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Blood genomic responses differ after stroke seizures hypoglycemia and hypoxia Blood genomic fingerprints of disease.

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ORIGINAL ARTICLES
Blood Genomic Responses Differ After
Stroke, Seizures, Hypoglycemia, and
Hypoxia: Blood Genomic Fingerprints
of Disease
Yang Tang, MD,1 Aigang Lu, MD,1 Bruce J. Aronow, PhD,2 and Frank R. Sharp, MD2
Using microarray technology, we investigated whether the gene expression profile in white blood cells could be used as
a fingerprint of different disease states. Adult rats were subjected to ischemic strokes, hemorrhagic strokes, sham surgeries, kainate-induced seizures, hypoxia, or insulin-induced hypoglycemia, and compared with controls. The white
blood cell RNA expression patterns were assessed 24 hours later using oligonucleotide microarrays. Results showed that
many genes were upregulated or downregulated at least twofold in white blood cells after each experimental condition.
Blood genomic response patterns were different for each condition. These results demonstrate the potential of blood gene
expression profiling for diagnostic, mechanistic, and therapeutic assessment of a wide variety of disease states.
Ann Neurol 2001;50:699 –707
With the rapid advance of DNA microarray technology and sequencing of rodent and human genomes
nearing completion, it is possible to examine the expression of thousands of genes in a single sample of
RNA derived from various tissues or organisms.1,2 Microarrays have been used to describe genomic responses
to a variety of different stimuli, including serum stimulation,3 iron,4 glucose deprivation,5 and hypoxia.6
Disease-related gene expression patterns are being
described, including genes specific for inflammatory
disease,7 evidence of toxic exposure,8 and multiple sclerosis.9 Gene expression patterns in tumor tissue are different from those in the host,10 as well as different patterns in different tumor types.11,12 Genomic responses
in tumor cells from patients with acute myeloid leukemia differ from responses in acute lymphoblastic leukemia.13 Of note has been the finding that even normal peripheral white blood cells demonstrate different
gene expression patterns when stimulated with different agents.14,15
These latter findings prompted us to wonder
whether the gene expression patterns in white blood
cells would be distinct in different disease states, and
whether the blood gene expression profile could serve
as a fingerprint for various disease states. We chose to
examine the blood gene expression profile because
From the 1Department of Neurology and Neuroscience Program;
and 2Division of Molecular Developmental Biology and Informatics, Children’s Hospital Research Foundation, University of Cincinnati, Cincinnati, OH.
Received Jul 9, 2001, and in revised form Aug 29, 2001. Accepted
for publication Aug 29, 2001.
blood is the most readily accessible tissue in patients.
Blood gene expression profiles might make it possible
to diagnose many medical and toxic conditions that are
currently difficult or impossible to assess. To begin to
address this, we subjected adult rats to various brainspecific or systemic stimuli, including ischemic strokes,
hemorrhagic strokes, sham surgeries, kainate-induced
seizures, hypoxia, and insulin-induced hypoglycemia,
and compared them with controls. RNA was isolated
24 hours later from peripheral blood mononuclear
cells. Gene expression patterns in blood were then surveyed, using triplicate Affymetrix oligonucleotide arrays. The results are consistent with the suggestion that
different disease states produce distinct genomic responses in peripheral white blood cells. We propose
that the blood genomic response will serve as a fingerprint for these and many other medical and neurological diseases.
Materials and Methods
Animal Models
Adult male Sprague-Dawley rats (Harlan, Indianapolis, IN),
weighing 250 to 300gm, were used. Animals were acclimated
to the animal quarters at least 3 days before study. Six rats
were used for each group: 6 for brain ischemic stroke, 6 for
brain hemorrhagic stroke, 6 for kainate-induced seizures, 6
Published online Nov 1, 2001; DOI/10.1002/ana.10042
Address correspondence to Dr Sharp, Department of Neurology and
Neuroscience Program, University of Cincinnati, Vontz Center for
Molecular Studies, Room 2327, 3125 Eden Avenue, Cincinnati,
OH 45267-0536. E-mail: frank.sharp@uc.edu
© 2001 Wiley-Liss, Inc.
