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Stochastic dynamics of Type-I interferon responses [1]

['Benjamin D. Maier', 'Department Of Modeling Of Biological Processes', 'Cos Heidelberg', 'Bioquant', 'Heidelberg University', 'Heidelberg', 'Luis U. Aguilera', 'Sven Sahle', 'Pascal Mutz', 'Division Virus-Associated Carcinogenesis']

Date: 2022-11 

Interferon (IFN) activates the transcription of several hundred of IFN stimulated genes (ISGs) that constitute a highly effective antiviral defense program. Cell-to-cell variability in the induction of ISGs is well documented, but its source and effects are not completely understood. The molecular mechanisms behind this heterogeneity have been related to randomness in molecular events taking place during the JAK-STAT signaling pathway. Here, we study the sources of variability in the induction of the IFN-alpha response by using MxA and IFIT1 activation as read-out. To this end, we integrate time-resolved flow cytometry data and stochastic modeling of the JAK-STAT signaling pathway. The complexity of the IFN response was matched by fitting probability distributions to time-course flow cytometry snapshots. Both, experimental data and simulations confirmed that the MxA and IFIT1 induction circuits generate graded responses rather than all-or-none responses. Subsequently, we quantify the size of the intrinsic variability at different steps in the pathway. We found that stochastic effects are transiently strong during the ligand-receptor activation steps and the formation of the ISGF3 complex, but negligible for the final induction of the studied ISGs. We conclude that the JAK-STAT signaling pathway is a robust biological circuit that efficiently transmits information under stochastic environments.

We investigate the impact of intrinsic and extrinsic noise on the reliability of interferon signaling. Information must be transduced robustly despite existing biochemical variability and at the same time the system has to allow for cellular variability to tune it against changing environments. Getting insights into stochasticity in signaling networks is crucial to understand cellular dynamics and decision-making processes. To this end, we developed a detailed stochastic computational model based on single cell data. We are able to show that reliability is achieved despite high noise at the receptor level.

Introduction

The interferon (IFN) system is the first line of innate immune defense. IFNs are polypeptides secreted by infected cells, inducing cell-intrinsic antimicrobial states that limit the spread of infectious agents. IFNs particularly act against viral pathogens in infected and neighboring cells. There are three distinct IFN families. The type-I IFN family comprises IFN-β and various subtypes of IFN-α. Most cell types produce IFN-β upon viral infection, whereas plasmacytoid dendritic cells are the predominant producers of IFN-α [1]. IFNγ is the only type-II IFN, and the type-III IFN family, the latest to be discovered, comprises IFNλ1–4. This study focuses on the widely studied type-I IFNs.

The IFN system is tightly regulated, as aberrant or excessive IFN responses can be detrimental and contribute to the pathogenesis of autoimmune diseases [2]. To prevent over-activation, multiple mechanisms determine the efficiency and flexibility in IFN-mediated responses, by amplifying or suppressing IFN-dependent signaling responses. Positive regulation of signaling includes the triggering of the JAK-STAT signaling pathway by binding of extracellular type-I IFN to the IFN-α receptor (IFNAR). Upon ligation, the two receptor chains, IFNAR1 and IFNAR2, heterodimerize and activate the receptor-associated protein tyrosine kinases Janus kinase 1 (JAK1) and tyrosine kinase 2 (TYK2), which in turn phosphorylate the signal transducers and activators of transcription 1 (STAT1) and STAT2. Phosphorylated STAT1 and STAT2 heterodimerize and together with IFN-regulatory factor 9 (IRF9) assemble the ternary complex called IFN-stimulated gene factor 3 (ISGF3) that translocates into the nucleus. ISGF3 acts as a transcription factor that directly activates the transcription of a set of several hundred IFN-stimulated genes (ISGs) and thus induces a cellular antiviral state [3]. IRF9 also constitutes a crucial positive feedback of JAK-STAT signaling, as its initial concentration is limiting and subsequently increased by ISGF3 activity [4]. Reciprocally, negative feedback regulation of the IFN response has also been well established and includes the induction of negative regulators in the JAK-STAT pathway, such as suppressor of cytokine signaling 1 (SOCS1) [3]. In addition to the receptor-dependent activation of ISGs, there is also an IFN-independent basal expression caused by constitutive formation of STAT2–IRF9 that translocates to the nucleus and initiates the transcription of many ISGs [5]. After IFN treatment, STAT2-IRF9 complexes are largely replaced by tyrosine-phosphorylated STAT1–STAT2 heterodimers, which have a lower dissociation rate and a higher quantity [5]. A graphical description of the IFN-induced response is given in Fig 1.

