MIST, a Novel Approach to Reveal Hidden Substrate Specificity in Aminoacyl-tRNA Synthetases

Identification of host tRNAs preferentially recognized by the Plasmodium surface protein tRip

Malaria is a life-threatening and devastating parasitic disease. Our previous work showed that parasite development requires the import of exogenous transfer RNAs (tRNAs), which represents a novel and unique form of host-pathogen interaction, as well as a potentially druggable target. This import is mediated by tRip (tRNA import protein), a membrane protein located on the parasite surface. tRip displays an extracellular domain homologous to the well-characterized OB-fold tRNA-binding domain, a structural motif known to indiscriminately interact with tRNAs. We used MIST (Microarray Identification of Shifted tRNAs), a previously established in vitro approach, to systematically assess the specificity of complexes between native Homo sapiens tRNAs and recombinant Plasmodium falciparum tRip. We demonstrate that tRip unexpectedly binds to host tRNAs with a wide range of affinities, suggesting that only a small subset of human tRNAs is preferentially imported into the parasite. In particular, we show with in vitro transcribed constructs that tRip does not bind specific tRNAs solely based on their primary sequence, hinting that post-transcriptional modifications modulate the formation of our host/parasite molecular complex. Finally, we discuss the potential utilization of the most efficient tRip ligands for the translation of the parasite's genetic information.

The nature of the purine at position 34 in tRNAs of 4-codon boxes is correlated with nucleotides at positions 32 and 38 to maintain decoding fidelity

Translation fidelity relies essentially on the ability of ribosomes to accurately recognize triplet interactions between codons on mRNAs and anticodons of tRNAs. To determine the codon-anticodon pairs that are efficiently accepted by the eukaryotic ribosome, we took advantage of the IRES from the intergenic region (IGR) of the Cricket Paralysis Virus. It contains an essential pseudoknot PKI that structurally and functionally mimics a codon-anticodon helix. We screened the entire set of 4096 possible combinations using ultrahigh-throughput screenings combining coupled transcription/translation and droplet-based microfluidics. Only 97 combinations are efficiently accepted and accommodated for translocation and further elongation: 38 combinations involve cognate recognition with Watson-Crick pairs and 59 involve near-cognate recognition pairs with at least one mismatch. More than half of the near-cognate combinations (36/59) contain a G at the first position of the anticodon (numbered 34 of tRNA). G34-containing tRNAs decoding 4-codon boxes are almost absent from eukaryotic genomes in contrast to bacterial genomes. We reconstructed these missing tRNAs and could demonstrate that these tRNAs are toxic to cells due to their miscoding capacity in eukaryotic translation systems. We also show that the nature of the purine at position 34 is correlated with the nucleotides present at 32 and 38.

Intrinsic Properties of tRNA Molecules as Deciphered via Bayesian Network and Distribution Divergence Analysis

The identity/recognition of tRNAs, in the context of aminoacyl tRNA synthetases (and other molecules), is a complex phenomenon that has major implications ranging from the origins and evolution of translation machinery and genetic code to the evolution and speciation of tRNAs themselves to human mitochondrial diseases to artificial genetic code engineering. Deciphering it via laboratory experiments, however, is difficult and necessarily time- and resource-consuming. In this study, we propose a mathematically rigorous two-pronged in silico approach to identifying and classifying tRNA positions important for tRNA identity/recognition, rooted in machine learning and information-theoretic methodology. We apply Bayesian Network modeling to elucidate the structure of intra-tRNA-molecule relationships, and distribution divergence analysis to identify meaningful inter-molecule differences between various tRNA subclasses. We illustrate the complementary application of these two approaches using tRNA examples across the three domains of life, and identify and discuss important (informative) positions therein. In summary, we deliver to the tRNA research community a novel, comprehensive methodology for identifying the specific elements of interest in various tRNA molecules, which can be followed up by the corresponding experimental work and/or high-resolution position-specific statistical analyses.

