A natural variant of the sole pyruvate kinase of fission yeast lowers glycolytic flux triggering increased respiration and oxidative-stress resistance but decreased growth

Cells balance glycolysis with respiration to support their energetic and biosynthetic needs in different environmental or physiological contexts. With abundant glucose, many cells prefer to grow by aerobic glycolysis, or fermentation in yeast. Using 161 natural isolates of fission yeast, we investigated the genetic basis and phenotypic effects of the fermentation-respiration balance. The laboratory and a few other strains were more dependent on respiration. This trait was associated with a missense variant in a highly conserved region of Pyk1. Pyk1 is the single pyruvate kinase in fission yeast, while most organisms possess isoforms with different activity. This variant reduced Pyk1 activity and glycolytic flux. Replacing the ‘low-activity’ pyk1 allele in the laboratory strain with the common ‘high-activity’ allele was sufficient to increase fermentation and decrease respiration. This metabolic reprogramming triggered systems-level adaptations in the transcriptome and proteome, and in cellular phenotypes, including increased growth and chronological lifespan, but decreased resistance to oxidative stress. Thus, low Pyk1 activity provided no growth advantage but stress tolerance, despite increased respiration. The genetic tuning of glycolytic flux by a single-nucleotide change might reflect an adaptive trade-off in a species lacking pyruvate-kinase isoforms.


Introduction
Inter-linked pathways for carbon metabolism generate energy in the form of ATP and fulfil key anabolic roles. Organisms tune their energy metabolism to environmental conditions, including stress or available nutrients, which affects fundamental biological processes such as cell proliferation, stress resistance and ageing 1,2 . Accordingly, aberrant energy metabolism is the cause of multiple human diseases [3][4][5] . Glycolysis converts glucose to pyruvate, which is further processed in alternate pathways; for example, pyruvate can be converted to ethanol (fermentation) or it can be metabolised in mitochondria via the citric acid cycle and oxidative phosphorylation (respiration). Fermentation and respiration are antagonistically regulated in response to glucose or physiological factors 6,7 . In the presence of ample glucose, many microbes suppress respiration and generate ATP preferentially by glycolysis, even with oxygen available. This metabolic state, called aerobic glycolysis 8 , appears paradoxical, because only full glucose oxidation via the citric acid cycle and respiration will maximize the ATP yield generated per glucose. Aerobic glycolysis, found in Crabtree-positive species, may have been selected because it enables higher rates of ATP production 9 . Analogously, human cancer cells typically grow by aerobic glycolysis, known as the Warburg effect 10 , thought to increase biosynthetic capacity [11][12][13] . Proposed explanations for how aerobic glycolysis allows faster proliferation involve efficient resource allocation 14,15 and molecular crowding [16][17][18][19] , among others [20][21][22] .
Crabtree-positive organisms, including the model yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe 23 , still require some oxygen and basal respiration for optimal cell proliferation 24 . S. cerevisiae cells without mitochondrial genome, and thus without respiratory capacity, feature a 'petite', slow-growth phenotype 25 . S. pombe cannot normally grow without a mitochondrial genome [26][27][28] , and blocking oxidative phosphorylation with antimycin A leads to moderate or strong growth inhibition, respectively, in rich or minimal glucose media 29 . In glucose-limiting conditions, such as stationary phase, the metabolism of yeast cells is reconfigured towards respiration 30,31 . Thus, cells tune the balance between respiration and fermentation to meet their energetic and biosynthetic needs 6 in a more nuanced way than captured by qualitative descriptions of aerobic glycolysis.
Given its impact in health and disease, it is important to understand the genetic and regulatory factors that affect cellular energy metabolism. Here we investigated the genetic basis and physiological implications for the regulatory balance between fermentation and respiration, using our collection of natural S. pombe isolates 32 . A few strains featured a higher reliance on respiration during cellular growth on glucose. This trait was associated with a missense variant in pyruvate kinase (PYK). PYK catalyses the final, ATP yielding step of glycolysis, the conversion of phosphoenolpyruvate to pyruvate. PYK can coordinate the activity of central metabolic pathways [33][34][35] . Most organisms encode several PYK isoforms that are expressed in specific tissues or developmental stages [36][37][38][39][40] . Work in budding yeast has implied that the switch from a high-to a low-activity PYK isoform causes increased oxygen uptake, triggering a shift from fermentative to oxidative metabolism 33,34 . S. pombe possesses only one PYK, Pyk1 41 . Nonetheless, exchanging the Pyk1 variant of the standard laboratory strain triggered increased glycolytic flux, which in turn led to substantial adjustments in the metabolome, transcriptome and proteome. These results show that altered PYK activity is self-sufficient to reprogram metabolism even in the absence of an evolved regulatory signaling system. These findings define a natural metabolic switch, consisting of a single amino-acid change, possibly reflecting an adaptation in a species lacking multiple PYK isoforms. Notably, the standard laboratory strain is among a minority of natural isolates locked in the low-activity state, and is thus metabolically and physiologically unusual. These findings highlight the importance of glycolysis in general, and PYK in particular, as a hub in cross-regulating metabolic pathways and coordinating energy metabolism with cell regulation and physiology, including growth and stress resistance.

Results
Increased respiration dependence is associated with a missense PYK variant Treating aerobic glycolysis as a complex, quantitative trait, we assessed the amount of residual respiration on glucose-rich media across a set of genotypically and phenotypically diverse wild S. pombe isolates. Resistance to antimycin A, which blocks the respiratory chain by inhibiting ubiquinol-cytochrome c oxidoreductase 42 , was used as a proxy readout for cellular dependence on oxidative phosphorylation. The standard laboratory strain 972 hshows a moderate reduction in maximum growth rate and biomass yield in this condition 29,43 . We applied a colony-based assay to determine relative fitness of each strain in rich glucose media with and without antimycin A. The resulting resistance scores showed a large diversity between strains (Fig. 1A, Supplementary Fig. 1, Supplementary Table 1). Notably, the laboratory strain was among the most sensitive (score=1, rank=10 of 154), while the mean score was 1.25±0.15 for all strains tested.

