Autotoxin-mediated latecomer killing in yeast communities

Arisa H Oda; Miki Tamura; Kunihiko Kaneko; Kunihiro Ohta; Tetsuhiro S Hatakeyama

doi:10.1371/journal.pbio.3001844

. 2022 Nov 7;20(11):e3001844. doi: 10.1371/journal.pbio.3001844

Autotoxin-mediated latecomer killing in yeast communities

Arisa H Oda ^1,^*, Miki Tamura ¹, Kunihiko Kaneko ^1,^2,³, Kunihiro Ohta ^1,², Tetsuhiro S Hatakeyama ^1,^*

Editor: Mark L Siegal⁴

PMCID: PMC9639812 PMID: 36342925

Abstract

Cellular adaptation to stressful environments such as starvation is essential to the survival of microbial communities, but the uniform response of the cell community may lead to entire cell death or severe damage to their fitness. Here, we demonstrate an elaborate response of the yeast community against glucose depletion, in which the first adapted cells kill the latecomer cells. During glucose depletion, yeast cells release autotoxins, such as leucic acid and L-2keto-3methylvalerate, which can even kill the clonal cells of the ones producing them. Although these autotoxins were likely to induce mass suicide, some cells differentiated to adapt to the autotoxins without genetic changes. If nondifferentiated latecomers tried to invade the habitat, autotoxins damaged or killed the latecomers, but the differentiated cells could selectively survive. Phylogenetically distant fission and budding yeast shared this behavior using the same autotoxins, suggesting that latecomer killing may be the universal system of intercellular communication, which may be relevant to the evolutional transition from unicellular to multicellular organisms.

Cellular adaptation to stressful environments is essential to the survival of microbial communities, but a uniform response runs the risk of extinction. This study reveals an elaborate response of yeast communities to glucose depletion, in which the early adapted cells kill the latecomers.

Introduction

When organisms face crises, such as starvation, they are forced to adapt at both individual [1,2] and population levels [3–5]. In unicellular organisms, the former has been intensively studied as an adaptation phenomenon [6–8], whereas the latter is poorly understood. Severe conditions decrease the carrying capacity [9], and unicellular organisms have to decrease the population size [10,11]. However, such an adjustment in cell number carries the risk of killing clonal cells. Thus, how cellular communities adapt to crises without decreasing the fitness of clonal cells remains unknown.

Here, we report an elaborate survival behavior in crisis. When fission yeast, Schizosaccharomyces pombe, is cultured under glucose-limited conditions, where the carrying capacity is expected to decrease, cells release toxic molecules into the medium. Such a medium kills even the clonal cells of the toxin-producing cells when they are transferred from glucose-rich conditions. This may look like mass suicide at first glance. However, cells precultured in glucose-depleted conditions continue to grow even in the conditioned medium, as they adapt to the toxins through glucose depletion, and such cellular state is inherited. In other words, cells autonomously differentiate into 2 types, adapted and nonadapted ones, and the cellular community selectively saves the former. Yeast cells in glucose-depleted media release toxins, which prevent an invasion of latecomers by killing them, as the Greek philosopher argued: the plank of Carneades [12]. Surprisingly, the same behavior was observed in the budding yeast, which are phylogenetically distant relatives of the fission yeast [13]. We also demonstrate results implicating the underlying molecular mechanisms of the latecomer killing.

Results

To detect interactions in the population, we prepared conditioned media (CM) by culturing wild-type (WT) fission yeast S. pombe referred to as WT CM. First, cells were cultured for 30 h in the minimal media (MM) without glucose, with 3% glycerol as a carbon source (see S1 Fig), and with 0.05% ethanol to induce genes to metabolize glycerol [14] (0% MM). Of note, we added 3% glycerol to every medium we used unless otherwise noted. Next, cells, which had been precultured in the MM with 3% glucose (3% MM), were cultured in the WT CM (see also Fig 1A for the procedure). Then, they stopped growing for approximately 20 h and resumed growing again (see the red line in Fig 1B). We termed this prolonged lag phase as the delay phase (see Text A in S1 Text for measurement of the delay phase). If incubation time to prepare CM was longer than 15 h, such media also induced the delay phase, while shorter incubation time did not introduce such a phase (Fig 1B). Although here we added leucine, adenine, uracil, and histidine to the MM as supplements for auxotroph mutant strains, the delay phase was induced regardless of whether we added these compounds or not (see S2 Fig). It indicated that the delay phase was not caused by the amino acid–rich environment. Those results indicated that in the early growth phase, cells might release inhibitors for growth or depleted some of the nutrients required for such a phase.

To determine whether cells release inhibitors or deplete essential nutrients, we constructed a conditioned medium using a 1,6 bis-phosphatase deletion mutant (fbp1Δ), which did not have a functional gluconeogenetic pathway [15]. Such a mutant strain could not grow without glucose (S3 Fig and [14,16]) and was expected not to consume the nutrients required for growth. The CM made using fbp1Δ cells (fbp1Δ CM) also caused the delay phase (Fig 1C), as shown with the WT CM. This suggested that the delay phase resulted from the release of inhibitory molecules by cells rather than the depletion of nutrients.

In addition, when we administered glucose at a sufficient concentration to the CM (Fig 1D) to recover the carrying capacity, cellular growth was not disrupted, and the delay phase was not observed, i.e., the growth curve of cells in such media was almost the same as that of those in MM with glucose (Fig 1E). This indicated that inhibitory molecules in the CM worked only in the absence of glucose. When we added other species of sugars, i.e., fructose, galactose, and mannose, they were able to reduce the length of the delay phase, but each of them showed different strength of effect; fructose had the same effect as glucose, and galactose and mannose reduced the delay phase only partially (S4A Fig). Furthermore, if we administrated 2-deoxy-D-glucose, a glucose analog not metabolized by the glycolysis, cells did not grow. These results suggest that the rescue of the growth was due to the influence of each sugar on the metabolism, and not to signaling by glucose. Further, when glutamate or a mix of 12 amino acids commonly used as supplements for yeast was added to the CM, they did not rescue the growth (S4B Fig).

After the delay phase, the growth rate in the CM returned to almost the same level as that in the glucose-depleted MM. This suggested that the cells were able to adapt to inhibitory molecules in the CM, and this adapted state was inherited; if cells do not memorize the adapted state, divided cells will not survive after the delay phase, and only a small portion of cells can escape death and grow. Then, the growth rate would decrease eventually. However, after the delay phase, the growth rate in the CM was stably maintained. Hence, those growing cells should inherit the adapted state from those mothers.

To verify the existence of the adapted state of cells, we precultured cells in the 0% MM for 24 h and measured their growth in WT and fbp1Δ CM (Fig 1F), and a delay phase was not observed (Fig 1G). Furthermore, to verify whether adaptation to the inhibitory compounds was due to genetic or not, we precultured the cells, which survived in the CM, in 3% MM and, again, cultured them in the CM; they showed a delay phase again (S5 Fig). Moreover, we performed genomic DNA sequencing (gDNA-seq) of surviving cells and identified no unique single-nucleotide polymorphism or insertion–deletion mutations (InDels), except for highly repetitive sequence loci, such as telomeres and centromeres (see S6 Fig and Text B in S1 Text for details). This indicated that the adapted state was inherited without genetic changes.

The plausible evolutionary significance of the release of inhibitory molecules and adapting to them is the inhibition of the growth of different lineages of cells. When sugars around cells are depleted, they start to release inhibitory molecules while simultaneously adapting to such inhibitors. Then, the modified environment will inhibit the growth of latecomers, even if they are closely related. We performed a competition assay by artificially mimicking the above conditions; we simultaneously added cells that were precultured in glucose-rich and glucose-depleted media into the CM at an equal amount in the beginning and observed their population dynamics (S7 Fig). Then, the fraction of adapted cells to unadapted cells continued to increase for 24 h and reached a steady-state (Figs 1H and S8A), while the 50–50 ratio was maintained in the competition assay between adapted cells (Figs 1I and S8B). In addition, the steady-state ratio of adapted and unadapted cells agreed with the ratio predicted from the growth curve observed in fresh and CM shown in Fig 1G (see also Text C in S1 Text). This implied that the combination of inhibitor release and adaptation caused population dynamics shown in Fig 1H and 1I and selected the offspring of inhibitor-producing cells to survive.

The characteristics of the inhibitory molecules observed in the CM helped us isolate them. Since treatments of the CM with autoclaving, DNase, RNase, and proteases did not affect its ability to cause the growth delay (S9 Fig), the inhibitory molecules in the CM should not be peptides, proteins, or nucleic acids, as well as volatile/thermolabile materials. Then, we identified chemical compounds in the freshly prepared MM, as well as WT and fbp1Δ CM, using capillary electrophoresis mass spectrometry (CE-MS). We identified 20 chemical compounds (see Table A in S1 Text). From these candidates, we chose 12 chemicals that were included in both types of CM but not the fresh medium (see the yellow hatching region in the Venn diagram in Fig 2A), because both types of CM initiated the delay phase. We further narrowed down 12 molecules by adding them as a single dose to the MM according to the following criteria: (1) They did not change the growth rate significantly after the delay phase. (2) They had little effect on growth in the presence of glucose. (3) They caused a shorter delay phase in cells that had already adapted to glucose depletion. Finally, we isolated 2 small molecules with similar structures: leucic acid (HICA; Fig 2B) and L-2keto-3methylvalerate (2K3MVA; Fig 2C). Note that some of the molecules in the candidate list were difficult to obtain commercially and could not be tested.

Fig 2 — (A) Venn diagram of compounds detected using CE-MS. Compounds in 0% MM, WT CM, and *fbp1*Δ CM were analysed. Twenty compounds were detected in both WT and *fbp1*Δ CM, and 8 of those were also detected in 0% MM. Thus, 12 compounds (hatching area) were detected uniquely in both types of CM (see Table A in S1 Text for details of the detected molecules). (B and C) The structure of (B) HICA and (C) 2K3MVA. (D and E) Growth curves in 0% MM in the presence of (D) HICA and (E) 2K3MVA. WT cells precultured in 3% MM were transferred to 0% MM with various concentrations of the inhibitory compound at 0 h. Each line represents the average of n≥6 samples. (F and G) Effects of adaptation and glucose administration on growth curves in the presence of (F) 30 mM HICA or (G) 25 mM 2K3MVA. The blue line indicates the growth curve of WT cells precultured in 0% MM in 0% MM with the inhibitory compound. The orange line is a growth curve of WT cells in MM with the inhibitory compound and 3% glucose. Pink and black lines indicate growth curves in 0% MM with the inhibitory compound and 3% MM as controls, respectively. Each line represents an average of n≥4 samples. (H and I) Cells precultured in 3% MM were inoculated to WT CM with additional HICA, 2K3MVA, or the mixture. (H) Growth curves and (I) length of the delay phase τ were plotted. A line for WT CM represents an average of n = 22 samples, and those for WT CM with the growth inhibitors represent an average of n = 3 samples. The data underlying this figure can be found in S1 Data. CE-MS, capillary electrophoresis mass spectrometry; CM, conditioned media; MM, minimal media; WT, wild-type.

The 2 inhibitory molecules had similar characteristics. When the concentration of these molecules was not sufficient, we did not observe the delay phase (Fig 2D and 2E). Then, the more inhibitors we administrated, the longer the delay phase took. Finally, if the concentration was higher than the critical concentrations (30 mM for HICA and 25 mM for 2K3MVA), cell growth was thoroughly repressed. Notably, there are 2 optical isomers of HICA, which are indistinguishable via CE-MS, both of which caused a growth delay at the same concentration (S10 Fig). When glucose was added to the MM simultaneously with inhibitory molecules, cell growth was not disrupted (see the orange line in Fig 2F and 2G). Moreover, even under the administration of such high concentration where cells stopped growing, cells that had been adapted to glucose depletion grew (see blue line in Fig 2F and 2G). These correspondences of inhibitory molecules with the CM implied that the release of HICA and 2K3MVA was one of the causes of growth inhibition during glucose depletion. Since adding leucine or the branched amino acids to the 0% MM did not cause the delay phase (S11 Fig), the growth inhibition was not due to ketogenic amino acids but to specific molecules we identified. Although the quantified concentrations of HICA and 2K3MVA in the CM were rather lower than the effective dose of each compound when only each was added to the medium (see Table B in S1 Text for the concentrations of those in the CM), the addition of HICA and 2K3MVA to the CM drastically reduced the effective dose by approximately 3 orders of magnitude (see Fig 2H and 2I). The addition of HICA and 2K3MVA at the physiological concentration (50 to 100 μM) to the WT CM led to drastic elongation of the delay phase, suggesting that HICA and 2K3MVA are indeed the important entities of the growth inhibitors that can work in a single dose, while they need other components for their full actions. It suggested that a combination of multiple secreted compounds might synergistically influence, while the main substances of growth inhibitors are HICA and 2K3MVA since other compounds detected did not cause the delay phase solely. The concentration of those molecules increased with time; from 10 to 20 h, they increased more than 5 times, and from 20 to 30 h, they increased more than 3 times. Accordingly, the delay phase appeared in the CM with a longer incubation time than 15 h (Fig 1B). It is consistent with the characteristics of the identified molecules, which repressed the cell growth above the critical concentrations (Fig 2D and 2E). In addition, since the cell doubling time in 0% MM was about 10 h (Table C in S1 Text), the concentrations of those molecules increased faster than cells, indicating that those molecules rapidly accumulated in the media. Note that the concentrations of identified growth inhibitors in fbp1Δ CM were lower than those in WT CM, but those 2 CMs showed almost the same length of the delay phase (Table C in S1 Text). It implies that the fbp1Δ CM contains substantial amounts of cofactors for growth inhibitors that are not effective alone but can enhance the action of HICA and 2K3MVA. Even if such compounds exist, they will not be observed by the method we adopted here, and future studies will be necessary to identify them.

We next explored the synthetic pathway of HICA and 2K3MVA. Although a synthetase for HICA (hydroxyisocaproic acid dehydrogenase) was identified in bacteria (see [17] for Lactococcus lactis), neither its homolog nor any enzymes for HICA and 2K3MVA synthesis have been reported in S. pombe. We, therefore, searched for genes that encode hydroxyacid dehydrogenases, whose targets are unknown in S. pombe, by using UniProt [18], and made their disruptant mutants. Then, the concentrations of HICA and 2K3MVA in media conditioned by all of these strains decreased but did not reach zero (e.g., 5.3 to 10.6 μM of HICA from 55.2 to 68.1 μM in WT measured in the same condition; Table B in S1 Text). Correspondingly, these CM caused a shorter delay phase than that caused by the WT CM (see S12 Fig). These results suggest that S. pombe synthesized HICA and 2K3MVA by using multiple pathways and synthetases. More importantly, adding back 25 to 50 μM of HICA or 2K3MVA, equivalent to their concentrations in the WT CM, to media conditioned by those mutants rescued the delay phase (see S13 Fig). These results again support that HICA and 2K3MVA are real substances of growth inhibitors and unidentified minor compounds in CM promote the action of HICA and 2K3MVA.

How do the inhibitory molecules cause the delay phase? There are 2 possible mechanisms: delay of initiation of growth in each cell or death of the majority of cells. In the latter, the concentration of living cells will be masked by that of dead cells in the optical density (OD) measurement, and an apparent delay phase will be observed until the concentration of living cells exceeds that of dead cells (see Text D in S1 Text and S14 Fig). To verify which hypothesis is correct, we counted the number of dead cells by staining them with phloxine B [19] (Fig 3A–3H). Then, over 80% of cells, which were cultured in the presence of a higher concentration of inhibitory molecules, were dyed in red (Fig 3G and 3H). Indeed, the majority of cells during the delay phase were stained red and showed a typical rod shape, as observed under a microscope, which showed the characteristics of dead cells [20,21] (Fig 3E and 3F). In contrast, only a small number of cells showed a spherical shape similar to cells cultured in MM without glucose. This indicated that only a small portion of the cells survived and continued to divide in the presence of inhibitory molecules. Similarly, the death rate in WT and fbp1Δ CM increased (Fig 3C, 3D and 3G). Furthermore, cells precultured under the glucose-depletion condition, which did not show a delay phase in the presence of the inhibitory molecules, were mostly alive (Fig 3H). This suggested that HICA and 2K3MVA induced cell death, which was the primary cause of the delay phase (see also Text D in S1 Text, S15 and S16 Figs and Table C in S1 Text).

