HIPCAT Meeting

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HIPCATMeeting

• January 27, 2006
• Stephen E. Harris

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Where are Our Computational Bottlenecks?

• Large collections of images such that layers of
  resolution are maintained ie. like a satellite
  image that can see a grass-blade in someones
  backyard.
• 3D imaging of biological processes with high
  resolution and animation
• Connecting, utilizing, displaying large gene
  expression datasets with all known information .
  
• Use of Natural Language Processing (NLP)
  technology.

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Natural Language Processing

• Our biological data is organized and abstracted
  in Medline and Pubmed
• NLP technology can be used to aid in the search
  of these large databases for context-dependent
  hits and links that have meaning in terms of a
  biological pathway
• Example x binds y resulting inc binding, z
  stimulates v.., a inhibits c, only when d is
  present, y is in the cytoplasm and moves to the
  plasma membrane after., resulting in

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Introduction to Our Biological Problem and Where
we can Use HIPCAT

• Mechanical loading of bone and Finite Element
  Analysis modelsassociate with select gene
  expression
• Osteocytes biology-mechanosenors in bone
• Imaging osteocytes at work in health and disease.
  
• Pathways and gene networks unique to osteocytes
  and the mechanical loading.
• Connect List of genes to large databases, such
  as Medline/Pubmed
• Derive Virtual Pathways that can lead to a
  deeper more systems biology approach to
  understanding a given biological system

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Bone is Formed Where the Biomechanical Demands
are Greatest
Robling, 2002
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• Osteocytes make up over 90 of all bone cells
• Osteocytes express long dendritic processes
• These cells are viable for decades in the bone
  matrix.

from the Primer on Metabolic Bone Disease and
Disorders of Metabolism editor Murray J. Favus
Mechanosensory Cell for Bone
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Fluid Flow Through the Osteocyte
Lacunae-Canalicular System-Procian
Red Injection Into the Tail
Vein of a Mouse
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Mouse Ulnae Loading Model
Courtesy of Alex Robling (Adapted from Torrance
et al., 1994)
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Pathway Assisthttp//www.ariadnegenomics.com

• Organize complex list of gene expression patterns
  and link to Medline/PubMed Databases
• NLP technology in MedScan, a ResNet database
  --includes comprehensive database of molecular
  networksie 500 pathways and over 1 million
  biological interactions
• Construct candidate interaction pathways, the
  data is directly linked to Medline and PubMed.
• Needs improvements and new ideas.

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DMP1-MEPE-SPP1-CDC42 Mechanical Loading
Responsive Gene Network
PathwayAssist
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http//129.111.78.243/HarrisLab/HarrisLab_home.htm

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Mouse Ulna Regions Analyzed for Gene Expression
of DMP1 and MEPE mRNA
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MEPE Expression at 3mm Distal to Mid-shaft
24hr after Loading at 2.4N at 60 cycles 2 Hz.
Lateral
Medial
U
U
Control-Left
Loaded-Right
Top In situ, darkfield Bottom-lightfield, HE,
U ulnae
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Quantitation of MEPE mRNA in Osteocytes after a
30 sec Load of 2.4N at 2Hz
In the Mouse Ulnae(N3)

Loaded Control




P lt 0.05

R2 0.63
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Strain Gradient Estimates Along the Diaphysis of
the Axially Loaded Mouse Ulna
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MEPE Gene Expression Threshold (GEt) and Relative
Gene Expression Change (rGE GEtx-GEctr) At 24
hr After 30 sec 2.4 N, 2Hz Load of Mouse Ulnae
P 0.038 slope
GEt 1350 /- 350 uE
uE
The Gene Expression Threshold(GEt) is similar to
the Estimated Bone Formation Threshold in the
Mouse and Rat Models
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Preliminary Finite Element Model of the Ulnae of
Mouse- 4month C57BL/6
A mCT image consisting of 1105 sections at 13
micrometer spacing of the C57BL/6 female ulnae
(4months). A coarse 2832 element model was then
constructed and analyzed using LS-DYNA. Proximal
and distal structures of the ulnae have been
removed in the model and idealized boundary
conditions imposed. (a) the course finite
element mesh superimposed on the CT image, (b)
the shaded finite element model with idealized
boundary conditions, and (c) representative
equivalent strain contours for a 2.4N idealized
static loading. Need more work. 1st
Reiteration.
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How can we study the osteocyte home and gene
expression patterns?Pathways and Gene Networks
in Osteocytes
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WT
Canaliculi
Osteocyte
Lacunae
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8KB DMP1 Cis-Regulatory Region Plus Intron
1-GFPtopaz and Conserved Non-Coding Sequences/
Mouse Human Comparison
A.
E2
E1
GFPtopaz
8kb Region
Intron 1
-7892bp
4439bp
B.
Use of the DMP1 cis-regulatory region to target
GFP to osteocytes. A. Contruct with the 8kb
plus Intron 1 region of DMP1 ligated to GFPtopaz.
Used to make stable osteoblast cells that
differentiate into osteocytes and transgenic
mice models. B. Conserved nucleotide
sequences(CNS) between mouse and human DMP1
genes. 8kb plus Intron 1 contains a large
portion of the CNS
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8KB Flanking Plus Intron 1 DMP1 Direct Expression
to Ostecytes
A
B
D
C
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E2
8kb Region
E1
Intron 1
DMP1 Gene
GFPtopaz
-7892bp
4439bp

• Fuorescent activated cell sorting was used to
  purify Primary osteocytes from Calvariae
• DMP1 mRNA expression in Unsorted, -GFP and GFP
  Cell Fractions.
• GFP expression in osteocytes of calvarial bone,
  driven by the 8kb Plus Intron 1 DMP1-GFP
  construct.

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Gene Expression Studies

• 500 ng of total RNA was 2x amplified.
• Affymetrix 430A mouse GeneChips
• GC-RMA was used for Normalization
• With N3, LIMMA in Bioconductor was used to
  determine significant genes at a max False
  Discovery Rate 5
• 723 Genes between GFP cells and GFP cells were
  analyzed, setting GFP 1.0

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Cluster 10
Gene Expressed 2-10 times higher in gfp Primary
Osteocytes
Muscle Secreted Differentiation Lipid Transcrip
tion
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Summary

• Need new HPC and Imaging tools for analysis of
  biological functions in vivo.
• Better tools for connecting complex dataset from
  microarray analysis to other databases, such as
  Medline, Pubmed, Protein interaction databases,
  and Pathway networks.

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Acknowledgements

• UTHSCSA UCONN
• Marie Harris
  Ivo Kalajzic
• 
  David Rowe
• Wuchen Yang
• Jelica Gluhak-Heinrich UMKC
• 
  Jian Q. Feng
• Indiana University Medical School
• Charles H. Turner
• Alex Robling
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•