Single-cell analysis of early antiviral gene expression reveals a determinant of stochastic<i>IFNB1</i>expression

Doğanay, Sultan; Lee, Maurice Youzong; Baum, Alina; Peh, Jessie; Hwang, Sun Young; Yoo, Joo Yeon; Hergenrother, Paul J.; Garcı́a-Sastre, Adolfo; Myong, Sua; Ha, Taekjip

doi:10.1039/c7ib00146k

Cited by 20 publications

(37 citation statements)

References 38 publications

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“…At 6 h post infection with the IFN inducer virus Clone 13, 21 genes were upregulated (Figures 2A and 2B). Among these were prototypical, IRF3-driven genes (IFNB1, CCL5, CXCL10, IFIT1 to 3, OAS1, and OASL), and others reported to be expressed by infected cells (DDX58, DDX60, DHX58, IFIH1, IFI44, ISG15) (Balogh et al., 2014, Doganay et al., 2017, Glennon et al., 2015, Grandvaux et al., 2002, McWhirter et al., 2004, Puthia et al., 2016, Schmid et al., 2010). By contrast, upregulated genes such as IRF7, MX1, MX2, OAS3, SAMD9, and USP18 are prototypical ISGs (Rusinova et al., 2013, Shaw et al., 2017) that most likely are activated by the Clone 13-induced IFN in the supernatant (see later discussion).…”

Section: Resultsmentioning

confidence: 99%

Virus- and Interferon Alpha-Induced Transcriptomes of Cells from the Microbat Myotis daubentonii

Hölzer

Schoen²,

Wulle

et al. 2019

iScience

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Summary Antiviral interferons (IFN-alpha/beta) are possibly responsible for the high tolerance of bats to zoonotic viruses. Previous studies focused on the IFN system of megabats (suborder Yinpterochiroptera ). We present statistically robust RNA sequencing (RNA-seq) data on transcriptomes of cells from the “microbat” Myotis daubentonii (suborder Yangochiroptera ) responding at 6 and 24 h to either an IFN-inducing virus or treatment with IFN. Our data reveal genes triggered only by virus, either in both humans and Myotis (CCL4, IFNL3, CH25H), or exclusively in Myotis (STEAP4). Myotis cells also express a series of conserved IFN-stimulated genes (ISGs) and an unusually high paralog number of the antiviral ISG BST2 (tetherin) but lack several ISGs that were described for megabats (EMC2, FILIP1, IL17RC, OTOGL, SLC24A1). Also, in contrast to megabats, we detected neither different IFN-alpha subtypes nor an unusually high baseline expression of IFNs. Thus, Yangochiroptera microbats, represented by Myotis , may possess an IFN system with distinctive features.

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Section: Resultsmentioning

confidence: 99%

Virus- and Interferon Alpha-Induced Transcriptomes of Cells from the Microbat Myotis daubentonii

Hölzer

Schoen²,

Wulle

et al. 2019

iScience

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“…However, the extent to which IFN induction and signaling within a specific cell is dependent on extracellular IFN levels is unclear. IFN independent pathways that upregulate ISGs exist [ 70 , 71 ]. Individual cells can alter their IFN secretion rates through autocrine-mediated IFN signaling, an effect that is not present in our model, which assumes that secretion rates are determined by a single global extracellular IFN level.…”

Section: Discussionmentioning

confidence: 99%

Investigating Functional Roles for Positive Feedback and Cellular Heterogeneity in the Type I Interferon Response to Viral Infection

Leviyang

Griva

2018

Viruses

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Secretion of type I interferons (IFN) by infected cells mediates protection against many viruses, but prolonged or excessive type I IFN secretion can lead to immune pathology. A proper type I IFN response must therefore maintain a balance between protection and excessive IFN secretion. It has been widely noted that the type I IFN response is driven by positive feedback and is heterogeneous, with only a fraction of infected cells upregulating IFN expression even in clonal cell lines, but the functional roles of feedback and heterogeneity in balancing protection and excessive IFN secretion are not clear. To investigate the functional roles for feedback and heterogeneity, we constructed a mathematical model coupling IFN and viral dynamics that extends existing mathematical models by accounting for feedback and heterogeneity. We fit our model to five existing datasets, reflecting different experimental systems. Fitting across datasets allowed us to compare the IFN response across the systems and suggested different signatures of feedback and heterogeneity in the different systems. Further, through numerical experiments, we generated hypotheses of functional roles for IFN feedback and heterogeneity consistent with our mathematical model. We hypothesize an inherent tradeoff in the IFN response: a positive feedback loop prevents excessive IFN secretion, but also makes the IFN response vulnerable to viral antagonism. We hypothesize that cellular heterogeneity of the IFN response functions to protect the feedback loop from viral antagonism. Verification of our hypotheses will require further experimental studies. Our work provides a basis for analyzing the type I IFN response across systems.

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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 [7,13,[16][17][18][19]. Of note, these fractions of first responders, also referred to as 'precocious cells' or 'early responding cells', are proposed to arise from all-or-nothing cellular decision-making, a phenomenon that has been observed for a variety of proinflammatory cytokines [7,[20][21][22]. In turn, IFN-Is prime surrounding cells via paracrine signaling to enhance IFN-I production upon viral recognition, a process initiated by a much larger fraction of so-called cellular second responders;…”

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confidence: 99%

Decoding the dynamics of multilayered stochastic antiviral IFN-I responses

Eyndhoven

Singh

Tel

2021

Trends in Immunology

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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.

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Single-cell analysis of early antiviral gene expression reveals a determinant of stochasticIFNB1expression

Cited by 20 publications

References 38 publications

Virus- and Interferon Alpha-Induced Transcriptomes of Cells from the Microbat Myotis daubentonii

Virus- and Interferon Alpha-Induced Transcriptomes of Cells from the Microbat Myotis daubentonii

Investigating Functional Roles for Positive Feedback and Cellular Heterogeneity in the Type I Interferon Response to Viral Infection

Decoding the dynamics of multilayered stochastic antiviral IFN-I responses

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