Evolutionary biology and the avoidance of antimicrobial resistance

Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs

There is growing interest in designing multidrug therapies that leverage tradeoffs to combat resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into 6 classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different underlying mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, while others do. And some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance. More generally speaking, by grouping mutants that likely affect fitness through similar underlying mechanisms, our work guides efforts to map the phenotypic effects of mutation.

Temporal and spatial dynamics of Plasmodium falciparum clonal lineages in Guyana

Plasmodium parasites, the causal agents of malaria, are eukaryotic organisms that obligately undergo sexual recombination within mosquitoes. In low transmission settings, parasites recombine with themselves, and the clonal lineage is propagated rather than broken up by outcrossing. We investigated whether stochastic/neutral factors drive the persistence and abundance of Plasmodium falciparum clonal lineages in Guyana, a country with relatively low malaria transmission, but the only setting in the Americas in which an important artemisinin resistance mutation (pfk13 C580Y) has been observed. We performed whole genome sequencing on 1,727 Plasmodium falciparum samples collected from infected patients across a five-year period (2016-2021). We characterized the relatedness between each pair of monoclonal infections (n = 1,409) through estimation of identity-by-descent (IBD) and also typed each sample for known or candidate drug resistance mutations. A total of 160 multi-isolate clones (mean IBD ≥ 0.90) were circulating in Guyana during the study period, comprising 13 highly related clusters (mean IBD ≥ 0.40). In the five-year study period, we observed a decrease in frequency of a mutation associated with artemisinin partner drug (piperaquine) resistance (pfcrt C350R) and limited co-occurence of pfcrt C350R with duplications of plasmepsin 2/3, an epistatic interaction associated with piperaquine resistance. We additionally observed 61 nonsynonymous substitutions that increased markedly in frequency over the study period as well as a novel pfk13 mutation (G718S). However, P. falciparum clonal dynamics in Guyana appear to be largely driven by stochastic factors, in contrast to other geographic regions, given that clones carrying drug resistance polymorphisms do not demonstrate enhanced persistence or higher abundance than clones carrying polymorphisms of comparable frequency that are unrelated to resistance. The use of multiple artemisinin combination therapies in Guyana may have contributed to the disappearance of the pfk13 C580Y mutation.

Temporal and spatial dynamics of Plasmodium falciparum clonal lineages in Guyana

Plasmodium parasites, the causal agents of malaria, are eukaryotic organisms that obligately undergo sexual recombination within mosquitoes. However, in low transmission settings where most mosquitoes become infected with only a single parasite clone, parasites recombine with themselves, and the clonal lineage is propagated rather than broken up by outcrossing. We investigated whether stochastic/neutral factors drive the persistence and abundance of Plasmodium falciparum clonal lineages in Guyana, a country with relatively low malaria transmission, but the only setting in the Americas in which an important artemisinin resistance mutation (pfk13 C580Y) has been observed. To investigate whether this clonality was potentially associated with the persistence and spatial spread of the mutation, we performed whole genome sequencing on 1,727 Plasmodium falciparum samples collected from infected patients across a five-year period (2016-2021). We characterized the relatedness between each pair of monoclonal infections (n=1,409) through estimation of identity by descent (IBD) and also typed each sample for known or candidate drug resistance mutations. A total of 160 clones (mean IBD ≥ 0.90) were circulating in Guyana during the study period, comprising 13 highly related clusters (mean IBD ≥ 0.40). In the five-year study period, we observed a decrease in frequency of a mutation associated with artemisinin partner drug (piperaquine) resistance (pfcrt C350R) and limited co-occurence of pfcrt C350R with duplications of plasmepsin 2/3, an epistatic interaction associated with piperaquine resistance. We additionally report polymorphisms exhibiting evidence of selection for drug resistance or other phenotypes and reported a novel pfk13 mutation (G718S) as well as 61 nonsynonymous substitutions that increased markedly in frequency. However, P. falciparum clonal dynamics in Guyana appear to be largely driven by stochastic factors, in contrast to other geographic regions. The use of multiple artemisinin combination therapies in Guyana may have contributed to the disappearance of the pfk13 C580Y mutation.

Within-Host Mathematical Models of Antibiotic Resistance

Mathematical models have been used to study the spread of infectious diseases from person to person. More recently studies are developing within-host modeling which provides an understanding of how pathogens-bacteria, fungi, parasites, or viruses-develop, spread, and evolve inside a single individual and their interaction with the host's immune system.Such models have the potential to provide a more detailed and complete description of the pathogenesis of diseases within-host and identify other influencing factors that may not be detected otherwise. Mathematical models can be used to aid understanding of the global antibiotic resistance (ABR) crisis and identify new ways of combating this threat.ABR occurs when bacteria respond to random or selective pressures and adapt to new environments through the acquisition of new genetic traits. This is usually through the acquisition of a piece of DNA from other bacteria, a process called horizontal gene transfer (HGT), the modification of a piece of DNA within a bacterium, or through. Bacteria have evolved mechanisms that enable them to respond to environmental threats by mutation, and horizontal gene transfer (HGT): conjugation; transduction; and transformation. A frequent mechanism of HGT responsible for spreading antibiotic resistance on the global scale is conjugation, as it allows the direct transfer of mobile genetic elements (MGEs). Although there are several MGEs, the most important MGEs which promote the development and rapid spread of antimicrobial resistance genes in bacterial populations are plasmids and transposons. Each of the resistance-spread-mechanisms mentioned above can be modeled allowing us to understand the process better and to define strategies to reduce resistance.

Collateral Sensitivity Interactions between Antibiotics Depend on Local Abiotic Conditions

Mutations conferring resistance to one antibiotic can increase (cross-resistance) or decrease (collateral sensitivity) resistance to others. Antibiotic combinations displaying collateral sensitivity could be used in treatments that slow resistance evolution. However, lab-to-clinic translation requires understanding whether collateral effects are robust across different environmental conditions. Here, we isolated and characterized resistant mutants of Escherichia coli using five antibiotics, before measuring collateral effects on resistance to other paired antibiotics. During both isolation and phenotyping, we varied conditions in ways relevant in nature (pH, temperature, and bile). This revealed that local abiotic conditions modified expression of resistance against both the antibiotic used during isolation and other antibiotics. Consequently, local conditions influenced collateral sensitivity in two ways: by favoring different sets of mutants (with different collateral sensitivities) and by modifying expression of collateral effects for individual mutants. These results place collateral sensitivity in the context of environmental variation, with important implications for translation to real-world applications. IMPORTANCE When bacteria become resistant to an antibiotic, the genetic changes involved sometimes increase (cross-resistance) or decrease (collateral sensitivity) their resistance to other antibiotics. Antibiotic combinations showing repeatable collateral sensitivity could be used in treatment to slow resistance evolution. However, collateral sensitivity interactions may depend on the local environmental conditions that bacteria experience, potentially reducing repeatability and clinical application. Here, we show that variation in local conditions (pH, temperature, and bile salts) can influence collateral sensitivity in two ways: by favoring different sets of mutants during bacterial resistance evolution (with different collateral sensitivities to other antibiotics) and by modifying expression of collateral effects for individual mutants. This suggests that translation from the lab to the clinic of new approaches exploiting collateral sensitivity will be influenced by local abiotic conditions.

