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HOMEWORK ASSIGNMENT #6
Part A: FIVE POINTS
RESEARCH ARTICLE READING:
Jacobssen JA, Schioth SB, and R Fredriksson. 2012. The impact of intronic single
nucleotide polymorphisms
and ethnic diversity for studies on the obesity gene FTO. Obesity Reviews
13:1096–1109.
1. What is meant by “exon”, “intron”, “FTO”, and “SNP”?
2. What are the functional roles of FTO with respect to adipose
tissue?
3. What is known regarding ethnic differences in FTO, i.e.
comparing Caucasian and
African populations?
4. What is the key result or outcome of this study that you
learned by reading this
paper?
Part B: FIVE POINTS
RESEARCH ARTICLE READING:
Parks BW et al. 2013. Genetic Control of Obesity and Gut Microbiota
Composition in Response to High-Fat, High-Sucrose Diet in Mice. Cell
Metabolism 17:141–152.
1. What were the primary objectives and hypotheses (stated or
implied) of this research?
2. What is the significance of monozygotic twins to obesity
research?
3. How did the researchers investigate gene-by-diet
interactions?
4. What did the authors discover with respect to the gut
microbiome in this study?
5. What is the key result or outcome of this study that you
learned by reading this paper?
Cell Metabolism
Resource
Genetic Control of Obesity and Gut Microbiota
Composition in Response to High-Fat,
High-Sucrose Diet in Mice
Brian W. Parks,1,* Elizabeth Nam,1 Elin Org,1 Emrah Kostem,2 Frode Norheim,1,5 Simon T. Hui,1 Calvin Pan,3
Mete Civelek,1 Christoph D. Rau,1 Brian J. Bennett,1,6 Margarete Mehrabian,1 Luke K. Ursell,7 Aiqing He,8
Lawrence W. Castellani,1 Bradley Zinker,9 Mark Kirby,9 Thomas A. Drake,4 Christian A. Drevon,5 Rob Knight,7,11
Peter Gargalovic,10 Todd Kirchgessner,10 Eleazar Eskin,2,3 and Aldons J. Lusis1,3,*
1Department
of Medicine/Division of Cardiology, David Geffen School of Medicine
of Computer Science
3Department of Human Genetics, David Geffen School of Medicine
4Department of Pathology and Laboratory Medicine, David Geffen School of Medicine
University of California, Los Angeles, Los Angeles, CA 90095, USA
5Department of Nutrition, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Norway
6Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
7Department of Chemistry and Biochemistry, University of Colorado Boulder, CO 80309, USA
8Department of Applied Genomics
9Department of Metabolic Diseases-Diabetes Drug Discovery
10Department of Atherosclerosis Drug Discovery
Bristol-Myers Squibb, Princeton, NJ 08543, USA
11Howard Hughes Medical Institute, Boulder, CO, 80309, USA
*Correspondence: bparks@mednet.ucla.edu (B.W.P.), jlusis@mednet.ucla.edu (A.J.L.)
http://dx.doi.org/10.1016/j.cmet.2012.12.007
2Department
SUMMARY
Obesity is a highly heritable disease driven by
complex interactions between genetic and environmental factors. Human genome-wide association
studies (GWAS) have identified a number of loci
contributing to obesity; however, a major limitation
of these studies is the inability to assess environmental interactions common to obesity. Using
a systems genetics approach, we measured obesity
traits, global gene expression, and gut microbiota
composition in response to a high-fat/high-sucrose
(HF/HS) diet of more than 100 inbred strains of
mice. Here we show that HF/HS feeding promotes
robust, strain-specific changes in obesity that are
not accounted for by food intake and provide
evidence for a genetically determined set point for
obesity. GWAS analysis identified 11 genome-wide
significant loci associated with obesity traits, several
of which overlap with loci identified in human studies.
We also show strong relationships between genotype and gut microbiota plasticity during HF/HS
feeding and identify gut microbial phylotypes associated with obesity.
