A reference map of the human binary protein interactome

Regulation of PCTAIRE1 protein stability by AKT1, LKB1 and BRCA1

PCTAIRE1, also known as CDK16, is a cyclin-dependent kinase that is regulated by cyclin Y. It is a member of the serine-threonine family of kinases and its functions have primarily been implicated in cellular processes like vesicular transport, neuronal growth and development, myogenesis, spermatogenesis and cell proliferation. However, as extensive studies on PCTAIRE1 have not yet been conducted, the signaling pathways for this kinase involved in governing many cellular processes are yet to be elucidated in detail. Here, we report the association of PCTAIRE1 with important cellular proteins involved in major cell signaling pathways, especially cell proliferation. In particular, here we show that PCTAIRE1 interacts with AKT1, a key player of the PI3K signaling pathway that is responsible for promoting cell survival and proliferation. Our studies show that PCTAIRE1 is a substrate of AKT1 that gets stabilized by it. Further, we show that PCTAIRE1 also interacts with and is degraded by LKB1, a kinase that is known to suppress cellular proliferation and also regulate cellular energy metabolism. Moreover, our results show that PCTAIRE1 is also degraded by BRCA1, a well-known tumor suppressor. Together, our studies highlight the regulation of PCTAIRE1 by key players of the major cell signaling pathways involved in regulating cell proliferation, and therefore, provide crucial links that could be explored further to elucidate the mechanistic role of PCTAIRE1 in cell proliferation and tumorigenesis.

The role of proteomics in defining autoimmunity

INTRODUCTION

Proteomics, i.e. the study of the set of proteins produced in a cell, tissue, organism, or biological entity, has made possible analyses and contextual comparisons of proteomes/proteins and biological functions among the most disparate entities, from viruses to the human being. In this way, proteomic scrutiny of tumor-associated proteins, autoantigens, and pathogen antigens offers the tools for fighting cancer, autoimmunity, and infections.

AREAS COVERED

Comparative proteomics and immunoproteomics, the new scientific disciplines generated by proteomics, are the main themes of the present review that describes how comparative analyses of pathogen and human proteomes led to re-modulate the molecular mimicry concept of the pre-proteomic era. I.e. before proteomics, molecular mimicry - the sharing of peptide sequences between two biological entities - was considered as intrinsically endowed with immunologic properties and was related to cross-reactivity. Proteomics allowed to redefine such an assumption using physicochemical parameters according to which frequency and hydrophobicity preferentially confer an immunologic potential to shared peptide sequences.

EXPERT OPINION

Proteomics is outlining peptide platforms to be used for the diagnostics and management of human diseases. A Molecular Medicine targeted to obtain healing without paying the price for adverse events is on the horizon. The next step is to take up the challenge and operate the paradigm shift that the current proteomic era requires.

The VRNetzer platform enables interactive network analysis in Virtual Reality

Networks provide a powerful representation of interacting components within complex systems, making them ideal for visually and analytically exploring big data. However, the size and complexity of many networks render static visualizations on typically-sized paper or screens impractical, resulting in proverbial ‘hairballs’. Here, we introduce a Virtual Reality (VR) platform that overcomes these limitations by facilitating the thorough visual, and interactive, exploration of large networks. Our platform allows maximal customization and extendibility, through the import of custom code for data analysis, integration of external databases, and design of arbitrary user interface elements, among other features. As a proof of concept, we show how our platform can be used to interactively explore genome-scale molecular networks to identify genes associated with rare diseases and understand how they might contribute to disease development. Our platform represents a general purpose, VR-based data exploration platform for large and diverse data types by providing an interface that facilitates the interaction between human intuition and state-of-the-art analysis methods.

Data-rich networks can be difficult to interpret beyond a certain size. Here, the authors introduce a platform that uses virtual reality to allow the visual exploration of large networks, while interfacing with data repositories and other analytical methods to improve the interpretation of big data.

Community Detection in Large-Scale Bipartite Biological Networks

Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks-such as those that represent regulatory interactions, drug-gene, or gene-disease associations-are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a "hairball" of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

Control Analysis of Protein-Protein Interaction Network Reveals Potential Regulatory Targets for MYCN

BACKGROUND

MYCN is an oncogenic transcription factor of the MYC family and plays an important role in the formation of tissues and organs during development before birth. Due to the difficulty in drugging MYCN directly, revealing the molecules in MYCN regulatory networks will help to identify effective therapeutic targets.

METHODS

We utilized network controllability theory, a recent developed powerful tool, to identify the potential drug target around MYCN based on Protein-Protein interaction network of MYCN. First, we constructed a Protein-Protein interaction network of MYCN based on public databases. Second, network control analysis was applied on network to identify driver genes and indispensable genes of the MYCN regulatory network. Finally, we developed a novel integrated approach to identify potential drug targets for regulating the function of the MYCN regulatory network.

RESULTS

We constructed an MYCN regulatory network that has 79 genes and 129 interactions. Based on network controllability theory, we analyzed driver genes which capable to fully control the network. We found 10 indispensable genes whose alternation will significantly change the regulatory pathways of the MYCN network. We evaluated the stability and correlation analysis of these genes and found EGFR may be the potential drug target which closely associated with MYCN.

CONCLUSION

Together, our findings indicate that EGFR plays an important role in the regulatory network and pathways of MYCN and therefore may represent an attractive therapeutic target for cancer treatment.

BBLN-1 is essential for intermediate filament organization and apical membrane morphology

Epithelial tubes are essential components of metazoan organ systems that control the flow of fluids and the exchange of materials between body compartments and the outside environment. The size and shape of the central lumen confer important characteristics to tubular organs and need to be carefully controlled. Here, we identify the small coiled-coil protein BBLN-1 as a regulator of lumen morphology in the C. elegans intestine. Loss of BBLN-1 causes the formation of bubble-shaped invaginations of the apical membrane into the cytoplasm of intestinal cells and abnormal aggregation of the subapical intermediate filament (IF) network. BBLN-1 interacts with IF proteins and localizes to the IF network in an IF-dependent manner. The appearance of invaginations is a result of the abnormal IF aggregation, indicating a direct role for the IF network in maintaining lumen homeostasis. Finally, we identify bublin (BBLN) as the mammalian ortholog of BBLN-1. When expressed in the C. elegans intestine, BBLN recapitulates the localization pattern of BBLN-1 and can compensate for the loss of BBLN-1 in early larvae. In mouse intestinal organoids, BBLN localizes subapically, together with the IF protein keratin 8. Our results therefore may have implications for understanding the role of IFs in regulating epithelial tube morphology in mammals.

Structure and thermodynamics of transient protein-protein complexes by chemometric decomposition of SAXS datasets

Transient biomolecular interactions play crucial roles in many cellular signaling and regulation processes. However, deciphering the structure of these assemblies is challenging owing to the difficulties in isolating complexes from the individual partners. The additive nature of small-angle X-ray scattering (SAXS) data allows for probing the species present in these mixtures, but decomposition into structural and thermodynamic information is difficult. We present a chemometric approach enabling the decomposition of titration SAXS data into species-specific information. Using extensive synthetic SAXS data, we demonstrate that robust decomposition can be achieved for titrations with a maximum fraction of complex of 0.5 that can be extended to 0.3 when two orthogonal titrations are simultaneously analyzed. The effect of the structural features, titration points, relative concentrations, and noise are thoroughly analyzed. The validation of the strategy with experimental data highlights the power of the approach to provide unique insights into this family of biomolecular assemblies.

