L. fusiformis is moderately virulent and leads to induction of genes involved in immune defense

We infected males from the standard D. melanogaster lab line, Canton S, with L. fusiformis at an optical density (OD₆₀₀) of 3.977 and measured survival for seven days post infection (DPI). At this dose, 76% of flies (51 of 67) survived to 7 days, while all flies pricked with a sterile needle survived (Fig 1A). This represents a significant, but moderate reduction in survival (Cox Proportional Hazard test, hazard ratio = 16.1, P = 0.007). Note that to achieve sensible hazard ratio, we called one sterile pricked fly dead at day 7. At this point, we could have elected to use a higher dose for subsequent infection experiments, but the OD₆₀₀ around 4.0 led to sizable variation in survival among different lines (both DGRP and DSPR had lines with survival rates close to zero), so we continued using this dosage throughout the remainder of the experiment.

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Fig 1. Lysinibacillus fusiformis infection is moderately virulent.

A) Canton S male survival after infect with L. fusiformis (OD₆₀₀ = 3.977, blue, n = 67) or sterile prick (gray, n = 59) with 95% confidence intervals. B) Volcano plot of gene expression in Canton S males 12 hours post infection with OD₆₀₀ = 3.977 L. fusiformis vs. uninfected (no sterile prick). Blue points are significantly differentially expressed (FDR-adjusted P-value<0.05) and genes of interest are labeled. C) Pearson correlation coefficients for Log₂ fold change between infections with L. fusiformis and microbes from Troha et al. 2018.

https://doi.org/10.1371/journal.ppat.1010934.g001

We performed RNA-sequencing of pools of males 12 hours post-infection with L. fusiformis to examine changes in gene expression upon infection (Fig 1B). The most highly induced genes are expected based on other studies [50–52] and are mostly antimicrobial peptides in the Toll and Imd pathways. We used an adjusted P-value threshold of 0.1 for Gene Ontology enrichment analysis, which yielded 36 genes differentially expressed. Gene Ontology categories significantly enriched included several related to immune defense: response to bacteria, response to external biotic stress, defense response, immune response, defense response to Gram-positive bacterium, defense response to Gram-negative bacterium (see the entire list in S1 Table).

It appears that the induced antimicrobial peptides are more squarely induced by the Imd pathway instead of the Toll pathway, suggesting induction of gene expression is more like Gram-negative bacteria than Gram-positive bacteria. The Lysinibacillus genus are Gram-positive bacteria, but like others in the Bacillales order, the peptidoglycan is not l-lysine-(lys) type. Instead, most Lysinibacillus examined have the A4alpha (Lys-Asp) peptidoglycan [53,54]. Since the humoral response in D. melanogaster is driven in part by recognition of peptidoglycan type [55,56], the expected relative responses of the Toll and Imd responses to Bacillales is less clear cut than for most bacteria that fit more neatly into Gram-positive and Gram-negative categories based on peptidoglycan [14]. To determine whether the transcriptional response to L. fusiformis infection is indeed more “Gram-negative-like”, we calculated Pearson correlation coefficients for Log₂ Fold Change (infected vs. uninfected) between our L. fusiformis data and 12-hour post infection gene expression for 11 bacterial infection treatments from the Troha et al. [50] data. Although all correlations were significant (P<0.0001), there was stronger correlation between L. fusiformis and Gram-negative bacteria (median = 0.4) than any of the Gram-positive bacteria (median = 0.32, Fig 1C). The same pattern is true when we only considered significantly differentially expressed genes (correlations for Gram-positive range from 0.798 to 0.832 and for Gram+ range from 0.910 to 0.949) or only significantly induced genes (correlations for Gram-positive range from 0.560 to 0.653 and for Gram+ range from 0.786 to 0.903), and these correlations are slightly stronger if we compare induction after L. fusiformis infection to the average induction after Gram-positive infection (Pearson correlation coefficient = 0.336) or Gram-negative infection (Pearson correlation coefficient = 0.433). Thus, L. fusiformis is a moderately virulent bacterial pathogen that induces a strong innate immune response that is more like the transcriptional response to Gram-negative than Gram-positive bacteria.

