Early cellular mechanisms of type I interferon-driven susceptibility to tuberculosis

1Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis disease, caused 1.6 million deaths in 2021. Treatment requires a 4–6 month course of antibiotics, or up to 2 years for increasingly prevalent multi-drug resistant strains. Moreover, the only approved vaccine for Mtb has variable or no efficacy in adults. The pathophysiology of tuberculosis remains poorly understood. The mouse model has been used to discover most of the host factors known to control tuberculosis in humans, including tumor necrosis (TNF) factor and interferon-γ–. Nevertheless, mouse models have been criticized for poorly recapitulating key aspects of human disease.

2In humans, active tuberculosis disease is reproducibly associated with the induction of type I interferons (IFNs)–, a family of cytokines that includes IFNβ and multiple IFNα isoforms. Type I IFNs signal via the type I IFN receptor (IFNAR) to elicit an anti-viral response that overlaps but is insufficient to recapitulate the protective anti-Mtb response elicited by IFNγ. Evidence that type I IFNs exacerbate tuberculosis in humans comes from observations that viral infections are associated with worse Mtb infection outcomes. For example, influenza infection correlates with an increased risk of death among pulmonary tuberculosis patients, and infants with cytomegalovirus have an increased risk of tuberculosis disease–. A causal role for type I IFNs in driving human tuberculosis is also supported by the finding that a partial loss-of-function mutation in IFNAR is associated with Mtb resistance in humans. How type I IFNs drive Mtb progression in humans is poorly understood, but one mechanism may involve antagonism of the critical, protective IFNγ response during infection,.

3Mice are able to model the virus-induced susceptibility to Mtb seen in humans and have established causality between type I IFN and loss of Mtb control. Chronic lymphocytic choriomeningitis virus (LCMV), acute LCMV, pneumonia virus of mice, and influenza A virus all exacerbate tuberculosis infection–. However, co-infection studies make it challenging to determine the cellular mechanism behind Mtb susceptibility, as perturbations such as type I IFN receptor blockade simultaneously impact viral and bacterial control. Therefore, an ideal platform to study type I IFN-driven Mtb susceptibility would be a mouse model where Mtb infection is itself sufficient to elicit the exacerbated type I IFN response observed in humans with active TB. However, C57BL/6 (B6) mice, the most used model for Mtb infection, generate a weak type I IFN response in response to Mtb infection; indeed, Ifnar1 deletion from unmanipulated B6 mice does not consistently impact Mtb lung burden–. Investigators have circumvented this issue by intranasal injection of B6 mice with polyI:C, an IFN-inducing viral mimic,,. Such studies demonstrate a convincing causal link between type I IFNs and Mtb susceptibility in mice, and have shown that a major detrimental effect of type I IFNs is to impair interleukin-1-dependent control of Mtb,. Despite these advances, it has been challenging to decipher the cellular mechanisms underlying the detrimental effects of type I IFN on bacterial control, and in particular, the cells required to produce or respond to type I IFNs to mediate Mtb-susceptibility remain unknown.

4Unlike B6 mice, C3H and 129 mice exhibit a type I interferon-driven susceptibility to Mtb–, but there are limited genetic tools in these mouse strains, making mechanistic studies difficult. We recently discovered that congenic B6 mice with the ‘super susceptibility to tuberculosis 1’ region from C3H mice (i.e., B6.Sst1S mice), are more susceptible to Mtb infection (compared to isogenic B6 mice) due to their strong type I IFN response. Ifnar1 deletion fully rescues the enhanced susceptibility of B6.Sst1S mice at early timepoints and increases survival. We further identified Sp140 as the gene within the Sst1 genetic interval that controls Mtb susceptibility, and confirmed that the early Mtb susceptibility of Sp140−/− mice is also rescued by Ifnar1 deletion. Our Sp140−/− mice were generated on a pure C57BL/6J background, enabling the use of existing genetic tools to dissect tuberculosis pathogenesis.

5In this study, we leveraged Sp140−/− mice to determine the cellular mechanisms of type I IFN-driven Mtb susceptibility. Single cell RNA-sequencing (scRNA-seq) identified interstitial macrophages (IMs) as a major type I IFN producer. A sensitive genetic reporter of type I IFN production corroborated the scRNA-seq findings, and also revealed that plasmacytoid dendritic cells (pDCs) are an additional source of type I IFN during Mtb infection. Type I IFN production by pDCs drives disease since pDC depletion rescued the enhanced susceptibility of Sp140−/− mice. Loss of bacterial control in Sp140−/− mice leads to neutrophil influx and abundant production of DNA-rich neutrophil extracellular traps (NETs), ligands described to promote type I IFN production by pDCs,. We developed transcriptional signatures that distinguish the response elicited by type I IFN from that elicited by IFNγ. Application of these signatures to our scRNA-seq data indicated that Mtb-infected type I IFN-responsive IMs in the lungs have impaired IFNγ responses,,. Cell-type specific deletion of Ifnar1 validated that type I IFN confers susceptibility by acting on IMs. Our findings suggest a model of tuberculosis pathogenesis in which type I IFNs drive an initial loss of bacterial control, possibly by impairing IFNγ responses, that in turn initiates a positive feedback loop of NET production and type I IFN expression by pDCs, leading to uncontrolled bacterial replication and active tuberculosis disease.

6As we previously demonstrated, the susceptibility of Sp140−/− mice to Mtb is driven by type I IFN, and mirrors the correlation between type I IFN and active tuberculosis disease seen in humans. Genetic or antibody-mediated depletion of IFNAR fully rescues the enhanced susceptibility of Sp140−/− animals (Fig. 1A). To better characterize the immune response of Sp140−/− mice to Mtb, we infected mice with Mtb expressing the fluorescent protein Wasabi (Mtb-Wasabi). With this approach, we identified Mtb-harboring cells in mouse lungs 25 days post-infection (Fig. 1B). Flow cytometry reliably reported overall lung bacterial burdens as the number of Mtb-infected cells detected by flow cytometry correlated with lung Mtb CFU (R = 0.63; Fig. 1C). Infected lungs from Sp140−/− animals contained significantly more neutrophils, IMs, and monocytes as compared to Sp140−/− Ifnar1−/− mice, with no difference in the number of AMs and a reduction in the number of cDC2 (Fig. 1D, Supplementary Fig. 1). Over 90% of infected cells were myeloid cells (Fig. 1B, 1E), in line with previous reports,. Consistent with their higher abundance in infected Sp140−/− lungs, neutrophils comprised a considerably larger percentage and absolute number of the Mtb-infected cells in Sp140−/− mice compared to Sp140–/–Ifnar1−/− animals (Fig. 1E, 1F). There were also more infected IMs and monocytes in Sp140−/− mice compared to Sp140−/− Ifnar1−/− animals, in line with the overall increase in these immune populations in the lungs of infected Sp140−/− mice (Fig. 1D, 1F). Importantly, we did not observe substantial alterations in the immune compartment of uninfected Sp140−/− mice, implying grossly normal hematopoietic development in these mice (Supplemental Fig. 2A-C) in contrast to prior suggestions–. However, the exact mechanisms causing the infection-induced differences in myeloid cells from Sp140−/− mice was unclear, and thus required more in-depth profiling of the myeloid compartment.

