Slower-growing species promote interspecific cooperation and coexistence under acid stress through cross-feeding

Domain: biology
Published: 2025-12-14
Editorial outcome: major_revision

Abstract
Acid stress is a central environmental factor shaping the structure and function of microbial communities worldwide. However, there is a lack of predictive understanding of how microbial communities respond physiologically and metabolically to acid stress. Here, we find that higher acid stress favors slower-growing species, promoting population growth and coexistence. Our experiments show that acid stress influences the spatial structure of communities, wherein coexistence is ordered over centimeter-length scales and determined by growth-tolerance trade-offs. We find that interspecific interactions are highly dynamic during acid stress changes, with shifts from competition to cooperation, enhancing resilience under high-stress intensities. Slower-growing species may bolster interspecific coexistence through stress-dependent excretion and cross-feeding of public goods. We construct a resource-consumer-based mathematical model to unravel the processes experienced by species in stress-induced coexistence and their distinct physiological states. Finally, our pairwise bacterial-fungal interaction experiments elucidate universalities in stress-induced coexistence between closely related and phylogenetically distant species with complementary phenotypic profiles. Overall, our work provides insights into how acid stress affects physiological and metabolic responses, as well as overall fitness, resilience, and coexistence.

Introduction
Microbial communities are ubiquitous in nature, shaped by intricate interactions among cohabitating species and their environments1–3. Synergistic interactions between different species perform advanced functions that surpass the capabilities of individual species alone4–6, such as food fermentation7,8, pollutant degradation9,10, and element cycling11. Understanding these interactions will help us decode the operating principles that support and sustain the stability and sustainability of microbial communities. Acid stress is a key environmental variable, shaping microbial community composition7,12. However, aside from the direct effects of acid stress itself, scant attention has been paid to how acid stress changes affect microbial dynamics, including colonization, growth, metabolism, and interactions among cohabitating species and their environments1–3. As global acidification intensifies, altering microbial community structure and stability13,14, it is of paramount importance to untangle the physiological and metabolic responses of microbial communities to environmental acidification and to determine whether there are general principles that can be used to explain these changes.

Although the specific effects of acid stress on microbial communities’ physiological and metabolic processes remain unclear, many investigations of culture-independent omics data in recent years can be used to characterize communities affected by acid stress. In the oceans, for example, profound cyclic variations in community structure are associated with seasonal and long-term acidity changes15,16. Variation in acid stress also drives the quality dynamics of microbially fermented foods, as both microbial diversity and metabolic function shift with varying acid stress7,17. Changes in bacterial community structure and function have also been observed in soils subject to acid stress changes12,18, with potentially important implications for biochemical cycling in a globally acidified environment. Moreover, in the animal gut, acid stress changes can lead to alterations in the structure and function of the gut/host microbiome, thereby affecting health and mood19,20. Thus, acid stress significantly influences ecological processes that control community structure and function.

One mechanism by which environmental change affects community structure and function is through regulating the types of interactions between microorganisms (e.g., competition, cooperation, etc.)21,22. These changes can alter the spatial arrangement of species in the community23–26. Importantly, these arrangements are determinants of community-level properties and specific behaviors, such as the metabolic processes of the microbial community, the resistance of the microbial community to invasive strains, its resilience to environmental stresses, and the evolution of microbial interactions27–30. Nevertheless, competitive interactions remain the dominant interaction type observed in studies27–30. Without a broad understanding of the physiological and metabolic responses of species within the community, it is impossible to determine whether species and communities can be favored in the face of environmental change, especially increasing environmental stress. In particular, few reports have specifically examined the effects of acid stress changes on the outcome of cooperation between microbial species. A small number of studies of synthetic communities and mathematical modeling have shown that microbial communities may cooperate and co-exist when enhanced environmental stress causes another species’ decline23,29. However, the universality of these findings is limited without taking into consideration dynamic feedbacks23,30, making it difficult to assess the natural behaviors of microbial communities. Little is known about how acid stress changes affect colonization, growth, metabolism, and interactions of free-living microbial communities and whether increased stress promotes microbial cooperation.

In this study, we investigate how lactate, produced by lactic acid bacteria (LAB), serves as a prevalent, natural acid stressor31,32. We simulate acid stress changes in many environments, ranging from gut and fermented substrates to soil and oceans, by lactate diffusion over time on multi-microwell agar plates, thus constructing dynamic feedbacks involving interactions within and between species, and with the environment. Specifically, this study systematically examines the growth dynamics, interaction, and stress-tolerant mechanisms of free-living co-cultured communities in response to acid stress using physiology, metabolomics, phenotypic analysis, and mathematical modeling. Our results reveal how the ecological outcomes of LAB communities with different growth rates are affected by acid stress changes, demonstrating that species with slower growth rates are favored under increased acid stress. We also explore lactate stress as a significant driver for interaction variability and metabolic excretion, elucidating that cross-feeding public goods promotes interspecies cooperation and coexistence. Finally, we report experimental results on the interactions between LAB and yeast, confirming the generality of this synergistic stress-tolerant mechanism. In summary, our study demonstrates that free-living microbial communities can respond to changing acid stress by altering physiology and metabolism to promote community-level cooperation and stress tolerance.

Results
As a system to study the spatial effect of acid stress on the colonization, growth, and interaction of LAB clones
To determine how complex environments affect the behavior and dynamics of free-living microbiota, we simulated a porous micromodel through solid agar media, including complex physical structures similar to those commonly found in fermentation substrates, intestines, soils, and sediments24,33. Using this porous micromodel as a basis, we constructed a system of acid stress changes where lactate diffuses through the physical structure, leading to dynamic changes in acid stress (Fig. 1A). To further simulate natural acid stress changes, we inoculated 2μL of the lactate producer at the center of chemically defined medium agar plates containing 2% glucose (CDMG) and an acidic chromogenic agent (see Methods, Fig. 1B). The results revealed that coloration commenced 10 hours post-inoculation, marking the onset of lactate spatial diffusion. Subsequent assessments at 14, 24, 32, and 40h indicated diffusion distances of 0.63±0.03, 1.67±0.04, 2.64±0.02, and 4.58±0.05 cm, respectively (Fig. 1B). Therefore, based on the spatial-temporal diffusion of lactate in agar plates, we could use this system to investigate microbial colonization, growth, and interactions during acid stress changes.

This study started with the colonization characterization of individual LAB clones, Leuconostoc mesenteroides C7M43 (LM), Leuconostoc citreum 4367 (LC4), and Leuconostoc citreum 200443 (LC2), isolated from fermented foods, in a stress-changed system containing CDMG agar (Fig. 1C and Table S2). After 1.5-2 days of growth at 30.0°C, all three naturally cultured LAB clones exhibited distinct spatial position dependencies, with higher local densities observed at the edges (Fig. 1C), contrasting sharply with the uniform distribution observed in other characterized strains34. We assessed the growth and acid-producing properties of populations located at the edges and center of the LAB clones (Fig. 1D). When inoculated in a CDMG liquid medium, populations were characterized by growth and acid-producing rates with distinct dependencies on the selection position, with the edge populations of all LAB clones exhibiting ~1.2-fold faster growth rates, but produced acid at a rate ~0.7-fold lower than the central populations (Fig. 1D, E).

To validate the impact of lactate stress on position-dependent colonization, we supplemented the medium with 100 mM 3-(N-morpholino)propanesulfonic acid (MOPS) to stabilize the acidity of agar. The results showed that upon removal of lactate stress, colonies exhibited a uniform distribution, abolishing position dependence (Fig. 1F). To eliminate the effects of other metabolites, we engineered a stress-silenced LAB strain, LMΔldh, incapable of lactate production (Fig. S1), and observed a similar absence of position dependence (Fig. 1G). The growth and acid production rates of clone populations at different spatial positions were analogous when the lactate stress was eliminated (Fig. 1H, I), starkly contrasting with the phenotypic variations where lactate stress persisted (Fig. 1E). These findings suggested that acid stress underlay position-dependent colonization, influencing the population response to chemical signals from the abiotic environment and regulating their growth and metabolic activities accordingly. In particular, these stress changes may influence the spatial-temporal dynamics of microbial interactions at the community level.

Spatial effects of acid stress on biomass
To elucidate the impact of lactate stress at the community level, we established microbial consortia containing producer LM and competitors using lactate diffusion-induced stress changes as a key factor (Fig. 2A). When other competitor strains (Table S2) were drop-plated at different distances from the lactate producer LM (See Table S1 for the definition of monoculture, Fig. 2B), we observed that all strains were inhibited by LM, exhibiting spatial-distance dependent responses in biomass (Fig. 2C, Comparison of colony growth). Specifically, the relative biomass of competitor strains increased with distance because when lactate was released, it diffused and suppressed nearby growth (Fig. 2D), which has been termed “lactate stress” or more generally “acid stress”. At a distance of 4.5 cm, biomass levels resembled those of plates inoculated with stress-silenced LMΔldh, with a relative biomass of 1 (Fig. 2D), revealing an interaction range from 0.0 to 4.5 cm (Fig. 2C, D).

To further investigate this phenomenon, we labeled the strains using fluorescent proteins, including eGFP, YFP, and mCherry (see “Methods”, Figs. S3A and S3B), and then plotted the transformation curve of the relationship between the obtained colony-forming unit (CFU) and fluorescence intensity (Fig. 2E, Fig. S4, and Table S6). Based on this quantitative relationship, we first evaluated the growth dynamics of binary consortia (See Table S1 for definitions), including a producer LM and Lactococcus lactis CCFM1092 (A01) from the same sample source (Fig. 2F, G). Fluorescence microscopy results revealed that in mixed co-inoculation (LM:A01=1:1) incubated for 33h (Cartoon diagram of Fig. 2F), A01 competitively suppressed the producer LM (Fig. 2F upper panel and 2I) owing to its rapid growth rate (Fig. S5). However, the competitive hierarchy of the consortia reversed in diffusion-based separated inoculations (See Table S1 for the definition of co-culture, Cartoon diagram of Fig. 2G), with LM prevailing over A01 due to lactate stress, yet this stress effect weakened with increasing diffusion distance (Fig. 2G upper panel and 2J). Similarly, fluorescent protein exchange yielded consistent results (Bottom panel of Fig. 2F, G). We further investigated this stress effect by utilizing the co-isolated strain L. lactis CCFM1032 (B01), which shares the same sample origin as A01, to construct different binary consortia (Fig. 2H). The results confirmed that the relative biomass of B01 increased with distance in diffusion-based separated inoculations, whereas the opposite was true for LM, as the stress effect decayed with distance (Fig. 2K). Furthermore, competitors A01 and B01 did not alter the lactate production level of producer LM (Fig. S6), suggesting that only unidirectional interference occurs in the binary consortia.

To confirm that these position-dependent findings in the consortia were also caused by lactate stress, we replicated the monoculture experiments on CDMG agar plates by adding 100 mM MOPS and employing stress-silenced engineered strain LMΔldh to focus on non-lactate stress (Fig. S7A–B). The results indicated that when lactate stress was eliminated, the growth of all strains was not inhibited, with relative biomass approaching 1, regardless of their spatial positions (Fig. S7C). Conversely, to focus on lactate stress, we replicated the inhibition effect of in vitro lactate stress on diffusion-based consortia by replacing lactate producer LM with lactate (Fig. S7D), and the results showed that the growth of competitors was inhibited (Figs. S7E and S7F). Thus, the observed position dependence in the monocultures was also predominantly derived from lactate diffusion-induced stress changes that determine biomass accumulation and distribution. However, the fluorescence images of A01 and B01 were significantly different in monocultures and co-cultures (Figs. S8, S9, and S10), indicating that the observed differences are indeed because of the interactions between the two co-cultured strains, and not merely the result of lactate stress-driven growth dynamics between LM and the other strains.

