Gerald Schoenknecht IOS Division Of Integrative Organismal Systems
BIO Direct For Biological Sciences
Start Date:
August 1, 2016
End Date:
July 31, 2021 (Estimated)
Awarded Amount to Date:
$2,175,310.00
Investigator(s):
Eve Wurtele mash@iastate.edu (Principal Investigator)
Kevin Bassler (Co-Principal Investigator)
Sponsor:
Iowa State University
1138 Pearson
AMES, IA
50011-2207
(515)294-5225
NSF Program(s):
Plant Genome Research Project,
Cross-BIO Activities
Program Reference Code(s):
7577, 9109, 9150, 9178, 9179, BIOT
Program Element Code(s):
1329, 7275
ABSTRACT
Genes confer the primary traits that are passed on from generation to generation in plants and animals. Where, when and how new genes arise are far-reaching biological questions with practical implications. If scientists could identify emergent genes that confer new traits, the breeding potential for crops could be greatly expanded. During the quest for understanding where new genes come from, it is now known that genes can arise anew from regions of the genome where there were none previously. These so-called "orphan genes" may be key ways that species can evolve and adapt to challenging environments through the expression of new traits. To uncover orphan genes, this project taps into the sequenced genomes of maize, a major crop of worldwide importance, and Brassica, another model crop. Both species have major genomic resources available for orphan gene discovery. The research will identify orphan genes and evaluate traits possibly conferred by the genes in maize lines adapted to particular conditions. Candidate orphan genes that influence agronomically important traits will be selected and functionally analyzed. Computational tools will be developed to mine the sequence datasets and the resulting data will be integrated into community databases. At all stages, students will be trained in computational and genomic science. To reach high school students and early undergraduate students, new computer games modules are being developed and tested with the intention of increasing an understanding of the function and potential of orphan genes. A long-term outcome is that researchers and society will be able to design new solutions to improving crops through harnessing orphan genes.
The premise that new genes can arise from non-genic DNA sequences is borne out from massive DNA and RNA sequencing data. This concept sharply contrasts with the long-accepted view that novel gene functions primarily arise from a slow process of accumulated mutations and rearrangements of already-established genes. A hypothesis is that a major role of orphan genes is to regulate the defense and metabolic responses that enable evolutionary adaptation to new environments. This research will identify orphan genes of major agronomic species, focusing first on maize and Brassica. These results will inform a systematic analysis of orphan genes at the level of subspecies, thus categorizing orphan genes in the context of the adaptation and selection that has occurred as the result of human intervention for improved agronomic traits. Based on the resultant data, specific orphan genes will be selected for experimental functional analysis. Data will be integrated into community databases, and code will be available to the public. New computer game modules will be targeted to high school and early undergraduate students. The goal is to develop data and computational tools that facilitate predictive understanding of the function of orphan genes in driving evolutionary adaptation, to harness these resources for improving crops, and to disseminate the information to researchers and students. These capabilities will empower researchers to explore the significance of recently-emerged orphan genes, and transform fundamental knowledge into innovative solutions that improve crop traits.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
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Rizhsky, Ludmila and Jin, Huanan and Shepard, Michael R and Scott, Harry W and Teitgen, Alicen M and Perera, M and Mhaske, Vandana and Jose, Adarsh and Zheng, Xiaobin and Crispin, Matt and others. "Integrating metabolomics and transcriptomics data to discover a biocatalyst that can generate the amine precursors for alkamide biosynthesis," The Plant Journal, v.88, 2016, p. 775.
Jones, Dallas C and Zheng, Wenguang and Huang, Sheng and Du, Chuanlong and Zhao, Xuefeng and Yennamalli, Ragothaman M and Sen, Taner Z and Nettleton, Dan and Wurtele, Eve S and Li, Ling. "A clade-specific Arabidopsis gene connects primary metabolism and senescence," Frontiers in plant science, v.7, 2016.
Singh, Pramesh and Chen, Tianlong and Arendsee, Zebulun and Wurtele, Eve S and Bassler, Kevin E. "A Regulatory Network Analysis of Orphan Genes in Arabidopsis Thaliana," Bulletin of the American Physical Society, v.62, 2017.
Bhandary, Priyanka and Seetharam, Arun S and Arendsee, Zebulun W and Hur, Manhoi and Wurtele, Eve Syrkin. "Raising orphans from a metadata morass: A researcher's guide to re-use of public?omics data," Plant science, v.267, 2018, p. 32--47.