7To further characterize the Mtb-infected myeloid cells in B6 and Sp140−/− mice, we performed scRNA-seq on myeloid cells from Mtb-infected or uninfected lungs 25 days after infection. For this experiment, CD64+ and Ly6G+ cells were magnetically enriched, sort purified, and processed for library generation with the 10X Genomics platform (Fig. 2A). Infected and uninfected (bystander) cells from Mtb-infected mice were sorted and barcoded separately. mRNA transcripts and protein expression for select lineage markers were simultaneously measured by CITE-seq, allowing for Weighted Nearest Neighbor (WNN) analysis to cluster cells on mRNA and protein expression and WNN uniform manifold approximation and projection (wnnUMAP) reductions for data visualization (Supplementary Fig. 1)–. The resulting dataset consists of 6,604 B6 and 13,668 Sp140−/− cells, almost exclusively consisting of myeloid cells (Fig. 2B). Each cluster is represented in the two datasets, however the proportions of some clusters are altered between genotypes. Most notably, the ratio of IFN stimulated gene (ISG)+ IM to ISG– IM was higher in the Sp140−/− mice, as expected from the exacerbated type I IFN response in this strain (Fig. 2B). The largest changes in composition were seen when comparing cells from naïve lungs to bystander and Mtb-infected cells from Mtb-infected lungs (Fig. 2B). For example, AMs are relatively abundant in naïve lungs but are rare among the Mtb-infected cells 25 days post-infection, as also seen by flow cytometry (Fig. 1F, 2B). B6 and Sp140−/− myeloid cells from uninfected mice were highly transcriptionally similar (Supplemental Fig. 2D-E), confirming that the type I IFN-driven changes in B6 and Sp140−/− mice occur after Mtb infection.

8To determine the cellular mechanism of type I IFN-driven Mtb susceptibility, we first sought to identify the type I IFN-producing cells. In general, our scRNA-seq analysis revealed that very few cells were Ifnb1 positive, which may reflect a lack of sensitivity of scRNA-seq, and/or the transient and stochastic expression pattern of this gene (Fig. 3A)–. Mtb infection resulted in increased expression of Ifnb1 in infected and bystander mononuclear phagocytes, with a slight bias towards Ifnb1 production by IMs compared to monocytes, and no production by AMs (Fig. 3A). While there was no major difference in the cell types producing Ifnb1 between B6 and Sp140−/− cells, a greater number and frequency of Sp140−/− cells expressed Ifnb1 (Fig. 3B). Additionally, Ifnb1-expressing cells in Sp140−/− mice trended towards a higher per cell expression of Ifnb1 than B6 cells (Fig. 3C). A prior scRNA-seq study of Mtb-infected and naïve lungs from non-human primates largely mirrors our findings in mice. Our analyses of these data indicate that IMs were also the dominant IFNB1-expressing cells in non-human primates with active tuberculosis, and IMs did not express IFNB1 in naïve or latently infected lungs (Fig. 3D). These results suggest that mice faithfully recapitulate the Mtb-induced type I IFN production seen in non-human primates.

9The type I IFN producers identified in the scRNA-seq datasets were validated using an Ifnb1 genetic reporter called I-Tomcat mice, which express TdTomato and Cre downstream of Ifnb1 (Fig. 3E, Supplementary Fig. 3). While TdTomato expression was sufficient to identify Ifnb1 expression by bone marrow-derived macrophages following in vitro stimulation with poly I:C (Supplementary Fig. 3), TdTomato+ cells were not detected 25 days after Mtb infection (Fig. 3F). TdTomato detection was not improved in I-Tomcat homozygous mice, examining an earlier timepoint of 19 days post-infection, or by gating on specific immune populations such as IMs (Supplementary Fig. 3). Even though type I IFN drives Mtb susceptibility in Sp140−/− mice, it is unclear when type I IFN production occurs (Fig. 1A). Type I IFN production may be an early and/or transient event, which would be missed by analyzing a single timepoint. To address this issue, we crossed I-Tomcat mice with the Ai6 Cre reporter mice (I-Tomcat Ai6) on B6 and Sp140−/− backgrounds (Fig. 3E). In these mice, any cell that has ever expressed Ifnb1 will constitutively express ZsGreen. Mtb-infected I-Tomcat Ai6 mice contained reporter-positive myeloid cells and low background was detected among cell populations that are not expected to express Ifnb1 (e.g., ~0.1% of T cells were Ai6+) (Fig. 3G). Consistent with the scRNA-seq analysis, IMs and monocytes were the primary Ifnb1-expressing cells in B6 and Sp140−/− mice (Fig. 3H, Supplementary Fig. 3). Interestingly, Sp140−/− mice exhibited elevated Ai6+ expression frequency in all cell types, suggesting SP140 broadly modulates the sensitivity for inducing Ifnb1 expression (Fig. 3H). In addition to corroborating the scRNA-seq data, the I-Tomcat mice also identified pDCs as a major type I IFN-producing cell population. Lung pDCs are very rare CD64–/Ly6G– cells and were therefore not represented in our scRNA-seq dataset. However, despite their scarcity, pDCs are extremely robust producers of type I IFNs on a per cell basis.

10While we expected IMs to be a major type I IFN-producing population given the scRNA-seq results, we were surprised that the majority of the Ai6+ IMs were Mtb– and most Mtb+ IMs were Ai6– (Fig. 3G). These results suggest that direct infection of IMs is neither required nor sufficient for IFN-β production. To examine this phenomenon in greater detail, we performed confocal microscopy and histo-cytometry analysis of Mtb-infected I-Tomcat Ai6 and Sp140−/− I-Tomcat Ai6 lungs,. While lesions of diseased tissue were clearly identifiable in I-Tomcat Ai6 mice, the size and myeloid cell influx into the diseased tissue were greatly exacerbated in Sp140−/− I-Tomcat Ai6 (Fig. 4A). Additionally, Ai6 expressing cells were identifiable throughout the lungs, with an increased propensity to localize in diseased rather than healthy tissue (Fig. 4A–4C). Within diseased tissue, Ai6 expressing cells were primarily located near Mtb harboring cells in I-Tomcat Ai6 and Sp140−/− I-Tomcat Ai6 lungs (Fig. 4B). Similar to the flow cytometry results, SIRPɑ+ cells, which are primarily macrophages in Mtb-infected lungs as they are ~100 fold more abundant than SIRPɑ expressing cDC2s, were a major Ai6 expressing cell population (Fig. 1D, 4B, 4D). The SIRPɑ+ macrophages expressed Ai6 at a higher frequency than CD4+ T cells in the diseased tissue but not healthy tissue (Fig. 3H, 4B, 4D). Direct infection by Mtb did not appear to be a major driver of IFN-β expression, as ~2–3% of infected macrophages were Ai6+ and ~12–15% of Ai6+ cells were Mtb-infected, in line with the frequencies seen in IMs by flow cytometry (Fig. 3H, 4E). These results suggest that IM localization to Mtb rich regions provides the activating signals required for IFN-β expression, while direct infection of IMs is not required for IFN-β expression.

11While pDCs have a well-established role in anti-viral immunity in the lung, limited work has assessed their contribution during Mtb infection,. pDCs may have been previously overlooked because of their scarcity in the lung. Indeed, we observe only ~20,000 pDCs in the lungs of naïve B6 and Sp140−/− mice, but this number increases 10-fold following Mtb infection and is modestly but significantly higher in Mtb-infected Sp140−/− than B6 mice (Fig. 5A). Despite their scarcity, pDCs can have major effects due to the extremely high levels of interferons produced per cell. Consistent with a role for pDCs during Mtb infection in mice, Khader and colleagues described the presence of pDCs in lungs of non-human primates with active pulmonary TB. However, the lack of genetic tools in non-human primates precluded functional studies of pDCs during TB. Therefore, we assessed whether pDCs affect Mtb control using our experimentally tractable mouse model and an anti-BST2 antibody–. This strategy efficiently depleted pDCs and resulted in a partial rescue of Mtb control in Sp140−/− mice (Fig. 5B, 5C). However, BST2 is known to be upregulated by cells other than pDCs in inflammatory environments; thus, antibody depletion could have been protective against Mtb by depleting non-pDC cells. We therefore also tested the contribution of pDCs by using a genetic pDC depletion strategy by crossing Sp140−/− mice with mice expressing the diphtheria toxin receptor (DTR) downstream of the human BDCA2 promoter (pDC-DTR). We used Sp140+/− pDC-DTR littermates as wild-type controls since a single copy of Sp140 is sufficient to restore Mtb control. DT administration efficiently ablated pDCs in Sp140−/− and Sp140+/− mice, with the depletion specifically affecting pDCs (Fig. 5D, Supplementary Fig. 4). pDC-DTR depletion of pDCs fully rescued bacterial control in Sp140−/− mice, while depletion in Sp140-sufficient animals did not affect lung bacterial burden, as expected (Fig. 5E). Additionally, pDC-DTR depletion of pDCs in Sp140−/− mice reduced expression of type I IFN stimulated genes to the level of Sp140-sufficient animals, while rescuing expression of the type II IFN stimulated gene H2-Ab1 (Fig. 5F, 5G). These results demonstrate a substantial contribution of pDCs in limiting Mtb control in animals with a hyper type I IFN response.

