Introduction

An effective immune response is essential for organisms to protect themselves from pathogens. However, an excessive immune response causes damage to self either directly (autoimmunity, cytokine storms) or through dysbiosis via disruption of the composition of beneficial microflora [1–4]. The dynamic suite of microbes that hosts face necessitates initiating a robust systemic immune response, avoiding self-harm, and maintaining a beneficial microbiome. The challenge of maintaining a balanced immune response is exacerbated because the threat of microorganisms is contextual; thus, the immune system must distinguish harmful versus beneficial microbes in each context [5,6]. For example, an ingested microbe may be harmless or even beneficial as a food source in the digestive tract, but, when in circulation, that same microbe may prove harmful to the organism [7]. One consequence of balancing a robust innate immune system is that it may lead to the maintenance of genetic polymorphism in genes that encode for proteins intimately involved in the immune response [8,9]. Such patterns of maintained polymorphisms contrast the standard coevolutionary arms race model where hosts and pathogen continually adapt to each other leading to rapid evolutionary change in immune genes [10–12]. Though the coevolutionary arms race model appears apt in many cases, the case for the adaptive maintenance of alleles, balancing selection in the broad sense, on genes involved in the immune defense stems from a more nuanced view of the delicate interplay of systemic immunity, life history traits and the beneficial microbiome [13,14].

Balancing selection is the general term for the adaptive maintenance of multiple alleles in a population. In contrast to the evolutionary arms race model, natural variation in many immune genes is maintained by balancing selection [15–18]. The inference of balancing selection from molecular population genetic data is difficult, especially when genes are small and in areas of high recombination. Nonetheless, several examples of balancing selection exist, many of which involve small effector proteins of the innate immune system [18–21].

Balancing selection on immune genes is likely to involve allelic benefits that are only conditionally beneficial. One way that an allele could be conditionally beneficial is if there is specificity between pathogen and allele [22] such that allele A better protects against pathogen 1 and allele B better protects against pathogen 2: pathogen specificity hypothesis. Another way that an allele can be conditionally beneficial in the context of immune defense is if resistance alleles are costly in the absence of infection. In this case, allele A better protects against both pathogen 1 and pathogen 2, but in the absence of infection allele A is costly for its host [23]. This cost could be energetic or through autoimmune-like damage: autoimmune hypothesis. In reality, there is likely a continuum between these alternative hypotheses.

Invertebrates lack an adaptive immune system, so the delicate balance between systemic and gut immunity is achieved only through adjustments to the innate immune response [24]. It is therefore important to understand how invertebrates optimize their systemic immune response with as little detriment to their beneficial gut microbiota as possible.

Though first characterized for its role in systemic immunity, the (IMD) pathway is also the main NF-κB immune pathway in the gut and contributes to the maintenance of the microbiome and protection from gut infections [25–27]. To date, there is little research into how individual AMPs help maintain a healthy microbiome composition in the fly, let alone how allelic variation may affect microbiome composition [28,29]. The standard gut microbiome of lab reared D. melanogaster consists of two prominent bacterial genera: Lactobacillus and Acetobacter [27]. Both genera are easily cultured in the lab, and the Drosophila gut can easily be manipulated, making it a good model to study the effects of immune gene variation on microbiome composition and how microbiome composition can, in turn, influence host fitness.

Antimicrobial peptides (AMPs) are a critical part of the innate immune system that act as broad-spectrum antimicrobials, combating bacteria, fungi, and viruses. In the coevolutionary arms race model, AMPs are on the front lines: directly interacting with microbes. AMPs have also been demonstrated to have critical roles in other physiological functions, for example, dysregulation of AMPs has been connected to diseases such as atopic dermatitis and Alzheimer’s disease [2,30,31]. AMPs also contribute to the aging process where the immune system becomes more active as organisms ages to compensate for a decline in its effectiveness. However, this leads to cytotoxicity that in turn shortens lifespan [3].

In Drosophila, AMPs play a crucial in both manage systemic infections and in the maintenance of the gut microbiota [32]. AMPs, however, appear to evolve more slowly than most immune gene families [33–36]. One explanation for this perceived lack of adaptive evolution is that Drosophila AMPs genetic variation is maintained adaptively through balancing selection [15,37–39], where different selective pressures favor different alleles. Naturally occurring allelic variation in AMP loci are sometimes associated with variability in pathogen resistance [19,40–42]. Diptericin A (hereon referred to as diptericin or Dpt) is one of the canonical effector genes of the Drosophila Immune deficiency (IMD) pathway, and is generally associated with defense against Gram-negative bacterial infection [43,44].

In both D. melanogaster and D. simulans, an amino acid polymorphism at the 69^th residue of the mature 83-residue Diptericin peptide segregates in most populations surveyed [19]. In both species, this polymorphism is due to a point mutation that changes the ancestral serine (S) allele to arginine (R), but the two species use different codons for arginine. Unckless et al. [19] found a significant difference in survival based on diptericin genotype after systemic infection with the Gram-negative bacteria, Providencia rettgeri, a natural pathogen of D. melanogaster. The study used inbred fly lines and found homozygous serine flies have a better survival rate 5 days post infection (∼60%) than homozygous arginine flies (∼20%) or flies with a premature stop codon in diptericin (0%). Later work by Hanson et al. took a more general approach to AMP specificity and found that Diptericin plays a disproportionate role in response to infection with P. rettgeri [45]. What remains unclear, however is why this presumably deleterious arginine allele persist in populations of two different species. We hypothesize that it either protects against a different suite of pathogens or it is beneficial in the absence of infection through some life history tradeoff.

