文献总结---The evolution of tit-for-tat in bacteria via the type VI secretion system----初稿

科学问题：善有善报恶有恶报是生物间常用的相互作用策略，但细菌之间的报复行为如何影响相互进化的研究还比较少，本文尝试探究以牙还牙策略在哪些情况有意义？如何起作用的？以及细菌之间不同策略的博弈？
1.前沿框架：
第一段：（研究背景）第六分泌系统（T6SS）是一种接触式依赖的细菌毒液注射武器，在生态环境中广泛的存在。
第二段：（研究对象特点）不同菌使用T6SS进攻的方式不同，主要分为两种：1.）表面持续存在进攻能力的随机进攻策略 例 如霍乱弧菌、马氏杆菌和巴伊不动杆菌 2）只在特定环境进行作用从特点部位进攻或者是应激报复策略 例如：铜绿假单胞菌。
第三段：（研究现状综述，引出研究的依据）铜绿假单胞菌的进攻方式已经有了一些生物机制的研究。研究发现多种的方式的刺激都能激发铜绿假单胞菌的反击行为其中也包括抵御外界其他种群的入侵行为。
第四段: (阐述目前研究还有待完善部分)目前的研究主要集中在这种机制的生物学基础，但缺乏研究T6SS不同的使用行为对细菌之间生态竞争的影响以及对生物适应性状和进化的意义。
第五段：（本文研究的内容与研究方法）本文基于代理（个体）模型来模拟这种策略的博弈收益，描述不同策略的优势。通过改变细菌的策略的方式和代价来解析这个进化意义并与实际环境过中的现象进行对比。
Abstract
【背景句1： 善有善报恶有恶报是的策略在生物相互作用中普遍存在。】Tit-for-tat is a familiar principle from animal behavior: individuals respond in kind to being helped or harmed by others.【问题句1：以牙还牙的这种策略在细菌中存在，但目前对这种行为对进化策略的影响意义尚不明确。】 Remarkably some bacteria appear to display tit-for-tat behavior, but how this evolved is not understood. 【方法说明1：本文使用结合进化博弈论与个体模型研究细菌在毒素上使用策略的选择差异。】Here we combine evolutionary game theory with agent-based modelling of bacterial tit-for-tat, whereby cells stab rivals with poisoned needles (the type VI secretion system) after being stabbed themselves. 【结果句1：通过模型模拟发现：当细菌选择只反击一次不会带来足够的优势，而且是一个糟糕的策略。因为反击一次缺乏先发优势（更容易被免疫）】Our modelling shows tit-fortat retaliation is a surprisingly poor evolutionary strategy, because tit-for-tat cells lack the fifirst-strike advantage of preemptive attackers. 【结果句2：如果细菌选择强烈的报复行为那么反击行为将获得更高的收益。（获得更多的生存资源提高生长的适应度/免疫多种类型毒的能力）】However, if cells retaliate strongly and fifire back multiple times, we fifind that reciprocation is highly effective. 【模型验证1：通过将模型推导的结果与实际铜绿单胞菌的反击模式对比，发现铜绿单胞菌实际过程中是多次反击霍乱弧菌的随机进攻。】We test our predictions by competing Pseudomonas aeruginosa (a tit-for-tat species) with Vibrio cholerae (random-fingering), revealing that P. aeruginosa does indeed fifire multiple times per incoming attack. 【研究的意义：强烈的报复行为和以牙还牙的策略在细菌进化过程中有特殊的回报收益。】Our work suggests bacterial competition has led to a particular form of reciprocation, where the principle is that of strong retaliation, or ‘tits-for-tat’.
Introduction
【背景句1：第六分泌系统的作用方式，主要的是通过接触才能起作用（具有空间特异性）。】The type VI secretion system (T6SS) is a contact-dependent bacterial weapon that is found in numerous bacterial species1,2,3,4 and used to inject toxic effector proteins into neighboring cells5,6,7. 【背景句2：T6SS的种类特征】Structurally and functionally homologous to a phages tail8, the T6SS consists of a membrane-bound base plate complex, an effector-tipped needle, and a surrounding sheath whose contraction drives the needle through the membranes of target cells9,10.【背景句3：T6SS的机制在自然界中广泛存在。】 Used by many notorious plant and animal pathogens, the T6SS is a potent anti-competitor weapon: the T6SS can determine whether a strain can invade, or defend, its niche in both environmental and host-associated microbial communities11,12,13,14,15,16.
