Title: Cross-linking Proteomics Indicates Effects of Simvastatin on the TLR2 Interactome and Reveals ACTR1A as a Novel Regulator of the TLR2 Signal Cascade
Abstract: Toll-like receptor 2 (TLR2) is a pattern recognition receptor that, upon ligation by microbial molecules, interacts with other proteins to initiate pro-inflammatory responses by the cell. Statins (hydroxymethylglutaryl coenzyme A reductase inhibitors), drugs widely prescribed to reduce hypercholesterolemia, are reported to have both pro- and anti-inflammatory effects upon cells. Some of these responses are presumed to be driven by effects on signaling proteins at the plasma membrane, but the underlying mechanisms remain obscure. We reasoned that profiling the effect of statins on the repertoire of TLR2-interacting proteins might provide novel insights into the mechanisms by which statins impact inflammation. In order to study the TLR2 interactome, we designed a coimmunoprecipitation (IP)-based cross-linking proteomics study. A hemagglutinin (HA)-tagged-TLR2 transfected HEK293 cell line was used to precipitate the TLR2 interactome upon cell exposure to the TLR2 agonist Pam3CSK4 and simvastatin, singly and in combination. To stabilize protein interactors, we used two different chemical cross-linkers with different spacer chain lengths. Proteomic analysis revealed important combinatorial effects of simvastatin and Pam3CSK4 on the TLR2 interactome. After stringent data filtering, we identified alpha-centractin (ACTR1A), an actin-related protein and subunit of the dynactin complex, as a potential interactor of TLR2. The interaction was validated using biochemical methods. RNA interference studies revealed an important role for ACTR1A in induction of pro-inflammatory cytokines. Taken together, we report that statins remodel the TLR2 interactome, and we identify ACTR1A, a part of the dynactin complex, as a novel regulator of TLR2-mediated immune signaling pathways. Toll-like receptor 2 (TLR2) is a pattern recognition receptor that, upon ligation by microbial molecules, interacts with other proteins to initiate pro-inflammatory responses by the cell. Statins (hydroxymethylglutaryl coenzyme A reductase inhibitors), drugs widely prescribed to reduce hypercholesterolemia, are reported to have both pro- and anti-inflammatory effects upon cells. Some of these responses are presumed to be driven by effects on signaling proteins at the plasma membrane, but the underlying mechanisms remain obscure. We reasoned that profiling the effect of statins on the repertoire of TLR2-interacting proteins might provide novel insights into the mechanisms by which statins impact inflammation. In order to study the TLR2 interactome, we designed a coimmunoprecipitation (IP)-based cross-linking proteomics study. A hemagglutinin (HA)-tagged-TLR2 transfected HEK293 cell line was used to precipitate the TLR2 interactome upon cell exposure to the TLR2 agonist Pam3CSK4 and simvastatin, singly and in combination. To stabilize protein interactors, we used two different chemical cross-linkers with different spacer chain lengths. Proteomic analysis revealed important combinatorial effects of simvastatin and Pam3CSK4 on the TLR2 interactome. After stringent data filtering, we identified alpha-centractin (ACTR1A), an actin-related protein and subunit of the dynactin complex, as a potential interactor of TLR2. The interaction was validated using biochemical methods. RNA interference studies revealed an important role for ACTR1A in induction of pro-inflammatory cytokines. Taken together, we report that statins remodel the TLR2 interactome, and we identify ACTR1A, a part of the dynactin complex, as a novel regulator of TLR2-mediated immune signaling pathways. Protein interactions have an important role in biological and cellular systems, including gene expression, signaling, and immune responses. The challenges associated with identifying specific protein-interacting partners in complex biological samples (1Ewing R.M. Chu P. Elisma F. Li H. Taylor P. Climie S. McBroom-Cerajewski L. Robinson M.D. O'Connor L. Li M. Taylor R. Dharsee M. Ho Y. Heilbut A. Moore L. Zhang S. Ornatsky O. Bukhman Y.V. Ethier M. Sheng Y. Vasilescu J. Abu-Farha M. Lambert J.