This study aimed to test two hypotheses about the maintenance of genetic variation in Diptericin. First, different Diptericin alleles might protect against different pathogens – we refer to this as the pathogen specificity hypothesis. It would be supported if the arginine allele were to be associated with higher survival than the serine allele in some infections, since the serine allele is already clearly more protective against P. rettgeri [19,42]. In contrast, flies with the serine allele might be generally better at surviving infection, but this may have a cost to the organism. We refer to this as the autoimmune hypothesis, where a protective immune allele is associated with a tradeoff that is costly in the absence of infection. We test whether there are tradeoffs between systemic immunity and other life history traits – particularly traits related to the gut microbiome utilizing genetically controlled lines for Diptericin alleles generated by CRISPR/Cas9-editing. Flies were systemically infected with a panel of six different bacteria and homozygous serine flies had a better 5-day survival with each bacterium. We then used axenic and gnotobiotic flies to look at the lifespan of flies since AMP overexpression and microbial proliferation are commonly observed in aging flies [46–49]. Lactobacillus plantarum is harmful to female flies with non-functional diptericin, while homozygous arginine flies poly-associated with L. plantarum and A. tropicalis had a longer lifespan than homozygous serine flies. In this way we found evidence for a tradeoff between the ability to fight systemic infection and the ability to control opportunistic gut infections.

Results

A single amino acid change drastically influences survival after infection

Unckless et al. [19] found that in inbred lines from the Drosophila Genetic Reference Panel (DGRP), flies homozygous for serine as position 69 of the mature Diptericin peptide survive systemic infection with P. rettgeri much better than those homozygous for the arginine peptide at the same position. This experiment showed only an association between immune defense and the serine/arginine polymorphism since these inbred lines were on several different genetic backgrounds. To control genetic background, we used CRISPR/Cas9 genome editing to create both an arginine allele (single nucleotide change, dpt^S69R) and multiple null alleles (1 or 3 base pair deletions, Δdpt flies₎, as well as control dpt^S69 (serine at position 69 of the mature peptide) in diptericin (Fig S1A, Table S1). The phenotype for our CRISPR/Cas9 edited flies showed striking similarity to the inbred lines. In systemic infection challenges with P. rettgeri, dpt^S69 flies are better protected from infection than dpt^S69R flies (p= 5.42e-08) and Δdpt flies (p=6.46e-09, Fig 1A). Remarkably, for inbred lines and CRISPR/Cas9 edited lines, survival for five days post infection with P. rettgeri for the serine allele lines is 50-60%, with the arginine allele is 10-20%, and less than 5% for null alleles.

Figure 1:

We next challenged the CRISPR/Cas9 edited flies with systemic infection using multiple other Gram-positive and Gram-negative bacteria to determine whether the arginine allele (dpt^S69R) protects against some infections better than the serine allele. Such a finding would support the hypothesis that allelic variation is maintained by different alleles providing specific protection against different microbes.

However, the differential response to systemic infection with P. rettgeri at an OD₆₀₀=0.1, as described above, remains the largest difference in immune response between Dpt genotypes. Dpt^S69flies survived better than dpt^S69R flies for all systemic infections tested (Fig 1, Table S2). In the Gram-positive bacterial infections (Enterococcus faecalis, S. succinus, L. fusiformis, and L. lactis), dpt^S69Rflies had lower survival than that of Δdpt or imd^- (null allele for the Imd gene) flies (Fig 1B-E). The only systemic infection where dpt genotype did not seem to matter was S. marcescens, where all flies survive infection well except for imd^-, which die very quickly (Fig 1F).

We also tested if males and females showed differences in systemic immunity based on genotype for P. rettgeri, E. faecalis and L. plantarum infections (Fig S2, Table S3). Generally, the Dpt genotypes have qualitatively similar survival patterns between males and females when infected with P. rettgeri. Females showed lower survival compared to males after systemic infection with E. faecalis (p=0.0003). Higher male survival post infection was observed previously for both E. faecalis and P. rettgeri with differences ascribed to the Toll pathway [50].

Overall, flies with the dpt^S69 allele are better equipped to survive a systemic bacterial infection than dpt^S69R flies, though this is most pronounced for P. rettgeri. Of the 6 bacteria tested, there was no case where dpt^S69R flies survived the infection better than dpt^S69 flies. These results do not support the hypothesis that alleles are maintained to better combat different pathogens.

Diptericin genotype affects lifespan of mono- and poly-associated gnotobiotically reared flies

Although our survey of systemic infections was not exhaustive, we did not find any instances where dpt^S69R flies were better able to fight infection, so we turned our attention to the role of Dpt in gut microbiome maintenance and immunity. The gut microbiome influences several life history traits in Drosophila and other organisms [51–54]. To dissect how diptericin genotype influences microbiome maintenance, we manipulated the microbiota in CRISPR/Cas9 flies, and measured longevity and bacterial load. We began with the longevity of axenically reared flies, since it represents a baseline survival without the presence of microbes. Flies with functional copies of diptericin (dpt^S69 or dpt^S69R) did not differ in lifespan for either sex (p_genotype=0.3762), and Δdpt lines show a similar lifespan. However, there was a difference in overall lifespan between the sexes among the CRISPR genome edited lines (p_sex=1.04e-6). In dpt^S69 or Δdpt female flies have a longer lifespan than male flies, as observed generally for D. melanogaster previously [55]. In contrast, imd^- male flies have a much longer lifespan than any of the other lines tested (Fig 2A – axenic row, Table S3).

