The subcellular organisation of Saccharomyces cerevisiae

Nightingale, Daniel J.H.; Geladaki, Aikaterini; Breckels, Lisa M.; Oliver, Stephen G.; Lilley, Kathryn S.

doi:10.1016/j.cbpa.2018.10.026

Cited by 27 publications

(30 citation statements)

References 78 publications

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“…We provide a brief description of the datasets used in this manuscript. We analyse hy-perLOPIT data, in which sub-cellular fractionation is performed using density-gradient centrifugation (Dunkley et al, 2004(Dunkley et al, , 2006Mulvey et al, 2017), on pluripotent mESCs (E14TG2a) (Christoforou et al, 2016), human bone osteosarcoma (U-2 OS) cells (Thul et al, 2017;Geladaki et al, 2019), and S. cerevisiae (bakers' yeast) cells (Nightingale et al, 2019). The mESC dataset combines two 10-plex biological replicates and quantitative information on 5032 proteins.…”

Section: Datasetsmentioning

confidence: 99%

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A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection

Crook

Geladaki

Nightingale

et al. 2020

Preprint

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The cell is compartmentalised into complex micro-environments allowing an array of specialised biological processes to be carried out in synchrony. Determining a protein's sub-cellular localisation to one or more of these compartments can therefore be a rst step in determining its function. High-throughput and high-accuracy mass spectrometry-based sub-cellular proteomic methods can now shed light on the localisation of thousands of proteins at once. Machine learning algorithms are then typically employed to make protein-organelle assignments. However, these algorithms are limited by insucient and incomplete annotation. We propose a semi-supervised Bayesian approach to novelty detection, allowing the discovery of additional, previously unannotated sub-cellular niches. Inference in our model is performed in a Bayesian framework, allowing us to quantify uncertainty in the allocation of proteins to new sub-cellular niches, as well as in the number of newly discovered compartments. We apply our approach across 10 mass spectrometry based spatial proteomic datasets, representing a diverse range of experimental protocols. Application of our approach to hyperLOPIT datasets validates its utility by recovering enrichment with chromatin-associated proteins without annotation and uncovers sub-nuclear compartmentalisation which was not identied in the original analysis. Moreover, using sub-cellular proteomics data from Saccharomyces cerevisiae, we uncover a novel group of proteins tracking from the ER to the early Golgi apparatus. Overall, we demonstrate the potential for novelty detection to yield biologically relevant niches that are missed by current approaches. * ksl23@cam.ac.uk

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Section: Datasetsmentioning

confidence: 99%

“…Novelty TAGM uncovers 8 putative phenotypes in the yeast hyperLOPIT data (Nightingale et al, 2019). Four of these phenotypes have no signicant over-represented annotations.…”

Section: Saccharomyces Cerevisiaementioning

confidence: 99%

A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection

Crook

Geladaki

Nightingale

et al. 2020

Preprint

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“…Qualitative and quantitative changes in the yeast proteome during CdS QDs treatments were obtained from the 2D-PAGE-based and gel-free iTRAQ approaches, respectively. (45,46) Common proteins identified with the two methods and after the two times of treatments are represented in Figs. 2 and 3 and in Table 1.…”

Section: Proteomic Variations In Response To Cds Qdsmentioning

confidence: 99%

“…Comparative proteomics utilizing gel-based and gel-free approaches has already been successfully applied to S. cerevisiae. (44)(45)(46) Our aim was to identify the proteomic alterations underlying the cell response to CdS QDs. The data facilitate understanding the detrimental effects that these ENMs have on the environment and human health.…”

Section: Introductionmentioning

confidence: 99%

Proteomic analysis to identify markers of exposure to cadmium sulphide quantum dots (CdS QDs)

Gallo

Srivast

Bulone

et al. 2020

Preprint

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Background The increasing use of cadmium sulphide (CdS) quantum dot (QD)-enabled products is expected to be accompanied by their release into the environment. In this study, the whole organism Saccharomyces cerevisiae was used as a model eukaryote to study protein modulations employing 2D- gel electrophoresis and gel-free iTRAQ (Isobaric Tags for Relative and Absolute Quantitation) proteomics following cell exposure to CdS QDs for 9 and 24 h. From both biotechnological and ecotoxicological perspectives, the use of S. cerevisiae as a model organism sheds light on the impact nanomaterials have on the biochemical responses of the exposed organism. Results Key proteins involved in essential biological pathways were downregulated, in particular after 24 h exposure. These include the major proteins of the glycolytic pathway, the components of mitochondrial respiratory chain complexes III, IV and V involved in the oxidative phosphorylation chain, the ATP-dependent molecular chaperone Hsc82 as well as other proteins responsible for protein folding and ubiquitination in the endoplasmic reticulum. Some of the proteins whose expression was altered have previously been described as strongly-adsorbed by CdS QD nanomaterial surfaces as hard corona, and involved in the cytotoxicity of this class of engineered nanomaterials. These data may be extrapolated to broader contexts and a wider range of organisms by allowing the identification of robust biomarkers of exposure to CdS QDs. Conclusions The work shows the power of the model organisms S. cerevisiae in biotechnology to ensure high levels of health and environmental safety. In fact, the double proteomic approach allowed to identify early markers of exposure to CdS QDs among all the proteins reprogrammed by the treatment.

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“…It is also used for functional toxicological studies—in identification of molecular biomarkers, unknown mechanisms of action, and novel potential targets . Nowadays, this yeast is excellent model for researching subcellular protein localization and mechanisms that govern cellular homeostasis …”

Section: Introductionmentioning

confidence: 99%

Combining proteomics and lipid analysis to unravel Confidor stress response in Saccharomyces cerevisiae

Justinić

Katić

Uršičić

et al. 2019

Environmental Toxicology

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The yeast Saccharomyces cerevisiae is a useful model for studying the influence of different stress factors on eukaryotic cells. In this work we used the pesticide imidacloprid, in the Confidor formulation, as the stress factor and analyzed its influence on the metabolic activity, proteome and lipid content and composition of Saccharomyces cerevisiae yeast. During the cultivation of yeast, the lowest recommended application dose of Confidor (0.025%, v/v) was added to the growth media and its influence on the mitochondria, cytosol with microsomes, and the whole yeast cells was monitored. The results show that under the stress provoked by the toxic effects of Confidor, yeast cells density significantly decreased and the percentage of metabolically disturbed cells significantly increased comparing with untreated control.Also, there was a downregulation of majority of glycolytic, gluconeogenesis, and TCA cycle enzymes (Fba1, Adh1, Hxk2, Tal1, Tdh1,Tdh3, Eno1) thus providing enough acetyl-CoA for the lipid restructuring and accumulation mechanism since we have found the changes in the cell and mitochondrial lipid content and FA composition.This data suggest that lipids could be the molecules that orchestrate the answer of the cells in the stress response to the Confidor treatment.

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The subcellular organisation of Saccharomyces cerevisiae

Cited by 27 publications

References 78 publications

A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection

A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection

Proteomic analysis to identify markers of exposure to cadmium sulphide quantum dots (CdS QDs)

Combining proteomics and lipid analysis to unravel Confidor stress response in Saccharomyces cerevisiae

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