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Natural variation in sugar tolerance associates with changes in signaling and mitochondrial ribosome biogenesis

  1. Richard G Melvin
  2. Nicole Lamichane
  3. Essi Havula
  4. Krista Kokki
  5. Charles Soeder
  6. Corbin D Jones
  7. Ville Hietakangas  Is a corresponding author
  1. University of Helsinki, Finland
  2. The University of North Carolina at Chapel Hill, United States
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Cite this article as: eLife 2018;7:e40841 doi: 10.7554/eLife.40841

Abstract

How dietary selection affects genome evolution to define the optimal range of nutrient intake is a poorly understood question with medical relevance. We have addressed this question by analyzing Drosophila simulans and sechellia, recently diverged species with differential diet choice. D. sechellia larvae, specialized to a nutrient scarce diet, did not survive on sugar-rich conditions, while the generalist species D. simulans was sugar tolerant. Sugar tolerance in D. simulans was a tradeoff for performance on low-energy diet and was associated with global reprogramming of metabolic gene expression. Hybridization and phenotype-based introgression revealed the genomic regions of D. simulans that were sufficient for sugar tolerance. These regions included genes that are involved in mitochondrial ribosome biogenesis and intracellular signaling, such as PPP1R15/Gadd34 and SERCA, which contributed to sugar tolerance. In conclusion, genomic variation affecting genes involved in global metabolic control defines the optimal range for dietary macronutrient composition.

https://doi.org/10.7554/eLife.40841.001

eLife digest

Animals meet their nutritional needs in a variety of ways. Some animals are specialists feeding only on one type of food; others are generalists that can choose many different kinds of food depending on the situation. Despite these differences in diet, animals have similar needs for basic cellular metabolism. This suggests that generalist and specialist species likely process the foods they eat in different ways in order to meet their basic needs. For example, the metabolism of generalist species may be more flexible to adapt to changing food sources.

To learn more about how metabolism evolves to respond to diet, scientists can study closely related species that eat different foods. For example, a species of fruit fly called Drosophila simulans is a generalist and its larvae can grow and develop by feeding on different kinds of decaying fruits and vegetables. Larvae of a closely related fruit fly called Drosophila sechellia are specialized to eat only the nutrient-poor Morinda fruit. Looking at how genetic differences between these species affect metabolism may provide scientists with clues about how these feeding strategies evolved.

Melvin et al. grew larvae of D. sechellia and D. simulans in different conditions. D. sechellia larvae thrived in low nutrient conditions, but died when exposed to high sugar foods. By contrast, D. simulans larvae tolerated high sugar levels, but did poorly in low-nutrient conditions.

Melvin et al. then bred the two species with each other, selecting flies that are genetically similar to D. sechellia but have the genes necessary for larvae to tolerate sugar. Analyzing the selected hybrid flies revealed genetic changes that explain the different survival abilities of each species. These changes suggest that D. sechellia rapidly evolved to thrive in low nutrient conditions, but the trade-off was losing their ability to tolerate high sugar levels.

Overall, the results presented by Melvin et al. suggest that genetic adaptions to food sources can occur quickly and drastically change metabolism. Further research will be needed to confirm if similar metabolic trade-offs developed as part of human evolution. If so, human populations that survived with limited nutrition for many generations may have a harder time adapting to high-sugar modern diets.

https://doi.org/10.7554/eLife.40841.002

Introduction

Animals require macronutrients to sustain growth, reproduction and repair over their lifetimes and the balance between nutrients has been shown to have significant effects on development, reproduction and longevity (Raubenheimer and Simpson, 1997; Lee et al., 2008; Arganda et al., 2017). Most animals consume a variety of different foods to meet their nutritional needs (Raubenheimer and Simpson, 1997; Lee et al., 2008) and even closely related species make highly distinct diet choices (Tinker et al., 2008; Goldman-Huertas et al., 2015; Salinas-Ramos et al., 2015; Costello et al., 2016; Han et al., 2016). Therefore, it is conceivable that the impact of nutrient composition on various life history traits depends on the genetic makeup of the animal. Some closely related species are distinguished by variation in morphological structures that are specialized for obtaining nutrients from unique resources (trophic morphology) (Malinsky et al., 2015; Parsons et al., 2016; Santana and Cheung, 2016). Darwin’s finches are the classic example of species that are differentiated in part by interspecific competition and specialization on an under-used food type (De León et al., 2014). Darwin’s finches are considered ‘imperfect dietary generalists’ (De León et al., 2014) having similar preferred diets that overlap among species but specialize on a unique food when the preferred diet is limiting. Such difference in diet flexibility suggests that in addition to morphological differences, animals might also display differential metabolic flexibility, that is the capacity to adapt nutrient use to nutrient availability. While much recent attention has been paid to the genetics that underlie plasticity of trophic morphology in animals (Malinsky et al., 2015; Ledogar et al., 2016; McGirr and Martin, 2017; Burress et al., 2017; Zelditch et al., 2017), less focus has been placed on metabolic regulators with regard to diet choice (Turner and Thompson, 2013). Metabolic phenotypes and diet tolerance is observed to vary with ecological diversification within and between species (Matzkin et al., 2009; Reed et al., 2010; Matzkin et al., 2011), and these phenotypic changes correlate with changes in gene expression (Nazario-Yepiz et al., 2017). However, it remains poorly understood, which genetic changes are causally important during evolution of diet choice and what kind of metabolic tradeoffs might emerge from adaptation to a new macronutrient composition.

Flexibility in the usage of metabolic pathways allows animals to accommodate changes in food nutrient content and availability. At the level of the organism, systemic nutrient levels are actively monitored by the so-called nutrient-sensing pathways composed of intra- and intercellular signaling pathways and gene regulatory networks, which ultimately control the activity of metabolic pathways (Mattila and Hietakangas, 2017). There are specific nutrient-sensing mechanisms for each type of macronutrient. For example, protein kinases mTOR complex 1 (mTORC1) and GCN2 respond to changes in amino acid availability (Efeyan et al., 2015), while the transcription factor complex Mondo/ChREBP-Mlx is activated in response to sugars (Havula and Hietakangas, 2012). In Drosophila, genetic mutations that impair nutrient-sensing pathways have revealed diet-specific phenotypes. Adult mutants of 4EBP, a target of mTORC1, are indistinguishable from controls when fed a protein-rich diet, but are sensitive to amino acid starvation (Teleman et al., 2005). On the other hand, mlx mutant larvae have impaired function of the sugar sensor Mondo/ChREBP-Mlx and grow normally when fed a low sugar diet (LSD), but are intolerant of high-sugar diet (HSD) (Havula et al., 2013). The mlx null mutants exhibit impaired growth, increased larval development time and reduced larvae to pupae survival when the high dietary sugar concentration is within the range available from natural food sources (Havula et al., 2013). Thus, nutrient-sensing pathways define the tolerated lower and upper limits of nutrient intake. These limits for each nutrient will further depend on the availability of other nutrients. We call the inclusive matrix of tolerated macronutrient contents the ‘macronutrient space’.

Here, we aimed to explore the natural variation of macronutrient space in closely related species. We hypothesized that the natural variation of diet choice as well as diet flexibility (specialist vs. generalist) is affected by genetically encoded differences that define the macronutrient space. To test this hypothesis, we studied two closely related Drosophila species that differ in diet choice, namely the generalist D. simulans and its specialist relative, D. sechellia. In nature, D. simulans larvae consume a range of decaying fruits that may contain high levels of sugars, whereas D. sechellia larvae grow on the unripe fruits of Morinda citrifolia, which has a low-sugar content (Singh et al., 2012). The two species occur together on islands of the Seychelles archipelago; however, D. sechellia adults and larvae are found infrequently on fruits other than that of Morinda (R'Kha et al., 1991; Matute and Ayroles, 2014). D. simulans and D. sechellia show strong dietary differentiation yet they are closely related and can form fertile female F1 hybrids (Lachaise et al., 1986). This makes the two species and their hybrids a tractable system for studying the genetics associated with determination of macronutrient space.

Results

Closely related Drosophila species have differential macronutrient spaces

Because the natural larval diet of the generalist species D. simulans may have a highly variable sugar content compared to that of the specialist species D. sechellia, we predicted that egg to pupa development time of these species would be dissociated along the sugar axis in a yeast × sugar macronutrient space. To test this prediction, we characterized larval development time and survival to pupa for both species across a macronutrient space consisting of a 5 × 5 grid of diets that varied in sucrose and yeast content. The species showed different phenotypes across the grid of diets (Figure 1A, Table 1). D. simulans larvae displayed rapid development and high larval survival on diets up to and including 20% sugar. In contrast, D. sechellia larvae displayed a more restricted space, with slowed development and reduced survival on high-protein diets containing >10% sugar and complete lethality on diet composed of 20% sucrose/20% yeast (Figure 1A, Table 1). For D. sechellia larvae, dietary sucrose concentration showed significant (p < 0.001) positive correlation with lengthened development time and significant (p < 0.001) negative correlation with survival, but such correlations were not observed for D. simulans larvae (Table 1). For both species, larval development time was negatively correlated and survival was positively correlated with dietary yeast concentration; however, the correlation was weaker for D. sechellia than for D. simulans (Table 1). To test the possibility that D. simulans and D. sechellia larvae differed by a behavioral feeding response to sugar, we assayed mouth hook extension rate for both species in the presence and absence of 20% sucrose (Shen, 2012; Scheiner et al., 2014). ANOVA showed no significant effect of species (F(1, 12) = 0.06, p = 0.81), sugar concentration (F(1, 12) = 0.42, p = 0.53), or their interaction (F(1,12) = 4.47, p = 0.06) on feeding behavior (Figure 1—figure supplement 1).

