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Figure S1. Summary of the experimental design. Table S1. The number of of inoculations per strain in each experiment block. Table S2. The total numbers of inoculations that germinated for each strain. Table S3. The total numbers of inoculations that resulted in immature and mature chasmothecia. Table S4. The number of Ampelomyces hyperparasite inoculations that reached state A1 for each powdery mildew strain. Table S5. The number of Ampelomyces hyperparasite inoculations that reached hyperparasite state A2 for each powdery mildew strain. Table S6. Typer4 Parameters for genotyping calling. Figure S2. Panel (A) displays the locations in which the studied strains were found in 2015. Majority (395) of all the strains found that year were only found in one location. Panel (B) shows the frequency distribution for the number of occupied locations for all the strains, for the SNP panel of 19 SNPs together with the locus with contig ID c6190. The study strains are shown in colors, and the amounts of colonized locations for each strain are shown in parenthesis. The shape_les used for generating the maps in panel A were downloaded from Stanford digital repository ( https://purl.stanford.edu/np067sb6776 ). Figure S3. Powdery mildew growth was scored using Bevan’s scale (adapted from [7]), ranging from 0 to 4 (0: no mycelium, 1: mycelium only, 2: mycelium and sparse sporulation visible only under a dissecting microscope, 3:abundant sporulation and lesion size 0.5 cm2). Figure S4. Ampelomyces infection was scored with a modi_ed version of the scale reported in [16]: A0: no pycnidia observed, A1: 1–20 pycnidia in each Ampelomyces cluster appearing and A2: 20–50 pycnidia in each powdery mildew lesion or between 30 and 50% of powdery mildew covered. This scale can re_ect either a set number of Ampelomyces pycnidia or an estimate of pycnidia coverage of the powdery mildew lesion. Hence, the scale controls for the di_erent amounts of powdery mildew tissue available for the hyperparasite to infect, i.e. small powdery mildew lesions can still support hyperparasite infection state A2 even if there is not enough tissue to produce abundant pycnidia. Table S7. The means and standard deviations of the measured life-history traits. Summary statistics for the timings of life-history events are computed only for inoculations for which the event actually occurred. The largest average value in each row is indicated in red, and the smallest in blue. Table S8. The results from pairwise model comparisons, for the survival models where the model with only strain id and the same model with both the experiment id and strain id as predictors are contrasted using anova. The presented p-value corresponds to the evidence in favor of the more rich model (Model 2). Table S9. The results from pairwise model comparisons, for the survival models where the model with only experiment id and the same model with both the experiment id and strain id as predictors are contrasted using anova. The presented p-value corresponds to the evidence in favor of the more rich model (Model 2). Table S10. The estimated relative rates and their 95% con_dence intervals for the di_erent studied infection event times, together with the associated test statistic for rejecting the null hypothesis of the corresponding factor having no e_ect on the rate of the event. Signi_cant deviations are shown in bold. Table S11. The results from pairwise model comparisons, for the ordinal regression models where the model with only experiment id and the same model with both the experiment id and strain id as predictors are contrasted using anova. The presented p-value corresponds to the evidence in favor of the more rich model (Model 2). Table S12. The results from pairwise model comparisons, for the ordinal regression models where the model with only strain id and the same model with both the experiment id and strain id as predictors are contrasted using anova. The presented p-value corresponds to the evidence in favor of the more rich model (Model 2). Table S13. Estimated e_ects for the ordinal regression model for the Bevan scale at day 15. Signi_cant deviations are shown in bold. Table S14. Estimated e_ects for the ordinal regression model for the (immature) chasmothecia category by the end of the follow-up. Table S15. The estimated relative rates (exponentials of the estimated coe_cients) for using the columns as predictor when predicting the event times indicated by the rows. The statistical signi_cancy of the estimated e_ect is shown in parenthesis and the signi_cant e_ects are shown in bold. With NA’s we have omitted the pairs of events occurring in wrong order, as they lead to non-intuitive modelling, as well as. Table S16. The estimated e_ects of event timings (columns) as predictor when predicting the abundance measureds indicated by the rows. The statistical signi_cancy of the estimated e_ect is shown in parenthesis and the signi_cant e_ects are shown in bold. Table S17. The estimated relative rates (exponentials of the estimated coe_cients) for using event times in the columns as predictor for the hyperparasite infection event times, indicated by the rows. The statistical signi_cancy of the estimated e_ect is shown in parenthesis and the signi_cant e_ects are shown in bold. Table S18. The results from pairwise model comparisons, for the survival models presented in where a survival model without any predictors and a model with the strain as a predictor are contrasted using anova. The presented p-value corresponds to the evidence in favor of the more rich model (Model 2). Table S19. The results from pairwise model comparisons, for survival models where a survival model with strain id as a predictor and a model with the strain and pathogen infection status at day 8 as a predictor are contrasted using anova. The presented p-value corresponds to the evidence in favor of the more rich model (Model 2). Table S20. The results from pairwise model comparisons, for the survival models where a survival model with pathogen infection status at day 8 as a predictor and a model with the strain id and pathogen infection status at day 8 as a predictor are contrasted using anova. The presented p-value corresponds to the evidence in favor of the more rich model (Model2). Table S21. The estimated relative rates and their 95% con_dence intervals for the di_erent studied hyperparasite infection event times, together with the associated test statistic for rejecting the null hypothesis of the corresponding factor having no e_ect on the rate of the event. Signi_cant deviations are shown in bold. Table S22. The estimated relative rates and their 95% con_dence intervals for the di_erent studied hyperparasite infection event times, where in the model the pathogen infection stage at day 8 was accounted for, together with the associated test statistic for rejecting the null hypothesis of the corresponding factor having no e_ect on the rate of the event. Signi_cant deviations are shown in bold. Figure S5. Panel A shows the frequency distribution for the number of occupied locations for all the observed strains in 2015. The majority of observed strains (395) in that year were only found in a single location, but 3 strains were found in > 25 discrete locations. Strains used to study life-history variation are shown in color. In panel B the prevalence of strains across the metapopulation is correlated with the mean _tness traits, and the corresponding p-values for the _tted rank correlations are shown in upright. (PDF 1859 kb)

