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The Effects of Osteoarthritis on the Genes of Mice
Jasmine George, 1Dr. Gregory Goins, 1Adrian Ambrose, 1Gregory Harper,
1Jasmine Silas 2Dr. Jacquelyn Fetrow, 2Amy Olex
1
Abstract
1 North Carolina Agricultural and Technical State University Department of Biology, Greensboro, NC,
2 Wake Forest University Department of Computer Science and Medical School
Osteoarthritis is a degenerative disease that attacks the
joints, bones and tendons causing the affected individual to
be unable to carry out normal mobile skills. This experiment,
conducted at Wake Forest University and North Carolina
A&T State University investigated the impact of osteoarthritic
genes in mice through Genetic Modeling and the study of
microarray technology. The experiment’s data was translated
through the Mouse 430 2.0 Array. The osteoarthritic genes
were injected into the knees of mice and translated into a
microarray chip. The Affymetrix IDs were converted into
Entrez IDs and set in Cytoscape. Cytoscape was
instrumental in mapping the old and young mice’s
progression in the disease. The genes that were given were
separated into three different categories; time zero, SHAM,
and DMM, under subcategories which varied by the ages of
the mice. It was observed early in the experiment that the
genes that were not affected as shown in the microarray
results were more common in the younger mice than in the
older mice.
Introduction
What is Osteoarthritis? Osteoarthritis is a degenerative
disease that attacks the joints, bones and tendons causing
the affected individual to be unable to carry out normal
mobile skills. Since Osteoarthritis is diagnosed in the later
stages, and in people in later ages, little help can be given to
the patient, causing adverse effects such as pain, stiffness,
and locking of the joints. Some of the causes include
diabetes, obesity, and it can be hereditary. The goal of this
experiment was to identify the key differences in gene
expression elucidate the mechanisms by which age
contributes to the development and progression of OA.
Methods
Conclusion
The data was sent from the Wake Forest University team and
put through Cytoscape, and calculated into various values
that corresponded to different factors such as elapsed time
and mouse age.
The data was normalized using SVN, and calculated into 3
different SLR values (expt - ctrl): (Time zero vs Sham,Time
zero vs. DMM, and Sham vs. DMM). The results needed
were expected to be significantly regulated and consistent
genes shown in the clustering of gene expression profiles.
In conclusion, there was seen a correlation to
the progression of Osteoarthritis to the age of
the mice (since, OA is closely associated with
age). When the results from the initial
experiment came back, the younger mice
appeared to have more up regulation (i.e.
more genes are expressed; the older the
mouse the less likely the genes will respond,
making it difficult to treat.) This proved that the
progression of the disease was common in
age. (Fig.3) Although this is an ongoing
process (several difficulties were encountered)
, through microarray analysis, and the
modeling of genes in Cytoscape, a correlation
between the age of the mice , the progression
of the disease, and which gene networks the
disease attacks (glycolysis/glucogenisis, insulin
signaling and starch sucrose metabolism, just
to name a few) can be seen.
Data
Fig.1. This figure shows two clusters
from the younger mice at the 8 wk
period of the experiment. The graph
shows the genes that were turned on
(depicted in yellow and blue) at the
time the mouse was injected and the
genes that did not respond (depicted
in black).
Fig.2. This figure shows two clusters
from the older mice at the 8 wk
period of the experiment. The graph
shows the genes that were turned on
(depicted in yellow and blue) at the
time the mouse was injected and the
genes that did not respond (depicted
in black).
Fig.3. Results from the JAM analysis (ran 10
times on different points of interests, or
seeds) shows that more significant genes
were expressed in younger mice than older
mice. The following shows the seed “Network
Union 0,6,7: ECM-Receptor Interaction”
ACKNOWLEDGEMENTS: BLEND is an Undergraduate BioMath (UBM) Project sponsored by NSF Grant No. 1029426