Abstract:The viral infectivity factor (Vif) was found to be essential for controlling HIV-1 virus infectivity. It targets cellular antiviral proteins in APOBEC family (APO) to trigger its fast degradation and inhibits APO packaging into nascent virion. In the present study, we propose a mathematical model to quantitatively study the intracellular dynamics of these typical virus-host interactions. Four sets of published experimental data were compared with simulation results to justify the model. Systematic parameter se… Show more
“… Figure 3 Combined model of HIV-1 infection and cell surface peptide presentation on MHCI molecules. Diagrammatic representation of the combination of the separate models that comprise the combined model: the HIV kinetics models 18 , 29 , 30 , and the peptide filtering model 33 . …”
Section: Resultsmentioning
confidence: 99%
“…The model incorporates three existing ODE (ordinary differential equation) models of HIV-1 intracellular kinetics: Kim & Yin 29 , Reddy & Yin 18 and Wang & LuHua 30 . HIV has been studied in detail and all three of these models are based on a wealth of experimental data that characterizes the rates of HIV mRNA transcription, ratios of HIV protein translation and half-lives, protein cytoplasmic concentrations and interactions with host proteins, and the mechanisms and kinetics of viral replication and virion budding.…”
The rate of progression of HIV infected individuals to AIDS is known to vary with the genotype of the host, and is linked to their allele of human leukocyte antigen (HLA) proteins, which present protein degradation products at the cell surface to circulating T-cells. HLA alleles are associated with Gag-specific T-cell responses that are protective against progression of the disease. While Pol is the most conserved HIV sequence, its association with immune control is not as strong. To gain a more thorough quantitative understanding of the factors that contribute to immunodominance, we have constructed a model of the recognition of HIV infection by the MHC class I pathway. Our model predicts surface presentation of HIV peptides over time, demonstrates the importance of viral protein kinetics, and provides evidence of the importance of Gag peptides in the long-term control of HIV infection. Furthermore, short-term dynamics are also predicted, with simulation of virion-derived peptides suggesting that efficient processing of Gag can lead to a 50% probability of presentation within 3 hours post-infection, as observed experimentally. In conjunction with epitope prediction algorithms, this modelling approach could be used to refine experimental targets for potential T-cell vaccines, both for HIV and other viruses.
“… Figure 3 Combined model of HIV-1 infection and cell surface peptide presentation on MHCI molecules. Diagrammatic representation of the combination of the separate models that comprise the combined model: the HIV kinetics models 18 , 29 , 30 , and the peptide filtering model 33 . …”
Section: Resultsmentioning
confidence: 99%
“…The model incorporates three existing ODE (ordinary differential equation) models of HIV-1 intracellular kinetics: Kim & Yin 29 , Reddy & Yin 18 and Wang & LuHua 30 . HIV has been studied in detail and all three of these models are based on a wealth of experimental data that characterizes the rates of HIV mRNA transcription, ratios of HIV protein translation and half-lives, protein cytoplasmic concentrations and interactions with host proteins, and the mechanisms and kinetics of viral replication and virion budding.…”
The rate of progression of HIV infected individuals to AIDS is known to vary with the genotype of the host, and is linked to their allele of human leukocyte antigen (HLA) proteins, which present protein degradation products at the cell surface to circulating T-cells. HLA alleles are associated with Gag-specific T-cell responses that are protective against progression of the disease. While Pol is the most conserved HIV sequence, its association with immune control is not as strong. To gain a more thorough quantitative understanding of the factors that contribute to immunodominance, we have constructed a model of the recognition of HIV infection by the MHC class I pathway. Our model predicts surface presentation of HIV peptides over time, demonstrates the importance of viral protein kinetics, and provides evidence of the importance of Gag peptides in the long-term control of HIV infection. Furthermore, short-term dynamics are also predicted, with simulation of virion-derived peptides suggesting that efficient processing of Gag can lead to a 50% probability of presentation within 3 hours post-infection, as observed experimentally. In conjunction with epitope prediction algorithms, this modelling approach could be used to refine experimental targets for potential T-cell vaccines, both for HIV and other viruses.
