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From the Department of Pathology, University of Michigan Medical School, Ann Arbor, Michigan
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Abstract |
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 Top Abstract IntroductionMaterials and MethodsResultsDiscussionReferences |
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Introduction |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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DNA microarray (or DNA chip) technology is promising to revolutionize the way fundamental biological questions are addressed in the postgenomic era. Rather than the traditional approach of focusing on one gene at a time, genomic-scale methodologies allow for a global perspective to be achieved. DNA microarrays have successfully been used to molecularly classify cancers, identify single nucleotide polymorphisms, genotype viruses, and monitor patterns of coordinated gene expression after a variety of biological stimuli. Obtaining large-scale gene expression profiles of cancers should theoretically allow for the identification of subsets of genes that function as prognostic disease markers or biological predictors of a therapeutic response. Oligonucleotide chips have been used for the molecular classification of acute leukemias,7 demonstrating the feasibility of using microarrays for identifying new cancer classes and for assigning tumors to known classes. Similarly, diffuse large B-cell lymphoma has been dissected into two prognostic categories by gene expression profiling.8
Although numerous studies have been undertaken to assess global gene expression patterns in cancer,7-10 few have been used in the context of inflammation or sepsis. In a proof of concept study, a 1000-element DNA microarray has been used to analyze gene expression changes in cytokine-activated monocytes, synovial fluid specimens from patients with rheumatoid arthritis, and intestinal mucosa biopsies from patients with inflammatory bowel disease.11 A similar global expression profiling study was performed characterizing transcript alterations in the lung using a rodent model of pulmonary fibrosis.12
As described above, a major complication of septic patients is
development of acute respiratory distress syndrome and onset of
multiple organ failure. It has been demonstrated both experimentally
and clinically that sepsis causes the appearance in plasma of a series
of cytokines, such as interleukin (IL)-1, tumor necrosis factor
(TNF)-
, and IL-6. This phenomenon seems to place organs (liver,
lung, and kidney) at risk of injury and failure. Why these organs
become targets of injury during sepsis is poorly understood.
Characterizing the molecular fingerprint (or gene profile) of sepsis in
this context may help elucidate the mechanism of sepsis-induced
multiple organ failure and suggest further approaches for therapeutic
intervention.
In the present study, we developed an 8064 element (8K) rat cDNA microarray to analyze multiorgan/multisystem gene expression patterns in a well-characterized rat CLP model of sepsis.13,14 We propose that the response to sepsis induces both distinct and shared gene expression programs in various organs—perhaps to minimize tissue injury by the host’s own immune system. Usually, these mediators are measured in plasma in the face of a very dynamic and rapidly changing environment of sepsis. Extrapolations to individual organs is not possible. Our hypothesis is that microarray analysis of genes expressed in organs during sepsis may be predictive of outcome, especially in organs that are known to be compromised during sepsis. Such studies may provide important insight into multiorgan failure during sepsis. Although several studies have successfully used DNA microarrays to molecularly classify malignancies,7,8 this is the first gene-profiling study to address an important disease process at a multiorgan, multisystem level.
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Materials and Methods |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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Sepsis was induced in rats by CLP as described in detail elsewhere.13,14 Briefly, male Long-Evans-specific pathogen-free rats (275 to 300 g; Harlan, Indianapolis, IN) were used in all studies. Anesthesia was induced by intraperitoneal administration of ketamine (20 mg/100 g body weight). Through a 2-cm abdominal midline incision, the cecum was ligated below the ileocecal valve without obstructing the ileum or colon. The cecum was then subjected to a single through and through perforation with a 21-gauge needle. After repositioning the bowel, the abdominal incision was closed with plain surgical suture 4-0 and metallic skin clips. Sham-operated rats underwent the same procedure except for ligation and puncture of the cecum. Lung, liver, thymus, spleen, kidneys, and brain were harvested from CLP rats, sham-rats, and control untreated rats. Various time points (6, 12, 18, and 24 hours) after surgery were used in the CLP and sham animals. Organs from three rats from each condition were pooled, snap-frozen, and stored at -80°C.
