A new efficient approach to fit stochastic models on the basis of high-throughput experimental data using a model of IRF7 gene expression as case study

Background: Mathematical models are used to gain an integrative understanding of biochemical processes and networks. Commonly the models are based on deterministic ordinary differential equations. When molecular counts are low, stochastic formalisms like Monte Carlo simulations are more appropriate and well established. However, compared to the wealth of computational methods used to fit and analyze deterministic models, there is only little available to quantify the exactness of the fit of stochastic models compared to experimental data or to analyze different aspects of the modeling results. Results: Here, we developed a method to fit stochastic simulations to experimental high-throughput data, meaning data that exhibits distributions. The method uses a comparison of the probability density functions that are computed based on Monte Carlo simulations and the experimental data. Multiple parameter values are iteratively evaluated using optimization routines. The method improves its performance by selecting parameters values after comparing the similitude between the deterministic stability of the system and the modes in the experimental data distribution. As a case study we fitted a model of the IRF7 gene expression circuit to time-course experimental data obtained by flow cytometry. IRF7 shows bimodal dynamics upon IFN stimulation. This dynamics occurs due to the switching between active and basal states of the IRF7 promoter. However, the exact molecular mechanisms responsible for the bimodality of IRF7 is not fully understood. Conclusions: Our results allow us to conclude that the activation of the IRF7 promoter by the combination of IRF7 and ISGF3 is sufficient to explain the observed bimodal dynamics

Investigations of the DNA-binding activity and gene regulatory properties of IRF3, IRF5, and IRF7 homodimers

The innate immune response is an essential component of the mammalian immune system that responds rapidly to pathogens. This response to pathogens is initiated by the detection of pathogen associated molecular patterns (PAMPs) by pathogen recognition receptors (PRRs). PRR signaling activates antipathogen gene programs via transcription factors (TFs) such as the interferon regulatory factors (IRFs). IRF3, IRF5, and IRF7 (IRF3/5/7) are key signal-dependent TFs that have overlapping, yet distinct, roles in the mammalian response to pathogens. To examine the role that DNA-binding specificity plays in delineating IRF3/5/7-specific gene regulation, we used protein-binding microarrays (PBMs) to characterize the DNA binding of IRF3/5/7 homodimers. We identified both common and dimer-specific DNA binding sites, and show that DNA-binding differences can translate into dimer-specific gene regulation. Central to the antiviral response, IRF3/5/7 regulate type I interferon (IFN) genes. We show that IRF3 and IRF7 bind to many interferon-stimulated response element (ISRE)-type sites in the virus-response elements (VREs) of IFN promoters. However, strikingly, IRF5 does not bind the VREs, suggesting evolutionary selection against IRF5 homodimer binding. Mutational analysis identified a a critical specificity-determining residue that inhibits IRF5 binding to the ISRE-variants present in the IFN gene promoters. Integrating PBM and reporter gene data we find that both DNA-binding affinity and affinity-independent mechanisms determine the transcriptional activation ability of DNA-bound IRF dimers, suggesting that DNA-based allostery plays a role in IRF binding site function. To assay the sequence determinants of IRF-dependent transcriptional regulation, we propose using a modified massively parallel reporter assay (MPRA). The proposed MPRA leverages unique molecular identifiers to improve the accuracy of reporter gene quantitation. This work provides new insights into the role and limitations of DNA-binding affinity in delineating IRF3/5/7-specific gene expression and lays groundwork for further understanding the complexities of IRF-dependent transcriptional regulation of innate immune genes

Integrative Modeling of Transcriptional Regulation in Response to Autoimmune Desease Therapies

