The human plasma membrane peripherome: visualization and analysis of interactions

Scratching the surface: native mass spectrometry of peripheral membrane protein complexes

A growing number of integral membrane proteins have been shown to tune their activity by selectively interacting with specific lipids. The ability to regulate biological functions via lipid interactions extends to the diverse group of proteins that associate only peripherally with the lipid bilayer. However, the structural basis of these interactions remains challenging to study due to their transient and promiscuous nature. Recently, native mass spectrometry has come into focus as a new tool to investigate lipid interactions in membrane proteins. Here, we outline how the native MS strategies developed for integral membrane proteins can be applied to generate insights into the structure and function of peripheral membrane proteins. Specifically, native MS studies of proteins in complex with detergent-solubilized lipids, bound to lipid nanodiscs, and released from native-like lipid vesicles all shed new light on the role of lipid interactions. The unique ability of native MS to capture and interrogate protein–protein, protein–ligand, and protein–lipid interactions opens exciting new avenues for the study of peripheral membrane protein biology.

Unraveling the surface glycoprotein interaction network by integrating chemical crosslinking with MS-based proteomics

The cell plasma membrane provides a highly interactive platform for the information transfer between the inside and outside of cells. The surface glycoprotein interaction network is extremely important in many extracellular events, and aberrant protein interactions are closely correlated with various diseases including cancer. Comprehensive analysis of cell surface protein interactions will deepen our understanding of the collaborations among surface proteins to regulate cellular activity. In this work, we developed a method integrating chemical crosslinking, an enzymatic reaction, and MS-based proteomics to systematically characterize proteins interacting with surface glycoproteins, and then constructed the surfaceome interaction network. Glycans covalently bound to proteins were employed as “baits”, and proteins that interact with surface glycoproteins were connected using chemical crosslinking. Glycans on surface glycoproteins were oxidized with galactose oxidase (GAO) and sequentially surface glycoproteins together with their interactors (“prey”) were enriched through hydrazide chemistry. In combination with quantitative proteomics, over 300 proteins interacting with surface glycoproteins were identified. Many important domains related to extracellular events were found on these proteins. Based on the protein–protein interaction database, we constructed the interaction network among the identified proteins, in which the hub proteins play more important roles in the interactome. Through analysis of crosslinked peptides, specific interactors were identified for glycoproteins on the cell surface. The newly developed method can be extensively applied to study glycoprotein interactions on the cell surface, including the dynamics of the surfaceome interactions in cells with external stimuli.

Proteins interacting with glycoproteins on the cell surface were systematically characterized by integrating chemical crosslinking, enzymatic oxidation, and MS-based proteomics. The surface glycoprotein interaction network was then constructed.

PH-domain-binding inhibitors of nucleotide exchange factor BRAG2 disrupt Arf GTPase signaling

Peripheral membrane proteins orchestrate many physiological and pathological processes, making regulation of their activities by small molecules highly desirable. However, they are often refractory to classical competitive inhibition. Here, we demonstrate that potent and selective inhibition of peripheral membrane proteins can be achieved by small molecules that target protein-membrane interactions by a noncompetitive mechanism. We show that the small molecule Bragsin inhibits BRAG2-mediated Arf GTPase activation in vitro in a manner that requires a membrane. In cells, Bragsin affects the trans-Golgi network in a BRAG2- and Arf-dependent manner. The crystal structure of the BRAG2-Bragsin complex and structure-activity relationship analysis reveal that Bragsin binds at the interface between the PH domain of BRAG2 and the lipid bilayer to render BRAG2 unable to activate lipidated Arf. Finally, Bragsin affects tumorsphere formation in breast cancer cell lines. Bragsin thus pioneers a novel class of drugs that function by altering protein-membrane interactions without disruption.

The SSU processome interactome in Saccharomyces cerevisiae reveals novel protein subcomplexes

Ribosome assembly is an evolutionarily conserved and energy intensive process required for cellular growth, proliferation, and maintenance. In yeast, assembly of the small ribosomal subunit (SSU) requires approximately 75 assembly factors that act in coordination to form the SSU processome, a 6 MDa ribonucleoprotein complex. The SSU processome is required for processing, modifying, and folding the preribosomal RNA (rRNA) to prepare it for incorporation into the mature SSU. Although the protein composition of the SSU processome has been known for some time, the interaction network of the proteins required for its assembly has remained poorly defined. Here, we have used a semi-high-throughput yeast two-hybrid (Y2H) assay and coimmunoprecipitation validation method to produce a high-confidence interactome of SSU processome assembly factors (SPAFs), providing essential insight into SSU assembly and ribosome biogenesis. Further, we used glycerol density-gradient sedimentation to reveal the presence of protein subcomplexes that have not previously been observed. Our work not only provides essential insight into SSU assembly and ribosome biogenesis, but also serves as an important resource for future investigations into how defects in biogenesis and assembly cause congenital disorders of ribosomes known as ribosomopathies.

