Disentangling the gene-by- environment interaction ...
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~~Laura K. Reed (lreed1@ua.edu)
Department of Biological SciencesUniversity of AlabamaTuscaloosa, AL USA
flygxe.ua.edu
Disentangling the gene-by-environment interaction architecture of metabolic disease through the lens
of evolutionary genetics and metabolomics
Reed et al, Current Opinion in Chemical Biology 2017
Mechanisms of Phenotypic Variation
Genome
Phenotype
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Source: CDC Behavior Risk Factor Surveillance System
Obesity Trends* Among U.S. AdultsBRFSS, 1999 - 2010
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
2960 Genetic Associations for Obesity in Humans* Only a tiny proportion of the genetic variation is explained by a specific
association.
*Retrieved 3/29/2018, NHGRI-EBI GWAS Catalog
Obesity and its comorbidities (metabolic syndrome- MetS) have both genetic and environmental influences
Outline
• The Fly as a MetS model
• Unlocking the black box of MetS using systems biology
• Determining genetic basis of Genotype x Diet Interactions
• Other environmental effects on MetS
Tori Nelko
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Fly-Human Homology
• Insulin signaling and other metabolic pathways largely homologous
• 77% OMIM human disease genes have cognates in Drosophila
Rulifson, et al. (2002). Science 296 , 118-20Reiter et al. (2001) Genome Research 11: 1114-1125
Hotamisligil (2006) Nature 444, 860-867O’Neill et al.,(2012) Biochemical Society Transactions 40:721-727;
Environmental Heterogeneity
How to Measure GxE
Wild Population
Inbred Lines
EnvironmentalVariation
Genetic VariationGenetic Variation
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Normal Control High Glucose High FatDiets
PhenotypesWeight
Blood SugarLipid Storage
Survival
MetabolomicsGC-MS
quantification of over 180
metabolites
Gene ExpressionWhole Genome
Expression Profiling
146 Isolines in initial screen
20 Isolines for Systems Analysis
20 Isolinesfor Systems
Analysis
20 Isolinesfor Systems
Analysis
Stephanie Williams, Kelly Dew-Budd, Kristen Davis, Julie Anderson, Kenda Freeman, Mastafa Springston
Wei
ght
Possible Outcomes
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Weight Genetic Variation
Reed et al., Genetics, 2010
Weight GxD
Reed et al., Genetics, 2010
Proportion of Variance Explained
18.9% Genetic1.9 % Dietary
12% GxDn = 14800
Significant decanalization on the high fat diet
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Pro
port
ion
of V
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nce
Exp
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Genetic x Diet
Diet
Genetic
MetS-like traitsGenetic and GxD effects greater than diet alone
Expression and metabolite principal
components do NOT correlate
GxD > diet for most MetS traits
Reed et al., Genetics 2014
Transcriptome and metabolome
significant GxD effects diet more important for
metabolome
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Expression Metabolites
WeightTAG
Sugar
Weight
Williams et al, G3 2015
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• Lack of conservation of correlated gene transcripts by diet
Ruth Bishop, Dana Davis, Katie Bray, Lauren Perkins, Joana Hubickey
Are Molecular Mechanisms for Phenotypes Conserved Across Environments?
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Metabolite PC2 highly predictive of MetS-like phenotypes
Arrhythmia Index correlated with MetPC2 and MetS
phenotypesReed et al. 2014 Genetics
MetPC2L-dopa
N-arachidonoyldopamineglucosevaline
leucineisoleucine
glycine methionine
phenylalanine
Branched Chain AA
Ocorr et al. Trends in Cardiovasc Med. 2007 17:177-182
What Links MetS Phenotypes?
Eigenvector Metabolite Analysis
(EvMA)
Clare Scott ChialvoRonglin CheDavid ReifAlison Motsinger-Reif
Scott Chialvo, Che, Reif, Motsinger-Reif, Reed 2016 Metabolomics
What Links MetS Phenotypes?
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Number of Correlated
Genes
Enriched GO Terms
Overt Phenotypes
0 -- Weight, Larval Survival
845 respiratory electron transport chain
Sugar, Weight, Pupal Survival
688 response to temperature stimulus, response to hypoxia
Weight
3134 Mitochondrion, generation of
precursor metabolites and energy, cellular
respiration, TCA cycle
Sugar, Lipid, Weight
9 -- Larval Survival, Pupal Survival, Development Time
3013 Mitochondrion, structural constituent
of ribosome
Weight, Development Time
0 -- --
227 ribosome biogenesis, rRNA metabolic
process
Larval Survival, Pupal Survival, Development Time
Fatty Acids (un- & saturated, l-dopa)
Sugars(disaccharides, maltose)
Amino Acids (BCAA, proline, serine)
Mixing Pot (monosaccharaides,
NADA, tyrosine)
Mixing Pot (unsaturated FA, AA)
Fatty Acids (unsaturated FA)
Amino Acids
Mixing Pot (sugars, FA, AA)
EvMA across all dietsWhat Links MetS Phenotypes?