Figure 1 with 1 supplement see all
Differential macronutrient spaces of Drosophila simulans and sechellia with respect to sugar tolerance.

(A) Larvae of D. simulans and D. sechellia showed differential pupariation time (h after egg-laying) and survival on high dietary sugar. Larval development was monitored on a 5 × 5 grid of varying yeast and sucrose levels. Pupariation index takes into account both survival and pupariation time. n = 5 replicates of 30 larvae/replicate for each genotype and diet. (B) Tolerance of high dietary carbohydrate was restored in the D. sechellia x D. simulans F1 hybrids. n = 5 replicates of 30 larvae/replicate for each diet.

https://doi.org/10.7554/eLife.40841.003
Table 1
Correlation analysis of nutrient space metrics.
https://doi.org/10.7554/eLife.40841.007
Pearson correlation coefficient
% Yeast% Sucrose
Larval development time
D. simulans−0.76***0.21
D. sechellia−0.45***0.59***
F1 hybrid−0.82***0.12
Larval survival
D. simulans0.88***−0.08
D. sechellia0.36***−0.58***
F1 hybrid0.69***−0.09
Pupariation Index
D. simulans0.93***−0.12
D. sechellia0.42***−0.69***
F1 hybrid0.76***−0.19*
  1. *P < 0.05; **P < 0.01, ***P < 0.001

Since nutrition affected both larval development time and survival similarly, we combined the data and calculated a so-called ‘pupariation index’ (Pupind) that takes both parameters into account. A high Pupind score is achieved with shorter larval development time and higher survival to pupal stage. Analysis of the Pupind of D. simulans and sechellia confirmed the poor performance of sechellia on high-sugar diets (Figure 1A, Table 1). We further analyzed our data by using a full generalized linear model (glm), which showed significant effects of genotype, percent sugar, percent yeast, and all interactions of the main effects on larval development time, larval survival, and Pupind (Table 2). However, while the effect of yeast on Pupind was stronger for D. simulans than for D. sechellia2 = 0.94 and 0.33, respectively), the effect of sugar on Pupind was substantially stronger for D. sechellia2 = 0.01 and 0.35 for D. simulans and D. sechellia, respectively) (Table 3). This further supports the conclusion that D. sechellia is sugar intolerant.

Table 2
Generalized linear model (GLM) details for pupariation index (Pupind), larval survival, and development time.

Models assumed a normal distribution and used an identity link function. Error d.f. = 300 for all comparisons.

https://doi.org/10.7554/eLife.40841.008
TraitEffectd.f.Log ratio χ2P
PupindGenotype2297.84<0.001***
Sugar4383.41<0.001***
Yeast41064.98<0.001***
Genotype × Sugar8177.07<0.001***
Genotype × Yeast8489.76<0.001***
Sugar × Yeast16286.75<0.001***
Genotype × Sugar × Yeast32218.40<0.001***
SurvivalGenotype2205.79<0.001***
Sugar4270.76<0.001***
Yeast4971.02<0.001***
Genotype × Sugar8240.94<0.001***
Genotype × Yeast8416.18<0.001***
Sugar × Yeast16135.45<0.001***
Genotype × Sugar × Yeast32303.48<0.001***
Dev. timeGenotype296.40<0.001***
Sugar4109.69<0.001***
Yeast4476.02<0.001***
Genotype × Sugar853.95<0.001***
Genotype × Yeast8150.31<0.001***
Sugar × Yeast1630.67<0.05 *
Genotype × Sugar × Yeast32125.34<0.001***
Table 3
Effect sizes by trait and genotype.
https://doi.org/10.7554/eLife.40841.009
TraitGenotypeEffect-log(P)ω2
PupindD. simulansSugar12.790.01
Yeast99.040.94
Sugar × Yeast9.430.02
D. sechelliaSugar43.520.35
Yeast41.830.33
Sugar × Yeast26.610.22
F1 hybridSugar18.620.04
Yeast83.580.86
Sugar × Yeast16.730.85
Larval survivalD. simulansSugar5.150.01
Yeast95.390.93
Sugar × Yeast13.710.03
D. sechelliaSugar40.150.34
Yeast41.580.36
Sugar × Yeast22.470.19
F1 hybridSugar4.150.01
Yeast76.780.88
Sugar × Yeast11.760.05
Dev. timeD. simulansSugar3.530.01
Yeast75.520.90
Sugar × Yeast7.000.03
D. sechelliaSugar11.340.18
Yeast18.280.34
Sugar × Yeast3.780.09
F1 hybridSugar5.580.05
Yeast36.390.62
Sugar × Yeast5.800.08

To confirm a genetic basis for the observed differential sugar tolerance between species, we assayed the larval development time and survival to pupa of D. simulans × D. sechellia F1 hybrid larvae across the 5 × 5 grid of diets. D. simulans and D. sechellia are closely related, having diverged from a common ancestor roughly 0.4 million years ago (Kliman et al., 2000), and hybrid females from the cross, D. sechellia male × D. simulans female, are fertile (Lachaise et al., 1986). We observed a clear rescue of sugar tolerant larval development and survival in the F1 hybrid (Figure 1B). This implied that the sugar intolerance phenotype of D. sechellia may be caused by altered function of genes underlying sugar tolerance

Introgression of sugar tolerance phenotype

To generate flies having the minimal D. simulans genomic regions essential for sugar tolerance in a mostly D. sechellia genomic background, we sought to introgress the sugar tolerance phenotype from D. simulans into a mostly D. sechellia genetic background. To do this, we used the phenotype-based introgression approach of Earley and Jones (Earley and Jones, 2011) (Figure 2A). Dietary sugar content of 20% provided a strong selection, since no survivors of the D. sechellia parental line were observed in these conditions. After 10 generations of backcrossing with selection, we observed that tolerance of an HSD (20% yeast/20% sugar) in the backcross larvae was equal to that of D. simulans (Figure 2B). A control fly line that was backcrossed in the same manner, but not selected on a high-sugar diet, showed only minimal tolerance for HSD (Figure 2B). Since the introgression was performed by repeated backcrossing of D. sechellia males with the hybrid line (maternally D. simulans), the mitochondrial genomes of the selected and control lines are the same. Therefore, the observed phenotypic differences are due to the nuclear genome. Morphologically, the introgressed lines resemble D. sechellia, including genital arch morphology (data not shown). Metabolic analysis of the parental and introgressed lines revealed that the sugar intolerant D. sechellia and no-selection control lines were less efficient in clearing glucose from circulation after challenge with high-sugar diet (Figure 2C). This suggests that the pathways controlling energy metabolism or their response toward high-sugar diet are affected by the genomic regions underlying sugar tolerance.

Introgression of D. simulans sugar tolerance into D. sechellia genome through repeated backcrosses on selective diet.

(A) Construction of the sugar selected and control backcross (B.C.) lines through phenotype-based introgression. Dietary sugar content of 20% provided a strong selection, since no survivors of the D. sechellia parental line were observed in these conditions. (B) Sugar tolerance of selected lines was similar to that in the parental D. simulans line, while the sugar tolerance of the control line resembled to that of D. sechellia. Error bars display standard error of the mean. n = 5 replicates of 30 larvae/replicate for each genotype. Dunnett’s test (|d| = 2.70, α = 0.05) showed that D. sechellia and the control backcross line had significantly reduced sugar tolerance compared to D. simulans while sugar tolerance of the two HSD-selected backcross lines did not differ from that of D. simulans. (C) The sugar intolerant control line showed impaired clearance of hemolymph glucose, similar to D. sechellia. Hemolymph glucose was measured from larvae on LSD, after 2 hr on HSD, and after 2 hr of transferring of HSD-fed larvae back to LSD. Error bars display standard error of the mean. n = 5 replicates of 10 larvae/replicate for each genotype and diet. Dunnett’s test (|d| = 2.62, α = 0.05) showed that after feeding for 2 hr on HSD, D. sechellia and the control backcross line had significantly elevated hemolymph glucose compared to that of D. simulans while that of the selected line did not differ from the D. simulans level. **p < 0.01, ***p < 0.001.

https://doi.org/10.7554/eLife.40841.010

Larval gene expression profiles are strongly associated with sugar tolerance

In order to achieve a genome-wide view of the gene expression profiles in the sugar tolerant and intolerant lines, we used RNAseq analysis and assayed 3rd instar larvae fed continuously on LSD (20% yeast) as well as following acute exposure to HSD (20% yeast/20% sugar for 8 hr) (Figure 3A). Global comparison of the gene expression by sample clustering revealed striking association between expression profiles and sugar tolerance. The gene expression profile of the sugar-selected hybrid had high similarity with that of D. simulans, while the sugar intolerant control hybrid clustered close to D. sechellia (Figure 3B). This implies, surprisingly, that the genetic factors underlying the differences in sugar tolerance explain the majority of the differential gene expression between the parental species. We further identified the genes that were differentially expressed in the tolerant vs. intolerant genotypes, focusing on genes that differed significantly (p < 0.05) when both sugar tolerant genotypes were compared to both sugar intolerant genotypes on HSD. Genes with reduced expression in both sugar intolerant lines displayed significant (p < 0.05) enrichment in functional categories related to mitochondrial ribosome, detoxification (e.g. cytochrome P450 and glutathione metabolism), growth control (ribosome biogenesis), carbohydrate metabolism (starch and sucrose metabolism) as well as several categories related to amino acid metabolism (Figure 3C). On the other hand, genes with high expression in sugar intolerant lines displayed overrepresentation of proteolysis and lysosome (Figure 3C).

Global gene expression changes associated with sugar tolerance.

