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Introduction

Carcinogenesis is a process where potential alterations in oncogenes, proto-oncogenes, conductive genes and loss in tumor suppressor genes accumulate over time (1). Nevertheless, recent evidence has shown that changes in the metabolism of cancer cells might be an important factor responsible for neoplastic development. In the Hanahan and Weinberg (2) model, the authors propose that changes in metabolism and in cellular microenvironment are important modulators of a tumor's sustainment and progression. Changes in the tissue microenvironment including lack of nutrients, hypoxia, and acidosis activate intracellular signaling pathways leading to a selective cell physiologic program to sustain adaptation to a new microenvironment (3). Also, recent reports have been identified that acidity modulates significantly the generation of metastasis (4). However, there is still a lack of knowledge regarding the potential role of hypoxia and acidity on intracellular transcriptomic phenotype.

During tumor development, the Hydrogen potential (pH) plays a potential role in the cellular division, the normal physiological pH it is estimated at a range between of 7.1–7.2 (1). In this context, when tumor development occurs, this pH decreases; acidifying the microenvironment intra or extracellularly, an acid microenvironment is favored, promoting a metastatic cell phenotype (5,6).

The normal atmospheric level of oxygen is 21%, (normoxia) (7), hypoxia occurs when there is a decrease in oxygen tension below the critical level (1 to 2%) (8). Cellular hypoxia can occur in both physiological conditions and in different pathological conditions (9), and cells adapt immediately by inducing the hypoxia-inducible factor 1α (HIF1α) (10). The heterodimer HIF1α is composed of alpha and beta subunits. In normoxia conditions, HIF1α is constitutively expressed and degraded in a short period of time (11–14). Under a hypoxic scenario HIF1α regulates cellular homeostasis and systemic response by positive or negatively modulating the transcriptional activation of selective genes, including those involved in energetic metabolism [glucose transporter 1 (GLUT1)], angiogenesis [vascular endothelial growth factor (VEGF)], survival [erythropoietin (EPO)], pH maintaining [carbonic anhydrase 9 (CA9)], cell death (CASP3 and CASP7), and others that facilitate oxygen supply or metabolic adaptation to hypoxia (11).

To address the effect of hypoxia on cell physiology, two classical cellular micro-environmental models have been carried out. The first model uses a hypoxia sealed chamber containing a mixture of gases where oxygen is at 1 to 2%. The second one uses a chemical agent, which is highly reactive with oxygen, for example, iron (deferoxamine); flavonoids, for example, quercetin; an inhibitor dependent of 2-oxoglutarate oxygenase; and transition metals, like cobalt (15). In fact, chemical induction with cobalt chloride (CoCl2) is the chemical model most frequently used (16). The gas chamber and chemical models have been used interchangeably to evaluate the effect of hypoxia in different cellular environments. In general, we sought to identify networks of selective metabolic genes activated by both hypoxic models, cobalt chloride and chamber sealed by hypoxia, using microarray analysis. We hypothesized that chemical or cellular hypoxia could regulate time-dependent genes involved in cell viability, apoptosis, and cell proliferation.

Materials and methods

Cell culture and maintenance

We used the cell line Caco-2 [adenocarcinoma colorectal; American Type Culture Collection (ATCC), Rockville, MD, USA] in all assays. Caco-2 cell line was cultivated in Dulbecco's modified Eagle's medium (DMEM; Gibco; Thermo Fisher Scientific, Inc., Waltham, MA, USA) supplemented with 10% fetal bovine serum, 1% penicillin-streptomycin, and was maintained at 37°C, with 5% CO2.

Gas chamber with induced hypoxia

A total of 150,000 Caco-2 cells were seeded in each well, incubated for 24 h to allow their adhesion. The cultures were then placed into the gas chamber (Stemcell Technologies, Inc., Vancouver, BC, Canada). The chamber was closed and the gas mixture (1% O2, 5% CO2 and 94% N2) was injected for 5 min (at a controlled pressure of 2 psi) to ensure equilibrium of the oxygen concentration in the cell medium, after which the chamber was sealed and finally placed in the incubator at 37°C. Hypoxic treatment was maintained for 0, 3, 6, 24, 48 and 72 h.

