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First published online April 20, 2007
Journal of Experimental Biology 210, 1613-1621 (2007)
Published by The Company of Biologists 2007
doi: 10.1242/jeb.005454
Review Article |
The biological importance of measuring individual variation
Rosenstiel School of Marine and Atmospheric Sciences, Marine Biology and Fisheries, 4600 Rickenbacker Causeway, Miami, FL 33149, USA
* Author for correspondence (e-mail: dcrawford{at}rsmas.miami.edu)
Accepted 6 March 2007
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Summary |
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Key words: functional genomics, cardiac metabolism, microarray, evolutionary selection, Fundulus heteroclitus, mRNA expression, gene expression, phenotypic variation
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Introduction |
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). There are good reasons to wonder about the utility of
quantifying the mRNA expression of specific genes, and most of these concerns
have to do with the multiple biochemical and physiological steps that modulate
gene expression other than at the level of the transcript (e.g. micro-RNAs,
translation, protein turnover, covalent modifications of enzymes and
protein–protein interactions). To better understand the validity of
transcriptomic data and whether mRNA expression underpins phenotypic
differences, we need to understand how gene expression varies within and among
taxa, whether the variation in mRNA co-varies with a specific phenotypic
character or biological process and whether these patterns of mRNA expression
are adaptively significant. All three are necessary for defining the
biological importance of gene expression because they each provide separate
information to confirm or reject the importance of gene expression. If mRNA expression regulates phenotypic variation, then we would expect the variation in gene expression to reflect the differences among populations and species. For example, one would expect that outbred, highly polymorphic species would have greater variance than inbred species. One would expect greater variance among taxa as the genetic distance is increased. This variation should also correlate with appropriate phenotypic variation. That is, with an increase in physiological performance, one would expect a change in the mRNA that affects this performance. Yet, and this is the crux of the problem, does the lack of correlation between mRNA and a physiological measure arise because one does not have the correct gene or because there are epistatic interactions, both of which make the patterns difficult to discern? Finally, if the pattern of mRNA variation is to be shown as adaptive, then it must affect a phenotype that is selectively (biologically) important.
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;
Cheung et al., 2003a;
Denver et al., 2005;
Gibson and Weir, 2005;
Jin et al., 2001;
Oleksiak et al., 2002;
Oleksiak et al., 2005;
Pritchard et al., 2001;
Schadt et al., 2003;
Townsend et al., 2003;
Whitehead and Crawford, 2005;
Whitehead and Crawford, 2006a;
Whitehead and Crawford,
2006b). Evolutionary analyses indicate that stabilizing selection
affects much of gene expression (Denver et
al., 2005; Lemos et al.,
2005). Stabilizing selection distinguishes between individuals
with phenotypes closer to the mean versus those that deviate from the
mean and selects against individuals that deviate. Thus, the observation that
stabilizing selection affects a majority of mRNA indicates that small changes
in gene expression have biological effects.
Although many genes have significant differences in expression among
individual F. heteroclitus within a population, the magnitude of
these differences is small, typically less than 1.5-fold
(Oleksiak et al., 2002;
Oleksiak et al., 2005;
Whitehead and Crawford, 2005;
Whitehead and Crawford, 2006a;
Whitehead and Crawford,
2006b). For Fundulus, it is the many small changes in
metabolic gene expression that together appear to be responsible for the
phenotypic variation in cardiac metabolism
(Oleksiak et al., 2005).
Additionally, although stabilizing selection eliminates many mutations
affecting gene expression because this variation is slightly deleterious,
there is significant additive genetic variation affecting gene expression that
can be the source of adaptive change. In Drosophila simulans
(Wayne et al., 2004), the
additive heritable variation is distributed among chromosomes, with much of
the effect acting in trans (Wayne
et al., 2004). Among 14 large human families, approximately 29% of
genes have significant additive variation due to both cis and
trans loci (Morley et al.,
2004). Among mice, 19% of all loci have significant additive
variation (Cui et al., 2006).
