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. 2021 Sep 8;12(9):805.
doi: 10.3390/insects12090805.

Frequent Drivers, Occasional Passengers: Signals of Symbiont-Driven Seasonal Adaptation and Hitchhiking in the Pea Aphid, Acyrthosiphon pisum

Melissa Carpenter  1 , Linyao Peng  2 , Andrew H Smith  1   2 , Jonah Joffe  2 , Michael O'Connor  1 , Kerry M Oliver  3 , Jacob A Russell  1   2
Affiliations

Frequent Drivers, Occasional Passengers: Signals of Symbiont-Driven Seasonal Adaptation and Hitchhiking in the Pea Aphid, Acyrthosiphon pisum

Melissa Carpenter et al. Insects. .

Abstract

Insects harbor a variety of maternally inherited bacterial symbionts. As such, variation in symbiont presence/absence, in the combinations of harbored symbionts, and in the genotypes of harbored symbiont species provide heritable genetic variation of potential use in the insects' adaptive repertoires. Understanding the natural importance of symbionts is challenging but studying their dynamics over time can help to elucidate the potential for such symbiont-driven insect adaptation. Toward this end, we studied the seasonal dynamics of six maternally transferred bacterial symbiont species in the multivoltine pea aphid (Acyrthosiphon pisum). Our sampling focused on six alfalfa fields in southeastern Pennsylvania, and spanned 14 timepoints within the 2012 growing season, in addition to two overwintering periods. To test and generate hypotheses on the natural relevance of these non-essential symbionts, we examined whether symbiont dynamics correlated with any of ten measured environmental variables from the 2012 growing season, including some of known importance in the lab. We found that five symbionts changed prevalence across one or both overwintering periods, and that the same five species underwent such frequency shifts across the 2012 growing season. Intriguingly, the frequencies of these dynamic symbionts showed robust correlations with a subset of our measured environmental variables. Several of these trends supported the natural relevance of lab-discovered symbiont roles, including anti-pathogen defense. For a seventh symbiont-Hamiltonella defensa-studied previously across the same study periods, we tested whether a reported correlation between prevalence and temperature stemmed not from thermally varying host-level fitness effects, but from selection on co-infecting symbionts or on aphid-encoded alleles associated with this bacterium. In general, such "hitchhiking" effects were not evident during times with strongly correlated Hamiltonella and temperature shifts. However, we did identify at least one time period in which Hamiltonella spread was likely driven by selection on a co-infecting symbiont-Rickettsiella viridis. Recognizing the broader potential for such hitchhiking, we explored selection on co-infecting symbionts as a possible driver behind the dynamics of the remaining six species. Out of twelve examined instances of symbiont dynamics unfolding across 2-week periods or overwintering spans, we found eight in which the focal symbiont underwent parallel frequency shifts under single infection and one or more co-infection contexts. This supported the idea that phenotypic variation created by the presence/absence of individual symbionts is a direct target for selection, and that symbiont effects can be robust under co-habitation with other symbionts. Contrastingly, in two cases, we found that selection may target phenotypes emerging from symbiont co-infections, with specific species combinations driving overall trends for the focal dynamic symbionts, without correlated change under single infection. Finally, in three cases-including the one described above for Hamiltonella-our data suggested that incidental co-infection with a (dis)favored symbiont could lead to large frequency shifts for "passenger" symbionts, conferring no apparent cost or benefit. Such hitchhiking has rarely been studied in heritable symbiont systems. We propose that it is more common than appreciated, given the widespread nature of maternally inherited bacteria, and the frequency of multi-species symbiotic communities across insects.

