Lysine acetylation is a widespread protein modification for diverse proteins in Arabidopsis

Lysine acetylation (LysAc), a form of reversible protein posttranslational modification previously known only for histone regulation in plants, is shown to be widespread in Arabidopsis (Arabidopsis thaliana). Sixty-four Lys modification sites were identified on 57 proteins, which operate in a wide variety of pathways/processes and are located in various cellular compartments. A number of photosynthesis-related proteins are among this group of LysAc proteins, including photosystem II (PSII) subunits, light-harvesting chlorophyll a/b-binding proteins (LHCb), Rubisco large and small subunits, and chloroplastic ATP synthase (β-subunit). Using two-dimensional native green/sodium dodecyl sulfate gels, the loosely PSII-bound LHCb was separated from the LHCb that is tightly bound to PSII and shown to have substantially higher level of LysAc, implying that LysAc may play a role in distributing the LHCb complexes. Several potential LysAc sites were identified on eukaryotic elongation factor-1A (eEF-1A) by liquid chromatography/mass spectrometry and using sequence- and modification-specific antibodies the acetylation of Lys-227 and Lys-306 was established. Lys-306 is contained within a predicted calmodulin-binding sequence and acetylation of Lys-306 strongly inhibited the interactions of eEF-1A synthetic peptides with calmodulin recombinant proteins in vitro. These results suggest that LysAc of eEF-1A may directly affect regulatory properties and localization of the protein within the cell. Overall, these findings reveal the possibility that reversible LysAc may be an important and previously unknown regulatory mechanism of a large number of nonhistone proteins affecting a wide range of pathways and processes in Arabidopsis and likely in all plants.     

A predicted protein-protein interaction network of the filamentous fungus Neurospora crassa

The filamentous fungus Neurospora crassa is a leading model organism for circadian clock studies. Computational identification of a protein-protein interaction (PPI) network (also known as an interactome) in N. crassa can provide new insights into the cellular functions of proteins. Using two well-established bioinformatics methods (the interolog method and the domain interaction-based method), we predicted 27,588 PPIs among 3006 N. crassa proteins. To the best of our knowledge, this is the first identified interactome for N. crassa, although it remains problematic because of incomplete interactions and false positives. In particular, the established PPI network has provided clues to further decipher the molecular mechanism of circadian rhythmicity. For instance, we found that clock-controlled genes (ccgs) are more likely to act as bottlenecks in the established PPI network. We also identified an important module related to circadian oscillators, and some functional unknown proteins in this module may serve as potential candidates for new oscillators. Finally, all predicted PPIs were compiled into a user-friendly database server (NCPI), which is freely available at .     

Probabilistic model of the human protein-protein interaction network

A catalog of all human protein-protein interactions would provide scientists with a framework to study protein deregulation in complex diseases such as cancer. Here we demonstrate that a probabilistic analysis integrating model organism interactome data, protein domain data, genome-wide gene expression data and functional annotation data predicts nearly 40,000 protein-protein interactions in humans-a result comparable to those obtained with experimental and computational approaches in model organisms. We validated the accuracy of the predictive model on an independent test set of known interactions and also experimentally confirmed two predicted interactions relevant to human cancer, implicating uncharacterized proteins into definitive pathways. We also applied the human interactome network to cancer genomics data and identified several interaction subnetworks activated in cancer. This integrative analysis provides a comprehensive framework for exploring the human protein interaction network.     

Greedily building protein networks with confidence

MOTIVATION: With genome sequences complete for human and model organisms, it is essential to understand how individual genes and proteins are organized into biological networks. Much of the organization is revealed by proteomics experiments that now generate torrents of data. Extracting relevant complexes and pathways from high-throughput proteomics data sets has posed a challenge, however, and new methods to identify and extract networks are essential. We focus on the problem of building pathways starting from known proteins of interest. RESULTS: We have developed an efficient, greedy algorithm, SEEDY, that extracts biologically relevant biological networks from protein-protein interaction data, building out from selected seed proteins. The algorithm relies on our previous study establishing statistical confidence levels for interactions generated by two-hybrid screens and inferred from mass spectrometric identification of protein complexes. We demonstrate the ability to extract known yeast complexes from high-throughput protein interaction data with a tunable parameter that governs the trade-off between sensitivity and selectivity. DNA damage repair pathways are presented as a detailed example. We highlight the ability to join heterogeneous data sets, in this case protein-protein interactions and genetic interactions, and the appearance of cross-talk between pathways caused by re-use of shared components. SIGNIFICANCE AND COMPARISON: The significance of the SEEDY algorithm is that it is fast, running time O[(E + V) log V] for V proteins and E interactions, a single adjustable parameter controls the size of the pathways that are generated, and an associated P-value indicates the statistical confidence that the pathways are enriched for proteins with a coherent function. Previous approaches have focused on extracting sub-networks by identifying motifs enriched in known biological networks. SEEDY provides the complementary ability to perform a directed search based on proteins of interest. AVAILABILITY: SEEDY software (Perl source), data tables and confidence score models (R source) are freely available from the author.     

