The effects of NOX2 and NOX4 inhibitors on panc-1 cell viability
Bachelor Thesis Project in Biomedicine 30 ECTS
Spring term 2020
Karl-Hermann Sielinou Kamgang (a17karsi@his.se) Supervisor: Ferenc Szekeres (ferenc.szekeres@his.se) Co-supervisor: Heléne Lindholm (helene.lindholm@his.se) Examiner: Anna Benrick (anna.benrick@his.se)
School of Bioscience University of skövde Box 408
54128 Skövde
School of Health and Education University of Skövde
Address Högskolevägen 1
PO Box 408 541 28 Skövde
Abstract
Pancreatic cancer is a leading cause of cancer related deaths worldwide and has a low survival rate 1 year after diagnosis. It is therfore important to identify new biomarkers and treatments to improve disease outcome. This experiment investigated the effects of different NOX2 and NOX4 inhibitors on Panc-1 cell lines. Following 48 hours treatment, Cell viability analysis, Caspase 3/7 as well as ROS levels where measured to see how these factors were affected by the treatments. As the PI3K/AKT/mTOR and MAPK/ERK pathways, as well as G6PD and VEGF have been shown to influence cancer metastasis and invasiveness, a gene expression analysis (VEGF, G6PD, AKT2, ERK2) was also conducted. Results obtained showed a general decrease in cell viability with increasing concentrations of inhibitors used. While a general increase in Caspase 3/7 activity was observed, M166-0.6μM treatment showed decreased Caspase 3/7 activity, suggesting that other non-apoptotic cell death pathways such as necrosis could have been at play. A general increase in ROS activity was also observed and unexpected. Gene expression showed an increase in G6PD activity which could mean, the cancer cells increased antioxidant system activity to achieve redox balance due to the increased ROS levels. Some results obtained where unexpected and make it difficult to conclude how the treatments decreased cell viability. The results also suggest that other currently unknown mechanisms were at play in influencing cell viability. Most importantly, the results show that completely inhibiting NOX2 and NOX4 lead to the most significant decrease in cell viability.
Popular scientific summary
Reactive oxygen species (ROS) are unstable and partially reduced oxygen derivatives. This includes superoxide (O2-) and hydrogen peroxide (H2O2), which normally act as signaling molecules. They have however been shown to be over expressed in various types of cancers and are linked with various processes advantageous to cancer cells. This includes increased tumor growth and chemotherapy resistance. Though mainly produced by the mitochondria, another major producer of ROS is the NADPH oxidase (NOX) family, and the ROS produced by them has been linked to activation of various biological pathways which promote cancer cell growth. This therefore suggests that inhibiting the NOX enzymes, thereby decreasing ROS levels would negatively affect cancer cell growth.
Three different inhibitors at different concentrations ((M114-0.15, 0.3 and 0.6 μM), (M159-0.15, 0.3 and 0.6 μM) and (M166-1.2, 2.4, 4.8 μM) where used as well as an untreated group and DMSO (4.8 μM) to make 11 treatment conditions. DMSO is a solvent widely used in experimentation. Emerging studies suggest that DMSO is physiologically active and might affect cells depending on the concentration used.
In this study, a treatment group with the highest concentration of DMSO (4.8 μM) used when dissolving the inhibitors was included to investigate if DMSO would influence cell viability. The concentrations used for each inhibitor represented half IC50 of each inhibitor, IC50, and 2x of IC50 respectively. This IC50 value is the value at which 50% of the enzyme is inhibited.
Various forms of analysis were conducted, including cell viability analysis (to investigate how treatments affected the cancer cells), ROS analysis (to investigate how ROS levels were affected), Caspase 3/7 analysis (to investigate if the intrinsic apoptosis, which is a type of cell death was involved in influencing cell viability analysis) and gene expression analysis of 4 genes (ERK2, AKT2, G6PD and VEGF), which have been shown to be important in cancer cell growth was also conducted.
The results show a significant decrease in cell viability in treatment conditions 155-0.3 and 0.60μM, as well as in all concentrations of M166 used. The caspase analysis also shows an increase in Caspase 3/7 levels (which would mean increased intrinsic apoptosis) in all the mentioned treatment conditions except for M166-0.6μM, which had the largest decrease in cell viability. This suggests the involvement of other cell death mechanisms such as necrosis. Information from previous studies, decreased cell viability and inhibition of NOX, suggest decrease in ROS levels in the 5 treatment conditions showing a decrease in cell viability. The opposite was however observed with all 5 groups showing an increase in ROS. Thus, the results suggest that NOX2 and NOX4 inhibition could be promising new treatment options in the fight against cancer.
Animals or human models were not used in this study. The only major social consideration would be informed consent of the individual from whom Panc-1 cell lines were derived, as well as anonymity. It should be noted that with the current ethical guidelines in place these two considerations would surely have been followed especially as the researchers using the cell lines do not know who they belong to.
Another issue that could be considered is the financial support from the government usually needed to carry out this type of research. It could be argued that though no magic drug has been discovered to treat the disease, the money used for research allows for incremental progress to be made towards better treatments. This cost is far less than the governmental and personal cost needed to treat a patient, and the benefit of this type of research outweighs the disadvantages.
Table of content
Introduction ... 1
Pancreatic cancer ... 1
Origins of cancer ... 1
Mitochondrial and NADPH oxidase derived ROS ... 2
ROS involvement in promoting cancer growth by upregulation of proliferative signaling pathways ... 3
Maintaining redox balance ... 5
Hypothesis and Aim ... 6
Method ... 7
Cell culture ... 7
NOX inhibitors ... 7
Cell viability ... 7
ROS detection ... 8
Apoptosis Assay ... 8
Quantitative Real-Time PCR Analysis ... 8
Statistical analysis ... 9
Results ... 10
Cell viability analysis ... 10
Caspase 3/7 and ROS analysis ... 11
Gene expression analysis... 12
Discussion ... 16
Caspase and ROS analysis ... 16
Gene expression analysis ... 17
Factors influencing results ... 18
Conclusion ... 20
Acknowledgments ... 20
Ethical aspects and importance of the project ... 21
References ... 22
1
Introduction Pancreatic cancer
Pancreatic cancer is the seventh leading cause of cancer related deaths worldwide, with approximately 450,000 new cases and causing about 430,000 deaths in 2018 (Rawla et al., 2019). The two main types are pancreatic adenocarcinoma, and pancreatic neuroendocrine tumor. Pancreatic adenocarcinoma is more common as it occurs in 85% of cases and has a 24% survival rate, one year after diagnosis (Rawla et al., 2019). Though the 5-year survival rate of pancreatic cancer has increased from 6% to 9%, prognosis of pancreatic cancer remains one of the lowest with a mortality-incidence ratio of 94% (Rawla et al., 2019).
Pancreatic cancer is generally considered to be a disease of the elderly, as it is seldom diagnosed before the age of 55 and the highest incidence occurring in people over 70 years of age (Ilic & Ilic, 2016). As an early diagnosis and small tumor size are two important prognostic factors for pancreatic cancer, primary prevention is considered of utmost importance. To accomplish this task, it is therefore important to better understand the mechanism of cancerous cells to identify new biomarkers that are present in the early stages of the disease. That would enable earlier diagnosis and treatment. A better understanding of the mechanisms of cancerous cells could also lead to development of better therapeutic treatments and discovery of novel therapeutics targets which would lead to lower mortality rates.