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TIFF original image Download: Fig 1. IFN activation of the JAK-STAT signaling pathway. Free IFN binds to the IFNAR subunits 1 and 2 to form an active complex. After IFNAR activation the signal is transduced inside the cytoplasm; here STAT1 and STAT2 are phosphorylated. Phosphorylated forms of STAT1 and STAT2 form a heterodimer (dimerSTAT). dimerSTAT interacts with IRF9 to form ISGF3 (a trimolecular complex). ISGF3 translocates to the nucleus. In the nucleus, ISGF3 binds to free transcription factor binding sites (ISRE), inducing transcriptional activity leading to production of more IRF9 as a positive feedback and SOCS as a negative feedback given by the SOCS1 degradation of active receptors. ISGF3 induces the expression of around 350 different ISGs, including MxA and IFIT1. Additionally, there is a constitutive formation of STAT2-IRF9 heterodimers, that stimulate the expression of interferon-induced genes (ISGs) without a signaling requirement (basal expression). The model consists of 42 species in two compartments and 62 kinetic reactions and is fully described in the methods section. In the figure, boxes represent the chemical species, empty symbols represent degradation processes, arrows represent the reactions described in Section B.1 in S1 Text, initial conditions and parameters are given in Tables 1, 2 and 3, respectively. The pathway diagram was created with the Newt Editor [26, 27] following the Systems Biology Graphical Notation (SBGN) [28]. https://doi.org/10.1371/journal.pcbi.1010623.g001

The expression of MxA and IFIT1 (previously referred to as ISG56) are gold standards for surrogate markers representing IFN-α’s biological activity in various experimental and clinical settings [6, 7]. MxA is one of the most commonly studied ISGs. Its expression is highly induced by IFN and it exerts strong antiviral activity against Orthomyxoviruses, most prominently Influenza A virus, but also other families of negative-strand RNA viruses such as Rhabdoviridae or Paramyxoviridae [8–10]. IFIT1 on the other hand was one of the first ISGs to be discovered, is the most prominent member of the viral stress-inducible gene family and is strongly induced in response to type-I IFN as well as various viruses [11–13].

As in many signalling pathways with low participating species concentrations, stochasticity certainly plays a role in the information processing. Even though our knowledge about the role of stochasticity in biological communication processes increased, the interpretation of stochastic signal transducing processes remains a challenge. The expression of many ISGs is believed to follow a digitalized (bi-modal all-or-none) expression pattern [14–18], however several studies are indicating that there are also ISGs that follow a graded (unimodal) pattern [19, 20]. Moreover, the number and percentage of ISGs displaying a bimodal expression varies between species and cell types [21–25], which is believed to reflect functionally important differences [15]. Despite evidence of all-or-nothing responses for MxA and IFIT1 gene induction [14, 15], the stochastic behavior of these genes cannot be fully explained yet. For instance, IFIT1 mRNA was shown to be bimodal in early time points before transitioning to a uniform response in Shalek et. al (2013) [15]. In order to answer this question and to better understand pathway information processing, the behaviour of these IFN activity markers has to be further verified. In the case of the IFN stimulated genes, neither the mechanisms nor the function of intrinsic variability in discrete reactions along the IFN activated pathway are well understood.