A network-based deep learning methodology for stratification of tumor mutations

MOTIVATION

Tumor stratification has a wide range of biomedical and clinical applications, including diagnosis, prognosis and personalized treatment. However, cancer is always driven by the combination of mutated genes, which are highly heterogeneous across patients. Accurately subdividing the tumors into subtypes is challenging.

RESULTS

We developed a network-embedding based stratification (NES) methodology to identify clinically relevant patient subtypes from large-scale patients' somatic mutation profiles. The central hypothesis of NES is that two tumors would be classified into the same subtypes if their somatic mutated genes located in the similar network regions of the human interactome. We encoded the genes on the human protein-protein interactome with a network embedding approach and constructed the patients' vectors by integrating the somatic mutation profiles of 7344 tumor exomes across 15 cancer types. We firstly adopted the lightGBM classification algorithm to train the patients' vectors. The AUC value is around 0.89 in the prediction of the patient's cancer type and around 0.78 in the prediction of the tumor stage within a specific cancer type. The high classification accuracy suggests that network embedding-based patients' features are reliable for dividing the patients. We conclude that we can cluster patients with a specific cancer type into several subtypes by using an unsupervised clustering algorithm to learn the patients' vectors. Among the 15 cancer types, the new patient clusters (subtypes) identified by the NES are significantly correlated with patient survival across 12 cancer types. In summary, this study offers a powerful network-based deep learning methodology for personalized cancer medicine.

AVAILABILITY AND IMPLEMENTATION

Source code and data can be downloaded from https://github.com/ChengF-Lab/NES.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Scanning Protein Surfaces with DNA-Encoded Libraries

Understanding the ligandability of a target protein, defined as the capability of a protein to bind drug-like compounds on any site, can give important stimuli to drug-development projects. For instance, inhibition of protein-protein interactions usually depends on the identification of protein surface binders. DNA-encoded chemical libraries (DELs) allow scanning of protein surfaces with large chemical space. Encoded library selection screens uncovered several protein-protein interaction inhibitors and compounds binding to the surface of G protein-coupled receptors (GPCRs) and kinases. The protein surface-binding chemotypes from DELs are predominantly chemically modified and cyclized peptides, and functional small-molecule peptidomimetics. Peptoid libraries and structural peptidomimetics have been less studied in the DEL field, hinting at hitherto less populated chemical space and suggesting alternative library designs. Roughly a third of bioactive molecules evolved from smaller, target-focused libraries. They showcase the potential of encoded libraries to identify more potent molecules from weak, for example, fragment-like, starting points.

N-Terminal Acetyltransferase Naa40p Whereabouts Put into N-Terminal Proteoform Perspective

The evolutionary conserved N-alpha acetyltransferase Naa40p is among the most selective N-terminal acetyltransferases (NATs) identified to date. Here we identified a conserved N-terminally truncated Naa40p proteoform named Naa40p25 or short Naa40p (Naa40^S). Intriguingly, although upon ectopic expression in yeast, both Naa40p proteoforms were capable of restoring N-terminal acetylation of the characterized yeast histone H2A Naa40p substrate, the Naa40p histone H4 substrate remained N-terminally free in human haploid cells specifically deleted for canonical Naa40p27 or 237 amino acid long Naa40p (Naa40^L), but expressing Naa40^S. Interestingly, human Naa40^L and Naa40^S displayed differential expression and subcellular localization patterns by exhibiting a principal nuclear and cytoplasmic localization, respectively. Furthermore, Naa40^L was shown to be N-terminally myristoylated and to interact with N-myristoyltransferase 1 (NMT1), implicating NMT1 in steering Naa40^L nuclear import. Differential interactomics data obtained by biotin-dependent proximity labeling (BioID) further hints to context-dependent roles of Naa40p proteoforms. More specifically, with Naa40^S representing the main co-translationally acting actor, the interactome of Naa40^L was enriched for nucleolar proteins implicated in ribosome biogenesis and the assembly of ribonucleoprotein particles, overall indicating a proteoform-specific segregation of previously reported Naa40p activities. Finally, the yeast histone variant H2A.Z and the transcriptionally regulatory protein Lge1 were identified as novel Naa40p substrates, expanding the restricted substrate repertoire of Naa40p with two additional members and further confirming Lge1 as being the first redundant yNatA and yNatD substrate identified to date.

Dynamic Module Detection in Temporal Attributed Networks of cancers

Tracking the dynamic modules during cancer progression is essential for studying cancer pathogenesis, diagnosis and therapy. However, current algorithms only focus on detecting dynamic modules from temporal cancer networks without integrating the heterogeneous genomic data, thereby resulting in undesirable performance. To attack this issue, a novel algorithm (aka TANMF) is proposed to detect dynamic modules in cancer temporal attributed networks, which integrates the temporal networks and gene attributes. To obtain the dynamic modules, the temporality and gene attributed are incorporated into an overall objective function, which transforms the dynamic module detection into an optimization problem. TANMF jointly decomposes the snapshots at two subsequent time steps to obtain the latent features of dynamic modules, where the attributes are fused via regulations. Furthermore, L₁ constraint is imposed to improve the robustness. Experimental results demonstrate that TANMF is more accurate than state-of-the-art methods in terms of accuracy. By applying TANMF to breast cancer data, the obtained dynamic modules are more enriched by the known pathways and associated with the survival time of patients. The proposed model and algorithm provide an effective way for the integrative analysis of heterogeneous omics.

Integrating 3D structural information into systems biology

Systems biology is a data-heavy field that focuses on systems-wide depictions of biological phenomena necessarily sacrificing a detailed characterization of individual components. As an example, genome-wide protein interaction networks are widely used in systems biology and continuously extended and refined as new sources of evidence become available. Despite the vast amount of information about individual protein structures and protein complexes that has accumulated in the past 50 years in the Protein Data Bank, the data, computational tools, and language of structural biology are not an integral part of systems biology. However, increasing effort has been devoted to this integration, and the related literature is reviewed here. Relationships between proteins that are detected via structural similarity offer a rich source of information not available from sequence similarity, and homology modeling can be used to leverage Protein Data Bank structures to produce 3D models for a significant fraction of many proteomes. A number of structure-informed genomic and cross-species (i.e., virus–host) interactomes will be described, and the unique information they provide will be illustrated with a number of examples. Tissue- and tumor-specific interactomes have also been developed through computational strategies that exploit patient information and through genetic interactions available from increasingly sensitive screens. Strategies to integrate structural information with these alternate data sources will be described. Finally, efforts to link protein structure space with chemical compound space offer novel sources of information in drug design, off-target identification, and the identification of targets for compounds found to be effective in phenotypic screens.

