One gene, multiple ecological strategies: A biofilm regulator is a capacitor for sustainable diversity | PNAS

Edited by Paul E. Turner, Yale University, New Haven, Connecticut, USA, and approved on July 27, 2020 (reviewed on May 1, 2020)

Many organisms (including bacteria) live in a variable environment that needs to attach and spread. These lifestyle decisions require processing of multiple external signals through several genetic pathways, but how to integrate these signals is largely unclear. We have conducted multiple evolution experiments, totaling> 20,000 generations

A population grown in a biofilm life cycle model, and a parallel mutation in a gene was identified,

, This is a conservative central regulatory agency. Because RpfR has multiple sensors and catalytic domains, different mutations can produce different ecological strategies, which can coexist and even increase net growth. This study shows that a single gene can coordinate the complex life history of bacteria residing in biofilms, and selection in a defined environment can reshape the breadth of the niche through a single mutation.

Many bacteria circulate between sessile and sporty types, and they must sense and respond to internal and external signals to coordinate proper physiology. Staying healthy requires a genetic network honed in a variable environment to integrate these signals. In most organisms, the identities of the main regulators and how their control mechanisms have evolved are still little known. In four different evolutionary experiments of the opportunist beta Proteus

In biofilm models, mutations in conservative genes are most commonly selected

. RpfR uniquely integrates two main signaling systems-quorum sensing and mediated by cyclo-di-GMP by sensing, responding and controlling the two domains of self-induced cis-2-dodecenoic acid (BDSF) synthesis Movement-sessile exchange. The BDSF response in turn modulates the activities of the diguanylate cyclase and phosphodiesterase domains that act on cyclic diGMP. Parallel adaptive substitutions have evolved in each of these domains to generate unique life history strategies by regulating ring two GMP content, global transcription response, biofilm production, and polysaccharide composition. These phenotypes translate into unique ecological and biofilm structures, allowing mutants to coexist and produce more biomass than would be expected from the components grown alone. This study shows that when selecting bacterial populations in environments with plastic limits, evolutionary mutations not only change the genes linked by the signaling network, but also reveal the scope of their regulatory functions.

Bacteria have gone through a powerful choice for billions of generations, and can effectively and reversibly transform from free swimming to surface restrained life. Records of this selection are engraved in the genomes of thousands of species, many of which have dozens or even hundreds of genes that control this lifestyle transition (

). In many bacteria, the key to this switch is the second messenger molecule cyclic biguanide monophosphate (c-di-GMP). Many genes are synthesized, degraded, or directly combined and respond to c-di-GMP. C-di-GMP promotes sessile lifestyle and biofilm production at high concentrations, and promotes lonely life at low concentrations. Those genomes with the greatest apparent redundancy in this signal network-sessile axis exhibit the highest plasticity (

). For example, in

, 41 different diguanylate cyclases (DGC) synthesize c-di-GMP and 31 different phosphodiesterases (PDEs) degrade the molecule (

).

Recent theories and experiments have shown that this apparently redundant development is due to the need to integrate many signal inputs generated in a fluctuating environment, and also need to generate appropriate response outputs (

). However, the question remains how many enzymes that produce or degrade c-di-GMP can be maintained to play different roles. One explanation is that certain DGCs or PDEs dominate the rest of the network under certain environmental conditions. Screening for a complete set of gene knockouts in a low temperature environment found that only six DGCs were the main factors leading to the increase in c-di-GMP levels.

(

,

). Similar method

, Usually contains 40 or more genes encoding DGC, PDE or two domains, which indicates that these enzymes form a complex suitable for universal sensing conditions (

). Now, active frontiers in this field are trying to define and characterize the external cues that activate these specific regulatory circuits, that is, how the one-second messenger c-di-GMP, as the decisive node of a clue-based variable bacterial life history strategy, originates from outside the cell ?

These two questions-what gene products dominate the c-di-GMP signal and how they integrate external signals-have driven the development of this research.

, An opportunistic β-proteobacteria with metabolic function, especially threatening patients with cystic fibrosis (

). More and more evidence shows that among the 25 potential DGC or PDE,

It’s essential in this network (

). Early mutant screening

Gene identified a gene,

As one of several ways to increase biofilm production (

). The gene was later renamed

(Modulator of pathogenic factors), and has been shown to have DGC and PDE domains (

). Importantly, this study also identified the PAS sensor domain in RpfR, which binds to the self-inducing molecule cis-2-dodecenoic acid, also known as

Diffusion Signal Factor (BDSF) (

). Recently, all hypothetical deletion mutants of DGC and PDE proteins have been studied.

