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Legume GRAS (GAI, RGA, SCR)-type transcription factors MODULATION SIGNALING PATHWAY1 (NSP1) and NSP2 are essential for rhizobium Nod factor-induced nodulation. Both proteins are considered to be Nod factor response factors regulating gene expression after symbiotic signaling. However, legume NSP1 and NSP2 can be functionally replaced by nonlegume orthologs, including rice (Oryza sativa) NSP1 and NSP2, indicating that both proteins are functionally conserved in higher plants. Here, we show that NSP1 and NSP2 are indispensable for strigolactone (SL) biosynthesis in the legume Medicago truncatula and in rice. Mutant nsp1 plants do not produce SLs, whereas in M. truncatula, NSP2 is essential for conversion of orobanchol into didehydro-orobanchol, which is the main SL produced by this species. The disturbed SL biosynthesis in nsp1 nsp2 mutant backgrounds correlates with reduced expression of DWARF27, a gene essential for SL biosynthesis. Rice and M. truncatula represent distinct phylogenetic lineages that split approximately 150 million years ago. Therefore, we conclude that regulation of SL biosynthesis by NSP1 and NSP2 is an ancestral function conserved in higher plants. NSP1 and NSP2 are single-copy genes in legumes, which implies that both proteins fulfill dual regulatory functions to control downstream targets after rhizobium-induced signaling as well as SL biosynthesis in nonsymbiotic conditions.

High-density oligonucleotide expression arrays are widely used in many areas of biomedical research. Affymetrix GeneChip arrays are the most popular. In the Affymetrix system, a fair amount of further preprocessing and data reduction occurs after the image-processing step. Statistical procedures developed by academic groups have been successful in improving the default algorithms provided by the Affymetrix system. In this article we present a solution to one of the preprocessing steps-background adjustment-based on a formal statistical framework. Our solution greatly improves the performance of the technology in various practical applications. These arrays use short oligonucleotides to probe for genes in an RNA sample. Typically, each gene is represented by 11-20 pairs of oligonucleotide probes. The first component of these pairs is referred to as a perfect match probe and is designed to hybridize only with transcripts from the intended gene (i. e., specific hybridization). However, hybridization by other sequences (i. e., nonspecific hybridization) is unavoidable. Furthermore, hybridization strengths are measured by a scanner that introduces optical noise. Therefore, the observed intensities need to be adjusted to give accurate measurements of specific hybridization. We have found that the default ad hoc adjustment, provided as part of the Affymetrix system, can be improved through the use of estimators derived from a statistical model that uses probe sequence information. A final step in preprocessing is to summarize the probe-level data for each gene to define a measure of expression that represents the amount of the corresponding mRNA species. In this article we illustrate the practical consequences of not adjusting appropriately for the presence of nonspecific hybridization and provide a solution based on our background adjustment procedure. Software that computes our adjustment is available as part of the Bioconductor Project ( http://www.bioconductor.org ).

As the most important natural raw material for textile industry, cotton fibres are an excellent model for studying single-cell development. Although expression profiling and functional genomics have provided some data, the mechanism of fibre development is still not well known. A class I TCP transcription factor (designated GbTCP), encoding 344 amino acids, was isolated from the normalized cDNA library of sea-island cotton fibre (from –2 to 25 days post anthesis). GbTCP was preferentially expressed in the elongating cotton fibre from 5 to 15 days post anthesis. Some expression was also observed in stems, apical buds, and petals. RNAi silencing of GbTCP produced shorter fibre, a reduced lint percentage, and a lower fibre quality than the wild-type plants. Overexpression of GbTCP enhanced root hair initiation and elongation in Arabidopsis and regulated branching. Solexa sequencing and Affymetrix GeneChip analysis indicated that GbTCP positively regulates the level of jasmonic acid (JA) and, as a result, activates downstream genes (reactive oxygen species, calcium signalling, ethylene biosynthesis and response, and several NAC and WRKY transcription factors) necessary for elongation of fibres and root hairs. JA content analysis in cotton also confirmed that GbTCP has a profound effect on JA biosynthesis. In vitro ovule culture showed that an appropriate concentration of JA promoted fibre elongation. The results suggest that GbTCP is an important transcription factor for fibre and root hair development by regulating JA biosynthesis and response and other pathways, including reactive oxygen species, calcium channel and ethylene signalling.

