Bacterial Methionine Metabolism Genes Influence Drosophilamelanogaster Starvation Resistance

Alec M. Judd,a Melinda K. Matthews,a Rachel Hughes,a Madeline Veloz,a Corinne E. Sexton,b John M. Chastona

aDepartment of Plant & Wildlife Sciences, Brigham Young University, Provo, Utah, USAbDepartment of Biology, Brigham Young University, Provo, Utah, USA

ABSTRACT Animal-associated microorganisms (microbiota) dramatically influencethe nutritional and physiological traits of their hosts. To expand our understandingof such influences, we predicted bacterial genes that influence a quantitative animaltrait by a comparative genomic approach, and we extended these predictions viamutant analysis. We focused on Drosophila melanogaster starvation resistance (SR).We first confirmed that D. melanogaster SR responds to the microbiota by demon-strating that bacterium-free flies have greater SR than flies bearing a standard5-species microbial community, and we extended this analysis by revealing thespecies-specific influences of 38 genome-sequenced bacterial species on D. mela-nogaster SR. A subsequent metagenome-wide association analysis predicted bac-terial genes with potential influence on D. melanogaster SR, among which weresignificant enrichments in bacterial genes for the metabolism of sulfur-containing amino acids and B vitamins. Dietary supplementation experiments es-tablished that the addition of methionine, but not B vitamins, to the diets signif-icantly lowered D. melanogaster SR in a way that was additive, but notinteractive, with the microbiota. A direct role for bacterial methionine metabo-lism genes in D. melanogaster SR was subsequently confirmed by analysis of fliesthat were reared individually with distinct methionine cycle Escherichia coli mu-tants. The correlated responses of D. melanogaster SR to bacterial methioninemetabolism mutants and dietary modification are consistent with the establishedfinding that bacteria can influence fly phenotypes through dietary modification,although we do not provide explicit evidence of this conclusion. Taken together,this work reveals that D. melanogaster SR is a microbiota-responsive trait, andspecific bacterial genes underlie these influences.

IMPORTANCE Extending descriptive studies of animal-associated microorganisms(microbiota) to define causal mechanistic bases for their influence on animaltraits is an emerging imperative. In this study, we reveal that D. melanogasterstarvation resistance (SR), a model quantitative trait in animal genetics, respondsto the presence and identity of the microbiota. Using a predictive analysis, wereveal that the amino acid methionine has a key influence on D. melanogaster SRand show that bacterial methionine metabolism mutants alter normal patterns ofSR in flies bearing the bacteria. Our data further suggest that these effects areadditive, and we propose the untested hypothesis that, similar to bacterial ef-fects on fruit fly triacylglyceride deposition, the bacterial influence may bethrough dietary modification. Together, these findings expand our understandingof the bacterial genetic basis for influence on a nutritionally relevant trait of amodel animal host.

KEYWORDS Acetobacter, Lactobacillus, Drosophila melanogaster, microbiota,metagenome-wide association, MGWA, starvation resistance, symbiosis

Received 24 March 2018 Accepted 25 May2018

Accepted manuscript posted online 22June 2018

Citation Judd AM, Matthews MK, Hughes R,Veloz M, Sexton CE, Chaston JM. 2018. Bacterialmethionine metabolism genes influenceDrosophila melanogaster starvation resistance.Appl Environ Microbiol 84:e00662-18. https://doi.org/10.1128/AEM.00662-18.

Editor Eric V. Stabb, University of Georgia

Copyright © 2018 American Society forMicrobiology. All Rights Reserved.

Address correspondence to John M. Chaston,john_chaston@byu.edu.

INVERTEBRATE MICROBIOLOGY

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The study of resident microorganisms (microbiota), including the ability of thesemicroorganisms to modulate organismal phenotypes, is a rapidly developing field

in animal biology (1–6). For example, the microbiota influence on nutrient metabolismsuggests a multiorganismal basis for these varied effects (7–13). An ongoing concern insuch studies is extending our understanding from description to mechanism. Here, weuse statistical genomic modeling to predict host-influencing bacterial genetic factors,and we verify some of these predictions by mutant analysis. We focus on the associ-ation between Drosophila melanogaster and its microbiota using D. melanogasterstarvation resistance (SR) as a representative quantitative trait.

D. melanogaster SR is a model quantitative trait. SR is rapidly scored and can beapplied to large population sizes (14–16). Insights from high-throughput (e.g., genome-wide association studies [GWAS]) and more mechanistic studies have identified numer-ous host genes that contribute to D. melanogaster SR. These primarily include genes incentral and fatty acid metabolism and have been identified by classic genetic ap-proaches, GWAS, and experimental evolution (15–21). For example, the gluconeogen-esis enzyme pepck gene is upregulated during D. melanogaster starvation (19). Becauseit is closely linked to host energy storage and metabolism, SR can also be used as anindicator or predictor of other host phenotypes. Variation in life history traits, such asdevelopment time, body weight and fat content, and life span (17, 22–25), are corre-lated with SR. SR has also been linked to other traits, including locomotion (26), heatshock response (27), cardiac function (28), and sleep patterns (29). The relative ease ofusing SR as a model phenotype has therefore enabled the dissection of its own geneticmechanisms and provided insight into mechanisms underlying other animal traits.