12We next sought to understand the role of lung pDCs during human Mtb infection by analyzing human lung and lymph node biopsies taken from Mtb culture-positive patients for the presence of pDCs near Mtb granulomas (Fig. 5H). Based on CD303 and CD123 staining, pDCs localized to the lymphocytic cuff surrounding Mtb granulomas in human lungs and lymph nodes (Fig. 5I, 5I, Supplementary Fig. 4). Of the 8 patient samples analyzed, 5 lung samples and 7 lymph node samples had pDCs in the same 400× field as an Mtb granuloma (Supplementary Table 1). The majority of the pDCs in the lung samples were distributed as individual cells, while lymph node pDCs were primarily grouped together in clusters of over 20 cells or scattered individually (Supplementary Table 1). These results demonstrate that pDCs, which are overall a rare cell type, cluster near Mtb-infected cells in granulomas in human lung and lymph nodes during infection. These results, along with our results in mice and previous studies in non-human primates, implicate pDCs as a plausible source of type I IFN that drives active tuberculosis in humans.

13We sought to understand why pDCs contribute to the susceptibility of Sp140−/− but not Sp140-sufficient animals. pDCs are not directly infected and typically produce type I IFNs after recognition of extracellular-derived nucleic acid ligands. Thus, we examined the availability of ligands that might potentially activate pDCs to produce type I IFNs. We focused on DNA-rich neutrophil extracellular traps (NETs) as a potential pDC-activating ligand because extracellular DNA can activate pDCs via TLR9, and NETs have been described as a stimulus for type I interferon production by pDCs in mice and humans in the context of autoimmunity,,. Additionally, another Mtb susceptible mouse model with a hyper type I interferon response identified the presence of NETs in the lungs of susceptible mice and humans with active Mtb disease. We found a 5-fold enrichment in lung neutrophils in Mtb-infected Sp140−/− relative to Sp140−/− Ifnar1−/− mice (Fig. 1D, 6A). Additionally, neutrophil depletion partially rescued the susceptibility of Sp140−/− mice at both lower and higher bacterial burdens (Fig. 6B-E). Given the importance of neutrophils, we assessed NET production in Sp140−/− and B6 mice by staining for citrullinated H3 in the lungs of Mtb-infected mice (Fig. 6F). Sp140−/− mice had over a 100-fold increase in NET staining as compared to B6 animals, indicating that the lungs of Sp140−/− mice harbor substantially more ligand to activate type I interferon production by pDCs as compared to wild-type hosts (Fig. 6G). As NETs are a source of nucleic acids, we hypothesized that pDCs would sense the NETs through endosomal TLRs. In line with this prediction, deletion of Unc93b1, which is an essential chaperone required for TLR7 and TLR9 function, partially rescued the Mtb susceptibility of Sp140−/− mice (Fig. 6H). By contrast, deletion of Ticam1, which encodes for TRIF, an adapter molecule critical for type I IFN production downstream of TLR3 and TLR4, had no effect on bacterial control in Sp140−/− mice (Fig. 6I). Together, these data suggest a model in which extracellular nucleic acid, potentially from NETs, is sensed by endosomal TLRs triggering pDC production of type I IFNs (Fig. 6J).

14Having identified pDCs, IMs, and monocytes as the main cells producing type I IFN during Mtb infection, we next sought to identify the cells responding to type I IFN. As expected, IFNAR was uniformly expressed by all lung myeloid cells, and was therefore not informative for identifying IFN responsive cells (Fig. 7A). However, comparing differentially expressed genes in B6 and Sp140−/− neutrophils and IMs showed a clear induction of IFN stimulated genes in cells from Sp140−/− over B6 animals (Fig. 7B). A major complication is that many genes induced by type I IFN are also induced by type II IFN (IFNγ), and most existing studies do not distinguish the two. Therefore, we sought to develop type I IFN-specific and IFNγ-specific transcriptional signatures. RNA-sequencing analysis of human macrophages and mouse bone marrow-derived macrophages stimulated with IFNγ, IFN-β, TNF, transforming growth factor-β, or nothing were used to define cytokine-induced genes (Supplementary Fig. 5, 6). Similar gene families were preferentially upregulated by type I or II IFNs in human and mouse macrophages. Genes included in the signatures for type I or II IFN were not strongly induced by a panel of other cytokines (Supplementary Fig. 5). We next applied the mouse gene signatures to our mouse lung myeloid scRNA-seq dataset. The strength of signature expression in naïve mice was used as the threshold for classifying cells as responding to IFNγ or type I IFN (Supplementary Fig. 6). As expected, naïve mice had very few cells responding to either cytokine, while bystander and Mtb-infected cells responded strongly to type I and/or II IFNs (Fig. 7C, Supplementary Fig. 6). Interestingly, the type I IFN response was limited to IMs and neutrophils, even though monocytes and AMs were responsive to IFNγ. Potentially, differences in the localization of these cells could explain their differences in cytokine responsiveness. As expected, Mtb-infected neutrophils and IMs from Sp140−/− mice exhibited a significant increase in type I IFN signaling relative to cells from B6 lungs (Fig. 7D). Consistent with considerable prior work demonstrating that type I IFNs impair responsiveness to IFNγ,,,, the Sp140−/− mice harbored a distinct population of infected IMs that exhibited the signature of type I IFN-responsiveness but lacked the signature of IFNγ responsiveness (note the distinct population of blue IMs among the Mtb-infected cells in Fig. 7C, 7D). The reduction in IFNγ signaling in Mtb-infected IMs in Sp140−/− mice correlated with reduced IFNγ receptor 1 expression on these cells in Sp140−/− relative to B6 mice (Supplementary Fig. 7). This reduced IFNγ receptor 1 expression also correlated with increased type I IFN stimulated genes and reduced type II IFN stimulated genes (Supplementary Fig. 7). Since IFNγ is critical for Mtb control, these results suggest that type I IFN impairs Mtb control at least in part by opening a niche of susceptible IMs that fail to respond to IFNγ.

15As neutrophils and IMs were the primary sensors of type I IFN, these cell types were tested as potential mediators of type I IFN-driven susceptibility in Sp140−/− mice. Neutrophil-specific deletion of Ifnar1 was insufficient to rescue the Mtb susceptibility or the increase in lung neutrophils exhibited by Sp140−/− mice (Fig. 7E, 7F). Conversely, deletion of Ifnar1 expression on myeloid cells with LysMCre Ifnar1fl/fl mice, or specifically in macrophages with CD64Cre Ifnar1fl/fl mice, rescued bacterial control and reduced lung neutrophil numbers to the same extent as global Ifnar1 deletion (Fig. 7G, 7H). Taken together, these results are consistent with a model in which type I IFNs act on IMs to inhibit IFNγ signaling in these cells, thereby reducing their ability to restrict Mtb growth.

