Competition for hosts modulates vast antigenic diversity to generate persistent strain structure in Plasmodium falciparum

Var genes, strain hyperdiversity, and malaria transmission dynamics

The microbiological paradigm for surveillance of diverse pathogens requires knowledge of the variation of the major surface antigen under the most intense immune selection as immune responses to these antigens drive transmission dynamics. This creates a pathway for population genetics/genomics to be combined with mathematical modelling to describe transmission dynamics to inform public health policy. Here we consider how we can bring population genetics and population dynamics together for a highly recombining pathogen like Plasmodium falciparum. We do this through the lens of what has been recently learnt about the population genetics of the var multigene family encoding the major surface antigen of the blood stages of Plasmodium falciparum, known as PfEMP1.

Metagenomic analysis reveals extreme complexity of Plasmodium spp. infections in high transmission in West Africa

Mixed-species, mixed-strain Plasmodia infections are known to occur in humans in malaria endemic areas. To date, the true extent of this complexity has not been explored in high-burden countries of sub-Saharan Africa. Here we take a metagenomic lens to infections obtained by sampling variable blood volumes from residents living in high, seasonal transmission in northern Ghana. We identified significantly higher prevalence of Plasmodium spp. and inter-/intra-species complexity in larger blood volumes. Overall, malaria infections displayed high levels of metagenomic complexity comprising single-, double-, and triple-species infections with varying levels of complexity for P. falciparum, P. malariae, P. ovale curtisi, and P. ovale wallikeri. We present evidence of individuals with greater susceptibility to highly-complex infections that cannot be explained by age or location. The implications of these findings to malaria epidemiology and control are illustrated by a geographic scaling exercise to district and region levels in Ghana.

From multiplicity of infection to force of infection for sparsely sampled Plasmodium falciparum populations at high transmission

High multiplicity of infection or MOI, the number of genetically distinct parasite strains co-infecting a single human host, characterizes infectious diseases including falciparum malaria at high transmission. This high MOI accompanies high asymptomatic Plasmodium falciparum prevalence despite high exposure, creating a large transmission reservoir challenging intervention. High MOI and asymptomatic prevalence are enabled by immune evasion of the parasite achieved via vast antigenic diversity. Force of infection or FOI, the number of new infections acquired by an individual host over a given time interval, is the dynamic sister quantity of MOI, and a key epidemiological parameter for monitoring antimalarial interventions and assessing vaccine or drug efficacy in clinical trials. FOI remains difficult, expensive, and labor-intensive to accurately measure, especially in high-transmission regions, whether directly via cohort studies or indirectly via the fitting of epidemiological models to repeated cross-sectional surveys. We propose here the application of queuing theory to obtain FOI from MOI, in the form of either a two-moment approximation method or Little's Law. We illustrate these two methods with MOI estimates obtained under sparse sampling schemes with the " var coding" approach. The two methods use infection duration data from naive malaria therapy patients with neurosyphilis. Consequently, they are suitable for FOI inference in subpopulations with a similar immune profile and the highest vulnerability, for example, infants or toddlers. Both methods are evaluated with simulation output from a stochastic agent-based model, and are applied to an interrupted time-series study from Bongo District in northern Ghana before and immediately after a three-round transient indoor residual spraying (IRS) intervention. The sampling of the simulation output incorporates limitations representative of those encountered in the collection of field data, including under-sampling of var genes, missing data, and antimalarial drug treatment. We address these limitations in MOI estimates with a Bayesian framework and an imputation bootstrap approach. Both methods yield good and replicable FOI estimates across various simulated scenarios. Applying these methods to the subpopulation of children aged 1-5 years in Ghana field surveys shows over a 70% reduction in annual FOI immediately post-intervention. The proposed methods should be applicable to geographical locations lacking cohort or cross-sectional studies with regular and frequent sampling but having single-time-point surveys under sparse sampling schemes, and for MOI estimates obtained in different ways. They should also be relevant to other pathogens whose immune evasion strategies are based on large antigenic variation resulting in high MOI.