Hur, Manhoi and Ware, Rebecca L and Park, Junkoo and McKenna, Amy M and Rodgers, Ryan P and Nikolau, Basil J and Wurtele, Eve S and Marshall, Alan G. "Statistically significant differences in composition of petroleum crude oils revealed by volcano plots generated from ultrahigh resolution fourier transform ion cyclotron resonance mass spectra," Energy \& fuels, v.32, 2018, p. 1206--121.
International Arabidopsis Informatics Consortium and Doherty, Colleen and Friesner, Joanna and Gregory, Brian and Loraine, Ann and Megraw, Molly and Provart, Nicholas and Slotkin, R Keith and Town, Chris and Assmann, Sarah M and others. "Arabidopsis bioinformatics resources: The current state, challenges, and priorities for the future," Plant Direct, v.3, 2019, p. e00109.
Qi, Mingsheng and Zheng, Wenguang and Zhao, Xuefeng and Hohenstein, Jessica D and Kandel, Yuba and O'Conner, Seth and Wang, Yifan and Du, Chuanlong and Nettleton, Dan and MacIntosh, Gustavo C and others. "QQS orphan gene and its interactor NF-YC 4 reduce susceptibility to pathogens and pests," Plant biotechnology journal, v.17, 2019, p. 252--263.
Reem, Nathan T and Chen, Han-Yi and Hur, Manhoi and Zhao, Xuefeng and Wurtele, Eve Syrkin and Li, Xu and Li, Ling and Zabotina, Olga. "Comprehensive transcriptome analyses correlated with untargeted metabolome reveal differentially expressed pathways in response to cell wall alterations," Plant molecular biology, v.96, 2018, p. 509--529.
Tianlong Chen, Pramesh Singh, Kevin E Bassler. "Network community detection using modularity density measures," Journal of Statistical Mechanics: Theory and Experiment, v.2018, 2018, p. 053406.
Arendsee, Zebulun and Li, Jing and Singh, Urminder and Seetharam, Arun and Dorman, Karin and Wurtele, Eve Syrkin. "phylostratr: A framework for phylostratigraphy," bioRxiv, 2018, p. 360164.
Bhandary, Priyanka and Seetharam, Arun S and Arendsee, Zebulun and Hur, Manhoi and Wurtele, Eve Syrkin. "Raising orphans from a metadata morass: a researcher's guide to re-use of public?omics data," Plant Science, 2017.
Hur, Manhoi and Ware, Rebecca L and Park, Junkoo and McKenna, Amy M and Rodgers, Ryan P and Nikolau, Basil J and Wurtele, Eve S and Marshall, Alan G. "Statistically Significant Differences in Composition of Petroleum Crude Oils Revealed by Volcano Plots Generated from Ultrahigh Resolution Fourier Transform Ion Cyclotron Resonance Mass Spectra," Energy \& Fuels, v.32, 2018, p. 1206--121.
Qi, Mingsheng and Zheng, Wenguang and Zhao, Xuefeng and Hohenstein, Jessica D and Kandel, Yuba and O'Conner, Seth and Wang, Yifan and Du, Chuanlong and Nettleton, Dan and MacIntosh, Gustavo C and others. "QQS orphan gene and its interactor NF-YC 4 reduce susceptibility to pathogens and pests," Plant biotechnology journal, 2018.
Reem, Nathan T and Chen, Han-Yi and Hur, Manhoi and Zhao, Xuefeng and Wurtele, Eve Syrkin and Li, Xu and Li, Ling and Zabotina, Olga. "Comprehensive transcriptome analyses correlated with untargeted metabolome reveal differentially expressed pathways in response to cell wall alterations," Plant molecular biology, v.96, 2018, p. 509--529.
Simon Stolarczyk, Manisha Bhardwaj, Kevin E. Bassler, Wei Ji Ma, and Kre?imir Josi?. "Loss of information in feedforward social networks," Journal of Complex Networks, v.6, 2018, p. 448.
Tianlong Chen, Pramesh Singh, Kevin E Bassler. "Network community detection using modularity density measures," Journal of Statistical Mechanics: Theory and Experiment, 2018, p. 053406.
Tong, Zheng and Wang, Dan and Sun, Yong and Yang, Qian and Meng, Xueru and Wang, Limin and Feng, Weiqiang and Li, Ling and Wurtele, Eve Syrkin and Wang, Xuchu. "Comparative proteomics of rubber latex revealed multiple protein species of REF/SRPP family respond diversely to ethylene stimulation among different rubber tree clones," International journal of molecular sciences, v.18, 2017, p. 958.