1 Bio-Trac 40 (Protein Bioinformatics) Bio-Trac 40 (Protein Bioinformatics) October 9, 2008 October 9, 2008 Zhang-Zhi Hu, M.D. Zhang-Zhi Hu, M.D. Research Associate Professor Research Associate Professor Protein Information Resource, Department of Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biochemistry and Molecular & Cellular Biology Biology Georgetown University Medical Center Georgetown University Medical Center Functional Interpretation of Large- scale Omics Data through Pathway and Network Analysis
58
Embed
Functional Interpretation of Large-scale Omics Data through Pathway and Network Analysis
Functional Interpretation of Large-scale Omics Data through Pathway and Network Analysis. Bio-Trac 40 (Protein Bioinformatics) October 9, 2008 Zhang-Zhi Hu, M.D. Research Associate Professor Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology - PowerPoint PPT Presentation
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Zhang-Zhi Hu, M.D. Zhang-Zhi Hu, M.D. Research Associate ProfessorResearch Associate ProfessorProtein Information Resource, Department of Protein Information Resource, Department of Biochemistry and Molecular & Cellular BiologyBiochemistry and Molecular & Cellular BiologyGeorgetown University Medical CenterGeorgetown University Medical Center
Functional Interpretation of Large-scale Omics Data through Pathway and Network Analysis
2
Overview• IntroductionIntroduction
- What are large-scale omics data?- What do they tell you? How to interpret?
• ApproachesApproaches- Omics data integration- Resources: databases and tools
• Case studiesCase studies• Systems biology Systems biology
• Population of moleculesPopulation of molecules– Genome, proteome and other “-omes”– Interactions, complexes– Pathways, processes– High level organizations
Genomics, Proteomics
4
From One Gene: multiple genetic variants, multiple transcripts,
Other “-omes”:ORFeomePromoterome InteractomeReceptomePhenomemore…
6
SPARC
COL3A1
SULF1
YARS
ABCA5
THY1
SIDT2
Corresponding to ECM cluster (Chen et al., 2003; Qiu et al, 2007)
ECM cluster
Gen
es
Potential Gene Markers
Global analysis
Gastric Cancer
7
Identification of novel MAP kinase pathway signaling targets
Twenty-five targets of this signaling pathway were identified, of which only five were previously characterized as MKK/ERK effectors. The remaining targets suggest novel roles for this signaling cascade in cellular processes of nuclear transport, nucleotide excision repair, nucleosome assembly, membrane trafficking, and cytoskeletal regulation. -- Mol Cell. 6:1343-54, 2000
~3500 spots ~91spot
changes reproducible
Digest of U-24
(PMA/TPA K562 cells MAPK pathway targets)
8
Drosophila Embryo Interaction Map
The proteins in the map that bear The proteins in the map that bear an RA (Ras Association) or RBD an RA (Ras Association) or RBD (Raf-like Ras-binding) domain (Raf-like Ras-binding) domain define a discrete subnetwork define a discrete subnetwork around Ras-like GTPases (colored around Ras-like GTPases (colored in yellow).in yellow).
The exploration of the present map leads to numerous biological hypothesis and expands our knowledge of regulatory protein networks important in human cancer as shown by the biological analysis of a particularly interesting network surrounding the Ras oncogene. Genome Res. 15:376-84, 2005.
Using Y2H technology, 102 bait protein homologous to human cancer genes, 2300 interactions detected, 710 high confidence.
• Metabolic Pathways– KEGG (Kyoto Encyclopedia of Genes and Genomes): Metabolic
Pathways– EcoCyc: Encyclopedia of E. coli Genes and Metabolism– MetaCyc: Metabolic Encyclopedia (Metabolic Pathways)
• Inter-Molecular Interactions and Regulatory Pathways– IntAct: Protein interaction data from literature and user submission– BIND: Descriptions of interactions, molecular complexes and pathways– DIP: Catalogs experimentally determined interactions between proteins – Reactome - A curated knowledgebase of biological pathways – Pathway Interaction Database (PID)– BioCarta: Biological pathways of human and mouse– Pathway Commons
RRM2 connected to other major DNA repair and cell cycle proteins, such as p53, BRCA1, HDAC1.
46
ATM
BRCA1
p53
p53
RRM2RRM1
DNA repair
HDAC1
RR complex
BRCA1
ATM
RRM2 in radiation-induced ATM-p53-mediated DNA repair pathway
47
III. Organelle Proteomes
Nucleus
Early endosome
Late endosome
Lysosome
Keratinocytes
Golgi
vATPaseG2
2
1
3
4
4
A
B
C
2
3
Stage I hybrid organelle
PEDF
MART1
TYR
Tyrp1
Flotillin-2
I1Matp
MGST3
Stage IIMART1
TYR
Tyrp1
Atp7a
Matp
Cu2+
AP-2a
SLC24A5 (golden)
vATPaseG2
Rab27a
Rab5c
P21-rac1
Tyrp1TYR
Molecularmotors:
kinesin, dynein/dynactin, dynaminMyosin V, myosin
Ic, Id, I4
Myo
-Va
Rab38
Lyst
DDT?
