Building blocks are synthesized on demand during the yeast cell cycle

Campbell, Kate; Westholm, Jakub Orzechowski; Kasvandik, Sergo; Bartolomeo, Francesca Di; Mormino, Maurizio; Nielsen, Jens

doi:10.1073/pnas.1919535117

Cited by 37 publications

(26 citation statements)

References 34 publications

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“…Our observation that TORC1/PKA activity fluctuates in synchrony with the cell cycle is in line with a recent report that metabolic precursors are synthesized at different rates during the cell cycle (e.g. amino acid synthesis increases in G1 and G2/M with a minimum in S) (Campbell et al, 2020), as well as the observation that metabolism operates as an autonomous oscillator that is coupled to the cell cycle (Papagiannakis et al, 2017). On the other hand, it becomes increasingly apparent that growth-controlling signaling pathways are in direct, bidirectional communication with the cell-cycle machinery (Moreno-Torres et al, 2017Odle et al, 2020;Romero-Pozuelo et al, 2020;Talarek et al, 2017).…”

Section: Discussionsupporting

confidence: 92%

TORC1 and PKA activity towards ribosome biogenesis oscillates in synchrony with the budding yeast cell cycle

Guerra

Been

et al. 2021

Preprint

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Recent studies have revealed that the growth rate of budding yeast and mammalian cells varies during the cell cycle. By linking a multitude of signals to cell growth, the highly conserved Target of Rapamycin Complex 1 (TORC1) and Protein Kinase A (PKA) pathways are prime candidates for mediating the dynamic coupling between growth and division. However, measurements of TORC1 and PKA activity during the cell cycle are still lacking. Following the localization dynamics of two TORC1 and PKA targets via time-lapse microscopy in hundreds of yeast cells, we found that the activity of these pathways towards ribosome biogenesis fluctuates in synchrony with the cell cycle even under constant external conditions. Mutations of upstream TORC1 and PKA regulators suggested that internal metabolic signals partially mediate these activity changes. Our study reveals a new aspect of TORC1 and PKA signaling, which will be important for understanding growth regulation during the cell cycle.

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Section: Discussionsupporting

confidence: 92%

TORC1 and PKA activity towards ribosome biogenesis oscillates in synchrony with the budding yeast cell cycle

Guerra

Been

et al. 2021

Preprint

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“…the cell cycle. As multi-omics circadian data becomes more prevalent (Collins et al, 2020;Campbell et al, 2020;Hughes et al, 2017), MOSAIC will provide an important role in finding and understanding both oscillatory and non-oscillatory trends in a variety of organisms and processes.…”

Section: Discussionmentioning

confidence: 99%

MOSAIC: a joint modeling methodology for combined circadian and non-circadian analysis of multi-omics data

2020

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Motivation Circadian rhythms are approximately 24 hour endogenous cycles that control many biological functions. To identify these rhythms, biological samples are taken over circadian time and analyzed using a single omics type, such as transcriptomics or proteomics. By comparing data from these single omics approaches, it has been shown that transcriptional rhythms are not necessarily conserved at the protein level, implying extensive circadian post-transcriptional regulation. However, as proteomics methods are known to be noisier than transcriptomic methods, this suggests that previously identified arrhythmic proteins with rhythmic transcripts could have been missed due to noise and may not be due to post-transcriptional regulation. Results To determine if one can use information from less-noisy transcriptomic data to inform rhythms in more-noisy proteomic data, and thus more accurately identify rhythms in the proteome, we have created the MOSAIC (Multi-Omics Selection with Amplitude Independent Criteria) application. MOSAIC combines model selection and joint modeling of multiple omics types to recover significant circadian and non-circadian trends. Using both synthetic data and proteomic data from Neurospora crassa, we showed that MOSAIC accurately recovers circadian rhythms at higher rates in not only the proteome but the transcriptome as well, outperforming existing methods for rhythm identification. In addition, by quantifying non-circadian trends in addition to circadian trends in data, our methodology allowed for the recognition of the diversity of circadian regulation as compared to non-circadian regulation. Availability MOSAIC’s full interface is available at https://github.com/delosh653/MOSAIC. An R package for this functionality, mosaic.find, can be downloaded at https://CRAN.R-project.org/package=mosaic.find. Supplementary information Supplementary data are available at Bioinformatics online.

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“…The previously mentioned module for integration of proteomics data generates a condition-dependent ecModel with proteomics constraints for each condition/replicate in a provided dataset of absolute protein abundances [mmol/gDw]. Even though absolute quantification of proteins is becoming more accessible and integrated into systems biology studies [58][59][60][61][62] , a major caveat of using proteomics data as constraints for quantitative models is their intrinsic high biological and technical variability 63 , therefore some of the incorporated data constraints need to be loosened in order to obtain functional ecModels. When needed, additional condition-dependent exchange fluxes of byproducts can also be used as constraints in order to limit the feasible solution space.…”

Section: Proteomics Constraints Refine Phenotype Predictions For Multiple Organisms and Conditionsmentioning

confidence: 99%

Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0

Domenzain

Sánchez

Anton

et al. 2021

Preprint

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Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into GEMs was first enabled by the GECKO method, allowing the study of phenotypes constrained by protein limitations. Here, we upgraded the GECKO toolbox in order to enhance models with enzyme and proteomics constraints for any organism with an available GEM reconstruction. With this, enzyme-constrained models (ecModels) for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus were generated, aiming to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions revealed that upregulation and high saturation of enzymes in amino acid metabolism were found to be common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO was further developed by the implementation of an automated framework for continuous and version-controlled update of ecModels, which was validated by producing additional high-quality ecModels for Escherichia coli and Homo sapiens. These efforts aim to facilitate the utilization of ecModels in basic science, metabolic engineering and synthetic biology purposes.

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Building blocks are synthesized on demand during the yeast cell cycle

Cited by 37 publications

References 34 publications

TORC1 and PKA activity towards ribosome biogenesis oscillates in synchrony with the budding yeast cell cycle

TORC1 and PKA activity towards ribosome biogenesis oscillates in synchrony with the budding yeast cell cycle

MOSAIC: a joint modeling methodology for combined circadian and non-circadian analysis of multi-omics data

Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0

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