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Endocrine Reviews 23(3):369 –381
Copyright © 2002 by The Endocrine Society
Hormonal Genomics
CHANDRA P. LEO, SHEAU YU HSU,
AND
AARON J. W. HSUEH
Division of Reproductive Biology, Department of Gynecology and Obstetrics, Stanford University School of Medicine,
Stanford, California 94305-5317
The availability of the human genomic sequence is changing
the way in which biological questions are addressed. Based on
the prediction of genes from nucleotide sequences, homologies among their encoded amino acids can be analyzed and
used to place them in distinct families. This serves as a first
step in building hypotheses for testing the structural and
functional properties of previously uncharacterized paralogous genes. As genomic information from more organisms
becomes available, these hypotheses can be refined through
comparative genomics and phylogenetic studies. Instead of
the traditional single-gene approach in endocrine research,
we are beginning to gain an understanding of entire mammalian genomes, thus providing the basis to reveal subfami-
lies and pathways for genes involved in ligand signaling. The
present review provides selective examples of postgenomic
approaches in the analysis of novel genes involved in hormonal signaling and their chromosomal locations, polymorphisms, splicing variants, differential expression, and physiological function. In the postgenomic era, scientists will be
able to move from a gene-by-gene approach to a reconstructionistic one by reading the encyclopedia of life from a global
perspective. Eventually, a community-based approach will
yield new insights into the complexity of intercellular communications, thereby offering us an understanding of hormonal physiology and pathophysiology. (Endocrine Reviews
23: 369 –381, 2002)
I.
II.
III.
IV.
V.
VI.
VII.
VIII.
IX.
needed for all functional assignments, it is becoming clear
that the emerging global perspective of all genes, as well as
the pathways and networks in which they are arranged, will
provide insights of a quality that was formerly unattainable
using the previous local view of a limited number of genes.
In the postgenomic era, the availability of all human gene
sequences allows us to study transcription profiles of genes
for specific cell types and developmental stages in response
to hormonal signaling. The introduction of new tools for the
parallel analysis of all gene transcripts and, in the future, the
expression of all proteins heralds the dawn of the new fields
of transcriptomics and proteomics, respectively.
A major impact of hormonal genomics that is evident
today is the method used for identifying new ligands and
receptors. In the classic endocrine approach, we first defined
a biological endpoint (such as body growth induced by pituitary extracts in hypophysectomized animals) to allow the
isolation of the potential ligand (in this case, GH) based on
bioassays. Only after the cloning of ligand genes could cognate receptors be identified. Theoretically, in the postgenomic era, all receptors could be predicted based on their
unique sequence features (e.g., the seven-transmembrane
stretches of hydrophobic amino acids characteristic for G
protein-coupled plasma membrane receptors). This reverse
endocrinology approach starts with orphan receptors to
eventually identify cognate ligands and physiological functions. Taking advantage of the sequence relatedness among
family genes, we can predict the functions of novel uncharacterized hormonal ligands, receptors, and intracellular signaling molecules.
Another important conceptual change for traditional endocrinologists is the realization that the global perspective of
biological systems will continue to blur established boundaries currently separating endocrinology, growth factor research, immunology, extracellular matrix research, and de-
Introduction
Gene Identification
Sequence Homology and Phylogenetic Relationships
Chromosomal Location of Genes
Gene Polymorphism
Alternative Splicing
Identification of the Regulatory Elements of Genes
mRNA Expression Profiling
The Postgenomic Challenges in Hormonal Research
I. Introduction
T
HE SEQUENCING OF the human genome has triggered
a revolution encompassing all fields of biomedicine (1,
2). Whereas traditional methods in genetics and molecular
biology focused on the characterization of individual genes
and their products, the new genomic approach takes advantage of knowledge concerning the totality of genes in an
organism. In the area of hormonal genomics, the functional
subgenomes of secreted extracellular signaling molecules
(peptide and protein ligands), transmembrane receptors, intracellular signaling molecules, and transcriptional factors
(including steroid receptors and related genes) can now be
analyzed in an integrated manner. With a global perspective
on the limited “parts list” of our body, we are able to predict
the structure of genes based on their sequence relationships,
to understand the functional redundancy of genes based on
their common position in gene networks, and to trace the
evolutionary roots of gene families based on comparative
genomic analyses of sequence conservation and chromosomal gene order. Although experimental evidence is
Abbreviations: CCR, C-C chemokine receptor; EST, expressed sequence tag; GDF, growth differentiation factor; LGR, leucine-rich repeatcontaining G protein-coupled receptor; ORF, open reading frame; SAGE,
serial analysis of gene expression.
369
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Endocrine Reviews, June 2002, 23(3):369 –381
velopmental biology. With the availability of a handful of
animal genomes for comparison, we can trace the evolutionary roots of all human endocrine genes. Also, increasing
evidence indicates that the endocrine mechanism is merely
a special case of long-range intercellular communication
mechanisms that developed during the course of evolution.
Indeed, one can find many cases in which endocrine hormones and receptors have paracrine roots; for example, homologs for insulin-signaling genes or glycoprotein hormone
receptors are already present in nematodes that are lacking
an enclosed circulatory system (3, 4).
Over the past decade, whole-genome sequencing projects
for numerous organisms have been completed, beginning
with viruses and prokaryotes such as Escherichia coli (5) and
continuing with the eukaryotic model organism’s baker’s
yeast, Saccharomyces cerevisiae (6), the nematode Caenorhabditis elegans (7), and the fruit fly Drosophila melanogaster (8).
Recently, working draft versions of the human genome have
been published (1, 2). Moreover, a private draft sequence of
the mouse genome (Mus musculus) and public draft sequences for two species of puffer fish (Fugu rubripes and
Tetraodon nigroviridis) have been completed. Additional sequencing projects for rat (Rattus norvegicus), zebra fish (Danio
rerio), and a number of other organisms are in progress.