Keywords: Wolbachia; adaptation; aphid; bacteria; hitchhiking; symbiont.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Hitchhiking or selection? Key for (co-)infection context deconstruction and its use in interpreting the symbiont dynamics shown in later figures. (A) Possible pattern expected if: there is positive selection on aphids with Symbiont A due to the symbiont’s fitness/phenotypic impacts, and if Symbiont B rarely lives with Symbiont A, showing, thus, no response via hitchhiking. (B,C) Patterns consistent with selection on aphids with one symbiont and hitchhiking by another. Under this scenario, selection acts on the phenotypic effects conferred by Symbiont A. This symbiont commonly lives with Symbiont B, which does not strongly impact Symbiont A’s fitness/phenotypic impacts, and which does not, itself, alter fitness at the given time. Symbiont B’s frequency dynamics, thus, extend from selection acting on its co-infecting Symbiont A partner. (D) Pattern expected if selection acts on a phenotype conferred due to the joint actions of two co-infecting symbionts. In this case, it is the co-infection that is under selection (only positive selection scenario shown for panel (D)—as done also for panel (A)). The boxed legend at the bottom decodes the line -color and -dashing schemes used to illustrate symbiont and (co-)infection context frequencies in (A–D). Note that y-axes represent the frequency of each symbiont or the particular (co-)infection type. Colored circles on the x-axis represent different sampling times.
Figure 2
Figure 2
Correlations between the prevalence of facultative symbionts and environmental variables across the 2012 field season. Here we graph all significant results from our assessments of whether environmental variables (time t) predict symbiont frequency dynamics (time t), ascertained through diagnostic PCR. Each plot shows the data for the environmental variable (left y-axis) and prevalence/frequency of each symbiont (proportion of aphids infected) showing a correlation with the given variable (right y-axis). The directions of each correlation are denoted with a “−” or “+” to the right of each symbiont trendline. (A) Correlations with mortality due to Aphidius ervi parasitoids (ascertained by mummification) and Pandora neoaphidis fungal pathogens (based on fungal cadaver morphology). (B) Correlations with insects counted from our sweep net samples, including A. ervi parasitoids, an alfalfa-feeding competitor (potato leafhopper—Empoasca fabae), predatory coccinellid beetles, and pea aphid density. (C) Correlations with climatic variables, i.e., vapor deficit (aridity) and temperature (for Hamiltonella—based on data originally reported in Smith et al., 2021 [60]). For each symbiont we use colors and symbols consistent with those used in other figures. Not shown here are significant correlations with environmental variables without clear a priori expectations (Tables S9 and S10). Environmental variables were plotted as averages, as were symbiont frequencies, in contrast with other figures where frequencies were shown as pooled estimates. At each time point PCR screening data were obtained from an average of 126 aphids and 5.5 fields.
Figure 3
Figure 3
Co-infection contexts for facultative symbionts across the 2012 field season. Shown here are results of diagnostic PCR screening for each of seven facultative symbionts—in total and in relation to the presence of other symbionts in the same aphid hosts. All datapoints represent pooled frequencies across all sampled fields from the given time. Solid black lines represent total frequencies. Colored lines represent the frequencies of aphids harboring co-infections with the focal symbiont and other facultative symbionts. Gray lines represent the frequencies of the focal symbionts in aphids with no other co-infecting facultative symbionts (“single infections”). Several symbionts underwent large frequency shifts across paired time points, separated by only 2-weeks. These showed frequent consistency/parallels across replicate fields (Figure S1). Among these, shifts for Hamiltonella at times 3–4 and 6–7 were at least partially comprised of changes in the prevalence of single infections, arguing against the possibility that Hamiltonella hitchhiked in aphids with a second symbiont yielding a phenotype under stronger selection. Single infection trajectories were somewhat muted, but parallel to those of overall symbiont prevalence for the rapid frequency shifts of Rickettsiella at times 4–5, 5–6 (to a weaker extent), 10–11, and 12–13. In contrast, single infection frequencies of Hamiltonella at times 4–5 and Regiella at times 6–7 did not track overall frequencies of these two bacterial species. We follow up on these trends with more detailed (co-)infection context graphing in Figure 4. Note that, for the present figure, y-axis scales differ across the varying symbionts. Additionally, time points on the x-axis are separated by 2 weeks, spanning late April through late October.
Figure 4
Figure 4