Fig. 1: A variant in conserved pyk1 region is associated with increased sensitivity to antimycin A. (A)
Distribution of antimycin A resistance scores for wild isolates, compared to standard laboratory strain (red vertical line). Resistance scores are the ratio of fitness on rich glucose media with vs without 500 µg/L antimycin A. Fitness was estimated based on colony size on solid media, corrected for spatial and plate effects (Methods). After quality control, we obtained quantitative fitness scores for 154 strains, with a signal-to-noise ratio of 29.8 and an unexplained variance of 0.12. (B) Volcano plot of a genome-wide association using mixed-model linear regression of antimycin A resistance for 118,527 genetic variants. Variants with moderate or high impact are shown in blue. Red dot: the variant at locus I:3845516, which causes a T343A change in the Pyk1 amino-acid sequence. This variant was among the top scoring (effect size=-0.645, rank 34; p=0.0008, rank 70). (C) Boxplot showing antimycin A resistance grouped by the two alleles at the pyk1 locus. (D) Sequence alignment of the region of interest in eukaryotic PYK proteins. The threonine residue at the highlighted position 343 is unique for the reference (laboratory) S. pombe strains. For this analysis, we collected 25 Pyk1 homologues from a wide variety of eukaryotes, including animals, plants and fungi, using the HomoloGene resource (Accession 37650). We manually expanded the homologous group to include three other species of the Schizosaccharomyces genus 44 . Sequences were aligned with MAFFT 45 and visualised with Jalview 46 . (E) Phylogenetic tree based on 31 biallelic SNPs in pyk1 and in 500bp up-and down-stream regions. Strains carrying the common A-allele are in orange, while strains carrying the unusual T-allele are in blue (strain names as in ref. 32 ). Strains JB22 and JB50 (underlined and bold) refer to heterothallic and homothallic versions of the laboratory strain, respectively. The pie chart shows the relative allele frequencies at the genomic locus across all wild strains. Genotype calls at the locus were checked manually and were conclusive, i.e. no strain produced reads with both alleles.
The antimycin A resistance trait showed an estimated narrow-sense heritability of 0.54, which is substantially higher than for most of the 223 phenotypes previously reported 32 . This result indicates a strong genetic basis for the dependence on respiration. We performed a genome-wide association study (GWAS) to identify loci linked to resistance among 118,527 small genetic variants, including single-nucleotide polymorphisms (SNPs) and small insertions or deletions (Fig. 1B). Compared to a recent GWAS in budding yeast 47 , our statistical power was lower, partly due to the low number of strains and the strong population substructure 32 . We therefore manually assessed the associated variants, based on p-value, effect size, and literature. Among the top-100 associations (by p-value, Supplementary Table  2), eight were predicted to have moderate or high impact as defined by SnpEff 48 , and six of those were located in genes with functional annotation in PomBase 49 . Two of these six variants were in S. pombe specific genes: wtf16 and wtf8 50 . Other variants were in ubp9, encoding a ubiquitin C-terminal hydrolase, in pfl5, encoding a cell-surface glycoprotein, and in jac1, encoding a mitochondrial 2Fe-2S cluster assembly co-chaperone. One missense variant caught our particular attention: this SNP was among the top scoring ( Fig. 1B) and leads to a T343A amino-acid sequence change in Pyk1, encoding the single PYK in S. pombe. Strains with a threonine residue at position 343 ('T-allele') showed a median resistance score of 1.07, while strains with an alanine ('A-allele') showed a ~15% higher median resistance of 1.28 (Fig. 1C).
An analysis of 26 PYK protein sequences from diverse eukaryotes revealed strong conservation, showing 45-93% agreement with the consensus sequence called from the alignment (Fig. 1D). The threonine residue in the reference (laboratory) strain of S. pombe was unique in this highly conserved region of all PYK sequences. All other species featured an alanine residue at this position, including three other Schizosaccharomyces species (Fig.  1D). The reference allele (T-allele) in the laboratory strain is the minor allele in our strain collection, found only in 18 of 161 strains (Fig. 1E). The rare T-allele occurred in four unique sequences in the phylogenetic tree, split up over two highly divergent lineages (Fig. 1E). This result was confirmed by considering a consensus tree based on the entire genome 32 . For 10 of the 18 T-allele strains, the geographical origin is known, with most being isolated in Europe but also one each from Asia and Australia. The substrate is predominantly fermenting grapes (as for most other strains), and one each from lychee and glace syrup, which reflects the fact that most S. pombe strains have been isolated from human-created niches. We conclude that the T-allele at position 343 is a rare, naturally occurring allele, which appears to have arisen, and been maintained, independently in two distant lineages.
Replacing the pyk1 allele in laboratory strain leads to higher PYK activity and metabolic reprogramming Many species possess two or more PYK isozymes with different activity and/or expression patterns. In S. cerevisiae, the minor isozymes can complement for the loss of the major isozymes 51 . The S. pombe reference genome 52 , based on the laboratory strain 972, features only one PYK isoform. To test whether this is also the case for the other strains, we searched the de novo assemblies of each wild strain genome 32 with tblastn 53 for Pyk1 homologues. This search consistently produced a single hit only (Pyk1 itself).
We set out to analyse the effect of the pyk1 SNP on cellular metabolism. Using seamless CRISPR/Cas9-based gene editing 54 , we replaced the pyk1 T-allele in the laboratory strain for the more common A-allele. We introduced the A-allele in both a heterothallic hand a homothallic h 90 laboratory strain, without any other genetic perturbations. We then selected three independently edited strains for both hand h 90 backgrounds to use as biological replicates throughout this study. Below, we use the abbreviations 'T-strain' for the normal laboratory strains and 'A-strains' for the edited pyk1 T343A h -/h 90 strains. These strains allowed us to study the impact of the pyk1 SNP in a controlled and well-characterized genetic background.
It has been reported that S. pombe features lower PYK activity than S. cerevisiae 41,55 . This conclusion was however derived from the laboratory strain, which contains the unusual pyk1 allele. To investigate the impact of the pyk1 SNP on metabolism, we applied a targeted metabolomics workflow based on liquid chromatography -selective reaction monitoring (LC-SRM). We quantified key metabolites potentially affected by PYK activity 56,57 , including glycolytic, pentose-phosphate pathway and citric-acid cycle intermediates as well as redox cofactors and adenine nucleotides. Data was obtained for nine replicates of the T-strain and eight replicates of the A-strain ( Fig Table 3, Supplementary  Fig. 2). Technical replicates sampled from the same culture, but prepared and measured separately, were highly correlated (Pearson r=0.93), indicating that our workflow is robust and that most of the observed variation is biological. Over the entire data set, the median coefficient of variation was 17.2%. A principal component analysis (PCA) of metabolite data distinguished all strains based on the SNP in pyk1 ( Fig. 2A). To visualise this high-dimensional data set, we divided the concentration of each metabolite by the median concentration of the T-strain. This normalisation corrects for the large differences in concentrations observed between metabolites but maintains the relative variance within each metabolite. Biological replicates of the T-and A-strains show distinct profiles, largely driven by concentrations of phosphoenolpyruvate, 2-/3-phosphoglyceric acid, NADH and NADPH, as indicated by top loading vectors for each principal component. The biological repeats for the three edited A-strains (circles, squares and triangles) behave similarly, with a variance comparable to that of the biological replicates of the single T-strain. (B) Left boxplot: two glycolytic intermediates directly upstream of PYK were strongly depleted in the A-strain. Right barplot: PYK activity was directly measured using a lactate dehydrogenase-coupled colorimetric enzyme activity assay, showing the mean and standard deviation of substrate conversion rate for 3 biological replicates measured in technical duplicates each. Significance keys: * p<0.05, ** p<0.005, *** p<0.0005 (Welch's t-test). (C) Concentrations of fructose-1,6-bisphosphate, which correlate with glycolytic flux, are significantly higher in A-strain (ratio = 1.35). (D) Boxplots for selected metabolomics data indicate differences in energy and redox status between Tand A-strains. (E) Heatmap of the 432 genes that are differentially expressed at the RNA level between the T-and Astrains (FDR <10%) and are measured in all four conditions (columns). First column: genes ordered by increasing fold-changes for RNAs (computed as log 2 Supplementary Fig. 3. The length of the bar represents the significance of the enrichment, while its color reflects the direction and magnitude of differential expression (A-vs T-strain) of individual genes annotated to this term. (G) Boxplot of oxygen consumption rates in A-and T-strains (ratio of means=0.65, p=1.31x10 -5 , Welch's T-test, 3 biological replicates, each measured in technical duplicates).
We observed a strong depletion of glycolytic intermediates upstream of PYK, with mean levels of phosphoenolpyruvate and 2-/3-phosphoglyceric acid in the A-strains only 25.9% and 12.7% of those in the T-strain (Fig. 2B, padj=8.7x10-8 and 3.7x10-6, Welch's t-test, Benjamini-Hochberg corrected). This result suggests that the A343T mutation reduces the activity of the Pyk1 enzyme. We directly tested this hypothesis by determining PYK activity in lysates of 3 biological replicates each of the A-and T-strain grown in rich glucose media, using a lactate dehydrogenase-coupled photometric assay in technical duplicates. With a buffer composition similar to those previously used 60 , the A-strains showed a 78.1% higher activity compared to the T-strain (p=0.0023, Welch's t-test) (Fig. 2B).
The metabolomics data also allowed us to estimate several physiological parameters. The A-strains exhibited significantly higher levels of the flux-signalling metabolite 61 fructose 1,6bisphosphate (ratio 1.35, padj=0.015) (Fig. 2C). The levels of this metabolite are known to strongly correlate with glycolytic flux in several yeast species 62,63 , consistent with a higher flux in the A-strain. Furthermore, cellular energy charge 64 was 4.7% higher in the A-strains ( Fig. 2D; p=0.002, Welch's t-test). These values are within the range reported for other organisms 65 . We used the ratio of the reduced to oxidised forms of NAD(H), NAD(P)H and Lglutathione as readouts for cellular redox status (Fig. 2D). For NAD(H), the A-strains showed an increase from 0.048 to 0.096 (p=0.0001, Welch's t-test). The same pattern was evident for NADP(H), where the median ratio was 3.24 in the A-strains, but only 2.43 in the T-strain (p=0.024). The A-strain had a significantly higher concentration of the reduced isoform of Lglutathione (ratio 1.30, padj=0.015, Welch's t-test, Fig. 2D), but no significant difference was apparent in the ratio of the reduced to oxidised isoforms, where the median for the A-strains was 2.01 vs 1.89 for the T-strain (p=0.289, Welch's t-test). These results are in line with the paradigm that NADH/NAD+ ratios are maintained at low levels to maximise availability of electron acceptors for catabolic processes, while NADPH/NADP+ ratios are maintained at high levels to provide electrons for anabolic processes and the antioxidant response 66 . As part of the antioxidant defence, which includes glutathione-, peroxiredoxin-and thioredoxindependent reduction systems, NADPH is limiting when cells are challenged with oxidative stress [67][68][69][70] . While analytical methods cannot distinguish between different compartments or sub-populations of these cofactors 71 , and our sample extraction method may allow some interconversion between reduced and oxidised isoforms 72 , these findings are consistent with the hypothesis that the A-strain respires less and thus has a lower oxidative burden.
