Perturbations to animal-associated microbial communities (the microbiota) have deleterious effects on various aspects of host fitness, but the molecular processes underlying these impacts are poorly understood. Here, we identify a connection between the microbiota and the neuronal factor Arc1 that affects growth and metabolism in Drosophila. We find that Arc1 exhibits tissue-specific microbiota-dependent expression changes, and that germ-free flies bearing a null mutation of Arc1 exhibit delayed and stunted larval growth, along with a variety of molecular, cellular and organismal traits indicative of metabolic dysregulation. Remarkably, we show that the majority of these phenotypes can be fully suppressed by mono-association with a single Acetobacter sp. isolate, through mechanisms involving both bacterial diet modification and live bacteria. Additionally, we provide evidence that Arc1 function in key neuroendocrine cells of the larval brain modulates growth and metabolic homeostasis under germ-free conditions. Our results reveal a role for Arc1 in modulating physiological responses to the microbial environment, and highlight how host-microbe interactions can profoundly impact the phenotypic consequences of genetic mutations in an animal host.
The physiology and life history traits of animals are shaped in remarkable ways by interactions with beneficial microorganisms (the microbiota). For many metazoans, microbial symbionts play integral roles in post-embryonic development and physiology to yield fit and fertile adults (McFall-Ngai et al., 2013; Robertson et al., 2019). Thus, perturbations to the microbiota can profoundly disrupt these processes. For example, germ-free (GF; microbiologically sterile) or antibiotic-treated mice exhibit decreased body fat (Smith et al., 2007), abnormal intestinal epithelial architecture (Hayes et al., 2018) and neurodevelopmental defects (Sampson and Mazmanian, 2015). In humans, gut bacterial dysbiosis has been implicated in the pathogenesis of type 2 diabetes (Larsen et al., 2010), obesity (Shen et al., 2013), autism (Gilbert et al., 2013) and other disorders. However, the molecular factors that actuate microbial influence on host physiology and development are incompletely understood. Drosophila melanogaster and its gut microbiota are an ideal model to discover such factors, given Drosophila’s extensive genetic resources and the technical ease of generating GF and gnotobiotic flies (Broderick and Lemaitre, 2012; Douglas, 2018).
From a screen to identify microbiota-responsive genes, we discovered altered levels of Drosophila Activity-regulated cytoskeleton associated protein 1 (Arc1) transcript in GF flies. Arc1 is a Drosophila homolog of mammalian Arc (also known as Arg3.1), a master regulator of synaptic plasticity in the brain (Carmichael and Henley, 2018; Shepherd and Bear, 2011). Arc transcription is highly upregulated during the encoding of novel information into neural circuits (Chen et al., 2020; Guzowski et al., 1999). Accordingly, reduced Arc expression impairs learning in rodents (Guzowski et al., 2000; Shandilya and Gautam, 2020), and defects in human ARC have been linked to neurological and neurodevelopmental disorders, including Alzheimer's disease (Bi et al., 2018), autism spectrum disorders (Alhowikan, 2016) and schizophrenia (Fromer et al., 2014). Both mammalian Arc and fly Arc1 encode retroviral group-specific antigen-like amino acid sequences, and are predicted to have evolved independently from ancient Ty3/Gypsy retrotransposons (Ashley et al., 2018; Campillos et al., 2006; Cottee et al., 2019; Pastuzyn et al., 2018). Recently, it was shown that Arc/Arc1 can self-assemble into capsid-like structures that package and transport mRNAs into cultured neuronal cell lines and across synapses in vivo, constituting a previously unknown mechanism of cell-cell communication (Ashley et al., 2018; Erlendsson et al., 2019; Pastuzyn et al., 2018).
As with mammalian Arc, synaptic activity leads to strong transcriptional upregulation of fly Arc1 (Guan et al., 2005; Mattaliano et al., 2007; Montana and Littleton, 2006; Mosher et al., 2015). At the larval neuromuscular junction (NMJ), synapse maturation and plasticity require Arc1 protein capsid-mediated transfer of Arc1 mRNA from motoneuronal boutons to post-synaptic myocytes (Ashley et al., 2018). Further, Arc1 loss-of-function mutants differentially express a repertoire of enzymes involved in central carbon metabolism, have altered metabolomic profiles, elevated fat levels, and are starvation resistant (Mattaliano et al., 2007; Mosher et al., 2015), suggesting that Arc1 controls metabolic homeostasis via unknown mechanisms.
In Drosophila, energy metabolism impacts developmental timing and whole-organism growth during the larval stages (Edgar, 2006). Developmental growth is diet dependent and genetically regulated by multiple nutrient-responsive, inter-organ signaling systems, including (but not limited to) the insect-specific steroid hormone 20-hydroxyecdysone (20E; Buhler et al., 2018; McBrayer et al., 2007) and the functionally conserved insulin/insulin-like growth factor (IIS) pathway (Brogiolo et al., 2001; Rulifson et al., 2002; Edgar, 2006). As in mammals, the Drosophila microbiota plays a crucial role in dietary influence on metabolic and developmental processes. Certain taxa of commensal bacteria (Lactobacillus and Acetobacter spp.) promote metabolic homeostasis and growth through mechanisms including nutritional provisioning and promoting host signaling pathways such as IIS (Chaston et al., 2014; Consuegra et al., 2020; Kamareddine et al., 2018; Keebaugh et al., 2018; Matos et al., 2017; Sannino et al., 2018; Shin et al., 2011). Interestingly, the microbiota can also reduce the severity of nutritional and developmental phenotypes resulting from host genetic mutations (Dobson et al., 2015; Ma et al., 2019). Although connections between the microbiota, host metabolism, and diet are widespread, an understanding of the molecular mechanisms that drive these connections is lacking.
Here, we uncover an interaction among Arc1, the microbiota and the host diet that impacts growth and metabolic traits in Drosophila. We show that Arc1 transcript and protein levels change in tissue-specific patterns in GF flies, and loss of Arc1 dramatically exacerbates the developmental growth delay of GF larvae. Furthermore, we found that a single Acetobacter isolate is sufficient to restore normal larval development and other hallmarks of metabolic health to Arc1 mutants, partly through a mechanism that involves conditioning the larval diet. Selective Arc1 expression in growth-regulating neuroendocrine cells suppresses the metabolic and developmental defects of GF Arc1 mutants. Lastly, we provide evidence of both microbiota-dependent and -independent IIS and 20E dysregulation in Arc1 null larvae. Together, our data reveal an experimental system wherein a single microbiota member supports the health of a metabolically destabilized host, and demonstrates a previously unknown role for Arc1 in mediating the animal's response to its microbial environment.
Arc1 transcript and protein are sensitive to microbial condition
To identify microbiota-responsive neuronal factors, we previously conducted a transcriptomic screen to identify Drosophila genes that are differentially expressed in GF adult heads. Arc1 was among the genes most responsive to host microbial condition (Keith et al., 2019 preprint). Specifically, Arc1 transcripts were elevated in the heads of adult wild-type Drosophila grown GF compared with flies grown in gnotobiotic (GNO) poly-association with a four-species bacterial community of two Acetobacter (Acetobacter sp., A. pasteurianus) and two Lactobacillus (L. brevis, L. plantarum) isolates (Fig. 1A). Arc1 was previously identified in published RNA-seq comparisons of microbiota-associated versus GF adult guts (Bost et al., 2017; Dobson et al., 2016; Guo et al., 2014; Petkau et al., 2017). Consistent with these reports, and in contrast to the adult head, we found that Arc1 transcripts are decreased in the adult gut following microbiota removal (Fig. 1A). These microbiota-dependent transcript changes occur in multiple wild-type lines, but both changes were not observed in every line (Fig. 1A).
