Note: Descriptions are shown in the official language in which they were submitted.
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COMPOSITIONS AND METHODS FOR DIAGNOSIS AND TREATMENT
OF TYPE 2 DIABETEES
FIELD OF THE INVENTION
The present invention relates generally to the identification of biological
markers
associated with an increased risk of developing Diabetes, as well as methods
of using such
=biological markers in diagnosis and prognosis of Diabetes. Furthermore,
selected biological
markers of the present invention present new targets for therapy and
constitute new therapeutics
for treatment or prevention of Diabetes_
BACKGROUND OF THE INVENTION
Diabetes mellitus comprises a cluster of diseases distinguished by chronic
hyperglycemia
that result from the body's failure to produce and/or use insulin, a hormone
produced by (3-cells
in the pancreas that plays a vital role in metabolism. Symptoms include
increased thirst and
urination, hunger, weight loss, chronic infections, slow wound healing,
fatigue, and blurred
vision. Often, however, symptoms are not severe, not recognized, or are
absent. Diabetes can
lead to debilitating and life-threatening complications including retinopathy
Ieading to blindness,
memory loss, nephropathy that may lead to renal failure, cardiovascular.
disease, neuropathy,
autonomic dysfunction, and limb amputation. Several.pathogenic processes are
involved in the
development of Diabetes, including but not limited to, processes which destroy
the insulin-
secreting P-cells with consequent insulin deficiency, and changes in liver and
smooth muscle
cells that result in resistance to insulin uptake. Diabetes can also eomprise
abnormalities of
carbohydrate, fat, and protein metabolism attY"ibuted to the deficient action
of insulin on target
tissues resulting from insulin insensitivity or lack-of itisulin.
Type 2 Diabetes is the most common form of Diabetes, which typically develops
as a
result of.a relative, rather than absolute, insulin deficiency, in combination
with the body's
failure to use insulin properly (also known in the art as "insulin
resistance"). Type 2 Diabetes
often manifests in persons, including children, who are overweight; other risk
factors include
high cholesterol, high blood pressure, ethnicity, and genetic factors, such as
a family history of
Diabetes. The majority of patients with Type 2 Diabetes are obese, and obesity
itself may cause
or aggravate insulin resistance_ Apart from adults, an increasing number of
children are also
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being diagnosed with Type 2 Diabetes. Due to the progressive nature of the
disease, Diabetes
complications often develop by the time these children become adults. A study
by the American
Diabetes Association (ADA) involved 51-children that were diagnosed with
Diabetes before the
age of 17. By the time these children reached their early 30s, three had
kidney failure, one was
blind, and two died of heart attacks while on dialysis. This study reinforces
the severity of the
disease, the serious damage inflictedby.Diabetes complications, and the need
for early diagnosis
of the disease.
The incidence of Diabetes has been rapidly escalating to alarming numbers.
Diabetes
currently affects approximately 170 nlillion people worldwide with the World
Health
Organization (WHO) predicting 300 znillion diabetics by 2025. The United
States alone has 20.8
rnillion people suffering from Diabetes (approximately 6% of population and
the 6th most
common cause of death). The annual direct healthcare costs of. Diabetes
worldwide for people in
the 20-79 age bracket are estimated at $153-286 billion and is expected to
rise to $2I3-396
billion in 2025.
Along with the expansion of the diagnosed diabetic population, the undiagnosed
diabetic
population has also continued to increase, primarily because Type 2 Diabetes
is often
asymptomatic in its early stages, or the hyperglycemia is often not severe
enough to provoke
noticeable symptoms of Diabetes. It is believed that approximately 33% of the
20.8 million
diabetics in the United States remain undiagnosed. Due to the delay in
diagnosis, Diabetes
complications have already advanced and thus, the future risk of further
complication and
derailment is severely increased = To obviate complications and irreversible
damage to multiple
organs, Diabetes management guidelines advocate initiation of therapeutic
intervention early in
the prognosis of the disease.
This modern epidenaic requires new tools for early detection of'T'ype 2
Diabetes, before
the disease instigates significant and irreparable damage. In addition, new
treatment paradigms
are needed to halt, delay, or ameliorate the massive deterioration in patient
health, ideally
reversing the course of the disease to partial or complete cure as an
alternative or a substitute for
current treatments, which merely address chronic management of disease
symptorns. Diabetic
hyperglycemuia can be decreased by weight reduction, increased physical
activity; and/or
therapeutic treatment modalities. Several biological mechanisms are associated
with
hyperglycemia, such as insulin resistance, insulin secretion, and
gluconeogenesis, and there are
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several agents available that act on one or more of these mechanisms, such as
but not linuted to
metfonnin, acarbose, and rosiglita.zone.
It is well documented that the pre-diabetic state can be present for ten or
more years
before the detection of glycemic disorders like Diabetes. Treatment of pre-
diabetics with
therapeutic agents can postpone or prevent Diabetes; yet few pre-diabetics are
identified and
treated. A major reason, as indicated above, is that no simple laboratory test
exists to deterrrune
the actual risk of an individual to develop Diabetes. Thus, there remains a
need in the art for
methods of identifying and diagnosing these individuals who are not yet
diabetics, but who are at
significant risk of developing Diabetes.
SUMMAR3.' OF THE INVENTION
The present invention is premised on the discovery that disease-associated
biomarkers
can be identified in serum or other bodily fluids long before overt disease is
apparent. The
presence or absence of these biomarkers from the serum footprints of patients
suffering from
Type 2 Diabetes precede disruptions in blood glucose control and can be used
as early diagnoSric
tools, for which treatment strategies can be devised and administered to
prevent, delay,
ameliorate, or reverse irreversible organ damage. One or several of the
disease-associated
biomarkers of the present invention can be used to diagnose subjects suffering
from Type 2
Diabetes or related diseases, or advantageously, to diagnose those subjects
who are
asymptomatic for Type 2 Diabetes and related diseases. The biomarkers of the
present inventiQn
can also be used for the design of new therapeutics. For instance, a biomarker
absent in a
diabetic patient and found in a healthy individual can constitute a new
protective or therapeuti"c
agent which, upon administration to the patient, may alleviate symptoms or
even reverse_the
disease.
Accordingly, in one aspect, the present invention provides a method of
diagnosing or
identifying type 2 Diabetes or a pre-diabetic condition in a subject,
corrrprising measuring an
effective amount of one or more DBMARKERS or a metabolite thereof in a sample
from the
subject, and comparing the amount to a reference value, wherein an increase or
decrease in the
amount of the one or more DBMARKERS relative to the reference value indicates
that the
subject suffers from the type 2 Diabetes or the pre-diabetic condition.
In one embodiment, the reference value comprises an index value, a value
derived from
one or more Diabetes risk prediction algorithms or computed indices, a value
derived from a
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subject not suffering from type 2 Diabetes or a pre-diabetic condition, or a
value derived from a
subject diagnosed with or identified as suffering from type 2 Diabetes or a.
pre-diabetic condition.
In another embodiment, the decrease is at least 10% greater than the reference
value. In
other embodiments, the increase is at least 10% greater than the reference
value.
The sample can be urine, serum, blood plasma, blood cells, endothelial cells,
tissue
biopsies, pancreatic juice, ascites fluid, bone marrow, interstitial fluid,
tears, sputum, or saliva.
The DBMARKERS oÃthe present invention can be detected electrophoretically,
immunochemically, by proteomics technology, or:by=genomic analysis. The
immunochemical
detection can be radioimmuno assay, immunoprecipitation, irnmunoblotting,
immunofluorescence assay, or enzyme-linked immunosorbent assay. The proteomics
technology
can comprise SELDI, MALDI, LC/MS, tandem LC/MS/MS, protein/peptide arrays, or
antibody
arrays. The genomic analysis can comprise polymerase chain reaction (PCR),
real-time PCR,
rnicroarray analysis, Northern blotting, or Southern blotting.
In another embodiment, the subject has not been previously diagnosed as having
type 2
Diabetes or a pre-diabetic condition. The subject can also be one who has been
previously
diagnosed as having type 2 Diabetes or a pre-diabetic condition.
Alternatively, the subject can
be asymptomatic for the type 2 Diabetes or the pre-diabetic condition.
Another aspect of the present invention provides a method for monitoring the
progression
of type 2 Diabetes or a pre-diabetic condition in a subject, comprising (a)
detecting an effective
amount of one or more DBMARKERS in a first sample from the subject at a first
period of time,
(b) detecting an effective amount of one or more DBMARKERS in a second sample
from the
subject at- a second period of time, and (c) comparing the amounts of the one
or more
DBMARKER.S detected in step (a) to the amount detected in step (b), or to- a
reference value.
In one embodiment, the subject has previously been treated for the type 2
Diabetes or the
pre-diabetic condition. In another embodiment, the first sample is taken from
the subject prior to
being treated for the type 2 Diabetes or the pre-diabetic condition. The
second sample can be
taken from the subject after being treated for the type 2 Diabetes or the pre-
diabetic condition. In
other.embodiments, the treatment for the type 2 Diabetes or the pre-diabetic
condition.comprises
exercise regimens, dietary supplements, surgical intervention, diabetes-
modulating agents, or
combinations thereof. The progression of type 2 Diabetes or pre-diabetic
conditions can be
monitored by detecting changes in body mass index (BMI), insulin levels, blood
glucose levels,
HDL levels, systolic and/or diastolic blood pressure, or combinations thereof.
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In another aspect of the present invention, a method of monitoring the
effectiveness of a
treatment regimen for type 2 Diabetes or a pre-diabetic condition in a subject
is provided,
comprising (a) detecting an effective amount of one or more DBMARKERS in a
first sample
from the subject prior to treatment of the type 2 Diabetes or the pre-diabetic
condition, (b)
detecting an effective amount of one or more DBMARKERS in a second sample from
the
subject after treatment of the type 2 Diabetes or the pre-diabetic condition,
and (c) comparing the
amount of the one or more DBMARKERS detected in step (a) to the amount
detected in step (b),
or to a reference value. In one embodiment, changes in blood glucose levels
can be detected by
oral glucose tolerance test_
Yet another aspect of the present invention provides a method of treating a
subject
diagnosed with or identified as suffering from type 2 Diabetes or a pre-
diabetic condition,
comprising detecting an effective amount of one or more DBIv1ARKERS or
metabolites thereof
present in a first sample from the subject at a first period of time, and
treating the subject with
one or more diabetes-modulating agents until the amounts of the one or more
DBMARKERS or
metabolites thereof return to a reference value measured in one or more
subjects at low risk for
-developing type 2 Diabetes or a pre-diabetic condition, or a reference value
measured in one or
more subjects who show improvements in Diabetes risk factors as'a result of
treatment with the
one or more diabetes-modulating agents.
In one embodiment, the one or more diabetes-modulating agents comprise
sulfonylureas,
biguanides, insulin, insulin analogs, peroxisome prolifereator-activated
receptor-y (PPAR-y)
agonists, dual-acting PPAR agonists, insulin secretagogues, analogs of
glucagon-like peptide-1
(GLP-1), inhibitors of dipeptidyl peptidase IV, pancreatic lipase inhibitors,
a-glucosidase
inhibitors, or combinations thereof. In another embodiment, the improvements
in Diabetes risk
factors as a result of treatment with one or more diabetes-modulating agents
comprise a
reduction in body mass index (BMI), a reduction in blood glucose levels, an
increase in insulin
levels, an increase in HDL levels, a reduction in systolic and/or diastolic
blood pressure, or
combinations thereof.
In another aspect of the present invention, a method of selecting a treatment
regimen for a
subject diagnosed with or identified as suffering from type 2 Diabetes or a
pre-diabetic condition
is provided, comprising (a) detecting an effective amount of one or more
DBMARKERS in a
first sample from the subject at a first period of time, (b) detecting an
effective amount of one or
more DBMARKERS in a second sample from the subject at a second period of time,
and
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comparing the amounts of the one or more DBMARKERS detected in step (a) to the
amount
detected in step (b), or to a reference value. In one embodiment, the
reference value is derived
from one or more subjects who show an improvement in Diabetes risk factors as
a result of one
or more treatments for type 2 Diabetes or the pre-diabetic condition.
Another aspect of the present invention provides a method of evaluating
changes in the
risk of developing type 2 Diabetes or a pre-diabetic condition in a subject,
comprising (a)
detecting an effective amount of one or more DBIVIARKERS in a first sample
from the subject at
a first period of time, (b) detecting an effective amount of one or more
DBMARY-ERS in a
second sample from the subject at a second period of time, and comparing the
amounts of the
one or more DBMARKERS detected in step (a) to the amount detected in step (b),
or to a
reference value.
In another aspect, a method of identifying one or more complications related
to type 2
Diabetes'in a subject is provided, comprising measuring an effective amount of
one or more
DBMARKERS or a metabolite thereof iri a sample from the subject and comparing
the amount
to a reference value, wherein an increase or decrease 'in the amount of the
one or more
DBMARKERS relative to the reference value indicates that the subject suffers
from or is at risk
for developing complications related to type 2 Diabetes.
In one embodiment, the complications comprise retinopathy, blindness, memory
loss,
nephropathy, renal failure, cardiovascular disease, neuropathy, autonomic
dysfunction,
hyperglycemic hyperosmolar coma, or combinations thereof. In another
embodiment, the
reference value comprises an index value, a value derived from one or more
diabetes risk-
prediction algorithms or computed indices, a value derived from a subject
diagnosed with or
identified as suffering from type 2 Diabetes or a value derived from a subject
previously
identified as having one or more complications related to type 2 Diabetes.
Another aspect of the present invention provides a type 2 Diabetes reference
expression
profile, comprising a pattern of expression levels of one or more DBMARKERS
detected in one
or more subjects who are not diagnosed with or identified as sufering from
type 2 Diabetes. In
another aspect, the present invention provides a pre-diabetic condition
reference expression
profile, comprising a pattern of expression levels of one or more DBMARKERS
detected in one
or more subjects who are not diagnosed with or identified as suffering from a
pre-diabetic
condition. The invention also provides a type 2 Diabetes subject expression
profile, comprising
a pattern, of expression levels detected in one or more subjects diagnosed
with or identified as
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suffering from type 2'Diabetes, are at risk for developing type 2 Diabetes, or
are being treated for
type 2 Diabetes. In another aspect, the present invention also provides a pre-
diabetic condition
subject expression profile, comprising a pattern of expression levels detected
in one or more
subjects diagnosed with or identified as suffering from a pre-diabetic
condition, are at risk for
developing a pre-diabetic condition, or are being treated for a pre-diabetic
condition.
The present invention also provides a kit comprising DBMARKER detection
reagents
that detect one or more DBMARKERS; a sample derived from a subject having
normal glucose
levels, and optionally instructions. for using the reagents to generate the
expression profiles
disclosed herein. The detection reagents can be, for example, one or more
antibodies or
fragments thereof, one or more aptamers, one or more oligonucleotides, or
combinations thereof.
In another aspect of the present invention, a pharmaceutical composition for
treating type
2 Diabetes or a pre-diabetic condition in a subject is provided, conmprising a
therapeutically
effective amount of one or more DBMARKERS or a metabolite thereof, and a
pharmaceutically
acceptable carrier or diluent. In some embodiments, the DBnRARKER metabolite
comprises
SF3Q ID NO: 1. In other embodiments, the DBMARKER metabolite comprises at
least 5, at least
10, at least 15, or at least 20 contiguous amino acid residues of SEQ ID NO:
1. Alternatively,
the DBMARKER metabolite can comprise an amino acid 'sequence at least 90%
identical to SEQ
IDNO: 1.
The present invention also provides a pharmaceutical composition consisting
essentially
of SEQ ID NO: 1 and a pharmaceutically acceptable carrier or diluent.
In yet another aspect, a method of treating type 2 Diabetes or a pre-diabetic
condition in a
subject in need thereof is provided, comprising administering to the subject a
therapeutically
effective amount of the pharmaceutical compositions of the invention.
Unless otherwise defined, all technical and scientific terms used herein have
the same
meaning as commonly understood by one of ordinary skill in the art to which
this invention
pertains. Although methods and materials similar or equivalent to those
described herein can be
used in the practice of the present invention, suitable methods and materials
are described below.
All publications, patent applications, patents, and other references mentioned
herein are
expressly incorporated by reference in their entirety. In cases of conflict,
the present
specification, including definitions, will control. In addition, materials,
methods, and examples
described herein are illustrative.only and are not intended to be limiting.
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Other features and advantages of the invention will be apparent from and are
encompassed by the following detailed description and claims.
BRIEF DESCRIPTION OF THE DRAWINGS
The following Detailed Description, given by way of example, but not intended
to limit
the invention to specific embodiments described, may be understood in
conjunction with the
accompanying Figures, incorporated herein by reference, in which:
: Figure 1 represents a protein expression profile of pancreatic-extracts from
Cohen
diabetic resistant (CDr) and sensitive (CDs) rats fed regular diet (RD) or
copper-poor high-
sucrose diet (HSD). Total protein extract (5 g) was prepared under reducing
conditions and run
on a 4-12% polyacrylamide gel.
Figure 2A is a graphical comparison of serum samples from CDr-RD, CDs-RD, CDr-
HSD, and CDs-HSD on a SELDI QI0 anion exchange surface chip. A median peak is
present in
CDr-RD and CDr-HSD (marked by an arrow), but not in CDs-RD and CDs-HSD. A
protein
fragment from this differentially expressed peak was identified as the C-
ternii.nal fragment of
Serpina 3M. -
Figure 2B is an MS/MS spectrum of the 4.2 kilodalton fragment identified by
SELDI.
Figure 3A depicts a BLAST alignment of the 38-amino acid Serpina 3M (also
referred to
as "D3") peptide and.proteins identified as having similar sequence identity.
Figure 3B shows a BLAST alignment of nucleic acid sequences encoding the 38-
amino
acid Serpina 3M peptide and proteins identified in 3A.
Figure 3C is a photograph of an agarose gel displaying the results of an RT-
PCR
experiment using degenerate primers designed to detect the conserved amino
acid motifs found
in the BLAST alignments of Figures 3A and 3B.
Figure 4A. is a photograph of two-dimensional maps of CDr-RD, CDs-RD, CDr-HSD
and
CDs-HSD serum samples analyzed by the 2D/LC fractionation system. The
intensity of the blue
bands represents the relative protein amount as detected at 214 nm by LN
absorbance.
Figure 4B shows a differential second-dimensional reverse-phase HPLC elution
profile of
CDr-RD (red) versus CDs-RD (green) of a selected first-dimensional isoelectric
point fraction
(Fraction 31). Proteins that were uniquely identified in CDs-RD samples are
listed at the bottom
of the graph.
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Figure 5A is a photograph of a protein gel representing differential protein
profiling of
CD rat serum samples using two-dimensional gel electrophoresis (2DE). The pH
for the first
dimension chromatofocusing was from pH 5-8, and the second dimensional
separation used a 4-
20% Tris-HCI SDS-PAGE gel. The gel was stained with BioSafe Coomassie Staining
(Bio-Rad)
for visualization.
Figure 5B is a magnified view of the spots identified in Figure 5A.
Figure 6 comprises graphical representations illustrating differentially
expressed proteins
found in the Cohen Diabetic rat models using 2DE.
Figure 7 is a histogram depicting the differentially expressed Cohen Diabetic
rat serum
, proteins identified by 2DE.
Figure 8 is a photograph of Western blots depicting the reactivity of the D3-
hyperimmune rabbit serurn with the -4kD protein fragment present in CDr-RD and
CDr-HSD rat
serum. In the left photograph, a higher molecular weight doublet (in the range
of 49 and 62 kD)
also reacted with the hyperimmune sera, indicating that a parent protein (and
a protein complex)
is expressed by all strains under both RD and HSD treatment modalities, while
the derivative of
smaller size is differentially expressed only in the CDr strain. As a negative
control, the right
photograph shows a Western blot membrane incubated in the absence of the D3
hyperimmune
rabbit serum.
Figure 9 depicts the concentration of the D3 peptide in CDr rat serum as
calculated from
SELDI analysis.
Figure 10 are photographs of gels containing liver extracts (10 g), which was
probed
with secondary goat anti-rabbit IgG conjugated to horseradish peroxidase
(HRP)(1:25000
dilution), in the presence (right panel) or absence (left panel) of primary
anti-D3 serum antibody
(1:200 dilution).
Figure 11 is a photograph of a Western blot analyzing human sera using D3
hyperimmune serum from rabbits. Lane 1 corresponds to the molecular weight
marker. Lanes
2-7 represent fractions of a single serum sample from a normal individual
(3045 NGT). Lanes
10-14 represent fractions of a single serum sample from a Type 2 Diabetes
patient (291).
Figures 12A and 12B show preparative gels that were run with. 100 g of CDr-
HSD and
CDs-HSD pancreatic extracts, respectively. The positive control was stained
with 20 g of anti-
actin antibody, and the subclone lanes were stained with 600 l of conditioned
culture
supematant.
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Figure 13 depicts the results of whole human serum profiled on an anionic Q10
protein
chip by SELDI.
Figure 14 is a photograph of a pseudogel showing the differentially expressed
protein
peaks identified in 13 T2D and 16 normal human serum samples. For the Ml2 15.2
kD marker,
the average peak intensity for T2D samples was 2.6, while for normal samples,
the average peak
intensity was 22.2. The difference between the two samples was about 9-fold.
For the M1Z 14.8
kD=marker, the average intensity for T2D samples was 4.4, and the average
intensity for normal
samples was 3.3. The relative intensity ratio was 1.47.
Figure 15 is a photograph of a pseudogel showing the differentially expressed
protein
peaks identified in 13 T2D and 16 normal human serum samples. The average peak
intensity for
T2D samples was 118, while for normal samples, the average peak intensity was
182. The ratio
of relative intensity was 0.65. Each dot represents the intensity of the
protein peak measured in
individual samples.
:. Figure 16 are graphs depicting differential albumin profiling in samples
obtained from
obese T2D subjects (Dr. Cheatham's samples) vs. non-obese T2D subjects (Dr.
Dankner's
samples).
Figures 17A and 17B are graphical representations of ELISA reactivity of CDs-
HSD and
CDr-HSD specific hybridoma colonies, as measured by absorbance at O.D. 450
rim.
Figures 18A, 18B, and 18C are photographs of Western blots depicting the
reactivity of
= the CDs-HSD and CDr-HSD specific hybridoma clones P2-10-B8-KA8, PI-14-A2-E-
H8, P2-4-
H5-K-B4, P1-20-B7-F-C1, P2-13-A9-P-A8, and P1-5-F11-XF5.
. Figure 19 is a photograph of a Coomassie-stained SDS-polyacrylamide gel
following
immunoprecipitation with the specific hybridoma clones derived from CDs-HSD
and CDr-HSD.
Figure 20A and 20B are screenshots of an MS spectrum analysis of the lower
bands
excised from the SDS-PAGE gel in Figure 18. A positive identification of the
lower band as
calnexin was made.
Figure 21 is a scatter plot of the 137 differentially expressed genes in Cohen
Type 2
Diabetes rat pancreas. Both upregulated and downregulated genes are shown on
the plot.
Figure 22A depicts Gene Tree microarray analysis of 12,729 genes present in
Cohen
Type 2 Diabetes rat pancreas.
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Figure 22B depicts Gene Tree microarray analysis of the 820 genes that were
found to
have 2-fold changes in expression, and the 137 genes shown to have 3-fold
changes in
expression in Cohen Type 2 Diabetes rat pancreas.
Figure 22C depicts the Sets 1-5 of the 137 genes exhibiting 3-fold changes in
expression,
as classified by K-mean clustering.
DETAILED DESCt2IPTION OF THE INVENTION
The present invention relates to the identification of biomarkers associated
with subjects
having Diabetes or a pre-diabetic condition, or who are pre-disposed to
developing Diabetes or a
pre-diabetic condition. Accordingly, the present invention features diagnostic
and prognostic
methods for"identifying subjects who are pre-disposed to developing Diabetes
or a pre-diabetic
condition, including those subjects who are asymptomatic for Diabetes or a pre-
diabetic
condition by detection of the biomarkers disclosed herein. The biomarkers can
also be used
advantageously to identify subjects having or at risk for developing
complications relating to
Type 2 Diabetes. These biomarkers are also useful for monitoring subjects
undergoing
treatments and therapies for Diabetes or pre-diabetic conditions, and for
selecting therapies and
treatments that would be effective in subjects having Diabetes or a pre-
diabetic condition,
wherein selection and use of such treatments and therapies slow the
progression of Diabetes or
pre-diabetic conditions, or substantially delay or prevent its onset. The
biomarkers of the present
invention can be in the form of a pharmaceutical composition used to treat
subjects having type 2
Diabetes or related conditions.
As used herein, "a," an" and "the" include singular and plural referents
unless the context
clearly dictates otherwise. Thus, for example, reference to "an active agent"
or "a
pharmacologically active agent" includes a single active agent as well as two
or more different
active agents in combination, reference to "a carrier" includes mixtures of
two or more carriers
as well as a single carrier, and the like.
"Diabetes Mellitus" in the context of the present invention encompasses Type 1
Diabetes,
both autoimmune and idiopathic and Type.2 Diabetes (together, "Diabetes"). The
World Health
Organization defines the diagnostic value of fasting plasma glucose
concentration to 7.0 mmol/1
(126 mg/dl) and above for Diabetes Mellitus (whole blood 6.1 mmol/l or 110
mg/dl), or 2-hour
glucose level >_11.1 mmol/L (?200 mg/dL). Other values suggestive of or
indicating high risk
for Diabetes Mellitus include elevated arterial pressure _ 140/90 mm.Hg;
elevated plasma
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triglycerides (_1.7 mmol/L; 150 mg/dL) and/or low HDL-cholesterol (<0.9
mmol/L, 35 mg/dl
for men; <1.0 mmol/L, 39 mg/dL women); central obesity (males: waist to hip
ratio >0.90;
females: waist to hip ratio > 0.85) and/or body mass index exceeding 30 kg/m2;
microalbuminuria, where the urinary albumin excretion rate -2:20 g/min or
albumin:creatinine
ratio _ 30 mg/g).