699
for insulin–glucose, 6 for hypoxia, 6 untouched controls, and
6 sham operations. The blood from 2 rats in each group was
combined into a single sample, so that enough RNA was
available for a single microarray. Brain samples from each
animal in each group were taken from the same region of
parietal neocortex. Therefore, three separate blood RNA
samples (from 6 rats) were placed on three microarrays and
three separate brain RNA samples (from 3 rats) on three microarrays for each group. We used triplicate microarrays because of greater reliability with triplicates.16 We did not perform replicates of the same samples of RNA because of the
manufacturer’s (Affymetrix, Santa Clara, CA) data demonstrating good reliability of the chips when analyses are performed on separate chips with the same sample of RNA.
Brain Ischemia
To produce brain ischemia, rats were anesthetized with
isoflurane (3% in 21% oxygen and 76% nitrogen), neck skin
and muscle were incised, and the left common carotid artery
was isolated. Body temperature was maintained at 37.0 ⫾
0.2°C with a rectal thermistor connected to a feedback
controller-driven heating pad. A 3-0 monofilament nylon suture was threaded through the external carotid artery stump
into the internal carotid artery and up to the stem of the
middle cerebral artery (MCA). The suture was anchored in
place with 4-0 silk to produce a permanent MCA occlusion.
Muscle and skin were sutured; once the animals recovered,
they were returned to their home cages with food and water
available ad libitum. This “suture” model of MCA occlusion
produces reliable infarction in the distribution of the
MCA.17,18
Intracerebral Hemorrhage
To produce brain hemorrhage, adult rats were anesthetized
with isoflurane. The scalp was incised and a burr hole drilled
0.5mm anterior and 4mm lateral to the bregma. A 25 gauge
needle was used to deliver 50␮l of lysed arterial blood 4mm
deep into the right striatum in 6 animals. The wound was
cleaned and sutured. Animals recovered in their home cages
with food and water ad libitum for 24 hours. This type of
brain hemorrhage results in cell death around the margins of
the hemorrhage.19
Sham Operations
These animals were anesthetized and neck and muscle incisions performed. The common carotid was isolated, but no
suture was inserted. The wounds were sutured. Once animals
recovered from anesthesia, they were returned to their home
cages with food and water available ad libitum.
Insulin and Glucose
Adult rats were injected with 10U/kg regular insulin subcutaneously. One-half of the subjects became obtunded, and
the other half became obtunded and had seizures. Approximately 4 to 6 hours after receiving insulin, animals were
given 20ml of 20% sterile glucose intraperitoneally, repeated
every hour for 2 hours. Of the 6 animals injected, 4 recovered and appeared to be normal. The other 2 animals continued to have intermittent seizures. All animals were sacrificed at 24 hours after the insulin injections.
Hypoxia
Adult rats were placed in a large plexiglass chamber (BioInstruments, Redfield, NY) through which 8% oxygen was circulated. The oxygen and carbon dioxide concentrations were
monitored continuously. After 6 hours of hypoxia, animals
were returned to normoxia in their home cages for 18 hours.
This degree and duration of hypoxia induces the hypoxiainducible factor (HIF-1) in brain.22
Untouched Controls
Rats that had not been handled in any way were used as
controls. These animals were allowed access to food and water ad libitum and were exposed to a 12 hour light/12 hour
dark cycle, as was the case for the all the other animals in
this study. All experimental and control animals were housed
in the same room both before and during the study.
