Modeling and dynamical analysis of virus-triggered innate immune signaling pathways

Data-driven multi-scale mathematical modeling of SARS-CoV-2 infection reveals heterogeneity among COVID-19 patients

Patients with coronavirus disease 2019 (COVID-19) often exhibit diverse disease progressions associated with various infectious ability, symptoms, and clinical treatments. To systematically and thoroughly understand the heterogeneous progression of COVID-19, we developed a multi-scale computational model to quantitatively understand the heterogeneous progression of COVID-19 patients infected with severe acute respiratory syndrome (SARS)-like coronavirus (SARS-CoV-2). The model consists of intracellular viral dynamics, multicellular infection process, and immune responses, and was formulated using a combination of differential equations and stochastic modeling. By integrating multi-source clinical data with model analysis, we quantified individual heterogeneity using two indexes, i.e., the ratio of infected cells and incubation period. Specifically, our simulations revealed that increasing the host antiviral state or virus induced type I interferon (IFN) production rate can prolong the incubation period and postpone the transition from asymptomatic to symptomatic outcomes. We further identified the threshold dynamics of T cell exhaustion in the transition between mild-moderate and severe symptoms, and that patients with severe symptoms exhibited a lack of naïve T cells at a late stage. In addition, we quantified the efficacy of treating COVID-19 patients and investigated the effects of various therapeutic strategies. Simulations results suggested that single antiviral therapy is sufficient for moderate patients, while combination therapies and prevention of T cell exhaustion are needed for severe patients. These results highlight the critical roles of IFN and T cell responses in regulating the stage transition during COVID-19 progression. Our study reveals a quantitative relationship underpinning the heterogeneity of transition stage during COVID-19 progression and can provide a potential guidance for personalized therapy in COVID-19 patients.

Coronavirus disease 2019 (COVID-19) is currently destroying both lives and economies. However, patients infected with severe acute respiratory syndrome (SARS)-like coronavirus (SARS-CoV-2) usually present heterogeneous and complicated progressions, such as different incubation periods (short and long), symptoms (asymptomatic and symptomatic) and severity (mild-moderate and severe). Currently, various clinical data and experimental data are available from different countries, which has great significance for integrating different types of data to comprehensively understand the diverse disease progression in COVID-19 patients and guide individual treatment strategies. Here, we developed a multi-scale computational model to describe the dynamical process of patients infected with SARS-CoV-2, including intracellular viral dynamics, multicellular infection process, and immune responses. By combining data integration, stochastic simulation and quantitative analysis based on the multi-scale mathematical model, we addressed an important question regarding how IFN response and T cell exhaustion quantitatively affect heterogeneous progression in patients with respect to incubation periods, symptoms and severity. Furthermore, the efficacy of various therapeutic strategies for treating COVID-19 patients with different severity degrees was evaluated and validated. The computational framework in this study can also be extended to explore the dynamical process of other coronavirus infections.

FBA reveals guanylate kinase as a potential target for antiviral therapies against SARS-CoV-2

Motivation

The novel coronavirus (SARS-CoV-2) currently spreads worldwide, causing the disease COVID-19. The number of infections increases daily, without any approved antiviral therapy. The recently released viral nucleotide sequence enables the identification of therapeutic targets, e.g. by analyzing integrated human-virus metabolic models. Investigations of changed metabolic processes after virus infections and the effect of knock-outs on the host and the virus can reveal new potential targets.

Results

We generated an integrated host–virus genome-scale metabolic model of human alveolar macrophages and SARS-CoV-2. Analyses of stoichiometric and metabolic changes between uninfected and infected host cells using flux balance analysis (FBA) highlighted the different requirements of host and virus. Consequently, alterations in the metabolism can have different effects on host and virus, leading to potential antiviral targets. One of these potential targets is guanylate kinase (GK1). In FBA analyses, the knock-out of the GK1 decreased the growth of the virus to zero, while not affecting the host. As GK1 inhibitors are described in the literature, its potential therapeutic effect for SARS-CoV-2 infections needs to be verified in in-vitro experiments.

Availability and implementation

The computational model is accessible at https://identifiers.org/biomodels.db/MODEL2003020001.