Chemical hypoxia induction with cobalt chloride (CoCl2)

For chemical hypoxia 150,000 Caco-2 cells were seeded in each well in the 6-well plates, incubated for 24 h to allow their adhesion and 100 µM CoCl2 (Sigma-Aldrich; Merck KGaA, Darmstadt, Germany) were added to the medium and were incubated for 0, 3, 6, 24, 48 and 72 h.

Acidosis assay

To simulate acidosis in our hypoxic model, the pH of the medium was determined by adding 25 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) or 25 mM 3-(N-morpholino) propanesulfonic acid (MOPS). Subsequently, the pH was measured using a SevenMulti standard potentiometer (Mettler Toledo, Greifensee, Switzerland), initially calibrated with reference buffer solutions (Hanna Instruments, Woonsocket, RI, USA), and was finally adjusted to 6.5, 6.7 or 6.9 with 1 M HCl.

Cell viability assay

Once pH and hypoxia conditions were selected, cell viability in the Caco-2 cell line was evaluated, with the chemiluminescent assay using the Cell Titer Glo Luminescent kit (Promega Corporation, Madison, WI, USA). Caco-2 cells were seeded in 96-well plates in a density of 15,000 cells/well and were cultivated for 24 h at 37°C in 5% CO2 to allow their adhesion. They were then exposed to pH and hypoxia protocols during 0, 24, 48 and 72 h and the cell viability was measured. This method is based on the quantification of ATP in the culture, and the levels are directly proportional to the presence of metabolically active cells.

Apoptosis chemiluminescent assay

Apoptosis was determined using the Caspase-GloR 3/7 Assay Protocol kit (Promega Corporation). This assay measured caspase 3/7 activity and the luminescent signal that are directly proportional to the apoptosis level in the evaluated cells.

RNA extraction, reverse transcription-quantitative polymerase chain reaction (RT-qPCR)

To evaluate the effect of cellular stress by hypoxia and acidity on cell metabolism, we carried out a Real-Time mRNA expression analysis of a panel of genes involved in hypoxia-metabolism: HIF1α, EPO, GLUT1, VEGF, LDH and CA9 by qPCR and SYBR-Green using GAPDH as gene normalizer. We previously performed an assay for the selection of the endogenous gene (data not shown) was made by the comparison of GAPDH, GPI and EMC7 using a gene stability assay (17). Our results showed that compared to the GPI and EMC7 genes, the GAPDH gene was the most stable gene. Cultures were maintained under the hypoxia and acid protocols and total RNA was isolated using TRIzol reagent (Invitrogen; Thermo Fisher Scientific, Inc.), following instructions from the fabricators, followed by a treatment with DNAse (Qiagen, Inc., Valencia, CA, USA). RNA integrity and concentration was evaluated by spectrophotometry with Nanodrop 8000 (Thermo Fisher Scientific, Inc.).

cDNA was synthesized from 1 µg of total RNA using High-Capacity cDNA Reverse Transcription kit (Applied Biosystems; Thermo Fisher Scientific, Inc.), following the fabricator's instructions. The primers for the metabolic genes and the constitutive gene were designed in accordance with the sequences from the database Gen Bank (Table I). The real-time PCR thermal cycler LightCycler 480 (Roche Diagnostics, Indianapolis, IN, USA) and the reagent SYBR-Green I Master (Roche Diagnostics) were used. All the qPCR reactions were triplicated and three independent assays were carried out. The expression analysis was carried out by the relative 2−ΔΔCq quantification method (18).

Table I. Primer sequence used in the quantification of the expression of genes sensible to stress.

Table I.

Primer sequence used in the quantification of the expression of genes sensible to stress.

