COMPOSITIONS AND METHODS FOR IMPROVING PLANT NITROGEN UTILIZATION EFFICIENCY (NUE) AND INCREASING PLANT BIOMASS

Abstract

Provided are machine learning methods for identifying genes that affect plant properties. Also provided are plant cell sand plants comprising genetic modifications that improve plant nitrogen utilization and increased biomass. Methods of making the modified plant cells and plants are also provided.

Description

SEQUENCE LISTING

The instant application contains a Sequence Listing which has been submitted in XML format and is hereby incorporated by reference in its entirety. Said XML copy, created on Aug. 8, 2022, is named “058636.00536.xml”, and is 3,084 bytes in size.

BACKGROUND

Being able exploit genomic data to predict organismal outcomes in response to changes in nutrition, toxin and pathogen exposure could inform crop improvement, disease prognosis, epidemiology, and public health. To this end, machine learning methods have been developed and applied to infer phenotypes from genomic and epigenetic features associated with such conditions using changes in mRNA/protein expression levels, single nucleotide polymorphisms, chromatin modifications, and more. Despite the compelling motivation and cumulative efforts, accurately predicting complex phenotypic traits from genome-scale information remains both a promise and a challenge. Several factors contribute to these challenges. First, in contrast to the increasing availability of omics data, collection of high-quality phenotypic data from a genetically diverse population that adequately represents the phenotypic diversity space has become a major limiting factor¹. In addition, phenotypic data is often collected from experiments that are distinct from those used to acquire the functional genomics data. To overcome these limitations, phenotyping efforts should be expanded and performed on the same materials that are the source of genetic/genomic information². Furthermore, the explosion of omics data means that the features (e.g. numbers of genes) collected from a single experiment inevitably outnumber the phenotype space (e.g. sample size), leading to problems in data sparsity, multicollinearity, multiple testing, and overfitting³. This can be counteracted with increasing sample size, dimension reduction, or feature selection methods such as Principal Component Analysis (PCA), Least Absolute Shrinkage and Selection Operator (LASSO) regularization, Canonical Correlation Analysis (CCA), and so forth⁴. Additionally, cross-species approaches have been adopted in machine learning context to improve the performance of model-to-human knowledge translation⁵. Thus, there is an ongoing and unmet need to provide improved methods for analyzing genomic data to predict organismal outcomes in response to environmental changes, and use the results from the analysis to identify and modify genes to improve plant function. The present disclosure is pertinent to these needs.

BRIEF SUMMARY

The present disclosure addresses a number of previous challenges in identifying and modifying genes to improve plant function by using an evolutionarily informed machine learning approach that exploits genetic diversity both within and across species. We employ transcriptome data of nitrogen response genes to predict nitrogen use efficiency (NUE), an agronomic outcome critical for worldwide food safety and sustainability^2,6. Nitrogen (N)—the main limiting macronutrient for plant growth—is supplemented in agricultural systems through application of N fertilizer. For major row crops such as maize (Zea mays), less than 40% of supplied N is taken up by the plants, while more than 60% of soil N is lost to the atmosphere or water bodies through multiple processes such as denitrification, ammonia volatilization, leaching etc⁷. Balancing the need to further increase crop yields, while also mitigating the environmental impacts associated with N fertilizer, is a challenge for sustainable agriculture. Considering the polygenic nature of NUE that involves the integration of developmental, physiological, and metabolic processes², machine learning was applied as a strategy to tackle the mechanisms underlying this complex trait. To this end, we collected transcriptomic and phenotypic NUE data from two species—maize (a crop) and Arabidopsis (a model)—each of which included a panel of genotypes with diverse genetic background and NUE variation. We used genes whose response to N-treatments (N-DEGs) was conserved within and across species as a dimension reduction approach for machine learning. As maize and Arabidopsis are highly divergent phylogenetically, these evolutionarily conserved N-response genes should represent essential/core functions contributing to NUE. We show that models constructed using these evolutionarily conserved N-DEGs significantly improved the prediction of NUE traits from gene expression values, compared to an equal number of top ranked N-DEGs or randomly selected expressed genes. The inclusion of the model species Arabidopsis enabled us to validate using mutants. This evidence validated that the genes whose expression levels are important in predicting NUE in the machine learning models are more than just markers, but functionally required for the trait. Moreover, we show that the described evolutionarily informed machine learning pipeline is transferable to other species and traits in plants and animals. Specifically, application of the described method to other matched transcriptome and phenotype datasets related to drought in field grown rice or disease in mouse models resulted in enhanced prediction accuracies of the learned models. As such, the described evolutionarily informed machine learning pipeline has the potential to identify genes of importance for complex phenotypes of interest across biology, agriculture, or medicine.

A result of the described analysis identified maize genes that can be modulated to improve plant function. In particular, the present disclosure shows that expression of certain identified genes can positively affect nitrogen utilization and increase plant biomass, including but not necessarily limited to maize grain mass. As such, the disclosure provides for inhibiting the expression and/or function of one or a combination of transcription factors (TFs) described herein. In embodiments, the expression and/or function of hb75, alone or in combination with another described TF, such as nf-ya3, is provided for use in improving plant function.

BRIEF DESCRIPTION OF THE FIGURES

The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

FIG. 1. Evolutionarily informed machine learning approach enhances the predictive power of gene-to-phenotype relationships. Step 1 Feature selection: Phenotypic and transcriptomic data of N-responses were generated from Arabidopsis (lab-grown) and maize (field-grown) under low- vs. high-N conditions. The expression levels of N-response differentially expressed genes (N-DEGs) conserved in both species were identified via ‘leave-out-one’ approach (FIG. 4) and used as gene features in the machine learning methods in Step 2. This biologically principled approach to reduce the feature dimensions ultimately improved the model performance (Table 1). Step 2 Feature importance: We ranked the genes based on i) the XGBoost-derived feature importance score (left) and ii) the TF connectivity in a GENIE3 regulatory network (right) constructed from the N-response TFs (Step 1) as regulators and the XGBoost important features as targets. Step 3 Feature validation: We validated the role of NUE for eight TFs in planta using Arabidopsis and maize loss-of-function mutants.

FIGS. 2a-2c. Nitrogen is the leading factor explaining the NUE variation across Arabidopsis natural accessions. (2a) Boxplot of NUE among the Arabidopsis genotypes measured in three independent batches. The coefficients of variation demonstrate the broad range of phenotype of this panel of genotypes, which has been widely used in NUE studies. The X-axis is ordered in the increasing value of average NUE. In the box plots, the box represents the 25th to 75th percentile and the line within the box marks the median. Whiskers above and below the box indicate the 10th and 90th percentiles. Points above and below the whiskers indicate outliers outside 10th and 90th percentiles. (2b) The correlation of traits measured in this study. NUE at the pre-bolting stage is highly correlated with NUpE. Biomass, g/plant; N uptake, mg N/plant; N %, N uptake/Biomass; E %, 15N uptake/N uptake; NUE, Biomass/applied N; NUpE, 15N uptake/applied 15N; NUtE, Biomass/N uptake. (2c) The NUE variation is primarily explained by nitrogen levels, followed by accession and nitrogen by accession interaction. Two-way ANOVA P-value: G, <2E-16; N, <2E-16; Gx N, 9.93E-07. For each genotype n>10 biologically independent plants examined over three independent experiments.

FIGS. 3a-3c. Genotype is the leading factor explaining the NUE variation in maize breeding lines. (3a) Boxplot of Total nitrogen utilization (NUtE) values among the maize genotype panel measured in three consecutive years. The X-axis is ordered by increasing value of average Total NUtE. The coefficients of variation demonstrate the broad range of phenotype of this smaller panel of maize genotypes, which spans the distribution of NutE values measured in a larger representative germplasm collection (FIG. 8). In the box plots, the box represents the 25th to 75th percentile and the line within the box marks the median. Whiskers above and below the box indicate the 10th and 90th percentiles. Points above and below the whiskers indicate outliers outside 10th and 90th percentiles. (3b) The correlation of traits measured in this study. (3c) The total NUtE variance of 2014, the year when the RNA samples were harvested, is primarily explained by Genotype (G), followed by N, and Gx N effect. Two-way ANOVA P-value: G, 8.6E-11; N, 2.9E-13; G×N, 2.28E-07. For each genotype n>5 biologically independent plants examined over three independent experiments.

FIG. 4. Evolutionarily conserved N-response genes across Arabidopsis-maize used as a biologically principled feature reduction method for the XGboost machine learning pipeline. The RNA-seq reads from leaves of Arabidopsis and maize N-treated samples were aligned to reference genome assemblies using BBMap and the read counts were generated using featureCounts. The N-response DEGs (N-DEGs) were identified using generalized linear models in edgeR and leave-out-one method: one genotype (out of 18) was left out during each round of analysis and the intersection of 18 DEG lists was used for feature reduction (For details, see FIG. 10). The overlap of N-DEGs from Arabidopsis (n=2,123) with maize (n=6,914) resulted in a set of evolutionarily conserved N-response Arabidopsis genes (n=610) which were used as features in the machine learning model. The corresponding conserved N-response genes in maize were further intersected with genes responding to nitrogen by genotype effects (n=3,664), resulting in 248 maize genes that were used as features in the machine learning model to predict NUE.

FIG. 5. Evolutionarily informed machine learning models uncover genes-of-importance and predictive of NUE. Step 1. The evolutionarily conserved N-DEGs between Arabidopsis and maize (see FIG. 4) and NUE data from n genotypes are split into training (n-1 genotypes) and test (left-out genotype) set (for details see FIG. 10). Step 2. The training set was used to optimize the XGBoost model, which then predicts the NUE using the gene expression in the test set. Step 3. The model performance was evaluated by calculating the Pearson's correlation coefficient r between the predicted and actual NUE values. In Arabidopsis, the dots indicate the Pearson's r of 100 individual iterations and the pointranges indicate mean+/−SD. In maize, there are only two data points for each genotype thus the Pearson's r was calculated from the pooled predicted and actual NUE from 100 iteration. Step 4. The TF features were ranked based on their contribution to the NUE. Certain of the genes are functionally validated in this disclosure.

FIGS. 6a-6c. Experimental validation of candidate TFs in NUE using loss-of-function mutants for Arabidopsis (lab) and maize (field). (6a) The Arabidopsis T-DNA mutants (Methods) in group I genes displayed higher NUE compared to wild-type under N-replete (yellow, 10 mM KNO₃) and N-deplete (grey, 2 mM KNO₃) conditions. This suggests their non-redundant role(s) in regulating NUE regardless of the environmental N levels. (6b) The Arabidopsis mutants in group II genes displayed higher NUE specifically under N-deplete conditions. This indicates that the group II genes are either only required under N-deplete conditions or are functionally redundant under N-replete conditions. The experiments were carried out three times with 10 or more plants per genotype per condition. (6c) Changes in NUE and component traits for the maize nfya3-1::Mu mutant compared to wild-type W22. Plants were grown in the field supplied additional N (150 kg N fertilizer/ha). Trait values are the average of five plants sampled from each of three replicate field plots, 15 plants per genotype (Methods). The higher total NUtE observed in the mutant was a combinatorial effect of lower stalk N (g/plant) (P=0.002), total N uptake (P=0.05) and higher grain biomass (P=0.1). The increased NUE phenotype was also observed in the Arabidopsis T-DNA mutant defective the homolog gene NF-YA6 (AT3G14020) (b). The pointrange indicates mean+/−SD. The P-value was calculated between WT and indicated mutant allele using one-sided t-test with unequal variance.

FIG. 7. Distribution of nitrogen utilization values among U.S. Corn Belt inbred diversity and the genotypes chosen for transcriptome-based prediction of this trait.

FIGS. 8a-8d. Schematic overviews of plant growth conditions and N-treatments.

FIG. 9. In maize, total NUtE is an optimal measure of NUE, compared to grain NUtE, the latter of which is confounded by maturity.

FIGS. 10a-10c. Comparison of XGBoost models created using a unified list of gene features (10a), or independent lists of gene features (10b). FIG. 10C provides a comparison of Arabidopsis and Mainze genotupes and correlation coeefieicents.

FIGS. 11a-11b. XGBoost-based feature importance ranking is marginally correlated with the edgeR-based P-value ranking.

FIGS. 12a-12b. The conserved N-DEGs can be used to predict multiple traits.

FIGS. 13a-13c. The Arabidopsis gene feature importance ranking is trait specific.

FIG. 14a-14c. The Arabidopsis gene feature importance ranking is trait specific.

FIG. 15. Use case: the pipeline proposed in this study can be applied on a different data set.

FIG. 16. Validation of candidate TFs in NUE using loss-of-function mutants in Arabidopsis.

FIG. 17. Expression of target genes in plant loss-of-function mutants used in this study.

DETAILED DESCRIPTION

Every numerical range given throughout this specification includes its upper and lower values, as well as every narrower numerical range that falls within it, as if such narrower numerical ranges were all expressly written herein.

As used in the specification and the appended claims, the singular forms “a” “and” and “the” include plural referents unless the context clearly dictates otherwise. Ranges may be expressed herein as from “about” one particular value, and/or to “about” another particular value. When such a range is expressed, another embodiment includes from the one particular value and/or to the other particular value. Similarly, when values are expressed as approximations, by the use of the antecedent “about” it will be understood that the particular value forms another embodiment. The term “about” in relation to a numerical value is optional and means for example+/−10%.

This disclosure includes every amino acid sequence described herein and all nucleotide sequences encoding the amino acid sequence. Polynucleotide and amino acid sequences having from 80-99% similarity, inclusive, and including and all numbers and ranges of numbers there between, with the sequences provided here are included in the invention. All of the amino acid sequences described herein can include amino acid substitutions, such as conservative substitutions, that do not adversely affect the function of the protein that comprises the amino acid sequences. The disclosure includes all polynucleotide and amino acid sequences described herein, and every polynucleotide sequence referred to herein includes its complementary DNA sequence, and also includes the RNA equivalents thereof to the extent an RNA sequence is not given. Any sequence referred to by a database entry is incorporated herein by reference as the sequence exists in the database as of the effective filing date of this application or patent, including but not limited to database entries that are signified by an alphanumeric indicator that starts with “Zm.”

The disclosure includes all described methods of analyzing transcriptome data to predict a phenotype described herein, all machine learning approaches described herein that are used for analysis of gene expression changes using Nitrogen (N)-treatment that influences expression of N responsive genes (N-DEGs), and extensions of those approaches to different genes, their protein products, and interspecies comparisons of transcriptome analysis and predictions of the influence of transcription factors on any phenotype. In a non-limiting embodiment, the disclosure includes the process as depicted in FIG. 4 and its accompanying description, and extensions thereof to other types of plants, as well as non-plant organisms.

In embodiments, based at least in part on the described analysis, the present disclosure provides compositions and methods for modifying plants and/or plant cells. The compositions and methods relate to altering expression of one or a combination of the TFs. Altering the expression can result in any change in the plant described herein. In embodiments, practicing a method of the disclosure results in an increase in N uptake, increased biomass, such as increased grain biomass, an increased harvest index, an increased Total nitrogen utilization (NUtE), an increased total Grain NUtE, or a combination thereof. Non-limiting demonstrations of these effects are summarized in FIG. 6, panel c, and its accompanying text. For instance, mutating Maize nyfa3-1 results in the described effects shown in FIG. 6. In this regard, Table 4 provides an analysis of select TFs, and includes analysis of nf-ya3 (also referred to herein as nyfa3-1) Ranksum scores. The ranksum, as described further below, is the sum of three rankings for each TF based on i) the number of TF-gene targets involved in the N-assimilation pathways, ii) the number of TF-gene targets comprising gene features predictive of N utilization (NUE), and iii) the number of TF-gene targets that are also transcription factors. Without intending to be bound by any particular theory, it is considered the ranksum value provides an indication of the importance of the described TFs in terms of N-assimilation pathways and NUE. As can be seen from Table 4, the ranksum of nf-ya3 (46) is similar to hb75 (41). Thus, based on the data presented in FIG. 6, the ranksum value for nf-ya3, and the positive changes in plant properties that are related to mutation of nf-ya3, it is expected that mutation of hb75 will have similar effects on plant N uptake, increased grain biomass, increased harvest index, increased NUt, and total Grain NUtE as observed for mutating nf-ya3. Thus, the disclosure, in one embodiment, provides for disrupting or inhibiting the expression of hb75, nf-ya3, or a combination thereof, in plant cells. In embodiments, the disclosure provides modified plant cells and plants, wherein the only genomic modification comprises modification of one or two of the described genes. In embodiments, modification of only one, or only two, of the describes genes is sufficient to produce the described improved properties, relative to the same properties in plants that do not comprise the same modifications.

Notwithstanding the foregoing description, the TFs of the present disclosure include any TF that is referenced in the description (including tables) or in the figures. Overexpression and underexpression of any one or combination of the described genes is included in the disclosure. Overexpression of a particular gene can be accomplished by any method known in the art. For example, a plant cell may be transformed with a nucleic acid vector comprising the coding sequences of the desired gene operably linked to a promoter active in a plant cell such that the desired gene is expressed at levels higher than normal (i.e., levels found in a control/nontransgenic plant). The promoters can be constitutively active in all or some plant tissues or can be inducible. The under-expression of a desired gene can be accomplished by any method known in the art. For example, a gene may be knocked out, or mutated such that lower than normal levels of the gene product is produced in the transgenic cells or plant. For example, such mutations include frame-shift mutations or mutations resulting in a stop codon in the wild-type coding sequence, thus preventing expression of the gene product. Another exemplary mutation is the removal of the transcribed sequences from the plant genome, for example, by homologous recombination. Another method for under-expressing a gene is transgenically introducing an insertion or deletion into the transcribed sequence or an insertion or deletion upstream or downstream of the transcribed sequence such that expression of the gene product is decreased as compared to wild-type or appropriate control. Additionally, microRNA (native or artificial) can be used to target a particular encoding mRNA for degradation, thus reducing the level of the expressed gene product in the transgenic plant cell. Another method for underexpression of a gene of interest is using clustered regularly interspaced short palindromic repeats (CRISPR) gene inactivation. A variety of suitable CRISPR systems for use in plants can be used, and include but are not necessarily limited to Cas3, Cas9, and Cas13 based systems, all of which are known in the art and can be adapted for the described purposes, such as by using a suitable CRISPR enzyme and guide RNA to target the described gene(s) and/or their regulatory elements, such as promoters.

The sequence of the protein encoded by maize nf-ya3 is:

(SEQ ID NO: 1)
MPVILREMEDHSVHPMSKSNHGSLSGNGYEMKHSGH
KVCDRDSSSESDRSHQEASAASESSPNEHTSTQSDN
DEDHGKDNQDTMKPVLSLGKEGSAFLAPKLHYSPSF
ACIPYTSDAYYSAVGVLTGYPPHAIVHPQQNDTTNT
PGMLPVEPAEEPIYVNAKQYHAILRRRQTRAKLEAQ
NKMVKNRKPYLHESRHRHAMKRARGSGGRFLNTKQL
QEQNQQYQASSGSLCSKIIANSIISQSGPTCTPSSG
TAGASTAGQDRSCLPSVGFRPTTNFSDQGRGGLKLA
VIGMQQRVSTIR

The sequence of the protein encoded by maize hb75 is:

(SEQ ID NO: 2)
MMIPARHMPPTMIVRNGGAAYGSSSALSLGQPNLMD
NQQLQFQQALQQQHLLLDQIPATTAESCDNTGRGGG
GRGSDPLADEFESKSGSENVDGVSVDDQDDPNQRPS
KKKRYHRHTLHQIQEMEA.

Those skilled in the art will recognize how to identify and modify DNA sequences that encode the described proteins based on the genetic code.

The described compositions and methods can be used for any type of plant, such as monocots, dicots, gymnosperms, or plant cells. The term “plant cell” as used herein refers to protoplasts, gamete producing cells, and includes cells which regenerate into whole plants. Plant cells include but are not necessarily limited to cells obtained from or found in: seeds, suspension cultures, embryos, meristematic regions, callus tissue, leaves, roots, shoots, gametophytes, sporophytes, pollen, and microspores. Plant cells can also be understood to include modified cells, such as protoplasts, obtained from the aforementioned tissues. In non-limiting embodiments, the method is used for any species of woody, ornamental, decorative, crop, cereal, fruit, or vegetable plant. The method can be used on intact plants, isolated plant parts, and plant cells. In embodiments, the method is used with a seed, a suspension culture, an embryo, a meristematic plant region, callus tissue, a leaf, a root, a shoot, a gametophyte, a sporophyte, pollen, a microspore, or a protoplast. In embodiments, the plant or plant cells that are modified according to the disclosure are any member of the following genera/group: Artemisia, Acorns, Aegilops, Allium, Amborella, Antirrhinum, Apium, Arabidopsis, Arachis, Beta, Betula, Brassica, Cannabis, Capsicum, Ceratopteris, Citrus, Coffea, Cryptomeria, Cycas, Descurainia, Eschscholzia, Eucalyptus, Glycine, Gossypium, Hedyotis, Helianthus, Hordeum, Ipomoea, Lactuca, Linum, Liriodendron, Lotus, Oryza, Lupinus, Lycopersicon, Medicago, Mesembryanthemum, Nicotiana, Nuphar, Pennisetum, Persea, Phaseolus, Physcomitrella, Picea, Pinus, Poncirus, Populus, Prunus, Robinia, Rosa, Saccharum, Schedonorus, Secale, Sesamum, Solanum, Sorghum, Stevia, Thellungiella, Theobroma, Triphysaria, Triticum, Vitis, Zea, or Zinnia. In non-limiting embodiments, the modified plant or plant cells are from one or more so-called “elite” varieties of maize. The disclosure includes seeds produced by any modified plant herein, and progeny of the plants and seeds. Articles of manufacture comprising the seeds and a container that contains the seeds are also provided. In embodiments, the articles of manufacture comprise kits.

The following Examples are intended but not limit the disclosure.

Example 1

We analyzed whether the prediction power of machine learning models could be enhanced by exploiting the genetic diversity of gene responses and phenotypes both within and across species. In non-limiting embodiments, we tested whether using N-DEGs conserved both within and across species as a biologically-principled means of dimension reduction, could enhance identification of genes of importance to predicting NUE phenotypes from gene expression data across a model (Arabidopsis) and crop (maize) plant. This model-to-crop machine learning approach enables more rapidly validation of conserved features of importance to NUE in the crop using the model species.

Within each species, we selected a set of genotypes that exhibit a broad spectrum of phenotypic variation in NUE. The data included 18 Arabidopsis accessions that were previously identified for their NUE diversity⁸ which originated from a nested collection of 265 accessions found in a wide range of habitats differing notably in soil nutrient richness⁹. The 23 maize genotypes analyzed in this disclosure correspond to 12 maize inbred lines and their 11 corresponding hybrids with B73. We selected these 12 maize inbred lines to represent the phenotypic diversity for NUE traits that we measured among a population of 318 field-grown maize inbreds (FIG. 7), which broadly represent the current germplasm base for U.S. Corn Belt hybrids. This maize population that we tested for NUE traits includes the parents of the Nested Association Mapping (NAM) population¹, improved inbreds from different breeding programs described in recently expired plant variety patents¹⁰, and the Illinois Protein Strains that display the known phenotypic extremes for NUE traits in maize²³. The B73 inbred maize line was chosen as the parent for the hybrids, because it is a major founder of the Stiff-Stalk heterotic group used in the production of nearly all commercial U.S. Corn Belt hybrids¹¹. Furthermore, B73 displays high nitrogen utilization efficiency (NUE), and also serves as the reference genome sequence assembly for maize¹².

To test whether genome-wide responses to N-treatments evolutionarily conserved across the model and crop could be a biologically principled approach to enhance the model performance of predicting NUE, we constructed a three-step machine learning pipeline (FIG. 1). (Step I) Feature selection: First, we collected and analyzed matched phenotypic and transcriptomic data from the same replicate plants for each N-treatment conducted in a controlled laboratory setting (Arabidopsis) or field conditions (maize) and (FIG. 8). Using linear models, we identified N-response differentially expressed genes (N-DEGs) in parallel for maize and Arabidopsis, and retained the N-DEGs conserved both within and across species as gene features used in machine learning. (Step II) Feature importance: We selectively used the expression levels of these evolutionarily conserved N-DEGs as a biologically-principled approach to feature reduction in the gradient boosting-based method XGBoost¹³ predictive models. The outcome of the machine learning enabled ranking the N-DEGs whose expression levels best predicted the NUE traits measured in the same set of plants. Moreover, we identified the transcription factors (TF) regulating these genes of importance to NUE and measured their connectivity in the NUE network by constructing a NUE gene regulatory network (GRN) using a Random Forest-based method GENIE3¹⁴. Through integration of the results of these complementary means, we generated ranked lists of: i) gene features based on their contribution to the trait prediction (XGBoost-based importance score), and ii) TFs based on their level of connectivity in the GRN for each species (GENIE3-based connectivity). (Step III) Feature validation: we validated the function of eight candidate TFs in Arabidopsis or maize based on their importance score to the NUE trait and/or their degree of connectivity in the GRN. We experimentally confirmed the function of these eight TFs in regulation of NUE in planta using loss-of-function mutants in Arabidopsis, as well as in maize, where available.

Example 2

Quantifying NUE Phenotypes Across Arabidopsis and Maize Varieties

In the described phenotypic analysis, we quantified nitrogen use efficiency (NUE) as the efficiency of converting supplied N to biomass/grain yield. For Arabidopsis, NUE was calculated as the efficiency with which each plant converted supplied N into shoot biomass (NUE=Above ground dry weight/Applied N). This measure of NUE is achieved by providing each plant with a trackable/contained amount of N in pots in a lab setting, as a proxy for the field agricultural setting². Indeed, we found the Arabidopsis accessions previously selected for NUE diversity⁸ present a broad range of NUE variation in our own experiments, as evidenced by the coefficient of variation (CV=0.58) (FIG. 2a). The correlation of traits shows that NUE at the pre-bolting stage is highly correlated with NUpE (r=0.88), and to a lesser extent with NUtE (r=0.39) (FIG. 2b). The NUE variation among the Arabidopsis accessions is primarily explained by nitrogen levels, followed by accession and nitrogen-by-accession interaction (Two-way ANOVA P-value: G, <2E-16; N, <2E-16; G×N, 9.93E-07). This indicates the N-level explains the phenotypic variation in NUE in this collection of Arabidopsis ecotypes.