INTRODUCTION
The dramatic increase in obesity during the past few decades is
tightly associated with the increase in obesity-related complica-
tions, such as type 2 diabetes, heart disease, and cancer.
Energy-rich diets containing high levels of fat and refined carbohydrates along with sedentary lifestyles are believed to be the
most significant environmental factors contributing to this
epidemic (Finucane et al., 2011; Malik et al., 2010). Understanding the genetic and environmental interactions contributing
to obesity is thus crucial for developing novel therapies and
preventive strategies. Human genome-wide association studies
(GWAS) and studies of rare monogenic forms of obesity, as well
as biochemical studies with cells and animal models, have identified relevant genes and pathways important in obesity;
however, given the complexity of the obesity phenotype and
the small amount of variance that can be explained by known
obesity alleles, it is clear that much remains to be discovered (Attie and Scherer, 2009; Bouchard et al., 1990; Knights et al., 2011;
Sandholt et al., 2010; Speakman et al., 2011).
Obesity is strongly heritable in humans, with estimates ranging
from 50% to 90% (Barsh et al., 2000; Stunkard et al., 1986).
Large human GWAS explain less than 3% of this heritable
component and environmental interactions with diet composition likely add significant complexity to such studies (Kilpeläinen
et al., 2011; Sonestedt et al., 2009; Speliotes et al., 2010).
Indeed, long-term overfeeding in monozygotic twins promotes
striking within-pair similarities in fat-mass gain, demonstrating
that gene-by-diet interactions may be highly heritable and have
a large impact on obesity (Bouchard et al., 1990). In addition to
host genetic contributions, the microbial community within the
gut has been shown to influence obesity in humans and mice
(Turnbaugh et al., 2006, 2009a). The gut microbiota is a transmissable trait and can undergo dynamic population shifts with
varied dietary composition (Benson et al., 2010; Turnbaugh
et al., 2009b; Yatsunenko et al., 2012). Obese subjects have an
Cell Metabolism 17, 141–152, January 8, 2013 ª2013 Elsevier Inc. 141
Cell Metabolism
Genetics of Dietary Responsiveness in Mice
altered gut microbiota compared to lean individuals, which may
be an important contributing factor to the obesity epidemic
(Turnbaugh et al., 2009a). To date, very little is known about
the genetic basis of gene-by-diet and gut microbiota-diet interactions to common obesogenic factors, such as the consumption of energy-rich diets.
Identification of genes contributing to complex traits, such as
obesity, in mice has been hampered by the poor mapping resolution of traditional genetic crosses (Bhatnagar et al., 2011; Burrage
et al., 2010; Dokmanovic-Chouinard et al., 2008; Ehrich et al.,
2005b; Flint et al., 2005; Lawson et al., 2011; York et al., 1999;
Zhang et al., 1994). Based on the genome sequencing of many
mouse strains, the identification of millions of single-nucleotide
polymorphisms (SNPs) (Keane et al., 2011), and the development
of an algorithm that corrects for population structure in association analysis (Kang et al., 2008), we recently developed a systems
genetics resource in the mouse capable of high-resolution
genome-wide association mapping (Bennett et al., 2010). This
resource, termed the hybrid mouse diversity panel (HMDP), is
composed of more than 100 commercially available mouse
strains and is ideal for systems-level analyses of gene-by-environment interactions. Association-based mapping approaches
in rodents have recently been reviewed (Flint and Eskin, 2012).
