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Abstract Regulatory networks coordinate metabolism to control how plants adapt to biotic and abiotic stresses. This coordination can align transcriptional shifts across metabolic pathways using cis-regulatory elements shared across the enzyme genes within these pathways. While the role of transcription factors (TFs) in controlling this process across pathways is well known, less is known regarding the role of shared cis-regulatory elements across the genes in a pathway. Sharing cis-regulatory elements across the genes in an enzyme complex or pathway, can create coordinated regulation of the pathway by a TF. However, it is unclear if all the genes in a pathway or enzyme complex need to be fully coordinated for maximal function. For example, if one gene in an enzyme complex loses a cis-regulatory element, it may not alter the function of the enzyme complexes function if post-transcriptional or compensatory transcriptional changes are sufficient to balance the complex. To test how cis-modular membership shapes the function of an enzyme complex, we used CRISPR/Cas9 to abolish a common cis-regulatory element across the promoters of nine genes required for the mitochondrial pyruvate dehydrogenase complex (mtPDC). This complex is composed of three apoenzymes and is a central hub coordinating carbon flow between glycolysis and the tricarboxylic acid (TCA) cycle. Different combinations of these cis-element mutations were tested across the genes in the complex inArabidopsis thalianaand the created genotypes were phenotyped for altered enzyme function using digital growth analysis, disease assays, metabolomics, and transcriptomics. This analysis revealed that mutating cis-element motifs of genes in this enzyme complex produced distinct phenotypes, displaying promoter-specific buffering and modularity. 

Abstract Data reduction methods are frequently employed in large genomics and phenomics studies to extract core patterns, reduce dimensionality, and alleviate multiple testing effects. Principal component analysis (PCA), in particular, identifies the components that capture the most variance within omics datasets. While data reduction can simplify complex datasets, it remains unclear how the use of PCA impacts downstream analyses such as quantitative trait loci (QTL) or genome-wide association (GWA) approaches and their biological interpretation. In QTL studies, an alternative to data reduction is the use of post-hoc data summarization approaches, such as hotspot analysis, which involves mapping individual traits and consolidating results based on shared genomic locations. To evaluate how different analytical approaches may alter the biological insights derived from multi-dimensional QTL datasets, we compared individual trait hotspots with PCA-based QTL mapping using transcriptomic and metabolomic data from a structured recombinant inbred line population. Interestingly, these two approaches identified different genomic regions and genetic architectures. These findings suggest that mapping PCA-reduced data does not merely streamline analyses but may generate a fundamentally different view of the underlying genetic architecture compared to individual trait mapping and hotspot analysis. Thus, the use of PCA and other data reduction techniques prior to QTL or GWAS mapping should be carefully considered to ensure alignment with the specific biological question being addressed. 

Abstract Nitrogen is an essential element required for plant growth and productivity. Understanding the mechanisms and natural genetic variation underlying nitrogen use in plants will facilitate the engineering of plant nitrogen use to maximize crop productivity while minimizing environmental costs. To understand the scope of natural variation that may influence nitrogen use, we grew 1,135 Arabidopsis thaliana natural genotypes on two nitrogen sources, nitrate and ammonium, and measured both developmental and defense metabolite traits. By using different environments and focusing on multiple traits, we identified a wide array of different nitrogen responses. These responses are associated with numerous genes, most of which were not previously associated with nitrogen responses. Only a small portion of these genes appear to be shared between environments or traits, while most are predominantly specific to a developmental or defense trait under a specific nitrogen source. Finally, by using a large population, we were able to identify unique nitrogen responses, such as preferring ammonium or nitrate, which appear to be generated by combinations of loci rather than a few large-effect loci. This suggests that it may be possible to obtain novel phenotypes in complex nitrogen responses by manipulating sets of genes with small effects rather than solely focusing on large-effect single gene manipulations.