For field-grown maize, we used Total NUtE, (stover biomass+grain biomass)/(stover N content +grain N content), as the target trait (FIG. 3a). We chose this because Total NUtE is more robust to the effects of maturity and photoperiod in the field¹⁵ (FIG. 9), and remains highly correlated to grain NUtE (FIG. 3b). We measured total NUtE across 318 maize inbred lines in a field experiment where soil N supply was not limiting, and observed a nearly three-fold range in total NUtE (56-156 kg biomass/g plant N) (FIG. 7). To illustrate the influence of soil N-supply on total NUtE, 25 inbred maize lines chosen to represent both historical (NAM parents)¹ and elite genetic diversity¹⁰ were grown in adjacent plots that received either no N fertilizer or were N-fertilized as the larger population. When grown with sufficient N, the distribution of NUtE values for these 25 maize inbreds overlaps with that observed from the larger population of 318 maize genotypes (FIG. 8). In this disclosure, we selected 12 (from the 25 above) maize inbreds, which exhibited a similar coefficient of variation for NUtE phenotypic values (CV=0.19) as the larger population of 318 genotypes (CV=0.15) for matched transcriptome profiling and detailed phenotyping in N-responsive field plots, over three field seasons.

ANOVA results revealed that 55% of the total NUtE variation in this maize experiment was attributed to genetic effects (FIG. 3c). Our two-way ANOVA analysis of the maize data shows that in addition to G (P-value=8.6E-11) and N (P-value=2.9E-13), G×N was also a significant factor (P-value=2.28E-07) explaining 19% of the variation in Total NUtE (FIG. 3c). This is distinct from our findings for Arabidopsis, where N is the main explanatory variable (FIG. 2c). This difference likely reflects not only the overall greater genetic diversity in the maize varieties, but also suggests that intensive breeding and selection for N-responsive grain yields in maize¹⁶ may have expanded the phenotypic variation for NUE beyond that observed among the Arabidopsis natural accessions. We therefore included these interactions of maize genotype with nitrogen supply on the NUE phenotype as a factor in our computational pipeline described below.

Example 3

Evolutionarily conserved transcriptome response to N-treatment used for feature reduction in machine learning

Feature reduction is an essential pre-processing step in machine learning, as too many irrelevant features may interfere with prediction performance³. Given the fact that the N level is a significant factor explaining NUE variation in both Arabidopsis and maize (FIGS. 2c and 3c), we used negative binomial Generalized Linear Mixed models (GLMs) in edgeR R-package¹⁷ and identified N-DEGs (Gene expression˜Condition+Genotype) in the training data (n-1 genotype). Importantly, we note that the testing data sets (the held-out genotype) were never used to select the N-DEGs. This was repeated in a round-robin manner across genotypes for each species (FIG. 10). Next, we retained the evolutionarily conserved N-DEGs by mapping the Arabidopsis N-DEGs to their corresponding maize homologs using Phytozome 10¹⁸ (FIG. 4). This cross-species analysis enabled us to i) apply an evolutionarily guided filter to reduce the dimensionality of gene features used in machine learning, and ii) enhance our ability to perform rapid validation testing of candidate NUE genes with relevance to the crop in the model species.

The resulting conserved N-DEGs from Arabidopsis (n=610) were used as gene features in the machine learning model (FIG. 5). We further subjected the conserved N-DEGs from maize to a second round of filtering to identify those also responding to N×G interaction (FIG. 4, Within-species Feature Reduction). This second filter aimed to account for the significant N×G effect that we observed in the maize NUE phenotypes (FIG. 3c), resulted in a list of maize N-DEGs responsive to N×G interaction (n=248). Next, these two sets of conserved N-DEGs from Arabidopsis and maize were used as features in the machine learning model (FIG. 5).

We then analyzed whether the expression levels of N-DEGs conserved across model and crop species could enhance identification of NUE phenotypes—compared to non-selected genes—using machine learning algorithms. This data-driven hypothesis is supported by the fact that: i) the expression levels of N-DEGs have been used as biomarkers of N status across maize genotypes¹⁹, and ii) the described phenotypic data shows that N level is a significant factor explaining the NUE variation in both maize and Arabidopsis (FIGS. 2c and 3c). Indeed, this analysis enabled determining that the predictive performance of the described models is significantly better at predicting NUE outcomes when the evolutionarily conserved N-DEGs are used, compared to the same number of top-ranked N-DEGs with the lowest P-value, or randomly selected expressed genes (Table 1), as detailed below.

Example 4

Evolutionarily Conserved N-Responsive Genes have Enhanced Predictive Power in Machine Learning

For each species, we used the gene expression values (N-DEGs) as features (also referred to as gene features) to predict NUE traits through XGBoost regression models. XGBoost¹³ is a implementation of the gradient boosting algorithm²⁰, that uses a boosting algorithm to combine multiple weak learners, i.e. shallow trees, into a strong one (FIG. 5, Step 2). Lastly, we used the trained XGBoost models to predict NUE for the left-out genotype and evaluated the model performance using correlation between the observed- and the predicted-NUE in the left-out test set (FIG. 5, Step 3). In summary, we repeated the above steps and constructed 18 models for Arabidopsis, and 16 models for maize, corresponding to each genotype analyzed (See FIG. 10 for an illustration).

For maize, using the N-DEGs (n=248) conserved with their Arabidopsis homologs, resulted in a mean Pearson's correlation coefficient r of 0.79 for the XGBoost models predicting NUE across 16 maize lines (FIG. 5, Step 3). The r was above 0.6 for all but two maize genotypes, Illinois High Protein (IHP1) and Illinois Low Protein (ILP1). These two maize inbred line are derived from more than 100 cycles of divergent selection for seed protein concentration and other component traits of nitrogen use efficiency^21,22. The models showed lower accuracy in predicting the NUE phenotypes of IHP1 and ILP1, compared to other maize inbreds and the hybrids that each share the B73 parent.

The described analysis showed that the overall predictive performance of learned models that used the evolutionarily conserved maize N-DEGs is significantly better than that obtained using the same number of top-ranked N-DEGs with the lowest P-value (0.68, Mann-Whitney U test P-value=1.06E-3), or ones randomly selected from total expressed genes (0.62, Mann-Whitney U test, P-value=1.5E-5) (Table 1). In addition, comparison of the feature importance score, an XGBoost¹³ output which reveals the influence of each feature (gene) in the predicted value (NUE)¹³, with the P-value in DEG analysis, uncovered only a weak correlation (Spearman's rank correlation coefficient rho=0.19, FIG. 11b). These comparisons support the interpretation that XGBoost models capture non-linear gene-trait relationships and our hypothesis that evolutionarily conserved N-DEGs enhance the machine learning outcome.

In parallel, we used the Arabidopsis N-DEGs (n=610) whose N-response is conserved with their maize homologs, as the features to predict NUE in the same XGBoost machine learning pipeline (FIG. 5). Our machine learning results show that the mean Pearson's correlation coefficient r across all 18 Arabidopsis genotypes was 0.65 (FIG. 5, Step 3). Moreover, we found that this overall model performance is significantly better than that obtained using the same number of top-ranked N-DEGs with the lowest P-value (r=0.59, Mann-Whitney U test P-value=1.64E-4), or ones randomly selected from total expressed genes (r=0.53, Mann-Whitney U test, P-value=3.82E-6) (Table 1). Similarly, we found that the feature importance ranking was weakly correlated with the edgeR-based P-value ranking of DEGs (Spearman's rank correlation coefficient rho=0.14, FIG. 11a).

The described results from both maize and Arabidopsis data show that using the evolutionarily conserved N-responsive differentially expressed genes significantly improved performance of the machine learning models predicting NUE significantly, and that this improvement is not due to a simple numerical reduction in the gene features (Table 1). Furthermore, the weak correlation between the XGBoost-based feature importance ranking and the edgeR-based P-value ranking (FIG. 11), indicates that XGBoost can capture non-linear gene-trait relationship beyond single variable DEG analysis. We used one set of hyperparameters for each species to achieve a consistent performance across genotypes, suggesting that the model is generalized and likely applicable to additional genotypes. Taken together, the results demonstrate that NUE—a polygenic trait—could be predicted from gene expression levels of N-DEGs, and that using an evolutionarily principled approach to feature reduction significantly improved the model performance.

Example 5

Predicting Additional Traits Demonstrates the General Applicability of the Evolutionarily Informed Machine Learning Pipeline

To further test whether our pipeline can be applied to predict additional traits from transcriptome data, we used the same conserved N-DEGs (FIG. 4), to predict two additional traits for each species. For Arabidopsis, we found that the mean Pearson's r for predicting biomass and N-uptake was 0.68 and 0.69, respectively (FIG. 12a), is comparable to that for predicting NUE (r=0.65). The feature importance ranking appeared to be trait-specific, as the gene ranking for NUE only weakly correlated with those for biomass (rho=0.09) and N-uptake (rho=0.08) (FIG. 13b, 12c). This result can be explained by the weak correlation between NUE and biomass (r=0.14), as well as that between NUE and N-uptake (r=0.01) (FIG. 2b). For highly correlated traits such as biomass and N-uptake (r=0.97), the feature importance rankings were also highly correlated (rho=0.94) (FIG. 13a). For maize, the mean Pearson's r for predicting biomass and grain yield was 0.72 and 0.52, respectively (FIG. 12b). As with Arabidopsis, the feature importance rankings for maize also appeared to be trait-specific, being greater (rho=0.59) for highly correlated traits such as biomass and grain yield (r=0.8), compared to Total NUtE—which is weakly correlated with either biomass (r=−0.14; rho=0.15) or grain yield (r=−0.19; rho=0.33) (FIG. 3b, FIG. 14). Taken together, these results indicate that the feature importance ranking can capture biological information represented by the degree of phenotypic correlation among different component traits.

We also applied the described evolutionarily informed machine learning pipeline to two additional matched transcriptome and phenotype datasets related to drought in field grown rice and disease response in mouse models.

The rice data comprises matched transcriptomic and phenotypic information collected from 220 rice genotypes subjected to drought treatment in field experiments²³. The 220 rice genotypes consist of two major subspecies, Indica and Japonica, which diverged ˜440,000 years ago, with the genotypic and phenotypic diversity of domesticated rice. From this large dataset, we retained 57 rice genotypes that had no missing data in the trait measurement. We then used this set of 57 rice genotypes, and randomly selected 20 genotypes to define drought-responsive DEGs and used them as gene features for predicting the fecundity in the 37 “left-out” rice genotypes. We repeated this process 10-times and the mean Pearson's r was 0.62. The model performance was consistent across the evolutionarily distant Japonica and Indica rice sub-species (FIG. 15), and better than using the same number of random expressed genes (Mann-Whitney U test, P-value <2.2e-16).

The mouse dataset comes from a highly genetically diverse Collaborative Cross (CC) population that comprises 90% of the genetic diversity across the entire laboratory Mus musculus genome²⁴. The dataset we selected comprises matched transcriptome and disease outcome after influenza virus infection of 11 genotypes from the CC mouse population study²⁴. We used DEGs (mock vs. infected) identified across the 11 mouse CC population genotypes to predict the disease outcome (asymptomatic vs. symptomatic) and found the mean Pearson's r to be 0.98. The models built using cross-genotype DEGs outperformed the model using the same number of random expressed genes (Mann-Whitney U test, P-value=3.3E-3).

Overall, the results for the matched transcriptome and phenotype datasets for the rice and mice models provide two use-case studies of evolutionarily informed machine learning pipeline applied to external data sets for traits in both plants and animals. They also show that transcript-based prediction can be achieved using a smaller population (20 and 11 genotypes in the case of rice and mice respectively), compared with the requirement of hundreds of lines which are needed for GWAS and eQTL studies²⁵.

Example 6

Validating the Function of Genes Whose Expression is Influential in Models Predicting NUE

The Examples above established the robustness of the evolutionarily informed machine learning models in predicting trait outcomes based on conserved gene responses within and across species. Next, we experimentally validated gene features that are most influential in our predictive models. To this end, we used the feature importance score, an XGBoost¹³ output which reveals the influence of each feature (gene) in the predicted value (NUE). We reasoned that if models built for multiple genotypes selected a common set of gene features, this would indicate that those gene features are robust to genotype in predicting NUE. In maize, over 81% (202/248) of the XGBoost “important gene features” for predicting NUE were shared by models built for 16 genotypes, and 91% (245/248) were shared by 10 or more maize genotypes. Similarly, for Arabidopsis 42% (257/610) of the “important features” for predicting NUE were shared by models built for 18 Arabidopsis accessions, and 85% (519/610) were shared by 10 or more Arabidopsis accessions. These results are not only consistent with the polygenic nature of NUE trait, but also reveal that there is a core set of influential N-DEGs whose expression levels can accurately predict NUE phenotypes for both species.

In maize, the top-ranked “important gene features” in predicting NUE outcomes include the transcription factors (NLP, MYB, WRKY), members of N-uptake/assimilation pathway (ammonium transporter, asparagine synthetase), and genes involved in photosynthesis and amino acid metabolism (FIG. 5, Step 4,). In Arabidopsis, the top-ranked “important gene features” in predicting NUE include transcription factors (NF-Y, NLP, MYB), members of the N-uptake/assimilation pathway (nitrate transporter, asparagine synthetase, glutamine synthetase), tubulins, and chlorophyll a-b binding proteins (FIG. 5, Step 4). Several of the important features including the transcription factors (NLPs, LBD37/LBD38) and genes involved in N-metabolism (glutamine and asparagine synthetase) have been implied or directly linked to affect NUE in planta^19,26-29. This consistency of our machine learning predictions of genes of “importance” to NUE with published results in planta not only validates the findings from the described machine learning pipeline, but also indicates the novel genes uncovered in this pipeline can shed light on additional previously unknown molecular components and mechanisms underlying NUE.

Further, we reasoned TFs controlling the levels of expression of multiple XGBoost important features for predicting NUE would be candidates for functional validation for their role in NUE in planta. To this end, we identified TFs predicted to regulate these XGBoost gene features of importance to NUE by constructing gene regulatory networks (GRNs) using GENIE3, which adopts the random forest machine learning algorithm and was the best performer in the DREAM4 and −5 Network Inference Challenge¹⁴.

To construct GRNs controlling NUE for each species, we first identified the N-responsive TFs in maize (545 TFs) and Arabidopsis (184 TFs) by intersecting the N-DEGs in this disclosure with the TFs for each species using published databases^30-32. Next, we used our N-response TFs in GENIE3 as the “regulatory genes” (GENIE3 term) whose influence on the evolutionarily conserved “target genes” in maize (248 gene features) or Arabidopsis (610 gene features) were weighed on a 0 to 1 scale, where 0=non-influential and 1=strongly influential. We kept the top 1% of the TF-target edges to construct the NUE regulatory network and calculated the number of TF-target edges (connectivity) for each TF as a measure to evaluate their influence within the GRN.

Next, we integrated our GRN analysis with the XGBoost results to select candidate TFs that regulate genes of importance to NUE phenotype for functional validation of their role in NUE (Table 2). The selection and prioritization of TFs was based on one or more of the following criteria: i) XGBoost-based importance score, ii) GENIE3-based TF connectivity in the NUE GRN, iii) curated knowledge from the literature, and iv) the availability of multiple mutant alleles. In Arabidopsis, the top TFs in the XGBoost-based importance ranking listed in Table 2 include NF-YA6 (AT3G14020), D1V1 (AT5G58900), UNE12 (AT4G02590), NLP5 (AT1G76350), and TCP2 (AT4G18390). The other two Arabidopsis TFs prioritized for in planta validation studies WRKY38 (AT5G22570) and WRKY50 (AT5G26170) (Table 2), were selected based on their high connectivity in the GENIE3-based GRN. For maize, we selected two candidate TFs (Zm00001d006293 nlp17, Zm00001d012544 myb74) for in planta validation studies that are hubs in the GENIE3-based GRN. Since no maize mutants were available for these genes, we took advantage of our cross-species approach by validating the function of their Arabidopsis homologs (AT1G76350 NLP5, AT5G06100 MY833) in NUE. With the goal of cross-species validation, we also selected the maize homolog (Zm00001d006835, nfya3) of the top-ranked Arabidopsis NF-YA6 (AT3G14020) for validation in NUE (Table 2). This choice took into consideration the fact that NF-Y transcription factors are enriched in Arabidopsis XGBoost gene features and in the maize GRN. Moreover, this selection was supported by previous studies which showed that overexpressing a member of the NF-YA family in wheat significantly increased N uptake and grain yield under different levels of N supply³³. To discern the function of maize NF-Y homologs in NUE, we characterized the nfya3-1::UfMu mutation with a Uniform Mu transposon insertion (mu1003041)³⁴ that does not produce a detectable full-length transcript.

Our results on the eight Arabidopsis TFs selected for in planta validation studies were classified into two groups based on our NUE phenotypic results (FIG. 6). The Group I “important gene features” in predicting NUE in Arabidopsis include MY833 (AT5G06100) and TCP2 (AT4G18390), which when mutated showed increased NUE phenotypes under both high- and low-N inputs (FIG. 6a). These validation results reveal that each TF plays a non-redundant role as negative regulators of NUE, as the loss-of-function T-DNA mutants displayed higher NUE under both N-deplete and N-replete conditions. The Group II “important gene features” in Arabidopsis include 6 TFs which when mutated show increased NUE phenotypes specifically under low-N input: UNE12 (AT4G02590), NLP5 (AT1G76350), NF-YA6 (AT3G14020), WRKY38 (AT5G22570), WRKY50 (AT5G26170), and D1V1 (AT5G58900) (FIG. 6b). These validation results reveal that each of these Class II TFs plays a non-redundant role as negative regulators of NUE, as the loss-of-function T-DNA mutants displayed higher NUE, specifically under N-deplete conditions (FIG. 6b, FIG. 16), suggesting that the function of these TFs in regulating NUE is only required when N is limiting. Alternatively, their function may be redundant with other TFs under N-replete conditions. For maize, the NNUE tests of the nfya3-1::UfMu mutant in the field showed that they accumulated less stalk and total N compared to wild-type, yet grain biomass and all other traits dependent on grain biomass (grain yield, harvest index, NUtE) increased when grown with sufficient N (FIG. 6c). These results show that loss of maize NFYA3 influences how developing seeds sense and respond to plant N status, with the mutation reducing the N requirement to promote grain, thereby enhancing the NUtE. Observing phenotypes in the grain is also consistent with the expression pattern of NFYA3, which is strongest in developing seeds³⁵. No significant differences were observed for NUE traits compared to wild-type maize (W22) when grown under N-limiting conditions, except for slightly lower grain yield and higher grain N concentration.

Taken together, the described evolutionarily informed machine learning predictions of genes of importance to NUE and validation results for TF mutants for both Arabidopsis and maize demonstrate that: i) Using evolutionarily conserved gene response significantly enhances the ability of the XGBoost machine learning models to predict NUE outcome across genotypes and species (plants and animals), and ii) The XGBoost-based important scores and GENIE3-based connectivity are informative in selecting functionally important features—including TFs—to control of a complex physiological trait in crops— NUE—which has important implications for sustainable agriculture.

It will be recognized from the foregoing Examples that the disclosure described a new genome-to-phenome analysis—namely, predicting phenotypic outcomes from genome-wide expression data. We show that exploiting evolutionary conserved gene expression datasets—within and across species—enhanced the machine learning model performance in predicting NUE phenotypes in a model (Arabidopsis) and a crop (maize), and also as applied to published matched transcriptome/phenotype datasets from another crop (rice) and model animal (mouse).

Our evolutionarily informed three-step machine learning pipeline (FIG. 1) which integrates phenotypic traits, transcriptome profiles, genetic variation, and environmental responses allowed us to; 1) preselect a subset of transcripts based on an evolutionarily conserved transcriptome responses within and across species, 2) employ this conservation as a biologically-principled way to reduce the feature dimensionality to improve the machine learning mmodel performance, and 3) rapidly validate the function of ‘important gene features’ identified from XGBoost models and GENIE3 gene regulatory network via the inclusion of a model and crop species.

The implementation of machine learning in predicting phenotypes has advanced in the past few years. However, the available datasets do not always; 1) exploit the genetic diversity of the organism(s) and 2) measure the phenotypes using same samples from which the transcriptome response was captured. The present disclosure advances the field in both points, as we utilized a panel of genotypes with diverse genetic backgrounds and measured phenotypes from the same batch of plants that the transcriptome was captured. We integrated genetic diversity, machine learning, and cross-species approaches to identify genes of importance to an agronomically important trait, NUE. The trait we selected for study on NUE has the challenge of its underlying polygenic nature and the difficulty in collecting high quality phenotypic data³⁶. To this end, we designed a sufficiently large experimental space of N-treatments across a set to ˜20 genotypes spanning NUE phenotypes in a model and crop species. The described results represent the largest matched phenotypic and transcriptomic datasets from both a model and a crop species. This dataset includes a large NUE phenotypic dataset resource of 318 maize genotypes for the plant community, and for 18 Arabidopsis accessions. We analyzed the genetic diversity in 18 Arabidopsis accessions and 23 maize genotypes selected for broad phenotypic variation in NUE and scored them for both transcriptomic and physiological responses in the same samples. Importantly, the selected maize genotypes represent the range of NUE diversity observed among a comprehensive collection of germplasm adapted to the U.S. Corn Belt, as confirmed empirically (FIG. 8).

To extend this analysis beyond NUE, we applied our evolutionarily informed machine learning approach to other agricultural traits (e.g. drought resistance) in another major crop, using published transcriptome and phenotype datasets of genetically diverse rice subspecies (Indica and Japonica)²³. In our application to animals, we exploited the growing awareness that host genetic variation has a major impact on pathogen susceptibility. To this end, we used matched transcriptome and phenotype data from a highly genetically diverse Collaborative Cross (CC) population that comprises 90% of the genetic diversity across the entire laboratory Mus musculus genome²⁴. Models that we built using cross-genotype DEGs from both these studies of these genetically diverse lines in plants (rice) and animals (mice) lines, significantly outperformed the model using the same number of random expressed genes. Importantly, in these two additional case studies, and in our proof-of-principle example, our evolutionary informed analysis of matched transcriptome and phenome data allowed us to use a considerably smaller sample size compared to those needed for GWAS or eQTL studies²⁵.

By providing accurate prediction, the predictive models reveal novel gene features for further investigation of causality³⁷. We demonstrate this principle using a reverse genetics approach to validate the function of eight transcription factors important to predicting NUE outcomes (Table 2). Notably, our two-way cross-species validation strategy enabled us to verify the function of genes involved in NUE for i) two maize candidate genes using mutants in their Arabidopsis homologs and ii) one Arabidopsis candidate TF via analysis of a mutant in its maize homolog grown in the field (Table 2, FIG. 6).

The learned model performance is more robust to maize genotype, compared with the models learned in Arabidopsis (FIG. 5). This outcome was obtained even though the maize genotypes used in the Examples possess greater genetic diversity of NUE (FIG. 3c). Many factors may contribute to this difference. For instance, the maize gene features were applied to forecast NUE traits measured at later development stages (FIG. 7). By contrast, the Arabidopsis gene features were applied to predict the NUE traits measured at the same time as RNA samples (FIG. 7).

The disclosure reveals that genes affecting NUE are involved in an array of processes (Table 2), including nutrient response and uptake (DIV1⁴⁰ and NLP5^19,41), anther and pollen development (NF-YA6⁴² and MYB33⁴³), juvenile-to-adult transition (MYB33⁴⁴), microRNA-mediated growth and responses (NF-YA⁴⁵, MYB33⁴⁴, TCP2⁴⁶), immune response (NF-YA6⁴², UNE12⁴⁷, WRKY38⁴⁸, and WRKY50⁴⁹), and photomorphogenesis (TCP2⁵⁰ and Zm00001d006835⁵¹). These results not only provide additional evidence supporting the notion that NUE is a polygenic trait and intertwined with diverse signaling pathways, but further reveal a novel role of these genes in regulating NUE. Notably, there are three transcription factor families, NF-Y, NLP, and WRKY, whose members are enriched as the gene features of XGBoost models and/or the regulators of GENIE3-based GRN.

Our results identified nine Arabidopsis and one maize NF-Y genes as the features in XGBoost models, as well as 12 Arabidopsis and 14 maize NF-Y genes, as potential regulators in the GENIE3 NUE GRN. Moreover, we validated the function of NF-YA6 in NUE—a top gene in Arabidopsis XGBoost model —using mutants in Arabidopsis NF-YA6 (AT3G14020), as well as its maize homolog nfya3 (FIG. 6) and expect similar results by inhibiting expression of hb7. The NF-Y family, found in nearly all eukaryotes⁵², encodes components of an evolutionarily conserved trimeric transcription factor complex. In humans, NF-Y binds to the CCAAT box in promoters of large sets of genes overexpressed in breast, colon, thyroid, and prostate cancer⁵³. In plants, the regulatory roles of NF-Y have been revealed in flowering-time, early seed development, nodulation, hormone signaling, and stress responses⁵². NF-Ys function as a multimeric protein complex (NF-YA/B/C(-CO/bZIP/bHLH) to bind its canonical motif CCAAT and/or the motif(s) of its partner TFs⁵⁴. It is possible that the flexible cis-binding capacity makes NF-Ys versatile and context-dependent TFs that can quickly adapt to nutrient fluctuations. It is noteworthy that several NF-Y genes are targeted and down-regulated by miR169⁵⁵ and miR169 members respond transcriptionally to N-starvation⁵⁶. Thus, the disclosure supports a new link between N-signaling, miRNA changes in N-responsive of NF-Ys, to the phenotypic output of NUE: Nitrogen→miR169→NF-Y→NUE.

We identified six Arabidopsis and two maize NLP genes as the features in XGBoost models to predict NUE, as well as five Arabidopsis and 14 NLP genes as potential regulators in the GENIE3 NUE GRN. Further, using mutants, we validated the role of NLP5—a top gene feature in maize XGBoost model and maize NUE GRN—as a negative regulator of NUE specifically under low-N conditions (FIG. 6b, FIG. 15). The NLPs—which are plant-specific TFs—are related to a core symbiotic gene Nin⁵⁷ and later identified as master regulators of nitrate signaling in Arabidopsis²⁶. Emerging evidence suggests their contribution to N-regulated gene expression and developmental processes is common across plant species⁵⁸. The results from our functional validation experiment indicated that NLP5 is a negative regulator of NUE under N-depleted conditions (FIG. 6B), which can be explained by the fact that NLP5 is a target of NIGT1/HRS1, a master regulator of N-starvation response genes^59,60. Thus, the loss of NLP5 in the Arabidopsis mutants could de-repress the N-starvation response, leading to higher NUE.