Differential transcript abundances between microbiota-associated and GF animals could be a primary consequence of microbial loss, or may be secondary to metabolic changes resulting from sterile rearing. To test this, we measured Arc1 levels in heads and guts of adult flies following starvation, which alters metabolic function (Wat et al., 2020; Zinke et al., 2002). Interestingly, Arc1 mRNA decreased ∼50% in the heads of flies starved for 24 h, but was unchanged in starved guts (Fig. 1B), suggesting that Arc1 expression may be sensitive to nutritional inputs as well as the microbiota.
Prompted by our transcript-level findings, we examined Arc1 protein in the adult brain and gut under GNO and GF conditions. Our analysis of the brain revealed a complex, cell type-specific effect of microbial condition. On the posterior brain surface, there was a significant increase in Arc1-positive cells (Fig. 1C,D). Although the level of Arc1 in the posterior central brain neurons was unaffected, Arc1 levels were decreased in GF animals in a neighboring cell type with distinct morphology (Fig. S1A,A″,C,D). By contrast, on the anterior brain surface, antennal lobe Arc1 expression was increased in GF animals (Fig. S1B,E). Arc1 transcript is reported to be highly enriched in the adult midgut (FlyAtlas; Leader et al., 2018), but its immunostaining pattern has not been reported. We found that Arc1 was broadly expressed throughout the foregut and midgut in GNO animals, with greatly reduced expression in the hindgut (Fig. 1E). Consistent with Arc1 mRNA (Fig. 1A), Arc1 protein in midgut cells was decreased ∼68% in GF animals (Fig. 1F).
Together, these results show that Arc1 transcript and protein levels change in complex, tissue- and cell type-specific ways in response to microbiota and nutritional deprivation.
Loss of the microbiota slows larval growth in Arc1 mutants and a single Acetobacter species is sufficient to restore normal growth rate
Arc1 regulates lipid homeostasis and central carbon metabolite levels in Drosophila larvae (Mosher et al., 2015), and loss of Arc1 results in enhanced adult starvation resistance (Mattaliano et al., 2007). These and other metabolic traits are also impacted by the microbiota, depending on dietary conditions and host genetic background (Dobson et al., 2015; Gnainsky et al., 2021; Judd et al., 2018; Yamauchi et al., 2020). One major consequence of GF-induced metabolic dysregulation in Drosophila is prolonged larval growth (Strigini and Leulier, 2016). To test for an interaction between the microbiota and Arc1, we raised wild-type flies (w1118) and an Arc1 deletion mutant (Arc1E8; Ashley et al., 2018) from embryo to adulthood either GF or GNO, and monitored larval growth rate. Consistent with many previous reports (reviewed by Strigini and Leulier, 2016), GNO wild-type animals completed larval development significantly faster than their GF siblings (∼7.3 days versus ∼8.8 days, respectively; Fig. 2A, Table S1). Strikingly, the GF larval growth delay was dramatically extended in larvae lacking Arc1. Whereas GNO Arc1E8 mutant larvae developed at a rate indistinguishable from GNO wild type, GF Arc1E8 animals took on average ∼12.8 days to pupariate (Fig. 2A). These differences in larval growth rate were reflected in the rate of adult emergence (Fig. S2A). We observed a similar, though less protracted, developmental delay for two independently generated Arc1 loss-of-function alleles (Arc1esm18 and Arc1esm113; Mattaliano et al., 2007) reared GF (Fig. S2B,C), and in GF Arc1E8/Arc1esm113 transheterozygotes (Fig. 2B). All larvae bearing deletions of Arc1 developed at the wild-type rate when grown with the GNO bacterial community. Arc2 is another Drosophila Arc homolog in a genomic locus adjacent to Arc1; the two proteins likely represent an ancestral duplication event (Pastuzyn et al., 2018). In contrast to Arc1 depletion, a P-element insertion in the Arc2 3′ UTR, which decreased Arc2 expression (Fig. S2D), had no effect on the developmental rate of GF larvae (Fig. S2E).
The microbiota-dependent growth delay of Arc1 mutants motivated us to examine Arc1 expression in the larval brain and gut under GNO and GF conditions. Arc1 transcripts were decreased slightly but significantly in the brains of GF compared with GNO larvae, but were unaffected in the larval gut (Fig. 2C). In the larval brain, there were multiple clusters of Arc1-positive neurons and other Arc1-positive cells in the central brain lobes (Fig. 2D, Fig. S3), as previously reported (Mattaliano et al., 2007; Mosher et al., 2015). In contrast to transcript levels, the number of Arc1-positive cells in the central brain lobes was increased in GF animals (Fig. 2E, Fig. S3).
We next investigated whether the polymicrobial GNO community's ability to promote Arc1 mutant growth rate was due to a single bacterial taxon or to the collective effects of the community. To test this, we mono-associated wild-type and Arc1 null larvae with each of the four bacteria and assessed time to pupariation. Association with either of the two Lactobacillus isolates or A. pasteurianus accelerated both wild-type and Arc1E8 larval growth, but was not sufficient to promote growth to the same extent as the GNO community (Fig. 2F,G). In contrast, Acetobacter sp. was indistinguishable from GNO in promoting growth rate of both wild-type and Arc1E8 animals (Fig. 2F,G). There were no significant differences in larval bacterial loads among the four isolates that might explain the growth-promoting action of Acetobacter sp. (Fig. S4A).
Because Acetobacter sp. alone was sufficient to achieve a growth rate comparable to wild-type in Arc1E8 larvae, we investigated whether it is also necessary in the four-species GNO community. Whereas mutant larvae associated with A. pasteurianus, L. brevis and L. plantarum individually developed substantially slower than those associated with the four-species group or Acetobacter sp., the three together in a GNO community lacking Acetobacter sp. were sufficient to promote normal development (GNO-3spp; Fig. 2G). For experimental tractability, we focused all subsequent investigation on larvae mono-associated with Acetobacter sp.
Although the delayed pupariation rate of wild-type GF larvae reflects a moderately extended duration of all three larval instars (Fig. S4B; Storelli et al., 2011), Arc1E8 GF larvae underwent prolonged L1 and L2 phases (Fig. S4B). Further, GF animals of both genotypes increased in size at a more gradual rate than those with Acetobacter sp., but this effect was exaggerated in Arc1 mutants, suggesting a longer time to attain the critical weight that triggers larval molts and metamorphosis (Fig. S4C; Mirth et al., 2005). The extended larval period could reflect reduced nutrient intake, but there were no differences in feeding behavior or food consumption (Fig. S4D,E).
