1. Ecology
  2. Evolutionary Biology

A toxin-mediated policing system in Bacillus optimizes division of labor via penalizing cheater-like nonproducers

  1. Rong Huang
  2. Jiahui Shao
  3. Zhihui Xu
  4. Yuqi Chen
  5. Yunpeng Liu
  6. Dandan Wang
  7. Haichao Feng
  8. Weibing Xun
  9. Qirong Shen
  10. Nan Zhang  Is a corresponding author
  11. Ruifu Zhang  Is a corresponding author
  1. Jiangsu Provincial Key Lab of Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center of Solid Organic Wastes, Nanjing Agricultural University, China
  2. State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultura Sciences, China
  3. National Engineering Research Center for Efficient Utilization of Soil and Fertilizer Resources, College of Resources and Environment, Shandong Agricultural University, China
https://doi.org/10.7554/eLife.84743
Share this article
Cite this article
  1. Rong Huang
  2. Jiahui Shao
  3. Zhihui Xu
  4. Yuqi Chen
  5. Yunpeng Liu
  6. Dandan Wang
  7. Haichao Feng
  8. Weibing Xun
  9. Qirong Shen
  10. Nan Zhang
  11. Ruifu Zhang
(2023)
A toxin-mediated policing system in Bacillus optimizes division of labor via penalizing cheater-like nonproducers
eLife 12:e84743.
https://doi.org/10.7554/eLife.84743
  2. Download BibTeX
  3. Download .RIS

Abstract

Division of labor, where subpopulations perform complementary tasks simultaneously within an assembly, characterizes major evolutionary transitions of cooperation in certain cases. Currently, the mechanism and significance of mediating the interaction between different cell types during the division of labor, remain largely unknown. Here, we investigated the molecular mechanism and ecological function of a policing system for optimizing the division of labor in Bacillus velezensis SQR9. During biofilm formation, cells differentiated into the extracellular matrix (ECM)-producers and cheater-like nonproducers. ECM-producers were also active in the biosynthesis of genomic island-governed toxic bacillunoic acids (BAs) and self-resistance; while the nonproducers were sensitive to this antibiotic and could be partially eliminated. Spo0A was identified to be the co-regulator for triggering both ECM production and BAs synthesis/immunity. Besides its well-known regulation of ECM secretion, Spo0A activates acetyl-CoA carboxylase to produce malonyl-CoA, which is essential for BAs biosynthesis, thereby stimulating BAs production and self-immunity. Finally, the policing system not only excluded ECM-nonproducing cheater-like individuals but also improved the production of other public goods such as protease and siderophore, consequently, enhancing the population stability and ecological fitness under stress conditions and in the rhizosphere. This study provides insights into our understanding of the maintenance and evolution of microbial cooperation.

Editor's evaluation

This manuscript reports notable findings regarding the potential for self-policing and a division of labor among biofilm-inhabiting Bacillus cells. Overall, this work is robust in its use of various techniques and provides solid insights into the intersections of well-understood regulatory controls and the suppression of cheaters. Colleagues interested in microbial social interactions should find this study's narrative about the internal mediation of cell differentiation valuable.

https://doi.org/10.7554/eLife.84743.sa0

Introduction

Cooperative interactions are not restricted to complex, higher organisms, but are also prevalent among microbial communities in many contexts (Kehe et al., 2021; Rakoff-Nahoum et al., 2016; Wenseleers and Ratnieks, 2006). Both natural selection and game theory predict that cooperative systems are vulnerable to non-cooperative cheaters that exploit the benefit, such as public goods including extracellular enzymes (Chen et al., 2019), siderophore (Griffin et al., 2004), or biofilm matrix (Dragoš and Kovács, 2017; Vlamakis et al., 2013) since these selfish individuals enjoy the common resources without paying their cost (Hardin, 1968; Martin et al., 2020; West et al., 2006). Intriguingly, cooperation principally survives cheating during evolutionary history (Travisano and Velicer, 2004), and a couple of mechanisms have been proposed to play significant roles in maintaining cooperation by preventing cheater invasion (Özkaya et al., 2017; Travisano and Velicer, 2004). These strategies mainly include kin selection/discrimination (Özkaya et al., 2017; Diggle et al., 2007; McNally et al., 2017), facultative cooperation regulated by a quorum-sensing (QS) system (Allen et al., 2016), or nutrient fitness cost (Sexton and Schuster, 2017), coupling production of public and private goods (Dandekar et al., 2012), punishment of cheating individuals by cooperator-produced antibiotics (García-Contreras et al., 2015; Wang et al., 2015), partial privatization of public goods under certain conditions (Jin et al., 2018; Otto et al., 2020), and spatial structuring (van Gestel et al., 2014). In general, the emergence of multiple sanction mechanisms is a consequence of natural selection, which suppresses social cheaters and enhances the altruistic behavior, thereby maintaining microbial community stability and improving their adaptation in different niches (Özkaya et al., 2017).

In certain cases, microbial cooperation involves the division of labor, where subpopulations of cells are specialized to perform different tasks (Dragoš et al., 2018a; Strassmann and Queller, 2011). Division of labor requires three basic conditions: individuals exhibit different tasks (phenotypic variation); some individuals carry out cooperative tasks that benefit other individuals (cooperation); all individuals gain an inclusive fitness benefit from the interaction (adaptation) (Dragoš et al., 2018b; West and Cooper, 2016). For instance, Bacillus subtilis colony will phenotypically differentiate into surfactin-producing and matrix-producing cells during sliding motility, where the surfactin reduces the friction between cells and their substrate, while the matrix assembles into van Gogh bundles that drive the migration (Jordi et al., 2015). Another typical case is in early-stage biofilms, an extracellular matrix (ECM)-the enclosed multicellular community that sustains bacterial survival in diverse natural environments; it is known that B. subtilis cells can differentiate into motile cells and matrix-producing cells during biofilm formation (Vlamakis et al., 2008; Chai et al., 2008; Shank and Kolter, 2011; López and Kolter, 2010; López et al., 2009c; López et al., 2009a; van Gestel et al., 2015; Vlamakis et al., 2013; Kearns, 2008). The advantage of the division of labor is to efficiently integrate distinct cellular activities, thereby endowing a community with higher fitness than undifferentiated clones (West and Cooper, 2016; Zhang et al., 2016).

Importantly, efficient division of labor relies on elaborate coordination of cell differentiation (Cremer et al., 2019; López et al., 2009b; Lord et al., 2019). In relative to the subpopulation producing a certain public good (e.g. cells producing ECM during biofilm formation), the nonproducing cells that can also enjoy this common good, actually become the ‘cheater-like’ individuals to some extent (although they may provide other contributions to the community) (Claessen et al., 2014; Otto et al., 2020; West and Cooper, 2016). Therefore, regulating the proportion of each cell type and alignment of interests, is important for maintaining the stability and fitness of the division of labor (West and Cooper, 2016), while an unbalanced cell differentiation will reduce the population productivity and even cause a collapse of the division of labor (Dragoš et al., 2018a). Despite the knowledge of pathways controlling cell differentiation in microbes, little is known about how the different cell types interact with each other and the fitness consequences of their interaction (van Gestel et al., 2015). Although a few studies have investigated the overlap between public goods production and cell cannibalism (González-Pastor et al., 2003; López et al., 2009c), as well as matrix privatization (Otto et al., 2020) during cell differentiation, the molecular mechanism involved in coordinating the cheater-like individuals in the division of labor, as well as the ecological significance of the policing system in regulating population stability and fitness, remain unclear. Accordingly, lacking these knowledge limits our understanding of cooperation and altruism within microbial social communities.

Bacillus velezensis SQR9 (formerly B. amyloliquefaciens SQR9) is a well-studied beneficial rhizobacterium that forms robust and highly structured biofilms on the air-liquid interface and plant roots (Qiu et al., 2014; Xu et al., 2019a; Xu et al., 2013; Cao et al., 2011). Strain SQR9 harbors a novel genomic island 3 (GI3) consisting of four operons, where the second, third, and fourth operons are responsible for the production of the novel branched-chain fatty acids, BAs, while the first operon encodes an ABC transporter to export toxic BAs for self-immunity (Wang et al., 2019). Production of toxic BAs was proved to occur in the subfraction of cells with the self-immunity ability induced by BAs during biofilm formation, where the nonproducing siblings will be lysed by BAs (Huang et al., 2021; Wang et al., 2019). Based on the manifestation that the BA-mediated cannibalism enhanced the biofilm formation of strain SQR9, we hypothesized the ECM and BAs synthesis can be co-regulated to restrain the cheater-like individuals that don’t produce ECM, thereby optimizing the division of labor and altruistic behavior. Using a combination of single-cell tracking techniques, molecular approaches, and ecological evaluation, we demonstrated that ECM and BAs production are coordinated in the same subpopulation by the same regulator during biofilm formation, which enforces punishment of the cheater-like nonproducers to maintain community stabilization; also this genomic island-governed policing system is significant to promote community fitness in various conditions.

Results

Coordinated production of ECM and autotoxin BAs punishes cheater-like nonproducers in the B. velezensis SQR9 community

Bacillus cells in early-stage biofilms are known to contain specialized groups as motile cells and matrix-producing cells (Kearns, 2008; van Gestel et al., 2015; Vlamakis et al., 2008). We hypothesized that secretion of cannibal toxin BAs can eliminate ECM nonproducers in B. velezensis SQR9 biofilm, and try to determine the subpopulation for ECM (public goods) production and BAs (autotoxin) biosynthesis/BAs-induced self-immunity, as well as their interactions. We fused promoters for genes related to extracellular polysaccharides (EPS) and TasA fibers (two dominant ECM components in Bacillus biofilm Vlamakis et al., 2013) biosynthesis with mCherry, while the promoters for genes related to the autotoxin BAs biosynthesis and the self-immunity with gfp, obtained the transcriptional reporter Peps-mCherry, PtapA-mCherry, PbnaF-gfp, and PbnaAB-gfp, respectively. Their expression patterns were monitored using confocal laser scanning microscopy (CLSM) during the biofilm community formation. Photographs show that expression of the Peps-mCherry, PtapA-mCherry, PbnaF-gfp, and PbnaAB-gfp were all observed in a subpopulation cells of the whole community (Figure 1), which suggests a functional division of labor during biofilm formation; this cell differentiation pattern also indicates the ECM-nonproducers can be recognized as the cheater-like individuals (Otto et al., 2020). Importantly, the overlay of the double fluorescent reporters indicates that ECM and BAs production is generally raised in the same subpopulation (Figure 1; the yellow cells represent co-expression of mCherry and gfp), the flow cytometry also confirms the positive correlation between the two reporters within the picked cells as expected (Figure 1—figure supplement 1), since the self-immunity gene bnaAB was reported to be specifically activated by endogenous BAs (Huang et al., 2021), it was also preferentially expressed in the same subpopulation with ECM-producers (Figure 1, Figure 1—figure supplement 1). These results demonstrate general coordination of ECM production and BAs synthesis/immunity in the same subpopulation of the B. velezensis SQR9 biofilm community.

Figure 1 with 1 supplement see all
Download asset Open asset
Expression of ECM production and BAs biosynthesis/immunity were located in the same subpopulation.