RNA Isolation from Blood and Brain
At 1 day (24 hours) after brain ischemia, intracerebral hemorrhage, sham surgery, kainic acid injection, hypoxia, or insulin–glucose injection, or assignment as an untouched control, all subjects were anesthetized with ketamine (100mg/kg)
and xylazine (20mg/kg). Once the animal was deeply anesthetized, a 20 gauge needle was used to withdraw ⱖ5ml of
whole (heparinized) blood from the left ventricle of the
heart. Immediately after this the animal was decapitated and
the brain rapidly removed. The brain was cut coronally at
the level of the bregma, and the motor and sensory cortex
dissected up to the cingulate dorsally and the whisker sensory
cortex ventrally.
Immediately after withdrawal of blood, the white cells
were isolated from whole blood using Ficoll-Paque Plus
(Amersham, Piscataway, NJ). The Ficoll method has been
used in previous microarray studies13 and is used to isolate
relatively pure populations of mononuclear cells. Total RNA
from both the parietal cortex and the white cells was isolated
with TRIZOL Reagent (Human Genome Sciences, Rockville, MD) and purified with RNeasy mini kit (Qiagen, Valencia, CA). RNA samples were quantified by spectrophotometry and stored at ⫺80°C for microarray and reverse
transcription–polymerase chain reaction (RT-PCR) studies.
Kainate Seizures
Rats were injected subcutaneously with 10mg/kg of kainic
acid dissolved in 0.9% sterile saline (Sigma, St Louis, MO).
Animals that had severe, prolonged generalized seizures were
studied.20 Animals remained in their home cages for 24
hours with food and water available ad libitum. Prolonged
seizures resulted in injury to neurons in cortex, hippocampus, entorhinal cortex, and other brain regions.20,21
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Microarray Analysis
Sample labeling, hybridization to arrays, and image scanning
were carried out according to the manufacturer’s instructions
(Affymetrix). The rat U34A array used contained more than
7,000 genes and 1,000 ESTs (expressed sequence tags, referred to as genes in the following text). Each gene on the
array was assessed using 16 probe pairs. Each probe pair con-
Table. Numbers of Regulated Genes for Each Condition Compared to Controls
No. of Regulated Genes
Brain hemorrhage vs
untouch
Kainate-induced seizure vs
untouch
Insulin–glucose vs
untouch
Hypoxia vs untouch
Sham vs untouch
⬎Twofoldb
⬎Twofoldc
Raw
⬎100
Raw
⬎1,000
Raw
⬎100
Raw
⬎1,000
Raw
⬎100
Raw
⬎1,000
Upregulated
Downregulated
Upregulated
2,507
2,538
2,109
25
98
27
20
73
23
15
33
10
12
24
6
8
10
4
8
9
3
Downregulated
Upregulated
2,538
1,681
193
106
121
97
89
63
63
55
23
16
17
13
Downregulated
Upregulated
2,538
1,894
311
94
214
86
195
43
139
40
27
25
22
25
Downregulated
Upregulated
Downregulated
Upregulated
Downregulated
2,538
1,761
2,538
2,408
2,538
231
139
294
40
162
161
134
214
34
105
165
78
145
26
82
116
75
99
23
64
52
19
12
18
50
45
18
9
18
43
Condition
Brain ischemia vs untouch
⬎Twofolda
No. of
Genes
Addressed
Numbers of white blood cell genes that were present and regulated 24 hours after brain ischemia, brain hemorrhage, kainate-induced seizures,
insulin–glucose, or hypoxia as compared with untouched control animals. The number of genes addressed indicates genes and ESTs that have
three “present” calls in three chips for that group. Upregulated genes have three “present” calls in experimental chips; downregulated genes have
three “present” calls in control chips (ie, 2,538 for every downregulated group). Genes are listed based on whether the raw average value
measured on the chips was either ⬎100 or ⬎1,000.
a
Genes up- or downregulated more than twofold.