Diet modulates the Arc1-dependent GF larval developmental delay
We demonstrated that Arc1 loss significantly exacerbates the developmental delay of GF larvae. The developmental rate of wild-type GF larvae is particularly sensitive to dietary yeast, the primary source of ingested amino acids and many other nutrients (Piper et al., 2014; Shin et al., 2011; Storelli et al., 2011; Wong et al., 2014). To determine how Arc1 deletion impacts microbe-dependent larval dietary sensitivity, we monitored time to pupariation of Acetobacter sp.-associated and GF wild-type and Arc1E8 larvae fed nine yeast-dextrose diets of varying protein:carbohydrate ratios (Fig. 3; Lesperance and Broderick, 2020a).
As expected, in wild-type animals ‘high-yeast’ (10%) diets eliminated the developmental gap between Acetobacter sp.-associated and GF larvae, regardless of glucose concentration, whereas on ‘low-yeast’ (3%) diets wild-type GF larvae were consistently delayed. The exception to this was the 3% yeast-10% glucose diet, which substantially slowed the developmental rate of all conditions, a known effect of high-sugar, low-protein diets (Musselman et al., 2011; Wong et al., 2014). Interestingly, on 5% yeast diets, increasing glucose concentration moderately accelerated the development of wild-type GF larvae, with a delay observed only at the lowest glucose level.
GF larvae lacking Arc1 generally showed enhanced developmental sensitivity to dietary composition; GF Arc1E8 animals developed more slowly than GF wild type on 5/9 diets tested, including 2/3 ‘high yeast’ diets (Fig. 3). On most diets, Acetobacter sp.-associated Arc1 mutants developed at the same rate as Acetobacter sp.-associated wild-type larvae. One exception was the 3% yeast-5% glucose diet, where Acetobacter sp. failed to have any growth rate-promoting activity for Arc1E8 larvae. Importantly, we did not find differences in Acetobacter sp. abundance on these diets that might correspond to the differences in growth rate of larvae of either genotype (Fig. S5).
Although GF Arc1-deficient larvae exhibited developmental delay on multiple diets, none of those tested yielded a magnitude of delay comparable to that on our laboratory's routine diet (protein:carbohydrate ratio 0.06; Fig. 2A). This may indicate that the full growth rate-promoting effects of Acetobacter sp. require the more complex cornmeal-yeast-molasses diet and are less dependent on the protein:carbohydrate ratio.
Together, these results indicate that the importance of Arc1 function during GF larval development is dependent on the host's nutritional environment, and suggest that Arc1 may be particularly important for GF larval growth dynamics in response to amino acid availability.
Live Acetobacter sp. populations are required for optimal Arc1 mutant growth rate
We next investigated how Acetobacter sp. enables Arc1-deficient animals to develop at the same rate as wild-type mono-associated animals. We hypothesized that consumed bacteria may be a supplemental food source supporting Arc1E8 development, as reported in wild-type Drosophila (Bing et al., 2018; Keebaugh et al., 2018; Storelli et al., 2018). To test this, we inoculated GF wild-type and Arc1E8 cultures with heat-killed Acetobacter sp. cells daily throughout the larval growth period. This had no significant effect on GF wild-type or Arc1 mutant larval developmental rates (Fig. 4A), providing no evidence for Acetobacter sp. cells as a nutritional supplement that affects growth.
The Acetobacteraceae generate acetic acid (Saichana et al., 2015), and two studies have shown that acetic acid/acetate consumption can accelerate the development of GF larvae or larvae associated with an acetic acid-deficient Acetobacter (Kamareddine et al., 2018; Shin et al., 2011). Acetic acid has also been reported to yield no effect on growth, or to exacerbate the GF developmental delay at higher concentrations (Kim et al., 2018; Shin et al., 2011). Consistent with the latter, we found that providing GF wild-type or Arc1E8 larvae with dietary acetic acid either had no impact on growth rate, or further slowed larval development (Fig. 4B). Feeding GF larvae acetic acid-supplemented diet in combination with heat-killed cells also had no effect (Fig. 4B). Additionally, we found that daily inoculation with filtered supernatant from planktonic Acetobacter sp. cultures had no effect on either wild-type or Arc1E8 GF larvae (Fig. 4C), suggesting that Acetobacter sp.-derived metabolite(s) do not promote growth.
Drosophila bacterial commensals proliferate on the flies' diet and are continually ingested (Ludington and Ja, 2020). The host and bacteria therefore share a dietary niche, with the bacteria utilizing the flies' food as a carbon source (Blum et al., 2013; Lesperance and Broderick, 2020b; Martino et al., 2018; Storelli et al., 2018). Certain microbe-dependent Drosophila traits, including growth rate, arise from bacterial utilization of dietary nutrients, producing an altered nutritional intake for the host (Consuegra et al., 2020; Huang and Douglas, 2015; Lesperance and Broderick, 2020b; Storelli et al., 2018). We therefore hypothesized that dietary modification might contribute to Acetobacter sp. support of Arc1 mutant development. To test this, we inoculated GF food with Acetobacter sp., allowed colonization of the diet for 5 days, and then heat-killed all bacteria, resulting in an Acetobacter sp.-conditioned but microbiologically sterile food substrate.
The conditioned diet did not impact the developmental rate of wild-type GF larvae (Fig. 4D). In contrast, the conditioned diet substantially accelerated GF Arc1E8 larval development, though these animals still developed at a significantly slower rate than Arc1 mutants associated with live Acetobacter sp. (Fig. 4D). These data suggest that Acetobacter sp. modification of the larval diet is an important, though not exclusive, mechanism by which Acetobacter sp. promotes Arc1E8 larval growth.
Acetobacter sp. ameliorates systemic metabolism-related Arc1 mutant phenotypes
We next investigated whether the microbial environment impinges on other hallmarks of aberrant metabolism observed in Arc1 mutants. Specifically, we assayed four organismal and cellular traits frequently associated with a larval growth delay and systemic metabolic dysregulation. In addition to slowed development, GF rearing stunts systemic growth yielding smaller pupae (Fig. 5A; Kamareddine et al., 2018; Storelli et al., 2018). This size reduction was exaggerated in GF Arc1 mutants (Fig. 5A). Microbiota perturbation can also compromise cellular growth, as measured by cell size in the adult wing (Shin et al., 2011). Trichome density in adult wings was increased in GF Arc1E8 adults, indicating reduced cell size compared with wild-type animals of either microbial condition (Fig. 5B). These data are consistent with impaired systemic and cellular growth capacity upon loss of both Arc1 and the microbiota.
Arc1 mutant larvae were previously shown to have elevated larval fat, and are starvation resistant as adults, the latter likely in part due to greater lipid stores accumulated during larval development (Mattaliano et al., 2007; Mosher et al., 2015). Consistent with these reports, we found that GF Arc1 mutants had significantly larger lipid droplets in the larval fat body (the primary adipose tissue; Fig. 5C,D), and adult females survived full nutrient deprivation for ∼2 days longer than wild type (Fig. 5E).
Importantly, we primarily observed the effects of Arc1 loss when Arc1 mutants were reared GF; all four phenotypes were suppressed or altered when Arc1 mutants were grown with Acetobacter sp. (Fig. 5). Interestingly, Acetobacter sp.-associated Arc1E8 larvae had significantly smaller lipid droplets in the fat body compared with wild-type animals (Fig. 5D). Comparable to published results (Mattaliano et al., 2007), Acetobacter sp.-associated Arc1 mutants were more starvation resistant than wild type, but succumbed considerably faster than GF mutants (Fig. 5E).
