On the Track of the Missing tRNA Genes: A Source of Non-Canonical Functions?

Codon decoding by orthogonal tRNAs interrogates the in vivo preferences of unmodified adenosine in the wobble position

The in vivo codon decoding preferences of tRNAs with an authentic adenosine residue at position 34 of the anticodon, the wobble position, are largely unexplored because very few unmodified A34 tRNA genes exist across the three domains of life. The expanded wobble rules suggest that unmodified adenosine pairs most strongly with uracil, modestly with cytosine, and weakly with guanosine and adenosine. Inosine, a modified adenosine, on the other hand, pairs strongly with both uracil and cytosine and to a lesser extent adenosine. Orthogonal pair directed sense codon reassignment experiments offer a tool with which to interrogate the translational activity of A34 tRNAs because the introduced tRNA can be engineered with any anticodon. Our fluorescence-based screen utilizes the absolute requirement of tyrosine at position 66 of superfolder GFP for autocatalytic fluorophore formation. The introduced orthogonal tRNA competes with the endogenous translation machinery to incorporate tyrosine in response to a codon typically assigned another meaning in the genetic code. We evaluated the codon reassignment efficiencies of 15 of the 16 possible orthogonal tRNAs with A34 anticodons. We examined the Sanger sequencing chromatograms for cDNAs from each of the reverse transcribed tRNAs for evidence of inosine modification. Despite several A34 tRNAs decoding closely-related C-ending codons, partial inosine modification was detected for only three species. These experiments employ a single tRNA body with a single attached amino acid to interrogate the behavior of different anticodons in the background of in vivo E. coli translation and greatly expand the set of experimental measurements of the in vivo function of A34 tRNAs in translation. For the most part, unmodified A34 tRNAs largely pair with only U3 codons as the original wobble rules suggest. In instances with GC pairs in the first two codon positions, unmodified A34 tRNAs decode the C- and G-ending codons as well as the expected U-ending codon. These observations support the "two-out-of-three" and "strong and weak" codon hypotheses.

Fitness Effects of Phenotypic Mutations at Proteome-Scale Reveal Optimality of Translation Machinery

Errors in protein translation can lead to non-genetic, phenotypic mutations, including amino acid misincorporations. While phenotypic mutations can increase protein diversity, the systematic characterization of their proteome-wide frequencies and their evolutionary impact has been lacking. Here, we developed a mechanistic model of translation errors to investigate how selection acts on protein populations produced by amino acid misincorporations. We fitted the model to empirical observations of misincorporations obtained from over a hundred mass spectrometry datasets of E. coli and S. cerevisiae. We found that on average 20% to 23% of proteins synthesized in the cell are expected to harbor at least one amino acid misincorporation, and that deleterious misincorporations are less likely to occur. Combining misincorporation probabilities and the estimated fitness effects of amino acid substitutions in a population genetics framework, we found 74% of mistranslation events in E. coli and 94% in S. cerevisiae to be neutral. We further show that the set of available synonymous tRNAs is subject to evolutionary pressure, as the presence of missing tRNAs would increase codon-anticodon cross-reactivity and misincorporation error rates. Overall, we find that the translation machinery is likely optimal in E. coli and S. cerevisiae and that both local solutions at the level of codons and a global solution such as the tRNA pool can mitigate the impact of translation errors. We provide a framework to study the evolutionary impact of codon-specific translation errors and a method for their proteome-wide detection across organisms and conditions.