Improved sequence-based prediction of interaction sites in α-helical transmembrane proteins by deep learning

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Fast and accurate prediction of transmembrane protein interaction sites.

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First ever computational survey of interaction sites in membrane proteins.

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10-30% of amino acid positions predicted to be involved in interactions.

Interactions between transmembrane (TM) proteins are fundamental for a wide spectrum of cellular functions, but precise molecular details of these interactions remain largely unknown due to the scarcity of experimentally determined three-dimensional complex structures. Computational techniques are therefore required for a large-scale annotation of interaction sites in TM proteins. Here, we present a novel deep-learning approach, DeepTMInter, for sequence-based prediction of interaction sites in α-helical TM proteins based on their topological, physiochemical, and evolutionary properties. Using a combination of ultra-deep residual neural networks with a stacked generalization ensemble technique DeepTMInter significantly outperforms existing methods, achieving the AUC/AUCPR values of 0.689/0.598. Across the main functional families of human transmembrane proteins, the percentage of amino acid sites predicted to be involved in interactions typically ranges between 10% and 25%, and up to 30% in ion channels. DeepTMInter is available as a standalone package at https://github.com/2003100127/deeptminter. The training and benchmarking datasets are available at https://data.mendeley.com/datasets/2t8kgwzp35.

A functional screen identifies transcriptional networks that regulate HIV-1 and HIV-2

The molecular networks involved in the regulation of HIV replication, transcription, and latency remain incompletely defined. To expand our understanding of these networks, we performed an unbiased high-throughput yeast one-hybrid screen, which identified 42 human transcription factors and 85 total protein-DNA interactions with HIV-1 and HIV-2 long terminal repeats. We investigated a subset of these transcription factors for transcriptional activity in cell-based models of infection. KLF2 and KLF3 repressed HIV-1 and HIV-2 transcription in CD4+ T cells, whereas PLAGL1 activated transcription of HIV-2 through direct protein-DNA interactions. Using computational modeling with interacting proteins, we leveraged the results from our screen to identify putative pathways that define intrinsic transcriptional networks. Overall, we used a high-throughput functional screen, computational modeling, and biochemical assays to identify and confirm several candidate transcription factors and biochemical processes that influence HIV-1 and HIV-2 transcription and latency.

Integrated intra- and intercellular signaling knowledge for multicellular omics analysis

Molecular knowledge of biological processes is a cornerstone in omics data analysis. Applied to single-cell data, such analyses provide mechanistic insights into individual cells and their interactions. However, knowledge of intercellular communication is scarce, scattered across resources, and not linked to intracellular processes. To address this gap, we combined over 100 resources covering interactions and roles of proteins in inter- and intracellular signaling, as well as transcriptional and post-transcriptional regulation. We added protein complex information and annotations on function, localization, and role in diseases for each protein. The resource is available for human, and via homology translation for mouse and rat. The data are accessible via OmniPath's web service (https://omnipathdb.org/), a Cytoscape plug-in, and packages in R/Bioconductor and Python, providing access options for computational and experimental scientists. We created workflows with tutorials to facilitate the analysis of cell-cell interactions and affected downstream intracellular signaling processes. OmniPath provides a single access point to knowledge spanning intra- and intercellular processes for data analysis, as we demonstrate in applications studying SARS-CoV-2 infection and ulcerative colitis.

Alternatively Splicing Interactomes Identify Novel Isoform-Specific Partners for NSD2

Nuclear receptor SET domain protein (NSD2) plays a fundamental role in the pathogenesis of Wolf-Hirschhorn Syndrome (WHS) and is overexpressed in multiple human myelomas, but its protein-protein interaction (PPI) patterns, particularly at the isoform/exon levels, are poorly understood. We explored the subcellular localizations of four representative NSD2 transcripts with immunofluorescence microscopy. Next, we used label-free quantification to perform immunoprecipitation mass spectrometry (IP-MS) analyses of the transcripts. Using the interaction partners for each transcript detected in the IP-MS results, we identified 890 isoform-specific PPI partners (83% are novel). These PPI networks were further divided into four categories of the exon-specific interactome. In these exon-specific PPI partners, two genes, RPL10 and HSPA8, were successfully confirmed by co-immunoprecipitation and Western blotting. RPL10 primarily interacted with Isoforms 1, 3, and 5, and HSPA8 interacted with all four isoforms, respectively. Using our extended NSD2 protein interactions, we constructed an isoform-level PPI landscape for NSD2 to serve as reference interactome data for NSD2 spliceosome-level studies. Furthermore, the RNA splicing processes supported by these isoform partners shed light on the diverse roles NSD2 plays in WHS and myeloma development. We also validated the interactions using Western blotting, RPL10, and the three NSD2 (Isoform 1, 3, and 5). Our results expand gene-level NSD2 PPI networks and provide a basis for the treatment of NSD2-related developmental diseases.

Meta-Analysis of Gene Popularity: Less Than Half of Gene Citations Stem from Gene Regulatory Networks

The reasons for selecting a gene for further study might vary from historical momentum to funding availability, thus leading to unequal attention distribution among all genes. However, certain biological features tend to be overlooked in evaluating a gene’s popularity. Here we present a meta-analysis of the reasons why different genes have been studied and to what extent, with a focus on the gene-specific biological features. From unbiased datasets we can define biological properties of genes that reasonably may affect their perceived importance. We make use of both linear and nonlinear computational approaches for estimating gene popularity to then compare their relative importance. We find that roughly 25% of the studies are the result of a historical positive feedback, which we may think of as social reinforcement. Of the remaining features, gene family membership is the most indicative followed by disease relevance and finally regulatory pathway association. Disease relevance has been an important driver until the 1990s, after which the focus shifted to exploring every single gene. We also present a resource that allows one to study the impact of reinforcement, which may guide our research toward genes that have not yet received proportional attention.

Role of PFKFB3 and PFKFB4 in Cancer: Genetic Basis, Impact on Disease Development/Progression, and Potential as Therapeutic Targets

Glycolysis is a crucial metabolic process in rapidly proliferating cells such as cancer cells. Phosphofructokinase-1 (PFK-1) is a key rate-limiting enzyme of glycolysis. Its efficiency is allosterically regulated by numerous substances occurring in the cytoplasm. However, the most potent regulator of PFK-1 is fructose-2,6-bisphosphate (F-2,6-BP), the level of which is strongly associated with 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase activity (PFK-2/FBPase-2, PFKFB). PFK-2/FBPase-2 is a bifunctional enzyme responsible for F-2,6-BP synthesis and degradation. Four isozymes of PFKFB (PFKFB1, PFKFB2, PFKFB3, and PFKFB4) have been identified. Alterations in the levels of all PFK-2/FBPase-2 isozymes have been reported in different diseases. However, most recent studies have focused on an increased expression of PFKFB3 and PFKFB4 in cancer tissues and their role in carcinogenesis. In this review, we summarize our current knowledge on all PFKFB genes and protein structures, and emphasize important differences between the isoenzymes, which likely affect their kinase/phosphatase activities. The main focus is on the latest reports in this field of cancer research, and in particular the impact of PFKFB3 and PFKFB4 on tumor progression, metastasis, angiogenesis, and autophagy. We also present the most recent achievements in the development of new drugs targeting these isozymes. Finally, we discuss potential combination therapies using PFKFB3 inhibitors, which may represent important future cancer treatment options.

