Human protein interaction networks across tissues and diseases

Improvements in viral gene annotation using large language models and soft alignments

BACKGROUND

The annotation of protein sequences in public databases has long posed a challenge in molecular biology. This issue is particularly acute for viral proteins, which demonstrate limited homology to known proteins when using alignment, k-mer, or profile-based homology search approaches. A novel methodology employing Large Language Models (LLMs) addresses this methodological challenge by annotating protein sequences based on embeddings.

RESULTS

Central to our contribution is the soft alignment algorithm, drawing from traditional protein alignment but leveraging embedding similarity at the amino acid level to bypass the need for conventional scoring matrices. This method not only surpasses pooled embedding-based models in efficiency but also in interpretability, enabling users to easily trace homologous amino acids and delve deeper into the alignments. Far from being a black box, our approach provides transparent, BLAST-like alignment visualizations, combining traditional biological research with AI advancements to elevate protein annotation through embedding-based analysis while ensuring interpretability. Tests using the Virus Orthologous Groups and ViralZone protein databases indicated that the novel soft alignment approach recognized and annotated sequences that both blastp and pooling-based methods, which are commonly used for sequence annotation, failed to detect.

CONCLUSION

The embeddings approach shows the great potential of LLMs for enhancing protein sequence annotation, especially in viral genomics. These findings present a promising avenue for more efficient and accurate protein function inference in molecular biology.

A STRP-ed definition of Structured Tandem Repeats in Proteins

Tandem Repeat Proteins (TRPs) are a class of proteins with repetitive amino acid sequences that have been studied extensively for over two decades. Different features at the level of sequence, structure, function and evolution have been attributed to them by various authors. And yet many of its salient features appear only when looking at specific subclasses of protein tandem repeats. Here, we attempt to rationalize the existing knowledge on Tandem Repeat Proteins (TRPs) by pointing out several dichotomies. The emerging picture is more nuanced than generally assumed and allows us to draw some boundaries of what is not a "proper" TRP. We conclude with an operational definition of a specific subset, which we have denominated STRPs (Structural Tandem Repeat Proteins), which separates a subclass of tandem repeats with distinctive features from several other less well-defined types of repeats. We believe that this definition will help researchers in the field to better characterize the biological meaning of this large yet largely understudied group of proteins.

Targeting stressor-induced dysfunctions in protein-protein interaction networks via epichaperomes

Diseases are manifestations of complex changes in protein-protein interaction (PPI) networks whereby stressors, genetic, environmental, and combinations thereof, alter molecular interactions and perturb the individual from the level of cells and tissues to the entire organism. Targeting stressor-induced dysfunctions in PPI networks has therefore become a promising but technically challenging frontier in therapeutics discovery. This opinion provides a new framework based upon disrupting epichaperomes - pathological entities that enable dysfunctional rewiring of PPI networks - as a mechanism to revert context-specific PPI network dysfunction to a normative state. We speculate on the implications of recent research in this area for a precision medicine approach to detecting and treating complex diseases, including cancer and neurodegenerative disorders.

Biological interacting units identified in human protein networks reveal tissue-functional diversification and its impact on disease

•

Biological processes are exerted by groups of physically interacting proteins.

•

Proteins display variable biological roles depending on tissue-interactomic context.

•

Tissue-specific protein-protein interaction networks reveal functional diversification.

•

Most disease associated genes/proteins display tissue-specific phenotypes.

•

Protein interaction network analysis is a valuable resource to identify disease genes.

Protein-protein interactions (PPI) play an essential role in the biological processes that occur in the cell. Therefore, the dissection of PPI networks becomes decisive to model functional coordination and predict pathological de-regulation. Cellular networks are dynamic and proteins display varying roles depending on the tissue-interactomic context. Thus, the use of centrality measures in individual proteins fall short to dissect the functional properties of the cell. For this reason, there is a need for more comprehensive, relational, and context-specific ways to analyze the multiple actions of proteins in different cells and identify specific functional assemblies within global biomolecular networks. Under this framework, we define Biological Interacting units (BioInt-U) as groups of proteins that interact physically and are enriched in a common Gene Ontology. A search strategy was applied on 33 tissue-specific (TS) PPI networks to generate BioInt libraries associated with each particular human tissue. The cross-tissue comparison showed that housekeeping assemblies incorporate different proteins and exhibit distinct network properties depending on the tissue. Furthermore, disease genes (DGs) of tissue-associated pathologies preferentially accumulate in units in the expected tissues, which in turn were more central in the TS networks. Overall, the study reveals a tissue-specific functional diversification based on the identification of specific protein units and suggests vulnerabilities specific of each tissue network, which can be applied to refine protein-disease association methods.

Supervised Prediction of Aging-Related Genes From a Context-Specific Protein Interaction Subnetwork

Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human aging-related genes. We focus on supervised prediction of such genes. Gene expression-based methods for this purpose study genes in isolation from each other. While protein-protein interaction (PPI) network-based methods for this purpose account for interactions between genes' protein products, current PPI network data are context-unspecific, spanning different biological conditions. Instead, here, we focus on an aging-specific subnetwork of the entire PPI network, obtained by integrating aging-specific gene expression data and PPI network data. The potential of such data integration has been recognized but mostly in the context of cancer. So, we are the first to propose a supervised learning framework for predicting aging-related genes from an aging-specific PPI subnetwork. In a systematic and comprehensive evaluation, we find that in many of the evaluation tests: (i) using an aging-specific subnetwork indeed yields more accurate aging-related gene predictions than using the entire network, and (ii) predictive methods from our framework that have not previously been used for supervised prediction of aging-related genes outperform existing prominent methods for the same purpose. These results justify the need for our framework.

Computational modeling of human-nCoV protein-protein interaction network

Novel coronavirus(SARS-CoV2) replicates the host cell's genome by interacting with the host proteins. Due to this fact, the identification of virus and host protein-protein interactions could be beneficial in understanding the disease transmission behavior of the virus as well as in potential COVID-19 drug identification. International Committee on Taxonomy of Viruses (ICTV) has declared that nCoV is highly genetically similar to the SARS-CoV epidemic in 2003 (∼89% similarity). With this hypothesis, the present work focuses on developing a computational model for the nCoV-Human protein interaction network, using the experimentally validated SARS-CoV-Human protein interactions. Initially, level-1 and level-2 human spreader proteins are identified in the SARS-CoV-Human interaction network, using Susceptible-Infected-Susceptible (SIS) model. These proteins are considered potential human targets for nCoV bait proteins. A gene-ontology-based fuzzy affinity function has been used to construct the nCoV-Human protein interaction network at a ∼99.98% specificity threshold. This also identifies 37 level-1 human spreaders for COVID-19 in the human protein-interaction network. 2474 level-2 human spreaders are subsequently identified using the SIS model. The derived host-pathogen interaction network is finally validated using six potential FDA-listed drugs for COVID-19 with significant overlap between the known drug target proteins and the identified spreader proteins.

