HIPPIE v2.0: enhancing meaningfulness and reliability of protein-protein interaction networks

Dynamic rewiring of the human interactome by interferon signaling

BACKGROUND

The type I interferon (IFN) response is an ancient pathway that protects cells against viral pathogens by inducing the transcription of hundreds of IFN-stimulated genes. Comprehensive catalogs of IFN-stimulated genes have been established across species and cell types by transcriptomic and biochemical approaches, but their antiviral mechanisms remain incompletely characterized. Here, we apply a combination of quantitative proteomic approaches to describe the effects of IFN signaling on the human proteome, and apply protein correlation profiling to map IFN-induced rearrangements in the human protein-protein interaction network.

RESULTS

We identify > 26,000 protein interactions in IFN-stimulated and unstimulated cells, many of which involve proteins associated with human disease and are observed exclusively within the IFN-stimulated network. Differential network analysis reveals interaction rewiring across a surprisingly broad spectrum of cellular pathways in the antiviral response. We identify IFN-dependent protein-protein interactions mediating novel regulatory mechanisms at the transcriptional and translational levels, with one such interaction modulating the transcriptional activity of STAT1. Moreover, we reveal IFN-dependent changes in ribosomal composition that act to buffer IFN-stimulated gene protein synthesis.

CONCLUSIONS

Our map of the IFN interactome provides a global view of the complex cellular networks activated during the antiviral response, placing IFN-stimulated genes in a functional context, and serves as a framework to understand how these networks are dysregulated in autoimmune or inflammatory disease.

PINOT: an intuitive resource for integrating protein-protein interactions

Background

The past decade has seen the rise of omics data for the understanding of biological systems in health and disease. This wealth of information includes protein-protein interaction (PPI) data derived from both low- and high-throughput assays, which are curated into multiple databases that capture the extent of available information from the peer-reviewed literature. Although these curation efforts are extremely useful, reliably downloading and integrating PPI data from the variety of available repositories is challenging and time consuming.

Methods

We here present a novel user-friendly web-resource called PINOT (Protein Interaction Network Online Tool; available at http://www.reading.ac.uk/bioinf/PINOT/PINOT_form.html) to optimise the collection and processing of PPI data from IMEx consortium associated repositories (members and observers) and WormBase, for constructing, respectively, human and Caenorhabditis elegans PPI networks.

Results

Users submit a query containing a list of proteins of interest for which PINOT extracts data describing PPIs. At every query submission PPI data are downloaded, merged and quality assessed. Then each PPI is confidence scored based on the number of distinct methods used for interaction detection and the number of publications that report the specific interaction. Examples of how PINOT can be applied are provided to highlight the performance, ease of use and potential utility of this tool.

Conclusions

PINOT is a tool that allows users to survey the curated literature, extracting PPI data in relation to a list of proteins of interest. PINOT extracts a similar numbers of PPIs as other, analogous, tools and incorporates a set of innovative features. PINOT is able to process large queries, it downloads human PPIs live through PSICQUIC and it applies quality control filters on the downloaded PPI data (i.e. removing the need for manual inspection by the user). PINOT provides the user with information on detection methods and publication history for each downloaded interaction data entry and outputs the results in a table format that can be straightforwardly further customised and/or directly uploaded into network visualization software.

Integrative analysis of large-scale loss-of-function screens identifies robust cancer-associated genetic interactions

Genetic interactions, including synthetic lethal effects, can now be systematically identified in cancer cell lines using high-throughput genetic perturbation screens. Despite this advance, few genetic interactions have been reproduced across multiple studies and many appear highly context-specific. Here, by developing a new computational approach, we identified 220 robust driver-gene associated genetic interactions that can be reproduced across independent experiments and across non-overlapping cell line panels. Analysis of these interactions demonstrated that: (i) oncogene addiction effects are more robust than oncogene-related synthetic lethal effects; and (ii) robust genetic interactions are enriched among gene pairs whose protein products physically interact. Exploiting the latter observation, we used a protein-protein interaction network to identify robust synthetic lethal effects associated with passenger gene alterations and validated two new synthetic lethal effects. Our results suggest that protein-protein interaction networks can be used to prioritise therapeutic targets that will be more robust to tumour heterogeneity.

Isoform-specific GSK3A activity is negatively correlated with human sperm motility

In mouse and bovine sperm, GSK3 activity is inversely proportional to motility. Targeted disruption of the GSK3A gene in testis results in normal spermatogenesis, but mature sperm present a reduced motility, rendering male mice infertile. On the other hand, GSK3B testis-specific KO is fertile. Yet in human sperm, an isoform-specific correlation between GSK3A and sperm motility was never established. In order to analyze GSK3 function in human sperm motility, normospermic and asthenozoospermic samples from adult males were used to correlate GSK3 expression and activity levels with human sperm motility profiles. Moreover, testicular and sperm GSK3 interactomes were identified using a yeast two-hybrid screen and coimmunoprecipitation, respectively. An extensive in-silico analysis of the GSK3 interactome was performed. The results proved that inhibited GSK3A (serine phosphorylated) presents a significant strong positive correlation (r = 0.822, P = 0.023) with the percentage of progressive human sperm, whereas inhibited GSK3B is not significantly correlated with sperm motility (r = 0.577, P = 0.175). The importance of GSK3 in human sperm motility was further reinforced by in-silico analysis of the GSK3 interactome, which revealed a high level of involvement of GSK3 interactors in sperm motility-related functions. The limitation of techniques used for GSK3 interactome identification can be a drawback, since none completely mimics the physiological environment. Our findings prove that human sperm motility relies on isoform-specific functions of GSK3A within this cell. Given the reported relevance of GSK3 protein-protein interactions in sperm motility, we hypothesized that they stand as potential targets for male contraceptive strategies based on sperm motility modulation.

