Metabolic Consequences of Polyphosphate Synthesis and Imminent Phosphate Limitation

Abstract

Cells stabilize intracellular inorganic phosphate (P_i) to compromise between large biosynthetic needs and detrimental bioenergetic effects of P_i. P_i homeostasis in eukaryotes uses Syg1/Pho81/Xpr1 (SPX) domains, which are receptors for inositol pyrophosphates. We explored how polymerization and storage of P_i in acidocalcisome-like vacuoles supports Saccharomyces cerevisiae metabolism and how these cells recognize P_i scarcity. Whereas P_i starvation affects numerous metabolic pathways, beginning P_i scarcity affects few metabolites. These include inositol pyrophosphates and ATP, a low-affinity substrate for inositol pyrophosphate-synthesizing kinases. Declining ATP and inositol pyrophosphates may thus be indicators of impending P_i limitation. Actual P_i starvation triggers accumulation of the purine synthesis intermediate 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR), which activates P_i-dependent transcription factors. Cells lacking inorganic polyphosphate show P_i starvation features already under P_i-replete conditions, suggesting that vacuolar polyphosphate supplies P_i for metabolism even when P_i is abundant. However, polyphosphate deficiency also generates unique metabolic changes that are not observed in starving wild-type cells. Polyphosphate in acidocalcisome-like vacuoles may hence be more than a global phosphate reserve and channel P_i to preferred cellular processes. IMPORTANCE Cells must strike a delicate balance between the high demand of inorganic phosphate (P_i) for synthesizing nucleic acids and phospholipids and its detrimental bioenergetic effects by reducing the free energy of nucleotide hydrolysis. The latter may stall metabolism. Therefore, microorganisms manage the import and export of phosphate, its conversion into osmotically inactive inorganic polyphosphates, and their storage in dedicated organelles (acidocalcisomes). Here, we provide novel insights into metabolic changes that yeast cells may use to signal declining phosphate availability in the cytosol and differentiate it from actual phosphate starvation. We also analyze the role of acidocalcisome-like organelles in phosphate homeostasis. This study uncovers an unexpected role of the polyphosphate pool in these organelles under phosphate-rich conditions, indicating that its metabolic roles go beyond that of a phosphate reserve for surviving starvation.

Keywords: SPX domains; Saccharomyces cerevisiae; acidocalcisome; phosphate signalling; polyphosphate.

FIG 1

Response of S. cerevisiae under different P_i starving conditions. (A) Growth curves of yeast cells in synthetic complete medium supplemented with different concentrations of P_i from 10 mM to 0 mM. Cells were inoculated at an OD₆₀₀ of 0.05 and cultured for 24 h. The means of triplicates are shown with standard deviation. (B) Acid phosphatase activities of yeast cells grown as in A. The means of triplicates are shown with standard deviation. (C) Concentrations of remaining P_i in the medium during cell growth. P_i concentration in the medium was monitored every 2 h for 8 h using the malachite green assay. The means of triplicates are shown with standard deviation; ***, P < 0.001; **, P < 0.01; *, P < 0.05; ns, not significant by Student’s t test; nd, not detected. (D) Fluorescence microscopy of live yeast cells producing Pho4 genomically tagged with GFP as the sole source of this protein. Cells were incubated for 8 h in 10 mM, 0.5 mM, and 0 mM P_i medium as in A before observation. (E and F) Relative gene expression levels of PHO5 (E) and PHO84 (F). Cells were grown in 10 mM, 0.5 mM, and 0 mM P_i medium for 8 h and harvested for RNA extraction and qRT-PCR. Fold change values were normalized with internal control TAF10. The means of three biological replicates are shown with standard deviation; ***, P < 0.001; **, P < 0.01; *, P < 0.05; ns, not significant by Student’s t test. (G) Polyphosphate levels in different P_i-containing medium. Cells were incubated for 8 h as in A and harvested for polyphosphate measurement. The means of triplicates are shown with standard deviation; ***, P < 0.001; **, P < 0.01; *, P < 0.05 by Student’s t test; a.u., arbitrary units.

FIG 2

Partial least-squares discriminant analysis (PLS-DA) of S. cerevisiae metabolites under different P_i conditions. (A) Score plot of PLS-DA. Red, green, and blue dots indicate the replicates of yeast metabolomic data incubated in 10 mM, 0.5 mM, and 0 mM P_i medium, respectively. The shaded regions represent the 95% confidence intervals. (B) Loading plot of PLS-DA. Red dots indicate P_i-containing metabolites. (C) Variable of importance in projection (VIP) scores of the top 20 metabolites generated from PLS-DA. The color code indicates the relative abundance of each metabolite under different P_i conditions.