Given the explosive growth of the genomic literature, we
have deliberately chosen to limit the scope of the present
review, highlighting basic principles in genomics and providing selected examples of their application to the field of
hormonal research. In contrast, the evolving field of hormonal proteomics and the impact of genomics on clinical
endocrinology will not be covered here.
II. Gene Identification
In small microbial genomes, the identification of genes is
relatively straightforward, as the gene-coding regions are not
interrupted by introns. However, as organisms and their
genomes increase in complexity, the task of gene finding
becomes more difficult. One main reason for the difficulty is
that the signal-to-noise ratio—and thus the accuracy of gene
predictions— drops as the proportion of noncoding DNA
increases. Whereas in prokaryotes, open reading frames
(ORFs) that could encode a stretch of uninterrupted amino
acids represent approximately 85% of the whole genome, this
number decreases to around 70% in yeast and to less than
25% in the worm and fly (9). The human genome is estimated
to have a total size of about 3.2 gigabases (i.e., 3.2 ⫻ 109 bp)
of DNA, of which less than 1.5% are thought to encode
proteins (10). Gene prediction in the human genome is further complicated by the presence of comparatively long and
variable intron sequences (1).
The transcription units, consisting of exons, introns, and
the regulatory region, are thought to constitute more than
20% of the entire human genome. To increase the sensitivity
and specificity of gene predictions in the draft sequence of
the human genome, both public and private groups are using
a combination of approaches based on: 1) identification of
matching mRNA-derived sequences, 2) searches for similarity to previously known genes and proteins, and 3) prediction based on features common to all genes.
Leo et al. • Hormonal Genomics
The first strategy relies on identifying regions of similarity
between genomic sequences and sequences that are known
to be transcribed, such as expressed sequence tags (ESTs)
derived from fragments of cDNA, and full-length cDNA
sequences (11). Even though this approach is based on direct
experimental evidence, it is still subject to errors resulting
from spurious EST data, e.g., from unspliced mRNAs, or
genomic DNA contamination. More importantly, genes expressed at low levels or those transcribed selectively in tissues or cell types underrepresented in EST databases may be
overlooked. Likewise, single-exon genes encoding small proteins may not be detected because the ESTs representing
them are sometimes indistinguishable from genomic DNA
contamination (12).
The second strategy identifies similarities between the
genomic sequence investigated and known gene or protein
sequences in human or other species. Although this approach can correctly predict genes belonging to larger gene
families or having orthologs in different species, it is not
capable of identifying genes having no sequence similarity to
known genes and is prone to picking up pseudogenes. Moreover, it is difficult to detect homologs of genes with small
ORFs because sequence similarities over short stretches may
not achieve the necessary level of statistical significance (12).
The third strategy, ab initio gene prediction, uses software
algorithms combining statistical information on splicing
sites, coding bias, and exon/intron lengths to detect putative
exons and genes (13). In a recent study involving the fruit fly
genome, such algorithms correctly predicted all exons of a
gene in about 40% of the cases, but entirely missed 5–10% of
the known genes (14). Due to the limitations discussed above,
the application of ab initio gene prediction methods to the
human genome is expected to yield an even lower sensitivity
and specificity (15).
Combining all three strategies, the total number of human
genes was estimated to be between 30,000 and 40,000. It is
important to note, however, that we are still some way from
having a complete human gene index. Indeed, initially, a
complete high-stringency alignment with the draft sequence
could only be achieved for half of the more than 10,000
known human genes stored in the curated RefSeq database
(1). This discrepancy is due to the remaining errors and gaps
in the draft sequence that are being addressed during the
ongoing process.
With the completion of the sequencing of several vertebrate genomes, analytical approaches to identify functionally
similar homologous genes have become feasible. The best
examples for hormonal research are the discoveries of more
than 400 G protein-coupled receptors in the human genome
with a potential hormonal ligand (16), and of diverse ligands
belonging to the cytokine (17), IL (18, 19), and cysteine-knot
gene families. New ligand/receptor systems have also been
discovered with the aid of genomic information. For example, a family of more than 10 Toll-like receptors has been
identified (20), and the crucial roles of these receptors in the
recognition of microbial components and innate immunity
have been elucidated (21).
As discussed above, the task of gene identification is not
trivial. Moreover, multiple gene messages can be derived
from a single stretch of DNA based on alternative uses of
Leo et al. • Hormonal Genomics
promoters, exons, and termination sites. Adding to these
overlapping transcription units, somatic recombination
events such as those found in some of the immune recognition loci, and the existence of highly similar gene families
and pseudogenes, render it difficult to clearly identify and
categorize the genes. Based on the classic definition that
genes are distinct transcription units that are translated to
generate one or a set of related proteins, many “genes” in the
genome are pseudogenes. The human genome is estimated
to encode approximately 900 olfactory receptor genes; however, 60% of them appear to be pseudogenes with disrupted
ORFs (22, 23). Likewise, the entire mouse olfactory receptor
gene repertoire comprising about 1300 genes may include
20% pseudogenes (24). Because not all genes with disruptions are pseudogenes, due to the possible splicing out of
regions with a termination codon, the exact number of functional genes remains to be determined using more advanced
experimental procedures.
Comparative genomic analyses of gene families across
different species can provide insights into the evolution and
associated adaptation of specific hormonal regulatory circuits among closely related species. For example, multiple
GnRHs and GnRH receptors have been identified in vertebrates from teleosts to primates (25); however, humans appear to have only one functional GnRH receptor due to the
apparent degeneration of the type II GnRH receptor gene
into a pseudogene (26 –28). Because the ortholog for the human type II GnRH receptor is functional in nonhuman primates (29), future investigation on the role of two types of
GnRH receptors in primates will have to address the evolutionary adaptation of these GnRH signaling systems.