Genes regulated more than twofold and where every value in the experimental group was either greater or every value was less than any of the
three measurements in the control group.
c
Genes regulated more than twofold. No overlap between the 3 experimental data and 3 control data points; Student’s t test showed p ⬍0.05
for comparison of experimental with control values.
b
sisted of an oligomer (25 bases long) perfectly complementary to a particular message (called the perfect match [PM])
and a companion oligomer identical to the PM probe except
for a single base difference in a central position (called the
mismatch [MM] probe). The mismatch probe served as a
control for hybridization specificity and helped subtract nonspecific hybridization. After hybridization intensity data were
captured, the Affymetrix Genechip software calculated intensity values for each probe cell and used these probe cell intensities to calculate an average intensity for each gene (average difference in intensities between PM and MM cells; the
average intensity directly correlates with mRNA abundance).
The software also gave each gene a qualitative assessment of
“present” or “absent” based on a voting scheme, with the
number of instances in which the PM signal is significantly
larger than the MM signal across the whole probe set. Only
those genes scored as present on three of three chips for each
experimental condition were used for the analysis.
Before comparing any two measurements, a scaling procedure was performed so that all signal intensities on an array
were multiplied by a factor that makes the average intensity
value for each array equal to a preset value of 1,500. Scaling
corrects for any interarray differences or small differences in
sample concentration, labeling efficiency, or fluorescence detection and makes interarray comparisons possible.
The ischemic stroke, hemorrhagic stroke, sham surgery,
hypoxia, kainate, and insulin–glucose results were all compared with the untouched controls. First, the numbers of
genes that had a twofold increase or more (upregulated,
greater than twofold) or a twofold decrease or more (downregulated, greater than twofold) average expression in the experimental compared with control samples were determined
(Table, ⬎twofold). The upregulated and downregulated
genes for each of the experimental conditions all had three
“present” calls, and their average difference had to be more
than 100 on all three experimental chips in the group. Because there is no agreed-upon detection threshold for the
chips, we have also listed genes that had three “present” calls
and that had an average difference value of more than 1,000
(see Table). This was done to ensure that the results were not
significantly affected or skewed by the lower value chosen for
the analysis of fold changes. To provide additional indices of
the reliability of the differences between the experimental
values and untouched control values, the “greater than twofold” list of genes was used to derive two additional lists (see
Table). In the ⬎twofoldb list, the numbers of genes are
shown when all of three measurements in a specific experimental condition were greater than those in the corresponding control condition, or likewise when all three were less
than in controls. Gene numbers for which a statistically significant difference ( p ⬍ 0.05, Student’s t test) was established between experimental and control conditions are listed
in the last column of the table.
A cluster analysis (Genespring software; Silicon Genetics,
Redwood City, CA) was performed on all the genes that (1)
were upregulated or downregulated more than twofold in the
Tang et al: Blood Genomic Fingerprints of Disease
701
Quantitative Reverse Transcriptase-Polymerase
Chain Reaction
Real-time RT-PCR was performed on five selected genes using the 5700 Sequence Detection System (Applied Biosystems, Foster City, CA). All primers and probes were designed using Primer Express 1.0. One-step RT-PCR was
performed according to manufacturer’s instructions (Taqman
gold RT-PCR Kit; Applied Biosystems).
Fig 1. Venn diagrams show the numbers of genes upregulated
(a) or downregulated (b) in several groups. (a) Numbers of
genes that were upregulated more than twofold in blood 24
hours after brain ischemia (BI), brain hemorrhage (BH), and
sham surgery (S), compared with untouched controls. (b)
Numbers of genes that were downregulated more than twofold
in blood 24 hours after kainate (K), insulin–glucose (IG),
and hypoxia (H), compared with untouched controls.
experimental conditions compared with the untouched control condition; and (2) showed no overlap of the values in
the experimental condition versus the values in the untouched controls (see Table). Separate cluster analyses were
performed for the 605 genes for which the raw values were
greater than 100; and for the 474 genes in which the raw
values were greater than 1,000 (see Table, ⬎twofold). Hierarchical clustering was performed, and a standard correlation
coefficient of 0.95 was used as the measure for significant
statistical similarity. Genes having similar expression patterns
across the 7 groups were clustered together. The branching
behavior of the tree was controlled using a separation ratio
setting of 0.5 and a minimum distance setting of 0.001.
