Sobre la implementación y seguimiento de la Hoja de Ruta
ENTE COORDINADORMesa 1:
more variance in the data was observed overall for Chr9ORFF44 than IP6K1 (Figure 4.7), most samples exhibited no change in methylation in response to intervention (Figure 4.8). Those in the placebo group did respond more dramatically than the intervention group for both an increase and decrease in methylation, but this difference did not reach statistical significance (p = 0.74). Net changes were measured from samples showing increased or decreased methylation, but when divided into their respective groups, folic acid (n=20) and placebo (n=28), the differences observed failed to reach statistical significance (p=0.33). Raw data supplied in Appendix E.3.
RAS p21 protein activator 4: MeDIP Group: ON/OFF methylation. Across the majority of
the cohort, no DNA methylation was observed for RASA4 (Figure 4.9). Similarly to the two examples above, the differences that were observed did not reach statistical significance (Figure 4.10 A, p = 0.9). For the folic acid (n=6) and placebo (n=7) groups, net methylation changes were calculated, but failed to reach statistical significance (p=0.67). Raw data supplied in Appendix E.4.
Along with examining differences in methylation between the folic acid and placebo group, the clinical data of samples that exhibited the most dramatic changes were compared to one another. For IP6K1, the single sample with the greatest decline in methylation was found to have low B12 levels when compared to the rest of the cohort. Likewise, the sample showing
the second-largest decline in methylation for Chr9ORF44 was also found to have low levels of B12. However, the sample with the largest decline in methylation for this region did not
match the emerging trend. For RASA4, two samples showed a considerable increase in DNA methylation. One of these two individuals was homozygous for the rare TT variant of the MTHFR polymorphism, while the other had the wild type CC variant.
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4.3.4: Assessment of HEK293 Growth Curve in the presence of varying concentrations of 5’Azacytidine
On a 24-well plate, a crystal violet assay was carried out to -find the optimum concentration of 5aC that maintained cells in the log phase of growth. At a concentration of 5µM, the drug was found to have the most significant effect on growing cells while keeping them in the exponential phase by Day 7. Higher concentrations lead to significant cell death, while lower concentrations arrested growth before the 7 day time-course was complete (Figures 4.11- 4.12).
4.3.5: Nucleic Acid Extraction and Integrity
After extractions were carried out based on the protocols outlined in Section 2.3.1, the concentrations of nucleic acid was assessed using the nanodrop ND-1000 spectrophotometer from Mason Technology measuring at A260nm, and integrity was assessed via gel
electrophoresis. Figure 4.13 shows intact, undegraded RNA on a 1% gel, while a demonstration that the resulting cDNA was free from contaminating genomic DNA was shown using an intron flanking PCR based assay, (Figure 4.14).
4.3.6: Gene Expression Analysis of HEK293 Cells treated with 5’Azacytidine by RT- qPCR
RT-qPCR assays for IP6K1, RASA4, and GPS2 were designed using the UPL assay design centre website from Roche (http://lifescience.roche.com/shop/products/universal- probelibrary-system-assay-design). An assay designed for GUS was used as an endogenous control, with a relative ratio between both experimental conditions established to be 0.92 (Appendix G.1). Standard curves and PCR efficiencies were obtained for each assay (Appendix G.2-G.4), which were incorporated into the Lightcycler 480’s E Calculations. Data is displayed from Figures 4.15 to 4.17. Experiments were carried out in biological and technical duplicates, with mean and standard deviations derived from four data-points. All assays showed the same results, with 5’Azacytidine increasing expression levels of each gene. Experiments for IP6K1 exhibited an increase in expression exceeding 10-fold (p=0.007). The increases in gene expression of RASA4 were not as dramatic as that for IP6K1, but was significantly increased (p=0.015). For GPS2, an increase in gene expression was also observed (p=0.003). The results indicate that 5aC treatment of HEK293 cells has a significant and consistent impact on gene expression at these particular loci.
4.3.7: Gene specific DNA methylation analysis of 5’Azacytidine treated HEK293 cells
The gene expression results from Section 4.3.6 cannot be concluded to be caused by changes in methylation alone, however. As described by Komashko et al., (2010)191, treatment of
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HEK293 cells with 5aC results in a genome-wide impact on expression, altering the transcription of genes regardless of their methylation status. Indeed, as demonstrated by the cytotoxicity assay in Section 4.3.4, exposure to the drug alone results in cell death. Given the plasticity of the transcriptome and its capacity to adapt to changes in the environment, more evidence that changes in gene expression observed through RT-qPCR were caused by demethylation of their promoter regions is required.
SMART-MSP assays IP6K1, RASA4, and GPS2 were carried out on each DNA sample extracted from HEK293 cells treated with 5aC. Results were obtained using the standard SMART MSP calculations outlined in Section 4.2.4. In each case, 5aC treatment of HEK293 cells successfully resulted in a decrease in DNA methylation (Figures 4.18-4.20).
