Infection prevalence and coinfection dynamics. Wigglesworthia
-specific DNA fragment amplification was used to rule out potential DNA extraction problems since all natural populations of tsetse carry Wigglesworthia
(data not shown). While all flies had Wigglesworthia
infections as expected, none of the 222 flies screened in our study carried Sodalis
despite using sequential PCRs to detect low-density infections (data not shown). This was similar to another study performed on G. fuscipes fuscipes
populations in Kenya where no Sodalis
infections were detected (39
The Wolbachia density in natural G. fuscipes fuscipes populations was unusually low and required an assay in which two sequential amplifications were performed for detection. Based on qPCR, the Wolbachia density in G. fuscipes fuscipes-infected individuals was found to be at least 20-fold lower than that in females from a laboratory line of G. morsitans morsitans (). Furthermore, flies shown to be negative by PCR amplification had no detectable levels of Wolbachia by a qPCR assay.
Fig 2 Comparison of Wolbachia density in G. fuscipes fuscipes (Gff) with that in a laboratory strain of G. morsitans morsitans (Gmm). Wolbachia densities from female Wolbachia-positive G. fuscipes fuscipes flies (GffWol+) (n = 12), Wolbachia-negative G. fuscipes (more ...)
To confirm that the parasite infection assay we performed on DNA extracted from whole flies reflects true trypanosome infections as opposed to those newly acquired in the last blood meal, we performed an experiment where we provided an infectious blood meal to laboratory flies, extracted the DNA from whole bodies, and amplified the DNA for parasites. We could detect trypanosomes in only 50% of the flies, which is similar to the level of mature parasite infections we typically obtain when we provide a trypanosome blood meal to newly emerged laboratory flies (see Fig. S1 in the supplemental material) (23
). Hence, the PCR-positive flies likely represent those that would have given rise to mature midgut infections. In fact, the levels of parasitemia in the infected mammalian hosts that flies typically feed on are likely to be significantly less than the level of 105
parasites/ml that we provided in the laboratory. Furthermore, the majority of flies that are trapped in the field have little residual blood meal in their midgut, unlike those we analyzed in the laboratory 48 h after the infected blood meal. Hence, although we did not perform midgut infections in the field, our prevalence data based on a whole-fly DNA amplification assay likely reflect true trypanosome infections in the field.
Prevalence of Wolbachia, SGHV, and trypanosomes was heterogeneous throughout the landscape (see Table S1a, b, and c in the supplemental material). Across Uganda, Wolbachia prevalence was 44.3%, while SGHV and trypanosome prevalences were 12% and 18%, respectively. Wolbachia prevalence in different populations ranged from uninfected to near fixation, whereas the prevalences for SGHV and trypanosomes ranged from 0% to 47% and from 0% to 40%, respectively.
In general, Wolbachia prevalence in different populations was higher than the corresponding prevalence of either SGHV or trypanosomes. However, across the 18 populations in our study, when Wolbachia infection prevalence was below 30%, SGHV and/or trypanosome infections tended to be at the same level or higher than that noted for Wolbachia (). Prevalences of Wolbachia and SGHV were more negatively correlated (r = −0.408, P = 0.046) than the prevalences of Wolbachia and trypanosomes (r = −0.176, P = 0.242), while prevalences of SGHV and trypanosomes were positively correlated (r = 0.257, P = 0.152). These results suggest that Wolbachia, on one hand, and SGHV and trypanosomes, on the other hand, have a negative impact on each other.
Fig 3 Prevalence of Wolbachia, SGHV, and Trypanosoma within G. fuscipes fuscipes populations. Populations, on the x axis, are arranged in ascending order of Wolbachia prevalence within the groups defined by microsatellites (n, north; s, south; w, west) (9). (more ...)
Single infections were more frequent than coinfections, with only 1% of flies harboring all three pathogens and 40% lacking all three pathogens. In contrast, 33%, 6%, and 8% of flies were singly infected with Wolbachia, SGHV, and trypanosomes, respectively. In terms of coinfections, 8% of flies were infected with Wolbachia and trypanosomes, 3% with Wolbachia and SGHV, and 2% with SGHV and trypanosomes. Among the pathogens, Wolbachia is the most common of the three, and its presence correlates most often with the presence of trypanosomes.
