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

Ehrlich, Ricardo; Davyt, Marcos; López, Ignacio; Chalar, Cora; Marı́n, Mónica

doi:10.3389/fmolb.2021.643701

Cited by 21 publications

(21 citation statements)

References 146 publications

(221 reference statements)

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“…This scoring system helps to classify if tRNAs are functional in translation and accounts for tRNA-derived short interspersed repeated elements (SINEs) [ 33 ]. A recent review summarized “missing tRNA genes” across different kingdoms, illustrating that Pro GGG , Leu GAG and Val CAG are absent in 60 eukarya species whereas Asn AUU is absent from 60 eukarya, 100 bacteria, and 50 archaea species [ 34 ].…”

Section: Resultsmentioning

confidence: 99%

Characterization of tRNA expression profiles in large offspring syndrome

Goldkamp

Rivera

et al. 2022

BMC Genomics

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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 97th 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 (SerGGA). 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.

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Section: Resultsmentioning

confidence: 99%

Characterization of tRNA expression profiles in large offspring syndrome

Goldkamp

Rivera

et al. 2022

BMC Genomics

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“…Like in other bacteria ( 42 ), the tRNA repertoires of the P. aeruginosa PAO1 ( 15 ) and clone C genomes (this work) consist of one, two, or three copies of 37 different isoacceptor genes plus three stop codons (Table S1). The ONT and Illumina platforms detected similar transcript levels of uncharged tRNA isoacceptors in the clone C biological replicates ( Fig.…”

Section: Resultsmentioning

confidence: 99%

Direct RNA Nanopore Sequencing of Pseudomonas aeruginosa Clone C Transcriptomes

et al. 2022

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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.

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“…Furthermore, the different Chargaff’s and Shannon’s scores of the tRNA highlighted in our analysis could take on several aspects, including the principle of purine ejection along the prevailing phylogeny affecting position 34 of the anticodon loop, the wobble position interfering with protein translation, or even tRNA modification depending on Guanosine replacement by Queuosine incorporation or Inosine use. This has probably led to an evolutionary reduction in ambiguous signals or tRNA adaptation phenomena at ribosome sites [ 31 ]. Recent findings have shown that tRNAs may interact with other macromolecules that have no translational functions.…”

Section: Discussionmentioning

confidence: 99%

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

Bergamini

Bianchi

Giaccone

et al. 2022

Biology

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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.

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

Cited by 21 publications

References 146 publications

Characterization of tRNA expression profiles in large offspring syndrome

Characterization of tRNA expression profiles in large offspring syndrome

Direct RNA Nanopore Sequencing of Pseudomonas aeruginosa Clone C Transcriptomes

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

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