Antibiotic resistance in microbes: History, mechanisms, therapeutic strategies and future prospects

Antibiotics have been used to cure bacterial infections for more than 70 years, and these low-molecular-weight bioactive agents have also been used for a variety of other medicinal applications. In the battle against microbes, antibiotics have certainly been a blessing to human civilization by saving millions of lives. Globally, infections caused by multidrug-resistant (MDR) bacteria are on the rise. Antibiotics are being used to combat diversified bacterial infections. Synthetic biology techniques, in combination with molecular, functional genomic, and metagenomic studies of bacteria, plants, and even marine invertebrates are aimed at unlocking the world's natural products faster than previous methods of antibiotic discovery. There are currently only few viable remedies, potential preventive techniques, and a limited number of antibiotics, thereby necessitating the discovery of innovative medicinal approaches and antimicrobial therapies. MDR is also facilitated by biofilms, which makes infection control more complex. In this review, we have spotlighted comprehensively various aspects of antibiotics viz. overview of antibiotics era, mode of actions of antibiotics, development and mechanisms of antibiotic resistance in bacteria, and future strategies to fight the emerging antimicrobial resistant threat.

Evolutionary Approaches to Combat Antibiotic Resistance: Opportunities and Challenges for Precision Medicine

The rise of antimicrobial resistance (AMR) in bacterial pathogens is acknowledged by the WHO as a major global health crisis. It is estimated that in 2050 annually up to 10 million people will die from infections with drug resistant pathogens if no efficient countermeasures are implemented. Evolution of pathogens lies at the core of this crisis, which enables rapid adaptation to the selective pressures imposed by antimicrobial usage in both medical treatment and agriculture, consequently promoting the spread of resistance genes or alleles in bacterial populations. Approaches developed in the field of Evolutionary Medicine attempt to exploit evolutionary insight into these adaptive processes, with the aim to improve diagnostics and the sustainability of antimicrobial therapy. Here, we review the concept of evolutionary trade-offs in the development of AMR as well as new therapeutic approaches and their impact on host-microbiome-pathogen interactions. We further discuss the possible translation of evolution-informed treatments into clinical practice, considering both the rapid cure of the individual patients and the prevention of AMR.

Dynamical footprints enable detection of disease emergence

Developing methods for anticipating the emergence or reemergence of infectious diseases is both important and timely; however, traditional model-based approaches are stymied by uncertainty surrounding the underlying drivers. Here, we demonstrate an operational, mechanism-agnostic detection algorithm for disease (re-)emergence based on early warning signals (EWSs) derived from the theory of critical slowing down. Specifically, we used computer simulations to train a supervised learning algorithm to detect the dynamical footprints of (re-)emergence present in epidemiological data. Our algorithm was then challenged to forecast the slowly manifesting, spatially replicated reemergence of mumps in England in the mid-2000s and pertussis post-1980 in the United States. Our method successfully anticipated mumps reemergence 4 years in advance, during which time mitigation efforts could have been implemented. From 1980 onwards, our model identified resurgent states with increasing accuracy, leading to reliable classification starting in 1992. Additionally, we successfully applied the detection algorithm to 2 vector-transmitted case studies, namely, outbreaks of dengue serotypes in Puerto Rico and a rapidly unfolding outbreak of plague in 2017 in Madagascar. Taken together, these findings illustrate the power of theoretically informed machine learning techniques to develop early warning systems for the (re-)emergence of infectious diseases.

Mosquitoes as a feasible sentinel group for anti-malarial resistance surveillance by Next Generation Sequencing of Plasmodium falciparum

Background

Plasmodium falciparum drug resistance surveillance is key to successful disease control and eradication. Contemporary methods that only allow determination of prevalence of resistance are expensive, time consuming and require ethical considerations. A newer method involving Next Generation Sequencing (NGS) permits obtaining frequency of resistance while allowing to detect minority variants in mixed infections. Here, NGS was tested for P. falciparum resistance marker detection in mosquito samples as a feasible and suitable alternative for molecular resistance surveillance. Anopheles funestus were collected in southern Mozambique using CDC light traps and manual collections. DNA was extracted from either whole mosquito, head-thorax and abdomen separately or pools of five mosquitoes. These samples were screened for P. falciparum and if positive for k13, pfcrt, pfmdr1, pfdhps and pfdhfr mutations related to anti-malarial drug resistance with Sanger sequencing and NGS.

Results

Among the 846 samples screened for P. falciparum, 122 were positive by 18S ssrDNA qPCR with an infection rate of 23.6%. No mutations were observed for k13 and pfcrt72-76 and almost zero for pfmdr86, but quintuple pfdhfr/pfdhps mutations were near fixation and about half of the isolates contained the pfmdr184F polymorphism. Similar allele frequencies of resistance markers were estimated with NGS in comparison with the prevalence of markers obtained with the gold standard Sanger sequencing.

Conclusions

Pooled deep sequencing of P. falciparum isolates extracted from mosquitoes is a promising, efficient and cost-effective method to quantify allele frequencies at population level which allows to detect known and unknown markers of resistance in single and mixed infections in a timelier manner. Using mosquitoes as sentinel group and focusing on allele frequency opposed to prevalence, permits active surveillance across a more homogeneous geographical range.

Pathology's historic 2019 incoming residents: Why "the internationalization of pathology" may markedly advance transfusion medicine and cellular therapeutics

OBJECTIVES

This study had two objectives: (1) to determine if, in the United States of America (US), the proportion of non-US citizen international medical graduates (non-US IMGs) entering pathology residencies had increased (again) in 2019 and (2) to assess how this multi-year trend might impact transfusion medicine in the US.

METHODS

The most recent (2019) "National Resident Matching Program" (NRMP) data were analyzed. To assess potential future impact, using controversies related to Plasmodium falciparum (Pf) malaria, conflicting US and non-US perspectives were reviewed. Differences between published US and non-US views were identified regarding, for example, the value of Pf-resistant ("variant") red blood cells (RBCs) and exchange transfusions.

RESULTS

Year 2019 is the first year non-US IMGs were the largest group to fill residency-training positions for a major US specialty via the "Main Residency Match." Also notable, US and non-US views were found to differ markedly regarding (1) the value and safety of Pf-resistant RBC variants and exchange transfusions, and (2) the threat of drug-resistant Pf-malaria parasites. Non-US clinicians and researchers seem more concerned about Pf-malaria, and their interest in cellular therapies seems greater and more optimistic.

