My next venture into bioinformatics would this time be directly with RNA splicing factors. I was still to look at what occurred when alpha 3 was knocked down but this time I was referencing that list against a file named Russ Carsten Uniprot. This file is named after the man that it came from and contained 396 genes. I quickly went through and found all the genes that were present in my file in alpha 3 as well as Russ Carsten's file.
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So in the lab that I'm working in the knocked down alpha 3 beta 1 integrin. They knocked down alpha 3 the same way that other lab from last week knocked down UPF1 by making the mRNA double stranded so the cell automatically degrades it. Alpha 3 in a integrin involved in a host of cell functions but for my purposes it happens to interact with Nonsense Mediated Decay. When alpha 3 is down regulated NMD functions normally, but when alpha 3 is upregulated NMD is suppressed. When UPF1 is down regulated NMD is also suppressed. My job was to compare the results from that two different labs got with regard to the fold change of other proteins, the thinking behind that being that if alpha 3 was upregulated and UPF1 was down regulated that means NMD won't be occurring so more mistakes would make into the protein formation stage. I was to look and see what proteins exactly were being upregulated while alpha 3 was being upregulated and UPF1 was being downregulated.
I did this by scanning through hundreds of lines of a spread sheet that had factors that I need like fold change. Fold changes are basically the difference in prevalence for when alpha 3 is knocked down versus when it wasn't. I ended up finding 6 that were upregulated while alpha 3 was upregulated and UPF1 was downregulated out of a possible 1,180 proteins. Oddly enough this exercise was more or less a warmup to get me acquainted with how to do a little bit of bioinformatics. So when DNA transcription (when messenger RNA copies strands of DNA) occurs sometimes mistakes occur. One way that eukaryotic cells defend against mistakes that can occur is called Nonsense Mediated Decay (NMD). Nonsense Mediated Decay is a process that eliminates mRNA transcripts that have premature stop codons.
It is known that UPF1 is a helicase that is essential for NMD to take place. This is because UPF1 precedes the end result of Nonsense Mediated Decay in the cell signaling cascade. Scientists in a different lab have been working with UPF1 because when UPF1 doesn't bind to the proper proteins in order to continue the cell signalling cascade cancer can occur since the mistake in the mRNA has gotten through and can form a tumor. So whenever UPF1 is downregulated NMD won't occur leading to higher chances of malignant growths. Splicing Factors bring to the junctions between introns and exons and marks it to be removed my the NMD process. Positives splicing factors promote splicing and negative splicing factors inhibit splicing. Sometimes you have more negative splicing factors than positive which stops introns from being removed causing premature termination. The same process noted by the lab studying UPF1 has been found in the breast cancer cell line the lab I'm in is working with. In the other lab they did experiments where they knocked down UPF1 to see what would happen. You knock down a protein by making the mRNA that would have passed the message along to the ribosomes so it would have been coded protein double stranded. When the cell produced double stranded RNA it is degraded. So the most important thing to grasp is that when UPF1 is knocked down it will cause some proteins to be upregulated when they would normally be downregulated because UPF1 helps carry out NMD. Today I sat down with Rakshitha for the first time and she basically gave me a mini cancer cell biology lesson with the pertinent information about what I would be doing.
The first thing she went over was how to use NCBI's Blast function on their website. I'll need this tool later on. Blast compares DNA or RNA against different databases to see how similar they are while allowing you to specify what organism you want to find that specific DNA strand in. You can also use NCBI for proteins which is helpful because I'll be looking at RNA splicing factors which are a type of protein. The example she took me through was with something called ptgas 2. I needed to choose Fasta which is a file format that gives you DNA. You put the DNA sequence into BLAST. In blood choose non-redundant proteins and then reference proteins. Query is what you asked for while Subject is basically a hit or result. The E-value is the expected value. The lower the better because the higher it is the more chance that the match is just by chance. For example 2 x 10^-6 is bad. All in all my STEAM internship was a truly fulfilling experience. I learned so much about clinical research and also had the opportunity of learning about things that I didn't know I would love such as statistics. Giving my presentation to my teachers and peers was simultaneously nerve wracking and enjoyable in that public speaking is always terrifying and it was also really nice to share what I'd been working on for a year. From learning the basics of hypertriglyceridemia and pancreatitis to learning about the prerequisites for a retrospective research project to exploring complex statistical software I am so grateful that I was able to take part in this enriching experience. I'm excited for what I'll undertake next year!
Today I got to sit in on an editing session of a research presentation. Dr. N, a medical student, is doing a retrospective research project on the effect of chest contusions and rib fractures on over all outcome, so it's in many ways similar and completely different from my project. She presented her power point to Dr. S, Dr. L, who I assume was her mentor on the project, Dr. A, Dr. T, as well as me. I think that Dr N, is a 3rd or 4th year medical student since before she began presenting she talked with Dr. S about receiving her desired "track". From what I gather, tracks are essentially the order of specialties that you rotate through as a medical student. I think that Dr. T was a surgery resident who helped with the project. Dr. N took us through her research which looked at how the severity of chest contusions correlated with the outcome of the patient. It was really fascinating and also incredibly helpful to see her give her presentation as this will be me in a few short weeks!
My mentor was back in town this week and completely redirected what I was looking at number wise. Instead of just looking at patients who have hypertriglyceridemia and acute pancreatitis, as well as patients that have hypertriglyceridemia and chronic pancreatitis separately he instructed me to look at those that have hypertriglyceridemia and any pancreatitis at all. I also discovered something tangentially interesting to my project. There was little to no statistical significance to the primary procedure the patients under went. Meaning of some 350 patients that had a primary procedure listed very few of them had anything to do with their hypertriglyceridemia or pancreatitis. Basically this means that patients came in with things from a broken leg to a chest pain and doctors did things to treat those main issues while still noting through lab work the variable that I'm looking at. I just found it really cool that hospitals have the resources to record things like triglyceride levels that allows for further research about tangential conditions.
This week my main mentor was out of town but I met with Dr. A. He had me perform more statistical analysis including cross tabulations of different factors. For example I looked at the subset of patients who not only had acute pancreatitis and hypertriglyceridemia but also had pseudocysts and we found that those patients were a lot more likely to have pseudocysts then those who only had hypertriglyceridemia. It was a very interesting meeting in that I learned what to do and what not do when doing write ups.
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Michaela BentonI'm lucky enough to go to this amazing school that has this amazing program that lets me learn amazing things. Archives
December 2017
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