A paradoxical population structure of var DBLα types in Africa

The var multigene family encodes Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1), central to host-parasite interactions. Genome structure studies have identified three major groups of var genes by specific upstream sequences (upsA, B, or C). Var with these ups groups have different chromosomal locations, transcriptional directions, and associations with disease severity. Here we explore temporal and spatial diversity of a region of var genes encoding the DBLα domain of PfEMP1 in Africa. By applying a novel ups classification algorithm (cUps) to publicly-available DBLα sequence datasets, we categorised DBLα according to association with the three ups groups, thereby avoiding the need to sequence complete genes. Data from deep sequencing of DBLα types in a local population in northern Ghana surveyed seven times from 2012 to 2017 found variants with rare-to-moderate-to-extreme frequencies, and the common variants were temporally stable in this local endemic area. Furthermore, we observed that every isolate repertoire, whether mono- or multiclonal, comprised DBLα types occurring with these frequency ranges implying a common genome structure. When comparing African countries of Ghana, Gabon, Malawi, and Uganda, we report that some DBLα types were consistently found at high frequencies in multiple African countries while others were common only at the country level. The implication of these local and pan-Africa population patterns is discussed in terms of advantage to the parasite with regards to within-host adaptation and resilience to malaria control.

Review of MrsFreqPhase methods: methods designed to estimate statistically malaria parasite multiplicity of infection, relatedness, frequency and phase

Malaria parasites are haploid within humans, but infections often contain genetically distinct groups of clonal parasites. When the per-infection number of genetically distinct clones (i.e., the multiplicity of infection, MOI) exceeds one, and per-infection genetic data are generated in bulk, important information are obfuscated. For example, the MOI, the phases of the haploid genotypes of genetically distinct clones (i.e., how the alleles concatenate into sequences), and their frequencies. This complicates many downstream analyses, including relatedness estimation. MOIs, parasite sequences, their frequencies, and degrees of relatedness are used ubiquitously in malaria studies: for example, to monitor anti-malarial drug resistance and to track changes in transmission. In this article, MrsFreqPhase methods designed to estimate statistically malaria parasite MOI, relatedness, frequency and phase are reviewed. An overview, a historical account of the literature, and a statistical description of contemporary software is provided for each method class. The article ends with a look towards future method development, needed to make best use of new data types generated by cutting-edge malaria studies reliant on MrsFreqPhase methods.

Hyper-diverse antigenic variation and resilience to transmission-reducing intervention in falciparum malaria

Intervention efforts against falciparum malaria in high-transmission regions remain challenging, with rapid resurgence typically following their relaxation. Such resilience co-occurs with incomplete immunity and a large transmission reservoir from high asymptomatic prevalence. Incomplete immunity relates to the large antigenic variation of the parasite, with the major surface antigen of the blood stage of infection encoded by the multigene and recombinant family known as var. With a stochastic agent-based model, we investigate the existence of a sharp transition in resurgence ability with intervention intensity and identify molecular indicators informative of its proximity. Their application to survey data with deep sampling of var sequences from individual isolates in northern Ghana suggests that the transmission system was brought close to transition by intervention with indoor residual spraying. These results indicate that sustaining and intensifying intervention would have pushed malaria dynamics to a slow-rebound regime with an increased probability of local parasite extinction.

Measuring changes in Plasmodium falciparum census population size in response to sequential malaria control interventions

Here we introduce a new endpoint ″census population size″ to evaluate the epidemiology and control of Plasmodium falciparum infections, where the parasite, rather than the infected human host, is the unit of measurement. To calculate census population size, we rely on a definition of parasite variation known as multiplicity of infection (MOI var ), based on the hyper-diversity of the var multigene family. We present a Bayesian approach to estimate MOI var from sequencing and counting the number of unique DBLα tags (or DBLα types) of var genes, and derive from it census population size by summation of MOI var in the human population. We track changes in this parasite population size and structure through sequential malaria interventions by indoor residual spraying (IRS) and seasonal malaria chemoprevention (SMC) from 2012 to 2017 in an area of high-seasonal malaria transmission in northern Ghana. Following IRS, which reduced transmission intensity by > 90% and decreased parasite prevalence by ~40-50%, significant reductions in var diversity, MOI var , and population size were observed in ~2,000 humans across all ages. These changes, consistent with the loss of diverse parasite genomes, were short lived and 32-months after IRS was discontinued and SMC was introduced, var diversity and population size rebounded in all age groups except for the younger children (1-5 years) targeted by SMC. Despite major perturbations from IRS and SMC interventions, the parasite population remained very large and retained the var population genetic characteristics of a high-transmission system (high var diversity; low var repertoire similarity) demonstrating the resilience of P. falciparum to short-term interventions in high-burden countries of sub-Saharan Africa.

