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Abstract Protein-level cellular dynamics, including multimerization, play a crucial role in the rapid adaptation of plants during developmental transitions and environmental stresses. The gaseous phytohormone ethylene is a key regulator of rapid cellular growth. During soil emergence, etiolated seedlings undergo crucial morphological changes to their apical hooks and hypocotyls, with ethylene inhibiting hypocotyl axial elongation while promoting radial expansion. Ethylene triggers these growth responses within 2 h; however, the protein machinery and cellular processes that control morphogenesis in Arabidopsis (Arabidopsis thaliana) are unknown. Here, we used quantitative proteomics and co-fractionation mass spectrometry to test for rapid ethylene-dependent changes in protein abundance and protein complex composition. Protein multimerization responses were numerous and diverse. There were instances of protein complex assembly and disassembly, with varying degrees of completeness. Small-scale validation tests indicated that the identified proteins play a role in hypocotyl development and suggested that this approach to gene discovery can identify potential targets for ethylene-mediated growth regulation and enhanced seedling adaptability during early development. 

Abstract Co-fractionation mass spectrometry (CFMS) enables the discovery of protein complexes and the systems-level analysis of multimer dynamics that facilitate responses to environmental and developmental conditions. A major challenge in CFMS data analysis, and omics approaches in general, is the development of reliable benchmarks for accurate evaluation of prediction methods. CORUM is commonly used as a source of benchmark complexes for protein complex composition predictions; however, its assumption of fully assembled subunit pools often conflicts with size exclusion chromatography (SEC) and interaction predictions from CFMS experiments. To address this, we developed an integrative analysis method that leverages cross-kingdom evolutionary conservation among specific CORUM complexes and high-resolution SEC profile data from cell extracts. The resulting benchmark complexes are supported by statistical significance and consistent sizes between calculated and measured apparent masses. The approach was robust, revealing both conserved and species-specific complexes. Designed specifically for benchmark identification, this method can be applied to any species and used to evaluate protein complex predictions from other studies. 

SUMMARY Cotton fibers are aerial trichoblasts that employ a highly polarized diffuse growth mechanism to emerge from the developing ovule epidermis. After executing a complicated morphogenetic program, the cells reach lengths over 2 cm and serve as the foundation of a multi‐billion‐dollar textile industry. Important traits such as fiber diameter, length, and strength are defined by the growth patterns and cell wall properties of individual cells. At present, the ability to engineer fiber traits is limited by our lack of understanding regarding the primary controls governing the rate, duration, and patterns of cell growth. To gain insights into the compartmentalized functions of proteins in cotton fiber cells, we developed a label‐free liquid chromatography mass spectrometry method for systems‐level analyses of fiber proteome. Purified fibers from a single locule were used to fractionate the fiber proteome into apoplast (APO_T), membrane‐associated (p200), and crude cytosolic (s200) fractions. Subsequently, proteins were identified, and their localizations and potential functions were analyzed using combinations of size exclusion chromatography, statistical and bioinformatic analyses. This method had good coverage of the p200 and APO_Tfractions, the latter of which was dominated by proteins associated with particulate membrane‐enclosed compartments. The apoplastic proteome was diverse, the proteins were not degraded, and some displayed distinct multimerization states compared to their cytosolic pool. This quantitative proteomic pipeline can be used to improve coverage and functional analyses of the cotton fiber proteome as a function of developmental time or differing genotypes. 

Abstract BackgroundMorphological properties of tissues and organs rely on cell growth. The growth of plant cells is determined by properties of a tough outer cell wall that deforms anisotropically in response to high turgor pressure. Cortical microtubules bias the mechanical anisotropy of a cell wall by affecting the trajectories of cellulose synthases in the wall that polymerize cellulose microfibrils. The microtubule cytoskeleton is often oriented in one direction at cellular length-scales to regulate growth direction, but the means by which cellular-scale microtubule patterns emerge has not been well understood. Correlations between the microtubule orientation and tensile forces in the cell wall have often been observed. However, the plausibility of stress as a determining factor for microtubule patterning has not been directly evaluated to date. ResultsHere, we simulated how different attributes of tensile forces in the cell wall can orient and pattern the microtubule array in the cortex. We implemented a discrete model with transient microtubule behaviors influenced by local mechanical stress in order to probe the mechanisms of stress-dependent patterning. Specifically, we varied the sensitivity of four types of dynamic behaviors observed on the plus end of microtubules – growth, shrinkage, catastrophe, and rescue – to local stress. Then, we evaluated the extent and rate of microtubule alignments in a two-dimensional computational domain that reflects the structural organization of the cortical array in plant cells. ConclusionOur modeling approaches reproduced microtubule patterns observed in simple cell types and demonstrated that a spatial variation in the magnitude and anisotropy of stress can mediate mechanical feedback between the wall and of the cortical microtubule array. 