From fuzziness to precision medicine: on the rapidly evolving proteomics with implications in mitochondrial connectivity to rare human disease

Mitochondrial (mt) dysfunction is linked to rare diseases (RDs) such as respiratory chain complex (RCC) deficiency, MELAS, and ARSACS. Yet, how altered mt protein networks contribute to these ailments remains understudied. In this perspective article, we identified 21 mt proteins from public repositories that associate with RCC deficiency, MELAS, or ARSACS, engaging in a relatively small number of protein-protein interactions (PPIs), underscoring the need for advanced proteomic and interactomic platforms to uncover the complete scope of mt connectivity to RDs. Accordingly, we discuss innovative untargeted label-free proteomics in identifying RD-specific mt or other macromolecular assemblies and mapping of protein networks in complex tissue, organoid, and stem cell-differentiated neurons. Furthermore, tag- and label-based proteomics, genealogical proteomics, and combinatorial affinity purification-mass spectrometry, along with advancements in detecting and integrating transient PPIs with single-cell proteomics and transcriptomics, collectively offer seminal follow-ups to enrich for RD-relevant networks, with implications in RD precision medicine.

Network Analysis Identifies Drug Targets and Small Molecules to Modulate Apoptosis Resistant Cancers

Programed cell death or apoptosis fails to induce cell death in many recalcitrant cancers. Thus, there is an emerging need to activate the alternate cell death pathways in such cancers. In this study, we analyzed the apoptosis-resistant colon adenocarcinoma, glioblastoma multiforme, and small cell lung cancers transcriptome profiles. We extracted clusters of non-apoptotic cell death genes from each cancer to understand functional networks affected by these genes and their role in the induction of cell death when apoptosis fails. We identified transcription factors regulating cell death genes and protein-protein interaction networks to understand their role in regulating cell death mechanisms. Topological analysis of networks yielded FANCD2 (ferroptosis, negative regulator, down), NCOA4 (ferroptosis, up), IKBKB (alkaliptosis, down), and RHOA (entotic cell death, down) as potential drug targets in colon adenocarcinoma, glioblastoma multiforme, small cell lung cancer phenotypes respectively. We also assessed the miRNA association with the drug targets. We identified tumor growth-related interacting partners based on the pathway information of drug-target interaction networks. The protein-protein interaction binding site between the drug targets and their interacting proteins provided an opportunity to identify small molecules that can modulate the activity of functional cell death interactions in each cancer. Overall, our systematic screening of non-apoptotic cell death-related genes uncovered targets helpful for cancer therapy.

A novel method for data fusion over Entity-Relation graphs and its application to protein-protein interaction prediction

MOTIVATION

Modern Bioinformatics is facing increasingly complex problems to solve, and we are indeed rapidly approaching an era in which the ability to seamlessly integrate heterogeneous sources of information will be crucial for the scientific progress. Here we present a novel non-linear data fusion framework that generalizes the conventional Matrix Factorization paradigm allowing inference over arbitrary Entity-Relation graphs, and we applied it to the prediction of Protein-Protein Interactions (PPIs). Improving our knowledge of Protein Protein Interaction (PPI) networks at the proteome scale is indeed crucial to understand protein function, physiological and disease states and cell life in general.

RESULTS

We devised three data-fusion based models for the proteome-level prediction of PPIs, and we show that our method outperforms state of the art approaches on common benchmarks. Moreover, we investigate its predictions on newly published PPIs, showing that this new data has a clear shift in its underlying distributions and we thus train and test our models on this extended dataset.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Comprehensive characterization of protein-protein interactions perturbed by disease mutations

Technological and computational advances in genomics and interactomics have made it possible to identify how disease mutations perturb protein-protein interaction (PPI) networks within human cells. Here, we show that disease-associated germline variants are significantly enriched in sequences encoding PPI interfaces compared to variants identified in healthy participants from the projects 1000 Genomes and ExAC. Somatic missense mutations are also significantly enriched in PPI interfaces compared to noninterfaces in 10,861 tumor exomes. We computationally identified 470 putative oncoPPIs in a pan-cancer analysis and demonstrate that oncoPPIs are highly correlated with patient survival and drug resistance/sensitivity. We experimentally validate the network effects of 13 oncoPPIs using a systematic binary interaction assay, and also demonstrate the functional consequences of two of these on tumor cell growth. In summary, this human interactome network framework provides a powerful tool for prioritization of alleles with PPI-perturbing mutations to inform pathobiological mechanism- and genotype-based therapeutic discovery.

The Ins and Outs of RAS Effector Complexes

RAS oncogenes are among the most commonly mutated proteins in human cancers. They regulate a wide range of effector pathways that control cell proliferation, survival, differentiation, migration and metabolic status. Including aberrations in these pathways, RAS-dependent signaling is altered in more than half of human cancers. Targeting mutant RAS proteins and their downstream oncogenic signaling pathways has been elusive. However, recent results comprising detailed molecular studies, large scale omics studies and computational modeling have painted a new and more comprehensive portrait of RAS signaling that helps us to understand the intricacies of RAS, how its physiological and pathophysiological functions are regulated, and how we can target them. Here, we review these efforts particularly trying to relate the detailed mechanistic studies with global functional studies. We highlight the importance of computational modeling and data integration to derive an actionable understanding of RAS signaling that will allow us to design new mechanism-based therapies for RAS mutated cancers.

Proteomic Analysis Uncovers Measles Virus Protein C Interaction With p65-iASPP Protein Complex

Viruses manipulate the central machineries of host cells to their advantage. They prevent host cell antiviral responses to create a favorable environment for their survival and propagation. Measles virus (MV) encodes two nonstructural proteins MV-V and MV-C known to counteract the host interferon response and to regulate cell death pathways. Several molecular mechanisms underlining MV-V regulation of innate immunity and cell death pathways have been proposed, whereas MV-C host-interacting proteins are less studied. We suggest that some cellular factors that are controlled by MV-C protein during viral replication could be components of innate immunity and the cell death pathways. To determine which host factors are targeted by MV-C, we captured both direct and indirect host-interacting proteins of MV-C protein. For this, we used a strategy based on recombinant viruses expressing tagged viral proteins followed by affinity purification and a bottom-up mass spectrometry analysis. From the list of host proteins specifically interacting with MV-C protein in different cell lines, we selected the host targets that belong to immunity and cell death pathways for further validation. Direct protein interaction partners of MV-C were determined by applying protein complementation assay and the bioluminescence resonance energy transfer approach. As a result, we found that MV-C protein specifically interacts with p65–iASPP protein complex that controls both cell death and innate immunity pathways and evaluated the significance of these host factors on virus replication.

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Measles virus controls immune response and cell death pathways to achieve replication.