Growth-tolerance trade-offs induced by acid stress drive community succession and interaction dynamics
To investigate microbial interaction dynamics during acid stress changes, we employed a 2µL mixed suspension (A01: B01=1:1, OD600=0.2–0.3) with distinct growth rates and tolerance abilities (Figs. S11 and S12) and co-cultivated at different distances from the LM to evaluate multispecies microbial colonization in extensively acidified habitats (Fig. 3A). The biomass ratio of the population (fold change R = B01 biomass/A01 biomass) in ternary microbial consortia was calculated by the biomass ratio of B01 to A01 (see Fig. 3A for biomass detection). With an initial strain ratio of 1:1, we found a spatial inflection (λx) at 2.5 cm, which was the spatial distance from the community to producer LM, where the behavior of spatial range expansion flipped (Fig. 3B, C, and Figure S13A left panel). Specifically, the fitness of A01 and B01 at λx was approximately equal, with fold change R approaching 1, while A01 and B01 dominated different spatial regions in both ends of λx, slower-growing A01 (green) dominated in the short-range (<2.5 cm) with its tolerance advantage, while low-tolerance B01 (red) dominated in the long-range (>2.5 cm) with its growth advantage (Fig. 3B–D), revealing the growth-tolerance trade-off pattern of microbial consortia at λx. Lactate stress-based changes in growth rate between two phenotypically different species appeared to explain this pattern (Fig. S12).

However, performing biomass comparisons between monocultures and co-cultures revealed not only the lactate stress effect, but also lactate stress-induced interspecific interactions in the spatial domain that manifested as cooperation close to λx and competition away from λx (Fig. 3B, C). To gain insight into this pattern, we manipulated the initial strain ratio of the A01 and B01 from 1:5 to 5:1 (Fig. S13A right panel). Although this change affected the relative abundance at a given time, it did not alter the growth distribution of A01 and B01 across spatial ranges, while spatial inflexion λx consistently fluctuated around 2.5 cm (Fig. S13A right panel and Fig. 3D), which highlighted that a strain’s fitness at a specific position depends on the presence of interactions and the state of the environment during the colonization of natural communities3,35.

The study on the lactate stress-induced community dynamics revealed unexpected colonization strategies under growth-tolerance trade-offs. To test the ecological significance of growth-tolerance trade-offs in habitat colonization, we employed the co-isolated invasive strain Enterobacter hormaechei 42D12 (EH1, Table S2), a prevalent spoilage bacterium in fermented foods36, to construct quaternary consortia (A01:B01:EH1=1:1:1, OD600=0.2–0.3, see Fig. S14). When still analyzing the fold change R (B01/A01), the consortia also exhibited a spatial inflection λx, and the behavior of range extension still flipped at λx of 2.5 cm, thereby exhibiting a growth-tolerance trade-off (Fig. S13B left panel and S15), as observed in ternary microbial consortia (Figs. 3B, C, and S13A left panel). Furthermore, the inoculation results for both equal and varying initial strain ratios indicated that maintaining the fold change R within 1.0 ± 0.5 was critical for population coexistence, since when deviating from this range, one population in the consortia was rapidly outcompeted and eliminated at all initial frequencies (5:1–1:5), as was the case for B01 below 0.5 or A01 above 1.5 (Fig. S13B right panel and 3E). Importantly, the spatial distances favoring the coexistence of quaternary consortia were also around the relatively stable value of 2.5 cm from the producer LM clone (Fig. 3E, Red frame). Microbial colonization habitats are intuitively perceived as complex and difficult to quantify3,37. Our results indicated that lactate stress-induced spatially structured communities follow a structural paradigm of ordered and stable coexistence over centimeter-long ranges.

To further reveal lactate stress-induced ecological succession in quaternary consortia, the biomass ratio of the population was modified (fold change R' = A/N). The result showed that lactate-induced growth-tolerance trade-offs drove range expansion and stable coexistence (Fig. 3F). Specifically, both competitors (A01 and B01) exhibited superior performance over the invader (EH1) when exposed to enhanced lactate stress near the producer, and one competitor was selected to occupy the habitat (Fig. 3F left). The consortia were then driven to a stable spatial distance (λx ~ 2.5 cm) at which both coexist (Fig. 3F center). However, diminishing lactate stress compromised the suppression of the faster-growing invasive strain EH1 (Fig. S12), leading to the elimination of A01 and B01 at all initial frequencies and subsequent invasion (Fig. 3F right). Interestingly, a comparison of biomass in monoculture and co-culture of quaternary consortia revealed that in addition to the lactate stress effect, A01 and B01 also exhibited competitive interactions at short range (<λx), while their cooperative interactions close to λx enabled them to coexist and resist EH1 invasion (Figs. S15 and S16). Furthermore, lactate supplementation and invasion experiments also demonstrated the existence of not only lactate stress effects but also cooperative interactions (Fig. S17). While previous studies have typically focused on lactate stress-based growth inhibition and one-way interactions3,37,38, our investigation highlighted that lactate diffusion-induced stress not only drove growth-based ecological succession but also influenced interaction dynamics, including competition and cooperation (Figs. 3, S13, and S15). These interaction dynamics were generally related to the intensity of lactate stress, as the intensity of acid stress varies with diffusion distance and time. However, the results observed above were all at a single time point (33 h of incubation), which may lead to differences in the interactions observed at different distances (Figs. 3, S13, and S15). Therefore, these interaction dynamics may be the result of a time series of interaction strengths.

Stress intensity drives microbial communities to shift from competition to cooperation
To test whether the interaction dynamics that emerge depend on acid stress changes and thus on the stress intensity, we hypothesized that this dynamic might be caused by stress-resource-mediated interactions between species and proposed a time-series model of interactions between lactate stress intensity and cross-feeding of public goods (See Table S1 for the definition): the producer LM consumes resources to produce lactate, leading to stress, and based on the stress degree, subsequently stressed species alter the excretion of beneficial metabolic by-products (defined as public goods see Table S1), which contribute to growth and stress tolerance among species (Fig. 4A). The interaction results at different spatial distances indicated that competitive and cooperative interaction variation in pluralistic microbial consortia typically occurred at 2.5 cm (Figs. 3B, C, and S15). Therefore, this distance was chosen to incubate microbial consortia to test the stress-resource-mediated physiological and metabolic responses in the above hypotheses. Firstly, ternary co-cultured microbial consortia 2.5 cm apart on CDMG agar plates were used as test subjects (see Fig. 3A for inoculation method), and quantitative methods for growth at different times and uptake/excretion characteristics extracted from different regions were constructed (see “Methods”, Figs. 4B and S18). The plates were then divided into three square regions (a, b, and c) based on the extraction region (Cartoon diagram of Figs. 4B and S18). The concentrations of lactate and glucose detected by high-performance liquid chromatography (HPLC) showed statistically significant differences across regions (lactate at 24h: region a vs. region b, df = 16, P < 0.05, Cohen’s d = 2.4, 95% CI of diff. = [98.8, 104.8]; region a vs. region c, df = 16, P < 0.05, Cohen’s d = 7.9, 95% CI of diff. = [84.4, 110.1]; region b vs. region c, df = 16, P < 0.05, Cohen’s d = 5.7, 95% CI of diff. = [86.0, 105.7]; glucose at 24h: region a vs. region b, df = 16, P < 0.05, Cohen’s d = 2.1, 95% CI of diff. = [22.8, 26.4]; region a vs. region c, df = 16, P < 0.05, Cohen’s d = 7.6, 95% CI of diff. = [19.1, 39.5]; region b vs. region c, df = 16, P < 0.05, Cohen’s d = 7.1, 95% CI of diff. = [21.6, 38.5]; unpaired two-tailed Student’s t-test; Fig. S19). To better understand the dynamics of the constituents, we used the measurements from the central region (region b) as the reference values to characterize the final concentrations.

In this way, we subsequently monitored each pure culture’s biomass and uptake/excretion characteristics on CDMG agar plates (See Table S1 for the definition of pure culture). LM, A01, and B01 experienced logarithmic growth reaching a stabilization period within 24–27h (Fig. S20A). Carbon-sourced metabolites such as amino acids can be used as public goods to promote cross-feeding between communities29,37,38, and these public goods were selected for potential analysis. Examination of excretion data using HPLC showed only small accumulations of pyruvate, aspartate, and glutamate in pure cultures LM, A01, and B01 (Figs. S20B–S20D). In LM pure culture, glucose was depleted at 27h (Fig. S21A, Orange square) and lactate reached a maximum of 121.7 mM (Fig. S21A, Red circle), significantly lower than 193.4 mM (df = 4, P < 0.05, Cohen’s d = 43.7, 95% CI of diff. = [89.6, 232.9]; unpaired two-tailed Student’s t-test) in well-mixed liquid culture (Fig. S21B), indicating that the lactate released during rapid growth on glucose diffuses directly into the medium leading to habitat acidification (Figs. S21 and 1B). As another test, we monocultured A01 and B01 separately at 2.5 cm to the right of the LM (Left panel of Fig. 4C), and found that slower-growing A01 underwent a logarithmic phase, reached maximum biomass at 30–33h, and then remained stable (Fig. 4D, Blue triangle), metabolically showing an increase in pyruvate, aspartate, and glutamate (Comparing Fig. S22A with S20C); faster-growing B01 also underwent a logarithmic period, reached maximum biomass at 27h, stabilized at 27–30h, and then began to decay (Fig. 4D, Purple triangle), metabolically exhibiting several key metabolites that did not increase (Comparing Fig. S22B with S20D). Moreover, 24h in monoculture was the inflection point of biomass dominance for both species, where biomass dominance flipped at this time (Fig. 4D). Specifically, the faster-growing B01 dominated within the initial 24h, while the slower-growing A01 outcompeted B01 at 24h (Fig. 4D, Blue and purple triangle), a shift attributed to increased stress intensity that inhibited the growth of the faster-growing, less stress-tolerant B01 (Fig. S12).

To determine the dynamic interactions of microbial consortia under lactate stress, a 2µL mixed suspension (OD600=0.2–0.3) containing equal amounts of A01 and B01 was co-cultured at 2.5 cm to the right of the producer LM to analyze the biomass of the two species at different times (Right panel of Fig. 4C). We first monitored the lactate content in agar plates to elucidate lactate stress. The results showed a gradual increase in 0–33h, reaching 68.8 mM at 24h and a maximum of 98.2 mM at 33h (Fig. 4E, Red open circle), and the inconsistency with the time of sugar exhaustion and maximum lactate level in the producer LM at 27h was due to the lag in diffusion time (Fig. S21A and Fig. 4E). When subsequently monitoring the growth dynamics of co-cultures under lactate stress, we found that 24h in co-culture remained the inflection point of biomass dominance for both species (Fig. 4D, Red and orange circles). However, the biomass of both species A01 and B01 in the co-culture was less than that of the monoculture in 0-24h (Fig. 4D), revealing that species also occupy niches through competitive interactions in addition to lactate stress. With increasing lactate stress, A01 and B01 reached maximum biomass at 30h and 24h, respectively, and then entered growth arrest (stable period), which was similar to the trend in the monocultures (Fig. 4D). Interestingly, within 30–33h of further increased lactate stress, there were significant differences in growth kinetics between monocultures and co-cultures, where the biomass of B01 increased significantly in co-cultures but decayed in monocultures (Comparison of biomass in Fig. 4D). Moreover, the biomass of both species A01 and B01 in the co-culture was higher than that of the monoculture in 30–33h (A01: df = 4, P < 0.05, Cohen’s d = 2.4, 95% CI of diff. = [592534084.7, 636534084.7]; B01: df = 4, P < 0.05, Cohen’s d = 12.3, 95% CI of diff. = [370897222.8, 656897222.8]; unpaired two-tailed Student’s t-test), suggesting that cooperative interactions were present under high lactate stress (Fig. 4D). These results suggested that stress intensity drove interaction dynamics that shifted microbial communities from competition to cooperation.