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Results
Blood Genomic Response
Of the 8,740 genes surveyed, 19% (1,681 of 8,740 in
the kainate group) to 29% (2,538 of 8,740 in the untouched group) are “present” in mononuclear white
blood cells (see Table). Of the “present” transcripts in
each group, some were induced or suppressed at least
twofold by different types of systemic and organspecific stimuli or injury. The Table shows the number
of genes that were upregulated or downregulated in
each condition based on whether the raw measurement
was greater than 100 or greater than 1,000. For example, if a twofold change criteria is used for genes with a
raw value of more than 100, as few as 25 of 2,507
transcripts are upregulated by brain ischemia in white
blood cells, and as many as 311 of 2,538 transcripts are
downregulated by kainate-induced seizures (see Table).
If more stringent criteria are used, as few as 6 of 2,109
(0.3%) transcripts are upregulated by brain hemorrhage and as many as 139 of 2,538 transcripts (5.5%)
downregulated by kainate-induced seizures (see Table).
No matter how stringent the criteria, including statistical analysis of genes expressed with raw values greater
than 1,000, significant numbers of transcripts were regulated by each condition.
Genes Regulated by Multiple Conditions
Many genes upregulated or downregulated by each experimental condition were modulated in two or more
of the groups. For example, of 25 transcripts upregulated twofold by brain ischemia, 6 were also induced
by brain hemorrhage and 4 by brain ischemia, brain
hemorrhage, and sham surgery (Fig 1a). Because animals subjected to ischemic and hemorrhagic stroke and
sham surgeries all had surgery while anesthetized, some
upregulated (see Fig 1a) and downregulated (see Table)
genes in these groups may be due to the anesthesia or
the surgery. Similarly, of 311 genes downregulated at
least twofold by kainate (see Table and Fig 1b), 102
are downregulated by insulin–glucose and 62 by kainate, insulin–glucose, and hypoxia. The 40 genes that
are regulated by kainate and insulin–glucose, but not
by hypoxia, may be accounted for by the fact that the
kainate animals and several of the insulin–glucose animals had seizures. Similar mechanisms may mediate
the induction of these genes when they are regulated in
2 or more groups. Some transcripts were induced or
ically regulated genes. For example, though there was
only one gene specifically upregulated by brain hemorrhage (Fig 4a), there were over a dozen genes specifiFig 3. Hierarchical clustering of genes with raw values of
⬎100 (A; n ⫽ 605) or ⬎1,000 (B; n ⫽ 474) that were regulated at least twofold after sham surgery, brain ischemia, brain
hemorrhage, kainate-induced seizures, hypoxia, and insulin–
glucose, compared with untouched controls. The clustered genes
were derived from those in the Table (⬎2-fold data), and
hence showed no overlap between experimental and untouched
control values. Genes that show similar expression patterns across
different treatments cluster together. Red ⫽ upregulation; yellow
green ⫽ little change; deep green ⫽ downregulation.
Fig 2. Microarray results compared with real-time reverse
transcription-polymerase (RT-PCR) results. Adult rats were
subjected to sham operations (S); brain ischemia (BI) produced by a permanent middle cerebral artery occlusion; brain
hemorrhage (BH) produced by intracerebral infusions of lysed
blood; kainate (K)-induced seizures; hypoxia (H) produced by
exposure to 8% oxygen for 6 hours; and insulin-induced hypoglycemia followed by glucose administration (IG). One day
later (24 hours), total RNA from the blood of three pairs of
animals for each condition was isolated. The RNA expression
levels of five selected genes were assessed by both microarrays
and real-time RT-PCR and normalized to that of the controls.