Contribution of tRNA sequence and modifications to the decoding preferences of E. coli and M. mycoides tRNAGlyUCC for synonymous glycine codons

tRNA superwobbling, used by certain bacteria and organelles, is an intriguing decoding concept in which a single tRNA isoacceptor is used to decode all synonymous codons of a four-fold degenerate codon box. While Escherichia coli relies on three tRNAGly isoacceptors to decode the four glycine codons (GGN), Mycoplasma mycoides requires only a single tRNAGly. Both organisms express tRNAGly with the anticodon UCC, which are remarkably similar in sequence but different in their decoding ability. By systematically introducing mutations and altering the number and type of tRNA modifications using chemically synthesized tRNAs, we elucidated the contribution of individual nucleotides and chemical groups to decoding by the E. coli and M. mycoides tRNAGly. The tRNA sequence was identified as the key factor for superwobbling, revealing the T-arm sequence as a novel pivotal element. In addition, the presence of tRNA modifications, although not essential for providing superwobbling, was shown to delicately fine-tune and balance the decoding of synonymous codons. This emphasizes that the tRNA sequence and its modifications together form an intricate system of high complexity that is indispensable for accurate and efficient decoding.

Implications of tRNA abundance on translation elongation across bovine tissues

Introduction: Translation is a crucial stage of gene expression. It may also act as an additional layer of regulation that plays an important role in gene expression and function. Highly expressed genes are believed to be codon-biased to support increased protein production, in which quickly translated codons correspond to highly abundant tRNAs. Synonymous SNPs, considered to be silent due to the degeneracy of the genetic code, may shift protein abundance and function through alterations in translational efficiency and suboptimal pairing to lowly abundant tRNAs. Methods: Here, we applied Quantitative Mature tRNA sequencing (QuantM-tRNAseq) and ribosome profiling across bovine tissues in order to investigate the relationship between tRNA expression and slowed translation. Results: Moreover, we have identified genes modulated at transcriptional and/or translational levels underlying tissue-specific biological processes. We have also successfully defined pausing sites that depict the regulatory information encoded within the open reading frame of transcripts, which could be related to translation rate and facilitate proper protein folding. This work offers an atlas of distinctive pausing sites across three bovine tissues, which provides an opportunity to predict codon optimality and understand tissue-specific mechanisms of regulating protein synthesis.

Effect of mRNA/tRNA mutations on translation speed: Implications for human diseases

Recent discoveries establish tRNAs as central regulators of mRNA translation dynamics, and therefore cotranslational folding and function of the encoded protein. The tRNA pool, whose composition and abundance change in a cell- and tissue-dependent manner, is the main factor which determines mRNA translation velocity. In this review, we discuss a group of pathogenic mutations, in the coding sequences of either protein-coding genes or in tRNA genes, that alter mRNA translation dynamics. We also summarize advances in tRNA biology that have uncovered how variations in tRNA levels on account of genetic mutations affect protein folding and function, and thereby contribute to phenotypic diversity in clinical manifestations.

Recent expansion of metabolic versatility in Diplonema papillatum, the model species of a highly speciose group of marine eukaryotes

BACKGROUND

Diplonemid flagellates are among the most abundant and species-rich of known marine microeukaryotes, colonizing all habitats, depths, and geographic regions of the world ocean. However, little is known about their genomes, biology, and ecological role.

RESULTS

We present the first nuclear genome sequence from a diplonemid, the type species Diplonema papillatum. The ~ 280-Mb genome assembly contains about 32,000 protein-coding genes, likely co-transcribed in groups of up to 100. Gene clusters are separated by long repetitive regions that include numerous transposable elements, which also reside within introns. Analysis of gene-family evolution reveals that the last common diplonemid ancestor underwent considerable metabolic expansion. D. papillatum-specific gains of carbohydrate-degradation capability were apparently acquired via horizontal gene transfer. The predicted breakdown of polysaccharides including pectin and xylan is at odds with reports of peptides being the predominant carbon source of this organism. Secretome analysis together with feeding experiments suggest that D. papillatum is predatory, able to degrade cell walls of live microeukaryotes, macroalgae, and water plants, not only for protoplast feeding but also for metabolizing cell-wall carbohydrates as an energy source. The analysis of environmental barcode samples shows that D. papillatum is confined to temperate coastal waters, presumably acting in bioremediation of eutrophication.