Collectively, these results indicate that loss of the microbiota uncovers or exacerbates growth and metabolic phenotypes in Arc1-deficient Drosophila, and association with a single bacterial taxon can strongly mitigate these defects.
Selective Arc1 expression in neuroendocrine cells mitigates growth and metabolic defects
We next investigated the tissues in which Arc1 function is important for regulation of growth rate and other metabolism-related traits in GF animals. To this end, we selectively restored wild-type Arc1 in GF Arc1E8 flies using a UAS-Arc1 transgene (Mattaliano et al., 2007) and a variety of GAL4 drivers expressed in organs/cell-types with known roles in growth regulation and/or where Arc1 is endogenously expressed.
Weak ubiquitous (armadillo-GAL4) expression of Arc1 significantly accelerated development in GF Arc1E8 larvae, whereas expression in the anterior midgut, hindgut and Malpighian tubules (drumstick-GAL4), somatic muscle (C57-GAL4) and fat body (r4-GAL4) had no effect (Fig. 6A, Fig. S6A-D). Interestingly, pan-neuronal Arc1 expression (Appl-GAL4) or strong expression in the foregut and midgut (mex1-GAL4) further extended the growth delay in GF mutants (Fig. 6A, Fig. S6E,F).
Arc1 expression has been previously described in the ring gland (Mattaliano et al., 2007), a neuroendocrine organ that controls the timing of larval molts and pupariation through ecdysone biosynthesis (Mirth et al., 2005; Uryu et al., 2018; Yamanaka et al., 2015). Interestingly, Arc1 expression was strongly increased in the ring gland once larvae stopped feeding and starting wandering in preparation for pupariation (Fig. S7A-B″). Consistent with Arc1 playing a functional role in this growth-promoting tissue, selective expression of Arc1 in the prothoracic gland (PG) cells of the ring gland moderately but significantly suppressed the growth delay of GF Arc1 mutants (Fig. 6B).
Larval growth is also systemically regulated by the production of insulin-like peptides (Ilps) in the insulin-producing cells (IPCs) of the larval brain (Géminard et al., 2009; Rulifson et al., 2002). In wild-type animals, clusters of strongly Arc1-positive neurons were observed lateral to the medial IPCs (Fig. 2D, Fig. S7C-D). Weakly Ilp2-positive cells sometimes could be observed adjacent to those Arc1-positive neurons (Fig. S7D, asterisks). Interestingly, in some cases, certain IPCs were weakly Arc1 positive (Fig. S7E-E″, arrowheads), and we found weaker Arc1-positive cells adjacent to the IPCs (Fig. S7F-F″, arrowheads). In the adult brain, Arc1 staining has been shown to fully colocalize with IPCs (Mattaliano et al., 2007). Overexpressing Arc1 in the IPCs of GF Arc1 null larvae (Fig. S7G-G″) significantly increased larval growth rate, but, as with selective expression in the PG, not to wild-type levels (Fig. 6C). Expressing Arc1 in the IPCs also suppressed the reduced pupal size (Fig. 6D), reduced wing cell size (Fig. 6E) and enlarged lipid droplets (Fig. 6F), but did not alter the starvation resistance of GF Arc1 mutants (Fig. 6G). Notably, ubiquitous, IPC-, and PG-specific overexpression of Arc1 had no effect on developmental rate or growth in GF wild-type larvae (Fig. S8A-D), whereas pan-neuronal and foregut-midgut overexpression exacerbated the GF developmental delay, similar to our observations in the Arc1E8 mutant background (Fig. S8E,F). Taken together, these data suggest that Arc1 expression in multiple tissues is necessary to control larval growth and other metabolic traits.
Evidence for microbe-dependent and -independent changes in insulin and ecdysone pathways in Arc1-deficient larvae
We next sought to identify molecular changes in Arc1 null animals that might yield mechanistic insight into their growth and metabolic defects. Collectively, the phenotypes we observed in GF Arc1E8 animals (Fig. 5) are reminiscent of those in IIS pathway mutants (Brogiolo et al., 2001; Broughton et al., 2005; DiAngelo and Birnbaum, 2009; Rulifson et al., 2002). For example, ablation of IPCs or loss of IIS signaling slows larval growth rate in GF or CV larvae (Fig. S9A; Rulifson et al., 2002). Surprisingly, we found that overactivation of IIS signaling in GF larvae through overexpression of Ilp2 or expression of activated PI3K also slows larval growth rate (Fig. S9B,C), similar to what has been previously described for overexpression of Ilp8 (Vallejo et al., 2015). Previous work did not reveal altered IIS function in Arc1 mutants (Mattaliano et al., 2007; Mosher et al., 2015), though under different dietary conditions and without microbial manipulation (see Discussion). Notably, Acetobacter strains sustain larval growth by activating IIS in wild-type flies fed nutrient-restrictive diets (Kamareddine et al., 2018; Shin et al., 2011; Storelli et al., 2011). We therefore hypothesized that IIS function might be perturbed in a microbe-dependent manner in Arc1E8 larvae.
In adequately fed larvae, Ilps are synthesized in the IPCs and secreted into the hemolymph; decreased Ilp expression and Ilp retention in the brain are associated with nutritional deficiency and impaired growth promotion (Géminard et al., 2009). We found that Ilp3 and Ilp5, but not Ilp2, transcripts were reduced ∼40% in the brains of feeding GF Arc1E8 larvae compared with wild-type Acetobacter sp.-associated larvae (Fig. 7A). Irrespective of these transcript level changes, both Ilp2 and Ilp5 protein levels were increased in Acetobacter sp.-associated Arc1 mutant brains compared with GF Arc1 mutant brains (Fig. 7B). Such accumulation is generally interpreted as evidence of decreased secretion and reduced IIS signaling.
Ilp secretion leads to systemic insulin signaling activation in key metabolic tissues, including the fat body (Brogiolo et al., 2001; DiAngelo and Birnbaum, 2009). Activation of IIS in these tissues leads to phosphorylation and activation of Akt (pAkt), nuclear exclusion of the transcription factor Foxo, and consequent upregulation of its negative targets InR and Thor (also known as 4E-BP; Baker and Thummel, 2007). In GF Arc1E8 fat body lysates, pAkt was decreased compared with Acetobacter sp.-associated fat bodies, and trended lower than GF wild type, though the difference was not significant (Fig. 7C,D). Despite reduced pAkt levels (consistent with reduced IIS signaling), InR and Thor expression were either decreased (consistent with elevated IIS signaling) or unaffected in Arc1E8 fat bodies and whole animals, regardless of microbial condition (Fig. 7E,F). Interestingly, we observed the same trends of decreased pAkt and decreased Foxo target gene expression in wild-type GF animals (Fig. 7C-F).