【背景句4：在不同类型的细菌中使用T6SS过程存在着明显的差异。】There is remarkable variation in the regulation and use of T6SS weaponry across species. Bacteria activate and deploy the T6SS across a range of environmental contexts16,17,18,19 and against both prokaryotic and eukaryotic targets20,21,22. 【背景句5：差异的方式主要分为两种：1）随机攻击，2）诱导反击。这两种攻击方式都有特定的模式生物。】The specific pattern of firing by cells also varies: whereas placement of T6SS assembly appears to be random in some species, such as Vibrio cholerae, Serratia marcescens, and Acinetobacter baylyi23,24,25, other bacteria are known to fire from specific locations on their cell membranes. Perhaps, the most striking example of this spatiotemporal control is the retaliatory firing strategy observed in Pseudomonas aeruginosa, whose T6SS apparatus (encoded at the HSI-1 locus) is specifically activated in cells that are themselves attacked by T6SS needles26,27.
【背景句6：目前对铜绿假单胞菌的反击策略的生物学反应过程已经有了一些基础性研究。】The regulatory pathway underpinning T6SS retaliation in P. aeruginosa is an active topic of study.【背景句7：铜绿假单胞菌的T6SS防御机制受多种的外界环境刺激，其中也包括外界入侵的菌种的影响。】 So far, it has been shown that various stimuli can trigger counterattacks in P. aeruginosa strain PAO1, including incoming T6SS attacks from multiple bacterial species23,27,28, conjugative T4SS pili29, and membrane-disrupting antibiotics like polymyxin B29. 【背景句8：有一些文献显示反击策略可能是一些特异性的毒素。】There is also evidence that retaliation can be toxin-specific—not all T6SS effectors trigger counterattacks, and in V. cholerae, only the lipase effector TseL triggers retaliation30. 【背景句9：铜绿假单胞菌的T6SS防御机制的生物学物质机理基础。】Across stimuli, P. aeruginosa appears to be responding to membrane perturbation, and a putative model is that this response is mediated post-transcriptionally via the TagQRST pathway. This signaling cascade leads to the localized phosphorylation of cytoplasmic Fha1 proteins, and subsequent T6SS activation31,32,33.
【背景句10：尽管对分子机制基础进行了广泛的研究，但这个行为差异对细菌适应性的影响和进化的策略的差异性没有对这个复杂问题进行解释。】Although the molecular regulation of retaliatory T6SS firing has received attention27,28,30,34, its evolution has not—leaving open the question of why such a complex strategy has evolved in bacteria, and only in some species.【背景句11：研究进化策略的意义，目前的研究主要集中在细菌的相互合作对竞争策略的研究比较少。】 At a broader level, while the evolution of reciprocation has a long history of study in evolutionary biology35,36,37,38, past efforts have focused largely on the evolution of reciprocal cooperation, rather than competition.【研究的意义：了解T6SS监管和报复的演变是非常重要的，这个行为对理解细菌之间的竞争关系是有意义，并作为进化生物学中的一个独特案例。】 Understanding the evolution of T6SS regulation and retaliation is therefore important, both for understanding bacterial warfare and as a distinct case in evolutionary biology.
【研究的方法：本文采用代理人模型研究不同T6SS策略对生物适应度的意义。】To address this, we used an agent-based modeling framework to simulate competition between different T6SS strategists.【研究内容：利用博弈策略研究不同报复策略的包括 回击次数，回击方式。探究生长过程中策略起作用的范围。】 By combining modeling with game theory, we explore the evolution of T6SS regulation, including tit-for-tat (TFT) firing, across a wide range of conditions. This reveals that TFT has significant limitations as a strategy for T6SS warfare. We found that it rarely wins in direct competition because it fails to fire against unarmed strains and always fires second against armed strains. However, we also found that a strong retaliator, which fires multiple times in response to an attack, is a powerful competitor against randomly firing T6SS attackers. Finally, by studying the retaliatory firing patterns of P. aeruginosa, we show that it does indeed fire multiple times in response to an incoming attack. 【研究的结果：T6SS的回报是最有益的战斗方式是以积极的反击的行为进行。】Our work suggests that T6SS reciprocation is most beneficial during combat when performed in an aggressive manner.
