BACKGROUND
the comprehensive definition of the mammalian transcriptome has altered our view of
genome complexity and the transcriptional landscape of tissues and cells.
systematic
analysis of the transcriptome is of central interest to the biology community, but
global coverage was not possible until the complete sequencing of the human and mouse
genomes and the advent of microarrays.
the pioneering work by su et al.
<cit>  provided the first comprehensive analysis of the protein-encoding
transcriptome of major organs of human and mouse.
others have used microarrays or
alternative methods to map expression in specific tissues or cell types  <cit> .
the work of the fantom and encode projects has revealed the true complexity
of the mammalian transcriptome, highlighting the impact of alternative initiation,
termination and splicing on the proteome, and the prevalence of multiple different
classes of non-coding rnas   <cit> .
the pace of data acquisition has continued to grow with the increasing
reliability and decreasing cost of the core technologies such as microarrays and the
sequencing of rna .
despite these efforts, knowledge of the human
transcriptional landscape is still sparse.
efforts to curate and analyze an 'atlas' from
the existing human microarray data are hindered by the fact that certain types of
samples have been analyzed extensively, for example hematopoietic cells and cancers,
while little or no data are available for many other tissues and cell types  <cit> .
studies of the non-pathological human transcriptome are compromised further
because most tissues can only be obtained post-mortem, the provenance of samples can be
variable and the health status of the individual from whom they were obtained is often
unknown.
with numerous predicted mammalian protein-coding loci still having no informative
functional annotation and even less insight into the function of the many
non-protein-coding genes, detailed knowledge of a transcript's expression pattern can
provide a valuable window on its function.
previously, we have used coexpression
analysis of large mouse datasets to provide functional annotation of genes,
characterization of cell types and discovery of candidate disease genes  <cit> .
isolated cell types may differ not only in their specialized function but
also in their engagement with 'housekeeping' processes, such as growth and
proliferation, mitochondrial biogenesis and oxidative phosphorylation, metabolism and
macromolecule synthesis, the cytoskeleton, the proteasome complex, endocytosis and
phagocytosis.
genes coding for proteins within pathways, both generic and cell-specific,
often form coexpression clusters  <cit> , so one can infer the function of a gene of unknown function from the
transcriptional company it keeps, by applying the principle of guilt-by-association.
the
identification of coexpression clusters can, in turn, inform the identification of
candidate genes within genomic intervals associated with specific traits from
genome-wide association studies  or classical linkage studies.
for example, we
identified a robust cluster of genes that is expressed specifically in cells of
mesenchymal lineages in the mouse  <cit> .
the cluster contained a large number of genes previously shown to be
causally associated with inherited abnormalities of the musculoskeletal system in humans  <cit> .
by inference, other genes within this cluster that have less informative
annotation or no known function, are likely to be involved in musculoskeletal
development.
as noted previously  <cit> , the conservation of coexpression clusters can provide an even more powerful
indicator of likely conserved function.
these authors mapped coexpressed clusters onto
<dig> human mendelian disease loci of unknown molecular basis from online mendelian
inheritance in man  and identified  <dig> candidate genes based upon their conserved
restricted expression within the affected organ.
the domestic pig  is economically important in its own right, and
has also been used increasingly as an alternative model for studying human health and
disease and for testing new surgical  and pharmacological
treatments .
compared to traditional rodent models, the pig is more closely-related to
humans in its size, growth, development, immunity and physiology as well as its genome
sequence  <cit> .
the translation of preclinical studies in rodents into clinical applications
in humans is frequently unsuccessful, especially for structures where rodents have very
different anatomy and physiology, such as the cardiovascular system  <cit> .
the recently released pig genome sequence   <cit>  and associated annotation will greatly enhance the utility of the pig as a
model  <cit> .
however, compared to the mouse, the knowledge of the pig transcriptome is
very limited partly due to a lack of commercial expression microarrays with
comprehensive gene coverage  <cit> .
while several est  sequencing projects have explored
gene expression across a range of tissues  <cit> , a systematic global study of the tissue expression landscape is not
available.
here we present a new microarray platform for the pig with greatly improved
gene coverage and annotation.
we have used this array to generate an expression atlas
for the pig, comparable to the human/mouse expression atlases, and, using advanced
visualization and clustering analysis techniques, we have identified networks of
co-expressed genes.
a detailed analysis of the porcine gastrointestinal tract
illustrates the power of the analytical approach and data.
these data will support
improved annotation of the pig and human genomes and increase the utility of the pig as
a model in medical research.
RESULTS
the pig is uniquely important both as a major source of food and an animal model for
human disease.
until recently the lack of a genome sequence for the pig and consequently
many of the functional-genomic analysis tools, have limited the kind of analyses now
routine in human and mouse systems.
here we report the design, annotation and validation
of a new comprehensive microarray for the analysis of gene expression in the pig and a
first attempt to produce a global map of the porcine protein coding transcriptome.
the new snowball array  is far more comprehensive in its gene coverage than the previous porcine
affymetrix array which was based on the available expressed sequence tag data circa
<dig>  it is also more extensive than the new porcine 'peg' array
recently released by affymetrix , with nearly twice as
many probes included on the snowball array, and draws on a larger cdna sequence
database.
the results from the analysis described here validate the performance and gene
annotation of the snowball array.
a major problem currently restricting genomic analysis
of production animals is the fact that many genes remain unannotated due to problems in
establishing orthology among homologous sequences from other species.
we adopted a 'best
match' approach to increase the number of annotated features on the array.
the repeated
finding that transcripts annotated in this way were expressed in a pattern that was
consistent with their proposed function  supports the validity of this
approach.
however, we would urge caution in accepting the orthology match of probes
annotated in this way without further verification.
we have aligned the probe sequences
from the snowball array with the recently released sscrofa <dig>  assembly.
we will publish
these alignments as a das track in ensembl in the short term and integrate the
alignments into ensembl and biomart in the next ensembl release.
these alignments enable
the expression data to be used to annotate the genome sequence further and the
interpretation of expression profiles for a gene/transcript in a genomic context.
transcriptome.
a comparison of different porcine expression arrays manufactured by affymetrix.
this table compares the major features of the two commercial affymetrix expression
arrays that have been designed for studies of the pig transcriptome with the array
designed and used for this study.
arrays still provide a very cost effective solution for producing a large amount of high
quality gene expression data.
in terms of speed of data acquisition and availability of
established analysis routines that can be run on desktop machines, arrays still have
many advantages over sequencing-based analyses.
with improvements in the assembly and
annotation of the genome and gene models and rnaseq analyses increasing our knowledge of
the transcriptional landscape of the transcriptome, there is no doubt the current array
design will be enhanced.
the primary cohort of animals used for this study was a group of three- to four-month
old juvenile pigs of both sexes.
we aimed to gather samples of every major pig tissue.
where possible biological replicates were analyzed that originated from different
animals of each sex.
regional analysis of the brain is clearly important, and more
feasible in pigs than in mice, but the method of killing  meant that
detailed dissection of brain was not possible.
the age/stage of the animals also meant
that certain tissues could not be collected and the panel of tissues was supplemented by
samples of placenta and a mature testis   <cit> .
since macrophages have proved to be one of the most complex sources of novel
mrnas  <cit> , we included a number of macrophage samples  stimulation) in the atlas.
for details of the tissues and cells
used for this study see additional file  <dig>  table s <dig>
biolayout express3d  <cit>  is a unique tool in the analysis of large complex expression datasets.
the
statistical approach employed centers on the principle of coexpression, based on the
transcript-to-transcript comparison of the expression signal across the samples
analyzed, by calculation of a pearson correlation matrix.
for any given comparison, the
pearson value can range from + <dig>  to - <dig> .
the correlation and clustering algorithms within biolayout
express3d, together with the ability to visualize and explore very
large network graphs, mean that it is uniquely positioned for the analysis of large
datasets and has been used extensively for this purpose  <cit> .
a graph derived from a given correlation cut-off value includes only those
genes that are related in expression to others above the selected threshold and more or
less complex graphs may be analyzed by decreasing or increasing this value,
respectively.
core topological structures that often form separate graph components at
high thresholds are robust and are maintained as correlation cut-off values are
lowered.
we used biolayout express3d to analyze the pig transcriptome data
generated using the snowball array .
