Abstract
Multiple Arabidopsis arogenate dehydratase (ADT) knock-out (KO) mutants, with phenotypes having variable lignin levels (up to circa 70% reduction), were studied to investigate how differential reductions in ADTs perturb its overall plant systems biology. Integrated “omics” analyses (metabolome, transcriptome, and proteome) of wild type (WT), single and multiple ADT KO lines were conducted. Transcriptome and proteome data were collapsed into gene ortholog (GO) data, with this allowing for enzymatic reaction and metabolome cross-comparisons to uncover dominant or likely metabolic biosynthesis reactions affected. Network analysis of enzymes–highly correlated to stem lignin levels–deduced the involvement of novel putative lignin related proteins or processes. These included those associated with ribosomes, the spliceosome, mRNA transport, aminoacyl tRNA biosynthesis, and phosphorylation. While prior work helped explain lignin biosynthesis regulation at the transcriptional level, our data here provide support for a new hypothesis that there are additional post-transcriptional and translational level processes that need to be considered. These findings are anticipated to lead to development of more accurate depictions of lignin/phenylpropanoid biosynthesis models in situ, with new protein targets identified for further biochemical analysis and/or plant bioengineering. Additionally, using KEGG defined functional categorization of proteomics and transcriptomics analyses, we detected significant changes to glucosinolate, α-linolenic acid, nitrogen, carotenoid, aromatic amino acid, phenylpropanoid, and photosynthesis-related metabolic pathways in ADT KO mutants. Metabolomics results also revealed that putative carotenoid and galactolipid levels were generally increased in amount, whereas many glucosinolates and phenylpropanoids (including flavonoids and lignans) were decreased in the KO mutants.
Highlights
MATERIALS AND METHODSArogenate is an important branch-point to either tyrosine (Tyr) or phenylalanine (Phe) in vascular plants, whose formation from arogenate (Agn) is catalyzed by arogenate dehydrogenase (ADH) and arogenate dehydratase (ADT), respectively (Figure 1)
We found that putative galactolipids and carotenoids generally increased in abundance in both tissues, whereas most of the identified flavonoids, 1-Oβ-D-glucopyranosyl sinapate, 5-hydroxyferuloyl malate and lignans, generally decreased in abundance most prominently in the stem tissues, with the greatest reductions being in stems with single or multiple KOs of ADT5
We examined what proportion of each KEGG functional category, on average, contained the most significantly changed proteins and transcripts in both leaf and stem tissues in all ADT KO lines (Supplementary Figure 6)
Summary
Arogenate is an important branch-point to either tyrosine (Tyr) or phenylalanine (Phe) in vascular plants, whose formation from arogenate (Agn) is catalyzed by arogenate dehydrogenase (ADH) and arogenate dehydratase (ADT), respectively (Figure 1). Dry residues were individually re-suspended in water:acetonitrile (500 μL, 1:1 v/v), and reextracted by sequential vortexing and sonication, followed by centrifugation at 15,000 × g for 5 min with supernatants removed and dried under vacuum. Peptide fractions were dried down and re-suspended in nanopure water at a concentration of 75 ng μL−1 for mass spectrometry analysis using an LTQ-Orbitrap Velos MS (Thermo Scientific) system as described below. Protein and Transcript Relative Fold-change, Z-score and P-value Determinations For the protein data, relative abundances of peptides were determined using iTRAQ reporter ion intensity ratios from each MS/MS spectrum. All proteins with a high Spearman rank correlation (rho > 0.85) to the monomeric guaiacyl (G) and syringyl (S) lignin-derived moieties, and which had a z-score ratio value no more than ±0.5 in the adt versus WT analysis, were identified in WT and adt, adt, adt, adt, adt4/5, adt1/4/5, adt3/4/5, and adt3/4/5/6 mutants, and were entered into the STRING network analysis algorithm (Szklarczyk et al, 2017) searching against. A minimum interaction score of 0.4 or higher was used for nodes with at least one interaction
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