The rapid decline in the biomass of faster-growing, low-tolerance B01 after 30h of monoculture was due to rapid cell death in strong lactate stress (Fig. 4D’s purple triangle and Fig. S12). Death originating from rapidly proliferating species may promote community coexistence in stressful environments39–41. Therefore, the preferential mortality of B01 might be expected to facilitate the coexistence of co-cultured A01 and B01 under high lactate stress. Typically, B01 death-mediated coexistence can strongly bias species proportions in favor of A01 and leave large amounts of unconsumed nutrients remaining39–41. However, our data showed a different result: the biomass of B01 instead increased in 30–33h, with the biomass ratio (fold change R = B01/A01) approaching 1 at 33h (Fig. 4D, Gray shaded region), consistent with the results of biomass ratio observed by single-cell fluorescence microscopy for coexistence at 33h (Fig. 3D, Green open circle). Moreover, the low amount of residual glucose (22.8 mM) at 30h and consumption was significantly reduced over the next 6h to 8.66 mM (Fig. 4E, Blue open square), yet it prompted regeneration of B01 and the coexistence of both A01 and B01 (Fig. 4D), suggesting that there are other mechanisms underlying coexistence in co-culture that are not caused by the preferential death of the rapidly proliferating B01.

Metabolic excretion by the slower-growing A01 favors regrowth of B01 and interspecies coexistence
To determine the reasons behind the B01 regrowth and the mechanistic basis of coexistence during the late growth phase, we further analyzed the co-culture consortia’s uptake/excretion properties. HPLC detection of the medium showed that other pivotal metabolites, including pyruvate, glutamate, and aspartate, also accumulated to high concentrations (Fig. 4F, Gray shaded area) when the lactate content was near maximum at 30h (Fig. 4E, Red open circle). The subsequent disappearance of these metabolites coincided with the recovery phase of B01 growth (Fig. 4D, F, Red arrow), while the density of A01 remained constant (Fig. 4D, Red circles). During its increase, the growth rate of B01 was significantly higher than when using lactate alone (Fig. 4G, Orange circles and blue triangles), slightly higher than that of B01 in a mixture of equal amounts of lactate, pyruvate, glutamate, and aspartate (Fig. 4G, Purple boxes), indicating that these additional metabolic by-products, namely public goods, may contribute to the B01 regrowth in lactate stress. The consumption of these public goods also explained the differences in their concentrations in B01 pure and monocultures (Figs. S20D and S22B), as well as in A01 monocultures and their co-culture with B01 (Figs. S22A and 4F). Despite being short-lived (approximately 3h), the increase of B01 was a key phase in co-culture dynamics, with its density increase of 1.16 × 10^8 for 126.19% of the 30-h biomass (Fig. 4F, Orange circles).

To understand how B01 successfully regrew at high lactate concentrations, we first prepared spent media from the agar plate of A01 monoculture and performed re-culturing of B01 (Schematic of Fig. S23). The results showed that B01 could grow in the spent medium (Fig. S23B, Blue circle), accompanied by a reduction of these public goods in the spent medium (Fig. S23C). B01 was subsequently monocultured in 100 mM and 140 mM lactate-acidified CDMG medium without and with supplements, including 6.0 mM pyruvate, 2.7 mM glutamate, and 2.0 mM aspartate (see “Methods” and Fig. 5A), the public goods which depleted significantly during the regrowth period (Fig. 4F). B01 grew only in the presence of supplements, which was attributed to the uptake of these public goods (Fig. 5B, C). The growth dynamics of B01 were monitored again using supplemental experiments in which different concentrations of public goods were added to 100 mM lactate-acidified CDMG medium. In this case, B01 biomass increased with increasing concentrations of the supplements (Fig. 5D). However, when these public goods productions were inhibited using the inhibitor 3-bromopyruvate (Fig. S24), a significant reduction in biomass was observed, and B01 regrowth was absent in co-culture growth dynamics (Comparing Figs. 5E with 4D), despite a small consumption of public goods (Fig. 5F). In addition, when the spent medium was prepared using A01 monocultured medium supplemented with inhibitor and then re-cultured with B01, B01 growth was reduced in the spent medium supplemented with the inhibitor compared to the spent medium not supplemented with the inhibitor (Fig. S25, Orange squares and red circles), which stemmed from the lack of these public goods in this spent medium, as supplementing public goods restored B01 growth to levels similar to those in spent medium without inhibitors (Fig. S25, Blue triangles and red circles). Thus, the accumulation and feeding of these public goods promoted the growth of B01 in high acid stress.

To elucidate the sources of these public goods, we maintained slower-growing A01 monocultures under lactate inhibition with a 100 mM lactate concentration to capture conditions during the lactate peak in a co-culture stable distance. Under these high lactate conditions, A01 could absorb glucose (Fig. 5G, Purple circles) and grow slowly (Fig. 5G, Orange circles), while aspartate, pyruvate, and glutamate accumulated (Fig. 5H), contrasting sharply with A01 monoculture without added lactate (Fig. S20C). The additional public goods were not mainly from dead/lysed cells, as A01 viability did not decreased during their accumulation (Fig. 5G, Orange circles). Importantly, the amount of carbon released (~18.5 mM C atoms in total within 6, including 1.2 mM aspartate, 4.3 mM pyruvate, and 2.2 mM glutamate) accounted for 60% of glucose consumption (5.2 mM) (Fig. 5H). Thus, under high lactate stress, A01 cells actively converted glucose into these public goods and excreted them, which B01 could then uptake to support its growth. Untargeted metabolomics analysis of the spent medium from A01 monoculture during self-acidification revealed an increase in other metabolite profiles besides those already mentioned (Table S5 and Fig. S26), explaining why the total C-atoms of the public goods accounted for only 60% of the glucose consumption (Fig. 5H). To determine if these metabolites could also be cross-fed in co-culture, we performed untargeted metabolomics analysis42,43 on spent medium collected during the lactate peak. Several metabolite profiles were found, exhibiting dynamics similar to those of aspartate, pyruvate, and glutamate (Table S5 and Fig. S26). In summary, these data suggested that various metabolites were excreted by slower-growing A01 and cross-fed into B01, with pyruvate, aspartate, and glutamate being the most predominant metabolites quantitatively (Table S5, Figs. S26 and 5I). Thus, the stress-tolerant, slower-growing species secreted public goods, and cross-feeding of these public goods promoted the persistence of the stress-sensitive species by enhancing interspecific cooperation.

Physiological processes and mechanisms underlying lactate stress-induced cross-feeding and coexistence
Next, we explored the process and the mechanisms by which the slower-growing species A01 secretes public goods to cross-feed B01 and promote their coexistence under lactate stress. Based on acid-tolerant mechanisms such as ATP energy synthesis, amino acid conversion, and TCA intermediates12,31,44, we hypothesized the following process and mechanism: for the slower-growing A01, it increased glycolytic flux for energy biosynthesis intracellularly in lactate stress and TCA intermediates (which amino acids are linked reversibly) were excreted to the extracellular as public goods (pyruvate, glutamate, and aspartate) to alleviate the increased intracellular glycolytic flux, thus favoring the growth and maintenance of A01 in acid stress; for the low-tolerant B01, it can utilize the excretory public goods of A01 to grow, which in turn promoted an increase in intracellular glycolytic flux of A01, and this bi-directional interaction led to coexistence of A01 and B01. The increase in glycolytic flux during lactate stress was supported by the fact that A01 under lactate stress excreted higher levels of public goods than A01 without lactate stress (Figs. S20C and S22A), and that, during the stabilization phase with high stress, A01 took up glucose and excreted 60% of its C-atoms as public goods (Fig. 5H). The experiments with spent media and supplementation of public goods also verified that public goods excreted by A01 in co-culture promoted the growth of B01, which hardly excreted public goods (Figs. 5D and S23B).

To further test this hypothesis, we monitored the intracellular ATP content of A01 in monocultures exposed to lactate stress. It was found that for the same biomass, the content of intracellular ATP in A01 at 60 mM and 100 mM lactate concentrations was 1.65 and 3.84-fold higher than that without lactate (Fig. S27A). Moreover, we also monitored the intracellular ATP content of A01 in co-culture exposed to 100 mM lactate stress, as well as the level of public goods in the medium. At 8h, ATP content in A01 in co-cultures was 2.32-fold higher than in monocultures (Fig. S27B), while the concentration of public goods in the co-culture was lower than in the monoculture, although they both reached their maximum at this time (Figs. 5H and S27C). As the incubation continued, A01 in the monoculture showed a decline over the next 6h (Fig. S27D), accompanied by a slight decrease in public goods (Fig. S27E), and glucose levels below 16 mM (Fig. 5G). However, A01 biomass in the co-culture remained stable, and the public goods decreased significantly (Fig. S27D and S27F). At this time, the intracellular ATP content of A01 in co-culture was 3.56-fold higher than that of A01 in mono-culture, and 1.24-fold higher than that at 8h (Figs. S27G and S27B), revealing that the biomass-stabilized A01 in co-culture remained metabolically active. These results suggested that consumption of public goods by B01 does promote an increase in glycolytic flux, excretion of TCA intermediates, and vitality maintenance of A01. Thus, although B01 did not excrete public goods beneficial to A01 growth, this bidirectional interaction nonetheless enhanced A01’s cellular activity and supported a slightly higher biomass in the co-culture versus the monoculture (Fig. 4D).

To investigate the reasons for the decrease in public goods in A01 monocultures, we exposed A01 to a 100 mM lactate-acidified medium supplemented with the same public goods concentration and different glucose concentrations (see ”Methods”). The results showed that A01 can also consume public goods to maintain activity, but only at glucose concentrations below 15 mM (Fig. S28), suggesting that A01 exhibited substrate preference and priority. However, despite this, in the later stages of the monoculture, the rate of cell decay was higher than the rate of growth, leading to a reduction in overall growth (Fig. S27D). The physiological processes described above revealed a synergistic mechanism between the two phenotypes, slower-growing, tolerant A01 and faster-growing, intolerant B01. This interspecific cooperation and coexistence under acid stress may be widespread among multiple species with complementary growth rates.

A mathematical model of lactate stress-induced cross-feeding and coexistence captures dynamic physiological processes
To further understand why lactate stress-induced cross-feeding can rescue co-cultures, enabling their stable coexistence, we constructed a dynamic model of growth and cross-feeding to provide a quantitative account of the dynamic features observed (Supplementary Notes 1 and 2). The model was first parameterized using measurements on individual strains. The growth rate of A01 (RA) depended on glucose concentrations (G) and lactate concentration (Cs), and the growth rate of B01 (RB) depended on G, Cs, and M (lactate-induced metabolites) in the co-culture (Fig. 6A). Further, B01 was more sensitive to lactate, and hence to increased lactate (Fig. S10). To illustrate intuitively, we believe that the change from growth to growth arrest is a difficult transition. Thus, for Cs below some threshold concentrations, Cs-A01=140 mM and Cs-B01=100 mM for A01 and B01, respectively, we consider the growth rates (RA and RB) to be of the Monod form, and if the lactate concentration (Cs) is above the thresholds (Supplemetanry Notes 1 and 2).

Subsequently, we explored the entire physiological process by constructing a dynamic model of acid-induced metabolic cross-feeding between A01 and B01 (Fig. 6A). This process not only integrates strain characteristics obtained in monoculture, containing the excretion/uptake properties of public goods, but also considers the stable period of A01 and the growth of B01 6h after the stable period (Fig. 6B, C). Additional experiments using A01 monoculture were conducted to reconstruct this stable period, demonstrating that A01 continues to metabolize nearly 60% of glucose into glutamate, aspartate, and pyruvate, secreting them, possibly providing growth for B01 in the stable period (Figs. 4F and 5E). Therefore, the emergence of the stable period stems from the secretion of these metabolites, along with potentially other compounds, in response to acid stress. This phenomenon has been elucidated as a memory effect exhibited by cells under conditions of elevated acid stress45.