Results
Agent-based modeling of different T6SS firing strategies
To study the interactions and evolution of different T6SS firing strategies, we began with an established agent-based modeling framework (CellModeller)39,40,41,42. The heart of this model is a realistic representation of physically interacting bacteria growing in dense communities. Agent-based models of this sort have proved to be a powerful means to explore cell–cell interactions in bacterial communities, generating a wide range of predictions that have been verified by empirical work (reviewed in ref. 43).
We recently reported a new version of this model44, designed specifically for the study of T6SS competition, in which cells can intoxicate neighbors by firing T6SS needles. Here, we extend this model, such that different modes of T6SS firing (Supplementary Table 1) can now be represented and compared: cells can be programmed not to fire, or to fire constantly and in random directions, or to fire in more elaborate patterns. Using this tool, one can then compare the effectiveness of T6SS firing strategies observed across different bacterial species, under tightly controlled conditions, while varying physiological parameters【模型的关键内容：攻击次数、每次攻击的代价、命中率、回击次数】 (T6SS firing rate kfire, carriage cost cupfront, pro rata cost c, and hit resilience Nhits）. Further details of our model are provided in the “Methods” section.
Random T6SS firing is effective against unarmed strains
（探究随机攻击与无攻击行为菌混合培养）
First, we used our agent-based model to study competition between bacterial strains with two basic strategies: Random-firing T6SS+ attackers ® and T6SS-susceptible Unarmed cells (U). We simulated community growth within 2-D patch environments, beginning with a randomly scattered, 1:1 mixture of R and U cells. Each patch simulation begins with a finite, uniform resource quota that is consumed as cells grow (exponentially, at rate kgrow), and simulations end once a patch becomes depleted of resources (Supplementary Fig. 1A). Would-be weapon users therefore face a trade-off: attacking one’s competitors prevents them from using up a patch’s resources, but at the costs of both reduced reproductive rate and efficiency. Here and throughout, we assume that T6SS+ strategists are immune to the toxins of their clonemates. We also assume that possessing and expressing T6SS genes is costly, such that the specific growth rate of a T6SS+ strain is reduced in proportion to its firing rate. However, we later show that our key conclusions do not rest upon the assumption that the T6SS is costly.
Figure 1a shows two patch simulations in which bacterial strains with R and U strategies compete, carried out for different starting cell densities. In the left example (at low cell density), T6SS-mediated killing marginally increases the final frequency of R strategists; to the right (high cell density), this competitive advantage is greatly enhanced. Strong density dependence is consistent with previous studies of T6SS competition—higher cell density results in increased (and earlier) contact between R and U cells, increasing overall killing45. Another benchmark of the model is that we also observe T6SS activity resulting in increased spatial segregation between competing strains (Fig. 1a and Supplementary Movie 2), compared with T6SS− controls (Supplementary Fig. 1A and Supplementary Movie 1), which is consistent with previous theoretical and empirical work46
To further explore the competitive value of Random T6SS firing, we compared R vs. U competition outcomes for a wide range of input parameters: varying initial cell density, T6SS firing rate, weapon cost, lysis rate, and toxin potency. These analyses confirmed that Random T6SS firing can indeed offer a competitive advantage (evidenced by increased R frequency after competition) under a broad set of conditions. As well as being favored by high cell density (Fig. 1b, Supplementary Figs. 2 and 7, and Supplementary Movie 2), which produces greater contact between R and U cells, natural selection for Random T6SS attackers is increased for low weapon costs (Supplementary Figs. 1C, D and 7), high toxin potency (Supplementary Figs. 1E and 2), and low victim cell lysis duration (Supplementary Fig. 1E). Similarly, we found that reducing weapon costs generally increases the optimal T6SS firing rate (Supplementary Fig. 7).
Our models confirm the intuition that the T6SS can help a bacterial strain to increase its frequency within a patch. But are Random T6SS attackers also expected to invade an Unarmed population when one also considers the competition between different patches to colonize new sites (global competition)? This question is important because while aggression may allow a strain to defeat a competitor, if this comes at a large personal cost, an aggressive strain may still end up producing very few dispersing cells. If other patches contain only passive strategies that make many dispersing cells, therefore, an aggressive strategy could win locally but lose globally by failing to colonize new patches. To address this question, we embedded our model in a game-theoretic framework that uses the principles of adaptive dynamics47. As detailed in the “Methods” section, this approach considers the fate of an initially rare, novel strategist placed in a metapopulation (large set of patches) dominated by another, resident strategist. If the relative fitness of the novel strategist is greater than that of the resident, we assume that its frequency in the metapopulation will increase until it supplants the resident as the common strategy. We then also check whether the resident can itself re-invade a population of the novel strategist from rarity, and when it cannot, we assume that the novel strategy will permanently replace the resident.