Fusion proteins mediate alternation of protein interaction networks in cancers

Fusions of two different genes could lead to the production of chimeric RNAs, which could be translated into novel fusion (or chimeric) proteins. Fusion proteins often act as oncoproteins and drive cancer development, particularly in leukemia and lymphomas. Fusion proteins modify the existing protein-protein interaction (PPI) networks, which could eliminate some PPIs by removing protein domains in such fusions. This alternation of protein interaction networks could impact the signaling pathways and switch on the cancer-promoting activity that could drive the generation of cancer phenotypes and/or loss of controlled apoptosis. Thus, knowledge of the fusion proteins and their protein interaction networks could facilitate a deeper molecular understanding of cancer development, which could help to design new approaches for cancer therapies. Here, we discuss the structural features of fusion proteins and how they impact the PPI networks in cancers. Further, we discuss how to analyze the fusion protein-mediated alternation of PPI networks in cancers.

Decoding multilevel relationships with the human tissue-cell-molecule network

Understanding the biological functions of molecules in specific human tissues or cell types is crucial for gaining insights into human physiology and disease. To address this issue, it is essential to systematically uncover associations among multilevel elements consisting of disease phenotypes, tissues, cell types and molecules, which could pose a challenge because of their heterogeneity and incompleteness. To address this challenge, we describe a new methodological framework, called Graph Local InfoMax (GLIM), based on a human multilevel network (HMLN) that we established by introducing multiple tissues and cell types on top of molecular networks. GLIM can systematically mine the potential relationships between multilevel elements by embedding the features of the HMLN through contrastive learning. Our simulation results demonstrated that GLIM consistently outperforms other state-of-the-art algorithms in disease gene prediction. Moreover, GLIM was also successfully used to infer cell markers and rewire intercellular and molecular interactions in the context of specific tissues or diseases. As a typical case, the tissue-cell-molecule network underlying gastritis and gastric cancer was first uncovered by GLIM, providing systematic insights into the mechanism underlying the occurrence and development of gastric cancer. Overall, our constructed methodological framework has the potential to systematically uncover complex disease mechanisms and mine high-quality relationships among phenotypical, tissue, cellular and molecular elements.

Open Structural Data in Precision Medicine

Three-dimensional protein structural data at the molecular level are pivotal for successful precision medicine. Such data are crucial not only for discovering drugs that act to block the active site of the target mutant protein but also for clarifying to the patient and the clinician how the mutations harbored by the patient work. The relative paucity of structural data reflects their cost, challenges in their interpretation, and lack of clinical guidelines for their utilization. Rapid technological advancements in experimental high-resolution structural determination increasingly generate structures. Computationally, modeling algorithms, including molecular dynamics simulations, are becoming more powerful, as are compute-intensive hardware, particularly graphics processing units, overlapping with the inception of the exascale era. Accessible, freely available, and detailed structural and dynamical data can be merged with big data to powerfully transform personalized pharmacology. Here we review protein and emerging genome high-resolution data, along with means, applications, and examples underscoring their usefulness in precision medicine. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Protein-Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation

Here, we present a Gaussian-based method for estimation of protein-protein binding entropy to augment the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) method for computational prediction of binding free energy (ΔG). The method is termed f5-MM/PBSA/E, where "E" stands for entropy and f5 for five adjustable parameters. The enthalpy components of ΔG (molecular mechanics, polar and non-polar solvation energies) are computed from a single implicit solvent generalized Born (GB) energy minimized structure of a protein-protein complex, while the binding entropy is computed using independently GB energy minimized unbound and bound structures. It should be emphasized that the f5-MM/PBSA/E method does not use snapshots, just energy minimized structures, and is thus very fast and computationally efficient. The method is trained and benchmarked in 5-fold validation test over a data set consisting of 46 protein-protein binding cases with experimentally determined dissociation constant K _d values. This data set has been used for benchmarking in recently published protein-protein binding studies that apply conventional MM/PBSA and MM/PBSA with an enhanced sampling method. The f5-MM/PBSA/E tested on the same data set achieves similar or better performance than these computationally demanding approaches, making it an excellent choice for high throughput protein-protein binding affinity prediction studies.

Inference of a Dynamic Aging-related Biological Subnetwork via Network Propagation

Gene expression (GE)data capture valuable condition-specific information ("condition" can mean a biological process, disease stage, age, patient, etc.)However, GE analyses ignore physical interactions between gene products, i.e., proteins. Because proteins function by interacting with each other, and because biological networks (BNs)capture these interactions, BN analyses are promising. However, current BN data fail to capture condition-specific information. Recently, GE and BN data have been integrated using network propagation (NP)to infer condition-specific BNs. However, existing NP-based studies result in a static condition-specific subnetwork, even though cellular processes are dynamic. A dynamic process of our interest is human aging. We use prominent existing NP methods in a new task of inferring a dynamic rather than static condition-specific (aging-related)subnetwork. Then, we study evolution of network structure with age - we identify proteins whose network positions significantly change with age and predict them as new aging-related candidates. We validate the predictions via e.g., functional enrichment analyses and literature search. Dynamic network inference via NP yields higher prediction quality than the only existing method for inferring a dynamic aging-related BN, which does not use NP. Our data and code are available at https://nd.edu/~cone/dynetinf.

Pharmacologically controlling protein-protein interactions through epichaperomes for therapeutic vulnerability in cancer

Cancer cell plasticity due to the dynamic architecture of interactome networks provides a vexing outlet for therapy evasion. Here, through chemical biology approaches for systems level exploration of protein connectivity changes applied to pancreatic cancer cell lines, patient biospecimens, and cell- and patient-derived xenografts in mice, we demonstrate interactomes can be re-engineered for vulnerability. By manipulating epichaperomes pharmacologically, we control and anticipate how thousands of proteins interact in real-time within tumours. Further, we can essentially force tumours into interactome hyperconnectivity and maximal protein-protein interaction capacity, a state whereby no rebound pathways can be deployed and where alternative signalling is supressed. This approach therefore primes interactomes to enhance vulnerability and improve treatment efficacy, enabling therapeutics with traditionally poor performance to become highly efficacious. These findings provide proof-of-principle for a paradigm to overcome drug resistance through pharmacologic manipulation of proteome-wide protein-protein interaction networks.