Fig 3 — (A–F) Fluorescent (upper) and brightfield (bottom) microscopic images of WT cells in various media after 24 h of incubation. Cells precultured in 3% MM were transferred to (A) 3% MM, (B) 0% MM, (C) WT CM, (D) *fbp1*Δ CM, (E) 0% MM with 25 mM HICA, and (F) 22.5 mM 2K3MVA. In fluorescent microscopic images, dead cells were stained with phloxine B. Scale bar indicates 10 μm. (G and H) The dyed cell ratio after 8 h of incubation. Cells were precultured in (G) 3% MM (n = 8–10) or (H) 0% MM (n = 3–6). Grey dots represent the dyed cell ratio in each sample, and red crosses represent the mean value. (I and J) Growth curves of cells precultured in the presence of one of the inhibitory molecules along with 3% glucose, in 0% MM with (I) 30 mM HICA or (J) 25 mM 2K3MVA. Cells were precultured in 0% MM (blue line), 3% MM (pink line), 3% MM with 30 mM HICA (black line), and 3% MM with 25 mM 2K3MVA (red line). (K) Calculated length of the delay phase in deletion mutant strains, where the relevant genes were up-regulated in adapted cells (see Table E in S1 Text for a list of the genes). The deletion mutants and WT cells were precultured in 3% MM with 30 mM HICA for 24 h and inoculated into 0% MM with or without 30 mM HICA. The time point when the initial concentration doubled, τ, for the SPAC57A7.05Δ strain was much longer than the observed time range, and thus we calculated the time when the initial concentration was 1.5 times higher. Then, we extrapolated by multiplying the value by log2/log1.5. (L) Growth curves of some deletion mutants that showed the significant prolongation of the delay phase longer than 30 h in 0% MM with 30 mM HICA. Each line represents an average of n≥4 samples. See S13 Fig for the growth curves of the rest of the mutants. The data underlying this figure can be found in S1 and S3 Data. CM, conditioned media; MM, minimal media; WT, wild-type.

We analyzed how cells were killed by the autotoxins or CM by costaining of annexin-V (AnnV) and propidium iodide (PI): Early apoptotic cells exhibit phosphatidylserine externalization and are stained by AnnV but not by PI. In contrast, primary necrotic cells show ruptured plasma membranes, which are stained by PI but not by AnnV [22]. Late apoptotic and secondary necrotic cells are stained by both AnnV and PI. As a result, cells in the autotoxins and CM caused cell death via 2 processes, i.e., apoptosis and necrosis, as shown in S17 Fig. It suggests that the mechanism of cell death is not simple but multifarious, and there is no uniformity in the way how cell dies.

In addition, we investigated what characterizes the tendency of death in the presence of autotoxins. One possibility is the difference in cell cycle stages. We thus measured the death rate of the temperature-sensitive cdc25-22 mutant strain, whose cell cycle can be arrested at the G2 phase at higher temperatures [23,24]. Using the cdc25-22 mutant, we prepared synchronously dividing cells and found that they showed higher death rates than asynchronously dividing cells, which will contain a substantial portion of cells arrested in the G0 phase. This suggests that the cell cycle stage is one of the major factors that determine the cell competency to survive or die, and the resting cells might show tolerance to autotoxins (S18 Fig). Note that the above mutant cells producing fewer autotoxins show the similar tolerance to the autotoxins with WT cells (S19 Fig).

The identified toxins also facilitated cell adaptation in a condition-dependent manner. When we precultured cells in 3% MM with an inhibitory molecule, HICA or 2K3MVA, they grew in 0% MM with a sufficiently high concentration of inhibitory molecules that stopped the growth of nonadapted cells (Fig 3I and 3J). This indicated that the molecules we identified had 2 distinguishable effects: to kill latecomer cells and help firstcomer cells to adapt. Then, using RNAseq, we explored genes responsible for adaptation. We analyzed differentially expressed genes between 3% MM and 3% MM with 30 mM HICA or 25 mM 2K3MVA (S20 Fig). Gene ontology analysis revealed that many of the up-regulated genes in the adapted cells were involved in the transmembrane transport (exporters) and small molecule metabolic process (detoxification) (see Table D in S1 Text). We, therefore, constructed deletion mutants for 17 genes, which were included in the top 20 up-regulated genes in response to HICA (see Table E in S1 Text for a list of up-regulated genes). They were precultured in 3% MM with 30 mM HICA for the adaptation to autotoxins, and their growth was measured in 0% MM with 30 mM HICA. Notably, we detected substantial delay of adaptation in many of the deletion mutants (Fig 3K). In particular, the growth of SPCC1739.08cΔ, SPBC1289.14Δ, SPBC359.06Δ, SPAC57A7.05Δ, ght4Δ, mfc1Δ, and map1Δ strains was drastically suppressed, while that of those in 0% MM was normal (Figs 3K, 3L and S21). Those genes are predicted to encode a short-chain dehydrogenase, adducin, adducin, transmembrane transporter, plasma membrane hexose:proton symporter, prospore membrane copper transmembrane transporter, and DNA-binding transcription factor, respectively. These results suggest that cells undergo adaptation by the mechanisms of both evacuation and detoxification to autotoxins. Those genes were also up-regulated in the WT CM with glucose and might contribute to the rescue of the cell growth by sugars (S22 Fig). In addition, those genes were up-regulated in survived cells in the WT CM or in the presence of the autotoxins (S23 Fig). At the same time, the lack of those genes did not affect the growth in 0% MM (S24 Fig). These results suggest that the adaptation to glucose depletion and autotoxins depends on several different processes.

Latecomer killing is not a unique characteristic of S. pombe but is widely observed in unicellular fungi. We cultured 2 strains of budding yeast Saccharomyces cerevisiae, phylogenetically distant from S. pombe, and prepared CM using them. Then, we found that such CM also initiated the delay phase in the growth of media producers (Figs 4A and S25). Moreover, we detected the same toxic molecules, HICA and 2K3MVA, in the media conditioned with S. cerevisiae (see Table A in S1 Text). In addition, the administration of such toxins to MM without glucose initiated the delay phase in a concentration-dependent manner (Figs 4B, 4C, S14B and S14C). Indeed, we confirmed that S. cerevisiae cells also died with the autotoxins similar to S. pombe (S26 Fig). Cells precultured under the glucose-depletion condition did not show a delay phase in their CM or 0% MM with an inhibitory molecule (Figs 4D, 4E S25D and S25E). This suggested that the same behavior with the same molecules, as observed in S. pombe, was evolutionarily conserved among distant species. In addition, media conditioned with distant species also initiated the delay phase (Fig 4F–4H), i.e., media conditioned with S. pombe inhibited the growth of S. cerevisiae and vice versa. Therefore, such a behavior was universally effective from closer to distant species.

Fig 4 — (A) Growth curves of S. *cerevisiae* (OC-2) cells in media conditioned with themselves. Different colored lines indicate growth curves of CM at different incubation times. Each line represents an average of n≥6 samples. (B) Growth curves of OC-2 in 0% MM with various concentrations of HICA. Each line represents an average of n≥5 samples. (C) Growth curves of OC-2 in 0% MM with various concentrations of 2K3MVA. Each line represents an average of n≥5 samples. (D) Growth curves of OC-2 precultured in 0% or 3% MM grown in OC-2 CM. Each line represents an average of n≥6 samples. (E) Growth curves of OC-2 precultured in 0% or 3% MM grown in 0% MM with 25 mM HICA or 25 mM 2K3MVA. Each line represents an average of n≥6 samples. (F, G, and H) Growth curves of (F) S. *pombe*, (G) OC-2, and (H) YEA8 in media conditioned with S. *pombe*, OC-2, or YEA8 for 30 h. Each line represents an average of n≥6 samples. The data underlying this figure can be found in S1 Data. CM, conditioned media; MM, minimal media.

Discussion

In this paper, we reported a new ecological behavior for microbes; the latecomer killing. It seems similar to the toxin-antitoxin system, such as bacteriocins in bacteria [25,26] and killer factors in yeast [27,28] and paramecium [29], but it is fundamentally different. Although in the toxin-antitoxin system, the clonal cells are not killed [30], even the clonal latecomer cells can be killed when they have not been adapted to toxins in the mechanism discovered here. Such a behavior might help the yeast to avoid a mass suicide of the entire cell population and select an appropriate offspring that produces toxins and selfishly purify their genome from closely related species. Moreover, the latecomer killing may overcome the problems of the toxin-antitoxin system. In such a system, toxin producers should continuously produce antitoxins to protect themselves, and the maintenance of this state is a heavy burden for them [31]. Thus, the toxin producer is lost to a cheater, which only has the antitoxin system, whereas the cheater loses to cells with neither toxins nor immunity [32]. In contrast, the latecomer killing does not cost much because the adaptation system can be turned off without the toxin; indeed, the cells precultured in 3% MM without the autotoxins died in the CMs (Fig 1). In contrast, cheaters have disadvantages in their stable expression of protection mechanisms. Then, when the environment fluctuates between glucose-rich and glucose-depleted conditions like real natural conditions, cells with adaptation can beat cheaters in a glucose-rich situation, and cheaters would be expelled eventually. Thus, latecomer killing may be resistant to cheaters.

We found that distant yeast species universally conserved latecomer killing, even at the molecular level. This might be because the toxins in the reported system are simple molecules, while toxins in the toxin-immunity systems in bacteria and yeast are highly evolved proteins. In the bacteriocin system of Escherichia coli, toxins and receptors set off an arms race between the diversification of toxins and enhancement of their recognition by modifying the structure of proteins [33]. One of the reasons may be that the toxin-antitoxin system is vulnerable to the invasion of cheaters, as described above, and, therefore, the toxin-antitoxin system cannot be maintained without evolving various toxins. In contrast, in the latecomer killing, the targets of toxins are insensitive to the detailed structure of molecules and are not specific receptors. Indeed, the enantiomers of HICA have the same activity, where both D- and L-forms of HICA cause the delay phase at the same concentration (S10 Fig). Moreover, HICA was first identified from fermented products of a bacterium, Lactobacillus plantarum [34]. HICA is reportedly useful for human health and food production: HICA is toxic to various bacterial species and pathogenic biofilms produced by Candida and Aspergillus species [35]. It is also produced by various lactic acid bacteria during ripening of Korean pickles, kimchi. The above facts suggest that HICA and 2K3MVA targets are universally conserved pathways. Therefore, the toxins we found were effective against a range of cells, from clones to distant species. Furthermore, such universality at the molecular level is consistent with the hypothesis discussed above that latecomer killing is resistant to the invasion of cheaters.

The latecomer killing we reported may play an important role in understanding the origin of multicellularity. Multicellular fungi fall into clades 8 to 11, while there are only 4 clades apart from fungi [36,37], indicating that transitions from unicellular to multicellular and multicellular to unicellular organisms occur easily in fungi. To form a complex multicellular body, mutual activation of growth, as well as growth inhibition and programmed cell death pathway, are essential [38]. Thus, the evolutionary origin or vestige of both mutual activation and inhibition of growth should be observed even in unicellular fungi. Indeed, multiple species of unicellular fungi use the former for quorum sensing [39]. However, the latter has not been reported. The mechanism we found here meets the criteria required for growth inhibition for multicellularity [40,41], i.e., the toxins cause cell death depending on the cell state and smoothly diffuse from cell to cell. The cell cycle dependency of cell death (S18 Fig) and the apoptotic pathway activation in a large fraction of dead cells (S17 Fig) may also suggest a possible relationship between latecomer killing and multicellularity. A recent artificial evolution experiment demonstrated that multicellular “snowflake” yeast repeatedly evolved 15 times from unicellular S. cerevisiae and underwent apoptosis to keep the original size constant [42]. This suggests that the origin or vestige of multicellularity is embedded in the unicellular yeast. The relationship between intercellular communication in unicellular cells and multicellularity is key to solving the enigma of major transitions in evolution [43], and our study provides a significant milestone.

Materials and methods

Continuous measurement of growth

Continuous measurement of the OD of yeast cultures was performed using ODBox-C/ODMonitor systems (Taitec, Saitama, Japan), a noncontact trubidimeter for shaking cultures. This apparatus detects and monitors the rate of transmitted light (950 nm) of shaken liquids in test tubes by voltage sensors every minute. ODBox-C was set in an incubator (BR-43FL, Taitec) and all the OD measurements were performed with 5 ml liquid cultures in 16.5 mm diameter test tubes at shaking speed of 180 rpm at 30° tilt angle. Following the manufacturer’s instruction, calibration for yeast culture was performed to convert the raw voltage data from ODMonitor into OD₆₀₀ OD at 600 nm (OD₆₀₀ was measured using iMark Microplate Absorbance Reader (Bio-Rad Laboratories, Hercules, CA, USA). For growth measurement, each test tubes with media without cells were shaken for more than 30 min to stabilize the aeration effect, then yeast cells were added and OD was monitored. Detailed delay phase and death rate analyses are described in Texts A and D in S1 Text.

Yeast strains and culture conditions

Haploid strains, L972 (h⁻ 972) and AR110 (h⁻ fbp1Δ), were used for the S. pombe experiments. Wine yeast OC-2 (IAM4274) and baker’s yeast YEA8 were used for the S. cerevisiae experiments. Strain information is also listed in Table F in S1 Text. Initially, cells were precultured at 30°C in yeast extract with supplements (YES; 3% Glucose, 0.5% yeast extract, 200 mg/L Adenine, 100 mg/L Uracil, 200 mg/L Histidine, 200 mg/L Leucine) liquid media. Then, cells were transferred to liquid MM with 3% glycerol and 3% glucose and precultured at 30°C for about 24 h until OD₆₀₀ reached to 0.15 to 0.2 unless otherwise noted. The final concentration of MM was as follows: 14.7 mM potassium hydrogen phthalate, 15.5 mM Na₂HPO₄, 93.5 mM NH₄CL, 0.05% EtOH, 5.2 mM MgCL₂, 0.1 mM CaCl₂·H₂O, 13.4 mM KCl, 0.28 mM Na₂SO₄, 4.2 μM pantothenic acid, 81.2 μM nicotinic acid, 55.5 μM inositol, 40.8 nM D-Biotin, 4.76 μM citric acid, 8.09 μM boric acid, 2.37 μM MnSO₄, 1.39 μM ZnSPO₄ 7H₂O, 0.74 μM FeCl₃ 6H₂O, 0.81 μM (NH₄)₆Mo₇O₂₄·4H₂O, 0.60 μM potassium iodide, 0.16 μM CuSO₄·5H₂O 200 mg/L adenine, 100 mg/L uracil, 200 mg/L histidine, 200 mg/L leucine. The culture was then inoculated into various media including MM with 3% glycerol without glucose (0% MM) and CM. Before cells were transferred, they were washed twice with the new media.

Preparation of conditioned media

Cells were precultured in 3% MM approximately to OD₆₀₀ 0.1 to 0.2, and they are washed twice with 0% MM, transferred to the same volume of 0% MM, and incuvated for 30 h. The final OD₆₀₀ was around 0.4 to 1.0 after 30-h incubation for S. pombe CM of WT, gene deletion mutants in Figs 3L and S12, S. cerevisiae CM as they grew in 0% MM. fbp1Δ CM is also prepared at starting OD₆₀₀ approximately 0.1 to 0.2, but the final OD₆₀₀ is still 0.1 to 0.2 after 30-h incubation. Cells were pelleted down by brief centrifugation (at room temperature, 3,000 · g for 1 min). The supernatants were then filtered using a 0.22-μm PVDF membrane (Millex Sterile Filter Unit, Merck Millipore, Burlington, MA, USA).

Flow cytometry analysis for competition assays

Cells were genetically tagged with mNeonGreen (HN98) or mCherry (HN101). Strain information is also listed in Table F in S1 Text. They were precultured in 3% or 0% MM and transferred to 0% MM and WT CM.

mCherry and mNeonGreen fluorescence were detected at 561 nm and 488 nm and collected with 615/20 and 530/30 emission filters of NovoCyte flow cytometer (ACEA Biosciences, San Diego, CA, USA), respectively. mNeonGreen-tagged strain was constructed from pFA6a-mNeonGreen-HIS3MX6, which was a gift from Wei-Lih Lee (Addgene plasmid #129100; http://n2t.net/addgene:129100; RRID:Addgene_129100) The gating strategy for competition assay is shown in S7 Fig.