(A) Schematic representation of RNAseq sample preparation. Parental lines and backcrossed hybrid lines were fed on LSD or transferred acutely (8 hr) on HSD, followed by RNA extraction and RNA sequencing. (B) Sample clustering reveals tight association between global gene expression profiles and sugar tolerance. Sample clustering was based on Pearson correlation and it was performed using R/Bioconductor package pvclust. Correlation was used as distance matrix. (C) Summary of selected functional groups significantly enriched among genes displaying differential expression in sugar tolerant vs. intolerant lines.

https://doi.org/10.7554/eLife.40841.011

We have earlier observed that the mlx1 null mutant larvae, lacking functional sugar sensing by Mondo-Mlx, display strong sugar intolerance, similar to D. sechellia (Havula et al., 2013). To test if the sugar intolerant D. sechellia lines show similarities to mlx1 mutants in gene regulation, we compared the simulans/sechellia RNAseq dataset with that of mlx1 null mutant, published earlier (Mattila et al., 2015). There was significant similarity between the gene expression profiles of the sugar intolerant genotypes (Figure 4A). For example, 30% (174/587; p = 1.2×10−69) of the genes downregulated in mlx1 mutants displayed reduced expression in D. sechellia and the control hybrid line (Figure 4A). Global comparison of all genes with similar gene expression differences in tolerant and intolerant lines revealed a high degree of similarity in the gene expression patterns (Figure 4B). These overlapping gene sets included several genes that were upregulated upon sugar feeding in sugar tolerant genotypes, but the activation was either absent or reduced in the sugar intolerant genotypes (Figure 4B). These include, for example the transcription factor sugarbabe and Phosphoserine phosphatase astray (Figure 4C,D), which we have earlier shown to be essential for sugar tolerance (Mattila et al., 2015).

Significant overlap between gene expression profiles of sugar intolerant lines and mlx mutants.

(A) Comparison of genes differentially expressed in sugar tolerant vs. intolerant lines with Mlx target genes. Gene expression profiles associated with sugar intolerance show highly significant overlap with profiles of mlx1 mutants. (B) Heat maps of the overlapping gene sets show similarities in gene sugar responsiveness in sugar intolerant lines and mlx1 mutants. Sugar tolerance/intolerance phenotypes of the analyzed lines are indicated in color. (C) Known sugar tolerance genes sugarbabe (sug) and astray (aay) show weaker sugar induction in sugar intolerant lines, resembling mlx1 mutants.

https://doi.org/10.7554/eLife.40841.012

Sugar tolerance associated with introgression of chromosome 2R

To determine the introgressed genomic regions and genes associated with the sugar tolerant phenotype, we sequenced the whole genomes of the two parental, sugar-selected and non-selected control fly lines and identified species-specific SNPs across the genome. SNP analysis showed three small and one large region of D. simulans SNPs on chromosome arm 2R, while all other regions of the genome showed an almost completely D. sechellia SNP signature (Figure 5A and B). Locations of the introgressions relative to nucleotide positions on the D. melanogaster chromosome arm 2R were from approximately 5,758,067 to 6,085,625 (spanning 25 annotated D. melanogaster genes); from 6,600,044 to 6,810,530 (spanning 35 annotated genes); and 21,774,876 to 24,092,079 (spanning 312 annotated genes) (Supplementary file 1). Majority of the introgressed genes showed no significant changes in gene expression (Figure 5C). In total, 40 introgressed genes were significantly (p < 0.05) downregulated in the intolerant lines, while 24 displayed elevated expression associated with sugar intolerance (Figure 5C; Supplementary file 1). This suggests that the global gene expression differences between sugar tolerant and intolerant lines are likely due to a small number of loci, which control the expression of a large number of downstream genes.

D. simulans SNP signature was introgressed into a mostly D. sechellia SNP signature background.

(A) Color shows the frequency distribution of D. simulans-specific SNPs displayed along the chromosomes. (B) Frequency distribution of D. simulans-specific SNPs displayed on chromosome arm 2R for the sugar selected (top) and not-selected control (bottom) backcross lines. Black lines above the heat maps indicate the three sugar tolerance-associated introgressed regions. (C) Limited overlap between introgressed genes and genes that are either up- or downregulated in sugar tolerant lines.

https://doi.org/10.7554/eLife.40841.013

Genes involved in mitochondrial ribosome biogenesis and intracellular signaling contribute to sugar tolerance

To identify genes from the introgressed chromosome 2R regions that were potentially responsible for the sugar tolerance phenotype, we utilized the genetic toolkit of D. melanogaster, a dietary generalist and close relative of D. simulans and sechellia with sugar tolerance similar to that of D. simulans (Figure 6—figure supplement 1). From the total number of 372 introgressed genes, we selected 102 genes based on their annotation, with a putative metabolic or regulatory function to be screened for survival on low (20% yeast) and high (20% yeast/20% sugar) sugar diets (Supplementary file 1).

The screen identified several genes with a sugar intolerant phenotype. Interestingly, three of the identified sugar tolerance genes, mRpL43, CG4882 and bonsai encode components of the mitochondrial ribosome (Figure 6A–C). Furthermore, all of them displayed reduced expression in sugar intolerant genotypes (Figure 6D–F), implying that reduced capacity mitochondrial protein biosynthesis contributes to the sugar intolerance phenotypes. In addition to mitochondrial genes, our screen identified several sugar tolerance genes with a role in signaling. RNAi knockdown of Sarco/endoplasmic reticulum Ca2+-ATPase (SERCA), Protein phosphatase 1 regulatory subunit 15 (PPP1R15, Gadd34), or Phosphotidylinositol 3 kinase 59F (Pi3K59F) led to strongly impaired larval growth on high-sugar diet, with only a few larvae surviving to pupae (Figure 7A–C). Furthermore, the expression of SERCA was downregulated in D. sechellia and in the sugar intolerant control line (Figure 7D–F). All these three genes have been linked to metabolic processes. SERCA pumps Ca2+ into endoplasmic reticulum (ER) and is involved in control of lipid homeostasis (Bi et al., 2014), while Pi3K59F is a known regulator of autophagy (Juhász et al., 2008). PPP1R15/Gadd34 is best known for its function as a negative regulator of the integrated stress response pathway, including amino acid sensing kinase GCN2 (Malzer et al., 2013). Furthermore, we found three additional genes (Taldo, Dpit47, GlcT-1) displaying milder phenotypes, namely reduced eclosion on high-sugar diet (Figure 7—figure supplement 1).

Figure 6 with 1 supplement see all
Low expression of mitochondrial ribosome genes contributes to sugar intolerance.

(A) Pupariation kinetics of control and mRpL43 RNAi larvae (Ubi-GAL4>), n = 7 replicates of 30 larvae/replicate for each genotype and diet. Error bars display standard deviation. (B) Pupariation kinetics of control and CG4882 RNAi larvae (Ubi-GAL4>), n = 8 replicates of 30 larvae/replicate for each genotype and diet. Error bars display standard deviation.(C) Pupariation kinetics of control and bonsai RNAi larvae (Fb-GAL4>), n = 3 replicates of 30 larvae/replicate for each genotype and diet. Error bars display standard deviation. (D–F) Relative expression of mRpL43, CG2882, and bonsai genes in sugar tolerant (hybrid and D. simulans) and intolerant (ctrl and D. sechellia) lines on low- and high-sugar diets identified by RNAseq. dAEL: days after egg laying.

https://doi.org/10.7554/eLife.40841.014
Figure 7 with 1 supplement see all
Several genes involved in signaling influence sugar tolerance.

(A) Pupariation kinetics of control and SERCA RNAi larvae (Ubi-GAL4>), n = 7 replicates of 30 larvae/replicate for each genotype and diet. Error bars display standard deviation. (B) Pupariation kinetics of control and PPP1R15 RNAi larvae (Ubi-GAL4>), n = 13 replicates of 30 larvae/replicate for each genotype and diet. Error bars display standard deviation. (C) Pupariation kinetics of control and Pi3K59F RNAi larvae (Tub-GAL4>), n = 5 replicates of 30 larvae/replicate for each genotype and diet. Error bars display standard deviation. (D–F) Relative expression of SERCA, PPP1R15, and PI3K59F genes in sugar tolerant (hybrid and D. simulans) and intolerant (ctrl and D. sechellia) lines on low- and high-sugar diets identified by RNAseq. dAEL: days after egg laying.

https://doi.org/10.7554/eLife.40841.019

Genomic changes in SERCA promoter lead to differential gene expression

Next we wanted to assess the level of genomic variation in the candidate genes identified, first focusing on the four genes (mRpL43, CG4882, bonsai, and SERCA) that displayed reduced expression in sugar intolerant lines. We mapped the density of nucleotide differences between D. simulans and sechellia using a sliding window of 100 bases within these specific gene regions. While all genomic regions displayed areas of high SNP density, the promoter region of the SERCA gene was found to be particularly variable (Figure 8A; Figure 8—figure supplement 1). To validate the functional impact of this variation, we cloned 1.2 kB fragments with putative promoter regions of D. simulans and sechellia SERCA gene in front of a lacZ reporter and generated in vivo reporter lines in D. melanogaster (Figure 8A). Indeed, the D. sechellia -derived promoter displayed significantly (p < 0.01) lower activity than the respective region of D. simulans, confirming the functional importance of the SNPs in the SERCA promoter (Figure 8B).

Figure 8 with 1 supplement see all
Genomic variation of SERCA promoter leads to differential promoter activity.