Previously, the dynamics of the JAK-STAT signaling pathway have been deterministically modeled and validated with time course data describing average dynamics in cell populations [4, 29–31]. Many questions could be answered with these studies. However, looking at the experimental data from single-cell measurements, it is obvious that stochastic influences play a major role. These data show distributions of behaviour rather than deterministic phenotypes. Obviously, the above efforts lack an exhaustive and systematic analysis of the influence of such stochastic fluctuations in the molecular reactions taking place along the pathway. The term “biochemical noise” is used to describe the inherently discrete and random interactions of molecules in the cellular environment [32]. Total biochemical noise is conventionally divided into two components: intrinsic and extrinsic noise. Intrinsic noise results from the probabilistic nature of molecular processes. Extrinsic noise results from (cell-to-cell) variations in the concentrations of cellular biomolecules such as ribosomes, enzymes, metabolites, overall proteins and nucleic acids [33]. Extrinsic and intrinsic noise have been proven to critically affect cellular decision-making processes [34]. Plenty of research has revealed genetic circuits that are subject to noise at the level of their components. In contrast, little is known about the effect of noise in cellular signaling pathways. However, previously, it has been shown that the information transduced in cellular signaling pathways is affected by noise [35]. Noise in cellular signaling pathways is conceptually different from the noise resulting from stochastic fluctuations of low-abundance genetic regulators. In cellular signaling pathways, noise can consist of low levels of enzyme activity through random transient interactions of receptors, binding of ligands to receptors in nonfunctional complexes, and background interactions along the pathway [36]. Noise plays an important role in IFN responses. It has been observed that within populations of nearly identical cells, not all cells that are infected by a virus or stimulated by IFN express ISGs [14, 37–41]. In all these cases the suggested mechanisms behind this heterogeneity has been related to stochastic events along the JAK-STAT signaling pathway.

Live-cell imaging data is indicating a multi-layered stochasticity (viral replication, IFN induction and IFN response), whereby heterogeneous IFN induction is largely determined by the translocation time of the key transcription factors and cell-intrinsic noise [14]. This result is challenged however by a recent integrated ChIP-seq and transcriptome analysis, which found that the molecular switch from an unstimulated homeostasis to a strong IFN-induced receptor-dependent response is caused by a rapid exchange of unphosphorylated transcription factors for the phosphorylated key transcription factors [5]. Furthermore, their results indicate that unstimulated and activated state are not only differing by intensity as previously assumed but also mechanistically as different signaling cascades are used, which has not been taken into account in earlier mathematical models and might be crucial to understand the above described (cell-intrinsic) noise. Hence, this study attempts to achieve greater detail than previous stochastic models, using more readily available flow cytometry data and novel literature findings.

Little is known about the sources and principles behind basal activity and expression in the JAK-STAT signaling pathway. Studies suggest that constitutive expression varies greatly between different cell types and occurs at different levels (receptor, signal transduction, gene expression) [42]. Basal STAT phosphorylation in diseased cells and tissues has been demonstrated by several groups [43–46]. Recently, it was shown that the basal expression of many interferon-induced genes (ISGs) is stimulated by STAT2–IRF9 complexes which form constitutively in the cytoplasm, shuttle between nucleus and cytoplasm and bind to the promoter region without a signaling requirement [5]. To which extent basal expression fluctuates within a population of genetically identical cells is, however, unknown so far.

Here, we aim to determine whether and how biochemical noise affects the information transduced in the JAK-STAT signaling pathway and investigate the transition between basal and activated state. To this end, we studied the stochastic responses of MxA and IFIT1 expression in Huh7.5 cells stimulated with IFN-α. Using fluorescent reporters under the control of the authentic promoter/enhancer region of IFIT1 and MxA we collected data displaying the differences between expressing and non-expressing cells for the marker genes in a time-course experiment. We hypothesize that the JAK-STAT signaling pathway efficiently transmits information under stochastic environments. To test our working hypothesis, we developed a detailed mathematical model using the obtained time-resolved flow cytometry data to describe the elements in the JAK-STAT signaling pathway at single-cell resolution. This model allowed us to systematically test the influence of intrinsic and extrinsic noise in the IFN response. We determined that the effects of intrinsic noise are particularly strong at the receptor level, during the formation of the transcription factor ISGF3 and during the transcription of the ISGs. We concluded that the JAK-STAT signaling pathway is a robust system that can filter extrinsic fluctuations.

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[1] Url: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010623

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