How infection-triggered pathobionts influence virulence evolution.

Host-pathogen interactions can be influenced by the host microbiota, as the microbiota can facilitate or prevent pathogen infections. In addition, members of the microbiota can become virulent. Such pathobionts can cause co-infections when a pathogen infection alters the host immune system and triggers dysbiosis. Here we performed a theoretical investigation of how pathobiont co-infections affect the evolution of pathogen virulence. We explored the possibility that the likelihood of pathobiont co-infection depends on the evolving virulence of the pathogen. We found that, in contrast to the expectation from classical theory, increased virulence is not always selected for. For an increasing likelihood of co-infection with increasing pathogen virulence, we found scenario-specific selection for either increased or decreased virulence. Evolutionary changes, however, in pathogen virulence do not always translate into similar changes in combined virulence of the pathogen and the pathobiont. Only in one of the scenarios where pathobiont co-infection is triggered above a specific virulence level we found a reduction in combined virulence. This was not the case when the probability of pathobiont co-infection linearly increased with pathogen virulence. Taken together, our study draws attention to the possibility that host-microbiota interactions can be both the driver and the target of pathogen evolution. This article is part of the theme issue 'Sculpting the microbiome: how host factors determine and respond to microbial colonization'.

Forecasting antimicrobial resistance evolution.

Antimicrobial resistance (AMR) is a major global health issue. Current measures for tackling it comprise mainly the prudent use of drugs, the development of new drugs, and rapid diagnostics. Relatively little attention has been given to forecasting the evolution of resistance. Here, we argue that forecasting has the potential to be a great asset in our arsenal of measures to tackle AMR. We argue that, if successfully implemented, forecasting resistance will help to resolve the antibiotic crisis in three ways: it will (i) guide a more sustainable use (and therefore lifespan) of antibiotics and incentivize investment in drug development, (ii) reduce the spread of AMR genes and pathogenic microbes in the environment and between patients, and (iii) allow more efficient treatment of persistent infections, reducing the continued evolution of resistance. We identify two important challenges that need to be addressed for the successful establishment of forecasting: (i) the development of bespoke technology that allows stakeholders to empirically assess the risks of resistance evolving during the process of drug development and therapeutic/preventive use, and (ii) the transformative shift in mindset from the current praxis of mostly addressing the problem of antibiotic resistance a posteriori to a concept of a priori estimating, and acting on, the risks of resistance.

The pharmacokinetic-pharmacodynamic modelling framework as a tool to predict drug resistance evolution.

Pharmacokinetic-pharmacodynamic (PKPD) models, which describe how drug concentrations change over time and how that affects pathogen growth, have proven highly valuable in designing optimal drug treatments aimed at bacterial eradication. However, the fast rise of antimicrobial resistance calls for increased focus on an additional treatment optimization criterion: avoidance of resistance evolution. We demonstrate here how coupling PKPD and population genetics models can be used to determine treatment regimens that minimize the potential for antimicrobial resistance evolution. Importantly, the resulting modelling framework enables the assessment of resistance evolution in response to dynamic selection pressures, including changes in antimicrobial concentration and the emergence of adaptive phenotypes. Using antibiotics and antimicrobial peptides as an example, we discuss the empirical evidence and intuition behind individual model parameters. We further suggest several extensions of this framework that allow a more comprehensive and realistic prediction of bacterial escape from antimicrobials through various phenotypic and genetic mechanisms.

Virulence decomposition for bifurcating infections.

A fundamental goal in infection biology is to understand the emergence of variation in pathogen virulence-here defined as the decrease in host fitness caused by a pathogen. To uncover the sources of such variation, virulence can be decomposed into both host- and pathogen-associated components. However, decomposing virulence can be challenging owing to complex within-host pathogen dynamics such as bifurcating infections, which recently received increased empirical and theoretical attention. Bifurcating infections are characterized by the emergence of two distinct infection types: (i) terminal infections with high pathogen loads resulting in rapid host death, and (ii) persistent infections with lower loads and delayed host death. Here, we propose to use discrete mixture models to perform separate virulence decompositions for each infection type. Using this approach, we reanalysed a recently published experimental dataset on bacterial load and survival in Drosophila melanogaster. This analysis revealed several advantages of the new approach, most importantly the generation of a more comprehensive picture of the varying sources of virulence in different bacterial species. Beyond this application, our approach could provide valuable information for ground-truthing and improving theoretical models of within-host infection dynamics, which are developed to predict variation in infection outcome and pathogen virulence.