In recent years, the D. melanogaster microbiota has emerged as a model forhost-microbiota interactions. The Drosophila microbiota is of low diversity, usuallydominated by readily cultured Lactobacillales, Acetobacteraceae, and Gammaproteobac-teria in either wild or laboratory-reared Drosophila species. As in mammals, there is no“core” microbiota; instead, the microbiota is inconstant with high interindividual vari-ability in terms of bacterial identity and localization (30–34). The D. melanogastermicrobiota also requires frequent dietary replenishment to maintain the normallyobserved bacterial loads, although there is also evidence that some bacteria persisteven after the bulk flow of food has passed through the gut (35, 36). Drosophila-associated bacterial communities are readily eliminated by bleach treatment (2, 37),and bacterium-free flies display developmental and health traits similar to those ofconventionally reared flies on nutrient-rich diets (38). The influence of bacterial com-munities or individual bacteria on different traits has shown that, among other traits,bacterium-associated flies have lower fat contents and shorter development times (3,5, 39–41). Because both of these traits are positively correlated with SR, we speculatedthe microbiota might also decrease fruit fly SR. The ready manipulation of the Dro-sophila microbiota enabled this work.

Here, we investigated the influence of the microbiota on D. melanogaster SR. Usinga metagenome-wide association (MGWA) approach, we predicted that microbial Bvitamin and methionine metabolism genes influence SR. Employing dietary supple-mentation and mutant analysis experiments, we confirmed a role for dietary methio-nine and bacterial methionine metabolism in D. melanogaster SR, including a nonin-teractive effect between the two. Together, these findings confirm that bacterialmethionine metabolism genes can influence the SR of D. melanogaster reared on anutrient-rich diet.

RESULTSAssociated bacteria influence D. melanogaster SR. To discern if associated bac-

teria influence D. melanogaster SR, we compared the period of survival under starvationconditions between bacterium-associated and bacterium-free Canton-S flies. Consistentwith the known influence of the microbiota to reduce the triacylglyceride content offlies, D. melanogaster flies reared in the presence of a defined 5-species microbialcommunity lived nearly 2 fewer days than flies reared free of associated microbes

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(Fig. 1; see also Table S1 in the supplemental material). This work demonstrates thatassociated microbes can contribute to the SR of laboratory-reared D. melanogaster fliesand raises questions if these effects are shared by all or a subset of the associatedmicrobes, what the genetic mechanisms for these effects are, and how they may berelated to known mechanisms for lipid storage in the flies.

There are species-specific bacterial effects on D. melanogaster SR. Because thebacteria influenced D. melanogaster SR, we reasoned that individual microbes mightconfer species-specific effects similar to the effects detected in previous studies of othermicrobiota-responsive Drosophila traits (3, 5, 39, 40). We measured SR in a singleCanton-S line that was monoassociated with 38 different bacterial species, with onespecies colonizing each Canton-S test group (Fig. 2 and Table S1). In general, flies that

FIG 1 The microbiota influences D. melanogaster SR. To test if the presence of associated microbessignificantly influenced SR in D. melanogaster flies, Canton-S flies were reared bacterium-free (Ax) or witha defined 5-species microbiota (5-sp). SR was measured as the number of surviving flies from vials of 10flies each in 8-h intervals. The difference between treatments was tested using a Cox mixed-effectssurvival model (96, 97). n � 9 vials of 10 flies per treatment (triplicate vials in each of 3 separateexperiments).

FIG 2 Species-specific bacterial influence on D. melanogaster SR. To determine the influence of different bacterial strainson D. melanogaster SR, Canton-S fly SR was measured when this fly line was monoassociated with 38 different strains ofgammaproteobacteria, alphaproteobacteria, or Firmicutes (4-letter codes from Table 2). Different letters over the barsrepresent statistically significant differences between treatments, as determined by a Cox mixed-effects survival model (foreach treatment, n � triplicate vials of 10 flies in each of three separate experiments, unless a vial was discarded withcontamination). Results are color-coded by taxonomic groups: red, Acetobacter; orange, non-Acetobacter acetic acidbacteria; green, gammaproteobacteria; blue, lactic acid bacteria; purple, non-lactic acid bacteria Firmicutes; black, axenic;red-blue, 5-species gnotobiotic (containing both Acetobacter and Lactobacillus isolates). The phylogeny was constructedfrom 16S sequences that were extracted from publicly available whole-genome sequences of these strains.

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bore lactic acid bacteria survived longer under starvation conditions than did flies thatwere monoassociated with acetic acid bacteria or gammaproteobacteria. There werecongeneric exceptions to these trends, including Acetobacter cerevisiae, which con-ferred greater SR than some Acetobacteraceae, and Bacillus subtilis and Lactobacillusbuchneri, which each shortened SR to a greater extent than other Firmicutes. Glucono-bacter species tended to confer the lowest average SR. No bacteria extended SR abovethe level observed in axenic flies. While we cannot rule out that untested strains couldfurther enhance D. melanogaster SR, these findings suggest that, on a nutrient-rich diet,individual members of the common Drosophila microbiota are generally antagonistic orneutral to but do not promote D. melanogaster SR above levels observed in bacterium-free flies.