Microarray Analysis
DNA microarray analysis of gene expression was done essentially as
described by the Brown and Derisi Labs (available at
www.microarrays.org). The sequence-verified cDNA clones on the rat cDNA
microarray are listed in the Supplementary Information and are
available from Research Genetics (www.resgen.com). Purified polymerase
chain reaction products, generated using the clone inserts as template,
were spotted onto poly-L-lysine-coated microscope slides
using an Omnigrid robotic arrayer (GeneMachines, CA) equipped
with quill-type pins (Majer Scientific, AZ). One full print run
generated 
100 DNA microarrays. All chips have various control
elements, which include human, rat, and yeast genomic DNAs,
standard saline citrate, yeast genes, housekeeping genes, among others.
In addition, we have separately obtained 500 inflammation- and
apoptosis- related cDNAs from Research Genetics to serve as independent
controls for clone tracking and function as duplicates for quality
control. Protocols for printing and postprocessing of arrays are
available in the public domain (www.microarrays.org) and described
previously.15
Pooled rat organs were homogenized and poly-(A)+ mRNA was isolated using a commercial kit (Fasttrack 2.0; Invitrogen, Carlsbad, CA). Once isolated, mRNA was used as a template for cDNA generation using reverse transcriptase. Inclusion of amino allyl-dUTP in the reverse transcriptase reaction allowed for subsequent fluorescent labeling of cDNA using monofunctional N-hydroxy succinimidyl (NHS) ester dyes (as described at www. microarrays.org). In each experiment, fluorescent cDNA probes were prepared from an experimental mRNA sample (Cy5 labeled) and a control mRNA sample (Cy3 labeled) isolated from untreated, control rat organs. For example, lung isolated from CLP and sham rats was compared against control lung from untreated rats (other organs were compared similarly). The experimental cDNA sample was coupled to a monofunctional Cy5-NHS ester and the reference cDNA sample to a Cy3-NHS ester (Amersham, Arlington Heights, IL). The labeled probes were then hybridized to 8K rat cDNA microarrays. Fluorescent images of hybridized microarrays were obtained using a GenePix 4000A microarray scanner (www.axon.com; Axon Instruments, CA).
Data Analysis
Primary analysis was done using the Genepix software package. Images of scanned microarrays were gridded and linked to a gene print list. Initially, data are viewed as a scatter plot of Cy3 versus Cy5 intensities. Cy3 to Cy5 ratios are determined for the individual genes along with various other quality control parameters (eg, intensity over local background). The Genepix software analysis package flags spots as absent based on spot characteristics (refer to web site). Additionally, bad spots or areas of the array with obvious defects were manually flagged. Spots with small diameters (<50 µm) and spots with low signal strengths <350 fluorescence intensity units over local background in the more intense channel were discarded. Flagged spots were not included in subsequent analyses. Data were scaled such that the average median ratio values for all spots were normalized to 1.0 (done separately for each array). An arbitrary cut-off ratio of twofold was used to select genes as significantly up- or down-regulated relative to the control sample.
Normalized fluorescence ratios of nonflagged array elements were uploaded to a Microsoft Access Database (Microsoft, WA). The data sets for each organ were individually queried for genes that were differentially expressed in the CLP organs as compared to control organs (ratios >2.0 or <0.5) but not in the sham-operated organs (ratios between 0.5 and 2.0). The data sets from individual organ analyses were then combined and imported into M. Eisen’s Gene Cluster Program and array elements that were not represented in at least 75% of the experimentals were excluded. The data were log2 transformed and hierarchically clustered with average linkage clustering and visualized using the TreeView Program.16 In some cases, inclusion thresholds were increased to focus the returned clusters.
Northern Blot Analysis
Five µg of poly A+ RNA were resolved by denaturing formaldehyde-agarose gel and transferred onto Hybond membrane (Amersham) by a capillary transfer set up. Hybridizations were performed by the method described by Church and Gilbert.17 Briefly, prehybridization was performed for 1 hour at 65°C in a solution containing 1% bovine serum albumin (fraction V), 8% sodium dodecyl sulfate, 0.5 mol/L phosphate buffer, pH 7.0, and 1 mmol/L ethylenediaminetetraacetic acid, pH 8.0. Hybridization was performed in prehybridization buffer for 16 hours at 65°C after adding the denatured probe at 2 to 3 x 106 cpm/ml concentration. Blots were washed with 2x standard saline citrate/0.1% sodium dodecyl sulfate at room temperature three times for a period of 30 minutes. Subsequently the blots were washed twice in 0.2x standard saline citrate/0.1% sodium dodecyl sulfate at room temperature at 65° twice for 10 minutes each. Signal was visualized and quantitated by phosphorimager. For relative fold estimation, the ratio of the intensity of the respective transcript in the CLP animal over the transcript intensity in the sham animal was determined. Similarly, for microarray analysis, the normalized Cy5/Cy3 ratio of the transcript in CLP animals is compared to the Cy5/Cy3 ratio in sham animals.
