Scielo RSS <![CDATA[Genetics and Molecular Biology]]> vol. 35 num. 1 lang. en <![CDATA[SciELO Logo]]> <![CDATA[<b>GENOSOJA - the Brazilian soybean genome consortium</b>: <b>high throughput omics and beyond</b>]]> <![CDATA[<b>A web-based bioinformatics interface applied to the GENOSOJA project</b>: <b>databases and pipelines</b>]]> 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. <![CDATA[<b>Expression pattern of drought stress marker genes in soybean roots under two water deficit systems</b>]]> The study of tolerance mechanisms for drought stress in soybean is fundamental to the understanding and development of tolerant varieties. Using in silico analysis, four marker genes involved in the classical ABA-dependent and ABA-independent pathways of drought response were identified in the Glycine max genome in the present work. The expression profiles of the marker genes ERD1-like, GmaxRD20A-like, GmaxRD22-like and GmaxRD29B-like were investigated by qPCR in root samples of drought sensitive and tolerant soybean cultivars (BR 16 and Embrapa 48, respectively), submitted to water deficit conditions in hydroponic and pot-based systems. Among the four putative soybean homologs to Arabidopsis genes investigated herein, only GmaxRD29B-like was not regulated by water deficit stress. Distinct expression profiles and different induction levels were observed among the genes, as well as between the two drought-inducing systems. Our results showed contrasting gene expression responses for the GmaxRD20A-like and GmaxRD22-like genes. GmaxRD20A-like was highly induced by continuous drought acclimating conditions, whereas GmaxRD22-like responses decreased after abrupt water deprivation. GmaxERD1-like showed a different expression profile for the cultivars in each system. Conversely, GmaxRD20A-like and GmaxRD22-like genes exhibited similar expression levels in tolerant plants in both systems. <![CDATA[<b>Expression analysis in response to drought stress in soybean</b>: <b>shedding light on the regulation of metabolic pathway genes</b>]]> Metabolomics analysis of wild type Arabidopsis thaliana plants, under control and drought stress conditions revealed several metabolic pathways that are induced under water deficit. The metabolic response to drought stress is also associated with ABA dependent and independent pathways, allowing a better understanding of the molecular mechanisms in this model plant. Through combining an in silico approach and gene expression analysis by quantitative real-time PCR, the present work aims at identifying genes of soybean metabolic pathways potentially associated with water deficit. Digital expression patterns of Arabidopsis genes, which were selected based on the basis of literature reports, were evaluated under drought stress condition by Genevestigator. Genes that showed strong induction under drought stress were selected and used as bait to identify orthologs in the soybean genome. This allowed us to select 354 genes of putative soybean orthologs of 79 Arabidopsis genes belonging to 38 distinct metabolic pathways. The expression pattern of the selected genes was verified in the subtractive libraries available in the GENOSOJA project. Subsequently, 13 genes from different metabolic pathways were selected for validation by qPCR experiments. The expression of six genes was validated in plants undergoing drought stress in both pot-based and hydroponic cultivation systems. The results suggest that the metabolic response to drought stress is conserved in Arabidopsis and soybean plants. <![CDATA[<b>Identification and in silico characterization of soybean trihelix-GT and bHLH transcription factors involved in stress responses</b>]]> Environmental stresses caused by either abiotic or biotic factors greatly affect agriculture. As for soybean [Glycine max (L.) Merril], one of the most important crop species in the world, the situation is not different. In order to deal with these stresses, plants have evolved a variety of sophisticated molecular mechanisms, to which the transcriptional regulation of target-genes by transcription factors is crucial. Even though the involvement of several transcription factor families has been widely reported in stress response, there still is a lot to be uncovered, especially in soybean. Therefore, the objective of this study was to investigate the role of bHLH and trihelix-GT transcription factors in soybean responses to environmental stresses. Gene annotation, data mining for stress response, and phylogenetic analysis of members from both families are presented herein. At least 45 bHLH (from subgroup 25) and 63 trihelix-GT putative genes reside in the soybean genome. Among them, at least 14 bHLH and 11 trihelix-GT seem to be involved in responses to abiotic/biotic stresses. Phylogenetic analysis successfully clustered these with members from other plant species. Nevertheless, bHLH and trihelix-GT genes encompass almost three times more members in soybean than in Arabidopsis or rice, with many of these grouping into new clades with no apparent near orthologs in the other analyzed species. Our results represent an important step towards unraveling the functional roles of plant bHLH and trihelix-GT transcription factors in response to