Wilco Ligterink

Wounding Induces the Rapid and Transient Activation of a Specific MAP Kinase Pathway.

Mechanical injury in plants induces responses that are involved not only in healing but also in defense against a potential pathogen. To understand the intracellular signaling mechanism of wounding, we have investigated the involvement of protein kinases. Using specific antibodies, we showed that wounding alfalfa leaves specifically induces the transient activation of the p44MMK4 kinase, which belongs to the family of mitogen-activated protein kinases. Whereas activation of the MMK4 pathway is a post-translational process and was not blocked by [alpha]-amanitin and cycloheximide, inactivation depends on de novo transcription and translation of a protein factor(s). After wound-induced activation, the MMK4 pathway was subject to a refractory period of 25 min, during which time restimulation was not possible, indicating that the inactivation mechanism is only transiently active. After activation of the p44MMK4 kinase by wounding, transcript levels of the MMK4 gene increased, suggesting that the MMK4 gene may be a direct target of the MMK4 pathway. In contrast, transcripts of the wound-inducible MsWIP gene, encoding a putative proteinase inhibitor, were detected only several hours after wounding. Abscisic acid, methyl jasmonic acid, and electrical activity are known to mediate wound signaling in plants. However, none of these factors was able to activate the p44MMK4 kinase in the absence of wounding, suggesting that the MMK4 pathway acts independently of these signals.

Differential activation of four specific MAPK pathways by distinct elicitors

Plant cells respond to elicitors by inducing a variety of defense responses. Some of these reactions are dependent on the activity of protein kinases. Recently, mitogen-activated protein kinases (MAPKs) have been identified to be activated by fungal and bacterial elicitors as well as by pathogen infection. In gel kinase assays of alfalfa cells treated with yeast cell wall-derived elicitor (YE) revealed that 44- and 46-kDa MAPKs are rapidly and transiently activated. Immunokinase assays with specific MAPK antibodies revealed that YE mainly activated the 46-kDa SIMK and the 44-kDa MMK3 and to a lesser extent the 44-kDa MMK2 and SAMK. When cells were treated with chemically defined elicitors potentially contained in the YE (chitin and N-acetylglucosamine oligomers, β-glucan, and ergosterol), the four MAPKs were found to be activated to different levels and with different kinetics. Whereas SIMK and SAMK have been found to be activated by a number of diverse stimuli, MMK3 is activated during mitosis and was therefore assumed to participate in cell division (22). No physiological process could be associated with MMK2 activity so far. This is the first report that MMK2 and MMK3 can be activated by external stimuli. Overall, our findings indicate that plant cells can sense different cues of a given microorganism through the activation of multiple MAPKs.

Identification of Reference Genes for RT–qPCR Expression Analysis in Arabidopsis and Tomato Seeds

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.

GERMINATOR: a software package for high-throughput scoring and curve fitting of Arabidopsis seed germination

Over the past few decades seed physiology research has contributed to many important scientific discoveries and has provided valuable tools for the production of high quality seeds. An important instrument for this type of research is the accurate quantification of germination; however gathering cumulative germination data is a very laborious task that is often prohibitive to the execution of large experiments. In this paper we present the germinator package: a simple, highly cost-efficient and flexible procedure for high-throughput automatic scoring and evaluation of germination that can be implemented without the use of complex robotics. The germinator package contains three modules: (i) design of experimental setup with various options to replicate and randomize samples; (ii) automatic scoring of germination based on the color contrast between the protruding radicle and seed coat on a single image; and (iii) curve fitting of cumulative germination data and the extraction, recap and visualization of the various germination parameters. The curve-fitting module enables analysis of general cumulative germination data and can be used for all plant species. We show that the automatic scoring system works for Arabidopsis thaliana and Brassica spp. seeds, but is likely to be applicable to other species, as well. In this paper we show the accuracy, reproducibility and flexibility of the germinator package. We have successfully applied it to evaluate natural variation for salt tolerance in a large population of recombinant inbred lines and were able to identify several quantitative trait loci for salt tolerance. Germinator is a low-cost package that allows the monitoring of several thousands of germination tests, several times a day by a single person.

A Gα subunit controls zoospore motility and virulence in the potato late blight pathogen Phytophthora infestans

The heterotrimeric G‐protein pathway is a ubiquitous eukaryotic signalling module that is known to regulate growth and differentiation in many plant pathogens. We previously identified Pigpa1, a gene encoding a G‐protein α subunit from the potato late blight pathogen Phytophthora infestans. P. infestans belongs to the class oomycetes, a group of organisms in which signal transduction processes have not yet been studied at the molecular level. To elucidate the function of Pigpa1, PiGPA1‐deficient mutants were obtained by homology‐dependent gene silencing. The Pigpa1‐silenced mutants produced zoospores that turned six to eight times more frequently, causing them to swim only short distances compared with wild type. Attraction to the surface, a phenomenon known as negative geotaxis, was impaired in the mutant zoospores, as well as autoaggregation and chemotaxis towards glutamic and aspartic acid. Zoospore production was reduced by 20–45% in different Pigpa1‐silenced mutants. Transformants expressing constitutively active forms of PiGPA1, containing amino acid substitutions (R177H and Q203L), showed no obvious phenotypic differences from the wild‐type strain. Infection efficiencies on potato leaves ranged from 3% to 14% in the Pigpa1‐silenced mutants, compared with 77% in wild type, showing that virulence is severely impaired. The results prove that PiGPA1 is crucial for zoospore motility and for pathogenicity in an important oomycete plant pathogen.