We identified six Arabidopsis and six maize WRKY genes as the features in XGBoost models, as well as 24 Arabidopsis and 11 WRKYgenes as the regulators in GENIE3 NUE GRN. Among them, WRKY38 and WRKY50 are the top-ranked TF hubs in the Arabidopsis NUE GRN. Our functional analysis using Arabidopsis mutants validated a role of WRKY38 and WRKY50 in mediating NUE (FIG. 6B). WRKY5, occurring primarily in plants⁶¹, are among the largest families of transcription factors. Cumulative evidence has demonstrated the important biological functions of WRKY5 in plant developmental processes (embryogenesis, germination, senescence etc.) as well as response to biotic and abiotic stresses including defense, salt, drought, nutrient starvation and more⁶². In addition to their known functions in defense responses^48,49, our results add a novel aspect to WRKY38 and WRK50 in regulating NUE and make them candidate TF hubs in coordinating plant responses to N levels as well as biotic stress.

The disclosure demonstrates that the integration of genetic diversity, cross-species transcriptome analysis and machine learning method enhances predictive modeling of genes affecting NUE. The results from reverse genetic analysis further show that those genes predictive of NUE are not only biomarkers but are functionally important in determining plant performance in response to environmental nutrition. The pipeline described herein could complement current approaches in identifying important genes in a multigenic trait. Our validation of the evolutionarily informed strategy for feature reduction across both genetically diverse crop and animal datasets, supports its potential to inform any system that seeks to uncover important genes controlling a complex phenotype in biology, agriculture, or medicine.

Example 7

This Example describes the materials and methods used to produce the described results.

Plant Materials, Growth Conditions, and Phenotypic Assays

Arabidopsis

All Arabidopsis seeds used in this disclosure were obtained from ABRC. The 18 Arabidopsis accessions are Akita, B1-1, Bur-0, Col-0, Ct-1, Edi-0, Ge-0, Kn-0, Mh-1, Mr-0, Mt-0, N13, Oy-0, Sakata, Shandara, St-0, Stw-0, and Tsu-0, as previously studied for NUE⁸. The T-DNA mutants are all in the Col-0 background. The mutant lines⁶³ are myb33-1 (SALK_056201), myb33-2 (SALK_065473), tcp2-2 (SALK_060818), une12-1 (SAILseq_711_E09.1), n1p5-1 (SALK_055211), n1p5-2 (SALK_063304), nfya6-1 (SALK_005942), nfya6-2 (SAIL_159_E03), wrky38-1 (WiscDsLox489-492C21), wrky38-3 (SAIL_749_B02), wrky50-1 (SAIL_115_C10), div1-1 (SALK_056735), and div1-2 (SALK_084867C). The mutants were genotyped to confirm the homozygosity. The expression level of the inserted gene in the homozygous mutants were below detection limit of real-time PCR (FIG. 17).

For growth experiments, the Arabidopsis seeds were germinated on ½ MS with MES Buffer and Vitamins (RPI cat M70800) plates for 7-10 days in on a 16h-light/8h-dark photoperiod. The seedlings were then transferred to pre-washed nutrient-poor matrix vermiculite under an 8 h light (120/μmol2/s)/16 h dark diurnal cycle, at temperatures 22 and 20° C. respectively and 40% humidity. We kept one plant per pot and carried out the entire experiment using Arasystem (https://www.arasystem.com/). To track the N supply for each plant, we treated each plant with the same amount of low N (LN, 2 mM KNO₃) (Sigma cat P6083) or high N (HN, 10 mM KNO₃) medium (Caisson Labs cat. no. MSP10) using a syringe and recorded the volume. The potassium concentration was maintained by supplementing KCl (Sigma cat P9333) to the LN medium. On 40 and 42 DAS, the treatment was enriched with 10% atom excess ¹⁵N for ¹⁵N influx analysis. To minimize the variation due to pot location in the growth chambers, the HN row was located adjacent to the LN row, and the flats were shuffled three times weekly. We repeated these experiments three times consecutively to obtain biological replicates for phenotypic and transcriptomic samples. For each of the 18 Arabidopsis accessions, mature leaves were harvested for transcriptome and the above ground tissues for physiological traits at 43 DAS. The dried tissues were ground and analyzed for total nitrogen using a PDZ Europa ANCA-GSL elemental analyzer interfaced to a PDZ Europa 20-20 isotope ratio mass spectrometer at UC Davis Stable Isotope Facility.

Maize

Seeds for all maize inbreds used in this disclosure were originally obtained from the USDA-ARS North Central Plant Introduction Station in Ames, Iowa, except for the inbreds derived from the Illinois Selection Experiment and FR1064 as described in Uribelarrea et al²². Inbred lines were subsequently increased by controlled self-pollination, and hybrid seed produced by controlled crosses. We grew the maize plants in N-managed field plots in Urbana, Ill. between May and September in 2014-2016. The soil type is a Drummer silty clay loam, pH 6.2, that received either 200 kg/Ha fertilizer N or no exogenous applied N when the plants reached the V3 growth stage. Subsequent soil testing and measures of plant N recovery estimate approximately 60 kg N/ha were made available from the soil alone. The N fertilizer was applied as granular ammonium sulfate banded adjacent to plants at the soil surface. Plants were grown in a split-plot design where individuals in each main plot (2 rows 5.3 m long, 76 cm row spacing) were paired in adjacent rows of N-replete or N depleted condition to a final density of 49,000 plants per hectare for inbreds and 77,000 plants per hectare for hybrids. Genotypes within main plots were arranged by relative maturity to minimize its impact on NUE traits. Plots were maintained weed free by a pre-plant application of herbicide (atrazine+metalochlor) followed by hand weeding as needed.

Maize phenotyping was performed at the R6 growth stage, when plants have reached physiological maturity, but may not yet have fully senesced. Five plants from each plot were cut at ground level, ears removed, and a fresh weight obtained on the entire remaining plant material (stover, comprising mostly stalk by weight, followed by leaves, tassels, and husks). The stover was then shredded in a Vermeer wood chipper, a subsample was collected into a tared cloth bag, and the subsample fresh weight was recorded. Stover samples were oven-dried to dryness at least three days at 65° C. and the subsample dry weight used to estimate stover biomass. The dried stover was further ground in a Wiley mill to pass through a 2 mm screen, and approximately 100 mg used to estimate total nitrogen concentration by combustion analysis with a Fisons EA-1108 N elemental analyzer. Grain samples were dried for approximately one week at 37° C., after which grain was shelled from the cobs, and the cob weight recorded. The moisture content and N concentration within each 5-plant grain sample was estimated using near-infrared reflectance spectroscopy on a Perten DA7200 analyzer, using a custom calibration built with samples possessing a broad range of variation in composition and color. The nitrogen concentration calibration was established using data from total combustion analysis of grain samples as described above for stover.

The nfya3-1::Mu loss-of-function allele was generated by the UniformMu insertion mu1003041::Mu in the 5′ untranslated region the annotated gene model Zm00001d006835. The UFMu-00332 seed stock was obtained from the Maize Genetics Cooperation Stock Center and genotyped⁶⁴ to identify homozygous for the nfya3-1::Mu mutant allele, which were then self-pollinated. The expression level of the nfya3 gene in the homozygous mutants was below detection limit of real-time PCR (CT>45) (FIG. 16). The nfya3 mutant and wildtype W22-Uniform Mu plants were grown in 2020 at the same field site and using the same experimental design, nitrogen treatments, and phenotyping methods described above.

RNA Extraction, Library Preparation, and Sequencing

For each of three Arabidopsis RNA replicates, we harvested mature leaves from pre-bolting plants on 43 DAS between 9 and 11 AM from two plants, flash froze in liquid nitrogen and stored in −80 C. We isolated RNA using Direct-zol RNA Kits following manufacturer's instructions (Zymo Research). RNA quality was assessed on an Agilent Tape station using RNA ScreenTape (Agilent cat 5067-5576). All 108 stranded RNA-seq libraries were made using the NEBNext® Ultra™ II Directional RNA Library Prep Kit for Illumina® (NEB cat E7768) and assessed using DNA high sensitivity D1000 ScreenTape system (Agilent cat 5067-5584). The RNA-Seq libraries were sequenced using Illumina HiSeq 2500 v4 with 1×75 bp single-end read chemistry at the GenCore Facility at New York University Center for Genomics and Systems Biology.

For each of three maize RNA replicates, we collected leaf tissues from two inches from the base of leaf 13 subtending the top ear at R1 stage between 9 and 11 AM, flash froze in liquid nitrogen and stored in −80 C. We extracted RNA from frozen leaf tissue using CTAB-chloroform method. Genomic DNA was removed using DNAse I (NEB cat M0303). RNA-seq libraries were prepared using a TruSeq Stranded mRNAseq Sample Prep kit (Illumina cat RS-122-2101) according to the protocol provided. Single-end 150 bp reads were generated using the Illumina HiSeq 4000 at the Roy J Carver Biotechnology Center in the University of Illinois at Urbana-Champaign.

Identification of N Response Differentially Expressed Genes (N-DEGs)

All RNA-seq raw reads were processed using the same pipeline to remove optical duplicates (Clumpify 37.24) and adapters (BBDuk 37.24)⁶⁵. The trimmed reads were aligned to the latest genome in 2018, TAIR10⁶⁶ for Arabidopsis and Zm-B73-REFERENCE-GRAMENE-4.0¹² for maize, using BBMap (37.24). The mapped reads were assigned by featureCounts (1.5.1)⁶⁷ using the latest annotation in 2018: Araport11⁶⁸ for Arabidopsis and AGPv4.32¹² for maize. The parameters and software versions for the above steps are available in GEO accession GSE152249. We identified N-DEGs in the training data set (n-1 genotypes) and repeated n times (n=number of genotypes in each species). In each round of analysis, we first filtered out the lowly expressed genes (CPM>1 in less than 10 samples) and normalized the data using upper-quantile (EDASeq 2.18.0)⁶⁹ and replicate samples (RUVSeq 1.18.0)⁷⁰. Subsequently, we used edgeR (3.26.8)¹⁷ to detect genes differentially expressed in high vs low N condition across genotypes (FDR <0.05). Lastly, we intersected the n lists of DEGs and only retained the ones occurring on n lists as a common set of N-DEGs. These analyses resulted in 2,123 Arabidopsis N-DEGs and 6,914 maize N-DEGs (FIG. 4). The Arabidopsis—Maize homolog mapping file is generated from Phytozome 10¹⁸.

We held out a testing genotype before the DEG stage; and only training genotypes (n-1 genotypes) were used in DEG analysis and XGBoost models. The held-out test genotypes were then used to validate the model performance. This round robin approach (FIGS. 10a(i) & 10b(i)), generated 18 and 16 independent DEG lists for Arabidopsis and maize, respectively. In approach a, we identified a unified list of gene features by intersecting these independent lists (e.g. 18 for Arabidopsis and 16 for maize) (FIG. 10a(ii)). By contrast, in approach b, cross species analysis was performed on each independent DEG list (e.g. 18 for Arabidopsis or 16 for maize).

To rule out the possibility that using the intersected DEGs (e.g. within species) would overly optimize the XGBoost results, we further compared the XGBoost performance using the intersected DEGs (FIG. 10a) with the alternative approach that did not go through the within species list intersection (FIG. 10b). The results of these two approaches are comparable (FIG. 10c). However, the advantage of conducting the cross-genotype intersection (FIG. 10a), which we used in this manuscript), has the benefit of resulting in a unified list of gene features, compared to multiple independent lists of gene features. Generating a unified list of gene features will enable the gene feature ranking across genotypes, rather than restricted to an individual genotype.

Construction and Evaluation of Predictive Machine Learning Models

We used a tree model with gradient boosting, XGBoost¹³ R implementation, to train and test the models. For each species, we split the data into training (n-1 phenotypes) and testing (left-out genotype) sets. We used five-fold internal cross-validation to select the optimized hyperparameters. We tuned “nrounds” (number of trees), “colsample_bytree” (the proportion of features for constructing each tree), “subsamples” (the portion of training data samples for training each additional tree), and “eta” (shrinkage of feature weights to make the boosting process more conservative and prevent overfitting) in an XGBoost:regression model. Subsequently, we made predictions on each of the left-out genotype, assessed the model accuracy by calculating the Pearson's correlation coefficient r between the predicted and actual values⁷¹, and reported the r from 100 iterations.

Selection of Candidate Genes for Functional Validation in NUE

We used two parallel procedures to select candidate genes for functional validation. First, we used the XGBoost-generated feature importance score that indicates how useful each feature was in the construction of model. We summed the score on a gene-by-gene basis from 18 models for Arabidopsis and 16 models for maize and generated a ranked list. Second, we used a Random Forest-based algorithm GENIE3 to infer the transcription factors regulating the gene features. We used the N-responsive TFs (184 Arabidopsis TFs and 545 maize TFs) as the regulators and the gene features (610 Arabidopsis genes and 248 maize genes) as the targets and kept the default parameters. We constructed the NUE regulatory network using the top 1% of the edges and ranked the TFs based on their connectivity (number of edges).

References—This reference listing is not an indication that any particular reference is material to patentability.

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TABLE 1
Evolutionary conservation of gene responsiveness enhances
machine learning outcomes. Comparison of the performance
of maize (top) and Arabidopsis (bottom) XGBoost models
using the same number of features from different sources:
randomly selected expressed genes, top N-DEGs based on FDR
ranking in edgeR analysis, and the evolutionarily conserved
N-DEGs. The numbers indicate the P-value
of one-tailed Mann-Whitney U test.
	Maize Features
	Random		Cross Species
	expressed genes	Top N-DEGs	N-DEGs
	Pearson′s r
	r = 0.62	r = 0.68	r = 0.79
Random		6.56e−04	1.5e−05
expressed genes
Top N-DEGs	6.56e−04		1.06E−03
Cross Species	1.5e−05	1.06−03
N-DEGs
	Arabidopsis Features
	Random		Cross Species
	expressed genes	Top N-DEGs	N-DEGs
	Pearson′s r
	r = 0.53	r = 0.59	r = 0.65
Random		7.63E−06	3.82E−06
expressed genes
Top N-DEGs	7.63E−06		1.64E−04
Cross Species	3.82E−06	1.64E−04
N-DEGs

TABLE 2
Candidate TFs identified from XGBoost feature importance ranking for predicting NUE and/or
hubs in GENIE3 network constructed from XGBoost important gene features. Our validation
results confirming the roles of these eight TFs in NUE are provided in FIG. 6, and FIG. 15.
Gene ID	Symbol	Published Functions	Selection Criteria
AT3G14020	NF-YA6	male gametogenesis,	At XGBoost gene-to-
		embryogenesis, seed morphology,	trait model
		and seed germination; ABA
		response42, NF-YAs are predicted
		target of miR16945
AT4G02590	UNE12	temperature-responsive SA	At and Zm XGBoost
		immunity regulator47	gene-to-trait model
AT5G58900	DIV1	Nitrogen-response gene in the	At and Zm XGBoost
		Arabidopsis seedling root and	gene-to-trait model
		shoot40
AT4G18390	TCP2	MicroRNA-mediated leaf	At XGBoost gene-to-
		morphogenesis46,	trait model
		photomorphogenesis in
		Arabidopsis50
AT5G22570	WRKY38	Basal defense48	At GENIE3 GRN
AT5G26170	WRKY50	Systemic Acquired Resistance49	At GENIE3 GRN
AT5G06100	MYB33	The Arabidopsis (MYB33), maize	Zm GENIE3 GRN, At and
		(Zm00001d012544) and rice	Zm XGBoost gene-to-
		(OsGAMYB) homologs are	trait model, conserved
		predicted target of miR15944,	cross-species function
		juvenile-to-adult transition44,	in anther development
		anther development43
AT1G76350	NLP5	The maize homolog of NLP5	Zm GENIE3 GRN, At and
		(Zm00001d006293) is a marker for	Zm XGBoost gene-to-
		N status19 and nutrient uptake41	trait model
Zm00001d006835	nfya3	photoperiod-dependent flowering	At XGBoost gene-to-
		and abiotic stress responses51	trait model

TABLE 3
25 MAIZE TRANSCRIPTION FACTORS AND THEIR ARABIDOPSIS HOMOLOGS
		Machine					Machine
		learning Gene					learning Gene
		Importance to					Importance to
		NUE (Cheng &					NUE (Cheng &
	Maize	Coruzzi 2021,			Published	Arabidopsis	Coruzzi 2021,			Published
Row	Gene	Table S3)	Symbol	Description	Function	Gene	Table S3)	Symbol	Description	Function
1	Zm00001	2.1	nf-ya3	CCAAT-HAP2-	NA	AT3G14020	38.0	NF-YA6	nuclear factor	Table 3
	d006835			transcription					Y, subunit A6	Row 1
				factor
2	Zm00001	41.2	hb75	Homeobox-	NA	AT4G04890	1.3	PDF2	protodermal	Table 3
	d002234			factor 75					factor 2	Row 46
				transcription		AT4G21750	3.4	ATM Li	Homeobox-	Table 3
									leucine zipper	Row 22
									family protein/
									lipid-binding
									START domain-
									containing
									protein
3	Zm00001	11.7	nlp17	NLP-	Table 2	AT1G20640	18.4	NLP4	Plant regulator	NA
	d006293			transcription	Row 3				RWP-RK family
				factor 17					protein
						AT1G76350	10.7	NLP5	Plant regulator	NA
									RWP-RK family
									protein
4	Zm00001	7.2	gras37	GRAS-	NA	AT3G54220	7.4	SCR	SGR1, SHOOT	Table 3
	d005029			transcription					GRAVITROPISM	Row 11
				factor 37					1
5	Zm00001	6.4	sbp23	SBP-	NA	AT3G57920	0.6	SPL15	squamosa	Table 3
	d006028			transcription					promoter	Row 54
				factor 23					binding
									protein-like 15
6	Zm00001	10.2	hb66	Homeobox-	NA	AT3G61890	0.6	HB-12	ATHB-12,	Table 3
	d002799			factor 66					homeobox 12	Row 55
				transcription		AT2G46680	0.6	HB-7	ATHB-7,	Table 3
									homeobox 7	Row 53
7	Zm00001	2.3	abi28	ABI3-VP1-	NA	AT2G24645	1.6		Transcriptional	NA
	d004358			transcription					factor B3
				factor 28					family protein
8	Zm00001	2.1	bbx6	b-box6	Table 2	AT2G21320	0.3	BBX18	B-box zinc	Table 3
	d006198				Row 8				finger family	Row 62
									protein
9	Zm00001	4.2	arr8	ARR-B-	NA	AT2G25180	2.3	RR12	ARR12,	Table 3
	d018380			transcription					response	Row 30
				factor 8					regulator 12
10	Zm00001	4.0	nf-ya11	CCAAT-HAP2-	NA	AT3G05690	4.0	NF-YA2	nuclear factor	Table 3
	d013676			factor 210					Y, subunit A2	Row 19
				transcription		AT5G06510	2.5	NF-YA10	nuclear factor	Table 3
									Y, subunit A10	Row 27
11	Zm00001	0.8	bhlh15	bHLH-	NA	AT1G03040	14.5		basic helix-	Table 3
	d013073		9	transcription					loop-helix	Row 6
				factor 159					(bHLH) DNA-
									binding
									superfamily
									protein
						AT4G02590	12.8	UNE12	basic helix-	Table 3
									loop-helix	Row 7
									(bHLH) DNA-
									binding
									superfamily
									protein
12	Zm00001	0.1	myb38	myb	Table 2	AT4G38620	1.8	MYB4	ATMYB4, myb	Table 3
	d032024			transcription	Row 12				domain protein	Row 37
				factor38					4
13	Zm00001	0.6	nlp13	NLP-	Table 2	AT1G20640	18.4	NLP4	Plant regulator	NA
	d021442			transcription	Row 13				RWP-RK family
				factor 13					protein
						AT1G76350	10.7	NLP5	Plant regulator	NA
									RWP-RK family
									protein
14	Zm00001	0.3	myb74	MYB-	Table 2	AT5G06100	1.3	MYB33		Table 3
	d012544			transcription	Row 14					Row 45
				factor 74
15	Zm00001	0.5	c3h39	C3H-	NA	AT2G19810	4.6	OZF1	AtOZF1, AtTZF2,	Table 3
	d037769			transcription					TZF2, tandem	Row 15
				factor 39					zinc finger 2
16	Zm00001	0.3	myb34	MYB-	NA	AT5G58900	15.2	DIV1	Homeodomain-	Table 3
	d042830			transcription					like	Row 5
				factor 34					transcriptional
									regulator
17	Zm00001	0.9	ereb81	AP2-EREBP-	Table 2	AT2G28550	1.8	RAP2.7	TO E1, TARGET	Table 3
	d035512			transcription	Row 17				OF EARLY	Row 35
				factor 81					ACTIVATION
									TAGGED (EAT)
									1
18	Zm00001	1.1	nactf10	NAC-	Table 2	AT1G01720	0.9	ATAF1	NAC (No Apical	Table 3
	d042609		9	transcription	Row 18				Meristem)	Row 50
				factor 109					domain
									transcriptional
									regulator
									superfamily
									protein
19	Zm00001	0.1	wrky40	WRKY-	Table 2	AT5G22570	1.8	WRKY38	ATWRKY38, AR	Table 3
	d043062			transcription	Row 19				ABIDOPSIS	Row 38
				factor 40					THALIANA
									WRKY DNA-
									BINDING
									PROTEIN 38
20	Zm00001	0.3	mybr3	MYB-related-		AT5G58900	15.2	DIV1	Homeodomain-	Table 3
	d038270			transcription					like	Row 5
				factor 3					transcriptional
									regulator
21	Zm00001	0.1	bzip10	bZIP-	Table 2	AT1G77920	2.4	TGA7	bZIP	Table 3
	d024160		7	transcription	Row 21				transcription	Row 29
				factor 107					factor family
									protein
22	Zm00001	0.3	wrky58	WRKY-	NA	AT1G13960	1.5	WRKY4	WRKY DNA-	Table 3
	d041740			transcription					binding protein	Row 42
				factor 58					4
23	Zm00001	0.1	nlp6	NLP-	NA	AT5G24310	4.9	ABIL3	ABL interactor-
	d039266			transcription					like protein 3
				factor 6
24	Zm00001	0.1	nactf44	NAC-	NA	AT3G04070	1.2	NAC047	ANAC047, NAC	Table 3
	d028999			transcription					domain	Row 47
				factor 44					containing
									protein 47,
									SHG, SPEEDY
									HYPONASTIC
									GROWTH
25	Zm00001	0.1	wrky12	WRKY-	NA	AT5G26170	0.2	WRKY50	ATWRKY50,	Table
	d037607		5	transcription					ARABIDOPSIS	Row 63
				factor 125					THALIANA
									WRKY DNA-
									BINDING
									PROTEIN 50

TABLE 4
25 MAIZE TRANSCRIPTION FACTORS
				Machine
				learning					Validation
				Gene			Ranking:	Ranking:	of role in
				Importance			# of	# of	NUE using
				to NUE		Ranking: #	target as	target as	mutant
				(Cheng &		of target in	gene	diff-	(Cheng &
				Coruzzi		N-	features	erentially	Coruzzi	Published	Published
				2021, Table	Rank-	assimilation	predictive	expressed	2021, FIG.	N	non-N
Row	Gene	Symbol	Description	S3)	sum	pathways	of NUE	TF	6)	function	function
1	Zm00001	nf-ya3	CCAAT-HAP2-	2.1	46	7	23	16	Yes	NA	Photoperiod-
	d006835		transcription								dependent
			factor4								flowering
											time control
											(Su et al.,
											2018)
2	Zm00001	hb75	Homeobox-	41.2	41	19	14	8		NA	NA
	d002234		transcription
			factor 75
3	Zm00001	nlp27	NLP-	11.7	17	7	3	7		N status	Ion Uptake in
	d006293		transcription							biomarker	the root
			factor 17							(Yang et	(Griffiths et
										al., 2011)	al., 2020)
4	Zm00001	gras37	GRAS-	7.2	54	12	22	20		NA	NA
	d005029		transcription
			factor 37
5	Zm00001	sbp23	SBP-	6.4	71	22	24	25		NA	NA
	d006028		transcription
			factor 23
6	Zm00001	hb66	Homeobox-	10.2	25	7	15	3		NA	NA
	d002799		transcription
			factor 66
7	Zm00001	abi28	ABI3-VP1-	2.3	27	3	13	11		NA	NA
	d004358		transcription
			factor 28
8	Zm00001	bbx6	b-box6	2.1	26	14	6	6		NA	Leaf
	d006198										senescence
											(Sekhon et
											al., 2019)
9	Zm00001	arr8	ARR-B-	4.2	8	2	5	1		NA	NA
	d018380		transcription
			factor 8
10	Zm00001	nf-ya11	CCAAT-HAP2-	4.0	39	14	11	14		NA	NA
	d013676		transcription
			factor 210
11	Zm00001	bhlh159	bHLH-	0.8	63	23	19	21		NA	NA
	d013073		transcription
			factor 159
12	Zm00001	myb38	myb	0.1	68	24	21	23		Misregulated	NA
	d032024		transcription							in
			factor38							mop1
										mutant
										(Vendra
										min et
										al., 2020)
13	Zm00001	nlp13	NLP-	0.6	29	7	8	14		Drought-	NA
	d021442		transcription							responsive
			factor 13							(Jin et
										al., 2019)
14	Zm00001	myb74	MYB-	0.3	39	6	16	17		Potential	NA
	d012544		transcription							targets of
			factor 74							microRNA
										(Li et
										al., 2019)
15	Zm00001	c3h39	C3H-	0.5	13	7	2	4		NA	NA
	d037769		transcription
			factor 39
16	Zm00001	myb34	MYB-	0.3	19	14	4	1		NA	NA
	d042830		transcription
			factor 34
17	Zm00001	ereb81	AP2-EREBP-	0.9	74	25	25	24		Stress-	NA
	d035512		transcription							responsive
			factor 81							(Du et
										al., 2014)
18	Zm00001	nactf10	NAC-	1.1	33	14	7	12		Overexpression	NA
	d042609	9	transcription							in
			factor 109							Arabidopsis
										enhance
										drought
										tolerance
										(Liu et
										al., 2019)
19	Zm00001	wrky40	WRKY-	0.1	34	3	18	13		Misregulated	NA
	d043062		transcription							in
			factor 40							mop1
										mutant
										(Vendramin
										et
										al., 2020)
20	Zm00001	mybr3	MYB-related-	0.3	13	3	1	9		NA	NA
	d038270		transcription
			factor 3
21	Zm00001	bzip107	bZIP-	0.1	33	12	12	9		Misregulated	NA
	d024160		transcription							in
			factor 107							mop1
										mutant
										(Vendramin
										et
										al., 2020)
22	Zm00001	wrky58	WRKY-	0.3	61	19	20	22		NA	NA
	d041740		transcription
			factor 58
23	Zm00001	nlp6	NLP-	0.1	54	19	17	18		NA	NA
	d039266		transcription
			factor 6
24	Zm00001	nactf44	NAC-	0.1	15	1	9	5		NA	NA
	d028999		transcription
			factor 44
25	Zm00001	wrky125	WRKY-	0.1	47	18	10	19		NA	NA
	d037607		transcription
			factor 125