Direct selection vs. hitchhiking effects for Hamiltonella, Rickettsiella, and Regiella across 2-week time intervals in the 2012 field season. We limited our focus to symbionts undergoing frequency shifts ≥20% in magnitude across such time intervals. (A) Schematic of our 2012 field sampling, with colored arrows emphasizing the time points spanning 2-week intervals with large (≥20%) symbiont frequency shifts. (B) Re-graphing of data from Figure S1, to show—in one panel—the total frequencies, pooled across fields and (co-)infection contexts, for all seven facultative symbionts. (C–H) Shown here are symbiont and (co-)infection type frequencies across the focal 2-week intervals, pooled across all replicate fields. Conclusions drawn, using the logic of Figure 1 and the patterns/significance seen for the below-described trend-lines, are stated at the top of each panel. Black lines illustrate the overall prevalence of each symbiont undergoing a ≥20% magnitude shift (Symbiont A, left or top graph) or the frequency of its most commonly co-infecting symbiont (Symbiont B, right or bottom graph). Solid, colored lines (left or top graph) show the frequencies of aphids harboring both co-infecting symbionts (A+B+). Dashed gray lines show the proportions of aphids harboring the focal symbiont without this co-infector (A+B−, left or top graph) or the proportion of aphids with Symbiont B but not Symbiont A (B+A− right or bottom graph). Results of mixed effects GzLM statistics (with binomial error and the logit link function) are indicated to the right of symbiont frequency trendlines. These statistics compared two models, one with only field as a random effect and one that also included time. Significant results indicate that the model with time was significantly better, suggesting a shift in the frequency of the symbiont or (co-)infection type. Abbreviations are as follows: “***” 0 < p < 0.001; “**” 0.001 < p < 0.01; “*” 0.01 < p < 0.05; “.”; 0.05 < p < 0.1; n.s. = not significant. Statistics are presented in detail in Tables S7 and S8. Cases of hitchhiking are emphasized with bolded box borders. Note that times and colors of circles on the x-axes correspond to those indicated with the colored arrows in (A) and with the same colored circles in (B). Y-axes represent the frequency of each symbiont or the particular (co-)infection type (i.e., the proportion of all examined aphids harboring the symbiont or that combination of symbionts).
Figure 5
Figure 5
Direct selection vs. hitchhiking effects for facultative symbionts of pea aphids across overwintering periods. As shown in Smith et al. 2021, Hamiltonella frequencies declined across two overwintering periods. Illustrated here, in addition to Hamiltonella, are dynamics for the other six symbionts. Unlike Hamiltonella, none showed consistent effects across the 2012–2013 and 2013–2014 overwintering periods, although the frequency of co-infections between Serratia and Rickettsiella seemed to rise across both (panels D,H; see also Figure S3). (A) Sampling scheme across overwintering periods, in addition to a description of the pea aphid lifecycle, across these spans, for cyclically parthenogenetic aphids. Colored arrows indicate sampling times (in October and May) and correspond to the colored circles/ovals used in B-H (in which “F” = fall; “S” = spring). (B) Symbiont frequencies, inferred from diagnostic PCR, across the sampling points indicated in (A). (C–H) Graphs of overall symbiont frequencies (black lines) and frequencies of aphids with particular (co-)infection types (colored and dashed gray lines), as done in Figure 4. Conclusions are stated at the top of each panel. These were reached using the logic of Figure 1 and patterns of statistical significance associated with the below-described trend-lines. In short—each panel shows the overwintering frequency trajectory of a focal symbiont (Symbiont A) and its most common co-infecting symbiont (Symbiont B) from this time. Results of mixed effects GzLM statistics (with binomial error and the logit link function) are indicated to the right of symbiont frequency trendlines. These statistics compared two models, one with only field as a random effect and one that also included time. Significant results indicate that the model with time was significantly better, suggesting a shift in the frequency of the symbiont or (co-)infection type. Abbreviations are as follows: “***” 0 < p < 0.001; “**” 0.001 < p < 0.01; “*” 0.01 < p < 0.05; “.” 0.05 < p < 0.1; n.s. = not significant. Statistics are presented in detail in Tables S5 and S8. Unlike our approach for the 2-week frequency shifts in 2012 in which only symbionts undergoing ≥20% frequency shifts were treated as focal symbionts (Figure 4), we performed statistics and graphical illustrations for all symbionts undergoing a significant (or marginally significant) frequency shift of ≥9% across an overwintering period. Most of these symbionts were modeled as the ‘focal symbionts’ for graphing (except Rickettsia—panel (C)).
Figure 6
Figure 6
No evidence for Hamiltonella hitchhiking with favored aphid clones at times 6–7 during our 2012 field season. Analyses performed to further assess whether Hamiltonella’s rise in frequency during a hot time period (times 6–7; Smith et al., 2021 [60]) was a product of hitchhiking. In brief, genotyping of pea aphids at six microsatellite loci provided no support for selection on common aphid clones with incidentally high Hamiltonella infection rates. (A) Infographic gives a unique color to each unique genotype for the n = 44 and n = 51 microsatellite-genotyped aphids from times 6 and 7. Aphids with resampled genotypes are labeled with letters (a, b, c, d, e, f, x, y, z). Boxes outlined in white represent aphids with only 5 genotyped loci. For these aphids we inferred a plausible 6-locus genotype assignment by declaring the aphid to most likely encode a singleton genotype (i.e., if it was not identical to any one aphid at all 5 of its genotyped loci) or to more likely belong to a resampled genotype/clone (i.e., if it was identical to one or more resampled genotypes at all 5 of its genotyped loci). For one aphid (Time 6), the genotype appeared equally likely to assign to genotype/clone “e” or “f”. (B) Frequencies of Hamiltonella in microsatellite genotyped aphids from times 6–7. If selection was operating on common clones with high propensities for Hamiltonella infection one would expect aphids with resampled genotypes to have a higher prevalence of Hamiltonella than those with unique genotypes. Yet, Fisher’s Exact Test statistics revealed that they did not (p > 0.05 for the comparisons at each time, indicated with the “n.s.” abbreviation, for not significant).
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