Fluorescence emission patterns of double-labeled strains. Colony cells of different double-labeled strains were visualized using CLSM to monitor the distribution of fluorescence signals from different reporters. Peps-mCherry and PtapA-mCherry were used to indicate cells expressing extracellular polysaccharides (EPS) and TasA fibers production, respectively; PbnaF-gfp and PbnaAB-gfp were used to indicate cells expressing BAs synthesis and self-immunity, respectively. The bar represents 5 μm.

Based on the co-expression pattern, we postulated that the ECM-nonproducing cheater-like cells, synchronously being sensitive to the BAs, could be killed by their siblings that produce both public goods ECM and the autotoxin BAs. Combining propidium iodide (a red-fluorescent dye for labeling dead cells) staining with reporter labeling, we monitored the cell death dynamics during the biofilm formation process in real-time. It was observed that a portion of the cells that didn’t produce public ECM or toxic BAs, or silenced in expression of the self-immunity gene bnaAB (cells without GFP signal), were killed by adjacent corresponding producers during the biofilm development process (Figure 2), while these producers remained alive throughout the incubation (Figure 2—videos 1–4); importantly, the number of dead cells adjacent to the producers was significantly higher than that closed to the non-producers (Figure 2—figure supplement 1). This lysis can be attributed to the BAs produced by the gfp-activated cells, as cannibalism of B. velezensis SQR9 was largely dependent on the production of this secondary metabolism (Huang et al., 2021). Taken together, the double-labeling observation and cell death dynamics detection indicate that the subpopulation of ECM and BAs producers selectively punish the nonproducing siblings, depending on a coordinately activated cell-differentiation pathway.

Figure 2 with 5 supplements see all
Download asset Open asset
ECM and BAs producing subpopulations eliminated the nonproducing cheaters.

The time-lapse experiment for observing the source and distribution of dead cells. Colony cells of different gfp-labeled strains were stained with propidium iodide (PI, a red-fluorescent dye for labeling dead cells) for 15 min, and then visualized by a CLSM to monitor the distribution of fluorescence signal from reporters and the PI dye. ‘0 min’ represents the time point at which cells are alive as shown by the arrow, ‘3 min’ or ‘6 min’ is the time point afterward, and the cells at the arrow die or even break apart. Peps-gfp and PtapA-gfp were used to indicate cells expressing extracellular polysaccharides (EPS) and TasA fibers production, respectively; PbnaF-gfp and PbnaAB-gfp were used to indicate cells expressing BAs synthesis and self-immunity, respectively. The total number of cells is 198 for strain SQR9-Peps-gfp, 71 for strain SQR9-PtasA-gfp, 88 for strain SQR9-PbnaF-gfp, and 162 for strain SQR9-PbnaAB-gfp. The bar represents 5 μm.

Spo0A is the co-regulator for triggering ECM production and BAs synthesis/immunity

To identify the potential co-regulator(s) of ECM production and BAs synthesis/immunity in B. velezensis SQR9, we evaluated the BAs production in an array of mutants that are known to be altered in ECM synthesis (ΔdegU, ΔcomPA, ΔabrB, ΔsinI, ΔsinR, and Δspo0A), by measuring their antagonism towards B. velezensis FZB42, a target strain specifically inhibited by BAs but no other antibiotics secreted by SQR9 (Wang et al., 2019). The BAs extract of wild-type SQR9 showed remarkable antagonism to the lawn of strain FZB42 (Figure 3A and B); only Δspo0A but no other mutants (all with the equal cell density of the wild-type), revealed a significantly reduced inhibition zone towards FZB42, and the complementary strain generally restored the antagonistic ability (Figure 3A and B). Spo0A is a well-investigated master regulator that governs multiple physiological behaviors in B. subtilis and closely-related species Hamon and Lazazzera, 2001; Molle et al., 2003; Xu et al., 2019b; as expected, the EPS production and biofilm formation was seriously impaired in Δspo0A (Figure 3—figure supplement 1). Intriguingly, Δspo0A but neither its complementary strain nor the wild-type, can be substantially inhibited by the BAs extract of strain SQR9, while Δspo0A was not inhibited by ΔGI3 that disabled in BAs production (Figure 3C), suggesting Spo0A does participate in the immunity to BAs. In addition, we constructed gfp transcriptional fusions to the promoter of genes involved in ECM production (eps & tapA) and BAs biosynthesis/immunity (bnaF/bnaAB) and discovered that under both liquid culture (Figure 3D) and plate colony conditions (Figure 3—figure supplement 2), their expression level was significantly decreased in Δspo0A as compared with the wild-type, which was restored in the complementary strain Δspo0A/spo0A. These results suggest that the global regulator Spo0A is the co-regulator for controlling ECM production and BAs biosynthesis/immunity in B. velezensis, which is probably dependent on the transcriptional regulation of certain relevant genes.

Figure 3 with 2 supplements see all
Download asset Open asset
Spo0A is the co-regulator for triggering ECM production and BAs synthesis/immunity.

(A) Oxford cup assay. Inhibition of the lawn of B. velezensis FZB42 by the BAs extract of wild-type SQR9, its different mutants altered in ECM production, and complementary strain Δspo0A/spo0A. (B) Quantification of inhibition zone. Diameter of the inhibition zones is observed in (A). (C) Oxford cup assay. Sensitivity of wild-type SQR9, Δspo0A, and Δspo0A/spo0A (as the lawn) to the extracellular extract of SQR9 and its mutant ΔGI3 that disable BAs synthesis. (D) Quantification of fluorescence in liquid culture. The expression level of eps, tapA, bnaF, and bnaAB in wild-type SQR9, Δspo0A, and Δspo0A/spo0A, as monitored by using gfp reporters fused to the corresponding promoters. Data are means and standard deviations from three biological replicates. * indicates a significant difference with the Control (SQR9) column as analyzed by Student’s t-test (p<0.05).

Figure 3—source data 1

Related to Figure 3B.

https://cdn.elifesciences.org/articles/84743/elife-84743-fig3-data1-v2.xlsx
Figure 3—source data 2

Related to Figure 3D.

https://cdn.elifesciences.org/articles/84743/elife-84743-fig3-data2-v2.xlsx

Spo0A activates acetyl-CoA carboxylase (ACC) to support BAs synthesis and self-immunity

In Bacillus, Spo0A governs the regulatory pathway for matrix gene (the eps and tapA-sipW-tasA operons) expression by controlling the activity of the regulators SinR and AbrB (Vlamakis et al., 2013), but how it mediates BAs synthesis and self-immunity remains unknown. We used biolayer interferometry analysis (BLI) for detecting molecular interaction signals between protein and DNA fragments (an increased signal during association and a decreased signal during dissociation). Results showed that the purified protein Spo0A cannot directly bind to the promoter of bnaF, suggesting it doesn’t induce BAs production through direct transcriptional activation (Figure 4—figure supplement 1). Alternatively, Spo0A has been reported to stimulate the expression of accDA that encodes ACC (Diomandé et al., 2015; Pedrido et al., 2013), which catalyzes acetyl-CoA to generate malonyl-CoA, an essential precursor for BAs biosynthesis (Figure 4A; Wang et al., 2019); therefore, we postulated accDA may be involved in the regulation of BAs production/immunity by Spo0A. We firstly verified the positive regulation of Spo0A on accDA expression in B. velezensis SQR9 by gfp fusion (Figure 4B, Figure 4—figure supplement 2 ). Since knockout of accDA, the essential gene for fatty acids biosynthesis, significantly impacts bacterial growth, we alternatively constructed a strain in which the original promoter of accDA was replaced by a xylose-inducible promoter (Pxyl), and monitored its BAs synthesis/immunity under different xylose induction conditions. The SQR9-Pxyl-accDA lost the antagonism ability towards target strain FZB42 in the absence of xylose, while the inhibition was significantly enhanced with the induction of xylose in a dose-dependent manner (Figure 4C and D). Since exogenous xylose didn’t influence the suppression of wild-type SQR9 on FZB42 (Figure 4C and D), these results suggest that accDA expression positively contributes to BAs production. Importantly, the SQR9-Pxyl-accDA was proved to be sensitive to SQR9-produced BAs without xylose addition, and the immunity was gradually restored with xylose supplement (Figure 4E, Figure 4—figure supplement 3). The xylose-induced transcription of accDA, also resulted in enhanced expression of genes involved in self-immunity (bnaAB; Figure 4F, for quantitative intensity please see Figure 4F, Figure 4—figure supplement 4A, C), but not BAs synthesis (bnaF; Figure 4F and Figure 4—figure supplement 4B, D), as the AccDA-derived malonyl-CoA accumulation affects BAs production in a post-transcriptional manner. The CLSM photographs and flow cytometry analysis also reveal that the activation of accDA (mCherry fusion) and bnaAB (gfp fusion) was located in the same subpopulation cells (Figure 4—figure supplement 5). Accordingly, these results indicate the positive regulation of Spo0A on BAs production/immunity in B. velezensis SQR9, is strongly dependent on accDA that encodes ACC.

Figure 4 with 5 supplements see all
Download asset Open asset
Spo0A activates ACC for BAs synthesis and self-immunity.

(A) Involvement of ACC in the biosynthesis of BAs in B. velezensis SQR9. ACC catalyzes acetyl-CoA to generate malonyl-CoA, which is transformed to malonyl-ACP under the catalyzation of ACP transacylase; then malonyl-ACP and acetyl-CoA are aggregated into a C5 primer, the precursor for BAs synthesis. (B) Quantification of fluorescence in liquid culture. The expression level of accDA in wild-type SQR9, Δspo0A, and Δspo0A/spo0A, as monitored by using the PaccDA-gfp reporter. (C) Oxford cup assay. Inhibition of the lawn of B. velezensis FZB42 by the BAs extract of wild-type SQR9 and SQR9-Pxyl-accDA, with the addition of different concentrations of xylose (0%, 0.1%, and 0.2%). (D) Quantification of inhibition zone. Diameter of the inhibition zones is observed in (C). (E) Oxford cup assay. Sensitivity of wild-type SQR9 and SQR9-Pxyl-accDA (as the lawn) to the BAs extract of SQR9 (100 μL (1x) or 200 μL (2x)), with the addition of different concentrations of xylose (0%, 0.1%, and 0.2%). (F) Colony fluorescence. Expression of bnaF and bnaAB in the colony cells of wild-type SQR9 and SQR9-Pxyl-accDA, with the addition of different concentrations of xylose (0%, 0.1%, and 0.2%). Colonies were observed under both bright fields (BF in the figure) and GFP channel, to monitor the fluorescence of PbnaF-gfp and PbnaAB-gfp reporters in different strains. The bar represents 1 mm. Data are means and standard deviations from three biological replicates. * in (B) indicates a significant difference (p<0.05) with the Control (SQR9) column as analyzed by Student’s t-test; columns with different letters in (D) are statistically different according to Duncan’s multiple range test (‘a’ for wild-type SQR9 under different concentrations of xylose and ‘a'’ for SQR9-Pxyl-accDA; p<0.05).