Employing a systems genetics approach in the mouse, we
integrated physical traits, molecular traits, and gut microbiota
composition data in response to an energy-rich diet. Using
GWAS rather than quantitative trait locus (QTL) analyses, we obtained biologically meaningful genetic mapping, such that
several of the genetic loci identified contained between one
and three genes, comparable to human GWAS. These genes
were prioritized with expression QTL (eQTL) analysis, and we
were able to show a significant overlap between mouse and
human GWAS loci. We measured the change in fat dynamically,
at five different points after high-fat/high-sucrose (HF/HS)
feeding, providing strong evidence for a genetically controlled
body fat set point. Our use of inbred mice strains also enabled
detailed analysis of the relationship between gut microbiota
composition, obesity traits, and diet. Overall, gene-by-diet interactions were highly reproducible and pervasive, providing
a partial explanation for the failure of human studies to explain
a larger fraction of the genetic basis of obesity. Our results indicate that mouse GWAS and systems genetics analyses provide
a powerful method to complement human studies and to
address factors, such as gene-by-diet interactions, that would
be difficult to study directly in humans.
RESULTS
Robust Variation in Gene-by-Diet Interactions
For assessment of gene-by-diet interactions common to
obesity, male mice were fed ad libitum a HF/HS diet that represents a typical fast food diet, in terms of fat and refined carbohydrates (32% kcal from fat and 25% kcal from sucrose). Mice
were maintained on a chow diet (6% kcal from fat) until 8 weeks
of age and subsequently placed on a HF/HS diet for 8 weeks.
Body fat percentage was assessed by magnetic resonance
imaging (MRI) every 2 weeks, and food intake was monitored
for a period of 1 week at the middle of the study timeline (study
schematic shown in Figure 1A). Altogether, about 100 inbred
strains of male mice were studied, with an average of six mice
of each strain (Table S1 available online).
A wide distribution in body fat percentage was observed in
male mice before HF/HS feeding (Figures 1B). Dietary responses,
as assessed by the body fat percentage increase during HF/HS
feeding, varied widely among the strains (Figures 1C and S1).
Although many strains exhibited a significant increase in body
fat percentage throughout the study timeline, their individual
responses differed significantly, from no change to a 200%
increase in body fat percentage within the first 2 weeks (Figures
1C). Most strains responded during the first 4 weeks of HF/HS
feeding and did not accumulate additional fat during the
remainder of the study, suggesting an upper set point whereby
continued body fat percentage growth is resisted (Figure 1C)
(Speakman et al., 2011). The large effect of the HF/HS feeding on
fat accumulation was confirmed with age-matched (16-weekold) male mice (Table S2) fed a chow diet, which displayed similar
body fat percentage to male mice before the HF/HS diet intervention (Figure S1A). Additionally, comparison of individual male
strains maintained on a chow diet or fed a HF/HS diet for 8 weeks
showed an average increase in body fat percentage from 0% to
more than 600% (Figure S1B).
We observed high heritability of about 80% for body fat
percentage across the study timeline (Table 1). Changes in
body fat percentage after HF/HS feeding were also highly heritable (>70%), suggesting that dietary responses are strongly
controlled by genetics. Our results are consistent with the heritability estimates for body mass index (BMI) and obesity in humans
(Barsh et al., 2000; Stunkard et al., 1986) and emphasize the
importance of genetics in controlling obesity traits, such as
gene-by-diet interactions.
Factors Contributing to Dietary Responsiveness
Overconsumption of high-calorie, energy-rich foods is a key
environmental factor contributing to the global obesity epidemic
(McCaffery et al., 2012). To understand the relationship between
food intake and obesity, we monitored food intake, and we found
it to range from 2–5 g per mouse per day. Total food intake per
day was significantly correlated with body weight and lean
mass (Figures 1D and 1E). In contrast, body fat percentage
and body fat percentage change after 4 weeks of HF/HS feeding
showed little to no correlation with food intake (Figures 1F and
1G). This suggests that factors outside of food intake largely
underlie the variation of obesity and fat mass gain between the
strains in response to HF/HS feeding.
To further define the contribution of energy consumption to the
differences in fat accumulation, we performed in vivo metabolic
chamber analyses of five inbred strains that are the progenitors
of the recombinant inbred strains (and therefore contribute
importantly to the overall genetic component) on chow diet.