from a pairwise transcript-to-transcript
correlation matrix a weighted, undirected network graph was constructed using a pearson
correlation threshold cut-off of r ≥  <dig> .
the resultant graph was large and
highly structured  with one large component of  <dig>  nodes and  <dig> smaller components
of between  <dig> and  <dig> nodes .
the topology
of the graph contained localized areas of high connectivity and high correlation
, dominated by groups of genes that
are coexpressed and form highly connected cliques within the network .
nodes representing different
probesets designed to the same gene were generally highly correlated and connected to
each other in the graph, confirming the validity of the probeset annotation and
approach.
three-dimensional visualization of a pearson correlation graph of data derived
from analysis of pig tissues and cells.
each node  in the graph represents
an individual probeset on the array and the edges  correspond to
correlations between individual measurements above the defined threshold.
the
graph is comprised of  <dig>  nodes  and  <dig> , <dig> edges .
the complex topology of the graph is a result of groups of
co-expressed genes forming cliques of high connectivity within the graph.
clustering of the graph using the mcl algorithm was used to assign genes to groups
based on coexpression.
by inspection of the underlying profiles, areas of the
graph can be associated with genes expressed by specific tissue or cell
populations.
plots of the average expression profile of genes in selected clusters
are given on the right: b. profile of cluster  <dig> genes whose expression is
restricted to brain and spinal cord; c. profile of cluster  <dig> genes whose
expression is highest in blood; d. profile of cluster  <dig> genes whose
expression is restricted to skeletal muscle; e. profile of cluster  <dig> genes
whose expression is highest in the adrenal gland.
mcl, markov cluster
algorithm.
shown here is a simplified view of the graph used for this analysis and shown inof the node being proportional to the number of individual nodes
within that cluster.
edges represent connections between clusters whereby nodes in
one cluster share edges with nodes in another.
the color of the nodes has been
selected to represent clusters of genes expressed in given types of tissues which
tend to group together with the overall topology of the network.
some highly expressed genes were not included in the graph.
the more unique a gene's
expression pattern, the fewer neighbors it will have in the network.
one example is the
protease inhibitor, alpha-2-macroglobulin .
there were five probesets on
the array designed to this gene and all showed a highly similar expression pattern,
albeit at a range of signal intensities.
these probesets formed a small correlation
network with themselves, but the expression pattern of this gene in the context of the
full atlas was essentially unique and no other porcine gene was expressed in this manner
.
in some cases, such isolation is a consequence of the
use of distinct cell-restricted promoters  <cit> .
for a2m, there is a single major transcription start site in both
mouse and human, and the pattern of expression is similar in these two species  and in pig, suggesting that a common set of
regulatory factors control this gene's expression across species.
for the majority of
other probesets not found in the graph described here, transcripts appear to be
expressed at very low levels .
these genes may be highly-expressed in
cells or tissues we have not sampled in this sample set.
for example, we would not
detect genes exclusively expressed during prenatal life as no samples from these stages
were represented in the current atlas.
within the biogps online portal.
all data used for this study are available
through the biogps database.
genes can be searched for and where found the full
expression profile is displayed as a bar chart.
samples are colored according to
type, for example, cns, gi tract, and so on, and the interface supports a number
of useful features including a zoom function on the profile viewer, searches for
genes with a similar profile, access to the raw data, links to external resources
and the potential to compare profiles across species, for example, human, mouse,
rat, zebrafish, frog.
cns, central nervous system; gi, gastrointestinal.
clustering of the graph using the markov clustering algorithm  resulted in  <dig>  clusters .
the largest consisted of  <dig>  transcripts
and the top  <dig> clusters , accounted for  <dig> % of the
nodes in the graph.
the remainder of the graph was of a sparser topology and subdivided
into numerous small clusters.
figure  <dig> shows the overall topology
of the network graph together with the expression profiles of selected clusters.
the
profile and gene content of each cluster was examined in detail, and the  <dig> largest
clusters are shown in table  <dig>  the full cluster list together with
gene membership is supplied in additional file  <dig>  table s <dig>
note that there may be a degree of variation in the expression pattern of individual
genes within a cluster which is masked when average profiles are displayed.
tissue/cells/pathway.
a list of the  <dig> largest coexpression clusters evident in the pig gene expression
atlas.
listed in the table are the  <dig> largest clusters of genes originating from
our analysis of the pig expression atlas.
clusters are numbered according to their
size  but are sorted here according to
their grouping within the graph and the biology they represent.
the first two
columns give the cluster id and number of transcripts present in the cluster and
the following column provides a description of the average expression profile of
all transcripts within the cluster.
the final two columns aim to group clusters
according to the class of tissues in which these genes are predominately expressed
and the tissue, cell or pathway we believe they represent.
several of the largest clusters showed relatively little tissue specificity in their
expression and might be considered to be 'housekeeping' genes since the proteins they
encode are likely to be functional in all cell types.
such clusters are a common feature
of large correlation graphs where a relatively low threshold has been employed.
genes/probes with limited informative nomenclature were over-represented in these
clusters, perhaps reflecting previous research focus on genes that demonstrate
tissue-restricted expression profiles  <cit> .
aside from these large, nondescript clusters, the majority of the
coexpression clusters were made up of transcripts that have a distinct tissue/cell
restricted expression pattern.
in each case, the cluster was named based upon the
tissue/cell in which the genes were most highly-expressed.
these data recapitulate
many of the known tissue restricted expression patterns that have been described for
human and mouse  <cit> .
for example, there were multiple large clusters of genes with strong
expression in the macrophage samples with a subset more highly-expressed in the alveolar
macrophages and another set induced by lps.
each of these clusters contained genes for
numerous well-studied macrophage surface markers and receptors, and proinflammatory
cytokines.
a detailed comparative analysis of human and pig macrophage gene expression
has been reported elsewhere  <cit> .
the present analysis did not identify the single large phagocytosis/lysosome
functional cluster that was evident in the analysis of mouse primary cell data  <cit> .
this cluster tends to be broken up when tissue samples are included in the
analysis because many of the components of this system are utilized more generally in
vesicle-trafficking and in other pathways.
a secondary feature of the network graph is that clusters with similar expression
patterns formed neighborhoods .
for instance, clusters
of genes selectively expressed in the reproductive tract, gastrointestinal tract,
central nervous system , mesenchymal-derived tissues, dermal tissues or blood cells
tended to occupy similar areas.
in this way the graph distributed the transcriptome into
groups of genes associated with tissues composed of cells of different embryonic
lineages.
because cells and tissues differ in their engagement with fundamental biochemical
processes, the graph also contained clusters that grouped together genes associated with
a particular cellular process  which may be active in a wide range of tissues
albeit not at the exact same level.
examples include clusters enriched for ribosomal
, cell cycle  and oxidative phosphorylation
genes.
the clusters of ribosomal genes form a separate graph
component which together contain  <dig> transcripts , including at
least  <dig> known ribosomal protein genes  gene identifiers), genes for eukaryotic translation initiation factors
, two members of the rnasep complex, naca , u <dig> and u <dig> small nuclear
ribonucleoproteins and at least  <dig> small nucleolar rnas .
snornas function to
guide modifications of other rnas, particularly ribosomal protein mrnas  <cit> , consistent with their co-clustering with components of the ribosome complex.
different tissues also vary in their rates of cell renewal and consequently in the
proportions of proliferating cells.
genes involved in the cell cycle, therefore, have a
pattern of expression that reflects the mitotic activity of the tissues and such genes
are readily identified in the graph.
cluster  <dig> contains many genes for proteins known
to be involved in the cell cycle  and supports the involvement of other cluster 14
genes in this pathway.
for example, the cluster includes vaccinia-related kinase 1
shown recently to play a role in the control of mitosis  <cit> , highlighting the importance of our approach for annotation of
uncharacterized genes.
to further illustrate the power of this approach in defining pathway systems, we show a
detailed analysis of the enrichment of genes associated with oxidative phosphorylation
and the tricarboxylic acid  cycle in clusters  <dig> and  <dig> .
clusters  <dig> and  <dig> were widely separated within the graph .
this separation represents a different regulation of these
two sets of genes.
