Biology subjects

Toronen, P.

Publications and source records attributed to Toronen, P..

3 recordsLinked to original sources

bioRxiv2023

Global analysis of aging-related protein structural changes uncovers enzyme polymerization-based control of longevity

Aging is associated with progressive phenotypic changes over time. Virtually all cellular phenotypes are produced by proteins and structural alterations in proteins can lead to age-related diseases. Nonetheless, comprehensive knowledge of proteins undergoing structural-functional changes during cellular aging and their contribution to age-related phenotypes is lacking. Here, we conducted proteome-wide analysis of early age-related protein structural changes in budding yeast using limited proteolysis-mass spectrometry. The results, compiled in online ProtAge-catalog, unravelled age-related functional changes in regulators of translation, protein folding and amino acid metabolism. Mechanistically, we found that folded glutamate synthase Glt1 polymerizes into supramolecular self-assemblies during aging causing breakdown of cellular amino acid homeostasis. Inhibiting Glt1 polymerization by mutating the polymerization interface restored amino acid levels in aged cells, attenuated mitochondrial dysfunction and led to life span extension. Altogether, this comprehensive map of protein structural changes enables identifying novel mechanisms of age-related phenotypes and offers opportunities for their reversal.

cell biology↗

bioRxiv2022

The giant diploid faba genome unlocks variation in a global protein crop

Increasing the proportion of locally produced plant protein in currently meat-rich diets could substantially reduce greenhouse gas emission and loss of biodiversity. However, plant protein production is hampered by the lack of a cool-season legume equivalent to soybean in agronomic value. Faba bean (Vicia faba L.) has a high yield potential and is well-suited for cultivation in temperate regions, but genomic resources are scarce. Here, we report a high-quality chromosome-scale assembly of the faba bean genome and show that it has grown to a massive 13 Gb in size through an imbalance between the rates of amplification and elimination of retrotransposons and satellite repeats. Genes and recombination events are evenly dispersed across chromosomes and the gene space is remarkably compact considering the genome size, though with significant copy number variation driven by tandem duplication. Demonstrating practical application of the genome sequence, we develop a targeted genotyping assay and use high-resolution genome-wide association (GWA) analysis to dissect the genetic basis of hilum colour. The resources presented constitute a genomics-based breeding platform for faba bean, enabling breeders and geneticists to accelerate improvement of sustainable protein production across Mediterranean, subtropical, and northern temperate agro-ecological zones.

plant biology↗

bioRxiv2022

Optimizing InterProScan feature processinggenerates a surprisingly good protein functionprediction method

MotivationAutomated protein Function Prediction (AFP) is an intensively studied topic. Most of this research focuses on methods that combine multiple data sources, while fewer articles look for the most efficient ways to use a single data source. Therefore, we wanted to test how different preprocessing methods and classifiers would perform in the AFP task when we process the output from the InterProscan (IPS). Especially, we present novel preprocessing methods, less used classifiers and inclusion of species taxonomy. We also test classifier stacking for combining tested classifier results. Methods are tested with in-house data and CAFA3 competition evaluation data. ResultsWe show that including IPS localisation and taxonomy to the data improves results. Also the stacking improves the performance. Surprisingly, our best performing methods outperformed all international CAFA3 competition participants in most tests. Altogether, the results show how preprocessing and classifier combinations are beneficial in the AFP task. Contactpetri.toronen(AT)helsinki.fi Supplementary informationSupplementary text is available at the project web site http://ekhidna2.biocenter.helsinki.fi/AFP/ and at the end of this document.

bioinformatics↗