CONCLUSIONS

In 2019, the historically high proportion of non-US IMGs among incoming pathology residents dramatically highlights the steady demographic shift that began years ago: "the internationalization of pathology" in the US. Fortunately, a review of publications related to exchange transfusion, Pf-malaria, and variant RBCs suggests non-US IMGs may markedly promote and advance cell therapies such as therapeutically-rational exchange (T-REX) of disease-resistant RBCs.

Implantable antimicrobial biomaterials for local drug delivery in bone infection models

Increased use of implantable biomedical devices demonstrates their potential in treating a wide variety of ailments and disorders in bone trauma and orthopaedic, reconstructive, and craniofacial applications. However, the number of cases involving implant failure or malfunction due to bacterial infection have also increased in recent years. Implanted devices can facilitate the growth of bacteria as these micro-organisms have the potential to adhere to the implant and grow and develop to form biofilms. In an effort to better understand and mitigate these occurrences, biomaterials containing antimicrobial agents that can be released or presented within the local microenvironment have become an important area of research. In this review, we discuss critical factors that regulate antimicrobial therapy to sites of bone infection, such as key biomolecular considerations and platforms for delivery, as well as current in vivo models and current advances in the field. STATEMENT OF SIGNIFICANCE: This review outlines the important factors that are taken into consideration for the development of biomaterials for local delivery of therapeutics to the site of bone infections. An overview of important criteria for development of this model (such as type of bone defect, antimicrobial therapeutic, and delivery vehicle) are provided, along with current research that utilizes these considerations. Additionally, this review highlights recent clinical trials that have utilized antimicrobial therapeutics for treatment of osteomyelitis.

The Kendrick modelling platform: language abstractions and tools for epidemiology

Background

Mathematical and computational models are widely used to study the transmission, pathogenicity, and propagation of infectious diseases. Unfortunately, complex mathematical models are difficult to define, reuse and reproduce because they are composed of several concerns that are intertwined. The problem is even worse for computational models because the epidemiological concerns are also intertwined with low-level implementation details that are not easily accessible to non-computing scientists. Our goal is to make compartmental epidemiological models easier to define, reuse and reproduce by facilitating implementation of different simulation approaches with only very little programming knowledge.

Results

We achieve our goal through the definition of a domain-specific language (DSL), Kendrick, that relies on a very general mathematical definition of epidemiological concerns as stochastic automata that are combined using tensor-algebra operators. A very large class of epidemiological concerns, including multi-species, spatial concerns, control policies, sex or age structures, are supported and can be defined independently of each other and combined into models to be simulated by different methods. Implementing models does not require sophisticated programming skills any more. The various concerns involved within a model can be changed independently of the others as well as reused within other models. They are not plagued by low-level implementation details.

Conclusions

Kendrick is one of the few DSLs for epidemiological modelling that does not burden its users with implementation details or required sophisticated programming skills. It is also currently the only language for epidemiology modelling that supports modularity through clear separation of concerns hence fostering reproducibility and reuse of models and simulations. Future work includes extending Kendrick to support non-compartmental models and improving its interoperability with existing complementary tools.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2843-0) contains supplementary material, which is available to authorized users.

Evolutionary epidemiology models to predict the dynamics of antibiotic resistance

The evolution of resistance to antibiotics is a major public health problem and an example of rapid adaptation under natural selection by antibiotics. The dynamics of antibiotic resistance within and between hosts can be understood in the light of mathematical models that describe the epidemiology and evolution of the bacterial population. "Between-host" models describe the spread of resistance in the host community, and in more specific settings such as hospitalized hosts (treated by antibiotics at a high rate), or farm animals. These models make predictions on the best strategies to limit the spread of resistance, such as reducing transmission or adapting the prescription of several antibiotics. Models can be fitted to epidemiological data in the context of intensive care units or hospitals to predict the impact of interventions on resistance. It has proven harder to explain the dynamics of resistance in the community at large, in particular because models often do not reproduce the observed coexistence of drug-sensitive and drug-resistant strains. "Within-host" models describe the evolution of resistance within the treated host. They show that the risk of resistance emergence is maximal at an intermediate antibiotic dose, and some models successfully explain experimental data. New models that include the complex host population structure, the interaction between resistance-determining loci and other loci, or integrating the within- and between-host levels will allow better interpretation of epidemiological and genomic data from common pathogens and better prediction of the evolution of resistance.

Why the evolution of vaccine resistance is less of a concern than the evolution of drug resistance

Vaccines and antimicrobial drugs both impose strong selection for resistance. Yet only drug resistance is a major challenge for 21st century medicine. Why is drug resistance ubiquitous and not vaccine resistance? Part of the answer is that vaccine resistance is far less likely to evolve than drug resistance. But what happens when vaccine resistance does evolve? We review six putative cases. We find that in contrast to drug resistance, vaccine resistance is harder to detect and harder to confirm and that the mechanistic basis is less well understood. Nevertheless, in the cases we examined, the pronounced health benefits associated with vaccination have largely been sustained. Thus, we contend that vaccine resistance is less of a concern than drug resistance because it is less likely to evolve and when it does, it is less harmful to human and animal health and well-being. Studies of pathogen strains that evolve the capacity to replicate and transmit from vaccinated hosts will enhance our ability to develop next-generation vaccines that minimize the risk of harmful pathogen evolution.

Putting evolution in elimination: Winning our ongoing battle with evolving malaria mosquitoes and parasites

Since 2000, the world has made significant progress in reducing malaria morbidity and mortality, and several countries in Africa, South America and South-East Asia are working hard to eliminate the disease. These elimination efforts continue to rely heavily on antimalarial drugs and insecticide-based interventions, which remain the cornerstones of malaria treatment and prevention. However, resistance has emerged against nearly every antimalarial drug and insecticide that is available. In this review we discuss the evolutionary consequences of the way we currently implement antimalarial interventions, which is leading to resistance and may ultimately lead to control failure, but also how evolutionary principles can be applied to extend the lifespan of current and novel interventions. A greater understanding of the general evolutionary principles that are at the core of emerging resistance is urgently needed if we are to develop improved resistance management strategies with the ultimate goal to achieve a malaria-free world.

The importance of Evolutionary Medicine in developing countries: A case for Pakistan's medical schools

Evolutionary Medicine (EM) is a fundamental science exploring why our bodies are plagued with disease and hindered by limitations. EM views the body as an assortment of benefits, mistakes, and compromises molded over millennia. It highlights the role of evolution in numerous diseases encountered in community and family medicine clinics of developing countries. It enables us to ask informed questions and develop novel responses to global health problems. An understanding of the field is thus crucial for budding doctors, but its study is currently limited to a handful of medical schools in high-income countries. For the developing world, Pakistan's medical schools may be excellent starting posts as the country is beset with communicable and non-communicable diseases that are shaped by evolution. Remarkably, Pakistani medical students are open to studying and incorporating EM into their training. Understanding the principles of EM could empower them to tackle growing health problems in the country. Additionally, some difficulties that western medical schools face in integrating EM into their curriculum may not be a hindrance in Pakistan. We propose solutions for the remaining challenges, including obstinate religious sentiments. Herein, we make the case that incorporating EM is particularly important in developing countries such as Pakistan and that it is achievable in its medical student body.