(A) SNP density maps comparing D. sechellia to D. simulans on regions surrounding the SERCA (Ca-P60A) gene. mRNA transcript models for each gene region are shown above SNP density heat maps with green and red representing coding regions on the (+) and (-) strand, respectively, and grey indicating non-coding regions. Direction of transcription is also indicated with a grey arrowhead. Heatmaps represent the density of SNP differences between D. sechellia and D. simulans in overlapping windows of 100 nt slid in 25 nt increments along the region. The promoter fragment cloned into the in vivo reporter is indicated as violet dashed line. (B) Relative mRNA (qPCR) expression of lacZ reporter gene downstream of 1.2 kB fragments of D. simulans and D. sechellia SERCA promoters reveals lower activity of D. sechellia-derived promoter. n = 8 replicates of 8 larvae/replicate for each genotype and diet. Error bars display standard deviation. **p < 0.01 (student’s t-test).

https://doi.org/10.7554/eLife.40841.024

High degree of amino acid changing coding region variation in the PPP1R15 gene

We also looked for potential coding region changes in the candidate genes. For all of the hits, the D. sechellia DNA sequence contained nucleotide substitutions that cause amino acid differences in the encoded protein as compared to D. simulans (Table 4). Most genes in the set of hits had substantially higher number of silent than amino acid altering nucleotide differences, which implies purifying selection. In contrast, there were 10 amino acid changing and only five silent nucleotide differences in the PPP1R15 sequence of D. sechellia compared to that of D. simulans. The rate of amino acid changing to silent mutations (Ka/Ks) in PPP1R15 was 0.58 indicating reduced purifying selection along the D. sechellia lineage (Table 4). Plausible alternative explanations for the higher Ka/Ks include the introduction of a new selective pressure on the founder population of D. sechellia.

Table 4
Nucleotide substitution differences between D. simulans and D. sechellia in genes identified as hits in the D. melanogaster RNAi screen.

AA: amino acid.

https://doi.org/10.7554/eLife.40841.026
GeneAA changingSilentka/ks
PPP1R15 (CG3825)1050.58
Pi3K59F (CG5373)2230.03
CG4882480.15
Taldo (CG2827)160.05
Dpit47 (CG3189)490.13
GlcT-1 (CG6437)3100.01
bonsai (CG4207)280.13
mRPL43 (CG5479)1130.03
SERCA (CG3725)6160.13

Trade-off of sugar tolerance with survival in a low nutrient environment

Low sugar tolerance in D. sechellia could be due to genetic drift in a low sugar dietary environment lacking selection or may be caused by a trade-off for an altered function that provides D. sechellia with selective advantage. To test if sugar tolerance is associated with Morinda toxin tolerance, we selected hybrid larvae on Morinda toxin and performed three generations of selection. Selection for tolerance of the Morinda toxins had no significant impact on sugar tolerance (Figure 9—figure supplement 1). Moreover, the sugar tolerant introgression lines were not sensitive to Morinda toxins (Figure 9—figure supplement 1), further implying that toxin tolerance is genetically independent of sugar tolerance.

Since no association between sugar tolerance and toxin tolerance was found, we hypothesized that poor sugar tolerance is associated with improved fitness in low-sugar nutrient space. Therefore, we determined regions of the diet space where D. sechellia larvae would have an advantage compared to those of D. simulans. We subtracted the pupariation rate of D. simulans from that of D. sechellia and plotted the difference across the diet space. The subtracted diet space surface shows that D. sechellia and D. simulans larvae have clearly separated peaks where they hold an advantage (Figure 9A). The strongest advantage for D. sechellia was observed when the yeast content was 5% or lower and sugar levels were close to zero. In order to test, whether the high fitness of D. sechellia on low energy diet is a tradeoff for sugar tolerance, we raised the sugar selected and control introgression lines on 2.5% yeast diet. Strikingly, the sugar tolerant introgression line performed like parental D. simulans, while the sugar intolerant control line displayed higher fitness on 2.5% yeast, similar to the parental D. sechellia larvae (Figure 9B). This implies that the genetic loci of D. simulans, which provide high sugar tolerance cause a disadvantage in conditions of low-energy diet.

Figure 9 with 1 supplement see all
Trade-off between sugar tolerance and growth on low-energy diet.

(A) Compared to D. simulans, D. sechellia larvae had lower tolerance of sugar, but showed an advantage in pupariation on low nutrient diets. Surface shows |(D. sechellia pupind) - (D. simulans pupind)|. (B) On a low nutrient (2.5% yeast) diet, D. sechellia and the sugar intolerant control lines had shorter egg to pupa time and greater larval survival than did D. simulans and the sugar-selected lines. Error bars display standard error of the mean. n = 5–9 replicates of 30 larvae/replicate for each genotype and diet. (C) Pupariation kinetics of PPP1R15 RNAi (cg-GAL4>) on 2% yeast diet, n = 28 replicates of 30 larvae/replicate for each genotype and diet. Error bars display standard deviation. dAEL: days after egg laying.

https://doi.org/10.7554/eLife.40841.027

Given the observed tradeoff in sugar tolerance and starvation tolerance, we next tested the Drosophila melanogaster RNAi lines with sugar-intolerant phenotype for their performance on low-energy diet. Interestingly, PPP1R15 knockdown animals showed elevated pupariation on low-energy diet when compared to corresponding control animals (Figure 9C). This implies that genetic changes affecting individual regulatory genes can contribute to the optimal macronutrient space of the animal.

Discussion

In this study, we show that the macronutrient space of two closely related species that have different dietary choices is dissociated in concordance with their natural diets. Larvae of the dietary specialist D. sechellia that feed on a low-sugar diet in nature exhibited intolerance of high-sugar diet. In contrast, the dietary generalist D. simulans broadly tolerated dietary sugar, but performed poorly on low-energy-content diets. Sugar intolerance was rescued in F1 hybrids suggesting complementation of D. sechellia alleles with those of D. simulans. To identify the genomic regions associated with sugar tolerance, we introgressed a sugar tolerant phenotype into a mostly D. sechellia genomic background through multiple rounds of backcrossing and selection on a high-sugar diet. The sugar selected fly lines exhibited sugar tolerance equal to that of the D. simulans parent while the introgression control lines that were not selected on high-sugar diet exhibited very poor sugar tolerance that was only slightly better than the D. sechellia parent. It remains a possibility that the dietary composition affects the growth of commensal microbes, which may differentially affect the growth of Drosophila species. However, we observed impaired clearance of circulating glucose in the sugar intolerant lines as well as differential gene expression response to sugar feeding. These data, together with our loss-of-function phenotypes on metabolic regulators, strongly suggests that differences in the regulation of energy metabolism were the primary causes for the observed differences in sugar tolerance. It should also be noted that our study relied on the use of single representative lines for D. simulans and D. sechellia and future studies with a larger number of lines are needed to determine the degree of natural variation of sugar tolerance within the species.

Our study represents evidence for rapid (~0.4 MY) evolution of macronutrient space in a multicellular animal. Evolution of metabolism is known to occur through multiple mechanisms, such as nonsynonymous coding region mutation, copy number variation or mutation of regulatory regions of a gene encoding a metabolic enzyme (Wagner, 2012). Examples of recent evolution of animal metabolism by altered function of a single enzyme include the lactase persistence in human populations (Gerbault et al., 2011) as well as increase in copy number of amylase-encoding gene upon dog domestication (Axelsson et al., 2013). In contrast to the aforementioned examples, D. simulans and D. sechellia display deviation of the macronutrient spaces along the carbohydrate/protein axis, likely requiring much more widespread reprogramming of core metabolic pathways. In line with this prediction, our RNAseq analysis revealed global changes in carbohydrate and amino acid metabolism, mitochondrial function, ribosome biogenesis and stress response pathways associated with sugar tolerance. In conclusion, our data demonstrates that global changes in macronutrient space caused by global rewiring of core metabolic pathways can occur in animals in relatively short evolutionary timeframe.

In order to reprogram large metabolic networks through mutations of genes encoding metabolic enzymes, a number of independent mutations would need to occur simultaneously, which is unlikely to occur. A plausible model for obtaining such rapid global changes in metabolic pathways is through genetic changes in metabolic ‘hub’ genes, including mitochondrial genes and signaling pathway components, whose activity is reflected to multiple metabolic pathways simultaneously. Several genes involved in mitochondrial ribosome were included into the introgression regions associated with sugar tolerance. The importance of mitochondrial ribosome biogenesis in survival on carbohydrate-rich food has been observed earlier (Kemppainen et al., 2016). Furthermore, reduced mitochondrial ribosome biogenesis is widely reflected to central carbon metabolism and redox balance of the animal (Kemppainen et al., 2016), which is consistent with the global gene expression differences observed in our sugar tolerant vs. intolerant lines.

One of the identified genetic determinants of sugar tolerance was SERCA, an ATP-dependent Ca2+ pump in the ER membrane. We observed that SERCA displayed significantly reduced gene expression in sugar intolerant lines and that RNAi-mediated knockdown of SERCA caused significant sugar intolerance D. melanogaster. Sequence comparison of genomic regions of D. simulans and sechellia led to identification of a high level of sequence variation at the promoter, which was sufficient to explain the lower expression of SERCA in D. sechellia, based on the in vivo reporter experiment. Previous evidence shows that SERCA has a critical role in metabolic control. In Drosophila, SERCA mutant fat body cells contain reduced number and size of lipid droplets compared to wild-type (Bi et al., 2014). SERCA2b expression is strongly downregulated in livers of obese mice and restoring its expression is sufficient to improve glucose tolerance (Park et al., 2010). Similar beneficial effects have been obtained by pharmacological activation of SERCA in ob/ob mice (Kang et al., 2016). Furthermore, mice mutant for sarcolipin, a muscle-specific regulator of SERCA, are obese and have poor glucose tolerance. Thus, regulation of SERCA expression and activity has a significant and conserved role in the control of glucose metabolism. While it remains unclear how intracellular calcium homeostasis mechanistically regulates energy metabolism, it has been proposed that Ca2+ transport from ER to mitochondria plays a key role (Kaufman and Malhotra, 2014). Interestingly, regulation of SERCA activity appears to be involved in human evolution as well. SNPs in the gene THADA, which encodes a regulator of SERCA activity, are among the most strongly positively selected SNPs during the evolution of modern humans, based on comparative analyses with the Neanderthal genome (Green et al., 2010). THADA interacts with SERCA and acts as a SERCA uncoupling protein, controlling lipid homeostasis and feeding as well as cold resistance in Drosophila (Moraru et al., 2017). In human, there is further evidence of THADA selection upon adaptation to cold climate (Cardona et al., 2014). Future studies should explore further the role of SERCA and its regulators in other evolutionary processes associated with global changes in energy metabolism. Moreover, considering the role of SERCA in thermogenesis, it will be interesting to test, whether the lower SERCA expression in D. sechellia affects its cold tolerance.

