Che-1 sustains hypoxic response of colorectal cancer cells by affecting Hif-1α stabilization
© The Author(s). 2017
Received: 12 January 2017
Accepted: 28 January 2017
Published: 18 February 2017
Solid tumours are less oxygenated than normal tissues. Consequently, cancer cells acquire to be adapted to a hypoxic environment. The poor oxygenation of solid tumours is also a major indicator of an adverse cancer prognosis and leads to resistance to conventional anticancer treatments. We previously showed the involvement of Che-1/AATF (Che-1) in cancer cell survival under stress conditions. Herein we hypothesized that Che-1 plays a role in the response of cancer cells to hypoxia.
The human colon adenocarcinoma HCT116 and HT29 cell lines undepleted or depleted for Che-1 expression by siRNA, were treated under normoxic and hypoxic conditions to perform studies regarding the role of this protein in metabolic adaptation and cell proliferation. Che-1 expression was detected using western blot assays; cell metabolism was assessed by NMR spectroscopy and functional assays. Additional molecular studies were performed by RNA seq, qRT-PCR and ChIP analyses.
Here we report that Che-1 expression is required for the adaptation of cells to hypoxia, playing an important role in metabolic modulation. Indeed, Che-1 depletion impacted on HIF-1α stabilization, thus downregulating the expression of several genes involved in the response to hypoxia and affecting glucose metabolism.
We show that Che-1 a novel player in the regulation of HIF-1α in response to hypoxia. Notably, we found that Che-1 is required for SIAH-2 expression, a member of E3 ubiquitin ligase family that is involved in the degradation of the hydroxylase PHD3, the master regulator of HIF-1α stability.
KeywordsChe-1/AATF Hypoxia Metabolism HIF-1α PHD3/EGLN3 SIAH-2
Hypoxia, i.e. low levels of O2, is a common feature of solid tumours, which leads to an aggressive tumor phenotype. Hypoxia is also associated with resistance to therapeutic treatments and with a poor clinical prognosis . The main adaptation of tumor cell to hypoxia is the switch from an aerobic metabolism to glycolysis .
The glycolytic pathway produces only 2 ATP (adenosine triphosphate) per molecule of glucose, but cancer cells compensate this reduced yield in ATP production increasing, by approximately ten fold the glycolytic flux of glucose to pyruvate, and then to lactate . Glycolysis also provides precursors for the synthesis of nucleic acids, lipids and amino acids that are crucial to enhance survival and growth of cancer cells both during carcinogenesis, and in metastatic tumours [4, 5].
The master regulator of hypoxia response is the hypoxia inducible factor (HIF) family of transcription factors, in particular HIF-1α . Indeed, in low oxygen tension HIF-1α is quickly stabilized and affects most of the cancer hallmarks involved in the hypoxic response . This transcription factor regulates the expression of multiple genes involved in proliferation, apoptosis, immune response, genomic instability, pH homeostasis, invasion and metastasis  so that, as a rule, over-expression of HIF-1α is associated with a poor clinical outcome [7, 8]. HIF-1α expression is mainly regulated through oxygen-dependent proteosomal degradation. Under normoxic conditions, specific prolyl-hydroxylase domain proteins (PHD) hydroxylate HIF-1α, at one or both conserved prolyl residues, promote the binding of HIF-1α to the von Hippel Lindau protein (pVHL)-E3-ubiquitin ligase complex and then consequently its ubiquitination and degradation . The inhibition of PHD enzymes by specific molecules leads to stabilization of HIF-1α, and constitutes a valid therapeutic approach in treating conditions of tissue stress, like inflammatory or ischemic events . Importantly, under hypoxic conditions HIF-1α activity contributes to shift cell glucose metabolism from oxidative to glycolytic, by increasing the expression of glucose transporters (SLC2A1 and SLC2A3) and downstream glycolytic enzymes, making glycolysis the main source of energy in hypoxic cells [11, 12].
Several studies have been conducted to investigate how to reduce the ability of cells adapting to hypoxic conditions, where HIF-1α has emerged to be an attractive target for new anti -cancer therapies to improve the current treatments of metastatic and resistant cancers .
