Identification of signaling components required for the prediction of cytokine release in RAW 264.7 macrophages
© 2006 Pradervand et al.; licensee BioMed Central Ltd. 2006
Received: 26 August 2005
Accepted: 18 January 2006
Published: 20 February 2006
Release of immuno-regulatory cytokines and chemokines during inflammatory response is mediated by a complex signaling network. Multiple stimuli produce different signals that generate different cytokine responses. Current knowledge does not provide a complete picture of these signaling pathways. However, using specific markers of signaling pathways, such as signaling proteins, it is possible to develop a 'coarse-grained network' map that can help understand common regulatory modules for various cytokine responses and help differentiate between the causes of their release.
Using a systematic profiling of signaling responses and cytokine release in RAW 264.7 macrophages made available by the Alliance for Cellular Signaling, an analysis strategy is presented that integrates principal component regression and exhaustive search-based model reduction to identify required signaling factors necessary and sufficient to predict the release of seven cytokines (G-CSF, IL-1α, IL-6, IL-10, MIP-1α, RANTES, and TNFα) in response to selected ligands. This study provides a model-based quantitative estimate of cytokine release and identifies ten signaling components involved in cytokine production. The models identified capture many of the known signaling pathways involved in cytokine release and predict potentially important novel signaling components, like p38 MAPK for G-CSF release, IFNγ- and IL-4-specific pathways for IL-1a release, and an M-CSF-specific pathway for TNFα release.
Using an integrative approach, we have identified the pathways responsible for the differential regulation of cytokine release in RAW 264.7 macrophages. Our results demonstrate the power of using heterogeneous cellular data to qualitatively and quantitatively map intermediate cellular phenotypes.
A main component of the inflammatory response is the production and release of immuno-regulatory cytokines and chemokines by macrophages. Pro-inflammatory cytokines, such as tumor necrosis factor (TNF)α, interleukin (IL)-1, IL-6, IL-12, granulocyte macrophage colony stimulating factor (GM-CSF) and interferon (IFN)γ, induce both acute and chronic inflammatory responses; the chemokines MIP(macrophage inflammatory protein)-1α and RANTES (Regulated on Activation, Normal T Expressed and Secreted) are involved in the chemotaxis of leucocytes; and anti-inflammatory cytokines, such as IL-4, IL-10 and transforming growth factor (TGF)β, limit the magnitude and the extent of inflammation [1, 2]. Activated macrophages synthesize and secrete cytokines . This process is mainly regulated transcriptionally, although post-transcriptional and translational mechanisms may also play a role [4, 5]. Several pathways transmit the signals that trigger cytokine production. Among them, the nuclear factor kappa B (NF-κB) pathway plays an essential role in activating genes encoding cytokines . Other signaling pathways, such as mitogen-activated protein kinases (MAPK), signal transducer and activator of transcription (STAT), cAMP-protein kinase A (PKA), interferon regulatory factor (IRF) or CAAT/enhancer-binding proteins (C/EBP), have also been described to be invoked in macrophages [1, 7]. These pathways are not distinct entities, but are part of a general network whose different signals are produced by multiple stimuli that generate different cytokine responses.
Systems Biology approaches to cellular networks are based on integration of diverse read-outs from cells. The contextual dependence of the pathways on the cell state and its response to specific inputs renders our ability to understand every network in entire detail a near impossibility. However, quantitative mapping of the input to response of a given phenotype often can be achieved in a more coarse-grained manner with appropriate analyses of the read-outs. This is our leitmotif in this work. Such an approach allows the elucidation of the common and different signaling modules required for the release of different cytokines, and the quantitative prediction of amounts of cytokines released.