A Human Pan-Cancer System Analysis of Procollagen-Lysine, 2-Oxoglutarate 5-Dioxygenase 3 (PLOD3)

The overexpression of the enzymes involved in the degradation of procollagen lysine is correlated with various tumor entities. Procollagen-lysine, 2-oxoglutarate 5-dioxygenase 3 (PLOD3) expression was found to be correlated to the progression and migration of cancer cells in gastric, lung and prostate cancer. Here, we analyzed the gene expression, protein expression, and the clinical parameters of survival across 33 cancers based on the Clinical Proteomic Tumor Analysis Consortium (CPTAC), function annotation of the mammalian genome 5 (FANTOM5), Gene Expression Omnibus (GEO), Genotype-Tissue Expression (GTEx), Human Protein Atlas (HPA) and The Cancer Genome Atlas (TCGA) databases. Genetic alteration, immune infiltration and relevant cellular pathways were analyzed in detail. PLOD3 expression negatively correlated with survival periods and the infiltration level of CD8⁺ T cells, but positively correlated to the infiltration of cancer associated fibroblasts in diverse cancers. Immunohistochemistry in colon carcinomas, glioblastomas, and soft tissue sarcomas further confirm PLOD 3 expression in human cancer tissue. Moreover, amplification and mutation accounted for the largest proportion in esophageal adenocarcinoma and uterine corpus endometrial carcinoma, respectively; the copy number alteration of PLOD3 appeared in all cancers from TCGA; and molecular mechanisms further proved the effect of PLOD3 on tumorigenesis. In particular, PLOD3 expression appears to have a tumor immunological effect, and is related to multiple immune cells. Furthermore, it is also associated with tumor mutation burden and microsatellite instability in various tumors. PLOD3 acts as an inducer of various cancers, and it could be a potential biomarker for prognosis and targeted treatment.

Detecting Rewiring Events in Protein-Protein Interaction Networks Based on Transcriptomic Data

Proteins rarely carry out their cellular functions in isolation. Instead, eukaryotic proteins engage in about six interactions with other proteins on average. The aggregated protein interactome of an organism forms a “hairy ball”-type protein-protein interaction (PPI) network. Yet, in a typical human cell, only about half of all proteins are expressed at a particular time. Hence, it has become common practice to prune the full PPI network to the subset of expressed proteins. If RNAseq data is available, one can further resolve the specific protein isoforms present in a cell or tissue. Here, we review various approaches, software tools and webservices that enable users to construct context-specific or tissue-specific PPI networks and how these are rewired between two cellular conditions. We illustrate their different functionalities on the example of the interactions involving the human TNR6 protein. In an outlook, we describe how PPI networks may be integrated with epigenetic data or with data on the activity of splicing factors.

FINER: enhancing the prediction of tissue-specific functions of isoforms by refining isoform interaction networks

Annotating the functions of gene products is a mainstay in biology. A variety of databases have been established to record functional knowledge at the gene level. However, functional annotations at the isoform resolution are in great demand in many biological applications. Although critical information in biological processes such as protein-protein interactions (PPIs) is often used to study gene functions, it does not directly help differentiate the functions of isoforms, as the 'proteins' in the existing PPIs generally refer to 'genes'. On the other hand, the prediction of isoform functions and prediction of isoform-isoform interactions, though inherently intertwined, have so far been treated as independent computational problems in the literature. Here, we present FINER, a unified framework to jointly predict isoform functions and refine PPIs from the gene level to the isoform level, enabling both tasks to benefit from each other. Extensive computational experiments on human tissue-specific data demonstrate that FINER is able to gain at least 5.16% in AUC and 15.1% in AUPRC for functional prediction across multiple tissues by refining noisy PPIs, resulting in significant improvement over the state-of-the-art methods. Some in-depth analyses reveal consistency between FINER's predictions and the tissue specificity as well as subcellular localization of isoforms.

Disease-specific interactome alterations via epichaperomics: the case for Alzheimer's disease

The increasingly appreciated prevalence of complicated stressor-to-phenotype associations in human disease requires a greater understanding of how specific stressors affect systems or interactome properties. Many currently untreatable diseases arise due to variations in, and through a combination of, multiple stressors of genetic, epigenetic, and environmental nature. Unfortunately, how such stressors lead to a specific disease phenotype or inflict a vulnerability to some cells and tissues but not others remains largely unknown and unsatisfactorily addressed. Analysis of cell- and tissue-specific interactome networks may shed light on organization of biological systems and subsequently to disease vulnerabilities. However, deriving human interactomes across different cell and disease contexts remains a challenge. To this end, this opinion article links stressor-induced protein interactome network perturbations to the formation of pathologic scaffolds termed epichaperomes, revealing a viable and reproducible experimental solution to obtaining rigorous context-dependent interactomes. This article presents our views on how a specialized 'omics platform called epichaperomics may complement and enhance the currently available conventional approaches and aid the scientific community in defining, understanding, and ultimately controlling interactome networks of complex diseases such as Alzheimer's disease. Ultimately, this approach may aid the transition from a limited single-alteration perspective in disease to a comprehensive network-based mindset, which we posit will result in precision medicine paradigms for disease diagnosis and treatment.

PINA 3.0: mining cancer interactome

Protein–protein interactions (PPIs) are crucial to mediate biological functions, and understanding PPIs in cancer type-specific context could help decipher the underlying molecular mechanisms of tumorigenesis and identify potential therapeutic options. Therefore, we update the Protein Interaction Network Analysis (PINA) platform to version 3.0, to integrate the unified human interactome with RNA-seq transcriptomes and mass spectrometry-based proteomes across tens of cancer types. A number of new analytical utilities were developed to help characterize the cancer context for a PPI network, which includes inferring proteins with expression specificity and identifying candidate prognosis biomarkers, putative cancer drivers, and therapeutic targets for a specific cancer type; as well as identifying pairs of co-expressing interacting proteins across cancer types. Furthermore, a brand-new web interface has been designed to integrate these new utilities within an interactive network visualization environment, which allows users to quickly and comprehensively investigate the roles of human interacting proteins in a cancer type-specific context. PINA is freely available at https://omics.bjcancer.org/pina/.

