Mouse kidney epithelial mIMCD3 cells derived from the inner medulla of a simian virus SV40 transgenic mouse were obtained from Maryland Polycystic Kidney Disease Research and Translation Core Center at the University of Maryland, and documented to be mycoplasma free. Cells were grown in T75 flasks with the RenaLife Epithelial Basal Medium (LM-0010, LIFELINE CELL TECHNOLOGY) supplemented with a RenaLife LifeFactors Kit (LS-1048, LIFELINE CELL TECHNOLOGY) and 5% FBS. This culture medium, which contains a nutrient blend of amino acids, vitamins, organic and inorganic supplements and salts, growth factors (0.5 μg/mL Insulin, 1 μM Epinephrine, 0.1 μg/mL Hydrocortisone, 10 nM Triiodothyronine, 10 ng/mL EGF, 5 μg/mL Transferrin, 2.4 mM L-Alanyl-L-Glutamine), and antibiotics (30 μg/mL Gentamicin, 15 ng/mL Amphotericin B), has become the choice for in vitro studies in the Maryland Cell engineering core of the NIH U54 funded Polycystic Kidney Disease Research Consortium. It was used here to provide a reference for on-going and future transcriptome profiling studies in the core. For RNA-Seq studies, cells were plated permeable filters (Polyester membrane) of a 6-well transwell plate (#3450, CORNING) and grown to confluence (10 days) before doxycycline treatment. Doxycycline (2 μg/mL) or vehicle (DMSO) added to both sides of the permeable filter and changed daily for 3 or 6 days.

Cells were lysed in Trizol reagent (15596018, Invitrogen). Total RNA was isolated from the Trizol lysate using Direct-Zol RNA Miniprep plus kit (R2070, ZYMO RESEARCH) and eluted in RNase-free water, and RNA concentration was measured using Qubit^TM RNA HS Assay Kit (Q32852, Invitrogen). To enrich mRNA, 1–3 μg of total RNA was applied to oligo dT-based mRNA isolation using NEBNext^® Poly(A) mRNA Magnetic Isolation Module (E7490, NEW ENGLAND BioLabs Inc.) according to manufacturer’s instructions. mRNA (20 ng) was used to create the cDNA libraries, using NEBNext^® Ultra II Directional RNA Library Prep Kit for Illumina (E7760, NEW ENGLAND BioLabs Inc.) and NEBNext^® Multiplex Oligos (E7335; E7500, NEW ENGLAND BioLabs Inc.). cDNA libraries were sequenced on the Illumina HiSeq 4,000 platform. Sequence reads (2 × 75 bp, paired-end) were aligned on Ensembl genome GRCm38p6 using STAR (2.6.0c).

To identify differentially expressed genes between vehicle- and doxycycline-treated cells, transcript abundance was quantified using salmon (Patro et al., 2017). Differential expression (DE) analysis was carried out using edgeR (Robinson et al., 2010) on R (3.6.0). Low abundant genes with CPM (Counts Per Million) less than 1 were removed from the data set for the DE analysis. Significance of DE was determined using a modified statistical test (edgeR “glmTreat”) with a threshold of expression changes above 20% at FDR < 0.05 (Chen et al., 2016). Plots were generated using R package ggplot2. Pathway enrichment analysis of DE genes was carried out using Gene Ontology (Biological process) on Metascape platform¹ (Zhou et al., 2019). The GO term analysis was performed using NaviGO (Wei et al., 2017).

Significance of DE was evaluated using edgeR and p-values were corrected using the Benjamini-Hochberg method. For significance of gene enrichment in the pathway analysis, q < 0.05 was considered as significant enrichment.

All fastq files and a raw count file from RNA-Seq were deposited in GEO (GSE171573²).

RNA-Seq analysis was performed to provide a transcript database of fully polarized mIMCD3 cells on permeable filter supports in the RenalLife medium which includes growth factors and antibiotics. For these studies, the cells were grown to confluence on the permeable filter supports for 10 days, and then were treated with DMSO or DOX in the RenalLife medium for 3 or 6 days.

