Transcriptome profiling of a cell line model for malignant transformation in response to moderate hypoxia
Frida Danielsson 1 , Erik Fasterius 2 , Kemal Sanli 1 , Cheng Zhang 1 , Adil Mardinoglu 1 , Cristina Al-Khalili 2 , Mikael Huss 3 , Mathias Uhlén 1 , Emma Lundberg 1
1. Science for Life Laboratory, KTH-Royal Institute of Technology, 171 21 Stockholm, Sweden
2. School of Biotechnology, Royal Institute of Technology, Stockholm, Sweden 3. Science for Life Laboratory, Department of Biochemistry and
Biophysics, Stockholm University, Box 1031, SE-171 21 Solna, Sweden
Abstract
The four-step BJ model for malignant transformation enables defined steps of
tumorigenesis to be studied, and is separated into normal, immortalized, transformed and metastasizing stages 1 . In human tumor tissues, hypoxia is a commonly
observed feature, as a result of rapidly proliferating cancer cells outgrowing their
surrounding vasculature network. Transformed cancer cells are known to exhibit
phenotypic alterations, enabling continuous proliferation despite a limited oxygen
supply. Here, we used RNA-sequencing (RNA-seq) to explore differences in gene
expression across the BJ model during cultivation in atmospheric oxygen compared
to a moderate hypoxic condition (3% O 2 ). We identified 909, 1090, 1247 and 794
significantly differentially expressed genes (DEGs) due to decreased oxygen supply,
in the four stages respectively. A majority of DEGs are downregulated in moderate
hypoxia in all stages except for the SV40-transformed stage. Several genes related
to proliferation and angiogenesis are upregulated across all stages in low oxygen,
such as E2F, BRCA1 and ANGPTL4. Even though the cell lines across the BJ model
are highly similar from a global view, the introduction of SV40 Large-T renders a
distinct adaptation to the altered condition. This is mainly manifested in altered lipid
metabolism and reversed expression patterns for several genes compared to the
previous stages within the model, such as the antioxidant SOD2 and VEGFA with its
up-stream regulator CXCL8.
Introduction
The transformation of primary cells into malignant counterparts capable of forming tumors is a multistep process where changes in the genome give rise to new characteristics 2 . These include increased rate of proliferation, the ability to escape survival
restrictions, increased vascularization and rewiring of the energy
metabolism 3 . To enable the study of defined steps of tumorigenesis, an isogenic model system for malignant transformation was generated by introducing genetic changes in the human fibroblast cell line BJ, thereby establishing an important foundation for future cancer research 1 . The model was generated by ectopic expression of the telomerase catalytic subunit (hTERT) followed by stepwise additions of two oncogenes, the Simian virus 40 Large-T and the oncogenic and hyperactive version of H-ras. With these sequentially added alterations, a model consisting of four cell lines was created, starting with the primary stage followed by an
immortalized stage (active telomerase, BJ hTERT), a transformed tumor forming stage (BJ hTERT SV40) and finally a fully transformed stage where cells are capable of metastasizing (BJ
hTERT+ SV40 RASG12V). Previous analysis of the transcriptome changes within this model showed that a
majority of genes are downregulated in the course of increased malignancy, and that most of the upregulated genes are directly related to
proliferation, whereas downregulation mainly affects a more functionally diverse set of extracellular proteins.
Analysis of differential gene
expression within the model revealed a number of interesting protein
candidates, for example BDH1, ANXA1, ANPEP and ANLN whose expression were compared in tumor tissues of matching malignancy
grades, indicating that the BJ model is a highly relevant model system for the study of human tumorigenesis and proliferative capability 4 .
One of the most important elements for the life of mammalian cells is oxygen. Oxygen is involved in almost all cellular functions and during normal oxygen conditions mammalian cells generate the energy that they require for molecular processes by oxidative phosphorylation in the mitochondria.
As first demonstrated by Otto Warburg in the 1920’s and thereafter confirmed in numerous different studies,
transformed cancer cells prefer to
generate energy through glycolysis instead of oxidative phosphorylation, even under aerobic conditions and regardless of the fact that it is relatively inefficient in terms of ATP
generation 5,6 . This change in cellular energetics is today known as one of the hallmarks of cancer and solid tumors often contain areas where no oxygen is present as a result of the tumor outgrowing the capacity of its surrounding vasculatory network.
Creation of oxygen-deprived microenvironments promotes
malignant progression through clonal selection of aggressive phenotypes and subsequent poor prognosis 7 . On a molecular level, decreased oxygen levels in tumors are associated with increased hypermethylation events with consequences on gene
expression 8 . Promoter regions of genes involved in cell-cycle arrest, DNA repair and apoptosis, glycolysis, metastasis and angiogenesis have been shown to be targets for hypoxia- induced hypermethylation.
