Proteotoxicity caused by perturbed protein complexes underlies hybrid incompatibility in yeast

Swamy, Krishna B S; Lee, Hsin‐Yi; Ladra, Carmina; Liu, Chien-Fu Jeff; Chao, Jung-Chi; Chen, Yi-Yun; Leu, Jun‐Yi

doi:10.1038/s41467-022-32107-4

Cited by 10 publications

(1 citation statement)

References 85 publications

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“…However, it is noteworthy that no hybrids were produced from one Bioethanol lineage strain. While we did not investigate the underlying reasons for this observation, we believe that certain genetic incompatibilities identified in interspecific hybridizations, particularly those related to mitochondrial and nuclear interactions, might account for the disparities in the mating rates of our hybrids (Blanckaert & Payseur, 2021; Lee et al, 2008; Moran et al, 2024; Swamy et al, 2022). Ploidy analysis revealed that rate mating led to a higher proportion of triploids hybrids.…”

Section: Discussionmentioning

confidence: 95%

Understanding brewing trait inheritance inde novolager yeast hybrids

Zavaleta,

Pérez-Través,

Saona

et al. 2024

Preprint

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Hybridization betweenSaccharomyces cerevisiaeandSaccharomyces eubayanusresulted in the emergence ofS. pastorianus, a crucial yeast for lager fermentation. However, our understanding of hybridization success and hybrid vigour between these two species remains limited due to the scarcity ofS. eubayanusparental strains. Here, we explore hybridization success and the impact of hybridization on fermentation performance and volatile compound profiles in newly formed lager hybrids. By selecting parental candidates spanning a diverse array of lineages from both species, we reveal that the Beer and PB-2 lineages exhibit high rates of hybridization success inS. cerevisiaeandS. eubayanus, respectively. Polyploid hybrids were generated through rare mating techniques, revealing a prevalence of triploids and diploids over tetraploids. Despite the absence of heterosis in fermentative capacity, hybrids displayed phenotypic variability, notably influenced by maltotriose consumption. Interestingly, ploidy levels did not significantly correlate with fermentative capacity, although triploids exhibited greater phenotypic variability. TheS. cerevisiaeparental lineages primarily influenced volatile compound profiles, with significant differences in aroma production. Interestingly, hybrids emerging from the BeerS. cerevisiaeparental lineages exhibited a volatile compound profile resembling the correspondingS. eubayanusparent. This pattern may result from the dominant inheritance of theS. eubayanusaroma profile, as suggested by the over-expression of genes related to alcohol metabolism and acetate synthesis in hybrids including the BeerS. cerevisiaelineage. Our findings suggest complex interactions between parental lineages and hybridization outcomes, highlighting the potential for creating yeasts with distinct brewing traits through hybridization strategies.

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Section: Discussionmentioning

confidence: 95%

Understanding brewing trait inheritance inde novolager yeast hybrids

Zavaleta,

Pérez-Través,

Saona

et al. 2024

Preprint

View full text Add to dashboard Cite

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A feature extraction free approach for protein interactome inference from co-elution data

Chen

Chao

Wong

et al. 2023

Briefings in Bioinformatics

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Protein complexes are key functional units in cellular processes. High-throughput techniques, such as co-fractionation coupled with mass spectrometry (CF-MS), have advanced protein complex studies by enabling global interactome inference. However, dealing with complex fractionation characteristics to define true interactions is not a simple task, since CF-MS is prone to false positives due to the co-elution of non-interacting proteins by chance. Several computational methods have been designed to analyze CF-MS data and construct probabilistic protein–protein interaction (PPI) networks. Current methods usually first infer PPIs based on handcrafted CF-MS features, and then use clustering algorithms to form potential protein complexes. While powerful, these methods suffer from the potential bias of handcrafted features and severely imbalanced data distribution. However, the handcrafted features based on domain knowledge might introduce bias, and current methods also tend to overfit due to the severely imbalanced PPI data. To address these issues, we present a balanced end-to-end learning architecture, Software for Prediction of Interactome with Feature-extraction Free Elution Data (SPIFFED), to integrate feature representation from raw CF-MS data and interactome prediction by convolutional neural network. SPIFFED outperforms the state-of-the-art methods in predicting PPIs under the conventional imbalanced training. When trained with balanced data, SPIFFED had greatly improved sensitivity for true PPIs. Moreover, the ensemble SPIFFED model provides different voting schemes to integrate predicted PPIs from multiple CF-MS data. Using the clustering software (i.e. ClusterONE), SPIFFED allows users to infer high-confidence protein complexes depending on the CF-MS experimental designs. The source code of SPIFFED is freely available at: https://github.com/bio-it-station/SPIFFED.

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ADTnorm: Robust Integration of Single-cell Protein Measurement across CITE-seq Datasets

Zheng

Jun

Tian

et al. 2022

Preprint

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CITE-seq technology enables the direct measurement of protein expression, known as antibody-derived tags (ADT), in addition to RNA expression. The increase in the copy number of protein molecules leads to a more robust detection of protein features compared to RNA, providing a deep definition of cell types. However, due to added discrepancies of antibodies, such as the different types or concentrations of IgG antibodies, the batch effects of the ADT component of CITE-seq can dominate over biological variations, especially for the across-study integration. We present ADTnorm as a normalization and integration method designed explicitly for the ADT counts of CITE-seq data. Benchmarking with existing scaling and normalization methods, ADTnorm achieves a fast and accurate matching of the negative and positive peaks of the ADT counts across samples, efficiently removing technical variations across batches. Further quantitative evaluations confirm that ADTnorm achieves the best cell-type separation while maintaining the minimal batch effect. Therefore, ADTnorm facilitates the scalable ADT count integration of massive public CITE-seq datasets with distinguished experimental designs, which are essential for creating a corpus of well-annotated single-cell data with deep and standardized annotations.Contactraphael.gottardo@chuv.chSupplementary informationSupplementary data are available online.

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Proteotoxicity caused by perturbed protein complexes underlies hybrid incompatibility in yeast

Cited by 10 publications

References 85 publications

Understanding brewing trait inheritance inde novolager yeast hybrids

Understanding brewing trait inheritance inde novolager yeast hybrids

A feature extraction free approach for protein interactome inference from co-elution data

ADTnorm: Robust Integration of Single-cell Protein Measurement across CITE-seq Datasets

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