Figure 2:

We next tested the influence of Diptericin genotype on mono-associations with standard constituents of the Drosophila gut microbiome. We found multiple sex effects when axenically reared flies were fed L. plantarum, L. brevis, or A. tropicalis (Figs 2A and 2B, S3). Flies that were fed L. plantarum show the most striking differences. Male flies continued to show similar lifespans to each other, but female Δdpt and female imd^- flies both succumbed quickly post-feeding, indicating functional Diptericin is important for gut immunity against opportunistic L. plantarum in females.

We also fed axenic flies P. rettgeri, which does not readily cause gut infection, to axenically reared flies. Recall that after systemic infection dpt^S69 flies are more resistant to P. rettgeri than dpt^S69R flies (Fig 1A). After monoassociation, however, we see no significant difference between the two genotypes with P. rettgeri in either sex (p=0.612, Fig 2B). There is also no significant difference between the Δdpt and lines with functional Diptericin (Fig 2B, S3). Surprisingly, however, the null allele for Imd again shows a significant effect on survival in a sex-specific manner: null Imd females die much earlier and null Imd males survive much longer when fed P. rettgeri.

Some of the sex-specific differences in survival after monoassociation with bacteria may be driven by intrinsic differences in feeding rates between the sexes (and potentially genotypes as well) [56]. To determine whether the differences in male and female survival and load were due in part to differences in feeding rates, we performed a feeding rate assay with blue dye mixed with media (Luria-Bertani broth) or P. rettgeri (OD₆₀₀=15.0). We noted that females did eat more in a single hour of feeding, but that there was also an effect of diptericin genotype (with null flies eating less, Figure S4). Thus, it is possible that differences in longevity after monoassociation are due to different rates of exposure to those bacteria because of different feeding rates.

We next looked at how poly-associations with bacteria affected lifespan. First, we fed flies a 1:1 mixture of L. plantarum and A. tropicalis, two common gut microbes found in lab-reared and wild caught flies [57,58]. We found that female Δdpt and imd^-flies had a much shorter lifespan than flies with functional Diptericin (Fig 2A and 2B). This is the same pattern observed in mono-association with L. plantarum. However, we observe that diptericin genotype influences survival when poly-associated with L. plantarum and A. tropicalis. Dpt^S69R female flies live longer than dpt^S69 female flies (Fig 2A and 2B; p=0.00782). This is the opposite of systemic infections, where dpt^S69flies always survived better than dpt^S69R flies, and may indicate a tradeoff between defense against systemic and gut immunity.

In poly-association with 1:1 L. plantarum and P. rettgeri, we observe many of the same patterns as in the poly-association with L. plantarum and A. tropicalis. Again, female Δdpt and imd^- flies have a shorter lifespan than female dpt^S69 and dpt^S69R flies (Fig 2B), and dpt genotype is associated with lifespan. Dpt^S69R females have a longer lifespan than dpt^S69 flies (p=0.000198, Fig 2B).

Given the genotypic and sex effects on survival after oral association with L. plantarum, we also looked at sex differences after systemically infecting conventionally reared flies with L. plantarum. We saw no difference in survival between the sexes, as observed for systemic infections with E. faecalis or P. rettgeri and saw no difference between Δdpt lines and lines with functional Diptericin, as observed for axenic flies mono-associated with L. plantarum (Fig S2). This could indicate that Diptericin plays different roles for systemic and gut immunity in relation to L. plantarum in each sex. Further, dpt^S69 male flies survived better than dpt^S69R male flies (p=0.00438), in line with observations from other systemic infections in males (Fig 1).

Overall, we found a role of both specific diptericin genotype (serine vs. arginine) and the presence of functional copies of diptericin for survival after introducing common gut microbes in controlled conditions. Most striking was the sexually dimorphic role of both Dpt and Imd-, with females being much more sensitive to genotype than males.

Gnotobiotic fly bacterial load

Given the differences in survival among sexes and genotype for different gnotobiotic associations with bacteria, we determined whether bacterial load after associations also were different among sexes and genotypes. To assess the influence of genotype on the immune response of aging flies, we studied how well common gut bacteria colonized the gut over 20 days, representing ∼20-33% of D. melanogaster’s normal lab lifespan [59,60]. We generated gnotobiotic flies by feeding specific bacteria for two days and observed the bacterial load 2-, 10-and 20-days post feeding.

When specifically looking at mono-association with L. plantarum we observe that bacterial load differences between genotypes occur within the first 2 days post feeding but begin to disappear by day 20 (p_{genotype(day2)}=2.6e-5, p_{genotype(day20)}=0.709, Fig 3A, S5). Note that in the first two days, the flies were raised on microbe-contaminated media, but after 2 days were moved onto sterile food and then transferred to new sterile food every 3 days. This corroborates what we saw in the longevity data in females. Within the first 15 days a large proportion of Δdpt female flies died, and we observed a higher bacterial load in these flies, especially on day 2 post feeding (Fig 2). By day 20, differences in bacterial load in females disappeared. Note that there is inherent sampling bias, as only the flies able to survive until day 20 are sampled at that time point. In the case of imd^- flies, no females survived until day 20, hence there is no data for imd^-flies on day 20 (Fig 3A, S5).