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Results |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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The glass slide cDNA microarrays developed here include
2000 known, named genes from the Research Genetics rat cDNA clone
set, 5000 expressed sequence tags (ESTs), and 500 control elements
(which include genomic human, rat, and yeast DNAs, yeast genes, and so
forth). We also included a separate set of 500 inflammation-related
genes to serve as replicates on the microarray and provide internal
controls for reproducibility of gene expression quantitation (See
Supplementary Information for the complete annotated list of these
cDNAs). Using this 8K-rat microarray, we profiled gene
expression across multiple time points in the lung, liver, kidney,
spleen, thymus, and brain of CLP and sham-operated rats. Organs were
pooled from at least three rats for each time point of the study, thus
minimizing variation between animals. Fluorescently labeled (Cy5) cDNA
was prepared from mRNA from each experimental sample. For each organ, a
reference sample was prepared from three unoperated, untreated rats
(control) and labeled using a second distinguishable fluorescent
nucleotide (Cy3).
In all, more than 40 8K rat cDNA microarrays were used to assess gene
expression in six different tissues (120 rat organs) at four time
points (6, 12, 18, and 24 hours) during CLP-induced sepsis. Figure 1
provides an overview of the variation
in gene expression across different organs/systems. Scatter plots of
Cy5 versus Cy3 intensities are shown for each organ at an
early time point (6 hours) and at a late time point (24 hours) of
CLP-induced sepsis. As expected, control lung cDNA labeled with Cy5
compared against control lung cDNA labeled with Cy3 revealed a strong
linear relationship (R2
=
0.97). Organs harvested from septic animals, however, displayed various
increases in scatter with R2
varying
from 0.89 (24-hour septic brain) to 0.43 (24-hour septic liver).
Differential gene expression was greatest in the early and late time
points of the sepsis liver (R2
= 0.50 at 6 hours, R2
= 0.43 at 24
hours), an organ known to produce large quantities of acute phase
reactants. Interestingly, the brain had very limited changes in gene
expression during sepsis (R2
=
0.86 and 0.89), presumably because of the blood-brain barrier that
prevents passage of blood components into brain tissue.
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Organs harvested from sham-operated rats displayed numerous
changes in gene expression when compared to organs from control rats
(see Supplementary Material). Sham animals underwent the same procedure
as CLP animals except for ligation and puncture of the cecum and thus,
these gene expression patterns are likely characteristic of the sham
operation that includes anesthesia, abdominal incision, and closure.
Although anesthesia- and surgery-induced changes are of interest, the
primary focus of this study was to monitor changes induced by the
sepsis state. Thus, the gene expression data sets (sham and
experimentals) for each organ were queried for genes that had at least
a twofold or higher variation of the Cy5/Cy3 ratio in the septic sample
but not in the matched sham samples. As sepsis induces a systemic
response involving multiple organs, we chose to explore the gene
expression data using a hierarchical clustering format in which
intraorgan and interorgan relationships could be evaluated (Figure 2)
. The colored bars on the right of
Figure 2
indicate clusters of coordinately expressed genes highlighting
interrelationships between organ systems. For example, cluster A
includes genes that were up-regulated in most if not all of the tissues
profiled. By contrast, cluster H highlights genes that were
coordinately down-regulated in both the lung and the spleen.
Interestingly, there were even groups with discordant gene expression,
as exemplified by cluster E, which includes genes that were
up-regulated in the spleen but down-regulated in the thymus.
Organ-specific gene expression alterations are also evident in clusters
B, F, and L (Figure 2)
. Taken together, Figure 2
illustrates the
diverse and interrelated gene expression patterns of a whole organism
responding to a systemic inflammatory stimulus (CLP). The entire data
set underlying Figure 2
can be obtained from the Supplementary
Materials.
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, were examined in more
detail (Figure 3)
. Cluster A is
especially interesting because it harbors genes that were up-regulated
in most of the organs profiled, defining a set of genes with a
relatively universal response to sepsis. As expected, well-known
mediators of the inflammatory response are included in cluster A
including IL-1ß, phospholipase A2
(PLA2), and complement components C1q and C3.