Channel H111 pointing

Particularly important(

). RpfR is now considered to be a bifunctional protein composed of DGC and PDE domains and two sensor domains. We recently discovered the second of them (

). One sensor is Per-Arnt-Sim (PAS) domain combined with BDSF (

), and then stimulate the cleavage of c-di-GMP into the PDE domain of pGpG and GMP (

). Therefore, BDSF, like other DSFs, promotes the spread of biofilms by reducing the level of cell c-di-GMP.

RpfR is the main target in the selection

Biofilm. (

) Hypothetical model of RpfR-RpfF modulator on BDSF signaling and c-di-GMP metabolism. RpfR contains four domains: 1) FI domain, 2) PAS sensor, 3) DGC domain with GGDEF motif and 4) PDE domain with EAL motif. Its neighboring gene product RpfF is the enoyl-CoA hydratase that produces BDSF. (

) When the DGC domain is activated (blue), c-di-GMP is synthesized, and c-di-GMP is the second messenger that regulates biofilm formation and movement. (

BDSF binds to the PAS domain and induces c-di-GMP degradation by activating the PDE domain (red). (

) The FI domain of RpfR binds to RpfF and forms a complex that inhibits BDSF production (

-

). (

)development of

Mutations occurred during the experimental selection of biofilms (blue), plankton growth (red), alternate growth of biofilms and plankton (purple), and chronic infection of cystic fibrosis airways (green). Mutations are disproportionately enriched in the linker region (gray shading) between the sensor and the catalytic domain, and at four residues (

The discovery of the second sensory area is partly through

In our biofilm bead model, bacteria are selected to colonize polystyrene beads, and then transferred to a new test tube containing medium and fresh beads every day (

). Evolution

The mutants of these studies led us to identify an additional N-terminal domain of the protein, which was not previously described in the protein database (

). We named this domain RpfF inhibitory domain or FI domain because it binds to RpfF, which is the thioesterase that produces BDSF encoded by adjacent genes. When RpfR-FI binds to RpfF, it will affect BDSF (

) (

). This finding leads us to hypothesize

Is the focus of selection, because it not only controls c-di-GMP-mediated biological processes, but also controls quorum sensing related to BDSF (

The integration of multiple regulatory roles within a gene raises an important evolutionary question: how does natural selection coordinate the functions of its protein domains (in view of its biochemical opposition (synthesize or degrade c-di-GMP) and its ability to produce different life histories (Stick) or swimming)? By using conventional methods of knockout or deletion mutations, it is difficult to solve this problem experimentally, because they usually mask the role of a single protein domain and cannot resolve widely conserved genes (such as

Affect specific functions (

). The point mutations in the different RpfR domains that have evolved in our long-term evolution experiments encode more subtle information. These mutants not only improved the adaptability of the biofilm lifestyle model, but also coexisted for hundreds of generations, indicating that they produced different phenotypes and were unable to compete for the same niche (

). In addition, in another study, we found

Mutations associated with increased biofilm production and genetic diversification during 20 years of chronic disease

Infections in patients with cystic fibrosis (

). In both cases,

Mutations occur simultaneously with other mutations along their evolutionary trajectory, so their independent contribution to fitness and gene function remains to be determined. Here, we use a combination of directed genetics, transcriptomics and detection methods of microbial ecology, physiology and adaptability in a variety of environments to understand how

Play the role of a regulatory node and why mutations in this system occur predictably in our biofilm models and perhaps during infection.

According to previous evolutionary experiments (

)versus

Growing in a bead model that simulates the life cycle of a biofilm, we found that there are at least 72

Mutations in 32 independent populations affecting multiple protein domains (

with

). By analyzing the mutations selected during evolution in the medium made from macerated onions, other mutations can be identified (

). mutation

In each laboratory experiment, they always appear first with high frequency mutations (> 25%), and are related to competitive fitness and increased biofilm production (

). The mutation profile shows a powerful choice for altered or eliminated protein functions: out of 46 nucleotide substitutions, 45 are synonymous, and the other 26 mutations produced missing or premature stop codons. Except for two deletions, all deletions are deleted

And adjacent

Gene, indicating that selection acts on the interaction between these two gene products. The distribution of single nucleotide polymorphisms (SNPs) is also non-random, and is obviously enriched in the linker region between the four domains, rather than in the catalytic or sensory site itself (X

= 10.47, degrees of freedom [df] = 1,

= 0.0012,

). This result indicates that changes in the interaction between the selected functional domains are not disrupted for BDSF sensing or c-di-GMP catalysis. Of the 13 mutations in the DGC domain, 8 occurred in Y355 or R377, indicating the functional importance of these residues. In addition, the 10 mutations affecting the EAL domain of phosphodiesterase only occurred in two positions, namely S570 (

= 3) and F589 (

= 7). In general, the choice is right

The high-precision sequence prompts further research on its function.