Signaling pathways and cytokine release after ligand stimulation
Correlations between signaling pathway activation and cytokine release
Identification of cytokine regulatory signals among measured signaling pathways
Analysis of the residuals to identify significant ligands
Minimal models of cytokine release
Predictors identified in the PCR minimal model
Cytokine gene regulation
Signaling pathways/transcription factors
NF-κB, C/EBPβ, Oct, post-transcriptional regulation
NF-κB, AP-1, Sp1
NF-κB, AP-1, Sp1, IRF-1, C/EBPβ
C/EBPβ, C/EBPδ, Sp1, cAMP/PKA
NF-κB, Ets, C/EBPβ, cAMP/PKA, posttranscriptional regulation
NF-κB, AP-1, IRF-1, IRF-3
Egr-1, Ets/Elk, NF-κB, c-jun/ATF-2, cAMP/PKA, post-transcriptional regulation (p38 dependent)
G-CSF specifically regulates the production of neutrophilic G granulocytes and enhances the functional activities of mature neutrophils . The expression of the gene encoding G-CSF is regulated by a combination of transcriptional and post-transcriptional mechanisms . Three conserved upstream regions have been identified in the G-CSF promoter, including binding sites for OCT (octamer), NF-κB and C/EBPβ. The last two have been shown to be required for the induction of the gene [13, 15]. Our model identified NF-κB, JNK and p38 pathways (Figure 8). C/EBPβ activation was not measured in our experimental data. However, its role may be inferred by the presence of JNK. Indeed, JNK was proposed to contribute to the transcriptional activation of C/EBPβ in macrophages . The presence of p38 in our minimal model may be related to post-transcriptional regulation. It has been shown that G-CSF mRNA contains AU-rich destabilizing elements (AREs) in the 3'-untranslated region  and recent evidence suggests a role for the p38 pathway in regulation of ARE mRNA stability .
IL-1α is a pro-inflammatory mediator distinct from IL-1β that is produced by monocytes after various stimulation . In contrast to IL-1β, few studies have investigated the mechanisms that mediate expression of the gene encoding IL-1α . Among transcription factors, AP-1 (a JNK target), NF-κB and Sp1 were shown to regulate expression of this gene [21–23]. In our model, these known activators are reflected through JNK and NF-κB (Figure 8). We also identified IFNγ and IL-4 as potential novel activators through independent pathways.
IL-6 is a pleiotropic cytokine whose expression is mediated by a wide range of signaling pathways that may vary depending on the cell type . In monocytes, a NF-κB site is crucial for LPS-induced expression of the gene encoding IL-6 . In these cells, it has also been shown that a synergistic induction by IFNγ and TNFα involves cooperation between IRF-1 and NF-κB p65 homodimers . IRF-1 is also a down-stream target of IFNβ  and has been designated as an immediate-early LPS-inducible gene . In order to activate IRF-1, LPS acts through a MyD88-independent pathway not shared by other TLR ligands . Therefore, in our model, IRF-1 may be represented both as the LPS- and as the IFNβ-specific pathway. The other important non-constitutive transcription factors involved in IL-6 gene activation include AP-1, C/EBPβ, which work synergistically with NF-κB and may be captured by the JNK component of our minimal model . IL-4 and cAMP are the remaining two components of our model (Figure 8). Using ANOVA analysis, we did not see any significant induction of IL-6 production by IL-4; neither did we see any interactive effect of IL-4 with other ligands. IL-4 is known for its inhibitory effects on pro-inflammatory cytokines, although it has been shown to stimulate IL-6 in osteoblast-like cells . Therefore, we may not give a high confidence to an effect of an IL-4 specific pathway on IL-6 cytokine release. A similar problem is observed with cAMP, which was identified as a negative predictor. Several reports have indicated activation of the IL-6 gene by cAMP in monocytes , although other reports have shown no response . In our PCR analysis, a lack of response may be translated to an anti-correlated predictor. Since the ligands that lead to elevated levels of cAMP did not decrease IL-6 production, the negative sign of cAMP may not reflect an inhibitory action.
IL-10 is a pleiotropic cytokine that has dominant suppressive effects on the production of pro-inflammatory cytokines by monocytes . Promoter analysis in RAW 264.7 macrophages stimulated by LPS showed a central role for a Sp1 binding site in the activation of the gene encoding IL-10 . On the other hand, this study and others suggest no contribution for NF-κB . The activation of the IL-10 gene by Sp1 was later suggested to be p38 dependant . In addition to Sp1, C/EBPβ and δ factors are also involved in LPS-induced gene expression of IL-10 . Thus, contrary to the other cytokines, TLR ligand pathways that activate IL-10 are p38-Sp1 and C/EBP dependent. Our model only partially reflects these facts through the presence of JNK (Figure 8). Another missing predictor is cAMP, since it is known to elevate IL-10 production . Two ligands (IL-4 and IL-6) were found to have specific pathways that activate IL-10 release. The effects of IL-4 on IL-10 production in macrophages have been contradictory . Indeed, IL-4 suppresses LPS-induced IL-10 production by peripheral blood mononuclear cells, but increases LPS-induced IL-10 production by monocyte-derived macrophages. Stimulation of IL-10 by IL-6 has been reported . It may involve C/EBPβ since several C/EBPβ binding sites are found in the IL-10 promoter  and C/EBPβ is a well known down-stream target of IL-6 signaling .