Our model was set up in the general framework of a consumer-resource model: LM takes up glucose with rate μM,G, excretes lactate with rate μM,E, respectively, and grows (or dies) at rate λM; A01 excretes acid-induced metabolites at rates μA,M and grows with rate λA; B01 grows with rate λB. A detailed explanation of population dynamics can be found in the Supplementary Notes 3-6. Depending on the intensity of acid stress and nutrient supplementation, our model demonstrated four generic physiological processes in co-culture resulting from growth and cross-feeding, as we describe below (Fig. 6C):

1. Logarithmic growth: at low lactate concentrations, B01 grows exponentially despite the presence of competitive interactions of A01, and these dependencies are characterized by the Monod growth functions; see Supplementary Note 3 for a full description.

2. Acidification-induced stable stage (growth arrest): at the initial stage of high lactate concentration and limited metabolite availability, B01 experiences a constant rate of decline without nutrient consumption; see Supplementary Note 4 for a full description.

3. Synergistic antacid tolerance: At the intermediate stage of high lactate concentration, crucial for cooperative stress tolerance, A01 exhibits a cooperative mechanism where it produces public goods such as pyruvate, glutamate, and aspartate, which are excreted. B01 can utilize these metabolites to maintain viability, accompanied by low glucose consumption. There is a lag period in the production and utilization of these metabolites, with a low rate of metabolite consumption during this stage; see Supplementary Note 5 for a full description.

4. Regrowth: at the later stage of high lactate concentration, B01 can grow but requires public goods. This simultaneous coexistence of lactate and cross-fed metabolites is modeled by the Monod functions; see Supplementary Note 6 for a full description.

The model quantitatively captured the processes and dynamics of co-culture consortia, showing distinct species dominance at different stages in the co-culture (Fig. 6C). These phases of differential dominance served as a pivotal factor in sustaining the coexistence of the two species, aligning with earlier investigations into growth dynamics (Figs. 3C, E, and 4D). Given that these phases arose from interactions between diverse physiological and metabolic states of the two species, encompassing factors such as the timing and quantity of major public goods proximal to the lactate peak and the population density of each species (Fig. 6B, C), their significance was likely amplified in more intricate communities. The observation of coexistence may depend on species abundance, interactions, nutrient levels, and environmental conditions during the process, which generates different species dominances at different time windows (Figs. 4D and 6C), facilitating stable coexistence across the community—a dynamic process that differs from the steady-state models commonly used in microbial ecology studies46,47.

The similarity of acid stress-induced cross-feeding and coexistence between bacteria and fungi
To verify the generality of the stress-induced cross-feeding scheme, we selected LABs and yeasts from the microbial consortium of the gut, another typical habitat for acid stress48, from different environmental sources than the previous experimental strains (Table S2). A species from the LAB was paired with a species from the Saccharomycetaceae family, with the former favoring growth on sugars and amino acids, while the latter thrives on sugars and acids49. When Lactococcus lactis JGB19 (LJ9), from the Lactococcaceae family, was grown in CDMG liquid medium with and without 100 mM lactate (see “Methods”), LJ9 monocultures grew at high ODs in lactate-free medium and ceased at low ODs in lactate-containing medium (Fig. S29A). Analysis of the spent medium from the monoculture of LJ9 revealed no accumulation of pyruvate acid, aspartate, and glutamate (Fig. S29B). Similarly, we cultured Saccharomyces cerevisiae Y2-7 (SY2-7) in CDMG liquid media with and without 100 mM lactate; the biomass of SY2-7 monoculture in the lactate-free medium was higher than that in the lactate-containing medium (Fig. S29C). Analysis of the spent medium from SY2-7 monoculture containing lactate revealed an accumulation of pyruvate acid, aspartate, and glutamate (Fig. S29D). Importantly, LJ9 had a growth rate higher than SY2-7 and a tolerance lower than SY2-7, characterized by complementary growth rates (Figs. S29A and S29C). Furthermore, co-culturing SY2-7 with LJ9 in CDMG liquid medium containing 100 mM lactate prolonged saturation OD by 1.1–1.4-fold and 2.3–2.6-fold, respectively, compared to monocultures of LJ9 and SY2-7. These results suggested the probability of lactate stress-induced cross-feeding in co-cultures (Fig. S30A).

To verify the occurrence of cross-feeding, a 2µL mixed suspension (OD600=0.2–0.3) containing equal amounts of LJ9 and SY2-7 was co-cultured at 2.5 cm to the right of the producer LM to analyze the growth and metabolic dynamics (see Fig. 3A for inoculation method). In marked contrast to the results of cooperative interactions in liquid cultures, competitive interactions were observed within 24h of co-cultivation on agar plates, with the biomass of the mixed co-cultures of LJ9 and SY2-7 being lower than that of their respective co-cultures with LM (Comparing Figs. 7A with S30B), consistent with the previous results of spatial structure environment promoting the interaction evolution25. The biomass dominance of the two species flipped at 24h of co-cultivation with a corresponding lactate content of 68.8 mM (Figs. 7A and 4E). Specifically, faster-growing, intolerant LJ9 dominated for the first 24h and slightly decayed in the following 6h (Fig. 7A, Orange circle), while slower-growing, tolerant SY2-7 surpassed LJ9 at 24h, then continued to rise and reached its maximum biomass at 30h (Fig. 7A, Red circle), at which time lactate was also close to its maximum content, 98.2 mM (Fig. 4E). Importantly, LJ9 still exhibited regrowth within 27–33h, with its density increasing by 1.54 × 10^8 for 132% of the 24-h biomass (Fig. 7A, Gray shaded area). HPLC analysis of the medium showed that several public goods, including pyruvate, glutamate, and aspartate, accumulated to high concentrations at 27h of co-culture (Fig. 7B). The subsequent disappearance of these public goods coincided with the recovery phase of LJ9 growth, while the density of SY2-7 remained unchanged (Fig. 7A, B, Gray shaded area). These observations closely resembled the dynamics observed in A01-B01 co-culture (Fig. 4D, F, Gray shaded area), despite the vastly different properties of these strains. To verify that these public goods produced by SY2-7 restored the growth of the LJ9 stalled by acid stress, we re-inoculated LJ9 using the spent medium from a 27-hour co-culture to monitor its growth and uptake characteristics (see “Methods”). The results showed that the spent medium was able to promote the growth of LJ9 (Fig. S30C), while the concentration of these public goods decreased (Fig. S30D). Moreover, we cultured monocultures of LJ9 in 100 mM lactate-acidified CDMG liquid medium with and without the addition of pyruvate, glutamate, and aspartate (see “Methods” and Fig. 5A). The results again followed that LJ9 can only grow under high lactate stress in the presence of public goods, which stems from uptake originating from public goods (Fig. 7C, D). Consistent with predicted universality, stress-induced cross-feeding of public goods facilitated interspecific cooperation and stress tolerance, leading to the coexistence of consortia under stress within 33–39h of co-culture, with a biomass ratio (fold change R = LJ9/SY2-7) close to 0.8 (Fig. 7A, Yellow shaded area).

Methods
Strains
Leuconostoc mesenteroides C7M43 (LM), Leuconostoc citreum 4367 (LC4), Leuconostoc citreum 200443 (LC2), Lactococcus lactis CCFM 1092 (A01), Lactococcus lactis CCFM 1032 (B01), and Enterobacter hormaechei 42D12 (EH1) were laboratory-preserved strains sourced from natural isolates from fermented chilies. In the previous work36, it was found that strains LM, A01, and B01 occupied the niche in fermented chiles for the first 4 days with a relative abundance of more than 1%. Strain LM is commonly used as a starter in fermented foods due to its ability to acidify the substrate rapidly. Strains A01 and B01 are producers of good flavor. Strain EH1 is a common spoilage bacterium in fermented foods that can easily invade foods and cause spoilage. They interact with each other in the pre-fermentation stage, leading to the normalization of fermented foods. Therefore, we chose LM as the producer, A01 and B01 as the competitors, and EH1 as the invader, which differ from each other in growth rate and acid tolerance as shown in Fig. S11, Fig. S12, and Table S2.

Other environmentally selected strains, including Lactococcus lactis JGB19 (LJ9), Saccharomyces cerevisiae Y2-7 (SY2-7), Saccharomyces cerevisiae JGF28 (SJ28), and Saccharomyces cerevisiae XZFM13-1 (SX131), used to validate acid-induced cross-feeding, were laboratory-preserved strains derived from natural isolates from the gut. The other strains used to test the lactate inhibition effect (Fig. 2C) were randomly selected from a library of laboratory-preserved strains. The detailed characterization of the strains is shown in Table S2.

Growth media
De Man Rogosa-Sharpe (MRS) liquid medium and preparation of MRS agar plate. MRS liquid medium was made as described by Liao et al.36. MRS agar plates were prepared by autoclaving a mixture of MRS liquid medium and 1.5% (w/v) agar. The temperature of the sterilized mixture was maintained above 50°C to prevent the agar from solidifying. Fifteen mL of the mixture was added to a petri dish (Sorfa, 100mm×15mm). Following solidification of the agar, the plates were stored at 4°C. Antibiotics were added, if required, at the following final concentrations: erythromycin (Em; 200ng mL−1) and chloramphenicol (Cm; 25µg mL−1).

Chemically defined medium (CDM)
CDM liquid medium was made as described by Olga et al.49. CDMG liquid medium was 2% glucose added to CDM liquid medium, and CDMS liquid medium was 2% sucrose added to CDM liquid medium. CDMG or CDMS agar plates were prepared in the same way as MRS agar plates, except that CDMG or CDMS liquid medium was mixed with 1.0% (w/v) agar.

Luria-Bertani (LB) medium
The components of the LB liquid medium contain peptone 10g L−1, yeast extract powder 5g L−1, and NaCl 10g L−1. LB agar plates were prepared in the same way as MRS agar plates, except that LB liquid medium was mixed with 1.5% (w/v) agar. Antibiotics were added, if required, at the following final concentrations: erythromycin (Em; 200ng mL−1) and chloramphenicol (Cm; 25µg mL−1).

Strongly buffered MOPS medium
We prepared a strongly buffered medium by mixing 3-(N-morpholino)propanesulfonic acid (MOPS) buffer with CDMG liquid medium, in which the final concentration of MOPS was 100 mM. Strongly buffered agar plates were prepared in the same way as MRS agar plates, except that CDMG medium containing 100 mM MOPS was mixed with 1.0% (w/v) agar.

Chromogenic CDMG medium
We prepared chromogenic CDMG medium by adding 1/10,000 (w/v) of the chemical indicator bromocresol purple to the CDMG liquid medium. Chromogenic agar plates were prepared in the same way as MRS agar plates, except that CDMG medium containing 1/10,000 (w/v) bromocresol purple was mixed with 1.0% (w/v) agar. Bromocresol purple was used in the range of 5.2–6.8; greater than 6.8 showed purple color, and less than 5.2 showed yellow color. Therefore, lactate production and diffusion can be determined based on the color change in the medium.

Spent medium generated in agar plates
For Fig. S23A, we prepared spent medium from the agar plate of monoculture A01 by the following steps: (1) after A01 was co-cultured with LM for 30h, the agar block (region b, see Fig. 4B) from A01 colony removal was excavated into 1.5 mL eppendorf (EP) tubes, and the size of the agar block was determined based on the statistical results in Fig. S19; (2) the resources and metabolites were extracted from the agar block, the as shown in the steps of Fig. 4B; (3) as the lactate concentration was close to 100 mM at 30h (see Fig. 4E), the concentration decreased after extraction and we added lactate to the EP tube to bring it up to 100 mM; (4) the above-mixed solution was filtered through a 0.22 µm filter membrane into a 2.0 mL EP tube and recorded as the spent medium.