Origins of cancer
The genetic theory for the origin of cancer was proposed by Theodor Boveri, who in 1914 suggested that cancer could arise from defects in the segregation of chromosomes during cell division (Seyfried et al., 2014). His hypothesis on the significant role chromosomes played in the origin of malignancy was heavily based on his observations of chromosomal behavior in sea urchins and nematodes as well as his considerations of von Hansemann’s earlier observations of abnormal chromosome behavior in tumors (Seyfried et al., 2014). This somatic gene theory is however plagued by several inconsistencies revealed through nuclear/cytoplasmic transfer experiments between tumorigenic and non-tumorigenic cells.
Several investigations have shown that combining cytoplasm containing normal mitochondria from non- tumorigenic cells with nuclei from tumor cells suppressed tumorigenicity (Israel & Schaeffer, 1987; Koura et al., 1982). Other studies have shown suppressed in vivo tumorigenicity of multiple human and animal tumor types when tumor cell nucleus was introduced into cytoplasm of non-tumorigenic cells (Hochedlinger et al., 2004; Li et al., 2003). These findings are not in accordance with the somatic gene theory, as tumors would be expected to develop from the tumor-derived nuclei which contained several tumor associated mutations.
2
The mitochondrial theory of cancer first proposed by Otto Warburg suggests that cancer should be considered as a metabolic disease related to disturbances in energy production through respiration and fermentation (Seyfried et al., 2014). He had observed an increased rate of glycolysis in cancer cells compared to healthy cells despite the availability of adequate oxygen levels, later termed “The Warburg Effect”. He theorized that insufficient respiration, initiated tumorigenesis and ultimately lead to cancer.
He also theorized that cancer cells compensated for the respiratory insufficiency by gradually increasing glycolysis even in the presence of oxygen, which he called “aerobic fermentation” due to the increased abnormal production of lactate (Seyfried et al., 2014). Later research driven by these observations have shown that mitochondria may contribute to malignant cell transformation via different mechanism. One such major mechanisms is due to mitochondrial reactive oxygen species (ROS), which favor accumulation of potentially oncogenic DNA defects and activation of potentially oncogenic pathways (Porporato et al., 2018).
Mitochondrial and NADPH oxidase derived ROS
ROS are unstable, reactive, and partially reduced oxygen derivatives such as hydrogen peroxide (H2O2) and superoxide anion (O2-). Signaling-associated ROS are primarily produced by the mitochondria during cellular respiration and act as important second messengers in cellular signaling of many biological processes in normal as well as cancer cells (Brewer et al., 2015; Yang et al., 2018).
Acceptance of electrons by the terminal electron acceptor oxygen (O2) during cellular respiration can lead to leakage of electrons from the electron transport chain. These electrons can then react with O2 to produce O2- (Goncalves et al., 2015). H2O2 is then derived from the rapid dismutation of O2- by the enzyme superoxide dismutase 1, 2 and 3 (SOD1, 2 and 3).The different forms of this enzyme can be found in the mitochondrial matrix, extracellular space, and the cytosol (Reczek & Chandel, 2017). O2- produced by the electron transport chain is released into the mitochondrial matrix where it is converted to H2O2 by SOD 2.
Mitochondrial O2- is also released into the cytosol where it is converted into H2O2 by SOD1. H2O2 in the mitochondria and cytosol can then be detoxified into H2O by antioxidants such as glutathione and peroxidase (Reczek & Chandel, 2017). Cytosolic H2O2 can also react with metal cations such as Fe2+ and Cu+ to produce hydroxyl radical (OH-), which readily react with and damage DNA, lipids and proteins (Reczek & Chandel, 2017).
Another important source of ROS is the NADPH oxidase (NOX) family. It consists of NOX1 to NOX5 as well as dual oxidase 1 (DUOX1) and DUOX2. They are a group of transmembrane proteins found on the plasma membrane as well as the membranes of the mitochondria, nucleus, and endoplasmic reticulum (Bedard
3
& Krause, 2007). They transport electrons from NADPH to reduce oxygen, thereby leading to production of O2-, which gets converted to H2O2 by SOD1. As mentioned, H2O2 detoxification is catalyzed by an antioxidant system. An imbalance between ROS production and its clearance by antioxidant enzymes often leads to oxidative stress. This is considered a hallmark of cancer, as tumors are distinguished by abnormal oxidative states and generation of extensive amounts of ROS (Kardeh et al., 2014).
Changes in ROS levels can affect signal transduction pathways differently as it can either lead to cell proliferation or apoptosis and necrosis. Under normal physiological conditions, mitochondrial and NOXs derived H2O2 can act as secondary messengers in major cellular signaling pathways (Reczek & Chandel, 2017) . An excessive increase in ROS can however lead to diffusion of H2O2 from where it was produced to distant parts of the cell where it induces oxidative damage and cell death (Reczek & Chandel, 2017).
Increased levels of ROS can react with lipids, proteins, and DNA especially, which eventually leads to activation of oncogenic signaling pathway, inhibitions of tumor suppressor genes and cancer progression.
Holbrook & Ikeyama (2002) showed that low doses of ROS can be mitogenic but at medium doses cause transient or permanent growth arrest and that high doses would usually lead to cell death caused by apoptosis or necrosis. ROS can therefore act as a signaling molecule or toxic agent depending on the type of ROS, its local concentration and abundance of antioxidants (Chandel & Tuveson, 2014).
Of the many inhibitors proposed to inhibit NOX activity, many have been shown to be unspecific due to several of target effects or due to inhibition of features not specific to NOX enzymes (Altenhöfer et al., 2015). The NOX inhibitors used in this study are GLX7013114 (M114), GLX7013159 (M159) and GLX7013166 (M166) provided by Glucox Biotech AB. Inhibitor M144 specifically inhibits NOX4 while inhibitors M159 and M166 both inhibit NOX2 and NOX4 with varying specificity at different concentrations. The maximal inhibitory concentration (IC50) represents the concentration needed for 50%
inhibitory effect in vitro (Altenhöfer et al., 2015). Inhibitor M144 and 159 both have an IC50 of 0.3 μM for NOX4 while that of M166 is 2.4 μM. As mentioned, M159 and M166 also inhibit NOX2 to varying degree with NOX2 IC50 of 1.5 and 0.9 μM, respectively.
ROS involvement in promoting cancer growth by upregulation of proliferative signaling pathways
ROS can influence major cell proliferative and apoptotic pathways by interacting with key regulatory signaling molecules along the pathways to the benefit of cancerous cells. Studies have shown that ROS promoted tumorigenesis (Safford et al., 1994), and that Rat Sarcoma (Ras) oncogenic mutations and growth factor signaling had the ability to induce H2O2 activation of PI3K/Akt/mTor and ERK/MAPK
4
signaling cascade (Reczek & Chandel, 2017). It was later shown that NOX4 increased ROS production to enhance proliferation due to oncogenic mutations in Ras. This NOX4 derived ROS could then activate the PI3K/Akt/mTOR survival pathway by oxidizing and inactivating the phosphatase and tensin homolog (PTEN) and the protein-tyrosine phosphatase 1B (PTP1B), which are key regulators of PI3K/Akt/mTor signaling (Lee et al., 2002; Salmeen et al., 2003). This is caused by H2O2 mediated reversible oxidation of cysteine thiol groups on the phosphatases. This oncogenic activation of Akt could increase ROS production to further promote cancer cell proliferation and survival (Los et al., 2009). Amplification and over expression of PI3K/Akt/mTor has been shown to occur frequently in many types of human cancers and is linked with the uncontrolled cellular proliferation and angiogenesis observed in cancer cells (Calvo et al., 2009). The proliferative effects are mainly due to the Akt kinases ability to directly phosphorylate proteins or indirectly regulate the expression of proteins involved in cell cycle progression at G1/s and G2/M transitions (Gan et al., 2015) .