Distribution

Domain mutation and statistical enrichment of linker regions

In the long-term evolution experiment, three

Mutations appear in the same population, coexist in the long-term biofilm selection process, and are related to different ecology, which indicates that these mutations are not the same in function (

). These mutants are A106P in the region connecting the FI and PAS domains, Y355D in the DGC domain, and deletion mutants of the two

(Or functionally equivalent de novo mutants) can also evolve in parallel between replicated populations, which has become the focus of this research. Taken together, these findings indicate that selection can generate multiple discrete phenotypes by changing the different domains of dominant c-di-GMP modulators.

We introduced evolutionary point mutations or targeted deletions into the ancestral HI2424 strain (

), and confirmed that they are syngeneic by whole-genome sequencing. From now on, we call these engineered genotypes evolutionary mutants. Further, in order to test the contribution of each sensor and enzymatic domain, we constructed deletions of FI domains (1-95aa) and alanine substitutions. These residues are expected to eliminate the diguanylate cyclase activity (GGDAF, Equivalent to E319A) or phosphodiesterase activity (AAL or E443A). We also deleted

Overall, the adjacent BDSF synthase

Or these two genes. Because a deletion of 95 genes was deleted at the same time

Developed repeatedly during our experiments and successfully constructed

Genotype, some experiments on this

Genotype +93 (

). Subsequently, we competed for these two genotypes and found that their fitness is statistically indistinguishable (

= 0.38, df = 10,

= 0.12).

An easy way to screen for c-di-GMP is to increase the uptake of Congo red dye or fold colony morphology, both of which are due to the increased production of polysaccharides usually associated with high c-di-GMP (

). The evolved point mutants (A106P and Y355D) showed increased absorption of Congo red dye on the morphological plate (

), and contrary to the smooth phenotype of wild type (WT), all evolved colonies produced characteristic stud centers and smooth periphery (

). These colony phenotypes are associated with increased biofilm production and decreased motility (

). Similar phenotypes are observed in engineered AAL and AAL.

mutant

), the activity of the PDE domain and the production of BDSF that activate the PDE domain should be eliminated, thereby increasing the level of c-di-GMP. To test these predictions, we quantified the intracellular c-di-GMP levels in the plankton and biofilm cultures of each mutant at 12 and 24 hours, respectively (

). First, we learned that in a dense biofilm, the absolute value of the signal is usually much larger at 24 h, but in our model, when the settlement of plastic beads accelerates, the relative difference (value divided by WT value ) Will be bigger (

). Secondly, the A106P mutant in the FI-PAS linker region will produce a moderate but continuous c-di-GMP increase under various conditions, indicating that the mutant will interfere with PAS-mediated PDE activation. Third, as expected, the PDE domain and

Mutants that did not produce BDSF to activate the PDE domain all increased c-di-GMP. Four, evolution

The +93 mutant produced elevated c-di-GMP in the 12-hour biofilm and the 24-hour planktonic culture, indicating that the loss of RpfR's PDE activity masked the contribution of other DGCs. Interestingly, only delete

It did not significantly change the level of c-di-GMP in the biofilm, but did increase the level in planktonic cultures, indicating that in the absence of RpfR, functional RpfF can affect the c-di-GMP library in an unknown way. Finally, the evolution of the DGC domain Y355D mutant produced the highest level of c-di-GMP, indicating that this is a gain-of-function mutation in a domain that is considered non-functional (

). To test this prediction, we constructed a GGDAF mutation that should disable the DGC domain in the Y355D mutant (Y355D-GGDAF), and found that, as expected, it produced c-di-GMP at WT levels (

). This result indicates that the RpfR DGC domain is directly responsible for the high c-di-GMP level in Y355D. Taken together, these results indicate that the evolved genotype produces different basal levels of c-di-GMP according to the affected domain, and changes the yield according to its environment. Broadly speaking,

In the cycle of biofilm attachment, formation and diffusion, the differentiation phenotype caused by a single plant can be selected

mutation.

Continuous development and design

Genotype produces a variety of c-di-GMP regulated phenotypes. (

) Congo red, the colony characteristics of different evolutionary and engineering mutants on protein try agar plates (due to the Congo red gradient, the colony background is different). (Scale bar, 5 mm.) (

) Biofilm productivity (adhesion) measured by crystal violet staining at 24 hours. Evolutionary mutants are shown in different colors, and engineering mutants are shown in gray. The relative level of c-di-GMP and WT is in (

) 12 and (

) 24 hours under biofilm and planktonic conditions. Error bars indicate 95% CI. Different letters in the same figure indicate significant differences between mutants (one-way analysis of variance and post-hoc comparison by Benjamini and Hochberg methods (

), q value <0.05). for

, Blue and black letters indicate plankton and biofilm conditions, respectively.