CONCLUSIONS

Nuclear genome information will allow systematic functional and cell-biology studies in D. papillatum. It will also serve as a reference for the highly diverse diplonemids and provide a point of comparison for studying gene complement evolution in the sister group of Kinetoplastida, including human-pathogenic taxa.

An old friend with a new face: tRNA-derived small RNAs with big regulatory potential in cancer biology

Transfer RNAs (tRNAs) are small non-coding RNAs (sncRNAs) essential for protein translation. Emerging evidence suggests that tRNAs can also be processed into smaller fragments, tRNA-derived small RNAs (tsRNAs), a novel class of sncRNAs with powerful applications and high biological relevance to cancer. tsRNAs biogenesis is heterogeneous and involves different ribonucleases, such as Angiogenin and Dicer. For many years, tsRNAs were thought to be just degradation products. However, accumulating evidence shows their roles in gene expression: either directly via destabilising the mRNA or the ribosomal machinery, or indirectly via regulating the expression of ribosomal components. Furthermore, tsRNAs participate in various biological processes linked to cancer, including apoptosis, cell cycle, immune response, and retroviral insertion into the human genome. It is emerging that tsRNAs have significant therapeutic potential. Endogenous tsRNAs can be used as cancer biomarkers, while synthetic tsRNAs and antisense oligonucleotides can be employed to regulate gene expression. In this review, we are recapitulating the regulatory roles of tsRNAs, with a focus on cancer biology.

On distinguishing between canonical tRNA genes and tRNA gene fragments in prokaryotes

Automated genome annotation is essential for extracting biological information from sequence data. The identification and annotation of tRNA genes is frequently performed by the software package tRNAscan-SE, the output of which is listed for selected genomes in the Genomic tRNA database (GtRNAdb). Here, we highlight a pervasive error in prokaryotic tRNA gene sets on GtRNAdb: the mis-categorization of partial, non-canonical tRNA genes as standard, canonical tRNA genes. Firstly, we demonstrate the issue using the tRNA gene sets of 20 organisms from the archaeal taxon Thermococcaceae. According to GtRNAdb, these organisms collectively deviate from the expected set of tRNA genes in 15 instances, including the listing of eleven putative canonical tRNA genes. However, after detailed manual annotation, only one of these eleven remains; the others are either partial, non-canonical tRNA genes resulting from the integration of genetic elements or CRISPR-Cas activity (seven instances), or attributable to ambiguities in input sequences (three instances). Secondly, we show that similar examples of the mis-categorization of predicted tRNA sequences occur throughout the prokaryotic sections of GtRNAdb. While both canonical and non-canonical prokaryotic tRNA gene sequences identified by tRNAscan-SE are biologically interesting, the challenge of reliably distinguishing between them remains. We recommend employing a combination of (i) screening input sequences for the genetic elements typically associated with non-canonical tRNA genes, and ambiguities, (ii) activating the tRNAscan-SE automated pseudogene detection function, and (iii) scrutinizing predicted tRNA genes with low isotype scores. These measures greatly reduce manual annotation efforts, and lead to improved prokaryotic tRNA gene set predictions.

Differentially expressed tRNA-derived fragments in bovine fetuses with assisted reproduction induced congenital overgrowth syndrome