A systematic review of differential rate of use of the word “evolve” across fields

Background

Although evolution is the driving force behind many of today’s major public health and agriculture issues, both journalists and scientific researchers often do not use the term “evolve” in discussions of these topics.

Methods

In a total of 1,066 articles and 716 papers selected from 25 US newspapers and 34 scientific journals, we assess usage of the word “evolve” and its substitute words in the contexts of cancer tumor drug resistance, HIV drug resistance, mosquito insecticide resistance, and weed pesticide resistance.

Results

We find significant differences in the use of “evolve” among fields and sources. “Evolve” is used most when discussing weed pesticide resistance (25.9% in newspapers, 52.4% in journals) and least when discussing cancer tumor drug resistance (3.9% in newspapers, 9.8% in journals). On average, scientific journals use “evolve” more often (22.2%) than newspapers (7.8%). Different types of journals (general science, general clinical, cancer specific, and drug resistance specific) show significantly different “evolve” usages when discussing cancer tumor drug resistance.

Discussion

We examine potential explanations of these findings, such as the relatively recent framing of cancer in evolutionary terms, before looking at consequences of low “evolve” usage and of differential “evolve” usage across fields. Use of the word “evolve” may not reflect current understanding of the problems we examine. However, given that our ability to tackle resistance issues relies upon accurate understandings of what causes and exacerbates resistance, use of the word “evolve” when called for may help us confront these issues in the future.

Antibiotic Cycling and Antibiotic Mixing: Which One Best Mitigates Antibiotic Resistance?

Can we exploit our burgeoning understanding of molecular evolution to slow the progress of drug resistance? One role of an infection clinician is exactly that: to foresee trajectories to resistance during antibiotic treatment and to hinder that evolutionary course. But can this be done at a hospital-wide scale? Clinicians and theoreticians tried to when they proposed two conflicting behavioral strategies that are expected to curb resistance evolution in the clinic, these are known as “antibiotic cycling” and “antibiotic mixing.” However, the accumulated data from clinical trials, now approaching 4 million patient days of treatment, is too variable for cycling or mixing to be deemed successful. The former implements the restriction and prioritization of different antibiotics at different times in hospitals in a manner said to “cycle” between them. In antibiotic mixing, appropriate antibiotics are allocated to patients but randomly. Mixing results in no correlation, in time or across patients, in the drugs used for treatment which is why theorists saw this as an optimal behavioral strategy. So while cycling and mixing were proposed as ways of controlling evolution, we show there is good reason why clinical datasets cannot choose between them: by re-examining the theoretical literature we show prior support for the theoretical optimality of mixing was misplaced. Our analysis is consistent with a pattern emerging in data: neither cycling or mixing is a priori better than the other at mitigating selection for antibiotic resistance in the clinic.

Key words: antibiotic cycling, antibiotic mixing, optimal control, stochastic models.

Introducing New Antimalarial Analogues of Chloroquine and Amodiaquine: A Narrative Review

Antimalarial drugs with the 4-aminoquinoline scaffold such as the important drugs, chloroquine (CQ) and amodiaquine (AQ), have been used to prevent and treat malaria for many years. The importance of these drugs is related to their simple usage, high efficacy, affordability, and cost-effectiveness of their synthesis. In recent years, with the spread of parasite resistance to CQ and cross-resistance to its other analogues have decreased their consumption in many geographical areas. On the other hand, AQ is an effective antimalarial drug which its usage has been restricted due to hepatic and hematological toxicities. The significance of the quinoline ring at quinoline-based antimalarial drugs has prompted research centers and pharmaceutical companies to focus on the design and synthesis of new analogues of these drugs, especially CQ and AQ analogues. Accordingly, various derivatives have been synthesized and evaluated in vitro and in vivo against the resistant strains of the malaria parasite to solve the problem of drug resistance. Also, the pharmacokinetic properties of these compounds have been evaluated to augment their efficacy and diminish their toxicity. Some of these analogues are currently in clinical and preclinical development. Consequently, the recent researches showed yet 4-aminoquinoline scaffold is active moiety in new compounds with antiplasmodial activity. Hence, the aim of this review article is to introduce of the novel synthetic analogues of CQ and AQ, which may constitute the next generation of antimalarial drugs with the 4-aminoquinoline scaffold.

A combined within-host and between-hosts modelling framework for the evolution of resistance to antimalarial drugs

The spread of drug resistance represents a significant challenge to many disease control efforts. The evolution of resistance is a complex process influenced by transmission dynamics between hosts as well as infection dynamics within these hosts. This study aims to investigate how these two processes combine to impact the evolution of resistance in malaria parasites. We introduce a stochastic modelling framework combining an epidemiological model of Plasmodium transmission and an explicit within-human infection model for two competing strains. Immunity, treatment and resistance costs are included in the within-host model. We show that the spread of resistance is generally less likely in areas of intense transmission, and therefore of increased competition between strains, an effect exacerbated when costs of resistance are higher. We also illustrate how treatment influences the spread of resistance, with a trade-off between slowing resistance and curbing disease incidence. We show that treatment coverage has a stronger impact on disease prevalence, whereas treatment efficacy primarily affects resistance spread, suggesting that coverage should constitute the primary focus of control efforts. Finally, we illustrate the importance of feedbacks between modelling scales. Overall, our results underline the importance of concomitantly modelling the evolution of resistance within and between hosts.

How Often Are Antibiotic-Resistant Bacteria Said to "Evolve" in the News?

Media plays an important role in informing the general public about scientific ideas. We examine whether the word "evolve," sometimes considered controversial by the general public, is frequently used in the popular press. Specifically, we ask how often articles discussing antibiotic resistance use the word "evolve" (or its lexemes) as opposed to alternative terms such as "emerge" or "develop." We chose the topic of antibiotic resistance because it is a medically important issue; bacterial evolution is a central player in human morbidity and mortality. We focused on the most widely-distributed newspapers written in English in the United States, United Kingdom, Canada, India, and Australia. We examined all articles that focused primarily on the evolution of antibiotic resistance, were published in 2014 or earlier, and were accessible in online archives, for a total of 1639 articles. The total years examined per newspaper ranged from 5 to 37 years with a median of 27 years, and the overall range was 1978-2014. We quantified how many articles included the term "evolve" and analyzed how this varied with newspaper, country, and time. We found that an overall rate of 18% of articles used the term "evolve" but with significant variation among countries. Newspapers in the United Kingdom had the highest rate (24%), more than double of those in India (9%), the country with the lowest rate. These frequencies were lower than those found in scientific papers from both evolutionary journals and biomedical journals. There were no statistically significant changes in frequency and no trends when "evolve" usage was compared against variables such as newspaper circulation, liberal/conservative bias, time, and state evolution acceptance in U.S. newspapers. This study highlights the globally low usage of the word "evolve" in the popular press. We suggest this low usage may affect public understanding and acceptance of evolutionary concepts.