Spent medium generated in liquid medium
After the monocultures were grown to the target time, the culture solution was filtered through a 0.22 µm filter membrane, and the filtered solution was recorded as the spent medium.

100 mM lactate-acidified CDMG or CDMS liquid medium
The lactate was first filtered through a 0.22 µm filter membrane and then added to sterilized CDMG liquid medium until the final concentration of lactate was 100 mM. Since the lactate concentration at 30h was close to 100 mM (see Fig. 4E), the final concentration of lactate for all acidified media was 100 mM, unless otherwise noted, for example, 60 mM in Fig. 4G, 140 mM in Fig. 5B. The preparation of 100 mM lactate-acidified CDMS liquid medium was the same. The glucose content in the acidified CDMG liquid medium is 25 mM, and the sucrose content in the acidified CDMS liquid medium is 20 mM, unless otherwise specified. For example, Fig. S28 contains three concentrations of glucose: 25 mM, 20 mM, and 15 mM.

100 mM lactate-acidified CDMG or CDMS liquid medium supplemented with public goods
Lactate and supplements (containing public goods: pyruvate, glutamate, and aspartate) were first filtered through a 0.22 µm filter membrane, then lactate was added to sterilized CDMG or CDMS medium until the final concentration of lactate was 100 mM, and finally, supplements were added to it until the final concentration. The final concentration of the supplement was determined based on the concentration of the public goods monitored after 27–30h of co-cultivation, as specified in Figs. 4F, 7B, F, and S27. For Figs. 5D, 7C, G, public goods in the medium were added at different concentrations; specific concentrations supplemented are shown in Table S4.

CDMG agar plates supplemented with the inhibitor 3-bromopyruvate
For Figs. 5E, F, and S24, we prepared an inhibitor-added CDMG medium by adding 120 mM inhibitor 3-bromopyruvate to the CDMG liquid medium. The inhibitor-added CDMG agar plates were prepared in the same way as MRS agar plates, except that CDMG medium containing 120 mM inhibitor 3-bromopyruvate was mixed with 1.0% (w/v) agar.

Spent medium generated in CDMG agar plates supplemented with the inhibitor 3-bromopyruvate
For Fig. S25, we prepared spent medium from CDMG agar plates supplemented with the inhibitor 3-bromopyruvate. A spent medium containing 3-bromopyruvic acid was prepared according to the methods of Fig. S23A. To remove the 3-bromopyruvic acid from the spent medium, we utilized Amberlite IRA-400 resin to adsorb the 3-bromopyruvic acid without affecting public goods. The specific steps were as follows: (1) mix the resin with a solution containing 3-bromopyruvic acid and public goods, stirring at 4×g/min for 60 min; (2) centrifuge the above solution at 5152×g/min for 2 min; and (3) collect the supernatant to be recorded as the spent medium.

Plasmids and strains construction
The fluorescent expression plasmids utilized in this study were developed from a lactic acid bacteria-Escherichia coli shuttle vector, pNZ8148, which contains a PnisA promoter, a Cm resistance gene, and an Em resistance gene (Table S3). The fluorescent protein sequence can be found in Supplementary Notes 7–9, and the primer sequences used for fluorescent plasmid construction are shown in Table S3. Gibson assembly was chosen to construct all fluorescent reporter plasmids, such as lcnA-eGFP, which enhances the expression of green fluorescent protein (eGFP) under the regulation of a constitutive promoter from the lcnA gene. Similarly, plasmids lcnA-mCherry and lcnA-YFP were constructed using mCherry and YFP, respectively, instead of eGFP, to allow for the constitutive expression of these fluorescent proteins.

The LAB was first purified by streaking onto an MRS agar plate and cultured overnight at 37°C. The monoclonal was inoculated into 2 mL fresh MRS medium and cultured at 37°C for 24h. 2 mL of culture was extracted with genomic DNA via a bacterial genomic DNA extraction kit. The extracted genome was used as a template to amplify the lactate dehydrogenase (LDH) gene by PCR primers (pGhostΔldh-F and pGhostΔldh-R). The amplified fragments were spliced using overlapping PCR and ligated to the pMD19T cloning vector, and then transformed into competent cells of E. coli DH5α receptor cells for positive clone screening. E. coli X-L-blue containing the pGhost4 plasmid was inoculated with LB medium containing erythromycin at 30°C for 12–16h to extract the pGhost4 plasmid. Subsequently, it was used for the construction of recombinant knockout plasmids. The construction system and reactions were as follows: 3 µL pGhost4, 4 µL splice fragment, 1 µL T4 ligase, and 2 µL 10× loading buffer. The recombinant knockout plasmids were transformed into competent E. coli X-L-Blue cells and coated with LB plates containing erythromycin. Positive validation of recombinant knockout plasmids was performed after overnight culture at 30°C. 10 µL of successful recombinant knockout plasmid was added to 100 µL of competent cells of LAB, then mixed and added to a 0.2 mm electroshock cup for electrotransfection. The electrotransfection conditions were as follows: 2500 V, 200 Ω, 25 µF, and 5 ms. 500 µL of recovery medium was added after electrotransfection, then coated in MRS solid medium containing erythromycin for 24h at 30°C. The monoclonal was picked and incubated in an MRS liquid medium containing erythromycin for 24h at 37°C. The genome was extracted and then amplified using lactate dehydrogenase gene primers and knockout primers, respectively, for positive knockout strain identification.

Culture conditions for microbial strains
All cultures (except for those grown in the plasmids and strains construction, see above) were grown statically at 30°C. Each growth/biomass assay experiment consisted of three steps: (1) seed cultures, (2) pre-cultures, and (3) experimental cultures. Strains stored from −80°C were individually inoculated in CDMG liquid medium to start a seed culture until an OD600 of 0.8 was achieved. This seed culture was inoculated at 1% (v/v) inoculum into fresh CDMG liquid medium and incubated until exponential growth. This pre-culture was re-inoculated at 1% (v/v) inoculum into fresh liquid CDMG medium and this monoculture was sampled for various measurements such as biomass, metabolites, spent medium, etc. Unless otherwise indicated, all liquid media used to prepare cultures had a volume of 2 mL.

Growth of pure culture
Growth of pure culture
The pure culture was defined when only one strain was cultured in the agar plate (Table S1).
For the CDMG solid culture in Fig. 1, three lactic acid bacteria (LAB), LM, LC4, and LC2, preserved in the laboratory, were inoculated in CDMG liquid medium at 1% inoculum (v/v%) for overnight culture, respectively. The overnight culture was diluted to an OD600 of 0.2–0.3, and each of the three LAB was inoculated in the center of CDMG solid medium at an inoculum of 2 µL and incubated at 30°C for 1.5–2 days.
For Figs. S5 and S20, a single strain (LM, B01, or A01) was inoculated in the center of the CDMG agar plate at an inoculum of 2 µL and incubated at 30°C for the corresponding time.
For the CDMS agar plate in Fig. S31A, LM was cultured in CDMS in the same way as in CDMG, except that the sugar added to the medium was different.

Biomass determination of pure culture in agar plates
For Fig. S2, firstly, the individual clones were mined from the agar plates into 1.5 mL EP tubes and rinsed three times using 500 µL of sterile water to obtain bacterial fluid. Subsequently, the bacterial fluid from the three rinses was collected in 2 mL EP tubes and centrifuged at 3578×g/min for 2 min to collect the cells. Next, the cells were resuspended using 500 µL of sterile water. Finally, 200 µL of resuspension was pipetted into a 96-well plate, and the absorbance value was measured by a photometer at 600 nm and recorded as biomass (OD600).

Growth of monoculture
Monoculture in CDMG liquid medium. The monoculture was defined when only one strain was cultured in the liquid medium (Table S1). For the CDMG liquid cultures in Fig. 1B, clones from different locations cultured on CDMG agar plates were inoculated into fresh CDMG medium and diluted to 0.6 of OD600, then inoculated at 1% (v/v%) into fresh CDMG medium and incubated for 16h at 30°C to assay for biomass and lactate production.

Biomass determination of monoculture in liquid medium
After the individual strains were cultured according to the above culture conditions, the monocultures were thoroughly mixed, and 1 mL was removed to a new EP tube. Then, the cells were collected by centrifugation at 3578×g/min for 5 min. The cells were washed three times with 500 µL of sterile water and then resuspended in 500 µL of sterile water. The 200 µL of resuspension was pipetted into a 96-well plate, and the absorbance value was measured by a photometer at 600 nm and recorded as biomass (OD600).

Monoculture in CDMG or CDMS agar plates
The monoculture was defined as inoculations of a single competitor strain at different distances to the right of the producer LM, with different distances being inoculated on separate agar plates. The producer LM and one other competitor were cultured overnight in CDMG or CDMS liquid medium. According to Fig. 1B, lactate started to diffuse at 10h. Unless otherwise stated, producer LM was inoculated for growth for 10h before inoculation with another competitor. A 2 µL (OD600=0.2–0.3) of competitor was drop-plated at 0.5, 1.5, 2.5, 3.5, and 4.5 cm to the right of the producer LM, and the growth of the competitor at different locations in different incubation times was monitored. For the inhibitor addition experiments, the steps were the same except for the addition of 120 mM 3-bromopyruvate.

Biomass determination of monoculture in agar plates
The biomass of monocultures on agar plates was determined in the same way as that of pure cultures.

Measurement of lactate-dependence of growth rate
For Fig. S12, A01, B01, and EH1 strains were inoculated in CDMG liquid medium containing 20–140 mM lactate and incubated for 24h to detect biomass. Growth rate = biomass/time.

Growth in spent medium
Strains were pre-incubated at 2.5 cm to the right of the producer LM in CDMG or CDMS agar plates for 30h. The colonies were then transferred to 1.5 mL EP tubes, and cells were collected following the steps in Fig. S2. To grow faster in the spent medium, we increased the inoculum amount inoculated into the spent medium (2 mL) to a final OD600 of 0.32 and incubated statically at 30°C. Every 1h interval, 50 µL of culture (well-mixed culture) was pipetted into a 96-well plate, and biomass (OD600) was measured. The value of biomass (OD600) was expressed as the assay value minus 0.32.

Growth in 100 lactate-acidified CDMG or CDMS liquid medium
The strains were cultured in CDMG or CDMS medium to an OD600 of 0.6–0.8, inoculated at 1% inoculum (v/v%) into CDMG or CDMS medium containing 100 mM lactate, and the biomass was monitored at different times. The final concentration of inoculation was an OD of 0.32. The value of biomass (OD600) was expressed as the assay value minus 0.32.

Growth in 100 mM lactate-acidified CDMG or CDMS liquid medium supplemented with public goods
The strains were pre-incubated at 2.5 cm to the right of the producer LM in CDMG or CDMS agar plates for 30h. Colonies were then transferred to 1.5 mL EP tubes and inoculated in an acidified CDMG liquid medium containing 25.0 mM glucose with the addition of supplements (pyruvate, aspartate, and glutamate). The final concentration of inoculation was an OD of 0.32. The value of biomass (OD600) was expressed as the assay value minus 0.32. For S28, the concentration of glucose in the medium was 25 mM, 20 mM, and 15 mM to verify that A01 was able to utilize public goods.

