Multi-omic analysis shows REVEILLE clock genes are involved in carbohydrate metabolism and proteasome function.

Plants are able to sense changes in their light environments, such as the onset of day and night, as well as anticipate these changes in order to adapt and survive. Central to this ability is the plant circadian clock, a molecular circuit that precisely orchestrates plant cell processes over the course of a day. REVEILLE (RVE) proteins are recently discovered members of the plant circadian circuitry that activate the evening complex and PSEUDO-RESPONSE REGULATOR genes to maintain regular circadian oscillation. The RVE8 protein and its two homologs, RVE 4 and 6 in Arabidopsis (Arabidopsis thaliana), have been shown to limit the length of the circadian period, with rve 4 6 8 triple-knockout plants possessing an elongated period along with increased leaf surface area, biomass, cell size, and delayed flowering relative to wild-type Col-0 plants. Here, using a multi-omics approach consisting of phenomics, transcriptomics, proteomics, and metabolomics we draw new connections between RVE8-like proteins and a number of core plant cell processes. In particular, we reveal that loss of RVE8-like proteins results in altered carbohydrate, organic acid, and lipid metabolism, including a starch excess phenotype at dawn. We further demonstrate that rve 4 6 8 plants have lower levels of 20S proteasome subunits and possess significantly reduced proteasome activity, potentially explaining the increase in cell-size observed in RVE8-like mutants. Overall, this robust, multi-omic dataset provides substantial insight into the far-reaching impact RVE8-like proteins have on the diel plant cell environment.

BoxCar and Library-Free Data-Independent Acquisition Substantially Improve the Depth, Range, and Completeness of Label-Free Quantitative Proteomics.

Data-dependent acquisition (DDA) methods are the current standard for quantitative proteomics in many biological systems. However, DDA preferentially measures highly abundant proteins and generates data that is plagued with missing values, requiring extensive imputation. Here, we demonstrate that library-free BoxCarDIA acquisition, combining MS1-level BoxCar acquisition with MS2-level data-independent acquisition (DIA) analysis, outperforms conventional DDA and other library-free DIA (directDIA) approaches. Using a combination of low- (HeLa cells) and high- (Arabidopsis thaliana cell culture) dynamic range sample types, we demonstrate that BoxCarDIA can achieve a 40% increase in protein quantification over DDA without offline fractionation or an increase in mass-spectrometer acquisition time. Further, we provide empirical evidence for substantial gains in dynamic range sampling that translates to deeper quantification of low-abundance protein classes under-represented in DDA and directDIA data. Unlike both DDA and directDIA, our new BoxCarDIA method does not require full MS1 scans while offering reproducible protein quantification between replicate injections and providing more robust biological inferences. Overall, our results advance the BoxCarDIA technique and establish it as the new method of choice for label-free quantitative proteomics across diverse sample types.

Glyoxalase Goes Green: The Expanding Roles of Glyoxalase in Plants.

The ubiquitous glyoxalase enzymatic pathway is involved in the detoxification of methylglyoxal (MG), a cytotoxic byproduct of glycolysis. The glyoxalase system has been more extensively studied in animals versus plants. Plant glyoxalases have been primarily associated with stress responses and their overexpression is known to impart tolerance to various abiotic stresses. In plants, glyoxalases exist as multigene families, and new roles for glyoxalases in various developmental and signaling pathways have started to emerge. Glyoxalase-based MG detoxification has now been shown to be important for pollination responses. During self-incompatibility response in Brassicaceae, MG is required to target compatibility factors for proteasomal degradation, while accumulation of glyoxalase leads to MG detoxification and efficient pollination. In this review, we discuss the importance of glyoxalase systems and their emerging biological roles in plants.

Suberin-associated fatty alcohols in Arabidopsis: distributions in roots and contributions to seed coat barrier properties.

Suberin is found in a variety of tissues, such as root endoderms and periderms, storage tuber periderms, tree cork layer, and seed coats. It acts as a hydrophobic barrier to control the movement of water, gases, and solutes as well as an antimicrobial barrier. Suberin consists of polymerized phenolics, glycerol, and a variety of fatty acid derivatives, including primary fatty alcohols. We have conducted an in-depth analysis of the distribution of the C18:0 to C22:0 fatty alcohols in Arabidopsis (Arabidopsis thaliana) roots and found that only 20% are part of the root suberin polymer, together representing about 5% of its aliphatic monomer composition, while the remaining 80% are found in the nonpolymeric (soluble) fraction. Down-regulation of Arabidopsis FATTY ACYL REDUCTASE1 (FAR1), FAR4, and FAR5, which collectively produce the fatty alcohols found in suberin, reduced their levels by 70% to 80% in (1) the polymeric and nonpolymeric fractions from roots of tissue culture-grown plants, (2) the suberin-associated root waxes from 7-week-old soil-grown plants, and (3) the seed coat suberin polymer. By contrast, the other main monomers of suberin were not altered, indicating that reduced levels of fatty alcohols did not influence the suberin polymerization process. Nevertheless, the 75% reduction in total fatty alcohol and diol loads in the seed coat resulted in increased permeability to tetrazolium salts and a higher sensitivity to abscisic acid. These results suggest that fatty alcohols and diols play an important role in determining the functional properties of the seed coat suberin barrier.

Twilight length alters growth and flowering time in Arabidopsis via LHY/CCA1.

