True versus false parasite interactions: a robust method to take risk factors into account and its application to feline viruses

Co-occurrence of pathogen assemblages in a keystone species the common cockle Cerastoderma edule on the Irish coast

Despite coinfections being recognized as the rule in animal populations, most studies focus on single pathogen systems. Pathogen interaction networks and the drivers of such associations are lacking in disease ecology studies. Common cockle Cerastoderma edule populations are exposed to a great diversity of pathogens, thus making them a good model system to investigate. This study examined the diversity and prevalence of pathogens from different taxonomic levels in wild and fished C. edule on the Irish coast. Potential interactions were tested focussing on abiotic (seawater temperature and salinity) and biotic (cockle size and age, and epiflora on shells) factors. No Microsporidia nor OsHV-1μVar were detected. Single infections with Haplosporidia (37.7%) or Vibrio (25.3%) were more common than two-pathogen coinfected individuals (9.5%), which may more easily succumb to infection. Fished C. edule populations with high cockle densities were more exposed to infections. Higher temperature and presence of epiflora on cockle shells promoted coinfection in warmer months. Low seawater salinity, host condition and proximity to other infected host species influenced coinfection distribution. A positive association between two Minchinia spp. was observed, most likely due to their different pathogenic effect. Findings highlight the major influence that ecological factors have on pathogen interactions and host–pathogen interplay.

Microhabitat distributions and species interactions of ectoparasites on the gills of cichlid fish in Lake Victoria, Tanzania

Heterogeneous exposure to parasites may contribute to host species differentiation. Hosts often harbour multiple parasite species which may interact and thus modify each other's effects on host fitness. Antagonistic or synergistic interactions between parasites may be detectable as niche segregation within hosts. Consequently, the within-host distribution of different parasite taxa may constitute an important axis of infection variation among host populations and species. We investigated the microhabitat distributions and species interactions of gill parasites (four genera) infecting 14 sympatric cichlid species in Lake Victoria, Tanzania. We found that the two most abundant ectoparasite genera (the monogenean Cichlidogyrus spp. and the copepod Lamproglena monodi) were non-randomly distributed across the host gills and their spatial distribution differed between host species. This may indicate microhabitat selection by the parasites and cryptic differences in the host-parasite interaction among host species. Relationships among ectoparasite genera were synergistic: the abundances of Cichlidogyrus spp. and the copepods L. monodi and Ergasilus lamellifer tended to be positively correlated. In contrast, relationships among morphospecies of Cichlidogyrus were antagonistic: the abundances of morphospecies were negatively correlated. Together with niche overlap, this suggests competition among morphospecies of Cichlidogyrus. We also assessed the reproductive activity of the copepod species (the proportion of individuals carrying egg clutches), as it may be affected by the presence of other parasites and provide another indicator of the species specificity of the host-parasite relationship. Copepod reproductive activity did not differ between host species and was not associated with the presence or abundance of other parasites, suggesting that these are generalist parasites, thriving in all cichlid species examined from Lake Victoria.

Coinfections by noninteracting pathogens are not independent and require new tests of interaction

If pathogen species, strains, or clones do not interact, intuition suggests the proportion of coinfected hosts should be the product of the individual prevalences. Independence consequently underpins the wide range of methods for detecting pathogen interactions from cross-sectional survey data. However, the very simplest of epidemiological models challenge the underlying assumption of statistical independence. Even if pathogens do not interact, death of coinfected hosts causes net prevalences of individual pathogens to decrease simultaneously. The induced positive correlation between prevalences means the proportion of coinfected hosts is expected to be higher than multiplication would suggest. By modelling the dynamics of multiple noninteracting pathogens causing chronic infections, we develop a pair of novel tests of interaction that properly account for nonindependence between pathogens causing lifelong infection. Our tests allow us to reinterpret data from previous studies including pathogens of humans, plants, and animals. Our work demonstrates how methods to identify interactions between pathogens can be updated using simple epidemic models.

If pathogen species, strains, or clones do not interact, intuition suggests the proportion of coinfected hosts can be obtained by simply multiplying the individual prevalences. However, even simple epidemiological models show this to be untrue. This study develops new tests for interaction between pathogens that account for this surprising lack of statistical independence.