Another interesting candidate gene identified in our study was PPP1R15, which had an impact on both sugar tolerance as well as survival on low-energy diet in D. melanogaster. Moreover, our data show that the PPP1R15 coding region contains several amino acid changing nucleotide changes, displaying a significantly higher number than expected when compared with the other candidate genes identified (χ2 = 17.03, p < 0.01, d.f. = 5). This indicates that there has been reduced pressure of purifying selection on the gene in D. sechellia compared to D. simulans, possibly reflecting a new habitat with reduced need to maintain sugar tolerance. However, since PPP1R15 affects both sugar tolerance and starvation resistance and these two traits form a trade-off, it is also possible that the pressure for development on low-energy diet has favored an alternative form of PPP1R15, explaining the high degree of amino acid changing mutations. Functionally PPP1R15 is an excellent candidate for a gene that enables rapid evolution of macronutrient space, since it controls major metabolic and energy consuming processes. PPP1R15 is a regulatory subunit of protein phosphatase one and it controls translation by dephosphorylating Ser51 of eIF2alpha (Novoa et al., 2001). eIF2alpha Ser51 is the target of the so-called integrated stress response pathway, including for example GCN2, the sensor of amino acid deprivation as well as PERK, a sensor of ER stress (Harding et al., 1999; Harding et al., 2000). It should be noted that SERCA-dependent ER Ca2+ homeostasis has a critical role in counteracting ER stress (Park et al., 2010; Lai et al., 2017), providing a possible functional link between PPP1R15A and SERCA. Downstream of eIF2alpha (and thus PPP1R15) is transcription factor Atf4, which controls carbohydrate metabolism in Drosophila melanogaster (Seo et al., 2009; Lee et al., 2015). Moreover, PPP1R15A-deficient mice develop obesity, nonalcoholic liver disease, insulin resistance and impaired glucose tolerance (Nishio and Isobe, 2015).

In conclusion, our study provides evidence for natural variation of organismal sugar tolerance and its association with diet choice. Furthermore, our data demonstrate that global changes in metabolic gene expression, substantially affecting macronutrient space can occur in relatively short evolutionary timeframe. Our findings indicate that adaptation to a new metabolic environment, such as one with low-level nutrition, may be broadly reflected to the macronutrient space, for example by lowering the tolerance to sugar overload. This may be conceptually relevant to human health, considering that human populations with distinct histories of diet choice may bear differential vulnerabilities to nutrient overload posed by modern lifestyles.

Materials and methods

Key resources table
Reagent type
(species) or
resource
DesignationSource or
reference
IdentifiersAdditional
information
Gene
(Drosophila melanogaster)
mRPL43NAFLYB:
FBgn0034893
Gene
(D. melanogaster)
CG4882NAFLYB:
FBgn0025336
Gene
(D. melanogaster)
bonsaiNAFLYB:
FBgn0026261
Gene
(D. melanogaster)
SERCANAFLYB:
FBgn0263006
Gene
(D. melanogaster)
PPP1R15NAFLYB:
FBgn0034948
Gene
(D. melanogaster)
Pi3K59FNAFLYB:
FBgn0015277
Strain,
strain background
(D. simulans C167.4)
D. Simulans
C167.4
The
National
Drosophila
Species Stock
Center, College
of Agriculture
and Life
Science,
Cornell
University
Drosophila
Species
Stock Center:
14021-0251-199
Strain,
strain background
(D. sechellia SynA)
D. sechellia
SynA
The
National
Drosophila
Species Stock
Center, College
of Agriculture
and Life
Science,
Cornell University
Drosophila Species
Stock Center:
14021-0248-28
Strain,
strain background
(D. melanogaster
Oregon R)
D. melanogasterotherA gift from
Tapio Heino,
University
of Helsinki
Strain,
strain background
(D. Simulans
and D. Sechellia
hybrid)
B.C. selected linethis paperSee Materials and methods
section
'Genetic
introgression'
Strain,
strain
background
(D. Simulans
and D. Sechellia
hybrid)
B.C. control linethis paperSee Materials and methods
section 'Genetic
introgression'
Genetic reagent
(D. melanogaster)
kk controlVienna
Drosophila
RNAi Center
VDRC: 60100Genotype:
y,w[1118];P{attP,y[+],w[3`]
Genetic reagent
(D. melanogaster)
mRPL43 RNAi
(VDRC 104466)
Vienna
Drosophila
RNAi Center
VDRC:
104466; FLYB:
FBgn0034893
FlyBase
symbol:
P{KK109027}
VIE-260B
Genetic reagent
(D. melanogaster)
CG4882 RNAi
(VDRC 106629)
Vienna
Drosophila
RNAi Center
VDRC:
106629; FLYB:
FBgn0025336
FlyBase symbol:
P{KK107150}
VIE-260B
Genetic reagent
(D. melanogaster)
bonsai RNAi
(VDRC 104412)
Vienna
Drosophila
RNAi Center
VDRC:
104412; FLYB:
FBgn0026261
FlyBase
symbol: P{KK108444}VIE-260B
Genetic reagent
(D. melanogaster)
SERCA RNAi
(VDRC 107446)
Vienna
Drosophila
RNAi Center
VDRC:
107446; FLYB:
FBgn0263006
FlyBase symbol:
P{KK107371}VIE-260B
Genetic reagent
(D. melanogaster)
PPP1R15 RNAi
(VDRC 107545)
Vienna
Drosophila
RNAi Center
VDRC:
107545; FLYB:
FBgn0034948
FlyBase symbol:
P{KK104106}
VIE-260B
Genetic reagent
(D. melanogaster)
Pi3K59F RNAi
(VDRC 100296)
Vienna
Drosophila
RNAi Center
VDRC:
100296; FLYB:
FBgn0015277
FlyBase symbol:
P{KK107602}VIE-260B
Genetic reagent
(D. melanogaster)
ubi-Gal4Bloomington Drosophila Stock CenterBDSC:
32551; FLYB:
FBst0032551
FlyBase symbol:
w*; P{Ubi-GAL4.U}2/CyO
Genetic reagent
(D. melanogaster)
tub-Gal4Bloomington Drosophila
Stock Center
BDSC:
5138; FLYB:
FBst0005138
FlyBase symbol:
y1 w*;
P{tubP-GAL4}
LL7/TM3,
Sb1 Ser1
Genetic reagent
(D. melanogaster)
cg-Gal4Bloomington Drosophila
Stock Center
BDSC:
7011; FLYB:
FBst0007011
FlyBase symbol:
w1118; P{Cg-GAL4.A}2
Genetic reagent
(D. melanogaster)
fb-Gal4PMID:
12676093
FLYB:
FBti0013267
Genotype:
P{GAL4}fat
Genetic reagent
(D. melanogaster)
D. simulans
SERCA
lacZ reporter
this paperProgenitors:
D. simulans SERCA
lacZ plasmid;
D. melanogaster
with landing site
attP2(3L)68A4
(GenetiVision)
Genetic reagent
(D. melanogaster)
D. sechellia
SERCA lacZ
reporter
this paperProgenitors:
D. sechellia SERCA
lacZ plasmid;
D. melanogaster
with landing site
attP2(3L)68A4
(GenetiVision)
Recombinant
DNA reagent
placZ-2.attB
(vector)
PMID: 23637332
Recombinant
DNA reagent
D. simulans
SERCA lacZ
plasmid
this paperProgenitors: PCR,
D. simulans
C167.4 flies; vector
placZ-2.attB
Recombinant
DNA reagent
D. sechellia
SERCA lacZ
plasmid
this paperProgenitors: PCR,
D. sechellia SynA flies;
vector placZ-2.attB
Sequence-based reagentSERCA F
(primer)
this paperSequence: 5’-
TAAGCGGCCGCTCTTCGTTCAGTGGCCTGTG-3’
Sequence-based reagentSERCA R
(primer)
this paperSequence:
5’-TAACTCGAGTCGTGATAAGGATTTCAGTTCG-3’
Sequence-based reagentLacZ F
(primer)
this paperSequence:
5’-CGAATCTCTA
TCGTGCGGTG-3’
Sequence-based reagentLacZ R
(primer)
this paperSequence:
5’-CCGTTCAGCA
GCAGCAGAC-3’
Sequence-based reagentAct42A F
(primer)
PMID: 23593032Sequence:
5’-CCGTACCACAG
GTATCGTGTTG-3’
Sequence-based reagentAct42A R
(primer)
PMID: 23593032Sequence: 5’-
GTCGGTTAAATC
GCGACCG-3’
Commercial
assay or kit
Glucose
Oxidase/
Peroxidase assay
kit (Sigma)
SigmaSigma:
GAGO20-1KT
Commercial
assay or kit
PureGene DNA
extraction kit
(Qiagen)
QiagenQiagen: 158667
Commercial
assay or kit
Nucleospin
RNA II kit
(Macherey-
Nagel)
Macherey-Nagel
Commercial
assay or kit
SensiFast cDNA
Synthesis kit
(Bioline)
Bioline
Commercial
assay or kit
SensiFAST
SYBR No-ROX kit
(Bioline)
Bioline
Software,
algorithm
JMPSAS Institute,
Cary, NC
RRID:SCR_014242
Software,
algorithm
PSI-seq methodPMID: 21940681
Software,
algorithm
FASTQC (v.0.11.2)RRID:SCR_014583
Software,
algorithm
Trimmomatic
(v.0.33)
RRID:SCR_011848
Software,
algorithm
Tophat (v.2.1.0)RRID:SCR_013035
Software,
algorithm
HTseq (v.2.7.6)RRID:SCR_005514
Software,
algorithm
R/Bioconductor
package limma
RRID:SCR_010943
Software,
algorithm
R/Bioconductor
package pvclust
URL:
https://CRAN.R-project.org/package=pvclust
Software,
algorithm
BWA-MEM
(Burrows-
Wheeler
alignment
software
package)
PMID: 19451168
Software,
algorithm
Geneious 11.1.5
software
Biomatters
Ltd., Aukland,
NZ
RRID:SCR_010519

