Erida Gjini

Critical Interplay between Parasite Differentiation, Host Immunity, and Antigenic Variation in Trypanosome Infections

Increasing availability of pathogen genomic data offers new opportunities to understand the fundamental mechanisms of immune evasion and pathogen population dynamics during chronic infection. Motivated by the growing knowledge on the antigenic variation system of the sleeping sickness parasite, the African trypanosome, we introduce a mechanistic framework for modeling within‐host infection dynamics. Our analysis focuses first on a single parasitemia peak and then on the dynamics of multiple peaks that rely on stochastic switching between groups of parasite variants. A major feature of trypanosome infections is the interaction between variant‐specific host immunity and density‐dependent parasite differentiation to transmission life stages. In this study, we investigate how the interplay between these two types of control depends on the modular structure of the parasite antigenic archive. Our model shows that the degree of synchronization in stochastic variant emergence determines the relative dominance of general over specific control within a single peak. A requirement for multiple‐peak dynamics is a critical switch rate between blocks of antigenic variants, which implies constraints on variant surface glycoprotein (VSG) archive genetic diversification. Our study illustrates the importance of quantifying the links between parasite genetics and within‐host dynamics and provides insights into the evolution of trypanosomes.

Unveiling time in dose-response models to infer host susceptibility to pathogens.

The biological effects of interventions to control infectious diseases typically depend on the intensity of pathogen challenge. As much as the levels of natural pathogen circulation vary over time and geographical location, the development of invariant efficacy measures is of major importance, even if only indirectly inferrable. Here a method is introduced to assess host susceptibility to pathogens, and applied to a detailed dataset generated by challenging groups of insect hosts (Drosophila melanogaster) with a range of pathogen (Drosophila C Virus) doses and recording survival over time. The experiment was replicated for flies carrying the Wolbachia symbiont, which is known to reduce host susceptibility to viral infections. The entire dataset is fitted by a novel quantitative framework that significantly extends classical methods for microbial risk assessment and provides accurate distributions of symbiont-induced protection. More generally, our data-driven modeling procedure provides novel insights for study design and analyses to assess interventions.

Integrating Antimicrobial Therapy with Host Immunity to Fight Drug-Resistant Infections: Classical vs. Adaptive Treatment

Antimicrobial resistance of infectious agents is a growing problem worldwide. To prevent the continuing selection and spread of drug resistance, rational design of antibiotic treatment is needed, and the question of aggressive vs. moderate therapies is currently heatedly debated. Host immunity is an important, but often-overlooked factor in the clearance of drug-resistant infections. In this work, we compare aggressive and moderate antibiotic treatment, accounting for host immunity effects. We use mathematical modelling of within-host infection dynamics to study the interplay between pathogen-dependent host immune responses and antibiotic treatment. We compare classical (fixed dose and duration) and adaptive (coupled to pathogen load) treatment regimes, exploring systematically infection outcomes such as time to clearance, immunopathology, host immunization, and selection of resistant bacteria. Our analysis and simulations uncover effective treatment strategies that promote synergy between the host immune system and the antimicrobial drug in clearing infection. Both in classical and adaptive treatment, we quantify how treatment timing and the strength of the immune response determine the success of moderate therapies. We explain key parameters and dimensions, where an adaptive regime differs from classical treatment, bringing new insight into the ongoing debate of resistance management. Emphasizing the sensitivity of treatment outcomes to the balance between external antibiotic intervention and endogenous natural defenses, our study calls for more empirical attention to host immunity processes.

The Impact of Mutation and Gene Conversion on the Local Diversification of Antigen Genes in African Trypanosomes

Patterns of genetic diversity in parasite antigen gene families hold important information about their potential to generate antigenic variation within and between hosts. The evolution of such gene families is typically driven by gene duplication, followed by point mutation and gene conversion. There is great interest in estimating the rates of these processes from molecular sequences for understanding the evolution of the pathogen and its significance for infection processes. In this study, a series of models are constructed to investigate hypotheses about the nucleotide diversity patterns between closely related gene sequences from the antigen gene archive of the African trypanosome, the protozoan parasite causative of human sleeping sickness in Equatorial Africa. We use a hidden Markov model approach to identify two scales of diversification: clustering of sequence mismatches, a putative indicator of gene conversion events with other lower-identity donor genes in the archive, and at a sparser scale, isolated mismatches, likely arising from independent point mutations. In addition to quantifying the respective probabilities of occurrence of these two processes, our approach yields estimates for the gene conversion tract length distribution and the average diversity contributed locally by conversion events. Model fitting is conducted using a Bayesian framework. We find that diversifying gene conversion events with lower-identity partners occur at least five times less frequently than point mutations on variant surface glycoprotein (VSG) pairs, and the average imported conversion tract is between 14 and 25 nucleotides long. However, because of the high diversity introduced by gene conversion, the two processes have almost equal impact on the per-nucleotide rate of sequence diversification between VSG subfamily members. We are able to disentangle the most likely locations of point mutations and conversions on each aligned gene pair.

How direct competition shapes coexistence and vaccine effects in multi-strain pathogen systems

We describe an integrated modeling framework for understanding strain coexistence in polymorphic pathogen systems. Previous studies have debated the utility of neutral formulations and focused on cross-immunity between strains as a major stabilizing mechanism. Here we convey that direct competition for colonization mediates stable coexistence only when competitive abilities amongst pathogen clones satisfy certain pairwise asymmetries. We illustrate our ideas with nested SIS models of single and dual colonization, applied to polymorphic pneumococcal bacteria. By fitting the models to cross-sectional prevalence data from Portugal (before and after the introduction of a seven-valent pneumococcal conjugate vaccine), we are able to not only statistically compare neutral and non-neutral epidemiological formulations, but also estimate vaccine efficacy, transmission and competition parameters simultaneously. Our study highlights that the response of polymorphic pathogen populations to interventions holds crucial information about strain interactions, which can be extracted by suitable nested modeling.