Bacterial B vitamins and amino acids are predicted to decrease D. melano-gaster SR. To predict bacterial genes that contribute to the varied influences of thedifferent species on D. melanogaster SR, we performed MGWA. We clustered amino acidsequences derived from whole-genome sequences available for each of the species wetested, identifying 14,225 orthologous groups (OGs) that contained more than 1 aminoacid sequence present in 5,855 phylogenetic distribution groups (PDGs; Table S2). Aphylogenetic distribution group is defined as a unique set of taxa in which an OG ispresent. A total of 4,822 (82%) of the OGs contained amino acid sequences that werepresent in just one PDG (median, 1 OG · PDG�1; mean � standard error of the mean[SEM], 2.4 � 0.15 OG · PDG�1), suggesting that genotype-phenotype associations couldbe readily attributed to one or a few genes in each PDG (Fig. S1). We associated eachgene presence-absence pattern with D. melanogaster SR for 4,297 PDGs and identified82 PDGs, collectively bearing 432 OGs (median, 1 OG · PDG�1; mean � SEM, 5.3 � 2.2OG · PDG�1), below a nominal Bonferroni-corrected threshold of a P value of �1 �

10�4 (Table S3).To focus on a subset of these top hits, we identified pathways with multiple genes

in the top predicted MGWA hits for functional analysis. A KEGG enrichment analysis of108 KEGG pathways containing at least one OG from the top hits list revealed that 7KEGG pathways bore significantly more OGs than expected by chance, including genesin vitamin B7 and vitamin B12, methionine, and glutathione metabolism (Table 1). Nopathways bore fewer genes than expected by chance. Additionally, many genesinvolved in B vitamin metabolism were among the most significant hits, includingvitamins B3, B5, B6, and B9 (Table S4). Together, these results predict that bacterialvitamin B and methionine metabolism, of which methionine metabolism is linked toglutathione metabolism through the transsulfuration pathway, involves vitamin B12 asa cofactor and is related to vitamin B6 metabolism, may influence D. melanogaster SR.To test this prediction, we adopted a 2-fold approach. First, we identified the nutrientswith the most dramatic influence on D. melanogaster by a dietary supplementationscreen. Then, by bacterial mutant analysis, we tested the prediction that bacterial genes

TABLE 1 KEGG enrichment analysis of significant MGWA predictionsa

KEGG pathway IDb KEGG pathway name No. of top genesc Total no. of genesd P value

ko00860 Porphyrin and chlorophyll metabolism 14 42 0.002ko00900 Terpenoid backbone biosynthesis 8 17 0.004ko00780 Biotin metabolism 6 10 0.004ko02020 Two-component system 7 157 0.01ko01120 Microbial metabolism in diverse environments 20 291 0.02ko00270 Cysteine and methionine metabolism 11 43 0.03ko00480 Glutathione metabolism 5 13 0.03aUsing a survival model that accounted for experimental block and the monoassociated bacterial strain as random effects, we detected the association between genepresence or absence for 13,343 clusters of orthologous groups (OGs) and D. melanogaster SR. KEGG enrichment analysis of OGs identified bacterial functions thatwere enriched in the MGWA predictions, resulting in the categories listed above. KEGG identification number and pathway type are identified on the left, withsignificant P values on the right. The genes listed in each category were among the top-ranked MGWA predictions; methionine metabolism is among these top hits.

bID, identification.cNumber of KEGG pathway genes in the 432 most significant OGs.dNumber of KEGG pathway genes in all 13,343 clustered OGs.

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involved in the metabolism of the most impactful metabolites would also influence D.melanogaster SR.

Dietary methionine decreases D. melanogaster SR. As a test of the prediction thatMGWA-predicted B vitamins or amino acids influence D. melanogaster SR, we comparedSR in bacterium-free flies reared on supplemented diets. We assayed bacterium-freeflies so that any differences in SR could be attributed exclusively to the supplement andnot to interactions with or an influence on the microbiota (e.g., nutrients promotingbacterial growth or bacteria catabolizing the nutrients). Nutrients were supplementedat molarities similar to those used on previous D. melanogaster diet studies (38, 42, 100).Similar to our inoculations with the 38-strain panel of bacteria, no supplementsextended fly SR above the levels observed in bacterium-free flies, but diets supple-mented with methionine substantially shortened fruit fly SR (Fig. 3A and S2 and TableS5). Initially, these findings suggested a role for methionine, but not other nutrients, inD. melanogaster SR. In a subsequent experiment, we confirmed the specificity of themethionine effect and ruled out that the effect was only due to its higher level ofsupplementation. When we inoculated the diets with 10 mM vitamin B6, vitamin B12, orcysteine (the three nutrients that conferred the next-lowest SR), the B vitamins did notconfer comparable SR to methionine, but cysteine did (Fig. S3 and Table S6). The effectsof methionine and cysteine were not specific to amino acids since supplementationwith glycine, an amino acid with an auxiliary relationship to the methionine cycle,conferred SR more similar to SR with supplementation of vitamins B6 and B12 thanto SR with supplementation of methionine (Fig. S3). Thus, while we cannot rule outthat the B vitamins would influence SR at other concentrations, these experimentsemphasize that increased dietary methionine has a specific effect to shorten D.melanogaster SR.

