Altering the content or composition of the cell wall polymer lignin is a favored approach to valorize lignin toward biomaterial and chemical production in the biorefinery. However, modifying lignin or cellulose in transgenic plants can induce expression of defense responses and negatively affect growth. Through genetic screening for suppressors of defense gene induction in the low lignin ccr1-3 mutant of Arabidopsis thaliana , we found that loss of function of the receptor-like kinase FERONIA, although not restoring growth, affected cell wall remodeling and blocked release of elicitor-active pectic polysaccharides as a result of the ccr1-3 mutation. Loss of function of multiple wall-associated kinases prevented perception of these elicitors. The elicitors are likely heterogeneous, with tri-galacturonic acid the smallest but not necessarily the most active component. Engineering of plant cell walls will require development of ways to bypass endogenous pectin signaling pathways.

Drought is one of the most hazardous environmental factors due to its severe damage on plant growth, development and productivity. Plants have evolved complex regulatory networks and resistance strategies for adaptation to drought stress. As a conserved epigenetic regulation, DNA methylation dynamically alters gene expression and chromosome interactions in plants’ response to abiotic stresses. The development of omics technologies on genomics, epigenomics and transcriptomics has led to a rapid increase in research on epigenetic variation in non-model crop species. In this review, we summarize the most recent findings on the roles of DNA methylation under drought stress in crops, including methylating and demethylating enzymes, the global methylation dynamics, the dual regulation of DNA methylation on gene expression, the RNA-dependent DNA methylation (RdDM) pathway, alternative splicing (AS) events and long non-coding RNAs (lnc RNAs). We also discuss drought-induced stress memory. These epigenomic findings provide valuable potential for developing strategies to improve crop drought tolerance.

Kudzu (Pueraria montana lobata) is used as a traditional medicine in China and Southeast Asia but is a noxious weed in the Southeastern United States. It produces both O‐ and C‐glycosylated isoflavones, with puerarin (C‐glucosyl daidzein) as an important bioactive compound. Currently, the stage of the isoflavone pathway at which the C‐glycosyl unit is added remains unclear, with a recent report of direct C‐glycosylation of daidzein contradicting earlier labeling studies supporting C‐glycosylation at the level of chalcone. We have employed comparative mRNA sequencing of the roots from two Pueraria species, one of which produces puerarin (field collected P. montana lobata) and one of which does not (commercial Pueraria phaseoloides), to identify candidate uridine diphosphate glycosyltransferase (UGT) enzymes involved in puerarin biosynthesis. Expression of recombinant UGTs in Escherichia coli and candidate C‐glycosyltransferases in Medicago truncatula were used to explore substrate specificities, and gene silencing of UGT and key isoflavone biosynthetic genes in kudzu hairy roots employed to test hypotheses concerning the substrate(s) for C‐glycosylation. Our results confirm UGT71T5 as a C‐glycosyltransferase of isoflavone biosynthesis in kudzu. Enzymatic, isotope labeling, and genetic analyses suggest that puerarin arises both from the direct action of UGT71T5 on daidzein and via a second route in which the C‐glycosidic linkage is introduced to the chalcone isoliquiritigenin.

Plants produce a diverse array of compounds that play crucial roles in growth, in development, and in responses to abiotic and biotic stresses. Understanding the fluxes within metabolic pathways is essential for guiding strategies aimed at directing metabolism for crop improvement and the plant natural product industry. Over the past decade, metabolic network modeling has emerged as a predominant tool for the integration, quantification, and prediction of the spatial and temporal distribution of metabolic flows. In this review, we present the primary methods for constructing mathematical models of metabolic systems and highlight recent achievements in plant metabolism using metabolic modeling. Furthermore, we discuss current challenges in applying network flux analysis in plants and explore the potential use of machine learning technologies in plant metabolic modeling. The practical application of mathematical modeling is expected to provide significant insights into the structure and regulation of plant metabolic networks.