Prevalence patterns of individual pathogens in relation to host groups.
Simple binomial logistic regression independently tested the effects of genetic groups, populations, and sex on the occurrence of one of the three infections. The contrast technique determined the statistical significance of individual infection differences between the 18 host populations, the three microsatellite groups, the two mtDNA groups, and the sexes (see Table S2a in the supplemental material). The following populations (white circles in ) were excluded from comparisons due to uninformative confidence intervals: KR and KL (Wolbachia), BU, KL, KB, and MS (SGHV), and BV and KL (trypanosomes).
Fig 4 Mean infection probabilities (circles) within G. fuscipes fuscipes populations and groups for Wolbachia (1), SGHV (2), and Trypanosoma (3), as determined by simple binomial logistic regression. Each panel shows infection probabilities within each population (more ...)
Wolbachia prevalence did not differ significantly between the mtDNA haplogroups. However, the host microsatellite groups showed large differences in prevalence of Wolbachia (south, 55%; north, 42%; west, 26%) (). The infection probabilities in these groups were significantly different (north-south, z = −2.49, P = 0.013; north-west, z = 2.73, P = 0.006; south-west, z = 4.63, P = 0.000) (see Table S2a in the supplemental material). At the population level, SS (87% prevalence) had a significantly higher infection probability than any other population (see Table S2a in the supplemental material). DK (64% prevalence) and KB (50% prevalence), despite being in lower-prevalence regions (north and west, respectively), had a significantly higher infection probability than some southern populations, namely, BU (29%) and NA (8%) (DK-BU, z = 2.68, P = 0.007; DK-NA, z = 4.32, P = 0.000; KB-NA, z = 3.46, P = 0.001). Males and females did not significantly differ for Wolbachia infections, whether compared across Uganda or within microsatellite and mtDNA groups.
Female and male infection prevalence by geographic region
The lack of a difference in Wolbachia prevalence between the mtDNA haplogroups suggests that unidirectional CI is not responsible for maintaining the genetic division identified between the mtDNA haplogroups () because Wolbachia is not absent from one of the two haplogroups. The significant differences in Wolbachia prevalence between the microsatellite groups and the high variation among populations within these groups suggest that the spread of the pathogen is dependent on the dispersal ability of the vector.
SGHV prevalence differed significantly (z = 2.28, P = 0.022) (see Table S2a in the supplemental material) between the north (15%) and south (8%) mtDNA haplogroups, but it did not differ based on microsatellite groups. At the population level, KF was the only population with a significantly higher infection probability (higher than eight other populations) (see Table S2a in the supplemental material). While in the population with the highest SGHV infection rate (KF [west microsatellite group]), most of the infected individuals were females, in the south, SGHV prevalence was highest in males (z = 2.25, P = 0.024) (see Table S2a in the supplemental material).
The significant difference we observed in SGHV prevalence between the mtDNA haplogroups and the lack of an association with microsatellite groups is not surprising, as it reflects vertical transmission coupled with reduced female dispersal (9
). Given that SGHV can affect tsetse reproduction, the biological significance of these associations between virus prevalence and host genetic makeup deserves further investigation. Furthermore, given that viral density can influence both the pathogenesis and fecundity outcomes (3
), future studies measuring viral densities from different populations are important.
Trypanosome prevalences were not significantly different between either mtDNA or microsatellite groups. At the population level, all significant differences in infection probability were found in pairwise comparisons of populations with higher prevalence than BK (9%) or lower prevalence than OS (40%) (see Table S2a in the supplemental material). The only other significant difference in infection probability was observed between males and females in the southern microsatellite group (higher in males; z = 2.39, P = 0.017).
Pathogen coinfection patterns in relation to host groups.
Multiple binomial logistic regression analyses independently tested the effect of populations and genetic groups (nuclear and mitochondrial) on an infection in the context of a second infection in two-pathogen comparisons. Multiple regressions also tested, independently, the effect of nuclear and mitochondrial groups on an infection in the context of the other two infections in three-pathogen comparisons.
