Decoding the dynamics of multilayered stochastic antiviral IFN-I responses
Authors: Laura C. Van Eyndhoven, Abhyudai Singh, Jurjen Tel
Journal: Trends in Immunology (2021)

The rise of single-cell technologies has highlighted a massive degree of cellular heterogeneity during mammalian interferon (IFN)-I responses.Multilayered stochasticity gives rise to all-or-nothing cellular decision-making, resulting in tightly regulated fractions of responding and nonresponding cells.Mathematical modeling has provided crucial insights into the origins of cellular heterogeneity and the complexity of IFN-I responses.Mathematical models can be considered as powerful tools to dissect and predict IFN-I-mediated physiological and pathological outcomes. Type I Interferon (IFN-I) responses were first recognized for their role in antiviral immunity, but it is now widely appreciated that IFN-Is have many immunomodulatory functions, influencing antitumor responses, autoimmune manifestations, and antimicrobial defenses. Given these pivotal roles, it may be surprising that multilayered stochastic events create highly heterogeneous, but tightly regulated, all-or-nothing cellular decisions. Recently, mathematical models have provided crucial insights into the stochastic nature of antiviral IFN-I responses, which we critically evaluate in this review. In this context, we emphasize the need for innovative single-cell technologies combined with mathematical models to further reveal, understand, and predict the complexity of the IFN-I system in physiological and pathological conditions that may be relevant to a plethora of diseases. Type I Interferon (IFN-I) responses were first recognized for their role in antiviral immunity, but it is now widely appreciated that IFN-Is have many immunomodulatory functions, influencing antitumor responses, autoimmune manifestations, and antimicrobial defenses. Given these pivotal roles, it may be surprising that multilayered stochastic events create highly heterogeneous, but tightly regulated, all-or-nothing cellular decisions. Recently, mathematical models have provided crucial insights into the stochastic nature of antiviral IFN-I responses, which we critically evaluate in this review. In this context, we emphasize the need for innovative single-cell technologies combined with mathematical models to further reveal, understand, and predict the complexity of the IFN-I system in physiological and pathological conditions that may be relevant to a plethora of diseases. Providing a robust first line of host defense, type I interferon (IFN-I; see Glossary) responses were first recognized for their role in antiviral immunity by Isaacs and Lindemann [1.Isaacs A. Lindemann J. Virus interference. I. The interferon.Proc. R. Soc. Lond. Ser. B Biol. Sci. 1957; 147: 258-267Crossref PubMed Google Scholar]. In addition, IFN-Is are known to have numerous immunomodulatory functions that go beyond the scope of antiviral immunity, with potent roles in antitumor responses, autoimmune diseases, and microbial infections [2.Musella M. et al.Type-I-interferons in infection and cancer: unanticipated dynamics with therapeutic implications.Oncoimmunology. 2017; 6e1314424Crossref PubMed Scopus (41) Google Scholar, 3.Psarras A. et al.Type I interferon-mediated autoimmune diseases: pathogenesis, diagnosis and targeted therapy.Rheumatology. 2017; 56: 1662-1675PubMed Google Scholar, 4.McNab F. et al.Type I interferons in infectious disease.Nat. Rev. Immunol. 2015; 15: 87-103Crossref PubMed Scopus (852) Google Scholar]. The expression and regulation of IFN-I has been extensively studied in mammals, and important molecular regulators have been characterized (Box 1; reviewed in [4.McNab F. et al.Type I interferons in infectious disease.Nat. Rev. Immunol. 2015; 15: 87-103Crossref PubMed Scopus (852) Google Scholar, 5.Ivashkiv L.B. Donlin L.T. Regulation of type i interferon responses.Nat. Rev. Immunol. 2014; 14: 36-49Crossref PubMed Scopus (1323) Google Scholar, 6.Mesev E.V. et al.Decoding type I and III interferon signalling during viral infection.Nat. Microbiol. 2019; 4: 914-924Crossref PubMed Scopus (92) Google Scholar]). Population-averaged measurements have generated not only a wealth of knowledge on IFN-I responses and regulation, but also contradictory results that remain hard to interpret or understand [7.Drayman N. et al.HSV-1 single-cell analysis reveals the activation of anti-viral and developmental programs in distinct sub-populations.Elife. 