FIG 3

Correlation analysis of S. cerevisiae metabolites under different P_i starving conditions. (A) Clustered correlation heatmap between metabolites under different P_i conditions. The correlation matrix was generated by Pearson correlation coefficients, which are represented by a color code. Red and blue indicate positive and negative correlations, respectively. Two representative metabolic groups showing strong correlations are marked as group 1 and group 2. (B and C) Relative abundance of metabolites included in group 1 (B) and group 2 (C) under different P_i conditions. The profiles of individual metabolites are shown in gray.

FIG 4

Metabolites differentially accumulated under P_i limitation or P_i starvation. Volcano plot analysis of changes after P_i limitation and P_i starvation. (A) Changes after P_i limitation (0.5 mM Pi). Red dots indicate differentially accumulated metabolites (fold change, |FC| > 1.5; P < 0.1). (B) Changes after P_i starvation (0 mM P_i). Red dots indicate differentially accumulated metabolites (fold change, |FC| > 2; P < 0.1). These metabolites are listed in Data Set S1. (C) Heatmap of P_i limitation. The list was selected by t test (P < 0.1), showing metabolites changing at least 1.5-fold under P_i limitation (0.5 mM P_i). (D) Heatmap of P_i starvation. Same as in C but shows metabolites changing at least 2-fold under P_i starvation (0 mM P_i). The relative abundance of metabolites is represented as log₂ (fold change) through a color code.

FIG 5

Pathway analysis of differentially accumulated metabolites under different P_i starving conditions. (A and B) Pathway analysis of differentially accumulated metabolites after P_i limitation (0.5 mM P_i) (A) and P_i starvation (0 mM P_i) (B). The size and color of the circle indicate impact value and P value, respectively. The annotated metabolic pathways have higher statistical significance (−log [P] > 1.5).

FIG 6

Inositol pyrophosphate profiles under different P_i conditions. (A to D) Inositol pyrophosphate levels of yeast cells grown in 10 mM, 0.5 mM, and 0 mM P_i medium for 8 h. The data were normalized by the number of cells. The amount of each inositol pyrophosphate in 10 mM P_i medium was set to 1. The means of triplicates are shown with standard deviation; ***, P < 0.001; **, P < 0.01; *, P < 0.05 by Student’s t test; nd, not detected; a.u., arbitrary unit.

FIG 7

Multivariate statistical analysis of metabolite profiling data from wild-type and Δvtc4 cells under 10 mM and 0 mM P_i conditions. (A) Score plot of partial least-squares discriminant analysis (PLS-DA). Red, green, blue, and light blue indicate the replicates of metabolomic data from wild-type 10 mM P_i, wild-type 0 mM P_i, Δvtc4 10 mM P_i, and Δvtc4 0 mM P_i, respectively. The shaded regions represent the 95% confidence intervals. (B) Loading plot of PLS-DA. Red dots indicate P_i-containing metabolites.

FIG 8

Interrelated effect of polyphosphate and P_i starvation on metabolic pathways. (A) Summary of two-way ANOVA analysis (adjusted P value of <0.05). Red, blue, and green represent the metabolites affected by polyphosphate (wild type and Δvtc4), P_i concentration (10 mM and 0 mM P_i), and interaction between both (polyphosphate and P_i concentration), respectively. (B) Metabolite set enrichment analysis of 43 metabolites simultaneously affected by polyphosphate, P_i concentration, and their interaction. The top 15 metabolite sets were selected based on P value. (C) A heatmap was generated based on the list of 43 metabolites affected by P_i concentration, poly(P), and their interaction from a two-way ANOVA. Features were clustered by Euclidean distance using Ward’s clustering method. The color code indicates the normalized intensity of metabolic features.

FIG 9

Working hypothesis on the translation of cytosolic P_i concentration into changes of PP-IPs. The scheme illustrates the inhibitory (red) and stimulatory (green) influences of P_i on key enzymes of ATP and PP-IP production, which are postulated to result from the high K_m and half-maximal inhibitory concentration (IC₅₀) values of GAP-DH, F-ATPase, IP₆ kinase, and PPIP5 kinase. Details are discussed in the main text.