Capillary electrophoresis time-of-flight mass spectrometry

Media composition was measured using an Agilent Capillary Electrophoresis System (CE-MS), and data were processed by Human Metabolome Technologies (Turuoka, Japan) in anion and cation analysis modes according to a previously published method [44]. Peaks with signal/noise ratios of more than 3-fold were extracted using MasterHands software ver2.17.1.11 (Keio University, Turuoka, Japan), and HMT’s metabolite libraries were mined, including more than 900 metabolites to search for metabolites in the media.

Search for growth inhibitors

Capillary electrophoresis-time-of-flight mass spectrometry (CE-MS) was performed in 2 biological replicates for 0% MM, and fbp1Δ CM, and in 3 biological replicates for WT CM. We extracted compounds found in both samples (compounds found in only 1 sample are described as N.R. in Table A in S1 Text), and 12 candidates of growth inhibitors were identified following the criteria described in the main text. Every detected compound was added to freshly prepared 0% and 3% MM at a final concentration of 10 to 40 mM, which was sufficiently high to certainly identify the candidates that are effective with a single dose. Then, the growth rate of cells in such media was measured using ODBox-C/ODMonitor systems. Purified reagents of the detected compounds were purchased and used for the experiments. Their information is as follows: L-Leucic Acid (Cat#L0026, Tokyo Chemical Industry (TCI), Tokyo, Japan; CAS RN: 13748-90-8), D-Leucic Acid (D—Hydroxyisocaproic acid; Cat#4026246, Bachem AG, Bubendorf, Switzerland; CAS RN: 20312-37-2), 3K2MVA (3-Methyl-2-oxovaleric Acid, Cat#K0020, TCI; CAS RN: 1460-34-0), 5-Oxoproline (DL-Pyroglutamic Acid; Cat#G0061, TCI; CAS RN: 149-87-1), 2-Hydroxyglutaric acid (Cat#H942575, Fujifilm Wako Pure Chemical Industries (Wako), Japan; CAS RN: 40951-21-1), Ala (L-Alanine; Cat#018–01043, Wako; CAS RN: 56-41-7), Gln (L(+)-Glutamine; Cat#076–00521, Wako; CAS RN: 56-85-9), Glu (L-Glutamic Acid; Cat#070–00502, Wako; CAS RN: 56-86-0), Glycerol 3-phosphate (Glycerophosphoric acid, aqueous solution 35% w/w; Cat#QA-1439, Combi-Blocks, San Diego, CA; CAS RN: 57-03-4), Hypoxanthine (Cat#H0311, TCI; CAS RN: 68-94-0), Inosine (Cat#099–00231, Wako; CAS RN: 58-63-9), Phe (L(-)-Phenylalanine; Cat#161–01302, Wako; CAS RN: 63-91-2), Succinic acid (Cat#190–04332, Wako; CAS RN: 110-15-6).

HICA and 2K3MVA quantification by a liquid chromatography-mass spectrometry

The concentrations of HICA and 2K3MVA in various media were quantified using liquid chromatography-mass spectrometry (LC-MS/MS), and data were processed by Chemicals Evaluation and Research Institute (CERI), Japan. Measurements were performed on a Shimadzu Nexera XR liquid chromatography coupled to a Sciex QTRAP 5500 mass spectrometer with L-columun2 ODS Metal-free column (150 mm × 2.0 mm, 3 m; CERI).

RNA sequencing and gene expression analysis by RNA sequencing

For gene expression analysis, 2 biological replicates were measured for each sample. As described above, 15 to 20 mL cultures 24 h after media change were prepared. Cells were pelleted down and frozen in liquid nitrogen. Frozen cell pellets were resuspended in 250 μL beads buffer (75 mM NH₄OAc, 10 mM EDTA, pH 8.0) at 65°C with 200 μL acid-washed glass beads (Sigma), 25 μL of 10% SDS, and 300 μL of acid-phenol:chloroform (pH 4.5, Thermo Fisher Scientific). The samples were vortexed 3 times 1 min each at 1-min intervals and incubated at 65°C, followed by a 10-min incubation at 65°C, 1-min vortexing, and 15-min centrifugation at room temperature (16,000 × g). Then, the upper aqueous phase was transferred to a fresh tube with 200 μL of beads buffer and 400 μL of phenol/chloroform/isoamyl alcohol (25:24:1, Sigma). Tubes were vortexed briefly and centrifuged at 4°C, 16,000 × g for 15 min. The upper aqueous phase was transferred to a fresh tube with 600 μL ice-cold isopropanol and 21 μL 7.5 M NH₄OAc. Tubes were vortexed briefly and centrifuged at 4°C, 16,000 × g for 30 min. After removing the supernatant, the pellet was washed with 70% ethanol, air-dried, and resuspended in nuclease-free water. RNA samples were treated with RQ1 DNase (Promega), followed by a ribosome RNA removal by the Ribo-Zero Gold rRNA Removal kit for yeast (Illumina).

cDNA libraries were prepared using NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (New England Biolabs) following the manufacturer’s instructions. cDNA libraries were sequenced using the Illumina paired-end technology on HiSeq (300-cycle). Two biological replicates were prepared for each sample. Salmon (version 1.1.0; [45]) was used to quantify RNA sequencing data. The reference genome and gene annotation list of S. pombe (version 2017.10.31) on the PomBase database [46] were used. For differential expression analysis, the R package “DESeq2” was used [47]. Likelihood ratio tests were performed to compare cells in 3% MM versus cells in 3% MM with HICA or 2K3MVA. Gene ontology enrichment analysis was performed for 464 genes that were significantly up-regulated or down-regulated (p-value < 0.01) in both 3% MM with 30 mM HICA and that with 25 mM 2k3MVA compared to 3% MM using GO Term Finder [48]. Sequences were deposited in DDBJ with DRA BioProject Acession Number: PRJDB11860.

Gene expression analysis using reverse transcriptase polymerase chain reaction

RNA was purified as described above. For reverse transcription, PrimeScriptRT Reagent Kit with gDNA eraser (Takara Bio, Japan) was used following the manufacturer’s instructions. The cDNA libraries were quantified using StepOne Real-Time PCR system (Thermo Fisher Scientific) with the Thunderbird SYBR qPCR Mix (Toyobo, Osaka, Japan) following the manufacturer’s instructions. PCR primers used for qPCR are listed in Table G in S1 Text.

Flow cytometry analysis to measure the dead cell ratio

Phloxine B (final concentration of 10 μg/ml, Sigma Aldrich) was used to dye dead cells. The fluorescent signal from phloxine B was detected at a wavelength of 488 nm and collected using 695/40 and 586/20 emission filters on a NovoCyte flow cytometer (ACEA Biosciences, San Diego, CA, USA). The gating strategy for the identification of dead cell population is shown in S16 Fig.

Microscopy and imaging analysis

All images were captured using a microscope equipped with UPlanSApo 100×/1.40 Oil Objective (Olympus, Tokyo, Japan) on an EVOS FL Imaging System (Thermo Fisher Scientific, Waltham, MA, USA). Images were acquired using a SonyICX285AL monochrome CCD camera controlled with built-in software for image acquisition. Fluorescence signal from phloxine B was detected at a wavelength of 530 nm and collected by EVOS Light Cube (Texas Red), which contains a 628/32 emission filter (Thermo Fisher Scientific).

Costaining of annexin V-FITC and propidium iodide

AnnV/PI staining was performed using the ApoAlert Annexin V-FITC Apoptosis Kit (Clontech) as previously described [49–51]. Approximately 1 × 10⁷ cells were first harvested and washed with Sorbitol buffer (1.2 M Sorbitol, 0.5 mM MgCl₂, and 35 mM potassium phosphate, pH 6.8), and then treated with 10 mg/ml Zymolyase-20T (Nacalai) for 2 h at room temperature. Spheroplasts were then washed and resuspended in 1 × Binding Buffer, followed by addition of 5 μl of Annexin V-FITC and 10 μl of PI according to the manufacturer’s instructions. After 15-min incubation at room temperature in the dark, the samples were analyzed by a flow cytometry (NovoCyte) using a single laser emitting excitation light at 488 nm.

Death rate analysis of synchronous culture of cdc25-22

Synchronous cultures were prepared by transient temperature shifts using cdc25-22, temperature-sensitive mutant as previously reported [23,24,52]. Cells were precultured in 15.5 ml of 3% MM at 25°C for 24 h to 5 × 10⁵ cells/ml. Then, the cells were shifted to 36°C for 4.25 h followed by shifting the temperature to the permissive temperature of 25°C to release the G2 block. The cells were kept culturing at 25°C in the 3% MM for 0 h (G2 phase), 1 h (M/G1 phase), and 2 h (S phase), and 450 μl of each culture were sampled for PI staining for cell synchronization analysis. Asynchronous cells were prepared without 36°C heat shock. The cells in the rest of the 15 ml cultures in each phase were dispensed into 3 aliquots, washed, and pelleted down followed by resuspension to 1 ml of 0% MM with 20, 22.5, or 25 mM HICA and 10 μg/ml phloxine B. After 8 h from the media change, the dead cell ratio in each aliquot was measured by a flow cytometer as described above. Cells were fixed with 50 μl formalin for 10 min on ice, washed twice in PBS, and treated with 0.2 mg/ml RNaseA in PBS/50mM EDTA for 2 h at 37°C. Then, PI was added to the final concentration of 5 μg/μl, detected by a flow cytometer as described above.

Measurement of surviving cell growth in conditioned media

WT cells were cultured in WT CM for 24 h, and the culture was spread on YES agar plates to isolate single colonies. After incubating at 30°C for 3 days, 24 colonies were picked and cultured in YES liquid medium for 24 h and then in 3% MM for 24 h. Then, the cells were cultured in WT CM again, and their growth curves were constructed using ODBox-C/ODMonitor systems.

Genome sequencing of surviving cells in conditioned media

WT cells were cultured in WT CM with phloxine B (final concentration of 10 μg/ml) for 24 h. Unstained living cells were selected using a cell sorter SH-800 (SONY) using a 488-nm excitation wavelength laser with a 525/50 emission filter and 561 nm excitation wavelength laser with a 617/30 emission filter. Then, they were cultured in YES liquid medium at 30°C for 3 days and pelleted down to extract genomic DNA. The cell pellet was resuspended in 150 μl of STES Buffer (0.5 M NaCl, 0.01 M EDTA, 1% SDS) with 150 μl of glass beads and 150 μl of phenol-chloroform. The samples were vortexed for 10 min, followed by a 10-min centrifugation at room temperature, 16,000 × g. The upper aqueous phase was transferred to a fresh tube with 150 μl of phenol-chloroform. The tubes were vortexed, followed by a 10-min centrifugation at 16,000 × g at room temperature. The upper aqueous phase was transferred to a fresh tube and mixed with 15 μl of 3 M NaOAc and 375 μl of ethanol. The samples were mixed briefly and centrifuged for 10 min at room temperature (16,000 × g). After the supernatant was removed, the pellet was rinsed with 70% ethanol, air-dried, and resuspended in nuclease-free water.

Genomic DNA libraries were prepared using the NEBNext Ultra II DNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, MA, USA), following the manufacturer’s instructions. Genomic DNA libraries were sequenced using the Illumina paired-end technology on MiSeq with MiSeq Reagent Kit v3 (600-cycle; Illumina, San Diego, CA, USA). Detailed information is provided in Text B in S1 Text. Sequences were deposited in DDBJ with DRA BioProject Acession Number: PRJDB10422.

Supporting information

S1 Fig. Glycerol is essential for growth in the 0% MM.

Growth curves of WT cells in the MM without glucose and with 3% glycerol (black) and that without both glucose and glycerol (red). Cells were precultured in the MM with 3% glucose and 3% glycerol. Each line represents an average of n = 2 samples. The data underlying this figure can be found in S2 Data.

(PDF)

Click here for additional data file.^{(2.4MB, pdf)}

S2 Fig. Effect of auxotrophic marker supplements; leucine, adenine, uracil, and histidine are not essential for the delay phase.

Growth curves of WT cells in the MM with and without leucine, adenine, uracil, and histidine, and those media conditioned by the WT cell. Each line represents an average of n = 4–8 samples. The data underlying this figure can be found in S2 Data.

(PDF)

Click here for additional data file.^{(461.4KB, pdf)}

S3 Fig. Growth curve of fbp1Δ without glucose.

fbp1Δ and WT cells were precultured in 3% MM and then transferred to 0% or 3% MM. Growth curves of fbp1Δ cells in 0% and 3% MM are shown in blue and light blue, respectively, and those of WT cells are shown in black and red, respectively. Each line represents an average of 3–7 samples. The data underlying this figure can be found in S2 Data.

(PDF)

Click here for additional data file.^{(611.6KB, pdf)}

S4 Fig. Delay in growth in the CM was rescued by sugars but not by glutamate or amino acid mix.

(A) Growth curves of WT cells in the WT CM with 3% glucose, 3% fructose, 3% galactose, 3% mannose, or 3% 2-deoxy-D-glucose (2-DG). Each line represents an average of n = 4–6 samples. (B) Growth curves of WT cells in the WT CM with 3.75 g/L (22.2 mM) monosodium glutamate or an amino acid mix. The final amino acid concentration in amino acid mix sample was as follows: Adenine 10 mg/L, L-Arginine HCl 50 mg/L, L-Aspartic Acid 80 mg/L, L-Histidine HCl 20 mg/L, L-Isoleucine 50 mg/L, L-Lysine HCl 50 mg/L, L-Methionine 20 mg/L, L-Phenylalanine 50 mg/L, L-Threonine 100 mg/L, L-Tryptophan 50 mg/L, Uracil 20 mg/L, L-Tyrosine 50 mg/L, L-Valine 140 mg/L. Each line represents an average of n = 2–4 samples. The data underlying this figure can be found in S2 Data.

(PDF)

Click here for additional data file.^{(616.2KB, pdf)}

S5 Fig. Growth curves of the cells that survived in WT CM.

The blue line shows the average growth curves of cells in 24 independent colonies isolated from surviving cells in WT CM. Red and magenta lines are the average growth curves of original WT cells precultured in 3% and 0% MM, respectively. The data underlying this figure can be found in S2 Data.

(PDF)

Click here for additional data file.^{(327.6KB, pdf)}

S6 Fig. Histograms of quality scores calculated using GATK for each mutation in original WT cells and cells that survived in WT CM.

The common mutations detected in all 4 samples are shown in yellow. Mutations detected in both original WT and surviving cells, but not in all 4, are shown in red. Unique mutations detected only in a certain replicate of the original WT, and surviving cells are shown in blue and cyan, respectively. The data underlying this figure can be found in S6 Data.

(PDF)

Click here for additional data file.^{(92.2KB, pdf)}

S7 Fig. Flow cytometry gating strategy for identification of the competition assay discussed in Fig 1H and 1I.

(A-D) Fission yeast cells were first gated on the red E1 gate (left panel). Then, cells in the E2 area were defined as mCherry-positive cells and cells in the E3 area were defined as mNeon-Green-positive cells (right panel). The total count of each sample is 500,000 counts. (A) An example image of competition assay between mCherry-tagged cells precultured in 0% MM and mNeonGreen-tagged cells precultured in 3% MM at 0 h. (B) An example image of competition assay between mCherry-tagged cells precultured in 0% MM and mNeonGreen-tagged cells precultured in 3% MM at 72 h. (C) An example image of competition assay between mCherry-tagged cells precultured in 0% MM and mNeonGreen-tagged cells precultured in 0% MM at 0 h. (D) An example image of competition assay between mCherry-tagged cells precultured in 0% MM and mNeonGreen-tagged cells precultured in 0% MM at 72 h.

(PDF)

Click here for additional data file.^{(235.1KB, pdf)}

S8 Fig. Release of inhibitory compounds and cell adaptation to them is beneficial for the competition among clonal cells.

mNeonGreen- and mCherry-labelled WT cells were mixed in equal fractions in the WT CM at 0 h, and then they showed population dynamics. Green and red areas indicate the fraction of mNeonGreen- and mCherry-labelled cells, respectively, and overwriting outline characters indicate preculture conditions, i.e., 3% and 0% indicate cells precultured in 3% and 0% MM, respectively. Black vertical bars between 2 areas indicate SEM (number of each sample is 12). (A) Competition assay between mNeonGreen-labelled cells precultured in 0% MM and mCherry-labelled cells precultured in 3% MM. (B) Competition assay between mNeonGreen- and mCherry-labelled cells precultured in 3% MM. The data underlying this figure can be found in S1 Data.

