Minireview

For the Greater (Bacterial) Good: Heterogeneous Expression of Energetically Costly Virulence Factors

Kimberly M. Davis
Anthony R. Richardson, Editor
DOI: 10.1128/IAI.00911-19

ABSTRACT

Bacterial populations are phenotypically heterogeneous, which allows subsets of cells to survive and thrive following changes in environmental conditions. For bacterial pathogens, changes within the host environment occur over the course of the immune response to infection and can result in exposure to host-derived, secreted antimicrobials or force direct interactions with immune cells. Many recent studies have shown host cell interactions promote virulence factor expression, forcing subsets of bacterial cells to battle the host response, while other bacteria reap the benefits of this pacification. It still remains unclear whether virulence factor expression is truly energetically costly within host tissues and whether expression is sufficient to impact the growth kinetics of virulence factor-expressing cells. However, it is clear that slow-growing subsets of bacteria emerge during infection and that these subsets are particularly difficult to eliminate with antibiotics. This minireview will focus on our current understanding of heterogenous virulence factor expression and discuss the evidence that supports or refutes the hypothesis that virulence factor expression is linked to slowed growth and antibiotic tolerance.

INTRODUCTION

Recent advances in imaging, fluorescence detection, and sequencing technologies have uncovered large amounts of heterogeneity within bacterial populations (1–3). Heterogeneity can occur at the genetic level, where genetic changes in subsets of bacteria promote genetic diversity, or the phenotypic level, where the population remains genetically homogeneous, but certain gene products are only expressed by subsets of the population (4–6). Heterogeneity can be highly advantageous, especially if a population experiences sudden, dramatic environmental changes (7). Heterogeneity ensures that some individual bacteria will survive and continue to thrive during these perturbations, thus promoting survival of the population as a whole (8). For bacterial pathogens, these changes could represent movement from the environment into a host organism or movement into different anatomical locations within a host.

Phenotypic heterogeneity occurs when an environmental perturbation forces a change in a subset of the population or through a bet-hedging approach, where a population exists in a phenotypically diverse state prior to any change in environmental conditions (4, 9–11). Phenotypic heterogeneity is particularly advantageous when production of a given protein or structure is energetically costly to an individual cell (12, 13). Production by a subset of cells may slow the growth of individuals but ensures that a factor is produced for the benefit of the population as a whole. This factor would then be considered a shared public good, and the production of this factor at a fitness cost to the individual would define cooperative behavior within this population (12, 14–16).

Several recent studies have shown that virulence factors, defined as factors required for a pathogen to establish infection in the host environment, are expressed heterogeneously within pathogenic bacterial populations. It remains less clear whether virulence factor expression comes at a fitness cost and whether these studies then represent examples of cooperative behavior (17). In contrast, it is known that slow-growing cells emerge during infection, and elimination of these subsets during antibiotic treatment can be very difficult due to reduced metabolism and reduced expression of antibiotic targets (18–20). These cells are considered antibiotic tolerant, which is a transient decrease in antibiotic sensitivity, typically linked to reduced metabolism (21). It still remains unclear whether virulence factor expression is costly enough to promote a slowed growth phenotype within expressing cells and whether virulence factor expression is then linked to antibiotic tolerance. A recent review discussed cooperative behavior in the context of Salmonella, Yersinia, and Mycobacterium virulence factor expression (17). This review will focus on the potential link between virulence factor production and slowed growth and discuss how heterogeneity in host and bacterial populations can impact disease progression.

HETEROGENEITY IN VIRULENCE FACTOR EXPRESSION

Virulence factors are defined as factors required for a pathogen to cause disease in a host, and these have typically been identified through mutant analyses. If a strain lacking a single gene product is attenuated during infection, and restoring a wild-type copy of the gene rescues virulence, then this gene product is defined as a virulence factor (22). There has been an effort to develop antimicrobial compounds and vaccine platforms that specifically target virulence factors as antibiotic resistance becomes increasingly more common within pathogenic bacterial populations (23, 24). By neutralizing virulence factors with one of these strategies, and thus eliminating virulence factor activity, this should attenuate bacteria and promote clearance of the infection. It remains unclear if this strategy will be efficacious; virulence factors may be only expressed by a subset of cells during infection or only expressed within a certain time frame or within a certain tissue during infection. Until very recently this was largely unknown, but in the last ten years, many studies have utilized novel single cell technologies to determine the extent of heterogeneity in virulence factor expression.