Background: As couples struggle with infertility and livestock producers wish to rapidly improve genetic merit in their herd, assisted reproductive technologies (ART) have become increasingly popular in human medicine as well as the livestock industry. Utilizing ART can cause an increased risk of congenital overgrowth syndromes, such as Large Offspring Syndrome (LOS) in ruminants. A dysregulation of transcripts has been observed in bovine fetuses with LOS, which is suggested to be a cause of the phenotype. Our recent study identified variations in tRNA expression in LOS individuals, leading us to hypothesize that variations in tRNA expression can influence the availability of their processed regulatory products, tRNA-derived fragments (tRFs). Due to their resemblance in size to microRNAs, studies suggest that tRFs target mRNA transcripts and regulate gene expression. Thus, we have sequenced small RNA isolated from skeletal muscle and liver of day 105 bovine fetuses to elucidate the mechanisms contributing to LOS. Moreover, we have utilized our previously generated tRNA sequencing data to analyze the contribution of tRNA availability to tRF abundance. Results: 22,289 and 7,737 unique tRFs were predicted in the liver and muscle tissue respectively. The greatest number of reads originated from 5' tRFs in muscle and 5' halves in liver. In addition, mitochondrial (MT) and nuclear derived tRF expression was tissue-specific with most MT-tRFs and nuclear tRFs derived from LysUUU and iMetCAU in muscle, and AsnGUU and GlyGCC in liver. Despite variation in tRF abundance within treatment groups, we identified differentially expressed (DE) tRFs across Control-AI, ART-Normal, and ART-LOS groups with the most DE tRFs between ART-Normal and ART-LOS groups. Many DE tRFs target transcripts enriched in pathways related to growth and development in the muscle and tumor development in the liver. Finally, we found positive correlation coefficients between tRNA availability and tRF expression in muscle (R = 0.47) and liver (0.6). Conclusion: Our results highlight the dysregulation of tRF expression and its regulatory roles in LOS. These tRFs were found to target both imprinted and non-imprinted genes in muscle as well as genes linked to tumor development in the liver. Furthermore, we found that tRNA transcription is a highly modulated event that plays a part in the biogenesis of tRFs. This study is the first to investigate the relationship between tRNA and tRF expression in combination with ART-induced LOS.

PlantRNA 2.0: an updated database dedicated to tRNAs of photosynthetic eukaryotes

PlantRNA (http://plantrna.ibmp.cnrs.fr/) is a comprehensive database of transfer RNA (tRNA) gene sequences retrieved from fully annotated nuclear, plastidial and mitochondrial genomes of photosynthetic organisms. In the first release (PlantRNA 1.0), tRNA genes from 11 organisms were annotated. In this second version, the annotation was implemented to 51 photosynthetic species covering the whole phylogenetic tree of photosynthetic organisms, from the most basal group of Archeplastida, the glaucophyte Cyanophora paradoxa, to various land plants. tRNA genes from lower photosynthetic organisms such as streptophyte algae or lycophytes as well as extremophile photosynthetic species such as Eutrema parvulum were incorporated in the database. As a whole, about 37 000 tRNA genes were accurately annotated. In the frame of the tRNA genes annotation from the genome of the Rhodophyte Chondrus crispus, non-canonical splicing sites in the D- or T-regions of tRNA molecules were identified and experimentally validated. As for PlantRNA 1.0, comprehensive biological information including 5'- and 3'-flanking sequences, A and B box sequences, region of transcription initiation and poly(T) transcription termination stretches, tRNA intron sequences and tRNA mitochondrial import are included.

Machine Learning Algorithms Highlight tRNA Information Content and Chargaff’s Second Parity Rule Score as Important Features in Discriminating Probiotics from Non-Probiotics

Simple Summary

Probiotics are a group of beneficial microorganisms that are symbionts of the human gut microbiome. The identification of new probiotics is therefore of paramount importance from both public health and commercial perspectives. In this study, we show for the first time that Artificial Intelligence algorithms can identify novel probiotics and also discriminate them from pathogenic organisms in the human gut. We were also able to determine the information content within tRNA sequences as the key genomic features capable of characterizing probiotics.