Dynamics of a Protein Interaction Network Associated to the Aggregation of polyQ-Expanded Ataxin-1

Background: Several experimental models of polyglutamine (polyQ) diseases have been previously developed that are useful for studying disease progression in the primarily affected central nervous system. However, there is a missing link between cellular and animal models that would indicate the molecular defects occurring in neurons and are responsible for the disease phenotype in vivo. Methods: Here, we used a computational approach to identify dysregulated pathways shared by an in vitro and an in vivo model of ATXN1(Q82) protein aggregation, the mutant protein that causes the neurodegenerative polyQ disease spinocerebellar ataxia type-1 (SCA1). Results: A set of common dysregulated pathways were identified, which were utilized to construct cerebellum-specific protein-protein interaction (PPI) networks at various time-points of protein aggregation. Analysis of a SCA1 network indicated important nodes which regulate its function and might represent potential pharmacological targets. Furthermore, a set of drugs interacting with these nodes and predicted to enter the blood–brain barrier (BBB) was identified. Conclusions: Our study points to molecular mechanisms of SCA1 linked from both cellular and animal models and suggests drugs that could be tested to determine whether they affect the aggregation of pathogenic ATXN1 and SCA1 disease progression.

Cytokines mapping for tissue-specific expression, eQTLs and GWAS traits

Dysregulation in cytokine production has been linked to the pathogenesis of various immune-mediated traits, in which genetic variability contributes to the etiopathogenesis. GWA studies have identified many genetic variants in or near cytokine genes, nonetheless, the translation of these findings into knowledge of functional determinants of complex traits remains a fundamental challenge. In this study we aimed at collection, analysis and interpretation of data on cytokines focused on their tissue-specific expression, eQTLs and GWAS traits. Using GO annotations, we generated a list of 314 cytokines and analyzed them with the GTEx resource. Cytokines were highly tissue-specific, 82.3% of cytokines had Tau expression metrics ≥ 0.8. In total, 3077 associations for 1760 unique SNPs in or near 244 cytokines were mapped in the NHGRI-EBI GWAS Catalog. According to the Experimental Factor Ontology resource, the largest numbers of disease associations were related to 'Inflammatory disease', 'Immune system disease' and 'Asthma'. The GTEx-based analysis revealed that among GWAS SNPs, 1142 SNPs had eQTL effects and influenced expression levels of 999 eGenes, among them 178 cytokines. Several types of enrichment analysis showed that it was cytokines expression variability that fundamentally contributed to the molecular origins of considered immune-mediated conditions.

Fully Automated Protein Proximity Assay in Formalin-Fixed, Paraffin-Embedded Tissue Using Caged Haptens

The ability to interrogate for the presence and distribution of protein-protein complexes (PPCs) is of high importance for the advancement of diagnostic capabilities such as determining therapeutic effects in the context of pharmaceutical development. Herein, we report a novel assay for detecting and visualizing PPCs on formalin-fixed, paraffin-embedded material using a caged hapten. To this end, we synthetically modified a nitropyrazole hapten with an alkaline phosphatase (AP)-responsive self-immolative caging group. The AP-labile caging group abrogates antibody binding; however, upon exposure to AP, the native hapten is regenerated. These caged haptens were applied in a proximity assay format by the use of a first antibody labeled with caged haptens that can be uncaged by AP conjugated to the second antibody. Only when the two epitopes of interest are in close proximity to one another will the AP interact with the caged hapten and uncage it. The native hapten, which represents the population of PPCs, was then visualized by an anti-hapten antibody conjugated to horseradish peroxidase, followed by diaminobenzidine detection. We provide proof of concept for the detection of protein proximity pairs (β-catenin-E-cadherin and EGFR-GRB2), and confirm assay specificity through technical controls involving reagent omission experiments, and biologically by treatment with small-molecule kinase inhibitors that interrupt kinase-adaptor complexes.

Complete Topological Mapping of a Cellular Protein Interactome Reveals Bow-Tie Motifs as Ubiquitous Connectors of Protein Complexes

The network topology of a protein interactome is shaped by the function of each protein, making it a resource of functional knowledge in tissues and in single cells. Today, this resource is underused, as complete network topology characterization has proved difficult for large protein interactomes. We apply a matrix visualization and decoding approach to a physical protein interactome of a dendritic cell, thereby characterizing its topology with no prior assumptions of structure. We discover 294 proteins, each forming topological motifs called "bow-ties" that tie together the majority of observed protein complexes. The central proteins of these bow-ties have unique network properties, display multifunctional capabilities, are enriched for essential proteins, and are widely expressed in other cells and tissues. Collectively, the bow-tie motifs are a pervasive and previously unnoted topological trend in cellular interactomes. As such, these results provide fundamental knowledge on how intracellular protein connectivity is organized and operates.

ResponseNet v.3: revealing signaling and regulatory pathways connecting your proteins and genes across human tissues

ResponseNet v.3 is an enhanced version of ResponseNet, a web server that is designed to highlight signaling and regulatory pathways connecting user-defined proteins and genes by using the ResponseNet network optimization approach (http://netbio.bgu.ac.il/respnet). Users run ResponseNet by defining source and target sets of proteins, genes and/or microRNAs, and by specifying a molecular interaction network (interactome). The output of ResponseNet is a sparse, high-probability interactome subnetwork that connects the two sets, thereby revealing additional molecules and interactions that are involved in the studied condition. In recent years, massive efforts were invested in profiling the transcriptomes of human tissues, enabling the inference of human tissue interactomes. ResponseNet v.3 expands ResponseNet2.0 by harnessing ∼11,600 RNA-sequenced human tissue profiles made available by the Genotype-Tissue Expression consortium, to support context-specific analysis of 44 human tissues. Thus, ResponseNet v.3 allows users to illuminate the signaling and regulatory pathways potentially active in the context of a specific tissue, and to compare them with active pathways in other tissues. In the era of precision medicine, such analyses open the door for tissue- and patient-specific analyses of pathways and diseases.

Pan-cancer mapping of differential protein-protein interactions

Deciphering the variations in the protein interactome is required to reach a systems-level understanding of tumorigenesis. To accomplish this task, we have considered the clinical and transcriptome data on >6000 samples from The Cancer Genome Atlas for 12 different cancers. Utilizing the gene expression levels as a proxy, we have identified the differential protein-protein interactions in each cancer type and presented a differential view of human protein interactome among the cancers. We clearly demonstrate that a certain fraction of proteins differentially interacts in the cancers, but there was no general protein interactome profile that applied to all cancers. The analysis also provided the characterization of differentially interacting proteins (DIPs) representing significant changes in their interaction patterns during tumorigenesis. In addition, DIP-centered protein modules with high diagnostic and prognostic performances were generated, which might potentially be valuable in not only understanding tumorigenesis, but also developing effective diagnosis, prognosis, and treatment strategies.