Genetic variation for immune defense against L. fusiformis in the DGRP

We aimed to map the genetic causes of variation in resistance to infection in three (DGRP and two DSPR) populations. All three populations showed genetic variation for survival after infection with L. fusiformis (Figs 2A, 3A and 4A). Raw survival in the DGRP population ranged from 10% to 100% surviving 5 days post infection (DPI). We also examined survival 2 days post infection, which was much higher overall and showed less variation (S1 Fig). Like several other studies of immune defense in the DGRP, we found that line 321 was particularly susceptible to infection, so we calculated line effects with and without line 321 (S2 Table and S2 Fig). Survival after infection in Drosophila is an inherently noisy phenotype, so it is important to note how much of the variation in survival is due to genetic variation (the heritability). The heritability in the DGRP was 0.802 for survival 5 DPI (0.759 if line 321 is removed) and 0.924 for survival 2 DPI (0.890 if line 321 is removed).

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Fig 2. Genetic variation in immune defense against L. fusiformis in the DGRP.

A) Sorted raw line mean proportion alive 5 days post infection with standard error of the proportion. B) Manhattan plot of genome wide associations with 5-day survival random effects. C) Correlation of line effects from several DGRP immune studies. Sample sizes in parentheses.

https://doi.org/10.1371/journal.ppat.1010934.g002

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Fig 3. Genetic architecture of immune defense against L. fusiformis in DSPR population A2.

A) Raw line means for proportion alive at day 5 post infection with standard error of the proportion sorted be mean proportion. B) QTL plot for line effects of survival post infection with L. fusiformis. Two QTLs are labeled, horizontal line shows a significance threshold with alpha = 0.05.

https://doi.org/10.1371/journal.ppat.1010934.g003

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Fig 4. Genetic architecture of immune defense against L. fusiformis in DSPR population B2.

A) Raw line means for proportion alive at day 5 post infection with standard error of the proportion sorted be mean proportion. B) QTL plot for line effects of survival post infection with L. fusiformis. Two QTLs are labeled, horizontal line shows a significance threshold with alpha = 0.2.

https://doi.org/10.1371/journal.ppat.1010934.g004

For asspciations in the DGRP, we used the online DGRP mapping tool because it accounts for inversions, Wolbachia infection status and the relatedness of lines. We performed the mapping for 5-day survival and 2-day survival both with and without line 321). We chose an FDR-adjusted cutoff of p<0.10 as our significance threshold, and few SNPs were significant at that cutoff (Figs 2B and S3 and S3–S6 Tables). Below we discuss the different mapping approaches but put the most faith in mapping results of 5-day survival when we discarded line 321. S4 Fig show that when line 321 is included (S4A and S4C Fig), the QQ plots show a characteristic inflation of significance (lower p-values) over expectations. This is likely due to the relatively small sample size in the DGRP meaning that sites that are shared between line 321 and other lines by chance (and not genetic linkage) will show inflated significance. In contrast, the omitting line 321 and examining survival 5 days post infection yields a QQ plot that mostly fits expectations with a few outliers that are more significant than expectations.

The FDR-adjusted significance threshold of 0.1 yielded only a handful of significant SNPs (S3–S6 Tables). Sixteen SNPs were significantly associated with survival 5 DPI when all lines were considered. Eleven intronic SNPs were in eys/CG9967, Pvr, CG44153, Trim9, CG15611 (4 SNPs), trio, CG32698 (actually a 2bp deletion), and Frq1. One SNP was in the exon of the noncoding RNA CR44310, one SNP caused a silent change in CG14971, and 3 SNPs were intergenic. When we omitted line 321, only 3 SNPs were significantly associated with 5-day survival–all three were intronic SNPs in Pvr. Note that the SNPs significantly associated when all lines were included but not significantly associated when 321 was omitted were mostly (13 out of 14) still tested but had low enough minor allele frequencies that power was lost and P-values were above our significance threshold. Eight SNPs were significantly associated with 2-day survival when all lines were considered. This included 4 intronic SNPs in Zasp52, trio, CG32364, and Plod as well as 4 SNPs in the 3’ UTR of CG15564. There were no significantly associated SNPs 2-days post infection when line 321 was omitted. The SNPs in Pvr significantly associated at 5-days post infection were nominally significant at 2-days post infection (~3.0 x 10⁻⁴ when all lines were considered and ~4.0 x 10⁻⁵ when line 321 was omitted). With our conservative cutoff for significance, only one SNP (3L:1030175, intronic in the Rho guanine exchange factor, trio) was significantly associated at both 2-days and 5-days post infection. Similarly, only one SNP (2L:8232234, intronic in Pvr) was significantly associated at 5-days post infection whether line 321 was included or not.