Background: Ticks and tick-borne diseases undermine cattle fitness and productivity in the whole of sub-Saharan Africa, including Nigeria. In this West African country, cattle are challenged by numerous tick species, especially during the wet season. Consequently, several TBDs are known to be endemic in Nigerian cattle, including anaplasmosis, babesiosis, cowdriosis and theilerioris (by Theileria mutans and Theileria velifera). To date, all investigations on cattle TBDs in Nigeria have been based on cytological examinations and/or on serological methods. This study aimed to ascertain the occurrence of tick-borne pathogens of veterinary and zoonotic importance in cattle in Nigeria using molecular approaches.Methods: In October 2008, 704 whole blood samples were collected from indigenous cattle in the Plateau State, Nigeria. Analysis for tick-borne pathogens was conducted by means of PCR-based reverse line blotting (RLB) and sequencing targeting a panel of five genera of microorganisms (i.e. Babesia, Theileria, Anaplasma, Ehrlichia and Rickettsia spp.).Results: In total, 561/704 (82.6 %) animals were found infected, with 465 (69.6 %) of them being infected by two or more microorganisms, with up to 77 possible combinations of pathogens detected. Theileria mutans was the most prevalent microorganism (66.3 %), followed by Theileria velifera (52.4 %), Theileria taurotragi (39.5 %), Anaplasma marginale (39.1 %), Anaplasma sp. (Omatjenne) (34.7 %), Babesia bigemina (7.9 %), Anaplasma centrale (6.3 %), Anaplasma platys (3.9 %), Rickettsia massiliae (3.5 %), Babesia bovis (2.0 %) and Ehrlichia ruminantium (1.1 %). Calves were found significantly less infected than juvenile and adult cattle.Conclusions: This study provides updated, molecular-based information on cattle TBDs in Nigeria. The molecular approach employed allowed the diagnosis of numerous positive cases including carrier statuses, multiple infections and novel pathogen detections within the indigenous cattle population. Moreover, the RLB method here described enabled the detection of veterinary agents not only pertaining to bovine health, including also those of zoonotic importance.The high prevalence recorded for T. mutans, T. velifera, A. marginale, T. taurotragi and Anaplasma sp. (Omatjenne), suggests they may be endemically established in Nigeria, whereas the lower prevalence recorded for other microorganisms (i.e. A. centrale and B. bovis) highlights a less stable epidemiological scenario, requiring further investigations.

