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This review discusses the intriguing yet controversial concept of metabolons, focusing on the malate dehydrogenase-citrate synthase (MDH-CISY) metabolon as a model. Metabolons are multienzyme complexes composed of enzymes that catalyze sequential reactions in metabolic pathways. Metabolons have been proposed to enhance metabolic pathway efficiency by facilitating substrate channeling. However, there is skepticism about the presence of metabolons and their functionality in physiological conditions in vivo. We address the skepticism by reviewing compelling evidence supporting the existence of the MDH-CISY metabolon and highlighting its potential functions in cellular metabolism. The electrostatic interaction between MDH and CISY and the intermediate oxaloacetate, channeled within the metabolon, has been demonstrated using various experimental techniques, including protein–protein interaction assays, isotope dilution studies, and enzyme coupling assays. Regardless of the wealth of in vitro evidence, further validation is required to elucidate the functionality of MDH-CISY metabolons in living systems using advanced structural and spatial analysis techniques. 

ABSTRACT Plants exhibit extensive environment-dependent intraspecific metabolic variation, which likely plays a role in determining variation in whole plant phenotypes. However, much of the work seeking to use natural variation to link genes and transcript’s impacts on plant metabolism has employed data from controlled environments. Here we generate and employ data on variation in the abundance of twenty-six metabolites across 660 maize inbred lines under field conditions. We employ these data and previously published transcript and whole plant phenotype data reported for the same field experiment to identify both genomic intervals (through genome-wide association studies) and transcripts (through both transcriptome-wide association studies and an explainable AI approach based on the random forest) associated with variation in metabolite abundance. Both genome-wide association and random forest-based methods identified substantial numbers of significant associations including genes with plausible links to the metabolites they are associated with. In contrast, the transcriptome-wide association identified only six significant associations. In three cases, genetic markers associated with metabolic variation in our study colocalized with markers linked to variation in non-metabolic traits scored in the same experiment. We speculate that the poor performance of transcriptome-wide association studies in identifying transcript-metabolite associations may reflect a high prevalence of non-linear interactions between transcripts and metabolites and/or a bias towards rare transcripts playing a large role in determining intraspecific metabolic variation. 

Abstract Mitochondrial cytochromecmaturation (CCM) requires heme attachment via distinct pathways termed systems I and III. The mosaic distribution of these systems in Archaeplastida raises questions about the genetic mechanisms and evolutionary forces promoting repeated evolution. Here, we show a recurrent shift from ancestral system I to the eukaryotic-specific holocytochromecsynthase (HCCS) of system III in 11 archaeplastid lineages. Archaeplastid HCCS is sufficient to rescue mutants of yeast system III and Arabidopsis system I. Algal HCCS mutants exhibit impaired growth and respiration, and altered biochemical and metabolic profiles, likely resulting from deficient CCM and reduced cytochromec-dependent respiratory activity. Our findings demonstrate that archaeplastid HCCS homologs function as system III components in the absence of system I. These results elucidate the evolutionary trajectory and functional divergence of CCM pathways in Archaeplastida, providing insight into the causes, mechanisms, and consequences of repeated cooption of an entire biological pathway. 

Abstract Mitochondrial malate dehydrogenase (MDH)-citrate synthase (CS) multi-enzyme complex is a part of the Krebs tricarboxylic acid (TCA) cycle ‘metabolon’ which is enzyme machinery catalyzing sequential reactions without diffusion of reaction intermediates into a bulk matrix. This complex is assumed to be a dynamic structure involved in the regulation of the cycle by enhancing metabolic flux. Microscale Thermophoresis analysis of the porcine heart MDH-CS complex revealed that substrates of the MDH and CS reactions, NAD⁺and acetyl-CoA, enhance complex association while products of the reactions, NADH and citrate, weaken the affinity of the complex. Oxaloacetate enhanced the interaction only when it was present together with acetyl-CoA. Structural modeling using published CS structures suggested that the binding of these substrates can stabilize the closed format of CS which favors the MDH-CS association. Two other TCA cycle intermediates, ATP, and low pH also enhanced the association of the complex. These results suggest that dynamic formation of the MDH-CS multi-enzyme complex is modulated by metabolic factors responding to respiratory metabolism, and it may function in the feedback regulation of the cycle and adjacent metabolic pathways. 

Abstract The asymmetric increase in average nighttime temperatures relative to increase in average daytime temperatures due to climate change is decreasing grain yield and quality in rice. Therefore, a better genome-level understanding of the impact of higher night temperature stress on the weight of individual grains is essential for future development of more resilient rice. We investigated the utility of metabolites obtained from grains to classify high night temperature (HNT) conditions of genotypes, and metabolites and single-nucleotide polymorphisms (SNPs) to predict grain length, width, and perimeter phenotypes using a rice diversity panel. We found that the metabolic profiles of rice genotypes alone could be used to classify control and HNT conditions with high accuracy using random forest or extreme gradient boosting. Best linear unbiased prediction and BayesC showed greater metabolic prediction performance than machine learning models for grain-size phenotypes. Metabolic prediction was most effective for grain width, resulting in the highest prediction performance. Genomic prediction performed better than metabolic prediction. Integrating metabolites and genomics simultaneously in a prediction model slightly improved prediction performance. We did not observe a difference in prediction between the control and HNT conditions. Several metabolites were identified as auxiliary phenotypes that could be used to enhance the multi-trait genomic prediction of grain-size phenotypes. Our results showed that, in addition to SNPs, metabolites collected from grains offer rich information to perform predictive analyses, including classification modeling of HNT responses and regression modeling of grain-size-related phenotypes in rice. 

Abstract A number of crop wild relatives can tolerate extreme stress to a degree outside the range observed in their domesticated relatives. However, it is unclear whether or how the molecular mechanisms employed by these species can be translated to domesticated crops. Paspalum (Paspalum vaginatum) is a self-incompatible and multiply stress-tolerant wild relative of maize and sorghum. Here, we describe the sequencing and pseudomolecule level assembly of a vegetatively propagated accession ofP. vaginatum. Phylogenetic analysis based on 6,151 single-copy syntenic orthologues conserved in 6 related grass species places paspalum as an outgroup of the maize-sorghum clade. In parallel metabolic experiments, paspalum, but neither maize nor sorghum, exhibits a significant increase in trehalose when grown under nutrient-deficit conditions. Inducing trehalose accumulation in maize, imitating the metabolic phenotype of paspalum, results in autophagy dependent increases in biomass accumulation. 

With growing populations and pressing environmental problems, future economies will be increasingly plant-based. Now is the time to reimagine plant science as a critical component of fundamental science, agriculture, environmental stewardship, energy, technology and healthcare. This effort requires a conceptual and technological framework to identify and map all cell types, and to comprehensively annotate the localization and organization of molecules at cellular and tissue levels. This framework, called the Plant Cell Atlas (PCA), will be critical for understanding and engineering plant development, physiology and environmental responses. A workshop was convened to discuss the purpose and utility of such an initiative, resulting in a roadmap that acknowledges the current knowledge gaps and technical challenges, and underscores how the PCA initiative can help to overcome them.