Increased PYK activity leads to transcriptome and proteome changes reflecting increased fermentation and decreased respiration To further analyse the effects of the pyk1 SNP, we characterised the transcriptomes and proteomes of the T-and A-strains using RNA-seq and mass spectrometry, respectively. We could quantify 7750 transcripts (including non-coding RNAs) and 3234 proteins in both strains, allowing for a broad analysis of genome regulation. The expression of pyk1 itself was similar in the T-and A-strains at both the transcript (log2[fold change]=0.016, padj=0.91) and protein level (log2[fold change]=-0.001, padj=0.94). This result means that the differences between the two strains were not caused by changes in pyk1 expression. Notably, the pyk1 allele replacement led to substantial changes in both the transcriptome and proteome. Overall, 960 transcripts and 434 proteins were differentially expressed between the T-and A-strains, at a false-discovery rate (FDR) of ≤10%. While changes at the transcriptome and proteome levels generally correlated well (r=0.65 for all genes with differentially expressed transcripts and/or proteins), we also found a large number of genes to be regulated exclusively on the protein level ( Supplementary Fig. 5). These proteins were enriched in functions related to cytoplasmic translation and depleted in functions related to ribosome biogenesis (Supplementary Tables 5 and 6). This result raises the possibility that posttranscriptional gene regulation plays an important role in controlling translation.
The differentially expressed genes were enriched in functions related to respiration and energy-demanding processes, like translation and ribosome biogenesis. These enrichments were evident at both the level of the transcriptome (Fig. 2F, Supplementary Fig. 3) and proteome ( Supplementary Fig. 4). Transcripts encoding respiratory chain and oxidative phosphorylation proteins were more highly expressed in the T-strain (Fig. 3), while those related to ribosome biogenesis and rRNA processing were more highly expressed in the Astrain. Some functional terms (e.g., NAD-binding) contained genes that were strongly regulated in either direction. Several of the most differentially expressed transcripts and proteins were directly involved in pyruvate metabolism (Fig. 3). The mae2 gene was most strongly regulated at both transcript and protein levels, being more highly expressed in the T-strain. Mae2 is an enzyme that catalyses the reaction from malate and oxaloacetate to pyruvate 73 . Thus, the T-strain may up-regulate Mae2 to replenish pyruvate using an alternate way that is largely independent of glycolytic flux. The pdc101 and atd1 genes, on the other hand, were expressed more highly in the A-strain. These genes encode pyruvate decarboxylase and aldehyde dehydrogenase, respectively, and their induction is consistent with higher glycolytic flux and increased fermentation 29 . These expression changes are consistent with the central role of PYK in glycolysis and the observed metabolic effects mediated by the Pyk1 variants.
The target of rapamycin complex 1 (TORC1) signalling pathway controls energy metabolism and promotes aerobic glycolysis in response to cellular nutrients 2 . Enrichment analysis using AnGeLi 74 revealed substantial overlaps between the differential expression signature of the T-and A-strains and the signature of TORC1 inhibition (Fig. 2E) 58 : 58 transcripts and 33 proteins induced by TORC1 inhibition were more highly expressed in the T-strain (p=6.1x10 -12 and 4.1x10 -7 , respectively), while 118 transcripts and 72 proteins repressed by TORC1 inhibition were more lowly expressed in the T-strain (p=1.8x10 -59 and 2.1x10 -30 , respectively). Thus, the expression signature of the T-strain resembles the signature caused by TORC1 inhibition, which leads to reduced glycolysis. Moreover, genes induced in response to oxidative stress were also differentially expressed; examples include gpx1, encoding glutathione peroxidase, and grx1, encoding glutaredoxin. Accordingly, the differential expression signature between the T-and A-strains also showed substantial overlaps with the core environmental stress response triggered by oxidants and other stresses (Fig. 2E) 59 : 63 transcripts induced by oxidative stress were higher expressed in the T-strain (p=7.0x10 -4 ), while 149 transcripts and 94 proteins repressed by stress were lower expressed in the Tstrain (p=2.8x10 -68 and 1.4x10 -37 , respectively. Thus, the expression signature of the T-strain also resembles the general signature of cells exposed to different types of stress, likely reflecting the higher load of reactive oxygen species from respiration. Together, the observed gene-expression reprogramming suggests that the T-strain features higher respiration and pentose-phosphate pathway activity (Fig. 3). Similarly, budding yeast strains genetically engineered to alter PYK activity reconfigure their metabolism, with a reduced activity leading to higher respiration (to meet energy demands) and pentose-phosphate metabolism (to increase reducing agents required to detoxify reactive oxygen species produced by respiration) 34 .
To independently confirm the shift from respiration to fermentation in the A-strain, we measured oxygen consumption in rich glucose media. Indeed, the A-strain consumed oxygen at a 34.1% lower rate than the T-strain (Fig. 2G). This result supports the prediction that the A-strain respires at lower levels than the T-strain. We conclude that the pyk1 allele replacement is sufficient for a systemic regulatory shift from respiration to fermentation in the A-strain, which is apparent by key metabolome changes and in turn is supported by adjustments in the transcriptome and proteome (Fig. 3). Increased glycolytic flux affects cellular growth, lifespan, and stress resistance Given the metabolic and gene-expression changes associated with the pyk1 allele replacement, we expected that the A-strain will feature phenotypic changes at the cellular level. We therefore interrogated diverse cellular traits. We confirmed that the T-strain was more sensitive than the A-strain to inhibition of respiration by antimycin A (Fig. 4A), which supports the prediction from the GWAS. We then recorded growth curves for A-and Tstrains. The A-strain showed a ~20% higher maximum growth rate than the T-strain during proliferation in rich glucose media (Fig. 4B). Thus, the SNP in pyk1 has a pronounced inhibitory effect on cell proliferation in the standard laboratory strain under a standard growth condition.
A natural SNP in a key metabolic enzyme could differentially affect fitness on different carbon sources. While the A-strain grows more rapidly on glucose, the T-strain might have fitness advantages in other conditions. To test for such a trade-off, we tested 12 common carbon sources in four different base media ( Fig. 4C and Supplementary Table 7). Both strains showed rapid cell growth on glucose, fructose and sucrose, intermediate growth on raffinose, mannose and maltose, and slow growth on the other carbon sources. Consistent with the result in Fig. 4B, the A-strain grew faster than the T-strain on the fermentable carbon sources glucose, fructose and sucrose. In the other carbon sources, the two strains showed similar growth. Thus, the T-strain did not show increased fitness in any of the tested conditions (Fig. 4C). We also tested for differential growth of the A-and T-strains on different nitrogen sources. Both strains showed substantial growth on 54 of the 95 nitrogen sources ( Fig. 4D and Supplementary Table 8). The A-strain grew about 2-fold better than the T-strain on L-phenylalanine but worse on L-cysteine. Validation by spot assays on solid media, however, could only confirm the difference for phenylalanine (Fig. 4F). In conclusion, these broad phenotypic assays did not provide support for the idea that the T-allele might represent an adaptation to specific carbon or nitrogen sources.
Only a fraction of natural environments might enable rapid proliferation as in the laboratory. Thus, resistance to stress could be a more important selection factor in determining fitness. Trade-offs are a key concept in evolutionary adaptation 75 , and microbes show an anticorrelation between growth rate and stress resistance 76,77 . In budding yeast, artificially reduced glycolytic flux leads to increased resistance to oxidative stress 34 , and mammalian cells show a similar feature 78 . We therefore assessed the ability of the A-and T-strains to endure oxidative stress triggered by hydrogen peroxide (H2O2) or diamide. Indeed, the Tstrain was substantially more resistant to both oxidants than the A-strain (Fig. 4A). Oxidative stress is a byproduct of cellular respiration, and the T-strain may feature a higher basal protection from oxidative stress due to higher respiratory activity. This protection is consistent with our observation that core environmental stress response genes were more highly expressed in the T-strain. The environment can also be a source of oxidants, e.g. in microbial communities with H2O2 producing lactic acid bacteria 79,80 . Thus, a natural SNP promoting oxidative-stress resistance may be beneficial. We conclude that the slower growth of the T-strain, compared to the A-strain, is offset by an increased resistance to oxidative stress. Both traits may be systemic properties emerging from the up-regulation of respiration at the cost of fermentation, triggered by the T-allele. We wondered whether the T-strain might feature fitness advantages in conditions other than oxidative stress. To this end, we screened for differential growth of the A-and T-strains on 72 different drugs and toxins. The strains appeared to be differentially sensitive to 9 compounds (Fig. 4E). The A-strain was more resistant to barium chloride, D-L-Alanine hydroxamate, caffeine (pleiotropic effects, including TOR inhibition) 58 , chlorpromazine (causes membrane stress) 81 , capreomycin (binds to ribosomes) 82 and phenylarsine oxide (inhibitor of tyrosine phosphatases) 83 . Notably, the A-strain was also more resistant to thallium (I) acetate, which is highly toxic due to its similarity to potassium ions and binds to mammalian PYK with a stronger affinity, but weaker activating effect, than potassium 84,85 .
The T-strain, on the other hand, was more resistant to acriflavine, an antiseptic, and EGTA, a chelator of bivalent cations (including Mg 2+ which activates S. cerevisiae PYK 86 ). We manually inspected dose-response curves ( Supplementary Fig. 6) to select four compounds for validation based on overall difference in maximum growth rate and consistency across concentrations. Using spot assays, we could validate the differential sensitivity to all selected compounds: caffeine, phenylarsine oxide, EGTA, and chlorpromazine (Fig. 4F). It is striking that the pyk1 SNP differentially affected the resistance to this broad range of stresses, suggesting a general role of glycolysis in stress resistance beyond the known role in oxidative stress.
The existence of a fundamental link between metabolism and lifespan is well known, e.g. through the observation that dietary restriction extends lifespan from yeast to humans 87 . In various model systems, increased respiration 30,88-90 and slower growth 91-94 correlate with increased lifespan. We therefore expected the T-strain to be longer lived than the A-strain and measured the chronological lifespan of both strains. Surprisingly, the A-strain was longer-lived, with a mean viability of 25.3% after 3 days, compared to 6.5% for the T-strain (Fig. 4G). Reduced glycolytic flux has been reported to shorten replicative life span in budding yeast 95 . We therefore speculate that unrestricted glycolytic flux generally promotes longevity, but mechanistic processes involved will require further investigation.
Our findings show that the pyk1 SNP has substantial effects on growth rate and oxidativestress resistance in the genetic background of the standard laboratory strain. Are such effects generally evident in other strain backgrounds? To address this question, we measured oxidative-stress resistance and growth rates for all wild strains. Indeed, the Tallele was significantly associated with higher resistance to H2O2 (Fig. 4H). With respect to growth rate, on the other hand, no significant difference was evident between strains containing the T-or A-alleles (Fig. 4I). These results suggest that the pyk1 SNP can play a substantial role in oxidative-stress resistance, while growth rate may be a more complex trait, which is controlled by many other loci or buffered by counteracting mutations.

