Subcellular Quantitative Proteomics Reveals Multiple Pathway Cross

Subcellular Quantitative Proteomics Reveals Multiple Pathway Cross-Talk That Coordinates Specific Signaling and Transcriptional Regulation for the Early Host Response to LPS Ruyun Du,†,‡ Jing Long,†,‡ Jun Yao,† Yun Dong,† Xiaoli Yang,† Siwei Tang,† Shuai Zuo,† Yufei He,† and Xian Chen*,†,§ Department of Chemistry and Institute of Biomedical Sciences, Fudan University, Shanghai, China, and Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, North Carolina Received October 25, 2009

Toll-like receptor 4 (TLR4) specifically recognizes lipopolysaccharide (LPS) to initiate signal transduction events that modulate host inflammatory responses. Although increasing numbers of genes have been characterized individually for their involvement in TLR4 signaling, the LPS-induced TLR4-mediated signaling pathway and connected networks are incompletely delineated. Given that most components of signaling pathways are activated at an early phase of the LPS-induced response, we have employed a subcellular, SILAC-based quantitative proteomics approach to identify proteins in LPS-stimulated macrophages showing either cytosolic- or nuclear-specific changes in abundance. Subcellular fractionation not only reduces the spectral complexity for identifying maximum numbers of proteins but also enriches for low-abundance proteins within the compartment in which they function. Following 10 min LPS stimulation, the abundances of 508 proteins were found elevated in the cytosol, while the elevated levels of 678 proteins together with the decreased abundances of another 80 proteins were quantified in nuclei. Coincident with observations that many key proteins involved in signal relays in the MAPK and NF-κB cascades were found simultaneously regulated in the cytosol, various transcriptional factors (TFs) such as IRFs were found activated in the nuclei. We also extended links between these intracellular pathways and various biological processes by identifying multiple pathway modules. For the first time, our combined data sets from quantitative proteomics and bioinformatics analyses provide a direct, system-wide insight into how cross-talk between upstream signaling pathways modulates the activities of particular TFs for regulating sets of pro-inflammatory genes. Keywords: amino acid-coded mass tagging • immune response • LC-MS/MS • lipopolysaccharides • toll like receptor 4 • nuclear factor-kappa B • mitogen-activated protein kinase • interferon regulatory factors • apoptosis • transcription factors

Introduction As a first line of defense, toll-like receptors (TLRs) recognize a broad spectrum of bacterial and viral products and play a critical role in the inflammatory response to pathogen invasion and in regulating concurrent adaptive immunity.1 To date, 11 human and 13 mouse TLRs have been identified, each of which responds to specific agonists derived from bacteria or viruses.2 Lipopolysaccharide (LPS), a major pro-inflammatory component of the gram-negative bacterial cell wall, is commonly present in airborne particles, including air pollution, organic dusts, and cigarette smoke. LPS specifically triggers expression of TLR4 molecules in major immune cells, especially macroph* To whom correspondence should be addressed. Dr. Xian Chen, Department of Biochemistry & Biophysics, UNC School of Medicine, 120 Mason Farm Road, Genetic Medicine, Ste 3010, CB # 7260, Chapel Hill, NC 275997260. E-mail: [email protected]. Phone: 919-843-5310. Fax: 919-9662852. † Fudan University. ‡ These authors contributed equally to this work. § University of North Carolina at Chapel Hill. 10.1021/pr900962c

 2010 American Chemical Society

ages, where TLR4 mediates pro-inflammatory responses to infection.3 Dys-regulated cytokine production via TLR4 could lead to a variety of pathogenic consequences such as sepsis, septic shock, or systemic inflammatory response syndrome.4 LPS binds a multiprotein receptor complex involving CD14, TLR4, and MD2 that triggers formation of intracellular cascades to transduce the signal in modulating the activity of particular transcriptional factors (TFs) for cytokine production.3,5,6 The Toll/IL-1 receptor (TIR) domain of TLR4 first interacts with the adaptor protein myeloid differentiation protein 88 (MyD88) and the TIR domain-containing adaptor protein inducing IFNβ (TRIF) that, in turn, recruits specific sets of signal proteins through either MyD88-dependent or -independent pathways.7 Upon LPS stimulation, MyD88 associates with IL-1 receptorassociated kinase-4 (IRAK4) through its death domain to activate TNF receptor-associated factor 6 (TRAF6).8 A series of ubiquitinylations on TRAF-6 further activate transforming growth factor-β-activated kinase 1 (TAK1), a candidate kinase for mediating proteasomal degradation of the nuclear factorkappa B (NF-κB) inhibiting kinase (IKK) complex, leading to Journal of Proteome Research 2010, 9, 1805–1821 1805 Published on Web 02/16/2010

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activation of the transcription factor NF-κB. NF-κB regulates a broad range of target genes involved in inflammation, cell proliferation, and cell death.10 In addition to activation of the IKK complex, activated TAK1 also triggers mitogen-activated protein kinases (MAPKs), serine-threonine kinases responsive to extracellular stimuli through p38 and JNK, leading to activation of another transcription factor, AP-1.2 LPS is a potent activator for all three MAPK pathways in human monocytic cells.11 The LPS-inducible transcription factors NF-κB and AP1, through the MAPKs signal cascades, control expression of genes encoding inflammatory cytokines such as TNF-R, IL-1, IL-6, IL-10, and IL-12.2 TLR4 can also interact with TRIF to activate both TANK-binding kinase 1 (TBK1) and inhibitor of kappaB kinase epsilon (IKKε), which mediate phosphorylation of interferon regulatory factor 3 (IRF3). Phosphorylated IRF3 dimerizes to translocate into the nucleus to induce expression of type I IFN and IFN-inducible genes.12 The diverse roles of IRFs in regulating immune responses have recently been found to associate with the signal transduction mediated by either TLRs or other PRRs.13 Until now, most of the knowledge about TLR4-mediated signaling and the ensuring regulated gene expression has been obtained one by one through various molecular and cellular biology approaches. Due to a lack of molecular understanding of these signaling and regulatory pathways, despite many years of effort, a mortality rate close to 50% is still associated with approximately 500 000 patients having sepsis and other LPS-inducible inflammation-associated syndromes.14 Given the highly complex nature of these TLR4 signaling pathways and the concurrent transcriptional activity, a systemwide proteomic investigation represents the most efficient way to dissect interconnected signaling and regulatory networks. To identify novel targets, for example, signal proteins or functional links that could serve as an initial alert of infection, we chose to investigate the early response to LPS stimulation because NF-κB is activated within 10 min following LPS challenge.15 Considering that longer LPS exposure could also activate stimulation-tolerant programs that are controlled by gene-specific TLR-induced chromatin modifications,16 the host response network was analyzed comprehensively at the early stage of response to LPS when only pro-inflammatory genes have been activated. Since many cytosolic proteins involved in ‘upstream’ signal transduction modulate the activities of transcription factors in or translocated to the nucleus,17,18 we extended the use of a subcellular quantitative proteomics method19 not only to identify functionally active proteins in their corresponding subcellular locations but also to reduce signal complexity in mass spectrometry (MS)-based analysis although the crosscompartment contamination is inevitable.20 In this regard, all procedures of protein extraction, subcellular fractionation, and protein/peptide separation in amino acid coded mass-tagging (AACT)21,22 or stable isotope labeling with amino acids (SILAC)23-based quantitative proteomics approaches can be carried out in a single experimental format so that accurate quantification of only those proteome changes caused by LPS stimulation is possible.24 As a result, we provide direct proteomic evidence for activation of not only multiple MyD88dependent and -independent signaling pathways but also the corresponding activity of particular TFs and cytokine production. Further, our combined data sets from quantitative subcellular proteomic and bioinformatics analyses provide the first global insight into how cross-talk between various signaling 1806

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pathways modulates the activity of particular TFs which in turn regulate the expression of signal proteins and particular sets of pro-inflammatory genes and the genes involved in a variety of biological processes.

