Towards a genome-wide transcriptogram: the Saccharomyces cerevisiae case

A genome modular classification that associates cellular processes to modules could lead to a method to quantify the differences in gene expression levels in different cellular stages or conditions: the transcriptogram, a powerful tool for assessing cell performance, would be at hand. Here we present a computational method to order genes on a line that clusters strongly interacting genes, defining functional modules associated with gene ontology terms. The starting point is a list of genes and a matrix specifying their interactions, available at large gene interaction databases. Considering the Saccharomyces cerevisiae genome we produced a succession of plots of gene transcription levels for a fermentation process. These plots discriminate the fermentation stage the cell is going through and may be regarded as the first versions of a transcriptogram. This method is useful for extracting information from cell stimuli/responses experiments, and may be applied with diagnostic purposes to different organisms

Evolutionary origins of human apoptosis and genome-stability gene networks

Apoptosis is essential for complex multicellular organisms and its failure is associated with genome instability and cancer. Interactions between apoptosis and genome-maintenance mechanisms have been extensively documented and include transactivation-independent and -dependent functions, in which the tumor-suppressor protein p53 works as a ‘molecular node’ in the DNA-damage response. Although apoptosis and genome stability have been identified as ancient pathways in eukaryote phylogeny, the biological evolution underlying the emergence of an integrated system remains largely unknown. Here, using computational methods, we reconstruct the evolutionary scenario that linked apoptosis with genome stability pathways in a functional human gene/protein association network. We found that the entanglement of DNA repair, chromosome stability and apoptosis gene networks appears with the caspase gene family and the antiapoptotic gene BCL2. Also, several critical nodes that entangle apoptosis and genome stability are cancer genes (e.g. ATM, BRCA1, BRCA2, MLH1, MSH2, MSH6 and TP53), although their orthologs have arisen in different points of evolution. Our results demonstrate how genome stability and apoptosis were co-opted during evolution recruiting genes that merge both systems. We also provide several examples to exploit this evolutionary platform, where we have judiciously extended information on gene essentiality inferred from model organisms to human

Comparison of topological measures for simulated networks.

<p>The black dots represent the superposed networks for all core organisms from string database with confidence score 0.800, the green lines are averages taken in intervals of , and the red lines are weighted averages of simulated networks. The upper, central, and lower rows show, respectively, degree distribution, clustering coefficient, and nearest neighbor mean degree. Each column refers to a simulated model: Barabási-Albert on the left, duplication-divergence on the center and duplication-acquisition on the right.</p

Evolution of simulated models. Barabási-Albert, duplication-divergence and duplication-acquisiton networks (red, blue and green lines, respectively).

<p>The black dots represent all core organisms from STRING database, where six well studied organisms are highlighted in orange. (a) Number of links, (c) mean degree and (e) maximum degree are shown as functions of the total number of nodes in the network. The degree distribution was calculated in five snapshots of the evolution of (b) Barabási-Albert, (d) duplication-divergence, and (f) duplication-acquisition models, in intervals of 2000 nodes.</p

Ordered association matrices.

<p>This figure presents the association matrices for <i>Homo sapiens</i>, <i>Mus musculus</i>, <i>Arabidopsis thaliana, Drosophila melanogaster, Saccharomyces cerevisiae, Gallus gallus</i>, Barbási-Albert model, duplication-divergence model and duplication-acquisition model after running the ordering algorithm. The black dots represent protein-protein association between two nodes.</p