Bacterial influence on D. melanogaster SR is additive, but not interactive, withdietary methionine supplementation. The dietary supplementation experimentsconfirmed that increasing the methionine content of the fly diet shortens D. melano-gaster SR. Because the MGWA predicted that bacterial methionine metabolism influ-ences D. melanogaster SR, we sought to determine if the methionine effect on SR wasdependent upon the presence of associated microorganisms. In a factorial design, we

FIG 3 Dietary methionine influences D. melanogaster SR and triglyceride content. (A) To test MGWA predictionsthat B vitamins and sulfur amino acids influence fruit fly SR, SR was measured in bacterium-free Canton-S fliesreared on a YG diet supplemented with B vitamins, cysteine (Cys), or methionine (Met). As controls, bacterium-freeflies (Ax-C) and 5-species gnotobiotic flies (5sp-C) were reared on an unsupplemented YG diet. (B) To test forinteractive effects between dietary methionine supplementation and the microbiota, SR was measured in fliesreared in a factorial design to compare bacterial treatment and methionine supplementation (none � nosupplement; Met � 10 mM supplemented methionine). D, diet; G, genotype. (C) Triacylglyceride (TAG) contents ofaxenic flies reared on methionine-supplemented versus normal YG diets. Light-gray bars, axenic flies; dark-graybars, 5-species gnotobiotic flies. Different letters over the bars represent statistically significant differences betweentreatments, as determined by a Cox mixed-effects survival model (A and B; for each treatment, n � triplicate vialsof 10 flies in each of three separate experiments, unless a vial was discarded with contamination) or a Wilcoxon test(C, n � 4 to 5 replicates in each of 2 experiments per treatment). W, Wilcoxon test statistic.

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measured SR in axenic and gnotobiotic flies that were reared on a normal ormethionine-supplemented diet. Both bacterial presence and diet supplementationinfluenced fly SR, but the interaction term was not significant, suggesting additive butnot interactive effects of the microbiota and diet (Fig. 3B and S4 and Table S7). Further,similar to the established effect of bacteria to lower the fat content of flies (5, 9, 39),adding methionine to the fly diet substantially lowered D. melanogaster fat content(Fig. 3C and Table S8). Thus, bacteria do not potentiate or buffer the SR-shorteningeffect of methionine on D. melanogaster, which is correlated with diet-dependentchanges in fly fat storage.

Bacterial methionine metabolism genes decrease D. melanogaster SR. To test ifbacterial methionine metabolism influences D. melanogaster SR, we individually asso-ciated a Canton-S fly line with bacterial mutants bearing transposon insertion muta-tions in methionine cycle genes. Because genes in cysteine and methionine metabolismwere enriched in our top MGWA hits and because methionine metabolism influencesSR, we selected genes based on their relationship to methionine metabolism, includinggenes in the transsulfuration pathway, one-carbon metabolism, or polyamine biosyn-thesis (Fig. 4A). All mutants were obtained from the KEIO collection, a library ofkanamycin-marked Escherichia coli transposon insertion mutants (43, 44), and were

FIG 4 Methionine cycle mutants decrease SR. SR of D. melanogaster that was monoassociated with E. colimethionine cycle mutants was compared to SR of D. melanogaster bearing a wild-type (WT) E. coli strain(4-letter codes from Table 2). (A) A simplified overview of the bacterial methionine biosynthesis pathwayand related contributing pathways. Each mutant tested is listed near its pathway location, and mutantsin the same subcycle are arranged by color. Gene name and MGWA PDG ranking (number to the right)are shown. The schematic is based on the work of Selhub (101). (B) Bacterial mutations that significantlyinfluenced SR relative to the background E. coli control (WT) are indicated by asterisks. Ax, axenic. *, P �0.05; **, P � 0.005; ***, P � 0.0005 (calculated by a Cox mixed-effects survival model). THF, tetrahydro-folate; PLP, pyridoxal 5=-phosphate.

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individually associated with D. melanogaster Canton-S. Each of the mutants associatedwith the flies, but bacterial load was not included as a covariate in the analysis since itdid not vary in the mutants or correlate with the starvation influence of the differentbacteria (Fig. S5 and Table S9). The SR of the flies associated with the different E. colimutants was compared to that of flies bearing wild-type E. coli strains. Consistent withthe idea that bacterial methionine metabolism influences D. melanogaster SR, several ofthe mutants bearing lesions in different methionine- or related-pathway genes led tolonger SR in the flies (Fig. 4B and S6 and Table S10). Conversely, mutations in threegenes that were not involved in methionine metabolism or predicted as top hits by ourMGWA analysis did not alter the SR of the flies relative to the wild-type E. coli strain.Taken together, these results confirm a role for some, but not all, bacterial methioninemetabolism genes in D. melanogaster SR.

DISCUSSION

The results of our study indicate a correlation between the presence of Drosophila-associated bacteria and host SR. By investigating the genetic influences of Drosophila-associated bacteria using MGWA, we found that the variability of SR is related to theidentity of associated bacterial species. Our computational analysis predicted thatbacterial B vitamin and amino acid biosynthesis genes influence SR, especially genesinvolved in methionine metabolism. Through dietary supplementation experiments, wedetermined that methionine decreases Drosophila SR. A nonsignificant interactionbetween methionine supplementation in the diet and microbiota composition sug-gested that the effect of dietary methionine supplementation was not suppressed orpotentiated by associated microbes. Additionally, a role for bacterial methionine me-tabolism was implicated rearing the flies with bacteria bearing lesions in methioninecycle genes. Together, these findings reveal that the microbial influence on D. mela-nogaster SR can be attributed, at least in part, to the activities of methionine meta-bolism genes in the associated microorganisms.