Among Drosophila (Nuzhdin et al.,
2004) and primates (Caceres,
2003; Enard, 2002;
Gilad et al., 2005;
Gilad et al., 2006;
Khaitovich, 2004a;
Khaitovich, 2004b) much of
this additive genetic variation for expression is evolving by natural
selection.
In Fundulus, by applying evolutionary analyses to natural
populations that have experienced the effects of selection, we were able to
document patterns of expression affected by directional, stabilizing and
balancing selection (Crawford et al.,
1999a; Crawford et al.,
1999b; Oleksiak et al.,
2002; Pierce and Crawford,
1997a; Whitehead and Crawford,
2006a; Whitehead and Crawford,
2006b). Our data on Fundulus, as well as other
investigators' data on Caenorhabditis elegans
(Denver et al., 2005),
Drosophila (Lemos et al.,
2005; Nuzhdin et al.,
2004) and humans (Caceres,
2003; Enard, 2002;
Gilad et al., 2006;
Gilad et al., 2005;
Khaitovich, 2004a;
Khaitovich, 2004b), suggest
that the variation in gene expression is selectively important; thus, this
variation is biologically important. This is only one, albeit an important,
criterion for establishing the importance of gene expression. It is also
important to define the heritability of gene expression and how it relates to
important phenotypic differences.
Genetics of gene expression
Much of gene expression measured by microarrays is genetic; it differs
between inbred lines, is associated with quantitative trait loci (QTLs) and
has narrow sense heritability (h2) greater than 30%
[narrow sense heritability is due only to the additive genetic variation
(Va), or
h2=Va/Vp, where
Vp is phenotypic variation]
(Cheung et al., 2003b;
Gibson and Weir, 2005;
Sharma et al., 2005;
Tan et al., 2005). Among F1
generations from two inbred mice strains, approximately two-thirds of all loci
have measurable h2 with a quarter having an
h2 of >50% (Cui et
al., 2006). In both humans and mice, the median
h2 is 34% among loci with measurable
h2 (Cui et al.,
2006). The variation in regulatory processes affecting gene
expression has been inferred by combining microarray and QTL studies
[expressed QTLs (eQTL)]. These studies identify both cis- and
trans-acting loci that are related to differences in gene expression
in Drosophila (Wang et al.,
2004; Wayne and McIntyre,
2002), yeast (Brem and
Kruglyak, 2005; Brem et al.,
2005; Ronald et al.,
2005; Yvert et al.,
2003), mice (Chesler et al.,
2005; Doss et al.,
2005; Ghazalpour et al.,
2005; Schadt et al.,
2003) and humans (Monks et
al., 2004; Morley et al.,
2004; Schadt et al.,
2005). In general, 20–30% of differential expressions are
due to a cis-eQTL (Doss et al.,
2005; Ronald et al.,
2005). Yet with more powerful analyses, gene expression becomes
more complex, involving many loci with a few loci that affect the expression
of many genes (Brem and Kruglyak,
2005; Brem et al.,
2005; Gibson and Weir,
2005; Schadt et al.,
2005; Stamatoyannopoulos,
2004). These data suggest a complex regulation of gene expression
in which polymorphisms among several loci affect the variation in gene
expression of a particular gene. It is important to realize that heritability
in gene expression indicates that gene expression is stable between
generations. This stability suggests that random biological variation or
`noise' is not the principal cause of variation in gene expression.
Although much progress has been made in understanding the variation in gene expression, we are unsure of its importance in affecting phenotypic variation. It is the phenotypic variation on which natural selection acts that defines human populations and humans' susceptibility to disease, drugs and stress, and thus is of scientific importance. To understand the importance of gene expression and its effect on phenotypic variation, more attention needs to be paid to the variation among individuals and whether there is variation in which genes are `important' in effecting a change in phenotype. We present here a summary of microarray data from Fundulus that supports our contention that mRNA expression affects physiological performance and is thus evolutionarily important.
