Global and local depletion of ternary complex limits translational elongation

The translation of genetic information according to the sequence of the mRNA template occurs with high accuracy and fidelity. Critical events in each single step of translation are selection of transfer RNA (tRNA), codon reading and tRNA-regeneration for a new cycle. We developed a model that accurately describes the dynamics of single elongation steps, thus providing a systematic insight into the sensitivity of the mRNA translation rate to dynamic environmental conditions. Alterations in the concentration of the aminoacylated tRNA can transiently stall the ribosomes during translation which results, as suggested by the model, in two outcomes: either stress-induced change in the tRNA availability triggers the premature termination of the translation and ribosomal dissociation, or extensive demand for one tRNA species results in a competition between frameshift to an aberrant open-reading frame and ribosomal drop-off. Using the bacterial Escherichia coli system, we experimentally draw parallels between these two possible mechanisms

FANSe: an accurate algorithm for quantitative mapping of large scale sequencing reads

The most crucial step in data processing from high-throughput sequencing applications is the accurate and sensitive alignment of the sequencing reads to reference genomes or transcriptomes. The accurate detection of insertions and deletions (indels) and errors introduced by the sequencing platform or by misreading of modified nucleotides is essential for the quantitative processing of the RNA-based sequencing (RNA-Seq) datasets and for the identification of genetic variations and modification patterns. We developed a new, fast and accurate algorithm for nucleic acid sequence analysis, FANSe, with adjustable mismatch allowance settings and ability to handle indels to accurately and quantitatively map millions of reads to small or large reference genomes. It is a seed-based algorithm which uses the whole read information for mapping and high sensitivity and low ambiguity are achieved by using short and non-overlapping reads. Furthermore, FANSe uses hotspot score to prioritize the processing of highly possible matches and implements modified Smith–Watermann refinement with reduced scoring matrix to accelerate the calculation without compromising its sensitivity. The FANSe algorithm stably processes datasets from various sequencing platforms, masked or unmasked and small or large genomes. It shows a remarkable coverage of low-abundance mRNAs which is important for quantitative processing of RNA-Seq datasets

Ribosomal pausing induced by secondary structure in CDS.

<p><i>(</i>A) Globally, ribosomal pausing is not significantly affected by the presence of secondary structure in the CDS. Box plot analysis of the ratio of RPF upstream (L1) calculated from <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005613#pgen.1005613.e002" target="_blank">Eq 2</a> and downstream (L2) calculated from <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005613#pgen.1005613.e003" target="_blank">Eq 3</a> of detected secondary structures (<i>P</i> = 0.1209, Kolmogorov-Smirnov test). (B,C) Ribosomal pausing is observed within coding sequences above the 80^th percentile (panel A). Examples of <i>ompF</i> transcript with previously validated secondary structure [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005613#pgen.1005613.ref065" target="_blank">65</a>] (B) as well as newly detected genes (C) for which a local secondary structure causes non-uniform ribosomal distribution. Aligned PARS score (upper panel, gray) with the RPF counts (bottom panel, red) at each nucleotide.</p

The stop codon of operon genes is more structured than non-operon genes.

<p>(A) Average PARS score and GC content for each position of genes terminating with UAA (black), UAG (red) and UGA (green) stop codons. (B) RPF coverage around the stop codon region for genes terminated by UAA (black), UAG (dashed red) and UGA (green) stop codons. Only genes with coverage over 60 reads (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005613#pgen.1005613.s004" target="_blank">S4D Fig</a>) were used; overlapping operon genes were excluded. Note, that UAG-terminated genes are included only for comparison; their low number prevents performing any statistical analysis. The inset shows, for both UAA- and UAG-terminated genes, the ratio between the RPFs downstream of the stop codon (3 to27 nt) and a mean of the CDS. The readthrough value for the majority of the genes was zero; only genes with a value higher than zero are plotted.</p

Secondary Structure across the Bacterial Transcriptome Reveals Versatile Roles in mRNA Regulation and Function

<div><p>Messenger RNA acts as an informational molecule between DNA and translating ribosomes. Emerging evidence places mRNA in central cellular processes beyond its major function as informational entity. Although individual examples show that specific structural features of mRNA regulate translation and transcript stability, their role and function throughout the bacterial transcriptome remains unknown. Combining three sequencing approaches to provide a high resolution view of global mRNA secondary structure, translation efficiency and mRNA abundance, we unraveled structural features in <i>E</i>. <i>coli</i> mRNA with implications in translation and mRNA degradation. A poorly structured site upstream of the coding sequence serves as an additional unspecific binding site of the ribosomes and the degree of its secondary structure propensity negatively correlates with gene expression. Secondary structures within coding sequences are highly dynamic and influence translation only within a very small subset of positions. A secondary structure upstream of the stop codon is enriched in genes terminated by UAA codon with likely implications in translation termination. The global analysis further substantiates a common recognition signature of RNase E to initiate endonucleolytic cleavage. This work determines for the first time the <i>E</i>. <i>coli</i> RNA structurome, highlighting the contribution of mRNA secondary structure as a direct effector of a variety of processes, including translation and mRNA degradation.</p></div

PARS analysis.

