Genic activity and pathway networks Embedding biological knowledge in genomic statistical analysis d

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Genic activity and pathway networks Embedding
biological knowledge in genomic statistical
analysisdaniel.remondini_at_unibo.it
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Collaborations
Brown University Brain Research Center Genomic
Proteomic Center Applied Math
Bologna University Dip. Fisica, CIG ISS -
Roma Unilever Research Center, UK ITB CNR Milano

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Our group

• Systems Biology (Genomics, IS, NS)
• EMF effects in biological tissues/cells/organisms
• F. Bersani - Physics
• G. Castellani - Physics
• M. Francesconi - Biotechnology
• P. Mesirca - Physics
• G. Procopio - Physics
• D. Remondini - Physics
• I. Zironi - Biology

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Typical genomic experiment

• Comparison between two sample (cell) "states"
  (one is the normal/basal state)
• 1) sample preparation
• 2) microarray hybridization
• 3) statistical analysis

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Genomic analysis drawbacks

• single gene analysis is not sufficient to
  understand cell mechanisms undergoing
  experimental conditions
• cell behaviour is a complex phenomenon several
  elements (e.g. genes) act together in order to
  generate it

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Single Gene Statistical Analysis

• Statistical significance is not necessarily
  related to biological significance because
• the most interesting genes often show the
  slightest variations in expression (low
  statistical significance)
• they often are poorly expressed at an absolute
  value (where the experimental signal is more
  noisy) - this also contributes to low significance

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"typical" microarray picture
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Introducing biological knowledge

• Gene significance must also rely on the known
  role that the gene has in cellular mechanisms
• Single gene statistical analysis must be
  integrated with a priori higher level biological
  knowledge
• -gt known biological pathways (e.g. KEGG)

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Improvements

• Robustness of statistical analysis is increased,
  since each pathway contains many genes.
• Single-gene significance threshold can be
  lowered, due to a following higher-level
  significance filtering
• A priori knowledge can be exploited for further
  analysis/comparison

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Pathway Network Structure

• Libraries like KEGG have also an interesting
  network structure it is possible that
  biologically relevant informations can be
  retrieved from the topological structure of nodes
  (pathways) and edges (common genes between two
  pathways)
• Topologically relevant edges can be focal areas
  from which biological messages are spread
  throughout the network (like hubs for the nodes)

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KEGG database

• Kyoto Encyclopedia of Genes and Genomes
• Gene relationships (interactions/pathway
  clustering) derive from knowledge of
  biochemical/physical interactions
• Several organisms Human, Mouse, Rat,
• Only a portion of whole genome is embedded in
  KEGG (continuously updated)

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KEGGPathway Network (Mouse)
104 probes 102 pathways
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KEGG (human) betweenness centrality
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Analysis pipeline

• 1) single gene statistical analysis
• 2) integration with a priori biological knowledge
  (KEGG pathway network)
• 3) larger scale (global) analysis
• pathway statistical significance
• pathway network structure (nodes and/or edges)

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Pathway significance analysis

• Node (pathway) or edge (intersection)
  significance analysis
• total number of genes represented in KEGG and
  total number of statistically significant genes
  compared with the significant genes found in a
  node or edge and its total number of elements
• hypergeometric distribution-based test

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Pathway network analysis

• Given staistically significant nodes and edges,
  the significant pathway network can be
  reconstructed.
• Edges and nodes can be ranked based on their
  centrality in the network (e.g. connectivity
  degree or betweenness)

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Betweenness Centrality BC

• BC of a network element (node/edge)
• sum (over al i,j) of shortest paths passing
  through the network element (node/edge)
  connecting element i and j, with respect to the
  total number of shortest paths connecting i and j

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Betweenness Centrality

• BC is a very interesting parameter because
• - it can be calculated both for nodes and edges
• - it is a measure of the possible information
  flow through that element, thus if it is affected
  by experimental conditions it is very likely that
  such perturbation can spread to the whole system
  more easily

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Case studies

• 1) c-myc induction (rat fibroblast cell line)
• 2) TAC induction (mouse heart cells)
• 3) Lifespan dataset (human lymphocytes)
• 4) Ewing Sarcoma dataset (human)

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c-Myc-triggered gene expression

• C-Myc encode for transcriptional regulators whose
  inappropriate expression is correlated with a
  wide array of human malignancies.
• Up-regulation of Myc enforces growth, antagonizes
  cell cycle withdrawal and differentiation, and in
  some situations promotes apoptosis.
• c-myc-/- cells reconstituted with the
  conditionally active, tamoxifen-specific
  c-Myc-estrogen receptor fusion protein (MycER)
  allows the fine and selective change of of c-Myc
  activity by Tamoxifen .
• Time series data 5 time points in triplicate
  (9000 probes)
• J.M. Sedivy lab (Brown Univ. USA) OConnel et
  al JBC 2004

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Network representation

• Significantly underrepresented (-1)
• Significantly overrepresented 1
• Not significant 0

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c-Myc off
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c-Myc on
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Is BC ranking affected by KEGG network structure?
Cmyc ON vs. KEGG
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Gene expression time correlation
c-Myc off
c-Myc on
Remondini et al, PNAS 2005
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Animal model (mouse) of Left Ventricular
Hypertrophy (LVH) induced by Transverse Aortic
Constriction (TAC).

• Time series experimental design
• 15 Affymetrix chips at T10, T22, T34 weeks
  after TAC.
• Each time point have been repeated with 5
  replicas

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TAC gene expression
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TAC 2 weeks
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TAC 4 weeks
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Lifespan Experiment

• Lymphocite mRNA extracted from 25 samples, age
  25-93 (20000 probes)
• 5 age groups with 5 samples each ANOVA
• Custom arrays (Unilever labs, UK)

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Lifespan gene expression
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Time correlation
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Lifespan KEGG network
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Pathway ranking by BC

• 1 PPAR SigPath 26 Apoptosis
• 2 Adipocytokine SigPath 27 Carbon fixation
• 3 Inositol phosphate Met 28 Colorectal cancer
• 4 Jak-STAT SigPath 29 Glutathione metabolism
• 5 Phosphatidylinositol SigSyst 30 g-ExaCloCE Degr
• 6 Purine metabolism 31 Antigen ProcAndPres
• 7 Glyo and Dicarbo xylate Met 32 Cyanoamino Ac
  Met
• 8 Cysteine metabolism 33 Gap junction
• 9 B cell receptor SigPath 34 Taur HypoTaur Met
• 10 Glycolysis-Gluconeogenesis 35 ALA-ASP Met
• Styrene degradation 36 Leuk tr-emigration
• 12 Long-term depression 37 Atrazine Deg

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Ewing Sarcoma Dataset
About 30 human samples of Ewing Sarcoma that
responded positively to therapy or
not Affymetrix absolute arrays U133_Plus_2
(about 50000 probes)
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Ewing Sarcoma Dataset
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Top 30 pathwaysranked by BC
Red overexpressed Grn - underexpressed
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DIGRESSIONE STATO BASALE (GENE EXPRESSION) DI
UNA CELLULA

• Modello di interazione genica spin glass/boolean
  net
• Ruolo della struttura delle interazioni
  (complessa)

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DIGRESSIONE STATO BASALE (GENE EXPRESSION) DI
UNA CELLULA

• Frustrazione? struttura ground state(s) bacini.
• Esiste una temperatura? (transregulation noise)
• Ruolo delle perturbazioni esterne (site-specific)