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Biology subjects

Pascual, M.

Publications and source records attributed to Pascual, M..

9 recordsLinked to original sources

Influenza incidence prediction for the United States: an update for the 2018-2019 season

IntroductionSeasonal influenza causes a high disease burden every year in the United States and worldwide. Anticipating epidemic size ahead of season can contribute to preparedness and more targetted control and prevention of seasonal influenza.\n\nMethodsA recently developed process-based epidemiological model that incorporates evolutionary change of the virus and generates incidence forecasts for the H3N2 subtype ahead of the season, was previously validated by several statistical criteria, including an accurate real-time prediction for the 2016-2017 influenza season. With this model, a new forecast is generated here for the upcoming 2018-2019 season. The accuracy of predictions published for the 2017-2018 season is also retrospectively evaluated.\n\nResultsFor 2017-2018, the model correctly predicted the dominance of the H3N2 subtype and its higher than average incidence. Based on surveillance and sequence data up to June 2018, the new forecast for the upcoming 2018-2019 season indicates low levels for H3N2, and suggests an H1N1 dominant season with low incidence of influenza B.\n\nDiscussionReal-time forecasts, those generated with a model that was parameterized based on data preceding the predicted season, allows valuable evaluation of the approach. Anticipating the dominant subtype and the size of the upcoming epidemic ahead of season informs disease control. Further studies are needed to promote more accurate ahead-of-season forecasts and extend the approach to multiple subtypes.

bioinformatics

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

In their competition for hosts, parasites with antigens that are novel to host immunity will be at a competitive advantage. The resulting frequency-dependent selection can structure parasite populations into strains of limited genetic overlap. For Plasmodium falciparum-the causative agent of malaria-in endemic regions, the high recombination rates and associated vast diversity of its highly antigenic and multicopy var genes preclude such clear clustering; this undermines the definition of strains as specific, temporally-persisting gene variant combinations. We use temporal multilayer networks to analyze the genetic similarity of parasites in both simulated data and in an extensively and longitudinally sampled population in Ghana. When viewed over time, populations are structured into modules (i.e., groups) of parasite genomes whose var gene combinations are more similar within, than between, the modules, and whose persistence is much longer than that of the individual genomes that compose them. Comparison to neutral models that retain parasite population dynamics but lack competition reveals that the selection imposed by host immunity promotes the persistence of these modules. The modular structure is in turn associated with a slower acquisition of immunity by individual hosts. Modules thus represent dynamically generated niches in host immune space, which can be interpreted as strains. Negative frequency-dependent selection therefore shapes the organization of the var diversity into parasite genomes, leaving a persistence signature over ecological time scales. Multilayer networks extend the scope of phylodynamics analyses by allowing quantification of temporal genetic structure in organisms that generate variation via recombination or other non-bifurcating processes. A strain structure similar to the one described here should apply to other pathogens with large antigenic spaces that evolve via recombination. For malaria, the temporal modular structure should enable the formulation of tractable epidemiological models that account for parasite antigenic diversity and its influence on intervention outcomes.\n\nSignificanceMany pathogens, including the causative agent of malaria Plasmodium falciparum, use antigenic variation, obtained via recombination, as a strategy to evade the human immune system. The vast diversity and multiplicity of genes encoding antigenic variation in high transmission regions challenge the notion of the existence of distinct strains: temporally-persistent and specific combinations of genes relevant to epidemiology. We examine the role of human immune selection in generating such genetic population structure in the major blood-stage antigen of Plasmodium falciparum. We show, using simulated and empirical data, that immune selection generates and maintains modules of genomes with higher genetic similarity within, than between, these groups. Selection further promotes the persistence of these modules for much longer times than those of their constituent genomes. Simulations show that the temporal modular structure reduces the speed at which hosts acquire immunity to the parasite. We argue that in P. falciparum modules can be viewed as dynamic strains occupying different niches in human immune space; they are thus relevant to formulating transmission models that encompass the antigenic diversity of the parasite. Our analyses may prove useful to understand the interplay between temporal genetic structure and epidemiology in other pathogens of human and wildlife importance.