Abstract This paper presents a method for time-lapse 3D cell analysis. Specifically, we consider the problem of accurately localizing and quantitatively analyzing sub-cellular features, and for tracking individual cells from time-lapse 3D confocal cell image stacks. The heterogeneity of cells and the volume of multi-dimensional images presents a major challenge for fully automated analysis of morphogenesis and development of cells. This paper is motivated by the pavement cell growth process, and building a quantitative morphogenesis model. We propose a deep feature based segmentation method to accurately detect and label each cell region. An adjacency graph based method is used to extract sub-cellular features of the segmented cells. Finally, the robust graph based tracking algorithm using multiple cell features is proposed for associating cells at different time instances. We also demonstrate the generality of our tracking method on C. elegans fluorescent nuclei imagery. Extensive experiment results are provided and demonstrate the robustness of the proposed method. The code is available on and the method is available as a service through the BisQue portal. 

Abstract Multiprotein complexes execute and coordinate diverse cellular processes such as organelle biogenesis, vesicle trafficking, cell signaling, and metabolism. Knowledge about their composition and localization provides useful clues about the mechanisms of cellular homeostasis and system-level control. This is of great biological importance and practical significance in heterotrophic rice (Oryza sativa) endosperm and aleurone–subaleurone tissues, which are a primary source of seed vitamins and stored energy. Dozens of protein complexes have been implicated in the synthesis, transport, and storage of seed proteins, lipids, vitamins, and minerals. Mutations in protein complexes that control RNA transport result in aberrant endosperm with shrunken and floury phenotypes, significantly reducing seed yield and quality. The purpose of this study was to broadly predict protein complex composition in the aleurone–subaleurone layers of developing rice seeds using co-fractionation mass spectrometry. Following orthogonal chromatographic separations of biological replicates, thousands of protein elution profiles were subjected to distance-based clustering to enable large-scale multimerization state measurements and protein complex predictions. The predicted complexes had predicted functions across diverse functional categories, including novel heteromeric RNA binding protein complexes that may influence seed quality. This effective and open-ended proteomics pipeline provides useful clues about system-level posttranslational control during the early stages of rice seed development. 

Whole-genome duplications are common during evolution, creating genetic redundancy that can enable cellular innovations. Novel protein-protein interactions provide a route to diversified gene functions, but, at present, there is limited proteome-scale knowledge on the extent to which variability in protein complex formation drives neofunctionalization. Here, we used protein correlation profiling to test for variability in apparent mass among thousands of orthologous proteins isolated from diverse species and cell types. Variants in protein complex size were unexpectedly common, in some cases appearing after relatively recent whole-genome duplications or an allopolyploidy event. In other instances, variants such as those in the carbonic anhydrase orthologous group reflected the neofunctionalization of ancient paralogs that have been preserved in extant species. Our results demonstrate that homo- and heteromer formation have the potential to drive neofunctionalization in diverse classes of enzymes, signaling, and structural proteins. 

ABSTRACT Multicellular organisms use dedicator of cytokinesis (DOCK) family guanine nucleotide exchange factors (GEFs) to activate Rac/Rho-of-plants small GTPases and coordinate cell shape change. In developing tissues, DOCK signals integrate cell-cell interactions with cytoskeleton remodeling, and the GEFs cluster reversibly at specific organelle surfaces to orchestrate cytoskeletal reorganization. The domain organizations among DOCK orthologs are diverse, and the mechanisms of localization control are poorly understood. Here, we use combinations of transgene complementation and live-cell imaging assays to uncover an evolutionarily conserved and essential localization determinant in the DOCK-GEF named SPIKE1. The SPIKE1-DHR3 domain is sufficient for organelle association in vivo, and displays a complicated lipid-binding selectivity for both phospholipid head groups and fatty acid chain saturation. SPIKE1-DHR3 is predicted to adopt a C2-domain structure and functions as part of a tandem C2 array that enables reversible clustering at the cell apex. This work provides mechanistic insight into how DOCK GEFs sense compositional and biophysical membrane properties at the interface of two organelle systems. 

Abstract Mechanical properties, size and geometry of cells, and internal turgor pressure greatly influence cell morphogenesis. Computational models of cell growth require values for wall elastic modulus and turgor pressure, but very few experiments have been designed to validate the results using measurements that deform the entire thickness of the cell wall. New wall material is synthesized at the inner surface of the cell such that full-thickness deformations are needed to quantify relevant changes associated with cell development. Here, we present an integrated, experimental–computational approach to analyze quantitatively the variation of elastic bending behavior in the primary cell wall of living Arabidopsis (Arabidopsis thaliana) pavement cells and to measure turgor pressure within cells under different osmotic conditions. This approach used laser scanning confocal microscopy to measure the 3D geometry of single pavement cells and indentation experiments to probe the local mechanical responses across the periclinal wall. The experimental results were matched iteratively using a finite element model of the experiment to determine the local mechanical properties and turgor pressure. The resulting modulus distribution along the periclinal wall was nonuniform across the leaf cells studied. These results were consistent with the characteristics of plant cell walls which have a heterogeneous organization. The results and model allowed the magnitude and orientation of cell wall stress to be predicted quantitatively. The methods also serve as a reference for future work to analyze the morphogenetic behaviors of plant cells in terms of the heterogeneity and anisotropy of cell walls.