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Genomic
technologies offer a significant advantage to the
identification of a biological biomarker, defined as
“a characteristic that is objectively measured and
evaluated as an indicator of normal biologic
processes, pathogenic processes, or pharmacologic
responses to a therapeutic intervention” (1); given
that, transcript profiling can monitor thousands of
possible endpoints simultaneously (2). Large genomics
databases, such as Gene Logic’s ToxExpress and
BioExpress System databases, can provide in silico
data on potential biomarkers, including distribution
in human normal and disease-state tissues, preclinical
compound treatment toxicity effects, and tissue
distributions across multiple species. While genomic
findings are only the first step in biomarker
identification and qualification (they must be
followed up with protein, enzyme, or metabolite
measurements and a solid validation strategy),
genomics is an enabling technology for this
application. A case study illustrating such is
provided by the identification and qualification of
the fetuin-B gene as indicative of chronic liver
disease.
Kellye
K. Daniels, M.S., Ph.D., D.A.B.T.1*, and
Donna L. Mendrick, Ph.D.2, Gene Logic
Introduction
Identification
of candidate safety biomarkers with utility in both
nonclinical and clinical studies (i.e., bridging
biomarkers) typically employs multiple
single-end-point analyses that often require
sequential staging to gain incremental information.
Gene expression analysis examines hundreds of pathways
and thousands of genes simultaneously, thus
compressing investigation time by months or years.
When coupled with data from multiple species and model
systems, a genomics approach can rapidly identify
species-specific and/or cross-species markers and help
determine those genes whose protein products are
secreted, allowing for monitoring in accessible
fluids.
Needle
biopsy of the liver, an invasive procedure, is the
gold-standard method for evaluating the presence,
type, and stage of liver fibrosis, the excessive
accumulation of extracellular matrix proteins that
occurs in most types of chronic liver diseases (3). In
addition to the invasive nature of liver biopsies,
they are costly and difficult to standardize (4,5);
consequently, noninvasive biomarkers would be of great
benefit to both patients and clinicians (5,6). Thus,
the following example focuses on candidate
cross-species biomarker identification and
qualification for liver fibrosis/cirrhosis.
Analysis
of Fetuin-B as a Candidate Bridging Biomarker
As
outlined in Figure 1, steps involved in accessing a
candidate biomarker from a genomics perspective
include: (i) focusing on genes whose protein
products are secreted and, thus, potentially
measurable in an accessible fluid; (ii)
assessing gene expression distribution across a panel
of diseased human tissues, establishing disease
specificity of the candidate biomarker; (iii)
evaluating the tissue distribution of candidate
expression in normal tissues across multiple species
to determine tissue specificity and species
commonality; and (iv) analyzing the tissue
distribution of candidate expression in
toxicant-treated rat liver samples to assess biomarker
relevance to cross-species adverse liver response.
For
this example, two groups of human liver samples were
selected from the BioExpress System database, which
were surgically accrued by biopsy according to
standard operating procedures and under strict
Institutional Review Board approval. Group 1 consisted
of 32 histopathologically confirmed normal liver
tissue samples, while Group 2 was comprised of 23
histopathologically confirmed fibrotic liver tissue
samples, specifically hepatitis C-positive cirrhosis
samples with evidence of septal fibrosis and
inflammatory infiltrate. Gene expression data for this
pairwise comparison was generated via Affymetrix
GeneChip HG_133(A,B) microarrays and analyzed for
differential expression (fold-change ≥ 1.8 and t-test
p value < 0.05), yielding more than 1,200
statistically significant altered probe sets with 80
of these genes encoding secreted proteins. Fetuin-B, a
member of the cystatin superfamily of proteins (7,8),
was included in the latter group with its expression
significantly down-regulated. Reports indicate
that fetuin-B mRNA is down-regulated during the acute
phase of experimentally induced inflammation in rats
(7) and that its overexpression in skin squamous
carcinoma cells suppresses tumor growth in nude mice
(9). When fetuin-B mRNA distribution was examined
across a panel of normal human tissues, its expression
was restricted to the liver (Figure 2). When examined
across various human diseased liver tissues, not only
was fetuin-B mRNA expression lower in
cirrhosis/fibrosis samples but also it was severely
decreased (t-test p value < 0.001) in
patients with liver malignancies (e.g., hepatocellular
carcinoma), though not significantly altered during
chronic inflammation (Figure 3). Given the
down-regulation of fetuin-B expression in human
fibrosis and in other chronic liver conditions,
combined with its restricted expression in only liver,
its mRNA distribution was examined across a panel of
normal rat (Figure 4; online) and canine (Figure 5;
online) tissues to address whether or not its
expression was likewise restricted to the liver in
other species. As was true for human tissue samples,
fetuin-B expression was restricted to the liver in
both rat and canine, demonstrating tissue specificity
across species.
