Natural variation in Arabidopsis thaliana: Molecular genetic architecture of stress tolerance under water deficit

Abstract only availableThe functional genomics tools available for studying Arabidopsis thaliana are a great resource for researchers trying to characterize and understand the genetic basis of natural variation. Genome wide transcript profiling can simultaneously monitor the gene expression programs regulated by growth and development and signal transduction pathways in response to environmental stress conditions. The responses of plants to water deficit depend on the extent and rate of water loss and its timing and duration. As a physical stress, water deficit triggers biochemical responses through a cascade that includes stress perception, signal transduction and regulation of gene expression. Arabidopsis accessions differ largely in their adaptation to stress tolerance. To understand the genetic basis of this intra-specific variation we analyzed five accessions under gradual water deficit leading to severe stress conditions. The changes in the gene expression profiles under water deficit conditions were studied using functional genomics tools, microarray and quantitative real time PCR and the regulatory roles of stress induced and developmental related transcripts will be discussed.Plant Genomics Internships @ M

Meeting Report: Soybean Genomics Assessment and Strategy Workshop

http://www.soybiotechcenter.org/archives/?id=195&id2=12On July 19 - 20, 2005, approximately 50 researchers and administrators with expert knowledge of soybean genomics participated in a workshop in St.Louis, MO, which was hosted by the Soybean Genetics Executive Committee and supported by the United Soybean Board. The workshop began with a series of presentations by experts in the topics discussed below. Each presentation was designed to update the audience on the current status of soybean resources and related genomics technologies. Following the presentations the participants divided into discussion groups to assess the status of soybean genomics, identify needs, and identify milestones to achieve objectives. The discussion groups included the general areas of Functional Genomics A (Transcriptome and Proteome), Functional Genomic B (Reverse Genetics), Physical and Genetic Maps, and Bioinformatics. After each discussion section the entire group reconvened to hear group reports and to further discuss each topic. The following is the report from this Workshop. It represents a consensus of the participants of the Workshop and it is structured to integrate with a White Paper generated in 2003 so that progress can be better monitored over time. The results of this report are consistent with those of a National Science Foundation soybean genomics workshop held in 2004 (St. Louis, MO) and a Cross-Legume workshop also held in 2004 (Santa Fe, NM).National Science Foundatio

Genetic mapping of QTL conditioning resistance to soybean cyst nematode in PI464925B

Abstract only availableSoybean cyst nematode (SCN) (Heterodera glycines Ichinohe) is estimated to cause the greatest yield losses to soybean [Glycine max (L.) Merr.] of any pest worldwide. It has been determined that host plant resistance is the most cost-effective and environmentally conscious method of controlling SCN. Phenotypic resistance appears to be quantitative and few cultivars exhibit resistance to one or more races of SCN. Identification of genetically resistant lines will be needed to compensate for various environmental SCN populations. Plant introductions (PIs) from the USDA Soybean Germplasm Collection have been screened for resistance to SCN and relatively few sources have been identified as new sources of SCN resistance. A wild soybean PI464925B (Glycine soja Siebold & Zucc.) is a soybean plant introduction from China shown to have resistance to SCN race 3. In this study, PI464925B was crossed with the SCN susceptible cultivar 'Hutcheson' to generate F1 hybrids. One hundred twenty-two F2 derived F3 progenies were evaluated for reaction to SCN race 3 in a thermo-regulated waterbath (27±1 ºC) in the greenhouse at the University of Missouri for reaction to SCN race 3. DNA from leaf tissue of the parents and progeny was extracted and one hundred seventeen of the progenies were used for construction of linkage maps and location of the QTL(quantitative trait loci) by using SSR(simple sequence repeats) markers. Multiplex PCR was performed using fluorescent labeled primers with subsequent analysis on an ABI 3100 DNA sequencer to increase high-throughput of genetic mapping. Genemapper (v3.5) was used for automatic allele sizing and genotyping. Parental testing showed 201 polymorphic SSR markers (56%), providing an average genomic coverage of 12 cM between two markers. Among them, genotypic data from 113 labeled SSR markers on the F3 progeny were collected to analyze association with the SCN response. QTL locations and genetic contribution of the favored alleles will be discussed.Plant Genomics Internship @ M

Genetic Mapping of Soybean Cyst Nematode (Heterodera glycines) Resistance to Enhance Soybean Production in the United States [abstract]