As tumor size increases, new blood vessels develop to provide oxygen and nutrients to the tumor (Liou &
Storz, 2010). This angiogenesis is important for tumor growth as well as metastasis, and evidence suggest a signaling role played by ROS in mediating various growth-related responses such as angiogenesis (Ushio- Fukai & Nakamura, 2008). Vascular endothelial growth factor (VEGF) is an important proangiogenic growth factor that facilitates angiogenesis via its receptor (VEGF receptor type 2), and its expression in tumors is correlated with poor prognosis in many cancers (Y. W. Kim & Byzova, 2014; Ushio-Fukai &
Nakamura, 2008). ROS play an important role in angiogenesis. Exogenous ROS have been shown to stimulate induction of VEGF by various cell type, as well as NOX derived ROS, which can directly activate the VEGF2 receptor without binding of the ligand (VEGF) (Ushio-Fukai & Nakamura, 2008).
Due to its role as an extra cellular signal censor, the ERK/MAPK pathway is readily exploited by cancer cells, which use the pathway to increase cell proliferation and migration. The ERK/MAPK signaling pathway is mainly activated by various growth factors such as VEGF and epidermal growth factor (EGF) (Jixiang Zhang et al., 2016a). ROS have been shown to activate receptors of the growth factors without the binding of the growth factor, thereby leading to activation of the ERK/MAPK pathway (Salaroglio et al., 2019). NOX derived ROS, seems to be involved in VEGFR2 autophosphorylation in signaling pathways such ERK/MAPK pathway, and eventually leading to activation of genes involved in angiogenesis (Ushio- Fukai & Nakamura, 2008). Activation of ERK/MAPK pathway has also been shown to be involved in chemotherapy resistance (Salaroglio et al., 2019).
5
Maintaining redox balance
In pancreatic cancer, ROS is suggested to promote pro-survival mutations of genomic DNA, resulting in oncogene activation as well as modification of gene expression and initiation of neoplastic transformation (Zhang et al., 2016). However, increase of ROS levels above desired threshold concentrations can lead to exhaustion of available antioxidant programs leading to ROS mediated oxidative damage and to cell death via apoptosis, necrosis and autophagy (Zhang et al., 2016). Maintaining the redox balance between ROS production and elimination is therefore important in proliferating cancer cells.
Nicotinamide adenine dinucleotide phosphate (NADPH) plays an important role in ROS detoxification by antioxidants as it promotes regeneration of reduced glutathione (GSH), which can convert harmful H2O2
into water (Stanton, 2012; Juan Zhang et al., 2016). NADPH is mainly produced by two enzymes of the pentose phosphate pathway(PPP). These are, 6-phosphogluconate dehydrogenase (6PG) and glucose-6- phosphate dehydrogenase (G6PD), which are rate limiting steps of the PPP (Fernandez-Marcos &
Nóbrega-Pereira, 2016). G6PD is considered a the key regulator of NADPH as an increase in G6PD expression would mean the expression of NADPH is also increased (Loscalzo et al., 2005). G6PD and NADPH are therefore important in cancer mediated antioxidant clearance as studies have linked increased G6PD with poor clinical outcomes in patients suffering from various types of cancer (Ju et al., 2017). Disruption of NADPH production in cancer cells have also been shown to have positive therapeutic impacts as it leads to increased cell sensitivity to ROS as well as to apoptosis (S. Y. Kim et al., 2007).
6
Hypothesis and Aim
Though the physiological role played by NOX have been well established in noncancerous cells, the effects of NOX-generated ROS on cancer cells are much less understood. Several studies show the important role played by ROS and the NOX family in cancer proliferation, relapse as well as chemotherapy resistance. It is therefore important to better understand how NOX derived ROS affect cancer cells. The aim of this thesis is to investigate how Panc-1 cells react to different concentrations of NOX2 and NOX4 inhibitors.
That is, to investigate how the inhibitors affect ROS levels, cell viability and different proliferative pathways. The hypothesis is that inhibiting NOX2 and NOX4 enzymes will reduce ROS levels enough to significantly influence Panc-1 cell viability.
7
Method Cell culture
The media used in this study was Dulbecco’s Modified Eagle´s Medium (Sigma Aldrich). 500ml bottles of this media where each supplemented with 10% Fetal Bovine Serum, and 1% penicillin streptomycin before use. Pand-1 cell lines (ATCC® CRL-1469™) where cultured on NuncTM EasYFlask 25cm2 NuclonTM Delta Surface containing 25 ml of the supplemented media and incubated at 370C with 5% CO2.
NOX inhibitors
The substances used in this experiment were M114 (which inhibits NOX4), M159 and M166 (which both inhibit NOX2 and NOX4 to various degrees) were obtained from Glucox Biotech AB. The substances were dissolved to 10mM in DMSO. The working concentrations for each inhibitor was obtained by performing serial dilutions with supplemented media. Three concentrations from each inhibitor where used as treatment groups. The concentrations where half IC50 of each inhibitor, IC50, and 2x of IC50: M114 (0.15, 0.3 and 0.6 μM), M159 (0.15, 0.3 and 0.6 μM) and M166 (1.2, 2.4, 4.8 μM). While M144 only inhibits NOX4, M159 has a NOX2 IC50 value of 1.5 μM and that of M166 is 0.9 μM. As DMSO was used to obtain working concentrations of the substances, a treatment group using DMSO was created to see if DMSO would significantly influence the cells. The concentration of DMSO used (4.8 μM), was much higher than the DMSO concentrations in the working concentrations of the substances (except for M166-4.8 μM) as they had undergone serial dilutions. This high concentration of DMSO was therefore used with the assumption that, if this concentration did not significantly influence cell viability, the smaller concentrations of DMSO found in the substances would therefore be considered to have negligible effects on the results obtained from the treatments.
Cell viability
Panc-1 cells, (6 x 103 cells per well), where seeded in a 96 well plate and incubated for 24hrs at 370C with 5% CO2. After the incubation period, they were treated for 24 or 48 hours with different substances at different concentrations (Control, Untreated, DMSO (4.8 μM), M114 (0.15, 0.3 and 0.6μM), M159 (0.15, 0.3 and 0.6 μM) and M166 (1.2, 2.4, 4.8 μM). Following the 24- or 48-hours incubation period, 20 μL of MTS (Cell Titer 96® AQueous One Solution Cell Proliferation Assay, Catalog No G3581) (Promega, Madison, Wisconsin, USA) was added to each well. It should be noted that before adding MTS to all wells the media from the control group was removed and placed in 4 empty wells. MTS was then added to all wells including these 4 wells but not the control wells with cells in them. The 96 well plate was then incubated for 1 hour at 370C and absorbance measured at 490nm using FLUOstar Omega microplate Reader (BMG
8
LABTECH, Allmendrgrun, Ortenberg, Germany) after 1 and 2 hours. The 1-hour measurement was used in all results.
ROS detection
Same seeding and treatment protocols for cell viability analysis was used, but the cells were however seeded in black-walled plate. After 48hrs treatment, a 6ml solution of 5 μM final concentration Cell ROXTM Green Reagent Catalog No. C10444 (Thermo Fisher Scientific, Van Allen Way Carlsbad, USA) was made using clear media and Cell ROX reagent. Media in each well of the 48hrs treated plate was removed and 100 μL of the 5 μM Cell ROXTM Green Reagent was added to the cells in each well. The treated plate was then incubated for 2 hrs at 37 0C and fluorescence measured at 520 nm using FLUOstar Omega microplate Reader (BMG LABTECH, Allmendrgrun, Ortenberg, Germany).
