Comparison of changes in fruit gene expression in tomato introgression lines provides evidence of genome-wide transcriptional changes and reveals links to mapped QTLs and described traits
ABSTRACT Total soluble solids content is a key determinant of tomato fruit quality for processing. Several tomato lines carrying defined introgressions from S. pennellii in a S. lycopersicum background produce fruit with elevated Brix, a refractive index measure of soluble solids. The genetic basis for this trait can be determined by fine-mapping each QTL to a single gene, but this is time-consuming and technically demanding. As an alternative, high-throughput analytical technologies can be used to provide useful information that helps characterize molecular changes in the introgression lines. This paper presents a study of transcriptomic changes in six introgression lines with increased fruit Brix. Each line also showed altered patterns of fruit carbohydrate accumulation. Transcriptomic changes in fruit at 20 d after anthesis (DAA) were assessed using a 12 000-element EST microarray and significant changes analysed by SAM (significance analysis of microarrays). Each non-overlapping introgression resulted in a unique set of transcriptomic changes with 78% of significant changes being unique to a single line. Principal components analysis allowed a clear separation of the six lines, but also revealed evidence of common changes; lines with quantitatively similar increases in Brix clustered together. A detailed examination of genes encoding enzymes of primary carbon metabolism demonstrated that few of the known introgressed alleles were altered in expression at the 20 DAA time point. However, the expression of other metabolic genes did change. Particularly striking was the co-ordinated up-regulation of enzymes of sucrose mobilization and respiration that occurred only in the two lines with the highest Brix increase. These common downstream changes suggest a similar mechanism is responsible for large Brix increases.
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ABSTRACT: Triticum monococcum (genome A(m)) and Triticum urartu (genome A(u)) are diploid wheats with the first having been domesticated in the Neolithic Era and the second being a wild species. In a germplasm collection rare wild T. urartu lines with the presence of T. monococcum alleles were found. This stimulated our interest to develop interspecific introgression lines of T. urartu in T. monococcum, a breeding tool currently implemented in several crop species. Moreover the experiments reported were designed to reveal the existence in nature of A(m)/A(u) intermediate forms and to clarify if the two species are at least marginally sexually compatible. From hand-made interspecific crosses, almost sterile F1 plants were obtained when the seed bearing parent was T. monococcum. A high degree of fertility was however evident in some advanced generations, particularly when T. urartu donors were molecularly more related to T. monococcum. Analysis of the marker populations demonstrated chromosome pairing and recombination in F1 hybrid plants. Forty-six introgression lines were developed using a line of T. monococcum with several positive agronomic traits as a recurrent parent. Microsatellite markers were tested on A(u) and A(m) genomes, ordered in a T. monococcum molecular map and used to characterize the exotic DNA fragments present in each introgression line. In a test based on 28 interspecific introgression lines, the existence of genetic variation associated with T. urartu chromosome fragments was proven for the seed content of carotenoids, lutein, β-cryptoxanthin and zinc. The molecular state of available introgression lines is summarized.G3-Genes Genomes Genetics 08/2014; DOI:10.1534/g3.114.013623 · 2.51 Impact Factor
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ABSTRACT: A successful development of herbivorous insects into plant tissues depends on coordination of metabolic processes. Plants have evolved complex mechanisms to recognize such attacks, and to trigger a defense response. To understand the transcriptional basis of this response, we compare gene expression profiles of two coffee genotypes, susceptible and resistant to leaf miner (Leucoptera coffella). A total of 22000 EST sequences from the Coffee Genome Database were selected for a microarray analysis. Fluorescence probes were synthesized using mRNA from the infested and non-infested coffee plants. Array hybridization, scanning and data normalization were performed using Nimble Scan(R) e ArrayStar(R) platforms. Genes with foldchange values +/-2 were considered differentially expressed. A validation of 18 differentially expressed genes was performed in infected plants using qRT-PCR approach. The microarray analysis indicated that resistant plants differ in gene expression profile. We identified relevant transcriptional changes in defense strategies before insect attack. Expression changes (>2.00-fold) were found in resistant plants for 2137 genes (1266 up-regulated and 873 down-regulated). Up-regulated genes include those responsible for defense mechanisms, hypersensitive response and genes involved with cellular function and maintenance. Also, our analyses indicated that differential expression profiles between resistant and susceptible genotypes are observed in the absence of leaf-miner, indicating that defense is already build up in resistant plants, as a priming mechanism. Validation of selected genes pointed to four selected genes as suitable candidates for markers in assisted-selection of novel cultivars. Our results show evidences that coffee defense responses against leaf-miner attack are balanced with other cellular functions. Also analyses suggest a major metabolic reconfiguration that highlights the complexity of this response.BMC Genomics 01/2014; 15(1):66. DOI:10.1186/1471-2164-15-66 · 4.04 Impact Factor
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ABSTRACT: Biology is in the middle of a data explosion. The technical advances achieved by the genomics, metabolomics, transcriptomics and proteomics technologies in recent years have significantly increased the amount of data that are available for biologists to analyze different aspects of an organism. However, *omics data sets have several additional problems: they have inherent biological complexity and may have significant amounts of noise as well as measurement artifacts. The need to extract information from such databases has once again become a challenge. This requires novel computational techniques and models to automatically perform data mining tasks such as integration of different data types, clustering and knowledge discovery, among others. In this article, we will present a novel integrated computational intelligence approach for biological data mining that involves neural networks and evolutionary computation. We propose the use of self-organizing maps for the identification of coordinated patterns variations; a new training algorithm that can include a priori biological information to obtain more biological meaningful clusters; a validation measure that can assess the biological significance of the clusters found; and finally, an evolutionary algorithm for the inference of unknown metabolic pathways involving the selected clusters.IEEE Computational Intelligence Magazine 11/2012; 7(4):22-34. DOI:10.1109/MCI.2012.2215122 · 2.71 Impact Factor