Figure 4—source data 1

Related to Figure 4B.

https://cdn.elifesciences.org/articles/84743/elife-84743-fig4-data1-v2.xlsx
Figure 4—source data 2

Related to Figure 4D.

https://cdn.elifesciences.org/articles/84743/elife-84743-fig4-data2-v2.xlsx

The co-regulation policing system optimizes the division of labor and promotes population fitness

Having illustrated the molecular mechanism of the co-regulation pathway for punishing nonproducing cheater-like cells in B. velezensis SQR9, we wondered about the broad-spectrum ecological significance of this policing system for B. velezensis SQR9 at a community level. We constructed two mutants with disabled sanction mechanism, the ΔbnaV deficient in BAs synthesis (loss of the punishing weapon) and the SQR9-P43-bnaAB that continually expresses the self-immunity genes (cheater-like individuals cannot be punished by the weapon BAs), both mutants showed similar growth characteristics with the wild-type (Figure 5—figure supplement 1). We first applied flow cytometry analysis to test whether the lack of the policing system (ΔbnaV and SQR9-P43-bnaAB) impairs the punishment of public goods-nonproducers during biofilm formation. The proportion of matrix-producing cooperators (eps & tapA active cells) in the wild-type community, as well as the average expression level of corresponding genes, were significantly higher than that in the ΔbnaV or SQR9-P43-bnaAB community (Figure 5A and B), suggesting the division of labor in the two mutants population was significantly different with the wild-type. Consequently, the wild-type established a more vigorous biofilm as compared with the two mutants, as shown by the earlier initial progress, larger maximum biomass, and delayed dispersal process (prolonged stationary phase) (Figure 5C and D). Additionally, the robust biofilm formed by the wild-type also endowed them with stronger resistance against different stresses, including antibiotics, salinity, acid-base, and oxidation (Figure 5D, Figure 5—figure supplement 2 and Figure 5—figure supplement 3). These data indicate the policing system in wild-type SQR9 ameliorates the division of labor during biofilm formation, thereby promoting community fitness.

Figure 5 with 4 supplements see all
Download asset Open asset
The co-regulation policing system optimizes the division of labor and enhances population fitness.

(A) Flow cytometry monitoring the expression of Peps-gfp and PtapA-gfp reporters in wild-type SQR9, SQR9ΔbnaV, and SQR9-P43-bnaAB. (B) Quantification of (A). The proportion of the active cells (%) and average FITC in wild-type SQR9, SQR9ΔbnaV, and SQR9-P43-bnaAB, as monitored by Peps-gfp and PtapA-gfp reporters using flow cytometry. (C) Pellicle morphology. Pellicle formation dynamics of wild-type SQR9, SQR9ΔbnaV, and SQR9-P43-bnaAB in MSgg medium. (D) Quantification of pellicles. Pellicle weight dynamics of wild-type SQR9, SQR9ΔbnaV, and SQR9-P43-bnaAB in MSgg medium under normal (corresponds to (C)) or stressed conditions (H2O2, tetracycline, or 7% NaCl). (E) Qualitative analysis of protease or siderophore yield. Production of proteases and siderophore by wild-type SQR9, SQR9ΔbnaV, and SQR9-P43-bnaAB colonies. (F) Root colonization assay. Comparison of root colonization of wild-type SQR9, SQR9ΔbnaV, and SQR9-P43-bnaAB. Data are means and standard deviations from three biological replicates; columns with different letters are significantly different according to Duncan’s multiple range tests, p<0.05.

Figure 5—source data 1

Related to Figure 5B.

https://cdn.elifesciences.org/articles/84743/elife-84743-fig5-data1-v2.xlsx
Figure 5—source data 2

Related to Figure 5D.

https://cdn.elifesciences.org/articles/84743/elife-84743-fig5-data2-v2.xlsx
Figure 5—source data 3

Related to Figure 5F.

https://cdn.elifesciences.org/articles/84743/elife-84743-fig5-data3-v2.xlsx

Besides the well-known regulation of biofilm matrix production, Spo0A also controls the production of other public goods such as proteases and siderophore (Fujita et al., 2005; Molle et al., 2003); it can be recognized as a critical switch that governs the cell transition from a free-living and fast-growing status (Spo0A-OFF), to a multicellular and cooperative style (Spo0A-ON) (Shank and Kolter, 2011; López et al., 2009c). Intrinsically, the punishing targets of this policing system are supposed not limited to the cheater-like matrix-nonproducers, but all of the Spo0A-OFF individuals (cells that don’t express the immune genes bnaAB, including protease-nonproducers and siderophore-nonproducers). Therefore, we determined the production of extracellular proteases and siderophore among the three strains, revealing that these public goods were also accumulated more in the wild-type than in these two mutants’ communities (Figure 5E, Figure 5—figure supplement 4). Importantly, the wild-type SQR9 demonstrated significantly stronger root colonization compared with the two mutant strains losing the cheater punishing system (Figure 5F). In summary, the Spo0A governed co-regulation punishment system effectively optimizes the division of labor and altruistic behavior in the B. velezensis population, by excluding the cheater-like nonproducers to a certain degree, consequently improving the population stability and ecological fitness under different conditions.

Discussion

Division of labor, where subpopulations perform complementary tasks simultaneously within an assembly, characterizes major evolutionary transitions of cooperation in certain cases (Babak, 2018). Unlike the diverse strategies for preventing obligate cheaters in cooperative systems (Özkaya et al., 2017; Smith and Schuster, 2019; Travisano and Velicer, 2004), division of labor requires an efficiency benefit and alignment of interests covering different specialized individuals (West and Cooper, 2016). For instance, compared with cells that produce a certain kind of public goods (e.g. ECM or extracellular hydrolases), the subpopulations that don’t perform these tasks (but still share these benefits) become cheater-like individuals, and their proportion needs to be controlled for maintaining community stability and fitness (Martin et al., 2020; West and Cooper, 2016). In the present study, we demonstrated that during biofilm formation, the beneficial rhizobacterium B. velezensis SQR9 engages a policing system that coordinately actives ECM production and autotoxin synthesis/immunity, to punish the cheater-like subpopulation silencing in public goods secretion and restrain their proportion in the community (Figure 6). Importantly, the optimized division of labor not only facilitates ECM accumulation but also contributes to elevated production of other public goods including proteases and siderophore, thereby improving the community fitness under different stressful conditions and in plant rhizosphere (Figure 5), which could be defined as an effective strategy for enhancing cooperation and altruism. The coordination policing system suppresses subpopulation that stays in a fast-growing, motility phase (Spo0A-OFF state), to promote the population to a stationary, resource-mining phase (Spo0A-ON state) when the environment required (Figure 6). Our finding coincides with the phenomenon that Spo0A-dependent toxin killing of Spo0A-OFF cells in B. subtilis enhances biofilm formation and delays sporulation progress, which can be attributed to both eliminating of matrix-nonproducers and releasing of available nutrients (González-Pastor et al., 2003; López et al., 2009c; Huang et al., 2021). It should be noted that the coordination system for optimizing the division of labor is relatively temperate than those for excluding obligate cheaters (Özkaya et al., 2018; Wang et al., 2015), as only a subpopulation of the cheater-like individuals were killed (Figure 2); we think this scene is a balance between restraining the cheater-like subpopulation and retaining the advantages of cell differentiation (Babak, 2018; Kaern et al., 2005; López et al., 2009c).

Figure 6
Download asset Open asset
A working model and ecological significance of the co-regulation policing system in B. velezensis.

In certain conditions (e.g. environmental or self-produced clues, surface attachments, etc.), Bacillus cells can differentiate into Spo0A-ON (~moderate phosphorylated) and Spo0A-OFF (unphosphorylated) subpopulations. The Spo0A-ON subpopulation is cooperators that produce public goods for the community, such as ECM or proteases; simultaneously they express AccDA to produce malonyl-CoA as the precursor for BAs biosynthesis, and the endogenous autotoxin activates immunity-required transporter BnaAB to pump them out. Comparatively, the Spo0A-OFF subpopulation is cheat-like individuals that are silenced in public goods secretion, which are also disabled in malonyl-CoA production and BAs biosynthesis/self-immunity. Consequently, the cooperators-produced BAs can effectively eliminate the cheater-like nonproducers, thereby optimizing the division of labor and enhancing population fitness.

The molecular working model of the present policing system, being Spo0A simultaneously regulates ECM production and also the toxin-antitoxin system (Figure 6), represents typical co-regulation machinery for mediating microbial social interactions (Dandekar et al., 2012; van Gestel et al., 2015; Wang et al., 2015). The opportunistic pathogen Pseudomonas aeruginosa engages the QS circuit (LasR-LasI and RhlI-RhlR systems) to couple the production of public and private goods for placing a metabolic restraint on cheaters (Dandekar et al., 2012; Whiteley et al., 2017); the P. aeruginosa cooperators can also punish LasR-null social cheaters by producing cyanide, where cooperators acquire immunity from the QS system while cheaters are sensitive to this toxin (Wang et al., 2015; Yan et al., 2019). During biofilm formation and sporulation by B. subtilis, the global regulator Spo0A simultaneously induces the production of matrix and cannibalism toxins (Skf and Sdp); since genes responsible for toxin synthesis and self-immunity are simultaneously expressed, the matrix producers can be resistant to these toxins while the sensitive nonproducers will be selectively penalized (Ellermeier et al., 2006; González-Pastor et al., 2003; López et al., 2009c). In the comparison of B. subtilis and B. velezensis SQR9, the similarity is that the global regulator Spo0A controls the synthesis of the ECM and the cannibalism toxin; the difference lies in the type of cannibalism toxin and its synthesis/regulation pathway. Specifically, the Spo0A-governed policing system in B. velezensis SQR9 is extremely unique: (i) The toxic BAs for punishment are novel antimicrobial fatty acids that firstly identified in strain SQR9, which mediate cannibalism and strongly inhibit the growth of closely related Bacillus strains; also its synthesis is encoded by a horizontal gene transfer (HGT)-acquired genomic island (Wang et al., 2019). (ii) Spo0A doesn’t mediate the BAs production/self-immunity in a direct transcriptional regulation way, but activates AccDA for accumulating the precursor for BAs biosynthesis (a post-transcriptional manner; Figure 4). The accumulated BA precursor may induce the expression of BA synthetase genes; additionally, the self-resistance is mainly induced by intracellular BAs through a two-component system (Huang et al., 2021). Therefore, the foreign genomic island and the indigenous Spo0A regulation pathways, constitute an ingenious coordination system for punishing cheater-like nonproducers and enhancing clonemate cooperation.

