Tag Archives: pathways

Announcement: Protein Signaling conference

This year I am once again involved in organizing an exclusive conference on protein signaling. There is no registration fee and accommodation is also free; all you have to pay yourself is your travel expenses.


Click the image to see the poster in full size.

This year we are fortunate to once again have an amazing lineup of invited speakers: Albert Heck, Anne-Claude Gavin, Bernd Bodenmiller, Brenda Schulman, Daniel Durocher, Gianni Cesareni, Giulio Superti-Furga, Ileana Cristea, Ivan Dickic, James Ferrell, Jason Chin, Jiri Lukas, Julio Saez-Rodriguez, Marc Kirschner, Matthias Mann, Nevan Krogan, Niels Mailand, Oskar Fernandez-Capetillo, Ray Deshaies, Ronald Hay, Steve Jackson, Søren Brunak, Titia Sixma, and Wade Harper.

Please note that although the poster says July 1, the application deadline is in fact June 20, which is only four days from now. To apply, please see the conference website.

Job: Postdoc in computational analysis of animal disease models

In collaboration with Jan Gorodkin at the Center for non-coding RNA in Technology and Health at University of Copenhagen, I will be starting up a project on cross-species network and pathway analysis of animal disease models. We have secured funding for the project and are now searching for the right person to fill a postdoc position.

The application deadline is February 27, 2016. For further details, including how to apply, please refer to the official job announcement.

Analysis: Three-dimensional DNA structure

A few months ago Bill Noble’s lab at University of Washington published a letter in Nature on a three-dimensional model of the complete nuclear genome of budding yeast:

A three-dimensional model of the yeast genome

Layered on top of information conveyed by DNA sequence and chromatin are higher order structures that encompass portions of chromosomes, entire chromosomes, and even whole genomes. Interphase chromosomes are not positioned randomly within the nucleus, but instead adopt preferred conformations. Disparate DNA elements co-localize into functionally defined aggregates or ‘factories’ for transcription and DNA replication. In budding yeast, Drosophila and many other eukaryotes, chromosomes adopt a Rabl configuration, with arms extending from centromeres adjacent to the spindle pole body to telomeres that abut the nuclear envelope. Nonetheless, the topologies and spatial relationships of chromosomes remain poorly understood. Here we developed a method to globally capture intra- and inter-chromosomal interactions, and applied it to generate a map at kilobase resolution of the haploid genome of Saccharomyces cerevisiae. The map recapitulates known features of genome organization, thereby validating the method, and identifies new features. Extensive regional and higher order folding of individual chromosomes is observed. Chromosome XII exhibits a striking conformation that implicates the nucleolus as a formidable barrier to interaction between DNA sequences at either end. Inter-chromosomal contacts are anchored by centromeres and include interactions among transfer RNA genes, among origins of early DNA replication and among sites where chromosomal breakpoints occur. Finally, we constructed a three-dimensional model of the yeast genome. Our findings provide a glimpse of the interface between the form and function of a eukaryotic genome.

Having previously worked with predicted 3D structure of DNA, such as intrinsic curvature, I was intrigued by the availability of a 3D structure of a complete eukaryotic genome. Based on past analyses of 1D distances in DNA, I expected that the 3D distance between two genes in the genome would correlate with expression, protein interactions, and metabolic pathways.

To test if 3D neighborhood correlates with function and/or regulation, I collected three large sets of protein pairs, namely pairs of co-expressed genes from the STRING database (Pearson correlation coefficient >0.7), interacting protein pairs from the BioGRID database, and pairs of genes assigned to the same pathway by the KEGG database. I subsequently mapped these onto the set of 3D neighbors listed in the supplementary information of the paper, including only 3D neighbors on different chromosomes (in order to eliminate correlations caused by 1D rather than 3D distance). I also mapped the three sets of gene pairs onto a shuffled version of the 3D neighbors, in order to estimate the overlaps that can be expected at random. The results are summarized in the table below:

3D neighbors Shuffled neighbors
Coexpressed (STRING) 58 61
Interacting (BioGRID) 2151 2122
Same pathway (KEGG) 357 344

To make a long story short, the numbers show that 3D genomic neighbors appear to be no more likely to be coexpressed, to interact, or to be involved in the same pathway than random pairs. It could be that they way I perform the analysis is too simplistic or that the data are too noisy to show a signal. However, it is also possible that the 3D structural organization of the genome simply doesn’t have much impact on gene regulation and function.