Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2015 Dec 3;11(12):e1004460.
doi: 10.1371/journal.pcbi.1004460. eCollection 2015 Dec.

Computational Identification of Mechanistic Factors That Determine the Timing and Intensity of the Inflammatory Response

Affiliations
Free PMC article

Computational Identification of Mechanistic Factors That Determine the Timing and Intensity of the Inflammatory Response

Sridevi Nagaraja et al. PLoS Comput Biol. .
Free PMC article

Abstract

Timely resolution of inflammation is critical for the restoration of homeostasis in injured or infected tissue. Chronic inflammation is often characterized by a persistent increase in the concentrations of inflammatory cells and molecular mediators, whose distinct amount and timing characteristics offer an opportunity to identify effective therapeutic regulatory targets. Here, we used our recently developed computational model of local inflammation to identify potential targets for molecular interventions and to investigate the effects of individual and combined inhibition of such targets. This was accomplished via the development and application of computational strategies involving the simulation and analysis of thousands of inflammatory scenarios. We found that modulation of macrophage influx and efflux is an effective potential strategy to regulate the amount of inflammatory cells and molecular mediators in both normal and chronic inflammatory scenarios. We identified three molecular mediators - tumor necrosis factor-α (TNF-α), transforming growth factor-β (TGF-β), and the chemokine CXCL8 - as potential molecular targets whose individual or combined inhibition may robustly regulate both the amount and timing properties of the kinetic trajectories for neutrophils and macrophages in chronic inflammation. Modulation of macrophage flux, as well as of the abundance of TNF-α, TGF-β, and CXCL8, may improve the resolution of chronic inflammation.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Quantitative indices of a typical inflammatory response curve.
The timing indices (Tact and Ri) and the amount indices (Ψmax and Rp) are defined as follows. Ψmax: peak height for a model output variable (molecular species or cell type); Tmax: time at which a model output variable reaches the peak height value; Tact: time to reach peak height after inflammation initiation; T50: time for a model output variable to decay from its peak height value to 50% of the peak height; Ri: difference between T50 and Tact; Rp: the value of a model output variable at the right-most time point of the simulation, as a percentage of the peak height value.
Fig 2
Fig 2. Correlation analysis for the model parameters (P#) and the amount indices of the model output variables.
The shades of grey represent the absolute values of the correlation coefficients (CCs). Subplots a and c show raw absolute CC values for Ψmax and Rp, respectively. Subplots b and d show only the strong (i.e., CC ≥ 0.5) index-parameter associations for subplots a and c, respectively. See Table 1 for a list of all model variables and parameters.
Fig 3
Fig 3. Correlation analysis for the model parameters (P#) and the timing indices of the model output variables.
The shades of grey represent the absolute values of the CCs. Subplot a shows the raw absolute CC values for Tact calculated using the full set of 10,000 simulations. Subplots b, c, and d show only the strong (i.e., CC ≥ 0.5) index-parameter associations for Tact in the full set of 10,000 simulations, Tact in the “chronic” (see Materials and Methods) subset of simulations, and Ri in the “chronic” subset of simulations, respectively. See Table 1 for a list of all model variables and parameters.
Fig 4
Fig 4. Inflammation index regulation by modifying the model parameters identified in the sensitivity and correlation analyses.
Shown are the normalized total neutrophil (a) and total macrophage (b) concentrations during chronic (solid red) inflammation. We simulated chronic inflammation by increasing the macrophage influx rate parameter by 5 fold, a strategy we employed in our previous model [11]. These curves appeared in our previous model results (see Figure 5 in [11]) and are reproduced here for comparison purposes. Neutrophil restoration modifications included increasing the CXCL8 degradation rate by 5 fold (solid black), increasing the TGF-β degradation rate by 15 fold (dashed black), and increasing TNF-α inhibition by TGF-β by 5 fold (dotted black). Macrophage restoration modifications comprised increasing the TNF-α degradation rate by 20 fold (black solid), increasing the macrophage efflux rate by 2 fold (dashed black), and increasing IL-10 production rate by 5 fold (dotted black). These parameter-specific fold changes were chosen so as to reduce the respective parameter-specific target inflammation indices by at least ~10% during a chronic inflammatory scenario (the target indices are defined in the description of the six parameter modification strategies in the Results Section). All the model-predicted values were normalized to the respective maximum values from the chronic inflammation simulations. The tables in the figure show the quantitative index values calculated from the respective simulated kinetic trajectories.
Fig 5
Fig 5. Total neutrophil and total macrophage concentrations for inflammatory mediator inhibition by different inhibitor concentrations.
Green and red lines represent model predictions for acute and chronic inflammation, respectively. We simulated chronic inflammation by increasing the initial platelet concentration by 100 fold, increasing the macrophage influx rate parameter by 1.5 fold, and simultaneously decreasing the platelet degradation rate parameter by 1.5 fold. We chose to increase the macrophage flux rate and decrease the platelet degradation rate because these rates were identified as robust regulators of the Ψmax and Tact indices for many inflammatory cellular and molecular components, as seen in our sensitivity and correlation analysis results (Table 2 and Figs 2 and 3). Moreover, the role of macrophage influx rate as a chronic inflammation driver has been shown in our previous computational analysis [11]. Experimental studies indicate that a relatively higher inflammation-inducing stimulus often results in chronic inflammation [17, 34]. Thus, we chose to modify the initial platelet concentration, which reflects injury severity in our model. The fold increase/decrease in these parameters was chosen based on the ability of the parametric changes to simulate the experimentally determined inflammatory trajectories from [34]. Black lines represent model predictions for different inhibitor concentrations for the respective mediators. Each inhibitor concentration was added at the time of inflammation initiation (t = 0) during chronic inflammation. a and d: Normalized neutrophil and macrophage concentrations during CXCL8 inhibition for inhibitor concentrations of 10 nM (dotted black), 100 nM (dashed black), and 500 nM (solid black). b and e: Normalized neutrophil and macrophage concentrations during TNF-α inhibition for inhibitor concentrations of 10 nM (dotted black), 100 nM (dashed black), and 500 nM (solid black). c and f: Normalized neutrophil and macrophage concentration during combined inhibition of both CXCL8 and TNF-α, with each inhibitor concentration set to 200 nM (solid black). Also shown are the normalized neutrophil and macrophage concentrations for individual inhibition of CXCL8 (dotted black) and TNF-α (dashed black), with the respective inhibitor concentrations equal to 200 nM. All model-predicted values were normalized to the respective maximum values from the chronic inflammation simulations.
Fig 6
Fig 6. Total neutrophil and total macrophage concentrations for inflammatory mediator inhibition at different time points.
Green and red lines represent model predictions for acute and chronic inflammation, respectively. We simulated chronic inflammation by increasing the initial platelet concentration by 100 fold, increasing the macrophage influx rate parameter by 1.5 fold, and simultaneously decreasing the platelet degradation rate parameter by 1.5 fold. In all the subplots, the time points of inhibitor addition (represented by the black lines) are as follows: dotted, 24 h; dashed, 48 h; and solid, 72 h. Shown are normalized neutrophil (a-c) and normalized macrophage (d-f) kinetics for CXCL8, TNF-α, and combined CXCL8 + TNF-α inhibition. The concentration of each of the considered inhibitors was equal to 200 nM. All model-predicted values were normalized to the respective maximum values from the chronic inflammation simulations. In our simulations, the 72 h time point was chosen for mediator inhibitor addition based on an experimental study that evaluated the efficacy of several pro-resolution drugs in a mouse peritoneal infection model [17]. Furthermore, we chose the 24 h and 48 h time points for mediator inhibitor addition because they represent the times at which the neutrophil and macrophage concentrations peak, respectively, in our acute inflammation simulation [11].