Differential network analysis of multiple human tissue interactomes highlights tissue-selective processes and genetic disorder genes

Motivation

Differential network analysis, designed to highlight network changes between conditions, is an important paradigm in network biology. However, differential network analysis methods have been typically designed to compare between two conditions and were rarely applied to multiple protein interaction networks (interactomes). Importantly, large-scale benchmarks for their evaluation have been lacking.

Results

Here, we present a framework for assessing the ability of differential network analysis of multiple human tissue interactomes to highlight tissue-selective processes and disorders. For this, we created a benchmark of 6499 curated tissue-specific Gene Ontology biological processes. We applied five methods, including four differential network analysis methods, to construct weighted interactomes for 34 tissues. Rigorous assessment of this benchmark revealed that differential analysis methods perform well in revealing tissue-selective processes (AUCs of 0.82–0.9). Next, we applied differential network analysis to illuminate the genes underlying tissue-selective hereditary disorders. For this, we curated a dataset of 1305 tissue-specific hereditary disorders and their manifesting tissues. Focusing on subnetworks containing the top 1% differential interactions in disease-relevant tissue interactomes revealed significant enrichment for disorder-causing genes in 18.6% of the cases, with a significantly high success rate for blood, nerve, muscle and heart diseases.

Summary

Altogether, we offer a framework that includes expansive manually curated datasets of tissue-selective processes and disorders to be used as benchmarks or to illuminate tissue-selective processes and genes. Our results demonstrate that differential analysis of multiple human tissue interactomes is a powerful tool for highlighting processes and genes with tissue-selective functionality and clinical impact.

Availability and implementation

Datasets are available as part of the Supplementary data.

Supplementary information

Supplementary data are available at Bioinformatics online.

Integrated querying and version control of context-specific biological networks

MOTIVATION

Biomolecular data stored in public databases is increasingly specialized to organisms, context/pathology and tissue type, potentially resulting in significant overhead for analyses. These networks are often specializations of generic interaction sets, presenting opportunities for reducing storage and computational cost. Therefore, it is desirable to develop effective compression and storage techniques, along with efficient algorithms and a flexible query interface capable of operating on compressed data structures. Current graph databases offer varying levels of support for network integration. However, these solutions do not provide efficient methods for the storage and querying of versioned networks.

RESULTS

We present VerTIoN, a framework consisting of novel data structures and associated query mechanisms for integrated querying of versioned context-specific biological networks. As a use case for our framework, we study network proximity queries in which the user can select and compose a combination of tissue-specific and generic networks. Using our compressed version tree data structure, in conjunction with state-of-the-art numerical techniques, we demonstrate real-time querying of large network databases.

CONCLUSION

Our results show that it is possible to support flexible queries defined on heterogeneous networks composed at query time while drastically reducing response time for multiple simultaneous queries. The flexibility offered by VerTIoN in composing integrated network versions opens significant new avenues for the utilization of ever increasing volume of context-specific network data in a broad range of biomedical applications.

AVAILABILITY AND IMPLEMENTATION

VerTIoN is implemented as a C++ library and is available at http://compbio.case.edu/omics/software/vertion and https://github.com/tjcowman/vertion.

CONTACT

tyler.cowman@case.edu.

Generation and Interpretation of Context-Specific Human Protein-Protein Interaction Networks with HIPPIE

High-throughput techniques for the detection of protein-protein interactions (PPIs) have enabled a systems approach for the study of the living cell. However, the increasing amount of protein interaction data, the varying quality of these measurements, and the lack of context information make it difficult to construct meaningful and reliable protein networks.The Human Integrated Protein-Protein Interaction rEference (HIPPIE) is a web tool that integrates and annotates experimentally supported human PPIs from a heterogeneous set of data sources. In HIPPIE, one can query for the interactors of one or more proteins and generate high-quality and context-specific networks. This chapter highlights HIPPIE's most important features and exemplifies its functionality through a proposed use case.

Tissue-Specific Subcellular Localization Prediction Using Multi-Label Markov Random Fields

The understanding of subcellular localization (SCL) of proteins and proteome variation in the different tissues and organs of the human body are two crucial aspects for increasing our knowledge of the dynamic rules of proteins, the cell biology, and the mechanism of diseases. Although there have been tremendous contributions to these two fields independently, the lack of knowledge of the variation of spatial distribution of proteins in the different tissues still exists. Here, we proposed an approach that allows predicting protein SCL on tissue specificity through the use of tissue-specific functional associations and physical protein-protein interactions (PPIs). We applied our previously developed Bayesian collective Markov random fields (BCMRFs) on tissue-specific protein-protein interaction network (PPI network) for nine types of tissues focusing on eight high-level SCL. The evaluated results demonstrate the strength of our approach in predicting tissue-specific SCL. We identified 1,314 proteins that their SCL were previously proven cell line dependent. We predicted 549 novel tissue-specific localized candidate proteins while some of them were validated via text-mining.

Applications of in Silico Methods for Design and Development of Drugs Targeting Protein-Protein Interactions

BACKGROUND

Identification of Protein-Protein Interactions (PPIs) is a major challenge in modern molecular biology and biochemistry research, due to the unquestionable role of proteins in cells, biological process and pathological states. Over the past decade, the PPIs have evolved from being considered a highly challenging field of research to being investigated and examined as targets for pharmacological intervention.

OBJECTIVE

Comprehension of protein interactions is crucial to known how proteins come together to build signalling pathways, to carry out their functions, or to cause diseases, when deregulated. Multiplicity and great amount of PPIs structures offer a huge number of new and potential targets for the treatment of different diseases.

METHODS

Computational techniques are becoming predominant in PPIs studies for their effectiveness, flexibility, accuracy and cost. As a matter of fact, there are effective in silico approaches which are able to identify PPIs and PPI site. Such methods for computational target prediction have been developed through molecular descriptors and data-mining procedures.

RESULTS

In this review, we present different types of interactions between protein-protein and the application of in silico methods for design and development of drugs targeting PPIs. We described computational approaches for the identification of possible targets on protein surface and to detect of stimulator/ inhibitor molecules.

CONCLUSION

A deeper study of the most recent bioinformatics methodologies for PPIs studies is vital for a better understanding of protein complexes and for discover new potential PPI modulators in therapeutic intervention.

