Within-host competition can delay evolution of drug resistance in malaria

Analysis of a diffusive two-strain malaria model with the carrying capacity of the environment for mosquitoes

We propose a malaria model involving the sensitive and resistant strains, which is described by reaction-diffusion equations. The model reflects the scenario that the vector and host populations disperse with distinct diffusion rates, susceptible individuals or vectors cannot be infected by both strains simultaneously, and the vector population satisfies the logistic growth. Our main purpose is to get a threshold type result on the model, especially the interaction effect of the two strains in the presence of spatial structure. To solve this issue, the basic reproduction number (BRN) R 0 i and invasion reproduction number (IRN) R ˆ 0 i of each strain (i = 1 and 2 are for the sensitive and resistant strains, respectively) are defined. Furthermore, we investigate the influence of the diffusion rates of populations and vectors on BRNs and IRNs.

Impact of different mutations on Kelch13 protein levels, ART resistance, and fitness cost in Plasmodium falciparum parasites

Reduced susceptibility to ART, the first-line treatment against malaria, is common in South East Asia (SEA). It is associated with point mutations, mostly in kelch13 (k13) but also in other genes, like ubp1. K13 and its compartment neighbors (KICs), including UBP1, are involved in endocytosis of host cell cytosol. We tested 135 mutations in KICs but none conferred ART resistance. Double mutations of k13C580Y with k13R539T or k13C580Y with ubp1R3138H, did also not increase resistance. In contrast, k13C580Y parasites subjected to consecutive RSAs did, but the k13 sequence was not altered. Using isogenic parasites with different k13 mutations, we found correlations between K13 protein amount, resistance, and fitness cost. Titration of K13 and KIC7 indicated that the cellular levels of these proteins determined resistance through the rate of endocytosis. While fitness cost of k13 mutations correlated with ART resistance, ubp1R3138H caused a disproportionately higher fitness cost.

IMPORTANCE

Parasites with lowered sensitivity to artemisinin-based drugs are becoming widespread. However, even in these "resistant" parasites not all parasites survive treatment. We found that the proportion of surviving parasites correlates with the fitness cost of resistance-inducing mutations which might indicate that the growth disadvantages prevents resistance levels where all parasites survive treatment. We also found that combining two common resistance mutations did not increase resistance levels. However, selection through repeated ART-exposure did, even-though the known resistance genes, including k13, were not further altered, suggesting other causes of increased resistance. We also observed a disproportionally high fitness cost of a resistance mutation in resistance gene ubp1. Such high fitness costs may explain why mutations in ubp1 and other genes functioning in the same pathway as k13 are rare. This highlights that k13 mutations are unique in their ability to cause resistance at a comparably low fitness cost.

Temporal genomic analysis of Plasmodium falciparum reveals increased prevalence of mutations associated with delayed clearance following treatment with artemisinin-lumefantrine in Choma District, Southern Province, Zambia

The emergence of antimalarial drug resistance is an impediment to malaria control and elimination in Africa. Analysis of temporal trends in molecular markers of resistance is critical to inform policy makers and guide malaria treatment guidelines. In a low and seasonal transmission region of southern Zambia, we successfully genotyped 85.5% (389/455) of Plasmodium falciparum samples collected between 2013-2018 from 8 spatially clustered health centres using molecular inversion probes (MIPs) targeting key drug resistance genes. Aside from one sample carrying K13 R622I, none of the isolates carried other World Health Organization-validated or candidate artemisinin partial resistance (ART-R) mutations in K13. However, 13% (CI, 9.6-17.2) of isolates had the AP2MU S160N mutation, which has been associated with delayed clearance following artemisinin combination therapy in Africa. This mutation increased in prevalence between 2015-2018 and bears a genomic signature of selection. During this time period, there was an increase in the MDR1 NFD haplotype that is associated with reduced susceptibility to lumefantrine. Sulfadoxine-pyrimethamine polymorphisms were near fixation. While validated ART-R mutations are rare, a mutation associated with slow parasite clearance in Africa appears to be under selection in southern Zambia.

SNP-slice resolves mixed infections: simultaneously unveiling strain haplotypes and linking them to hosts

MOTIVATION

Multi-strain infection is a common yet under-investigated phenomenon of many pathogens. Currently, biologists analyzing SNP information sometimes have to discard mixed infection samples as many downstream analyses require monogenomic inputs. Such a protocol impedes our understanding of the underlying genetic diversity, co-infection patterns, and genomic relatedness of pathogens. A scalable tool to learn and resolve the SNP-haplotypes from polygenomic data is an urgent need in molecular epidemiology.

RESULTS

We develop a slice sampling Markov Chain Monte Carlo algorithm, named SNP-Slice, to learn not only the SNP-haplotypes of all strains in the populations but also which strains infect which hosts. Our method reconstructs SNP-haplotypes and individual heterozygosities accurately without reference panels and outperforms the state-of-the-art methods at estimating the multiplicity of infections and allele frequencies. Thus, SNP-Slice introduces a novel approach to address polygenomic data and opens a new avenue for resolving complex infection patterns in molecular surveillance. We illustrate the performance of SNP-Slice on empirical malaria and HIV datasets and provide recommendations for using our method on empirical datasets.

AVAILABILITY AND IMPLEMENTATION

The implementation of the SNP-Slice algorithm, as well as scripts to analyze SNP-Slice outputs, are available at https://github.com/nianqiaoju/snp-slice.