The effect of combined knockdowns of Attacins on survival and bacterial load in Tenebrio molitor.

Introduction: Upon infection, insect hosts simultaneously express a cocktail of antimicrobial peptides (AMPs) which can impede pathogen colonization and increase host fitness. It has been proposed that such a cocktail might be adaptive if the effects of co-expressed AMPs are greater than the sum of individual activities. This could potentially prevent the evolution of bacterial resistance. However, in vivo studies on AMPs in combination are scarce. Attacins are one of the relatively large AMP families, which show anti-Gram-negative activity in vitro. Material and methods: Here, we used RNA interference (RNAi) to silence three members of the Attacin family genes in the mealworm beetle, Tenebrio molitor: (TmAttacin1a (TmAtt1a), TmAttacin1b (TmAtt1b), and TmAttacin2 (TmAtt2) both individually and in combination. We then infected T. molitor with the Gram negative entomopathogen Pseudomonas entomophila. Results: We found that survival of the beetles was only affected by the knockdown of TmAttacin1b, TmAttacin2 and the knockdown of all three Attacins together. Triple knockdown, rather than individual or double knockdowns of AMPs, changes the temporal dynamics of their efficiency in controlling the colonization of P. entomophila in the insect body. Discussion: More precisely, AMP gene expression influences P. entomophila load early in the infection process, resulting in differences in host survival. Our results highlight the importance of studying AMP-interactions in vivo.

Complete metamorphosis and microbiota turnover in insects.

The insects constitute the majority of animal diversity. Most insects are holometabolous: during complete metamorphosis their bodies are radically reorganized. This reorganization poses a significant challenge to the gut microbiota, as the gut is replaced during pupation, a process that does not occur in hemimetabolous insects. In holometabolous hosts, it offers the opportunity to decouple the gut microbiota between the larval and adult life stages resulting in high beta diversity whilst limiting alpha diversity. Here, we studied 18 different herbivorous insect species from five orders of holometabolous and three orders of hemimetabolous insects. Comparing larval and adult specimens, we find a much higher beta-diversity and hence microbiota turnover in holometabolous insects compared to hemimetabolous insects. Alpha diversity did not differ between holo- and hemimetabolous insects nor between developmental stages within these groups. Our results support the idea that pupation offers the opportunity to change the gut microbiota and hence might facilitate ecological niche shifts. This possible effect of niche shift facilitation could explain a selective advantage of the evolution of complete metamorphosis, which is a defining trait of the most speciose insect taxon, the holometabola.

Antimicrobial activity of cationic antimicrobial peptides against stationary phase bacteria.

Antimicrobial peptides (AMPs) are ancient antimicrobial weapons used by multicellular organisms as components of their innate immune defenses. Because of the antibiotic crisis, AMPs have also become candidates for developing new drugs. Here, we show that five different AMPs of different classes are effective against non-dividing Escherichia coli and Staphylococcus aureus. By comparison, three conventional antibiotics from the main three classes of antibiotics poorly kill non-dividing bacteria at clinically relevant doses. The killing of fast-growing bacteria by AMPs is faster than that of slow-dividing bacteria and, in some cases, without any difference. Still, non-dividing bacteria are effectively killed over time. Our results point to a general property of AMPs, which might explain why selection has favored AMPs in the evolution of metazoan immune systems. The ability to kill non-dividing cells is another reason that makes AMPs exciting candidates for drug development.

Tachykinin-related peptides modulate immune-gene expression in the mealworm beetle Tenebrio molitor L.

Tachykinins (TKs) are a group of conserved neuropeptides. In insects, tachykinin-related peptides (TRPs) are important modulators of several functions such as nociception and lipid metabolism. Recently, it has become clear that TRPs also play a role in regulating the insect immune system. Here, we report a transcriptomic analysis of changes in the expression levels of immune-related genes in the storage pest Tenebrio molitor after treatment with Tenmo-TRP-7. We tested two concentrations (10-8 and 10-6 M) at two time points, 6 and 24 h post-injection. We found significant changes in the transcript levels of a wide spectrum of immune-related genes. Some changes were observed 6 h after the injection of Tenmo-TRP-7, especially in relation to its putative anti-apoptotic action. Interestingly, 24 h after the injection of 10-8 M Tenmo-TRP-7, most changes were related to the regulation of the cellular response. Applying 10-6 M Tenmo-TRP-7 resulted in the downregulation of genes associated with humoral responses. Injecting Tenmo-TRP-7 did not affect beetle survival but led to a reduction in haemolymph lysozyme-like antibacterial activity, consistent with the transcriptomic data. The results confirmed the immunomodulatory role of TRP and shed new light on the functional homology between TRPs and TKs.