-P. Duewel H.S. Stewart I.I. Kuehl B. Hogue K. Colwill K. Gladwish K. Muskat B. Kinach R. Adams S.-L. Moran M.F. Morin G.B. Topaloglou T. Figeys D. Large-scale mapping of human protein-protein interactions by mass spectrometry.Mol. Syst. Biol. 2007; 3: 89Crossref PubMed Scopus (760) Google Scholar, 2Stelzl U. Worm U. Lalowski M. Haenig C. Brembeck F.H. Goehler H. Stroedicke M. Zenkner M. Schoenherr A. Koeppen S. Timm J. Mintzlaff S. Abraham C. Bock N. Kietzmann S. Goedde A. Toksoz E. Droege A. 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Coimmunoprecipitation (IP)-based identification of protein interactions is a gold standard technique for defining protein complexes in native biological systems (4Lambert J.-P. Picaud S. Fujisawa T. Hou H. Savitsky P. Uusküla-Reimand L. Gupta G.D. Abdouni H. Lin Z.-Y. Tucholska M. Knight J.D.R. Gonzalez-Badillo B. St-Denis N. Newman J.A. Stucki M. Pelletier L. Bandeira N. Wilson M.D. Filippakopoulos P. Gingras A.-C. Interactome rewiring following pharmacological targeting of BET bromodomains.Mol. Cell. 2019; 73: 621-638.e17Abstract Full Text Full Text PDF PubMed Scopus (82) Google Scholar). In this method, a protein of interest is subjected to affinity- or antibody-based purifications along with its interacting partners. Optimization of wash conditions that remove nonspecific interactions but preserve transient and weak interactions is a major challenge that renders this method most amenable to identifying stable protein-protein interactions. In order to improve co-IP proteomics, protein cross-linking methods that covalently attach proximal protein binding partners have recently been employed (5Chowdhury S.M. Shi L. Yoon H. Ansong C. Rommereim L.M. Norbeck A.D. Auberry K.J. Moore R.J. Adkins J.N. Heffron F. Smith R.D. A method for investigating protein-protein interactions related to salmonella typhimurium pathogenesis.J. Proteome Res. 2009; 8: 1504-1514Crossref PubMed Scopus (21) Google Scholar, 6Yu C. Huang L. Cross-Linking Mass Spectrometry: An Emerging Technology for Interactomics and Structural Biology.Anal. Chem. 2018; 90: 144-165Crossref PubMed Scopus (171) Google Scholar). Cross-linking theoretically captures transient and weak protein interactions, permitting the subsequent use of strong denaturing washing conditions that preserve specificity. A further advantage of cross-linking methods is that interactions can be defined either through identifying the proteins or in some cases through specifically examining cross-linked peptides. Although domain-specific cross-linking data analysis is hindered because of the complexity of bioinformatics software, several software packages are currently available for specific cross-linkers. Nonetheless, because confident protein identification is still very challenging for large-scale data sets, identifying the interaction of cross-linked proteins by examining unmodified peptides has become a very popular approach. The toll-like receptors (TLRs) are a family are type I transmembrane proteins of the innate immune system that trigger a stereotypical pro-inflammatory cytokine induction response upon ligation. 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Recently, statins have been shown to have additional immunomodulatory activities that are relevant to the pathogenesis of cardiovascular and other diseases (23Yilmaz A. Reiss C. Weng A. Cicha I. Stumpf C. Steinkasserer A. Daniel W.G. Garlichs C.D. Differential effects of statins on relevant functions of human monocyte-derived dendritic cells.J. Leukoc. Biol. 2006; 79: 529-538Crossref PubMed Scopus (107) Google Scholar). For example, statins suppress maturation of human monocyte-derived dendritic cells (24Yilmaz A. Reiss C. Tantawi O. Weng A. Stumpf C. Raaz D. Ludwig J. Berger T. Steinkasserer A. Daniel W.G. Garlichs C.D. HMG-CoA reductase inhibitors suppress maturation of human dendritic cells: new implications for atherosclerosis.Atherosclerosis. 