Figure 1c shows the fate of an invading Random T6SS attacker (summarized by an invasion index, Iinv) as a function of its attack rate, kfire,R, for when there is purely local competition (“local competition”) and when there is both local and global competition (“global competition”, see “Methods”). For local competition, Random attackers compete only within patches with the resident Unarmed strain. For global competition, they must also compete with Unarmed cells in neighboring patches where Random attackers are absent. In both scenarios, we find that R can successfully invade U for all non-zero firing rates, assuming a high initial cell density (Fig. 1c, 200:200 cells). For lower cell densities, the range of viable kfire,R values narrows, and is generally smaller for global competition than for local competition (Supplementary Fig. 3). In sum, in this system, we find that local and global competition scenarios give qualitatively similar results.
An evolutionarily stable rate of random T6SS firing 【随机攻击对没有反击行为的细菌是有效的攻击策略】Our models predict that Random T6SS attackers will readily invade a population of Unarmed cells, under a range of conditions. As Random attackers become more abundant, they will begin to encounter one another, and so we next consider the outcomes of battles between different R-type strategists. When can one Random attacker invade another’s patch? Here we studied competition between pairs of R-type strategists, R1 and R2, each having its own attack rate kfire,R1, kfire,R2, and each being susceptible to the other’s toxins. Figure 1d shows a pairwise invasion plot, commonly used in adaptive dynamics47, indicating which of R1 and R2 invades the other as a function of their respective attack rates, for local (within-patch) competition. We find that either competitor can invade the other by firing faster than it (Supplementary Movie 3 shows an example of this), but only up to a point. Beyond the yellow diagonal line, having a higher attack rate than one’s competitor makes one vulnerable to invasion, since the increased costs of the higher attack rate outweigh any additional benefits conferred44. Figure 1e shows a similar pairwise invasion plot, this time computed for the case of global competition.
What firing rate kfire,R is predicted to evolve during competition between Random T6SS attackers? Here, we compute the evolutionarily stable strategy (ESS) as the value of kfire,R, denoted ESSfire,Rkfire,RESS, for which a resident strategist cannot be invaded by mutants with a higher or lower firing rate. In Fig. 1d, the ESS for local R1 vs. R2 competition is shown as a white circle—if a resident strategist (R1) adopts this firing rate, then a mutant strategist (R2) cannot invade irrespective of its firing rate.
Moreover, one can show that any resident population will evolve towards this strategy. For example, suppose we begin with a resident R1, which possesses the T6SS but does not use it (kfire,R1 = 0, Fig. 1d). Then suppose a mutant R2 appears in this population with kfire,R2 = δ, where δ > 0 represents some small increment in firing rate. Since the local invasion index localinv(0,)>1,Iinvlocal(0,δ)>1, R2 can invade R1, and kfire,R1 = δ becomes the resident strategy. The same outcome occurs with kfire,R2 = 2δ, 3δ … such that successive invasions by incrementally more aggressive mutants increase the firing rate in the resident population (Fig. 1d, black arrows), eventually converging on ESS,localfire,Rkfire,RESS,local. Similarly, a resident population with a very high firing rate (e.g., kfire,R1 = 250 firings cell−1 h−1) will be displaced by mutants with lower firing rates (Fig. 1d, yellow arrows), again converging on ESS,localfire,Rkfire,RESS,local. Global competition (Fig. 1e) favors a reduced level of aggression than local competition (i.e., ESS,localfire,Rkfire,RESS,local > ESS,globalfire,Rkfire,RESS,global), a trend also seen for other strategist pairs at various initial densities (Supplementary Fig. 3). This follows one of the core results of social evolution: between-group selection can select against competition, and for cooperation, because global (between-group) competition makes group productivity important for fitness48,49.
TFT retaliation fails to beat a random attacker【TFT报复未能击败随机攻击者，单次/轻微反击不是一个良好的策略行为。】 Our results indicate that Random T6SS firing can often be a successful strategy, both enabling invasion of Unarmed populations and achieving higher cell frequencies against other Random T6SS attackers than Unarmed strategists attain. From this baseline, we can evaluate the evolutionary costs and benefits of the more complex T6SS firing strategy of TFT. Based on published empirical work on P. aeruginosa23,27, we assume that TFT differs from R in two key respects: (i) TFT does not fire its T6SS continuously, but counterattacks once per incoming attack (retaliatory firing); (ii) TFT does not fire from randomly chosen sites on its cell membrane, but instead from the points where incoming attacks struck (spatial sensing). To provide a fair basis for strategy comparison, we assume TFT to be identical to R in all other respects (toxin potency, lysis delay, weapon costs per T6SS firing, costs of weapon carriage).