environmental cues. <![CDATA[<b>Overall picture of expressed Heat Shock Factors in <i>Glycine max, Lotus japonicus</i> and <i>Medicago truncatula</i></b>]]> Heat shock (HS) leads to the activation of molecular mechanisms, known as HS-response, that prevent damage and enhance survival under stress. Plants have a flexible and specialized network of Heat Shock Factors (HSFs), which are transcription factors that induce the expression of heat shock proteins. The present work aimed to identify and characterize the Glycine max HSF repertory in the Soybean Genome Project (GENOSOJA platform), comparing them with other legumes (Medicago truncatula and Lotus japonicus) in view of current knowledge of Arabidopsis thaliana. The HSF characterization in leguminous plants led to the identification of 25, 19 and 21 candidate ESTs in soybean, Lotus and Medicago, respectively. A search in the SuperSAGE libraries revealed 68 tags distributed in seven HSF gene types. From the total number of obtained tags, more than 70% were related to root tissues (water deficit stress libraries vs. controls), indicating their role in abiotic stress responses, since the root is the first tissue to sense and respond to abiotic stress. Moreover, as heat stress is related to the pressure of dryness, a higher HSF expression was expected at the water deficit libraries. On the other hand, expressive HSF candidates were obtained from the library inoculated with Asian Soybean Rust, inferring crosstalk among genes associated with abiotic and biotic stresses. Evolutionary relationships among sequences were consistent with different HSF classes and subclasses. Expression profiling indicated that regulation of specific genes is associated with the stage of plant development and also with stimuli from other abiotic stresses pointing to the maintenance of HSF expression at a basal level in soybean, favoring its activation under heat-stress conditions. <![CDATA[<b>An overall evaluation of the resistance (<i>R</i>) and pathogenesis-related (<i>PR</i>) superfamilies in soybean, as compared with <i>Medicago</i> and <i>Arabidopsis</i></b>]]> Plants have the ability to recognize and respond to a multitude of pathogens, resulting in a massive reprogramming of the plant to activate defense responses including Resistance (R) and Pathogenesis-Related (PR) genes. Abiotic stresses can also activate PR genes and enhance pathogen resistance, representing valuable genes for breeding purposes. The present work offers an overview of soybean R and PR genes present in the GENOSOJA (Brazilian Soybean Genome Consortium) platform, regarding their structure, abundance, evolution and role in the plantpathogen metabolic pathway, as compared with Medicago and Arabidopsis. Searches revealed 3,065 R candidates (756 in Soybean, 1,142 in Medicago and 1,167 in Arabidopsis), and PR candidates matching to 1,261 sequences (310, 585 and 366 for the three species, respectively). The identified transcripts were also evaluated regarding their expression pattern in 65 libraries, showing prevalence in seeds and developing tissues. Upon consulting the SuperSAGE libraries, 1,072 R and 481 PR tags were identified in association with the different libraries. Multiple alignments were generated for Xa21 and PR-2 genes, allowing inferences about their evolution. The results revealed interesting insights regarding the variability and complexity of defense genes in soybean, as compared with Medicago and Arabidopsis. <![CDATA[<b>Transcriptome analysis of resistant soybean roots infected by <i>Meloidogyne</i> javanica</b>]]> Soybean is an important crop for Brazilian agribusiness. However, many factors can limit its production, especially root-knot nematode infection. Studies on the mechanisms employed by the resistant soybean genotypes to prevent infection by these nematodes are of great interest for breeders. For these reasons, the aim of this work is to characterize the transcriptome of soybean line PI 595099-Meloidogyne javanica interaction through expression analysis. Two cDNA libraries were obtained using a pool of RNA from PI 595099 uninfected and M. javanica (J2) infected roots, collected at 6, 12, 24, 48, 96, 144 and 192 h after inoculation. Around 800 ESTs (Expressed Sequence Tags) were sequenced and clustered into 195 clusters. In silico subtraction analysis identified eleven differentially expressed genes encoding putative proteins sharing amino acid sequence similarities by using BlastX: metallothionein, SLAH4 (SLAC1 Homologue 4), SLAH1 (SLAC1 Homologue 1), zinc-finger proteins, AN1-type proteins, auxin-repressed proteins, thioredoxin and nuclear transport factor 2 (NTF-2). Other genes were also found exclusively in nematode stressed soybean roots, such as NAC domain-containing proteins, MADS-box proteins, SOC1 (suppressor of overexpression of constans 1) proteins, thioredoxin-like protein 4-Coumarate-CoA ligase and the transcription factor (TF) MYBZ2. Among the genes identified in non-stressed roots only were Ser/Thr protein kinases, wound-induced basic protein, ethylene-responsive family protein, metallothionein-like protein cysteine proteinase inhibitor (cystatin) and Putative Kunitz trypsin protease inhibitor. An understanding of the roles of these differentially