Heterogeneity in symbiotic effects facilitates Wolbachia establishment in insect populations

Facultative vertically transmitted bacterial symbionts often manipulate its host’s reproductive biology and thus facilitate their persistence. Wolbachia is one such symbiont where frequency-dependent reproductive benefits are opposed by frequency-independent fitness costs leading to bistable dynamics. Introduction of carriers does not assure invasion unless the initial frequency is above a threshold determined by the balance of costs and benefits. Recent laboratory experiments have uncovered that Wolbachia also protects their hosts from pathogens. The expected consequence of this phenotype in natural environments is to lower the invasion threshold by a factor that increases with the extent of pathogen exposure. Here, we introduce a series of mathematical models to address how pathogen protection affects Wolbachia invasion. First, under homogeneous symbiotic effects, we obtain an analytical expression for the invasion threshold in terms of pathogen exposure, and find a regime where symbiont releases may result in elimination of the entire host population provided that abundance of virulent pathogens is high. Second, we distribute Wolbachia effects such that some carriers are totally protected and others not at all, and explore how this interplays with different pathogen intensities, to conclude that heterogeneity further lowers the threshold for Wolbachia invasion. Third, we replicate the analysis using a realistic distribution of protective effects and confirm that heterogeneity increases system resilience by reducing the odds of population collapse.

Linking the antigen archive structure to pathogen fitness in African trypanosomes

Systems that generate antigenic variation enable pathogens to evade host immune responses and are intricately interwoven with major pathogen traits, such as host choice, growth, virulence and transmission. Although much is understood about antigen switching at the molecular level, little is known about the cross-scale links between these molecular processes and the larger-scale within and between host population dynamics that they must ultimately drive. Inspired by the antigenic variation system of African trypanosomes, we apply modelling approaches to our expanding understanding of the organization and expression of antigen repertoires, and explore links across these scales. We predict how pathogen population processes are determined by underlying molecular genetics and infer resulting selective pressures on important emergent repertoire traits.

Geographic variation in pneumococcal vaccine efficacy estimated from dynamic modeling of epidemiological data post-PCV7

Although mean efficacy of multivalent pneumococcus vaccines has been intensively studied, variance in vaccine efficacy (VE) has been overlooked. Different net individual protection across settings can be driven by environmental conditions, local serotype and clonal composition, as well as by socio-demographic and genetic host factors. Understanding efficacy variation has implications for population-level effectiveness and other eco-evolutionary feedbacks. Here I show that realized VE can vary across epidemiological settings, by applying a multi-site-one-model approach to data post-vaccination. I analyse serotype prevalence dynamics following PCV7, in asymptomatic carriage in children attending day care in Portugal, Norway, France, Greece, Hungary and Hong-Kong. Model fitting to each dataset provides site-specific estimates for vaccine efficacy against acquisition, and pneumococcal transmission parameters. According to this model, variable serotype replacement across sites can be explained through variable PCV7 efficacy, ranging from 40% in Norway to 10% in Hong-Kong. While the details of how this effect is achieved remain to be determined, here I report three factors negatively associated with the VE readout, including initial prevalence of serotype 19F, daily mean temperature, and the Gini index. The study warrants more attention on local modulators of vaccine performance and calls for predictive frameworks within and across populations.

A theoretical framework to identify invariant thresholds in infectious disease epidemiology.

Setting global strategies and targets for disease prevention and control often involves mathematical models. Model structure is typically subject to intense scrutiny, such as confrontation with empirical data and alternative formulations, while a less frequently challenged aspect is the widely adopted reduction of parameters to their average values. Focusing on endemic diseases, we use a general transmission model to explain how mean field approximations decrease the estimated R0 from prevalence data, while threshold phenomena - such as the epidemic and reinfection thresholds - remain invariant. This results in an underestimation of the effort required to control disease, which may be particularly severe when the approximation inappropriately places transmission estimates below important thresholds. These concepts are widely applicable across endemic pathogen systems.

Expanding vaccine efficacy estimation with dynamic models fitted to cross-sectional prevalence data post-licensure

The efficacy of vaccines is typically estimated prior to implementation, on the basis of randomized controlled trials. This does not preclude, however, subsequent assessment post-licensure, while mass-immunization and nonlinear transmission feedbacks are in place. In this paper we show how cross-sectional prevalence data post-vaccination can be interpreted in terms of pathogen transmission processes and vaccine parameters, using a dynamic epidemiological model. We advocate the use of such frameworks for model-based vaccine evaluation in the field, fitting trajectories of cross-sectional prevalence of pathogen strains before and after intervention. Using SI and SIS models, we illustrate how prevalence ratios in vaccinated and non-vaccinated hosts depend on true vaccine efficacy, the absolute and relative strength of competition between target and non-target strains, the time post follow-up, and transmission intensity. We argue that a mechanistic approach should be added to vaccine efficacy estimation against multi-type pathogens, because it naturally accounts for inter-strain competition and indirect effects, leading to a robust measure of individual protection per contact. Our study calls for systematic attention to epidemiological feedbacks when interpreting population level impact. At a broader level, our parameter estimation procedure provides a promising proof of principle for a generalizable framework to infer vaccine efficacy post-licensure.