The authors have declared that no competing interests exist.
Colorectal cancer is one of the most commonly diagnosed cancers worldwide and its prevalence can be reduced by changes to lifestyle and diet. Fermentation of dietary fibre by the gut microbiota and formation of short chain fatty acids, in particular butyrate, is widely thought to play a role in preventing development of the disease. Despite butyrate’s known pro-apoptotic effects, a subpopulation of cancer cells is able to overcome these anti-neoplastic effects of colonic luminal butyrate to proliferate and establish tumours in vivo. In this study, a time course analysis of HT29 and HT29-BR cells treated with butyrate was conducted and global gene expression analysis was used to identify novel mechanisms associated with butyrate-induced apoptosis and in the acquisition of butyrate resistance. Bioinformatic analysis of the data identified deregulated O-GlcNAcylation activity and disruption to gene transcription by BRD4 as possible factors involved with butyrate-induced apoptosis. EGF signalling was identified as being potentially involved in the acquisition of butyrate resistance. Furthermore, the expression of the minichromosome maintenance protein family was significantly reduced in the HT29-BR cell line reflecting disruptions to the DNA replication process. Together, this may confer a unique survival advantage for cells with acquired butyrate resistance.
Colorectal cancer (CRC) is one of the most commonly diagnosed cancers and the fourth most common cause of cancer death worldwide
Butyrate is an established histone deacetylase inhibitor (HDI) and induces apoptosis via activation of both the mitochondrial-dependent and –independent pathways. Many studies have attempted to identify the mechanisms involved in butyrate-induced apoptosis and have highlighted the complexities involved in the cellular pathways potentially responsible for butyrate’s effects
There is also growing interest in understanding the cellular mechanisms involved with the development or acquisition of resistance to butyrate’s pro-apoptotic effects. By circumventing apoptosis, it has been shown in vitro that tumorigenic cells are able to proliferate in the presence of butyrate
In this study, we have conducted gene expression analysis to understand the early events in butyrate-induced apoptosis and the potential mechanisms involved in the acquisition of butyrate resistance in HT29 cells. This will provide insight into factors unique to butyrate resistant cells that enable tumorigenic potential in this subpopulation of cells.
HT29 colorectal cancer cells (ATCC, Rockville, MD) were maintained in Dulbecco’s Modified Eagle’s Medium/F-12 Nutrient Mixture Media with L-glutamine (1:1; 370C; 5% CO2; Invitrogen) containing 5% fetal calf serum (FCS; Invitrogen) and 1% penicillin/streptomycin. HT29-BR cells were generated and maintained as previously described
Cell culture experiments and subsequent gene expression analyses were performed in triplicate for each time point to minimize technical variability. At the appropriate time points following butyrate addition, HT29 and HT29-BR cells were harvested and RNA extracted. Untreated HT29 cells were used as the control group. Total RNA was prepared using the QIAGEN RNeasy Plus Mini Kit (QIAGEN, Valencia, CA) according to the manufacturer protocols. The RNA integrity and concentration for each sample was assessed using the Agilent BioAnalyzer. Samples with RNA integrity greater than 7 were used for analysis.
