Biology subjects

Tassios, E.

Publications and source records attributed to Tassios, E..

4 recordsLinked to original sources

bioRxiv2026

Evolutionary origins of protein novelty across an entire yeast subphylum

Novel protein-coding sequences fuel molecular and cellular evolutionary innovations and frequently contribute to species-defining characteristics. They can originate either de novo from previously noncoding sequences or through extreme divergence of already coding ones. How frequently each mechanism occurs and how they shape the structural and functional potential of the resulting proteins remains unclear. Here, we conducted a broad computational investigation of genetic and protein novelty throughout the entire subphylum of Saccharomycotina yeasts. We detected more than 5,000 robust de novo genes across 332 species and compared them to more than 6,000 novel genes resulting from extreme sequence divergence, revealing two quantitatively similar but qualitatively distinct modes of evolution of novelty. A remarkable 40% of de novo proteins are predicted to localize to mitochondria compared to only 20% of divergent, with the latter also being substantially longer and more disordered. A detailed analysis of conservatively predicted tertiary structures of novel proteins shows that ''invention'' of new folds occurs more frequently through de novo emergence. We also illustrate cases of evolutionary ''re-invention'' of existing protein folds from noncoding sequences. Our work deepens our understanding of the origins and importance of novel proteins, opening new directions for further structural and functional characterization.

genomics↗

bioRxiv2025

Machine learning can identify orphans that have diverged into the ''twilight zone'' of sequence similarity

Species-specific orphan genes (orphans) lack homologues outside of a given taxon and frequently underlie unique species traits. It is thus important to elucidate the evolutionary origins of orphans. Orphan genes can result from sequence divergence beyond recognition, when homologous proteins diverge to an extent at which tools that rely on sequence similarity to establish homology can no longer identify them as homologues. Orphans can also result from other processes, including de novo gene emergence from previously noncoding sequences, in which a homologous protein-coding gene truly does not exist. Here we propose that orphans resulting from divergence might be recognizable from their patterns of non-statistically significant similarity hits which are almost always discarded. To test this, we simulated diverged orphan protein sequences based on conserved proteins from the Unified Human Gastrointestinal Protein catalogue (UHGP) and used reversed protein sequences as negative data sets. We trained four machine learning classifiers on features extracted from the similarity search tool DIAMONDs output, like total query coverage or maximum bit score. We tested the influence of evolutionary parameters such as simulation tree branch length, indel rate and among-site rate heterogeneity. We found that the performance of the models depends on the simulation parameters: when the underlying simulated divergence was moderate, accuracy reached [~]90%, but when extremely diverged scenarios where simulated accuracy dropped to [~]70%. The most important features for the classification were the number of alignments (hits) and the minimum hit E-value. When applying our classifier on a set of [~]170,000 eligible simulated orphans from the UHGP dataset, we found that [~]30% of them are predicted to be divergent and these are shorter and more disordered than the rest. Our classifiers and pre-processing python scripts are available at https://github.com/emiliostassios/Classification-of-divergent-genes-using-ML and can be readily used as a computationally fast means to obtain a candidate set of diverged orphans from any similarity search output. Therefore, our work allows to study such orphans across the tree of life and in doing so to recover cases of remote homology, get better estimates of the evolutionary age of protein families, detect cases of rapid divergence and more generally better understand how genetic novelty arises.

bioinformatics↗

bioRxiv2022

Intergenic regions of Saccharomycotina yeasts are enriched in potential to form transmembrane domains

Intergenic genomic regions have essential regulatory and structural roles that impose constraints on their sequences. But regions that do not currently encode proteins, also carry the potential to do so in the future. De novo gene emergence, the evolution of novel genes out of previously non-coding sequences has now been established as a potent force for genomic novelty. Recently, it was shown that intergenic regions in the genome of S. cerevisiae harbor pervasive cryptic potential to, if theoretically translated, form transmembrane domains (TM domains) more frequently than expected by chance, a property that we refer to as TM-forming enrichment. The source and biological relevance of this property is unknown. Here we expand the investigation into the TM-forming potential of intergenic regions to the entire Saccharomycotina budding yeast subphylum, in an effort to explain this property and understand its importance. We find pervasive but variable enrichment in TM-forming potential across the subphylum, regardless of the composition and average size of intergenic regions. This cryptic property is evenly spread across the genome, cannot be explained by the hydrophobic content of the sequence, and does not appear to localize to regions containing regulatory motifs. This TM-forming enrichment specifically, and not the actual TM-forming potential, is associated, across genomes, with more TM domains in evolutionarily young genes. Our findings shed light on this newly discovered feature of yeast genomes and constitute a first step towards understanding its evolutionary importance.

genomics↗

bioRxiv2022

Distinct chromosomal niches in the genome of S. cerevisiae provide the background for genomic innovation and shape the fate of gene duplicates

Gene duplication is a major source of genomic innovation in all eukaryotes, with large proportions of genes being the result of either small-scale (SSD) or genome-wide duplication (WGD) events. In the model unicellular eukaryote Saccharomyces cerevisiae, of which nearly one third of the genome corresponds to gene duplicates, the two modes of duplication have been shown to follow different evolutionary fates, with SSD genes being more prone to acquire novel functionalities (neofunctionalization) and WGD more likely to retain different parts of the original ancestral function (subfunctionalization). Having previously described aspects of functional compartmentalization for the genes of S. cerevisiae, in this work we set out to investigate the existence of positional preferences of gene duplicates. We found that SSD and WGD genes are organized in distinct gene clusters that are, furthermore, segregated, occupying different regions, with SSD being more peripheral and WGD more centrally positioned close to centromeric chromatin. Duplicate gene clusters differ from the rest of the genome in terms of gene size and spacing, gene expression variability and regulatory complexity. What is more interesting, some of these properties are also shared by singleton genes residing in duplicate-rich regions in a position-dependent manner. Our analysis further reveals particular chromatin architectures in the promoters of duplicate genes, which are generally longer, with less pronounced nucleosome-free regions, strong structural constraints and a larger number of regulatory elements. Such structural features appear to be important for gene evolution as we find SSD gene-pair co-expression to be strongly associated with the similarity of nucleosome positioning patterns. We propose that specific regions of the yeast genome provide a favourable environment for the generation and maintenance of small-scale gene duplicates. The existence of such genomic "niches" is supported by the enrichment of these regions in singleton genes bearing similarities with gene "relics", remnants of recent duplications that have reverted to single gene status. Our findings provide a valuable framework for the study of genomic innovation and suggest taking into account positional preferences in the study of gene emergence and fixation in experimentally and naturally evolving populations.

genomics↗