Protein interaction analysis of SCF ubiquitin E3 ligase subunits from Arabidopsis

Ubiquitin E3 ligases are a diverse family of protein complexes that mediate the ubiquitination and subsequent proteolytic turnover of proteins in a highly specific manner. Among the several classes of ubiquitin E3 ligases, the Skp1-Cullin-F-box (SCF) class is generally comprised of three 'core' subunits: Skp1 and Cullin, plus at least one F-box protein (FBP) subunit that imparts specificity for the ubiquitination of selected target proteins. Recent genetic and biochemical evidence in Arabidopsis thaliana suggests that post-translational turnover of proteins mediated by SCF complexes is important for the regulation of diverse developmental and environmental response pathways. In this report, we extend upon a previous annotation of the Arabidopsis Skp1-like (ASK) and FBP gene families to include the Cullin family of proteins. Analysis of the protein interaction profiles involving the products of all three gene families suggests a functional distinction between ASK proteins in that selected members of the protein family interact generally while others interact more specifically with members of the F-box protein family. Analysis of the interaction of Cullins with FBPs indicates that CUL1 and CUL2, but not CUL3A, persist as components of selected SCF complexes, suggesting some degree of functional specialization for these proteins. Yeast two-hybrid analyses also revealed binary protein interactions between selected members of the FBP family in Arabidopsis. These and related results are discussed in terms of their implications for subunit composition, stoichiometry and functional diversity of SCF complexes in Arabidopsis.     

Strategies for membrane interaction proteomics: no mass spectrometry required

Membrane-bound proteins are one of the most important protein types in the cell, and are involved in many major cell processes and signaling pathways. Most proteins, including those at membranes, must interact with other proteins to form complexes, which are essential for their function(s). In this review, we describe some of the major non-mass spectrometry-based methods and technologies used for the investigation of intracellular membrane protein complexes including Tango, fluorescence/bioluminescence resonance energy transfer (F/BRET), luminescence-based mammalian interactome mapping (LUMIER), protein-fragment complementation assay (PCA), and membrane yeast two-hybrid assay (MYTH). We highlight the advantages and drawbacks of these methods, describe recent studies utilizing these methods, and discuss some of the major findings in the study of membrane protein-based cell pathways.     

Isolation and proteomic characterization of the Arabidopsis Golgi defines functional and novel components involved in plant cell wall biosynthesis

The plant Golgi plays a pivotal role in the biosynthesis of cell wall matrix polysaccharides, protein glycosylation, and vesicle trafficking. Golgi-localized proteins have become prospective targets for reengineering cell wall biosynthetic pathways for the efficient production of biofuels from plant cell walls. However, proteomic characterization of the Golgi has so far been limited, owing to the technical challenges inherent in Golgi purification. In this study, a combination of density centrifugation and surface charge separation techniques have allowed the reproducible isolation of Golgi membranes from Arabidopsis (Arabidopsis thaliana) at sufficiently high purity levels for in-depth proteomic analysis. Quantitative proteomic analysis, immunoblotting, enzyme activity assays, and electron microscopy all confirm high purity levels. A composition analysis indicated that approximately 19% of proteins were likely derived from contaminating compartments and ribosomes. The localization of 13 newly assigned proteins to the Golgi using transient fluorescent markers further validated the proteome. A collection of 371 proteins consistently identified in all replicates has been proposed to represent the Golgi proteome, marking an appreciable advancement in numbers of Golgi-localized proteins. A significant proportion of proteins likely involved in matrix polysaccharide biosynthesis were identified. The potential within this proteome for advances in understanding Golgi processes has been demonstrated by the identification and functional characterization of the first plant Golgi-resident nucleoside diphosphatase, using a yeast complementation assay. Overall, these data show key proteins involved in primary cell wall synthesis and include a mixture of well-characterized and unknown proteins whose biological roles and importance as targets for future research can now be realized.     