We investigated genes connected to significantly associated SNPs in a few ways. First, we examined whether any of these genes were differentially expressed 12 hours after infection based on our RNA-seq data. None were significantly differentially expressed even if we consider the nominal P-values, so genes connected to significantly associated SNPs are not significantly induced or repressed upon infection at the 12-hour time point. Relying on transcriptional responses to infection assumes that genes must be induced to have a role in infection. This is clearly not always true but provides one line of corroborating evidence.

Next, we examined annotated functions of these genes. Two stand out. First, Pvr (PDGF- and VEGF-receptor related) is involved in the regulation of several innate immune functions including apoptosis, humoral immune response, JNK signaling and wound healing [57–61]. Pvr is a negative regulator of humoral immune response, so if these SNPs are associated with alternative splicing or otherwise influence Pvr expression, it would be a plausible connection. We performed RNAi knockdowns of Pvr using ubiquitous drivers (which produced no offspring), and hemocyte and fat body drivers. Though not significant, the knockdown genotypes survived better after infection with L. fusiformis than controls for the hemocyte driver and one of the fat body drivers (S5 Fig). We used a higher dose (OD₆₀₀~10) in these experiments after pilot experiments suggested Pvr knockdown enhanced survival. Given that Pvr is a negative regulator of humoral immunity, the increased survival makes sense, but we hesitate to draw any conclusions from this data since the differences were not significant. The other gene of interest is CG44153, which is predicted to enable cell-cell adhesion mediator activity and is active in the plasma membrane [62]. It stands out because it was also identified in two other DGRP immunity association studies: survival time after infection with Pseudomonas aeruginosa [21], and microbiota-related diet shift [63]. However, CG44153 also was reported to harbor genetic variation associated with several other non-immune phenotypes, so it is possible that there is something about genetic variation at the locus that makes it prone to false positive associations.

Little evidence for correlations among immune phenotypes in the DGRP

The DGRP lines have been available to the public for more than a decade, and in that time, there have been several studies attempting to map variation in immune defense against different pathogens. This allows us to begin to determine whether immune defense against different pathogens is correlated. Are lines that are better at fighting infection against one pathogen, generally better at fighting infection? If so, is this true universally or within categories such as Gram-positive bacteria? A general correlation in immune defense would suggest that at least some of the genetic basis of that variation is also shared for these different pathogens. There is some evidence for this in the overlap in associations in this study and those of Chapman et al. and Wang et al. [1,21]. However, it is also possible that resistance is much more specific for each pathogen. This is likely given some of the specificity of associations–particularly for Providencia rettgeri and Diptericin [5,20,38].

We calculated Spearman’s rank correlations for 14 DGRP immune-related data sets plus an additional 6 non-immune phenotypes (Fig 2C). Though there are several positive correlations, few are significantly correlated after Benjamini & Hochberg correction. Correlations tend to be between the same microbe using different experimental design (different sex or survival vs. bacterial load) or between different microbes measured in the same laboratory (E. faecalis and L. fusiformis were both measured in our laboratory at the same basic time; M. anisopliae and P. aeruginosa were reported as part of the same experiment in Wang et al. [21]). However, there is an almost complete lack of significant correlation between immune phenotypes and any of the other phenotypes, the exception being a positive correlation between female survival after P. aeruginosa and male starvation survival. Given the general lack of correlation between phenotypes, we decided not to look at the overlap in significantly associated SNPs between immune phenotypes. Most of the phenotypes we chose utilized nearly all DGRP lines (170–200 of the 205 DGRP) lines. However, the number of pairwise observations for some of these correlations might be small enough that power to detect significant correlations is reduced. Differences in the lines used in different experiments also enhances the likelihood that traits measured in the same (or related) labs show correlation since those labs are likely to use a similar set of lines. Nevertheless, Pseudomonas infection phenotypes (n = 81 for P. aeruginosa) were correlated with each other and with an enteric Pseudomonas entomophila infection. Our correlation results suggest that the DGRP immune phenotypes that have been reported do show some correlation, but it is difficult with the available data to determine if these correlations are due to phylogenetic relatedness of the microbes used or are an artifact of something about the experimental design (how phenotypes were defined, dose, overlap in lines utilized, etc.) or labs where the experiments were performed.

