Transfer RNAs (tRNA) through their abundance and modification pattern
significantly influence protein translation. Here, we present a systematic
analysis of the tRNAome of Lactococcus lactis. Using the
next-generation sequencing approach, we identified 40 tRNAs which carry 16
different posttranscriptional modifications as revealed by mass spectrometry
analysis. While small modifications are located in the tRNA body, hypermodified
nucleotides are mainly present in the anticodon loop, which through wobbling
expand the decoding potential of the tRNAs. Using tRNA-based microarrays, we
also determined the dynamics in tRNA abundance upon changes in the growth rate
and heterologous protein overexpression stress. With a four-fold increase in the
growth rate, the relative abundance of tRNAs cognate to low abundance codons
decrease, while the tRNAs cognate to major codons remain mostly unchanged.
Significant changes in the tRNA abundances are observed upon protein
overexpression stress, which does not correlate with the codon usage of the
overexpressed gene but rather reflects the altered expression of housekeeping
genes.
Understanding how genetic variation contributes to phenotypic differences is a fundamental question in biology. Combining high-throughput gene function assays with mechanistic models of the impact of genetic variants is a promising alternative to genome-wide association studies. Here we have assembled a large panel of 696 Escherichia coli strains, which we have genotyped and measured their phenotypic profile across 214 growth conditions. We integrated variant effect predictors to derive gene-level probabilities of loss of function for every gene across all strains. Finally, we combined these probabilities with information on conditional gene essentiality in the reference K-12 strain to compute the growth defects of each strain. Not only could we reliably predict these defects in up to 38% of tested conditions, but we could also directly identify the causal variants that were validated through complementation assays. Our work demonstrates the power of forward predictive models and the possibility of precision genetic interventions.
Translation is a central cellular process and is optimized for speed and fidelity. The speed of translation of a single codon depends on the concentration of aminoacyl-tRNAs. Here, we used microarray-based approaches to analyze the charging levels of tRNAs in Escherichia coli growing at different growth rates. Strikingly, we observed a non-uniform aminoacylation of tRNAs in complex media. In contrast, in minimal medium, the level of aminoacyl-tRNAs is more uniform and rises to approximately 60%. Particularly, the charging level of tRNASer, tRNACys, tRNAThr and tRNAHis is below 50% in complex medium and their aminoacylation levels mirror the degree that amino acids inhibit growth when individually added to minimal medium. Serine is among the most toxic amino acids for bacteria and tRNAsSer exhibit the lowest charging levels, below 10%, at high growth rate although intracellular serine concentration is plentiful. As a result some serine codons are among the most slowly translated codons. A large fraction of the serine is most likely degraded by L-serine-deaminase, which competes with the seryl-tRNA-synthetase that charges the tRNAsSer. These results indicate that the level of aminoacylation in complex media might be a competition between charging for translation and degradation of amino acids that inhibit growth.
Predictive biomarkers of trabectedin represents an unmet need in advanced soft-tissue sarcomas (STS). DNA damage repair (DDR) genes, involved in homologous recombination or nucleotide excision repair, had been previously described as biomarkers of trabectedin resistance or sensitivity, respectively. The majority of these studies only focused on specific factors (ERCC1, ERCC5 and BRCA1) and did not evaluate several other DDR-related genes that could have a
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