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.

Hyper-diverse antigenic variation and resilience to transmission-reducing intervention in falciparum malaria

Intervention against falciparum malaria in high transmission regions remains challenging, with relaxation of control efforts typically followed by rapid resurgence. Resilience to intervention co-occurs with incomplete immunity, whereby children eventually become protected from severe disease but not infection and a large transmission reservoir results from high asymptomatic prevalence across all ages. Incomplete immunity relates to the vast antigenic variation of the parasite, with the major surface antigen of the blood stage of infection encoded by the multigene family known as var. Recent deep sampling of var sequences from individual isolates in northern Ghana showed that parasite population structure exhibited persistent features of high-transmission regions despite the considerable decrease in prevalence during transient intervention with indoor residual spraying (IRS). We ask whether despite such apparent limited impact, the transmission system had been brought close to a transition in both prevalence and resurgence ability. With a stochastic agent-based model, we investigate the existence of such a transition to pre-elimination with intervention intensity, and of molecular indicators informative of its approach. We show that resurgence ability decreases sharply and nonlinearly across a narrow region of intervention intensities in model simulations, and identify informative molecular indicators based on var gene sequences. Their application to the survey data indicates that the transmission system in northern Ghana was brought close to transition by IRS. These results suggest that sustaining and intensifying intervention would have pushed malaria dynamics to a slow-rebound regime with an increased probability of local parasite extinction.

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.

An immune memory-structured SIS epidemiological model for hyperdiverse pathogens

A hyperdiverse class of pathogens of humans and wildlife, including the malaria parasite Plasmodium falciparum, relies on multigene families to encode antigenic variation. As a result, high (asymptomatic) prevalence is observed despite high immunity in local populations under high-transmission settings. The vast diversity of "strains" and genes encoding this variation challenges the application of established models for the population dynamics of such infectious diseases. Agent-based models have been formulated to address theory on strain coexistence and structure, but their complexity can limit application to gain insights into population dynamics. Motivated by P. falciparum malaria, we develop an alternative formulation in the form of a structured susceptible-infected-susceptible population model in continuous time, where individuals are classified not only by age, as is standard, but also by the diversity of parasites they have been exposed to and retain in their specific immune memory. We analyze the population dynamics and bifurcation structure of this system of partial-differential equations, showing the existence of alternative steady states and an associated tipping point with transmission intensity. We attribute the critical transition to the positive feedback between parasite genetic diversity and force of infection. Basins of attraction show that intervention must drastically reduce diversity to prevent a rebound to high infection levels. Results emphasize the importance of explicitly considering pathogen diversity and associated specific immune memory in the population dynamics of hyperdiverse epidemiological systems. This statement is discussed in a more general context for ecological competition systems with hyperdiverse trait spaces.

SNP-Slice Resolves Mixed Infections: Simultaneously Unveiling Strain Haplotypes and Linking Them to Hosts

Multi-strain infection is a common yet under-investigated phenomenon of many pathogens. Currently, biologists analyzing SNP information have to discard mixed infection samples, because existing 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 reliable tool to learn and resolve the SNP haplotypes from polygenomic data is an urgent need in molecular epidemiology. In this work, 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 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 the practical use of the method.

Molecular epidemiology of continued Plasmodium falciparum disease transmission after an outbreak in Ecuador

To better understand the factors underlying the continued incidence of clinical episodes of falciparum malaria in E-2025 countries targeting elimination, we characterized the molecular epidemiology of Plasmodium falciparum disease transmission after a clonal outbreak in Ecuador. Here we study disease transmission by documenting the diversity and population structure of the major variant surface antigen of the blood stages of P. falciparum encoded by the var multigene family. We used a high-resolution genotyping method, "varcoding", involving targeted amplicon sequencing to fingerprint the DBLα encoding region of var genes to describe both antigenic var diversity and var repertoire similarity or relatedness in parasite isolates from clinical cases. We identified nine genetic varcodes in 58 P. falciparum isolates causing clinical disease in 2013-2015. Network analyses revealed that four of the varcodes were highly related to the outbreak varcode, with identification of possible diversification of the outbreak parasites by recombination as seen in three of those varcodes. The majority of clinical cases in Ecuador were associated with parasites with highly related or recombinant varcodes to the outbreak clone and due to local transmission rather than recent importation of parasites from other endemic countries. Sharing of types in Ecuadorian varcodes to those sampled in South American varcodes reflects historical parasite importation of some varcodes, especially from Colombia and Peru. Our findings highlight the translational application of varcoding for outbreak surveillance in epidemic/unstable malaria transmission, such as in E-2025 countries, and point to the need for surveillance of local reservoirs of infection in Ecuador to achieve the malaria elimination goal by 2025.