MIP-1α belongs to the group of CC chemokines that modulate several aspects of the inflammatory response, including trafficking, adhesion and activation of leukocytes, as well as the fever response . Our minimal model identified four regulatory modules for MIP-1α: JNK, p38-PAF, cAMP and STAT1 (Figure 8). In macrophages, MIP-1α mRNA is rapidly induced by TLR ligands and IFNγ (whose effect could be represented by STAT1 in our model), and this effect can be down-regulated by dibutyryl cAMP [43, 44]. DNA-binding studies revealed a role for C/EBPβ, NF-κB and c-Ets transcription factors . As discussed earlier, C/EBPβ may be inferred by the presence of JNK in our model. NF-κB may have been omitted due to the high variability of the MIP-1α data leading to a less precise model. Since NF-κB seems to be a false negative predictor and is retained with JNK for all other minimal models, the JNK-NF-κB module is shown activating MIP-1α in Figure 8. MIP-1α mRNA also contains ARE motifs known to be implicated in mRNA stability and translational control . This process is under the control of p38  and, therefore, may be reflected in the p38-PAF component of our model.
RANTES/CCL5 is a CC chemokine that is predominantly chemotactic for monocytes/macrophages and lymphocytes . Three main pathways have been demonstrated to be important for its gene induction in macrophages: JNK, NF-κB and interferon regulatory factors (IRFs) . Transcriptional activation of the RANTES promoter is dependent on specific AP-1 and NF-κB response elements, which are regulated by JNK and NF-κB kinase cascades, respectively . It is well established that IFNγ and TNFα cooperatively induce RANTES gene expression, although no STAT binding elements have been identified in the promoter [48, 49]. The synergy between IFNγ and TNFα may involve IRFs since it was demonstrated to require STAT1 activation and to be dependent on protein synthesis . Indeed, IRF-1 was shown to bind the RANTES promoter . As seen previously, LPS, but not the other TLR ligand, activates IRFs via a MyD88-independent pathway . Therefore, the STAT1 and LPS-dependent pathway identified in our minimal model can be explained by the role of IRF-1/IRF-3 (Figure 8).
TNFα is essential for normal host defense in mediating inflammatory and immune responses . Signal transduction mechanisms that regulate TNFα production have been of considerable interest. In macrophages, TNFα production has been shown to undergo transcriptional and post-transcriptional controls . NF-κB is the best described transcriptional activator, with three binding sites on the TNFα promoter . Its inhibition by overexpression of its natural inhibitor IκB alpha reduced LPS-induced TNFα production by 80% . The other transcription factors recruited to the TNFα promoter involve Sp1, the ERK targets Egr-1, Ets and Elk-1 , as well as the JNK targets c-Jun and ATF-2 . Transcription of TNFα is augmented by IFNγ  and inhibited by the cAMP/PKA pathway . Post-transcriptional regulation of TNFα production also involves ARE elements under the control of p38 [45, 60, 61]. Therefore, except for the ERK pathway, our minimal model identified the known signaling mechanism responsible for the regulation of TNFα (Figure 8). Moreover, it also identified an independent M-CSF specific pathway. M-CSF treatment was shown to trigger TNFα production by monocytes . However, to our knowledge, the underlying mechanism is not known. This study suggests that it follows a pathway independent of NF-κB, JNK or p38.
