Decoding the dynamics of multilayered stochastic antiviral IFN-I responses

Abstract

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. From population averages to single-cell resolutionProviding 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]. 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][3][4]. The expression and regulation of IFN-I has been extensively studied in mammals, and important molecular regulators have been characterized (Box 1;). 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]. 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. HighlightsThe rise of single-cell technologies has highlighted a massive degree of cellular heterogeneity during mammalian interferon (IFN)-I responses.

The evolution of powerful yet perilous immune systems

“…For example, in fruit flies [ 71 ] and flour beetles [ 72 ], subtly divergent early dynamics ultimately determine whether the host will survive or die; in the fruit flies ( Drosophila melanogaster ) the relative rates of bacterial proliferation versus Imd -driven antimicrobial peptide production [ 73 ] immediately following Providencia rettgeri injection determined whether or not the fly would survive [ 71 ]. Mathematical modeling of mammalian immune dynamics suggests that such bifurcations also determine organism-scale Th cell phenotype [ 66 ], type I IFN responsiveness [ 74 ], and ultimately the chronicity of infection [ 75 , 76 ]. We eagerly look forward to experimental tests of these predictions in mammals.…”

“…We expect that the evolutionary legacy of multicellularity will affect susceptibility to immunopathology due to such system dynamics. In particular, genetics and environmental conditions, although important, are insufficient predictors of immunopathology: perfect matching to the optimum (striking the perfect balance between protection and immunopathology) for every individual becomes impossible because small stochastic fluctuations propagated through feedback loops in the cellular communication circuits generate variation in the realized cytokine response [ 66 , 74 ]. Furthermore, genetic variants that even subtly modulate feedback strengths (such as the Imd -deficient and other fruit fly variants in a seminal study [ 71 ]) can also drastically alter the probabilistic cytokine output in response to similar stimuli ( Figure 3 B,C) [ 77 ].…”

Post-transcriptional regulatory feedback encodes JAK-STAT signal memory of interferon stimulation

“…The second, positive feedback involved regulation of STAT1 expression, trough STAT1-mediated activation of Interferon Regulatory Factor 1 (IRF1) (Hu et al 2002; Pertsovskaya et al 2013). In the model, we assume that IFN-γ and IFN-α/β1 bind their cognate receptors, forming an active receptor complexes, which phosphorylate STAT1 and STAT2 in the cytoplasm (Van Eyndhoven, Singh, and Tel 2021). Phosphorylated STATs (pSTAT1 and pSTAT2) undergo homo- and hetero-dimerization.…”

“…IFN-γ binding leads to the nuclear translocation of Signal Transducers and Regulators of Transcription 1 (STAT) homodimers, which directly activate expression of hundreds of interferon-regulated genes (ISGs) via conserved sequences in their promoters (Barrat, Crow, and Ivashkiv 2019). Functionally related type I interferons, such as IFN-α and IFN-β regulate overlapping sets of genes via ISGs (in part through STAT1 homodimers), but also utilise IFN-stimulated response elements (trough STAT1-STAT2-IRF9 complexes) (Van Eyndhoven, Singh, and Tel 2021). Regulation of IFN signalling exemplifies the intricate balance within the immune system to produce appropriate responses.…”

“…Functionally related type I interferons, such as IFN- and IFN- regulate overlapping sets of genes via ISGs (in part through STAT1 homodimers), but also utilise IFN-stimulated response elements (trough STAT1-STAT2-IRF9 complexes) (Van Eyndhoven, Singh, and Tel 2021).…”