Determinants of translation efficiency and accuracy

A given protein sequence can be encoded by an astronomical number of alternative nucleotide sequences. Recent research has revealed that this flexibility provides evolution with multiple ways to tune the efficiency and fidelity of protein translation and folding

RNAlysis: analyze your RNA sequencing data without writing a single line of code

Abstract Background Among the major challenges in next-generation sequencing experiments are exploratory data analysis, interpreting trends, identifying potential targets/candidates, and visualizing the results clearly and intuitively. These hurdles are further heightened for researchers who are not experienced in writing computer code since most available analysis tools require programming skills. Even for proficient computational biologists, an efficient and replicable system is warranted to generate standardized results. Results We have developed RNAlysis, a modular Python-based analysis software for RNA sequencing data. RNAlysis allows users to build customized analysis pipelines suiting their specific research questions, going all the way from raw FASTQ files (adapter trimming, alignment, and feature counting), through exploratory data analysis and data visualization, clustering analysis, and gene set enrichment analysis. RNAlysis provides a friendly graphical user interface, allowing researchers to analyze data without writing code. We demonstrate the use of RNAlysis by analyzing RNA sequencing data from different studies using C. elegans nematodes. We note that the software applies equally to data obtained from any organism with an existing reference genome. Conclusions RNAlysis is suitable for investigating various biological questions, allowing researchers to more accurately and reproducibly run comprehensive bioinformatic analyses. It functions as a gateway into RNA sequencing analysis for less computer-savvy researchers, but can also help experienced bioinformaticians make their analyses more robust and efficient, as it offers diverse tools, scalability, automation, and standardization between analyses

A Comprehensive tRNA Deletion Library Unravels the Genetic Architecture of the tRNA Pool

<div><p>Deciphering the architecture of the tRNA pool is a prime challenge in translation research, as tRNAs govern the efficiency and accuracy of the process. Towards this challenge, we created a systematic tRNA deletion library in <i>Saccharomyces cerevisiae</i>, aimed at dissecting the specific contribution of each tRNA gene to the tRNA pool and to the cell's fitness. By harnessing this resource, we observed that the majority of tRNA deletions show no appreciable phenotype in rich medium, yet under more challenging conditions, additional phenotypes were observed. Robustness to tRNA gene deletion was often facilitated through extensive backup compensation within and between tRNA families. Interestingly, we found that within tRNA families, genes carrying identical anti-codons can contribute differently to the cellular fitness, suggesting the importance of the genomic surrounding to tRNA expression. Characterization of the transcriptome response to deletions of tRNA genes exposed two disparate patterns: in single-copy families, deletions elicited a stress response; in deletions of genes from multi-copy families, expression of the translation machinery increased. Our results uncover the complex architecture of the tRNA pool and pave the way towards complete understanding of their role in cell physiology.</p></div

KEGG pathways differentiating between tRNA deletion sets.

<p>KEGG pathways <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004084#pgen.1004084-Kanehisa1" target="_blank">[49]</a> for which changes in genes expression are significantly different between the two groups of tRNA deletion strains: MC (multi-copy) group (<i>ΔtH(GUG)G1</i> and <i>ΔtR(UCU)M2</i>) vs. SC (single-copy) group (<i>ΔtL(GAG)G</i>, <i>ΔtR(CCU)J</i>, <i>ΔtiM(CAU)C</i>) calculated with GSEA <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004084#pgen.1004084-Subramanian1" target="_blank">[51]</a>, <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004084#pgen.1004084-Mootha1" target="_blank">[52]</a>. In the first column are pathways, which are higher in SC vs. MC and vice versa in the second column. The values are corrected for multiple hypothesis and the FDR q-values are indicated next to each pathway.</p

Changes in the tRNA pool affect protein folding.