Experimental Procedures Chemicals and Antibodies. All components of cell culture media and protease inhibitor cocktails were purchased from Sigma (St, Louis, MO). Fetal bovine serum (FBS) was obtained from PAA Laboratories GmbH (Linz, Austria), and dialyzed FBS was purchased from Invitrogen. Deuterium-labeled leucine (leucine-d3) was obtained from Cambridge Isotope (Andover, MA). Ultra Pure Escherichia coli LPS was purchased from Invivogen (San Diego, CA). Trypsin was purchased from Promega (Madison, WI). All chemicals were sequence- or HPLC-grade unless specifically indicated. Antibodies (Abs) against BAX and p21 were obtained from Beyotime (AB026-1, AP021, China); Abs for NCL and MCM were purchased from ProteinTech Group, Inc. (10556-1AP,11225-1-AP); Abs for β-Actin and 14-3-3ε were obtained from Abcam (ab6276, ab40117); Abs for NF-κB p65 was purchased from Biolegend (#622602); Ab for PCNA was ordered from Thermo Fisher (MS-106-P); and Ab for Annexin A5 was purchased from Sigma (A8604). Cell Culture and AACT/SILAC Labeling. Following a previous protocol,15,19 the murine alveolar macrophage cell line AMJ2-C8 (ATCC CRL-2455) was maintained in high glucose DMEM with 10% FBS, 100 units/mL penicillin, and 100 µg/mL streptomycin and incubated at 37 °C in a 5% CO2 atmosphere. Similar to the procedures of AACT/SILAC-labeling previously reported by our group25,26 and other groups,23 the incorporation of heavy amino acids could reach close to over 97% for this particular type of AMJ macrophage cells after 4-5 passages as we previously demonstrated.15 To reach the highest level of labeling, that is, over 98%, which could indirectly reduce possible errors in the concurrent quantitation, we labeled the cells through 7 passages while the same response to LPS of the fully labeled cells was maintained. For those leu-d3-containing peptides derived from β-actin which was extracted from the labeled cells, the efficiency/extent of the labeling was examined by the precursor analysis by using MALDITOF 4700 (Applied Biosystem) (Suppl. Figure 1, Supporting Information). Nonlabeled AMJ2-C8 cells were treated with LPS (1 µg/mL) for 10 min. Subcellular Fractionation. Cytosolic and nuclear fractions were prepared as previously described with minor modifications.27 Briefly, cells were harvested and washed twice with icecold PBS. Similar amounts of LPS-stimulated and unstimulated cells were suspended in buffer A (10 mM HEPES pH 7.9, 1.5 mM MgCl2, 10 mM KCl, 0.5 mM DTT, and a protease inhibitor cocktail). NP-40 was added to 0.1%, the cells were mixed and incubated on ice for 10 min, and the nuclei (P1 fraction) were collected by centrifugation (10 min, 6000 rpm, 4 °C). The supernatant (S1 fraction) was isolated, and the P1 nuclei were washed once in buffer A and centrifuged again. The pelleted nuclei (P2 fraction) were lysed for 30 min on ice in buffer B (20 mM HEPES pH 7.9, 25% glycerol, 420 mM NaCl, 0.2 mM EDTA, 1.5 mM MgCl2, 10 mM KCl, 0.5 mM DTT, and protease inhibitor cocktail). Insoluble chromatin (fraction P3) and the soluble nuclear protein (fraction S3) were separated by centrifugation (15 min, 14 000 rpm, 4 °C). The procedure is shown in Figure 1C. Fractions S1 and S3 were separated by 12% SDSPAGE and stained using Coomassie Brilliant Blue (CBB) (Figure 2A).

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Figure 1. Time course- and dose-dependent LPS-induced changes of NF-κB activity and the experimental design for subcellular quantitative proteomics. (A) Time course-dependent NF-κB activation. AMJ2-C8 cells were collected at 0, 5, 10, 15, 20, and 30 min following the stimulation with 1 µg/mL LPS. Nuclear proteins (40 µg) were loaded on each lane and immunoblotting (IB) was performed with an anti-NF-κB p65 antibody. (B) Dose-dependent NF-κB activation. AMJ2-C8 cells were stimulated by LPS with different concentrations of 50, 100, 500, and 1000 ng/mL for 10 min and harvested to extract nuclear proteins for similar IB experiments above. (C) Strategy for the identification of the LPS-induced differentially expressed proteins in the cytosol and the nuclear fractions.

Figure 2. Purity and identification of the proteins isolated through subcellular fractionation. (A) SDS-PAGE of S1 and S3 subcellular fractions. The S1 and S3 fractions were separated on a 1D SDS-gel and stained with CBB-R250. The separation patterns for S1 or S3 were found to be different. Cytoplasmic actin (left lane) or a nuclear high mobility group (HMG) protein (right lane) are identified in their corresponding subcellular locations. (B) Venn diagram representing the protein identified in their corresponding subcellular compartments.. Approximately 25% of total proteins were found solely in either the cytosol or the nuclear fraction. Journal of Proteome Research • Vol. 9, No. 4, 2010 1807

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In-Gel Trypsin Digestion. Following a similar protocol, the SDS-PAGE bands were cut into 19 sections of S1 and 20 sections of S3. The gel pieces were washed once with Milli-Q water, and CBB dye was removed by three rinses in 50% ACN, 50 mM ammonium bicarbonate for 15 min. Gel pieces were dehydrated twice in 100% ACN for 30 min and reconstituted overnight at 37 °C with an in-gel digestion reagent containing 10 ng/µL sequencing grade trypsin. The tryptic peptides were extracted from the gel pieces with 50% ACN and 0.1% TFA and lyophilized for 4 h. LC-MS/MS. LC-MS/MS experiments were performed on a hybrid linear quadrupole ion trap/Orbitrap (LTQ Orbitrap) mass spectrometer (Thermo Finnigan, Bremen, Germany) coupled to a Shimadzu LC-20AD LC system (Shimadzu, Japan) and SIL-20AC autosampler (Shimadzu, Japan). Tryptic peptides were redissolved in 30 µL of 0.1% FA solution and chromatographically separated by a HyPurity Aquastar column (C18 0.1 × 150 mm 5 µ 100A; Thermo Electron, Bellefonte, PA) thermostatted at 50 °C. Each sample was loaded in solvent A (95% H2O, 5% ACN, 0.1% FA) and followed by gradient elution of 5-45% solvent B (5% H2O, 95% ACN, 0.1% FA) over 38 min with a flow rate of 500 nL/min. The entire eluant was sprayed into the mass spectrometer via a dynamic nanospray probe (Thermo Fisher Scientific) and analyzed in positive mode. The 3 most abundant precursor ions detected in the full MS survey scan (m/z range of 400-2000, R ) 60 000) were isolated for further MS/MS analyzing. Spectra were acquired under automatic gain control (AGC) in one microscan for survey spectra (AGC: 106) and in three microscans for MS/MS spectra (AGC: 104). Database Searching, Protein Identification, and Quantification. Acquired data (Xcalibur RAW-files) were converted into DTA-files using Extract_msn 3.0 in Bioworks 3.2 (Thermo Electron, San Jose, CA) and submitted to the NCBI mouse database (2006.12.04, 44 188 entries) using TurboSequest V.27 (rev.12) search engine. The search criteria were used as follows: full tryptic enzyme specificity; 2 missed cleavage; 10 ppm peptide tolerance and 0.5 Da fragment ion tolerance were allowed; dynamic modification of 3.0188 Da on leucine. The MS/MS spectra were also searched reversed sequence database with the same setting. The reversed database was generated from NCBI mouse database (2006.12.04, 44 188 entries) to assess the false-assignment. After searching the database and reversed database, the threshold of dCn and XCorr were optimized to yield false positive rate within 1%. Data were further filtered with Xcorr of 1.9, 2.70, and 3.50 for +1, +2, and +3 charge state peptides, respectively, delta correlation (delta Cn) score > 0.1, and peptide probability < 0.001. Quantification was performed using XPRESS software that automatically isolates the light- and heavy-isotope peptide elution profiles, determines the area of each peptide peak, and calculates the abundance ratio based on these areas.28 These quantification ratios were confirmed by manual validation. We have carefully examined possible offsets between nondeuterated and deuterated forms of those peptides containing more than one leucine residues in LC retention time as we previously observed.29 We did not encounter similar offset problems probably due to the faster ‘duty cycle’ of LTQ than qTOF as the MS spectra of some multiple leucine-containing peptides were demonstrated (Suppl. Figure 2, Supporting Information). To find the quantitative baseline that could be used to distinguish those LPS-induced differentially displayed proteins, we normalized the ratio of β-actin and nucleolin (NCL) identified in the 1808