Role of seasonal importation and genetic drift on selection for drug-resistant genotypes of Plasmodium falciparum in high-transmission settings

Historically Plasmodium falciparum has followed a pattern of drug resistance first appearing in low-transmission settings before spreading to high-transmission settings. Several features of low-transmission regions are hypothesized as explanations: higher chance of symptoms and treatment seeking, better treatment access, less within-host competition among clones and lower rates of recombination. Here, we test whether importation of drug-resistant parasites is more likely to lead to successful emergence and establishment in low-transmission or high-transmission periods of the same epidemiological setting, using a spatial, individual-based stochastic model of malaria and drug-resistance evolution calibrated for Burkina Faso. Upon controlling for the timing of importation of drug-resistant genotypes and examination of key model variables, we found that drug-resistant genotypes imported during the low-transmission season were (i) more susceptible to stochastic extinction due to the action of genetic drift, and (ii) more likely to lead to establishment of drug resistance when parasites are able to survive early stochastic loss due to drift. This implies that rare importation events are more likely to lead to establishment if they occur during a high-transmission season, but that constant importation (e.g. neighbouring countries with high levels of resistance) may produce a greater risk during low-transmission periods.

Antigenic strain diversity predicts different biogeographic patterns of maintenance and decline of antimalarial drug resistance

The establishment and spread of antimalarial drug resistance vary drastically across different biogeographic regions. Though most infections occur in sub-Saharan Africa, resistant strains often emerge in low-transmission regions. Existing models on resistance evolution lack consensus on the relationship between transmission intensity and drug resistance, possibly due to overlooking the feedback between antigenic diversity, host immunity, and selection for resistance. To address this, we developed a novel compartmental model that tracks sensitive and resistant parasite strains, as well as the host dynamics of generalized and antigen-specific immunity. Our results show a negative correlation between parasite prevalence and resistance frequency, regardless of resistance cost or efficacy. Validation using chloroquine-resistant marker data supports this trend. Post discontinuation of drugs, resistance remains high in low-diversity, low-transmission regions, while it steadily decreases in high-diversity, high-transmission regions. Our study underscores the critical role of malaria strain diversity in the biogeographic patterns of resistance evolution.

A Comparative Study of Genetic Diversity and Multiplicity of Infection in Uncomplicated Plasmodium falciparum Infections in Selected Regions of Pre-Elimination and High Transmission Settings Using MSP1 and MSP2 Genes

BACKGROUND

Understanding the genetic structure of P. falciparum population in different regions is pivotal to malaria elimination. Genetic diversity and the multiplicity of infection are indicators used for measuring malaria endemicity across different transmission settings. Therefore, this study characterized P. falciparum infections from selected areas constituting pre-elimination and high transmission settings in South Africa and Nigeria, respectively.

METHODS

Parasite genomic DNA was extracted from 129 participants with uncomplicated P. falciparum infections. Isolates were collected from 78 participants in South Africa (southern Africa) and 51 in Nigeria (western Africa). Allelic typing of the msp1 and msp2 genes was carried out using nested PCR.

RESULTS

In msp1, the K1 allele (39.7%) was the most common allele among the South African isolates, while the RO33 allele (90.2%) was the most common allele among the Nigerian isolates. In the msp2 gene, FC27 and IC3D7 showed almost the same percentage distribution (44.9% and 43.6%) in the South African isolates, whereas FC27 had the highest percentage distribution (60.8%) in the Nigerian isolates. The msp2 gene showed highly distinctive genotypes, indicating high genetic diversity in the South African isolates, whereas msp1 showed high genetic diversity in the Nigerian isolates. The RO33 allelic family displayed an inverse relationship with participants' age in the Nigerian isolates. The overall multiplicity of infection (MOI) was significantly higher in Nigeria (2.87) than in South Africa (2.44) (p < 0.000 *). In addition, heterozygosity was moderately higher in South Africa (1.46) than in Nigeria (1.13).

CONCLUSIONS

The high genetic diversity and MOI in P. falciparum that were observed in this study could provide surveillance data, on the basis of which appropriate control strategies should be adopted.

Antigenic strain diversity predicts different biogeographic patterns of maintenance and decline of anti-malarial drug resistance

The establishment and spread of anti-malarial drug resistance vary drastically across different biogeographic regions. Though most infections occur in Sub-Saharan Africa, resistant strains often emerge in low-transmission regions. Existing models on resistance evolution lack consensus on the relationship between transmission intensity and drug resistance, possibly due to overlooking the feedback between antigenic diversity, host immunity, and selection for resistance. To address this, we developed a novel compartmental model that tracks sensitive and resistant parasite strains, as well as the host dynamics of generalized and antigen-specific immunity. Our results show a negative correlation between parasite prevalence and resistance frequency, regardless of resistance cost or efficacy. Validation using chloroquine-resistant marker data supports this trend. Post discontinuation of drugs, resistance remains high in low-diversity, low-transmission regions, while it steadily decreases in high-diversity, high-transmission regions. Our study underscores the critical role of malaria strain diversity in the biogeographic patterns of resistance evolution.

Sampling for malaria molecular surveillance

Strategic use of Plasmodium falciparum genetic variation has great potential to inform public health actions for malaria control and elimination. Malaria molecular surveillance (MMS) begins with a strategy to identify and collect parasite samples, guided by public-health priorities. In this review we discuss sampling design practices for MMS and point out epidemiological, biological, and statistical factors that need to be considered. We present examples for different use cases, including detecting emergence and spread of rare variants, establishing transmission sources and inferring changes in malaria transmission intensity. This review will potentially guide the collection of samples and data, serve as a starting point for further methodological innovation, and enhance utilization of MMS to support malaria elimination.

Critical interdependencies between Plasmodium nutrient flux and drugs

Nutrient import and waste efflux are critical dependencies for intracellular Plasmodium falciparum parasites. Nutrient transport proteins are often lineage specific and can provide unique targets for antimalarial drug development. P. falciparum nutrient transport pathways can be a double-edged sword for the parasite, not only mediating the import of nutrients and excretion of waste products but also providing an access route for drugs. Here we briefly summarise the nutrient acquisition pathways of intracellular P. falciparum blood-stage parasites and then highlight how these pathways influence many aspects relevant to antimalarial drugs, resulting in complex and often underappreciated interdependencies.