Genetic variation for immune defense against L. fusiformis in the DSPR

The DSPR consists of two populations (A and B) that were derived from eight different founder genotypes. Each of those populations was further split into two parallel sets (e.g. A1 and A2) which were derived from the same founders, but different interbreeding populations. We infected 343 lines from population A2 with L. fusiformis (OD₆₀₀ ~ 4.0, mean = 22.3 male flies per line, median = 20 male flies per line, standard deviation = 6.47 male flies per line). The overall survival rate 5 days post infection was 74.76% and the line mean raw survival was 75.16% (median = 80%, standard deviation = 21.11%, Figs 3A and S6A for Day 2). Heritability was 0.828 for survival 5 DPI and 0.928 2 DPI.

We used the same logistic regression model as described above to calculate line effects for each DSPR population A line (S7A Fig and S7 Table), and used the DSPRqtl package [64] to determine regions of the genome associated with variation in immune defense against L. fusiformis. We determined a significance threshold (alpha = 0.05) by permuting the data for 1000 iterations. This yielded several QTL peaks with LOD scores above the threshold, but they concentrate in two regions (Fig 3B). The first peak (qtl A5.1) was centered at Chr2R:3,420,000 (LOD drop 2 range 3,360,000 to 3,470,000, note this is release 5 of the D. melanogaster genome, LOD = 8.59) and was associated with 10.96% of the variation in the phenotype. We extracted the effects of individual founder genotypes (S8A Fig) and found that founder A1 genotypes at this region had significantly higher survival than the other four genotypes represented. This includes all or part of 21 genes, none of which have obvious immune function, nor were any differentially expressed in our RNA-seq data after p-value correction. One gene, Cyt-b5 showed an almost 50% reduction in expression upon infection, leading to a nominal p-value of 0.02. Importantly it is annotated to be involved in metabolism of xenobiotic compound, but the naïve expectation if it is breaking down microbial toxins would be induction, not suppression upon infection.

The second peak (qtl A5.2) had a higher LOD score and was also much broader. We focused on the main peak (highest LOD score) which was centered at Chr2R:14,720,000 (LOD drop range from 14,680,000 to 14,770,000, LOD = 21.19) and was associated with 24.89% of the variation in phenotype. This peak contains all or part of 19 genes including two genes involved in immune defense: DptA and DptB. The founder genotype from A5 is associated with significantly lower survival than the other represented genotypes (S8B Fig). If we expand to look at genes under the three adjacent (actually overlapping) peaks with LOD scores above 20, we also include the gene, Jabba, which functions in immune defense. One more QTL peak still overlapping these but with a LOD score of 14.7 (peak 20) contains the Bomanin gene cluster (eight small, secreted peptides involved in immune defense) as well as Imd (adaptor protein for the Imd pathway). Line A5 harbors two unique nonsynonymous SNPs in Jabba exon 6 as well as a loss of stop codon in BomT2 (a Bomanin). Line A2 (not susceptible) contains a 1748bp insertion of a Jockey element in the intron of BomS4 (the intron is 64bp in the other lines). The two Diptericins are tempting candidates, but neither have any variation unique to line A5 in either coding or regulatory regions. Furthermore, the DGRP lines segregate for null alleles of DptA and those lines are right in the middle of the line effects (51^st and 53^rd percentile). There is also a single line with a null mutation (premature stop) in DptB in the DGRP (line 223) and it showed 100% survival. In the DSPR, line A3 has a 37bp deletion that starts in exon 1 of DptB and ends in the intron, and that genotype has no apparent deficit in immune defense against L. fusiformis. We also investigated variation in other genes with known immune functions. Prophenoloxidase gene, PPO1, is involved in the melanization reaction. While line A5 (susceptible DSPR line) did have a single amino acid difference compared to other lines (a conservative arginine to lysine at the 420^th residue), line A7 includes single base deletion leading to a frameshift and a premature stop codon only 44 amino acids into the otherwise 690 residue protein. We chose not to investigate PPO1 further since prior work showed no effect of null PPO1 mutants except in response to infection with Staphylococcus aureus [65,66]. Finally, we examined the coding sequence of Imd and found only a single amino acid difference (A2 has a glycine to serine change at residue 103 compared to the others) among any of the four lines (A2, A5, A6 and A7) with representation at QTL A5.2.

Line effects and mapping in the DSPR A2 population were correlated when comparing Day 2 and Day 5 survival (S7A, S7B, S7F and S8 Figs and S8 Table, Pearson correlation coefficient for line effects = 0.908, Pearson correlation coefficient for LOD scores = 0.957). While the peaks overlapped significantly, the Day 2 survival did nothing to further filter to a smaller set of candidates. We therefore utilize Day 5 survival data for population A2.