Interactome disassembly during apoptosis occurs independent of caspase cleavage

Protein–protein interaction networks (interactomes) define the functionality of all biological systems. In apoptosis, proteolysis by caspases is thought to initiate disassembly of protein complexes and cell death. Here we used a quantitative proteomics approach, protein correlation profiling (PCP), to explore changes in cytoplasmic and mitochondrial interactomes in response to apoptosis initiation as a function of caspase activity. We measured the response to initiation of Fas‐mediated apoptosis in 17,991 interactions among 2,779 proteins, comprising the largest dynamic interactome to date. The majority of interactions were unaffected early in apoptosis, but multiple complexes containing known caspase targets were disassembled. Nonetheless, proteome‐wide analysis of proteolytic processing by terminal amine isotopic labeling of substrates (TAILS) revealed little correlation between proteolytic and interactome changes. Our findings show that, in apoptosis, significant interactome alterations occur before and independently of caspase activity. Thus, apoptosis initiation includes a tight program of interactome rearrangement, leading to disassembly of relatively few, select complexes. These early interactome alterations occur independently of cleavage of these protein by caspases.     

Experimental and computational approaches for the study of calmodulin interactions

Ca²⁺, a universal messenger in eukaryotes, plays a major role in signaling pathways that control many growth and developmental processes in plants as well as their responses to various biotic and abiotic stresses. Cellular changes in Ca²⁺ in response to diverse signals are recognized by protein sensors that either have their activity modulated or that interact with other proteins and modulate their activity. Calmodulins (CaMs) and CaM-like proteins (CMLs) are Ca²⁺ sensors that have no enzymatic activity of their own but upon binding Ca²⁺ interact and modulate the activity of other proteins involved in a large number of plant processes. Protein-protein interactions play a key role in Ca²⁺/CaM-mediated in signaling pathways. In this review, using CaM as an example, we discuss various experimental approaches and computational tools to identify protein-protein interactions. During the last two decades hundreds of CaM-binding proteins in plants have been identified using a variety of approaches ranging from simple screening of expression libraries with labeled CaM to high-throughput screens using protein chips. However, the high-throughput methods have not been applied to the entire proteome of any plant system. Nevertheless, the data provided by these screens allows the development of computational tools to predict CaM-interacting proteins. Using all known binding sites of CaM, we developed a computational method that predicted over 700 high confidence CaM interactors in the Arabidopsis proteome. Most (>600) of these are not known to bind calmodulin, suggesting that there are likely many more CaM targets than previously known. Functional analyses of some of the experimentally identified Ca²⁺ sensor target proteins have uncovered their precise role in Ca²⁺-mediated processes. Further studies on identifying novel targets of CaM and CMLs and generating their interaction network - “calcium sensor interactome” - will help us in understanding how Ca²⁺ regulates a myriad of cellular and physiological processes.     

A tandem affinity purification-based technology platform to study the cell cycle interactome in Arabidopsis thaliana

Defining protein complexes is critical to virtually all aspects of cell biology because many cellular processes are regulated by stable protein complexes, and their identification often provides insights into their function. We describe the development and application of a high throughput tandem affinity purification/mass spectrometry platform for cell suspension cultures to analyze cell cycle-related protein complexes in Arabidopsis thaliana. Elucidation of this protein-protein interaction network is essential to fully understand the functional differences between the highly redundant cyclin-dependent kinase/cyclin modules, which are generally accepted to play a central role in cell cycle control, in all eukaryotes. Cell suspension cultures were chosen because they provide an unlimited supply of protein extracts of actively dividing and undifferentiated cells, which is crucial for a systematic study of the cell cycle interactome in the absence of plant development. Here we report the mapping of a protein interaction network around six known core cell cycle proteins by an integrated approach comprising generic Gateway-based vectors with high cloning flexibility, the fast generation of transgenic suspension cultures, tandem affinity purification adapted for plant cells, matrix-assisted laser desorption ionization tandem mass spectrometry, data analysis, and functional assays. We identified 28 new molecular associations and confirmed 14 previously described interactions. This systemic approach provides new insights into the basic cell cycle control mechanisms and is generally applicable to other pathways in plants.     

