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Jingyuan Fu

Publications and source records attributed to Jingyuan Fu.

5 recordsLinked to original sources
PubMed2006

Mapping determinants of gene expression plasticity by genetical genomics in C. elegans.

Recent genetical genomics studies have provided intimate views on gene regulatory networks. Gene expression variations between genetically different individuals have been mapped to the causal regulatory regions, termed expression quantitative trait loci. Whether the environment-induced plastic response of gene expression also shows heritable difference has not yet been studied. Here we show that differential expression induced by temperatures of 16 degrees C and 24 degrees C has a strong genetic component in Caenorhabditis elegans recombinant inbred strains derived from a cross between strains CB4856 (Hawaii) and N2 (Bristol). No less than 59% of 308 trans-acting genes showed a significant eQTL-by-environment interaction, here termed plasticity quantitative trait loci. In contrast, only 8% of an estimated 188 cis-acting genes showed such interaction. This indicates that heritable differences in plastic responses of gene expression are largely regulated in trans. This regulation is spread over many different regulators. However, for one group of trans-genes we found prominent evidence for a common master regulator: a transband of 66 coregulated genes appeared at 24 degrees C. Our results suggest widespread genetic variation of differential expression responses to environmental impacts and demonstrate the potential of genetical genomics for mapping the molecular determinants of phenotypic plasticity.

Animals↗
PubMed2006

The genetics of plant metabolism.

Variation for metabolite composition and content is often observed in plants. However, it is poorly understood to what extent this variation has a genetic basis. Here, we describe the genetic analysis of natural variation in the metabolite composition in Arabidopsis thaliana. Instead of focusing on specific metabolites, we have applied empirical untargeted metabolomics using liquid chromatography-time of flight mass spectrometry (LC-QTOF MS). This uncovered many qualitative and quantitative differences in metabolite accumulation between A. thaliana accessions. Only 13.4% of the mass peaks were detected in all 14 accessions analyzed. Quantitative trait locus (QTL) analysis of more than 2,000 mass peaks, detected in a recombinant inbred line (RIL) population derived from the two most divergent accessions, enabled the identification of QTLs for about 75% of the mass signals. More than one-third of the signals were not detected in either parent, indicating the large potential for modification of metabolic composition through classical breeding.

Arabidopsis↗
PubMed2005

Optimal design and analysis of genetic studies on gene expression.

Whole-genome profiling of gene expression in a segregating population has the potential to identify the regulatory consequences of natural allelic variation. Costs of such studies are high and require that resources--microarrays and population--are used as efficiently as possible. We show that current studies can be improved significantly by a new design for two-color microarrays. Our "distant pair design" profiles twice as many individuals as there are arrays, cohybridizes individuals with dissimilar genomes, gives more weight to known regulatory loci if wished, and therewith maximizes the power for decomposing expression variation into regulatory factors. It can also exploit a large population (larger than twice the number of available microarrays) as a useful resource to select the most dissimilar pairs of individuals from. Our approach identifies more regulatory factors than alternative strategies do in computer simulations for realistic genome sizes, and similar promising results are obtained in an application on Arabidopsis thaliana. Our results will aid the design and analysis of future studies on gene expression and will help to shed more light on gene regulatory networks.

Arabidopsis↗
PubMed2005

Combining microarrays and genetic analysis.

Gene expression can be studied at a genome-wide scale with the aid of modern microarray technologies. Expression profiling of tens to hundreds of individuals in a genetic population can reveal the consequences of genetic variation. In this paper it is argued that the design and analysis of such a study is not a matter of simply applying the existing and more-or-less standard computational tools for microarrays to a new type of experimental data. It is shown how to fully exploit the power of genetics through optimal experimental design and analysis for two major microarray technologies, cDNA two-colour arrays and Affymetrix short oligonucleotide arrays.

Chromosome Mapping↗