Conceptualizing microbe-plasmid communities as complex adaptive systems

Plasmids shape microbial communities' diversity, structure, and function. Nevertheless, we lack a mechanistic understanding of how community structure and dynamics emerge from local microbe-plasmid interactions and coevolution. Addressing this gap is challenging because multiple processes operate simultaneously at multiple levels of organization. For example, immunity operates between a plasmid and a cell, but incompatibility mechanisms regulate coexistence between plasmids. Conceptualizing microbe-plasmid communities as complex adaptive systems is a promising approach to overcoming these challenges. I illustrate how agent-based evolutionary modeling, extended by network analysis, can be used to quantify the relative importance of local processes governing community dynamics. These theoretical developments can advance our understanding of plasmid ecology and evolution, especially when combined with empirical data.

Multilayer networks of plasmid genetic similarity reveal potential pathways of gene transmission

Antimicrobial resistance (AMR) is a significant threat to public health. Plasmids are principal vectors of AMR genes, significantly contributing to their spread and mobility across hosts. Nevertheless, little is known about the dynamics of plasmid genetic exchange across animal hosts. Here, we use theory and methodology from network and disease ecology to investigate the potential of gene transmission between plasmids using a data set of 21 plasmidomes from a single dairy cow population. We constructed a multilayer network based on pairwise plasmid genetic similarity. Genetic similarity is a signature of past genetic exchange that can aid in identifying potential routes and mechanisms of gene transmission within and between cows. Links between cows dominated the transmission network, and plasmids containing mobility genes were more connected. Modularity analysis revealed a network cluster where all plasmids contained a mobM gene, and one where all plasmids contained a beta-lactamase gene. Cows that contain both clusters also share transmission pathways with many other cows, making them candidates for super-spreading. In support, we found signatures of gene super-spreading in which a few plasmids and cows are responsible for most gene exchange. An agent-based transmission model showed that a new gene invading the cow population will likely reach all cows. Finally, we showed that edge weights contain a non-random signature for the mechanisms of gene transmission, allowing us to differentiate between dispersal and genetic exchange. These results provide insights into how genes, including those providing AMR, spread across animal hosts.

Unravelling var complexity: Relationship between DBLα types and var genes in Plasmodium falciparum

The enormous diversity and complexity of var genes that diversify rapidly by recombination has led to the exclusion of assembly of these genes from major genome initiatives (e.g., Pf6). A scalable solution in epidemiological surveillance of var genes is to use a small 'tag' region encoding the immunogenic DBLα domain as a marker to estimate var diversity. As var genes diversify by recombination, it is not clear the extent to which the same tag can appear in multiple var genes. This relationship between marker and gene has not been investigated in natural populations. Analyses of in vitro recombination within and between var genes have suggested that this relationship would not be exclusive. Using a dataset of publicly-available assembled var sequences, we test this hypothesis by studying DBLα-var relationships for four study sites in four countries: Pursat (Cambodia) and Mae Sot (Thailand), representing low malaria transmission, and Navrongo (Ghana) and Chikwawa (Malawi), representing high malaria transmission. In all study sites, DBLα-var relationships were shown to be predominantly 1-to-1, followed by a second largest proportion of 1-to-2 DBLα-var relationships. This finding indicates that DBLα tags can be used to estimate not just DBLα diversity but var gene diversity when applied in a local endemic area. Epidemiological applications of this result are discussed.

