recall across all five bacterial species, and it outperformed previous published approaches on two benchmark datasets in terms of precision and recall. Our results indicate that even though there is limited sRNA sequence conservation across different bacterial species, there are intrinsic features in the genomic context of sRNAs that are conserved across taxa. We show that these features are utilized by machine learning approaches to learn a species-independent model to prioritize bona fide bacterial sRNAs.

An sRNA and Cold Shock Protein Homolog-Based Feedforward Loop Post-transcriptionally Controls Cell Cycle Master Regulator CtrA

Adjustment of cell cycle progression is crucial for bacterial survival and adaptation under adverse conditions. However, the understanding of modulation of cell cycle control in response to environmental changes is rather incomplete. In α-proteobacteria, the broadly conserved cell cycle master regulator CtrA underlies multiple levels of control, including coupling of cell cycle and cell differentiation. CtrA levels are known to be tightly controlled through diverse transcriptional and post-translational mechanisms. Here, small RNA (sRNA)-mediated post-transcriptional regulation is uncovered as an additional level of CtrA fine-tuning. Computational predictions as well as transcriptome and proteome studies consistently suggested targeting of ctrA and the putative cold shock chaperone cspA5 mRNAs by the trans-encoded sRNA (trans-sRNA) GspR (formerly SmelC775) in several Sinorhizobium species. GspR strongly accumulated in the stationary growth phase, especially in minimal medium (MM) cultures. Lack of the gspR locus confers a fitness disadvantage in competition with the wild type, while its overproduction hampers cell growth, suggesting that this riboregulator interferes with cell cycle progression. An eGFP-based reporter in vivo assay, involving wild-type and mutant sRNA and mRNA pairs, experimentally confirmed GspR-dependent post-transcriptional down-regulation of ctrA and cspA5 expression, which most likely occurs through base-pairing to the respective mRNA. The energetically favored secondary structure of GspR is predicted to comprise three stem-loop domains, with stem-loop 1 and stem-loop 3 targeting ctrA and cspA5 mRNA, respectively. Moreover, this work reports evidence for post-transcriptional control of ctrA by CspA5. Thus, this regulation and GspR-mediated post-transcriptional repression of ctrA and cspA5 expression constitute a coherent feed-forward loop, which may enhance the negative effect of GspR on CtrA levels. This novel regulatory circuit involving the riboregulator GspR, CtrA, and a cold shock chaperone may contribute to fine-tuning of ctrA expression.