To investigate the transcriptional changes induced by decreased
oxygen supply, and how the response differ between the four isogenically matched cell lines at different degree of malignancy, we evaluated the whole
transcriptome using RNA-seq after cultivation in both the standard in vitro setup of atmospheric oxygen and at moderate hypoxia (3% O 2 ) for six passages. By comparing differential gene expression across the model, we explored how the different cell lines, despite their overall resemblance, respond differently to cultivation in 3%
oxygen. The SV40-transformation appears again 4 to induce a relatively dramatic phenotypic shift, reflected in upregulation of several genes that are downregulated in the previous stages, as well as increased lipid metabolism.
Materials & Methods
Cell cultivation
All four cell lines in the model were cultivated in Dulbecco’s modified Eagle’s Medium (Sigma-Aldrich) supplemented with 10% Fetal Bovine Serum (Sigma-Aldrich) for six
passages at 37 °C. Cultivation was performed in a humidified atmosphere containing 5% CO 2 and both under atmospheric oxygen pressure and 3%
oxygen for six passages. The cells were grown up to 80% confluence and counted with a Scepter 2.0 Cell
Counter (Merck Millipore, Billerica, MA,
USA). Cells were cultivated in
duplicate plates in parallel for each cell line and oxygen condition.
RNA sequencing
RNA was extracted from the cells using the RNeasy kit (Qiagen),
generating high quality total RNA (i.e.
RIN>8) that was used as input material for library construction with Illumina TruSeq Stranded mRNA reagents (Illumina). Duplicate samples for each cell line were sequenced on the Illumina HiSeq2500 platform.
Raw sequences were mapped to the Human reference genome GrCh37 and further quantified using the Kallisto software 9 to generate normalized Transcript Per Million (TPM) values.
TPM values for genes were generated by summing up TPM values for the corresponding transcripts generated by Kallisto. Genes with a TPM value greater than 1 were considered as expressed. Differential expression analysis was performed with the Kallisto software (v0.42.1) 9 on the raw FASTQ files with default parameters, followed by differential expression analysis using the TXimport (v1.0.3) 10 and edgeR (v3.14.0) 11 R packages.
Detailed code is available as an RMarkdown document in the supplementary data. The GRCh37
human reference genome assembly was used in all steps of the analyses.
Cell authentication
Cell line authenticity and mutational analysis was performed as previously described in 12 . Briefly, the raw RNA- seq data was aligned using the 2-pass method of the STAR (v2.5.1b) aligner, followed by de-duplication, re-
calibration and variant calling with the Genome Analysis Toolkit (GATK) Best Practices workflow (v3.5.0) 13 . The resulting variant calls were annotated using SnpEff and SnpSift (v4.2)
followed by filtering and analysis using in-house Python and R scripts 14,15 .
Metabolic profilingMetabolic profiling of the BJ model was elaborated by following a top-down analysis
approach within the broad category of metabolism in the KEGG Orthology database 16 . The program PACFM (v.
0.2) 17 was used to plot the log fold
change data between the atmospheric
and 3% oxygen treatments of the cell
lines at different hierarchy levels within
metabolism. Data was plotted by
preserving the absent functional
categories in each cancer cell line, in
order to facilitate intuitive comparison
between the different stages of the BJ model.
Results
Different model stages show varying responses to moderate hypoxia
In order to generate a complete overview of the transcriptome at the different stages in the BJ model and under atmospheric versus 3% oxygen levels, we performed RNA-seq
analysis. An overview of the isogenic model system and the experimental setup can be seen in Figure 1A. Cell authentication was performed as previously described to confirm the isogenic nature of the cells and confirm the model’s stage-specific mutations 12 . The results corroborate both the identity of the cells and the presence of mutations only in expected cell lines (see Supplementary Data). Gene
expression was estimated using the Kallisto software 9 , followed by hierarchical clustering of Spearman correlations for all expressed protein coding genes across the model (N=14,994). While there are high correlations between all the samples (Spearman correlation coefficients >
0.9, Figure 1B), there is a significant pattern within the model itself: the primary and immortalized samples cluster by model stage whereas the two transformed cell lines cluster according to oxygen level, indicating a higher degree of adaptation to the change in oxygen supply. To investigate this adaptation further, differential gene expression was analyzed with edgeR, using a cutoff of 0.01 for adjusted p-value, a fold
change of 2 and expression of 1 TPM in at least one of the samples being compared. Going from the primary cells to the immortalized cells, to the transformed cells and finally to the metastasizing cells, we identified 909, 1090, 1247 and 794 differentially expressed genes (DEGs), respectively between the two oxygen levels (Figure 2A). The largest difference is observed in the SV40-transformed cells,
indicating that this change promotes
alteration of gene expression as a
consequence of low oxygen. The
transcriptional response to oxygen
depravation is more accentuated in the
transformed tumor-forming cells (BJ
SV40) than in any of the other isogenic
cells analyzed. A majority of DEGs are
downregulated at low levels of oxygen
in all cell lines within the model, except
for the SV40-transformed cell line (Figure 2B).
