Martijn van Zanten | PubMed Health

PubMed2006

Abscisic acid antagonizes ethylene-induced hyponastic growth in Arabidopsis.

Ethylene induces enhanced differential growth in petioles of Arabidopsis (Arabidopsis thaliana), resulting in an upward movement of the leaf blades (hyponastic growth). The amplitude of this effect differs between accessions, with Columbia-0 (Col-0) showing a large response, while in Landsberg erecta (Ler), hyponastic growth is minimal. Abscisic acid (ABA) was found to act as an inhibitory factor of this response in both accessions, but the relationship between ethylene and ABA differed between the two; the ability of ABA to inhibit ethylene-induced hyponasty was significantly more pronounced in Col-0. Mutations in ABI1 or ABI3 induced a strong ethylene-regulated hyponastic growth in the less responsive accession Ler, while the response was abolished in the ABA-hypersensitive era1 in Col-0. Modifications in ABA levels altered petiole angles in the absence of applied ethylene, indicating that ABA influences petiole angles also independently from ethylene. A model is proposed whereby the negative effect of ABA on hyponastic growth is overcome by ethylene in Col-0 but not in Ler. However, when ABA signaling is artificially released in Ler, this regulatory mechanism is bypassed, resulting in a strong hyponastic response in this accession.

Abscisic Acid↗

PubMed2006

How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results.

BACKGROUND: Short oligonucleotide arrays for transcript profiling have been available for several years. Generally, raw data from these arrays are analysed with the aid of the Microarray Analysis Suite or GeneChip Operating Software (MAS or GCOS) from Affymetrix. Recently, more methods to analyse the raw data have become available. Ideally all these methods should come up with more or less the same results. We set out to evaluate the different methods and include work on our own data set, in order to test which method gives the most reliable results. RESULTS: Calculating gene expression with 6 different algorithms (MAS5, dChip PMMM, dChip PM, RMA, GC-RMA and PDNN) using the same (Arabidopsis) data, results in different calculated gene expression levels. Consequently, depending on the method used, different genes will be identified as differentially regulated. Surprisingly, there was only 27 to 36% overlap between the different methods. Furthermore, 47.5% of the genes/probe sets showed good correlation between the mismatch and perfect match intensities. CONCLUSION: After comparing six algorithms, RMA gave the most reproducible results and showed the highest correlation coefficients with Real Time RT-PCR data on genes identified as differentially expressed by all methods. However, we were not able to verify, by Real Time RT-PCR, the microarray results for most genes that were solely calculated by RMA. Furthermore, we conclude that subtraction of the mismatch intensity from the perfect match intensity results most likely in a significant underestimation for at least 47.5% of the expression values. Not one algorithm produced significant expression values for genes present in quantities below 1 pmol. If the only purpose of the microarray experiment is to find new candidate genes, and too many genes are found, then mutual exclusion of the genes predicted by contrasting methods can be used to narrow down the list of new candidate genes by 64 to 73%.

Algorithms↗