Figure 2a shows our implementation of a TFT strategist in the agent-based model. To assess conditions favoring TFT strategists, we competed TFT against R for different initial cell densities, as before (Fig. 2b, c, Supplementary Fig. 2, and Supplementary Movie 4). We were surprised to find that, while TFT generally does better against R than U (cf. Figure 1b), R is nevertheless predicted to outcompete TFT in a wide range of conditions. Specifically, we see that R can always evolve to a firing rate kfire,R that makes it equal or better than TFT (Fig. 2d). We also see that it is at the higher initial cell densities that R performs the best against TFT. This effect is telling: increasing initial cell density simultaneously creates more fronts between competing cell groups and increases the time for which competing strains are in physical contact—both of which favor the strain with the best contact-dependent attack (above, Fig. 1a). Overall, our model suggests that R can invade and displace TFT simply by evolving relatively low kfire,R values, for both local and global competition scales (Fig. 2d and Supplementary Figs. 2 and 3), provided cell density is sufficient.
Retaliation can evolve by investing in attack and defense【报复可以通过选择将能力改变为用于攻击和防御来进化。】
We found that a wide range of conditions preclude the evolution of TFT retaliatory T6SS firing from a population of Random attackers. Trivially, TFT is also guaranteed to lose against U, since the latter never triggers retaliatory T6SS attacks, and is spared the cost of T6SS carriage24. How then could TFT have evolved in P. aeruginosa if it is predicted to be typically outcompeted by other less sophisticated strategies?
To resolve this apparent paradox, we considered ways in which the TFT strategist might evolve to improve its competitive ability. This revealed that increasing the number of counterattacks launched by retaliators can pay great dividends. Specifically, we found that a strong retaliator strategist—dubbed 2-tits-for-tat (2TFT)—is highly successful against a Random T6SS attacker (Fig. 2e), outcompeting it for all T6SS firing rates and cell densities studied (Fig. 2f and Supplementary Movie 5). Swapping TFT for 2TFT also reversed the trend in competition outcome with respect to initial cell density, with higher cell densities now favoring 2TFT instead of R (Fig. 2f, cf. Fig. 2c). This again illustrates that high cell density tends to intensify contact-dependent warfare, and thereby favor whichever strain has the best contact-dependent attack (Fig. 1a, last section).
Accordingly, we also found that 2TFT is able to invade a population of R cells for all kfire,R > 0 (Fig. 2g), and for all cell densities studied (Supplementary Fig. 3). However, this robust competitive advantage disappeared when we reduced the resilience of both strategists (Nhitsreduced to 1 from 2), such that a single T6SS hit is sufficient to kill any non-clonemate cell (Supplementary Fig. 3): here, 2TFT performs no better than TFT. 【结论句：这表明，对T6SS中毒的防御和通过增加反击次数进行攻击的充分投资对于报复的演变是重要的】This suggests that sufficient investment in both defense against T6SS intoxication and attack via increased firing is important for the evolution of retaliation.
While 2TFT is very successful in competition with R cells, it is expected to lose against unarmed (U) strains, just like TFT. This raises the possibility of rock-paper-scissors dynamics, also suggested in a recent study50, where non-transitive interactions between competing bacterial species stabilizes variation in T6SS firing patterns51,52. Consistent with this possibility, we found that parameter combinations exist (Supplementary Fig. 5) where unarmed U strains are beaten by random R attackers (T6SS killing trumps growth advantage), who are beaten by 2TFT (superior killing and growth advantage trumps T6SS aggression), and who can be beaten in turn by unarmed strains (growth advantage trumps unused costly T6SS).
We also tested the robustness of 2TFT’s supremacy across a range of additional biological scenarios, including low diversity in T6SS toxins in the population (Supplementary Fig. 8), the potential for cheating strategies that do not use the T6SS but which are immune to some T6SS toxins (Supplementary Figs. 9 and 10), and conditions with high within-patch relatedness (Supplementary Figs. 11 and 12). We discuss the effects of these scenarios in detail in the supplement, but across all conditions, 2TFT was predicted to be equivalent or superior to both TFT and R.