Functional enrichment reveals changes in proliferation and angiogenesis as response to hypoxia
Functional enrichment analysis was performed using the Database for Annotation, Visualization and
Integrative Discovery (DAVID) tool, for upregulated and downregulated genes separately 18 . The three functional clusters with highest significance, involving upregulated genes in the primary cell line, are related to angiogenesis, proliferation and response to decrease in
oxygen/hypoxia respectively.
Interestingly, functions related to angiogenesis and proliferation are also among the top three functions
involving downregulated genes in the primary cell line (Figure 3). This reflects the generalized view provided by functional enrichment analysis, and the complexity of the genetic circuitry affected by the change in oxygen supply. The upregulated genes in the primary cell line that are known
mediators of hypoxic response include PLOD2 that is a known prognostic factor upregulated in several tumors and PDK1 that is known to be critical
in the adaptation to hypoxia, by attenuation of mitochondrial ROS production 19,20 . PDK1 is significantly upregulated in low oxygen in the three first steps in the BJ model, where it reaches the same expression level as in the metastasizing cell under
atmospheric oxygen pressure. Genes
involved in angiogenesis such as E2F,
BRCA1 and ANGPTL4 are also among
the significantly upregulated genes in
the primary cell line. The top three
upregulated clusters in the SV40-
transformed cell line are mainly related
to responses to external stimuli and
hypoxia, while the corresponding
clusters of the last stage of the model
are more related to cell migration,
proliferation and apoptosis. The
downregulated clusters of the third
stage are related to various RNA
processing functions, as well as
regulation of apoptosis, while the
fourth stage has downregulated
clusters related to interferon signaling
and the immune response. Taken
together, the enrichment analyses
indicates that the four-stage model not
only acquires important cancer-related
milestones with decreased degree of
differentiation as previously shown, but
also that these have a large effect on
the cells’ response to lower oxygen
levels.
SV40-transformation leads to increased adaptation to lowered oxygen supply
By comparing DEGs shared across the model stages, the impact of the Large- T oncogene becomes even clearer:
not only are more genes differentially regulated in the transformed stage than in the others, the DEGs shared with the previous immortalized stage are also more often regulated in the opposite direction (Figure 4a). Out of the 216 DEGs that are shared
between the immortalized and transformed cells, 157 genes are regulated in opposite directions, and a majority of these are upregulated in the SV40-transformed cells. For example, Interleukin 8 (CXCL8) is upregulated more than five-fold in the SV40 transformed cells (31 TPM to 788 TPM) but is downregulated in the immortalized cells (84 TPM to 65 TPM). This protein is known to promote the expression of VEGFA which mediates homeostatic adaptation to hypoxic condition by promoting vascularization to compensate for the decrease in oxygen supply 21 . VEGFA is differentially expressed and
upregulated in the SV40 transformed cells and displays expression levels
changing from 30 TPM in atmospheric oxygen to 150 TPM in 3% oxygen.
Another interesting protein,
upregulated from 74 TPM to 345 TPM in the SV40-transformed cells, and downregulated from 1994 TPM to 166 TPM in the immortalized cells, is the mitochondrial superoxide dismutase, SOD2. This enzyme catalyzes the transformation of superoxide radicals (O 2 -) into either oxygen (O 2 ) or peroxide (H 2 O 2 ), and has been
considered one of the most important regulators of cellular redox state in normal and cancer cells. Due to its anti-apoptotic effect, overexpression of SOD2 has been linked to increased invasiveness of tumor metastases 22,23 . Increased levels of SOD2 has been linked to increased metastatic
capability by enabling cancer cells to maintain an increased growth potential and stay protected from excess ROS that would otherwise lead to apoptosis and necrosis 24 .