Relevant to how such a complex system could evolve, a possible scenario is a gradual evolution through transitional states. Perhaps homogeneous biofilm formation was the ancestral state, where all cells in the community are matrix producers (Khare and Shaulsky, 2006). Thereafter, heterogeneous biofilm raised as cells specialize in motile or matrix-producing subpopulations; it would be favored if the benefit of quickly responding to drop-in nutrients outweighed the cost of having cheater-like nonproducers that reduced the ability to form a biofilm (Hamilton, 1964; Smits et al., 2006; López et al., 2009c; Joan et al., 2011; West and Cooper, 2016). Furthermore, the heterogeneous biofilm strategy provides the evolutionary context of sanctioning behavior (Acar et al., 2008; López et al., 2009c; West and Cooper, 2016; Spratt and Lane, 2022). Interestingly, the genomic island responsible for BAs synthesis in B. velezensis SQR9 acquired through HGT, not only acts as a weapon for antagonizing closely related competitors (Wang et al., 2019), but also establishes a policing system for punishing cheater-like individuals within the biofilm community. Considering that bacterial biofilm is a major lifestyle in the natural environment (Hall-Stoodley et al., 2004), the dual ecological benefits probably explain why this large cluster was integrated into the genome of strain SQR9; also this case can provide inspiration for discovering novel molecular regulatory mechanisms and understanding microbial evolution events (Strassmann et al., 2000). Alternatively, this sanction system can work in concert with a privatization strategy to collectively enhance cooperation during biofilm formation (Otto et al., 2020).

In conclusion, the present study highlights the beneficial rhizobacterium B. velezensis SQR9 engages a policing system that coordinately actives ECM production and autotoxin synthesis/immunity, to penalize the cheater-like subpopulation silencing in public goods secretion, thereby enhancing the division of labor and community fitness. This study provides insights into the molecular mechanism involved in controlling cell differentiation, as well as the ecological roles of the policing system, which deepens our understanding of the maintenance and evolution of microbial cooperation and altruistic behavior.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Strain, strain background (Bacillus velezensis)SQR9Lab strainCGMCC accession No. 5808
Strain, strain background (Bacillus velezensis)FZB42Chen et al., 2007BGSC accession no. 10A6
Strain, strain background (Escherichia coli)Top 10InvitrogenHost for plasmids
Strain, strain background (Escherichia coli)BL21 (DE3)InvitrogenFor recombinant protein expression
Recombinant DNA reagentpNW33n
(plasmid)		Zhou et al., 2018B. subtilis-E. coli shuttle vector
Gene (Bacillus velezensis)spo0AGenBankV529_25300
Gene (Bacillus velezensis)bnaAGenBankV529_06410
Gene (Bacillus velezensis)bnaBGenBankV529_06420
Gene (Bacillus velezensis)bnaVGenBankV529_06620
Software, algorithmFlowJo V10FlowJo V10
Software, algorithmSPSSSPSS
OtherPropidium iodideInvitrogenL7012(20 mM)

Bacterial strains and growth conditions

Request a detailed protocol

The strains and plasmids used in this study are listed in Supplementary file 1a. Bacillus velezensis SQR9 (formerly B. amyloliquefaciens SQR9, China General Microbiology Culture Collection Center (CGMCC) accession no. 5808) was used throughout this study. B. velezensis FZB42 (Bacillus Genetic Stock Center (BGSC) accession no. 10A6) was used to test the BAs production by wild-type SQR9 and its mutants. Escherichia coli TOP 10 (Invitrogen, Shanghai, China) was used as the host for all plasmids. E. coli BL21 (DE3) (Invitrogen, Shanghai, China) was used as the host for recombinant protein expression. All strains were routinely grown at 37 °C in low-salt Luria-Bertani (LLB) medium (10 g L–1 peptone, 5 g L–1 yeast extract, 3 g L–1 NaCl). For biofilm formation, B. velezensis SQR9 and its mutants were cultivated in MSgg medium (5 mM potassium phosphate, 100 mM morpholine propanesulfonic acid, 2 mM MgCl2, 700 μM CaCl2, 50 μM MnCl2, 50 μM FeCl3, 1 μM ZnCl2, 2 mM thiamine, 0.5% glycerol, 0.5% glutamate, 50 μg of tryptophan per milliliter, 50 μg of phenylalanine per milliliter, and 50 μg of threonine per milliliter) at 37 °C (Branda et al., 2001). To collect the fermentation supernatant for antagonism assessment, B. velezensis SQR9 and its mutants were cultured in Landy medium (Landy et al., 1947) containing 20 g L–1 glucose and 1 g L–1 yeast extract. When necessary, antibiotics were added to the medium at the following final concentrations: zeocin, 20 μg mL–1; spectinomycin, 100 μg mL–1; kanamycin, 30 μg mL–1; ampicillin, 100 μg mL–1; chloramphenicol, 5 μg mL–1 for B. velezensis strains and 12.5 μg mL–1 for E. coli strains; erythromycin, 1 μg mL–1 for B. velezensis strains and 200 μg mL–1 for E. coli strains. The medium was solidified with 2% agar.

Reporter construction

Request a detailed protocol

For single-labeled strain, the promoter region of the testing gene and gfp fragment were fused through overlap PCR, and this transcriptional fusion was cloned into vector pNW33n using primers listed in Supplementary file 1b. For double-labeled strains, one promoter region was fused with gfp fragment, and the other promoter region was fused with the mCherry fragment. The two fusions were then fused in opposite transcription directions and cloned into vector pNW33n using primers listed in Supplementary file 1b. All constructions were transferred into competent cells of B. velezensis SQR9 and mutants when required.

Promoter replacement

Request a detailed protocol

Strain SQR9-Pxyl-accDA was constructed by replacing the original promoter of accDA (PaccDA) with a xylose-inducible promoter Pxyl. The approximately 800 bp fragments upstream and downstream of the PaccDA region were amplified from the genomic DNA of strain SQR9; the Spcr fragment was amplified from plasmid P7S6 (Feng et al., 2018), and the Pxyl promoter was amplified from the plasmid PWH1510 (Xu et al., 2019a). The four fragments were fused using overlap PCR in the order of the upstream fragment, Spcr, Pxyl, and the downstream fragment. The fusion was transferred into competent cells of B. velezensis SQR9 for generating transformants. Strain SQR9-P43-bnaAB was obtained by replacing the original promoter (PbnaAB) with a constitutive promoter P43. The primers used for constructing the four-fragment fusion are listed in Supplementary file 1b.

Fluorescence microscopy

Request a detailed protocol

Cells were inoculated from a fresh pre-culture and grown to mid-exponential growth at 37 °C in an LLB medium. Bacterial cultures were centrifuged at 4000 × g for 5 min, the pellets were washed and suspended in liquid MSgg to reach an OD600 of 1.0. One μL suspension was placed on a solid MSgg medium and was cultured at 37 °C for 12 h. Agarose MSgg pads were then inverted on a glass bottom dish (Nest). Cells were imaged using the Leica TCS SP8 microscope with the 63x oil-immersion objective lens. For GFP observation, the excitation wavelength was 488 nm and the emission wavelength was 500~560 nm; for mCherry observation, the excitation wavelength was 587 nm and the emission wavelength was 590~630 nm. Wild-type biofilms containing no fluorescent fusions were analyzed to determine the background fluorescence. The number of cells emitting mCherry, GFP, or both fluorescence was also collected for calculating the proportion; each treatment includes six biological replicates.

For the time-lapse experiment, after staining with propidium iodide (PI) for 15 min, images of early-stage biofilms on the agarose pad were recorded for 3 hr, with an interval of 3 min. Image acquisitions were also performed with the Leica TCS SP8 microscope with the 63x oil-immersion objective lens. Detectors and filter sets for monitoring of GFP and PI (excitation wavelength of 536 nm and emission wavelength of 608~652 nm) were used.

Flow cytometry

Request a detailed protocol

Biofilms of 16 hr were collected and re-suspended in 1 mL PBS buffer, and single cells were obtained after mild sonication. Cells were centrifuged at 4000 × g for 5 min and washed briefly with PBS. For flow cytometry, cells were diluted to 1:100 in PBS and measured on BD FACSCanto II. For GFP fluorescence, the laser excitation was 488 nm and coupled with 500–560 nm.

For assessment of the double-labeled strains, cells were diluted to 1:100 in PBS and measured on BD FACS Symphony SORP. For GFP fluorescence, the laser excitation was 488 nm coupled with 530/30 and 505LP sequential filters; for mCherry fluorescence, the laser excitation was 561 nm coupled with 610/20 and 600LP sequential filters.

Every replicate was analyzed for 20,000 events. FlowJo V10 software was used for data analysis and graph creating. Three replicates were analyzed for each treatment.

Preparation of the BAs extract

Request a detailed protocol

The BAs extract was prepared by thin layer chromatography (TLC). According to a previous study (Wang et al., 2019), the fermentation supernatant of strain SQR9 was separated on a TLC plate, and the inhibition zone on the lawn of strain FZB42 indicated the position of BAs. Then, silica gel powder with BAs was scraped and extracted by MeOH, which was used as the BAs extract.

Oxford cup assay

Request a detailed protocol

Inhibition of different SQR9-derived mutants on B. velezensis FZB42 was evaluated by the Oxford cup method. The suspension of strain FZB42 (~106 CFU mL–1) was spread onto LLB plates (10 × 10 cm) to grow as a bacterial lawn. A volume of 100 μL BAs extract produced by different mutants was injected into an Oxford cup on the lawn of strain FZB42. The plates were placed at 22 °C until a clear zone formed around the cup, and the inhibition diameter was scored. Each treatment includes three biological replicates.

BAs-sensitivity assessment

Request a detailed protocol

Cells were inoculated from a fresh pre-culture and grown to mid-exponential growth at 37 °C in an LLB medium. Afterward, diluted cell suspension (~106 CFU mL–1) was spread onto LLB plates to grow as a bacterial lawn. A volume of 100 μL BAs extracts from the wild-type SQR9 was injected into an Oxford cup on the lawn. The plates were placed at 22 °C for observation and determination of the inhibition zone. Each treatment includes three biological replicates.

Biolayer interferometry (BLI) measurements

Request a detailed protocol

To confirm whether Spo0A can bind PbnaF directly, determination of binding kinetics was performed on an Octet RED96 device (ForteBio, Inc, Menlo Park, US) at 25 °C with orbital sensor agitation at 1000 rpm. Streptavidin (SA) sensor tips (ForteBio) were used to immobilize 100 nM biotin-labeled PbnaF. Then, a baseline measurement was performed in the buffer PBST (PBS, 0.1% BSA, 0.02% Tween-20) for 300 s. The binding of Spo0A at different concentrations (100 nM, 250 nM, 500 nM, and 1000 nM) to PbnaF was recorded for 600 s followed by monitoring protein dissociation using PBST for another 600 s. The BLI data for each binding event was summarized as an ‘nm shift’ (the wavelength/spectral shift in nanometers) and KD values were determined by fitting to a 1:1 binding model.

Promoter activity testing via fluorescence intensity

Request a detailed protocol

For colony fluorescence, cells were inoculated from a pre-culture into a fresh LLB medium and grown at 37 °C with 170 rpm shaking until OD600 reached 0.5. One μL of the suspension was inoculated on a solid LLB medium and cultured at 37 °C. Colony morphology and fluorescence were recorded by the stereoscope. ImageJ software was used to measure GFP intensity. For liquid culture fluorescence, overnight cultures were transferred to a fresh LLB medium. Fluorescence intensity was determined by a microtiter plate reader. Each treatment includes three biological replicates.