Only abstract of poster available.Track V: BiomassSoybean cyst nematode (SCN, Heterodera glycines) is the most destructive pest of soybean in the United States, resulting in an annual extensive yield loss of approximately $1.5 billion in the United States alone. Breeding for resistance to SCN is the most effective approach to control this pest. However, most of commercial soybean varieties resistant to SCN were mainly derived from a few common resistant sources. The continuation of growing the same resistant cultivar(s) have resulted in SCN population shifts and loss of SCN resistance; thus it highlights a need of further investigation to mine new resistant genes from new resistant sources for soybean improvement. As a leading group on SCN research in the United States, the University of Missouri SCN researchers have been continuing the evaluation of exotic soybean germplasm for broad-based resistance to multi-HG types of SCN, the identification and mapping of novel quantitative trait loci (QTL)/gene(s), and the discovery of genetic markers for marker-assisted selection (MAS) programs. Using many plant introductions (PIs) with high resistance to multi-SCN HG types, we have developed genetic populations for molecular characterization and QTL mapping. These efforts led to the discovery of many novel QTL underlying the resistance to multi-SCN HG types. With sequence information using the genome-wide Illumina/Solexa sequencing technology, we have developed hundreds of genetic markers associated with the target QTL. Along with the soybean physical and genetic maps, these markers will provide a powerful genomics tool facilitating our efforts toward fine-mapping and positional cloning of candidate genes for SCN resistance. Moreover, the QTL associated genetic markers are greatly useful to incorporate novel resistant genes into new soybean varieties through the MAS approach. With SCN resistant soybean varieties, soybean yield and productivity will be increased and, in turn, enhance the seed oil production; which will significantly be an important source for the development of biofuel

SNP discovery by high-throughput sequencing in soybean

<p>Abstract</p> <p>Background</p> <p>With the advance of new massively parallel genotyping technologies, quantitative trait loci (QTL) fine mapping and map-based cloning become more achievable in identifying genes for important and complex traits. Development of high-density genetic markers in the QTL regions of specific mapping populations is essential for fine-mapping and map-based cloning of economically important genes. Single nucleotide polymorphisms (SNPs) are the most abundant form of genetic variation existing between any diverse genotypes that are usually used for QTL mapping studies. The massively parallel sequencing technologies (Roche GS/454, Illumina GA/Solexa, and ABI/SOLiD), have been widely applied to identify genome-wide sequence variations. However, it is still remains unclear whether sequence data at a low sequencing depth are enough to detect the variations existing in any QTL regions of interest in a crop genome, and how to prepare sequencing samples for a complex genome such as soybean. Therefore, with the aims of identifying SNP markers in a cost effective way for fine-mapping several QTL regions, and testing the validation rate of the putative SNPs predicted with Solexa short sequence reads at a low sequencing depth, we evaluated a pooled DNA fragment reduced representation library and SNP detection methods applied to short read sequences generated by Solexa high-throughput sequencing technology.</p> <p>Results</p> <p>A total of 39,022 putative SNPs were identified by the Illumina/Solexa sequencing system using a reduced representation DNA library of two parental lines of a mapping population. The validation rates of these putative SNPs predicted with low and high stringency were 72% and 85%, respectively. One hundred sixty four SNP markers resulted from the validation of putative SNPs and have been selectively chosen to target a known QTL, thereby increasing the marker density of the targeted region to one marker per 42 K bp.</p> <p>Conclusions</p> <p>We have demonstrated how to quickly identify large numbers of SNPs for fine mapping of QTL regions by applying massively parallel sequencing combined with genome complexity reduction techniques. This SNP discovery approach is more efficient for targeting multiple QTL regions in a same genetic population, which can be applied to other crops.</p

Genetic marker anchoring by six-dimensional pools for development of a soybean physical map