In two-pathogen comparisons, the influence of Wolbachia was such that differences in prevalence between host groups of either SGHV or trypanosomes were significant only when uninfected with Wolbachia. SGHV prevalence was higher in the north than in the south mtDNA group (P = 0.019), and trypanosome prevalence was higher in three out of five mixed populations in the region around Lake Kyoga (BN, JN, and KF) (see Table S2b in the supplemental material). When uninfected with SGHV, we found that the prevalence differences in Wolbachia, identified by simple logistic regressions (see Table S2a in the supplemental material), persisted between the microsatellite groups and populations. When infected with SGHV, however, the only significant difference was found in trypanosome prevalence between the south and north microsatellite groups (z = 2.17, P = 0,030) (see Table S2b in the supplemental material). When uninfected with trypanosomes, Wolbachia prevalence differences found using simple logistic regression (see Table S2a in the supplemental material) persisted. For SGHV, significant differences were found between microsatellite groups (west-south, z = 2.1, P = 0.035; north-south, z = 1.98, P = 0.048) (see Table S2b in the supplemental material). At the population level, the significantly higher SGHV prevalence in KF persisted. When infected with trypanosomes, however, the only significant differences were found with respect to Wolbachia prevalence (BK-OS, z = 1.98, P = 0.048; SS-JN, z = 2.17, P = 0.030; SS-OS, z = 2.84, P = 0.005) (see Table S2b in the supplemental material).
In three-pathogen comparisons, when uninfected with both SGHV and trypanosomes (see Table S2b in the supplemental material, SGHV− and Tryp−), Wolbachia prevalence was significantly different between microsatellite host groups (north-south, z = −2.01, P = 0.045; north-west, z = 2.57, P = 0.010; south-west, z = 4.04, P = 0.000). When infected with Wolbachia but uninfected with trypanosomes (see Table S2a in the supplemental material, Wol+ and Tryp−), SGHV infection prevalence was higher in the northern than the southern mtDNA haplogroup (z = 2.16, P = 0.031).
We used MCA to simultaneously investigate cooccurrence patterns of the three pathogens at the population level, with microsatellite and mtDNA host groups and host sex plotted post hoc to facilitate the interpretation of similarities or dissimilarities between populations (). The two axes of MCA represent 63% of the total variation (proportions of explained inertia, τ1 = 46.9% and τ2 = 16.1%). Infection status (presence or absence) () of Wolbachia varied along the first axis, trypanosomes varied along the second, and SGHV differed along both axes. Given the variance explained by each axis (46.9% versus 16.1%), we can infer that a majority of the differences between populations can be attributed to differences in Wolbachia prevalence rather than differences in SGHV or trypanosomes. MCA showed a strong association between the absence of SGHV and the absence of trypanosome infections across populations. Although there is an association between SGHV and trypanosome infections, this is mostly driven by two populations (KF and OS) (). MCA revealed exceptions to coinfection patterns found in microsatellite- and mtDNA-defined regions. For instance, BK and DK (north) and KB (west) were more associated with the presence of Wolbachia infection than with its absence, a pattern which was not generally true of the northern or the western microsatellite population groups. The opposite was true for BU (south), which was an exception to the rest of the southern microsatellite group, inasmuch as it was associated with the absence rather than the presence of Wolbachia infection. In concordance with the general pattern of the southern microsatellite group, the two southern populations JN and SS were associated with the presence of Wolbachia infection, but they also had a higher probability of SGHV and trypanosome infection than did the other populations in the southern microsatellite group.
Fig 5 Multiple correspondence analysis plot. The first two axes, representing 63% of the total variation, are plotted. Eigenvalues (λ1 = 0.038 and λ2 = 0.013) and proportions of explained inertia (τ1 = 46.9% and τ2 = 16.1%) have (more ...)
The Mantel test between coinfection dissimilarity and genetic distance was not significant (r = −0.111). However, the Mantel test between coinfection and geographic distance revealed a significant negative correlation (Pearson's correlation coefficient; r = −0.261; α = 0.01). When Wolbachia data were excluded, geography and coinfection were not significantly negatively correlated (r = −0.125). Thus, geographically proximate populations are more dissimilar in coinfections than are distant populations. The fact that the exclusion of Wolbachia influences the negative correlation between coinfection dissimilarity and geographic distance indicates that no spatial heterogeneity between close populations exists without Wolbachia.