cytosol (S1) and nuclear (S3) fraction respectively to 1.0 from the observed ratio at 1.1. With these controls of subcellular markers, the ratios of all other proteins were calibrated accordingly. Immunoblotting. The cytosol (S1) and nuclear (S3) fractions from either stimulated or nonstimulated cells were separated by SDS-PAGE, transferred to PVDF membranes at 15 V for 60 min using a semidry transferring device, blocked with TBST (5% nonfat milk in TBS with 0.1% Tween-20) for 1 h at room temperature, washed twice with TBST, and then incubated with primary antibodies dissolved in blocking solution for 1 h. After washing four times for 10 min in TBST, the membranes were incubated for 1 h with horseradish peroxidase-linked secondary antibodies. After four washes, proteins were detected with a luminol reagent and imaged with the LAS-3000 (FUJIFILM, Japan). Functional Clustering and Network Analysis. The quantified proteins were submitted to Amigo Slimmer tool (http:// amigo.geneontology.org) to obtain their corresponding subcellular locations. Biological processes and molecular functions were categorized by PANTHER (http://www.pantherdb.org/) and DAVID (http://david.abcc.ncifcrf.gov/). According to their categorized function, a network was constructed by the bioinformatics analysis tool STRING and modified by the software Cytoscape.

Results and Discussion Identification of Induced Cytosolic and Nuclear Proteins During the Macrophage Early Response to LPS Stimulation. As shown in Figure 1A and B, the results of both time- and dose-dependent LPS stimulation experiments indicated that sufficient changes in nuclear translocation of NF-κB occurred 10 min after LPS stimulation at a dose level of 1 µg/mL. We chose these stimulation conditions, defined as the early stage of the macrophage response to LPS, to profile the corresponding proteomic changes in cytosolic and nuclear fractions. Figure 1C illustrates the strategy for identifying differentially displayed proteins in the cytosol and the nuclear fractions, which is similar to a previously described approach.19 Briefly, macrophages subjected to LPS treatment were maintained in a ‘regular’ or ‘light’ medium, whereas nonstimulated cells were cultured in the ‘heavy’ medium containing leucine-d3. Following LPS treatment, cell fractionation was performed on a 1:1 mixture of stimulated, or “light”, and nonstimulated, or “heavy”, macrophages to obtain cytosol (S1) and nuclear (S3) fractions. The fractions were then separated on a 1D SDS-gel and gel slices were subjected to tryptic digestion and peptide extraction followed by nano-LC-MS/MS analysis (Figure 1C). The purity of subcellular fractionation was first assessed by 1D SDS gel separation, wherein particular differentially displayed bands from S1 (cytosol) or S3 (nuclear) were identified as cytoplasmic actin (Figure 2A, left lane) or nuclear high mobility group (HMG) protein (Figure 2A, right lane), respectively. Using the threshold for protein identification established as described previously,30,31 a total of 2144 nonredundant proteins, including 1107 in the cytosol and 1037 in nucleus, were identified with high confidence. On the basis of a previous criteria19,32,33 and MS data quality with higher signal-to-noise (S/N), a threshold of 20% in LPS-induced changes in protein abundance, that is, a light-to-heavy ratio (L/H) higher than 1.2 or less than 0.8, was conservatively selected to identify differentially expressed proteins. By this criterion, after 10 min LPS stimulation, the abundance of 508 proteins was found elevated with the


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Figure 3. Functional categorization and the distribution of LPS-induced differentially expressed proteins identified by MS. Those proteins showing the L/H either higher than 1.2 or less than 0.8 in S1 and S3, respectively, were submitted to PANTHER (http:// www.pantherdb.org/) to obtain the information about their previously known functions.

decreased levels of 103 proteins in the cytosol (S1) fraction and 678 proteins with elevated abundances and 80 proteins with decreased abundances in the nuclear (S3) fraction. The distribution of S1- or S3-unqiue proteins is given in Figure 2B as approximately 25% of total protein found solely in either the cytosol or the nuclear fraction. Although cross-compartment contamination in the subcellular fractionation step is inevitable,34 our fractionation indeed reduced the complexity of the proteome, which indirectly increased the dynamic range of protein detection19 and enriched for proteins with relatively low abundance. In our case, certain cytoplasmic kinases such as IKKβ, MAP2K4, and MAP2K3, which are known to be of low abundance, were identified only in the cytosol fraction. Likewise, a number of low-abundance nuclear transcription factors such as Rb1 and Sp1 were identified only in the nuclear fraction, suggesting the effectiveness and purity of our fractionation. The details about subcellular compartment-associated functions are discussed below. Functional Categorization and Validation of LPS-Inducible Proteins Identified in the Cytosol and Nuclear Fractions. All of the proteins subject to LPS-induced changes in expression were categorized according to their known subcellular locations and functions using Amigo GO Slimmer tool (http://amigo.geneontology.org) (Suppl. Figure 3A, Supporting Information). The biological processes and functions of cytosolic and nuclear proteins were further clustered using PANTHER (http://www.pantherdb.org/). The clustering analysis (Suppl. Figure 3B, Supporting Information) revealed that most of the cytosolic proteins (S1) are mainly engaged in protein metabolism and modification while nuclear proteins (S3) are

primarily involved in nucleic acid metabolism, nucleic acid binding, and transcription. These results also confirmed the relatively high purity of our subcellular fractionation. Cytosol or nuclear proteins involved in nucleic acid metabolism, transport, and mRNA translation represented the largest groups up-regulated following 10 min of LPS stimulation (Figure 3), suggesting the energetic processes for transcription, possible DNA repair, translation, and protein synthesis in the early phase of the host response to LPS. Importantly, compared to our previous results,19 we identified more low-abundance proteins involved in a variety of signaling cascades associated with innate immune/inflammatory responses and host defense. Among all proteins identified and quantified, these signal proteins with LPS-inducible expression changes represent another large functional category, indicating that the early response to LPS could simultaneously elicit a series of intracellular signaling cascades such as calcium-mediated signaling, MAPK-mediated signal transduction, and NF-κB regulated signaling. LPS/TLR4-Mediated Cross-Talk among Activated Signaling Pathways Downstream of TLR4. On the basis of our comprehensive data set of LPS-inducible proteomic changes within the cytosol and nucleus, we employed bioinformatics tools such as PANTHER (http://www.pantherdb.org/) and DAVID (http://david.abcc.ncifcrf.gov/) to cluster those proteins associated with known signal cascades (Suppl. Table 1, Supporting Information). Toll-interacting protein (Tollip) was down-regulated by approximately 30% in S1 (Suppl. Table 1, Supporting Information, also in Figure 4). Tollip negatively regulates TLR signaling Journal of Proteome Research • Vol. 9, No. 4, 2010 1809

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Figure 4. Reconstructed network involving multiple signal cascades activated in early response to LPS stimulation. Some key components (Suppl. Table 1, Supporting Information) were found and submitted to String (http://string.embl.de/) for network construction. Crosstalks among particular signaling pathways interconnected to TFs such as NF-κB, MAPK and IRF were found to regulate specific cytokine expression. The network links were edited by using Cytoscape software.