Abstract

Probiotic bacteria are microorganisms with beneficial effects on human health and are currently used in numerous food supplements. However, no selection process is able to effectively distinguish probiotics from non-probiotic organisms on the basis of their genomic characteristics. In the current study, four Machine Learning algorithms were employed to accurately identify probiotic bacteria based on their DNA characteristics. Although the prediction accuracies of all algorithms were excellent, the Neural Network returned the highest scores in all the evaluation metrics, managing to discriminate probiotics from non-probiotics with an accuracy greater than 90%. Interestingly, our analysis also highlighted the information content of the tRNA sequences as the most important feature in distinguishing the two groups of organisms probably because tRNAs have regulatory functions and might have allowed probiotics to evolve faster in the human gut environment. Through the methodology presented here, it was also possible to identify seven promising new probiotics that have a higher information content in their tRNA sequences compared to non-probiotics. In conclusion, we prove for the first time that Machine Learning methods can discriminate human probiotic from non-probiotic organisms underlining information within tRNA sequences as the most important genomic feature in distinguishing them.

Bacterial tRNA landscape revisited

The updated Wobble Hypothesis reasonably explains why some 40 tRNA species are sufficient to decode the 61 amino acid codons of the Universal Genetic Code. However, we still have no clue why eubacteria lack tRNA isoacceptors with ANN anticodons, whereas eukaryotes universally lack eight GNN anticodons, only one of which is also absent in bacteria. Direct tRNA sequencing could resolve the patterns of nucleoside modification that had been driving the divergent evolution in prokaryotes and eukaryotes, but this task will require the development of AI-supported base-callers that can recognize modified nucleosides without any subsequent analytical verification. Our knowledge of the bacterial tRNA landscape is moreover broadened by the recent discovery of antisense tRNAs and tRNA-derived fragments that should be examined in their roles for gene expression, translation, bacterial physiology or metabolism.

Characterization of tRNA expression profiles in large offspring syndrome

Background

Assisted Reproductive Technologies (ART) use can increase the risk of congenital overgrowth syndromes, such as large offspring syndrome (LOS) in ruminants. Epigenetic variations are known to influence gene expression and differentially methylated regions (DMRs) were previously determined to be associated with LOS in cattle. We observed DMRs overlapping tRNA clusters which could affect tRNA abundance and be associated with tissue specificity or overgrowth. Variations in tRNA expression have been identified in several disease pathways suggesting an important role in the regulation of biological processes. Understanding the role of tRNA expression in cattle offers an opportunity to reveal mechanisms of regulation at the translational level. We analyzed tRNA expression in the skeletal muscle and liver tissues of day 105 artificial insemination-conceived, ART-conceived with a normal body weight, and ART-conceived bovine fetuses with a body weight above the 97^th percentile compared to Control-AI.

Results

Despite the centrality of tRNAs to translation, in silico predictions have revealed dramatic differences in the number of tRNA genes between humans and cattle (597 vs 1,659). Consistent with reports in human, only a fraction of predicted tRNA genes are expressed. We detected the expression of 474 and 487 bovine tRNA genes in the muscle and liver with the remainder being unexpressed. 193 and 198 unique tRNA sequences were expressed in all treatment groups within muscle and liver respectively. In addition, an average of 193 tRNA sequences were expressed within the same treatment group in different tissues. Some tRNA isodecoders were differentially expressed between treatment groups. In the skeletal muscle and liver, we categorized 11 tRNA isoacceptors with undetected expression as well as an isodecoder that was unexpressed in the liver (Ser^GGA). Our results identified variation in the proportion of tRNA gene copies expressed between tissues and differences in the highest contributing tRNA anticodon within an amino acid family due to treatment and tissue type. Out of all amino acid families, roughly half of the most highly expressed tRNA isoacceptors correlated to their most frequent codon in the bovine genome.

Conclusion

Although the number of bovine tRNA genes is nearly triple of that of the tRNA genes in human, there is a shared occurrence of transcriptionally inactive tRNA genes in both species. We detected differential expression of tRNA genes as well as tissue- and treatment- specific tRNA transcripts with unique sequence variations that could modulate translation during protein homeostasis or cellular stress, and give rise to regulatory products targeting genes related to overgrowth in the skeletal muscle and/or tumor development in the liver of LOS individuals. While the absence of certain isodecoders may be relieved by wobble base pairing, missing tRNA species could increase the likelihood of mistranslation or mRNA degradation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08496-7.