Although we found that multiple IIS signaling steps are impacted by microbial condition and/or Arc1 loss, these changes do not suggest simple increased or decreased IIS pathway activity in GF Arc1E8 larvae. This might indicate that the combined effects of Arc1 and microbiota loss on IIS function are more complex than the effects of either manipulation alone, and thus could involve dysregulation of other pathways that impinge on IIS to affect metabolism and growth. The steroid hormone 20E regulates the timing of larval maturation, and is also nutrient responsive and intersects with IIS signaling (Boulan et al., 2013; Buhler et al., 2018; Yamanaka et al., 2015). Notably, the 20E biosynthetic pathway begins in the PG, where targeted Arc1 expression suppresses the growth delay of GF Arc1E8 larvae (Fig. 6B). shroud (sro), a short-chain dehydrogenase/reductase that functions in this pathway in the PG (Niwa et al., 2010), was moderately decreased (∼35%) in larval brain/ring glands of feeding GF Arc1E8 animals, whereas transcripts of other ecdysone biosynthetic enzymes were unaffected (Fig. 7G). The monooxygenase shade (shd) converts ecdysone to bioactive 20E in the fat body (Petryk et al., 2003), and reduced shade expression delays pupariation, stunts growth, reduces Ilp3 expression and induces Ilp2 retention (Buhler et al., 2018). Interestingly, fat body expression of shade decreased ∼50% in Arc1 mutants under both microbial conditions (Fig. 7G).
Collectively, these data suggest that GF Arc1 mutants experience complex dysregulation of two primary endocrine axes that regulate growth and metabolism in Drosophila, and some, but not all, of these defects are mitigated by Acetobacter sp.
Many physiological traits in animals are shaped by highly complex, poorly understood interactions between a host's genotype and its microbiota. Here, we report an unexpected connection between the host gene Arc1 and commensal bacteria that affects metabolism and growth in Drosophila. We initially identified Arc1 from a screen for transcripts showing differential abundance in the heads of GF versus CV/GNO adult flies. Notably, Arc1 was recently designated a ‘core’ microbiome-response gene in Drosophila, as it consistently appears in published RNA-seq studies focused on the gut or whole animals (Delbare et al., 2020). Our study further reveals that microbiota-dependent Arc1 mRNA and protein changes vary in direction and magnitude between the gut and the head/brain, and among different populations of Arc1-positive cells in the brain (Figs 1 and 2, Figs S1 and S3). Arc1 expression increases substantially following neuronal activation, similar to mammalian Arc (Guan et al., 2005; Mattaliano et al., 2007; Montana and Littleton, 2006), and this response to activity can be brain region specific (Mosher et al., 2015), comparable to the microbiota-dependent cell type-specific variance we observed. Genetic manipulations that reduce IIS activity also yield tissue-specific differential Arc1 expression (Musselman et al., 2018; Tain et al., 2021), as does rearing flies on a high-fat diet (Rivera et al., 2019) or under starved conditions (Fig. 1B).
Together, this complexity suggests that: (1) Arc1 expression is highly sensitive to microbial and nutritional cues, and (2) Arc1 might play mechanistically distinct roles in different tissues and cell types, necessitating precise spatiotemporal regulation of its transcript and protein levels. Consistent with the latter idea, targeted tissue-specific expression of Arc1 in Arc1E8 GF animals had a range of effects depending on the driver (Fig. 6, Fig. S6). Notably, overexpressing Arc1 pan-neuronally and in the foregut-midgut was deleterious, significantly exacerbating the GF growth delay of both wild-type and Arc1 mutant larvae (Fig. 6A, Figs S6E,F and S8E,F). Although IPC-specific and PG-specific expression both accelerated GF Arc1E8 development, neither fully rescued the defect to wild-type GF growth rates (Fig. 6B,C). Similarly, IPC rescue only partially suppressed the reduced wing cell size and increased lipid droplet size of GF Arc1 mutants, but resulted in pupae that were larger than wild-type animals, while having no impact on starvation resistance, in contrast to previous findings (Fig. 6D-G; Mattaliano et al., 2007). These results suggest that metabolic and growth homeostasis require precise modulation of Arc1 levels in multiple tissues. Also, Arc1 may function in mechanistically distinct ways in different tissues, with varying outcomes for the specific phenotypes we assayed. For example, Arc1 transcript is abundant in wing imaginal discs (FlyAtlas; Leader et al., 2018), raising the possibility that Arc1 might have a cell-autonomous function in this tissue that contributes to wing cell size (Fig. 5B). To date, both Drosophila and mammalian Arc proteins have been studied primarily in the brain and neurons. Our work suggests that Arc proteins may play important roles in other organs and cell types, and this should be examined in vertebrate systems as well.
Despite its consistently reported microbe-dependent transcript changes, evidence for a functional interaction between Arc1 and the microbiota had not been previously investigated. We found that loss of Arc1 in GF Drosophila resulted in deleterious phenotypes at the molecular, cellular and organismal levels that were all indicative of metabolic dysregulation and compromised growth (Figs 2, 5 and 7). Some of these findings are similar to previously reported Arc1 mutant phenotypes, which suggested that Arc1 regulates systemic metabolism. Specifically, Mosher et al. (2015) showed that Arc1esm18 larvae have elevated whole-animal fat levels, which is consistent with our finding that GF Arc1E8 larvae have enlarged lipid droplets in the fat body (Fig. 5C,D). Mattaliano et al. (2007) reported that Arc1esm18 adults are starvation resistant, which we also observed in Arc1E8 adults (Fig. 5E). Importantly, as with developmental rate, pupal size and wing cell size (Figs 2A and 5A,B), we only observed these Arc1 phenotypes (or, in the case of starvation resistance, observed the strongest effect) when Arc1 mutants were also GF; Arc1E8 animals mono-associated with Acetobacter sp. were more similar to, if not indistinguishable from, wild-type animals in most of our assays. It should also be noted that our study and those mentioned above utilized considerably different diet formulations, and, in fact, we found that the microbe-dependent Arc1 larval growth rate was extremely sensitive to diet (Fig. 3). These results highlight the need to consider diet and microbial condition as key factors that can interact strongly with host genotype to greatly influence phenotypic outcomes, in Drosophila and other model systems.
Although Arc1 was previously shown to impact systemic energy metabolism (Mosher et al., 2015), and our study has expanded the range of Arc1 mutant phenotypes to include organismal and cellular growth defects, the exact molecular mechanisms through which Arc1 exerts these effects is unknown. Mammalian Arc promotes endocytosis of AMPA receptors via direct interactions with endocytic machinery (Chowdhury et al., 2006; DaSilva et al., 2016; Wall and Corrêa, 2018), and both Drosophila Arc1 and mammalian Arc encode polypeptides that can self-assemble into mRNA-containing capsid-like structures, which are released in extracellular vesicles and taken up by recipient cells (Ashley et al., 2018; Erlendsson et al., 2019; Pastuzyn et al., 2018). Both of these mechanisms appear to be crucial for the role of Arc as a master regulator of synaptic plasticity. Their potential contributions towards its metabolic and growth-promoting functions have not been investigated, but it is possible that distinct molecular functions dependent on cellular and tissue contexts may contribute to the role of Arc1 in metabolism and growth regulation.