H+
Na+/K+/Ca2+
AP-2a
-actin ARPC4
Stage IV
Vinculin
Drebrin
Rab5
Pmel17
Pmel17
Pmel17
V
V
V
V
V
V
P
M
M
M
M
M
M
P
PP P
P
P
PM
MP
P
Newly identified and validated
Mouse color gene homolog
Proposed new protein
P
P
P
Sec24P
VAP-AP
* Untagged are known melanosome proteins
OA1
DCT
Melanocyte
Nucleus
Early endosome
Early endosome
Late endosome
LysosomeLysosome
Keratinocytes
Golgi
vATPaseG2
2
1
3
4
4
A
B
C
2
3
Stage I hybrid organelle
PEDF
MART1
TYRTYR
Tyrp1
Flotillin-2
I1Matp
MGST3
Stage IIMART1
TYRTYR
Tyrp1
Atp7a
Matp
Cu2+
AP-2a
SLC24A5 (golden)
vATPaseG2
Rab27a
Rab5c
P21-rac1
Tyrp1TYRTYR
Molecularmotors:
kinesin, dynein/dynactin, dynaminMyosin V, myosin
Ic, Id, I4
Myo
-Va
Rab38
Lyst
DDT?
H+
Na+/K+/Ca2+
AP-2a
-actin ARPC4
Stage IV
Vinculin
Drebrin
Rab5
Pmel17
Pmel17
Pmel17
V
V
V
V
V
V
P
M
M
M
M
M
M
P
PP P
P
P
PM
MP
P
Newly identified and validated
Mouse color gene homolog
Proposed new protein
P
P
P
Sec24P
VAP-AP
* Untagged are known melanosome proteins
OA1OA1
DCTDCT
Melanocyte
Comparative organelle proteome profiling allows to propose key proteins potentially involved in regulation of organelle biogenesis
Schematic drawing of melanosome biogenesis pathway and key proteins involved in each stage.
Chi A, et al. (2006) J. Prot. Res.
48
Towards Systems Biology
(Nature 422:193, 2003)
GenomicsTranscriptomics
ProteomicsMetabolomics Bioinformatics
Bibliomics
…mics…mics…omics
Literature MiningIntegrated knowledge and tools are needed for Systems Biology’s research
49
What is Systems Biology?
‘Systems biology defines and analyses the interrelationships of all of the elements in a functioning system in order to understand how the system works.’ -- Leroy Hood
Systems Biology, 2004, 1(1):19-27.
• How an organism works from an overall perspective.
• Interactions of parts of biological systems
– how molecules work together to serve a regulator function in cells or between cells.
– how cells work to make organs, how organs work to make a person.
• Systems biology is the converse of reductionist biology.
50
Reductionist vs. Systems Biology
The driving force in 20th century biology has been reductionism:
From the population to the individual From the individual to the cell
From the cell to the biomolecule From the biomolecule to the genome
From the genome to the genome sequence
With the publication of genome sequences, reductionist biology has
reached its endpoint
The driving force for 21st century biology will be integration:
Integrating the activity of genes and regulators into regulatory networks
Integrating the interactions of amino acids into protein folding predictions
Integrating the interactions of metabolites into metabolic networks
Integrating the interactions of cells into organisms
Integrating the interactions of individuals into ecosystems
51
Although the individual components are unique to a given organism, the topologic properties of cellular networks share surprising similarities with those of natural and social networks
• top-down: systemic-data driven, to discover or refine pre-existing models that describe the measured data (more on regulatory models). Emerges as dominant method due to “-omics”.
• bottom-up: starts with the molecular properties to construct models to predict systemic properties followed by validation and model refinement (more on kinetic models) (Silicon cell program: http://www.siliconcell.net/)
EGFR signaling network model is constructed based on the reaction stoichiometry and kinetic constants Bottom-up
The model allows predictions of temporal patterns of cellular responses to EGF under diverse perturbations (e.g., EGF doses):
• The dynamics of GAB1 tyr-phosphorylation is controlled by positive GAB1-PI3K and negative MAPK-GAB1 feedbacks. • The essential function of GAB1 is to enhance PI3K/Akt activation and extend the duration of Ras/MAPK signaling. • GAB1 plays a critical role in cell proliferation and tumorigenesis by amplifying positive interactions between survival and
mitogenic pathways
55
Gene regulatory networks (GRNs)
Reprod Toxicol. 19:281-90, 2005
WIRED Systems biology looks at the connections between components in cells.
Essential elements of the role of Dorsal in establishing dorsoventral polarity in Drosophila embryonic development
56
Modeling of the main modules of cell-cycle progression
Chembiochem 5:1322-33, 2004
Three functional Three functional units:units:
• Start function: onset of S-phase• Cyclin cascades (C1, C2, C3)• End function: onset of mitosis to cell division
57
Challenges to Systems Biology
• A complete characterization of an organism (molecular constituents interactions cell function)
• Spatial-temporal molecular characterization of a cell• A thorough systems analysis of “molecular response”
of a cell to external/internal perturbations• Information must be integrated into mathematical
models to enable knowledge testing by formulating hypothesis and discovery of new biological mechanisms…