@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 {750, title = {Arabidopsis bioinformatics resources: The current state, challenges, and priorities for the future}, journal = {Plant Direct}, volume = {3}, year = {2019}, month = {01/2019}, url = {https://onlinelibrary.wiley.com/doi/full/10.1002/pld3.109}, author = {Colleen Doherty and Joanna Friesner and Brian Gregory and Ann Loraine and Molly Megraw and Nicholas Provart and R Keith Slotkin and Chris Town and Sarah M Assmann and Michael Axtell and Tanya Berardini and Sixue Chen and Malia Gehan and Eva Huala and Pankaj Jaiswal and Stephen Larson and Song Li and Sean May and Todd Michael and Chris Pires and Chris Topp and Justin Walley and Eve Wurtele} } @article {749, title = {Identification of transcription factors from NF-Y, NAC, and SPL families responding to osmotic stress in multiple tomato varieties}, journal = {Plant Science}, volume = {274}, year = {2018}, month = {09/2018}, pages = {441-450}, abstract = {

Identifying osmotic stress-responsive transcription factors (TFs) can facilitate discovery of master regulators mediating salt and/or drought tolerance. To date, few RNA-seq datasets for high resolution time course of salt or drought stress treatments are publicly available for certain crop species. However, such datasets may be available for other crops, and in combination with orthology analysis may be used to infer candidate osmotic stress regulators across distantly related species. Here, we demonstrate the utility of this approach for identification and validation of osmotic stress-responsive transcription factors in tomato. First, we developed physiologically calibrated salt and dehydration-responsive systems for tomato cultivars using real time measurements of transpiration rate and photosynthetic efficiency. Next, we identified differentially expressed TFs in rice using raw RNA-seq datasets for a publicly available salt stress time course. Putative salt stress-responsive TFs in tomato were then inferred based on their orthology with the transcription factors upregulated by salt in rice. Finally, using our osmotic stress system, we experimentally validated stress-responsive expression of predicted tomato candidates representing NUCLEAR FACTOR Y, SQUAMOSA PROMOTER BINDING, and NAC domain TF families. Quantification of transcript copy numbers confirmed that mRNAs encoding all three TFs were strongly upregulated not only by salt but also by drought stress. Induction by both salt and dehydration occurred in a temporal manner across diverse tomato cultivars, suggesting that the identified TFs may play important roles in regulating osmotic stress responses.

}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0168945218303534}, author = {Sergei A Filichkin and Mitra Ansariola and Valerie N Fraser and Molly Megraw} } @article {747, title = {IndeCut evaluates performance of network motif discovery algorithms}, journal = {Bioinformatics}, volume = {34}, year = {2018}, month = {05/2018}, pages = {1514-1521}, abstract = {
Motivation
Genomic networks represent a complex map of molecular interactions which are descriptive of the biological processes occurring in living cells. Identifying the small over-represented circuitry patterns in these networks helps generate hypotheses about the functional basis of such complex processes. Network motif discovery is a systematic way of achieving this goal. However, a reliable network motif discovery outcome requires generating random background networks which are the result of a uniform and independent graph sampling method. To date, there has been no method to numerically evaluate whether any network motif discovery algorithm performs as intended on realistically sized datasets\—thus it was not possible to assess the validity of resulting network motifs.
Results
In this work, we present IndeCut, the first method to date that characterizes network motif finding algorithm performance in terms of uniform sampling on realistically sized networks. We demonstrate that it is critical to use IndeCut prior to running any network motif finder for two reasons. First, IndeCut indicates the number of samples needed for a tool to produce an outcome that is both reproducible and accurate. Second, IndeCut allows users to choose the tool that generates samples in the most independent fashion for their network of interest among many available options.
}, url = {https://academic.oup.com/bioinformatics/article/34/9/1514/4721785}, author = {Mitra Ansariola and Molly Megraw and David Koslicki} } @article {746, title = {The Next Generation of Training for Arabidopsis Researchers: Bioinformatics and Quantitative Biology}, journal = {Plant Physiology}, volume = {175}, year = {2017}, month = {12/2017}, pages = {1499-1509}, abstract = {
It has been more than 50 years since Arabidopsis (Arabidopsis thaliana) was first introduced as a model organism to understand basic processes in plant biology. A well-organized scientific community has used this small reference plant species to make numerous fundamental plant biology discoveries (Provart et al., 2016). Due to an extremely well-annotated genome and advances in high-throughput sequencing, our understanding of this organism and other plant species has become even more intricate and complex. Computational resources, including CyVerse,3 Araport,4 The Arabidopsis Information Resource (TAIR),5 and BAR,6 have further facilitated novel findings with just the click of a mouse. As we move toward understanding biological systems, Arabidopsis researchers will need to use more quantitative and computational approaches to extract novel biological findings from these data. Here, we discuss guidelines, skill sets, and core competencies that should be considered when developing curricula or training undergraduate or graduate students, postdoctoral researchers, and faculty. A selected case study provides more specificity as to the concrete issues plant biologists face and how best to address such challenges.
}, url = {http://www.plantphysiol.org/content/175/4/1499}, author = {Joanna Friesner and Sarah M. Assmann and Ruth Bastow and Julia Bailey-Serres and Jim Beynon and Volker Brendel and C. Robin Buell and Alexander Bucksch and Wolfgang Busch and Taku Demura and Jose R. Dinneny and Colleen J. Doherty and Andrea L. Eveland and Pascal Falter-Braun and Malia A. Gehan and Michael Gonzales and Erich Grotewold and Rodrigo Gutierrez and Ute Kramer and Gabriel Krouk and Shisong Ma and R.J. Cody Markelz and Molly Megraw and Blake C. Meyers and James A.H. Murray and Nicholas J. Provart and Sue Rhee and Roger Smith and Edgar P. Spalding and Crispin Taylor and Tracy K. Teal and Keiko U. Torii and Chris Town and Matthew Vaughn and Richard Vierstra and Doreen Ware and Olivia Wilkins and Cranos Williams and Siobhan M. Brady} }