Live: Lecture by Nobel Laurate Avram Hershko

Today I am at the the symposium “Protein Chemistry ‐ Applications to Combat Diseases”, which takes place in Copenhagen a few minutes walk from where I work.

This morning started with a keynote lecture by Nobel Laurate Avram Hershko on regulation of the cell division cycle by ubiquitin‐mediated protein degradation. This post is just a very quick write-up and a few photos made during and immediately after his presentation.

Most of the early work on ubiquitylation was done on model proteins, most of which were extracellular. Interestingly, what spurred Avram Hershko on to study ubiquitylation of physiologically relevant proteins was the early work on cyclin degradation for which Tim Hunt received the Nobel Prize. Tim Hunt speculated speculated that there was a cyclin protease that would break down cyclins. However, Avram Hershko showed in 1991 that cyclins are in fact not degraded by a specific protease, but are rather targeted for proteasomal degradation by a specific ubiquitin ligase. Showed this in JBC papers in 1991 and 1994. One year later his group identified this ubiquitin ligase to be what is now known as the Anaphase Promoting Complex (APC) / Cyclosome (APC/C).

In addition to being crucial for degradation of cyclins, APC/C is also required for entry into anaphase of the cell cycle (hence the name Anaphase Promoting Complex). This because it is responsible for targeting the securin protein for degradation, which in turns releases separase activity to degrade the cohesin rings that hold together sister chromatids.

Having worked on other cell-cycle proteins for many years, Avram Hershko has in recent years returned his interest to APC/C, more specifically to understand how the inhibition of APC/C is released, which in turn leads to the whole series of events described above.

Analysis: Toward doing science

Yesterday, Rangarajan and coworkers published a paper in BMC Bioinformtatics entitled “Toward an interactive article: integrating journals and biological databases”. Not many hours later Neil Saunders made the following tweet commenting on it:

This reminded me of a draft blog post that I wrote in 2008 on the use of the word “toward(s)” in article titles, and I decided that it was time to update the plot and finally publish it. The background was that I had the gut feeling that there was a somewhat disturbing trend, namely that more and more papers use these words in the title. I thus went to Medline and counted the fraction of papers from each year having a title starting with “toward” or “towards” (I also included them if towards appeared inside the title following a colon, semicolon, or dash):

The plot shows that fraction of articles with “toward(s)” in the title is rapidly rising; it has more than tripled over the past two decades. There is thus no doubt that the use of “toward(s)” in article titles is a trend in biomedical publishing.

As is often the case with statistics, though, this analysis answers only one question but leads to several new ones. Are we increasingly selling our papers on what we hope to do soon rather than on what we have actually done? Or have we just become more honest by now adding the word “toward(s)” where we might have left it out in the past?

Analysis: 10butnotMe

About five years ago George Church announced the Personal Genome Project (PGP). A very interesting aspect of this project is that all data are released under the Creative Commons Zero waiver. This includes not only the genetic data, but also some medical information and even the identity of each individual.

Although PGP has enrolled more than a thousand individuals, it is presently only possible to download data on ten individuals. It is obviously pointless to attempt to link genotype to phenotype based on such a small number of individuals. However, I wondered if any meaningful structure would emerge if I calculated the Hamming distances for all pairs of individuals, that is the number of SNPs by which they differ (download).

Like said so done. I downloaded all available SNP data from PGP (including array and exome sequencing data), calculated all pairwise SNP distances, and visualized the results as a heatmap along with the faces of the individuals (click for a larger version of the figure):

Individual #10 stands out as being genetically most dissimilar from everyone else, which is unsurprising as he is the only African American in the study. I next tried to similarly define the genetically most average individual, that is the individual that is most similar to everyone else. If one defines this as the individual with the lowest sum of differences, the answer is individual #7. However, because the origins of his grandparents are unknown, it is difficult to conclude anything interesting based on this.