Visualizing the Selectivity and Dynamics of Interferon Signaling In Vivo

Article

Visualizing the Selectivity and Dynamics of Interferon Signaling In Vivo Graphical Abstract

Authors Sebastian A. Stifter, Nayan Bhattacharyya, Andrew J. Sawyer, ..., Warwick J. Britton, Alan Sher, Carl G. Feng

Correspondence [email protected]

In Brief Stifter et al. generated an Irgm1 reporter mouse sensitive to induction by all three types of IFNs. They show that cellular responses to IFNs are highly heterogenous in vivo. Furthermore, different types of IFNs act in a cell-typedependent manner to convey synergistic, antagonistic, or non-redundant signaling during influenza infection.

Highlights d

M1Red mouse reports on cellular responses to all three types of interferons (IFNs)

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Hematopoietic progenitors and myeloid cells highly respond to IFN stimulation

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Respiratory influenza virus infection induces local and systemic IFN signaling

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Influenza-susceptible pneumocytes respond poorly to IFNs early during infection

Stifter et al., 2019, Cell Reports 29, 3539–3550 December 10, 2019 ª 2019 The Author(s). https://doi.org/10.1016/j.celrep.2019.11.021

Cell Reports

Article Visualizing the Selectivity and Dynamics of Interferon Signaling In Vivo Sebastian A. Stifter,1,2,9 Nayan Bhattacharyya,1,2 Andrew J. Sawyer,1,2 Taylor A. Cootes,1,2 John Stambas,3 Sean E. Doyle,4 Lionel Feigenbaum,5 William E. Paul,6,10 Warwick J. Britton,2,7 Alan Sher,8 and Carl G. Feng1,2,11,* 1Immunology

and Host Defense Group, Discipline of Infectious Diseases and Immunology, Faculty of Medicine and Health, The University of Sydney, NSW 2006, Australia 2Centenary Institute, The University of Sydney, NSW 2050, Australia 3School of Medicine, Deakin University, Geelong, VIC 3216, Australia 4Bristol-Myers Squibb, Seattle, WA 98102, USA 5Laboratory Animal Sciences Program, National Cancer Institute, Frederick, MD 21702, USA 6Cytokine Biology Unit, Laboratory of Immunology, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892, USA 7Central Clinical School, Faculty of Medicine and Health, The University of Sydney, NSW 2006, Australia 8Immunobiology Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892-3202, USA 9Present address: Institute of Experimental Immunology, University of Zurich, Zurich 8057, Switzerland 10Deceased 11Lead Contact *Correspondence: [email protected] https://doi.org/10.1016/j.celrep.2019.11.021

SUMMARY

Interferons (IFN) are pleiotropic cytokines essential for defense against infection, but the identity and tissue distribution of IFN-responsive cells in vivo are poorly defined. In this study, we generate a mouse strain capable of reporting IFN-signaling activated by all three types of IFNs and investigate the spatio-temporal dynamics and identity of IFN-responding cells following IFN injection and influenza virus infection. Despite ubiquitous expression of IFN receptors, cellular responses to IFNs are highly heterogenous in vivo and are determined by anatomical site, cell type, cellular preference to individual IFNs, and activation status. Unexpectedly, type I and II pneumocytes, the primary target of influenza infection, exhibit striking differences in the strength and temporal dynamics of IFN signaling associated with differential susceptibility to the viral infection. Our findings suggest that time- and cell-type-dependent integration of distinct IFN signals govern the specificity and magnitude of IFN responses in vivo. INTRODUCTION The interferon (IFN) family comprises more than 20 members and is divided into type I, II, and III IFNs based on their respective cell surface receptors. With the exception of type III IFNs, IFN receptors are expressed ubiquitously on all nucleated cells. IFNs signal via the JAK-STAT pathway, and the common anti-viral, anti-proliferative, and pro-apoptotic functions of IFNs specifically depend on STAT1 (van Boxel-Dezaire et al., 2006). Although IFN function has been studied in great detail in vitro and in vivo using recombinant cytokines and genetically modi-

fied mice, many of these studies fail to provide information on the hierarchy of cellular sensitivity to different types of IFNs or anatomic distribution of IFN-responsive cells in tissues, which limits the elucidation of cell-type-specific IFN signaling and impact of cellular composition on the overall IFN response in inflamed tissues. Until recently, the only IFN signaling reporter mouse available was the Mx2-luciferase mouse. Although these mice have proven invaluable for the visualization of the IFN response in vivo at the organ/tissue level, they are not suited for the analysis of spatial dynamics of the IFN response at the level of individual cells (Pulverer et al., 2010). While preparing our manuscript, an Mx1-GFP mouse was published (Uccellini and Garcia-Sastre, 2018). Mx1-GFP mice provide an excellent tool for the interrogation of type I/III IFN responses, however, by design, they are not able to identify IFN-g responsive cells. Because type I/III IFNs and IFN-g utilize similar yet distinct signaling pathways (Liu et al., 2012) and many diseases are characterized by IFN-g production, identification of cells sensitive to IFN-g, in addition to type I/III IFN, will provide a more complete picture of how individual cells respond to distinct IFNs in vivo. Influenza, caused by infection with influenza virus, remains among the most significant global infectious diseases owing to its high infectivity, variable efficacy of vaccines, and the limitations of anti-viral therapy. Infection induces profound pulmonary inflammation, characterized by infiltration of monocytes, neutrophils, and lymphocytes, and the production of pro-inflammatory cytokines, including IFNs (Newton et al., 2016). However, how the different IFNs mediate their protective functions is incompletely understood. In this study, we have generated an immunity-related GTPase m1 (Irgm1) reporter mouse strain (M1Red) to visualize IFNresponsive cells in vivo following inoculation with individual recombinant IFNs or influenza A virus (IAV). Irgm1 was selected because this GTPase is widely expressed in cells of both hematopoietic and non-hematopoietic origins (Bafica et al., 2007; Taylor et al., 2004; Hunn et al., 2011) and is highly sensitive to

Cell Reports 29, 3539–3550, December 10, 2019 ª 2019 The Author(s). 3539 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Figure 1. M1Red Mice Report the Activation of All Three Types of IFN Signaling In Vitro and In Vivo (A and B) Irgm1/DsRed intensity (MFI) in (A) PBMCs and (B) bone marrow Ly6G+ neutrophils isolated from M1Red mice 24 h after stimulation with medium or 100 ng/mL IFNs in vitro. Data shown are mean MFI ± SD (n = 3) and are representative of two independent experiments. (C) Representative flow cytometry plots showing Irgm1/DsRed expression in blood leukocytes of M1Red mice 24 h after i.v. injection with 2 mg of the indicated IFN. (D) Irgm1/DsRed expression in blood leukocytes isolated from M1Red mice 6 and 24 h after i.v. injection with 2 mg of the indicated IFN. Data shown are mean MFI ± SD (n = 3). Dotted line indicates Irgm1/DsRed expression levels in PBS injected M1Red animals.

induction by different types of IFNs in a STAT1 dependent manner (MacMicking et al., 2003; Bafica et al., 2007; Feng et al., 2008). All three types of IFNs are highly expressed during IAV infection and each of the IFNs contribute to host resistance to IAV infection in the murine model (Galani et al., 2017; Klinkhammer et al., 2018). The M1Red reporter mice utilized in this study have allowed us to visualize the spatio-temporal dynamics of STAT1-dependent IFN-signaling in vivo. Unexpectedly, although IFN receptors are ubiquitously expressed, only a subset of host cells responded to injected or pathogen-induced IFNs in vivo. Furthermore, by analyzing wild-type (WT) M1Red mice crossed to those deficient in either Ifnar1 or Ifngr1, we were able to discern the contributions of IFN-a/b and IFN-g to the overall IFN response in immune and alveolar epithelial cell populations following influenza infection in vivo. Our findings demonstrate that in infection in vivo the IFN response selectively incorporates one or multiple unique IFN signals in a cell-typedependent manner. RESULTS Irgm1/DsRed Is Induced in Leukocytes in Response to IFN Stimulation In Vitro We utilized a bacterial artificial chromosome (BAC) approach to generate an Irgm1 reporter mouse strain. The DsRed2 coding sequence was inserted at the translational start site of the Irgm1 gene in the BAC clone RP23-305A21 using recombineering technology (Figure S1), so that DsRed expression reports