Cardiac performance
Cardiac metabolism in Fundulus is measured using isolated heart
ventricles (Oleksiak et al.,
2005; Podrabsky et al.,
2000), and individual determinations were made by alternating
between populations. Metabolic rates are measured in triplicate by determining
oxygen consumption in a well-mixed chamber for each of three different
substrates: 5 mmol l–1 glucose, 1 mmol l–1
fatty acid (FA; palmitic acid–bovine serum albumin) or LKA [5 mmol
l–1 lactate, ketones (5 mmol l–1 each
hydroxybutyrate and acetoacetate) and 0.1% ethanol]. Two inhibitors of glucose
metabolism (20 mmol l–1 2-deoxy-glucose and 10 mmol
l–1 iodoacetic acid) were used when measuring FA and LKA
metabolism. The addition of inhibitors reduces metabolism to less than 15% of
the rate with glucose only. Adding FA or LKA caused a significant increase in
metabolism compared with metabolic rates of hearts with inhibitors and
glucose, indicating that much of glucose metabolism was inhibited when using
both inhibitors. Although determination of metabolism for all three substrates
took approximately 15 min, isolated heart ventricles from Fundulus
are able to maintain stable metabolic rates for greater than 45 min. All
metabolic rates from isolated heart ventricles were a function of body mass,
which is highly correlated (r=0.7–0.85) with heart mass (see
below). These data indicate that our measures of metabolism are substrate
dependent.
Individual ventricles were splayed open for all metabolic measures.
Fundulus hearts, like those of most small fish, lack coronary
circulation, and thus oxygen is supplied by diffusion from the internal blood
flow (Farrell, 1993).
Dissecting or splaying open the heart provided greater access and more uniform
interfaces for the Ringer solution and the internal surfaces of the heart.
When measuring oxygen consumption in isolated ventricles, we were primarily concerned with the variation between individuals that can arise from genetic, developmental or physiological mechanisms (e.g. due to acclimation to different temperatures). All individuals were acclimated to a common laboratory environment (temperature, salinity, light:dark cycle, feeding regime, etc.) and were assayed at similar times of day. Despite minimizing physiological sources of variation, there can be other biological differences. For example, a fish could be sick or stressed due to social interactions or `unhappy' for unknown reasons. These sources of variation are difficult to ascertain, and their importance remains unknown. Technical variation can arise due to poor heart preparation, electrical interference with the oxygen electrode, poor mixing of Ringer solution or poor maintenance of the oxygen electrode. Multiple determinations provide some measure of consistency and thus estimates technical variance. However, because a single heart cannot be measured on separate days, the replicate measures are dependent on the status of the electrode, heart preparation and electronic noise during the 15–20 min measurement period. One can measure `day effects' among individuals, and this was not significant (P>0.5). That is, among the 3 days that F. heteroclitus cardiac ventricles were measured, hearts had approximately the same mean. However, because there is much variation among individuals it would be difficult to detect a day effect. Thus, the lack of day effect does not eliminate this as a source of variation but only makes it less probable.