TABLE 5
63 ARABIDOPSIS TRANSCRIPTION FACTORS
				Machine	Validation
				learning	of role in
				Gene	NUE using	Published
				Importance	mutant	Nitrogen
				to NUE	(Cheng &	function	Published
				(Cheng &	Coruzzi	using	non-Nitrogen function
				Coruzzi 2021,	2021,	mutants/	using
Row	Gene	Symbol	Description	Table S3)	FIG. 6)	transgenics	mutants/transgenics
1	AT3G14020	NF-YA6	nuclear factor Y, subunit A6	38.0	Yes	NA	Male gametogenesis,
							embryogenesis, and
							seed development
							(Mu et al., 2013)
2	AT1G54160	NF-YA5	NFYA5, NUCLEAR FACTOR Y A5	22.8		NA	Drought resistance (Li
							et al., 2008)
3	AT1G20640	NLP4	Plant regulator RWP-RK family protein	18.4		NA	NA
4	AT3G09370	MYB3R-3	AtMYB3R3, myb domain protein 3R3	16.8		NA	DNA repair
							(Bourbousse et al.,
							2018)
5	AT5G58900	DIV1	Homeodomain-like transcriptional	15.2	Yes	NA	NA
			regulator
6	AT1G03040		basic helix-loop-helix (bHLH) DNA-	14.5		NA	Thermoresponsive
			binding superfamily protein				regulator (Bruessow et
							al., 2021)
7	AT4G02590	UNE12	basic helix-loop-helix (bHLH) DNA-	12.8	Yes	NA	Thermoresponsive
			binding superfamily protein				regulator (Bruessow et
							al., 2021)
8	AT1G76350	NLP5	Plant regulator RWP-RK family protein	10.7	Yes	NA	NA
9	AT5G08190	NF-YB12	nuclear factor Y, subunit B12	9.4		NA	NA
10	AT5G20510	AL5	alfin-like 5	8.1		NA	Abiotic stress
							tolerance (Wei ei al.,
							2015)
11	AT3G54220	SCR	SGR1, SHOOT GRAVITROPISM 1	7.4		NA	Root development (Di
							Laurenzio et al., 1996),
							Bundle sheath
							differentiation (Cui et
							al., 2014)
12	AT4G39250	RL1	ATRL1, RAD-like 1, RSM2,	6.9		NA	NA
			RADIALIS-LIKE SANT/MYB 2
13	AT3G46130	MYB48	ATMYB48-1, ATMYB48-2,	6.8		NA	NA
			ATMYB48-3, ATMYB48,
			myb domain protein 48
14	AT4G26150	CGA1	GATA22, GATA TRANSCRIPTION	4.7		Nitrate-	Flowering time and
			FACTOR 22, GNL, GNC-LIKE			responsive	cold tolerance (Ritcher
						and	et al., 2013)
						chlorophyll
						synthesis
						(Bi et all,
						2005)
15	AT2G19810	OZF1	AtOZF1, AtTZF2, TZF2, tandem zinc	4.6		NA	JA and ABA response
			finger 2				(Lee et al., 2012)
16	AT4G35270	NLP2	Plant regulator RWP-RK family protein	4.5		NA	NA
17	AT5G6541O	HB25	ATHB25, ARABIDOPSIS THALIANA	4.4		NA	Gibberellin signalling
			HOMEOBOX PROTEIN				in seed longevity
			25, ZFHD2, ZINC				(Bueso et al., 2012)
			FINGER HOMEODOMAIN
			2, ZHD1, ZINC
			FINGER HOMEODOMAIN 1
18	AT1G78600	LZF1	BBX22, B-box domain protein	4.0		NA	Photomorphogenesis
			22, DBB3, DOUBLE				(Gangappa et al.,
			B-BOX 3, STH3,SALT				2013)
			TOLERANCE HOMOLOG 3				NA
19	AT3G05690	NF-YA2	ATHAP2B, HEME	4.0		NA
			ACTIVATOR PROTEIN
			(YEAST) HOMOLOG 2B, AtNF-
			YA2, HAP2B, HEME ACTIVATOR
			PROTEIN (YEAST) HOMOLOG
			2B, UNE8, UNFERTILIZED
			EMBRYO SAC 8
20	AT1G30500	NF-YA7	nuclear factor Y, subunit A7	3.8		NA	Abiotic stress
							tolerance (Leyva-
							González et al., 2012)
21	AT3G20640		basic helix-loop-helix (bHLH) DNA-	3.6		NA	Cell elongation and
			binding superfamily protein				seed germination (Lee
							et al., 2005)
22	AT4G21750	ATML1	Homeobox-leucine zipper family	3.4		NA	Shoot epidermal cell
			protein/lipid-binding START domain-				differentiation (Takada
			containing protein				et al., 2013)
23	AT3G59580	NLP9	Plant regulator RWP-RK family protein	3.3		NA	NA
24	AT2G34720	NF-YA4	nuclear factor Y, subunit A4	3.0		NA	NA
25	AT2G43500	NLP8	Plant regulator RWP-RK	3.0		Nitrate-	NA
			family protein			promoted
						seed
						germination
						(Yan et al.,
						2016)
26	AT3G15270	SPL5	squamosa promoter binding protein-	2.7		Nitrate-	Flowering time (Lal et
			like 5			mediated	al., 2011)
						flowering time
						control
						(Olas et al.,
						2019)
27	AT5G06510	NF-YA10	nuclear factor Y, subunit A10	2.5		NA	Leaf growth via auxin
							signaling (Zhang et al.,
							2017)
28	AT5G24930	COL4	ATCOL4, BBX5, B-box	2.4		NA	Abiotic stress
			domain protein 5				tolerance (Min et al.,
							2015)
29	AT1G77920	TGA7	bZIP transcription factor family	2.4		NA	Disease resistance
			protein				(Kesarwani et al.,
							2007)
30	AT2G25180	RR12	ARR12, response regulator	2.3		NA	Cytokinin signal
			12, AtARR12				transduction (Mason
							et al., 2005)
31	AT3G20910	NF-YA9	nuclear factor Y, subunit A9	2.2		NA	Male gametogenesis,
							embryogenesis, and
							seed development
							(Mu et al., 2013)
32	AT4G18390	TCP2	TEOSINTE BRANCHED 1, cycloidea	2.2	Yes	NA	Photomorphogenesis
			and PCF transcription factor 2				(He et al., 2016)
33	AT1G19510	RL5	ATRL5, RAD-like 5, RSM4,	2.0		NA	NA
			RADIALIS-LIKE SANT/MYB 4
34	AT1G72650	TRFL6	TRF-like 6	1.9		NA	NA
35	AT2G28550	RAP2.7	TOE1, TARGET OF	1.8		NA	Flowering time and
			EARLY ACTIVATION				innate immunity (Zhai
			TAGGED (EAT) 1				et al., 2015)
36	AT2G42280	FBH4	AKS3, ABA-responsive kinase	1.8		NA	Flowering time (Ito et
			substrate 3				al., 2012)
37	AT4G38620	MYB4	ATMYB4, myb domain protein 4	1.8		NA	Flavonoid biosynthesis
							(Wang et al., 2020)
38	AT5G22570	WRKY38	ATWRKY38,	1.8	Yes	NA	Plant defense (Kim et
			ARABIDOPSIS THALIANA				al., 2008)
			WRKY DNA-BINDING PROTEIN 38
39	AT5G59780	MYB59	ATMYB59-1, ATMYB59-2,	1.7		NA	NA
			ATMYB59-3, ATMYB59, MYB
			DOMAIN PROTEIN 59
40	AT1G30210	TCP24	ATTCP24	1.7		NA	Secondary cell wall
							thickening and anther
							endothecium (Wang et
							al., 2015)
41	AT2G24645		Transcriptional factor B3 family	1.6		NA	NA
			protein
42	AT1G13960	WRKY4	WRKY DNA-binding protein 4	1.5		NA	Plant resistance to
							biotrophic pathogens
							(Lai et al., 2008)
43	AT5G67420	LBD37	ASL39, ASYMMETRIC	1.4		Anthocyanin	NA
			LEAVES2-LIKE 39			synthesis and
						nitrogen
						responses
						(Rubin et
						al., 200)
44	AT2G16770	bZIP23	Basic-leucine zipper (bZIP)	1.4		NA	Zinc sensor (Lilav et al.,
			transcription factor family protein				2021)
45	AT5G06100	MYB33	ATMYB33	1.3	Yes	NA	Regulated by miR159
							in anther development
							(Miller and Gubler,
							2005)
46	AT4G04890	PDF2	protodermal factor 2	1.3		NA	Embryo development
							(Ogawa et al., 2015)
47	AT3G04070	NAC047	ANAC047, NAC domain containing	1.2		NA	Waterlogging-induced
			protein 47, SHG, SPEEDY				hyponastic leaf growth
			HYPONASTIC GROWTH				(Rauf et al., 2013)
48	AT3G42790	AL3	alfin-like 3	0.9		NA	NA
49	AT2G02470	AL6	alfin-like 6	0.9		NA	Abiotic stress (Wei et
							al., 2015)
50	AT1G01720	ATAF1	NAC (No Apical Meristem) domain	0.9		NA	Embryogenesis
			transcriptional regulator superfamily				(Kunieda et al., 2008)
			protein
51	AT3G19510	HAT3.1	Homeodomain-like protein with	0.8		NA	NA
			RING/FYVE/PHD-type zinc finger
			domain-containing protein
52	AT1G56170	NF-YC2	ATHAP5B, HAP5B	0.7		NA	Flowering (Hackenberg
							et al., 2012)
53	AT2G46680	HB-7	ATHB-7, homeobox	0.6		NA	Drought response (Re
			7, ATHB7, ARABIDOPSIS THALIANA				et al., 2014)
			HOMEOBOX7
54	AT3G57920	SPL15	squamosa promoter binding protein-	0.6		NA	Flowering (Hyun et al.,
			like 15				2016)
55	AT3G61890	HB-12	ATHB-12, homeobox	0.6		NA	Drought response (Re
			12, ATHB12, ARABIDOPSIS THALIANA				et al., 2014)
			HOMEOBOX 12
56	AT1G53160	SPL4	FTM6, FLORAL TRANSITION	0.5		NA	Flowering (Jung et al.,
			AT THE MERISTEM6				2016)
57	AT5G11510	MYB3R-4	AtMYB3R4, myb domain protein 3R4	0.5		NA	Cell cycle (Haga et al.,
							2011)
58	AT1G14920	GAI	RGA2, RESTORATION ON	0.5		NA	Gibberellin
			GROWTH ON AMMONIA 2				responses (Peng et al.,
							1997)
59	AT2G21230	bZIP30	Basic-leucine zipper (bZIP)	0.4		NA	Reproductive
			transcription factor family protein				development (Lozano-
							Sotomayor et al.,
							2016)
60	AT2G27230	LHW	transcription factor-like protein	0.4		NA	Epidermal responses
							to phosophate
							deprivation (Wendrich
							et al., 2020)
61	AT3G49940	LBD38	LOB domain-containing protein 38	0.4		Anthocyanin
						synthesis and
						nitrogen
						responses
						(Rubin et
						al., 200)
62	AT2G21320	BBX18	B-box zinc finger family protein	0.3		NA	Thermomorphogenesis
							(Ding et al., 2018)
63	AT5G26170	WRKY50	ATWRKY50,	0.2	Yes	NA	Plant defense
			ARABIDOPSIS THALIANA				(Hussain
			WRKY DNA-BINDING PROTEIN 50				et al., 2018)