expressed genes will provide insights into the resistance mechanisms and candidate genes involved in soybean-M. javanica interaction and contribute to more effective control of this pathogen. <![CDATA[<b>Separomics applied to the proteomics and peptidomics of low-abundance proteins</b>: <b>choice of methods and challenges - a review</b>]]> The enrichment and isolation of proteins are considered limiting steps in proteomic studies. Identification of proteins whose expression is transient, those that are of low-abundance, and of natural peptides not described in databases, is still a great challenge. Plant extracts are in general complex, and contaminants interfere with the identification of proteins involved in important physiological processes, such as plant defense against pathogens. This review discusses the challenges and strategies of separomics applied to the identification of low-abundance proteins and peptides in plants, especially in plants challenged by pathogens. Separomics is described as a group of methodological strategies for the separation of protein molecules for proteomics. Several tools have been used to remove highly abundant proteins from samples and also non-protein contaminants. The use of chromatographic techniques, the partition of the proteome into subproteomes, and an effort to isolate proteins in their native form have allowed the isolation and identification of rare proteins involved in different processes. <![CDATA[<b>Metatranscriptomic analysis of small RNAs present in soybean deep sequencing libraries</b>]]> A large number of small RNAs unrelated to the soybean genome were identified after deep sequencing of soybean small RNA libraries. A metatranscriptomic analysis was carried out to identify the origin of these sequences. Comparative analyses of small interference RNAs (siRNAs) present in samples collected in open areas corresponding to soybean field plantations and samples from soybean cultivated in greenhouses under a controlled environment were made. Different pathogenic, symbiotic and free-living organisms were identified from samples of both growth systems. They included viruses, bacteria and different groups of fungi. This approach can be useful not only to identify potentially unknown pathogens and pests, but also to understand the relations that soybean plants establish with microorganisms that may affect, directly or indirectly, plant health and crop production. <![CDATA[<b>Subtractive libraries for prospecting differentially expressed genes in the soybean under water deficit</b>]]> 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. <![CDATA[<b><i>In silico</i></b><b> identification of known osmotic stress responsive genes from <i>Arabidopsis</i> in soybean and <i>Medicago</i></b>]]> Plants experience various environmental stresses, but tolerance to these adverse conditions is a very complex phenomenon. The present research aimed to evaluate a set of genes involved in osmotic response, comparing soybean and medicago with the well-described Arabidopsis thaliana model plant. Based on 103 Arabidopsis proteins from 27 categories of osmotic stress response, comparative analyses against Genosoja and Medicago truncatula databases allowed the identification of 1,088 soybean and 1,210 Medicago sequences. The analysis showed a high number of sequences and high diversity, comprising genes from all categories in both organisms. Genes with unknown function were among the most representative, followed by transcription factors, ion transport proteins, water channel, plant defense, protein degradation, cellular structure, organization & biogenesis and senescence. An analysis of sequences with unknown function allowed the annotation of 174 soybean and 217 Medicago sequences, most of them concerning transcription factors. However, for about 30% of the sequences no function could be attributed using in silico procedures. The establishment of a gene set involved in osmotic stress responses in soybean and barrel medic will help to better understand the survival mechanisms for this type of stress condition in legumes. <![CDATA[<b>Cell wall, lignin and fatty acid-related transcriptome in soybean</b>: <b>achieving gene expression patterns for bioenergy legume</b>]]> Increasing efforts to preserve environmental resources have included the development of more efficient technologies to produce energy from renewable sources such as plant biomass, notably through biofuels and cellulosic residues. The relevance of the soybean industry is due mostly to oil and protein production which, although interdependent, results from coordinated gene expression in primary metabolism. Concerning biomass and biodiesel, a comprehensive analysis of gene regulation associated with cell wall components (as polysaccharides and lignin) and fatty acid metabolism may be very useful for finding new strategies in soybean breeding for the expanding bioenergy industry. Searching the Genosoja transcriptional database for enzymes and proteins directly involved in cell wall, lignin and fatty acid metabolism provides gene expression datasets with frequency distribution and specific regulation that is shared among several cultivars and organs, and also in response to different biotic/abiotic