2004; 172: 85-93Abstract Full Text Full Text PDF PubMed Scopus (131) Google Scholar), and have been shown to ameliorate inflammation in a wide range of animal models of immunological disorders such as autoimmune encephalomyelitis (25Aktas O. 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Paradoxically, in other settings, proinflammatory effects of statins have also been identified on several cell types, including endothelial cells, peripheral blood mononuclear cells, and dendritic cells (28Matsumoto M. Einhaus D. Gold E.S. Aderem A. Simvastatin augments lipopolysaccharide-induced proinflammatory responses in macrophages by differential regulation of the c-Fos and c-Jun transcription factors.J. Immunol. 2004; 172: 7377-7384Crossref PubMed Scopus (83) Google Scholar). The mechanisms that determine the pro- versus anti-inflammatory actions of statins in different settings remain poorly understood, but in many cases have been proposed to stem from indirect effects on membrane proteins (e.g. receptors). We reasoned that P3C activation of TLR2 would serve as a well-defined model system amenable to unbiased proteomic-based profiling of the receptor-proximal effects of statins upon inflammatory signaling. To study the combinatorial effects of P3C and statins on the TLR2 protein interactome, we designed a cross-linking-based co-IP MS strategy. HEK293 1The abbreviations used are:HEK293THA-TLR2-MD2-CD14-HEK293 cellsXLcross-linkerP3CPam3CSK4ACTR1Aalpha-centractinDUCCTdual cleavable cross-linking technology. 1The abbreviations used are:HEK293THA-TLR2-MD2-CD14-HEK293 cellsXLcross-linkerP3CPam3CSK4ACTR1Aalpha-centractinDUCCTdual cleavable cross-linking technology. cells stably expressing HA-tagged TLR2 were used to pull down TLR2 along with its interactors following crosslinker treatment. Given that smaller cross-linking agents may miss covalent attachment of surface receptors and cytosolic proteins near the membrane, we used two cross-linker agents with different spacer-chain lengths, our recently developed a dual cleavable cross-linker (DUCCT; spacer chain distance ∼16.3 Å) (29Chakrabarty J.K. Naik A.G. Fessler M.B. Munske G.R. Chowdhury S.M. Differential tandem mass spectrometry-based cross-linker: a new approach for high confidence in identifying protein cross-linking.Anal. Chem. 2016; 88: 10215-10222Crossref PubMed Scopus (21) Google Scholar) and a commercial cross-linker BS3 (spacer chain distance, 11.4 Å). After cross-linking and affinity pulldown, proteins were separated by SDS-PAGE, trypsin-digested, and the resultant peptides were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by data filtering. HA-TLR2-MD2-CD14-HEK293 cells cross-linker Pam3CSK4 alpha-centractin dual cleavable cross-linking technology. HA-TLR2-MD2-CD14-HEK293 cells cross-linker Pam3CSK4 alpha-centractin dual cleavable cross-linking technology. Two proteins, alpha-centractin (ACTR1A) and myristoylated alanine-rich protein kinase C substrate-like protein 1 (MARCKSL1), were identified as novel interactors of TLR2 exclusively in statin-treated cells under DUCCT cross-linker treatment. We followed this discovery up with biochemical validation studies. We show that ACTR1A has important modulatory actions on the TLR2 pro-inflammatory signaling cascade. Taken together, these data identify for the first time that ACTRA1 is a statin-responsive protein that serves to modulate TLR2-mediated signaling. Given the prevalence of statin use in human populations, these mechanistic studies may have important translational implications. A plasmid expressing the HA-tagged human TLR2 gene (Catalogue # puno-htlr2ha, Invivogen, San Diego, CA) was transiently transfected into HEK293-hMD2-CD14 cells (Invivogen) using Lipofectamine 2000. Standard antibiotic selection procedures using Blasticidin S hydrochloride (Invivogen) were used together with Western blotting and immunostaining verification, to generate stable cell lines that strongly expressed (driven by a composite promoter hEF1/HTLV) the hTLR2-HA protein. In order to maintain selective pressure, the cell line was grown and maintained in DMEM containing 10‥ FBS, 100 U/ml penicillin, and 100 μg/ml