Pea (Pisum sativum L.) was the original model organism used in Mendel’s discovery (1866) of the laws of inheritance, making it the foundation of modern plant genetics. However, subsequent progress in pea genomics has lagged behind many other plant species. Although the size and repetitive nature of the pea genome has so far restricted its sequencing, comprehensive genomic and post genomic resources already exist. These include BAC libraries, several types of molecular marker sets, both transcriptome and proteome datasets and mutant populations for reverse genetics. The availability of the full genome sequences of three legume species has offered significant opportunities for genome wide comparison revealing synteny and co-linearity to pea. A combination of a candidate gene and colinearity approach has successfully led to the identification of genes underlying agronomically important traits including virus resistances and plant architecture. Some of this knowledge has already been applied to marker assisted selection (MAS) programs, increasing precision and shortening the breeding cycle. Yet, complete translation of marker discovery to pea breeding is still to be achieved. Molecular analysis of pea collections has shown that although substantial variation is present within the cultivated genepool, wild material offers the possibility to incorporate novel traits that may have been inadvertently eliminated. Association mapping analysis of diverse pea germplasm promises to identify genetic variation related to desirable agronomic traits, which are historically difficult to breed for in a traditional manner. The availability of high throughput ‘omics’ methodologies offers great promise for the development of novel, highly accurate selective breeding tools for improved pea genotypes that are sustainable under current and future climates and farming systems.

Background: Cultivated chickpea (Cicer arietinum) has a narrow genetic base making it difficult for breeders to produce new elite cultivars with durable resistance to major biotic and abiotic stresses. As an alternative to genome mapping, microarrays have recently been applied in crop species to identify and assess the function of putative genes thought to be involved in plant abiotic stress and defence responses. In the present study, a cDNA microarray approach was taken in order to determine if the transcription of genes, from a set of previously identified putative stress-responsive genes from chickpea and its close relativeLathyrus sativus, were altered in chickpea by the three abiotic stresses; drought, cold and high-salinity. For this, chickpea genotypes known to be tolerant and susceptible to each abiotic stress were challenged and gene expression in the leaf, root and/or flower tissues was studied. The transcripts that were differentially expressed among stressed and unstressed plants in response to the particular stress were analysed in the context of tolerant/susceptible genotypes.Results: The transcriptional change of more than two fold was observed for 109, 210 and 386 genes after drought, cold and high-salinity treatments, respectively. Among these, two, 15 and 30 genes were consensually differentially expressed (DE) between tolerant and susceptible genotypes studied for drought, cold and high-salinity, respectively. The genes that were DE in tolerant and susceptible genotypes under abiotic stresses code for various functional and regulatory proteins. Significant differences in stress responses were observed within and between tolerant and susceptible genotypes highlighting the multiple gene control and complexity of abiotic stress response mechanism in chickpea.Conclusion: The annotation of these genes suggests that they may have a role in abiotic stress response and are potential candidates for tolerance/susceptibility.

Plant pathogens are a major reason of reduced crop productivity and may lead to a shortage of food for both human and animal consumption. Although chemical control remains the main method to reduce foliar fungal disease incidence, frequent use can lead to loss of susceptibility in the fungal population. Furthermore, over-spraying can cause environmental contamination and poses a heavy financial burden on growers. To prevent or control disease epidemics, it is important for growers to be able to detect causal pathogen accurately, sensitively, and rapidly, so that the best practice disease management strategies can be chosen and enacted. To reach this goal, many culture-dependent, biochemical, and molecular methods have been developed for plant pathogen detection. However, these methods lack accuracy, specificity, reliability, and rapidity, and they are generally not suitable for in-situ analysis. Accordingly, there is strong interest in developing biosensing systems for early and accurate pathogen detection. There is also great scope to translate innovative nanoparticle-based biosensor approaches developed initially for human disease diagnostics for early detection of plant disease-causing pathogens. In this review, we compare conventional methods used in plant disease diagnostics with new sensing technologies in particular with deeper focus on electrochemical and optical biosensors that may be applied for plant pathogen detection and management. In addition, we discuss challenges facing biosensors and new capability the technology provides to informing disease management strategies.

The tribe Fabeae (formerly Vicieae) contains some of humanity's most important grain legume crops, namely Lathyrus (grass pea/sweet pea/chickling vetches; about 160 species); Lens (lentils; 4 species); Pisum (peas; 3 species); Vicia (vetches; about 140 species); and the monotypic genus Vavilovia. Reconstructing the phylogenetic relationships within this group is essential for understanding the origin and diversification of these crops. Our study, based on molecular data, has positioned Pisum genetically between Vicia and Lathyrus and shows it to be closely allied to Vavilovia. A study of phylogeography, using a combination of plastid and nuclear markers, suggested that wild pea spread from its centre of origin, the Middle East, eastwards to the Caucasus, Iran and Afghanistan, and westwards to the Mediterranean. To allow for direct data comparison, we utilized model-based Bayesian Analysis of Population structure (BAPS) software on 4429 Pisum accessions from three large world germplasm collections that include both wild and domesticated pea analyzed by retrotransposon-based markers. An analysis of genetic diversity identified separate clusters containing wild material, distinguishing Pisum fulvum, P. elatius and P. abyssinicum, supporting the view of separate species or subspecies. Moreover, accessions of domesticated peas of Afghan, Ethiopian and Chinese origin were distinguished. In addition to revealing the genetic relationships, these results also provided insight into geographical and phylogenetic partitioning of genetic diversity. This study provides the framework for defining global Pisum germplasm diversity as well as suggesting a model for the domestication of the cultivated species. These findings, together with gene-based sequence analysis, show that although introgression from wild species has been common throughout pea domestication, much of the diversity still resides in wild material and could be used further in breeding. Moreover, although existing collections contain over 10,000 pea accessions, effort should be directed towards collecting more wild material in order to preserve the genetic diversity of the species.