III. Sequence Homology and Phylogenetic
Relationships
Although the human genome has been sequenced, only a
small fraction of its genes have been studied experimentally
to reveal their physiological roles. Consequently, a major
challenge for functional genomics is to predict and then
elucidate the function of all genes in the human body. For
hormonal genomics, we can take advantage of the unique
sequence features of known ligands, receptors, and intracellular signaling molecules. Using sequence relatedness as a
criterion, one can define subgenomes encompassing specific
groups of ligand-signaling genes. Although we do not have
knowledge of all hormones and receptors in the human genome, most of the encoded G protein-coupled receptors have
been identified. Likewise, attempts were made to predict all
cystine knot-containing ligands and related extracellular signaling molecules among the human ORFs by searching for
their unique pattern of cysteine signatures (30). Although a
large number of secreted polypeptide hormones can potentially be identified based on the presence of a signal peptide
for secretion and the lack of transmembrane regions, this
group of predicted genes also includes adhesion molecules,
secreted enzymes, and other uncharacterized extracellular
proteins.
Despite the lack of a convenient global approach to understand genes involved in ligand signaling, one can define
Endocrine Reviews, June 2002, 23(3):369 –381
371
subgroups of these genes based on their evolutionary relationships. Two types of relationships between genes deserve
specific attention. The first type is the orthologous gene that
did not significantly change in either structure or function
during speciation. For instance, it has been estimated that
40% and 60% of human genes responsible for genetic diseases have orthologs in yeast and fly, respectively. The second group of human genes derived as the result of geneduplication events during evolution are called paralogs.
Although these genes belong to the same family and usually
have similar structural motifs, they can serve related but
nonidentical physiological functions. It has been hypothesized that during early chordate evolution, before the Cambrian explosion and during the early Devonian period, two
entire genome duplications took place (31, 32), leading to the
generation of sets of multiple mammalian paralogs, concomitant with the opportunity to develop complex regulatory
mechanisms and functions. Thus, most human genes belong
to a family as the result of gene duplication and domain
shuffling. These evolutionary mechanisms allow for the amplification and diversification of existing domains, the recruitment of existing domains for new functions, and the
development of new domains through domain combination
and shuffling.
Comparative genomic analysis reveals the evolution of
polypeptide ligands, receptors, and intracellular signaling
molecules. Only four orthologs of human insulin-like genes
have been found in D. melanogaster, whereas C. elegans possesses a large number of insulin gene homologs, including
one that was shown to determine the life span of the worm
(33). In contrast, three insulin paralogs are present in the
human genome, including insulin controlling carbohydrate
metabolism, and IGF-I and IGF-II regulating organ and body
growth. A related group of hormonal ligands that also consist of B and A subunits connected by a long-C-domain
peptide is constituted by the relaxin-related proteins. The
human paralogs of both insulin and relaxin all have highly
conserved cysteine residues essential for maintaining a similar secondary structure. In the relaxin group, at least seven
human paralogs have been identified, including three relaxin
genes, Leydig cell relaxin (INSL3), INSL4, INSL5/RIF2, and
INSL6/RIF1 involved in reproductive tract maintenance and
other uncharacterized functions (34, 35).
Genomic studies on the presence of genes with sequence
similarities to the human gonadotropin and TSH receptors
also enhanced our understanding of their evolution. All of
these genes encode proteins with a large N-terminal extracellular region important for ligand binding followed by a
seven-transmembrane region known to be essential for G
protein coupling. Based on the evolutionary conservation of
these genes, five orphan receptors were identified in humans
and named LGRs (leucine-rich repeat-containing, G proteincoupled receptors) (36, 37). Based on the structural comparison of LGRs from diverse species, and the evolutionary
relationship of these animals, a putative evolutionary tree for
the LGR family of proteins can be proposed. The primitive
LGR gene likely replicated before the emergence of the cniderian (sea anemone) to form three LGRs (named as LGRA,
LGRB, and LGRC), each with homologs in modern vertebrates. Based on their structural similarity and putative evo-
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Endocrine Reviews, June 2002, 23(3):369 –381
lutionary origins, mammalian LGRs can be divided into
three subgroups: LGRA (LH, FSH, and TSH receptors),
LGRB (LGR4, LGR5, and LGR6), and LGRC (LGR7 and
LGR8) (38). In fly, there are at least three LGRs, each belonging to one of these subgroups. Because only one LGR
with similarities to the LGRA subgroup could be identified
in C. elegans (4), it is likely that a gene loss occurred during
evolution. Interestingly, recent studies indicated that LGR7
and LGR8 are receptors for relaxin (39).
Initially discovered based on their sequence similarity to
a fly ortholog, cyclic nucleotide phosphodiesterases represent a large group of genes encoding enzymes that hydrolyze
and inactivate cAMP and cGMP (40). Animal cyclic nucleotide phosphodiesterases comprise at least seven subtypes as
well as orthologs corresponding to four subtypes that can be
found even in freshwater sponges. Accordingly, phylogenetic analysis revealed that most gene duplications and domain shuffling that gave rise to different subtypes had been
completed in the early evolution of animals before the separation of sponges and eumetazoans (41).
Another example of the use of comparative genomics for
hormone discovery is the recent identification of CRHrelated peptides (42– 44). Using phylogenetic and structure
profile analyses, two novel peptides, stresscopin/urocortin II
and stresscopin-related peptide/urocortin III, with limited
sequence relatedness to frog sauvagine and mammalian
CRH, were isolated from the human and puffer fish genomic
databases and shown to function as selective type 2 CRH
receptor agonists. These studies suggest that the two novel
peptides are important in stress-coping responses in both
central and peripheral tissues for maintaining homeostasis
after the initial fight-or-flight responses induced by stress.
