networks and context: identifying differences between macrophage and macrophage derived cell types

UCSD-Bioinformatics & Systems Biology Group

Networks and Context:Identifying differences between Macrophage and Macrophage

Derived Cell Types

Benner, Subramaniam and Glass. 2003


Macrophage Cell Types

• RAW – Macrophage cell line

• TM - Thioglycolate elicited macrophages

• BM – Bone Marrow derived macrophages

• ES – Embryonic Stem Cells (not macrophage)


Chromosome 1


High Throughput Analysis

Gene Ontology

Biocarta.org

KEGG, others…

15160161711694822329

10421520482

2323141214213169717042166520912679211910611630223291298523872205291249416859664031969

15160161711694822329104215204822323141214213169717042166520912679211910611630223291298523872205291249416859664031969

15160161711694822329104215204822323141214213169717042166520912679211910611630223291298523872205291249416859664031969

BM resposive Groups (vs. TM) P-ValueMouse Breast Cancer and Estrogen Receptor Signaling 2.23E-34Mouse TGFb BMP Signaling Pathway 2.37E-31Common Diseases 3.28E-29Mouse JAK / STAT Signaling Pathway 3.05E-28Mouse Angiogenesis 3.71E-28Growth Factors and Receptors 4.52E-28Mouse Hypoxia Signaling Pathway 4.82E-28Molecular Markers for Stem and Differentiated Cells 5.49E-28Mouse Extracellular Matrix & Adhesion Molecules 5.86E-28Extracellular Matrix and Adhesion Molecules 5.86E-28Cancer 4.07E-25Growth factors and Cytokines 5.11E-25Mouse cAMP/Ca2+ PathwayFinder 5.11E-25Mouse Nitric Oxide 5.11E-25Stem Cell 8.31E-25

TM reposive Groups (vs. BM) P-ValueSmall Inducible Cytokines 1.19E-11Molecules Regulating Inflammation 8.38E-11NFkB Responsive Genes 1.01E-10Interleukins & Receptors 5.29E-10Endothelial cell injure (apoptosis) 1.24E-09Cytokine Production 2.62E-09Chemokines 2.87E-09Cytokines 3.15E-09Smad target gene 4.80E-09Cell-cycle control 6.39E-09CD4+T cell markers 1.24E-08G1 Phase 6.35E-08Other Related Genes 7.69E-08Genes induced by Stat proteins 8.46E-08

Significant Categories are

Assigned

Inspect Results


Differential Response to LPS• Example: NF-kB Responsive Genes (possible Acquired Immunity Response)

BM TM


Investigating DifferencesCell Cycle Genes

Common Transcription Factors


Common cellular and pathway phenotypes lead to distinct

regulatory networks in primary B-cells

Mock and Subramaniam. 2003


ALLIANCE FOR CELULAR SIGNALING


Cell Lab in Dallas

Produces Cells

Treats Cellswith Ligands

Molecular Biology Lab

Microarray analysis

Protein Lab

P-proteins

Antibody Lab

P-proteins

Lipid Lab

Lipid analysis

cAMPCalcium

Ligand Screen: Perturbing Cells


Group Ligand Calcium Cyclic AMP P-Protein Blots Genes Other

Gi S1P ++ + E/A/R SomeSDF ++ + E/A/R SomeBLC ++ + E/A/R SomeELC ++ + E/A/R SomeSLC ++ + E/A/R Some

Gs Terbutaline 0 ++++ 0 SomePGE2 0 ++ (sust) 0 Some

2-MA (?) 0 + 0 Some

Gq & Gs LPA ++ (fast) ++ 0 Some

BCR Antigen ++++ 0 E/A/R Lots Proliferation

IL4 IL4 0 0 Stat 6 Lots

CD40 Anti-CD40 0 0 Delayed E/A/R Lots Proliferation

IL10 IL10 0 0 Stat 3 Yes

BAFF BAFF 0 0 0 Yes Viability

TNF TNFalpha 0 0 0 YesIGF IGF1 0 0 0 YesBOMB BOM 0 0 0 YesfMLP fMLP 0 0 0 YesIFN IFG 0 0 0 YesLeukotriene LTB4 0 0 0 YesNeurokinin NKB 0 0 0NGF NGF 0 0 0 Yes

Summary of Ligand Screen Responses


Reconstructing Networks

Legacy Data AfCS Data

Protein Interactions Microarray Data

Biochemical Pathways Yeast Two-Hybrid Data

RNAi Data

Protein Data

Perturbation Data

Microscopy Data

Published Literature

Reconstructing Networks


from Downward, Nature, August (2001)

Signal Transduction in a Cell


• B cell samples prepared by Cell Lab (Dallas).• Cultured for different time periods (.5, 1, 2, and 4 hr) in

the presence or absence of ligands before harvesting for total RNA isolation.

