When I moved to UCSD, we began the Alliance for Cellular Signaling and our initial focus was B- and T-lymphocytes. Very soon, it became obvious that B-cells cannot be transfected easily and perturbation experiments were not an option. We shifted to studying a macrophage cell line, the RAW 264.7. This cell, which is a great model system for studying inflammation, became our mammalian systems biology learning playground for several years. We first used the phosphoproteomic and cytokine measurements using a multitude of ligands acting on the macrophages and developed a suite of novel computational methods. These methods led to very interesting biological insights. We mapped the common and distinct signaling pathways in macrophages, investigated the transcriptional response that were end points of the signaling cascade. Several papers resulted from this “learning experience” both in terms of methods development and mechanistic biology. Lipidmaps was initiated a few years after the Cell Signaling Project and the logical choice for the model system to study lipidomics was again the macrophage cell. Macrophages while being cytokine factories were also involved in physiological lipid changes especially in producing lipid mediators of pain and inflammation. The connection with atherosclerosis and insulin resistance was also an important factor in this choice. Given the mass spectrometric expertise in the consortium and the ever-improving transcriptomics methods, we were able to map completely the cellular lipidome in macrophage activation. We published the first mammalian lipidome paper and along with it came a host of statistical methodology that we developed to analyze integratively the transcriptome and the lipidome.
RELATED PUBLICATIONS
Information theoretic approach to complex biological network reconstruction: application to cytokine release in RAW 264.7 macrophages.
BMC Systems Biology. 2014 Jun 25; 8: 77 PubMed
Farhangmehr F, Maurya MR, Tartakovsky DM, Subramaniam S.
Time-varying causal inference from phosphoproteomic measurements in macrophage cells.
IEEE Transaction on Biomedical Circuits Systems. 2014 Feb; 8 (1):74-86 PubMed
Masnadi-Shirazi M, Maurya MR, Subramaniam S.
Modeling of eicosanoid fluxes reveals functional coupling between cyclooxygenases and terminal synthases.
Biophysical Journal. 2014 Feb 18; 106 (4): 966-75 PubMed
Kihara Y, Gupta S, Maurya MR, Armando A, Shah I, Quehenberger O, Glass CK, Dennis EA, Subramaniam S.
Omics approaches to microphage biology. Macrophages: Biology and Role in the Pathology of Diseases.
Gupta S, Dinasarapu AR, Gersten M, Maurya MR, Subramaniam S. (2014)
25-Hydroxycholesterol activates the integrated stress response to reprogram transcription and translation in macrophages.
Journal of Biological Chemistry. 2013 Dec 13; 288 (50): 35812–35823 PubMed
Shibata N, Carlin AF, Spann NJ, Saijo K, Morello CS, McDonald JG, Romanoski CE, Maurya MR, Kaikkonen MU, Lam MT, Crotti A, Reichart D, Fox JN, Quehenberger O, Raetz CR, Sullards MC, Murphy RC, Merrill AH Jr, Brown HA, Dennis EA, Fahy E, Subramaniam S, Cavener DR, Spector DH, Russell DW, Glass CK.
A combined omics study on activated macrophages–enhanced role of STATs in apoptosis, immunity and lipid metabolism.
Bioinformatics. 2013 Nov 1; 29 (21): 2735-2743 PubMed
Analysis of inflammatory and lipid metabolic networks across RAW264.7 and thioglycolate-elicited macrophages.
Journal of Lipid Research. 2013 Sep; 54 (9): 2525-2542 PubMed
Maurya MR, Gupta S, Li X, Fahy E, Dinasarapu AR, Sud M, Brown HA, Glass CK, Murphy RC, Russell DW, Dennis EA, Subramaniam S.
Regulated accumulation of desmosterol integrates macrophage lipid metabolism and inflammatory responses.
Cell. 2012 Sep 28; 151 (1): 138-52 PubMed
Spann NJ, Garmire LX, McDonald JG, Myers DS, Milne SB, Shibata N, Reichart D, Fox JN, Shaked I, Heudobler D, Raetz CR, Wang EW, Kelly SL, Sullards MC, Murphy RC, Merrill AH Jr, Brown HA, Dennis EA, Li AC, Ley K, Tsimikas S, Fahy E, Subramaniam S, Quehenberger O, Russell DW, Glass CK.
Mechanisms Establishing TLR4-Responsive Activation States of Inflammatory Response Genes.
