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Background: Glioblastoma (GBM) is the most common type of primary malignant brain tumor. Molecular hydrogen has been considered a preventive and therapeutic medical gas in many diseases including cancer. In our study, we sought to assess the potential role of molecular hydrogen on GBM., Methods: The in vivo studies were performed using a rat orthotopic glioma model and a mouse subcutaneous xenograft model. Animals inhaled hydrogen gas (67%) 1 h two times per day. MR imaging studies were performed to determine the tumor volume. Immunohistochemistry (IHC), immunofluorescence staining, and flow cytometry analysis were conducted to determine the expression of surface markers. Sphere formation assay was performed to assess the cancer stem cell self-renewal capacity. Assays for cell migration, invasion, and colony formation were conducted., Results: The in vivo study showed that hydrogen inhalation could effectively suppress GBM tumor growth and prolong the survival of mice with GBM. IHC and immunofluorescence staining demonstrated that hydrogen treatment markedly downregulated the expression of markers involved in stemness (CD133, Nestin), proliferation (ki67), and angiogenesis (CD34) and also upregulated GFAP expression, a marker of differentiation. Similar results were obtained in the in vitro studies. The sphere-forming ability of glioma cells was also suppressed by hydrogen treatment. Moreover, hydrogen treatment also suppressed the migration, invasion, and colony-forming ability of glioma cells., Conclusions: Together, these results indicated that molecular hydrogen may serve as a potential anti-tumor agent in the treatment of GBM.

Background: Alfalfa (Medicago sativa L.) is the primary forage legume crop species in the United States and plays essential economic and ecological roles in agricultural systems across the country. Modern alfalfa is the result of hybridization between tetraploid M. sativa ssp. sativa and M. sativa ssp. falcata. Due to its large and complex genome, there are few genomic resources available for alfalfa improvement., Results: A de novo transcriptome assembly from two alfalfa subspecies, M. sativa ssp. sativa (B47) and M. sativa ssp. falcata (F56) was developed using Illumina RNA-seq technology. Transcripts from roots, nitrogen-fixing root nodules, leaves, flowers, elongating stem internodes, and post-elongation stem internodes were assembled into the Medicago sativa Gene Index 1.2 (MSGI 1.2) representing 112,626 unique transcript sequences. Nodule-specific and transcripts involved in cell wall biosynthesis were identified. Statistical analyses identified 20,447 transcripts differentially expressed between the two subspecies. Pair-wise comparisons of each tissue combination identified 58,932 sequences differentially expressed in B47 and 69,143 sequences differentially expressed in F56. Comparing transcript abundance in floral tissues of B47 and F56 identified expression differences in sequences involved in anthocyanin and carotenoid synthesis, which determine flower pigmentation. Single nucleotide polymorphisms (SNPs) unique to each M. sativa subspecies (110,241) were identified., Conclusions: The Medicago sativa Gene Index 1.2 increases the expressed sequence data available for alfalfa by ninefold and can be expanded as additional experiments are performed. The MSGI 1.2 transcriptome sequences, annotations, expression profiles, and SNPs were assembled into the Alfalfa Gene Index and Expression Database (AGED) at http://plantgrn.noble.org/AGED/ , a publicly available genomic resource for alfalfa improvement and legume research.

Background: Common bean (Phaseolus vulgaris) is grown throughout the world and comprises roughly 50% of the grain legumes consumed worldwide. Despite this, genetic resources for common beans have been lacking. Next generation sequencing, has facilitated our investigation of the gene expression profiles associated with biologically important traits in common bean. An increased understanding of gene expression in common bean will improve our understanding of gene expression patterns in other legume species., Results: Combining recently developed genomic resources for Phaseolus vulgaris, including predicted gene calls, with RNA-Seq technology, we measured the gene expression patterns from 24 samples collected from seven tissues at developmentally important stages and from three nitrogen treatments. Gene expression patterns throughout the plant were analyzed to better understand changes due to nodulation, seed development, and nitrogen utilization. We have identified 11,010 genes differentially expressed with a fold change ≥ 2 and a P-value < 0.05 between different tissues at the same time point, 15,752 genes differentially expressed within a tissue due to changes in development, and 2,315 genes expressed only in a single tissue. These analyses identified 2,970 genes with expression patterns that appear to be directly dependent on the source of available nitrogen. Finally, we have assembled this data in a publicly available database, The Phaseolus vulgaris Gene Expression Atlas (Pv GEA), http://plantgrn.noble.org/PvGEA/ . Using the website, researchers can query gene expression profiles of their gene of interest, search for genes expressed in different tissues, or download the dataset in a tabular form., Conclusions: These data provide the basis for a gene expression atlas, which will facilitate functional genomic studies in common bean. Analysis of this dataset has identified genes important in regulating seed composition and has increased our understanding of nodulation and impact of the nitrogen source on assimilation and distribution throughout the plant.