Xylose induction assay

Request a detailed protocol

For the xylose-induced BAs production assay, 30 μL overnight culture of SQR9-Pxyl-accDA or wild-type SQR9 was transferred respectively into 3 mL fresh LLB liquid with different concentrations of xylose (0%, 0.1%, and 0.2%) and incubated at 37 °C, 170 rpm for 24 hr. Cell suspensions were adjusted to the same OD600 and were centrifuged at 12,000 × g for 1 min. The cell-free supernatant was mixed with MeOH (volume ratio 2:1) to extract BAs. A volume of 100 μL extract was injected into an Oxford cup on the lawn of strain FZB42 (as described above). The plates were placed at 22 °C.

For the xylose-induced self-immunity assay, strain SQR9-Pxyl-accDA was grown in LLB without xylose for 24 hr. The cell suspension was spread onto LLB plates containing different concentrations of xylose (0%, 0.1%, and 0.2%) to grow as the lawn. A volume of 100 μL (1x) or 200 μL (2x) BAs extract from the wild-type SQR9 was injected into an Oxford cup on the lawn. The plates were placed at 22 °C.

For xylose-induced gene expression assay, cells were inoculated from a pre-culture into fresh LLB medium with different concentrations of xylose (0%, 0.1%, and 0.2%), and were grown at 37 °C with 170 rpm shaking until OD600 reached 0.5. One μL of suspension was inoculated on a solid LLB medium and was cultured at 37 °C, colony morphology and fluorescence were recorded by the stereoscope.

Each treatment in these assays includes three biological replicates.

Biofilm formation

Request a detailed protocol

Cells were inoculated from a fresh pre-culture and grown to mid-exponential growth at 37 °C in an LLB medium. Bacterial cultures were centrifuged at 4000 × g for 5 min, the pellets were washed and suspended in MSgg medium to an OD600 of 1.0. For colony observation, 1 μL of suspension was inoculated on a solid MSgg medium and cultured at 37 °C, then the colony morphology was recorded by the stereoscope. For pellicle observation, the suspension was inoculated into MSgg medium with a final concentration of 1% in a microtiter plate well, and the cultures were incubated at 37 °C without shaking.

Besides, the ability of the strain to form biofilm under stress was measured in the 48-well microtiter plate according to the method described above. When required, reagents that simulate stress were supplemented in the MSgg medium before inoculating, including oxidative stress (0.0025% H2O2), salt stress (7% NaCl), acid stress (pH 5), alkaline stress (pH 8), and antibiotic stress (4 µg mL–1 tetracycline or 20 µg mL–1 streptomycin). The amount of reagent added was determined according to a concentration gradient in the pre-experiment, and a concentration was chosen to inhibit wild-type growth without killing it. At different stages of biofilm development (initiation, progress, maturity, and dispersal), the MSgg medium underneath the biofilm was carefully removed by pipetting, and then the biofilm was taken and weighed.

Each treatment includes three biological replicates.

Root colonization assay in hydroponic culture

Request a detailed protocol

The bacterial suspension was inoculated into 1/4 Murashige-Skoog medium to make the final OD600 value to be 0.1, into which sterile cucumber seedlings with three true leaves were immersed. After being cultured with slowly shaking for two days, cells colonized on cucumber roots were determined by plate colony counting. In detail, roots were washed eight times in PBS to remove free and weakly attached bacterial cells. After vortexing for 5 min, until colonized bacteria were detached from roots, 100 μL of the bacterial suspension was plated onto LLB agar plates for quantification. Each treatment includes three biological replicates.

Measurement of public goods production

Request a detailed protocol

Qualitative measurement of proteases production was done by inoculating 1 μL of bacterial suspension on a solid 2% skim milk medium and cultured at 30 °C until a transparent zone formed around colonies; quantitative measurements of alkaline protease and neutral protease activity were conducted according to a previous study (Kunitz, 1947). Qualitative and quantitative measurements of siderophore production were based on the universal chemical assay described by Schwyn and Neilands, 1987. Each treatment includes three biological replicates.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting file.

References

    1. Landy M
    2. Roseman SB
    3. Warren GH
    (1947)
    An antibiotic from Bacillus subtilis active against pathogenic fungi
    Journal of Bacteriology 54:24.

Decision letter

  1. Karine A Gibbs
    Reviewing Editor; University of California, Berkeley, United States
  2. Aleksandra M Walczak
    Senior Editor; CNRS, France
  3. Bin Ni
    Reviewer; China Agricultural University, China

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "A toxin-mediated policing system in Bacillus optimizes division of labor via penalizing cheater-like nonproducers" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Aleksandra Walczak as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Bin Ni (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

Addressing the following list of major suggestions is essential for substantiating the manuscript's conclusions. (Note: The reviewing editor has lightly edited the reviewers' statements for clarity.)

1) The microscopy needs additional quantitative analysis.

Item #(1) All of the microscopy needs total cell count numbers.

Item #(2) Although Figure 1 depicts the co-expression pattern of ECM production (eps and tapA) and policing system (bnaF and bnaA) as an individual-level property, it lacks statistical analysis and a spatial correlation test. The authors should provide flow-cytometry data that quantifies the overlap between different subpopulations; with this data, one could understand the cell differentiation pattern from a population perspective.

Item #(3) For the data presented in Figure 1, the authors need a more concrete discussion of methods and could expand upon their existing quantification to improve the understanding of the co-expression of EPS and BA genes. How were cells segmented? What were the thresholds for whether a cell emitted fluorescence, and how was such a threshold chosen? The authors should perform a more sophisticated colocalization/correlation analysis. e.g., if the cells have a high Peps signal, does that correlate with a high PbnaF signal?

Item #(4) In the CLSM photographs (figure supplement 6 and lines 195 – 199), only one frame and no population sizes were provided. Additional frames and population counts are needed.

2) The gfp/mCherry fusions need transcriptional validation. For example, use qPCR to ascertain the relationship between the transcription of the reporter genes and the genes (eps, tapA, etc.) to which they correspond.

3) Figure 2 is not definitive.

#(1) The PI staining microscopy (as shown in Figure 2 and the supplemental videos) does not demonstrate that only non-expressing cells perish. Also, the presented data shows many non-labeled cells without PI; why do some nearby non-gfp-expressing cells remain alive? Consider using a third reporter that is a marker for all living cells (constitutive expression).

Question #(2) How do the authors determine whether the PI-stained cells were producers or cheaters before they died based on the data shown in Figure 2? If a Peps-GFP cell dies, I presume it loses its GFP signal? Do the authors identify the cell types before death? Furthermore, in the figure legend, the authors should define what they mean by "near" an EPS- producing cell. Is this quantitatively defined?

Item #(3) Figure 2 shows no visual evidence that the distance to producer cells matters. Also, the counts for "Statistics" in Figure 2 lack total population sizes, error bars, and statistical analysis; these data are needed to determine whether "selectively punish" is an accurate conclusion or whether other cellular factors could also explain the PI-stained cells.

3) The authors need to standardize their measurements of gene expression. Currently, the authors determine gene expression in different ways. For example, in Figure 3D, the ratio between fluorescence intensity and cell density (RFU/OD600) is used to show the transcription of target genes. While in Figure 4F, only RFU is shown but OD600 is ignored. Using only the fluorescence data may not reflect the objective gene expression pattern if the xylose concentrations can affect the growth of wild-type SQR9 or the constructed strains.

4) The spo0A complementation is a partial rescue, most visible in cells with the eps promoter driving gfp expression. The authors need to address this discrepancy and discuss its implications.

5) The data in Figure 4E should be quantified, as similar data are in other figures.

Reviewer #1 (Recommendations for the authors):

– Figure 2 shows no visual evidence that the distance to producer cells matters. Also, the counts for "Statistics" in Figure 2 lack total population sizes, error bars, and statistical analysis; these data are needed to determine whether "selectively punish" is an accurate conclusion or whether other cellular factors could also explain the PI-stained cells.

– In general, the total cell count numbers (in all microscopy) are needed. I could not fully validate the imaging conclusions with the data and images provided.

– The spo0A complementation is a partial rescue, most visible in cells with the eps promoter driving gfp expression. The authors need to address this discrepancy and discuss its implications.

– I could not verify the conclusions from the CLSM photographs (figure supplement 6 and lines 195 – 199) as only one frame and no population sizes were provided.

– B. subtilis is a well-studied organism. It would be helpful for the authors to include how the pathways in this Bacillus strain compare to the known ones in B. subtilis. As written, it seems that accDA might be a direct regulator, but it's not clear from the results shown. (I took away that it's an upstream regulator, but is it indirect?)

– For all photography, explicitly state that the image represents ## experiments (in the legend).

– The supplemental videos do not show more than the still images and are not needed for this manuscript.

Reviewer #2 (Recommendations for the authors):

Although the manuscript is concise and brings a detailed study of social interactions within microbial populations, I nevertheless have a few major concerns. Some of my main concerns would require additional experiments and a re-write of some part of the manuscript.

1) The co-expression pattern of ECM production (eps and tapA) and policing system (bnaF and bnaA) was shown in Figure 1 in an individual-level property, but lacks statistical analysis or spatial correlation test. I suggest authors provide a flow-cytometry data that quantifies the overlap between different subpopulations, thus we can understand the cell differentiation pattern in a population perspective.

2) The gfp/mCherry fusions need a transcriptional validation, such as the correspondence between the transcription of the reporter genes and corresponding genes (eps, tapA, etc.) determined by qPCR.

3) Determination of gene expression are performed in different ways. For example, in Figure 3D, the ratio between fluorescence intensity and cell density (RFU/OD600) is used to show the transcription of target genes. While in Figure 4F, only RFU is shown but OD600 is ignored; if the xylose concentrations can affect the growth of wild-type SQR9 or the constructed strains, using only the fluorescence data may not reflect the objective gene expression pattern.

4) According to the working model raised by the authors, it seems that expression of ECM-related genes (eps and tapA) and BAs-synthesis genes (bnaF) are not directly relevant, since Spo0A enhances ECM production in a transcriptional-regulation pattern but activates BAs biosynthesis in a post-transcriptional manner (by promoting the accumulation of the precursor). Interestingly, the CLSM graph shows that expression of these two sorts of genes is also likely to be overlapped. Authors should discuss or comment on this phenomenon in the manuscript.

5) Line 111-114, one additional sentence is needed here for introducing why the autotoxin system is involved in this scene.

6) Line 261, the reference citation format is incorrect.

The two references in Table S1 (Chen et al., 2007; Zhou et al., 2017) are missing in the Reference List.

7) Figures need to be standardized, such as font size and initial capitalization in Figure 5D.

8) There are a couple of grammatical and semantic errors in the manuscript. Please carefully go through the manuscript to correct them.

Reviewer #3 (Recommendations for the authors):

1. For the data presented in Figure 1, the authors need more concrete discussion of methods and could expand upon their existing quantification to improve the understanding of the co-expression of EPS and BA genes. How were cells segmented? What were the thresholds for whether a cell emitted fluorescence or not, and how was such a threshold chosen? The authors should perform a more sophisticated colocalization/correlation analysis. e.g. if the cells have high Peps signal, does that correlate with high PbnaF signal?