Heterogeneity during chronic respiratory infection.Bacterial populations reach high numbers within respiratory tissues and can also establish long-term, chronic infections, which lead to increased heterogeneity within bacterial populations. Chronic Pseudomonas aeruginosa infections occur frequently in cystic fibrosis patients; the long-term duration of these infections allows research to observe dynamics within the bacterial population longitudinally (25, 26). Several mutations in virulence factors have been observed within patient populations during chronic infection, which suggests that expression of these gene products occurs at a fitness cost to individual bacteria. Adaptation to the host environment also occurs during chronic P. aeruginosa infection, and it is likely that many of these gene products aid in establishing infection in the host but are not needed during long-term infection (25).

Production of iron-scavenging siderophores by P. aeruginosa is energetically costly and is defined as a cooperative trait, where subsets of bacteria produce this common good for the benefit of the population (12). Loss-of-function mutations occur in iron acquisition genes within patients, eliminating production of the iron-scavenging siderophore pyoverdine in subsets of the bacterial population (15). P. aeruginosa with mutations in the type III secretion system (T3SS), which is critical for mitigating the host immune response, is also commonly isolated from patients. T3SS mutants have a selective advantage in the murine lung when producing cells are present, suggesting that T3SS expression reduces bacterial fitness (27, 28). Increased mutant fitness was specifically linked to the ability of T3SS-expressing bacteria to produce exotoxin U (ExoU), which killed recruited neutrophils, relieving the need for T3SS expression (27). Host sensing of T3SS components likely also contributes to the cost of T3SS expression, as sensing of the P. aeruginosa T3SS rod components activates the NLRC4 inflammasome and promotes inflammation (29, 30). Bacterial clones with mutations in LasR, the central quorum-sensing regulator that promotes virulence gene expression, are commonly isolated from patients and emerge within culture conditions that require quorum-sensing activity (31, 32). LasR mutations either eliminate LasR activity or alter LasR-dependent gene expression, which is thought to reduce energetically costly virulence gene expression (32). Collectively, these results suggest that virulence factor expression is required in at least a subpopulation of bacteria during the early establishment of infection but that these factors are no longer required once bacteria have adapted to the host environment.

Nonproducing cells within the population are defined as cheaters, since they benefit from the production of costly gene products by other cells within the population. Interestingly, P. aeruginosa also has mechanisms to regulate the emergence of cheater populations and prevent their outgrowth, thus maintaining the wild-type (WT) phenotype within the majority of the population (33, 34). Adenosine metabolism is controlled by quorum sensing in P. aeruginosa, and the addition of adenosine to cells growing on casein protein was sufficient to prevent the emergence of cheaters lacking quorum-sensing function (33). P. aeruginosa can also actively prevent the emergence of cheater cells. Cooperating cells participating in quorum sensing can produce energetically costly cyanide, which specifically impacts sensitive cheater cells lacking quorum-sensing ability (34). This ensures that the bacterial population is largely capable of infecting a new host, since many of the mutants that emerge during chronic infection are avirulent (27, 28).

