@article {753, title = {Metabolomics analysis reveals both plant variety and choice of hormone treatment modulate vinca alkaloid production in Catharanthus roseus}, journal = {Plant Direct}, volume = {4}, year = {2020}, month = {09/2020}, abstract = {

The medicinal plant\ Catharanthus roseus\ produces numerous secondary metabolites of interest for the treatment of many diseases \– most notably for the terpene indole alkaloid (TIA) vinblastine, which is used in the treatment of leukemia and Hodgkin\&$\#$39;s lymphoma. Historically, methyl jasmonate (MeJA) has been used to induce TIA production, but in the past, this has only been investigated in whole seedlings, cell culture, or hairy root culture. This study examines the effects of the phytohormones MeJA and ethylene on the induction of TIA biosynthesis and accumulation in the shoots and roots of 8-day-old seedlings of two varieties of\ C. roseus. Using LCMS and RT-qPCR, we demonstrate the importance of variety selection, as we observe markedly different induction patterns of important TIA precursor compounds. Additionally, both phytohormone choice and concentration have significant effects on TIA biosynthesis. Finally, our study suggests that several early-induction pathway steps as well as pathway-specific genes are likely to be transcriptionally regulated. Our findings highlight the need for a complete set of\&$\#$39;omics resources in commonly used\ C. roseus\ varieties and the need for caution when extrapolating results from one cultivar to another.

}, url = {https://onlinelibrary.wiley.com/doi/10.1002/pld3.267}, author = {Valerie N. Fraser and Benjamin Philmus and Molly Megraw} } @article {325, title = {MicroRNA promoter analysis.}, journal = {Methods Mol Biol}, volume = {592}, year = {2010}, month = {2010}, pages = {149-61}, abstract = {

In this chapter, we present a brief overview of current knowledge about the promoters of plant microRNAs (miRNAs), and provide a step-by-step guide for predicting plant miRNA promoter elements using known transcription factor binding motifs. The approach to promoter element prediction is based on a carefully constructed collection of Positional Weight Matrices (PWMs) for known transcription factors (TFs) in Arabidopsis. A key concept of the method is to use scoring thresholds for potential binding sites that are appropriate to each individual transcription factor. While the procedure can be applied to search for Transcription Factor Binding Sites (TFBSs) in any pol-II promoter region, it is particularly practical for the case of plant miRNA promoters where upstream sequence regions and binding sites are not readily available in existing databases. The majority of the material described in this chapter is available for download at http://microrna.gr.

[Link to Tools and Supplementary Materials]

}, keywords = {MicroRNAs, Promoter Regions, Genetic, Transcription Factors}, issn = {1940-6029}, doi = {10.1007/978-1-60327-005-2_11}, author = {Megraw, Molly and Hatzigeorgiou, Artemis G} } @article {324, title = {miRGen 2.0: a database of microRNA genomic information and regulation.}, journal = {Nucleic Acids Res}, volume = {38}, year = {2010}, month = {2010 Jan}, pages = {D137-41}, abstract = {

MicroRNAs are small, non-protein coding RNA molecules known to regulate the expression of genes by binding to the 3\&$\#$39;UTR region of mRNAs. MicroRNAs are produced from longer transcripts which can code for more than one mature miRNAs. miRGen 2.0 is a database that aims to provide comprehensive information about the position of human and mouse microRNA coding transcripts and their regulation by transcription factors, including a unique compilation of both predicted and experimentally supported data. Expression profiles of microRNAs in several tissues and cell lines, single nucleotide polymorphism locations, microRNA target prediction on protein coding genes and mapping of miRNA targets of co-regulated miRNAs on biological pathways are also integrated into the database and user interface. The miRGen database will be continuously maintained and freely available at http://www.microrna.gr/mirgen/.

}, keywords = {3{\textquoteright} Untranslated Regions, Algorithms, Animals, Cell Line, Tumor, Computational Biology, Databases, Genetic, Databases, Nucleic Acid, Humans, Information Storage and Retrieval, Internet, Mice, MicroRNAs, Polymorphism, Single Nucleotide, Software, Transcription Factors}, issn = {1362-4962}, doi = {10.1093/nar/gkp888}, author = {Alexiou, Panagiotis and Vergoulis, Thanasis and Gleditzsch, Martin and Prekas, George and Dalamagas, Theodore and Megraw, Molly and Grosse, Ivo and Sellis, Timos and Hatzigeorgiou, Artemis G} } @article {329, title = {miRGen: a database for the study of animal microRNA genomic organization and function.}, journal = {Nucleic Acids Res}, volume = {35}, year = {2007}, month = {2007 Jan}, pages = {D149-55}, abstract = {

