Sulfur Metabolism Actively Promotes Initiation of Cell Division in Yeast

Blank, Heidi M.; Gajjar, Shefali; Belyanin, Andrey; Polymenis, Michael

doi:10.1371/journal.pone.0008018

Cited by 18 publications

(24 citation statements)

References 42 publications

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“…However, the CBS activity in the wt‐HepG2 was relatively low (9.8 mU/mg/h) compared with the wt‐HEK293T (501.1 mU/mg/h; Figure ). This is consistent with previous reports, which concluded that the CBS activity positively correlates with the rate of cell proliferation (Blank, Gajjar, Belyanin, & Polymenis, ). These findings could be further supported by the fact that the doubling time of wt‐HEK293T cells (16–24 h) is about three times shorter than that of wt‐HepG2 cells (48–72 h; Amann et al., ; López‐Terrada, Cheung, Finegold, & Knowles, ; Soni & Lai, ).…”

Section: Discussionsupporting

confidence: 94%

In silico and in vivo models for Qatari-specific classical homocystinuria as basis for development of novel therapies

et al. 2018

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Homocystinuria is a rare inborn error of methionine metabolism caused by cystathionine β‐synthase (CBS) deficiency. The prevalence of homocystinuria in Qatar is 1:1,800 births, mainly due to a founder Qatari missense mutation, c.1006C>T; p.R336C (p.Arg336Cys). We characterized the structure–function relationship of the p.R336C‐mutant protein and investigated the effect of different chemical chaperones to restore p.R336C‐CBS activity using three models: in silico, ΔCBS yeast, and CRISPR/Cas9 p.R336C knock‐in HEK293T and HepG2 cell lines. Protein modeling suggested that the p.R336C induces severe conformational and structural changes, perhaps influencing CBS activity. Wild‐type CBS, but not the p.R336C mutant, was able to restore the yeast growth in ΔCBS‐deficient yeast in a complementation assay. The p.R336C knock‐in HEK293T and HepG2 cells decreased the level of CBS expression and reduced its structural stability; however, treatment of the p.R336C knock‐in HEK293T cells with betaine, a chemical chaperone, restored the stability and tetrameric conformation of CBS, but not its activity. Collectively, these results indicate that the p.R336C mutation has a deleterious effect on CBS structure, stability, and activity, and using the chemical chaperones approach for treatment could be ineffective in restoring p.R336C CBS activity.

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Section: Discussionsupporting

confidence: 94%

In silico and in vivo models for Qatari-specific classical homocystinuria as basis for development of novel therapies

et al. 2018

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“…We found that ire1Δ cells display regular metabolic oscillations (Fig. 5A), with a period similar to IRE1 + cells [17],[22],[23],[24]. We also examined the timing of DNA replication during the metabolic cycle, by monitoring the DNA content of cells during these oscillations.…”

Section: Resultsmentioning

confidence: 91%

The Unfolded Protein Response Is Not Necessary for the G1/S Transition, but It Is Required for Chromosome Maintenance in Saccharomyces cerevisiae

et al. 2010

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BackgroundThe unfolded protein response (UPR) is a eukaryotic signaling pathway, from the endoplasmic reticulum (ER) to the nucleus. Protein misfolding in the ER triggers the UPR. Accumulating evidence links the UPR in diverse aspects of cellular homeostasis. The UPR responds to the overall protein synthesis capacity and metabolic fluxes of the cell. Because the coupling of metabolism with cell division governs when cells start dividing, here we examined the role of UPR signaling in the timing of initiation of cell division and cell cycle progression, in the yeast Saccharomyces cerevisiae.Methodology/Principal FindingsWe report that cells lacking the ER-resident stress sensor Ire1p, which cannot trigger the UPR, nonetheless completed the G1/S transition on time. Furthermore, loss of UPR signaling neither affected the nutrient and growth rate dependence of the G1/S transition, nor the metabolic oscillations that yeast cells display in defined steady-state conditions. Remarkably, however, loss of UPR signaling led to hypersensitivity to genotoxic stress and a ten-fold increase in chromosome loss.Conclusions/SignificanceTaken together, our results strongly suggest that UPR signaling is not necessary for the normal coupling of metabolism with cell division, but it has a role in genome maintenance. These results add to previous work that linked the UPR with cytokinesis in yeast. UPR signaling is conserved in all eukaryotes, and it malfunctions in a variety of diseases, including cancer. Therefore, our findings may be relevant to other systems, including humans.

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“…Other highly-correlated pathways act as intermediaries between processes that are important for cell growth, such as C5-Branched dibasic acid and galactose metabolism. Furthermore, we identified: purine metabolism, which has been found to regulate cell growth (31); RNA degradation, which has been shown to be strongly correlated with yeast growth rates (32); sulfur metabolism, which can actively promote initial cell division (33). Finally, the fact that growth rate is also correlated with pyrimidine supports recent research suggesting that its limitation causes the depletion of UTP and CTP, which in turn limits RNA biosynthesis, a limiting factor for cell growth (34).…”

Section: Strain-specific Metabolic Modeling Of Yeast Mutantsmentioning

confidence: 99%

A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth

Culley

Vijayakumar

Zampieri

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype–phenotype–environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning–based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143 Saccharomyces cerevisiae mutants and we test 27 machine-learning methods, incorporating state-of-the-art feature selection and multiview learning approaches. We propose a multiview neural network using fluxomic and transcriptomic data, showing that the former increases the predictive accuracy of the latter and reveals functional patterns that are not directly deducible from gene expression alone. We test the proposed neural network on a further 86 strains generated in a different experiment, therefore verifying its robustness to an additional independent dataset. Finally, we show that introducing mechanistic flux features improves the predictions also for knockout strains whose genes were not modeled in the metabolic reconstruction. Our results thus demonstrate that fusing experimental cues with in silico models, based on known biochemistry, can contribute with disjoint information toward biologically informed and interpretable machine learning. Overall, this study provides tools for understanding and manipulating complex phenotypes, increasing both the prediction accuracy and the extent of discernible mechanistic biological insights.

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Sulfur Metabolism Actively Promotes Initiation of Cell Division in Yeast

Cited by 18 publications

References 42 publications

In silico and in vivo models for Qatari-specific classical homocystinuria as basis for development of novel therapies

In silico and in vivo models for Qatari-specific classical homocystinuria as basis for development of novel therapies

The Unfolded Protein Response Is Not Necessary for the G1/S Transition, but It Is Required for Chromosome Maintenance in Saccharomyces cerevisiae

A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth

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