Proteomics and phosphoproteomics in precision medicine: applications and challenges

Recent advances in proteomics allow the accurate measurement of abundances for thousands of proteins and phosphoproteins from multiple samples in parallel. Therefore, for the first time, we have the opportunity to measure the proteomic profiles of thousands of patient samples or disease model cell lines in a systematic way, to identify the precise underlying molecular mechanism and discover personalized biomarkers, networks and treatments. Here, we review examples of successful use of proteomics and phosphoproteomics data sets in as well as their integration other omics data sets with the aim of precision medicine. We will discuss the bioinformatics challenges posed by the generation, analysis and integration of such large data sets and present potential reasons why proteomics profiling and biomarkers are not currently widely used in the clinical setting. We will finally discuss ways to contribute to the better use of proteomics data in precision medicine and the clinical setting.

Molecular Signatures of Fusion Proteins in Cancer

Although gene fusions are recognized as driver mutations in a wide variety of cancers, the general molecular mechanisms underlying oncogenic fusion proteins are insufficiently understood. Here, we employ large-scale data integration and machine learning and (1) identify three functionally distinct subgroups of gene fusions and their molecular signatures; (2) characterize the cellular pathways rewired by fusion events across different cancers; and (3) analyze the relative importance of over 100 structural, functional, and regulatory features of ∼2200 gene fusions. We report subgroups of fusions that likely act as driver mutations and find that gene fusions disproportionately affect pathways regulating cellular shape and movement. Although fusion proteins are similar across different cancer types, they affect cancer type-specific pathways. Key indicators of fusion-forming proteins include high and nontissue specific expression, numerous splice sites, and higher centrality in protein-interaction networks. Together, these findings provide unifying and cancer type-specific trends across diverse oncogenic fusion proteins.

A data-driven interactome of synergistic genes improves network-based cancer outcome prediction

Robustly predicting outcome for cancer patients from gene expression is an important challenge on the road to better personalized treatment. Network-based outcome predictors (NOPs), which considers the cellular wiring diagram in the classification, hold much promise to improve performance, stability and interpretability of identified marker genes. Problematically, reports on the efficacy of NOPs are conflicting and for instance suggest that utilizing random networks performs on par to networks that describe biologically relevant interactions. In this paper we turn the prediction problem around: instead of using a given biological network in the NOP, we aim to identify the network of genes that truly improves outcome prediction. To this end, we propose SyNet, a gene network constructed ab initio from synergistic gene pairs derived from survival-labelled gene expression data. To obtain SyNet, we evaluate synergy for all 69 million pairwise combinations of genes resulting in a network that is specific to the dataset and phenotype under study and can be used to in a NOP model. We evaluated SyNet and 11 other networks on a compendium dataset of >4000 survival-labelled breast cancer samples. For this purpose, we used cross-study validation which more closely emulates real world application of these outcome predictors. We find that SyNet is the only network that truly improves performance, stability and interpretability in several existing NOPs. We show that SyNet overlaps significantly with existing gene networks, and can be confidently predicted (~85% AUC) from graph-topological descriptions of these networks, in particular the breast tissue-specific network. Due to its data-driven nature, SyNet is not biased to well-studied genes and thus facilitates post-hoc interpretation. We find that SyNet is highly enriched for known breast cancer genes and genes related to e.g. histological grade and tamoxifen resistance, suggestive of a role in determining breast cancer outcome.

Visualizing Post-Translational Modifications in Protein Interaction Networks Using PTMOracle

Post-translational modifications (PTMs) of proteins act as key regulators of protein activity, including the regulation of protein-protein interactions (PPIs). However, exploring functional links between PTMs and PPIs can be difficult. PTMOracle is a Cytoscape app that facilitates the co-visualization and co-analysis of PTMs in the context of PPI networks. PTMOracle also allows extensive data to be integrated and co-analyzed, allowing the role of domains, motifs, and disordered regions to be considered. Here, we describe several PTMOracle protocols investigating complex PTM-associated relationships and their role in PPIs. This is assisted by OraclePainter for coloring proteins by the modifications present and visualizing these in the context of networks, by OracleTools for cross-matching PTMs with sequence feature for all nodes in the network, and by OracleResults for exploring specific proteins and visualizing their PTMs in the context of protein sequences. This unit aims to demonstrate how PTMOracle can be used to systematically explore network visualizations and generate testable hypotheses regarding the functional role of PTMs in PPIs, and how the results can be analyzed to better understand the regulatory role of PTMs in PPIs. © 2019 by John Wiley & Sons, Inc.

The BioGRID interaction database: 2019 update

The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the curation and archival storage of protein, genetic and chemical interactions for all major model organism species and humans. As of September 2018 (build 3.4.164), BioGRID contains records for 1 598 688 biological interactions manually annotated from 55 809 publications for 71 species, as classified by an updated set of controlled vocabularies for experimental detection methods. BioGRID also houses records for >700 000 post-translational modification sites. BioGRID now captures chemical interaction data, including chemical-protein interactions for human drug targets drawn from the DrugBank database and manually curated bioactive compounds reported in the literature. A new dedicated aspect of BioGRID annotates genome-wide CRISPR/Cas9-based screens that report gene-phenotype and gene-gene relationships. An extension of the BioGRID resource called the Open Repository for CRISPR Screens (ORCS) database (https://orcs.thebiogrid.org) currently contains over 500 genome-wide screens carried out in human or mouse cell lines. All data in BioGRID is made freely available without restriction, is directly downloadable in standard formats and can be readily incorporated into existing applications via our web service platforms. BioGRID data are also freely distributed through partner model organism databases and meta-databases.

Differential HDAC1/2 network analysis reveals a role for prefoldin/CCT in HDAC1/2 complex assembly

HDAC1 and HDAC2 are components of several corepressor complexes (NuRD, Sin3, CoREST and MiDAC) that regulate transcription by deacetylating histones resulting in a more compact chromatin environment. This limits access of transcriptional machinery to genes and silences transcription. While using an AP-MS approach to map HDAC1/2 protein interaction networks, we noticed that N-terminally tagged versions of HDAC1 and HDAC2 did not assemble into HDAC corepressor complexes as expected, but instead appeared to be stalled with components of the prefoldin-CCT chaperonin pathway. These N-terminally tagged HDACs were also catalytically inactive. In contrast to the N-terminally tagged HDACs, C-terminally tagged HDAC1 and HDAC2 captured complete histone deacetylase complexes and the purified proteins had deacetylation activity that could be inhibited by SAHA (Vorinostat), a Class I/II HDAC inhibitor. This tag-mediated reprogramming of the HDAC1/2 protein interaction network suggests a mechanism whereby HDAC1 is first loaded into the CCT complex by prefoldin to complete folding, and then assembled into active, functional HDAC complexes. Imaging revealed that the prefoldin subunit VBP1 colocalises with nuclear HDAC1, suggesting that delivery of HDAC1 to the CCT complex happens in the nucleus.