The dramatic change due to the SV40-
transformation is further shown among
the number of DEGs by comparing
expression within the model, rather
than between oxygen levels for the
same stages. Here, we observe 1330,
2976 and 732 DEGs between primary
versus immortalized, immortalized
versus transformed and transformed versus metastasizing, respectively, at atmospheric oxygen concentration, under which the model was initially created. Interestingly, the
corresponding numbers at low oxygen levels are 1072, 2000 and 288. All stages of the model show a larger amount of variation in terms of number of DEGs for atmospheric oxygen levels, while the differences become less pronounced in low oxygen. Again, the comparison between the last two stages of the model is clearly different from the first two, showing very few DEGs. This is in line with the
hypothesis that the model achieves most of its oxygen adaptations in the third stage. This could be explained by the fact that both of the two stages affect highly interconnected
oncogenes p53 and HRAS. Among the 264 DEGs across oxygen conditions that are shared between the primary and the immortalized cells, 89%
(N=236) are regulated in the same direction. Several of the upregulated genes are related to proliferation, for example the E2F receptor, MCM10 and the most commonly used marker for proliferation used in clinical
settings, MKI67 (Figure 4b). Among DEGs shared between the
transformed and metastasizing cells, a
large majority (78%) is regulated in the same direction. Several genes related to lipid metabolism and increased motility are upregulated, for example several chemokines and colony stimulating factors CSF2 and CSF3 (Figure 3c).
Transcriptome changes in low oxygen resembles the process of malignant transformation
Of all the 909 DEGs identified in the primary cell line, over a third are also DE within the model during the
atmospheric condition under which the model system was created, and a majority of these are regulated in the same direction. Several genes involved in proliferation and vasculature development that are upregulated due to the introduction of hTERT, are also up-regulated in 3%
oxygen, such as E2F receptors and several chemochines. ALDH1A1 that was previously shown, both on transcript and protein level, to completely disappear after
immortalization in atmospheric oxygen,
is also downregulated in 3% oxygen,
going from 429 TPM in atmospheric
condition to 54 TPM in 3% oxygen. For
the immortalized cell line this is even
clearer, sharing 60% of the DEGs
across oxygen conditions with DEGs that are differentially expressed due to the SV40 transformation. Several genes display similar patterns, for example upregulation of ANLN, AURKA and ADAM19.
SV40 triggers capability of metabolic switching
Among the DEGs observed across the oxygen conditions, the totality of
enzymes related to metabolic pathways exhibit an overall upregulation in low oxygen concentration compared to the atmospheric conditions (Figure 5).
Across the different stages of the model, the transformed tumor forming state (SV40-transformed) displays a relatively higher number of
upregulated metabolic genes
compared to the rest. Downregulation of genes belonging to lipid,
carbohydrate and amino acid metabolism in the immortalized cell line under moderate hypoxia is conspicuous, as shown in Figure 5b.
As shown in Figure 5c-d, upregulation of biosynthetic pathways during low oxygen supply in the transformed and metastasizing cell lines is evident. The downregulated enzymes in the
immortalized cell line are almost entirely reversed in favor of lipid,
carbohydrate and amino acid
metabolic pathways in the transformed cell lines. For example the fatty acid desaturase FADS1 is downregulated from 155 TPM to 71 TPM in the immortalized cells, but upregulated from 65 TPM to 137 TPM in the transformed cells. This protein regulated the unsaturation of fatty acids and in a recent study by others, reduced expression of this protein was shown to correlate with poor prognosis in small lung cancer, contradictive to our results and indicating a more complex role of this protein 25 . The aldo-keto reductase AKR1B1, which is involved in glycerolipid metabolism and often over-expressed in human cancers, show dramatic upregulation from 237 TPM to 1156 TPM in the SV40-transformed cells, whereas it is downregulated from 2490 TPM to 546 TPM in the immortalized cells.
Stearoyl-CoA (SCD), involved in the metabolism of fatty acids is another example of a protein that shows reversed regulation between the immortalized and transformed cells.
Knock down of this protein has
previously shown to induce apoptosis
in tumor cells grown in vitro, indicating
the ability for the SV40-transformed
cells to evade apoptosis through
upregulation of this metabolic process 26,27 . Another prominent
difference between the different stages of the model is the downregulation of energy metabolism in metastasizing cell lines (Figure 5d). Figure 5 also shows that enzymes taking role in lipid, carbohydrate and amino-acid metabolic pathways are largely shared within these pathways, resulting in the dissemination of the up or
downregulation to the entirety of these broad groups of pathways throughout the functional hierarchy (see the central lines in Figures 5 a-d).