P. aeruginosa mutants are also isolated from patients that have heightened antibiotic tolerance and increased expression of biofilm-promoting genes (35). These and previous results suggest that the role of biofilm formation in promoting antibiotic tolerance could be 2-fold; if the expression of biofilm is energetically costly, then this could slow the growth of expressing cells, promoting tolerance. Alternatively, biofilm production could prevent antibiotics from accessing bacterial cells, limiting efficacy. Gene expression analyses have shown that P. aeruginosa biofilms have decreased ribosomal content, increased stress responses, and altered metabolism due to oxygen limitation; however, it remains unclear whether these responses occurred within cells in spatially distinct locations (36). Interestingly, it has recently been shown that bacterial aggregation in the absence of biofilm formation is linked to P. aeruginosa antibiotic persistence, suggesting that the spatial constraints within the lung may play a very important role in phenotypic changes and treatment efficacy (26, 37). Decreased ATP levels and flux through the tricarboxylic acid cycle are also sufficient to induce persistence, which can occur within individual cells in spatially distinct subpopulations during biofilm production (20, 38–40). Incorporation of single cell assays into models of P. aeruginosa growth and biofilm formation could allow researchers to determine whether the individual cells with reduced antibiotic susceptibility are the same cells expressing stress response genes or quorum-sensing molecules and whether these individuals are growth arrested.

Staphylococcus aureus is known to bifurcate into two subpopulations during infection: biofilm-forming cells, which dominate during chronic infection, and planktonic, toxin-producing cells critical for establishing infection (41, 42). The agr quorum-sensing system, which controls the expression of virulence factors and toxin production (43), directly controls this switch through environmental perturbations (44). Although agr expression is required to establish infection, isolates that lack Agr function are frequently isolated from cystic fibrosis patients with chronic infections, suggesting the expression may come at a fitness cost (45, 46). Agr inactivation is associated with increased biofilm formation, which is likely important for long-term lung colonization (42). Loss of capsule production is also associated with chronic infection, while the capsule locus is intact during acute infection, suggesting this loss also occurs during S. aureus adaptation to the host environment (45, 47, 48).

Slow-growing S. aureus has been identified within host tissues, but it remains unclear whether this is related to virulence factor expression or whether this represents a distinct transition into a dormant phase. S. aureus is known to form small colony variants (SCVs), which are cells with altered metabolism and cellular morphology and slowed growth rates and associated with antibiotic persistence (49, 50). SCVs represent a distinct morphotype of S. aureus cells that express low levels of agr and some virulence factors, including toxins, but produce heightened levels of adhesins, resulting in increased intracellular persistence (51). SCVs are also known to be isolated at heightened frequency from patients with chronic infections relative to acute infection (46, 50). Differentially growing S. aureus can be identified following uptake by respiratory epithelial cells in culture, where nonreplicating S. aureus cells are associated with live host cells and actively replicating bacteria trigger host cell death (52). It remains unclear if these persisting cells are SCVs, and it will be interesting to determine how, or if, this is linked to virulence gene expression. Uptake by neutrophils is known to trigger S. aureus virulence factor expression, although it remains unclear whether virulence factor expression is heterogeneous, and it is also unclear whether uptake by any host cell is sufficient to trigger virulence factor expression (53). The differential outcomes following uptake could also be due to heterogeneity in the host cell population, as described with macrophage populations (54, 55).

Heterogeneity during acute infection.Isolation of mutants confirms genetic changes occur in bacterial populations during chronic infection and also suggests that there is some fitness cost incurred by expressing specific virulence genes. However, during acute infection, bacterial populations exhibit phenotypic diversity and are typically closely related genetically.

Salmonella enterica serovar Typhimurium (S. Typhimurium) utilizes two T3SSs, SPI-1 and SPI-2, to establish infection within host tissues. SPI-1 expression is required for invasion into host cells, and SPI-2 promotes establishment of an intracellular vacuolar niche to promote bacterial replication (56–59). Several studies have suggested that expression of one of these systems, SPI-1, comes at a fitness cost to individual expressing cells (14, 16, 60). SPI-1 expression is controlled at two levels, first by environmental and cellular signals and then by a positive-feedback loop to amplify expression and establish the “ON” state (61). Following uptake by host cells, Salmonella senses the intracellular environment through the PhoPQ two-component system and downregulates SPI-1 expression (62). The intricacy of SPI-1 regulation, as well as the lengths cells go to regulate expression, may suggest that expression of this system is energetically costly. Immune recognition likely also contributes to the pressure to downregulated T3SS expression, as SPI-1 T3SS components activate the NLRC4 inflammasome and promote inflammation (29, 63, 64).