Background: Extracted ion chromatogram (EIC) extraction and chromatographic peak detection are two important processing procedures in liquid chromatography/mass spectrometry (LC/MS)-based metabolomics data analysis. Most commonly, the LC/MS technique employs electrospray ionization as the ionization method. The EICs from LC/MS data are often noisy and contain high background signals. Furthermore, the chromatographic peak quality varies with respect to its location in the chromatogram and most peaks have zigzag shapes. Therefore, there is a critical need to develop effective metrics for quality evaluation of EICs and chromatographic peaks in LC/MS based metabolomics data analysis., Results: We investigated a comprehensive set of potential quality evaluation metrics for extracted EICs and detected chromatographic peaks. Specifically, for EIC quality evaluation, we analyzed the mass chromatographic quality index (MCQ index) and propose a novel quality evaluation metric, the EIC-related global zigzag index, which is based on an EIC's first order derivatives. For chromatographic peak quality evaluation, we analyzed and compared six metrics: sharpness, Gaussian similarity, signal-to-noise ratio, peak significance level, triangle peak area similarity ratio and the local peak-related local zigzag index., Conclusions: Although the MCQ index is suited for selecting and aligning analyte components, it cannot fairly evaluate EICs with high background signals or those containing only a single peak. Our proposed EIC related global zigzag index is robust enough to evaluate EIC qualities in both scenarios. Of the six peak quality evaluation metrics, the sharpness, peak significance level, and zigzag index outperform the others due to the zigzag nature of LC/MS chromatographic peaks. Furthermore, using several peak quality metrics in combination is more efficient than individual metrics in peak quality evaluation.

Background: Plants regulate intrinsic gene expression through transcription factors (TFs), transcriptional regulators (TRs), chromatin regulators (CRs), and the basal transcription machinery. An understanding of plant gene regulatory mechanisms at a systems level requires the identification of these regulatory elements on a genomic scale., Results: Here, we present PlantTFcat, a high-performance web-based analysis tool that is designed to identify and categorize plant TF/TR/CR genes from genome-scale protein and nucleic acid sequences by systematically analyzing InterProScan domain patterns in protein sequences. The comprehensive prediction logics that are included in PlantTFcat are based on relationships between gene families and conserved domains from 108 published plant TF/TR/CR families. These prediction logics effectively distinguish TF/TR/CR families with common conserved domains. Our systematic performance evaluations indicate that PlantTFcat annotates known TF/TR/CR families with high coverage and sensitivity., Conclusions: PlantTFcat provides an analysis tool to identify and categorize plant TF/TR/CR genes on a genomic scale. PlantTFcat is freely available to the public at http://plantgrn.noble.org/PlantTFcat/.

Background: Single nucleotide polymorphisms (SNPs) are the most common type of sequence variation among plants and are often functionally important. We describe the use of 454 technology and high resolution melting analysis (HRM) for high throughput SNP discovery in tetraploid alfalfa (Medicago sativa L.), a species with high economic value but limited genomic resources., Results: The alfalfa genotypes selected from M. sativa subsp. sativa var. 'Chilean' and M. sativa subsp. falcata var. 'Wisfal', which differ in water stress sensitivity, were used to prepare cDNA from tissue of clonally-propagated plants grown under either well-watered or water-stressed conditions, and then pooled for 454 sequencing. Based on 125.2 Mb of raw sequence, a total of 54,216 unique sequences were obtained including 24,144 tentative consensus (TCs) sequences and 30,072 singletons, ranging from 100 bp to 6,662 bp in length, with an average length of 541 bp. We identified 40,661 candidate SNPs distributed throughout the genome. A sample of candidate SNPs were evaluated and validated using high resolution melting (HRM) analysis. A total of 3,491 TCs harboring 20,270 candidate SNPs were located on the M. truncatula (MT 3.5.1) chromosomes. Gene Ontology assignments indicate that sequences obtained cover a broad range of GO categories., Conclusions: We describe an efficient method to identify thousands of SNPs distributed throughout the alfalfa genome covering a broad range of GO categories. Validated SNPs represent valuable molecular marker resources that can be used to enhance marker density in linkage maps, identify potential factors involved in heterosis and genetic variation, and as tools for association mapping and genomic selection in alfalfa.