through its association with IRAK, by preventing IRAK from being phosphorylated, which could result in a block in recruiting critical downstream signaling molecules.35 Our observation of LPS-induced down-regulation of Tollip suggests that the activity of IRAK could be activated for relaying the signal. Ube2n, which showed a similar increase in both the cytosol and nuclei, (Suppl. Table 1, Supporting Information, also in Figure 4), is a ubiquitin-conjugating enzyme promoting polyubiquitination.36 It was reported that, after LPS stimulation, Ube2n in collaboration with a cofactor, Uve1A, facilitates TRAF6 polyubiquitination, resulting in activation of TAK1, further stimulating mitogen-activated protein kinase kinase (MAPKK) and the IΚB kinase (IKK) complex.37,38 The IKK complex is essential for catalyzing IΚB phosphorylation, culminating in NF-κB translocation from cytoplasm to nucleus.39 One key component of the IKK complex, IKKβ/ Ikbkb was found in the S1 cytosol with its expression up-regulated by 70% in response to LPS stimulation (Suppl. Table 1, Supporting Information, also in Figure 4). Although NF-κB was not detected in our experiment, the LPS-induced differential regulation of components like Tollip, Ube2n, and IKKβ suggests activation of the overall NF-κB-regulated signaling pathway (Figure 4). The MAPK cascade is one of the most evolutionarily conserved signaling pathways, composed of the four subfamilies of the extracellular-signal-regulated kinases (ERKs), ERK5 (also known as BMK1 and MAPK7), JUN N-terminal kinases (JNKs), and p38 kinases.40 Here, we found that MAP2K4 (Suppl. Table 1, Supporting Information, also in Figure 4), an upstream kinase that activates JNK,41 was up-regulated by approximately 40% in the cytosol. MAP2K3 (Suppl. Table 1, Supporting Information, also in Figure 4), an upstream kinase linked to p38 activation,41 was also identified in the cytosol with 30% upregulation. MAPK14 (also known as p38R) was identified in 1810


both S1 and S3 fractions with significant LPS-induced changes in abundance, that is, 3.8- and 5.2-fold increase, respectively (Suppl. Table 1, Supporting Information, also in Figure 4). All of this evidence indicates that both the JNK and p38 cascades were triggered in the early response to LPS stimulation. Activation of JNK and p38 results in nuclear translocation of another transcription factor AP-140 (Figure 4). Further ERK can be activated through RAS/RAF/MEK/ERK signaling cascades.42 GRB2 is an adaptor that binds constitutively to guanine nucleotide-exchange factors (GEFs) through its SH3 domain to activate RAS. We found that LPS induced a 2-fold upregulation of GRB2 in both the cytosol and nuclear fractions (Suppl. Table 1, Supporting Information, also in Figure 4). RAF1, which is recruited by GRB2 upon RAS activation, showed a 2.7- and 1.7-fold increase in the S1 and S3 fractions, respectively (Suppl. Table 1, Supporting Information, also in Figure 4). RAF1 is a serine/threonine kinase whose activation leads to phosphorylation and stimulation of the dual specificity tyrosine/threonine kinase MAP2K1,2 that, in turn, phosphorylates ERK1,2, resulting in its activation (Figure 4). We also found a 1.6-1.8-fold up-regulated expression of MAP2K1 and MAPK1/ERK1 in the cytosol (Suppl. Table 1, Supporting Information, also in Figure 4). The simultaneous elevation of these key components involved in the ERK signaling cascade40 suggests activation of the corresponding ERK signaling pathway for subsequent regulation of downstream effectors in the early LPS response. Additionally, we found LPS-induced up-regulation of some members of the Rho GTPase family, which includes proteins with highly conserved structures found in most eukaryotic cell types and regulating a variety of biological processes.43 Further, activation of RAC1 and CDC42 is apparently capable of stimulating JNK and p38.43,44 In our studies, both RAC1 and

Early Host Response to LPS CDC42 showed elevated expression in both fractions (Suppl. Table 1, Supporting Information, also in Figure 4). The p21activated kinases (PAKs) are the mammalian RAC/CDC42associated serine/threonine protein kinases.45 PAK2, one of the PAK family proteins, was found elevated by 1.3-fold in the cytosol (Suppl. Table 1, Supporting Information, also in Figure 4). PAKs stimulate JNK and p38 activity in certain cell types through phosphorylation of upstream components in the JNK/ p38 signaling cascades.45 It has also been reported that this RAC/CDC42/PAKs functional link has an impact on regulation of certain ERK pathway components through activating RAF1 and phosphorylation of MAP2K1.46 Evidence also shows that the Rho-family proteins including RAC and CDC42 can activate NF-κB in response to a variety of stimuli.47,48 Our comprehensive results indicate that a possible ‘cross-cascade’ connection involving key components of the MAPK, Rho family G proteins, and ERK pathways could coordinate modulation of NF-κB activity for the LPS-induced TLR4-mediated early response. In addition to our proteomic data indicating coactivation of the NF-κB and MAPKs signal cascades, we also identified two interferon regulatory factors (IRFs), IRF3 and IRF5, which were up-regulated by 2.6- and 1.5-fold, respectively, in the nucleus (Suppl. Table 1, Supporting Information, also in Figure 4). IRF3 is a key transcription factor responsible, upon LPS stimulation, for IFN-β production and the delayed-phase of NF-κB activation via the TRIF-TBK1-IKKε dependent pathway.49 A recent report demonstrated that the TAK1-JNK cascade is required for IRF3 function in addition to TBK1-IKKε, which uncovers a novel role of MAPK pathways in regulating innate immunity.50 Unlike IRF3, IRF5 was shown to be a regulator downstream from MyD88 and TRAF6.51 After being triggered through a MyD88dependent pathway, IRF5 translocates to the nucleus to activate transcription of cytokine genes like IFN-R.51 Given the fact that IRFs are located downstream of TRIF, another TLR4 adaptor, the LPS-induced activation of both NF-κB and IRFs suggests that both MyD88-dependent and -independent signal cascades communicate to coordinate the specificity of the inflammatory response (Figure 4). As a consequence of modulation of the activities of particular transcription factors such as NF-κB, AP-1, IRFs, expression of specific cytokines is regulated. In this regard, we did not detect typical cytokines such as TNFR, IL6, IFN-R or IFN-β, probably due to their being secreted. Instead, we identified a number of other cytokines specifically regulated in the LPS-induced response. For example, macrophage migration inhibitory factor (MIF), one of the pleiotropic cytokines playing an essential role in both innate and acquired immunity,52 was found in both S1 and S3 fraction with 1.3- and 2.2-fold increase in abundance, respectively (Suppl. Table 1, Supporting Information, also in Figure 4). MIF regulates the macrophage response to LPS or other products of Gram-negative bacteria53 as MIF promotes recognition of LPS, leading to the activation of pro-inflammatory cytokines such as TNF-R. In our study, we found that activation of MIF occurred with a short exposure of LPS. A possible mediator of innate immune response, pre-B-cell colony-enhancing factor 1 (PBEF1), was found in both fractions up-regulated by 1.3- and 2.3-fold (Suppl. Table 1, Supporting Information, also in Figure 4). PBEF was initially described as a growth factor for early stage B cells54 and later found associated with a variety of inflammation-related chronic or acute disorders such as rheumatoid arthritis,55 Type 2 diabetes,56 acute lung injury,57 and sepsis.58 PBEF was also linked to tumorigenesis.59 PBEF was found to induce transcription and

research articles translation of IL-1β, IL-1ra, IL-6, IL-10, and TNF-R in PBMCs and IL-1β, IL-6, and TNF-R in CD14+ monocytes by involving either p38 pathways in monocytes or activation of NF-κB in human leukocytes.60 Our results suggest that activation of PBEF occurred immediately upon LPS stimulation, implying its possible role in early TLR4-mediated response. The member 1 of small inducible cytokine subfamily E (Scye1) along with another pleiotropic cytokine was found up-regulated by 1.3fold in the cytosol (Suppl. Table 1, Supporting Information, also in Figure 4). Scye1 (also known as p43) is associated with the mammalian macromolecular aminoacyl tRNA synthetase complex.61 p43 is secreted to act as a cytokine on both endothelial and immune cells by inducing TNF-R production in macrophages via NF-κB activation,62 implying roles in mediating tumor angiogenesis and escape from immune surveillance during tumor progression.63 Using a bioinformatics tool, String (http://string.embl.de/), to mine our quantitative proteomic data set, we performed a data-dependent network analysis to map the possible connectivity among individual pathway components, signaling cascades, and concurrent signaling-modulated regulation for transcription. As shown in Figure 4, the dynamic linkages among signal proteins showing LPS-regulated expression could be first established in their corresponding major signaling pathways such as MAPK and NF-κB. Second, as discussed above, due to our identification of some regulated proteins previously known to be involved in multiple signal cascades, we were able to identify pathway cross-talk that coordinates the activity of downstream transcription factors such as NFκB or IRF, which is essential to understand the direction of signal transmission. Third, the LPS-induced expression of specific cytokines could be detected from the effects on upstream regulatory networks. A Short LPS Challenge Induces TLR4-Mediated Apoptosis Through Both Extrinsic and Intrinsic Death Pathways. Apoptosis is a conserved and essential genetic program for development and homeostasis of the immune system.64 There are two major pro-apoptotic pathways, the extrinsic pathway, initiated by ligation of death receptors, and the intrinsic pathway, regulated by the BCL-2 family.64,65 In our study, we identified a number of apoptosis-associated proteins (Table 1). The Fas-associated death domain (FADD) protein, a death domain-containing receptor involved in the extrinsic pathway, was identified in the cytosol with a 2.3-fold increase in its LPSinduced expression (Table 1). It is known that TLR2 triggers apoptosis through an interaction between the death domain of MyD88 with that of FADD.66 Since LPS-triggered TLR4 signaling uses both MyD88-dependent and -independent routes, the functional role of FADD could be complex, in that FADD could interact with IRAK1 along with MyD88. Recent evidence suggests that these interactions negatively regulate the LPS-induced multiple signaling pathways related to apoptosis whereas FADD was required to propagate TRIF-dependent apoptosis through formation of the TRIF/RIP1/FADD complex.67 Therefore, the signal through FADD could mediate both induction and inhibition of apoptosis through both the TLR4 adaptor proteins TRIF and MyD88 to maintain the balance between protecting healthy cells from apoptosis and promoting apoptosis of damaged cells. A similar DD-containing adaptor protein to FADD, CRADD, was also identified with 1.6fold expression increase in the S1 fraction (Table 1). CRADD induces apoptosis through its dual-domain structure including Journal of Proteome Research • Vol. 9, No. 4, 2010 1811