all cluster  <dig> genes  were highly expressed in all
tissues  and are core
components of the mitochondrial oxidative phosphorylation complexes encoded by the
mitochondrial genome.
in contrast, the genes in cluster  <dig> are encoded by the nuclear
genome and showed a marked elevation in their expression in the heart, reflecting the
high rates of respiration in this tissue.
the  <dig> transcripts in this cluster include
multiple members of every one of the five complexes associated with the generation of
atp by the mitochondria and most of the enzymes driving the tca cycle.
the coexpression
of multiple members of pathways for long chain fatty acid oxidation, mitochondrial
membrane transport and ubiquinone and cytochrome c biosynthesis supports the functional
link between these pathways  <cit> .
on the basis of guilt-by-association the unnannotated/poorly characterized
transcripts within this cluster are prime candidates for a functional association with
the oxidative respiration process.
for example, gbas and chchd <dig> were
recently identified by coexpression analysis and shown to be associated with
mitochondrial complex iv  <cit> .
there are numerous other clusters within this dataset which cannot easily be
associated with an obvious functional role but likely represent clusters of genes with
shared or related functions.
and  <dig>
ndufb <dig>  ndufb <dig>  ndufb <dig>  ndufb <dig>  ndufb <dig>  ndufc <dig>  ndufc <dig>  ndufs <dig>  ndufs <dig>
coexpression of members of oxidative phosphorylation and related pathways.
nuclear
and mitochondrial  gene clusters associated with
oxidative phosphorylation.
the known association with pathway/protein complex is
listed for each gene in the cluster.
list of all genes present in clusters  <dig> and
<dig> and the function of the encoded protein, where known.
the pig's size and the feasibility of obtaining fresh tissues from healthy individuals
offer a unique opportunity to study the expression landscape of important organ systems.
in common with humans, the pig is an omnivore and its gastrointestinal tract  has
evolved to be able to masticate, digest and absorb a wide range of foodstuffs.
in this
study, we collected samples along the entire length of the gi tract from the tongue to
the rectum, a total of  <dig> distinct regions , as shown in figure 4a.
the gi tract is lined with an epithelial layer whose cellular
composition changes in line with the functional role of the gi compartment.
the upper gi
tract is lined with a stratified squamous epithelium which transitions in the stomach to
a columnar epithelium that runs through to the rectum.
even within the small intestine,
enterocyte expression of solute transporters and digestive enzymes is tightly regulated
to reflect the changing nature of the luminal contents, as well as the migration of
cells up the crypt-villus axis  <cit> .
associated with the epithelium are various glandular cell types involved
with enzyme secretion, lubrication, and endocrine control, and specialized structures,
such as the pyloric and fundic glands of the stomach and sub-mucosal brunner's glands of
the duodenum.
the lamina propria, which lies beneath the epithelium, is itself a complex
mix of cells made up of endothelial, immune and connective tissues.
the gi tract is
almost entirely surrounded by musculature  and regulated by
the enteric neural plexus.
therefore, the gi tract is composed of five major classes of
cell types: epithelia, glandular/endocrine epithelia, immune cells, neuronal cells and
mesenchymal cells .
the region-specific cellular composition
of the gi tract is summarized in figure 4b.
with specific regions.
a. schematic of the different regions of the
pig gi tract with areas sampled for this study marked with a red dot.
b.
table of the five main cell types and subdivisions thereof that make up the gi
tract and their expected presence in the samples analyzed here.
gi,
gastrointestinal.
to validate the gi-specific analysis, we initially selected a number of gene
families/classes where expression is known to be specific to certain cell populations in
other mammals .
keratins are
structural proteins that distinguish different classes of epithelial cells  <cit> .
we looked at eight keratin gene family members .
all but
krt <dig> and krt <dig> were heavily expressed in the tongue, krt <dig>
krt <dig> and krt <dig> were also expressed in the lower esophagus and fundus,
both of which are lined with a stratified squamous epithelium.
krt <dig> and
krt <dig>  markers of columnar epithelium  <cit> , showed the anticipated inverse pattern, with strong expression in the
salivary gland, antrum and along the entire length of the small and large intestine.
to
confirm region-specific epithelial function, we examined the expression of four
well-characterized brush border hydrolases: lactase , sucrose-isomaltase
, aminopeptidase n  and dipeptidyl-peptidase 4
.
lct is responsible for the enzymatic cleavage
of the milk sugar lactose and was detected in the duodenum and jejunum but not in the
ileum.
si expression was low in the duodenum and peaked in jejunum, with lower
expression in the ileum.
anpep and dpp <dig> were expressed all along the
small intestine.
dpp <dig> was also highly expressed in the salivary gland and in
the distal colon.
these observations fit the known expression patterns for these genes
in post-weaned rabbits  <cit> .
associated with the role of the intestine in nutrient uptake, there were a
large number of solute transporters included in the gi tract data , and many showed region-specific expression patterns consistent with their
known functions .
for example, ferroportin , a protein
involved in iron export from duodenal epithelial cells and found to be defective in
patients with iron overload  <cit> , was restricted to duodenum.
the expression of the enterocyte sodium/glucose
cotransporter  was restricted to the small intestine, expression levels
peaking in the jejunum  <cit>  and the chloride transporter of apical membrane of columnar epithelium of the
colon   <cit>  which when mutated results in congenital chloride diarrhea, was largely
restricted to the large bowel samples.
other cell-specific 'marker' genes, for example,
mucins , gut hormones , lymphocyte markers , myosins  and collagens  were also enriched in samples where they would be expected
.
the gi tract data were prefiltered to remove low intensity signals and technical
artefacts, and the remaining data  subjected to network analysis.
a collapsed cluster diagram of the network is shown in figure 5a
and screenshots of the transcript level network in additional file  <dig>  figure s <dig>  annotated '.expression' and '.layout' files are given in
additional files  <dig> and  <dig>
respectively.
the data divided into  <dig> clusters of coexpressed genes .
a listing of the main clusters and an interpretation of the gene
signatures is shown in table  <dig> and a full listing of the genes
within those clusters is provided in additional file  <dig>  table
s <dig>
the average gene profile of transcripts within selected clusters.
a.
collapsed cluster diagram shown here is a simplified view of the graph used for
the analysis of the gi tract [see additional file  <dig>
figure s <dig> for screenshot of transcript level graph].
each node represents a
cluster of genes, the size of the node being proportional to the number of
individual nodes  with that cluster.
edges represent connections
between clusters whereby nodes in one cluster share edges with nodes in another.
the color of the nodes has been selected to represent clusters of genes expressed
in similar tissue types.
b. block diagrams of the average gene expression
profile of the major gi clusters.
expression levels are indicated as a % of
maximum with values rounded to the nearest 10%, each gi compartment analyzed being
represented as a separate block.
a key to the size of each block is shown in the
bottom right hand corner.
gene clusters have been grouped according to cell type
of origin: purple, stratified squamous epithelia; brown, salivary stratified
columnar epithelia; light green, ciliated/glandular columnar epithelia; dark
green, immune cells/cell cycle; blue, musculature ; grey,
neuronal.
gi, gastrointestinal.
along the porcine gi tract.
gi, gastrointestinal.
in analyzing these data we have attempted to relate the clusters to the cell composition
of the gi tact, based on the gene membership of clusters and their expression pattern.
the different samples varied significantly in their muscle content, so some of the
largest clusters contained muscle-specific genes.
gi-cluster  <dig> was enriched for genes
known to be expressed specifically in skeletal muscle and were highly expressed in the
tongue and esophageal samples .
in contrast, the genes
in gi-cluster  <dig> were highly expressed throughout the gi tract, peaking in the pylorus
sample.
the cluster contained not only genes associated with smooth muscle but also many
extra-cellular matrix -associated genes identified previously from mouse data  <cit> .
expression of these genes was shared with other mesenchymal lineages  and they formed a separate cluster in the whole atlas data.
gi-cluster 9
sits between gi-clusters  <dig> and  <dig> and comprises a set of genes expressed in both muscle
types.
another cluster in this region of the graph  contained many of the
genes associated with oxidative phosphorylation  with a number of
interesting and plausible new additions to this pathway.
finally, gi-cluster  <dig> genes
were highly-expressed in the pylorus sample.
the cluster contained numerous
neuron-associated genes and may derive from neuronal/supporting cells that make up the
enteric plexus.