Our analysis demonstrates that the gut microbiota has strain-specific influence on D.melanogaster SR. For example, in our study, flies that were monoassociated withLactobacillus species tended to be more starvation resistant than flies bearing individ-ual Acetobacter strains, but several strains, such as Acetobacter cerevisiae and Gluconac-etobacter europaeus, conferred SR on flies at levels that were more consistent with thelevels observed in Lactobacillus-associated flies. We previously observed similar trendsin bacterial influence on D. melanogaster fat content (3), and strain-specific effects havebeen observed in other studies of monoassociated flies (5, 39–41) and mice (45–47), aswell as well-documented examples in mono- or oligospecific animal-microbe associa-tions (48–50). Findings from the MGWA and follow-up validation experiments suggestthat the bacterium-dependent differences can be attributed in part to specific meta-bolic functions of the bacteria, underscoring the limitations of attributing phenotype-conferring characteristics based on taxonomic or limited genomic information (e.g., fullor partial 16S rRNA gene sequences).

Our current findings combined with previous work lead us to hypothesize thatassociated bacteria may be able to influence host SR through dietary modification. Ithas previously been shown that microbial catabolism of dietary glucose causally lowersthe triacylglyceride content of the flies, an effect that could be recapitulated by rearingbacterium-free flies on diets of reduced glucose content (3, 51). In this work, we revealthat increasing the methionine content of the diet decreases triacylglyceride levels andSR in flies reared on those diets, in an additive relationship with the presence orabsence of associated microbes. The impact of methionine supplementation on fatstorage in other organisms varies (52–56), suggesting that more work is needed tounderstand the basis for the varied effects; one factor may be how nutritionallycomplete the original diet is, but this idea has not been tested explicitly. The correlatedinfluence of triacylglyceride content and SR, together with the responsiveness of fruitfly triacylglyceride content to bacterium-dependent nutrient acquisition (3, 51), sug-gests the possibility that methionine production by associated microbes may influence

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the flies through its accumulation in the diet (nutrient acquisition). Alternatively oradditionally, bacterial methionine metabolism may influence nutrient allocation, as hasbeen shown for other bacterial influences on D. melanogaster nutrition (5, 39), or itseffects may be correlated with but not causal of SR influence. For example, methioninemay not be the key metabolite of interest. A recent analysis of the D. melanogaster lifespan, a trait that is positively correlated with SR in many natural fruit fly populationsand is negatively influenced by dietary methionine (57), showed that levels of methi-onine cycle intermediates S-(5=-adenosyl)-L-homocysteine (SAH) and S-(5=-adenosyl)-L-methionine (SAM), but not methionine, were more directly associated with negative lifespan influence. Metabolomic and/or pulse-chase experiments are necessary to furthertest and develop this proposed hypothesis.

A role for methionine in D. melanogaster SR is not surprising given the influence ofmethionine metabolism on organismal life history traits (58). Life history traits influencean animal’s fitness and reproduction, including organismal longevity, fecundity, time todevelopment, energy storage, and survival under stress (including SR in Drosophilaflies). A hallmark relationship between these traits is the trade-off between organismalresource investment in somatic maintenance (i.e., life span) and reproduction-relatedtraits (59–62). In. D. melanogaster, SR is commonly positively associated with somaticmaintenance but negatively associated with early fecundity traits (63–67), although thiscorrelation is not apparent in all fly populations (68–71) and can be disrupted bygenetic manipulation or selection (71–74). Methionine restriction is an establishedmethod for extending organismal life span, and increasing or decreasing the dietarymethionine content of an isogenic host line can influence its adoption of somaticmaintenance or reproduction-related traits (75–78). Therefore, the established relation-ships between life span and SR, and of methionine on the D. melanogaster life span, areconsistent with the negative impact of methionine on SR reported in this study.Additionally, we observed that Acetobacteraceae tended to confer a shorter period ofSR on Drosophila flies than the Lactobacillales strains we tested, an effect correlatedwith a trend toward Acetobacteraceae also shortening the period of fly developmentand lowering fly triacylglyceride content, as reported previously (3). Taken togetherwith an established relationship between methionine restriction and D. melanogasterlife span extension, our data confirm the influences of methionine on life span-correlated life history traits. Interesting directions for future research include testing thehypothesis that microbial methionine metabolism influences organismal life span. Wenote that this idea is speculative, as it has not been supported by screens for bacterialinfluence on Caenorhabditis elegans longevity (79, 80), and the manipulation of dietarymethionine or methionine metabolism genes does not necessarily lead to tradeoffsbetween longevity and reproduction (58, 81, 82). Regardless, these data provide keyhypotheses to pursue and support a previous assertion that future work investigatingthe relationship between Drosophila flies, the microbiota, and life history tradeoffs is ofinterest (83). Our data suggest that bacterial methionine metabolism is a possiblemechanism for such influences.