Utilizing
ToxExpress System database content, fetuin-B
expression was analyzed in selected liver samples from
male Sprague-Dawley rats treated with compounds that
have been previously shown to induce
inflammation/hepatitis [i.e., lipopolysaccharide (LPS;
10), diclofenac (11,12), and indomethacin (12)] or
fibrosis [i.e., dimethylnitrosamine (DMN; 13)] in
humans and/or rats. Fibrosis was histopathologically
observed in rats at eight days following DMN exposure
accompanied by an active inflammatory process. It
should be noted that the chemically induced fibrosis
in the rats is in contrast to the previously discussed
human cases of fibrosis that reflect virus infection.
Furthermore, in contrast to the human cases,
inflammatory infiltrate in rodent samples was presumed
secondary to hepatic injury.
Gene
expression data from the aforementioned rat samples
were generated via Affymetrix RG_U34A GeneChip
microarrays and analyzed for differential expression
(fold-change magnitude ≥ 1.8 and t-test p
value < 0.05) of fetuin-B. While neither diclofenac
nor indomethacin treatments resulted in a
statistically significant alteration in fetuin-B mRNA
levels, both LPS and DMN exposures yielded
differential suppression in its expression (Figure 6).
These observations were consistent with the
down-regulation in fetuin-B expression observed in
human fibrosis/cirrhosis samples, yet not seen in
human chronic inflammation (Figure 6). Taken together,
these findings provide reasonable evidence supportive
of the cross-compound and cross-species
down-regulation of fetuin-B expression during chronic
liver disease.
Conclusions
This
analysis demonstrates one approach to identify and to
qualify candidate biomarkers that takes advantage of
large gene expression databases tied to clinical
attributes. In the absence of such databases,
biomarker discovery has to be conducted de novo,
often with the additional challenge of obtaining
appropriate human samples. By mining the in silico data
resident in both the ToxExpress and BioExpress System
databases, fetuin-B mRNA has been shown to be (i)
significantly down-regulated in human liver fibrotic/cirrhotic
samples with severe loss of expression in patients
with liver malignancies (e.g., hepatocellular
carcinoma), (ii) specifically expressed in the
liver of normal rat, canine, and human tissues, and (iii)
differentially modulated in rat livers exposed to
specific compound treatments. Given all of the
aforementioned, the fetuin-B gene, whose protein
product is secreted, may be an effective cross-species
blood marker of chronic liver disease with some level
of disease specificity. Additional analyses are
underway on the remaining genes that were
significantly perturbed in the human liver diseased
samples, potentially expanding the number of qualified
bridging biomarker candidates.
(AUTHOR
REFERENCES)
1 Department of Toxicogenomics Services;
2
Department of Toxicogenomics, Gene Logic, 610
Professional Drive, Gaithersburg , MD 20879
, USA
References
1. Biomarkers
Definitions Working Group. “Biomarkers and surrogate
endpoints: preferred definitions and conceptual
framework.” Clin Pharmacol Ther 69, 89-95;
2001.
2.
Farr, S., and R.T. Dunn, 2nd. “Concise review: gene
expression applied to toxicology.” Toxicol Sci
50, 1-9; 1999.
3.Bataller,
R., and D.A. Brenner. “Liver fibrosis.” J Clin
Invest 115, 209-18; 2005.
4.
Colloredo, G., et al. “Impact of liver biopsy size
on histological evaluation of chronic viral hepatitis:
the smaller the sample, the milder the disease.” J
Hepatol 39, 239-44; 2003.
5.
Fontana , R.J., and A.S. Lok. “Noninvasive
monitoring of patients with chronic hepatitis C.” Hepatology
36(5 Suppl 1), S57-64; 2002.
6.
Grigorescu, M. “Noninvasive biochemical markers of
liver fibrosis.” J Gastrointestin Liver Dis
15, 149-59; 2006.
7.
Olivier, E. et al. “Fetuin-B, a second member of the
fetuin family in mammals.” Biochem J 350(Pt
2), 589-97; 2000.
8.
