Strain Dependent Genetic Networks for Antibiotic-Sensitivity in a

RESEARCH ARTICLE

Strain Dependent Genetic Networks for Antibiotic-Sensitivity in a Bacterial Pathogen with a Large Pan-Genome Tim van Opijnen1*, Sandra Dedrick1, José Bento2 1 Boston College, Biology Department, Chestnut Hill, Massachusetts, United States of America, 2 Boston College, Computer Science Department, Massachusetts, United States of America * [email protected]

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OPEN ACCESS Citation: van Opijnen T, Dedrick S, Bento J (2016) Strain Dependent Genetic Networks for AntibioticSensitivity in a Bacterial Pathogen with a Large PanGenome. PLoS Pathog 12(9): e1005869. doi:10.1371/journal.ppat.1005869 Editor: Andreas Peschel, University of Tubingen, GERMANY Received: June 1, 2016 Accepted: August 15, 2016 Published: September 8, 2016 Copyright: © 2016 van Opijnen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Tn-Seq sequencing data is deposited at the Sequence Read Archive under BioProject PRJNA318012. Funding: TvO and SD are supported by NIH grant R01AI110724, and TvO and JB are supported by NIH grant U01AI124302. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist.

Abstract The interaction between an antibiotic and bacterium is not merely restricted to the drug and its direct target, rather antibiotic induced stress seems to resonate through the bacterium, creating selective pressures that drive the emergence of adaptive mutations not only in the direct target, but in genes involved in many different fundamental processes as well. Surprisingly, it has been shown that adaptive mutations do not necessarily have the same effect in all species, indicating that the genetic background influences how phenotypes are manifested. However, to what extent the genetic background affects the manner in which a bacterium experiences antibiotic stress, and how this stress is processed is unclear. Here we employ the genome-wide tool Tn-Seq to construct daptomycin-sensitivity profiles for two strains of the bacterial pathogen Streptococcus pneumoniae. Remarkably, over half of the genes that are important for dealing with antibiotic-induced stress in one strain are dispensable in another. By confirming over 100 genotype-phenotype relationships, probing potassium-loss, employing genetic interaction mapping as well as temporal gene-expression experiments we reveal genome-wide conditionally important/essential genes, we discover roles for genes with unknown function, and uncover parts of the antibiotic’s mode-of-action. Moreover, by mapping the underlying genomic network for two query genes we encounter little conservation in network connectivity between strains as well as profound differences in regulatory relationships. Our approach uniquely enables genome-wide fitness comparisons across strains, facilitating the discovery that antibiotic responses are complex events that can vary widely between strains, which suggests that in some cases the emergence of resistance could be strain specific and at least for species with a large pan-genome less predictable.

Author Summary While antibiotic resistant bacterial pathogens cause millions of deaths each year it remains largely unclear how a bacterium deals with antibiotic-induced stress and how this leads to the emergence of resistance. Moreover, many bacterial species are composed of strains whose genomes vary considerably, and while this variation may significantly affect

PLOS Pathogens | DOI:10.1371/journal.ppat.1005869 September 8, 2016

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Strain Dependent Genetic Networks for Antibiotic-Sensitivity

phenotypes such as antibiotic sensitivity, its importance is unknown. Here we apply the method Tn-Seq, showing it is feasible to develop a detailed view of how a bacterium experiences antibiotic stress, while simultaneously determining the influence of the genomic-background. We show for two strains of the bacterial pathogen Streptococcus pneumoniae that, even though they experience the same stress triggered by daptomycin, they use a majority of different genes to withstand this stress, including genes important for integrity of the membrane, potassium uptake and protein turnover. Additionally, by untangling underlying genomic networks we unexpectedly expose large differences in genetic-interactions as well as transcriptional regulation. Our study provides not only a sensitive approach to untangle the influence of the genomic-background on phenotypes such as antibiotic sensitivity, but also highlights that this knowledge is instrumental in understanding how bacteria respond to environmental stress, which in turn influences the manner in which they evolve.