STAM Adaptor Proteins Interact with COPII Complexes and Function in ER-to-Golgi Trafficking

Signal-transducing adaptor molecules (STAMs) are involved in growth factor and cytokine signaling as well as receptor degradation, and they form complexes with a number of endocytic proteins, including Hrs and Eps15. In this study, we demonstrate that STAM proteins also localize prominently to early exocytic compartments and profoundly regulate Golgi morphology. Upon STAM overexpression in cells, the Golgi apparatus becomes extensively fragmented and dispersed, but when STAMs are depleted, the Golgi becomes highly condensed. Under both scenarios, vesicular stomatitis virus G protein–green fluorescent protein trafficking to the plasma membrane is markedly inhibited, and recovery of Golgi morphology after Brefeldin A treatment is substantially impaired in STAM-depleted cells. Furthermore, STAM proteins interact with coat protein II (COPII) proteins, probably at endoplasmic reticulum (ER) exit sites, and Sar1 activity is required to maintain the localization of STAMs at discrete sites. Thus, in addition to their roles in signaling and endocytosis, STAMs function prominently in ER-to-Golgi trafficking, most likely through direct interactions with the COPII complex.     

Systematic prediction of human membrane receptor interactions

Membrane receptor‐activated signal transduction pathways are integral to cellular functions and disease mechanisms in humans. Identification of the full set of proteins interacting with membrane receptors by high‐throughput experimental means is difficult because methods to directly identify protein interactions are largely not applicable to membrane proteins. Unlike prior approaches that attempted to predict the global human interactome, we used a computational strategy that only focused on discovering the interacting partners of human membrane receptors leading to improved results for these proteins. We predict specific interactions based on statistical integration of biological data containing highly informative direct and indirect evidences together with feedback from experts. The predicted membrane receptor interactome provides a system‐wide view, and generates new biological hypotheses regarding interactions between membrane receptors and other proteins. We have experimentally validated a number of these interactions. The results suggest that a framework of systematically integrating computational predictions, global analyses, biological experimentation and expert feedback is a feasible strategy to study the human membrane receptor interactome.     

Information Flow Analysis of Interactome Networks

Recent studies of cellular networks have revealed modular organizations of genes and proteins. For example, in interactome networks, a module refers to a group of interacting proteins that form molecular complexes and/or biochemical pathways and together mediate a biological process. However, it is still poorly understood how biological information is transmitted between different modules. We have developed information flow analysis, a new computational approach that identifies proteins central to the transmission of biological information throughout the network. In the information flow analysis, we represent an interactome network as an electrical circuit, where interactions are modeled as resistors and proteins as interconnecting junctions. Construing the propagation of biological signals as flow of electrical current, our method calculates an information flow score for every protein. Unlike previous metrics of network centrality such as degree or betweenness that only consider topological features, our approach incorporates confidence scores of protein–protein interactions and automatically considers all possible paths in a network when evaluating the importance of each protein. We apply our method to the interactome networks of Saccharomyces cerevisiae and Caenorhabditis elegans. We find that the likelihood of observing lethality and pleiotropy when a protein is eliminated is positively correlated with the protein's information flow score. Even among proteins of low degree or low betweenness, high information scores serve as a strong predictor of loss-of-function lethality or pleiotropy. The correlation between information flow scores and phenotypes supports our hypothesis that the proteins of high information flow reside in central positions in interactome networks. We also show that the ranks of information flow scores are more consistent than that of betweenness when a large amount of noisy data is added to an interactome. Finally, we combine gene expression data with interaction data in C. elegans and construct an interactome network for muscle-specific genes. We find that genes that rank high in terms of information flow in the muscle interactome network but not in the entire network tend to play important roles in muscle function. This framework for studying tissue-specific networks by the information flow model can be applied to other tissues and other organisms as well.     