The importance of multiparasitism: examining the consequences of co-infections for human and animal health

Most parasites co-occur with other parasites, although the importance of such multiparasitism has only recently been recognised. Co-infections may result when hosts are independently infected by different parasites at the same time or when interactions among parasite species facilitate co-occurrence. Such interactions can have important repercussions on human or animal health because they can alter host susceptibility, infection duration, transmission risks, and clinical symptoms. These interactions may be synergistic or antagonistic and thus produce diverse effects in infected humans and animals. Interactions among parasites strongly influence parasite dynamics and therefore play a major role in structuring parasite populations (both within and among hosts) as well as host populations. However, several methodological challenges remain when it comes to detecting parasite interactions. The goal of this review is to summarise current knowledge on the causes and consequences of multiparasitism and to discuss the different methods and tools that researchers have developed to study the factors that lead to multiparasitism. It also identifies new research directions to pursue.

Parasite-Parasite Interactions in the Wild: How To Detect Them?

Inter-specific interactions between parasites impact on parasite intra-host dynamics, host health, and disease management. Identifying and understanding interaction mechanisms in the wild is crucial for wildlife disease management. It is however complex because several scales are interlaced. Parasite-parasite interactions are likely to occur via mechanisms at the within-host level, but also at upper levels (host population and community). Furthermore, interactions occurring at one level of organization spread to upper levels through cascade effects. Even if cascade effects are important confounding factors, we argue that we can also benefit from them because upper scales often provide a way to survey a wider range of parasites at lower cost. New protocols and theoretical studies (especially across scales) are necessary to take advantage of this opportunity.

Interactions among urogenital, intestinal, skin, and oral infections in pregnant and lactating Panamanian Ngäbe women: a neglected public health challenge

Interrelationships among bacteria, protozoa, helminths, and ectoparasites were explored in a cross-sectional survey of 213 pregnant and 99 lactating indigenous women. Prevalences in pregnancy and lactation, respectively, were: vaginitis (89.2%; 46.8%), vaginal trichomoniasis (75.3%; 91.1%), bacterial vaginosis (BV; 60.6%; 63.3%), hookworm (56.6%; 47.8%), asymptomatic bacteriuria/urinary tract infection (AB/UTI; 56.2%; 36.2%), cervicitis (33.3%; 6.3%), vaginal yeast (24.9%; 11.4%), Ascaris (32.5%; 17.4%), vaginal diplococci (20.4%; 31.6%), caries (19.7%; 18.2%), scabies (17.4%; 8.1%), and Trichuris (12.5%; 8.7%). Multiple regressions revealed positive associations during pregnancy (trichomoniasis and AB/UTI; diplococci and Ascaris) and lactation (yeast and scabies). Negative associations were detected in pregnancy (BV and trichomoniasis; hookworm and diplococci) and lactation (BV and yeast). Vaginal Lactobacillus reduced odds of diplococci in pregnancy and lactation, but increased Ascaris eggs per gram (epg) and odds of trichomoniasis in pregnancy and yeast in lactation. These associations raised a concern that treatment of one condition may increase the risk of another.

Unknown age in health disorders: A method to account for its cumulative effect and an application to feline viruses interactions

Parasite interactions have been widely evidenced experimentally but field studies remain rare. Such studies are essential to detect interactions of interest and access (co)infection probabilities but face methodological obstacles. Confounding factors can create statistical associations, i.e. false parasite interactions. Among them, host age is a crucial covariate. It influences host exposition and susceptibility to many infections, and has a mechanical effect, older individuals being more at risk because of a longer exposure time. However, age is difficult to estimate in natural populations. Hence, one should be able to deal at least with its cumulative effect. Using a SI type dynamic model, we showed that the cumulative effect of age can generate false interactions theoretically (deterministic modeling) and with a real dataset of feline viruses (stochastic modeling). The risk to wrongly conclude to an association was maximal when parasites induced long-lasting antibodies and had similar forces of infection. We then proposed a method to correct for this effect (and for other potentially confounding shared risk factors) and made it available in a new R package, Interatrix. We also applied the correction to the feline viruses. It offers a way to account for an often neglected confounding factor and should help identifying parasite interactions in the field, a necessary step towards a better understanding of their mechanisms and consequences.