TABLE 6
209 MAIZE NON-TRANSCRIPTION FACTORS AND THEIR ARABIDOPSIS HOMOLOGS
		Machine learning				Machine learning
		Gene Im- portance				Gene Im- portance
		to NUE				to NUE
		(Cheng & Coruzzi				(Cheng & Coruzzi
Row	Maize Gene	2021, Table S3)	Symbol	Description	Arabidopsis Gene	2021, Table S3)	Symbol	Description
1	Zm00001d0	128.9	morf2	multiple	AT4G09010	0.2	TL29	APX4, ascorbate peroxidase 4
	02426			organellar RNA
2	Zm00001d0	96.5		editing factor2	AT1G15820	3.1	LHCB6	CP24
	01857
3	Zm00001d0	90.8			AT3G15360	1.2	TRX-M4	ATHM4, ATM4, ARABIDOPSIS
	02854							THIOREDOXIN M-TYPE 4
4	Zm00001d0	74.4	mlo9	barley mlo defense	AT5G53760	2.6	MLO11	ATM LO11, MILDEW
	01804			gene homolog9				RESISTANCE LOCUS O 11
5	Zm00001d0	71.4	imd2	isopropylmalate	AT5G14200	0.5	IMD1	ATIMD1, ARABIDOPSIS
	02880			dehydrogenase2				ISOPROPYLMALATE
								DEHYDROGENASE 1
6	Zm00001d0	59.7	pco139896	Photosystem I	AT1G08380	1.9	PSAO	photosystem I subunit O
	03767			subunit O
7	Zm00001d0	51.0		Probable	AT1G08630	18.0	THA1	threonine aldolase 1
	03059			low-specificity
				L-threonine aldolase 1
8	Zm00001d0	40.8		Peroxisomal (S)-2-	AT4G18360	1.5	GOX3	Aldolase-type TIM barrel
	02261			hydroxy-acid oxidase				family protein
				GLO1
9	Zm00001d0	39.3		OJ000126_13.10	AT4G26950	2.0		senescence regulator
	02798			protein				(Protein of unknown
								function, DUF584)
10	Zm00001d0	38.5		ABC transporter	AT3G60160	0.3	ABCC9	ATMRP9, multidrug
	02503			C family				resistance-associated protein
				member 9				9, MRP9, multidrug
								resistance-associated protein
								9
11	Zm00001d0	36.7		Photosystem	AT4G28750	0.8	PSAE-1	Photosystem I reaction
	05446			I reaction				centre subunit IV/PsaE
				center subunit IV A				protein
					AT2G20260	0.4	PSAE-2	photosystem I subunit E-2
12	Zm00001d0	30.3	SAUR11	auxin-responsive	AT2G45210	2.0	SAUR36	SAG201, senescence-
	02826			SAUR				associated gene 201
				family member	AT3G60690	0.2	SAUR59	SMALL AUXIN
								UPREGULATED
								RNA 59
13	Zm00001d0	26.9		Cyclopropane fatty	AT3G23530	4.3		Cyclopropane-fatty-acyl-
	06098			acid synthase				phospholipid synthase
14	Zm00001d0	26.1	cys1	cysteine synthase1	AT2G43750	1.4	OASB	ACS1, ARABIDOPSIS
	08379							CYSTEINE
								SYNTHASE 1, ATCS-
								B, ARABIDOPSIS THALIANA
								CYSTEIN SYNTHASE-
								B, CPACS1, CHLOROPLAST
								O-ACETYLSERINE
								SULFHYDRYLASE 1
15	Zm00001d0	20.8		Probable metal-	AT5G41000	0.7	YSL4	AtYSL4
	03941			nicotianamine
				transporter YSL6
16	Zm00001d0	18.6	hct5	hydroxycinnamoyl-	AT5G48930	5.1	HCT	hydroxycinnamoyl-CoA
	03129			transferase5				shikimate/quinate
								hydroxycinnamoyl
								transferase
17	Zm00001d0	17.5		PLAT domain-	AT2G22170	0.6	PLAT2	Lipase/lipooxygenase,
	03457			containing				PLAT/LH2 family protein
				protein 3
18	Zm00001d0	17.1	pco080190	Amino acid binding	AT2G36840	2.0	ACR10	ACT-like superfamily protein
	05317			protein
19	Zm00001d0	16.1		Cysteine-rich	AT4G23180	2.4	CRK10	RLK4
	06793			receptor-like	AT4G23150	1.3	CRK7	cysteine-rich RLK (RECEPTOR-
				protein kinase 10				like protein kinase) 7
					AT4G23130	0.3	CRK5	RLK6, RECEPTOR-LIKE
								PROTEIN KINASE 6
					AT4G23140	0.1	CRK6	cysteine-rich RLK (RECEPTOR-
								like protein kinase) 6
					AT4G11530	0.7	CRK34	cysteine-rich RLK (RECEPTOR-
								like protein kinase) 34
					AT4G23230	0.9	CRK15	cysteine-rich RECEPTOR-like
								kinase
20	Zm00001d0	15.3	ga2ox2	gibberellin	AT4G21200	9.1	GA20X8	ATGA20X8, ARABIDOPSIS
	02999			2-oxidase2				THALIANA GIBBERELLIN 2-
								OXIDASE 8
21	Zm00001d0	15.0	elip1	early light inducible	AT3G22840	0.2	ELIP1	ELIP
	07827			protein1	AT4G14690	0.0	ELIP2	Chlorophyll A-B binding
								family protein
22	Zm00001d0	14.6		Photosystem	AT1G55670	0.6	PSAG	photosystem I subunit G
	05996			I reaction
				center subunit V
23	Zm00001d0	13.4	pspb1	photosystem	AT1G06680	2.6	PSBP-1	OE23, OXYGEN EVOLVING
	07857			II oxygen				COMPLEX SUBUNIT 23
				evolving				KDA, OEE2, OXYGEN-
				polypeptide1				EVOLVING ENHANCER
								PROTEIN 2, PSII-
								P, PHOTOSYSTEM II
								SUBUNIT P
24	Zm00001d0	12.6		Serine/threonine-	AT4G38470	4.9	STY46	ACT-like protein tyrosine
	06267			protein kinase				kinase family protein
				STY46
25	Zm00001d0	12.5		cytochrome P450	AT2G46660	1.8	CYP78A6	EOD3, enhancer of da1-1
	06193			family 78 subfamily A
				polypeptide 8
26	Zm00001d0	12.3		Protein LURP1	AT1G33840	1.3		LURP-one-like protein
	05193							(DUF567)
27	Zm00001d0	12.0	IDP2449	Gamma-	AT4G39640	1.0	GGT1	gamma-glutamyl
	03446			glutamyltrans- peptidase				transpeptidase 1
				1
28	Zm00001d0	11.7		Probable alpha-	AT5G13980	1.6		Glycosyl hydrolase family 38
	07383			mannosidase				protein
29	Zm00001d0	8.6	cl11315_1a	Protein disulfide-	AT1G75690	0.8	LQY1	DnaJ/Hsp40 cysteine-rich
	03459			isomerase LQY1				domain superfamily protein
				chloroplastic
30	Zm00001d0	8.1		Protein kinase Kelch	AT2G44130	1.2	KFB39	Galactose oxidase/kelch
	07274			repeat:Kelch				repeat superfamily protein,
								Kelch-domain-containing F-
								box protein 39, KMD3, KISS
								ME DEADLY 3
31	Zm00001d0	7.5		UDP-	AT2G43840	2.5	UGT74F1	UDP-glycosyltransferase 74
	06140			glycosyltransferase				F1
				74B1	AT2G43820	0.6	UGT74F2	ATSAGT1, Arabidopsis
								thaliana salicylic acid
								glucosyltransferase l
32	Zm00001d0	7.1	pza03240	Proline oxidase	AT3G30775	4.5	ERD5	AT-
	29853							POX, ATPDH, ATPOX,
								ARABIDOPSIS
								THALIANA PROLINE
								OXIDASE, PDH1, proline
								dehydrogenase
								1, PRO1, PRODH, PROLINE
								DEHYDROGENASE
33	Zm00001d0	5.9	pco112665	Bifunctional protein	AT3G12290	0.8		Amino acid dehydrogenase
	10867			FolD 2				family protein
34	Zm00001d0	5.7		Oxygen-evolving	AT4G21280	1.5	PSBQA	PSBQ-1, PHOTOSYSTEM II
	06540			enhancer protein 3-1				SUBUNIT Q-
								1, PSBQ, PHOTOSYSTEM II
								SUBUNIT Q
					AT4G05180	1.7	PS BQ-2	PS BQ, PHOTOSYSTEM II
								SUBUNIT Q, PSII-Q
35	Zm00001d0	5.7		3′-5′ exonuclease	AT2G25910	2.9		3′-5′ exonuclease domain-
	11188			domain-containing				containing protein/K
				protein/K homology				homology domain-containing
				domain-containing				protein/KH domain-
				protein/KH domain-				containing protein
				containing protein
36	Zm00001d0	5.4		Mitochondrial	AT1G79900	2.8	BAC2	Mitochondrial substrate
	08974			arginine				carrier family protein
				transporter BAC2
37	Zm00001d0	5.4	Ihca1	light harvesting	AT3G61470	0.5	LHCA2	photosystem l light
	06663			complex A1				harvesting complex protein
38	Zm00001d0	4.7		Serinc-domain	AT4G13345	1.1	MEE55	Serinc-domain containing
	08772			containing serine and				serine and sphingolipid
				sphingolipid biosynthesis				biosynthesis protein
				protein
39	Zm00001d0	4.2		L-type lectin-domain	AT4G02420	0.2	LecRK-	Concanavalin A-like lectin
	24637			containing receptor			IV.4, L-type	protein kinase family protein
				kinase V.9			lectin
							receptor
							kinase IV.4
40	Zm00001d0	4.1	umc1272	Probable amino acid	AT5G23810	0.7	AAP7	amino acid permease 7
	25665			permease 7
41	Zm00001d0	4.0	hct6	hydroxycinnamoyl-	AT5G48930	5.1	HCT	hydroxycinnamoyl-CoA
	17186			transferase6				shikimate/quinate
								hydroxycinnamoyl
								transferase
42	Zm00001d0	3.9		Tetratricopeptide	AT4G10840	0.9	KLCR1	Tetratricopeptide repeat
	14961			repeat				(TPR)-like superfamily
				(TPR)-like				protein
				superfamily
				protein
43	Zm00001d0	3.7	AY109733	40S ribosomal protein	AT4G09800	8.2	RPS18C	S18 ribosomal protein
	13086			S18
44	Zm00001d0	3.7		UDP-	AT2G43840	2.5	UGT74F1	UDP-glycosyltransferase 74
	06137			glycosyltransferase				F1
				74B1	AT2G43820	0.6	UGT74F2	ATSAGT1, Arabidopsis
								thaliana salicylic acid
								glucosyltransferase
								1, GT, SAGT1, salicylic acid
								glucosyltransferase
								l, SGTl, UDP-glucose: salicylic
								acid glucosyltransferase l
45	Zm00001d0	3.6	mlkp3	Maize LINC KASH	AT3G13360	3.7	WIP3	WPP domain interacting
	05997			AtWIP-like3				protein 3
46	Zm00001d0	3.5	lhcb6	light harvesting	AT1G15820	3.1	LHCB6	CP24
	26599			chlorophyll a/b binding
				protein6
47	Zm00001d0	3.4	TIDP2961	Auxin-responsive	AT1G29450	2.6	SAUR64	SMALL AUXIN
	06274			protein				UPREGULATED
								RNA 64
				SAUR61	AT1G29510	1.4	SAUR68	SMALL AUXIN
								UPREGULATED
								RNA 68
					AT1G29500	2.1	SAUR66	SMALL AUXIN
								UPREGULATED
								RNA 66
48	Zm00001d0	3.4		Thioredoxin-like	AT1G76080	1.3	CDSP32	ATCDSP32, ARABIDOPSIS
	21334			protein				THALIANA CHLOROPLASTIC
				CDSP32				DROUGHT-INDUCED STRESS
				chloroplastic				PROTEIN OF 32 KD
49	Zm00001d0	3.3		DEAD-box ATP-	AT5G62190	2.4	PRH75	DEAD box RNA helicase
	06160			dependent RNA				(PRH75)
				helicase 7
50	Zm00001d0	3.0		Sm-like protein	AT5G48870	2.9	SADI	AtLSM5, AtSAD1, LSM5, SM-
	38088			LSM5				like 5
51	Zm00001d0	3.0		Ultraviolet-	AT2G06520	0.7	PSBX	photosystem II subunit X
	08681			B-repressible
				protein
52	Zm00001d0	2.9	fdh1	formaldehyde	AT5G43940	0.7	HOT5	ADH2, ALCOHOL
	18468			dehydrogenase				DEHYDROGENASE
				homolog1				2, ATGSNOR1, GSNOR,S-
								NITROSOGLUTATHIONE
								REDUCTASE, PAR2, PARAQUAT
								RESISTANT 2
53	Zm00001d0	2.9	mkkk27	MAP kinase kinase	AT5G28080	0.7	WNK9	Protein kinase superfamily
	06644			kinase27				protein
54	Zm00001d0	2.9	IhcblO	light harvesting	AT2G34430	0.4	LHB1B1	LHCB1.4, LIGHT-
	11285			chlorophyll a/b				HARVESTING
				binding				CHLOROPHYLL-PROTEIN
								COMPLEX II SUBUNIT Bl
				protein10	AT2G34420	0.6	LHB1B2	LHCB1.5, PHOTOSYSTEM II
								LIGHT HARVESTING
								COMPLEX GENE 1.5
					AT1G29910	0.4	CAB3	AB180, LHCB1.2, LIGHT
								HARVESTING CHLOROPHYLL
								A/B BINDING PROTEIN 1.2
55	Zm00001d0	2.9		Serine	AT3G17180	0.5	scpl33	serine carboxypeptidase-like
	09178			carboxypeptidase- like 33				33
56	Zm00001d0	2.8	pco070301	MtN19-like protein	AT5G61820	1.4		stress up-regulated Nod 19
	31677							protein
57	Zm00001d0	2.8		Rhodanese-like	AT4G01050	2.3	TROL	thylakoid rhodanese-like
	16100			domain-				protein
				containing protein 4
				chloroplastic
58	Zm00001d0	2.6		Phospholipase A1-	AT2G30550	0.6	DALL3	alpha/beta-Hydrolases
	10463			Igamma1				superfamily protein, DAD1-
				chloroplastic				Like Lipase 3
59	Zm00001d0	2.5	stcl	sesquiterpene	AT4G20230	0.3		terpenoid synthase
	45054			cyclase1				superfamily protein
60	Zm00001d0	2.5	nrt5	nitrate transports	AT1G12940	7.9	NRT2.5	ATNRT2.5, nitrate
	11679							transporter2.5
61	Zm00001d0	2.5	npi447a	agal1; alpha-	AT5G08370	9.0	AGAL2	AtAGAL2, alpha-galactosidase
	32605			galactosidase1: Entrez				2
				Gene relates to alpha-
				galactosidase 1 (AGAL)
				of Arabidopsis
62	Zm00001d0	2.5	oec33	oxygen evolving	AT5G66570	0.4	PSBO1	MSP-1, MANGANESE-
	36535			complex, 33 kDa				STABILIZING PROTEIN
				subunit				1, OE33, OXYGEN EVOLVING
								COMPLEX 33 KILODALTON
								PROTEIN, OEE1, 33 KDA
								OXYGEN EVOLVING
								POLYPEPTIDE
								1, OEE33, OXYGEN EVOLVING
								ENHANCER PROTEIN
								33, PSBO-1, PS II OXYGEN-
								EVOLVING COMPLEX 1
					AT3G50820	1.7	PSBO2	OEC33, OXYGEN EVOLVING
								COMPLEX SUBUNIT 33
								KDA, PSBO-2, PHOTOSYSTEM
								II SUBUNIT O-2
63	Zm00001d0	2.4	pco129777	Phospho-	AT1G48600	0.6	PMEAMT	AtPMEAMT
	11642		b	ethanolamine
				N-methyl-
				transferase 3
64	Zm00001d0	2.4		cytochrome P450	AT3G14690	0.7	CYP72A15	cytochrome P450, family 72,
	11418			family 72 subfamily				subfamily A, polypeptide 15
				A polypeptide 8
65	Zm00001d0	2.4		Agmatine deiminase	AT5G08170	2.5	EMB1873	ATAIH, AGMATINE
	25644							IMINOHYDROLASE
66	Zm00001d0	2.4		Syntaxin-81	AT1G51740	2.4	SYP81	ATSYP81, ATUFE1,
	25915							ARABIDOPSIS
								THALIANA ORTHOLOG OF
								YEAST UFE1 (UNKNOWN
								FUNCTION-ESSENTIAL
								1), UFE1, ORTHOLOG OF
								YEAST UFE1 (UNKNOWN
								FUNCTION-ESSENTIAL 1),
								Rhodanese/Cell cycle control
								phosphatase superfamily
								protein
67	Zm00001d0	2.4		Rhodanese-like	AT2G42220	0.7
	19899			domain-
				containing protein 9
				chloroplastic
68	Zm00001d0	2.3		Vacuolar-sorting	AT2G14740	0.4	VSR3	ATVSR3, vaculolar sorting
	14303			receptor 4				receptor 3, BP80-2; 2, binding
								protein of 80 kDa
								2; 2, VSR2; 2, VACUOLAR
								SORTING RECEPTOR 2; 2
69	Zm00001d0	2.3	amo1	amine oxidase1	AT4G12290	1.5		Copper amine oxidase family
	25103							protein
70	Zm00001d0	2.3		Photosystem	AT1G31330	1.4	PSAF	photosystem I subunit F
	13146			I reaction
				center subunit III
				chloroplastic
71	Zm00001d0	2.3	psah1	photosystem I H	AT3G16140	0.3	PSAH-1	photosystem I subunit H-1
	38984			subunit1
72	Zm00001d0	2.3	atg18d	autophagy18d	AT3G56440	0.3	ATG18D	ATATG18D, homolog of yeast
	08691							autophagy 18 (ATG18) D
73	Zm00001d0	2.1	Iox5	lipoxygenase5	AT3G22400	4.6	LOX5	PLAT/LH2 domain-containing
	13493							lipoxygenase family protein
74	Zm00001d0	2.1	cdc2	cell division control	AT3G48750	1.9	CDC2	CDC2A, CDC2AAT, CDK2,
	27373			protein homolog2				CDKA 1, CDKA; 1
75	Zm00001d0	2.1	Iox1	lipoxygenase1	AT3G22400	4.6	LOX5	PLAT/LH2 domain-containing
	42541							lipoxygenase family protein
76	Zm00001d0	2.1	psb29	photosystem II	AT3G08940	3.8	LHCB4.2	light harvesting complex
	21763			subunit29				photosystem II
					AT5G01530	2.0	LHCB4.1	light harvesting complex
								photosystem II
77	Zm00001d0	2.1	hex3	hexokinase3	AT2G19860	3.3	HXK2	ATHXK2, ARABIDOPSIS
	10796							THALIANA HEXOKINASE 2
78	Zm00001d0	2.1		Vacuolar protein	AT3G49645	1.4	FAD-
	34796			sorting-			binding
				associated protein 9A			protein
79	Zm00001d0	2.0		Peptidyl-prolyl	AT3G25220	6.2	FKBP15-1	FK506-binding protein 15 kD-
	21021			cis-trans				1
				isomerase
80	Zm00001d0	2.0		S-adenosyl-L-	AT2G41380	1.7		S-adenosyl-L-methionine-
	09084			methionine-				dependent
				dependent				methyltransferases
				methyltransferases				superfamily protein
				superfamily protein
81	Zm00001d0	1.9		alpha/beta-Hydrolases	AT5G38220	9.1		alpha/beta-Hydrolases
	11624			superfamily protein				superfamily protein
82	Zm00001d0	1.9	peamt2	Phosphoethanolamine	AT1G48600	0.6	PMEAMT	AtPMEAMT
	38891			N-methyltransferase 3
83	Zm00001d0	1.8		Methyl-CpG-binding	AT5G52230	1.1	MBD13	methyl-CPG-binding domain
	24306			domain-containing				protein 13
				protein 13
84	Zm00001d0	1.8		chaperone protein	AT5G43260	1.4		chaperone protein dnaJ-like
	16561			dnaJ-related				protein
85	Zm00001d0	1.8	sqd1	sulfolipid	AT4G33030	9.8	SQD1	sulfoquinovosyldiacylglycerol
	09967			biosynthesis1				1
86	Zm00001d0	1.8	mpk4	MAP kinase4	AT4G01370	1.3	MPK4	ATMPK4, MAP kinase
	24568							4, MAPK4
					AT1G01560	1.0	MPK11	ATMPK11, MAP kinase 11
87	Zm00001d0	1.7		Phospho-2-dehydro-	AT1G22410	4.3		Class-11 DAHP synthetase
	06900			3-deoxyheptonate				family protein
				aldolase 2 chloroplastic
88	Zm00001d0	1.7		actin binding protein	AT1G52080	9.7	AR791	actin binding protein family
	37695			family
89	Zm00001d0	1.7	rl1	radialis homolog1	AT1G19510	2.0
	39118				AT4G39250	6.9
90	Zm00001d0	1.5		Histidine-containing	AT3G16360	4.1	AHP4	HPT phosphotransmitter 4
	10791			phosphotransfer
				protein 4
91	Zm00001d0	1.5		Protein	AT4G11910	3.6	NYE2,	STAY-GREEN-like protein
	06211			STAY-GREEN 1			NONY
				chloroplastic			ELLOWING
							2, SGR2,
							STAY-
							GREEN 2
					AT4G22920	0.6	NYE1	ATNYE1, NON-YELLOWING
								1, SGR1, STAY-GREEN
								1, SGR, STAY-GREEN
92	Zm00001d0	1.5		Histone deacetylase	AT2G45640	6.3	SAP18	ATSAP18, SIN3 ASSOCIATED
	15058			complex subunit				POLYPEPTIDE 18
				SAP18
93	Zm00001d0	1.5		Probable	AT3G23790	7.7	AAE16	AMP-dependent synthetase
	34832			acyl-activating				and ligase family protein
				enzyme 16
				chloroplastic
94	Zm00001d0	1.5		Chlorophyll	AT3G47470	1.0	LHCA4	CAB4
	50403			a-b binding
				protein 4
95	Zm00001d0	1.4	mrpa10	multidrug resistance	AT3G59140	4.1	ABCC10	ATMRP14, multidrug
	31447			protein associated10				resistance-associated protein
								14, MRP14, multidrug
								resistance-associated protein
								14
96	Zm00001d0	1.4		Photosystem	AT1G55670	0.6	PSAG	photosystem I subunit G
	20877			I reaction
				center subunit V
				chloroplastic
97	Zm00001d0	1.3		Nuclear pore	AT3G15970	3.1	NUP50	(Nucleoporin 50 kDa) protein
	43757			complex
				protein NUP50A
98	Zm00001d0	1.3	gpm930	Photosystem	AT4G28750	0.8	PSAE-1	Photosystem I reaction
	19518			I reaction				centre subunit IV/PsaE
				center subunit IV A				protein
					AT2G20260	0.4	PSAE-2	photosystem I subunit E-2
99	Zm00001d0	1.3	IDP755	D111/G-patch	AT1G63980	12.5		D111/G-patch domain-
	27444			domain-				containing protein
				containing protein
100	Zm00001d0	1.3		GTP-binding protein	AT5G57960	2.3	HfIx	GTP-binding protein, HfIX
	48944			hfIX
101	Zm00001d0	1.3		Glucomannan	AT5G22740	1.1	CSLA02	ATCSLA02, ARABIDOPSIS
	53696			4-beta-				THALIANA CELLULOSE
				mannosyl-				SYNTHASE-LIKE
				transferase 2				A02, ATCSLA2, ARABIDOPSIS
								THALIANA CELLULOSE
								SYNTHASE-LIKE
								A2, CSLA2, CELLULOSE
								SYNTHASE-LIKE A 2
102	Zm00001d0	1.3	umc1383	lhcb9; light	AT2G05070	4.2	LHCB2.2	LHCB2, LIGHT-HARVESTING
	33132			harvesting				CHLOROPHYLL B-BINDING 2
				chlorophyll binding
				protein9: cDNA
				sequence is a classll
				Ihcb, unlike previously
				characterized lhcb genes
				which are class1
				(Viret et al 1993)
103	Zm00001d0	1.2	lhcb2	light harvesting	AT2G34430	0.4	LHB1B1	LHCB1.4,
	21435			chlorophyll a/b				LIGHT-HARVESTING
				binding				CHLOROPHYLL-PROTEIN
				protein2				COMPLEX II SUBUNIT B1
					AT2G34420	0.6	LHB1B2	LHCB1.5, PHOTOSYSTEM II
								LIGHT HARVESTING
								COMPLEX GENE 1.5
					AT1G29910	0.4	CAB3	AB180, LHCB1.2, LIGHT
								HARVESTING CHLOROPHYLL
								A/B BINDING PROTEIN 1.2
104	Zm00001d0	1.2		Nuclear transport	AT5G04830	1.2		Nuclear transport factor 2
	11799			factor				(NTF2) family protein
				2 (NTF2) family
				protein
105	Zm00001d0	1.2		Pollen Ole e 1	AT5G15780	1.1		Pollen Ole e 1 allergen and
	52518			allergen				extensin family protein
				and extensin family
				protein
106	Zm00001d0	1.1		Chlorophyll a-b	AT3G61470	0.5	LHCA2	photosystem I light
	21906			binding protein				harvesting complex protein
107	Zm00001d0	1.1	pip1b	plasma membrane	AT4G23400	0.6	PIP1; 5	PIP1D
	17526			intrinsic protein1	AT1G01620	0.9	PIP1C	PIP1; 3, PLASMA MEMBRANE
								INTRINSIC PROTEIN 1; 3,
								TMP-B
108	Zm00001d0	1.1		D-xylose-proton	AT5G17010	2.8		Major facilitator superfamily
	14435			symporter-like l				protein
109	Zm00001d0	1.1	psad1	photosystem I	AT4G02770	0.5	PSAD-1	photosystem I subunit D-1
	13039			subunit d1
110	Zm00001d0	1.1		photosystem II light	AT2G34430	0.4	LHB1B1	LHCB1.4,
	44401			harvesting complex				LIGHT-HARVESTING
				gene B1B2				CHLOROPHYLL-PROTEIN
								COMPLEX II SUBUNIT B1
					AT2G34420	0.6	LHB1B2	LHCB1.5, PHOTOSYSTEM II
								LIGHT HARVESTING
								COMPLEX GENE 1.5
					AT1G29910	0.4	CAB3	AB180, LHCB1.2, LIGHT
								HARVESTING CHLOROPHYLL
								A/B BINDING PROTEIN 1.2
ill	Zm00001d0	1.1		Phospho-2-dehydro-	AT1G22410	4.3		Class-II DAHP synthetase
	22181			3-deoxyheptonate				family protein
				aldolase 1
112	Zm00001d0	1.1	AY111834	Cytochrome P450	AT2G46660	1.8	CYP78A6	EOD3, enhancer of da1-1
	32042			CYP78A53
113	Zm00001d0	1.1	elip2	early light inducible	AT3G22840	0.2	ELIP1	ELIP
	18940			protein2	AT4G14690	0.0	ELIP2	Chlorophyll A-B binding
								family protein
114	Zm00001d0	1.1		Chlorophyll	AT3G47470	1.0	LHCA4	CAB4
	32197			a-b binding
				protein 4
				chloroplastic
115	Zm00001d0	1.1	d9	dwarf plant9	AT1G14920	0.5
	13465
116	Zm00001d0	1.1		hydroxyproline-rich	AT2G39050	1.8	EULS3	ArathEULS3
	40190			glycoprotein family
				protein
117	Zm00001d0	1.1		405 ribosomal	AT4G09800	8.2	RPS18C	S18 ribosomal protein
	34422			protein 518
118	Zm00001d0	1.0		Transcription factor	AT3G20640	3.6
	43248			bHLH112
119	Zm00001d0	1.0	alia1	allantoinase1	AT4G04955	15.7	ALN	ATALN, allantoinase
	26635
120	Zm00001d0	1.0	cncr1	cinnamoyl CoA	AT1G80820	4.6	CCR2	ATCCR2
	32152			reductase1
121	Zm00001d0	1.0		BTB/POZ domain-	AT1G55760	10.5	SIBP1	BTB/POZ domain-containing
	52837			containing protein				protein
122	Zm00001d0	1.0	pspb2	photosystem	AT1G06680	2.6	PSBP-1	OE23, OXYGEN EVOLVING
	18779			II oxygen				COMPLEX SUBUNIT 23
				evolving				KDA, OEE2, OXYGEN-
				polypeptide2				EVOLVING ENHANCER
								PROTEIN 2, PSII-
								P, PHOTOSYSTEM II SUBUNIT
								P
123	Zm00001d0	1.0		Alanine-glyoxylate	AT4G39660	14.4	AGT2	alanine: glyoxylate
	27861			aminotransferase 2				aminotransferase 2
				homolog 1
				mitochondrial
124	Zm00001d0	0.9		Serine/threonine-	AT1G65800	0.8	RK2	ARK2, receptor kinase
	12609			protein kinase				2, AtARK2
125	Zm00001d0	0.9		Serine/threonine-	AT4G21380	0.3	RK3	ARK3, receptor kinase 3
	12609			protein kinase	AT1G65790	0.1	RK1	ARK1, receptor kinase 1
126	Zm00001d0	0.9		Encodes a protein	AT1G66480	11.5		plastid movement impaired 2
	32233			whose expression
				is responsive
				to nematode infection.
127	Zm00001d0	0.9		Putative	AT5G60900	0.1	RLK1	receptor-like protein kinase 1
	25035			D-mannose
				binding lectin family
				receptor-like protein
				kinase
128	Zm00001d0	0.9		ubiquitin-associated	AT1G04850	5.0		ubiquitin-associated
	50551			(UBA)/TS-N				(UBA)/TS-N domain-
				domain-				containing protein
				containing protein
129	Zm00001d0	0.8		Peptide transporter	AT1G52190	0.4	AtNPF1.2,	Major facilitator superfamily
	43374			PTR2			NP	protein
							F1.2, NRT1/
							PTR family
							1.2,
							NRT1.11
130	Zm00001d0	0.8	psan1	photosystem I N	AT5G64040	0.8	PSAN	photosystem I reaction
	41819			subunit1				center subunit PSI-N,
								chloroplast, putative/PSI-N,
								putative (PSAN)
131	Zm00001d0	0.8	pba1	PBA1 homolog1	AT4G01150	0.8	CURT1A	CURVATURE THYLAKOID
	27456							1A-like protein
132	Zm00001d0	0.8	psan2	photosystem I N	AT5G64040	0.8	PSAN	photosystem I reaction
	23713			subunit2				center subunit PSI-N,
								chloroplast, putative/PSI-N,
								putative (PSAN)
133	Zm00001d0	0.8	TIDP3460	cytochrome	AT1G57750	2.5	CYP96A15	MAH1, MID-CHAIN ALKANE
	27601			P450 family				HYDROXYLASE 1
				96 subfamily A
				polypeptide 1
134	Zm00001d0	0.8		Zn-dependent	AT4G33540	0.6		met allo-beta-lactamase
	51842			hydrolase%2C				family protein
				including
				glyoxylase
135	Zm00001d0	0.8		Peroxiredoxin-5	AT3G52960	1.1		Thioredoxin superfamily
	46682							protein
136	Zm00001d0	0.8	ago101	argonaute101	AT5G43810	1.8	AGO10	PNH, PINHEAD, ZLL,
	46438							ZWILLE
137	Zm00001d0	0.8		alpha/beta-	AT4G39955	9.1		alpha/beta-Hydrolases
	22182			Hydrolases				superfamily protein
				superfamily protein
138	Zm00001d0	0.8		Thioredoxin M1	AT3G15360	1.2	TRX-M4	ATHM4, ATM4, ARABIDOPSIS
	17379			chloroplastic				THIOREDOXIN M-TYPE 4
139	Zm00001d0	0.8		SPIa/RYanodine	AT1G35470	15.3	RanBPM	SPIa/RYanodine receptor
	16825			receptor				(SPRY) domain-containing
				(SPRY) domain-				protein
				containing protein	AT4G09340	1.5		SPIa/RYanodine receptor
								(SPRY) domain-containing
								protein
140	Zm00001d0	0.7		Phosphatase	AT1G17710	0.6	PEPC1	AtPEPC1, Arabidopsis thaliana
	43621			phospho1				phosphoethanolamine/phos
								phocholine phosphatase 1
141	Zm00001d0	0.7		UDP-glucuronic acid	AT5G59290	0.5	UXS3	ATUXS3
	47797			decarboxylase 5
142	Zm00001d0	0.7		Probable	AT1G79110	3.1	BRG2	zinc ion binding protein
	33419			BOI-related
				E3 ubiquitin-protein
				ligase 2
143	Zm00001d0	0.7	IDP518	Chlorophyll	AT2G34430	0.4	LHB1B1	LHCB1.4,
	44396			a-b binding				LIGHT-HARVESTING
				protein 48% 2C				CHLOROPHYLL-PROTEIN
				chloroplastic				COMPLEX II SUBUNIT B1
					AT2G34420	0.6	LHB1B2	LHCB1.5, PHOTOSYSTEM II
								LIGHT HARVESTING
								COMPLEX GENE 1.5
					AT1G2991O	0.4	CAB3	AB180, LHCB1.2, LIGHT
								HARVESTING CHLOROPHYLL
								A/B BINDING PROTEIN 1.2
144	Zm00001d0	0.7	gst31	glutathione	AT1G59700	0.4	GSTU16	ATGSTU16, glutathione S-
	27557			transferase31				transferase TAU 16
					AT1G59670	0.6	GSTU15	ATGSTU15, glutathione S-
								transferase TAU 15
145	Zm00001d0	0.7	pco123453	S-adenosyl-L-	AT4G28830	1.4		S-adenosyl-L-methionine-
	36274			methionine-				dependent
				dependent				methyltransferases
				methyltransferases				superfamily protein
				superfamily protein
146	Zm00001d0	0.6	idh1	isocitrate	AT1G65930	0.8	cICDH	cytosolic NADP+-dependent
	11487			dehydrogenase1				isocitrate dehydrogenase
147	Zm00001d0	0.6	pip1e	plasma membrane	AT4G23400	0.6	PIP1; 5	PIP1D
	51872			intrinsic proteinl	AT1G01620	0.9	PIP1C	PIP1; 3, PLASMA MEMBRANE
								INTRINSIC PROTEIN 1; 3,
								TMP-B
148	Zm00001d0	0.6		Putative leucine-rich	AT1G28440	0.5	HSL1	HAESA-like 1
	09029			repeat receptor-like
				protein kinase family
				protein
149	Zm00001d0	0.6		Metallothionein-like	AT5G02380	1.9	MT2B	metallothionein 2B
	39914			protein type 2
150	Zm00001d0	0.6		Snf1-related kinase	AT1G80940	2.6		Snf1 kinase interactor-like
	18364			interacting protein SKI1				protein
151	Zm00001d0	0.6		ROTUNDIFOLIA	AT2G39705	0.6	RTFL8	DVL11, DEVIL 11
	28598			like 8
152	Zm00001d0	0.6		ATPase	AT4G28070	0.6		AFG1-like ATPase family
	25892							protein
153	Zm00001d0	0.6		Ultraviolet-B-	AT2G06520	0.7	PSBX	photosystem II subunit X
	39715			repressible
				protein
154	Zm00001d0	0.6		Protein LRP16	AT2G40600	0.3		appr-1-p processing enzyme
	29065							family protein
155	Zm00001d0	0.6		Proline oxidase	AT3G30775	4.5	ERD5	AT-
	47124							POX, ATPDH, ATPOX,
								ARABIDOPSIS
								THALIANA PROLINE
								OXIDASE, PDH1, proline
								dehydrogenase
								1, PRO1, PRODH, PROLINE
								DEHYDROGENASE
156	Zm00001d0	0.6		NAD(P)-linked	AT1G59950	1.1		NAD(P)-linked
	28360			oxidoreductase				oxidoreductase superfamily
				superfamily protein				protein
157	Zm00001d0	0.6	gdh1	glutamic	AT3G03910	4.1	GDH3	glutamate dehydrogenase 3
	34420			dehydrogenase1
158	Zm00001d0	0.6		Putative calcium-	AT4G09570	0.7	CPK4	ATCPK4
	23560			dependent protein
				kinase family protein
159	Zm00001d0	0.5	gpm345	NAD(P)H	AT4G27270	1.1		Quinone reductase family
	12607			dehydrogenase				protein
				(quinone) FQR1
160	Zm00001d0	0.5	oec33b	oxygen-evolving	AT5G66570	0.4	PSBO1	MSP-1, MANGANESE-
	14564			complex 33 kda				STABILIZING PROTEIN
				protein b				1, OE33, OXYGEN EVOLVING
								COMPLEX 33 KILODALTON
								PROTEIN, OEE1, 33 KDA
								OXYGEN EVOLVING
								POLYPEPTIDE
								1, OEE33, OXYGEN EVOLVING
								ENHANCER PROTEIN
								33, PSBO-1, PS II OXYGEN-
								EVOLVING COMPLEX 1
					AT3G50820	1.7	PSBO2	OEC33, OXYGEN EVOLVING
								COMPLEX SUBUNIT 33
								KDA, PSBO-2, PHOTOSYSTEM
								II SUBUNIT O-2
161	Zm00001d0	0.5	kch1	potassium	AT2G26650	1.8	KT1	AKT1, K+ transporter
	44056			channel 1				1, ATAKT1
162	Zm00001d0	0.5		3′-5′ exonuclease	AT2G25910	2.9		3′-5′ exonuclease domain-
	44243			domain-containing				containing protein/K
				protein/K homology				homology domain-containing
				domain-containing				protein/KH domain-
				protein/KH domain-				containing protein
				containing protein
163	Zm00001d0	0.5	abh3	abscisic acid 8′-	AT3G19270	0.5	CYP707A4	cytochrome P450, family
	50021			hydroxylase3				707, subfamily A,
								polypeptide 4
164	Zm00001d0	0.5		protein; Expressed	AT5G16110	6.5		hypothetical protein
	41410			protein
165	Zm00001d0	0.5	see2b	senescence	AT4G32940	1.7		GAMMAVPE
	44495			enhanced2b
166	Zm00001d0	0.5		Chaperone	AT1G16680	5.6		Chaperone DnaJ-domain
	41488			DnaJ-domain				superfamily protein
				superfamily protein
167	Zm00001d0	0.4		Serine	AT4G12910	6.9	scpl20	serine carboxypeptidase-like
	41769			carboxypeptidase-				20
				like20
168	Zm00001d0	0.4		FAD/NAD(P)-	AT4G38540	0.4		FAD/NAD(P)-binding
	48416			binding				oxidoreductase family
				oxidoreductase family				protein
				protein
169	Zm00001d0	0.4		chaperone protein	AT2G24395	0.7		chaperone protein dnaJ-like
	31514			dnaJ-related				protein
170	Zm00001d0	0.4		Ultraviolet-B-	AT2G06520	0.7	PSBX	photosystem II subunit X
	22464			repressible
				protein
171	Zm00001d0	0.4		Photosystem II repair	AT1G03600	2.6	PSB27	photosystem II family protein
	29049			protein PSB27-H1
				chloroplastic
172	Zm00001d0	0.4			AT4G11910	3.6	NYE2,	STAY-GREEN-like protein
	21288						NONY
							ELLOWING
				Senescence-inducible			2, SGR2,
				chloroplast			STAY-
				stay-green			GREEN 2
				protein 1	AT4G22920	0.6	NYE1	ATNYE1, NON-YELLOWING
								1, SGR1, STAY-GREEN
								1, SGR, STAY-GREEN
173	Zm00001d0	0.4		RNase L inhibitor	AT5G10070	30.6		RNase L inhibitor protein-like
	48190			protein-related				protein
174	Zm00001d0	0.4		GDSL	AT1G28580	0.3		GDSL-like
	44465			esterase/lipase				Lipase/Acylhydrolase
								superfamily protein
					AT1G28570	13.3		SGNH hydrolase-type
								esterase superfamily protein
175	Zm00001d0	0.4	rte2	rotten ear2	AT3G62270	2.1	BOR2,	HCO3-transporter family
	41590						REQUIRES
							HIGH
							BORON 2
176	Zm00001d0	0.4	gst19	glutathione	AT1G17170	0.8	GSTU24	ATGSTU24, glutathione S-
	36951			transferase19				transferase TAU
								24, GST, Arabidopsis thaliana
								Glutathione S-transferase
								(class tau) 24
177	Zm00001d0	0.3	amt1	ammonium	AT4G13510	0.4	AMT1; 1	ATAMT1; 1, ATAMT1,
	25831			transporter1				ARABIDOPSIS THALIANA
								AMMONIUM TRANSPORT 1
178	Zm00001d0	0.3	mdh4	malate	AT1G04410	6.9	c-NAD-	Lactate/malate
	32695			dehydrogenase4			MDH1	dehydrogenase family
								protein
179	Zm00001d0	0.3		Cytochrome c	AT1G53030	11.8	COX17	Cytochrome C oxidase
	52040			oxidase copper				copper chaperone (COX17)
				chaperone%3B
				Cytochrome c oxidase
				copper chaperone
				isoform 1% 3B
				Cytochrome c oxidase
				copper chaperone
				isoform 2
180	Zm00001d0	0.3		ATPase%2C	AT1G71960	3.6	ABCG25	ATABCG25, Arabidopsis
	53049			coupled				thaliana ATP-binding
				to transmembrane				cassette G25
				movement of
				substance%3B
				ATPase%2C coupled
				to transmembrane
				movement of
				substances
181	Zm00001d0	0.3		SGF29 tudor-like	AT3G27460	16.0	SGF29a	AtSGF29a
	23689			domain
182	Zm00001d0	0.3		Transmembrane 9	AT5G25100	0.1		Endomembrane protein 70
	24141			superfamily				protein family
				member 9	AT5G10840	1.3	EMP1	Endomembrane protein 70
								protein family
					AT2G24170	0.5		Endomembrane protein 70
								protein family
183	Zm00001d0	0.3		Serine	AT3G17180	0.5	scpl33	serine carboxypeptidase-like
	40741			carboxypeptidase- like 33				33
184	Zm00001d0	0.3		Protein FREE1	AT1G20110	0.5	FREE1	RING/FYVE/PHD zinc finger
	21878							superfamily protein
185	Zm00001d0	0.3	cys2	cysteine synthase2	AT4G14880	1.2	OASA1	ATCYS-
	31136							3A, CYTACS1, OLD3, ONSET
								OF LEAF DEATH 3
186	Zm00001d0	0.3	mate6	multidrug and toxic	AT4G39030	0.3	EDS5	SCORD3, susceptible to
	15060			compound				coronatine-deficient Pst
				extrusion6				DC3000 3, SID1, SALICYLIC
								ACID INDUCTION
								DEFICIENT 1
187	Zm00001d0	0.3		evolutionarily	AT1G79270	0.4	ECT8	evolutionarily conserved C-
	43860			conserved				terminal region 8
				C-terminal region 8
188	Zm00001d0	0.3		Photosystem II repair	AT1G03600	2.6	PSB27	photosystem II family protein
	47532			protein PSB27-H1
				chloroplastic
189	Zm00001d0	0.3		NAD(P)H	AT4G27270	1.1		Quinone reductase family
	43249			dehydrogenase				protein
				(quinone) FQR1
190	Zm00001d0	0.3		Cytochrome	AT2G40890	3.4	CYP98A3	REF8, REDUCED EPIDERMAL
	43174			P450 98A3				FLUORESCENCE 8
191	Zm00001d0	0.2		Tryptophan	AT1G34060	19.5		Pyridoxal phosphate (PLP)-
	43651			aminotransferase-				dependent transferases
				related protein 4				superfamily protein
192	Zm00001d0	0.2		ROTUNDIFOLIA	AT2G39705	0.6	RTFL8	DVL11, DEVIL 11
	47820			like 8
193	Zm00001d0	0.2		Phosphatidylinositol	AT4G00440	4.5	TRM15	GPI-anchored adhesin-like
	48540			N-acety-				protein, putative (DUF3741)
				glucosaminly-
				transferase subunit P-related
194	Zm00001d0	0.2	cyp11	cytochrome P450 11	AT3G14690	0.7	CYP72A15	cytochrome P450, family 72,
	44159							subfamily A, polypeptide 15
195	Zm00001d0	0.2		F11F12.5 protein	AT3G20300	4.4		extracellular ligand-gated ion
	46652							channel protein (DUF3537)
196	Zm00001d0	0.2		Photosystem II	AT2G30570	0.4	PSBW	photosystem II reaction
	43299			reaction				center W
				center W protein
				chloroplastic
197	Zm00001d0	0.2		Ribosomal protein	AT4G22380	0.3		Ribosomal protein
	47958			L7Ae/L30e/S12e/				L7Ae/L30e/S12e/Gadd45
				Gadd4				family protein
				5 family protein
198	Zm00001d0	0.2		Grx_A2-gluta	AT4G33040	0.8		Thioredoxin superfamily
	39468			redoxin				protein
				subgroup III
199	Zm00001d0	0.2		Putative membrane	AT5G59350	8.9		transmembrane protein
	37644			lipoprotein
200	Zm00001d0	0.2		HIT-type Zinc finger	AT4G28820	14.1		HIT-type Zinc finger family
	42997			family protein				protein
201	Zm00001d0	0.2		PIF/Ping-Pong	AT5G12010	0.5		nuclease
	44300			family
				of plant transposases
202	Zm00001d0	0.2		Glutathione S-	AT1G59700	0.4	GSTU16	ATGSTU16, glutathione S-
	43795			transferase GSTU6				transferase TAU 16
					AT1G59670	0.6	GSTU15	ATGSTU15, glutathione S-
								transferase TAU 15
203	Zm00001d0	0.1		GINS complex	AT1G19080	10.2	TTN10	PSF3, Partner of SLD5 3
	52742			protein
204	Zm00001d0	0.1		Photosystem II core	AT1G67740	4.9	PSBY	YCF32
	49650			complex protein psbY
205	Zm00001d0	0.1		5-hydroxyisourate	AT5G58220	0.3	TTL	ALNS, allantoin synthase
	47217			hydrolase
206	Zm00001d0	0.1		Tryptophan	AT1G34060	19.5		Pyridoxal phosphate (PLP)-
	43650			aminotransferase-				dependent transferases
				related protein 4				superfamily protein
207	Zm00001d0	0.1		F-box/kelch-repeat	AT2G44130	1.2	KFB39,	Galactose oxidase/kelch
	49016			protein SKIP20			KMD3,	repeat superfamily protein,
							KISS	Kelch-domain-containing F-
							ME	box protein 39
							DEADLY
							3
208	Zm00001d0	0.1	gid1	gibberellin-	AT3G05120	1.4	GID1A	ATGIDIA, GA INSENSITIVE
	38165			insensitive				DWARF1A
				dwarf protein
				homolog1	AT3G63010	0.5	GID1B	ATGID1B
209	Zm00001d0	0.0		cytoplasmic	AT1G33490	0.7		E3 ubiquitin-protein ligase
	53786			membrane
				protein