(PDF)

Click here for additional data file.^{(698.5KB, pdf)}

S9 Fig. Growth curves of WT cells in the CM treated with autoclave, nucleases, or proteases.

WT cells were precultured in 3% MM, then growth was measured in the various CM treated as follows: Autoclaved: WT CM was autoclaved at 121°C for 20 min. Papain treated: WT CM was treated with 2.5 mg/ml papain (Cat#166–00171, Wako) at 37°C for 24 h. RNase A treatment: WT CM was treated with 50 μg/ml RNaseA (Cat#318–06391, Nippon gene) at 37°C for 24 h. DNase treatment: WT CM was treated with 100 U/ml DNase I (Cat#18047019, Invitrogen) at 37°C for 24 h. Proteinase K treatment: WT CM was treated with 8 μg/ml Proteinase K (Cat# 25530049, Invitrogen) at 37°C for 24 h. Each line represents an average of n = 2–4 samples. The data underlying this figure can be found in S2 Data.

(PDF)

Click here for additional data file.^{(546.1KB, pdf)}

S10 Fig. Growth curves of WT cells in 0% MM with enantiomers of HICA.

(A and B) Structures of (A) L- and (B) D-forms of HICA. (C and D) Growth curves of WT cells in 0% MM with various concentrations of (C) L-form HICA and (D) D-form HICA. For the L-form of HICA, growth curves are an average of 15–18 samples, and for the D-form of HICA, it is an average of 2 samples. The data underlying this figure can be found in S2 Data.

(PDF)

Click here for additional data file.^{(654.3KB, pdf)}

S11 Fig. Adding of BCAAs to the 0% MM did not inhibit the growth.

Growth curves of WT cells in 0% MM without auxotrophic marker supplements with 30 mM, 60 mM leucine or mixture of branched chain amino acids (BCAA: 30 mM leucine + 30 mM valine + 30 mM isoleucine). WT cells were precultured in 3% MM without auxotrophic marker supplements. Each line represents an average of n = 2 samples. The data underlying this figure can be found in S2 Data.

(PDF)

Click here for additional data file.^{(452.5KB, pdf)}

S12 Fig. Growth curves of WT cells in media conditioned by deletion mutant strains of genes encoding putative hydroxyacid dehydrogenase.

Different colored lines indicate growth curves of WT in media conditioned by different deletion mutants. Each deleted gene is predicted as putative hydroxyacid dehydrogenase or hydroxyacid dehydrogenase homolog in the UniProt database. WT cells were precultured in 3% MM. Each line represents an average of n = 4–7 samples. The data underlying this figure can be found in S2 Data.

(PDF)

Click here for additional data file.^{(644.2KB, pdf)}

S13 Fig. Add-back experiments of HICA and 2K3MVA into conditioned media from deletion mutants for hydroxyacid dehydrogenases.

Cells precultured in 3% MM were inoculated to media conditioned by (A) SPACUNK4.10Δ, (B) SPBC1773.17cΔ, (C) SPCC364. 07Δ, (D) SPAC186.07cΔ, and (E) SPAC186.02Δ mutants. The conditioned media from those cultures were added with HICA or 2K3MVA or both of them. (F) Length of the delay phase τ for each growth curve. A line for each conditioned media represents an average of n = 4–7 samples, and those for conditioned media with the autotoxins represents an average of n = 2. The data underlying this figure can be found in S2 Data.

(PDF)

Click here for additional data file.^{(1.3MB, pdf)}

S14 Fig. Schematic representation of growth curves with delay phase.

a₀ is the OD at time = 0, τ is the time when the OD reaches twice of a₀, and r is the growth rate at the steady growth phase. The blue dotted line and orange dashed line are the concentrations of dead and living cells, respectively; thus, the solid magenta line, which is the summation of dead and living cells, is observed as OD.

(EPS)

Click here for additional data file.^{(650.1KB, eps)}

S15 Fig. Measured and estimated death rate in various media.

Open circles represent the death rate measured using phloxine B, as shown in Fig 3. Blue crosses represent the average of each sample. Filled circles represent the estimated death rate as described in Text B in S1 Text. Red crosses represent the average of each sample. The data underlying this figure can be found in S1 and S2 Data.

(EPS)

Click here for additional data file.^{(708.5KB, eps)}

S16 Fig. Flow cytometry gating strategy for identification of the dead cell populations discussed in Fig 3G and 3H.

(A-D) Fission yeast cells were first gated on a FSC/SSC scatter plot as the red P1 gate (left panel). Then, cells gated on the blue and yellow gates were defined as phloxine B-stained dead cells and unstained living cells, respectively (right panel). The total count of each sample is 100,000 counts. Example images of flow cytometry data for cells in (A) 3% MM, (B) 0% MM, (C) WT CM, and (D) 30 mM HICA.

(PDF)

Click here for additional data file.^{(5.6MB, pdf)}

S17 Fig. Discrimination between early apoptosis and early necrosis by using costaining with annexin V (AnnV) and propidium iodide (PI).

(A) AnnV/PI staining of WT cells in various media. Cells were inoculated to 0% MM, WT CM, 0% MM with 20/25/30 mM HICA, and 0% MM with 15/20/25 mM 2K3MVA from 3% MM (0h), and then stained at 2 h and 4 h after the inoculation. The bottom left panel shows the schematic image of the gate setting and their biological annotations for the panel (B). Early apoptotic cells exhibit phosphatidylserine externalization, which is detected by AnnV staining (AnnV+/PI−). Primary necrotic cells show a ruptured plasma membrane, which is detected by PI staining (AnnV−/PI+). Late apoptotic/secondary necrotic cells show both phosphatidylserine externalization and membrane permeability (AnnV+/PI+), while living cells are not stained with both AnnV and PI (AnnV−/PI−). B) Proportion of the cells in each phase. Percentages of cells in the early apoptosis (blue), in the primary necrosis (yellow), in the late apoptosis/secondary necrotic phase (orange), and alive cells (grey) are shown. The data underlying this figure can be found in S2 Data.

(PDF)

Click here for additional data file.^{(1.6MB, pdf)}

S18 Fig. Death rate of synchronous and asynchronous cells.

(A) Death rate of the cdc25-22 strain with and without a transient heat shock in 0% MM with HICA. G2, M/G1, and S cells were transferred to 0% MM with HICA 0, 1, and 2 h after a transient increase in temperature to 36°C. Death rate was measured after 8 h from media change. Asynchronous cells were transferred without a heat shock. The number of biological replicates is n = 2. (B) Flow cytometry analysis of synchronous cells. The first peak indicates the 1c cells, whose DNA amount is that of an interphase cell containing a G2 nucleus or a mitotic cell containing two G1 nuclei. The second peak indicates the 2c cells, whose DNA amount is that a septated cell containing 2 G2 nuclei. The ratio of 1c and 2c cells in each sample is given as: 87.8 ± 2.1%: 12.2 ± 2.1% for G2 cells, 76.0 ± 6.6%: 24.0 ± 6.6% for M/G1 cells, and 66.0 ± 1.7%: 34.0 ± 1.7% for S cells. The data underlying this figure can be found in S2 Data.

(PDF)

Click here for additional data file.^{(772.4KB, pdf)}

S19 Fig. Growth curves of deletion mutant strains of genes encoding putative hydroxyacid dehydrogenase in 0% MM with HICA or 2K3MVA.

The strains in S12 Fig cells were precultured in 3% MM and then shifted to 0% MM, 0% MM with 25, 27.5 mM HICA or 20, 25 mM 2K3MVA. Each line represents an average of n = 2–4 samples. The data underlying this figure can be found in S2 Data.

(PDF)

Click here for additional data file.^{(1.3MB, pdf)}

S20 Fig. Genes up-regulated or down-regulated in the adapted cells.

MA-plot for the log fold change of all genes between cells cultured in 3% MM with 30 mM HICA vs. those in 3%MM (A), and between cells cultured in 3% MM with 25 mM 2K3MVA vs. those in 3% MM (B). Cells precultured in 3% MM were cultured 3% MM with 30 mM HICA, 3% MM with 25 mM 2k3MVAm, and 3% MM for 24 h. Then, a difference in the gene expression was analyzed by RNA-seq. Red points indicate genes with a p-value less than 0.01. The data underlying this figure can be found in S7 Data.

(PDF)

Click here for additional data file.^{(1.1MB, pdf)}

S21 Fig. Growth curves of some deletion mutants that showed the prolongation of the delay phase less than 30 h in 0% MM with 30 mM HICA.

Each line represents an average of n≥4 samples. See Fig 3L for growth curves of the mutants that showed the significant prolongation of the delay phase longer than 30 h. The data underlying this figure can be found in S1 Data.

(PDF)

Click here for additional data file.^{(987.9KB, pdf)}

S22 Fig. Relative changes in gene expression in 3% MM with HICA/2K3MVA or in the WT CM with 3% glucose.

WT cells were precultured in 3% MM and inoculated to 3% MM with 30 mM HICA or 25 mM 2K3MVA and CM with 3% glucose. Expression of genes of which deletion mutants showed the significant prolongation of the delay phase, as shown in Fig 3L, was quantified by RT-PCR at times 0, 1, 4, 8, and 24 h after a change of media. Each line represents an average of n = 2 samples. The data underlying this figure can be found in S2 Data.

(PDF)

Click here for additional data file.^{(445.6KB, pdf)}

S23 Fig. Relative changes in gene expression of survived cells in the CM or media with the autotoxins.

WT cells were precultured in 3% MM and inoculated to WT MC, 0% MM with or without 10, 25 mM HICA, 10, or 20 mM 2K3MVA, and 3% MM with 30 mM HICA or 25 mM 2K3MVA. Expression of genes of which deletion mutants showed the significant prolongation of the delay phase, as shown in Fig 3L, was quantified by RNAseq at times 0, 8, and 24 h after a change of media. Each line represents an average of n = 2 samples. The data underlying this figure can be found in S2 Data.

(PDF)

Click here for additional data file.^{(443.3KB, pdf)}

S24 Fig. Growth curves of the deletion mutants of genes for the adaptation in the 0% MM.

Mutant cells that showed the significant prolongation of the delay phase in Fig 3L were precultured in 0% MM, and their growth was measured in WT CM. Each line represents an average of n = 2 samples. The data underlying this figure can be found in S2 Data.

(PDF)

Click here for additional data file.^{(587.5KB, pdf)}

S25 Fig. Media conditioned with various yeast strains caused the delay phase.

(A) Growth curves of 2 strains of S. cerevisiae (YEA8) in media conditioned by itself. Different colored lines indicate growth curves in CM with different incubation times. Each line represents an average of n≥6 samples. (B) Growth curves of YEA8 in 0% MM with various concentrations of HICA. Each line represents an average of n≥5 samples. (C) Growth curves of YEA8 in 0% MM with various concentrations of 2K3MVA. Each line represents an average of n≥5 samples. (D) Growth curves of YEA8 precultured in 0% MM or 3% MM in CM of OC-2. Each line represents an average of n≥6 samples. (E) Growth curves of YEA8 precultured in 0% MM or 3% MM in 0% MM with 25 mM HICA or 25 mM 2K3MVA. Each line represents an average of n≥6 samples. The data underlying this figure can be found in S2 Data.

(PDF)

Click here for additional data file.^{(843.3KB, pdf)}

S26 Fig. The autotoxins also killed S. cerevisiae cells.

(A and B) Fluorescent (upper) and brightfield (bottom) microscopic images of WT S. cerevisiae strains (A) OC2, (B) YEA8 in various media after 8 h of incubation. Cells precultured in 3% MM were transferred to 3% MM, 0% MM, CM of each strain, 0% MM with 15 mM HICA, and 15 mM 2K3MVA. In fluorescent microscopic images, dead cells were stained with phloxine B. Scale bar indicates 10 μm. (C and D) Box plot of the phloxine B stained cell ratio after 8 h of incubation. S. cerevisiae (C) OC2, (D) YEA8 cells were precultured in 3% MM and shifted to various media. Grey areas represent the interquartile ranges of dyed cell ratio in each sample, and crosses represent the mean value. Fluorescence of over 50,000 cells were measured for each sample using FACS. (n = 2–4) The data underlying this figure can be found in S2, S4, and S5 Data.

(PDF)

Click here for additional data file.^{(889.5KB, pdf)}

S1 Text

Text A-D and Table A-G.

(PDF)

Click here for additional data file.^{(183.4KB, pdf)}

S1 Data. The individual numeric values in Figs 1B, 1C, 1E, 1G–1I, 2D–2I, 3G–3L, 4A–4H, S8A, S8B and S21.

(XLSX)

Click here for additional data file.^{(19.6MB, xlsx)}

S2 Data. The individual numeric values in S1–S5, S9–S13, S15–S19 and S22–S26 Figs.

(XLSX)

Click here for additional data file.^{(13.5MB, xlsx)}

S3 Data. Raw microscopic images of Fig 3.

(PS)

Click here for additional data file.^{(48.7MB, ps)}

S4 Data. Raw microscopic images of S26A Fig.

(PS)

Click here for additional data file.^{(42.3MB, ps)}

S5 Data. Raw microscopic images of S26B Fig.

(PS)

Click here for additional data file.^{(42.1MB, ps)}

S6 Data. Raw VCF files for S6 Fig.

(ZIP)

Click here for additional data file.^{(49.9KB, zip)}

S7 Data. Raw differential genome expression data for S20 Fig.

(ZIP)

Click here for additional data file.^{(700.7KB, zip)}

Acknowledgments

The authors would like to thank H. Nakaoka for his help in preparing S. pombe strains HN98 and HN101. The authors would like to thank C. Furusawa for fruitful research discussion, and E. Takaya for experimental supports. Yeast strain FY7755 was provided by the National Bio-Resource Project (NBRP), Japan.

Abbreviations

AnnV: annexin-V
CE-MS: capillary electrophoresis mass spectrometry
CM: conditioned media
gDNA-seq: genomic DNA sequencing
InDel: insertion–deletion
LC-MS/MS: liquid chromatography-mass spectrometry
MM: minimal media
OD: optical density
PI: propidium iodide
WT: wild-type

Data Availability

All sequence data are available from the DDBJ with DRA BioProject (accession numbers PRJDB11860, PRJDB10422; https://ddbj.nig.ac.jp/resource/bioproject/PRJDB10422 https://ddbj.nig.ac.jp/resource/bioproject/PRJDB11860 All other relevant data are within the paper and its Supporting Information files.

