From Arabidopsis to Crops: The Arabidopsis QQS Orphan Gene Modulates Nitrogen Allocation Across Species

  • Seth O’Conner
  • Andrea Neudorf
  • Wenguang Zheng
  • Mingsheng Qi
  • Xuefeng Zhao
  • Chuanlong Du
  • Dan Nettleton
  • Ling LiEmail author


To enhance the nitrogen use efficiency (NUE) of crops to increase yields, one approach is to develop crops with improved NUE. Qua quine starch (QQS), a species-specific orphan gene present only in Arabidopsis thaliana, has a novel, unexpected functionality in regulation of carbon and nitrogen allocation. Approximately 0.5–8% of genes in a given species are uniquely present in that species, having no homologs in other species. They represent a significant fraction of eukaryotic and prokaryotic genomes, and are thought to be a determinant of the character of a species. However, little is known about their functional significance. QQS can affect the important trait of protein content when expressed in other species, including soybean, maize, and rice. Understanding QQS functions has multiple impacts, revealing how plants allocate precious carbon and nitrogen resources. Here, we report that QQS interactor nuclear factor Y subunit C4 (NF-YC4) affects carbon and nitrogen allocation to protein in soybean and maize. RNA-sequencing analyses of the QQS mutant materials have identified candidate genes involved in regulation of nitrogen allocation. QQS and its related network may be used as a tool to increase the protein content in crops and to study the nitrogen allocation network.


QQS Orphan Nitrogen allocation Arabidopsis thaliana Glycine max Zea mays 



We thank Diane Luth, Bronwyn Frame, and Kan Wang at ISU PTF for soybean and maize transformation; Kent Berns for field management; Charles Hurburgh and Glen Rippke at ISU Grain Quality Laboratory for NIRS analysis of soybean and maize seed composition; and Eurofins for Kjeldahl analysis. The transcriptome sequencing was conducted in conjunction with BGI. We thank BGI for contributing its expertise in genomic sequencing and bioinformatics analysis to provide processed sequencing data. This material is based in part upon work supported by: National Science Foundation (L.L. as Co-PI on MCB-0951170), United Soybean Board (2287 to L.L.), Iowa Soybean Association (to L.L.), Amfora Inc (to L.L.), ISU Research Foundation (to L.L.), and Center for Metabolic Biology at ISU.


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Seth O’Conner
    • 1
    • 2
  • Andrea Neudorf
    • 2
  • Wenguang Zheng
    • 2
  • Mingsheng Qi
    • 3
  • Xuefeng Zhao
    • 4
  • Chuanlong Du
    • 5
  • Dan Nettleton
    • 5
  • Ling Li
    • 1
    • 2
    Email author
  1. 1.Department of Biological SciencesMississippi State UniversityStarkvilleUSA
  2. 2.Department of Genetics, Development and Cell BiologyIowa State UniversityAmesUSA
  3. 3.Department of Plant Pathology and MicrobiologyIowa State UniversityAmesUSA
  4. 4.Laurence H. Baker Center for Bioinformatics and Biological StatisticsIowa State UniversityAmesUSA
  5. 5.Department of StatisticsIowa State UniversityAmesUSA

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