Introduction Bacteria evolve antibiotic resistance in response to selective pressures that emerge from the interaction between the antibiotic and the bacterium. Routes of escape often lead through ways that diminish the interaction with the direct target. For instance, escape from penicillin, whose direct target are the Penicillin Binding Proteins (PBPs), can often be found in mutations in PBPs that decrease the affinity for the drug, or in functionally related genes that compensate for diminished function [1–4]. However, it has become clear that the relationship between a bacterium and an antibiotic reaches far beyond its direct target. Instead an antibiotic triggers a complex, multi-factorial process that may begin with the physical interaction between the drug and its target but quickly propagates into the involvement of a variety of processes that can include regulation, metabolism and/or energy generation [5–12]. These system-wide selective pressures could explain why clinical strains often contain multiple alterations that may contribute to resistance but are located in genes whose primary role is not resistance but rather are involved in fundamental bacterial processes [13–17]. The adaptive sequence space thus seems to lie well beyond the antibiotic’s direct target, which contributes to the complexity of determining and predicting how and where resistance evolves. Moreover, adaptive mutations can be species-specific. For instance, the direct target of fluoroquinolones in gram-negatives including Escherichia coli is DNA gyrase, while in gram positives such as Staphylococcus aureus it is topoisomerase IV, which may explain why mutations in gyrA such as S83L in E. coli can increase the MIC to fluoroquinolones while the equivalent mutation in gyrA in S. aureus does not necessarily have an effect on the MIC [18–23]. Additionally, some gyrA mutations are associated with a fitness cost in E. coli [24, 25], while a positive fitness effect can be observed in Campylobacter jejuni and Salmonella enterica [26, 27]. These contrasting phenotypes suggest that the manner in which a bacterium experiences antibiotic-induced stress may differ depending on the genomic background and the underlying genomic network. While detailed insights into these factors could help in designing novel antimicrobial strategies, the importance of the genomic background and to which extent antibiotic-sensitivity and resistance depend on network architecture is currently unclear. In this study we use Streptococcus pneumoniae to explore the importance of the genomic background on antibiotic-sensitivity and the manner in which stress is experienced and processed. S. pneumoniae is a human nasopharyngeal commensal and respiratory pathogen. It triggers pneumococcal pneumonia, meningitis, and septicemia, which results in ~1 million deaths annually among children 65 yrs.), making it one of the most important bacterial pathogens worldwide [28–30]. Although vaccination has been successful, we and others have shown that it does not result in complete protection, and that some groups, such as children with Sickle Cell Disease, remain especially vulnerable [31]. Antibiotics thus continue to be extremely important as a treatment option, especially in acute disease. However, as with almost any clinically important bacterial pathogen, the emergence of multidrug-resistant (MDR) strains is a global problem [32–37] and with 1.2 million drug-resistant pneumococcal infections annually in the US, and $96 million in excess medical costs, S. pneumoniae is a serious concern [30]. S. pneumoniae is one of several species for which the availability of complete bacterial genomes has demonstrated that a distinction can be made between its core-genome (the pool of genes shared by all members of a species) and pan-genome (a species’ global gene repertoire) [38–41]. On average two pneumococcal strains may differ by ~300 genes in their genomic content, i.e. the presence and absence of genes [42, 43], which highlights the genome’s plasticity to retain function in the presence of variation. Such plasticity is remarkable because no genomic element, gene, or pathway exists in a vacuum; rather they are connected through networks resulting in specific organismal properties [44, 45]. A newly acquired element thus needs to be integrated thereby possibly affecting existing connections and creating new ones. Consequently, no two genomes may function in the same manner, potentially affecting phenotypes ranging from drug tolerance to virulence to evolutionary potential. By employing genome-wide approaches we, and others, have shown that it is possible to determine, upon exposure to an environmental perturbation, where stress in the bacterial genome is experienced [31, 46–55]. Here we apply Tn-Seq, a tool for systems-level analysis of microorganisms, which combines transposon mutagenesis with massively parallel sequencing to determine genome-wide fitness in a single experiment. We develop daptomycin-sensitivity profiles for two strains of the bacterial pathogen S. pneumoniae. Although the exact mechanism of action of daptomycin is not completely clear it seems to insert itself into the membrane for which the presence of phosphatidylglycerol in the membrane is required. Following insertion, membrane structure and curvature may be distorted leading to cells with altered cell shapes. These distortions in the membrane at the site of daptomycin insertion may lead to leakage of ions and loss of membrane potential and local dysregulation of cell division and/or cell wall-biosynthesis [56–61]. The daptomycin-sensitivity profiles generated in this study illustrate how the antibiotic’s effects ripple through the organism and how the bacterium deals with this stress with a diverse set of genes from different functional categories and organizational levels including: cell-wall organization, membrane integrity and transport, control and regulation of fundamental processes, and metabolism. Surprisingly, the sensitivity profiles turn out to be highly strain-specific highlighted by over 50% of sensitivity-profile genes that increase antibiotic sensitivity in one strain but have no effect, or even decrease sensitivity in the other strain. We show that these differences are partially the result of a network architecture that is not well conserved, exemplified by strain-specific differences in Potassium (K+)-release and ClpP functionality, as well as differences in regulatory relationships between genes from different organizational levels. Importantly, we present a generally applicable, and highly sensitive approach that enables comparisons of environment-induced fitness effects on a genome-wide scale and species-wide level.