TABLE 7
224 MAIZE NON-TRANSCRIPTION FACTORS
				Machine
				learning Gene
				Importance to
				NUE
				(Cheng &
				Coruzzi 2021,
Row	Gene	Symbol	Description	Table S3)
1	Zm00001d002530			169.7
2	Zm00001d002426	morf2	multiple organellar RNA editing	128.9
			factor2
3	Zm00001d001857			96.5
4	Zm00001d002854			90.8
5	Zm00001d001804	mlo9	barley mio defense gene	74.4
			homolog9
6	Zm00001d002880	imd2	isopropylmalate dehydrogenase2	71.4
7	Zm00001d003767	pco139896	Photosystem I subunit O	59.7
8	Zm00001d003059		Probable low-specificity	51.0
			L-threonine aldolase 1
9	Zm00001d002261		Peroxisomal (S)-2-hydroxy-acid	40.8
			oxidase GLO1
10	Zm00001d002798		OJ000126_13.10 protein; protein	39.3
11	Zm00001d002503		ABC transporter C family member 9	38.5
12	Zm00001d005446		Photosystem I reaction center	36.7
			subunit IV A
13	Zm00001d002826		SAUR11-auxin-responsive SAUR	30.3
			family member
14	Zm00001d006098		Cyclopropane fatty acid synthase	26.9
15	Zm00001d008379	cys1	cysteine synthasel	26.1
16	Zm00001d003941		Probable metal-nicotianamine	20.8
			transporter YSL6
17	Zm00001d003129	hct5	hydroxycinnamoyltransferase5	18.6
18	Zm00001d003457		PLAT domain-containing protein 3	17.5
19	Zm00001d005317	pco080190	Amino acid binding protein	17.1
20	Zm00001d006793		Cysteine-rich receptor-like	16.1
			protein kinase 10
21	Zm00001d002999	ga2ox2	gibberellin 2-oxidase2	15.3
22	Zm00001d007827	elip1	early light inducible proteinl	15.0
23	Zm00001d005996		Photosystem I reaction center	14.6
			subunit V
24	Zm00001d007857	pspb1	photosystem II oxygen evolving	13.4
			polypeptide1
25	Zm00001d006267		Serine/threonine-protein kinase	12.6
			STY46
26	Zm00001d006193		cytochrome P450 family 78	12.5
			subfamily A polypeptide 8
27	Zm00001d005193		Protein LURP1	12.3
28	Zm00001d003446	IDP2449	Gamma-glutamyltranspeptidase 1	12.0
29	Zm00001d007383		Probable alpha-mannosidase	11.7
30	Zm00001d003459	cl11315_1a	Protein disulfide-isomerase LQY1	8.6
			chloroplastic
31	Zm00001d007274		Protein kinase Kelch repeat: Kelch	8.1
32	Zm00001d006140		UDP-glycosyltransferase 74B1	7.5
33	Zm00001d029853	pza03240	Proline oxidase	7.1
34	Zm00001d010867	pco112665	Bifunctional protein FolD 2	5.9
35	Zm00001d005657			5.9
36	Zm00001d020348			5.7
37	Zm00001d006540		Oxygen-evolving enhancer	5.7
			protein 3-1
38	Zm00001d011188		3′-5′ exonuclease domain-	5.7
			containing protein/K homology
			domain-containing protein/KH
			domain-containing protein
39	Zm00001d008974		Mitochondrial arginine	5.4
			transporter BAC2
40	Zm00001d006663	lhca1	light harvesting complex A1	5.4
41	Zm00001d008772		Serinc-domain containing serine	4.7
			and sphingolipid biosynthesis
			protein
42	Zm00001d017768			4.2
43	Zm00001d024637		L-type lectin-domain containing	4.2
			receptor kinase V.9
44	Zm00001d025665	umc1272	Probable amino acid permease 7	4.1
45	Zm00001d017186	hct6	hydroxycinnamoyltransferase6	4.0
46	Zm00001d014961		Tetratricopeptide repeat (TPR)-	3.9
			like superfamily protein
47	Zm00001d013086	AY109733	40S ribosomal protein S18	3.7
48	Zm00001d006137		UDP-glycosyltransferase 74B1	3.7
49	Zm00001d005997	mlkp3	Maize LINC KASH AtWIP-like3	3.6
50	Zm00001d026599	lhcb6	light harvesting chlorophyll a/b	3.5
			binding protein6
51	Zm00001d006274	TIDP2961	Auxin-responsive protein SAUR61	3.4
52	Zm00001d021334		Thioredoxin-like protein CDSP32	3.4
			chloroplastic
53	Zm00001d006160		DEAD-box ATP-dependent RNA	3.3
			helicase 7
54	Zm00001d038088		Sm-like protein LSM5	3.0
55	Zm00001d008681	GRMZM2G380414	Ultraviolet-B-repressible protein	3.0
56	Zm00001d018468	fdh1	formaldehyde dehydrogenase	2.9
			homolog1
57	Zm00001d006644	mkkk27	MAP kinase kinase kinase27	2.9
58	Zm00001d011285	lhcb10	light harvesting chlorophyll a/b	2.9
			binding protein10
59	Zm00001d009178		Serine carboxypeptidase-like 33	2.9
60	Zm00001d031677	pco070301	MtN19-like protein	2.8
61	Zm00001d016100		Rhodanese-like domain-	2.8
			containing protein 4 chloroplastic
62	Zm00001d006521			2.7
63	Zm00001d010463		Phospholipase A1-lgamma1	2.6
			chloroplastic
64	Zm00001d045054	stc1	sesquiterpene cyclase1	2.5
65	Zm00001d011679	nrt5	nitrate transports	2.5
66	Zm00001d032605	npi447a	agal1; alpha-galactosidase1:	2.5
			Entrez Gene relates to alpha-
			galactosidase 1 (AGAL) of
			Arabidopsis
67	Zm00001d036535	oec33	oxygen evolving complex, 33 kDa	2.5
			subunit
68	Zm00001d011642	pco129777b	Phosphoethanolamine N-	2.4
			methyltransferase 3
69	Zm00001d011418		cytochrome P450 family 72	2.4
			subfamily A polypeptide 8
70	Zm00001d025644		Agmatine deiminase	2.4
71	Zm00001d025915		Syntaxin-81	2.4
72	Zm00001d019899		Rhodanese-like domain-	2.4
			containing protein 9 chloroplastic
73	Zm00001d014303		Vacuolar-sorting receptor 4	2.3
74	Zm00001d025103	amo1	amine oxidase1	2.3
75	Zm00001d013146		Photosystem I reaction center	2.3
			subunit III chloroplastic
76	Zm00001d038984	psah1	photosystem I H subunit1	2.3
77	Zm00001d008691	atgl8d	autophagy18d	2.3
78	Zm00001d026026			2.3
79	Zm00001d013493	lox5	lipoxygenases	2.1
80	Zm00001d027373	cdc2	cell division control protein	2.1
			homolog2
81	Zm00001d042541	lox1	lipoxygenase1	2.1
82	Zm00001d021763	psb29	photosystem II subunit29	2.1
83	Zm00001d010796	hex3	hexokinase3	2.1
84	Zm00001d034796		Vacuolar protein sorting-	2.1
			associated protein 9A
85	Zm00001d021021		Peptidyl-prolyl cis-trans	2.0
			isomerase
86	Zm00001d009084		S-adenosyl-L-methionine-	2.0
			dependent methyltransferases
			superfamily protein
87	Zm00001d011624		alpha/beta-Hydrolases	1.9
			superfamily protein
88	Zm00001d038891	peamt2	Phosphoethanolamine	1.9
			N-methyltransferase 3
89	Zm00001d024306		Methyl-CpG-binding domain-	1.8
			containing protein 13
90	Zm00001d016561		chaperone protein dnaJ-related	1.8
91	Zm00001d009967	sqd1	sulfolipid biosynthesis1	1.8
92	Zm00001d030766			1.8
93	Zm00001d024568	mpk4	MAP kinase4	1.8
94	Zm00001d006900		Phospho-2-dehydro-3-	1.7
			deoxyheptonate aldolase 2
			chloroplastic
95	Zm00001d037695		actin binding protein family	1.7
96	Zm00001d032306			1.7
97	Zm00001d039118	rl1	radialis homolog1	1.7
98	Zm00001d010791		Histidine-containing	1.5
			phosphotransfer protein 4
99	Zm00001d006211		Protein STAY-GREEN 1	1.5
			chloroplastic
100	Zm00001d048497			1.5
101	Zm00001d015058		Histone deacetylase complex	1.5
			subunit SAP18
102	Zm00001d034832		Probable acyl-activating enzyme	1.5
			16 chloroplastic
103	Zm00001d050403		Chlorophyll a-b binding protein 4	1.5
104	Zm00001d031447	mrpa10	multidrug resistance protein	1.4
			associated10
105	Zm00001d016800			1.4
106	Zm00001d020877		Photosystem I reaction center	1.4
			subunit V chloroplastic
107	Zm00001d043757		Nuclear pore complex protein	1.3
			NUP50A
108	Zm00001d019518	gpm930	Photosystem I reaction center	1.3
			subunit IV A
109	Zm00001d027444	IDP755	D111/G-patch domain-containing	1.3
			protein
110	Zm00001d048944		GTP-binding protein hfIX	1.3
111	Zm00001d053696		Glucomannan 4-beta-	1.3
			mannosyltransferase 2
112	Zm00001d033132	umc1383	lhcb9; light harvesting chlorophyll	1.3
			binding protein9: cDNA sequence
			is a classII lhcb, unlike previously
			characterized lhcb genes which
			are class1 (Viret et al 1993)
113	Zm00001d021435	lhcb2	light harvesting chlorophyll a/b	1.2
			binding protein2
114	Zm00001d011799		Nuclear transport factor 2 (NTF2)	1.2
			family protein
115	Zm00001d052518		Pollen Ole e 1 allergen and	1.2
			extensin family protein
116	Zm00001d021906		Chlorophyll a-b binding protein	1.1
117	Zm00001d020264			1.1
118	Zm00001d017526	pip1b	plasma membrane intrinsic	1.1
			proteinl
119	Zm00001d016991			1.1
120	Zm00001d014435		D-xylose-proton symporter-like l	1.1
121	Zm00001d013039	psad1	photosystem I subunit d1	1.1
122	Zm00001d044401		photosystem II light harvesting	1.1
			complex gene B1B2
123	Zm00001d022181		Phospho-2-dehydro-3-	1.1
			deoxyheptonate aldolase 1
124	Zm00001d032042	AY111834	Cytochrome P450 CYP78A53	1.1
125	Zm00001d018940	elip2	early light inducible protein2	1.1
126	Zm00001d032197		Chlorophyll a-b binding protein 4	1.1
			chloroplastic
127	Zm00001d013465	d9	dwarf plant9	1.1
128	Zm00001d040190		hydroxyproline-rich glycoprotein	1.1
			family protein
129	Zm00001d034422		40S ribosomal protein S18	1.1
130	Zm00001d043248		Transcription factor bHLH112	1.0
131	Zm00001d026635	alla1	allantoinase1	1.0
132	Zm00001d032152	cncr1	cinnamoyl CoA reductasel	1.0
133	Zm00001d052837		BTB/POZ domain-containing	1.0
			protein
134	Zm00001d018779	pspb2	photosystem II oxygen evolving	1.0
			polypeptide2
135	Zm00001d027861		Alanine--glyoxylate	1.0
			aminotransferase 2 homolog l
			mitochondrial
136	Zm00001d012609		Serine/threonine-protein kinase	0.9
137	Zm00001d032233		Encodes a protein whose	0.9
			expression is responsive to
			nematode infection.
138	Zm00001d025035		Putative D-mannose binding	0.9
			lectin family receptor-like protein
			kinase
139	Zm00001d050551		ubiquitin-associated (UBA)/TS-N	0.9
			domain-containing protein
140	Zm00001d038346			0.9
141	Zm00001d019117			0.9
142	Zm00001d043374		Peptide transporter PTR2	0.8
143	Zm00001d041819	psan1	photosystem I N subunitl	0.8
144	Zm00001d027456	pba1	PBA1 homolog1	0.8
145	Zm00001d023713	psan2	photosystem I N subunit2	0.8
146	Zm00001d027601	TIDP3460	cytochrome P450 family 96	0.8
			subfamily A polypeptide 1
147	Zm00001d051842		Zn-dependent hydrolase % 2C	0.8
			including glyoxylase
148	Zm00001d046682		Peroxiredoxin-5	0.8
149	Zm00001d046438	ago101	argonaute101	0.8
150	Zm00001d022182		alpha/beta-Hydrolases	0.8
			superfamily protein
151	Zm00001d017379		Thioredoxin M1 chloroplastic	0.8
152	Zm00001d016825		SPIa/RYanodine receptor (SPRY)	0.8
			domain-containing protein
153	Zm00001d043621		Phosphatase phospho1	0.7
154	Zm00001d047797		UDP-glucuronic acid	0.7
			decarboxylase 5
155	Zm00001d033419		Probable BOI-related E3	0.7
			ubiquitin-protein ligase 2
156	Zm00001d044396	IDP518	Chlorophyll a-b binding protein	0.7
			48% 2C chloroplastic
157	Zm00001d027557	gst31	glutathione transferase31	0.7
158	Zm00001d036274	pco123453	S-adenosyl-L-methionine-	0.7
			dependent methyltransferases
			superfamily protein
159	Zm00001d011487	idh1	isocitrate dehydrogenasel	0.6
160	Zm00001d051872	pip1e	plasma membrane intrinsic	0.6
			protein1
161	Zm00001d009029		Putative leucine-rich repeat	0.6
			receptor-like protein kinase
			family protein
162	Zm00001d039914		Metallothionein-like protein type 2	0.6
163	Zm00001d018364		Snfl-related kinase interacting	0.6
			protein SKI1
164	Zm00001d028598		ROTUNDIFOLIA like 8	0.6
165	Zm00001d025892		ATPase	0.6
166	Zm00001d039715		Ultraviolet-B-repressible protein	0.6
167	Zm00001d029065		Protein LRP16	0.6
168	Zm00001d047124		Proline oxidase	0.6
169	Zm00001d028360		NAD(P)-linked oxidoreductase	0.6
			superfamily protein
170	Zm00001d034420	gdh1	glutamic dehydrogenasel	0.6
171	Zm00001d023560		Putative calcium-dependent	0.6
			protein kinase family protein
172	Zm00001d012607	gpm345	NAD(P)H dehydrogenase	0.5
			(quinone) FQR1
173	Zm00001d014564	oec33b	oxygen-evolving complex 33 kda	0.5
			protein b
174	Zm00001d044056	kch1	potassium channel 1	0.5
175	Zm00001d044243		3′-5′ exonuclease domain-	0.5
			containing protein/K homology
			domain-containing protein/KH
			domain-containing protein
176	Zm00001d050021	abh3	abscisic acid 8′-hydroxylase3	0.5
177	Zm00001d041410		protein; Expressed protein	0.5
178	Zm00001d044495	see2b	senescence enhanced2b	0.5
179	Zm00001d041488		Chaperone DnaJ-domain	0.5
			superfamily protein
180	Zm00001d041769		Serine carboxypeptidase-like 20	0.4
181	Zm00001d048416		FAD/NAD(P)-binding	0.4
			oxidoreductase family protein
182	Zm00001d031514		chaperone protein dnaJ-related	0.4
183	Zm00001d022464		Ultraviolet-B-repressible protein	0.4
184	Zm00001d029049		Photosystem II repair protein	0.4
			PSB27-H1 chloroplastic
185	Zm00001d021288		Senescence-inducible chloroplast	0.4
			stay-green protein 1
186	Zm00001d048190		RNase L inhibitor protein-related	0.4
187	Zm00001d044465		GDSL esterase/lipase	0.4
188	Zm00001d041590	rte2	rotten ear2	0.4
189	Zm00001d036951	gst19	glutathione transferase19	0.4
190	Zm00001d025831	amt1	ammonium transporter1	0.3
191	Zm00001d032695	mdh4	malate dehydrogenase4	0.3
192	Zm00001d052040		Cytochrome c oxidase copper	0.3
			chaperone % 3B Cytochrome c
			oxidase copper chaperone
			isoform 1% 3B Cytochrome c
			oxidase copper chaperone
			isoform 2
193	Zm00001d053049		ATPase % 2C coupled to	0.3
			transmembrane movement of
			substance % 3B ATPase % 2C
			coupled to transmembrane
			movement of substances
194	Zm00001d023689		SGF29 tudor-like domain	0.3
195	Zm00001d024141		Transmembrane 9 superfamily	0.3
			member 9
196	Zm00001d040741		Serine carboxypeptidase-like 33	0.3
197	Zm00001d021878		Protein FREE1	0.3
198	Zm00001d031136	cys2	cysteine synthase2	0.3
199	Zm00001d015060	mate6	multidrug and toxic compound	0.3
			extrusion6
200	Zm00001d043860		evolutionarily conserved	0.3
			C-terminal region 8
201	Zm00001d047532		Photosystem II repair protein	0.3
			PSB27-H1 chloroplastic
202	Zm00001d043249		NAD(P)H dehydrogenase	0.3
			(quinone) FQR1
203	Zm00001d043174	GRMZM2G138074	Cytochrome P450 98A3	0.3
204	Zm00001d043651		Tryptophan aminotransferase-	0.2
			related protein 4
205	Zm00001d047820		ROTUNDIFOLIA like 8	0.2
206	Zm00001d048540		Phosphatidylinositol N-	0.2
			acetyglucosaminlytransferase
			subunit P-related
207	Zm00001d048135			0.2
208	Zm00001d044159	cyp11	cytochrome P450 11	0.2
209	Zm00001d046652		F11F12.5 protein	0.2
210	Zm00001d043299		Photosystem II reaction center W	0.2
			protein chloroplastic
211	Zm00001d047958		Ribosomal protein	0.2
			L7Ae/L30e/S12e/Gadd45 family
			protein
212	Zm00001d039468		Grx_A2-glutaredoxin subgroup III	0.2
213	Zm00001d037644		Putative membrane lipoprotein	0.2
214	Zm00001d042997		HIT-type Zinc finger family	0.2
			protein
215	Zm00001d044300		PIF/Ping-Pong family of plant	0.2
			transposases
216	Zm00001d043795		Glutathione S-transferase GSTU6	0.2
217	Zm00001d038964			0.2
218	Zm00001d052742		GINS complex protein	0.1
219	Zm00001d049650		Photosystem II core complex	0.1
			protein psbY
220	Zm00001d047217		5-hydroxyisourate hydrolase	0.1
221	Zm00001d043650		Tryptophan aminotransferase-	0.1
			related protein 4
222	Zm00001d049016		F-box/kelch-repeat protein	0.1
			SKIP20
223	Zm00001d038165	gid1	gibberellin-insensitive dwarf	0.1
			protein homolog1
224	Zm00001d053786		cytoplasmic membrane protein	0.0