[Screening of biomarkers related with leukocyte responses early after burn injury in mice by differential gene expression profiling]

OBJECTIVE

To screen the genes related with leukocyte responses in mice early after burn injury by bioinformatic analysis of the gene expression profiling data.

METHODS

The gene expression profiles were obtained from GEO (GSE7404, Mouse musculus, 25% TBSA, full-thickness) database. T test, fold changes and GO functional enrichment analysis were used to identify the differentially expressed genes (DEGs) related to leukocyte responses to burns; the interacting genes were transferred to STRING to construct the protein-protein interaction (PPI) network. Biological annotation of the sub-networks was executed using the software Cytoscape. Real-time PCR and Western blotting were used to verify the DEGs in mice.

RESULTS

In mice at 1 day post-burn, a total of 658 genes were up-regulated and 1167 were down-regulated. PPI network and module analysis suggested that some of the genes (Stat1, Cdk1, Cd19, Lck and Jun) may play critical roles in the PPI network post-burn. Real-time PCR and Western blotting results in mice were consistent with those of bioinformatic analysis of Stat1, Cdk1 and Jun.

CONCLUSION

Stat1, Cdk1 and Jun might be critical players in the development of leukocyte response in mice early after burn injury. Our finding provides new insights into the pathogenesis of leukocyte response to burn injury and identifies several biomarkers as potential targets for burn treatment.

[Screening of biomarkers related with leukocyte responses early after burn injury in mice by differential gene expression profiling]

OBJECTIVE

To screen the genes related with leukocyte responses in mice early after burn injury by bioinformatic analysis of the gene expression profiling data.

METHODS

The gene expression profiles were obtained from GEO (GSE7404, Mouse musculus, 25% TBSA, full-thickness) database. T test, fold changes and GO functional enrichment analysis were used to identify the differentially expressed genes (DEGs) related to leukocyte responses to burns; the interacting genes were transferred to STRING to construct the protein-protein interaction (PPI) network. Biological annotation of the sub-networks was executed using the software Cytoscape. Real-time PCR and Western blotting were used to verify the DEGs in mice.

RESULTS

In mice at 1 day post-burn, a total of 658 genes were up-regulated and 1167 were down-regulated. PPI network and module analysis suggested that some of the genes (Stat1, Cdk1, Cd19, Lck and Jun) may play critical roles in the PPI network post-burn. Real-time PCR and Western blotting results in mice were consistent with those of bioinformatic analysis of Stat1, Cdk1 and Jun.

CONCLUSION

Stat1, Cdk1 and Jun might be critical players in the development of leukocyte response in mice early after burn injury. Our finding provides new insights into the pathogenesis of leukocyte response to burn injury and identifies several biomarkers as potential targets for burn treatment.

The amyloid interactome: Exploring protein aggregation

Protein-protein interactions are the quintessence of physiological activities, but also participate in pathological conditions. Amyloid formation, an abnormal protein-protein interaction process, is a widespread phenomenon in divergent proteins and peptides, resulting in a variety of aggregation disorders. The complexity of the mechanisms underlying amyloid formation/amyloidogenicity is a matter of great scientific interest, since their revelation will provide important insight on principles governing protein misfolding, self-assembly and aggregation. The implication of more than one protein in the progression of different aggregation disorders, together with the cited synergistic occurrence between amyloidogenic proteins, highlights the necessity for a more universal approach, during the study of these proteins. In an attempt to address this pivotal need we constructed and analyzed the human amyloid interactome, a protein-protein interaction network of amyloidogenic proteins and their experimentally verified interactors. This network assembled known interconnections between well-characterized amyloidogenic proteins and proteins related to amyloid fibril formation. The consecutive extended computational analysis revealed significant topological characteristics and unraveled the functional roles of all constituent elements. This study introduces a detailed protein map of amyloidogenicity that will aid immensely towards separate intervention strategies, specifically targeting sub-networks of significant nodes, in an attempt to design possible novel therapeutics for aggregation disorders.

Global profiling of protein complexes: current approaches and their perspective in biomedical research

INTRODUCTION

Despite the rapid evolution of proteomic methods, protein interactions and their participation in protein complexes - an important aspect of their function - has rarely been investigated on the proteome-wide level. Disease states, such as muscular dystrophy or viral infection, are induced by interference in protein-protein interactions within complexes. The purpose of this review is to describe the current methods for global complexome analysis and to critically discuss the challenges and opportunities for the application of these methods in biomedical research. Areas covered: We discuss advancements in experimental techniques and computational tools that facilitate profiling of the complexome. The main focus is on the separation of native protein complexes via size exclusion chromatography and gel electrophoresis, which has recently been combined with quantitative mass spectrometry, for a global protein-complex profiling. The development of this approach has been supported by advanced bioinformatics strategies and fast and sensitive mass spectrometers that have allowed the analysis of whole cell lysates. The application of this technique to biomedical research is assessed, and future directions are anticipated. Expert commentary: The methodology is quite new, and has already shown great potential when combined with complementary methods for detection of protein complexes.