Evolutionary genomic approaches have facilitated the
analysis of large families of polypeptide ligands. A recent
review summarizes the origin and function of the pituitary
adenylate cyclase-activating polypeptide/glucagon superfamily in vertebrates and shows that the ancestral superfamily members can be traced to a common origin with the
invertebrates. It also suggests that the superfamily began
with gene or exon duplication and then continued to diverge
with some gene duplications in vertebrates (45). Likewise,
recent duplication of the LH␤ gene in ancestral primates led
to the derivation of multiple human chorionic gonadotropin-␤ genes (46). Furthermore, the coevolution of GH and its
receptor in primates has been analyzed. These studies demonstrated that GH evolution is very conservative among
most mammals but not in primates. The human GH receptor
displays species specificity and interacts only with human or
rhesus monkey GH, but not with nonprimate GH. Sequence
analysis revealed that species specificity of the human GH
receptor emerged in the common ancestor of Old World
primates in a relatively short period (47). Phylogenetic analysis also suggested the evolution of vertebrate steroid receptors from an ancestral estrogen receptor, followed by the
progesterone receptor and other related proteins (48).
As these examples illustrate, the availability of genomic
sequences from an increasing number of organisms will enable hormonal researchers to decipher the evolutionary origin of diverse human genes important for ligand signaling.
In turn, these findings will significantly facilitate the exper-
Leo et al. • Hormonal Genomics
imental verification of functions for novel genes based on
their relationship to characterized genes as well as studies in
model organisms.
IV. Chromosomal Location of Genes
For decades, genetic mapping of the chromosomal positions of genes has been performed based on patients’ family
histories or cross-breeding in animals. Linkage analyses utilize the frequency of meiotic recombination between genetic
markers such as microsatellite DNAs or known genes to
estimate the position of genes with a unique phenotype. The
determination of gene positions in chromosomes has also
been assisted by physical mapping based on fluorescence in
situ hybridization, restriction enzyme length polymorphism,
and sequence-tagged sites. In the postgenomic era, chromosomal location is known for all human genes with known
sequences.
Investigations of gene order in chromosomes of closely
related species indicate that chromosomes underwent duplication, fusion, translocation, and inversion events during
evolution. In syntenic regions of related species, a partial or
complete conservation of gene order during chromosome
evolution is evident. With the availability of complete genomes of increasing numbers of vertebrate species, the
genomic analysis of syntenic regions becomes a valuable tool
for understanding the function of human orthologs based on
the conservation of gene locations. Recently, syntenic mapping of human and mouse chromosomes was performed by
the National Center for Biotechnology Information (http://
www.ncbi.nlm.nih.gov/Homology/). With the well established technology of gene engineering in the mouse, comparisons of human and mouse phenotypes and the
elucidation of syntenic genes will become an increasingly
useful approach in the future. Similar syntenic mapping
projects are being performed between human and rat (49) as
well as between other species.
One example of the impact of synteny studies on endocrine research is the role of certain members of the TGF␤
family in ovarian follicle development. Mutant mice lacking
the oocyte hormone growth differentiation factor (GDF)-9
showed an arrest of ovarian follicles at the secondary stage
(50). Furthermore, another oocyte gene with high homology
to GDF-9, named GDF-9B or bone morphogenetic protein-15
(51, 52), was also found to stimulate granulosa cell proliferation (53) and mapped to human chromosome Xp11.2–11.4.
In sheep, a naturally occurring X-linked mutation was
known to cause an increased ovulation rate and twin and
triplet births in heterozygotes but primary ovarian failure in
homozygotes. Further studies identified the genetic locus of
this Inverdale sheep mutation as syntenic to the human Xp
region containing the GDF-9B/bone morphogenetic protein-15 gene. Thus, this oocyte gene is essential for female
fertility, and its natural mutations can cause both an increased ovulation rate and infertility phenotypes in a dosagesensitive manner (54).
In addition to studies of syntenic chromosome regions,
analyses of unique phenotypes in patients have also confirmed the disruption of linked genes in the human genome.
Leo et al. • Hormonal Genomics
Kallmann syndrome is characterized by hypogonadotropic
hypogonadism and an inability to smell as the result of a
defect in the migration of olfactory and GnRH neurons (55).
Although this disease has been associated with both X-linked
and autosomal inheritance, some infertile patients also
showed skin lesions of scalp, ears, and neck, representing a
contiguous gene syndrome involving two adjacent genes in
chromosome Xp22.3 (56). In this region, the KAL1 gene responsible for Kallmann syndrome is situated adjacent to the
steroid sulfatase gene responsible for X-linked ichthyosis.
Therefore, the analysis of patients with microdeletions of
neighboring chromosomal regions containing linked genes
can reveal the location of disease genes.
V. Gene Polymorphism
The out-of-Africa hypothesis regards all modern human
populations as descended from a small group of approximately 100,000 individuals that migrated from Africa less
than 150,000 yr ago and replaced archaic populations (57).
Assuming the common origin of modern humans, nucleotide
variations are shared at specific positions in the genome. Base
pair changes that lead to minimal alterations in the property
of encoded amino acids usually do not significantly affect the
overall function of the protein. Consequently, variations in
nucleotide sequences that are associated with silent mutations or minimal phenotypic changes are tolerated during
evolution and inherited over generations. These single nucleotide polymorphisms represent minor alterations in DNA
that occur with varying frequencies in different ethnic populations and are a central focus of pharmacogenomic studies
(58). A random pair of human haploid genomes differed at
an average rate of 1 per 1250 bp, but there was marked
heterogeneity in the occurrence of polymorphisms across the
genome. It has been estimated that there are 300,000 potential
single nucleotide polymorphisms in the human genome
(59, 60).
