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Title Dissecting the cellular specificity of smoking effects and reconstructing lineages in the human airway epithelium

Authors/Affiliations Katherine C. Goldfarbmuren1#, Nathan D. Jackson1#, Satria P. Sajuthi1, Nathan Dyjack1, Katie S. Li1, Cydney L. Rios1, Elizabeth G. Plender1, Michael T. Montgomery1, Jamie L. Everman1, Eszter K. Vladar3,4, Max A. Seibold1,2,3,*

1Center for Genes, Environment, and Health, National Jewish Health, Denver, CO, 80206 USA; 2Department of Pediatrics, National Jewish Health, Denver, CO, 80206 USA; 3Division of Pulmonary Sciences and Critical Care Medicine and 4Department of Cell and Developmental Biology, University of Colorado-AMC, Aurora, CO, 80045 USA, #these authors contributed equally to this work, *correspondence: [email protected]

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Abstract Cigarette smoke first interacts with the lung through the cellularly diverse airway epithelium and goes on to drive development of most chronic lung diseases. Here, through single cell RNA-sequencing analysis of the tracheal epithelium from smokers and nonsmokers, we generated a comprehensive atlas of epithelial cell types and states, connected these into lineages, and defined cell-specific responses to smoking. Our analysis inferred multi-state lineages that develop into surface mucus secretory and ciliated cells and contrasted these to the unique lineage and specialization of submucosal gland (SMG) cells. Our analysis also suggests a lineage relationship between tuft, pulmonary neuroendocrine, and the newly discovered CFTR-rich ionocyte cells. Our smoking analysis found that all cell types, including protected stem and SMG populations, are affected by smoking, through both pan-epithelial smoking response networks and hundreds of cell type-specific response genes, redefining the penetrance and cellular specificity of smoking effects on the human airway epithelium.

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Introduction The human airway epithelium is a complex, cellularly diverse tissue that plays a critical role in respiratory health by facilitating air transport, barrier function, mucociliary clearance, and the regulation of lung immune responses. These airway functions are accomplished through interactions among a functionally diverse set of both common (ciliated, mucus secretory, basal stem) and rare cell types (pulmonary neuroendocrine, tuft, ionocyte) which compose the airway surface epithelium. This remarkable cell diversity derives from the basal airway stem cell by way of multiple branching lineages1,2, yet, the nature of these lineages, their transcriptional regulation and the functional heterogeneity to which they lead, remain incompletely defined in humans. Equally important to airway function, if even more poorly understood on both a molecular and cellular level, is the epithelium of the airway submucosal glands (SMG), a network that is contiguous with the surface epithelium and a critical source of airway mucus and defensive secretions.

Gene expression and histological studies of the airway epithelium have demonstrated that both molecular dysfunction and cellular imbalance due to shifting cell composition in the epithelium are common features of most chronic lung diseases, including asthma3 and chronic obstructive pulmonary disease4 (COPD). This cellular remodeling is largely mediated by interaction of the epithelium with inhaled agents such as air pollution, allergens, and cigarette smoke, which are risk factors for these diseases. Among these exposures, cigarette smoke is the most detrimental and, as the primary driver of COPD5 and a common trigger of asthma exacerbations6, constitutes the leading cause of preventable death in the U.S.7 Smoking is known to induce mucus metaplasia8 and gene expression studies based on bulk RNA-sequencing have established the dramatic influence of this exposure on airway epithelial gene expression9-12. However, these bulk expression changes are a composite of all cell type expression changes, frequency shifts, and emergent metaplastic cell states, making it impossible to determine the precise cellular and molecular changes induced by smoke exposure using this type of expression data.

Here, we use single cell RNA-sequencing (scRNA-seq) to define the transcriptional cell types and states of the tracheal airway epithelium in smokers and nonsmokers, infer the lineage relationship among these cells, and determine the influence of cigarette smoke on individual surface and SMG airway epithelial cell types with single cell resolution.

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Results Smoking induces both shared and unique gene expression responses across diverse airway epithelial cell types

To interrogate the cellular diversity of the human tracheal epithelium, we enzymatically dissociated tracheal specimens from seven donors and subjected these cells to scRNA- seq (Figure 1a). These donors included never-smokers, light smokers and heavy smokers (Supplementary Table S1), allowing us to evaluate the transcriptional effects of smoking habit on each epithelial cell type. Shared nearest neighbor (SNN) clustering of expression profiles from 13,840 epithelial cells identified eight broad cell clusters, each containing the full range of donors and smoking habits (Figure 1b, Supplementary Figure S1ab). Between 200-1500 differentially expressed genes (DEGs) distinguished these clusters from one another (Figure 1c, Supplementary Figure S1c).

Assignment of clusters to cell types or states was accomplished by examining expression of known epithelial cell type markers13,14 (Figure 1c). High KRT5 expression identified two basal cell populations, distinguished by the presence/absence of proliferation markers (e.g. MKI67, Supplementary Figure S1c). Proliferative heterogeneity within KRT5high basal cells was confirmed by immunofluorescence (IF) labeling in tracheal tissue sections (Figure 1d). Expression of the two major airway gel- forming mucins15, MUC5AC and MUC5B, identified the mucus secretory population via fluorescence in situ hybridization (FISH) (Figure 1ce). The ciliated cell population was identified through expression of ciliogenesis and ciliary function markers, including FOXJ116 and DNAH1117 (Figure 1ce). We also identified a cluster characterized by high KRT8 expression. Consistent with KRT8 being a differentiating epithelial cell marker18, KRT8high cells localized to the mid-to-upper epithelium, above KRT5high basal cells and often reaching the airway surface by IF (Figure 1f). Gene expression across KRT8high cells was highly heterogeneous, with a wide range of expression for both basal (KRT5, TP63) and early secretory cell (SCGB1A1, WFDC2) markers. The smallest cluster contained cells expressing markers diagnostic for pulmonary neuroendocrine cells or PNECs19 (CHGA, ASCL1), ionocytes20,21 (FOXI1, CFTR) and tuft cells22 (POU2F3). Although these cells comprised less than 1% of the total epithelium, FISH of tracheal tissue identified cells expressing each of these markers (Figure 1g).

In addition to surface epithelial populations, two clusters highly expressed known glandular genes23 (Figure 1c, Supplementary Figure S1d), suggesting our digest isolated SMG epithelial cells. One of these SMG clusters highly expressed basal

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markers (e.g. KRT5, KRT14), while the other exhibited a mucus secretory cell character, including high MUC5B expression. IF labeling of tracheal tissue using these markers allowed visualization of SMG secretory and basal cells (Figure 1h, Supplementary Figure S1e).

Each identified population contained cells from all three smoking groups (Supplementary Figure S1b) and thus, we investigated the transcriptomic effects of smoking habit in each of the cell populations independently. We first examined smoking effects of genes previously reported to be differentially expressed between current and never-smokers using bulk RNA-seq data from bronchial airway epithelial brushings9. For both the upregulated and downregulated gene lists reported, a significant shift in mean expression between heavy smokers and nonsmokers was observed in six of seven populations that matched the direction of effect in the bulk data (Figure 1i, Supplementary Figure S1f). These effects, however, were not consistently observed in light smokers (Supplementary Figure S1f), leading us to focus further investigation of smoking effects on heavy smoker vs. nonsmoker cells.

Transcriptome-wide single-gene differential expression analysis identified over 150 DEGs between heavy and nonsmokers in each cell population (Supplementary Figure S1g, Supplementary Table S2). Importantly, 7%-87% of the smoking DEGs for each population were unique to that population, revealing a previously unappreciated cell type-specific aspect to the smoking response, discussed below (Figure 1j, Supplementary Figure S1g). Additionally, we identified a “core” response to heavy smoking, encompassing genes consistently up- or downregulated in at least four populations. Among these, MUC5AC was notably upregulated in six of seven (non-rare) cell types, while protein-protein interaction (PPI) network analysis of upregulated core genes revealed a pan-epithelial induction of nine interacting secretion-related genes with heavy smoking (Figure 1k). The upregulated core response also included genes enriched for xenobiotic metabolism and chemokine signaling (Figure 1k), suggesting that known airway responses to smoking, like toxin metabolism and macrophage recruitment10,24, are a joint effort conducted across epithelial cell types. The downregulated core response largely involved deactivation of immune function, such as the complement system, which helps clear microbes and damaged cells, and secretoglobin 1A1 (SCGB1A1) production, important for airway defense25 (Supplementary Figure S1i). Notably, the downregulated core response contained multiple HLA type I and II genes (Supplementary Figure S1i), possibly signaling an

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underappreciated role for antigen presentation in the epithelium, which is suppressed by smoking.

Secretory cells form a continuous lineage that culminates in mucus secretory cells

Airway secretory cells canonically include club cells, which produce SCGB1A1-laden defensive secretions, and mucus secretory cells, which varyingly express the major gel- forming mucins (MUC5AC and MUC5B). Although inflammatory stimuli have been shown to induce conversion of club cells into mucus cells in mice26, the lineage relationship between these cells in the homeostatic human airway is unclear. Moreover, while NOTCH signaling is a likely mediator of secretory cell fate in the differentiating airway27,28, and the transcription factor (TF), SPDEF29,30, specifically drives inflammation-induced mucus metaplasia, little else is known regarding regulation of human secretory cell development. To investigate this area, we reconstructed the human secretory cell lineage using pseudotime trajectory analysis31 of the mucus secretory cells and KRT8high populations, which contained cells with both an intermediate basal/secretory profile and club-like cells. This analysis aligned most cells along a single lineage (Supplementary Figure S2a) in which basal-like cells transitioned into mucus secretory cells through expression of three successive gene modules (Figure 2a). These modules included TFs and signaling molecules that may drive their expression (Figure 2b). The first of these modules (secretory preparation) was highly enriched for genes involved in ATP production and protein translation elongation, likely reflecting necessary preparation for the high energy demands of secretory protein production (Figure 2a). Secretory preparation genes were enriched for NOTCH signaling and included the NOTCH3 receptor, as well as potential novel TF regulators (BTF3, KLF3) (Figure 2ab).

A second module followed that was characteristic of club cells32 including expression of SCGB1A1, WFDC2, and CYP2F1. This module was highly enriched for O-linked glycosylation of mucins and xenobiotic metabolism, and contained airway transmembrane mucin genes33 (MUC1, MUC4, MUC16). In this club secretory phase of pseudotime, an array of known and novel TFs increased expression, eventually reaching a crescendo in the mucus secretory cells, consistent with these cells transitioning into mucus secretory cells. The first of these to appear was the novel cAMP responsive TF, CREB3L1, which was followed by expression of both SPDEF and another novel TF, MAGED2. Expression of SCGB1A1 began later and was coincident

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with expression of XBP1, a TF likely driving the cellular stress response to the initial secretion of secretoglobin and accompanying secreted proteins34,35 (Figure 2b). The secretory cell trajectory terminated with the mucus secretory module, containing both MUC5AC, MUC5B, and the TF, FOXA3, while being highly enriched for genes involved in O-linked glycosylation, vesicle coating, SLC-mediated membrane transport, and unfolded protein response, consistent with these cells actively producing and secreting mucus (Figure 2ab). Together, these data support a single developmental lineage of human secretory cells, driven by sequentially activated TFs, which transitions through functional intermediates (club cells) to culminate in a multi-functional mucus secretory cell.

Heavy smoking drives mucus secretory cells to express a MUC5AC secretory program

We investigated whether transcriptionally functional subsets of mucus secretory cells exist that may carry out the known mucociliary and airway defense responsibilities of these cells. Agnostic subclustering yielded two subpopulations (Supplementary Figure S2b), one of which contained only 15% of secretory cells and was surprisingly distinguished by its expression of many known ciliated cell markers, including critical regulator FOXJ116 (Supplementary Figure S2c). We speculated that this hybrid secretory/ciliated population was undergoing transdifferentiation, discussed later. The larger subpopulation, consisting of mucus secretory cells, exhibited no functionally distinct transcriptome-level subgroups and failed to reflect goblet cell subtypes previously reported36 (Supplementary Figure S2d).

To further explore the heterogeneity in this mucus secretory subpopulation, we inspected the distribution of the canonical secretory genes, SCGB1A1, MUC5AC and MUC5B, and observed high co-expression of all three of genes, with 94% of cells expressing SCGB1A1 and 78% of cells expressing both MUC5AC and MUC5B (Figure 2c). Pervasive co-expression was confirmed in tracheal sections at both the mRNA and protein level (Figures 1e, 2d). These patterns are consistent with the transcriptome-wide homogeneity observed and suggest these genes all reach peak expression in this mucus secretory state.

Considering that MUC5AC was a core smoking response gene, we next examined whether mucin co-expression differed between nonsmokers (NS) and heavy smokers (HS). We found a sharp increase in the frequency of MUC5AC+ only cells (NS=1%,

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HS=10%) with heavy smoking and a corresponding decrease in both MUC5B+ only (NS=17%, HS=5%) and MUC5AC-/MUC5B- double negative cells (NS=8%, HS=2%; Supplementary Figure S2e), consistent with published data showing that MUC5B is more homeostatic and defensive37, whereas MUC5AC is more inducible and characteristic of inflammatory disease states15,30. Moreover, we found little overlap in genes correlated with MUC5AC and MUC5B, suggesting these mucins are associated with distinct functional programs (Figure 2e). For example, MUC5B-specific correlated genes encoded known secretory defense proteins, including C3, CFB, SAA1, SAA2, and LCN2, whereas MUC5AC-specific correlated genes contained a different set of defensive proteins (MSMB, LYZ, TFF1, BPIFB2, CEACAM5) while also being enriched for pro-secretory pathways related to ER-based protein processing and glycosylation (Figure 2e). Notably, among genes uniquely co-expressed with MUC5AC was XBP1, a TF previously implicated in both mucus production and its associated unfolded protein responses34,38. Not only were the two mucins themselves anti-correlated with smoking, but mean expression of MUC5AC- or MUC5B-correlated genes also increased or decreased, respectively, with smoke exposure (Figure 2f). Both IL33, a master regulator of type 2 mucus metaplasia39-41, and NKX3-1 are potential regulators of these smoking- induced changes in secretory cells (Supplementary Figure S2f). Together, these data further support the concept of a continuous secretory cell lineage and show how smoking may mediate an additional transition, from mucin-balanced terminal secretory cells into an extended endpoint where MUC5AC (and its co-expressed program) dominate.