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Among the 16 individuals, there were 4.9-, 15.9- and 4.8-fold differences between the highest and lowest metabolic rates for glucose, FA and LKA, respectively. These individuals differed in body mass, and body mass affected all three measures of metabolism (Fig. 1). Thus, this range of metabolism was due both to differences in body mass and to individual variation. The effect of body mass was removed by using the residual from the log regression, resulting in 2.0-, 11.2- and 2.5-fold differences between the highest and lowest metabolic rates for glucose, FA and LKA, respectively. With the caveats described above concerning technical variation, replicate measures enabled the statistical testing of inter-individual differences in substrate-specific metabolism among 16 individuals: P<0.0001, P<0.005 and P<0.02 for glucose-, FA- and LKA-dependent metabolism, respectively (Fig. 2). These data suggest that there was significant variation in metabolism among individuals. These differences also exist if one compares just the eight individuals within the Maine population (P<0.05 for all three measures) and glucose-specific metabolism in the Georgia population (P<0.005). Fig. 2 illustrates these differences, but more importantly is the variation in the relative use of each substrate within an individual (Fig. 2B). For most individuals, metabolism fuelled by FA was greater than with either glucose or LKA (Fig. 2). For example, for individual ME06, FA metabolism was more than 10-fold greater than the other two substrates. Yet, in other individuals (ME10 and ME03), glucose metabolism was greater than FA metabolism. These measures of substrate-specific metabolism within an individual are unlikely to be due to technical variation because they are measured within a short time (less than 20 min) on the same heart preparation.
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Fundulus gene expression and metabolism
For F. heteroclitus, we have demonstrated that gene expression
explained the variation in cardiac metabolism among 16 male individuals from
natural outbred populations raised in a common environment
(Fig. 3)
(r2=0.65–0.81)
(Oleksiak et al., 2005). But
the relationship was complex, a vast majority of genes were different between
individuals and these expression patterns cluster the 16 individuals into
three groups. It is within these three groups that one can show an association
between gene expression and metabolic rates.
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). Using a very conservative multiple correction (Bonferroni's
or Fmax permutations), 84% were significantly different.
These differences were not due to one or a few individuals: all possible
permutations of six out of eight individuals within each population had on
average 78.9% of genes that were significantly different among individuals
(P<0.01). This high frequency of differences was among male
individuals raised in a common environment and was unrelated to body mass or
any other obvious physiological or experimental condition
(Oleksiak et al., 2005). What
is apparent is that the three groups of individuals share different patterns
of expression (Oleksiak et al.,
2005).
Among the 16 individuals examined, shared gene expression patterns cluster the 16 individuals into three groups of 5, 5 and 6 individuals. However, there was no difference in the mean or variation in substrate-specific metabolism among these three groups (P>0.4 for glucose and FA metabolism, and P>0.05 for LKA metabolisms) (Fig. 2). Yet, gene expression differences among the three groups were statistically robust. Differences in gene expression within each of the three groups were less than those found within a single population (62–73% vs 94% of genes are statistically significant) and were significantly less (P<0.01) than those found in the 2052 random combinations of five individuals (average 85%). The number of genes that were significantly different among the three groups (50 genes) was considerably more than the 12 genes that were different between populations and significantly greater than the average of one significant difference found among three random groups formed by 1000 random permutations. These data indicate that the three groups were functionally distinctive and the differences were robust.
To explore the relationship between metabolism and gene expression within the three groups, the variation in gene expression was reduced to the principal components (PCs) for glycolytic, tricarboxylic acid (TCA) and oxidative-phosphorylation metabolic pathways (Table 1) (see Table S1 in supplementary material for a list of genes). A PC is a linear equation that sums the measures of gene expression where each gene is multiplied by a coefficient. The weight of the coefficient is chosen to maximize the explained variation among individuals without, in this case, reference to metabolism. For example, the first PC for glucose-related enzymes explains 54% of the variation among these 15 genes. Additionally, there were both strong positive and negative weighting factors [e.g. glyceraldehyde 3-phosphate dehydrogenase (GAPDH) expression is multiplied by –0.63, and aldehyde dehydrogenase (ALDH) by 0.37] (Table 1).
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These PCs summarize the metabolic pathway-dependent RNA expression and statistically explain significant proportions of the variation in cardiac metabolism, but only within the three defined groups of fish (Fig. 3). For glucose metabolism, 81% of the variation in group 1 individuals was explained by changes in expression of genes involved in glucose. However, in groups 2 and 3, these glycolytic genes had little power to explain the differences among individual metabolic rates. Instead, genes of the oxidative-phosphorylation pathway explained the variation in glucose-specific metabolic rates. Similarly, gene expression in different pathways explained FA- and LKA-specific metabolism. These patterns, where gene expression from a pathway explains substrate-specific metabolism, only occur if one examines these groups of individuals. Permutation analyses indicate that few other random sets of five individuals share a common relationship between gene expression and phenotype. There is no relationship of one or more genes to metabolism among all 16 individuals. Nor do any of the PCs explain metabolism among all 16 individuals.