Transmembrane protein 2 inhibits Zika virus replication through activation of the Janus kinase/signal transducers and activators of transcription signaling pathway

Aim: TMEM2 has been demonstrated to suppress HBV infection by activating the Jak/STAT pathway. In this study, we sought to explore the mechanism by which TMEM2 effects on Zika virus (ZIKV) replication. Materials & methods: TMEM2 was overexpressed. Selected gene mRNA, p-STAT1 levels and interferon stimulated response element activity were examined by qRT-PCR, Western blot and luciferase assay respectively. Results: Overexpression of TMEM2 significantly inhibited ZIKV replication, upregulated MDA5 and RIG-I expression, increased IFN-β promoter activity and IFN-β expression. Overexpression of TMEM2 enhanced the Jak/STAT signaling including increased p-STAT1 level, ISRE activity as well as the expression of several antiviral interferon-stimulated genes. Conclusion: TMEM2 inhibited ZIKV replication through increased IFN-β production and enhanced activation of the Jak/STAT signaling pathway.

Effects of randomness on viral infection model with application

Virus population disease dynamics in various species of ecosystem keep the research interests alive for many centuries. In this research article, an attempt has been made to understand the qualitative behavior of a virus infection model with Lytic and Non-Lytic Immune Responses by perturbing with randomness (white noise) via Lyapunov technique. The conditions for the extinction and permanence of the viral infection in the interacting populations has been found, analyzed and supported with numerical simulations. An application to HIV infection model has also been presented for drawing a comparative study of the model under various modeling methods. The research findings of this paper reveal that a study that includes random fluctuations of the environment prove to be the ideal way to bring out the qualitative analysis of a mathematical model that will depict the real world scenario.

Avian Interferon-Inducible Transmembrane Protein Family Effectively Restricts Avian Tembusu Virus Infection

Avian Tembusu virus (ATMUV) is a highly pathogenic flavivirus that causes significant economic losses to the Chinese poultry industry. Our previous experiments demonstrated that ATMUV infection effectively triggered host innate immune response through MDA5 and TLR3-dependent signaling pathways. However, little information is available on the role of interferon-stimulated genes (ISGs) in defending against ATMUV infection. In this study, we found that ATMUV infection induced robust expression of type I and type III interferon (IFNs) in duck tissues. Furthermore, we observed that expression of interferon-inducible transmembrane proteins (IFITMs) was significantly upregulated in DEF and DF-1 cells after infection with ATMUV. Similar results were obtained from in vivo studies using ATMUV-infected ducklings. Importantly, we showed that knockdown of endogenous IFITM1 or IFITM3 by specific shRNA markedly enhanced ATMUV replication in DF-1 cells. However, disruption of IFITM2 expression had no obvious effect on the ATMUV replication. In addition, overexpression of chicken or duck IFITM1 and IFITM3 in DF-1 cells impaired the replication of ATMUV. Taken together, these results reveal that induced expression of avian IFITM1 and IFITM3 in response to ATMUV infection can effectively restrict the virus replication, and suggest that increasing IFITM proteins in host may be a useful strategy for control of ATMUV infection.

Mathematical Models for Immunology: Current State of the Art and Future Research Directions

The advances in genetics and biochemistry that have taken place over the last 10 years led to significant advances in experimental and clinical immunology. In turn, this has led to the development of new mathematical models to investigate qualitatively and quantitatively various open questions in immunology. In this study we present a review of some research areas in mathematical immunology that evolved over the last 10 years. To this end, we take a step-by-step approach in discussing a range of models derived to study the dynamics of both the innate and immune responses at the molecular, cellular and tissue scales. To emphasise the use of mathematics in modelling in this area, we also review some of the mathematical tools used to investigate these models. Finally, we discuss some future trends in both experimental immunology and mathematical immunology for the upcoming years.

Avian Tembusu virus infection effectively triggers host innate immune response through MDA5 and TLR3-dependent signaling pathways

Avian Tembusu virus (ATMUV) is a newly emerged flavivirus that belongs to the Ntaya virus group. ATMUV is a highly pathogenic virus causing significant economic loss to the Chinese poultry industry. However, little is known about the role of host innate immune mechanism in defending against ATMUV infection. In this study, we found that ATMUV infection significantly up-regulated the expression of type I and type III interferons (IFN) and some critical IFN-stimulated genes (ISG) in vivo and in vitro. This innate immune response was induced by genomic RNA of ATMUV. Furthermore, we observed that ATMUV infection triggered IFN response mainly through MDA5 and TLR3-dependent signaling pathways. Strikingly, shRNA-based disruption of IPS-1, IRF3 or IRF7 expression significantly reduced the production of IFN in the 293T cell model. Moreover, NF-κB was shown to be activated in both chicken and human cells during the ATMUV infection. Inhibition of NF-κB signaling also resulted in a clear decrease in expression of IFN. Importantly, experiments revealed that treatment with IFN significantly impaired ATMUV replication in the chicken cell. Consistently, type I IFN also exhibited promising antiviral activity against ATMUV replication in the human cell. Together, these data indicate that ATMUV infection triggers host innate immune response through MDA5 and TLR3-dependent signaling that controls IFN production, and thereby induces an effective antiviral immunity.