Discussion
Many eukaryotes, from budding yeast to humans, possess low-and high-activity PYK isozymes. PYK activity has been implicated in coordinating fermentation with respiration in synthetic S. cerevisiae models 34,35 . The low-activity isoform of S. cerevisiae is expressed under respiratory growth conditions 51 . Using an unbiased, genome-wide approach, we identified a naturally occurring SNP in the sole S. pombe PYK gene. Our findings show that this SNP affects PYK activity and glycolytic flux and is sufficient to cause a shift in the respiration-fermentation balance. When we replaced the T-allele of the laboratory strain with the common A-allele, which is broadly conserved in most S. pombe strains all other eukaryotes examined, glycolytic flux increased and oxygen consumption decreased.This metabolic re-programming led to changes in gene expression at the transcriptome and proteome levels, resembling the signatures of rapidly proliferating cells with high TORC1 activity and no stress exposure. At the cellular level, the allele replacement led to increased growth and chronological lifespan but decreased resistance to oxidative stress. Cellular growth and stress resistance are linked with gene regulation, although cause-effect relationships are not really understood 77,96-100 . The extraordinary plasticity in response to altered glycolytic flux, triggered by a single-nucleotide change, highlights the fundamental impact of the glycolysis on cellular control, physiology and adaptation. Possessing only a single PYK isoform, S. pombe is unlikely to have a pre-existing genetic or signalling programme for the regulation of high-and low-activity PYK states. Yet, a mutation in Pyk1 that changes its activity is sufficient to induce coherent metabolic, regulatory and cellular responses. A PYK-induced change in glycolytic flux is hence the cause, not a consequence, of major changes in cell regulation and physiology.
What might be the evolutionary and ecological role of the pyk1 SNP? We propose that the mutation in the laboratory strain is beneficial given its maintenance at a strongly conserved position, its occurrence in two independent S. pombe lineages, its associated phenotypes, and the use of low-activity isoforms in other organisms. The literature suggests that low PYK activity could help cells to retain more carbon intermediates for biosynthesis [101][102][103] . Our results, and other recent research 104 , do not support this hypothesis as cells with low Pyk1 activity grow slower and form less biomass. Our phenotypic assays also do not support the possibility that the pyk1 SNP is adaptive on specific carbon or nitrogen sources. However, we have identified several stress conditions where the laboratory strain exhibits higher fitness than the allele-replacement strain, most notably oxidative stress. It is plausible that stress resistance has provided the selection factor for the low-activity Pyk1 allele. Accordingly, we propose that altered stress tolerance provides a biological rationale for the evolution of systems that allow conditional switching between high-and low-activity PYK isozymes.

