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Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is an accurate method for quantifying gene expression levels. Choosing appropriate reference genes to normalize the data is essential for reducing errors. Gelsemium elegans is a highly poisonous but important medicinal plant used for analgesic and anti-swelling purposes. Gelsenicine is one of the vital active ingredients, and its biosynthesis pathway remains to be determined. In this study, G. elegans leaf tissue with and without the application of one of four hormones (SA, MeJA, ETH, and ABA) known to affect gelsenicine synthesis, was analyzed using ten candidate reference genes. The gene stability was evaluated using GeNorm, NormFinder, BestKeeper, ∆CT, and RefFinder. The results showed that the optimal stable reference genes varied among the different treatments and that at least two reference genes were required for accurate quantification. The expression patterns of 15 genes related to the gelsenicine upstream biosynthesis pathway was determined by RT-qPCR using the relevant reference genes identified. Three genes 8-HGO, LAMT, and STR, were found to have a strong correlation with the amount of gelsenicine measured in the different samples. This research is the first study to examine the reference genes of G. elegans under different hormone treatments and will be useful for future molecular analyses of this medically important plant species. [ABSTRACT FROM AUTHOR]

Multiple myeloma (MM) is a cancer of antibody-making plasma cells. It frequently harbors alterations in DNA and chromosome copy numbers, and can be divided into two major subtypes, hyperdiploid (HMM) and non-hyperdiploid multiple myeloma (NHMM). The two subtypes have different survival prognosis, possibly due to different but converging paths to oncogenesis. Existing methods for identifying the two subtypes are fluorescence in situ hybridization (FISH) and copy number microarrays, with increased cost and sample requirements. We hypothesize that chromosome alterations have their imprint in gene expression through dosage effect. Using five MM expression datasets that have HMM status measured by FISH and copy number microarrays, we have developed and validated a K-nearest-neighbor method to classify MM into HMM and NHMM based on gene expression profiles. Classification accuracy for test datasets ranges from 0.83 to 0.88. This classification will enable researchers to study differences and commonalities of the two MM subtypes in disease biology and prognosis using expression datasets without need for additional subtype measurements. Our study also supports the advantages of using cancer specific characteristics in feature design and pooling multiple rounds of classification results to improve accuracy. We provide R source code and processed datasets at www.ChengLiLab.org/software. [ABSTRACT FROM AUTHOR]

Background & Objective: Genome-wide profiles of tumors obtained using functional genomics platforms are being deposited to the public repositories at an astronomical scale, as a result of focused efforts by individual laboratories and large projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium. Consequently, there is an urgent need for reliable tools that integrate and interpret these data in light of current knowledge and disseminate results to biomedical researchers in a user-friendly manner. We have built the canEvolve web portal to meet this need. Results: canEvolve query functionalities are designed to fulfill most frequent analysis needs of cancer researchers with a view to generate novel hypotheses. canEvolve stores gene, microRNA (miRNA) and protein expression profiles, copy number alterations for multiple cancer types, and protein-protein interaction information. canEvolve allows querying of results of primary analysis, integrative analysis and network analysis of oncogenomics data. The querying for primary analysis includes differential gene and miRNA expression as well as changes in gene copy number measured with SNP microarrays. canEvolve provides results of integrative analysis of gene expression profiles with copy number alterations and with miRNA profiles as well as generalized integrative analysis using gene set enrichment analysis. The network analysis capability includes storage and visualization of gene co-expression, inferred gene regulatory networks and protein-protein interaction information. Finally, canEvolve provides correlations between gene expression and clinical outcomes in terms of univariate survival analysis. Conclusion: At present canEvolve provides different types of information extracted from 90 cancer genomics studies comprising of more than 10,000 patients. The presence of multiple data types, novel integrative analysis for identifying regulators of oncogenesis, network analysis and ability to query gene lists/pathways are distinctive features of canEvolve. canEvolve will facilitate integrative and meta-analysis of oncogenomics datasets. Availability: The canEvolve web portal is available at http://www.canevolve.org/. [ABSTRACT FROM AUTHOR]

RNAs localizing to the outer cell surface have been recently identified in mammalian cells, including RNAs with glycan modifications known as glycoRNAs. However, the functional significance of cell surface RNAs and their production are poorly known. We report that cell surface RNAs are critical for neutrophil recruitment and that the mammalian homologs of the sid-1 RNA transporter are required for glycoRNA expression. Cell surface RNAs can be readily detected in murine neutrophils, the elimination of which substantially impairs neutrophil recruitment to inflammatory sites in vivo and reduces neutrophils' adhesion to and migration through endothelial cells. Neutrophil glycoRNAs are predominantly on cell surface, important for neutrophil-endothelial interactions, and can be recognized by P-selectin (Selp). Knockdown of the murine Sidt genes abolishes neutrophil glycoRNAs and functionally mimics the loss of cell surface RNAs. Our data demonstrate the biological importance of cell surface glycoRNAs and highlight a noncanonical dimension of RNA-mediated cellular functions. [Display omitted] • GlycoRNAs are present in neutrophils and primarily locate on the cell surface • Cell surface RNAs facilitate neutrophil recruitment to inflammatory sites in vivo • Neutrophil glycoRNAs bind to P-selectin on endothelial cells • Sidt genes are required for glycoRNA expression and cell surface RNA function RNAs present on the outer cell surface have been recently identified in mammalian cells, but the functional significance of these cell surface RNAs hasn't been clear. This study reveals roles of cell surface RNAs in mediating neutrophil recruitment to inflammatory sites in mice and the mechanisms regulating cell surface glycoRNA functions and levels. [ABSTRACT FROM AUTHOR]