<p>(A–C) Relative growth rate (compare to wild-type) of the following five deletion strain: <i>tL(GAG)G</i> (blue), <i>tR(CCU)J</i> (red), <i>tiM(CAU)C</i> (green), <i>tH(GUG)G1</i> (magenta) and <i>tR(UCU)M2</i> (cyan). Strains were grown in media supplemented with increasing concentrations of the following proteotoxic agent: AZC (A) Tunicamycin (B) DTT (C). (D) Percentage of cells that contain puncta in the populations of the above strains. (E) Percentage of cells that contain puncta in the populations of the above strains following treatment with 2.5 mM AZC. Data are presented as mean of 3 biological repetitions +/− SEM, in each repetition 500 cells were counted. (*) P<0.001 by Students <i>t</i>-test. (F–G) Images of representative fields for the wild-type and <i>tR(CCU)J</i> deletion strain, without treatment (F) and following treatment with 2.5 mM AZC (G).</p

Screening the tRNA deletion library across various growth conditions.

<p>(A) Percent of strains exhibiting a growth yield phenotype in various conditions. The color indicates the type of phenotype: impaired (blue) or improved (red). (B) Percent of strains exhibiting a growth rate phenotype in various conditions. (C–D) The σ values measured for both the growth yield (C) and the growth rate (D) for all deletion strains across six conditions. The color bar indicates the σ values, red denoting improvement and blue impairment. Each row denotes a tRNA deletion strain and each column denotes different growth condition. Strains are ordered on the y-axis according to amino acids (denoted by letter) and further separated into families (denoted by lines within the amino-acid box). Black rows denote lethal strains. Gray rows indicate strains for which the respective value was not measured.</p

Extensive redundancy underlies robustness to tRNA gene deletion.

<p>(A) Schematic representation of the genetic interactions within and between tRNA families. Families are denoted by dark grey circles and grouped (black dashed line) according to their tRNA copy number. Each family is denoted by its anti-codon and amino-acid. A protein-coding gene i.e. <i>TRM9</i> is denoted by a grey box. Each filled circle indicates a tRNA deletion strain. The lines connecting the deletion strains denote a co-deletion of these genes (a multi-tRNAs deletion strain). The color of the filled circles and lines denote the severity of the growth phenotype for the respective strain: blue for normal growth, purple for impaired growth (worse than wild-type) and red for lethality. (B) Epistasis values for multi-tRNAs deletion strains which contain the deletion of <i>tL(GAG)</i> and either: one <i>tL(UAG)</i> gene, two <i>tL(UAG)</i> genes, <i>tL(CAA)</i> (which is a tRNA of different Leucine family), and <i>tW(CCA)</i> (which is a non-Leucine tRNA) as controls. (C) Epistasis values for multi-tRNAs deletion strains which contain the deletion of <i>trm9</i> with: the singleton <i>tR(CCU)</i>, and <i>tR(ACG)</i> which is a tRNA of different Arginine family and <i>tW(CCA)</i> which is a non- Arginine tRNA as controls. In both (B) and (C) epistasis values of the relative growth yield and growth rate are indicated in grey and green respectively. Data is presented as mean of 3 biological repetitions +/− SEM.</p

Creation and analysis of tRNA deletion library.

<p>(A) Schematic representation of the deletion process. 204 different tRNA strains were created using homologous recombination. In each strain, a different tRNA gene was replaced by a hygromycin B resistance marker. (B) Schematic representation of growth measurements, analysis, and scoring. For each strain, relative-growth-rate and relative-growth-yield are calculated in relation to the wild-type strain. These parameters are then projected on a distribution of the wild-type growth parameters. Sigma (σ) is calculated according to the formula and denotes the number of standard deviations from the mean of the wild-type (see also Supplemental <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004084#pgen.1004084.s001" target="_blank">figure S1A</a>). The color in the histogram are areas were: σ<−3 (blue), −3<σ<−2 (cyan), 2<σ<3 (yellow) and 3<σ (red). The same color code is used to define phenotypes in the pie charts (C and D). (C–D) Distribution of phenotypes for the tRNA deletion library in rich medium, according to two growth parameters: relative growth yield (C) relative growth rate (D). Deletion strains were assigned to categories according to their σ values. Any absolute σ value larger than 2 was considered as non-normal phenotype, where negative sigma denotes impairment (worse than the wild-type) and positive sigma denotes improvement (better than the wild-type). Any absolute σ value larger than 3 was considered as a strong phenotype. Thus, highly impaired for σ<−3, impaired for −2>σ>−3, improved for 2<σ<3, and highly improved for σ>3, see also Supplemental <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004084#pgen.1004084.s001" target="_blank">figure S1B</a>.</p