although the motile and hormonal activity of the gi tract is controlled
by a complex nervous system, neurons actually represent only a small percentage of the
cells that make up the organ.
hence, their expression signature would appear to be
relatively weak compared with other cell types.
the gi tract is also a major immune organ.
it represents one of the main battle grounds
in an animal's defense against invading pathogens because of the large surface area, the
nutrient rich luminal environment and the requirement for a thin lining permeable to
nutrients.
it is, therefore, unsurprising that the largest cluster of genes  contained many genes associated with the immune system, their expression being two-
to three-fold higher in the ileum than other regions.
the lower small intestine is known
to be associated with increased immune surveillance and the presence of peyer's patches
.
the cluster analysis did not separate the immune cell types which are largely
co-located in the lamina propria and lymphoid aggregates.
included in gi-cluster  <dig> were
genes encoding many of the protein components of the b cell receptor complex
but also numerous
genes identified in the full atlas analysis as being expressed specifically by t cells
or macrophages.
also evident in this cluster were many of the core components of the
cell cycle, for example cyclins, dna polymerases, kinesins, and so on, again identified
in the whole atlas as a discrete cluster .
the association of cell
cycle genes with an immune signature is most likely due to the high level of lymphocyte
proliferation  <cit> , which increases the proportion of cells undergoing mitosis relative to the
rest of the organ.
in the neighborhood of the main gi immune cluster were smaller
clusters of immune-associated genes that were expressed in a distinct but related
manner, perhaps connected to regional immune specialization.
gi-cluster  <dig> contains many
of the components of the t cell receptor complex  which could be aligned with the distribution of intraepithelial
lymphocytes.
the analysis also detected a small, heavily expressed cluster of plasma b
cell genes  and two small clusters of immune response
genes  that varied significantly in their level of expression
between animals.
other clusters were enriched for mhc class  <dig>  and class
<dig>  antigen presentation pathway genes.
although the lamina propria of the gut contains the largest macrophage population in the
body  <cit> , many of the macrophage-specific genes identified in the whole atlas were not
detectable in gi-cluster  <dig>  for each of the genes in the macrophage cluster as defined
in the full atlas dataset, we calculated the ratio of their highest expression in
macrophages to their highest expression across gi tract samples.
the average ratio was
around  <dig>  suggesting that macrophages provide around 20% of the total mrna yield from
the gut.
the genes that were under-expressed based upon this ratio were derived mainly
from atlas cluster  <dig>  the subset of macrophage-expressed genes that was enriched in
alveolar macrophages.
the most repressed was cyp7a <dig>  the
cholesterol-7-hydroxylase, which metabolizes bile acids.
the other striking feature was
the large number of genes for c-type lectins, including clec5a ,
clec7a , cd <dig> , clec4d ,
siglec <dig> , clec13d , clec4e
and clec12b, that are highly-expressed in alveolar macrophages
but appeared down-regulated in the gi tract.
this pattern indicates that macrophages of
the gut are distinct from those of the lung and blood, perhaps adapted to be
hypo-responsive to food-derived glycoproteins where those of the lung must use the same
receptors to recognize and engulf potential pathogens.
the phenotype of lamina propria
macrophages may also vary within different regions of the gi tract thereby breaking up
their expression signature.
the epithelial layer exhibits a great diversity between different gi compartments, its
structure and function changing in line with requirements.
many clusters correlated with
the known region-specific expression of structural proteins and solute carriers
described above.
gi-clusters  <dig> and  <dig>  containing specific keratin genes, are related to
the stratified squamous epithelial populations that protect against abrasion and
mechanical damage to the underlying tissues in the tongue and esophagus.
genes in
gi-cluster  <dig> tended to be expressed in equal levels in the tongue and lower esophagus,
whereas genes in gi-cluster  <dig> were more restricted in their expression to the tongue.
these genes define the specific signature of stratified squamous epithelial populations
present in this organ.
similarly gi-clusters  <dig> and  <dig> which were high in the salivary
gland or along the entire length of the gut, respectively, likely represent genes
specifically expressed in the stratified or ciliated columnar epithelium present in
these organs.
among the columnar epithelium populations, which line the gut from the
stomach to the rectum, there was region-specific differentiation, reflected by the
differing levels of expression of genes along the longitudinal axis of the intestine and
the presence of specific populations of glandular cells.
enriched in gi-cluster  <dig> were
many transcripts  that were expressed specifically in
the small intestine and encode the machinery for the digestion and absorption of
nutrients.
in contrast, there were relatively few genes expressed specifically in the
colon  and little evidence of
functional compartmentalization of expression along that organ.
among these genes many
matched the known markers of this tissue but others were novel.
there are various
glandular and endocrine cell populations that are integral to the columnar epithelial
lining and in many cases have their origins in the same epithelial stem cell populations
located at the base of the crypts.
because they inhabit specific niches within the gi
tract, genes expressed specifically within them have a unique expression pattern.
for
this reason, we can assign the genes in gi-cluster  <dig> with some confidence to expression
in the fundic glands, gi-cluster  <dig> genes to pyloric glands and gi-cluster  <dig> genes to
mucous secreting superficial gastric glands.
these assignments are also strongly
supported by the gene membership of these clusters and the lists expand the complement
of genes known to be expressed in these specialized glandular systems.
the genes in
gi-cluster  <dig> were likely expressed in glandular/endocrine cells present only in the
duodenum.
finally, genes expressed in the salivary gland could be segregated to those
expressed in serosal  or mucosal  acini.
while both were
exclusively expressed in the salivary gland they separate the two salivary gland
samples, presumably due to chance sampling of different regions of the gland.
in our previous analysis of a mouse cell atlas, specific clusters frequently contained
the transcription factors that regulated them, and their promoters were over-represented
with the motifs that are the targets of those factors  <cit> .
we analyzed a set of candidate transcription factors  encoded by the
human genome  <cit>  as a correlation network .
clusters of tfs that had a preference in their expression for one or
multiple regions of the gi tract grouped together.
the expression patterns of numerous
other tfs imply previously unrecognized roles in regulating cell differentiation in this
organ.
rfx <dig> is classically associated with regulating insulin expression and has
recently been shown to be essential for islet cell differentiation in the murine
pancreas  <cit> .
in the pig gi tract, the rfx <dig> gene was highly expressed in the
salivary gland, with significant expression in the duodenum .
we suggest that the rfx <dig> protein could also contribute to
epithelial/endocrine differentiation in these organs.
this suggestion is supported by
protein expression data  <cit> , and the discovery that mutations in this gene in human mitchell-riley
syndrome are associated with duodenal and jejunal atresia  <cit> .
the onecut <dig> protein is a member of a small tf family that contains a cut
domain and an atypical homeodomain.
onecut <dig> has been associated with the regulation of
retinal development  <cit>  and pancreatic and enteric endocrine differentiation  <cit> .
in the pig gut, the gene was highly and specifically expressed in the
duodenum  and was tightly coexpressed with the tf
pdx <dig> , a gene which is expressed by
duodenal enterocytes  <cit> , suggesting a role in defining epithelial differentiation in the region of
the intestine.
finally, satb <dig> is a homeobox protein with known roles in osteoblast  <cit>  and neuronal  <cit>  differentiation.
the recently characterized hsa2q <dig>  microdeletion syndrome
is associated with genomic deletion of all or part of the human satb <dig> gene  <cit> .
in the pig, expression of this gene was exclusively found in the lower
bowel, consistent with human protein expression data  <cit>  and its utility as a marker of colorectal derived cancers  <cit> .
this specific expression in the epithelium of the large intestine would
predict a defining role in this region.
expression among the complement of transcription factors  expressed in the
porcine gi tract.
tfs with similar expression profiles group together and
groupings likely represent regulatory units that work together to control cellular
differentiation within regions of the organ.
a number of tfs have been selected
that are expressed in a very region-specific manner but whose known biology has
not previously been associated with a functional role within this organ.
gi,
gastrointestinal.