Polymorphisms of individual genes can account for differences in an individual’s responsiveness to hormonal and
other ligands. For example, polymorphisms in the coding
region of the C-C chemokine receptor (CCR)-5 gene, and in
the promoter region of its ligand RANTES, are associated
with aberrant ligand-receptor interactions. Patients with an
altered CCR-5 receptor, the coreceptor for the viral infection
of immune cells, or patients who exhibit overproduction of
RANTES, can be exposed to HIV with reduced infection risk
or delayed disease progression (61, 62). In both cases, the
viral particles fail to infect cells either because they cannot
enter using the defective receptor or because the functional
receptor is already occupied by high levels of the endogenous ligand. Likewise, a polymorphism of the ␤-adrenergic
receptor is associated with altered sensitivity to ␤-agonists in
asthmatics (63). Moreover, a single-nucleotide polymorphism of the IL-1B promoter has been found to be associated
with a chronic hypochlorhydric response to Helicobacter pylori infection and the risk of gastric cancer, presumably by
altering IL-1 levels in the stomach (64). Recently, two linked
polymorphisms of the FSH receptor gene were found to alter
FSH responsiveness of gonadal cells as the amount of FSH
Endocrine Reviews, June 2002, 23(3):369 –381
373
required for ovarian stimulation in patients with different
polymorphic alleles differed (65). Estrogen receptor variants
in normal and neoplastic mammary tissues and other target
cells have also been identified (66, 67). These transcripts
could be responsible for aberrant ligand-signaling events. A
recent study analyzed polymorphisms and genetic variations of multiple G protein-coupled receptors and documented the functional importance of specific residues for the
function of these receptors (68).
Studies on gene polymorphisms could also facilitate the
mapping of disease genes based on their chromosomal location. Studies on polymorphic differences in coding and
noncoding regions in large populations with a particular
phenotype facilitate genome-wide linkage scans of polygenic
diseases such as type 1 and type 2 diabetes. This approach
is complicated by the dependence of such complex diseases
on a combination of genetic variations that may be common
in the general population, and environmental factors. However, increasing documentation of single-nucleotide polymorphism variation in humans could allow genome-wide
linkage and association scans for complex endocrine diseases
(69). Recently, advanced analytical tools have been applied
to delineate the relationship between a common polymorphism in the peroxisome proliferator- activated receptor-␥
gene and type 2 diabetes (70). Because the chromosomal
locations of human genes in the known ligand-signaling
pathways are clear, efforts to identify genetic loci predisposing for hypertension have also focused on candidate gene
strategies. Genetic linkage and association methods have
correlated hypertension with polymorphisms in the genes of
the renin-angiotensin-aldosterone system, the catecholaminergic/adrenergic receptors, and other hormonal factors.
These studies have yielded promising leads by suggesting a
number of candidate genes for hypertension susceptibility
(71).
VI. Alternative Splicing
To deduce the complete set of proteins encoded by a genome, more information than the sequence of all its genes is
required. In higher eukaryotes, the primary transcripts of
individual genes are processed in the nucleus before being
transported to the cytoplasm as mRNAs that direct ribosomal protein synthesis. Alternative mRNA processing is a
mechanism for creating protein diversity through selective
inclusion or exclusion of nucleotide sequences during posttranscriptional processing. Although human, fruit fly, and
worm appear to have similar numbers of alternative splicing
forms per gene, it is clear that alternative splicing results in
a significant increase in coding capacity and protein diversity
(72). Different studies imply that alternative splicing occurs
in about 35–50% of human genes (73–76) and may be more
frequent in certain types of genes, e.g., those encoding cell
surface receptors (77). Although it is not unusual for individual genes to have as many as 12 different mRNA species,
a small subset of genes (such as adhesion molecules or ion
channels) have been found to possess more than 100 splicing
variants (78).
Sequence alignment of multiple ESTs with the correspond-
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Endocrine Reviews, June 2002, 23(3):369 –381
ing genomic sequence is the most common method to predict
the presence of different mRNA species (76, 79). Intronic
sequences at splice junctions are highly conserved, and 99%
of introns have a GT-AG at their 5⬘- and 3⬘-ends, respectively.
These characteristic patterns can be used to verify candidate
splices. Other approaches for the prediction of alternative
splicing variants include interspecies comparisons of
genomic sequences (80) and the computational analysis of
mRNA expression data measured with oligonucleotide microarrays (81).
The phenomenon of alternative splicing affects hormonal
systems in two ways. In one case, the components of the
machinery—polypeptide ligands, receptors (plasma membrane and nuclear), intracellular mediators of signaling pathways, etc.—are themselves subject to alternative splicing. On
the other hand, the processing of pre-mRNAs in the nucleus
may be subject to hormonal regulation (82). For example,
hypophysectomy in the rat results in the expression of alternative splice variants of a calcium-dependent potassium
channel, an effect that can be prevented by injection of ACTH
(83).
A number of hormone and growth factor isoforms are
derived from alternative gene splicing (84). One prominent
example is vascular endothelial growth factor, a factor that
induces microvascular permeability and plays a central role
in both angiogenesis and vasculogenesis. Through alternative mRNA splicing, the vascular endothelial growth factor
gene gives rise to several distinct isoforms differing in their
expression patterns as well as their biochemical and biological properties (85– 87).
There are also many examples of receptor gene splicing.
For instance, inclusion or exclusion of exon 11 of the insulin
receptor gene results in two different mRNA isoforms encoding receptor proteins with differing biological properties
(88). Similarly, there are at least eight human CRH-receptor
type 1 mRNA variants, and the splicing pattern varies in
response to environmental stress. Functional analysis indicated that some of these CRH-receptor type 1 variants differ
in their ability to transduce ligand signals in the cAMPmediated pathways, whereas others could function as binding proteins for CRH and related ligands (89). The alternative
splicing mechanism also underlies the generation of diverse
decoy receptors belong to the TNF receptor, the chemokine
receptor, and cytokine receptor families. These soluble or
membrane-anchored decoy receptors bind and sequester
their respective ligands with high affinity and specificity but
are incapable of signaling or presenting the agonist to the
functional receptor complexes, thus acting as a molecular
trap for endogenous agonists (90).