Crosstalk between pathways enhances the controllability of signalling networks

The control of complex networks is one of the most challenging problems in the fields of biology and engineering. In this study, the authors explored the controllability and control energy of several signalling networks, which consisted of many interconnected pathways, including networks with a bow-tie architecture. On the basis of the theory of structure controllability, they revealed that biological mechanisms, such as cross-pathway interactions, compartmentalisation and so on make the networks easier to fully control. Furthermore, using numerical simulations for two realistic examples, they demonstrated that the control energy of normal networks with crosstalk is lower than in networks without crosstalk. These results indicate that the biological networks are optimally designed to achieve their normal functions from the viewpoint of the control theory. The authors' work provides a comprehensive understanding of the impact of network structures and properties on controllability.

Live Cell Analysis and Mathematical Modeling Identify Determinants of Attenuation of Dengue Virus 2'-O-Methylation Mutant

Dengue virus (DENV) is the most common mosquito-transmitted virus infecting ~390 million people worldwide. In spite of this high medical relevance, neither a vaccine nor antiviral therapy is currently available. DENV elicits a strong interferon (IFN) response in infected cells, but at the same time actively counteracts IFN production and signaling. Although the kinetics of activation of this innate antiviral defense and the timing of viral counteraction critically determine the magnitude of infection and thus disease, quantitative and kinetic analyses are lacking and it remains poorly understood how DENV spreads in IFN-competent cell systems. To dissect the dynamics of replication versus antiviral defense at the single cell level, we generated a fully viable reporter DENV and host cells with authentic reporters for IFN-stimulated antiviral genes. We find that IFN controls DENV infection in a kinetically determined manner that at the single cell level is highly heterogeneous and stochastic. Even at high-dose, IFN does not fully protect all cells in the culture and, therefore, viral spread occurs even in the face of antiviral protection of naïve cells by IFN. By contrast, a vaccine candidate DENV mutant, which lacks 2’-O-methylation of viral RNA is profoundly attenuated in IFN-competent cells. Through mathematical modeling of time-resolved data and validation experiments we show that the primary determinant for attenuation is the accelerated kinetics of IFN production. This rapid induction triggered by mutant DENV precedes establishment of IFN-resistance in infected cells, thus causing a massive reduction of virus production rate. In contrast, accelerated protection of naïve cells by paracrine IFN action has negligible impact. In conclusion, these results show that attenuation of the 2’-O-methylation DENV mutant is primarily determined by kinetics of autocrine IFN action on infected cells.

Dengue virus (DENV) infection is a global health problem for which no selective therapy or vaccine exists. The magnitude of infection critically depends on the induction kinetics of the interferon (IFN) response and the kinetics of viral countermeasures. Here we established a novel live cell imaging system to dissect the dynamics of this interplay. We find that IFN controls DENV infection in a kinetically determined manner. At the single cell level, the IFN response is highly heterogeneous and stochastic, likely accounting for viral spread in the presence of IFN. Mathematical modeling and validation experiments show that the kinetics of activation of the IFN response critically determines control of virus replication and spread. A vaccine candidate DENV mutant lacking 2’-O-methylation of viral RNA is profoundly attenuated in IFN-competent cells. This attenuation is primarily due to accelerated kinetics of IFN production acting on infected cells in an autocrine manner. In contrast, accelerated protection of naïve cells by paracrine IFN action has negligible impact. Thus, attenuation of the 2’-O-methylation DENV mutant is primarily determined by kinetics of autocrine IFN action on infected cells.

A New Method for Detecting Protein Complexes based on the Three Node Cliques

The identification of protein complexes in protein-protein interaction (PPI) networks is fundamental for understanding biological processes and cellular molecular mechanisms. Many graph computational algorithms have been proposed to identify protein complexes from PPI networks by detecting densely connected groups of proteins. These algorithms assess the density of subgraphs through evaluation of the sum of individual edges or nodes; thus, incomplete and inaccurate measures may miss meaningful biological protein complexes with functional significance. In this study, we propose a novel method for assessing the compactness of local subnetworks by measuring the number of three node cliques. The present method detects each optimal cluster by growing a seed and maximizing the compactness function. To demonstrate the efficacy of the new proposed method, we evaluate its performance using five PPI networks on three reference sets of yeast protein complexes with five different measurements and compare the performance of the proposed method with four state-of-the-art methods. The results show that the protein complexes generated by the proposed method are of better quality than those generated by four classic methods. Therefore, the new proposed method is effective and useful for detecting protein complexes in PPI networks.