Evaluation of our models using literature data shows good agreement, although a precise assessment should be done in vitro in RAW264.7 macrophages since regulation of cytokine production is cell-type and sometimes cell-state dependent. Our minimal model covers all known mechanisms of activation of G-CSF and highlights a potential role for p38 in its post-transcriptional regulation. For IL-1α release, besides all known activators, IFNγ and IL-4 are identified as potential novel independent activators. For IL-6 release, four predictors were corroborated by literature data whereas cAMP and IL-4 may be false positives, although the role of IL-4 is controversial. IL-10 response yielded the least convincing model, with a misidentification of NF-κB and a non-identification of p38 and cAMP as positive predictors. Another obvious missing predictor was NF-κB for MIP-1α release. However, in this model, all other important signaling pathways were represented. For RANTES release, all known mechanisms of activation were found. Finally, all known signaling pathways with the exception of ERK were found for TNFα release. This last minimal model also identified a potentially new M-CSF specific pathway for the activation of TNFα. Overall, the performance of our strategy is excellent, with a 1.2% false positive rate and a 13% false negative rate.
We designed an input-output modeling approach that integrates PCR and exhaustive-search-based model reduction. We have demonstrated that this approach is applicable to heterogeneous types of data through combining western blot phosphorylation and cAMP measurements, and is extendable to other types of data, such as those measured by mass spectrometry. Regarding the issue of scalability to much larger data sets, we note that the PCR part solves a set of linear equations and hence scales well for large systems with thousands of predictors. The minimization part warrants combinatorial optimization, is computationally intensive and hence can go up to exponential complexity in the number of predictors. Nevertheless, it is tractable for up to a few hundred predictors, which is adequate for most cellular intermediate phenotype measurements.
Cytokines mediate pathogenesis of many diseases (for example, chronic inflammatory diseases, autoimmune diseases, cancer). With increasing quantitative knowledge about the important pathways in the production of cytokines, model building as presented in this study will help identify novel targets in order to maximize the efficacy of a drug such that it affects one or few cytokines while minimizing the effect on the homeostasis of other cytokines. The results of the present study demonstrate the power of using heterogeneous cellular data to qualitatively and quantitatively map intermediate cellular phenotypes. These predictive models of the physiological process of cytokine release are important for a quantitative understanding of macrophage activation during the inflammation process.
Materials and methods
Single- and double-ligand screen experimental data were obtained from the AfCS Data Center . To generate these data, RAW 264.7 macrophages were stimulated with a variety of receptor-specific ligands applied alone or in combinations of two. Time-dependent changes in signaling-protein phosphorylations, intracellular cAMP concentrations and extracellular cytokines released were measured. Assays included immunoblots to detect phosphorylation of signaling proteins at 1, 3, 10 and 30 minutes after stimulation (AfCS protocols #PP00000177 and #PP00000181 ), competitive enzyme-linked immunosorbant assays to measure cAMP concentrations at 20, 40, 90, 300 and 1,200 seconds after stimulation (AfCS protocol #PP00000175 ), and a multiplex suspension array system (Bio-Plex, Bio-Rad, #171-F11181) to measure concentrations of cytokines in the extracellular medium at 2 hours, 3 hours and 4 hours after stimulation (AfCS protocols #PP00000209 and #PP00000223 ).
To quantitatively estimate the contributions of various experimental and biological factors to signaling-protein phosphorylations and cytokine release, statistical models of single-ligand screens are defined as:
cijk = μ + Ti + Lj + Ek + TLij + TEik + LEjk + eijk
where cijk is the measured response at time Ti for ligand condition Lj in experiment Ek. L is defined as a particular ligand being present or absent (the corresponding control). Interaction term TLK is included in the random error (e). ANOVA were performed on log transformed data (base e). Significant terms were identified after correction for multiple testing (Dunn-Sidak method). In the case of protein phosphorylation data, the 30 minutes time point was discarded and the remaining time points (1, 3 and 10 minutes) were each randomly paired to one of the three measurements of basal phosphorylation. Studentized residuals were assessed on residual and quantile-quantile (Q-Q) plots.