Role of Seasonal Importation and Random Genetic Drift on Selection for Drug-Resistant Genotypes of Plasmodium falciparum in High Transmission Settings

Historically Plasmodium falciparum has followed a pattern of drug resistance first appearing in low transmission settings before spreading to high transmission settings. Several features of low-transmission regions are hypothesized as explanations: higher chance of symptoms and treatment seeking, better treatment access, less within-host competition among clones, and lower rates of recombination. Here, we test whether importation of drug-resistant parasites is more likely to lead to successful emergence and establishment in low-transmission or high-transmission periods of the same epidemiological setting, using a spatial, individual-based stochastic model of malaria and drug-resistance evolution calibrated for Burkina Faso. Upon controlling for the timing of importation of drug-resistant genotypes and examination of key model variables, we found that drug-resistant genotypes imported during the low transmission season were, (1) more susceptible to stochastic extinction due to the action of random genetic drift, and (2) more likely to lead to establishment of drug resistance when parasites are able to survive early stochastic loss due to drift. This implies that rare importation events are more likely to lead to establishment if they occur during a high-transmission season, but that constant importation (e.g., neighboring countries with high levels of resistance) may produce a greater risk during low-transmission periods.

In vitro and in vivo antiplasmodial evaluation of sugar-modified nucleoside analogues

Drug-resistant Plasmodium falciparum (Pf) infections are a major burden on the population and the healthcare system. The establishment of Pf resistance to most existing antimalarial therapies has complicated the problem, and the emergence of resistance to artemisinin derivatives is even more concerning. It is increasingly difficult to cure malaria patients due to the limited availability of effective antimalarial drugs, resulting in an urgent need for more efficacious and affordable treatments to eradicate this disease. Herein, new nucleoside analogues including morpholino-nucleoside hybrids and thio-substituted nucleoside derivatives were prepared and evaluated for in vitro and in vivo antiparasitic activity that led a few hits especially nucleoside-thiopyranoside conjugates, which are highly effective against Pf3D7 and PfRKL-9 strains in submicromolar concentration. One adenosine derivative and four pyrimidine nucleoside analogues significantly reduced the parasite burden in mouse models infected with Plasmodium berghei ANKA. Importantly, no significant hemolysis and cytotoxicity towards human cell line (RAW) was observed for the hits, suggesting their safety profile. Preliminary research suggested that these thiosugar-nucleoside conjugates could be used to accelerate the antimalarial drug development pipeline and thus deserve further investigation.

Targeted and whole-genome sequencing reveal a north-south divide in P. falciparum drug resistance markers and genetic structure in Mozambique

Mozambique is one of the four African countries which account for over half of all malaria deaths worldwide, yet little is known about the parasite genetic structure in that country. We performed P. falciparum amplicon and whole genome sequencing on 2251 malaria-infected blood samples collected in 2015 and 2018 in seven provinces of Mozambique to genotype antimalarial resistance markers and interrogate parasite population structure using genome-wide microhaplotyes. Here we show that the only resistance-associated markers observed at frequencies above 5% were pfmdr1-184F (59%), pfdhfr-51I/59 R/108 N (99%) and pfdhps-437G/540E (89%). The frequency of pfdhfr/pfdhps quintuple mutants associated with sulfadoxine-pyrimethamine resistance increased from 80% in 2015 to 89% in 2018 (p < 0.001), with a lower expected heterozygosity and higher relatedness of microhaplotypes surrounding pfdhps mutants than wild-type parasites suggestive of recent selection. pfdhfr/pfdhps quintuple mutants also increased from 72% in the north to 95% in the south (2018; p < 0.001). This resistance gradient was accompanied by a concentration of mutations at pfdhps-436 (17%) in the north, a south-to-north increase in the genetic complexity of P. falciparum infections (p = 0.001) and a microhaplotype signature of regional differentiation. The parasite population structure identified here offers insights to guide antimalarial interventions and epidemiological surveys.

Resolving drug selection and migration in an inbred South American Plasmodium falciparum population with identity-by-descent analysis

The human malaria parasite Plasmodium falciparum is globally widespread, but its prevalence varies significantly between and even within countries. Most population genetic studies in P. falciparum focus on regions of high transmission where parasite populations are large and genetically diverse, such as sub-Saharan Africa. Understanding population dynamics in low transmission settings, however, is of particular importance as these are often where drug resistance first evolves. Here, we use the Pacific Coast of Colombia and Ecuador as a model for understanding the population structure and evolution of Plasmodium parasites in small populations harboring less genetic diversity. The combination of low transmission and a high proportion of monoclonal infections means there are few outcrossing events and clonal lineages persist for long periods of time. Yet despite this, the population is evolutionarily labile and has successfully adapted to changes in drug regime. Using newly sequenced whole genomes, we measure relatedness between 166 parasites, calculated as identity by descent (IBD), and find 17 distinct but highly related clonal lineages, six of which have persisted in the region for at least a decade. This inbred population structure is captured in more detail with IBD than with other common population structure analyses like PCA, ADMIXTURE, and distance-based trees. We additionally use patterns of intra-chromosomal IBD and an analysis of haplotypic variation to explore past selection events in the region. Two genes associated with chloroquine resistance, crt and aat1, show evidence of hard selective sweeps, while selection appears soft and/or incomplete at three other key resistance loci (dhps, mdr1, and dhfr). Overall, this work highlights the strength of IBD analyses for studying parasite population structure and resistance evolution in regions of low transmission, and emphasizes that drug resistance can evolve and spread in small populations, as will occur in any region nearing malaria elimination.

