Large-scale screening for novel low-affinity extracellular protein interactions

The Impact of Gene Expression Regulation on Evolution of Extracellular Signaling Pathways

Cell growth selection system to detect extracellular and transmembrane protein interactions.

Vascular progenitor cells (VPCs) facilitate angiogenesis and vascular repair in damaged tissues where inflammatory cytokines coordinate healing through VPC mobilization. VPCs also facilitate tumor angiogenesis and induce angiogenic switching in metastatic sites. VPC binding to extracellular matrix proteins enriched in injured healing tissues and metastatic sites and the participation of integrins in this binding was investigated. VPC binding profiles on fibronectin and laminin of "healthy" controls was different from those of "cancer" patients and patients with acute tissue damage. Specific integrin-mediated binding of cells in VPC clusters were matrix protein-dependent. Patients with oral cancer and lymphoma had elevated plasma vascular endothelial growth factor (VEGF) levels compared to healthy controls. VEGF facilitated VPC clustering on fibronectin and incubating cells from healthy controls with VEGF switched binding profiles from "healthy" to "cancer". This study provided new insights regarding VPC-matrix interactions that help distinguish VPC involvement in vascular repair from vascular-mediated metastasis.

The RNA folding trajectory features numerous off-pathway folding traps, which represent conformations that are often equally as stable as the native functional ones. Therefore, the conversion between these off-pathway structures and the native correctly folded ones is the critical step in RNA folding. This process, referred to as RNA refolding, is slow, and is represented by a transition state that has a characteristic high free energy. Because this kinetically limiting process occurs in vivo, proteins (called RNA chaperones) have evolved that facilitate the (re)folding of RNA molecules. Here, we present an overview of how proteins interact with RNA molecules in order to achieve properly folded states. In this respect, the discrimination between static and transient interactions is crucial, as different proteins have evolved a multitude of mechanisms for RNA remodeling. For RNA chaperones that act in a sequence-unspecific manner and without the use of external sources of energy, such as ATP, transient RNA–protein interactions represent the basis of the mode of action. By presenting stretches of positively charged amino acids that are positioned in defined spatial configurations, RNA chaperones enable the RNA backbone, via transient electrostatic interactions, to sample a wider conformational space that opens the route for efficient refolding reactions.

Globally, the tenth most common cancer is the oral squamous cell carcinoma (OSCC) and the treatment strategy for improving of OSCC patients survival rate still remains a challenging one. Aberrant regulation of cell to extracellular matrix protein interactions leads to progression of human cancers. The focal adhesion kinase (FAK) and its downstream target paxillin have been implicated in cancer growth, migration, invasion and metastasis of different cancers. However, the clinical significance of FAK and paxillin in OSCC is not well characterized so far. In the present work, we showed that relative mRNA and protein expressions of FAK and paxillin are significantly higher in side population (SP) cells of OSCC cell line SCC-55. Concomitantly, the matrix metalloproteinase-11 (MMP-11) level is also significantly elevated in SP cells. The enhanced expression of paxillin is strongly correlated with increased chemoresistance, proliferation rate, migration and invasion potential of SP cells. In addition, inhibition of paxillin expression by RNAi makes SP cells more sensitive to chemotherapy drugs. Therefore, our results suggest that paxillin over expression might play a significant role in cancer progression, invasion and chemoresistance of OSCC.