The input matrix was constructed from cAMP and signaling-protein phosphorylation data and the output matrix was constructed from cytokine release data. For signaling-protein phosphorylation, a fold change over basal was calculated (AfCS protocol #PP00000181 ). For cAMP, the corresponding control concentration was subtracted and one was added. In both cases, the natural logarithm was taken and data were averaged across time points after removing time-series with missing values. Means and standard deviations were obtained from replicate experiments. Most of the measurements had three or more replicates. A few measurements did not have any replicates, but were still incorporated. Extracellular cytokine concentrations were log-transformed after subtraction of the corresponding controls concentration and addition of one. Signal-to-noise ratios were also calculated as the difference between treated and control measurements divided by the standard deviation of the control measurements. Cytokines with an average signal-to-noise ratio lower than five were discarded. The remaining seven cytokines (G-CSF, IL-1a, IL-6, IL-10, MIP-1α, RANTES and TNFα) were retained for further analysis. Time-series with missing values were discarded and outliers, defined as repeats with z-scores outside a 95% confidence interval, were removed. Data were averaged across time points. Means, variances and standard deviations were obtained from replicate experiments. For each cytokine, variance distributions were assessed and stimulation conditions with large variances (outside a 95% confidence interval) were discarded. A matrix of m stimulation conditions × n1 predictors (independent block) was constructed from the mean values (across time-points and repeats) for cAMP and protein phosphorylation measurements. A matrix of m stimulation conditions × n2 responses (dependent block) was constructed from the mean values for cytokines release.
Identification of significant predictors
Significant predictors (that is, phosphoproteins and ligands) were identified through a PCR  and significance-test based procedure. The significant-test was carried out by comparing the predictor coefficients in the PCR model with the standard deviation in the coefficients corresponding to a PCR model with random outputs. The predictors with a ratio higher than a threshold, r th = 1.96 corresponding to 95% confidence, were considered significant. In principal, the methodology is similar to the bootstrap method in which randomly shuffled outputs are used to develop random models , but in our novel procedure these random models are never actually identified. Instead, an indirect procedure is used in which the desired standard deviation is calculated implicitly by utilizing the latent variables of the input data and the standard deviation of the population of output data. The procedure is given below.
Step 1: Principal component decomposition of the input data
Let X be the normalized input data (zero-mean, unit-standard deviation), of size m × n1 and Y be the normalized output data (zero-mean), of size m × n2. Compute the eigen values (λ i , i = 1,..., n1) and eigen vectors (loadings, v i ) of the covariance matrix of X, S. Calculate the scores (latent variables, T i ): T i = X *v i .
Step 2: PCR model
For k latent variables, let V = [v1 v2 ... v k ], Λ k = diag([λ1 λ2 ... λ k ]), and T = [T1 T2 ... T k ] = X*V. Then, the PCR model for jth output (Y j ) is:
Y j = X*B j,k
with the coefficients
B j,k = V*(Λ k *(m - 1))-1*T T *Y j
Step 3: Ratio of the coefficients B j,k to the standard deviation of coefficients for random models (σ j,k )
In a boot-strap approach, many random shufflings of the output are considered. For each, a model is built. Then the standard deviation (σ j,k ) of the coefficients in these models is calculated. Here we use a novel implicit (indirect) approach to estimate σ j,k . Consider a random model with coefficients corresponding to the output values , the lth random shuffling of the jth output Y j . Then:
σ j,k = std( ) = diag(V * (Λ k * (m - 1))-1 * V T )* std( )
where std refers to standard deviation and diag (A), A being a square matrix, is a column vector containing the diagonal elements of A. Since (∀ l) belong to the same population as Y j , std( ) ≈ std(Y j ) (observed computationally too), and hence:
σ j,k = std( ) ≈ diag(V * (Λ k * (m - 1))-1 * V T )* std(Y j )·r j,k = B j,k /σ j,k .
Step 5: Identification of significant predictors
Repeat Steps 2 and 3 for k = kmin ,..., kmax, where kmin and kmax are the number of latent variables needed to capture 80% and 95%, respectively, variance in X. Compute the average of rj,k, , and the threshold r th = the confidence interval of normal distribution for a specified significance (r th = 1.96 for 95% confidence, t test with infinite degree of freedom). The ith predictor is significant if > r th ( is the ith element of ).
Step 6: Development of a model based upon PCR
Choose the number of latent variables (k) corresponding to the minimum fit-error to develop a model.