The molecular and cellular processes impacted by the Arc1-microbiota-diet axis that might result in metabolic and growth phenotypes are also unclear. In Drosophila, these processes are largely coordinated by the intertwined activities of IIS and 20E signaling, which are both affected by the microbiota (Bing et al., 2018; Kamareddine et al., 2018; Shin et al., 2011; Storelli et al., 2011). Notably, we found that molecular readouts of IIS function at multiple points in the pathway, and in multiple tissues, were altered in Arc1 mutants, though in both microbiota-dependent and -independent ways, and not consistent with simple IIS over- or under-activation (Fig. 7A-F). In addition to changes in the IIS pathway, two of the enzymes required for 20E biosynthesis are decreased in Arc1 mutants (Fig. 7G). We think it is likely that the interplay between microbial condition and Arc1 mutation has complex and multifarious impacts on many growth-regulating cellular processes, each of which may be a primary consequence of Arc1 loss, or secondary to the metabolic defects induced by Arc1 and microbiota removal. Interestingly, mammalian Arc has also been linked to insulin and metabolism. Arc expression can be strongly induced in cultured human neuroblasts by exogenous insulin (Kremerskothen et al., 2002), and mice fed a high-fat diet, which induces insulin resistance and diabetic-like phenotypes, have suppressed Arc expression in the cerebral cortex and hippocampus (Chen et al., 2020; Mateos et al., 2009). Our data suggest that links between Arc proteins and insulin signaling might be evolutionarily conserved, and that the influence of the microbiota may be an integral component of this relationship.
One of the major findings of this study is that mono-association with a single member of the bacterial microbiota, Acetobacter sp., was sufficient to fully or partially suppress most of the phenotypes observed in GF Arc1 mutants. We further found that pre-conditioning the larval diet with Acetobacter sp. was sufficient to accelerate GF Arc1E8 development, suggesting that dietary modification is a key element of Acetobacter sp. growth-promoting activity (Fig. 4D). There is precedent for similar mechanisms in wild-type Drosophila associated with other microbiota members. For example, on nutritionally poor diets, L. plantarum depletes the levels of sugars and branched-chain amino acids, and increases the levels of glycolysis and fermentation products to promote larval growth (Storelli et al., 2018). Importantly, we found that live Acetobacter sp. populations are required for full growth rate promotion of Arc1 mutants. In wild-type flies, commensal bacteria promote growth through interactions between bacterial cell wall components and gut cells, including induction of intestinal peptidase expression by D-alanylated teichoic acids (Consuegra et al., 2020; Matos et al., 2017), and innate immune signaling activation by DAP-type peptidoglycan (Davoodi et al., 2018; Kamareddine et al., 2018). Notably, these two modes of microbiota activity are reminiscent of microbial influence on mammalian physiology. Bacterial breakdown of macro-nutrients, such as complex polysaccharides, in the human gut has been linked to health and disease states (Cockburn and Koropatkin, 2016). Other functions, such as immune cell maturation and maintenance of gut epithelial architecture, appear to require bacterial cell-derived antigens (Sekirov et al., 2010).
The ability of Acetobacter sp. to largely restore metabolic and developmental homeostasis to Arc1 mutant Drosophila is a representative example of a bacterial symbiont mitigating the deleterious effects of genetic aberrations in the host, a prevalent feature of animal-microbe interactions (Douglas, 2019; Lynch and Hsiao, 2019; Ussar et al., 2016). The potential to harness these phenotypic buffering functions has motivated increasing efforts to develop microbial therapies for human diseases with a genetic basis (Baruch et al., 2021; Davar et al., 2021; Helmink et al., 2019). However, the success of these endeavors will require a more thorough understanding of the highly complex, modular interactions among host genotype, microbial metagenomes, diet, and other factors. This study reveals a tractable system in which to explore how a single host gene and the microbiota converge on conserved cellular pathways to regulate nutritional responsiveness, metabolic health and developmental growth.
MATERIALS AND METHODS
Drosophila stocks and diets
The following fly stocks were used: w1118, Canton-S and Oregon-R are long-term lab stocks originally from the Bloomington Drosophila Stock Center (BDSC), Top Banana (derived from flies wild-caught in Seattle, Washington in 2013; kind gift from Dr Michael Dickinson; Giraldo et al., 2018), y1w1 (BDSC #1495), w1118; Arc1E8 (kind gift from Dr Vivian Budnik and Dr Travis Thomson, University of Massachusetts Medical School, USA), w*;Arc1esm113 (BDSC #37531), w*;Arc1esm18 (BDSC #37530), y1w67c23;Arc2EY21260 (BDSC #22466), w*;;UAS-Arc1-WT (BDSC #37532), w*;;arm-Gal4 (BDSC #1561), Appl-Gal4,y1w* (BDSC #32040), w1118;;Ilp296A08-GAL4 (BDSC #48030; Jenett et al., 2012), y1w*;;phm-GAL4 (BDSC #80577), w1118;;drm-GAL4 (BDSC #7098), mex1-GAL4,y1w* (kind gift from Dr Claire Thomas, Pennsylvania State University, USA), w*;;C57-GAL4 (BDSC #32556), y1w*;;r4-GAL4 (BDSC #33832), w*;;Hs-GAL4 (BDSC #1799), y1w*;;UAS-Ilp2 (BDSC #80936), w1118;UAS-rpr (BDSC #5824), UAS-PI3K92ECAAX,y1w1118 (BDSC #8294). Additional mutations or transgenes present in GAL4 stocks obtained from the BDSC were removed by standard crossing methods. We use a yeast-cornmeal-molasses diet consisting of (percentages given as wt/vol or vol/vol throughout): 8.5% molasses (Millhouse molasses; Domino Foods), 7% cornmeal (Enriched yellow cornmeal; Prairie Mills Products), 1.1% active dry yeast (Genesee Scientific 62-103), 0.86% gelidium agar (MoorAgar 41074), 0.27% propionic acid (Fisher Scientific A258) and 0.27% methylparaben (Sigma-Aldrich 47889). All experiments utilized this diet except those in Fig. 3, which were conducted on yeast-glucose diets. Yeast-glucose diets were prepared as described (Koyle et al., 2016) and consisted of the indicated proportions of active dry yeast and dextrose (Fisher Scientific BP350-1), with gelidium agar, propionic acid and methylparaben as above.
The Acetobacter sp., Acetobacter pasteurianus, Lactobacillus plantarum and Lactobacillus brevis stocks utilized in this study were all isolated from conventionally reared Top Banana Drosophila cultures in our laboratory. Adult flies were surface sterilized in 10% bleach and 70% ethanol, rinsed three times and homogenized in PBS. Serial dilutions of fly homogenates were plated on de Man, Rogosa and Sharpe (MRS; Weber Scientific 3113-60) and acetic acid-ethanol (AE; Blum et al., 2013) agar plates: 0.8% yeast extract (Fisher Scientific 212750), 1.5% peptone (Fisher Scientific 211677), 1% dextrose, 0.5% ethanol, 0.3% acetic acid (Fisher Scientific A38-212). Colonies with distinct morphology were streaked for isolation. Bacterial taxonomies were assigned by PCR amplification and sequencing of the 16S rRNA gene using universal bacterial primers 8F (5′-AGAGTTTGATCTGGCTCAG-3′) and 1492R (5′-GGMTACCTTGTTACGACTT-3′; Eden et al., 1991). Sequences were searched against the NCBI nr/nt database via blastn (Altschul et al., 1990; Camacho et al., 2009; Morgulis et al., 2008) and taxonomies were assigned based on >97% sequence homology. Because the 16S rRNA sequence for isolate A22 (Acetobacter sp.) bore >97% similarity with more than five different Acetobacter species, we did not assign a species-level taxonomic classification.