Apoptosis Assay
Same seeding and treatment protocols for cell viability analysis was used, except cells were seeded in 96 well black walled plate. After 48-hour treatment of the cell, apoptosis assay was performed using CellEventTM Caspase-3/7 Green Detection Reagent Catalog No. C10423 (Thermo Fisher Scientific, Van Allen Way Carlsbad, USA), according to the manufacturer protocol. A 5ml solution of 5μM working concentration of CellEventTM Caspase-3/7 Green mixture was made by diluting with clear media. Following 48 hrs treatment, 100 μL of the 5 μM CellEventTM Caspase-3/7 Green was added to each well. The plate was then incubated for 1hr at 37 0C and fluorescence measured at 520 nm using FLUOstar Omega microplate Reader (BMG LABTECH, Allmendrgrun, Ortenberg, Germany).
Quantitative Real-Time PCR Analysis
Two 6-well plates where seeded with cells (1.5 x 105 cells per well) and incubated for 24 hrs at 370C. The plates where then classified and treated: Untreated, DMSO (4.8 μM), M114 (0.15, 0.3 and 0.6 μM), M159 (0.15, 0.3 and 0.6 μM) and 166 (1.2, 2.4, 4.8 μM) for 48 hours (1 well for each treatment group), after which the plates (treated cells) where stored in a -800C freezer for later RNA extraction. RNA from the 6- well plates (treated cells) was extracted and isolated using Qiagen RNeasy plus Mini Kit (QIAGEN AB, Sollentuna, Sweden) according to the manufacturer manual. RNA concentration/ purity was measured using a Nano Drop spectrophotometer. 2 μg of RNA from each sample was then reverse-transcribed using High-Capacity cDNA Reverse Transcription Kit Catalog No. C4368813, (Thermo Fisher Scientific, Van Allen Way Carlsbad, USA) according to the manufacturer protocol.
Quantitative real-time PCR (qPCR) analysis of the different genes of interest (NOX2, NOX4, G6PD, ERK2, VEGF, AKT2 and PMM1). The mRNA levels where performed using PikoReal Real-Time PCR System
9
(Thermo Fisher Scientific, Van Allen Way Carlsbad, USA). Expression of some genes was according to SYBR Green and TaqMan (Table 1).
Table.1 Forward and reverse primers for different genes in 5 to 3 direction
GENE FORWARD REVERSE
ERK2 5`TGGATTCCCTGGTTCTCTCTAAAG´3 5`GGGTCTGTTTTCCGAGGATGA´3 G6PD 5`ATGGCAGAGCAGGTGGCCCT´3 5`TCATGCAGGA CTCGTGAATG´3 VEGF 5`CAGCTGCGTGACTGTGCAGCGCTG´3 5`TCAGGGCGCTGGTGGTGCTG`3 NOX2 5`TGCCAGTCTGTCGAAATCTGC´3 5`ACTCGGGCATTCACACACC´3 NOX4 5`TGTGCCGAACACTCTTGGC´3 5`ACATGCACGCCTGAGAAAATA´3 Table 2. SYBR green qPCR reaction components (per reaction).
Volume (μL per reaction) Final concentration
cDNA 1 5 ng/μL
Forward primer 0.75 200 nM
Reverse Primer 0.75 200 nM
Master mix (2X) 2.5 …..
PMM1 and AKT2 were obtained as probes from ThermoFisher, and their gene expression analysis where run using TaqMan. PMM1 was run as a reference gene. GAPDH gene expression was conducted using both SYBR green and TaqMan. Results of both gene expressions showed significant variation in GAPDH expression between treatment conditions. The other reference gene available at hand was PMM1.
TaqMan gene expression of PMM1 showed that it was more stable then GAPDH, so it was used as reference gene. The PMM1 CT values were then used to obtain fold change expression of AKT2 using the 2-ΔΔCT method.
The analysis for the other genes was conducted using SYBR green protocol (Table 2), and cDNA and primer dilutions performed using nuclease free water. The cDNA (5ng/μL) for the 11 treatment conditions where run as triplicates together with one No Template Control (NTC) triplicate, and a standard curve of 4 different cDNA concentrations 25, 6.5, 1.6, 0.4ng/μL (triplicates of each) from the untreated condition.
This was done to obtain a standard curve that allowed for eventual CT values to be converted into concentrations. Once obtained, these concentrations were normalized to control by subtracting the control concentrations from the treatment concentrations. The results obtained from this, was used to represent gene expression of the various genes.
Statistical analysis
Statistical analysis used for MTS results was Student t-test while Caspase 3/7 analysis and ROS statistical analysis were conducted using Mann Whitney-U test, a P-value =<0.05 was considered significant.
10
Results
Cell viability analysis
There was a significant decrease in cell viability of five treatment conditions compared to that of the untreated (Figure 1). These treatment conditions where inhibitor M159 (0.3 and 0.6 μM), and inhibitor M166 (1.2, 2.4, 4.8 μM). Both M159 treatments resulted in18.5% and 19.5% decrease in cell viability, while the cell viability of the three M166 inhibitors treatment decreased by 18.1%, 18.9% and 24.4%.
Figure 1. Percentage cell viability of Panc-1 cells treated with different NOX-inhibitors at different concentrations (Untreated, DMSO (4.8 μM), M114 (0.15, 0.3 and 0.6μM), M159 (0.15, 0.3 and 0.6 μM) and 166 (1.2, 2.4, 4.8 μM) for 48hours. Statistical analysis was conducted using student T-test (n=18 per group). Asterisks represent significant difference from control (P=<0.01**). Error bars show standard error of mean (SEM).
11
Caspase 3/7 and ROS analysis
The caspase 3/7 activity increased significantly for several treatment conditions: DMSO(4.8), M114(0.15, 0.3, 0.6 μM), and M159(0.15, 0.6 μM) (Figure 2). In contrast, there was a significant decrease in Caspase 3/7 activity for treatment condition M166-4.8 μM compared to the untreated condition (Figure 2).
Figure 2. Caspase 3/7 levels (arbitrary values) in the different treatment conditions relative to the Untreated condition after 48hrs. Statistical analysis was conducted using Mann Whitney U test (n=3 per group). Asterisks represent significant difference from control, (P=<0.05*). Error bars show standard error of mean (SEM).
Figure 3 shows a significant increase in ROS levels for treatment conditions M114(0.15, 0.3, 0.6μM) and M159-0.3μM. There was also a significant ROS decrease in treatment condition M166-4.8μM.
12
Figure 3. ROS levels (arbitrary values) in the different treatment conditions relative to the Untreated after 48hrs.
Statistical analysis was conducted using Mann Whitney U test, (n=3 per group). Asterisks represent significant difference from control (P=<0.05*).Error bars show standard error of mean (SEM).
Gene expression analysis
Figures 4-7 show the gene expression results of the various genes of interest. All gene analysis used SYBR green except for AKT2 and PMM1.The latter ones were obtained as probes and therefore used TaqMan.
Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was the reference gene used for the SYBR green analysis. However, gene expression analysis using both SYBR green and TaqMan revealed that the GAPDH expression was not consistent across treatment conditions. The Ct-value difference between treatment conditions varied from 1-5 Ct.
Though not perfect, TaqMan gene expression analysis of PMM1 showed more consistent Ct-values between groups with the lowest Ct-value being 27.88 and the largest being 29.49 (Figure 7). As PMM1 was the only other reference gene available and variation of its expression in the different treatment conditions was acceptable, it was used as reference gene.
13
The fold change gene expression results could be directly obtained for AKT2 since it also used TaqMan.