An inverse association between West Nile virus serostatus and avian malaria infection status

Background

Various ecological and physiological mechanisms might influence the probability that two or more pathogens may simultaneously or sequentially infect a host individual. Concurrent infections can have important consequences for host condition and fitness, including elevated mortality risks. In addition, interactions between coinfecting pathogens may have important implications for transmission dynamics.

Methods

Here, we explore patterns of association between two common avian pathogens (West Nile virus and avian malaria parasites) among a suburban bird community in Chicago, IL, USA that share mosquito vectors. We surveyed 1714 individual birds across 13 species for both pathogens through established molecular protocols.

Results

Field investigations of haemosporidian and West Nile virus (WNV) infections among sampled birds yielded an inverse association between WNV serostatus and Plasmodium infection status. This relationship occurred in adult birds but not in juveniles. There was no evidence for a relationship between Haemoproteus infection and WNV serostatus. We detected similar prevalence of Plasmodium among birds captured with active WNV infections and spatiotemporally paired WNV-naïve individuals of the same species, demonstrating that the two pathogens can co-infect hosts.

Conclusions

Mechanisms explaining the negative association between WNV serostatus and Plasmodium infection status remain unclear and must be resolved through experimental infection procedures. However, our results highlight potential interactions between two common avian pathogens that may influence their transmission among hosts. This is especially relevant considering that West Nile virus is a common zoonotic pathogen with public health implications. Moreover, both pathogens are instructive models in infectious disease ecology, and infection with either has fitness consequences for their avian hosts.

Electronic supplementary material

The online version of this article (doi:10.1186/1756-3305-7-415) contains supplementary material, which is available to authorized users.

Infection-interactions in Ethiopian village chickens

Chickens raised under village production systems are exposed to a wide variety of pathogens, and current or previous infections may affect their susceptibility to further infections with another parasite, and/or can alter the manifestation of each infection. It is possible that co-infections may be as important as environmental risk factors. However, in cross-sectional studies, where the timing of infection is unknown, apparent associations between infections may be observed due to parasites sharing common risk factors. This study measured antibody titres to 3 viral (Newcastle disease, Marek's disease and infectious bursal disease) and 2 bacterial (Pasteurella multocida and Salmonella) diseases, and the infection prevalence of 3 families of endo- and ecto-parasites (Ascaridida, Eimeria and lice) in 1056 village chickens from two geographically distinct populations in Ethiopia. Samples were collected during 4 cross-sectional surveys, each approximately 6 months apart. Constrained ordination, a technique for analysis of ecological community data, was used to explore this complex dataset and enabled potential relationships to be uncovered and tested despite the different measurements used for the different parasites. It was found that only a small proportion of variation in the data could be explained by the risk factors measured. Very few birds (9/1280) were found to be seropositive to Newcastle disease. Positive relationships were identified between Pasteurella and Salmonella titres; and between Marek's disease and parasitic infections, and these two groups of diseases were correlated with females and males, respectively. This may suggest differences in the way that the immune systems of male and female chickens interact with these parasites. In conclusion, we find that a number of infectious pathogens and their interactions are likely to impact village chicken health and production. Control of these infections is likely to be of importance in future development planning.

Felis catus gammaherpesvirus 1; a widely endemic potential pathogen of domestic cats

Felis catus gammaherpesvirus 1 (FcaGHV1), recently discovered in the USA, was detected in domestic cats in Australia (11.4%, 95% confidence interval 5.9-19.1, n=110) and Singapore (9.6%, 95% confidence interval 5.9-14.6, n=176) using qPCR. FcaGHV1 qPCR positive cats were 2.8 times more likely to be sick than healthy. Risk factors for FcaGHV1 detection included being male, increasing age and coinfection with pathogenic retroviruses, feline immunodeficiency virus (FIV) or feline leukaemia virus. FcaGHV1 DNA was detected in multiple tissues from infected cats with consistently high virus loads in the small intestine. FcaGHV1 viral load was significantly higher in FIV-infected cats compared with matched controls, mimicking increased Epstein-Barr virus loads in human immunodeficiency virus-infected humans. FcaGHV1 is endemic in distant geographic regions and is associated with being sick and with coinfections. Horizontal transmission of FcaGHV1 is supported, with biting being a plausible route. A pathogenic role for FcaGHV1 in domestic cats is supported.

