Quantifying absolute gene expression profiles reveals distinct regulation of central carbon metabolism genes in yeast

Yu, Rong; Vorontsov, Egor; Sihlbom, Carina; Nielsen, Jens

doi:10.7554/elife.65722

Cited by 29 publications

(39 citation statements)

References 44 publications

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“…To generate the yeast k app dataset we collected absolute proteomics data of S. cerevisiae under diverse conditions ( 4 – 7 ) ( Dataset S1 ). The absolute protein abundance can be directly adopted as enzyme abundance in Eq.…”

Section: Resultsmentioning

confidence: 99%

In vitro turnover numbers do not reflect in vivo activities of yeast enzymes

Nielsen

2021

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

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Turnover numbers (kcat values) quantitatively represent the activity of enzymes, which are mostly measured in vitro. While a few studies have reported in vivo catalytic rates (kapp values) in bacteria, a large-scale estimation of kapp in eukaryotes is lacking. Here, we estimated kapp of the yeast Saccharomyces cerevisiae under diverse conditions. By comparing the maximum kapp across conditions with in vitro kcat we found a weak correlation in log scale of R2 = 0.28, which is lower than for Escherichia coli (R2 = 0.62). The weak correlation is caused by the fact that many in vitro kcat values were measured for enzymes obtained through heterologous expression. Removal of these enzymes improved the correlation to R2 = 0.41 but still not as good as for E. coli, suggesting considerable deviations between in vitro and in vivo enzyme activities in yeast. By parameterizing an enzyme-constrained metabolic model with our kapp dataset we observed better performance than the default model with in vitro kcat in predicting proteomics data, demonstrating the strength of using the dataset generated here.

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Section: Resultsmentioning

confidence: 99%

In vitro turnover numbers do not reflect in vivo activities of yeast enzymes

Nielsen

2021

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

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“…This could result in different cellular states that feedback globally on gene expression (Keren et al 2013). It is of note that the growthrate-dependent component defined in this study might in some cases complicate the interpretation of condition-specific responses and should then be taken into account (Pancaldi, Schubert, and Bähler 2010;Yu et al 2021).…”

Section: Discussionmentioning

confidence: 99%

Growth-Rate Dependent And Nutrient-Specific Gene Expression Resource Allocation In Fission Yeast

Kleijn

Martínez-Segura

Bertaux

et al. 2021

Preprint

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Cellular resources are limited and their relative allocation to gene expression programmes determines physiological states and global properties such as the growth rate. Quantitative studies using various growth conditions have singled out growth rate as a major physiological variable explaining relative protein abundances. Here, we used the simple eukaryote Schizosaccharomyces pombe to determine the importance of growth rate in explaining relative changes in protein and mRNA levels during growth on a series of non-limiting nitrogen sources. Although half of fission yeast genes were significantly correlated with the growth rate, this came alongside wide-spread nutrient-specific regulation. Proteome and transcriptome often showed coordinated regulation but with notable exceptions, such as metabolic enzymes. Genes positively correlated with growth rate participated in every level of protein production with the notable exception of RNA polymerase II, whereas those negatively correlated mainly belonged to the environmental stress response programme. Critically, metabolic enzymes, which represent ~55-70% of the proteome by mass, showed mainly condition-specific regulation. Specifically, many enzymes involved in glycolysis and NAD-dependent metabolism as well as the fermentative and respiratory pathways were condition-dependent and not consistently correlated with growth. In summary, we provide a rich account of resource allocation to gene expression in a simple eukaryote, advancing our basic understanding of the interplay between growth-rate dependent and nutrient-specific gene expression.

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“…The former might involve truncations, knockouts, transcription factor engineering (see later), mRNA stability, RNA polymerase engineering, transcription factor binding sites, and direct promoter engineering. A suite of such approaches has been referred to as global transcription machinery engineering (gTME) Data are replotted from supplementary table S1a of [218]. The dilution rate from 0.1 to 0.35 h −1 is encoded by the style of symbol, and the nitrogen source by the colour indicated (optimised for colour-blind legibility via the palettes at http:// colorbrewer.org/).…”

Section: Transcription Vs Translation Engineeringmentioning

confidence: 99%

“…While transcript levels were an important contributor to this variation, the number of protein molecules per transcript in the same study also showed an impressive range, from 40 to 180 000 proteins per transcript [ 217 ], implying considerable post-transcriptional control. A more recent study in the same organism detailed absolute protein abundances under some 42 conditions [ 218 ], with data replotted therefrom in Figure 7 . This serves to show the relatively limited range available in both the total transcriptome and the total proteome in growing cell cultures.…”

Section: The Potential For Host Proteome Optimisationmentioning

confidence: 99%

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Intelligent host engineering for metabolic flux optimisation in biotechnology

Munro

Kell

2021

Biochemical Journal

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Optimising the function of a protein of length N amino acids by directed evolution involves navigating a ‘search space’ of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is ‘making such biology predictable’. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

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Quantifying absolute gene expression profiles reveals distinct regulation of central carbon metabolism genes in yeast

Cited by 29 publications

References 44 publications

In vitro turnover numbers do not reflect in vivo activities of yeast enzymes

In vitro turnover numbers do not reflect in vivo activities of yeast enzymes

Growth-Rate Dependent And Nutrient-Specific Gene Expression Resource Allocation In Fission Yeast

Intelligent host engineering for metabolic flux optimisation in biotechnology

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