MUC5Bhigh SMG secretory cells shift toward MUC5AC production and away from specialized defensive secretions with heavy smoke exposure

Human airway mucus is formed from the composite of secretions produced by both surface and SMG mucus secretory cells42. We thus compared expression profiles between these two populations to examine similarities and differences in their secretory products and the molecular mechanisms that underlie them.

We identified over 100 DEGs defining mucus secretory cells in both SMG and surface populations, which were enriched for transmembrane transport and mucosal defense (Supplementary Figure S3a). Despite these similarities, an even larger number of genes were uniquely characteristic of one or the other cell type (Figure 3a, Supplementary Figure S3b). The SMG population specifically expressed a highly unique repertoire of secretory proteins with strong enrichment for bacterial defense and innate immunity

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functions (Figure 3a). Furthermore, we found that while both populations highly expressed MUC5B (Figure 3b), expression of MUC5AC in SMG secretory cells was much lower (18.4-fold reduction) and less ubiquitous (SMG=30% vs. surface=84%). Reduced MUC5AC within SMG cells was accompanied by significantly reduced or absent expression of a host of genes involved in ER-to-Golgi vesicle-mediated transport, protein processing in the ER, and both O-linked and N-linked mucin glycosylation (Figure 3a). Distinct panels of TFs in the two groups likely govern these different expression states. For example, CREB3L1 and SPDEF, the canonical secretory cell TF, were most predominant on the surface, while SMG cells uniquely expressed the SMG TF, SOX9, as well as FOXC1 and BARX2, which are known to be involved in lacrimal gland development43-45 (Figure 3a). Together, our data suggest that unique TF drivers in SMG cells result in MUC5B-dominated mucus, which requires considerably less post-translational processing and glycosylation than surface mucus production, and is equipped with specialized defensive functions. This is consistent with recent studies detailing distinct physical properties of mucus from the SMG compared to epithelial surface46,47 .

Examining smoking effects in SMG mucus secretory cells, we found that MUC5AC was induced and MUC5B was suppressed by heavy smoking (Figure 3c), echoing mucin responses on the surface. Heavy smoking also increased levels of inflammatory cytokine interleukin-6 (IL6) uniquely in these cells, which has been implicated in lung disease48 (Supplementary Figure S3c). However, most notable was the unique downregulation of 48 genes in SMG cells with smoking, which were related to multiple functions, including calcium ion binding (S100A6, S100A16) and secretion (e.g., DMBT1, TF, and GNAS) (Supplementary Figure S3c). Heavy smoking thus appears to induce inflammation, shift the balance of mucins, and diminish the diversity of specialized proteins produced by SMG mucus cells, likely compromising barrier and defense functions of the airway.

Human SMG basal cell states include myoepithelial cells and are modified by heavy smoking

Recent work in mice has established the myoepithelial cell population as the SMG stem cell, which can differentiate into luminal cells through a basal cell intermediate. These cells were also shown to regenerate the surface epithelium in settings of severe injury. How these observations translate to the human airway is unclear. Investigating this, we identified a cell population with high glandular gene expression which highly expressed

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KRT14, a marker of murine SMG basal cells2,49,50. Also upregulated in this population were several other genes associated with glandular basal cells, including CAV1, CAV2, IFITM3, ACTN1, and VIM51-53 (Figure 3d, Supplementary Figure 3d).

Subclustering of this group revealed additional heterogeneity, with three major states identified (Supplementary Figure S3defg). The smallest of these expressed over 200 genes related to muscle function absent from the other two subpopulations (Supplementary Figure S3dg), a profile highly similar to murine SMG myoepithelial (ACTA2+) cells54,55. This population was not proliferating (MKI67-) and poorly expressed KRT5, suggesting it represents a quiescent human myoepithelial population. Compared to myoepithelial cells, the other two major SMG basal states exhibited expression more typical of surface basal cells, including high KRT5 expression. One of these KRT5high populations was proliferating (MKI67+) and in fact clustered with surface proliferating basal cells upon epithelium-wide clustering (Figure 1b). The second state was non- proliferative and appeared to be differentiating in that it highly expressed, IL33, a marker of surface differentiating basal cells in our dataset (Figure 3d).

A pseudotime trajectory31 of all (except proliferating) SMG populations proceeded from myoepithelial cells into differentiating basal, and then SMG mucus secretory cells (Figure 3e, Supplementary Figure S3h). Transitioning out of the myoepithelial state involved losing expression of ACTA2 and muscle-related genes while simultaneously gaining expression of basal cell genes (KRT5, IL33). TFs distinctively characteristic of SMG (compared to surface) mucus cells (SOX9, FOXC1, and BARX2) initiated high expression in the differentiating basal population, consistent with this state being the precursor to mucus SMG cells (Figure 3e). IF labeling of tracheal sections further supported these transitions as well as the presence of these populations at the protein level (Figure 3f).

Even these SMG basal cells at the base of glands were affected by prolonged smoking, exhibiting a total of 174 DEGs (Supplementary Figure S3ij). Notably, smoker SMG basal cells uniquely upregulated TSLP, a major driver of type 2 airway inflammation56,57, which we confirmed with IF labeling (Figure 3g), suggesting a potentially unrecognized role for these cells in the onset of chronic inflammatory airway disease.

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Sequential transcriptional programs drive motile ciliogenesis

Upon cell fate acquisition, nascent ciliated cells activate expression of a large ciliary program, precipitating the generation of hundreds of cytoplasmic basal bodies which traffic to and dock with the apical membrane where they then elongate motile axonemes58. As our in vivo scRNA-seq data did not wholly capture the heterogeneity reflective of this progression, we studied the process by culturing basal tracheal epithelial cells from a subset of the donors at air-liquid interface (ALI) and harvesting replicate cultures at 20 timepoints across mucociliary differentiation for scRNA-seq sequencing analysis (Supplementary Figure S4abc). Clustering of 5,976 cells yielded three in vitro populations distinguished by their high expression of ciliary genes (Figure 4a, Supplementary Figure S5a). Trajectory reconstruction59 identified two major lineages, one of which transitioned from basal through early secretory cells, culminating in the three ciliated cell populations (Supplementary Figure S4d), whose ordering matched the real-time appearance of states across ALI differentiation (Supplementary Figure S5b). The first state to appear was highly enriched for genes involved in basal body assembly60,61 (DEUP1, STIL, PLK4) (Figure 4a) and also contained known early transcriptional drivers of ciliogenesis62-64 (MCIDAS, MYB, and TP73) (Figure 4b). We also found that TF, E2F7, was highly expressed in this state. Since E2F4 and E2F5 act at the top of the ciliogenesis program62, other family members may also be involved in this initial early ciliating stage.

The two subsequent states were both highly enriched for mature ciliated cell genes, but the first of these in pseudotime was distinguished by the presence of basal body docking (CEP290, TTBK2)65 and axoneme assembly (IFT52) genes (Figure 4a), as well as peak expression of known ciliogenesis TFs (GRHL2, RFX2 and RFX3) 17,66,67. These TFs were downregulated in the third and final state, which displayed the highest expression of another canonical ciliogenesis and ciliary maintenance TF, FOXJ116 (Figure 4b). This state also showed high expression of mitochondrial formation and ATP synthesis genes, consistent with the significant energy requirements of axonemal motility68 (Supplementary Figure S5c). Finally, 233 known ciliary genes displayed higher unspliced-to-spliced ratios69 in the second compared to the third state, while only 10 genes showed the inverse pattern (Supplementary Figure S5de), supporting the trajectory’s ordering of states and illustrating a putative role of mRNA processing during the final completion of ciliogenesis.

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Interestingly, forkhead box N4 (FOXN4), a known regulator of ciliogenesis in Xenopus70, was highly and nearly exclusively expressed in the early state, and may thus be a novel driver of this population. Consistent with its early expression, we detected nuclear FOXN4 at ALI Day 9 but no signal in mature ciliated cells at ALI Day 21 (Figure 4c). CRISPR-Cas9 knockout (KO) of FOXN4 carried out in basal cells resulted in a partially penetrant block to ciliogenesis upon differentiation. At Day 21, 76% of ciliated cells had no or only short, sparse cilia compared to only 2% in the control (Figure 4d, left; Supplementary Figure S5f). The abnormal KO cells retained basal bodies and deuterosomes60 in the cytoplasm (Figure 4d, right), indicating that the basal body generation machinery was intact, but basal body docking and deuterosome disassembly was blocked. Thus, our data are consistent with FOXN4 regulating this later step in early ciliogenesis.

In vivo, most ciliated cells were mature and the only cluster resembling the early ciliating state (Figure 4e, top), including high FOXN4 expression, was the hybrid secretory/ciliated cell population that subclustered out of mature mucus secretory cells (Supplementary Figure S2cd). These hybrid cells expressed SPDEF, MUC5AC, and mature mucus secretory genes (Figure 4e, bottom), in contrast to the non-mucus producing early secretory cells that gave rise to the FOXN4+ early ciliating state in vitro (Supplementary Figure S4d). Together these data suggest that during de novo epithelization, ciliated cells derive from early secretory cells, but in the homeostatic airway, mature mucus cells transdifferentiate into ciliated cells, possibly in response to stimulus.

Mature ciliated cells exhibited 42 genes uniquely upregulated in heavy smokers which included the TF, XBP1, and genes involved in ER processing and unfolded protein and heat shock responses (Figure 4f). As heat shock family chaperonins were recently shown to be required for axonemal protein complex assembly71, prolonged smoking may enhance the ciliated cell-specific protein-folding program to counteract smoking- related protein damage and misfolding.

Smoking leads to decreased ciliary function and ciliated cell loss72-75, yet we found that genes downregulated by heavy smoking in mature ciliated cells were not related to ciliogenesis or ciliary function (Supplementary Table S2). However, these genes were strongly downregulated in the hybrid secretory/ciliated cells (Figure 4g), suggesting that the ciliogenesis program in this hybrid population is uniquely vulnerable to prolonged

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smoking. Thus, smoking may hinder the regeneration of ciliated cells rather than impairing their function once fully developed.

Decoupled FOXI1 and CFTR expression and a potential lineage relationship among rare epithelial cell types

Subclustering of the rare cell population identified three distinct groups, each expressing canonical markers of highly disease-relevant epithelial cell types: PNECs19 (CALCA), tuft cells22,76,77 (POU2F3), or ionocytes21,36,78 (CFTR) (Figure 5a). We transcriptionally defined the function of these cells in humans using differential expression analyses, which confirmed highly enriched expression of Achaete-Scute family BHLH TFs, ASCL1, ASCL2 or ASCL3, in PNECs, tuft cells or ionocytes, respectively36,76,79 (Figure 5b, Supplementary Figure S6a). These data confirm that ionocytes populate the human tracheal epithelium and highly express CFTR, as recently recognized36,78. On a per cell basis, ionocyte CFTR expression was between 17- and 467-fold higher than in other cell types, yet low frequency of these cells (average 0.2%) means only 11% of the total CFTR expressed by the epithelium was derived from ionocytes, whereas other more abundant cells contribute more, such as the KRT8high population which supplied 56% of epithelial CFTR (Figure 5c). Exploring whether this result was due to a scRNA-seq sampling bias, we examined bulk RNA-seq data from our ALI differentiation time course, finding that CFTR expression began and peaked much earlier than ionocyte marker genes (Figure 5d), further supporting a significant CFTR contribution from other epithelial cell types.

The expression signature in our human PNECs revealed neurotransmitter processing pathway genes employed by these cells as well as a host of secreted neurotransmitters (Figure 5b). Despite characteristic POU2F3 and ASCL2 expression in our human tuft cell population, expression signatures in these cells were distinct from those reported in murine tuft cells36 (Figure 5b, Supplementary Figure S6a). For example, many diagnostic markers in mice (GNAT3, TRPM5, GNG13, HMX2, etc.) were not well- represented in our human scRNA-seq dataset, while other murine markers were present (HOXA5, HCK, and LRMP). Therefore, we classified our POU2F3+/ASCL2+ cells as “tuft-like” to signal the uniqueness of their transcriptional profile compared to previously described tuft cells.

Along with murine lineage tracing results36, the appearance of all these populations in ALI cultures (Supplementary Figure S6bcd) demonstrates that rare cells all ultimately

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derive from basal airway epithelial cells. Yet, little is known about the paths a basal cell takes to differentiate into these cell types. That these three rare populations clustered together when the entire epithelium was analyzed (in vitro and in vivo, and by others78) potentially indicates a shared origin as well as phenotype. Supporting this, differential expression analysis identified 67 genes highly expressed across only these three populations, as well as hundreds of genes uniquely shared between pairs of rare cell types (Supplementary Table S3). Interestingly, HES6 was one of the 67 genes uniting rare cells, suggesting a possible role for NOTCH competition80 during fate determination of these cell types, and also potentially reflecting a shared neuronal character81 (Supplementary Figure S6e).