The fold change (y axis) was calculated as the expression of
the gene in each condition versus the controls, so that the fold
change for control in every case is 1. (A) X06827 prophobilinogen deaminase; (B) M60666 ␣-tropomyosin; (C) U39875
EF-hand calcium binding protein; (D) AF045464 aflatoxin
B1 aldehyde reductase; (E) L00603 monoamine transporter.
suppressed by all the experimental conditions when
compared with controls. For example, porphobilinogen
deaminase (Accession no. X06827) was induced in
blood white cells after all the stimuli mentioned above
(Fig 2). It is possible that systemic stress and catecholamines induce a number of common genes for
each condition (Fig 3).
Genes Specifically Regulated by Each Condition
Genes that were upregulated or downregulated in only
one condition were then identified to determine
whether there is a specific blood genomic response for
each experimental condition. For most experimental
conditions examined here, there was a group of specif-
Tang et al: Blood Genomic Fingerprints of Disease
703
Figure 4
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cally downregulated by brain hemorrhage compared
with the other groups (see Fig 4b). Similarly, there
were specifically upregulated and downregulated transcripts for kainate-induced seizures (see Fig 4c and d),
hypoxia (see Fig 4e and f), and insulin–glucose treatment (see Fig 4g and h), as compared with the other
experimental groups. However, there was no differentially expressed gene specific for brain ischemia (see Fig
4i and j).
Unique Gene Expression Profile in Peripheral
White Cells
Because there may not be specifically regulated genes
for many disease conditions, we looked for a unique
pattern of gene expression for each of the experimental
conditions, including brain ischemia. For example, Figure 4 shows gene profiles that could be used to distinguish gene expression between sham surgery, brain
ischemia, and brain hemorrhage (see Fig 4i and j). A
broader view of global expression profiling was accomplished using cluster analysis. This approach showed
that each experimental condition produced a unique
gene expression pattern in white cells (see Fig 3). To
ensure that our conclusions were not affected by the
threshold of gene expression chosen, separate cluster
analyses were performed for the genes that had raw values of greater than 100 or greater than 1,000 and were
regulated more than twofold without any overlap between experimental and control values (see Table). For
both analyses, the overall clustering pattern was similar.
Some gene clusters were regulated by only one treatment, while others were regulated by two or more
treatments. For the seven global expression profiles
shown, no profile was identical. This was true whether
the raw value threshold was greater than 100 or greater
than 1,000. The different gene expression patterns, including sham versus untouched controls, emphasize the
uniqueness of the different blood genomic responses
for each condition. The overall similarities in the blood
genomic response patterns in each of the six experimental conditions compared with the untouched controls suggest that similar genes are induced in response
to stress in all of these conditions.
Examples of the upregulated genes point to the
unique pattern of gene expression in each condition.
Š
Some of the genes upregulated greater than twofold in
blood mononuclear cells after brain ischemia compared
with untouched controls included transferrin receptor
(M58040), protease 1 (S69206), pituitary transforming gene (U73030), vesicular monoamine transporter
(L00603), junD (D26307), patched (AF079162), and
thromboxane A2 receptor (D32080). Transcripts upregulated greater than twofold in the blood after brain
hemorrhage included transferrin receptor (M58040),
rat-related rab 1B protein (X13905), replication factor C
(AF030050), and vascular protein tyrosine phosphatase-1
rDEP-1 (U40790). Genes upregulated by insulin–
glucose in blood included glucose-regulated 94 related
protein (AB003515), NaPi-2 ␤ (AB013454), dihydrolipoamide acetyltransferase (D10655), 12-lipoxygenase
(L06040), asparagine synthetase (U07201), carbonic anhydrase II (U60578), ADP-ribosylation factor (L12384),
syntaxin 5 (L20822), and RNA polymerase II transcription factor SIII p18 subunit (L42855). Transcripts upregulated more than twofold by hypoxia in blood included ATP synthase subunit (D13120), mitochondrial
long-chain enoyl-CoA hydratase (D16478), mitochondrial long-chain 3-ketoacyl-CoA thiolase (D16479),
NADH-ubiquinone oxidoreductase (D86215), longchain acyl-CoA synthetase (D90109), phospholipid
hydroperoxide glutathione peroxidase (L24896), ␣tropomyosin (M15474), proton pump polypeptide
(M58758), bcl-xs (S78284), asparagine synthetase
(U07201), EF-hand calcium binding protein p22
(U39875), and a putative chloride channel (Z36944).