We predict that different c-di-GMP levels and related differences in biofilm matrix production contribute to fitness. The evolved and modified mutants compete with WT strains in equal proportions and show significant changes in adaptability. Among them, the Y355D mutant is the most suitable (

). In general, when mature, the adaptability of the biofilm model at 24 h is positively correlated with the level of c-di-GMP when the adhesion rate increases at 12 h (

). However, as the level of c-di-GMP increases, the rate of increase in adaptability decreases, especially in evolutionary mutants, which indicates diminishing returns (

). The A106P mutant is more suitable at 24 h, indicating that this genotype affects other advantages of the FI-PAS linker region, but it is suitable

Although the content of c-di-GMP is high, it is equivalent to WT in biofilm (

). Further evolution

+93 and engineering

Although c-di-GMP increased moderately, the genotype was more suitable for WT. Under planktonic growth conditions, many mutants are also more suitable for wild-type, which is an essential part of our bead model and needs to be dispersed, but compared with biofilm conditions, the adaptive benefits between mutants are lower and Small changes (

). As expected, the loss of PDE activity (AAL) increased the level of c-di-GMP and also greatly improved adaptability (

). This powerful advantage indicates that the main role of RpfR is its PDE activity, as shown by the orthologs of other species (

)

Fitness

The relationship between genotype and c-di-GMP level. The fitness is calculated by the selection rate, and the neutral fitness=0. The colored symbols are evolutionary genotypes; the gray is carefully designed. (

) Non-linear relationship (piecewise linear regression,

= 0.77) between 12 hours of c-di-GMP production and 24 hours of biofilm adaptability. Engineering mutants shown in gray are not included in the function. (

) Relative adaptability and WT under the conditions of biofilm growth for 24 and 48 h. The experiment included four different biofilm stages: attachment, assembly (measured at 24 hours), reattachment, and reassembly measured at 48 hours. Through post-hoc testing after ANOVA, different letters indicate significant differences between genotypes. (

) Fitness difference between

Mutants after 24 hours of competition started with different starting frequencies. Regression line and

The axis is the predicted sudden change frequency at equilibrium. The slope of the game between A106P and Y355D and between A106P and Y355D

+93 is different from zero (

<0.001). The error bar is 95% CI.

Different continuous coexistence

Mutants in the evolved biofilm population (

) Can be explained by niche differentiation in the life cycle of biofilms. If these niches support populations of different sizes, the adaptability of different genotypes should depend on their relative frequency, and when rare, the genotypes should be able to invade each other, also known as negative frequency dependent selection (NFDS) (

). We tested this hypothesis by competing each evolved genotype with other genotypes after 24 hours in the biofilm and found support for the model (

). When low frequency is introduced, both A106P and Y355D can invade each other, and the estimated balance ratio is 1:4 A106P: Y355D (

, Linear regression analysis

= −0.0149×

+ 0.3599,

= 0.98). In addition, A106P and

+93 or

The mutant showed higher adaptability in the competition with WT (

), but can coexist through NFDS during co-cultivation (

, Linear regression

= −0.01164×

+ 0.8477,

= 0.94 and

= −0.02265×

+ 0.945,

= 0.64). However, the Y355D mutant is more

The +93 genotype was eventually replaced in a long-term evolution experiment (

, Yellow, linear regression

= −0.0029×

+ 1.074,

= 0.03). The high adaptability of Y355D is consistent with its sweep to high frequency (

) And parallel evolution (

) But this does not explain why

+93 eventually replaced Y355D. Previous research has shown that other mutations are

+93 pedigree increases its relative fitness and excludes others

Descent (

), we will explore other explanations below. in contrast,

And WT cannot invade each other in rare cases (

), which is the same as

The phenotype is produced by wild-type BSDF. In short, different

Avoiding scattered genotypes mediated by BDSF in various ways can easily replace WT ancestors in our biofilm models and can be coexisted by NFDS for hundreds of generations.

By forming aggregates of different compositions and forms, different genotypes in biofilms can continue to coexist. We tested the potential mechanism of this niche differentiation using fluorescently labeled genotypes to measure their co-localization and total volume by confocal microscopy (

). When cultured alone, both A106P and Y355D will form large and dense aggregates, which are well dispersed (

),and

+93 produces a thinner, more uniform biofilm, arranged in small clusters. This result indicates that the loss of RPFRF complex and/or the production of BDSF changed the form of biofilm formation, and the point mutations appeared to be larger than the clusters produced by WT (

). Different combinations of genotypes produced aggregates of different sizes and biofilm thickness (

). Differences in biofilm development

When this mutant is co-cultured with Y355D or A106P, +93 is even more pronounced, resulting in a thinner, more uniform structure and showing

Biofilm development +93 (

). By the way, we observe

It will form small clusters when mixed with other mutants, but will form larger aggregates when grown together, which is complementary to crossover (

Co-cultures of evolutionary mutants exhibit complementary interactions. (

Confocal images describe specific structural differences between single strains and co-cultured biofilms. Large aggregates with spaces were observed in Y355D, A106P and Y355D x A106P mutant biofilms, and

