Publications by authors named "Jessica Foret"

Article Synopsis
  • * This research identifies the rapid transcriptional changes that occur shortly after NHP treatment, revealing that WRKY transcription factor genes respond first, followed by defense-related genes in a secondary response.
  • * The study also finds that while the initial response relies on low salicylic acid (SA) levels, the later response does not depend on new SA production, indicating the importance of WRKY70 in regulating these gene expressions related to SAR and reactive oxygen species (ROS) production.
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Nitrogen is an essential macronutrient for plant growth and basic metabolic processes. The application of nitrogen-containing fertilizer increases yield, which has been a substantial factor in the green revolution. Ecologically, however, excessive application of fertilizer has disastrous effects such as eutrophication.

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Sensitivity, dynamic and detection range as well as exclusion of expression and instrumental artifacts are critical for the quantitation of data obtained with fluorescent protein (FP)-based biosensors in vivo. Current biosensors designs are, in general, unable to simultaneously meet all these criteria. Here, we describe a generalizable platform to create dual-FP biosensors with large dynamic ranges by employing a single FP-cassette, named GO-(Green-Orange) Matryoshka.

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Tissue-specific gene expression is often thought to arise from spatially restricted transcriptional cascades. However, it is unclear how expression is established at the top of these cascades in the absence of pre-existing specificity. We generated a transcriptional network to explore how transcription factor expression is established in the Arabidopsis thaliana root ground tissue.

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Time course transcriptome datasets are commonly used to predict key gene regulators associated with stress responses and to explore gene functionality. Techniques developed to extract causal relationships between genes from high throughput time course expression data are limited by low signal levels coupled with noise and sparseness in time points. We deal with these limitations by proposing the Cluster and Differential Alignment Algorithm (CDAA).

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