Microbe-induced changes in macronutrient content are required to generate immune responses via a kinase and potassium transporter.

A surge in the accumulation of oxidants generates shifts in the cellular redox potential during early stages of plant infection with pathogens and activation of effector-triggered immunity (ETI). The redoxome, defined as the proteome-wide oxidative modifications of proteins caused by oxidants, has a well-known impact on stress responses in metazoans. However, the identity of proteins and the residues sensitive to oxidation during the plant immune response remain largely unknown. Previous studies of the thimet oligopeptidases TOP1 and TOP2 placed them in the salicylic acid dependent branch of ETI, with a current model wherein TOPs sustain interconnected organellar and cytosolic pathways that modulate the oxidative burst and development of cell death. Herein, we characterized the ETI redoxomes in Arabidopsis (Arabidopsis thaliana) wild-type Col-0 and top1top2 mutant plants using a differential alkylation-based enrichment technique coupled with label-free mass spectrometry-based quantification. We identified cysteines sensitive to oxidation in a wide range of protein families at multiple time points after pathogen infection. Differences were detected between Col-0 and top1top2 redoxomes regarding the identity and number of oxidized cysteines, and the amplitude of time-dependent fluctuations in protein oxidation. Our results support a determining role for TOPs in maintaining the proper level and dynamics of proteome oxidation during ETI. This study significantly expands the repertoire of oxidation-sensitive plant proteins and can guide future mechanistic studies.

Kinases facilitate detection of extracellular signals and set in motion cellular responses for plant adaptation and survival. Some of the energy utilized for kinase signal processing is produced through the activity of ion transporters. Additionally, the synergy between cellular ions and signal transduction influences plant response to pathogens, and their growth and development. In plants, the signaling elements that connect cell wall and membrane sensors with ion homeostasis and transport-mediated processes are largely unknown. Current research indicates that plant Integrin-Linked Kinases (ILKs), a subfamily Raf-like MAP2K Kinases, may have evolved to fulfill this role. In this review, we explore new findings on plant ILKs placing a particular focus on the connection between ILKs proteins unique structural features and ILKs functions. The ankyrin repeat motifs and the kinase domains of ILKs in Arabidopsis and land plants lineage, respectively, are analyzed and discussed as potential determinants of ILKs’ metal ion cofactor specificity and their enzymatic and interaction activities. Further, ILKs regulation through gene expression, subcellular localization, and ions and ion transporters is reviewed in the context of recent studies. Finally, using evidence from literature and interactomics databanks, we infer ILKs-dependent cellular pathways and highlight their potential in transmitting multiple types of signals originating at the interface between the cell wall and plasma membrane. cessing.

Waterlogging is an important environmental stress limiting the productivity of crops worldwide. Cowpea (Vigna unguiculata L.) is particularly sensitive to waterlogging stress during the reproductive stage, with a consequent decline in pod formation and yield. However, little is known about the critical processes underlying cowpea’s responses to waterlogging during the reproductive stage. Thus, we investigated the key parameters influencing carbon fixation, including stomatal conductance (g_s), intercellular CO₂ concentration, chlorophyll content, and chlorophyll fluorescence, of two cowpea genotypes with contrasting waterlogging tolerance. These closely related genotypes have starkly contrasting responses to waterlogging during and after 7 days of waterlogging stress (DOW). In the intolerant genotype (‘EpicSelect.4’), waterlogging resulted in a gradual loss of pigment and decreased photosynthetic capacity as a consequent decline in shoot biomass. On the other hand, the waterlogging-tolerant genotype (‘UCR 369’) maintained CO₂ assimilation rate (A), stomatal conductance (g_s), biomass, and chlorophyll content until 5 DOW. Moreover, there was a highly specific downregulation of the mesophyll conductance (g_m), maximum rate of Rubisco (V_cmax), and photosynthetic electron transport rate (J_max) as non-stomatal limiting factors decreasing A in EpicSelect.4. Exposure of EpicSelect.4 to 2 DOW resulted in the loss of PSII photochemistry by downregulating the PSII quantum yield (F_v/F_m), photochemical efficiency (Φ_PSII), and photochemical quenching (qP). In contrast, we found no substantial change in the photosynthesis and chlorophyll fluorescence of UCR 369 in the first 5 DOW. Instead, UCR 369 maintained biomass accumulation, chlorophyll content, and Rubisco activity, enabling the genotype to maintain nutrient absorption and photosynthesis during the early period of waterlogging. However, compared to the control, both cowpea genotypes could not fully recover their photosynthetic capacity after 7 DOW, with a more significant decline in EpicSelect.4. Overall, our findings suggest that the tolerant UCR 369 genotype maintains higher photosynthesis under waterlogging stress attributable to higher photochemical efficiency, Rubisco activity, and less stomatal restriction. After recovery, the incomplete recovery of A can be attributed to the reduced g_s caused by severe waterlogging damage in both genotypes. Thus, promoting the rapid recovery of stomata from waterlogging stress may be crucial for the complete restoration of carbon fixation in cowpeas during the reproductive stage.

Several methods to handle data generated from bottom-up proteomics via liquid chromatography-mass spectrometry, particularly for peptide-centric quantification dealing with post-translational modification (PTM) analysis like reversible cysteine oxidation are evaluated. The paper proposes a pipeline based on the R programming language to analyze PTMs from peptide-centric label-free quantitative proteomics data. Our methodology includes variance stabilization, normalization, and missing data imputation to account for the large dynamic range of PTM measurements. It also corrects biases from an enrichment protocol and reduces the random and systematic errors associated with label-free quantification. The performance of the methodology is tested by performing proteome-wide differential PTM quantitation using linear models analysis (limma). We objectively compare two imputation methods along with significance testing when using multiple-imputation for missing data. Identifying PTMs in large-scale datasets is a problem with distinct characteristics that require new methods for handling missing data imputation and differential proteome analysis. Linear models in combination with multiple-imputation could significantly outperform a t-test-based decision method.