Scott Chialvo, Che, Reif, Motsinger-Reif, Reed 2016 Metabolomics
High FatNormal
350 metabolites • 270 with IDs • Metabolon Inc.Top 30 diet differentiating metabolites• 9 unknowns • medium chain fatty
acids• short chain fatty
acids• dicarboxylic/
monohydroxy fatty acids
• Glycolysis/TCA cycle metabolites
Random forest identifies metabolites that differentiate based on diet
*metabolites potentially involved in omega FA oxidation
*
Oza, Aicher, & Reed, Metabolomics, 2018 Vishal Oza – now postdoc Lasseigne lab
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Long/ Very long Chain Fatty Acids
Fatty Acids
Beta Fatty Acid Oxidation
TCA Cycle
Omega Fatty Acid Oxidation
Elo
ng
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n
Carnitineshuttle
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No difference between ND and HFD
Elevated in HFD
? No evidence found in our study
Pathway suggested in this study
Hypothesized shift toward Omega fatty acid oxidation on a High Fat Diet
?
Oza, Aicher, & Reed, Metabolomics, 2018
King, McDonald, Long, 2012
Drosophila Synthetic Population Resource
raised larvae on normal
and high fat diets
Measure phenotypes and genetically map
association
What loci control genotype-by-diet interactions?
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Dew-Budd et al. in revision
Main Genetic Effect QTLs
What loci control genotype-by-diet interactions?
Male Pupae Weight
Dew-Budd et al. in revision
Genotype-by-Diet Interacting QTLs (Plastic QTLs)
What loci control genotype-by-diet interactions?
Male Pupae Weight
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Model●
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Diet
MainGxDGenetic
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2L 2R 3L 3R X
QTL Position (Mb)
Main Genetic Effect QTLs
and Plastic (GxD) QTLs are different!
Dew-Budd et al. in revisionWhat loci control genotype-by-diet interactions?
Tre-GTre-DTG-GTG-DmWt-GmWt-DfWt-GfWt-D
Epistatic Interactions Occur Throughout the Genome
Dew-Budd et al. in revision
What loci control genotype-by-diet interactions?
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Male Weight- Main
1011 2
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Male Weight- GxD
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Diet 1 Diet 2
Main Genetic Effect (Gene A)
Main GxD Effect (Gene A)
EpistaticGenetic Effect
EpistaticGxD Effect
AA
Aa
aa
BB Bb bb
AA
Aa
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BB Bb bb
AA
Aa
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BB Bb bb
AA
Aa
aa
BB Bb bb
AA
Aa
aa
BB Bb bb
AA
Aa
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BB Bb bb
AA
Aa
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BB Bb bb
AA
Aa
aa
BB Bb bb
What loci control genotype-by-diet interactions?
Dew-Budd et al. in revision
Gene ontology enrichment - Many loci linked to
metabolic functions- miR310 cluster
(regulation in hedgehog pathway)
- Pathways regulating growth and development (neurodevelopment)
R. Mather, A. Motsinger-Reif NCSU
NIH: West Coast Metabolomics Center
• Fiehn lab (GCTOF MS)
• 421 metabolites detected
• 169 with confirmed
chemical ids
raised larvae on normal
and high fat diets
What loci control metabolite profiles?
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Metabolite 1
Gene 1Simple Genetic
Architecture
Architecture of Main Genetic Effect mQTLs
Hub Gene
Metabolite 2 Metabolite 3
Gene 1
Metabolite 1
Hub MetaboliteGene 1 Gene 2 Gene 3
Metabolite 1
Genetic red, GxD blue Genomic Position
Most mQTLs for Genetic effects are different from those with GxD effect
Met
abo
lite
Ind
ex
2L 2R 3L 3R X
What loci control metabolite profiles?
