Yeast and animals use mitogen-activated protein (MAP) kinase cascades to mediate stress and extracellular signals. We have tested whether MAP kinases are involved in mediating environmental stress responses in plants. Using specific peptide antibodies that were raised against different alfalfa MAP kinases, we found exclusive activation of p44MMK4 kinase in drought- and cold-treated plants. p44MMK4 kinase was transiently activated by these treatments and was correlated with a shift in the electrophoretic mobility of the p44MMK4 protein. Although transcript levels of the MMK4 gene accumulated after drought and cold treatment, no changes in p44MMK4 steady state protein levels were observed, indicating a posttranslational activation mechanism. Extreme temperatures, drought, and salt stress are considered to be different forms of osmotic stress. However, high salt concentrations or heat shock did not induce activation of p44MMK4, indicating the existence of distinct mechanisms to mediate different stresses in alfalfa. Stress adaptation in plants is mediated by abscisic acid (ABA)-dependent and ABA-independent processes. Although ABA rapidly induced the transcription of an ABA-inducible marker gene, MMK4 transcript levels did not increase and p44MMK4 kinase was not activated. These data indicate that the MMK4 kinase pathway mediates drought and cold signaling independently of ABA.

Plants are fascinating and complex organisms. A comprehensive understanding of the organization, function and evolution of plant genes is essential to disentangle important biological processes and to advance crop engineering and breeding strategies. The ultimate aim in deciphering complex biological processes is the discovery of causal genes and regulatory mechanisms controlling these processes. The recent surge of omics data has opened the door to a system-wide understanding of the flow of biological information underlying complex traits. However, dealing with the corresponding large data sets represents a challenging endeavour that calls for the development of powerful bioinformatics methods. A popular approach is the construction and analysis of gene networks. Such networks are often used for genome-wide representation of the complex functional organization of biological systems. Network based on similarity in gene expression are called (gene) co-expression networks. One of the major application of gene co-expression networks is the functional annotation of unknown genes. Constructing co-expression networks is generally straightforward. In contrast, the resulting network of connected genes can become very complex, which limits its biological interpretation. Several strategies can be employed to enhance the interpretation of the networks. A strategy in coherence with the biological question addressed needs to be established to infer reliable networks. Additional benefits can be gained from network-based strategies using prior knowledge and data integration to further enhance the elucidation of gene regulatory relationships. As a result, biological networks provide many more applications beyond the simple visualization of co-expressed genes. In this study we review the different approaches for co-expression network inference in plants. We analyse integrative genomics strategies used in recent studies that successfully identified candidate genes taking advantage of gene co-expression networks. Additionally, we discuss promising bioinformatics approaches that predict networks for specific purposes.

Parsley cells recognize the fungal plant pathogen Phytophthora sojae through a plasma membrane receptor. A pathogen-derived oligopeptide elicitor binds to this receptor and thereby stimulates a multicomponent defense response through sequential activation of ion channels and an oxidative burst. An elicitor-responsive mitogen-activated protein (MAP) kinase was identified that acts downstream of the ion channels but independently or upstream of the oxidative burst. Upon receptor-mediated activation, the MAP kinase is translocated to the nucleus where it might interact with transcription factors that induce expression of defense genes.

Quantifying gene expression levels is an important research tool to understand biological systems. Reverse transcription–quantitative real-time PCR (RT–qPCR) is the preferred method for targeted gene expression measurements because of its sensitivity and reproducibility. However, normalization, necessary to correct for sample input and reverse transcriptase efficiency, is a crucial step to obtain reliable RT–qPCR results. Stably expressed genes (i.e. genes whose expression is not affected by the treatment or developmental stage under study) are indispensable for accurate normalization of RT–qPCR experiments. Lack of accurate normalization could affect the results and may lead to false conclusions. Since transcriptomes of seeds are different from other plant tissues, we aimed to identify reference genes specifically for RT–qPCR analyses in seeds of two important seed model species, i.e. Arabidopsis and tomato. We mined Arabidopsis seed microarray data to identify stably expressed genes and analyzed these together with putative reference genes from other sources. In total, the expression stability of 24 putative reference genes was validated by RT–qPCR in Arabidopsis seed samples. For tomato, we lacked transcriptome data sets of seeds and therefore we tested the tomato homologs of the reference genes found for Arabidopsis seeds. In conclusion, we identified 14 Arabidopsis and nine tomato reference genes. This provides a valuable resource for accurate normalization of gene expression experiments in seed research for two important seed model species.

Summary: Plants are exposed to combinations of various biotic and abiotic stresses, but stress responses are usually investigated for single stresses only. Here, we investigated the genetic architecture underlying plant responses to 11 single stresses and several of their combinations by phenotyping 350 Arabidopsis thaliana accessions. A set of 214 000 single nucleotide polymorphisms (SNPs) was screened for marker‐trait associations in genome‐wide association (GWA) analyses using tailored multi‐trait mixed models. Stress responses that share phytohormonal signaling pathways also share genetic architecture underlying these responses. After removing the effects of general robustness, for the 30 most significant SNPs, average quantitative trait locus (QTL) effect sizes were larger for dual stresses than for single stresses. Plants appear to deploy broad‐spectrum defensive mechanisms influencing multiple traits in response to combined stresses. Association analyses identified QTLs with contrasting and with similar responses to biotic vs abiotic stresses, and below‐ground vs above‐ground stresses. Our approach allowed for an unprecedented comprehensive genetic analysis of how plants deal with a wide spectrum of stress conditions.