Genome-wide survey for biologically functional pseudogenes by Orjan Svensson, Lars Arvestad, and Jens Lagergren, PLoS computational biology, early online release



A pseudogene is a piece of noncoding DNA very similar in sequence to a normal "true" gene but containing disablements that prevent its expression into a functional protein. Pseudogenes were long thought to be relatives of functional genes that were formerly active in the ancient genome but now constitute part of the "junk" DNA. Recently, there has been speculation that some pseudogenes may actually be functional at the RNA level. The RNA of one mouse pseudogene was found to be critical for the translation of its homologous coding gene, and mice in which the pseudogene was knocked out died or had nasty deformities (see figure above).

This paper uses a comparative genomics approach to identify the pseudogenes most likely to be functional. The authors say it can be applied to any pair of genomes, but they use human-mouse and human-chimp (though the results here were too noisy as we are too closely related). The filters they used are plausible evolutionary history (ancient origin of pseudogene + high conservation), synteny (true gene and corresponding pseudogene are in conserved order in human and mouse), transcriptional evidence, and sequence conservation.

Criticisms: 1) no wet lab experimental confirmation of any predictions!, 2) I don't think synteny should be a required filter for functionality, and 3) they looked for transcriptional evidence by BLASTing against the EST database, which may not be a comprehensive catalog of all transcripts.

Another possible filter could be conservation in the promoter regions because if any portion of the pseudogene gets transcribed, it needs a functional promoter (i.e. disablements here could be more significant than disablements in the coding sequence). Maybe they could have added a complementary section with a detailed look at one predicted functional pseudogene. But overall, it was a solid approach with well designed filters. It also prompts a call for the sequencing of more primate genomes to get a sense of varying evolutionary distance. Chimp was great, but it would help to have gorilla and orangutan in addition to rhesus macaque.

5th Annual Systems Biology Symposium will be webcast around the world!

From Lee Hood:
Dear colleagues,

I have the exciting opportunity to announce that we will be conducting a
live webcast during this years symposium this Sunday and Monday, April 23
and 24. The free webcast will be available to viewers both inside and
outside ISB.

To register for the webcast please go to
http://www.systemsbiology.org/symposium/WebcastRegIndex.html . Please
save this link and remember your password to access the webcast.


Enjoy the symposium!

Lee

Bioinf PhD program rankings

For the first time since 2002, U.S. News also surveyed Ph.D. programs in the sciences. This is the first time that Genetics/Genomics/Bioinformatics was included in the study. The results are not terribly surprising, but should give people thinking about graduate school something to think about.
Here are the top 10, including links to their bioinformatics or similar programs:

1. Harvard (HST)
2. MIT (HST)
Stanford (BMI)
4. Cal Tech (Biology)
5. UCSF (BMI)
6. Berkeley (Comp Bio)
UCSD (Bioinformatics)
Wash U - St. Louis (CBP)
9. Hopkins (Biomedical Engineering)
10.Yale (CBB)
US News rankings

Please post the complete list if you have it!

Stochastic protein expression in individual cells at the single molecule level. Cai, Friedman, Xie in Nature March 16, 2006.
Stochasticity in gene expression is a well known phenomenon, and mechanisms by which cells attenuate stochastic fluxuations to achieve deterministic outcomes is a topic of current research. Here for the first time, Cai, Friedman and Xie characterize the magnitude and variance in single expression events for the beta-galactosidase gene in E. coli and S. cerevisiae. This is achieved using a microfluidic device in which single or very few cells are trapped in 100 picoliter chambers, such that the fluorescent product of beta-gal-catalyzed cleavage of FDG can not diffuse away after being exported from the cell.
By measuring the fluorescence of these chambers using indirect laser excitation, the authors observe discrete changes in the slope of fluorescent intensity over time. The number of beta-gal molecules is then calculated from the time derivative of the fluorescence (compensating for photobleaching) using a calibration factor determined from a previous experiment in which the authors correlate specific values of the intensity slope to integer numbers of beta-gal enzymes.
The authors find that the number of beta-gal enzymes produced in E. coli per expression event follows a decaying exponential distribution, in agreement with the theoretically predicted lifetime of the mRNA molecule. The average number of beta-gal expression events per cell cycle under repressed lacZ conditions was observed to be 0.11, and the average number of beta-gal monomers per event was observed to be 20 (or 5 functional tetramers), also in agreement with previous biochemical estimates. Making the assumption that protein copy numbers are halved at each cell division, this low number of expression events per cell cycle was theoretically shown to give rise to a heterogeneous “all-or-none” population, in which some cells would have copies of the beta-gal enzyme and Lac permease (expressed on the same operon) and thus would be responsive to rises in lactose concentration, whereas other cells would not. This itself is a known and previously unexplained phenomenon.
The only thing I can take away from this paper is that the authors’ method relies on a enzyme-catalyzed cleavage of a fluorescent substrate in order to amplify the fluorescent signal. Thus it is not generalizable to other gene products, although the method can be used to characterize a particular promoter. Hopefully, Cai et al.’s new method will open up research into how regulatory mechanisms can reliably maintain small copy counts of molecules essential for, e.g., signaling in mammalian systems.


*Thanks to Paul L for reporting on this interesting article