A, Hypoxia sensitive genes
Gene	Sequence
HIF1α
F	ATCCATGTGACCATGAGGAAATG
R	TCGGCTAGTTAGGGTACACTTC
EPO
F	GGAGGCCGAGAATATCACGAC
R	CCCTGCCAGACTTCTACGG
CA9
F	GGATCTACCTACTGTTGAGGCT
R	CATAGCGCCAATGACTCTGGT
VEGF
F	AGGGCAGAATCATCACGAAGT
R	AGGGTCTCGATTGGATGGCA
GLUT1
F	TCTGGCATCAACGCTGTCTTC
R	CGATACCGGAGCCAATGGT
B, Endogenous gene
Gene	Sequence
GAPDH
F	ACAACTTTGGTATCGTGGAAGG
R	GCCATCACGCCACAGTTTC

[i] GLUT1, glucose transporter 1; VEGF, vascular endothelial growth factor; HIF1α, hypoxia-inducible factor 1A; EPO, erythropoietin.

Microarrays analysis (expression profiles)

The RNA, sample processing, microarray hybridization, and gene expression analysis were conducted using the GeneChip 3′ IVT Express kit (Affymetrix; Thermo Fisher Scientific, Inc.). The hybridization mixture was prepared and applied to the GeneChip Human Genome U133 Plus 2.0 Array (Affymetrix; Thermo Fisher Scientific, Inc.), measuring >43,000 transcripts representing >20,000 human genes. Washing and scanning processes were realized in the Fluidics Station 400 and GeneChip Scanner 3000 7G, respectively, and preliminary data analysis was completed using Microarray Suite software version 5.0.0.032. Normalization was performed using robust microarray analysis and quantile normalization.

Statistical analysis

Data were compared using a two-way ANOVA (treatment and time) and Dunnet's test was performed to compare viability and apoptosis in all trials. The variables were previously normalized (Kolmogorov-Smirnov). The analysis was performed using SPSS software v20.0 (SPSS, Inc., Chicago, IL, USA). All graphs were done with GraphPad Prism v6.0 (GraphPad Software, Inc., La Jolla, CA, USA). All assays were performed in triplicate. Results are expressed as the mean ± standard deviation.

Results

Viability assays in chemical and sealed gas chamber hypoxia models

To determine the effect of hypoxia on cell viability, cells treated with CoCl2 or maintained in the gas chamber were quantified as described. When comparing viability based on time and treatment, a highly significant difference was found in time (F=15.396, P<0.01) and for the treatment (F=17.288, P<0.01). We found a significant decrease in cell viability in cells treated with CoCl2 at 48 h and 72 h compared to the control without chemical hypoxia (Fig. 1A). In contrast, hypoxia-induced in sealed gas chamber shows a substantial increase in cell viability at a similar time frame to chemical hypoxia, 48 and 72 h (Fig. 1B).

Figure 1. Cell viability and apoptosis under both models of hypoxia. (A) Chemical model and (B) Gas chamber. It is observed that in the (A) chemical model there is cell death starting at 48 h while in the (B) gas chamber model there is an increase in cell viability, compared to the control. There is the same cell death (apoptosis) in the (C) chemical model with respect to the normal control (statistical significance *P<0.01), while the contrary occurred in the (D) cells maintained in the gas chamber at 72 h.

Apoptosis evaluation in chemical and sealed gas chamber hypoxia models

The caspase 3/7 activity ratio was measured to evaluate apoptosis in both hypoxia models. The results of apoptosis displayed significant changes between the control at time 72 h (F=964.923, P<0.01) and for the treatment of hypoxia chamber (F=1,154.081, P<0.01). It was observed that chemically induced hypoxia does not promote apoptotic activation when compared to the normoxia control in all the times (Fig. 1C). We did find that hypoxia induced by the gas chamber decreased apoptosis significantly at 72 h compared to the normal control (Fig. 1D). This data suggests that a hypoxic environment induced by gas chamber incubation may have a severe effect on oxygen depletion leading to cell death and does not favor proliferation.