Results and Discussion Two S. pneumoniae strains with variable gene content have the same susceptibility to daptomycin A major goal of this study is to determine whether bacterial strains from the same species that differ in their genomic content, respond in an identical manner to antibiotic stress. On a


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Fig 1. Daptomycin growth rate reduction in T4 and 19F. The left panel shows that the reduction in the growth rate with increasing daptomycin concentration is the same for strains T4 (circles) and 19F (squares). For instance a concentration of 25 μg/ml daptomycin results in a relative fitness (W ± standard deviation) of 0.83 ± 0.04 for T4 and 0.85 ± 0.11 for 19F, which means that the growth rate under these circumstances increases on average by 17% and 15% respectively. For illustrative purposes, a fitness of 0.5 indicates that the relative growth rate increases by 50% and that the doubling time is 2-fold longer compared to a ‘standard’ or in this case the no-antibiotic condition. The right two panels depict representative growth curves for each strain in the absence and presence of increasing daptomycin concentrations. Colors refer to the used daptomycin concentration and are the same for all three figures, with red being no daptomycin (W = 1.0), and turquoise depicting the highest concentration of daptomycin at 47ug/ml (W = 0). doi:10.1371/journal.ppat.1005869.g001

species level the genome of S. pneumoniae can be divided up in a core genome consisting of ~1600 genes, and a pan genome of ~4000 genes, while a genome on average has approximately 2000 genes. We selected two strains, TIGR4 (T4) and Taiwan-19F (19F) (S1 Fig), which can both cause invasive disease: T4 is a serotype 4 strain that was originally isolated from a patient from Norway with Invasive Pneumococcal Disease (IPD) [62, 63], while 19F is a multi-drug resistant (MDR) strain isolated from a patient with IPD from Taiwan [64, 65]. With respect to genomic content the strains share 1711 genes, while T4 has 324 genes that are absent in 19F, and 19F has 204 genes that are absent in T4. On average two pneumococcal strains may differ by ~15% in their genomic content, and thus the amount of variation between these two strains is representative for differences observed between strains within the species [42, 43]. All genes were split into 17 functional categories and except for the number of genes with unknown function, both strains share a similar distribution over these categories (S2 Fig; S1 Table). Both strains are differentially susceptible to different antibiotics, for instance 19F is approximately 25-fold less susceptible to penicillin than T4, while they are equally sensitive to daptomycin (Fig 1). Because equal sensitivity creates the simplest opportunity to test whether strains use the same genes in dealing with antibiotic induced stress, daptomycin is used here.

Antibiotic sensitivity profiles reveal antibiotic functionality and leads to gene function To identify in detail which genes in the genome are involved in dealing with daptomycinstress we employed transposon insertion sequencing (Tn-Seq), which enables high-throughput and accurate calculations of the growth rate for each possible gene-knockout in the genome [31, 46, 48, 49]. Six independent transposon libraries, each consisting of ~10,000 mutants were created in T4 and were grown in the absence and presence of daptomycin at a concentration of 25 μg/ml, which moderately slows the growth rate by ~15% (Fig 1). For each


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Fig 2. Strain-specific daptomycin-sensitivity profiles for T4 (A) and 19F (B). Each color-coded circle (node) represents a gene depicted by its gene number (note that all numbering is according to strain T4; for strain specific numbers see the correspondence table in S8 Table), that is connected by a line (edge) to daptomycin in the center of the network. The edge indicates that the gene’s fitness significantly changes in the presence of daptomycin; a black edge indicates fitness is significantly lower, while a green edge indicates fitness increases. A node with a dotted border indicates that its phenotype is not conserved in the other strain, while a solid black border indicates its phenotype is conserved, unless there is a dash underneath the gene-number which means it was impossible to determine whether the phenotype was conserved in the other strain due to too little insertions. If a gene-number starts with SP or SPT, the gene is strain-specific in T4 or 19F respectively. Some sets of genes are highlighted in the networks by indicating their functional role (e.g. peptidoglycan, ClpP) or by arrows that represent relationships (e.g. SP1732/SP1733 have a regulatory effect on peptidoglycan). Genes with unknown function are depicted in white, and if they have a close neighbor with an annotated function in the network they are placed next to that neighbor, possibly suggesting a shared role. Next to each network the importance of each functional category in the network is ranked by the number of genes present in the network. Finally, as described in more detail in the main text, and to illustrate that the antibiotic interacts with the bacterium on different organizational levels, each functional category belongs to one of four hierarchical layers which combines the physical location of the gene-product with its molecular function. The first layer consists of capsule and peptidoglycan genes and is both the first layer of protection from, and interaction with, the outside world. The second layer is important for membrane integrity and transport across the membrane represented by membrane, lipoprotein and transporter genes. The third layer controls and orchestrates fundamental processes including cell division, transcription and translation and regulation, while the fourth combines all metabolism genes. The importance of each layer is highlighted by the thickness of the layer, which is determined by the number of genes in that layer, while within each layer the importance of each category is represented as a percentage. doi:10.1371/journal.ppat.1005869.g002