Adaptive Landscape by Environment Interactions Dictate Evolutionary Dynamics in Models of Drug Resistance

The adaptive landscape analogy has found practical use in recent years, as many have explored how their understanding can inform therapeutic strategies that subvert the evolution of drug resistance. A major barrier to applications of these concepts is a lack of detail concerning how the environment affects adaptive landscape topography, and consequently, the outcome of drug treatment. Here we combine empirical data, evolutionary theory, and computer simulations towards dissecting adaptive landscape by environment interactions for the evolution of drug resistance in two dimensions-drug concentration and drug type. We do so by studying the resistance mediated by Plasmodium falciparum dihydrofolate reductase (DHFR) to two related inhibitors-pyrimethamine and cycloguanil-across a breadth of drug concentrations. We first examine whether the adaptive landscapes for the two drugs are consistent with common definitions of cross-resistance. We then reconstruct all accessible pathways across the landscape, observing how their structure changes with drug environment. We offer a mechanism for non-linearity in the topography of accessible pathways by calculating of the interaction between mutation effects and drug environment, which reveals rampant patterns of epistasis. We then simulate evolution in several different drug environments to observe how these individual mutation effects (and patterns of epistasis) influence paths taken at evolutionary "forks in the road" that dictate adaptive dynamics in silico. In doing so, we reveal how classic metrics like the IC50 and minimal inhibitory concentration (MIC) are dubious proxies for understanding how evolution will occur across drug environments. We also consider how the findings reveal ambiguities in the cross-resistance concept, as subtle differences in adaptive landscape topography between otherwise equivalent drugs can drive drastically different evolutionary outcomes. Summarizing, we discuss the results with regards to their basic contribution to the study of empirical adaptive landscapes, and in terms of how they inform new models for the evolution of drug resistance.

The population genetics of drug resistance evolution in natural populations of viral, bacterial and eukaryotic pathogens

Drug resistance is a costly consequence of pathogen evolution and a major concern in public health. In this review, we show how population genetics can be used to study the evolution of drug resistance and also how drug resistance evolution is informative as an evolutionary model system. We highlight five examples from diverse organisms with particular focus on: (i) identifying drug resistance loci in the malaria parasite Plasmodium falciparum using the genomic signatures of selective sweeps, (ii) determining the role of epistasis in drug resistance evolution in influenza, (iii) quantifying the role of standing genetic variation in the evolution of drug resistance in HIV, (iv) using drug resistance mutations to study clonal interference dynamics in tuberculosis and (v) analysing the population structure of the core and accessory genome of Staphylococcus aureus to understand the spread of methicillin resistance. Throughout this review, we discuss the uses of sequence data and population genetic theory in studying the evolution of drug resistance.

How competition governs whether moderate or aggressive treatment minimizes antibiotic resistance

Understanding how our use of antimicrobial drugs shapes future levels of drug resistance is crucial. Recently, there has been debate over whether an aggressive (i.e., high dose) or more moderate (i.e., lower dose) treatment of individuals will most limit the emergence and spread of resistant bacteria. In this study, we demonstrate how one can understand and resolve these apparently contradictory conclusions. We show that a key determinant of which treatment strategy will perform best at the individual level is the extent of effective competition between resistant and sensitive pathogens within a host. We extend our analysis to the community level, exploring the spectrum between strict inter-strain competition and strain independence. From this perspective as well, we find that the magnitude of effective competition between resistant and sensitive strains determines whether an aggressive approach or moderate approach minimizes the burden of resistance in the population.

DOI:http://dx.doi.org/10.7554/eLife.10559.001

Antibiotics are chemical compounds used to treat bacterial infections. The discovery of antibiotics, starting with penicillin in 1929, revolutionized medicine, making it possible to cure or prevent life-threatening infections such as tetanus and pneumonia. However, many bacteria have become resistant to one or more antibiotics and so can no longer be killed by these drugs.

The emergence of antibiotic resistance reflects an evolutionary process that occurs during antibiotic treatment. While the antibiotic will kill most bacteria, some bacteria may naturally have a feature or genetic mutation that allows them to survive in the presence of the antibiotic. These bacteria then reproduce and pass on their resistant traits, eventually leading to the emergence of a new antibiotic-resistant strain of bacteria. Once a resistant strain exists it may be able to spread from one person to another.

There is conflicting evidence about how best to prevent antibiotic-resistant bacteria from evolving and spreading. The results of some experiments suggest that treating bacteria with large doses of antibiotics early in an infection is the most effective way to optimize treatment and minimize the risk of an antibiotic-resistant strain developing. However, other studies suggest that exposing bacteria to high levels of antibiotics more efficiently selects for resistance; in this case a more moderate approach should be used when treating bacterial infections.

Here, Colijn and Cohen present a mathematical model that suggests that the natural competition between the antibiotic-resistant and antibiotic-sensitive strains of bacteria influence which treatment strategy should be taken. Strains were modeled both within individual hosts and spreading in a community of individuals. In the models, aggressive antibiotic treatment is most effective (in that it minimizes antibiotic resistance) when the antibiotic-resistant strain either does not experience strong competition from the non-resistant strains of bacteria or is not fit enough to be a good competitor. However, a more moderate treatment is appropriate when the two strains are competing and the antibiotic-resistant strain is a fit competitor. Competition may mean that moderate treatment is best to avoid resistance at the community level, even in situations when aggressive treatment is likely best for individuals.

Two important future challenges are to better understand the diversity of strains in bacterial infections, and to develop tools to measure to what extent strains are effectively competing with each other.

DOI:http://dx.doi.org/10.7554/eLife.10559.002

Therapeutic strategies to combat antibiotic resistance

With multidrug resistant bacteria on the rise, new antibiotic approaches are required. Although a number of new small molecule antibiotics are currently in the development pipeline with many more in preclinical development, the clinical options and practices for infection control must be expanded. Biologics and non-antibiotic adjuvants offer this opportunity for expansion. Nevertheless, to avoid known mechanisms of resistance, intelligent combination approaches for multiple simultaneous and complimentary therapies must be designed. Combination approaches should extend beyond biologically active molecules to include smart controlled delivery strategies. Infection control must integrate antimicrobial stewardship, new antibiotic molecules, biologics, and delivery strategies into effective combination therapies designed to 1) fight the infection, 2) avoid resistance, and 3) protect the natural microbiome. This review explores these developing strategies in the context of circumventing current mechanisms of resistance.