A "pre-diabetic condition" refers to a metabolic state that is intermediate
between normal
glucose homeostasis, metabolism, and states seen in frank Diabetes Mellitus.
Pre-diabetic
conditions include, without limitation, Metabolic Syndrome ("Syndrome X"),
Impaired Glucose
Tolerance (IGT), and Impaired Fasting Glycemia (IFG). IGT refers to post-
prandial
abnormalities of glucose regulation, while IFG refers to abnormalities that
are measured in a
fasting state. The World Health Organization defines values for IFG as a
fasting plasma glucose
concentration of 6.1 mmol/L (100 mg/dL) or greater (whole blood 5.6 mmol/L;
100 mg/dL), but
less than 7.0 mmol/L (126 mg/dL)(whole blood 6.1 mmol/L; 110 mg/dL). Metabolic
Syndrome
according to National Cholesterol Education Program (NCEP) criteria are
defined as having at
least three of the following: blood pressure _130/85 mm Hg; fasting plasma
glucose _6.1
mmol/L; waist circumference >102 cm (men) or >88 cm (women); triglycerides
>_1.7 mmol/L;
and HDL cholesterol <1.0 mmol/L (men) or 1.3 mmol/L (women).
"Impaired glucose tolerance" (IGT) is defined as having a blood,glucose level
that is
higher than normal, but not high enough to be classified as Diabetes Mellitus.
A subject with
IGT will have two-hour glucose levels of 140 to 199 mg/dL (7.8 to 11.0 mmol)
on the 75 g oral
glucose tolerance test. These glucose levels are above normal but below the
level that is
diagnostic for Diabetes. Subjects with irnpaired glucose tolerance or impaired
fasting glucose
have a significant risk of developing Diabetes and thus are an im.portant
target group for primary
prevention.
"Insulin resistance" refers to a condition in which the cells of the body
become resistant
to the effects of insulin, that is, the normal response to a given amount of
insulin is reduced. As a
result, higher levels of insulin are needed in order for insulin to exert its
effects. '
"Complications related to type 2 Diabetes" or "complications related to a pre-
diabetic
condition" can include, without limitation, diabetic retinopathy, diabetic
nephropathy, blindness,
memory loss, renal failure, cardiovascular disease (including coronary artery
disease, peripheral
artery disease, cerebrovascular disease, atherosclerosis, and hypertension),
neuropathy,
autononuc dysfunction, hyperglycemic hyperosmolar coma, or combinations
thereof.
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"Normal glucose levels" is used interchangeably with the term "normoglyceniic"
and
refers to a fasting venous plasma glucose concentration of less than 6.1
mmol/L (110 mg/dL).
Although this amount is arbitrary, such values have been observed in subjects
with proven
normal glucose tolerance, although some may have IGT as measured by oral
glucose tolerance
test (OGTT). A baseline value, index value, or reference value in the context
of the present
invention and defined herein can comprise, for example, "normal glucose
levels."
One hundred and fifty-eight (1.58) biomarkers have'been identified as having
altered or
modified presence or concentration levels in subjects who have Diabetes, or
who exhibit
symptoms characteristic of a pre-diabetic condition, such as those subjects
who are insulin
resistant, have altered beta cell function or are at risk of developing
Diabetes based upon known
clinical parameters or risk factors, such as fanlily history of Diabetes, low
activity level, poor
diet, excess body weight (especially around the waist), age greater than 45
years, high blood
pressure, high levels of triglycerides, HDL cholesterol of less than 35,
previously identified
impaired glucose tolerance, previous Diabetes during pregnancy ("gestational
Diabetes
Mellitus") or giving birth to a baby weighing more than nine pounds, and
ethnicity.
The biomarkers and methods. of the present invention allow one of skill in the
art to
identify, diagnose, or otherwise assess those subjects who do not exhibit any
syinptoms of
Diabetes or a pre-diabetic condition, but who nonetheless may be at risk for
developing Diabetes
or experiencing symptoms characteristic of a pre-diabetic condition.
The term "biomarker" in the context of the present invention encompasses,
without
limitation, proteins, peptides, nucleic acids, polymorphisms of proteins and
nucleic acids, splice
variants, fragments of proteins or nucleic acids, elements, metabolites, and
other analytes.
Biomarkers can also include mutated proteins or mutated nucleic acids. The
term "analyte" as
used herein can mean any substance to be measured and can encompass
electrolytes and
elements, such as calcium. Finally, biomarkers can also refer to non-analyte
physiological
markers of health status encompassing other clinical characteristics such as,
without limitation,
age, ethnicity, diastolic and systolic blood pressure, body-mass index, and
resting heart rate.
Proteins, peptides, nucleic acids, polymorphisms, and metabolites whose levels
are
changed in subjects who have Diabetes or a pre-diabetic condition, or are
predisposed to
developing Diabetes or a pre-diabetic condition are summarized in Table 1 and
are collectively
referred to herein as, inter alia, "Diabetes-associated proteins", "DBMARIr.ER
polypeptides", or
"DBIVIARKER proteins". The corresponding nucleic acids encoding the
polypeptides are
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referred to as "Diabetes-associated nucleic acids", "Diabetes-associated
genes", "DBMARKER
nucleic acids", or "DBMARKER genes". Unless indicated otherwise, "DBMARKER",
"Diabetes-associated proteins", "Diabetes-associated nucleic acids" are meant
to refer to any of
the sequences disclosed herein. The corresponding metabolites of the DBMARKER
proteins or
nucleic acids can also be measured, herein referred to as "DBMA.RKER
metabolites".
Calculated indices created from mathematically combining measurements of one
or more,
preferably two or more of the aforementioned clas"ses of DBMA.RKERS are
i=eferred to as
"DBMAR.KER indices". Proteins, nucleic acids, polymorphisms, mutated proteins
and mutated
nucleic acids, metabolites, and other analytes are, as well as common
physiological
measurements and indices constructed from any of the preceding entities, are
included in the
broad category of "DBMARKERS".
One DBMARKER of interest, which has a molecular weight of about 4.2kD and was
further identified as a C-terminal fiagment of a serine protease inhibitor,
Serpina 3M. This
marker was shown to be upregulated in CDr-RD and CDr-HSD rats. Amino acid
sequencing of
.15 this fragment revealed that this fragment comprises the amino acid
sequence
SGRPPMNWFNRPFLIAVSHTHGQTILFMAKVINPVGA (SEQ ID NO: 1)
A DBMARKER "metabolite" in the context of the present invention comprises a
portion
of a full length polypeptide. No particular length is implied by the term
"portion.'~ A
DBMARKER metabolite can be less than 500 amino acids in length, e.g., less
than or equal to
400, 350, 300, 250, 200, 150, 100, 75; 50, 35, 26, 25, 15, or 10 amino acids
in length. An
exemplary DBMARKER metabolite includes a peptide, which can include (in whole
or in part)
the sequence of SEQ ID NO: 1. Preferably, the DBMARKER metabolite includes at
least 5, 10,
15, 20, 25 or more contiguous amino acids of SEQ ID NO:1..
A"subject" in the context of the present invention is preferably a mammal. The
mammal
can be a human, non-human primate, mouse, rat, dog, cat, horse, or cow, but
are not limited to
these examples. Mammals other than humans can be advantageously used as
subjects that
represent animal models of Diabetes Mellitus or pre-diabetic conditions. A
subject can be male
or female. A subject can be one who has been previously diagnosed withor
identified as
suffering from or having Diabetes or a pre-diabetic condition, and optionally,
but need not have
already undergone treatment for the Diabetes or pre-diabetic condition. A
subject can also be
one who is not stiffering from type 2 Diabetes or a pre-diabetic condition. A
subject can also be
one who has been diagnosed with or identified as suffering from type 2
Diabetes or a pre-
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diabetic condition, but who show improvements in known Diabetes risk factors
as a result of
receiving one or more treatments for type 2 Diabetes or the pre-diabetic
condition. Alternatively,
a subject can also be one who has not been previously diagnosed as having
Diabetes or a pre-
diabetic condition. For example, a subject can be one who exhibits one or.more
risk factors for
Diabetes or a pre-diabetic condition, or a subject who does not exhibit
Diabetes risk factors, or a
subject who is asymptomatic for Diabetes or a pre-diabetic condition. A
subject can also be one
who is suffering from or at risk of developing Diabetes or a pre-diabetic
condition. A subject
can also be one who has been diagnosed with or identified as.having one or
more complications
related to type 2 Diabetes or a pre-diabetic condition as defined herein, or
alternatively, 'a subject
can be one who has not been previously diagnosed with or identified as having
one or more
complications related to type 2 Diabetes or a pre-diabetic condition.
A"saxnple" in the context of the present invention is a biological sample
isolated from a
subject and can include, for example, serum, blood plasma, blood cells,
endothelial cells, tissue
biopsies, lymphatic fluid, pancreatic juice, ascites fluid, iriterstitital
fluid (also known as
"extracellular fluid" and encompasses the fluid found in spaces between cells,
including, inter
alia, gingival crevicular fluid), bone marrow, sputurn, saliva, tears, or
urine.
One or more, preferably two or more DBIVIARK-ERS can be detected in the
practice of
the present invention. For example, one (1), two (2), five (5), ten (10),
fifteen (15), twenty (20),
twenty-five (25), thirty (30), thirty-five (35), forty (40), forty-five (45),
fifty (50), fifty-five (55),
sixty (60), sixty-five (65), seventy (70), seventy-five (75), eighty (80),
eighty-five (85), ninety
(90), ninety-five (95), one hundred (100), one hundred and five (105), one
hundred and ten (110),
one hundred and fifteen (115), one hundred and twenty (120), one hundred and
twenty-five (125),
one hundred and thirty (130), one hundred and thirty-five (135), one hundred
and forty (140),
one hundred and forty-five (145), one hundred and fifty (150), one hundred and
fifty-five (155)
or more DBMARKERS can be detected. In some aspects, all 158 DBMARKERS
disclosed
herein can be detected. Preferred ranges from which the number of DBNIARK.ERS
can be
detected include ranges bounded by any minimum selected from between one and
158,
particularly two, five, ten, fifteen, twenty, twenty-five, thirty, forty,
fifty, sixty, seventy, eighty,
ninety, one hundred, one hundred and ten, one hundred and twenty, one hundred
and thirty, one
hundred and forty, one hundred and fifty, paired with any maximum up to the
total known
DBMARKERS, particularly one, two, five, ten, twenty, and twenty-five.
Particularly preferred
ranges include one to two (1-2), one to five (1-5), one to ten (1-10), one to
fifteen (1-15), one to
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twenty (1-20), one to twenty-five (1-25), one to tliirty (1-30), one to thirty-
five (1-35), one to
forty (1-40), one to forty-five (1-45), one to fifty (1-50), one to fifty-five
(1-55), one to sixty (1-
60), one to sixty-five (1-65), one to seventy (1-70), one to seventy-five (1-
75), one to eighty (1-
80), one to eighty-five (1-85), one to ninety (1-90), one to ninety-five (1-
95), one to one hundred
(1-100), one to one hundred and twenty (1-120), one to one hundred and twenty-
five (1-125),
one to one-hundred and thirty (1-130), one to one hundred and forty (1-140),
one to one hundred
and fifty (1450), one to one hundred and fifty-eight (1-158), two- to five (2-
5), two to teri (2:-10),
two to fifteen (2-15), two to twenty (2-20), two to twenty-five (2-25), two to
thirty (2-30), two to
thirty-five (2-35), two to forty (2-40), two to forty-five (2-45), two to
fifty (2-50), two to fifty-
five (2-55), two to sixty (2-60), two to sixty-five (2-65), two to seventy (2-
70), two to seventy-
five (2-75), two to eighty (2-80), two to eighty-five (2-85), two to ninety (2-
90), two to ninety-
five (2-95), two to one hundred (2-100), two to one hundred and'tvrenty (2-
120), two to one
hundred and twenty-five (2-125), two to one hundred and thirty (2-130), two to
one hundred and
forty (2-140), two to one hundred and fifty (2-150), two to one hundred and
fifty-eight (2-158),
five to ten (5-10), five to fifteen (5-15), five to twenty (5-20), five to,
twenty-five (5-25), five to
thirty (5-30), five to thirty-five (5-35), five to forty (5-40), five to forty-
five (5-45), five to fifty
(5-50), five to fifty-five (5-55), five to sixty (5-60), five to sixty-five (5-
65), five to seventy (5-
70), five to seventy-five (5-75), five to eighty (5-80), five to eighty-five
(5-85), five to ninety (5-
90), five to ninety-five (5-95), five to one hundred (5-100), five to one
hundred and twenty (5-
120), five to one hundred and twenty-five (5-125), five to one hundred and
thirty (5-130), five to
one hundred and forty (5-140), five to one hundred and fifty (5-150), five to
one hundred and
fifty-eight (5-158), ten to fifteen (10-15), ten to twenty (10-20), ten to
twenty-five (10-25), and
ten to thirty (10-30), ten to thirty-five (10-35), ten to forty (10-40), ten
to forty-five (10-45), ten
to fifty (10-50), ten to fifty-five (10-55), ten to sixty (10-60), ten to
sixty-five (10-65), ten to
seventy (10-70), ten to seventy-five (10-75), ten to eighty (10-80), ten to
eighty-five (10-85), ten
to ninety (10-90), ten to ninety-five (10-95), ten to one hundred (10-100),
ten to one hundred and
twenty (10-120), ten to one hundred and twertty-five (10-125), ten to one
hundred and thirty (10-
130), ten to one hundred and forty (10-140), ten to one hundred and fifty
(10,150), ten to one
hundred and fifty-eight (10-158), twenty to fifty (20-50), twenty to seventy-
five (20-75), twenty
to one hundred (20-100), twenty to one-hundred and twenty (20-120), twenty to
one hundred and
twenty-five (20-125), twenty to one hundred and thirty (20-130), twenty to one
hundred and
forty (20-140), twenty to one hundred and fifty (20-150), twenty to
one.hundred and fifty-eight
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(20-158), fifty to seventy-five (50-75), fifty to one hundred (50-100), fifty
to one hundred and
twenty (50-120), fifty to one hundred and twenty-five (50-125), fifty to one
hundred and thirty
(50-130), fifty to one hundred and forty (50-140), fifty to one hundred and
fifty (50-150), fifty to
one hundred and fifty-eight (50-158), one hundred to one hundred and twenty-
five (100-125),
one hundred and twenty-five to one hundred and fifty (125-150), and one
hundred and fifty to
one hundred and fifty eight (150-158).
Diagnostic and Prognostic Methods
The risk of developing Diabetes or Pre-diabetic condition can be detected by
examining
an "effective amount" of DBMARKER proteins, peptides, nucleic acids,
polymorphisms,
metabolites, and other analytes in a test sample (e.g., a subject denived
sample) and comparing
the effective amounts to*reference or index values. An "effective amount" can
be the total
amount or levels of DBMARKERS that are detected in a sample, or it can be
a"normalized"
amount, e.g., the difference between DBMARKERS- detected in a sample and
background noise.
Normalization methods and normalized values will differ depending on the
method of detection.
Preferably, mathematical algorithms can be used to combine information from
results of multiple
individual DBMARKERS into a single measurement or index. Subjects identified
as having an
increased risk of Diabetes or a pre-diabetic condition can optionally be
selected to receive
treatment regimens, such as administration of prophylactic or therapeutic
compounds such as
"diabetes-modulating agents" as defined herein, or implementation of exercise
regimens or
dietary supplements to prevent or delay the onset of Diabetes or a pre-
diabetic condition. A
sample isolated from the subject can comprise, for example, blood, plasma,
blood cells,
endothelial cells, tissue biopsies, lymphatic fluid, pancreatic juice, serum,
bone marrow, ascites
fluid, interstitial fluid (including, for example, gingival crevicular fluid),
urine, sputum, saliva,
tears, or other bodily fluids.
The amount of the DBMARKER protein, peptide, nucleic acid, polymorphism,
metabolite, or other analyte can be measured in a test sample and compared to
the normal control
level. The term "normal control level", means the level of one or more
DBIVIARKER proteins,
nucleic acids, polymorphisms, metabolites, or other analytes, or DBM:ARKER
indices, typically
found in a subject not suffering from Diabetes or a pre-diabetic condition and
not likely to have
Diabetes or a pre-diabetic condition, e.g., relative to samples collected from
longitudinal studies
of young subjects who were monitored until advanced age and were found not to
develop
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Diabetes or a pre-diabetic condition. The "normal control level" can encompass
values. obtained
from a subject having "normal glucose levels" or "normoglycemic levels" as
defined herein.
Altematively, the normal control level can mean the level of one or more
DBMARKER protein,
peptide, nucleic acid, polymorphism, metabolite, or other analyte typically
found in a subject
suffering from Diabetes or a pre-diabetic condition. The normal control level
can be a range or
an index. Alteznatively, the normal control level can be a database of pattems
from previously
tested subjects. A change in the level in the subject-derived sample of one or
more
DBMARKER protein, nucleic acid, polyinorphism, metabolite, or other analyte
compared to the
normal control level can indicate that the subject is suffering from or is at
risk of developing
Diabetes or a pre-diabetic condition. In contrast, when the methods are
applied prophylactically,
a similar level compared to the normal control level in the subject-derived
sample of one or more
DBMARKER proteins, nucleic acids, polymorphisms, metabolites, or other
analytes can indicate
that the subject is not suffering from, is not at risk or is at low risk of
developing Diabetes-or a
pre-diabetic condition.
. A reference value can refer to values obtained from a control subject or
population whose
diabetic state is known (i.e., has been diagnosed with or identified as
suffering from type 2
Diabetes or a pre-diabetic condition, or has not been diagnosed with or
identified as suffering
from type 2 Diabetes or a pre-diabetic condition) or can be an index value or
baseline value. The
reference sample or index value or baseline value may be taken or derived from
one or more
subjects who have been exposed to the treatment, or may be taken or derived
from one or more
subjects who are at low risk of developing Diabetes or a pre-diabetic
condition, or may be taken
or derived from subjects who have shown improvements in Diabetes risk factors
as a result of
exposure to treatment. Altematively, the reference sample or index value or
baseline value may
be taken or derived from one or more subjects who have not been exposed to the
treatment. For
example, samples may be collected from subjects who have received initial
treatment for
Diabetes or a pre-diabetic condition and subsequent treatment for Diabetes or
a pre-diabetic
condition to monitor the progress of the treatment. A reference value can also
comprise a value
derived from risk prediction algorithms or computed indices from population
studies such as
those disclosed herein. A reference value can also be a value derived from a
subject previously
identified as having one or more complications related to type 2 Diabetes or a
pre-diabetic
condition, or altematively, a value derived from a subject who has not
developed complications,
or has not been previously diagnosed with or identified as having
complications relating to type
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2 Diabetes or a pre-diabetic condition. A reference value can also comprise a
value
corresponding to the normal control level or derived from one or more subjects
having "normal
glucose levels" as defined herein.
Differences in the level or amounts (which can be an "effective amount") of
DBMARKERS measured by the methods of the present invention can comprise
increases or
decreases in the level or amounts of DBMARKERS. The increase or decrease in
the amounts of
DBMARKI RS relative to a reference value can be indicative of progression
of.type 2 Diabetes
or a pre-diabetic=condition, delay, progressioii, development, or amelioration
of complications
related to type 2 Diabetes or a pre-diabetic condition, an increase or
decrease in the risk of
developing type 2 Diabetes or a pre-diabetic condition, or complications
relating thereto. The
increase or decrease can be indicative of the success of one or more treatment
regimens for type
2 Diabetes or a pre-diabetic condition, or can indicate improvements or
regression of Diabetes
risk factors. = The increase or decrease can be, for example, at least 5%, at
least 10%, at least 15%,
at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least
45%, or at least 50%
of the reference value or normal control level. =
The difference in the level (or amounts) of DBACARKERS is preferably
statistically
significant. By "statistically significant", it is meant that the alteration
is greater than what might
be expected to happen by chance alone. Statistical significance can be
determined by any
method known in the art. For example, statistical significance can be
determined byp-value.
The p-value is a measure of probability that a difference between groups
during an experiment
happened by chance. (P(z>zobserved)). For example, ap-value of 0.01 means that
there is a 1 in
100 chance the result occurred by chance. The lower thep-value, the more
likely it is that the
difference between groups was caused by treatment. An alteration is
statistically significant if
the p-value is at least 0.05. Preferably, thep-value is 0.04, 0.03, 0.02,
0.01, 0.005, 0.001 or less.
As noted below, and without any limitation of the invention, achieving
statistical significance
generally but not always requires that combinations of several DBMARKERS be
used together
in panels and combined with mathematical algorithms in order to achieve a
statistically
significant DBMARKER index.
The "diagnostic accuracy" of a test, assay, or method concerns the ability of
the test,
assay, or method to distinguish between subjects having Diabetes or a pre-
diabetic condition, or
at risk for Diabetes or a pre-diabetic condition is based on whether the
subjects have a "clinically
significant presence" or a "clinically significant alteration" in the levels
of one or more
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DBMARKERS. By "clinically signifcant presence" or "clinically significant
alteration", it is
meant that the presence of the DBMARKER (e.g., mass, such as milligrams,
nanograms, or mass
per volume, such as milligrams per deciliter or copy number of a transcript
per unit volume) or
an alteration in.the presence of the DBMARKER in the subject (typically in a
sample from the
subject) is higher than the predetermined cut-off point (or threshold value)
for that DBMARKER
and therefore indicates that the subject has Diabetes or a pre-diabetic
condition for which the
sufficiently high presence of that protein, peptide, nucleic acid,
polymorphism, metabolite or
analyte is a marker.
The present inventioix may be used to make categorical or continuous
measurements of
the risk of conversion to Type 2 Diabetes, thus diagnosing a category of
subjects defined as pre-
Diabetic.
In the categorical scenario, the methods of the present invention can be used
to
discriminate between normal and pre-diabetic condition subject cohorts. In
this categorical use
of the invention, the terms "high degree of diagnostic accuracy" and "very
high degree of
diagnostic accuracy" refer to the test or assay for that DBMARKER (or DBMARKER
index;
wherein DBMARKER value encompasses any individual measurement whether from a
single
DBMARKER or derived from an index of DBMARKERS) with the predetermined cut-off
point
correctly (accurately). indicating the presence or absence of a pre-diabetic
condition. A perfect
test would have perfect accuracy. Thus, for subjects who have a pre-diabetic
condition, the test
would indicate only positive test results and would not report any of those
subjects as being
"negative" (there would be no "false negatives"). In other words, the
"sensitivity" of the test (the
true positive rate) would be 100%. On the other hand, for subjects who did not
have a pre-
diabetic condition, the test would indicate only negative test results and
would not report any of
those subjects as being "positive" (there would be no "false positives"). In
other words, the
"specificity" (the true negative rate) would be 100%. See, e.g., O'Marcaigh
AS, Jacobson RM,
"Estimating The Predictive Value Of A Diagnostic Test, How To Prevent
Misleading Or
Confusing Results," Clin. Ped. 1993, 32(8): 485-491, which discusses
specificity, sensitivity, and
positive and negative predictive values of a test, e.g., a clinical diagnostic
test. In other
embodiments, the present invention may be used to discriminate a pre-diabetic
condition from
Diabetes, or Diabetes from Normal. Such use may require different subsets of
DBMARKERS(out of the total DBMARKERS as disclosed in Table 1), mathematical
algorithm,
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and/or cut-off point, but be subject. to the same aforementioned measurements
of diagnostic
accuracy for the intended use.
In the categorical diagnosis of a disease, changing the cut point or threshold
value of a
test (or assay) usually changes the sensitivity and specificity, but in a
qualitatively inverse
relationship. For example, if the cut point is lowered, more subjects in the
population tested will
typically have test results over the cut point or threshold value. Because
subjects who have test
results above the cut point are reported as having the disease, condition, or
syndrome for which
the test is conducted, lowering the cut point will cause more subjects to be
reported as having
positive results (e.g., that they have Diabetes or a pre-diabetic condition).
Thus, a higher
proportion of those who have Diabetes or a pre-diabetic condition will be
indicated by the test to
have it. Accordingly, the sensitivity (true positive rate) of the test will be
increased. However, 'at
the same time, there will be more false positives because more people who do
not have the
disease, condition, or syndrome (e.g., people who are truly "negative") will
be indicated by the
test to have DBMARKER values above the cut point and therefore to be reported
as positive
(e.g., to have the disease, condition, or. syndrome) rather than being
correctly indicated by the
test to be negative. Accordingly, the specificity (true negative rate).of the
test will be decreased.
Similarly, raising the cut point will tend to decrease the sensitivity and
increase the specificity.
Therefore, in assessing the accuracy and usefulness of a proposed medical
test, assay, or method
for assessing a subject's condition, one should always take both sensitivity
and specificity into
account and be mindful of what the cut point is at which the sensitivity and
specificity are being
reported because sensitivity and specificity may vary significantly over the
range of cut points.
There is, however, an indicator that allows representation of the sensitivity
and specificity
of a test, assay, or method over the entire range of test (or assay) cut
points with just a single
value. That indicator is derived from a Receiver Operating Characteristics
("ROC") curve for
the test, assay, or method in question. See, e.g., Shultz, "Clinical
Interpretation Of Laboratory
Procedures," chapter 14 in Teitz, Fundamentals of Clinical Chemistry, Burtis
and Ashwood
(eds.), 4`hedition 1996, W.B. Saunders Company, pages 192-199; and Zweig et
al., "ROC Curve
Analysis: An Example Showing.The Relationships Among.Serum Lipid And
Apolipoprotein
Concentrations In Identifying Subjects With Coronory Artery Disease," Clin.