Modeling Polygenic Antibiotic Resistance Evolution in Biofilms.

The recalcitrance of biofilms to antimicrobials is a multi-factorial phenomenon, including genetic, physical, and physiological changes. Individually, they often cannot account for biofilm recalcitrance. However, their combination can increase the minimal inhibitory concentration of antibiotics needed to kill bacterial cells by three orders of magnitude, explaining bacterial survival under otherwise lethal drug treatment. The relative contributions of these factors depend on the specific antibiotics, bacterial strain, as well as environmental and growth conditions. An emerging population genetic property-increased biofilm genetic diversity-further enhances biofilm recalcitrance. Here, we develop a polygenic model of biofilm recalcitrance accounting for multiple phenotypic mechanisms proposed to explain biofilm recalcitrance. The model can be used to generate predictions about the emergence of resistance-its timing and population genetic consequences. We use the model to simulate various treatments and experimental setups. Our simulations predict that the evolution of resistance is impaired in biofilms at low antimicrobial concentrations while it is facilitated at higher concentrations. In scenarios that allow bacteria exchange between planktonic and biofilm compartments, the evolution of resistance is further facilitated compared to scenarios without exchange. We compare these predictions to published experimental observations.

Antimicrobial Peptide Combination Can Hinder Resistance Evolution.

Antibiotic-resistant microbial pathogens are becoming a major threat to human health. Therefore, there is an urgent need to develop new alternatives to conventional antibiotics. One such promising alternative is antimicrobial peptides (AMPs), which are produced by virtually all organisms and typically inhibit bacteria via membrane disruption. However, previous studies demonstrated that bacteria can rapidly develop AMP resistance. Here, we study whether combination therapy, known to be able to inhibit the evolution of resistance to conventional antibiotics, can also hinder the evolution of AMP resistance. To do so, we evolved the opportunistic pathogen Staphylococcus aureus in the presence of individual AMPs, AMP pairs, and a combinatorial antimicrobial peptide library. Treatment with some AMP pairs indeed hindered the evolution of resistance compared with individual AMPs. In particular, resistance to pairs was delayed when resistance to the individual AMPs came at a cost of impaired bacterial growth and did not confer cross-resistance to other tested AMPs. The lowest level of resistance evolved during treatment with the combinatorial antimicrobial peptide library termed random antimicrobial peptide mixture, which contains more than a million different peptides. A better understanding of how AMP combinations affect the evolution of resistance is a crucial step in order to design "resistant proof" AMP cocktails that will offer a sustainable treatment option for antibiotic-resistant pathogens. IMPORTANCE The main insights gleaned from this study are the following. (i) AMP combination treatment can delay the evolution of resistance in S. aureus. Treatment with some AMP pairs resulted in significantly lower resistance then treatment with either of the individual AMPs. Treatment with a random AMP library resulted in no detectable resistance. (ii) The rate at which resistance to combination arises correlates with the cost of resistance to individual AMPs and their cross-resistance. In particular, combinations to which the least resistance arose involved AMPs with high fitness cost of resistance and low cross-resistance. (iii) No broad-range AMP resistance evolved. Strains that evolved resistance to some AMPs typically remained sensitive to other AMPs, alleviating concerns regarding the evolution of resistance to immune system AMPs in response to AMP treatment.

Biocides used as material preservatives modify rates of de novo mutation and horizontal gene transfer in bacteria.

Antimicrobial resistance (AMR) is a global health problem with the environment being an important compartment for the evolution and transmission of AMR. Previous studies showed that de-novo mutagenesis and horizontal gene transfer (HGT) by conjugation or transformation - important processes underlying resistance evolution and spread - are affected by antibiotics, metals and pesticides. However, natural microbial communities are also frequently exposed to biocides used as material preservatives, but it is unknown if these substances induce mutagenesis and HGT. Here, we show that active substances used in material preservatives can increase rates of mutation and conjugation in a species- and substance-dependent manner, while rates of transformation are not increased. The bisbiguanide chlorhexidine digluconate, the quaternary ammonium compound didecyldimethylammonium chloride, the metal copper, the pyrethroid-insecticide permethrin, and the azole-fungicide propiconazole increase mutation rates in Escherichia coli, whereas no increases were identified for Bacillus subtilis and Acinetobacter baylyi. Benzalkonium chloride, chlorhexidine and permethrin increased conjugation in E. coli. Moreover, our results show a connection between the RpoS-mediated general stress and the RecA-linked SOS response with increased rates of mutation and conjugation, but not for all biocides. Taken together, our data show the importance of assessing the contribution of material preservatives on AMR evolution and spread.