streptomycin, supplemented with Blasticidin 10 μg/ml and Hygromycin 50 μg/ml. Hemagglutinin (HA) tagged-TLR2-MD2-CD14-human embryonic kidney (HEK)293 cells were maintained in DMEM supplemented with 10‥ fetal bovine serum, 1‥ penicillin/streptomycin in a humidified atmosphere of 5‥ CO2, and antibiotics (50 μg/ml hygromycin and 10 μg/ml blasticidin). Cells were treated with 10 μm simvastatin (Sigma) for 24 h, then stimulated with 1 μg/ml Pam3CSK4 (P3C; InvivoGen) for 24 h in fresh medium. The cells were then treated with Dual Cleavable Cross-linking Technology (DUCCT) (29Chakrabarty J.K. Naik A.G. Fessler M.B. Munske G.R. Chowdhury S.M. Differential tandem mass spectrometry-based cross-linker: a new approach for high confidence in identifying protein cross-linking.Anal. Chem. 2016; 88: 10215-10222Crossref PubMed Scopus (21) Google Scholar) or commercial bissulfosuccinimidyl suberate (BS3) cross-linker (XL), added at a final concentration of 1μmol/ml for 30 min, followed by quenching the reaction with 50 mm Tris-HCl, pH 8.0. For IP-pull down for proteomics, the cells were lysed with immunoprecipitation (IP)- lysis buffer supplemented with protease inhibitors at 4 °C for 15 mins, then sonicated for another 15 mins. Finally, the suspended cells were kept at 4 °C for 30 mins, then centrifuged (20,000 × g, 4 °C, 30 min). The supernatant was collected for measuring the protein concentration with a BCA protein assay kit, using bovine serum albumin as a standard. Anti-HA magnetic beads (Thermo Scientific, MA) were washed with 0.05‥ TBS-T buffer and gently vortexed. Suspended magnetic beads were collected using magnetic stand for 5 min at room temperature (RT). HA-tagged protein samples were mixed into the pre-washed beads and gently rotated at 4 °C overnight. The beads were then collected with a magnetic stand and washed with TBS-T buffer and ultrapure-water twice, followed by elution in Laemmli buffer (95 °C, 5 min). After centrifugation, the reduced samples were loaded onto SDS-PAGE gels (12‥) for separation, followed by staining with Sypro Ruby in the dark for 12 h (supplemental Fig. S1). For reverse coimmunoprecipitation (IP), protein samples were mixed with 5–10 μg anti-ACTR1A or -MARCKSL1 antibody, after volume adjustment to 500 μl with IP lysis buffer. The samples were incubated for overnight at 4 °C with continuous mixing, then exposed to washed protein G magnetic beads (Thermo Scientific, MA) and incubated overnight at 4 °C with continuous mixing. Beads were collected using a magnetic stand, washed with washing buffer and ultra-pure water, then eluted in Laemmli buffer (95 °C, 5 min). The protein eluent was then separated by SDS-PAGE for immunoblotting. SDS-PAGE gel bands were excised and minced (six pieces for each gel band), squeezed with acetonitrile, and dried at room temperature. Proteins were then reduced and alkylated and digested with trypsin (porcine) (MS Grade) at 37 °C for overnight (30Kamal A.H.M. Chakrabarty J.K. Udden S.M.N. Zaki M.H. Chowdhury S.M. Inflammatory Proteomic Network Analysis of Statin-treated and Lipopolysaccharide-activated Macrophages.Sci. Rep. 2018; 8: 164Crossref PubMed Scopus (17) Google Scholar). Formic acid to pH < 3 was added to the resulting peptides, followed by drying by speed vacuum, and then dissolution in 0.1‥ formic acid. Finally, the peptides were centrifuged at 20,000 × g for 30 min at 4 °C. Digested peptides were analyzed by nano LC-MS/MS using a Velos Pro Dual-Pressure Linear Ion Trap Mass Spectrometer (ThermoFisher Scientific, MA) coupled to an UltiMate 3000 UHPLC (ThermoFisher Scientific, MA). Peptides were loaded onto the analytical column and separated by reverse-phase chromatography using a 15-cm column (Acclaim PepMap RSLC) with an inner diameter of 75 μm and packed with 2 μm C18 particles (Thermo Fisher Scientific, MA). The peptide samples were eluted from the nano column with multi-step gradients of 4–90‥ solvent B (A: 0.1‥ formic acid in water; B: 95‥ acetonitrile and 0.1‥ formic acid in water) over 70 min with a flow rate of 300 nL/min with a total run time of 90 min. The mass spectrometer was operated in positive