Avoidance of stochastic RNA interactions can be harnessed to control protein expression levels in bacteria and archaea

A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools

RNA biology has risen to prominence after a remarkable discovery of diverse functions of noncoding RNA (ncRNA). Most untranslated transcripts often exert their regulatory functions into RNA-RNA complexes via base pairing with complementary sequences in other RNAs. An interplay between RNAs is essential, as it possesses various functional roles in human cells, including genetic translation, RNA splicing, editing, ribosomal RNA maturation, RNA degradation and the regulation of metabolic pathways/riboswitches. Moreover, the pervasive transcription of the human genome allows for the discovery of novel genomic functions via RNA interactome investigation. The advancement of experimental procedures has resulted in an explosion of documented data, necessitating the development of efficient and precise computational tools and algorithms. This review provides an extensive update on RNA-RNA interaction (RRI) analysis via thermodynamic- and comparative-based RNA secondary structure prediction (RSP) and RNA-RNA interaction prediction (RIP) tools and their general functions. We also highlighted the current knowledge of RRIs and the limitations of RNA interactome mapping via experimental data. Then, the gap between RSP and RIP, the importance of RNA homologues, the relationship between pseudoknots, and RNA folding thermodynamics are discussed. It is hoped that these emerging prediction tools will deepen the understanding of RNA-associated interactions in human diseases and hasten treatment processes.

Analysis of 11,430 recombinant protein production experiments reveals that protein yield is tunable by synonymous codon changes of translation initiation sites

Recombinant protein production is a key process in generating proteins of interest in the pharmaceutical industry and biomedical research. However, about 50% of recombinant proteins fail to be expressed in a variety of host cells. Here we show that the accessibility of translation initiation sites modelled using the mRNA base-unpairing across the Boltzmann's ensemble significantly outperforms alternative features. This approach accurately predicts the successes or failures of expression experiments, which utilised Escherichia coli cells to express 11,430 recombinant proteins from over 189 diverse species. On this basis, we develop TIsigner that uses simulated annealing to modify up to the first nine codons of mRNAs with synonymous substitutions. We show that accessibility captures the key propensity beyond the target region (initiation sites in this case), as a modest number of synonymous changes is sufficient to tune the recombinant protein expression levels. We build a stochastic simulation model and show that higher accessibility leads to higher protein production and slower cell growth, supporting the idea of protein cost, where cell growth is constrained by protein circuits during overexpression.

TISIGNER.com: web services for improving recombinant protein production

Experiments that are planned using accurate prediction algorithms will mitigate failures in recombinant protein production. We have developed TISIGNER (https://tisigner.com) with the aim of addressing technical challenges to recombinant protein production. We offer three web services, TIsigner (Translation Initiation coding region designer), SoDoPE (Soluble Domain for Protein Expression) and Razor, which are specialised in synonymous optimisation of recombinant protein expression, solubility and signal peptide analysis, respectively. Importantly, TIsigner, SoDoPE and Razor are linked, which allows users to switch between the tools when optimising genes of interest.

Fitness landscape of a dynamic RNA structure

RNA structures are dynamic. As a consequence, mutational effects can be hard to rationalize with reference to a single static native structure. We reasoned that deep mutational scanning experiments, which couple molecular function to fitness, should capture mutational effects across multiple conformational states simultaneously. Here, we provide a proof-of-principle that this is indeed the case, using the self-splicing group I intron from Tetrahymena thermophila as a model system. We comprehensively mutagenized two 4-bp segments of the intron. These segments first come together to form the P1 extension (P1ex) helix at the 5' splice site. Following cleavage at the 5' splice site, the two halves of the helix dissociate to allow formation of an alternative helix (P10) at the 3' splice site. Using an in vivo reporter system that couples splicing activity to fitness in E. coli, we demonstrate that fitness is driven jointly by constraints on P1ex and P10 formation. We further show that patterns of epistasis can be used to infer the presence of intramolecular pleiotropy. Using a machine learning approach that allows quantification of mutational effects in a genotype-specific manner, we demonstrate that the fitness landscape can be deconvoluted to implicate P1ex or P10 as the effective genetic background in which molecular fitness is compromised or enhanced. Our results highlight deep mutational scanning as a tool to study alternative conformational states, with the capacity to provide critical insights into the structure, evolution and evolvability of RNAs as dynamic ensembles. Our findings also suggest that, in the future, deep mutational scanning approaches might help reverse-engineer multiple alternative or successive conformations from a single fitness landscape.