+93 and co-culture with the mutant (A106P x

+93 and Y355D x

+93) shows small clusters and uniform thickness. In order to improve the viewing effect, the cells marked with RFP are colored magenta as the false color, and the cells marked with YFP are colored yellow. White dots indicate co-aggregation of different marker strains. (Scale bar, 10μm.). (

) The correlation between the average aggregate size of the attached aggregates and the thickness of the biofilm (the symbol represents the genotype, and the color represents the paired genotype). (

) Total biofilm productivity expressed as CFU of a single strain in co-cultivation. Stacked bars with the same color indicate competition between oppositely labeled cells of the same mutant, while bars with different colors indicate co-culture. The mutants in the picture are color-coded (green, A106P; blue, Y355D; red,

+93). The expected (exp) value is obtained from each game, and the observed (obs) value is determined through experiments. Letters indicate different paired statistical groups, such as

) The aggregation in the biofilm, where the positive coefficient indicates the degree of overlap between the two channels (* value is significantly different from zero).

Interactions between genotypes can range from antagonism (which reduces the net productivity of both types) to synergy (which increases the productivity of both types). We measure productivity in terms of attached colony forming units (CFU) per milliliter and microscopic biological volume for all genotype combinations. In most cases, evolutionary co-cultivation

Mutants grown on polystyrene beads are more productive than mutants grown alone (

). This shows the difference

Genotypes are conducive to attachment and growth to each other, which supports the conclusions of previous studies on long-term biofilm populations involving more complex genotypes (

). It is worth noting that the biofilm productivity of Y355D and A106P co-culture is higher than that of a single genotype, but lower than that of two genotypes.

+93, which increases the co-aggregation of two point mutants (Pearson’s

> 0.5) (

). These results indicate that

Mutants with different levels of c-di-GMP and BDSF signaling ability are more productive than when grown alone and together produce a more uniform biofilm structure. We speculate that the increased uniformity of the hybrid biomembrane structure may be an adaptation to maintain the adhesion to the polystyrene beads, which often collide in the test tube.

Encoding the ability to produce various polysaccharides. The most famous of these is cepacian (consisting of rhamnose, mannose, glucose, galactose and glucuronic acid) (

), but others include Bep (

Extracellular polysaccharide) and galactan-deoxy-d-mannose caprylic acid (

). We assume that the different combination and aggregation characteristics

These mutants are related to the production of components in these extracellular polysaccharides with different compositions. We use fluorescein-labeled lectins to bind different sugars to visualize and quantify the differences in the composition of extracellular polysaccharides (EPS) of evolutionary mutants (

). All genotypes, including wild type, produced a matrix composed of mannose, which is especially elevated in plants.

+93 genotype. But only in

Mutant instead of

). Galactose

-Acetyl Glucosamine

-Acetylgalactosamine was not detected in EPS produced by any genotype. Then, we dyed the cellulose with fluorescent white calcium and found that Y355D produced more cellulose than any other mutant (

). Therefore, the different biofilm phenotypes

Mutants may be due to different genotypes secreting different polymers, which can be used as shared products for beneficial collective attachment.

Evolution of various EPS components

mutant. Calculate the biological volume from the fluorescence intensity of the labeled cells and the bound fluorescent labeled lectin,

) Mannose and (

) Fucose or calcium fluoride

) Use IMARIS 9.0 cellulose. The biomass of the EPS staining channel is divided by the biomass of the bacteria to approximate EPS/bacteria cluster. Use different letters to indicate significant differences between mutants (Tukey's post hoc test after ANOVA).

mutation

Obviously it is pleiotropic, so to check the degree of its regulation, we performed 6 genotypes (A106P, Y355D,

And WT) grow under selective biofilm conditions. Hundreds of genes distinguished the expression of WT mutants (q value<0.05), Y355D recorded the largest number (about 930 genes, fold change <1.5), and dozens of genes separated mutants from each other (

). As expected from the increase in the c-di-GMP level of the mutant, the motility and chemotaxis processes were down-regulated (except

, C-di-GMP levels close to wild-type were also produced in biofilms), while in the mutants with the highest c-di-GMP levels, Y355D and other PDEs (such as Bcen2424_5027) were upregulated (

). A gene cluster encoding Bep synthesis showed the greatest increase in expression among all mutants, providing strong evidence that this polymer is responsible for the increase in biofilm production. In addition,

The genes that bind to c-di-GMP and activate Bep production and cellulose synthesis (Bcen2424_4216) are up-regulated in all mutants, but

). It is worth noting that the expression of genes in the Bep cluster is different in the mutants, and the most up-regulated one is Bcen2424_4206 (a

Homolog), which encodes mannose-1-phosphate guanylate transferase. This enzyme plays a dual role, acting as a transferase to convert mannose-1-phosphate into GDP-mannose, which is the precursor of other sugar nucleotides such as GDP-fucose and GDP-rhamnose, and As an isomerase production of mannose 6-phosphate. 6-fructose phosphate is used for gluconeogenesis (

). We hypothesized that increased expression of this gene might activate fucose synthesis (

) Through the intermediate GDP-mannose. Interestingly, both

It is the most active in Y355D, which can explain the high fucose and cellulose in the EPS of this mutant. Another up-regulated gene in Y355D is expected to encode Flp/Fap fimbriae protein (Bcen2424_5868,

), as we all know, this kind of bacteria will cause the surface attachment of many bacteria (

). These differences strongly suggest the genetic pathway of functional differentiation among humans.