Generation of GF and GNO fly cultures
GF and GNO Drosophila cultures were generated according to established methods (Koyle et al., 2016). Synchronous populations of embryos were collected on apple juice agar plates. In a sterile biosafety cabinet, embryos were treated with 50% bleach solution for 2 min to eliminate exogenous microbes and remove the chorion. Embryos were then rinsed twice in 70% ethanol, twice in sterilized milliQ water, and once in sterilized embryo wash: 2% Triton X-100 (MP Biomedicals 807426), 7% NaCl (Fisher Scientific BP358-212). Sterilized embryos were then pipetted into autoclaved food vials to generate GF cultures. To generate GNO flies, overnight cultures of bacterial isolates were grown for ∼16-18 h in MRS broth (30°C with shaking for Acetobacter isolates and 37°C static for Lactobacilli). Sterile food vials were inoculated with 40 µl of overnight cultures (OD∼1) immediately prior to the addition of sterilized fly embryos. For poly-associated (GNO) flies, vials were inoculated with 40 µl of a 1:1:1:1 mixture of overnight cultures of the four indicated bacteria.
For mono-association experiments (Fig. 2), bacterial inoculum was standardized to ∼108 colony-forming units (CFU) for each strain tested using the following empirically determined CFU/ml constants: Acetobacter sp.: 4.5×108; A. pasteurianus: 6.12×108; L. brevis: 4.28×108; L. plantarum: 1.14×108 (Koyle et al., 2016).
All experimental Drosophila cultures were maintained in an insect incubator at 23°C, 70% humidity, on a 12:12 light-dark cycle.
Larval and adult animals were confirmed as GF or mono-/poly-associated by homogenization in sterile PBS and plating on MRS and AE agar plates.
We did not maintain GF, GNO or mono-associated flies over multiple generations; all experiments utilized independently derived GF or GNO animals.
Developmental timing measurements
Synchronous populations of embryos were collected in a 4-6 h time window and treated as described above to generate cultures of defined microbial conditions. For pupariation and eclosion rate analysis, the number of pupae formed or empty pupal cases, respectively, were counted daily until 100% of the population had pupariated or eclosed. The duration of larval development is strongly affected by crowding conditions in the food (Klepsatel et al., 2018). Also, variable and unpredictable numbers of embryos do not survive the bleach and ethanol washes employed to generate GF and GNO cultures (Koyle et al., 2016; Troha and Buchon, 2019; S.A.K. and B.M.M., unpublished observations). We found that 10-30 larvae per vial was optimal for growth, and that within-treatment developmental rates were comparable for vials with larval densities in this range (see data provided in Peer Review History linked to this paper). Therefore, vials containing fewer than ten and greater than 30 animals were omitted from analyses as either under- or over-crowded, respectively.
Larval instars were determined via mouth hook and/or posterior spiracle morphologies (Bodenstein, 1950).
Larval feeding assays
Larval feeding was assessed via dye consumption (Buhler et al., 2018; Libert et al., 2007; Mosher et al., 2015; Shin et al., 2011). Pre-wandering third instar larvae were transferred to apple juice plates coated with dyed yeast paste consisting of a 2:1 mixture of 0.5% FD&C Red #40 dye (Ward's Science 470301-004) and active dry yeast, and allowed to feed for 30 min at 23°C. Guts were then dissected from 20 larvae, homogenized in 1 ml PBS, and absorbance of the supernatant was measured at 490 nm with a Tecan Spark microplate reader.
Mouth hook contraction rates were assayed as described (Bhatt and Neckameyer, 2013). Pre-wandering third instar larvae were transferred to apple juice plates thinly coated with yeast paste, given 30 s to acclimate, and contractions were counted manually for 30 s.
Quantifying bacterial loads for mono-associated larvae
Pre-wandering third instar larvae (eight to ten animals per replicate) were removed from the food and surface sterilized in 10% bleach for 1 min. Larvae were then rinsed three times in PBS and homogenized in 125 µl PBS by bead beating for 30 s. Homogenates were serially diluted in PBS and dilutions were plated on AE (for Acetobacter) or MRS (for Lactobacilli) agar plates. Plates were incubated for 2-3 days at either 30°C (AE) or 37°C (MRS), and resultant colonies were counted manually from dilution plates bearing ∼50-400 colonies. Bacterial loads were calculated as CFU per larva, as previously described (Koyle et al., 2016), and log-transformed for analysis.
Heat-killed bacterial feeding (Fig. 4A)
Overnight cultures of Acetobacter sp. (culture density ∼2.5×108-109 CFU/ml) were heat-killed at 65°C for 3 h and autoclaved food vials were inoculated with 40 µl of heat-killed suspension prior to the addition of GF embryos. Vials were further inoculated with 40 µl heat-killed bacterial suspension daily until 100% of the population had pupariated. Successful heat-killing was confirmed for each daily inoculum by plating undiluted heat-treated bacterial suspension on AE plates, and by plating larval homogenates.
Acetic acid-supplemented diets (Fig. 4B)
Fly food was autoclaved and allowed to cool to ∼50-60°C. Glacial acetic acid (Fisher Scientific A38-212) was added to a final concentration of 0.1% or 0.2%, or sodium acetate (Sigma-Aldrich S2889) was added to a final concentration of 50 mM.
Cell-free supernatant feeding (Fig. 4C)
Overnight cultures of Acetobacter sp. (3 ml in MRS, grown as described above) were filtered twice through 0.22 µm PVDF sterile membrane filters (Genesee Scientific 25-240). Autoclaved food vials were inoculated with 40 µl of filtered media immediately prior to addition of GF embryos, and vials were further inoculated with 40 µl of filtered media daily until 100% of the population pupariated. Absence of live bacterial cells was confirmed by plating daily filtered media on AE plates, and plating larval homogenates.
Acetobacter sp.-conditioned diet (Fig. 4D)
Autoclaved food vials were inoculated with Acetobacter sp. overnight culture, as described above. Inoculated vials were incubated for 5 days at 23°C. Vials were then incubated at 65°C for 1 h to kill bacteria. Sterility of the conditioned diet was confirmed by plating food and larval homogenates on AE plates. Heated diet controls consisted of un-inoculated, autoclaved GF vials incubated at 65°C for 1 h.
Wings from adult females were dissected, mounted in Aqua-mount (Thermo Scientific 14-390-5), and imaged with a QICAM-IR Fast 1394 camera (Q-Imaging) on a Zeiss Axioskop2 Plus microscope. Trichome density was measured with FijiWings2.3 software using the 150px trichome density feature (Dobens and Dobens, 2013).
Starvation experiments were conducted by transferring eight to ten 5-day-old adult females to 1% agar-water vials. Approximately 50-120 animals per condition per replicate were assessed. Flies were transferred to fresh agar-water vials daily and survival monitored daily until 100% of the population succumbed. Data from multiple replicates (two or three per condition) were combined for analysis.
Tissues were dissected in cold PBS and homogenized immediately in Trizol reagent (Thermo Fisher 15596026). RNA was extracted using the Direct-zol RNA Miniprep kit (Zymo Research 11-330) exactly following the manufacturer's protocol. High-quality RNA (A260nm/280nm ∼2; 250-500 ng) was used as template for cDNA synthesis using the qScript cDNA synthesis kit (QuantaBio 95048). Product from cDNA synthesis reactions was used for qPCR with PerfeCTa SYBR Green Supermix (QuantaBio 95055) in an Applied Biosystems 7300 Real Time PCR System instrument. Data were normalized to Rpl32 and expression fold changes were calculated using the 2−ΔΔCt method. Primer sequences are listed in Table S2.