Nevertheless, this was not the case for all other genes which used SYBR green. A standard curve method was therefore used for the SYBR green genes which converted obtained CT values into concentrations.
These values were then normalized to the untreated samples to obtain “arbitrary values”, which would be like the results of the fold change method.
Figure 4 shows NOX2 and NOX4 expression in various treatment conditions. NOX4 and NOX2 gene expression was mainly conducted to see if there was any NOX available for the inhibitors to bind to, as there would be no point in running the experiments if their expression were not available. This was done with the assumption that if gene expression showed available NOX2 and NOX4, it would also mean NOX expression at the protein level. NOX2 gene analysis shows a significant increase of NOX2 expression in treatment conditions M114-0.6 and M159-0.3μM, while M166-4.8μM showed a significant decrease. The NOX4 expression was significantly increased in treatment condition M166-2.4 and significantly decreased in M166-1.2 μM
Figure 4. The NOX4 and NOX2 expressions in all treatment conditions. The Y-axis represent fold change. The arbitrary values were obtained from the standard curve after normalizing to untreated condition. Statistical analysis was conducted using Mann Whitney U test(n=3 per group). Asterisks represent significant difference from control, (P=<0.01**). The error bars represent standard error of mean (SEM).
The pathways of G6PD, VEGF, ERK2 and AKT2 have been shown to play a role in influencing cancer metastasis and progression in various ways. Therefore, the expression of these genes was investigated to study how the treatment conditions affected them, and how they related to the analysis of cell viability, Caspase 3/7, and ROS. The G6PD gene analysis showed increased G6PD expression in all treatment conditions except for M166-4.8 μM and M114-0.3 (Figure 5). The expressions of the latter ones were
14
inconclusive due to possible contamination of the sample. The VEGF analysis showed a significant decrease of VEGF expression in sample M166-4.8 μM (Figure 5). The VEGF expression of M114-0.3 μM could not be used due to contamination of the sample.
Figure 5. The G6PD and VEGF expressions are shown for all treatment conditions. The Y-axes represent fold change. The arbitrary values were obtained from the standard curve after normalizing to untreated condition.
Statistical analysis was conducted using Mann Whitney U test (n=3 per group). The error bars represent standard error of mean (SEM). Asterisks represent significant difference from control (P=<0.05*) and (P=<0.01**).
Figure 6. ERK2 and AKT2 expression shown for all treatment conditions. The Y-axis for the AKT2 represents fold change. The arbitrary values on the Y-axis for ERK2 represents concentrations obtained from the standard curve after normalizing to untreated condition. Statistical analysis was conducted using Mann Whitney U test, (n=3 per group). Asterisks represent significant difference from control, (P=<0.05*) and (P=<0.01**). The error bars represent standard error of mean (SEM).
15
Figure 7 The Ct-values obtained from the TaqMan gene expression analysis of the reference gene PMM1 across all treatment conditions. The largest difference in Ct-values was 1.61Ct, observed between DMSO-4.8 (27.88 Ct) and M166-4.8 (29.49 Ct).
16
Discussion
As NOX2 and NOX4 derived ROS are known to play an important role in progression of cancer, inhibition of these enzymes would have some effect on cancer cells. Though all inhibitors used decreased cell viability, it was interesting to note that only substances M159 and M166 significantly decreased cell viability. By simply looking at these observations and since the NOX4 inhibitor M114 did not have a significant influence on cell viability, one could assume that inhibition of both NOX2 and NOX4 is needed to substantially affect cell viability as both inhibitors M159 and M166 inhibit both NOX2 and NOX4 to varying degrees. As the NOX4 IC50 of M166 is 2.4 μM while its NOX2 IC50 is 0.9 μM, it suggests that a partial or complete inhibition of NOX2 alone can also have significant effects on cell viability. As inhibitor M159 and M166 inhibit both NOX4 and NOX2 to different extents depending on concentration, it is hard to know if this lesser inhibition might have played a contributing role in affecting cell viability.
Caspase and ROS analysis
The main objective of treating the cells was to investigate how the treatments would affect cell viability.
As Figure 1 showed, there was a general decrease in cell viability for all groups though not all where significant. Due to the Caspase 3/7 tight link with the intrinsic apoptotic pathway, an increase in Caspase activity would be expected in the groups with the most significant decrease in cell viability. Also, since the inhibitors used blocked NOX4 and NOX2, a decrease in ROS levels would also be expected.
There were significant increases in Caspase 3/7 for all treatment conditions except M166-1.2 and 2.4μM while a significant decrease was observed in treatment condition M166-4.8 μM (see Figure 2). Though Caspases(including Caspase 3 and Caspase 7) are mostly known for their proapoptotic role. Studies have shown that their activation does not necessarily mean or lead to cell death (McComb et al., 2010; Pérez- Garijo, 2018). It has been shown that they can in the cell of origin, as well as in surrounding cells induce cell proliferation (Pérez-Garijo, 2018). Also, the decreased Caspase 3/7 activity observed in M166-4.8μM suggests that other non-apoptotic cell death pathways such as necrosis or autophagy could have been involved.
ROS analysis (see Figure 3) showed a significant ROS increase in treatment conditions M114 (0.15, 0.3, 0.6 μM) and M159-0.3 μM, while a significant decrease in ROS was observed in M166-4.8 μM. An increase in ROS levels is however not unheard off. Studies have shown that a disproportional increase in intracellular ROS, a mechanism used in cancer treatments such as chemotherapy, can induce cell cycle arrest and apoptosis in cancer cells (Liou & Storz, 2010).
17
Cancerous cells have been shown to maintain a redox balance (L. Zhang et al., 2016). This balance keeps ROS at levels suitable enough to enhance metastasis and invasion without leading to ROS mediated cell death. This therefore proposes the idea that ROS levels might have been increased as a response to counter act the increased NOX2 and NOX4 inhibition. It should be noted that other than the NOX enzymes, cancerous cells can use various other mechanisms and cellular components such as the mitochondria to elevate ROS levels when required. NOX2 and NOX4 production could have decreased while an increased production of these molecules at other sites could have obscured this decrease. As the kit for ROS analysis (see Methods) does not measure the specific ROS produced by both NOX enzymes, the ROS analysis shown in Figure 3 cannot be fully considered as accurate representations of how treating Panc-1 cells with different inhibitors for 48 hours affected NOX derived ROS levels. Altogether, the ROS results make it difficult to draw conclusions about how the changes in ROS levels could have influenced the decrease in cell viability observed. It should also be taken into consideration that both the Caspase analysis and the ROS analysis where only run once, and as triplicates which makes the analysis less reliable.
Gene expression analysis
As ROS has been shown to increase VEGF expression (Y. Kim & Byzova, 2014), inhibition of NOX2 and NOX4 should lead to VEGF downregulation. While both upregulation and downregulation can be observed in the different treatment conditions in Figure 5, the only significant change in VEGF expression was observed in treatment condition M166-4.8. It showed a significant decrease in VEGF compared to the untreated condition. Since the highest concentration of M166 show the greatest decrease in VEGF expression, it can therefore be suggested that a complete inhibition of both NOX2 and NOX4 seems optimal for downregulating VEGF expression.