Characterization of the mitogenome of Gongronella sp. w5 reveals substantial variation in Mucoromycota

Gongronella is a genus of fungi in Mucorales (Mucoromycota). Some of its members have important biotechnological applications, but until now, not a single mitogenome has been characterized in Gongronella. Here, we present the complete mitogenome assembly of Gongronella sp. w5, a soil isolate known to interact with plants and several fungi. Its 36,593-bp circular mitogenome encodes the large and small subunit rRNAs, 14 standard mitochondrial proteins, 24 tRNAs, three free-standing ORF proteins, and the RNA subunit of RNase P (rnpB). These genes arrange in an order novel to known fungal mitogenomes. Three group I introns are present in the cob, cox1, and nad5 genes, respectively, and they are probably acquired by horizontal gene transfer. Phylogenetic analysis based on mitochondrion-encoded proteins supports the grouping of Gongronella sp. w5 with Absidia glauca, forming the Cunninghamellaceae clade within Mucoromycota. Gongronella and most other Mucoromycota species are predicted to use the standard genetic code in mitochondrial translation, rather than code 4 assigned by GenBank. A comparison among seven publicly available mitogenomes in Mucoromycota reveals the presence of the same 14 typical protein-coding genes plus rnpB, yet substantial variation in mitogenome size, intron number, gene order, and orientation. In this comparison, the uniqueness of Gongronella is evident from similarly large differences to its closest phylogenetic neighbor, A. glauca. This study promotes our understanding of fungal evolution in Mucoromycota. KEY POINTS: • This study reports the first mitogenome in Gongronella, which presents a novel gene order. • Different Mucoromycota mitogenomes show substantial variation of gene organizations. • Most Mucoromycota species use the standard genetic code to translate mitochondrial genes.

Direct RNA nanopore sequencing of Pseudomonas aeruginosa clone C transcriptomes

The transcriptomes of Pseudomonas aeruginosa clone C isolates NN2 and SG17M during the mid-exponential and early stationary phase of planktonic growth were evaluated by direct RNA sequencing on the nanopore platform and compared with established short-read cDNA sequencing on the Illumina platform. Fifty to ninety percent of the sense RNAs turned out to be rRNA molecules followed by similar proportions of mRNA transcripts and non-coding RNAs. Both platforms detected similar proportions of uncharged tRNAs and 29 yet undescribed antisense tRNAs. For example, the rarest arginine codon was paired with the most abundant tRNA^Arg, and the tRNA^Arg gene is missing for the most frequent arginine codon. More than 90% of the antisense RNA molecules were complementary to a coding sequence. The antisense RNAs were evenly distributed in the genomes. Direct RNA sequencing identified more than 4,000 distinct non-overlapping antisense RNAs during exponential and stationary growth. Besides highly expressed small antisense RNAs less than 200 bases in size, a population of longer antisense RNAs was sequenced that covered a broad range of a few hundred to thousands of bases and could be complementary to a contig of several genes. In summary, direct RNA sequencing identified yet undescribed RNA molecules and an unexpected composition of the pools of tRNAs, sense and antisense RNAs. IMPORTANCE Genome-wide gene expression of bacteria is commonly studied by high-throughput sequencing of size-selected cDNA fragment libraries of reverse-transcribed RNA preparations. However, the depletion of ribosomal RNAs, enzymatic reverse transcription and the fragmentation, size selection and amplification during library preparation lead to inevitable losses of information about the initial composition of the RNA pool. We demonstrate that direct RNA sequencing on the nanopore platform can overcome these limitations. Nanopore sequencing of total RNA yielded novel insights into the Pseudomonas aeruginosa transcriptome that - if replicated in other species - will change our view of the bacterial RNA world. The discovery of sense - antisense pairs of tmRNA, tRNAs and mRNAs indicates a further and unknown level of gene regulation in bacteria.