Figure 3:

P. rettgeri is the only canonical systemic pathogen we included in our gnotobiotic study, and the patterns based on genotype were different between systemic infection and the gnotobiotic oral infection data. When flies were fed P. rettgeri, we saw a range of bacterial loads, from zero colonies to bacterial load levels on par with common gut bacteria. We also observed a genotype effect between dpt^S69 and dpt^S69R on day 10- and 20-post feeding in males (p_{genotype(day10)}=0.0044, p_{genotype(day20)}=0.0004, model only included dpt^S69 and dpt^S69R, Table S5). In both instances, bacterial load in dpt^S69R flies is higher than in dpt^S69 flies. This may indicate that dpt^S69flies are better equipped to deal with both systemic and oral infection from P. rettgeri. Whether this is due to a greater ability of dpt^S69flies to withstand the effects of infection (tolerance) by P. rettgeri by remains a question. It is also important to note that P. rettgeri establishes poorly in the gut of wildtype flies (personal observation), which may explain the noisy results for mono associations after oral infection.

A range of bacterial loads were also observed when flies were poly-associated with L. plantarum and P. rettgeri (Fig 3B, S6). There were no statistically significant differences between dpt^S69 and dpt^S69R bacterial for this poly association (Table S6). In fact, there were no significant differences between any of the P. rettgeri bacterial loads, however L. plantarum does show differences on day 2-post feeding (p=7.97e-6). These differences are mainly between flies with non-functional diptericin (imd- and Δdpt) and flies with functional diptericin. Both Δdpt and imd- flies had higher bacterial load than dpt^S69 and dpt^S69R flies on day 2 post feeding which may be an indication of the reason both these lines quickly succumb to feeding with L. plantarum at least in the context of the poly-association with P. rettgeri.

We observed larger differences in bacterial load shortly after feeding with bacteria and those differences became less by day 20-post feeding. We did not see any differences in P. rettgeri load when mono-associated or when part of a poly-association, indicating the flies respond differently to the same pathogen when introduced systemically or orally.

Evidence for life history tradeoffs mediated by diptericin genotype

Proteins involved in a robust immune response may have pleiotropic effects on other traits either because of a direct interaction with that trait or because of inherent costs of immune defense (self-damage, energy expenditure, etc.) [61–63]. We examined three life history traits (desiccation stress survival, starvation stress survival, and uninfected longevity) to determine whether Dpt genotype had such pleiotropic effects.

The ability to survive desiccation stress is an important life history trait for wild Drosophila survival [64,65]. When subjecting our male CRISPR/Cas9 flies to desiccation, we observe conventionally reared flies succumb faster to desiccation stress than axenically reared flies (Fig 4A, p_genotype< 2e-16). In conventionally reared males, dpt^S69 flies have similar desiccation resistance to dpt^S69R flies (p=0.917). However, dpt^S69 flies survive desiccation stress better than dpt^S69R flies when reared axenically (p_genotype=0.04126). We also compared Drosophila OreR and W1118 (both homozygous for the serine allele of Diptericin) to the dpt^S69 line (Fig S7, Table S7). Unsurprisingly, despite the same diptericin genotype, all 3 lines show dramatically different desiccation resistance. Therefore, diptericin genotype plays limited, if any role in variation in desiccation resistance, as all 3 wildtype lines were derived from different genetic backgrounds.

Figure 4:

We next looked at the effect of genetic variation in diptericin on male ability to survive under starvation stress. As with desiccation resistance, we found axenically reared flies survive starvation stress better than conventionally reared flies (Fig 4B, S8, p_treatment < 2e-16). However, unlike the desiccation stress conditions, both conventionally reared and axenically reared dpt^S69Rflies survive starvation stress longer than dpt^S69 flies (conventional: p_genotype < 2e-16, axenic: p_genotype < 2e-16). In conventionally reared flies there is no difference in survival between dpt^S69 flies and Δdpt flies (Fig 4B, Table S8). However, in axenically reared flies, Δdpt flies have an intermediate survival phenotype between dpt^S69 and dpt^S69R flies. This may suggest an interaction between functional diptericin and the microbiome that influences starvation.

Finally, we looked at overall longevity of female and male conventionally reared flies (in the absence of any infection or other significant selection pressure). Female flies have a longer lifespan than males (Fig 4C, S9, p_sex= 5.80e-13) regardless of dpt genotype. In males, dpt^S69 flies had a significantly longer lifespan than male dpt^S69R flies (mean of 60.3 days for dpt^S69 and 54.1 for dpt^S69R, p= 0.0072), but not in female flies (mean of 61.9 days for dpt^S69 and 59.0 for dpt^S69R, p= 0.6434). However, in axenically reared flies only female dpt^S69 flies have a longer lifespan than male dpt^S69R flies. Interestingly, the female flies with the longest lifespan have non-functional Diptericin (Fig 4C, Δdpt line and imd^-line, Table S9). Males lacking functional Diptericin show the same effect, but to a lesser extent. These results provide evidence that, in the presence of a standard gut microbiota, both Dpt and a functional Imd pathway may decrease longevity. This is consistent with others who found downregulation of NF-κB pathways and AMPs increased lifespan in Drosophila [63,66], but the fact that flies with Dpt null alleles alone are sufficient to increase lifespan is noteworthy.