Population genetics, biofilm recalcitrance, and antibiotic resistance evolution.

Biofilms are communities of bacteria forming high-density sessile colonies. Such a lifestyle comes associated with costs and benefits: while the growth rate of biofilms is often lower than that of their free-living counterparts, this cost is readily repaid once the colony is subjected to antibiotics. Biofilms can grow in antibiotic concentrations a thousand times higher than planktonic bacteria. While numerous mechanisms have been proposed to explain biofilm recalcitrance towards antibiotics, little is yet known about their effect on the evolution of resistance. We synthesize the current understanding of biofilm recalcitrance from a pharmacodynamic and a population genetics perspective. Using the pharmacodynamic framework, we discuss the effects of various mechanisms and show that biofilms can either promote or impede resistance evolution.

Tripartite interactions: how immunity, microbiota and pathogens interact and affect pathogen virulence evolution.

The bipartite interactions between insect hosts and their bacterial gut microbiota, or their bacterial pathogens, are empirically and theoretically well-explored. However, direct, and indirect tripartite interactions will also likely occur inside a host. These interactions will almost certainly affect the trajectory of pathogen virulence evolution, an area that is currently under researched. The interactions within tripartite associations can be competitive, that is, exploitative-competition, interference-competition or apparent-competition. Competitive interactions will be significantly influenced by non-competitive effects, for example, immunopathology, immunosuppression, and microbiota-mediated tolerance. Considering a combination of these interactions and effects, will enable an increased understanding of the evolution of pathogen virulence. This new perspective allows us to identify several novel research questions, which we hope will be a useful framework for future research.

Multi-step vs. single-step resistance evolution under different drugs, pharmacokinetics, and treatment regimens.

The success of antimicrobial treatment is threatened by the evolution of drug resistance. Population genetic models are an important tool in mitigating that threat. However, most such models consider resistance emergence via a single mutational step. Here, we assembled experimental evidence that drug resistance evolution follows two patterns: (i) a single mutation, which provides a large resistance benefit, or (ii) multiple mutations, each conferring a small benefit, which combine to yield high-level resistance. Using stochastic modeling, we then investigated the consequences of these two patterns for treatment failure and population diversity under various treatments. We find that resistance evolution is substantially limited if more than two mutations are required and that the extent of this limitation depends on the combination of drug type and pharmacokinetic profile. Further, if multiple mutations are necessary, adaptive treatment, which only suppresses the bacterial population, delays treatment failure due to resistance for a longer time than aggressive treatment, which aims at eradication.

The rise in antibiotic resistance is threatening our ability to treat bacterial infections. Bacteria often evolve resistance by acquiring new genetic mutations during the treatment period. Understanding how resistance emerges and spreads through a bacterial population is crucial to prevent antibiotic drugs from failing. Mathematical models are a useful tool for exploring how bacteria will respond to antibiotics and assessing the risk of resistance. Usually, these models only consider instances where bacteria acquire one genetic mutation that makes them virtually impervious to treatment. But, in nature, this is not the only possibility. Although some mutations do give bacteria a high level of resistance, numerous others only provide small amounts of protection against the drug. If these mutations accumulate in the same bacterial cell, their effects can combine to make the strain highly resistant to treatment. But it was unclear how the emergence of multiple mutations affects the risk of treatment failure and the diversity of the bacterial population. To answer this question, Igler et al. devised a mathematical model in which each bacterium is able to mutate multiple times during the treatment period. The model revealed that if one mutation provides a high level of resistance on its own, the risk of bacteria surviving treatment is very high. But, if it takes more than two mutations to achieve a high level of resistance, the risk drops to almost nothing. Igler et al. also found that the chance of bacteria evolving high enough resistance is affected by the type of antibiotics used and how fast the drug decays. With low-level resistance mutations, adapting treatment to maintain an acceptable number of sensitive bacteria as competitors for (a small number of) resistant bacteria was more effective at delaying treatment failure than trying to kill all the bacteria at once. These findings suggest that adjusting the treatment strategy used for bacterial infections according to the proportion of low- and high-level resistance mutations could slow down the evolution of resistance. To apply these models in the real world, it will be important to measure the level of resistance conferred by single mutations. The type of models used here could also predict the response of other diseases that resist treatment, such as cancer.