2. For the data presented in Figure 2, how do the authors determine that the PI-stained cells were producers or cheaters before death? If a Peps-GFP cell dies, I presume it loses its GFP signal? Do the authors identify the cell types before death? Furthermore, in the figure legend, the authors should define what they mean by "near" an EPS- producing cell. Is this quantitatively defined?

3. The data in Figure 4E should be quantified, as similar data are in other figures.

https://doi.org/10.7554/eLife.84743.sa1

Author response

Essential revisions:

Addressing the following list of major suggestions is essential for substantiating the manuscript's conclusions. (Note: The reviewing editor has lightly edited the reviewers' statements for clarity.)

Thank you very much for sorting out and summarizing the problems existing in the manuscript, we have carefully addressed all these comments to improve this manuscript.

1) The microscopy needs additional quantitative analysis.

Item #(1) All of the microscopy needs total cell count numbers.

Item #(2) Although Figure 1 depicts the co-expression pattern of ECM production (eps and tapA) and policing system (bnaF and bnaA) as an individual-level property, it lacks statistical analysis and a spatial correlation test. The authors should provide flow-cytometry data that quantifies the overlap between different subpopulations; with this data, one could understand the cell differentiation pattern from a population perspective.

Item #(3) For the data presented in Figure 1, the authors need a more concrete discussion of methods and could expand upon their existing quantification to improve the understanding of the co-expression of EPS and BA genes. How were cells segmented? What were the thresholds for whether a cell emitted fluorescence, and how was such a threshold chosen? The authors should perform a more sophisticated colocalization/correlation analysis. e.g., if the cells have a high Peps signal, does that correlate with a high Pbnf signal?

Item #(4) In the CLSM photographs (figure supplement 6 and lines 195 – 199), only one frame and no population sizes were provided. Additional frames and population counts are needed.

Thank you very much for summarizing the problems of missing quantitative analysis from the microscopic observations. We have performed additional experiments and analyses to address all these comments. Please see the specific responses as follows.

Item #(1) The statistical analyses in the original Figure 1 were removed. A more accurate and objective method, flow cytometry, was used to analyze the fluorescence expression patterns of different double-labeled strains (revised Figure 1—figure supplement 1). The total cell number observed for each strain was 20,000, and this information is included in the caption of revised Figure 1—figure supplement 1.

The statistical analyses in the original Figure 2 were also removed. We re-observed cells in biofilms for 3 hours (revised Figure 2-video 1-4), the source and distribution of newly emerged dead cells during this period was counted and analyzed in detail (revised Figure 2—figure supplement 1). During the 3 hours, pictures covering a 6 minutes stage that show the elimination process of several non-gfp expressing cells, are selected for display in revised Figure 2; the total number of cells in this figure is 198 for strain SQR9-Peps-gfp, 71 for strain SQR9-PtasA-gfp, 88 for strain SQR9-PbnaF-gfp, and 162 for strain SQR9-PbnaAB-gfp, and this information is included in the caption.

Item #(2) We further monitored the expression pattern of mCherry and GFP signals in each single double-labeled cell by flow cytometry. The results revealed that both fluorescence was generally located in the same subpopulation in the community (revised Figure 1—figure supplement 1), which confirmed the positive correlation between the two reporters within the picked cells from a population perspective. Thus, ECM production (eps and tapA) and the policing system (bnaF and bnaA) are co-expressed.

Item #(3) The statistical analysis in original Figure 1 was replaced by flow cytometry data that can accurately access the fluorescence emitted by each cell of double-labeled strains and determine their distribution pattern (revised Figure 1—figure supplement 1). Flow cytometry showed that in all double-labeled strains, mCherry fluorescence intensity generally correlated with GFP fluorescence intensity. For example, in strain Peps-mCherry PbnaF-gfp, the Peps signal was correlated with the PbnaF signal to a certain extent, highlighting the co-expression of EPS and BA synthesis genes in the same subpopulation (revised Figure 1—figure supplement 1). With regards to the experimental method of flow cytometry, (1) mild sonication of the biofilms was performed to segment cells for flow cytometry analysis; (2) the wild-type strain expressing no fluorescence protein was used as a negative control; (3) the negative control establishes the background fluorescence of the experimental samples and is used to set the threshold by adjusting baseline PMT (photomultiplier tube) voltages of the instrument.

For CLSM observation, the wild-type strain without fluorescent proteins was used as a negative control to estimate the fluorescence threshold. Besides, early-stage biofilms with monolayer cells were selected for observation.

Item #(4) Additional frames have been provided (revised Figure 4—figure supplement 5A) and the fluorescence expression patterns of 20,000 cells has been analyzed by flow cytometry (revised Figure 4—figure supplement 5B). In detail, the fluorescent signals of both mCherry and GFP were detected to be in the same subpopulation of the double-labeled strain PaccDA-mCherry PbnaAB-gfp (revised Figure 4—figure supplement 5B). Thus, the activation of accDA and bnaAB was located in the same subpopulation cells. Accordingly, the “Statistics” of the original figure was replaced by the flow cytometry analysis.

2) The gfp/mCherry fusions need transcriptional validation. For example, use qPCR to ascertain the relationship between the transcription of the reporter genes and the genes (eps, tapA, etc.) to which they correspond.

Thank you very much for listing this comment. We performed a qPCR validation and showed that the reporter gene expression was correlated with the corresponding genes (epsD, tasA, accD, bnaF, and bnaA).

Author response image 1
Download asset Open asset

3) Figure 2 is not definitive.

#(1) The PI staining microscopy (as shown in Figure 2 and the supplemental videos) does not demonstrate that only non-expressing cells perish. Also, the presented data shows many non-labeled cells without PI; why do some nearby non-gfp-expressing cells remain alive? Consider using a third reporter that is a marker for all living cells (constitutive expression).

Question #(2) How do the authors determine whether the PI-stained cells were producers or cheaters before they died based on the data shown in Figure 2? If a Peps-GFP cell dies, I presume it loses its GFP signal? Do the authors identify the cell types before death? Furthermore, in the figure legend, the authors should define what they mean by "near" an EPS- producing cell. Is this quantitatively defined?

Item #(3) Figure 2 shows no visual evidence that the distance to producer cells matters. Also, the counts for "Statistics" in Figure 2 lack total population sizes, error bars, and statistical analysis; these data are needed to determine whether "selectively punish" is an accurate conclusion or whether other cellular factors could also explain the PI-stained cells.

Thank you very much for summarizing the problems existed in Figure 2. We have addressed all these comments carefully. Please see the specific responses as follows.

Item #(1) According to the reviewer's suggestion, an observation covering more complete biofilm forming process, as well as a more convinced data statistics, should be performed. We then re-conducted microscope observation lasting for 3 h during biofilm formation, and assess the source and location of dead cells for statistical analysis. The results showed that all dead cells were originated from the subpopulation that didn't express the gfp (the nonproducers), and the number of dead cells adjacent to the producers was significantly higher than that closed to the non-producers (please see the pictures in Figure 2 and revised Figure 2—figure supplement 1).

In addition, regarding the survival of some non-gfp-expressing cells near the producers, based on several relevant literatures1-3 and the observation in the present study, we speculate that the coordination system for optimizing the division of labor is relatively temperate, thus only a part of the nonproducers (relative sensitive cells or facing higher concentrations of the toxin) are eliminated. We think this scene is a balance between restraining the cheater-like subpopulation and retaining the advantages of cell differentiation.

Item #(2) The SQR9-Peps-gfp cells emitting GFP signal are supposed to be producers because their EPS synthesis gene is activated; conversely, cells that don’t emit GFP signal are nonproducing cheaters. Based on this criterion, we determined the source of dead cells in the newly captured time-lapse images. The results showed that all dead cells were from non-gfp expressing cells (revised Figure 2—figure supplement 1), while the active Peps-gfp cells kept alive and didn’t lose their GFP signal during the cannibalism progress. Additionally, the distribution of dead cells in the newly captured time-lapse images were also analyzed. We define "near" an EPS- producing cell as cells that were in contact with an EPS-producing cell, also this information has been included in the figure legend. The number of dead cells adjacent to producers was significantly higher than that of non-adjacent cells (revised Figure 2—figure supplement 1).

Item #(3) We re-observed the cell differentiation and live-dead distribution in early-stage biofilms for 3 hours (revised Figure 2-video 1-4). The progress of individual nonproducers from alive to initial death and even disappearance in a biofilm population is shown in Figure 2 of the revised manuscript; also the source (from gfp-expressing or non-expressing cells) and distribution (near GFP-emitting cells or silent cells) of the dead cells in the newly captured time-lapse images were statistically analyzed. The results showed that all dead cells were from non-gfp expressing cells, and the number of dead cells adjacent to producers (in contact with a gfp expressing cell) was significantly higher than that of non-adjacent cells (revised Figure 2—figure supplement 1). We think the re-collected data can demonstrate the "selectively punish" in the biofilm population of strain SQR9. Also, total population sizes, error bars, and statistical analysis have been provided in revised Figure 2—figure supplement 1.

3) The authors need to standardize their measurements of gene expression. Currently, the authors determine gene expression in different ways. For example, in Figure 3D, the ratio between fluorescence intensity and cell density (RFU/OD600) is used to show the transcription of target genes. While in Figure 4F, only RFU is shown but OD600 is ignored. Using only the fluorescence data may not reflect the objective gene expression pattern if the xylose concentrations can affect the growth of wild-type SQR9 or the constructed strains.

Thank you very much for this suggestion and sorry for missing the cell density. The ratio between fluorescence intensity and cell density (RFU/OD600) has been provided to show the transcription of target genes in the xylose-induced strain (SQR9-Pxyl-accDA) during liquid culture. The xylose-induced transcription of accDA resulted in enhanced expression of genes involved in self-immunity (revised Figure 4—figure supplement 4C) and BAs synthesis (revised Figure 4—figure supplement 4D).

4) The spo0A complementation is a partial rescue, most visible in cells with the eps promoter driving gfp expression. The authors need to address this discrepancy and discuss its implications.

Thank you for the comments. We have sequenced gene spo0A and its promoter of the complementation strain, ensuring the sequence is correct and it indeed inserted into the amyE site of strain SQR9 chromosome. Thus, for unknown reasons, ex-situ replacement of gene spo0A might result a slight impairment as compared with the wild-type.

In addition, the fluorescent intensity emitted by the strain Δspo0A/spo0A-Peps-gfp has been rechecked for several times, and the expression level was significantly enhanced compared with the original data and close to the wild-type (revised Figure 3—figure supplement 2).

5) The data in Figure 4E should be quantified, as similar data are in other figures.

Sorry for lack of quantification of data in Figure 4E. We have supplemented the statistical information (Duncan's multiple rang tests) in the revised Figure 4—figure supplement 3.

Reviewer #1 (Recommendations for the authors):

– Figure 2 shows no visual evidence that the distance to producer cells matters. Also, the counts for "Statistics" in Figure 2 lack total population sizes, error bars, and statistical analysis; these data are needed to determine whether "selectively punish" is an accurate conclusion or whether other cellular factors could also explain the PI-stained cells.