The influence of biological, epidemiological, and treatment factors on the establishment and spread of drug-resistant Plasmodium falciparum

The effectiveness of artemisinin-based combination therapies (ACTs) to treat Plasmodium falciparum malaria is threatened by resistance. The complex interplay between sources of selective pressure-treatment properties, biological factors, transmission intensity, and access to treatment-obscures understanding how, when, and why resistance establishes and spreads across different locations. We developed a disease modelling approach with emulator-based global sensitivity analysis to systematically quantify which of these factors drive establishment and spread of drug resistance. Drug resistance was more likely to evolve in low transmission settings due to the lower levels of (i) immunity and (ii) within-host competition between genotypes. Spread of parasites resistant to artemisinin partner drugs depended on the period of low drug concentration (known as the selection window). Spread of partial artemisinin resistance was slowed with prolonged parasite exposure to artemisinin derivatives and accelerated when the parasite was also resistant to the partner drug. Thus, to slow the spread of partial artemisinin resistance, molecular surveillance should be supported to detect resistance to partner drugs and to change ACTs accordingly. Furthermore, implementing more sustainable artemisinin-based therapies will require extending parasite exposure to artemisinin derivatives, and mitigating the selection windows of partner drugs, which could be achieved by including an additional long-acting drug.

Genetic Diversity of Plasmodium falciparum and Distribution of Antimalarial Drug Resistance Mutations in Symptomatic and Asymptomatic Infections

Malaria control relies on passive case detection, and this strategy fails detecting asymptomatic infections. In addition, infections in endemic areas harbor multiple parasite genotypes that could affect case management and malaria epidemiology.

Whole Genome Sequencing Contributions and Challenges in Disease Reduction Focused on Malaria

Malaria elimination remains an important goal that requires the adoption of sophisticated science and management strategies in the era of the COVID-19 pandemic. The advent of next generation sequencing (NGS) is making whole genome sequencing (WGS) a standard today in the field of life sciences, as PCR genotyping and targeted sequencing provide insufficient information compared to the whole genome. Thus, adapting WGS approaches to malaria parasites is pertinent to studying the epidemiology of the disease, as different regions are at different phases in their malaria elimination agenda. Therefore, this review highlights the applications of WGS in disease management, challenges of WGS in controlling malaria parasites, and in furtherance, provides the roles of WGS in pursuit of malaria reduction and elimination. WGS has invaluable impacts in malaria research and has helped countries to reach elimination phase rapidly by providing required information needed to thwart transmission, pathology, and drug resistance. However, to eliminate malaria in sub-Saharan Africa (SSA), with high malaria transmission, we recommend that WGS machines should be readily available and affordable in the region.

Swarming morlet wavelet neural network procedures for the mathematical robot system

The task of this work is to present the solutions of the mathematical robot system (MRS) to examine the positive coronavirus cases through the artificial intelligence (AI) based Morlet wavelet neural network (MWNN). The MRS is divided into two classes, infected I ( θ ) and Robots R ( θ ) . The design of the fitness function is presented by using the differential MRS and then optimized by the hybrid of the global swarming computational particle swarm optimization (PSO) and local active set procedure (ASP). For the exactness of the AI based MWNN-PSOIPS, the comparison of the results is presented by using the proposed and reference solutions. The reliability of the MWNN-PSOASP is authenticated by extending the data into 20 trials to check the performance of the scheme by using the statistical operators with 10 hidden numbers of neurons to solve the MRS.

Evolutionary Dynamics of Treatment-Induced Resistance in Cancer Informs Understanding of Rapid Evolution in Natural Systems

Rapid evolution is ubiquitous in nature. We briefly review some of this quite broadly, particularly in the context of response to anthropogenic disturbances. Nowhere is this more evident, replicated and accessible to study than in cancer. Curiously cancer has been late - relative to fisheries, antibiotic resistance, pest management and evolution in human dominated landscapes - in recognizing the need for evolutionarily informed management strategies. The speed of evolution matters. Here, we employ game-theoretic modeling to compare time to progression with continuous maximum tolerable dose to that of adaptive therapy where treatment is discontinued when the population of cancer cells gets below half of its initial size and re-administered when the cancer cells recover, forming cycles with and without treatment. We show that the success of adaptive therapy relative to continuous maximum tolerable dose therapy is much higher if the population of cancer cells is defined by two cell types (sensitive vs. resistant in a polymorphic population). Additionally, the relative increase in time to progression increases with the speed of evolution. These results hold with and without a cost of resistance in cancer cells. On the other hand, treatment-induced resistance can be modeled as a quantitative trait in a monomorphic population of cancer cells. In that case, when evolution is rapid, there is no advantage to adaptive therapy. Initial responses to therapy are blunted by the cancer cells evolving too quickly. Our study emphasizes how cancer provides a unique system for studying rapid evolutionary changes within tumor ecosystems in response to human interventions; and allows us to contrast and compare this system to other human managed or dominated systems in nature.

Immune selection suppresses the emergence of drug resistance in malaria parasites but facilitates its spread

Although drug resistance in Plasmodium falciparum typically evolves in regions of low transmission, resistance spreads readily following introduction to regions with a heavier disease burden. This suggests that the origin and the spread of resistance are governed by different processes, and that high transmission intensity specifically impedes the origin. Factors associated with high transmission, such as highly immune hosts and competition within genetically diverse infections, are associated with suppression of resistant lineages within hosts. However, interactions between these factors have rarely been investigated and the specific relationship between adaptive immunity and selection for resistance has not been explored. Here, we developed a multiscale, agent-based model of Plasmodium parasites, hosts, and vectors to examine how host and parasite dynamics shape the evolution of resistance in populations with different transmission intensities. We found that selection for antigenic novelty (“immune selection”) suppressed the evolution of resistance in high transmission settings. We show that high levels of population immunity increased the strength of immune selection relative to selection for resistance. As a result, immune selection delayed the evolution of resistance in high transmission populations by allowing novel, sensitive lineages to remain in circulation at the expense of the spread of a resistant lineage.

In contrast, in low transmission settings, we observed that resistant strains were able to sweep to high population prevalence without interference. Additionally, we found that the relationship between immune selection and resistance changed when resistance was widespread. Once resistance was common enough to be found on many antigenic backgrounds, immune selection stably maintained resistant parasites in the population by allowing them to proliferate, even in untreated hosts, when resistance was linked to a novel epitope. Our results suggest that immune selection plays a role in the global pattern of resistance evolution.