To establish the baseline, we first examined the control cells, treated with DMSO. We found a high correlation between the transcriptome of cells treated with DMSO at 3 and 6 days (Pearson correlation: 0.9956, Figure 1A), consistent with stable gene expression and lack of transient DMSO responses. Based comparison of normalized expression values (transcripts per million, TPM), the transcriptome exhibited a high correlation with native mIMCD3 cells grown on plastic solid supports (GSE97770) as reported by Chan et al. (2018) (Figures 1B,C). Whether grown on permeable supports or plastic solid supports, mIMCD3 equally express the conventional epithelial cell markers (Jedroszka et al., 2017), such as Tjp1, Krt18, Dsp, Muc1, and Sdc1 (Figure 1D). Other extracellular matrix components, Col4a2 and Sparc, were also more abundantly expressed in the mIMCD3 cells grown on the solid support compared to mIMCD3 cells on the filter support (Figure 1G). Interestingly, mesenchymal cell marker genes, namely Cdh2, Fn1, Sparc, and Vim, were also more abundant in mIMCD3 cells grown on the solid support compared to mIMCD3 cells grown on the filter support (Figure 1E). By contrast, in mIMCD3 cells grown on the filter supports, tight junction proteins, Cldn4, Cldn7, and Epcam, were more abundant than in cells on the solid support (Figure 1F). Thus, transcriptomic signature of mIMCD3 cells is most compatible with a differentiated epithelial state when cells are grown on permeable supports compared to cells grown on plastic supports. Yet the transcriptome profiles of mIMCD3 cells grown on permeable and non-permeable are more similar than different. Interestingly, we did not identify the enrichment of specific transcripts of medullary collecting duct in this cell line.

Nevertheless, the dataset of comprehensive transcriptome profile in mIMCD3 provides an informative resource for further utilization as the in vitro kidney epithelial cell model. To expand understanding of intracellular signaling pathways in mIMCD3 cells grown on solid supports (Valkova and Kultz, 2006; Chan et al., 2018) compared to cells grown on filters, we classified transcriptome profile into genes encoding transcription factors, ion channels, transporters, and G protein-coupled receptors (GPCRs) in Tables 1–4 (Supplementary Table 1). Many of the listed genes have been identified in previous studies. For example, Hnf1b (hepatocyte nuclear factor-1 beta, TPM: 43.0) (Aboudehen et al., 2017), Pax2 (Paired box 2, TPM: 20.8) (Torban et al., 2000; Cai et al., 2005), and Egr1 (Early Growth Response 1, TPM: 49.7) (Cohen et al., 1994, 1996) are transcription factors reliably expressed in the mIMCD3 cells (Supplementary Table 1, “Transcription factors”). In addition to known expression of transcription factors, expression of transcription factors that could be involved in the kidney nephron development, such as Emx2, Pax8, Tfap2a, Hmga2, Hmgb2, and Hoxa11, were identified (Ribes et al., 2003; Schwab et al., 2003; Chambers et al., 2019).

As shown in the previous studies (Slaats et al., 2015; Liu et al., 2018), expression of two ion transport proteins, Pkd2 (Polycystic kidney disease 2, TPM: 20.6) and Clcn4 (H⁺/Cl^– exchange transporter 4, TPM: 9.5), were found in the current transcriptome (Table 2). We also identified the mechanosensitive cation channel Piezo1 (piezo-type mechanosensitive ion channel component 1) (Coste et al., 2010; Table 2). Expression of ion channels, Trpm4 (Transient Receptor Potential Cation Channel Subfamily M Member 4, TPM: 13.9), Trpm6 (Transient Receptor Potential Cation Channel Subfamily M Member 6, TPM: 3.3), and Trpm7 (Transient Receptor Potential Cation Channel Subfamily M Member 7, TPM: 23.7), as well as a Mg⁺ transporter, Magt1 (TPM: 20.1), suggests further utilization as an in vitro model for studying transepithelial Mg⁺ transport mechanisms in kidney epithelial cells (Groenestege et al., 2006; Tables 2, 3 and Supplementary Table 1, “Ion channels” and “Transporters”).