We infected 391 lines from the B2 DSPR panel with a mean of 18.81 (range 10–20, median = 20) individuals infected per line. The mean survival was 0.828 (median = 0.85, st. dev. = 0.134). There was again ample variation in survival across lines (Figs 4A and S7C and S9 Table). Heritability was 0.674 for survival 5 DPI and 0.760 for survival 2 DPI. Thus, both heritability and the range of survival post infection are higher in the DSPR A panel (Figs 3A vs 4A), which may have resulted in reduced power for mapping in the B panel. The QTL scan did not result in any significant peaks with alpha = 0.05 (Fig 4B). In fact, we had to relax alpha to 0.20 to get significant peaks. There were 3 overlapping peaks on Chr3R between 15,470,000 and 16,770,000 –a region with 145 genes (qtl B5.1). This peak explained 7.35% of the variance. There are several genes plausibly associated with immune defense. First is Stat92E, the transcription factor of the JAK/STAT pathway (not significantly induced, but expression was up 25.7%). Second are a cluster of Turandots, immune/stress effectors regulated by JAK/STAT. Of the five Turandots under the peak, TotA, TotC and TotX were all significantly upregulated after infection and there was no expression data for TotB or TotC. The founder genotypes under these peaks show that lines B5 and B7 have significantly lower survival than the other lines (S9C Fig). Scanning through variation in these genes, we did not find any sites limited to B5 and B7 that were immediately good candidates for being causal. For panel B2, the correlation between LOD scores for Day 2 and Day 5 was lower and what we observed in population A (S9 and S10 Tables). In fact, there was an additional peak at Day 2 (qtl B2.1, S10 Fig). This peak is only significant with alpha = 0.1 and is on 3L at 2,880,000 (LOD score = 7.57, 2 LOD drop range: 2,850,000 to 3,100,000, 8.53% of variance explained). Founder line B3 genotypes had a significantly lower survival than others (S8D Fig). The peak includes 27 genes including spz-5 (a paralog of the canonical Toll pathway Spaetzle gene). These genes were not differentially expressed in the transcriptome data.

To further investigate overlap between the DGRP and DSPR populations, we looked for DGRP SNPs significantly associated with survival after L. fusiformis infection that were under the three focal QTL peaks (A5.1, A5.2 and B5.1). For the A populations, there were no SNPs that were significantly associated with survival after infection (adjusted P>0.85 where adjustment accounted for just those SNPs under the QTL peak). For the B population, there was a single SNP that, after multiple test correction was associated at p = 0.091, but with multiple corrections for the entire data set, the adjusted p-value was 1.0. We conclude that any causative genetic variation segregating in the DGRP in the regions of the three DSPR peaks was either absent or at too low frequency to result in a significant association.

Defense against L. fusiformis shares a similar genetic architecture with defense against a fungal pathogen

We reanalyzed the data from Shahrestani et al. [44], where DSPR panel A1 was infected with Beauveria bassiana (a fungal pathogen) and survival proportion was subsequently mapped. We performed the same logistic regression (described above), which is different from their approach, and used other variables (all random effects) as they described. Our heritability estimate was 0.921 which is much higher than what they estimated (0.53). We cannot examine correlations among lines for immune defense against B. bassiana and L. fusiformis because the experiments were done in different lines (set A1 vs. A2). However, we can determine whether there is significant overlap in QTLs, which would suggest a shared architecture for immune defense. Like L. fusiformis, there was significant variation in the random effects of line for survival after B. bassiana infection (S7E Fig and S11 Table). In contrast to what Shahrestani et al. [44] reported, we did find one peak significantly associated with survival after B. bassiana infection at alpha = 0.05 (Fig 5A and S12 Table). The peak centered at chromosome 2R:14,660,000 (Fig 5B, qtl Bbas.1, 2 LOD drop range 14,590,000–14,700,000, LOD score = 9.06, 13.15% of variance explained). The A5 founder genotype had significantly lower survival than other lines (Fig 5C).

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Fig 5. A shared genetic architecture of immune defense between L. fusiformis and B. bassiana.