Wild strain phenotyping and GWAS
We constructed two arrays of 384 strains, each containing a reference grid of 96 JB22 colonies (standard laboratory strain 972), around which 159 wild isolates from our collection 32 were randomly arranged in triplicates, with additional internal, interspersed JB22 controls. A RoToR HDA pinning robot (Singer Instruments) was used to copy the arrayed strains onto various growth media. Plates were grown for 2 days at 32°C, and images were acquired by transmission scanning (Epson V800 Photo). Colony sizes were determined with gitter 105 and corrected for spatial biases using reference-grid normalisation 106 , as implemented in our freely available pipeline (github.com/Bahler-Lab/pyphe-slim). Further, strains which did not grow at all (colony size <10 pixels at 600 dpi scanning resolution) or showed abnormal circularity values (>1.1 or <0.85) were excluded from further analysis. Strains for which no consistent fitness estimate could be obtained were also excluded (standard deviation of triplicates greater than standard deviation of all colonies of all strains), which removed two strains from our data set. For the rest, individual corrected colony sizes were averaged and condition-specific resistance/fitness scores were determined by dividing the corrected colony size in the condition of interest by that of the control condition without drug. Signal to noise ratios were determined by dividing the mean fitness of the internal controls by their standard deviation. The fraction of unexplained variance was determined by dividing the standard deviation of the internal controls by that of the entire dataset.
For GWAS, phenotype values were transformed to normal shape using the Box-Cox method, mean-centred at zero and normalised to unit variance, using PowerTransformer of scikit-learn 107 . Genomic variants were called from published and aligned sequence data 32 using freebayes 108 , with the following options: --ploidy 1 --standard-filters --min-coverage 10 --min-alternate-count 3. The version of the reference genome used was ASM294v2. SNPs within 3bp of an indel were filtered out using the --SnpGap option of bcftools 109 . Low quality calls and loci where >50% of the population was not genotyped were removed using the -max-missing 0.5 --minQ 30 --remove-filtered-all options of vcftools 110 . Variant effects were predicted using SnpEff 48 . Variants were filtered for a minor allele frequency of >5% and converted to plink format using plink 111 . A kinship matrix was constructed in LDAK5 112,113 by first cutting and thinning predictors, then calculating their weights, and finally using the direct method for obtaining the kinship matrix. All steps used default options. Heritability estimates were obtained by REML as implemented in LDAK5. Linear mixed model association was performed in LDAK5, using the previously generated kinship matrix to correct for population structure.