Chem., 1992,
38(8): 1425-1428.
An ROC curve is an x-y plot of sensitivity on the y-axis, on a scale of zero
to one (e.g.,
100%), against a value equal to one minus specificity on the x-axis, on a
scale of zero to one
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(e.g., 100%). In other words, it is a plot of the true positive rate against
the false positive rate for
that test, assay, or method. To construct the ROC curve for the test, assay,
or method in question,
subjects can be assessed using a perfectly accurate or "gold standard" method
that is independent
of the test, assay, or method in question to determine whether the subjects
are truly positive or
negative for the disease, condition, or syndrome (for example, coronary
angiography is a gold
standard test for the presence of coronary atherosclerosis). The subjects can
also be tested using
the test, assay, or method in que'stion, and for varying cut points, the
subjects are reported as
being positive or negative according to the test, assay, or method. The
sensitivity (true positive
rate) and the value equal to one minus the spedificity (which value equals the
false positive rate)
are determined for each cut point, and each pair of x-y values is plotted as a
single point on the
x-y diagram. The "curve" connecting those points is the ROC curve.
The ROC curve is often used in order to determine the optimal single clinical
cut-off or
treatment threshold value where sensitivity and specificity are maximized;
such a situation -
represents the point on the ROC curve which describes the upper left corner of
the single largest
rectangle which can be drawn under the curve.
The total area under the curve ("AUC") is the indicator that allows
represerccation of the
sensitivity and specificity of a test, assay, or method over the entire range
of cut points witli just
a single value. The rnaximum AUC is one (a perfect test) and the minimum area
is one half (e.g.
the area where there is no discrimination of nonnal versus disease). The
closer the AUC is to one,
the better is the accuracy of the test. It =should be noted that implicit in
all ROC and AUC is the
definition of the disease and the post-test time horizon of interest.
By a "high degree of diagnostic accuracy", it is meant a test or assay in
which.the AUC
(area under the ROC curve for the test or assay) is at least 0.70, desirably
at least 0.75, more
desirably at least 0.80, preferably at least 0.85, more preferably at least
0.90, and most preferably
at least 0.95.
By a "very high degree of diagnostic accuracy", it is meant a test or assay in
which the
AUC (area under the ROC curve for the test or assay) is at least 0.80,
desirably at least 0.85,
more desirably at least 0.875, preferably at least 0.90, more preferably at
least 0.925, and most
preferably at least 0.95.
Alternatively, in low disease prevalence tested populations (defined as those
with less
than 1% rate of occurrences per annum), ROC and AUC can be misleading as to
the clinical
utility of a test, and absolute and relative risk ratios as defined elsewhere
in this disclosure can be
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employed to deternune the degree of diagnostic accuracy. Populations of
subjects to be tested
can also be categorized into quartiles, where the top quartile (25% of the
population) comprises
the group of subjects with the highest relative risk for developing or
suffering from Diabetes or a
pre-diabetic condition and the bottom quartile comprising the group of
subjects having the
lowest relative risk for developing Diabetes or a pre-diabetic condition.
Generally, values
derived from tests or assays having over 2.5 times the relative risk from top
to bottom quartile in
a low prevalence population are considered to have a"high degree of diagnostic
accuracy," and
those with five to seven times the relative risk for each quartile are
considered to have a very
high degree of diagnostic accuracy. Nonetheless, values derived from tests or
assays having only
1.2 to 2.5 times the relative risk for each quartile remain clinically useful
are widely used as risk
factors for a disease; such is the case with insulin levels or blood glucose
levels with respect to
their prediction of future type 2 Diabetes.
The predictive value of any test depends on the-sensitivity and specificity of
the test, and
on the prevalence of the condition in the population being tested. This
notion, based on Bayes'
theorem, provides that the greater the likelihood that the' condition being
screened for is present
in a subject or in the population (pre-test probability), the greater the
validity of a positive test--
and the greater the likelihood that the result is a true positive. Thus, the
problem with using a
test in any population where there is a low likelihood of the condition being
present is that a
positive result has limited value (i.e., more likely to be a false positive).
Similarly, in
populations at very high risk, a negative test result is more likely to be a
false negative. By
defining the degree of diagnostic accuracy, i.e., cut points on a ROC curve,
defining an
acceptable AUC value, and determining the acceptable ranges in relative
concentration of what
constitutes an effective amount of the DBMARKERS of the invention allows one
of skill in the
art to use the DBMARKERS to diagnose or identify subjects with a pre-
detezmined level of
predictability.
Alternative methods of determining diagnostic accuracy must be used with
continuous
measurements of risk, which are commonly used when a disease category or risk
category (such
as a pre-diabetic condition) has not yet been clearly defined by the relevant
medical societies and
practice of medicine.
"Risk" in the context of the present invention can mean "absolute" risk, which
refers to
that percentage probability that an event will occur over a specific time
period. Absolute risk
can be measured with reference to either actual observation post-measurement
for the relevant
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time cohort, or with reference to index values developed from statistically
valid historical cohorts
that have been followed for the relevant time period. "Relative" risk refers
to the ratio of
absolute risks of a subject's risk compared either to low risk cohorts or
average population risk,
which can vary by how clinical risk factors are assessed. Odds ratios, the
proportion of positive
events to negative events for a given test result, are also commonly used
(odds are according to
the formula p/(1-p) where p is the probability of event and (1- p) is the
probability of no event) to
no-conversion: Alternative continuous measures which may be assessed'in the
context of the
present.invention include time to Diabetes conversion and therapeutic'
Diabetes conversion risk
reduction ratios.
For such continuous measures, measures of diagnostic accuracy for a calculated
index are=
typically based on linear regression curve fits between the predicted
continuous value and the
actual observed values (or historical index calculated value) and utilize
measures such as R
squared, p values and confidence intervals. It is not unusual for predicted
values using such
algorithms to be reported including a confidence interval (usually 90% or 95%
CI) based on a
historical observed cohort's predictions,. as in the test for risk of future
breast cancer recurrence
commercialized by Genomic Health (Redwood City, Caiifornia).
The ultimate determinant and gold standard of true risk conversion to Diabetes
is actual
conversions within a sufficiently large population and observed over a
particular length of time.
However, this is problematic, as it is necessarily a retrospective point of
view, coming after any
opportunity for preventive interventions. As a result, subjects suffering from
or at.risk of
developing Diabetes or a pre-diabetic condition are commonly diagnosed or
identified by
methods known in the art, and future risk is estimated based on historical
experience and registry
studies. Such methods include, but are not Iimited to, measurement of systolic
and diastolic
blood pressure, measurements of body mass index, in vitro determination of
total cholesterol,
LDL, HDL, insulin, and glucose levels from blood samples, oral glucose
tolerance tests, stress
tests, measurement of human serum C-reactive protein (hsCRP),
electrocardiogram (ECG), c-
peptide levels, anti-insulin antibodies, anti-beta cell-antibodies, and
glycosylated hemoglobin
(HbAIJ. Additionally, any of the aforementioned methods can be used separately
or in
combination to assess if a subject has shown an "improvement in Diabetes risk
factors." Such
improvements include, without limitation, a reduction in body mass index
(BMI), a reduction in
blood glucose levels, an increase in HDL levels, a reduction in systolic
and/or diastolic blood
pressure, an increase in insulin levels, or combinations thereof.
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The oral glucose tolerance test (OGTT) is principally used for diagnosis of
Diabetes
Mellitus or pre-diabetic conditions when blood glucose levels are equivocal,
during pregnancy,
or in epidemiological studies (Definition, Diagnosis and Classification of
Diabetes Mellitus and
its Complications, Part 1, World Health Organization, 1999). The OGTT should
be administered
in the morning after at least 3 days of unrestricted diet (greater than 150 g
of carbohydrate daily)
and usual physical activity. A reasonable (30-50 g) carbohydrate-containing
meal should be
consumed on the evening before the test: The test should be preceded by an
ovemight fast of 8-
14 hours, during which water may be consumed. After collection of the fasting
blood sample,
the subject should drink 75 g of anhydrous glucose or 82.5 g of glucose
monohydrate in 250-300
-X 0 ml of water over the course of 5 minutes. For children, the test load
should be 1.75 g of glucose
per kg body weight up to a total of 75 g of glucose. Timing of the test is
from the beginning of
the drink. Blood samples must be collected 2 hou'rs after the test load. As
previously noted, a
diagnosis of impaired glucose tolerance (IGT) has been noted as being only 50%
sensitive, with
a >10% false positive rate, for a 7.5 year conversion to Diabetes when used at
the WHO cut-off
1:5 points. This is a significant problem for the clinical utility of the
test, as even relatively high risk
ethnic groups have only a 10% rate of conversion to Diabetes over such a
period unless
otherwise enriched by other risk factors; in an unselected general population,
the rate of
conversion over such periods is typically estimated at 5-6%, or less than 1 lo
per annum.
Other methods of measuring glucose in blood include reductiometric methods
known in
20 the art such as, but not limited to, the Somogyi-Nelson method, methods
using hexokinase and
glucose dehydrogenase, immobilized glucose oxidase electrodes, the o-toluidine
method, the
ferricyanide method and the neocuprine autoanalyzer method. Whole blood
glucose values are
usually about 15% lower than corresponding plasma values in patients with a
normal hematocrit
reading, and arterial values are generally about 7% higher than corresponding
venous values.
25 Subjects taking insulin are frequently requested to build up a"glycemic
profile" by self-
measurement of blood glucose at specific times of the day. A"7-poin.t profile"
is useful, with
samples taken before and 90 minutes after each meal, and just before going to
bed.
A subject suffering from or at risk of developing Diabetes or a pre-diabetic
condition may
also be suffering from or at risk of developing cardiovascular disease,
hypertension or obesity.
30 Type 2 Diabetes in particular and cardiovascular disease have many risk
factors in common, and
many of these risk factors are highly correlated with one another. The
relationships among these
risk factors may be attributable to a small number of physiological phenomena,
perhaps even a
CA 02661332 2009-02-20
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single phenomenon. In addition to detecting levels of one or more DBMARKERS of
the'
invention, subjects suffering from or at risk of developing Diabetes,
cardiovascular disease,
hypertension or obesity.can be identified by methods known in the art. For
example, Diabetes is
frequently diagnosed by measuring fasting blood glucose levels or insulin.
Normal adult glucose
levels are 60-126 mg/dl. Normal insulin levels are 7 mU/ml 3mU. Hypertension
is diagnosed
by a blood pressure consistently at or above 140/90. Risk of cardiovascular
disease can also be _
diagaosed by measuring cholesterol levels. For example, .LDL cholesterol above
137 or total
cholesterol above 200 is indicative of a heighteined risk of cardiovascular
disease. Obesity4s.
diagnosed for example, by body mass index. Body mass index (BMI) is measured
(kg/m2 (or=
lb/in2 X 704.5)). Alternatively, waist circumference (estimates fat
distribution), waist-to-hip
ratio (estimates fat distribution), skinfold thickness (if measured at several
sites, estimates fat
distribution), or bioirim.pedance (based on principle that lean mass conducts
current better than fat
mass (i.e. fat mass impedes current), estimates % fat) can be measured. The
parameters' for..
normal, overweight, or obese individuals is as follows: Underweight: BMI <1
8.5; Normal: 13MI
18.5 to 24.9; Overweight: BMI = 25 to 29.9. Overweight individuals are
characterized as having
a waist circumference of >94 cm for men or >80 cm for women and waist to hip
ratios of > 0.95
in men and > 0.80 in women. Obese individuals are characterized as having a
BMI of 30 to:34.9,
being greater than 20% above "normal" weight for height, having a body fat
percentage > 30%
for women and 25% for men, and having a waist circumference >102 cm (40
inches) for men or
~ 88 cm (35 inches) for women. Individuals with severe or morbid obesity are
char'acterized as
having a BMI of> 35_ Because of the interrelationship between Diabetes and
cardiovascular
disease, some or all of the individual DBMARKERS and DBMARKER expression
profiles of
the present invention may overlap or be encompassed by biomarkers of
cardiovascular disease,
and indeed may be useful in the diagnosis of the risk of cardiovascular
disease.
Risk prediction for Diabetes Mellitus or a pre-diabetic condition can also
encompass risk
prediction algorithms and computed indices that assess and estimate a
subject's absolute risk for
developing Diabetes or a pre-diabetic condition with reference to a historical
cohort. Risk
assessment using such predictive mathematical algorithms and computed indices
has =
increasingly been incorporated into guidelines for diagnostic testing and
treatment, and
encompass indices obtained from and validated with, inter alia, multi-stage,
stratified samples
from a representative population. A plurality of conventional Diabetes risk
factors are
incorpoiated into predictive models. A notable example of such algorithms
include the
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Framingham.Heart Study (Kannel, W.B., et al, (1976) Am. J. Cardiol. 38: 46-51)
and
modifications of the Framingham Study, such as the National Cholesterol
Education Program
Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol
in Adults
(Adult Treatment Panel III), also know as NCEP/ATP III, which incorporates a
patient's age,
total cholesterol concentration, HDL cholesterol concentration, smoking
status, and systolic
blood pressure to estimate a person's 10-year risk of developing
cardiovascular disease, which is
conunonly found- in subjects=suffering from or at risk for developing Diabetes
Mellitus, or a pre-
diabetic condition. The Framingham algorithm has been found to be modestly
predictive of the :.'
risk for develQping Diabetes Mellitus, or a pre-diabetic condition.
Other Diabetes risk prediction algorithms include, without limitation, the San
Antonio
Heart Study (Stern, M.P. et al, (1984) Am. J. Epidemiol. 120: 834-851; Stem,
M.P. et al, (1993)
Diabetes 42: 706-714; Burke, J.P. et al, (1999) Arch. Intern. Med. 159: 1450-
1456), Archimedes
(Eddy, D.M..and Schlessinger, L. (2003) Diabetes Care 26(11): 3.093-3101;
Eddy, D.M. and
Schlessinger, L. (2003) Diabetes Care 26(11): 3102-3110), the Finnish-based
Diabetes Risk
Score (Lindstrom, J. and Tuomilehto, J. (2003) Diabetes Care 26(3): 725-731),
and the Ely Study
(Griffin, S.J. et al, (2000) Diabetes Metab. Res. Rev. 16: 164-171), the
contents of which are
expressly incorporated.herein by reference.
Archimedes is a mathematical model of Diabetes that simulates the disease
state person-
by-person, object-by-object and comprises biological=details that are
continuous in reality, such
as the pertinent organ systems, more than 50 continuously interacting
biological variables, and
the major symptoms, tests, treatments, and outcomes commonly associated with
Diabetes.
Archimedes includes many diseases simultaneously and interactively in a single
integrated physiology, enabling it to address features such as co-morbidities,
syndromes,
treatments and other multiple effects. The Archimedes model includes Diabetes
and its
complications, such as coronary artery disease, congestive heart failure, and
asthma. The model
is written in differential equations, using object-oriented programming and a
construct called
"features". The model comprises the anatomy of a subject (all simulated
subjects have organs,
such as hearts, livers, pancreases, gastrointestinal tracts, fat, muscles,
kidneys, eyes, limbs,
circulatory systems, brains, skin, and peripheral nervous systems), the
"features" that determine
the course of the disease and representing real physical phenomena (e.g., the
number of
milligrams of glucose in a deciliter of plasma, behavioral phenomena, or
conceptual phenomena
(e.g., the `progression" of disease), risk factors, incidence, and
progression of the disease,
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glucose metabolism, signs and tests, diagnosis, symptoms, health outcomes of
glucose
metabolism, treatments, complications, deaths from Diabetes and its
complications, deaths from
other causes, care processes, and medical system resources. For a typical
application of the
model, there are thousands of simulated subjects, each with a simulated
anatomy and physiology,
who will get simulated diseases, can seek care at simulated health care
facilities, will be seen by
simulated health care personnel in simulated facilities, will be given
simulated tests and
treatments, and will have simulated outcomes. As in reality, each of the
siniulated patients is
different, with different characteristics, physiologies,. behaviors, and
responses to treatments, all
designed to match the individual variations seen in reality.
= The model is built by development of a=non-quantitative or conceptual
description of the
pertinent biology and pathology - the variables and relationships - as best
they are understood
with current information. Studies are then identified that pertain to the
variables and
relationships, and typically comprise basic research, epidemiological, and
clinical studies that
experts in the field identify as the foundations of their own understanding of
the disease. That
information is used to develop differential equations that relate the
variables. The development
of any particular equation in the Archimedes model involves finding the form
and coefficients
that best fit the available information about the variables, after which the
equations are
programmed into an object-oriented language. This is followed by a series of
exercises in which
the parts of the model are tested and debugged, first one at a time, and then
in appropriate
combinations, using inputs that have known outputs. The entire model can then
be used to
simulate a complex trial, which demonstrates not only the individual parts of
the model, but also
the connections between all the parts. The Archimedes calculations are
performed using
distributed computing techniques. Archimedes has been validated as a realistic
representation of
the anatomy, pathophysiology, treatrnents and outcomes pertinent to Diabetes
and its
complications (Eddy, D.M. and Schlessinger, L. (2003) Diabetes Care 26(11)
3102-3110).
The Finland-based Diabetes Risk Score is designed as a screening tool for
identifying
high-risk.subjects in the population and for increasing awareness of the
modifiable risk factors
and healthy lifestyle. The Diabetes Risk Score was deternzined from a random
population
sample of 35- to 64-year old Finnish men and women with no anti-diabetic drug
treatment at
baseline, and followed for 10 years. Multivariate logistic regression model
coefficients were
used to assign each variable category a score. The Diabetes Risk Score
comprises the sum of
these individual scores and validated in an independent population survey
performed in 1992
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with a prospective follow-up for 5 years. Age, BMI, waist circumference,
history of anti-
hypertensive drug treatment and high blood glucose, physical activity, and
daily consumption of
fruits, berries, or vegetables were selected as categorical variables.
The Finland-based Diabetes Risk Score values are derived from the coefficients
of the
logistic model by classifying them into five categories. The estimated
probability (p) of drug-
treated Diabetes over a 10-year span of time for any combination of risk
factors can be calculated
from the followirig coefficients:
e((30+Pixl+p2x2+'==)
P(Diabetes) = ----------------------------
1 + e(a0+plxt +0 2x2+ )
where (3o is the intercept and (31a P2, and so on represent the regression
coefficients of the
various categories of the risk factors xl, x2, and so on.
The sensitivity relates to the probability that the test is positive for
subjects who will get
drug-treated Diabetes in the future and the specificity reflects the
probability that the test is
negative for subjects without drug-treated Diabetes. The sensitivity and the
specificity with 95%
confidence interval (CI) were calculated for each Diabetes Risk Score level in
differentiating the
subjects' who developed drug-treated Diabetes from those who did not. ROC
curves vvere plotted
-for the Diabetes Risk score, the sensitivity was plotted on the y-axis and
the false-positive rate
(1-specificity) was plotted on the x-axis. The more accurately discriminatory
the test, the steeper
the upward portion of the ROC curve, and the higher the AUC, the optimal cut
point being the
peak of the curve. =
Statistically significant independent predictors of future drug-treated
Diabetes in the
Diabetes Risk Score are age, BMI, waist circumference, antihypertensive drug
therapy, and
history of high blood glucose levels. The Diabetes Risk Score model comprises
a concise model
that includes only these statistically significant variables and a full model,
which includes
physical activity and fruit and'vegetable consumption.
The San Antonio Heart Study is a long-term, connnunity-based prospective
observational
study of Diabetes and cardiovascular disease in Mexican Americans and non-
Hispanic
Caucasians. The study initially enrolled 3,301 Mexican-American and 1,857 non-
Hispanic
Caucasian men and non-pregnant women in two phases between 1979 and 1988.
Participants
were 25-64 years of age at enrollment and were randomly selected from low,
middle, and high-
income neighborhoods in San Antonio, Texas. A 7-8 year follow-up exam followed
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approximately 73% of the surviving individuals initially enrolled in the
study. Baseline
characteristics such as medical history of Diabetes, age, sex, ethnicity, BMI,
systolic and
diastolic blood pressure, fasting and 2-hour plasma glucose levels, fasting
serum total cholesterol,
LDL, and HDL cholesterol levels, as well as triglyceride levels, were compiled
and assessed. A
multiple logistic regression model with incident Diabetes as the dependent
variable and the
aforementioned baseline characteristics were applied as independent.variables.
Using this model,
univariate odds ratios can be computed for each potetitial risk factor for men
and women *
separately and for both sexes combined. , For continuous risk factors, the
odds ratios can be
presented for a 1-SD increment. A multivari ate predicting model with both
sexes combined can
be developed using a stepwise logistic regression procedure in which the
variables that had
shown statistically significarit odds ratios when examined individually were
allowed to enter the
model. This multivariable model is then analyzed by ROC curves and 95% CIs of
the areas
under the ROC curves estimated by non-parametrie algorithms such as those
described by
DeLong (DeLong E.R. et al, (1988) Biometrics 44: 837-45). The results of
the'San Antonio
Heart Study indicate that pre-diabetic subjects have an atherogenic pattem of
risk factors
(possibly caused by obesity, hyperglycemia, and especially hyperinsulinemia),
which may be
present for many years and may contribute to the :nsk of macrovascular disease
as much as the
duration of clinical Diabetes itself.
Despite the numerous studies and algorithms that have been used to assess the
risk of
Diabetes or a pre-diabetic condition, the evidence-based, multiple risk factor
assessment
approach is only moderately accurate for the prediction of short- and long-
term risk of
manifesting Diabetes or a pre-diabetic condition in individual asymptomatic or
otherwise healthy
subjects. Such risk prediction algorithms can be advantageously used in
combination with the
DBNs1ARKERS of the present invention to distinguish between subjects in a
population of
interest to determine the risk stratification of developing Diabetes or a pre-
diabetic condition.
The DBMARKERS and methods of use disclosed herein provide tools that can be
used in
combination with such risk prediction algorithms to assess, identify, or
diagnose subjects who
are asymptomatic and do not exhibit the conventional risk factors. =
The data derived from risk prediction algorithms and from the methods of the
present
invention can be compared by linear regression. Linear regression analysis
models the
relationship between two variables by fitting a linear equation to observed
data. One variable is
considered to be an explanatory variable, and the other is considered to be a
dependent variable.
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For example, values obtained from the Archimedes or San Antonio Heart analysis
can be used as
a dependent variable and analyzed against levels of one or more DBMARKERS as
the
explanatory variables in an effort to more fully define the underlying biology
implicit in the
calculated algorithm score (see Examples). Alternatively, such risk prediction
algorithms, or
their individual inputs, which are generally DBMARKERS themselves, can be
directly '
incorporated into the practice of the present invention, with the combined
algorithm compared
against actual observed results'in a historical cohort.
A linear regression line has an equation of the form Y = a + bX, where X is
the
explanatory variable and Y is the dependent variable. The slope of the line is
b, and a is the
intercept (the value of y when x = 0). A numerical measure of association
between two variables
is the "correlation coefficient,"or R, which is a value between -1 and I
indicating the strength of
the'association of the observed data for the two variables. This is also often
reported as the
square of the correlation coefficient, as the "coefficient, of. determination"
or Rz; in this form it is
the proportion of the total variation in Y explained by fi:tting the line. The
most connnon method
for fitting a regression line is the method of least-squares. Tlus method
calculates the best-fitting
line for the observed data by minimizing the sum of the squares of the
vertical deviations from
each data-pciint to the line (if a point lies on the fitted line exactly, then
its vertical deviation is 0).
Because the deviations are first squared, then summed, there are no
cancellations between
positive and negative values.
After a regression line has been computed for a group of data, a point which
lies far from
the line (and thus has a large residual value) is known as an outlier. Such
points may represent
erroneous data, or may indicate a poorly fitting regression line. If a point
lies far from the other
data in the horizontal direction, it is known as an influential observation.
The reason for this
distinction is that these points have rnay have a significant impact on the
slope of the regression
line. Once a regression model has been fit to a group of data, examination of
the residuals (the
deviations from the fitted line to the observed values) allows one of skill in
the art to investigate
the validity of the assumption that a linear relationship exists. Plotting the
residuals on the y-axis
against the explanatory variable on the x-axis reveals any possible non-linear
relationship among
the variables, or might alert the skilled artisan to investigate "lurking
variables." A "lurking
variable" exists when the relationship between two variables is significantly
affected by the
presence of a third variable which has not been included in the modeling
effort.
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Linear regression analyses can be used, inter alia, to predict.the risk o
developing
Diabetes or a pre-diabetic condition based upon correlating the levels of one
or more
DBMARKERS in a sample from a subject to that subjects' actual observed
clinical outcomes, or
in combination with, for example, calculated Archimedes risk scores, San
Antonio Heart risk
scores, or other known methods of diagnosing or predicting the prevalence of
Diabetes or a pre=
diabetic condition. Of particular use, however, are non-linear equations and
analyses to
deternzine the relationship between known predictive models of Diabetes and
levels of
DBMARKERS detected in a subject sample. Of particular interest are structural
and synactic
classification -algorithms, and methods of risk index construction; utilizing
pattern recognition-
features, including established techniques such as the Kth-Nearest Neighbor,
Boosting, Decision
Trees, Neural -Networks, Bayesian Networks, Support Vector Machines, and
Hidden Markov
Models. Most commonly used are classification algorithms using logistic
regression, which are
the basis for the Framingham, Finnish, and San Antonio Hearfrisk. scores.
Furthermore, the.
application o.f.such techniques to panels of multiple DBMA.RKERS is
encompassed by or within
the ambit of the present invention, as is the use of such combination to
create single numerical
"risk indices"-or "risk scores" encompassing information from multiple
DBMARKER inputs.