Fly food, fly stocks and husbandry

Drosophila simulans line C167.4 (14021-0251-199) and D. sechellia line SynA (14021-0248-28) were obtained from the Drosophila Species Stock Center, University of California, San Diego (now located at the College of Agriculture and Life Science, Cornell University). D. melanogaster strain Oregon R was a gift from Tapio Heino, University of Helsinki. The following VDRC RNAi lines were used: mRpL43: 104466, CG4882: 106629, bonsai: 104412, SERCA: 107446, PPP1R15 (Gadd34): 107545, Pi3K59F: 100296, Taldo: 106308, Dpit47: 110401, GlcT-1: 108064 (see VDRC web site for specific information). Ubi-GAL4, tub-GAL4 and cg-GAL4 were obtained from the Bloomington Drosophila Stock Center. Fb-GAL4 (FBti0013267 (Grönke et al., 2003) was also used. All fly stocks and parents of the experimental flies were maintained on a common laboratory diet containing 2.4% (v/v) nipagin, 0.7% (v/v) propionic acid. All experiments took place at 25°C, 50% RH with 12 hr light, 12 hr dark daily cycle and at controlled density (30 larvae per vial). Driver lines crossed with w1118 containing landing site VIE-260B (VDRC ID: 60100) were used as controls for the RNAi experiments. Different driver lines were used in the knockdown, depending on the strength of the phenotypes. For selection on Morinda toxins flies were reared on 0.5% agar 20% yeast diet supplemented with 0.5% hexanoic acid and 0.01% octanoic acid (Earley and Jones, 2011).

Nutrient space pupariation assay

We determined the optimal nutrient space for larval growth for each species. Parents of the experimental flies were released into egg-laying chambers provided with apple-juice-agar plates supplemented with yeast and allowed to lay eggs for 2 hr intervals. Yeast was removed from the egg-laying plates and they were incubated 24 hr at 25°C, 50% RH. Thirty 1st instar larvae were placed into five replicate vials of 0.5% agar-based media in a 5 × 5 grid of baker’s yeast (1.25, 2.5, 5, 10% and 20%) and sucrose (0, 5, 10, 15, and 20%). Estimated caloric contents of the diets are presented in Table 5. Vials were scored for the number of larvae pupariated at 24 hr intervals for 408 hr total. Because nutrition affects both developmental timing and larval survival, we calculated the pupariation index (Pupind) that is the maximum rate of pupariation. Specifically, Pupind = max({(pt/t): t = 24, 48, …, 408}); where pt = number of larvae that have pupariated at observation time t hours after egg-laying (hAEL). The maximum Pupind was analyzed using a full general linearized model with main effects of genotype, yeast, sucrose and their interactions using JMP software (SAS Institute). The strengths of the main effects were determined by calculating ω2 (Yigit and Mendes, 2018).

Table 5
Estimated caloric content of the 25 Yeast-Sugar diets (kcal/100 g).
https://doi.org/10.7554/eLife.40841.031
% Yeast
1.252.551020
% Sugar04.18.116.332.565.0
524.428.436.652.885.3
1044.748.756.973.1105.6
1565.069.077.293.4125.9
2085.389.397.5113.7146.2

Feeding behavior

Feeding behavior was assayed by quantifying the rate of larval mouth hook extensions using the method described by Shen (2012). Parents of the experimental larvae were allowed to lay eggs for 2 hr on apple juice agar plates spread with yeast paste. Plates were incubated at 25°C overnight and then first instar larvae were transferred to vials of 20% yeast in groups of 30 and raised to pre-wandering third larval instar. On the morning of the assay, larvae were transferred to Petri plates containing 20% yeast colored with blue food dye and were allowed to feed for 1 hr. Actively feeding larvae with visible blue food in the gut were selected for assay.

Media for the mouth hook extension assay was prepared by mixing 12 g dry agar with a solution of 1 × PBS buffer or with 1 × PBS buffer containing 20% sucrose to final volume of 100 ml. The mixtures were incubated overnight at 4°C to hydrate the agar completely. The thickened assay media was poured into small Petri plates and allowed to equilibrate to RT for 3 hr.

To begin the assay, 30 larvae were transferred from the blue-dyed LSD plate onto a plate of assay media. Larvae were viewed on the plate using a dissecting microscope and the number of mouth hook extensions in 1 min was counted for 10 individual larvae. Larvae float on the assay media and are unable to move from where they are placed. Four no sucrose and four 20% sucrose plates were observed. Counts were recorded using a handheld cell counter and each observed larva was removed from the plate before proceeding to the next. Mean mouth hook extension rate was calculated per plate from the 10 larvae and compared by ANOVA with species and sucrose percent as main effects.

Phenotype-based selection and introgression with resequencing

We introgressed the phenotype of sugar tolerance from D. simulans into a mostly D. sechellia genomic background using the crossing scheme outlined in Figure 2A. To produce F1 hybrids, groups of 100 unmated D. simulans females were collected and crossed to 100 D. sechellia males in 1-litre plastic population cages. Females were allowed to lay eggs on apple-juice agar plates for 24 hr, then eggs were collected and seeded at approximately 200 eggs per 240 ml bottle onto 20% yeast media (Clancy and Kennington, 2001). For the first backcross generation ≥100 unmated F1 hybrid females were collected from the bottles and crossed to 100 D. sechellia males in 1-liter plastic population cages. Females from the cross were allowed to lay eggs on apple-juice agar plates for 24 hr then eggs were collected and seeded at approximately 200 eggs per 240 ml bottle onto 20% yeast/20% sucrose media to select the sugar tolerant phenotype or onto 20% yeast for the control backcross. Bottles of eggs that were collected from a single crossing cage were kept together, separate from those collected from replicate crosses. For generation 2 through 10, virgin females were collected from the bottles, crossed to D. sechellia males, eggs were collected and seeded into fresh 20% yeast/20% sucrose (selected) or 20% yeast (control) media. Crossing populations consisted of ≥100 backcross females from the previous generation and 100 D. sechellia males. After 10 generations of backcrossing the lines were sibling mated for three generations before beginning experiments. Backcross lines were continuously maintained on 20% yeast/20% sucrose (selected) or 20% yeast (control) diet throughout sibling mating.

Genomic DNA was extracted from 30 female flies for two no-selection control lines, three sugar-selected lines, and from each parental D. sechellia and D. simulans line using a PureGene DNA extraction kit (Qiagen). Resequencing was performed at the University of North Carolina DNA sequencing facility. Regions of introgression were mapped using the PSI-seq method of Earley and Jones (2011).

Original datasets have been placed into a public repository (NCBI): https://www.ncbi.nlm.nih.gov/bioproject/PRJNA486014/

Hemolymph glucose clearance

We determined the hemolymph glucose concentration of the parental and introgression lines after feeding on a high sugar diet. Larvae were collected onto 20% yeast media and raised to pre-wandering third larval instar. Five replicates of 50 pre-wandering third instar larvae were transferred to media containing 20% sucrose for 2 hr and then to 0% sucrose media for 2 hr. Hemolymph was collected from 10 larvae at time points 0 hr - 0%, 2 hr - 20% sucrose and 2 hr - clearance (0% sucrose). Hemolymph glucose was assayed using a Glucose Oxidase/Peroxidase assay kit (Sigma) (Havula et al., 2013) and data were analyzed by comparing to D. simulans using Dunnett’s test implemented in JMP software (SAS institute). Dunnett’s test queries whether the difference between the mean of the control group and an experimental group differs by greater than a critical value.

RNA sequencing and data analysis

We extracted and sequenced total RNA from third instar D. simulans (C167.4), D. sechellia (SynA), backcross control, and backcross selected larvae that were fed LSD (20% yeast) or HSD (20% yeast/20% sucrose) for 8 hr using a Nucleospin RNA II kit (Macherey-Nagel). Larvae were prepared from three replicates of each fly line. Parents of the experimental larvae were released into egg collection cages and allowed to lay eggs for 2 hr on apple juice agar plates supplemented with yeast paste. Plates of eggs were placed at 25°C for 24 hr. Following incubation, 100 first instar larvae were transferred to plates containing LSD and placed at 25°C for 48 hr after which pre-wandering third instar larvae were transferred to plates of LSD or HSD that contained 2% blue food dye. Larvae were allowed to feed for 2 hr at 25°C then larvae that did not have blue dye in their gut were removed from the plates. Feeding larvae that had blue dye in their gut were kept 6 hr at 25°C and then collected for RNA extraction. RNA sequencing libraries we prepared using the TruSeq Stranded mRNA Library Prep Kit (Illumina) and sequenced (single-end 76 bp reads) using Illumina NextSeq 500 technology.