Negative feedback contributes to the stochastic expression of the interferon-β gene in virus-triggered type I interferon signaling pathways

Type I interferon (IFN) signaling pathways play an essential role in the defense against early viral infections; however, the diverse and intricate molecular mechanisms of virus-triggered type I IFN responses are still poorly understood. In this study, we analyzed and compared two classes of models i.e., deterministic ordinary differential equations (ODEs) and stochastic models to elucidate the dynamics and stochasticity of type I IFN signaling pathways. Bifurcation analysis based on an ODE model reveals that the system exhibits a bistable switch and a one-way switch at high or low levels when the strengths of the negative and positive feedbacks are tuned. Furthermore, we compared the stochastic simulation results under the Master and Langevin equations. Both of the stochastic equations generate the bistable switch phenomenon, and the distance between two stable states are smaller than normal under the simulation of the Langevin equation. The quantitative computations also show that a moderate ratio between positive and negative feedback strengths is required to ensure a reliable switch between the different IFN concentrations that regulate the immune response. Moreover, we propose a multi-state stochastic model based on the above deterministic model to describe the multi-cellular system coupled with the diffusion of IFNs. The perturbation and inhibition analysis showed that the positive feedback, as well as noises, has little effect on the stochastic expression of IFNs, but the negative feedback of ISG56 on the activation of IRF7 has a great influence on IFN stochastic expression. Together, these results reveal that positive feedback stabilizes IFN gene expression, and negative feedback may be the main contribution to the stochastic expression of the IFN gene in the virus-triggered type I IFN response. These findings will provide new insight into the molecular mechanisms of virus-triggered type I IFN signaling pathways.

Optimal control strategy for abnormal innate immune response

Innate immune response plays an important role in control and clearance of pathogens following viral infection. However, in the majority of virus-infected individuals, the response is insufficient because viruses are known to use different evasion strategies to escape immune response. In this study, we use optimal control theory to investigate how to control the innate immune response. We present an optimal control model based on an ordinary-differential-equation system from a previous study, which investigated the dynamics and regulation of virus-triggered innate immune signaling pathways, and we prove the existence of a solution to the optimal control problem involving antiviral treatment or/and interferon therapy. We conduct numerical experiments to investigate the treatment effects of different control strategies through varying the cost function and control efficiency. The results show that a separate treatment, that is, only inhibiting viral replication (u₁(t)) or enhancing interferon activity (u₂(t)), has more advantages for controlling viral infection than a mixed treatment, that is, controlling both (u₁(t)) and (u₂(t)) simultaneously, including the smallest cost and operability. These findings would provide new insight for developing effective strategies for treatment of viral infectious diseases.

A new asynchronous parallel algorithm for inferring large-scale gene regulatory networks

The reconstruction of gene regulatory networks (GRNs) from high-throughput experimental data has been considered one of the most important issues in systems biology research. With the development of high-throughput technology and the complexity of biological problems, we need to reconstruct GRNs that contain thousands of genes. However, when many existing algorithms are used to handle these large-scale problems, they will encounter two important issues: low accuracy and high computational cost. To overcome these difficulties, the main goal of this study is to design an effective parallel algorithm to infer large-scale GRNs based on high-performance parallel computing environments. In this study, we proposed a novel asynchronous parallel framework to improve the accuracy and lower the time complexity of large-scale GRN inference by combining splitting technology and ordinary differential equation (ODE)-based optimization. The presented algorithm uses the sparsity and modularity of GRNs to split whole large-scale GRNs into many small-scale modular subnetworks. Through the ODE-based optimization of all subnetworks in parallel and their asynchronous communications, we can easily obtain the parameters of the whole network. To test the performance of the proposed approach, we used well-known benchmark datasets from Dialogue for Reverse Engineering Assessments and Methods challenge (DREAM), experimentally determined GRN of Escherichia coli and one published dataset that contains more than 10 thousand genes to compare the proposed approach with several popular algorithms on the same high-performance computing environments in terms of both accuracy and time complexity. The numerical results demonstrate that our parallel algorithm exhibits obvious superiority in inferring large-scale GRNs.

