# Exploration of the macrophage – virus interactions during a PRRSV infection by a modelling approach

Natacha Go, Caroline C. Bidot, Catherine C. Belloc, Suzanne Touzeau

► To cite this version:

Natacha Go, Caroline C. Bidot, Catherine C. Belloc, Suzanne Touzeau. Exploration of the macrophage – virus interactions during a PRRSV infection by a modelling approach. 64th Annual Meeting of the European Association for Animal Production (EAAP), Aug 2013, Nantes, France., 2013. hal-02744986

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Submitted on 7 Aug 2023

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Distributed under a Creative Commons Attribution 4.0 International License
# EXPLORATION OF THE MACROPHAGE – VIRUS INTERACTIONS
## DURING PRRSV INFECTION BY A MODELLING APPROACH

Natacha Go¹,², Caroline Bidot¹, Catherine Belloc², Suzanne Touzeau³

natacha.go@jouy.inra.fr

1. INRA, UR341 MIA, F-78350 Jouy-en-Josas, France
2. Oniris / INRA, UMR1300 BioEpAR, F-44307 Nantes, France

3. INRA, UMR1355 ISA, F-06900 Sophia-Antipolis, France. Inria, Biocore, F-06900 Sophia-Antipolis, France

## Introduction

The Porcine Respiratory and Reproductive Syndrome Virus (PRRSV) responsible for reproductive failures and production losses is a major concern for swine industry. To improve the efficiency of control measures, one challenge is the identification of the immune mechanisms allowing to reduce the infection severity and duration. Several studies discuss the influence of the immune functions of the target cells (macrophages), the role of the cytotoxic cells involving a key anti-viral cytokine (IFNγ) and the humoral response involving a key immuno-modulatory cytokine (IL₁₀). The high between-host variability and the heterogeneity of viral strains in the field increase the uncertainties. To unravel these limits, we proposed here a modelling approach.

**Aims**: (i) Provide a detailed representation of the immune and infection dynamics in the infection place, the lung.

(ii) Estimate the model parameters resulting in simulations covering the response variability.

(iii) Identify the immune mechanisms determining the infection duration in various scenarios of virulence and susceptibility.

## Modelling Approach

**Summary** Deterministic ODE model, 14 state variables, 28 parameters, complex and non-linear interactions ⇒ difficult to calibrate. Detailed representation of the macrophage – virus interactions and its regulations involving cytokines and adaptive cells.

<figure><img src="image_2.png" /></figure>

### Cells

**Macrophages** : susceptible macrophages $M_s$ activation by virus $V \Rightarrow$ phagocytosis $M_p$ or infection $M_e$.

**Natural killers** : NK destroy the infected cells.

**Adaptive cells** : the cellular $cR$ destroy infected cells, the humoral $hR$ neutralize free viral particles and the regulatory $rR$ inhibit all immune mechanisms.

### Cytokines

**Inflammatory** : $P_i = IL_{1\beta} + IL_6 + IL_8$ amplify the recruitment of $M_s$ and NK.

**Anti-viral** : $IFN_\gamma$ and $A_i = TNF_\alpha + IFN_\alpha$ promote phagocytosis, inhibit viral replication, orientate towards the $cR$ response.

**Immuno-regulatory** : $IL_{12}$ has a similar effect than $IFN_\gamma$, $IL_{10}$ and $TGF\beta$ have a nearly opposite effect.

$$ \begin{aligned} \dot{M}_s = & A_m \left(1 + \frac{V_m P_i}{k_m + P_i}\right) \\ & - \eta M_s V \frac{k_m}{k_m + IL_{10} + TGF\beta} \left(1 + \frac{V_m (A_i + IFN_\gamma)}{k_m + A_i + IFN_\gamma}\right) \\ & + \gamma M_p \frac{k_m}{k_m + IL_{10}} \left(1 + \frac{V_m (A_i + IFN_\gamma)}{k_m + A_i + IFN_\gamma}\right) \\ & - \beta M_s V \frac{k_m}{k_m + A_i + IFN_\gamma + TGF\beta} \left(1 + \frac{V_m IL_{10}}{k_m + IL_{10}}\right) \\ & - M_s \left( \mu_M^{\text{nat}} + \mu_M^{\text{inf}} \frac{A_i}{k_m + A_i} \right) \end{aligned} $$

EQUATION of the dynamic of susceptible macrophages

<figure><img src="image_3.png" /></figure>

## Parameter Range Estimation

**Method** Few experimental data, high variability and uncertainty ⇒ ad hoc method :

(i) Simulations from large parameter range (10 000, LHS design) – FIG. a

(ii) Definition of quantitative criteria for realistic dynamics based on experimental data – FIG. b, grey area

(iii) Identification of realistic parameter sets (17) – FIG. a in grey

(iv) Selection of representative parameter sets (2) – FIG. b

(v) Robustness : parameters ± 5% (2 sets x 10 000 simulations, LHS designs) – FIG. b

<caption>FIGURE b.</caption>

### Results

<figure><img src="image_4.png" /><figcaption>FIGURE a.</figcaption></figure>

<figure><img src="image_5.png" /></figure>

**Conclusions** 2 parameter ranges generating contrasted viral dynamics (short duration & high peak vs long duration & low peak) associated with realistic immune dynamics (results not shown).

## Infection Duration

**Method** Comparison of scenarios. Reference scenario **S0**. Low **S1** vs high **S2** infection capacity. Low **SA** vs high **SB** anti-viral capacity. High **S2A** vs low **S1B** virulence and susceptibility – See **Viral titer**

**Results** Immune mechanisms determining the infection duration :

- **SA & S2A** ⇒ long duration, % $IL_{10} \ge 90$, strong $hR$ and $rR$.

- **SB & S1B** ⇒ short duration, % $IFN_\gamma \ge 80$, strong $cR$.

- **S2** (high rate of viral replication – e) ⇒ long duration, high $IFN_\gamma$ and $cR$.

- **S1** (low e) ⇒ short duration, high $IL_{10}$ and $hR$.

**Conclusions** Various mechanisms can determine the infection duration ⇒ Take into account the variability between hosts and strains to improve the control measures.

<figure><img src="image_6.png" /><figcaption>TABLE. Correlations with the infection duration ($R^2 \ge 0.7$).</figcaption></figure>

<table><thead><tr><th>Groups</th><th>% IFN<sub>γ</sub></th><th>% IL<sub>10</sub></th><th>% TGF<sub>β</sub></th><th>M<sub>p</sub></th><th>M<sub>i</sub></th><th>P<sub>i</sub></th><th>A<sub>i</sub></th></tr></thead><tbody><tr><td>S1, S2</td><td>+</td><td>-</td><td>--</td><td>--</td><td>+</td><td>+</td><td>++</td></tr><tr><td>SA, SB</td><td>--</td><td>++</td><td>++</td><td>++</td><td>+++</td><td>++</td><td>--</td></tr><tr><td>S1B, S2A</td><td>-</td><td>+</td><td>+</td><td>++</td><td>+++</td><td>-</td><td></td></tr></tbody></table>

## Perspectives

**Model extension** to represent the dynamics not only in the lung but in whole pig ⇒ integration of dendritic cells and more detailed representation of the adaptive response.

Use of the model to :

(i) Test the efficiency of **individual control measures** such as vaccination and propose directions to better control the infection

(iii) Identify the key immune mechanisms that drive the infection resolution, to integrate them in an **immuno-epidemiological model at the population scale**.

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>