Background: Legumes (Leguminosae or Fabaceae) play a major role in agriculture. Transcriptomics studies in the model legume species, Medicago truncatula, are instrumental in helping to formulate hypotheses about the role of legume genes. With the rapid growth of publically available Affymetrix GeneChip Medicago Genome Array GeneChip data from a great range of tissues, cell types, growth conditions, and stress treatments, the legume research community desires an effective bioinformatics system to aid efforts to interpret the Medicago genome through functional genomics. We developed the Medicago truncatula Gene Expression Atlas (MtGEA) web server for this purpose., Description: The Medicago truncatula Gene Expression Atlas (MtGEA) web server is a centralized platform for analyzing the Medicago transcriptome. Currently, the web server hosts gene expression data from 156 Affymetrix GeneChip(R) Medicago genome arrays in 64 different experiments, covering a broad range of developmental and environmental conditions. The server enables flexible, multifaceted analyses of transcript data and provides a range of additional information about genes, including different types of annotation and links to the genome sequence, which help users formulate hypotheses about gene function. Transcript data can be accessed using Affymetrix probe identification number, DNA sequence, gene name, functional description in natural language, GO and KEGG annotation terms, and InterPro domain number. Transcripts can also be discovered through co-expression or differential expression analysis. Flexible tools to select a subset of experiments and to visualize and compare expression profiles of multiple genes have been implemented. Data can be downloaded, in part or full, in a tabular form compatible with common analytical and visualization software. The web server will be updated on a regular basis to incorporate new gene expression data and genome annotation, and is accessible at: http://bioinfo.noble.org/gene-atlas/., Conclusions: The MtGEA web server has a well managed rich data set, and offers data retrieval and analysis tools provided in the web platform. It's proven to be a powerful resource for plant biologists to effectively and efficiently identify Medicago transcripts of interest from a multitude of aspects, formulate hypothesis about gene function, and overall interpret the Medicago genome from a systematic point of view.

Background: Membrane transporters play crucial roles in living cells. Experimental characterization of transporters is costly and time-consuming. Current computational methods for transporter characterization still require extensive curation efforts, especially for eukaryotic organisms. We developed a novel genome-scale transporter prediction and characterization system called TransportTP that combined homology-based and machine learning methods in a two-phase classification approach. First, traditional homology methods were employed to predict novel transporters based on sequence similarity to known classified proteins in the Transporter Classification Database (TCDB). Second, machine learning methods were used to integrate a variety of features to refine the initial predictions. A set of rules based on transporter features was developed by machine learning using well-curated proteomes as guides., Results: In a cross-validation using the yeast proteome for training and the proteomes of ten other organisms for testing, TransportTP achieved an equivalent recall and precision of 81.8%, based on TransportDB, a manually annotated transporter database. In an independent test using the Arabidopsis proteome for training and four recently sequenced plant proteomes for testing, it achieved a recall of 74.6% and a precision of 73.4%, according to our manual curation., Conclusions: TransportTP is the most effective tool for eukaryotic transporter characterization up to date.

Background: White lupin (Lupinus albus L.) roots efficiently take up and accumulate (heavy) metals, adapt to phosphate deficiency by forming cluster roots, and secrete antimicrobial prenylated isoflavones during development. Genomic and proteomic approaches were applied to identify candidate genes and proteins involved in antimicrobial defense and (heavy) metal uptake and translocation., Results: A cDNA library was constructed from roots of white lupin seedlings. Eight thousand clones were randomly sequenced and assembled into 2,455 unigenes, which were annotated based on homologous matches in the NCBInr protein database. A reference map of developing white lupin root proteins was established through 2-D gel electrophoresis and peptide mass fingerprinting. High quality peptide mass spectra were obtained for 170 proteins. Microsomal membrane proteins were separated by 1-D gel electrophoresis and identified by LC-MS/MS. A total of 74 proteins were putatively identified by the peptide mass fingerprinting and the LC-MS/MS methods. Genomic and proteomic analyses identified candidate genes and proteins encoding metal binding and/or transport proteins, transcription factors, ABC transporters and phenylpropanoid biosynthetic enzymes., Conclusion: The combined EST and protein datasets will facilitate the understanding of white lupin's response to biotic and abiotic stresses and its utility for phytoremediation. The root ESTs provided 82 perfect simple sequence repeat (SSR) markers with potential utility in breeding white lupin for enhanced agronomic traits.

