Recipient Organization
IOWA STATE UNIVERSITY
2229 Lincoln Way
AMES,IA 50011
Performing Department
GDCB
Non Technical Summary
Nitrogen (N) is a key nutrient required for plant growth. In intensively farmed regions, such as Iowa, many farmers apply N fertilizer to their fields to ensure that maize has plentiful N to sustain high yields. However, the productionand excess use of N fertilizer can pollute our air and waterways. In contrast, much of the world's agricultural systems lack sufficient N to sustain maize production. Therefore, developing maize lines that can grow well with less N are beneficial to agriculture worldwide.Since N is most often acquired by the roots in the soil, many root traits have been shown to improve N acquisition, particularly when the amount of N is reduced. For instance, roots that grow at a steep angle and enable deep rooting more quickly improve N acquisition. Though, little is known about how this trait is controlled.Fortunately, we can make use of the existing diversity within maize to tackle this problem. Much of the genotypic diversity we observe in maize is derived from transposable elements (TEs), which are small bits of DNA that are able to replicate and move around the genome. TEs have long been thought to be "junk DNA" accumulating over evolutionary time. New evidence has shown that some TEs are responsive to environmental stress and modulate the activity of neighboring genes potentially inducing changes in phenotype. Therefore, this project seeks to harness the potential of TEs as regulators of stress responses in order to develop maize lines that optimize N capture through better designed root systems.
Animal Health Component
20%
Research Effort Categories
Basic
60%
Applied
20%
Developmental
20%
Goals / Objectives
Our overarching goal is to bridge the gaps between the plant genome, transcriptome, and phenome by studying how transposable elements may regulate transcriptional and phenotypic responses to variable nitrogen availability.Goal 1: Assess the effect of N stress on root transcriptional networksBuild RNAseq libraries to measure transcript abundance over time in response to nitrogen stress in two maize lines with contrasting root growth angleIdentify genes responsive to nitrogen deficiency or differentially regulated by root phenotypeConstruct gene co-expression and regulatory networks to infer nitrogen stress effects on gene-gene relationshipsGoal 2:Relate TE variation in genomes to changes in gene expressionQuantify TE expression under high and low nitrogenCorrelate TE expression with differentially expressed genesCharacterize the activity of key nitrogen-responsive TEs or TE familiesCreate maize mutant where key TE or TE family is knocked out using CRISPR/Cas9Evaluate maize mutant under nitrogen stress in the greenhouse or field to evaluate transcriptional, phenotypic, and functional differences?Goal 3:Integrate genome, transcriptome, and phenome to predict phenotypesPhenotype root growth angle across a NAM family in the fieldQuantify expression of key genes and TEs that contribute to root phenotypes and N stress responses in the NAM familyDevelop a predictive model for root phenotypes and N-stress responses based on genome-scale TE presence/absence data
Project Methods
Methods:Build RNAseq libraries and RNA sequencing: Maize root tissue will be collected from 20-day-old seedling grown in sand culture with nutrient solution containing either high nitrogen (N) (15ppm) or low N (0.5 ppm) concentrations. Two genotypes will be evaluated: B73 and Oh7B. Three different tissue types will be collected from second and third whorl roots: the root tip (most distal 1.2 cm), the young root tissue from which lateral roots have not yet emerged, and the mature root tissue that contains lateral roots. Total RNA will be isolated from these samples using a Qiagen RNeasy Plant Mini Prep kit. A NEBNext Ulta II RNA kit will be used to prepare libraries for RNA sequencing.RNA sequencing will be performed at the Iowa State DNA Facility using an Illumina HiSeq 3000 with a target of >5 million reads per sample. Clean reads will be aligned to B73 to quantify transcripts. A secondary analysis will instead align all reads to Oh7B.Differential expression analysis: I will identify differentially expressed genes and transposable elements (TEs) using the package "DESeq" and R. Raw reads will be normalized to reads per million. Differentially expressed genes/TEs will be defined as those that exhibited at least a 10-fold change in expression (i.e. absolute values of the log(change) is equal to 1). Gene ontology enrichment using AgriGO will be used to infer the functional roles of differentially expressed genes.Network construction: To examine the interactivity of genes associated with root phenotypes and limiting N, I will construct a weighted gene co-expression network using the package "WGCNA" in R. I will assess differences in network topology and module membership between each genotype, N treatment, and root tissue type. I will also examine modules containing known N-responsive genes. Separate networks will also be constructed for each genotype to parse treatment-specific responses.I will also construct a gene regulatory network to test the role of TFs regulating root phenotypes. Our group is still evaluating the best tools to build regulatory