Pervasive Selection against MicroRNA Target Sites in Human Populations

MicroRNA target sites are often conserved during evolution and purifying selection to maintain such sites is expected. On the other hand, comparative analyses identified a paucity of microRNA target sites in coexpressed transcripts, and novel target sites can potentially be deleterious. We proposed that selection against novel target sites pervasive. The analysis of derived allele frequencies revealed that, when the derived allele is a target site, the proportion of nontarget sites is higher than expected, particularly for highly expressed microRNAs. Thus, new alleles generating novel microRNA target sites can be deleterious and selected against. When we analyzed ancestral target sites, the derived (nontarget) allele frequency does not show statistical support for microRNA target allele conservation. We investigated the joint effects of microRNA conservation and expression and found that selection against microRNA target sites depends mostly on the expression level of the microRNA. We identified microRNA target sites with relatively high levels of population differentiation. However, when we analyze separately target sites in which the target allele is ancestral to the population, the proportion of single-nucleotide polymorphisms with high F_st significantly increases. These findings support that population differentiation is more likely in target sites that are lost than in the gain of new target sites. Our results indicate that selection against novel microRNA target sites is prevalent and, although individual sites may have a weak selective pressure, the overall effect across untranslated regions is not negligible and should be accounted when studying the evolution of genomic sequences.

Determinants of translation efficiency in the evolutionarily-divergent protist Trichomonas vaginalis

BACKGROUND

Trichomonas vaginalis, the causative agent of a prevalent urogenital infection in humans, is an evolutionarily divergent protozoan. Protein-coding genes in T. vaginalis are largely controlled by two core promoter elements, producing mRNAs with short 5' UTRs. The specific mechanisms adopted by T. vaginalis to fine-tune the translation efficiency (TE) of mRNAs remain largely unknown.

RESULTS

Using both computational and experimental approaches, this study investigated two key factors influencing TE in T. vaginalis: codon usage and mRNA secondary structure. Statistical dependence between TE and codon adaptation index (CAI) highlighted the impact of codon usage on mRNA translation in T. vaginalis. A genome-wide interrogation revealed that low structural complexity at the 5' end of mRNA followed closely by a highly structured downstream region correlates with TE variation in this organism. To validate these findings, a synthetic library of 15 synonymous iLOV genes was created, representing five mRNA folding profiles and three codon usage profiles. Fluorescence signals produced by the expression of these synonymous iLOV genes in T. vaginalis were consistent with and validated our in silico predictions.

CONCLUSIONS

This study demonstrates the role of codon usage bias and mRNA secondary structure in TE of T. vaginalis mRNAs, contributing to a better understanding of the factors that influence, and possibly regulate, gene expression in this human pathogen.

A Nested 2-Level Cross-Validation Ensemble Learning Pipeline Suggests a Negative Pressure Against Crosstalk snoRNA-mRNA Interactions in Saccharomyces cerevisiae

The growing number of RNA-mediated regulation mechanisms identified in the past decades suggests a widespread impact of RNA-RNA interactions. The efficiency of the regulation relies on highly specific and coordinated interactions while simultaneously repressing the formation of opportunistic complexes. However, the analysis of RNA interactomes is highly challenging because of the large number of potential partners, discrepancy of the size of RNA families, and the inherent noise in interaction predictions. We designed a recursive two-step cross-validation pipeline to capture the specificity of noncoding RNA (ncRNA) messenger RNA (mRNA) interactomes. Our method has been designed to detect significant loss or gain of specificity between ncRNA-mRNA interaction profiles. Applied to small nucleolar RNA-mRNA in Saccharomyces cerevisiae, our results suggest the existence of a repression of ncRNA affinities with mRNAs and thus the existence of an evolutionary pressure leveling down such interactions.

Within-Gene Shine-Dalgarno Sequences Are Not Selected for Function

The Shine-Dalgarno (SD) sequence motif facilitates translation initiation and is frequently found upstream of bacterial start codons. However, thousands of instances of this motif occur throughout the middle of protein coding genes in a typical bacterial genome. Here, we use comparative evolutionary analysis to test whether SD sequences located within genes are functionally constrained. We measure the conservation of SD sequences across Enterobacteriales, and find that they are significantly less conserved than expected. Further, the strongest SD sequences are the least conserved whereas we find evidence of conservation for the weakest possible SD sequences given amino acid constraints. Our findings indicate that most SD sequences within genes are likely to be deleterious and removed via selection. To illustrate the origin of these deleterious costs, we show that ATG start codons are significantly depleted downstream of SD sequences within genes, highlighting the constraint that these sequences impose on the surrounding nucleotides to minimize the potential for erroneous translation initiation.