<p>(A) Overview of modified PARS approach. RNase V1 cleaves double-stranded RNA and combination of RNases A/T1 the single stranded RNA with optimal activities at physiological pH (7.0). RNAse A/T1 usage requires an additional phosphorylation step prior to library generation. (B) The PARS score of the <i>rpoS</i> leader sequence (inset) was overlaid with the experimentally determined structure [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005613#pgen.1005613.ref064" target="_blank">64</a>]. Double-stranded nucleotides with positive PARS score are colored red, single-stranded nucleotides with negative PARS score–blue, nucleotides with missing PARS score or equal to zero–green. The color intensity of the <i>rpoS</i> nucleotides reflects the PARS scores (rainbow legend). (C) Metagene analysis of protein-coding transcripts. Average PARS score for each nucleotide (top) and GC content (bottom) across the 5’UTRs, CDS and 3’UTRs of all protein-coding transcripts, aligned at the start or stop codon, respectively. For the shaded areas the average PARS scores or GC content is calculated; thus note the deviations from the total GC content of 51% in <i>E</i>. <i>coli</i>. Unstructured region upstream of the start codon and structured sequence preceding the stop codon are marked by arrows with filled and open arrow heads, respectively.</p

mRNA structure correlates with mRNA abundance.

<p>(A) Distribution of transcript abundance, expressed in gene read counts normalized by the length of CDS per kilobase and the total mapped reads per million (rpkM). The 30% least (blue) and most (green) abundant genes from the reliably detected genes (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005613#pgen.1005613.s004" target="_blank">S4 Fig</a>) are highlighted. (B) Dependence of the mean PARS score on the mRNA abundance of the middle (black) and most (green) abundant transcripts as defined in panel A. R = 0.777, Pearson correlation coefficient. (C) Average PARS score (top) and GC content (bottom) for each position of all transcripts (black curve) as well as the 30% most (green) and least (blue) abundant. (D) Average PARS score (top) and GC content (bottom) for each position around the top 64 RNase E cleavage sites (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005613#pgen.1005613.s010" target="_blank">S2 Table</a>). Inset, the sequence logo of the aligned RNase E cleavage sites, spanning from -10 to +10 nt.</p

Stronger SD sequence has a higher propensity to form secondary structure which does not correlate with the translation efficiency.

<p>(A) SD strength does not correlate with translation efficiency (i.e. the total RPFs per coding mRNA) of a gene. SD hybridization energies fall into four major distributions: strong SD, MHE < -8.5 kcal/mol; medium SD, -8.5 < MHE < -4.4 kcal/mol; weak SD, -4.4 < MHE < -2 kcal/mol; no SD, MHE > -2.0 kcal/mol. For each gene (dot) the MHE (horizontal axis) of the SD sequence is plotted against SD spacing (vertical axis), defined as the distance between the second to last nucleotide of the SD and the start codon. Genes with the 30% highest ribosomal density are highlighted as black dots. (B) Cumulative plots of ribosomal density for all genes grouped by SD strength. Genes were aligned by the first nucleotide of the start codon. (C) Average PARS score smoothed over 3 nt (top) and GC content (bottom) for each position of the four SD strength classes, aligned by the start codon. The four different SD groups are color coded as in panel A. (D) FACS expression analysis of <i>adhE</i> whose original docking site was replaced by three other docking sites with clearly different sequence (AU-rich or GC-rich) and different PARS score. Only the sequence upstream of the SD (green on the PARS profiles) was replaced. The common part of <i>adhE</i> which is fused to YFP (schematic inset) is shadowed on the PARS profiles. The average PARS score over the docking site (12 to 30 nt upstream of the start codon, red on the PARS profiles are): <i>adhE</i>–-0.564, <i>ppiD</i>–-0.495, <i>cspE</i>–0.724, <i>accD</i>–0.665. Data are means (n = 3) ± standard error of the mean (s.e.m.). *, <i>P</i> <0.05; **, <i>P</i> <0.01.</p