Evaluation of the effect of pH and pH+ hypoxia on viability and apoptosis in both models of hypoxia

We evaluated the acidity effect in the chemical and sealed gas chamber hypoxia models by setting three pH values: 6.5, 6.7 and 6.9. An increase in cell viability was observed at 48 and 72 h when the cells were exposed to the most acidic pH (Ph 6.7 and 6.9), compared against normoxia conditions (Fig. 2A and B). We also found that viability and apoptosis showed time-dependent significant changes (F=18.759, F=55.840, P<0.01), whereas at pH 6.5 there was a decrease in cell proliferation when compared to normoxia. This indicates that pH 6.5 significantly affects cell viability (Fig. 2A). We also found that changes in pH do not affect the number of apoptotic cells at 24, 48 and 72 h when compared to normoxia (Fig. 2B).

Figure 2. Acidity effect on cell viability. Cell viability was measured at different pH: 6.5, 6.7 and 6.9 in (A) standard (normoxia) culture conditions, (B) in chemically induced hypoxia, and (C) by gas chamber. (A) In normoxia conditions and (B) chemical model induced cell viability and behavior in cell viability has a pattern similar to the increase in plate confluence. (C) In the hypoxia induced by chamber an increase in viability at 48 or 72 h with pH 6.7 was observed. Apoptosis induction (D) in the Caco-2 cell line in normoxia conditions at acid pH increases with time but is independent of pH concentration (statistical significance *P<0.01). 6.9-Hco, Hypoxia chemically induced with pH 6.9; 6.7-Hco, hypoxia chemically induced whit pH 6.7; 6.5-Hco, hypoxia chemically induced with pH 6.5; 6.9-Hca, hypoxia-induced in gas chamber with pH 6.9; 6.7-Hca, hypoxia-induced in gas chamber whit pH 6.7 and 6.5-Hca, hypoxia-induced in gas chamber with pH 6.5.

Next, we determined cell viability in the pH+chemical hypoxia model. We found no changes at any of the three pH values analyzed (Fig. 2C). Finally, we observed that the pH+sealed chamber hypoxia model promotes an increase in viability at pH 6.9 and 6.7 when compared to normoxia at 48 h (Fig. 2D). After 72 h, we still found an increase in cell viability at pH 6.7 and no changes at pH 6.9 and 6.5 (Fig. 2D). These results reproduce, at least in part; those observed in the independent measurements of hypoxia or acidity and support the effect of pH on cell viability during hypoxia.

Effect of pH on the expression of hypoxia response genes

To implement the standardization of the hypoxia model, we initially evaluated the effect of pH on six genes related to hypoxia response: HIF1α, EPO, VEGF, GLUT1, CA9, and LDH. These results suggest that pH changes are not involved as genetic modulators of HIF1α, EPO, VEGF, GLUT1, CA9, and LDH (Fig. 3).

Figure 3. pH effect on the expression of six genes. Expression of HIF1α, EPO, VEGF, GLUT1, CA9 and LDH in Caco-2 cells at different induction of pH measured by qPCR. Expression levels are normalized to expression of the housekeeping gene GAPDH.

Gene expression comparison in the two models of hypoxia

We evaluated the expression of the same six genes in two hypoxia models. When we compared the two models we observed that the hypoxia induced by CoCl2 promotes an acute time specific increase in gene expression at 6 h and later showed a decrease to control values (Fig. 4). Observations of the hypoxia-induced sealed gas chamber show a stable effect on gene expression displaying a lower, gradual and maintained elevation. At 72 h, the effect is maintained and HIF1α, EPO, and CA9 are still overexpressed. Finally, the gene GLUT1 stands out in that it only maintains basal levels of expression in both models (Fig. 4). At this stage, our results suggest that chemical hypoxia modulates gene expression profile at 6 h whereas hypoxia-dependent chamber leads its major effects at 72 h.

Figure 4. Expression of six genes in two hypoxia models. Expression of (A) HIF1α, (B) EPO, (C) VEGF, (D) GLUT1, (E) CA9 and (F) LDH in Caco-2 cells at different induction of hypoxia measured by qPCR. Expression levels are normalized to expression of the housekeeping gene GAPDH. Hco, hypoxia chemically induced; Hca, hypoxia-induced in gas chamber.