CONCLUSIONS
this work describes the first detailed analysis of the transcriptional landscape of the
pig.
since the pig is a large animal with a physiology that is closer to man's than is
that of mouse, this analysis provides a major new resource for understanding gene
expression with respect to the known physiology of mammalian tissues and cells.
at the
single gene level, this dataset represents a comprehensive survey of gene expression
across a large range of pig tissues.
in instances where the expression of a gene is
regulated in a tissue-specific manner it represents a good starting point for
understanding its likely cellular expression pattern and, therefore, its functional
role.
the availability of the data on the biogps web portal renders the data amenable to
such queries.
however, it is the ability to understand the expression of a gene in the
context of others that makes this analysis unique.
correlation analysis and the use of
advanced network visualization and clustering techniques go beyond standard pairwise
hierarchical approaches in defining coexpression relationships between genes.
the
approach used here allows us to capture and visualize the complexity of these
relationships in high dimensional data, rendering large proportions of the data
available for analysis.
using this network clustering approach we have been able to
recapitulate known expression and functional relationships between genes as well as
infer new ones based on guilt-by-association.
the detailed analysis of the
transcriptional landscape of the gastrointestinal tract provides the first comprehensive
view of the regional specialization of this organ in a large animal, and has highlighted
numerous candidate genes that may underlie genetic diseases of the human
gastrointestinal tract such as colitis and cancer.

 the comprehensive definition of the mammalian transcriptome has altered our view of genome complexity and the transcriptional landscape of tissues and cells. systematic analysis of the transcriptome is of central interest to the biology community, but global coverage was not possible until the complete sequencing of the human and mouse genomes and the advent of microarrays. the pioneering work by su et al. provided the first comprehensive analysis of the protein-encoding transcriptome of major organs of human and mouse. others have used microarrays or alternative methods to map expression in specific tissues or cell types. the work of the fantom and encode projects has revealed the true complexity of the mammalian transcriptome, highlighting the impact of alternative initiation, termination and splicing on the proteome, and the prevalence of multiple different classes of non-coding rnas. the pace of data acquisition has continued to grow with the increasing reliability and decreasing cost of the core technologies such as microarrays and the sequencing of rna. despite these efforts, knowledge of the human transcriptional landscape is still sparse. efforts to curate and analyze an'atlas' from the existing human microarray data are hindered by the fact that certain types of samples have been analyzed extensively, for example hematopoietic cells and cancers, while little or no data are available for many other tissues and cell types. studies of the non-pathological human transcriptome are compromised further because most tissues can only be obtained post-mortem, the provenance of samples can be variable and the health status of the individual from whom they were obtained is often unknown. with numerous predicted mammalian protein-coding loci still having no informative functional annotation and even less insight into the function of the many non-protein-coding genes, detailed knowledge of a transcript's expression pattern can provide a valuable window on its function. previously, we have used coexpression analysis of large mouse datasets to provide functional annotation of genes, characterization of cell types and discovery of candidate disease genes. isolated cell types may differ not only in their specialized function but also in their engagement with'housekeeping' processes, such as growth and proliferation, mitochondrial biogenesis and oxidative phosphorylation, metabolism and macromolecule synthesis, the cytoskeleton, the proteasome complex, endocytosis and phagocytosis. genes coding for proteins within pathways, both generic and cell-specific, often form coexpression clusters, so one can infer the function of a gene of unknown function from the transcriptional company it keeps, by applying the principle of guilt-by-association. the identification of coexpression clusters can, in turn, inform the identification of candidate genes within genomic intervals associated with specific traits from genome-wide association studies or classical linkage studies. for example, we identified a robust cluster of genes that is expressed specifically in cells of mesenchymal lineages in the mouse. the cluster contained a large number of genes previously shown to be causally associated with inherited abnormalities of the musculoskeletal system in humans. by inference, other genes within this cluster that have less informative annotation or no known function, are likely to be involved in musculoskeletal development. as noted previously, the conservation of coexpression clusters can provide an even more powerful indicator of likely conserved function. these authors mapped coexpressed clusters onto human mendelian disease loci of unknown molecular basis from online mendelian inheritance in man and identified candidate genes based upon their conserved restricted expression within the affected organ. the domestic pig is economically important in its own right, and has also been used increasingly as an alternative model for studying human health and disease and for testing new surgical and pharmacological treatments. compared to traditional rodent models, the pig is more closely-related to humans in its size, growth, development, immunity and physiology as well as its genome sequence. the translation of preclinical studies in rodents into clinical applications in humans is frequently unsuccessful, especially for structures where rodents have very different anatomy and physiology, such as the cardiovascular system. the recently released pig genome sequence and associated annotation will greatly enhance the utility of the pig as a model. however, compared to the mouse, the knowledge of the pig transcriptome is very limited partly due to a lack of commercial expression microarrays with comprehensive gene coverage. while several est sequencing projects have explored gene expression across a range of tissues, a systematic global study of the tissue expression landscape is not available. here we present a new microarray platform for the pig with greatly improved gene coverage and annotation. we have used this array to generate an expression atlas for the pig, comparable to the human/mouse expression atlases, and, using advanced visualization and clustering analysis techniques, we have identified networks of co-expressed genes. a detailed analysis of the porcine gastrointestinal tract illustrates the power of the analytical approach and data. these data will support improved annotation of the pig and human genomes and increase the utility of the pig as a model in medical research.  the pig is uniquely important both as a major source of food and an animal model for human disease. until recently the lack of a genome sequence for the pig and consequently many of the functional-genomic analysis tools, have limited the kind of analyses now routine in human and mouse systems. here we report the design, annotation and validation of a new comprehensive microarray for the analysis of gene expression in the pig and a first attempt to produce a global map of the porcine protein coding transcriptome. the new snowball array is far more comprehensive in its gene coverage than the previous porcine affymetrix array which was based on the available expressed sequence tag data circa it is also more extensive than the new porcine'peg' array recently released by affymetrix, with nearly twice as many probes included on the snowball array, and draws on a larger cdna sequence database. the results from the analysis described here validate the performance and gene annotation of the snowball array. a major problem currently restricting genomic analysis of production animals is the fact that many genes remain unannotated due to problems in establishing orthology among homologous sequences from other species. we adopted a'best match' approach to increase the number of annotated features on the array. the repeated finding that transcripts annotated in this way were expressed in a pattern that was consistent with their proposed function supports the validity of this approach. however, we would urge caution in accepting the orthology match of probes annotated in this way without further verification. we have aligned the probe sequences from the snowball array with the recently released sscrofa assembly. we will publish these alignments as a das track in ensembl in the short term and integrate the alignments into ensembl and biomart in the next ensembl release. these alignments enable the expression data to be used to annotate the genome sequence further and the interpretation of expression profiles for a gene/transcript in a genomic context. transcriptome. a comparison of different porcine expression arrays manufactured by affymetrix. this table compares the major features of the two commercial affymetrix expression arrays that have been designed for studies of the pig transcriptome with the array designed and used for this study. arrays still provide a very cost effective solution for producing a large amount of high quality gene expression data. in terms of speed of data acquisition and availability of established analysis routines that can be run on desktop machines, arrays still have many advantages over sequencing-based analyses. with improvements in the assembly and annotation of the genome and gene models and rnaseq analyses increasing our knowledge of the transcriptional landscape of the transcriptome, there is no doubt the current array design will be enhanced. the primary cohort of animals used for this study was a group of three- to four-month old juvenile pigs of both sexes. we aimed to gather samples of every major pig tissue. where possible biological replicates were analyzed that originated from different animals of each sex. regional analysis of the brain is clearly important, and more feasible in pigs than in mice, but the method of killing meant that detailed dissection of brain was not possible. the age/stage of the animals also meant that certain tissues could not be collected and the panel of tissues was supplemented by samples of placenta and a mature testis. since macrophages have proved to be one of the most complex sources of novel mrnas, we included a number of macrophage samples stimulation) in the atlas. for details of the tissues and cells used for this study see additional file table s biolayout express3d is a unique tool in the analysis of large complex expression datasets. the statistical approach employed