A novel phenylpropanoid methyl esterase enables catabolism of aromatic compounds that inhibit biological nitrification
Wilson, A.; Van Fossen, E. M.; Shrestha, R.; Trotter, V. V.; Poirier, B.; Frank, A.; Nelson, W. C.; Kim, Y.-M.; Deutschbauer, A.; Egbert, R.; Elmore, J. R.
Show abstract
Agriculture is the largest source of greenhouse gases (GHG) production. Conversion of nitrogen fertilizers into more reduced forms by microbes through a process known as biological nitrification drives GHG production, enhances proliferation of toxic algal blooms, and increases cost of crop production. Some plants reduce biological nitrification in soils by exuding a diverse array of biological nitrification inhibitors (BNIs) that inhibit the ammonium oxidizing microbes responsible for nitrification. Applying synthetic biology to enhance and transfer BNI production into food and bioenergy crops is a promising approach to reduce nitrification but the success of this strategy is contingent upon improving our limited understanding of BNIs mechanisms-of-action and degradation in the soil. We addressed this gap by investigating the previously unknown metabolic route through which a prominent class of aromatic BNIs known as phenylpropanoid methyl esters (PPMEs) are catabolized. While neither transcriptomics (RNAseq) or high-throughput functional genomics (RB-TnSeq) methods reduced the genetic search space for pathway discovery into a tenable number of genes, the combination of narrowed the search space to a collection of 4 proteins of unknown function. Using genetic and biochemical analyses we found that two previously uncharacterized enzymes, including a novel phenylpropanoid methyl esterase, funnel PPMEs into an established phenylpropanoid catabolism pathway. Transfer of these two enzymes into bacteria capable of using other phenylpropanoids use of PPMEs as carbon sources. This work both provided insight into BNI catabolism and is the first step towards development of model in vivo plant-microbe systems for studying BNI mechanisms under well controlled conditions.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Aromatic acid metabolism in Methylobacterium extorquens reveals interplay between methylotrophic and heterotrophic pathways 97%
- High Enzyme Promiscuity in Lignin Degradation Mechanisms in Rhodopseudomonas palustris CGA009 97%
- Evolutionary divergence of the Wsp signal transduction system in β- and γ-proteobacteria 96%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Establishing Vibrio natriegens as a high-performance host for acetate-based poly-3-hydroxybutyrate production 97%
- High Throughput Fitness Profiling Reveals Loss Of GacS-GacA Regulation Improves Indigoidine Production In Pseudomonas putida 97%
- Replacing the Calvin cycle with the reductive glycine pathway in Cupriavidus necator 97%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.