ABSTRACTStaphylococcus aureus infections have become a major challenge in health care due to increasing antibiotic resistance. We aimed to design small molecule inhibitors of S. aureus surface proteins to be developed as colonization inhibitors. We identified allantodapsone in an initial screen searching for inhibitors of clumping factors A and B (ClfA and ClfB). We used microbial adhesion assays to investigate the effect of allantodapsone on extracellular matrix protein interactions. Allantodapsone inhibited S. aureus Newman adhesion to fibrinogen with an IC50 of 21.3 μM (95% CI 4.5-102 μM), minimum adhesion inhibitory concentration (MAIC) of 100 μM (40.2 μg/mL). Additionally, allantodapsone inhibited adhesion of Lactococcus lactis strains exogenously expressing the clumping factors to fibrinogen (L. lactis ClfA, IC50 of 3.8 μM [95% CI 1.0–14.3 μM], MAIC 10 μM, 4.0 μg/mL; and L. lactis ClfB, IC50 of 11.0 μM [95% CI 0.9–13.6 μM], MAIC 33 μM, 13.3 μg/mL), indicating specific inhibition. Furthermore, the dapsone and alloxan fragments of allantodapsone did not have any inhibitory effect. Adhesion of S. aureus Newman to L2v loricrin is dependent on the expression of ClfB. Allantodapsone caused a dose dependent inhibition of S. aureus adhesion to the L2v loricrin fragment, with full inhibition at 40 μM (OD600 0.11 ± 0.01). Furthermore, recombinant ClfB protein binding to L2v loricrin was inhibited by allantodapsone (P < 0.0001). Allantodapsone also demonstrated dose dependent inhibition of S. aureus Newman adhesion to cytokeratin 10 (CK10). Allantodapsone is the first small molecule inhibitor of the S. aureus clumping factors with potential for development as a colonization inhibitor.IMPORTANCE S. aureus colonization of the nares and the skin provide a reservoir of bacteria that can be transferred to wounds that can ultimately result in systemic infections. Antibiotic resistance can make these infections difficult to treat with significant associated morbidity and mortality. We have identified and characterized a first-in-class small molecule inhibitor of the S. aureus clumping factors A and B, which has the potential to be developed further as a colonization inhibitor.

The interaction of extracellular matrix proteins with lipid bilayers is fundamental to cell adhesion and related processes such as tissue formation. In this paper we use atomic force microscopy to examine the interaction between fibronectin and supported lipid bilayers. For binary lipid mixtures that display phase separation, atomic force microscopy shows that fibronectin preferentially and almost exclusively interacts with the gel phase domains, sitting on top of these raised regions. Single molecule force spectroscopy confirms the location of fibronectin on the bilayer and reveals that the mechanical properties of fibronectin are affected by aggregation of the protein on the lipid. We demonstrate that it is possible to measure the thickness of the protein aggregates by identifying a protein layer breakthrough event. Of particular interest are force–distance curves that exhibit a combination of three features, a protein layer breakthrough event, a lipid bilayer breakthrough event, and a sawtooth pattern attributed to fibronectin unfolding. The combination of lipid- and protein-related events on a single force–distance profile, characterizes in detail the lipid/protein interaction. Preferential interactions of fibronectin with lipids suggest that non-specific binding could be important in extracellular matrix protein-cell interactions and that the lateral organization of the cell membrane may play an even greater role in cell adhesion than previously thought.

Characterization of protein-protein complexes within a family of synaptic surface receptors

Plants uniquely contain large numbers of protein kinases, and for the vast majority of the 1,429 kinases predicted in the rice (Oryza sativa) genome, little is known of their functions. Genetic approaches often fail to produce observable phenotypes; thus, new strategies are needed to delineate kinase function. We previously developed a cost-effective high-throughput yeast two-hybrid system. Using this system, we have generated a protein interaction map of 116 representative rice kinases and 254 of their interacting proteins. Overall, the resulting interaction map supports a large number of known or predicted kinase-protein interactions from both plants and animals and reveals many new functional insights. Notably, we found a potential widespread role for E3 ubiquitin ligases in pathogen defense signaling mediated by receptor-like kinases, particularly by the kinases that may have evolved from recently expanded kinase subfamilies in rice. We anticipate that the data provided here will serve as a foundation for targeted functional studies in rice and other plants. The application of yeast two-hybrid and TAPtag analyses for large-scale plant protein interaction studies is also discussed.