Transcripts induced more than twofold after kainate
induced seizures included PSD-Zip45 (AB017140),
muscarinic receptor m2 (AB017655), putative pheromone receptor (AF016178), neuron glucose transporter
GLUT3 (D13962), NMDA receptor glutamate-binding
subunit (S61973), AMP-regulated phosphoprotein
(S65091), and dopamine D3 receptor (X53944). A
number of transcripts, including the 50 kDa glycoprotein (RH50; AB015194), were upregulated by several conditions. Although space precludes the listing
of downregulated transcripts, these may be as important as those that are upregulated. For example,
poly(ADP-ribose)polymerase (U94340), notch (X57405),
tricarboxylate carrier (S70011), ␣-fodrin (AF084186),
retinoblastoma protein (D25233), p38 mitogen acti-
Fig 4. Selected genes that were specifically upregulated (a, c, e, g, i) or downregulated (b, d, f, h, j) greater than twofold in blood
after various experimental conditions, compared with controls (C). Adult rats were subjected to sham operations, brain ischemia
produced by a permanent middle cerebral artery occlusion, brain hemorrhage produced by intracerebral infusions of lysed blood,
kainate-induced seizures, hypoxia produced by exposure to 8% oxygen for 6 hours, and hypoglycemia produced by insulin injections
that was reversed by glucose (x-axis labels as in Fig 2). Twenty four hours later, total RNA from the blood of three pairs of animals for each condition was isolated and gene expression assessed using Affymetrix rat oligonucleotide microarrays. Each of the panels
shows the fold changes of gene expression for one or more genes that were upregulated or downregulated as compared with controls
after brain hemorrhage (a and b), kainic acid (c and d), hypoxia (e and f), insulin–glucose (g and h), and sham operations, brain
ischemia, and brain hemorrhage (I and j). Error bars demonstrate the range of values measured in each of the 3 subjects.
Tang et al: Blood Genomic Fingerprints of Disease
705
vated protein kinase (U91847), ␤-adrenergic receptor
kinase-1 (M87854), and others are downregulated in
mononuclear white blood cells 24 hours after 6 hours
of 8% hypoxia. Lists of all the upregulated and downregulated genes derived from the Table are provided as
supplementary information at http://www.interscience.
wiley.com/jpages/0364-5134/suppmat/index/html.
Blood Genomic Markers for Brain Injury
One of the original hypotheses that stimulated these
studies was that cell death in the brain might modulate
specific transcripts in white blood cells that would be
different from stimuli that do not lead to cell death.
Two transcripts were induced after brain ischemia
and kainate, and variably in insulin–glucose and brain
hemorrhage: vascular protein tyrosine phosphatase-1
rDEP-1 (U40790) and vesicular monoamine transporter (L00603). In addition, the histamine H2
receptor (S57565) was downregulated after brain ischemia, brain hemorrhage, kainate-induced seizures, and
insulin–glucose.