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Table 1. Quantified Proteins Involved in Apoptosis gi number

locationa

protein name

7304889 6753060 6753060 94158994 94158994 31981004 21450341 31982030 31982030 34398362 21703784 33147082 6680770 6680770 31981310 58037550 58037550 31542228 31542228 10048468 41281940 41281940 6753284 31981865 33563288 31542366 31542366 33695123 6680908 6671726 94384476 19527376 6753516

S1 S1 S3 S1 S3 S1 S1 S1 S3 S1 S1 S1 S1 S3 S3 S1 S3 S1 S3 S1 S1 S3 S1 S1 S3 S1 S3 S3 S1 S3 S1 S1 S1

12963737 12963737 31981221 31981221 7549752 7549752 19482162 7710014 7710014 22164790 28076981 28076981 12667808 85677504 76096375 87299637 87299637 6755314 6681273 6681273 13384756 13384756 31712036 31712036 17933766 17933766 6753812 40789280 17998694 33468897 31981282 31981282 15723368

S1 S3 S1 S3 S1 S3 S1 S1 S3 S1 S1 S3 S3 S1 S3 S1 S3 S3 S1 S3 S1 S3 S1 S3 S1 S3 S1 S3 S1 S1 S1 S3 S1

annexin A4 annexin A5 annexin A5 apoptosis inhibitor 5 apoptosis inhibitor 5 APAF1 interacting protein amyloid beta precursor protein binding protein 1 Rho GDP dissociation inhibitor (GDI) alpha Rho GDP dissociation inhibitor (GDI) alpha Bcl2-associated athanogene 1 isoform 1 L Bcl2-associated athanogene 2 HLA-B-associated transcript 3 Bcl2-associated X protein Bcl2-associated X protein B-cell receptor-associated protein 31 Bcl2-like 1 Bcl2-like 1 BH3 interacting domain death agonist BH3 interacting domain death agonist baculoviral IAP repeat-containing 6 brain and reproductive organ-expressed protein isoform IV brain and reproductive organ-expressed protein isoform IV caspase 3, apoptosis related cysteine protease caspase 6 cell division cycle and apoptosis regulator 1 cell division cycle 2 homologue A cell division cycle 2 homologue A cell division cycle 2-like 1 cyclin-dependent kinase 5 cyclin-dependent kinase inhibitor 1A (P21) PREDICTED: similar to craniofacial development protein 1 cytokine induced apoptosis inhibitor 1 CASP2 and RIPK1 domain containing adaptor with death domain chromosome segregation 1-like chromosome segregation 1-like nuclear protein NAP nuclear protein NAP cullin 1 cullin 1 cullin 2 cullin 3 cullin 3 cullin 4A cullin 5 cullin 5 death-associated protein 3 diablo death inducer-obliterator 1 isoform 3 dynamin 2 dynamin 2 D4, zinc and double PHD fingers family 2 eukaryotic translation elongation factor 1 alpha 2 eukaryotic translation elongation factor 1 alpha 2 eukaryotic translation elongation factor 1 epsilon 1 eukaryotic translation elongation factor 1 epsilon 1 eukaryotic translation initiation factor 5A eukaryotic translation initiation factor 5A engulfment and cell motility 1 isoform 1 engulfment and cell motility 1 isoform 1 Fas-associated via death domain Fas-associated factor 1 fragile X mental retardation gene 1, autosomal homologue glutamate-cysteine ligase, catalytic subunit glyoxalase 1 glyoxalase 1 HIV-1 tat interactive protein 2, homologue

1812


coverage

Pepb (pepc)

SDd

ratioe

Anxa4 Anxa5 Anxa5 Api5 Api5 Apip Appbp1 Arhgdia Arhgdia Bag1 Bag2 Bat3 Bax Bax Bcap31 Bcl2l1 Bcl2l1 Bid Bid Birc6 Bre Bre Casp3 Casp6 Ccar1 Cdc2a Cdc2a Cdc2l1 Cdk5 Cdkn1a Cfdp1 Ciapin1 Cradd

40.44 51.72 44.20 6.55 31.55 24.07 5.62 39.71 23.04 8.17 5.24 2.60 48.44 42.71 7.76 16.74 23.18 45.64 27.18 0.31 7.75 7.75 9.03 5.80 5.24 11.11 27.27 3.32 8.22 11.95 17.29 12.94 13.57

9(8) 13(10) 10(7) 2(2) 10(9) 4(2) 2(1) 9(6) 5(1) 2(1) 1(1) 2(2) 7(3) 6(5) 1(1) 2(2) 3(1) 5(3) 3(1) 1(1) 2(1) 2(1) 2(1) 1(1) 4(4) 2(1) 5(2) 2(1) 2(1) 1(1) 3(2) 2(2) 2(1)

0.05 0.11 0.14 0.04 0.14 0.06 / / 0.13 / / 0.21 0.05 0.16 / / 0.05 / 0.11 / / / / / 0.02 / 0.16 / / / 0.01 0 /

2.3 1.1 2.0 1.3 1.1 1.2 1.8 1.3 2.5 1.6 2.5 1.9 2.1 2.0 1.4 1.5 1.5 1.6 2.9 1.2 1.5 2.1 0.9 1.3 1.0 3.2 2.7 1.8 1.5 2.8 1.4 0.8 1.6

Cse1l Cse1l Ctnnbl1 Ctnnbl1 Cul1 Cul1 Cul2 Cul3 Cul3 Cul4a Cul5 Cul5 Dap3 Diablo Dido1 Dnm2 Dnm2 Dpf2 Eef1a2 Eef1a2 Eef1e1 Eef1e1 Eif5a Eif5a Elmo1 Elmo1 Fadd Faf1 Fxr1h Gclc Glo1 Glo1 Htatip2

28.01 13.18 5.68 8.88 6.57 4.51 3.12 3.26 8.33 5.54 11.03 2.69 3.07 32.07 0.84 8.17 5.52 4.09 9.94 7.56 28.74 5.75 15.58 15.58 13.89 22.70 17.56 2.00 2.60 10.05 34.24 22.28 14.46

19(14) 7(5) 2(1) 3(2) 4(2) 3(2) 2(1) 2(1) 5(2) 2(2) 5(4) 1(1) 1(1) 4(3) 1(1) 5(3) 3(2) 1(1) 4(2) 3(2) 4(4) 1(1) 2(1) 2(1) 6(3) 9(7) 2(1) 1(1) 1(1) 4(2) 6(6) 3(2) 2(2)

0.14 0.2 / 0.13 0.01 0.06 / / 0.03 0.04 / 0.15 / 0.08 / 0.09 0.15 / 0.07 0.14 / 0.09 / / 0.13 0.16 / / / 0.19 0.08 0.17 0.01

1.4 1.3 1.5 1.3 1.3 2.0 1.1 1.5 2.2 1.2 0.8 1.3 1.2 0.9 1.0 1.0 1.2 1.7 1.0 1.0 1.0 1.5 1.2 1.4 0.9 1.5 2.3 6.5 1.4 0.9 1.9 4.2 0.8

gene symbol

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Early Host Response to LPS Table 1 Continued gi number

locationa

protein name

15723368 31980991 6754356 6754356 11225264 84579909 19387852 6678938 6678938 6754744 86198335 31560391 31560391 54607128 9790259 6755000 6755000 6755002 6755002 31560120 112293264 112293264 21426847 21729749 8394027 8394027 31560539 31560539 94405266