Funding Statement

This work was partially supported by the Ohsumi Frontier Science Foundation (https://www.ofsf.or.jp/), the Basic Science Research Projects of Sumitomo Foundation (http://www.sumitomo.or.jp/e/), Japan Society for the Promotion of Science (JSPS) (https://www.jsps.go.jp/english/) KAKENHI (19K16070) to A.H.O., by JSPS KAKENHI (17H06386) to K.K., by JSPS KAKENHI (20H04862) to T.S.H., by Japan Science and Technology Agency (JST) (https://www.jst.go.jp/EN/) CREST, Japan, Grant Number JPMJCR18S3, Japan Agency for Medical Research and Development (AMED) (https://www.amed.go.jp/en/) Grant Number JP20wm0325003 to K.O. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Takeshige K, Baba M, Tsuboi S, Noda T, Ohsumi Y. Autophagy in yeast demonstrated with proteinase-deficient mutants and conditions for its induction. J Cell Biol. 1992;119(2):301–311. doi: 10.1083/jcb.119.2.301 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Barkai N, Leibler S. Robustness in simple biochemical networks. Nature. 1997;387(6636):913–917. doi: 10.1038/43199 [DOI] [PubMed] [Google Scholar]
3.Nguyen D, Joshi-Datar A, Lepine F, Bauerle E, Olakanmi O, Beer K, et al. Active starvation responses mediate antibiotic tolerance in biofilms and nutrient-limited bacteria. Science. 2011;334(6058):982–986. doi: 10.1126/science.1211037 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Aertsen A, Michiels CW. Stress and how bacteria cope with death and survival. Crit Rev Microbiol. 2004;30(4):263–273. doi: 10.1080/10408410490884757 [DOI] [PubMed] [Google Scholar]
5.Opalek M, Wloch-Salamon D. Aspects of Multicellularity in Saccharomyces cerevisiae Yeast: A Review of Evolutionary and Physiological Mechanisms. Genes. 2020;11(6):690. doi: 10.3390/genes11060690 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Gurvich Y, Leshkowitz D, Barkai N. Dual role of starvation signaling in promoting growth and recovery. PLoS Biol. 2017;15(12):e2002039. doi: 10.1371/journal.pbio.2002039 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Hohmann S, Mager WH., editors. Yeast Stress Responses. Vol. 1. Berlin, Germany: Springer Science & Business Media; 2007. [Google Scholar]
8.Huang WP, Klionsky DJ. Autophagy in yeast: a review of the molecular machinery. Cell Struct Funct. 2002;27(6):409–420. doi: 10.1247/csf.27.409 [DOI] [PubMed] [Google Scholar]
9.Monod J. The growth of bacterial cultures. Annu Rev Microbiol. 1949;3(1):371–394. [Google Scholar]
10.Bren A, Hart Y, Dekel E, Koster D, Alon U. The last generation of bacterial growth in limiting nutrient. BMC Syst Biol. 2013;7(1):1–9. doi: 10.1186/1752-0509-7-27 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Kjelleberg S, editor. Starvation in bacteria. New York: Plenum Press; 1993. [Google Scholar]
12.Cicero MT. De officiis, Book 3, Part 89. transl. Brutus: On the Nature of the Gods. On Divination. On Duties. Chicago, IL: University of Chicago Press; 1950. [Google Scholar]
13.Kurtzman CP, Robnett CJ. Phylogenetic relationships among yeasts of the ‘Saccharomyces complex’ determined from multigene sequence analyses. FEMS Yeast Res. 2003;3(4):417–432. doi: 10.1016/S1567-1356(03)00012-6 [DOI] [PubMed] [Google Scholar]
14.Matsuzawa T, Hara F, Tohda H, Uemura H, Takegawa K. Promotion of glycerol utilization using ethanol and 1-propanol in Schizosaccharomyces pombe. Appl Microbiol Biotechnol. 2012;95(2):441–449. [DOI] [PubMed] [Google Scholar]
15.Hoffman CS, Winston F. Isolation and characterization of mutants constitutive for expression of the fbp1 gene of Schizosaccharomyces pombe. Genetics. 1990;124(4):807–816. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Malecki M, Bitton DA, Rodríguez-López M, Rallis C, Calavia NG, Smith GC, et al. Functional and regulatory profiling of energy metabolism in fission yeast. Genome Biol. 2016;17(1):1–18. doi: 10.1186/s13059-016-1101-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Chambellon E, Rijnen L, Lorquet F, Gitton C, van Hylckama Vlieg JET, et al. The D-2-hydroxyacid dehydrogenase incorrectly annotated PanE is the sole reduction system for branched-chain 2-keto acids in Lactococcus lactis. J Bacteriol. 2009;191(3):873–881. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.The UniProt Consortium. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 2020;49(D1):D480–D489. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Nagai S. Diagnostic Color Differentiation Plates for Hereditary Respiration Deficiency in Yeast. J Bacteriol. 1963;86(2):299–302. doi: 10.1128/jb.86.2.299-302.1963 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Middelhoven WJ, Broekhuizen B, van Eijk J. Detection, with the dye phloxine B, of yeast mutants unable to utilize nitrogenous substances as the sole nitrogen source. J Bacteriol. 1976;128(3):851–852. doi: 10.1128/jb.128.3.851-852.1976 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Noda T. Viability assays to monitor yeast autophagy. Methods Enzymol. 2008;451:27–32. doi: 10.1016/S0076-6879(08)03202-3 [DOI] [PubMed] [Google Scholar]
22.Carmona-Gutierrez D, Eisenberg T, Büttner S, Meisinger C, Kroemer G, Madeo F. Apoptosis in yeast: triggers, pathways, subroutines. Cell Death Differ. 2010;17:763–773. doi: 10.1038/cdd.2009.219 [DOI] [PubMed] [Google Scholar]
23.Yoshida S, Al-Amodi H, Nakamura T, McInerny CJ, Shimoda C. The Schizosaccharomyces pombe cdt2(+) gene, a target of G1-S phase-specific transcription factor complex DSC1, is required for mitotic and premeiotic DNA replication. Genetics. 2003;164(3):881–893. doi: 10.1093/genetics/164.3.881 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Peng X, Karuturi RK, Miller LD, Lin K, Jia Y, Kondu P, et al. Identification of cell cycle-regulated genes in fission yeast. Mol Biol Cell. 2005;3:1026–1042. doi: 10.1091/mbc.e04-04-0299 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Kommineni S, Bretl DJ, Lam V, Chakraborty R, Hayward M, Simpson P, et al. Bacteriocin production augments niche competition by enterococci in the mammalian gastrointestinal tract. Nature. 2015;526(7575):719–722. doi: 10.1038/nature15524 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Gratia A. Sur un remarquable exemple d’antagonisme entre deux souches de coilbacille. CR Seances Soc Biol Fil. 1925;93:1040–1041. [Google Scholar]
27.Bevan EA. The physiological basis of the killer character in yeast. Proc. 11th Int. Cong. Gene. 1963;1:202–203. [Google Scholar]
28.Hara S, Iimura Y, Oyama H, Kozeki T, Kitano K, Otsuka K. The breeding of cryophilic killer wine yeasts. Agric Biol Chem. 1981;45(6):1327–1334. [Google Scholar]
29.Sonneborn TM. Mating types in Paramecium aurelia: diverse conditions for mating in different stocks; occurrence, number and interrelations of the types. Proc Am Philos Soc. 1938;79(3):411–434. [Google Scholar]
30.Riley MA, Wertz JE. Bacteriocins: evolution, ecology, and application. Annu Rev Microbiol. 2002;56(1):117–137. [DOI] [PubMed] [Google Scholar]
31.Kerr B, Riley MA, Feldman MW, Bohannan BJ. Local dispersal promotes biodiversity in a real-life game of rock-paper-scissors. Nature. 2002;418(6894):171–174. doi: 10.1038/nature00823 [DOI] [PubMed] [Google Scholar]
32.Kirkup BC, Riley MA. Antibiotic-mediated antagonism leads to a bacterial game of rock-paper-scissors in vivo. Nature. 2004;428(6981):412–414. doi: 10.1038/nature02429 [DOI] [PubMed] [Google Scholar]
33.Riley MA. Molecular mechanisms of bacteriocin evolution. Annu Rev Genet. 1998;32(1):255–278. doi: 10.1146/annurev.genet.32.1.255 [DOI] [PubMed] [Google Scholar]
34.Hietala PK, Westermarck HW, Jaarma M. Identification of antimicrobial alpha-hydroxyacids in Lactobacillus plantarum-fermented animal protein. Ann Nutr Metab. 1979;23(3):227–234. [DOI] [PubMed] [Google Scholar]
35.Sakko M, Moore C, Novak-Frazer L, Rautemaa V, Sorsa T, Hietala P, et al. Rautemaa, 2-hydroxyisocaproic acid is fungicidal for Candida and Aspergillus species. Mycoses. 2014;57(4):214–221. [DOI] [PubMed] [Google Scholar]
36.Nagy LG, Kovács GM, Krizsán K. Complex multicellularity in fungi: evolutionary convergence, single origin, or both? Biol Rev Camb Philos Soc. 2018;93(4):1778–1794. doi: 10.1111/brv.12418 [DOI] [PubMed] [Google Scholar]
37.Niklas KJ, Newman SA. The origins of multicellular organisms. Evol Dev. 2013;15(1):41–52. doi: 10.1111/ede.12013 [DOI] [PubMed] [Google Scholar]
38.Ameisen JC. On the origin, evolution, and nature of programmed cell death: a timeline of four billion years. Cell Death Differ. 2002;9(4):367–393. doi: 10.1038/sj.cdd.4400950 [DOI] [PubMed] [Google Scholar]
39.Albuquerque P, Casadevall A. Quorum sensing in fungi–a review. Med Mycol. 2012;50(4):337–345. doi: 10.3109/13693786.2011.652201 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Koonin EV, Aravind L. Origin and evolution of eukaryotic apoptosis: the bacterial connection. Cell Death Differ. 2002;9(4):394–404. doi: 10.1038/sj.cdd.4400991 [DOI] [PubMed] [Google Scholar]
41.Claessen D, Rozen DE, Kuipers OP, Søgaard-Andersen L, Van Wezel GP. Bacterial solutions to multicellularity: a tale of biofilms, filaments and fruiting bodies. Nat Rev Microbiol. 2014;12(2):115–124. doi: 10.1038/nrmicro3178 [DOI] [PubMed] [Google Scholar]
42.Ratcliff WC, Denison RF, Borrello M, Travisano M. Experimental evolution of multicellularity. Proc Natl Acad Sci U S A. 2012;109(5):1595–1600. doi: 10.1073/pnas.1115323109 [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Smith JM, Szathmáry E. The major transitions in evolution. Oxford University Press; 1997. [Google Scholar]
44.Soga T, Ohashi Y, Ueno Y, Naraoka H, Tomita M, Nishioka T. Quantitative metabolome analysis using capillary electrophoresis mass spectrometry. J Proteome Res. 2003;2(5):488–494. doi: 10.1021/pr034020m [DOI] [PubMed] [Google Scholar]
45.Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14:417–419. doi: 10.1038/nmeth.4197 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Lock A, Rutherford K, Harris MA, Hayles J, Oliver SG, Bähler J, et al. PomBase 2018: user-driven reimplementation of the fission yeast database provides rapid and intuitive access to diverse, interconnected information. Nucleic Acids Res. 2018;47(D1):D821–D827. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550. doi: 10.1186/s13059-014-0550-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Boyle EI, Weng S, Gollub J, Jin H, Botstein D, Cherry JM, et al. GO::TermFinder–open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes. Bioinformatics. 2004;20(18):3710–3715. doi: 10.1093/bioinformatics/bth456 [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Madeo F, Fröhlich E, Fröhlich KU. A yeast mutant showing diagnostic markers of early and late apoptosis. J Cell Biol. 1997;139(3):729–734. doi: 10.1083/jcb.139.3.729 [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Huard S, Chen M, Burdette K, Fenyvuesvolgyi C, Yu M, Elder RT, et al. HIV-1 Vpr-induced cell death in Schizosaccharomyces pombe is reminiscent of apoptosis. Cell Res. 2008;18:961–973. [DOI] [PubMed] [Google Scholar]
51.Guérin R, Arseneault G, Dumont S, Rokeach LA. Calnexin is involved in apoptosis induced by endoplasmic reticulum stress in the fission yeast. Mol Biol Cell. 2008;10:4404–4420. doi: 10.1091/mbc.e08-02-0188 [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Hagan I, Carr AM, Grallert A, Nurse P. Fission Yeast: A Laboratory Manual. Cold Spring Harbor Laboratory Press; 2016. [Google Scholar]

PLoS Biol. doi: 10.1371/journal.pbio.3001844.r001

Decision Letter 0

Roland G Roberts

22 Mar 2022

Dear Dr Hatakeyama,

Thank you for submitting your manuscript entitled "Autotoxin-mediated voluntary triage in starved yeast community" for consideration as a Research Article by PLOS Biology.

Your manuscript has now been evaluated by the PLOS Biology editorial staff, as well as by an academic editor with relevant expertise, and I'm writing to let you know that we would like to send your submission out for external peer review.

However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.

Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. Once your manuscript has passed the checks it will be sent out for review. To provide the metadata for your submission, please Login to Editorial Manager (https://www.editorialmanager.com/pbiology) within two working days, i.e. by Mar 24 2022 11:59PM.

If your manuscript has been previously reviewed at another journal, PLOS Biology is willing to work with those reviews in order to avoid re-starting the process. Submission of the previous reviews is entirely optional and our ability to use them effectively will depend on the willingness of the previous journal to confirm the content of the reports and share the reviewer identities. Please note that we reserve the right to invite additional reviewers if we consider that additional/independent reviewers are needed, although we aim to avoid this as far as possible. In our experience, working with previous reviews does save time.

If you would like to send previous reviewer reports to us, please email me at rroberts@plos.org to let me know, including the name of the previous journal and the manuscript ID the study was given, as well as attaching a point-by-point response to reviewers that details how you have or plan to address the reviewers' concerns.

During the process of completing your manuscript submission, you will be invited to opt-in to posting your pre-review manuscript as a bioRxiv preprint. Visit http://journals.plos.org/plosbiology/s/preprints for full details. If you consent to posting your current manuscript as a preprint, please upload a single Preprint PDF.

Given the disruptions resulting from the ongoing COVID-19 pandemic, please expect some delays in the editorial process. We apologise in advance for any inconvenience caused and will do our best to minimize impact as far as possible.

Feel free to email us at plosbiology@plos.org if you have any queries relating to your submission.

Kind regards,

Roli Roberts

Roland Roberts

Senior Editor

PLOS Biology

rroberts@plos.org

PLoS Biol. doi: 10.1371/journal.pbio.3001844.r002

Decision Letter 1

Roland G Roberts

6 May 2022

Dear Dr Hatakeyama,

Thank you for your patience while your manuscript "Autotoxin-mediated voluntary triage in starved yeast community" was peer-reviewed at PLOS Biology. It has now been evaluated by the PLOS Biology editors, an Academic Editor with relevant expertise, and by four independent reviewers.

In light of the reviews, which you will find at the end of this email, we would like to invite you to revise the work to thoroughly address the reviewers' reports.

IMPORTANT: You'll see that all of the reviewers are intrigued by the phenomenon that you describe, but each of them has a significant number of requests, some involving further experimental work. We note that some of reviewer #1's requests, while they would strengthen our understanding of the implications of your observations, go somewhat beyond the scope of the current study. However, reviewer #1 also makes the interesting suggestion that your paper might work better as a Short Report; indeed, when invited to cross-comment on the other reviews, reviewer #3 also made this suggestion. We therefore give you two choices:

EITHER: Address all concerns, including all of those raised by reviewer #1, and resubmit your revised version as a full Research Article...

OR: Address all of the concerns of reviewers #2, #3 and #4, plus those of reviewer #1 that are needed to fully support the current claims, and resubmit your revised version as a Short Report. IMPORTANT: If you go down this route, you need to reduce the number of Figures to a maximum of 4 (either by combining the current Figures or moving material to the Supplement); if you cannot change the article type in our system, we can do so on your behalf.

Given the extent of revision needed, we cannot make a decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is likely to be sent for further evaluation by all or a subset of the reviewers.

We expect to receive your revised manuscript within 3 months. Please email us (plosbiology@plos.org) if you have any questions or concerns, or would like to request an extension.

At this stage, your manuscript remains formally under active consideration at our journal; please notify us by email if you do not intend to submit a revision so that we may withdraw it.

**IMPORTANT - SUBMITTING YOUR REVISION**

Your revisions should address the specific points made by each reviewer. Please submit the following files along with your revised manuscript:

1. A 'Response to Reviewers' file - this should detail your responses to the editorial requests, present a point-by-point response to all of the reviewers' comments, and indicate the changes made to the manuscript.

*NOTE: In your point-by-point response to the reviewers, please provide the full context of each review. Do not selectively quote paragraphs or sentences to reply to. The entire set of reviewer comments should be present in full and each specific point should be responded to individually, point by point.

You should also cite any additional relevant literature that has been published since the original submission and mention any additional citations in your response.

2. In addition to a clean copy of the manuscript, please also upload a 'track-changes' version of your manuscript that specifies the edits made. This should be uploaded as a "Revised Article with Changes Highlighted" file type.

*Re-submission Checklist*

When you are ready to resubmit your revised manuscript, please refer to this re-submission checklist: https://plos.io/Biology_Checklist

To submit a revised version of your manuscript, please go to https://www.editorialmanager.com/pbiology/ and log in as an Author. Click the link labelled 'Submissions Needing Revision' where you will find your submission record.