The growth, development, and differentiation of multicellular organisms are primarily driven by intercellular communication, which coordinates the activities of diverse cell types. This cell-to-cell signaling is typically mediated by various types of protein-protein interactions, including ligand-receptor; receptor-receptor, and extracellular matrix-receptor interactions. Currently, computational methods for inferring ligand-receptor communication primarily depend on gene expression data of ligand-receptor pairs and spatial information of cells. Some approaches integrate protein complexes; transcription factors; or pathway information to construct cell communication networks. However, few methods consider the critical role of protein-protein interactions (PPIs) in intercellular communication networks, especially when predicting communication between different cell types in the absence of cell type information. These methods often rely on ligand-receptor pairs that lack PPI evidence, potentially compromising the accuracy of their predictions. To address this issue, we propose CellGAT, a framework that infers intercellular communication by integrating gene expression data of ligand-receptor pairs, PPI information, protein complex data, and experimentally validated pathway information. CellGAT not only builds a priori models but also uses node embedding algorithms and graph attention networks to build cell communication networks based on scRNA-seq (single-cell RNA sequencing) datasets and includes a built-in cell clustering algorithm. Through comparisons with various methods, CellGAT accurately predicts cell-cell communication (CCC) and analyzes its impact on downstream pathways; neighboring cells; and drug interventions.

Extracellular expression is essential for the function of secreted and cell surface proteins. Proper intracellular trafficking depends on protein interactions in multiple subcellular compartments. Co-immunoprecipitation and the yeast two-hybrid system are commonly used to investigate protein-protein interactions. These methods, however, depend on high-affinity protein interactions. In many glycoproteins, glycans are important for protein intracellular trafficking and extracellular expression. If glycoprotein interactions are transient and relatively weak, it may be challenging to use co-immunoprecipitation or the two-hybrid system to identify glycoprotein-binding partners. To circumvent this problem, protein cross-linking can be applied first to immobilize the transient and/or low-affinity protein interactions. Here we describe a protocol of protein cross-linking, co-immunoprecipitation, and proteomic analysis, which was used to identify endoplasmic reticulum (ER) chaperones critical for the folding and ER exiting of N-glycosylated serine proteases in human embryonic kidney (HEK) 293 cells. This approach can be used to identify other protein interactions in a variety of cells.

Surfaceome nanoscale organization and extracellular interaction networks

Many essential cellular functions are carried out by multi-protein complexes that can be characterized by their protein–protein interactions. The interactions between protein subunits are critically dependent on the strengths of their interactions and their cellular abundances, both of which span orders of magnitude. Despite many efforts devoted to the global discovery of protein complexes by integrating large-scale protein abundance and interaction features, there is still room for improvement. Here, we integrated >7000 quantitative proteomic samples with three published affinity purification/co-fractionation mass spectrometry datasets into a deep learning framework to predict protein–protein interactions (PPIs), followed by the identification of protein complexes using a two-stage clustering strategy. Our deep-learning-technique-based classifier significantly outperformed recently published machine learning prediction models and in the process captured 5010 complexes containing over 9000 unique proteins. The vast majority of proteins in our predicted complexes exhibited low or no tissue specificity, which is an indication that the observed complexes tend to be ubiquitously expressed throughout all cell types and tissues. Interestingly, our combined approach increased the model sensitivity for low abundant proteins, which amongst other things allowed us to detect the interaction of MCM10, which connects to the replicative helicase complex via the MCM6 protein. The integration of protein abundances and their interaction features using a deep learning approach provided a comprehensive map of protein–protein interactions and a unique perspective on possible novel protein complexes.

CiRS-7/CDR1as; An oncogenic circular RNA as a potential cancer biomarker

While researchers have known the importance of the protein-protein interaction for decades, recent innovations in large-scale screening techniques have caused a shift in the paradigm of protein function analysis. Where the focus was once on the individual protein, attention is now directed to the surrounding network of protein associations. As protein interaction networks can provide useful insights into the potential function of and phenotypes associated with proteins, the increasing availability of large-scale protein interaction data suggests that molecular biologists can extract more meaningful hypotheses through examination of these large networks. Further, increasing availability of high-quality protein interaction data in multiple species has allowed interpretation of the properties of networks (i.e., the presence of hubs and modularity) from an evolutionary perspective. In this chapter, we discuss major previous findings derived from analyses of large-scale protein interaction data, focusing on approaches taken by landmark assays in evaluating the structure and evolution of these networks. We then outline basic techniques for protein interaction network analysis with the goal of pointing out the benefits and potential limitations of these approaches. As the majority of large-scale protein interaction data has been generated in budding yeast, literature described here focuses on this important model organism with references to other species included where possible.