Background: Allotetraploid white clover (Trifolium repens L.) is an important forage legume widely cultivated in most temperate regions. Only a small number of microsatellite markers are publicly available and can be utilized in white clover breeding programs. The objectives of this study were to develop an integrated approach for microsatellite development and to evaluate the approach for the development of new SSR markers for white clover., Results: Genomic libraries containing simple sequence repeat (SSR) sequences were constructed using a modified Fast Isolation by AFLP of Sequences COntaining repeats (FIASCO) procedure and phpSSRMiner was used to develop the microsatellite markers. SSR motifs were isolated using two biotin-labeled probes, (CA)17 and (ATG)12. The sequences of 6,816 clones were assembled into 1,698 contigs, 32% of which represented novel sequences based on BLASTN searches. Approximately 32%, 28%, and 16% of these SSRs contained hexa-, tri-, and di-nucleotide repeats, respectively. The most frequent motifs were the CA and ATG complementary repeats and the associated compound sequences. Primer pairs were designed for 859 SSR loci based on sequences from these genomic libraries and from GenBank white clover nucleotide sequences. A total of 191 SSR primers developed from the two libraries were tested for polymorphism in individual clones from the parental genotypes GA43 ('Durana'), 'SRVR' and six F1 progeny from a mapping population. Ninety two percent produced amplicons and 66% of these were polymorphic., Conclusion: The combined approach of identifying SSR-enriched fragments by FIASCO coupled with the primer design and in silico amplification using phpSSRMiner represents an efficient and low cost pipeline for the large-scale development of microsatellite markers in plants.The approach described here could be readily adapted and utilized in other non-related species with none or limited genomic resources.

Background: Guar, Cyamopsis tetragonoloba (L.) Taub, is a member of the Leguminosae (Fabaceae) family and is economically the most important of the four species in the genus. The endosperm of guar seed is a rich source of mucilage or gum, which forms a viscous gel in cold water, and is used as an emulsifier, thickener and stabilizer in a wide range of foods and industrial applications. Guar gum is a galactomannan, consisting of a linear (1-->4)-beta-linked D-mannan backbone with single-unit, (1-->6)-linked, alpha-D-galactopyranosyl side chains. To better understand regulation of guar seed development and galactomannan metabolism we created cDNA libraries and a resulting EST dataset from different developmental stages of guar seeds., Results: A database of 16,476 guar seed ESTs was constructed, with 8,163 and 8,313 ESTs derived from cDNA libraries I and II, respectively. Library I was constructed from seeds at an early developmental stage (15-25 days after flowering, DAF), and library II from seeds at 30-40 DAF. Quite different sets of genes were represented in these two libraries. Approximately 27% of the clones were not similar to known sequences, suggesting that these ESTs represent novel genes or may represent non-coding RNA. The high flux of energy into carbohydrate and storage protein synthesis in guar seeds was reflected by a high representation of genes annotated as involved in signal transduction, carbohydrate metabolism, chaperone and proteolytic processes, and translation and ribosome structure. Guar unigenes involved in galactomannan metabolism were identified. Among the seed storage proteins, the most abundant contig represented a conglutin accounting for 3.7% of the total ESTs from both libraries., Conclusion: The present EST collection and its annotation provide a resource for understanding guar seed biology and galactomannan metabolism.

Background: The economic and biological implications of plant invasion are overwhelming; however, the processes by which plants become successful invaders are not well understood. Limited genetic resources are available for most invasive and weedy species, making it difficult to study molecular and genetic aspects that may be associated with invasion., Results: As an initial step towards understanding the molecular mechanisms by which plants become invasive, we have generated a normalized Expressed Sequence Tag (EST) library comprising seven invasive populations of Centaurea maculosa, an invasive aster in North America. Seventy-seven percent of the 4423 unique transcripts showed significant similarity to existing proteins in the NCBI database and could be grouped based on gene ontology assignments., Conclusion: The C. maculosa EST library represents an initial step towards looking at gene-specific expression in this species, and will pave the way for creation of other resources such as microarray chips that can help provide a view of global gene expression in invasive C. maculosa and its native counterparts. To our knowledge, this is the first published set of ESTs derived from an invasive weed that will be targeted to study invasive behavior. Understanding the genetic basis of evolution for increased invasiveness in exotic plants is critical to understanding the mechanisms through which exotic invasions occur.