The quality of the reads was assessed with FASTQC (v.0.11.2) (Babraham Bioinformatics, Cambridge, UK). The reads were trimmed with Trimmomatic (v.0.33) (Bolger et al., 2014). Reads were scanned with sliding window of 20 and if the average quality per base dropped below 20, the read was discarded. Additionally, base length of 40 was required for the reads and for both leading and trailing ends quality score of 30 was required. For the mlx mutant RNAseq dataset (Mattila et al., 2015), trimming was performed with sliding window of 4 bases with average per base quality requirement of 15. Required base length was set to 36 and required strand quality in both at the end and start was set to 36. Tophat (v.2.1.0) (Trapnell et al., 2009) was used for aligning the reads to D. melanogaster reference genome (Flybase R6.10). HTseq (v.2.7.6) (Anders et al., 2015) was used for strand-specific quantification of exons. Reads with quality score below 10 were discarded.

The quality of the samples was assessed with multi-dimensional scaling and variety of sample clustering methods (pearson correlation, euclidean ward, euclidean complete) using R/Bioconductor's package pvclust (Suzuki and Shimodaira, 2006). Based on these results, one sample (sim.0.1) was defined as outlier and thus removed from the analysis. The differential expression analysis was performed with R/Bioconductor package limma (Ritchie et al., 2015; Law et al., 2014). The samples were required to have >1 CPM (counts per million) in all replicates in either tolerant or intolerant group. For mlx mutant datasets, no outliers were discovered, and >1 CPM was required in majority of samples in at least one of the conditions. The Benjamini-Hochberg correction was used for adjusting p values (Benjamini and Hochberg, 1995).

Sample clustering was performed using R/Bioconductor package pvclust (Suzuki and Shimodaira, 2006). Correlation was used as distance matrix. The gene set enrichment was performed with hypergeometric test for the manually downloaded pathway sets from KEGG and GO. The pathway was defined as enriched if the adjusted p value < 0.05. The heatmaps were generated using scaled log2CPM values for means of each sample group. The scaling was performed separately for the two datasets. The row-wise clustering was performed using correlation distance.

Original datasets have been placed into a public repository (NCBI): https://www.ncbi.nlm.nih.gov/bioproject/PRJNA486014/.

Comparative analysis of genomic regions of candidate genes

Species-specific nucleotide sequences for SERCA, CG4882, RpL49, and bonsai genomic regions were compiled by mapping Illumina sequence reads from D. simulans (C167.4) and D. sechellia (SynA) to D. melanogaster sequences using BWA-MEM in the Burrows-Wheeler alignment software package (Li and Durbin, 2009). Sequence read alignments were edited by hand using Geneious 11.1.5 software (Biomatters Ltd., Aukland, NZ) to produce simple majority consensus sequences. For each gene, the consensus sequences were aligned and nucleotide differences between species called using Geneious 11.1.5 software. The average frequency of nucleotide differences was calculated in 100 base windows slid forward in steps of 25 bases. The genomic average frequency of nucleotide differences between D. simulans and D. sechellia was calculated for three randomly chosen 20 kb regions on chromosomes 2R, 2L, and X, and was subtracted from each window to correct for background noise. Frequencies were charted using JMP Pro 14 software (SAS Institute Cary, NC).

Generation of transgenic reporter flies

The likely SERCA promoter region was identified based on the nine chromatin stage model (Kharchenko et al., 2011), the selected fragment corresponds to the ‘red’ chromatin type (Active promoter/transcription start site region). The selected 1.2 kB promoter regions of SERCA from D. simulans (C167.4) and D. sechellia (SynA) were cloned into the placZ-2.attB vector using restriction enzyme sites NotI and XhoI and ligase-dependent cloning (Bischof et al., 2013). Successful generation of plasmids was verified with Sanger sequencing. Injection was performed by GenetiVision (Houston, TX) into a D. melanogaster w1118 line with landing site attP2(3L)68A4.

Cloning was performed using the following primers:

SERCA F:

5’-TAAGCGGCCGCTCTTCGTTCAGTGGCCTGTG-3’

SERCA R:

5’-TAACTCGAGTCGTGATAAGGATTTCAGTTCG-3’

RNA extraction and quantitative RT-PCR

Eight early 3rd instar larvae were collected for each sample and RNA was extracted using the Nucleospin RNA kit (Macherey-Nagel) according to the manufacturer’s protocol. RNA was reverse-transcribed using the SensiFAST cDNA Synthesis kit (Bioline) according to the manufacturer’s protocol, and qPCR was performed using SensiFAST SYBR No-ROX kit (Bioline) with Light cycler 480 Real-Time PCR System (Roche) with three technical replicates per sample. Actin 42A was used as a reference gene.

Following primers were used:

LacZ F: 5’-CGAATCTCTATCGTGCGGTG-3’

LacZ R: 5’-CCGTTCAGCAGCAGCAGAC-3’

Act42A F: 5’-CCGTACCACAGGTATCGTGTTG-3’

Act42A R: 5’-GTCGGTTAAATCGCGACCG-3’

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Decision letter

  1. Patricia J Wittkopp
    Senior and Reviewing Editor; University of Michigan, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Natural variation in sugar tolerance associates with changes in signaling and mitochondrial ribosome biogenesis" for consideration by eLife. Your article has been reviewed by Patricia Wittkopp as the Senior Editor, a Reviewing Editor, and three reviewers. The following individual involved in review of your submission has agreed to reveal her identity: Christen K Mirth (Reviewer #3).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

The manuscript by Melvin and co-authors outlines a thorough and elegant study to explore the genetic nature of differences in dietary requirements between species. This study uses the Nutritional Geometry framework to explore how two closely related species, Drosophila simulans and D. sechellia differ in their tolerance for sugar. They further extend this beyond the scope of previous nutritional geometry studies to understand the metabolic differences underpinning differences in sugar tolerance, using a clever backcrossing approach to introgress D. simulans sugar tolerance into the D. sechellia genomic background. In their final experiment, they examine trade-offs between high sugar intolerance and the ability to survive on a low nutrient diet. They provide evidence that at the cost of high-sugar intolerance, D. sechellia survive better on low energy diets than D. simulans.

This is a colossal study, with far-reaching implications on how dietary requirements are determined. Each of these experiments represent an impressive amount of work, and as a reward for their efforts the authors have generated one of the most complete pictures of how differences in dietary ranges are encoded in the genome. The study is of exceptionally high quality, but the reviewers did raise a few questions about the analysis and interpretations that will need to be addressed before publication. Addressing this and the other comments shown below (which are combined from the three reviewers and thus not always in the order in which the issues appear in the manuscript) will require extensive changes to the text.

Especially important to address among these comments are the requests for (1) more specific methodological details including for the pupariation index, the experimental parameters for RNAseq libraries etc. and the comments relating to the discussion, (2) clearer aims and reworking of the document for better logical flow, and (3) revisiting the nutritional geometry analysis and presenting the results with a greater degree of caution in the Discussion section (acknowledging that because you used a single inbred line from each species, it is difficult to be certain that these are truly differences between species).

For the response surface analysis, you should be more cautionary with your interpretation. For a response surface to be considered strikingly different, the optima is expected to be at opposite ends of the nutrient space. The main differences that you see are that responses for survival, development time, and pupariation index are much more restricted in D. sechellia than in D. simulans. In other words, the optima overlap but occupy a much broader region in D. simulans.

To infer significant differences between responses you need to do a formal test. It's true you find significant correlations between your trait and sucrose concentrations only in D. sechellia (and F1 hybrids for pupariation index) but given that variance in these datasets tends to be high this does not necessarily mean that the responses are significantly different. To test this, you should include a full glm model with all interactions: trait ~ sucrose + sucrose2 + yeast + yeast2 + species + sucrose*yeast + sucrose*species + yeast*species + sucrose*yeast*species. If any of the interaction terms between diet and species are significant (sucrose*species + yeast*species + sucrose*yeast*species), you can be confident that there is a significant difference in the way each species responds to the composition of the diet. It also provides additional information as it tells you what is driving the difference; the sucrose*species term would be expected to be significant but not the yeast*species term – meaning that the differences in how traits respond to the diet are driving by differences in sucrose tolerance (but not protein requirements) between D. simulans and D. sechellia.

The rationale for comparing expression between your introgression lines to mlx mutants was not clear. Was this done to show that the intolerant lines show similar expression profiles as previously identified sugar-intolerant mutants?

When using a single inbred line for each species, it's impossible to say whether the differences observed are truly due to differences between species. For example, without knowing how D. simulans populations vary in sugar tolerance, it's entirely possible that the inbred line selected showed high tolerance relative to the population mean (or that the D. sechellia line chosen had low sugar tolerance). There are two ways around this problem, you can conduct your nutritional geometry experiments on genetically diverse outbred lines or you can repeat these experiments on multiple inbred lines of the same species (to get an estimate for genetic variation for your trait within a species). Given the size of nutritional geometry experiments, using outbred lines is usually preferred. All this to say, it is difficult to be sure from your experiments that the differences you've mapped really represent differences between species. While it is not necessary to directly address this problem within the context of this work, this caveat should be mentioned in the Discussion section. It doesn't detract from your main message: that you've explored the difference in sugar tolerance between two genomes and identified genomic regions that contribute to this variation.

The order of the Materials and methods section does not parallel the order of the Results section. In addition, the order of the methods does not follow a logical progression (ex. Hemolymph glucose clearance assay which requires the introgressed lines is presented before Genomic Introgression).

Each section within the Results section ends with a sentence that begins "In conclusion”. The authors then make a statement that discusses the results and should be incorporated in the Discussion section not the Results section.

Nowhere in the Introduction do the authors state what they aim to do with the study. They present their hypothesis but don't provide context as to how they intend to test it in the study.