2019; 8: 1-25Crossref Scopus (37) Google Scholar]. Over the past few years, an overwhelming wave of single-cell data has changed the dogma on how systemic immune responses are generated, challenging the traditional assumption that all cells of a given lineage display nearly identical behaviors upon stimulation.Box 1Basics of IFN-I responsesIFN-I-mediated immune responses provide a robust first line of host defense against viruses and other invading pathogens [5.Ivashkiv L.B. Donlin L.T. Regulation of type i interferon responses.Nat. Rev. Immunol. 2014; 14: 36-49Crossref PubMed Scopus (1323) Google Scholar]. Given their long history of coevolution, these responses are hardwired within mammalian genomes and are complex. This complexity ensures that IFN responses can be tightly regulated, which is needed to avoid tissue damage and potential autoimmunity [100.Trinchieri G. Type I interferon: friend or foe?.J. Exp. Med. 2010; 207: 2053-2063Crossref PubMed Scopus (579) Google Scholar]. IFN-Is constitute the largest IFN class, comprising IFN-α, IFN-β, IFN-ε, IFN-κ, and IFN-ω in humans. Different subtypes display differential tissue expression and binding affinities to their receptors (reviewed in [101.Pestka S. The interferons: 50 years after their discovery, there is much more to learn.J. Biol. Chem. 2007; 282: 20047-20051Abstract Full Text Full Text PDF PubMed Scopus (256) Google Scholar]). Consequently, various outcomes, involving antiviral, immunomodulatory, and antiproliferative activity, can occur [102.Jaitin D.A. et al.Inquiring into the differential action of interferons (IFNs): an IFN- 2 mutant with enhanced affinity to IFNAR1 is functionally similar to IFN-beta.Mol. Cell. Biol. 2006; 26: 1888-1897Crossref PubMed Scopus (167) Google Scholar, 103.Kalie E. et al.The stability of the ternary interferon-receptor complex rather than the affinity to the individual subunits dictates differential biological activities.J. Biol. Chem. 2008; 283: 32925-32936Abstract Full Text Full Text PDF PubMed Scopus (80) Google Scholar, 104.Moraga I. et al.Receptor density is key to the Alpha2/Beta interferon differential activities.Mol. Cell. Biol. 2009; 29: 4778-4787Crossref PubMed Scopus (65) Google Scholar]. IFN-I production can be elicited in almost every cell type, simply because viruses have evolved to successfully infect almost every cell type. Similarly, IFN-α/β receptors (IFNARs) are constitutively expressed on almost all cells, allowing cells to acquire an antiviral state.The prevalent pathway of IFN-I induction is initiated when viral, xenogeneic, or autologous nucleic acids are recognized by the cytosolic receptors, RNA helicases, retinoic acid-inducible gene I (RIG-I, and melanoma differentiation-associated gene 5 (MDA5). Upon activation, these receptors, also known as RIG-I-like receptors (RLRs), initiate a signaling cascade leading to the translocation and activation of the transcription factor Interferon regulatory factor 3 (IRF3), which induces the transcription of IFN-Is (reviewed in [4.McNab F. et al.Type I interferons in infectious disease.Nat. Rev. Immunol. 2015; 15: 87-103Crossref PubMed Scopus (852) Google Scholar]). Via the expression of Toll-like receptors (TLR3/7/8/9) in endosomes, phagocytic immune cells, such as macrophages, dendritic cells, and pDCs, can recognize endosomal viral nucleic acids, resulting in the activation of the transcription factor IRF7, which functions similarly to IRF3 [105.Honda K. et al.Type I inteferon gene induction by the interferon regulatory factor family of transcription factors.Immunity. 2006; 25: 349-360Abstract Full Text Full Text PDF PubMed Scopus (810) Google Scholar]. IFN-I production is further enhanced via autocrine signaling of IFN-α and IFN-β predominantly, resulting in the upregulation IRF7 and other signaling components [105.Honda K. et al.Type I inteferon gene induction by the interferon regulatory factor family of transcription factors.Immunity. 