The mIMCD3 cell line has been widely used in several studies of primary cilia conformation and function. The current transcriptome profile provides information about ciliary GPCRs expressed in mIMCD3 cells. Among the ciliary GPCRs, as previously reported (Hilgendorf et al., 2016; Mykytyn and Askwith, 2017), we identified expression of Gpr161 (G protein-coupled receptor 161, TPM: 11.5), Ptger4 (prostaglandin E receptor 4, TPM: 11.7), and Smo (Smoothened homolog, TPM: 22.86) in mIMCD3 cells (Table 4). In addition to ciliary GPCRs, expression of two adenylyl cyclases, Adcy1 (TPM: 8.9) and Adcy6 (TPM: 31.5), was found in the transcriptome profile. These adenylyl cyclases could be considered in further studies for examining cAMP responses in mIMCD3 cells (Strait et al., 2010).

To characterize the DOX response in mIMCD3 cells, we compared the transcriptomes of DOX-treated mIMCD3 cells and DMSO-treated mIMCD3 cells (Figure 2 and Supplementary Table 2). We found DOX changed the gene expression profile, reflecting a change in abundance of 1,662 genes at 3 days (Figure 2A) and 2,858 genes at 6 days of treatment (Figure 2B). A total of 1,157 genes were consistently changed at both times. Downstream analysis identified GO Biological Processes (GOBPs) that are enriched in DOX-treated cells (Figure 2C). The NaviGO based GO term association analysis, which classifies similarity within GO term hierarchies, revealed three major clusters of GOBPs: (1) cell proliferation/differentiation; (2) signal transduction; and (3) immune responses (Figure 2D). In the cells treated with DOX for 3 days, differentially expressed genes were enriched for signal transduction pathways and cell proliferation/differentiation (Figures 2C,D). In particular, this involved significant changes of genes associated with ERK, cAMP, and Notch signaling pathways, known to mainly involve cell proliferation and development processes (Figure 2C). Differentially expressed genes from the 6 days-dataset were also especially enriched in biological processes associated with cell proliferation/differentiation but were also enriched in immune response pathways (Figures 2C,D). As shown in the heatmap (Figure 2E), expression of genes involved in cell proliferation/differentiation processes and immune responses were mostly decreased at both times. The results indicate that DOX attenuates gene expression associated with cell proliferation and immune responses. Full list of genes associated with these pathways are provided in Supplementary Table 3.

Examination of genes encoding downstream factors in signal transduction processes, including transcription factors and kinases, revealed alterations in three different signaling pathways (cAMP, ERK, Notch). Remarkable downregulation of several transcription factors and protein kinases was found in DOX-treated cells (Figure 3A). Especially, Hnf1b (Chung et al., 2017), Egr1 (Sukhatme et al., 1988), Tead1 (Zhao et al., 2008), and members of the Fox family (Kume et al., 2000; Aschauer et al., 2013), which have been proposed previously as key players in epithelial cell proliferation. Additionally, several protein kinases downregulated in response to DOX, namely Tgfbr2 (LeBleu et al., 2013), Kit (Gomes et al., 2018), and protein kinase A (Amsler et al., 1991), are highly related to regulation of epithelial cell function (Figure 3A). Furthermore, the effects of DOX to decrease the expression of genes involved in each step of the cell cycle and cell cycle progression suggest that DOX may have proclivity to inhibit cell proliferation (Figure 3B).

The transcriptome dataset showing the attenuated cell cycle progression also exhibited that several genes known as cell proliferation markers were consistently downregulated by DOX treatment at 6 days (Figure 4). The result confirmed that DOX treatment suppresses cell proliferation. In addition to cell proliferation markers, DOX treatment for 3 and 6 days induced significant changes of immune response-associated cellular pathways (Figure 2), largely reflecting DOX-responsive reduction in cytokine production at day 3 and comprehensive alteration of cellular inflammatory response at day 6. In particularly, we identified several chemokines that were significantly downregulated at 3 days after DOX treatment from the literature-based gene sets of immunologic mediators (Commins et al., 2010; Figure 4), corresponding the pathways involved in the repression of epithelial cell proliferation, which are associated with epithelial responses to cytokines (Stadnyk, 1994).