HyPR-MS for Multiplexed Discovery of MALAT1, NEAT1, and NORAD lncRNA Protein Interactomes

RNA-protein interactions are integral to the regulation of gene expression. RNAs have diverse functions and the protein interactomes of individual RNAs vary temporally, spatially, and with physiological context. These factors make the global acquisition of individual RNA-protein interactomes an essential endeavor. Although techniques have been reported for discovery of the protein interactomes of specific RNAs they are largely laborious, costly, and accomplished singly in individual experiments. We developed HyPR-MS for the discovery and analysis of the protein interactomes of multiple RNAs in a single experiment while also reducing design time and improving efficiencies. Presented here is the application of HyPR-MS to simultaneously and selectively isolate the interactomes of lncRNAs MALAT1, NEAT1, and NORAD. Our analysis features the proteins that potentially contribute to both known and previously undiscovered roles of each lncRNA. This platform provides a powerful new multiplexing tool for the efficient and cost-effective elucidation of specific RNA-protein interactomes.

Predicting multicellular function through multi-layer tissue networks

Motivation

Understanding functions of proteins in specific human tissues is essential for insights into disease diagnostics and therapeutics, yet prediction of tissue-specific cellular function remains a critical challenge for biomedicine.

Results

Here, we present OhmNet, a hierarchy-aware unsupervised node feature learning approach for multi-layer networks. We build a multi-layer network, where each layer represents molecular interactions in a different human tissue. OhmNet then automatically learns a mapping of proteins, represented as nodes, to a neural embedding-based low-dimensional space of features. OhmNet encourages sharing of similar features among proteins with similar network neighborhoods and among proteins activated in similar tissues. The algorithm generalizes prior work, which generally ignores relationships between tissues, by modeling tissue organization with a rich multiscale tissue hierarchy. We use OhmNet to study multicellular function in a multi-layer protein interaction network of 107 human tissues. In 48 tissues with known tissue-specific cellular functions, OhmNet provides more accurate predictions of cellular function than alternative approaches, and also generates more accurate hypotheses about tissue-specific protein actions. We show that taking into account the tissue hierarchy leads to improved predictive power. Remarkably, we also demonstrate that it is possible to leverage the tissue hierarchy in order to effectively transfer cellular functions to a functionally uncharacterized tissue. Overall, OhmNet moves from flat networks to multiscale models able to predict a range of phenotypes spanning cellular subsystems.

Availability and implementation

Source code and datasets are available at http://snap.stanford.edu/ohmnet.

Systematic Evaluation of Molecular Networks for Discovery of Disease Genes

Gene networks are rapidly growing in size and number, raising the question of which networks are most appropriate for particular applications. Here, we evaluate 21 human genome-wide interaction networks for their ability to recover 446 disease gene sets identified through literature curation, gene expression profiling, or genome-wide association studies. While all networks have some ability to recover disease genes, we observe a wide range of performance with STRING, ConsensusPathDB, and GIANT networks having the best performance overall. A general tendency is that performance scales with network size, suggesting that new interaction discovery currently outweighs the detrimental effects of false positives. Correcting for size, we find that the DIP network provides the highest efficiency (value per interaction). Based on these results, we create a parsimonious composite network with both high efficiency and performance. This work provides a benchmark for selection of molecular networks in human disease research.

The DifferentialNet database of differential protein-protein interactions in human tissues

DifferentialNet is a novel database that provides users with differential interactome analysis of human tissues (http://netbio.bgu.ac.il/diffnet/). Users query DifferentialNet by protein, and retrieve its differential protein-protein interactions (PPIs) per tissue via an interactive graphical interface. To compute differential PPIs, we integrated available data of experimentally detected PPIs with RNA-sequencing profiles of tens of human tissues gathered by the Genotype-Tissue Expression consortium (GTEx) and by the Human Protein Atlas (HPA). We associated each PPI with a score that reflects whether its corresponding genes were expressed similarly across tissues, or were up- or down-regulated in the selected tissue. By this, users can identify tissue-specific interactions, filter out PPIs that are relatively stable across tissues, and highlight PPIs that show relative changes across tissues. The differential PPIs can be used to identify tissue-specific processes and to decipher tissue-specific phenotypes. Moreover, they unravel processes that are tissue-wide yet tailored to the specific demands of each tissue.

Genetic variants and protein-protein interactions: a multidimensional network-centric view

We review recent progress in the mapping of genetic variants to proteins, in the context of their interactions, as measured from experiments and/or computational predictions. Such variants can impact on the molecular mechanisms underlying an interaction and its stability. We highlight recent work which relies on the effective use of protein-protein interaction networks (PPINs), integrated with 3D structural information, for evaluating disease-associated variants. Furthermore, we discuss how the integration of multiple layers of biological information, in the context of PPINs, can improve the interpretation of genetic variants and inspire new therapeutic strategies.

Protein complexes, big data, machine learning and integrative proteomics: lessons learned over a decade of systematic analysis of protein interaction networks

OVERVIEW

Elucidation of the networks of physical (functional) interactions present in cells and tissues is fundamental for understanding the molecular organization of biological systems, the mechanistic basis of essential and disease-related processes, and for functional annotation of previously uncharacterized proteins (via guilt-by-association or -correlation). After a decade in the field, we felt it timely to document our own experiences in the systematic analysis of protein interaction networks. Areas covered: Researchers worldwide have contributed innovative experimental and computational approaches that have driven the rapidly evolving field of 'functional proteomics'. These include mass spectrometry-based methods to characterize macromolecular complexes on a global-scale and sophisticated data analysis tools - most notably machine learning - that allow for the generation of high-quality protein association maps. Expert commentary: Here, we recount some key lessons learned, with an emphasis on successful workflows, and challenges, arising from our own and other groups' ongoing efforts to generate, interpret and report proteome-scale interaction networks in increasingly diverse biological contexts.

A convex optimization approach for identification of human tissue-specific interactomes

Motivation: Analysis of organism-specific interactomes has yielded novel insights into cellular function and coordination, understanding of pathology, and identification of markers and drug targets. Genes, however, can exhibit varying levels of cell type specificity in their expression, and their coordinated expression manifests in tissue-specific function and pathology. Tissue-specific/tissue-selective interaction mechanisms have significant applications in drug discovery, as they are more likely to reveal drug targets. Furthermore, tissue-specific transcription factors (tsTFs) are significantly implicated in human disease, including cancers. Finally, disease genes and protein complexes have the tendency to be differentially expressed in tissues in which defects cause pathology. These observations motivate the construction of refined tissue-specific interactomes from organism-specific interactomes.

