 Good morning, I am Smithi and I have been working in the summer under the Korana program in the lab of Dr. Norma Drinkwater in Makata Laboratory for Cancer Research. And this is the project that I have been doing which is the identification of the liver cancer susceptibility modifier in the chromosome 17 of the C57 brown mice. Okay, the basic question of why work on liver cancer or hepatocelular carcinoma? It is the fifth most common neoplasm in the world. The third most common cause for cancer related death causing over 500,000 deaths per year. The incidence is 2 to 5 times higher in men than women. The reason for this has not been attributed very clearly but it is attributed to harmonial environments and exposure to other risk factors. This just gives a geographical distribution of the liver cancer mortality rate in the world. You can see clearly that the rate is very high in countries like Africa and also in Asia. And this graph also indicates clearly that in all regions the mortality rate in males is much higher than females. Just to give a little background, my lab has been using mouse models to work in liver cancer. The reason for this primarily is that in mice the incidence in males is generally much higher than females which is the same thing which we see in humans. But there is this specific strain of mice in which I am working on which is the brown mice which is quite different in the fact that the females are almost as susceptible as the males. So this makes it an interesting model. And generally the in red mice strains differ dramatically in their susceptibility to liver cancer. So the brown mice which I am working on is 550 fold more susceptible than most of the resistance strains which would include the C57BL6J which I would refer to as B6 and the C3H strain. Both these strains are resistance strains. So I mean just to give a small background previously linkage studies were done between the brown and the B6 mice. And the HCF1 locus which is present on the chromosome 17 was identified as the predominant locus mainly because it accounts for two-thirds of the susceptibility in the brown mice. And by further analyzing the recombinants that were obtained the region of the locus was narrowed down to a 1.43 megabase region which is this region, this is the region in which I am working on. It spans from 34.05 megabases to 35.48. It's a 1.43 megabase region. Just to talk a little bit about the region it contains 160 genes and it partially overlaps with the major histocompatibility complex and it is rich in immune response genes. So that makes it very interesting because information is attributed in liver cancer. So and it also corresponds to the region on the human chromosome 6B which is amplified in half of the liver cancer cases. So that also makes it a very interesting region to study. So the objective of my study here was to identify genes in the brown in the HCF1 minimal region which are unique to brown. Like in this case as you can see the B6 and the C3H are resistance strains whereas the brown is a susceptible strain. So we are looking for genes that are unique from both C3H as well as B6 because we believe I mean that would be the reason for that would be causing the susceptibility. And this I propose to do by two methods. Primarily by sequencing and SNP analysis, SNP single nucleotide polymorphisms. And the second part of my project was based on differential expression analysis. So coming to the first part the methodology that I followed was the basic sequencing protocol. I ran PCRs and this we did by designing overlapping primers for the genes present in that region. And the primers were designed using the B6 information which the B6 is a strain whose genome has been sequenced. So we use the B6 information to design overlapping primers of about 500 to 700 base pairs. For example in this particular gene here which is the LY6G6C. This would be one set of primers and this would be the second and this would be the third. And mainly we concentrated on sequencing the exons and the flanking region around it. So this was a point where I had a lot of troubleshooting to do because many of the PCRs did not work due to lot of reasons. And for the reactions that worked we went on to purify the PCR products, did the big die sequencing PCR and then analyzed for the presence of any SNPs. Just to talk a little bit about the troubleshooting that I did. One of the things that I tried was the failsafe buffers. There are 12 failsafe buffers which have a varying concentration of magnesium chloride whose concentration varies between 3 to 7 millimolar in the buffers. And it also contains a PCR enhancer that has beaten and this is supposed to reduce the nonspecific binding of the primers. So we tried using the failsafe buffers for the reactions where normal buffers did not work. So this was one case where I did get a few bands. Like here you can see that this is the failsafe buffer A, B, C and D. Here the B and C have given distinct bands while the A and D did not. So another thing which I did was the gradient PCR which was varying the temperature, the annealing temperatures. So I varied the annealing temperatures from 50 to 61 degrees and performed the reactions. As you can see here at the lower temperatures of 50 to 54 degrees you can see a very clear band whereas the band becomes weaker as the temperature increases. So these are the results that I got for the sequencing part. So these were 7 genes which have been completed at the end of the sequencing and these are some of the many genes in which the major gaps have been filled but still small region of the exons have to be filled so you can't say that it's entirely complete. So this is just to show an analysis of one of the genes that have been completed. This is the Ly6G5C. As you can see here these are the exon regions. These are these darker red ones and these are the regions which we have sequenced and you can see that it's complete because all the exons and the franking