Die rheumatoide Arthritis (RA) und die Multiple Sklerose (MS) werden allgemein als Autoimmunkrankheiten eingestuft. Zur Behandlung dieser Krankheiten werden immunmodulatorische Medikamente eingesetzt, etwa TNF-alpha-Blocker (z.B. Etanercept) im Falle der RA und IFN-beta-Präparate (z.B. Betaferon und Avonex) im Falle der MS. Bis heute sind die molekularen Mechanismen dieser Therapien weitestgehend unbekannt. Zudem ist ihre Wirksamkeit und Verträglichkeit bei einigen Patienten unzureichend. In dieser Arbeit wurde die transkriptionelle Antwort im Blut von Patienten auf jede dieser drei Therapien untersucht, um die Wirkungsweise dieser Medikamente besser zu verstehen. Dabei wurden Methoden der Netzwerkinferenz eingesetzt, mit dem Ziel, die genregulatorischen Netzwerke (GRNs) der in ihrer Expression veränderten Gene zu rekonstruieren. Ausgangspunkt dieser Analysen war jeweils ein Genexpressions- Datensatz. Daraus wurden zunächst Gene gefiltert, die nach Therapiebeginn hoch- oder herunterreguliert sind. Anschließend wurden die genregulatorischen Regionen dieser Gene auf Transkriptionsfaktor-Bindestellen (TFBS) analysiert. Um schließlich GRN-Modelle abzuleiten, wurde ein neuer Netzwerkinferenz-Algorithmus (TILAR) verwendet. TILAR unterscheidet zwischen Genen und TF und beschreibt die regulatorischen Effekte zwischen diesen durch ein lineares Gleichungssystem. TILAR erlaubt dabei Vorwissen über Gen-TF- und TF-Gen-Interaktionen einzubeziehen. Im Ergebnis wurden komplexe Netzwerkstrukturen rekonstruiert, welche die regulatorischen Beziehungen zwischen den Genen beschreiben, die im Verlauf der Therapien differentiell exprimiert sind. Für die Etanercept-Therapie wurde ein Teilnetz gefunden, das Gene enthält, die niedrigere Expressionslevel bei RA-Patienten zeigen, die sehr gut auf das Medikament ansprechen. Die Analyse von GRNs kann somit zu einem besseren Verständnis Therapie-assoziierter Prozesse beitragen und transkriptionelle Unterschiede zwischen Patienten aufzeigen

Revealing the vectors of cellular identity with single-cell genomics

Single-cell genomics has now made it possible to create a comprehensive atlas of human cells. At the same time, it has reopened definitions of a cell's identity and of the ways in which identity is regulated by the cell's molecular circuitry. Emerging computational analysis methods, especially in single-cell RNA sequencing (scRNA-seq), have already begun to reveal, in a data-driven way, the diverse simultaneous facets of a cell's identity, from discrete cell types to continuous dynamic transitions and spatial locations. These developments will eventually allow a cell to be represented as a superposition of 'basis vectors', each determining a different (but possibly dependent) aspect of cellular organization and function. However, computational methods must also overcome considerable challenges-from handling technical noise and data scale to forming new abstractions of biology. As the scale of single-cell experiments continues to increase, new computational approaches will be essential for constructing and characterizing a reference map of cell identities.National Institutes of Health (U.S.) (grant P50 HG006193)BRAIN Initiative (grant U01 MH105979)National Institutes of Health (U.S.) (BRAIN grant 1U01MH105960-01)National Cancer Institute (U.S.) (grant 1U24CA180922)National Institute of Allergy and Infectious Diseases (U.S.) (grant 1U24AI118672-01

A systems biology approach- quantification and molecular insights into influenza-A virus infection

Influenza A virus (IAV) circulating worldwide are highly contagious and can cause acute to severe respiratory disease. Annually, around 500 million individuals are infected by influenza, which causes about 500,000 deaths worldwide, including 5000-8000 deaths in Germany. In addition to,yearly epidemics, several pandemics are reported globally, recently in 2009 (influenza A/H1N1/pdm2009) which resulted in around 50 millions of deaths globally. The biological basis for the increased severity of some IAVs remains unclear. Unpredicted mutation which leads to intra-host evolution of quasi-species, and strong inflammation are important hallmarks of severe pandemic IAV infection. Understanding the differences in the pathogenicity of virus strains is an important aspect of influenza kinetic. Modeling influenza kinetics plays an integral role to understand the differences and potential mechanisms of a virulent strain compared to a less pathogenic one. Furthermore, investigating the molecular mechanisms of severe pandemic IAV (pdmIAV) is of great importance in controlling the complications and reducing the pulmonary damage. Comprehensive genome wide expression data involving both innate and adaptive immune response helps to understand molecular mechanisms of host response during severe influenza infection