Transcriptional noise and exaptation as sources for bacterial sRNAs

Understanding how new genes originate and integrate into cellular networks is key to understanding evolution. Bacteria present unique opportunities for both the natural history and experimental study of gene origins, due to their large effective population sizes, rapid generation times, and ease of genetic manipulation. Bacterial small non-coding RNAs (sRNAs), in particular, many of which operate through a simple antisense regulatory logic, may serve as tractable models for exploring processes of gene origin and adaptation. Understanding how and on what timescales these regulatory molecules arise has important implications for understanding the evolution of bacterial regulatory networks, in particular, for the design of comparative studies of sRNA function. Here, we introduce relevant concepts from evolutionary biology and review recent work that has begun to shed light on the timescales and processes through which non-functional transcriptional noise is co-opted to provide regulatory functions. We explore possible scenarios for sRNA origin, focusing on the co-option, or exaptation, of existing genomic structures which may provide protected spaces for sRNA evolution.

RNA⁻Protein Interactions Prevent Long RNA Duplex Formation: Implications for the Design of RNA-Based Therapeutics

Cells frequently simultaneously express RNAs and cognate antisense transcripts without necessarily leading to the formation of RNA duplexes. Here, we present a novel transcriptome-wide experimental approach to ascertain the presence of accessible double-stranded RNA structures based on sequencing of RNA fragments longer than 18 nucleotides that were not degraded by single-strand cutting nucleases. We applied this approach to four different cell lines with respect to three different treatments (native cell lysate, removal of proteins, and removal of ribosomal RNA and proteins). We found that long accessible RNA duplexes were largely absent in native cell lysates, while the number of RNA duplexes was dramatically higher when proteins were removed. The majority of RNA duplexes involved ribosomal transcripts. The duplex formation between different non-ribosomal transcripts appears to be largely of a stochastic nature. These results suggest that cells are—via RNA-binding proteins—mostly devoid of long RNA duplexes, leading to low “noise” in the molecular patterns that are utilized by the innate immune system. These findings have implications for the design of RNA interference (RNAi)-based therapeutics by imposing structural constraints on designed RNA complexes that are intended to have specific properties with respect to Dicer cleavage and target gene downregulation.

Manifold Routes to a Nucleus

It is widely assumed that there is a clear distinction between eukaryotes, with cell nuclei, and prokaryotes, which lack nuclei. This suggests the evolution of nuclear compartmentation is a singular event. However, emerging knowledge of the diversity of bacterial internal cell structures suggests the picture may not be as black-and-white as previously thought. For instance, some members of the bacterial PVC superphylum appear to have nucleus-like compartmentation, where transcription and translation are physically separated, and some jumbophages have recently been shown to create nucleus-like structures within their Pseudomonad hosts. Moreover, there is also tantalizing metagenomic identification of new Archaea that carry homologs of genes associated with internal cell membrane structure in eukaryotes. All these cases invite comparison with eukaryote cell biology. While the bacterial cases of genetic compartmentation are likely convergent, and thus viewed by many as not germane to the question of eukaryote origins, we argue here that, in addressing the broader question of the evolution of compartmentation, other instances are at least as important: they provide us with a point of comparison which is critical for a more general understanding of both the conditions favoring the emergence of intracellular compartmentation of DNA and the evolutionary consequences of such cellular architecture. Finally, we consider three classes of explanation for the emergence of compartmentation: physical protection, crosstalk avoidance and nonadaptive origins.

Local genic base composition impacts protein production and cellular fitness

The maintenance of a G + C content that is higher than the mutational input to a genome provides support for the view that selection serves to increase G + C contents in bacteria. Recent experimental evidence from Escherichia coli demonstrated that selection for increasing G + C content operates at the level of translation, but the precise mechanism by which this occurs is unknown. To determine the substrate of selection, we asked whether selection on G + C content acts across all sites within a gene or is confined to particular genic regions or nucleotide positions. We systematically altered the G + C contents of the GFP gene and assayed its effects on the fitness of strains harboring each variant. Fitness differences were attributable to the base compositional variation in the terminal portion of the gene, suggesting a connection to the folding of a specific protein feature. Variants containing sequence features that are thought to result in rapid translation, such as low G + C content and high levels of codon adaptation, displayed highly reduced growth rates. Taken together, our results show that purifying selection acting against A and T mutations most likely results from their tendency to increase the rate of translation, which can perturb the dynamics of protein folding.

