altering immune-related gene levels in MS by targeting regulatory microRNAs

Members of the Australia and New Zealand MS Genetics Consortium (ANZgene) compared miRNA patterns in blood samples from 59 MS patients and 37 healthy controls (PLoS ONE link soon to come). Two specific microRNAs, miR-17 and miR-20a, have been found at significantly lower levels in the blood of MS patients (RR-MS, PP-MS and SP-MS) than in that of unaffected individuals. Both of these miRNAs are thought to regulate immune genes in humans by curbing the expression of genes involved in T-cell activation in the immune system. Therefore, these miRNAs could be very useful in the development of novel MS treatments.

Other than recent usage of miRNA technologies, MS research has focused a lot on HLA-loci and epigenetic factors associated with MS incidence. However, it seems as though miRNA associations could be much more helpful in understanding more about MS development and therapeutic approaches. Results from the aforementioned study suggest that miR-17 and miR-20a down-regulation is linked to enhanced mRNA levels for some of the same T-cell related genes that get over-expressed in MS patient blood samples, leading us to believe that the miRNAs contribute to MS development.

The ANZgene team concluded that:

Even if the miRNAs under-expressed in MS were not directly contributing to the immune cell signature observed in MS whole blood, the excessive T-cell activation signature seen in MS and other autoimmune diseases suggest agents which can reduce this activity may be therapeutically beneficial.

Whether or not the miRNAs contribute directly to the disease, it might be possible to target these kinds of regulatory miRNAs and therefore tweak immune related gene levels in MS.

post-grad thoughts: academia vs. industry

Lately, the pressure has been on to begin thinking about "what i'm going to do with my life". I've known, probably since 9th grade, that I've had this passion for genomics/neuroscience/pharmacogenomics. I would be very happy sitting at a bench with test tubes, gels, sequencing technology - all of it. Unfortunately, word on the street is that jobs like that don't really "pay well", and aren't worthy of the next six years of grad school tuition and education. Soooo, now I find myself asking - do I want to be in academia or do I want to get into pharmacogenomics and be in an industry job?

The deciding factor for me appears to be that industry apparently tends to offer more/better financial opportunities, but academia would most likely foster more flexibility in terms of being able to explore my own research interests. And while these fields seem to be pretty divergent, it would seem as though you need the same fundamental skills - writing well, being a good scientist (broad, i know) and keeping up with current research trends.

Sometimes I wonder if in my future the two will have to be mutually exclusive. And I really wonder if I will actually be given the opportunity to choose for myself. I'm going to have to find a way to make a living somehow in research, and most likely I'll end up in whichever lets me in.

more accurate SNP identification by using population sequence data: the SNP-seq method

Researchers at the Scripps Research Institute, located in La Jolla CA, have devised a cutting-edge program to identify SNPs and sequence individuals' genotypes called SNIP-seq. This program was designed to utilize population sequence data (when a number of samples/individuals n>=20 have been sequenced across the same genomic regions) to identify SNPs. In addition, SNIP-seq assigns genotypes for each SNP to each sample.

This method has been deemed highly accurate and reduces the rate of false positives that have been caused by sequencing errors. Vikas Bansal, the first author of the paper from Genome Research (abstract), explained the motivation to develop this technology:

...You have a lot of tools for aligning the short reads generated by the next-gen sequencing platforms to a reference genome and you also have tools for identifying SNPs, but when you have population sequence data, you can leverage the fact that you have multiple individuals' sequences across the same genomic regions to improve both the accuracy of SNPs and the genotype calling.

The researchers evaluated the accuracy of their method in a really cool way - they used sequence data from a 200kb region on chromosome 9p21 (location of genes that add to a person's risk of developing coronary artery disease and diabetes) from 48 individuals. The SNIP-seq method proved accurate for detecting variants and filtered out false SNPs. The even cooler thing is that the researchers "stumbled" across novel SNPs in this chromosomal region, which they later validated using pooled sequencing data and confirmed using Sanger sequencing.

This new, more accurate method (false-positive rate ~2%, down from ~5%) can help us to re-sequence genomic regions known to be associated with disease, and therefore detect rare variants that might contribute to disease progression. Previous, less accurate sequencing methods that identified false positive SNPs potentially impeded disease research. SNP-seq will potentially distinguish false SNPs from real ones. This breakthrough use of population sequencing data will hopefully lead to more accurate studying of disease-causing genetic variants and viable therapeutic/pharmacogenomic targets.

to publish or not to publish, that is the question...

GenomeWeb posted a pretty cool article this month about the dilemma of whether or not to publish in Science. I think it's pretty well written and makes very valid points, so instead of reiterating it, here's the link.

When I read articles assigned in class, or journals on my own, I always tend to be drawn to finding out how the researchers could potentially be displaying their conclusions to appear that there is actually more of an effect than there really is. This article adequately brings up that same question, and alludes to Daniele Fanelli, who's research supposedly determined that this 'positive bias' tends to get larger as you move away from the physical/chemical sciences and closer to the behavioral sciences along the "spectrum of science". I would just expect that to be the case though, being as chemical science is largely based on strict numbers and calculations where behavioral studies are largely based on observations and drawing (often subjective) conclusions.

They talk about "changing the system". Having journals specifically for negative results!? That just seems kind of unnecessary and weird to me. I essentially don't think this positive bias can ever be removed, but I think it's necessary to make the scientific community aware of this bias when they read newly published literature.

My own proposal? More websites like the Faculty of 1000 Biology website. Here, established professionals can read new literature and mark specific ones that they think are especially cutting edge or valuable for future research. They also take validity into consideration, and most of the people rating these articles have a pretty good eye for true results when they see 'em. It's a great help for gathering sources for a paper, or just for pointing you in the right direction for leisurely reading about new things in the field.