Gibberellins Promote Trichome Formation by Up-Regulating GLABROUS1 in Arabidopsis1

Trichome development is dependent on gibberellin (GA) signaling in Arabidopsis thaliana. Using the GA-deficient mutant ga1–3, the GA-response mutant spy-5, and uniconazol (a GA-biosynthesis inhibitor), we show that the GA level response correlates positively with both trichome number and trichome branch number. Two genes, GL1 and TTG, are required for trichome initiation. In ga1–3, coexpression of GL1 and R, the maize TTG functional homolog, under control of the constitutive 35S promoter, restored trichome development, whereas overexpression of neither GL1 nor R alone was sufficient to significantly suppress the glabrous phenotype. We next focused on GL1 regulation by GAs. In the double mutant the gl1–1 glabrous phenotype is epistatic to the spy-5 phenotype, suggesting that GL1 acts downstream of the GA signal transduction pathway. The activity of a β-glucuronidase reporter gene driven by the GL1 promoter was decreased in the wild type grown on uniconazol and showed a clear GA-dependent activation in ga1–3. Finally, quantification of GL1 transcript levels by reverse transcriptase-polymerase chain reaction demonstrated that relative to wild type, ga1–3 plants contained less transcript. These data support the hypothesis that GAs induce trichome development through up-regulation of GL1 and possibly TTG genes.

The optimization principle in phylogenetic analysis tends to give incorrect topologies when the number of nucleotides or amino acids used is small

In the maximum parsimony (MP) and minimum evolution (ME) methods of phylogenetic inference, evolutionary trees are constructed by searching for the topology that shows the minimum number of mutational changes required (M) and the smallest sum of branch lengths (S), respectively, whereas in the maximum likelihood (ML) method the topology showing the highest maximum likelihood (A) of observing a given data set is chosen. However, the theoretical basis of the optimization principle remains unclear. We therefore examined the relationships of M, S, and A for the MP, ME, and ML trees with those for the true tree by using computer simulation. The results show that M and S are generally greater for the true tree than for the MP and ME trees when the number of nucleotides examined (n) is relatively small, whereas A is generally lower for the true tree than for the ML tree. This finding indicates that the optimization principle tends to give incorrect topologies when n is small. To deal with this disturbing property of the optimization principle, we suggest that more attention should be given to testing the statistical reliability of an estimated tree rather than to finding the optimal tree with excessive efforts. When a reliability test is conducted, simplified MP, ME, and ML algorithms such as the neighbor-joining method generally give conclusions about phylogenetic inference very similar to those obtained by the more extensive tree search algorithms.

Differences in quantal amplitude reflect GluR4- subunit number at corticothalamic synapses on two populations of thalamic neurons

Low-frequency thalamocortical oscillations that underlie drowsiness and slow-wave sleep depend on rhythmic inhibition of relay cells by neurons in the reticular nucleus (RTN) under the influence of corticothalamic fibers that branch to innervate RTN neurons and relay neurons. To generate oscillations, input to RTN predictably should be stronger so disynaptic inhibition of relay cells overcomes direct corticothalamic excitation. Amplitudes of excitatory postsynaptic conductances (EPSCs) evoked in RTN neurons by minimal stimulation of corticothalamic fibers were 2.4 times larger than in relay neurons, and quantal size of RTN EPSCs was 2.6 times greater. GluR4-receptor subunits labeled at corticothalamic synapses on RTN neurons outnumbered those on relay cells by 3.7 times, providing a basis for differences in synaptic strength.