Results: We present a novel technique for constructing human tissue-specific interactomes. Using a variety of validation tests (Edge Set Enrichment Analysis, Gene Ontology Enrichment, Disease-Gene Subnetwork Compactness), we show that our proposed approach significantly outperforms state-of-the-art techniques. Finally, using case studies of Alzheimer’s and Parkinson’s diseases, we show that tissue-specific interactomes derived from our study can be used to construct pathways implicated in pathology and demonstrate the use of these pathways in identifying novel targets.

Availability and implementation:http://www.cs.purdue.edu/homes/mohammas/projects/ActPro.html

Contact:mohammadi@purdue.edu

Network-based Approaches in Pharmacology

In drug discovery, network-based approaches are expected to spotlight our understanding of drug action across multiple layers of information. On one hand, network pharmacology considers the drug response in the context of a cellular or phenotypic network. On the other hand, a chemical-based network is a promising alternative for characterizing the chemical space. Both can provide complementary support for the development of rational drug design and better knowledge of the mechanisms underlying the multiple actions of drugs. Recent progress in both concepts is discussed here. In addition, a network-based approach using drug-target-therapy data is introduced as an example.

Rewiring of the inferred protein interactome during blood development studied with the tool PPICompare

BACKGROUND

Differential analysis of cellular conditions is a key approach towards understanding the consequences and driving causes behind biological processes such as developmental transitions or diseases. The progress of whole-genome expression profiling enabled to conveniently capture the state of a cell's transcriptome and to detect the characteristic features that distinguish cells in specific conditions. In contrast, mapping the physical protein interactome for many samples is experimentally infeasible at the moment. For the understanding of the whole system, however, it is equally important how the interactions of proteins are rewired between cellular states. To overcome this deficiency, we recently showed how condition-specific protein interaction networks that even consider alternative splicing can be inferred from transcript expression data. Here, we present the differential network analysis tool PPICompare that was specifically designed for isoform-sensitive protein interaction networks.

RESULTS

Besides detecting significant rewiring events between the interactomes of grouped samples, PPICompare infers which alterations to the transcriptome caused each rewiring event and what is the minimal set of alterations necessary to explain all between-group changes. When applied to the development of blood cells, we verified that a reasonable amount of rewiring events were reported by the tool and found that differential gene expression was the major determinant of cellular adjustments to the interactome. Alternative splicing events were consistently necessary in each developmental step to explain all significant alterations and were especially important for rewiring in the context of transcriptional control.

CONCLUSIONS

Applying PPICompare enabled us to investigate the dynamics of the human protein interactome during developmental transitions. A platform-independent implementation of the tool PPICompare is available at https://sourceforge.net/projects/ppicompare/ .

The integration of weighted human gene association networks based on link prediction

Background

Physical and functional interplays between genes or proteins have important biological meaning for cellular functions. Some efforts have been made to construct weighted gene association meta-networks by integrating multiple biological resources, where the weight indicates the confidence of the interaction. However, it is found that these existing human gene association networks share only quite limited overlapped interactions, suggesting their incompleteness and noise.

Results

Here we proposed a workflow to construct a weighted human gene association network using information of six existing networks, including two weighted specific PPI networks and four gene association meta-networks. We applied link prediction algorithm to predict possible missing links of the networks, cross-validation approach to refine each network and finally integrated the refined networks to get the final integrated network.

Conclusions

The common information among the refined networks increases notably, suggesting their higher reliability. Our final integrated network owns much more links than most of the original networks, meanwhile its links still keep high functional relevance. Being used as background network in a case study of disease gene prediction, the final integrated network presents good performance, implying its reliability and application significance. Our workflow could be insightful for integrating and refining existing gene association data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0398-0) contains supplementary material, which is available to authorized users.

The TissueNet v.2 database: A quantitative view of protein-protein interactions across human tissues

Knowledge of the molecular interactions of human proteins within tissues is important for identifying their tissue-specific roles and for shedding light on tissue phenotypes. However, many protein–protein interactions (PPIs) have no tissue-contexts. The TissueNet database bridges this gap by associating experimentally-identified PPIs with human tissues that were shown to express both pair-mates. Users can select a protein and a tissue, and obtain a network view of the query protein and its tissue-associated PPIs. TissueNet v.2 is an updated version of the TissueNet database previously featured in NAR. It includes over 40 human tissues profiled via RNA-sequencing or protein-based assays. Users can select their preferred expression data source and interactively set the expression threshold for determining tissue-association. The output of TissueNet v.2 emphasizes qualitative and quantitative features of query proteins and their PPIs. The tissue-specificity view highlights tissue-specific and globally-expressed proteins, and the quantitative view highlights proteins that were differentially expressed in the selected tissue relative to all other tissues. Together, these views allow users to quickly assess the unique versus global functionality of query proteins. Thus, TissueNet v.2 offers an extensive, quantitative and user-friendly interface to study the roles of human proteins across tissues. TissueNet v.2 is available at http://netbio.bgu.ac.il/tissuenet.

Dynamic Protein Interactions of the Polycomb Repressive Complex 2 during Differentiation of Pluripotent Cells

Polycomb proteins assemble to form complexes with important roles in epigenetic regulation. The Polycomb Repressive Complex 2 (PRC2) modulates the di- and tri-methylation of lysine 27 on histone H3, each of which are associated with gene repression. Although three subunits, EZH1/2, SUZ12, and EED, form the catalytic core of PRC2, a wider group of proteins associate with low stoichiometry. This raises the question of whether dynamic variation of the PRC2 interactome results in alternative forms of the complex during differentiation. Here we compared the physical interactions of PRC2 in undifferentiated and differentiated states of NTERA2 pluripotent embryonic carcinoma cells. Label-free quantitative proteomics was used to assess endogenous immunoprecipitation of the EZH2 and SUZ12 subunits of PRC2. A high stringency data set reflecting the endogenous state of PRC2 was produced that included all previously reported core and associated PRC2 components, and several novel interacting proteins. Comparison of the interactomes obtained in undifferentiated and differentiated cells revealed candidate proteins that were enriched in complexes isolated from one of the two states. For example, SALL4 and ZNF281 associate with PRC2 in pluripotent cells, whereas PCL1 and SMAD3 preferentially associate with PRC2 in differentiating cells. Analysis of the mRNA and protein levels of these factors revealed that their association with PRC2 correlated with their cell state-specific expression. Taken together, we propose that dynamic changes to the PRC2 interactome during differentiation may contribute to directing its activity during cell fate transitions.