An Arabidopsis Phenomics Study in T-DNA Insertion Mutant Population
Authors: N. C. Gupta, M. Rao

With the completion of the Arabidopsis genome sequencing project, the next major challenge was the large scale determination of gene function. As an important model organism, Arabidopsis provides major insights into gene functions, important for plants growth or production in related crop species. Phenomics with detailed information about tagged populations provides a good tool for functional genomics analysis. By a T-DNA insertional mutagenesis approach, we have generated an Arabidopsis thaliana mutant population containing 5,000 activation tagged for gene activation or knockout lines. Approximately 500 of these lines have known integration sites. The T1 and T2 plants were grown in glass houses for subsequent generations, with the mutant phenotypes recorded. Detailed data describing growth and development of these plants in different categories and subcategories, over the entire three-month growing period along with the genetic segregation information and flanking sequence data. The correlations among different mutation phenotypes are also calculated. Together, the information about mutant lines, their integration sites, and the phenotypes make this collection, a good resource for Arabidopsis phenomics study.

Arabidopsis thaliana is one of the most important model plant species in the world. Rice (Oryza sativa) and Brassica crops share a large degree of genic similarity, making Arabidopsis an excellent model plant for genomics research studies. Arabidopsis thaliana was the first plant, and the third multicellular organism after Caenorhabditis elegans (The C. elegans Sequencing Consortium 1998) and Drosophila melanogaster (Adams et al. 2000), to be completely sequenced (The Arabidopsis Genome Initiative 2000). Arabidopsis was the first model plant chosen for complete genome sequencing because (1) it has the smallest genome size (125 Mb) among the other dicot species, (2) it can undergo large-scale transformation on a routine basis, (3) the isolation of genes from Arabidopsis could facilitate isolation of homologous genes from other cereal crops, and (4) much molecular and genetic information (ESTs, markers, genetic, and physical maps, etc.) about Arabidopsis are available.

With the completion of genomic sequencing of Arabidopsis the challenge of the post-genomic era is to systematically analyze the functions of all genes (25000 +) in the genome. Computation- and curation-based annotation of the genome was initiated to predict the locations of the genes, including exons, introns, and their putative functions. In the case of Arabidopsis genome annotation at least 40% of the gene predictions were subsequently found erroneous. Thus, further validation of the present Arabidopsis gene model and identification of additional genes must be achieved by other computational and experimental approaches.

A variety of investigations have explored Arabidopsis gene structures and functions, such as full-length cDNA sequences, whole genome tiling microarrays, gene expression arrays, serial analysis of gene expression (SAGE, massively parallel signature sequence (MPSS), proteomics and generation of large-scale chemical and irradiation-induced mutants. Among these techniques, an important and direct approach of defining the function of a novel gene is to eliminate or activate its function by insertional mutagenesis. Insertional mutagenesis, with T-DNA or a transposable element, provides opportunities to assign a function to a particular DNA sequence and to isolate the target gene causing a specific phenotype. In comparison with transposons, T-DNA produces a stable mutant which makes it an ideal material for use in a tagged population. T-DNA is usually found preferentially integrated into the genic region, with no obvious insertion ‘‘hot spot,’’ thus generating high tagging efficiency. The activation tagging, offers a highly valuable resource for high-throughput Arabidopsis functional analyses with both forward and reverse genetic approaches. There are few major steps to follow precautiously during phenotypic evaluation of the Arabidopsis mutant population

(a) Management of growth conditions for phenotype scoring: Temperature regime during light and dark hours, light intensity during day and night, relative humidity at various stage of development needs to be monitored throughout the growth and development. T1 plants screened over the selection media should be planted individually into pot and T2 plants should be checked for phenotypic reproducibility. Results should be recorded by phenotype code and photography. Quantitative traits must be measured before harvest. T2 seeds from the plants belonging to the same phenotype group should be harvested separately.

(b) Phenotypic profiling

• (i) Mutation subcategories: Once or twice a week during the growing season, needs to

Examine the T1 plants for defined phenotypic categories, including overall growth condition, leaf color, leaf morphology, plant morphology, mimic response, branching pattern, inflorescence emergence date, flower morphology, siliques, seed fertility, and seed morphology. These categories if needed can further be divided into subcategories.

• (ii) Correlation between the phenotypic traits: During the field observation, the highly related mutant phenotypes should be analyzed carefully and establish the correlation between the subcategories of mutations.

• (iii) Variations in quantitative traits in wild type and mutant plants: The important agronomic quantitative traits like plant height, numbers of primary, secondary branches and their patterns and inflorescence emerging date for each T1 plant in each generation. Analysis of the quantitative traits of wild type and mutant lines during the subsequent generation revealed more variation in the mutant lines than that for the wild type

• (iv) Variations in plant type and seed morphology: Quantitative traits varied greatly in T-DNA mutant population.


With intensive phenomics studies of the T-DNA tagged population of Arabidopsis functional genomic analysis becomes easier for revealing the secret of the genome.

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