Letter
Transcriptome analysis in various cell lines exposed to nitric oxide
2024 Volume 49 Issue 6 Pages 281-288
Details
2024 Volume 49 Issue 6 Pages 281-288
Nitric oxide (NO) plays a physiological role in signal transduction and excess or chronic NO has toxic effects as an inflammatory mediator. NO reversibly forms protein S-nitrosylation and exerts toxicological functions related to disease progression. DNA methyltransferases, epigenome-related enzymes, are inhibited in enzymatic activity by S-nitrosylation. Therefore, excess or chronic NO exposure may cause disease by altering gene expression. However, the effects of chronic NO exposure on transcriptome are poorly understood. Here, we performed transcriptome analysis of A549, AGS, HEK293T, and SW48 cells exposed to NO (100 μM) for 48 hr. We showed that the differentially expressed genes were cell-specific. Gene ontology analysis showed that the functional signature of differentially expressed genes related to cell adhesion or migration was upregulated in several cell lines. Gene set enrichment analysis indicated that NO stimulated inflammation-related gene expression in various cell lines. This finding supports previous studies showing that NO is closely involved in inflammatory diseases. Overall, this study elucidates the pathogenesis of NO-associated inflammatory diseases by focusing on changes in gene expression.
Nitric oxide (NO), a gaseous molecule produced in vivo, regulates various functions in the body, including vasodilation and memory formation (Mustafa et al., 2009). However, excessive or chronic production of NO induces toxic consequences such as protein misfolding, mitochondrial dysfunction, and neuronal injury (Uehara et al., 2006; Cho et al., 2009). One cause of this toxic effect is the promotion of protein S-nitrosylation, which is a reversible post-translational modification induced by NO (Yun et al., 2011; Stamler et al., 2001; Foster et al., 2009). S-Nitrosylated proteins change their enzymatic activities, ion channel permeation, and protein-protein interactions; they can acquire toxic functions that differ from their original functions (Foster et al., 2009; Gonzalez et al., 2009; Marozkina and Gaston, 2012). A previous study demonstrated that chronic inflammation-derived NO causes colorectal cancer progression (Tazawa et al., 2013). In recent decades, research has focused on the toxic effects of chronic NO exposure.
The epigenetic control of gene transcription, such as histone modifications and DNA methylation, is important for many cellular and developmental processes (Allis and Jenuwein, 2016). Recently, we reported that S-nitrosylated DNA methyltransferase (DNMT) partially lost its enzymatic activity and altered the expression of several genes (Okuda et al., 2023). Histone deacetylase (HDAC), an enzyme that contributes to transcriptional repression by acetylating chromatin, has also been reported as S-nitrosylated (Nott et al., 2008; Okuda et al., 2015).
Transcriptome and epigenome signatures are completely dependent on the cell type and developmental stage (Garcia-Alonso et al., 2022; Roadmap Epigenomics Consortium et al., 2015). Therefore, we hypothesized that chronic exposure to NO might be involved in disease pathogenesis by altering epigenetic regulation and gene expression. However, few studies have examined the genes that are altered in expression across various cell lines following exposure to NO (Hemish et al., 2003; Vasudevan et al., 2015). The mechanisms of disease pathogenesis linked to chronic NO exposure remain unclear, as there has been no focus on transcriptome changes resulting from such exposure.
This study aimed to gain insight into the mechanisms underlying the pathogenesis of chronic NO exposure. This was achieved by identifying the differentially expressed genes in multiple cell lines exposed to NO. In this study, we showed that the NO-induced changes in gene expression were cell type-specific.
Human lung adenocarcinoma A549, human embryonic kidney (HEK) 293T, and human colorectal adenocarcinoma SW48 cells were maintained in Dulbecco’s modified Eagle’s medium (DMEM; FUJIFILM Wako Pure Chemical Corporation, Osaka, Japan or Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 10% (v/v) heat-inactivated fetal bovine serum (FBS; Sigma-Aldrich or Biosera, Cholet, France) and 1% penicillin/streptomycin (FUJIFILM Wako Pure Chemical Corporation) at 37 °C in a humidified atmosphere containing 5% CO2/95% air. Human gastric adenocarcinoma AGS cells were maintained in Ham’s F-12 medium (FUJIFILM Wako Pure Chemical Corporation or Thermo Fisher Scientific), supplemented with 10% (v/v) heat-inactivated FBS and 1% penicillin/streptomycin, at 37 °C in a humidified atmosphere containing 5% CO2/95% air.
NO treatmentThe NO donor, S-nitrosoglutathione (GSNO), was freshly prepared before use and stored in the dark. NaNO2 was purchased from Sigma-Aldrich (St. Louis, MO, USA) and glutathione (FUJIFILM Wako Pure Chemical Corporation) were dissolved in equimolar amounts (50 μmol) in ddH2O. After the solution was acidified with 3N HCl, it was diluted 10-fold with 100 mM HEPES-NaOH (pH 7.7) (Tsikas et al., 2001). A549, AGS, HEK293T, and SW48 cells were treated with GSNO at a concentration of 100 μM. After 24 hr of treatment, the medium was changed and the cells were treated with GSNO for an additional 24 hr under the same conditions.