Phylogenetics
The phylogenetic tree in Fig. 1 was constructed by filtering biallelic SNPs in the region ±500bp around the pyk1 gene (I:1:3844243-3847145) using bcftools. This vcf was converted to a pseudo alignment in fasta format with VCF-kit 114 . The tree was constructed by the neighbour-joining method, implemented in ClustalW2 115 , accessed through the EBI webinterface 116 , and drawn in seaview 117 .

Construction of allele replacement strains
The allele replacement strains were generated using the CRISPR-Cas9 system. The plasmid containing the gRNA targeting the pyk1 SNP in 968 h 90 (homothallic) and 972 h -(heterothallic) was generated as described 54 using the following primers: gRNA.JB50-F: GCTTTCCGGTGAGACTACCAgttttagagctagaaatagc and gRNA.JB50-R: TGGTAGTCTCACCGGAAAGCttcttcggtacaggttatgt Proper gRNA cloning was assessed by Sanger sequencing. The template for homologous recombination was generated using the following primers (underlined is the point mutation introduced in the T strain to convert it to the A strain): HR_JB50-F: ACCCTCGTCCTACTCGTGCCGAGGTTTCCGATGTTGGTAACGCCGTTCTCGATGGTGC TGACTTGGTCATGCTTTCCGGTGAGACTGCCAAGGGTTCTTA HR_JB50-R: GTAAGGGATGGAAGCCTCAGCAACACGGGCAGTCTCAGCCATGTAGGTAACGGCTTCA ACGGGGTAAGAACCCTTGGCAGTCTCACCGGAAAGCATGACC The following primers were used to identify and confirm successful mutants, of which three were kept and used for experiments in order to reduce the risk of observing the effects of offtarget mutations: Pyk1ck-F: GATGTTGGTAACGCCGTTCT Pyk1ck-R: GGACGGTACTTGGAGCAGAG Cell culture for multi-omics experiments Transcriptomes and proteomes were measured from the same cell culture, in five biological repeats per strain. Strains were woken up on yeast extract with supplement (YES) agar and incubated for two days at 32°C. Then, 50ml pre-cultures (YES medium, 32°C, 170 rpm) were grown overnight and used to inoculate 200 ml cultures (YES medium) at an OD600 of 0.1 These cells were then grown until OD600 of 0.8 and harvested as described below.

Transcriptomics experiments
When cells reached OD 0.8, 25 ml were collected by centrifugation and snap frozen in liquid nitrogen. RNA was extracted with a hot phenol method described 118 . RNA was further purified with Qiagen RNAeasy columns, and DNAse treatment was performed in the columns (as suggested by manufacturer) prior to library preparation. RNA quality was assessed with a Bioanalyzer instrument (Agilent), and all samples presented a RIN (RNA Integrity Number) >9. cDNA libraries were prepared with the Illumina TruSeq stranded mRNA kit, according to the manufacturer's specifications, by the Cologne Centre for Genomics (CCG) facility. The samples were sequenced on a single lane of an Illumina Hiseq4000 to produce 2x75nt reads.
Reads were trimmed with Trimmomatric 119 v0.36, with the following parameters differing from default settings: LEADING:0 TRAILING:0 SLIDINGWINDOW:4:15 MINLEN:25. The reference genome was indexed with bowtie2-build with default settings. Paired reads were aligned to the reference genome using bowtie2 with default settings (v2.3.4.1) 120 . In the case of the A-strain, the reference genome was edited to reflect the base substitution within pyk1. Aligned reads were counted using intersect from the bedtools package (v2.27.1) 121 , with the parameters -wb -f 0.55 -s -bed. Identical reads were only counted once. Readcounts were tested for differential expression between strains using DESeq2 v1.18.1, with default settings 122 .