Upregulation of AKT and ERK is generally associated with cell proliferation. The upregulation of AKT and ERK has been observed in multiple cancer types and is generally considered to be proteogenic (Los et al., 2009; Manning & Cantley, 2007; Worster et al., 2012). ROS as well as VEGF have been shown to activate the ERK1/2 pathway (Baldwin, 2012; Narasimhan et al., 2009; Reczek & Chandel, 2017). NOX4 derived ROS has also been shown to activate the PI3K/AKT/mTOR pathway directly by inactivation of phosphatase PTEN and PTP1B (Lee et al., 2002; Salmeen et al., 2003). These findings therefore suggest that inhibiting NOX2 and NOX4 as well as the general decrease in VEGF expression would lead to downregulation of both AKT2 and ERK2. Observations from Figure 6 shows over-expression in ERK2, even in the treatment conditions with the significant decrease in cell viability. AKT2 was shown to be both upregulated (M159- 0.6 μM, M166-2.4 and 4.8μM) and downregulated (M159-0.3 and M166-1.2) in the treatments with
18
significant decrease in cell viability. As upregulation of both genes are observed in most cancers and generally associated with increased cell proliferation, it would make sense to think that these treatment conditions showing the most significant decreases in cell viability would show decreased ERK2 and AKT2 activity. Though not in accordance with the cell viability results obtained, it is interesting to note that upregulations observed in ERK are like those observed in ROS analysis. ERK is generally activated by the binding of growth factors to their receptors. As mentioned, NOX-derived ROS can directly activate these receptors without ligand binding, thereby leading to downstream activation of ERK. It should also be noted that though both AKT and ERK are generally associated with cell proliferation their expression does not always mean proliferation. Nogueira et al (2008) however showed that the AKT upregulation generally and widely noticed in various cancer cells is not always beneficial for cell proliferation. It was shown that AKT activation could increase oxidative stress and increased the cells susceptibility to ROS triggered cell death (Nogueira et al., 2008).
An upregulation of G6PD was also observed. Since G6PD regulates NADPH, which is needed for GSH regeneration, it can be theoretically assumed that an increase it G6PD expression would also mean an upregulation of NADPH. This would if true, signify and increase in antioxidant activity. This would be interesting, as this upregulation could be said to have been due to the increase in ROS levels causing the cancer cells to increase activity of the oxidative systems in an attempt to regain redox balance, as the increase in ROS could lead to cell death (Zhang et al., 2016).
Factors influencing results
As mentioned, DMSO was used as a solvent for the inhibitors. It as a commonly used solvent in biological studies that is generally accepted as non-toxic at very low concentrations (Verheijen et al., 2019). Multiple studies have shown that it does to some extent influence various biological processes such as gene expression and can in certain concentrations negatively or positively influence cell viability (Hammoudeh et al., 2019). DMSO was been shown to have cytotoxic effects on human fibroblast at concentrations above 3% and has also been shown to influence synthesis of glycosaminoglycans (GAG) (Moskot et al., 2019).
Cell viability, ROS and Caspase analysis all show a difference between the untreated samples and DMSO treatment samples (Figure 1). The gene analysis results obtained (Figure 4 to 6) show that DMSO had a greater effect on expression that certain treatments. This finding altogether suggests that DMSO did have some effects on the Panc-1 cell lines. It should however be noted that the concentration of DMSO used
19
4.8μM was far more than that in the actual different inhibitor concentrations which had been diluted with media several times.
Since the only condition that changed for the cells during experimentation was the different treatment conditions and time, it can be concluded that the changes in expression observed resulted from the different treatment, DMSO and handling.
Multiple reports comparing mRNA and protein abundance show rather weak correlations between the two (Maier et al., 2009), and other studies like Jiang et al (2001) showed that the PI3K/AKT/mTOR pathway could increase HIF-1α protein levels via AKT activation without altering HIF-1α mRNA levels. This is important as it also suggests mRNA levels are not always representative of protein expression, thereby signifying that it is not certain if the gene expressions observed are representative of the actual protein expression. Validity of results obtained, would therefore be to some extent improved if a method that directly measures protein expression is used, such as western blot.
Another influencer of results would be the experimental errors that occur on the journey to obtain results.
Gene expression studies and experimentation involves various steps sensitive to human and equipment error which add up to affects the end results obtained to varying degree. Though experimental methods and techniques have vastly improved in terms of accuracy and minimizing of human error, experimental such as pipetting, seeding and loading plates for gene analysis are not always free of errors with humans involved.
20
Conclusion
Results obtained from this study revealed findings that are in accordance with previous research as well as other unexpected finding. Cell viability analysis revealed that inhibition of NOX2 and NOX4 generally decreased cell viability which was expected. Though an expected general increase in caspase activity was also observed, the decreased caspase activity in treatment group M166-4.8μM was unexpected and could possibly mean that other nonapoptotic cell death mechanisms such as autophagy and necrosis where at play. Though not uncommon, a general increase in ROS levels was observed when a decrease was expected since the NOX2 and NOX4 enzymes where inhibited. Increased expression of G6PD observed would suggest an interplay between ROS levels and antioxidant systems to achieve redox balance. These unexpected findings coupled with previous studies showing multiple interactions between genes analyzed make it definite conclusions difficult on how ROS and caspase3/7 could have affected cell viability. That said, the result obtained (cell viability, ROS, caspase, gene analysis) shows that the NOX inhibitors M159 and M166 do significantly decrease cell viability as well as DMSO (4.8μM) to some extent.
Acknowledgments
I would like to thank my supervisors Ferenc Szekeres and Heléne Lindholm for their continuous help this past few months, as the thesis would not be possible without their help. I would also like to thank the school for the equipment used and good research environment, as well as Glucox AB for the NOX inhibitors provided. Finally, I would like to say thank you to all my family and friends who have been with me and supported me through every step of this valuable, exciting, and informative journey.
21
Ethical aspects and importance of the project
Though animal testing can provide useful pathological information, it is accompanied by many ethical concerns and guidelines, which need to be followed. In vitro testing of specific cell types allows for testing of substance toxicity as well as studying sub-cellular pathways. It should however be noted that though in vivo experiments are conducted, and their results are often used to make inference of in vivo environments. The conditions are not the same, and the same stimulus can lead to responses differing from those observed in in vitro conditions. In vitro studies such as the use of cell lines, however come with multiple advantages including reduced ethical burden on the researcher compared to the use of animal models, easier to use, less expensive and easier to replicate as a cell line can be used multiple times. It should be noted that in vitro assays cannot fully substitute animal models as we are not completely certain of the difference in cellular signaling and interaction of cells laboratory conditions and in human or animal models.
In considerations to the ethical aspects, using cell lines would be considered more ethically appropriate than the use of animal or human models. Firstly, the cell lines are obtained from a patient suffering with a certain condition. The ethical guidelines and rules (Geraghty et al., 2014), state that consent should be obtained and patients identity concealed. The origin and identity of patients from whom the Panc-1 cell lines used in this study was obtained was not known, so ethical guidelines were followed as best possible from the part of the researcher.
This and other studies highlight the importance of ROS and the NOX family in cancer development and treatment. It is therefore important to devote more research to further understand their role in cancer development to create better treatments and therapeutic targets for the large number of individuals suffering from pancreatic cancer and other forms of cancer.
Though more research needs to be conducted, results from this study suggest that the NOX inhibitors used (M159 and M166) could be potential treatments for cancer, which would greatly benefit individuals as well as the society. Also, the unexpected decrease in Caspase activity observed in treatments condition M166-4.8μM as well as the increase in ROS levels both suggest that other cell death pathways apart from intrinsic apoptosis could be involved in decreasing cell viability.
22
References
Altenhöfer, S., Radermacher, K. A., Kleikers, P. W. M., Wingler, K., & Schmidt, H. H. H. W. (2015).