Spatial-temporal diffusion of lactate in CDMG agar plates
For in vitro lactate diffusion in agar plates: lactate was first drop-plated in the center of the chromogenic agar plate at 2 µL per drop until the final lactate level was 194 mM, a concentration that is the maximum production concentration of producer LM in CDMG liquid medium. After drop-plating, the plates were allowed to stand at 30°C, and the lactate diffusion distance was recorded every 3h through a straight edge and repeated three times for each plate.
Diffusion of lactate produced by producer LM in agar plates: 2 µL of producer LM (OD600=0.2–0.3) was drop-plated in the center of the chromogenic CDMG agar plates. After drop-plating, the plates were placed at 30°C for resting incubation, and the lactate diffusion distance was recorded once at different intervals through a straight edge and repeated three times for each plate.

Construction of fluorescence intensity versus biomass (CFU/mL) transformation curves
Firstly, an individual strain containing fluorescent protein was cultured on agar plates. Subsequently, clones with different growth periods were extracted from the agar plates into 1.5 mL EP tubes and rinsed three times with 500 µL of sterile water to obtain the cell solution. Next, the cell solution from the three rinses was collected in 2 mL EP tubes and centrifuged at 2290×g/min for 2 min to collect the cells. Finally, the cells were resuspended with 500 µL of sterile water, and dilutions were performed until the final dilutions had an OD600 between 0.2 and 0.8. The above dilutions with different growth periods were coated, and the biomass (CFU/mL) of the solution was calculated based on the principle that the colony counts were in the range of 30-300. Simultaneously, the fluorescence intensity of the dilutions was measured using a fluorescence photometer. The biomass and fluorescence intensity of different growth periods were fitted to construct the relationship equation (relationship curve). Different strains carried different fluorescent proteins, and the same steps were used to construct the relationship equation. Based on this relationship equation, in ternary and quaternary microbial consortia, the biomass of the corresponding strain can be calculated by measuring the fluorescence intensity of fluorescent proteins carried by different strains. The relationship curve is shown in Table S6.

Determination of biomass when different strains are present in a clone
When a single clone contains different strains, such as ternary and quaternary interactions, the method of quantifying biomass by OD600 is not applicable. Therefore, biomass (CFU/mL) was calculated by measuring fluorescence intensity. The details are as follows: the colonies in the agar plates at different distances were transferred separately to the gradient dilution solution until the OD600 of the final mixed colonies was between 0.2 and 0.8. The fluorescence intensity of the dilutions was measured using a fluorescence photometer in different fluorescence channels, such as yellow fluorescence (YFP), green fluorescence (eGFP), or red fluorescence (mCherry). The fluorescence intensity of different strains can be obtained because the strains carry different fluorescent proteins. Based on the above relationship equation between fluorescence intensity and biomass, the biomass of different strains in mixed cultures can be calculated.

Interaction experiments at coexistence distances
Ternary and quaternary interaction experiments revealed that microbial consortia coexisted near a fixed distance of 2.5 cm. In the later time-series interaction experiments and metabolite detection experiments exploring this distance, the steps were the same as in the interaction experiments, except that the inoculation distance was fixed at 2.5 cm.

Growth of gut cocultures
Binary and ternary interactions of producer LM with gut strains (LJ9, SY2-7, LJ9, and SX131) occurred at a fixed distance of 2.5 cm in the same steps as described above, except that the sample sources differed.

Intracellular metabolites
The intracellular metabolites in the culture were obtained according to the method of Ikeda et al.71 with some modifications. The cultures were centrifuged at 3578×g/min for 5 min, and the cells and spent medium were collected separately. The cells were resuspended and diluted to an OD600 of 0.4, and 200 µL of the diluent, 600 µL of methanol, and 30 µL of a-aminoadipate (50 µM, internal standard) were mixed into a 2.0 mL EP tube. It was immediately frozen in dry ice for 10s, then stored at −80°C. Replace the dilution with 200 µL of spent medium and follow the same procedure. The two frozen samples were concentrated and dried in a vacuum dryer for 15h. The resulting precipitate was resuspended in 150 µL of sterile water. After ultrasonication and centrifugation at 53578×g/min for 10 min, the supernatant was collected for HPLC detection.

Detection of resources and metabolites
HPLC detection of resources and lactates. Resource depletion and lactate excretion in the medium were quantified by HPLC. The colonies were first transferred to 1.5 mL EP tubes, and cells were collected following the steps in Fig. S2. Subsequently, the agar plate where the competitor was located was divided into three regions, a, b, and c (see Fig. S18). Agar blocks from regions a, b, and c (volumes of 108±5 mL, 240 mL±8 mL, and 418 mL±13 mL for a, b, and c, respectively; volumes were converted according to 1 mg = 1 mL) were transferred to 1.5 mL EP tubes and extracted with an equal volume of 5 mM dilute sulfuric acid for 10 min in ultrasound for a total of three times. The extracts were frozen at −80°C and then dried and concentrated in a vacuum desiccator for at least 3 days. Subsequently, the residue was redissolved in 10 mL of 5 mM dilute sulfuric acid. Before high-performance liquid chromatography (HPLC) analysis, the above-dissolved extracts were diluted to the same volume as the original volume of the agar block in regions a-c, respectively, for normalization. HPLC detection of the resources and lactate in solution was performed as reported in our previous report36. All measurements were repeated three times.

HPLC detection of amino acids
Amino acids in the agar block were extracted with methanol concerning the above procedure. The amino acids were detected via HPLC outfitted with a Gemini 5mm C18 110Å column and fluorescence detection. Samples were automatically derivatized by o-phthalaldehyde solution before HPLC detection. The separation was carried out using solvent A (9.5% methanol and 0.5% tetrahydrofuran) and solvent B (100% methanol). Gradient elution assay was performed at an injection volume of 20 µL and a flow rate of 1.0 mL/min, with the following procedure: 0 min (0% of B, v/v), 6 min (10% of B, v/v), 22 min (10%, v/v), 23 min (80%, v/v), 36 min (0% of B, v/v). All measurements were repeated three times.

Detection of intracellular ATP content
The intracellular ATP content is measured by the BacTiter-LumiTM Luminescent Microbial Cell Viability Assay Kit (COO52S, Beyotime Biotechnology, China), referring to the manufacturer’s instructions for the assay procedure. The specific steps are as follows: (1) The cultured cells were washed three times with phosphate buffer and diluted to an OD of 0.2–0.3; (2) 100 µL of the above cells solution was added to the wells of a 96-well plate; (3) 100 µL BacTiter-LumiTM assay reagent was added to the wells of a 96-well plate; (4) The 96-well plate was incubated at room temperature (25°C) for 10 min at 2×g/min; (5) The chemiluminescence assay was performed using a microplate reader and the ATP content of the cell samples was calculated from their chemiluminescence readings.

Untargeted metabolic mass spectrometry
The cultures were centrifuged at 2290×g/min for 5 min to collect the supernatant, which was diluted 20-fold with sterile water and then subjected to non-targeted metabolomics using a Agilent 6520 time-of-flight mass spectrometer equipped with an Agilent Poroshell 120 EC-CN column. The mobile phase consisted of 10 mM ammonium acetate (pH 5.9) at a flow rate of 250 µL/min, and 2 µL of sample was injected every 2.5 min. After each 30 injections, the column was washed for 5 min with a buffer containing 40% water (v/v), 30% isopropanol, and 30% acetonitrile. The mass-to-charge ratio (m/z) ranged from 20 to 450. The raw data were processed by China Sangong Bioengineering Co, Ltd, a testing organization, using MATLAB, whereas the compounds were selected from the KEGG and Metlin databases; data are provided in the source data file. The relative intensity of a single metabolite time course was calculated by taking the difference between the intensity of the first time point and the intensity and dividing by the maximum value of that time process. Fig. 5F was plotted based on the relative intensity of the individual metabolite time processes.

Mathematical simulation
Detailed details on the mathematical model can be found in the Supplementary Notes, and the mathematical simulations in Fig. 6 were carried out using Python 3.10.1 or Matlab R2020a.

