1. Genetics and Genomics

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
https://doi.org/10.7554/eLife.40841
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Cite this article
  1. Richard G Melvin
  2. Nicole Lamichane
  3. Essi Havula
  4. Krista Kokki
  5. Charles Soeder
  6. Corbin D Jones
  7. Ville Hietakangas
(2018)
Natural variation in sugar tolerance associates with changes in signaling and mitochondrial ribosome biogenesis
eLife 7:e40841.
https://doi.org/10.7554/eLife.40841
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9 figures, 6 tables and 2 additional files

Figures

Figure 1 with 1 supplement
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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
Figure 1—source data 1

Source data for Figure 1A.

https://doi.org/10.7554/eLife.40841.005
Figure 1—source data 2

Source data for Figure 1B.

https://doi.org/10.7554/eLife.40841.006
Figure 1—figure supplement 1
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Feeding behavior did not differ significantly between the species.

The rate of mouth hook extensions was quantified in the presence and absence of 20% sucrose. n = 4 replicates of 10 larvae/replicate for each genotype and diet. Error bars display standard error of the mean. ANOVA showed no significant difference between the groups.

https://doi.org/10.7554/eLife.40841.004
Figure 2
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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
Figure 3
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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
Figure 4
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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
Figure 5
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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
Figure 6 with 1 supplement
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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 6—source data 1

Source data for Figure 6A.

https://doi.org/10.7554/eLife.40841.016
Figure 6—source data 2

Source data for Figure 6B.

https://doi.org/10.7554/eLife.40841.017
Figure 6—source data 3

Source data for Figure 6C.

https://doi.org/10.7554/eLife.40841.018
Figure 6—figure supplement 1
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Macronutrient space of Drosophila melanogaster shows high sugar tolerance.

Larval development was monitored on a 5 × 5 grid of varying yeast and sucrose levels. Pupind: pupariation index. n = 5 replicates of 30 larvae/replicate for each diet.

https://doi.org/10.7554/eLife.40841.015
Figure 7 with 1 supplement
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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
Figure 7—source data 1

Source data for Figure 7A.

https://doi.org/10.7554/eLife.40841.021
Figure 7—source data 2

Source data for Figure 7B.

https://doi.org/10.7554/eLife.40841.022
Figure 7—source data 3

Source data for Figure 7C.

https://doi.org/10.7554/eLife.40841.023
Figure 7—figure supplement 1
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Genes with a modest impact on sugar tolerance.

(A) Total eclosion % of control and GlcT-1 RNAi larvae (tub-GAL4>), n = 4 replicates of 30 larvae/replicate for each genotype and diet. (B) Total eclosion % of control and Dpit47 RNAi larvae (Ubi-GAL4>), n = 4 replicates of 30 larvae/replicate for each genotype and diet. (C) Total eclosion % of control and Taldo RNAi larvae (Ubi-GAL4>), n = 19 replicates of 30 larvae/replicate for each genotype and diet. Error bars display standard deviation.

https://doi.org/10.7554/eLife.40841.020
Figure 8 with 1 supplement
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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
Figure 8—figure supplement 1
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Genomic variation in the mitochondrial ribosome encoding gene regions.

SNP density maps comparing D. sechellia to D. simulans on regions surrounding the CG4882, mRpL43, and bonsai genes. 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.

https://doi.org/10.7554/eLife.40841.025
Figure 9 with 1 supplement
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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
Figure 9—source data 1

Source data for Figure 9B.

https://doi.org/10.7554/eLife.40841.029
Figure 9—source data 2

Source data for Figure 9C.

https://doi.org/10.7554/eLife.40841.030
Figure 9—figure supplement 1
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Morinda toxin tolerance is not associated with sugar tolerance.

(A) Pupariation indices on HSD of lines after three generations of introgression and selection on HSD, LSD, and LSD + Morinda toxins. Mean of 5 replicate (30 larvae/replicate for each genotype and diet) vials were compared by Student’s t-test. ** p < 0.01. (B) Two sugar-selected introgression lines show full tolerance to Morinda toxins, in contrast to parental D. simulans line. n = 5 replicates of 30 larvae/replicate for each genotype and diet.

https://doi.org/10.7554/eLife.40841.028

Tables

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

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
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
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
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

Additional files

Supplementary file 1

D. melanogaster genes corresponding to the D. simulans/sechellia genes in the introgressed genomic regions.

Genes which were up- or downregulated in both sugar intolerant genotypes when compared to both sugar tolerant genotypes are indicated as well as the genes that were screened by RNAi.

https://doi.org/10.7554/eLife.40841.032
Transparent reporting form
https://doi.org/10.7554/eLife.40841.033

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  1. Richard G Melvin
  2. Nicole Lamichane
  3. Essi Havula
  4. Krista Kokki
  5. Charles Soeder
  6. Corbin D Jones
  7. Ville Hietakangas
(2018)
Natural variation in sugar tolerance associates with changes in signaling and mitochondrial ribosome biogenesis
eLife 7:e40841.
https://doi.org/10.7554/eLife.40841