Thank you for your suggestions. We re-observed the cell differentiation and live-dead distribution in early-stage biofilms for 3 hours (revised Figure 2-video 1-4). The progress of individual nonproducers from alive to initial death and even disappearance in a biofilm population is shown in Figure 2 of the revised manuscript; also the source (from gfp-expressing or non-expressing cells) and distribution (near GFP-emitting cells or silent cells) of the dead cells in the newly captured time-lapse images were statistically analyzed. The results showed that all dead cells were from non-gfp expressing cells, and the number of dead cells adjacent to producers (in contact with a gfp expressing cell) was significantly higher than that of non-adjacent cells (revised Figure 2—figure supplement 1). We think the re-collected data can demonstrate the "selectively punish" in the biofilm population of strain SQR9. Also, total population sizes, error bars, and statistical analysis have been provided in revised Figure 2—figure supplement 1.

– In general, the total cell count numbers (in all microscopy) are needed. I could not fully validate the imaging conclusions with the data and images provided.

Thank you for your comments. The statistical analyses in the original Figure 1 were removed. A more accurate and objective method, flow cytometry, was used to analyze the fluorescence expression patterns of different double-labeled strains (revised Figure 1—figure supplement 1). The total cell number observed for each strain was 20,000, and this information is included in the caption of revised Figure 1—figure supplement 1.

The statistical analyses in the original Figure 2 were also removed. We re-observed cells in biofilms for 3 hours (revised Figure 2-video 1-4), the source and distribution of newly emerged dead cells during this period was counted and analyzed in detail (revised Figure 2—figure supplement 1). During the 3 hours, pictures covering a 6 minutes stage that show the elimination process of several non-gfp expressing cells, are selected for display in revised Figure 2; the total number of cells in this figure is 198 for strain SQR9-Peps-gfp, 71 for strain SQR9-PtasA-gfp, 88 for strain SQR9-PbnaF-gfp, and 162 for strain SQR9-PbnaAB-gfp, and this information is included in the caption.

– The spo0A complementation is a partial rescue, most visible in cells with the eps promoter driving gfp expression. The authors need to address this discrepancy and discuss its implications.

Thank you for your comments. We have sequenced gene spo0A and its promoter of the complementation strain, ensuring the sequence is correct and it indeed inserted into the amyE site of strain SQR9 chromosome. Thus, for unknown reasons, ex-situ replacement of gene spo0A might result a slight impairment as compared with the wild-type.

In addition, the fluorescent intensity emitted by the strain Δspo0A/spo0A-Peps-gfp has been rechecked for several times, and the expression level was significantly enhanced compared with the original data and close to the wild-type (revised Figure 3—figure supplement 2).

– I could not verify the conclusions from the CLSM photographs (figure supplement 6 and lines 195 – 199) as only one frame and no population sizes were provided.

Thank you for your comments. Additional frames have been provided (revised Figure 4—figure supplement 5A) and the fluorescence expression patterns of 20,000 cells has been analyzed by flow cytometry (revised Figure 4—figure supplement 5B). In detail, the fluorescent signals of both mCherry and GFP were detected to be in the same subpopulation of the double-labeled strain PaccDA-mCherry PbnaAB-gfp (revised Figure 4—figure supplement 5B). Thus, the activation of accDA and bnaAB was located in the same subpopulation cells. Accordingly, the “Statistics” of the original figure was replaced by the flow cytometry analysis.

– B. subtilis is a well-studied organism. It would be helpful for the authors to include how the pathways in this Bacillus strain compare to the known ones in B. subtilis. As written, it seems that accDA might be a direct regulator, but it's not clear from the results shown. (I took away that it's an upstream regulator, but is it indirect?)

Thank you for your suggestions. In B. subtilis, as a global regulator, Spo0A simultaneously induces the production of extracellular matrix and toxic peptides (Skf and Sdp, sporulation killing factor and sporulation delaying protein, respectively)7. Thus, in the comparison of B. subtilis and B. velezensis SQR9, the similarity is that the global regulator Spo0A controls the synthesis of extracellular matrix and the cannibalism toxin. The difference lies in the type of cannibalism toxin and its synthesis/regulation pathway (Line 278~281 of the revised Discussion). For B. subtilis, Spo0A directly activate the peptide Skf synthesis genes skfABH. The first gene, skfA, encodes the 56-amino acid long pro-antimicrobial peptide, which is post-translationally modified by SkfB, a radical S-adenosyl-methionine enzyme8,9. The resulting pre-SkfA is further processed to its active state by the putative thioredoxin oxidoreductase SkfH10. In contrast to the directly controlling of skf expression, Spo0A only plays an indirect role for regulating the sdp locus by repressing the global regulator AbrB that inhibits sdp expression11. Upon transcription of the sdpABC operon, the toxic peptide SdpC is post-translationally modified by SdpAB to its active 63-amino acids-long form.

For B. velezensis SQR9, the global regulator Spo0A activates acetyl-coA carboxylase AccDA through a direct manner, and this regulation is also conserved in B. subtilis. AccDA catalyzes the production of malonyl-CoA, which is essential for the synthesis of BAs precursors. As a result, AccDA is not a direct regulator for BAs synthesis, but works as a critical enzyme that controls the synthesis of BAs precursors.

– For all photography, explicitly state that the image represents ## experiments (in the legend).

Thank you for your suggestion and sorry for unclearness. In the revised legends, each image is annotated with the experiment the image represents.

– The supplemental videos do not show more than the still images and are not needed for this manuscript.

Sorry for unclearness. The original videos provide little new information as compared with the corresponding figures. New supplemental videos shown in the revised version lasted longer period (3 hours) (revised Figure 2-video 1-4); specifically, pictures covering a 6 minutes stage that show the elimination process of several non-gfp expressing cells, are selected for display in revised Figure 2.

Reviewer #2 (Recommendations for the authors):

Although the manuscript is concise and brings a detailed study of social interactions within microbial populations, I nevertheless have a few major concerns. Some of my main concerns would require additional experiments and a re-write of some part of the manuscript.

Thank you very much for your positive comments. The detailed response to your recommendations and suggestions are as follows.

1) The co-expression pattern of ECM production (eps and tapA) and policing system (bnaF and bnaA) was shown in Figure 1 in an individual-level property, but lacks statistical analysis or spatial correlation test. I suggest authors provide a flow-cytometry data that quantifies the overlap between different subpopulations, thus we can understand the cell differentiation pattern in a population perspective.

Thank you very much for your constructive suggestion. We further monitored the expression pattern of mCherry and GFP signals in each single double-labeled cell by flow cytometry. The results revealed that both fluorescence was generally located in the same subpopulation in the community (revised Figure 1—figure supplement 1), which confirmed the positive correlation between the two reporters within the picked cells from a population perspective. Thus, ECM production (eps and tapA) and the policing system (bnaF and bnaA) are co-expressed.

2) The gfp/mCherry fusions need a transcriptional validation, such as the correspondence between the transcription of the reporter genes and corresponding genes (eps, tapA, etc.) determined by qPCR.

Thank you for your suggestion. We performed a qPCR validation and showed that the reporter gene expression was correlated with the corresponding genes (epsD, tasA, accD, bnaF, and bnaA).

3) Determination of gene expression are performed in different ways. For example, in Figure 3D, the ratio between fluorescence intensity and cell density (RFU/OD600) is used to show the transcription of target genes. While in Figure 4F, only RFU is shown but OD600 is ignored; if the xylose concentrations can affect the growth of wild-type SQR9 or the constructed strains, using only the fluorescence data may not reflect the objective gene expression pattern.

Thank you very much for your comments and sorry for missing the cell density. The ratio between fluorescence intensity and cell density (RFU/OD600) has been provided to show the transcription of target genes in the xylose-induced strain (SQR9-Pxyl-accDA) during liquid culture. The xylose-induced transcription of accDA resulted in enhanced expression of genes involved in self-immunity (revised Figure 4—figure supplement 4C) and BAs synthesis (revised Figure 4—figure supplement 4D).

4) According to the working model raised by the authors, it seems that expression of ECM-related genes (eps and tapA) and BAs-synthesis genes (bnaF) are not directly relevant, since Spo0A enhances ECM production in a transcriptional-regulation pattern but activates BAs biosynthesis in a post-transcriptional manner (by promoting the accumulation of the precursor). Interestingly, the CLSM graph shows that expression of these two sorts of genes is also likely to be overlapped. Authors should discuss or comment on this phenomenon in the manuscript.

Thank you very much for your comments. According to the manner that Spo0A controls ECM and BA synthesis, that expression of ECM-related genes (eps and tapA) and BAs-synthesis genes (bnaF) may not be directly relevant. Interestingly, both CLSM and flow cytometry data show that the expression of these two sorts of genes is also likely to be overlapped. It's common for a substrate or precursor to induce functional enzyme expression12, thus we speculated that the accumulated BA precursor (catalyzed by AccDA under the activation of Spo0A) may induce expression of BA synthetase genes, thus exhibiting such expression pattern. We have added this discussion in the revised manuscript (Line 287~288).

5) Line 111-114, one additional sentence is needed here for introducing why the autotoxin system is involved in this scene.

Thank you for your suggestion. One sentence as "We hypothesized that secretion of cannibal toxin BAs can eliminate ECM nonproducers in B. velezensis SQR9 biofilm" has been added in the revised manuscript (Line 109~111).

6) Line 261, the reference citation format is incorrect.

The two references in Table S1 (Chen et al., 2007; Zhou et al., 2017) are missing in the Reference List.

Sorry for the mistakes. We have corrected the reference citation format in the revised manuscript and added references in the revised Supplementary File 1.

7) Figures need to be standardized, such as font size and initial capitalization in Figure 5D.

Thanks for your comments and sorry for the mistakes. We have carefully gone through all figures to standardize font size and initial capitalization, and have made correction in Figure 5D and Figure 5—figure supplement 3 of the revised version.

8) There are a couple of grammatical and semantic errors in the manuscript. Please carefully go through the manuscript to correct them.

Thank you for your comments and sorry for these errors. We have carefully gone through the whole manuscript to revise grammatical and semantic errors.

Reviewer #3 (Recommendations for the authors):

1. For the data presented in Figure 1, the authors need more concrete discussion of methods and could expand upon their existing quantification to improve the understanding of the co-expression of EPS and BA genes. How were cells segmented? What were the thresholds for whether a cell emitted fluorescence or not, and how was such a threshold chosen? The authors should perform a more sophisticated colocalization/correlation analysis. e.g. if the cells have high Peps signal, does that correlate with high Pbnf signal?

Thank you very much for your constructive suggestions. The statistical analysis in original Figure 1 was replaced by flow cytometry data that can accurately access the fluorescence emitted by each cell of double-labeled strains and determine their distribution patterns (revised Figure 1—figure supplement 1). Flow cytometry showed that in all double-labeled strains, mCherry fluorescence intensity generally correlated with GFP fluorescence intensity. For example, in strain Peps-mCherry PbnaF-gfp, the Peps signal was correlated with the PbnaF signal to a certain extent, highlighting the co-expression of EPS and BA synthesis genes in the same subpopulation (revised Figure 1—figure supplement 1). With regards to the experimental method of flow cytometry, (1) mild sonication of the biofilms was performed to segment cells for flow cytometry analysis; (2) the wild-type strain expressing no fluorescence protein was used as a negative control; (3) the negative control establishes the background fluorescence of the experimental samples and is used to set the threshold by adjusting baseline PMT (photomultiplier tube) voltages of the instrument.