IL-1ß has a central role in the pathogenesis of sepsis and mediates
its proinflammatory effects by binding to its cognate receptor (IL-1R)
and subsequently activating the transcription factor nuclear
factor-
B.18
By microarray analysis, IL-1ß transcript
was found to be increased in all of the organs tested excluding the
brain. Interestingly, IL-1 receptor type II (IL-1RII), which functions
as a decoy receptor for IL-1,19,20
was shown to be
up-regulated in a similar set of tissues (Figure 3)
. Cluster A also
includes PLA2, an important mediator of
arachidonic acid metabolism and recently implicated in the pathogenesis
of sepsis-induced lung injury.21
Because its transcript
was increased in most of the tissues we examined,
PLA2 may have a more encompassing role in
sepsis-induced tissue injury than previously recognized. Many other
known inflammatory markers are contained in cluster A, including
complement proteins, proteases, anti-proteases, and oxidant scavenging
enzymes, among others. These mediators are discussed in more detail in
the context of the functional clusters described in Figure 4
. In addition to uncharacterized ESTs,
there are numerous genes that were identified by our screen but not
previously known to be involved in the sepsis response. For example, a
member of cluster A, n-chimaerin, a p21rac-GTPase-activating protein
and phorbol ester receptor22
presumed to have an important
role in neuronal signaling, is highly induced in many of the tissues
from animals with sepsis. N-chimaerin has previously been shown to
enhance the biological activities of p21rac, including actin
cytoskeletal mobilization and superoxide generation (in conjunction
with NADPH oxidase assembly).23
However, the precise role
of n-chimaerin up-regulation during sepsis is unclear. A number of
previously uncharacterized ESTs are also presented in this cluster.
Guilt by association would suggest that they potentially have a role in
the septic response.
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depicts genes that are repressed during the
response to sepsis, an area that has received little attention in the
study of inflammation. Specifically, these are transcripts that are
coordinately decreased in the lung and spleen. Several genes in this
cluster are involved in maintaining the extracellular matrix and are
repressed during sepsis, including pro-2 collagen I, pro-1
collagen Type III, tenascin X, and protein-lysine 6-oxidase. Genes
involved in maintaining the extracellular matrix presumably have more
of a role in tissue repair and/or chronic inflammation than in sepsis,
at least during the time interval studied. Aquaporin 5, a gene involved
in alveolar fluid clearance,24
was consistently
down-regulated in the CLP lung and thus may account for extravascular
fluid accumulation in the injured lung.25
It is also
interesting to note that Ca2+-independent
PLA2 and phospholipase D are markedly repressed
in a number of tissues, which is in stark contrast to increased
cytoplasmic PLA2 transcript levels (Figure 3
,
cluster A). The spleen and thymus express an interesting set of genes (cluster E) that collectively exhibited diametrically opposed patterns of gene expression. Tyrosine/tryptophan monooxygenase (14-3-3 eta) is one member of this cluster and is involved in cell signaling mediated by Ca2+/calmodulin-dependent protein kinases and protein kinase C.26 This member of the 14-3-3 family may also function as an inhibitor of apoptosis by repressing the activity of p38MAP kinase.27 Interestingly, our previous work in CLP rats demonstrated marked decreases in thymic weight correlating with thymocyte apoptosis that was not the case in the spleen or the other organs tested.28 An interesting pair of genes in this cluster are ESTs highly similar to HSP-90 ß and the p59 protein (FK506-BP4), which have been shown to physically interact, and in concert, modulate intracellular trafficking of steroid hormone receptors.29,30 Both proteins exhibit increased transcript expression in the spleen and decreased expression in the thymus. Platelet-activating factor is a bioactive phospholipid with numerous proinflammatory activities including increasing vascular permeability and promoting leukocyte aggregation, adhesion, and chemotaxis. Of note, cluster E contains an EST with high similarity to the platelet-activating factor acetylhydrolase, an enzyme that functions to inactivate platelet-activating factor. The reason for discordant expression of this transcript in the thymus and the spleen remains to be determined.
Validation of Selected Genes Identified by Microarray Analysis
Selected genes identified by our microarray screen were
corroborated by Northern analysis of the six organs studied (Figure 4)
.
For example, TIMP1 was found to be 3.8-, 4.4-, 3.1-, 3.7-, 1.2-, and
2.3-fold up-regulated by microarray in the CLP liver, lung, spleen,
thymus, brain, and kidney, respectively (Figure 4A)
. Similarly by
Northern analysis TIMP1 transcript was up-regulated in the same set of
organs 12.3-, 2.5-, 4.6-, 17.8-, 2.3-, and 2.2-fold, respectively.