For immunostaining of adult and larval brains and adult guts, tissues were dissected in cold PBS and fixed in 4% paraformaldehyde-PBS (Electron Microscopy Sciences 157-8) for 30 min at room temperature. Samples were washed in PBS with 0.3% Triton X-100 (PBT) and blocked for 1 h in PBS with 0.3% Triton X-100 and 5% normal goat serum (PNT; Invitrogen 10000C). Samples were then incubated with primary antibodies in PNT at 4°C overnight with agitation. After washing in PBT, samples were incubated with secondary antibodies in PNT for 3 h at room temperature. Samples were washed in PBS and mounted in aqua-poly/mount (Polysciences 18606) or ProLong Gold antifade mounting medium (Invitrogen P36934).
Primary antibodies were: rabbit anti-Arc1 (1:250; Ashley et al., 2018), rat anti-Ilp2 and rabbit anti-Ilp5 (1:800; Géminard et al., 2009), mouse anti-EcR common [1:100; Developmental Studies Hybridoma Bank (DSHB) DDA2.7]. Secondary antibodies were: Alexa Fluor 488-, Alexa Fluor 546- or Alexa Fluor 647-conjugated (1:1000; Invitrogen A32723, A11035, A48265).
For lipid droplet analysis, larval fat bodies were dissected in PBS, transferred to 6 mm-well Shandon multi-spot slides (Fisher Scientific 99-910-90) and fixed for 30 min in 4% paraformaldehyde-PBS. Fat bodies were then rinsed three times in PBS, incubated with BODIPY 493/503 (1 mg/ml; Invitrogen D3922) diluted 1:1000 in PBS for 30 min at room temperature, rinsed three times in PBS, and mounted as above.
Image acquisition and analysis
Images were captured on a spinning disk microscope with a Celesta 1 W light engine (Lumencor), an X-Light V2 scan head (Crest Optics), and a Prime95B CMOS camera (Photometrics) on a Zeiss Axiovert 200 M using Metamorph software (Molecular Devices).
All analyses were conducted using Fiji. Fluorescence intensity was calculated as the percentage fluorescence above background. Lipid droplets ≥5 µm were measured in a single z-plane that represented the largest diameter droplets between the tissue surface and the nuclei. Low-magnification images of adult guts (Fig. 1E) were stitched together manually using Fiji.
Fat bodies from ten pre-wandering third instar larvae were dissected in PBS, homogenized by bead beating in 180 µl ice-cold PBS, and immediately frozen until analysis. Lysates were centrifuged at 16,000 g for 5 min to pellet tissue debris and equal volumes mixed 1:1 with 2× Laemmli sample buffer (Bio-Rad 161-0737). Samples were boiled, resolved on 10% SDS-PAGE gel, and transferred to 0.22 µm pore-size nitrocellulose membrane (Bio-Rad 1620112). Blots were blocked in 5% bovine serum albumin (Sigma-Aldrich BSAV-RO) in tris-buffered saline with 0.1% Tween 20 (TBS-T; Sigma-Aldrich 9416) and incubated overnight at 4°C with rabbit anti-phospho-Ser505-Akt (Cell Signaling Technology 4054) or mouse anti-α-Tubulin (DSHB 12G10) diluted 1:1000 in blocking buffer. Incubations with horseradish peroxidase-conjugated secondary antibodies (1:20,000; Jackson ImmunoResearch 111-035-003, 115-035-003) were performed for 3 h at room temperature, and signal detected with Pierce ECL substrate (Thermo Scientific 34580) on a ChemiDoc MP imaging system (Bio-Rad). Quantification of relative protein amounts was conducted using Fiji.
Statistical tests were conducted and figures generated using Prism 9 (GraphPad) and R version 3.5.1 (https://R-project.org). Except where noted, RT-qPCR and immunostaining data were analyzed using unpaired, two-tailed Student's t-test for comparison of two groups, or two-way ANOVA for comparison of genotype and microbial condition. For developmental rate data, the average time to pupariation was calculated for each vial from the number of individuals pupariating on each day until the entire population completed larval development. These per-vial values from at least three replicates were used for statistical analyses; full sample sizes and statistical test output for all development experiments are reported in Table S1. Within-genotype comparisons among different treatments were conducted using one-way analysis-of-variance (ANOVA), whereas comparisons among different genotypes and treatments were conducted via two-way ANOVA with Tukey's test used for post-hoc analysis. Data that did not meet parametric test assumptions (normal distribution assessed by Shapiro–Wilk test and homogeneity of variance assessed by Levene's test) were analyzed via Mann–Whitney test or Kruskal–Wallis test with Dunn's post-hoc comparison (Bonferroni correction). Tests used for each experiment are indicated in the figure legends. Starvation survival data were compared by Cox proportional-hazards model analysis using the ‘survival’ package (version 3.2-11, https://CRAN.R-project.org/package=survival). In all box-and-whisker plots, the box comprises two hinges around the median representing the first and third quartiles (the 25th and 75th percentiles). Upper whisker represents largest value no further than 1.5*inter-quartile range from the hinge. Lower whisker represents smallest value no further than 1.5*inter-quartile range. Outlying values beyond the whiskers are plotted as individual points. In figures, lowercase letters are used to indicate post-hoc statistical comparisons: conditions that share a letter are not statistically different from one another. Throughout, the threshold of statistical significance was considered P<0.05.
We thank members of the McCartney, Hiller and Mitchell labs for helpful discussions while conducting the study. We thank Rory Eutsey (Hiller lab, Carnegie Mellon University) for technical assistance with qPCR experiments. We would like to thank Dr John Woolford for providing feedback on the manuscript. The Top Banana fly stock was a generous gift from Dr Michael Dickinson's lab (CalTech). The Arc1E8 fly stock and Arc1 antibody were generous gifts from Dr Vivian Budnik and Dr Travis Thomson (University of Massachusetts Medical School). Mex1-GAL4 flies were a kind gift from Dr Claire Thomas (Pennsylvania State University). The Ilp2 and Ilp5 antibodies were generous gifts from Dr Pierre Léopold (Institut Curie). We thank the Bloomington Drosophila Stock Center for providing other fly stocks. We would like to thank the Woolford, Mitchell, Linstedt and Hinman labs, and the Molecular Biosensor and Imaging Center at Carnegie Mellon University for reagents and equipment.
Conceptualization: S.A.K., B.M.M.; Validation: B.M.M.; Formal analysis: S.A.K., C.B., S.F., B.M.M.; Investigation: S.A.K., C.B., S.F., B.M.M.; Writing - original draft: S.A.K.; Writing - review & editing: B.M.M.; Visualization: S.A.K.; Supervision: B.M.M.; Project administration: B.M.M.; Funding acquisition: B.M.M.
Funding for this work was provided by a Charles E. Kaufman Foundation New Initiative Grant and a Carnegie Mellon University ProSEED/BrainHub seed grant (to B.M.M.). S.A.K. was supported by a National Science Foundation Graduate Research Fellowship (under grant numbers DGE 1252522 and DGE 1745016).
Peer review history
The peer review history is available online at https://journals.biologists.com/dev/article-lookup/doi/10.1242/dev.195222
The authors declare no competing or financial interests.