Evolution of NADPH oxidase inhibitors: Selectivity and mechanisms for target engagement. In Antioxidants and Redox Signaling (Vol. 23, Issue 5, pp. 406–427). Mary Ann Liebert Inc.
https://doi.org/10.1089/ars.2013.5814
Baldwin, A. S. (2012). Regulation of cell death and autophagy by IKK and NF-κB: Critical mechanisms in immune function and cancer. In Immunological Reviews (Vol. 246, Issue 1, pp. 327–345). Blackwell Publishing Ltd. https://doi.org/10.1111/j.1600-065X.2012.01095.x
Bedard, K., & Krause, K. H. (2007). The NOX family of ROS-generating NADPH oxidases: Physiology and pathophysiology. In Physiological Reviews (Vol. 87, Issue 1, pp. 245–313). American Physiological Society. https://doi.org/10.1152/physrev.00044.2005
Brewer, T. F., Garcia, F. J., Onak, C. S., Carroll, K. S., & Chang, C. J. (2015). Chemical Approaches to Discovery and Study of Sources and Targets of Hydrogen Peroxide Redox Signaling Through NADPH Oxidase Proteins. Annual Review of Biochemistry, 84(1), 765–790.
https://doi.org/10.1146/annurev-biochem-060614-034018
Calvo, E., Bolós, M. V., & Grande, E. (2009). Multiple roles and therapeutic implications of Akt signaling in cancer. In OncoTargets and Therapy (Vol. 2, pp. 135–150). Dove Press.
https://doi.org/10.2147/ott.s4943
Chandel, N. S., & Tuveson, D. A. (2014). The promise and perils of antioxidants for cancer patients. New England Journal of Medicine, 371(2), 177–178. https://doi.org/10.1056/NEJMcibr1405701
Fernandez-Marcos, P. J., & Nóbrega-Pereira, S. (2016). NADPH: New oxygen for the ROS theory of aging.
In Oncotarget (Vol. 7, Issue 32, pp. 50814–50815). Impact Journals LLC.
https://doi.org/10.18632/oncotarget.10744
Gan, W., Liu, P., & Wei, W. (2015). Akt promotes tumorigenesis in part through modulating genomic instability via phosphorylating XLF. Nucleus, 6(4), 261–265.
https://doi.org/10.1080/19491034.2015.1074365
Geraghty, R. J., Capes-Davis, A., Davis, J. M., Downward, J., Freshney, R. I., Knezevic, I., Lovell-Badge, R., Masters, J. R., Meredith, J., Stacey, G. N., Thraves, P., Vias, M., & Cancer Research UK. (2014).
Guidelines for the use of cell lines in biomedical research. British Journal of Cancer, 111(6), 1021–
1046. https://doi.org/10.1038/bjc.2014.166
Goncalves, R. L. S., Quinlan, C. L., Perevoshchikova, I. V., Hey-Mogensen, M., & Brand, M. D. (2015). Sites of superoxide and hydrogen peroxide production by muscle mitochondria assessed ex vivo under conditions mimicking rest and exercise. In Journal of Biological Chemistry (Vol. 290, Issue 1, pp.
209–227). American Society for Biochemistry and Molecular Biology Inc.
https://doi.org/10.1074/jbc.M114.619072
Hammoudeh, S. M., Hammoudeh, A. M., & Hamoudi, R. (2019). High-throughput quantification of the effect of DMSO on the viability of lung and breast cancer cells using an easy-to-use
spectrophotometric trypan blue-based assay. Histochemistry and Cell Biology, 152(1), 75–84.
https://doi.org/10.1007/s00418-019-01775-7
Hochedlinger, K., Blelloch, R., Brennan, C., Yamada, Y., Kim, M., Chin, L., & Jaenisch, R. (2004).
23
Reprogramming of a melanoma genome by nuclear transplantation. Genes and Development, 18(15), 1875–1885. https://doi.org/10.1101/gad.1213504
Holbrook, N. J., & Ikeyama, S. (2002). Age-related decline in cellular response to oxidative stress: Links to growth factor signaling pathways with common defects. Biochemical Pharmacology, 64(5–6), 999–
1005. https://doi.org/10.1016/S0006-2952(02)01169-3
Ilic, M., & Ilic, I. (2016). Epidemiology of pancreatic cancer. In World Journal of Gastroenterology (Vol.
22, Issue 44, pp. 9694–9705). Baishideng Publishing Group Co., Limited.
https://doi.org/10.3748/wjg.v22.i44.9694
Israel, B. A., & Schaeffer, W. I. (1987). Cytoplasmic suppression of malignancy. In Vitro Cellular &
Developmental Biology : Journal of the Tissue Culture Association, 23(9), 627–632.
https://doi.org/10.1007/bf02621071
Ju, H. Q., Lu, Y. X., Wu, Q. N., Liu, J., Zeng, Z. L., Mo, H. Y., Chen, Y., Tian, T., Wang, Y., Kang, T. B., Xie, D., Zeng, M. S., Huang, P., & Xu, R. H. (2017). Disrupting G6PD-mediated Redox homeostasis enhances chemosensitivity in colorectal cancer. Oncogene, 36(45), 6282–6292.
https://doi.org/10.1038/onc.2017.227
Kardeh, S., Ashkani-Esfahani, S., & Alizadeh, A. M. (2014). Paradoxical action of reactive oxygen species in creation and therapy of cancer. In European Journal of Pharmacology (Vol. 735, Issue 1, pp. 150–
168). Elsevier B.V. https://doi.org/10.1016/j.ejphar.2014.04.023
Kim, S. Y., Lee, S. M., Tak, J. K., Choi, K. S., Kwon, T. K., & Park, J. W. (2007). Regulation of singlet oxygen- induced apoptosis by cytosolic NADP+-dependent isocitrate dehydrogenase. Molecular and Cellular Biochemistry, 302(1–2), 27–34. https://doi.org/10.1007/s11010-007-9421-x
Kim, Y. W., & Byzova, T. V. (2014). Oxidative stress in angiogenesis and vascular disease. In Blood (Vol.
123, Issue 5, pp. 625–631). American Society of Hematology. https://doi.org/10.1182/blood-2013- 09-512749
Koura, M., Isaka, H., Yoshida, M. C., Tosu, M., & Sekiguchi, T. (1982). Suppression of tumorigenicity in interspecific reconstituted cells and cybrids. Gann, The Japanese Journal of Cancer Research, 73(4), 574–580.
Lee, S. R., Yang, K. S., Kwon, J., Lee, C., Jeong, W., & Rhee, S. G. (2002). Reversible inactivation of the tumor suppressor PTEN by H2O2. Journal of Biological Chemistry, 277(23), 20336–20342.
https://doi.org/10.1074/jbc.M111899200
Li, L., Connelly, M. C., Wetmore, C., Curran, T., & Morgan, J. I. (2003). Mouse embryos cloned from brain tumors. Cancer Research, 63(11), 2733–2736. http://www.ncbi.nlm.nih.gov/pubmed/12782575 Liou, G. Y., & Storz, P. (2010). Reactive oxygen species in cancer. In Free Radical Research (Vol. 44, Issue
5, pp. 479–496). NIH Public Access. https://doi.org/10.3109/10715761003667554
Los, M., Maddika, S., Erb, B., & Schulze-Osthoff, K. (2009). Switching Akt: From survival signaling to deadly response. BioEssays, 31(5), 492–495. https://doi.org/10.1002/bies.200900005
Loscalzo, J., Voetsch, B., Liao, R., & Leopold, J. (2005). Genetic Determinants of Vascular Oxidant Stress and Endothelial Dysfunction. Congestive Heart Failure, 11(2), 73–79.
https://doi.org/10.1111/j.1527-5299.2005.03854.x
24
Maier, T., Güell, M., & Serrano, L. (2009). Correlation of mRNA and protein in complex biological samples. In FEBS Letters (Vol. 583, Issue 24, pp. 3966–3973). No longer published by Elsevier.
https://doi.org/10.1016/j.febslet.2009.10.036
Manning, B. D., & Cantley, L. C. (2007). AKT/PKB Signaling: Navigating Downstream. In Cell (Vol. 129, Issue 7, pp. 1261–1274). NIH Public Access. https://doi.org/10.1016/j.cell.2007.06.009
McComb, S., Mulligan, R., & Sad, S. (2010). Caspase-3 is transiently activated without cell death during early antigen driven expansion of CD8+ T cells in vivo. PLoS ONE, 5(12).
https://doi.org/10.1371/journal.pone.0015328
Moskot, M., Jakóbkiewicz-Banecka, J., Kloska, A., Piotrowska, E., Narajczyk, M., & Gabig-Cimińska, M.