Neutral vs. non-neutral genetic footprints of Plasmodium falciparum multiclonal infections

At a time when effective tools for monitoring malaria control and eradication efforts are crucial, the increasing availability of molecular data motivates their application to epidemiology. The multiplicity of infection (MOI), defined as the number of genetically distinct parasite strains co-infecting a host, is one key epidemiological parameter for evaluating malaria interventions. Estimating MOI remains a challenge for high-transmission settings where individuals typically carry multiple co-occurring infections. Several quantitative approaches have been developed to estimate MOI, including two cost-effective ones relying on molecular data: i) THE REAL McCOIL method is based on putatively neutral single nucleotide polymorphism loci, and ii) the varcoding method is a fingerprinting approach that relies on the diversity and limited repertoire overlap of the var multigene family encoding the major Plasmodium falciparum blood-stage antigen PfEMP1 and is therefore under selection. In this study, we assess the robustness of the MOI estimates generated with these two approaches by simulating P. falciparum malaria dynamics under three transmission conditions using an extension of a previously developed stochastic agent-based model. We demonstrate that these approaches are complementary and best considered across distinct transmission intensities. While varcoding can underestimate MOI, it allows robust estimation, especially under high transmission where repertoire overlap is extremely limited from frequency-dependent selection. In contrast, THE REAL McCOIL often considerably overestimates MOI, but still provides reasonable estimates for low and moderate transmission. Regardless of transmission intensity, results for THE REAL McCOIL indicate that an inaccurate tail at high MOI values is generated, and that at high transmission, an apparently reasonable estimated MOI distribution can arise from some degree of compensation between overestimation and underestimation. As many countries pursue malaria elimination targets, defining the most suitable approach to estimate MOI based on sample size and local transmission intensity is highly recommended for monitoring the impact of intervention programs.

Ecological and evolutionary dynamics of multi-strain RNA viruses

Potential interactions among co-circulating viral strains in host populations are often overlooked in the study of virus transmission. However, these interactions probably shape transmission dynamics by influencing host immune responses or altering the relative fitness among co-circulating strains. In this Review, we describe multi-strain dynamics from ecological and evolutionary perspectives, outline scales in which multi-strain dynamics occur and summarize important immunological, phylogenetic and mathematical modelling approaches used to quantify interactions among strains. We also discuss how host-pathogen interactions influence the co-circulation of pathogens. Finally, we highlight outstanding questions and knowledge gaps in the current theory and study of ecological and evolutionary dynamics of multi-strain viruses.

Bayesian estimation of community size and overlap from random subsamples

Counting the number of species, items, or genes that are shared between two groups, sets, or communities is a simple calculation when sampling is complete. However, when only partial samples are available, quantifying the overlap between two communities becomes an estimation problem. Furthermore, to calculate normalized measures of β-diversity, such as the Jaccard and Sorenson-Dice indices, one must also estimate the total sizes of the communities being compared. Previous efforts to address these problems have assumed knowledge of total community sizes and then used Bayesian methods to produce unbiased estimates with quantified uncertainty. Here, we address communities of unknown size and show that this produces systematically better estimates—both in terms of central estimates and quantification of uncertainty in those estimates. We further show how to use species, item, or gene count data to refine estimates of community size in a Bayesian joint model of community size and overlap.

Maximum antigen diversification in a lyme bacterial population and evolutionary strategies to overcome pathogen diversity

Natural populations of pathogens and their hosts are engaged in an arms race in which the pathogens diversify to escape host immunity while the hosts evolve novel immunity. This co-evolutionary process poses a fundamental challenge to the development of broadly effective vaccines and diagnostics against a diversifying pathogen. Based on surveys of natural allele frequencies and experimental immunization of mice, we show high antigenic specificities of natural variants of the outer surface protein C (OspC), a dominant antigen of a Lyme Disease-causing bacterium (Borrelia burgdorferi). To overcome the challenge of OspC antigenic diversity to clinical development of preventive measures, we implemented a number of evolution-informed strategies to broaden OspC antigenic reactivity. In particular, the centroid algorithm-a genetic algorithm to generate sequences that minimize amino-acid differences with natural variants-generated synthetic OspC analogs with the greatest promise as diagnostic and vaccine candidates against diverse Lyme pathogen strains co-existing in the Northeast United States. Mechanistically, we propose a model of maximum antigen diversification (MAD) mediated by amino-acid variations distributed across the hypervariable regions on the OspC molecule. Under the MAD hypothesis, evolutionary centroids display broad cross-reactivity by occupying the central void in the antigenic space excavated by diversifying natural variants. In contrast to vaccine designs based on concatenated epitopes, the evolutionary algorithms generate analogs of natural antigens and are automated. The novel centroid algorithm and the evolutionary antigen designs based on consensus and ancestral sequences have broad implications for combating diversifying pathogens driven by pathogen-host co-evolution.

