Resolving the spatial distribution of the human proteome at a subcellular level can greatly increase our understanding of human biology and disease. Here we present a comprehensive image-based map ...

http://science.sciencemag.org/content/sci/356/6340/eaal3321.full.pdf

A subcellular map of the human proteome

Protein subcellular localization is tightly controlled and intimately linked to protein function in health and disease. Capturing the spatial proteome - that is, the localizations of proteins and their dynamics at the subcellular level - is therefore essential for a complete understanding of cell biology. Owing to substantial advances in microscopy, mass spectrometry and machine learning applications for data analysis, the field is now mature for proteome-wide investigations of spatial cellular regulation. Studies of the human proteome have begun to reveal a complex architecture, including single-cell variations, dynamic protein translocations, changing interaction networks and proteins localizing to multiple compartments. Furthermore, several studies have successfully harnessed the power of comparative spatial proteomics as a discovery tool to unravel disease mechanisms. We are at the beginning of an era in which spatial proteomics finally integrates with cell biology and medical research, thereby paving the way for unbiased systems-level insights into cellular processes. Here, we discuss current methods for spatial proteomics using imaging or mass spectrometry and specifically highlight global comparative applications. The aim of this Review is to survey the state of the field and also to encourage more cell biologists to apply spatial proteomics approaches.

Spatial proteomics: a powerful discovery tool for cell biology.

Compartmentalization is a defining characteristic of eukaryotic cells, and partitions distinct biochemical processes into discrete subcellular locations. Microscopy1 and biochemical fractionation coupled with mass spectrometry2–4 have defined the proteomes of a variety of different organelles, but many intracellular compartments have remained refractory to such approaches. Proximity-dependent biotinylation techniques such as BioID provide an alternative approach to define the composition of cellular compartments in living cells5–7. Here we present a BioID-based map of a human cell on the basis of 192 subcellular markers, and define the intracellular locations of 4,145 unique proteins in HEK293 cells. Our localization predictions exceed the specificity of previous approaches, and enabled the discovery of proteins at the interface between the mitochondrial outer membrane and the endoplasmic reticulum that are crucial for mitochondrial homeostasis. On the basis of this dataset, we created humancellmap.org as a community resource that provides online tools for localization analysis of user BioID data, and demonstrate how this resource can be used to understand BioID results better. A proximity-dependent biotinylation technique defines the location of more than 4,000 proteins in a human cell, and almost 36,000 proximal interactions between proteins, including those at the interface of the mitochondria and ER.

A proximity-dependent biotinylation map of a human cell.

The intratumoral microenvironment, or stroma, is of major importance in the pathobiology of pancreatic ductal adenocarcinoma (PDA), and specific conditions in the stroma may promote increased cancer aggressiveness. We hypothesized that this heterogeneous and evolving compartment drastically influences tumor cell abilities, which in turn influences PDA aggressiveness through crosstalk that is mediated by extracellular vesicles (EVs). Here, we have analyzed the PDA proteomic stromal signature and identified a contribution of the annexin A6/LDL receptor-related protein 1/thrombospondin 1 (ANXA6/LRP1/TSP1) complex in tumor cell crosstalk. Formation of the ANXA6/LRP1/TSP1 complex was restricted to cancer-associated fibroblasts (CAFs) and required physiopathologic culture conditions that improved tumor cell survival and migration. Increased PDA aggressiveness was dependent on tumor cell-mediated uptake of CAF-derived ANXA6+ EVs carrying the ANXA6/LRP1/TSP1 complex. Depletion of ANXA6 in CAFs impaired complex formation and subsequently impaired PDA and metastasis occurrence, while injection of CAF-derived ANXA6+ EVs enhanced tumorigenesis. We found that the presence of ANXA6+ EVs in serum was restricted to PDA patients and represents a potential biomarker for PDA grade. These findings suggest that CAF-tumor cell crosstalk supported by ANXA6+ EVs is predictive of PDA aggressiveness, highlighting a therapeutic target and potential biomarker for PDA.

