Two Genetically Separable Phases of Growth Inhibition Induced by Blue Light in Arabidopsis Seedlings1

High fluence-rate blue light (BL) rapidly inhibits hypocotyl growth in Arabidopsis, as in other species, after a lag time of 30 s. This growth inhibition is always preceded by the activation of anion channels. The membrane depolarization that results from the activation of anion channels by BL was only 30% of the wild-type magnitude in hy4, a mutant lacking the HY4 BL receptor. High-resolution measurements of growth made with a computer-linked displacement transducer or digitized images revealed that BL caused a rapid inhibition of growth in wild-type and hy4 seedlings. This inhibition persisted in wild-type seedlings during more than 40 h of continuous BL. By contrast, hy4 escaped from the initial inhibition after approximately 1 h of BL and grew faster than wild type for approximately 30 h. Wild-type seedlings treated with 5-nitro-2-(3-phenylpropylamino)-benzoic acid, a potent blocker of the BL-activated anion channel, displayed rapid growth inhibition, but, similar to hy4, these seedlings escaped from inhibition after approximately 1 h of BL and phenocopied the mutant for at least 2.5 h. The effects of 5-nitro-2-(3-phenylpropylamino)-benzoic acid and the HY4 mutation were not additive. Taken together, the results indicate that BL acts through HY4 to activate anion channels at the plasma membrane, causing growth inhibition that begins after approximately 1 h. Neither HY4 nor anion channels appear to participate greatly in the initial phase of inhibition.

Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object. It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network-generated SARs. The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.