Airway epithelial basal cells generate pseudostratified airway epithelia at an air liquid interface
To induce airway epithelial differentiation from iPSC derived and ex vivo primary basal cells, the latter were allowed to grow to confluency on 3T3-J2 feeder layers then harvested as a single cell population by trypsinisation after removal of the bulk of the feeder cells by exposure to 0.48mM sodium EDTA (Versene, Gibco). The single cells were plated on a transwell insert (ThinCerts™, Greiner bio-one, 662610), left to grow to confluency and then they were fed from the basal side leaving the apical side in contact with the air. To differentiate the cells into airway epithelia, the commercial PneumaCult™ media was used with the addition of 10µM DAPT between differentiation days 10 and 14 as shown in the schematic diagram (Figure 1A). Structures with mucous layers present on the apical surface were derived after this differentiation period (Figure 1B) and sections of these structures indicate the presence of a pseudostratified epithelium (Figure 1C). Differentiation of the basal cells into the other cell types present in the pseudostratified epithelium is indicated by the presence of club cell protein 10 (club cells, Figure 1C), and expression of MUC5AC (goblet cells, Figure 1C). The presence of putative pulmonary neuroendocrine cells is indicated by expression of synaptophysin (Figure 1C). In addition, the apical surface of the pseudostratified epithelium comprises ciliated epithelial cells capable of forming tight junction indicated by the presence of ZO-1 and cilia indicated by the presence of acetylated tubulin (Figure 1D). The ability to form tight junctions between the ciliated epithelial cells probably contributes to the TEER values in the range of 250-550 Ω/cm2 by day 60 of culture indicating establishment of an epithelial barrier. The expression of additional markers of airway phenotype was quantified by qRT-PCR showing expression of cytochrome P450 enzymes characteristic of the upper airway. In some instances (CYP2J2, CYP2S1, MUC5AC) expression levels were comparable between the iBAE and pBAE airway constructs, but for most genes (FOXJ1, CC10, CYP2A6, CYP2F1, CYP2B6) expression was lower in iBAE constructs (Figure 1E). Expression of the pulmonary neuroendocrine cell marker ASCL1 was higher.
Detectable expression of cytochrome P450 enzymes suggests that the iBAE airway epithelial construct might be capable of metabolism parallel to primary basal cell derived constructs albeit, perhaps at lower levels of enzyme activity. In view of this we proceeded to analyse the transcriptomic response of both types of construct to xenobiotic compounds using TempoSeq analysis.
iPSC derived and ex vivo primary basal cell derived airway constructs show comparable transcriptomes with respect to genes included in TempoSeq analysis
TemposeqTM transcriptome data were analysed using the Deseq2 package. To obtain log2fold change, TempoSeqR analysis software was used to generate the log2fold differences. The similarity between samples and the heatmaps were generated by Morpheus, (https://software.broadinstitute.org/morpheus). The normalised count comparison of xenobiotic metabolism was graphed using Prism GraphPad. Heatmaps of log2fold changes were generated using R package Pheatmap. Using principal component analysis (PCA), we observed that the transcriptomes of the iBAE constructs cluster together with those of the pBAE construct model and moreover, have the lowest variance between each other when compared with PCA analysis of other iPSC derived cell types produced by the IN3 consortium under which our current project was performed (Figure 2A). We then compared the normalised values between the transcriptome data of undifferentiated iPSCs, iBAE and pBAE constructs and analysed the samples using Pearson Correlation (Figure 2B). This indicated low variability between samples (between 0.93 and 1) and similarity between the iBAE and pBAE models (R=0.69¬0.76). Comparison of these models was extended to 60 highest expressed genes present in both models; most of the top 60 highest expressing genes in the iBAE model (Figure 2C) are also highly expressed in the pBAE model except for Mt-ND6, CXCL14, TP53I3, EIF4G1 and SLC3A2, which are expressed either at lower levels or not at all in the pBAE model. Conversely, four genes (NEAT1, HSP90AA1, EPHX1, TPPP3) are expressed in the pBAE model that are not expressed by iBAE constructs. In both models we can find highly expressed keratins (KRT6A, KRT15, KRT17) and annexins (ANXA2P2, ANXA1), which are characteristic of lung tissue. Mucin 1 (MUC1) was also found among the top 50 genes expressed in the primary basal cell model and is also highly expressed in the iBAE model. Other common genes that come up in both lists are the eukaryotic translation initiation factors (EIF1, EIF3E) and S100 Calcium Binding Protein P (S100P). The similarities observed in both models and expression of lung specific genes suggests that iPSC differentiation was successful to obtain airway epithelium, however there are still differences in terms of gene expression which could affect the metabolism and toxic response to xenobiotic chemicals.