Retaliation brings both geometric and economic benefits【报复既带来几何/形状效益，也带来经济效益。】
【结论段：通过回击策略有利于提高毒素的命中率，同时有利于反击的细菌节约能量减少毒素失效和生成代价。】We next sought to characterize the origin of 2TFT’s advantage over R, in contrast to the standard model of T6SS retaliation (TFT). We identified two key advantages offered by retaliatory T6SS firing, and used our models to compare their relative contributions to 2TFT’s fitness in competition with R (Fig. 3). First, the ability to sense where incoming attacks are coming from allows T6SS counterattacks to be aimed specifically at attackers. By contrast, Random attackers have no information on where target cells are9, and so miss most of the time (Fig. 3a). We confirmed this principle by measuring T6SS hit:miss ratios in fixed, well-mixed configurations of cells, showing that attacks by 2TFT cells were significantly more likely to hit R cells than vice versa (Fig. 3b, see “Methods”).
Second, the ability to sense when one is being attacked prevents costly use of the T6SS when it is not needed. Examination of cell growth rates during R vs. 2TFT competitions confirmed that only 2TFT cells that are in contact with competitors pay for T6SS firing—compared with R cells, which pay for constant T6SS firing whether or not competitors are actually in range (Fig. 3c). We found that this resulted in significantly higher specific growth rates for 2TFT cells than for R cells (Fig. 3d).
To determine which of these advantages—improved aim or lower cost—drives 2TFT’s success in a given scenario, we created three new retaliator phenotypes with one or both advantages removed (Fig. 3e). To remove the advantage of T6SS aiming through spatial sensing, we configured 2TFT cells to counterattack from randomly chosen sites on their membranes, instead of from the points at which incoming attacks struck (Fig. 3e, bottom row). To remove the advantage of reduced T6SS cost, we configured 2TFT cells to pay the same growth costs as Random T6SS attackers, for any given attack rate kfire,R (Fig. 3e, right column). Comparing the single knockout cases (loss of aiming or loss of cost saving) against a normal R vs. 2TFT competition, we found that removing cost saving still allowed 2TFT to beat R (albeit by a reduced margin) irrespective of weapon cost factor c, including the limit in which weapon use is cost-free (Fig. 3e, top right). By contrast, eliminating only T6SS aiming (Fig. 3e, bottom left) allowed R to beat 2TFT, except where weapon costs were very high. Similar results were seen when cell density was varied instead of weapon costs (Supplementary Fig. 4). In sum, a 2TFT strategist can accrue benefits from both advantages, but it is improved aim that appears most critical to their success.
Pseudomonas aeruginosa launches multiple counterattacks
【实例分析：铜绿假单胞菌发起多重反击】
【实例验证段：通过观察铜绿单胞菌与霍乱弧菌的混合生长观察到，在有霍乱弧菌的随机攻击刺激下铜绿假单胞菌进行了多次回击。】
Our model suggests that the evolution of retaliation via the T6SS rests upon at least three specific characteristics of a retaliating cell: (1) intrinsic resistance to T6SS attack such that a cell can survive more than one hit (Supplementary Fig. 3), (2) the ability to reciprocate with multiple counterattacks (Fig. 2), and (3) the ability to aim counterattacks towards aggressors (Fig. 3). Predictions 1 and 3 are already supported by published work. An opportunistic pathogen found in a wide variety of environments, P. aeruginosa is a notably resilient species with a high natural tolerance to many antibiotics and other toxins53. More specifically, P. aeruginosa cells are known to regularly tolerate multiple hits from the T6SSs of other species, including the human pathogen V. cholerae27. In addition, the firing behavior of P. aeruginosa in response to an incoming hit is visibly non-random, occurring reliably on the same side of the cell as the incoming hit23. However, prediction 2 has not been examined empirically, offering us an opportunity to test our model against an unknown aspect of T6SS biology.