Che-1/AATF (Che-1) is a RNA polymerase II-binding protein mainly involved in different and fundamental processes in controlling transcription, cell cycle regulation, DNA damage response, and apoptosis . This protein is regulated by several post-translational modifications, and in response to several kinds of stress, Che-1 not only activates p53 expression, but it also binds this onco-suppressor in such way that it represses the apoptotic arm of the p53 response [15, 16]. Moreover, Che-1 sustains mutant p53 expression, contributing to the growth of tumor cells . More recently, Che-1 was found to be phosphorylated in response to several stresses, regulating mTORC1 and mTORC2 activity and activating autophagy, thus providing a new link between different cellular stress factors and mTOR signalling . In this study, we explored the role of Che-1 in hypoxic response. We found that Che-1 expression is required for the adaptation of cancer cells to hypoxia. In particular, our results demonstrated that Che-1 depletion in the presence of hypoxia strongly reduces metabolic adaption and induces cell death. Taken together these findings reinforce the notion that Che-1 could be an attractive target for cancer therapy.
Cell line and culture conditions
HCT116 colon cancer cells were cultured in Dulbecco’s Modified Eagle Medium High Glucose (DMEM), HT29 and A549 cancer cell lines in RPMI 1640, supplemented with 10% fetal bovine serum (FBS). Cells were plated in 60 or 100 mm2 dishes and maintained at 37 °C in a humidified atmosphere of 5% CO2–95% air. For hypoxia treatments, cell culture dishes were exposed to a mix of 1% O2 saturated with 5% CO2 and 94% N2 for the time indicated, using hypoxia modulator chamber (Billups-Rothenberg). The temperature in the anaerobic chamber was set at 37 °C. The pH in the cell culture medium was determinated by CyberScan pH 510 Bench pH/mV Meter with ATC probe, integral electrode holder & power adapter (Thermofisher).
Transfections, western blot analysis and antibodies
Che-1 siRNA was performed by transfecting a specific pool of double-stranded RNA oligonucleotides (siChe-1) (Stealth, Life Technologies-Thermofisher). Stealth siRNA Negative Control oligos were purchased from Life Technologies (Thermofisher). Transfections were carried out by Lipofectamine 3000 (Life Technologies-Thermofisher) in accordance with the manufacturer’s instructions.
Cell extract purifications and western blotting analyses were performed as previously described  by using the following antibodies: anti-Che-1 , anti-β-actin (Sigma, A5441, clone AC-15); anti-HIF-1α (BD Transduction Laboratories-610958); anti-HIF-1α (Bethyl Laboratories A300-286A); anti-Caspase-7 (Cell Signaling Technologies-12827); anti-EGLN3/PHD3 (Novus Biologicals NB100-139); anti-SIAH-2 (N14) (Santacruz sc-5507). Densitometric analyses of immunoblots were performed using Alliance system by UVITECH Cambridge, and Ratios were calculated by the following formula:
intensity sample/intensity β Actin
intensity control/intensity β Actin
Assay of oxygen consumption
Cells were plated in 15 cm2 dishes and after 24 h they were transfected by specific siRNA. Then, the cells were exposed to hypoxia for 16 h. At the end of incubation, cells were trypsinized and suspended in DMEM at a concentration of 1 × 108/ml. Oxygen consumption was measured with a Clark oxygen electrode (Yellow Spring Instruments Company) at 37 C°; the concentration of dissolved oxygen was taken to be 406 ng-atoms O/ml . The rate of oxygen consumption was determined by adding 0.1 ml of cell suspension (1 × 107 cells) to 1.9 ml of DMEM in a closed glass chamber of 2 ml capacity (Gilson Medical Electronics). The final concentrations of oligomycin, FCCP and antimycin A were respectively, 1 μg/ml, 0.25 μM, and 2 μM.
Hexokinase activity assay
Cells were transfected with siControl or siChe-1, and after hypoxia treatment collected and processed. Cells were lysed and the activity of the Hexokinase was measured in accordance with the Hexokinase Assay Kit protocol (Cat# E-111) from Biomedical Research Service Center, using a SpectraMax M2 MultiMode Microplate Reader (Molecular Devices, Sunnyvale, CA, USA), with OD 492 nm. The activity was calculated in IU/L unit.
