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Modelling pathogen load dynamics to elucidate mechanistic determinants of host-Plasmodium falciparum interactions.

ABSTRACT: During infection, increasing pathogen load stimulates both protective and harmful aspects of the host response. The dynamics of this interaction are hard to quantify in humans, but doing so could improve understanding of the mechanisms of disease and protection. We sought to model the contributions of the parasite multiplication rate and host response to observed parasite load in individual subjects infected with Plasmodium falciparum malaria, using only data obtained at the time of clinical presentation, and then to identify their mechanistic correlates. We predicted higher parasite multiplication rates and lower host responsiveness in cases of severe malaria, with severe anaemia being more insidious than cerebral malaria. We predicted that parasite-growth inhibition was associated with platelet consumption, lower expression of CXCL10 and type 1 interferon-associated genes, but increased cathepsin G and matrix metallopeptidase 9 expression. We found that cathepsin G and matrix metallopeptidase 9 directly inhibit parasite invasion into erythrocytes. The parasite multiplication rate was associated with host iron availability and higher complement factor H levels, lower expression of gametocyte-associated genes but higher expression of translation-associated genes in the parasite. Our findings demonstrate the potential of using explicit modelling of pathogen load dynamics to deepen understanding of host-pathogen interactions and identify mechanistic correlates of protection.

SUBMITTER: Georgiadou A

PROVIDER: S-EPMC6708439 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Modelling pathogen load dynamics to elucidate mechanistic determinants of host-Plasmodium falciparum interactions.

Georgiadou Athina A Lee Hyun Jae HJ Walther Michael M van Beek Anna E AE Fitriani Fadlila F Wouters Diana D Kuijpers Taco W TW Nwakanma Davis D D'Alessandro Umberto U Riley Eleanor M EM Otto Thomas D TD Ghani Azra A Levin Michael M Coin Lachlan J LJ Conway David J DJ Bretscher Michael T MT Cunnington Aubrey J AJ

Nature microbiology 20190617 9

During infection, increasing pathogen load stimulates both protective and harmful aspects of the host response. The dynamics of this interaction are hard to quantify in humans, but doing so could improve understanding of the mechanisms of disease and protection. We sought to model the contributions of the parasite multiplication rate and host response to observed parasite load in individual subjects infected with Plasmodium falciparum malaria, using only data obtained at the time of clinical pre ...[more]

PMID: 31209307

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Project description:BackgroundMalaria 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.MethodsMechanistic 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.ResultsThe 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.ConclusionsThis 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.

Project description:BackgroundParasites of the genus Trichinella are the pathogenic agents of trichinellosis, which is a widespread and severe foodborne parasitic disease. Trichinella spiralis resides primarily in mammalian skeletal muscle cells. After invading the cells of the host organism, T. spiralis must elude or invalidate the host's innate and adaptive immune responses to survive. It is necessary to characterize the pathogenesis of trichinellosis to help to prevent the occurrence and further progression of this disease. The aims of this study were to elucidate the mechanisms of nurse cell formation, pathogenesis and immune evasion of T. spiralis, to provide valuable information for further research investigating the basic cell biology of Trichinella-infected muscle cells and the interaction between T. spiralis and its host.MethodsWe performed transcriptome profiling by RNA sequencing to identify global changes at 1, 3, 7, 10 and 15 days post-infection (dpi) in gene expression in the diaphragm after the parasite entered and persisted within the murine myocytes; the mice were infected by intravenous injection of newborn larvae. Gene expression analysis was based on the alignment results. Differentially expressed genes (DEGs) were identified based on their expression levels in various samples, and functional annotation and enrichment analysis were performed.ResultsThe most extensive and dynamic gene expression responses in host diaphragms were observed during early infection (1 dpi). The number of DEGs and genes annotated in the Kyoto Encyclopedia of Genes and Genomes and Gene Ontology databases decreased significantly in the infected mice compared to the uninfected mice at 3 and 7 dpi, suddenly increased sharply at 10 dpi, and then decreased to a lower level at 15 dpi, similar to that observed at 3 and 7 dpi. The massive initial reaction of the murine muscle cells to Trichinella infection steadied in the later stages of infection, with little additional changes detected for the remaining duration of the studied process. Although there were hundreds of DEGs at each time point, only 11 genes were consistently up- or downregulated at all 5 time points.ConclusionsThe gene expression patterns identified in this study can be employed to characterize the coordinated response of T. spiralis-infected myocytes in a time-resolved manner. This comprehensive dataset presents a distinct and sensitive picture of the interaction between host and parasite during intracellular infection, which can help to elucidate how pathogens evade host defenses and coordinate the biological functions of host cells to survive in the mammalian environment.

Dataset Information

Modelling pathogen load dynamics to elucidate mechanistic determinants of host-Plasmodium falciparum interactions.

Publications

Modelling pathogen load dynamics to elucidate mechanistic determinants of host-Plasmodium falciparum interactions.

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