Proteomic analyses reveal distinct chromatin-associated and soluble transcription factor complexes.

The current knowledge on how transcription factors (TFs), the ultimate targets and executors of cellular signalling pathways, are regulated by protein-protein interactions remains limited. Here, we performed proteomics analyses of soluble and chromatin-associated complexes of 56 TFs, including the targets of many signalling pathways involved in development and cancer, and 37 members of the Forkhead box (FOX) TF family. Using tandem affinity purification followed by mass spectrometry (TAP/MS), we performed 214 purifications and identified 2,156 high-confident protein-protein interactions. We found that most TFs form very distinct protein complexes on and off chromatin. Using this data set, we categorized the transcription-related or unrelated regulators for general or specific TFs. Our study offers a valuable resource of protein-protein interaction networks for a large number of TFs and underscores the general principle that TFs form distinct location-specific protein complexes that are associated with the different regulation and diverse functions of these TFs

The ever-evolving concept of the gene: The use of RNA/Protein experimental techniques to understand genome functions

The completion of the human genome sequence together with advances in sequencing technologies have shifted the paradigm of the genome, as composed of discrete and hereditable coding entities, and have shown the abundance of functional noncoding DNA. This part of the genome, previously dismissed as "junk" DNA, increases proportionally with organismal complexity and contributes to gene regulation beyond the boundaries of known protein-coding genes. Different classes of functionally relevant nonprotein-coding RNAs are transcribed from noncoding DNA sequences. Among them are the long noncoding RNAs (lncRNAs), which are thought to participate in the basal regulation of protein-coding genes at both transcriptional and post-transcriptional levels. Although knowledge of this field is still limited, the ability of lncRNAs to localize in different cellular compartments, to fold into specific secondary structures and to interact with different molecules (RNA or proteins) endows them with multiple regulatory mechanisms. It is becoming evident that lncRNAs may play a crucial role in most biological processes such as the control of development, differentiation and cell growth. This review places the evolution of the concept of the gene in its historical context, from Darwin's hypothetical mechanism of heredity to the post-genomic era. We discuss how the original idea of protein-coding genes as unique determinants of phenotypic traits has been reconsidered in light of the existence of noncoding RNAs. We summarize the technological developments which have been made in the genome-wide identification and study of lncRNAs and emphasize the methodologies that have aided our understanding of the complexity of lncRNA-protein interactions in recent years

Functional modules in the Arabidopsis core cell cycle binary protein-protein interaction network

As in other eukaryotes, cell division in plants is highly conserved and regulated by cyclin-dependent kinases (CDKs) that are themselves predominantly regulated at the posttranscriptional level by their association with proteins such as cyclins. Although over the last years the knowledge of the plant cell cycle has considerably increased, little is known on the assembly and regulation of the different CDK complexes. To map protein-protein interactions between core cell cycle proteins of Arabidopsis thaliana, a binary protein-protein interactome network was generated using two complementary high-throughput interaction assays, yeast two-hybrid and bimolecular fluorescence complementation. Pairwise interactions among 58 core cell cycle proteins were tested, resulting in 357 interactions, of which 293 have not been reported before. Integration of the binary interaction results with cell cycle phase-dependent expression information and localization data allowed the construction of a dynamic interaction network. The obtained interaction map constitutes a framework for further in-depth analysis of the cell cycle machinery

Methods for protein complex prediction and their contributions towards understanding the organization, function and dynamics of complexes

Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organization of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight challenges faced by these methods, in particular detection of sparse and small or sub- complexes and discerning of overlapping complexes. We describe methods for integrating diverse information including expression profiles and 3D structures of proteins with PPI networks to understand the dynamics of complex formation, for instance, of time-based assembly of complex subunits and formation of fuzzy complexes from intrinsically disordered proteins. Finally, we discuss methods for identifying dysfunctional complexes in human diseases, an application that is proving invaluable to understand disease mechanisms and to discover novel therapeutic targets. We hope this review aptly commemorates a decade of research on computational prediction of complexes and constitutes a valuable reference for further advancements in this exciting area.Comment: 1 Tabl