Applying evolutionary biology to address global challenges

Two categories of evolutionary challenges result from escalating human impacts on the planet. The first arises from cancers, pathogens, and pests that evolve too quickly and the second, from the inability of many valued species to adapt quickly enough. Applied evolutionary biology provides a suite of strategies to address these global challenges that threaten human health, food security, and biodiversity. This Review highlights both progress and gaps in genetic, developmental, and environmental manipulations across the life sciences that either target the rate and direction of evolution or reduce the mismatch between organisms and human-altered environments. Increased development and application of these underused tools will be vital in meeting current and future targets for sustainable development.

Expanding the eco-evolutionary context of herbicide resistance research

The potential for human-driven evolution in economically and environmentally important organisms in medicine, agriculture and conservation management is now widely recognised. The evolution of herbicide resistance in weeds is a classic example of rapid adaptation in the face of human-mediated selection. Management strategies that aim to slow or prevent the evolution of herbicide resistance must be informed by an understanding of the ecological and evolutionary factors that drive selection in weed populations. Here, we argue for a greater focus on the ultimate causes of selection for resistance in herbicide resistance studies. The emerging fields of eco-evolutionary dynamics and applied evolutionary biology offer a means to achieve this goal and to consider herbicide resistance in a broader and sometimes novel context. Four relevant research questions are presented, which examine (i) the impact of herbicide dose on selection for resistance, (ii) plant fitness in herbicide resistance studies, (iii) the efficacy of herbicide rotations and mixtures and (iv) the impacts of gene flow on resistance evolution and spread. In all cases, fundamental ecology and evolution have the potential to offer new insights into herbicide resistance evolution and management.

Amphiphilic dendritic derivatives as nanocarriers for the targeted delivery of antimalarial drugs

It can be foreseen that in a future scenario of malaria eradication, a varied armamentarium will be required, including strategies for the targeted administration of antimalarial compounds. The development of nanovectors capable of encapsulating drugs and of delivering them to Plasmodium-infected cells with high specificity and efficacy and at an affordable cost is of particular interest. With this objective, dendritic derivatives based on 2,2-bis(hydroxymethyl)propionic acid (bis-MPA) and Pluronic(®) polymers have been herein explored. Four different dendritic derivatives have been tested for their capacity to encapsulate the antimalarial drugs chloroquine (CQ) and primaquine (PQ), their specific targeting to Plasmodium-infected red blood cells (pRBCs), and their antimalarial activity in vitro against the human pathogen Plasmodium falciparum and in vivo against the rodent malaria species Plasmodium yoelii. The results obtained have allowed the identification of two dendritic derivatives exhibiting specific targeting to pRBCs vs. non-infected RBCs, which reduce the in vitro IC50 of CQ and PQ by ca. 3- and 4-fold down to 4.0 nm and 1.1 μm, respectively. This work on the application of dendritic derivatives to antimalarial targeted drug delivery opens the way for the use of this new type of chemicals in future malaria eradication programs.

Exposure to phages has little impact on the evolution of bacterial antibiotic resistance on drug concentration gradients

The use of phages for treating bacterial pathogens has recently been advocated as an alternative to antibiotic therapy. Here, we test a hypothesis that bacteria treated with phages may show more limited evolution of antibiotic resistance as the fitness costs of resistance to phages may add to those of antibiotic resistance, further reducing the growth performance of antibiotic-resistant bacteria. We did this by studying the evolution of phage-exposed and phage-free Pseudomonas fluorescens cultures on concentration gradients of single drugs, including cefotaxime, chloramphenicol, and kanamycin. During drug treatment, the level of bacterial antibiotic resistance increased through time and was not affected by the phage treatment. Exposure to phages did not cause slower growth in antibiotic-resistant bacteria, although it did so in antibiotic-susceptible bacteria. We observed significant reversion of antibiotic resistance after drug use being terminated, and the rate of reversion was not affected by the phage treatment. The results suggest that the fitness costs caused by resistance to phages are unlikely to be an important constraint on the evolution of bacterial antibiotic resistance in heterogeneous drug environments. Further studies are needed for the interaction of fitness costs of antibiotic resistance with other factors.

Drug resistance in vectorborne parasites: multiple actors and scenarios for an evolutionary arms race

Drug-resistant pathogens emerge faster than new drugs come out of drug discovery pipelines. Current and future drug options should therefore be better protected, requiring a clear understanding of the factors that contribute to the natural history of drug resistance. Although many of these factors are relatively well understood for most bacteria, this proves to be more complex for vectorborne parasites. In this review, we discuss considering three key models (Plasmodium, Leishmania and Schistosoma) how drug resistance can emerge, spread and persist. We demonstrate a multiplicity of scenarios, clearly resulting from the biological diversity of the different organisms, but also from the different modes of action of the drugs used, the specific within- and between-host ecology of the parasites, and environmental factors that may have direct or indirect effects. We conclude that integrated control of drug-resistant vectorborne parasites is not dependent upon chemotherapy only, but also requires a better insight into the ecology of these parasites and how their transmission can be impaired.

HIV Drug Resistance: Problems and Perspectives

Access to combination antiretroviral treatment (ART) has improved greatly over recent years. At the end of 2011, more than eight million HIV-infected people were receiving ART in low-income and middle-income countries. ART generally works well in keeping the virus suppressed and the patient healthy. However, treatment only works as long as the virus is not resistant against the drugs used. In the last decades, HIV treatments have become better and better at slowing down the evolution of drug resistance, so that some patients are treated for many years without having any resistance problems. However, for some patients, especially in low-income countries, drug resistance is still a serious threat to their health. This essay will review what is known about transmitted and acquired drug resistance, multi-class drug resistance, resistance to newer drugs, resistance due to treatment for the prevention of mother-to-child transmission, the role of minority variants (low-frequency drug-resistance mutations), and resistance due to pre-exposure prophylaxis.

When the most potent combination of antibiotics selects for the greatest bacterial load: the smile-frown transition

Conventional wisdom holds that the best way to treat infection with antibiotics is to 'hit early and hit hard'. A favoured strategy is to deploy two antibiotics that produce a stronger effect in combination than if either drug were used alone. But are such synergistic combinations necessarily optimal? We combine mathematical modelling, evolution experiments, whole genome sequencing and genetic manipulation of a resistance mechanism to demonstrate that deploying synergistic antibiotics can, in practice, be the worst strategy if bacterial clearance is not achieved after the first treatment phase. As treatment proceeds, it is only to be expected that the strength of antibiotic synergy will diminish as the frequency of drug-resistant bacteria increases. Indeed, antibiotic efficacy decays exponentially in our five-day evolution experiments. However, as the theory of competitive release predicts, drug-resistant bacteria replicate fastest when their drug-susceptible competitors are eliminated by overly-aggressive treatment. Here, synergy exerts such strong selection for resistance that an antagonism consistently emerges by day 1 and the initially most aggressive treatment produces the greatest bacterial load, a fortiori greater than if just one drug were given. Whole genome sequencing reveals that such rapid evolution is the result of the amplification of a genomic region containing four drug-resistance mechanisms, including the acrAB efflux operon. When this operon is deleted in genetically manipulated mutants and the evolution experiment repeated, antagonism fails to emerge in five days and antibiotic synergy is maintained for longer. We therefore conclude that unless super-inhibitory doses are achieved and maintained until the pathogen is successfully cleared, synergistic antibiotics can have the opposite effect to that intended by helping to increase pathogen load where, and when, the drugs are found at sub-inhibitory concentrations.

