Biology subjects

Fufezan, C.

Publications and source records attributed to Fufezan, C..

4 recordsLinked to original sources

bioRxiv2024

Codon Pair-Specific Translation Defects Trigger Ribosome-Associated Quality Control to Avoid Proteotoxic Stress

tRNA modifications tune translation rates and codon optimality, thereby optimizing co-translational protein folding, but how codon optimality defects trigger cellular phenotypes remains unclear. Here, we show that ribosomes stall at specific modification-dependent codon pairs, triggering ribosome collisions and inducing a coordinated and hierarchical response of cellular quality control pathways. Ribosome profiling reveals an unexpected functional diversity for wobble-uridine (U34) modifications during decoding. The same modification can have different effects at the A and P sites. Furthermore, modification-dependent stalling codon pairs induce ribosome collisions, triggering ribosome-associated quality control (RQC) to prevent protein aggregation by degrading aberrant nascent peptides and mRNAs. RQC inactivation stimulates the expression of molecular chaperones to remove protein aggregates. Our results show that loss of tRNA modifications primarily disrupts translation rates of suboptimal codon pairs and reveal the coordinated regulation and adaptability of cellular surveillance systems to ensure efficient and accurate protein synthesis and maintain protein homeostasis.

biochemistry↗

bioRxiv2022

PeptideForest: Semisupervised machine learning integrating multiple search engines for peptide identification

The first step in bottom-up proteomics is the assignment of measured fragmentation mass spectra to peptide sequences, also known as peptide spectrum matches. In recent years novel algorithms have pushed the assignment to new heights, unfortunately, different algorithms come with different strengths and weaknesses and choosing the appropriate algorithm poses a challenge for the user. Here we introduce PeptideForest, a semi-supervised machine learning approach that integrates the assignments of multiple algorithms to train a random forest classifier to elevate that issue. Additionally, PeptideForest increases the number of peptide-to-spectrum matches that exhibit a q-value lower than 1% by 25.2 {+/-} 1.6% compared to MS-GF+ data on samples containing mixed HEK and E. coli proteomes. However, an increase in quantity does not necessarily reflect an increase in quality and this is why we devised a novel approach to determine the quality of the assigned spectra through TMT quantification of samples with known ground truths. Thereby, we could show that the increase in PSMs below 1% q-value does not come with a decrease in quantification quality and as such PeptideForest offers a possibility to gain deeper insights into bottom-up proteomics. PeptideForest has been integrated into our pipeline framework Ursgal and can therefore be combined with a wide array of algorithms.

bioinformatics↗

bioRxiv2020

SugarPy facilitates the universal, discovery-driven analysis of intact glycopeptides

MotivationProtein glycosylation is a complex post-translational modification with crucial cellular functions in all domains of life. Currently, large-scale glycoproteomics approaches rely on glycan database dependent algorithms and are thus unsuitable for discovery-driven analyses of glycoproteomes. ResultsTherefore, we devised SugarPy, a glycan database independent Python module, and validated it on the glycoproteome of human breast milk. We further demonstrated its applicability by analyzing glycoproteomes with uncommon glycans stemming from the green alga Chlamydomonas reinhardtii and the archaeon Haloferax volcanii. SugarPy also facilitated the novel characterization of glycoproteins from the red alga Cyanidioschyzon merolae. AvailabilityThe source code is freely available on GitHub (https://github.com/SugarPy/SugarPy), and its implementation in Python ensures support for all operating systems. Contactmhippler@uni-muenster.de and pohlschr@uni-muenster.de Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2017

Attomol-level quantification of chemically modified ribonucleosides enabled by capillary porous graphitic carbon columns in nano LC-MS

Post-transcriptional chemical modifications of (t)RNA molecules are crucial in fundamental biological processes, such as translation. Despite their biological importance and accumulating evidence linking them to various human diseases, technical challenges have limited the development of methods for reliable detection and accurate quantification of these modifications. Here, we present a sensitive capillary nanoflow liquid chromatography mass spectrometry (nLC-MS) pipeline for quantitative high-resolution analysis of ribonucleoside modifications from complex biological samples. We evaluated two porous graphitic carbon (PGC) materials as stationary phases for reversed-phase separation of ribonucleosides and found that both PGC matrices have excellent retention and separation characteristics, as well as the capability to separate structural isomers. Using PGC matrices in nLC-MS yielded excellent signal-to-noise ratios in a detection range spanning up to six orders of magnitude, allowing for the analysis of individual ribonucleosides down to attomol concentrations. Furthermore, normalizing the obtained signal intensities to a stable isotope labeled spike-in enabled direct comparison of ribonucleoside levels between different samples. In conclusion, capillary PGC columns coupled to nLC-MS constitute a powerful and sensitive tool for quantitative analysis of chemically modified ribonucleosides in complex biological samples. This setup will be invaluable for further unraveling the intriguing and multifaceted biological roles of RNA modifications.

biochemistry↗