Similar articles

See all similar articles

Cited by 2 articles

References

    1. Bannenberg GL, Chiang N, Ariel A, Arita M, Tjonahen E, Gotlinger KH, et al. Molecular circuits of resolution: formation and actions of resolvins and protectins. J Immunol. 2005;174: 4345–4355. - PubMed
    1. Nathan C, Ding A. Nonresolving inflammation. Cell. 2010;140: 871–882. 10.1016/j.cell.2010.02.029 - DOI - PubMed
    1. Kamaly N, Fredman G, Subramanian M, Gadde S, Pesic A, Cheung L, et al. Development and in vivo efficacy of targeted polymeric inflammation-resolving nanoparticles. Proc Natl Acad Sci U S A. 2013;110: 6506–6511. 10.1073/pnas.1303377110 - DOI - PMC - PubMed
    1. Ortega-Gomez A, Perretti M, Soehnlein O. Resolution of inflammation: an integrated view. EMBO Mol Med. 2013;5: 661–674. 10.1002/emmm.201202382 - DOI - PMC - PubMed
    1. Maskrey BH, Megson IL, Whitfield PD, Rossi AG. Mechanisms of resolution of inflammation: a focus on cardiovascular disease. Arterioscler Thromb Vasc Biol. 2011;31: 1001–1006. 10.1161/ATVBAHA.110.213850 - DOI - PubMed

Publication types

Substances

Grant support

The authors were supported by the Clinical and Rehabilitative Medicine Research Program of the U.S. Army Medical Research and Materiel Command, Fort Detrick, MD (http://mrmc.amedd.army.mil/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Feedback