Identification of Microproteins in Saccharomyces cerevisiae under Different Stress Conditions

Phylogenomics and functional annotation of 530 non-Saccharomyces yeasts from winemaking environments reveals their fermentome and flavorome

The winemaking industry faces unprecedented challenges due to climate change and market shifts, with profound commercial and socioeconomic repercussions. In response, non-Saccharomyces yeasts have gained attention for their potential to both mitigate these challenges and enhance the complexity of winemaking. This study builds upon our previous cataloguing of 293 non-Saccharomyces yeast species associated with winemaking environments by rigorously analysing 661 publicly available genomes. By employing a bioinformatics pipeline with stringent quality control checkpoints, we annotated and evaluated these genomes, culminating in a robust dataset of 530 non-Saccharomyces proteomes, belonging to 134 species, accessible to the research community. Employing this dataset, we conducted a comparative phylogenomic analysis to decipher metabolic networks related to fermentation capacity and flavor/aroma modulation. Our functional annotation has uncovered distinctive metabolic traits of non-Saccharomyces yeasts, elucidating their unique contributions to enology. Crucially, this work pioneers the identification of a non-Saccharomyces 'fermentome', a specific set of six genes uniquely present in fermentative species and absent in non-fermentative ones, and an expanded set of 35 genes constituting the complete fermentome. Moreover, we delineated a 'flavorome' by examining 96 genes across 19 metabolic categories implicated in wine aroma and flavour enhancement. These discoveries provide valuable genomic insights, offering new avenues for innovative winemaking practices and research. Citation: Franco-Duarte R, Fernandes T, Sousa MJ, Sampaio P, Rito T, Soares P (2025). Phylogenomics and functional annotation of 530 non-Saccharomyces yeasts from winemaking environments reveals their fermentome and flavorome. Studies in Mycology 111: 1-17. doi: 10.3114/sim.2025.111.01.

Discovering the hidden function in fungal genomes

New molecular technologies have helped unveil previously unexplored facets of the genome beyond the canonical proteome, including microproteins and short ORFs, products of alternative splicing, regulatory non-coding RNAs, as well as transposable elements, cis-regulatory DNA, and other highly repetitive regions of DNA. In this Review, we highlight what is known about this 'hidden genome' within the fungal kingdom. Using well-established model systems as a contextual framework, we describe key elements of this hidden genome in diverse fungal species, and explore how these factors perform critical functions in regulating fungal metabolism, stress tolerance, and pathogenesis. Finally, we discuss new technologies that may be adapted to further characterize the hidden genome in fungi.

Biological factors and statistical limitations prevent detection of most noncanonical proteins by mass spectrometry

Ribosome profiling experiments indicate pervasive translation of short open reading frames (ORFs) outside of annotated protein-coding genes. However, shotgun mass spectrometry (MS) experiments typically detect only a small fraction of the predicted protein products of this noncanonical translation. The rarity of detection could indicate that most predicted noncanonical proteins are rapidly degraded and not present in the cell; alternatively, it could reflect technical limitations. Here, we leveraged recent advances in ribosome profiling and MS to investigate the factors limiting detection of noncanonical proteins in yeast. We show that the low detection rate of noncanonical ORF products can largely be explained by small size and low translation levels and does not indicate that they are unstable or biologically insignificant. In particular, proteins encoded by evolutionarily young genes, including those with well-characterized biological roles, are too short and too lowly expressed to be detected by shotgun MS at current detection sensitivities. Additionally, we find that decoy biases can give misleading estimates of noncanonical protein false discovery rates, potentially leading to false detections. After accounting for these issues, we found strong evidence for 4 noncanonical proteins in MS data, which were also supported by evolution and translation data. These results illustrate the power of MS to validate unannotated genes predicted by ribosome profiling, but also its substantial limitations in finding many biologically relevant lowly expressed proteins.

Biological Factors and Statistical Limitations Prevent Detection of Most Noncanonical Proteins by Mass Spectrometry

Ribosome profiling experiments indicate pervasive translation of short open reading frames (ORFs) outside of annotated protein-coding genes. However, shotgun mass spectrometry experiments typically detect only a small fraction of the predicted protein products of this noncanonical translation. The rarity of detection could indicate that most predicted noncanonical proteins are rapidly degraded and not present in the cell; alternatively, it could reflect technical limitations. Here we leveraged recent advances in ribosome profiling and mass spectrometry to investigate the factors limiting detection of noncanonical proteins in yeast. We show that the low detection rate of noncanonical ORF products can largely be explained by small size and low translation levels and does not indicate that they are unstable or biologically insignificant. In particular, proteins encoded by evolutionarily young genes, including those with well-characterized biological roles, are too short and too lowly-expressed to be detected by shotgun mass spectrometry at current detection sensitivities. Additionally, we find that decoy biases can give misleading estimates of noncanonical protein false discovery rates, potentially leading to false detections. After accounting for these issues, we found strong evidence for four noncanonical proteins in mass spectrometry data, which were also supported by evolution and translation data. These results illustrate the power of mass spectrometry to validate unannotated genes predicted by ribosome profiling, but also its substantial limitations in finding many biologically relevant lowly-expressed proteins.