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Bioinformatic and Experimental Exploration of MNIO Enzymes in Natural Product Biosynthesis

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dc.contributor.advisorSeyedsayamdost, Mohammad R.
dc.contributor.authorCai, Amanda
dc.date.accessioned2025-08-12T13:31:40Z
dc.date.available2025-08-12T13:31:40Z
dc.date.issued2025-04-21
dc.description.abstractThe rhizosphere is a dynamic chemical environment shaped by secondary metabolites that influence microbial interactions, nutrient availability, and plant health. Among these, ribosomally synthesized, post-translationally modified peptides (RiPPs) represent a largely unexplored class of natural products. DUF692 multinuclear non-heme iron-dependent oxidative enzymes (MNIOs) are an emerging family of tailoring enzymes involved in the biosynthesis of RiPPs. These MNIO-modified RiPPs have shown growing evidence for roles in copper chelation, antivirulence functions, and microbial community dynamics, though their full potential remains underexplored. To address this gap, this study presents a scalable bioinformatic pipeline for identifying MNIO-associated biosynthetic gene clusters (BGCs) across bacterial genomes. Application of this framework uncovered over 1,200 putative MNIO-RiPP BGCs. To probe their functional relevance, we selected a candidate cluster from [Actinomadura] parvosata subsp. kistnae for experimental study. Mass spectrometry analysis revealed a unique mass shift consistent with MNIO-mediated modification, and a putative structure has been proposed. This work expands the known chemical space of MNIO-RiPPs and underscores their potential as bioactive mediators of rhizosphere interactions with translational relevance in environmental and agricultural contexts.
dc.identifier.urihttps://theses-dissertations.princeton.edu/handle/88435/dsp0176537481q
dc.language.isoen_US
dc.titleBioinformatic and Experimental Exploration of MNIO Enzymes in Natural Product Biosynthesis
dc.typePrinceton University Senior Theses
dspace.entity.typePublication
dspace.workflow.startDateTime2025-05-07T20:36:13.846Z
pu.certificateQuantitative and Computational Biology
pu.contributor.authorid920278947
pu.date.classyear2025
pu.departmentChemical and Biological Engr

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