S1 S3 S1 S3 S1 S3 S3 S1 S3 S1 S1 S1 S3 S3 S1 S1 S3 S1 S3 S1 S1 S3 S1 S3 S1 S3 S1 S3 S1

94405266

S3

6755252 6755252 18497290 18497290 21703900 60097929 31541819 31541819 114145487 7106427 7106427 27369569 6754954 31559988 20589521 23956332 6678349 6678437 6678437 83921612 31543902 31543902 11968158 6755963 31981647 31981647 6756037

S1 S3 S1 S3 S1 S3 S1 S3 S1 S1 S3 S3 S1 S3 S3 S3 S3 S1 S3 S1 S1 S3 S1 S3 S1 S3 S1

6756037

S3

27370072 27370072

S1 S3

HIV-1 tat interactive protein 2, homologue HtrA serine peptidase 2 inositol polyphosphate-5-phosphatase D inositol polyphosphate-5-phosphatase D lymphocyte specific 1 mitogen activated protein kinase 1 MutL protein homologue 1 mutS homologue 2 mutS homologue 2 mutS homologue 6 nudix (nucleoside diphosphate linked moiety X)-type motif 2 programmed cell death 10 programmed cell death 10 programmed cell death protein 11 programmed cell death 5 programmed cell death 6 programmed cell death 6 programmed cell death 6 interacting protein programmed cell death 6 interacting protein phosducin-like 3 protein disulfide isomerase associated 3 protein disulfide isomerase associated 3 phosphoprotein enriched in astrocytes 15 isoform 2 polymerase (DNA directed), beta alpha isoform of regulatory subunit A, protein phosphatase 2 alpha isoform of regulatory subunit A, protein phosphatase 2 peroxiredoxin 2 peroxiredoxin 2 PREDICTED: similar to Transcriptional activator protein Pur-alpha PREDICTED: similar to Transcriptional activator protein Pur-alpha purine rich element binding protein B purine rich element binding protein B protein kinase raf 1 protein kinase raf 1 RAS p21 protein activator 1 Ras association (RalGDS/AF-6) domain family 5 Ras-related GTP binding A Ras-related GTP binding A small GTPase homologue STE20-like kinase STE20-like kinase survival motor neuron domain containing 1 sequestosome 1 serine/threonine kinase 17b (apoptosis-inducing) transforming growth factor beta regulated gene 4 THO complex 1 Tial1 cytotoxic granule-associated RNA binding protein-like 1 tumor protein, translationally controlled 1 tumor protein, translationally controlled 1 thioredoxin domain containing 5 thioredoxin-like 1 thioredoxin-like 1 ubiquitination factor E4B voltage-dependent anion channel 1 tyrosyl-tRNA synthetase tyrosyl-tRNA synthetase tyrosine 3-monooxygenase/tryptophan 6-monooxygenase activation protein, eta polypeptide tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, eta polypeptide nuclear interacting partner of ALK nuclear interacting partner of ALK

coverage

Pepb (pepc)

SDd

ratioe

Htatip2 Htra2 Inpp5d Inpp5d Lsp1 Mapk1 Mlh1 Msh2 Msh2 Msh6 Nudt2 Pdcd10 Pdcd10 Pdcd11 Pdcd5 Pdcd6 Pdcd6 Pdcd6ip Pdcd6ip Pdcl3 Pdia3 Pdia3 Pea15 Polb Ppp2r1a Ppp2r1a Prdx2 Prdx2 Pura

14.46 14.19 3.53 7.31 11.89 15.08 1.58 10.59 19.25 10.38 17.01 16.51 16.51 0.59 19.84 46.07 46.07 23.36 12.77 18.75 29.31 32.48 10.77 14.93 6.62 8.66 22.73 16.67 19.66

2(2) 4(3) 3(2) 5(4) 2(1) 3(2) 1(1) 7(4) 11(6) 8(4) 2(2) 3(2) 3(1) 1(1) 2(2) 5(3) 5(3) 13(11) 6(4) 3(3) 13(8) 13(8) 1(1) 3(2) 3(3) 4(4) 4(3) 2(1) 3(2)

0.01 0.15 0.08 0.11 / 0.11 / 0.13 0.17 0.2 0.11 / 0.06 / 0.13 0.09 0.18 0.17 0.18 0.21 0.04 0.12 / 0.1 0.15 0.2 / 0.17 0.08

0.8 2.1 1.4 1.3 1.1 2.4 3.0 2.7 2.5 3.4 1.2 1.8 1.8 0.7 1.3 1.2 1.1 1.2 1.5 2.6 0.4 0.8 1.3 0.8 1.3 1.9 1.1 2.0 1.1

Pura

29.49

5(3)

0.18

1.2

Purb Purb Raf1 Raf1 Rasa1 Rassf5 Rraga Rraga Rragc Slk Slk Smndc1 Sqstm1 Stk17b Tbrg4 Thoc1 Tial1 Tpt1 Tpt1 Txndc5 Txnl1 Txnl1 Ube4b Vdac1 Yars Yars Ywhah

9.57 19.44 3.70 3.70 15.01 3.63 8.31 5.11 5.28 3.08 1.70 4.20 3.39 3.50 3.02 15.07 10.20 8.14 8.14 6.00 26.99 12.11 8.35 10.60 8.52 8.52 26.42

1(1) 2(2) 2(1) 2(1) 8(5) 1(1) 2(1) 1(1) 1(1) 2(2) 1(1) 1(1) 1(1) 1(1) 1(1) 7(6) 3(1) 1(1) 1(1) 2(1) 5(2) 2(1) 5(3) 2(2) 3(2) 3(2) 6(3)

/ 0.09 / / 0.21 / / / / / 0.06 / / / / 0.15 / / / / / 0.18 0.11 0.02 0.02 0.05 /

1.1 1.0 2.7 1.7 1.1 2.9 1.9 2.7 1.5 1.2 1.9 1.1 0.6 4.8 1.7 0.8 1.5 0.9 0.9 0.9 1.7 1.4 1.2 0.9 0.9 1.3 0.8

Ywhah

18.29

3(1)

0.14

1.0

Zc3hc1 Zc3hc1

3.95 3.95

1(1) 1(1)

/ /

2.0 1.7

gene symbol

a Notes: Protein identified in S1 or S3 fraction. b Peptide number matched to the protein. c Peptide number used to quantify the protein. d Standard deviation was calculated by the isotope intensity ratio from multiple leucine-containing peptides. e Protein expression changes of stimulation versus nonstimulation with LPS.

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Figure 5. Data-dependent reconstructed network to elucidate how the activated signal pathways coordinate the apoptosis balance. The LPS-induced differentially expressed proteins identified in our study were submitted to String (http://string.embl.de/) for network construction. The network links were edited by using Cytoscape software.

an N-terminal caspase-homology domain that interacts with caspase-2 and a C-terminal DD that interacts with RIP.68 We also identified certain BCL-2 family proteins possessing either pro- or antiapoptotic activity (Table 1). With LPS stimulation, the pro-apoptotic BH3-containing protein, BID,65 was upregulated by 1.6- or 2.9-fold in the S1 or S3 fraction, respectively (Table 1). Activated BID binds to and activates the pro-apoptotic protein BAX to promote release of cytochrome c from mitochondria so as to seed apoptosome assembly69 and expression of BAX was also elevated by LPS challenge (Table 1). Interestingly, BCL2L1, the inhibitor of BCL-2 family proteins, was also up-regulated in both fractions (Table 1). Similar to our previous finding of a TLR2 agonist-stimulated host inflammatory response,19 here we found both pro-apoptotic proteins such as Htra2, Msh6, Stk17b, and Mlh1 and antiapoptotic proteins such as Bre, Cdc2a, Bag1, and Pea15 all up-regulated (Table 1). This phenomenon suggests that the host responses to different TLR agonists in general could trigger both extrinsic and intrinsic apoptotic pathways. Our networking results using String indicate that all LPSactivated signal cascades including MAPKs, NF-κB, and IRF interconnect with the apoptotic regulatory pathways (Figure 5). We have therefore provided a system-wide view on how both activated signal pathways and the regulated processes coordinate a balance between protecting infected but healthy cell from apoptosis and eliminating infection-damaged cells. Data-Dependent Analysis Revealed a Global Network Describing the Transcriptional Machinery Responsible for the LPS-Induced Early Response. Approximately 50 transcription factors or cofactors were identified and quantified in the nuclear fraction (Suppl. Table 2, Supporting Information). By using PANTHER (http://www.pantherdb.org/), these proteins 1814