One model is developed for each cytokine (output). First, all the measured phosphoproteins and cAMP are used to develop a phosphoproteins model (PP-model) to explain extracellular cytokine levels from signaling pathway activation. Then, the residuals are calculated and used to identify if the inclusion of one or more ligands in the model can significantly improve the fit of the data. If so, it is inferred that the PP-model alone does not capture all the important pathways and that the inclusion of ligands captures pathways from the ligands to the output through unmeasured signaling-proteins (Figure 1). Here ligands serve as predictors and residuals serve as outputs. In the residuals-model, r th = * 1.96 = 2.7719 is used since residuals themselves have a strong random component. The factor corresponds to the standard deviation of difference of two random variables (that is, mean of random coefficients – random coefficients) drawn from standard normal distribution.
Development of minimal models
To reduce the number of false-positives, a model with a minimal number of predictors (minimal model) is developed that has a statistically similar fit-error as the detailed model with all the predictors. A two-level procedure is used. At level one, using the significant phosphoproteins identified based upon the detailed model, one or more minimal PP-models are developed by a combined sequential and combinatorial (exhaustive search) model-reduction procedure. Once a minimal PP-model is generated, the residuals are generated for this minimal PP-model. At level two, the residuals are used to identify important ligands by developing a minimal residuals model using the same approach. The overall minimal model is the combination of the minimal PP-model and the minimal residuals model. The procedure for the identification of the minimal model containing the necessary and sufficient set of predictors is summarized below. This procedure is used at both level one and level two for each cytokine.
Starting with a model that includes all the significant predictors, to test if the model is good, the following criteria are used:
1. Statistically same fit-error for the minimal models and the detailed model (F-test): let e d and e r be the root-mean-squared-errors (RMSE) for the detailed and the candidate minimal model. This criterion is satisfied (that is, null hypothesis H0 is accepted) if / <finv(p, d1, d2) where p = 1 - α, α is the significance-level (0.05), and d1 and d2 are the degrees of freedom for and , respectively. For the residuals model, instead of e d , the fit-error for the significant-predictors model (es) is used to avoid over-fitting.
2. The fit-error for minimal models should be statistically lesser (F-test used) than the fit-error for a zero-predictor model (mean-model), that is, the alternative hypothesis (H1) is accepted. Else, the mean-model is the minimal model. The logic behind this criterion is that if a model with one or more predictors does not improve the fit over a trivial model, then those predictors should not be included in the minimal model. For this test, p = 0.95 is used for the PP-model and p = 0.68 (that is, somewhat lesser improvements also are accepted) is used for the residuals model.
If the model satisfies the two criteria listed above, eliminate the least significant predictor from the current list of predictors (based upon the original ranking from the detailed model). Develop a model using the remaining predictors and test if the model satisfies the two criteria. Repeat until no further reduction is possible. If this minimal model has more than one predictor then test all possible combinations of one or more predictors (from the original list of all significant predictors). During this phase, it is also required that the signs of the coefficients of the predictors in the minimal model be the same as the sign of the coefficients of the corresponding predictors in the detailed model. The smallest good model(s) are the minimal model(s). If multiple minimal models are generated, then the model with least fit-error is considered.
To validate the minimal models, test data are used. If validation fails, the test data are also included in the training set and the model-reduction procedure is repeated. Additional details are provided in Additional data file 1.
Matlab code and the data can be obtained upon request.
Additional data files
The following additional data are available with the online version of this paper. Additional data file 1 contains a detailed description of the procedure for the validation of the model.
We would like to acknowledge the Cell Preparation and Analysis Laboratory of the Alliance for Cellular Signaling (University of Texas Southwestern Medical Center) and the Antibody Laboratory of the Alliance for Cellular Signaling (University of Texas Southwestern Medical Center) for experimental data. We would like to acknowledge Robert C Hsueh (Cell Preparation and Analysis Laboratory, Alliance for Cellular Signaling) and Ronald Taussig (Cell Preparation and Analysis Laboratory, Alliance for Cellular Signaling, and Department of Pharmacology, University of Texas Southwestern Medical Center) for a preliminary review of this manuscript and insightful discussions. This research was supported by National Institute of Health Collaborative Grant U54 GM62114 (Alliance for Cellular Signaling), National Institute of Health/Purdue University grant 1 R01-GM068959 (SS) and a grant from the Hilblom foundation (SS).
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