De Novo Transcriptome Analysis of Allium cepa L. (Onion) Bulb to Identify Allergens and Epitopes.

Allium cepa (onion) is a diploid plant with one of the largest nuclear genomes among all diploids. Onion is an example of an under-researched crop which has a complex heterozygous genome. There are no allergenic proteins and genomic data available for onions. This study was conducted to establish a transcriptome catalogue of onion bulb that will enable us to study onion related genes involved in medicinal use and allergies. Transcriptome dataset generated from onion bulb using the Illumina HiSeq 2000 technology showed a total of 99,074,309 high quality raw reads (~20 Gb). Based on sequence homology onion genes were categorized into 49 different functional groups. Most of the genes however, were classified under 'unknown' in all three gene ontology categories. Of the categorized genes, 61.2% showed metabolic functions followed by cellular components such as binding, cellular processes; catalytic activity and cell part. With BLASTx top hit analysis, a total of 2,511 homologous allergenic sequences were found, which had 37-100% similarity with 46 different types of allergens existing in the database. From the 46 contigs or allergens, 521 B-cell linear epitopes were identified using BepiPred linear epitope prediction tool. This is the first comprehensive insight into the transcriptome of onion bulb tissue using the NGS technology, which can be used to map IgE epitopes and prediction of structures and functions of various proteins.

Evaluation of Bar, Barnase, and Barstar recombinant proteins expressed in genetically engineered Brassica juncea (Indian mustard) for potential risks of food allergy using bioinformatics and literature searches.

The potential allergenicity of Bar, Barnase, and Barstar recombinant proteins expressed in genetically engineered mustard for pollination control in plant breeding was evaluated for regulatory review. To evaluate the potential allergenicity of the Bar, Barnase and Barstar proteins amino acid sequence comparisons were made to those of known and putative allergens, and search for published evidence to the sources of the genes using the AllergenOnline.org database. Initial comparisons in 2012 were performed with version 12 by methods recommended by the Codex Alimentarius Commission and the Indian Council of Medical Research, Government of India. Searches were repeated with version 15 in 2015. A literature search was performed using PubMed to identify reports of allergy associated with the sources of the three transgenes. Potential open reading frames at the DNA insertion site were evaluated for matches to allergens. No significant sequence identity matches were identified with Bar, Barnase or Barstar proteins or potential fusion peptides at the genomic-insert junctions compared to known allergens. No references were identified that associated the sources of the genes with allergy. Based on these results we conclude that the Bar, Barnase and Barstar proteins are unlikely to present any significant risk of food allergy to consumers.

Ellagic acid inhibits VEGF/VEGFR2, PI3K/Akt and MAPK signaling cascades in the hamster cheek pouch carcinogenesis model.

BACKGROUND: Blocking vascular endothelial growth factor (VEGF) mediated tumor angiogenesis by phytochemicals has emerged as an attractive strategy for cancer prevention and therapy. METHODS: We investigated the anti-angiogenic effects of ellagic acid in a hamster model of oral oncogenesis by examining the transcript and protein expression of hypoxia-inducible factor-1alpha (HIF-1α), VEGF, VEGFR2, and the members of the PI3K/Akt and MAPK signaling cascades. Molecular docking studies and cell culture experiments with the endothelial cell line ECV304 were performed to delineate the mechanism by which ellagic acid regulates VEGF signaling. RESULTS: We found that ellagic acid significantly inhibits HIF-1α-induced VEGF/VEGFR2 signalling in the hamster buccal pouch by abrogating PI3K/Akt and MAPK signaling via downregulation of PI3K, PDK-1, p-Akt(ser473), mTOR, p-ERK, and p-JNK. Ellagic acid was also found to reduce the expression of histone deacetylases that could inhibit neovascularization. Analysis of the mechanism revealed that ellagic acid inhibits hypoxia-induced angiogenesis via suppression of HDAC-6 in ECV304 cells. Furthermore, knockdown of endogenous HDAC6 via small interfering RNA abrogated hypoxia-induced expression of HIF-1α and VEGF and blocked Akt activation. Molecular docking studies confirmed interaction of ellagic acid with upstream kinases that regulate angiogenic signaling. CONCLUSIONS: Taken together, these findings demonstrate that the anti-angiogenic activity of ellagic acid may be mediated by abrogation of hypoxia driven PI3K/Akt/mTOR, MAPK and VEGF/VEGFR2 signaling pathways involving suppression of HDAC6 and HIF-1α responses. GENERAL SIGNIFICANCE: Ellagic acid offers promise as a lead compound for anticancer therapeutics by virtue of its ability to inhibit key oncogenic signaling cascades and HDACs.

Identification of LOGP values and Electronegativities as structural insights to model inhibitory activity of HIV-1 capsid inhibitors - a SVM and MLR aided QSAR studies.

Linear and non-linear QSAR studies have been performed in present investigation with multiple linear regressions (MLR) analysis and Support vector machine (SVM) using different kernels. Three relevant descriptors out of fifteen descriptors calculated are identified as LOGP values, G3e and Rte+. Their relationship with biological activity IC50 have provided structural insights in interpretation and serializing the results into a pragmatic approachable technique. QSAR models obtained show statistical fitness and good predictability. SVM using Gaussian kernel function was found more efficient in prediction of IC50 of training set of thirty small molecules HIV-1 capsid inhibitors. Y-scrambling, PRESS and test set were used as validation parameters. SVM was found superior to training set prediction and internal validations and found inferior to external test set (11 molecules) predictions. Wherein MLR analysis it was vice-versa. Mechanistic interpretation of selected descriptors from both the models actuates further research.

High-throughput phenotyping of plant populations using a personal digital assistant.

During many biological experiments voluminous data is acquired, which can be best collected with -portable data acquisition devices and later analyzed with a personal computer (PC). Public domain software catering to data acquisition and analysis is currently limited. The necessity of phenotyping large plant populations led to the development of the application "PHENOME" to manage the data. PHENOME allows acquisition of phenotypic data using a personal digital assistant (PDA) with a built-in barcode scanner. The acquired data can be exported to a customized database on a PC for further analysis and cataloging. PHENOME can be used for a variety of applications, for example high-throughput phenotyping of a mutagenized or mapping population, or phenotyping of several individuals in one or more ecological niches.