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Irgm1 transcription in this system. Embryos were manipulated by pro-nuclear injection and the resulting reporter mouse strain was designated Irgm1/DsRed (M1Red). To characterize the induction of Irgm1/ DsRed by IFNs in mature leukocytes, we first stimulated peripheral blood mononuclear cells (PBMCs) isolated from naive M1Red mice with type I, II, and III IFNs in vitro for 24 h and measured induction of DsRed by flow cytometry. Although IFN stimulation (100 ng/mL) induced Irgm1/DsRed expression in all leukocyte populations, the levels of expression (quantified by mean fluorescence intensity [MFI]) varied depending on the type of leukocytes and IFNs used (Figure 1A). Monocytes expressed the highest level of DsRed among PBMCs regardless of IFN stimuli. As expected, IFN-l did not induce DsRed expression in PBMCs in vitro, because the expression of the IFN-l receptor is restricted to neutrophils and epithelial cells (Mordstein et al., 2010; Sommereyns et al., 2008; Blazek et al., 2015). Indeed, IFN-l induced Irgm1/DsRed in bone marrow (BM) neutrophils isolated from naive M1Red mice (Figure 1B), demonstrating that IFN-l is able to induce DsRed expression in leukocytes in our reporter system. M1Red Mice Reveal Distinct and Overlapping Activities of Individual IFNs in Leukocyte Populations following IFN Injection In Vivo To examine Irgm1/M1Red induction by individual IFNs in vivo, we next injected M1Red mice intravenously with graded doses (0.22 mg, 0.66 mg, and 2 mg) of type I, II, and III IFNs and measured Irgm1/DsRed expression in various tissues using flow cytometry. Consistent with our in vitro findings, Irgm1/DsRed was markedly induced in circulating leukocytes by both IFN-b and IFN-g with the strongest response in monocytes (Figure 1C). Natural killer (NK) cells appeared to respond only to type I IFN. Interestingly, we observed that the in vivo induction of Irgm1/Dsred in circulating leukocytes was dynamic following a single IFN injection.

Figure 2. M1Red Mice Reveal Distinct and Overlapping Activities of Individual IFNs in Leukocyte Populations (A–C) M1Red animals were i.v. injected with PBS, 0.22 mg, 0.66 mg, or 2 mg of the indicated IFNs and Irgm1/DsRed expression determined 24 h later using flow cytometry. Heatmaps show DsRed expression on leukocytes in (A) blood and (B) bone marrow (BM), as well as (C) progenitor/stem cells in BM. Data shown are the mean fold change in DsRed MFI compared to PBS injected animals (n = 2–3). (D) DsRed expression in Ly6G+ neutrophils isolated from the blood and BM of M1Red mice 24 h after i.v. injection with the indicated doses of IFN-l. Data shown are mean fold change in DsRed MFI compared to PBS injected animals ± SD (n = 3).

In almost all cell types, DsRed expression, clearly elevated at 6 h after IFN injection, began to decline by 24 h, suggesting that DsRed-expressing cells were diluted by newly arrived mature leukocytes or cleared from the circulation due to cell death or migration to tissues at this time point (Figure 1D). An exception to this observation was the induction of DsRed in neutrophils by IFN-l, which showed increased expression at 24 h compared to 6 h. While this may be due to the increased half-life of the pegylated IFN-l used for these experiments, IFN-l has recently been shown to have delayed induction of ISGs compared to type I IFNs in human hepatocytes (Forero et al., 2019). We observed that when the same quantity of IFNs was injected, IFN-b was consistently more potent at inducing Irgm1/ DsRed in circulating mature leukocytes than IFN-g (Figures 1D and 2A). This difference was unlikely to be due to insufficient quantities of injected IFN-g, as DsRed induction in all cells but eosinophils peaked at the intermediate dose (0.66 mg) of IFN-g (Figure 2A). We also noted that in mice injected with IFN-b, DsRed expression in monocytes, neutrophils and T cells peaked at the intermediate dose, whereas its expression in eosinophils and B cells increased further at the highest dose (2 mg). These data therefore indicate that the sensitivity of cells to IFN stimulation is both IFN and cell-type-dependent. Moreover, the strength of IFN signaling may vary significantly depending on tissue site, possibly due to differential IFN sensitivity or response kinetics, as a more robust Irgm1/DsRed induction was observed in BM leukocytes compared to blood leukocytes at 24 h (Figure 2B). IFNs have previously been shown to signal on progenitor/hematopoietic stem cells (HSCs) (Essers et al., 2009; Sato et al., 2009; de Bruin et al., 2013), however, how this sensitivity compares to mature leukocytes is unknown. We found among undifferentiated cell populations in the BM (negative for lineage surface marker expression, lineage ), multi-potent progenitors as well as short-term HSCs (MPP and ST-HSC: Lin , cKit+ Sca1+, CD150 ) and long-term HSCs (LT-HSC: Lin , cKit+ Sca1+, CD150+) responded strongly to IFN-b and IFN-g (Figure 2C). Interestingly, the increase in Irgm1/DsRed in BM progenitor populations was more profound than that observed in mature leukocytes (Figure 2B). In response to IFN-l, only neutrophils showed a dose-dependent increase in DsRed signal in both blood and BM. DsRed expression could be further elevated by the injection of 10-fold

higher IFN-l (20 mg) (Figure 2D), suggesting that the quantity of IFN-l required for saturating Irgm1/DsRed expression is much higher than that of type I and II IFNs. M1Red Mice Reveal that Pulmonary Epithelial Cells Exhibit Differential Sensitivity to IFN Stimulation Epithelial barrier surfaces provide the first line of defense against invading pathogens. In the airways, cilia cells line the upper airways and two alveolar epithelial cell populations, type I and type II epithelial cells (AT-I and AT-II), are key components of the alveoli. AT-I facilitate gas exchange and form the structural component of the alveoli and AT-II produce surfactant. These cells can be identified in enzyme-digested lung tissue using flow cytometry. We defined AT-I as CD45 , T1a+ MHC-I+, AT-II as CD45 , T1a , EpCAM+ MHC-IIhigh, and cilia cells as CD45 , T1a , EpCAM+ MHC-IIint as previously described (Stegemann-Koniszewski et al., 2016; Cardani et al., 2017; Hasegawa et al., 2017) (Figure 3A). Although two recent transcriptomic studies identified an IFN gene signature in AT-II (Stegemann-Koniszewski et al., 2016; Steuerman et al., 2018), to the best of our knowledge, the IFN-response in this and other pulmonary epithelial cells has yet to be compared. Following intravenous (i.v.) injection with IFNs, we observed that AT-I were highly sensitive to both type I and II IFNs (Figure 3B). In comparison to AT-I, however, AT-II cells were much less sensitive to IFN stimulation. We could only detect significant DsRed induction by IFN-l in cilia cells. Similar results were obtained when IFN-l was delivered via the respiratory route (Figure 3C). The high sensitivity of cilia cells to type III IFN supports the findings that IFN-lambda signals strongly on primary tracheal airway cultures (Davidson et al., 2016; Crotta et al., 2013), as well as the previous reported key role for this cytokine in restricting IAV spread from the upper airways to the deep lung (Klinkhammer et al., 2018). Irgm1/DsRed Expressing Cells Are in Close Proximity to Viral Infection Loci in IAV Infected Lungs To investigate the cellular response to endogenously produced IFNs, we employed the PR8 H1N1 influenza virus infection model, as it leads to potent production of all three types of IFNs, yet viral replication is largely restricted to the lungs. We first determined the kinetics of Irgm1/DsRed induction in lung cells using flow cytometry. Although Irgm1/DsRed induction was not

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Figure 3. Pulmonary Epithelial Cells Exhibit Differential Sensitivity to IFN Stimulation (A) AT-I, AT-II, and cilia cell populations in lung single-cell suspensions defined by flow cytometry. (B) Irgm1/DsRed expression in lung epithelial cells of M1Red mice 24 h after i.v. injection with 0.22 mg, 0.66 mg, or 2 mg of the indicated IFNs. Data shown are the percentage of DsRed+ cells ± SD (2–3 mice/group). Dotted line indicates DsRed expression level in PBS injected animals. (C) Percentage of DsRed+ lung epithelial cells of M1Red at 24 h after intratracheal administration with 10 mg IFN-l. Data shown are mean MFI ± SD (n = 3).

detectable 1 day post-infection (dpi) (data not shown), by 2 dpi Irgm1/DsRed expression could be reliably detected in both hematopoietic (CD45+) and non-hematopoietic (CD45 ) cell populations (Figure S2). By 3 dpi, Irgm1/DsRed was induced consistently in ~30% of cells and increased to a maximum of ~70%–100% by 7 dpi, coinciding with the peak lung cellular response (Stifter et al., 2016) (Figure 4A). Importantly, DsRed expression levels closely corresponded to those of endogenous Irgm1 in the lungs up to 7 dpi (Figure 4B). At 10 dpi, while the DsRed signal remained high, Irgm1 gene expression decreased, possibly due to the half-life of the fluorescent protein exceeding that of the controlling gene, which is a common limitation of many reporter systems. Because of this reason, along with the finding that Irgm1/DsRed expression is very low before day 3, we have chosen day 3 and 7 time points for the remainder of the study. We observed that the induction of Irgm1/DsRed correlated to that of IFNs and other IFN stimulated genes (ISGs) in the lung, peaking between 3 and 7 dpi. In the lungs, Ifna and Ifnb could be detected at 3 dpi, whereas Ifng expression was highest at day 7, the time at which T cells start to infiltrate the lungs (Stifter et al., 2016) (Figure 4C). The expression profiles of Isg15 and Irf7, two genes commonly used to measure type I IFN-responses, largely correlated with the expression of Ifna and Ifnb, while Nos2, a gene potently induced by IFN-g, mirrored the induction kinetics of Ifng. Lung viral loads as measured by IAV nucleoprotein (NP) mRNA expression remained stable between 3 and 7 dpi (Figure 4D). Although the identity of type I/III IFN-responsive cells in the lung has been reported previously (Uccellini and Garcia-Sastre, 2018), the spatio-temporal dynamics of IFN signaling in the lungs during IAV infection remains unknown. Microscopic analysis of the IFNresponse in situ provides essential information as to the localization of cells that is lost upon processing for flow cytometry or gene expression analysis. We next visualized simultaneously the progression of viral infection and host IFN response in tissue sections of M1Red mice at 3 and 7 dpi. Foci enriched with IAV-infected (NP+) and IFN-responsive (DsRed+) cells in lung tissues were readily detected in the lungs 3 dpi (Figure 4E). These lesions