Diptericin’s influence on gut microbial diversity

To determine whether Diptericin genotype influences the composition of the bacterial community in the gut, we sequenced amplicons of 16s ribosomal rRNA in conventionally reared lab flies under two different rearing conditions: flies reared in standard Drosophila vials, and the progeny of the cross between dpt^S69 and dpt^S69R flies reared in cages for more than 2 generations. The vials may capture the long-term impact of genotype on microbiota but makes ruling out stochastic changes in communities difficult. The cages ensure identical microbiota during development, but do not allow gradual effects of genotype on microbiota to accumulate. First, we found flies that were co-reared in cages had similar microbiomes, with no discernable differences by the alpha diversity metric, Shannon diversity (p=0.5239, Fig 5A). On the other hand, the microbiomes of flies reared in vials was distinctly different overall compared to the microbiomes of flies reared in the cages (p<0.001, Fig 5B). However, the differences between genotypes were still minimal in the vial-reared flies and vial may be a large factor in differences between lines. There were 2 dpt^S69R lines used and even though these are the same genotype and genetic background there was a significant difference between the 2 lines (Fig 5B). This difference is likely due to the within-vial drift in gut microbiome communities [67], which we attempted to control for using flies of different genotypes reared in the same cage (Fig 5A).

Figure 5

The lab is a controlled environment that allows for excellent control of variables but does not replicate the conditions found in nature. Thus, we looked at the microbiomes of wild caught flies collected from the decaying fruit of an apple orchard. We found flies with homozygous for dpt^S69R, homozygous for dpt^S69, and heterozygous genotypes at our collection location (Table S10). Out of the 955 D. melanogaster flies successfully genotyped, only 20 flies were homozygous for dpt^S69Rwere identified. All 20 homozygous dpt^S69R flies were profiled along with 36 each of dpt^S69 homozygous flies and heterozygous flies for a total of 92 flies profiled (amplicon sequences of both Dpt and 16s rRNA are in Table S2).

We found that dpt genotype does not affect the overall composition of the microbiome of wild caught flies based on Shannon diversity (alpha diversity) and Bray-Curtis dissimilarity (beta diversity) (Fig 5C). However, when looking at differential abundance of individual bacterial families there are differences based on genotype. We tested specific association between diptericin genotype and normalized counts from 9 microbial families. Most intriguing is the difference in abundance of Morganellaceae family reads since, P. rettgeri belongs to this family. Heterozygous and homozygous serine flies have a slightly higher abundance than homozygous arginine flies (Fig 5D), but these differences were not significant.

Overall, our results for associations between Dpt genotype and gut microbiome in both lab and wild flies are weak. Such associations may require much larger sample sizes if the effects are small – particularly given the noisy phenotype in the wild. Alternatively, the influence of Dpt genotype on gut microbiome diversity may be specific to developmental life stages or microbes that are relatively rare in these populations.

Discussion

The adaptive maintenance of multiple alleles of a single gene has been posited for nearly a century [68–72] and evidence for its pervasiveness continues to grow [73–78]. However, there are relatively few cases where we understand the adaptive benefits of both allele (sickle-cell anemia and malaria resistance being the best studied example [79]). In this study, we generated CRISPR/Cas9 genome-edited flies of different diptericin genotypes to investigate mechanisms of maintenance of allelic variation in AMPs. This allowed us to specifically study a single amino acid change on an otherwise genetically-controlled background. Overall, we found evidence that dpt^S69 flies survive systemic infection better, while dpt^S69Rflies survive some opportunistic gut infections better. Thus, more robust defense against systemic infection appears to interfere with maintaining a balanced gut microbiota – this tradeoff may result in natural populations maintaining both alleles. Importantly, we note that while our results are consistent with the adaptive maintenance of alleles in natural populations, these lab assays do not prove the adaptive value of different alleles in the field. Systemic infections with P. rettgeri in CRISPR/Cas9 genome-edited lines confirmed the amino acid polymorphism in Diptericin as the basis for the difference in immune response of flies with different diptericin genotypes previously hypothesized via an association study [19].

Surprisingly, dpt^S69 flies had a higher survival five days post infection than dpt^S69R flies in all systemic infections tested. This may mean either that the serine allele provides improved defense against all the tested systemic infections, or that alterations to other aspects of fly physiology (notably the gut microbiota) could predispose arginine flies to poorer survival. It is also possible that some of diptericin’s influence on survival is mediated by changes in the microbiome. We did not perform systemic infections on axenically-reared flies, but if Dpt genotype influences the microbiome, which in turn influences survival after infection with other pathogens, Dpt should not influence survival after systemic infection if flies are reared axenically.

Our study also reveals an important role of Diptericin in preventing opportunistic gut infections by common gut microbes. This is especially evident when looking at lifespan after associating axenic flies with L. plantarum in both mono- and poly-association contexts. In both sexes, dpt^S69R flies have a longer lifespan than dpt^S69 flies, and this effect is enhanced in poly-association with L. plantarum and A. tropicalis. Other studies have posited a role for individual AMPs in the maintenance of gut microbes in Drosophila [29], but this appears to be the first example linking a functional copy of a single AMP to survival differences.

One of our most striking and unexpected results was the distinct sex differences in longevity after association with common gut bacteria. Furthermore, fly genotype plays a significant role in these differences. After association with L. plantarum, imd- females had the shortest mean survival, while imd- males had the longest mean survival. The effect for Δdpt was similar: females had much shorter average survival than the other two dpt alleles, but the three alleles showed equivalent longevity in males. While the imd- sexual dimorphism is relatively consistent across experiments with different microbes, the Δdpt effect seems to be more limited to associations involving L. plantarum. This result not only highlights the importance of using both sexes in microbiome research, but also confirms that functional Diptericin is important for Drosophila melanogaster gut health. There is a growing body of literature on the effects of sexual dimorphisms in immune response, but it is lacking for sexual dimorphisms in gut immunity and our findings only emphasize the need to fill this gap (Drosophila immunity sexual dimorphisms reviewed in Belmonte et al. 2020; also see [80]). Our feeding assay provides some evidence that the differences between male and female survival after exposure to microbes is due the amount of food (and therefore the number of microbes) consumed, but these differences should be examined in much more detail. More generally, the sex by genotype effect suggests a likely explanation for the maintenance of genetic variation: different fitness effects of alleles in the different sexes [81]. Note that while our study focuses on the serine/arginine polymorphism, at least 6 null alleles of dpt segregate in natural populations of D. melanogaster and the frequency of those nulls shows clinal variation in both North American and Africa [82], thus the nulls are likely maintained selectively too.