Most intriguing of the rare cell pair relationships was that observed between ionocytes and tuft-like cells, which uniquely shared expression of 114 genes, including the reported ionocyte TF, ASCL336,78 (Supplementary Table S3). Moreover, we found that ionocyte marker, FOXI120, whose expression has been reported to be sufficient to produce CFTRhigh ionocytes36,78, was expressed by roughly half of POU2F3+ tuft-like cells (Figure 5e). Despite FOXI1 expression levels comparable to ionocytes, these tuft- like cells lacked detectable CFTR expression. Confirming this, FOXI1 was present by FISH in nearly all CFTRhigh cells and about half of POU2F3+ cells (Figure 5f). Quantifying the FISH data, on average 48% of FOXI1+ cells exhibited an ionocyte expression pattern (CFTR+/POU2F3-), while 38% of FOXI1+ cells exhibited a tuft-like pattern (CFTR-/POU2F3+) (Figure 5g, Supplementary Figure S6f). Consistent with this, bulk RNA-seq data from the ALI differentiation time course shows that the tuft-like expression signature begins and peaks early in differentiation, whereas signatures of both ionocytes and PNECs appear much later, continuing to increase after much of the epithelium has matured (Day 21) (Figure 5h). Tuft-like cell expression was also most similar to that in basal cells (Supplementary Figure S6g), further supporting the possibility that they could serve as precursor cells. Together, these data suggest that tuft-like cells may be a precursor to ionocytes, and possibly PNECs.

We observed fewer tuft-like cells and more ionocytes in heavy smokers (Supplementary Figure S6h), suggesting that smoking may alter fate choice in favor of ionocytes over tuft-like cells and lending further evidence for a possible lineal relationship among these populations. In ionocytes, we observed 307 genes downregulated in heavy smokers, which included many genes highly specific to this cell type (Supplementary Figure S6ij), suggesting that ionocytes present in smokers, while not decreasing in number, may exhibit compromised function.

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Discussion In this study, we have generated an agnostic atlas of the human in vivo tracheal airway epithelium, identifying and characterizing cell types, cell states, and lineage relationships among them. As such our study expands on the mouse in vivo and human in vitro airway epithelial atlases published recently36,78, and we also provide a much more densely sampled in vitro time course of human airway epithelial differentiation. Our data reveal that during both in vitro differentiation and in vivo homeostasis, ciliated cells derive from a secretory progenitor through multiple, discrete, transcriptional states, regulated by a suite of TFs that include FOXN4, which we identify as a novel regulator of the earliest ciliating state. Similarly, we show that the heterogeneity in secretory cells (club, mucus secretory cells expressing one or both of MUC5B and MUC5AC) is likely all part of a continuous secretory lineage that culminates in a multi-mucin producing mucus secretory cell.

Our atlas also produces the first transcriptional picture of human airway SMG cells, allowing us to identify a human equivalent to the recently described murine myoepithelial stem cell54,55. Our analysis suggests this human counterpart also exhibits stem function, as it silences its muscle expression program to assume both surface basal (KRT5, TP63) and unique glandular expression (SOX9), as well as engage in proliferation. This basal cell state can then differentiate into a mucus secretory cell, as orchestrated by TFs distinct from those involved in surface mucus secretory cell differentiation. The uniqueness of this program produces a vastly different secretory cell, with distinct mucin expression and processing and a specialized repertoire of protein secretions. It remains unclear whether these SMG stem cells can repopulate the surface epithelium in humans as in mice54,55. We confirm that the homeostatic human airway epithelium does contain ionocytes and that they highly express CFTR. However, the large proportion of CFTR expression deriving from other epithelial cell types and our observation of FOXI1/CFTR decoupling, cautions against the simple FOXI1 -> CFTR -> cystic fibrosis model. Lastly, our data suggest an unrecognized lineage relationship between at least tuft cells and ionocytes, if not also PNECs, which may relate to recently reported tuft-like variants of small cell lung cancer, generally thought to be a PNEC-derived tumor76.

Importantly, we use scRNA-seq to deconstruct smoking effects on the epithelium to the cell type level, which we can then reassemble into a comprehensive model of how smoking modifies epithelial function as a whole (Figure 6). To summarize, pan-epithelial

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effects of smoking reach the basal stem cells and include induction of chemokine signaling and xenobiotic metabolism at the expense of antigen presentation and innate immune signaling. Surface and SMG secretory cells shift their mucin programs toward a MUC5AC-dominated inflammatory state while SMG secretory cells lose many of their distinctive defensive secretions and SMG basal cells upregulate the type 2 inflammatory cytokine, TSLP. Early ciliating cells preferentially lose ciliary function, potentially hindering regeneration of ciliated cells upon injury, and tuft-like cells are being depleted in conjunction with an increase in functionally-impaired ionocytes. Taken together, these data paint a smoker epithelium that has been rendered more functionally monochromatic, carrying out a MUC5AC inflammatory program at the expense of performing its normal defensive, interactive and reparative roles essential to lung health and homeostasis.

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Acknowledgements This work was supported by the National Jewish Health Regenerative Medicine and Genome Editing Program (REGEN) and NIH grants R01 HL135156, R01 MD010443, R01 HL128439, P01HL132821, P01 HL107202. The authors would like to thank Dr. HongWei Chu, Dr. Reem Al Mubarak and Nicole Pavelka in the NJH Live Cell Core, as well as Dr. Carolyn Morris, Dr. Yingchun Li, Ari Stoner and Dr. Meghan Cromie in the Seibold Lab and Dave Heinz, Katrina Diener and Todd Woessner for assistance with tissue processing, sequencing and useful discussion.

Author contributions Conceptualization, K.C.G. and M.A.S.; Methodology, K.C.G., N.D.J., S.P.S. and M.A.S.; Software, N.D.J., S.P.S., N.D. and K.S.L.; Validation, K.C.G., N.D.J., E.K.V. and M.A.S.; Formal Analysis, N.D.J, S.P.S., N.D., E.G.P. and K.S.L.; Investigation, K.C.G., C.L.R., M.T.M., J.L.E. and E.K.V.; Resources, K.C.G., C.L.R., J.L.E., E.K.V. and M.A.S.; Writing – Original Draft, K.C.G., N.D.J. and M.A.S.; Writing – Review & Editing, K.C.G., N.D.J., E.K.V. and M.A.S.; Visualization, K.C.G., N.D.J., S.P.S., N.D., K.S.L., E.G.P., E.K.V. and M.A.S.; Supervision, K.C.G., N.D.J., E.K.V. and M.A.S.; Funding Acquisition, M.A.S.

Declaration of interests The authors have no competing interests.

26 bioRxiv preprint doi: https://doi.org/10.1101/612747; this version posted April 17, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Figure legends Figure 1: Shared and cell type-specific gene expression responses to smoking across diverse airway epithelial cell types a. Schematic for studying human tracheal epithelium by scRNA-seq. b. tSNE visualization of cells in the human trachea depicts unsupervised clusters defining broad cell types present. c. Dot plots highlight known and novel markers distinguishing broad cell categories based on average expression level (color) and ubiquity (size). Colored bar corresponds to the cell types/states in b. d. Immunofluorescence (IF) labeling of human tracheal sections shows MKI67 (red) and TP63 (green) in a subset of KRT5high (white) basal cells. Dotted line denotes the approximate apical surface of epithelium. Scale bar = 25 μm. DAPI labeling of nuclei (blue). e. Fluorescence in situ hybridization (FISH) co-localizes MUC5B (white) and MUC5AC (red) mRNA to non-ciliated cells. FOXJ1 (ciliated marker, green). f. IF labeling localizes KRT8 (green) to mid-upper epithelium, MUC5AC (red), KRT5 (white). g. FISH distinguishes rare cell mRNA markers: Left, PNECs (CHGA, white) and ionocytes (CFTR, red); Right, ionocytes and tuft-like cells (POU2F3, green). h. IF labeling localizes MUC5B (white) and KRT14 (green) to distinct cells in the SMGs. i. Published bulk RNA-seq upregulated smoking genes9 are upregulated in most heavy smoker cell populations. Box plots show distributions of mean normalized expression, p-values are based on one-sided t-tests comparing means for heavy and non-smokers. Rare cells were excluded due to small cell numbers. Box plots for downregulated genes from the same published study are in Supplementary Figure S1f. j. Venn diagram summarizes core and unique smoking responses across seven broad cell populations (colors match those in b), with number of up and downregulated genes unique to populations given in the tips and number of core genes affected in ≥ four populations in the center. Note degree of overlap in the diagram is not proportional to gene overlap for readability. Detailed percentages in Supplementary Figure S1g. k. Protein-protein interaction (PPI) networks show shared function across core smoking upregulated genes. Redness indicates number of cell populations where a gene was significantly upregulated (FDR < 0.05). See also Supplementary Figure S1, Supplementary Tables S1-S2.

27 bioRxiv preprint doi: https://doi.org/10.1101/612747; this version posted April 17, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Figure 2: In vivo secretory cells form a continuous lineage and exhibit MUC5AC- correlated smoking effects a. Heat map of smoothed expression across a Monocle-inferred lineage trajectory shows transitions in transcriptional programs that underlie differentiation in the in vivo human airway epithelium, from basal-like pre-secretory (KRT8high) cells into mucus secretory cells. Select genes that represent these programs are shown, all significantly correlated with pseudotime. Key enrichment pathways and genes belonging to each block are indicated at right. Subcluster colors are the same as those in the pseudotime trajectory shown in Supplementary Figure S2a. b. Scaled, smoothed expression of select transcriptional regulators (colored lines) and canonical markers (black dashed/solid lines) across pseudotime differentiation of human tracheal secretory cells in vivo. The x-axis corresponds to the x-axis in a. c. Pie chart depicts proportions of all mucus secretory cells exhibiting different MUC5AC and MUC5B mucin co-expression profiles. d. Co-expression of common secretory markers at the mRNA level (Left, FISH with SCGB1A1 in red, MUC5B in green) and protein level (Right, IF labeling with MUC5AC in red, MUC5B in green and KRT5 in white). Scale bar 25 μm. e. Smoking-independent correlation coefficients of MUC5B-correlated and MUC5AC-correlated genes. Genes are colored based on whether they were significantly correlated with only MUC5B (green), only MUC5AC (blue), or both (orange). Select genes are labeled. f. Box plots illustrate the converse effects of smoking on the mean expression of MUC5B and MUC5AC-correlated genes. P-values are from one-sided Wilcoxon tests. See also Supplementary Figure S2.

Figure 3: SMG mucus secretory cells are predicted to derive from a myoepithelial cell and exhibit specialized functions which are modified by smoking a. Heat map depicts select genes, functional terms and TFs that distinguish surface and SMG secretory cells. Detailed heat map in Supplementary Figure S3a. b. Box plots of normalized mucin expression across surface secretory (orange), SMG secretory (brown) and non-secretory cells (grey). Median fold change between surface and SMG secretory cells is indicated. c. Top, Pie chart depicts proportions of SMG secretory cells exhibiting different MUC5AC and MUC5B mucin co-expression profiles. Bottom, bar plot showing

28 bioRxiv preprint doi: https://doi.org/10.1101/612747; this version posted April 17, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

how proportions of cells belonging to each mucin co-expression class differ between nonsmokers (black; n = 3) and heavy smokers (red; n = 2). d. Dot plot showing the expression of markers that unite and distinguish SMG basal cell substates, relative to surface populations and each other. e. Scaled, smoothed expression of key genes and regulators across a pseudotime trajectory that models the differentiation process of SMG cells. MEC, myoepithelial cells. A minimum spanning tree of the trajectory can be found in Supplementary Figure S3h. f. IF labeling illustrates myoepithelial cells (ACTA2+, green) transitioning to SMG basal cells (KRT5+, red). Example myoepithelial (green arrows), SMG basal (red arrow) and transitioning (yellow arrow) cells are highlighted. DMBT1 is in white and scale bar is 50 μm. g. IF labeling illustrates presence of TSLP (red) in SMG basal cells of heavy smokers. ACTA2, green; MUC5B, white. Scale bar, 25 μm. See also Supplementary Figure S3.

Figure 4: Sequential transcriptional programs drive motile ciliogenesis and smoking inhibits the early ciliating cell state a. Heat map depicts gene signatures of three ciliated cell states (function summarized in schematic above) in human airway epithelial ALI cultures sampled across differentiation. Select genes from each state are indicated. b. Dot plots reveal TFs exhibiting expression associated with ciliated states in vitro. c. Wholemount IF labeling of FOXN4 knockout in human tracheal epithelial ALI cultures is consistent with early expression of FOXN4 (green). Acetylated α- Tubulin (ACT), red, identifies immature (white arrows) and mature ciliated cells (yellow arrows) in control cultures at ALI timepoints indicated. Scale bar = 25 μm. d. Left, Quantification of mature and immature ciliated cells on Day 21 as determined by ACT labeling morphology (see Methods). Right, Wholemount IF labeling of FOXN4 knockout illustrates aberrant ciliogenesis where basal bodies are generated (γ-Tubulin, green), but fail to dock (white arrows) and deuterosomes are assembled (DEUP1, red), but retained (yellow arrows). Scale bar = 25 μm. e. Average expression of markers from two in vitro ciliogenesis states (top and middle) and in vivo mature mucus secretory cells (bottom) reveals that the hybrid secretory/ciliated state contains both early ciliating and mature secretory character. Marker expression for the later ciliating in vitro state in Supplementary Figure S5e.

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f. Left, box plots summarize expression of genes uniquely upregulated in mature ciliated cells with heavy smoking, Right, functional gene network (FGN) of non- core upregulated genes in mature ciliated cells. Colored edges indicate shared enrichment annotations between genes that belong to functional categories summarized by the exemplar terms listed, grey edges indicate shared annotations across different functional categories. Edge thickness corresponds to the number of shared terms. Genes in bold are those uniquely upregulated in mature ciliated cells. g. Smoking downregulates ciliogenesis in hybrid secretory/ciliating cells but not mature ciliated cells. Left, box plots summarize expression of genes uniquely downregulated in hybrid ciliating cells, Right, FGN summarizes functional relatedness among these genes and is as described for f except that genes in bold are those annotated as cilia-related by CiliaCarta82. See Supplementary Figures S4-S5.

