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On April 15, 2024, Kristina Gruden's team from the National Institute of Biology in Slovenia published an article titled "Stress Knowledge Map: A knowledge graph resource for systems biology analysis of plant stress responses" in Plant Communications. This study constructed a Stress Knowledge Map (SKM) of plant and established a database for knowledge map retrieval and analysis.
In terms of content, SKM contains four layers: stimulus signal, signal perception, signal transduction and execution. The first layer of stimulus signals includes not only abiotic stresses such as drought, temperature and flooding, but also biotic stresses such as pathogenic bacteria and insects. At the second layer of perception, signal sensing receptors and ion channels are mainly responsible. At the third layer of signal transduction, it is relatively complex and rich in content. It includes ROS and Ca2+-mediated signal transduction, as well as signal transduction mediated by a variety of plant hormones and protein kinases. Finally, at the execution layer, there are downstream biological processes such as the synthesis of some stress-responsive metabolites and stomatal closure. Compared with previous plant protein interaction databases, SKM's data content is more systematic and richer.
As an open-available resource, SKM contains two complementary knowledge maps describing current knowledge about biochemical, signaling and regulatory molecular interactions in plants. One is a highly edited plant stress signaling model (PSS) containing 543 responses. The other is a large comprehensive knowledge network (CKN), which contains 488,390 interactions. CKN’s interaction data includes not only protein-protein interactions, but also transcription factor-DNA interactions, as well as non-coding RNA and mRNA interactions and other types of macromolecule interactions. Both models were constructed by domain experts through systematic compilation of various literature and database resources. Among them, PSS can also be used as a qualitative and quantitative model of systems biology. Therefore, SKM provides an excellent entry point for the study of plant stress responses and related growth processes, as well as for the interactive exploration of current knowledge.
Finally, the author illustrates the application method of the knowledge map with examples. Based on the data from the knowledge map, the authors used the promoter of the stress-responsive gene RD29 to drive luciferase as a reporter system and analyzed the response pattern of the abscisic acid (ABA)-responsive gene RD29 in Arabidopsis and potato respectively. Experimental results found that ABA treatment induced the expression of RD29, while the simultaneous application of ABA and methyl jasmonate (MeJA) or the simultaneous application of ABA and salicylic acid (SA) inhibited the expression of RD29. According to the SKM, this may be due to the previous inhibition of these three hormone receptors. Based on this hypothesis, the author processed ABA, MeJA and SA together and found that the inhibitory effect on RD29 was the strongest, which verified this hypothesis.
In summary, this study constructed a plant stress knowledge map SKM and used case studies to illustrate how to use it for complex biological analysis, including the formulation of hypotheses and the design of verification experiments. Ultimately, this knowledge map will help gain new insights into experimental observations in plant biology.
| Cat# | Product Name | Purity |
| EXT-1805 | Abscisic acid | ≥ 98% |
| GRO-521 | (±)-Abscisic acid | ≥98.5 % |
| PHAS-100 | ABA Analytical Standard | ≥98.0% (HPLC) |
| PHAS-110 | MEJA Analytical Standard | ≥98% |
| PHAS-114 | SA Analytical Standard | ≥99.0% |
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