Two BCB students presented at International Conference

March 28, 2018
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Two BCB PhD students presented at the 60th Annual Maize Genetics Conference in Palais du Grand Large, Saint-Malo, France which took place March 22 to March 25, 2018.   Gokul Wimalanathan and Keting Chen presented their research and their abstracts are below.

Others from Iowa State in attendance included: Carson Andorf, BCB faculty member and USDA-ARS (MaizeGDB), Ian Braun, BCB student, Darwin Campbell, Ethy Cannon, Lisa Harper, USDA-ARS (MaizeGDB), Travis Hattery, Matt Hufford, EEOB Department and BCB Faculty member, Aaron Kusmec, Nick Lauter, USDA-ARS, Plant Pathology and Microbiology Department, Carolyn Lawrence-Dill, GDCB and BCB Chair, Xianran Li, Morgan Mccaw, Tes Posekany, Pat Schnable, Agronomy and GDCB and BCB Faculty member, Aimee Schulz, Adam Vanous, Erik Vollbrecht, (2011 Chair of this conference), GDCB Department, and BCB Faculty member; Jesse Walsh, MaizeGDB, Jinyu Wang, Kan Wang, Margaret Woodhouse, MaizeGDB, Eve Wurtele, GDCB and BCB Faculty member, Marna Yandeau-Nelson, GDCB, and BCB Faculty member, and Jianming Yu

Keting Chen, Major Professors Basil Nikolau, BBMB, Marna Yandeau-Nelson, GDCB, and Karin Dorman, Statistics, presented:

Interrogating metabolic and transcriptomic networks to explain genetic, developmental and environmental variation in the cuticular lipid landscape on maize silks (Computational and Large-Scale Biology)
Chen, Keting1 2; Maghoub, Umnia2; Loneman, Derek2; Peddicord, Layton3 4; Lopez, Miriam4 5; McNinch, Colton4; Chudalayandi, Siva4; Dorman, Karin1 2 6; Lauter, Nick3 4 5; Nikolau, Basil J.1 3 7; Yandeau-Nelson, Marna D.1 2 3
1 Bioinformatics and Computational Biology Graduate Program; Iowa State University, Ames, IA, 50011
2 Department of Genetics, Development and Cell Biology; Iowa State University, Ames, IA, 50011
3 Genetics and Genomics Graduate Program; Iowa State University, Ames, IA, 50011
4 Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA, 50011
5 USDA-ARS Corn Insect and Crop Genetics Research Unit, Ames, IA, 50011
6 Department of Statistics; Iowa State University, Ames, IA, 50011
7 Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology; Iowa State University, Ames, IA, 50011

Keting ChenThe plant cuticle is infused with and coated by non-polar and amphipathic lipids that form a hydrophobic layer that is protective against environmental stresses. These extracellular surface lipids (SLs) are comprised primarily of long-chain saturated and unsaturated fatty acids, aldehydes, and hydrocarbons, which are metabolically linked by enzymatic reactions as the hypothesized precursors, intermediates, and end products in hydrocarbon biosynthesis. To investigate this biosynthetic pathway, we employed a systems approach to query the metabolomes and transcriptomes of silks from four genotypes (B73, Mo17 and their reciprocal hybrids) across a spatio-temporal gradient that captures acropetal silk development and the environmental transition as silks emerge from the husks.

Supervised and un-supervised network analyses were pursued to address key questions: 1) Which metabolites explain the dynamic variations in SL composition? 2) Which enzymatic processes lead to variation in these metabolites? and 3) What genes explain the differential metabolome compositions? Our results show that silk SL composition is dynamic and significantly impacted by encasement status, genotype, and development. Discriminant analysis revealed that differential utilization of fatty acid precursors likely contributes to the observed variation in hydrocarbon composition among genotypes. Product-precursor ratio investigations showed that hydrocarbon abundances are elevated relative to their associated fatty acid precursors at longer chain-lengths, suggesting increased recruitment of longer-chain fatty acid precursors into the biosynthetic pathway. Metabolome-transcriptome associations impacting hydrocarbon production under varied conditions were identified from a partial least squares regression model built from a set of informative metabolites. Preliminary analysis identified candidate genes associated with genotype-based variation in the metabolic network, including 3-ketoacyl-CoA synthases involved in generating fatty acid precursors, and acyl desaturases involved in production of unsaturated SLs. Analyses are being conducted to interrogate the transcriptome in the context of product-precursor, product-intermediate and intermediate-precursor relationships to identify candidate genes associated with specific biochemical reactions in the network.

Funding acknowledgement: National Science Foundation (NSF), United States Department of Agriculture (USDA)

 

Gokul Wimalanathan, Major Professors: Carolyn Lawrence-Dill, GDCB and Erik Vollbrecht, GDCB presented:

GO Annotation Methods Evaluation and Review (Maize - GAMER) (submitted by Kokulapalan Wimalanathan)

Full Author List: Wimalanathan, Kokulapalan1 2; Friedberg, Iddo1 3; Andorf, Carson M 1 4 5; Lawrence-Dill,Carolyn J 1 2 6

1Bioinformatics and Computational Biology, Iowa State University, Ames, IA 50011, USA
2Department of Genetics Development and Cell Biology, Iowa State University, Ames, IA 50011, USA
3Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA 50011, USA
4USDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames,IA 50011,USA
5Department of Computer Science, Iowa State University, Ames, IA 50011, USA
6Department of Agronomy, Iowa State University, Ames, IA 50011, USA

We created a new high-coverage, robust, and reproducible functional annotation of maize protein coding genes based on Gene Ontology (GO) term assignments. Whereas the existing Phytozome and Gramene maize GO annotation sets only cover 41% and 56% of maize protein coding genes, respectively, this study provides annotations for 100% of the genes. We also compared the quality of our newly-derived annotations with the existing Gramene and Phytozome functional annotation sets by comparing all three to a manually annotated gold standard set of 1,619 genes where annotations were primarily inferred from direct assay or mutant phenotype. Evaluations based on the gold standard indicate that our new annotation set is measurably more accurate than those from Phytozome and Gramene. To derive this new high-coverage, high-confidence annotation set we used sequence similarity and protein-domain-presence methods as well as mixed - method pipelines that developed for the Critical Assessment of Function Annotation (CAFA) challenge. Our project to improve maize annotations is called maize-GAMER (GO Annotation Method, Evaluation, and Review) and the newly-derived annotations are accessible via MaizeGDB (http://download.maizegdb.org/maize - GAMER) and CyVerse (B73 RefGen_v3 5b+ at doi.org/10.7946/P2S62P and B73 RefGen_v4 Zm00001 d.2 at doi.org/10.7946/P2M925).

Funding acknowledgement: National Science Foundation (NSF), United States Department of Agriculture (USDA)

Congratulations Keting and Gokul !!

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