Correlation of Microarray Data With Polymerase
Chain Reaction Results
Quantitative RT-PCR was performed on five selected
genes (see Fig 2). The RT-PCR results showed excellent agreement with the corresponding microarray results (porphobilingen deaminase; ␣-tropomyosin; EFhand calcium binding protein; and the monoamine
transporter). For one gene (aflatoxin B1 aldehyde reductase), RT-PCR results showed a much greater
change as compared with the microarray results (see
Fig 2D). However, both the microarray and the RTPCR results for aflatoxin B1 aldehyde reductase
showed the same pattern of expression in each of the
conditions: low in control; somewhat higher in sham;
little change in brain ischemia, brain hemorrhage, and
hypoxia; and the highest levels of expression in kainate
and insulin–glucose (see Fig 2).
Discussion
The major finding of this study is that different patterns of gene expression occur in the peripheral white
blood cells 1 day after ischemic stroke, hemorrhagic
stroke, seizures, hypoxia, and hypoglycemia, as compared with control animals. The results imply that differences of gene expression in peripheral white blood
cells could be used in the clinic to diagnose the occurrence of these events 1 day (or possibly many days)
previously. It might be possible to diagnose recent hypoglycemia in diabetic patients, recent seizures in patients with epilepsy, and recent hypoxia in patients
with syncope or cardiovascular disease. Because of the
large numbers of genes expressed by white blood cells,
and differences that would be manifested over time, it
is possible that there is a genomic fingerprint for a
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wide variety of diseases and toxic states that could be
used to diagnose and monitor these conditions.
It is not too surprising that systemic hypoxia and
insulin produce changes of gene expression in white
blood cells, since most cells in the body including the
white cells would have been hypoxic or hypoglycemic.
It is also not surprising that there would be genes specifically induced by hypoxia and insulin–glucose, as
there are both oxygen or hypoxia-inducible genes23 and
glucose-regulated proteins.24 The greatest surprise was
the upregulation and downregulation of genes with
organ-specific injury. That is, genes are induced or
suppressed in white blood cells after brain ischemia,
brain hemorrhage, and kainate-induced seizures. The
mechanisms by which different brain injuries might affect specific gene expression in white blood cells may
be related to immune surveillance. However, the specific factors that would lead to the downregulation of
so many genes in each of the conditions remain unclear. There appears to be considerable specificity, as
ischemic and hemorrhagic stroke and kainate-induced
seizures produced different patterns of gene downregulation in the blood. This could relate to inhibition of
specific signaling pathways. Alternatively, the patterns
of altered gene expression could reflect altered function
or apoptosis of certain classes of white blood cells. As
no attempt was made to examine gene expression patterns in different classes of white blood cells, it is possible that the gene expression changes reflect different
classes of white blood cells that are affected differentially by each condition.
Alternatively, the differences of mononuclear cell
gene expression after ischemic stroke, hemorrhage, seizures, and hypoglycemia could relate to unique immune responses to injury. There is likely a unique immune response to dying neurons, glia, and vasculature
after ischemic infarction; to blood elements after hemorrhage; and possibly to the death of different selectively vulnerable neurons produced by seizures and hypoglycemia.
Although only a limited number of disease conditions were studied, the data show that the blood
genomic response was unique for each. The best example of this difference was the unique blood genomic
response observed in sham-operated control animals
that differed from untouched controls. Even though
there are genes that are specifically regulated by each of
the conditions, we propose that it is the composite
blood genome response—all the upregulated and
downregulated genes—that will provide a genomic fingerprint that can be distinguished for many different
disease-related states.
These studies add to a growing literature demonstrating the use of microarray-based gene expression
patterning in the molecular classification of disease.13,14 However, to our knowledge, this is the first
effort to show the potential of using blood genomic
responses as markers for end-organ diseases. Extending
these studies to humans is the next step. However, the
diverse genetic background and multiple environmental factors that affect humans could result in more variation than we have seen in rats, which could make pattern recognition of blood genomic responses to disease
in humans more difficult.
These studies were supported by the National Institutes of Health
(NS28167, NS38084, and NS38743) and by the Bugher Foundation (0070006N) from the American Heart Association (to F.R.S.).
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