An accurate method for identifying recent recombinants from unaligned sequences

Motivation

Recombination is a fundamental process in molecular evolution, and the identification of recombinant sequences is thus of major interest. However, current methods for detecting recombinants are primarily designed for aligned sequences. Thus they struggle with analyses of highly diverse genes, such as the var genes of the malaria parasite Plasmodium falciparum, which are known to diversify primarily through recombination.

Results

We introduce an algorithm to detect recent recombinant sequences from a dataset without a full multiple alignment. Our algorithm can handle thousands of gene-length sequences without the need for a reference panel. We demonstrate the accuracy of our algorithm through extensive numerical simulations; in particular, it maintains its effectiveness in the presence of insertions and deletions. We apply our algorithm to a dataset of 17,335 DBLα types in var genes from Ghana, observing that sequences belonging to the same ups group or domain subclass recombine amongst themselves more frequently, and that non-recombinant DBLα types are more conserved than recombinant ones.

Availability

Source code is freely available at https://github.com/qianfeng2/detREC_program.

Supplementary information

Supplementary data are available at Bioinformatics online.

Evolution of transcriptional control of antigenic variation and virulence in human and ape malaria parasites

Background

The most severe form of human malaria is caused by the protozoan parasite Plasmodium falciparum. This unicellular organism is a member of a subgenus of Plasmodium called the Laverania that infects apes, with P. falciparum being the only member that infects humans. The exceptional virulence of this species to humans can be largely attributed to a family of variant surface antigens placed by the parasites onto the surface of infected red blood cells that mediate adherence to the vascular endothelium. These proteins are encoded by a large, multicopy gene family called var, with each var gene encoding a different form of the protein. By changing which var gene is expressed, parasites avoid immune recognition, a process called antigenic variation that underlies the chronic nature of malaria infections.

Results

Here we show that the common ancestor of the branch of the Laverania lineage that includes the human parasite underwent a remarkable change in the organization and structure of elements linked to the complex transcriptional regulation displayed by the var gene family. Unlike the other members of the Laverania, the clade that gave rise to P. falciparum evolved distinct subsets of var genes distinguishable by different upstream transcriptional regulatory regions that have been associated with different expression profiles and virulence properties. In addition, two uniquely conserved var genes that have been proposed to play a role in coordinating transcriptional switching similarly arose uniquely within this clade. We hypothesize that these changes originated at a time of dramatic climatic change on the African continent that is predicted to have led to significant changes in transmission dynamics, thus selecting for patterns of antigenic variation that enabled lengthier, more chronic infections.

Conclusions

These observations suggest that changes in transmission dynamics selected for significant alterations in the transcriptional regulatory mechanisms that mediate antigenic variation in the parasite lineage that includes P. falciparum. These changes likely underlie the chronic nature of these infections as well as their exceptional virulence.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12862-021-01872-z.

An antigenic diversification threshold for falciparum malaria transmission at high endemicity

In malaria and several other important infectious diseases, high prevalence occurs concomitantly with incomplete immunity. This apparent paradox poses major challenges to malaria elimination in highly endemic regions, where asymptomatic Plasmodium falciparum infections are present across all age classes creating a large reservoir that maintains transmission. This reservoir is in turn enabled by extreme antigenic diversity of the parasite and turnover of new variants. We present here the concept of a threshold in local pathogen diversification that defines a sharp transition in transmission intensity below which new antigen-encoding genes generated by either recombination or migration cannot establish. Transmission still occurs below this threshold, but diversity of these genes can neither accumulate nor recover from interventions that further reduce it. An analytical expectation for this threshold is derived and compared to numerical results from a stochastic individual-based model of malaria transmission that incorporates the major antigen-encoding multigene family known as var. This threshold corresponds to an “innovation” number we call R_div; it is different from, and complementary to, the one defined by the classic basic reproductive number of infectious diseases, R₀, which does not readily is better apply under large and dynamic strain diversity. This new threshold concept can be exploited for effective malaria control and applied more broadly to other pathogens with large multilocus antigenic diversity.