Metabolic Labeling and Profiling of Transfer RNAs Using Macroarrays

Transfer RNAs (tRNA) are abundant short non-coding RNA species that are typically 76 to 90 nucleotides in length. tRNAs are directly responsible for protein synthesis by translating codons in mRNA into amino acid sequences. tRNAs were long considered as house-keeping molecules that lacked regulatory functions. However, a growing body of evidence indicates that cellular tRNA levels fluctuate in correspondence to varying conditions such as cell type, environment, and stress. The fluctuation of tRNA expression directly influences gene translation, favoring or repressing the expression of particular proteins. Ultimately comprehending the dynamic of protein synthesis requires the development of methods able to deliver high-quality tRNA profiles. The method that we present here is named SPOt, which stands for Streamlined Platform for Observing tRNA. SPOt consists of three steps starting with metabolic labeling of cell cultures with radioactive orthophosphate, followed by guanidinium thiocyanate-phenol-chloroform extraction of radioactive total RNAs and finally hybridization on in-house printed macroarrays. tRNA levels are estimated by quantifying the radioactivity intensities at each probe spot. In the protocol presented here we profile tRNAs in Mycobacterium smegmatis mc²155, a nonpathogenic bacterium often used as a model organism to study tuberculosis.

SPOt: A novel and streamlined microarray platform for observing cellular tRNA levels

Recent studies have placed transfer RNA (tRNA), a housekeeping molecule, in the heart of fundamental cellular processes such as embryonic development and tumor progression. Such discoveries were contingent on the concomitant development of methods able to deliver high-quality tRNA profiles. The present study describes the proof of concept obtained in Escherichia coli (E. coli) for an original tRNA analysis platform named SPOt (Streamlined Platform for Observing tRNA). This approach comprises three steps. First, E. coli cultures are spiked with radioactive orthophosphate; second, labeled total RNAs are trizol-extracted; third, RNA samples are hybridized on in-house printed microarrays and spot signals, the proxy for tRNA levels, are quantified by phosphorimaging. Features such as reproducibility and specificity were assessed using several tRNA subpopulations. Dynamic range and sensitivity were evaluated by overexpressing specific tRNA species. SPOt does not require any amplification or post-extraction labeling and can be adapted to any organism. It is modular and easily streamlined with popular techniques such as polysome fractionation to profile tRNAs interacting with ribosomes and actively engaged in translation. The biological relevance of these data is discussed in regards to codon usage, tRNA gene copy number, and position on the genome.

The Enzymatic Paradox of Yeast Arginyl-tRNA Synthetase: Exclusive Arginine Transfer Controlled by a Flexible Mechanism of tRNA Recognition

Identity determinants are essential for the accurate recognition of transfer RNAs by aminoacyl-tRNA synthetases. To date, arginine determinants in the yeast Saccharomyces cerevisiae have been identified exclusively in vitro and only on a limited number of tRNA Arginine isoacceptors. In the current study, we favor a full cellular approach and expand the investigation of arginine determinants to all four tRNA Arg isoacceptors. More precisely, this work scrutinizes the relevance of the tRNA nucleotides at position 20, 35 and 36 in the yeast arginylation reaction. We built 21 mutants by site-directed mutagenesis and tested their functionality in YAL5, a previously engineered yeast knockout deficient for the expression of tRNA Arg CCG. Arginylation levels were also monitored using Northern blot. Our data collected in vivo correlate with previous observations. C35 is the prominent arginine determinant followed by G36 or U36 (G/U36). In addition, although there is no major arginine determinant in the D loop, the recognition of tRNA Arg ICG relies to some extent on the nucleotide at position 20. This work refines the existing model for tRNA Arg recognition. Our observations indicate that yeast Arginyl-tRNA synthetase (yArgRS) relies on distinct mechanisms to aminoacylate the four isoacceptors. Finally, according to our refined model, yArgRS is able to accommodate tRNA Arg scaffolds presenting N34, C/G35 and G/A/U36 anticodons while maintaining specificity. We discuss the mechanistic and potential physiological implications of these findings.