Chromatin immunoprecipitation assay (ChIP)
Chromatin immunoprecipitation assays in HCT116 cells were performed as previously described , by using anti-HIF-1α (Bethyl). Immunoprecipitations with no specific immunoglobulins (Santa Cruz) were performed as negative controls. For quantitative ChIP analysis (ChIP-qPCR), 1ul of purified DNA was used for amplification on an Applied Biosystems 7500 Fast Real Time PCR system (with Applied Biosystem SYBR GREEN). The following human promoter-specific primers were employed in PCR amplifications:
PDK1 forward 5′- GAGCCTTTTGGCTGAGATTG -3′
PDK1 reverse 5′- GATGGGACTGGGGACACTAA -3′
PFKFB4 forward 5′- CCCTAGCAAGGAGGTAGCAG -3′
PFKFB4 reverse 5′- AGGCCAGGATCGAGAATGCG -3′
RNA isolation and quantitative RT–PCR analysis
Total RNA was isolated using TRIzol reagent (Life Technologies-Thermofisher) in accordance with the manufacturer’s instructions, and the first-strand cDNA was synthesized with random primers and M-MLV reverse transcriptase (Life Technologies-Thermofisher). The cDNA was used for quantitative real-time PCR (qRT–PCR) experiments carried out in a 7500 Fast Real Time PCR System (Applied Biosystem-Thermofisher) using SYBR GREEN PCR Master Mix (Applied Biosystem-Thermofisher). ΔΔCt values were normalized with those obtained from the amplification of the endogenous RPL19 gene. Data are presented as the mean ± SD from three independent experiments performed in duplicate. The following human-specific primers were employed in PCR amplifications:
RPL19 forward 5′-CGGAAGGGCAGGCACAT -3′
RPL19 reverse 5′- GGCGCAAAATCCTCATTCTC -3′
Che-1 forward 5′- CCGGAATTCGGATAAGACCAAACTGGCT -3′
Che-1 reverse 5′- CCGCTCGAGGAGTTCTCGAAGGAGCTG -3′
SLC2A1 forward 5′- GTGGGAGGAGCAGTGCTTGG -3′
SLC2A1 reverse 5′- GACGATGCCCAGCTGGTGCA -3′
SLC2A3 forward 5′- TGCGGGGTGCCTTTGGCACT -3′
SLC2A3 reverse 5′- TGGGCTGTCGGTAGCTGGAC -3′
PFKFB4 forward 5′- GCGCATTGAGTGCTATGAGA -3′
PFKFB4 reverse 5′- AATATACGATGCGGCTCTGG -3′
PDK1 forward 5′- GCCCAGGGTGTGATTGAATA -3′
PDK1 reverse 5′- GGACTTCCTTTGCCTTTTCC -3′
SIAH-2 forward 5′- CATCAGGAACCTGGCTAT -3′
SIAH-2 reverse 5′- GGACGGTATTCACATATG -3′
PHD3 forward 5′- TCCCTGGGCTGGACTGACCTTT -3′
PHD3 reverse 5′- CCTCCCCCAAGAAGCCACTGAAA -3′
HIF1-α forward 5′- CCAGTTAGGTTCCTTCGATCAGT -3′
HIF1-α reverse 5′- TTTGAGGACTTGCGCTTTCA -3′
Each medium sample (2 ml) was lyophilized and then dissolved in 700 μl of 1 mM TSP [sodium salt of 3 (trimethylsilyl) propionic-2,2,3,3-d4 acid], 10 mM sodium azide D2O phosphate buffer solution (pH = 7.4) and finally homogenized by vortex mixing for 1 min. After centrifugation (10 min, 10.000 RCF at 22 °C), 600 μl of each resulting supernatant was transferred to a 5-mm NMR tube and used for the NMR analysis2D. 1H J-resolved (JRES) NMR spectra were acquired on a 500 MHz Varian/Agilent spectrometer (Agilent, Santa Clara, CA) using a double spin echo sequence with 4 transients per increment for a total of 32 increments. These were collected into 16 k data points using spectral widths of 6 kHz in F2 and 40 Hz in F1. There was a 2.0 s relaxation delay. Each FID was Fourier transformed after a multiplication with sine-bell/exponential function in the F2 dimension and a sine-bell function in the F1 dimension. JRES spectra were tilted by 45°, symmetrised about F1, referenced to TSP at δH = 0.0 ppm and the proton-decoupled skyline projections (p-JRES) exported using Agilent VNMRJ 3.2 software. Metabolites responsible for the separation between samples from cells treated with hypoxia in the presence or absence of siChe-1 were identified using an in-house NMR database and Chenomx NMR suite v. 7.7 (Chenomx Inc., Alberta, Canada).