Factor'analysis is a mathematical technique by which a large number of
correlated
variables (such as Diabetes risk factors) can be reduced to fewer "factors"
that represent distinct
attributes that account for a large proportion of the variance -in the
original variables (Hanson,
R.L. et al, (2002) Diabetes 51: 3120-3127). Thus, factor analysis is well
suited for identifying
components of Diabetes Mellitus and pre-diabetic conditions such as IGT, IFG,
and Metabolic Syndrome. Epiderniological studies of factor "scores" from these
analyses can further determine
relations between components of the metabolic syndrome and incidence of
Diabetes. The
premise underlying factor analysis is that correlations observed among a set
of variables can be
explained by a small number of unique unxneasured variables, or "factors".
Factor analysis
involves two procedures: 1) factor extraction to estimate the number of
factors, and 2) factor
rotation to determine constituents of each factor in terms of the original
variables.
Factor extraction can be conducted by the.method of principal components.
These
components are linear combinations of the original variables that are
constructed so that each
component has a correlation of zero with each of the other components. Each
principal
component is associated with an "eigen-value," which represents the variance
in the original
variables explained by that component (with each original variable
standardized to have a
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variance of I.). The number of principal components that can be constructed is
equal to the
number of original variables. In factor analysis, the number of factors is
customarily determined
by retention of only those components that account for more of the total
variance than any single
original variable (i.e., those components with eigen-values of>1).
Once the number of factors has been established, then factor rotation is
conducted to
determine the composition of factors that has the most parsimonious
interpretation in terms of
the original variables. In factor rotation, "faotor loadings," which represent
correlations of each
=-factor with the original variables, are changed so that these factor
loadings are made as close to 0
or 1 as possible (with the constraint that the total amount of variance
explained by the factors
' remains unchanged). A number of methods for factor rotation have been
developed and can be
distinguished by whether they require the fmal set of factors to remain
uncorrelated with one
another (also known as "orthogonal methods") or by whether they allow factors
to be correlated
("oblique methods"). In interpretation of.fxctor analysis, the pattern of
factor loadings is
examined to determine which original variables represent primary constituents
of each factor.
15. Coiiventionaily, variables that have a factor loading of >0.4 (or less
than -0.4) with a particular
factor are considered to be its major constituents. Factor analysis can be
very useful in
constructing DBMARKER panels from their constituent components, and in
grouping
substitutable groups of markers.
Comparison can be performed on test ("subject") and reference ("control")
samples
measured concurrently or at temporally distinct times. An example of the
latter is the use of
compiled expression information, e.g., a sequence database, which assembles
information about
expression levels of DBMARKERS. If the reference sample, e.g., a control
sample is from a
subject that does not have Diabetes a similarity in the amount of the
DBMARKERS in the
subject test sample and the control reference sample indicates that the
treatment is efficacious.
However, a change in the amount of one or more DBMARKERS in the test sample
and the
reference sample can reflect a less favorable clinical outcome or prognosis.
"Efficacious" or
"effective" means that the treatment leads to an decrease or increase in the
amount of one or
more DBIVIA.RKERS, or decrease of serum insulin levels or blood glucose levels
in a subject.
Assessment of serum insulin or blood glucose levels can be analyzed using
standard clinical
protocols. Efficacy can be determined in association with any known method for
diagnosing or
treating Diabetes.
33
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Levels of an effective amount of DBMARKER proteins, peptides, nucleic acids,
polymorphisms, metabolites, or other analytes also allows for the course of
treatment of Diabetes
or a pre-diabetic condition to be monitored. In this method, a biological
sample can be provided
from a subject undergoing treatment regimens, e.g., drug treatments, for
Diabetes. 'Such
treatment regimens can include, but are not limited to, exercise regimens,
dietary
supplementation (including without limitation, alpha-lipoic acid, chromium,
coenzyme Q 10,
garlic., inagnesium, and omega-3 fatty acids), surgical intervention (such as
but not limited to
gastric bypass, angioplasty, etc.), and treatment with therapeutics or
prophylactics used-in ..
subjects diagnosed or identified with Diabetes or a pre-diabetic condition,
such as for example,
diabetes-modulating agents as defined herein. If desired, biological samples
are obtained from
the subject at various time points before, during, or after treatmeint. Levels
of an effective
amount of DBMARKER proteins, peptides, nucleic acids, polymorphisms,
metabolites, or other
analytes can then be determined and compared to a'reference value, e.g. a
control subject or.
population whose diabetic state is known or an index value or baseline value.
The reference
sample or index value or baseline value may be taken or derived from one or
more subjects who
have been exposed to the treatment, or may be taken or derived from one or
more subjects who
are at low risk of developing Diabetes or a pre-diabetic condition, or may be
taken or derived
from subjects who have shown improvements in Diabetes risk factors as a result
of exposure to
treatment. Alternatively, the reference sample or index value or baseline
value may be taken or
20'= derived from one or more subjects who have not been exposed to the
treatment. For example,
samples may be collected from subjects who have received initial treatment for
Diabetes or a
pre-diabetic condition and subsequent treatment for Diabetes or a pre-diabetic
condition to
monitor the progress of the treatment. A reference value can also comprise a
value derived from
risk prediction algorithms or computed indices from population studies such as
those disclosed
herein.
The DBMARKERS of the present invention can thus be used to generate a
"reference
expression profile" which comprises a pattern of expression levels of
DBMARKERS detected in
those subjects who do not have Diabetes or a pre-diabetic condition such as
impaired glucose
tolerance, and would not be expected to develop Diabetes or a pre-diabetic
condition. The
DBMARKERS disclosed herein can also be used to generate a "subject expression
profile"
comprising a pattern of expression levels of DBMARKERS taken from subjects who
have
Diabetes or a pre-diabetic condition like impaired glucose tolerance. The
subject expression
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profiles can be compared to a reference expression profile to diagnose or
identify subjects at risk
for developing Diabetes or a pre-diabetic condition, to monitor the
progression of disease, as
well as the rate of progression of disease, including development or risk of
development of
complications related to type 2 Diabetes or pre-diabetic conditions, and to
monitor the
effectiveness of Diabetes or pre-diabetic condition treatment modalities. The
reference and
subject expression profiles of the present invention can be contained in a
machine-readable
medium, such as but not limited to, analog tapes or digital xnedia like those
readable by a VCR,
CD-ROM, DVD-ROM, USB flash media, among others. Such machine-readable media
can also
contain additional test results, such as, -without linnitation, measurements
of conventional
Diabetes risk factors like systolic and diastolic blood pressure, blood
glucose levels, insulin
levels, BMI indices, and cholesterol (LDL and HDL) levels. Altematively or
additionally, the
machine-readable media can also comprise subject information such as medical
history and any
relevant family history. The machine-readable media can also *contain
information relating to
other Diabetes-risk algorithms and computed indices such as those described
herein.
Differences in the genetic makeup of subjects can result in differences in
their relative
abilities to metabolize various agents, which may modulate the symptoms or
risk factors of
Diabetes or a pre-..eiiabetic condition. Subjects that have Diabetes or a pre-
diabetic condition, or
at risk for developing Diabetes or a pre-diabetic condition can vary in age,
ethnicity, body mass
index (BMI), total cholesterol levels, blood glucose levels, blood pressure,
LDL atnd HDL levels,
and other parameters. Accordingly, use of the DBIVIA.RKERS disclosed herein
allow for a pre-
determined level of predictability that a putative therapeutic or=prophylactic
to be tested in a
selected subject will be suitable for treating or preventing Diabetes, a pre-
diabetic condition, or
complications thereof in the subject.
To identify therapeutics or agents that are appropriate for a specific
subject, a test sample
from the subject can be exposed to a therapeutic agent or a drug, and the
level of one or more of
DBMARKER proteins, nucleic acids, polymorphisms, metabolites or other analytes
can be
determined. The level of one or more DBMARKERS can be compared to a sample
derived
from the subject at a first period of time before and at a second period of
time after treatment or
exposure to a therapeutic agent or a drug, or can be compared to samples
derived from one or
more subjects who have shown improvements in Diabetes or pre-diabetic
condition risk factors
as a result of such treatment or exposure. Examples of such therapeutics or
agents frequently
used in Diabetes treatments, and may modulate the symptoms or risk factors of
Diabetes include,
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b.ut are not limited to, sulfonylureas like glimepiride, glyburide (also known
in the art as
glibenclamide), glipizide, gliclazide; biguanides such as metformin; insulin
(including inhaled
formulations such as Exubera), and insulin analogs such as insulin lispro
(Humalog), insulin
glargine (Lantus), insulin detemir, and insulin glulisine; peroxisome
proliferator-activated
receptor-y (PPAR-y) agonists such as the thiazolidinediones including
troglitazone (Rezulin),
pioglitazone (Actos), rosiglitazone (Avandia), and isaglitzone (also known as
netoglitazone);
dual-acting PPAR agonists such as BMS-298585 and tesaglitazar; insulin
secretagogues
including metglitinides such as repaglinide and nateglinide; analogs of
glucagon-like peptide-1
(GLP-1) such as exenatide (AC-2993) and liraglutide (insulinotropin);
inhibitors of dipeptidyl
peptidase IV like LAF-237; pancreatic lipase inhibitors such as orlistat; a-
glucosidase inhibitors
such as* acarbose; miglitol, and voglibose; and combinations. thereof,
particularly metformin and
glyburide (Glucovance), metformin and rosiglitazone (Avandamet), and metformin
and glipizide
(Metaglip). Such=therapeutics or agents have been prescribed for subjects
diagnosed with
Diabetes or a pre-diabetic condition, and may modulate tfie symptoms or risk
factors of Diabetes
or a pre-diabetic condition (herein, `diabetes-modulating agents").
A subject sample can be incubated in the presence of a candidate agent and the
pattern of
DBMARKER expression in the test sample is measured and compared to a reference
profile, e.g.,
a Diabetes reference expression profile or a non-Diabetes reference expression
profile or an
index value or baseline value. The test agent can be any compound or
composition or
combination thered"f. For example, the test agents are agents frequently used
in Diabetes
treatment regimens and are described herein.
Table 1 comprises the one hundred and fifty-eight (158) DBMARKERS of the
present
invention. One skilled in the art will recognize that the DBMARKERS presented
herein
encompasses all forms and variants, including but not limited to,
polymorphisms, isoforms,
mutants, derivatives, precursors including nucleic acids, receptors (including
soluble and
transmembrane receptors), ligands, and post-translationally modified variants,
as well as any
multi-unit nucleic acid, protein, and glycoprotein structures comprised of any
of the
DBMARKERS as constituent subunits of the fully assembled structure.
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Table 1: DBMARKERS
DBMARKER Common Name Alternative Name
I Serpina 3M C-terminal fragment of a predicted protein, similar
to serine protease inhibitor 2.4
2 S in2a
3 Fetuin beta Fetub; Fetuin (3; Fetuin B
4 Apolipoprotein C-Ill Apoc3
precursor
Predicted protein, similar to Apoc2, predicted
A oli o rotein C2
6 Alpha-2-HS-glycoprotein = a-2-HS-glycoprotein; =Ahsg; Fetuin a; Fetuin A;
Aa2-066
7 T-]cininogen II precursor
8 Alpha-l-macroglobulin a-l-macroglobulin; A2MG; Pzp;.pregnancy-zone
protein
9 Serpin Cl Serine/cysteine proteinase inhibitor, clade C,
member I ( redicted)
Coagulation factor 2 = F2..
11 Inter-alpha-inhibitor H4 ITIH4
heavy chain
12 Vitamin D binding protein Gc; VTDB
r e tide
13 Low-molecular weight T- Kininogen; LMW T-kininogen I precursor; major
kininogen I recursor acute phase al ha-1 protein precursor
14 Apoli o rotein A-1 Prea oli o rotein A-1; ApoAl
Predicted protein, similar to Apoc2
apolipoprotein C-II
precursor
16 Thrombin Prothrombin pTdcursor; THRB
17 A oli o roteinE A oE
18 Liver regeneration-related Tf
rotein LRRG03
19 Apoli o rotein A-IV ApoA4
Alpha-I -inhibitor 3 LOC297568
precursor
21 XP 579384
22 Histidine-rich gl co rotein Hrg
23 XP 579477
24 Complement component C9 C9
recursor
A oli o rotein H ApoH
26 B-factor, properdin Cfb
27 Hemopexin Hpx
28 Calnexin Ca(2+)-binding hos ho rotein p90
29 Reg3a Rn. 11222; regenerating islet-derived 3 alpha
LOC680945 Rn.1414; similar to stromal cell-derived factor 2-
like 1
31 Pap Rn.9727; pancreatitis-associated protein
32 Ptfl a Rn.10536; Pancreas specific transcription factor,
la
33 Matl a Rn.10418; methionine adenosyltransferase I, al ha
34 Nu rl Rn.l 1182; nuclear rotein 1
Rn.128013
36 Chacl (predicted) Rn.23367; ChaC; cation transport regulator-like 1
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DBMARKER Common Name Alternative Name
37 Slc7a3 Rn.9804; solute carrier family 7 (cationic amino
acid transporter, y+ system), member 3
38 LOC312273 Rn.13006; trypsin V-A
39 Rn.47821
40 Ptger3 Rn.10361; prostaglandin E receptor 3 (subtype
EP3
41 RGD 1562451 Rn.199400; similar to Pabpc4 predicted protein
42 RGD 1566242 Rn.24858; similar to RIKEN cDNA 1500009M05
43 Cyp2d26 Rn.91355; Cytochrome P450, family 2, subfamily
d, polypeptide 26
44 Rn. 17900 Similar to aldehyde dehydrogenase 1 family,
member L2
45 LOC286960 Rn.10387; reprotrypsinogen IV
46 Gls2 Rn.10202; glutaminase 2 (liver, mitochondrial)
47 Nme2 Rn.927; expressed in non-metastatic cells 2
48 Rn.165714
49 P2rx1 Rn.91176; purinergic receptor PX2, ligand-gated
ion channel, I
50 Pdk4 Rn.30070; pyruvate dehydrogenase kinase,
isoenzyme 4
51 Amyl Rn.116361; amylase 1, salivary
52 Cbs Rn.87853; cystathionine beta synthase
53 Mtel Rn.37524; mitochondrial acyl-CoA thioesterase I
54 Spinki Rn.9767; serine protease inhibitor, Kazal type 1
= 55 Gatm =Rn.1766 1; glycine amidinetransferase (L-
arginine:glycine amidinotransferase)
=56 Tmed6_predicted Rn.19837; transmembrane emp24 protein transport
domain containing 6
57 Tff2 Rn.34367; trefoil factor 2(spasmolytic protein 1)
58 Hsdl7bl3 Rn.25104; hydroxysteroid (17-beta)
dehydro enase 13
150 Rn.11766 Similar to LRRGT00012
61 Gnmt Rn. 11142; glycine N-methyltransferase
62 Pah Rn. 1652; phenylalanine h droxylase
63 Serpini2 Rn.54500; serine/cysteine proteinase inhibitor,
ciade 1, member 2
64 RGD1309615 Rn.167687
65 LOC691307 Rn.79735; similar to leucine rich repeat containing
39 isoform 2
66 Eprs Rn.21240; glutamyl-prolyl-tRNA synthetase
67 Pck2_predicted Rn.35508; phosphoenolpyruvate carboxykinase 2
mitochondrial
68 Chd2_predicted Rn. 162437; chromodomain helicase DNA binding
rotein 2
69 Rn.53085
70 Rn. l 2530
71 NIPK Rn.22325; tribbles homolog; cDNA clone
RPCAG66 3' end, mRNA sequence
72 SIc30a2 . Rn.1 1135; solute carrier family 30 (zinc
trans orter), member 2
73 SerpinalO Rn.10502; serine/cysteine peptidase inhibitor,
clade A, member 10
74 Cfi Rn.7424; com lement factor I
75 Cckar Rn.10184;cholecystokinin A receptor
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DBMARKER Common Name Alternative Name
76 LOC689755 Rn.151728; LOC689755
77 BhlhbB Rn.9897; basic helix-loop-helix domain containing
class B, 8
78 An e Rn.11132; alanyl (membrane) aminopeptidase)
79 Asns Rn.11172; asparagine syntbetase
80 S1c7a5 Rn.3226 1; solute carrier family 7 (cationic amino
acid transporter, y+ system), member 5
81 Us 3 redicted Rn. 12678; ubiguitin specific protease 43
82 Csnklal Rn.238I0; casein kinase 1, alpha I
83 Cml2 Rn.160578; camello-like 2
84 Pab c4 = Rn.199602
85 Gjb2 Rn.198991; gap junction membrane channel
protein beta 2
86 N f Rn.1.1331;=inerve growth factor, gamma
87 Clca2 redicted ,Rn.48629
88 RGD1565381 Rn.16083; similar to RIKEN cDNA 181003M07
89 Qscn6 Rn.44920; uiescin Q6
90 CldnlOpredicted Rn.99994; claudin 10
91 Spink3 Rn.144683; serine protease inhibitor, Kazal type 3
92 LOC498174 ., Rn.163210; similar to NipSnap2 protein
lioblastoma amplified sequence)
93 Rn.140163 Similar to methionine-tRNA synthetase
94 Cyr6I Rn.22129; cysteine rich protein 61
95 RGD1307736 Rn.162140; Similar to KIA.A0152
3
96 Ddit3 Rn.1 1183; DNA damage inducible transcript
97 Re 1 Rn.11332; regenerating islet derived I
98 Eif4b Rn.95954; eukaryotic translation initiation factor
4B
99 Rnase4 Rn.I742; ribonuclease, RNase A family 4
.100 Cebpg Rn.10332; CCAAT/enhancer binding protein
C/EBP , amma
1.01 siat7D Rn.195322; alpha-2,6-sialyltransferase
ST6Ga1NAc IV
102 Herpudl Rn.4028; homocysteine-inducible, ubiquitin-like
domain member I
103 Unknown rat cDNA
104 Gcat Rn.43940; glycine C-acetyltransferase (2-amino-3-
ketobutyrate-coenzyme A ligase)
105 RGD1562860 Rn.75246; similar to RIKEN cDNA 2310045A20
106 Hs a9a redicted Rn.7535; heat shock 70 kD protein 9A
107 Dbt Rn.198610; dihydrolipoamide branched chain
transacylase E2
108 Bspry Rn.53996; B-box and SPRY domain containing
109 Putl Rn.1 1382; fucosyltransferase I
110 Rp13 Rn.107726; ribosomal protein L3
111 Rn.22481 Similar to NP_083520.1 acylphosphatase 2,
muscle type
112 Unknown rat cDNA
113 Vldlr Rn.9975; very low density li o rotein receptor
114 RGD1311937 Rn.33652; similar to MGC17299
115 RGD1563144 Rn.14702; Similar to EMeg32 protein
116 Rn.43268
117 pre-mtHSP70 Rn.7535; 70 kD heat shock protein precursor
118 Ddahl Rn.7398; dimethylarginine
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DBMARKER Common Name Alternative Name
dimeth laminohydrolase 1
119 RAMP4 Rn.2119; ribosome associated membrane protein 4
120 Rn.169405
121 Ccbel_predicted Rn.199045; collagen and calcium binding EGF
domains I
122 Dnajc3 Rn.162234; DnaJ (Hsp40) homolog, subfamily C,
member 3
123 Mtac2dl Rn.43919; membrane targeting (tandem )C2
domain containing 1
124 RGD1563461 Rn.199308
125 Gimap4 Rn.198155; GTPase, IMAP family member 4
126 S100b Rn.8937; S100 protein, beta ol e tide
127 Klf2_predicted Rn.92653; Kruppel-like factor 2 (lung)
128 RGD1309561 Rn.102005; similar to FLH31951
129 NAP22 Rn.163581
130 Sfrs3_predicted Rn.9002; splicing factor,;arginine/serine-rich 3
SR 30
131 Rn.6731
132 Cd53 Rn.31988; CD53 antigen
133 RGD 1561419 Rn.131539; similar to RIKEN cDNA 6030405P05
gene
134 I12rg Rn.14508; interleukin=2 receptor, gamma
135 LOC361346 Rn.31250; simiiar to chromosome 18 open reading
frame 54
136 Cd38 Rn:11414; CD38 antigen
137 Plac8 redicted Rn.2649; lacenta-s ecific 8
.138 LOC498335 Rn.6917; similar to small inducible cytokine B13
precursor (CXCL13)
139 Igfbp3 Rn.26369; insulin-like growth factor binding
protein 3
140 Ptprc Rn.90166; protein tyrosine phosphatase, receptor
type C
141 RTI-Aw2 Rn.40130; RTI class Ib, locus Aw2
142 Rac2 Rn.2863; RAS-related C3 botulinum substrate
= 143 Rn.9461
144 Fos Rn.103750; FBJ murine osteosarcoma viral
oncogene homolog
145 Arhgdib Rn.15842; Rho, GDP dissociation inhibitor (CDI)
beta
146 Sgnel Rn.6173; secretory granule neuroendocrine protein
1
147 Lck_mapped Rn.22791; lymphocyte protein tyrosine kinase
(mapped)
148 Fcgr2b Rn.33323; Fc receptor, IgG, low affinity Ilb
149 SIfn8 Rn.137139; Schlafen 8
150 Rab8b Rn.10995; RAB8B, member RAS oncogene
family
151 Rn.4287
152 RGD1306939 Rn.95357; similar to mKIAA0386 protein
153 Tnfrsf26_predicted Rn.162508; tumor necrosis factor receptor
superfamily, member 26
154 Ythdf2_predicted Rn.21737; YTH domain family 2
155 RGD1359202 Rn. 10956; similar to immunoglobulin heavy chain
6 (Igh-6)
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DBMARKER = Common Name Alternative Name
156 RGD1562855 Rn_117926; similar to I ka a chain
157 Igha mapped Rn.109625; imYnunoglobulin heavy chain (alpha
ol e tide) (ma ed)
158 Cc121b Rn.39658; chemokine (C-C motit) ligand 21b
(serine)
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Levels of the DBMARKERS can be determined at the protein or nucleic acid level
using
any method known in the art. DBMARKER amounts can be detected, inter alia,
electrophoretically (such as by agarose gel electrophoresis, sodium dodecyl
sulfate-
polyacrylamide gel electrophoresis (SDS-PAGE), Tris-HCl polyacrylamide gels,
non-denaturing
protein gels, two-dimensional gel electrophoresis (2DE), and the like),
immunochernically (i.e.,
radioimmunoassay, immunoblotting, immunoprecipitation, immunofluorescence,
enzyme-linked
immunosorbent assay), by "proteomics technology", or by "genomic analysis."
For exainple; at
the nucleic acid level, Northern and Southern hybridization analysis, as well
as ribonuclease
protection assays using probes which specifically recognize one or more of
these sequences-can
be used to determine gene expression. Alternatively, expression can be
measured using reverse-
transcription-based PCR assays (RT-PCR), e.g., using primers specific for the
differeintially =
expressed sequence of genes. Expression can also be determined at the protein
level, e.g., by
measuring the levels of peptides encoded by the gene products described
herein, or activities
thereof. Such methods are well known in the art and include, e.g.,
immunoassays based on'
antibodies to proteins encoded by the genes, aptamers or molecular imprints..
Any biological
material can be used for the detection/quantification of the protein or its
activity. Alternatively, a
suitable method can be selected to determine the activity of proteins encoded
by the marker
genes according to the activity of each protein analyzed.
"Proteomics technology" includes, but is not limited to, surface enhanced
laser
desorption ionization (SELDI), matrix-assisted laser desorption ionization-
time of flight
(MALDI-TOF), high performance liquid chromatography (HPLC), liquid
chromatography with
or without mass spectrometry (LC/MS), tandem LC/MS, protein arrays, peptide
arrays, and
antibody arrays.
"Genome analysis" can comprise, for example, polymerase chain reaction (PCR),
real-
time PCR (such as by Light CyclerM, available from Roche Applied Sciences),
serial analysis of
gene expression (SAGE), Northern blot analysis, and Southern blot analysis.
Microarray technology can be used as a tool for analyzing gene or protein
expression,
comprising a small membrane or solid, support (such as but not limited to
microscope glass slides,
plastic supports, silicon chips or wafers with or without fiber optic
detection means, and
membranes including nitrocellulose, nylon, or polyvinylidene fluoride). The
solid support can
be chemically (such as silanes, streptavidin, and numerous other examples) or
physically
derivatized (for example, photolithography) to enable binding of the analyte
of interest, usually
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nucleic acids, .proteins, or metabolites or fragments thereof. The nucleic
acid or protein can be
printed (i.e., inkjet printing), spotted, or synthesized in situ. Deposition
of the nucleic acid or
protein of interest can be achieved by xyz robotic niicroarrayers, which
utilize automated-
spotting devices with very precise movement controls on the x-, y-, and z-
axes, in combination
with pin technology to provide accurate, reproducible spots on the arrays. The
analytes of
interest are placed on the solid support in an orderly or fixed arrangement so
as to facilitate easy
identification of a particularly desired analyte. A number of microarray
formats are
comniercially available from, inter alia, Affymetrix, Arraylt, Agilent
Technologies, Asper
Biotech, BioMicro, CombiMatrix, GeiiePix, Nanogen, and Roche Diagnostics.
The nucleic acid or protein of interest can be synthesized in the presence of
nucleotides
or amino acids tagged with one or more detectable labels. Such labels include,
for example,
fluorescent=dyes and chemilununescent'labels_ In particular, for microarray
detection,
fluorescent dyes such as but not limited to rhodarnine, fluorescein,
phycoerythrin, cyanine dyes
like Cy3 and Cy5', and conjugates like streptavidin-phycoerythrin (when
nucleic acids or proteins
are tagged with biotin) are frequently used.
