Dataset Information

Analysis of time-resolved gene expression measurements across individuals.

ABSTRACT: Genetic and environmental determinants of altered cellular function, disease state, and drug response are increasingly studied using time-resolved transcriptomic profiles. While it is widely acknowledged that the rate of biological processes may vary between individuals, data analysis approaches that go beyond evaluating differential expression of single genes have so far not taken this variability into account. To this end, we introduce here a robust multi-gene data analysis approach and evaluate it in a biomarker discovery scenario across four publicly available datasets. In our evaluation, existing methods perform surprisingly poorly on time-resolved data; only the approach taking the variability into account yields reproducible and biologically plausible results. Our results indicate the need to capture gene expression between potentially heterogeneous individuals at multiple time points, and highlight the importance of robust data analysis in the presence of heterogeneous gene expression responses.

SUBMITTER: Elo LL

PROVIDER: S-EPMC3857324 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Analysis of time-resolved gene expression measurements across individuals.

Elo Laura L LL Schwikowski Benno B

PloS one 20131209 12

Genetic and environmental determinants of altered cellular function, disease state, and drug response are increasingly studied using time-resolved transcriptomic profiles. While it is widely acknowledged that the rate of biological processes may vary between individuals, data analysis approaches that go beyond evaluating differential expression of single genes have so far not taken this variability into account. To this end, we introduce here a robust multi-gene data analysis approach and evalua ...[more]

PMID: 24349258

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Project description:Winter dormancy – a period of low metabolic activity and no visible growth – appears as an adaptation to harsh winter conditions and can be divided into different phases. It is tightly controlled by environmental cues, with ambient temperature playing a major role. During endodormancy, a cultivar-specific amount of cold needs to be perceived, and during ecodormancy, heat hours accumulate before bud burst and anthesis in spring. Expression analysis, performed in several key fruit tree species, proved to be very useful in elucidating the molecular control of onset and release of dormancy. However, the time resolution of these experiments has been limited. Therefore, in this study, dense time-series expression analysis was conducted for 40 candidate genes involved in dormancy control, under the cool-temperate climate conditions in Dresden. Samples were taken from the cultivars ‘Pinova’ and ‘Gala,’ which differ in flowering time. The set of candidate genes included well-established dormancy genes such as DAM genes, MdFLC-like, MdICE1, MdPRE 1, and MdPIF4. Furthermore, we tested genes from dormancy-associated pathways including the brassinosteroid, gibberellic acid, abscisic acid (ABA), cytokinin response, and respiratory stress pathways. The expression patterns of well-established dormancy genes were confirmed and could be associated with specific dormancy phases. In addition, less well-known transcription factors and genes of the ABA signaling pathway showed associations with dormancy progression. The three ABA signaling genes HAB1_chr15, HAI3, and ABF2 showed a local minimum of gene expression in proximity of the endodormancy to ecodormancy transition. The number of sampling points allowed us to correlate expression values with temperature data, which revealed significant correlations of ambient temperature with the expression of the Malus domestica genes MdICE1, MdPIF4, MdFLC-like, HAB1chr15, and the type-B cytokinin response regulator BRR9. Interestingly, the slope of the linear correlation of temperature with the expression of MdPIF4 differed between cultivars. Whether the strength of inducibility of MdPIF4 expression by low temperature differs between the ‘Pinova’ and ‘Gala’ alleles needs to be tested further.

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Analysis of time-resolved gene expression measurements across individuals.

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Analysis of time-resolved gene expression measurements across individuals.

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