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Host proteins interaction network with measles virulence factor C protein.

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Cellular p65–iASPP complex is targeted by measles virus C protein.

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/.

Role of Antizyme Inhibitor Proteins in Cancers and Beyond

Polyamines are multivalent organic cations essential for many cellular functions, including cell growth, differentiation, and proliferation. However, elevated polyamine levels are associated with a slew of pathological conditions, including multiple cancers. Intracellular polyamine levels are primarily controlled by the autoregulatory circuit comprising two different protein types, Antizymes (OAZ) and Antizyme Inhibitors (AZIN), which regulate the activity of the polyamine biosynthetic enzyme ornithine decarboxylase (ODC). While OAZ functions to decrease the intracellular polyamine levels by inhibiting ODC activity and exerting a negative control of polyamine uptake, AZIN operates to increase intracellular polyamine levels by binding and sequestering OAZ to relieve ODC inhibition and to increase polyamine uptake. Interestingly, OAZ and AZIN exhibit autoregulatory functions on polyamine independent pathways as well. A growing body of evidence demonstrates the dysregulation of AZIN expression in multiple cancers. Additionally, RNA editing of the Azin1 transcript results in a "gain-of-function" phenotype, which is shown to drive aggressive tumor types. This review will discuss the recent advances in AZIN's role in cancers via aberrant polyamine upregulation and its polyamine-independent protein regulation. This report will also highlight AZIN interaction with proteins outside the polyamine biosynthetic pathway and its potential implication to cancer pathogenesis. Finally, this review will reveal the protein interaction network of AZIN isoforms by analyzing three different interactome databases.

Zinc transporters and their functional integration in mammalian cells

Zinc is a ubiquitous biological metal in all living organisms. The spatiotemporal zinc dynamics in cells provide crucial cellular signaling opportunities, but also challenges for intracellular zinc homeostasis with broad disease implications. Zinc transporters play a central role in regulating cellular zinc balance and subcellular zinc distributions. The discoveries of two complementary families of mammalian zinc transporters (ZnTs and ZIPs) in the mid-1990s spurred much speculation on their metal selectivity and cellular functions. After two decades of research, we have arrived at a biochemical description of zinc transport. However, in vitro functions are fundamentally different from those in living cells, where mammalian zinc transporters are directed to specific subcellular locations, engaged in dedicated macromolecular machineries, and connected with diverse cellular processes. Hence, the molecular functions of individual zinc transporters are reshaped and deeply integrated in cells to promote the utilization of zinc chemistry to perform enzymatic reactions, tune cellular responsiveness to pathophysiologic signals, and safeguard cellular homeostasis. At present, the underlying mechanisms driving the functional integration of mammalian zinc transporters are largely unknown. This knowledge gap has motivated a shift of the research focus from in vitro studies of purified zinc transporters to in cell studies of mammalian zinc transporters in the context of their subcellular locations and protein interactions. In this review, we will outline how knowledge of zinc transporters has been accumulated from in-test-tube to in-cell studies, highlighting new insights and paradigm shifts in our understanding of the molecular and cellular basis of mammalian zinc transporter functions.

Common genes and pathways involved in the response to stressful stimuli by astrocytes: A meta-analysis of genome-wide expression studies

Astrocytes play pivotal roles in the brain and they become reactive under stress conditions. Here, we carried out, for the first time, an integrative meta-analysis of genome-wide expression profiling of astrocytes from human and mouse exposed to different stressful stimuli (hypoxia, infections by virus and bacteria, cytokines, ethanol, among others). We identified common differentially expressed genes and pathways in human and murine astrocytes. Our results showed that astrocytes induce expression of genes associated with stress response and immune system regulation when they are exposed to stressful stimuli, whereas genes related to neurogenesis are found as downregulated. Several of the identified genes showed to be important hubs in the protein-protein interaction analysis (TRAF2, CDC37 and PAX6). This work demonstrates that despite astrocytes are highly heterogeneous and complex, there are common gene expression signatures that can be triggered under distinct detrimental stimuli, which opens an opportunity for exploring other possible markers of reactivity.

Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery

In this study, we integrate the outcomes of co-expression network analysis with the human interactome network to predict novel putative disease genes and modules. We first apply the SWItch Miner (SWIM) methodology, which predicts important (switch) genes within the co-expression network that regulate disease state transitions, then map them to the human protein-protein interaction network (PPI, or interactome) to predict novel disease-disease relationships (i.e., a SWIM-informed diseasome). Although the relevance of switch genes to an observed phenotype has been recently assessed, their performance at the system or network level constitutes a new, potentially fascinating territory yet to be explored. Quantifying the interplay between switch genes and human diseases in the interactome network, we found that switch genes associated with specific disorders are closer to each other than to other nodes in the network, and tend to form localized connected subnetworks. These subnetworks overlap between similar diseases and are situated in different neighborhoods for pathologically distinct phenotypes, consistent with the well-known topological proximity property of disease genes. These findings allow us to demonstrate how SWIM-based correlation network analysis can serve as a useful tool for efficient screening of potentially new disease gene associations. When integrated with an interactome-based network analysis, it not only identifies novel candidate disease genes, but also may offer testable hypotheses by which to elucidate the molecular underpinnings of human disease and reveal commonalities between seemingly unrelated diseases.

Chronic exposure of humans to high level natural background radiation leads to robust expression of protective stress response proteins

Understanding exposures to low doses of ionizing radiation are relevant since most environmental, diagnostic radiology and occupational exposures lie in this region. However, the molecular mechanisms that drive cellular responses at these doses, and the subsequent health outcomes, remain unclear. A local monazite-rich high level natural radiation area (HLNRA) in the state of Kerala on the south-west coast of Indian subcontinent show radiation doses extending from ≤ 1 to ≥ 45 mGy/y and thus, serve as a model resource to understand low dose mechanisms directly on healthy humans. We performed quantitative discovery proteomics based on multiplexed isobaric tags (iTRAQ) coupled with LC–MS/MS on human peripheral blood mononuclear cells from HLNRA individuals. Several proteins involved in diverse biological processes such as DNA repair, RNA processing, chromatin modifications and cytoskeletal organization showed distinct expression in HLNRA individuals, suggestive of both recovery and adaptation to low dose radiation. In protein–protein interaction (PPI) networks, YWHAZ (14-3-3ζ) emerged as the top-most hub protein that may direct phosphorylation driven pro-survival cellular processes against radiation stress. PPI networks also identified an integral role for the cytoskeletal protein ACTB, signaling protein PRKACA; and the molecular chaperone HSPA8. The data will allow better integration of radiation biology and epidemiology for risk assessment [Data are available via ProteomeXchange with identifier PXD022380].

canSAR: update to the cancer translational research and drug discovery knowledgebase

canSAR (http://cansar.icr.ac.uk) is the largest, public, freely available, integrative translational research and drug discovery knowledgebase for oncology. canSAR integrates vast multidisciplinary data from across genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and more. It also provides unique data, curation and annotation and crucially, AI-informed target assessment for drug discovery. canSAR is widely used internationally by academia and industry. Here we describe significant developments and enhancements to the data, web interface and infrastructure of canSAR in the form of the new implementation of the system: canSARblack. We demonstrate new functionality in aiding translation hypothesis generation and experimental design, and show how canSAR can be adapted and utilised outside oncology.

