Statement Of Hypothesis and Specific Aims

websterhissBiotechnology

Oct 1, 2013 (3 years and 10 months ago)

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A. STATEMENT OF HYPOTHESIS
AND

SPECIFIC AIMS:

This proposal
responds

to program announcement PA
-
06
-
094 that
supports

i
nformatics research

in
health related

areas
.

M
ajor advances in genomi
cs and proteomics

have led to an explosive growth in the
volume of

bi
ological information
.
The
field of bioinformatics

can assist both informatics and experimental
scientists in optimizing their use of these vast data resources.
We have

been active in
HIV
research
since the
disease was first discovered
.
Our

informatics coll
aborators

are computational scientists
in the

Data Analytical
Bio
informatics Core at the
NY

State Center of Excellence in Bioinformatics and the Life Sciences (CoE)
and

the Buffalo Center for Biomedical Computing (BCBC)

of

the

Dep
ar
t
ment

of Computer Scienc
e and Engineering
at
UB; the

BCBC
is

a member

of the

NIH

supported

National P
rograms of Excellence in Biomedical
C
omputing
.

They will

assist us in analyzing
the

large, complex
, clinical,

genomic and proteomic database

that

we are building

from our HIV
-
1 in
fected patient
s
.

This

project, using data mining methods, will contribute to
a

fundamental understanding of the pathogenesis of HIV infections and identify new markers of disease
progression and potential
new

targets for therapy. In this proposal, we focus

on a
unique cohort

of

HIV
-
1
infected

patients who are
long term non
-
progressors (LTNP) in comparison with normal progressors (NP)
.

Susceptibility to and p
rogression of HIV
-
1

infection is dependent on

different

vir
us

and host factors

(
Yue
et al.,2005;
Cam
pbell
et al.
, 2004., Anzala
et al.
, 1995; Cao
et al.
, 1995; Buchbinder et al., 1994)
. Host genetic
factors such
as
human allelic variants,

and
HLA

and

HIV co
-
receptor polymorphism
s

significantly influence
disease

outcomes
. As
the course of an HIV
-
1 infecti
on is a complex, orchestrated action of many different
mechanisms,

we propose that a

database containing clinical, genomic and proteomic data
comparing
different
patient cohorts

may yield significant new biomarkers for susceptibility to HIV
-
1 infection and

disease
progression.

Thus we
hypothesize that using innovative informatics technology we shall develop a large,
interactive database containing clinical, genomic and proteomic information on HIV
-
1 infected patients that
can
be used to guide rationale, evi
dence
-
based, decision making at both the clinical and public health levels.


Specific Aim I.

To identify functional genes and proteins that are unique to
our

NP

and LTNP patient cohorts.

This will be accomplished
by:




Genomic analysis of peripheral blood
mononuclear cells (PBMC) from
N
P and LTNP subjects using
cDNA microarrays.




Proteomic analysis of
PBMC

from
N
P and LTNP subjects using
2
-
dimensional difference gel
electrophoresis (2D
-
DIGE) and protein identification using
liquid
c
hromatography
-
tandem mass

s
pectrometry
(
LC
-
MS/MS).

Additionally, label
-
free proteomic quantification methods such as
isotope coded affinity tag
(
ICAT
),

isobaric tag for relative and absolute quantitation

(
ITRAQ
),
18
O
-
incorporation, or

stable
-
isotope labeling
by amino acids in cell

culture

(
SILAC
) will be explored.


Specific Aim II
.

To evaluate the
role
of human

allelic
variants in

influencing the

rate
of HIV
-
1 disease
progression.

This will be accomplished
by:




Quantitating

the gene expression of the allelic variants, RANTES In1.1c
, CCR2b
-
641, CCR5
-
∆32,

IL10
-
5'A,

IL
-
4
-
589T, TNF
-

-
238A and HLA
-
B27, in
N
P and LTNP patient cohorts

using single nucleotide
polymorphism (SNP) analysis and real time, quantitative

PCR (Q
PCR).


Specific Aim III
.
To develop new computational tools to analyze and integrate ge
nomic, proteomic, and clinical
data from these HIV
-
1 patient cohorts and convert them into clinically useful information relating to the
pathogenesis, transmission and therapeutic response of HIV
-
1 infected patients
.This will be accomplished
by:




Design
fl
exible, data mining
-
oriented schemas for integrating
genomic, proteomic, and clinical data

in
a
data warehouse
. Design and implement flexible and scalable analytical functionality for the data warehouse.



Develop novel algorithms
that

are speci
fically suited for mining interesting and significant expression
patterns for
gene and
protein expression data. Design

advanced
algorithms to
jointly explore
genomic and
proteomic data sets
,

and

to integrate expert

knowledge and exploit the annotation of k
nown genes

and
proteins

to guide the mining process.



Design metrics and methods to e
xplore the association between genetic variations and haplotype
patterns
underlying
HIV
disease
.