/pdf/cancer-associated-fibroblast-derived-annexin-a6-3u0nbas6i2.pdf

Cancer-associated fibroblast-derived annexin A6+ extracellular vesicles support pancreatic cancer aggressiveness

Motivation Recent years have witnessed an exponential growth in the number of identified interactions between biological molecules. These interactions are usually represented as large and complex networks, calling for the development of appropriated tools to exploit the functional information they contain. Random walk with restart (RWR) is the state-of-the-art guilt-by-association approach. It explores the network vicinity of gene/protein seeds to study their functions, based on the premise that nodes related to similar functions tend to lie close to each other in the networks. Results In this study, we extended the RWR algorithm to multiplex and heterogeneous networks. The walk can now explore different layers of physical and functional interactions between genes and proteins, such as protein-protein interactions and co-expression associations. In addition, the walk can also jump to a network containing different sets of edges and nodes, such as phenotype similarities between diseases. We devised a leave-one-out cross-validation strategy to evaluate the algorithms abilities to predict disease-associated genes. We demonstrate the increased performances of the multiplex-heterogeneous RWR as compared to several random walks on monoplex or heterogeneous networks. Overall, our framework is able to leverage the different interaction sources to outperform current approaches. Finally, we applied the algorithm to predict candidate genes for the Wiedemann-Rautenstrauch syndrome, and to explore the network vicinity of the SHORT syndrome. Availability and implementation The source code is available on GitHub at: https://github.com/alberto-valdeolivas/RWR-MH. In addition, an R package is freely available through Bioconductor at: http://bioconductor.org/packages/RandomWalkRestartMH/. Supplementary information Supplementary data are available at Bioinformatics online.

https://hal.archives-ouvertes.fr/hal-01946427/document

Random walk with restart on multiplex and heterogeneous biological networks

Motivation: Multifunctional proteins perform several functions. They are expected to interact specifically with distinct sets of partners, simultaneously or not, depending on the function performed. Current graph clustering methods usually allow a protein to belong to only one cluster, therefore impeding a realistic assignment of multifunctional proteins to clusters.

Results: Here, we present Overlapping Cluster Generator (OCG), a novel clustering method which decomposes a network into overlapping clusters and which is, therefore, capable of correct assignment of multifunctional proteins. The principle of OCG is to cover the graph with initial overlapping classes that are iteratively fused into a hierarchy according to an extension of Newman's modularity function. By applying OCG to a human protein–protein interaction network, we show that multifunctional proteins are revealed at the intersection of clusters and demonstrate that the method outperforms other existing methods on simulated graphs and PPI networks.

Availability: This software can be downloaded from http://tagc.univ-mrs.fr/welcome/spip.php?rubrique197

Contact: brun@tagc.univ-mrs.fr

Supplementary information:Supplementary data are available at Bioinformatics online.

/pdf/multifunctional-proteins-revealed-by-overlapping-clustering-1wb1upv20q.pdf

Multifunctional proteins revealed by overlapping clustering in protein interaction network

Moonlighting proteins are a subclass of multifunctional proteins whose functions are unrelated. Although they may play important roles in cells, there has been no large-scale method to identify them, nor any effort to characterize them as a group. Here, we propose the first method for the identification of ‘extreme multifunctional' proteins from an interactome as a first step to characterize moonlighting proteins. By combining network topological information with protein annotations, we identify 430 extreme multifunctional proteins (3% of the human interactome). We show that the candidates form a distinct sub-group of proteins, characterized by specific features, which form a signature of extreme multifunctionality. Overall, extreme multifunctional proteins are enriched in linear motifs and less intrinsically disordered than network hubs. We also provide MoonDB, a database containing information on all the candidates identified in the analysis and a set of manually curated human moonlighting proteins.

/pdf/extreme-multifunctional-proteins-identified-from-a-human-26pqbh2qjg.pdf

Extreme multifunctional proteins identified from a human protein interaction network.

Background and scope Large networks, such as protein interaction networks, are extremely difficult to analyze as a whole. We developed Clust&See, a Cytoscape plugin dedicated to the identification, visualization and analysis of clusters extracted from such networks. Implementation and performance Clust&See provides the ability to apply three different, recently developed graph clustering algorithms to networks and to visualize: (i) the obtained partition as a quotient graph in which nodes correspond to clusters and (ii) the obtained clusters as their corresponding subnetworks. Importantly, tools for investigating the relationships between clusters and vertices as well as their organization within the whole graph are supplied.

https://hal-upec-upem.archives-ouvertes.fr/hal-00832028/document

Benoît Robisson

Papers

Multifunctional proteins revealed by overlapping clustering in protein interaction network

Extreme multifunctional proteins identified from a human protein interaction network.

Clust&See: a Cytoscape plugin for the identification, visualization and manipulation of network clusters.