For the fun of it, and to help with my visualization exercises, I wrote a mock Ruby script to instruct my various mono-nuclear blood cells about their duty. For the non-initiated, it requires a number of different bits of useful code — including NK cells, M1 Marcophage responses, Il-2, and IL-15. Then, it defines a function that judges (a la St. Michael) whether any given cell is dangerous or not. My entire frontal cortex is loaded as a tissue file, then Ruby iterates through every single cell in my frontal cortex, asking whether or not each cell is a glioma, and if so, instructing it to undergo apoptosis. Ruby pros will note that among other things, I’ve assumed Ruby has a class called ‘cell’ and that within this class, there’s a function called “.apoptosis”. Considering I am writing this for the hell of it, I don’t think I necessarily need to construct an entire Ruby class from scratch. But, that being said, I do appreciate the image of my immune system acting with the grim efficiency of a computer script, systematically checking every single cell in my frontal cortex for signs of danger, and then permanently deleting anything remotely dangerous. Outliers must be eliminated.
In the autumn semester on alternate years, I teach a course titled “Practical Next Generation Sequencing.” This course is the pride and joy of my teaching portfolio. I have a small group of graduate students who start with purified RNA, build Illumina sequencing libraries, personally run them on the Department’s MiSeq, and perform all the follow-up statistics and analysis (including writing their own Ruby scripts, like the one above, only real) on the university’s high performance computing cluster. In many ways, this is a unique class in that students get hands-on training for next-generation sequencing wetlab and bioinformatics pipelines. Last autumn, all eight students successfully generated workable sequencing libraries, and all eight generated usable data. Because this exercise is more interesting and more valuable for the students if it’s a part of a novel research project, the samples we analyzed came from flies in which I knocked-down the expression of a heretofore nondescript circadian clock-controlled gene, and we compared global gene expression in these flies to wild-type controls. The results were striking: of the top 15 differentially-expressed genes, 11 were involved in innate immune responses. And the changes weren’t subtle: in our mutants, some of these genes were up-regulated by 40 to 200-fold. That’s a Result with a capital “R“. The nearby image is of one of my students from this class, Diana Gamba, presenting our work at the Missouri Academy of Sciences about the time I was having my cranium cut open.
Of course, none of this is real unless it independently replicates, so my PhD student, Jiajia Li (who was also in Biology 6320), has set about verifying this basic result. I’m happy to say that after months of working on the fly genetics, performing crosses, and doing gene expression analyses, the result has held up. There’s a point in every research project where you cross an invisible line from “let’s see how this turns out” to “we have a manuscript we’re working on“. I remember how happy I was in graduate school when I realized I’d crossed that line, and I actually had something on which I could write my dissertation. Jiajia’s validation experiments have pushed this project past this invisible line. We still don’t know how or why this clock-controlled gene is regulating immune response, or whether it’s important for host defense, but we now have a robust and reproducible phenomenon, and the rest are just details which can be worked out with the application of time and energy. Jiajia now has the kernel of a dissertation, which is one of my happy thoughts on which I reflect from time to time. Also, my Bio6320 students will all be co-authors on the eventual publication, which I think it a pretty special outcome from a unique class.