For intracellular signaling proteins, gene splicing can also
lead to multiple gene products with diverse functions. For
genes in the apoptosis pathway, alternative splicing can affect both the functional activity and the intracellular distribution of protein isoforms (91). For example, a splicing variant of the antiapoptotic Mcl-1 protein has proapoptotic
activity and antagonizes the prosurvival action of the known
Mcl-1 protein by forming noncovalent dimers (92). In this
example, proteins with diametrically opposed functions are
generated under different physiological conditions through
alternative splicing mechanisms.
Leo et al. • Hormonal Genomics
Splicing of mRNA constitutes an essential molecular
mechanism in the ongoing evolution of eukaryotes and is
pivotal to the understanding of hormonal genomics. Currently available information on alternative splice variants for
numerous genes can be found in databases such as ASDB
(93), AsMamDB (94), or PALS db (95). Such resources will
allow us to gain a better understanding of the complexity of
alternative splicing and its role in hormonal research. Because approximately 15% of the point mutations that cause
diseases in humans are thought to alter the normal splicing
pattern, studies on gene splicing mechanisms could also
provide important insights into human pathophysiology.
VII. Identification of the Regulatory Elements
of Genes
Hormones often effect changes in their target tissues by
altering the expression of specific sets of genes. At the DNA
level, these temporal, spatial, and quantitative changes in
gene expression are controlled by various regulatory DNA
sequences such as promoters, enhancers, insulators, and silencers (96, 97). The identification and characterization of
regulatory DNA sequences that mediate the actions of hormones and their signaling pathway genes therefore constitute an important focus of hormonal research.
Traditionally, regulatory DNA sequences have been investigated on a gene-by-gene basis, using a variety of approaches including deletion constructs, DNA footprinting,
and gel shift assays. With the availability of genome sequences for human, rodent, and other species, novel strategies for the large-scale, computer-based in silico identification
of regulatory sites are becoming feasible (98).
cis-Regulatory elements in the promoters and enhancers of
genes can be defined based on their DNA sequences, position
relative to coding sequences, and the potential to interact
with transcription factors. Because an exhaustive experimental characterization of all potential regulatory sites in a given
genome is difficult to generate, a variety of computational
approaches to this problem have been developed. These
methods include 1) analyses using databases of experimentally defined transcription factor binding sites, 2) approaches
based on comparative genomics, and 3) sequence analyses of
genes with similar expression profiles.
Experimentally derived data on transcription factors, their
consensus DNA binding sites, and DNA-binding profiles
have been compiled in a number of publicly accessible databases (99 –102). By comparing a given genomic DNA sequence to the consensus target sites for transcription factors
stored in the database, potential regulatory sites can be identified. Because many of these sites are short in length and
contain degenerate consensus sequences, a relatively large
number of false-positive predictions are generated.
Cross-species comparisons, also known as phylogenetic
profiling, provide another approach for identifying regulatory DNA sequences. These methods align noncoding sequences of orthologous genes from different species, taking
advantage of the high degree of conservation for regulatory
sequences. On a functional level, this finding is matched by
the observation that genomic transgenes largely retain their
Leo et al. • Hormonal Genomics
natural expression patterns even when introduced into mice
from other mammalian species (103). With the availability of
working drafts of the human and mouse genome, large-scale
interspecies comparisons to identify conserved noncoding
sequences have become possible (104, 105). Moreover, alignments of large genomic regions can be easily performed and
visualized using Internet-based tools such as PipMaker (106).
The last common ancestor of humans and mice lived about
80 –100 million years ago, and it appears that in many regions
of the human genome, this evolutionary distance will allow
the identification of conserved regulatory sites through comparative genomics (98, 107).
Another strategy to predict regulatory elements of genes
is based on the use of expression profiling to identify coregulated genes (cf. Section VIII). Transcription factors often
coordinate the expression of whole sets of genes such as those
induced by a given hormonal ligand. This coordinated regulation results from the presence of common transcription
factor binding sites in these genes and, hence, similar or
identical sequence motifs in their noncoding regions. For
hormonally regulated genes, large-scale identification of coexpressed mRNAs after hormonal treatment can be used to
identify genes that are coregulated by the same transcription
factors. A detailed analysis of the upstream regions of these
functionally clustered genes for shared sequence motifs can
then serve as a starting point for the characterization of novel
regulatory sequences. This approach has already been successfully applied to infer regulatory sites in yeast from microarray- derived expression data (108 –110). Because the
expression of a single gene can be controlled by various
synergistic and/or antagonistic factors acting on different
binding sites in its regulatory region, additional algorithms
are needed to model relationships between multiple transcription factors and their binding sites to unravel complex
transcriptional networks (111–113).
The combination of bioinformatics and novel experimental
approaches allows new insights into the function of regulatory elements. For example, nuclear receptor signaling is
transduced by homodimeric or heterodimeric receptor complexes that bind to specific sequences of DNA, termed response elements, in the promoter regions of target genes.
Because considerable flexibility exists in the types of binding
sites the nuclear receptors are capable of recognizing, the
precision of predicting regulatory elements based on a bioinformatic approach is limited. Screening methods involving
immunoselection and PCR amplification or microarray binding assays in combination with bioinformatic analyses now
can be used to examine genomic binding sites for different
nuclear receptors. The integrated approach for isolating
functional binding sites for nuclear receptors from genomic
DNA should aid in the discovery of genes regulated by
steroid hormones (114). The large-scale prediction of regulatory regions in mammalian genomes will likely require a
combination of all the approaches mentioned above. Importantly, in silico prediction of regulatory DNA sequences will
need to be verified by functional data from high-throughput
experimental methods. Although the area of regulatory element analysis remains a major challenge for hormonal researchers, recent studies on the promoter of the human
RANTES/CCR5 ligand gene have allowed the identification
Endocrine Reviews, June 2002, 23(3):369 –381
375
of promoter “modules” that are important for cell type-specific expression of this gene (115, 116) and will serve as a
model for future bioinformatic approaches in this field.
