Transcriptional response of human umbilical vein endothelial cells to low doses of ionizing radiation.

We used cDNA microarray hybridization technology to monitor the transcriptional response of Human Umbilical Vein Endothelial (HUVEC) cells to x-rays doses ranging from 2 to 200 cGy. An early time window from irradiation (4h) was selected in order to minimize the effects of the cell cycle blockage eventually induced at high doses of irradiation. Three different gene-clustering algorithms have been used to group the 4134 monitored ORF based on their transcriptional response in function of the irradiation dose. The results show that while few genes exhibit a typical dose-dependent modulation with a variable threshold, most of them have a different modulation pattern, peaking at the two intermediate doses. Strikingly even the lowest dose used (2 cGy) seems to be very effective in transcriptional modulation. These results confirm the physiological relevance of sublethal-dose exposures of endothelial cells and strengthens the hypothesis that alternative dose-specific pathways of radioadaptive response exist in the mammalian cells. 111 genes were found to be modulated at all doses of irradiation. These genes were functionally classified by cellular process or by molecular function. Genes involved in coagulation and peroxidase activity and structural constituent of ribosomes were over-represented among the up-regulated genes as compared with their expected statistical occurrence. Three genes coding for regulatory kinase activities (CDK6; PRCKB1 and TIE) are found down-regulated at all doses of irradiation.

Ancestral maximum likelihood of evolutionary trees is hard.

Maximum likelihood (ML) (Neyman, 1971) is an increasingly popular optimality criterion for selecting evolutionary trees. Finding optimal ML trees appears to be a very hard computational task--in particular, algorithms and heuristics for ML take longer to run than algorithms and heuristics for maximum parsimony (MP). However, while MP has been known to be NP-complete for over 20 years, no such hardness result has been obtained so far for ML. In this work we make a first step in this direction by proving that ancestral maximum likelihood (AML) is NP-complete. The input to this problem is a set of aligned sequences of equal length and the goal is to find a tree and an assignment of ancestral sequences for all of that tree's internal vertices such that the likelihood of generating both the ancestral and contemporary sequences is maximized. Our NP-hardness proof follows that for MP given in (Day, Johnson and Sankoff, 1986) in that we use the same reduction from Vertex Cover; however, the proof of correctness for this reduction relative to AML is different and substantially more involved.