Figure 5: Rare cell types are highly related and CFTR and FOXI1 expression is decoupled in the human airway epithelium a. Left, tSNE depicts subclustering of rare cells found in the human tracheal epithelium, Right, violin plots show expression of rare cell markers identify the three subclusters. b. Heat map of unique gene signatures across rare cell types. Select genes in each block are indicated at left and select gene ontology terms enriched by the genes in each block are indicated at right. c. Left, Level of CFTR expression is shown across the tSNE plot of cells in the in vivo human tracheal epithelium. Grey points indicate cells with zero expression. Rare cell cluster is circled. Right, Table details the distribution of CFTR UMIs across major cell populations in the in vivo human tracheal epithelium. d. Geometric mean of scaled bulk RNA-seq expression for in vivo marker genes of non-rare cells, ionocytes, and CFTR across samples from 20 timepoints of human epithelial ALI differentiation indicate that bulk CFTR appears days before ionocytes in culture. e. Violin plots show that FOXI1 is expressed in both tuft-like cells and ionocytes in vivo. Point color indicates co-expression of FOXI1 with CFTR (red), POU2F3 (green) or neither (white). f. FISH of FOXI1 (white), CFTR (red) and POU2F3 (green) illustrates overlap of FOXI1 in ionocytes (FOXI1+/CFTR+, pink arrows) and tuft-like cells (FOXI1+/POU2F3+, yellow arrows) in the human tracheal epithelium in vivo.

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Green arrow, FOXI1-/POU2F3+ tuft-like cell. Representative images from more than 8.4 cm of basolateral membrane across four donors are shown. g. Average co-expression quantification of FISH for the three markers in f across 561 total ionocytes and tuft-like cells imaged in 4 donors. Number of cells is indicated in each pie. Break down of each donor’s profile can be found in Supplementary Figure S6f. h. Tuft-like cell markers appear days before ionocyte or PNEC markers in vitro. The geometric mean of scaled bulk RNA-seq expression from the top 25 in vivo markers for each rare cell type are shown. See also Supplementary Figure S6.

Figure 6: Whole epithelium smoking responses reconstructed from cell specific scRNA-seq analyses a. Functional Gene Network (FGN) based on all genes upregulated with heavy smoking shows how genes that respond to smoking in distinct cell types of the airway epithelium may collaborate in carrying out dysregulated function. Node (i.e. gene) colors in Node Key refer to the cell type in which a gene was differentially expressed if “unique”; nodes for “semi-unique” and “core” DEGs are white and black, respectively. Edges connect genes annotated for the same enriched term. Exemplar enriched functions are given next to each functional metagroup (or category), which are indicated by the underlay colors that encompass all genes annotated only for the terms within the metagroup. Nodes without colored underlay represent genes in multiple metagroups. Other properties of the network, including node size, connecting edge thickness, and label size/redness are defined in the Network key. b. FGN as in a, but for all genes downregulated with heavy smoking in the airway epithelium. Legend serves for both a and b. c. Schematic summarizes the smoking response of the whole epithelium.

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Methods MATERIALS AND CORRESPONDENCE Further information and requests for resources and reagents should be directed to and will be fulfilled by Max A. Seibold ([email protected])

EXPERIMENTAL METHODS Key Resources All reagents and resources referred to below are summarized with vendors and identifiers in Supplementary Table S4.

Human trachea samples Human tracheal airway epithelia were isolated from de-identified donors whose lungs were not suitable for transplantation. Lung specimens were obtained from the International Institute for the Advancement of Medicine (Edison, NJ) and the Donor Alliance of Colorado. The National Jewish Health Institutional Review Board (IRB) approved the research under IRB protocols HS-3209 and HS-2240. Smokers with at least 15 pack years were classified as “heavy”, while smokers with fewer than 5 pack years were classified as “light”. See table in Supplementary Table S1 for donor details.

In vivo harvest for scRNA-seq (10X Genomics) Human tracheas were wet in Stock solution (DMEM-F + 1X PSA) and fat and connective tissue were removed, before cutting into small sections. Sections were rinsed in Stock solution to remove mucus before proteolytic digest (0.2% Protease in Stock solution) overnight at 4°C, with rocking. Protease was neutralized with FBS, the supernatant was saved (tube 1), and tracheal sections washed (5 mM HEPES, 5 mM EDTA, 150 mM NaCl) for 20 min at 37°C. The supernatant was also saved (tube 2) and the loosened epithelium was then manually scraped off into stock solution with 10% FBS (tube 3), and all cells were collected by centrifugation for 10 min at 1000 rpm, 4°C (tubes 1, 2, and 3). Cell pellets resuspended in BEGM+0.5X PSA were filtered using a 70um cell strainer, collected by centrifugation (5 min, 1000 rpm, 4°C) and cryopreserved in freeze media (F-media, 30% FBS, 10% DMSO). On the day of capture, cells were quick thawed, washed twice in 1X PBS/BSA (0.04%) and resuspended at 1200 cells/μL for capture on the 10X Genomics platform.

Primary cell culture Primary human basal airway epithelial cells from tracheal digests were expanded at 37°C on NIH 3T3 fibroblast feeders in F-media (67.5% DMEM-F, 25% Ham’s F-12,

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7.5% FBS, 1.5 mM L-glutamine, 25 ng/mL hydrocortisone, 12 5ng/mL EGF, 8.6 ng/mL cholera toxin, 24 μg/mL Adenine, 0.1% insulin, 75 U/mL pen/strep) with ROCK1 Inhibitor (RI, 16 μg/mL) and antibiotics (1.25 μg/mL amphotericin B, 2 μg/mL fluconazole, 50 μg/mL gentamicin)83 and cryopreserved in freeze media upon initial passaging (P1).

In vitro ALI culture Tracheal cells (P1) were expanded a second time on feeders in F-media/RI and harvested by differential trypsinization with FBS neutralization. After washing in 1X PBS, cells were resuspended in 1X HBSS and subjected to DNase digest for 5 min at 37°C. DNase was diluted 2-fold with HBSS, cells were centrifuged 1000 rpm, 5 min, 4°C and seeded onto bovine collagen-coated 6.5mm transwell inserts (2 x104 cells/insert) in ALI Expansion medium (50% BEBM, 50% DMEM-C, 0.5 mg/mL BSA, 80 μM ethanolamine, 10 ng/mL hEGF, 0.4 μM MgSO4, 0.3 μM MgCl2, 1 μM CaCl2, 30 ng/mL retinoic acid, 0.8X insulin*, 0.5X transferrin*, 1X hydrocortisone*, 1X epinephrine*, 1X bovine pituitary extract*, 1X gentamicin/amphotericin*, *relative to BEGM Bullet Kit aliquot) with RI (Day -5). RI was removed after 24 h, and ALI expansion medium was changed 48 hlater (Day -2). After another 48 h(Day 0), apical medium was removed and basolateral medium was replaced with PneumaCult (PC)-ALI media. Basolateral medium was exchanged for fresh PC-ALI every 48 or 72 h for the subsequent 11 days, and then daily for the following 22 days along with an apical wash of 20 μL PC-ALI.

In vitro harvest for scRNA-seq (WaferGen) For each timepoint, medium was removed from endpoint ALI cultures, and the apical chamber was washed with warm PBS/DTT (10mM) for 5 min at 37°C, followed by a warm PBS wash of both chambers. Cultures were dislodged from the insert with 200 μL apical dissociation solution (Accutase with 5mM EDTA and 5mM EGTA) for 30 min at 37°C with occasional manual agitation84. Single cell suspensions were diluted, centrifuged, washed once with PBS/DTT and twice with PBS, before cryopreservation (F-media, 40% FBS, 10% DMSO). On the day of capture, cells were quick thawed, washed with 1X PBS (no BSA) and counted before proceeding with WaferGen capture according to the manufacturer’s instructions with the following modifications: 5 or 10 x 104 cells were stained per sample, single cell candidates were confirmed by manual visual triage.

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FOXN4 KO in human tracheal basal cells Two CRISPR RNA (crRNA) guides targeting human FOXN4 annealed with universal tracrRNA and complexed with Alt-R HiFi Cas9 nuclease (1:1.2 duplex:nuclease), were electroporated (3.1uM RNP) into expanded human tracheal basal cells (5 x 105 cells/transfection) with the Amaxa Nucleofector II (pulse code W-001). RNP-containing basal cells were then expanded on rat collagen-coated dishes and seeded onto transwell inserts (1 x 105 cells/insert) in PC-Ex Plus expansion media with RI. RI was removed after 24 h, and 48 h later apical medium was removed and basolateral medium was replaced with PC-ALI medium. Basolateral media was replaced with fresh PC-ALI every 48 or 72 h until harvest for wholemount IF labeling on the day indicated.

Immunofluorescence microscopy Tissue and ALI section histology Adjacent cross-sections of human trachea were fixed in 10% neutral buffered formalin for >48 h at 4°C. ALI cultures were washed with warm 1X PBS (5 min, 37°C), fixed in PFA (1X PBS, 3.2% PFA, 3% sucrose) for 20 min on ice and washed twice with ice cold PBS. Tissue or ALI cultures were cut out of plastic supports, paraffin-embedded and sectioned onto microscope slides. Rehydration was performed with two 3 min washes in HistoChoice, followed by a standard ethanol dilution series, and antigen retrieval in Antigen Unmasking Solution for three 4-min boiling intervals. Slides were then cooled to room temperature on ice and washed three times with TBST (1X TBS, 0.5% TritonX- 100) before blocking in Block buffer (1X TBS, 3% BSA, 0.1% TritonX-100) for 30 min at room temperature. Double or triple primary antibody applications were performed in Block buffer overnight at 4°C with dilutions as follows: KRT5 (1:500), TP63 (1:200), MKI67 (1:100), KRT8 (1:100), MUC5AC (1:500), MUC5B (1:200), KRT14 (1:200), DMBT1 (1:50), SCGB3A1 (1:20), ACTA2 (1:100), TSLP (1:30). Slides were washed three times in TBST before concurrent secondary application (1:500) in Block buffer with DAPI for 30 min and room temperature. Slides were again washed three times in TBST, mounted with ProLong Diamond Mount and imaged on an Echo Revolve R4 microscope.

ALI wholemount ALI cultures inserts were rinsed with 1X PBS, fixed for 15 min in 3.2% PFA (no sucrose) at room temperature or 10 min in methanol at -20°C, rinsed twice more with 1X PBS and stored at 4°C. Membranes were cut out of plastic supports and placed cell-side up on parafilm in a humid chamber. After three brief washes in TBST, cells were blocked in Block buffer for 30 min at room temperature and primary antibodies were applied in

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Block buffer for 2 h at room temperature at the following dilutions: FOXN4 (1:50), Acetlyated α-Tubulin (1:1000), γ-Tubulin (1:500), DEUP1 (1:500). Membranes were washed 3 times in TBST before concurrent secondary application (1:500) with DAPI for 30 min at room temperature. Membranes were again washed three times in TBST, mounted with ProLong Diamond and imaged on a Leica TCS SP8 confocal microscope. Ciliated cells were quantitated based on anti-acetylated α-Tubulin antibody labeling in maximum projections of confocal image stacks of scramble (584) or FOXN4 KO (854) cells. “Mature” ciliated cells display numerous well-formed cilia, and “immature” cells display one of the indicated phenotypes: short, sparse or bulging.

Fluorescence in situ hybridization (RNAScope) Adjacent cross-sections of human trachea or PBS rinsed ALI cultures were fixed in 10% neutral buffered formalin for 24+/- 8 h at room temperature, washed with 1X PBS and paraffin-embedded immediately. Paraffin blocks were sectioned onto SuperFrost Plus slides, dried overnight and baked for 1 h at 60°C before immediately proceeding with the RNAScope Multiplex Fluorescent v2 assay according to the manufacturer’s instructions with the following modifications: Target retrieval was performed for 15 min in a boiling beaker, protease III was used for 30 min pre-treatment at 40°C and hybridized slides were left overnight in 5X SSC before proceeding with the amplification steps. Opal fluors were applied at 1 in 1500 dilution, and slides were image on an Echo Revolve R4 microscope. Ionocytes and tuft-like cells were quantified by presence of grouped FOXI1, POU2F3 and/or CFTR puncta on triple labeled sections. Basolateral membrane length was quantified with the freehand line tool and Measure functions in ImageJ.

AmpliSeq of bulk RNA samples Bulk RNA was extracted with the Quick RNA MicroPrep Kit and 0.5 – 3 x 105 cells were harvested alongside those used for scRNA-seq. Isolated RNA was normalized to 3.5 ng input/sample for automated library preparation with the Ion AmpliSeq Transcriptome Human Gene Expression Kit, using 12 cycles of amplification. Libraries were sequenced with Ion Proton System (ThermoFisher Scientific).

QUANTIFICATION AND STATISTICAL ANALYSIS Pre-processing of in vivo scRNA-seq data Initial pre-processing of the 10X in vivo scRNA-seq data, including demultiplexing, alignment to the hg38 human genome, and UMI-based gene expression quantification, was performed using Cell Ranger (version 2.1, 10X Genomics).

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We next carried out donor-specific filtering of cells to ensure that high quality single cells were used for downstream analysis. Although samples comprising multiple cells were removed during the cell selection stage, we safeguarded against doublets by removing 465 cells with either a gene count over the 99th percentile or a UMI count over the 97th percentile. Furthermore, 223 cells were removed that either exhibited fewer than 1500 genes or fell within the 1st percentile of gene counts and 170 cells were removed that contained more than 30% of mapped reads originating from the mitochondrial genome. Prior to downstream analysis, select mitochondrial and ribosomal genes (genes beginning with MTAT, MT-, MTCO, MTCY, MTERF, MTND, MTRF, MTRN, MRPL, MRPS, RPL, or RPS), or very lowly expressed genes (expressed in < 0.1% of cells) were also removed. The final quality-controlled dataset consisted of 14,324 cells, and 19,339 genes. After initial clustering and visualization, which allowed for identification of 11 major cell populations (see Supplementary Figure S1a), 484 cells were removed that we characterized as non-epithelial (smooth muscle cells, endothelial cells, and immune cells), leaving 13,840 epithelial cells for further analysis.

