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HIPCAT Meeting January 27, 2006 Stephen E. Harris 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. 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 contextdependent hits and links that have meaning in terms of a biological pathway • Example: x binds y resulting in…c 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… Introduction to Our Biological Problem and Where we can Use HIPCAT • Mechanical loading of bone and Finite Element Analysis models—associate 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 Bone is Formed Where the Biomechanical Demands are Greatest Robling, 2002 •Osteocytes make up over 90% of all bone cells •Osteocytes express long dendritic processes •These cells are viable for decades in the bone matrix. Mechanosensory Cell for Bone from the “Primer on Metabolic Bone Disease and Disorders of Metabolism” editor Murray J. Favus Fluid Flow Through the Osteocyte Lacunae-Canalicular System-Procian Red Injection Into the Tail Vein of a Mouse Mouse Ulnae Loading Model Courtesy of Alex Robling (Adapted from Torrance et al., 1994) Pathway Assist http://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 networks—ie 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. DMP1-MEPE-SPP1-CDC42 Mechanical Loading Responsive Gene Network PathwayAssist http://129.111.78.243/HarrisLab/HarrisLab_home.htm Mouse Ulna Regions Analyzed for Gene Expression of DMP1 and MEPE mRNA MEPE Expression at 3mm Distal to Mid-shaft 24hr after Loading at 2.4N at 60 cycles 2 Hz. Lateral Medial U Control-Left Top – In situ, darkfield Bottom-lightfield, HE, U Loaded-Right U =ulnae Fold Change Relative to Control Quantitation of MEPE mRNA in Osteocytes after a 30 sec Load of 2.4N at 2Hz In the Mouse Ulnae(N=3) * 2.5 Loaded Control 2.0 * * 1.5 * 1.0 0.5 * * P < 0.05 R2 = 0.63 d1 d2 0.0 0 p2 p1 MS d3 Position along Ulnae d4 8 Strain Gradient Estimates Along the Diaphysis of the Axially Loaded Mouse Ulna MEPE mRNA Control Loaded P = 0.038 slope 1.0 GEt = 1350 +/- 350 uE 0.5 40 39 33 59 26 76 20 00 -0.5 13 08 0.0 44 4 Gene Expression Change GEtx-GEctr 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 1.5 Microstrain uE The Gene Expression Threshold(GEt) is similar to the Estimated Bone Formation Threshold in the Mouse and Rat Models 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. How can we study the osteocyte home and gene expression patterns? Pathways and Gene Networks in Osteocytes WT Canaliculi Osteocyte Lacunae 8KB DMP1 Cis-Regulatory Region Plus Intron 1-GFPtopaz and Conserved Non-Coding Sequences/ Mouse Human Comparison A. E1 8kb Region E2 GFPtopaz Intron 1 -7892bp +4439bp A Exon1 B. -10kb -7.3kb -4.6kb -2.0kb Exon2 & 3 +3.4kb +6.1kb +1.0kb Exon6 +8.7kb +10.4kb 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 B osteocytes and transgenic mice models. B. Conserved nucleotide sequences(CNS) between mouse and DMP1 cis-REGULATORY REGION - GFPtopaz CONSTRUCTS human DMP1 genes. 8kb plus Intron 1 contains a large portion of the CNS 10kb REGION E1 INTRON1 GFPtopaz 8KB Flanking Plus Intron 1 DMP1 Direct Expression to Ostecytes A B C D 8kb Region -7892bp E1 Intron 1 DMP1 Gene E2 +4439bp GFPtopaz Fuorescent activated cell sorting was used to purify Primary osteocytes from Calvariae A. DMP1 mRNA expression in Unsorted, -GFP and +GFP Cell Fractions. B. GFP expression in osteocytes of calvarial bone, driven by the 8kb Plus Intron 1 DMP1-GFP construct. Gene Expression Studies • • • • 500 ng of total RNA was 2x amplified. Affymetrix 430A mouse GeneChips GC-RMA was used for Normalization With N=3, 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 Cluster 10 Gene Expressed 2-10 times higher in +gfp Primary Osteocytes Muscle Secreted Differentiation Lipid Transcription 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. 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