RNA-seq analysisTotal RNA was extracted from the cells using the RNeasy Mini Kit (Qiagen, GmbH, Hilden, Germany). Quality control of the RNA was strictly checked using a 2100 Bioanalyzer (Agilent Technologies, Inc., Santa Clara, CA, USA). Whole-transcriptome RNA sequencing was performed using the SureSelect strand-specific RNA library preparation kit (Agilent) as previously described (Miyagawa et al., 2018). Libraries were pooled and paired-end sequenced (2 × 75) using the Illumina NextSeq 550 system. All sequence data files were analyzed with default parameters using the CLC Genomics Workbench (Qiagen, GmbH, Hilden, Germany). Quantitative analysis was performed on the transcripts per million. Differential expression analysis was performed using BaseSpace RNA-seq Differential Expression (Illumina, Inc., San Diego, CA, USA). The sequence data files were deposited in the DNA Data Bank of Japan (DRA017624).
Data analysismRNA expression analysis was performed using the CLC Genomics Workbench. Differentially expressed genes (DEGs) were identified with an absolute value of fold change greater than 2.0, and a p-value less than 0.01. The commonality of DEGs is shown in a Venn diagram. Gene ontology (GO) analysis was performed using HOMER software with default value settings. Biological process (BP) and KEGG network analysis results are shown as bar plots. Gene set enrichment analysis (GSEA) was performed for the HALLMARK gene sets, and the normalized enrichment score (NES) and FDR q-values were obtained.
We first performed RNA-seq in four cell lines to analyze the gene expression in response to chronic NO exposure. We selected the lung (A549), stomach (AGS), kidney (HEK293T), and colon (SW48) cell lines for this study. These cell lines were selected from tissues in which NO production and pathogenesis (e.g., inflammatory carcinogenesis) are thought to be involved. The cell lines were treated with 100 μM GSNO, an NO donor, for 48 hr to mimic the possible concentrations of physiological NO exposure (Hussain et al., 2008). Total RNA was collected and analyzed using RNA-seq (Fig. 1A). In total, 18,999 transcripts were subjected to gene expression analysis, and differential expression genes (DEGs) were defined as p-value < 0.01 with |log2-fold change| ≥ 1 (Fig. 1B, Supplemental Table 1). We successfully observed 69-142 upregulated genes and 68-73 downregulated genes in the four cell lines. HEK293T cells exhibited the largest number of upregulated genes (0.75% of all transcripts) and AGS cells showed the largest number of downregulated genes (0.38% of all transcripts) upon exposure to NO.
Gene expression changes by NO-exposure in various cell lines. (A) Schematic representation of the experimental design used for RNA-seq. The cells were treated with 100 μM GSNO for 48 hr. (B) Volcano plots of gene expression after GSNO treatment in the indicated cell lines. Upregulated genes are shown in red (p < 0.01, log2-fold change ≥ 1) and downregulated genes are shown in blue (p < 0.01, log2-fold change ≤ -1). Other genes are shown in gray. The magenta and cyan lines indicate the fold-change threshold and the gray line indicates the p-value threshold.
Next, we analyzed the commonalities of the DEGs in the examined cell lines. Venn diagrams showing the overlap of upregulated and downregulated genes in individual cell lines revealed that there were no common upregulated or downregulated DEGs in these cell lines (Fig. 2A, B). These results indicated that NO induces changes in gene expression in a cell-specific manner. K-means clustering for all expressed genes revealed that gene clusters with a high fold-change in a specific cell line differed from those with a high fold-change in other cell lines (Fig. 2C). These results indicated that the transcriptome changes following NO exposure in the different cell lines were not closely matched.
Commonly upregulated or downregulated genes in four NO-exposed cell lines. (A) Venn diagram of overlap among the significantly upregulated genes in the four cell lines. (B) Venn diagram showing overlap among the significantly downregulated genes in the four cell lines. (C) Heatmap showing the z-scores of fold-changes in expression before and after GSNO treatment in the indicated cell lines. Five clusters were formed based on k-means clustering. Untreated cell samples were integrated and analyzed as control samples.
Since NO induced gene expression changes in different cell lines, we analyzed the DEGs from a phenotypic perspective. The DEGs were subjected to Gene Ontology analysis using HOMER software. The results of the upregulated and downregulated genes in each cell line were compared. We found that different pathways were enriched in the differentially expressed genes in a cell line-specific manner (Fig. 3). However, the upregulated genes in A549 and HEK293T cells indicated that pathways related to cell adhesion were activated. These results suggested that NO alters gene clusters with distinct functions in various cell lines.
Commonly activated or suppressed pathways in four NO-exposed cell lines. (A) Gene ontology (GO) analysis of upregulated and (B) downregulated genes using the biological process (BP) and KEGG pathways. The top five enriched pathways are shown in bar plots.