Delineating WWOX protein interactome by tandem affinity purification-mass spectrometry : Identification of top interactors and key metabolic pathways involved

It has become clear from multiple studies that WWOX (WW domain-containing oxidoreductase) operates as a “non-classical” tumor suppressor of significant relevance in cancer progression. Additionally, WWOX has been recognized for its role in a much wider array of human pathologies including metabolic conditions and central nervous system related syndromes. A myriad of putative functional roles has been attributed to WWOX mostly through the identification of various binding proteins. However, the reality is that much remains to be learned on the key relevant functions of WWOX in the normal cell. Here we employed a Tandem Affinity Purification-Mass Spectrometry (TAP-MS) approach in order to better define direct WWOX protein interactors and by extension interaction with multiprotein complexes under physiological conditions on a proteomic scale. This work led to the identification of both well-known, but more importantly novel high confidence WWOX interactors, suggesting the involvement of WWOX in specific biological and molecular processes while delineating a comprehensive portrait of WWOX protein interactome. Of particular relevance is WWOX interaction with key proteins from the endoplasmic reticulum (ER), Golgi, late endosomes, protein transport, and lysosomes networks such as SEC23IP, SCAMP3, and VOPP1. These binding partners harbor specific PPXY motifs which directly interact with the amino-terminal WW1 domain of WWOX. Pathway analysis of WWOX interactors identified a significant enrichment of metabolic pathways associated with proteins, carbohydrates, and lipids breakdown. Thus, suggesting that WWOX likely plays relevant roles in glycolysis, fatty acid degradation and other pathways that converge primarily in Acetyl-CoA generation, a fundamental molecule not only as the entry point to the tricarboxylic acid (TCA) cycle for energy production, but also as the key building block for de novo synthesis of lipids and amino acids. Our results provide a significant lead on subsets of protein partners and enzymatic complexes with which full-length WWOX protein interacts with in order to carry out its metabolic and other biological functions while also becoming a valuable resource for further mechanistic studies.Facultad de Ciencias MédicasCentro de Investigaciones Inmunológicas Básicas y Aplicada

Tyrosine-Based Signals Regulate the Assembly of Daple⋅PARD3 Complex at Cell-Cell Junctions.

Polarized distribution of organelles and molecules inside a cell is vital for a range of cellular processes and its loss is frequently encountered in disease. Polarization during planar cell migration is a special condition in which cellular orientation is triggered by cell-cell contact. We demonstrate that the protein Daple (CCDC88C) is a component of cell junctions in epithelial cells which serves like a cellular "compass" for establishing and maintaining contact-triggered planar polarity. Furthermore, these processes may be mediated through interaction with the polarity regulator PARD3. This interaction, mediated by Daple's PDZ-binding motif (PBM) and the third PDZ domain of PARD3, is fine-tuned by tyrosine phosphorylation on Daple's PBM by receptor and non-receptor tyrosine kinases, such as Src. Hypophosphorylation strengthens the interaction, whereas hyperphosphorylation disrupts it, thereby revealing an unexpected role of Daple as a platform for signal integration and gradient sensing for tyrosine-based signals within the planar cell polarity pathway

Recovering Protein-Protein and Domain-Domain Interactions from Aggregation of IP-MS Proteomics of Coregulator Complexes