Significant strain differences were observed in total food intake,
activity, heat production, and utilization of different energy
substrates, as indicated by respiratory exchange ratio (RER)
(Figures S2A–S2D), all of which can influence dietary responses
and subsequent fat accumulation.
GWAS and Systems Genetics Analysis
Association analysis was performed using about 100,000 informative SNPs, spaced throughout the genome, with efficient
142 Cell Metabolism 17, 141–152, January 8, 2013 ª2013 Elsevier Inc.
Cell Metabolism
Genetics of Dietary Responsiveness in Mice
A
B
C
D
E
F
G
Figure 1. Natural Variation in Gene-by-Diet Interactions
(A) Schematic of study design with indicated time points for HF/HS feeding (red), magnetic resonance imaging (MRI; blue), food intake monitoring (yellow), and
end of study (red).
(B) Body fat percentage in male mice (108 strains) before (red) and after (blue) 8 weeks of HF/HS feeding. Error bars (black) represent SEM.
(C) Biweekly percent body fat percentage increase in male mice with indicated body fat percentage increase after 8 weeks of HF/HS feeding.
(D–G) Correlation of food intake (grams/day/mouse) with body weight (D), lean mass (E), body fat percentage—4 weeks on HF/HS diet (F), and body fat
percentage growth—0 to 4 weeks (G), regression line (red). r, biweight midcorrelation; p, p value.
See also Figures S1 and S2 and Table S1.
mixed model association (EMMA) adjusting for population structure (Kang et al., 2008). The threshold for genome-wide significance was based on simulation and permutations, as previously
described (Farber et al., 2011). This approach has been validated
with transgenic analyses and by comparison with linkage
analysis (Bennett et al., 2010). Altogether, 11 genome-wide
significant loci were found to be associated with obesity traits
(Table 2). Loci averaged 500 kb to 2 Mb in size and in most cases
contained 1 to 20 genes within a linkage disequilibrium (LD)
block, an improvement of more than an order of magnitude as
compared to traditional linkage analysis in mice which has
a resolution of 10 to 20 Mb (Flint et al., 2005).
In order to help identify candidate genes at loci, we carried out
global expression analyses of epididymal adipose tissue in male
mice (16 weeks old) fed a chow diet to determine genetic regulation and correlation between gene expression and body fat
percentage. The loci controlling transcript levels in adipose
tissue were mapped with EMMA and are referred to as expression quantitative trait loci (eQTL). Loci are termed ‘‘cis’’ if the
locus maps within 1 Mb of the gene encoding the transcript
and ‘‘trans’’ if the locus is outside 1 Mb. Overall, 3,960 cis and
4,496 trans eQTL were identified to have a genome-wide significance threshold (cis threshold: p < 1.4 3 10 3 and trans
threshold: p < 6.1 3 10 6) (Figure S3A). Cis regulation indicates
a potential functional genomic variation within or near a gene that
significantly influences gene expression of a given gene. For
example, Fto, the most widely replicated gene in human
GWAS for obesity shows a strong cis eQTL in the adipose tissue
of mice (Figure S5D), indicating genetic variation of this gene.