Network propagation of rare variants in Alzheimer's disease reveals tissue-specific hub genes and communities

State-of-the-art rare variant association testing methods aggregate the contribution of rare variants in biologically relevant genomic regions to boost statistical power. However, testing single genes separately does not consider the complex interaction landscape of genes, nor the downstream effects of non-synonymous variants on protein structure and function. Here we present the NETwork Propagation-based Assessment of Genetic Events (NETPAGE), an integrative approach aimed at investigating the biological pathways through which rare variation results in complex disease phenotypes. We applied NETPAGE to sporadic, late-onset Alzheimer's disease (AD), using whole-genome sequencing from the AD Neuroimaging Initiative (ADNI) cohort, as well as whole-exome sequencing from the AD Sequencing Project (ADSP). NETPAGE is based on network propagation, a framework that models information flow on a graph and simulates the percolation of genetic variation through tissue-specific gene interaction networks. The result of network propagation is a set of smoothed gene scores that can be tested for association with disease status through sparse regression. The application of NETPAGE to AD enabled the identification of a set of connected genes whose smoothed variation profile was robustly associated to case-control status, based on gene interactions in the hippocampus. Additionally, smoothed scores significantly correlated with risk of conversion to AD in Mild Cognitive Impairment (MCI) subjects. Lastly, we investigated tissue-specific transcriptional dysregulation of the core genes in two independent RNA-seq datasets, as well as significant enrichments in terms of gene sets with known connections to AD. We present a framework that enables enhanced genetic association testing for a wide range of traits, diseases, and sample sizes.

TRIM-NHL as RNA Binding Ubiquitin E3 Ligase (RBUL): Implication in development and disease pathogenesis

TRIM proteins are RING domain-containing modular ubiquitin ligases, unique due to their stimuli specific expression, localization, and turnover. The TRIM family consists of more than 76 proteins, including the TRIM-NHL sub-family which possesses RNA binding ability along with the inherent E3 Ligase activity, hence can be classified as a unique class of RNA Binding Ubiquitin Ligases (RBULs). Having these two abilities, TRIM-NHL proteins can play important role in a wide variety of cellular processes and their dysregulation can lead to complex and systemic pathological conditions. Increasing evidence suggests that TRIM-NHL proteins regulate RNA at the transcriptional and post-transcriptional level having implications in differentiation, development, and many pathological conditions. This review explores the evolving role of TRIM-NHL proteins as TRIM-RBULs, their ubiquitin ligase and RNA binding ability regulating cellular processes, and their possible role in different pathophysiological conditions.

DOMINO: a network-based active module identification algorithm with reduced rate of false calls

Algorithms for active module identification (AMI) are central to analysis of omics data. Such algorithms receive a gene network and nodes' activity scores as input and report subnetworks that show significant over-representation of accrued activity signal ("active modules"), thus representing biological processes that presumably play key roles in the analyzed conditions. Here, we systematically evaluated six popular AMI methods on gene expression and GWAS data. We observed that GO terms enriched in modules detected on the real data were often also enriched on modules found on randomly permuted data. This indicated that AMI methods frequently report modules that are not specific to the biological context measured by the analyzed omics dataset. To tackle this bias, we designed a permutation-based method that empirically evaluates GO terms reported by AMI methods. We used the method to fashion five novel AMI performance criteria. Last, we developed DOMINO, a novel AMI algorithm, that outperformed the other six algorithms in extensive testing on GE and GWAS data. Software is available at https://github.com/Shamir-Lab.

Deconvolution of Multiple Rab Binding Domains Using the Batch Yeast 2-Hybrid Method DEEPN

A hallmark of functionally significant interactions between Rab proteins and their targets is whether that binding depends on the type of nucleotide bound to the Rab GTPase. A system that can directly compare those sets of interactions mediated by a Rab in its GTP-bound conformation versus its GDP bound conformation would provide a direct route to finding biologically relevant partners. Comprehensive large-scale yeast 2-hybrid assays allow a potential method to compare one interactome against another provided that the same set of potentially interacting partners is interrogated between samples. Here we describe the use of such a yeast 2-hybrid system that lends itself toward comparing pairs of Rab mutants, locked in either their GTP or GDP conformation. Importantly, using a complex library of protein fragments as potential binding ("prey") partners, identification of interacting proteins as well as the domain(s) mediating those interactions can be determined using a series of sequence analyses and binary validation experiments.

Neuregulins in Neurodegenerative Diseases

Neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS), are typically characterized by progressive neuronal loss and neurological dysfunctions in the nervous system, affecting both memory and motor functions. Neuregulins (NRGs) belong to the epidermal growth factor (EGF)-like family of extracellular ligands and they play an important role in the development, maintenance, and repair of both the central nervous system (CNS) and peripheral nervous system (PNS) through the ErbB signaling pathway. They also regulate multiple intercellular signal transduction and participate in a wide range of biological processes, such as differentiation, migration, and myelination. In this review article, we summarized research on the changes and roles of NRGs in neurodegenerative diseases, especially in AD. We elaborated on the structural features of each NRG subtype and roles of NRG/ErbB signaling networks in neurodegenerative diseases. We also discussed the therapeutic potential of NRGs in the symptom remission of neurodegenerative diseases, which may offer hope for advancing related treatment.

Emerging Methods and Resources for Biological Interrogation of Neuropsychiatric Polygenic Signal

Most neuropsychiatric disorders are highly polygenic, implicating hundreds to thousands of causal genetic variants that span much of the genome. This widespread polygenicity complicates biological understanding because no single variant can explain disease etiology. A strategy to advance biological insight is to seek convergent functions among the large set of variants and map them to a smaller set of disease-relevant genes and pathways. Accordingly, functional genomic resources that provide data on intermediate molecular phenotypes, such as gene-expression and methylation status, can be leveraged to functionally annotate variants and map them to genes. Such molecular quantitative trait locus mappings can be integrated with genome-wide association studies to make sense of the polygenic signal that underlies complex disease. Other resources that provide data on the 3-dimensional structure of chromatin and functional importance of specific genomic regions can be integrated similarly. In addition, mapped genes can then be tested for convergence in biological function, tissue, cell type, or developmental stage. In this review, we provide an overview of functional genomic resources and methods that can be used to interpret results from genome-wide association studies, and we discuss current challenges for biological understanding and future requirements to overcome them.

Integration of gene interaction information into a reweighted Lasso-Cox model for accurate survival prediction

Motivation

Accurately predicting the risk of cancer patients is a central challenge for clinical cancer research. For high-dimensional gene expression data, Cox proportional hazard model with the least absolute shrinkage and selection operator for variable selection (Lasso-Cox) is one of the most popular feature selection and risk prediction algorithms. However, the Lasso-Cox model treats all genes equally, ignoring the biological characteristics of the genes themselves. This often encounters the problem of poor prognostic performance on independent datasets.