Expressions of nuclear receptors and phase I/II metabolic enzymes in iPSC derived and ex vivo primary basal cell derived airway constructs
Organisms have developed a general strategy to protect themselves from possible toxic effects of xenobiotic molecules, which comprises two groups of molecular systems. The first line of defence involves nuclear receptors or xenosensors to detect the presence of potentially harmful materials and xenobiotic metabolizing and transporter systems to break them down to substances that are more easily eliminated. We interrogated TempoSeqTM data to compare expression levels of key genes in these mechanisms. The aryl hydrocarbon receptor (encoded by AHR) which normally resides as an inactive complex with heat shock protein 9p chaperone protein and responds to the presence of planar aromatic hydrocarbons by translocation of the receptor protein to the nucleus where it can activate expression of xenobiotic metabolism genes [Gottlicher M. 1999]. The translocation requires dimerization between the AHR protein and the gene product of ARNT, so it is encouraging that these two genes in addition to the feedback repressor AHRR are expressed in iBAE constructs (Figure 3A). Interestingly, AHRR is not detected in the primary basal cell derived constructs. Genes encoding the functionally similar Estrogen receptor ESR1 are present in both pBAE and iBAE constructs while ESR2 is detectable only in the latter. Several members of the nuclear receptor superfamily are expressed at similar levels in both models while three members of the peroxisome proliferator activated group of nuclear receptors (PPARA, PPARG and PPARGC1A), retinoic acid receptor alpha (RARA), thyroid hormone receptor β (THRB) and its interacting protein (TRIP13) and the vitamin D receptor gene (VDR) are also present at comparable levels Together, these data suggest that the iBAE model may be a useful tool for quantifying the response of human airway epithelia to xenobiotic substances.
Phase I metabolism of xenobiotic substances aims to introduce or expose functional groups with the goal of increasing the polarity of the compound and broadly cover oxidation, reduction, and hydrolysis reactions. The iBAE model expresses most of the enzymes involved in phase I metabolism that are present on the TempoSeq library (Figure 3B) albeit these are mostly present at lower levels than in pBAE. The first group of genes presented in Figure 3A are members of GDP- glucuronosyltransferase class (UGT1A1, UGT1A10, UGT1A8, UGT2B7). The aldo-keto reductases (AKR1B1, AKR1B10, AKR1C2, AKR1C3, AKR7A2) are also expressed while several enzymes such as UGT1A5, the alcohol dehydrogenases (ADH1A and ADH1B) and AKR1C2 were expressed at much higher levels in the pBAE model. The cytochrome P450 enzymes are important contributors to phase I metabolism due to their activity as monoxygenases capable of oxidising diverse xenobiotic substrates [Danielson, PB. 2002]. CYP1A1 is reported to be expressed in human lung [Shimada et al. 1996; Shimada, T. 2006] and to undergo significant induction by exposure to tobacco smoke [Kim et l. 2004] so it is interesting that it is only expressed by the iBAE model (Figure 3C), however the expression of other Cytochrome P450 genes correlates with published reports of their expression in human lung [Carlson, GP. 2008]. High expression of CYP1A1 and CYP4B1 relative to other members of the CYP class is reported in the literature [Gene Set - Lung (maayanlab.cloud)] so it is useful to note that the iBAE model demonstrates greater expression levels than the pBAE model, however CYP2B7P is the exception being detected only in the primary models. The iBAE model also shows lower expression of CYP2B6, CYP26A1 and CYP2C8 than pBAE but also expresses CYP2W1 which is one of the CYPs expressed in the bronchial mucosa in vivo [Courcot et al 2012]. That notwithstanding, the mRNA expression of phase I enzymes is comparable between the two models
In summary, these data suggest that both iBAE and pBAE airway models should be capable of phase I/II metabolism of xenobiotic molecules.