Drug resistance in the malaria parasite, Plasmodium falciparum, presents an ongoing public health challenge, but aspects of its evolution are poorly understood. Although antimalarial resistance is common worldwide, it can typically be traced to just a handful of evolutionary origins. Counterintuitively, although Sub Saharan Africa bears 90% of the global malaria burden, resistance typically originates in regions where transmission intensity is low. In high transmission regions, infections are genetically diverse, and hosts have significant standing adaptive immunity, both of which are known to suppress the frequency of resistance within infections. However, interactions between immune-driven selection, transmission intensity, and resistance have not been investigated. Using a multiscale, agent-based model, we found that high transmission intensity slowed the evolution of resistance via its effect on host population immunity. High host immunity strengthened selection for antigenic novelty, interfering with selection for resistance and allowing sensitive lineages to suppress resistant lineages in untreated hosts. However, once resistance was common in the circulating parasite population, immune selection maintained it in the population at a high prevalence. Our findings provide a novel explanation for observations about the origin of resistance and suggest that adaptive immunity is a critical component of selection.

Mechanistic within-host models of the asexual Plasmodium falciparum infection: a review and analytical assessment

Background

Malaria blood-stage infection length and intensity are important drivers of disease and transmission; however, the underlying mechanisms of parasite growth and the host’s immune response during infection remain largely unknown. Over the last 30 years, several mechanistic mathematical models of malaria parasite within-host dynamics have been published and used in malaria transmission models.

Methods

Mechanistic within-host models of parasite dynamics were identified through a review of published literature. For a subset of these, model code was reproduced and descriptive statistics compared between the models using fitted data. Through simulation and model analysis, key features of the models were compared, including assumptions on growth, immune response components, variant switching mechanisms, and inter-individual variability.

Results

The assessed within-host malaria models generally replicate infection dynamics in malaria-naïve individuals. However, there are substantial differences between the model dynamics after disease onset, and models do not always reproduce late infection parasitaemia data used for calibration of the within host infections. Models have attempted to capture the considerable variability in parasite dynamics between individuals by including stochastic parasite multiplication rates; variant switching dynamics leading to immune escape; variable effects of the host immune responses; or via probabilistic events. For models that capture realistic length of infections, model representations of innate immunity explain early peaks in infection density that cause clinical symptoms, and model representations of antibody immune responses control the length of infection. Models differed in their assumptions concerning variant switching dynamics, reflecting uncertainty in the underlying mechanisms of variant switching revealed by recent clinical data during early infection. Overall, given the scarce availability of the biological evidence there is limited support for complex models.

Conclusions

This study suggests that much of the inter-individual variability observed in clinical malaria infections has traditionally been attributed in models to random variability, rather than mechanistic disease dynamics. Thus, it is proposed that newly developed models should assume simple immune dynamics that minimally capture mechanistic understandings and avoid over-parameterization and large stochasticity which inaccurately represent unknown disease mechanisms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-021-03813-z.

Host-targeted Interventions as an Exciting Opportunity to Combat Malaria

Terminal and benign diseases alike in adults, children, pregnant women, and others are successfully treated by pharmacological inhibitors that target human enzymes. Despite extensive global efforts to fight malaria, the disease continues to be a massive worldwide health burden, and new interventional strategies are needed. Current drugs and vector control strategies have contributed to the reduction in malaria deaths over the past 10 years, but progress toward eradication has waned in recent years. Resistance to antimalarial drugs is a substantial and growing problem. Moreover, targeting dormant forms of the malaria parasite Plasmodium vivax is only possible with two approved drugs, which are both contraindicated for individuals with glucose-6-phosphate dehydrogenase deficiency and in pregnant women. Plasmodium parasites are obligate intracellular parasites and thus have specific and absolute requirements of their hosts. Growing evidence has described these host necessities, paving the way for opportunities to pharmacologically target host factors to eliminate Plasmodium infection. Here, we describe progress in malaria research and adjacent fields and discuss key challenges that remain in implementing host-directed therapy against malaria.

Implicating extracellular vesicles in Plasmodium falciparum artemisinin resistance development

Plasmodium falciparum malaria remains a disease of significant public health impact today. With the risk of emerging artemisinin resistance stalling malaria control efforts, the need to deepen our understanding of the parasite's biology is dire. Extracellular vesicles (EVs) are vital to the biology of P. falciparum and play a role in the pathogenesis of malaria. Recent studies have also shown that EVs may play a role in the development of artemisinin resistance in P. falciparum. Here, we highlight evidence on EVs in P. falciparum biology and malaria pathogenesis and argue that there is sufficient ground to propose a role for EVs in the development of P. falciparum artemisinin resistance. We suggest that EVs are actively secreted functional organelles that contribute to cellular homeostasis in P. falciparum-infected red blood cells under artemisinin pressure. Further exploration of this hypothesized EVs-based molecular mechanism of artemisinin resistance will aid the discovery of novel antimalarial therapies.

Considerations for anthelmintic resistance emergence in hookworm at a single locus

Over 800 million people are infected with hookworms around the world. Hookworms of the genus Ancylostoma and Necator are examples of nematodes that harbor the ability to enter a host by penetrating the skin, and after entry the infective larvae migrate to the small intestine where they encounter host-specific signals that initiate developmental pathways and culminate in maturation to the adult stage. Currently no vaccine is available for the treatment of hookworm infection. The control strategy is limited to anthelmintic drugs, which run the risk of losing efficacy as resistance grows. Genetic resistance has developed against all classes of anthelmintic drugs against livestock parasites, and recently markers of anthelmintic resistance in human hookworm populations have been reported. As anthelmintic resistance develops in human populations of hookworm, new drugs and novel control methods like vaccines will be required in the future to control hookworm transmission. This review outlines how population genetics and anthelmintic resistance could interact at a single locus to influence current control strategies.

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This review outlines how population genetics and anthelmintic resistance may interact to influence current control strategies.

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Altering mutation rate modifies rate of emergence of anthelmintic resistance.

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Genotypic fitness changes the efficacy of anthelmintic administration.