To account for differences in coverage across cells, UMI counts were normalized for each donor, dividing each count by the sum of its cell’s UMIs, multiplying by 10,000, and then taking the natural log. For input into dimensionality reduction and clustering analyses and for plotting relative expression in heat maps and dot plots, we also fitted normalized expression of each gene to the sum of the UMI counts per cell, and then mean centered (subtracted from each gene count the average expression of that gene) and scaled (divided each centered gene count by that gene’s standard deviation) the residuals. The Seurat R package85 was used to carry out all data normalization and scaling as well as downstream dimensionality reduction, clustering, tSNE plot overlaying, and differential expression.

Pre-processing of in vitro scRNA-seq data We trimmed and culled raw demultiplexed cDNA reads in FASTQ files using Cutadapt86, trimming poly A tails and 5’ and 3’ ends with q < 20 and removing any reads shorter than 25 base pairs. Trimmed reads were then aligned to the hg38 human genome with GSNAP87, setting “max-mismatches=0.05” and accounting for both known Ensembl splice sites and SNPs. Gene expression was quantified using HTSeq88 with “stranded=yes”, “mode=intersection-nonempty”, and “t=gene” and then summed the number of unique molecular identifiers (UMIs) for each gene across runs for each cell to obtain a UMI count matrix used for all downstream analysis.

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We carried out quality-control filtering using a similar approach to that used for the in vivo dataset, removing 100 cells for which the percentage of reads mapping to genes was <50%, 2,262 cells with > 25% of mapped reads being mitochondrial, and 384 cells with UMI counts outside the 3rd and 97th percentiles. We filtered genes using the procedure described above for the in vivo dataset. The final quality-controlled dataset consisted of 5,976 cells, and 23,825 genes.

Pre-processing of in vitro bulk AmpliSeq data Reads sequenced on the Ion Torrent Proton sequencer were mapped to AmpliSeq transcriptome target regions with the torrent mapping alignment program (TMAP) and gene count tables were generated for uniquely mapped reads using the Ion Torrent ampliSeqRNA plugin. After removing duplicated sequences our dataset contained 20,869 genes for 20 sampled time points. We normalized expression based on size factors calculated using DESeq289.

Dimensionality reduction, clustering, and visualization Prior to clustering and visualizing each of the two scRNA-seq datasets (in vitro and in vivo), we reduced the dimensionality of variation in a way that accounted for batch- based shifts in expression among donors. To do this, we used canonical correlation analysis (CCA) to identify the strongest components of gene correlation structure that were shared across donors (using Seurat’s RunMultiCCA function). For the in vivo dataset, this was based on the 5,009 genes contained within the union of the top 8,000 most informative genes from each donor that involved at least five of them, where “informativeness” was defined by gene dispersion (i.e., the log of the ratio of expression variance to its mean) across cells, calculated after accounting for its relationship with mean expression (using the FindVariableGenes function). Correlated expression across donors based on the top 25 CCA dimensions was then projected into a common subspace using Seurat’s AlignSubspace function, allowing us to identify and visualize universal populations of cells, rather than those driven by technical batch or donor- specific variation. We further reduced dimensionality of these 25 subspace-aligned CCA dimensions using the Barnes-Hut implementation of t-distributed neighborhood embedding (tSNE) and then plotted cell coordinates based on the first two dimensions (perplexity = 100). We further carried out unsupervised clustering by first constructing a shared nearest neighbor (SNN) graph based on k-nearest neighbors (k = 30) calculated from the aligned CCA dimensions. The number and composition of clusters were then

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determined using a modularity function optimizer based on the Louvain algorithm (resolution = 0.3).

A similar approach was followed for the in vitro dataset, except that the union of the top 10,000 most informative genes involving two or more of the three donors was used for CCA (9,542 genes total), while SNN clustering (resolution = 0.4, k = 15) based on an SLM optimizer and tSNE visualization (perplexity = 80) were performed using the top 20 subspace-aligned CCA dimensions. Specifications for additional subclustering and visualization performed in the study are summarized in Supplementary Table S5.

Plotting expression across cells For overlaying expression onto the tSNE plot or dot plots for single genes or for the average across a panel of genes, we plotted normalized expression along a continuous color scale. All heat maps showing gene expression across cells (except Figure 2a and Supplementary Figure S3h, which were created using Monocle) were produced using Heatmap390 and also show scaled normalized expression along a continuous color scale, with break scales set as indicated.

Differential expression analysis Differential expression for each gene between various groups specified in the text was tested using a non-parametric Wilcoxon rank sum test carried out with Seurat. We limited each comparison to genes exhibiting both an estimated log fold change > +0.25 and detectable expression in > 10% of cells in one of the two clusters being compared. We corrected for multiple hypothesis testing by calculating FDR-adjusted p-values. Genes were considered to be differentially expressed when FDR < 0.05.

Functional enrichment analysis We tested for gene overrepresentation of all target lists within a panel of annotated gene databases (Gene Ontology [GO] Biological Process [BP] 2017, GO Molecular Function [MF] 2017, GO Cellular Component [CC] 2017, Kyoto Encyclopedia of Genes and Genomes [KEGG] 2016, and Reactome 2016) using hypergeometric tests implemented with Enrichr91, as automated using the python script, EnrichrAPI (https://github.com/russell-stewart/enrichrAPI). We report only terms and pathways that were enriched with FDR < 0.05.

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Identification and plotting of cell type-specific markers To identify cell type-specific markers for both the in vitro and in vivo datasets, we first carried out pairwise differential expression analysis between each of the major clusters, downsampling large clusters to the median cluster size. Markers for each cluster were those genes exhibiting significant upregulation (FDR < 0.05) when compared against all other clusters. Markers for each cluster were sorted by FDR as calculated based on largest p-value observed for each gene across comparisons.

For plotting expression of in vivo rare cell types across differentiating AmpliSeq bulk samples in vitro, we first obtained in vivo cell type markers for each of the three rare cells by isolating genes that were both significantly upregulated in each rare cell type relative to one another (with FDR < 1e-5) and when compared to all non-rare cells (with FDR < 1e-5). We then plotted the geometric mean of log-normalized expression in bulk across the top 25 in vivo markers for each cell type (based on FDRs in the non-rare cell comparisons), after scaling values to be between zero and one (see Figure 5h). We added a small value (0.01) to expression to avoid calculating the geometric mean with zeros. For the expression of non-rare in vivo cluster markers shown in Figure 5d, we took the geometric mean of log-normalized expression across the top 25 markers for each of the main non-rare in vivo cell clusters.

For the marker tSNE overlay plots in Supplementary Figure S4e, for each in vitro and in vivo cluster, we calculated average expression across the top 100 markers (or as many as were available, if fewer than 100) and then used these values to show characteristic expression of select in vitro cell types on the in vivo cells and vice versa. Because the Secretory cell 1 in vitro cluster was transitionary and consequently had only a single marker, we used the top 25 most distinct genes for this population, despite these markers not being significant by FDR < 0.05. Cells on the tSNE plots were identified as being characteristic of a given cell type if marker mean expression for that cell type was at least in the 85th percentile while marker mean expression for all other cell types was below the 95th percentile. To render the in vitro Secretory cell 1 and 2 populations more distinguishable, we reduced stringency for expression of Secretory cell 1 markers to the 75th percentile and increased stringency for expression of Secretory cell 2 markers to the 95th percentile.

Defining core and unique smoking response genes Core smoking response genes were defined as those significantly differentially expressed in heavy smokers compared to nonsmokers in four or more of the main in

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vivo populations. For this analysis, we excluded the rare cell population, which generally contained too few cells to detect significant smoking DEGs, and used a mucus secretory population from which the small subpopulation of hybrid secretory/ciliated cells was removed, as the response in this small population was so distinct. Unique response genes were those that were significantly differentially expressed in only one population while exhibiting either a log fold change < 0.25 and/or an FDR > 0.2 in all other populations. Populations assessed for uniqueness were the two basal populations, KRT8high, ciliated, SMG basal and SMG secretory populations, the hybrid secretory/ciliated subpopulation, surface secretory cells (with the hybrid subpopulation removed), and ionocytes (there were too few of the other two rare cell types to assess smoking response). Genes responding to heavy smoking in a least one cell population but not considered unique or core were defined as semi-unique.

Calculating correlations with MUC5AC and MUC5B To find genes in surface secretory cells (excluding the hybrid secretory/ciliated subpopulation) whose expression was correlated with that of MUC5AC or MUC5B, we used Spearman partial correlation analysis, which calculated gene correlations while controlling for differences in expression due to smoking. Light smoker cells and cells that did not express both MUC5AC and MUC5B were excluded from this analysis.

Lineage trajectories For the in vivo dataset, we constructed lineage trajectories for KRT8high and mucus secretory cell populations combined and for SMG cells (myoepithelial, SMG differentiating basal, and SMG secretory populations) to better understand the genes and processes that regulate and transition across these two lineages. For the KRT8high- to-secretory cell trajectory, we first aligned donors from these two populations using the strategy outlined above and re-clustered them (alignment and clustering specifications are given in Supplementary Table S5), resulting in nine clusters. One of these clusters corresponded to a hybrid secretory/ciliated population, which we removed prior to trajectory construction. Then, using Monocle v2.831, we carried out dimensionality reduction using the DDRTree algorithm, regressing out both donor identity and the number of genes per cell, and then ordered cells along a trajectory of pseudotime (using Monocle’s orderCells function; see Supplementary Figure S2a) based on their expression across the 3,000 most differentially expressed genes (sorted by q-value), as inferred using tests of gene differential expression as a function of cluster membership (using Monocle’s differentialGeneTest function). We then tested each gene for differential expression as a function of pseudotime, hierarchically clustered the

40 bioRxiv preprint doi: https://doi.org/10.1101/612747; this version posted April 17, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

significantly correlated genes (with q-value < 0.05), and then used the plot_pseudotime_heatmap function to plot smoothed scaled expression of genes belonging to four major modules across cells sorted by pseudotime, assuming that the most basal-like cells occupy the initial state (see Figure 2a). To view the expression of key regulators across the trajectory on a shared scale, we normalized all smoothed expression values to be between zero and one, and then plotted these normalized expression curves across pseudotime (see Figure 2b).

The same approach was followed for constructing the SMG cell trajectory. To order the subclustered cells (see Supplementary Figure S3d), we used the top 3,000 genes that were most differentially expressed as a function of cluster membership to one of the three SMG basal substates, the myoepithelial state, or the SMG secretory population (proliferating basal SMG cells were excluded from this analysis). Smoothed expression for 14 (hand-sorted) modules of genes that were significantly associated with pseudotime were plotted across the trajectory, assuming that the myoepithelial cells occupy the root state (see Supplementary Figure S3h).

Additionally, we constructed lineage trajectories for the in vitro dataset, allowing us to capitalize on the known real-time appearance of cell states across differentiation of ALI cultures. Applying the previously calculated tSNE dimensions, we used Slingshot59 to build lineages of cells that link in vitro SNN cell clusters by fitting a minimum spanning tree (MST) onto the clusters. When constructing these lineages, we only used differentiating basal, secretory and ciliating/ciliated populations, where lineages were constrained to begin with the differentiating basal population and to end with either the mature secretory (Secretory cell 2) or mature ciliated populations. We inferred two major lineages, one defining the transition from differentiating basal to Secretory cell 1 then Secretory cell 2, and the other defining the transition from differentiating basal to early and late ciliating cells, and then on to mature ciliated cells. Pseudotime values for cells were obtained for each lineage by projecting cells onto smoothed lineages constructed using Slingshot’s simultaneous principle curves method. For the two lineages, we then plotted smoothed scaled expression (as a weighted average across a 100 cell window) of select genes that were significantly associated with pseudotime based on Monocle’s differential gene test (q-value < 0.05) (see Supplementary Figure S4d).

41 bioRxiv preprint doi: https://doi.org/10.1101/612747; this version posted April 17, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Spliced/unspliced ratios It has been shown that unspliced and spliced mRNA molecules capture earlier and later expression states, respectively, thus providing temporal information that is distinct from expression of combined RNA-seq data92. Thus, to further test the polarity of the later ciliating and mature ciliated expression states in the ALI cultured scRNA-seq dataset, we used the Velocyto pipeline92 (applying default options) to identify unspliced and spliced mRNA reads for each gene in the original in vitro BAM files.

Gene networks We constructed functional gene networks (FGNs) in order to summarize the major processes being carried out by selected gene sets in a way that shows the genes involved and their interconnectivity. FGNs were created by finding enriched terms for the given gene set (based on Gene Ontology and KEGG Pathway libraries), filtering and consolidating these enrichments into categories (i.e. metagroups) using GeneTerm Linker93, and then constructing gene networks based on select metagroups using FGNet94, which connects genes via edges with shared annotations that fall within a particular metagroup. Genes (i.e., nodes) uniquely involved in distinct processes (i.e., metagroups; each with a different colored border) can be distinguished from those involved in multiple processes (nodes belonging to multiple metagroups, indicated with white borders). Edges indicate at least one shared annotation.

Protein-protein interaction (PPI) networks were also constructed for some gene sets, enabling us to visualize how genes within sets may produce proteins that interact within the cell to carry out particular functions. We used STRNGdb95 to create PPI networks, where edges are drawn between gene nodes that have predicted interactions, and where edge thickness is proportional to the predicted interaction strength. Networks were split into densely connected sub-networks containing three or more genes using the cluster_leading_eigen or cluster_edge_betweenness functions in iGraph96.

DATA AND SOFTWARE AVAILABILITY Gene lists associated with Figure and Supplementary Figure panels can be found in Supplementary Table S6. All raw and processed scRNA-seq data used in this study are in the process of being deposited in the National Center for Biotechnology Information/Gene Expression Omnibus (GEO).