16The dominant gene signature identified in humans with active tuberculosis disease is a type I IFN signature,,,. Type I IFNs are critical for effective anti-viral immunity but impair Mtb control in human and animal models,,,,,. For example, viral co-infections or chemical interferon inducers exacerbate Mtb disease in mice–. Interestingly, although type I IFN and IFNγ induce a highly overlapping set of target genes (Supplementary Fig. 5), type I IFNs promote Mtb disease while IFNγ potently protects against mycobacterial infections in mice and humans,–. Indeed, type I IFNs can exacerbate bacterial infections in mice and humans by directly antagonizing IFNγ signaling,,. The underlying mechanism is poorly understood but may in part be due to downregulation of the IFNγ receptor,. Type I IFNs can also impair IL-1 signaling, an additional pathway critical for Mtb control, through the induction of IL-1 receptor antagonist and eicosanoid imbalance,,,. Thus, the antagonism of protective IFNγ and IL-1 responses by type I IFNs may be a key driver of progression to active tuberculosis. Therefore, we sought a genetically tractable model of Mtb infection to establish the cellular mechanisms by which type I IFN drives Mtb susceptibility.

17The commonly used B6 mouse model does not exhibit a strong type I IFN response after Mtb infection,,. Consistent with the modest type I IFN response of B6 mice, Ifnar1 deletion on the B6 background does not reliably impact survival or lung bacterial burden after Mtb infection–,. Therefore, we sought a different mouse model that recapitulated two key aspects of human disease: the hyper type I IFN response, and the accompanying neutrophilic inflammation,,. Previously, we identified B6.Sst1S mice as a mouse model that exhibits type I IFN-driven susceptibility to Mtb infection. We then demonstrated that the absence of Sp140 in B6.Sst1S mice explains their susceptibility to Mtb. SP140 is a member of the Speckled Protein family of epigenetic readers and is widely expressed in leukocytes. Sp140-deficient macrophages have been hypothesized to exhibit inherent defects in bacterial control causing increased susceptibility to dextran sulfate sodium-induced colitis,. However, an inherent defect in bacterial control is not evident during Mtb infection, as Sp140−/− mice lacking Ifnar1 restrict Mtb as well as B6 animals 25 days after infection, a time point at which macrophages are critical for Mtb control. This result suggests that the early susceptibility of Sp140−/− mice is due to their strong type I IFN response rather than an inherent defect in bacterial killing by macrophages. In addition to their hyper type I IFN response, Sp140−/− mice have more lung neutrophils after Mtb infection than Sp140−/−Ifnar1−/− animals (Fig. 1D, 7B, 7D). Therefore, Sp140−/− mice recapitulate the fundamental type I IFN and neutrophilic character of human active Mtb disease, and can serve as a platform for understanding the cellular mechanism of type I IFN-driven Mtb susceptibility.

18Sp140−/− mice provide an ideal model of the aberrant type I IFN response, as they are on a pure B6 genetic background, and do not require repeated administration of TLR agonists, viral co-infection, or other perturbations of the innate immune system for type I IFN production,,,,. Other groups have also modeled the type I IFN response by infecting B6 mice with a lineage 2 clinical Mtb strain, such as HN878,. However, Ifnar1 deletion had no impact on survival or bacterial control at early time points in B6 mice infected with HN878, unlike Sp140−/− mice infected with Mtb Erdman,,. Thus, we believe the Sp140−/− mouse model recapitulates the hyper type I IFN response exhibited by humans, and provides a tool to study the mechanistic basis of the aberrant type I IFN response.

19We used Sp140−/− mice to identify the type I IFN producers and responders that mediate Mtb disease. Flow cytometry and imaging of I-Tomcat Ai6 Ifnb1 reporter mice identified IMs and pDCs as the major IFN-β producers during Mtb infection (Figure 3, 4). Imaging provided insight into why these cells expressed type I IFN, as the frequency of type I IFN-expressing macrophages was enriched relative to CD4+ T cells in diseased tissue but not in healthy tissue. This result suggests that proximity to Mtb dictates access to activating signals required to induce IFN-β expression by macrophages. However, most IFN-β expressing IMs were not infected with Mtb, and most infected IMs were not Ifnb1 or reporter positive, indicating direct infection is insufficient and may not be the main driver of type I IFN expression in vivo. In vitro studies have shown that bone marrow-derived macrophages infected with Mtb induce type I IFN via the cytosolic DNA-sensing cGAS-STING pathway. However, this pathway does not appear to play a major role in vivo,–. Instead, our results suggest that uninfected bystander cells responding to extracellular ligands may be the primary producers of type I IFN during Mtb infection. Consistent with this hypothesis, we found that mice lacking Unc93b1, a chaperone required for TLRs that sense extracellular nucleic acids, exhibit enhanced control of Mtb (Figure 6I). Of note, pDCs, which we found to be important type I IFN producers during Mtb infection, are robust producers of type I IFN after TLR7/9 sensing of exogenous nucleic acids,,.

20Expression of type I IFNs by pDCs during Mtb infection was particularly noteworthy as limited work exists on the effect of pDCs on Mtb control. Production of type I IFNs by pDCs is important for control of viral infections,,. pDCs also demonstrate a protective function against bacterial infections such as Citrobacter rodentium, Chlamydia pneumoniae, and Klebsiella pneumoniae–. However, the contribution of pDCs during Mtb infection remains unclear. Blood pDC numbers were reduced in Mtb-infected humans, but lung pDC numbers or function were not assessed,. In non-human primates, active pulmonary Mtb correlated with pDC influx and IFN-responsive macrophages into the lungs of rhesus macaques. The granulomas of Mtb-infected cynomolgus macaques also contained pDCs, but the pDC frequency did not correlate with bacterial burdens in the granulomas. A major issue in the studies using NHPs or humans is the difficulty in depleting or otherwise functionally assessing the role of pDCs. To address this limitation, we generated Sp140−/− pDC-DTR mice,. Using these mice, we found that pDCs contribute significantly to the susceptibility of Sp140−/− mice (Figure 5). Additionally, we identified pDCs in the lymphocytic cuff surrounding Mtb granulomas in human lungs and lymph nodes. Together, these results suggest that type I IFN produced by pDCs drives tuberculosis disease in mice, and is likely conserved across non-human primates and humans.

21While pDC depletion rescued Sp140−/− mice, it had no impact on bacterial burden in B6 animals. This result was expected given that very few myeloid cells in B6 mice expressed a type I IFN signature and Ifnar1 deficiency also has only modest effects in the B6 background,. As pDCs are present in B6 and Sp140−/− mice, we speculated that the difference in pDC type I IFN production in these mouse strains could be due in part to differences in the availability of activating ligands. As seen in another Mtb-susceptible mouse model, and in Mtb-infected human lungs, Sp140−/− mice had a significant enrichment in NET production compared to B6 mice. NETs are DNA-rich products of neutrophils and may act as ligands for TLR9 on the pDCs, as described in mouse and human autoimmunity,,,. In support of this hypothesis, neutrophil depletion partially rescued the susceptibility of Sp140−/− mice. Given that NET formation was also identified in Mtb granulomas in human lung sections, pDC sensing of NETs may contribute to the type I IFN response detected in humans with active Mtb disease.