Human Exon 1.0ST arrays (Affymetrix Inc., Santa Clara, CA) were used for gene expression analysis and processed according to manufacturer protocols. Analysis of microarray data was performed using the Partek Genomics Suite (v6.6, Partek Inc., St Louis, MO) as previously described
Butyrate-induced apoptosis was observed in the parental HT29 cells but not in the HT29-BR cells (data not shown), consistent with our previously published results
To identify early responding genes involved in butyrate-induced apoptosis, we compared the expression of genes at 0.5 and 6 hrs with that of untreated control cells. At 0.5 hr, no changes in gene expression were detected (FDR <1%, fold change >±2). At 6 hrs, 476 genes were identified as being differentially expressed. However, 1516 and 1091 genes were differentially expressed at 15 hrs (intermediate response) and 48 hrs (late responding) respectively when compared with the control population (Supplementary table 2). Further analysis determined that 227 genes were common to all three time points. A Venn diagram showing the distribution of the number of genes differentially expressed at each time point is shown in
Bioinformatic analysis (Ingenuity Pathway Analysis) was conducted to determine possible relationships between differentially expressed genes and revealed deregulated activity of several regulatory molecules which can potentially influence the expression of their respective downstream targets including transcription regulators, enzymes, translation regulators, transporters and kinases. The activity of several regulatory molecules were predicted to be deregulated at specific time points including MGEA5 at 6 hrs, the kinase BRD4 at 15 hrs, and FOXO1, CCNK, S100A6 and CD24 which were deregulated at 48 hrs only (
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enzyme | 2.058 | 2.813 | 2.630 | BCL6,CFLAR,HIP1,MERTK,POR,PTGS1,SMPD1,TTC27,UBXN8 | |
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enzyme | -3.317 | AKT1S1,CAV1,CREB3L2,CYFIP2,EPHB3,KREMEN1,RAP2A,SERPINB9,TAF5L,TIMP2,TUBA1A | |||
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transcription regulator | 3.330 | 3.970 | 3.601 | AURKA,BUB1B,CCNB1,CDCA3,DLGAP5,FBXO5,HMMR,KIF2C, MCM2,MCM3,NCAPH,NDC80,SAT1,SWAP70,TTK | |
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transcription regulator | 4.517 | 5.012 | 5.745 | AURKA,BIRC5,BUB1,BUB1B,CCNB1,CDC20,CDC25A,CDC6,CDKN1A,CHEK1,FEN1,HMMR,KIF23,MCM2,MCM3,MCM4,MCM6,MCM7,NDC80,NDRG1,PEG10,PRC1,PROM1,RFC3,TNFRSF10B,TNFSF9 | |
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kinase | 2.183 | 2.395 | BIRC5,CCNB1,CDC25A,CDKN1A,CHEK1,DUSP1,FOXM1,RRM1 | ||
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transcription regulator | -2.583 | -3.075 | -3.075 | BIRC5,BUB1B,CCNB1,CDC20,CDKN1A,FOXM1,PRC1 | |
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translation regulator | -2.000 | -2.236 | -2.449 | BIRC5,BRCA2,CHEK1,RAD51 | |
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kinase | -2.236 | CDC25A,MYB,SLC38A5,SRM,TTC27 | |||
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transcription regulator | -2.891 | -3.302 | BIRC5,CCNB1,CENPF,DLGAP5,KIF11,KIF18A,MCM5,NEK2,NUSAP1,PRC1,SPC25,TXNIP | ||
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other | -2.376 | -2.376 | BRCA1,FANCD2,FANCI,POLA2,RFC5,UBE2T | ||
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transporter | -2.236 | -2.236 | AURKA,CENPA,NCAPH,NEK2,TYMS | ||
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other | -2.236 | -2.236 | DEPDC1B,HMMR,KIF18A,SMC4,TOP2A | ||
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other | 2.214 | BUB1,CDC6,CHEK1,GTSE1,TTK | |||
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transcription regulator | -2.000 | CCDC80,CDC25A,CDC6,MCM7 |
ANOVA analysis of gene expression profiles indicated that 942 genes were differentially expressed at 0hr between HT29 and HT29-BR cells (FDR <1%, fold change >±2) (Supplementary table 3). Gene ontology analysis classified these genes into the four functional categories including enzyme activity (19%), transporter activity (9%), transcription regulation (8%), and kinase activity (5%) (Supplementary figure 2). This indicates that a range of different cellular processes are involved in the acquisition of butyrate resistance in HT29 cells. A large proportion of differentially expressed genes (43%) could not be accurately classified into any known functional category.
Pathway analysis determined that the majority of genes were involved in tumorigenesis (125 genes, 13%) and that 40 genes (4%) were involved in CRC specifically (Supplementary table 4). Furthermore, EGF was identified as a core node, implicating the EGF signalling network as a potentially significant factor in the acquisition of butyrate resistance in HT29 cells (
ANOVA analysis revealed that for HT29-BR cells at 15 and 48 hrs, 17 genes and 227 genes were differentially expressed respectively when compared to HT29-BR cells at 0 hr (FDR <1%, fold change >±2) (Supplementary table 3). Comparison of the 0.5 and 6 hr time points to 0 hr, showed no significant differences in gene expression. These results indicate that the greatest observable difference in gene expression occurs between HT29 and HT29-BR cells (i.e. t=0 hr) and that minimal change in gene expression occur over time (i.e. up to t=48 hr) in the HT29-BR cells under the cell culture conditions used in this study (Supplementary figure 1).