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represent the initial sites of virus infection and IFN signaling/production in the distal lung. We observed a remarkable level of colocalization of IFN-responsive and IAV-infected cells at day 3, suggesting that IFN signaling reported by Irgm1/DsRed expression at this time-point is likely induced by autocrine or paracrine IFNs. This is consistent with findings from IFN-b reporter mice, which show high expression of IFN-b in regions of IAV infection (Lienenklaus et al., 2009). The size and number of DsRed+ areas increased by day 7, corresponding with the progression of IAV infection and further development of the IFN response in the lung (Figure 4F). Overall, these data show that Irgm1/DsRed is strongly induced in a time-dependent manner in lungs during influenza infection, and IFN-responsive cells are in close proximity to infection loci. Respiratory IAV Infection Induces Local and Systemic Expression of Irgm1/DsRed in a STAT1-Dependent Manner To evaluate whether or not lung influenza infection leads to a systemic IFN-response in our system, we infected animals with IAV and analyzed singe-cell suspensions from a number of distal organs at 7 dpi (Figure 5A). Consistent with the fact that the lungs are the primary target of IAV infection, Irgm1/DsRed expression was highest in cells from this organ. In contrast to findings reported previously (Uccellini and Garcia-Sastre, 2018), we found that Irgm1/DsRed was strongly induced on a substantial proportion of cells in the blood, spleen, bone marrow, lung draining mediastinal (MLN), and non-lung draining iliac lymph nodes (ILN) in infected but not naive M1Red mice (Figures 5A and 5B). Interestingly, we did not detect a DsRed signal in the thymus above that measured in uninfected control M1Red animals, suggesting that cells at this site are protected from the systemic effects of IFN signaling. We next investigated whether the differentiation state of BM cells affected their ability to respond to IFNs during infection, as we found that progenitor cells were highly sensitive to i.v. administered IFN (Figure 2). Although differentiated cells (stained positively with an antibody cocktail containing monoclonal antibodies [mAbs] to the surface molecules CD3, CD11b, B220, Ly-76, and Gr1) displayed moderate levels of

Figure 4. Irgm1/DsRed Expressing Cells Are in Close Proximity to Viral Infection Loci in the Lungs (A) Flow cytometry plots and summary data showing percentage of DsRed+ cells in lung cells at 0, 3, 7, and 10 dpi. Data shown are mean percentage ± SD (3 mice/time point) and are representative of 2 independent experiments. (B–D) Relative mRNA expression of host genes and (D) IAV nucleoprotein (NP) copy number as measured by qRT-PCR from lungs of day 0, 3, 7 and 10 infected WT mice. Data shown in (B) and (C) are mean fold changes ± SD (7–10 mice/time point) compared to uninfected WT for host genes and in (D) are total NP copy numbers ± SD (3–4 mice/time point). Data are representative of 2 independent experiments. Red line denotes the limit of detection as determined using uninfected animals. (E and F) Single and merged color fluorescent images of lungs showing IFN responsive (DsRed+) and IAV-infected (NP+) cells at (E) 3 and (F) 7 dpi. Data are representative of >5 mice/time point.

Irgm1/DsRed, strikingly, lineage progenitor cells displayed the highest level of DsRed expression among all BM cells during infection (Figure 5C). Indeed, consistent with our IFN injection data, Sca-1+ cKit+ progenitor and stem cell populations were highly represented among the lineage DsRed+ population when compared to their lineage DsRed counterparts in infected mice. To evaluate which IFN was responsible for Irgm1/ DsRed induction in BM progenitor populations, we next compared Irgm1/DsRed expression in lineage cells of WT M1Red, Ifnar1 / M1Red and Ifngr1 / M1Red mice 7 dpi and found that although deficiency in either IFN receptor signaling resulted in decreased Irgm1/DsRed expression in lineage cells, the reduction was more pronounced in Ifngr1 / M1Red mice (Figure S3A), suggesting that systemic IFN-signaling is mediated mainly by IFN-g. This could be explained partly by the differences in the levels of individual IFNs in circulation, as at 7 dpi large quantities of circulating IFN-g were detected in the IAV-in-

fected mice (Figure S3B), whereas levels of serum type I and III IFNs were very low, either close to the limit of detection (IFN-a) or under the limit of detection (IFN-b and IFN-l, data not shown). Interestingly, IFN-g levels were significantly elevated in the serum of Ifnar1- and Ifngr1-deficient animals compared to WT mice. We next compared DsRed expression in STAT1-sufficient (WT) and Stat1 / M1Red mice following IAV infection. In contrast to WT reporter mice, infected Stat1 / M1Red mice failed to upregulate DsRed expression in either the lungs or the BM (Figure 5D). Moreover, lung mRNA expression of DsRed and Irgm1 was completely abrogated in infected Stat1 / M1Red mice, confirming that transcription of both genes required STAT1 (Figure 5E). Together, these data indicate that influenza infection in the lung triggers local and systemic IFN signaling and that Irgm1/DsRed induction is STAT1-dependent. M1Red Mice Reveal Cellular Heterogeneity in IFN Signaling in IAV Infection In Vivo To gain a better understanding of which cells in the lung respond to IFNs during infection, we analyzed tissue sections stained with a pan-leukocyte marker, CD45, and an AT-I marker, Aquaporin 5 (AQP5) 7 dpi (Figure 6A), a time point at which all major leukocyte populations are present in the lungs. While Irgm1/DsRed was highly and uniformly expressed in AT-I, only a subset of CD45+ cells expressed Irgm1/DsRed, suggesting that not all cells at the site of infection were IFN-responsive. To further investigate this heterogeneity in leukocytes, cells from day 7 infected M1Red mice were comprehensively analyzed

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Figure 5. IAV Infection Induces Local and Systemic Expression of Irgm1/DsRed in a STAT1-Dependent Manner (A) Representative flow cytometry histograms showing DsRed expression in total cells isolated from lung, blood, BM, spleen, iliac lymph nodes (ILN), and thymus of naive (gray lines) and day 7 infected (red lines) M1Red mice. (B) Flow cytometry summary data showing percentage of DsRed+ cells in the indicated organs at 7 dpi. Data shown are mean percentage ± SD (n = 3) and are representative of 2 independent experiments. (C) Representative flow cytometry plots showing DsRed expression in total and lineage marker negative BM cells from naive and day 7 IAV-infected M1Red mice. (D) Flow cytometry histograms showing DsRed expression in cells isolated from lungs and BM of WT M1Red (red lines) and Stat1 / M1Red (dashed lines) mice at 7 dpi. (E) mRNA expression of Dsred and Irgm1 as measured by qRT-PCR from lungs of day 0, 3, and 7 infected WT M1Red and Stat1 / M1Red mice. Data shown are mean fold increase ± SD compared to uninfected WT (3 mice/genotype/ time point) and are representative of 2 independent experiments.

using multi-parameter flow cytometry and t-distributed stochastic neighbor embedding (t-SNE) (Figure 6B). This analysis revealed that the highest IFN responsive cells after IAV infection were of the myeloid lineage, particularly monocytes/macrophages (CD11b+, Ly6G ). Alveolar macrophages (CD11c+, SiglecF+). Eosinophils (CD11c , CD11b+, SiglecF+) and neutrophils (CD11b+, Ly6Cint, Ly6G+) expressed intermediate levels of DsRed. In comparison to myeloid cells, T lymphocytes had a much weaker response to IFNs and NK and B cells expressed the lowest levels of DsRed. Cellular Response to IFNs in Infection Is Determined by Cellular Activation Status and Differential Integration of Distinct IFN-Receptor Signals Given the finding that myeloid and lymphoid cells were in effect on opposite ends of the IFN-response spectrum, we next sought to determine possible factors governing this heterogeneity. Analysis of lung Ly6Chi monocytes/macrophages revealed that those expressing high levels of MHC-II also expressed higher level of DsRed (Figure 6C), suggesting that the activation of these cells enhanced their IFN-responsiveness. In contrast, we found that for T lymphocytes, the majority of DsRed+ cells were CD44low naive T cells in lung draining mediastinal LNs, suggesting that T cell activation inhibits IFN signaling in lymphocytes during infection (Figure 6D). We previously reported that the interplay between type I and II IFNs regulates monocyte/macrophage recruitment and function (Stifter et al., 2016). Importantly, as Irgm1 is inducible by all IFNs, the Irgm1/Dsred reporter mice could be employed to more comprehensively investigate the contributions of type I IFNs and IFN-g to the overall IFN-response in lung leukocytes during IAV infection. We therefore crossed M1Red mice to Ifnar1 / and