In addition to methionine metabolism, other bacterial functions were predicted toinfluence D. melanogaster SR. We focused on methionine metabolism, since methioninehad the most significant effect on SR in our dietary supplementation experiments.Conversely, supplementation with other B vitamins and amino acids did not influenceD. melanogaster SR, possibly suggesting a high false-positive rate among MGWApredictions. Alternatively, we cannot rule out that other experiments that vary nutrientconcentrations, e.g., by supplementing at different concentrations, removing the nu-trients in defined diets, or using a different background diet, would lead us to detectan effect of these predicted genes. Additionally, there were many highly rankedindividual genes on which we did not focus. For example, the top ranked genes were2 genes involved in pyrroloquinoline quinone biosynthesis and utilization, pqqE andqdbA. Pyrroloquinoline quinone (PQQ) plays a key role in microbiota-dependent nutri-ent allocation and development rate in D. melanogaster, two traits that are positivelyassociated with SR in natural D. melanogaster populations (22), supporting the idea that

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bacterial PQQ biosynthesis and utilization may influence D. melanogaster SR. The mostsignificant result was for a phosphoglycerate mutase, suggesting a possible role ofbacterial central carbon metabolism on D. melanogaster SR. Several genes with links topolyamine biosynthesis were also predicted to influence fruit fly SR. One significantMGWA prediction was a bacterial spermidine synthase. The presence of ornithinedecarboxylase, the first and rate-limiting enzyme in polyamine biosynthesis, was asso-ciated with SR. Also, atoS, encoding a spermidine-responsive two-component systemsensor kinase that regulates the expression of the fatty acid degradation operon,atoDAEB (84, 85), was associated with lower SR. Dietary intervention or genetic manip-ulation of D. melanogaster spermidine biosynthesis genes has shown that spermidinelevels are positively associated with an increase in D. melanogaster life span (86–88),stress resistance (89, 90), and triacylglyceride content (90). Our data confirm a role forbacterial speG, an acetyltransferase gene that provides resistance to potentially toxicpolyamines, in D. melanogaster SR (91, 92). Although the mechanism of such effects isunknown, if bacterial spermidine supplements Drosophila spermidine levels, it wouldbe consistent with the established idea that bacterial machinery can complement or beredundant with host enzymatic functions. Taken together, these findings suggest a richreservoir of additional bacterial functions that can be interrogated for influence on D.melanogaster SR.

In summary, our findings support a multiorganismal basis for D. melanogaster SR.While the primary focus of this work has been to demonstrate a role for methionine inD. melanogaster SR, which to our knowledge has not been previously demonstrated,our association analysis predicted that bacteria are likely to influence D. melanogasterSR through multiple functions. The methionine influence on SR is consistent with thelife span-extending influence of methionine restriction on D. melanogaster and thepositively correlated relationship of SR and life span as life history traits in D. melano-gaster. We suspect that bacterial methionine metabolism influences D. melanogaster SRthrough dietary modification and nutrient acquisition, but this prediction comes withthe caveat that we have not performed the necessary metabolomic experiments to testthis idea. As a quantitative trait that has expanded our understanding of how a modelanimal responds to nutrient storage, we expect that better understanding the interac-tions between the microbiota and D. melanogaster SR has the potential to improve ourunderstanding of the multiorganismal basis for animal traits.

MATERIALS AND METHODSBacterial and fly cultures. All experiments were performed using a Wolbachia-free stock of

Drosophila melanogaster Canton-S, obtained from Mariana Wolfner, Cornell University. The standard flyculture was a 12-h light-dark cycle at 25°C on a yeast-glucose (YG) diet (1 liter H2O, 100 g glucose [catalogno. 158968; Sigma], 100 g inactive brewer’s yeast [catalog no. 02903312; MP Biomedicals], 1.2% agar[catalog no. A2530; Apex], 0.84% propionic acid, and 0.08% phosphoric acid).

The bacterial strains used are listed in Table 2, with accompanying growth media, temperature, andoxygen conditions. The media used included brain heart infusion medium (BHI; catalog no. 101480172;Sigma), lysogeny broth (LB; 1% tryptone, 0.5% yeast extract, 0.5% sodium chloride [93]), modified MRSmedium (mMRS; 1.25% peptone, 0.75% yeast extract, 2% glucose, 0.5% sodium acetate, 0.2% dipotas-sium hydrogen phosphate, 0.2% triammonium citrate, 0.02% magnesium sulfate heptahydrate, 0.005%manganese sulfate tetrahydrate, 1.2% agar [40]), and potato medium (catalog no. P6685; Sigma-Aldrich).E. coli transposon insertion mutants obtained from the KEIO collection were cultured in the presence of50 �g/ml kanamycin. Auxotroph identity was confirmed on either 22.7 �M pantothenate-supplementedor 10 mM methionine-supplemented M9 medium. Microoxic conditions were achieved in liquid cultureby static incubation and in solid culture by flooding an airtight container with CO2 before sealing thecontainer. Oxic strains were grown with shaking (liquid) or ambient atmosphere (solid).

Axenic and gnotobiotic flies. Axenic and gnotobiotic fly cultures were derived after bleachsterilization of fly eggs (2). D. melanogaster Canton-S embryos �20 h post-egg deposition were collectedfrom grape juice agar plates (YG diet plus 10% grape juice) by gentle scraping with a paintbrush, filteredthrough a 10-�m nylon mesh filter (catalog no. 57-102; Genesee Scientific), and dechorionated by rinsingthe embryos twice for 150 s each in 0.6% sodium hypochlorite. Three rinses in sterile H2O concluded thedechorionation process. Thirty to 60 eggs were then transferred to a sterile YG diet (preservative omitted)in 50-ml centrifuge tubes. Axenic flies were left unmanipulated. Monoassociated flies were created byinoculating the sterile embryos with 50 �l of a single strain that, after 24 to 72 h of culture, was washedin phosphate-buffered saline (PBS) and normalized to an optical density at 600 nm (OD600) of 0.1. The5-species gnotobiotic flies were reared by inoculating sterile eggs with 50 �l of a mixed culture (1:1:1:1:1

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ratio) of OD600 of 0.1-normalized Acetobacter tropicalis DmCS_006, Acetobacter pomorum DmCS_004,Lactobacillus brevis DmCS_003, Lactobacillus fructivorans DmCS_002, and Lactobacillus plantarumDmCS_001 strains.