ecology

Critical transitions in malaria transmission models are consistently generated by superinfection

The history of infectious disease modelling essentially begins with the papers by Ross on malaria [1-5]. These models assume that the dynamics of malaria can most simply be characterized by two equations that describe the prevalence of malaria in the human and mosquito hosts. This structure has formed the central core of models for malaria and most other vector-borne diseases for the last century with occasional additions acknowledging important aetiological details. We partially add to this tradition by describing a malaria model that provides for vital dynamics in the vector and the possibility of super-infection in the human host; reinfection of asymptomatic hosts before they have cleared a prior infection. These key features of malaria aetiology create the potential for break points in the prevalence of infected hosts, sudden transitions that seem to characterize malarias response to control in different locations. We show that this potential for critical transitions is a general and underappreciated feature of any model for vector borne diseases with incomplete immunity and asymptomatic patients, including the canonical Ross-McDonald model. Ignoring these details of the hosts immune response to infection can potentially lead to serious misunderstanding in the interpretation of malaria distribution patterns and the design of control schemes for other vector-borne diseases.

epidemiology

Genomic analysis of European Drosophila melanogaster populations on a dense spatial scale reveals longitudinal population structure and continent-wide selection

Genetic variation is the fuel of evolution, with standing genetic variation especially important for short-term evolution and local adaptation. To date, studies of spatio-temporal patterns of genetic variation in natural populations have been challenging, as comprehensive sampling is logistically difficult, and sequencing of entire populations costly. Here, we address these issues using a collaborative approach, sequencing 48 pooled population samples from 32 locations, and perform the first continent-wide genomic analysis of genetic variation in European Drosophila melanogaster. Our analyses uncover longitudinal population structure, provide evidence for continent-wide selective sweeps, identify candidate genes for local climate adaptation, and document clines in chromosomal inversion and transposable element frequencies. We also characterise variation among populations in the composition of the fly microbiome, and identify five new DNA viruses in our samples.

evolutionary biology

Evolution-informed forecasting of seasonal influenza A (H3N2)

Inter-pandemic or seasonal influenza exacts an enormous annual burden both in terms of human health and economic impact. Incidence prediction ahead of season remains a challenge largely because of the virus antigenic evolution. We propose here a forecasting approach that incorporates evolutionary change into a mechanistic epidemiological model. The proposed models are simple enough that their parameters can be estimated from retrospective surveillance data. These models link amino-acid sequences of hemagglutinin epitopes with a transmission model for seasonal H3N2 influenza, also informed by H1N1 levels. With a monthly time series of H3N2 incidence in the United States over 10 years, we demonstrate the feasibility of prediction ahead of season and an accurate real-time forecast for the 2016/2017 influenza season.\n\nSUMMARYSkillful forecasting of seasonal (H3N2) influenza incidence ahead of the season is shown to be possible by means of a transmission model that explicitly tracks evolutionary change in the virus, integrating information from both epidemiological surveillance and readily available genetic sequences.

systems biology

Networks of genetic similarity reveal non-neutral processes shape strain structure in Plasmodium falciparum

Pathogens compete for hosts through patterns of cross-protection conferred by immune responses to antigens. In Plasmodium falciparum malaria, the var multigene family encoding for the major blood-stage antigen PfEMP1 has evolved enormous genetic diversity through ectopic recombination and mutation. With 50-60 var genes per genome, it is unclear whether immune selection can act as a dominant force in structuring var repertoires of local populations. The combinatorial complexity of the var system remains beyond the reach of existing strain theory, and previous evidence for non-random structure cannot demonstrate immune selection without comparison to neutral models. We develop two neutral models that encompass malaria epidemiology but exclude competitive interactions between parasites. These models, combined with networks of genetic similarity, reveal non-neutral strain structure in both simulated systems and an extensively sampled population in Ghana. The unique population structure we identify underlies the large transmission reservoir characteristic of highly endemic regions in Africa.