To be or not to be associated: power study of four statistical modeling approaches to identify parasite associations in cross-sectional studies

A growing number of studies are reporting simultaneous infections by parasites in many different hosts. The detection of whether these parasites are significantly associated is important in medicine and epidemiology. Numerous approaches to detect associations are available, but only a few provide statistical tests. Furthermore, they generally test for an overall detection of association and do not identify which parasite is associated with which other one. Here, we developed a new approach, the association screening approach, to detect the overall and the detail of multi-parasite associations. We studied the power of this new approach and of three other known ones (i.e., the generalized chi-square, the network and the multinomial GLM approaches) to identify parasite associations either due to parasite interactions or to confounding factors. We applied these four approaches to detect associations within two populations of multi-infected hosts: (1) rodents infected with Bartonella sp., Babesia microti and Anaplasma phagocytophilum and (2) bovine population infected with Theileria sp. and Babesia sp. We found that the best power is obtained with the screening model and the generalized chi-square test. The differentiation between associations, which are due to confounding factors and parasite interactions was not possible. The screening approach significantly identified associations between Bartonella doshiae and B. microti, and between T. parva, T. mutans, and T. velifera. Thus, the screening approach was relevant to test the overall presence of parasite associations and identify the parasite combinations that are significantly over- or under-represented. Unraveling whether the associations are due to real biological interactions or confounding factors should be further investigated. Nevertheless, in the age of genomics and the advent of new technologies, it is a considerable asset to speed up researches focusing on the mechanisms driving interactions between parasites.

A probabilistic model in cross-sectional studies for identifying interactions between two persistent vector-borne pathogens in reservoir populations

BACKGROUND

In natural populations, individuals are infected more often by several pathogens than by just one. In such a context, pathogens can interact. This interaction could modify the probability of infection by subsequent pathogens. Identifying when pathogen associations correspond to biological interactions is a challenge in cross-sectional studies where the sequence of infection cannot be demonstrated.

METHODOLOGY/PRINCIPAL FINDINGS

Here we modelled the probability of an individual being infected by one and then another pathogen, using a probabilistic model and maximum likelihood statistics. Our model was developed to apply to cross-sectional data, vector-borne and persistent pathogens, and to take into account confounding factors. Our modelling approach was more powerful than the commonly used Chi-square test of independence. Our model was applied to detect potential interaction between Borrelia afzelii and Bartonella spp. that infected a bank vole population at 11% and 57% respectively. No interaction was identified.

CONCLUSIONS/SIGNIFICANCE

The modelling approach we proposed is powerful and can identify the direction of potential interaction. Such an approach can be adapted to other types of pathogens, such as non-persistents. The model can be used to identify when co-occurrence patterns correspond to pathogen interactions, which will contribute to understanding how organism communities are assembled and structured. In the long term, the model's capacity to better identify pathogen interactions will improve understanding of infectious risk.

Prevalence, genomic and metabolic profiles of Acinetobacter and Asaia associated with field-caught Aedes albopictus from Madagascar

The presence of cultivable bacteria Acinetobacter and Asaia was recently demonstrated in the mosquito vector Aedes albopictus. However, it is not known how prevalent these bacteria are in field populations. Here, the presence of these bacteria in Ae. albopictus populations from Madagascar was diagnosed by amplification of 16S rRNA gene fragments. Both genera were detected at relatively high frequencies, 46% for Asaia and 74% for Acinetobacter. The prevalence of Acinetobacter correlated significantly with mosquito gender, and the prevalence of Asaia with the interaction between mosquito gender and the sampling site. For each bacterial genus, more male than female mosquitoes were infected. Using pulse field gel electrophoresis, no significant difference in genome size was found between Acinetobacter isolates from mosquitoes compared with free-living Acinetobacter. However, a great diversity was observed in plasmid numbers (from 1 to 12) and sizes (from < 8 to 690 kb). Mosquito isolates utilized fewer substrates than free-living isolates, but some substrates known as blood or plant components were specifically utilized by mosquito isolates. Therefore it is likely that a specific subpopulation of Acinetobacter is selected by Ae. albopictus. Overall, this study emphasizes the need to gain a global view on the bacterial partners in mosquito vectors.