regions have been sequenced. First thing what we did was we did a sequence alignment with the B6 strain which is the resistance strain and this is just showing one of the three context. So like this when we aligned it we found that there were several SNPs. Like in this particular context we found that there were three SNPs, three changes. So when we analyzed all of the context we found that there were about 14 SNPs. Next we went on to see if these SNPs where all of them were critical to our experiment which would be because we were concentrating only on the exons and the regions surrounding it. So we looked whether these SNPs were in those regions and we found out that out of the 14 only eight of them were in the exon and in the franking regions. So for these eight SNPs we went on to confirm whether the quality of the sequencing which we had done was good. Like in this particular case here the, I mean this is the B6, this is the Brown. So the G has been changed to C and this is the corresponding peak for the sequencing and as you can see it is very clear. So like when we did this we found out that there was no problem with the sequencing. We went on to do this for all the eight and the quality of all the eight C8 points were good. And then we went on to compare it with another resistance strain which is the C3H. The sequence data is available in the Sanger database so we used that and we found out and in this case we found that the change that had occurred in the Brown had also occurred in the C3H. It was pretty consistent. So the G which has changed to C, this change is also found in C3H but C3H is a resistance strain. So this shows that this change is not what is causing the susceptibility. When we did similar analysis to the other SNPs present in this particular gene we found that none of them were significant because we saw the same changes in the C3H. So as a result we eliminated L by 6G5C gene as the level of susceptibility modified. So the future directions for this part of the project would be to sequence the remaining genes and then analyze the genes for the presence of any interesting SNPs and as a result of this identify genes which have mutations unique to the Brown strain. So coming to the next part of my project which was differential expression analysis. Just to talk about the methodology that was used. The liver cells from the mice were taken and it was purified and the mRNA was prepared. It was reverse transcribed to give the CDNA and from the CDNA previous microarray studies had been done. So from the microarray studies there were a few carotid genes that were identified. These carotid genes had been differentially expressed in the Brown mice as against the B6 mice. So my part of the experiment was to verify these carotid genes by using a quantitative real-time PCR and also another thing was there were a few genes which were not expressed in the microarray. So we wanted to verify whether any of those genes would be a candidate gene. This is the results that was obtained as a result of microarray between the Brown and the B6. So there were six genes that were identified as candidate genes as they were differentially expressed. One gene here which I would just point out to is the C4A which has a 3.2 fold higher in the Brown as against the B6. I'm just pointing out this because I'll be talking about this in the next slide. So this is a result of the quantitative PCR that we obtained. Out of the 14 genes and the 3 housekeeping genes that we tested, we found that five of these genes are differentially expressed in Brown and C3H as relative to B6. So all these genes could be possible candidates but one very interesting candidate was this one which is the C4A transcript 6. As I had mentioned the C4A is seen to be, I mean from the microarray we saw that it was 3.2 fold higher, I mean highly expressed in the Brown as against the B6. So in this case it was seen that it was the, it was about 2.6 fold higher. So that is pretty consistent and as you can see here the expression of C3H relative to B6 was, it was down-regulated. So this shows that the Brown is different from the B6, the expression and the, as well as the C3H. So this makes it an interesting candidate. So this probably could be a candidate gene that is causing the liver cancer susceptibility. So future directions for this part would be to do the expression analysis for the other candidates and also do the quantitative PCR and analyze the genes that were not expressed in the microarray. As a result we aim to identify few potential candidate genes and just to summarize my project, the aim of my project was to identify unique genes in the Brown mice which are causing the susceptibility by using SNP and expression analysis. As a result of the sequencing analysis seven genes were completed and several gaps were filled based on the analysis of the SNPs the Ly6G5C gene was eliminated and as a result of the expression analysis using QPCR we identified five differently expressed genes or transcripts out of which one which is the C4A which we believe could be a potential candidate. Knowledgemen's, I would like to thank Dr. Norman Ringwater for hosting me in the lab and Dr. Andrea Bilger and Rebecca Boss for guiding me through the project. So and I would also like to thank the Department of Biotechnology, Government of India, the IUSSTF and UW-Miracine for providing the funding for the Corona program. Thank you. Look at the nature of the SNPs themselves. I mean, do some of them have any code for just a U.S. of changes or sacrodawns or anything like that? No, actually we haven't had the chance to do that. The other thing is the C486. Was that in the 34 megabase region? Yeah, it was. Okay, all those genes were... Yeah, we tested the genes in that particular region. Factors, what are you talking about when you said that we have a higher incidence? Risk factors as in more of environmental factors such as exposure, I mean, could be afrotoxins, alcohol, those kind of...