Please make sure to read the following important policies and guidelines while preparing your revision:

*Published Peer Review*

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out. Please see here for more details:

https://blogs.plos.org/plos/2019/05/plos-journals-now-open-for-published-peer-review/

*PLOS Data Policy*

Please note that as a condition of publication PLOS' data policy (http://journals.plos.org/plosbiology/s/data-availability) requires that you make available all data used to draw the conclusions arrived at in your manuscript. If you have not already done so, you must include any data used in your manuscript either in appropriate repositories, within the body of the manuscript, or as supporting information (N.B. this includes any numerical values that were used to generate graphs, histograms etc.). For an example see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5

*Blot and Gel Data Policy*

We require the original, uncropped and minimally adjusted images supporting all blot and gel results reported in an article's figures or Supporting Information files. We will require these files before a manuscript can be accepted so please prepare them now, if you have not already uploaded them. Please carefully read our guidelines for how to prepare and upload this data: https://journals.plos.org/plosbiology/s/figures#loc-blot-and-gel-reporting-requirements

*Protocols deposition*

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Thank you again for your submission to our journal. We hope that our editorial process has been constructive thus far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Roli Roberts

Roland Roberts

Senior Editor

PLOS Biology

rroberts@plos.org

------------------------------------

REVIEWERS' COMMENTS:

Reviewer #1:

In this study titled 'Autotoxin-mediated voluntary triage in starved yeast community', the authors identify a novel phenomenon wherein S. pombe cell populations adapt to glucose starvation by releasing autotoxins. The authors identify two compounds (HICA and 2K3MVA) that putatively act as autotoxins driving this collective cellular phenomenon. Only a fraction of the cells in the population survive as these autotoxins build up. Interestingly, this phenomenon seems to be evolutionarily conserved, even in the distantly related S. cerevisiae cells, suggesting some universality.

This study reports a novel, interesting phenomenon. However, this study definitely overstates many claims, or makes plausible inferences without sufficient data.

Upfront, the highlight of the study is the discovery of this collective phenomenon mediated by produced metabolites that are accessible to all cells, but leading to some cells dying and others selectively surviving. Three major points are unclear: (i) whether such a phenomenon provides any advantage to cells within the population (and what it might mean to a population of clonal cells), (ii) while there is no molecular/mechanistic insight (and let me state that there need not always be molecular/mechanistic insight) into how this phenomenon might be regulated, it is not clear even from the data presented whether the phenomenon is purely a reproducible, stochastic event, or if there are any deterministic features that would determine which cells might be resistant to auto-toxins, or might benefit from these molecules, and (iii) is there a separation within the population into cells that produce the toxins vs others that do not produce toxins/are resistant to toxins/consume toxins. In may ways, this study works better as a 'short report' that highlights this phenomenon, rather than a full article that does not provide mechanistic or conceptual insight into how such a phenomenon might occur.

The following are the major concerns with this study.

Major concerns

1. The authors suggest that the presence of inhibitory compounds and not nutrient depletion is responsible for the delay in growth observed in cells in CM. They show that glucose addition rescues this delay in growth suggesting that the inhibitory effect is absent when glucose is present (fig 2A). However, this experiment does not rule out the possibility that glucose-depletion itself is responsible for the delay in growth after shift to CM. The fbp deletion experiment does not answer this directly. What happens if an alternate 'good' carbon source (eg. glycerol) or an amino acid mix/glutamate is added to the CM? Will the delay in growth be rescued? This experiment will tell us whether the growth delay is independent of all nutrient availability or not. It will also strengthen the observation that the rescue in growth is glucose specific.

2. It is not clear from the data at what point in time do cells in 0% MM start producing HICA and 2K3MVA. Are these present only in CM or do cells which are shifted to CM continue to produce these compounds? Can the amount of these compounds be measured over time to address this?

3. Relatedly, is there a build up and then a decrease in these metabolites? If the compounds only accumulate, then the interpretation is different from if the metabolites accumulate and then start decreasing (suggesting some utilization and/or breakdown), which holds even if the metabolites in the medium flat-line. An additional possibility is that there is a feeder-utilizer type phenomenon that is established, where some cells continue to put out these metabolites, while others do not produce it (and possibly also start utilizing these metabolites). Can the authors establish which of these possibilities exist? If so, this becomes conceptually very interesting (from the perspective of collective cell behavior), regardless of which process occurs.

4. Relatedly, the authors can definitely identify cells that now are able to grow in medium which as these autoinhibitors. Can these cells actually start utilizing/breaking down these metabolites? A possible experiment would be to separate the cells that are now growing in this medium, and then add conditioned medium (exogenously) to these cells, and see if these metabolites are taken up and consumed. This experiment would be quite important in order to understand what is happening with this autoinhibitory system (and interpretations therin).

5. (points 5-7 are related queries) Do the authors have any idea how these compounds (eg. HICA) are produced in cells? Eg. is there any evidence of high branched-chain amino acid catabolism or keto-genesis, or is this just a reflection of an accumulation of BCAAs (which are all ketogenic)? Can, for example, just the addition of leucine/BCAAs after glucose depletion result in this phenomenon of cells dying?

6. Can the production of these compounds be disrupted genetically? Will these mutant cells behave differently from the WT cells in terms of glucose starvation response?

7. Relatedly, in complementary experiments, if the authors keep replacing the medium with glucose depleted medium without the HICA and the keto-acids, will cells in the population now start dying/growing in a similar manner? This relates to the idea of whether it is actually HICA that causes cell death, or is it just the accumulation of leucine that is sufficient for this phenomenon. Leucine is a ketogenic acid, and also can be metabolized to HICA. So this question of whether this phenomenon is a consequence of just high accumulation of leucine, or whether this is truly a produced metabolite specific phenomenon becomes interesting and important. It can also help separate possible signaling roles of leucine vs metabolic roles, that cause this phenomenon.

8. The authors show that addition of glucose prevents the growth delay in cells shifted to CM (Figure 2A). Is this because of upregulating the same set of genes that help the cells cultured in HICA and 2K3MVA to reduce the growth delay (Figure 5A)? Can the authors determine if the major genes identified for adaptation in this study (SPCC1739.08c etc) are upregulated once glucose is added to the CM? This will strengthen the story and also address point number 1.

9. There is no data to establish unambiguously that the adaptation to autotoxins is epigenetic. Can it be just a transcriptional regulation where the adaptive genes are upregulated? Is this an inheritable phenotype (i.e. the resistant cells which grow remain resistant across generations)? This is the classical definition of epigenetic, and if this is not the case, then much of this can be just attributed to stochasticity in gene expression (and therefore heterogeneity). Is there a specific type of gene expression signature (that can come from stochastic events) that will provide resistance to cells?

10. The authors suggest that a few cells in the population adapt to the autotoxins epigenetically, possibly by upregulating genes for detoxification and toxin export. However, it is not clear whether these genes are specifically upregulated only in a few cells in the population. The data presented shows that the cells that are pre-exposed to toxins have these genes upregulated. However, it is possible that all the cells in CM have high expression of these genes, or there is stochasticity in gene expression (see point 7). Can the authors do conclusive experiments to determine if these genes are specifically upregulated in the few cells that have better survival in CM? One possibility is to use a fluorescent reporter under promoters of the major upregulated genes, and correlate the expression of the reporters with the cells that live/die. This would more unambiguously establish the phenomenon and conceptual mechanism that enables the phenomenon.

11. "Thus, the toxin producer is lost to a cheater, which only has the antitoxin system, whereas the cheater loses to cells having neither any toxins nor immunity [27]. In contrast, the voluntary triage does not cost much because the adaptation mechanism is usually offed without the toxin. This suggests that voluntary triage is resistant to cheaters" lines 187-191

In theory, cheaters which have upregulated genes for export and detoxification (the genes identified in this study) can survive the autotoxins without necessarily producing the autotoxins themselves. Can the authors clarify this? This goes back to the earlier question of whether the population actually splits into toxin producers vs non-producers/consumers, vs all cells producing (and sensing) the putative auto-toxins.

Minor points

1. The figures, especially the graphs can be labelled in more detail to help the readers understand the figures. The figures 3 B, C, D, E could be labelled in a better way. Since there are multiple plots in this paper, proper labelling will help the readers understand the figures and graphs better.

2. The title seems incomplete. There is some kind of autotoxin mediated triage….but? Triage leads to what? Triage exists? Is it really voluntary? I hope the authors give this some more thought.

Reviewer #2:

In this article, the authors describe a phenomenon where fission yeast release toxins (identified here using mass spectrometry as primarily leucic acid--2-hydrozyisocaproic acid, HICA and L-2keto-3methyvalerate--2K3MVA) under glucose starvation conditions. The discovery of HICA is interesting, as it has been shown to have antifungal and antimicrobial populations in other microbial studies, including of Lactobacillus. The authors term this toxin release voluntary triage. Cells that adapt to conditioned media (CM) containing the toxins can outcompete unadapted isogenic cells. The authors perform RNA-seq and find evidence that adaptation is due to expression of genes involved in transport (in addition the authors verified with genomic DNA-sequencing that survival in conditioned media was not due to accumulation of mutations). In addition, comparison with two S. cerevisiae strains indicates that these autotoxins and voluntary triage may be a conserved mechanism between distant yeast species.

This is an interesting paper that may have implications for understanding microbial community dynamics, population dynamics, and multicellularity. However, I feel that the paper is underdeveloped and I do not have a good sense for the importance of the autotoxins in dictating population-level behavior (including heterogeneity in cell death) nor whether I should think about toxin production as a regulated process or a chance byproduct of metabolism under glucose-deprived conditions. I have some suggestions that should be addressed prior to publication:

Major Comments:

* The paper is written in a way that seems to imply the release of HICA and 2K3MVA are regulated processes or cellular "decisions", but is this an appropriate way to think about this process? I'm guessing that these molecules are being generated due to catabolism occurring in conjunction with starvation, so should I be thinking of this as anything more sophisticated than yeast having to take less desirable metabolic routes as the environment worsens, leading to the generation of toxic byproducts?

* What is the mechanism of toxicity by the identified autotoxins? Why are the toxins only toxic in the absence of glucose?

* Given that there seems to be the suggestion that toxin production and adaptation prevents mass-suicide (see Intro, Discussion) it would seem important to understand the nature of heterogeneity of entry into the stress-resistant states. The introductory sentences (line 17-22) would seem to suggest that as glucose is depleted cells differentiate into adapted and non-adapted cells, and the non-adapted cells will go on to die. Is there evidence for this? For example, how coherent is expression of the identified genes as cells progress into glucose starvation?

* I find the choice of the term "voluntary triage" confusing. It seems to imply that cells are choosing to make toxic molecules. Or that this is an evolved mechanism.

* How should I understand the similarity between Figure 1B and 1C? Since the fbp1 mutant cannot grow further in glycerol, when you switch to 0% MM are you adding more cells for fbp1 than for WT? Or are the fbp1 cells somehow producing the same amount of toxin over the many hours (up to 30 h) of incubation in 0% MM to produce CM despite presumably growing less in the 0% MM media?

* Can you clarify how the search for the inhibitors was done? The methods say that the 12 were added at 10-40mM. Why was this the relevant concentration range? Particularly, given that later in the paper it looks like HIC and 2K3MVA were added at substantially lower concentrations.

* Its not clear to me that the statements made in line 180-183 are supported by the evidence. What counts as a mass suicide? Is there evidence that more cells would die without the production of the autotoxins? Similarly, the following comments about relative susceptibility to invasion by cheaters (lines 187-190) do not seem supported nor tested in the current work.

Minor Comments:

* Line 15-It is not clear in this context what it means for cells to "tag" themselves. (Also line 67).

* The shorthand for the media (3% MM is MM with both 3% glucose and 3% glycerol) is confusing.

* Supplemental Figure 2: Does the delay phase really look the same? It seems longer without amino acids.

* Figure 2: It is very hard to see some of the lines, especially the light blue line for fbp1CM+GLC (in both panel B and D)

* Line 112: Is the point of this line that the effective concentration of HIC and 2K3MVA required to inhibit cell growth is higher than one would expect given the mass spec data? The meaning of this sentence is not totally clear to me. And when the molecules are combined, how do the necessary inhibitory concentrations compare to the "natural condition"?

* Line 190: What does it mean "the adaptation mechanism is usually offed without the toxin?"

* Line 228-234—these methods are not clear

* Line 254—I think that the strain info is actually in Supplementary Table 5

* Line 266—missing Figure reference

* Why do so many of the growth curves show periodic fluctuations? (e.g. red line Supp Fig 1)

* this be? And secretion does not require growth?

* Line 216—Reference 34 does not seem to be the correct reference here.

Reviewer #3:

In this manuscript, the authors report an interesting phenomenon of autotoxin-mediated killing in yeasts. They show that switching the carbon source from glucose to glycerol results in the secretion of molecules that can kill yeast cells not previously exposed to the glycerol medium. This novel discovery opens an unexpected avenue into understanding intercellular communications in unicellular organisms.

I have the following suggestions for the authors to consider.

Major point

I am not fully convinced that HICA and 2K3MVA are the major components of autotoxins. The authors should provide measurements or estimation of the concentrations of HICA and 2K3MVA in the conditioned media as this information is important for interpreting the experiments performed using synthetic compounds. In addition, the authors did not rule out secreted proteins in the conditioned media as contributing factors. I suggest the authors test whether denaturing proteins by heating affects the cell-killing activity of conditioned media.

Minor points

(1) The term "voluntary triage"

I think the term "voluntary triage" gives an impression that a fraction of cells shifted to the glycerol medium are killed by autotoxins. But this is not be the case. Rather, cells that can be killed by autotoxins are naive "latecomer" cells not previously exposed to the glycerol medium. I suggest the authors consider using a different term, for example, "latecomer killing", to more accurately describe the phenomenon.

(2) The words "starved", "starvation", and "starving"

Title

"Autotoxin-mediated voluntary triage in starved yeast community"

Abstract

"we demonstrate an elaborate response of the yeast community against glucose starvation, named the voluntary triage"

"Glucose starvation" usually refers to shifting cells from glucose-containing medium to a medium lacking any carbon source. In this study, the condition used to induce autotoxins is shifting cells from glucose-containing medium to glycerol-containing medium. I think it is not appropriate to call this treatment "glucose starvation".

The authors may want to consider changing "starved yeast community" in the title to "yeast community adapting to a poor carbon source".

For the above sentence in the abstract, I suggest changing it to "we demonstrate an elaborate response of the yeast community to a shift to a poor carbon source and name this response the voluntary triage".

Elsewhere in the manuscript, the words "starved", "starvation", and "starving" also need to be re-considered.

(3) Are cells producing autotoxins sensitive to autotoxins?

Abstract

"yeast cells release autotoxins, such as leucic acid and L-2keto-3methylvalerate, which can even kill the cells producing them"

It is not clear to me how the authors can conclude that autotoxins kill the cells producing them. I think it is likely that cells producing autotoxins are resistant to killing.

(4) How are conditioned media prepared?

The following description is insufficient.

"Cells were precultured in 3% MM and transferred to 0% MM and cultured. The supernatants were then filtered using a 0.22 μm PVDF membrane (Millex Sterile Filter Unit, Merck Millipore, Burlington, MA, USA)."

The authors should provide more details, including the starting OD600 in 0% MM and the OD600 at the 30 h time point. Because fbp1Δ cells do not grow in 0% MM, I wonder whether the authors might have used different starting OD600 for WT and fbp1Δ.

(5) The sources of synthetic autotoxin compounds

The authors should provide information on the sources from which enantiomers of HICA and 2K3MVA were obtained or how they were synthesized.

(6) Are genes important for adaptation to HICA also important for acquiring resistance in 0% MM?

It is useful to test whether genes such as SPAC57A7.05Δ are important for acquiring the ability to resist to conditioned media through preculturing in 0% MM.

(7) Are S. cerevisiae killed by auxotoxins?

I suggest the authors examine whether S. cerevisiae are killed by auxotoxins, for example, by performing a vital dye staining.

(8) A typo

Page 14

"cDNA libraries were prepared using the NEBNext Ultra II DNA Library Prep Kit 351 for Illumina (New England Biolabs, Inc. Ipswich, MA, USA), following the 352 manufacturer's instructions. cDNA libraries were sequenced using the Illumina 353 paired-end technology on MiSeq with MiSeq Reagent Kit v3"

In the above two sentences, I think "cDNA libraries" should be "genomic DNA libraries".

(9) How are HICA and 2K3MVA synthesized in the cells?

The authors should discuss how HICA and 2K3MVA may be synthesized in yeast cells.

Reviewer #4:

The manuscript describes results of extensive studies examining the phenomenon of voluntary triage, an yeast response to glucose starvation. This response involves the production of autotoxins which serve double functions: while a subset of cells is killed by these autotoxins, the remaining cells adapt to their presence epigenetically and can grow efficiently under conditions of glucose starvation.

The authors find convincing evidence for this phenomenon in the related yeast S. cerevisiae and argue for a conserved mechanism. This is a well done and solid study for which I only have a few suggestions to improve the quality of the manuscript.