SPI-1 expression occurs specifically in a subset of individuals within the population in culture and is also expressed in a heterogeneous manner in mouse models of intestinal infection (14, 60, 65). Consistent with the linkage between SPI-1 expression and slowed growth, SPI-1-expressing cells were found to be more tolerant to antibiotics (65). Mutants that lack SPI-1 expression also arise during mouse intestinal infection, and mutants are able to benefit from the gene products generated by WT, SPI-1-expressing cells (16). However, SPI-1 expression was not specifically heightened within slow-growing cells, suggesting that expression may not be sufficient to slow the growth of bacteria within host tissues (18).

Alternatively, it is possible that the detection method used to isolate slow growing cells may not have captured this virulence factor-expressing population. A slow-folding DsRed derivative, called TIMER, was used to isolate slow-growing cells within host tissues, showing that the slow-growing subset was enriched in stationary-phase markers but did not have heightened expression of virulence genes, such as SPI-1 (18). It remains possible that SPI-1 expression is not sufficient for a transition into stationary phase but could have more subtle impacts on cell fitness. Many different markers have been used to detect slow-growing cell subsets; some markers detect transient growth arrest, others detect a lack of cell division during a certain time period, and other markers detect altered metabolism or an overall decrease in translational activity. One would predict that virulence factor expression could fall into any of these categories, depending on the metabolic cost of producing a given virulence factor.

Fluorescent constructs have been critical in identifying differences in growth rates between individual S. Typhimurium cells. Long-term live cell imaging has been used to visualize heterogeneity within Salmonella-containing vacuoles in epithelial cell culture and showed that cytosolic bacteria were hyperreplicating, whereas vacuolar replication occurred at a slower rate (66). In these experiments, fluorescent protein expression was used to detect and track bacterial cells, but reporters were not utilized. Live imaging of single bacterial cells has also been used with fluorescent reporters to show that T3SS expression stochastically varies during growth in culture and that this is associated with slowed growth and transient antibiotic tolerance (65). In addition to live cell imaging, several fluorescent reporter strategies have been used to identify and isolate differentially growing subpopulations. These approaches have primarily been used to study acute infection models but could also be applied to study chronic infection. TIMER, the slow-folding fluorescent protein, has been utilized to differentially mark slow- and fast-growing bacteria within host tissues (18, 67). Slow-growing bacteria had decreased antibiotic susceptibility but were relatively rare within the bacterial population, and a majority of the cells surviving antibiotic treatment had an intermediate growth phenotype (18).

Fluorescence dilution approaches have also been utilized to differentiate between actively replicating and nonreplicating bacterial cells and have revealed high levels of heterogeneity within both the bacterial and host cell populations (68–70). Fluorescence dilution involves inducing fluorescent protein production, removing the inducer, and following fluorescent protein partitioning into daughter cells (68, 71, 72). Use of this approach has shown that subsets of S. Typhimurium transition into a nonreplicating state following uptake by macrophages, while other bacteria continue to actively replicate intracellularly (68, 71). This result mirrors results with S. aureus and epithelial cells, suggesting these divergent replication outcomes may frequently occur following pathogen uptake (52). Single cell transcriptome sequencing (scRNA-Seq) of macrophages was used to show proinflammatory M1 macrophages harbor nonreplicating Salmonella, correlating the host response with bacterial growth arrest (69). It remains unclear if bacterial virulence gene expression could have also contributed to this growth arrest, especially given results that suggest bacterial gene expression can modulate host cell phenotypes (70). Recently, it has been shown that Salmonella SPI-2 effectors modulate macrophages by promoting their transition from an M1 to an anti-inflammatory M2 phenotype (73). Subsets of SPI-2-expressing bacteria remained metabolically active following uptake, suggesting that there may be actively growing cells and also nongrowing persister cells within the SPI-2-expressing population (73).