22Having defined the cells producing type I IFNs in vivo after Mtb infection, we then sought to identify the type I IFN responders. To do this, we first developed transcriptional signatures that distinguish the response to type I IFN from the closely related response to IFNγ. Applying these signatures to our scRNA-seq data, we identified neutrophils and IMs as type I IFN sensors. Both IMs and neutrophils harbor Mtb, so the effect of type I IFN could be on either or both cell types. In a GM-CSF blockade model of type I IFN-driven Mtb susceptibility, neutrophil-specific deletion of Ifnar1 rescued bacterial control. By contrast, we were unable to detect any rescue of Sp140−/− mice when neutrophils lacked Ifnar1 (Figure 7E-F). Instead we found that deletion of Ifnar1 on macrophages rescued Sp140−/− mouse bacterial control (Figure 7G-H). GM-CSF is critical for maintaining lung alveolar macrophages and enhances the responsiveness of lung monocytes and macrophages to infections, including Mtb infection–. Therefore, it is possible that impairing lung macrophages by GM-CSF blockade shifted the impact of type I IFN on Mtb control from macrophages to neutrophils. Our scRNA-seq dataset only contains myeloid cells and therefore cannot address the contribution of type I IFN signaling in other cell types. However, it is likely that type I IFN largely acts through myeloid cells to drive Mtb susceptibility, as macrophage-specific deletion of Ifnar1 rescued Mtb control to a similar extent as global Ifnar1 deficiency. These results suggest that during a GM-CSF sufficient response, type I IFN signaling in macrophages reduces their ability to restrict Mtb. The mechanism by which type I IFNs impair Mtb clearance by IMs remains unknown. However, we observed that infected IMs in Sp140−/− mice express lower levels of IFNγ receptor and IFNγ target genes, and instead primarily exhibited a type I IFN signature, consistent with the exacerbated type I IFN response in these mice (Supplementary Fig. 7). The transcriptional response of Sp140−/− IMs contrasted dramatically with that of infected IMs in B6 mice, which control Mtb and which exhibited a uniform signature of responsiveness to IFNγ (Figure 7). Given the essential role of IFNγ in controlling Mtb in mice and humans, our results suggest that one detrimental effect of type I IFNs is inhibition of IFNγ signaling in infected macrophages,,.

23Taken together, our results identify the cell types that produce and respond to type I IFNs during IFN-driven tuberculosis disease. We propose that in addition to its previously described role in inhibition of IL-1 signaling, type I IFNs also impair responsiveness to IFNγ, leading to an initial loss of bacterial control. Bacterial replication then leads to neutrophil influx and NET production within the diseased tissue. DNA-rich NETs may be one source of extracellular-derived ligands sensed by endosomal TLRs in pDCs, though cellular RNA or bacterial DNA may be additional sources. Activation of pDCs results in very robust per-cell production of type I IFNs, which we propose acts in a positive feedback loop to further antagonize IFNγ signaling and reduce the ability of IMs to restrict Mtb growth. Given the correlations between our results and findings in rhesus macaques and humans with active Mtb, we believe that our proposed mechanism of type I IFN-driven loss of Mtb control is conserved across species. These findings open the door for the development of therapies targeting NET production or pDC function as host-directed strategies for treating active Mtb infection.

24Although we see a strong correlation between NET formation and type I IFN-driven Mtb susceptibility, the current study does not directly test the contribution of NETs in this response. Additionally, the present study provides data suggesting that type I IFN signaling correlates with a loss of type II IFN signaling in IMs during Mtb infection, but does not directly examine whether Mtb-harboring IMs in Sp140−/− mice are unable to control Mtb infection because of a lack of response to IFNγ. We also limited our studies to ~25 days post-infection, which is an early time point for Mtb infection. It is possible that the cellular sources and targets of type I IFN shift to other cell types at later time points in the infection.

25Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Russell Vance (rvance@berkeley.edu).

26Materials used in this study will be provided upon request and available upon publication.

27Raw and processed bulk RNA- and single cell RNA-sequencing data is deposited in the NCBI Gene Expression Omnibus: GSE216023, GSE232827, GSE232922. This paper also analyzes existing, publicly available data, for which the accession numbers are listed in the key resources table.

28Code for bulk RNA- and scRNA-sequencing analysis is available on Github: https://github.com/dmitrikotov/Sp140-Type-I-Inteferon.

29Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

30Mice were maintained under specific pathogen-free conditions and housed at 23°C with a 12 hour light-dark cycle in accordance with the regulatory standards of the University of California Berkeley Institutional Animal Care and Use Committee. All mice were sex- and age-matched and were 6–12 weeks old at the start of infections. Male and female mice were used in all experiments. Littermate controls were used when possible, as indicated in the figure legends. B6, B6.129S2-Ifnar1tm1Agt/Mmjax (Ifnar1−/−), B6.Cg-Gt(ROSA)26Sortm6(CAG-ZsGreen1)Hze/J (Ai6), B6(Cg)-Ifnar1tm1.1Ees/J (Ifnar1fl), B6.Cg-Tg(S100A8-cre,-EGFP)1Ilw/J (Mrp8Cre), B6.129P2-Lyz2tm1(cre)Ifo/J (LysMCre), C57BL/6J-Ticam1Lps2/J (Ticam1−/−), and B6N.129S4-Gt(ROSA)26Sortm1(FLP1)Dym/J (FLPer) mice were purchased from Jackson Laboratories. C57BL/6N-Unc93b1tm1(KOMP)Vlcg/Mmucd (Unc93b1−/−) mice were obtained from the Mutant Mouse Resource and Research Center (MMRRC) at the University of California, Davis, was donated to the MMRRC by the KOMP repository at University of California, Davis, originated from David Valenzuela of Regeneron Pharmaceuticals, and were provided by Gregory Barton at the University of California, Berkeley. Ifnb1-Tomato-Cre-pA Terminator (I-Tomcat) mice were generated by Daniel Stetson at the University of Washington as described below. B6-Fcgr1tm2Ciphe (CD64Cre) mice were generated by Bernard Malissen at Centre d’Immunologie de Marseille-Luminy and provided by Yasmine Belkaid at the National Institutes of Health. B6-Tg(CLEC4C-HBEGF)956Cln/J (pDC-DTR) mice were provided by Adam Lacy-Hulbert at the Benaroya Research Institute. Sp140−/− mice were previously generated in-house. Sp140−/− Ifnar1−/− mice were generated by crossing Sp140−/− mice with Ifnar1−/− mice in-house. I-Tomcat Ai6 mice were generated by crossing I-Tomcat mice with Ai6 mice, while Sp140−/− I-Tomcat Ai6 mice were the result of crossing I-Tomcat mice with Sp140−/− and Ai6 mice in-house. Sp140−/− mice were crossed in-house with pDC-DTR mice to generate Sp140−/− pDC-DTR mice. Sp140−/− Ifnar1fl LysMcre, Sp140−/− Ifnar1fl Mrp8Cre, and Sp140−/− Ifnar1fl CD64Cre mice were generated by crossing Sp140−/− mice with Ifnar1fl and LysMCre or Mrp8Cre or CD64Cre mice in-house.

31Mtb strain Erdman was a gift from Sarah Stanley at the University of California, Berkeley. Frozen aliquoted stocks were produced after passing the strain in vivo to ensure virulence. Mtb expressing Wasabi (Mtb-Wasabi) and Mtb-mCherry were generated using Mtb that had been passaged 2 or fewer times in vitro. For these fluorescent strains, Mtb was grown in Middlebrook 7H9 liquid medium supplemented with 10% albumin-dextrose-saline, 0.4% glycerol, and 0.05% Tween-80 for 5 days at 37°C. The cells were pelleted and washed in 10% glycerol to remove salt. The bacteria were then electroporated with 1 μg DNA using a 2 mm electroporation cuvette and the following settings: 2500 volts, 1000 Ohms, 25 μF. The pTEC15 plasmid (Addgene plasmid # 30174), which expresses Wasabi under the control of the Mycobacterium Strong Promoter, was electroporated into Mtb to generate Mtb-Wasabi. The pMSP12::mCherry plasmid (a gift from Lalita Ramakrishnan, University of Cambridge; Addgene plasmid # 30167), which expresses mCherry under the control of the Mycobacterium Strong Promoter, was electroporated into Mtb to generate Mtb-mCherry. Following electroporation, bacteria were grown on 7H11 plates supplemented with 10% oleic acid, albumin, dextrose, and catalase, 0.5% glycerol, and either 200 μg / mL Hygromycin for Mtb-Wasabi or 50 μg / mL Kanamycin for Mtb-mCherry for 3–4 weeks at 37°C. Individual colonies where then propagated in 10 mL inkwell flask cultures using 7H9 medium supplemented with 10% albumin-dextrose-saline, 0.4% glycerol, 0.05% Tween-80, and either Hygromycin for Mtb-Wasabi or Kanamycin for Mtb-mCherry for 7 days at 37°C. The inkwell cultures were expanded into a 100 mL culture using the same 7H9 supplemented media with antibiotics and cultured for 4–5 days at 37°C. Once the bacteria were in log phase, the culture was filtered with a 5 μm syringe filter and frozen in 1 mL aliquots in 10% glycerol.

