A clinical screening tool to detect genetic cancer predisposition in pediatric oncology shows high sensitivity but can miss a substantial percentage of affected children.

PURPOSE: Clinical checklists are the standard of care to determine whether a child with cancer shows indications for genetic testing. Nevertheless, the efficacy of these tests to reliably detect genetic cancer predisposition in children with cancer is still insufficiently investigated. METHODS: We assessed the validity of clinically recognizable signs to identify cancer predisposition by correlating a state-of-the-art clinical checklist to the corresponding exome sequencing analysis in an unselected single-center cohort of 139 child-parent data sets. RESULTS: In total, one-third of patients had a clinical indication for genetic testing according to current recommendations, and 10.1% (14 of 139) of children harbored a cancer predisposition. Of these, 71.4% (10 of 14) were identified through the clinical checklist. In addition, >2 clinical findings in the checklist increased the likelihood to identifying genetic predisposition from 12.5% to 50%. Furthermore, our data revealed a high rate of genetic predisposition (40%, 4 of 10) in myelodysplastic syndrome cases, while no (likely) pathogenic variants were identified in the sarcoma and lymphoma group. CONCLUSION: In summary, our data show high checklist sensitivity, particularly in identifying childhood cancer predisposition syndromes. Nevertheless, the checklist used here also missed 29% of children with a cancer predisposition, highlighting the drawbacks of sole clinical evaluation and underlining the need for routine germline sequencing in pediatric oncology.

Pairs of amino acids at the P- and A-sites of the ribosome predictably and causally modulate translation-elongation rates.

Variation in translation-elongation kinetics along a transcript's coding sequence plays an important role in the maintenance of cellular protein homeostasis by regulating co-translational protein folding, localization, and maturation. Translation-elongation speed is influenced by molecular factors within mRNA and protein sequences. For example, the presence of proline in the ribosome's P- or A-site slows down translation, but the effect of other pairs of amino acids, in the context of all 400 possible pairs, has not been characterized. Here, we study Saccharomyces cerevisiae using a combination of bioinformatics, mutational experiments, and evolutionary analyses, and show that many different pairs of amino acids and their associated tRNA molecules predictably and causally encode translation rate information when these pairs are present in the A- and P-sites of the ribosome independent of other factors known to influence translation speed including mRNA structure, wobble base pairing, tripeptide motifs, positively charged upstream nascent chain residues, and cognate tRNA concentration. The fast-translating pairs of amino acids that we identify are enriched four-fold relative to the slow-translating pairs across Saccharomyces cerevisiae's proteome, while the slow-translating pairs are enriched downstream of domain boundaries. Thus, the chemical identity of amino acid pairs contributes to variability in translation rates, elongation kinetics are causally encoded in the primary structure of proteins, and signatures of evolutionary selection indicate their potential role in co-translational processes.

Selective ribosome profiling to study interactions of translating ribosomes in yeast.

A number of enzymes, targeting factors and chaperones engage ribosomes to support fundamental steps of nascent protein maturation, including enzymatic processing, membrane targeting and co-translational folding. The selective ribosome profiling (SeRP) method is a new tool for studying the co-translational activity of maturation factors that provides proteome-wide information on a factor's nascent interactome, the onset and duration of binding and the mechanisms controlling factor engagement. SeRP is based on the combination of two ribosome-profiling (RP) experiments, sequencing the ribosome-protected mRNA fragments from all ribosomes (total translatome) and the ribosome subpopulation engaged by the factor of interest (factor-bound translatome). We provide a detailed SeRP protocol, exemplified for the yeast Hsp70 chaperone Ssb (stress 70 B), for studying factor interactions with nascent proteins that is readily adaptable to identifying nascent interactomes of other co-translationally acting eukaryotic factors. The protocol provides general guidance for experimental design and optimization, as well as detailed instructions for cell growth and harvest, the isolation of (factor-engaged) monosomes, the generation of a cDNA library and data analysis. Experience in biochemistry and RNA handling, as well as basic programing knowledge, is necessary to perform SeRP. Execution of a SeRP experiment takes 8-10 working days, and initial data analysis can be completed within 1-2 d. This protocol is an extension of the originally developed protocol describing SeRP in bacteria.