Hub Metabolite
Hub Gene
Simple QTL
Mather et al., in prep
Fiehn lab (GCTOF MS) 421 metabolites detected (169 chemical ids)
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Genomic Position
NL
P
2L 2R 3L 3R X
Linking Metabolites through mQTLs
• mQTL shared between glycolic acid and citrulline
• pyruvate kinase rate limiting step in glycolysis
glycolate phosphoenolpyruvate pyruvate
pyruvate kinase
citrulline
Mather et al., in prep
Met 1 unknown
Met 2 known
Met 3 unknown
Met 4 known
Met 5 known
Gaussian Graphical Model
Correlation Structure
Conditional Dependence
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Normal Diet GGM
High Fat Diet GGM
Phospholipid subnetworks
Dipeptide subnetworks
Fatty acid subnetworks
Overlaid Network
Gaussian graphical models by diet reveals distinct portions of sub-networks
Oza, Aicher, & Reed, Metabolomics, 2018
Correlation networkGaussian graphical model
Edges in the GGM are between biologically (and chemically) similar metabolites, but correlation network misses known biological
relationships
R > 0.9235
C (8:0) C (10:0) C (12:0)
Fatty Acid Synthesis“positive control”
Oza, Aicher, & Reed, Metabolomics, 2018
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Unknown
Dipeptide “pathways” - GGM Dipeptide “hairball” - correlation
R > 0.9235
*GGM provides testable hypotheses
Oza, Aicher, & Reed, Metabolomics, 2018
Treatment Control
TreadWheel
Sean Mendez
Uses negative geotaxis to exercise
adult flies
Rachel Hill
Exercise
Mendez et al. 2016 PlosOne
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1.75
2.25
2.75G
lyco
gen
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Tri
gly
ceri
de
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Pro
tein
0.82
0.84
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0.88
0.9
0.92
Wei
gh
t
Control ExerciseControl Exercise
p =10-7.52 p =10-2.10
p =10-4.27 p =10-46.66
Exercise Works in Flies
Control ExerciseControl Exercise
Mendez et al. 2016 PlosOne
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1.9
2.1
2.3
2.5
2.7
2.9
3.1
Clim
bin
g
Control Exercise
p =10- 36.87
Climbing Performance Improves
Nicole Riddle, Louis Watanabe, Maria DeLuca UAB
Mendez et al. 2016 PlosOne
• Exercise interacts with genetic line and sex • Mitochondrial function genes show exercise and
genotype-by-exercise interactions• Adult exercise interacts with larval diet and genotype
ExerciseControl
Normal High Fat Normal High Fat
Exercise Works in Flies(for some better than others)
Nicole Riddle, Louis Watanabe, Maria DeLuca UAB
Clim
bin
g H
eig
ht
Lowman et al, in prep Genotype x Diet x Exercise p=5.7e-17
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www.motherjones.com
Larval bacterial communities from natural conditions are interact with genetic and dietary effects
Bombin et al, 2020, Microorganisms 8(12), 1972
P,NS
P,S
PA,SPA,NS
R,NS
R,S
100 most abundant microbial ZOTU
Discriminate analysis
1. Maternal microbes shape bacterial community2. Community on natural peach diets differ from lab and
autoclaved diets 3. Genotype-by-diet interactions on bacterial
communities4. Correlations between dominant taxa and metabolic
traits (consistent with the literature)
High Fat (NS) High Sugar (NS)
Bacterial communities more strongly influenced by environmental bacteria on a high fat diet
Bombin et al, in prep
Autoclaved Peach
Lab and non-autoclaved peach
The bacterial species correlated with metabolic phenotype vary with diet and maternal microbes
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• Flies are great models!
• Genotype-by-diet interactions are a substantial contributor to MetS variation
• Metabolite profiles have predictive power for MetS phenotypes
• Quantitative data can be used to categorize unknown compounds and hypothesize new pathways
• Loci for genetic and genotype-by-diet interactions are different
• Additional environmental factors can be modeled in Drosophila Tori Nelko
The Take Home Messages
lreed1@ua.edu flygxe.ua.edu
Flies, More Flies, and Statistics….
flygxe.ua.edulreed1@ua.edu
Funding:NSF 1915544 to LKRNIH R25GM130517 to LKR NIH-R01 GM098856 to LKRNIH-NRSA Fellowship to LKR
NSF DEB 1737869 to LKRAustralian ARC DP0880204 to GGNIH R01-GM61600 to GG UA Office of Research UA College of Arts and Sciences
Collaborators:Greg Gibson Alison Motsinger-ReifSiddharth RoyRavi MatherRonglin CheOliver FiehnRolf BodmerNorm GlassbrookMaria DeLucaDavid ReifRonglin CheOliver FiehnThomas WernerSong SongThomas MerrittKelly DyerArt EddisonNicole RiddleLouis Watanabe
Kelly Dew-BuddDana DavisKatie BrayNick IzorSean MendezLeah LeonardMatt KiefferCheyenne PaivaAndrea DavidsonSteph Williams Julie BrownRuth BishopLevi MillerOlivia SorrellAlison AdamsAshley GilchristJulie JarniganCigdem Tunckanat
Alison AdamsAshley GilchristJulie JarniganCigdem TunckanatJordyn MerriamChristie TalleyTanner HallmanRachel HillMeredith OwensJaron NixJohn HendersonJoseph AicherMichael MooreAndrew DavisZavier MasonBeau SchafferNelson BrownKara Macintyre
Kelsey LowmanLaura MaflaLeigh Ann PounceyAndrei BombinCourtney MooreClare Scott ChialvoVishal OzaTreavor HearingLogan GriffinCarson FosterBrandon MoyeOwen CunneelyKatie SandlinOlivia FishKira EickmanSarah-Ashley GiambroneMichelle TanRosemary HartlineTyler Hale
Reed Lab Motto: When it comes to genes, our flies are always open.
Jordan BeveridgeBailey LoseRyan O’RourkeAbigail RuesyLauren HaynesMengting SueRachel CowanMaddison SharpCole KiserAnna Grace PriceCaroline HartAbigail MyersMcKenzie ChamberlainGrace KeirnSam HoffmanSean Shelley-TremblayJohn YordyInes MartinandJordan AlbriechtAnnie BacklundJade Miller
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