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Ifngr1 / animals and characterized Irgm1/DsRed induction in pulmonary leukocytes. By comparing the percentage and fluorescence intensity (MFI) of DsRed-positive cells in myeloid and lymphoid populations in the lungs of WT and IFN receptor-deficient M1Red mice, we were able to deduce the hierarchy of IFNresponsive immune cells in the infected lung, as well as the contributions of type I and type II IFNs to each cell’s overall IFN response. Pulmonary immune cells could be grouped based on DsRed expression as high (MFI >5,000), intermediate (MFI 1,000–5,000) and low (MFI <1,000) IFN responder groups (Figure 6E). In IFN-receptor sufficient mice, monocytes and macrophages, particularly Ly6Chi monocytes, were the most IFNresponsive cells defined by both the proportion of responsive cells and MFI, whereas lymphocytes showed the weakest response to IFN signaling. Granulocytes, including eosinophils and neutrophils, and pDCs displayed intermediate level of IFN responsiveness. With the exception of Ly6Clo monocytes, type I and type II IFNs showed synergistic activity in high responding cell types. In contrast, IFN signaling in intermediate and low responder groups were predominantly driven by single IFN type. In these cell populations, type I and type II IFNs played a key role in inducing Irgm1/ DsRed in granulocytes and lymphocytes, respectively. We found lung viral load as measured by IAV NP transcript expression to be unaltered in WT, Ifnar1 / , and Ifngr1 / mice at 7 dpi, which is consistent with a number of reports showing little or no changes in viral load in WT, Ifnar1 / , Ifngr1 / , and Stat1 / mice (Durbin et al., 2000; Turner et al., 2007; Garcı´aSastre et al., 1998; Stifter et al., 2016), suggesting that the observed differences in cellular IFN-responses were not due to changes in viral load (Figure 6F). Furthermore, although there was a trend for reduced Ifnb and Ifng expression in Ifnar1 / and Ifngr1 / compared to WT mice, this difference was not

Figure 6. Cell-Type-Specific IFN Signaling in Infection Is Regulated by Cellular Activation Status and Differential Preference to Distinct IFN-Receptor Signals (A) Fluorescent image showing DsRed expression in leukocytes and epithelial cells in the lungs at 7 dpi. Data are representative of at least 3 mice. (B) t-SNE plot of lung populations in day 7 IAV-infected M1Red mice. Plot was generated from concatenating 3 individual data files. Intensity staining correlates to DsRed expression in indicated cell populations. (C) The expression of MHC-II and Ly6C on pulmonary monocytes in naive and day 7 infected M1Red mice determined by flow cytometry. The intensity of DsRed expression on MHC-IIlow (red box) and MHC-IIhi (blue box) Ly6C+ populations was further compared (histograms). The gray peaks depict DsRed expression level in naive M1Red mice. Data are representative of at least 6 mice. (D) Representative flow cytometry plots showing the expression of DsRed and CD44 on CD4+ and CD8+ T cells in mediastinal lymph nodes of day 7 IAV-infected M1Red mice. Data are representative of 7 mice from 2 independent experiments. (E) Bubble plot generated from flow cytometry data showing the proportion and MFI of DsRed+ leukocytes from lungs of WT, Ifnar1 / , and Ifngr1 / M1Red mice at 7 dpi. Data shown are the mean MFI and percentage values (3 mice/genotype). (F and G) IAV NP copy number (F) and mRNA expression of indicated genes (G) as measured by qRT-PCR from lungs of WT, Ifnar1 / , and Ifngr1 / mice 7 dpi. Data shown in (F) are total copy number ± SD (8–11 mice/genotype), and in (G) are mean fold change ± SD compared to WT (4–7 mice/genotype). (H) IFN protein levels measured in BALF of WT, Ifnar1 / , and Ifngr1 / mice at 7 dpi. ND, not detectable. Data shown are mean ± SD (5 mice/ genotype). Statistical analysis were performed using Kruskal-Wallis test.

statistically significant. Similarly, Ifna gene expression was unchanged among the different mouse strains (Figure 6G). We further measured IFN protein levels in the bronchoalveolar lavage fluid (BALF) 7 dpi. Overall, type I IFN levels were low or undetectable at this time point (Figure 6H), which is consistent with previously published data (Davidson et al., 2014). Type III IFN (IFN-l3) protein was not detected (data not shown). In contrast, high concentrations of IFN-g were measured in the BALF of WT and IFN-receptor-deficient mice, which correlated with the elevated level of IFN-g in the circulation at the same time point (Figure S3B). These data indicate that differential Irgm1/DsRed expression was unlikely to be due solely to differences in cytokine availability in the various mouse strains. Together, these data reveal that the cellular heterogeneity in IFN signaling observed in vivo is dictated by a number of factors including developmental stage (Figures 2 and 5C), activation state (Figures 6C and 6D) and cell-type-dependent integration of distinct IFN-receptor signals (Figures 2 and 6E). Importantly, the strength of IFN signaling does not always correlate with the quantity of IFNs present.

AT-I Cells Are the Major IFN-Responding Cells during Early IAV Infection To examine the epithelial cell response to IFN signaling during IAV infection, we first analyzed lung tissue sections from M1Red mice 3dpi by confocal microscopy. AT-I cells (podoplanin/T1a+) were highly responsive to IFN induction (Figure 7A). In contrast, the majority of cuboidal IAV+, CD45 T1a cells in infection foci only exhibited dim DsRed expression. We observed that expression of pro-surfactant protein C (PSPC), a marker for ATII, was dim or absent in regions with strong NP and DsRed fluorescence, indicating a reduction in PSPC production due to the viral infection in these regions (Figures 4E and 4F). Thus, based on morphology, alveolar location and abundance, susceptibility to IAV infection (Ibricevic et al., 2006), and lack of CD45 expression, we hypothesized that these cuboidal cells with weak IFN responsiveness to be AT-II. Given the technical challenges of definitively identifying AT-II in tissue sections owing to the loss of PSPC expression following IAV infection, we decided to compare Irgm1/DsRed expression levels in AT-I and AT-II cells isolated from digested lung tissues

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Figure 7. AT-I Are the Major IFN-Responsive Pneumocytes in Early IAV Infection (A) Merged color fluorescent deconvolution z stack image showing IFN responsive (DsRed+) and IAV-infected (NP+) cells in the lungs of M1Red mice 3 dpi. Data are representative of at least 4 mice. (B) Representative flow cytometry histograms showing the expression of Irgm1/DsRed in AT-I and AT-II of naive and day 3 infected M1Red animals. (C) Paired analysis of the percentage and MFI of DsRed+ AT-I and AT-II from day 3 infected mice (n = 9) using flow cytometry. Data points shown were pooled from three independent experiments. (D and E) IAV NP copy number (D) and host IFN-related gene expression (E) in AT-I and AT-II sorted from day 3 infected mice measured by qRT-PCR. Data points shown were pooled from two separate flow cytometry sorting experiments. In (C)–(E), lines between data points indicate paired samples. Statistical analysis was performed using Wilcoxon signed-rank test. (F and G) Percentage of DsRed+ AT-I and AT-II in the lungs of WT, Ifnar1 / , and Ifngr1 / M1Red reporter mice determined by flow cytometry at (F) 3 and (G) 7 dpi. Data points shown are mice pooled from two to three independent experiments (n = 5–12). Statistical analysis were performed using Kruskal-Wallis test. (H) Percentage of DsRed+ AT-II epithelial cells in the lungs of M1Red and Rag2 / M1Red mice at 7 dpi determined using flow cytometry. Data points shown are pooled from 5 independent experiments (14 mice/genotype). Statistical analysis was performed using Mann-Whitney test. In (F)–(H), each symbol represents one individual mouse. (I) mRNA expression of Ifng as measured by qRT-PCR from lungs of WT and Rag2 / mice at 7 dpi. Data shown are mean fold change ± SD compared to naive WT (6–7 mice/genotype). Statistical analysis was performed using Mann-Whitney test.

by flow cytometry using our cellular identification strategy (Figure 3A). To confirm the identity of these cells, we sorted the populations using flow cytometry and measured Podoplanin (the gene for T1a) and Sftpc (the gene for surfactant protein C) expression by qRT-PCR (Figure S4A). Paired analysis of these two cell types by flow cytometry confirmed AT-I to be much more responsive to IFN signaling than AT-II, both in terms of percentage and intensity of DsRed expression at 3 dpi (Figures 7B and 7C). Intriguingly, the relative differences in Irgm1/DsRed expression in the two cell types inversely correlated with their infectious state, as AT-II exhibited almost 100-fold increased viral NP expression compared to AT-I (Figure 7D). The difference was not due to the reduced expression of type I IFNs in AT-II cells compared to their AT-I counterparts (Figure S4B).

