Project description:Alfalfa, [Medicago sativa (L.) sativa], a widely-grown perennial forage has potential for development as a cellulosic ethanol feedstock. The application of genomic approaches would advance development of alfalfa as a cellulosic feedstock. However, the genomics of alfalfa, a non-model species, is still in its infancy. The recent advent of RNA-Seq, a massively parallel sequencing method for transcriptome analysis, provides an opportunity to expand the identification of alfalfa genes and polymorphisms, and conduct in-depth transcript profiling. Cell walls in stems of alfalfa genotype 708 have higher cellulose and lower lignin concentrations compared to cell walls in stems of genotype 773. Using the Illumina GA-II platform, a total of 198,861,304 expression sequence tags (ESTs, 76 bp in length) were generated from cDNA libraries derived from elongating stem (ES) and post-elongation stem (PES) internodes of 708 and 773. These ESTs were de novo assembled into 132,153 unique sequences. By combining the de novo assembled ESTs (132,153 sequences) with our previously identified EST sequences (341,984 sequences, unpublished data), and the ESTs available from GenBank (12,371 sequences), we built the first Alfalfa Gene Index (MSGI 1.0). MSGI 1.0 contains 124,025 unique sequences including 22,729 tentative consensus sequences (TCs), 22,315 singletons and 78,981 pseudo-singletons. We identified a total of 1, 294 simple sequence repeats (SSR) among the sequences in MSGI 1.0. In addition, a total of 10,826 single nucleotide polymorphisms (SNPs) were predicted between the two genotypes. Transcript profiling of stem internodes of genotypes 708 and 773 was conducted by quantifying the number of Illumina EST reads that were mapped to sequences in MSGI 1.0. We identified numerous candidate genes that may play a role in stem development as well as candidate genes that may contribute to the differences in cell wall composition in stems of the two genotypes. Our results demonstrate that RNA-Seq can be successfully used for gene identification, polymorphism detection and transcript profiling in alfalfa, a non-model, allogamous, autotetraploid species. The alfalfa gene index (MSGI 1.0) assembled in this study, and the SNPs, SSRs and candidate genes identified can be used to improve alfalfa as a cellulosic feedstock. Examination of 2 different tissue types at different developmental stages (Elongating vs. post-elongation stem internodes) in two alfalfa genotypes (708 and 773) with divergent cell wall composition in stems.

Project description:Alfalfa, [Medicago sativa (L.) sativa], a widely-grown perennial forage has potential for development as a cellulosic ethanol feedstock. The application of genomic approaches would advance development of alfalfa as a cellulosic feedstock. However, the genomics of alfalfa, a non-model species, is still in its infancy. The recent advent of RNA-Seq, a massively parallel sequencing method for transcriptome analysis, provides an opportunity to expand the identification of alfalfa genes and polymorphisms, and conduct in-depth transcript profiling. Cell walls in stems of alfalfa genotype 708 have higher cellulose and lower lignin concentrations compared to cell walls in stems of genotype 773. Using the Illumina GA-II platform, a total of 198,861,304 expression sequence tags (ESTs, 76 bp in length) were generated from cDNA libraries derived from elongating stem (ES) and post-elongation stem (PES) internodes of 708 and 773. These ESTs were de novo assembled into 132,153 unique sequences. By combining the de novo assembled ESTs (132,153 sequences) with our previously identified EST sequences (341,984 sequences, unpublished data), and the ESTs available from GenBank (12,371 sequences), we built the first Alfalfa Gene Index (MSGI 1.0). MSGI 1.0 contains 124,025 unique sequences including 22,729 tentative consensus sequences (TCs), 22,315 singletons and 78,981 pseudo-singletons. We identified a total of 1, 294 simple sequence repeats (SSR) among the sequences in MSGI 1.0. In addition, a total of 10,826 single nucleotide polymorphisms (SNPs) were predicted between the two genotypes. Transcript profiling of stem internodes of genotypes 708 and 773 was conducted by quantifying the number of Illumina EST reads that were mapped to sequences in MSGI 1.0. We identified numerous candidate genes that may play a role in stem development as well as candidate genes that may contribute to the differences in cell wall composition in stems of the two genotypes. Our results demonstrate that RNA-Seq can be successfully used for gene identification, polymorphism detection and transcript profiling in alfalfa, a non-model, allogamous, autotetraploid species. The alfalfa gene index (MSGI 1.0) assembled in this study, and the SNPs, SSRs and candidate genes identified can be used to improve alfalfa as a cellulosic feedstock.

Project description:Stem internodes of grasses function in mechanical support, transport, and, in some species are a major sink organ for carbon in the form of cell wall polymers. To establish the rice elongating internode as a model for secondary cell wall development, we conducted cell wall composition, proteomic and metabolomic analyses of the second rice internode at booting stage. We measured major secondary cell wall components along eight segments of an elongating internode. Cellulose, lignin, and xylose increase as a percentage of cell wall material from the younger to the older internode segments, indicating active cell wall synthesis. With the whole elongating internode, we measured peptides via liquid-chromatography mass spectrometry (LC-MS) following trypsin digestion of size fractionated proteins. This identified a total of 3249 protein groups with at least two unique peptides, including many glycosyltransferases, acyltransferases, glycohyrolases, cell wall-localized proteins, and protein kinases that have or may have functions in cell wall biosynthesis or remodeling. In addition, GO over-representation analysis and KO pathway analysis indicate many proteins involved in biosynthetic processes, especially the synthesis of secondary metabolites such as phenylpropanoids, flavonoids, and tepenoids. Therefore, we also used LC-MS to measure methanol-extracted secondary metabolites from the whole internode at the elongation stage and three post-elongation stages, and from leaf and root at the second post-elongation stage. The results indicate secondary metabolites in stems are distinct from those of roots and leaves, and show different profiles during stem development. This study fills a void of knowledge of proteomics and metabolomics data for grass stems, especially of rice, and provides baseline knowledge for more detailed studies of cell wall synthesis and other biological processes during internode development. This and future work is aimed at optimizing stem development and cell wall composition of grasses to improve agronomic properties and biofuel production.