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Correlated patterns of gene expression
Among the PCs, some genes have greater importance, as indicated by their
larger coefficients. These coefficients are both negative and positive
(Table 1). These coefficients
reflect the strong correlation in expression among metabolic genes within and
between pathways. In Fig. 4,
the correlations between two genes among the 16 individuals are displayed as
green and blue boxes in an all-against-all matrix. A significant positive
correlation (a green cell) indicates that the expression for these two genes
would be similar among all individuals (i.e. both would be high or low in the
same individuals). Surprisingly, the expressions of most metabolic genes
(Fig. 4) are either negatively
or positively correlated. For example, an individual with a high expression of
phosphofructokinase (PFK) also has high expression of
GAPDH but low expression for phosphoglucoisomerase B
(PGI-B) (Fig. 4C).
These correlation patterns repeat themselves in each of the three pathways
(Fig. 4B–D). In the
linear pathway of glycolysis, an increase in aldolase, Ldh-B and
enolase among 16 individuals is matched with a decrease in PFK, GAPDH
and pyruvate kinase. These enzymes are interspersed along the
pathway. What is more difficult to see are the correlations among proteins
that form enzyme complexes or pathways. In the first enzyme complex of
oxidative phosphorylation, NADH dehydrogenase, the subunits have significant
positive or negative correlations for expression with each other (correlations
are significant for 11 of the 20 NADH subunits). This pattern is repeated for
complex 4 (cytochrome c oxidase) and complex 5 (ATP synthetase).
These subunits have to form at a stoichiometric ratio for each enzyme, yet
they have negative correlations for expression! Although these opposite
patterns of gene expression present a biochemical conundrum, an obvious
molecular reason is that many of these enzymes share common transcription
factors.
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Discussion |
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). The strength of this study and our other cardiac microarray
studies (Oleksiak et al.,
2002; Oleksiak et al.,
2005; Whitehead and Crawford,
2005; Whitehead and Crawford,
2006a; Whitehead and Crawford,
2006b) is that they used an outbred vertebrate population (such as
humans) where the variation among individuals was substantial enough to
statistically define meaningful patterns. The weakness of these studies is
that they used outbred populations, which left unresolved the genetic
contributions to the variation in gene expression that explain cardiac
metabolism. For the studies provided here, only males were used, and these
individuals were acclimated to the same environment. Thus, physiologically
induced differences due to temperature, hypoxia or any other environmental
parameter do not explain the observed differences.
Our conclusions that mRNA expression was statistically related to cardiac
performance, that it is evolving by natural selection and that it varies in a
predictable manner depending on genetic distance
(Oleksiak et al., 2002;
Oleksiak et al., 2005;
Whitehead and Crawford, 2006a;
Whitehead and Crawford, 2006b)
suggest that microarray experiments can be meaningful. Thus, in contrast to
Feder (Feder and Walser,
2005), we argue that there is much utility in measuring
genome-wide patterns of gene expression. We suggest that these microarrays
present patterns of mRNA expression that are both informative and provide
unexpected relationships. However, they are not simple and thus are subject to
misinterpretation.
Individual variation
We present several observations concerning individual variation: (1) there
are significant differences in cardiac metabolism among individuals; (2) there
are large differences in which substrate is preferentially used, (3) a vast
majority of genes have significant differences in expression and (4) the genes
that statistically explain the variation in substrate utilization differ among
individuals. Our ability to explain these patterns depends on the delineation
of three distinct groups among the 16 individuals, each group exhibiting a
distinctive but consistent pattern of gene expression. The biological
importance of these three groups is related to the correlated patterns of gene
expression among all 16 individuals (Fig.