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To confirm that the differential Irgm1/DsRed expression in AT-I and AT-II was not due to Irgm1-specific regulation, but rather reflected the differences in cellular IFN responses, we measured the expression of other ISGs in the sorted epithelial cell populations using qRT-PCR. We observed that AT-I expressed significantly more Isg15, Oas1a, and Socs1 transcripts, and had trends of increased Irf7 and Stat1 expression than AT-II, validating the differences in DsRed expression observed in our imaging and flow cytometry analysis (Figure 7E). The increased IFN response seen in AT-I over AT-II was unlikely to be due to differential expression of IFN receptors, as Ifnar1 and Ifngr1 expression were comparable in AT-II and AT-I (Figure S4C). In addition, Il28ra transcript levels in AT-II were significantly higher compared to those in AT-I, a surprising finding given the lower

overall IFN-response in AT-II and the fact that type III IFN is produced in large abundance following IAV infection (Jewell et al., 2010). Overall, these data indicate that in the IAV infected lung, AT-I are more responsive to IFNs than their AT-II counterparts during the early stage of IAV infection, and this difference is independent of IFN-receptor expression. AT-I and AT-II Cells Exhibit Marked Differences in the Strength and Temporal Dynamics of IFN Signaling Associated with Differential Susceptibility to IAV Infection Our in vivo IFN administration experiments demonstrated that both AT-I and AT-II are responsive to a single injection of type I or type II IFNs, although the former cells are more sensitive to IFN stimulation than the latter (Figure 3). However, during IAV infection, multiple types of IFNs are produced continuously but with different kinetics. Thus, type I and III IFNs are produced early by 2–3 dpi, whereas IFN-g production peaks later at days 6–7 pi (Figure 4C). We next employed M1Red mice to investigate how temporally produced distinct types of IFNs coordinate to control overall IFN signaling in alveolar epithelial cells during the infection. WT, Ifnar1-, and Ifngr1-deficient M1Red animals were infected with IAV and DsRed expression in AT-I and ATII, at both 3 and 7 dpi analyzed. Consistent with our previous findings, at 3 dpi Irgm1/DsRed was markedly induced in AT-I of WT M1Red mice (Figure 7F). The Irgm1/DsRed expression was driven primarily by type I IFNs, as the signal was significantly reduced in Ifnar1 / but not Ifngr1 / AT-I. In contrast, although DsRed was induced to a lower level in AT-II than in their AT-I counterparts, lack of either Ifngr1 or Ifnar1 signaling led to a reduction in DsRed expression in AT-II, albeit only the former was statistically significant. These data therefore suggest that during infection, AT-I and AT-II preferentially respond to type I and type II IFNs, respectively. This dichotomy in cell-type-dependent signaling was further accentuated when Irgm1/DsRed in AT-I and AT-II cells was analyzed at a later time point when large quantities of IFN-g are produced. Specifically, Irgm1/DsRed expression was higher in both AT-I and AT-II cells at day 7 than day 3 pi (Figure 7G). However, the magnitude of increase was more pronounced in the latter cell type. Interestingly, lack of type I or II IFN signaling had a minimal impact on DsRed expression in AT-I, revealing a redundancy in type I and II IFN signaling in that cell type during the later stages of infection. In contrast, deficiency in Ifngr1, but not Ifnar, almost completely abolished DsRed expression in AT-II cells (Figure 7G), suggesting a critical non-redundant role for IFN-g signaling in stimulating IFN responses in this cell type. The slight increase in Irgm1/Dsred in Ifnar1 / compared to WT AT-I and AT-II cells was likely due to enhanced IFN-g activity in the absence of type I IFN receptor signaling, as we reported previously (Stifter et al., 2016). Although cilia cells were highly responsive to in vivo administered IFN-l (Figure 3), during IAV infection these cells exhibited Irgm1/DsRed expression levels similar to those of AT-II cells, where the IFN-response was comparatively low at 3 dpi and increased by 7 dpi (Figure S5). We next sought to determine whether T cell derived IFN-g was responsible for driving the high levels of Irgm1/DsRed expres-

sion seen in AT-II at 7 dpi. We crossed M1Red mice to adaptive lymphocyte-deficient Rag2 / animals and analyzed Irgm1/ DsRed expression in AT-II at 7 dpi. In support of our hypothesis, we found Irgm1/DsRed expression to be greatly reduced in AT-II of Rag2 / M1Red compared to WT mice (Figure 7H). Indeed, the loss of DsRed signal in this cell type corresponded to the substantial decrease in Ifng expression in Rag2 / mice (Figure 7I), suggesting that T cells are the major source of IFN-g driving Irgm1/DsRed expression in AT-II. DISCUSSION While analysis of ISG expression in tissue homogenates reports the overall level of IFN-dependent responses, it does not provide information on the cellular heterogeneity and spatial dynamics of IFN responses in situ. Moreover, it is unclear how different IFNs cooperate or antagonize in individual hematopoietic and non-hematopoietic cells within infected tissues. By tracking IFNresponsive cells following IFN injection and IAV infection, we uncovered an unexpected cellular heterogeneity in the IFN response, suggesting that cellular composition at the tissue site, which may alter significantly during infection and inflammation, could be a key element dictating the biological function of distinct IFNs and their relative contribution to host responses to infection in vivo. The discovery that type I and II alveolar epithelial cells show significant differences in their response to IFN stimulation early following respiratory influenza virus infection suggests that cell-type-dependent selectivity in IFN receptor signaling coupled with temporal availability of the different IFNs shapes the kinetics and magnitude of antiviral IFN responses during IAV infection. Identification of IFN-responsive cells in disease models is informative for understanding disease pathogenesis and developing host-directed therapies. There were no reporter mice available for interrogating IFN signaling at the single-cell level until the recently described Mx1-GFP reporter mouse (Uccellini and Garcia-Sastre, 2018). Some of our data, namely that inflammatory monocytes are highly responsive to IFN while lymphocytes are not, strongly agree with those published by Uccellini and Garcia-Sastre (2018). Nevertheless, there are key differences in our findings, probably due to the differences in the reporter systems and experimental approaches employed. Unlike the Mx1GFP mouse, which reports type I and III IFNs only, Irgm1/DsRed reporter mice are capable of reporting on the activity of type I, II, and III IFNs and thus provide a more comprehensive information of cellular IFN-responses. The ability to report IFN-g signaling is critical for revealing the breadth of IFN signaling in the influenza model employed, as we show IAV infection induces robust IFN-g production and systemic IFN-signaling is mediated mainly by IFN-g. This current and the Uccellini and Garcia-Sastre (2018) studies differed in the conclusions on IFN responses in epithelial cells. The discrepancy could be due to IFN-g signaling not being reported in Mx1-GFP mice as discussed above, or to the approach used to analyze epithelial cells in the Uccellini and Garcia-Sastre (2018) study. AT-I cells are known to be difficult to isolate and are frequently under-represented in single-cell analysis by flow cytometry. Therefore, in our study, we have combined flow