Denecke, B. et al. “Tissue distribution and activity
testing suggest a similar but not identical function
of fetuin-B and fetuin-A.” Biochem J 2376(Pt
1), 135-45; 2003.
9.
Hsu, S.J. et al. “Identification of Fetuin-B as a
member of a cystatin-like gene family on mouse
chromosome 16 with tumor suppressor activity.” Genome
47, 931-46; 2004.
10.
Jirillo, E. et al. “The rol
e of the liver in the response to LPS: experimental
and clinical findings.” J Endotoxin Res 8,
319-27; 2002.
11.
Iveson, T.J. et al. “Diclofenac associated
hepatitis.” J Hepatol 10, 85-9; 1990.
12.
Manoukian, A.V., and J.L. Carson. “Nonsteroidal
anti-inflammatory drug-induced hepatic disorders.
Incidence and prevention.” Drug Saf 15,
64-71; 1996.
13.
George, J. et al. “Dimethylnitrosamine-induced liver
injury in rats: the early deposition of collagen.” Toxicology
156, 129-38; 2001.
FIGURE
1. EXAMPLE WORKFLOW FOR IDENTIFICATION AND
QUALIFICATION OF CANDIDATE BRIDGING BIOMARKER.
Overview of an analysis approach that utilizes large
gene expression databases coupled with clinical
attributes to enable the in silico discovery
and qualification of bridging biomarkers.
FIGURE
2. E-NORTHERN ANALYSIS OF FETUIN-B: NORMAL HUMAN
TISSUES. e-Northern of fetuin-B (210521_s_at
represented on Affymetrix GeneChip HG_U133A) in normal
human tissues as generated by the Genesis Enterprise
System Software. Tissues are listed on the far right.
The scatter plot graphs each individual sample as a
line. A blue line is indicative of a present call, and
a red line is an absent call for the gene expression
response for an individual sample. The absent and
present calls are derived from Affymetrix’s
algorithms. The percentage value denotes the present
call for fragment expression across the sample set.
The gray rectangles and whisker plots to the left
indicate the overall average expression values of
fetuin-B in each sample set. The central black bar in
each gray rectangle is the median expression intensity
and is surrounded by the 25th and 75th percentile gray
rectangle limits. The whiskers approximate 3 standard
deviations, assuming a normal data distribution.
Each e-Northern blot is scaled to show the
expression intensity of the most highly expressed
sample set.
FIGURE
3. E-NORTHERN ANALYSIS OF FETUIN-B: HUMAN DISEASED
LIVER TISSUES.
e-Northern of fetuin-B (210521_s_at represented on
Affymetrix GeneChip HG_U133A) in normal and diseased
human liver tissues as generated by the Genesis
Enterprise System Software. [Same conditions as in
Figure 2.] Asterisk denotes t-test p
value < 0.001 for pairwise comparisons of diseased
tissues to normal human liver tissues.
FIGURE
4. E-NORTHERN ANALYSIS OF FETUIN-B: NORMAL RAT TISSUES.
e-Northern of Fetuin-B (rcAI169740_at represented on
Affymetrix GeneChip RG_U34C) in normal rat tissues as
generated by the Genesis Enterprise System Software.
Normal rat tissues are listed on the far right. [Same
conditions as in Figure 2.]
FIGURE
5. E-NORTHERN ANALYSIS OF FETUIN-B: NORMAL CANINE
TISSUES.
e-Northern of Fetuin-B (Cfa.19146.1.S1_at represented
on Affymetrix GeneChip Canine 2) in normal canine
tissues as generated by the Genesis Enterprise System
Software. Normal canine tissues are listed on the far
right. The gray rectangles and whisker plots to the
left indicate the overall average expression values of
Arg1 in each sample set. [Other conditions same as in
Figure 2.]
FIGURE
6. CROSS-COMPOUND AND -SPECIES COMPARISON OF FETUIN-B
EXPRESSION. Rat
liver samples obtained at 24 hours after treatment
with indomethacin (10 mg/kg), diclofenac (200 mg/kg),
lipopolysaccharide (LPS; 8 mg/kg), or
dimethylnitrosamine (DMN; 10 mg/kg) with expression
data generated via Affymetrix GeneChip RG_U34A
microarray. Human liver data obtained from
histopathologically confirmed normal, hepatitis
C-induced fibrosis or chronic inflammation samples and
generated via Affymetrix GeneChip HG_U133(A,B)
microarray. Asterisk denotes magnitude fold-change
≥ 1.8 and t-test p value <
0.05.
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