<p>Abstract</p> <p>Background</p> <p>Integrated genetic and physical maps are extremely valuable for genomic studies and as important references for assembling whole genome shotgun sequences. Screening of a BAC library using molecular markers is an indispensable procedure for integration of both physical and genetic maps of a genome. Molecular markers provide anchor points for integration of genetic and physical maps and also validate BAC contigs assembled based solely on BAC fingerprints. We employed a six-dimensional BAC pooling strategy and an <it>in silico </it>approach to anchor molecular markers onto the soybean physical map.</p> <p>Results</p> <p>A total of 1,470 markers (580 SSRs and 890 STSs) were anchored by PCR on a subset of a Williams 82 <it>Bst</it>Y I BAC library pooled into 208 pools in six dimensions. This resulted in 7,463 clones (~1× genome equivalent) associated with 1470 markers, of which the majority of clones (6,157, 82.5%) were anchored by one marker and 1106 (17.5%) individual clones contained two or more markers. This contributed to 1184 contigs having anchor points through this 6-D pool screening effort. In parallel, the 21,700 soybean Unigene set from NCBI was used to perform <it>in silico </it>mapping on 80,700 Williams 82 BAC end sequences (BES). This <it>in silico </it>analysis yielded 9,835 positive results anchored by 4152 unigenes that contributed to 1305 contigs and 1624 singletons. Among the 1305 contigs, 305 have not been previously anchored by PCR. Therefore, 1489 (78.8%) of 1893 contigs are anchored with molecular markers. These results are being integrated with BAC fingerprints to assemble the BAC contigs. Ultimately, these efforts will lead to an integrated physical and genetic map resource.</p> <p>Conclusion</p> <p>We demonstrated that the six-dimensional soybean BAC pools can be efficiently used to anchor markers to soybean BACs despite the complexity of the soybean genome. In addition to anchoring markers, the 6-D pooling method was also effective for targeting BAC clones for investigating gene families and duplicated regions in the genome, as well as for extending physical map contigs.</p

Constructing proteome and metabolome maps for genetic improvement of energy-related traits in soybean [abstract]

Only abstract of poster available.Track V: BiomassAlthough the genetic blueprint of soybean is represented by the genome, its phenotype is a product of that blueprint manifested as the production of proteins and metabolites influencing growth characteristics, stress responses, seed composition, and yield. We are using various tools of genomics and molecular breeding with an aim towards development of value-added soybeans that will help United States farmers to maintain their competitiveness and expand utilization of soybean crops (e.g. functional foods, industrial uses, biodiesel, etc). Profiling soybean gene products will lay the foundation for a systems biology approach to key processes such as seed development, which will lead to the genetic improvement of yield and seed composition. Being one of the major bio-energy crops, building a comprehensive map of proteins and metabolites for soybean will help make connections between regulatory or metabolic pathways not previously characterized. Another major benefit from these studies is the discovery of energy related traits including plant productivity and seed compositional traits for the genetic improvement of soybean. It is well known that environmental cues influence developmental phenotypes in plants. Different biotic stresses such as fungal diseases and abiotic stresses, such as drought and flooding, also elicit phenotypic responses from the genome. Thus, by studying the gene products, a direct correlation between response and specific peptides/metabolites can be made. This will lead to crop improvement either through breeding or transgenic efforts. Major objectives of this study are: a) to identify key soybean seed, leaf, and root proteins involved in development and biotic and abiotic stress responses; b) to establish a comprehensive set of chemical standards for soybean metabolites moving toward construction of a metabolome map with a focus on seed and drought effects on seed development and, c) to compile a database linking proteomic and metabolite information and associate this information to value-added soybean traits and markers for assisted breeding. We are utilizing GC/MS, LC/MS, and NMR approaches to identify key molecules for further characterization

Natural genetic variation for root traits among diversity lines of maize (Zea Mays L.)

Maize (Z. mays L.) is the third most important food grain for humankind after rice and wheat. Maize is mostly grown under rain-fed conditions and among the cereals, it is the second most susceptible to drought next to rice. Constitutive variation for root traits is an important adaptation under drought prone conditions. The objective of this study is to screen the twenty five diverse parental lines used in the maize nested association mapping panel along with the common parental line, B73, for constitutive root traits (including rooting depth and root biomass) and shoot traits. All the lines were grown with five replications in 72 cm deep pots containing a turface:sand mixture (2:1 v/v) for 30 days under well-watered conditions in a temperature and humidity controlled green house. Significant variation existed among the diverse lines for root length, root biomass, shoot length, and leaf area. The average root length ranged from 17.5 to 106 cm. The genotypes with a deep root system also recorded greater root biomass and leaf area. The natural genetic variation exhibited by these lines could be exploited to identify potential quantitative trait loci controlling root architecture. Using the nested association mapping populations that were developed from these diverse lines, would allow for in-depth analysis and fine-mapping of prospective candidate genes for root architecture in maize

Soybean transcription factor ORFeome associated with drought resistance: a valuable resource to accelerate research on abiotic stress resistance

Tissue/organ expression pattern of TF genes. The expression of soybean TF-ORFeome candidates in seven soybean organs including root, root tip, leaf, shoot apical meristem (SAM), nodule, flower and green pod were based on published RNA-Seq data [26]. The color scale indicates the degree of gene expression levels (yellow, low expression level; red, high expression level)