centers on the principle of coexpression, based on the transcript-to-transcript comparison of the expression signal across the samples analyzed, by calculation of a pearson correlation matrix. for any given comparison, the pearson value can range from + to -. the correlation and clustering algorithms within biolayout express3d, together with the ability to visualize and explore very large network graphs, mean that it is uniquely positioned for the analysis of large datasets and has been used extensively for this purpose. a graph derived from a given correlation cut-off value includes only those genes that are related in expression to others above the selected threshold and more or less complex graphs may be analyzed by decreasing or increasing this value, respectively. core topological structures that often form separate graph components at high thresholds are robust and are maintained as correlation cut-off values are lowered. we used biolayout express3d to analyze the pig transcriptome data generated using the snowball array. from a pairwise transcript-to-transcript correlation matrix a weighted, undirected network graph was constructed using a pearson correlation threshold cut-off of r ≥. the resultant graph was large and highly structured with one large component of nodes and smaller components of between and nodes. the topology of the graph contained localized areas of high connectivity and high correlation, dominated by groups of genes that are coexpressed and form highly connected cliques within the network. nodes representing different probesets designed to the same gene were generally highly correlated and connected to each other in the graph, confirming the validity of the probeset annotation and approach. three-dimensional visualization of a pearson correlation graph of data derived from analysis of pig tissues and cells. each node in the graph represents an individual probeset on the array and the edges correspond to correlations between individual measurements above the defined threshold. the graph is comprised of nodes and, edges. the complex topology of the graph is a result of groups of co-expressed genes forming cliques of high connectivity within the graph. clustering of the graph using the mcl algorithm was used to assign genes to groups based on coexpression. by inspection of the underlying profiles, areas of the graph can be associated with genes expressed by specific tissue or cell populations. plots of the average expression profile of genes in selected clusters are given on the right: b. profile of cluster genes whose expression is restricted to brain and spinal cord; c. profile of cluster genes whose expression is highest in blood; d. profile of cluster genes whose expression is restricted to skeletal muscle; e. profile of cluster genes whose expression is highest in the adrenal gland. mcl, markov cluster algorithm. shown here is a simplified view of the graph used for this analysis and shown inof the node being proportional to the number of individual nodes within that cluster. edges represent connections between clusters whereby nodes in one cluster share edges with nodes in another. the color of the nodes has been selected to represent clusters of genes expressed in given types of tissues which tend to group together with the overall topology of the network. some highly expressed genes were not included in the graph. the more unique a gene's expression pattern, the fewer neighbors it will have in the network. one example is the protease inhibitor, alpha-2-macroglobulin. there were five probesets on the array designed to this gene and all showed a highly similar expression pattern, albeit at a range of signal intensities. these probesets formed a small correlation network with themselves, but the expression pattern of this gene in the context of the full atlas was essentially unique and no other porcine gene was expressed in this manner. in some cases, such isolation is a consequence of the use of distinct cell-restricted promoters. for a2m, there is a single major transcription start site in both mouse and human, and the pattern of expression is similar in these two species and in pig, suggesting that a common set of regulatory factors control this gene's expression across species. for the majority of other probesets not found in the graph described here, transcripts appear to be expressed at very low levels. these genes may be highly-expressed in cells or tissues we have not sampled in this sample set. for example, we would not detect genes exclusively expressed during prenatal life as no samples from these stages were represented in the current atlas. within the biogps online portal. all data used for this study are available through the biogps database. genes can be searched for and where found the full expression profile is displayed as a bar chart. samples are colored according to type, for example, cns, gi tract, and so on, and the interface supports a number of useful features including a zoom function on the profile viewer, searches for genes with a similar profile, access to the raw data, links to external resources and the potential to compare profiles across species, for example, human, mouse, rat, zebrafish, frog. cns, central nervous system; gi, gastrointestinal. clustering of the graph using the markov clustering algorithm resulted in clusters. the largest consisted of transcripts and the top clusters, accounted for % of the nodes in the graph. the remainder of the graph was of a sparser topology and subdivided into numerous small clusters. figure shows the overall topology of the network graph together with the expression profiles of selected clusters. the profile and gene content of each cluster was examined in detail, and the largest clusters are shown in table the full cluster list together with gene membership is supplied in additional file table s note that there may be a degree of variation in the expression pattern of individual genes within a cluster which is masked when average profiles are displayed. tissue/cells/pathway. a list of the largest coexpression clusters evident in the pig gene expression atlas. listed in the table are the largest clusters of genes originating from our analysis of the pig expression atlas. clusters are numbered according to their size but are sorted here according to their grouping within the graph and the biology they represent. the first two columns give the cluster id and number of transcripts present in the cluster and the following column provides a description of the average expression profile of all transcripts within the cluster. the final two columns aim to group clusters according to the class of tissues in which these genes are predominately expressed and the tissue, cell or pathway we believe they represent. several of the largest clusters showed relatively little tissue specificity in their expression and might be considered to be'housekeeping' genes since the proteins they encode are likely to be functional in all cell types. such clusters are a common feature of large correlation graphs where a relatively low threshold has been employed. genes/probes with limited informative nomenclature were over-represented in these clusters, perhaps reflecting previous research focus on genes that demonstrate tissue-restricted expression profiles. aside from these large, nondescript clusters, the majority of the coexpression clusters were made up of transcripts that have a distinct tissue/cell restricted expression pattern. in each case, the cluster was named based upon the tissue/cell in which the genes were most highly-expressed. these data recapitulate many of the known tissue restricted expression patterns that have been described for human and mouse. for example, there were multiple large clusters of genes with strong expression in the macrophage samples with a subset more highly-expressed in the alveolar macrophages and another set induced by lps. each of these clusters contained genes for numerous well-studied macrophage surface markers and receptors, and proinflammatory cytokines. a detailed comparative analysis of human and pig macrophage gene expression has been reported elsewhere. the present analysis did not identify the single large phagocytosis/lysosome functional cluster that was evident in the analysis of mouse primary cell data. this cluster tends to be broken up when tissue samples are included in the analysis because many of the components of this system are utilized more generally in vesicle-trafficking and in other pathways. a secondary feature of the network graph is that clusters with similar expression patterns formed neighborhoods. for instance, clusters of genes selectively expressed in the reproductive tract, gastrointestinal tract, central nervous system, mesenchymal-derived tissues, dermal tissues or blood cells tended to occupy similar areas. in this way the graph distributed the transcriptome into groups of genes associated with tissues composed of cells of different embryonic lineages. because cells and tissues differ in their engagement with fundamental biochemical processes, the graph also contained clusters that grouped together genes associated with a particular cellular process which may be active in a wide range of tissues albeit not at the exact same level. examples include clusters enriched for ribosomal, cell cycle and oxidative phosphorylation genes. the clusters of ribosomal genes form a separate graph component which together contain transcripts, including at least known ribosomal protein genes gene identifiers), genes for eukaryotic translation initiation factors, two members of the rnasep complex, naca, u and u small nuclear ribonucleoproteins and at least small nucleolar rnas. snornas function to guide modifications of other rnas, particularly ribosomal protein mrnas, consistent with their co-clustering with components of the ribosome complex. different tissues also vary in their rates of cell renewal and consequently in the proportions of proliferating cells. genes involved in the cell cycle, therefore, have a pattern of expression that reflects the mitotic activity of the tissues and such genes are readily identified in the graph. cluster contains many genes for proteins known to be involved in the cell cycle and supports the involvement of other cluster 14 genes in this pathway. for example, the cluster includes vaccinia-related kinase 1 shown recently to play a role in the control of mitosis, highlighting the importance of our approach for annotation of uncharacterized genes. to further illustrate the power of this approach in defining pathway systems, we show a detailed analysis of the enrichment of genes associated with oxidative phosphorylation and the tricarboxylic acid cycle in clusters and. clusters and were widely separated within the graph. this separation represents a different regulation of these two sets of genes. all cluster genes were highly expressed in all tissues and are core components of the mitochondrial oxidative phosphorylation complexes encoded by the mitochondrial genome. in contrast, the genes in cluster are encoded by the nuclear genome and showed a marked elevation in their expression in the heart, reflecting the