key: cord-0278619-23qaiwin authors: Ewald, Jan; Rivieccio, Flora; Radosa, Lukáš; Schuster, Stefan; Brakhage, Axel A.; Kaleta, Christoph title: Dynamic optimization reveals alveolar epithelial cells as key mediators of host defense in invasive aspergillosis date: 2021-05-14 journal: bioRxiv DOI: 10.1101/2021.05.12.443764 sha: a64d8c3bbf7986f703bacbc4aa44ae1e8ae14784 doc_id: 278619 cord_uid: 23qaiwin Aspergillus fumigatus is an important human fungal pathogen and its conidia are constantly inhaled by humans. In immunocompromised individuals, conidia can grow out as hyphae that damage lung epithelium. The resulting invasive aspergillosis is associated with devastating mortality rates. Since infection is a race between the innate immune system and the outgrowth of A. fumigatus conidia, we use dynamic optimization to obtain insight into the recruitment and depletion of alveolar macrophages and neutrophils. Using this model, we obtain key insights into major determinants of infection outcome on host and pathogen side. On the pathogen side, we predict in silico and confirm in vitro that germination speed is a key virulence trait of fungal pathogens due to the vulnerability of conidia against host defense. On the host side, we find that epithelial cells play a so far underappreciated role in fungal clearance and are potent mediators of cytokine release which we confirm ex vivo. Further, our model affirms the importance of neutrophils in invasive aspergillosis and underlines that the role of macrophages remains elusive. We expect that our model will contribute to improvement of treatment protocols by focusing on the critical components of immune response to fungi but also fungal virulence. Since we constantly inhale microorganisms, the human lung is an entry point for opportunistic Fig. 1 ). In addition, we explicitly model AEC 84 as interactive cells and consider a single dosage scenario of fungal conidia exposure. The latter 85 modeling decision enables comparison of results and parameters to experimental animal models, 86 which mainly use single dosage regimes [34, 35] . 87 Our model based on ordinary differential equations (ODE) considers time-dependent transition 88 kinetics to describe the process of swelling and germination of conidia (detailed description in 89 Subsection 4.1). After germination, growth of A. fumigatus as hyphae depends on the presence 90 of AEC as resource. Importantly, we accurately model interaction of each cell type with all host 91 cell types. Resting conidia are not recognized or killed due to their coating and swollen conidia 92 are phagocytosed and killed by AM, neutrophils and AEC. Since AM and AEC are unable to 93 phagocytose and kill larger hyphae at reasonable rates [36, 37] , we only model killing of hyphae 94 by neutrophils. 95 Host cell dynamics are characterized by damage, host or pathogen mediated, and transmigra-96 tion of immune cells. In our dynamic optimization model, AEC face lysis by hyphae and damage 97 by activated immune cells. While AM and neutrophils like AEC show cell death upon interaction 98 with fungal cells, their transmigration upon infection is modeled by recruitment and depletion. The 99 maximal rate of recruitment and depletion is linked to the presence of pro-inflammatory cytokines 100 and is optimized in our dynamic optimization approach via the control variables u 1−4 (see Figure 101 1 and cf. Material and Methods 4.1). In our model we capture the release of cytokines by AM as 102 well as AEC to reveal their respective contribution during infection. 103 As host objectives we define two main goals, which are optimized during infection. Firstly, Figure 3 : Influence of parameters on the outcome of infection depicted by the contribution to variance (colored from white, yellow to red from no to high influence). This relative contribution is based on a Spearman rank correlation of the parameter value and the objective value of the optimal solution (see Material and Methods 4.2). Parameters are grouped based on their relation to the cell types: alveolar epithelial cells (AEC), resting (RES) or swollen conidia (SWO), hyphae (HYP), alveolar macrophages (AM) and neutrophils (NEU). We list in addition the cytokine (CYT) property c3 (decay rate). We determined decisive parameters for the outcome of infection by calculating the contribution 140 to variance in the objective function of each randomized parameter (see Material and Methods 141 4.2). Further, we simulated healthy mice and a lack of immune cells as well as the influence of low 142 or high doses of conidia. Across all scenarios we see that the fungal parameters s 1 (germination 143 time of a swollen conidium) and h 1 (hyphal growth rate) are most decisive for infection outcome 144 since they explain more than 50% of the variance in the objective function (see Figure 3 ). This 145 illustrates that a fast germination of swollen conidia is a strong virulence factor, because it is the 146 most vulnerable growth state of the pathogen. The growth rate of hyphae is in addition crucial in 147 the race between neutrophils (recruitment and hyphal killing) and A. fumigatus. On the host side, we see that the importance of some parameters differs significantly when Figure 4A and B). Our model predicts 176 here a non-linear but distinctive relationship between the germination time and epithelial damage 177 after 24h (see Figure 4C ). comparable results (see Figure 4E ). This underlines the importance of further investigations to 185 understand differences between the human host and rodent model organisms. Further, it suggests 186 an avoidance of elevated epithelial damage by A. fumigatus and its ability to hide and escape in 187 AEC during the human immune response. 188 The accordance of modeling prediction with experimental data on germination as well as cyto-189 toxicity shows that our dynamic optimization approach is able to identify key parameters of invasive In the multicellular growth state of hyphae the number of A. fumigatus cells depends on the 315 rate of germination as well as the growth rate (h 1 ) of hyphae, which we link to the presence of 316 AEC as a resource for growth. We model killing of hyphae by neutrophils and obtain the following 317 description of hyphal dynamics: The number of AEC is influenced in our model by the lysis induced by fungal hyphae and the 319 damage originating from active AM and neutrophils. The tissue damage by immune cells is often 320 ignored in computational models, but is crucial to reflect recruitment and depletion of immune cells 321 [60]. To this end, we further connect tissue damage with the pro-inflammatory cytokine level C: To determine the optimal innate immune response during invasive aspergillosis, the above 348 described dynamic system has to fulfill the following constraints and objectives. Intuitively, state 349 variables describing cell numbers are positive, pro-inflammatory cytokine level as well as control 350 variables describing recruitment and depletion of immune cells range between 0 and 1: alveolar epithelial cells resting conidia swollen conidia hyphae alveolar macrophages neutrophils Lung infection-a public health priority Lung infections after cancer chemotherapy Menacing mold: the molecular biology of Aspergillus fumigatus Aspergillus fumigatus: saprophyte or pathogen? 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