Inflammation is critical for immunity but it is also a key determinant of a large number of human pathologies, if not as cause, then as promoter of progression. The expression of hundreds of genes is induced during inflammation, encompassing pro-inflammatory cytokines that function systemically, chemokines that recruit specific cell types to the site of infection, cell surface proteases for local tissue remodeling, itra-cellular innate immune effectors, regulators of cell survival and division, anti-inflammatory mediators and numerous signaling proteins that determine the progression of the inflammatory response and cellular responsiveness. Indeed, each pathologic threat, injury or inflammatory insult requires an appropriate and specific blend of these gene products to maximize the effectiveness of the inflammatory response and minimize its toxicity. Inflammatory responses are described as being stimulus-specific, mediated by stimulus-specific gene expression programs. It is well established that NF?B is the key regulator of inflammatory gene expression, and that the control of NF?B activity is highly dynamic due to several prominent delayed negative feedback loops. Prompted by our finding that NF?B dynamics are stimulus-specific, we posited the hypothesis that NF?B dynamics constitute a signaling code. According to this hypothesis, the dynamics of NF?B activity encode information about the stimulus that is then decoded by the nuclear gene regulatory network to produce stimulus-specific gene expression. While much progress has been made in elucidating the stimulus-encoding mechanisms, it has been remarkably difficult to develop a mechanistic understanding of how gene regulatory networks may decode stimulus-specific NF?B dynamics to produce stimulus-specific gene expression. Some reasons for the slow progress are the large numbers of possible stimulation conditions and the substantial cell-to-cell variability in NF?B dynamics - these necessitate high throughput single studies and novel data analysis approaches. Here we will combine (a) experimentally validated mathematical models (Hoffmann), (b) comprehensive microfluidics-enabled experimentation (Tay), and (c) new information theoretic approaches (Wollman) to (i) identify the dynamical features that mediate stimulus-specific cellular responses, (ii) quantify their reliability and information carrying capacity, and (iii) determine the gene regulatory strategies that decode these specific NF?B dynamical features. As a concerted collaborative effort, the proposed work will break through the current impasse in understanding how inflammatory gene expression programs are specified, thus revealing novel opportunities for therapeutic modulation in myriad pathologic settings. Further, as a model system for the field of signal transduction in general, the work will have broad impact in establishing concepts and workflow for the analysis of dynamical codes found also in other biological signaling systems.

Public Health Relevance

The transcription factor NF?B is the key regulator of inflammatory gene expression. NF?B activity is known to be highly dynamics, but it is unclear whether and how these dynamics mediate stimulus-specific responses, a question further complicated by the fact that there is substantial cell-to-cell variability in these dynamics. Here we will combine (a) experimentally validated mathematical models (Hoffmann), (b) comprehensive microfluidics-enabled experimentation (Tay), and (c) new information theoretic approaches (Wollman) to (i) identify the dynamical features that mediate stimulus-specific cellular responses, (ii) quantify their reliability and information carrying capacity, and (iii) determine the gene regulatory strategies that decode these specific NF?B dynamical features.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
1R01GM117134-01
Application #
9010757
Study Section
Modeling and Analysis of Biological Systems Study Section (MABS)
Program Officer
Resat, Haluk
Project Start
2016-02-15
Project End
2020-01-31
Budget Start
2016-02-15
Budget End
2017-01-31
Support Year
1
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of California Los Angeles
Department
Microbiology/Immun/Virology
Type
Schools of Medicine
DUNS #
092530369
City
Los Angeles
State
CA
Country
United States
Zip Code
90095
Kellogg, Ryan A; Tian, Chengzhe; Etzrodt, Martin et al. (2017) Cellular Decision Making by Non-Integrative Processing of TLR Inputs. Cell Rep 19:125-135
Piehler, Andreas; Ghorashian, Navid; Zhang, Ce et al. (2017) Universal signal generator for dynamic cell stimulation. Lab Chip 17:2218-2224
Cheng, Christine S; Behar, Marcelo S; Suryawanshi, Gajendra W et al. (2017) Iterative Modeling Reveals Evidence of Sequential Transcriptional Control Mechanisms. Cell Syst 4:330-343.e5
Heltberg, Mathias; Kellogg, Ryan A; Krishna, Sandeep et al. (2016) Noise Induces Hopping between NF-?B Entrainment Modes. Cell Syst 3:532-539.e3
Handly, L Naomi; Yao, Jason; Wollman, Roy (2016) Signal Transduction at the Single-Cell Level: Approaches to Study the Dynamic Nature of Signaling Networks. J Mol Biol 428:3669-82
Hoffmann, Alexander (2016) Immune Response Signaling: Combinatorial and Dynamic Control. Trends Immunol 37:570-2