Proteomics experiments
Sample preparation for mass spectrometry: Cells were washed with 1X PBS and centrifuged for 3 min at 600 x g at room temperature. Subsequently, cells were washed with 1 ml room temperature lysis buffer (LB: 100 mM HEPES, 1 mM MgCl2, 150 mM KCl, pH 7.5), transferred to 1.5 ml tubes and centrifuged at 600 x g for 5 min at room temperature. After discarding the supernatant, the cell pellets were flash-frozen at -80°C and stored until use. To lyse the cells, pellets were resuspended in 400 μl cold LB and mixed with the same volume of acid-washed glass beads (Sigma Aldrich). The mixture was then transferred to a FastPrep-24TM 5G Instrument (MP Biomedicals) where the cells were disrupted at 4°C by 8 rounds of beads-beating at 30 sec with 200 sec pause between the runs. Samples were then centrifuged at 1000 x g for 2 min at 4°C. The supernatant was collected and protein concentration determined with the bicinchoninic acid assay (Thermo Fisher Scientific). Then, 100 μg of proteome samples were subjected to the sample preparation workflow for MS analysis. In the first step, sodium deoxycholate (Sigma Aldrich) was added to the samples to a final concentration of 5% followed by the addition of Tris(2-carboxyethyl)phosphine (Thermo Fisher Scientific) to a final concentration of 10 mM and incubation at 37°C for 30 min under shaking at 550 rpm to reduce disulfide bridges. Next, the alkylation of free cysteine residues was achieved by adding iodoacetamide (Sigma Aldrich) at 40 mM final concentration. After 45 min incubation at room temperature in the dark, samples were diluted 1:5 with freshly prepared 0.1M ammonium bicarbonate and pre-digested with lysyl endopeptidase LysC (Wako Chemicals) at an enzyme to substrate (E/S) ratio of 1:100 for 4 h at 37°C under shaking at 800 rpm. Digestion was completed by treatment with sequencing-grade trypsin (promega) at an E/S ratio of 1:100 for 16h at 37°C, under shaking at 180 rpm. Protease digestions were quenched by lowering the reaction pH to <3 with the addition of formic acid to a final concentration of 1%. Peptides were desalted using Sep-Pak C18 cartridges according to manufacturer's instructions (Waters). After elution from the cartridges, peptides were dried using a vacuum centrifuge and resuspended in 50 µl 0.1% formic acid for MS measurement. LC-MS/MS data acquisition: Peptide samples were analyzed on an Orbitrap Q Exactive Plus mass spectrometer (Thermo Fisher Scientific) equipped with a nano-electrospray ion source and a nano-flow LC system (Easy-nLC 1000, Thermo Fisher Scientific). Peptides were separated on a 40 cm x 0.75 µm i.d. column (New Objective, PF360-75-10-N-5) packed in house with 1.9 um C18 beads (Dr. Maisch Reprosil-Pur 120). For LC fractionation, buffer A was 0.1% formic acid and buffer B was 0.1% formic acid in 100% acetonitrile and the following gradient was employed: linear from 5% to 25% buffer B over 100 min, linear from 25% to 40% buffer B over 10 min, linear from 40% to 90 % buffer B over 5 min. and isocratic with buffer B concentration fixed at 90% for 5 min. The flow rate was 300 nL/min and the column was heated to 50 °C. For shotgun LC-MS/MS data acquisition (DDA), 1 μL peptide digests from each sample were injected independently at a concentration of 1 μg/uL. MS1 spectra were acquired from 350 to 1500 m/z at a resolution of 70000. The 20 most intense precursors that exceeded 1300 ion counts were selected for fragmentation at 25 eV normalized collision energy and the corresponding MS2 spectra were acquired at a resolution of 17500 using maximally 100,000 ions, collected for maximally 55 msec. All multiply charged ions were used to trigger MS-MS scans followed by a dynamic exclusion for 30 sec. Singly charged precursor ions and ions of undefinable charged states were excluded from fragmentation. One μL peptide digest from the same samples were also measured in data-independent acquisition (DIA) mode on an Orbitrap QExactive Plus mass spectrometer (Thermo Fisher Scientific) using the DIA settings reported 123 .
Peptide identification and spectral library generation: The collected DDA spectra were searched against the S. pombe fasta database (PMID:25432776), using the Sorcerer™-SEQUEST® database search engine (Thermo Electron), allowing up to two missed cleavages. Cysteine carboxyamidomethylation (+57.0214 Da) and methionine oxidation (+15.99492) were allowed as fixed and variable modifications, respectively. Monoisotopic peptide tolerance was set to 10 ppm, and fragment mass tolerance was set to 0.02 Da. The identified proteins were filtered using the high peptide confidence setting in Protein Discoverer (version 2.2, Thermo Fisher Scientific), which correspond to a filter for 1% FDR on peptide level. For generation of spectral libraries, the DDA spectra were analyzed with Proteome Discoverer 2.2 as described above and imported in the Spectronaut software (version 8,Biognosys AG) 124 . DIA-MS targeted data extraction was performed with Spectronaut version 8 (Biognosys AG) 124 with default settings. Retention time prediction type was set to dynamic iRT with correction factor 1 for determination of XIC extraction windows. Retention time correction was performed with a non-linear calibration strategy, and interference correction on MS2 level was enabled 125 . The false discovery rate (FDR) was estimated with the mProphet approach 126 and set to 1% at peptide precursor level.
Analysis of protein abundances: Analyses of protein abundance were performed with the MSstat package 127 . Unless stated otherwise, default parameters were used. Spectronaut output was converted to the input format of MSstats with the SpectronauttoMSstatsFormat function. The normalised peak areas were further processed with the function dataProcess and intensity = "NormalizedPeakArea". This included log2 transformation, median normalization, the summary of fragments to peptides, and the summary of peptides to proteins. The parameter featureSubset was set to "all". We used the groupComparison function with linear mixed models to compare protein abundances between the replicates for the two strains. The FDRs and log2 fold changes between strains (l2FCs) returned by groupComparison were used for further analyses.

Functional enrichment analyses
Gene ontology (GO) enrichment analysis was performed with the topGO package (v2.30.1) 128 . The annotations were downloaded from pombase.com (uploaded on 1st Sept 2015) 49 . The transcriptome and proteome were tested separately. All genes with an FDR ≤10% were included in the test set, while all other genes formed the background. Importantly, only those genes with available measurements were included in the background to avoid false positive enrichments. The nodeSize was set to 10. We performed Fisher's exact tests with the elim-algorithm. All terms with p ≤0.01 were included in the plots. We used AnGeLi 74 for functional enrichment analysis to confirm the GO enrichments and to reveal overlap with core environmental stress and TORC1 response genes.

Metabolomics experiments
Overnight, cell pre-cultures were diluted to OD600 of 0.1, and 5 ml were quenched in 20 ml dry-ice-cold methanol when an OD600 of 0.8 was reached. This suspension was spun down (600g, 3 min, 4°C) and the supernatant was discarded by inversion. The pellet was resuspended in the remaining liquid and transferred to a small tube, and spun down again with the same parameters. The supernatant was removed completely and the pellet was frozen in liquid nitrogen and stored at -80°C until further processing.
The samples were extracted as described 129 . Acid washed Zirkonia beads were added to the pellet, together with 140 µl of 10:4 MeOH/water, and cells were lysed mechanically (FastPrep Instrument, 40 sec, 6.5 m/s). Then, 50 µl chloroform were added and mixed thoroughly, followed by 50 µl water and 50 µl chloroform. Insoluble components were removed by centrifugation at 5000g for 10 min. The aqueous phase was recovered and used without further conditioning. One microlitre was injected for LC-MS/MS analysis. The sample was diluted 1:20 for the analysis of free amino acids, except for glutamine which was quantified without dilution.
The compounds were resolved on an Agilent 1290 liquid chromatography system, using a HILIC amide column (Waters BEH Amide, 2.1 x 100 mm, 1.7 µm particle size) with acetonitrile (solvent A) and 100 mM aqueous ammonium carbonate (solvent B) for gradient elution at a constant flow rate of 0.3 ml/min and column temperature of 35°C. The gradient program started at 30% B and was kept constant for 3 min before a steady increase to 60% B over 4 min. Solvent B was maintained at 60% for 1 min before returning to initial conditions. The column was washed and equilibrated for 2 min resulting in a total analysis time of 10 min. Compounds were identified by comparing retention time and fragmentation patterns with analytical standards. The samples were analysed by tandem mass spectrometry coupled to an Agilent 6470 triple quadrupole. The sample was acquired using the Agilent dynamic MRM (dMRM) approach with polarity switching. Peak areas were converted to concentrations using external calibration by standard curves and corrected for the optical density of the culture at the time of harvesting. Enzyme assays PYK activity was assayed as described 60 . The homothallic A-and T-strains were grown over-night in 3 ml of YES pre-cultures, diluted to OD 0.15 and grown for a further 5 hrs. The OD600 of cultures at the time of sampling was approximately 1. Lysate was prepared by spinning 2 ml of culture (800g, 3 min, RT), discarding the supernatant, adding a small amount of glass beads and 200 μL of lysis buffer (10 mM Tris at pH 7, 100 mM KCl, 5 mM MgCl2, 1 mM DTT), breaking the cells with a FastPrep instrument (MP Biomedicals), operated at 4°C three times for 40 sec with 1 min breaks in between, spinning at 8000g (3 min, 4°C), and finally transferring the supernatant to a fresh tube kept on ice and used fresh.
Reactions with a total volume of 200 μl in a 96 well plate contained 10 mM Tris at pH 7, 100 mM KCl, 5 mM MgCl2, 20 µg L-lactate dehydrogenase from rabbit muscle, 5 mM ADP (all by Sigma-Aldrich), 10 mM PEP (from Molekula) and 200μM NADH (from Bioworld). The reaction mix was warmed to 32°C for 1 min and the reaction was started by adding 4 μl of lysate. The absorbance at 340nm was measured every ~15 sec in a Tecan Infinite M200 Pro plate reader set to 32°C. Absorbance values below 0.2 were set to NA. Absorbance values were converted to concentration using the extinction coefficient 6220 M −1 cm −1 and a path length of 0.5411cm (calculated from reaction volume and well diameter). The slope of the concentration trace was determined using the linregress function from the scipy python package, background subtracted (reaction mix without lysate), and divided by the OD of the culture at the time of sampling.
Oxygen consumption rate measurements YES cell cultures (100ml) were grown overnight to an OD between 1 and 3. A ~25ml sample was put into a 25ml Erlenmeyer flask and stirred at 900rpm using a magnetic stirrer bar. An oxygen probe (Hanna HI 98193), held with a clamp, was inserted into the flask, resulting in it being completely filled, and the flask was sealed with multiple layers of parafilm. The oxygen saturation of the culture was followed over 7 min and recorded every ~1 min. The slope of the concentration trace was determined using the linregress function from the scipy python package and divided by the OD of the culture.