ionization mode with nano spray voltage set at 2.50–3.00 kV and source temperature at 275 °C. The three precursor ions with the most intense signal in a full MS scan were consecutively isolated and fragmented to acquire their corresponding MS2 scans. Full MS scans were performed with 1 micro scan at resolution of 3000, and a mass range of m/z 350–1500. Normalized collision energy (NCE) was set at 35‥. Fragment ion spectra produced via high-energy collision-induced dissociation (CID) was acquired in the Linear Ion Trap with a resolution of 0.05 FWHM (full-width half maximum) with an Ultra Zoom-Scan between m/z 50–2000. A maximum injection volume of 5 μl was used during data acquisition with partial injection mode. The mass spectrometer was controlled in a data-dependent mode that toggled automatically between MS and MS/MS acquisition. MS/MS data acquisition and processing were performed by XcaliburTM software, ver. 2.2 (ThermoFisher Scientific, MA). Proteins were identified through Proteome Discoverer software (ver. 2.1, Thermo Fisher Scientific) using UniProt human (Homo sapiens) protein sequence database (120,672 sequences, and 44,548,111 residues). The reviewed protein sequences of human were downloaded from UniProt protein database (www.uniprot.org) on August 12, 2016. The considerations in SEQUEST searches for normal peptides were used with carbamidomethylation of cysteine as the static modification and oxidation of methionine as the dynamic modification. Trypsin was indicated as the proteolytic enzyme with two missed cleavages. Peptide and fragment mass tolerance were set at ± 1.6 and 0.6 Da and precursor mass range of 350–3500 Da, and peptide charges were set excluding +1 charge state. SEQUEST results were filtered with the target PSM validator to improve the sensitivity and accuracy of the peptide identification. Using a decoy search strategy, target false discovery rates for peptide identification of all searches were < 1‥ with at least two peptides per protein, a maximum of two missed cleavage, and the results were strictly filtered by ΔCn (< 0.01), Xcorr (≥ 1.5) for peptides, and peptide spectral matches (PSMs) with high confidence, that is, with q-value of ≤ 0.05. Proteins quantifications were conducted using the total spectrum count of identified proteins. Additional criteria were applied to increase confidence that PSMs must be present in all three biological replicates samples. Normalization of identified PSMs among LC-MS/MS runs was performed by dividing individual PSMs of proteins with total PSMs and average of ‥ PSM count was used for calculating fold changes for different treatment conditions (30Kamal A.H.M. Chakrabarty J.K. Udden S.M.N. Zaki M.H. Chowdhury S.M. Inflammatory Proteomic Network Analysis of Statin-treated and Lipopolysaccharide-activated Macrophages.Sci. Rep. 2018; 8: 164Crossref PubMed Scopus (17) Google Scholar, 31Kamal A.H.M. Fessler M.B. Chowdhury S.M. Comparative and network-based proteomic analysis of low dose ethanol- and lipopolysaccharide-induced macrophages.PLoS ONE. 2018; 13: e0193104Crossref PubMed Scopus (16) Google Scholar). For contrasting relative intensities of proteins between control, P3C, statin-P3C, and statin groups, samples were evaluated using cumulative confident normalized PSMs value. Protein-encoding genes were functionally categorized using gene ontology systems by PANTHER classification system-based biological processes, molecular activities, and cellular components (32Mi H. Poudel S. Muruganujan A. Casagrande J.T. Thomas P.D. PANTHER version 10: expanded protein families and functions, and analysis tools.Nucleic Acids Res. 2016; 44: D336-D342Crossref PubMed Scopus (648) Google Scholar). Protein abundances were visualized as a heat map. The cluster was generated by MeV software (ver. 4.9; http://www.tm4.org/) (33Saeed A.I. Sharov V. White J. Li J. Liang W. Bhagabati N. Braisted J. Klapa M. Currier T. Thiagarajan M. Sturn A. Snuffin M. Rezantsev A. Popov D. Ryltsov A. Kostukovich E. Borisovsky I. Liu Z. Vinsavich A. Trush V. Quackenbush J. 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