The mutant responds to transcription via c-di-GMP.

Global changes expressed in evolution and design

Mutants growing in biofilms. The genes that distinguish four or five mutants from WT are shown and classified by function (q value <0.05). The processes of up-regulation and down-regulation are drawn in shades of blue and orange, respectively. The results come from three biological replicates and follow

Most consistently down-regulated among gene clusters

The mutant encodes three lectins that bind to fucose (

). According to reports, these lectins have a high affinity for galactose and fucose, and can bind to the carbohydrates in the mucus or sugar conjugates on the surface of epithelial cells, allowing them to specifically adhere to the host surface as a single cell (

), but this form of attachment is not available in our laboratory system. The result also shows

The balance is based on the individual attachment of lectins and the formation of aggregates through polysaccharide synthesis. Another downgraded cluster

The mutant presumably encodes fatty acid biosynthesis (

). Overall, the choices seem to favor these

Mutants because they have a global regulatory effect that produces multiple phenotypes related to attachment and biofilm production, as well as dispersion and reattachment. Although many of them can usually be explained as the classic result of high c-di-GMP, the way the mutants are expressed is also different. Finally, in a strong demonstration of the power of experimental evolution as forward genetic screening,

Delete the least expression change (

); This deletion did not evolve in our experiments, and the benefits of this mutant are no less than that of an evolutionary SNP that has evolved but has not eliminated the function of RpfR.

Many microorganisms living at the surface-liquid interface have experienced cycles of attachment, biofilm assembly, diffusion, and reattachment, and therefore have experienced long-term heterogeneity. At the beginning of these evolutionary experiments, we expect to produce multiple genotypes to adapt to a subset of these conditions (

). However, to our surprise, mutations in one gene are selected more frequently than any other gene (

). The evolutionary experiment summarized here spans more than 20,000 generations, but only one of the 25 genes in this gene has a mutation.

The HI2424 genome with DGC or PDE domain that synthesizes or degrades c-di-GMP has reached a high frequency. This key choice

And excellent parallelism at a few residues (

) Indicates that the central regulatory agency controls the conversion to biofilm growth. It’s even more surprising because

Mutations are usually the first mutations in an evolutionary population to reach a high frequency. We can infer that there is only one gene among the 6,812 genes predicted in humans.

The genome encodes the potential for optimal adaptation in our laboratory's biofilm system. This parallelism is at least partly a product of our strain choices and specific experimental conditions, but despite this, we still hope

It plays a similar core role in many other species because the gene is very conserved (>60% identical, >80% similar) among dozens of beta and gamma Proteus genera, and is usually

). Our evolutionary experiments have identified the core regulators of c-di-GMP signaling and life history decision-making, which involve many bacterial species, including many medical and agricultural implications.

The discovery of molecular parallelism in evolutionary experiments has become more and more common and can indicate the functional importance of certain residues. Here, we observed the horizontal parallelism of residues Y355 and R377 in the DGC domain and S570 and F589 in the PDE domain, as well as the disproportionate number of mutations in the linker region connecting the binding domain of RpfR and the catalytic domain (

). Please note that the homology of Y355 between homologues is 99%, and the homology of R377 is 97%, providing evidence of its functional importance (

). Taken together, these results indicate that selection can increase biofilm-related adaptability by changing but not eliminating the regulation of RpfR function. These residues may be important for understanding how RpfR is. RpfR is the first reported c-di-GMP modulator directly activated by a diffusible autoinducer, and how to coordinate various responses (

Contrary to the previous report (

), we found to delete

It did not cause growth defects, but increased the adaptability of our biofilm model and reduced exercise capacity (

). Similarly, delete homologues

(previously

)in

It also reduces exercise capacity (

). We concluded that RpfR is mainly a PDE with restricted DGC activity, so deleting this gene has no measurable effect on the level of c-di-GMP in the biofilm, and the overall level of the biofilm is much higher (

), but it plays an important role in floating conditions. We predict that non-functional PDE activity will increase the level of c-di-GMP in the early growth stage and induce the expression of polysaccharide genes, leading to increased biofilm production in the deletion mutant. In addition, we speculate that Y355 and R377 play a role in the conformational changes that control RpfR DGC activity. Similarly, S570 and F589 are 100% identical

It is homologous and contains a conserved "loop 6" domain, which can dimerize the EAL domain and bind c-di-GMP and magnesium ion cofactor (

). This study of loop 6 shows that S570 is particularly critical for c-di-GMP hydrolytic binding, therefore, a point mutation at this site will almost certainly destroy PDE activity.