To confirm the identity of associated microbes and to test for bacterial contamination in the flies,pools of five female flies were homogenized in 125 �l of bacterium-specific growth medium and 125 �lLysing Matrix D ceramic beads (catalog no. 11654034; MP Biomedicals) on a FastPrep-24 for 30 to 60 s

TABLE 2 Metagenome-wide association and KEGG enrichment analysis show that bacterial pathways significantly influenceD. melanogaster SR

Identifier Relevant characteristics (strain name; resistance; PMID or accession no.)a

Preferredmediumb

Oxygenconditionsc

7636 Escherichia coli BW25113 (CGSC wild type); 10829079, 16738554 LB Oxic8399 CGSC 7636 ΔspeE739::kan; Kmr; 10829079, 16738554 LB Oxic8422 CGSC 7636 Δpfs-773::kan; Kmr; 10829079, 16738554 LB Oxic8713 CGSC 7636 ΔahpC744::kan; Kmr; 10829079, 16738554 LB Oxic9346 CGSC 7636 ΔspeG732::kan; Kmr; 10829079, 16738554 LB Oxic9386 CGSC 7636 ΔgstA785::kan; Kmr; 10829079, 16738554 LB Oxic9453 CGSC 7636 ΔkatE731::kan; Kmr; 10829079, 16738554 LB Oxic9859 CGSC 7636 ΔpdxB729::kan; Kmr; 10829079, 16738554 LB Oxic9920 CGSC 7636 ΔpdxK747::kan; Kmr; 10829079, 16738554 LB Oxic10018 CGSC 7636 ΔglyA725::kan; Kmr; 10829079, 16738554 LB Oxic10100 CGSC 7636 ΔluxS768::kan; Kmr; 10829079, 16738554 LB Oxic10758 CGSC 7636 ΔmetE774::kan; Kmr; 10829079, 16738554 LB Oxic10798 CGSC 7636 ΔsodA768::kan; Kmr; 10829079, 16738554 LB Oxic10826 CGSC 7636 ΔmetF728::kan; Kmr; 10829079, 16738554 LB Oxic10856 CGSC 7636 ΔmetA780::kan; Kmr; 10829079, 16738554 LB Oxic10862 CGSC 7636 ΔmetH786::kan; Kmr; 10829079, 16738554 LB Oxic11994 CGSC 7636 ΔyjiA750::kan; Kmr; 10829079, 16738554 LB Oxicaace Acetobacter aceti NBRC 14818; BABW00000000 mMRS Oxicacew Acetobacter orientalis DmW_048; JOOY00000000 mMRS Oxicaci5 Acetobacter sp. strain DmW_043; JOMN00000000 mMRS Oxicain2 Acetobacter indonesiensis DmW_046; JOMP00000000 mMRS Oxicamac Acetobacter malorum DmCS_005; JOJU00000000 mMRS Oxicaori Acetobacter orientalis DmW_045; JOMO00000000 mMRS Oxicapa3 Acetobacter pasteurianus 3P3; CADQ00000000 mMRS Oxicapan Acetobacter pasteurianus NBRC 101655; BACF00000000 mMRS Oxicapnb Acetobacter pasteurianus NBRC 106471 or LMG 1262; PRJDA65547 mMRS Oxicapoc Acetobacter pomorum DmCS_004; JOKL00000000 mMRS Oxicatrc Acetobacter tropicalis DmCS_006; JOKM00000000 mMRS Oxicatrn Acetobacter tropicalis NBRC 101654; BABS00000000 mMRS Oxicbsub Bacillus subtilis subsp. subtilis strain 168; NC_000964.3 LB Oxicecok Escherichia coli strain K-12 substrain MG1655; NC_000913.3 LB Oxicefav Enterococcus faecalis V583; NC_004668.1 BHI Oxicefog Enterococcus faecalis OG1RF; NC_017316.1 BHI Oxicehor Enterobacter hormaechei ATCC 49162; AFHR00000000 LB Oxicgalb Gluconobacter sp. strain DsW_056; JOPF00000000 Potato Oxicge5p Gluconacetobacter europaeus 5p3; CADS00000000 Potato Oxicgfra Gluconobacter frateurii NBRC 101659; BADZ00000000 Potato Oxicghan Gluconacetobacter hansenii ATCC 23769; ADTV01000000 Potato Oxicgobo Gluconacetobacter oboediens 174Bp2; CADT00000000 Potato Oxicgxyl Gluconacetobacter xylinus NBRC 3288; NC_016037.1 Potato Oxiclani Lactobacillus animalis KCTC 3501; AEOF00000000 mMRS Microoxiclbga Lactobacillus brevis subsp. gravesensis ATCC 27305; NZ_ACGG01000000 mMRS Microoxiclbrc Lactobacillus brevis DmCS_003; JOKA00000000 mMRS Microoxiclbuc Lactobacillus buchneri NRRLB-30929; ACGG00000000 mMRS Microoxiclfal Leuconostoc fallax KCTC 3537; AEIZ00000000 mMRS Microoxiclfer Lactobacillus fermentum ATCC 14931; ACGI00000000 mMRS Microoxiclfrc Lactobacillus fructivorans DmCS_002; JOJZ00000000 mMRS Microoxiclfrk Lactobacillus fructivorans KCTC 3543; AEQY00000000 mMRS Microoxicllac Lactococcus lactis BPL1; JRFX00000000 mMRS Microoxiclmli Lactobacillus mali KCTC 3596 � DSM 20444; BACP00000000 mMRS Microoxiclplc Lactobacillus plantarum DmCS_001; JOJT00000000 mMRS Microoxiclplw Lactobacillus plantarum WCFS1; NC_004567.2 mMRS Microoxiclrha Lactobacillus rhamnosus GG; NC_013198.1 mMRS Microoxicpbur Providencia burhodogranariea DSM 19968; AKKL00000000 LB OxicaKmr, kanamycin resistance.bAbbreviations and media are described in Materials and Methods.cSolid and liquid conditions for oxic and microoxic conditions are described in Materials and Methods.