42 iue1 Figure k d g a i bioRxiv preprint eltp rstate: or type Cell C

Mean normalized F heavy light TR MKI67 SERPI expression of smokers smokers nonsmokers

smoking genes CHGA p-value: 0. 0. 0. 0. 1. KR N BPI 2 4 6 8 0 B POU2 T 3 F 5 doi: SLP B T scRNA-seq certified bypeerreview)istheauthor/funder.Allrightsreserved.Noreuseallowedwithoutpermission. Secretion 1 P63 3.0e-60 F SERPI https://doi.org/10.1101/612747 RA I prolif. basal S1 3 R MUC 0 R 0 E P N 4.3e-74 S B basal PSC 5 diff. 1 4 P A R ● ● C ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● f ● e ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● b ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● SS ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● A ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● <1e-99 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● KRT8 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● high ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 3 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● MUC5AC ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● C ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5.3e-18 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● MG ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● F ● ● ● ● ● ● ● mucus ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● TI ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● sec. ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● TR ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● M ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● M ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ST ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● CHGA ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● P ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● UC5B ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 3 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ; ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ALDH3A ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 3.7e-67 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ciliated ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ADH ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● this versionpostedApril17,2019. ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● GS ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● AKR1C ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● F ● ● ● ● ● POU2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● OXJ1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● metabolism ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● T ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Xenobiotic ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● A ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● B ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1.3e- ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● F ● ● ● ● ● ● basal ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● SMG ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ALDH1A ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 3 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 3 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● GS ● ● ● ● ● ● ● ● ● ● ● ● ● ● GP ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ev smoker Heavy ● ● ● ● ● ● ● ● ● Nonsmoker ● ● ● ● Light ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 4 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● SCG ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● T ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● X ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● h ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● f ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● P ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● NQ ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● SMG ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● sec. ● ● ● ● ● ● ● 0.87 ● ● ● ● ● ● ● ● ● ● ● SQST ● ● ● ● ● ● ● ● B ● smoker ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 3 1 O FT A 1 1 L M MUC5AC 1 M secretory SMG uu secretory mucus basal proliferating KRT8 ciliated M basal SMG cells rare basal differentiating MUC5B high CX KRT5 intermediate The copyrightholderforthispreprint(whichwasnot DEFB C j L2 23 42 KR ⬆ ⬆ KRT8 1 CX CX T Chemokine 14 signaling 48 CCL C C 9 L3 L1 ⬆ ⬆ 12 9 2 CX TM4SF ⬆ ⬆ GA 0 c SCGB3A1 SCGB1A1 MUC5AC DNAH11 C POU2F3 WFDC2 MUC5B CDHR3 DMBT1 FOLR1 D ASCL1 KRT14 FOXJ1 MKI67 FOXI1 CHGA L8 CFTR CAV1 KRT8 KRT5 TP63 D IL33 response 1 KL 4 expression Core 5 51 37 Average A F scaled 6 ⬆ ⬆ 21 13 -1.0 -0.5 1.0 0.0 0.5 ⬆ ⬆ 17 11 ihsignifcant with ⬆ ⬆ upregulation elstates cell # ● ● ● ● expressing % responses Unique 7 6 5 4 cells 5 43 7 100 75 50 25 0 27 17 ⬆ ⬆ ⬆ ⬆ bioRxiv preprint doi: https://doi.org/10.1101/612747; this version posted April 17, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Figure 2

a b Scaled Pseudotime smoothed Subclusters Club expression Basal-like Secretory preparation Mucus secretory sec. Secretory Basal-like Club Mucus preparation sec. sec. -3 -2 -1 0 1 2 3 1.00 -Extracellularmatrixorganization -Collagenformation -Cellcycle 0.75

-ATPsynthesis 0.50 -Proteintranslationelongation -NOTCHsignaling Scaled expression 0.25

-Metabolismofxenobiotics Pseudotime -O-linkedglycosylationofmucins 0 10 20 SCGB1A1, WFDC2, CYP2F1 Subclusters MUC1, MUC4, MUC16 HIF1A CREB3L1 FOXA3 -Vesiclecoating BTF3 SPDEF SCGB1A1 -ERmembrane NOTCH3 MAGED2 MUC5B -Unfoldedproteinresponse KLF3 XBP1 MUC5AC MUC5AC, MUC5B

c d SCGB1A1 MUC5B MUC5AC MUC5B KRT5 5AC+/5B- - - 5AC-/5B+ 5AC /5B

5AC+/5B+

e 0.4 f MUC5AC MUC5B correlates correlates ●CP ●C3 1.6 0.3 ●BPIFB1

s ●SAA1 1.4 BPIFA1 ●●●LCN2 PIGR ● ●● ● CFB ● ● ● SAA2 ● ● ●●● ●●RABAC1 0.2 ● ●● ● ● TFF3 1.2 PNPLA3 ● ●●● ● ● ● ● ● ● ●QSOX1 ●●●● ● correlation ● ● 1.0 ● ● ● ● expression ●●●● ● XBP1 0.1 ●● ● ● BPIFB2 TFF1

●● ● Mean normalized ●●● ●●●● ● 0.8 ●●●●● ● ● ●LYZ MUC5B ● ● ● ●CEACAM5 0.6 ● ● ADRA2A ●MSMB 0.0 ACSS1 ● ● ● ● SERPINB11 0.4 PDIA3 ● TSPAN8 VSIG2 ●● ●SCGB2A1 p = 3.69e-06 4.92e-14 -0.1 ●LMAN2 S100P ● Nonsmoker -0.1 0.0 0.1 0.2 0.3 0.4 0.5 Heavy smoker

MUC5AC correlations

44 bioRxiv preprint doi: https://doi.org/10.1101/612747; this version posted April 17, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Figure 3 a b c + - Surface secretory SMG secretory 5AC /5B DNAJC10 MUC5AC MUC5B - - EDEM3 Protein processing 5AC /5B ERLEC1 SEC62 in the ER Fold SEC61A1 18.5↓ 1.22↓ SERPINA1 change SEC24D CTSC ER to Golgivesicle- SEC22B mediated transport ERGIC1 5 5AC+/5B+ B3GNT6 5AC-/5B+ GALNT6 MUC13 O-linked glycosylation MUC16 of mucins GALNT7 KDELR3 4 FUT3 Asparagine N-linked GFPT1 GNE glycosylation KDELR2 Nonsmoker 3 ANPEP 0.8 Heavy smoker SERPINA3 DMBT1 C2 PIP 0.6 FOLR1 Regulation of 2 CCL28 MMP7 complement activation MIA 0.4 CHI3L2 TF KDR 1 STATH Antimicrobial humoral PROM1 0.2 SLCO2A1 response Normalized expression LTF PPP1R1B NOXO1 0 Proportion of cells 0.0 - MGP - + +

- - SPDEF SMG SMG + /5B /5B + /5B /5B MAGED1 CREB3L1 surface surface CREB3L4 5AC PRRX2 Transcription 5AC 5AC 5AC BARX2 CREB3L2 factors non-secretory non-secretory FOXC1 SOX9 TFCP2L1

-1.6 0 1.6 MEC Basal SMG Secretory SMG d SMG basal cells e 1.00 KRT5 MKI67 n IL33 KRT14 0.75 ACTA2 SOX9 CAV2 0.50 ANXA5 IFITM3

surface differentiating basal Scaled expressio 0.25 surface proliferatingSMG proliferating basal basal SMG myoepithelial surface KRT8 SMG basal SMG secretorsurface other Average scaled 0 10 20 30 expression high 0.50 y Pseudotime 0.25 0.00 % cells ACTA2 KRT5 ELF3 -0.25 expressing MEF2C IL33 FUT2 -0.50 0 KRT14 SOX9 SCGB3A1 25 BARX2 MUC5B 50 FOXC1 DMBT1 75 100 f g Nonsmoker Smoker

DMBT1 ACTA2 KRT5 MUC5B ACTA2 TSLP

45 bioRxiv preprint doi: https://doi.org/10.1101/612747; this version posted April 17, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Figure 4 merge with merge with a b c ac. α-Tubulin ac. α-Tubulin early later mature FOXN4 FOXN4 ciliating ciliating and DAPI and DAPI E2F4 Average E2F5 scaled E2F7 expression GMNC 1.0 axonemal basal body basal body GMNN 0.0 assembly docking & function & MCIDAS control maintenance −1.0 axonemal MYB generation TP73

GRHL2 % expressing KO

STIL KDELC2 STIL TBC1D30

YPEL1 BRCA2 cells DEUP1 SASS6 RFX2 CCDC18 RTTN DEUP1 CEP120

CEP295 CEP78 0 CENPF

CCDC14 RANBP9 PLK4 PLK4 RFX3 CDC20B

FOXN4

CEP152 E2F7 25 POC1A

CENPJ POC1A

AGO2 PCNT FOXJ1 WDR62

CCNO

CEP85 EZH2 CDC20B MCIDAS 50 FOXN4 GLI2

TRAF3IP1 BBOF1 FOXN4 CFAP100 KIAA0556 CCNO TEKT1

CASC1 RSPH9 75 CFAP74

LRRC23

IFT52 KIF19 CENPJ CFAP46 DNAAF1 Day 9 Day 21 DNAH3

DZIP1L

WDR60

DNAH10

WDR66

RP1

DNAI1

RFX2

NEK5

DRC3

CROCC

CCDC114

DNAI2

DRC7

DNAH1

CCDC13

AGBL2

RABL2B RABL2A CEP290 RSPH10B

EFCAB2

WDR63

ARMC4

CC2D2A

NEK1 CEP290 SYNE1 RAB28

CEP44

LRRC49

DYNC2H1

SAXO2 PPP1R42 SYNE2 RPGR

CEP162 FBXL13 d BBS9

TTBK2 INTU TTBK2 γ-Tubulin merge KIAA0319 DEUP1 TOGARAM2

WDR78

TCTEX1D1

CFAP61

SYNE1 CCDC39 IFT52 MAATS1

SYNE2

PROM1

CFAP221

CFAP52 DNAH2 CFAP61 CFAP157

IFT88 CFAP73 1.0 DNAH7 Mature RFX3 WDPCP DNAAF1 WDR27

SPAG17

SPEF2

CSPP1

DNAH6 SPAG16 Immature DNAH5

DNAH9

DNAH12

HYDIN

CDHR3

PACRG TTC29 0.8 CFAP54

DNAH11

ATP6V1D

CDK5RAP2

WDR34

MAP9

BCCIP

FOXJ1

HSPB11

LZTFL1

ZMYND12

STK36 control PIH1D3

ROPN1L

ZMYND10 TMEM107 0.6 TTC30B TTC30A RSPH1 IFT172

IFT20

TMEM216

MKS1

RIBC2

CCDC181 GAS8 SPAG1 TUBGCP2

CLUAP1

LRRC6

IQCB1

DYNLRB1 FAM92B DNALI1 DYDC2 0.4

TEKT2

MNS1

IQCD

CCDC65 SPAG1 IFT57 DYNLRB2

CCDC170

DNALI1

CFAP298

RSPH4A

DYNLL1 KO CALM1 GAS8 CETN2

DYNLT1 ARL3 0.2 CFAP126

PIFO AGR3 ATP6V1D SNTN

RSPH1

IFT57

IFT22

ENKUR CCDC113 SNTN

Fraction ciliated0.0 cells control FOXN4 KO -1.6 0 1.6 FOXN4 Day 21 e early ciliating f markers 0.8 Glycolysis Xenobiotic 1.5 metabolism AKR1B10

0.6 s AKR1C1 GAPDH Transferase AKR1B1 0.4 GSTA2 activity TKT ADH7 CLDN10 ZFP36 Tight junction 1.0 focal adhesion 0.2 TALDO1 ALDOA ACTG1 Cholesterol CTNNB1 biosynthesis FDPS HMGCS1 JUN 0.0 MGST3 GCLM

0.5 SQLE DHCR24 CRLF1 Response to Platelet CD9 GPX3 drug ciliary maintenance activation CLU JUNB markers CALR TXNRD1 2.0 HSPA5 HBB in mature ciliated cell XBP1 PRDX1 0.0 Protein Peroxidase processing SDF2L1 1.5 upregulated smoking genes hybrid mature non- STOM activity Average expression of unique ciliated in ER HSP90B1 CES1 1.0

0.5 g

RSPH9 0.0 DNALI1 SPEF1 POLR2I MAP1A NME7 1.5 SNTN CEP89 Cilium axoneme SDHC NDUFB1 NUDC ARL3 mature secretory ROPN1L IFT74 ATP binding SLC25A4 GAS2L2 DNAL4 ODF2L TULP3 HSPB11 CEP95

markers s CAMSAP1 DLG1 RSPH4A DNAH3 TEKT1 FGFR1OP Average marker gene expression DYNLT1 KIF19 2.0 1.0 Cell projection TTLL7 STK11IP KATNAL2 DYNLL1 organization CBY1 DNAH11 IK FUZ TEKT4 SPA17 YWHAQ SERPINE1 Oocyte 1.5 DYNC2LI1 ENKUR KRAS IFT43 DNAH10 IARS maturation DNAI2 EFHC1 REC8 CCNA1 1.0 0.5 TCP1 CC2D2A SPAG17 DNAAF1 Unfolded CCT7 TEKT2 DNAH2 PTTG1 PSMA4 in hybrid cell CCNB1 protein PFDN6 SPAG6 KIF27 RAD9A PSMD10 0.5 binding NEK2 CDC26 SHFM1 Proteosome CCT3 RUVBL2 VCP UBR1 HSPE1 TUBA1A PPP2R1A 0.0 0.0 HSP90AA1 PSMC4 HIST1H2BJ HSPA8 HSPD1 RUVBL1 LRWD1

dowregulated smoking genes hybrid mature non- Average expression of unique SYTL2 HSPBP1 NSFL1C CLOCK HIST1H2BC Chromosome ciliated ER protein HIST1H1C assembly processing BSG DERL3 HIST2H2BE SSRP1 PDCD6IP HIST1H2AE HIST1H2AC hybrid Nonsmoker mature mucus HIST1H2BD secretory Heavy smoker non-ciliated

46 bioRxiv preprint doi: https://doi.org/10.1101/612747; this version posted April 17, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure 5 a b Ionocyte Tuft-like PNEC CFTR