Genetic background and PfKelch13 affect artemisinin susceptibility of PfCoronin mutants in Plasmodium falciparum

Malaria continues to impose a significant health burden in the continent of Africa with 213 million cases in 2018 alone, representing 93% of cases worldwide. Because of high transmission of malaria within the continent, the selection pressures to develop drug resistance in African parasites are distinct compared to the rest of the world. In light of the spread of resistance to artemisinin conferred by the C580Y mutation in the PfKelch13 propeller domain in Southeast Asia, and its independent emergence in South America, it is important to study genetic determinants of resistance in the African context using African parasites. Through in vitro evolution of Senegalese parasites, we had previously generated the artemisinin-resistant parasites Pikine_R and Thiès_R and established pfcoronin mutations to be sufficient to confer artemisinin resistance in the standard ring-stage survival assay (RSA). In the current study, we used genetic analysis of revertants to demonstrate pfcoronin to be the major driver of elevated RSA in the artemisinin-resistant parasites Pikine_R and Thiès_R evolved in vitro. We interrogated the role of a second gene PF3D7_1433800, which also had mutations in both the Pikine_R and Thiès_R selected lines, but found no evidence of a contribution to reduced susceptibility in the RSA survival assay. Nevertheless, our genetic analysis demonstrates that parasite genetic background is important in the level of pfcoronin mediated RSA survival, and therefore we cannot rule out a role for PF3D7_1433800 in other genetic backgrounds. Finally, we tested the potential synergy between the mutations of pfcoronin and pfkelch13 through the generation of single and double mutants in the Pikine genetic background and found that the contribution of pfcoronin to reduced susceptibility is masked by the presence of pfkelch13. This phenomenon was also observed in the 3D7 background, suggesting that pfcoronin may mediate its effects via the same pathway as pfkelch13. Investigating the biology of proteins containing the beta-propeller domain could further elucidate the different pathways that the parasite could use to attain resistance.

Treatment timing shifts the benefits of short and long antibiotic treatment over infection

Antibiotics are the major tool for treating bacterial infections. Rising antibiotic resistance, however, calls for a better use of antibiotics. While classical recommendations favor long and aggressive treatments, more recent clinical trials advocate for moderate regimens. In this debate, two axes of 'aggression' have typically been conflated: treatment intensity (dose) and treatment duration. The third dimension of treatment timing along each individual's infection course has rarely been addressed. By using a generic mathematical model of bacterial infection controlled by immune response, we examine how the relative effectiveness of antibiotic treatment varies with its timing, duration and antibiotic kill rate. We show that short or long treatments may both be beneficial depending on treatment onset, the target criterion for success and on antibiotic efficacy. This results from the dynamic trade-off between immune response build-up and resistance risk in acute, self-limiting infections, and uncertainty relating symptoms to infection variables. We show that in our model early optimal treatments tend to be 'short and strong', while late optimal treatments tend to be 'mild and long'. This suggests a shift in the aggression axis depending on the timing of treatment. We find that any specific optimal treatment schedule may perform more poorly if evaluated by other criteria, or under different host-specific conditions. Our results suggest that major advances in antibiotic stewardship must come from a deeper empirical understanding of bacterial infection processes in individual hosts. To guide rational therapy, mathematical models need to be constrained by data, including a better quantification of personal disease trajectory in humans. Lay summary: Bacterial infections are becoming more difficult to treat worldwide because bacteria are becoming resistant to the antibiotics used. Addressing this problem requires a better understanding of how treatment along with other host factors impact antibiotic resistance. Until recently, most theoretical research has focused on the importance of antibiotic dosing on antibiotic resistance, however, duration and timing of treatment remain less explored. Here, we use a mathematical model of a generic bacterial infection to study three aspects of treatment: treatment dose/efficacy (defined by the antibiotic kill rate), duration, and timing, and their impact on several infection endpoints. We show that short and long treatment success strongly depends on when treatment begins (defined by the symptom threshold), the target criterion to optimize, and on antibiotic efficacy. We find that if administered early in an infection, "strong and short" therapy performs better, while if treatment begins at higher bacterial densities, a "mild and long" course of antibiotics is favored. In the model host immune defenses are key in preventing relapses, controlling antibiotic resistant bacteria and increasing the effectiveness of moderate intervention. In order to improve rational treatments of human infections, we call for a better quantification of individual disease trajectories in bacteria-immunity space.

Leveraging Computational Modeling to Understand Infectious Diseases

Purpose of Review

Computational and mathematical modeling have become a critical part of understanding in-host infectious disease dynamics and predicting effective treatments. In this review, we discuss recent findings pertaining to the biological mechanisms underlying infectious diseases, including etiology, pathogenesis, and the cellular interactions with infectious agents. We present advances in modeling techniques that have led to fundamental disease discoveries and impacted clinical translation.

Recent Findings

Combining mechanistic models and machine learning algorithms has led to improvements in the treatment of Shigella and tuberculosis through the development of novel compounds. Modeling of the epidemic dynamics of malaria at the within-host and between-host level has afforded the development of more effective vaccination and antimalarial therapies. Similarly, in-host and host-host models have supported the development of new HIV treatment modalities and an improved understanding of the immune involvement in influenza. In addition, large-scale transmission models of SARS-CoV-2 have furthered the understanding of coronavirus disease and allowed for rapid policy implementations on travel restrictions and contract tracing apps.

Summary

Computational modeling is now more than ever at the forefront of infectious disease research due to the COVID-19 pandemic. This review highlights how infectious diseases can be better understood by connecting scientists from medicine and molecular biology with those in computer science and applied mathematics.

Molecular surveillance of antimalarial partner drug resistance in sub-Saharan Africa: a spatial-temporal evidence mapping study

Background

Molecular markers for antimalarial drug resistance can be used to rapidly monitor the emergence and spatial distribution of resistance to artemisinin-based combination therapies (ACTs). Little has been done to analyse molecular surveillance efforts or to assess surveillance coverage. This study aimed to develop an evidence map to characterise the spatial-temporal distribution and sampling methodologies of drug resistance surveillance in sub-Saharan Africa, specifically focusing on markers associated with ACT partner drugs.