Integration of pharmacokinetic and intracellular models of interferon administration and induced responses

A thorough understanding of drug-target relationships is essential in preclinical and clinical translation studies. However, there is a gap of knowledge in the quantitative understanding of dose-response relationships at the target site. To fill that gap, a particularly promising approach is quantitative systems pharmacology (QSP) where, mechanistic and hence comprehensive models of dose-effect relationships are used to guide the design of clinical and translational studies. In this thesis, I present for the first time a QSP approach for a therapeutic protein, interferon alpha (IFN-a), by coupling physiologically based pharmacokinetic (PBPK) models at the whole-body level with intracellular models of signal transduction in the liver. Whole-body distribution models of an injected dose of IFN-a calibrated to quantitative measurements of the plasma concentration are established for humans and mice. They are then coupled to mechanistic intracellular models of the triggered JAK/STAT signalling cascade that describes the dynamic response in the expression of the antiviral mRNAc of IRF9 for humans and antiviral protein Mx2 for mice on the cellular scale. By doing so, I am able to establish the quantitative dose-effect relationship of the injected IFN-a dose to the responding interferon stimulated genes (ISGs) triggered at the target site, the liver. The established multi-scale physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model of human predict a reduced response of IRF9 mRNAc to IFN-a under physiological in vivo conditions as compared to in vitro. The QSP model also elicits the large impact of the IFN-receptors on the clearance of IFN-a in the liver, thus, not only providing mechanistic insights into the pharmacodynamic (PD) response but also elucidating the influence of receptor variability on the response. Although IFN-a is specifically used in humans, in preclinical studies, it is also tested in mice for understanding the medical impact of IFN-a for other diseases. Therefore, I elaborate an analogous QSP model for the IFN-a response in mice to illustrate possibilities of model-based cross species translation. Like the human model, a whole body PBPK/PD mouse model was also established to follow the response of antiviral protein Mx2. The model clarified the differences between the pharmacokinetics of human and murine IFN-a injection in mice and will support quantitative crossspecies extrapolation in the future. Finally, as heterogeneity in ISGs reflects inter-cell variability in response to IFN-a, I study the impact of sources of this heterogeneity by implementing the mechanistic stochastic model of the JAK/STAT signalling pathway. The model was developed on the basis of time-resolved flow cytometry data of two ISGs, MxA and IFIT1, in Huh7.5 cells. The model analysed intrinsic variability in the concentration of the molecules of the pathway and generated a graded response of MxA and IFIT1 instead of an all-or-none response. Ultimately, the model concludes that the stochasticity in the initiation of the signalling pathway, i.e., at the receptor level, can be buffered by the system and a more robust response of ISGs, MxA and IFIT1 is induced