4). Most genes were positively or negatively correlated. Thus,
individuals with high expression of genes `A' to `E' also had low expression
of genes `W' to `Z', and the opposite pattern happened for other individuals
(high expression of W–Z and low expression of A–E). Thus, the
groups (especially groups 2 and 3) represent individuals with opposite
patterns of gene expression. The surprising observation is that these patterns
occur within pathways. Thus, individuals in any one group share patterns of
expression within a pathway that are different (opposite) from those in other
groups. For example, among individuals in group 2, relative to group 3, there
was greater expression of GAPDH and PFK but lower expression
of aldolase, aldehyde dehydrogenase, pyruvate kinase and
PGI.
What is the meaning of these large differences among individuals?
There were significant differences among individuals in (1) metabolism, (2)
which metabolic substrate was preferentially used, (3) the pattern of
metabolic gene expression and, consequently, (4) the relationship between gene
expression and metabolism. The greater metabolic rate among some individuals
was due to a greater utilization of glucose whilst in others it was the
utilization of FA. The variation in specific substrates was explained by the
expression of different metabolic pathways, but these explanations differ
among groups of individuals. For example, fatty acid utilization can be
explained by changes in oxidative, TCA or glycolytic enzyme expression
depending on which group of individuals is examined.
These data have several implications. The first is that to examine one or a few inbred individuals could lead to misleading conclusions. Imagine having an inbred line from one F. heteroclitus individual. Cardiac metabolism in this imaginary strain could be primarily dependent on glucose and not on fatty acid utilization. Thus, one would conclude that glucose was the primary energy source for the heart. Yet, this is only correct for a subset of individuals. Similarly, the investigation of fatty acid utilization in cardiac tissue using one individual would reveal that gene expression in glycolytic enzymes regulates FA utilization as opposed to the TCA pathway, which would be found using another individual. Neither conclusion is incorrect, just misleading because it makes conclusions based on too few individuals.
The second implication from these data is that many genes or different pathways can affect substrate-specific metabolism. It is not surprising that the expression of glycolytic enzymes explains glucose metabolism (Fig. 3, group 1). However, the observation that the relative expression of genes in the oxidative phosphorylation pathway explained glucose metabolism among some individuals is somewhat surprising. More surprising was that glycolytic enzyme expression explained 75% of the variation in fatty acid utilization in group 3 individuals (Fig. 3). If these data are correct, it suggests that the activity of one pathway affects the flux through other pathways. Thus, we cannot measure one or a few enzymes and expect it to always explain metabolic variation among many different individuals.
The third implication is that it is unlikely that one gene, or a set of
genes, is responsible for the phenotypic variation among all individuals. For
example, if cardiac metabolism is related to health or fitness, then which
genes affect the health of an individual is dependent on the status of other
genes and pathways, an outcome that is entirely consistent with studies of
metabolic epistasis, where metabolism is dependent on variation at other loci
(Clark and Wang, 1997;
Segrè et al., 2005).
Thus, we should not expect a `magic bullet' that will cure everyone of a
specific disease. Instead, there will need to be different cures for different
individuals, supporting the concept of personalized medicine that is currently
driving many pharmaceutical research programs
(Nadeau and Topol, 2006).
Finally, the lack of correlation between gene expression and phenotype is not necessarily due to the lack of importance of gene expression. Instead, if the importance of gene expression is context dependent then a significant relationship will only be discernable from within a specific context. Thus, the inability to accept an alternative hypothesis that there is a relationship between gene expression and phenotype does not support the null hypothesis. Instead, one has failed to address the proper hypothesis.
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Footnotes |
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Supplementary material available online at http://jeb.biologists.org/cgi/content/full/210/9/1613/DC2
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