Monitoring Cell-Type-Specific Gene Expression Using Ribosome Profiling In Vivo During Cardiac Hemodynamic Stress.

RATIONALE: Gene expression profiles have been mainly determined by analysis of transcript abundance. However, these analyses cannot capture posttranscriptional gene expression control at the level of translation, which is a key step in the regulation of gene expression, as evidenced by the fact that transcript levels often poorly correlate with protein levels. Furthermore, genome-wide transcript profiling of distinct cell types is challenging due to the fact that lysates from tissues always represent a mixture of cells. OBJECTIVES: This study aimed to develop a new experimental method that overcomes both limitations and to apply this method to perform a genome-wide analysis of gene expression on the translational level in response to pressure overload. METHODS AND RESULTS: By combining ribosome profiling (Ribo-seq) with a ribosome-tagging approach (Ribo-tag), it was possible to determine the translated transcriptome in specific cell types from the heart. After pressure overload, we monitored the cardiac myocyte translatome by purifying tagged cardiac myocyte ribosomes from cardiac lysates and subjecting the ribosome-protected mRNA fragments to deep sequencing. We identified subsets of mRNAs that are regulated at the translational level and found that translational control determines early changes in gene expression in response to cardiac stress in cardiac myocytes. Translationally controlled transcripts are associated with specific biological processes related to translation, protein quality control, and metabolism. Mechanistically, Ribo-seq allowed for the identification of upstream open reading frames in transcripts, which we predict to be important regulators of translation. CONCLUSIONS: This method has the potential to (1) provide a new tool for studying cell-specific gene expression at the level of translation in tissues, (2) reveal new therapeutic targets to prevent cellular remodeling, and (3) trigger follow-up studies that address both, the molecular mechanisms involved in the posttranscriptional control of gene expression in cardiac cells, and the protective functions of proteins expressed in response to cellular stress.

A chemical kinetic basis for measuring translation initiation and elongation rates from ribosome profiling data.

Analysis methods based on simulations and optimization have been previously developed to estimate relative translation rates from next-generation sequencing data. Translation involves molecules and chemical reactions, hence bioinformatics methods consistent with the laws of chemistry and physics are more likely to produce accurate results. Here, we derive simple equations based on chemical kinetic principles to measure the translation-initiation rate, transcriptome-wide elongation rate, and individual codon translation rates from ribosome profiling experiments. Our methods reproduce the known rates from ribosome profiles generated from detailed simulations of translation. By applying our methods to data from S. cerevisiae and mouse embryonic stem cells, we find that the extracted rates reproduce expected correlations with various molecular properties, and we also find that mouse embryonic stem cells have a global translation speed of 5.2 AA/s, in agreement with previous reports that used other approaches. Our analysis further reveals that a codon can exhibit up to 26-fold variability in its translation rate depending upon its context within a transcript. This broad distribution means that the average translation rate of a codon is not representative of the rate at which most instances of that codon are translated, and it suggests that translational regulation might be used by cells to a greater degree than previously thought.

Accurate prediction of cellular co-translational folding indicates proteins can switch from post- to co-translational folding.

The rates at which domains fold and codons are translated are important factors in determining whether a nascent protein will co-translationally fold and function or misfold and malfunction. Here we develop a chemical kinetic model that calculates a protein domain's co-translational folding curve during synthesis using only the domain's bulk folding and unfolding rates and codon translation rates. We show that this model accurately predicts the course of co-translational folding measured in vivo for four different protein molecules. We then make predictions for a number of different proteins in yeast and find that synonymous codon substitutions, which change translation-elongation rates, can switch some protein domains from folding post-translationally to folding co-translationally--a result consistent with previous experimental studies. Our approach explains essential features of co-translational folding curves and predicts how varying the translation rate at different codon positions along a transcript's coding sequence affects this self-assembly process.