We also found that RpfR interacts directly with RpfF (the enzyme that synthesizes BDSF), and the RpfR-RpfF interaction inhibits the synthesis of BDSF (

). We hypothesize that BDSF, RpfR and RpfF can form a feedback inhibitory device, so the combination of BDSF and RpfR will limit the activity of RpfF and further BDSF production. In addition, we predict that RpfR

In the long term, RpfF interaction is essential for these bacteria, but it is essential in these short-term experiments. RpfF forms BDSF by dehydrating 3-hydroxydodecanoyl-acyl carrier protein (ACP)

-2-Dodecenoyl-ACP and hydrolyze the thioester bond connecting the acyl chain and ACP, releasing free BDSF (

). However, RpfF is promiscuous and can target other acyl ACP substrates, such as hindering membrane lipid synthesis. Some bacteria like

spp. The antagonist proteins RpfB and RpfC are also produced to control RpfF activity (

),but

Lack of these proteins. Therefore, the RpfR-FI domain is the key to dominate RpfF activities. This regulation is complementary to the interaction between BDSF and RpfR, which activates its PDE domain after binding to the PAS domain (

On the basis of this model, we predict that the A106P mutation in the junction region between FI and PAS domains will interfere with conformational changes, which will activate the PDE domain after BDSF binding (

). The mutant in this linker can be regarded as "signal blind" and maintain basic DGC activity, which is consistent with the intermediate c-di-GMP and adaptive effects of this mutant (

). Another common mutation is completely deleted

As well as other 93 genes, these genes eliminate BDSF synthesis and RpfR-mediated c-di-GMP regulation of its main PDE. This will lead to the net increase in biofilm production and biofilm-related fitness that we have observed, but it will also not be able to produce or detect BDSF, so it is relatively insensitive to the functions of other genotypes. This predicted signal blindness and signal muting function is consistent with the ability of the genotype to persist and eventually invade (with other mutations).

Genotypes in long-term evolutionary experiments

), although its initial adaptability is low.

Expected effect

The mutant adapts to the life cycle of the biofilm. Established

The model shows how the selected mutants alter the RpfR/RPF signal network to alter the production of BDSF and c-di-GMP and the resulting biofilm phenotype. (

) WT genotype produces sparse biofilm due to its production (

) And detect BDSF, which binds to RpfR-PAS and activates the RpfR-EAL phosphodiesterase domain, which hydrolyzes c-di-GMP (orange gradient,

). The RpfR-FI domain restricts RpfF activity by binding to and inhibiting BDSF synthesis, thereby restoring c-di-GMP levels to low levels (

The Y355D genotype will over-activate the GGDEF domain and increase c-di-GMP, leading to large-scale biofilm aggregation. Although the combination of BDSF and RpfR-PAS can activate the phosphodiesterase domain, the level of c-di-GMP is still high (blue gradient). (

) We hypothesized that A106P in the linker region between the FI and PAS domains can prevent the conformational changes caused by the BDSF-PAS interaction, so that the genotype is blind to BDSF. Therefore, the level of c-di-GMP was slightly increased by the action of RpfR or other DGCs, and the mutant formed larger biofilm aggregates. (

) Without RpfF and RpfR, BDSF will not be produced, and c-di-GMP produced by other enzymes will accumulate, resulting in a biofilm composed of small aggregates. This trait is dominant in other genotypes, and the mixture exhibits a more uniform biofilm phenotype.

Integrating these findings can allow us to expand our understanding of the mechanisms by which RpfF/R regulatory nodes control c-di-GMP signaling and BDSF quorum sensing to achieve "decisions" in the life cycle of biofilms (

). The selection of conditions for increasing the biofilm will directly inactivate PDE activity by mutating S570/F589 or by restricting BDSF binding to activate PDE (A106P) or by RpfF to eliminate BDSF synthesis and indirectly inactivate PDE activity (Δ

). Alternatively, you can choose a mutant that activates DGC (such as Y355D) (

). We tested these predictions by making targeted mutations to the functional domains of the system. First, in the previous study, we designed point mutations in the PAS domain at the sites expected to bind to BDSF. These mutations produced higher c-di-GMP and adaptability because the PDE domain was not activated (

). Here, we also deleted the FI domain thought to control the activity of RpfF, and as expected, the mutant has lower c-di-GMP and is harmful under biofilm conditions (

). On the other hand, delete

c-di-GMP has greatly increased, but this single gene deletion is not beneficial in competing with WT, possibly because the BDSF produced by WT supplements Δ

Defects of co-cultivation. So alone

The mutant was never selected in our experiment (

). This means that the RpfR-F complex and maybe other partners (

), has been retained by choosing to retain as a functional unit, and is not conducive to destroying only one component. Hengge Lab improved the model of RpfR orthologs

, PdeR, acts as a "trigger enzyme" in the c-di-GMP signaling hub to control the synthesis of curl and other biofilm-related traits (

). although

If RpfF is not encoded, the FI domain of PdeR and its orthologs in different species may bind to other triggering factors.