TABLE 8
547 ARABIDOPSIS NON-TRANSCRIPTION FACTORS
				Machine
				learning
				Gene
				Importance
				to NUE
				(Cheng &
				Coruzzi
				2021,
Row	Gene	Symbol	Description	Table S3)
1	AT5G10070		RNase L inhibitor protein-like protein	30.6
2	AT1G20550		O-fucosyltransferase family protein	21.0
3	AT3G59800		stress response protein	20.4
4	AT4G03240	FH	ATFH	20.3
5	AT1G34060		Pyridoxal phosphate (PLP)-dependent transferases superfamily	19.5
			protein
6	AT5G04940	SUVH1	SU(VAR)3-9 homolog 1	19.0
7	AT1G08630	THA1	threonine aldolase 1	18.0
8	AT4G22340	CDS2	cytidinediphosphate diacylglycerol synthase 2	17.6
9	AT2G34200		RING/FYVE/PHD zinc finger superfamily protein	17.6
10	AT1G79390		centrosomal protein	17.4
11	AT3G10220	EMB2804	tubulin folding cofactor B	16.6
12	AT2G29960	CYP5	ATCYP5, ARABIDOPSIS THALIANA CYCLOPHILIN 5, CYP19-4,	16.2
			CYCLOPHILIN 19-4
13	AT3G27460	SGF29a	AtSGF29a	16.0
14	AT4G04955	ALN	ATALN,allantoinase	15.7
15	AT5G53920		ribosomal protein L11 methyltransferase-like protein	15.3
16	AT5G59210		myosin heavy chain-like protein	15.3
17	AT1G35470	RanBPM	SPIa/RYanodine receptor (SPRY) domain-containing protein	15.3
18	AT4G39660	AGT2	alanine: glyoxylate aminotransferase 2	14.4
19	AT4G28820		HIT-type Zinc finger family protein	14.1
20	AT5G56460		Protein kinase superfamily protein	13.9
21	AT1G28570		SGNH hydrolase-type esterase superfamily protein	13.3
22	AT5G46620		hypothetical protein	13.3
23	AT5G57000		DEAD-box ATP-dependent RNA helicase	12.5
24	AT1G63980		D111/G-patch domain-containing protein	12.5
25	AT1G53030		Cytochrome C oxidase copper chaperone (COX17)	11.8
26	AT5G65480		CCI1, Clavata complex interactor 1	11.7
27	AT4G32660	AME3	Protein kinase superfamily protein	11.6
28	AT1G66480		plastid movement impaired 2	11.5
29	AT3G13224		RNA-binding (RRM/RBD/RNP motifs) family protein	11.0
30	AT3G20650		mRNA capping enzyme family protein	10.6
31	AT1G55760	SIBP1	BTB/POZ domain-containing protein	10.5
32	AT1G22160		senescence-associated family protein (DUF581)	10.4
33	AT1G19080	TTN10	PSF3, Partner of SLD5 3	10.2
34	AT4G33030	SQD1	sulfoquinovosyldiacylglycerol l	9.8
35	AT1G52080	AR791	actin binding protein family	9.7
36	AT4G01320	ATSTE24	Peptidase family M48 family protein	9.6
37	AT5G10100	TPPI	Haloacid dehalogenase-like hydrolase (HAD) superfamily protein	9.6
38	AT3G01560		proline-rich receptor-like kinase, putative (DUF1421)	9.3
39	AT4G39955		alpha/beta-Hydrolases superfamily protein	9.1
40	AT4G21200	GA20X8	ATGA2OX8, ARABIDOPSIS THALIANA GIBBERELLIN 2-OXIDASE 8	9.1
41	AT5G38220		alpha/beta-Hydrolases superfamily protein	9.1
42	AT5G64740	CESA6	E112, IXR2, ISOXABEN RESISTANT 2, PRC1, PROCUSTE 1	9.1
43	AT5G08370	AGAL2	AtAGAL2, alpha-galactosidase 2	9.0
44	AT1G14560	CoAc1, CoA Carrier 1	Mitochondrial substrate carrier family protein	9.0
45	AT5G59350		transmembrane protein	8.9
46	AT5G18130		transmembrane protein	8.6
47	AT5G64160		plant/protein	8.4
48	AT4G09800	RPS18C	S18 ribosomal protein	8.2
49	AT4G11560		bromo-adjacent homology (BAH) domain-containing protein	8.0
50	AT1G12940	NRT2.5	ATNRT2.5, nitrate transporter2.5	7.9
51	AT3G23790	AAE16	AMP-dependent synthetase and ligase family protein	7.7
52	AT4G25850	ORP4B	OSBP(oxysterol binding protein)-related protein 4B	7.5
53	AT1G58080	ATP-PRT1	ATATP-PRT1, ATP phosphoribosyl transferase 1, HISN1A	7.2
54	AT1G15110	PSS1	AtPSS1	7.0
55	AT1G04410	c-NAD-MDHl	Lactate/malate dehydrogenase family protein	6.9
56	AT4G12910	scpl20	serine carboxypeptidase-like 20	6.9
57	AT5G04090		histidine-tRNA ligase	6.8
58	AT1G03340		hypothetical protein	6.8
59	AT1G05560	UGT75B1	UGT1, UDP-GLUCOSE TRANSFERASE 1	6.8
60	AT5G09300		Thiamin diphosphate-binding fold (THDP-binding) superfamily	6.7
			protein
61	AT2G44950	HUB1	RDO4, REDUCED DORMANCY 4	6.5
62	AT5G1611O		hypothetical protein	6.5
63	AT3G13730	CYP90D1	cytochrome P450, family 90, subfamily D, polypeptide 1	6.4
64	AT5G59050		G patch domain protein	6.4
65	AT4G38250		Transmembrane amino acid transporter family protein	6.3
66	AT2G45640	SAP18	ATSAP18, SIN3 ASSOCIATED POLYPEPTIDE 18	6.3
67	AT1G73720	SMU1	transducin family protein/WD-40 repeat family protein	6.3
68	AT3G25220	FKBP15-1	FK506-binding protein 15 kD-1	6.2
69	AT4G00330	CRCK2	calmodulin-binding receptor-like cytoplasmic kinase 2	6.2
70	AT1G51560		Pyridoxamine 5′-phosphate oxidase family protein	6.1
71	AT5G19420		Regulator of chromosome condensation (RCC1) family with FYVE zinc	6.1
			finger domain-containing protein
72	AT3G47010		Glycosyl hydrolase family protein	6.0
73	AT1G42430		inactive purple acid phosphatase-like protein	5.8
74	AT1G15710		prephenate dehydrogenase family protein	5.6
75	AT1G16680		Chaperone DnaJ-domain superfamily protein	5.6
76	AT4G33180		alpha/beta-Hydrolases superfamily protein	5.6
77	AT1G08730	XIC	Myosin family protein with Dil domain-containing protein	5.5
78	AT5G54160	OMT1	ATOMT1, O-methyltransferase 1, AtCOMT, COMT1, caffeate	5.5
			O-methyltransferase 1, OMT3, O-methyltransferase 3
79	AT5G09830	BolA2	BolA-like family protein, homolog of E. coli BolA 2	5.2
80	AT1G63970	ISPF	MECPS, 2C-METHYL-D-ERYTHRITOL 2,4-CYCLODIPHOSPHATE	5.1
			SYNTHASE
81	AT5G48930	HCT	hydroxycinnamoyl-CoA shikimate/quinate hydroxycinnamoyl	5.1
			transferase
82	AT1G20696	HMGB3	NFD03, NFD3	5.0
83	AT3G57680		Peptidase S41 family protein	5.0
84	AT1G04850		ubiquitin-associated (UBA)/TS-N domain-containing protein	5.0
85	AT1G67740	PSBY	YCF32	4.9
86	AT1G13440	GAPC2	GAPC-2, GLYCERALDEHYDE-3-PHOSPHATE DEHYDROGENASE C-2	4.9
87	AT5G24310	ABIL3	ABL interactor-like protein 3	4.9
88	AT4G38470	STY46	ACT-like protein tyrosine kinase family protein	4.9
89	AT3G20550	DDL	SMAD/FHA domain-containing protein	4.9
90	AT2G33770	PHO2	ATUBC24, UBIQUITIN-CONJUGATING ENZYME 24, UBC24, UBIQUITIN-	4.8
			CONJUGATING ENZYME 24
91	AT4G11110	SPA2	SPA1-related 2	4.8
92	AT1G73700		MATE efflux family protein	4.7
93	AT3G22400	LOX5	PLAT/LH2 domain-containing lipoxygenase family protein	4.6
94	AT1G80820	CCR2	ATCCR2	4.6
95	AT3G30775	ERD5	AT-POX, ATPDH, ATPOX, ARABIDOPSIS THALIANA PROLINE	4.5
			OXIDASE, PDH1, proline dehydrogenase 1, PRO1, PRODH, PROLINE
			DEHYDROGENASE
96	AT4G00440	TRM15	GPI-anchored adhesin-like protein, putative (DUF3741)	4.5
97	ATIG10600	AMSH2	associated molecule with the SH3 domain of STAM 2	4.5
98	AT3G08620		RNA-binding KH domain-containing protein	4.4
99	AT5G09920	NRPB4	RNA polymerase II, Rpb4, core protein	4.4
100	AT1G76940		RNA-binding (RRM/RBD/RNP motifs) family protein	4.4
101	AT3G20300		extracellular ligand-gated ion channel protein (DUF3537)	4.4
102	AT1G22410		Class-II DAHP synthetase family protein	4.3
103	AT3G23530		Cyclopropane-fatty-acyl-phospholipid synthase	4.3
104	AT3G54460		SNF2 domain-containing protein/helicase domain-containing	4.3
			protein/F-box family protein
105	AT4G21110		G10 family protein	4.3
106	AT3G24315	AtSec20	Sec20 family protein	4.2
107	AT2G22190	TPPE	Haloacid dehalogenase-like hydrolase (HAD) superfamily protein	4.2
108	AT2G05070	LHCB2.2	LHCB2, LIGHT-HARVESTING CHLOROPHYLL B-BINDING 2	4.2
109	AT3G03910	GDH3	glutamate dehydrogenase 3	4.1
110	AT5G51940	NRPB6A	RNA polymerase Rpb6	4.1
111	AT3G59140	ABCC10	ATMRP14, multidrug resistance-associated protein 14,	4.1
			MRP14, multidrug resistance-associated protein 14
112	AT3G16360	AHP4	HPT phosphotransmitter 4	4.1
113	AT4G11920	CCS52A2	FZRI, FIZZY-RELATED 1	4.0
114	AT5G15260		ribosomal protein L34e superfamily protein	3.9
115	AT5G10350		RNA-binding (RRM/RBD/RNP motifs) family protein	3.9
116	AT5G67320	HOS15	WD-40 repeat family protein	3.9
117	AT3G08940	LHCB4.2	light harvesting complex photosystem II	3.8
118	AT3G15095	HCF243	Serine/Threonine-kinase pakA-like protein	3.8
119	AT5G17920	ATMS1	ATCIMS, COBALAMIN-INDEPENDENT METHIONINE SYNTHASE, ATMETS	3.8
120	AT4G12800	PSAL	photosystem I subunit 1	3.7
121	AT3G13360	WIP3	WPP domain interacting protein 3	3.7
122	AT1G47330		methyltransferase, putative (DUF21)	3.6
123	AT4G11910	NYE2,	STAY-GREEN-like protein	3.6
		NONYELLOWING 2,
		SGR2, STAY-GREEN 2
124	AT2G26810		Putative methyltransferase family protein	3.6
125	AT1G52570	PLDALPHA2	phospholipase D alpha 2	3.6
126	AT1G71960	ABCG25	ATABCG25, Arabidopsis thaliana ATP-binding cassette G25	3.6
127	AT1G36380		transmembrane protein	3.5
128	AT4G32750		transmembrane protein	3.5
129	AT2G36750	UGT73C1	UDP-glucosyl transferase 73C1	3.4
130	AT2G40890	CYP98A3	REF8, REDUCED EPIDERMAL FLUORESCENCE 8	3.4
131	AT2G18196		Heavy metal transport/detoxification superfamily protein	3.4
132	AT2G28060	KINβ3	5′-AMP-activated protein kinase beta-2 subunit protein	3.3
133	AT2G19860	HXK2	ATHXK2, ARABIDOPSIS THALIANA HEXOKINASE 2	3.3
134	AT5G51040	SDHAF2	succinate dehydrogenase assembly factor	3.2
135	AT5G53400	BOB1	HSP20-like chaperones superfamily protein	3.2
136	AT1G49350		pfkB-like carbohydrate kinase family protein	3.2
137	AT2G30950	VAR2	FTSH2	3.2
138	AT3G15970	NUP50 protein	Nucleoporin 50 kDa	3.1
139	AT1G15820	LHCB6	CP24	3.1
140	AT2G45695	URM11	Ubiquitin related modifier 1	3.1
141	AT1G79110	BRG2	zinc ion binding protein	3.1
142	AT2G36380	ABCG34	ATPDR6, PLEIOTROPIC DRUG RESISTANCE 6, PDR6, pleiotropic drug	3.0
			resistance 6
143	AT3G05170		Phosphoglycerate mutase family protein	2.9
144	AT5G16570	GLN1; 4	glutamine synthetase 1; 4	2.9
145	AT1G79630		Protein phosphatase 2C family protein	2.9
146	AT3G46450	SEC14 cytosolic factor		2.9
		family protein/
		phosphoglyceride
		transfer family protein
147	AT5G48870	SAD1	AtLSM5, AtSAD1, LSM5, SM-like 5	2.9
148	AT2G25910		3′-5′ exonuclease domain-containing protein/K homology domain-	2.9
			containing protein/KH domain-containing protein
149	AT5G11760		stress response protein	2.8
150	AT4G23840		Leucine-rich repeat (LRR) family protein	2.8
151	AT5G17010		Major facilitator superfamily protein	2.8
152	AT3G09560	PAH1	ATPAH1, PHOSPHATIDIC ACID PHOSPHOHYDROLASE 1	2.8
153	AT1G79900	BAC2	Mitochondrial substrate carrier family protein	2.8
154	AT1G67250		Proteasome maturation factor UMP1	2.7
155	AT3G50360	CEN2	ATCEN2, centrin2, CEN1, CENTRIN 1	2.7
156	AT5G53760	MLO11	ATMLO11, MILDEW RESISTANCE LOCUS O 11	2.6
157	AT5G40640		transmembrane protein	2.6
158	AT1G06680	PSBP-1	OE23, OXYGEN EVOLVING COMPLEX SUBUNIT 23 KDA, OEE2, OXYGEN-	2.6
			EVOLVING ENHANCER PROTEIN 2, PSII-P, PHOTOSYSTEM II SUBUNIT P
159	AT1G03600	PSB27	photosystem II family protein	2.6
160	AT1G29450	SAUR64	SMALL AUXIN UPREGULATED RNA64	2.6
161	AT3G20760	Nse4	component of Smc5/6 DNA repair complex	2.6
162	AT1G80940		Snfl kinase interactor-like protein	2.6
163	AT2G47980	SCC3	ATSCC3, SISTER-CHROMATID COHESION PROTEIN 3	2.6
164	AT2G43840	UGT74F1	UDP-glycosyltransferase 74 F1	2.5
165	AT5G08170	EMB1873	ATAIH, AGMATINE IMINOHYDROLASE	2.5
166	AT1G57750	CYP96A15	MAH1, MID-CHAIN ALKANE HYDROXYLASE 1	2.5
167	AT1G77590	LACS9	long chain acyl-CoA synthetase 9	2.5
168	AT1G55610	BRL1	BRI1 like	2.5
169	AT1G51740	SYP81	ATSYP81, ATUFE1, ARABIDOPSIS THALIANA ORTHOLOG OF YEAST	2.4
			UFEI (UNKNOWN FUNCTION-ESSENTIAL 1), UFE1, ORTHOLOG OF
			YEAST UFE1 (UNKNOWN FUNCTION-ESSENTIAL 1)
170	AT5G65010	ASN2	asparagine synthetase 2	2.4
171	AT2G25220		Protein kinase superfamily protein	2.4
172	AT5G62190	PRH75	DEAD box RNA helicase (PRH75)	2.4
173	AT4G23180	CRK10	RLK4	2.4
174	AT4G01050	TROL	thylakoid rhodanese-like protein	2.3
175	AT5G57960	Hflx	GTP-binding protein, HflX	2.3
176	AT5G61040		hypothetical protein	2.3
177	AT2G32320		tRNAHis guanylyltransferase	2.3
178	AT3G06270		Protein phosphatase 2C family protein	2.2
179	AT4G21215		transmembrane protein	2.2
180	AT3G14840	LIK1, LysM RLK1-	Leucine-rich repeat transmembrane protein kinase	2.2
		interacting kinase 1
181	AT5G46840		RNA-binding (RRM/RBD/RNP motifs) family protein	2.2
182	AT5G50580	SAE1B	AT-SAE1-2	2.2
183	AT4G17670		senescence-associated family protein (DUF581)	2.2
184	AT2G07050	CAS1	cycloartenol synthase 1	2.2
185	AT4G14960	TUA6	Tubulin/FtsZ family protein	2.2
186	AT1G01420	UGT72B3	UDP-glucosyl transferase 72B3	2.2
187	AT1G10140		Uncharacterized conserved protein UCP031279	2.2
188	AT2G18040	PIN1AT	peptidylprolyl cis/trans isomerase, NIMA-interacting 1	2.1
189	AT3G62270	BOR2	HCO3-transporter family, REQUIRES HIGH BORON 2	2.1
190	AT5G44070	CAD1	ARA8, ATPCS1, ARABIDOPSISTHALIANA PHYTOCHELATIN SYNTHASE	2.1
			1, PCS1, PHYTOCHELATIN SYNTHASE 1
191	AT1G29500	SAUR66	SAUR-like auxin-responsive protein family, , SMALL AUXIN	2.1
			UPREGULATED RNA66
192	AT1G11170		lysine ketoglutarate reductase trans-splicing-like protein (DUF707)	2.1
193	AT1G02850	BGLU11	beta glucosidase 11	2.1
194	AT5G47060		hypothetical protein (DUF581)	2.1
195	AT3G63140	CSP41A	chloroplast stem-loop binding protein of 41 kDa	2.0
196	AT5G50110		S-adenosyl-L-methionine-dependent methyltransferases superfamily protein	2.0
197	AT5G57700		BNR/Asp-box repeat family protein	2.0
198	AT5G57230			2.0
199	AT2G45210	SAUR36	SAG201, senescence-associated gene 201	2.0
200	AT5G01530	LHCB4.1	light harvesting complex photosystem II	2.0
201	AT4G39720		VQ motif-containing protein	2.0
202	AT4G26950		senescence regulator (Protein of unknown function, DUF584)	2.0
203	AT2G36840	ACR10	ACT-like superfamily protein	2.0
204	AT5G13290	CRN	SOL2, SUPPRESSOR OF LLP1 2	2.0
205	AT2G35170		Histone H3 K4-specific methyltransferase SET7/9 family protein	2.0
206	AT5G02380	MT2B	metallothionein 2B	1.9
207	AT5G63135		transcription termination factor	1.9
208	AT3G03780	MS2	ATMS2, methionine synthase 2	1.9
209	AT3G48750	CDC2	CDC2A, CDC2AAT, CDK2, CDKA1, CDKA; 1	1.9
210	AT1G11220		cotton fiber, putative (DUF761)	1.9
211	AT1G08380	PSAO	photosystem I subunit O	1.9
212	AT2G40980		Protein kinase superfamily protein	1.9
213	AT4G16146		cAMP-regulated phosphoprotein 19-related protein	1.9
214	AT1G16860		Ubiquitin-specific protease family C19-related protein	1.8
215	AT1G56190		Phosphoglycerate kinase family protein	1.8
216	AT2G44280		Major facilitator superfamily protein	1.8
217	AT2G18740		Small nuclear ribonucleoprotein family protein	1.8
218	AT2G39050	EULS3	ArathEULS3	1.8
219	AT2G46660	CYP78A6	EOD3, enhancer of da1-1	1.8
220	AT1G76520	PILS3, PIN-LIKES 3	Auxin efflux carrier family protein	1.8
221	AT1G76270		O-fucosyltransferase family protein	1.8
222	AT2G26650	KT1	AKT1, K+ transporter 1, ATAKT1	1.8
223	AT5G43810	AGO10	PNH, PINHEAD, ZLL, ZWILLE	1.8
224	AT2G41380		S-adenosyl-L-methionine-dependent methyltransferases superfamily	1.7
			protein
225	AT2G46200		U11/U12 small nuclear ribonucleoprotein	1.7
226	AT3G50820	PSBO2	OEC33, OXYGEN EVOLVING COMPLEX SUBUNIT 33 KDA, PSBO-2,	1.7
			PHOTOSYSTEM II SUBUNIT O-2
227	AT4G05180	PSBQ-2	PSBQ, PHOTOSYSTEM II SUBUNIT Q, PSII-Q	1.7
228	AT4G32940	GAMMA-VPE	GAMMAVPE	1.7
229	AT1G02640	BXL2	ATBXL2, BETA-XYLOSIDASE 2	1.6
230	AT5G52450		MATE efflux family protein	1.6
231	AT3G25860	LTA2	2-oxoacid dehydrogenases acyltransferase family protein	1.6
232	AT2G32415		Polynucleotidyl transferase, ribonuclease H fold protein with HRDC	1.6
			domain-containing protein
233	AT1G73350		ankyrin repeat protein	1.6
234	AT1G03475	LIN2	ATCPO-I, HEMF1	1.6
235	AT4G27820	BGLU9	beta glucosidase 9	1.6
236	AT1G12840	DET3	ATVH A-C, ARABIDOPSIS THALIANA VACUOLAR ATP SYNTHASE	1.6
			SUBUNIT C
237	AT4G01670		hypothetical protein	1.6
238	AT4G36940	NAPRT1	nicotinate phosphoribosyltransferase 1	1.6
239	AT5G13980		Glycosyl hydrolase family 38 protein	1.6
240	AT4G18360	GOX3	Aldolase-type TIM barrel family protein	1.5
241	AT4G21280	PSBQA	PSBQ-1, PHOTOSYSTEM II SUBUNIT Q-1, PSBQ, PHOTOSYSTEM II	1.5
			SUBUNIT Q
242	AT4G12290		Copper amine oxidase family protein	1.5
243	AT4G38400	EXLA2	ATEXLA2, expansin-like A2, ATEXPL2, ATHEXP BETA	1.5
			2.2, EXPL2, EXPANSIN L2
244	AT5G57330		Galactose mutarotase-like superfamily protein	1.5
245	AT2G43780		cytochrome oxidase assembly protein	1.5
246	AT4G09340		SPIa/RYanodine receptor (SPRY) domain-containing protein	1.5
247	AT4G36720	HVA22K	HVA22-like protein K	1.5
248	AT4G21660		proline-rich spliceosome-associated (PSP) family protein	1.5
249	AT5G12250	TUB6	beta-6 tubulin	1.5
250	AT1G01790	KEA1	ATKEA1, K+ EFFLUX ANTI PORTER 1	1.5
251	AT3G06350	MEE32	EMB3004, EMBRYO DEFECTIVE 3004	1.4
252	AT5G61820		stress up-regulated Nod 19 protein	1.4
253	AT2G43750	OASB	ACS1, ARABIDOPSIS CYSTEINE SYNTHASE 1, ATCS-B, ARABIDOPSIS	1.4
			THALIANA CYSTEIN SYNTHASE-B, CPACS1, CHLOROPLAST
			O-ACETYLSERINE SULFHYDRYLASE 1
254	AT1G07470		Transcription factor IIA, alpha/beta subunit	1.4
255	AT2G37770	ChlAKR	AKR4C9, Aldo-keto reductase family 4 member C9	1.4
256	AT1G29510	SAUR68	SAUR67, SMALL AUXIN UPREGULATED RNA 67	1.4
257	AT4G14600		Target SNARE coiled-coil domain protein	1.4
258	AT1G31330	PSAF	photosystem I subunit F	1.4
259	AT5G43260		chaperone protein dnaJ-like protein	1.4
260	AT2G38360	PRA1.B4	prenylated RAB acceptor 1.B4	1.4
261	AT3G05120	GID1A	ATGID1A, GA INSENSITIVE DWARF1A	1.4
262	AT4G28830		S-adenosyl-L-methionine-dependent methyltransferases superfamily	1.4
			protein
263	AT3G49645		FAD-binding protein	1.4
264	AT1G76080	CDSP32	ATCDSP32, ARABIDOPSIS THALIANA CHLOROPLASTIC DROUGHT-	1.3
			INDUCED STRESS PROTEIN OF 32 KD
265	AT2G01490	PAHX	phytanoyl-CoA dioxygenase (PhyH) family protein	1.3
266	AT2G07680	ABCC13	ATMRP11, multidrug resistance-associated protein 11, AtABCC13,	1.3
			MRP11, multidrug resistance-associated protein 11
267	AT4G23150	CRK7	cysteine-rich RLK (RECEPTOR-like protein kinase) 7	1.3
268	AT2G47060	PTI1-4	Protein kinase superfamily protein	1.3
269	AT4G01370	MPK4	ATMPK4, MAP kinase 4, MAPK4	1.3
270	AT1G5445O		Calcium-binding EF-hand family protein	1.3
271	AT2G41830		Uncharacterized protein	1.3
272	AT2G18600		Ubiquitin-conjugating enzyme family protein	1.3
273	AT4G24400	CIPK8	ATCIPK8, PKS11, PROTEIN KINASE 11, SnRK3.13, SNF1-RELATED	1.3
			PROTEIN KINASE 3.13
274	AT4G28706		pfkB-like carbohydrate kinase family protein	1.3
275	AT1G21210	WAK4	wall associated kinase 4	1.3
276	AT5G53800		nucleic acid-binding protein	1.3
277	AT5G10840	EMP1	Endomembrane protein 70 protein family	1.3
278	AT1G33840		LURP-one-like protein (DUF567)	1.3
279	AT5G63030	GRXC1	Thioredoxin superfamily protein	1.3
280	AT2G44130		KFB39, Kelch-domain-containing F-box protein 39, KMD3, KISS ME	1.2
			DEADLY 3
281	AT5G04830		Nuclear transport factor 2 (NTF2) family protein	1.2
282	AT3G15360	TRX-M4	ATHM4, ATM4, ARABIDOPSIS THIOREDOXIN M-TYPE 4	1.2
283	AT4G14880	OASA1	ATCYS-3A, CYTACS1, OLD3, ONSET OF LEAF DEATH 3	1.2
284	AT2G21390		Coatomer, alpha subunit	1.2
285	AT5G10780		ER membrane protein complex subunit-like protein	1.2
286	AT5G46910		Transcription factor jumonji (jmj) family protein/zinc finger (C5HC2 type)	1.2
			family protein
287	AT1G65820		microsomal glutathione s-transferase	1.2
288	AT1G65840	PAO4	ATPAO4, polyamine oxidase 4	1.2
289	AT1G64640	ENODL8	AtENODL8	1.2
290	AT5G49830	EXO84B	exocyst complex component 84B	1.2
291	AT4G13345	MEE55	Serinc-domain containing serine and sphingolipid biosynthesis	1.1
			protein
292	AT5G63890	HDH	ATHDH, histidinol dehydrogenase, HISN8, HISTIDINE BIOSYNTHESIS 8	1.1
293	AT3G52960		Thioredoxin superfamily protein	1.1
294	AT1G59950		NAD(P)-linked oxidoreductase superfamily protein	1.1
295	AT3G57050	CBL	cystathionine beta-lyase	1.1
296	AT5G52230	MBD13	methyl-CPG-binding domain protein 13	1.1
297	AT2G27510	FD3	ATFD3, ferredoxin 3	1.1
298	AT5G15780		Pollen Ole e l allergen and extensin family protein	1.1
299	AT1G67570		zinc finger CONSTANS-like protein (DUF3537)	1.1
300	AT5G06230	TBL9	TRICHOME BIREFRINGENCE-LIKE 9	1.1
301	AT4G27270		Quinone reductase family protein	1.1
302	AT1G15410		aspartate-glutamate racemase family	1.1
303	AT3G47000		Glycosyl hydrolase family protein	1.1
304	AT5G22740	CSLA02	ATCSLA02, ARABIDOPSIS THALIANA CELLULOSE SYNTHASE-LIKE	1.1
			A02, ATCSLA2, ARABIDOPSIS THALIANA CELLULOSE SYNTHASE-LIKE
			A2, CSLA2, CELLULOSE SYNTHASE-LIKE A 2
305	AT4G39400	BRI1	ATBRI1, BIN1, BR INSENSITIVE 1, CBB2, CABBAGE 2, DWF2, DWARF 2	1.1
306	AT1G77460	CSI3	CELLULOSE SYNTHASE INTERACTIVE 3	1.0
307	AT5G14880	KUP8	Potassium transporter family protein	1.0
308	AT3G47470	LHCA4	CAB4	1.0
309	AT4G39640	GGT1	gamma-glutamyl transpeptidase 1	1.0
310	AT2G06010	ORG4	OBP3-responsive protein 4 (ORG4)	1.0
311	AT5G61220		LYR family of Fe/S cluster biogenesis protein	1.0
312	AT3G28740	CYP81D11	Cytochrome P450 superfamily protein	1.0
313	AT4G33950	OST1	ATOST1, OPEN STOMATA l, P44, SNRK2-6, SUCROSE NONFERMENTING	1.0
			1-RELATED PROTEIN KINASE 2-6, SNRK2.6, SNFl-RELATED PROTEIN
			KINASE 2.6, SRK2E
314	AT1G01560	MPK11	ATMPK11, MAP kinase 11	1.0
315	AT3G23090	WDL3	TPX2 (targeting protein for Xklp2) protein family	1.0
316	AT4G09750		NAD(P)-binding Rossmann-fold superfamily protein	1.0
317	AT3G54890	LHCA1	chlorophyll a-b binding protein 6	1.0
318	AT3G46780	PTAC16	plastid transcriptionally active 16	0.9
319	AT5G40440	MKK3	ATMKK3, mitogen-activated protein kinase kinase 3	0.9
320	AT4G23230	CRK15	cysteine-rich RECEPTOR-like kinase	0.9
321	AT4G10840	KLCR1	Tetratricopeptide repeat (TPR)-like superfamily protein	0.9
322	AT4G37400	CYP81F3	cytochrome P450, family 81, subfamily F, polypeptide 3	0.9
323	AT5G67570	DG1	EMB1408, embryo defective 1408, EMB246, EMBRYO DEFECTIVE 246	0.9
324	AT2G41050		PQ-loop repeat family protein/transmembrane family protein	0.9
325	AT1G01620	PIP1C	PIP1; 3, PLASMA MEMBRANE INTRINSIC PROTEIN 1; 3, TMP-B	0.9
326	AT3G21055	PSBTN	photosystem II subunit T	0.9
327	AT4G15910	DI21	ATDI21, drought-induced 21	0.9
328	AT1G53470	MSL4	mechanosensitive channel of small conductance-like 4	0.9
329	AT1G14290	SBH2	AtSBH2	0.9
330	AT2G42960		Protein kinase superfamily protein	0.9
331	AT1G71080		RNA polymerase II transcription elongation factor	0.9
332	AT5G63970	RGLG3	Copine (Calcium-dependent phospholipid-binding protein) family	0.9
333	AT5G35100		Cyclophilin-like peptidyl-prolyl cis-trans isomerase family protein	0.9
334	AT1G14000	VIK	VHl-interacting kinase	0.9
335	AT3G47050		Glycosyl hydrolase family protein	0.8
336	AT1G17600		Disease resistance protein (TIR-NBS-LRR class) family	0.8
337	AT3G12290		Amino acid dehydrogenase family protein	0.8
338	AT5G65620	OOP	Zincin-like metalloproteases family protein, organellar	0.8
			oligopeptidase, TOP1, thimet metalloendopeptidase 1
339	AT4G28750	PSAE-1	Photosystem 1 reaction centre subunit IV/PsaE protein	0.8
340	AT1G65800	RK2	ARK2, receptor kinase 2, AtARK2	0.8
341	AT1G75690	LQY1	DnaJ/Hsp40 cysteine-rich domain superfamily protein	0.8
342	AT4G01150	CURT1A	CURVATURE THYLAKOID 1A-like protein	0.8
343	AT1G03680	THM1	ATHM1, thioredoxin M-type 1, ATM1, ARABIDOPSIS THIOREDOXIN	0.8
			M-TYPE 1, TRX-M1, THIOREDOXIN M-TYPE 1
344	AT4G33040		Thioredoxin superfamily protein	0.8
345	AT4G32260	PDE334	ATPase, F0 complex, subunit B/B′, bacterial/chloroplast	0.8
346	AT2G34730		myosin heavy chain-like protein	0.8
347	AT3G09830	PCRK1	Protein kinase superfamily protein	0.8
348	AT4G09510	CINV2	A/N-lnvl, alkaline/neutral invertase 1	0.8
349	AT1G04820	TUA4	TOR2, TORTIFOLIA 2	0.8
350	AT5G62200		Embryo-specific protein 3, (ATS3)	0.8
351	AT1G17170	GSTU24	ATGSTU24, glutathione S-transferase TAU 24, GST, Arabidopsis thaliana	0.8
			Glutathione S-transferase (class tau) 24
352	AT5G4647O	RPS6	disease resistance protein (TIR-NBS-LRR class) family	0.8
353	AT5G6514O	TPPJ	Haloacid dehalogenase-like hydrolase (HAD) superfamily protein	0.8
354	AT5G64040	PSAN	photosystem 1 reaction center subunit PSI-N, chloroplast, putative/	0.8
			PSI-N, putative (PSAN)
355	AT1G65930	cICDH	cytosolic NADP+-dependent isocitrate dehydrogenase	0.8
356	AT5G2381O	AAP7	amino acid permease 7	0.7
357	AT2G24395		chaperone protein dnaJ-like protein	0.7
358	AT2G42220		Rhodanese/Cell cycle control phosphatase superfamily protein	0.7
359	AT1G14910		ENTH/ANTH/VHS superfamily protein	0.7
360	AT1G72230		Cupredoxin superfamily protein	0.7
361	AT4G11530	CRK34	cysteine-rich RLK (RECEPTOR-like protein kinase) 34	0.7
362	AT3G14690	CYP72A15	cytochrome P450, family 72, subfamily A, polypeptide 15	0.7
363	AT4G09570	CPK4	ATCPK4	0.7
364	AT5G28080	WNK9	Protein kinase superfamily protein	0.7
365	AT1G01180		S-adenosyl-L-methionine-dependent methyltransferases superfamily protein	0.7
366	AT1G67500	REV3	ATREV3, recovery protein 3	0.7
367	AT3G47090		Leucine-rich repeat protein kinase family protein	0.7
368	AT2G36800	DOGT1	UGT73C5, UDP-GLUCOSYL TRANSFERASE 73C5	0.7
369	AT5G43940	HOT5	ADH2, ALCOHOL DEHYDROGENASE 2, ATGSNOR1, GSNOR, S-	0.7
			NITROSOGLUTATHIONE REDUCTASE, PAR2, PARAQUAT RESISTANT 2
370	AT1G22610		C2 calcium/lipid-binding plant phosphoribosyltransferase family protein	0.7
371	AT2G41740	VLN2	ATVLN2	0.7
372	AT5G50100		Putative thiol-disulfide oxidoreductase DCC	0.7
373	AT4G03110	RBP-DR1	AtBRN1, AtRBP-DR1, RNA-binding protein-defense related 1,	0.7
			BRN1, Bruno-like 1
374	AT5G47770	FPS1	farnesyl diphosphate synthase 1	0.7
375	AT1G78460		SOUL heme-binding family protein	0.7
376	AT5G41000	YSL4	AtYSL4	0.7
377	AT1G33490		E3 ubiquitin-protein ligase	0.7
378	AT2G06520	PSBX	photosystem II subunit X	0.7
379	AT1G76140		Prolyl oligopeptidase family protein	0.7
380	AT1G55670	PSAG	photosystem I subunit G	0.6
381	AT5G01880	DAFL2,	RING/U-box superfamily protein	0.6
		DAF-Like gene 2
382	AT4G22920	NYE1	ATNYE1, NON-YELLOWING 1, SGR1, STAY-GREEN 1, SGR, STAY-GREEN	0.6
383	AT2G26910	ABCG32	ATPDR4, PLEIOTROPIC DRUG RESISTANCE 4, AtABCG32, PDR4,	0.6
			pleiotropic drug resistance 4, PEC1, PERMEABLE CUTICLE 1
384	AT1G80380		P-loop containing nucleoside triphosphate hydrolases superfamily protein	0.6
385	AT3G06890		transmembrane protein	0.6
386	AT3G46460	UBC13	ubiquitin-conjugating enzyme 13	0.6
387	AT1G13195		RING/U-box superfamily protein	0.6
388	AT1G17710	PEPC1	AtPEPCl, Arabidopsis thaliana phosphoethanolamine/phosphocholine	0.6
			phosphatase 1
389	AT4G28070		AFGl-like ATPase family protein	0.6
390	AT3G13620	PUT4	Amino acid permease family protein	0.6
391	AT4G33540		metallo-beta-lactamase family protein	0.6
392	AT2G39705	RTFL8	DVL11, DEVIL 11	0.6
393	AT2G43820	UGT74F2	ATSAGTl1 Arabidopsis thaliana salicylic acid glucosyltransferase 1, GT,	0.6
			SAGT1, salicylic acid glucosyltransferase 1, SGT1, UDP-glucose:
			salicylic acid glucosyltransferase l
394	AT1G74410		RING/U-box superfamily protein	0.6
395	AT4G24670	TAR2	tryptophan aminotransferase related 2	0.6
396	AT1G59670	GSTU15	ATGSTU15, glutathione S-transferase TAU 15	0.6
397	AT2G22170	PLAT2	Lipase/lipooxygenase, PLAT/LH2 family protein	0.6
398	AT1G80860	PLMT	ATPLMT, ARABIDOPSIS PHOSPHOLIPID N-METHYLTRANSFERASE	0.6
399	AT2G35130		Tetratricopeptide repeat (TPR)-like superfamily protein	0.6
400	AT1G16110	WAKL6	wall associated kinase-like 6	0.6
401	AT4G38060	CCI2, Clavata complex	hypothetical protein	0.6
		interactor 2
402	AT1G7653O	PILS4, PIN-LIKES 4	Auxin efflux carrier family protein	0.6
403	AT2G26400	ARD3	ARD, ACIREDUCTONE DIOXYGENASE, ATARD3, acireductone	0.6
			dioxygenase 3
404	AT1G48600	PMEAMT	AtPMEAMT	0.6
405	AT1G16260		Wall-associated kinase family protein	0.6
406	AT2G34420	LHB1B2	LHCB1.5, PHOTOSYSTEM II LIGHT HARVESTING COMPLEX GENE 1.5	0.6
407	AT4G23400	PIP1; 5	PIPID	0.6
408	AT2G30550	DALL3, DAD1-Like	alpha/beta-Hydrolases superfamily protein	0.6
		Lipase 3
409	AT1G24170	LGT9	Nucleotide-diphospho-sugar transferases superfamily protein	0.6
410	AT1G13750	ATPAP1,	Purple acid phosphatases superfamily protein	0.5
		ARABIDOPSIS
		THALIANA
		PURPLE ACID
		PHOSPHATASE 1,
		PAP1, PURPLE ACID
		PHOSPHATASE 1
411	AT4G04640	ATPC1	ATPase, F1 complex, gamma subunit protein	0.5
412	AT4G24460	CLT2	CRT (chloroquine-resistance transporter)-like transporter 2	0.5
413	AT3G63010	GID1B	ATGIDIB	0.5
414	AT5G59290	UXS3	ATUXS3	0.5
415	AT5G66850	MAPKKK5	mitogen-activated protein kinase kinase kinase 5	0.5
416	AT4G02770	PSAD-1	photosystem I subunit D-1	0.5
417	AT1G30380	PSAK	photosystem I subunit K	0.5
418	AT1G28440	HSL1	HAESA-like 1	0.5
419	AT3G21600		Senescence/dehydration-associated protein-like protein	0.5
420	AT1G21270	WAK2	wall-associated kinase 2	0.5
421	AT4G34150		Calcium-dependent lipid-binding (CaLB domain) family protein	0.5
422	AT3G19270	CYP707A4	cytochrome P450, family 707, subfamily A, polypeptide 4	0.5
423	AT5G12010		nuclease	0.5
424	AT2G24170		Endomembrane protein 70 protein family	0.5
425	AT1G07650		Leucine-rich repeat transmembrane protein kinase	0.5
426	AT5G13120	Pnsl5	ATCYP20-2, ARABIDOPSIS THALIANA CYCLOPHILIN 20-2, CYP20-2,	0.5
			cyclophilin 20-2
427	AT5G33320	CUE1	ARAPPT, ARABIDOPSIS THALIANA	0.5
			PHOSPHATE/PHOSPHOENOLPYRUVATE
			TRANSLOCATOR, PPT, PHOSPHOENOLPYRUVATE/PHOSPHATE
			TRANSLOCATOR
428	AT4G10340	LHCB5	light harvesting complex of photosystem II 5	0.5
429	AT3G61470	LHCA2	photosystem 1 light harvesting complex protein	0.5
430	AT5G43150		elongation factor	0.5
431	AT5G44060		embryo sac development arrest protein	0.5
432	AT5G16400	TRXF2	ATF2	0.5
433	AT5G14200	IMD1	ATIMD1, ARABIDOPSIS ISOPROPYLMALATE DEHYDROGENASE 1	0.5
434	AT1G05630	5PTASE13	AT5PTASE13, Arabidopsis thaliana inositol-polyphosphate 5-	0.5
			phosphatase 13
435	AT1G50010	TUA2	tubulin alpha-2 chain	0.5
436	AT3G17180	scpl33	serine carboxypeptidase-like 33	0.5
437	AT2G36310	URH1	NSH1, nucleoside hydrolase 1	0.5
438	AT1G63840		RING/U-box superfamily protein	0.5
439	AT1G20110		FREE1, FYVE domain protein required for endosomal sorting 1, FYVE1,	0.5
			FYVE-domain protein 1
440	AT1G79270	ECT8	evolutionarily conserved C-terminal region 8	0.4
441	AT2G30570	PSBW	photosystem II reaction center W	0.4
442	AT3G50910		netrin receptor DCC	0.4
443	AT2G42070	NUDX23	ATNUDT23, ARABIDOPSIS THALIANA NUDIX HYDROLASE HOMOLOG	0.4
			23, ATNUDX23, nudix hydrolase homolog 23
444	AT5G66570	PSBO1	MSP-1, MANGANESE-STABILIZING PROTEIN 1, OE33, OXYGEN	0.4
			EVOLVING COMPLEX 33 KILODALTON PROTEIN, OEE1, 33 KDA OXYGEN
			EVOLVING POLYPEPTIDE 1, OEE33, OXYGEN EVOLVING ENHANCER
			PROTEIN 33, PSBO-1, PS II OXYGEN-EVOLVING COMPLEX 1
445	AT1G67060		peptidase M50B-like protein	0.4
446	AT1G34210	SERK2	ATSERK2	0.4
447	AT2G34430	LHB1B1	LHCB1.4, LIGHT-HARVESTING CHLOROPHYLL-PROTEIN COMPLEX II	0.4
			SUBUNIT B1
448	AT3G52750	FTSZ2-2	Tubulin/FtsZ family protein	0.4
449	AT3G12780	PGK1	phosphoglycerate kinase 1	0.4
450	AT4G34490	CAP1	ATCAP1, cyclase associated protein 1, CAP 1	0.4
451	AT2G38120	AUX1	AtAUX1, MAP1, MODIFIER OF ARF7/NPH4 PHENOTYPES 1, PIR1,	0.4
			WAV5, WAVY ROOTS 5
452	AT1G12990		beta-1, 4-N-acetylglucosaminyltransferase family protein	0.4
453	AT5G39320	UDG4	UDP-glucose 6-dehydrogenase family protein	0.4
454	AT5G06750	APD8	Protein phosphatase 2C family protein	0.4
455	AT5G11000		hypothetical protein (DUF868)	0.4
456	AT1G61520	LHCA3	PSI type III chlorophyll a/b-binding protein	0.4
457	AT1G07000	EXO70B2	ATEXO70B2, exocyst subunit exo70 family protein B2	0.4
458	AT5G07030		Eukaryotic aspartyl protease family protein	0.4
459	AT1G59700	GSTU16	ATGSTU16, glutathione S-transferase TAU 16	0.4
460	AT2G20260	PSAE-2	photosystem I subunit E-2	0.4
461	AT2G39740	HESO1	Nucleotidyltransferase family protein	0.4
462	AT3G15760		cytochrome P450 family protein	0.4
463	AT2G33730		P-loop containing nucleoside triphosphate hydrolases superfamily protein	0.4
464	AT2G36330	CASPL4A3	CASP-like protein 4A3, Uncharacterized protein family (UPFO497)	0.4
465	AT5G14540		basic salivary proline-rich-like protein (DUF1421)	0.4
466	AT4G13510	AMT1;1	ATAMT1;1, ATAMT1, ARABIDOPSIS THALIANA AMMONIUM	0.4
			TRANSPORT 1
467	AT1G29910	CAB3	AB180, LHCB1.2, LIGHT HARVESTING CHLOROPHYLL A/B BINDING	0.4
			PROTEIN 1.2
468	AT2G31810		ACT domain-containing small subunit of acetolactate synthase	0.4
			protein
469	AT1G52190	AtNPF1.2, NPF1.2,	Major facilitator superfamily protein	0.4
		NRT1/PTR family 1.2,
		NRT1.11, nitrate
		transporter 1.11
470	AT5G01240	LAX1	like AUXIN RESISTANT 1	0.4
471	AT5G64090		hyccin	0.4
472	AT4G38540		FAD/NAD(P)-binding oxidoreductase family protein	0.4
473	AT3G25510		disease resistance protein (TIR-NBS-LRR class) family protein	0.4
474	AT1G75590	SAUR52	SAUR-like auxin-responsive protein family, SMALL AUXIN	0.4
			UPREGULATED RNA 52
475	AT5G09930	ABCF2	ABC transporter family protein	0.4
476	AT2G14740	VSR3	ATVSR3, vaculolar sorting receptor 3, BP80-2;2, binding protein of 80 kDa 2;2,	0.4
			VSR2;2, VACUOLAR SORTING RECEPTOR 2;2
477	AT5G66200	ARO2	armadillo repeat only 2	0.4
478	AT1G31540		Disease resistance protein (TIR-NBS-LRR class) family	0.4
479	AT5G62900		basic-leucine zipper transcription factor K	0.3
480	AT3G49350		Ypt/Rab-GAP domain of gyp1p superfamily protein	0.3
481	AT5G50375	CPI1	cyclopropyl isomerase	0.3
482	AT3G05520	CPA	AtCPA	0.3
483	AT4G36640		Sec14p-like phosphatidylinositol transfer family protein	0.3
484	AT3G62110		Pectin lyase-like superfamily protein	0.3
485	AT4G36040	J11	DJC23, DNA J protein C23	0.3
486	AT3G56440	ATG18D	ATATG18D, homolog of yeast autophagy 18 (ATG18) D	0.3
487	AT3G05350		Metallopeptidase M24 family protein	0.3
488	AT3G52340	SPP2	ATSPP2, SUCROSE-PHOSPHATASE 2	0.3
489	AT1G34750		Protein phosphatase 2C family protein	0.3
490	AT5G47870	RAD52-2	ODB2, Organellar DNA-Binding protein 2, RAD52-2B	0.3
491	AT4G22380		Ribosomal protein L7Ae/L30e/S12e/Gadd45 family protein	0.3
492	AT5G46110	APE2	TPT, triose-phosphate ⁄ phosphate translocator	0.3
493	AT3G63470	scpl40	serine carboxypeptidase-like 40	0.3
494	AT4G39030	EDS5	SCORD3, susceptible to coronatine-deficient Pst DC3000 3, SID1,	0.3
			SALICYLIC ACID INDUCTION DEFICIENT 1
495	AT3G60160	ABCC9	ATMRP9, multidrug resistance-associated protein 9, MRP9, multidrug	0.3
			resistance-associated protein 9
496	AT5G53550	YSL3	ATYSL3, YELLOW STRIPE LIKE 3	0.3
497	AT4G21190	emb1417	Pentatricopeptide repeat (PPR) superfamily protein	0.3
498	AT3G16140	PSAH-1	photosystem I subunit H-1	0.3
499	AT2G36360		Galactose oxidase/kelch repeat superfamily protein	0.3
500	AT2G04630	NRPB6B	RNA polymerase Rpb6	0.3
501	AT5G58220	TTL	ALNS, allantoin synthase	0.3
502	AT2G45290	TKL2	Transketolase	0.3
503	AT1G13320	PP2AA3	protein phosphatase 2A subunit A3	0.3
504	AT3G58100	PDCB5	plasmodesmata callose-binding protein 5	0.3
505	AT1G20780	SAUL1	ATPUB44, ARABIDOPSIS THALIANA PLANT U-BOX 44, PUB44,	0.3
			PLANT U-BOX 44
506	AT4G21380	RK3	ARK3, receptor kinase 3	0.3
507	AT4G20230		terpenoid synthase superfamily protein	0.3
508	AT3G17410		Protein kinase superfamily protein	0.3
509	AT2G40600		appr-1-p processing enzyme family protein	0.3
510	AT1G28580		GDSL-like Lipase/Acylhydrolase superfamily protein	0.3
511	AT4G23130	CRK5	RLK6, RECEPTOR-LIKE PROTEIN KINASE 6	0.3
512	AT4G27830	BGLU10	AtBGLU10	0.3
513	AT2G25520		Drug/metabolite transporter superfamily protein	0.3
514	AT1G34130	STT3B	staurosporin and temperature sensitive 3-like b	0.2
515	AT4G29440		Regulator of Vps4 activity in the MVB pathway protein	0.2
516	AT1G77490	TAPX	thylakoidal ascorbate peroxidase	0.2
517	AT4G38660		Pathogenesis-related thaumatin superfamily protein	0.2
518	AT3G29360	UGD2	UDP-glucose 6-dehydrogenase family protein	0.2
519	AT5G62580		ARM repeat superfamily protein	0.2
520	AT1G16670		Protein kinase superfamily protein	0.2
521	AT4G09010	TL29	APX4, ascorbate peroxidase 4	0.2
522	AT3G60690	SAUR59	SAUR-like auxin-responsive protein family, SMALL AUXIN	0.2
			UPREGULATED RNA 59
523	AT2G37550	AGD7	ASP1, yeast pde1 sup, pressor 1	0.2
524	AT5G11250		Disease resistance protein (TIR-NBS-LRR class)	0.2
525	AT5G19780	TUA5	tubulin alpha-5	0.2
526	AT1G55910	ZIP11	zinc transporter 11 precursor	0.2
527	AT5G24870		RING/U-box superfamily protein	0.2
528	AT3G22840	ELIP1	ELIP	0.2
529	AT5G19770	TUA3	tubulin alpha-3	0.2
530	AT1G34630		transmembrane protein	0.2
531	AT3G55260	HEXO1	ATHEX2	0.2
532	AT4G02420		LecRK-IV.4, L-type lectin receptor kinase IV.4	0.2
533	AT1G69730		Wall-associated kinase family protein	0.2
534	AT1G66880		Protein kinase superfamily protein	0.1
535	AT4G23140	CRK6	cysteine-rich RLK (RECEPTOR-like protein kinase) 6	0.1
536	AT2G31020	ORP1A	OSBP(oxysterol binding protein)-related protein 1A	0.1
537	AT2G16950	TRN1	ATTRN1, TRANSPORTIN 1	0.1
538	AT5G48380	BIR1	BAK1-interacting receptor-like kinase 1	0.1
539	AT5G25100		Endomembrane protein 70 protein family	0.1
540	AT1G21250	WAK1	AtWAK1, PRO25	0.1
541	AT5G22770	alpha-ADR	alpha-adaptin	0.1
542	AT5G60900	RLK1	receptor-like protein kinase 1	0.1
543	AT1G65790	RK1	ARK1, receptor kinase 1	0.1
544	AT5G35200		ENTH/ANTH/VHS superfamily protein	0.1
545	AT2G42900		Plant basic secretory protein (BSP) family protein	0.1
546	AT3G54100		O-fucosyltransferase family protein	0.0
547	AT4G14690	ELIP2	Chlorophyll A-B binding family protein	0.0