Project description:Background:; Cowpea (Vigna unguiculata L. Walp) is an important food and fodder legume of the semiarid tropics and subtropics worldwide, especially in sub-Saharan Africa. High density genetic linkage maps are needed for marker assisted breeding but are not available for cowpea. A single feature polymorphism (SFP) is a microarray-based marker which can be used for high throughput genotyping and high density mapping. Results:; Here we report detection and validation of SFPs in cowpea using a readily available soybean (Glycine max) genome array. Robustified projection pursuit (RPP) was used for statistical analysis using RNA as a surrogate for DNA. Using a 15% outlying score cut-off, 1058 potential SFPs were enumerated between two parents of a recombinant inbred line (RIL) population segregating for several important traits including drought tolerance, Fusarium and brown blotch resistance, grain size and photoperiod sensitivity. Sequencing of 25 putative polymorphism-containing amplicons yielded a SFP probe set validation rate of 68%. Conclusions:; We conclude that the Affymetrix soybean genome array is a satisfactory platform for identification of some 1000âs of SFPs for cowpea. This study provides an example of extension of genomic resources from a well supported species to an orphan crop. Presumably, other legume systems are similarly tractable to SFP marker development using existing legume array resources. SUBMITTER_CITATION: Detection and validation of single feature polymorphisms in cowpea (Vigna unguiculata L. Walp) using a soybean genome array Sayan Das, Prasanna R. Bhat, Chinta Sudhakar, Jeffrey D. Ehlers, Steve Wanamaker, Philip A. Roberts, Xinping Cui, Timothy J. Close BMC Genomics 2008, 9:107 Experiment Overall Design: Expression data were generated by hybridizing cowpea cRNA to the soybean genome array. A statistical method called robustified projection pursuit (RPP) was used for Single Feature Polymorphism(SFP) analysis. Only the values from the PM probes were utilized. The use of RNA as a surrogate for genomic DNA eliminated interference from highly repetitive DNA as a technical impediment to SFP detection. An important aspect of the RPP method is that it first utilizes a probe set level analysis to identify SFP-containing probe sets and then chooses individual probes from within each SFP-containing probe set. The net result is the identification of probes that directly overlay polymorphic sequences. Experiment Overall Design: Separate comparisons were made between two genotypes (with two replicates each) for unstressed and drought stressed treatments, resulting in two SFP lists. In the context of SFPs, there is no necessity to have separate stress and control lists; in fact it would be simpler and less costly to have only one SFP list from highly complex RNA made by blending stressed and unstressed RNA. In our case, two separate lists were available as a consequence of another study not described here which compared gene expression patterns in stressed and control plants (data not shown). At 15% outlying score cut-off, we detected 488 SFP probes in stressed and 661 SFP probes in unstressed treatments. The union of these two lists contained 1058 SFP probes and the intersection contained 91. A total of 37 primer pairs targeting 37 putative SFP probe sets were initially tested, of which 25 yielded single amplicons of the expected sizes from both parents. These 25 amplicons targeted 14 probe sets selected from the intersection of the two SFP probe set lists and 11 from the remaining SFP probe sets. 9 of the 14 SFP probe sets (64%) from the intersection list were validated at the DNA sequence level and 8 of the other 11 (73%) were validated.

Project description:Seminal fluid contains some of the fastest evolving proteins currently known. These seminal fluid proteins (Sfps) play crucial roles in reproduction, such as supporting sperm function, and – particularly in insects – modifying female physiology and behaviour. Identification of Sfps in small animals is challenging, and often relies on samples taken from the female reproductive tract after mating. A key pitfall of this method is that it might miss Sfps that are of low abundance due to dilution in the female-derived sample or rapid processing in females. Here we present a new and complimentary method, which provides added sensitivity to Sfp identification. We applied label-free quantitative proteomics to Drosophila melanogaster male reproductive tissue – where Sfps are unprocessed, and highly abundant – and quantified Sfps before and immediately after mating, to infer those transferred during copulation. We also analysed female reproductive tracts immediately before and after copulation to confirm the presence and abundance of known and candidate Sfps, where possible. Results were cross-referenced with transcriptomic and sequence databases to improve confidence in Sfp detection. Our data was consistent with 124 previously reported Sfps. We found 8 high-confidence novel candidate Sfps, which were both depleted in mated versus unmated males and identified within the reproductive tract of mated but not virgin females. We also identified 31 more candidates that are likely Sfps based on their abundance, known expression and predicted characteristics, and revealed that four proteins previously identified as Sfps are at best minor contributors to the ejaculate. The estimated copy numbers for our candidate Sfps were lower than for previously identified Sfps, supporting the idea that our technique provides a deeper analysis of the Sfp proteome than previous studies. Our results demonstrate a novel, high-sensitivity approach to the analysis of seminal fluid proteomes, whose application will further our understanding of reproductive biology.

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Transcription profiling of Medicago stem internodes

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