Measurement of growth rates
Growth curves shown in Fig. 4A were obtained by pre-culturing cells overnight (5 ml), diluting 1 to 50, growing for an additional 6 hours, and then diluting to OD600 0.2. Growth curves were recorded with a BioLector instrument (m2p-labs). Strains grew in 48 flowershaped well plates, shaken at 1000 rpm at 85% humidity. Biomass measurements use light scattering at 620 nm and were taken every 10 min, with the signal gain set to 50. The maximum slope was extracted by fitting all possible linear regressions over 12 consecutive timepoints and retaining the one with the highest slope.

Carbon source screen
We used Edinburgh minimal medium (EMM) or YES, depending on the nitrogen source in the final assay medium 130 . Cells were pre-cultured overnight (5 ml), diluted to approximately OD600 0.2 in the morning, grown for 6 hours, spun down (400g, 4 min, RT), washed once in EMM without glucose or YE, resuspended and diluted to OD600 0.2 in EMM without glucose or YE. Carbon sources were used at the same molarity as glucose in standard EMM (2% w/w, 111 mM), except for sucrose, maltose and raffinose (where amount was corrected for number of monosaccharides they contain), pectin (where a saturated solution was used), and glutamine (16% w/w due to low solubility). The OD600 was recorded in 384-well plates, every 15 min, with short shaking (15 sec) before each measurement in a plate reader (Tecan Infinite M200 Pro). Growth curves were smoothed by first applying a median filter of size 5 and a Gaussian filter with sigma=3. We obtained maximum slopes for each well by fitting all linear regression models for 12 timepoints over the course of the growth curve and retaining the best one.

Biolog phenotyping screen
The resistance to various chemical compounds was assessed using Biolog Phenotype MicroArray plates PM22, PM23 and PM25. JB50 and pyk1-A-allele h 90 were grown overnight in EMM, diluted to OD600 0.15 in fresh EMM, and grown for 6 hours at 25°C. Cultures were then diluted to OD600 0.05 and 100 μl were added to each well. For each plate type, two individual plates were used (one per strain). Growth curves were recorded by measuring the absorbance at 610 nm every 30 min in an EnVision 2104 plate reader (PerkinElmer) with stacker module. The room was not strictly temperature controlled but was stable at 23.5±1°C over the course of the experiment. Growth curves exhibited considerable noise levels and were smoothed by first applying a median filter of size 5 and a Gaussian filter with sigma=3. We obtained maximum slopes for each well by fitting all linear regression models for 12 timepoints over the course of the growth curve and retaining the best one. Each plate contained multiple concentrations of the same compound, and dose-response curves were plotted for all (Supplement), with hits identified by manual inspection.
To assess the ability of both strains for using different nitrogen sources (Biolog Phenotype MicroArray plate PM3), we applied the same strategy with the following modifications. We used strains with the heterothallic hmating type to prevent mating and sporulation in poor nutrient conditions. Pre-cultures were diluted to OD600 0.2 and grown for 6 hours at 25°C. Cultures were then spun down (300g, 3 min, RT), washed in EMM without nitrogen or carbon (EMM-N-C), resuspended in EMM-N at an OD600 of 0.2 and 100 μl of cells were added to each well of the assay plates. OD610 was recorded in 15 min intervals, and the fit range for maximum slope extraction was accordingly doubled to 24 timepoints. Growth curves were otherwise acquired and analysed similarly. Nitrogen sources in which both strains had a maximum growth rate <0.015 were excluded from further analysis.

Spot assays
Three independent pre-cultures were used for the T-strain and one pre-culture per independent CRISPR-engineered mutant. Pre-cultures were grown in either YES or EMM media depending on the plate used for the actual assay. Overnight cultures were diluted to OD600 of 0.15 and grown for an additional ~6 hours. Cultures were then diluted to OD600 of 0.4, with a 3-fold dilution series prepared in a 96-well plate (one culture per column). The '1 to 16 array single source' program of the RoToR HDA (Singer Instruments) was used to create the readout plates. For each batch, a control plate without toxin was prepared to check for any accidental bias in strain dilutions.

Chronological lifespan assay
Chronological lifespan assays were performed as previously described 58 . Single colonies were picked and inoculated in YES. Cells were grown for 48 hours, which was treated as the beginning of stationary phase (Day 0). Error bars in Fig. 4G represent the standard deviation of 3 independent biological repeats, which consist of 3 technical repeats each, averaged previously. For the A-strain, 3 different mutants were used as biological repeats.

Supplementary information and availability of materials
The homothallic and heterothallic A-strains (972 h-pyk1 T343A and 968 h90 pyk1 T343A respectively) are available from the Bähler Laboratory on request.