“…Wang and Lai (14) modeled the transcription of viral mRNA, translation of Gag, Tat, and the accessory protein Vif, the creation of Vif-Gag, Vif-APO, and Gag-APO complexes, where APO is a cellular antiviral protein, together with incorporation of these complexes into budding virions. The model was verified using several experimental data sets.…”
Section: Mechanistic Pathway Models Their Construction and The Datamentioning
T lymphocytes are stimulated when they recognize short peptides bound to class I proteins of the major histocompatibility complex (MHC) protein, as peptide–MHC complexes. Due to the diversity in T-cell receptor (TCR) molecules together with both the peptides and MHC proteins they bind to, it has been difficult to design vaccines and treatments based on these interactions. Machine learning has made some progress in trying to predict the immunogenicity of peptide sequences in the context of specific MHC class I alleles but, as such approaches cannot integrate temporal information and lack explanatory power, their scope will always be limited. Here, we advocate a mechanistic description of antigen presentation and TCR activation which is explanatory, predictive, and quantitative, drawing on modeling approaches that collectively span several length and time scales, being capable of furnishing reliable biological descriptions that are difficult for experimentalists to provide. It is a form of multiscale systems biology. We propose the use of chemical rate equations to describe the time evolution of the foreign and host proteins to explain how the original proteins end up being presented on the cell surface as peptide fragments, while we invoke molecular dynamics to describe the key binding processes on the molecular level, including those of peptide–MHC complexes with TCRs which lie at the heart of the immune response. On each level, complementary methods based on machine learning are available, and we discuss the relationship between these divergent approaches. The pursuit of predictive mechanistic modeling approaches requires experimentalists to adapt their work so as to acquire, store, and expose data that can be used to verify and validate such models.
“…The results indicate that the detection method can successfully detect most particles in an image with accuracy of 98.61% and 97.65% for adaptor and clathrin images, respectively, and that the tracking algorithm can resolve merging and splitting issues encountered when analyzing dynamic, live-cell images of clathrin assemblies. Recently, much attention has been focused on the development of segmentation and particle tracking techniques for the analysis of large volumes of microscopy fluorescence images acquired during cellular level cell imaging [1,2]. Particle tracking is one method of acquiring information about cellular dynamics.…”
mentioning
confidence: 99%
“…Recently, much attention has been focused on the development of segmentation and particle tracking techniques for the analysis of large volumes of microscopy fluorescence images acquired during cellular level cell imaging [1,2]. Particle tracking is one method of acquiring information about cellular dynamics.…”
Dynamically tracking hundreds of individual pits is essential to determine whether there exist "hot spots" for the formation of clathrin-coated pits or if the pits formed randomly on the plasma membrane. We propose an automated approach to detect these particles based on an improved á trous wavelet transform decomposition with automatic threshold selection and post processing solution, and to track the dynamic process with a greedy algorithm. The results indicate that the detection method can successfully detect most particles in an image with accuracy of 98.61% and 97.65% for adaptor and clathrin images, respectively, and that the tracking algorithm can resolve merging and splitting issues encountered when analyzing dynamic, live-cell images of clathrin assemblies. Recently, much attention has been focused on the development of segmentation and particle tracking techniques for the analysis of large volumes of microscopy fluorescence images acquired during cellular level cell imaging [1,2]. Particle tracking is one method of acquiring information about cellular dynamics. Clathrin-coated pits can be found in all nucleated cells, and provide an important means by which proteins and lipids are removed from the plasma membrane (endocytosis) and transported to an internal compartment (endosome) [3]. Using electronic microscopes, fluorescent-labeled versions of a variety of marker proteins have provided a tantalizing glimpse into the dynamics of the system in living cells. The clathrin pathway has thus acquired special status for analyzing molecular mechanisms in membrane traffic. A single image may capture hundreds or thousands of coated pits forming at the surface of a cell, all of which may not behave in the same way in time and space. Thus, information about these objects may need to be extracted and assembled at different time points and correlated with information from other images obtained at different spectral frequencies. For that purpose, clathrin light chains can be tagged with fluorescent proteins such as enhanced green fluorescent protein (EGFP) or yellow fluorescent protein (YFP) [1], and long time-series data can then be acquired using wide-field, confocal and total internal reflection fluorescence (TIRF) microscopy. Owing to the large quantity of images and hundreds of coated pits in each obtained image, an automated analysis approach is therefore necessary to visualize such molecular imaging data.The process for computerized analysis of cellular microscopy images generally consists of auto-segmentation, tracking and feature extraction. The detection of particles is the most critical step in molecular or sub-cellular image analysis where knowledge of the morphology of particles and the distribution of fluorescence signals in the particles is required. Although several particle detection methods based on segmentation have been developed [4,5], results have varied. Under-segmentation and/or over-segmentation can occur because these methods are based only on the intensity of the images or the morphologica...
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