In cells treated with 5µM 5aC for 7 days, methylation at the IP6K1 locus decreased significantly (p = 2.23E-05). For RASA4, the decrease in methylation was less pronounced, but still statistically significant (p = 0.009). For GPS2, the loss of methylation was also statistically significant (p=0.019). In each of these cases, a decrease in DNA methylation at a promoter region is mirrored by an increase in gene expression.
It is clear that 5’azacytidine treatment did not completely eliminate DNA methylation across the genome, as predicted by Falck et al., (2012)190. Only the region analysed by the IP6K1
assay reached levels lower than 10% when exposed to the drug. Nonetheless, high levels of demethylation were found in each region, each consistent with an increase in gene expression for their associated genes.
4.4: Discussion
The primary aim of this project was to assess the effect of folic acid supplementation on DNA methylation during pregnancy. As a methyl donor for DNA methyltransferase reactions, folic acid and its folate derivatives have been hypothesised and demonstrated to influence DNA methylation on both a genome-wide and gene-specific level3,111,196,197. With the beneficial
effects of folic acid during pregnancy being well established but poorly understood, DNA methylation has been considered to be a candidate for linking the cause and effect of periconceptional supplementation.
The FASSTT study was designed to study the effects of folic acid supplementation after week 12 of pregnancy4. During pregnancy, higher levels of circulating homocysteine have been
associated with an increased risk of conditions including NTDs, preeclampsia, and recurrent early pregnancy loss198–200 relative to unaffected pregnancies198–200. Folate status is a major
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determinant of homocysteine levels, as it is required for the methylation of homocysteine to methionine201. Accelerated folate catabolism has been reported to occur during a typical
pregnancy202, which may contribute to the decrease in circulating folate levels found in other
studies26.Recommended folic acid supplementation procedures during the first trimester aim
to prevent this decline, but little has been known about its past week 12 of pregnancy. The FASSTT study has found that folic acid supplementation can prevent the decline in serum and red cell folate levels and the increase in homocysteine levels that occurs otherwise during pregnancy4.
In order to examine the methylation levels of DNA extracted from the blood samples used in this study, a literature survey was first carried out to examine the different methods of genome- wide and gene-specific DNA methylation analysis used in the field5. Although successful in
finding methods suitable for the scope and scale of this project, it was clear from the review that the vast majority of methods established for DNA methylation analysis were conceived for the purpose of finding aberrant methylation patterns in cancer134,138,146. For each of the
three methods discussed in this thesis – MMSDK, MeDIP, and SMART MSP – the “proof- of-concept” experiments carried out in all of their respective publications were on DNA derived from cancerous tissue where dramatic DNA methylation differences are known to occur.
From the subset of the FASSTT cohort subjected to MeDIP and promoter DNA microarray hybridisation, a preliminary list of FS-DMRs was obtained. Using highly astringent selection criteria, this list was narrowed down to five; far fewer than what is typically reported in studies using MeDIP to find DMRs between cancerous and healthy cell-lines170,203,204. In the
subsequent analysis, when three of these FS-DMRs were examined over an extended cohort using a different, gene-specific method, no significant difference was observed. Some individual samples exhibited major changes in DNA methylation levels for these sites in comparison to the rest of the cohort, and clinical data for each was examined. Of the five individuals examined, 2 were found to have lower B12 levels when compared to the rest of the
cohort, while 1 was a TT homozygote for the MTHFR 677 C>T polymorphism. Without a consistent trend amongst these samples, however, no conclusion can be made from these findings.
Although these methods were not specific for examining tumourigeneis, the changes in methylation observed in the FASSTT study were not as significant as those presented in the cancer case-studies used to demonstrate their efficiency and specificity. The difficulty lies in the nature of the FASSTT samples themselves: the differences in DNA methylation levels
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affected by supplementation in a healthy individual over a short period of time would never be as dramatic as those reported from tumourgenesis32,169,171,175. This, along with the
differences in DNA methylation patterns that exist between individuals prior to intervention, has made analysing the effect of folic acid supplementation on DNA methylation particularly challenging.
The cell culture analysis did yield conclusive results, however. When the promoter regions of IP6K1, RASA4, and GPS2 are subjected to demethylation in vitro, a consistent increase in gene expression is observed demonstrating that DNA methylation changes have a direct impact on the gene expression of these genes.
The DNA methylation analysis on the FASSTT cohort did not find measurable changes in DNA methylation in response to folic acid supplementation, but if those differences did exist on a smaller scale, other methods of genome-wide methylation analysis using next generation sequencing and sodium bisulfite treatment to generate data at a single-base-pair resolution could be used to elucidate them. With evidence here that DNA methylation does in fact affect the expression of these genes, there is potential for a follow-up study to re-examine these proposed FS-DMRs using more sophisticated technology, such as sodium bisulfite pyrosequencing.