high rates of respiration in this tissue. the transcripts in this cluster include multiple members of every one of the five complexes associated with the generation of atp by the mitochondria and most of the enzymes driving the tca cycle. the coexpression of multiple members of pathways for long chain fatty acid oxidation, mitochondrial membrane transport and ubiquinone and cytochrome c biosynthesis supports the functional link between these pathways. on the basis of guilt-by-association the unnannotated/poorly characterized transcripts within this cluster are prime candidates for a functional association with the oxidative respiration process. for example, gbas and chchd were recently identified by coexpression analysis and shown to be associated with mitochondrial complex iv. there are numerous other clusters within this dataset which cannot easily be associated with an obvious functional role but likely represent clusters of genes with shared or related functions. and ndufb ndufb ndufb ndufb ndufb ndufc ndufc ndufs ndufs coexpression of members of oxidative phosphorylation and related pathways. nuclear and mitochondrial gene clusters associated with oxidative phosphorylation. the known association with pathway/protein complex is listed for each gene in the cluster. list of all genes present in clusters and and the function of the encoded protein, where known. the pig's size and the feasibility of obtaining fresh tissues from healthy individuals offer a unique opportunity to study the expression landscape of important organ systems. in common with humans, the pig is an omnivore and its gastrointestinal tract has evolved to be able to masticate, digest and absorb a wide range of foodstuffs. in this study, we collected samples along the entire length of the gi tract from the tongue to the rectum, a total of distinct regions, as shown in figure 4a. the gi tract is lined with an epithelial layer whose cellular composition changes in line with the functional role of the gi compartment. the upper gi tract is lined with a stratified squamous epithelium which transitions in the stomach to a columnar epithelium that runs through to the rectum. even within the small intestine, enterocyte expression of solute transporters and digestive enzymes is tightly regulated to reflect the changing nature of the luminal contents, as well as the migration of cells up the crypt-villus axis. associated with the epithelium are various glandular cell types involved with enzyme secretion, lubrication, and endocrine control, and specialized structures, such as the pyloric and fundic glands of the stomach and sub-mucosal brunner's glands of the duodenum. the lamina propria, which lies beneath the epithelium, is itself a complex mix of cells made up of endothelial, immune and connective tissues. the gi tract is almost entirely surrounded by musculature and regulated by the enteric neural plexus. therefore, the gi tract is composed of five major classes of cell types: epithelia, glandular/endocrine epithelia, immune cells, neuronal cells and mesenchymal cells. the region-specific cellular composition of the gi tract is summarized in figure 4b. with specific regions. a. schematic of the different regions of the pig gi tract with areas sampled for this study marked with a red dot. b. table of the five main cell types and subdivisions thereof that make up the gi tract and their expected presence in the samples analyzed here. gi, gastrointestinal. to validate the gi-specific analysis, we initially selected a number of gene families/classes where expression is known to be specific to certain cell populations in other mammals. keratins are structural proteins that distinguish different classes of epithelial cells. we looked at eight keratin gene family members. all but krt and krt were heavily expressed in the tongue, krt krt and krt were also expressed in the lower esophagus and fundus, both of which are lined with a stratified squamous epithelium. krt and krt markers of columnar epithelium, showed the anticipated inverse pattern, with strong expression in the salivary gland, antrum and along the entire length of the small and large intestine. to confirm region-specific epithelial function, we examined the expression of four well-characterized brush border hydrolases: lactase, sucrose-isomaltase, aminopeptidase n and dipeptidyl-peptidase 4. lct is responsible for the enzymatic cleavage of the milk sugar lactose and was detected in the duodenum and jejunum but not in the ileum. si expression was low in the duodenum and peaked in jejunum, with lower expression in the ileum. anpep and dpp were expressed all along the small intestine. dpp was also highly expressed in the salivary gland and in the distal colon. these observations fit the known expression patterns for these genes in post-weaned rabbits. associated with the role of the intestine in nutrient uptake, there were a large number of solute transporters included in the gi tract data, and many showed region-specific expression patterns consistent with their known functions. for example, ferroportin, a protein involved in iron export from duodenal epithelial cells and found to be defective in patients with iron overload, was restricted to duodenum. the expression of the enterocyte sodium/glucose cotransporter was restricted to the small intestine, expression levels peaking in the jejunum and the chloride transporter of apical membrane of columnar epithelium of the colon which when mutated results in congenital chloride diarrhea, was largely restricted to the large bowel samples. other cell-specific'marker' genes, for example, mucins, gut hormones, lymphocyte markers, myosins and collagens were also enriched in samples where they would be expected. the gi tract data were prefiltered to remove low intensity signals and technical artefacts, and the remaining data subjected to network analysis. a collapsed cluster diagram of the network is shown in figure 5a and screenshots of the transcript level network in additional file figure s annotated'.expression' and'.layout' files are given in additional files and respectively. the data divided into clusters of coexpressed genes. a listing of the main clusters and an interpretation of the gene signatures is shown in table and a full listing of the genes within those clusters is provided in additional file table s the average gene profile of transcripts within selected clusters. a. collapsed cluster diagram shown here is a simplified view of the graph used for the analysis of the gi tract [see additional file figure s for screenshot of transcript level graph]. each node represents a cluster of genes, the size of the node being proportional to the number of individual nodes with that cluster. edges represent connections between clusters whereby nodes in one cluster share edges with nodes in another. the color of the nodes has been selected to represent clusters of genes expressed in similar tissue types. b. block diagrams of the average gene expression profile of the major gi clusters. expression levels are indicated as a % of maximum with values rounded to the nearest 10%, each gi compartment analyzed being represented as a separate block. a key to the size of each block is shown in the bottom right hand corner. gene clusters have been grouped according to cell type of origin: purple, stratified squamous epithelia; brown, salivary stratified columnar epithelia; light green, ciliated/glandular columnar epithelia; dark green, immune cells/cell cycle; blue, musculature; grey, neuronal. gi, gastrointestinal. along the porcine gi tract. gi, gastrointestinal. in analyzing these data we have attempted to relate the clusters to the cell composition of the gi tact, based on the gene membership of clusters and their expression pattern. the different samples varied significantly in their muscle content, so some of the largest clusters contained muscle-specific genes. gi-cluster was enriched for genes known to be expressed specifically in skeletal muscle and were highly expressed in the tongue and esophageal samples. in contrast, the genes in gi-cluster were highly expressed throughout the gi tract, peaking in the pylorus sample. the cluster contained not only genes associated with smooth muscle but also many extra-cellular matrix -associated genes identified previously from mouse data. expression of these genes was shared with other mesenchymal lineages and they formed a separate cluster in the whole atlas data. gi-cluster 9 sits between gi-clusters and and comprises a set of genes expressed in both muscle types. another cluster in this region of the graph contained many of the genes associated with oxidative phosphorylation with a number of interesting and plausible new additions to this pathway. finally, gi-cluster genes were highly-expressed in the pylorus sample. the cluster contained numerous neuron-associated genes and may derive from neuronal/supporting cells that make up the enteric plexus. although the motile and hormonal activity of the gi tract is controlled by a complex nervous system, neurons actually represent only a small percentage of the cells that make up the organ. hence, their expression signature would appear to be relatively weak compared with other cell types. the gi tract is also a major immune organ. it represents one of the main battle grounds in an animal's defense against invading pathogens because of the large surface area, the nutrient rich luminal environment and the requirement for a thin lining permeable to nutrients. it is, therefore, unsurprising that the largest cluster of genes contained many genes associated with the immune system, their expression being two- to three-fold higher in the ileum than other regions. the lower small intestine is known to be associated with increased immune surveillance and the presence of peyer's patches. the cluster analysis did not separate the immune cell types which are largely co-located in the lamina propria and lymphoid aggregates. included in gi-cluster were genes encoding many of the protein components of the b cell receptor complex but also numerous genes identified in the full atlas analysis as being expressed specifically by t cells or macrophages. also evident in this cluster were many of the core components of the cell cycle, for example cyclins, dna polymerases, kinesins, and so on, again identified in the whole atlas as a discrete cluster. the association of cell cycle genes with an immune signature is most likely due to the high level of lymphocyte proliferation, which increases the proportion of cells undergoing mitosis relative to the rest of the organ. in the neighborhood of the main gi immune cluster were smaller clusters of immune-associated genes that were expressed in a distinct but related manner, perhaps connected to regional immune specialization. gi-cluster contains many of the components of the t cell receptor complex which could be aligned with the distribution of intraepithelial lymphocytes. the analysis also detected a small, heavily expressed cluster of plasma b cell genes and two small clusters of immune response genes that varied significantly in their level of expression between animals. other clusters were enriched for