Masthead-image

Asparagine Synthetase Gene Regulatory Network and Plant Nitrogen Metabolism

Asparagine Synthetase Gene Regulatory Network and Plant Nitrogen Metabolism

DOE Grant DE-FG02-92ER20071
P.I. Gloria Coruzzi


ABSTRACT

Significance: The goal of this DOE project is to model and alter gene regulatory networks affecting N-assimilation into asparagine (Asn), a C- and N-efficient amino acid used to transport and store nitrogen in seeds. Altering transcription of ASN1, the major gene controlling Asn synthesis in Arabidopsis, effects increases in seed-N, and this technology is in field trials of corn and other crops. Using a combination of genetics, genomics and systems biology, we uncovered regulatory mechanisms operating at the level of chromatin and transcription. These regulatory mechanisms coordinate ASN1 expression with processes required for N-assimilation into Asn (an energy/ATP-dependent reaction) including C-metabolism, photosynthesis, and energy production. Our goal is to study and modify these regulatory mechanisms, with the aim of increasing C- and N-use efficiency in plants.

Approach: Using a combined genetic, genomic and systems biology approach, we have uncovered components regulating Asn synthesis and metabolism in response to carbon (C), light (L) and nitrogen (N) signals. In a positive genetic selection, Arabidopsis mutants impaired in C and L repression of an ASN1promoter::hygromycin resistance transgene were selected. Transcriptome analysis of one mutant, cli186, uncovered a metabolic network of genes integrating N and C metabolism, photosynthesis and energy production. Map-based cloning revealed that CLI186 encodes a histone lysine methyltransferase involved in H3K4/K36 methylation, which was previously shown to control FLC, a repressor of flowering. In this renewal, we will explore whether and how CLI186/EFS coordinates methylation of H3K4me3 (at promoters) and K36me2 (in gene body) of genes in this Asn-metabolic regulatory network in vivo (Aim 1). We next explore the downstream TF networks that coordinate the reciprocal regulation of Asn synthesis (via ASN1) and Asn metabolism (via ANS1) with related processes in metabolism, photosynthesis & energy (Aim 2). We expand our studies to the level of N-metabolites, to test whether mutants in structural and regulatory genes controlling Asn synthesis and metabolism affect flux of 15NO3- into Asn, and test whether Asn functions in N-signaling in vivo (Aim 3), as follows:

AIM 1. CHROMATIN: Role of histone methylation in the ASN1- metabolic regulatory network.
Methylation of histones at H3K4/36 is believed to “poise” transcriptionally active genes for regulation in response to environmental stimuli in plants & animals. Here, we will discover the mechanisms that coordinate its K3K4 and H3K36 methylation activities in vivo using sequential Chromatin-IP analysis of H3K4me3 and H3K36me2 in wild-type vs. cli186 mutant plants (Aim 1A). We next exploit the unique genetic selection of cli186, to test mutants in the unique zf-CW domain, postulated to “read” H3K4m3 marks and target specific loci for H3K36 methylation in vivo (Aim 1B).

AIM 2: TRANSCRIPTION: Role of Transcription Factors (TFs) in the ASN1-metabolic regulatory network. Here, we will derive and validate hypotheses for transcription factors (TFs) that control the ASN1 regulatory network in response to C, L and N signaling. We derive and test hypotheses for TFs that are targets of CLI186/EFS based on H3K4/36 methylation data from WT and cli186 (Aim 2A). We also derive and validate new hypotheses for TFs that reciprocally regulate Asn synthesis and Asn metabolism using T-DNAs and 35S::TF lines (Aim 2B).

AIM 3: METABOLITES: Role of metabolic control of transcription in the ASN1-metabolic regulatory network. Here, we integrate studies on transcriptional control of Asn synthesis/metabolism to include amino acid metabolites. We will use mutants/transgenics in structural and regulatory genes affecting ASN1/ANS1 to examine changes in 15N-flux into Asn (Aim 3A). We will also infer relationships between transcriptional control and metabolic output using machine learning by stochastic gradient descent with regularization (Aim 3B). This analysis will globally test the hypothesis that the Gln/Asn ratio alters the regulation of gene expression, and also derive new hypotheses for genome-wide metabolic regulation in response to Asn-metabolite signaling.