1. One major aspect of the study that I see lacking is the understanding of why some cells become resistant to HICA and 2K3MVA while others do not. And whether the fate of these subpopulations is related in any way to their ability to produce these chemicals. So would cells that produce more HICA/2K3MVA are also more resistant to them. This could be tested by testing the MIC (minimum inhibitory concentration) of mutants defective in the production of HICA/2K3MVA.

2. How specific is this effect to glucose? It would be interesting to see if the absence of other fermentative sugars would lead to a similar effect (e.g. galactose, fructose, mannose etc).

3. Is the effect due to glucose signaling or glucose metabolism? This can be easily tested using 2-DOG.

4. I found the discussion on multicellularity highly speculative and somewhat unrelated to the topic. Instead, I suggest the authors allocate more space to discussing population heterogeneity and the possible reasons for why some cells are killed by or adapt to the autotoxins and the evolutionary implications of this type of selection.

PLoS Biol. 2022 Nov 7;20(11):e3001844. doi: 10.1371/journal.pbio.3001844.r003

Author response to Decision Letter 1

6 Aug 2022

Attachment

Submitted filename: 220804_response.pdf

Click here for additional data file.^{(258.7KB, pdf)}

PLoS Biol. doi: 10.1371/journal.pbio.3001844.r004

Decision Letter 2

Roland G Roberts

9 Sep 2022

Dear Dr Hatakeyama,

Thank you for your patience while we considered your revised manuscript "Autotoxin-mediated latecomer killing in yeast community during glucose depletion" for publication as a Short Reports at PLOS Biology. This revised version of your manuscript has been evaluated by the PLOS Biology editors, the Academic Editor, and the original reviewers.

Based on the reviews, we are likely to accept this manuscript for publication, provided you satisfactorily address the remaining points raised by the reviewers. Please also make sure to address the following data and other policy-related requests.

IMPORTANT: Please attend to the following:

a) Please address the remaining requests from the reviewers.

b) Note that reviewers #1 and #2 both feel that some of your claims are "oversold" - please go through your manuscript carefully and ensure that your claims are commensurate with the supporting evidence.

c) Reviewer #1 also draws attention to some grammatical errors; we tend to agree, and recommend that you ask a native English-speaking colleague to read through your manuscript and correct these.

d) We recommend that you truncate your title to: "Autotoxin-mediated latecomer killing in yeast communities."

e) I can't find your supplementary Figures in the current version of the manuscript; please ensure that you include these!

f) Please address my Data Policy requests below; specifically, we need you to supply the numerical values underlying Figs 1BCEGHI, 2BCDEFGHI, 3GHIJKL, 4ABCDEFGH and all of the Supplementary Figures, either as a supplementary data file or as a permanent DOI’d deposition.

g) Please cite the location of the data clearly in all relevant main and supplementary Figure legends, e.g. “The data underlying this Figure can be found in S1 Data” or “The data underlying this Figure can be found in https://zenodoXXXXX”

As you address these items, please take this last chance to review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the cover letter that accompanies your revised manuscript.

We expect to receive your revised manuscript within two weeks.

To submit your revision, please go to https://www.editorialmanager.com/pbiology/ and log in as an Author. Click the link labelled 'Submissions Needing Revision' to find your submission record. Your revised submission must include the following:

- a cover letter that should detail your responses to any editorial requests, if applicable, and whether changes have been made to the reference list

- a Response to Reviewers file that provides a detailed response to the reviewers' comments (if applicable)

- a track-changes file indicating any changes that you have made to the manuscript.

NOTE: If Supporting Information files are included with your article, note that these are not copyedited and will be published as they are submitted. Please ensure that these files are legible and of high quality (at least 300 dpi) in an easily accessible file format. For this reason, please be aware that any references listed in an SI file will not be indexed. For more information, see our Supporting Information guidelines:

https://journals.plos.org/plosbiology/s/supporting-information

*Published Peer Review History*

Please note that you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out. Please see here for more details:

https://blogs.plos.org/plos/2019/05/plos-journals-now-open-for-published-peer-review/

*Press*

Should you, your institution's press office or the journal office choose to press release your paper, please ensure you have opted out of Early Article Posting on the submission form. We ask that you notify us as soon as possible if you or your institution is planning to press release the article.

*Protocols deposition*

Please do not hesitate to contact me should you have any questions.

Sincerely,

Roland

Roland Roberts, PhD

Senior Editor,

rroberts@plos.org,

PLOS Biology

------------------------------------------------------------------------

DATA POLICY:

You may be aware of the PLOS Data Policy, which requires that all data be made available without restriction: http://journals.plos.org/plosbiology/s/data-availability. For more information, please also see this editorial: http://dx.doi.org/10.1371/journal.pbio.1001797

Note that we do not require all raw data. Rather, we ask that all individual quantitative observations that underlie the data summarized in the figures and results of your paper be made available in one of the following forms:

1) Supplementary files (e.g., excel). Please ensure that all data files are uploaded as 'Supporting Information' and are invariably referred to (in the manuscript, figure legends, and the Description field when uploading your files) using the following format verbatim: S1 Data, S2 Data, etc. Multiple panels of a single or even several figures can be included as multiple sheets in one excel file that is saved using exactly the following convention: S1_Data.xlsx (using an underscore).

2) Deposition in a publicly available repository. Please also provide the accession code or a reviewer link so that we may view your data before publication.

Regardless of the method selected, please ensure that you provide the individual numerical values that underlie the summary data displayed in the following figure panels as they are essential for readers to assess your analysis and to reproduce it: Figs 1BCEGHI, 2BCDEFGHI, 3GHIJKL, 4ABCDEFGH and all of the Supplementary Figures. NOTE: the numerical data provided should include all replicates AND the way in which the plotted mean and errors were derived (it should not present only the mean/average values).

IMPORTANT: Please also ensure that figure legends in your manuscript include information on where the underlying data can be found, and ensure your supplemental data file/s has a legend.

Please ensure that your Data Statement in the submission system accurately describes where your data can be found.

------------------------------------------------------------------------

SPECIES INDICATED IN THE ABSTRACT?

- Please note that per journal policy, the model system/species studied should be clearly stated in the abstract of your manuscript.

------------------------------------------------------------------------

DATA NOT SHOWN?

- Please note that per journal policy, we do not allow the mention of "data not shown", "personal communication", "manuscript in preparation" or other references to data that is not publicly available or contained within this manuscript. Please either remove mention of these data or provide figures presenting the results and the data underlying the figure(s).

------------------------------------------------------------------------

REVIEWERS' COMMENTS:

Reviewer #1:

[identifies himself as Sunil Laxman]

This version of a manuscript is a substantial improved and more readable version, primarily establishing a biochemical basis for killing cells within a clonal population of yeast. The use of 'latecomer-killing' also seems more appropriate than involuntary triage.

I enjoyed reading this version of the manuscript, and it is very effective as a short report. The manuscript needs a careful proofreading (to remove some minor grammatical errors, as well as slightly difficult to read sentences), but I will not suggest any further experiments.

The last paragraph of the discussion (on the origins of multicellularity) is overly suggestive (especially with the liberal use of 'imply'). Of course, some speculation is encouraged in the discussion, but for example "The cell cycle dependency of cell death (Fig. S18) and the apoptotic pathway activation in a large fraction of dead cells (Fig. S17) also imply a relationship between latecomer killing and multicellularity." is quite optimistic, since it is still not clear how meaningful these data are with respect to say the artificial experiments forming snowflake yeasts. I would recommend a careful toning down of the last paragraph, but without eliminating the speculative element.

Reviewer #2:

In this revision, the authors have converted their paper to a short report focused on the phenomenon of toxin release (identified here using mass spectrometry as primarily leucic acid--2-hydrozyisocaproic acid, HICA and L-2keto-3methyvalerate--2K3MVA) under glucose starvation conditions which they now term "latecomer killing". In addition, they have performed additional important and interesting experiments including measuring HICA and 2K3MVA concentration in conditioned media, assessing additional mutants, assessing additional nutrient add-backs, performing a 2-DOG experiment, analyzing cell death, and determining the effect of the cell cycle on killing.

The manuscript is improved over the previous version and functions better as a short report. Indeed, the observation of the autotoxin phenomenon is extremely interesting and worth reporting. However, although the authors have performed additional experiments questions remain about whether the phenomenon provides a population-level advantage, why there is so much variability in the mechanism of cell death (if being caused by the same autotoxins), and mechanistically what is causing resistance or lack there-of. In addition, while the authors have dialed back some of their language the connections to cheater dynamics and multicellularity are tenuous at best. In addition, there is a tendency to oversell the results (for example, while the cell cycle data is intriguing it does not directly show that it is G0 cells that are resistant and indeed the idea that it is G0 cells also somewhat contradicts that idea that there is a dividing, resistant population.) Furthermore, lines 63-69 suggest that cells are inheriting an adapted state because their growth rate remains constant in CM. Since adapted cells are being inoculated into CM, I am unconvinced of this explanation. Of course, there is continuous pressure for cells to maintain the adapted state. That doesn't mean that they are necessarily remembering it as they divide, particularly in the epigenetic sense (although the authors did remove reference to epigenetics).

This paper reports a very interesting phenomena that is deserving of publication with interesting experiments that hint at potential mechanisms and population-level effects. However, in some places the results are oversold and mechanistic insight is lacking.

Reviewer #3:

The revision has addressed all my concerns. I support the publication of this paper.

Reviewer #4:

The authors have addressed most of the points raised by all 4 reviewers, either through additional experiments or through changes to the text.

Some issues could not be addressed due to the extensive work required which would be greatly beyond the scope of a Short Report.

The results with the different sugars and the cell cycle mutant are interesting and worth pursuing.

PLoS Biol. 2022 Nov 7;20(11):e3001844. doi: 10.1371/journal.pbio.3001844.r005

Author response to Decision Letter 2

20 Sep 2022

Attachment

Submitted filename: response.pdf

Click here for additional data file.^{(38KB, pdf)}

PLoS Biol. doi: 10.1371/journal.pbio.3001844.r006

Decision Letter 3

Roland G Roberts

22 Sep 2022

Dear Dr Hatakeyama,

Thank you for the submission of your revised Short Report "Autotoxin-mediated latecomer killing in yeast community" for publication in PLOS Biology. On behalf of my colleagues and the Academic Editor, Mark Siegal, I'm pleased to say that we can in principle accept your manuscript for publication, provided you address any remaining formatting and reporting issues. These will be detailed in an email you should receive within 2-3 business days from our colleagues in the journal operations team; no action is required from you until then. Please note that we will not be able to formally accept your manuscript and schedule it for publication until you have completed any requested changes.

Note: I have asked my colleagues to include the following request to you: "Thank you for changing the Title. However, please can you change the last word in the Title from 'community' to 'communities,' as previously requested?"

Please take a minute to log into Editorial Manager at http://www.editorialmanager.com/pbiology/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process.

PRESS: We frequently collaborate with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, we would be grateful if they could coordinate with biologypress@plos.org. If you have previously opted in to the early version process, we ask that you notify us immediately of any press plans so that we may opt out on your behalf.

We also ask that you take this opportunity to read our Embargo Policy regarding the discussion, promotion and media coverage of work that is yet to be published by PLOS. As your manuscript is not yet published, it is bound by the conditions of our Embargo Policy. Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. For full details of our Embargo Policy, please visit http://www.plos.org/about/media-inquiries/embargo-policy/.

Thank you again for choosing PLOS Biology for publication and supporting Open Access publishing. We look forward to publishing your study.

Sincerely,

Roli Roberts

Roland G Roberts, PhD, PhD

Senior Editor

PLOS Biology

rroberts@plos.org

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Glycerol is essential for growth in the 0% MM.

(PDF)

Click here for additional data file.^{(2.4MB, pdf)}

S2 Fig. Effect of auxotrophic marker supplements; leucine, adenine, uracil, and histidine are not essential for the delay phase.

(PDF)

Click here for additional data file.^{(461.4KB, pdf)}

S3 Fig. Growth curve of fbp1Δ without glucose.

(PDF)

Click here for additional data file.^{(611.6KB, pdf)}

S4 Fig. Delay in growth in the CM was rescued by sugars but not by glutamate or amino acid mix.

(PDF)

Click here for additional data file.^{(616.2KB, pdf)}

S5 Fig. Growth curves of the cells that survived in WT CM.

(PDF)

Click here for additional data file.^{(327.6KB, pdf)}

S6 Fig. Histograms of quality scores calculated using GATK for each mutation in original WT cells and cells that survived in WT CM.

(PDF)

Click here for additional data file.^{(92.2KB, pdf)}

S7 Fig. Flow cytometry gating strategy for identification of the competition assay discussed in Fig 1H and 1I.

(PDF)

Click here for additional data file.^{(235.1KB, pdf)}

S8 Fig. Release of inhibitory compounds and cell adaptation to them is beneficial for the competition among clonal cells.

(PDF)

Click here for additional data file.^{(698.5KB, pdf)}

S9 Fig. Growth curves of WT cells in the CM treated with autoclave, nucleases, or proteases.

(PDF)

Click here for additional data file.^{(546.1KB, pdf)}

S10 Fig. Growth curves of WT cells in 0% MM with enantiomers of HICA.

(PDF)

Click here for additional data file.^{(654.3KB, pdf)}

S11 Fig. Adding of BCAAs to the 0% MM did not inhibit the growth.

(PDF)

Click here for additional data file.^{(452.5KB, pdf)}

S12 Fig. Growth curves of WT cells in media conditioned by deletion mutant strains of genes encoding putative hydroxyacid dehydrogenase.

(PDF)

Click here for additional data file.^{(644.2KB, pdf)}

S13 Fig. Add-back experiments of HICA and 2K3MVA into conditioned media from deletion mutants for hydroxyacid dehydrogenases.

(PDF)

Click here for additional data file.^{(1.3MB, pdf)}

S14 Fig. Schematic representation of growth curves with delay phase.

(EPS)

Click here for additional data file.^{(650.1KB, eps)}

S15 Fig. Measured and estimated death rate in various media.

(EPS)

Click here for additional data file.^{(708.5KB, eps)}

S16 Fig. Flow cytometry gating strategy for identification of the dead cell populations discussed in Fig 3G and 3H.

(PDF)

Click here for additional data file.^{(5.6MB, pdf)}

S17 Fig. Discrimination between early apoptosis and early necrosis by using costaining with annexin V (AnnV) and propidium iodide (PI).

(PDF)

Click here for additional data file.^{(1.6MB, pdf)}

S18 Fig. Death rate of synchronous and asynchronous cells.

(PDF)

Click here for additional data file.^{(772.4KB, pdf)}

S19 Fig. Growth curves of deletion mutant strains of genes encoding putative hydroxyacid dehydrogenase in 0% MM with HICA or 2K3MVA.

(PDF)

Click here for additional data file.^{(1.3MB, pdf)}

S20 Fig. Genes up-regulated or down-regulated in the adapted cells.

(PDF)

Click here for additional data file.^{(1.1MB, pdf)}

S21 Fig. Growth curves of some deletion mutants that showed the prolongation of the delay phase less than 30 h in 0% MM with 30 mM HICA.

(PDF)

Click here for additional data file.^{(987.9KB, pdf)}

S22 Fig. Relative changes in gene expression in 3% MM with HICA/2K3MVA or in the WT CM with 3% glucose.

(PDF)

Click here for additional data file.^{(445.6KB, pdf)}

S23 Fig. Relative changes in gene expression of survived cells in the CM or media with the autotoxins.

(PDF)

Click here for additional data file.^{(443.3KB, pdf)}

S24 Fig. Growth curves of the deletion mutants of genes for the adaptation in the 0% MM.

(PDF)

Click here for additional data file.^{(587.5KB, pdf)}

S25 Fig. Media conditioned with various yeast strains caused the delay phase.

(PDF)

Click here for additional data file.^{(843.3KB, pdf)}

S26 Fig. The autotoxins also killed S. cerevisiae cells.

(PDF)

Click here for additional data file.^{(889.5KB, pdf)}

S1 Text

Text A-D and Table A-G.

(PDF)

Click here for additional data file.^{(183.4KB, pdf)}

S1 Data. The individual numeric values in Figs 1B, 1C, 1E, 1G–1I, 2D–2I, 3G–3L, 4A–4H, S8A, S8B and S21.