2006; 25: 349-360Abstract Full Text Full Text PDF PubMed Scopus (810) Google Scholar].Upon vast production, IFN-Is diffuse and subsequently bind to IFNARs expressed on neighboring cells. Upon activation, the IFNAR1-associated protein tyrosine kinase 2 (Tyk2) and the IFNAR2-associated protein tyrosine kinases Janus kinase 1 (JAK1) regulate the phosphorylation and activation of different signal transducer and activator of transcription (STAT) proteins [5.Ivashkiv L.B. Donlin L.T. Regulation of type i interferon responses.Nat. Rev. Immunol. 2014; 14: 36-49Crossref PubMed Scopus (1323) Google Scholar]. Activated STAT proteins homo- or heterodimerize and translocate to the nucleus, where they promote the expression of several target genes, such as IFN-stimulated genes (ISGs), including IRF7 [105.Honda K. et al.Type I inteferon gene induction by the interferon regulatory factor family of transcription factors.Immunity. 2006; 25: 349-360Abstract Full Text Full Text PDF PubMed Scopus (810) Google Scholar]. In addition, IFN-Is can directly activate nuclear factor-κB (NF-κB) signaling pathways, resulting in the production of additional proinflammatory cytokines, thereby promoting the population-wide anti-viral responses in both infected and uninfected cells [105.Honda K. et al.Type I inteferon gene induction by the interferon regulatory factor family of transcription factors.Immunity. 2006; 25: 349-360Abstract Full Text Full Text PDF PubMed Scopus (810) Google Scholar]. IFN-I-mediated immune responses provide a robust first line of host defense against viruses and other invading pathogens [5.Ivashkiv L.B. Donlin L.T. Regulation of type i interferon responses.Nat. Rev. Immunol. 2014; 14: 36-49Crossref PubMed Scopus (1323) Google Scholar]. Given their long history of coevolution, these responses are hardwired within mammalian genomes and are complex. This complexity ensures that IFN responses can be tightly regulated, which is needed to avoid tissue damage and potential autoimmunity [100.Trinchieri G. Type I interferon: friend or foe?.J. Exp. Med. 2010; 207: 2053-2063Crossref PubMed Scopus (579) Google Scholar]. IFN-Is constitute the largest IFN class, comprising IFN-α, IFN-β, IFN-ε, IFN-κ, and IFN-ω in humans. Different subtypes display differential tissue expression and binding affinities to their receptors (reviewed in [101.Pestka S. The interferons: 50 years after their discovery, there is much more to learn.J. Biol. Chem. 2007; 282: 20047-20051Abstract Full Text Full Text PDF PubMed Scopus (256) Google Scholar]). Consequently, various outcomes, involving antiviral, immunomodulatory, and antiproliferative activity, can occur [102.Jaitin D.A. et al.Inquiring into the differential action of interferons (IFNs): an IFN- 2 mutant with enhanced affinity to IFNAR1 is functionally similar to IFN-beta.Mol. Cell. Biol. 2006; 26: 1888-1897Crossref PubMed Scopus (167) Google Scholar, 103.Kalie E. et al.The stability of the ternary interferon-receptor complex rather than the affinity to the individual subunits dictates differential biological activities.J. Biol. Chem. 2008; 283: 32925-32936Abstract Full Text Full Text PDF PubMed Scopus (80) Google Scholar, 104.Moraga I. et al.Receptor density is key to the Alpha2/Beta interferon differential activities.Mol. Cell. Biol. 2009; 29: 4778-4787Crossref PubMed Scopus (65) Google Scholar]. IFN-I production can be elicited in almost every cell type, simply because viruses have evolved to successfully infect almost every cell type. Similarly, IFN-α/β receptors (IFNARs) are constitutively expressed on almost all cells, allowing cells to acquire an antiviral state. The prevalent pathway of IFN-I induction is initiated when viral, xenogeneic, or autologous nucleic acids are recognized by the cytosolic receptors, RNA helicases, retinoic acid-inducible gene I (RIG-I, and melanoma differentiation-associated gene 5 (MDA5). Upon activation, these receptors, also known as RIG-I-like receptors (RLRs), initiate a signaling cascade leading to the translocation and activation of the transcription factor Interferon regulatory factor 3 (IRF3), which induces the transcription of IFN-Is (reviewed in [4.McNab F. et al.Type I interferons in infectious disease.Nat. Rev. Immunol. 2015; 15: 87-103Crossref PubMed Scopus (852) Google Scholar]). Via the expression of Toll-like receptors (TLR3/7/8/9) in endosomes, phagocytic immune cells, such as macrophages, dendritic cells, and pDCs, can recognize endosomal viral nucleic acids, resulting in the activation of the transcription factor IRF7, which functions similarly to IRF3 [105.Honda K. et al.Type I inteferon gene induction by the interferon regulatory factor family of transcription factors.Immunity. 