PubMed
Escoubet-Lozach L, Benner C, Kaikkonen MU, Lozach J, Heinz S, Spann NJ, Crotti A, Stender J, Ghisletti S, Reichart D, Cheng CS, Luna R, Ludka C, Sasik R, Garcia-Bassets I, Hoffmann A, Subramaniam S, Hardiman G, Rosenfeld MG, Glass CK. (2011)
A global clustering algorithm to identify long intergenic non-coding RNA–with applications in mouse macrophages.
PLoS One. 2011; 6 (9): e24051 PubMed
Garmire LX, Garmire DG, Huang W, Yao J, Glass CK, Subramaniam S.
Integration of lipidomics and transcriptomics data towards a systems biology model of sphingolipid metabolism.
BMC Systems Biology. 2011 Feb 8; 5:26 PubMed
Gupta S, Maurya MR, Merrill AH Jr, Glass CK, Subramaniam S.
A mouse macrophage lipidome.
The Journal of Biological Chemistry. 2010 Dec 17; 285 (51): 39976-39985 PubMed
Dennis EA, Deems RA, Harkewicz R, Quehenberger O, Brown HA, Milne SB, Myers DS, Glass CK, Hardiman G, Reichart D, Merrill AH Jr, Sullards MC, Wang E, Murphy RC, Raetz CR, Garrett TA, Guan Z, Ryan AC, Russell DW, McDonald JG, Thompson BM, Shaw WA, Sud M, Zhao Y, Gupta S, Maurya MR, Fahy E, Subramaniam S.
Regulatory network of microRNAs in RAW 264.7 macrophage cells.
PubMed
Garmire LX, Shen Z, Briggs S, Yeo G, Subramaniam S, Glass C. (2010)
A Clustering Approach to Identify Intergenic Non-coding RNA in Mouse Macrophages.
PubMed
Garmire LX, Subramaniam S, Garmire DG, Glass CK. (2010)
Lipidomics reveals a remarkable diversity of lipids in human plasma.
PubMed
Quehenberger O, Armando AM, Brown AH, Milne SB, Myers DS, Merrill AH, Bandyopadhyay S, Jones KN, Kelly S, Shaner RL, Sullards CM, Wang E, Murphy RC, Barkley RM, Leiker TJ, Raetz CR, Guan Z, Laird GM, Six DA, Russell DW, McDonald JG, Subramaniam S, Fahy E, Dennis EA. (2010)
Subcellular organelle lipidomics in TLR-4-activated macrophages.
PubMed
Andreyev AY, Fahy E, Guan Z, Kelly S, Li X, McDonald JG, Milne S, Myers D, Park H, Ryan A, Thompson BM, Wang E, Zhao Y, Brown HA, Merrill AH, Raetz CR, Russell DW, Subramaniam S, Dennis EA. (2010)
Identification of crosstalk between phosphoprotein signaling pathways in RAW 264.7 macrophage cells.
PubMed
Gupta S, Maurya MR, Subramaniam S. (2010)
Application of proteomic marker ensembles to subcellular organelle identification.
PubMed
Andreyev AY, Shen Z, Guan Z, Ryan A, Fahy E, Subramaniam S, Raetz CR, Briggs S, Dennis EA. (2010)
An integrated model of eicosanoid metabolism and signaling based on lipidomics flux analysis.
PubMed
Gupta S, Maurya MR, Stephens DL, Dennis EA, Subramaniam S. (2009)
Systems biology of macrophages.
PubMed
Maurya MR, Benner C, Pradervand S, Glass C, Subramaniam S. (2007)
Identification of signaling components required for the prediction of cytokine release in RAW 264.7 macrophages.
PubMed
Pradervand S, Maurya MR, Subramaniam S. (2006)
Kdo2-Lipid A of Escherichia coli, a defined endotoxin that activates macrophages via TLR-4.
PubMed
Raetz CR, Garrett TA, Reynolds CM, Shaw WA, Moore JD, Smith DC Jr, Ribeiro AA, Murphy RC, Ulevitch RJ, Fearns C, Reichart D, Glass CK, Benner C, Subramaniam S, Harkewicz R, Bowers-Gentry RC, Buczynski MW, Cooper JA, Deems RA, Dennis EA. (2006)
Molecular determinants of crosstalk between nuclear receptors and toll-like receptors.
PubMed
Ogawa S, Lozach J, Benner C, Pascual G, Tangirala RK, Westin S, Hoffmann A, Subramaniam S, David M, Rosenfeld MG, Glass CK. (2005)