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Cp Cp Average STDEV Tm Tm Average STDEV
100% 26.10 26.19 26.15 0.0636 76.63 76.64 76.64 0.0071 50% 27.30 27.09 27.20 0.1485 76.62 76.49 76.56 0.0919 10% 29.73 29.61 29.67 0.0849 76.49 76.42 76.46 0.0495 1% 32.09 33.52 32.81 1.0112 76.65 76.56 76.61 0.0636 0% 35.62 36.09 35.86 0.3323 74.79 74.50 74.65 0.2051 neg 35.50 35.85 35.68 0.2475 74.99 74.96 74.98 0.0212
Figure 4.1: Melting profile of IP6K1 SMART MSP Assay
Melting peaks of products from IP6K1 assay. Blue Peak = methylated DNA (~76.6oC); Red
peak = unmethylated DNA (74.7oC); Grey peak = no-template negative control (~74.7oC).
Methylated and unmethylated DNA PCR products generate distinct melting peaks. The product of the no-template negative control has a peak similar to that of the unmethylated standard, both arising from primer-dimer formation. Only Cp values from the methylated peak (~76.6oC) were considered for the SMART-MSP methylation percentage calculation.
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Cp Cp Average STDEV Tm Tm Average STDEV
100% 27.43 27.90 27.67 0.3323 78.41 78.35 78.38 0.0424
50% 28.64 29.19 28.92 0.3889 78.32 78.30 78.31 0.0141
10% 30.22 30.74 30.48 0.3677 78.17 78.22 78.20 0.0354
1% 31.08 31.12 31.10 0.0283 77.51 77.31 77.41 0.1414
0% 30.28 30.33 30.31 0.0354 77.37 77.32 77.35 0.0354
neg N/A N/A N/A N/A N/A N/A N/A N/A
Figure 4.2: Melting profile of Chr9ORF44 SMART MSP Assay
Melting peaks of products from Chr9ORF44 assay. Blue Peak = methylated DNA (~78.3oC);
Grey peak = unmethylated DNA (77.5oC); Yellow peak = no-template negative control (no
amplification). Methylated and unmethylated DNA PCR products generate distinct melting peaks. Tm scores indicate that samples with 100% to 10% methylated DNA will generate a
product at ~78.3oC, while those from 1% to 0% produced a peak at 77.5oC. Only Cp values
from the methylated peak (~78.3oC) were considered for the SMART-MSP methylation
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Cp Cp Average STDEV Tm Tm Average STDEV
100% 24.54 24.8 24.67 0.1838 76.96 76.8 76.88 0.1131 50% 26.47 26.21 26.34 0.1838 76.77 76.69 76.73 0.0566 10% 27.48 27.36 27.42 0.0849 76.02 76.08 76.05 0.0425 1% 28.86 28.22 28.54 0.4526 Early Peak Early Peak Early Peak Early Peak 0% 35.65 35.91 35.78 0.1838 Early Peak Early Peak Early Peak Early Peak neg 34.83 33.50 34.165 0.9405 Early Peak Early Peak Early Peak Early Peak
Figure 4.3: Melting profile of RASA4 SMART MSP Assay
Melting peaks of products from RASA4 assay. Blue Peak = methylated DNA (~77.8oC, with
a shoulder of ~71.5oC); Red peak = unmethylated DNA and no template negative controls
(71.5oC); Orange peak = 10% methylated DNA, (peaks at 76.8oC and 71.5oC). Only Cp values
from the methylated peak (~76.8oC) were considered for the SMART-MSP methylation
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Cp Cp Average STDEV Tm Tm Average STDEV
100% 31.67 31.79 31.73 0.0849 80.86 80.83 80.85 0.0212
50% 32.56 32.52 32.54 0.0283 80.71 80.66 80.69 0.0354
10% 35.72 35.53 35.63 0.1344 79.97 79.98 79.98 0.0071
1% 35.32 37.14 36.23 1.2869 78.98 78.92 78.95 0.0424
0% 35.02 35.71 35.37 0.4879 78.83 78.89 78.86 0.0424
neg N/A N/A N/A N/A N/A N/A N/A N/A
Figure 4.4: Melting profile of GPS2 SMART MSP Assay
Melting peaks of products from GPS2 assay. Blue Peak = methylated DNA (~80.8oC); Red
peak = unmethylated DNA (78.5oC); Grey peak = 10% methylated DNA, (peaks at 81oC and
78.5oC). Only Cp values from the methylated peak (~81oC) were considered for the SMART-
MSP methylation percentage calculation. No template negative controls exhibit zero amplification.
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Figure 4.5: Scatterplot of IP6K1 methylation changes for all FASSTT samples
Regardless of their grouping, the change in methylation at the IP6K1 locus before and after intervention (Y-axis) was plotted for each participant of the FASSTT cohort. Changes of 10% increase or decrease (red lines) were deemed no change due to the insensitivity of the assay to detect consistent changes in this range. The single patient that showed the greatest decline in methylation was found to also have low B12 levels when compared to the rest of the cohort.
A
-80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 0 20 40 60 80 100 M e th yl ation % FASSTT Participents126