First steps in experimental cancer evolution

Evolutionary processes play a central role in the development, progression and response to treatment of cancers. The current challenge facing researchers is to harness evolutionary theory to further our understanding of the clinical progression of cancers. Central to this endeavour will be the development of experimental systems and approaches by which theories of cancer evolution can be effectively tested. We argue here that the experimental evolution approach - whereby evolution is observed in real time and which has typically employed microorganisms - can be usefully applied to cancer. This approach allows us to disentangle the ecological causes of natural selection, identify the genetic basis of evolutionary changes and determine their repeatability. Cell cultures used in cancer research share many of the desirable traits that make microorganisms ideal for studying evolution. As such, experimental cancer evolution is feasible and likely to give great insight into the selective pressures driving the evolution of clinically destructive cancer traits. We highlight three areas of evolutionary theory with importance to cancer biology that are amenable to experimental evolution: drug resistance, social evolution and resource competition. Understanding the diversity, persistence and evolution of cancers is vital for treatment and drug development, and an experimental evolution approach could provide strategic directions and focus for future research.

Enhanced transmission of drug-resistant parasites to mosquitoes following drug treatment in rodent malaria

The evolution of drug resistant Plasmodium parasites is a major challenge to effective malaria control. In theory, competitive interactions between sensitive parasites and resistant parasites within infections are a major determinant of the rate at which parasite evolution undermines drug efficacy. Competitive suppression of resistant parasites in untreated hosts slows the spread of resistance; competitive release following treatment enhances it. Here we report that for the murine model Plasmodium chabaudi, co-infection with drug-sensitive parasites can prevent the transmission of initially rare resistant parasites to mosquitoes. Removal of drug-sensitive parasites following chemotherapy enabled resistant parasites to transmit to mosquitoes as successfully as sensitive parasites in the absence of treatment. We also show that the genetic composition of gametocyte populations in host venous blood accurately reflects the genetic composition of gametocytes taken up by mosquitoes. Our data demonstrate that, at least for this mouse model, aggressive chemotherapy leads to very effective transmission of highly resistant parasites that are present in an infection, the very parasites which undermine the long term efficacy of front-line drugs.

Plasticity in parasite phenotypes: evolutionary and ecological implications for disease

Preventing disease is a major goal of applied bioscience and explaining variation in the harm caused by parasites, and their infectiousness, are major goals of evolutionary biology. The emerging field of evolutionary medicine integrates these two ambitions to inform the development of control strategies that retard or withstand unfavorable parasite evolution. However, as parasites live in hostile and changeable environments – the bodies of other organisms – the success of integrating evolutionary biology with medicine requires a better understanding of how natural selection has solved the problems parasites face. There is increasing appreciation that natural selection shapes parasite strategies to survive in the host and transmit between hosts through facultative (plastic) shifts in parasite traits expressed during infections and in different hosts. This article describes how integrating parasite plasticity into biomedical thinking is central to explaining disease outcomes and transmission patterns, as well as predicting the success of control measures.

Causes of variation in malaria infection dynamics: insights from theory and data

Parasite strategies for exploiting host resources are key determinants of disease severity (i.e., virulence) and infectiousness (i.e., transmission between hosts). By iterating the development of theory and empirical tests, we investigated whether variation in parasite traits across two genetically distinct clones of the rodent malaria parasite, Plasmodium chabaudi, explains differences in within-host infection dynamics and virulence. First, we experimentally tested key predictions of our earlier modeling work. As predicted, the more virulent genotype produced more progeny parasites per infected cell (burst size), but in contrast to predictions, invasion rates of red blood cells (RBCs) did not differ between the genotypes studied. Second, we further developed theory by confronting our earlier model with these new data, testing a new set of models that incorporate more biological realism, and developing novel theoretical tools for identifying differences between parasite genotypes. Overall, we found robust evidence that differences in burst sizes contribute to variation in dynamics and that differential interactions between parasites and host immune responses also play a role. In contrast to previous work, our model predicts that RBC age structure is not important for explaining dynamics. Integrating theory and empirical tests is a potentially powerful way of progressing understanding of disease biology.

Evolutionary approaches to sexually transmitted infections

Evolutionary approaches are particularly valuable for studies of sexually transmitted diseases. Methods for tracing evolutionary phylogenies have powerful new applications that use genetic data to trace the history of pathogens across millions of years, within outbreaks lasting years, and even within individuals. Equally valuable are less widely appreciated evolutionary methods for analyzing how host-pathogen co-evolution shapes extreme traits whose costs can be substantial. These and other applications of Darwinian medicine will improve understanding and treatment of sexually transmitted diseases.

The fitness of drug-resistant malaria parasites in a rodent model: multiplicity of infection

Malaria infections normally consist of more than one clonally replicating lineage. Within-host interactions between sensitive and resistant parasites can have profound effects on the evolution of drug resistance. Here, using the Plasmodium chabaudi mouse malaria model, we ask whether the costs and benefits of resistance are affected by the number of co-infecting strains competing with a resistant clone. We found strong competitive suppression of resistant parasites in untreated infections and marked competitive release following treatment. The magnitude of competitive suppression depended on competitor identity. However, there was no overall effect of the diversity of susceptible parasites on the extent of competitive suppression or release. If these findings generalize, then transmission intensity will impact on resistance evolution because of its effect on the frequency of mixed infections, not because of its effect on the distribution of clones per host. This would greatly simplify the computational problems of adequately capturing within-host ecology in models of drug resistance evolution in malaria.

The evolution of drug resistance and the curious orthodoxy of aggressive chemotherapy

The evolution of drug-resistant pathogens is a major challenge for 21st century medicine. Drug use practices vigorously advocated as resistance management tools by professional bodies, public health agencies, and medical schools represent some of humankind's largest attempts to manage evolution. It is our contention that these practices have poor theoretical and empirical justification for a broad spectrum of diseases. For instance, rapid elimination of pathogens can reduce the probability that de novo resistance mutations occur. This idea often motivates the medical orthodoxy that patients should complete drug courses even when they no longer feel sick. Yet "radical pathogen cure" maximizes the evolutionary advantage of any resistant pathogens that are present. It could promote the very evolution it is intended to retard. The guiding principle should be to impose no more selection than is absolutely necessary. We illustrate these arguments in the context of malaria; they likely apply to a wide range of infections as well as cancer and public health insecticides. Intuition is unreliable even in simple evolutionary contexts; in a social milieu where in-host competition can radically alter the fitness costs and benefits of resistance, expert opinion will be insufficient. An evidence-based approach to resistance management is required.