were further clustered according to their known roles in particular processes such as nucleic acid metabolism, DNA repair and replication, chromatin packaging and remodeling, mRNA transcription, cell developmental processes, cell cycle control, and immunity and host response (Suppl. Figure 4A, Supporting Information). On the basis of our quantitative proteomic data set for a data-dependent network analysis using String, we obtained a detailed map of global functional links among these transcription factors, cofactors, and their associated proteins, which elucidates how the TF-regulatory network operates in an interconnected way with upstream signal cascades for the LPS-induced early response (Suppl. Figure 3, Supporting Information). In this global regulatory network, as shown in Figure 6, a number of key subnetworks or pathway modules were found to be interconnected to a variety of biological processes. An example of constructing a multifunctional pathway module is given in Figure 6A. LPS stimulation led to a 1.5-fold increase in the abundance of the transcription factor Sp1 (Suppl. Table 2, Supporting Information), a ubiquitously expressed, prototypic C2H2-type zinc finger-containing DNA binding protein that serves mainly as a constitutive activator of housekeeping genes.70 Recent evidence suggests that Sp1 is a more versatile partner of other transcription factors functioning as oncogenes and tumor suppressors that regulate genes associated with cell growth, apoptosis, and tumorigenesis.71 Interestingly, it was reported that, upon LPS stimulation, MAPKs including ERK, p38, and JNK triggered phosphorylation of Sp1 for the production of IL10 in human alveolar macrophages.72 Others have found that Sp1 can serve as a bridge in binding of NF-κB and C/EBP isoforms to the IL-6 promoter for rapid response to inflammatory stimuli.73 Sp1 overexpression induced apoptosis whereas


research articles

Figure 6. Continued.

a dominant-negative form of Sp1 suppressed apoptosis.71 Our finding of an interacting network implicates that LPS-induced activation of Sp1 may be interconnected with the NF-κB and MAPKs signaling cascades to regulate both pro- and antiapoptotic genes and genes involved in other biological processes (Figure 6A).

Because another ubiquitously expressed TF, nuclear transcription factor Y alpha (NFYA), which interacts with Sp1,74 showed an LPS-inducible increase of 2.8-fold together with another NFY subunit, NFYB, (Suppl. Table 2, Supporting Information), we were able to extend the network to the NFY-associated regulatory network (Figure 6A). The CCAATJournal of Proteome Research • Vol. 9, No. 4, 2010 1815

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Figure 6. Subnetworks/pathway modules identified by data-dependent network analysis and their functional links to different biological processes. The previously known function of the proteins in the network were obtained from the bioinformatics analysis based on PANTHER (http://www.pantherdb.org/) and GeneOntoloty (http://www.geneontology.org/). The “zoom-in” maps of these subnetworks extracted from the global regulatory network (Suppl. Figure 3, Supporting Information) are given as follows: (A) the subnetwork involving Sp1, NFYA, NFYB, Usf1, and Rb1 and their associating proteins, and (B) the subnetwork involving NF-κB p65, AP-1, and IRF3. Note that although neither of NF-κB p65 or AP-1 was detected in our experiment, some key signal proteins related to these signal cascades identified have suggested their activated status, (C) the subnetwork involving Sin3a, Etv6, Ruvbl1, Ruvbl2, and Ubtf, and (D) the subnetwork involving Cand1, Tceb2, and Mant1. 1816


Early Host Response to LPS binding NF-Y factors function as trimers containing H2A/H2Blike subunits could embed in both positive and negative methyl histone marks to serve as an activator or as a repressor of transcription regulation respectively.75 NF-Y is also a target of IL-6, which plays a role in transcriptional regulation of myeloid differentiation primary response genes, and is required for optimum myeloid differentiation.76 NF-Y also interacts with an activator of the JNK kinase pathway to induce apoptosis and is involved in regulation of Gadd45 induction by DNA damage.77 As a target of TGF-β signaling, NF-Y activation and nuclear translocation are cell type-dependent and involve interplay among MAPK pathways to regulate expression of cell cycle genes such as cyclin A2.78 Although there is no evidence to show a functional link between NF-Y and TLR signaling, cyclin A2 (Ccna2) a known target gene of NF-Y (Figure 6A), was found up-regulated by 14-fold in our study (Suppl. Table 5, Supporting Information), implicating a possible role of NF-Y in the TLR4 signaling pathway triggered by a short LPS challenge. Our network analysis also suggested the LPS-induced rapid accumulation of NF-Y in nuclei could result from crosstalk among MAPKs signaling cascades involving p38, JNK, and ERK, which together regulate expression of cell cycle-related genes (Figure 6A). In the same network (Figure 6A), upstream stimulatory factor 1 (Usf1), a member of the evolutionarily conserved basicHelix-loop-helix-Leucine Zipper transcription factor family, was up-regulated by 1.6-fold (Suppl. Table 2, Supporting Information). Usf1 interacts with Sp1 via binding to juxtaposed E-box and GC-elements.79 Usf1 regulates a large number of genes involved in different regulatory networks relating to stress and immune response, cell cycle, and cell proliferation.80 Recent evidence demonstrated that Usf1 was subjected to p38mediated phosphorylation-dependent acetylation for altered gene regulation, providing new insight into Usf1 targeted gene regulation associated with DNA damage and carcinogenesis.81 Usf1 and 2 together with IRFs and NF-κB are also important for constitutive and cytokine-induced levels of β2-microglobulin, whose transactivation is under control of activated transcriptional pathways during injury, infection, and inflammation in lymphoid and myeloid cells.82 Here, the activation of Usf1 upon LPS stimulation of macrophages may also mediate various transcriptional activities via its interaction with other TFs (Figure 6A). Interestingly, retinoblastoma 1 (Rb1), which showed a similar LPS-induced up-regulation (Suppl. Table 2, Supporting Information), was found in the same regulatory complex with Usf1, NFYB, and Sp1 (Figure 6A). As a tumor suppressor, Rb1 exerts its antiproliferative effects by mediating transcriptional repression of genes required for DNA replication and mitosis.83 The Rb1 family also plays a role in stabilizing histone methylation at constitutive heterochromatin, linking tumor suppression and the epigenetic definition of chromatin84 as well as in differentiation and apoptosis.85 The Rb-associated induction of the transcriptionally inactive p50 homodimer represents an important pathway for regulating NF-κB activity in nuclei.86 Here, through the established links to Rb1 and its associated protein factors, the regulatory network of LPS-induced transcription was extended to the regulation related to cell cycle, DNA repair and apoptosis (Figure 6A). Similar to the construction of the subnetwork/pathway module involving the functional links of Sp1-NFYA-Usf1-Rb1 (Figure 6A), in a stepwise manner, other subnetworks were also found responsible for regulating a variety of cellular processes