Detection of fluorescent signals and image acquisition are typically achieved
using confocal
fluorescence laser scanning or photomultiplier tube, which provide relative
signal intensities and
ratios of analyte abundance for the nucleic acids or proteins represented on
the array. A wide
variety of different scanning instnuments are available, and a number of image
acquisition and
quantification packages are associated with them, which allow for numerical
evaluation of - -
combined selection criteria to define optimal scanning conditions, such as
median value, inter-
quartile range (IQR), count of saturated spots, and linear regression between
pair-wise scans (r2
and P). Reproducibility of the scans, as well as optimization of scanning
conditions,
background correction, and normalization, are assessed prior to data analysis.
Normalization refers to a collection of processes that are used to adjust data
means or
variances for effects resulting from systematic non-biological differences
between arrays,
subarrays (or print-tip groups), and dye-label channels. An array is defined
as the entire set of
target probes on the chip or solid support. A subarray or print-tip group
refers to a subset of .
those target probes deposited by the same print-tip, which can be identified
as distinct, smaller
arrays of proves within the full array. The dye-label channel refers to the
fluorescence frequency
of the target sample hybridized to the chip. Experiments where two differently
dye-labeled
samples are mixed and hybridized to the same chip are referred to in the art
as "dual-dye
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experiments", which result in a relative, rather than absolute, expression
value for each target on
the array, often represented as the log of the ratio between "red" channel and
"green channel."
Normalization can be performed according to ratiometric or absolute value
methods.
Ratiometric analyses are mainly employed in dual-dye experiments where one
channel or array
is considered in relation to a common reference. A ratio of expression for
each target probe is
calculated between test and reference sample, followed by a transformation of
the ratio into
log2(ratio) to symmetrically represent relative changes. Absolute value
methods are used
frequently in single-dye experiments or dual-dye experiments where there is no
suitable
reference for a channel or array. Relevant "hits"'are -defi~n:ed as expression
levels or amounts that
characterize a specific experimental condition. Usually, these are nucleic
acids or proteins in
which the expression levels differ significantly between different
experimental conditions,
usually by comparison of the expression levels of a nucleic acid or protein in
the different
conditions and analyzing.the relative expression ("fold change") of the
nucleic acid or protein
and the ratio of its expression level in one set of samples to its expression
in another set.
Data obtained from microarray experiments can be analyzed by any one of
numerous
statistical analyses, such as clustering methods and scoring methods.
Clustering methods attempt
to identify targets (such as nucleic acids and/or proteins) that behave
similarly across a range of
conditions or samples. The motivation to fmd such targets is driven by the
assumption that
.targets that demonstrate similar patterns of expression shar..e common
characteristics, such as
common regulatory elements, comm.on functions, or common cellular origins.
Hierarchical clustering is an agglomerative process in which single-member
clusters are
fused to bigger and bigger clusters. The procedure begins by computing a
pairwise distance
matrix between all the target molecules, the distance matrix is explored for
the nearest genes, and
they are defined as a cluster. After a new cluster is formed by agglomeration
of two clusters, the
distance matrix is updated to reflect its distance from all other clusters.
Then, the procedure
searches for the nearest pair of clusters to agglomerate, and so on. This
procedure results in a
hierarchical dendrogram in which multiple clusters are fused to nodes
according to their
similarity, resulting in a single hierarchical tree. Hierarchical clustering
software algorithms
include Cluster and Treeview.
K-means clustering is an iterative procedure that searches for clusters that
are defined in
terms of their "center" points or means. Once a set of cluster centers is
defined, each target
molecule is assigned to the cluster it is closest to. The clustering algorithm
then adjusts the
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center of each cluster of genes to minimize the sum of distances of target
molecules in each
cluster to the center. This results in a new choice of cluster ceiiters, and
target molecules can be
reassigned to clusters. These iterations are applied until convergence is
observed. Self-
organizing maps (SOMs) are related in part to the k-means procedure, in that
the data is assigned
to a predetermined set of clusters. However, unlike k-means, what follows is
an iterative process
in which gene expression vectors in each cluster are "trained" to find the
best distinctions
between the different clusters. 'In other words, a partial siructure
is=imposed on the 'data and then
this structure is iteratively modified according to the data. SOM is included
in many software
packages, such as, for instance, GeneClu$ter. Other clustering- methods
include. graph-theoretic
clustering, which utilizes graph-theoretic and statistical techniques to
identify tight groups of
highly similar elements (kernels), which are likely to belong to the same true
cluster. ' Several
heuristic procedures are then used to expand the kernels into the full
clustering. An example of
software utilizing graph-theoretic clustering includes CLICK in combination
with the-Expander
visualization tool.
Data obtained from high-throughput expression analyses cary be scored using
statistical
methods such as parametric and non-parametric methods. Parametric approaches
model
expression profiles within a parametric representation and ask how different
the parameters of
the experimental groups are. Examples of parametric methods include, without
limitation, 1-tests,
separation scores, and Bayesian t-tests. Non-parametric methods involve
analysis of the data,
wherein no a priori assumptions are made about the distribution of expression
profiles in the
data, and the degree to which the two groups of expression measurements are
distinguished is
directly examined. Another method uses the TNOM, -or the threshold number of
misclassifications, which measures the success in separation two groups of
samples by a simple
threshold over the expression values.
= SAGE (serial analysis of gene expression) can also be used to systematically
determine
the levels of gene expression. In SAGE, short sequence tags within a defined
position containing
sufficient information to uniquely identify a transcript are used, followed by
concatenation of
tags in a serial fashion. See, for example, Velculescu V.E. et al, (1995)
Science 270: 484-487.
Polyadenylated RNA is isolated by oligo-dT priming, and cDNA is then
synthesized using a
biotin-labeled primer. The cDNA is subsequently cleaved with an anchoring
restriction
endonucleases, and the 3'-terminal cDNA fragments are bound to streptaviding-
coated beads.
.An oligonucleotide linker containing recognition sites for a tagging enzyme
is linked to the
CA 02661332 2009-02-20
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bound cDNA. The tagging enzyme can be a class II restriction endonucleases
that cleaves the
DNA at a constant number of bases 3' to the recognition site, resulting in the
release of a short
tag and the linker from the beads after digestion with the enzyme. The 3' ends
of the released
tags plus linkers are then blunt-ended and ligated to one another to form
linked ditags that are
approximately 100 base pairs in length. The ditags are then subjected to PCR
amplification,
after which the linkers and tags are released by digestion with the anchoring
restriction
endonucleases. Thereafter, the tags (usually ranging iii size from 25-30-mers)
are gel purified,
concatenated, and cloned into a sequence vector. Sequencing the coneatemers
enables individual
tags to be identified and the abundanee:of the transcripts for a given cell or
tissue type can be
determined. . `
The DBMARKER proteins, polypeptides, mutations, and polymorphisms thereof can
be
detected in any iuanner known to those skilled in the art. Of particular
utility are two-
dimensional gel =electrophoresis, which separates a mixture of proteins (such
as found in
biological samples such as serum) in one dimension according to the
isoelectric point (such as,
for example, a pH range from 5-8), and according.to molecular weight in a
second dimension.
Two-dimensional liquid chromatography is also advantageously used to identify
or detect
DBMARKER proteins, polypeptides, mutations, and polymorphisms of the
invention, and one
specific example, the ProteomeLab PF 2D Protein Fractionation System is
detailed in the
..Examples. The PF 2D system resolves proteins in one dimension by isoelectric
point and by
hydrophobicity in the second dimension: Another advantageous method of
detecting proteins,
polypeptides, mutations, and polymorphisms include SELDI (disclosed herein)
and other high-
throughput proteomic arrays.
DBMARKER proteins, polypeptides, mutations, and polymorphisms can be typically
detected by contacting a sample from the subject with an antibody which binds
the DBMARKER
protein, polypeptide, mutation, or polymorphism and then detecting the
presence or absence of a
reaction=product. The antibody may be monoclonal, polyclonal, chimeric, or a
fragment of the
foregoing, as discussed in detail above, and the step of detecting the
reaction product may be
carried out with any suitable immunoassay. The sample from the subject is
typically a biological
fluid as described above, and may be the same sample of biological fluid used
to conduct the
methodi=described above.
Imznunoassays carried out in accordance with the present invention may be
homogeneous
assays or heterogeneous assays. In a homogeneous assay, the immunological
reaction usually
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involves the specific antibody (e.g., anti-DBIVIARKER protein antibody), a
labeled analyte, and
the sample of interest. The signal arising from the label is modified,
directly or indirectly, upon
the binding of the antibody to the labeled analyte. Both the immunological
reaction and
detection of the extent thereof can be carried out in a homogeneous solution.
Immunochemical
labels which may be employed include free radicals, radioisotopes, fluorescent
dyes, enzymes,
bacteriophages, or coenzymes.
In a heterogeneous assay approach, the reagents are usually the sample, the
antibody, and
means for producing a. detectable signal. Samples as described above may be
used. The antiliody
can be immobilized on a support, such as a bead (such as:protein A agarose,
protein G agarose,
latex, polystyrene, magnetic or paramagnetic beads), plate or slide, and
contacted with the
specimen suspected of containing the antigen in a liquid phase. The support is
then separated
from the liquid phase and either the support phase or the-liquid phase is
examined for a
detectable signal employing means for producing such signal. = The signal is
related to the-:
presence of the analyte in the sample. Means for producing a detectable signal
include the use of
radioactive labels, fluorescent labels, or enzyme labels. For :exarnple, if
the antigen to be
detected contains a second binding site, an antibody which binds to that site
can be conjugated to
a detectable group and added to the liquid phase reaction solution before the
separation step.
The presence of the detectable group on the solid support indicates the
presence of the antigeii in
the test sample. Examples of suitable immunoassays are oligonucleotides,
immunoblotting,
immunoprecipitation, immunofluorescence methods, chemiluminescence methods,
electrochemiluminescence or enzyme-linked immunoassays.
Those skilled in the art will be familiar with numerous specific immunoassay
formats and
variations theredf which may be useful for carrying out the method disclosed
herein. See
generally E. Maggio, Enzyme-Immunoassay, (1980) (CRC Press, Inc., Boca Raton,
Fla.); see
also U.S. Pat. No. 4,727,022 to Skold et al. titled "Methods for Modulating
Ligand-Receptor
Interactions and their Application," U.S. Pat. No. 4,659,678 to Forrest et al.
titled "Irnrnunoassay
of Antigens," U.S. Pat. No. 4,376,110 to David et al., titled "Immunometric
Assays Using
Monoclonal Antibodies," U.S. Pat. No. 4,275,149 to Litman et al., titled
`:Macromolecular
Environment Control in Specific Receptor Assays," U.S. Pat. No. 4,233,402 to
Maggio et al.,
titled "Reagents and Method Employing Channeling," and U.S. Pat. No. 4,230,767
to Boguslasld
et al., titled "Heterogenous Specific Binding Assay Employing a Coenzyme as
Label."
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Antibodies can be conjugated to=-a solid support suitable for a diagnostic
assay (e.g.,
beads such as protein A or protein G agarose, microspheres, plates, slides or
wells formed from
materials such as latex or polystyrene) in accordance with known techniques,
such as passive
binding. Antibodies as described herein may likewise be conjugated to
detectable labels or
groups such as radiolabels (e.g., 355, 125I, 131I), enzyme labels (e.g.,
horseradish peroxidase,
alkaline phosphatase), and fluorescent labels (e.g., fluorescein, Alexa, green
fluorescent protein)
in accordance W-ith known techniques.
Antibodies can also be useful for detecting post-translational modifications
of
DBMARKER proteins, polypeptides, mutations, and polyinorphisms, such as
tyrosine
phosphorylation, threonine phosphorylation, serine phosphorylation,
glycosylation (e.g., 0-
G1cNAc), Such antibodies specifically detect the phosphorylated amino acids in
a protein or
proteins of interest, and can be used in immunoblotting, immunofluorescence,
and ELISA assays
described herein. These antibodies are well-known to those skilled in the art,
and commercially
available: Post-translational modifications can also be determined using
metastable ions in
reflector matrix-assisted laser desorption ionization-time of flight mass
spectrometry (MALDI-
TOF) (Wirth, U. et al. (2002) Proteomics 2(10): 1445-51).
For DBMARKE.R proteins, polypeptides, mutations, and polymorphisms known to
have
enzymatic activity, the activities can be determined in vitro using enzyme
assays known in the
art. Such assays include, without limitation, kinase assays, phosphatase
assays, reductase assays,
among many others. Modulation of the kinetics of enzyme activities can be
determined by
measuring the rate constant Km using known algorithms, such as the Hill plot,
Michaelis-Menten
equation, linear regression plots such as Lineweaver-Burk analysis, and
Scatchard plot.
Using sequence information provided by the database entries for the DBMARKER
sequences, expression of the DBMARKER sequences can be detected (if present)
and measured
using techniques well known to one of ordinary skill in the art. For example,
sequences within
the sequence database entries corresponding to DBMARKER sequences, or within
the sequences
disclosed herein, can be used to construct probes for detecting DBMARKER RNA
sequences in,
e.g., Northern blot hybridization analyses or methods which specifically, and,
preferably,
quantitatively amplify specific nucleic acid sequences. As another example,
the sequences can
be used to construct primers for specifically amplifying the DBIVIARKER
sequences in, e.g.,
amplification-based detection methods such as reverse-transcription based
polymerase chain
reaction (RT-PCR). When alterations in gene expression are associated with
gene amplification,
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WO 2008/030273 PCT/US2007/007875
deletion, polyrnorphisms, and mutations, sequence comparisons in test and
reference populations
can be made by comparing relative amounts of the examined DNA sequences in the
test and
reference cell populations.
Expression of the genes disclosed herein can be measured at the RNA level
using any
method known in the art. For example, Northern hybridization analysis using
probes which
specifically recognize one or more of these sequences can be used to determine
gene expression.
Alternatively, expression can be measured using reverse-transcription-based
PCR assays (RT-
PCR), e.g., using primers specific for the differentiallyexpressed sequences.
Alternatively, DBMARKER protein and niacle.ic acid metabolites or fragments
can be
measured. The term "metabolite" includes any chemical or biochemical product
of a metabolic
process, such as any compound produced by the processing, cleavage or
consumption, of a
biological molecule (e.g., -a protein, nucleic acid, carbohydrate, or lipid).
Metabolites can be
detected in a variety of ways known to one of skill in the art, including the
refractive index
-spectroscopy (RI), ultra-violet spectroscopy (UV), fluorescence analysis,
radiochemical analysis,
near-infrared spectroscopy (near-IR), nuclear magnetic resonance spectroscopy
(NMR), light
scattering analysis (LS), mass -spectrometry, pyrolysis inass spectrometry,
nephelometry,
dispersive Raman spectroscopy, gas chromatography combined with mass
spectrometry, liquid
chromatography combined with mass spectrometry, matrix-assisted laser
desorption ionization-
time of flight (MALDI-TOF) combined with ma.ss spectrometry, surface-enhanced
laser
desorption ionization (SELDI), ion spray spectroscopy combined with mass
spectrometry,
capillary electrophoresis, NMR and IR detection. (See, WO 04/056456 and WO
04/088309,
each of which are hereby incorporated by reference in their entireties) In
this regard, other
DBMARI{ER analytes can be measured using the above-mentioned detection
methods, or other
methods known to the skilled artisan.
Kits
The invention also includes a DBMARKER-detection reagent, e.g., nucleic acids
that
specifically identify one or more DBMARKER nucleic acids by having homologous
nucleic acid
sequences, such as oligonucleotide sequences, complementary to a portion of
the DBMARKER
nucleic acids or antibodies to proteins encoded by the DBMARI~CER nucleic
acids packaged
together in the form of a kit. The oligonucleotides can be fragments of the
DBMARKER genes.
For example the oligonucleotides can be 200, 150, 100, 50, 25, 10 or less
nucleotides in length.
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WO 2008/030273 PCT/US2007/007875
The:DBIv1ARKER-detection reagents can also comprise, inter alia, antibodies or
fragments of
antibodies, and aptamers. The kit may contain in separate containers a nucleic
acid or antibody
(either already bound to a solid matrix or packaged separately with reagents
for binding them to
the matrix), control formulations (positive and/or negative), and/or a
detectable label.
Instructions (e.g., written, tape, VCR, CD-ROM, etc.) for carrying out the
assay may be included
in the kit. The assay may for example be in the fonn of a Northern blot
hybridization or a
saiidwich ELISA as known in the art: Alternatively, the kit can be in the form
of amicroarray as =..
known in the art.
Diagnostic kits for carrying out -the methods described herein are produced in
a. number
of ways: Preferably, the kits of the present invention comprise a control (or
reference) sample.
derived from a subject having normal glucose levels. Altematively, the kits
can comprise a
control sample derived from a siu.bject who has been diagnosed with or
identified as suffering
frotn type 2 Diabetes or a pre-diabetic condition. In one embodiment, the
diagnostic kit
comprises (a) an antibody (e.g., fibrinogen aC domain peptide) conjugated to a
solid support
and (b) a second antibody of the invention conjugated'to a detectable group.
The reagents may
also include ancillary agents such as-buffering agents.and. protein
stabilizing agents, e.g.,
polysaccharides and the like. The diagnostic kit may further include, where
necessary, other
members of the signal-producing system of which system the detectable group is
a member (e.g.,
enzyme substrates), agents for reducing background interference in a test,
control reagents;
apparatus for conducting a test, and the like. Alternatively, a test kit
contains (a) an antibody ,
and (b) a specific binding partner for the antibody conjugated to a detectable
group. The test kit
may be packaged in any suitable manner, typically with all elements in a
single container,
optionally with a sheet of printed instructions for carrying out the test.
For example, DBMARKER detection reagents can be immobilized on a solid matrix
such
as a porous strip to form at least one DBMARKER detection site. The
measurement or detection
region of the porous strip may include a plurality of sites containing a
nucleic acid. A test strip
may also contain sites for negative and/or positive controls. Alternatively,
control sites can be
located on a separate strip from the test strip. Optionally, the different
detection sites may
contain different amounts of immobilized nucleic acids, e.g., a higher amount
in the first
detection site and lesser amounts in subsequent sites. Upon the addition of
test sample, the
number of sites displaying a detectable signal provides a quantitative
indication of the amount of
CA 02661332 2009-02-20
WO 2008/030273 PCT/US2007/007875
DBMARKERS..,present in the sample. The detection sites may be configured in
any suitably
detectable shape and are typically in the shape of a bar or dot spanning the
width of a test strip.
Alternatively, the kit contains a nucleic acid substrate array comprising one
or more
nucl eic acid sequences. The nucleic acids on the array specifically identify
one or more nucleic
acid sequences represented by DBMARKERS 1-158. In various embodiments, the
expression of
2, 3,4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, or more of the DBMARKERS 1-
158 can be
identified by virtue of binding to the array. The substrate array can be on,
e.g.,'a solid substrate,
e.g., a "chip" as described in U.S. Patent 14o.5,744,305. Alternatively, the
substrate array can be,
a solution array, e.g., xMAP (Luminex, Austin,.TX), Cyvera (Illumina, San
Diego, CA),
CellCard- (Vitra Bioscience, Mountain View, CA) and Quantum Dots' Mosaic
(Invitrogen,
Carlsbad, CA).. The skilled artisan can routinely make antibodies, nucleic
acid probes, e.g.,
oligonuclebtides, aptamers, siRNAs, antisense oligonucleotides, against any of
the
DBMARKERS in:Table 1. The Examples presented herein describe generation of
monoclonal
antibodies in mice,.,as well as generation of polyclonal hyperimmune serum
from rabbits. Such
techniques are well-known to those of ordinary skill in the art.
Pharmaceutical Cotnpositions and Methods of Treatment
The terrn "treating" in its various grammatical forms in relation to the
present invention
refers to preventing (i.e. chemoprevention), curing, reversing, attenuating,
alleviating,
minimizing, suppressing or halting the deleterious effects of a disease state,
disease progression,
disease causative agent (e.g., bacteria or viruses) or other abnormal
condition. For example,
treatment may involve alleviating a symptom (i.e., not necessarily all
symptoms) of a disease or
attenuating the progression of a disease.
As used herein, the term "therapeutically effective amount" is intended to
qualify the
amount or amounts of DBMARKERS or other diabetes-modulating agents that will
achieve a
desired biological response. In the context of the present invention, the
desired biological
response can be partial or.total inhibition, delay or prevention of the
progression of type 2
Diabetes, pre-diabetic conditions, and complications associated with type 2
Diabetes or pre-
diabetic conditions; inhibition, delay or prevention of the recurrence of type
2 Diabetes, pre-
diabetic conditions, or complications associated with type 2 Diabetes or pre-
diabetic conditions;
or the prevention of the onset or development of type 2 Diabetes, pre-diabetic
conditions, or
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complications associated with type 2 Diabetes or pre-diabetic conditions
(chemoprevention) in a
subject, for example a human.
The DBMARKERS, preferably included as part of a pharmaceutical composition,
can be
adnainistered by any known administration method knbwn to a person skilled in
the art.
Examples of routes of administration include but are not limited to oral,
parenteral,
intraperitoneal, intravenous, intraarterial, transderrnal, topical,
sublingual, intramuscular, rectal,
transbuccal, intranasal, liposomal, via inhalation, vagirial, intraoccular,
via local delivery by
catheter or stent, subcutaneous, intraadiposal, intraarticular; intrathecal,
or in a slow release
dosage form. The DBMARKERS or pharrnaceutical compositions comprising the
DBMARKERS- can be administered in accordance with any dose and dosing schedule
that
achieves a dose effective to treat disease.
As examples, DBMARKERS or pharmaceutical compositions comprising
DBMARKERS of the invention can be administeted in such oral forms as tablets,
capsules (eacli
of which includes sustained release or timed release formulations), pills,
powders, granules,
elixirs, tinctures, suspensions, syrups, and emulsions. Likewise, the.
DBMARKERS or
- -pharmaceutical compositions comprising DBMARI{ERS can be administered by
intravenous
(e.g., bolus or infusion), intraperitoneal, subcutaneous, intramuscular, or
other routes using forms
well known to those of ordinary skill in the pharmaceutical arts.
DBNIA.RKERS and pharmaceutical compositions comprising DBMARKERS can also be
administered in the form of a depot injection or implant preparation, which
may be formulated in
such a manner as to permit a sustained release of the active ingredient. The
active ingredient can
be compressed into pellets or small cylinders and implanted subcutaneously or
intramuscularly
as depot injections or implants. hnplants may employ inert materials such as
biodegradable
polymers or synthetic silicones, for example, Silastic, silicone rubber or
other polymers
manufactured by the Dow-Corning Corporation.
DBMARKERS or pharmaceutical compositions comprising DBMARKERS can also be
administered in the form of liposome delivery systems, such as small
unilamellar vesicles, large
i.unilamellar vesicles and miiltilamellar vesicles. Liposomes can be formed
from a variety of
phospholipids, such as cholesterol, stearylamine, or phosphatidylcholines.
Liposomal
preparations of diabetes-modulating agents may also be used in the methods of
the invention.
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DBMARKERS or pharmaceutical compositions-_comprising DBNIARKERS can also be
delivered by the use of monoclonal antibodies as individual carriers to which
the compound
molecules are coupled.
DBMARKERS or pharmaceutical compositions comprising DBMARKERS can also be
prepared with soluble polymers as targetable drug carriers. Such polymers can
include
polyvinylpyrrolidone, pyran copolymer, polyhydroxy-propyi-methacrylamide-
phenol,
polyhydroxyethyl-aspartamide-phenol,.or polyethylerieoxide-polylysine
substituted with
pahnitoyl residues. Furtherrnore, DBMARKERS or pharmaceutical compositions
comprising
DBMARKERS can be prepared with biodegradable polymers useful in achieving
controlled
release of a drug, for example, polylactic acid, polyglycolic acid, copolymers
of polylactic and
polyglycolic acid, polyepsilon caprolactone, polyhydroxy butyric acid,
polyorthoesters,
polyacetals, polydihydropyrans, polycyanoacrylates and cross linked or
amphipathic block
copolymers of hydrogels.
The DBMARKERS or pharmaceutical compositions comprising DBMARKERS can also
be administered in intranasal form-via topical use of suitable intranasal
vehicles, or via
transdermal routes, using those forms of transdermal skin patches well known
to those of
ordinary skill in that art. To be administered in the form of a transdermal
delivery system, the
dosage administration will, or course, be continuous rather than intennittent
throughout the
dosage regime.
Suitable pharmaceutically acceptable salts of the agents described herein and
suitable for
use in the method of the invention, are conventional non-toxic salts and can
include a salt with a
hase or an acid addition salt such as a salt with an inorganic base, for
example, an alkali metal
salt (e.g., lithium salt, sodium salt, potassium salt, etc.), an alkaline
earth metal salt (e.g., calcium
salt, magnesium salt, etc.), an anunonium salt; a salt with an organic base,
for example, an
organic amine salt (e.g., triethylamine salt, pyridine salt, picoline salt,
ethanolamine salt,
triethanolamine salt, dicyclohexylantine salt, N,N'-dibenzylethylenediamine
salt, etc.) etc.; an
inorganic acid addition salt (e.g., hydrochloride, hydrobromide, sulfate,
phosphate, etc.); an
or.ganic carboxylic or sulfonic acid addition salt (e.g., forrnate, acetate,
trifluoroacetate, maleate,
tartrate, methanesulfonate, benzenesulfonate, p-toluenesulfonate, etc.); a
salt with a basic or
acidic amino acid (e.g., arginine, aspartic acid, glutamic acid, etc.) and the
like.
