Mechanisms of antibiotic action shape the fitness landscapes of resistance mutations.

Antibiotic-resistant pathogens are a major public health threat. A deeper understanding of how an antibiotic's mechanism of action influences the emergence of resistance would aid in the design of new drugs and help to preserve the effectiveness of existing ones. To this end, we developed a model that links bacterial population dynamics with antibiotic-target binding kinetics. Our approach allows us to derive mechanistic insights on drug activity from population-scale experimental data and to quantify the interplay between drug mechanism and resistance selection. We find that both bacteriostatic and bactericidal agents can be equally effective at suppressing the selection of resistant mutants, but that key determinants of resistance selection are the relationships between the number of drug-inactivated targets within a cell and the rates of cellular growth and death. We also show that heterogeneous drug-target binding within a population enables resistant bacteria to evolve fitness-improving secondary mutations even when drug doses remain above the resistant strain's minimum inhibitory concentration. Our work suggests that antibiotic doses beyond this "secondary mutation selection window" could safeguard against the emergence of high-fitness resistant strains during treatment.

The Infectious Dose Shapes Vibrio cholerae Within-Host Dynamics.

During infection, the rates of pathogen replication, death, and migration affect disease progression, dissemination, transmission, and resistance evolution. Here, we follow the population dynamics of Vibrio cholerae in a mouse model by labeling individual bacteria with one of >500 unique, fitness-neutral genomic tags. Using the changes in tag frequencies and CFU numbers, we inform a mathematical model that describes the within-host spatiotemporal bacterial dynamics. This allows us to disentangle growth, death, forward, and retrograde migration rates continuously during infection. Our model has robust predictive power across various experimental setups. The population dynamics of V. cholerae shows substantial spatiotemporal heterogeneity in replication, death, and migration. Importantly, we find that the niche available to V. cholerae in the host increases with inoculum size, suggesting cooperative effects during infection. Therefore, it is not enough to consider just the likelihood of exposure (50% infectious dose) but rather the magnitude of exposure to predict outbreaks. IMPORTANCE Determining the rates of bacterial migration, replication, and death during infection is important for understanding how infections progress. Separately measuring these rates is often difficult in systems where multiple processes happen simultaneously. Here, we use next-generation sequencing to measure V. cholerae migration, replication, death, and niche size along the mouse gastrointestinal tract. We show that the small intestine of the mouse is a heterogeneous environment, and the population dynamic characteristics change substantially between adjacent gut sections. Our approach also allows us to characterize the effect of inoculum size on these processes. We find that the niche size in mice increases with the infectious dose, hinting at cooperative effects in larger inocula. The dose-response relationship between inoculum size and final pathogen burden is important for the infected individual and is thought to influence the progression of V. cholerae epidemics.

A single step three-strain in vivo Gateway reaction.

We developed a simplified, highly efficient Gateway reaction that recombines target DNA to expression (destination) plasmids in vivo and subsequently conjugates the final vector into a recipient strain, all in a single step. This recipient strain does not need to contain any selective marker and can be freely chosen as long as it is sensitive to ccdB counterselection and can be targeted by the RP4α conjugation system. Our protocol is simple, robust, and cost effective. It works in 96-well plate format and performs across a range of temperatures. We designed modular, minimal destination vectors containing a modified Gateway insert to ease vector design by providing locations for insertion of tags, promoters, or conjugations. To demonstrate the utility of our system, we created destination vectors with split adenylate cyclase tags for bacterial two-hybrid (B2H) studies and screened a library of diguanylate cyclases for protein-protein interactions in a single step.

RESTAMP - Rate estimates by sequence-tag analysis of microbial populations.

Microbial division rates determine the speed of mutation accumulation and thus the emergence of antimicrobial resistance. Microbial death rates are affected by antibiotic action and the immune system. Therefore, measuring these rates has advanced our understanding of host-pathogen interactions and antibiotic action. Several methods based on marker-loss or few inheritable neutral markers exist that allow estimating microbial division and death rates, each of which has advantages and limitations. Technical bottlenecks, i.e., experimental sampling events, during the experiment can distort the rate estimates and are typically unaccounted for or require additional calibration experiments. In this work, we introduce RESTAMP (Rate Estimates by Sequence Tag Analysis of Microbial Populations) as a method for determining bacterial division and death rates. This method uses hundreds of fitness neutral sequence barcodes to measure the rates and account for experimental bottlenecks at the same time. We experimentally validate RESTAMP and compare it to established plasmid loss methods. We find that RESTAMP has a number of advantages over plasmid loss or previous marker based techniques. (i) It enables to correct the distortion of rate estimates by technical bottlenecks. (ii) Rate estimates are independent of the sequence tag distribution in the starting culture allowing the use of an arbitrary number of tags. (iii) It introduces a bottleneck sensitivity measure that can be used to maximize the accuracy of the experiment. RESTAMP allows studying microbial population dynamics with great resolution over a wide dynamic range and can thus advance our understanding of host-pathogen interactions or the mechanisms of antibiotic action.

Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones.

Antibiotic resistance is rising and we urgently need to gain a better quantitative understanding of how antibiotics act, which in turn would also speed up the development of new antibiotics. Here, we describe a computational model (COMBAT-COmputational Model of Bacterial Antibiotic Target-binding) that can quantitatively predict antibiotic dose-response relationships. Our goal is dual: We address a fundamental biological question and investigate how drug-target binding shapes antibiotic action. We also create a tool that can predict antibiotic efficacy a priori. COMBAT requires measurable biochemical parameters of drug-target interaction and can be directly fitted to time-kill curves. As a proof-of-concept, we first investigate the utility of COMBAT with antibiotics belonging to the widely used quinolone class. COMBAT can predict antibiotic efficacy in clinical isolates for quinolones from drug affinity (R2>0.9). To further challenge our approach, we also do the reverse: estimate the magnitude of changes in drug-target binding based on antibiotic dose-response curves. We overexpress target molecules to infer changes in antibiotic-target binding from changes in antimicrobial efficacy of ciprofloxacin with 92-94% accuracy. To test the generality of our approach, we use the beta-lactam ampicillin to predict target molecule occupancy at MIC from antimicrobial action with 90% accuracy. Finally, we apply COMBAT to predict antibiotic concentrations that can select for resistance due to novel resistance mutations. Using ciprofloxacin and ampicillin as well defined test cases, our work demonstrates that drug-target binding is a major predictor of bacterial responses to antibiotics. This is surprising because antibiotic action involves many additional effects downstream of drug-target binding. In addition, COMBAT provides a framework to inform optimal antibiotic dose levels that maximize efficacy and minimize the rise of resistant mutants.

Selection or drift: The population biology underlying transposon insertion sequencing experiments.

Transposon insertion sequencing methods such as Tn-seq revolutionized microbiology by allowing the identification of genomic loci that are critical for viability in a specific environment on a genome-wide scale. While powerful, transposon insertion sequencing suffers from limited reproducibility when different analysis methods are compared. From the perspective of population biology, this may be explained by changes in mutant frequency due to chance (drift) rather than differential fitness (selection). Here, we develop a mathematical model of the population biology of transposon insertion sequencing experiments, i.e. the changes in size and composition of the transposon-mutagenized population during the experiment. We use this model to investigate mutagenesis, the growth of the mutant library, and its passage through bottlenecks. Specifically, we study how these processes can lead to extinction of individual mutants depending on their fitness and the distribution of fitness effects (DFE) of the entire mutant population. We find that in typical in vitro experiments few mutants with high fitness go extinct. However, bottlenecks of a size that is common in animal infection models lead to so much random extinction that a large number of viable mutants would be misclassified. While mutants with low fitness are more likely to be lost during the experiment, mutants with intermediate fitness are expected to be much more abundant and can constitute a large proportion of detected hits, i.e. false positives. Thus, incorporating the DFEs of randomly generated mutations in the analysis may improve the reproducibility of transposon insertion experiments, especially when strong bottlenecks are encountered.

Transposon-insertion sequencing screens unveil requirements for EHEC growth and intestinal colonization.