We therefore analyzed the T6SS counterattacks of P. aeruginosa (strain PAO1) cells, in response to random attacks by V. cholerae (strain 2740-80) bacteria, as in the original T6SS retaliation study27 and subsequent work29,54,55,56. In our experiments, both cell types express functional T6SS apparatus, the sheaths of which (TssB subunits in the case of P. aeruginosa and VipA subunits in the case of V. cholerae) carry fluorescent tags (see “Methods”). These tags allow individual T6SS firing events to be tracked using time-lapse fluorescence microscopy, as described in previous studies25,27,57. When the two are grown together on agarose pads, V. cholerae antagonizes P. aeruginosa and causes it to launch counterattacks (Supplementary Fig. 6 and Supplementary Movie 6), such that T6SS dynamics of the two species can be compared directly in the same setting. By contrast, control experiments using a T6SS− V. cholerae mutant resulted in no P. aeruginosa T6SS activity, reproducing behavior reported in previous studies27,28,29, and confirming the retaliatory nature of P. aeruginosa attacks (Supplementary Movie 7).
Figure 4 shows example kymographs, tracking sheath lengths in individual T6SS apparatus imaged in P. aeruginosa cells (Fig. 4a) during these co-culture experiments. We observed that, following an incoming attack, P. aeruginosa cells fire repeatedly (between 1 and 6 firings over a 5-min time-lapse, with median 2 firings per site; see Fig. 4b and Supplementary Movie 8). As predicted by our model, therefore, we found that retaliatory firing in P. aeruginosa is associated with multiple counterattacks from the same T6SS site. Conversely, we could detect no instances of repeated T6SS firing by V. cholerae within the same time window (Fig. 4c), confirming that repeated T6SS firing is not simply a universal trait among γ-Proteobacteria.
Discussion（讨论）
【结论句1：随机攻击策略有效/有较大优势的情况主要为1）每次进攻的成本不高的情况下2）初始接触适合的接触面积（较高的菌种密度）。】We found that random constitutive firing of the T6SS can readily evolve in unarmed populations, provided that (i) weapon costs are not excessive and (ii) initial mixing provides enough inter-strain contact. 【结论句2：反击策略主要是适合于与其他攻击者相比时存在优势。优势的主要来源于在反击的鲁棒性（有效性、广谱性）、提高了命中几率以及强烈的报复行为。】By contrast, retaliatory firing is successful only against other T6SS users, and then only if the retaliator is robust to incoming attack, gains an aiming advantage, and deals more damage than it sustains (strong retaliation). 【总结句3：报复行为需要加倍奉还才能起作用，使主动攻击的细菌处于不利地位。】Ultimately, these additional constraints stem from the first-strike advantage possessed by random attackers: having already been struck by at least one T6SS needle, a retaliator always enters combat at a disadvantage, requiring that retaliation be disproportionate to be generally successful.
【总结句1：这些特定的限制条件解释了反击报复行为在自然界中如此罕见，大多数细菌都选择随机攻击的方式。】The additional constraints limiting retaliator evolution may explain why P. aeruginosa is, to our knowledge, the only example of a T6SS retaliator found so far—whereas many species appear to use random T6SS firing23,24,25.【总结句2：研究也清晰的展示出铜绿假单胞菌采用非常适合的报复策略。同时也进化出与这个策略相互适应生理基础。】 It is also clear that P. aeruginosa is very well suited for T6SS retaliation. First, P. aeruginosa can resist oncoming T6SS attacks from other species like V. cholerae27. Second, P. aeruginosa’s ability to aim T6SS firing—through spatially resolved, TagQRST-mediated attack sensing—is likely to be a key contributor to its success as a retalitor (Fig. 3e), because it provides cells with additional information on the location of attackers. This contrasts with other forms of T6SS regulation, where the rate of T6SS firing is increased in response to cellular damage18,24,58, but with T6SS placement occurring apparently at random. By placing T6SS assemblies at attack sites, P. aeruginosa can substantially improve its hit efficiency, compared with a random firer that has no information on the location of its target.
【结论句3：对于铜绿假单胞菌需要更加强烈的报复行为获取更多生长资源，使消耗的个体数目小于获得的生长优势。当充分混合时最后能在全局竞争中获取优势。】Third, our models predict that P.aeruginosa cells can only fully exploit this aiming advantage if they also launch multiple counterattacks from a given site of impact. Otherwise, they still stand to lose more cells per pairwise T6SS battle than their competitors, such that the latter can still win overall if the two strains are sufficiently well mixed.Our experiments confirmed that P.aeruginosa does indeed fire repeatedly from T6SS assemblies placed at hit sites, a pattern not observed in random-firing V.cholerae. In light of this observation, P.aeruginosa’s retaliatory firing appears better characterised as a tits-for-tat strategy than a tit-for-tat one36.