Finally, we found an early increase in HIF1α expression that peaked at 6 h following a decrease later on (Fig. 4A). At 6 h, we also identified an increase of EPO, VEGF, GLUT1 and LDH genes (Fig. 4B-D and F). No significant changes were found in CA9 (Fig. 4E).

Global gene expression profile in the two hypoxia models using microarrays

We used microarray analysis to determine the effect of global gene expression in the two hypoxia models: Chemical (6 h) and sealed chamber (72 h) induced hypoxia. We identified a gene expression profile for each model (Fig. 5A and B). The chemical hypoxia model promotes downregulation of 3 genes (CYP1A1, CYP1B1, and CYCS) and the overexpression of 27 genes: TXNIP, ALDH1L2, FAM169A, ZBTB20, RORA, CREBRF, FBXL20, LOX, ZNF846, YPEL2, PPM1A, FAM225A, NGFR, VLDLR-AS1, MT1M, ANKRD37, SPAG4, BNIP3L, ATP8B2, AKR1C1, SLC7A11, AQP10, AKR1C1, KLHL24, DDIT4, PCK1, JMY. For the sealed gas chamber model, we found the subexpression of 2 genes: UTS2 and MMP1 and the overexpression of 28 genes: LINC01293, CASP5, RORA, SLC6A8, ZG16, GNRH2, CREBRF, FAM129A, ANKRD37, CLIC3, ENO2, PCAT6, BHLHE40, BNIP3L, TMEM45A, TMEM27, DDIT4, FRMD3, CXCR4, NRN1, ATP8B2, SPAG4, LGALS14, AQP10, LOX, BNIP3, PDK1, NDRG1. We also observed that nine genes were shared between both models (SPAG4, LOX, BNIP3L, ATP8B2, AQP10, RORA, DDIT4, CREBRF, and ANKRD37) (Fig. 5C).

Figure 5. Gene expression levels in normoxic and hypoxic cells. (A and B) Heatmap of the genes from the hypoxia profile in both models. The color bar shows the fold change relative to the median expression of each gene across all conditions. The data shows the mean value of two independent experiments, and are log2 transformed. (C) Venn Diagram shows genes from hypoxia profile in both models with the deregulated genes obtained in microarray expression. Left: Signature hypoxia chamber shows 21 deregulated genes. Right: Signature hypoxia CoCl2 shows 21 deregulated genes. Intersection: Nine genes shared in both models.

On the other hand, we compared the gene signatures obtained from both models with the report of Sørensen et al (19) that reported a signature of 15 genes sensitive to hypoxia, finding 3 common genes in all the genetic signatures, suggesting that our data correlates with a universal genetic signature for the hypoxia profile (Fig. 6).

Figure 6. Deregulated genes. Venn diagram with the deregulated genes obtained in microarray expression. Left: Signature hypoxia chamber shows 30 deregulated genes. Right: Signature hypoxia CoCl2 shows 30 deregulated genes. Center: Signature Sorensen 2015 shows 15 deregulated genes. Intersection: Three genes, which are shared in three signatures.

Discussion

Experimental evidence has shown the effect of hypoxia induction in vitro using the technical strategies implemented in our study (20–23). In general terms, one of the best candidates to identify the hypoxia model is the evaluation of HIF1α expression. However, additional potential candidates are emerging to validate sensor genes in hypoxia and its metabolic effect, but they are not yet validated (13,24,25). In this study, we evaluated five target genes of HIF1α along with parameters of viability and apoptosis in two models of hypoxia, widely cited in the literature and with the potential to be interchangeable. We found that both models show similarities in the final induction of hypoxia and show no significant differences. However, we identified selective time-dependent changes particularly related to the induction of HIF1α as a response to hypoxia. In addition, when the expression of the rest of the genes is analyzed, a variable expression is observed in the different times evaluated. For example, at 6 h in the CoCl2 model, we found the highest expression of all the genes evaluated, followed by a low expression and tendency to reach normal values in later times, such as 48 and 72 h. In contrast, the gas chamber model showed increased and gradual gene expression, which was maintained for up to 72 h. This indicates that in the chemical model, the cells normalize the system rapidly at 3 to 6 h and in the hypoxia CoCl2 model the effect is rapidly reversed. On the other hand, in the gas chamber model, the cells slowly adapted to the change in a time-dependent manner and the effect was observed and maintained in response to hypoxia.