Finally, we note that we have framed our argument in terms of balancing selection in the broad sense because we are agnostic as to the population dynamics upon which these different selective pressures might act. For example, it may be that some populations are disproportionately infected by P. rettgeri or other microbes for which the serine allele is favored, while other populations are relatively pathogen-free and the arginine allele is favored. This scenario is partly supported by the evidence of clinal patterns in allele frequency in Africa and (to a lesser extent) North America [83]. Similarly, these selective pressures could vary temporally. On the other hand, these pressures could act all in one population where serine is favored in infected individuals and the arginine allele is favored in uninfected individuals.

Herein we highlight 3 main roles of Diptericin in Drosophila. 1) Diptericin genotype influences systemic immune defense; 2) in certain conditions Diptericin genotype influences gut immunity; and 3) Diptericin has sex-specific effects in the gut. These results highlight the need for individuals and populations to modulate the immune system to balance systemic and gut immunity, and how the different needs of females and males complicate this balance. The dramatic differences in survival of males and females in response to oral infection by common gut bacteria underlines the importance of looking at both sexes when examining the maintenance of genetic diversity, as balancing selection may be caused by sexual dimorphism. Overall, our results suggest that a complex interaction between sex, environmental context (starvation, pathogen exposure – both systemic and oral), and genotype may contribute to the long-term maintenance of immune alleles.

Materials and Methods

Drosophila Lines and Rearing

Conventionally reared flies were maintained in a 23°C incubator with a 12h light:12h dark schedule on a cornmeal-molasses-yeast diet (64.3g/L cornmeal, 79.7mL/L molasses, 35.9g/L yeast, 8g/L agar, 15.4mL of food acid mix (50mL Phosphoric Acid + 418mL Propionic Acid + 532mL deionized water) and 1g/L Tegosept. We used CRISPR/Cas9 genome editing to modify the diptericin A gene. Briefly, DNA coding for guide RNA (gRNA) was inserted into the pUS-BbsI plasmid (Table S1). A single stranded donor DNA (120bp) containing the desired edit (to change from serine to arginine) and a silent mutated PAM site was synthesized by IDT (Coralville, IA, USA). The plasmid and ssODN were then injected into Bloomington stock #55821, which expresses Cas9 driven by the vasa promoter, by Genetivision, Inc. (Houston, TX, USA). Individual flies developed from the injected embryos were collected and crossed with a modified version of Bloomington stock # 7198 (a line with CyO/Kruppel balanced on the 2^nd chromosome, and serrate/Dichaete balanced on the 3^rd chromosome). Our version, 7198^A4, was provided by Stuart Macdonald and has the DSPR [84] A4 line’s X chromosome instead of the w[*] from the original 7198. The F1s were collected and again individually crossed with 7198^A4 for F2 crosses yielding individuals with a homozygous 2^nd chromosome representing one of the chromosomes carried by the original injected embryo. The Dpt gene was sequenced from the F2 cross progeny to determine whether edits occurred. This yielded several classes of alleles including homozygous serine dpt (wildtype, dpt^S69), homozygous arginine dpt (dpt^S69R), and dpt null (Δdpt, refers to lines with either 1 or 3 base pair deletion) (Fig S1A). Balancers were removed, and lines were moved into the same genetic background through a series of crosses as shown in Fig S1B. An imd- line was used as an IMD pathway negative control but note that this line was from a completely different genetic background from the rest of the lines.

Axenic Fly Preparation

Microbe-free (axenic) lines were generated by first washing embryos in a 10% bleach solution to dissolve the chorion for 2 minutes. The embryos were then washed in 70% ethanol for 30 seconds and water for another 30 seconds, then transferred to autoclaved molasses food (see above). Some embryos from each treatment were placed onto De Man, Rogosa and Sharpe (MRS) agar plates and incubated at 30°C for 48 hours to check that they did not contain viable microbes. Axenic lines were continuously checked for the presence of contaminating microbes (every 3-4 generations) by homogenizing flies and plating the homogenate on MRS agar.

Axenically and gnotobiotically (see below) reared flies were maintained in an incubator that was isolated from conventionally reared flies. The incubator was kept at 23°C with a 12h light:12h dark schedule. Axenic and gnotobiotic flies were kept on the same molasses diet that had been autoclaved before dispensing into autoclaved vials. Axenic and gnotobiotic flies were only handled inside a sterile hood (Baker SG 400, The Baker Company Inc., Sanford, ME).

Bacterial Strains

The following bacteria were used for systemic infection assays: Providencia rettgeri [85], Providencia burhodegraneria Strain B [85], Enterococcus faecalis [85]), Serratia marcescens [40], Lysinibacillus fusiformis Strain Juneja [86], and Staphylococcus succinus (isolated from wild Drosophila, Unckless lab). Bacteria were grown from glycerol stocks on LB plates at 37°C overnight.