For CLSM observation, the wild-type strain without fluorescent proteins was used as a negative control to estimate the fluorescence threshold. Besides, early-stage biofilms with monolayer cells were selected for observation.

2. For the data presented in Figure 2, how do the authors determine that the PI-stained cells were producers or cheaters before death? If a Peps-GFP cell dies, I presume it loses its GFP signal? Do the authors identify the cell types before death? Furthermore, in the figure legend, the authors should define what they mean by "near" an EPS- producing cell. Is this quantitatively defined?

Thank you for your comments. The SQR9-Peps-gfp cells emitting GFP signal are supposed to be producers because their EPS synthesis gene is activated; conversely, cells that don’t emit GFP signal are nonproducing cheaters. Based on this criterion, we determined the source of dead cells in the newly captured time-lapse images. The results showed that all dead cells were from non-gfp expressing cells (revised Figure 2—figure supplement 1), while the active Peps-gfp cells kept alive and didn’t lose their GFP signal during the cannibalism progress. Additionally, the distribution of dead cells in the newly captured time-lapse images were also analyzed. We define "near" an EPS- producing cell as cells that were in contact with an EPS-producing cell, also this information has been included in the figure legend. The number of dead cells adjacent to producers was significantly higher than that of non-adjacent cells (revised Figure 2—figure supplement 1).

3. The data in Figure 4E should be quantified, as similar data are in other figures.

Sorry for lack of quantification of data in Figure 4E. We have supplemented the statistical information (Duncan's multiple rang tests) in the revised Figure 4—figure supplement 3.

https://doi.org/10.7554/eLife.84743.sa2

Article and author information

Author details

  1. Rong Huang

    Jiangsu Provincial Key Lab of Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center of Solid Organic Wastes, Nanjing Agricultural University, Nanjing, China
    Contribution
    Software, Validation, Visualization, Methodology, Writing – original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1347-841X

  2. Jiahui Shao

    Jiangsu Provincial Key Lab of Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center of Solid Organic Wastes, Nanjing Agricultural University, Nanjing, China
    Contribution
    Resources, Investigation
    Competing interests
    No competing interests declared

  3. Zhihui Xu

    Jiangsu Provincial Key Lab of Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center of Solid Organic Wastes, Nanjing Agricultural University, Nanjing, China
    Contribution
    Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3987-8836

  4. Yuqi Chen

    Jiangsu Provincial Key Lab of Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center of Solid Organic Wastes, Nanjing Agricultural University, Nanjing, China
    Contribution
    Investigation
    Competing interests
    No competing interests declared

  5. Yunpeng Liu

    State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultura Sciences, Beijing, China
    Contribution
    Validation
    Competing interests
    No competing interests declared

  6. Dandan Wang

    National Engineering Research Center for Efficient Utilization of Soil and Fertilizer Resources, College of Resources and Environment, Shandong Agricultural University, Tai’an, China
    Contribution
    Methodology
    Competing interests
    No competing interests declared

  7. Haichao Feng

    Jiangsu Provincial Key Lab of Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center of Solid Organic Wastes, Nanjing Agricultural University, Nanjing, China
    Contribution
    Validation
    Competing interests
    No competing interests declared

  8. Weibing Xun

    Jiangsu Provincial Key Lab of Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center of Solid Organic Wastes, Nanjing Agricultural University, Nanjing, China
    Contribution
    Validation
    Competing interests
    No competing interests declared

  9. Qirong Shen

    Jiangsu Provincial Key Lab of Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center of Solid Organic Wastes, Nanjing Agricultural University, Nanjing, China
    Contribution
    Conceptualization
    Competing interests
    No competing interests declared

  10. Nan Zhang

    Jiangsu Provincial Key Lab of Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center of Solid Organic Wastes, Nanjing Agricultural University, Nanjing, China
    Contribution
    Conceptualization, Funding acquisition, Validation, Writing – original draft, Writing - review and editing
    For correspondence
    nanzhang@njau.edu.cn
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8444-7456

  11. Ruifu Zhang

    1. Jiangsu Provincial Key Lab of Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center of Solid Organic Wastes, Nanjing Agricultural University, Nanjing, China
    2. State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultura Sciences, Beijing, China
    Contribution
    Conceptualization, Funding acquisition, Writing - review and editing
    For correspondence
    rfzhang@njau.edu.cn
    Competing interests
    No competing interests declared

Funding

National Natural Science Foundation of China (31870096)

  • Ruifu Zhang

National Natural Science Foundation of China (42090064)

  • Qirong Shen

National Natural Science Foundation of China (31972512)

  • Zhihui Xu

National Natural Science Foundation of China (32072665)

  • Nan Zhang

National Natural Science Foundation of China (32072675)

  • Weibing Xun

National Key Research and Development Program of China (2022YFD1901300)

  • Nan Zhang

Fundamental Research Funds for the Central Universities (KYZZ2022003)

  • Ruifu Zhang

National Key Research and Development Program of China (2021YFD1900300)

  • Weibing Xun

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (31870096, 42090064, 31972512, 32072665, and 32072675), the National Key R&D Program of China (2022YFD1901300), the Fundamental Research Funds for the Central Universities (KYZZ2022003), and the National Key Research and Development Program (2021YFD1900300).

Senior Editor

  1. Aleksandra M Walczak, CNRS, France

Reviewing Editor

  1. Karine A Gibbs, University of California, Berkeley, United States

Reviewer

  1. Bin Ni, China Agricultural University, China

Publication history

  1. Preprint posted: May 14, 2022 (view preprint)
  2. Received: November 7, 2022
  3. Accepted: April 24, 2023
  4. Accepted Manuscript published: April 25, 2023 (version 1)
  5. Version of Record published: May 10, 2023 (version 2)

Copyright

© 2023, Huang et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 358
    Page views
  • 101
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Rong Huang
  2. Jiahui Shao
  3. Zhihui Xu
  4. Yuqi Chen
  5. Yunpeng Liu
  6. Dandan Wang
  7. Haichao Feng
  8. Weibing Xun
  9. Qirong Shen
  10. Nan Zhang
  11. Ruifu Zhang
(2023)
A toxin-mediated policing system in Bacillus optimizes division of labor via penalizing cheater-like nonproducers
eLife 12:e84743.
https://doi.org/10.7554/eLife.84743

Categories and tags

Further reading

    1. Ecology
    2. Evolutionary Biology

    Thermal phenotypic plasticity of pre- and post-copulatory male harm buffers sexual conflict in wild Drosophila melanogaster

    Claudia Londoño-Nieto, Roberto García-Roa ... Pau Carazo
    Research Article Updated

    Strong sexual selection frequently leads to sexual conflict and ensuing male harm, whereby males increase their reproductive success at the expense of harming females. Male harm is a widespread evolutionary phenomenon with a strong bearing on population viability. Thus, understanding how it unfolds in the wild is a current priority. Here, we sampled a wild Drosophila melanogaster population and studied male harm across the normal range of temperatures under which it reproduces optimally in nature by comparing female lifetime reproductive success and underlying male harm mechanisms under monogamy (i.e. low male competition/harm) vs. polyandry (i.e. high male competition/harm). While females had equal lifetime reproductive success across temperatures under monogamy, polyandry resulted in a maximum decrease of female fitness at 24°C (35%), reducing its impact at both 20°C (22%), and 28°C (10%). Furthermore, female fitness components and pre- (i.e. harassment) and post-copulatory (i.e. ejaculate toxicity) mechanisms of male harm were asymmetrically affected by temperature. At 20°C, male harassment of females was reduced, and polyandry accelerated female actuarial aging. In contrast, the effect of mating on female receptivity (a component of ejaculate toxicity) was affected at 28°C, where the mating costs for females decreased and polyandry mostly resulted in accelerated reproductive aging. We thus show that, across a natural thermal range, sexual conflict processes and their effects on female fitness components are plastic and complex. As a result, the net effect of male harm on overall population viability is likely to be lower than previously surmised. We discuss how such plasticity may affect selection, adaptation and, ultimately, evolutionary rescue under a warming climate.

    1. Ecology
    2. Evolutionary Biology

    Sensory conflict disrupts circadian rhythms in the sea anemone Nematostella vectensis

    Cory A Berger, Ann M Tarrant
    Research Article Updated

    Circadian clocks infer time of day by integrating information from cyclic environmental factors called zeitgebers, including light and temperature. Single zeitgebers entrain circadian rhythms, but few studies have addressed how multiple, simultaneous zeitgeber cycles interact to affect clock behavior. Misalignment between zeitgebers (‘sensory conflict’) can disrupt circadian rhythms, or alternatively clocks may privilege information from one zeitgeber over another. Here, we show that temperature cycles modulate circadian locomotor rhythms in Nematostella vectensis, a model system for cnidarian circadian biology. We conduct behavioral experiments across a comprehensive range of light and temperature cycles and find that Nematostella’s circadian behavior is disrupted by chronic misalignment between light and temperature, which involves disruption of the endogenous clock itself rather than a simple masking effect. Sensory conflict also disrupts the rhythmic transcriptome, with numerous genes losing rhythmic expression. However, many metabolic genes remained rhythmic and in-phase with temperature, and other genes even gained rhythmicity, implying that some rhythmic metabolic processes persist even when behavior is disrupted. Our results show that a cnidarian clock relies on information from light and temperature, rather than prioritizing one signal over the other. Although we identify limits to the clock’s ability to integrate conflicting sensory information, there is also a surprising robustness of behavioral and transcriptional rhythmicity.

    1. Ecology

    Larger but younger fish when growth outpaces mortality in heated ecosystem

    Max Lindmark, Malin Karlsson, Anna Gårdmark
    Research Article

    Ectotherms are predicted to ‘shrink’ with global warming, in line with general growth models and the temperature-size rule (TSR), both predicting smaller adult sizes with warming. However, they also predict faster juvenile growth rates and thus larger size-at-age of young organisms. Hence, the result of warming on the size-structure of a population depends on the interplay between how mortality rate, juvenile- and adult growth rates are affected by warming. Here, we use two-decade long time series of biological samples from a unique enclosed bay heated by cooling water from a nearby nuclear power plant to become 5–10 °C warmer than its reference area. We used growth-increment biochronologies (12,658 reconstructed length-at-age estimates from 2426 individuals) to quantify how >20 years of warming has affected body growth, size-at-age, and catch to quantify mortality rates and population size- and age structure of Eurasian perch (Perca fluviatilis). In the heated area, growth rates were faster for all sizes, and hence size-at-age was larger for all ages, compared to the reference area. While mortality rates were also higher (lowering mean age by 0.4 years), the faster growth rates lead to a 2 cm larger mean size in the heated area. Differences in the size-spectrum exponent (describing how the abundance declines with size) were less clear statistically. Our analyses reveal that mortality, in addition to plastic growth and size-responses, is a key factor determining the size structure of populations exposed to warming. Understanding the mechanisms by which warming affects the size- and the age structure of populations is critical for predicting the impacts of climate change on ecological functions, interactions, and dynamics.