Many studies have now identified heterogeneity in bacterial virulence factor expression during infection of host tissues and have shown that dynamics between expressing and nonexpressing cells are required for virulence. A bistable switch controls invasion in Yersinia pseudotuberculosis, resulting in expressing and nonexpressing subpopulations in Peyer’s patches and the cecum (74). Y. pseudotuberculosis required the ability to switch between invasive and noninvasive phenotypes for full virulence, suggesting that dynamics in expression occur over the course of infection (74, 75). Y. pseudotuberculosis also differentiates into spatially distinct, virulence factor-expressing and nonexpressing subsets as it grows within lesions in the spleen (76). This differentiation was a response to host-derived nitric oxide, and both overexpression and a lack of stress response genes attenuated Y. pseudotuberculosis, suggesting that expression specifically in a subset of cells resulted in optimal virulence (76). It was recently shown that phase variation generates two morphotypes of Clostridium difficile through the activity of the CmrRST gene products, which results in smooth and rough colonies with differing motility and virulence phenotypes. Full virulence required the ability to switch between morphotypes during infection, and mixed populations were also identified during hamster colonization, suggesting that different morphotypes may cooperate to establish infection within the host environment (77).

HETEROGENEITY BOTH WITHIN AND BETWEEN LESIONS AND IN INITIAL HOST CELL INTERACTIONS

When infections are initially established within a host organism, few bacterial cells establish themselves at the site of infection. This means that initial interactions between very few bacterial cells and few host cells may dictate infection outcome. This clearly contributes in several different infection scenarios where individual lesions are established across tissues and, due to host cell interactions, results in either establishment of colonization or host clearance of the pathogen. These differential outcomes can occur within the same organ, which is likely due to heterogeneity in both bacterial and host cell populations (78).

Heterogeneity between lesions has been observed during Mycobacterium tuberculosis infection of cynomolgus macaques, where granuloma clearance occurs in some lesions and other lesions progress within the same lung (79). When M. tuberculosis is inhaled, it first encounters alveolar macrophages in the host lung. Early interactions with alveolar or interstitial macrophages in the lung likely play an important role in the establishment or clearance of bacterial infection and ultimately in determining whether a granuloma forms (54, 80). Heterogeneity within the bacterial population can also help dictate whether a lesion forms or bacteria are cleared. Heterogeneity develops as M. tuberculosis divides asymmetrically, which introduces variation within the bacterial population with each cell division cycle and promotes the formation of antibiotic tolerant cells (81, 82). Interestingly, eliminating asymmetric cell division collapses heterogeneity within M. tuberculosis populations and results in more uniform bacterial killing with antimicrobials, suggesting that much of the heterogeneity within this population is generated during cell division (82). Differential drug penetration across M. tuberculosis lesions also contributes to differential outcomes during drug treatment, and the immune activation status of the host can determine whether infection progresses or is eliminated (83, 84).

Streptococcus pneumoniae exhibits heterogeneous expression of one of its critical virulence factors, the cholesterol-dependent cytolysin pneumolysin (Ply). Ply expression varies stochastically, which leads to differing outcomes for low- and high-expressing bacteria. Low expression promoted intracellular survival within human brain endothelial cells and passage through the blood-brain barrier, whereas high Ply expression promoted vacuolar escape from the phagolysosome and destruction within the host cell cytosol (85). These differential outcomes can result in bacterial movement across the endothelium into brain tissue and the initiation of meningitis or in clearance of bacteria within the host cell cytosol.