NMR spectra pre-processing treatment
The 1D skyline projections exported were aligned and then reduced into spectral bins with widths ranging from 0.01 to 0.02 ppm by using the ACD intelligent bucketing method (1D NMR Manager software (ACD/Labs, Toronto, Canada). To compare the spectra, the integrals derived from the binning procedure were normalized to the total integral region, following exclusion of bins representing the residual water peak (δ 4.33–5.17 ppm) and the TSP peak (δ 0.5–0.5 ppm). The resulting data was used as input for Principal Component Analysis (PCA)  and was performed by using SIMCA-P + version 12 (Umetrics, Umea, Sweden).
HCT116 cell line was transfected with siChe-1 or siRNA negative control using Lipofectamine 3000 (Stealth Life Technologies-Thermofisher), and exposed to hypoxia for 4 h where indicated. Total RNA was extracted from cells using Trizol (Life Technologies-Thermofisher), purified and enriched by Qiagen RNeasy columns for gene expression profiling (Qiagen). Quantity and integrity of the extracted RNA were assessed by NanoDrop Spectrophotometer (NanoDrop Technologies) and by Agilent 2100 Bioanalyzer (Agilent Technologies), respectively. RNA libraries for sequencing were generated in accordance with the standard Illumina TruSeq RNA Library Prep Kit protocol using 2 μg total RNA as starting material. The resulting library was controlled qualitatively with the High Sensitivity DNA Kit (Agilent Technologies, Santa Clara, CA, USA) and quantitatively with real-time analysis employing a SYBR Green qPCR protocol with specific primers complementary to adapter sequences. Based on the qPCR quantification, libraries were normalized to 1 nM and denatured by using 0.1 N NaOH. Cluster amplification of denatured templates was carried out according to the manufacturer’s protocol (Illumina, Inc., San Diego, CA, USA). Sequencing was performed on a Genome Analyzer IIx (Illumina) in paired-end mode, sequencing from each side 51 bp. For each sample generated by the Illumina platform, a pre-process step for quality control was performed to assess sequence data quality and to discard low quality reads.
The analyses were performed by exploiting the RNA-seq analysis workflow RAP  that comprises of read mapping, transcript assembly and abundancy estimation followed by transcript-based differential expression via the Tuxedo suite . Paired-end reads were mapped to the human genome assembly hg19 with TopHat and further analyzed by the Cufflinks-Cuffdiff pipeline to identify differentially expressed genes. We ran the pipeline without novel transcript discovery. Raw data (BAM files) were submitted to the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus database (http://www.ncbi.nlm.gov/geo), with accession ID GSE90599. UCSC Genome Browser screenshots were created through loading bigWig files generated from the accepted_hits Tophat BAM file. Scatterplots were created thanks to the CummeRbund library. Custom plots such as barplots were created via custom R scripting.
Statistical analyses were performed by using the Student two-tailed t-test to compare in vitro experiments. All statistical tests were carried out using GraphPad Prism version 5.0 for Windows, Graphpad Software, San Diego California USA (www.graphpad.com). Probability value of <0.05 was considered statistically significant.
Che-1 protects colon cancer cells from apoptosis induced by hypoxia
Che-1 is required for metabolic adaption to hypoxia
Che-1 modulates genes involved in the response to hypoxia
Che-1 promotes the degradation of HIF-1α through the transcriptional regulation of the RING E3 ubiquitin ligase SIAH2
In response to hypoxia, the cell has devised numerous adaptation strategies enabling it to survive. These mechanisms are mainly implemented at transcriptional level, through the activation and stabilization of the transcription factor HIF-1α . Its activity is mainly reflected in a change of glucose metabolism, which switches from mitochondrial respiration to increased anaerobic glycolysis .
