Transient Transfection
Transfection is a method to aid delivery of genetic material (DNA or RNA) through the cell membrane by the use of an agent called transfection reagent. In general, transfection reagents can be divided into two groups; cationic lipids or cationic polycations. Cationic lipids such as Lipofectamine 2000 and RNAiMAX are effective transfection reagents for the transfer of mRNA and siRNA molecules into the cell.
However, possible toxicity must be controlled. Even though the toxicity of these agents (Lipofectamine 2000 and RNAiMAX) is reported to be limited, the transfection protocol may promote unspecific responses that can interfere with cell viability. These effects are cell line dependent and some cell lines may also show increased proliferation as a result of the transfection procedure. Previously, it has been reported that the transfection reagents (cationic lipids) may interfere with the functions of the cell membrane [165, 166], and the cells can become more sensitive to adverse effects such as immune activation, inflammation and cytotoxicity, thus inhibiting cell cycle progression [167]. The differences in the amount of oligonucleotide uptake between experiments as well as differences in uptake between cell lines can affect the results. In this study, siRNA molecules were used to reduce expression of MITF-M and p16INK4A mRNA levels. Gene silencing by the use of siRNA molecules will never be 100% efficient, keeping small amounts of targeted protein present within the cell. In contrast to modulations at the DNA level (CRISPR) where gene expression can be completely abolished. When using siRNA molecules it
69 is important to consider the protein turnover with respect to choosing an appropriate transfection time. In addition siRNA molecules have a restricted duration of efficacy before they are degraded and/or are diluted into daughter cells. Many of these challenges when using siRNA molecules can to some extent be avoided by stably transducing cell lines with vectors containing short hairpin RNA (shRNA), a method that is also reported to have a lower probability of off-target effects [168].
Nevertheless, the use of siRNA molecules and transient transfection was selected in this study, based on previously optimized transfection protocols and the ease of use.
Cell viability
The MTS One Solution Cell Proliferation Assay from Promega is easy to use, and requires only a spectrophometer. The values measured after running a MTS assay are often used to assess cell viability, since the amount of formazan color produced are proportional to the count of viable cells [169]. However, it is important to recognize that the MTS assay measures metabolically active cells, and not cell viability in form of the amount of e.g. apoptotic cells. Thus, the difference measured in absorbance might not reflect a difference in only proliferation, as senescent cells also are metabolically active. Moreover, the MTS assay can be influenced by changes in culture conditions such as pH and nutrients (glucose and fatty acid content) [170].
Therefore, it is beneficial to compare the results from several cell viability assays to strengthen the data. In this study we also investigated cell proliferation rates after various treatments by using time-laps microscopy assay (IncuCyte).
Gene expression measured by qPCR
qPCR was used to study potential differential gene expression. One of the important considerations in this method is the normalization of the results. Reference genes correct the variation between the samples and serve as loading control. Choosing reference genes that are stable among the samples is therefore important.
Housekeeping genes TBP and RPLPO were used in this study, as their gene expression was not altered by experimental conditions. The Ct values obtained from qPCR are the normalized value of the target gene. The normalizer is a mean Ct value from the two reference genes.
70 A comparison between different genes in the same conditions using Ct values is challenging. In this study the same target genes with the same reference genes (TBP and RPLPO) were used on the same samples from different cell lines. These Ct values can then be compared as they have the same reference point. To ensure that the threshold does not influence the Ct values, the threshold was set at the same point (in the gene analysis program, Bio-Rad) where the amplification curves had an exponential increase.
71
5 FUTURE PERSPECTIVES
To further investigate the role of MITF upon the CDKN2A gene regulation in melanoma, it would be mandatory to investigate the potential binding of MITF protein to the INK4A and ARF promoter in various cell backgrounds spanning the disease. This could be achieved by running a ChIP-PCR of the relevant promoter. If MITF binds to the promoter of p16INK4A and/or p14ARF, it would be interesting to mutate the MITF binding site by CRISP. Then, overexpress p16INK4A/p14ARF and compare against wild type (non-mutated promoter) for downstream effects such as senescence (by using a β-galactosidase assay). Since MITF has been reported to activate p16INK4A, and they both have been found to induce senescence (after modulation), it is challenging to separate downstream effects from the two genes. We find it interesting that depletion of MITF alone and induction of p16 both induces senescence, while depletion of MITF has been found to inhibit p16INK4A expression. Is MITF using an alternative pathway to induce senescence (p21/p53) or do our results make sense (imply that MITF could inhibit p16INK4A expression)? Several questions need to be addressed to elucidate the role of MITF and p16INK4A in the senescence program. A better understanding of the senescence program could give rise to new treatment modalities involving e.g. natural killer (NK) cells that can eliminate malignant senescent cells [171].
72
6 CONCLUSIONS
- Positive association between p16INK4A and p14ARF mRNA expression in an extended melanoma cell line panel.
- MITF-M depletion up-regulates mRNA levels of p16INK4A and p14ARF the majority of melanoma cell lines tested. The exception found in the WM983B cell line indicates that regulation of p16INK4A and p14ARF expression by MITF-M is cell line specific.
- Knockdown of p16INK4A have no specific effect on proliferation rate in melanoma cell line WM1382.
- Overexpression of p16 wild type mRNA have no significant effect on proliferation rate, compared to overexpression of p16 P81T mutated mRNA in cell lines WM9 and WM983B.