During systemic infection and colonization of the mouse spleen, S. Typhimurium interacts with multiple different host cell types, resulting in bacterial responses to reactive oxygen species (ROS) within neutrophils and reactive nitrogen species (RNS) within macrophages (86). However, isolation and characterization of slow-growing Salmonella within the spleen suggest that these stress responses were not sufficient to slow the growth of individual cells (18). In contrast, RNS-driven stress responses were sufficient to promote antibiotic tolerance of intracellular Burkholderia pseudomallei and S. Typhimurium within primary activated macrophages, suggesting potential differences in the phenotypes of S. Typhimurium within different macrophage subsets (87). M. tuberculosis associated with activated macrophages from the mouse lung also showed enhanced antibiotic tolerance, suggesting that macrophages activated in vivo can also promote antibiotic tolerance (84). However, it was unclear whether tolerance was linked to stress responses. Recently, it has been shown that ROS exposure within activated macrophages is sufficient to reduce S. aureus antibiotic susceptibility due to decreased bacterial respiration and ATP production, thus linking stress to reduced bacterial metabolism and antibiotic susceptibility (88). These differences in results may be due to interactions with different subsets of macrophages within the splenic environment, which could impact the degree or longevity of stress imparted by the host cell and the subsequent impact on bacteria. High levels of nitric oxide may be sufficient to induce antibiotic tolerance, if the dose and longevity of the stress are sufficient to disrupt the electron transport chain (89). Intriguingly, because the ROS and RNS stress response genes are virulence factors in many different bacterial pathogens (76, 90–93), the studies discussed above provide a potential link between virulence factor expression, slowed growth, and antibiotic tolerance.

SINGLE CELL APPROACHES TO DETECT HETEROGENEITY

The first descriptions of heterogeneity within bacterial populations were associated with antibiotic treatment of patients and the observations that bacterial cells had differential susceptibility to antibiotics (94, 95). In 1967, researchers showed that macrophage populations exhibit heterogeneity in their ability to clear intracellular bacteria (96), which was likely the first study to describe heterogeneity within host cell populations. Although it was known that host immune cell and bacterial populations were heterogeneous, it was difficult to isolate and study subpopulations in detail, and for decades many studies averaged cell behavior across the population. With recent advances in imaging, isolation, and sequencing technologies, we now have the tools available to study individual cells and to determine whether small subpopulations of cells impact disease outcome. Some of the recent advances will be briefly described here, since these techniques have been recently reviewed in detail elsewhere (1, 2, 97, 98).

Several years ago researchers developed single cell RNA-Seq to detect gene expression differences within single mammalian cells, which greatly increased our understanding of the complexity of host cell populations (99, 100). This technology has been applied to bacterial populations, which can uncover gene expression-level heterogeneity in individual bacterial cells as well (3, 101). Dual RNA-Seq was originally developed to simultaneously detect gene expression changes in both host cell and pathogen populations during infection, and these techniques have recently been adapted to detect heterogeneity in the host and pathogen populations at the single cell level (55, 70, 102, 103). Many single cell assays utilize droplet-based microfluidics approaches to capture individual bacteria for downstream analyses, which include single cell whole-genome sequencing of individual bacteria and transcriptional profiling (104–106). Microfluidics systems allow researchers to isolate individual bacterial cells in an unbiased way and profile the composition of the entire population.

Fluorescence detection techniques can also be used to isolate or quantify specific subpopulations of interest and can be incorporated into assays upstream of single cell sequencing. Fluorescent reporter constructs have been generated for many different genes of interest to distinguish between expressing and nonexpressing cells and can be utilized to isolate specific subpopulations (18, 19, 69, 70, 86). Single cell RNA-FISH (fluorescence in situ hybridization) uses fluorescent probes to quantify the number of specific mRNA transcripts within an individual cell; this method has been used to visualize stochastic expression of highly expressed genes and also to compare transcript levels to protein levels (107, 108). Photoconvertible fluorescent proteins have been utilized in combination with fluorescence recovery after photoconversion to identify differential metabolism of the parasite Leishmania major in a mouse model of infection, but this technology has not yet been applied to bacterial infections (109). Fluorescent in situ sequencing (FISSEQ) utilizes fluorescent probes to detect and sequence individual transcripts within intact tissues, but it has not yet been applied to bacterial systems (3, 110).

Many of these assays were initially established with bacteria grown in bacteriological media and different culture systems, and the challenge is applying these technologies to identify differences between individual bacteria in the host environment. The ability to detect gene expression changes at the single cell level will now allow researchers to detect heterogeneity without first using additional methods to identify subpopulations of interest, although subpopulation isolation can still be a first step to home in on populations of interest.