We propose that these observations must be considered when choosing one of this hypoxia models in order to define if the effect that is desired to quantify is analyzed from 3 to 6 h or even a more prolonged time, as was proposed recently (26).

One of the major achievements of our study is that we show, for the first time, a comparison in both chemical and chamber hypoxia models in different scenarios and identify a selective expression of the genes induced by HIF1α. Our results also show that the effect of hypoxia induction is time sensitive and selective for each model. For example, a study reported by Danli Wu in 2011 using both hypoxia models quantifies HIF1α as the only hypoxia marker in this scenario (16).

Potentials pathways mainly affected by hypoxia are glycolysis and oxidative phosphorylation (27). In this scenario cell viability assays may have technical limitations when the main goal is to quantify cell viability (28–30). For instance, no changes in oxidative phosphorylation and cellular ATP levels have been reported in in vitro trials trying to simulate conditions of lack of nutrients and hypoxia models (28–30). In our project we determined cell viability by using ATP quantification as a reliable marker of cell viability. However, we also agree that ATP levels can be altered by hypoxic conditions and this measurement might not be an indicator of cell viability so metabolic activity marker in cells.

Despite that our two hypoxia models show a selective time leading to HIF1α upregulation, we found 9 common hypoxia-inducible genes between both models, as observed in the Venn diagram (Fig. 5C). Our data correlate with previous results showing that hypoxia regulates similar metabolic pathways such as: Glycolysis, pyruvate metabolism, ATP synthesis, regulation of processes (catalytic activity, hydrolases, oxidoreductases) and other cellular processes like adhesion or transport (Fig. 7) (23,24). In particular, Sørensen et al (19) in 2015 reported a 15 hypoxia-regulated genes signature as a universal hypoxia profile in various cancer cell types. Our data correlate with the LOX, ANKRD37, BNIP3L genes (Fig. 6), previously reported by Sørensen et al (19). This indicates that these three genes are highly regulated by the effect of hypoxia regardless of the hypoxia model induced with. At this stage, we propose that LOX, ANKRD37, BNIP3L genes might potentially be added as universal hypoxia genes along with HIF1α, EPO, VEGF, GLUT1, CA9 and LDH previously characterized.

Figure 7. Genes regulated by hypoxia. Genes that are regulated by hypoxia and acidity in our work. The first line shows genes previously described. In blue: Genes traditionally referred to as genes of response to hypoxia; in red: A group of genes that have recently been considered as new hypoxia response genes; in the second line: Gas chamber model; and in the third line: Chemical model, we describe genes that were identified in this work and have not been previously reported.

In summary, in this study, we show that the effect of hypoxia is time-dependent regarding the hypoxia model used. Chemical induction simulates an acute hypoxia whereas chamber of gases maintains the effect of hypoxia in extended periods. These two hypoxia models share 9 susceptible genes and when comparing the two models of hypoxia with that reported by Sørensen et al (19), we found 3 genes (LOX, ANKRD37, BNIP3L) that could be considered as universal hypoxia genes along with HIF1α, EPO, VEGF, GLUT1, CA9, and LDH.

Our study opens a new research area showing the significant value of identifying potential oxygen sensors and key genes involved in metabolism and stress conditions during hypoxia. Finally, it might be required to study the expression and methylation of these genes to amplify the knowledge in this area and to better evaluate the effect of hypoxia in the mechanisms of cancer progression and metabolism of tumor cells.

References