Methods

By use of a systematic search, we identified studies that reported data on the following mutations associated with ACT partner drug resistance: pfmdr1 Asn86Tyr, Tyr184Phe, Asp1246Tyr, and copy number variation and pfcrt Lys76Thr, with sample collection occurring in sub-Saharan Africa between Jan 1, 2004, and Dec 31, 2018, corresponding to the uptake of ACTs. For each identified study, we extracted information on its sampling and laboratory methods, author and publication affiliations, years of sampling and of publication, geographic coordinates of the study sites, and prevalence of the partner drug resistance-associated markers. We used linear models to test whether urbanicity, population density, and endemicity were predictors of drug resistance survey sites and linear regressions to identify associations between the number of resistance surveys within a given country and the at-risk malaria population in 2010, the per-capita GDP in 2010, and the mean amount of funding directed to malaria and to determine trends in marker prevalence over time. For country case studies with three or more datapoints, we assessed global spatial autocorrelation using Moran's I.

Findings

Our search yielded 254 studies encompassing 492 year-specific and location-specific surveys from 35 malaria-endemic countries, the most complete set of molecular partner drug surveillance data to date. We observed a median time lag of 3·1 years (95% CI 1·0-7·7) from final sample acquisition to publication. 22 (49%) of the 44 countries in the study region conducted, on average, one or fewer studies every 3 years. The locations of surveillance sites were positively associated with urbanicity (p<0·0001), and the abundance of country-level data was associated with reported donor funding in 2004-18 (p=0·0011) and local government funding in 2004-09 (p=0·014). Nearly all molecular markers displayed significant regional trends over time and global spatial autocorrelation in space. For selected countries with more widespread coverage of surveillance data, some markers also displayed spatial heterogeneity.

Interpretation

In most sub-Saharan countries, molecular data on antimalarial resistance might not be representative of the temporal and geographic heterogeneity of partner drug resistance, and likely do not represent the true spatially dependent distribution of partner drug resistance. Our results highlight several inefficiencies that can be improved upon to develop more accurate data landscapes, including the expansion of sentinel surveillance systems, syndemic usage of research samples, and increased participation in reporting published and unpublished data to centralised platforms.

Population structure across scales facilitates coexistence and spatial heterogeneity of antibiotic-resistant infections

Antibiotic-resistant infections are a growing threat to human health, but basic features of the eco-evolutionary dynamics remain unexplained. Most prominently, there is no clear mechanism for the long-term coexistence of both drug-sensitive and resistant strains at intermediate levels, a ubiquitous pattern seen in surveillance data. Here we show that accounting for structured or spatially-heterogeneous host populations and variability in antibiotic consumption can lead to persistent coexistence over a wide range of treatment coverages, drug efficacies, costs of resistance, and mixing patterns. Moreover, this mechanism can explain other puzzling spatiotemporal features of drug-resistance epidemiology that have received less attention, such as large differences in the prevalence of resistance between geographical regions with similar antibiotic consumption or that neighbor one another. We find that the same amount of antibiotic use can lead to very different levels of resistance depending on how treatment is distributed in a transmission network. We also identify parameter regimes in which population structure alone cannot support coexistence, suggesting the need for other mechanisms to explain the epidemiology of antibiotic resistance. Our analysis identifies key features of host population structure that can be used to assess resistance risk and highlights the need to include spatial or demographic heterogeneity in models to guide resistance management.

The Economic Burden of Malaria: Revisiting the Evidence

A portion of the economics literature has long debated about the relative importance of historical, institutional, geographical, and health determinants of economic growth. In 2001, Gallup and Sachs quantified the association between malaria and the level and growth of per capita income over the period 1965–1995 in a cross-country regression framework. We took a contemporary look at Gallup and Sachs’ seminal work in the context of significant progress in malaria control achieved globally since 2000. Focusing on the period 2000–2017, we used the latest data available on malaria case incidence and other determinants of economic growth, as well as macro-econometric methods that are now the professional norm. In our preferred specification using a fixed-effects model, a 10% decrease in malaria incidence was associated with an increase in income per capita of nearly 0.3% on average and a 0.11 percentage point faster per capita growth per annum. Greater average income gains were expected among higher burden countries and those with lower income. Growth of industries with the same level of labor intensity was found to be significantly slower in countries with higher malaria incidence. To analyze the causal impact of malaria on economic outcomes, we used malaria treatment failure and pyrethroid-only insecticide resistance as exogeneous instruments in two-stage least squares estimations. Despite several methodological challenges, as expected in these types of analyses, our findings confirm the intrinsic link between malaria and economic growth and underscore the importance of malaria control in the agenda for sustainable development.

Molecular surveillance of anti-malarial drug resistance in Democratic Republic of Congo: high variability of chloroquinoresistance and lack of amodiaquinoresistance

Background

The loss of chloroquine (CQ) effectiveness has led to its withdrawal from national policies as a first-line treatment for uncomplicated malaria in several endemic countries, such as the Democratic Republic of Congo (DRC). The K76T mutation on the pfcrt gene has been identified as a marker of CQ resistance and the SVMNT haplotype in codons 72–76 on the same gene has been associated with resistance to amodiaquine (AQ). In the DRC, the prevalence of K76T has decreased from 100% in 2000 to 63.9% in 2014. The purpose of this study was to determine the prevalence of K76T mutations in circulating strains of Plasmodium falciparum, 16 years after CQ withdrawal in the DRC and to investigate the presence of the SVMNT haplotype.

Methods

In 2017, ten geographical sites across the DRC were selected. Dried blood samples were collected from patients attending health centres. Malaria was first detected by a rapid diagnostic test (RDT) available on site (SD Bioline Malaria Ag Pf or CareStart Malaria Pf) or thick blood smear and then confirmed by a P. falciparum species-specific real-time PCR assay. A pfcrt gene segment containing a fragment that encodes amino acids at positions 72–76 was amplified by conventional PCR before sequencing.