The following bacteria were used in gnotobiotic experiments: L. plantarum, L. brevis, A. tropicalis. All these strains were isolated from plating conventionally reared flies on MRS agar in the Unckless lab at the University of Kansas. Individual colonies from plated fly homogenate were grown overnight in MRS for DNA isolation. Bacterial species were identified using Sanger sequencing with the 16S rRNA region primers 27F (AGAGTTTGATCCTGGCTCAG) and 1492R (CGGTTACCTTGTTACGACTT). We also utilized the same P. rettgeri as described for systemic infection.

Survival Assays

For systemic infections, individual colonies of bacteria were picked and grown in 2mL LB broth shaking overnight at 37°C. Bacterial suspensions were then diluted or concentrated to OD₆₀₀=0.1 for P. rettgeri, OD₆₀₀=10 for S. succinus, OD₆₀₀=1.5 for E. faecalis, OD₆₀₀=3.5 for L. fusiformis, OD₆₀₀=1.0 for L. lactis, and OD₆₀₀=4.0 for S. marcescens. L. plantarum was grown in 5mL MRS at 30°C overnight and was concentrated to OD₆₀₀=10 for systemic infections. To induce systemic infection, 5-9 day old, conventionally reared flies were pricked in the thorax with a needle dipped in a bacterial suspension [87]. Infections were done in triplicate with at least 20 flies for each replicate per line for a total of 60 flies per genotype per condition. Flies were incubated at 23°C with a 12h light:12h dark schedule and survival was tracked daily for 5 days post infection.

Gnotobiotic Longevity

Axenically reared flies were collected within 24 hours of eclosion. Flies were then kept on sterile food for 2 days before sorting for longevity experiments. To begin longevity experiments, flies were separating into groups of 10 individuals of each sex and put onto sterile food seeded with 50uL of bacterial suspension at an OD600 of 15 +/-1. For each replicate, we used 2 vials of 10 flies each per sex per line for a total of 20 flies for each sex per genotype for a total of 60 flies across all replicates. Flies were allowed to feed in the inoculated vials for 3 days before being transferred to uninoculated sterile food vials. Flies were flipped to new sterile media every 4-5 days for the remainder of the experiment. Surviving flies were counted every 1-3 days until all flies were dead.

Gnotobiotic Bacterial Load

To determine whether microbes became established in the gut, we homogenized flies during and after the exposure and plated the homogenate. We measured bacterial load by inoculating flies in the same manner as gnotobiotic longevity. Flies were separated into groups of 5 females and 5 males per vial. For 2-day experiments flies were kept on the seeded food for the entire experiment. For 10– and 20-day experiments, flies were allowed to feed on the seeded food for 3 days before being transferred to sterile food. Flies continued to be transferred to new sterile media every 3-4 days until day 10 or 20 post feeding. After the experimental (2, 10 or 20 day) time period, flies were surface sterilized by washing in 70% ethanol followed by molecular grade water. Flies were separated by sex and three individuals were homogenized together in 300uL of sterile 1x PBS and the homogenate was plated on the appropriate media using a Whitley WASP Touch® spiral plater (Don Whitley Scientific, UK). When there were not 3 flies still alive then all remaining flies of a sex were used and squished in 100uL of 1x PBS per fly collected instead of 300uL to keep all samples the same concentration per fly. Counts were adjusted accordingly.

Feeding Rate Assays

To measure the amount of food and bacteria consumed by males and females of different genotypes, we made food containing blue dye by adding 11.2g FCF blue dye (Erioglaucine disodium salt) per liter of food. We used newly eclosed flies (1-2 days post eclosion / 14 days post oviposition) and separated sexes (in sterile conditions) and keep flies at a density of 10 flies/vial in fresh food vials. We held these flies in incubators for one day, so they would recover stress of anesthesia during sexing. To introduce bacteria (or control media) into the food, we pipetted 50uL of suspension or LB at an OD600 of 15 (+/-1) into each vial and allowed the suspension to absorbed into the food for 30-40 mins by keeping the vials open inside a sterile hood. We next added the experimental flies and allowed them to feed for one hour. After 1 hour, we anesthetized the flies on ice, then rinsed in ethanol and sterile water. Flies were homogenized in 300µl of 1X PBS with a glass bead (maximum speed for 4 minutes). The homogenate was centrifuged at 14000 RPM for 4 minutes and 200µl of the supernatant was used to measure absorbance at 630nm. Absorbance differences were analyzed using the natural log of absorbance as the response variable with genotype, sex, genotype by sex interaction, and block as independent variables. Due to the blocking structure of the experiment, each treatment (no media control, LB control, P. rettgeri) was analyzed separately.

Desiccation

Desiccation survival assays were performed on dpt^S69and dpt^S69R adult males 4-7 days post-eclosion in conventionally and axenically reared flies. Ten males were placed into an empty vial and closed off with a cotton flug. Each genotype had 5 vials for a total of 50 flies per line per rearing condition in each replicate. The flugs were topped with silica gel (Fisher, #S684) and sealed with parafilm to prevent any moisture from entering the vials. Vials were kept at 24°C on 12 hour day/night cycles. Survival was measured by counting flies hourly until the entire population died. This was repeated once more with 50 flies per line per rearing condition in each trial for a total of 100 flies with the exception for the Δdpt line which in total only had 30 flies per condition in total across trials.

Starvation

Starvation survival assays were performed on dpt^S69and dpt^S69R adult males 4-7 days after eclosion for axenically and conventionally reared flies. Ten males were placed into a vial with autoclaved starvation media (1% agar). The 1% agar was used to starve flies of nutrition but not desiccate them. Vials were kept at 24°C on 12-hour day/night cycles. Survival was measured by counting surviving flies at three 8-hour intervals (8 am, 4 pm, and 12 am) until all flies died. This was repeated twice more for a total of 3 trials with at least 40 flies per line per rearing condition in each trial for a total of at least 120 flies.