research articles or pathways (Figure 6B-D). For example, a subnetwork (Figure 6B) involving NF-κB p65, AP-1 and IRF3 is mainly associated with DNA repair, MAPK signaling, apoptosis etc. Figure 6C illustrates how Sin3a, Etv6, Ruvbl1, Ruvbl2 and Ubtf are interconnected for regulating transcription, chromatin modification, MAPK signaling as well. Meanwhile a subnetwork involving Cand1, Tceb2 and Mant1 is primarily associated with cell cycle control, transcription, protein folding, protein phosphorylation and apoptosis (Figure 6D). In line with the notion that transcriptional regulation coordinately controlled by multiple transcription factors, transcriptional cofactors, and chromatin modifications could play the central role in the inflammatory response,87,88 our global network (Suppl. Figure 5, Supporting Information) derived from quantitative proteomic data-dependent bioinformatics analysis provides direct evidence as to how these TFs each regulate defined biological process(es) or pathway(s) to coordinate the early response to LPS challenge. Based on our findings the LPSinducible regulatory network is now extended to signal transduction, protein synthesis, nucleotide metabolism and DNA repair, apoptosis, cell differentiation and proliferation, chromatin remodeling, and post-transcriptional control (Suppl. Figure 4B, Supporting Information). Validation of the Accuracy of AACT/SILAC-Based Quantitative Proteomics and Concurrent Network Analysis. We have examined the quantitative accuracy in distinguishing LPSinducible differentially expressed proteins. Due to their known roles in major pathways/biological processes/networks such as MAPK-mediated signaling, apoptosis, and cell cycle pathways, the following proteins were selected to represent LPSinduced activation of the corresponding pathways/network: 3-monooxygenase/tryptophan 5-monooxygenase activation protein, epsilon polypeptide (14-3-3ε) (Figure 5), Annexin A5 (Anxa5) (Figure 5), Bcl2-associated X protein (BAX) (Figure 5), proliferating cell nuclear antigen (PCNA) (Figure 6A), cyclindependent kinase inhibitor 1A (P21) (Figure 6A), and minichromosome maintenance protein 7 (MCM7) (Figure 6A). As shown in Figure 7, the LPS-induced changes in abundance of these proteins were consistent between AACT/SILAC-based quantitative proteomics measurement and immunoblotting. 14-3-3 proteins are a family of highly conserved, ubiquitous, multifunctional factors involved in cell cycle regulation, apoptosis and RAS/RAF/MAPK cascade signaling. We found 6 out of 7 mammalian isoforms89 which were up-regulated in response to LPS stimulation (Suppl. Table 3, Supporting Information), implicating the 14-3-3 protein family in TLR4mediated signaling. Annexins are a class of Ca+-dependent phospholipid-binding proteins with 13 known members, A1 to A13.90 We quantified the changes in this class of proteins in both the cytosol and nucleus (Suppl. Table 3, Supporting Information). As modulators, annexin participates in multiple cellular processes such as inflammation, cell differentiation and proliferation, tumorigenesis, and apoptosis.91,92 Annexin A5, having nanomolar affinity for negatively charged phosphatidylserine, has been used frequently for detection of early apoptosis in vitro and in vivo.93 Taken together from our results showing LPS-induced up-regulation of both Annexin A5 (Anxa5) (Figure 7) and a proapoptotic protein, BAX (Figure 7), we conclude that various proapoptotic pathways are activated in the early immune response mediated by TLR signaling in macrophages. Proliferating cell nuclear antigen (PCNA) is involved in a variety of processes including nucleotide excision repair, posJournal of Proteome Research • Vol. 9, No. 4, 2010 1817

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Figure 7. Immunoblotting validation of the representing proteins in networks. 14-3-3ε, also known as Ywhae (Figure 5), Anxa5 (Figure 5), and BAX (Figure 5), and PCNA (Figure 6A), p21 (Figure 6A), and MCM7 (Figure 6A) were selected for immunoblotting to determine the accuracy of our data generation and analysis. Following 10 min LPS stimulation at 1 µg/mL, the S1 and S3 fraction proteins (from stimulated and nonstimulated cells) were separated by SDS-PAGE and submitted to immunoblotting. β-Actin and NCL were used as the internal controls for the proteins in S1 and S3 fractions, respectively. L/H ratio refers to the protein expression changes of stimulated versus nonstimulated measured by AACT/SILAC-based quantitative MS experiments (Suppl. Table 3, Supporting Information).

treplication mismatch repair, base excision repair, cell cycle control, DNA replication, and at least one apoptotic pathway.94 The LPS-induced inflammatory response may induce to some extent oxidative DNA damage, which subsequently causes cell cycle arrest and requires DNA repair; PCNA regulates these processes through coordination with partners such as Fen1,95 Msh296 and p21/Cdkn1a.97 We find LPS-induced up-regulation of not only PCNA (Figure 7) but also its interacting proteins Fen1 (Suppl. Table 3, Supporting Information) by 3.6-fold in cytosol and by 1.5-fold in nucleus, Msh2 (Suppl. Table 3, Supporting Information) by 2.7-fold in cytosol and 2.5-fold in nucleus, and p21/Cdkn1a (Suppl. Table 3, Supporting Information) by 2.8-fold in nucleus. The expression change of p21, a CDK inhibitor involved in DNA damage-induced cell cycle arrest,98 was also validated by immunoblotting (Figure 7). Previous studies indicated that p21 is induced by LPS injection in the mouse central nervous system as a part of the acute response to inflammation and induction was most likely a downstream event of cytokine signaling involving the TLR4 recptor.98 Taken together, these results implicate PCNA and its partners in the downstream events of the inflammatory response modulated by TLR4 signaling pathways (Figure 6A). Minichromosome maintenance (MCM) proteins were first identified as required for minichromosome maintenance in Saccharomyces cerevisiae, and a total of six structurally related MCM proteins, that is, MCM2-7, form a hexametric complex, which is evolutionally conserved in all eukaryotes.99 All MCM proteins were identified in our experiments as being upregulated upon LPS stimulation (Suppl. Table 3, Supporting Information). We selected MCM7 to represent the protein family for validation experiments (Figure 7). MCM2-7 are essential for initiation and elongation of eukaryotic DNA replication,100 and links between the Notch pathway and the MCM complex are established during cell cycle control.101 Notch signaling was activated in LPS-stimulated macrophages which, in turn, could regulate genes involved in the inflam1818


matory response.102 Our data therefore suggests that a function of the MCM complex in the LPS triggered inflammatory response could be regulated through the Notch pathway. Not all of the proteins previously known to be involved in TLR4-mediated signaling were identified in our study. Reasons for this outcome are that some proteins were previously identified singly in overexpressing conditions and some may be present at levels below the detection limit of mass spectrometric measurements.

Conclusions We have performed a data-dependent quantitative proteomic analysis to reveal both signaling and regulatory networks that systematically operate in the early response to LPS. The combination of subcellular fractionation and AACT/SILACbased quantitative analysis resulted in the identification of many low-abundance proteins including signal-related proteins, activated transcription factors, and their partners. We observed simultaneous activation of signaling cascades involving NF-κB, MAPK, and IRF and their crosstalks in coordinating TLR4-mediated responses were revealed by data-dependent bioinformatics analysis. More importantly, the links between LPS-inducible signaling pathways were extended to the regulatory networks responsible for a variety of biological processes and pathways including protein and nucleic acid metabolism, apoptosis, DNA damage recognition and repair, cell cycle control, and host cell defense. The cross-talk among multiple signaling cascades represents critical points of convergence for TLR4 signaling for modulating the activity of multiple transcriptional factors where pharmacological targets for therapeutic intervention could be located systematically. Abbreviations: LPS, lipopolysaccharides; AACT, amino acidcoded mass tagging; SILAC, stable isotope labeling with amino acids in cell culture; PRRs, pattern recognition receptors; PAMPs, pathogen-associated molecular patterns; TLR, toll-like

research articles

Early Host Response to LPS receptor; MyD88, myeloid differentiation protein 88; TRIF, TIR domain-containing adaptor protein inducing IFNβ; TAK1, transforming growth factor-β-activated kinase 1; AP-1, Jun oncogene; TBK1, TANK-binding kinase 1; IKK, inhibitor of kappa B kinase; IRF, interferon regulatory factor; NF-κB, nuclear factor-kappa B; MAPK, mitogen-activated protein kinase; TFs, transcription factors; 14-3-3ε/Ywhae, 3-monooxygenase/tryptophan 5-monooxygenase activation protein, epsilon polypeptide; Anxa5, Annexin A5; BAX, Bcl2-associated X protein; PCNA, proliferating cell nuclear antigen; P21/Cdkn1a, cyclin-dependent kinase inhibitor 1A (P21); MCM7, minichromosome maintenance protein 7.

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Acknowledgment. This work was supported by grants from Shanghai Science and Technology Development Program (Grants 03DZ14024) and the 863 High Technology Foundation of China (Grant 2006AA02A310). This work was also supported by the U.S. NIH 1R01AI064806-01A2 and U.S. Department of Energy, the Office of Science (BER), Grant No.DE-FG02-07ER64422. We also thank Dr. Howard Fried for proofreading our manuscript.

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Supporting Information Available: Supplementary figures, MS/MS spectra of the identified S1 and S3 proteins based on a single peptide, and supplementary tables. This material is available free of charge via the Internet at http:// pubs.acs.org.

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