2006; 25: 349-360Abstract Full Text Full Text PDF PubMed Scopus (810) Google Scholar]. IFN-I production is further enhanced via autocrine signaling of IFN-α and IFN-β predominantly, resulting in the upregulation IRF7 and other signaling components [105.Honda K. et al.Type I inteferon gene induction by the interferon regulatory factor family of transcription factors.Immunity. 2006; 25: 349-360Abstract Full Text Full Text PDF PubMed Scopus (810) Google Scholar]. Upon vast production, IFN-Is diffuse and subsequently bind to IFNARs expressed on neighboring cells. Upon activation, the IFNAR1-associated protein tyrosine kinase 2 (Tyk2) and the IFNAR2-associated protein tyrosine kinases Janus kinase 1 (JAK1) regulate the phosphorylation and activation of different signal transducer and activator of transcription (STAT) proteins [5.Ivashkiv L.B. Donlin L.T. Regulation of type i interferon responses.Nat. Rev. Immunol. 2014; 14: 36-49Crossref PubMed Scopus (1323) Google Scholar]. Activated STAT proteins homo- or heterodimerize and translocate to the nucleus, where they promote the expression of several target genes, such as IFN-stimulated genes (ISGs), including IRF7 [105.Honda K. et al.Type I inteferon gene induction by the interferon regulatory factor family of transcription factors.Immunity. 2006; 25: 349-360Abstract Full Text Full Text PDF PubMed Scopus (810) Google Scholar]. In addition, IFN-Is can directly activate nuclear factor-κB (NF-κB) signaling pathways, resulting in the production of additional proinflammatory cytokines, thereby promoting the population-wide anti-viral responses in both infected and uninfected cells [105.Honda K. et al.Type I inteferon gene induction by the interferon regulatory factor family of transcription factors.Immunity. 2006; 25: 349-360Abstract Full Text Full Text PDF PubMed Scopus (810) Google Scholar]. Improving technologies and experimental approaches, such as the use of fluorescently tagged cells, reporter mice, single-mRNA molecule in situ hybridization, single-cell quantitative PCR, single-cell RNA sequencing (scRNA-seq), and microfluidic approaches, have revealed a massive degree of mammalian cellular heterogeneity during IFN-I responses, along with multiple layers of stochasticity, which in turn yield cellular heterogeneity [7.Drayman N. et al.HSV-1 single-cell analysis reveals the activation of anti-viral and developmental programs in distinct sub-populations.Elife. 2019; 8: 1-25Crossref Scopus (37) Google Scholar, 8.Steuerman Y. et al.Dissection of Influenza infection in vivo by single-cell RNA sequencing.Cell Syst. 2018; 6: 679-691Abstract Full Text Full Text PDF PubMed Scopus (64) Google Scholar, 9.Zanini F. et al.Single-cell transcriptional dynamics of flavivirus infection.Elife. 2018; 7e32942Crossref PubMed Scopus (55) Google Scholar, 10.Russell A.B. et al.Extreme heterogeneity of influenza virus infection in single cells.Elife. 2018; 7: 1-26Crossref Scopus (91) Google Scholar, 11.Shnayder M. et al.Defining the transcriptional landscape during cytomegalovirus latency with single-cell RNA sequencing.MBio. 2018; 9e00013-18Crossref PubMed Scopus (78) Google Scholar, 12.Hu J. et al.Role of cell-to-cell variability in activating a positive feedback antiviral response in human dendritic cells.PLoS ONE. 2011; 6e16614Crossref PubMed Scopus (28) Google Scholar, 13.Patil S. et al.Single-cell analysis shows that paracrine signaling by first responder cells shapes the interferon-β response to viral infection.Sci. 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Given the pivotal roles of IFN-Is in multiple physiological and pathological conditions, it may be surprising that only small fractions of cells begin producing IFN-I upon viral infection, in vitro, ex vivo, and in vivo [7.Drayman N. et al.HSV-1 single-cell analysis reveals the activation of anti-viral and developmental programs in distinct sub-populations.Elife. 2019; 8: 1-25Crossref Scopus (37) Google Scholar,13.Patil S. et al.Single-cell analysis shows that paracrine signaling by first responder cells shapes the interferon-β response to viral infection.Sci. Signal. 