73
7 APPENDIX
Table A1. Cell culturing Producer
RPMI-1640 medium Sigma@ Life Sciences
L-alanyl-L-glutamine (Glutamax) Sigma@ Life Sciences Fetal Bovine Serum (FBS) Sigma@ Life Sciences
Culture EasY-flasks Nunc
6-well plates Nunclon Thermo-fisher
Trypsin-EDTA Sigma@ Life Sciences
CountessTM automated cell counter Invitrogen CountessTM cell counter chamber slides Invitrogen
Dimethyl-sulphoxide-hybri-max (DMSO) Sigma@ Life Sciences 1420 multilable counter victor2 Wallac
PBS Sigma@ Life Sciences
96-well plates Corning Costar
254 medium Gibco Invitrogen
Phorbol-12-myristate-13-acetate Sigma-Altdich
Cholera toxin Sigma-Altdich
Human stem cell factor (hSCF) Thermo-Fisher
Endothilin (EDN1) Milipore
VenorGEM Mycoplasma detection kit Biolabs
Cell lines Source
WM35 Coriell Institute (Wistar collection)
WM1366 Coriell Institute (Wistar collection)
WM983 Coriell Institute (Wistar collection)
WM115 Coriell Institute (Wistar collection)
WM1341 Coriell Institute (Wistar collection)
FMX-I Norwegian Radium Hospital
FMX-V Norwegian Radium Hospital
LOXIMVI Norwegian Radium Hospital
MEWO American Type Culture Collection
(ATCC)
WM9 Coriell Institute (Wistar collection)
WM1382 Coriell Institute (Wistar collection)
WM239A Coriell Institute (Wistar collection)
WM45.1 Coriell Institute (Wistar collection)
SKMEL-28 American Type Culture Collection
(ATCC)
WM266-4 Coriell Institute (Wistar collection)
A375 American Type Culture Collection
(ATCC)
74
WM852 Coriell Institute (Wistar collection)
Hermes 3C The welcome trust genomic cell bank
Hermes 4C The welcome trust genomic cell bank
Table A2. Transfection Producer
siRNA MITF Eurogenetic
siRNA p16 Medprobe
EGFP mRNA Medprobe
Lipofectamine RNAiMAX Invitrogen
Lipofectamine 2000 Invitrogen
Negative control #1 random sequence Eurogenetic
Table A3.Real time PCR Producer
NANODROP 2000 Thermo Scientific
qScriptTM DNA synthesis kit Quanta Biosciences Total RNA Miniprep kit Sigma-Aldrich
Thermo-cycler Gene-amp@ PE Applied Biosystems Nucleotide-Perfecta@ super mix Quanta Biosciences Real time PCR CFX-connectTM Bio-RAD
Perfecta TM SYBR@ Green supermix Quanta Biosciences CFX manager 3.1 softweare Bio-Rad
Table A4. Western immunoblotting
Antibodies Dilution Producer
Anti-β-actin (mouse) 1:10 000 (primary) Sigma-Aldrich Anti-α-tubulin (mouse) 1:10 000 (primary) Sigma-Aldrich Anti-mouse (rabbit) 1:10 000 (secundary) Daco
Anti-MITF (mouse) 1:10 00 (primary) Cell signaling
Anti-p16 (mouse) 1:10 00 (primary) Millipore
Phosphatase inhibitors 1:100 Sigma-Aldrich
Sulotion buffers
Lysis buffer:
NaCl 150mM
Tris-HCL pH 7,5 50mM
NP-40 0,1% (v/v)
EDTA 1 med mer
ddH2O Adjust
TBS-T:
NaCl (150mM) 5M - 30ml Tris-HCL pH 7,5
(20mM) 1M - 20ml
Tween-20 (0,1%) 20% - 5ml
ddH2O Adjust to 1
75 Liter
Table A5. PCR primers (producer IDT)
Primer target Sequence Direction
MITF-M
5'-CATTGT-TAT-GCT-GGA-AAT-GCT-AGA-3' Forward
5'-GC-TAA-AGT-GGT-AGA-AAG-GTA-CTG-C-3' Reverse
p16v3
5'-GTCTG-TGA-TTA-CAA-ACC-CCT-TCT-G-3' Forward
5’-GTCCC-CTT-GCC-TGG-AAA-GAT-AC-3’ Reverse
p14 5’-TCCCA-GTC-TGC-AGT-TAA-GGG-3’ Forward
5’-TGAAC-CAC-GAA-AAC-CCT-CAG-3’ Reverse
TBP 5’-GCCCGA-AAC-GCC-GAA-TAT-3’ Forward
5’-CGTGGC-TCT-CTT-ATC-CTC-ATG-A-3’ Reverse
RPLPO 5’-CGC-TGC-TGA-ACA-TGC-TCA-AC-3’ Forward
5’-TCG-AAC-ACC-TGC-TGG-ATG-AC-3’ Reverse
Primer efficiency tests was conducted with the following results: p16v3 95,1%, p14 97,6% and MITF-M 93,8%.
76
8 SUPPLEMENTARY FIGURES
Supplementary figure S1: images (20x magnification) captured by phase contrast microscopy (X-Cite®, Life Science) after siRNA silencing of MITF in the WM1382 cell line (120 hours). Image A represents untreated control, B: negative control (NC siRNA), and C: MITF target siRNA to the right.
Supplementary figure S2: the WM1382 cell line were transfected with p16 siRNA and monitored for 120 hours by time-lapse microscopy (incuCyteZoom). A: untreated control. B: negative control (NC siRNA) and C: p16 siRNA.
A B C
A B C
77
Supplementary figure S3: show representative growth curves of WM9 and WM1382. Cells were monitored for 120 hours by time-lapse microscopy (incuCyteZoom) in two independent experiments.
The program was set to take contrast images every 3 hours. The incorporated software analyzed the phase contrast images, and calculated confluence over time, by measuring the area covered with cells.
A much higher growth rate was observed for the WM9 cells (grey curve) compared to WM1382 cells (black curve).
0 10 20 30 40 50 60 70 80 90 100
0 24 48 72 96 120
Cell density (%)
Time (h) WM1382
WM9
78
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