Analysis of a mathematical model of immune response to fungal infection

2021 ◽  
Vol 83 (1) ◽  
Author(s):  
Avner Friedman ◽  
King-Yeung Lam
2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Julie M. Steinbrink ◽  
Rachel A. Myers ◽  
Kaiyuan Hua ◽  
Melissa D. Johnson ◽  
Jessica L. Seidelman ◽  
...  

Abstract Background Candidemia is one of the most common nosocomial bloodstream infections in the United States, causing significant morbidity and mortality in hospitalized patients, but the breadth of the host response to Candida infections in human patients remains poorly defined. Methods In order to better define the host response to Candida infection at the transcriptional level, we performed RNA sequencing on serial peripheral blood samples from 48 hospitalized patients with blood cultures positive for Candida species and compared them to patients with other acute viral, bacterial, and non-infectious illnesses. Regularized multinomial regression was utilized to develop pathogen class-specific gene expression classifiers. Results Candidemia triggers a unique, robust, and conserved transcriptomic response in human hosts with 1641 genes differentially upregulated compared to healthy controls. Many of these genes corresponded to components of the immune response to fungal infection, heavily weighted toward neutrophil activation, heme biosynthesis, and T cell signaling. We developed pathogen class-specific classifiers from these unique signals capable of identifying and differentiating candidemia, viral, or bacterial infection across a variety of hosts with a high degree of accuracy (auROC 0.98 for candidemia, 0.99 for viral and bacterial infection). This classifier was validated on two separate human cohorts (auROC 0.88 for viral infection and 0.87 for bacterial infection in one cohort; auROC 0.97 in another cohort) and an in vitro model (auROC 0.94 for fungal infection, 0.96 for bacterial, and 0.90 for viral infection). Conclusions Transcriptional analysis of circulating leukocytes in patients with acute Candida infections defines novel aspects of the breadth of the human immune response during candidemia and suggests promising diagnostic approaches for simultaneously differentiating multiple types of clinical illnesses in at-risk, acutely ill patients.


1995 ◽  
Vol 03 (02) ◽  
pp. 429-439 ◽  
Author(s):  
S. G. RUDNEV ◽  
A. A. ROMANYUKHA

Using ordinary differential equations, we propose a mathematical model describing an “averaged” dynamics of variables involved in which some parameters are shown to be important characteristics of lung resistance. The model consists of modified D.A. Lauffenburger’s mathematical model for inflammatory reaction in lungs, and the model of humoral immune response (G. I. Marchuk). Coefficients are identified against clinical and experimental data. We attempt to elucidate some disease characteristics in terms of sensitivity analysis of model solutions with respect to parameters variations.


2016 ◽  
Vol 283 (1831) ◽  
pp. 20160499 ◽  
Author(s):  
Rebecca H. Chisholm ◽  
Mark M. Tanaka

Mycobacterium tuberculosis has an unusual natural history in that the vast majority of its human hosts enter a latent state that is both non-infectious and devoid of any symptoms of disease. From the pathogen perspective, it seems counterproductive to relinquish reproductive opportunities to achieve a détente with the host immune response. However, a small fraction of latent infections reactivate to the disease state. Thus, latency has been argued to provide a safe harbour for future infections which optimizes the persistence of M. tuberculosis in human populations. Yet, if a pathogen begins interactions with humans as an active disease without latency, how could it begin to evolve latency properties without incurring an immediate reproductive disadvantage? We address this question with a mathematical model. Results suggest that the emergence of tuberculosis latency may have been enabled by a mechanism akin to cryptic genetic variation in that detrimental latency properties were hidden from natural selection until their expression became evolutionarily favoured.


2018 ◽  
Vol 36 (3) ◽  
pp. 381-410 ◽  
Author(s):  
Angela M Jarrett ◽  
Meghan J Bloom ◽  
Wesley Godfrey ◽  
Anum K Syed ◽  
David A Ekrut ◽  
...  

Abstract The goal of this study is to develop an integrated, mathematical–experimental approach for understanding the interactions between the immune system and the effects of trastuzumab on breast cancer that overexpresses the human epidermal growth factor receptor 2 (HER2+). A system of coupled, ordinary differential equations was constructed to describe the temporal changes in tumour growth, along with intratumoural changes in the immune response, vascularity, necrosis and hypoxia. The mathematical model is calibrated with serially acquired experimental data of tumour volume, vascularity, necrosis and hypoxia obtained from either imaging or histology from a murine model of HER2+ breast cancer. Sensitivity analysis shows that model components are sensitive for 12 of 13 parameters, but accounting for uncertainty in the parameter values, model simulations still agree with the experimental data. Given theinitial conditions, the mathematical model predicts an increase in the immune infiltrates over time in the treated animals. Immunofluorescent staining results are presented that validate this prediction by showing an increased co-staining of CD11c and F4/80 (proteins expressed by dendritic cells and/or macrophages) in the total tissue for the treated tumours compared to the controls ($p < 0.03$). We posit that the proposed mathematical–experimental approach can be used to elucidate driving interactions between the trastuzumab-induced responses in the tumour and the immune system that drive the stabilization of vasculature while simultaneously decreasing tumour growth—conclusions revealed by the mathematical model that were not deducible from the experimental data alone.


2018 ◽  
Vol 5 (sup1) ◽  
pp. S178-S200
Author(s):  
Mohamed CH-Chaoui ◽  
Amina Eladdadi ◽  
Karima Mokni

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