ecology

Enriched pathogen diversity under host-type heterogeneity and immune-mediated competition

Pathogen strains can stably coexist if they specialize on different hosts. Multiple strains can also coexist on a single host through negative frequency-dependent interactions mediated by partial cross-immunity. Understanding pathogen diversity remains a challenge however when both host specificity and cross-immunity are acting and may be functionally linked, as has been proposed for rotavirus, where a single protein is both antigenically important and determines host specificity by binding to the genetically encoded human blood group antigens. This situation is akin to the more general question in ecology of species coexistence when stabilizing and equalizing mechanisms interact. We examine this interaction with a theoretical model motivated by rotavirus and apply an adaptive dynamics framework to show how these two kinds of competition, typically considered separately, affect diversity. When cross-immunity depends on host-pathogen affinity, diversity is magnified as long-term evolution allows for the coexistence of multiple semi-specialized strains, similar to observations in rotavirus. In contrast, the simultaneous co-occurrence of several semi-specialized individuals is not observed when the degree of cross-immunity is independent from affinity distance among strains. The interplay of equalizing and stabilizing mechanisms fundamentally modifies diversity patterns and should be considered when addressing strain coexistence.

ecology

Role Of Competition In The Strain Structure Of Rotavirus Under Invasion And Reassortment

The role of competitive interactions in the formation and coexistence of viral strains remains unresolved. Neglected aspects of existing strain theory are that viral pathogens are repeatedly introduced from animal sources and readily exchange their genes. The combined effect of introduction and reassortment opposes strain structure, in particular the predicted stable coexistence of antigenically differentiated strains under strong frequency-dependent selection mediated by cross-immunity. Here we use a stochastic model motivated by rotavirus, the most common cause of childhood diarrheal mortality, to investigate serotype structure under these conditions. We describe a regime in which the transient coexistence of distinct strains emerges despite only weak cross-immunity, but is disturbed by invasions of new antigenic segments that reassort into existing backgrounds. We find support for this behavior in global rotavirus sequence data and present evidence for the displacement of new strains towards open antigenic niches. Our work extends previous work to bacterial and viral pathogens that share these rotavirus-like characteristics, with important implications for the effects of interventions such as vaccination on strain composition, and for the understanding of the factors promoting emergence of new subtypes.

evolutionary biology

Conservation of single amino-acid polymorphisms in Plasmodium falciparum erythrocyte membrane protein 1 and association with severe pathophysiology

Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) is a parasite protein encoded by a multigene family known as var. Expressed on the surface of infected red blood cells, PfEMP1 plays a central role in parasite virulence. The DBL domain of PfEMP1 contains short sequence motifs termed homology blocks. Variation within homology blocks, at the level of single amino-acid modifications, has not been considered before in association with severe disease. Here we identify a total of 2701 amino-acid polymorphisms within DBL homology blocks, the majority of which are shared between two geographically distant study populations in existing transcription data from Kenya and in a new genomic dataset sampled in Ghana. Parasitemia levels and the transcription levels of specific polymorphisms are as predictive of severe disease (AUC=0.83) and of the degree of rosetting (forecast skill SS=0.45) as the transcription of classic var groups. 11 newly categorized polymorphisms were strongly correlated with grpA var gene expression (SS=0.93) and a different set of 16 polymorphisms was associated with the H3 subset (SS=0.20). These associations provide the basis for a novel method of relating pathophysiology to parasite gene expression levels--one that, being site-specific, has more molecular detail than previous models based on var groups or homology blocks. This newly described variation influences disease outcome, and can help develop anti-malarial intervention strategies such as vaccines that target severe disease. Further replication of this analysis in geographically disparate populations and for larger sample sizes can help improve the identification of the molecular causes of severe disease.

genomics