Gene Set Enrichment Analysis (GSEA), which can analyze all the genes in the analysis without a cutoff, was performed to explore potential commonly induced phenotypes. We focused on the hallmark gene set in which NES was positive in two or more cell lines. As anticipated, the NES of the gene set involved in the activation of inflammation was significant (Fig. 4). In particular, the NES of TNFα signaling via NFκB, inflammatory response, interferon alpha response, and the IL2 STAT5 signaling pathway showed positive tendencies across all cell lines. In addition, across all cell lines, gene sets associated with epithelial-mesenchymal transition (EMT), response to estrogen, response to toxic substances, and fatty acid metabolism were also induced. Overall, these results suggested that NO alters the expression of inflammation-activating genes in various cell lines.
Activated gene sets by GSEA in two or more NO-exposed cell lines. Heatmap showing GSEA for GSNO-treated versus untreated cells. Comparisons of the results for the four cell lines are also presented. Gene sets with a positive normalized enrichment score (NES) in two or more cells are shown. *, **, and *** represent p-values less than 0.05 with FDR q-values less than 0.25, 0.1, and 0.01, respectively.
This study aimed to elucidate the toxicity of chronic NO exposure by examining the transcriptome changes. We initially hypothesized that NO alters the expression of gene clusters in specific pathways. Contrary to our expectations, we showed that NO changed different gene clusters in various cell lines (Fig. 2). GO analysis indicated that NO altered the expression of genes related to cell adhesion and migration in the several cell lines (Fig. 3). GSEA revealed that NO altered the expression of genes involved in the activation of the inflammatory pathway (Fig. 4).
Since we performed RNA-seq with a single concentration in cultured cells, our research has the limitation of not completely reflecting in vivo conditions. The NO exposure time was set to 48 hr because of the chemical behavior of GSNO (Broniowska et al., 2013). However, the effects of gene expression after longer exposure to NO should be examined in the future study. To our knowledge, this is the first study to compare the transcriptomes of various cell lines treated with NO under similar conditions. We clarified that there are no common marker genes for nitric oxide response across the cell line in this condition. Despite these limitations, it is noteworthy that NO alters different gene clusters in various cell lines.
GO analysis of DEGs suggested that the expression of cell adhesion-related genes was upregulated in several cell types (Fig. 3), and previous studies suggested that NO inhibits cell adhesion in mesangial cells and brain microvascular endothelial cells, which were different cell lines we used in this research (Yao et al., 1998; Wong et al., 2004). Consequently, it is postulated that NO induces cell adhesion in some cell types and inhibits it in others. The GSEA results showed a significant pathway involved in inflammatory responses, although there were no commonly upregulated genes (Figs. 2 and 4). Thus, the inflammatory phenotype may have been induced as a result of the altered expression of different inflammatory genes in each cell line. Indeed, GO analysis of A549 cells showed the pathway of immature B cell differentiation, and SW48 cells showed the pathway of regulation of natural killer cell proliferation by GSNO treatment (Fig. 3A). It is reported that excessive levels of NO derived from iNOS can exacerbate such responses and inflammation-associated diseases (Sharma et al., 2007; Tsuge et al., 2023; Jiang et al., 2023). Several reports have suggested that EMT is triggered by inflammation (Ricciardi et al., 2015; Suarez‐Carmona et al., 2017), and that fatty acids worsen inflammation (Provenzano et al., 2014; Fritsche, 2015). Hence, it is likely that the shared pathways activated by NO signify the causes and effects of the inflammation. Inflammation has a protective role in living organisms. However, excessive or chronic inflammation can contribute to the onset and progression of diseases, such as cancer, cardiovascular disease, and neurodegenerative diseases (Arulselvan et al., 2016). Therefore, our findings suggest that NO contributes to the onset and progression of inflammatory diseases.
Our findings suggested that NO alters the expression of different gene clusters in various tissues during pathogenesis. DNA methylation is deeply involved in the regulation of gene expression, and the pattern of DNA methylation depends on tissue type. We recently reported that NO alters DNA methylation (Okuda et al., 2023). As the location of hypo/hypermethylation sites depends on the tissue type (Loyfer et al., 2023), NO-induced DNA methylation changes are also likely to differ between tissues. We found that changes in gene expression induced by NO were cell type-dependent (Fig. 2), which may be due to the DNA methylation status of each cell line. Further studies comparing the transcriptome and DNA methylome in various cell types are required to confirm this hypothesis.
In conclusion, our study showed that NO-induced changes in gene expression were cell type-specific. In the future, further analyses focusing on genes related to pathogenesis may help to understand the mechanisms underlying pathogenesis involving NO and DNA methylation. In addition, our results suggest that NO induces inflammation in various cell lines. Therefore, future studies should elucidate the mechanisms underlying the development of systemic inflammatory diseases involving NO.
We thank Ms. Mari Matsumoto and Ms. Kotoe Sueyoshi for their technical assistance. This work was supported in part by Grant-in-Aid for Challenging Research (Exploratory) (KAKENHI JP22K19380) and Scientific Research (A) (KAKENHI JP24H00678) (to T.U.) from the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan and JST SPRING (JPMJSP2126 to T.M.).
Conflict of interestThe authors declare that there is no conflict of interest.