The vast diversity of the falciparum malaria parasite, as seen by the immune system of hosts in high transmission regions, underlies both high prevalence of asymptomatic infections and partial protection to re-infection despite previous exposure. This large antigenic diversity of the parasite challenges control and elimination efforts. We propose a threshold quantity for antigenic innovation, we call R_div, measuring the potential of transmission to accumulate new antigenic variants over time. When R_div is pushed below one by reduced transmission intensity, new genes encoding this variation can no longer accumulate, resulting in a lower number of strains and facilitating further intervention. This innovation number can be applied to other infectious diseases with fast turnover of antigens, where large standing diversity similarly opposes successful intervention.

Molecular approaches to Malaria 2020

Twenty years ago the Molecular Approaches to Malaria conference was conceived as a forum to present the very latest advances in malaria research and to consolidate and forge new collaborative links between international researchers. The 6th MAM conference, held in February 2020 in Australia, provided 5 days of stimulating scientific exchange and highlighted the incredible malaria research conducted globally that is providing the critical knowledge and cutting-edge technological tools needed to control and ultimately eliminate malaria.

Species functional traits and abundance as drivers of multiplex ecological networks: first empirical quantification of inter-layer edge weights

Many vertebrate species act as both plant pollinators and seed-dispersers, thus interconnecting these processes, particularly on islands. Ecological multilayer networks are a powerful tool to explore interdependencies between processes; however, quantifying the links between species engaging in different types of interactions (i.e. inter-layer edges) remains a great challenge. Here, we empirically measured inter-layer edge weights by quantifying the role of individually marked birds as both pollinators and seed-dispersers of Galápagos plant species over an entire year. Although most species (80%) engaged in both functions, we show that only a small proportion of individuals actually linked the two processes, highlighting the need to further consider intra-specific variability in individuals' functional roles. Furthermore, we found a high variation among species in linking both processes, i.e. some species contribute more than others to the modular organization of the multilayer network. Small and abundant species are particularly important for the cohesion of pollinator seed-dispersal networks, demonstrating the interplay between species traits and neutral processes structuring natural communities.

The network structure and eco-evolutionary dynamics of CRISPR-induced immune diversification

As a heritable sequence-specific adaptive immune system, CRISPR-Cas is a powerful force shaping strain diversity in host-virus systems. While the diversity of CRISPR alleles has been explored, the associated structure and dynamics of host-virus interactions have not. We explore the role of CRISPR in mediating the interplay between host-virus interaction structure and eco-evolutionary dynamics in a computational model and compare the results with three empirical datasets from natural systems. We show that the structure of the networks describing who infects whom and the degree to which strains are immune, are respectively modular (containing groups of hosts and viruses that interact strongly) and weighted-nested (specialist hosts are more susceptible to subsets of viruses that in turn also infect the more generalist hosts with many spacers matching many viruses). The dynamic interplay between these networks influences transitions between dynamical regimes of virus diversification and host control. The three empirical systems exhibit weighted-nested immunity networks, a pattern our theory shows is indicative of hosts able to suppress virus diversification. Previously missing from studies of microbial host-pathogen systems, the immunity network plays a key role in the coevolutionary dynamics.

Interactions among Triatoma sanguisuga blood feeding sources, gut microbiota and Trypanosoma cruzi diversity in southern Louisiana

Integrating how biodiversity and infectious disease dynamics are linked at multiple levels and scales is highly challenging. Chagas disease is a vector-borne disease, with specificities of the triatomine vectors and Trypanosoma cruzi parasite life histories resulting in a complex multihost and multistrain life cycle. Here, we tested the hypothesis that T. cruzi transmission cycles are shaped by triatomine host communities and gut microbiota composition by comparing the integrated interactions of Triatoma sanguisuga in southern Louisiana with feeding hosts, T. cruzi parasite and bacterial microbiota in two habitats. Bugs were collected from resident's houses and animal shelters and analysed for genetic structure, blood feeding sources, T. cruzi parasites, and bacterial diversity by PCR amplification of specific DNA markers followed by next-generation sequencing, in an integrative metabarcoding approach. T. sanguisuga feeding host communities appeared opportunistic and defined by host abundance in each habitat, yielding distinct parasite transmission networks among hosts. The circulation of a large diversity of T. cruzi DTUs was also detected, with TcII and TcV detected for the first time in triatomines in the US. The bacterial microbiota was highly diverse and varied significantly according to the DTU infecting the bugs, indicating specific interactions among them in the gut. Expanding such studies to multiple habitats and additional triatomine species would be key to further refine our understanding of the complex life cycles of multihost, multistrain parasites such as T. cruzi, and may lead to improved disease control strategies.