• Treated and untreated time-course samples hybridized against a spleen reference.

• After removing the common spleen denominator, comparison to 0 time point data reflects the changes in mRNA levels due to ligand treatment and/or time in culture.

• One of the largest mammalian array sets (33 ligands).• All of the experiments were done in triplicate. Including in controls >450 arrays (Caltech)

Ligand Screen Transcript Analysis


The mitogenic response from the ligands AIG, 40L, I04, LPS, CPG dominate at the center of the plot. This is too dense for a clear view (see histogram to the left).

IF, GRH, CGS, PAF, TGF, M3A, 2MA also showed a significant gene response.

Graph association map (4hr)

Differentially expressed genes for ligands vs UNTREATED @ 4hr [ SAM ; False Discovery Rate ( ) ]

ligand (4hr)

40L

(1%

)

LPS

(1%

)

AIG

(1%

)

IL4

(1%

)

CP

G (1

%)

IFB

(1.5

%)

GR

H (1

%)

2MA

(18%

)

LPA

(17%

)

CG

S (2

.9%

)

BOM

(35%

)

IGF

(8%

)

S1P

(38%

)

PAF

(2.4

%)

70L

(6%

)

NP

Y (1

0%)

DIM

(9%

)

LB4

(23%

)

M3A

(3.5

%)

FML

(11%

)

TGF

(2.5

%)

TER

(35%

)

IL10

(20%

)

ELC

(26%

)

PG

E (1

1%)

BAFF

(11%

)

BLC

(57%

)

NG

F (4

2%)

TNF

(33%

)

SDF

(20%

)

IFG

(25%

)

NE

B (2

5%)

SLC

(NA

)

num

ber o

f gen

es (p

robe

s)di

ffere

ntia

lly e

xpre

ssed

0

50

100

150

200

500

600

700

800

900

1000

1100

down-regulated up-regulated


Similarity measures between genes under different conditions with respect to expression levels for…

… groups of genes clustering methods

… pairs of genes correlation methods

Linear correlation (x – xmean) (y – ymean) [ (x – xmean)2 (y- ymean)2 ]½

Partial correlation

= r2 xy

= r xy.z

r xy - r

xz r yz

[(1- r2xz

) (1- r2yz

)]½

“marginal” global correlation (for ligand j ) r2

all xy - r2 all xy except ligand j


Two-way hierarchical cluster:

mean ratio (vs control) of phosphoprotein levels and ligand

Several ligands that elicit an ERK response (chemokines + AIG, CD40L) clustered together.


ERK-MAPK p38 JNK-SAPK

STAT1 P53

ETS.v5

N.MYC1 NFATC1ETS.v6

MEF2C Gadd45aH3F3A

CHOP Gadd45bCREB1

Max Gadd45gC.FOS

Bcl2l11 Egr1H3F3B

Bcl2l2 CHOPSocs3MaxCREB3

JUN JUNSTAT1Egr1 C.FOSN.MYC1

Bcl2l11Bcl2l2

Diagrams are from …

“Mitogen-Activated Protein Kinase Pathways Mediated by ERK, JNK, and p38 Protein Kinases”

G. L. Johnson and R. Lapadat Science 2002 December 6; 298: 1911-1912. (in Review)SRF

Three main pathways of MAPK and their respective target genes and transcription factors.


Level plots “Marginal” correlation of genes in MAPK pathways


“marginal” global correlation (for ligand j )

difference in correlation = r2

all xy - r2 all xy except ligand j

Red indicates positive influence on the gene upon removing ligand j

Green indicates negative influence on the gene upon removing ligand j

Highly responsive genes from MAPK-ERK pathway

B cells respond to AIG through the MAPK-ERK pathway.


We see the correlation results of removing ligands CD40L (40L) and interleukin 4 (I04) separately from the pool of 33 ligands. The colors red and green refer to decreases/increases in the subsequent correlation similarity matrix respectively. The absolute differential effects are almost uniform across CD40L (with a slightly smaller marginal difference from the ERK related genes h3f3b, ets-v6,c-fos), in contrast to interleukin 4 which shows darker shades, with the color black showing no differences, except for a few p38 (chop, jun) and JNK-SAPK (gadd45q) related genes.

lesser effect in ERK pathway than AIG

cytokine stress-related genes


B cells do not show any response to NGF but respond to LPS. Note: LPS has more response genes in p38 & JNK-SAPK than ERK.

No marginal changes in the pairwise gene correlations in the MAPK pathways from the addition or subtraction of this ligand NGF.


Positive pairwise correlation was more positive by the

additional ligand

Negative pairwise correlation was less negative by the

additional ligand

Positive pairwise correlation was more negative by the

additional ligand

Negative pairwise correlation was less positive by the

additional ligand

Marginal Correlations Connection Maps for MAPK Pathways

40L

Legendtarget genes only

transcription factors

This shows the marginal changes [eg edge threshold =0.1] in the significant pairwise correlation [95% confidence interval for the Fisher transformed distribution] between genes after the addition of the four timepoints of a particular ligand [40L] to the low, intermediate-response ligands (n =112, 28 ligands).



additional ligand


additional ligand


additional ligand


additional ligand


Legend

AIG

target genes only


This shows the marginal changes [eg edge threshold =0.1] in the significant pairwise correlation [95% confidence interval for the Fisher transformed distribution] between genes after the addition of the four timepoints of a particular ligand [AIG] to the low, intermediate-response ligands (n =112, 28 ligands).



additional ligand


additional ligand


additional ligand


additional ligand


Legend

LPS

target genes only


This shows the marginal changes [eg edge threshold =0.1] in the significant pairwise correlation [95% confidence interval for the Fisher transformed distribution] between genes after the addition of the four timepoints of a particular ligand [LPS] to the low, intermediate-response ligands (n =112, 28 ligands).


Cell Cycle

Kohn Map


Subcluster of“mitogenic” ligand(late time periods)

Myc box genes (cell cycle)


MYC Connection Map

Ap2a1Ap2a2Arhgef6

Cdc16

Cdc42ep3

Cdc42ep4.

Cdc42ep5

Cdgap

Cdh11

Cks1

Clk

Clk2Clk3 Clk4 Mad4

Max

Mga

Mina.

Mycbp

Rb1

Rbbp9.

Rbl1

Spec1

Srprb..

Tcfap2aTcfap2b

abc

d

e

f

g

h

i

j

kl

m n op

q

r

s

t

u

v

w

xy

z

Genetic regulatory module generated by partial correlations critical value = 10-6


Syk A000040Lyn

A001441

CD22 A000542

Immunoglobulin B-cell receptor

PIP3 A003330

CaM A000452

NFATA000024

cytosol

nucleus

some known Immediate-early

genes /transcription

factors

c-Jun A001300Max

A001480

NFB A002936-7

Bclxl A000373

Oct-2 A002281

Bfl-1

some known transcription regulators

SMAD3 A002175

p53 A001721

c-Fos A000404

PPARA001888 ETS-1 A000889

ER-a A000884

Bcl-6 A000369

Egr-1A003269

ATF-2A000347

CHOP A000655MEF2c

A001503

c-Myc A001568

CREB A003216

Stat1 A002230

SRF A002147

NFATc1A000024

Elk-1 A000830

phosphorylated

Ras A000004

Grb2 A001088Sos

A002030-1

PLC-2 A001809-10

Blnk A000381

DAG A000764-7

PKC A001919-

Endoplasmicreticulum IP3

A000077

Ca++

Rap A002022

Btk A000038

Shc A002150-3

ERK 1-2 A000874-5

p38 A001717

JNK1 A001296

Vav A002360-2

Rac1 A002001

calcineurin A000420-4

Rho

FcRIIB

Shp A2156-7

CD45 A000567

Akt A000249

PTEN A001941

GSK-3 A001105-6

PKC - A001920

IKK A001170

TRAF3 A002309

TRAF2 A002308

TRAF5 A002311

dimer

CD40 Toll-like 4receptor

MyD88 A003535

dimerJak1

A001290

Interleukin 4R

dimer

STAT6 A002236

Plasma membrane

Shp A2156-7

MEK1/2 A001512-3

Raf1 A002008

Growth proliferation signals

Cyclin D1 /cdk 4,6 A000721 A000608,10

Rb A002035

p130 A002038

Rb - P p130 - P

E2F1 A002962

E2F2 A002963

E2F3a A002964

Cyclin EA000724 / Cdk2 A000602

S Phase genes

CKIs

E2F4,5-p130 A002965-6 E2F3b-Rb A002964

p19ARF

A001711

Mdm2 A001498

p53

Cytochrome Crelease

DNA damage

ATMA000349 /ATR

Chk2

Apaf1 A000302

p73 A002935 Apoptosis

Caspase 9

Survival signals

Bad

A000358

MDM2 A001498

Bclxl A000373

GDP

s

GTP

iGTP

G-protein-coupled receptor

PYK2

cAMP

qGTP

PKA A0

0

GTP

PI3K A0

GDP

PLC- A0

+others

Dennis Mock-UCSD

Connection matrix cytosol only

Signaling pathways of primary B cell (mouse)

networks and context: identifying differences between macrophage and macrophage derived cell types

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