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cytometry and imaging analysis of epithelial cells. The latter approach also provided spatial dynamics of IFN signaling in IAV-infected lungs. The difference in which alveolar epithelial cells responded to IFNs is unexpected, given that AT-I and AT-II are known to share a close developmental relationship, and damaged AT-I are repaired by the proliferation and trans-differentiation of AT-II (reviewed in McElroy and Kasper, 2004). While IAV preferentially infects AT-II (Weinheimer et al., 2012; Ibricevic et al., 2006), lethal influenza is commonly associated with the infection and loss of AT-I (Sanders et al., 2013; Cardani et al., 2017). By analyzing IAV-infected M1Red mice, we found that AT-I are highly sensitive to early type I IFN signaling and subsequently incorporate IFN-g signaling when the latter cytokine becomes available. Importantly, neither IFN-g nor type III IFNs were able to compensate for the loss of type I IFNs in AT-I early during infection, revealing an indispensable role for type I IFN in orchestrating the ISG program in this epithelial cell type. In contrast, AT-II responded poorly to type I IFNs early during infection. We cannot exclude the possibility that the heightened susceptibility of AT-II to IAV infection is responsible for the impaired ISG expression. Indeed, we observed a loss of PSPC expression in lungs of infected animals and in addition, IAV NS1 protein is known to antagonize IFN-responses (Haye et al., 2009). It would be interesting to see if IFN-responses in AT-II are restored following infection with NS1-deficient IAV. A recent study found activation of IFNsignaling pathways in individual IAV-infected human A549 cells to be rare and to not contribute to viral heterogeneity in these cells (Russell et al., 2018). It is noteworthy, however, that A549 cells are a type II alveolar epithelial cell type and may thus not reflect the true heterogeneity of viral infection and IFN-responses. Regardless and irrespective of the mechanism, the consequence of the delayed IFN response in AT-II versus AT-I could explain the discrepancy in IAV susceptibility between the cell types; however, this requires further investigation. An obvious explanation for the observed cellular heterogeneity of IFN responses in vivo is the availability of type I and II IFNs during IAV-infection. However, by simultaneous analysis of IFN production and signaling (as measured by DsRed expression), we demonstrated that respiratory epithelial cells and leukocytes exhibit distinct preferences for activation by different types of IFNs. These findings strongly suggest that the quantity of IFNs does not always consistently predict its overall signaling strength in vivo and additional cell-intrinsic mechanisms contribute to the IFN-responsiveness of individual cells. Importantly, IFN activity is known to be affected by factors including distinct receptor engagement by individual cytokines (Piehler et al., 2012), differential utilization of STAT phosphorylation pathways (van BoxelDezaire et al., 2010), the activation state of cells (Van De Wiele et al., 2004; Gil et al., 2006), epigenetic programming (Fang et al., 2012), and the induction and utilization of different negative regulators, such as Usp18 and the SOCS family of proteins (Porritt and Hertzog, 2015). One or combinations of these proposed mechanisms could contribute to the heterogeneity of IFN responses observed within IAV-infected lungs. Additionally, the viral burden among cells has recently been shown to be associated with the expression of different clusters of anti-viral responses (Sjaastad et al., 2018), suggesting that there may be a

3548 Cell Reports 29, 3539–3550, December 10, 2019

cell-type-specific switch that regulates antiviral gene expression depending on severity of infection. M1Red mice, like any reporter system, have limitations. First, the fluorescent protein DsRed has a longer half-life than the gene it reports on. Second, M1Red reporter mice may not identify cell populations in which IFN-signaling does not trigger Irgm1 expression, or cells in which IFN pathways are activated entirely by STAT1-independent mechanisms. Lastly, the individual contribution of any IFN to the overall response requires the use of receptor-deficient animals, as we have done in this study. Ultimately, crossing the Mx1-GFP and M1Red strains to one another would provide an elegant solution to investigating IFN signaling in vivo. Biochemical studies suggest that all IFNs utilize a common signaling pathway and induce almost identical transcriptomic changes in cells. However, we show here that cellular responses to IFNs in vivo are highly dynamic and heterogeneous. Our findings reveal that integration of time- and cell-type-dependent IFN receptor signaling is critical for specifying the function of IFNs during IAV infection in vivo. Further analysis of this interplay should be valuable both in defining critical elements of IFNdependent host resistance and in the design of cytokine based interventions. STAR+METHODS Detailed methods are provided in the online version of this paper and include the following: d d d

d

d d

KEY RESOURCES TABLE LEAD CONTACT AND MATERIALS AVAILABILITY EXPERIMENTAL MODEL AND SUBJECT DETAILS B Mouse Peripheral blood mononuclear cells (PBMC) B Mouse Model METHOD DETAILS B Generation of M1Red reporter mice B Influenza A virus infection and in vivo IFN stimulations B Cell isolations B Broncho-alveolar lavage fluid (BALF) collection B Lung tissue processing, antibody staining and imaging B Flow Cytometry B Cell sorting, mRNA preparation and qRT-PCR B Quantification of type I, II and III IFNs QUANTIFICATION AND STATISTICAL ANALYSIS DATA AND CODE AVAILABILITY B Key Resources Table

SUPPLEMENTAL INFORMATION Supplemental Information can be found online at https://doi.org/10.1016/j. celrep.2019.11.021. ACKNOWLEDGMENTS We thank C. Watson and J. Zhu for their advice in constructing the M1Red reporter mouse strain and B. Roediger and M. Coleman for critical reading of this manuscript. We acknowledge the Centenary Institute animal and flow cytometry core facilities for their contributions to this work. This work was supported by a National Health and Medical Research Council (NHMRC) of Australia

Project (APP1051742). N.B., A.J.S., and T.A.C are supported by Australian Postgraduate Awards. AUTHOR CONTRIBUTIONS Conceptualization, S.A.S. and C.G.F.; Methodology, S.A.S., L.F., W.E.P., and C.G.F.; Investigation, S.A.S, A.J.S., N.B., and T.A.C.; Resources, J.S. and S.E.D.; Writing – Original Draft, S.A.S., and C.G.F.; Writing – Review & Editing, S.A.S., J.S., W.J.B., A.S., and C.G.F.; Supervision, W.J.B., A.S., and C.G.F.; Funding Acquisition, C.G.F. DECLARATION OF INTERESTS S.E.D. is an employee and stock-holder of Bristol-Myers Squibb, which owns intellectual property on IFN-l.

Galani, I.E., Triantafyllia, V., Eleminiadou, E.E., Koltsida, O., Stavropoulos, A., Manioudaki, M., Thanos, D., Doyle, S.E., Kotenko, S.V., Thanopoulou, K., and Andreakos, E. (2017). Interferon-lambda Mediates Non-redundant Front-Line Antiviral Protection against Influenza Virus Infection without Compromising Host Fitness. Immunity 46, 875–890. Garcı´a-Sastre, A., Durbin, R.K., Zheng, H., Palese, P., Gertner, R., Levy, D.E., and Durbin, J.E. (1998). The role of interferon in influenza virus tissue tropism. J. Virol. 72, 8550–8558. Gil, M.P., Salomon, R., Louten, J., and Biron, C.A. (2006). Modulation of STAT1 protein levels: a mechanism shaping CD8 T-cell responses in vivo. Blood 107, 987–993. Hasegawa, K., Sato, A., Tanimura, K., Uemasu, K., Hamakawa, Y., Fuseya, Y., Sato, S., Muro, S., and Hirai, T. (2017). Fraction of MHCII and EpCAM expression characterizes distal lung epithelial cells for alveolar type 2 cell isolation. Respir. Res. 18, 150.

Received: January 21, 2019 Revised: September 25, 2019 Accepted: November 6, 2019 Published: December 10, 2019

Haye, K., Burmakina, S., Moran, T., Garcı´a-Sastre, A., and Fernandez-Sesma, A. (2009). The NS1 protein of a human influenza virus inhibits type I interferon production and the induction of antiviral responses in primary human dendritic and respiratory epithelial cells. J. Virol. 83, 6849–6862.

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Sommereyns, C., Paul, S., Staeheli, P., and Michiels, T. (2008). IFN-lambda (IFN-lambda) is expressed in a tissue-dependent fashion and primarily acts on epithelial cells in vivo. PLoS Pathog. 4, e1000017. Stegemann-Koniszewski, S., Jeron, A., Gereke, M., Geffers, R., Kro¨ger, A., Gunzer, M., and Bruder, D. (2016). Alveolar Type II Epithelial Cells Contribute to the Anti-Influenza A Virus Response in the Lung by Integrating Pathogenand Microenvironment-Derived Signals. MBio 7, e00276-16. Steuerman, Y., Cohen, M., Peshes-Yaloz, N., Valadarsky, L., Cohn, O., David, E., Frishberg, A., Mayo, L., Bacharach, E., Amit, I., and Gat-Viks, I. (2018). Dissection of Influenza Infection In Vivo by Single-Cell RNA Sequencing. Cell Syst. 6, 679–691. Stifter, S.A., Bhattacharyya, N., Pillay, R., Flo´rido, M., Triccas, J.A., Britton, W.J., and Feng, C.G. (2016). Functional Interplay between Type I and II Inter-

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Turner, S.J., Olivas, E., Gutierrez, A., Diaz, G., and Doherty, P.C. (2007). Disregulated influenza A virus-specific CD8+ T cell homeostasis in the absence of IFN-gamma signaling. J. Immunol. 178, 7616–7622.

van Boxel-Dezaire, A.H., Zula, J.A., Xu, Y., Ransohoff, R.M., Jacobberger, J.W., and Stark, G.R. (2010). Major differences in the responses of primary human leukocyte subsets to IFN-beta. J. Immunol. 185, 5888–5899. Van De Wiele, C.J., Marino, J.H., Whetsell, M.E., Vo, S.S., Masengale, R.M., and Teague, T.K. (2004). Loss of interferon-induced Stat1 phosphorylation in activated T cells. J. Interferon Cytokine Res. 24, 169–178. Weinheimer, V.K., Becher, A., To¨nnies, M., Holland, G., Knepper, J., Bauer, €ckert, J.C., Szymanski, K., et al. T.T., Schneider, P., Neudecker, J., Ru (2012). Influenza A viruses target type II pneumocytes in the human lung. J. Infect. Dis. 206, 1685–1694. Zhu, J., Jankovic, D., Oler, A.J., Wei, G., Sharma, S., Hu, G., Guo, L., Yagi, R., Yamane, H., Punkosdy, G., et al. (2012). The transcription factor T-bet is induced by multiple pathways and prevents an endogenous Th2 cell program during Th1 cell responses. Immunity 37, 660–673.