Results

Here, we propose a Reweighted Lasso-Cox (RLasso-Cox) model to ameliorate this problem by integrating gene interaction information. It is based on the hypothesis that topologically important genes in the gene interaction network tend to have stable expression changes. We used random walk to evaluate the topological weight of genes, and then highlighted topologically important genes to improve the generalization ability of the RLasso-Cox model. Experiments on datasets of three cancer types showed that the RLasso-Cox model improves the prognostic accuracy and robustness compared with the Lasso-Cox model and several existing network-based methods. More importantly, the RLasso-Cox model has the advantage of identifying small gene sets with high prognostic performance on independent datasets, which may play an important role in identifying robust survival biomarkers for various cancer types.

Availability and implementation

http://bioconductor.org/packages/devel/bioc/html/RLassoCox.html

Supplementary information

Supplementary data are available at Bioinformatics online.

MaveQuest: a web resource for planning experimental tests of human variant effects

Summary

Fully realizing the promise of personalized medicine will require rapid and accurate classification of pathogenic human variation. Multiplexed assays of variant effect (MAVEs) can experimentally test nearly all possible variants in selected gene targets. Planning a MAVE study involves identifying target genes with clinical impact, and identifying scalable functional assays for that target. Here, we describe MaveQuest, a web-based resource enabling systematic variant effect mapping studies by identifying potential functional assays, disease phenotypes and clinical relevance for nearly all human protein-coding genes.

Availability and implementation

MaveQuest service: https://mavequest.varianteffect.org/. MaveQuest source code: https://github.com/kvnkuang/mavequest-front-end/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Coupling of autism genes to tissue-wide expression and dysfunction of synapse, calcium signalling and transcriptional regulation

Autism Spectrum Disorder (ASD) is a heterogeneous disorder that is often accompanied with many co-morbidities. Recent genetic studies have identified various pathways from hundreds of candidate risk genes with varying levels of association to ASD. However, it is unknown which pathways are specific to the core symptoms or which are shared by the co-morbidities. We hypothesised that critical ASD candidates should appear widely across different scoring systems, and that comorbidity pathways should be constituted by genes expressed in the relevant tissues. We analysed the Simons Foundation for Autism Research Initiative (SFARI) database and four independently published scoring systems and identified 292 overlapping genes. We examined their mRNA expression using the Genotype-Tissue Expression (GTEx) database and validated protein expression levels using the human protein atlas (HPA) dataset. This led to clustering of the overlapping ASD genes into 2 groups; one with 91 genes primarily expressed in the central nervous system (CNS geneset) and another with 201 genes expressed in both CNS and peripheral tissues (CNS+PT geneset). Bioinformatic analyses showed a high enrichment of CNS development and synaptic transmission in the CNS geneset, and an enrichment of synapse, chromatin remodelling, gene regulation and endocrine signalling in the CNS+PT geneset. Calcium signalling and the glutamatergic synapse were found to be highly interconnected among pathways in the combined geneset. Our analyses demonstrate that 2/3 of ASD genes are expressed beyond the brain, which may impact peripheral function and involve in ASD co-morbidities, and relevant pathways may be explored for the treatment of ASD co-morbidities.

SECAT: Quantifying Protein Complex Dynamics across Cell States by Network-Centric Analysis of SEC-SWATH-MS Profiles

Protein-protein interactions (PPIs) play critical functional and regulatory roles in cellular processes. They are essential for macromolecular complex formation, which in turn constitutes the basis for protein interaction networks that determine the functional state of a cell. We and others have previously shown that chromatographic fractionation of native protein complexes in combination with bottom-up mass spectrometric analysis of consecutive fractions supports the multiplexed characterization and detection of state-specific changes of protein complexes. In this study, we extend co-fractionation and mass spectrometric data analysis to perform quantitative, network-based studies of proteome organization, via the size-exclusion chromatography algorithmic toolkit (SECAT). This framework explicitly accounts for the dynamic nature and rewiring of protein complexes across multiple cell states and samples, thus, elucidating molecular mechanisms that are differentially implemented across different experimental settings. Systematic analysis of multiple datasets shows that SECAT represents a highly scalable and effective methodology to assess condition/state-specific protein-network state. A record of this paper's transparent peer review process is included in the Supplemental Information.

Epidermal PAR-6 and PKC-3 are essential for larval development of C. elegans and organize non-centrosomal microtubules

The cortical polarity regulators PAR-6, PKC-3, and PAR-3 are essential for the polarization of a broad variety of cell types in multicellular animals. In C. elegans, the roles of the PAR proteins in embryonic development have been extensively studied, yet little is known about their functions during larval development. Using inducible protein degradation, we show that PAR-6 and PKC-3, but not PAR-3, are essential for postembryonic development. PAR-6 and PKC-3 are required in the epidermal epithelium for animal growth, molting, and the proper pattern of seam-cell divisions. Finally, we uncovered a novel role for PAR-6 in organizing non-centrosomal microtubule arrays in the epidermis. PAR-6 was required for the localization of the microtubule organizer NOCA-1/Ninein, and defects in a noca-1 mutant are highly similar to those caused by epidermal PAR-6 depletion. As NOCA-1 physically interacts with PAR-6, we propose that PAR-6 promotes non-centrosomal microtubule organization through localization of NOCA-1/Ninein.

SkipGNN: predicting molecular interactions with skip-graph networks

Molecular interaction networks are powerful resources for molecular discovery. They are increasingly used with machine learning methods to predict biologically meaningful interactions. While deep learning on graphs has dramatically advanced the prediction prowess, current graph neural network (GNN) methods are mainly optimized for prediction on the basis of direct similarity between interacting nodes. In biological networks, however, similarity between nodes that do not directly interact has proved incredibly useful in the last decade across a variety of interaction networks. Here, we present SkipGNN, a graph neural network approach for the prediction of molecular interactions. SkipGNN predicts molecular interactions by not only aggregating information from direct interactions but also from second-order interactions, which we call skip similarity. In contrast to existing GNNs, SkipGNN receives neural messages from two-hop neighbors as well as immediate neighbors in the interaction network and non-linearly transforms the messages to obtain useful information for prediction. To inject skip similarity into a GNN, we construct a modified version of the original network, called the skip graph. We then develop an iterative fusion scheme that optimizes a GNN using both the skip graph and the original graph. Experiments on four interaction networks, including drug–drug, drug–target, protein–protein, and gene–disease interactions, show that SkipGNN achieves superior and robust performance. Furthermore, we show that unlike popular GNNs, SkipGNN learns biologically meaningful embeddings and performs especially well on noisy, incomplete interaction networks.