Introduction Ticks are the most common arthropod vectors of both human and animal diseases in Europe, and the Ixodes ricinus tick species is able to transmit a large number of bacteria, viruses and parasites. Ticks may also be co-infected with several pathogens, with a subsequent high likelihood of co-transmission to humans or animals. However few data exist regarding co-infection prevalences, and these studies only focus on certain well-known pathogens. In addition to pathogens, ticks also carry symbionts that may play important roles in tick biology, and could interfere with pathogen maintenance and transmission. In this study we evaluated the prevalence of 38 pathogens and four symbionts and their co-infection levels as well as possible interactions between pathogens, or between pathogens and symbionts. Methodology/principal findings A total of 267 Ixodes ricinus female specimens were collected in the French Ardennes and analyzed by high-throughput real-time PCR for the presence of 37 pathogens (bacteria and parasites), by rRT-PCR to detect the presence of Tick-Borne encephalitis virus (TBEV) and by nested PCR to detect four symbionts. Possible multipartite interactions between pathogens, or between pathogens and symbionts were statistically evaluated. Among the infected ticks, 45% were co-infected, and carried up to five different pathogens. When adding symbiont prevalences, all ticks were infected by at least one microorganism, and up to eight microorganisms were identified in the same tick. When considering possible interactions between pathogens, the results suggested a strong association between Borrelia garinii and B. afzelii, whereas there were no significant interactions between symbionts and pathogens. Conclusion/significance Our study reveals high pathogen co-infection rates in ticks, raising questions about possible co-transmission of these agents to humans or animals, and their consequences to human and animal health. We also demonstrated high prevalence rates of symbionts co-existing with pathogens, opening new avenues of enquiry regarding their effects on pathogen transmission and vector competence.

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Multiparasitism and specifically statistical associations among parasites, have a strong influence on the ecology of parasites. This influence reinforced when parasites interact and thus modify their effect on hosts. However, the identification of associations and interactions between parasites is complex at the population level. Our aim was to model multi-parasite associations and interactions, in particular for parasites of medical, veterinary or agricultural importance. We first present a review of the literature on the different causes and consequences of multiparasitism and the methods and tools available to better understand the phenomena that generate them. In a second step we worked on the detection of multi-parasite associations. We developed a new approach « association screening » to statistically test the presence of multi-parasite associations on a global scale. We used this method to identify associations and to reveal precisely associated parasites in different host populations. Then, we focused on the study of interactions between parasites. We developed a model to identify the interactions between two vector-borne and persistent parasites in a host, using data from cross-sectional studies. One way to increase our capacity to detect parasite interactions in populations is the taking into account common risk factors. Taking into account interactions increases diagnosis, treatments and prevention of infectious diseases.; Le multiparasitisme et surtout les associations qui en découlent, ont une grande influence sur l’écologie des parasites concernés. Elle est d’autant plus grande que les parasites peuvent interagir et ainsi modifier leurs effets sur les hôtes. Cependant, l’identification des associations et interactions de parasites est complexe au niveau populationnel. Notre objectif était de modéliser les associations et les interactions multiparasitaires, notamment dans le cadre des parasites d’importance médicale, vétérinaire ou agronomique. Nous avons tout d’abord réalisé une revue bibliographique sur les différentes causes et conséquences du multiparasitisme ainsi que sur les méthodes et outils disponibles pour comprendre les phénomènes qui le génèrent. Dans un second temps, nous nous sommes intéressés tout particulièrement à la détection des associations multiparasitaires. Nous avons développé une nouvelle méthode « de screening des associations » pour tester statistiquement la présence d’associations de parasites à une échelle globale. Nous avons utilisé cette méthode pour identifier les associations et pour révéler précisément les parasites associés au sein de diverses populations d’hôtes. Puis, nous avons étudié les interactions entre les parasites. Nous avons développé un modèle pour identifier les interactions entre deux parasites vectorisés et persistants chez un hôte, à partir de données issues d’études transversales. L’une des voies de progression de la connaissance sur les interactions est la prise en compte des facteurs de risque communs. La prise en compte des interactions contribue à améliorer le diagnostic, les traitements et la prévention des maladies infectieuses.

International audience

L’article originel a été catalogué par le Comité d’EDENext comme EDENext090

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National audience

Multiple infections by tick-borne pathogens according to age classes and overall number of animals. (PDF 19 kb)

Multiple infections by tick-borne pathogens according to age classes and overall number of animals. (PDF 19 kb)

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.