mhc class and class antigen presentation pathway genes. although the lamina propria of the gut contains the largest macrophage population in the body, many of the macrophage-specific genes identified in the whole atlas were not detectable in gi-cluster for each of the genes in the macrophage cluster as defined in the full atlas dataset, we calculated the ratio of their highest expression in macrophages to their highest expression across gi tract samples. the average ratio was around suggesting that macrophages provide around 20% of the total mrna yield from the gut. the genes that were under-expressed based upon this ratio were derived mainly from atlas cluster the subset of macrophage-expressed genes that was enriched in alveolar macrophages. the most repressed was cyp7a the cholesterol-7-hydroxylase, which metabolizes bile acids. the other striking feature was the large number of genes for c-type lectins, including clec5a, clec7a, cd, clec4d, siglec, clec13d, clec4e and clec12b, that are highly-expressed in alveolar macrophages but appeared down-regulated in the gi tract. this pattern indicates that macrophages of the gut are distinct from those of the lung and blood, perhaps adapted to be hypo-responsive to food-derived glycoproteins where those of the lung must use the same receptors to recognize and engulf potential pathogens. the phenotype of lamina propria macrophages may also vary within different regions of the gi tract thereby breaking up their expression signature. the epithelial layer exhibits a great diversity between different gi compartments, its structure and function changing in line with requirements. many clusters correlated with the known region-specific expression of structural proteins and solute carriers described above. gi-clusters and containing specific keratin genes, are related to the stratified squamous epithelial populations that protect against abrasion and mechanical damage to the underlying tissues in the tongue and esophagus. genes in gi-cluster tended to be expressed in equal levels in the tongue and lower esophagus, whereas genes in gi-cluster were more restricted in their expression to the tongue. these genes define the specific signature of stratified squamous epithelial populations present in this organ. similarly gi-clusters and which were high in the salivary gland or along the entire length of the gut, respectively, likely represent genes specifically expressed in the stratified or ciliated columnar epithelium present in these organs. among the columnar epithelium populations, which line the gut from the stomach to the rectum, there was region-specific differentiation, reflected by the differing levels of expression of genes along the longitudinal axis of the intestine and the presence of specific populations of glandular cells. enriched in gi-cluster were many transcripts that were expressed specifically in the small intestine and encode the machinery for the digestion and absorption of nutrients. in contrast, there were relatively few genes expressed specifically in the colon and little evidence of functional compartmentalization of expression along that organ. among these genes many matched the known markers of this tissue but others were novel. there are various glandular and endocrine cell populations that are integral to the columnar epithelial lining and in many cases have their origins in the same epithelial stem cell populations located at the base of the crypts. because they inhabit specific niches within the gi tract, genes expressed specifically within them have a unique expression pattern. for this reason, we can assign the genes in gi-cluster with some confidence to expression in the fundic glands, gi-cluster genes to pyloric glands and gi-cluster genes to mucous secreting superficial gastric glands. these assignments are also strongly supported by the gene membership of these clusters and the lists expand the complement of genes known to be expressed in these specialized glandular systems. the genes in gi-cluster were likely expressed in glandular/endocrine cells present only in the duodenum. finally, genes expressed in the salivary gland could be segregated to those expressed in serosal or mucosal acini. while both were exclusively expressed in the salivary gland they separate the two salivary gland samples, presumably due to chance sampling of different regions of the gland. in our previous analysis of a mouse cell atlas, specific clusters frequently contained the transcription factors that regulated them, and their promoters were over-represented with the motifs that are the targets of those factors. we analyzed a set of candidate transcription factors encoded by the human genome as a correlation network. clusters of tfs that had a preference in their expression for one or multiple regions of the gi tract grouped together. the expression patterns of numerous other tfs imply previously unrecognized roles in regulating cell differentiation in this organ. rfx is classically associated with regulating insulin expression and has recently been shown to be essential for islet cell differentiation in the murine pancreas. in the pig gi tract, the rfx gene was highly expressed in the salivary gland, with significant expression in the duodenum. we suggest that the rfx protein could also contribute to epithelial/endocrine differentiation in these organs. this suggestion is supported by protein expression data, and the discovery that mutations in this gene in human mitchell-riley syndrome are associated with duodenal and jejunal atresia. the onecut protein is a member of a small tf family that contains a cut domain and an atypical homeodomain. onecut has been associated with the regulation of retinal development and pancreatic and enteric endocrine differentiation. in the pig gut, the gene was highly and specifically expressed in the duodenum and was tightly coexpressed with the tf pdx, a gene which is expressed by duodenal enterocytes, suggesting a role in defining epithelial differentiation in the region of the intestine. finally, satb is a homeobox protein with known roles in osteoblast and neuronal differentiation. the recently characterized hsa2q microdeletion syndrome is associated with genomic deletion of all or part of the human satb gene. in the pig, expression of this gene was exclusively found in the lower bowel, consistent with human protein expression data and its utility as a marker of colorectal derived cancers. this specific expression in the epithelium of the large intestine would predict a defining role in this region. expression among the complement of transcription factors expressed in the porcine gi tract. tfs with similar expression profiles group together and groupings likely represent regulatory units that work together to control cellular differentiation within regions of the organ. a number of tfs have been selected that are expressed in a very region-specific manner but whose known biology has not previously been associated with a functional role within this organ. gi, gastrointestinal.  this work describes the first detailed analysis of the transcriptional landscape of the pig. since the pig is a large animal with a physiology that is closer to man's than is that of mouse, this analysis provides a major new resource for understanding gene expression with respect to the known physiology of mammalian tissues and cells. at the single gene level, this dataset represents a comprehensive survey of gene expression across a large range of pig tissues. in instances where the expression of a gene is regulated in a tissue-specific manner it represents a good starting point for understanding its likely cellular expression pattern and, therefore, its functional role. the availability of the data on the biogps web portal renders the data amenable to such queries. however, it is the ability to understand the expression of a gene in the context of others that makes this analysis unique. correlation analysis and the use of advanced network visualization and clustering techniques go beyond standard pairwise hierarchical approaches in defining coexpression relationships between genes. the approach used here allows us to capture and visualize the complexity of these relationships in high dimensional data, rendering large proportions of the data available for analysis. using this network clustering approach we have been able to recapitulate known expression and functional relationships between genes as well as infer new ones based on guilt-by-association. the detailed analysis of the transcriptional landscape of the gastrointestinal tract provides the first comprehensive view of the regional specialization of this organ in a large animal, and has highlighted numerous candidate genes that may underlie genetic diseases of the human gastrointestinal tract such as colitis and cancer. 
network analysis of the transcription factors expressed in the gastrointestinal order to provide comprehensive coverage of the known pig transcriptome. array was used to generate a genome-wide expression atlas of pig tissues derived respect to the known physiology of mammalian tissues and cells. discuss the expression signatures associated with the gastrointestinal tract, an to man, we provide a major new resource for understanding gene expression with transcriptome where transcripts are grouped according to their expression pattern,
 this work describes the first genome-wide analysis of the transcriptional landscape of the pig. a new porcine affymetrix expression array was designed in order to provide comprehensive coverage of the known pig transcriptome. the new array was used to generate a genome-wide expression atlas of pig tissues derived from tissue/cell types. these data were subjected to network correlation analysis and clustering.  the analysis presented here provides a detailed functional clustering of the pig transcriptome where transcripts are grouped according to their expression pattern, so one can infer the function of an uncharacterized gene from the company it keeps and the locations in which it is expressed. we describe the overall transcriptional signatures present in the tissue atlas, where possible assigning those signatures to specific cell populations or pathways. in particular, we discuss the expression signatures associated with the gastrointestinal tract, an organ that was sampled at sites along its length and whose biology in the pig is similar to human. we identify sets of genes that define specialized cellular compartments and region-specific digestive functions. finally, we performed a network analysis of the transcription factors expressed in the gastrointestinal tract and demonstrate how they sub-divide into functional groups that may control cellular gastrointestinal development.  as an important livestock animal with a physiology that is more similar than mouse to man, we provide a major new resource for understanding gene expression with respect to the known physiology of mammalian tissues and cells. the data and analyses are available on the websites http://biogps.org and http://www.macrophages.com/pig-atlas. pigporcinesus scrofamicroarraytranscriptometranscription networkpathwaygastrointestinal tract 