stress treatments. These results may be useful as a starting point to depict the Genosoja database regarding gene expression directly associated with potential applications of soybean biomass and/or residues for bioenergy-producing technologies. <![CDATA[<b>Identification of SNPs in RNA-seq data of two cultivars of <i>Glycine max</i> (soybean) differing in drought resistance</b>]]> 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. <![CDATA[<b>Mining plant genome browsers as a means for efficient connection of physical, genetic and cytogenetic mapping</b>: <b>an example using soybean</b>]]> Physical maps are important tools to uncover general chromosome structure as well as to compare different plant lineages and species, helping to elucidate genome structure, evolution and possibilities regarding synteny and colinearity. The increasing production of sequence data has opened an opportunity to link information from mapping studies to the underlying sequences. Genome browsers are invaluable platforms that provide access to these sequences, including tools for genome analysis, allowing the integration of multivariate information, and thus aiding to explain the emergence of complex genomes. The present work presents a tutorial regarding the use of genome browsers to develop targeted physical mapping, providing also a general overview and examples about the possibilities regarding the use of Fluorescent In Situ Hybridization (FISH) using bacterial artificial chromosomes (BAC), simple sequence repeats (SSR) and rDNA probes, highlighting the potential of such studies for map integration and comparative genetics. As a case study, the available genome of soybean was accessed to show how the physical and in silico distribution of such sequences may be compared at different levels. Such evaluations may also be complemented by the identification of sequences beyond the detection level of cytological methods, here using members of the aquaporin gene family as an example. The proposed approach highlights the complementation power of the combination of molecular cytogenetics and computational approaches for the anchoring of coding or repetitive sequences in plant genomes using available genome browsers, helping in the determination of sequence location, arrangement and number of repeats, and also filling gaps found in computational pseudochromosome assemblies. <![CDATA[<b>A simple, economical and reproducible protein extraction protocol for proteomics studies of soybean roots</b>]]> Sample preparation is a critical step in two-dimensional gel electrophoresis (2-DE) of plant tissues. Here we describe a phenol/SDS procedure that, although greatly simplified, produced well-resolved and reproducible 2-DE profiles of protein extracts from soybean [Glycine max (L.) Merril] roots. Extractions were made in three replicates using both the original and simplified procedure. To evaluate the quality of the extracted proteins, ten spots were randomly selected and identified by mass spectrometry (MS). The 2-DE gels were equally well resolved, with no streaks or smears, and no significant differences were observed in protein yield, reproducibility, resolution or number of spots. Mass spectra of the ten selected spots were compared with database entries and allowed high-quality identification of proteins. The simplified protocol described here presents considerable savings of time and reagents without compromising the quality of 2-DE protein profiles and compatibility with MS analysis, and may facilitate the progress of proteomics studies of legume-rhizobia interactions. <![CDATA[<b>Method optimization for proteomic analysis of soybean leaf</b>: <b>improvements in identification of new and low-abundance proteins</b>]]> The most critical step in any proteomic study is protein extraction and sample preparation. Better solubilization increases the separation and resolution of gels, allowing identification of a higher number of proteins and more accurate quantitation of differences in gene expression. Despite the existence of published results for the optimization of proteomic analyses of soybean seeds, no comparable data are available for proteomic studies of soybean leaf tissue. In this work we have tested the effects of modification of a TCA-acetone method on the resolution of 2-DE gels of leaves and roots of soybean. Better focusing was obtained when both mercaptoethanol and dithiothreitol were used in the extraction buffer simultaneously. Increasing the number of washes of TCA precipitated protein with acetone, using a final wash with 80% ethanol and using sonication to ressuspend the pellet increased the number of detected proteins as well the resolution of the 2-DE gels. Using this approach we have constructed a soybean protein map. The major group of identified proteins corresponded to genes of unknown function. The second and third most abundant groups of proteins were composed of photosynthesis and metabolism related genes. The resulting protocol improved protein solubility and gel resolution allowing the identification of 122 soybean leaf proteins, 72 of which were not detected in other published soybean leaf 2-DE gel datasets, including a transcription factor and several signaling proteins.