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Link to original content: http://pubmed.ncbi.nlm.nih.gov/30861289/
Endemic infection can shape exposure to novel pathogens: Pathogen co-occurrence networks in the Serengeti lions - PubMed Skip to main page content
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. 2019 Jun;22(6):904-913.
doi: 10.1111/ele.13250. Epub 2019 Mar 12.

Endemic infection can shape exposure to novel pathogens: Pathogen co-occurrence networks in the Serengeti lions

Nicholas M Fountain-Jones  1 , Craig Packer  2 , Maude Jacquot  3 , F Guillaume Blanchet  4 , Karen Terio  5 , Meggan E Craft  1
Affiliations

Endemic infection can shape exposure to novel pathogens: Pathogen co-occurrence networks in the Serengeti lions

Nicholas M Fountain-Jones et al. Ecol Lett. 2019 Jun.

Abstract

Pathogens are embedded in a complex network of microparasites that can collectively or individually alter disease dynamics and outcomes. Endemic pathogens that infect an individual in the first years of life, for example, can either facilitate or compete with subsequent pathogens thereby exacerbating or ameliorating morbidity and mortality. Pathogen associations are ubiquitous but poorly understood, particularly in wild populations. We report here on 10 years of serological and molecular data in African lions, leveraging comprehensive demographic and behavioural data to test if endemic pathogens shape subsequent infection by epidemic pathogens. We combine network and community ecology approaches to assess broad network structure and characterise associations between pathogens across spatial and temporal scales. We found significant non-random structure in the lion-pathogen co-occurrence network and identified both positive and negative associations between endemic and epidemic pathogens. Our results provide novel insights on the complex associations underlying pathogen co-occurrence networks.

Keywords: Babesia; calicivirus; canine distemper virus; co-infection; community assembly; coronavirus; feline immunodeficiency virus; parvovirus.

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Figures

Figure 1
Figure 1
Pathogen summary co‐occurrence network for (a) high taxonomic resolution and (b) medium taxonomic resolution data, where nodes are pathogens and edges reflect co‐occurrence. Edges are shown only when there were ≥ 3 co‐occurrences. Node colours reflect separate clusters. Edge weights are proportional to the number of co‐occurrences. Pathogen labels in bold (in boxes) were considered epidemic. See Fig. S4 for networks of pathogens detected via qPCR and serology separately.
Figure 2
Figure 2
Pathogen–pathogen associations detected at (a) individual, (b) pride‐year and (c) landscape‐year level after controlling for individual, pride and environmental variables in high and medium taxonomic resolution models. Blue represents negative correlations and red indicates positive associations. Only associations with posterior coefficient estimates ≥ 0.4 with 95% credible intervals that do not cross 0 are shown. The light red line indicates the association between Hepatozoon felis and CDV that was ≥ 0.4 in the medium resolution model but was below the threshold (0.38) in the high‐resolution model. Pathogens in bold and in boxes are the epidemic viruses (all other pathogens are likely endemic). This figure was drawn using the R package ‘circleplot’ (Westgate 2016). See Fig. S6 for association matrices and Figs S9/S10 for covariate partitioning and effect size.
Figure 3
Figure 3
Summary of the strong positive (red line/arrows) and negative (blue lines/arrows) associations between endemic (grey circles) and epidemic (orange circles) pathogens in the Serengeti lions; dark‐grey borders indicate protozoa. The direction of the red or blue arrows indicates the potential sequence of infection events. The black arrow along the X‐axis represents age; the circles reflect the ages when lions were likely to be infected by each pathogen (based on age‐exposure data rather than longitudinal data, see Fig. S1). Dashed circles indicate major co‐occurrence clusters identified at the landscape‐year scale.
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