IDENTIFICATION OF SLOW-GROWING, PERSISTENT BACTERIA

Many of the efforts to identify slow-growing bacterial subpopulations were initiated to better understand individual bacterial cells that are less susceptible to antibiotics. For decades, we have known that antibiotic treatment may not eliminate all bacteria present within a patient, even if the bacterial cells are fully susceptible to antibiotics (94, 95). These subsets of bacteria were termed “persisters” due to their propensity to persist in the presence of antibiotic treatment. This incomplete elimination of bacteria may be partially due to limited diffusion of antibiotics into host tissues, preventing the accumulation of inhibitory levels of drug (83, 111). The presence of subsets of bacteria with altered metabolic activity also clearly limits drug efficacy (112, 113).

Persister cell formation can occur through multiple mechanisms, which include toxin/antitoxin expression, nutrient deprivation, stress responses, asymmetric cell division, and interactions with the host immune response (81, 112, 114). Interestingly, it has recently been shown that bacterial aggregation in the absence of biofilm formation is linked to P. aeruginosa antibiotic persistence, suggesting that the spatial constraints within the lung could play a very important role in phenotypic changes and treatment efficacy (37). Decreased ATP levels and flux through the tricarboxylic acid cycle are also sufficient to induce persistence, which has important implications for the treatment of biofilms (20). Collectively, persister cell formation has been linked to reduced metabolism and slowed growth, and it has been difficult to determine the distinct contribution of each. Slowed growth occurs during infection, although the underlying mechanism is not always clear and may vary among individual bacterial cells due to different microenvironments and host cell interactions. Recent results have shown that, when uncoupled, slowed growth may not be the best predictor of antibiotic susceptibility, and instead the metabolic state of an individual cell more accurately predicts whether a cell will survive antibiotic treatment (115).

It is known that bacteria vary in terms of growth rates and that subsets of bacteria are replicating at a slowed rate relative to other individual cells. Many of the reporter strategies described above were utilized to identify these slow-growing cells to study their antibiotic susceptibility in more detail. Antibiotics generally target conserved bacterial structures, such as DNA replication machinery, RNA polymerase required for transcription, ribosomes required for translation of proteins, cell wall peptidoglycan that provides cell surface integrity, or surface structures. Many of these targets are present at increased abundance in actively replicating cells and at low abundance in slow-growing cells. In M. tuberculosis, a ribosomal reporter was used to show slow-growing cells had decreased ribosomal content, and this decrease in target abundance correlated with decreased drug susceptibility (19). A reporter for single-stranded binding protein has also been utilized in M. tuberculosis to identify bacteria actively completing DNA replication cycles and to visualize bacteria with decreased DNA replication rates (116, 117).

CONCLUSIONS AND FUTURE PERSPECTIVES

Many studies have now shown that there is extensive heterogeneity within pathogenic bacterial populations and that virulence factors may only be expressed by subsets of the population. Accumulation of mutations that limits virulence factor production suggests that expression comes at a fitness cost to individual cells, and the application of different techniques to identify slow-growing cells is beginning to support this hypothesis. The slowed growth detection method likely plays a major role in these conclusions, since some methods detect severe growth defects, while other methods may detect transient growth arrest. It is likely that virulence factor expression impacts growth to a certain extent, but whether this is sufficient to impact the antibiotic susceptibility of an individual cell may depend on the energetic fitness cost of the particular virulence factor. Virulence factor expression can also be a response to cellular damage or stress within the host environment, which may trigger a growth arrest simultaneously with virulence factor expression but by a distinct mechanism. It will be important to tease apart these details to develop better strategies to eliminate slow-growing cells.

Much of the discussion here has focused on secreted factors that could be utilized as shared public goods by the bacterial population and how this promotes the development of producer and cheater subpopulations. However, heterogeneous virulence factor expression is also clearly driven by environmental changes, and small differences in microenvironments or specific host cell interactions can also drive heterogeneity (17, 76). This is especially true of virulence factors expressed within the cytoplasm of bacteria, which alter the phenotype of individual cells but cannot be directly accessed by nonproducing cells.