Global gene expression in epididymal adipose tissue was
correlated with body fat percentage in chow fed mice (top 50
genes shown in Table S3). Many genes known to play a vital
role in adipose biology showed significant correlations with
body fat percentage. Leptin is a key adipose-derived hormone
correlating with adipose tissue mass (Ioffe et al., 1998) and is
strongly correlated (r = 0.75; p < 2.2 3 10 16) with body fat
percentage (Figure 2A). Other important adipose tissue genes,
such as Sfrp5 (Ouchi et al., 2010) (r = 0.76; p < 2.2 3 10 16),
Chrebp (Herman et al., 2012) (r = 0.57; p = 2.28 3 10 9), and
Tmem160 (r = 0.71; p = 4.21 3 10 16), a recently identified
Cell Metabolism 17, 141–152, January 8, 2013 ª2013 Elsevier Inc. 143
Cell Metabolism
Genetics of Dietary Responsiveness in Mice
Table 1. Heritability Estimates for Obesity and Dietary
Responsiveness
Trait
Heritability (%)
Body fat percentage —0 weeks on HF/HS diet
80
Body fat percentage —2 weeks on HF/HS diet
82
Body fat percentage —4 weeks on HF/HS diet
83
Body fat percentage —6 weeks on HF/HS diet
83
Body fat percentage —8 weeks on HF/HS diet
85
Body fat percentage growth—0 to 2 weeks
63
Body fat percentage growth—0 to 4 weeks
63
Body fat percentage growth—0 to 6 weeks
67
Body fat percentage growth—0 to 8 weeks
73
Body fat percentage growth calculated by quantifying the percentage
increase of body fat after beginning HF/HS diet. Heritability calculated
as described in the Experimental Procedures.
gene from a human GWAS for BMI (Speliotes et al., 2010), were
also found to be highly correlated with body fat percentage
(Figures 2B, S3B, and S3C).
Genetic Control of Obesity and Dietary Responsiveness
Most strains in the study showed a striking increase in body fat
percentage within the first 2 weeks of HF/HS feeding (Figure 1C). Association analysis with body fat percent increase
after 2 weeks of HF/HS feeding identified genome-wide significant loci on chromosomes 2 and 6 (Table 2 and Figure S4A).
The chromosome 2 locus (rs13476804; p = 2.95 3 10 6)
contains one gene within the LD block, Sptlc3, which has
been implicated in biogenesis of sphingolipids (Demirkan
et al., 2012; Hornemann et al., 2009). The locus on chromosome 6 contains 11 genes within LD and the peak SNP,
(rs13478690; p = 2.8 3 10 7) is 33.5 kb upstream of Klf14,
a primary candidate causal gene at this locus (Table 2 and Figure S4B). Klf14 has previously been identified in human GWAS
for type 2 diabetes (Voight et al., 2010) and has recently been
shown to be a master regulator of gene expression in adipose
tissue (Small et al., 2011). Our results support a role of Klf14 in
regulating changes in adipose tissue and indicate that Klf14
may also regulate dietary interactions.
Eight genome-wide significant loci were associated with
body fat percentage growth after 8 weeks of HF/HS feeding
(Table 2 and Figure 2C). The most significant signal
(rs31849980; p = 1.4 3 10 8) maps to chromosome 1 and
has genome-wide significant SNPs spanning a 5 Mb region
with 60 genes within LD (Table 2). A primary candidate gene
within this locus is Degs1, a fatty acid desaturase involved in
the metabolism of important bioactive sphingolipids (Ternes
et al., 2002). Degs1 expression in adipose tissue of chow fed
male mice is strongly correlated (within top ten genes) with
body fat percentage (r = 0.7; p = 1.3 3 10 15) (Figure 3E).
Previous linkage studies in mice have identified distal chromosome 1 as contributing importantly to obesity (Chen et al.,
2008) and our results greatly refine this region and suggest
Degs1 as a high-confidence candidate gene in the locus,
although given the size of the locus multiple genes may be
contributing to the signal.
Of the eight loci associated with body fat percent growth after
8 weeks of HF/HS feeding both loci on chromosomes 16 and 18
contained genes with genome-wide cis eQTL and strong expression correlation with body fat percentage in epididymal adipose
tissue (Figure 3C). The peak SNP at chromosome 18
(rs30078681; p = 4.3 3 10 8) contained 26 genes within LD,
and one gene, Npc1, was previously identified in a human
GWAS for obesity (Meyre et al., 2009). Gene expression analysis
of Npc1 indicated a strong negative correlation with body fat
percentage (r = 0.4; p = 1.5 3 10 5) (Figure S3D) and the presence of a genome-wide significant cis eQTL (Fig ...
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