ABRACL ACADSB ACBD6 ACSF3 ACTL6A ADA ADNP AKAP1 ANKRD65 ANXA1 APEX1 ARF5 ARL9 ASCL2 ASRGL1 ATP11C ATP2C1 BAZ1A BBOX1 BDH2 BTF3L4 BUD31 BYSL BZW1 BZW2 C14orf166 C20orf27 C6orf132 C6orf48 CADM4 CAPRIN1 CBX3 CCDC112 CCDC12 CCDC167 CCSER1 CCT2 CCT3 CCT6A CCT7 CCT8 CD276 CD320 CDK12 CDK17 CDK2AP1 CDK4 CHORDC1 CHPT1 CKAP5 CKS2 CLPP CMTM3 COA4 COIL COLCA2 COPS5 CPT1A CSDE1 CTD−2089N3.2 CUEDC2 CUL4B CXCL14 CYB5A DAB1 DAXX DCP2 DDB1 DDX21 DDX46 DDX5 DDX6 DEDD2 DENR DGUOK DKC1 DLST DNAAF2 DNAJA1 DNTTIP2 DRG1 DSTYK DTYMK ASCL2 DUS1L DUSP10 DYNLT1 E2F3 EEF1G 3 EEF2 EHF EHMT1 EHMT2 EIF4A1 EIF4E2 EIF4EBP1 EIF5A EIF5AL1 ELF3 EMC4 ENAH ENY2 EPB41L4A−AS1 EPHB3 EWSR1 EXOSC4 FAM60A FAM89B FANCF FMNL2 FNBP1 Cell cycle FRAT2 FUBP1 GABARAP GABPB1−AS1 GDI2 GNL3 GPBP1 GPRC5B GRB7 GRPEL1 GTF2B GTF2I GTF3C3 GUCA1A H2AFY2 H2AFZ H3F3A HCK HDAC2 HDAC7 HIST1H2AC HIST3H2A POU2F3 HMGA1 HMGB1 HMGN1 HN1 HNRNPA0 HNRNPA1 HNRNPA1L2 HNRNPA3 HNRNPAB HNRNPC HNRNPK HNRNPM HOMER3 HOTAIRM1 HPCAL1 HSD17B11 HSD17B13 HSP90AA1 HSP90AB1 HSPD1 HSPE1 IDO1 IGFBP2 IL23A ILF3 IMP4 IRGQ DNA replication & repair ITGB1 2 KDM1A KDM5B KHDRB S1 KLHL24 KNOP1 KPNA2 KYNU LENG9 LETM1 LGALS9 LIMD2 LRMP LRWD1 LSM3 LSM7 LYRM1 MAD2L2 MARCKS MAU2 MCM7 MDK METTL13 HCK METTL9 MEX3A MFAP2 MFGE8 MGEA5 MIDN MIS18BP1 MLXIP MORC3 MRTO4 MSI1 MTHFD1L MYB MYBPC1 NCBP2−AS2 NDUFA10 NDUFA9 NDUFB11 NELFA NELFCD NFATC3 NFE2L3 NGDN NHP2 NHSL1 NID1 NIFK Gene expression NLN NME1 NMU NONO NOS2 NPEPL1 NREP NSG1 NUDT5 ODC1 OSTC PABPN1 PAFAH1B2 PAFAH1B3 PAPD5 PCGF2 PCID2 PDCL3 PHF14 PILRB PLPP2 PNN 1 SOX4 POLR2B POLR2H POLR3E POU2F3 PPM1G PRCC PRIM2 PRKX PRMT1 PRPF38B PRPF40A PRRC2C PSMA1 PSMB1 PSMB5 PSMB7 PSMD3 PTK7 PTMA PTPRG RAN RARB RARS RBBP4 RBBP5 RBM15B RBM25 Splicing RBMX RCC2 RCN2 REC8 RNASE1 RNF44 RNPS1 RP9 RPAIN RPRD1A RQCD1 RSL24D1 RSPRY1 RTF1 SCAF4 SDC4 SET SF1 SF3B5 SFPQ SH2B2 SKIV2L2 LRMP SLC16A1 SLC19A1 SLC25A33 SLC25A6 SLC26A4 SLC26A4−AS1 SLC7A6 SMARCC1 SMARCE1 SMNDC1 SNN SNRPA SNRPE SORD SOX4 SOX9 SPSB3 SRSF1 SRSF2 SRSF6 SRSF7 SSBP3 0 SSNA1 SSR2 SSSCA1 STAP2 STARD4−AS1 Translation STAU1 STK38 STK38L SUMO2 SUPT16H SYNCRIP TBPL1 TCERG1 TCF25 TCP1 TDG TERF1 TFDP2 TGIF2 THOC5 THUMPD1 THYN1 TIAL1 TIMM13 TIMM8B TINCR TJP2 TMA16 NREP TMEM132A TMEM258 TMSB10 TNFAIP2 TOMM22 TOMM40 TOP1 TP53 TPD52L1 PNEC TPGS2 TPM2 TRA2A TRAF7 TRAP1 TRIM13 TRMT61B TSN TSPAN14 TSPAN6 TTYH3 TUBB TUFM TXNL1 UBE2G1 UBE2I UBE2R2 UBXN1 UQCRC2 USP42 WDR33 WHSC1 XIST XRN2 YBX1 YTHDC1 YTHDF2 ZBED5 ZBTB10 ZFAS1 ZMYM2 ZNF146 ZNF195 ZNF286A ZNF397 ZNF428 ZNF43 ZNF444 ZNF462 ZNF609 ZNF738 ZPR1 A1BG ABCA5 ABHD16A ACYP1 ACYP2 ADAM28 ADAMTSL1 ADCY1 ADGRG1 ADGRV1 AES AGR3 AGT AIDA AKR7A3 AMPH ANK3 ANXA6 AP3B2 APLP1 APOL6 ARFGEF3 ASAH1 ASCL1 ASNS ASPHD1 ATF7IP2 POU2F3 ATP6V1B2 ATP8A1 B3GALT5 B3GAT3 BAALC BAALC−AS2 BAIAP3 BASP1 BBIP1 BCAM BEX1 BEX2 BEX4 BEX5 BLCAP BOLA3 BTG1 C10orf10 C11orf49 C16orf45 C17orf58 C1orf210 C2CD4B C2orf74 C3orf14 C3orf52 C7orf73 CA6 CA8 CACNA1A CACNA1H CACNA2D1 CADM1 CADM2 CADPS CADPS2 CALCA CALD1 CALM3 CALY CAMK1D CAMK2N1 CANT1 CANX CBFA2T2 CBX6 CCDC159 CCDC24 CCDC88A CD302 CD47 CDH2 CDK5R2 CDK5RAP2 CDKN2C CELF4 CERK CERS4 CFAP36 CFAP65 CGA CHGA CHGB CHN2 CHPF CHST11 CHST8 GRP CISD3 CLGN CLSTN3 CMIP CMYA5 CNKSR3 CNTNAP2 COBL COPA COPZ1 CPE CPEB4 CPLX2 CPNE4 CREM CRIP2 CRMP1 CRNDE CSNK1G3 CSRP1 CST3 CTB−171A8.1 CTB−55O6.8 CTSF CYB561 CYHR1 DAPL1 DCTN4 DDC DDHD1 DDR1 DDT DISC1 DMPK DNAJB2 DNAJB9 DNAJC12 DNAJC3 2 DNAJC5B DNER DPM3 DPP10 DPP10−AS1 DPYSL2 DRAIC DUSP26 EBLN3 ECM1 EFR3B CALCA EID1 ELAVL3 ELAVL4 ENPP4 Post-translational modifcation ENTPD8 EPB41 EPB41L4B ERI3 ERICH3 ERICH5 ESPL1 ETV1 EVA1A EXTL3 FAAH FAIM2 FAM127A FAM155A FAM167A FAM171A1 FAM171B FAM172A FAM174A FAM58A FBLN7 FGF14 FGFR1 FIBCD1 FKBP2 FKBP9 FMO5 FNDC3A FOXA2 FRMD3 FRY FXYD5 FXYD6 FZD9 GALR2 GDAP1 GFI1 GFRA3 GJB1 GK5 GLS GMDS GNB5 GNG4 GOLT1A CHGA GP2 GPX3 GRAMD4 GRP Protein processing in ER/Golgi GSTT2B GUCY1A3 GUK1 HHIP HHIP−AS1 HK1 HLA−E HLA−G HOXB−AS1 HOXB−AS3 HOXB2 1 HOXB5 HOXB6 HSD11B1L HSD17B14 IBTK ID2 IFI27 IFI6 IFNGR2 IFT172 IFT57 IL10RB IL10RB−AS1 ILDR1 IMMP1L INA INHBA INSM1 ISL1 ITFG1 ITGAE ITPK1 IZUMO4 JAG2 JAKMIP1 JAKMIP2 JTB KCND3 KCNH2 KCNH6 KCNJ6 KCNK3 KCTD12 KCTD17 KDM1B CHGB KIAA1324 KIAA1456 KIF19 KIF1A N-linked glycosylation KIF3A KIF3B KIF5C KLF13 KLHDC8A KLHL2 LAMP2 LAT2 LCOR LDOC1L LGALS3BP LINC00261 LINC00869 LINC00998 LINC01003 LINC01315 LINC01420 LINC01481 LINGO2 LIPA LMBRD1 LONRF2 LRPAP1 LRRC24 LY6H Tuft-like LYG2 LYRM4 LYSMD2 LYST MAGED1 MAN1A1 MAP1A MAP1B MAP2 MAP2K1 MAPK10 0 MAPK8IP1 MAPK8IP2 MAPRE3 MARCH3 MARCH4 MB MBOAT2 MEGF6 MFSD12 GP2 MGAT4C MIAT MINCR MIR4458HG Neurotransmitter secretion MMP24−AS1 MOAP1 MS4A8 MTHFD2 MTURN MXRA7 MYEF2 MYH7B MYO15A NAAA NARS2 NAV2 NBEA NEB NELL1 NENF NFASC NGFRAP1 NMNAT3 NMUR2 NOL4 NPTN Log of expression NPTX1 NR0B1 NR0B2 NR2F1 NR2F2 NRXN1 NUCB1 NUCB2 NUDT14 NUP210 OR51E1 OTUD3 OVOS2 P4HA2 P4HTM PAK3 PAM PARD6A PCDHB10 PCLO PCSK1 PCSK5 PDIA3 PDIA6 ASLC1 PEG10 PEPD PEX5L Neuronal system PIGT PIK3IP1 PJA2 PKIB PKIG PLD3 PLEKHA6 PLEKHB1 PLLP PLS1 PNMA1 POMGNT1 PPFIA3 PPIF PPM1K PPP1R1A PPP1R37 PPP2R2B PPP3CA PREPL PRKCDBP PRUNE2 PSD PSIP1 PTGFR PTPRN PTPRN2 PYCR2 QDPR RAB2A RAB30 RAB3A RAB3B RALYL RBFOX1 RDH14 REV3L RGS16 RGS17 RGS4 RHBDL1 RHOBTB3 RIMBP2 RIMKLA RNASEH2C ROBO1 SYT1 RP11−385F7.1 CALCA RP11−421L21.3 RP11−423H2.3 RP11−467L13.7 RP11−522B15.3 RP11−61J19.5 RP11−983P16.4 RPN2 RTBDN RUNDC3A S100A13 SCG3 SCG5 Ionocyte SCGB2A1 SCGN SCN1B SCN3A SDF2L1 SDK2 SDR16C5 SEC14L5 SEC22B SEC62 SEPW1 SERGEF SERPINF1 SERPINI1 SEZ6 SEZ6L2 SGCE SGSM1 SLC12A5 SLC17A5 SLC26A9 SLC29A4 SLC35D3 SLC36A4 SLC38A10 SLC6A17 SLC7A5 SLITRK6 SMAP1 SMCO4 SMIM14 SMIM22 SMOC2 SMPD3 SMYD3 SNAP25 SPOCK1 SNHG19 SOX21−AS1 SPINT2 SPOCK1 SPOCK3 SPP1 SPTB SPTSSA SRPK3 SRRM4 SST ST6GAL1 ST6GALNAC4 ST8SIA5 STARD10 STMN3 7.5 STX1A STXBP1 SUCO SUMF2 SUSD6 SV2A SV2B SVIP SYN2 SYP SYT1 SYT11 SYT13 SYT5 SYT7 TCEAL1 TCEAL4 TCEAL8 TCERG1L TGOLN2 TIMP1 TLE6 TM7SF3 TMED10 TMED4 TMED9 TMEM178B TMEM205 TMEM255B TMEM263 TMEM9 TNNT1 TOX TPGS1 TPH1 TRIM44 TRIM9 TRPM8 TSPYL1 TTC37 TTC39A TTLL7 TXNDC15 U47924.27 UAP1 UBB UBE2E2 UBL4A USH1C USP11 UTP3 VEGFB VGF VKORC1 VWA5A WAC−AS1 WBP5 XXbac−B135H6.15 ZACN ZDHHC2 ZMYND11 ZSCAN16−AS1 ABHD12 AC009948.5 ACADVL ACAT2 ACSL1 ACTB ACTR3C ACVR1 ADCY5 ADGRF1 ADGRF5 ADI1 ADIPOR2 ADRB1 5.0 AKAP7 AKIRIN1 AMACR AMFR AMN ANKRD12 ANKRD9 APBB2 APLP2 APOPT1 ARF1 ARHGAP18 ARHGEF38 ARL15 ARL3 CFTR ARL4A ARL6IP5 ARL6IP6 ARPC5 ARSJ ATP10D ATP1B1 ATP5D ATP5G3 ATP6V0A4 ATP6V0D2 ATP6V1B1 ATP6V1C2 ATP6V1D ATP6V1E1 ATP6V1F ATP6V1G3 ATP7A ATPAF1 BBX BEST3 BPIFA2 BRWD1 BSND C1orf168 C1orf21 C1orf64 C20orf24 C21orf2 CAB39L CAMK1 CAMSAP2 CAPZA2 CAPZB CARD19 CAT CBR1 CCDC34 CCDC68 CCND3 CCNI CD59 CD9 CDA CEL CELF2 CENPW CFTR CHAC2 ATP6V1B1 CHCHD10 CHMP2A CHMP4A CHST15 CIRBP CLCNKA CLCNKB CLDN25 CLNK CMTM8 CNN3 COPS8 2.5 COX20 COX5B COX7B Mitochondrion COX8A CRACR2A CSPG5 CTSL CTSS CUEDC1 CYCS CYP26A1 CYP51A1 DBI DDX4 DEFB1 DEGS1 DEGS2 DGKI DIDO1 DOCK1 DPY19L2 DSG2 DSTN EBP ECI1 EFHD1 EIF1AY ELF2 ELP6 ENSA EPS8 FAM114A1 FAM131C FAM13C FAM153B FAM210B FAM24B STAP1 FAM50A FAM73A FBXO32 FDPS FGF13 FKBP5 FOXI2 FTH1 GABARAPL1 GABRB2 GALK1 GAREM1 GAS6 GHITM GNAL GNB2 TCA/Electron transport chain GNG11 GPI GPSM2 GSN H2AFJ HADHA HMGB3 HMGCR HMGCS1 HPS5 HSD11B2 ID3 ID4 IDH3A IDI1 IGF1 IL18 IL6ST ILK IMPA2 INSIG1 IQGAP2 ISG20 ITIH5 0.0 ITSN1 JAK1 KAZN KBTBD4 KCNH1 KCNQ1 KIAA1522 KITLG KLF7 KLHL23 RARRES2 KMT2C KRT8 KTN1 LAMB2 LAPTM4B LDHA LGALS1 LGALS3 LGR4 LIMS1 LINC01187 LLGL2 LPAR1 LPP LRRFIP2 Oxidative Phosphorylation LTBP2 MAD1L1 MAF MAL MAMDC2 MAN2A1 MAP3K2 MAPK6 MAPRE1 MARCH10 MCUR1 MDH1 MEA1 MEF2D MEGF9 METTL21A MFSD6L MLEC MMAB MOB3B MPC1 MPHOSPH8 MTL5 MTMR3 MUC1 MUC16 MVD MYL6 MYZAP NADK NCKAP5 NDUFA1 NDUFA2 NDUFA4 DEFB1 NDUFA6 NDUFB9 NDUFS1 NDUFS2 NECAB1 NEDD4L NGFR NPC2 NR6A1 NRIP3 NRP1 OLMALINC OPTN OXCT1 P3H2 P4HB Iron uptake & transport PACRG PACS1 PANK3 PARK7 PARP16 PCDH17 PCGF5 PCYOX1 PEF1 PFKP PGAM1 PGD PGRMC2 PHF1 PHYH PICALM PIM3 PITPNA PKP2 PLCG2 PLCL1 PLEKHB2 PLEKHF2 PLK2 PNPLA8 POSTN PPARGC1A PPCS PPDPF PPP1CA PPP1R12B PRADC1 PRELID1 PRKAB1 SCNN1B PRKAR1A PSD3 PSMD7 PSMD8 PTGER3 PTGS2 PTK2 PTPN9 PTRH1 PYGB RAB20 RAB24 RAB5C RACGAP1 RARRES2 Cholesterol biosynthesis RARRES3 RASD1 RASL12 RCAN2 RERG RGCC RHEB RHOA RICTOR RILP RNF152 ROGDI RP11−138A9.1 RP11−451G4.2 RP11−553L6.5 RP11−617F23.1 RPA2 RRAGD Other RSRC1 RTN4IP1 RUNX2 SCD SCD5 SCIN SCNN1B SCNN1G SCRIB SCUBE2 SDC2 SEL1L SELENBP1 SEMA3C SEPT9 SERPINI2 BPIFA2 SFTPB PNEC SGK223 SH2D4A SHTN1 SIRT2 SLC12A2 SLC14A1 SLC16A7 SLC25A1 SLC25A11 SLC25A37 SLC25A5 SLC35F5 SLC6A4 SLC9A2 Focal adhesion SMAD1 SMAD4 SMIM24 SMIM5 SMIM6 SMPD1 SMPDL3A SNF8 Tuft-like SNTB1 SOCS1 SORBS2 SP100 SPTBN1 SQLE SSFA2 ST3GAL4 Ionocyte STAC2 STAMBP STAP1 STIP1 SUCLG1 SULT1C2 SUN2 SUSD3 SYNE2 SYVN1 TAPBP TBC1D14 TBC1D4 TDRP TET2 TFCP2L1 TFG TFPI CEL THBS1 THEMIS2 THRB TM7SF2 TM9SF2 TMBIM4 TMEM141 TMEM171 TMEM19 TMEM211 TMEM38A TMEM45B TMEM5 TMEM53 TMEM64 TMPRSS11E TMPRSS13 TMSB4X TMTC3 TNNC2 TOM1 TRIM47 TRIM8 TRNAU1AP TRPM2 TSG101 TST TSTD1 TWSG1 TXNRD2 UBL3 UST VAPA VKORC1L1 VWA1 WBP2 WDR1 WLS WSB1 ZBTB20 ZNF652 CLCNKB ZNF827