Characterisation of the Regulation and Dynamics of the RIG-I Signalling Network

RIG-I is a pattern recognition receptor that is responsible for the initiation of an antiviral response to a variety of virus infections. Cytoplasmic nucleic acid derived from virus replication is detected by RIG-I resulting in the initiation of a signalling cascade that ultimately leads to the activation of transcription factors, namely IRF3 and NFkB. Activation of these transcription factors leads to the production and secretion of type I and III interferons (IFN), which act in an auto- and paracrine manner to induce the expression of a large array of IFN stimulated genes (ISGs). In concert, these ISGs promote an antiviral state of the cell, limiting viral replication and spread. Much is known about the individual steps and proteins involved in the RIG-I signalling pathway, however, much less is understood about its regulation and dynamics. Upon virus entry into a cell, the initial response phase is critical to the outcome and determines if the infection is cleared or established. Understanding the dynamics of these processes will be key to comprehend and possibly predict the outcome of a viral infection. The main goal of this thesis, therefore, was to identify novel regulators and to kinetically characterize the rapid induction of the cellular antiviral signalling cascade. As for the identification of previously unrecognized regulators of the RIG-I pathway, we have performed an siRNA-based high-throughput screen of over 600 known and putative E3 ubiquitin ligase genes. Post-translational ubiquitination has been shown to constitute a major regulatory process in innate immune signalling. From our screening approach, we were able to identify several genes that significantly impacted IRF3 activation upon silencing during viral infection. The main part of this thesis deals with the temporal characterisation of RIG-I-initiated antiviral signalling. For that purpose, we developed an approach which permitted the completely synchronous stimulation of cells with virus-like double-stranded RNA. In contrast to authentic infections or classical liposome-based transfections, this method allows for a very high degree of time-resolution in measuring the flow of the signal along the cascade. Quantitative, time-resolved activation measurements of critical proteins in the RIG-I pathway showed that RIG-I signalling is rapid and, in contrary to previous reports, strictly deterministic with very little variability from cell to cell. Furthermore, we extensively characterized the transcriptional program triggered by RIG-I in a time-resolved manner by full-genomic transcriptional profiling. We found that the panel of genes upregulated directly by the primary IRF3 response is surprisingly large and congruent with the ISGs classically known to be induced only by IFN signalling, with only very few genes exhibiting a strict dependence on IFN/JAK/STAT signalling. This work represents the first detailed molecular characterization of the kinetics of host cellular processes triggered in the first few minutes after virus infection. The comprehensive quantitative and time-resolved data generated can serve as a solid basis for a mathematical model that combines viral replication dynamics and host antiviral responses. Such a model will be an unprecedented and powerful tool to study the principles governing the outcome of viral infection and to help understanding how certain viruses manage to overcome host immunity and cause fulminant disease or even establish life-long persistence

mRNA Splicing-Mediated Gene Expression Regulation in Innate Immunity

At the heart of an inflammatory response lies a tightly regulated gene expression program. Perturbations to this finely tuned response can result in unchecked or inappropriately scaled inflammation, shifting the balance from protective to destructive immunity. A variety of post-transcriptional mechanisms play a role in the fine-tuning of an inflammatory gene expression program. One such mechanism involves unproductive RNA splicing, whereby alternative splicing can frameshift the transcript or introduce a premature termination codon (PTC). These effects render the transcript nonfunctional and/or subject it to nonsense-mediated decay. We observed such an event in Irf7, the master regulator of the type I interferon response. We found a single intron was consistently retained at a level much greater than other introns in the Irf7 transcript. In an effort to understand trans-acting factors that regulate this retention, we used RNA-antisense purification followed by mass spectrometry (RAP-MS) to identify the factor BUD13 as a highly enriched protein on Irf7 transcripts. Deficiency in BUD13 was associated with increased retention, decreased mature Irf7 transcript and protein levels, and consequently a dampened type I interferon response, which compromised the ability of BUD13-deficient macrophages to withstand vesicular stomatitis virus (VSV) infection. Beyond this intron retention event in Irf7, we identified a variety of other unproductive splicing events in a number of important genes involved with the innate immune response. This unproductive splicing was not restricted to intron retention events. For example, we identified a frequently used alternative splice site in the crucial murine antiviral response gene, oligoadenylate synthetase 1g (Oas1g) that led to both a frameshift and incorporation of a PTC. Genome editing was used to remove the alternative splice site in a macrophage cell line, which led to both increased Oas1g expression and improved viral clearance. We hypothesize these events exist as a means of mitigation for what might otherwise be an inappropriately scaled response. In doing so, they represent a previously underappreciated layer of gene expression regulation in innate immunity.</p