The expression of inulinolytic genes in Aspergillus niger is co-regulated and induced by inulin and sucrose. We have identified a positive acting transcription factor InuR, which is required for the induced expression of inulinolytic genes. InuR is a member of the fungal specific class of transcription factors of the Zn(II)2Cys6 type. Involvement of InuR in inulin and sucrose metabolism was suspected because of the clustering of inuR gene with sucB, which encodes an intracellular invertase with transfructosylation activity and a putative sugar transporter encoding gene (An15g00310). Deletion of the inuR gene resulted in a strain displaying a severe reduction in growth on inulin and sucrose medium. Northern analysis revealed that expression of inulinolytic and sucrolytic genes, e.g., inuE, inuA, sucA, as well as the putative sugar transporter gene (An15g00310) is dependent on InuR. Genome-wide expression analysis revealed, three additional putative sugar transporters encoding genes (An15g04060, An15g03940 and An17g01710), which were strongly induced by sucrose in an InuR dependent way. In silico analysis of the promoter sequences of strongly InuR regulated genes suggests that InuR might bind as dimer to two CGG triplets, which are separated by eight nucleotides.

Microarrays offer a powerful approach to the analysis of gene expression that can be used for a wide variety of experimental purposes. However, there are several types of microarray platforms that are available. In addition, microarray experiments are expensive and generate complicated data sets that can be difficult to interpret. Success with microarray approaches requires a sound experimental design and a coordinated and appropriate use of statistical tools. Here, the advantages and pitfalls of utilizing microarrays are discussed, as are practical strategies to help novice users succeed with this method that can empower them with the ability to assay changes in gene expression at the whole genome level.

The number of global gene expression studies has increased significantly in recent years. It is assumed that the different techniques employed report similar levels of gene expression for each sequence type. While this may be true for many species, polyploids containing homoeologous and paralogous gene copies represent a unique situation. In this paper, we describe the comparison of the Affymetrix GeneChip® Wheat Genome Array, an in-house custom-spotted complementary DNA array and quantitative reverse transcription-polymerase chain reaction (PCR) for the study of gene expression in hexaploid wheat. Analysis of the data generated from each platform revealed little concordance and suggested that global comparisons are not possible. Potential causes of these inter-platform discrepancies were investigated and revealed to be due to the inability of the platforms to discriminate between different but related transcripts. Our results also showed that the traditionally used array validation technique, quantitative reverse transcription PCR, differs in its discriminatory ability, resulting in the poor confirmation rates seen in previous polyploid studies. These findings have implications for gene expression studies in polyploid organisms and highlight the need for homoeologous- and paralogous-specific arrays when investigating polyploid gene expression.

A number of methods are currently used for gene expression profiling. They differ in scale, economy and sensitivity. We present the results of a direct comparison between serial analysis of gene expression (SAGE) and the Barley1 Affymetrix GeneChip. Both technology platforms were used to obtain quantitative measurements of transcript abundance using identical RNA samples and assessed for their ability to quantify differential gene expression. For SAGE, a total of 82,122 tags were generated from two independent libraries representing whole developing barley caryopsis and dissected embryos. The Barley1 GeneChip contains 22,791 probe sets. Results obtained from both methods are generally comparable, indicating that both will lead to similar conclusions regarding transcript levels and differential gene expression. However, excluding singletons, 24.4% of the unique SAGE tags had no corresponding probe set on the Barley1 array indicating that a broader snapshot of gene expression was obtained by SAGE. Discrepancies were observed for a number of \"genes\" and these are discussed.