Construction of an immunorelated protein-protein interaction network for clarifying the mechanism of burn

BACKGROUND AND AIM

Severe burn is known to induce a series of pathological responses resulting in increased susceptibility to systemic inflammatory response and multiple organ failure, but the underlying molecular mechanism remains unclear at present. The main aim of this study was to expand our understanding of the events leading to circulating leukocyte response after burn by subjecting the gene expression profiles to a bioinformatic analysis.

MATERIALS AND METHODS

Comprehensive gene expression analysis was performed to identify differentially expressed genes (DEGs) using the expression profile GSE7404 (Mus musculus, circulating leukocyte, 25% of total body surface area (TBSA), full thickness) downloaded from the Gene Expression Omnibus, followed by the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. In addition, a postburn protein-protein interaction (PPI) network was constructed to identify potential biomarkers.

RESULTS

Maximum changes in the gene expression profile were detected 1 day post burn. Separate Gene Ontology (GO) functional enrichment analysis for upregulated and downregulated DEGs revealed significant alterations of genes related to biological process such as "response to stimuli," "metabolic," "cellular and immune system processes," "biological regulation," and "death" in the leukocyte transcriptome after the burn. The KEGG pathway enrichment analysis showed that the upregulated DEGs were significantly enriched in the nodes of immunorelated and signal transduction-related pathways, and the downregulated genes were significantly enriched for the immunorelated pathways. The PPI network and module analysis revealed that, 1 day after the burn, lymphocyte-specific protein tyrosine kinase (Lck) (downregulated), Jun (upregulated), Cd19 (downregulated), Stat1 (downregulated), and Cdk1 (upregulated) were located centrally in both the PPI network and modules.

CONCLUSIONS

Based on an integrated bioinformatic analysis, we concluded that Lck, Jun, Cd19, Stat1, and Cdk1 may be critical 1 day after the burn. These findings expand our understanding of the molecular mechanisms of this important pathological process. Further studies are needed to support our work, focused on identifying candidate biomarkers with sufficient predictive power to act as prognostic and therapeutic biomarkers for burn injury.

A protein interaction map of the LSU processome

Maturation of the large ribosomal subunit (LSU) in eukaryotes is a complex and highly coordinated process that requires the concerted action of a large, dynamic, ribonucleoprotein complex, the LSU processome. To interrogate its organization and architecture, McCann et al. assayed 4800 protein–protein interactions and identified 232 high-confidence, binary-interacting protein pairs, representing a fourfold increase from current knowledge. The resulting LSU processome interactome map enhances our understanding of the organization and function of the biogenesis factors within the LSU processome.

Maturation of the large ribosomal subunit (LSU) in eukaryotes is a complex and highly coordinated process that requires the concerted action of a large, dynamic, ribonucleoprotein complex, the LSU processome. While we know that >80 ribosome biogenesis factors are required throughout the course of LSU assembly, little is known about how these factors interact with each other within the LSU processome. To interrogate its organization and architecture, we took a systems biology approach and performed a semi-high-throughput, array-based, directed yeast two-hybrid assay. Assaying 4800 protein–protein interactions, we identified 232 high-confidence, binary-interacting protein pairs, representing a fourfold increase from current knowledge. The resulting LSU processome interactome map has enhanced our understanding of the organization and function of the biogenesis factors within the LSU processome, revealing both novel and previously identified subcomplexes and hub proteins, including Nop4.

ComPPI: a cellular compartment-specific database for protein-protein interaction network analysis

Here we present ComPPI, a cellular compartment-specific database of proteins and their interactions enabling an extensive, compartmentalized protein–protein interaction network analysis (URL: http://ComPPI.LinkGroup.hu). ComPPI enables the user to filter biologically unlikely interactions, where the two interacting proteins have no common subcellular localizations and to predict novel properties, such as compartment-specific biological functions. ComPPI is an integrated database covering four species (S. cerevisiae, C. elegans, D. melanogaster and H. sapiens). The compilation of nine protein–protein interaction and eight subcellular localization data sets had four curation steps including a manually built, comprehensive hierarchical structure of >1600 subcellular localizations. ComPPI provides confidence scores for protein subcellular localizations and protein–protein interactions. ComPPI has user-friendly search options for individual proteins giving their subcellular localization, their interactions and the likelihood of their interactions considering the subcellular localization of their interacting partners. Download options of search results, whole-proteomes, organelle-specific interactomes and subcellular localization data are available on its website. Due to its novel features, ComPPI is useful for the analysis of experimental results in biochemistry and molecular biology, as well as for proteome-wide studies in bioinformatics and network science helping cellular biology, medicine and drug design.