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at 4.0 M/s. The CFU load was determined by serial dilutions on the same preferred growth medium after24 to 72 h. Strain identity was confirmed by visual inspection of the colonies. White opaque colonieswere classified as lactobacilli (minor differences in color and texture aided in strain-specific identification),and tan semitransparent colonies were classified as Acetobacter species. Many Acetobacteraceae strainscould not be distinguished visually, and we therefore cannot rule out cross-contamination betweenAcetobacter species. Any vials containing greater than 100 CFU · fly�1 of bacterial species not adminis-tered were discarded, and vials containing the appropriate load of administered bacteria were includedin fly SR analysis. Significant differences in the bacterial loads of the different mutants were tested by aKruskal-Wallis test, and multiple comparisons were performed using the PMCMR (94) and multcompView(95) packages in R by a Tukey honest significant difference (HSD) test.

SR assay. The SR assay was conducted using pools of 10 5- to 7-day-old female flies. Flies were lightlyanesthetized with CO2 and transferred to foam-capped vials containing 5 ml 1% agarose. Fly survival wasmonitored daily at 0, 4, 8, 12, and 16 h into the light/dark cycle, with the 16-h readings performed undera red light. At each time point, the number of dead flies was recorded, and the assay continued until allflies in a vial were dead. Treatment-dependent differences in D. melanogaster SR were analyzed using Coxmixed-survival models in R, with experimental replicate and, when it lowered the Akaike informationcriterion (AIC), vial included as random effects (96, 97). An R markdown file for the statistical outputscorresponding to each figure is available in File S2 in the supplemental material.

Metagenome-wide association. To predict bacterial genes that influence D. melanogaster SR, weperformed a metagenome-wide association (MGWA) analysis, as in our previous work (3). SR wasmeasured in D. melanogaster flies that were individually reared with one of 38 genome-sequencedbacterial strains. The strains were selected to represent bacterial taxa commonly found in flies, togetherwith a few neighboring taxa to provide genetic diversity (Table 2). Most of the taxa in this study werethe same as in a previous MGWA analysis we conducted. Orthologous groups (OGs) of genes wereidentified in the strain panel using OrthoMCL, with an inflation factor of 1.5, as described previously (3).To perform the MGWA, OG presence-absence patterns were statistically associated with D. melanogasterSR using a Cox mixed-survival model with experimental block and bacterial strain as random effects inR (98). A nominal Bonferroni-corrected threshold of a P value of �1 � 10�4 was used to determine thesignificance of the predictions.

To test for bacterial functions that were enriched in the significant MGWA hits, the representation ofKEGG categories in the top hits was determined by a chi-square test. KEGG functions were assigned toa representative sequence from each OG using BlastKOALA. KEGG pathway assignments for the total dataset (13,343 OGs) and the top hits (432 OGs) were retrieved in KEGG Pathway (http://www.genome.jp/kegg/tool/map_pathway1.html). Pathway enrichment was determined by a significant false-discoveryrate (FDR)-corrected chi-square test. Chi-square tests and FDR correction were performed in R (99).

Dietary supplementation. Dietary supplementations were performed by rearing bacterium-free orbacterium-associated D. melanogaster on a YG diet inoculated with nutritional supplements. Supplementconcentrations were modeled after previous work (38, 42, 100) (Table S11) and added at 1.86 �M (B2),1.62 �M (B3), 22.7 �M (B5), 8.27 �M (B6), 0.409 �M (B7), 20.4 �M (B9), 7.38 �M (B12), 1 mM (L-cysteine),and 10 mM (L-methionine). In a second experiment, all nutrients were supplemented at 10 mM.

SUPPLEMENTAL MATERIAL

Supplemental material for this article may be found at https://doi.org/10.1128/AEM.00662-18.

SUPPLEMENTAL FILE 1, XLSX file, 4.8 MB.SUPPLEMENTAL FILE 2, PDF file, 1.3 MB.

ACKNOWLEDGMENTSWe acknowledge members of the Chaston lab for assistance with SR assays, Allen

Gibbs and Chris Hardy (UNLV) for helpful discussions, and 3 anonymous reviewers forfeedback.

This work was supported by startup funds and a mentoring environment grant fromBrigham Young University to J.M.C., and an ORCA award from BYU for undergraduateresearch to A.M.J.

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