miRGen is an integrated database of (i) positional relationships between animal miRNAs and genomic annotation sets and (ii) animal miRNA targets according to combinations of widely used target prediction programs. A major goal of the database is the study of the relationship between miRNA genomic organization and miRNA function. This is made possible by three integrated and user friendly interfaces. The Genomics interface allows the user to explore where whole-genome collections of miRNAs are located with respect to UCSC genome browser annotation sets such as Known Genes, Refseq Genes, Genscan predicted genes, CpG islands and pseudogenes. These miRNAs are connected through the Targets interface to their experimentally supported target genes from TarBase, as well as computationally predicted target genes from optimized intersections and unions of several widely used mammalian target prediction programs. Finally, the Clusters interface provides predicted miRNA clusters at any given inter-miRNA distance and provides specific functional information on the targets of miRNAs within each cluster. All of these unique features of miRGen are designed to facilitate investigations into miRNA genomic organization, co-transcription and targeting. miRGen can be freely accessed at http://www.diana.pcbi.upenn.edu/miRGen.

}, keywords = {Animals, Data Interpretation, Statistical, Databases, Nucleic Acid, Genomics, Humans, Internet, Mice, MicroRNAs, Rats, User-Computer Interface}, issn = {1362-4962}, doi = {10.1093/nar/gkl904}, author = {Megraw, Molly and Sethupathy, Praveen and Corda, Benoit and Hatzigeorgiou, Artemis G} } @article {331, title = {MicroRNA promoter element discovery in Arabidopsis.}, journal = {RNA}, volume = {12}, year = {2006}, month = {2006 Sep}, pages = {1612-9}, abstract = {

In this study we present a method of identifying Arabidopsis miRNA promoter elements using known transcription factor binding motifs. We provide a comparative analysis of the representation of these elements in miRNA promoters, protein-coding gene promoters, and random genomic sequences. We report five transcription factor (TF) binding motifs that show evidence of overrepresentation in miRNA promoter regions relative to the promoter regions of protein-coding genes. This investigation is based on the analysis of 800-nucleotide regions upstream of 63 experimentally verified Transcription Start Sites (TSS) for miRNA primary transcripts in Arabidopsis. While the TATA-box binding motif was also previously reported by Xie and colleagues, the transcription factors AtMYC2, ARF, SORLREP3, and LFY are identified for the first time as overrepresented binding motifs in miRNA promoters.

}, keywords = {Arabidopsis, Base Sequence, Binding Sites, Databases, Genetic, Feedback, Physiological, Genes, Plant, MicroRNAs, Promoter Regions, Genetic, TATA Box, Transcription Factors, Transcription Initiation Site}, issn = {1355-8382}, doi = {10.1261/rna.130506}, author = {Megraw, Molly and Baev, Vesselin and Rusinov, Ventsislav and Jensen, Shane T and Kalantidis, Kriton and Hatzigeorgiou, Artemis G} } @article {332, title = {microRNAs exhibit high frequency genomic alterations in human cancer.}, journal = {Proc Natl Acad Sci U S A}, volume = {103}, year = {2006}, month = {2006 Jun 13}, pages = {9136-41}, abstract = {

MicroRNAs (miRNAs) are endogenous noncoding RNAs, which negatively regulate gene expression. To determine genomewide miRNA DNA copy number abnormalities in cancer, 283 known human miRNA genes were analyzed by high-resolution array-based comparative genomic hybridization in 227 human ovarian cancer, breast cancer, and melanoma specimens. A high proportion of genomic loci containing miRNA genes exhibited DNA copy number alterations in ovarian cancer (37.1\%), breast cancer (72.8\%), and melanoma (85.9\%), where copy number alterations observed in \>15\% tumors were considered significant for each miRNA gene. We identified 41 miRNA genes with gene copy number changes that were shared among the three cancer types (26 with gains and 15 with losses) as well as miRNA genes with copy number changes that were unique to each tumor type. Importantly, we show that miRNA copy changes correlate with miRNA expression. Finally, we identified high frequency copy number abnormalities of Dicer1, Argonaute2, and other miRNA-associated genes in breast and ovarian cancer as well as melanoma. These findings support the notion that copy number alterations of miRNAs and their regulatory genes are highly prevalent in cancer and may account partly for the frequent miRNA gene deregulation reported in several tumor types.

}, keywords = {Breast Neoplasms, Female, Gene Dosage, Gene Expression Profiling, Humans, MicroRNAs, Neoplasms, Nucleic Acid Hybridization, Oligonucleotide Array Sequence Analysis, Ovarian Neoplasms, Statistics as Topic}, issn = {0027-8424}, doi = {10.1073/pnas.0508889103}, author = {Zhang, Lin and Huang, Jia and Yang, Nuo and Greshock, Joel and Megraw, Molly S and Giannakakis, Antonis and Liang, Shun and Naylor, Tara L and Barchetti, Andrea and Ward, Michelle R and Yao, George and Medina, Angelica and O{\textquoteright}brien-Jenkins, Ann and Katsaros, Dionyssios and Hatzigeorgiou, Artemis and Gimotty, Phyllis A and Weber, Barbara L and Coukos, George} }