Decades of research have uncovered how plants respond to two environmental variables that change across latitudes and over seasons: photoperiod and temperature. However, a third such variable, twilight length, has so far gone unstudied. Here, using controlled growth setups, we show that the duration of twilight affects growth and flowering time via the LHY/CCA1 clock genes in the model plant Arabidopsis. Using a series of progressively truncated no-twilight photoperiods, we also found that plants are more sensitive to twilight length compared to equivalent changes in solely photoperiods. Transcriptome and proteome analyses showed that twilight length affects reactive oxygen species metabolism, photosynthesis, and carbon metabolism. Genetic analyses suggested a twilight sensing pathway from the photoreceptors PHY E, PHY B, PHY D, and CRY2 through LHY/CCA1 to flowering modulation through the GI-FT pathway. Overall, our findings call for more nuanced models of day-length perception in plants and posit that twilight is an important determinant of plant growth and development.

Systems-level proteomics and metabolomics reveals the diel molecular landscape of diverse kale cultivars.

Kale is a group of diverse Brassicaceae species that are nutritious leafy greens consumed for their abundance of vitamins and micronutrients. Typified by their curly, serrated and/or wavy leaves, kale varieties have been primarily defined based on their leaf morphology and geographic origin, despite having complex genetic backgrounds. Kale is a very promising crop for vertical farming due to its high nutritional content; however, being a non-model organism, foundational, systems-level analyses of kale are lacking. Previous studies in kale have shown that time-of-day harvesting can affect its nutritional composition. Therefore, to gain a systems-level diel understanding of kale across its wide-ranging and diverse genetic landscape, we selected nine publicly available and commercially grown kale cultivars for growth under near-sunlight LED light conditions ideal for vertical farming. We then analyzed changes in morphology, growth and nutrition using a combination of plant phenotyping, proteomics and metabolomics. As the diel molecular activities of plants drive their daily growth and development, ultimately determining their productivity as a crop, we harvested kale leaf tissue at both end-of-day (ED) and end-of-night (EN) time-points for all molecular analyses. Our results reveal that diel proteome and metabolome signatures divide the selected kale cultivars into two groups defined by their amino acid and sugar content, along with significant proteome differences involving carbon and nitrogen metabolism, mRNA splicing, protein translation and light harvesting. Together, our multi-cultivar, multi-omic analysis provides new insights into the molecular underpinnings of the diel growth and development landscape of kale, advancing our fundamental understanding of this nutritious leafy green super-food for horticulture/vertical farming applications.

Machine learning classification of plant genotypes grown under different light conditions through the integration of multi-scale time-series data.

In order to mitigate the effects of a changing climate, agriculture requires more effective evaluation, selection, and production of crop cultivars in order to accelerate genotype-to-phenotype connections and the selection of beneficial traits. Critically, plant growth and development are highly dependent on sunlight, with light energy providing plants with the energy required to photosynthesize as well as a means to directly intersect with the environment in order to develop. In plant analyses, machine learning and deep learning techniques have a proven ability to learn plant growth patterns, including detection of disease, plant stress, and growth using a variety of image data. To date, however, studies have not assessed machine learning and deep learning algorithms for their ability to differentiate a large cohort of genotypes grown under several growth conditions using time-series data automatically acquired across multiple scales (daily and developmentally). Here, we extensively evaluate a wide range of machine learning and deep learning algorithms for their ability to differentiate 17 well-characterized photoreceptor deficient genotypes differing in their light detection capabilities grown under several different light conditions. Using algorithm performance measurements of precision, recall, F1-Score, and accuracy, we find that Suport Vector Machine (SVM) maintains the greatest classification accuracy, while a combined ConvLSTM2D deep learning model produces the best genotype classification results across the different growth conditions. Our successful integration of time-series growth data across multiple scales, genotypes and growth conditions sets a new foundational baseline from which more complex plant science traits can be assessed for genotype-to-phenotype connections.

A Flower-Specific Phospholipase D Is a Stigmatic Compatibility Factor Targeted by the Self-Incompatibility Response in Brassica napus.

Self-incompatibility (SI) is a genetic mechanism in hermaphroditic flowers that prevents inbreeding by rejection of self-pollen, while allowing cross- or genetically diverse pollen to germinate on the stigma to successfully fertilize the ovules. In Brassica, SI is initiated by the allele-specific recognition of pollen-encoded, secreted ligand (SCR/SP11) by the stigmatic receptor kinase S-locus receptor kinase (SRK), resulting in activation of SRK through phosphorylation [1-3]. Once activated, this phospho-relay converges on intracellular compatibility factors, which are immediately targeted for degradation by the E3 ligase, ARC1, resulting in the pollen rejection response [4, 5]. Through proteomics approach using proteins from SI activated stigmas of Brassica napus, we identified phospholipase D α1 (PLDα1) as one of the candidates that is most likely targeted for degradation after SI [6]. PLDα1 is enriched in the stigmas and functions as a stigmatic compatibility factor as loss of PLDα1 compromised compatible pollination, while overexpression of PLDα1 in self-incompatible stigmas led to breakdown of SI response. PLDα1 can be ubiquitinated by ARC1 and accumulate in ARC1-suppressed lines, confirming PLDα1 as a target of ARC1 during SI response. Addition of phosphatidic acid (PA) to PLDα1-deficient stigmas was sufficient to rescue compatibility, suggesting an essential role for PA generated by PLDα1 for compatible interactions. We propose that PA produced by PLDα1 activity during compatible pollination promotes vesicle fusion at the membrane to facilitate exocytosis necessary for pollen germination to occur, while SI response could abrogate this process by targeting PLDα1 for degradation.