In vivo selection of sfGFP variants with improved and reliable functionality in industrially important thermophilic bacteria

BACKGROUND

Fluorescent reporter proteins (FP) have become an indispensable tool for the optimization of microbial cell factories and in synthetic biology per se. The applicability of the currently available FPs is, however, constrained by species-dependent performance and misfolding at elevated temperatures. To obtain functional reporters for thermophilic, biotechnologically important bacteria such as Parageobacillus thermoglucosidasius, an in vivo screening approach based on a mutational library of superfolder GFP was applied.

RESULTS

Flow cytometry-based benchmarking of a set of GFPs, sfGFPs and species-specific codon-optimized variants revealed that none of the proteins was satisfyingly detectable in P. thermoglucosidasius at its optimal growth temperature of 60 °C. An undirected mutagenesis approach coupled to fluorescence-activated cell sorting allowed the isolation of sfGFP variants that were extremely well expressed in the chassis background at 60 °C. Notably, a few nucleotide substitutions, including silent mutations, significantly improved the functionality and brightness. The best mutant sfGFP(N39D/A179A) showed an 885-fold enhanced mean fluorescence intensity (MFI) at 60 °C and is the most reliable reporter protein with respect to cell-to-cell variation and signal intensity reported so far. The in vitro spectral and thermostability properties were unaltered as compared to the parental sfGFP protein, strongly indicating that the combination of the amino acid exchange and an altered translation or folding speed, or protection from degradation, contribute to the strongly improved in vivo performance. Furthermore, sfGFP(N39D/A179A) and the newly developed cyan and yellow derivatives were successfully used for labeling several industrially relevant thermophilic bacilli, thus proving their broad applicability.

CONCLUSIONS

This study illustrates the power of in vivo isolation of thermostable proteins to obtain reporters for highly efficient fluorescence labeling. Successful expression in a variety of thermophilic bacteria proved that the novel FPs are highly suitable for imaging and flow cytometry-based studies. This enables a reliable cell tracking and single-cell-based real-time monitoring of biological processes that are of industrial and biotechnological interest.

RNA search engines empower the bacterial intranet

RNA acts not only as an information bearer in the biogenesis of proteins from genes, but also as a regulator that participates in the control of gene expression. In bacteria, small RNA molecules (sRNAs) play controlling roles in numerous processes and help to orchestrate complex regulatory networks. Such processes include cell growth and development, response to stress and metabolic change, transcription termination, cell-to-cell communication, and the launching of programmes for host invasion. All these processes require recognition of target messenger RNAs by the sRNAs. This review summarizes recent results that have provided insights into how bacterial sRNAs are recruited into effector ribonucleoprotein complexes that can seek out and act upon target transcripts. The results hint at how sRNAs and their protein partners act as pattern-matching search engines that efficaciously regulate gene expression, by performing with specificity and speed while avoiding off-target effects. The requirements for efficient searches of RNA patterns appear to be common to all domains of life.

A comprehensive benchmark of RNA-RNA interaction prediction tools for all domains of life

Motivation

The aim of this study is to assess the performance of RNA-RNA interaction prediction tools for all domains of life.

Results

Minimum free energy (MFE) and alignment methods constitute most of the current RNA interaction prediction algorithms. The MFE tools that include accessibility (i.e. RNAup, IntaRNA and RNAplex) to the final predicted binding energy have better true positive rates (TPRs) with a high positive predictive values (PPVs) in all datasets than other methods. They can also differentiate almost half of the native interactions from background. The algorithms that include effects of internal binding energies to their model and alignment methods seem to have high TPR but relatively low associated PPV compared to accessibility based methods.

Availability and Implementation

We shared our wrapper scripts and datasets at Github (github.com/UCanCompBio/RNA_Interactions_Benchmark). All parameters are documented for personal use.

Contact

sinan.umu@pg.canterbury.ac.nz.

Supplementary information

Supplementary data are available at Bioinformatics online.

Selecting against accidental RNA interactions

Random base-pairing interactions between messenger RNAs and noncoding RNAs can reduce translation efficiency.