In addition, this invention also encompasses pharmaceutical compositions
comprising
any solid or liquid physical form of one or more of the DBMARKERS of the
invention. For
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WO 2008/030273 PCT/US2007/007875
example, the DBMARKERS can be in a crystalline form, in amorphous form, and
have any
particle size. The DBMARI.~ER particles may be micronized, or may be
agglomerated,
particulate granules, powders, oils, oily suspensions or any other form of
solid or liquid physical
form.
For oral administration, the pharmaceutical compositions can be liquid or
solid. Suitable
solid oral formulations include tablets, capsules, pills, granules, pellets,
and the like. Suitable
liquid oral formulations include solutions, suspensions, .dispersions,
emulsions, oils, and tlie like.
Any inert excipient that is commonly usecl as a carrier or diluent may be used
in the
formulations of the present invention, such as= for example, a gum, a starch,
a sugar, a cellulosic
material; an acrylate, or mixtures thereof. The.compositions may fuxther
comprise a
disintegrating agent and a lubricant, and in addition may coinprise one or
more additives selected
frbm a binder, a buffer, a protease inhibitor, a surfactant, a solubilizing
agent, a plasticizer, an
emulsifier, a stabilizing agent, a viscosity increasing agent, a sweetener, a
film forming agent, or
any combination thereof. Furthermore, the compositions of the present
invention. may be in the
fomi of controlled release or imrnediate release. foxmulations.
DBMARKERS can be administered as active ingredients in admixture with suitable
pharmaceutical diluents, excipients or carriers (collectively referred to
herein as "carrier"
materials or "pharmaceutically acceptable carriers") suitably selected with
respect to the
intended form of administration. As used herein, "pharmaceutically acceptable
carrier or
.20 diluent" is intended to include any and all=solvents, dispersion media,
coatings, antibacterial and
antifungal agents, isotonic and absorption delaying agents, and the like,
compatible with
pharmaceutical administration. Suitable carriers are described in the most
recent edition of
Remington's Pharmaceutical Sciences, a standard reference text in the field,
which is
incorporated herein by reference.
For liquid formulations, pharmaceutically acceptable carriers may be aqueous
or non-
aqueous solutions, suspensions, emulsions or oils. Examples of non-aqueous
solvents are
propylene glycol, polyethylene glycol, and injectable organic esters such as
ethyl oleate.
Aqueous carriers include water, alcoholic/aqueous solutions, emulsions, or
suspensions,
including saline and buffered media. Examples of oils are those of petroleum,
animal, vegetable,
or synthetic origin, for example, peanut oil, soybean oil, mineral oil, olive
oil, sunflower oil, and
fish-liver oil. Solutions or suspensions can also include the following
components: a sterile
diluent such as water for injection, saline solution, fixed oils, polyethylene
glycols, glycerine,
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WO 2008/030273 PCT/US2007/007875
propylene glycol or other synthetic solvents; antibacterial agents such as
benzyl alcohol or
methyl parabens; antioxidants such as ascorbic acid or sodium bisulfite;
chelating agents such as
ethylenediaxninetetraacetic acid (EDTA); buffers such as acetates, citrates or
phosphates, and
agents for the adjustment of tonicity such as sodium chloride or dextrose. The
pH can be
adjusted with acids or bases, such as hydrochloric acid or sodium hydroxide.
Liposomes and non-aqueous vehicles such as fixed oils may also be used. The
use of
..such media and agents for pharmaceutically active substances is well known
in the art. Except
=insofar as any conventional media or agent is incompatible ~vith the active
compound, use
thereof in the- compositions is contemplated: Supplementary active compounds
can also be,
incorporated into the compositions.
Solid carriers/diluents include, but are not limited to, =a gum, a starch
(e.g., corn starch,
pregelatinized starch), a sugar (e.g., lactose, mannitol, sucrose, dextrose),
a cellulosic material
:(e:g.,:microcrystalline cellulose), an acrylate (e.g.; polymeth.ylacrylate),
calcium carbonate,
magnesium oxide, talc, or mixtures thereof.
In addition, the compositions may further comprise binders (e.g., acacia,
conistarch,
gelatin, carbomer, ethyl cellulose, guar gum, hydroxypropyl cellulose,
hydroxypropyl meth.yl'
celluiose, povidone), disintegrating agents (e.g., comstarch, potato starch,
alginic acid; silicon
dioxide, croscarmellose sodium, crospovidone, guar gum, sodium starch
glycolate, Primogel),
buffers (e.g., tris-HCI, acetate, phosphate) of various pH and ionic strength,
additives such as
albumin or gelatin to prevent absorption to surfaces; =detergents (e.g., Tween
20, Tween 80,
Pluronic F68, bile acid salts), protease inhibitors, surfactants (e.g., sodium
lauryl sulfate),
permeation enhancers, solubilizing agents (e.g., glycerol, polyethylene
glycerol), a glidant (e.g.,
colloidal silicon dioxide), anti-oxidants (e.g., ascorbic acid, sodium
metabisulfite, butylated
hydroxyanisole), stabilizers (e.g., hydroxypropyl cellulose,
hydroxypropylmethyl cellulose),
viscosity increasing agents (e.g., carbomer, colloidal silicon dioxide, ethyl
cellulose, guar gum),
sweeteners (e.g., sucrose, aspartame, citric acid), flavoring agents (e.g.,
peppermint, methyl
salicylate, or orange flavoring), preservatives (e.g., Thimerosal, benzyl
alcohol, parabens),
lubricants (e.g., stearic acid, magnesium stearate, polyethylene glycol,
sodium lauryl sulfate),
flow-aids (e.g., colloidal silicon dioxide), plasticizers (e.g., diethyl
phthalate, triethyl citrate),
emulsifiers (e.g., carbomer, hydroxypropyl cellulose, sodium lauryl sulfate),
polymer coatings
(e.g., poloxamers or poloxamines), coating and film forming agents (e.g.,
ethyl cellulose,
acrylates, polymethacrylates) and/or adjuvants.
CA 02661332 2009-02-20
WO 2008/030273 PCT/US2007/007875
In one embodiment, the active compounds are prepared with carriers that will
protect the
compound against rapid elirnination from the body, such as a controlled
release formulation,
including implants and microencapsulated delivery systems. Biodegradable,
biocompatible
polymers can be used, such as ethylene vinyl acetate, polyanhydrides,
polyglycolic acid, collagen,
polyorthoesters, and polylactic acid. Methods for preparation of such
formulations will be
apparent to those skilled in the art. The materials can also be obtained
commercially from Alza.
Corporation and Nova Pharmaceuticals, Inc. Liposomal suspensions (including
liposomes
targeted"to infected cells with monoclonal antibodies to viral antigens) can
also b.e used as
pharmaceuticall.y acceptable carriers. These can,be prepared according to
methods known to
those skilled in the art, for example, as described in U.S. Patent No.
4,522,811.
It is especially advantageous to formulate oral compositions in dosage unit
form for ease
of adxninistration and uniformity of dosage. Dosage unit form as used herein
refers to physically'
discrete units- suited as unitary dosages for the subject to be treated; each
unit containing a
predetermined quantity of active compound calculated to produce the desired
therapeutic effect
in association with the required pharmaceutical carrier. The specification for
the dosage unit . ..
forms of the invention are dictated by and directly dependent on the unique
characteristics of the
active compound and the particular therapeutic effect to be achieved, and the
limitations inherent
in the art of compounding such an active compound for the treatment of
individuals. The
pharmaceutical compositions can be included in a container, pack, or dispenser
together with
instructions for administration.
The preparation of pharmaceutical compositions that contain an active
component is well
understood in the art, for example, by mixing, granulating, or tablet-forming
processes. The
active therapeutic ingredient is often mixed with excipients that are
pharmaceutically acceptable
and compatible with the active ingredient. For oral administration, the active
agents are mixed
with additives customary for this purpose, such as vehicles, stabilizers, or
inert diluents, and
converted by customary methods into suitable forms for administration, such as
tablets, coated
tablets, hard or soft gelatin capsules, aqueous, alcoholic, or oily solutions
and the like as detailed
above. For IV administration, Glucuronic acid, L-lactic acid, acetic acid,
citric acid or any
pharmaceutically acceptable acid/conjugate base with reasonable buffering
capacity in the pH
range acceptable for intravenous admixiistration can be used as buffers.
Sodium chloride solution
wherein the pH has been adjusted to the desired range with either acid or
base, for example,
56
CA 02661332 2009-02-20
WO 2008/030273 PCT/US2007/007875
hydrochloric acid or sodium hydroxide,. can also be employed. Typically, a pH
range for the
intravenous formulation can be in the range of from about 5 to about 12. A
particular pH range
for intravenous formulation comprising an HDAC inhibitor, wherein the HDAC
inhibitor has a
hydroxamic acid moiety, can be about 9 to about 12.
Subcutaneous formulations can be prepared according to procedures well kriown
in the
art at a pH in the range between about 5 and about 12, which include suitable
buffers and
isotonicity agents. They can be fornmuiated to deliver a daily dose of the
active agent in one or
more daily subcutaneous administrations. The choice of appropriate. buffer and
pH of a
formulation, depending on solubility of one or more DBnIARKERS to be
ad.rrministered, is
: readily made by a person having ordinary skill in the art. Sodium chloride
solution wherein the
pH has been adjusted to the desired range with either acid or base, for
example, hydrochloric
acid or sodium hydroxide, can also be employed in the subcutaneous
formulation. Typically, a
pH range for the subcutaneous formulation can be in the range of from about 5-
to about 12.
The compositions of the present invention can also be administered in
intranasal form via
15.. topical use of suitable intranasal vehicles, or via transdermal routes,
using those fonms of
transdermal skin patches well known to those of ordinary skill in that art. To
be administered in
the form of a transdermal delivery system, the dosage administration will, or
course, be
continuous rather than intermittent throughout the dosage regime.
EXAMPLES
Example 1: Biomarker Identification in the Cohen rat model of Type 2 Diabetes
The Cohen diabetic (CD) rat is a well-known and versatile animal model of Type
2
Diabetes, and is comprised of 2 rodent strains that manifest many of the
common features of
Type 2 Diabetes (T2D) in humans. The sensitive strain (CDs) develops Diabetes
within 30 days
when maintained on a high sucrose/copper-poor diet (HSD), whereas the
resistant strain (CDr)
retains normal blood glucose levels. When maintained indefinitely on regular
rodent diet (RD),
neither strain develop symptoms of T2D.
Sample Preparation
Serum, urine, and tissue samples (including splenic tissue, pancreatic tissue,
and liver
tissue) were obtained from both CDr and CDs rats that were fed either RD or
HSD for 30 days.
The samples were flash-frozen and stored at -80 C.
57
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WO 2008/030273 PCT/US2007/007875
Whole protein extracts were prepared for each of the 4 experimental
conditions, utilizing
individual organs per group. Pancreatic tissues were processing using a
mechanical shearing
device (Polytron). To preserve protein integrity in processed samples, tissues
were kept on dry
ice until processing commenced and all buffers and equipriment were pre-
chilled. Samples were
5 also kept on ice during the homogenization process.
T-Per buffer (Pierce) was pre-Lchilled on ice and two tablets of Complete
Protease
Inhibitor (Roche Applied Sciences) were added per 50 m,l of buffer prior to
use. * Once protease
inhibitors, were added, any unused buffer was discarded. T-Per buffer was used
at 20 nil per
gtam of.tissue. For each group, pancreatic samples were weighed and the amount
of lysis buffer
10 required was calculated and added to each tissue sample in a 50 ml tube.
Each sample was
homogenized on ice for 10 seconds, followed by a 30 second rest period to
allow the sample to
cool. If gross debris was still apparent, the cycle was'repeated until the
homogenate was smooth.
The horriogenization probe was inserted into the samples approximately I cm
from the bottom of
the-tube to rninimize foaming. When homogenization was complete, the extract
was centrifuged
at 10,000 x g for 15 minutes at 4 C. Following centrifugation, the supernatant
was harvested and a bicinchoninic acid (B CA)
assay was: performed to determine the total protein content. Table 2 provides
the mean protein
content of the samples corresponding to CDr rats fed either RD or HSD, and CDs
rats fed either
RD or HSD.
Table 2: Total Protein Content of Pancreatic Extracts from Cohen Diabetic Rats
Mean Protein Content (Rgtml)
Tissue CDr-RD CDr-HSD CDs-RD CDs-HSD
Pancreas 7969.2 6061.9 6876.4 3387.8
Supernatants were dispensed into aliquots and stored at -80 C. Pelleted
material was also
kept and stored at -80 C.
Protein expression profiling of the CDr and CDs phenotypes was conducted on
the
pancreatic extracts using one-dimensional SDS-PAGE. A sample of each extract
containing 6
gg of total protein was prepared in sample buffer and loaded onto a 4-12%
acrylamide gel.
Following completion of the electrophoretic run, the gel was soaked with
Coomassie stain for 1
hour and destained in distilled water overnight. The resulting protein
expression profile allowed
58
CA 02661332 2009-02-20
WO 2008/030273 PCT/US2007/007875
an empirical visual comparison of each extract (Figure 1). These pancreatic
extracts were then
used for bi-directional immunological contrasting, disclosed herein.
Since albumin, immunoglobulin and other abundant proteins constitute about 95-
97% of
the total proteins in serum, the detection of less abundant proteins and
peptides markers are
masked if the whole serum were analyzed directly. Therefore, fractionation of
serum samples
was necessary to reduce masking of low abundance protein and to increase the
number of peaks
available= for analysis.
To increase the detection of a larger nurriber of peaks as well as to
alleviate signal
suppression e:ffects`on low abundance proteins from high abundant proteins
such as albumin,
immunoglobulin etc., the crude serum samples from CDr and CDs rats fed RD or
HSD were
fractionated into=six fractions. The fractionation was carried out using anion
exchange bead
based serum fractionate kit purchased from Ciphergen (Fremont, CA). In brief,
the serum
samples were diluted with a 9M urea denaturant-solution; the diluted.samples
were then loaded ='
onto a 96-well -filter micropfate pre-filled with an anion exchange sorbent.
Using this process,
samples were allowed to bind to the active surface of the beads, and after 30
minutes incubation
at 4 C, the samples'were eluted using.stepwise pH gradient buffers. The
process allowed the
collection of 6 fractions including pH 9, pH 7, pH 5, pH 4, pH 3 and an
organic eluent. After the
fractionation, the serum samples were analyzed in the following formats on
SELDI chips.
SELDI (Surface Enhanced Laser Desorption Ionization)
SELDI Proteinchip Technology (Ciphergen) is designed to perform mass
spectrometric
analysis of protein mixtures retained on chromatographie chip surfaces. The
SELDI mass
spectrometer produces spectra of complex protein mixtures based on the
mass/charge ratio of the
proteins in the mixture and their binding affinity to the chip surface.
Differentially expressed
proteins are determined from these protein profiles by comparing peak
intensity. This technique
utilizes aluminum-based supports, or chips, engineered with cheznical modified
surfaces
(hydrophilic, hydrophobic, pre-activated, normal-phase, immobilized metal
affinity, cationic or
anionic), or biological (antibody, antigen binding fragments (e.g.; scFv),
DNA, enzyme, or =
receptor) bait surfaces. These varied chemical and biochexnical surfaces allow
differential
capture of proteins based on the intrinsic properties of the proteins
themselves. Tissue extractions
or body fluids in volumes as small as I l are directly applied to these
surfaces, where proteins
with affinities to the bait surface will bind. Following a series of washes to
remove non-
59
CA 02661332 2009-02-20
WO 2008/030273 PCT/US2007/007875
specifically or weakly bo-und proteins, the bound proteins are laser desorbed
and ionized for MS
analysis. Molecular weights of proteins ranging from small peptides to
proteins (1000 Dalton to
200 kD) are measured. These mass spectral patterns are then used to
differentiate one sample
from another, and identify lead candidate markers for fu.rther analysis.
Candidate marker have
been identified by comparing the protein profiles of conditioned versus
conditioned stem cell
culture medium. Once candidate markers are identified, the.y are purifzed and
sequenced.
The fractionated serum samples were applied to different chemically modified
surface
chips (cationic exchange, anionic exchange, metal-affinity binding,
hydrophobic and normal
phase).and profiled by SELDI, two-dimensional-PAGE (2DE) and two-dimensional
liquid
chromatography (2D/LC).
Two-dimensional Liquid Chromatography (2D/LC)
The ProteomeLab PF-2D Protein Fractionation System is a fully automated, two-
dimensional fractionation system (in liquid phase) that.resolves and collects
proteins by
isoelectric point (pI) iri the first dimension and by hydrophobicity in the
second'dimension. The.
system visualizes the complex pattern with a two dimensional protein map that
allows the direct
comparison of protein profiling between different samples.: Since all
components are isolated
and collected in liquid phase, it is ideal for downstream protein
identification using mass
spectrometry and/or protein extraction for antibody production.
The PF 2D system addresses many of the problems associated with traditional
proteomics
research, such as detection of low abundance proteins, run-to-run
reproducibility, quantitation,
detection of membrane or hydrophobic proteins, detection of basic proteins and
detection of very
low and very high molecular weight proteins. Since the dynamic range of
proteins in serum
spans over 10 orders of magnitude, and the relatively few abundant proteins
make up over 95%
of the total protein contents, this makes it very difficult to detect low
abundant proteins that are
candidate markers. In order to enrich and identify the less abundant proteins,
the serum samples
were partitioned using IgY-R7 rodent optimized partition column to separate
the seven abundant
proteins (Albumin, IgG, Transferrin, Fibrinogen, IgM, at-Antitrypsin,
Haptoglobin) from the.
less abundant ones.
The partitioned serum was applied to the PF-2D. The first dimensional
chromatofocusing was performed on an HPCF column with a linear pH gradient
generated using
start buffer (pH 8.5) and eluent buffer (pH 4.0). The proteins were separated
based on the pT.
CA 02661332 2009-02-20
WO 2008/030273 PCT/US2007/007875
Fractions were collected and applied:to a reverse-phase HPRP column for a
second dimensional
separation. The 2D map generated from each sample was then compared and
differential peak
patterns were identified. The fraction was subsequently selected and subjected
to trypsin
digestion. The digested samples were sequenced using LC/MS for protein
identification.
2-D GeI Electrophoresis
Two-dimensional electrophoresis has the ability to resolve complex mixtures of
'
thousands of proteins simultaneously iii -a single gel. In the first
dimension, proteins are
separated by pI, while in the second dimension, proteins are sepaiated by MW.
Applications of
2D gel electrophoresis include proteome analysis, cell differentiation,
detection of disease
markers, monitoring response to treatment etc.
The IgY partitioned serum samples were applied to imrniobilized pH gradient
(IPG) strips
with different pH gradients, pH 3-10,=pH 3-6 and pH 5-8. After the first
dimensional run, the
IPG strip was laid on top of an 8-16% or 4-20% SDS-PAGE gradient. gel for
second dimensional
separation.
Results
A peak protein of approximately 4200 daltons was present in the senum of CDr-
RD and
CDr-HSD, but not in the serum of CDs-RD or CDs-HSD, as shown in Figure 2A.
Figure 2B is a
MS/MS spectrum of the 4200 dalton fragment. This protein was sequenced and
following
extensive database searches, was found to be a novel protein. The peptide was
designed "D3"
and its sequence was found to be SGRPP MIVWF NRPFL IAVSH THGQT ILFMA KVINP
VGA (SEQ ID NO: 1). The D3 peptide.is a 38-mer peptide sequence that
corresponds to the
first bibmarker discovered in the Cohen diabetic rat. Sequence alignment using
the BLAST
algorithm available from the National Center for Biotechnology Information
(NCBI) was
performed and the 38-amino acid fragment was found to have sequence identity
with at least ten
different amino acid sequences. Notably, BLAST alignment revealed that the 38-
amino acid D3
peptide contains conserved motifs corresponding to: "FNRPFL" and
"FMS/GKVT/VNP".
Figure 3A shows the results of the BLAST alignment of aniino acid sequences
related to the D3
peptide fragment, and Figure 3B shows the results of a BLAST alignment of
nucleic acid
sequences encoding the D3 peptide and the peptides identified by protein
BLAST. Degenerate
primers were designed to target the conserved motifs and comprise the
following sequences:
61
CA 02661332 2009-02-20
WO 2008/030273 PCT/US2007/007875
Forward primer (targeting regions containing the amino acid sequence "FNRPFL":
5'-TTC AAC
MRR CCY TTY ST-3' (SEQ ID NO: 2) and Reverse primer (targeting regions
containing the
sequence "FMS/.GKVT/VNP"): 5'-YVA CYT TKC=YMA KRA AGA-3' (SEQ ID NO: 3);
wherein M= A or C; R= A or G; Y= C or T; S= C. or G; K= G or T; and V= A, C,
or G.
These degenerate primers were used in reverse-transcription polymerase chain
reactions (RT-
PCR) to amplify human SERPINA 3 in liver and pancreas. A 1.3 Kb fragment was
identified in
humari liver and pancreas, as shown in Figure 3C.
Table 3 represents additional identified candidate markers identified by SELDI
analysis.
Array = CM10 (Anion
Type exchange)
Sample Fractioned Serum Fl
CDs-
M/Z CDr-RD RD CDr-HSD CDs-HSD
-2156 + . +
-2270 + + - +
-3875 + + - -
Sample Fractioned Serum F3
CDs-
M/Z CDr-RD RD CDr-HSD CDs-HSD
-3408 - + - +
-3422 + - + -
-3848 - + - - +
-3861 + - + -
Sample Fractioned Serum F4
CDs-
M/Z CDr-RD RD CDr-HSD CDs-HSD
-4202 + - + -
~4423 + +
Sample Fractioned Serum F5
CDs-
M/Z CDr-RD RD CDr-HSD CDs-HSD
-5377 ++ = ++ ++ +
-5790 +/- = '+/- - a-
-8813 +
Sample Fractioned Serum F6
CDs-
M/Z CDr-RD RD CDr-HSD CDs-HSD
62
CA 02661332 2009-02-20
WO 2008/030273 PCT/US2007/007875
-4200 + - + Sample Whole Serum
CDs-
M/Z CDr-RD RD CDr-HSD CDs-HSD
-6631 - + - -
-7013 - - + +
-7027 + + - -
-7811 - + -
Array
Type Q10
Sample Fractioned =Serum Fl
CDs-
M/Z CDr-RD RD CDr-HSD CDs-HSD
-2627 + - + : -
-2705 + - + -
-4290 + + -+-+= . _ +
=~5058 - - + -
-5220 + ++ +
-5789 - - +
-8818 + +/- ++ ++
Sample Fractioned Serum F2
CDs-
MIZ CDr-RD RD CDr-HSD CDs-HSD
-2359 + +I- - -
-2587 + + - +/-
-2879 + + - +/-
---2298 - + - -
Sample Fractioned Serum F4
CDs-
M/Z CDr-RD RD CDr-HSD CDs-HSD
-4200 + - + -
-2067 - - + +
-2092 - - + +
-2042 - ~ + +=
-8810 - - + +
-8850 + + - -
Sample Fractioned Serum F5
63
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WO 2008/030273 PCT/US2007/007875
CDs-
IVllZ CDr-RD RI) CDr-HSD CDs-HSD
-3977 + - + -
-4200 + - + -
-2102 + - + -
-4030 + -+-t- + ++
Sample Fractioned Serum F6
CDs- -
M/Z CDr-RD RD CDr-HSD. CDs-HSD
-4200 + - 4= - --17645 + - + -
Sample Whole Serum
CDs-
M/Z CDr-RD RD CDr-HSD CDs-HSD
-6632 - + - -
-3419 + +
-3435 . + + -
-4074 + + - -
-4090 + + - -
-4200 + - + -
--5152 + + - -
-8915 + + - -
Array
Type H50
Samplc Fractioned Serum F2
CDs-
M/Z CDr-RD RD CDr-HSD CDs-HSD
-5521 - + - -
Sample Fractioned Serum F5
CDs-
M/Z CDr-RD RD CDr-HSD CDs-HSD
-34224 - - - +
Array
Type IMAC
Sample Whole Serum =
CDs-
M/Z CDr-RD RD CDr-HSD CDs-HSD
-2714 + + +
- 4330 - + + +
64
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WO 2008/030273 PCT/US2007/007875
The differences among Cohen diabetic rats are shown in Figures 4A and 4B,
which
represent gels depicting biomarkers identified by LC/MS technology and a graph
showing an
elution profile obtained by differential two-dimensional reverse-phase HPLC or
CDr-RD (red)
versus CDs-RD (green) of a selected first dimension pI fraction (fraction 31).
Figure 5A
represent 2DE gels of samples derived from each of the four Cohen diabetic rat
models, while
Figure 5B is a magnified view of spots identified in Figure 4A identified "as
apolipoprotein E,
liver regeneration-related protein, and a previously unidentified protein.
Figure 6 is a graphical
representation illustrating the differentially expressed proteizis found in
the four Cohen Diabetic
rat models using 2DE technology. Figure 7 is a histogram showing the
differentially expressed
Cohen Diabetic rat serum proteins identified by 2DE.
The D3 peptide was used for the production of hyper-immune serum in rabbits.
Figure 8
'depicts Western blots showing the reactivity of the D3 hyper-immune serum
with a--4 kD
protein isolated from CDr-RD and CDr-HSD rat serum fraction 6. Fractionated CD
rat serum
samples were run on a 10% SDS-PAGE gel, then transferred to PVDF membranes. A
higher
molecular weight doublet (in the range of 49 and 62 kD) also reacted with the
hyper-immune
sera, indicated that a parent protein is expressed by all strains under
treatment modalities RD or
HSD, however a derivative of smaller size (-4 kD) corresponding to the D3
fragment is
differentially expressed only in the CDr strain. These results are consistent
with the results
obtained by SELDI profiling. The concentration of the D3 fragment in CDr rat
serum was
subsequently analyzed by SELDI. A series of synthetic D3 peptide standards
(0.1, 0.033, 0.011,
0.0037, 0.0012 and 0 mg/ml) and lOX diluted CDr-serum were spotted in
duplicate on Q10
protein chip arrays. The peak intensity was plotted against the concentration
of D3 peptide
standards. Based on the plot (Figure 9), the linear range for concentration
determination is from
0 to 0.01 mg/ml. Accordingly, the concentration of D3 in CDr-RD serum is
around 0.04 mg/mi,
based on the peak intensity of the CDr-RD serum sample.
