The following post is a departure from my usual reporting on an interesting primate related tidbit of research. I’ll be posting about how I have thought about how to study primate brain evolution research. These are just ideas I have brainstormed. It is very probable that people are doing this out in their respective labs but I’m not in the know of what’s totally current. I hope you are interested in what scope of primate brain evolution research I will be discussion… I’ll be mostly taking in a functional genomic and computational biology approach, but that’s not to say more objective sciences such as psychology can’t fit into this game plan.
To start off, understanding primate brain evolution, specifically the biological mechanisms by how the primate brains have been positively selected for by size involves two complementary aspects of research. One of it is to identify the genes involved in brain growth and development, as well as their expression patterns. This is wet lab work, a whole lot of tissue sampling, mRNA isolation, cDNA synthesis and RT-PCR amplification, gene quantification and qualification and ultimately sequencing. At this level, one would need to sample multiple samples of representative primates (that have their genomes sequenced) and different developmental stages and populations.
Once these key players can be identified, the functions of these genes need to be well understood. Of course making knockout monkeys will be a costly and time consuming endeavor full of ethical issues, so I imagine having knockout neuron cultures can help understand the function of these genes better when they aren’t expressed. That’s a bit hard, neurons are awfully fickle to grown in culture. Maybe reporter constructs? Also, other non-traditional research such as sequence homology to other known proteins can help isolate potential functions based on structure.
Now once these key developmental genes have been classified, their relative importance should be noted… or in other words, one needs to organize which genes are specific to all primates and which are specific to fewer primates. Do these genes correlate with the known lineage of primates? If a unique pattern can be extracted, this will make the second aspect of research much easier and conclusive. This is the computational biology approach, using computers, statistics, and other mathematical models to identify what genes were mutated the most to yield the most growth. What genes remained fairly consistent? Can we estimate ages of coalescence or divergence, are there unique mutations to populations or species of primates… ultimately can we begin to make a phylogenetic tree of these genes and their changes throughout evolutionary time?
As I currently laid it out, these two field complement each other and if anything one is dependent on the other. Currently, I know of computational studies that seem to search high and low to find genes that have been positively selected for in primates by scanning and comparing entire genomes. If a hit is found, the research then shifts backwards to estimate functions based on the sequence homology to other known genes and their functions. While that maybe a useful, quick and easy approach, it’s all sorts of wrong. It is wrong because it is the needle in the haystack method. I advise one first narrow down the list, by doing the functional genomic screens, which is arduous and tedious, but much more quantitative and thorough. That way, one can limit things down to candidate genes specific to a species, developmental stage, etc. The playing field will be much more narrow and the computations will be much more conclusive.
What do you think? Do I have it right, do I have it wrong? Not to be rubbing my ego, but I think I have a thorough plan here — one that would make the most killer dissertation ever. Do you know of any researchers doing it this way? If any one out there, who reads this blog, carries out primate brain evolution research please feel free to comment and discuss. I’m really curious to know if what I have been thinking is even right.