Phenotypic heterogeneity is generally thought to be promoted either by distinct microenvironments or because of stochastic variation in transcription factors, polymerases, or ribosomes (9, 10). A recent study also shows that translation of specific mRNAs in Vibrio vulnificus by divergent I-ribosomes could generate phenotypic diversity within a population (118). These analyses were performed at the population level and showed that 10% of ribosomes were I-ribosomes. It would be very interesting to pursue additional studies at the single cell level to determine whether these divergent ribosomes are predominant in some cells or if all cells contain 10% I-ribosomes. It also remains unclear how widespread this may be and whether many pathogens have a similar mechanism to generate heterogeneity.

While we typically think of heterogeneity as advantageous to a population, bacteria also have mechanisms in place to limit the extent of heterogeneity, suggesting this diversification is not always beneficial to the population. In Salmonella, bistable on/off expression of SPI-1 limits the emergence of avirulent genetic mutants lacking SPI-1 expression (16). Interestingly, a recent study has shown that interactions between bacterial species during coculture reduce the amount of phenotypic heterogeneity within a bacterial population, which suggests that communities with mixed species composition, such as different microbiota communities, may be relatively homogeneous phenotypically within a single species (119). This has interesting implications for many different scientific studies, where we tend to grow cultures of single strains, and may actually be introducing heterogeneity that would not have occurred within natural, multispecies communities.

This review has focused primarily on phenotypic heterogeneity, but genetic heterogeneity plays an equally important role during infection. The discussion above has included examples of mutations isolated from patient samples, which clearly demonstrate genetic heterogeneity over the course of chronic infection. Selective pressure within the host environment can promote the emergence of mutations in genes that are no longer needed under circumstances where a mutation provides some fitness benefit to the individual cell. T3SS mutations are one potential example of this: T3SS expression is initially required to establish infection, but the host can mount an adaptive response to Yersinia T3SS components and may eliminate expressing cells at later time points or during challenge (120–122). Phenotypic heterogeneity may also contribute to the emergence of genetic heterogeneity within a population by enhancing the effects of beneficial mutations in the presence of selective pressure (123). Phenotypic heterogeneity can also result in activation of DNA damage responses within individual cells, which has been shown to increase mutation rates, and may also introduce heterogeneity within a population at the genetic level (124).

Ultimately, it will be very important to consider both genetic and phenotypic heterogeneity in future studies and to utilize novel single cell assays to identify heterogeneity within both host and bacterial populations. These approaches will allow us to identify cellular interactions that may occur at a fairly low frequency, but through follow-up studies, we may find some of these rare interactions play a critical role in tipping the balance in infection outcome, between establishing infection and clearance of bacterial cells. Single cell approaches will also allow researchers to better understand the pathways utilized by slow-growing, antibiotic tolerant subsets of bacteria and to determine whether these individuals are also expressing virulence factors. If so, then virulence factors may be viable targets for future therapeutics. Better understanding of slow-growing, tolerant cells and the gene products they produce will allow us to develop novel, targeted therapeutics to more efficiently eliminate this subpopulation and more effectively treat bacterial infections.

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Author Bios


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Kimberly M. Davis received her B.S. and M.S. in Cell and Molecular Biology from the University of Michigan. She completed her Ph.D. in Cell and Molecular Biology at the University of Pennsylvania in the laboratory of Dr. Jeffrey Weiser and studied the contribution of host sensing of peptidoglycan to clearance of pneumococcal colonization. She completed her postdoctoral research in the laboratory of Dr. Ralph Isberg at Tufts University, where her research utilized Yersinia pseudotuberculosis mouse models of infection to identify the spatial location of phenotypically distinct bacterial cells. She is currently an Assistant Professor at the Johns Hopkins Bloomberg School of Public Health and has continued to use Y. pseudotuberculosis mouse models to better understand how bacteria replicate within deep tissues, how subsets of bacteria cooperate to establish infection, and whether gene expression comes at a fitness cost to individual cells, resulting in differential antibiotic susceptibility.

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KEYWORDS

antibiotic tolerance
bacterial pathogenesis
cooperation
single cell assays
virulence

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