Mitogen-activated protein [MAP] kinase pathways in plants: versatile signaling tools.

Mitogen-activated protein kinases (MAPKs) are important signaling tools in all eukaryotes, and function in mediating an enormous variety of external signals to appropriate cellular responses. MAPK pathways have been studied extensively in yeast and mammalian cells, and a large body of knowledge on their functioning has accumulated, which is summarized briefly. Plant MAPK pathways have attracted increasing interest, resulting in the isolation of a large number of different components of MAPK cascades. Studies on the functions of these components have revealed that MAPKs play important roles in the response to a broad variety of stresses, as well as in the signaling of most plant hormones and in developmental processes. Finally, the involvement of various plant phosphatases in the inactivation of MAPKs is discussed.

Gene expression profiling during asexual development of the late blight pathogen Phytophthora infestans reveals a highly dynamic transcriptome.

Much of the pathogenic success of Phytophthora infestans, the potato and tomato late blight agent, relies on its ability to generate from mycelia large amounts of sporangia, which release zoospores that encyst and form infection structures. To better understand these stages, Affymetrix GeneChips based on 15,650 unigenes were designed and used to profile the life cycle. Approximately half of P. infestans genes were found to exhibit significant differential expression between developmental transitions, with approximately (1)/(10) being stage-specific and most changes occurring during zoosporogenesis. Quantitative reverse-transcription polymerase chain reaction assays confirmed the robustness of the array results and showed that similar patterns of differential expression were obtained regardless of whether hyphae were from laboratory media or infected tomato. Differentially expressed genes encode potential cellular regulators, especially protein kinases; metabolic enzymes such as those involved in glycolysis, gluconeogenesis, or the biosynthesis of amino acids or lipids; regulators of DNA synthesis; structural proteins, including predicted flagellar proteins; and pathogenicity factors, including cell-wall-degrading enzymes, RXLR effector proteins, and enzymes protecting against plant defense responses. Curiously, some stage-specific transcripts do not appear to encode functional proteins. These findings reveal many new aspects of oomycete biology, as well as potential targets for crop protection chemicals.

The Re-Establishment of Desiccation Tolerance in Germinated Arabidopsis thaliana Seeds and Its Associated Transcriptome

The combination of robust physiological models with “omics” studies holds promise for the discovery of genes and pathways linked to how organisms deal with drying. Here we used a transcriptomics approach in combination with an in vivo physiological model of re-establishment of desiccation tolerance (DT) in Arabidopsis thaliana seeds. We show that the incubation of desiccation sensitive (DS) germinated Arabidopsis seeds in a polyethylene glycol (PEG) solution re-induces the mechanisms necessary for expression of DT. Based on a SNP-tile array gene expression profile, our data indicates that the re-establishment of DT, in this system, is related to a programmed reversion from a metabolic active to a quiescent state similar to prior to germination. Our findings show that transcripts of germinated seeds after the PEG-treatment are dominated by those encoding LEA, seed storage and dormancy related proteins. On the other hand, a massive repression of genes belonging to many other classes such as photosynthesis, cell wall modification and energy metabolism occurs in parallel. Furthermore, comparison with a similar system for Medicago truncatula reveals a significant overlap between the two transcriptomes. Such overlap may highlight core mechanisms and key regulators of the trait DT. Taking into account the availability of the many genetic and molecular resources for Arabidopsis, the described system may prove useful for unraveling DT in higher plants.

Learning from co-expression networks : Possibilities and challenges

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 endeavor 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.

Advances in genetical genomics of plants.

Natural variation provides a valuable resource to study the genetic regulation of quantitative traits. In quantitative trait locus (QTL) analyses this variation, captured in segregating mapping populations, is used to identify the genomic regions affecting these traits. The identification of the causal genes underlying QTLs is a major challenge for which the detection of gene expression differences is of major importance. By combining genetics with large scale expression profiling (i.e. genetical genomics), resulting in expression QTLs (eQTLs), great progress can be made in connecting phenotypic variation to genotypic diversity. In this review we discuss examples from human, mouse, Drosophila, yeast and plant research to illustrate the advances in genetical genomics, with a focus on understanding the regulatory mechanisms underlying natural variation. With their tolerance to inbreeding, short generation time and ease to generate large families, plants are ideal subjects to test new concepts in genetics. The comprehensive resources which are available for Arabidopsis make it a favorite model plant but genetical genomics also found its way to important crop species like rice, barley and wheat. We discuss eQTL profiling with respect to cis and trans regulation and show how combined studies with other ‘omics’ technologies, such as metabolomics and proteomics may further augment current information on transcriptional, translational and metabolomic signaling pathways and enable reconstruction of detailed regulatory networks. The fast developments in the ‘omics’ area will offer great potential for genetical genomics to elucidate the genotype-phenotype relationships for both fundamental and applied research.