32Targeting of C57BL6/J embryonic stem (ES) cells and generation of chimeric mice was performed by Biocytogen. A construct was targeted immediately downstream of the endogenous Ifnb stop codon using Cas9 and a gRNA targeting the genomic site TGCAACCACCACTCATTCTGAGG; the underlined sequence represents the protospacer adjacent motif (PAM). The construct included an encephalomyocarditis virus (EMCV) internal ribosome entry site (IRES), coding sequence for the TdTomato red fluorescent protein, a picornavirus T2A “self-cleaving” peptide, a nuclear localization sequence (NLS)-containing Cre recombinase, and a bovine growth hormone (BGH) polyadenylation (pA) sequence that bypasses the endogenous polyadenylation site in the 3’ untranslated region of the Ifnb gene.

33After the BGH pA sequence, the construct contained a FRT site-flanked phosphoglycerate kinase (PGK)-neomycin resistance cassette. The insertion cassette was flanked by ~2 kilobase homology arms on either side, and a Diptheria Toxin A (DTA) gene at the 3’ end to select against random insertions. Successful targeting of ES cells was confirmed by PCR. After germline transmission, the knockin mice were confirmed by sequencing and then bred to FLPer mice to remove the FRT-flanked neo cassette.

34For infection, a frozen aliquot of Mtb-Wasabi or Mtb-mCherry was diluted in distilled H2O, and 9 mL of diluted culture was loaded into the nebulizer of a inhalation exposure system (Glas-Col, Terre Haute, IN) to deliver ~20–100 bacteria per mouse as determined by measuring CFU in lungs 1 day post-infection.

35Mice were harvested at various days post-infection (as described in figure legends) to measure CFUs by plating and innate immune populations by flow cytometry. All lung lobes were harvested into a gentleMACS C tube (Miltenyi Biotec) containing 3 mL of RPMI media with 70 μg / mL of Liberase TM (Roche) and 30 μg / mL of Dnase I (Roche). Samples were processed into chunks using the lung_01 setting on the gentleMACS (Miltenyi Biotec) and incubated for 30 minutes at 37°C. Tissue was then homogenized into a single cell suspension by running the samples on the lung_02 setting on the gentleMACS. The digestion was quenched by adding 2 mL of PBS with 20% Newborn Calf Serum (Thermo Fisher Scientific) and filtered through 70 μm SmartStrainers (Miltenyi Biotec).

36For measuring plasmacytoid dendritic cell numbers, spleens were harvested into a 12 well plate with 1 mL of PBS with 2% Newborn Calf Serum and 0.05% sodium azide in each well. The spleens were sandwiched between 100 uM mesh filters and mashed into a single cell suspension with the back of a syringe plunger. The single cell suspensions were filtered through 70 μm SmartStrainers (Miltenyi Biotec).

37To measure CFU, 50 μL was taken from each single cell suspension and then serially diluted in phosphate-buffered saline (PBS) with 0.05% Tween-80. Serial dilutions were plated on 7H11 plates supplemented with 10% oleic acid, albumin, dextrose, and catalase and 0.5% glycerol. Colonies were counted after 3 weeks.

38For flow cytometry, lung single cell suspensions were pelleted and resuspended in 500 μL of PBS with 2% Newborn Calf Serum and 0.05% Sodium azide and 100–150 μL were stained with antibodies for analysis. Spleen single cell suspensions were pelleted and resuspended in 5 mL of PBS with 2% Newborn Calf Serum and 0.05% Sodium azide, of which 50 μL were stained with antibodies. Single cell suspensions were stained for 45 minutes to an hour at room temperature with the following antibodies: TruStain FcX PLUS (S17011E, BioLegend), BUV496-labeled CD45 (30-F11, BD Biosciences), APC-labeled CD64 (X54–5/7.1, BioLegend), BV480-labeled B220 (RA3–6B2, BD Biosciences), BV480-labeled CD90.2 (53–2.1, BD Biosciences), APC-Fire 750-labeled Ly6G (1A8, BioLegend), BUV395-labeled CD11b (M1/70, BD Biosciences), BUV737-labeled CD11c (HL3, BD Biosciences), APC-R700-labeled Siglec F (E50–2440, BD Biosciences), PE-labeled MerTK (DS5MMER, Thermo Fisher Scientific), Super Bright 645-labeled MHC II (M5/114.15.2, Thermo Fisher Scientific), BV421-labeled PD-L1 (MIH5, BD Biosciences), BV711-labeled Ly6C (HK1.4, BioLegend), PE-labeled IFNAR-1 (MAR1–5A3, BioLegend), PE-Cy7-labeled MerTK (DS5MMER, Thermo Fisher Scientific), APC-eFluor 780-labeled CD11b (M1/70, Thermo Fisher Scientific), BUV395-labeled CCRL2 (BZ2E3, BD Biosciences), BUV563-labeled Ly6G (1A8, BD Biosciences), Percp-Cy5.5-labeled B220 (RA3–6B2, BioLegend), BV421-labeled Siglec H (440c, BD Biosciences), BV480-labeled CD19 (1D3, BD Biosciences), BV605-labeled MHC II (M5/114.15.2, BioLegend), BV785-labeled Ly6C (HK1.4, BioLegend), BV605-labeled CD4 (GK1.5, BioLegend), BUV805-labeled CD8ɑ (53–6.7, BD Biosciences), and PE-Cy7-labeled PDCA-1 (eBio927, Thermo Fisher Scientific). All samples also received fixable viability dye (Ghost Dye Violet 510; Tonbo Biosciences), Super Bright Complete Staining Buffer (Thermo Fisher Scientific), and True-Stain Monocyte Blocker (BioLegend) at the same time as the antibodies. Stained samples were fixed with cytofix/cytoperm (BD biosciences) for 20 minutes at room temperature before samples were removed from the BSL3. For intracellular staining, the fixed samples were stained with the following antibodies: PE-Cy7-labeled CD63 (NVG-2, BioLegend), Percp-eFluor 710-labeled iNOS (CXNFT, Thermo Fisher Scientific), BV785-labeled CD206 (C068C2, BioLegend). Cell numbers were calculated by adding fluorescent AccuCheck Counting Beads (Invitrogen) to each sample. Cells were then analyzed on a Fortessa (BD Biosciences) or an Aurora (Cytek) flow cytometer. Data were analyzed with Flowjo version 10 (BD Biosciences).

39Injections for cell depletions were started 12 days after Mtb infection and continued every other day until the mice were harvested 25 days post-infection. Antibody depletion of pDCs was performed by administering 200 μg of anti-BST2 (927, BioXCell) or rat IgG2b isotype control antibody (LTF-2, BioXCell) in 200 μL PBS via intraperitoneal injection. Genetic depletion involved administering 100 ng diphtheria toxin (Millipore Sigma) in 100 μL PBS via intraperitoneal injection into Sp140−/− pDC-DTR mice and littermate controls. Antibody depletion of neutrophils was performed by administering 200 μg of anti-Ly6G (1A8, BioXCell) or rat IgG2a isotype control antibody (2A3, BioXCell) in 200 μL PBS via intraperitoneal injection.