Similar qualitative concordance between Northern and microarray
analysis was achieved with other genes tested including N-chimaerin,
PLA2, and Ca2+-independent
PLA2 (Figure 4, B and C)
.
Functional Analysis of Sepsis-Induced Gene Expression Patterns
We next assessed the data by examining functional groups of known,
named genes (Figure 5)
. During the
response to sepsis, bacterially derived lipopolysaccharide induces the
appearance of a number of cytokines in circulation that mediate the
systemic response including TNF-, IL-1, and IL-6. In the organs
analyzed, increased TNF- expression was only observed at the 6-hour
time point in the CLP lung. This may be because of the higher
concentrations of mononuclear phagocytes found in lung tissue or
because of an increase in TNF- that may occur before the 6-hour time
point in CLP animals. As discussed earlier, it is interesting to note
the coordinated gene expression of IL-1ß and its negative regulator,
IL-1RII (Figure 5)
. Coordinated up-regulation of these proteins is seen
in the liver, lung, spleen, and thymus. Although IL-1ß transcript is
increased in the kidney, the corresponding transcript for
IL-1RII is not. Differential regulation of either protein does not
occur in the brain. Up-regulation of an inflammatory agonist (IL-1ß)
and its decoy receptor (IL-1RII) in the diverse organs tested likely
represents a physiological mechanism to tightly regulate the
inflammatory response. Although IL-6 is notably absent from our chip,
IL-6 ST(gp130), which in conjunction with IL-6 receptor mediates IL-6
signaling, is up-regulated in the kidney, lung, and spleen.
Interestingly, STAT3, which is a transcription factor integral to the
gp130-signaling pathway, exhibited increased transcript expression in
the liver, lung, and spleen. Thus, our data suggest that several
tissues mobilize downstream components of the IL-6 signaling pathway in
response to sepsis and are presumably primed for activation by IL-6.
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Tissue injury during sepsis occurs by a variety of mechanisms including
that mediated by reactive oxygen species and proteolytic enzymes. In
the protease and anti-protease groups it is evident that the rat serine
protease inhibitor, Cpi-26 (contrapsin-like inhibitor) and the
metalloproteinase inhibitor, TIMP-1 are induced during sepsis in most
of the tissues studied, suggesting that this functions as a mechanism
for attenuating protease-mediated tissue damage. It is also reassuring
to observe up-regulation of -2 macroglobulin and -2
anti-proteinase inhibitor in the liver, two classic acute-phase
reactants. Other less characterized proteases and anti-proteases
described in the rat are also differentially regulated during the
septic response (Figure 5)
. Enzymes known to produce oxidative
metabolites such as deamine oxidase (amiloride binding protein 1) and
xanthine dehydrogenase are transcriptionally up-regulated during sepsis
in most of the tissues studied (Figure 5)
. In addition to generating
oxidants, deamine oxidase degrades histamine,31
which has
a central role in promoting allergic and acute inflammatory states.
Xanthine dehydrogenase is produced by both epithelial cells and
neutrophils and has been shown to be a major source of injurious
reactive oxygen metabolites during tissue injury.32,33
Similarly, proteins that have anti-oxidant effects such as
metallothionein and ceruloplasmin are also up-regulated in a similar
set of tissues. Both proteins have the ability to scavenge superoxide
anion and may represent a defense mechanism against oxidant-mediated
tissue injury.26,34
Activation of the complement system together with assembly of the
membrane attack complex C5b-9 plays an important role in host defense
and sepsis.35
Components of the complement system such as
C1q, C3, and to a lesser extent C4 are up-regulated in many tissues of
the CLP animal (Figure 5)
. By contrast, C6, C8, and C9 do not display a
similar gene expression pattern. Interestingly, the complement
regulatory protein, Factor I (CFI), a serine protease that inactivates
C3b and C4b,36,37
is also up-regulated in many of the
tissues tested. Concordant CFI up-regulation may serve as a defense
mechanism against renegade activation of the complement system during
the systemic response to sepsis.