Results

A total of 1070 patients were enrolled. Of the 806 PCR-confirmed P. falciparum positive samples, 764 were successfully sequenced. The K76T mutation was detected in 218 samples (28.5%; 95% CI 25.4%–31.9%), mainly (96%) with the CVIET haplotype. Prevalence of CQ resistance marker was unequally distributed across the country, ranging from 1.5% in Fungurume to 89.5% in Katana. The SVMNT haplotype, related to AQ resistance, was not detected.

Conclusion

Overall, the frequency of the P. falciparum CQ resistance marker has decreased significantly and no resistance marker to AQ was detected in the DRC in 2017. However, the between regions variability of CQ resistance remains high in the country. Further studies are needed for continuous monitoring of the CQ resistance level for its prospective re-use in malaria management. The absence of the AQ resistance marker is in line with the use of this drug in the current DRC malaria treatment policy.

Within-host priority effects and epidemic timing determine outbreak severity in co-infected populations

Co-infections of hosts by multiple pathogen species are ubiquitous, but predicting their impact on disease remains challenging. Interactions between co-infecting pathogens within hosts can alter pathogen transmission, with the impact on transmission typically dependent on the relative arrival order of pathogens within hosts (within-host priority effects). However, it is unclear how these within-host priority effects influence multi-pathogen epidemics, particularly when the arrival order of pathogens at the host-population scale varies. Here, we combined models and experiments with zooplankton and their naturally co-occurring fungal and bacterial pathogens to examine how within-host priority effects influence multi-pathogen epidemics. Epidemiological models parametrized with within-host priority effects measured at the single-host scale predicted that advancing the start date of bacterial epidemics relative to fungal epidemics would decrease the mean bacterial prevalence in a multi-pathogen setting, while models without within-host priority effects predicted the opposite effect. We tested these predictions with experimental multi-pathogen epidemics. Empirical dynamics matched predictions from the model including within-host priority effects, providing evidence that within-host priority effects influenced epidemic dynamics. Overall, within-host priority effects may be a key element of predicting multi-pathogen epidemic dynamics in the future, particularly as shifting disease phenology alters the order of infection within hosts.

Evolutionary consequences of feedbacks between within-host competition and disease control

Lay Summary: Competition often occurs among diverse parasites within a single host, but control efforts could change its strength. We examined how the interplay between competition and control could shape the evolution of parasite traits like drug resistance and disease severity.

Partitioning the influence of ecology across scales on parasite evolution

Vector-borne parasites must succeed at three scales to persist: they must proliferate within a host, establish in vectors, and transmit back to hosts. Ecology outside the host undergoes dramatic seasonal and human-induced changes, but predicting parasite evolutionary responses requires integrating their success across scales. We develop a novel, data-driven model to titrate the evolutionary impact of ecology at multiple scales on human malaria parasites. We investigate how parasites invest in transmission versus proliferation, a life-history trait that influences disease severity and spread. We find that transmission investment controls the pattern of host infectiousness over the course of infection: a trade-off emerges between early and late infectiousness, and the optimal resolution of that trade-off depends on ecology outside the host. An expanding epidemic favors rapid proliferation, and can overwhelm the evolutionary influence of host recovery rates and mosquito population dynamics. If transmission investment and recovery rate are positively correlated, then ecology outside the host imposes potent selection for aggressive parasite proliferation at the expense of transmission. Any association between transmission investment and recovery represents a key unknown, one that is likely to influence whether the evolutionary consequences of interventions are beneficial or costly for human health.

Multiple-Strain Malaria Infection and Its Impacts on Plasmodium falciparum Resistance to Antimalarial Therapy: A Mathematical Modelling Perspective

The emergence of parasite resistance to antimalarial drugs has contributed significantly to global human mortality and morbidity due to malaria infection. The impacts of multiple-strain malarial parasite infection have further generated a lot of scientific interest. In this paper, we demonstrate, using the epidemiological model, the effects of parasite resistance and competition between the strains on the dynamics and control of Plasmodium falciparum malaria. The analysed model has a trivial equilibrium point which is locally asymptotically stable when the parasite's effective reproduction number is less than unity. Using contour plots, we observed that the efficacy of antimalarial drugs used, the rate of development of resistance, and the rate of infection by merozoites are the most important parameters in the multiple-strain P. falciparum infection and control model. Although the drug-resistant strain is shown to be less fit, the presence of both strains in the human host has a huge impact on the cost and success of antimalarial treatment. To reduce the emergence of resistant strains, it is vital that only effective antimalarial drugs are administered to patients in hospitals, especially in malaria-endemic regions. Our results emphasize the call for regular and strict surveillance on the use and distribution of antimalarial drugs in health facilities in malaria-endemic countries.

A general framework for modelling the impact of co-infections on pathogen evolution

Theoretical models suggest that mixed-strain infections, or co-infections, are an important driver of pathogen evolution. However, the within-host dynamics of co-infections vary enormously, which complicates efforts to develop a general understanding of how co-infections affect evolution. Here, we develop a general framework which condenses the within-host dynamics of co-infections into a few key outcomes, the most important of which is the overall R₀ of the co-infection. Similar to how fitness is determined by two different alleles in a heterozygote, the R₀ of a co-infection is a product of the R₀ values of the co-infecting strains, shaped by the interaction of those strains at the within-host level. Extending the analogy, we propose that the overall R₀ reflects the dominance of the co-infecting strains, and that the ability of a mutant strain to invade a population is a function of its dominance in co-infections. To illustrate the utility of these concepts, we use a within-host model to show how dominance arises from the within-host dynamics of a co-infection, and then use an epidemiological model to demonstrate that dominance is a robust predictor of the ability of a mutant strain to save a maladapted wild-type strain from extinction (evolutionary emergence).