(XLSX)

Click here for additional data file.^{(19.6MB, xlsx)}

S2 Data. The individual numeric values in S1–S5, S9–S13, S15–S19 and S22–S26 Figs.

(XLSX)

Click here for additional data file.^{(13.5MB, xlsx)}

S3 Data. Raw microscopic images of Fig 3.

(PS)

Click here for additional data file.^{(48.7MB, ps)}

S4 Data. Raw microscopic images of S26A Fig.

(PS)

Click here for additional data file.^{(42.3MB, ps)}

S5 Data. Raw microscopic images of S26B Fig.

(PS)

Click here for additional data file.^{(42.1MB, ps)}

S6 Data. Raw VCF files for S6 Fig.

(ZIP)

Click here for additional data file.^{(49.9KB, zip)}

S7 Data. Raw differential genome expression data for S20 Fig.

(ZIP)

Click here for additional data file.^{(700.7KB, zip)}

Attachment

Submitted filename: 220804_response.pdf

Click here for additional data file.^{(258.7KB, pdf)}

Attachment

Submitted filename: response.pdf

Click here for additional data file.^{(38KB, pdf)}

Data Availability Statement

[pbio.3001844.ref001] 1.Takeshige K, Baba M, Tsuboi S, Noda T, Ohsumi Y. Autophagy in yeast demonstrated with proteinase-deficient mutants and conditions for its induction. J Cell Biol. 1992;119(2):301–311. doi: 10.1083/jcb.119.2.301 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pbio.3001844.ref002] 2.Barkai N, Leibler S. Robustness in simple biochemical networks. Nature. 1997;387(6636):913–917. doi: 10.1038/43199 [DOI] [PubMed] [Google Scholar]

[pbio.3001844.ref003] 3.Nguyen D, Joshi-Datar A, Lepine F, Bauerle E, Olakanmi O, Beer K, et al. Active starvation responses mediate antibiotic tolerance in biofilms and nutrient-limited bacteria. Science. 2011;334(6058):982–986. doi: 10.1126/science.1211037 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pbio.3001844.ref004] 4.Aertsen A, Michiels CW. Stress and how bacteria cope with death and survival. Crit Rev Microbiol. 2004;30(4):263–273. doi: 10.1080/10408410490884757 [DOI] [PubMed] [Google Scholar]

[pbio.3001844.ref005] 5.Opalek M, Wloch-Salamon D. Aspects of Multicellularity in Saccharomyces cerevisiae Yeast: A Review of Evolutionary and Physiological Mechanisms. Genes. 2020;11(6):690. doi: 10.3390/genes11060690 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pbio.3001844.ref006] 6.Gurvich Y, Leshkowitz D, Barkai N. Dual role of starvation signaling in promoting growth and recovery. PLoS Biol. 2017;15(12):e2002039. doi: 10.1371/journal.pbio.2002039 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pbio.3001844.ref007] 7.Hohmann S, Mager WH., editors. Yeast Stress Responses. Vol. 1. Berlin, Germany: Springer Science & Business Media; 2007. [Google Scholar]

[pbio.3001844.ref008] 8.Huang WP, Klionsky DJ. Autophagy in yeast: a review of the molecular machinery. Cell Struct Funct. 2002;27(6):409–420. doi: 10.1247/csf.27.409 [DOI] [PubMed] [Google Scholar]

[pbio.3001844.ref009] 9.Monod J. The growth of bacterial cultures. Annu Rev Microbiol. 1949;3(1):371–394. [Google Scholar]

[pbio.3001844.ref010] 10.Bren A, Hart Y, Dekel E, Koster D, Alon U. The last generation of bacterial growth in limiting nutrient. BMC Syst Biol. 2013;7(1):1–9. doi: 10.1186/1752-0509-7-27 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pbio.3001844.ref011] 11.Kjelleberg S, editor. Starvation in bacteria. New York: Plenum Press; 1993. [Google Scholar]

[pbio.3001844.ref012] 12.Cicero MT. De officiis, Book 3, Part 89. transl. Brutus: On the Nature of the Gods. On Divination. On Duties. Chicago, IL: University of Chicago Press; 1950. [Google Scholar]

[pbio.3001844.ref013] 13.Kurtzman CP, Robnett CJ. Phylogenetic relationships among yeasts of the ‘Saccharomyces complex’ determined from multigene sequence analyses. FEMS Yeast Res. 2003;3(4):417–432. doi: 10.1016/S1567-1356(03)00012-6 [DOI] [PubMed] [Google Scholar]

[pbio.3001844.ref014] 14.Matsuzawa T, Hara F, Tohda H, Uemura H, Takegawa K. Promotion of glycerol utilization using ethanol and 1-propanol in Schizosaccharomyces pombe. Appl Microbiol Biotechnol. 2012;95(2):441–449. [DOI] [PubMed] [Google Scholar]

[pbio.3001844.ref015] 15.Hoffman CS, Winston F. Isolation and characterization of mutants constitutive for expression of the fbp1 gene of Schizosaccharomyces pombe. Genetics. 1990;124(4):807–816. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pbio.3001844.ref016] 16.Malecki M, Bitton DA, Rodríguez-López M, Rallis C, Calavia NG, Smith GC, et al. Functional and regulatory profiling of energy metabolism in fission yeast. Genome Biol. 2016;17(1):1–18. doi: 10.1186/s13059-016-1101-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pbio.3001844.ref017] 17.Chambellon E, Rijnen L, Lorquet F, Gitton C, van Hylckama Vlieg JET, et al. The D-2-hydroxyacid dehydrogenase incorrectly annotated PanE is the sole reduction system for branched-chain 2-keto acids in Lactococcus lactis. J Bacteriol. 2009;191(3):873–881. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pbio.3001844.ref018] 18.The UniProt Consortium. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 2020;49(D1):D480–D489. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pbio.3001844.ref019] 19.Nagai S. Diagnostic Color Differentiation Plates for Hereditary Respiration Deficiency in Yeast. J Bacteriol. 1963;86(2):299–302. doi: 10.1128/jb.86.2.299-302.1963 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pbio.3001844.ref020] 20.Middelhoven WJ, Broekhuizen B, van Eijk J. Detection, with the dye phloxine B, of yeast mutants unable to utilize nitrogenous substances as the sole nitrogen source. J Bacteriol. 1976;128(3):851–852. doi: 10.1128/jb.128.3.851-852.1976 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pbio.3001844.ref021] 21.Noda T. Viability assays to monitor yeast autophagy. Methods Enzymol. 2008;451:27–32. doi: 10.1016/S0076-6879(08)03202-3 [DOI] [PubMed] [Google Scholar]

[pbio.3001844.ref022] 22.Carmona-Gutierrez D, Eisenberg T, Büttner S, Meisinger C, Kroemer G, Madeo F. Apoptosis in yeast: triggers, pathways, subroutines. Cell Death Differ. 2010;17:763–773. doi: 10.1038/cdd.2009.219 [DOI] [PubMed] [Google Scholar]

[pbio.3001844.ref023] 23.Yoshida S, Al-Amodi H, Nakamura T, McInerny CJ, Shimoda C. The Schizosaccharomyces pombe cdt2(+) gene, a target of G1-S phase-specific transcription factor complex DSC1, is required for mitotic and premeiotic DNA replication. Genetics. 2003;164(3):881–893. doi: 10.1093/genetics/164.3.881 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pbio.3001844.ref024] 24.Peng X, Karuturi RK, Miller LD, Lin K, Jia Y, Kondu P, et al. Identification of cell cycle-regulated genes in fission yeast. Mol Biol Cell. 2005;3:1026–1042. doi: 10.1091/mbc.e04-04-0299 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pbio.3001844.ref025] 25.Kommineni S, Bretl DJ, Lam V, Chakraborty R, Hayward M, Simpson P, et al. Bacteriocin production augments niche competition by enterococci in the mammalian gastrointestinal tract. Nature. 2015;526(7575):719–722. doi: 10.1038/nature15524 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pbio.3001844.ref026] 26.Gratia A. Sur un remarquable exemple d’antagonisme entre deux souches de coilbacille. CR Seances Soc Biol Fil. 1925;93:1040–1041. [Google Scholar]

[pbio.3001844.ref027] 27.Bevan EA. The physiological basis of the killer character in yeast. Proc. 11th Int. Cong. Gene. 1963;1:202–203. [Google Scholar]

[pbio.3001844.ref028] 28.Hara S, Iimura Y, Oyama H, Kozeki T, Kitano K, Otsuka K. The breeding of cryophilic killer wine yeasts. Agric Biol Chem. 1981;45(6):1327–1334. [Google Scholar]

[pbio.3001844.ref029] 29.Sonneborn TM. Mating types in Paramecium aurelia: diverse conditions for mating in different stocks; occurrence, number and interrelations of the types. Proc Am Philos Soc. 1938;79(3):411–434. [Google Scholar]

[pbio.3001844.ref030] 30.Riley MA, Wertz JE. Bacteriocins: evolution, ecology, and application. Annu Rev Microbiol. 2002;56(1):117–137. [DOI] [PubMed] [Google Scholar]

[pbio.3001844.ref031] 31.Kerr B, Riley MA, Feldman MW, Bohannan BJ. Local dispersal promotes biodiversity in a real-life game of rock-paper-scissors. Nature. 2002;418(6894):171–174. doi: 10.1038/nature00823 [DOI] [PubMed] [Google Scholar]

[pbio.3001844.ref032] 32.Kirkup BC, Riley MA. Antibiotic-mediated antagonism leads to a bacterial game of rock-paper-scissors in vivo. Nature. 2004;428(6981):412–414. doi: 10.1038/nature02429 [DOI] [PubMed] [Google Scholar]

[pbio.3001844.ref033] 33.Riley MA. Molecular mechanisms of bacteriocin evolution. Annu Rev Genet. 1998;32(1):255–278. doi: 10.1146/annurev.genet.32.1.255 [DOI] [PubMed] [Google Scholar]

[pbio.3001844.ref034] 34.Hietala PK, Westermarck HW, Jaarma M. Identification of antimicrobial alpha-hydroxyacids in Lactobacillus plantarum-fermented animal protein. Ann Nutr Metab. 1979;23(3):227–234. [DOI] [PubMed] [Google Scholar]

[pbio.3001844.ref035] 35.Sakko M, Moore C, Novak-Frazer L, Rautemaa V, Sorsa T, Hietala P, et al. Rautemaa, 2-hydroxyisocaproic acid is fungicidal for Candida and Aspergillus species. Mycoses. 2014;57(4):214–221. [DOI] [PubMed] [Google Scholar]

[pbio.3001844.ref036] 36.Nagy LG, Kovács GM, Krizsán K. Complex multicellularity in fungi: evolutionary convergence, single origin, or both? Biol Rev Camb Philos Soc. 2018;93(4):1778–1794. doi: 10.1111/brv.12418 [DOI] [PubMed] [Google Scholar]

[pbio.3001844.ref037] 37.Niklas KJ, Newman SA. The origins of multicellular organisms. Evol Dev. 2013;15(1):41–52. doi: 10.1111/ede.12013 [DOI] [PubMed] [Google Scholar]

[pbio.3001844.ref038] 38.Ameisen JC. On the origin, evolution, and nature of programmed cell death: a timeline of four billion years. Cell Death Differ. 2002;9(4):367–393. doi: 10.1038/sj.cdd.4400950 [DOI] [PubMed] [Google Scholar]

[pbio.3001844.ref039] 39.Albuquerque P, Casadevall A. Quorum sensing in fungi–a review. Med Mycol. 2012;50(4):337–345. doi: 10.3109/13693786.2011.652201 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pbio.3001844.ref040] 40.Koonin EV, Aravind L. Origin and evolution of eukaryotic apoptosis: the bacterial connection. Cell Death Differ. 2002;9(4):394–404. doi: 10.1038/sj.cdd.4400991 [DOI] [PubMed] [Google Scholar]

[pbio.3001844.ref041] 41.Claessen D, Rozen DE, Kuipers OP, Søgaard-Andersen L, Van Wezel GP. Bacterial solutions to multicellularity: a tale of biofilms, filaments and fruiting bodies. Nat Rev Microbiol. 2014;12(2):115–124. doi: 10.1038/nrmicro3178 [DOI] [PubMed] [Google Scholar]

[pbio.3001844.ref042] 42.Ratcliff WC, Denison RF, Borrello M, Travisano M. Experimental evolution of multicellularity. Proc Natl Acad Sci U S A. 2012;109(5):1595–1600. doi: 10.1073/pnas.1115323109 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pbio.3001844.ref043] 43.Smith JM, Szathmáry E. The major transitions in evolution. Oxford University Press; 1997. [Google Scholar]

[pbio.3001844.ref044] 44.Soga T, Ohashi Y, Ueno Y, Naraoka H, Tomita M, Nishioka T. Quantitative metabolome analysis using capillary electrophoresis mass spectrometry. J Proteome Res. 2003;2(5):488–494. doi: 10.1021/pr034020m [DOI] [PubMed] [Google Scholar]

[pbio.3001844.ref045] 45.Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14:417–419. doi: 10.1038/nmeth.4197 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pbio.3001844.ref046] 46.Lock A, Rutherford K, Harris MA, Hayles J, Oliver SG, Bähler J, et al. PomBase 2018: user-driven reimplementation of the fission yeast database provides rapid and intuitive access to diverse, interconnected information. Nucleic Acids Res. 2018;47(D1):D821–D827. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pbio.3001844.ref047] 47.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550. doi: 10.1186/s13059-014-0550-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pbio.3001844.ref048] 48.Boyle EI, Weng S, Gollub J, Jin H, Botstein D, Cherry JM, et al. GO::TermFinder–open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes. Bioinformatics. 2004;20(18):3710–3715. doi: 10.1093/bioinformatics/bth456 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pbio.3001844.ref049] 49.Madeo F, Fröhlich E, Fröhlich KU. A yeast mutant showing diagnostic markers of early and late apoptosis. J Cell Biol. 1997;139(3):729–734. doi: 10.1083/jcb.139.3.729 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pbio.3001844.ref050] 50.Huard S, Chen M, Burdette K, Fenyvuesvolgyi C, Yu M, Elder RT, et al. HIV-1 Vpr-induced cell death in Schizosaccharomyces pombe is reminiscent of apoptosis. Cell Res. 2008;18:961–973. [DOI] [PubMed] [Google Scholar]

[pbio.3001844.ref051] 51.Guérin R, Arseneault G, Dumont S, Rokeach LA. Calnexin is involved in apoptosis induced by endoplasmic reticulum stress in the fission yeast. Mol Biol Cell. 2008;10:4404–4420. doi: 10.1091/mbc.e08-02-0188 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pbio.3001844.ref052] 52.Hagan I, Carr AM, Grallert A, Nurse P. Fission Yeast: A Laboratory Manual. Cold Spring Harbor Laboratory Press; 2016. [Google Scholar]

PERMALINK

Autotoxin-mediated latecomer killing in yeast communities

Arisa H Oda

Miki Tamura

Kunihiko Kaneko

Kunihiro Ohta

Tetsuhiro S Hatakeyama

Roles

Abstract

Introduction

Results

Fig 1. Conditioned media caused a delay phase of cell growth and latecomer killing during glucose depletion.

Fig 2. Identification of growth inhibitors.

Fig 3. Identified molecules kill cells and also facilitate cell adaptation to the molecule and deletion of some genes up-regulated in adapted cells prolonged the delay phase.

Fig 4. Media conditioned with various strains of yeasts initiated the delay phase.

Discussion

Materials and methods

Continuous measurement of growth

Yeast strains and culture conditions

Preparation of conditioned media

Flow cytometry analysis for competition assays

Capillary electrophoresis time-of-flight mass spectrometry

Search for growth inhibitors

HICA and 2K3MVA quantification by a liquid chromatography-mass spectrometry

RNA sequencing and gene expression analysis by RNA sequencing

Gene expression analysis using reverse transcriptase polymerase chain reaction

Flow cytometry analysis to measure the dead cell ratio

Microscopy and imaging analysis

Costaining of annexin V-FITC and propidium iodide

Death rate analysis of synchronous culture of cdc25-22

Measurement of surviving cell growth in conditioned media

Genome sequencing of surviving cells in conditioned media

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Roland G Roberts

Roles

Decision Letter 1

Roland G Roberts

Roles

Author response to Decision Letter 1

Decision Letter 2

Roland G Roberts

Roles

Author response to Decision Letter 2

Decision Letter 3

Roland G Roberts

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases