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Identification of Important Signaling Proteins and Stimulants for the Production of Cytokines in RAW 264.7 Macrophages Sylvain Pradervand 1, Mano Ram Maurya.

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Presentation on theme: "Identification of Important Signaling Proteins and Stimulants for the Production of Cytokines in RAW 264.7 Macrophages Sylvain Pradervand 1, Mano Ram Maurya."— Presentation transcript:

1 Identification of Important Signaling Proteins and Stimulants for the Production of Cytokines in RAW Macrophages Sylvain Pradervand 1, Mano Ram Maurya 2, Shankar Subramaniam 1,2 1 San Diego Supercomputer Center 2 Department of Bioengineering University of California, San Diego AIChE Annual Meeting, Wednesday, November 02, 2005

2 AIChE Annual Meeting 2005 Outline  Production and release of cytokines in macrophage  Identification of significant correlations between signaling proteins and cytokines  Quantitative input/output modeling using Principal Component Regression (PCR)  Results  Summary and conclusions

3 AIChE Annual Meeting 2005 Cytokine Production and Release in Macrophages  Cytokines  Proteins for communication between immune cells  Important players of the immune system  Apoptosis of infected cells  Initiation of inflammation  Control of inflammation  Secreted by immune cells  Complex signaling activities followed by gene-expression

4 AIChE Annual Meeting 2005 Cytokine Production and Release in Macrophages Multiple Stimuli Cytokines Complex signaling network of signaling proteins (phosphoproteins) Macrophage Paracrine cytokinesEndocrine cytokines Autocrine cytokines (ligands)

5 Acknowledgement: Each of the signaling proteins or 2 nd messengers are a marker of a pathway: overall very complex

6 AIChE Annual Meeting 2005 Clustering and Correlation Analysis  Hierarchical clustering (using R)  Reveals which inputs (ligands) have similar effect on the signaling proteins and on cytokine release  Correlation analysis (using R)  Neuman-Pearson correlation method

7 AIChE Annual Meeting 2005 Signaling Pathway Activations and Cytokine Release Singaling proteins/2 nd messengers Ligands cytokines Increase Decrease Toll-like receptor: pattern recognition receptors (PRRs), binding to pathogen-associated molecular patterns, for immediate action without antibody Toll like receptor (TLR) ligands

8 AIChE Annual Meeting 2005 Correlation between Signaling Activity and Cytokines  With TLRL, similar pattern of correlations for the many cytokines  TLRL are dominant, effect of others less visible  Without TLRL data, only few positives correlations, most involved TNF  Without TLRL data, STATs show stronger correlations Significant correlations are displayed With Toll-like receptor (TLR) ligandsWithout TLR ligands Cytokine Signaling proteins Positive correlation Negative correlation

9 AIChE Annual Meeting 2005 Further (Quantitative) Analysis  Hierarchical clustering and correlation analysis are only qualitative  Detailed signaling map is not available  Develop simplified linear input/output models  Elucidate common and different signaling modules  Predict cytokine release

10 AIChE Annual Meeting 2005 Further (Quantitative) Analysis L Y = Y 1 + Y 2 X B1B1 B2B2 Y1Y1 Y2Y2  Two-part model  Part-I: Capture most of the output as: Y 1 = X*B 1 (PP-model)  Part-II: Residual, Y – Y 1, as Y 2 = L*B 2  X’s highly correlated  User principal component regression (PCR)  PLS not used since the number of data points > > the number of outputs

11 AIChE Annual Meeting 2005 PCR-Based Approach  Estimate B s/t Y = X*B, using known X and Y 1.Calculate T = X*V 2.Calculate Q with least-square 3.Predicte Y: Y p = T*Q 4.Repeat the procedure for the residual, Y – Y p, with L as input X: input or predictor V = matrix of eigen vectors of cov(X) T = matrix of latent variables k latent variables

12 AIChE Annual Meeting 2005 Statistical Significance of the Coefficients  Most coefficients non-zero  Identify the significant coefficients  Estimate the coefficients for many random models  Randomly shuffle Y (the data points), Y s, calculate coefficients  Calculate standard deviation of the random coefficients (  j )  Calculate the ratio: r j = b j /  j  Significance test 95% confidence level: r th = 1.96  Null hypothesis true if r j < r th  Use higher threshold for the residuals: r th =  2*1.96 = 2.77  standard deviation of the difference of two samples from N(0,1)

13 AIChE Annual Meeting 2005 Cytokines Regulatory Signals  JNK, p38, NF-kB strongest coefficients  ERK1/2 and RSK similar profile  cAMP the only significant negative

14 AIChE Annual Meeting 2005 Cytokines Regulatory Signals Without TLRL Data  cAMP kept its negative strength  STATs became more significant  Remaining positive coefficients: p38 (G-CSF and TNF), RSK (TNF)

15 AIChE Annual Meeting 2005 Cytokines Regulatory Signals in Residuals  Only few ligands statistically significant

16 AIChE Annual Meeting 2005 Cytokines Regulatory Signals in Residuals Without TLRL Data  IL-4 is strong for IL-1a, IL-6 and IL-10  2MA is strong for G-CSF and TNFa  G-CSF and TNFa have a similar pattern of coefficients

17 AIChE Annual Meeting 2005 Minimal PCR Model  Many predictors flagged as significant due to correlation with other important predictors  Identifies most known pathways but high false positive rate  Identify necessary and sufficient set of signaling pathways that would predict cytokine release  Generate minimal models  Find the least number of predictors with statistically same fit as the full model  Must be better than a zero predictor (average) model  Use F-test for each of these

18 AIChE Annual Meeting 2005 Procedure for PCR Minimal Models Full (detailed) model with all significant predictors (e d ) Zero-predictor model (e 0 ) F-test: As good as the full-model: Better than the trivial model: Decreasing number of predictors Keep eliminating the least significant predictor: R 1 increases, R 2 decreases Initial minimal model Final minimal model If more than one predictor left, use combinatorial selection (integer programming) for exhaustive testing

19 AIChE Annual Meeting 2005 Combined Minimal Model and Validation  Integrate validation with the model development  Build a network combining the results from model +/- TLRL data Pathways: p38, cAMP, NF-kB, JNK, STAT1 Ligands: others  10 regulatory modules  JNK/NF-kB translates TLRL dependency  p38/PAF post-transcriptional controls?  STAT1 affects the chemokines  cAMP is anti-correlated (inhibitory?)

20 AIChE Annual Meeting 2005 Validation with the Literature  With minimal model  Overall 1.2% false positive rate (FPR) and 13% false negative rate (FNR)  With full model  Overall 11% FPR (10 times higher) and 3% FNR (4 times lower)  Relative gain with minimization: a factor of 2.5 CytokineFalse PositiveFalse Negative G-CSF00 IL-1a00 IL-65% (2 extra)0 IL-102.5% (1 extra)40% (missed 2) MIP-1a033% (missed 2) RANTES00 TNFa017% (missed 1)

21 AIChE Annual Meeting 2005 New Hypothesis for G-CSF from Network Reconstruction  All known regulatory pathways found  New hypothesis: p38 involved in post- transcriptional regulation of G-CSF (stimulates production of neutrophils)? p38 LPS, P2C P3C, R-848 G- CSF ISO Adrb2 NF-  B JNK TLR2/1, TLR2/6 TLR4, TLR7 2MA P2X, P2Y

22 AIChE Annual Meeting 2005 Summary Ligand screen data set Statistical analysisModeling Collection of hypothesis Design in vitro assays

23 AIChE Annual Meeting 2005 Cytokine Production and Release in Macrophages  Cytokines  Messengers proteins in communication between immune cells  Important players of the immune system

24 AIChE Annual Meeting 2005 Glossary of Cytokine names  IL: interleukin  TGF: transforming growth factor  TNF: tumor necrosis factor  GM-CSF: granulocyte/macrophage colony stimulating factor, also M-CSF and G-CSF  MIP: macrophage inflammatory protein  RANTES: Regulated on Activation, Normal T Expressed and Secreted (also known as CCL5, binds to CCR5 which is a coreceptor of HIV, thus blocks HIV from entering the cell)

25 AIChE Annual Meeting 2005 Glossary of Ligand Names  GM-CSF: granulocyte/macrophage colony stimulating factor, also M-CSF and G-CSF  IL: interleukin  IFN: interferon (induce cells to resist viral replication)  C5a: cleavage product from C5 (a protein of the complement pathway/system)  R-848: Resiquimod: potent antiviral regent  LPS: lipopolysaccharide  P2C: PAM2CSK4 (synthetic diacylated lipopeptide; AfCS)  2MA: 2-Methylthio-ATP is a synthetic analog of ATP (acts through P2X (ligand-gated) and P2Y (GPCR))  LPA: Lysophosphatidic acid (derived from phospholipid)  UDP: Uridine diphosphate (a nucleotide)  S1P: Sphingosine-1-phosphate  PAF:Platelet activating factor (PAF) is a proinflammatory phospholipid  ISO:Isoproterenol  PGE:Prostaglandin E2, a lipid product of arachidonic acid metabolism, has an immunosuppressive effect

26 AIChE Annual Meeting 2005 Glossary of Signaling Proteins  cAMP  Akt: protein kinase B  ERK & JNK: MAPKs (from wikipedia.com; To date, four distinct groups of MAPKs have been characterized in mammals: (1) extracellular signal-regulated kinases (ERKs), (2) c-Jun N-terminal kinases (JNKs), (3) p38 isoforms, and (4) ERK5)  RSK:ribosomal S6 kinase  GSK: Glycogen synthase kinase-3 (overexpressed in Alzheimer’s disease)  nF-KB  p40Phox (Neutrophil cytosolic factor 4; an oxidoreductase)  SMAD SMAD-1 is the human homologue of Drosophila Mad (Mad =Mothers against decapentaplegic)  STAT: Signal Transducers and Activator of Transcription  Rps6: ribosomal protein S6

27 AIChE Annual Meeting 2005 Measurement of Signaling Proteins and Cytokines  2 nd messengers  Enzyme-linked immunoassay to measure cAMP concentrations  Fluorescent dye to measure intracellular free calcium  Signaling proteins  Immunoblots to detect signaling proteins phosphorylations  Responses  Agilent inkjet-deposited presynthesized oligo arrays to assess gene expression  Multiplex suspension array system to measure concentrations of cytokines in the extracellular medium  Data is log-transformed after subtraction of basal response observed in control data  Stimulation by a single or double ligands at a fixed strength

28 AIChE Annual Meeting 2005 Procedure for Normalization of Data  Data processing  Signaling proteins  Log2(Fold-change (response/basal-response))  Except for cAMP for which basal was subtracted, then log2  Cytokines  Log2(response – basal + 1), basal is close to 0  Signal-to-noise ratio calculated Cytokine not analyzed if SNR < 5

29 AIChE Annual Meeting 2005 Main Regulation at the mRNA Level  Most of the regulatory mechanisms at the gene- transcription level  Except for IL-1a, good overall correlation with coefficients > 0.9: 0.92 (MIP-1a) to 0.99 (IL-10)

30 AIChE Annual Meeting 2005 Statistical Analysis of Ligands Interactions  Is there more than additive effect of ligands on the cytokine release (output)?  Use of linear model (a similar model with lesser terms used to identify significant ligands in single-ligand data)  Null hypothesis: no synergism (more than additive effect) of the ligands on the cytokine release, i.e., L1L2 hi = 0  Used ANOVA (Analysis of Variance) Time-effect Random-error Constant term Effect of ligand 1 Effect of ligand 2 Nonlinear-term These terms are either 0 or non-zero but fixed Since the data corresponds to fixed strength of the stimulus Gel-effect

31 AIChE Annual Meeting 2005 An Example of Hypothesis from Interaction Analysis  IL-4 enhances STAT1  /  activation by IFN  Pathway A IFNgIL-4 Pathway B + STAT1a/b

32 AIChE Annual Meeting 2005  G  s ligands enhance G-CSF, IL-1 , IL-6, IL-10 releases by TLRL Pathway A G-CSF, IL-1a IL-6, IL-10 TLRLISO/PGE Pathway B + Examples of Hypothesis from Interaction Analysis

33 AIChE Annual Meeting 2005  Synergism between IL-6 and TLRL on IL-10 release is mediated via a ERK1/2-dependent pathway ERK1/2 TLRLIL-6 Pathway B + IL-10 Examples of Hypothesis from Interaction Analysis

34 AIChE Annual Meeting 2005 PCR-Based Approach  Estimate B s/t Y = X*B, using known X and Y 1.V k = [V 1 V 2 …V k ],  k = matrix of eigen-values =diag[ 1 2 … k ] 2.Calculate T = X*V; T’*T =  *(m-1);  k -1 = diag(1/ 1…. 1/ k ) 3.Calculate Q with least-square method: Q =  k -1 /(m-1) *(T’*Y) 4.Calculate B = V*Q, predicted Y, Y p = T*Q = T*  k -1 /(m-1) *(T’*Y) 5.Repeat the procedure for the residual, Y – Y p, with L as input X: input or predictor V = matrix of eigen vectors of cov(X) T = matrix of latent variables k latent variables

35 AIChE Annual Meeting 2005 Statistical Significance of the Coefficients  Most coefficients (b j ; with respect to j th input) are non-zero  Identify the significant coefficients  Estimate the coefficients for a random model  Randomly shuffle Y (the data points), Y s, calculate coefficients  Repeat many times (1000 times)  Calculate standard deviation of the random coefficients (  j )  Approximation:  Calculate the ratio: r j = b j /  j  Significance test at a confidence level of 95%: r th = 1.96  Null hypothesis (coefficient not significant) true if r i < r th  Use higher threshold for the residuals: r th =  2*1.96 = 2.77  standard deviation of the difference of two samples from N(0,1)

36 AIChE Annual Meeting 2005 Cytokines Regulatory Signals  JNK, p38, NF-kB strongest coefficients  ERK1/2 and RSK similar profile  cAMP the only significant negative Average of the ratios for models with different number of predictors to capture 80% - 95% variation in input data

37 AIChE Annual Meeting 2005 Minimal PCR Model  Many predictors flagged as significant because of their correlation with other important predictors  Identifies most of the known pathways but results in high number of false positives  Identification of necessary and sufficient set of signaling pathways that would predict cytokine release  Algorithm to generate minimal models  Essential idea:  Form the list of significant predictors, find the least number of predictors with fit statistically equal to the fit for the full model  The minimal model should be better than a zero predictor (average of the output) model  Use F-test for each of these

38 AIChE Annual Meeting 2005 Procedure for PCR Minimal Models Full (detailed) model with all significant predictors (e d ) Zero-predictor (average output) model (e 0 ) Intermediate model-1 (e 1 ) Intermediate model-2 (e 2 ) F-test: As good as the full-model: Better than the trivial model: Decreasing number of predictors If full model itself is no better than the trivial model: Accept the trivial model Keep eliminating the least significant predictor: R 1 increases, R 2 decreases Initial minimal model Final minimal model If more than one predictor left, use combinatorial selection (integer programming) for exhaustive testing

39 AIChE Annual Meeting 2005 New hypothesis for IL-1 from network reconstruction  All known regulatory pathways found  New hypothesis: IFNg regulates IL-1a through an IRFs-dependent pathway?  New hypothesis: IL-4 regulates IL-1a through a STAT6 pathway?

40 AIChE Annual Meeting 2005 New hypothesis for TNFa from network reconstruction  All known regulatory pathways except ERK1/2 found  New hypothesis: M-CSF- specific pathway regulates TNFa?

41 AIChE Annual Meeting 2005 New hypothesis for RANTES from network reconstruction  All known regulatory pathways found  New hypothesis: Synergisms between LPS specific pathway (IRF-1?) and NF-kB on RANTES regulation? Similar hypothesis for IL-6 release

42 AIChE Annual Meeting 2005 Validation with the Literature True positives identified (both) (1,1)False positive (1,0) False negative (0,1)True negative identified (both) (0,0) literature Count ER1/2 as 1, stat1a/b as 1 JNK sh/lg as 1, GSK 3a/3b as 1 18 PP and 22 ligands as total (false positive + none) = 40 Total true negative = (negative identified + not-identified-but- reported-in literature) Our model  With minimal model  Overall 1.2% false positive rate (FPR) and 13% false negative rate (FNR)  With full model  Overall 11% FPR (10 times higher) and 3% FNR (4 times lower)  Relative gain with minimization: a factor of 2.5

43 AIChE Annual Meeting 2005 Validation with the Literature CytokineFalse PositiveFalse Negative G-CSF00 IL-1a00 IL-65% (2 extra)0 IL-102.5% (1 extra)40% (missed 2) MIP-1a033% (missed 2) RANTES00 TNFa017% (missed 1) Full model missed only cAMP for IL-10, but it has more false positives FPR (type-I error) = FP/(FP + none (true negative)) FNR (type-II error) = FN/(true positives = positives_identified + FN)

44 AIChE Annual Meeting 2005 Validation with the Literature


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