A) B. bassiana QTL analysis yields a single peak. B) The peaks on 2R for B. bassiana (blue, qtl Bbas.1) and L. fusiformis (gray, qtl A5.2) overlap. Horizontal lines represent significance threshold (alpha = 0.05), dashed vertical lines show the locations of the highest peaks. C) Founder genotype effects (plus standard error) for both experiments. Note that founder genotypes not present at that locus in one experiment are over a gray background.

https://doi.org/10.1371/journal.ppat.1010934.g005

Thus, the B. bassiana peak is quite similar to the location of qtl A5.2 for L. fusiformis, even though the two experiments were performed in different subsets of the A population (Fig 5B). To test whether this overlap is more than expected by chance, we performed a Fisher’s Exact Test with the fraction of physical positions with LOD score greater than the threshold score (alpha = 0.05). Day 5 survival for L. fusiformis had 289 of 11768 significant, while B. bassiana had a single position significant of 11768. That one position significant for B. bassiana was also significant for L. fusiformis, which is a significant excess over the expectation (expected overlap = 0.0246, Fisher Exact Test P-value < 0.0001). Furthermore, the founder genotype effects for both phenotypes clearly show reduced survival for the A5 genotype compared to all other genotypes present (this is true whether we match the peaks as in Fig 5C or pick the most significant peak for each genotype). The DSPR experiments lead us to conclude that different panels (DSPR A2 and B2), which have different founder genotypes, have different genetic architectures for immune defense against L. fusiformis, but in two different mapping experiments performed in two different labs with very different pathogens, there appears to be a shared genetic architecture for resistance to infection (DSPR A2 L. fusiformis vs. DSPR A1 for B. bassiana).

Infections of mutant and RNAi knockdown flies show the potential role of individual genes in immune defense against both L. fusiformis and B. bassiana

The number of genes under the shared QTL peak in the DSPR A population are too numerous (96 genes plus tRNAs and lncRNAs) to test individually. We therefore tested several reasonable candidate genes including 4 genes in the Bomanin cluster and Jabba by RNAi knockdown plus knockout lines for genes encoding all Bomanins a subset of Bomanins, Imd, and Diptericin A. The BomΔ^55C line contains an excision of all 10 Bomanin paralogs in the cluster on chromosome 2R, and the BomΔ^left contains an excision of the six “left-most” paralogs: BomT1, BomBc1, BomS1, BomS4, BomBc2, and BomS2. Infections with L. fusiformis confirm a role for Bomanin genes in survival after infection since both BomΔ^55C and BomΔ^left show significantly reduced survival compared to control (Fig 6A). In contrast, neither Dpt nor Imd null mutants had any effect on survival after L. fusiformis infection (Fig 6B). B. bassiana infections also revealed an important role for the Bomanin cluster as again both mutants showed severely reduced survival after infection (Fig 6C). Interestingly, Imd null mutants also showed reduced survival after infection with B. bassiana compared to controls, suggesting a role for Imd in defense against fungal pathogens (Fig 6D).

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Fig 6. CRISPR/Cas9 and classical mutants reveal roles of Bomanin cluster and Imd in immune defense against both L. fusiformis (A and B) and B. bassiana (C and D).

In all cases, controls are in red, other and knockouts of genes of interest are in other tones.

https://doi.org/10.1371/journal.ppat.1010934.g006

With evidence that the Bomanin cluster was generally important for infection with L. fusiformis and B. bassiana, we sought to determine whether specific Bomanin copies had specific protective benefits against infection using fat body RNAi of individual Bomanins. After infection with L. fusiformis or B. bassiana, both BomS2 and BomBC2 knockdown flies stand out as being more susceptible to infection than controls (Fig 7, S13–S17 Tables, P<0.001 in each comparison). Other members of the Bomanin cluster had little to no influence on survival after infection with either pathogen. Given the similarity in sequence of some of these paralogs, we tested whether RNAi knockdowns were specific to specific Bomanins. While the two bicipital forms (BomBc1 and BomBc2) were specific, BomS1 did show some reduced induction even when BomS2 was targeted by RNAi (see methods). Jabba fat body knockdowns also had little impact on survival after infection. Note that the two AttP40 RFP RNAi knockdowns, used as additional controls, showed intermediate survival. Each still show significantly higher survival compared to BomBc2 (P<0.001) for both L. fusiformis and B. bassiana. Taken together, it appears that specific Bomanin paralogs (BomS2 and BomBc2) provides some defense against L. fusiformis and B. bassiana infection. However, the best approach would be confirmed CRISPR knockouts of each specific Bomanin paralog, or even better, recreation of the specific mutations observed in the DSPR lines.

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Fig 7. Fat body RNAi knockdowns reveal roles of specific Bomanin genes in immune defense against both L. fusiformis (A and B) and B. bassiana (C and D).