The subsection “Introgression of sugar tolerance phenotype” is primarily describing methods and would be better placed in the Materials and methods section. Also, in the Materials and methods section, the authors do not provide enough detail to replicate this method.

The Abstract primarily presents results but does not offer sufficient background detail to frame the importance of the study.

https://doi.org/10.7554/eLife.40841.039

Author response

The manuscript by Melvin and co-authors outlines a thorough and elegant study to explore the genetic nature of differences in dietary requirements between species. This study uses the Nutritional Geometry framework to explore how two closely related species, Drosophila simulans and D. sechellia differ in their tolerance for sugar. They further extend this beyond the scope of previous nutritional geometry studies to understand the metabolic differences underpinning differences in sugar tolerance, using a clever backcrossing approach to introgress D. simulans sugar tolerance into the D. sechellia genomic background. In their final experiment, they examine trade-offs between high sugar intolerance and the ability to survive on a low nutrient diet. They provide evidence that at the cost of high-sugar intolerance, D. sechellia survive better on low energy diets than D. simulans.

This is a colossal study, with far-reaching implications on how dietary requirements are determined. Each of these experiments represent an impressive amount of work, and as a reward for their efforts the authors have generated one of the most complete pictures of how differences in dietary ranges are encoded in the genome. The study is of exceptionally high quality, but the reviewers did raise a few questions about the analysis and interpretations that will need to be addressed before publication. Addressing this and the other comments shown below (which are combined from the three reviewers and thus not always in the order in which the issues appear in the manuscript) will require extensive changes to the text.

Especially important to address among these comments are the requests for (1) more specific methodological details including for the pupariation index, the experimental parameters for RNAseq libraries etc. and the comments relating to the discussion, (2) clearer aims and reworking of the document for better logical flow, and (3) revisiting the nutritional geometry analysis and presenting the results with a greater degree of caution in the Discussion section (acknowledging that because you used a single inbred line from each species, it is difficult to be certain that these are truly differences between species).

We thank the reviewers for a thorough and constructive feedback, which has helped us to significantly improve our study. We have now revised the manuscript according to the comments of the reviewers, including the three points highlighted above. Below, we provide a point-by-point response to the reviewers.

For the response surface analysis, you should be more cautionary with your interpretation. For a response surface to be considered strikingly different, the optima is expected to be at opposite ends of the nutrient space. The main differences that you see are that responses for survival, development time, and pupariation index are much more restricted in D. sechellia than in D. simulans. In other words, the optima overlap but occupy a much broader region in D. simulans.

We have now modified the Results section to more precisely describe the phenotypes, e.g. “In contrast, D. sechellia larvae displayed a more restricted space, with slowed development and reduced survival on high protein diets containing >10% sugar and complete lethality on diet composed of 20% sucrose/20% yeast (Figure 1A, Table 1).”

To infer significant differences between responses you need to do a formal test. It's true you find significant correlations between your trait and sucrose concentrations only in D. sechellia (and F1 hybrids for pupariation index) but given that variance in these datasets tends to be high this does not necessarily mean that the responses are significantly different. To test this, you should include a full glm model with all interactions: trait ~ sucrose + sucrose2 + yeast + yeast2 + species + sucrose*yeast + sucrose*species + yeast*species + sucrose*yeast*species. If any of the interaction terms between diet and species are significant (sucrose*species + yeast*species + sucrose*yeast*species), you can be confident that there is a significant difference in the way each species responds to the composition of the diet. It also provides additional information as it tells you what is driving the difference; the sucrose*species term would be expected to be significant but not the yeast*species term – meaning that the differences in how traits respond to the diet are driving by differences in sucrose tolerance (but not protein requirements) between D. simulans and D. sechellia.

We have now tested for significant differences in the responses using the full glm model with all interactions. The results of the analysis are presented in the new Table 2 and show that all effects and their interactions are significant. To get at the question of what drives differences in trait values we calculated ω2 to estimate the strength of the sucrose, yeast, and sucrose × yeast interaction effects for each species and the F1 hybrid separately. We find that the effect of sucrose on Pupind, survival, and development time is substantially stronger in D. sechellia than in D. simulans (Table 3). Conversely, the effect of yeast is stronger in D. simulans than sechellia (reflecting the better performance of sechellia in low yeast). However, the effects indicate that the species difference is much greater for sucrose, further underlining the prominent sugar intolerance in D. sechellia.

The rationale for comparing expression between your introgression lines to mlx mutants was not clear. Was this done to show that the intolerant lines show similar expression profiles as previously identified sugar-intolerant mutants?

We have now better clarified the rationale in the Results section.

When using a single inbred line for each species, it's impossible to say whether the differences observed are truly due to differences between species. For example, without knowing how D. simulans populations vary in sugar tolerance it's entirely possible that the inbred line selected showed high tolerance relative to the population mean (or that the D. sechellia line chosen had low sugar tolerance). There are two ways around this problem, you can conduct your nutritional geometry experiments on genetically diverse outbred lines or you can repeat these experiments on multiple inbred lines of the same species (to get an estimate for genetic variation for your trait within a species). Given the size of nutritional geometry experiments, using outbred lines is usually preferred. All this to say, it is difficult to be sure from your experiments that the differences you've mapped really represent differences between species. While it is not necessary to directly address this problem within the context of this work, this caveat should be mentioned in the Discussion section. It doesn't detract from your main message: that you've explored the difference in sugar tolerance between two genomes and identified genomic regions that contribute to this variation.

As suggested, we have now mentioned in the Discussion section the need to determine the variation of sugar tolerance within the species by using multiple lines.

The order of the Materials and methods section does not parallel the order of the Results section. In addition, the order of the methods does not follow a logical progression (ex. Hemolymph glucose clearance assay which requires the introgressed lines is presented before Genomic Introgression).

We have now reorganized the Materials and methods section, to match the order of the Results section.

Each section within the Results section ends with a sentence that begins "In conclusion”. The authors then make a statement that discusses the results and should be incorporated in the Discussion section not the Results section.

As suggested, we have omitted the concluding sentences from the Results section.

Nowhere in the Introduction do the authors state what they aim to do with the study. They present their hypothesis but don't provide context as to how they intend to test it in the study.

We have now stated the aim of the study in the Introduction: “Here we aimed to explore the natural variation of macronutrient space in closely related species.”

The subsection “Introgression of sugar tolerance phenotype” is primarily describing methods and would be better placed in the Materials and methods section. Also, in the Materials and methods section, the authors do not provide enough detail to replicate this method.

As suggested, we have reduced the methodological descriptions in the Results section and better described the details of the introgression experiment in the Materials and methods section.

The Abstract primarily presents results but does not offer sufficient background detail to frame the importance of the study.

We have now revised the Abstract to better frame the importance of the study, some details of results were omitted due to length restriction.

https://doi.org/10.7554/eLife.40841.040

Article and author information

Author details

  1. Richard G Melvin

    1. Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
    2. Institute of Biotechnology, University of Helsinki, Helsinki, Finland
    Present address
    University of Minnesota School of Medicine, Minnesota, United States
    Contribution
    Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Writing—original draft, Project administration
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6428-6763
  2. Nicole Lamichane

    1. Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
    2. Institute of Biotechnology, University of Helsinki, Helsinki, Finland
    Contribution
    Conceptualization, Formal analysis, Investigation, Visualization, Writing—original draft, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1746-0332
  3. Essi Havula

    1. Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
    2. Institute of Biotechnology, University of Helsinki, Helsinki, Finland
    Present address
    Charles Perkins Centre, University of Sydney, Sydney, Australia
    Contribution
    Formal analysis, Investigation, Visualization, Writing—review and editing
    Competing interests
    No competing interests declared
  4. Krista Kokki

    1. Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
    2. Institute of Biotechnology, University of Helsinki, Helsinki, Finland
    Contribution
    Formal analysis, Visualization, Writing—review and editing
    Competing interests
    No competing interests declared
  5. Charles Soeder

    Biology Department, The University of North Carolina at Chapel Hill, Carolina, United States
    Contribution
    Formal analysis, Writing—review and editing
    Competing interests
    No competing interests declared
  6. Corbin D Jones

    Biology Department, The University of North Carolina at Chapel Hill, Carolina, United States
    Contribution
    Resources, Methodology, Writing—review and editing
    Competing interests
    No competing interests declared
  7. Ville Hietakangas

    1. Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
    2. Institute of Biotechnology, University of Helsinki, Helsinki, Finland
    Contribution
    Conceptualization, Supervision, Funding acquisition, Investigation, Writing—original draft, Project administration, Writing—review and editing
    For correspondence
    ville.hietakangas@helsinki.fi
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9900-7549

Funding

Suomen Akatemia (286767)

  • Ville Hietakangas

Novo Nordisk Fonden (NNF16OC0021460)

  • Ville Hietakangas

Sigrid Juséliuksen Säätiö

  • Ville Hietakangas

Diabetestutkimussäätiö

  • Ville Hietakangas

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Christen Mirth, Ronald Regal, and members of our group for advice and comments. Sequencing was performed by the core facility (DNA Sequencing and Genomics Laboratory) of the Institute of Biotechnology, University of Helsinki. Funding was provided by the Academy of Finland (grant no. 286767 to VH), Novo Nordisk Foundation (NNF16OC0021460 to VH), Sigrid Juselius Foundation and Finnish Diabetes Foundation.

Senior and Reviewing Editor

  1. Patricia J Wittkopp, University of Michigan, United States

Publication history

  1. Received: August 6, 2018
  2. Accepted: November 27, 2018
  3. Accepted Manuscript published: November 27, 2018 (version 1)
  4. Version of Record published: December 20, 2018 (version 2)

Copyright

© 2018, Melvin et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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