Abstract Asian soybean rust (ASR) is one of the most destructive diseases affecting soybeans. The causative agent of ASR, the fungus Phakopsora pachyrhizi, presents characteristics that make it difficult to study in vitro, limiting our knowledge of plant-pathogen dynamics. Therefore, this work used leaf lesion laser microdissection associated with deep sequencing to determine the pathogen transcriptome during compatible and incompatible interactions with soybean. The 36,350 generated unisequences provided an overview of the main genes and biological pathways that were active in the fungus during the infection cycle. We also identified the most expressed transcripts, including sequences similar to other fungal virulence and signaling proteins. Enriched P. pachyrhizi transcripts in the resistant (PI561356) soybean genotype were related to extracellular matrix organization and metabolic signaling pathways and, among infection structures, in amino acid metabolism and intracellular transport. Unisequences were further grouped into gene families along predicted sequences from 15 other fungi and oomycetes, including rust fungi, allowing the identification of conserved multigenic families, as well as being specific to P. pachyrhizi. The results revealed important biological processes observed in P. pachyrhizi, contributing with information related to fungal biology and, consequently, a better understanding of ASR.

Soybean has a wide range of applications in the industry and, due to its crop potential, its improvement is widely desirable. During drought conditions, soybean crops suffer significant losses in productivity. Therefore, understanding the responses of the soybean under this stress is an effective way of targeting crop improvement techniques. In this study, we employed the Suppressive Subtractive Hybridization (SSH) technique to investigate differentially expressed genes under water deficit conditions. Embrapa 48 and BR 16 soybean lines, known as drought-tolerant and -sensitive, respectively, were grown hydroponically and subjected to different short-term periods of stress by withholding the nutrient solution. Using this approach, we have identified genes expressed during the early response to water deficit in roots and leaves. These genes were compared among the lines to assess probable differences in the plant transcriptomes. In general, similar biochemical processes were predominant in both cultivars; however, there were more considerable differences between roots and leaves of Embrapa 48. Moreover, we present here a fast, clean and straightforward method to obtain drought-stressed root tissues and a large enriched collection of transcripts expressed by soybean plants under water deficit that can be useful for further studies towards the understanding of plant responses to stress.

The legume Glycine max (soybean) plays an important economic role in the international commodities market, with a world production of almost 260 million tons for the 2009/2010 harvest. The increase in drought events in the last decade has caused production losses in recent harvests. This fact compels us to understand the drought tolerance mechanisms in soybean, taking into account its variability among commercial and developing cultivars. In order to identify single nucleotide polymorphisms (SNPs) in genes up-regulated during drought stress, we evaluated suppression subtractive libraries (SSH) from two contrasting cultivars upon water deprivation: sensitive (BR 16) and tolerant (Embrapa 48). A total of 2,222 soybean genes were up-regulated in both cultivars. Our method identified more than 6,000 SNPs in tolerant and sensitive Brazilian cultivars in those drought stress related genes. Among these SNPs, 165 (in 127 genes) are positioned at soybean chromosome ends, including transcription factors (MYB, WRKY) related to tolerance to abiotic stress.

The Genosoja consortium is an initiative to integrate different omics research approaches carried out in Brazil. Basically, the aim of the project is to improve the plant by identifying genes involved in responses against stresses that affect domestic production, like drought stress and Asian Rust fungal disease. To do so, the project generated several types of sequence data using different methodologies, most of them sequenced by next generation sequencers. The initial stage of the project is highly dependent on bioinformatics analysis, providing suitable tools and integrated databases. In this work, we describe the main features of the Genosoja web database, including the pipelines to analyze some kinds of data (ESTs, SuperSAGE, microRNAs, subtractive cDNA libraries), as well as web interfaces to access information about soybean gene annotation and expression.