STAR+METHODS KEY RESOURCES TABLE

REAGENT or RESOURCE

SOURCE

IDENTIFIER

Fc Block (2.4G2)

BD

553142; RRID: AB_394657

CD4-AF700 (RM4-5)

BD

557956; RRID: AB_396956

CD8-BV711 (53-6.7)

BD

563046; RRID:AB_2737972

B220-BUV737 (RA3-6B2)

BD

564449; RRID:AB_2738813

I-A/I-E-BV510 (M5-114.15.2)

Biolegend

107635; RRID:AB_2561397

H2kb-BV421 (AF6-88.5)

BD

562942; RRID:AB_2737908

EpCAM-APC (G8.8)

BD

563478; RRID:AB_2738234

CD31-PerCPCy5.5 (MEC13.3)

BD

562861; RRID:AB_2737847

F4/80-FITC (BM8)

Biolegend

123108; RRID:AB_893502

T1a/Podoplanin-PE/Cy7 (8.1.1)

Biolegend

127411; RRID:AB_10613294

T1a/Podoplanin biotin (8.1.1)

Biolegend

127403; RRID:AB_1134221

Ly6G-BUV395 (1A8)

BD

563978; RRID:AB_2716852

Ly6C-PerCPCy5.5 (HK1.4)

Thermo Fisher

45-5932-82; RRID:AB_2723343

CD11b-BV650 (M1/70)

Biolegend

101239; RRID:AB_11125575

NK1.1-APC (PK136)

Biolegend

108709; RRID:AB_313396

NK1.1-PE/Cy7 (PK136)

eBioscience

25-5941-82; RRID:AB_469665

SiglecF-BV421 (E50-2440)

BD

562681; RRID:AB_2722581

SiglecH-biotin (551.3D3)

Miltenyi Biotec

130-101-858; RRID:AB_2660879

CD11c-BV786 (HL3)

BD

563735; RRID:AB_2738394

CD45-APC/Cy7 (30-F11)

BD

557659; RRID:AB_396774

CD45.2-FITC (104)

Biolegend

109806; RRID:AB_313443

CD45-BV421 (30-F11)

BD

563890; RRID:AB_2651151

CD44-BV786 (IM7)

BD

563736; RRID:AB_2738395

Lineage Cocktail V450

BD

561301; RRID:AB_10611731

CD117-biotin (2B8)

BD

553353; RRID:AB_394804

Sca1-PE/Cy7 (D7)

Biolegend

108114; RRID:AB_493596

IAV NP-FITC (D67J)

Thermo Fisher

MA1-7322; RRID:AB_1017747

Polyclonal rabbit Prosurfactant protein C

Abcam

Ab90716; RRID:AB_10674024

Polyclonal rabbit aquaporin 5

Abcam

ab104751; RRID:AB_10712314

anti-rabbit AF647

Thermo Fisher

A21245; RRID:AB_2535813

Anti-hamster AF594

Thermo Fisher

A21113; RRID:AB_2535762

Common passaged lab strain - grown in house

N/A

UV Live/Dead

Life Technologies

L34962

Streptavidin-BV510

BD

563261

Streptavidin-BUV395

BD

564176

mIFN-a2

Thermo Fisher

14-8312-62

mIFN-b

In Vitro Technologies

8234-MB-010

mIFN-g

Peprotech

315-05

mIFN-l

Peprotech

250-33

PEG-mIFN-l2

Bristol-Myers Squibb

N/A

Antibodies

Bacterial and Virus Strains PR8: A/PR/8/34 (H1N1) Chemicals, Peptides, and Recombinant Proteins

(Continued on next page)

Cell Reports 29, 3539–3550.e1–e4, December 10, 2019 e1

Continued REAGENT or RESOURCE

SOURCE

IDENTIFIER

RNAlater

Thermo Fisher

AM7021

Trisure

Bioline

BIO-38033

DNase I

Sigma Aldrich

DN25-100MG

Collagenase type IV

Sigma Aldrich

C5138

Dispase II

Sigma Aldrich

D4693

Prolong Gold Antifade

Thermo Fisher

P36934

16% Methanol Free Paraformaldehyde

VWR

AA43368-9M

Optimum cutting temperature compound

VWR

00411243

Tetro cDNA synthesis Kit

Bioline

BIO-65043

SYBR No-ROX Master Mix

Bioline

BIO-98020

Isolate II RNA Kit

Bioline

BIO-52073

LegendPlex Mouse Type I/II IFN Panel

Biolegend

740635

IL-28B/(IFN lambda 3) Mouse Uncoated ELISA Kit

Thermo Fisher

88-7284-22

M1Red mice (Tg(Irgm1-DsRed)Nci)

This paper

N/A

C57BL/6J

Australian BioResources

N/A

Critical Commercial Assays

Experimental Models: Organisms/Strains

Ifnar1

/

(B6.129S2-Ifnar1tm1Agt)

Taconic Farms

N/A

Ifngr1

/

(B6.129S7-Ifngr1tm1Agt/J)

The Jackson Laboratory

003288

Stat1

/

(B6.129S6/SvEv-Stat1tm1Rds)

Taconic Farms

N/A

Rag2

/

(B6.129S6-Rag2tm1Fwa)

Taconic Farms

N/A

Sigma-Aldrich

N/A

Children’s Hospital Oakland Research Institute

N/A

https://github.com/sydneycytometry/ tSNEplots

N/A

Oligonucleotides See Table S1 Recombinant DNA BAC clone RP23-305A21 Software and Algorithms tSNE plot generator

LEAD CONTACT AND MATERIALS AVAILABILITY Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Carl G. Feng ([email protected]). Mouse lines generated in this study have not been deposited to commercial vendors but will be shared upon request. EXPERIMENTAL MODEL AND SUBJECT DETAILS Mouse Peripheral blood mononuclear cells (PBMC) PBMCs from 8 to 12 week old M1Red mice were prepared by Histopaque purification (GE Healthcare). Isolated PBMCs were stimulated in vitro for 24 hours with 100 ng/mL of the following cytokines: mIFN-a2 (Thermo Fisher), mIFN-b (In Vitro Technologies), mIFN-g (Peprotech) and mIFN-l (Peprotech). Mouse Model M1Red mice were generated by pronuclear injection of a bacterial artificial chromosome (BAC) as described below. C57BL/6, M1Red, M1Red Rag2—/—, M1Red Ifnar1—/—, M1Red Ifngr1—/— and M1Red Stat1—/— (all on C57BL/6 background) were bred and maintained under specific pathogen free conditions at either the University of Sydney Bosch Rodent Facility or the Centenary Institute Animal Facility. Animals were provided food and water ad libitum and housed in a temperature and humidity controlled environment with a 12 hour light/12 hour dark cycle. Male and female animals aged between 8 and 20 weeks were used for experiments. No obvious sex or age differences were noted.

