Mechanistic modeling explains the dsRNA length-dependent activation of the RIG-I mediated immune response

2020 ◽  
Vol 500 ◽  
pp. 110336
Author(s):  
Darius Schweinoch ◽  
Pia Bachmann ◽  
Diana Clausznitzer ◽  
Marco Binder ◽  
Lars Kaderali
Keyword(s):  
Immune Response ◽  
Mechanistic Modeling ◽  
Length Dependent Activation

Mechanistic modeling of the SARS-CoV-2 and immune system interplay unravels design principles for diverse clinicopathological outcomes

2020 ◽  
Author(s):  
Sarthak Sahoo ◽  
Kishore Hari ◽  
Siddharth Jhunjhunwala ◽  
Mohit Kumar Jolly
Keyword(s):  
Immune Response ◽  
Immune System ◽  
Tissue Damage ◽  
Systems Approach ◽  
Wide Spectrum ◽  
Mechanistic Modeling ◽  
Public Healthcare ◽  
Adaptive Immune ◽  
Co Morbidity ◽  
Infected Cells

AbstractThe disease caused by SARS-CoV-2 is a global pandemic that threatens to bring long-term changes worldwide. Approximately 80% of infected patients are asymptomatic or have mild symptoms such as fever or cough, while rest of the patients have varying degrees of severity of symptoms, with 3-4% mortality rate. Severe symptoms such as pneumonia and Acute Respiratory Distress Syndrome can be caused by tissue damage mostly due to aggravated and unresolved innate and adaptive immune response, often resulting from a cytokine storm. However, the mechanistic underpinnings of such responses remain elusive, with an incomplete understanding of how an intricate interplay among infected cells and cells of innate and adaptive immune system can lead to such diverse clinicopathological outcomes. Here, we use a dynamical systems approach to dissect the emergent nonlinear intra-host dynamics among virally infected cells, the immune response to it and the consequent immunopathology. By mechanistic analysis of cell-cell interactions, we have identified key parameters affecting the diverse clinical phenotypes associated with COVID-19. This minimalistic yet rigorous model can explain the various phenotypes observed across the clinical spectrum of COVID-19, various co-morbidity risk factors such as age and obesity, and the effect of antiviral drugs on different phenotypes. It also reveals how a fine-tuned balance of infected cell killing and resolution of inflammation can lead to infection clearance, while disruptions can drive different severe phenotypes. These results will help further the case of rational selection of drug combinations that can effectively balance viral clearance and minimize tissue damage simultaneously.Significance StatementThe SARS-CoV-2 pandemic has already infected millions of people, and thousands of lives have been lost to it. The pandemic has already tested the limits of our public healthcare systems with a wide spectrum of clinicopathological symptoms and outcomes. The mechanistic underpinnings of the resultant immunopathology caused by the viral infection still remains to be elucidated. Here we propose a minimalistic but rigorous description of the interactions of the virus infected cells and the core components of the immune system that can potentially explain such diversity in the observed clinical outcomes. Our proposed framework could enable a platform to determine the efficacy of various treatment combinations and can contributes a conceptual understanding of dynamics of disease pathogenesis in SARS-CoV-2 infections.

scholarly journals Mechanistic Modeling of Mycobacterium tuberculosis Infection in Murine Models for Drug and Vaccine Efficacy Studies

2020 ◽  
Vol 64 (3) ◽  
Author(s):  
Nan Zhang ◽  
Natasha Strydom ◽  
Sandeep Tyagi ◽  
Heena Soni ◽  
Rokeya Tasneen ◽  
...  
Keyword(s):  
Immune Response ◽  
Mycobacterium Tuberculosis ◽  
Vaccine Efficacy ◽  
Vaccine Development ◽  
Bacterial Load ◽  
Mechanistic Modeling ◽  
Response Dynamics ◽  
Content Type ◽  
Immune Effect ◽  
Adaptive Immune

ABSTRACT Tuberculosis (TB) drug, regimen, and vaccine development rely heavily on preclinical animal experiments, and quantification of bacterial and immune response dynamics is essential for understanding drug and vaccine efficacy. A mechanism-based model was built to describe Mycobacterium tuberculosis H37Rv infection over time in BALB/c and athymic nude mice, which consisted of bacterial replication, bacterial death, and adaptive immune effects. The adaptive immune effect was best described by a sigmoidal function on both bacterial load and incubation time. Applications to demonstrate the utility of this baseline model showed (i) the important influence of the adaptive immune response on pyrazinamide (PZA) drug efficacy, (ii) a persistent adaptive immune effect in mice relapsing after chemotherapy cessation, and (iii) the protective effect of vaccines after M. tuberculosis challenge. These findings demonstrate the utility of our model for describing M. tuberculosis infection and corresponding adaptive immune dynamics for evaluating the efficacy of TB drugs, regimens, and vaccines.

scholarly journals Genetic Algorithms for model refinement and rule discovery in a high-dimensional agent-based model of inflammation

2019 ◽  
Author(s):  
R Chase Cockrell ◽  
Gary An
Keyword(s):  
Immune Response ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Innate Immune ◽  
Mechanistic Modeling ◽  
Agent Based ◽  
Multi Scale ◽  
Model Rule ◽  
Rule Set ◽  
Biological Heterogeneity

