Mapping the plant proteome: tools for surveying coordinating pathways

Abstract

Plants rapidly respond to environmental fluctuations through coordinated, multi-scalar regulation, enabling complex reactions despite their inherently sessile nature. In particular, protein post-translational signaling and protein–protein interactions combine to manipulate cellular responses and regulate plant homeostasis with precise temporal and spatial control. Understanding these proteomic networks are essential to addressing ongoing global crises, including those of food security, rising global temperatures, and the need for renewable materials and fuels. Technological advances in mass spectrometry-based proteomics are enabling investigations of unprecedented depth, and are increasingly being optimized for and applied to plant systems. This review highlights recent advances in plant proteomics, with an emphasis on spatially and temporally resolved analysis of post-translational modifications and protein interactions. It also details the necessity for generation of a comprehensive plant cell atlas while highlighting recent accomplishments within the field.

Abscisic acid‐controlled redox proteome of Arabidopsis and its regulation by heterotrimeric Gβ protein

“…Levels of transcripts, proteins and metabolites are carefully and dynamically controlled to ensure proper plant adaptive responses (Takahashi et al, 2018;van der Reest et al, 2018;Cimini et al, 2019;Mergner et al, 2020). The proteins are not only affected in their overall abundance, but also at the level of multiple posttranslational modifications that have critical roles in controlling their stability, activity and function (Liu et al, 2014;McConnell et al, 2019;Mergner et al, 2020;Smythers & Hicks, 2021). Exposure to various abiotic stresses leads to generation of ROS (Zandalinas et al, 2018;Xu et al, 2019), which then proliferate signaling across protein pathways through reversible oxidation of cysteine thiols, resulting in sulfenylation, nitrosylation and glutathionylation, among others (Liu et al, 2014;van der Reest et al, 2018;Ameztoy et al, 2019;McConnell et al, 2019).…”

Integration of data‐independent acquisition (DIA) with co‐fractionation mass spectrometry (CF‐MS) to enhance interactome mapping capabilities

“…DIA typically uses a wider isolation m/z range than DDA, meaning that more peptides tend to get co-fragmented. To reduce the FDR, it can be useful to repeat co-elution across multiple, distinct separations to strengthen statistical power [76]. A tutorial is available for researchers who are new to DIA [39] and automated workflows can be used to analyze high-throughput data [77,78].…”

Representing and describing nanomaterials in predictive nanoinformatics

“…At the first level, such descriptors would include characterization of the interfacial ENM contact with biomolecules in terms of the binding energies of biomolecule elements (amino acids, lipid headgroups and so on). Such descriptors should be organized into a bio-nano interactions database linked with initiatives such as The Human Protein Atlas 68 and equivalents for ecotoxicity species and plants 69,70 . The Human Protein Atlas contains detailed quantitative information on the relative abundances of proteins in different cellular and tissue environments that can be integrated and used to predict ENM corona formation, including characterization of the outer corona surface and prediction of the likelihood of a particular hazardous effect.…”