networks, but a strong candidate is GENIST, which combines clustering with dynamic Bayesian network inference.Generation and evaluation of CRISPR/Cas9 maize mutants: Using the differential expression and network data, I will identify TE candidates that are associated with responses to N stress. For 2-4 of our top candidates, I will knockout the activity of targeted TEs using CRISPR/Cas9. For candidates that are individual TEs, we will design guide RNAs that target a unique sequence of the individual TE to compromise its activity. For candidates that are a TE family, we will design guide RNAs that target the target site duplication specific to that family and knockout the activity of multiple TEs across the genome.Maize mutants will be evaluated in a controlled environment in the greenhouse or in Iowa State's transgenic field for responses to N stress. Leaf and seed N content, shoot biomass, leaf greenness, and photosynthesis rates are some metrics that will be recorded to assess physiological function. I will also quantify the expression of known N-responsive genes and other novel N-responsive genes selected from previous research. If funds allow, transcriptomic and network analyses may be repeated in these mutants.Field design: Maize genotypes will be grown under high and low N conditions in the field in a split-plot design. The high N treatment will follow yearly N fertilizer recommendations for field corn in central Iowa while the low N treatment will not have any supplemental fertilizer applied. Two replicates of each genotype will be planted in each treatment. Each replicate consists of a 15 ft long, single-rowed plot. Each plot contains 20 individuals planted at 9 in spacing and 2 in depth. Small-scale experiments will be jab planted while large-scale experiments (>80 genotypes) will be planted using a cone planter.Plant performance measures: Data will be collected throughout the whole field season to assess N stress effects on plant growth and development. Plant height and leaf greenness will be regularly checked every 2 weeks. We will collect flowering notes. Shoot biomass may be collected at anthesis. Yield will be collected from 3 plants per plot at the end of the season to calculate total yield and 100 seed weight.Root phenotyping: Root crowns will be collected using "shovelomics". In short, a standard spade is used to excavate the volume of soil that is one spade head deep and in radius around the base of the plant. The root crown is soaked and washed with low pressure water to remove remaining dirt and imaged in a photo booth. ImageJ is used to measure root growth angle, which is the angle at which the roots grow relative to the soil surface.Building a model to predict root phenotypes and N stress responses: I will integrate all transcriptomic, genomic, and phenomic data to build a partial least squares regression model to predict root responses to N stress. TE presence/absence variation will first be quantified in the NAM founders using scripts published in Anderson et al. (2019). These scripts will facilitate pairwise comparisons between each NAM founder and B73. First, TE annotations will be lifted from B73v4 to B73v5. Second, 400 bp flanking sequences centered on the start and stop points of each TE will be collected and locally aligned on a NAM founder genome. "Present" TEs are those where the full flank mapped successfully to the NAM founder in a given search window. "Absent" TEs are those where only the outside 200 bp portions of the flank successfully map within the search window. "Unresolved" TEs are those that were unable to be determined as present or absent. This data will be used to identify structural variants.To build a model, I will integrate the structural variant and differential expression data to predict root phenotypes. PLS regression will create latent variables to best predict our phenotypic outcomes, for which several genotypic traits may contribute to. 80% of our field data will be used to train the model while the remaining 20% will test our model with 5-fold cross-validation.Efforts: TEs had long been called "junk DNA", but this research will demonstrate the important role that TEs potentially play in plant genomes. This work may be shown in educational settings to provide tangible examples on the function of TEs. This project as a whole will also provide experiential learning opportunities for undergraduate research assistants employed as part of this grant and public school teachers that participate in hands-on activities in various labs across Iowa State University.Evaluation:*key milestonesTotal RNA isolated from greenhouse samples is high quality.RNA sequencing achieved >5 million reads per sample with adequate coverage (at least 10x).Generate count tables of all samples.*Differentially expressed genes, TEs, and TE families are identified.Gene networks show topological differences between genotypes, root tissue types, and N treatments.*Identify 2-4 TE candidates based on differential expression and network analyses.*Develop and evaluate maize mutant lines where TE activity is knocked out.*Phenotype roots of B73 x Oh7B NAM family in the field under high and low N conditions.Quantify TE presence/absence variation in the NAM founders.Re-format NAM family genotype data to B73- or Oh7B-like.*Train and test PLS regression model.