40Human lung and lymph node samples were acquired from the surgical pathology archives of Emory University Hospital with appropriate institutional approval. 8 lung samples and 8 lymph node samples were analyzed. Each sample had been previously culture verified for Mycobacterium tuberculosis infection. The samples were formalin-fixed and paraffin-embedded. Sections were cut and stained with anti-CD123 (6h6, Thermo Fisher Scientific), anti-CD303 (124B3.13, Dendritics), or hematoxylin and eosin,. Primary antibodies were detected by immunoperoxidase staining with the LSAB+ System and a standard DAB reaction following manufacturer’s instructions (DakoCytomation). Sections were counterstained with hematoxlyin prior to mounting and microscopy. pDCs were assessed in multiple 400× fields for each section to calculate the frequency of samples containing pDCs and the clustering of pDCs within each sample, defined as either singe cells, loose clusters of 5–20 cells, or tight clusters of more than 20 cells.

41Confocal Microscopy was performed using a Zeiss LSM 880 laser scanning confocal microscope (Zeiss) equipped with two photomultiplier detectors, a 34-channel GaASP spectral detector system, and a 2-channel AiryScan detector as well as 405, 458, 488, 514, 561, 594, and 633 lasers. 20 μm paraformaldehyde fixed lung sections from Mtb-mCherry infected I-Tomcat Ai6 and Sp140−/− I-Tomcat Ai6 mice were stained at 4°C overnight with BV421-labeled SIRPα (P84, BD Biosciences), Pacific Blue–labeled B220 (RA3–6B2, BioLegend), eF506-labeled CD4 (RM4–5, BioLegend), and AF647-labeled Ly6G (1A8, BioLegend). Sections detecting the presence of Neutrophil extracellular traps (NETs) were stained with BV421-labeled Ly6G (1A8, BioLegend) and rabbit polyclonal anti-citrullinated histone-H3 (citrulline R2, R8, R17; Abcam), stained with AF488 donkey anti-rabbit secondary (Poly4064, BioLegend). Stained sections were inspected with a 5× air objective to find representative lesions and distal sites and then imaged using a 63× oil immersion objective lens with a numerical aperture of 1.4. For each infected lung, one Mtb-heavy lesion image and one distal site image was taken consisting of 20 μm z-stacks acquired at a 1.5 μm step size. For representative NET images, 4×4 tiled images were captured without a z-stack. Additionally, the Zeiss LSM 880 microscope was used to image single color-stained Ultracomp eBeads Plus (Thermo Fisher Scientific) for generating a compensation matrix.

42Image analysis was performed using Chrysalis software. Briefly, a compensation matrix was generated by automatic image-based spectral measurements on single color-stained controls in ImageJ by using Generate Compensation Matrix script. This compensation matrix was used to perform linear unmixing on three-dimensional images with Chrysalis. Chrysalis was also used for further image processing, including rescaling data and generating new channels by performing mathematical operations using existing channels. For histo-cytometry analysis, Imaris 9.9.1 (Bitplane) was used for surface creation to digitally identify cells in images based on protein expression. Statistics for the identified cells were exported from Imaris and then imported into FlowJo version 10 (BD Biosciences) for quantitative image analysis.

43RNA-seq of Mtb-infected Sp140-sufficient and -deficient mouse lungs genetically depleted of pDCs was performed on 20% of each lung single cell suspension, prepared as described for CFU and flow cytometry analysis. Single cell suspensions were preserved in Trizol LS (Thermo Fisher Scientific) and stored at −80°C. The samples were thawed at room temperature for 5 minutes, then 200 μL of chloroform (Thermo Fisher Scientific) was added per 0.75 mL of Trizol LS to each sample and the samples were removed from the BSL3. Samples were centrifuged in Phasemaker tubes (Thermo Fisher Scientific) to isolate total RNA, which was then purified following the RNeasy Micro (Qiagen) protocol starting at the ethanol addition step. Library preparation, sequencing, and read alignment to the mouse genome was performed by Azenta Life Sciences. Libraries were prepared from total RNA using an Illumina kit for Poly(A) selection. Samples were sequenced on an Illumina HiSeq sequencer with paired 150 bp reads to a depth of 20–30 million reads per sample. Sequence reads were trimmed of adapter sequences and low quality nucleotides with Trimmomatic v.0.36 and then mapped to the Mus musculus GRCm38 reference genome with STAR aligner v.2.5.2b. The raw counts were used as input for DESeq2 analysis of differential gene expression.

44RNA-seq of cytokine stimulated bone marrow-derived macrophages was performed by differentiating bone marrow from B6 mice in DMEM supplemented with 10% FBS, PenStrep, Glutamin, HEPES, and M-CSF for 7 days then reseeding the cells in a 6-well plate and incubating for 5 days at 37°C and 5% CO2. The macrophages were left untreated or stimulated with 10 ng / mL of IFN-β (BioLegend), IFNγ (Abcam), TNF (Peprotech), or transforming growth factor-β (BioLegend) for 6 hours. Cells were lysed with TRK lysis buffer (Omega Bio-Tek) and 2-mercaptoethanol (Thermo Fisher Scientific) followed by total RNA isolation using the E.Z.N.A Total RNA Kit I (Omega Bio-Tek). The library preparation, sequencing, and read alignment to the mouse genome was performed by Azenta Life Sciences as described for the Mtb-infected mouse lung samples. Raw counts were used as input for analysis with DESeq2.

453 B6 and 3 Sp140−/− mice were infected with less than 100 CFU of Mtb-Wasabi bacteria. Lungs from infected animals as well as 2 naïve control mice per genotype were harvested 25 days post-infection and processed as described for CFU and flow cytometry analysis. Single cell suspensions were resuspended in PBS with 2% Newborn Calf Serum and stained with TruStain FcX PLUS (S17011E, BioLegend), APC-labeled Ly6G (1A8, BioLegend), APC-labeled CD64 (X54–5/7.1, BioLegend), and TotalSeq-A-labeled Ly6G (1A8, BioLegend) anti-mouse antibodies on ice for 30 minutes. Myeloid cells were then magnetically enriched using an EasySep APC Positive Selection Kit II (StemCell Technologies) and MojoSort Magnets (BioLegend). All enriched samples were stained with the following panel of TotalSeq-A-labeled anti-mouse antibodies to detect protein expression in the scRNA-seq dataset: Ly6C (HK1.4, BioLegend), CD44 (IM7, BioLegend), CD169 (3D6.112, BioLegend), CD274 (MIH6, BioLegend), Siglec F (S17007L, BioLegend), CSF1R (AFS98, BioLegend), CD11b (M1/70, BioLegend), CD86 (GL-1, BioLegend), MHC II (M5/114.15.2, BioLegend), and CX3CR1 (SA011F11, BioLegend). Each sample was also stained with a unique anti-mouse TotalSeq-A Hashtag antibody (1–6; BioLegend) to allow up to 6 populations to be multiplexed in a single lane on a 10X Genomics Chromium Next GEM Chip. Enriched cells from infected mice were also stained with PE-labeled B220 (RA3–6B2, Tonbo), PE-labeled CD90.2 (30-H12, Tonbo), and BV785-labeled CD45.2 (104, BioLegend) anti-mouse antibodies. Enriched cells from naïve mice were stained with Pacific Blue-labeled B220 (RA3–6B2, BioLegend), Pacific Blue-labeled CD90.2 (53–2.1, BioLegend), PE-labeled F4/80 (BM8, Thermo Fisher Scientific), and BV785-labeled CD45.2 (104, BioLegend) anti-mouse antibodies. All post-enrichment antibody staining was performed on ice for 45 minutes in the presence of True-Stain Monocyte Blocker (BioLegend). Following staining, cells were resuspended in PBS with 2% Newborn Calf Serum and Sytox Blue Dead Cell Stain (Thermo Fisher Scientific). Cells were sort purified using a 100 μm microfluidic sorting chip in a 4 laser SH-800 cell sorter (Sony) on the purity setting. The isolated populations from infected lungs were Mtb-infected cells and bystander myeloid cells. Macrophages and a mixture of neutrophils and monocytes were isolated from the naïve lungs and the macrophages were combined with neutrophil/monocyte mixture at a 1:2 ratio for better macrophage representation in the resulting dataset.