VIII. mRNA Expression Profiling
The majority of cells in an individual organism (with exceptions such as haploid germ cells or neoplastic cells) share
an identical set of genes. Nevertheless, these cells display an
astonishing phenotypic diversity that is further enhanced by
a multiplicity of developmental stages and physiological or
pathological states. This diversity of cellular phenotypes encountered within an individual is almost entirely due to
differences in the patterns of gene expression. Consequently,
the study of changes in gene expression over time and
space—as well as in response to hormones—represents a
challenging area of hormonal research.
Traditional experimental methods, such as Northern blot
analysis, differential display, and subtractive hybridization,
were the standard tools that researchers used to identify
differentially expressed genes. These approaches produced
either lists of differentially expressed genes or, at most, semiquantitative estimates of transcript frequencies in different
biological samples. This situation changed with highthroughput sequencing of cDNAs, including studies of ESTs
and the serial analysis of gene expression (SAGE). These
methods allowed scientists to quantify the occurrence of
thousands of transcripts in libraries prepared from different
cells or tissues. Based on the resulting data, the relative
expression of different genes within a biological sample
could be assessed. Databases containing information on ESTs
derived from different tissues or cell lines can be analyzed to
estimate the relative abundance of transcripts (117, 118). Using digital differential display or electronic subtraction, a
computational assessment of transcript frequencies in different samples (e.g., with or without nerve growth factor
treatment) can be used to explore differential gene expression (119). Digital differential display has been used to identify genes differentially expressed in diverse hormoneproducing cells and tissues, including murine oocytes (120),
the human prostate (121, 122), and the human hypothalamus-pituitary-adrenal axis (79). By comparing the genomic
localizations of the genes encoding testis-specific transcripts
with known translocation breakpoints from infertile males,
a set of candidate genes for male infertility was identified
(123).
In SAGE, short diagnostic sequence tags are isolated from
multiple cDNAs in the sample of interest, concatenated, and
cloned. In this manner, the sequencing of thousands of socalled SAGE tags can be performed with relative ease. By
calculating the frequencies of tags in a given sample, the
expression patterns of the corresponding genes can be determined (124, 125). A SAGE analysis of the molecular phenotype of the human oocyte identified a variety of surface
receptors, components of second messenger systems, and
secreted proteins, many of which were not previously known
to be expressed in mammalian oocytes (126). Other applications of SAGE technology to the field of endocrinology
include the identification of corticosteroid-responsive genes
376
Endocrine Reviews, June 2002, 23(3):369 –381
in the rat hippocampus (127), the discovery of human thyroid-specific transcripts (128, 129), and the characterization
of gene expression in PRL receptor knockout mice (130).
With the introduction of DNA array technologies, powerful tools for global studies of gene expression have become
available to individual laboratories (131). In this approach,
gene-specific sequences (probes) are immobilized on solid
substrates in ordered arrays. Subsequently, mRNA from the
cells or tissues to be studied is used to generate labeled
samples (targets) that are hybridized to the arrays. By quantifying the amount of labeled target hybridized to the array,
large-scale analyses of gene expression can be performed.
The available DNA array platforms differ in a number of
ways including the substrate on which the array is produced
(glass slides or silicon wafers), the type of arrayed material
(cDNAs or oligonucleotides), the way in which the probe is
fixed onto the array (printing or in situ synthesis), and the
type of labeling used [fluorescent dyes or radioactive tracers
(132)].
As a single DNA array experiment yields thousands of
data points, new computational tools have been developed
for their management and analysis (133). In particular, a
variety of clustering algorithms have been designed to classify and group genes on the basis of similarities or differences
in their expression across different samples and thus help
users to recognize global gene expression patterns (134). The
popular hierarchical clustering approach uses data from a set
of array experiments to place genes on a single hierarchical
tree. Genes with similar expression profiles become neighbors on the tree, whereas genes with larger differences in
their expression patterns appear on more distant branches
(135).
One basic assumption in interpreting gene expression profiles is that genes with similar transcription patterns are
likely to share aspects of their regulation and/or biological
functions. As a consequence, previously uncharacterized
genes have been attributed putative functions based on the
characteristics of known genes that are found in the same
cluster. This “guilt-by-association” approach has been successfully used to predict the function of yeast genes (131) and
is now being applied to the study of mammalian genomes,
e.g., the identification of prostate cancer marker genes (136).
Microarray technologies have also been applied to investigate a number of endocrine tissues and hormone-regulated
processes. Studies performed to date have addressed—
among others— questions involving gene expression in the
human adrenal (137), the effect of hypophysectomy in rats
(138), development of the mouse pituitary gland (139), adipose tissue in obese and diabetic mice (140), preovulatory
gene expression in the rat ovary (141), implantation in the
mouse (142), and gene expression in the murine placenta and
embryo (143). Another important area of DNA array studies
is the classification of disease phenotypes based on expression profiling of genes in normal and pathological samples
(144 –147). In the future, transcript profiling of patient specimens could be used to subclassify hormonal disorders allowing for more precise therapeutic interventions.
As gene expression patterns may vary widely between
adjacent but biologically distinct tissues and cell types, a
meaningful microarray analysis often depends on the precise
Leo et al. • Hormonal Genomics
selection of defined cell populations from a sample. The
investigation of gene expression patterns can be refined by
laser capture microdissection, often in combination with
methods for target amplification (148, 149). In the future,
global analyses of gene expression may be performed even
in single cells (150).