AbstractIntroductionAgent-based modeling frequently used modeling method for multi-scale mechanistic modeling. However, the same properties that make agent-based models (ABMs) well suited to representing biological systems also present significant challenges with respect to their construction and calibration, particularly with respect to the selection of potential mechanistic rules and the large number of free parameters often present in these models. We have proposed that various machine learning approaches (such as genetic algorithms (GAs)) can be used to more effectively and efficiently deal with rule selection and parameter space characterization; the current work applies GAs to the challenge of calibrating a complex ABM to a specific data set, while preserving biological heterogeneity.MethodsThis project uses a GA to augment the rule-set for a previously validated ABM of acute systemic inflammation, the Innate Immune Response ABM (IIRABM) to clinical time series data of systemic cytokine levels from a population of burn patients. The genome for the GA is a vector generated from the IIRABM’s Model Rule Matrix (MRM), which is a matrix representation of not only the constants/parameters associated with the IIRABM’s cytokine interaction rules, but also the existence of rules themselves. Capturing heterogeneity is accomplished by a fitness function that incorporates the sample value range (“error bars”) of the clinical data.ResultsThe GA-enabled parameter space exploration resulted in a set of putative MRM rules and associated parameterizations which closely match the cytokine time course data used to design the fitness function. The number of non-zero elements in the MRM increases significantly as the model parameterizations evolve towards a fitness function minimum, transitioning from a sparse to a dense matrix. This results in a model structure that more closely resembles (at a superficial level) the structure of data generated by a standard differential gene expression experimental study.ConclusionWe present an HPC-enabled evolutionary computing approach to calibrate a complex ABM to clinical data while preserving biological heterogeneity. The integration of machine learning, HPC, and multi-scale mechanistic modeling provides a pathway forward to effectively represent the heterogeneity of clinical populations and their data.Author SummaryIn this work, we utilize genetic algorithms (GA) to operate on the internal rule set of a computational of the human immune response to injury, the Innate Immune Response Agent-Based Model (IIRABM), such that it is iteratively refined to generate cytokine time series that closely match what is seen in a clinical cohort of burn patients. At the termination of the GA, there exists an ensemble of candidate model rule-sets/parameterizations which are validated by the experimental data;

Humoral and cellular immune response to a crude exo-antigen and purified keratinase of Microsporum canis in experimentally infected guinea pigs

1999 ◽  
Vol 37 (2) ◽  
pp. 123-129 ◽  
Author(s):  
B. R. Mignon ◽  
T. Leclipteux ◽  
CH. Focant ◽  
A. J. Nikkels ◽  
G. E. PIErard ◽  
...  
Keyword(s):  
Immune Response ◽  
Cellular Immune Response ◽  
Guinea Pigs ◽  
Microsporum Canis ◽  
Cellular Immune

Author response for "Diabetes alters immune response patterns to acute melioidosis in humans"

2019 ◽  
Author(s):  
Barbara Kronsteiner ◽  
Panjaporn Chaichana ◽  
Manutsanun Sumonwiriya ◽  
Kemajitra Jenjaroen ◽  
Fazle Rabbi Chowdhury ◽  
...  
Keyword(s):  
Immune Response ◽  
Author Response ◽  
Response Patterns

The humoral and cellular immune response of sheep against Borna disease virus in endemic and non-endemic areas

2004 ◽  
Vol 146 (4) ◽  
pp. 159-172 ◽  
Author(s):  
D. Müller-Doblies ◽  
S. Baumann ◽  
P. Grob ◽  
A. Hülsmeier ◽  
U. Müller-Doblies ◽  
...  
Keyword(s):  
Immune Response ◽  
Disease Virus ◽  
Cellular Immune Response ◽  
Borna Disease Virus ◽  
Endemic Areas ◽  
Cellular Immune ◽  
Borna Disease

Oral Immune Activation by Disgust and Disease-Related Pictures

2015 ◽  
Vol 29 (3) ◽  
pp. 119-129 ◽  
Author(s):  
Richard J. Stevenson ◽  
Deborah Hodgson ◽  
Megan J. Oaten ◽  
Luba Sominsky ◽  
Mehmet Mahmut ◽  
...  
Keyword(s):  
Immune Response ◽  
Immune Responses ◽  
Innate Immune Response ◽  
Negative Control ◽  
Innate Immune ◽  
Innate Immune Responses ◽  
Immune Markers ◽  
Before And After ◽  
Secondary Analyses ◽  
Image Set

Abstract. Both disgust and disease-related images appear able to induce an innate immune response but it is unclear whether these effects are independent or rely upon a common shared factor (e.g., disgust or disease-related cognitions). In this study we directly compared these two inductions using specifically generated sets of images. One set was disease-related but evoked little disgust, while the other set was disgust evoking but with less disease-relatedness. These two image sets were then compared to a third set, a negative control condition. Using a wholly within-subject design, participants viewed one image set per week, and provided saliva samples, before and after each viewing occasion, which were later analyzed for innate immune markers. We found that both the disease related and disgust images, relative to the negative control images, were not able to generate an innate immune response. However, secondary analyses revealed innate immune responses in participants with greater propensity to feel disgust following exposure to disease-related and disgusting images. These findings suggest that disgust images relatively free of disease-related themes, and disease-related images relatively free of disgust may be suboptimal cues for generating an innate immune response. Not only may this explain why disgust propensity mediates these effects, it may also imply a common pathway.

Sex differences in immune response to a social stressor

2013 ◽  
Author(s):  
Christopher Gelety ◽  
Lauren Johnson ◽  
Melissa Birkett
Keyword(s):  
Immune Response ◽  
Sex Differences ◽  
Social Stressor
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