Linking functional and molecular mechanisms of host resilience to malaria infection

Abstract

It remains challenging to understand why some hosts suffer severe illnesses, while others are unscathed by the same infection. We fitted a mathematical model to longitudinal measurements of parasite and red blood cell density in murine hosts from diverse genetic backgrounds to identify aspects of within-host interactions that explain variation in host resilience and survival during acute malaria infection. Among eight mouse strains that collectively span 90% of the common genetic diversity of laboratory mice, we found that high host mortality was associated with either weak parasite clearance, or a strong, yet imprecise response that inadvertently removes uninfected cells in excess. Subsequent cross-sectional cytokine assays revealed that the two distinct functional mechanisms of poor survival were underpinned by low expression of either pro- or anti-inflammatory cytokines, respectively. By combining mathematical modelling and molecular immunology assays, our study uncovered proximate mechanisms of diverse infection outcomes across multiple host strains and biological scales.

Red blood cell dynamics during malaria infection challenge the assumptions of mathematical models of infection dynamics

“…Second, new model structures could prove valuable. For example, RBC dynamics may be described as time-varying functions as in e.g., Thakre et al (2018), Wale et al (2019) and Kamiya et al (2021). It is unlikely that infection dynamics are determined by time per se but rather vary with a suite of underlying biological mechanisms that happen to vary with time, and the “ looped ” trajectory of the R − E curve (and Q un − E curve, not shown) implies that a time-dependent model will not be predictive.…”

Using the Collaborative Cross and Diversity Outbred Mice in Immunology

“…The CC and DO mouse models have shown exceptional promise in identifying loci that contribute to our understanding of the genetic regulation of the differential outcomes to various infectious diseases caused by viral (influenza, SARS‐CoV1, SARS‐CoV2, Ebola, Zika, West Nile, TMEV, etc. ), bacterial (Pseudomonas, Klebsiella pneumoniae, Salmonella enterica Typhimurium ), mycobacterial (TB), fungal ( Aspergillus fumigatus, Blastomyces dermatitidis ), prion, protist ( Plasmodium chabaudi ), or helminth parasitic pathogens ( S. venezuelensis ) (Durrant et al., 2011; Graham et al., 2021; Green et al., 2016; Kamiya, Davis, Greischar, Schneider, & Mideo, 2021; Kohn et al., 2022; Lorè et al., 2020; Manet et al., 2020; Matsushita et al., 2021; Noll et al., 2020; Perez Gomez et al., 2021; Price et al., 2020; Schäfer et al., 2021; Smith et al., 2022; Vered, Durrant, Mott, & Iraqi, 2014; Xiong et al., 2014; Zhang et al., 2018).…”

Targeting malaria parasites inside mosquitoes: ecoevolutionary consequences

“…Fourth, estimating the risk of de novo resistance emergence against compounds targeting parasites during sporogony will require a mathematical model that explicitly tracks parasite development from the ingestion of a bloodmeal to onward transmission to humans, and all stages in between. Although the population dynamics of malaria parasites within mammalian hosts have been studied extensively (e.g., [ 71 – 74 ]), mechanistic modelling of within-mosquito parasite dynamics is a relatively recent development (e.g., [ 31 , 62 , 75 – 77 ]). Future studies should extend these models to provide a quantitative understanding of the origin and fate of resistance mutations in mosquitoes.…”