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SUMMARY Protein homeostasis (proteostasis) is crucial for proper cellular function, including the production of peptides with biological functions through controlled proteolysis. Proteostasis has roles in maintenance of cellular functions and plant interactions with the environment under physiological conditions. Plant stress continues to reduce agricultural yields causing substantial economic losses; thus, it is critical to understand how plants perceive stress signals to elicit responses for survival. As previously shown inArabidopsis thaliana, thimet oligopeptidases (TOPs) TOP1 (also referred to as organellar oligopeptidase) and TOP2 (also referred to as cytosolic oligopeptidase) are essential components in plant response to pathogens, but further characterization of TOPs and their peptide substrates is required to understand their contributions to stress perception and defense signaling. Herein, label‐free peptidomics via liquid chromatography‐tandem mass spectrometry was used to differentially quantify 1111 peptides, originating from 369 proteins, between the Arabidopsis Col‐0 wild type andtop1top2knock‐out mutant. This revealed 350 peptides as significantly more abundant in the mutant, representing accumulation of these potential TOP substrates. Ten direct substrates were validated usingin vitroenzyme assays with recombinant TOPs and synthetic candidate peptides. These TOP substrates are derived from proteins involved in photosynthesis, glycolysis, protein folding, biogenesis, and antioxidant defense, implicating TOP involvement in processes aside from defense signaling. Sequence motif analysis revealed TOP cleavage preference for non‐polar residues in the positions surrounding the cleavage site. Identification of these substrates provides a framework for TOP signaling networks, through which the interplay between proteolytic pathways and defense signaling can be further characterized. 

Abstract Background Recent development of bioinformatics tools for Next Generation Sequencing data has facilitated complex analyses and prompted large scale experimental designs for comparative genomics. When combined with the advances in network inference tools, this can lead to powerful methodologies for mining genomics data, allowing development of pipelines that stretch from sequence reads mapping to network inference. However, integrating various methods and tools available over different platforms requires a programmatic framework to fully exploit their analytic capabilities. Integrating multiple genomic analysis tools faces challenges from standardization of input and output formats, normalization of results for performing comparative analyses, to developing intuitive and easy to control scripts and interfaces for the genomic analysis pipeline. Results We describe here NetSeekR, a network analysis R package that includes the capacity to analyze time series of RNA-Seq data, to perform correlation and regulatory network inferences and to use network analysis methods to summarize the results of a comparative genomics study. The software pipeline includes alignment of reads, differential gene expression analysis, correlation network analysis, regulatory network analysis, gene ontology enrichment analysis and network visualization of differentially expressed genes. The implementation provides support for multiple RNA-Seq read mapping methods and allows comparative analysis of the results obtained by different bioinformatics methods. Conclusion Our methodology increases the level of integration of genomics data analysis tools to network inference, facilitating hypothesis building, functional analysis and genomics discovery from large scale NGS data. When combined with network analysis and simulation tools, the pipeline allows for developing systems biology methods using large scale genomics data. 

Pseudokinases are thought to lack phosphotransfer activity due to altered canonical catalytic residues within their kinase domain. However, a subset of pseudokinases maintain activity through atypical phosphotransfer mechanisms. The Arabidopsis ILK1 is a pseudokinase from the Raf-like MAP3K family and is the only known plant pseudokinase with confirmed protein kinase activity. ILK1 activity promotes disease resistance and molecular pattern-induced root growth inhibition through its stabilization of the HAK5 potassium transporter with the calmodulin-like protein CML9. ILK1 also has a kinase-independent function in salt stress suggesting that it interacts with additional proteins. We determined that members of the ILK subfamily are the sole pseudokinases within the Raf-like MAP3K family and identified 179 novel putative ILK1 protein interactors. We also identified 70 novel peptide targets for ILK1, the majority of which were phosphorylated in the presence of Mn 2+ instead of Mg 2+ in line with modifications in ILK1’s DFG cofactor binding domain. Overall, the ILK1-targeted or interacting proteins included diverse protein types including transporters (HAK5, STP1), protein kinases (MEKK1, MEKK3), and a cytokinin receptor (AHK2). The expression of 31 genes encoding putative ILK1-interacting or phosphorylated proteins, including AHK2, were altered in the root and shoot in response to molecular patterns suggesting a role for these genes in immunity. We describe a potential role for ILK1 interactors in the context of cation-dependent immune signaling, highlighting the importance of K + in MAMP responses. This work further supports the notion that ILK1 is an atypical kinase with an unusual cofactor dependence that may interact with multiple proteins in the cell. 

After a theory of morphogenesis in chemical cells was introduced in the 1950s, much attention had been devoted to the numerical solution of reaction-diffusion (RD) partial differential equations (PDEs). The Crank–Nicolson (CN) method has been a common second-order time-stepping procedure. However, the CN method may introduce spurious oscillations for nonsmooth data unless the time step size is sufficiently small. This article studies a nonoscillatory second-order time-stepping procedure for RD equations, called a variable- θ method , as a perturbation of the CN method. In each time level, the new method detects points of potential oscillations to implicitly resolve the solution there. The proposed time-stepping procedure is nonoscillatory and of a second-order temporal accuracy. Various examples are given to show effectiveness of the method. The article also performs a sensitivity analysis for the numerical solution of biological pattern forming models to conclude that the numerical solution is much more sensitive to the spatial mesh resolution than the temporal one. As the spatial resolution becomes higher for an improved accuracy, the CN method may produce spurious oscillations, while the proposed method results in stable solutions. 

Background 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. Results 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. Conclusion 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. 

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. 

Plant protein kinases form redundant signaling pathways to perceive microbial pathogens and activate immunity. Bacterial pathogens repress cellular immune responses by secreting effectors, some of which bind and inhibit multiple host kinases. To understand how broadly bacterial effectors may bind protein kinases and the function of these kinase interactors, we first tested kinase–effector (K-E) interactions using the Pseudomonas syringae pv. tomato–tomato pathosystem. We tested interactions between five individual effectors (HopAI1, AvrPto, HopA1, HopM1, and HopAF1) and 279 tomato kinases in tomato cells. Over half of the tested kinases interacted with at least one effector, and 48% of these kinases interacted with more than three effectors, suggesting a role in the defense. Next, we characterized the role of select multi-effector–interacting kinases and revealed their roles in basal resistance, effector-triggered immunity (ETI), or programmed cell death (PCD). The immune function of several of these kinases was only detectable in the presence of effectors, suggesting that these kinases are critical when particular cell functions are perturbed or that their role is typically masked. To visualize the kinase networks underlying the cellular responses, we derived signal-specific networks. A comparison of the networks revealed a limited overlap between ETI and basal immunity networks. In addition, the basal immune network complexity increased when exposed to some of the effectors. The networks were used to successfully predict the role of a new set of kinases in basal immunity. Our work indicates the complexity of the larger kinase-based defense network and demonstrates how virulence- and avirulence-associated bacterial effectors alter sectors of the defense network.