Enterohemorrhagic Escherichia coli O157:H7 (EHEC) is an important food-borne pathogen that colonizes the colon. Transposon-insertion sequencing (TIS) was used to identify genes required for EHEC and E. coli K-12 growth in vitro and for EHEC growth in vivo in the infant rabbit colon. Surprisingly, many conserved loci contribute to EHEC's but not to K-12's growth in vitro. There was a restrictive bottleneck for EHEC colonization of the rabbit colon, which complicated identification of EHEC genes facilitating growth in vivo. Both a refined version of an existing analytic framework as well as PCA-based analysis were used to compensate for the effects of the infection bottleneck. These analyses confirmed that the EHEC LEE-encoded type III secretion apparatus is required for growth in vivo and revealed that only a few effectors are critical for in vivo fitness. Over 200 mutants not previously associated with EHEC survival/growth in vivo also appeared attenuated in vivo, and a subset of these putative in vivo fitness factors were validated. Some were found to contribute to efficient type-three secretion while others, including tatABC, oxyR, envC, acrAB, and cvpA, promote EHEC resistance to host-derived stresses. cvpA is also required for intestinal growth of several other enteric pathogens, and proved to be required for EHEC, Vibrio cholerae and Vibrio parahaemolyticus resistance to the bile salt deoxycholate, highlighting the important role of this previously uncharacterized protein in pathogen survival. Collectively, our findings provide a comprehensive framework for understanding EHEC growth in the intestine.

Reaction Kinetic Models of Antibiotic Heteroresistance.

Bacterial heteroresistance (i.e., the co-existence of several subpopulations with different antibiotic susceptibilities) can delay the clearance of bacteria even with long antibiotic exposure. Some proposed mechanisms have been successfully described with mathematical models of drug-target binding where the mechanism's downstream of drug-target binding are not explicitly modeled and subsumed in an empirical function, connecting target occupancy to antibiotic action. However, with current approaches it is difficult to model mechanisms that involve multi-step reactions that lead to bacterial killing. Here, we have a dual aim: first, to establish pharmacodynamic models that include multi-step reaction pathways, and second, to model heteroresistance and investigate which molecular heterogeneities can lead to delayed bacterial killing. We show that simulations based on Gillespie algorithms, which have been employed to model reaction kinetics for decades, can be useful tools to model antibiotic action via multi-step reactions. We highlight the strengths and weaknesses of current models and Gillespie simulations. Finally, we show that in our models, slight normally distributed variances in the rates of any event leading to bacterial death can (depending on parameter choices) lead to delayed bacterial killing (i.e., heteroresistance). This means that a slowly declining residual bacterial population due to heteroresistance is most likely the default scenario and should be taken into account when planning treatment length.

Deciphering the landscape of host barriers to Listeria monocytogenes infection.

Listeria monocytogenes is a common food-borne pathogen that can disseminate from the intestine and infect multiple organs. Here, we used sequence tag-based analysis of microbial populations (STAMP) to investigate Lmonocytogenes population dynamics during infection. We created a genetically barcoded library of murinized Lmonocytogenes and then used deep sequencing to track the pathogen's dissemination routes and quantify its founding population (Nb) sizes in different organs. We found that the pathogen disseminates from the gastrointestinal tract to distal sites through multiple independent routes and that Nb sizes vary greatly among tissues, indicative of diverse host barriers to infection. Unexpectedly, comparative analyses of sequence tags revealed that fecally excreted organisms are largely derived from the very small number of L. monocytogenes cells that colonize the gallbladder. Immune depletion studies suggest that distinct innate immune cells restrict the pathogen's capacity to establish replicative niches in the spleen and liver. Finally, studies in germ-free mice suggest that the microbiota plays a critical role in the development of the splenic, but not the hepatic, barriers that prevent L. monocytogenes from seeding these organs. Collectively, these observations illustrate the potency of the STAMP approach to decipher the impact of host factors on population dynamics of pathogens during infection.

Genetic analysis of Vibrio parahaemolyticus intestinal colonization.

Vibrio parahaemolyticus is the most common cause of seafood-borne gastroenteritis worldwide and a blight on global aquaculture. This organism requires a horizontally acquired type III secretion system (T3SS2) to infect the small intestine, but knowledge of additional factors that underlie V. parahaemolyticus pathogenicity is limited. We used transposon-insertion sequencing to screen for genes that contribute to viability of V. parahaemolyticus in vitro and in the mammalian intestine. Our analysis enumerated and controlled for the host infection bottleneck, enabling robust assessment of genetic contributions to in vivo fitness. We identified genes that contribute to V. parahaemolyticus colonization of the intestine independent of known virulence mechanisms in addition to uncharacterized components of T3SS2. Our study revealed that toxR, an ancestral locus in Vibrio species, is required for V. parahaemolyticus fitness in vivo and for induction of T3SS2 gene expression. The regulatory mechanism by which V. parahaemolyticus ToxR activates expression of T3SS2 resembles Vibrio cholerae ToxR regulation of distinct virulence elements acquired via lateral gene transfer. Thus, disparate horizontally acquired virulence systems have been placed under the control of this ancestral transcription factor across independently evolved human pathogens.