So far, most of these DNA array studies have taken a local
approach to the study of gene expression and have focused
on identifying individual genes connected with certain biological phenomena. Because gene products in a given cell
usually function in a concerted manner and belong to specific
pathways for carrying out unique cellular functions, future
studies will focus on a global analysis of gene expression. In
a recent study, the results of more than 500 DNA microarray
experiments were analyzed to group coregulated genes in C.
elegans. To visualize the relationships between more than
17,000 genes in the nematode genome, clusters of coexpressed transcripts were displayed as mountains in a threedimensional expression map that corresponds to established,
as well as previously unknown, biological relationships
(151). In addition, a number of projects focus on the analysis
and presentation of gene pathway maps. For example, GenMapp provides a graphic representation of the relative expression of selected groups of genes categorized by their
known positions in physiological pathways (152) (http://
www.genmapp.org). Thus, RNA transcripts for thousands of
genes from defined cell populations can be probed for their
relative abundance based on prearranged maps representing
functional pathways.
DNA array experiments generate data sets of sizes that are
unprecedented in most areas of biological research (153). In
the future, it will become increasingly useful for these data
to be available in electronic form to the scientific community
at large. On the one hand, these data cannot be published by
traditional methods because of their unwieldy quantity and
because they can be more easily visualized and manipulated
using a computer. On the other hand, the constant improvement of algorithms for the analysis of microarray data means
that data sets will be frequently revisited to reinterpret their
meaning or to formulate new hypotheses and relationships.
Most importantly, however, the true power of gene expression data can only be unleashed when the expression of a
given set of transcripts can be compared over as many different tissues and cell types, developmental and physiological states, and diseases and experimental conditions as possible. Therefore, public repositories and common data
formats for gene expression data have been proposed (154).
IX. The Postgenomic Challenges in
Hormonal Research
The application of postgenomic approaches to the field of
hormone research will likely result in major changes in the
way endocrinologists perform their work. In the traditional
endocrine laboratory, the single-gene/single-protein approach dominates, providing an integrated view of sequence, mRNA, and protein expression, as well as cellular
and organismal function for a limited number of genes. This
type of research often starts from functional studies where
Leo et al. • Hormonal Genomics
individual laboratories function independently with a limited degree of collaboration. In the current early phase of the
postgenomic era, new methods provide massive amounts of
data, but these are still largely interpreted using a classic
paradigm. For example, DNA microarrays are often used as
a sophisticated substitute for differential display or subtractive hybridization. Consequently, out of thousands analyzed, only a few genes with the most pronounced changes
are selected and studied using the single-gene approach. In
spite of the accelerated pace of discovery, the independent
laboratory approach is still most common.
In the next phase, the large data sets created by the application of a single high-throughput method will be analyzed and interpreted with regard to the complete underlying set of biological entities (e.g., genes). The characterization
of gene function will also expand from the single-gene
knockout approach to random or targeted gene-trapping
approaches (155, 156) (http://socrates.berkeley.edu/⬃skarnes/resource.html; http://www.lexgen.com/). A secretory
trap method has allowed the generation of mice with a deletion of genes encoding membrane and secreted proteins,
including those for polypeptide ligands and plasma membrane receptors. After the validation of functions for individual genes, comprehensive gene functional maps will be
integrated into a biological atlas (157). As a result, relationships between separate entities in this set are recognized,
patterns are discovered, and new testable hypotheses regarding gene pathways and networks are created. This
global phase will be reached when a large number of data
sets become available in a compatible electronic format and
new computational methods for their analysis, visualization,
and interpretation become available (e.g., the use of microarray data in the yeast research community). For understanding hormonal signaling, data on all ligands and receptors will
be deposited and categorized in searchable databases with
links to biomedical literature and gene sequences, protein
structures, and other resources. These resources will be complemented by databases for specific endocrine organs [e.g.,
the Ovarian Kaleidoscope database, (158)] to provide an integrated view of organ physiology and pathophysiology.
We are moving from a gene-by-gene approach to a reconstructionistic one. Genes in the hormonal signaling system
can be analyzed from the combined perspectives of traditional endocrinologists, molecular biologists, developmental
biologists, cell biologists, evolution biologists, structural biologists, and geneticists. In the postgenomic era, the scientific
community as a whole, as opposed to an individual laboratory, plays an increasingly important role in biomedical research. Eventually, the massive genomic data gathered with
different methods (e.g., DNA arrays and proteomics) will be
integrated to allow a global view of the system (157). Recently, steroid receptors and related orphan receptors have
been grouped in the context of all transcription factors (159),
whereas diverse plasma membrane receptors have been classified based on their evolutionary origin and relationship to
other plasma membrane signaling molecules (I. Ben-Shlomo,
S. Y. Hsu, and A. J. W. Hsueh, submitted). In this way, new
metapatterns can be discovered, and pathways spanning the
scope of different methods can be understood ranging from
regulatory DNA sequences to gene transcript expression to
Endocrine Reviews, June 2002, 23(3):369 –381
377
protein function. This phase relies heavily on collaboration
within the biomedical community because no single laboratory is likely to have the expertise, time, and resources to
generate such large datasets using different high-throughput
methods. This transition of hormonal research from a single
laboratory approach to an integrated community effort will
pose an important challenge for the next generation of endocrine researchers.
Acknowledgments
Address all correspondence and requests for reprints to: Aaron J. W.
Hseuh, Ph.D., Division of Reproductive Biology, Department of Gynecology and Obstetrics, Stanford University Medical Center, 300 Pasteur
Drive, Room A344, Stanford, California 94305-5317. E-mail: aaron.
[email protected]
This work was supported by NIH Grants HD-23273, HD-31398, and
DK-58534.
* Current address for C.P.L.: MBA Programme, Institute Europeen
d’Administration des Affaires, Boulevard de Constance, 77305 Fontainebleau Cedex, France.
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STADY III—International Symposium on Signal Transduction in Health and Disease
1– 4 October, 2002
Tel Aviv, Israel
For further information, contact Professor Zvi Naor at the following address:
Prof. Zvi Naor
Department of Biochemistry
Tel Aviv University
Ramat Aviv 69978
Israel
Telephone: ⫹972-3-6409032/6417057
Fax: ⫹972-3-6406834
E-mail: [email protected] or [email protected]
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