Coregulator proteins (CoRegs) are part of multi-protein complexes that transiently assemble with transcription factors and chromatin modifiers to regulate gene expression. In this study we analyzed data from 3,290 immuno-precipitations (IP) followed by mass spectrometry (MS) applied to human cell lines aimed at identifying CoRegs complexes. Using the semi-quantitative spectral counts, we scored binary protein-protein and domain-domain associations with several equations. Unlike previous applications, our methods scored prey-prey protein-protein interactions regardless of the baits used. We also predicted domain-domain interactions underlying predicted protein-protein interactions. The quality of predicted protein-protein and domain-domain interactions was evaluated using known binary interactions from the literature, whereas one protein-protein interaction, between STRN and CTTNBP2NL, was validated experimentally; and one domain-domain interaction, between the HEAT domain of PPP2R1A and the Pkinase domain of STK25, was validated using molecular docking simulations. The scoring schemes presented here recovered known, and predicted many new, complexes, protein-protein, and domain-domain interactions. The networks that resulted from the predictions are provided as a web-based interactive application at http://maayanlab.net/HT-IP-MS-2-PPI-DDI/

The quantitative protein interactome in yeast and human

Cellular function is closely tied to protein-protein interactions. Mapping these on a large scale, therefore, provides fundamental knowledge about the regulation and structure of biological systems. With the onset of proteomics, the use of affinity purification coupled to mass spectrometry (MS) has become the major tool to map protein interactions. Already twenty years ago, researchers endeavored to build interaction maps of model organisms such as yeast. However, previous large-scale interaction studies in Saccharomyces cerevisiae date back more than ten years, covered only about half of all genes, and made use of non-quantitative MS and tandem-affinity purification strategies. These approaches were limited by harsh purification protocols and required large amounts of cell lysate. Additionally large false positive and negative rates hampered their use as a fully reliable source for network studies. Building on recent improvements in sensitivity and speed of MS technology and the introduction of the concept of ‘affinity enrichment coupled to MS,’ I developed a fast, robust, and highly reproducible workflow for proteome-wide interaction studies. I applied and optimized the approach for a first full screen in S. cerevisiae. The workflow starts from only a few hundred µg of proteins per pull-down and is performed entirely in 96-well format, including cell growth, lysis, and affinity enrichment of GFP-tagged proteins. To increase sample throughput and minimize MS idle time between injections, I turned to the high throughput Evosep One liquid chromatography system. This allowed me to obtain data on 60 baits per day. The system is coupled online to a timsTOF Pro mass spectrometer capable of fragmenting over 100 peptides per second using the parallel accumulation – serial fragmentation (PASEF) technology. This combination of miniaturization and standardization ensured high sample throughput, sensitivity, and robustness. Altogether, I successfully performed over 4150 pull-downs and completed more than 8300 measurements for the yeast interactome using this next-generation workflow, all in less than 20 weeks of mass spectrometer running time. The dataset has a very high success rate for pull-downs. The near-complete coverage of expressed proteins in our study enabled a novel two-dimensional analysis strategy that efficiently scores interactions. We examined well-known protein complexes, which confirmed very high data quality. Although the yeast interactome has been studied by large-scale methods for decades, the majority of interactions were novel compared to known high-quality interaction databases. Among many striking novel discoveries - I found compelling evidence for interactions between the conserved chromatin remodeler SWI/SNF and SPX-domain-containing plasma-transporters. Using the common GFP-tag for quantification of protein abundance confirmed that our workflow covers a wide range of cellular protein abundances down to a few copies per cell. Redefining the yeast interactome with very high data quality and completeness enabled the study of its fundamental network properties that have been controversially discussed over many years. In total, our protein-protein interaction network encompasses about 4,000 proteins connected via about 30,000 interactions. A full browsable web application is accessible at yeast-interactome.org and allows (sub-) network exploration, interactor validation via volcano plots and correlation maps, and sample quality control. In a collaboration with the CZ Biohub, we set out to implement the mass spectrometry pipeline developed here to an interaction screen with CRISPR GFP-tagged human HEK293T cells. The reduced sample amount allowed us to screen cell cultures grown in 12-well plates for high throughput. The interaction and localization results of 1,311 processed interactomes in biological triplicates can be accessed at opencell.czbiohub.org