Chemoproteomic profiling of host and pathogen enzymes active in cholera.

Activity-based protein profiling (ABPP) is a chemoproteomic tool for detecting active enzymes in complex biological systems. We used ABPP to identify secreted bacterial and host serine hydrolases that are active in animals infected with the cholera pathogen Vibrio cholerae. Four V. cholerae proteases were consistently active in infected rabbits, and one, VC0157 (renamed IvaP), was also active in human choleric stool. Inactivation of IvaP influenced the activity of other secreted V. cholerae and rabbit enzymes in vivo, and genetic disruption of all four proteases increased the abundance of intelectin, an intestinal lectin, and its binding to V. cholerae in infected rabbits. Intelectin also bound to other enteric bacterial pathogens, suggesting that it may constitute a previously unrecognized mechanism of bacterial surveillance in the intestine that is inhibited by pathogen-secreted proteases. Our work demonstrates the power of activity-based proteomics to reveal host-pathogen enzymatic dialog in an animal model of infection.

The design and analysis of transposon insertion sequencing experiments.

Transposon insertion sequencing (TIS) is a powerful approach that can be extensively applied to the genome-wide definition of loci that are required for bacterial growth under diverse conditions. However, experimental design choices and stochastic biological processes can heavily influence the results of TIS experiments and affect downstream statistical analysis. In this Opinion article, we discuss TIS experimental parameters and how these factors relate to the benefits and limitations of the various statistical frameworks that can be applied to the computational analysis of TIS data.

Infant Rabbit Model for Diarrheal Diseases.

Vibrio cholerae is the agent of cholera, a potentially lethal diarrheal disease that remains a significant threat to populations in developing nations. The infant rabbit model of cholera is the only non-surgical small animal model system that closely mimics human cholera. Following orogastric inoculation, V. cholerae colonizes the intestines of infant rabbits, and the animals develop severe cholera-like diarrhea. In this unit, we provide a detailed description of the preparation of the V. cholerae inoculum, the inoculation process and the collection and processing of tissue samples. This infection model is useful for studies of V. cholerae factors and mechanisms that promote its intestinal colonization and enterotoxicity, as well as the host response to infection. The infant rabbit model of cholera enables investigations that will further our understanding of the pathophysiology of cholera and provides a platform for testing new therapeutics.

Classic reaction kinetics can explain complex patterns of antibiotic action.

Finding optimal dosing strategies for treating bacterial infections is extremely difficult, and improving therapy requires costly and time-intensive experiments. To date, an incomplete mechanistic understanding of drug effects has limited our ability to make accurate quantitative predictions of drug-mediated bacterial killing and impeded the rational design of antibiotic treatment strategies. Three poorly understood phenomena complicate predictions of antibiotic activity: post-antibiotic growth suppression, density-dependent antibiotic effects, and persister cell formation. We show that chemical binding kinetics alone are sufficient to explain these three phenomena, using single-cell data and time-kill curves of Escherichia coli and Vibrio cholerae exposed to a variety of antibiotics in combination with a theoretical model that links chemical reaction kinetics to bacterial population biology. Our model reproduces existing observations, has a high predictive power across different experimental setups (R(2) = 0.86), and makes several testable predictions, which we verified in new experiments and by analyzing published data from a clinical trial on tuberculosis therapy. Although a variety of biological mechanisms have previously been invoked to explain post-antibiotic growth suppression, density-dependent antibiotic effects, and especially persister cell formation, our findings reveal that a simple model that considers only binding kinetics provides a parsimonious and unifying explanation for these three complex, phenotypically distinct behaviours. Current antibiotic and other chemotherapeutic regimens are often based on trial and error or expert opinion. Our "chemical reaction kinetics"-based approach may inform new strategies, which are based on rational design.

Sequence tag-based analysis of microbial population dynamics.

We describe sequence tag-based analysis of microbial populations (STAMP) for characterization of pathogen population dynamics during infection. STAMP analyzes the frequency changes of genetically 'barcoded' organisms to quantify population bottlenecks and infer the founding population size. Analyses of intraintestinal Vibrio cholerae revealed infection-stage and region-specific host barriers to infection and showed unexpected V. cholerae migration counter to intestinal flow. STAMP provides a robust, widely applicable analytical framework for high-confidence characterization of in vivo microbial dissemination.