2015; 8: 1-13Crossref Scopus (39) Google Scholar,16.Shalek A.K. et al.Single-cell RNA-seq reveals dynamic paracrine control of cellular variation.Nature. 2014; 510: 363-369Crossref PubMed Scopus (506) Google Scholar, 17.Wimmers F. et al.Single-cell analysis reveals that stochasticity and paracrine signaling control interferon-alpha production by plasmacytoid dendritic cells.Nat. 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The IFN-I system is evidently complex and, therefore, the need to apply advanced technologies, innovative approaches, and different ways to interpret large amounts of single-cell and systems immunology data, has been magnified. Mathematical models enable scientists to dissect complex biological systems into smaller pieces that are easier to understand. Additionally, simulations that generate hypothetical outcomes can be validated with experimental data to improve the quality and the complexity of the models. Moreover, mathematical models can be used as powerful tools to predict certain experimental outcomes during interventional studies. For the IFN-I system, as for many other biological systems, mathematical models have provided crucial insights regarding the complexity of the system, which we address in this review. These models have enabled scientists to better understand the highly heterogenous cellular behaviors and cellular decisions made during the course of a viral infection. Here, we provide a brief overview of the multilayered stochasticity arising from virus-induced signaling, how it causes cellular heterogeneity, how it leads to the three aforementioned cell fates, and how mathematical models can aid in the interpretation, understanding, and prediction of outcomes. Altogether, these models can serve as powerful tools to reveal and understand the complexity of IFN-I signaling pathways, in a manner that may surpass knowledge of antiviral immune responses alone. As cellular heterogeneity in the IFN-I system has become more evident, its origins are being studied more extensively. Cellular heterogeneity in the IFN-I system has been deemed to be the result of multiple different stochastic events, divided approximately into three distinct, but intertwined layers (Figure 1) [7.Drayman N. et al.HSV-1 single-cell analysis reveals the activation of anti-viral and developmental programs in distinct sub-populations.Elife. 2019; 8: 1-25Crossref Scopus (37) Google Scholar,14.Rand U. et al.Multi-layered stochasticity and paracrine signal propagation shape the type-l interferon response.Mol. Syst. Biol. 2012; 8: 1-13Crossref Scopus (86) Google Scholar]. Each layer of stochasticity originates chronologically over the course of a viral infection and involves different biological, biochemical, and biophysical processes. Consequently, these three layers of stochasticity can result in widely variable cellular outcomes in terms of infection onset time, viral load, IFN-I expression, IFN-I signaling, antiviral response, and so on. Even before the first cells become infected, the first layer of stochasticity may originate from the random distribution of viral particles that the individual cells encounter [29.Cohen E.M. Kobiler O. Gene expression correlates with the number of herpes viral genomes initiating infection in single cells.PLoS Pathog. 2016; 12e1006082Crossref PubMed Scopus (21) Google Scholar]. Additionally, genetic variability within the virus population may lead to even more variation, with some virus particles being more or less successful in establishing infection [30.Lauring A.S. et al.The role of mutational robustness in RNA virus evolution.Nat. Rev. Microbiol. 2013; 11: 327-336Crossref PubMed Scopus (210) Google Scholar,31.Stern A. et al.Costs and benefits of mutational robustness in RNA viruses.Cell Rep. 2014; 8: 1026-1036Abstract Full Text Full Text PDF PubMed Scopus (34) Google Scholar]. 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It is thought that intrinsic gene expression noise results from the stochastic nature of biochemical reactions, whereas extrinsic gene expression noise results from cell–cell fluctuations of components that are involved in generating the response [38.Dey S.S. et al.Orthogonal control of expression mean and variance by epigenetic features at different genomic loci.Mol. Syst. Biol. 2015; 11: 806Crossr