* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Use of methylation profiling to identify genes involved in relapse in
Human genome wikipedia , lookup
No-SCAR (Scarless Cas9 Assisted Recombineering) Genome Editing wikipedia , lookup
Genetic engineering wikipedia , lookup
Gene expression programming wikipedia , lookup
Non-coding DNA wikipedia , lookup
Quantitative trait locus wikipedia , lookup
Vectors in gene therapy wikipedia , lookup
Long non-coding RNA wikipedia , lookup
Transgenerational epigenetic inheritance wikipedia , lookup
X-inactivation wikipedia , lookup
Epitranscriptome wikipedia , lookup
Metagenomics wikipedia , lookup
Ridge (biology) wikipedia , lookup
Biology and consumer behaviour wikipedia , lookup
Minimal genome wikipedia , lookup
Genome evolution wikipedia , lookup
Genome (book) wikipedia , lookup
Epigenetics of neurodegenerative diseases wikipedia , lookup
Microevolution wikipedia , lookup
History of genetic engineering wikipedia , lookup
Mir-92 microRNA precursor family wikipedia , lookup
Therapeutic gene modulation wikipedia , lookup
Site-specific recombinase technology wikipedia , lookup
Epigenetics wikipedia , lookup
Designer baby wikipedia , lookup
Epigenetic clock wikipedia , lookup
Gene expression profiling wikipedia , lookup
Polycomb Group Proteins and Cancer wikipedia , lookup
Genomic imprinting wikipedia , lookup
Artificial gene synthesis wikipedia , lookup
Epigenetics of human development wikipedia , lookup
Behavioral epigenetics wikipedia , lookup
Epigenetics of depression wikipedia , lookup
Oncogenomics wikipedia , lookup
Secreted frizzled-related protein 1 wikipedia , lookup
Cancer epigenetics wikipedia , lookup
Epigenetics in stem-cell differentiation wikipedia , lookup
DNA methylation wikipedia , lookup
Epigenomics wikipedia , lookup
Epigenetics of diabetes Type 2 wikipedia , lookup
Epigenetics in learning and memory wikipedia , lookup
The use of methylation profiling to identify genes involved in relapse of adult ALL. Vanessa Williams*, 110188267, Biomedical Genetics, [email protected] Supervisors: Dr Gordon Strathdee and Fadhel Lafta, Northern Institute for Cancer Research. Introduction Introduction Methods In healthy cells CpG islands that are based at a gene promoter are usually methylation free. In cancerous cells these same regions frequently exhibit hypermethylation, leading to stable gene inactivation. • The conditions for the primers of each gene were found by carrying out PCR at multiple annealing temperatures (5863°C) and varying MgCl2 levels (1-4mM). The final conditions were 63°C and 2mM for MSC1 and 58°C and 3.5mM TTC12 It is known that genes important in the development of cancer are switched off due to this process and therefore identifying altered DNA methylation patterns can lead to the identification of genes important in cancer development. This project focussed specifically on genes that might be important in causing relapse in acute lymphoblastic leukaemia (ALL). Based on previous genome wide screening for changes in methylation and gene expression a set of 12 genes had been identified as candidates for a role in the development of relapse (based on their methylation levels in diagnostic and relapse samples and a negative association between methylation and expression). These included the MSC1 and TTC12 genes. Aims • To investigate the methylation patterns of the genes MSC1 and TTC12 in paired patient samples at diagnosis and at relapse • Compare the methylation level in these samples looking for changes in methylation between diagnosis and relapse Acknowledgements Thanks to Newcastle University student vacation scholarship for the funding to allow this research. Figures 3 and 4 show two pyrograms produced from a patients modified diagnostic and relapse DNA samples. The number in blue is the percentage of methylation at that specific CpG site. The final value for each samples is determined by averaging the values at the 3 CpG sites covered by the assay. • After comparing the results for TTC12 there are some samples that show variation however the majority only have a small difference between diagnostic and relapse. Figure 1 and 2 show electrophoresis gel images of the conditions using gel red staining. The brightest band suggests the most PCR product and therefore shows which conditions are optimum for the primers. • The patient genomic DNA samples were modified by bisulphite modification which allows subsequent assessment of methylation status by PCR based methods • PCR was carried out to amplify the regions of interest in the modified DNA Samples • Pyrosequencing was then carried out to determine the methylation levels at specific CpG sites Results • For the TTC12 locus good quality pyrograms were achieved which allowed determination of methylation levels in most of the sample set • However, for the MSC1 locus the pyrograms produced were frequently weak and inconsistent. Further optimisation of conditions were unable to generate consistent results for this locus and so investigation was not continued Figure 5 shows a bar chart comparing the levels of methylation between the diagnostic and relapse samples of patients. Although some individual samples do show variation between diagnosis and relapse, overall there is no clear pattern of increased or decreased TTC12 methylation between diagnostic and relapse samples (p=0.70, paired T-test) Conclusions • The TTC12 loci is frequently methylated in ALL samples at booth diagnosis and relapse. While methylation levels can vary within paired samples there was no apparent association with disease progression