Discussion
By profiling transcriptome changes under atmospheric and 3% oxygen conditions, we observe that even though the four cell lines in the BJ model for malignant transformation are highly similar, they display striking differences in their response to decreased oxygen supply. These differences are most prominent after the introduction of SV40 Large-T. The transformation with the SV40 Large-T oncogene has a large effect on the RNA expression of the cells, while the addition of the mutated HRAS has a smaller, relative effect. This separation
of the BJ model system into two groups representing pre and post transformation states is in accordance with our previous findings 4 .
The overall upregulation of the enzymes belonging to metabolic pathways throughout the different stages of cancer cell lines indicates that 3% oxygen supply, which is
considered moderate hypoxia, triggers metabolic activity. Relatively high upregulation of lipid, carbohydrate and amino acid metabolic pathways in the transformed tumor forming state in comparison to the previous cell lines may suggest a potential confirmation for the suggested mechanism, by which the cells increase the
consumption of ATP to trigger elevated fluxes of glycolytic pathways 28 . The metastasizing cell line expands the ATP consumption to other biosynthetic pathways, including glycan
biosynthesis and the metabolism of other amino acids in addition to a prominent increase in the
carbohydrate metabolism (Figure 5d).
The downregulation of oxidative
phosphorylation in metastasizing cells
during decreased oxygen supply
indicates the adaptation capability of
these cells to anaerobic conditions. In
contrast to the debated Warburg
effect, according to our results, the
metastasizing cells might prefer oxidative phosphorylation under
aerobic conditions whereas they down- regulate the genes taking role in oxidative phosphorylation favoring glycolytic pathways during low oxygen supply. Oxygen concentrations in human tumors are highly
heterogeneous, often containing regions where the oxygen
concentration reaches zero, known as anoxic regions 29 . Even though tumor hypoxia often manifests itself with lower oxygen concentrations than used in this study, we observe large differences in gene expression across the conditions. The BJ model has several inherent limitations in its ability to mimic the in vivo response to
hypoxia, mainly by being of fibroblast origin and created under atmospheric oxygen pressure. This greatly limits its use to studying the effects of oxygen levels on already transformed cells, and not the process in which oxygen levels may interplay in the process of
malignant transformation itself. With this study we show that the BJ model is not only a suitable model to study the mechanisms underlying malignant transformation. The potential of this model system expands beyond this and offers possibilities to expose the cells to perturbations such as drugs, under varying environmental
conditions to study the molecular effects and rewiring of signaling pathways. The BJ model offers a system to enable a deeper
understanding of the link between cancerous stage and response to environmental changes, which will add strength to the over-all mechanistic understanding of tumor development and behavior.
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Figure 1 A. Experimental setup B. Spearman correlations between all samples.
Figure 2 A. Number of DEGs across oxygen conditions in the four stages of the model respectively. B. Up- and downregulation across the four stages of the model, showed as percentage.
BJ BJ
hTERT+ BJ
hTERT+ SV40
hTERT+ BJ BJ hTERT+ SV40
RasG12V
Malignant transformation
Atmospheric oxygen 3% oxygen
RNA-seq
A B
Cell Oxygen
CellOxygen
Oxygen Low Atm Cell
Primary Immortalized Transformed Metastasizing
0.9 0.92 0.94 0.96 0.98 1
Figure 3 Top three significantly enriched clusters based on the Gene Ontology domain Biological Function in DAVID, for up- and downregulated genes separately at each stage in the BJ model.
BJ BJ
hTERT+ BJ
hTERT+ SV40 BJ
hTERT+ SV40 RasG12V
Proliferation Angiogenesis Differentiation
Angiogenesis Proliferation Hypoxia
Collagen Migration
Response to lipid Renal dev.
Angiogenesis Cell cycle
tRNA metabolism Apoptosis
ncRNA process External stimulus Hypoxia
Angiogenesis
Immune response IFNγ response Stress response Migration Proliferation Apoptosis Regulation
in 3% oxygen
Figure 4 Shared DEGs across oxygen conditions between the cell lines in the BJ model.
A. Shared DEGs between immortalized and SV40-transformed cells. B. Shared DEGs between primary and immortalized cells. C. Shared DEGs between SV40-transformed and metastasizing cells.
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log2FC BJ primary
log2FC BJ hTERT
−6 −4 −2 2 4 6
−6−4−2246
AMPD3
CXCL1 CXCL8
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log2FC BJ hTERT
log2FC BJ SV40
−6 −4 −2 2 4 6
−6−4−2246
CD82 AKR1B1 SOD2
ID3 IL1B
ASS1
GPNMB IL6
IGFBP3 STC1
IL24
CXCL1 CXCL8
MME
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log2FC BJ SV40
log2FC BJ Ras
−6 −4 −2 2 4 6
−6−4−2246
ACTG2 HRAS
