Principles of cellular resource allocation revealed by condition-dependent proteome profiling

Metzl-Raz, Eyal; Kafri, Moshe; Yaakov, Gilad; Soifer, Ilya; Gurvich, Yonat; Barkai, Naama

doi:10.7554/elife.28034

Cited by 208 publications

(333 citation statements)

References 62 publications

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“…Discussion A central theme emphasized in this study is mechanisms by which cells manage resource allocation, supply and demand during cell growth. Recent studies in model organisms like yeast and E. coli focus on protein translation, and the need to 'buffer' translation capacity during cell growth (Hui et al, 2015;Metzl--Raz et al, 2017). These studies alter our perception of how translation is regulated during high cell growth.…”

Section: Gcn4 Dependent Lysine and Arginine Supply Is Essential To Mamentioning

confidence: 99%

“…Such a balanced cellular economy therefore entails multiple regulatory systems that sense nutrients, and control global responses in order to manage metabolic resources and ensure coordinated growth outputs. Here, the model eukaryote, Saccharomyces cerevisiae, has been instrumental in building our general understanding of global nutrient--dependent responses, for identifying what are 'limiting metabolites' for a cell, addressing resource allocations within cells, as well as to uncover conserved mechanisms of nutrient--sensing (Boer et al, 2010;Brauer et al, 2006;Dikicioglu et al, 2011;Gresham et al, 2011;Gutteridge et al, 2010;Metzl--Raz et al, 2017;Saldanha et al, 2004;Zaman et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Genome-scale reconstruction of Gcn4/ATF4 networks driving a growth program

Srinivasan

Walvekar

Seshasayee

et al. 2020

Preprint

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How cells manage biomolecule supply to enable constructive metabolism for growth is a fundamental question. Methionine directly activates genomic programs where translation and metabolism are induced. In otherwise amino acid limitations, methionine induced ribosomal biogenesis, carbon metabolism, amino acid and nucleotide biosynthesis. Here, we find that the methionine--induced anabolic program is carbon--source independent, and requires the 'starvation-responsive' transcription factor Gcn4/ATF4. Integrating transcriptome and ChIP--Seq analysis, we decipher direct and indirect, methionine--dependent roles for Gcn4. Methionine--induced genes enabling metabolic precursor biosynthesis critically require Gcn4; contrastingly ribosomal genes are partly repressed by Gcn4. Gcn4 binds promoter--regions and transcribes a subset of metabolic genes, driving amino acid (particularly lysine and arginine) biosynthesis. This sustained lys/arg supply maintains high translation capacity, by allowing the translation of lys/arg enriched transcripts, notably the translation machinery itself. Thus, we discover how Gcn4 enabled metabolic--precursor supply bolsters protein synthesis demand, and drive a methionine--mediated anabolic program.

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Section: Gcn4 Dependent Lysine and Arginine Supply Is Essential To Mamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genome-scale reconstruction of Gcn4/ATF4 networks driving a growth program

Srinivasan

Walvekar

Seshasayee

et al. 2020

Preprint

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“…For instance, the proteins involved in the synthesis of the major lipid components, the fatty acids and sterols, were enriched among the high and moderate abundant proteins, whereas the proteins related to the synthesis of specific lipid head groups were enriched in the low to very low abundant proteins. This might be a consequence of cell resource optimization as protein synthesis is one of the most energetically expensive tasks in cells 34 . Our lipid map also showed that TG, PC and PE are the most diverse classes of lipids.…”

Section: Discussionmentioning

confidence: 99%

AHistoplasma capsulatumlipid metabolic map identifies antifungal targets

Zamith-Miranda

Heyman

Burnet

et al. 2020

Preprint

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Lipids play a fundamental role in fungal cell biology, being components of the cell membrane as well as targets of antifungal drugs. A deeper knowledge of lipid metabolism is key for developing new drugs and a better understanding of fungal pathogenesis. Here we built a comprehensive map of the Histoplasma capsulatum lipid metabolic pathway by integrating proteomic and lipidomic analyses. The map led to the identification of both the fatty acid desaturation and the sphingolipid biosynthesis pathways as targets for drug development. We also found that H. capsulatum produces analogs of platelet-activating factor, a potent regulator of the human immune response. The H. capsulatum platelet-activating factor analogs induced platelet aggregation and stimulated the production of the cytokines interleukin-10 and tumor necrosis factor-α by J774 macrophages. Overall, this lipid metabolic map revealed pathways that can be targeted for drug development, in addition to identifying a regulator of the host immune response. decrease in aged yeast cells 25 . In terms of sphingolipids, H. capsulatum also produces glucosylceramides, inositolphosphorylceramides and galactosyl-dimannosylinositolphosphorylceramides, which are components of membrane microdomains and are important for infection 20,26,27 . Considering the importance of lipids, we reasoned that a deeper characterization of H. capsulatum lipid metabolism would result in the discovery of virulence factors and drug targets. In this study, we performed an in-depth characterization of the H. capsulatum lipid biosynthetic pathway by profiling its lipids and the associated proteins, which were integrated into a metabolic map. By comparing our results to Saccharomyces cerevisiae and humans, we show unique features of the lipid biosynthetic pathway of H. capsulatum that can be targeted for drug development. This analysis also led to the discovery that H.capsulatum produces analogs of platelet-activating factor (PAF), which are biologically active and regulate different aspects of the human immune response.

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“…indicates an increasing overcapacity of ribosomes with a decreasing growth rate µ Metzl-Raz et al, 2017). Both parameters w R and φ R,0 were determined for E. coli by fitting equation (2) to measured, cross-conditional concentration data of the ribosomal proteome sector ( Fig 2B).…”

Section: The Ribosomal Sectormentioning

confidence: 99%

Protein allocation and enzymatic constraints explainEscherichia coliwildtype and mutant phenotypes

Alter

Blank

Ebert

2020

Preprint

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Proteins have generally been recognized to constitute the key cellular component in shaping microbial phenotypes. Due to limited cellular resources and space, optimal allocation of proteins is crucial for microbes to facilitate maximum proliferation rates while allowing a flexible response to environmental changes. Regulatory patterns of protein allocation were utilized to account for the condition-dependent proteome in a genome-scale metabolic reconstruction of Escherichia coli by linearly linking mass concentrations of protein sectors and single metabolic enzymes to flux variables. The resulting protein allocation model (PAM) correctly approximates wildtype phenotypes and flux distributions for various substrates, even under data scarcity. Moreover, we showed the ability of the PAM to predict metabolic responses of single gene deletion mutants by additionally assuming growth-limiting, transcriptional restrictions. Thus, we promote the integration of protein allocation constraints into classical constraint-based models to foster their predictive capabilities and application for strain analysis and metabolic engineering purposes. K E Y W O R D S constraint-based modeling, enzyme kinetics, protein allocation, transcriptional control, Escherichia coli 1

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Principles of cellular resource allocation revealed by condition-dependent proteome profiling

Cited by 208 publications

References 62 publications

Genome-scale reconstruction of Gcn4/ATF4 networks driving a growth program

Genome-scale reconstruction of Gcn4/ATF4 networks driving a growth program

AHistoplasma capsulatumlipid metabolic map identifies antifungal targets

Protein allocation and enzymatic constraints explainEscherichia coliwildtype and mutant phenotypes

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