Statistical analysis
All experiments were repeated at least 2 or 3 times. For more information on replications and statistical analyses for each experiment, see the figure legends and source data. Statistical analysis was performed using XLSTAT 2011.1.04 software (Microsoft, USA) or GraphPad Prism 8.0 (GraphPad Software, USA), which involved one-way analysis of variance (ANOVA) or an unpaired two-tailed Student’s t-test to determine p-values. Figures were generated using the R software (https://cran.r-project.org/), GraphPad Prism 8.0, or Origin 2023 (OriginLab, USA).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The study focuses on the influence of an abiotic stress gradient on the spatial and temporal interactions between microbial partners. The authors demonstrate that the intensity of acid stress can influence the outcome of competition between bacteria occupying similar niches. Furthermore, the study claims that the secretion of metabolic intermediates from one of the interacting partners can support the growth of co-occurring bacteria, potentially promoting mutualism and resilience to acid stress. The authors confirm the generality of the observed patterns by repeating the experiments with bacterial-fungal pairs. I find the research questions addressed in the manuscript to be interesting. However, I did not find any evidence that demonstrates the emergence of cross-feeding in the manuscript. Further, the manuscript lacks clarity in writing with missing details, for example, in the methods section and statistics.\nMajor Comments:\n• For most of the range-based assays, the bacterial communities were spotted at different locations on the same agar plate. Has it been verified that the presence of communities closer to the centre does not influence the stress gradient experienced by the communities farther from the center? It would be better to have the spots at different distances on separate plates. The current experimental design may suffer from the possibility that neighboring colonies at varying distances from the center of the plate could affect each other’s biology.\n• For all microscopy images, it is essential for the authors to report each fluorescence channel separately, rather than providing only combined images.\n• In the Materials and Methods section, more details are needed. For example, how much media was used to perform the experiments etc.\n• General colony competition is normal. In this case, the authors demonstrate that one mechanism involves lactate-based growth suppression. However, this seems like a passive interaction, as the producer does not increase lactate production in the presence of a competitor.\n• Certain sections need rewriting for clarity. For example, in the legend for Figure 2c, it is impossible to determine which competitor is present at each location on the plate. Using typed labels would be more effective than relying solely on the original handwritten labels.\n• It is unclear how many replicates were performed for each experiment. In some cases, error bars are used, indicating the number of replicates, but in others, error bars are not shown. Is this because the errors are too small to be visible? For example, Figures 3e and 2d do not seem to include error bars. Upon further reading, it appears that the authors have not specified the number of replicates performed, making it difficult to interpret the presented data. This issue is further compounded by the lack of clarity regarding the specific statistical analyses used to support their claims. In the statistics section, the authors mention reporting standard deviation (SD) and using one-way ANOVA to calculate p-values, but this information is too generic, and one-way ANOVA may not be appropriate for all the analyses presented. The statistics section also refers to “box plot analysis and other figures…” (line 1121). It is unclear which box plot analyses the authors are referring to. Overall, the manuscript seems to suffer from limited application of statistical approaches to support the conclusions drawn.\n• Isn’t the observation of stable coexistence of both interacting partners at around 2.5 cm a possible explanation for the lactate-based growth rate changes among the two species being the responsible factor? I am not sure why the authors decided to look for evidence of cross-feeding.Even in that case, authors need to demonstrate colony counts for both partners and analyse them relative to pure culture permance to demonstrate cross-feeding\n• The authors present fluorescent images of the LM and A01 combinations, but not of the LM and B01 combination. It may be useful to include these, especially since the growth suppression by LM differs for A01 and B01. These results are important to argue that the differences observed are indeed because of some form of interaction between A01 and B0! And not simply an outcome of growth dynamics primarily driven by interaction between LM and the other two focal strains.\n• Supplementary Figure S10 shows that the reduction in the growth rate of individual strains (A01, B01, and EH1) in the presence of higher concentrations of lactate captures the phenotypes observed in terms of invasion and coexistence dynamics. Therefore, the role of interactions between partners in maintenance is unclear.\n• While the B01 counts might recover due to the availability of pyruvate or glutamate in the environment, this is likely an effect of byproducts and may not constitute mutualistic interaction, as A01 does not benefit from this interaction.\nMinor Comments:\n• Line 47-49: The sentence is unclear and may need rephrasing.\n• Line 137: \"Collected at positions\"—missing information in the sentence.\n• Line 472-473: Is it correct to say that A01 is secreting byproducts to support the regrowth of B01?\n• There are grammatical errors in multiple locations throughout the manuscript.\n• Spelling mistake: \"coating count\" in Figure 2e.", "reviewer_2": "Manuscript Title: “Stress-Selected Slower-Growing Species Promote Interspecies Stress-Resistant Mutualism via Cross-Feeding Public Goods: Dynamic Mechanisms of Competitive-to-Cooperative Variation”\nAuthors: Hui Liao, Liming Wu, Yi Luo, Hussain Asif, Xinlei Huang, Xiaole Xia\nAffiliations: Jiangnan University, Tianjin University of Science & Technology\nSummary\nThis manuscript investigates how lactate-induced acid stress influences microbial interactions, emphasizing the transition from competitive to cooperative behaviors within microbial communities. The authors argue that stress-induced heterogeneity selects for slower-growing species, which subsequently enhance mutualism through the cross-feeding of public goods, such as glutamate and pyruvate, thereby promoting coexistence and increasing stress resistance. The study combines experimental approaches, including microbial cultures and metabolomics, with mathematical modeling to elucidate the ecological and physiological mechanisms underpinning stress-induced microbial mutualism.\nUpon review, several aspects of the experimental design and interpretation require significant improvement. Specifically, there is insufficient experimental support for some of the claims, incomplete methodological details, and a need for additional testing to validate the conclusions. While the manuscript provides an interesting perspective on microbial ecology, it suffers from conceptual and structural weaknesses that compromise its impact. The research addresses a key question—how microbial communities adapt and shift interactions under environmental stress—but does not adequately support its conclusions and lacks precise definitions for critical terms and mechanisms. Additional experimentation is required to confirm the proposed cross-feeding mechanism and elucidate the underlying metabolic pathways. Overall, the paper shows potential but necessitates substantial revisions to improve precision, scientific rigor, and robustness.\nMajor Issues:\n1. Metabolite Dynamics and Cross-Feeding:\nThe paper’s central claim revolves around metabolite cross-feeding as a mechanism driving mutualistic coexistence under lactate stress. While the HPLC data provide insight into the presence of key metabolites such as pyruvate, glutamate, and aspartate, they do not fully establish a direct link between these metabolites and their effects on microbial growth. More direct experimentation is required to confirm the proposed cross-feeding mechanism. For instance, supplementation experiments where different concentrations of these metabolites are added to mono-cultures or consortia would allow for a more robust validation of their role in driving mutualism. Additionally, the use of knockouts or inhibitors that block the production of these public goods could help confirm their necessity in the observed cooperative dynamics.\n2. Mechanistic Gaps in Discussion:\nThe study provides evidence of a shift from competition to cooperation between microbial species under lactate stress, driven by metabolite exchange. However, the metabolic mechanisms underlying this shift are not sufficiently explored. While the paper shows that certain metabolites are excreted and cross-fed, the reasons for their secretion and how they influence species growth remain unclear. The mathematical model used to simulate these interactions, while valuable for predicting dynamics, does not provide biological insights into the metabolic pathways involved. Further experiments are needed to investigate the metabolic basis of this cross-feeding. Specifically, detailed time-resolved metabolomics or metabolite supplementation studies could offer deeper insight into how these processes evolve and whether they can be generalized to other microbial systems or stressors.\n3. Insufficient Statistical Transparency:\nThe statistical methods used in the manuscript are not adequately explained. The authors reference differences between experimental groups without detailing key aspects such as the statistical tests performed, the assumptions underlying these tests, sample sizes, or exact p-values. Furthermore, figures and tables should include more information on the number of replicates, statistical significance, and comparisons made between groups. Providing clearer and more comprehensive details on the statistical analyses would improve the transparency and reliability of the conclusions.\n4. Overgeneralization of Findings:\nWhile the experimental data on microbial interactions under lactate stress are intriguing, the authors overgeneralize their findings, suggesting that the principles observed in their limited model system can be broadly applied to microbial communities in diverse environments. The study focuses on a small number of species and a single stress condition (lactate), yet the conclusions are extended to suggest general principles for microbial community behavior under stress. The authors should acknowledge the limitations of their system and temper their conclusions, avoiding broad claims that are not supported by the data.\n5. Conceptual Ambiguity:\nThe manuscript lacks clarity on key concepts such as \"public goods,\" \"cross-feeding,\" and \"mutualism.\" Although cross-feeding is often presented as a cooperative interaction, it can also result in competition depending on the context. The authors do not clearly define these terms or explain how these metabolic exchanges lead to cooperation in this particular case. Additionally, the paper assumes that slower-growing species under stress universally secrete public goods without exploring the mechanisms behind this process. A more thorough definition of these terms and a detailed explanation of how and why public goods are produced under stress would help clarify the dynamics being described.\nMinor Issues:\n1. Abstract and Title:\nThe abstract is comprehensive but could be more concise, focusing on the study's main findings and implications to avoid overwhelming readers. Similarly, the title could be simplified to better reflect the core findings. Although it captures the study's scope, a shorter, more direct title may be more impactful.\n2. Figures and Legends:\nThe figures, while visually appealing, need more detailed legends to enhance comprehensibility. For instance, Figure 1, which describes the experimental system, lacks sufficient labeling and explanation of key elements, making it challenging for readers to follow the setup and results. I had a particularly difficult time following the figures, which significantly hindered my understanding of the data presented. To address these issues, I have created a detailed set of comments specifically for the figures as the last section of my review. Additionally, improving the use of colors and data representations could increase accessibility, ensuring that each visual element's meaning is clear to all readers. Including error bars and specifying statistical comparisons in figure legends would also improve result interpretation.\n3. Terminology and Language:\nThe manuscript frequently uses technical terms without sufficient explanation. Terms such as \"stress heterogeneity,\" \"interspecific mutualism,\" and \"cross-feeding\" are critical to the paper but are not clearly defined or contextualized. Introducing a short glossary or, at a minimum, providing explicit definitions of these terms within the text would prevent confusion, particularly for readers who are not specialists in microbial ecology or evolutionary biology.\n4. Simplistic Model System:\nThe authors use a well-controlled agar-based model system to simulate lactate stress. While this allows for precise manipulation and monitoring, it does not fully capture the complexity of natural microbial communities, which often face multiple overlapping stressors and interact with a wider diversity of species. Acknowledging these limitations in the discussion would strengthen the paper. Additionally, discussing how these results might apply to more complex, real-world environments (e.g., soils, gut microbiomes) would enhance the ecological relevance of the findings.\nFigures\nFigure 1\nIn Figure 1, a series of Agar Plates labeled A and B display stages from 0 to 18 hours in A and from 0 to 40 hours in B, presumably representing different lactate concentrations over time. However, this distinction is not clearly conveyed. Although the method section is referenced to clarify these differences, the figure should be understandable on its own without requiring cross-reference to the methods section.\nThe text describing panels A and B is repetitive, reducing clarity. Instead of duplicating the description, it would be more effective to highlight the differences and specific measurements presented. In panel B, a ruler is used to measure colony growth from the center to the edge, showing distances from 1 to 6.5 cm. However, by the 40-hour mark, when the plate is fully colonized, the ruler indicates a total growth of only 4.58 cm, which appears inconsistent. The authors should clarify this discrepancy.\nIn panel C, there is a noticeable color difference in the first image of the isolated clones, but the source of this variation is not explained. Clarifying the origin of this color would improve the figure's interpretation. The key message becomes clearer in panel E, where clones at the edge have higher growth rates but lower lactate production rates, while the opposite is true for those at the center. This message could be communicated more explicitly for better understanding.\nFinally, panel G, which aims to illustrate the morphological characteristics of the modified strain, is unclear about which specific features should be noted. Providing a more detailed description of the key morphological traits would significantly assist in guiding the reader's interpretation.\nFigure 2\nThe ruler presented here extends from 0 to 6.5 cm, which is inconsistent with the length indicated in Figure 1 (5.5 cm).\nAdditionally, there is an unnecessary repetition in the text structure between panels (F) and (E), which should be revised for greater coherence.\nFigure 3\nThe experiment involves multiple microbial consortia (ternary and quaternary) with varying initial inoculation ratios. The caption outlines the experimental steps, but it would benefit from greater clarity on the specific methodology for calculating biomass from fluorescence intensity, which is referenced but not sufficiently explained. Since the procedure heavily relies on fluorescence intensity measurements, the figure would be more informative if it included a brief summary of the calculations or a reference to the relevant section in the methods where this is explained in detail.\nThe experiment analyzes microbial growth across spatial distances, specifically mentioning the inflection point (λx) where the fitnesses of strains A01 and B01 equalize. This term could be confusing without a visual marker in the figure clearly identifying the inflection point. Adding visual cues, such as arrows or markers indicating λx or distances where shifts in fitness occur, would help in understanding the findings. The blue and orange dashed lines in panels B and D are difficult to distinguish. Enhancing their contrast or using more distinct colors would improve visibility.\nThe caption notes that each experiment was repeated at least three times. While this adds credibility, specifying whether the presented data are means or representative images and whether error bars or confidence intervals are shown on the ratio curves would improve interpretation of the findings.