In retrospect, perhaps we shouldn’t be surprised that multi-domain protein mutants with sensory and catalytic activity have different functions. It is worth noting that due to its unique ecology, different mutants can evolve and coexist in the same population. For example, small aggregate phenotype

+93 can grow among a large number of co-cultivating partners, improve overall biofilm productivity, and maintain genetic diversity despite having the main Y355D genotype (

). These mixed biofilms are composed of genotypes that produce large aggregates or small clusters, which seem to reduce competition and increase the carrying capacity of the environment, which is consistent with the character replacement process described in our previous long-term evolution experiments (

). In general, higher c-di-GMP levels are associated with more EPS production and larger aggregates, and their own beneficial traits, but the co-cultured evolutionary mutants produced novel structures (

) And a mixture of stable strains maintained by mutual frequency dependence (

). The evolution of phenotypic diversity has previously been reported to increase the productivity of biofilm populations (

), and the antagonism between strains can also increase the total yield of biofilm (

). The positive interaction revealed here is most consistent with the process of functional differentiation, which can achieve three different

Mutants can coexist for hundreds of generations (

Phenotype

Lead to important differences in affinity and pathogenic potential for different surfaces. The WT strain produces an effective BclACB lectin that binds fucose and mannose residues on the host cell (

), the evolved mutants will down-regulate these lectins, thereby up-regulating EPS production, the composition of which varies from mutant to mutant (

). EPS synthesis is related to upregulation

Gene, reported to be the causative agent of cystic fibrosis infection caused by bacteria

complex(

). The synthesis of lectins is controlled by quorum sensing molecules (including BDSF(

) And the protein that binds to GtrR

Promoter and induce the expression of these genes. RpfR enhances this expression by forming a complex with GtrR, but not when it binds to c-di-GMP (

). It can be seen that the production of high c-di-GMP

The mutant down-regulates lectin production. However, these mutations can lead to trade-offs that limit other dimensions of the niche, such as reduced mobility and suppression of lectin-based attachment (

), and may not last in the long run.

of

The complex is known for causing opportunistic infections in the airways of cystic fibrosis, where the population encounters a more restrictive subset of its original niche, and thus chooses a diverse set of traits regulated by aggregation.

). Research on the evolution of maize genome.

spp. Still relatively limited during chronic infection (

), and occasionally mutations are found in this locus (

). However, by

Such as the production of EPS, lectins, fatty acid synthesis and motility often require diversified choices during infection, and may reflect the importance of this network in the body (

). Constantly study the changing population

And many other species

, Will provide valuable tests for the genetic model introduced here, and will determine whether this regulatory point can ultimately be used for antibacterial strategies or microbiome engineering.

The strains and plasmids used in the research are listed in

. Unless otherwise stated, all experiments were performed in 3% galactose M9 minimal medium. The evolution experiment follows the method described in the reference.

Modified

. The method described by Fazli et al. was used to create syngeneic mutants. (

). Adaptability is measured as described in Reference 1.

But in

. The level of c-di-GMP, motility and biofilm production were quantified as described in the references.

, And noted some modifications. For detailed instructions on microscopy, RNA-seq and statistics, as well as details on other steps, see

in

.

The RNA sequencing data has been saved in the BioProject database of the National Center for Biotechnology Information (NCBI) under the accession number.

This research was supported by Grants NIH R01GM110444 and NASA Institute of Astrobiology CAN-7 NNA15BB04A (to VSC). We thank Professor Simon C. Watkins (University of Pittsburgh Bioimaging Center) for his support and help in image analysis, VSC laboratory members and Evan Waldron (Rutgers University) for helpful discussions and proofreading, and Christopher Deitrick in bioinformatics Help and help. Store RNAseq files in NCBI database.

Current address: Department of Biology, Loyola University Chicago, Illinois 60660.

Author contributions: EM, DJS, SWB, MBN, CMW and VSC design research; EM, DJS, ES, CBT, SWB, NLF and VSC conducted research; EM, DJS, CBT, SWB, NLF, MBN and CMW contributed New reagents/analysis tools; EM, DJS, ES, CBT, SWB, NLF and VSC analysis data; EM and VSC wrote the paper.

The author declares that there are no competing interests.

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