16S Sequencing

Flies were reared in the lab in two distinct ways for 16S rRNA sequencing of conventionally reared flies. First, flies were taken from vials of individual genotypes. This allows for any moderate effects of fly genotype to equilibrate over time. Second, flies were taken from cages that were started with heterozygous diptericin flies. These cages were started with the F1 progeny from crosses of dpt^S69 and dpt^S69R flies and allowed to continue for 3 discrete generations before flies were collected for 16S rRNA sequencing. This ensures that the genotypes are exposed to the same microbes and any differences in microbiome are due to genetic differences manifest in that generation.

We isolated DNA from individual flies using Gentra PureGene Tissue DNA Isolation Kit (Qiagen #158388, Qiagen, Germantown, MD) following manufacturer’s instructions. DNA pellets were rehydrated in 10 μl DNA hydration solution. Flies from cage populations were genotyped by using the dpt_cw primer pair (Table S1) followed by a restriction enzyme digest using enzymes that cut DNA sequences for arginine (BccI, NEB # R0704S) or serine (AluI, NEB # R0137S).

DNA concentration was measured with Qubit fluorimeter (Invitrogen). Ten flies from each inbred CRISPR genome edited line (5 females and 5 males from dpt^S69, dpt^S69R, dpt^S69R-2(a second homozygous arginine line from CRISPR/Cas9 genome editing), and Δdpt (1 base pair deletion) and ten flies of each genotype from the cage population (5 females and 5 males of SS, SR, RR genotypes) were each brought to a DNA concentration of 5 ng/μl. Libraries were prepped in accordance with the 16S Metagenomic Sequencing Library Preparation protocol from Illumina for the 16S V3/V4 region. Sequencing was performed on the Illumina MiSeq platform using v3 300bp paired end reads. Library preparation and sequencing was performed at the University of Kansas Genome Sequencing Core (Lawrence, KS, USA).

Wild flies were collected from decaying apples and pears in an apple orchard in Kansas City, KS (3341 N 139^th St, Kansas City, KS 66109). Flies were immediately transported back to the lab and sorted by species on CO₂ and frozen at -20°C. DNA was extracted from individual flies using Gentra PureGene Cell & Tissue DNA Isolation Kit (Qiagen #158388). The samples were tested for species (D. simulans vs. D. melanogaster), Wolbachia status, and Dpt genotype using primers listed in Table S1. Collections are summarized in Table S10. Libraries were prepped in the same manner as the conventionally reared flies.

16S Bioinformatic Analysis

Demultiplexed reads were processed with QIIME2, v 2019.10 [88]. Primers were removed from 5’ ends with Cutadapt using default parameters [89]. Reads were de-noised and trimmed for quality with Divisive Amplicon Denoising Algorithm (DADA2) within the QIIME2 bioinformatics pipeline [90]. Forward and reverse reads were truncated at 280 bp and 245 bp respectively. The remaining ASV table was exported from QIIME2 for further processing in R [91]. Taxonomy was assigned to the ASV table using SILVA 16S rRNA gene reference database, v.138 [92–94]. Reads assigned to Genus level Wolbachia (a Drosophila endosymbiont) and Kingdom level Eukaryota were removed from further analysis. We also removed reads not observed at least 3 times in at least 10 samples. Then, conventionally reared flies were rarefied to 17066 reads, while wild flies were rarefied to 10771 reads.

All statistical analysis of 16S data was performed in R (4.1.2) using the Phyloseq [95] package and ggplot2 [96] package for visualization. The CRISPR and cage population data were analyzed separately. For each population, alpha diversity was estimated using Shannon diversity in Phyloseq using the estimate_richness() function. Bray-Curtis dissimilarity was calculated to look at overall patterns of microbiome composition in Phyloseq using the ordinate() function.

Significance was determined at α=0.05. Fixed effects models were fit with the package lme4 [97] with the fixed effects genotype + sex + genotype*sex. When necessary, P-values were adjusted for multiple comparisons using FDR correction method.

Statistical Analysis

R (version 4.1.2) was used to run statistical analyses. Survival data were plotted using the R package survminer [98]. The analysis was performed using the Cox proportional-hazards regression model in R [99]. For longevity, significance was determined using the model: Lifespan∼(Genotype*Sex)/Vial+Block. For gnotobiotic bacterial load, significance was determined using the model: Colonies.mL∼(Genotype)*Sex+Block.

Competing Interests

The authors state no competing interests.

Acknowledgements

We thank the Cider Hill Family Orchard for allowing us to collect wild flies on their property; S. Macdonald lab for providing the modified 7198 and A4 lines; Kistie Brunsell for help with DNA extractions of wild flies; and Elizabeth Everman for assistance with the feeding assay. The University of Kansas Genome Sequencing Center Core (supported by NIH CMADP COBRE P20-GM103638) performed 16s sequencing. SRM was supported by a University of Kansas Self Graduate Fellowship, the work was supported by NIH R01-AI139154 to RLU.

Supplemental tables

Table S1.

Table S2.

Table S3.

Table S4.

Table S5.

Table S6.

Table S7.

Table S8.

Table S9.

Table S10.

Supplemental Figures

Figure S1.

Figure S2.

Figure S3.

Figure S4.

Figure S5.

Figure S6.

Figure S7.

Figure S8.

Figure S9.