ARTIST: high-resolution genome-wide assessment of fitness using transposon-insertion sequencing.

Transposon-insertion sequencing (TIS) is a powerful approach for deciphering genetic requirements for bacterial growth in different conditions, as it enables simultaneous genome-wide analysis of the fitness of thousands of mutants. However, current methods for comparative analysis of TIS data do not adjust for stochastic experimental variation between datasets and are limited to interrogation of annotated genomic elements. Here, we present ARTIST, an accessible TIS analysis pipeline for identifying essential regions that are required for growth under optimal conditions as well as conditionally essential loci that participate in survival only under specific conditions. ARTIST uses simulation-based normalization to model and compensate for experimental noise, and thereby enhances the statistical power in conditional TIS analyses. ARTIST also employs a novel adaptation of the hidden Markov model to generate statistically robust, high-resolution, annotation-independent maps of fitness-linked loci across the entire genome. Using ARTIST, we sensitively and comprehensively define Mycobacterium tuberculosis and Vibrio cholerae loci required for host infection while limiting inclusion of false positive loci. ARTIST is applicable to a broad range of organisms and will facilitate TIS-based dissection of pathways required for microbial growth and survival under a multitude of conditions.

Cell cycle transition from S-phase to G1 in Caulobacter is mediated by ancestral virulence regulators.

Zinc-finger domain transcriptional regulators regulate a myriad of functions in eukaryotes. Interestingly, ancestral versions (MucR) from Alpha-proteobacteria control bacterial virulence/symbiosis. Whether virulence regulators can also control cell cycle transcription is unknown. Here we report that MucR proteins implement a hitherto elusive primordial S→G1 transcriptional switch. After charting G1-specific promoters in the cell cycle model Caulobacter crescentus by comparative ChIP-seq, we use one such promoter as genetic proxy to unearth two MucR paralogs, MucR1/2, as constituents of a quadripartite and homeostatic regulatory module directing the S→G1 transcriptional switch. Surprisingly, MucR orthologues that regulate virulence and symbiosis gene transcription in Brucella, Agrobacterium or Sinorhizobium support this S→G1 switch in Caulobacter. Pan-genomic ChIP-seq analyses in Sinorhizobium and Caulobacter show that this module indeed targets orthologous genes. We propose that MucR proteins and possibly other virulence regulators primarily control bacterial cell cycle (G1-phase) transcription, rendering expression of target (virulence) genes periodic and in tune with the cell cycle.

Cycling empirical antibiotic therapy in hospitals: meta-analysis and models.

The rise of resistance together with the shortage of new broad-spectrum antibiotics underlines the urgency of optimizing the use of available drugs to minimize disease burden. Theoretical studies suggest that coordinating empirical usage of antibiotics in a hospital ward can contain the spread of resistance. However, theoretical and clinical studies came to different conclusions regarding the usefulness of rotating first-line therapy (cycling). Here, we performed a quantitative pathogen-specific meta-analysis of clinical studies comparing cycling to standard practice. We searched PubMed and Google Scholar and identified 46 clinical studies addressing the effect of cycling on nosocomial infections, of which 11 met our selection criteria. We employed a method for multivariate meta-analysis using incidence rates as endpoints and find that cycling reduced the incidence rate/1000 patient days of both total infections by 4.95 [9.43-0.48] and resistant infections by 7.2 [14.00-0.44]. This positive effect was observed in most pathogens despite a large variance between individual species. Our findings remain robust in uni- and multivariate metaregressions. We used theoretical models that reflect various infections and hospital settings to compare cycling to random assignment to different drugs (mixing). We make the realistic assumption that therapy is changed when first line treatment is ineffective, which we call "adjustable cycling/mixing". In concordance with earlier theoretical studies, we find that in strict regimens, cycling is detrimental. However, in adjustable regimens single resistance is suppressed and cycling is successful in most settings. Both a meta-regression and our theoretical model indicate that "adjustable cycling" is especially useful to suppress emergence of multiple resistance. While our model predicts that cycling periods of one month perform well, we expect that too long cycling periods are detrimental. Our results suggest that "adjustable cycling" suppresses multiple resistance and warrants further investigations that allow comparing various diseases and hospital settings.