The Use of Translational Modelling and Simulation to Develop Immunomodulatory Therapy as an Adjunct to Antibiotic Treatment in the Context of Pneumonia.

The treatment of respiratory tract infections is threatened by the emergence of bacterial resistance. Immunomodulatory drugs, which enhance airway innate immune defenses, may improve therapeutic outcome. In this concept paper, we aim to highlight the utility of pharmacometrics and Bayesian inference in the development of immunomodulatory therapeutic agents as an adjunct to antibiotics in the context of pneumonia. For this, two case studies of translational modelling and simulation frameworks are introduced for these types of drugs up to clinical use. First, we evaluate the pharmacokinetic/pharmacodynamic relationship of an experimental combination of amoxicillin and a TLR4 agonist, monophosphoryl lipid A, by developing a pharmacometric model accounting for interaction and potential translation to humans. Capitalizing on this knowledge and associating clinical trial extrapolation and statistical modelling approaches, we then investigate the TLR5 agonist flagellin. The resulting workflow combines expert and prior knowledge on the compound with the in vitro and in vivo data generated during exploratory studies in order to construct high-dimensional models considering the pharmacokinetics and pharmacodynamics of the compound. This workflow can be used to refine preclinical experiments, estimate the best doses for human studies, and create an adaptive knowledge-based design for the next phases of clinical development.

Bacteria primed by antimicrobial peptides develop tolerance and persist.

Antimicrobial peptides (AMPs) are key components of innate immune defenses. Because of the antibiotic crisis, AMPs have also come into focus as new drugs. Here, we explore whether prior exposure to sub-lethal doses of AMPs increases bacterial survival and abets the evolution of resistance. We show that Escherichia coli primed by sub-lethal doses of AMPs develop tolerance and increase persistence by producing curli or colanic acid, responses linked to biofilm formation. We develop a population dynamic model that predicts that priming delays the clearance of infections and fuels the evolution of resistance. The effects we describe should apply to many AMPs and other drugs that target the cell surface. The optimal strategy to tackle tolerant or persistent cells requires high concentrations of AMPs and fast and long-lasting expression. Our findings also offer a new understanding of non-inherited drug resistance as an adaptive response and could lead to measures that slow the evolution of resistance.

Antimicrobial Peptide Induced-Stress Renders Staphylococcus aureus Susceptible to Toxic Nucleoside Analogs.

Cationic antimicrobial peptides (AMPs) are active immune effectors of multicellular organisms and are also considered as new antimicrobial drug candidates. One of the problems encountered when developing AMPs as drugs is the difficulty of reaching sufficient killing concentrations under physiological conditions. Here, using pexiganan, a cationic peptide derived from a host defense peptide of the African clawed frog and the first AMP developed into an antibacterial drug, we studied whether sub-lethal effects of AMPs can be harnessed to devise treatment combinations. We studied the pexiganan stress response of Staphylococcus aureus at sub-lethal concentrations using quantitative proteomics. Several proteins involved in nucleotide metabolism were elevated, suggesting a metabolic demand. We then show that Staphylococcus aureus is highly susceptible to antimetabolite nucleoside analogs when exposed to pexiganan, even at sub-inhibitory concentrations. These findings could be used to enhance pexiganan potency while decreasing the risk of resistance emergence, and our findings can likely be extended to other antimicrobial peptides.

Antimicrobial peptides: Application informed by evolution.

Antimicrobial peptides (AMPs) are essential components of immune defenses of multicellular organisms and are currently in development as anti-infective drugs. AMPs have been classically assumed to have broad-spectrum activity and simple kinetics, but recent evidence suggests an unexpected degree of specificity and a high capacity for synergies. Deeper evaluation of the molecular evolution and population genetics of AMP genes reveals more evidence for adaptive maintenance of polymorphism in AMP genes than has previously been appreciated, as well as adaptive loss of AMP activity. AMPs exhibit pharmacodynamic properties that reduce the evolution of resistance in target microbes, and AMPs may synergize with one another and with conventional antibiotics. Both of these properties make AMPs attractive for translational applications. However, if AMPs are to be used clinically, it is crucial to understand their natural biology in order to lessen the risk of collateral harm and avoid the crisis of resistance now facing conventional antibiotics.