Analysis of Serpina expression by Western blot analysis was performed in Cohen
rat
liver extracts using anti D3 rabbit serum (1:200) and secondary goat anti-
rabbit IgG conjugated
to HRP (1:25,000 dilution). Controls containing liver extracts (10 g) and
secondary goat anti-
rabbit IgG antibodies conjugated to HRP (1:25,000 dilution), but no primary
antibody were also
analyzed (Figure 10). A cluster of proteins (41, 45 and 47 kD) were visualized
following
reaction of liver extracts with D3 hyper immune serum. The 41 and 45 kD
proteins were
CA 02661332 2009-02-20
WO 2008/030273 PCT/US2007/007875
expressed at approximately the same level while the 47kD protein is not
detected in the diabetic
rat-i.e., CDs-HSD (diabetic).
Table 4 contains a suirunary of biomarker data obtained from CD rat serum
samples.
66
CA 02661332 2009-02-20
WO 2008/030273 PCT/US2007/007875
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t- ~ o~o o c~ a~i fl-
8
o
X "a U a co~ ~
rl 2
N N N N N N N
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Example 2: Biomarker ldentification in Human Sera
Analysis of human sera was performed using D3 hyper immune serum (rabbit;
Figure 11).
The primary antibody used was rabbit polyclonal antibodies produced following
immunization
with D3 peptide_ A protein with molecular weight of 20 kD (between the 14 kD
and 28 kD
markers) is expressed in human serum at a higher intensity in the normal
individual as compared
with Type 2 diabetic patient. A pair of proteins with MW of 60-80 kD appear to
be present in
both (normal and diabetic) samples. Interestingly, the intensity of the
proteins in tlie doublet
seemed to be inverted; an observation that was made using monoclonal
antibodies derived from a
~ ==.
subtractive immunization with CDr-HSD and CDs-HSD pancreas. Figures 12A and
12B show
preparative gels containing 100 g of CDr-HSD or'.CDs-HSD pancreatic extracts.
The positive
control was stained with 20 g of anti-actin antibodies, and subclone lanes
were stained with 600
l of conditioned culture supernatant (described elsewhere in this disclosure).
Human serum samples corresponding to=samples taken from normal, diabetic and
insulin-
resistant subjects were obtained from three differerit sources and subjected
to SELDI analysis:
Dr. Itamar Raz, Dr. Wendell Cheatham, and Dr.= Rachel Danlrner. Dr. Raz's
samples (hereinafter
"Raz samples") comprised 11 T2D human serum and plasma samples, and 9 normal
human
serum and plasma samples. The Cheatham samples comprised a total of 51 serum
and urine
samples, 12 of which were derived from Type 1 Diabetic individuals, 13 from
T2D individuals,
10 insulin=resistant subjects, and 16 normal subjects. The Dankner samples
comprised 23 T2D
human serum samples and 25 normal human serum samples. SELDI analysis revealed
the
significant peaks from the Raz and Dankner samples, shown in Tables 5 and 6
below. Figure 13
is an example of whole human serum profiled on anionic Q10 chips by SELDI.
Table 5: Selected significant peaks present in Raz samples
Sample No. Peak (MIZ) P-value Fold Change
T2D/N)
1 12900 9.90E-07 3.24
2 134500 4.75E-06 0.55
3 44500 1.75E-05 2.21
4 4260 1.84E-05 0.4
5 4260 2.1=3E-05 0.49
6 56500 2.84E-05 0.55
7 6640 8.08E-05 2.14
8 12600 1.96E-04 2.64
9 2505 2.09E-04 1.71
10 29000 2.46E-04 0.63
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Sample No. Peak (M/Z) P-value Fold Change
(T2D/N)
11 3300 3.44E-04 0.65
12 14070 3.58E-04 0.69
13 11750 5.22E-Q4 2.81
14 6875 7.49E-04 2.2
15 13750 1.05E-03 0.66
16 9715 2.69E=03 1.89
17 9375 3.88E=03 1.61
18 6440 6.04E-03 2.1
Table 6: Selected significant peaks present in Dankner samples
Sample No. Peak (M/Z) F-value . Fold Change
(T2D/N)
1 10075 4:81E=04 3.63
2 9310 1.87E-03 1.9
3 4160 3.68E-03 1.74
4 6450 1.59E-04 0.76
9310 8.25E-04 1.36
6 7770 8.25E=04 0.66
7 6430 1.32E-'05 0.7
8 10650 2.25E-04 2.58
SELDI analysis revealed differentially expressed protein peaks identified in
13 T2D
5 human samples and 16 normal human samples. Figure 14 depicts a pseudogel
view of SELDI
analysis of Fraction 1 of the samples. Each lane represents a spectrum of an
individual sample
from M/Z 14.0 kD to 16.0 kD. The M/Z for the protein bands are approximately
15.2, 14.8, and
14.5 kD, respectively. Figure 15 is another pseudogel view of SELDI analysis
performed on 13
T2D and 16 normal fractionated serum samples (Fraction 3), profiled on a Q10
protein chip. =
Each lane represents the spectrum of an individual sample from M/Z 8.0 kD to
10.0 kD. The
M/Z for the protein marker is approximately 9.3 kD. The graph below in Figure
15 is a cluster
view of a marker (M/Z -6430) thaf is downregulated in T2D samples. Levels of
albumin were
profiled using SELDI on the Cheatham samples and were compared to the Dankner
samples, as
shown in Figure 16.
Example 3: Bi-Directional Imrnunolo ical Contrasting and Generation of
Monoclonal
Antibodies
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From the pancreatic extract protein profiles obtained by SDS-PAGE, obvious
differences
in the banding patterns were noted between CDr-HSD and CDs-HSD samples (Figure
1). Bi-
directional immunological contrast was-performed between these two samples.
This technique
involves injecting two pancreatic extracts from the Cohen diabetic rats to be
contrasted
separately into the footpads of an experimental animal (e.g. a Balb/c mouse).
Following uptake
and processing..of the antigen at the site of injection by antigen presenting
cells (APCs), the
activated APCs migrate to the local lymph nodes (popliteal) to initiate an
immune response. As
these lymph nodes are located in each leg, they are anatomically separated.
from each other,
which prevents mixing of antigen-specific lymphocytes.at this point. Later in
the immune
response, these activated lymphocytes migrate from the local lymph nodes to
the spleen where
they become mixed, and from where they may circulate systemically.
Two weeks after footpad injection, the animals were boosted by injecting each
footpad
with the same antigen as before. This boost-recalls antigen specific=
lymphocytes back to the site
of injection, again subsequently draining to the popliteal lymph nodes. This
technique uses the
natural proliferation and cell migration processes as a filtering mechanism to
separate and enrich
specific lymphocytes in'each lymph node, where they are anatomically
segregated to minimize
mixing of cells that are specific for antigen(s) expressed in only one of the
extracts. Three days
after boosting; the popliteal lymph nodes were removed and separated into
pools derived from
each side of the animals. When boosting, it is imperative not to switch the
antigenic material, as
this will cause specific lymphocytes to migrate to both sets of popliteal
lymph nodes and the
anatomical segregation of specific cells, and hence the advantage of the
technique, will be lost.
Fifteen female Balb/c mice ages 6-8 weeks were ordered from Harlan. Each
animal was
injected with 25 g of CDr-HSD pancreatic extract into the left hind footpad,
and 25 g of CDs-
HSD pancreatic extract into the right hind footpad. Antigens were prepared in
20% Ribi
adjuvant in a final volume of 50 i as follows:
Table 7:
Right footpad Left footpad
375 mg of CDs-HSD 110 1 -----------
375 mg of CDr-HSD ----------- 62 1 PBS 490 1 538 1
Ribi adjuvant 150 1 150 gl
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Ribi adjuvant was warmed to 37 C and reconstituted with I ml of sterile PBS.
The bottle
was vortexed for at least 1 nvnute to fully reconstitute the material. The
correct volume of Ribi
adjuvant was then added to the antigen preparation, and the mixture was again
vortexed for 1
minute. Any unused formulated material was discarded, and -any unused Ribi
adjuvant was
stored at 4 C and used to formulate booster injections. Animals were primed on
day 1 and
boosted on day 14. Animals were euthanized on day 17, when popliteal lymph
nodes were
.=excised post mortem.and returned to the lab for processing.
Generation of Hybridomas
Hybridoma cell lines were created essentially as described by Kohler and
Milstein (1975).
Lymphocytes derived from inununized animals were fused with a murine myeloma
cell line
(Sp2/0) by incubation with polyethylene glycol (PEG). Following fusion, cells
were maintained
in selective medium containing hypoxanthine, aminopterin and thymidine (HA.T
medium) that
facilitates only the outgrowth of chimeric fused cells.
On the day before the fusion, the fusion partner (Sp2/Ox Agl4 cells in
dividing stage with
viability above 95%) was split at 1 X 105 viable cells/ml, 24 hours before the
fusion. On the day
of the fusion, the mice were sacrificed and the lymph nodes were excised and
placed in a Petri
dish containing pre-warmed room temperature DMEM supplemented with 10% fetal
bovine
serum (FBS). Using sterile n=ii.croscope slides, the lymph nodes were placed
between the 2 frosty
sides of the slides and crushed into a single cell suspension. The cell
suspension was then
transferred to a 15 ml tube and centrifuged for 1 minute at 1000 rpm. The
supematant was
removed by aspiration, and the cell pellet gently resuspended in 12 ml of
serum-free DMEM,
after which they were subjected to another round of centrifugation for 10
minutes at 1000 rpm.
The process was repeated twice more to ensure that the serum was completely
removed. After
washing, the cells were resuspended in 5 ml of senun-free DMEM and counted
under the
microscope.
The fusion partner was collected by spinning in a centrifuge for 10 minutes at
1000 rpm.
The cells were washed three times in serum-free DMEM, and finally resuspended
in sesum-free
DMEM and counted. The number of fusion partner cells were calculated based on
the nurnber of
lymph node cells. For every myeloma cell (fusion partner), 2 lymph nodes cells
is needed (ratio
1:2 of myeloma to lyrnph node cells; e.g. for l Ox 1061ymph node cells, 5x 106
fusion partner cells
are needed). The appropriate number of myeloma cells to the LN cells were
added and the total
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volume of cells was adjusted to 25 ml using serum free DMEM, and 25 ml of 3%
dextran was
then added to the cells. The mixture was spun for 10 minutes at 1000 rpm, and
the supernatant
aspirated as much as possible from the cell pellet. Once the lid was placed
onto the tube
containing the cells, the bottom of the tube was gently tapped the bottom of
the tube to resuspend
the cells and 1 ml of pre-warmed 50% (v/v) PEG was added to the tube. The
agglutinated cells
were,allowed to sit for I minute, after. which 20 ml of serum free DMEM,
followed by 25 ml of
20% FBS, DMEM with 25 mM Hepes was added. The tube was inverted once to mix
and then -
centrifuged for 10 minutes at 1000 rpm. The media was aspirated and the cells
were gently
resuspended by tapping. HAT selection media was added such_that the cell
suspension was
either at 0.125 x 106 cells/ml or 0.0625 x 106 cells/ml. -One hundred* l of
cells per well were
added to a 96=wel1 flat bottom plate and incubated at 37 C with COZ at 8.5%_
After 2 days, the
cells were fed with 100 l of fresh HAT selection media. Cells were checked
for colony growth
after *7.days.
Hybrido.ma Screening
Once visible colonies were observed in the 96 well plates, 100 l of
conditioned
supematatit was harvested from each colony for screening by ELISA.
Supernatants were
screened for the presence of detectable levels of antigen-specific IgG against
both CDr-HSD and
CDs-HSD extracts. Only colonies exhibiting a positive ELISA reaction against
one of the two
-extracts with at least a 2-fold difference were selected for expansion and
further characterization.
Pancreas extract at a concentration of 25 g/ml to be tested was diluted in
carbonate
bicarbonate buffer (1 capsule of carbonate-bicarbonate was dissolved in 100 ml
of deionized
water). .Two extra wells for the positive control and two extra wells for the
negative control of a
96-well plate were reserved. The plate was then covered using adhesive film
and incubated at
4 C overnight.
The plate was washed once with 200 l of PBS/Tween. The well content was
removed
by flicking the plate into a sink, and then gently tapping the plate against
absorbent paper to
remove remaining liquid. Approximately 200 l of washing buffer (PBS/Tween)
was added and
subsequently discarded as previously described. The entire plate was then
blocked for 1 hour at
37 C in 200 l of 5% powdered milk/PBS/Tween. The plate was then washed 3
times using
PBS/Tween as previously described.
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The fusion culture supernatant was diluted 1:1 in 0.5% milklPBS/Tween and each
sample
added to the wells (50 l; final volume is 100 l per well) with 50 l of anti-
actin Ab (Sigma) at
20 g/ml to well containing 50 1 of buffer. Fifty l of buffer was added to
the negative control
well. The plate was covered and incubated overnight at 4 C. The plate was
washed 3 times
using PBS/Tween as previously described, and anti-HRP anti-mouse IgG in 0.5%
milk/PBS/Tween at 1:20000 (100 1) was added to each well. The plate was
covered and
incubated at 37 C for two hours.
- After incubation with secondary antibody; the plates were washed 4 to 5
times as
previously described. On'the last wash, the washing buffer was left on the
plate for a couple of
= 10 minutes before discarding it. One hundred l of pre-warmed room
temperature TMB (VWR;
stored in the dark) was added to each well while riminimizing the introduction
of bubbles, until
the color developed (20-30 minutes). The reaction was stopped by adding 50 g1
of 2M sulfuric
acid. The plate'vvas read using a spectrophotometer at 450 nm.
Thirteen clones produced monoclonal antibodies (mAbs) that met the
experimental
criteria outlined above, 9 against CDs-HSD and 4 against CDr-HSD. The ELISA
data for these
colonies is summarized in Table 5 and graphically represented in Figures 17A
and 17B. Table 8
shows ELISA screening data for monospecific CDr-HSD and CDs-HSD hybridomas.
Absolute
-absorbance values, and fold difference at OD 450 nm is shown for each colony.
To verify
primary screening data, some clones were retested during expansion to confirm
the experimental
observations from the initial screen.
Table 8
Primary Screen Confirmatory Screen
Clone ID CDR-HSD CDS-HSD DiffeFold rence CDR-HSD CDS-HSD DiffeFold
rence
P1-5-1711 0.021 0.426 20.29 0.013 0.192 14.77
P1-14-A2 0.363 0.714 1.97 NT NT -
P1-17-E4 0.042 0.398 9.48 NT NT -
P1-18-C12 0.021 0.183 8.71 NT NT -
P1-20-B7 0.065 0.192 2.95 0.025 0.110 4.40
P1-23-F7 0.039 0.912 23.38 0.046 0.547 11.89
P2-1-E8 0.001 0.139 139.00 0.019 0.252 13.26
P2-10-E3 0.007 0.249 35.57 0.017 0.153 9.00
P2-14-C6 0.006 0.353 58.8 0.054 0.143 2.65
P2-4-H5 0.214 0.058 3.69 0.217 0.065 3.34
P2-8-A3 0.184 0.095 1.94 0.227 0.065 3.49
P2-10-B8 0.101 0.055 1.84 0.12] 0.029 4.17
P2-13-A9 0.114 0.004 28.5 0.213 0.035 6.09
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To derive monoclonal hybridoma lines, each colony was subcloned by limiting
dilution.
The resulting clonal lines derived from each parent colony were rescreened and
ranked by O.D.
450 nm to determine the best clones. The top 10 antibody secreting clones were
expanded and
archived in liquid nitrogen storage. Cells were couinted and:ensured that the
viability was at least
80%. Cells were prepared in subcloning media containing 10% FBS and 10%
hybridoma
cloning factor (bioVeris) in DMEM at 5 cells/ml (about 60 ml for 3 plates).
Another set of the
same cells was prepared at a coricentration of -1.6 cells/ml (about 60 ml for
3 plates). Two
hundred l of cells were plated per well in a 96 well round bottom plate. One
set of 3 plates
contained 1 cell/well, and another contained, on average, I cell every 3
wells. After 10 days,
cells were visible, and the subclones were tested for specificity. Cells of
interest were expanded
in a 24 well plate in 10% FBS DMEM containing 5% of hybridoma cloning factor.
The composition of each n-iA.b was defined by determining-the class of heavy
and light
chains, as well as the molecular- weight, of each component. Isotyping was
performed using the
Immunopure monoclonal antibody isotyping kit I (Pierce) according to the
manufacturer's
instructions. The molecular weight of heavy and light chains was determined
using the Experion
automated electroph.oresis. system from Bio-Rad. The Experion system
automatically performs
the multiple steps of gel-based electrophoresis: separation, stairiing,
destaining, band detection,
imaging, and data analysis. The results of these analyses are shown in Table
9, which shows the
physical characterization of CDr-HSD and CDs-HSD specific monoclonal
antibodies.
Identification of both heavy and light chains was performed using the
Immunopure monoclonal
antibody isotyping kit I (Pierce), and molecular weights (in kD) were
determined using the
Experion automated electrophoresis system (Bio-Rad).
Table 9
Light chain Hea chain Whole IgG
Clone ID Subtype Mol.Wt. Subclass Mol.Wt. Mo1.Wt.
- -
P1-5-FIi kappa - IgG2b
Pl-14-A2 Kappa/ lambda - I G1 - -
- -
P1-17-E4 Kappa - IgGI
- -
PI-18-C12 Kappa - IgG2b
P1-20-B7 Kappa - - IgGl - -
P1-23-F7 Kappa - IgG2b - -
P2-1-E8 Kappa - IgGI - -
- -
P2-10-E3 Kappa - IgG2a
- -
P2-14-C6 Kappa - IgGI
P2-4-H5 Ka a ~ - IgG2b - -
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P2-8-A3 Kappa - I G2b -
P2-10-B8 Kappa. - IgG2b
- -
P2-13-A9 kappa - IgGI
- -
To determine the specific 'antigen for each clone, each rnAb was tested by
Western
Blotting to ascertain the molecular weight of the corresponding antigen. Data
obtained from
reactive clones is shown in Figures 18A-18C.
To purify the antigen specific for P240-B8-KA8, an immunoprecipitation was
performed.
Specific antibody was bound to Protein G beads and used to pan for antigen
from CDr-HSD
pancreatic extract containing 6 mg of total protein. In an Eppendorf tube, CDR-
HSD pancreatic
extract was centrifuged for 5 minutes at 13,000 rpm, and the deposit on the
top of the extract was
removed. Without removing any of the pellet, 6mg of extract was transferred to
3 clean
centrifuge tubes and the volume adjusted I ml by addition of T-per buffer. To
tube 1, 100 ~ig of
purified P2-10-B8-KA8 was added to:the.diluted sample, 200 g of purified P2-
10-B8-KA,8 was
added to tube 2, and 300 g of purified P2-10-B8-KA.B was added to tube 3. The
tubes were
rotated at 4 C overnight.
Protein G beads slurry (1 ml) were centrifuged for 3 minutes at 500 x g in an
Eppendorf
centrifuge, and washed twice with pre-chilled T-per buffer by diluting the
beads 1:1 with the
buffer. The slurry (200 l) was transferred to each tube containing the
antibody-antigen mixture.
A control tube was set up by preparing a tube with 200 l of slurry in I ml of
T-Per buffer and
300 g of"antibody. The tubes were rotated at 4 C for 2 hours. Thereafter, the
beads were
washed twice using pre-chilled T-per buffer (centrifuged at 500 x g for 3
minutes) and the
supematants retained. After one final wash in cold PBS, the supernatant was
removed as much
as possible and 100 l of 2X sample buffer (Pierce 5X loading buffer: 200 I
of loading buffer,
100 1 of reducing agent, complete with 200 l of water) was added. The
samples were boiled
for 5 minutes at 95 C and subsequently cooled on ice for 5 minutes. After
spinning the sarnples
for 3 minutes, each sample was loaded in an amount of 20 1 per lane on a 4-
12% SDS-PAGE
rnini gel for electrophoresis.
Following precipitation, several.bands were visible on the gel after staining
for total
protein with Coomassie. A faint doublet band was observed in the molecular
weight range of 70
to 8,0 kD (see Figure 19). The doublet was confirmed to be the bands of
interest by probing a
Western Blot prepared from a similar gel with the same mAb (data not shown).
The doublet
bands were excised individually from the SDS-PAGE gel and submitted for
identification by
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mass spectrometry. An positive identification of the lower band as calnexin
was made.
Calnexin is a molecular chaperone associated with the endoplasmic reticulum.
Calnexin is a 90 kD integral protein of the endoplasmic reticulum (ER). It
consists of a
large (50 kD) N-terminal calcium-binding lumenal domain, a single
transmembrane helix and a.
short (90 residues), acidic cytoplasmic tail. Calnexin belongs to a family of
proteins known as
"chaperones," which are characterized by their main function of assisting
protein folding and
quality control, ensuring that only properly folded and assembled proteiris
proceed further along
the secretory pathway. The function of calnexin is to retain unfolded or
unassembled N-linked
glycoproteins in the endoplasnuc reticuluin. Calnexiin binds only those N-
glycoproteins that
have GlcNAc2Man9Glc l oligosaccharides. Oligosaccharides with three sequential
glucose
residues are added to asparagine residues of the-nascent proteins in the ER.
The
monoglucosylated oligosaccharides that are recognized by calnexin result from
the trinvming of
two glucose residues by the sequential- action of two glucosidases, I and II.
Glucosidase II can
also remove the third and last glucose residue. If the glycoprotein is not
properly folded, an
.l5 enzynie called UGGT will add the glucose residue back onto the
oligosaccharide -thus
regenerating the glycoprotein ability to bind to calnexin. The glycoprotein
chain which for some
reason has difficulty folding up properly thus loiters:in the ER, risking the
encounter with MNS 1
(a-mannosidase), which eventually sentences the underperforming glycoprotein
to degradation
by rernoving its mannose residue. ATP and Ca2+ are two of the cofactors
involved in -substrate
binding for calnexin. Figures 20A and 20B are screen shots depicting the read-
out of the MS
spectrograms identifying the protein of interest as calnexin. .
Example 2: Microarray Analysis of Gene Expression in Tissues from Cohen Type 2
Diabetic Rats
The microarray data were analyzed through Phase I and Phase II analyses. Phase
I is
based on the processed data from Gene Logic. Phase II corresponds to data
analysis using
GeneSpring GX. Additional criteria including statistics, signaling pathways
and clustering were
used for the analyses. =
The microarray results from Gene Logic (Phase I) that were derived from
comparisons of
pancreatic total RNA of Cohen Type 2 Diabetes rats (CDs-HSD, CDr-HSD) were
analyzed using
MAS5.0 software from Affymetrix, Inc. The global gene expression analysis
showed that there
were 1178 genes upregulated in CDr-HSD and 803 genes were downregulated in
compared to
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CDs-HSD. Many of these transcripts are involved in several signaling pathways
related to Type
2 Diabtes such as insulin signaling, beta-cell dysfunction and lipid and
glucose metabolisms.
Also, several serpin fan-iily members (serine proteinase inhibitors) are
expressed differently in
the two models.
Table 10 provides a summary of the data derived from Gene Logic, wherein
changes greater
than 3-fold were observed.
Signaling Pathways Upregulated genes Downregulated genes
CDR-HS vs. CDS- CDR-HS vs. CDS-HS
HS
sulin signaling 39 1
(3 cell dysfunction (a o tosis, survival) 17 6
flarrunation and immune system 5. _ 92
Mitochondrial dysfunction and reactive 0 8
xygen species
i id and glucose metabolisms 17 . 13
roteinase and proteinase inhibitors 8 17
Aiiiino acid, nucleic acid transporters aind 13 9
etabolisms
otassium channels 3 6
PR and Golgi body related genes 8 8
Other unclassified genes 1028 603
otal 1178 803
Phase II data analysis was performed using G.eneSpring GX, which used
normalized data
(ratio = transcript signal/control signal) to improve cross-chip comparison.
GeneSpring GX
allows for gene lists to be filtered according to genes exhibiting a 2-fold or
3-fold change in the
expression levels. GeneSpring GX also comprises statistical algorithms, such
as ANOVA, Post-
Hoc Test, and Cross-Gene Error Modeling, as well as gene clustering algorithms
like Gene Tree,
K-mean clustering, and Self-Organizing Map (SOM) clustering. GeneSpring GX
also has the
ability to integrate with pathways that are published in the art, such as the
Kyoto Encyclopedia of
Genes and Genomes ("KEGG pathways") and Gen Map Annotator and Pathway Profiler
(GenMA-PP).
The microarray results analyzed by GeneSpring GX show that among the
transcripts with
changes higher than three fold in the two groups, 137 transcripts have a p-
value of less than 0.05.
These genes are involved in several signaling pathways such as the insulin
signaling pathway,
serpin protein family, basic metabolism, pancreas function and inflammation.
Figure 21 shows a
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scatter plot of differentially expressed genes. The 137 transcripts whose
levels show a change of
three-fold or higher are shown in Figure 22B and are also grouped in Tables 11
and 12.