Of the 17 genes differentially expressed at 15 hrs, 11 of these are involved in DNA replication and the DNA damage response, including 6 genes that are components of the minichromosome maintenance complex (MCM) (
In addition to those genes involved in DNA repair and replication at 15 hrs, the expression of three genes was up-regulated: GPR155 (up-regulated 2.1 fold at 15 hrs and 2.3 fold at 48 hrs), LAMP3 (up-regulated 2.1 fold at 15 hrs and 2 fold at 48 hrs) and SYT11 (up-regulated 2.1 fold at 15hrs and 2.6 fold at 48 hrs). Down-regulated genes include FAM111B (down-regulated 3.2 fold at 15 hrs and 4.1 fold at 48 hrs) and SLC26A9 (down-regulated 2.3 fold at 15 hrs and 1.8 fold at 48 hrs).
Of the 227 genes identified as being differentially expressed at 48 hrs, 137 (61%) were nuclear proteins, 42 genes (19%) localised to the cytoplasm, 16 genes (7%) were located at the plasma membrane, 13 genes (6%) were confined to the extracellular space and the remaining 16 genes (7%) could not be classified (Supplementary figure 3). Of the nuclear proteins, the expression of nine (6.5%) genes was up-regulated, and the remaining genes were down-regulated. Thirty-one genes (23%) were classified as enzymes, 14 genes (10%) were transcription regulators while 78 genes (57%) could not be classified into any functional category (Supplementary figure 4).
Further bioinformatic analysis also revealed that the activity of seven regulatory molecules to be potentially altered at 48 hrs (
At 6 hrs, 476 genes were differentially expressed in response to butyrate (FDR <1%, fold change >±2), and over 1000 genes were differentially expressed over the 48 hrs time period. Based on gene expression changes, bioinformatic analysis predicted that the activity of 12 regulatory molecules was deregulated with butyrate treatment. Of note, the activity of MGEA5 was predicted to be inhibited early in the apoptotic process (6 hrs) only, i.e., its activity had returned to levels comparable with the control group at 15 hrs. MGEA5 is a highly conserved enzyme that catalyses the removal of the O-GlcNAc functional group from serine and threonine residues of proteins. Regulation of O-GlcNAcylation of proteins is highly dependent on the hexosamine biosynthetic pathway where it requires metabolic intermediates such as glutamine, glucose and acetyl-CoA to generate the donor sugar, UDP-N-acetylglucosamine, and it is therefore intimately linked with a cell’s nutritional status. Reduced activity of MGEA5, as observed at 6 hrs, may be indicative of low turnover of O-GlcNAcylated proteins. Perturbed cycling of protein O-GlcNAcylation has been implicated in the development of a number of malignancies, including CRC
The activity of BRD4 was down-regulated by butyrate at 15 hrs specifically. BRD4 regulates gene transcription by binding to acetylated H3 and H4 histone proteins; however, its role in tumorigenesis is not understood. Inhibition of BRD4 by small molecule inhibitors has been shown to have anti-tumour effects and BRD4 has been identified as a potential therapeutic target in cancer
Our analysis identified 942 genes as being differentially expressed in the HT29-BR cells that potentially contribute to the development of butyrate resistance (Supplementary table 3), including 40 genes specifically involved in CRC development (Supplementary table 4). Of particular interest is the possible activation of the EGF signalling network in the HT29-BR cell line which may also have implications in cancer therapeutics. Recent reports indicate potential synergistic effects of combined HDI and tyrosine kinase inhibitor therapy, including EGF receptor (EGFR) inhibitors, in cancer patients with EGFR-expressing tumours
Our studies have also indicated an association between butyrate resistance and down-regulated expression of members of the MCM protein family. This is supported by a recent report indicating down-regulation of MCM proteins in CRC cell lines in response to trichostatin A
Understanding the mechanisms involved with the acquisition of resistance to butyrate’s apoptotic effects will provide novel insights into the development of CRC. Using gene expression analysis, we have described novel processes regulated by butyrate which may be important in the development of butyrate resistance and that potentially contributes to the tumorigenic process. Activation of the EGF signalling network may enhance proliferation and cell survival in cancer cells and deregulated expression of members of the MCM protein family may contribute to genomic instability. In addition, we have identified possible mechanisms contributing to butyrate’s pro-apoptotic effects. Further studies in vivo are required to define the role of these processes in CRC progression.
This work was funded by the CSIRO Preventative Health National Research Flagship.