Bacteria use structural imperfect mimicry to hijack the host interactome

Bacteria use protein-protein interactions to infect their hosts and hijack fundamental pathways, which ensures their survival and proliferation. Hence, the infectious capacity of the pathogen is closely related to its ability to interact with host proteins. Here, we show that hubs in the host-pathogen interactome are isolated in the pathogen network by adapting the geometry of the interacting interfaces. An imperfect mimicry of the eukaryotic interfaces allows pathogen proteins to actively bind to the host’s target while preventing deleterious effects on the pathogen interactome. Understanding how bacteria recognize eukaryotic proteins may pave the way for the rational design of new antibiotic molecules.     

The Golgi-localized Arabidopsis endomembrane protein12 contains both endoplasmic reticulum export and Golgi retention signals at its C terminus

Endomembrane proteins (EMPs), belonging to the evolutionarily conserved transmembrane nine superfamily in yeast and mammalian cells, are characterized by the presence of a large lumenal N terminus, nine transmembrane domains, and a short cytoplasmic tail. The Arabidopsis thaliana genome contains 12 EMP members (EMP1 to EMP12), but little is known about their protein subcellular localization and function. Here, we studied the subcellular localization and targeting mechanism of EMP12 in Arabidopsis and demonstrated that (1) both endogenous EMP12 (detected by EMP12 antibodies) and green fluorescent protein (GFP)-EMP12 fusion localized to the Golgi apparatus in transgenic Arabidopsis plants; (2) GFP fusion at the C terminus of EMP12 caused mislocalization of EMP12-GFP to reach post-Golgi compartments and vacuoles for degradation in Arabidopsis cells; (3) the EMP12 cytoplasmic tail contained dual sorting signals (i.e., an endoplasmic reticulum export motif and a Golgi retention signal that interacted with COPII and COPI subunits, respectively); and (4) the Golgi retention motif of EMP12 retained several post-Golgi membrane proteins within the Golgi apparatus in gain-of-function analysis. These sorting signals are highly conserved in all plant EMP isoforms and, thus, likely represent a general mechanism for EMP targeting in plant cells.     

Glycosaminoglycan–Protein Interactions: The First Draft of the Glycosaminoglycan Interactome

The six mammalian glycosaminoglycans (GAGs), chondroitin sulfate, dermatan sulfate, heparin, heparan sulfate, hyaluronan, and keratan sulfate, are linear polysaccharides. Except for hyaluronan, they are sulfated to various extent, and covalently attached to proteins to form proteoglycans. GAGs interact with growth factors, morphogens, chemokines, extracellular matrix proteins and their bioactive fragments, receptors, lipoproteins, and pathogens. These interactions mediate their functions, from embryonic development to extracellular matrix assembly and regulation of cell signaling in various physiological and pathological contexts such as angiogenesis, cancer, neurodegenerative diseases, and infections. We give an overview of GAG–protein interactions (i.e., specificity and chemical features of GAG- and protein-binding sequences), and review the available GAG–protein interaction networks. We also provide the first comprehensive draft of the GAG interactome composed of 832 biomolecules (827 proteins and five GAGs) and 932 protein–GAG interactions. This network is a scaffold, which in the future should integrate structures of GAG–protein complexes, quantitative data of the abundance of GAGs in tissues to build tissue-specific interactomes, and GAG interactions with metal ions such as calcium, which plays a major role in the assembly of the extracellular matrix and its interactions with cells. This contextualized interactome will be useful to identify druggable GAG–protein interactions for therapeutic purpose:     

Dynamic remodeling of Escherichia coli interactome in response to environmental perturbations

Proteins play an essential role in the vital biological processes governing cellular functions. Most proteins function as members of macromolecular machines, with the network of interacting proteins revealing the molecular mechanisms driving the formation of these complexes. Profiling the physiology-driven remodeling of these interactions within different contexts constitutes a crucial component to achieving a comprehensive systems-level understanding of interactome dynamics. Here, we apply co-fractionation mass spectrometry and computational modeling to quantify and profile the interactions of ∼2000 proteins in the bacterium Escherichia coli cultured under 10 distinct culture conditions. The resulting quantitative co-elution patterns revealed large-scale condition-dependent interaction remodeling among protein complexes involved in diverse biochemical pathways in response to the unique environmental challenges. The network-level analysis highlighted interactome-wide biophysical properties and structural patterns governing interaction remodeling. Our results provide evidence of the local and global plasticity of the E. coli interactome along with a rigorous generalizable framework to define protein interaction specificity. We provide an accompanying interactive web application to facilitate the exploration of these rewired networks.