condition reproducibility was determined by comparing fitness between different libraries, which in each case was high (R2 = 0.78–0.89). Fitness values for each insertion in each gene were averaged and genes with a significant antibiotic-specific response were visualized in a network with Cytoscape [66] and grouped according to their functional category (Fig 2A; S2 Table). Previously we showed how such a visual network approach provides a detailed overview of how an environmental disturbance can affect a bacterium on multiple different levels [31, 48]. The same is true for this network, demonstrating how a large variety of genes from different functional categories become important for the survival of T4 in the presence of daptomycin (Fig 2A; S2 Table), which highlights several aspects of daptomycin’s modus operandi:


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1) Our results show that any gene that affects peptidoglycan (PG) biosynthesis, stability, or regulation can, upon its removal, make the bacterium more susceptible to daptomycin. Even a decrease in PG acetylation (mediated by SP1479) or a decrease in the speed and efficiency of Penicillin Binding Protein (PBP) folding, which has been shown to be mediated by PrsA in B. subtilis [67] (prsA/SP0981), increases susceptibility to daptomycin. Associations of daptomycin with the cell wall have also been shown in other bacteria: in B. subtilis, and S. aureus, daptomycin induces a cell wall stress response [59, 68], and mutations that increase cell wall thickening have been associated with resistance in S. aureus. [69, 70]; 2) Lipo- and membrane proteins that provide structural support to the membrane become important in the presence of daptomycin, suggesting that the interaction of daptomycin with the membrane has a destabilizing effect on membrane integrity. The importance of the membrane anchored protease FtsH (SP0013), whose function includes proteolysis of aberrant membrane proteins and thereby influences membrane turnover [71, 72], further suggests that daptomycin negatively affects membrane-protein stability and thus membrane integrity. In B. subtilis, daptomycin preferentially interacts with regions of the membrane enriched in phosphatidylglycerol (PhG) [59], it has been physically associated with sites of membrane distortion [56] and resistance is linked to the overall PhG content [73], in S. aureus mutations in mprF increase daptomycin resistance by changing membrane lipid composition and charge [74–76], while in Enterococci changes in cardiolipin synthesis can increase daptomycin resistance [77, 78]; 3) A Trk-system (SP0479-0480), which mediates K+-uptake [79], becomes important in T4 in the presence of daptomycin, which suggests that T4 suffers from daptomycin-induced K+-loss. Indeed, it is assumed that daptomycin triggers potassium loss through its interaction with the membrane [57, 60, 61]. Besides K+, other ions may be leaking out as well [61], or at least other ions become more important and may compensate for K+-loss, which is highlighted by the importance of several ion transport systems in our network including SP1623 (annotated as a cation-transporter), which we previously associated with pH-homeostasis [48]; 4) The sensitivity profile highlights the importance of a diverse set of cell division, RNA and protein turnover, signaling and regulation, and metabolism genes, indicating that the antibiotic’s effects resonate throughout some of the most embedded systems in the bacterium. Importantly, these profiles can also suggest roles for genes with unknown or unclear functions in at least two ways: 1) A gene with unknown function adjacent to a gene with a defined function and a similar sensitivity suggests that the two genes are involved in the same process. For instance SP1730 and SP1731 are hypothetical genes and have a similar fitness as their regulatory neighbors SP1732 (stkP) and SP1733 (phpP; the cognate phosphatase of stkP), which sense intracellular peptidoglycan and have regulatory control over cell-division [80]. 2) Genes with domains that suggest a function or association with a specific functional category are more likely to be correct if they fit within the sensitivity profile. For instance BLAST searches and protein domain predictions predict that both SP1505 and SP1720 are membrane proteins. These predictions fit well with the sensitivity profile where membrane proteins make up one of the most important categories. Moreover, with GFP-fusions we confirmed the localization of both proteins in the membrane.

Daptomycin sensitivity profiles are not conserved between strains We expected to uncover highly similar sensitivity profiles due to the strains’ equivalent susceptibility to daptomycin. However, less than 50% of the responsive genes have a conserved phenotype between strains (Fig 2B; S3 Table), and based on a Jaccard similarity index, the networks are significantly different (J = 0.24, p