Various proteins involved in arachidonic acid metabolism are induced in
tissues of CLP rats. Cytosolic PLA2, which is
responsible for the release of arachidonic acid from phospholipid
stores, is activated by submicromolar concentrations of
Ca2+ and has recently been implicated in
sepsis-induced lung injury.21
Here we discover that
PLA2 is up-regulated at the gene expression level
in many of the tissues we analyzed in the CLP rat including the lung
(Figure 5)
. Interestingly, we observed coordinate decreases in the
transcript levels of the Ca2+-independent forms
of PLA2. This may represent another site of
physiological regulatory control in the systemic inflammatory pathway.
A number of other named genes (inflammation-related or otherwise) with
twofold increases or decreases in transcripts relative to the
respective control organs are also displayed in Figure 5
(Inflammation
and Other). Notable examples of genes with increased expression in this
group include Fc
receptor, MRP14, p19
cytosolic protein, and matrix G1a protein. Similarly, genes with
decreased expression, in selected organs of this arbitrary grouping,
include c-erb-A--2-related protein, negative acute phase
apha-1 protein, and MHC class I proteins.
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Discussion |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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, IL-1), suggesting that regulation of the inflammatory response
has been compromised.4,45
There is evidence of complement
activation, as reflected by falling levels in plasma of the hemolytic
activity of complement (CH50) and the appearance in plasma of
complement activation products such as C3a and C5a, together with the
membrane attack complex, C5b-9.45,46
Although the
complement system (especially complement activation products, C3b,
iC3b, and C5b-9) is a vital defensive system against invasion by
bacteria, there also exists the possibility that excessive complement
activation can result in compromised host defenses.
Several overriding themes emerge from our multiorgan gene expression
study of sepsis. Microbial infection and the associated systemic sepsis
response triggers a massive activation of transcriptional programs in
the individual organs/tissues of a whole organism. Not only is an array
of genes induced during sepsis but an equally interesting set of genes
is repressed. One of the most intriguing aspects of this study is the
comparison of gene expression patterns of different tissues to a
systemic stimulus. Although there are subsets of genes that share
similar expression patterns in many organs (with the brain being a
frequent exception), each organ has a distinctive molecular response to
systemic inflammation. The blood-brain barrier may be responsible for
the apparent lack of the common sepsis signature in the brain.
There are also interesting associations between organs. For example, a
distinct set of genes is up-regulated in the thymus and coordinately
down-regulated in the spleen (Figure 3)
. Does this have to do with
different sepsis responses by thymocytes versus B and T
lymphocytes? Further experimentation will be needed to decipher this
interaction between the thymus and the spleen. Finally, it is also
quite evident that a specific set of genes is differentially expressed
in an organ-specific manner (Figure 3)
. The molecular basis for these
tissue-common and tissue-specific responses remains to be discovered.
Genes with proinflammatory effects were often balanced by genes with anti-inflammatory effects illustrating the regulatory controls embedded in this complex pathway. Examples of this Yin-Yang gene expression include: 1) IL-1ß and its decoy receptor; 2) reactive oxygen metabolite generating enzymes and superoxide destroying enzymes; 3) complement components (C1q, C3, C4) and an inactivator of complement components (CFI); and 4) induction of Ca2+-dependent PLA2 and coordinated repression of Ca2+-independent PLA2.
Differential gene expression was observed in proteins responsible for
preventing tissue injury and promoting homeostasis including
anti-proteases (TIMP-1, Cpi-26), oxidant neutralizing enzymes
(metallothionein), cytokine decoy receptors (IL-1RII), and
tissue/vascular permeability factors (aquaporin 5, VEGF). Genes
previously implicated in the inflammatory process were studied in the
context of sepsis at a multiorgan level. Likewise, genes not known to
be involved in sepsis were also characterized. Numerous ESTs were
assigned by gene expression patterns to the sepsis clusters described
in Figure 2
(guilt by association). Further characterization of the
sepsis-induced gene expression profiles obtained here may identify
novel sepsis biomarkers and shed light into the etiology of multiple
organ failure, an often-fatal complication of systemic inflammation. By
profiling gene expression at a multiorgan level in an animal model of
systemic inflammation, it will soon be possible to determine potential
anti-inflammatory effects of emerging therapeutics.
Supplementary Information
Sepsis profiling datasets (DNA microarray datasets) will be available at the author’s website: http://chinnaiyan.path.med.umich.edu/.
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Acknowledgements |
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Footnotes |
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Supported by the National Institutes of Health (grants HL-31963 and GM-29507 to P. A. W.).
A. M. C. and M. H.-L. contributed equally to this work.
Accepted for publication June 18, 2001.
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