iPSC derived and ex vivo primary basal cell derived airway constructs show comparable expression of solute transporter protein genes
The solute carrier superfamily comprises over 400 transport proteins responsible for movement of diverse substrates across membranes of the cell or its organelles [Hediger et al 2004]. Several transporter protein genes relevant for toxicological examination are expressed at comparable levels in both airway models (Figure 4A) including neutral amino acid transporters (SLC1A2, SLC1A3, SLC1A4) [Kanai et al. 2013], glucose transporters (SLC2A6, SLC2A1) [Lizak et al. 2019], members of the mitochondrial carrier gene family (SLC25A13, SLC25A14, SLC25A27, SLC25A4, SLC25A46) [Gutierrez-Aquilar, M. 2013] fatty acid transporters (SLC27A1, SLC27A2, SLC27A3) [Anderson et al. 2013], (Figure 4A) with a few notable exceptions. The sulphate ion transporter (SLC26A2) [Heneghan et al. 2010] is absent in the iBAE airway constructs as is choline transporter like protein 4 (SLC44A4) [Nabokina et al. 2015]. Conversely, two glucose transporter proteins (SLC2A14 and SLC2A3) are absent from the pBAE models although the impact of these discrepancies on the relative metabolic activities in not clear.
Transcriptomic Response Of Airway Constructs To Xenobiotic Molecules
iPSC and primary basal cell derived models were exposed to five xenobiotic substances with known toxicity against lung /airway tissues at sub-lethal concentrations over 24 hours. Cytotoxicity was assessed by quantifying TEER and LDH although we did not find any significant increase in the LDH activity relative to the untreated control suggesting that the concentrations of chemicals used do not kills cells in the airway constructs. Measurement of TEER before and after treatment indicated that only Amiodarone and Paraquat caused significant reduction in TEER suggesting breakdown of the epithelial barrier (Figure 5A, B). Data obtained from TempoSeq analysis of the transcriptomes of xenobiotic treated cells were processed using the Deseq2 package to generate lists of statistically significant log fold change lists of genes affected by chemical treatment relative to the untreated control [Love et al. 2014] (Figure 6A) and subjected to principal component analyses to quantify the similarity of response to the applied compounds of the iBAE and pBAE models (Figure S1A-E) In performing this analysis, we aimed to show the correlation between the untreated iBAE and pBAE models and between the individual models after treatment with the xenobiotic compounds. The analyses in Figure S1 show that principal components 1 and 2 demonstrate the variance between the iBAE and pBAE models before and after xenobiotic treatment while principal components 3 and 4 account for the variance within the models. These data indicate significant differences between the iBAE and pBAE models, however given the degree of similarity in expression of genes involved in phase 1 and 2 metabolism, we considered the possibility that variance may have arisen from the genetic background of the patient derived cells used to generate the pBAE model and the iPSC used to generate the iBAE model.
We then selected the top 20 upregulated and 10 top downregulated genes with the highest sum of log2fold changes with significantly low p-value adjusted (p< 0.05). We applied gene ontology analysis to the top 20 genes (up and downregulated), however the limitation of gene ontology analysis is that it is not specific to toxicological processes so we manually annotated the genes identified by TempoSeq that are participants in the major toxicity associated pathways (ATF4 [HU et al. 2019; Lee et al. 2003], Nrf2 [ B’Chir et al. 2013], p53 [Kwak et al. 2003], NFkB [Gassmann et al. 2010; Harris et al. 2005] Ahr [Guerrina et al. 2018], PPARG [Michalik et al. 2006], HIF1a [Masoud et al. 2015], STAT1/2 [Xu et al. 2012], MTF1 [Laity et al. 2007] and XBP1) [Boei et al. 2017] (see Supplementary Table 1 for list of genes assigned to each toxicity pathway). These data were analysed using PathVisio3 [Kutmon et al. 2015] to analyse the log2fold changes of genes induced by the treatment of the chemicals and calculate a Z-score based on the annotation of genes. The pathways are ranked based on their Z-Score in which a positive Z-Score (Z > 1.96) indicates a pathway with more genes meeting the criterion than expected based on the complete dataset. A negative Z-Score (Z < 1.96) indicates that less genes meet the criterion than expected. An examination of Z-score data suggests that iBAE airway models are marginally more susceptible to toxicological pathway activation than pBAE models (Figure 6B). After application of cerium nanoparticles, the only pathway that shows statistically significant activation in the iBAE model is STAT2 although 87 genes show differential expression between test and control samples. Among the top 20 upregulated genes we can see genes involved several different processes such as apoptotic response (BIRC5, PHLA2), cell cycle arrest and differentiation (INHBA), NFkB inhibition, ciliogenesis (SLC26A) Supplementary Table 1).