\nFigure 4\nPanel A visually represents the hypothesis of resource-metabolite-mediated coexistence, showing lactate diffusion and metabolite exchange between A01 and B01. While helpful, it lacks sufficient detail about experimental specifics. Including explicit markers to show inoculation points and colony arrangements would better illustrate these spatial interactions, which are central to the hypothesis.\nPanel B provides a flowchart outlining the method used to extract and detect metabolites via HPLC from different areas of the agar plate. The plate is divided into regions a, b, and c, each representing increasing distances from the inoculated colonies. While the methodology is sound, the panel would benefit from clearer labeling to indicate which areas are closer to the producer LM and which are further away, as this spatial differentiation is key to interpreting diffusion and cross-feeding processes. The caption mentions \"normalization\" of extracts but does not explain how this was performed, which is crucial for understanding the reliability of the measurements.\nPanels C-F present quantitative data on cell counts and metabolite concentrations over time. In Panel C, the temporal analysis of A01 and B01 growth is displayed, with the regrowth of B01 highlighted by a grey-shaded area. Adding context regarding the biological significance of this regrowth phase and the conditions triggering it would improve understanding. Clearer annotations on the graph, such as arrows or labels marking critical time points or growth phases, would also aid in readability.\nPanel D depicts HPLC-detected concentrations of glucose and lactate, while Panel E shows levels of pyruvate, glutamate, and aspartate in the co-culture areas. These panels provide detailed insights into how metabolite levels fluctuate over time in response to microbial growth, but the connection between these trends and microbial behaviors (such as regrowth or stasis) could be better illustrated. Explicitly linking data in these panels to the biological processes occurring in Panel C would create a more cohesive narrative.\nPanel F investigates B01 cell counts under different conditions, such as lactate stress alone and in the presence of supplemented metabolites. While color coding for different conditions is useful, adding symbols or patterns to distinguish treatments would be especially helpful for readers with color vision deficiencies.\nStructurally, the caption is somewhat disorganized. Dividing the caption into subsections for each panel (A, B, C, etc.) and including well-defined legends, axis labels, and direct annotations on the graphs would make the data easier to interpret.\nFigure 5\nPanel A presents a schematic diagram summarizing the experimental setup used to assess whether B01 can regrow using public goods excreted by A01. Including details about sampling intervals and the rationale behind using 30 hours of pre-incubation would make the schematic more informative.\nIn Panel B, the optical density (OD) values for B01 monocultures in the acidified medium are displayed, with or without public goods supplementation. The filled and open symbols effectively differentiate conditions, but the baseline OD600 value of 0.32 should be further explained to clarify its relevance.\nPanels C and E illustrate changes in metabolite concentrations over time. Explicit labeling of key time points, such as the onset of B01 regrowth, would provide better context. A clearer description of the HPLC analysis, including normalization methods, would also strengthen the findings.\nIn Panel D, the relationship between A01’s biomass and glucose consumption over time is depicted. More prominent markers indicating significant time points would improve interpretability.\nPanel F showcases metabolomics data, presenting relative intensities of various metabolites in the CDMG medium. Including a legend or labels directly on the graph would help clarify which metabolites are most important.\nFigure 6\nIn Panel A, the model integrates multiple parameters, such as glucose and lactate levels, as well as changes in A01’s internal state due to metabolite depletion. The use of multiple arrows and interacting variables makes the visual layout overwhelming. Adding more detailed labels or a clearer explanation of what each arrow and color scheme represents would improve interpretability.\nPanel B provides a cartoon representation of the lactate stress-induced cross-feeding mechanism, which effectively communicates the basic concept but could be improved by linking it more explicitly to the variables and processes defined in Panel A.\nIn Panel C, the growth dynamics of A01 and B01 are presented over time, with color bands indicating different physiological states. Explicit references to the mathematical model components that drive these phases would enhance explanatory power.\nThe caption accompanying Figure 6 should be more structured, with distinct subsections for each panel and detailed guidance on how components relate to each other.\nFigure 7\nIn Panel A, the growth dynamics of SY2-7 and LJ9 are shown over time when co-cultured on CDMG agar plates. Providing more insight into why a specific distance was chosen for inoculation and how it affects metabolite diffusion would improve clarity.\nPanel B presents HPLC-detected concentrations of pyruvate, glutamate, and aspartate in co-culture areas. Explicitly connecting metabolite concentrations with specific biological outcomes would strengthen the interpretation of the data.\nPanel C depicts OD values of LJ9 monocultures in acidified medium. Providing more information on why a specific baseline OD value was used as a reference point would help clarify its significance.\nPanel D shows changes in concentrations of public goods in LJ9 monocultures over time. A more thorough explanation of time intervals and how metabolites influence growth behavior at specific points would improve comprehension.\nPanels E–H mirror the experiments from Panels A–D, but with sucrose as the carbon source instead of glucose. A more direct comparison of the outcomes would highlight any differences in metabolite production or microbial growth.\nImproving the visual organization of the figure, adding annotations to show connections between metabolite concentrations and growth phases, and increasing the font size of axis labels would enhance readability.\nThe caption could be better structured by breaking down explanations for each panel into subsections to make it easier to follow.\nFigure 8\nFigure 8 provides a schematic illustration of a general model depicting cross-feeding interactions that promote interspecies resistance and mutualistic coexistence under acid stress. While the figure conveys the overall concept effectively, it lacks sufficient detail in terms of the exact chemical reactions and how acid stress impacts these processes. The interactions shown are somewhat abstract, with minimal labeling of key components, making it difficult to discern the specific mechanisms at play. Clearer annotations, particularly for the movement of metabolites and the effects of stress on these interactions, would significantly improve clarity.\nThe model does not seem to be directly linked to the experimental results presented in the manuscript, which weakens its overall impact. Incorporating references to specific experimental data or findings would help to ground the conceptual model in empirical evidence. Additionally, the layout of the figure could benefit from a more structured visual hierarchy, which would emphasize the cause-and-effect relationships between acid stress, metabolite exchange, and mutualistic growth. Currently, the figure presents these elements in a somewhat flat manner, which does not clearly communicate the progression of these interactions.\nLastly, the caption accompanying this figure is too brief, failing to provide the necessary context or explanation for the processes depicted. Expanding the caption to explain the assumptions of the model and its connection to the study’s results would enhance the figure’s interpretability. Overall, the figure offers a useful conceptual overview but requires more detail and stronger integration with the experimental data to fully support the conclusions of the manuscript.", "reviewer_3": "The manuscript provides a systematic investigation of how microbial species can achieve coexistence in heterogeneous and stressful environments. The discovery of a metabolic strategy switch under acid stress, which shifts interspecies interactions from competition to cooperation, is novel and potentially significant. This finding, alongside with its underlying mechanisms, offers important insights into the remarkable diversity of gut microbial communities in the intestines.\nQuestion:\nOne of the highlights of the study is the interaction between the two Lactococcus lactis strains, A01 and B01, which are portrayed as a typical \"cooperator\" and \"cheater\" pair. B01 benefits from A01 without reciprocating any advantages. Are these niches always exclusive, or is there any possibility that both strains might secrete and consume public goods under certain conditions?\nComment:\nIt looks B01 tends to outcompete A01 in non-acidic environments(Fig. 3B), while A01 demonstrates greater acid tolerance, provides a explanation for their coexistence in nature. It may be interesting to explore how niche differentiation between these strains might have evolved from a common ancestor in evolutionary experiments in future studies.\nThe experiments are well designed and carefully executed to support the hypothesis, and the findings are presented clearly. I would strongly support the publication of this work in Nature Communications."}, "author_response": "General responses to the reviewers' comments\nWe sincerely appreciate the careful way the reviewers examined the manuscript and are pleased with the positive comments. Meanwhile, their constructive criticisms and valuable suggestions have greatly benefited the precision, scientific rigor, and robustness of the manuscript. Accordingly, strictly following the reviewers' comments, we have performed extensive additional experiments, clearly defined terminology, analyzed interaction mechanisms, and refined figure presentations. Substantial revisions have been made to the manuscript, greatly enhancing the robustness and reliability of our study. The precise amendments are elaborated upon below and are highlighted in red in the revised manuscript.\nPoint-to-point responses to the reviewers' comments\nReviewer #1 (Remarks to the Author):\n[...]"}, {"round": 2, "reviewer_comments": {"reviewer_2": "Review of Revised Manuscript: \"Acid Stress-Selected Slower-Growing Species Promote Interspecies Cooperation and Stress-Resistant Coexistence through Cross Feeding\"\nI thank the authors for their diligent efforts in revising the manuscript based on the comments provided in the previous review round. The authors have made substantial changes to the manuscript and supplementary materials, which have significantly improved the clarity, detail, and overall quality of the work. The inclusion of additional experiments, clarifications of methodology and terminology, and refinements to the figures are appreciated.\nI will now assess how the specific concerns raised previously have been addressed:\nMajor Issue 1: Metabolite Dynamics and Cross-Feeding\nI requested more direct evidence that pyruvate, glutamate, and aspartate drive the shift to cooperation—e.g., supplementation assays and inhibition of public‐goods production. In their rebuttal, the authors report three new lines of evidence: (1) re-culturing B01 in A01’s spent medium (Figure S23) showing both B01 regrowth and depletion of the public goods; (2) mono-cultures of B01 in lactate-acidified medium supplemented with graded concentrations of those metabolites, demonstrating dose-dependent biomass increases (Figure 5D); and (3) use of 3-bromopyruvate to inhibit public-goods production, which abolishes B01 regrowth in co-culture (Figures 5E, 5F, and S25). These complementary experiments convincingly establish a causal role for the identified metabolites in cross-feeding and satisfactorily address this concern.\nMajor Issue 2: Mechanistic Gaps in Discussion\nI noted the need for deeper insight into why and how these metabolites are secreted under stress and how they feed back on A01. The authors added temporal metabolomics (Figure S26), showed that B01’s uptake of A01’s metabolites enhances A01’s glycolytic flux and ATP synthesis (Figures S27B,G), and expanded their discussion of metabolic pathways in the Results and Discussion sections. These additions substantially deepen the mechanistic narrative and appropriately integrate model and empirical data.\nMajor Issue 3: Statistical Transparency\nI asked for clearer reporting of replicates, error bars, and statistical tests. The authors clarified that some panels plot means ± SD (n = 3) while others show single averages, updated all figure legends to specify the number of replicates and statistical comparisons, and detailed their use of one-way ANOVA where applied (e.g. Figures 2D, 3E). This now provides the necessary transparency.\nMajor Issue 4: Overgeneralization of Findings\nI cautioned against broad ecological extrapolations from this simplified agar model. The authors have tempered their conclusions, added a dedicated paragraph in the Discussion acknowledging the limitations of single-stress, few-species systems, and suggested directions for validating these principles in more complex environments (soils, gut microbiomes). This appropriately confines their claims.\nMajor Issue 5: Conceptual Ambiguity\nI pointed out undefined terms (“public goods,” “cross-feeding,” etc.). The authors now include a clearly defined glossary (Table S1) and an explicit review of cross-feeding types—unidirectional and reciprocal—supported by literature citations. This resolves the ambiguity.\nMinor Issues\nAll of the minor points—rewriting unclear sentences (Lines 47–49; 137), correcting “coating count” to “colony counts,” improving figure legends and labels, and eliminating grammatical errors—have been addressed through targeted revisions in the text and figures .\n--------\nRecommendation\nThe authors have thoroughly and effectively addressed the substantive and editorial concerns I raised. I therefore recommend that this manuscript be accepted pending minor revisions (solely to incorporate any final editorial polish), as all major and minor issues have now been satisfactorily resolved.", "reviewer_3": "I appreciate the thorough revisions made in response to my question. My concern is well addressed.", "reviewer_4": "The authors have put extensive work into addressing reviewer comments. I believe that there are still several issues that need to be dealt with to provide clarity.\n1) The data in figure 4 is unclear. I assume that the samples must be destructively sampled, but the legend states that samples are all “measured from the same co-culture.” This should be clarified. It is also unclear whether the data represent the average of three biological replicates or three technical replicates (i.e. 3 repeated measurements) of a single biological replicate. Similarly, the statement “data are presented as two experimental values” is unclear. Does that mean 2 biological replicates? I would encourage the author to state that error bars are not shown because they are too small to see. In panel D, I would encourage the authors to make the x axis scale with time. Currently the 6-hour intervals are represented as the same size as the 3-hour intervals.\n2) In lines 189 and 868 I do not believe it is accurate to say this represents the largest distance over which communication has been observed. For example, PMCID: PMC7211448 has documented chemical effects at larger distances.\n3) Line 287 it is not clear what “game-like” means.\n4) The term “evolutionary interactions” is used repeatedly (e.g. lines 326, 327, 373) and is unclear. Perhaps the intent is to suggest that the response acid is an adaptation to optimize species interactions. I do not believe there is data to support that claim if that is the intent.\n5) In lines 425 and 433 statements are made about differences in biomass but it is not clear that these differences are statistically significant. This is particularly relevant as the statement in 433 is used to suggest that the acid-stress generates a mutualism rather than unidirectional facilitation. In the absence of a significant increase in biomass in A01 I do not think that bidirectional cooperation has been documented even with the ATP measurements.\n6) In line 892 I would encourage the authors not to use the term “ecological intelligence” and it is likely to cause confusion."}, "author_response": "General responses to the reviewers' comments\nWe would like to thank the editor and reviewers for their helpful and constructive feedback. We have revised the manuscript accordingly. Below, please find the list of changes made. In the attached revised document, all the changes are marked in a red font to make it easier for the reviewers to examine the modifications.\nPoint-to-point responses to the reviewers' comments\nReviewer #4 (Remarks to the Author):\n[...]"}, {"round": 3, "reviewer_comments": {"reviewer_4": "The authors have sufficient addressed my concerns."}, "author_response": "We sincerely appreciate your comments and suggestions, which are very constructive for improving the quality and understandability of this manuscript. Thank you very much again! Hope you everything goes well."}]

Decision letter