Table 11 Upregulated genes Tota1= 101 Transcripts)
Common UniGene Descri tion
Reg3a Rn.11222 Regenerating islet-derived 3 alpha
LOC680945 Rn.1414 Similar to stromal cell-derived factor 2-like I
Pa Rn.9727 Pancreatitis-associated protein
Ptfl a Rn.10536 Pancreas specific transcription factor, 1 a
Matla Rn.10418 Methionine adenosyltransferase I, alpha
Nu r=1 Rn.11182 Nuclearprotein 1
Rn.128013 unknown cDNA
Chacl redicted Rn.23367 ChaC, cation transport regulator-like 1 (E. coli) (
redicted '
Solute carrier family 7(cationic amino acid transporter, y+
Slc7a3 Rn.9804 system), member 3
LOC312273 Rn.I3006 T sin V-A
Rn.47821 Transcribed locus
Pt er3 Rn. 10361 Prostaglan din E receptor 3 (subtype EP3)
RGD1562451 redicted Rn.199400 - Similar to Pab c4 redicted protein ( redicted
RGD15~6242 redicted Rn.24858 Similar to RIKEN cDNA 1500009M05 (predicted)
C 2d26 Rn.91355 Cytochrome P450, family 2, subfamily d, polypeptide 26
Rn. 17900 similar to aldehyde dehydro enase 1 family, member L2
LOC286960 Rn.10387 Pre rot sino en IV
Gls2 Rn.10202 Glutaminase 2 (liver, mitochondrial
Nme2 Rn.927 Expressed in non-metastatic cells 2
Rn.165714 Transcribed locus
P2rx1 R.n.91176 Purinergic receptor P2X, li and- ated ion channel, I
Pdk4 Rn.30070 Pyruvate dehydrogenase kinase, isoenzyrne 4
Amyl Rn.1 16361 Amylase 1, salivary
Cbs Rn.87853 Cystathionine beta synthase
Mtel Rn.37524 Mitochondrial acyl-CoA thioesterase I
Spink] Rn.9767 Serine protease inhibitor, Kazal type 1
Glycine amidinotransferase (L-arginine:glycine
Gatm Rn.17661 amidinotransferase)
Transmembrane emp24 protein transport domain
Tmed6 redicted Rn.19837 containing 6 (redicted
Tff2 Rn.34367 Trefoil factor 2 s asmol 'c protein 1)
Hsd17b]3 Rn.25104 Hydroxysteroid (17-beta) dehydrogenase 13
Rn.11766 imilar to LRRGT00012 [Rattus norvegicus]
Gnmt Rn.11142 Glycine N-methyltransferase
Pah R.n.1652 Phenylalanine hydroxylase
Serpini2 Rn.54500 serine (or cysteine) proteinase inhibitor, clade 1, member 2
RGD1309615 Rn.167687 unknown cDNA
LOC691307 Rn.79735 Similar to leucine rich repeat containing 39 isoform 2
Eprs = Rn.21240 Glutamyl-prolyl-tRNA synthetase
Phosphoenolpyruvate carboxykinase 2 (mitochondrial)
Pck2 redicted Rn.35508 (predicted)
Chromodomain helicase DNA binding protein 2
Chd2 redicted Rn.162437 ( redicted)
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Common UniGene Descri tion
Rn.53085 Transcribed locus
Rn.12530 Transcribed locus
NIPK Rn.22325 tribbles homolog 3 (Drosophila)
S1c30a2 Rn.111=35 Solute carrier family 30 (zinc transporter), member 2
Serine (or cysteine) peptidase inhibitor, clade A, member
Se inal0 Rn.10502 10
Cfi Rn.7424 Complement factor I
.=Cckar Rn.10184 Cholecystokinin A receptor
LOC689755 Rn.151728 Hypothetical protein LOC689755
Bhlhb8 Rn.9897 Basic helix-loo -helix domain containing, class B, 8
'Anpep Rn.11132 Alanyl (membrane) aminopeptidase.
Asns Rn.11172 Asparagine synthetase
Solute carrier family 7 (cationic amino acid transporter, y+
Slc7a5 Rn.32261= system), member 5 - =
Us 43 redicted Rn. 12678 Ubiquitin specific proiease 43 (predicted) =
Csnklal Rn.23810 Casein kinase 1, alpha 1
Phosphoenolpyruvate carboxykinase 2 (mitochondrial)
Pck2 redicted Rn.35508 (predicted)
S inkl Rn.9767 Setine protease inhibitor, Kazal type 1
Cml2 = . Rn.160578 Camello-like 2
Pab c4 Rn.199602 Transcribed locus
Gjb2 Rn.198991 Gap 'unction membrane channel protein beta 2
. Ngfg Rn.11331 Nerve growth factor, gamma
Clca2 redicted Rn.48629 Transcribed locus
RGD1565381 redicted = Rn.16083 Similar to RIKEN cDNA 1810033M07 (predicted)
Qscn6 Rn.44920 Quiescin Q6
Cldn10 redicted Rn.99994 Claudin 10 (predicted)
Spink3 Rn. 144683 Serine protease inhibitor, Kazal type 3
Similar to NipSnap2 protein (Glioblastoma amplified
LOC498174 Rn.163210 sequence)
similar to Methionine-tRNA synthetase (Rattus
Rn. 140163 norvegicus)
Cyr61 Rn.22129 Cysteine rich protein 61
RGD1307736 Rn.162140 Similar to Hypothetical protein KIAA0152
Ddit3 Rn. 11183 DNA-damage inducible transcript 3
Regl Rn.11332 Regenerating islet-derived 1
E rs Rn.21240 Glutam 1- rolyl-tRNA synthetase
NIPK Rn.22325 =cDNA clone RPCAG66 3' end, mRNA sequence.
Eif4b Rn.95954 Eukaryotic translation initiation factor 4B
Spinkl Rn.9767 Serine protease inhibitor, Kazal type I
Rnase4 Rn.1742 Ribonuclease, RNase A family 4
Ceb g Rn.10332 CCAAT/enhancer binding protein C/EBP), gamma
siat7D Rn.195322 AI ha-2,6-sial ltransferase ST6GalNAc IV
He udl Rn.4028 Homocysteine-inducible, ubiguitin-like domain member I
unknown rat cDNA
Glycine C-acetyltransferase (2-amino-3-ketobutyrate-
Gcat Rn.43940 coenzyme A ligase)
RGD1562860 redicted Rn.75246 Similar to RIKEN cDNA 2310045A20 redicted)
Hs a9a redicted Rn.7535 Heat shock 70kD protein 9A ( redicted
Dbt Rn.198610 Dihydroli oamide branched chain transacylase E2
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Common UniGene Description
Bspry Rn.53996 B-box and SPRY domain containing
Futl Rn.11382 Fucosyltransferase 1
Rp13 Rn.107726 Ribosomal protein L3
similar to NP_083620.1 acylphosphatase 2, muscle type
Rn.22481 [Mus musculus]
unknow rat cDNA
Vldlr Rn.9975 Very low density lipoprotein receptor
RGD1311937 redicted Rn.33652 Similar to hypothetical protein MGC 17299 (
redicted
RGD1563144 redicted Rn. 14702 Similar to EMeg32 protein ( redicte.d)
Rn.43268 Transcribed locus
re-mtHSP70 Rn.7535 70 kD heat shock protein precursor;
Ddah1 Rn.7398 Dimethylarginine dimethylaminohydrolase I
RGD1307_736 Rn.162=140 Similar to Hypothetical protein KIAA0152
RAMP4 Rn.2119 ' Ribosome associated menibrane protein 4
Pt er3 ._.' Rn.10361 Prosta landin E reoe tor 3 (subte EP3)
Rn.169405 =.. Transcribed locus
CCbel redicfed Rn.199045 . Collagen and calcium binding EGF domains 1
(predicted)
Dna'c3 Rn. 162234 DnaJ (H0) homolog, subfamily C, member 3
Mtae2dl = Rn.43919 - Membrane targeting (tandem) C2 domain containing 1
Table 12: Downregulated enes (Total = 36 transcripts)
Common UniGene Description
RGD1563461_predicted .Rn.199308 . Transcribed locus
Gimap4 Rn.198155. GTPase, IMAP family member 4
S100b Rn.8937 S100 protein, beta polypeptide
KIf2_predicted Rn.92653 Kruppel-like factor 2 (lung) (predicted)
RGD1309561_predicted Rn.102005 Similar to hypothetical protein FLJ31951
(predicted)
NAP22 Rn.163581 Transcribed locus
Sfrs3_predicted Rn.9002 Splicing factor, arginine/serine-rich 3(SRp20)
( redicted)
Rn.6731 Transcribed locus
Cd53 Rn.31988 CD53 antigen
RGD1561419_predicted Rn.131539 Similar to RIKEN cDNA 6030405P05 gene
(predicted)
I12rg Rn. 14508 Interleukin 2 receptor, gamma
LOC361346 Rn.31250 Similar to chromosome 18 open reading frame 54
Cd38 Rn.11414 CD38 antigen
KIf2_predicted Rn.92653 Kruppel-like factor 2(lung) (predicted)
Plac8_predicted Rn.2649 Placenta-specific 8 (predicted)
LOC498335 Rn.6917 Similar to Small inducible cytokine B13 precursor
(CXCL13)
Igfbp3 Rn.26369 Insulin-like growth factor binding protein 3
Ptprc Rn.90166 Protein tyrosine phosphatase, receptor type, C
RTI-Aw2 Rn.40130 RTI class Ib, locus Aw2 =
Rac2 Rn.2863 RAS-related C3 botulinum substrate 2
Rn.9461 Transcribed locus
Fos Rn.103750 FBJ murine osteosarcoma viral oncogene homolog
Arhgdib Rn.15842 Rho, GDP dissociation inhibitor (GDI) beta
Sgnel Rn.6173 = Secretory granule neuroendocrine protein I
Lck mapped Rn.22791 Lymphocyte protein tyrosine kinase (mapped)
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Common UniGene Description
Fcgr2b Rn.33323 Fc receptor, IgG, low affinity IIb
SifnB Rn.137139 Schlafen 8
Rab8b Rn. 10995 RA.B8B, member RAS oncogene family
Rn.4287 unknown cDNA
RGD 1306939 Rn.95357 Similar to mKIAA0386 protein
Tnfrsf26_aredicted Rn. [62508 Tumor necrosis factor receptor superfamily,
member 26
redicted
Ythdf2predicted Rn.21737 YTH domain family 2 (predicted)
RGD1359202 Rn.10956 Similar to immunoglobulin heavy chain 6(Igh-6)
RGD1562855~redicted Rn. ] 17926 Similar to Ig kappa chain (predicted)
Igha_mapped Rn. 109625 Immtinbglobulin heavy chain (alpha polypeptide) ma ped)
Ccl2lb Rn.39658 Chemokine (C-C motif) ligand 21b (serine)
Gene Tree gene clustering analysis, represented in Figure 22A, shows the
12,729 genes
that are present in all six samples. As discussed.above, 820 genes showed 2-
fold changes in
expression, whi(e 13.7 genes showed 3-fold changes in expressi4n., aad a Gene
Tree
representation 'is shown in Figure 22B.. Of the 137 genes that showed 3-fold
changes, K-mean
clustering analysis further divided these 137 genes into 5 sets, based on the.
greatest similarities
between the genes within the sets (Figure 21 C). These 5 sets are desigriated
"Up-l ", "Up-2",
"Up-3", "Up-4", and "Up-5" and are summarized in Tables 13-17 below.
Table 13: Up-I
--=.,
-
10 [Total genes:91 ! .,= ( Fold
..._..___._..
_.._._...___.....~._._..___.,..._..__.____._.._..__....________...-_----
_._._._......._...____...__. ..__ _._...___...- =---_._..__.._...._._.~
Common = Description ( Changes
Reg3a Regenerating islet-derived 3 alpha = 75.08
LOC680945, Similar to stromal cellilerived factor 2-like 1 32.31
Pap Pancreatitis-associated protein 19.53
PtP9a Pancreasspecifictranscriptionlactor, 1a 11.59
Mat1a Methionine adenosyltransferase 1, alpha 8.67
Nupri Nuclear protein 1 7.53
unknown cDNA 7.52
Chacl_predicted ChaC, cation transport regulator-like 1(E. coii) (predicted)
7.41
Slc7a3 Solute canier famity 7 (cationic amino acid transporter, y+ system),
member 3 6.68
L0C312273 Trypsin V-A = 6.38
Transcribed locus 6.08
Ptger3 Prostaglandin E receptor 3 (subtype EP3) 6.01
RGD1562451_predicte Similar to Pabpc4_predicted protein (predicted) 5.88
RGD1566242__predict Similar to RIKEN cDNA 1500009M05 (predicted) 5.62
Cyp2d26 Cytochrome P450, famity 2, subfamily d, polypeptide 26 5.59
similar to aidehyde dehydrogenase 1 family, member L2 [Canis familiaris] 5.37
LOC286960 Preprotrypsinogen IV 5.19
GIs2 Glutaminase 2(lker, mitochondrial) 5.10
Table 14: Up-2
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iTotal genes: 91 I I Fold
Common Description Changes
Transcribed locus 4.92
P2rxl Purinergic receptor P2)~ ligand-gated ion channel, 1 4.85
Pdk4 Pyruvate dehydrogenase kinase, isoenzyme 4 4.72
Amyl Amylase 1, salivary 4.70
Cbs Cystathionine beta synthase 4.67
Mtel Mitochondrial acyl-CoA thioesterase 1 4.49
Spink1 Serine protease inhibitor, Kazal type 1 4.43
Gatm Glycine amidinotransferase (L-arginine:glycine amidinotransferase) 4.40
Tmed6_predicted Transmembrane emp24 protein transport domain containing 6
(predicted) 4.38
TfR Trefoil factor 2(spasmolytic protein 1) 4.36
Hsdl7bl3 Hydroxysteroid (17-beta) dehydrogenase 13 4.34
similar to LRRGT00012 [Rattus nomgicus] 4.30
Gnmt Glycine N-methyltransfeidse = 4.30
Pah Phenylatanine hydroxylase 4.29
Serpini2 serine (or cysteine) proteinase inhibitor, clade I, member 2 4.28
RGD1309615 = unknown cDNA 4.16
LOC691307 Similar to leucine rich repeat containing 39 isoform 2 4.12
Eprs Glutamyl-prolyi-tRNA synthetase = = = 4.03
PCk2_predicted Phosphoenolpyruvate carboxykinase 2(mitochondrial) (predicted)
4.01
Table 15: Up-3
Total genes: 91 Fold
Common Description = Changes
I 5 Transcribed locus == 3.97
Transcribed locus 3.96
SIc3Oa2 Solute canier family 30 (zinc transporter), member 2 3.77
SerpinalO Serine (or cysteine) peptidase inhibitor, clade A, mem&er 10 3.77
Cfi Complement factor I = 3.69
Cckar Cholecystokinin A receptor 3.68
LOC689755 Hypothetical protein LOC689755 3_68
Bhlhb8 Basic helix-loop-helix domain containing, class B, 8 = 3.66
Anpep Alanyl (membrane) aminopeptidase 3.65
Asns Asparagine synthetase 3.65
Usp43_predicted Ubiquitin specific protease 43 (predicted) 3.62
SIc7a5 Solute carrierfamily 7 (cationic amino acid transporter, y+ system),
member 5 3.62=
Csnklal Casein kinase 1, alpha 1 3.58
Cm12 Camello-like 2 3.51
Pabpc4 Transcribed locus 3.50
Gjb2 Gap junction membrane channel protein beta 2 3.49
Ngfg Nem growth factor, gamma 3.47
CIca2_predicted Transcribed locus 3=46
RGD1565381_predict Similar to RIKEN cDNA 1810033M07 (predicted) 3.42
Qscn6 Quiescin Q6 = 3.41
Table 16: Up-4
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Total genes: 91 Fold ;
Common Description Changes
CidnlO_predicted Claudin 10 (predicted) 3.40
Spink3 Serine protease inhibitor, Kazal type 3 3.38
LOC498174 Similar to NipSnap2 protein (Glioblastoma amplified sequence) 3.36
similar to Methionine-tRNA synthetase [Rattus norvegicus] 3.35
Cyr61 Cysteine rich protein 61 3.33
RGD1307736 Similar to Hypothetical protein KIAA0152 3.32
Ddit3 DNA-damage inducible transcript 3 3.32
Regi Regenerating islet-derned 1-= =- . 3.22
NIPK unicnowp cDNA ' . . 3.19
Eif4b Eukaryotic transiation initiation factor 4B 3.17
Rnase4 Ribonuclease, RNase A famil~ 4 3_16
Cebpg CCAAT/enhancer binding protein (C/EBP), gamma.. 3.16
siat7D Alpha-2,6-siaiyltransferase STG.GaIN/ac IV' 3.15.
Herpud1 Homocysteine-inducibie,.,=ubiquitin-iike domain member 1 3.15
Gcat Glycine C-acetyltransferase (2-amino-3-ketobutyrate-coenzyme A ligase)
3.13
RGD1562860_predicte Similar to RIKEN cDNA 2310045A20 (predicted) 3.11
Hspa9a_predicted Heat shock 7OkDa protein9A (predicted) ' 3.10
Dbt Dihydrolipoamide branched. chain transacylase E2 3.10
Bspry B-box and SPRY domain containing 3.10
Table 17: Up-5 ;Totai genes: 91 Fold
Common Description Changes
Fut1 Fucosyitransferase 1 _ .3.09
Rpl3 = Ribosomat protein L3 = = 3:08
strongty simifar to iYP 083620.R acyiphosphatase 2, muscle type [Mus muscu
3.08
Vidlr = Very low density lipoprotein receptor 3.07
RGD1311937_predicte Similar to hypothetical protein MGC17299 (predicted) 3.04
RGD1563144_predicte Similar to EMeg32 protein (predicted) 3.04
Transcribed iocus 3.04
Ddahl Dimethylarginine dimethylaminohydrolase 1 3.03
RAMP4 Ribosome associated membrane protein 4 3.01
. Transcribed locus.. 3.01
Gcbe1_predicted Collagen and calcium binding EGF domains 1(predicted) 3.01
Dnajc3 DnaJ (Hsp4O) homoiog, subfamily C, member 3 3.00
Mtac2dl Membrane targeting (tandem) C2 domain containing 1 3.00
Two additional sets, named "Down-1" and "Down-2" represent genes that were
found by
GeneSpring GX analysis to be downregulated in the Cohen diabetic rat samples.
The following
Tables 18 and 19 summarize the results obtained in the "Down-1" and "Down-2"
sets-
Table 18: Down-1
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__.___-_-_ __-____.__._.___._..----.___..---... _---__..__-,-.___._ ~.,
~Total genes: 35 genes ( I Fold
Common Description Change
Ccl2lb Chemokine (C-C motif) ligand 21b (serine) 11.33
Igha_mapped Immunoglobulin heavy chain (alpha polypeptide) (mapped) 7.63
RGD1562855_predicted Similar to Ig kappa chain (predicted) .4.98
RGD1359202 Similar to immunoglobulin heavy chain 6(lgh-6) 4.78
Ythdt2_predicted YTH domain family 2 (predicted) = 4.63
Tnfrst26_predicted Tumor necrosis factor receptor superfamily, member 26
(predicted) 4.37
RGD1306939 Similar to mKIAA0386 protein 4.33
unknown cDNA 4.24
Rab8b RAB8B, member RAS oncogene family = 4.10
51fi8 Schlafen 8 3.91
Fcgr2b Fc receptor, IgG, low affinity llb 3.79
Lck_mapped Lymphocyte protein tyrosine kinase (mapped), 3.66
Sgnel Secretory granule neuroehdo'6rine protein 1 = 3.56
Fos FBJ murine osteosarcoma viral bncogene homolog = 3.55
Arhgdib Rho, GDP dissociation inhibitor:(GDI) beta 3.55
Transcribed locus = 3.51
Rac2 RAS-related C3 botulinum substtate 2 3.42
Table 19: Down-2 ~_.....---..._.._.__. - __..___.._
.............._....._.__._...._...... .... ...---...... ....__--.
Motal genes: 35 genes Fold, ;
Common Description = = -=== = Change
RT1 Aw2 RTI class lb, locus Aw2- = 3.39
Ptprc Protein tyrosine phosphatase, receptor type, C 3.39
lgtbp3 Insulin-like growth factor binding=protein 3 3.37
LOC498335 Similar to Small inducible cytokine B13 precursor (CXCL13) 3.27
PlacB_predicted Placenta-specific 8 (Predicted) 3.25
Cd38 CD38 antigen " 3.24
LOC361346 Similar to chromosome 18 open reading frame 54 3.24
RGD1561419_predicted Similar to RIKEN cDNA 6030405P05 gene (predicted) 3.19
112rg Interleukin 2 receptor, gamma (setiere' combined immunodeficiency 3.19
Cd53 = CD53 antigen 3.18
Transcribed locus . 3.16
Sfrs3_predicted Splicing factor, arginine/serine-rich 3(SRp20) (predicted)
3.15
RGD1309561_predicted Similar to hypothetical protein FLJ31951 (predicted) 3.13
NAP22 Transcribed locus 3.13
KIfi2_predicted Kruppel-like factor 2 (lung) (predicted) 3.11
S100b S100 protein, beta polypeptide 3.08
Gimap4 GTPase, IMAP family member 4 3.07
RGD1 563461 _predicted Transcribed locus 3.07
Finally, gene expression analyses obtained by microarray were confirmed using
quantitative RT-PCR according to standard methods. The table below provides a
summary of
the genes of interest identified by microarray analysis and whose fold changes
in expression
.were verified using Q-RT-PCR.
Table 20: Quantitative RT-PCR Analysis on Selected Genes
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Downreguiated fABI, S. Common Genbank UniGene t)escriptlon Foid Chainge
Cci21b BI282920 Rn.39658 Chemokine (C-C moti$ ligand 21b (serine) 11.33 250
TnfisP26_predicted BE098317 Rn.162508 Tumor necrosis factor receptor
superfamily, member 26 (predicted) 4.37 250~
Igfbp3 NM 012588 Rn.26369 Insulin-Ilke growth factor binding protein 3 .37 150
112rg A1178808 Rn.14508 Interleukin 2 receptor, gamma sewre combined
immunodeficiency) 3.19 V 250}
L ~_.__._._._ ____. {_._-___.._--_=_- ... ._...__...._._...._.._..
_._..____.__.__.....:-__.__._._ .__.._...._.. _ ___._.__..__......__---- ^-=---
+
Upregulated
Common Genbank UniGene Description Fold Change
Reg3a L10229 Rn.11222 Regenerating Isiet-derfred 3 alpha == 75.08 -~ 250't
LOC680945 BI275923 Rn.1414 Similar to stromal cell-derimed factor 2-like 1
32.31 - 25~I
Ptffa NM053964 Rn.10536 Pancreas s ific transctiption factor, 1a 11.59
LOC312273 A1178581 Rn.13006 Trypsin V-A 6.38 = 250.
LOC286960 = X15679 Rn.10387 Preprotrypsinogen IV 5.19 _ 25~i
S inkl NM_012674 Rn.9767 Serine protease inhibitor, Kazal type 1" 4.43 150;
Serp1n12= = NM 133612 Rn.54500 serine (orcysteine) proteinase inhibilor; clade
1; member 2 4.28 250i
Serpinal0 NM;133617 Rn.10502 Serine'(or cysteine) peptidase Inhibitor,
clade.A, member 10 3.77 25D~
Spink3 M27883 Rn.144663 Serine protease inhibitor, Kaz.al type 3= :' _ :,'=
,.. 3.38 150; .
Reg1 NM_012641 Rn.11332 Regenerating istet-deriued 1 ~ 3.22 50
Eif4b B1278814 Rn.95954 = Eukaryotic lranslation initiation Eactor463.17 250I;
Rpt BG057530 Rn.107726 Ribosomal protein L3 3.08 =2501
.
RAMP4= A1103695 Rn.2119 Ribosome associated membrane protein 4 3.01 2501
..
, ... .I . . ._. . .
__.__ _.......-..__...___..._._.:.__..__.._..._..__..:....'..__...--.
.....__....._......._.._....__......- -_...__._..,..-.---=
._.t__..._.ont_ __.ls . 14352338E __ :. _ __... ..... ..__._ _._..-_ GAPOH 50~
~Racro _ _ !
.. _ ---_...__...__..._._.._ .........._.__.. _.-.__. .. ..... . .... ._.... _
-........_....... __........._....._- !:.._.
...,~.__. ..... ....._ .
14352340E +AGTIN, BETA _...._._=-=---_
r .- ._. _..-......._..._... _...__....-...... _.-__....._.. .._..__.._.__.-..
__......._ ~ _.._.
4750
The protein encoded by the CD53 gene is a member of the transmembrane 4
superfa.m'ily,
also known as the tetraspanin family. Most of these members.are cell-surface
proteins that are=.
characterized by the presence of four hydrophobic domains. The proteins
mediate signal
=transduction events that play a role in the regulation of cell development,
activation, growth and
motility_ This encoded protein is a cell surface glycoprotein that is known to
complex with
integriiis. It contributes to the transduction of CD2-generated signals in T
cells and natural killer
cells and has been suggested to play a role in growth regulation. Familial
deficiency of this gene
has been linked to an immunodeficiency associated with recurrent infectious
diseases caused by
bacteria, fungi and viriuses. Alternative splicing results in multiple
transcript variants encoding
the same protein. CD.38 is a novel multifunctional ectoenzyme widely expressed
in cells and
tissues especially in leukocytes. CD38 also functions in cell adhesion, signal
transduction and
calcium signaling-
It is to be understood that while the invention has been described in
conjunction with the
detailed description thereof, the foregoing description.is intended to
illustrate and not limit the
scope of the invention, which is defined by the scope of the appended claims.
Other aspects,
advantages, and modifications are within the ambit of the following claims. -
87