Amiodarone is a potassium channel blocking agent indicated for life threatening supraventricular and ventricular arrythmias [Baritussio et al 2001] but use of this drug is associated with significant toxic effects against lung tissue, primarily the development of pulmonary fibrosis. As an amphiphilic cationic molecule, amiodarone inhibits lysosomal phospholipases leading to disruption of the lysosome membranes [Haliwell, WH. 1997] and release of reactive oxygen species which may lead to stress pathway activation and lung epithelial cell apoptosis. Surprisingly, our TempoSeqTM analysis of amiodarone exposure did not indicate significant activation of toxicological pathways in either the iBAE or pBAE models. Conversely, significant downregulation of pathways involving NF-kB, p53, NRF2 and ATF4 is observed in the iBAE model, meaning that other genes outside the pathways are strongly affected. Differentially expressed genes were detected for other GO classes, HAVCR1 (cilia function), BUB1 (apoptosis), CCL4 and PECAM1 (cell adhesion) (Supplementary Table 1).
Busulfan is an alkylating anti-neoplastic agent used to treat chronic myeloid leukaemia [Chen et al. 2013] which can cause interstitial lung fibrosis in humans [Oliner et al. 1961]. In both models Nrf2 was activated (with Z-score 6.9 in iBAE and 4.5 in the pBAE) and p53 (with Z-score 3.2 in iBAE and 2.97 in the pBAE), while NFkB was only affected in the iBAE (z-score=2.07). The most upregulated genes in the iBAE are NMRALP2 (redox sensor), FOSL1 (involved in cilia), NQO1 (NADP(H)dehydrogenase involved in Nrf2), DNM1, GPX2, CCL4 and BRICS. In the pBAE it as NMRALP2, TRIM16L, GPHT, UHCL1 (Supplementary Table 1). Activation of p53 and NRF2 is consistent with a response to DNA damage induced by an alkylating agent such as Busulfan.
Paraquat (N,N′-dimethyl-4,4′-bipyridinium dichloride) is a redox active heterocyclic compound which functions as an electron transport chain blocking agent. Paraquat has been widely employed to trigger oxidative stress given its unique potency to generate superoxide anion in nearly all experimental systems ranging from isolated mitochondria and mammalian cells [Blanco-Ayala et al. 2014] and as such its mechanism of toxicity is thought to arise by exceeding the capacity of cells to eliminate reactive oxygen species. Several pathways are downstream targets of this imbalance such as NF-KB [Gonzalez-Polo et al. 2004] and UPR [Mottis et al. 2014] so it is interesting to note activation of XBP1, HIF1α, NF-KB, NRF2 and ATF4 in the iBAE model following paraquat exposure (Figure 6B). It noteworthy that NF-KB and HIF1α activation in the pBAE airway model fall short of statistical significance of their z-scores while the p53 DNA damage response which correlates with reported increases of double strand break formation following paraquat exposure in cultured mouse lymphoblasts albeit at a 10x greater concentration than used in our study [Ross et al. 1979]. Differentially expressed genes in the iBAE and pBAE models following Paraquat exposure are shown (Supplementary Table 1).
The polycyclic aromatic hydrocarbon, Benzo(a) pyrene has very similar effects on both models with activation of the NF-KB, NRF2 and aryl hydrocarbon response pathways. The iBAE model shows activation of STAT2 that is on the boundary of statistical significance (Z-score=2.06), which was not seen in the pBAE model while the latter also shows exclusive but modest activation of HIF1α. It is surprising that activation of the DNA damage response, p53 was not observed in either model given the reported ability of Benzo(a)pyrene to induce double strand DNA breaks [Wang et al. 2005], however increased activation of AHR, NRF2 and AT4 are consistent with reported toxicity mechanisms of this molecule [Jin et al 2021]. Differentially expressed genes in the iBAE and pBAE models following Benzo(a) pyrene are shown (Supplementary Table 1).