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims

1. A modified plant cell, said modified plant cell comprising a modification that inhibits expression of hb75.

2. The modified plant cell of claim 1, further comprising a modification such that expression of a gene selected from Table 4 is altered.

3. The modified plant cell of claim 2, wherein the gene is nf-ya3, and wherein expression of nf-ya3 is inhibited.

4. A plant comprising plant cells of claim 1.

5. The plant of claim 4, wherein the plant exhibits at least one of: an increase in Nitrogen (N) uptake, increased biomass, an increased harvest index, an increased Total nitrogen utilization (NUtE), or an increased total Grain NUtE, relative to a plant that does not comprise a modification that inhibits expression of hb75.

6. The plant of claim 5, wherein the plant exhibits the increased biomass.

7. The plant of claim 5, wherein the plant is a maize plant.

8. The plant of claim 7, wherein the increased biomass comprises increased grain mass.

9. A method comprising modifying a plant comprising disrupting expression hb75 such that the plant exhibits at least one of an increase in Nitrogen (N) uptake, increased biomass, an increased harvest index, an increased Total nitrogen utilization (NUtE), or an increased total Grain NUtE, relative to a plant that does not comprise a modification that inhibits expression of hb75.

10. The method of claim 9, wherein the plant further comprises a such that expression of a gene selected from Table 4 is altered.

11. The method of claim 10, wherein the gene is nf-ya3, and wherein expression of nf-ya3 is inhibited.

12. The method of claim 9, wherein the plant is a maize plant.

13. The method of claim 10, wherein the plant is a maize plant.

14. The method of claim 11, wherein the plant is a maize plant.