Network and pathway expansion of genetic disease associations identifies successful drug targets

Genetic evidence of disease association has often been used as a basis for selecting of drug targets for complex common diseases. Likewise, the propagation of genetic evidence through gene or protein interaction networks has been shown to accurately infer novel disease associations at genes for which no direct genetic evidence can be observed. However, an empirical test of the utility of combining these approaches for drug discovery has been lacking. In this study, we examine genetic associations arising from an analysis of 648 UK Biobank GWAS and evaluate whether targets identified as proxies of direct genetic hits are enriched for successful drug targets, as measured by historical clinical trial data. We find that protein networks formed from specific functional linkages such as protein complexes and ligand–receptor pairs are suitable for even naïve guilt-by-association network propagation approaches. In addition, more sophisticated approaches applied to global protein–protein interaction networks and pathway databases, also successfully retrieve targets enriched for clinically successful drug targets. We conclude that network propagation of genetic evidence can be used for drug target identification.

Edge-based analysis of networks: curvatures of graphs and hypergraphs

The relations, rather than the elements, constitute the structure of networks. We therefore develop a systematic approach to the analysis of networks, modelled as graphs or hypergraphs, that is based on structural properties of (hyper)edges, instead of vertices. For that purpose, we utilize so-called network curvatures. These curvatures quantify the local structural properties of (hyper)edges, that is, how, and how well, they are connected to others. In the case of directed networks, they assess the input they receive and the output they produce, and relations between them. With those tools, we can investigate biological networks. As examples, we apply our methods here to protein-protein interaction, transcriptional regulatory and metabolic networks.

Prohibitin, STAT3 and SH2D4A physically and functionally interact in tumor cell mitochondria

Chromosome 8p is frequently deleted in various cancer entities and has been shown to correlate with poor patient survival. SH2D4A is located on chromosome 8p and prevents the nuclear translocation of the pro-tumorigenic transcription factor STAT3. Here, we investigated the interaction of SH2D4A and STAT3 to shed light on the non-canonical functions of STAT3 in cooperation with the tumor suppressor SH2D4A. Using an immunoprecipitation-mass spectrometry (IP-MS) approach, we identified the mitochondrial scaffold proteins prohibitin 1 (PHB1) and prohibitin 2 (PHB2) among other proteins to potentially bind to SH2D4A. Co-immunoprecipitation and proximity ligation assays confirmed direct interactions of STAT3, PHB1, and SH2D4A in situ and in vitro. In addition, cell fractionation and immunofluorescence staining revealed co-localization of these proteins with mitochondria. These interactions were selectively interrupted by the small molecule and PHB ligand FL3. Furthermore, FL3 led to a reduction of STAT3 protein levels, STAT3 transcriptional activity, and HIF1α protein stabilization upon dimethyloxalylglycine (DMOG) treatment. Besides, mitochondrial fusion and fission markers, L-OPA1, Mfn1, and FIS1, were dysregulated upon FL3 treatment. This dysregulated morphology was accompanied by significant reduction of mitochondrial respiration, thus, FL3 significantly diminished mitochondrial respirational capacity. In contrast, SH2D4A knockout increased mitochondrial respiration, whereas FL3 reversed the effect of SH2D4A knockout. The here described results indicate that the interaction of SH2D4A and PHB1 is involved in the mitochondrial function and integrity. The demonstrated interaction with STAT3, accompanied by its reduction of transcriptional activity, further suggests that SH2D4A is linking STAT3 to its mitochondrial functions, and inhibition of PHB-interaction may have therapeutic effects in tumor cells with STAT3 activation.

Proteome-Scale Analysis of Protein S-Acylation Comes of Age

Protein S-acylation (commonly known as palmitoylation) is a widespread reversible lipid modification, which plays critical roles in regulating protein localization, activity, stability, and complex formation. The deregulation of protein S-acylation contributes to many diseases such as cancer and neurodegenerative disorders. The past decade has witnessed substantial progress in proteomic analysis of protein S-acylation, which significantly advanced our understanding of S-acylation biology. In this review, we summarized the techniques for the enrichment of S-acylated proteins or peptides, critically reviewed proteomic studies of protein S-acylation at eight different levels, and proposed major challenges for the S-acylproteomics field. In summary, proteome-scale analysis of protein S-acylation comes of age and will play increasingly important roles in discovering new disease mechanisms, biomarkers, and therapeutic targets.

On the Robustness of Graph-Based Clustering to Random Network Alterations

Biological functions emerge from complex and dynamic networks of protein-protein interactions. Because these protein-protein interaction networks, or interactomes, represent pairwise connections within a hierarchically organized system, it is often useful to identify higher-order associations embedded within them, such as multimember protein complexes. Graph-based clustering techniques are widely used to accomplish this goal, and dozens of field-specific and general clustering algorithms exist. However, interactomes can be prone to errors, especially when inferred from high-throughput biochemical assays. Therefore, robustness to network-level noise is an important criterion. Here, we tested the robustness of a range of graph-based clustering algorithms in the presence of noise, including algorithms common across domains and those specific to protein networks. Strikingly, we found that all of the clustering algorithms tested here markedly amplified network-level noise. Randomly rewiring only 1% of network edges yielded more than a 50% change in clustering results. Moreover, we found the impact of network noise on individual clusters was not uniform: some clusters were consistently robust to injected noise, whereas others were not. Therefore we developed the clust.perturb R package and Shiny web application to measure the reproducibility of clusters by randomly perturbing the network. We show that clust.perturb results are predictive of real-world cluster stability: poorly reproducible clusters as identified by clust.perturb are significantly less likely to be reclustered across experiments. We conclude that graph-based clustering amplifies noise in protein interaction networks, but quantifying the robustness of a cluster to network noise can separate stable protein complexes from spurious associations.

Transcriptome assisted label free proteomics of hepatic tissue in response to both dietary restriction and compensatory growth in cattle

Compensatory growth (CG) is a naturally occurring phenomenon where, following a period of under nutrition, an animal exhibits accelerated growth upon re-alimentation. The objective was to identify and quantify hepatic proteins involved in the regulation of CG in cattle. Forty Holstein Friesian bulls were equally assigned to one of four groups. Groups; A1 and A2 had ad libitum access to feed for 125 days, groups R1 and R2 were feed restricted. Following this, R1 and A1 animals were slaughtered. Remaining animals (R2 and A2) were slaughtered following ad libitum feeding for a successive 55 days. At slaughter hepatic tissue samples were collected and label-free quantitative proteomics undertaken with spectra searched against a custom built transcriptome database specific to the animals in this study. 24 differentially abundant proteins were identified during CG (R2 vs. R1) including; PSPH, ASNS and GSTM1, which are involved in nutrient metabolism, immune response and cellular growth. Proteins involved in biochemical pathways related to nutrient metabolism were down-regulated during CG, indicating a possible adaptive response by the liver to a period of fluctuating nutrient availability. The livers ability to regulate its metabolic activity may have profound effects on the efficiency of whole body energy utilization during CG. SIGNIFICANCE: This study is the first to unravel the effect of compensatory growth on the hepatic proteome of cattle using transcriptome-assisted shot gun proteomics. Proteins identified as being affected by dietary restriction and subsequent expression of compensatory growth in this study may, following appropriate validation, contribute to the identification of functional genetic variants. Such information could be harnessed within the context of genomic selection in cattle breeding programs to identify animals with a greater genetic potential to undergo compensatory growth, thus increasing the profitability of the beef sector and accelerating genetic gain.