In this work we show how Che-1 plays an important role in the adaptation of the cell to hypoxia. Indeed, Che-1 results activated after hypoxic treatment in HCT116 and HT29 cells (Fig. 1a), and its depletion increases apoptosis following hypoxia, underlying the relevance of Che-1 in cell survival (Fig. 1c and d). Our group, as well as other researchers, has previously shown how numerous types of stress affect the half-life of Che-1, its localization and its functions [16–18]. These effects are mediated through specific Che-1 phosphorylations by DDR checkpoint kinases such as ATM, Chk2 and MK2 . Although hypoxia does not induce DDR, it has been recently shown that it can still induce ATM activity , leaving to assume that even in these stressful conditions for the cell, this kinase may be responsible for the phosphorylation and stabilization of Che-1. Notably, unlike other cellular stresses such as genotoxic insult , in response to hypoxic stress Che-1 is also activated at the transcriptional level, especially during the early hours (Fig. 1b). These findings suggest that Che-1 may be a direct target of HIF-1α, but further studies will be needed to confirm this hypothesis and to characterize the mechanisms of this regulation.
Previous findings have demonstrated that hypoxia elicits alterations in cell metabolism through several mechanisms . In this study we demonstrate an involvement of Che-1 in cell metabolic adaptation upon hypoxic conditions. Indeed, its depletion leads to an inhibition of the metabolic switch observed in cells subjected to hypoxic treatment (Fig. 2a and b). The relevance of the involvement of Che-1 in cell adaption is confirmed by the evidence that its depletion produces a markedly reduction of glucose and glutamine consumption (Fig. 2c), associated with reduced inhibition of oxygen consumption (Fig. 2d) and with less activation of glycolytic enzymes (Fig. 2e).
Hypoxia is one of the most important pathological characteristics of solid tumors, and is clinically associated with a poor prognosis as it may adversely affect the outcome of radiotherapy and chemotherapy [37, 38]. Another important feature of cancer cells is the high glycolytic activity even in the presence of normal oxygen level (Warburg effect) . Our findings show how Che-1, in presence of hypoxia, is required for the production of primary metabolic substrates utilized from cancer cells for ATP production and macromolecules biosynthesis, needed for their proliferation and progression . Preliminary results have shown that Che-1 depletion produces metabolic alterations in cancer cells even in absence of the hypoxic stress, leading us to speculate its possible role played in the Warburg effect [39, 41]. However, further experiments will be required to understand the Che-1’s true role in these complex pathways. Moreover, it also possible that the high levels of Che-1 observed in numerous tumor cell lines, not only contribute to cell proliferation, but also play an important role in the metabolic adaptation of these cells.
Che-1 exerts its functions mainly as a coactivator of transcription , and even in the case of cellular response to hypoxic stress, its depletion produces a decrease in the transcriptional activity of HIF-1α (Fig. 3). This might imply a direct action of Che-1 on transcription along with this transcriptional factor. However, although some preliminary experiments showed the presence of Che-1 on the promoters of genes regulated by HIF-1α (data not shown), co-immunoprecipitation experiments have failed to demonstrate the direct interaction between these two proteins.
Our results, however, show that Che-1 is required for HIF-1α stabilization in response to hypoxia (Fig. 4a). In fact, in Che-1 depleted cells there is a strong decrease in the protein levels of this transcription factor but not of its RNA messenger (Fig. 4b).
In conclusion, our data from these studies reveal a novel role for Che-1 as molecular determinant in the response to hypoxia of colorectal cancer cells. The ability of Che-1 to regulate HIF1-α stabilization, provides a novel metabolic target for tumor treatment.
DNA Damage Response
Hypoxia Inducible Factor 1α
Next Generation Sequencing
Nuclear Magnetic Resonance
Principal Component Analysis
Prolyl Hydroxylase Domain
Protein Von Hippel Lindau
RNA-Seq Analysis Pipeline
Ribonucleic acid sequencing
Seven in Absentia Homolog 2
Solute carrier family
We are grateful to Tania Merlino for the English language editing of this manuscript.
We acknowledge the CINECA award under the ISCRA initiative, for the availability of high performance computing resources and support (project: IscrC_ROCIMM).
This study was supported by the Italian Association for Cancer research (AIRC) to MF (Grant number 15255).
Availability of data and materials
All data generated or analyzed during this study are included in this published article. Raw and processed data are stored in the laboratory of the corresponding authors and are available upon request.
TB, MF, AF, GB, CM conceived and designed the experiments. TB, MCV, LC, AD, VC, SI, CS performed most of the experiments. FDN, FG performed RNA-seq experiments and MP analyzed data. TB and MF contributed to the writing of the manuscript. All the authors read and approved the final version of this manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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