-1.6 0 1.6 c d CFTR cell % of Non-rare cell 1 3 5 average 1.00 expression in vivo cell equivalents total markers populations CFTR needed per CFTR UMI/cell Ionocyte UMI 0.75 Proliferating basal 0.06 77.8 7.2 CFTR Ionocyte Differentiating basal 0.27 17.3 10.9 markers KRT8high intermediate 0.12 38.9 56 0.50 Mucus secretory 0.18 25.9 9.6 Ciliated 0.02 233.5 1.8 0.25 SMG basal 0.01 467.0 0.5 Geometric mean of SMG secretory 0.14 33.4 3.1 0.00 No expression Ionocytes 4.67 1.0Text 11 scaled AmpliSeq expression Text -5 -2 0 1 2 4 5 7 9 11 12 14 16 18 21 23 25 28 30 32 Day of ALI culture e f

FOXI1

4 3 2 1 0 Ionocyte Tuft- PNEC other Log of expression like CFTR+ FOXI1+ only POU2F3+ all negative g FOXI1+ CFTRhigh POU2F3+ cells cells cells 7 7 7 FOXI1 CFTR POU2F3 53 22 merge with DAPI 102 h 210 1.00 Tuft-like 167 167 markers 210 0.75 Ionocyte markers 0.50 FOXI1+/CFTR+ CFTR+ only PNEC FOXI1+/POU2F3+ POU2F3+ only markers 0.25

CFTR+/POU2F3+ FOXI1+ only Geometric mean of

FOXI1+/CFTR+/POU2F3+ scaled AmpliSeq expression 0.00

-5 -2 0 1 2 4 5 7 9 11 12 14 16 18 21 23 25 28 30 32 Day of ALI culture 47 bioRxiv preprint doi: https://doi.org/10.1101/612747; this version posted April 17, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure 6 a Apical plasma membrane Transporter activity

LCN2 SLC26A2 CMTM7 IFNGR1 KCNK1 AQP5 PI3 GALNT6 ZG16B Chemokine activity CYR61 SYT8 CCL20 SLC11A2 SPARCL1 CXCL14 IL20RB LDLR HES1 Proteinacious extracellular matrix Chemotaxis CLEC2B CSF3 CXCL1 IL19 TIMP1 SPRY1 Protein stabilization TSLP CAP1 CXCL17 SPON2 CHI3L1 ATP6V1B1 CD9 SOX4 SLPI CHI3L1 CXCL3 IL18 PLAT CXCL6 EFEMP1 CX3CL1 SCGB3A1 SLC5A1 ATP6V1G1 ABCC3 LGALS3 CXCL2 LGALS7B HNRNPH2 IL6 DEFB1 CALR ASNA1 SLC6A14 CCL2 MYL12A ITGA2 Tight junction S100A9 S100A16 ACTG1 CITED2 GNAL SNCA Actin cytoskeleton Response to: S100A8 PLAUR CTSD FBLN1 CLDN1 NCL LPS, ethanol, toxin, CDKN1A S100A6 CD63 NFKBIA HSPA5 CCND1 CLDN10 SQSTM1 SET PIK3R3 CTGF RAB2A ZFP36 organic cyclic substance ALDOA JUN TMSB4X TNFAIP3 SOD2 TIMP3 BTG2 F3 SERPINF1 PMAIP1 GSN JUNB CLU ERRFI1 CCNO TSPO MCM3 LONP2 TACC2 STOM WEE1 HMGCS1 POLR2J3 ADH7 ID3 TKT GADD45A CAV2 ALDH1A1 SDF2L1 SND1 EI24 CTNNB1 GCLM PRDX1 SLC1A5 HSP90B1 XBP1 FOSB RHEB NFE2L2 EPHX1 TALDO1 ERGIC1 ER UPR TUBB YWHAH ALDH3A1 P4HB FMO2 JTB FMO3 SSR3 Protein processing GSTP1 GPX3 RDH10 TMED9 TFF1 SQLE IRS2 AKR1C1 ALDH3A2 TXNRD1 TFF3 MGST3 GAPDH NQO1 PTGES AKR1C2 HSD17B7 MGST1 CES1 TXNIP NTS ID1 AKR1C3 Cholesterol biosynthesis KLK12 HSD11B2 CYB5A CYP4B1 AKR1B1 DHCR24 CYP1B1 GCLC CLIC1 ISOC1 Aldehyde metabolism SORL1 RP1 CYP26A1 PRDX6 Steroid metabolism PMVK FDPS CYP2A6 STEAP4 HBB Xenobiotic metabolism AKR1B10 GSTA1 GPX2 SH3BGRL3 MUC5AC EPAS1 Glutathione metabolism SDHB SRM GSTA2 CBR1 ACADVL Response to oxidative stress

b Antigen processing & presentation MHC Class I/II protein Cilium axoneme Interferon-gamma-mediated signaling Ciliary motility

CD200R1 Microtubule-based movement IGLC3 IFI6 SOCS3 HGS TEKT1 IP6K2 AZGP1 RUVBL1 IFITM3 SREBF1 MYH14 ROPN1L DNAL4 Unfolded protein response CLDN7 TEKT2 TGM2 PDCD6IP CD74 HLA-DQA1 DNAH2 KDR Chaperone binding HLA-DRA HLA-DRA HLA-DMA CLDN3 TEKT4 RSPH9 HLA-DMB DNAH11 Protein folding and refolding HLA-DRB1 TTLL7 DNAI2 TULP3 HLA-DPB1 HLA-DRB5 DNAH10 HSPE1 IFI27 HLA-DRB5 SPAG6 DNAH3 HLA-DQA2 KIF27 CC2D2A RSPH4A PFDN6 DNAJA1 HLA-C SPEF1 NAP1L4 HLA-F DNAH5 PYCARD DYNC2LI1 RUVBL2 HLA-E SPAG17 HERPUD1 HLA-B TIMM10 SPA17 CP HSPA6 CAV1 DNAAF1 DNALI1 PPIB HLA-A Response to toxin HSPA1A AHSA2 VCP B2M KIF9 PPP3CA NEK2 KATNAL2 CCT3 HSPB1 P53 signaling pathway PPIA TCP1 HSP90B1 TNFSF10 KIF19 CDC42 KRAS G2/M transition of mitotic cell cycle CCT7 MIF PPP2R1A HSPH1 DYNLL1 Tissue regeneration HSPA5 HSPA8 HSPA2 RHOB HSPD1 PTTG1 CCNA1 COMT SAT1 CTSB YWHAQ CDC26 DLG1 Node Key BDH1 FHL2 TCEB2 IGFBP7 HSP90AA1 ITGA6 FOS CEP89 CETN2 STMN1 DNAJB1 REC8 HIF1A CEP95 Proliferating basal CLU SERPINE2 CAPG DNAJC10 HES1 CES1 MGMT Differentiating basal CCNB1 GADD45B LGALS1 JUND SERPINE1 CALR IARS Complement SLPI SERPING1 DEGs KRT8high intermediate LXN CLOCK SLC9A3R1 ID1 unique &coagulation TFPI SERPINB1 IGFBP3 SRI TP53AIP1 Mucus secretory SPOCK3 DMBT1 FGFR1OP to one cascades APP RAD9A PKIB Hybrid secretory/ciliated SERPINA1 IGFBP5 DUSP1 NUDC cluster C3 ARL3 RAMP1 Ciliated WFDC6 RAMP1 NOTCH3 INSR SORD C1S WFDC2 CTNNA1 SMG basal CFH SHFM1 KRT14 CFD PSMC4 KRT5 SMG secretory DSP CD151 TUBA1A PSMB9 TTBK2 PSMD10 UBR1 Core response gene (≥ 4) KRT15 COL17A1 Mitotic cell cycle PSMB8 PNN Neither core nor unique PSMA4 KRT7 KRT6A Network Key DNA damage response VIM SPTBN4 LMNA KRT17 underlay = genes only in particular enrichment metagroups KRT13 MNS1 UGCG FABP5 no underlay = genes in at least two metagroups (hub genes) Cell junction assembly PTHLH Intermediate flament Circle size = gene connectivity within the network Structural constituent of the cytoskeleton Edge thickness = number of shared annotations between two genes Label size = increases with mean log fold change Label redness = increases with decreasing FDR

S S cxc 5B 5AC S Schematic Key * * 5B S * 5AC cxc c * *5AC 5B * * * cxc 5B 5AC S 5AC 5AC 5B 5AC 5AC* 5AC * 5B antigen presenting * S* * 5AC cxc innate immune signaling * * S * cxc * Proliferating basal secretion enchanced Differentiating basal xenobiotic metabolizing KRT8high intermediate Surface Mucus secretory epithelium Secretions Hybrid secretory/ciliated 5AC MUC5AC Ciliated 5B MUC5B SMG basal S SCGB1A1 SMG secretory surface defensive 5B 5B * Ionocytes 5B 5AC 5AC * SMG defensive SMG 5AC cxc chemokine 5B 5B 5B 5AC

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