Analysis of a summary network of co-infection in humans reveals that parasites interact most via shared resources

Abstract

Simultaneous infection by multiple parasite species (viruses, bacteria, helminths, protozoa or fungi) is commonplace. Most reports show co-infected humans to have worse health than those with single infections. However, we have little understanding of how co-infecting parasites interact within human hosts. We used data from over 300 published studies to construct a network that offers the first broad indications of how groups of co-infecting parasites tend to interact. The network had three levels comprising parasites, the resources they consume and the immune responses they elicit, connected by potential, observed and experimentally proved links. Pairs of parasite species had most potential to interact indirectly through shared resources, rather than through immune responses or other parasites. In addition, the network comprised 10 tightly knit groups, eight of which were associated with particular body parts, and seven of which were dominated by parasite–resource links. Reported co-infection in humans is therefore structured by physical location within the body, with bottom-up, resource-mediated processes most often influencing how, where and which co-infecting parasites interact. The many indirect interactions show how treating an infection could affect other infections in co-infected patients, but the compartmentalized structure of the network will limit how far these indirect effects are likely to spread.

Synergistic effects of dual parasitism in Daphnia magna under nutrient limitation

“…Virulence effects in the highest food quality treatment were small to absent, but parasite-induced effects were more pronounced when nutrient limitation was more severe and/or under multiple parasite species exposure, depending on the considered time point in the experiment. This result is in line with many studies that show that parasite virulence resembles the virulence of the most virulent competitor or increases under co-infection (Ben-ami et al 2008;schmiD-hemPeL 2011;aLizon et al 2013;griffiths et al 2015). However, we here show that this especially occurs upon food quality (nutrient) manipulation.…”

“…In most cases, multiple infections will be more virulent than single infections, given that the most competitive strains gain a disproportional share of the host (Ben-ami et al 2011;schmiD-hemPeL 2011;aLizon et al 2013;griffiths et al 2015). However, the net outcome of multiple infections depends, inter alia, on the mode of competition between different parasite species, the involvement of the host immune system (graham 2008;miDeo 2009;cressLer et al 2014), host demography (izhar et al 2015) and the environmental dependency of multiple infections (PenczyKowsKi et al 2016).…”

Resource limitation alters the consequences of co-infection for both hosts and parasites

“…SEM analysis allowed us to determine that indirect, immune-mediated interactions had the strongest effects on helminth egg shedding in our study. Interestingly, a recent meta-analysis found that resourcemediated interactions are most common in human co-infections, but the strength of different interactions could not be accounted for due to a lack of requisite data (Griffiths et al, 2014). We also found that parasite fitness and host weight strongly depended on resources and the combination of co-infecting parasites, but we also recognise the need for further studies with different infection orders and dosing regimes.…”

Invasions of Host-Associated Microbiome Networks

“…Griffiths et al (2014) assemble a network of such interactions, drawing links between pairs of species on which some association has been published. Analysing the resulting three-trophic layer network, they argue that WH pathogen interactions tend to be predominantly indirect; that the network has several identifiable and repeated motifs; that most nodes have less than 10 links, i.e.…”

“…Analysing the resulting three-trophic layer network, they argue that WH pathogen interactions tend to be predominantly indirect; that the network has several identifiable and repeated motifs; that most nodes have less than 10 links, i.e. degrees (nodes are connected to few other nodes); and that there is bottom-up control (the community dynamics is driven mostly by nutrient availability Griffiths et al 2014). Unfortunately, this analysis did not include resident taxa (e.g., microbiome) which may change these results; nor did they study how the model responds to perturbations.…”