Deciphering deterioration mechanisms of complex diseases based on the construction of dynamic networks and systems analysis

The early diagnosis and investigation of the pathogenic mechanisms of complex diseases are the most challenging problems in the fields of biology and medicine. Network-based systems biology is an important technique for the study of complex diseases. The present study constructed dynamic protein-protein interaction (PPI) networks to identify dynamical network biomarkers (DNBs) and analyze the underlying mechanisms of complex diseases from a systems level. We developed a model-based framework for the construction of a series of time-sequenced networks by integrating high-throughput gene expression data into PPI data. By combining the dynamic networks and molecular modules, we identified significant DNBs for four complex diseases, including influenza caused by either H3N2 or H1N1, acute lung injury and type 2 diabetes mellitus, which can serve as warning signals for disease deterioration. Function and pathway analyses revealed that the identified DNBs were significantly enriched during key events in early disease development. Correlation and information flow analyses revealed that DNBs effectively discriminated between different disease processes and that dysfunctional regulation and disproportional information flow may contribute to the increased disease severity. This study provides a general paradigm for revealing the deterioration mechanisms of complex diseases and offers new insights into their early diagnoses.

Computational Studies of the Intestinal Host-Microbiota Interactome

A large and growing body of research implicates aberrant immune response and compositional shifts of the intestinal microbiota in the pathogenesis of many intestinal disorders. The molecular and physical interaction between the host and the microbiota, known as the host-microbiota interactome, is one of the key drivers in the pathophysiology of many of these disorders. This host-microbiota interactome is a set of dynamic and complex processes, and needs to be treated as a distinct entity and subject for study. Disentangling this complex web of interactions will require novel approaches, using a combination of data-driven bioinformatics with knowledge-driven computational modeling. This review describes the computational approaches for investigating the host-microbiota interactome, with emphasis on the human intestinal tract and innate immunity, and highlights open challenges and existing gaps in the computation methodology for advancing our knowledge about this important facet of human health.

Mathematical modeling for intracellular transport and binding of HIV-1 Gag proteins

This paper presents a modeling study for the intracellular trafficking and trimerization of the HIV-1 Gag proteins. A set of differential equations including initial and boundary conditions is used to characterize the transport, diffusion, association and dissociation of Gag monomers and trimers for the time period from the initial production of Gag protein monomers to the initial appearance of immature HIV-1 virions near the cell membrane (the time duration Ta). The existence and stability of the steady-state solution of the initial boundary value problems provide a quantitative characterization of the tendency and equilibrium of Gag protein movement. The numerical simulation results further demonstrate Gag trimerization near the cell membrane. Our calculations of Ta are in good agreement with published experimental data. Sensitivity analysis of Ta to the model parameters indicates that the timing of the initial appearance of HIV-1 virions on the cell membrane is affected by the diffusion and transport processes. These results provide important information and insight into the Gag protein transport and binding and HIV-1 virion formation.

Systematic analysis of the mechanisms of virus-triggered type I IFN signaling pathways through mathematical modeling

Based on biological experimental data, we developed a mathematical model of the virus-triggered signaling pathways that lead to induction of type I IFNs and systematically analyzed the mechanisms of the cellular antiviral innate immune responses, including the negative feedback regulation of ISG56 and the positive feedback regulation of IFNs. We found that the time between 5 and 48 hours after viral infection is vital for the control and/or elimination of the virus from the host cells and demonstrated that the ISG56-induced inhibition of MITA activation is stronger than the ISG56-induced inhibition of TBK1 activation. The global parameter sensitivity analysis suggests that the positive feedback regulation of IFNs is very important in the innate antiviral system. Furthermore, the robustness of the innate immune signaling network was demonstrated using a new robustness index. These results can help us understand the mechanisms of the virus-induced innate immune response at a system level and provide instruction for further biological experiments.

What role (if any) does the highly conserved CSB-PGBD3 fusion protein play in Cockayne syndrome?

The PGBD3 piggyBac transposon inserted into CSB intron 5 early in the primate lineage. As a result of alternative splicing, the human CSB gene now encodes three proteins: CSB, a CSB-PGBD3 fusion protein that joins the N-terminal CSB domain to the C-terminal PGBD3 transposase domain, and PGBD3 transposase. The fusion protein is as highly conserved as CSB, suggesting that it is advantageous in health; however, expression of the fusion protein in CSB-null cells induces a constitutive interferon (IFN) response. The fusion protein binds in vivo to PGBD3-related MER85 elements, but is also tethered to c-Jun, TEAD1, and CTCF motifs by interactions with the cognate transcription factors. The fusion protein regulates nearby genes from the c-Jun (and to a lesser extent TEAD1 and CTCF) motifs, but not from MER85 elements. We speculate that the fusion protein interferes with CSB-dependent chromatin remodeling, generating double-stranded RNA (dsRNA) that induces an IFN response through endosomal TLR or cytoplasmic RIG-I and/or MDA5 RNA sensors. We suggest that the fusion protein was fixed in primates because an elevated IFN response may help to fight viral infection. We also speculate that an inappropriate IFN response may contribute to the clinical presentation of CS.