Population biological principles of drug-resistance evolution in infectious diseases

The emergence of resistant pathogens in response to selection pressure by drugs and their possible disappearance when drug use is discontinued are evolutionary processes common to many pathogens. Population biological models have been used to study the dynamics of resistance in viruses, bacteria, and eukaryotic microparasites both at the level of the individual treated host and of the treated host population. Despite the existence of generic features that underlie such evolutionary dynamics, different conclusions have been reached about the key factors affecting the rate of resistance evolution and how to best use drugs to minimise the risk of generating high levels of resistance. Improved understanding of generic versus specific population biological aspects will help to translate results between different studies, and allow development of a more rational basis for sustainable drug use than exists at present.

Transmission intensity and drug resistance in malaria population dynamics: implications for climate change

Although the spread of drug resistance and the influence of climate change on malaria are most often considered separately, these factors have the potential to interact through altered levels of transmission intensity. The influence of transmission intensity on the evolution of drug resistance has been addressed in theoretical studies from a population genetics' perspective; less is known however on how epidemiological dynamics at the population level modulates this influence. We ask from a theoretical perspective, whether population dynamics can explain non-trivial, non-monotonic, patterns of treatment failure with transmission intensity, and, if so, under what conditions. We then address the implications of warmer temperatures in an East African highland, where, as in other similar regions at the altitudinal edge of malaria's distribution, there has been a pronounced increase of cases from the 1970s to the 1990s. Our theoretical analyses, with a transmission model that includes different levels of immunity, demonstrate that an increase in transmission beyond a threshold can lead to a decrease in drug resistance, as previously shown [1], but that a second threshold may occur and lead to the re-establishment of drug resistance. Estimates of the increase in transmission intensity from the 1970s to the 1990s for the Kenyan time series, obtained by fitting the two-stage version of the model with an explicit representation of vector dynamics, suggest that warmer temperatures are likely to have moved the system towards the first threshold, and in so doing, to have promoted the faster spread of drug resistance. Climate change and drug resistance can interact and need not be considered as alternative explanations for trends in disease incidence in this region. Non-monotonic patterns of treatment failure with transmission intensity similar to those described as the ‘valley phenomenon’ for Uganda can result from epidemiological dynamics but under poorly understood assumptions.

Chemotherapy, within-host ecology and the fitness of drug-resistant malaria parasites

A major determinant of the rate at which drug-resistant malaria parasites spread through a population is the ecology of resistant and sensitive parasites sharing the same host. Drug treatment can significantly alter this ecology by removing the drug-sensitive parasites, leading to competitive release of resistant parasites. Here, we test the hypothesis that the spread of resistance can be slowed by reducing drug treatment and hence restricting competitive release. Using the rodent malaria model Plasmodium chabaudi, we found that low-dose chemotherapy did reduce competitive release. A higher drug dose regimen exerted stronger positive selection on resistant parasites for no detectable clinical gain. We estimated instantaneous selection coefficients throughout the course of replicate infections to analyze the temporal pattern of the strength and direction of within-host selection. The strength of selection on resistance varied through the course of infections, even in untreated infections, but increased immediately following drug treatment, particularly in the high-dose groups. Resistance remained under positive selection for much longer than expected from the half life of the drug. Although there are many differences between mice and people, our data do raise the question whether the aggressive treatment regimens aimed at complete parasite clearance are the best resistance-management strategies for humans.

Intensive Farming: Evolutionary Implications for Parasites and Pathogens

An increasing number of scientists have recently raised concerns about the threat posed by human intervention on the evolution of parasites and disease agents. New parasites (including pathogens) keep emerging and parasites which previously were considered to be ‘under control’ are re-emerging, sometimes in highly virulent forms. This re-emergence may be parasite evolution, driven by human activity, including ecological changes related to modern agricultural practices. Intensive farming creates conditions for parasite growth and transmission drastically different from what parasites experience in wild host populations and may therefore alter selection on various traits, such as life-history traits and virulence. Although recent epidemic outbreaks highlight the risks associated with intensive farming practices, most work has focused on reducing the short-term economic losses imposed by parasites, such as application of chemotherapy. Most of the research on parasite evolution has been conducted using laboratory model systems, often unrelated to economically important systems. Here, we review the possible evolutionary consequences of intensive farming by relating current knowledge of the evolution of parasite life-history and virulence with specific conditions experienced by parasites on farms. We show that intensive farming practices are likely to select for fast-growing, early-transmitted, and hence probably more virulent parasites. As an illustration, we consider the case of the fish farming industry, a branch of intensive farming which has dramatically expanded recently and present evidence that supports the idea that intensive farming conditions increase parasite virulence. We suggest that more studies should focus on the impact of intensive farming on parasite evolution in order to build currently lacking, but necessary bridges between academia and decision-makers.

Stress, drugs and the evolution of reproductive restraint in malaria parasites

Life-history theory predicts that sexually reproducing organisms have evolved to resolve resource-allocation trade-offs between growth/survival versus reproduction, and current versus future reproduction. Malaria parasites replicate asexually in their vertebrate hosts, but must reproduce sexually to infect vectors and be transmitted to new hosts. As different specialized stages are required for these functions, the division of resources between these life-history components is a fundamental evolutionary problem. Here, we test how drug-sensitive and drug-resistant isolates of the human malaria parasite Plasmodium falciparum resolve the trade-off between in-host replication and between-host transmission when exposed to treatment with anti-malarial drugs. Previous studies have shown that parasites increase their investment in sexual stages when exposed to stressful conditions, such as drugs. However, we demonstrate that sensitive parasites facultatively decrease their investment in sexual stages when exposed to drugs. In contrast to previous studies, we tested parasites from a region where treatment with anti-malarial drugs is common and transmission is seasonal. We hypothesize that when exposed to drugs, parasites invest in their survival and future transmission by diverting resources from reproduction to replication. Furthermore, as drug-resistant parasites did not adjust their investment when exposed to drugs, we suggest that parasites respond to changes in their proliferation (state) rather the presence of drugs.

Seventy-five years of Resochin in the fight against malaria

The four different forms of human malaria have threatened humanity since time immemorial and to this day, they exact a death toll of one to three million people annually. Synthetic anti-malarial agents have been in development since early 1900. Perhaps the most successful and widely used drug, Resochin (chloroquine), was discovered 75 years ago; for a long time, it was the drug of choice and to this day, it is still used in many regions of the world as a reliable treatment against simpler forms of malaria. In regions where it has not been in use against malaria tropica for quite some time due to the development of resistances, it has regained some of its efficacy. This review traces the discovery and the mechanism of action of this substance, illustrates the significance of malaria today, and underlines the need for controlled and reliable therapeutic measures.