e2 Cell Reports 29, 3539–3550.e1–e4, December 10, 2019

All mouse work was performed according to ethical guidelines determined by the University of Sydney Animal Ethics Committee and the Sydney Local Health District Animal Ethics Committee, in accordance with the Australian Code for the Care and Use of Animals for Scientific Purposes (2013) established by the National Health and Medical Research Council of Australia. All experiments within this manuscript were approved under protocol numbers 2013/5847, 2013/5848 and 2015/037. METHOD DETAILS Generation of M1Red reporter mice Mice were generated using methods published previously (Zhu et al., 2012). In brief the protein coding sequence of DsRed2 was inserted into the ATG translational start site of the sequence encoding Irgm1 in the BAC clone RP23-305A21 by the galK replacement method. Following sequence verification of the modified BAC-construct, transgenic mice were generated by pronuclear injection of the BAC construct into fertilized C57BL/6 eggs. Transgene positive founder mice were crossed to C57BL/6 animals to verify germline transmission. M1Red mice were genotyped for the DsRed2 gene by standard endpoint PCR using forward (GCTCCAAGGTGTACGT GAAG) and reverse (GCTTGGAGTCCACGTAGTAG) primers. Influenza A virus infection and in vivo IFN stimulations Mice were anaesthetized by intraperitoneal (i.p.) injection with 80mg/kg ketamine and 10 mg/kg xylazine and inoculated intranasally (i.n.) with 20 plaque forming units (PFU) of influenza A virus strain PR8 (A/Puerto Rico/8/1934 H1N1) in a volume of 40-50 mL. Mice were injected intravenously with mIFN-g (Peprotech), mIFN-b (In Vitro Technologies) or PEG-mIFN-l2 (a kind gift of BristolMyers Squibb) and blood collected 6 hours post injection. 24 hours after injection, blood, BM and lungs were collected and processed as described below. For intratracheal IFN stimulation, mice were anaesthetized with ketamine/xylazine as described above and inoculated intratracheally with 25 mL of sterile PBS or PBS containing 10 mg IFN-l2. Cell isolations For leukocyte cell isolations from lungs and spleens, euthanized animals were perfused with 10 mL PBS and organs removed into 1 mL cold 2% FCS/RPMI supplemented with 2 mg/mL of DNaseI and Collagenase IV. Organs were minced using scissors and the homogenates incubated for 30 minutes at 37 C. The digested organs were then dissociated through a 70 mm cell strainer and red blood cells lysed with ACK lysis buffer. Thymus and lymph nodes were placed into DNaseI and Collagenase IV supplemented 2% FCS/RPMI for 30 minutes before being dissociated through a 70 mm cell strainer. Cells were counted using trypan blue exclusion on a haemocytometer. Lung epithelial cells were isolated based on a previously described protocol with modifications (Stegemann-Koniszewski et al., 2016). Following perfusion, lungs were inflated through an incision in the trachea with a digestion cocktail containing 2.5 mg/mL Dispase II, 500 mg/mL DNaseI and 500 mg/mL Collagenase IV. Lungs were excised and placed in 3 mL of digestion cocktail and incubated for 30 minutes in a 37 C humidified tissue culture incubator. Lungs lobes were then separated from the trachea and heart and minced using scissors. Minced lungs were then incubated for a further 15 minutes in a 37 C humidified incubator. Following digestion, lungs were repeatedly passed through a 19G needle and then strained through a 70 mm cell strainer. Red blood cells were lysed with ACK lysis buffer and the lungs resuspended in 1 mL of 2% FCS/RPMI. For blood leukocytes, 100-200 mL of blood was collected from the tail vein into tubes containing 20 mL 0.5M EDTA. Antibody cocktail for flow cytometry was added directly to the blood for 30 minutes for cell staining. Red blood cells were lysed using Pharmlyse buffer (BD Biosciences) and cells resuspended in 2% FCS/PBS before analysis. For bone marrow cells, femurs and tibias were removed and cleaned of flesh before the bone marrow was flushed with 2% FCS/ RPMI. Cells were pelleted at 300 g for 5 minutes and resuspended in ACK lysis buffer for 1 minute to lyse red blood cells. Cells were washed and resuspended in 2% FCS/RPMI before counting by trypan blue exclusion. Broncho-alveolar lavage fluid (BALF) collection BALF was prepared by flushing lungs with 2 mL of cold, sterile PBS. Supernatant was collected by pelleting cells at 350 g for 10 minutes at 4 C. BALF was stored at 80 C until analyzed. Lung tissue processing, antibody staining and imaging To collect lungs for microscopic imaging, the lungs were inflated with 1-1.5 mL of 4% paraformaldehyde (PFA) through a small incision in the trachea. The trachea was tied off using suture and the lungs and connected MLNs removed and placed into 5 mL of 4% PFA. After 8 hours, immersion fixed lungs were transferred to a 25% Sucrose/PBS solution for 48 hours. Individual lung lobes were then frozen in OCT blocks and sectioned on a Shandon Cryotome E (Thermo Fisher). Sections were stored at 80 C until use. Frozen sections were allowed to equilibrate to room temperature for 10 minutes before staining. Sections were treated with 1% PFA/PBS for 10 minutes, followed by blocking/permeabilization with 3% normal goat serum / 0.1% Triton X-100 diluted in PBS for 30 minutes at room temperature. All incubation steps were carried out in a humidified chamber. Following blocking, sections were washed 1x in PBS before labeling with primary antibodies diluted in 3% normal goat serum/PBS overnight in the dark at

Cell Reports 29, 3539–3550.e1–e4, December 10, 2019 e3

4 C. The following day, sections were washed 3x in PBS and then stained with secondary antibodies diluted in 3% normal goat serum/PBS for 2 hours at room temperature. The following antibodies (and clone number) were used in this study: Pro-surfactant Protein C (ab90716), Influenza A virus NP (D67J), Aquaporin 5 (ab104751), CD45 (30-F11), goat anti rabbit AF647 (A21245), anti-hamster AF594 (A21113). Sections were again washed 3x in PBS and then mounted using ProLong Gold (Life Technologies). Sections were imaged on a Deltavision Personal (GE Healthcare) or an Olympus BX51 upright fluorescent microscope. Z stacks were deconvolved using Huygens Software package. Image analysis was performed using FIJI software v1.51w (NIH). Flow Cytometry Lung, spleen, lymph nodes, thymus, whole blood or BM cells were stained according to standard procedures. Briefly, cells were stained in FACS wash (2%FCS/PBS/2mM EDTA) containing FcBlock (clone 2.4G2), UV Live/Dead (Life Technologies) and primary antibody cocktail for 30 minutes at 4 C in the dark. The following antibodies and clones were used throughout this project: CD4 (clone GK1.5), CD8 (53-6.7), B220 (RA3-6B2), I-A/I-E (M5-114.15.2), H2kb (AF6-88.5), EpCAM (G8.8), CD31 (MEC13.3), Podoplanin/T1a/ gp38 (8.1.1), Ly6G (1A8), Ly6C (HK1.4), CD11b (M1/70), NK1.1 (PK136), Siglec-F (E50-2440), Siglec-H (REA819), CD11c (N418), CD44 (IM7), CD45 (30-F11). BM was stained using a mAb cocktail containing antibodies to the following lineage markers CD3e, CD11b, B220, Ly-76 and Gr1. The gating strategy used for identifying leukocyte cell populations is as previously reported in Stifter et al. (2016). All flow cytometry data acquisition was performed on a LSRII using FACSDiva software (BD Biosciences) and analysis was performed using FlowJo 10 (TreeStar). tSNE plots were generated in RStudio using the script available at https://github.com/ sydneycytometry/tSNEplots. Cell sorting, mRNA preparation and qRT-PCR For gene expression analysis of sorted pneumocytes, epithelial cells were isolated as described above and stained with fluorophoreconjugated antibodies for 30 minutes at 4 C in the dark. Cells were washed twice in 2% FCS/PBS and then resuspended in 2 mL of 2% FCS/PBS containing 40 mg/ml DNase I. AT-I (CD45-, T1a+, MHC-I+) and AT-II (CD45-, EpCAM+, MHC-II+) were flow cytometrically sorted on a BD Influx using 100 mm nozzle into tubes containing 100% FCS. Following sorting, cells were pelleted by centrifugation for 45 minutes at 300 g and immediately lysed in RLY buffer (Bioline). RNA was prepared from FACS-sorted pneumocyte populations using the Isolate II RNA Kit according to the manufacturer’s instructions (Bioline). Total RNA (200 ng) was reverse transcribed using the Tetro cDNA synthesis Kit with random primers according to the manufacturer’s instructions (Bioline). To isolate total lung RNA, mouse lung tissue was collected and submerged in RNAlater (Ambion) for 24 hours prior to long term storage at 80 C. RNA was prepared from mouse lungs using Trisure (Bioline) according to the manufacturer’s instructions (Bioline). RNA (2 mg) was reverse transcribed using the Tetro cDNA synthesis Kit with random primers according to the manufacturer’s instructions (Bioline). Viral nucleoprotein quantification was carried out as previously described (Stifter et al., 2016). In brief, a PCR-amplified 216bp nucleoprotein DNA fragment was serially diluted to generate a standard curve to enumerate absolute viral nucleoprotein mRNA copy number among sample mRNA. All quantitative reverse-transcriptase PCR (qRT-PCR) was performed using SYBR NoROX master mix (Bioline) on a Roche LightCycler480. Data are expressed as fold change over either uninfected control lungs (for whole lung qPCR) or gene expression levels in AT-I (for comparisons between sorted pneumocytes) and were calculated by the DDCT method using 18S as the reference gene. Forward and reverse qRT-PCR primers used in this study are listed in Table S1. Quantification of type I, II and III IFNs For IFN-a and IFN-b, cytokine levels in BALF and serum were determined using a LegendPlex Type 1/2 Interferon Panel according to the manufacturer’s instructions (Biolegend). For IFN-l3 (IL-28B), the cykine level was quantified using an ELSA kit according to the manufacturer’s instructions (Thermo Fisher). QUANTIFICATION AND STATISTICAL ANALYSIS All statistical analyses were performed in Prism 7 (GraphPad Software). Significance was determined using the tests outlined in the figure legends. Results with p < 0.05 were deemed statistically significant, and are labeled as *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001. DATA AND CODE AVAILABILITY This study did not generate new datasets or code. Key Resources Table See Table S1 for qPCR primers used in this study.

e4 Cell Reports 29, 3539–3550.e1–e4, December 10, 2019