In all cases, empty vector controls are in gray, other controls are in reddish tones and knockdowns of genes of interest are in blue/green tones.

https://doi.org/10.1371/journal.ppat.1010934.g007

Drosophila strains and husbandry

Drosophila Synthetic Population Resource (DSPR) lines from populations A1 and B1 are maintained by Stuart Macdonald at the University of Kansas [40]. The DSPR population lines were derived from two different sets of founder inbred lines. Each population was interbred among lines for several generations, then inbred to create the recombinant inbred lines used in these experiments. Drosophila Genetic Reference Panel (DGRP) lines were obtained from Brian Lazzaro at Cornell University and from the Bloomington Drosophila Stock Center (Bloomington, IN). Canton S is a standard laboratory Drosophila line. All Drosophila stocks were maintained on standard molasses media at 23 degrees Celsius on a cycle of 12 hours light to 12 hours dark. We refer to both sets of lines by their numeric assignment (for the DGRP we refer to line 321, not RAL_321 or line_321).

Microbes and microbiology

Lysinibacillus fusiformis (strain Juneja) is a natural pathogen of Drosophila obtained from Brian Lazzaro at Cornell University [46]. Bacteria were grown from single colonies in LB liquid media overnight to stationary phase, then concentrated to an optical density (600nm) of about 4.0. Beauvaria bassiana (strain GHA) was obtained from Parvin Shahrestani (California State University at Fullerton). Spores were grown in malt extract agar, then resuspended with a 10ml syringe, filtered through a 0.22 μm Miracell filter and resuspended in 20% glycerol at 4.3e08 spores per mL as measured on a hemocytometer.

General infection protocol

Flies were tipped to new media and cleared after 2–4 days of egg laying to control larval density. Ten to fourteen days later, emerging adults were moved to new media vials. Three- to seven-day old males were infected after light CO₂ anesthesia by pricking in the thorax with a needle dipped in bacteria or fungal suspension or LB broth (or 20% glycerol) as a sterile control. The number surviving 2- and 5-days post infection was noted. For Canton S infections and RNAi knockdowns, we noted survival daily. Infections were performed over several weeks with 3–4 days of infection each week. A single researcher performed all infections (n = 22174) for the GWAS and QTL experiments, and a different researcher performed all infections for the RNAi experiments. These experiments were done in batches of subsets of lines since it would not be possible to infect all lines in one batch. Each subset of lines was chosen randomly and batch effects were included in statistical models.

Infection dynamics in Canton S

The effect of L. fusiformis infection on Canton S survival was assessed using a Cox Proportional Hazard test implemented in the R packages survival and survminer [69,70] with treatment (infected vs. sterile prick) as the independent variable.

RNA-sequencing of infection in Canton-S males

We used Canton S flies to compare expression 12 hours after infection with L. fusiformis to those uninfected (no sterile prick). Each treatment contained 3 replicates of 3 flies per replicate. We flash froze flies in liquid nitrogen, then extracted RNA using a Zymo Quick-RNA Microprep kit (R1050, Zymo Research, Irvine, CA). We then constructed RNA libraries using the Lexogen QuantSeq 3’ mRNA-Seq Library Prep kit (Lexogen GmbH, Vienna, Austria). We sequenced the samples on an Illumina MiSeq Micro SR100 run. Raw Reads were quality filtered using the fastx toolkit (http://hannonlab.cshl.edu/fastx_toolkit/; RRID:SCR_005534), then mapped with STAR version 2.7.10b [71,72] to the Drosophila melanogaster (r6.18) genome, processed with samtools version 1.9 [73,74] and analyzed with DESeq2 [75] using a simple infection vs. no prick design.

Genetic variation for immune defense in the DGRP

We infected male DGRP flies with OD₆₀₀ ~ 4.0 L. fusiformis. Each vial consisted of eight to ten infected flies with one to five vials per line for the DGRP. This corresponds to an average of 36.0 flies (3–4 vials per line) infected per line in 199 DGRP lines.

We performed the DGRP genome wide associations with and without line 321. We used both approaches because line 321 showed unusually low survival at 5 days post infection (DPI) and others [1,21] noted and excluded 321 in their analysis of resistance to Metarhizium anisopliae and Enterococcus faecalis infections. However, line 309 actually had lower survival (0% at 5 DPI) and we hesitate to start dropping all lines with low survival since variation is the point of this type of mapping. We measured survival both 2- and 5-days post infection and performed the mapping experiment at both time points. We fit a mixed effects logistic regression model using the lmer function in lme4 [76] and line effects were extracted using the ranef function. These values were used with the DGRP website [31] to perform association mapping. Thus, there were four total mapping experiments for the DGRP: 2-and 5-days post infection and with and without line 321. We calculated heritabilities using line variance and total variance from the VarrCorr function in lme4.