(2019). The role of dimethyl sulfoxide (DMSO) in gene expression modulation and glycosaminoglycan metabolism in lysosomal storage disorders on an example of mucopolysaccharidosis. International Journal of Molecular Sciences, 20(2).
https://doi.org/10.3390/ijms20020304
Narasimhan, P., Liu, J., Song, Y. S., Massengale, J. L., & Chan, P. H. (2009). VEGF stimulates the ERK 1/2 signaling pathway and apoptosis in cerebral endothelial cells after ischemic conditions. Stroke, 40(4), 1467–1473. https://doi.org/10.1161/STROKEAHA.108.534644
Nogueira, V., Park, Y., Chen, C. C., Xu, P. Z., Chen, M. L., Tonic, I., Unterman, T., & Hay, N. (2008). Akt Determines Replicative Senescence and Oxidative or Oncogenic Premature Senescence and Sensitizes Cells to Oxidative Apoptosis. Cancer Cell, 14(6), 458–470.
https://doi.org/10.1016/j.ccr.2008.11.003
Pérez-Garijo, A. (2018). When dying is not the end: Apoptotic caspases as drivers of proliferation. In Seminars in Cell and Developmental Biology (Vol. 82, pp. 86–95). Elsevier Ltd.
https://doi.org/10.1016/j.semcdb.2017.11.036
Porporato, P. E., Filigheddu, N., Pedro, J. M. B. S., Kroemer, G., & Galluzzi, L. (2018). Mitochondrial metabolism and cancer. In Cell Research (Vol. 28, Issue 3, pp. 265–280). Nature Publishing Group.
https://doi.org/10.1038/cr.2017.155
Rawla, P., Sunkara, T., & Gaduputi, V. (2019). Epidemiology of Pancreatic Cancer: Global Trends, Etiology and Risk Factors. World Journal of Oncology, 10(1), 10–27. https://doi.org/10.14740/wjon1166 Reczek, C. R., & Chandel, N. S. (2017). The Two Faces of Reactive Oxygen Species in Cancer. Annual
Review of Cancer Biology, 1(1), 79–98. https://doi.org/10.1146/annurev-cancerbio-041916-065808 Safford, S. E., Oberley, T. D., Urano, M., & St Clair, D. K. (1994). Suppression of fibrosarcoma metastasis
by elevated expression of manganese superoxide dismutase. Cancer Research, 54(16), 4261–4265.
http://www.ncbi.nlm.nih.gov/pubmed/8044768
Salaroglio, I. C., Mungo, E., Gazzano, E., Kopecka, J., & Riganti, C. (2019). ERK is a pivotal player of chemo-immune-resistance in cancer. In International Journal of Molecular Sciences (Vol. 20, Issue 10). MDPI AG. https://doi.org/10.3390/ijms20102505
Salmeen, A., Andersen, J. N., Myers, M. P., Meng, T. C., Hinks, J. A., Tonks, N. K., & Barford, D. (2003).
Redox regulation of protein tyrosine phosphatase 1B involves a sulphenyl-amide intermediate.
Nature, 423(6941), 769–773. https://doi.org/10.1038/nature01680
Seyfried, T. N., Flores, R. E., Poff, A. M., & D’Agostino, D. P. (2014). Cancer as a metabolic disease:
25
Implications for novel therapeutics. In Carcinogenesis (Vol. 35, Issue 3, pp. 515–527). Oxford University Press. https://doi.org/10.1093/carcin/bgt480
Stanton, R. C. (2012). Glucose-6-phosphate dehydrogenase, NADPH, and cell survival. In IUBMB Life (Vol.
64, Issue 5, pp. 362–369). NIH Public Access. https://doi.org/10.1002/iub.1017
Ushio-Fukai, M., & Nakamura, Y. (2008). Reactive oxygen species and angiogenesis: NADPH oxidase as target for cancer therapy. In Cancer Letters (Vol. 266, Issue 1, pp. 37–52). NIH Public Access.
https://doi.org/10.1016/j.canlet.2008.02.044
Verheijen, M., Lienhard, M., Schrooders, Y., Clayton, O., Nudischer, R., Boerno, S., Timmermann, B., Selevsek, N., Schlapbach, R., Gmuender, H., Gotta, S., Geraedts, J., Herwig, R., Kleinjans, J., &
Caiment, F. (2019). DMSO induces drastic changes in human cellular processes and epigenetic landscape in vitro. Scientific Reports, 9(1), 1–12. https://doi.org/10.1038/s41598-019-40660-0 Worster, D. T., Schmelzle, T., Solimini, N. L., Lightcap, E. S., Millard, B., Mills, G. B., Brugge, J. S., & Albeck,
J. G. (2012). Akt and ERK control the proliferative response of mammary epithelial cells to the growth factors IGF-1 and EGF through the cell cycle inhibitor p57 Kip2. Science Signaling, 5(214), ra19–ra19. https://doi.org/10.1126/scisignal.2001986
Yang, H., Villani, R. M., Wang, H., Simpson, M. J., Roberts, M. S., Tang, M., & Liang, X. (2018). The role of cellular reactive oxygen species in cancer chemotherapy. Journal of Experimental & Clinical Cancer Research, 37(1), 266. https://doi.org/10.1186/s13046-018-0909-x
Zhang, Jixiang, Wang, X., Vikash, V., Ye, Q., Wu, D., Liu, Y., & Dong, W. (2016a). ROS and ROS-Mediated Cellular Signaling. Oxidative Medicine and Cellular Longevity, 2016, 4350965.
https://doi.org/10.1155/2016/4350965
Zhang, Jixiang, Wang, X., Vikash, V., Ye, Q., Wu, D., Liu, Y., & Dong, W. (2016b). ROS and ROS-Mediated Cellular Signaling. In Oxidative Medicine and Cellular Longevity (Vol. 2016). Hindawi Limited.
https://doi.org/10.1155/2016/4350965
Zhang, Juan, Cao, M., Yang, W., Sun, F., Xu, C., Yin, L., & Pu, Y. (2016). Inhibition of Glucose-6-Phosphate Dehydrogenase Could Enhance 1,4-Benzoquinone-Induced Oxidative Damage in K562 Cells.
Oxidative Medicine and Cellular Longevity, 2016. https://doi.org/10.1155/2016/3912515 Zhang, L., Li, J., Zong, L., Chen, X., Chen, K., Jiang, Z., Nan, L., Li, X., Li, W., Shan, T., Ma, Q., & Ma, Z.
(2016). Reactive Oxygen Species and Targeted Therapy for Pancreatic Cancer. Oxidative Medicine and Cellular Longevity, 2016, 1616781. https://doi.org/10.1155/2016/1616781