46The scRNA-sequencing libraries were generated using the v3.1 chemistry Chromium Single Cell 3’ Reagent Kit (10X Genomics) largely following the manufacturer protocol with the following minor modifications. Cells were loaded into 3 different lanes on a Chromium Next GEM Chip. Lane 1 was loaded with Mtb-infected cells from all 3 B6 and 3 Sp140−/− lungs. Lane 2 was loaded with bystander myeloid cells from the 3 infected B6 lungs as well as the myeloid cell mixture from the 2 naïve B6 lungs. Lane 3 was loaded with bystander myeloid cells from the 3 infected Sp140−/− lungs as well as the myeloid cell mixture from the 2 naïve Sp140−/− lungs. All 3 lanes of the Chromium Next GEM Chip were super-loaded with 29000 cells with a target of 14800 single cells per lane, as hashtag barcoding allows for a lower effective multiplet rate due to the ability to identify most of the multiplets (https://satijalab.org/costpercell/). 0.5 U/μL RNaseOUT Recombinant Ribonuclease Inhibitor (Invitrogen) was added to single cell RT master mix during the loading step and 1 μL of ADT and HTO additive primers (0.2 μM stock) were added during the cDNA amplification, as recommended by the CITE-seq and Cell Hashing Protocol (https://cite-seq.com/protocols/). Following cDNA, ADT, and HTO purification, samples were decontaminated by 2 rounds of centrifugation through 0.2 μM filter microcentrifuge tubes and then removed from the BSL3. Library preparations were completed outside of the BSL3 following the 10X Genomics protocol for the cDNA and the CITE-seq and Cell Hashing Protocol for the ADT and HTO libraries. Quality control of the libraries was performed with a Fragment Analyzer (Agilent). The mRNA, ADT, and HTO libraries were pooled at the following proportions: 85% mRNA, 9% ADT, and 6% HTO. Libraries were sequenced on a NovaSeq 6000 (Illumina) using two lanes of a S1 flow cell and the following cycles read 1 (28 cycles), i7 index (10 cycles), i5 index (10 cycles), read 2 (90 cycles).

47Raw sequencing reads for the mRNA libraries were processed into raw count matrices with CellRanger version 4.0.0 (10X Genomics). The ADT and HTO libraries were processed into raw count matrices with CITE-Seq-Count version 1.4.3 (https://hoohm.github.io/CITE-seq-Count/). The raw counts for mRNA, ADT, and HTO were analyzed in R via the RStudio integrated development environment with Seurat v4.1.1 using default settings for normalizing the data, finding variable features, and scaling the data. HTO demultiplexing was performed with the HTODemux function. Data was filtered to only include single cells with between 200 and 4500 genes and less than 5% mitochondrial reads. The resulting datasets were integrated together using 30 dimensions for the FindIntegrationAnchors function and 30 dimensions for the IntegrateData function. The data was then scaled and analyzed by PCA with 30 principal components followed by UMAP analysis with 30 dimensions. Clustering was performed by using 30 dimensions with the FindNeighbors function and a resolution of 0.8 for the FindClusters function.

48To improve resolution for clustering innate immune cells, weighted nearest neighbor analysis was used to combine the protein data (ADTs) and the mRNA data when clustering cells. For this analysis, variable ADT features were identified and then normalized using centered log ratio transformation and a margin of 2. The normalized ADT data was then scaled and analyzed by PCA. The ADT and mRNA data was then combined with the FindMultiModalNeighbors function using 30 dimensions for the mRNA and 10 for the protein. The resulting dataset was analyzed by UMAP and clusters were identified with the FindClusters function using algorithm 3 and a resolution of 1.5. Tidyverse, EnhancedVolcano, and various Seurat functions were used for plotting the scRNA-seq data.

49For generating the type I IFN and IFNγ gene signatures, we utilized a published RNA-seq dataset (GEO: GSE20251) of primary human macrophages that were unstimulated or stimulated with 10 ng/mL of TNF, IFNγ, IFN-β, transforming growth factor-β, or other ligands for 24 hours and then processed for RNA-sequencing. We also generated an RNA-seq dataset mouse bone marrow-derived macrophages left unstimulated or stimulated with 10 ng/mL of TNF, IFNγ, IFN-β, or transforming growth factor-β for 6 hours. For both datasets, counts were normalized by DESeq2’s median of ratios method and biological replicates were averaged to generate average normalized counts for each cytokine condition. The average normalized counts were used to calculate a max to second-max ratio for the 4 cytokine conditions to determine gene specificity. Next, gene expression was compared across all stimulation conditions and filtered to only include genes that were induced relative to the untreated cells by a log2 fold change of 1 by IFNγ or IFN-β and to exclude genes induced by a log2 fold change of 1.5 by TNF or transforming growth factor-β. Using this filtered gene list, a ratio was calculated of the log2 fold change following IFNγ stimulation to the log2 fold change following IFN-β stimulation. For the human dataset, IFN-β specific genes were defined as those having a fold change ratio < 0, a log2 fold change upon IFN-β stimulation > 1, an average normalized count following IFN-β stimulation > 2000 and a max to second-max ratio > 2.5. Human IFNγ specific genes were defined as those with a fold change ratio > 1.5, a log2 fold change upon IFNγ stimulation > 2, an average normalized count following IFNγ stimulation > 1000 and a max to second-max ratio > 2.5. Mouse macrophage IFN-β specific genes were defined as those with a fold change ratio < 0.66, a log2 fold change upon IFN-β stimulation > 4, an average normalized count following IFN-β stimulation > 4000 and a max to second-max ratio > 3. Mouse IFNγ specific genes were defined as those that had a fold change ratio > 1.5, a log2 fold change upon IFNγ stimulation > 2, an average normalized count following IFNγ stimulation > 500 and a max to second-max ratio > 3.

50Human IFN-β and IFNγ gene signatures were validated by examining their induction following human macrophage stimulation with a variety of cytokines, such as IL-4, IL-6 and IL-10, using the published RNA-seq dataset (GEO: GSE20251). The specificity of the mouse macrophage IFNγ signature was validated by stimulating bone marrow-derived macrophages for >16 hours with 10 ng/mL of IFNγ, IFN-β, or nothing and then examining CXCL9 expression by flow cytometric analysis of intracellular staining with PE-labeled anti-mouse CXCL9 antibody (MIG-2F5.5; BioLegend). The mouse gene signatures were then used to score cells in the mouse myeloid scRNA-seq dataset based on their gene expression with the UCell R package.

51Group sizes were informed by the results of preliminary experiments and power calculations. The number of animals in each figure is indicated in the legends as n = x mice per group. Statistical significance was determined using Prism (GraphPad) software for unpaired one-tailed or two-tailed Student t test when comparing two populations, one-way or two-way ANOVA tests with Tukey’s or Sidak’s multiple comparisons test when comparing multiple groups. Prism (GraphPad) was also used to calculate linear correlations and R2. R was used to calculate statistical significance for bulk RNA- and scRNA-sequencing datasets using the Wald test with multiple testing correction by the Benjamini and Hochberg method and the Wilcoxon Rank-Sum test with Bonferroni correction, respectively. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, n.s. = not significant. See figure legends for more information on statistical tests.