We next asked whether line effects correlated between DGRP infection experiments (and a few other traits). We retrieved line effects for both male and female 24 hour bacterial load and female survival post Providencia rettgeri infection [5,38], male and female Coxiella burnetii hazard ratio [77], female Pseudomonas entomophila enteric survival [78], male and female Pseudomonas aeruginosa survival [21], male and female Metarhizium anisopliae survival [21], Staphylococcus aureus phagocyte mobilization [79], Listeria monocytogenes bacterial load [80], Enterococcus faecalis survival [1], male aggression [81], male starvation resistance [31], pigmentation (tergite 5) [82], lifespan and fecundity [83], and weight [35]. We calculated Spearman’s rank correlations for these 20 phenotypes and grouped by non-immune, Gram-positive/fungal and Gram-negative pathogens. In these analyses, we used the negative of the load values which means that all immune phenotypes have higher values indicating better immune defense and lower values meaning worse immune defense.

Genetic variation for immune defense in the DSPR

The DSPR experiment was similar to that for the DGRP, but with more lines and fewer individuals infected per line. We used from one to four vials (usually 2–3 vials), consisting of 8 to 10 individuals, per line in the DSPR population. This corresponds to an average of 22.3 flies infected per line in 343 lines from DSPR population A2 and 18.8 flies infected per line in 391 lines from DSPR population B2. For each population, we used a logistic regression for proportion alive at day 2 and day 5 with line and infection date as random effects.

These random effects of line were used directly in the DSPRqtl package in R [40,41]. We used the DSPRscan function with the model effect~1. To determine a significance threshold, we performed 1000 permutations using the same model. Peaks were determined using the LOD drop 2 method in the DSPRPeaks function and nearby and overlapping peaks were curated by hand.

We also retrieved raw data for survival after B. bassiana infection [44] and performed a logistic regression analysis with random effects of line, replicate nested in line, group and group nested in set (as in their analysis). This differs from the authors’ approach who used arcsin-square root transformed survival. All other analyses were performed as above.

Mutant and RNAi knockdown experiments to examine the role of individual genes in immune defense

We followed up our QTL mapping by examining several genes that occur putatively associated with immune defense. These included null mutants (Imd, Diptericin A, BomΔ^55C and BomΔ^left) and their respective controls. We obtained the TRIP RNAi lines [84] from the Bloomington Drosophila Stock Center (Bloomington, Indiana, USA) and crossed to the C564 (RRID: BDSC_6982) fat body-specific driver [85]. In the case of Pvr, we used two additional drivers. We also crossed the driver to an empty vector and RFP RNAi (when available) as negative controls. All lines are described in S18 Table. Infections were performed as described above for L. fusiformis, but B. bassiana were monitored for 21 days instead of 7 days, with flies transferred to new food every 3–4 days. In addition, we mock infected lines with either Luria-Bertani broth or 20% glycerol as a sterile wound control. Very few of these flies died from the wound. We assessed significant differences in survival using the coxph function from the survival package in R [69,86] with genotype and date as variables and the empty vector as the reference. Since the RNAi lines are on different backgrounds (AttP2 and AttP40), we performed separate analyses based on background. Since these Bomanin genes are paralogous and have some degree of sequence similarity, we performed qPCR to examine the degree of specificity for each knockdown. To do so, we first determined the RNAi target sequence for each focal Bomanin gene, then found the paralog with the sequence closest to that target sequence. We then asked whether both target gene and closest paralog (based on target sequence) were differentially expressed 8 hours post infection or no-infection control and between knockdown and RNAi control (BDSC_36303 or BDSC_36304). qPCR primers are listed in S19 Table. S10A Fig shows that the two short form Bomanins are nonspecific. BomS1 shows reduced induction compared to control whether BomS1 or BomS2 was targeted. However, BomS2 only shows reduced induction compared to control when BomS2 was targeted. For the bicipital form Bomanins (S10B Fig), both were specific–only showing decreased induction when the focal gene was targeted. We assessed significance with a simple t test of the difference in relative expression in infected vs. uninfected conditions: (RP49_infected-target_infected)–(RP49_uninfected-target_uninfected).