Protein changes in synaptosomes of Huntington's disease knock-in mice are dependent on age and brain region

Molecular changes at synapses are thought to underly the deficits in motor and cognitive dysfunction seen in Huntington's disease (HD). Previously we showed in synaptosome preparations age dependent changes in levels of selected proteins examined by western blot assay in the striatum of Q140/Q140 HD mice. To assess if CAG repeat length influenced protein changes at the synapse, we examined synaptosomes from 6-month old heterozygote HD mice with CAG repeat lengths ranging from 50 to 175. Analysis of 19 selected proteins showed that increasing CAG repeat length in huntingtin (HTT) increased the number of affected proteins in HD striatal synaptosomes. Moreover, SDS-soluble total HTT (WT plus mutant HTT) and pThr3 HTT were reduced with increasing CAG repeat length, and there was no pSer421 mutant HTT detected in any HD mice. A LC-MS/MS and bioinfomatics study of synaptosomes from 2 and 6-month old striatum and cortex of Q140/Q7 HD mice showed enrichment of synaptic proteins and an influence of age, gender and brain region on the number of protein changes. HD striatum at 6 months had the most protein changes that included many HTT protein interactors, followed by 2-month old HD striatum, 2-month old HD cortex and 6-month HD cortex. SDS-insoluble mutant HTT was detected in HD striatal synaptosomes consistent with the presence of aggregates. Proteins changed in cortex differed from those in striatum. Pathways affected in HD striatal synaptosomes that were not identified in whole striatal lysates of the same HD mouse model included axon guidance, focal adhesion, neurotrophin signaling, regulation of actin cytoskeleton, endocytosis, and synaptic vesicle cycle. Results suggest that synaptosomes prepared from HD mice are highly informative for monitoring protein changes at the synapse and may be preferred for assessing the effects of experimental therapies on synaptic function in HD.

Post-translational Modification Crosstalk and Hotspots in Sirtuin Interactors Implicated in Cardiovascular Diseases

Sirtuins are protein deacetylases that play a protective role in cardiovascular diseases (CVDs), as well as many other diseases. Absence of sirtuins can lead to hyperacetylation of both nuclear and mitochondrial proteins leading to metabolic dysregulation. The protein post-translational modifications (PTMs) are known to crosstalk among each other to bring about complex phenotypic outcomes. Various PTM types such as acetylation, ubiquitination, and phosphorylation, and so on, drive transcriptional regulation and metabolism, but such crosstalks are poorly understood. We integrated protein–protein interactions (PPI) and PTMs from several databases to integrate information on 1,251 sirtuin-interacting proteins, of which 544 are associated with cardiac diseases. Based on the ∼100,000 PTM sites obtained for sirtuin interactors, we observed that the frequency of PTM sites (83 per protein), as well as PTM types (five per protein), is higher than the global average for human proteome. We found that ∼60–70% PTM sites fall into ordered regions. Approximately 83% of the sirtuin interactors contained at least one competitive crosstalk (in situ) site, with half of the sites occurring in CVD-associated proteins. A large proportion of identified crosstalk sites were observed for acetylation and ubiquitination competition. We identified 614 proteins containing PTM hotspots (≥5 PTM sites) and 133 proteins containing crosstalk hotspots (≥3 crosstalk sites). We observed that a large proportion of disease-associated sequence variants were found in PTM motifs of CVD proteins. We identified seven proteins (TP53, LMNA, MAPT, ATP2A2, NCL, APEX1, and HIST1H3A) containing disease-associated variants in PTM and crosstalk hotspots. This is the first comprehensive bioinformatics analysis on sirtuin interactors with respect to PTMs and their crosstalks. This study forms a platform for generating interesting hypotheses that can be tested for a deeper mechanistic understanding gained or derived from big-data analytics.

Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis

Recent advances in mass spectrometry (MS)-based proteomics have enabled tremendous progress in the understanding of cellular mechanisms, disease progression, and the relationship between genotype and phenotype. Though many popular bioinformatics methods in proteomics are derived from other omics studies, novel analysis strategies are required to deal with the unique characteristics of proteomics data. In this review, we discuss the current developments in the bioinformatics methods used in proteomics and how they facilitate the mechanistic understanding of biological processes. We first introduce bioinformatics software and tools designed for mass spectrometry-based protein identification and quantification, and then we review the different statistical and machine learning methods that have been developed to perform comprehensive analysis in proteomics studies. We conclude with a discussion of how quantitative protein data can be used to reconstruct protein interactions and signaling networks.

Potential target identification for breast cancer and screening of small molecule inhibitors: A bioinformatics approach

In the current study, we investigated the role of PAK1 (P21 (RAC1) Activated Kinase 1) gene in breast cancer and to this end, we performed differential gene expression analysis of PAK1 in breast cancer tissues compared to the normal adjacent tissue. We also studied its significance in protein-protein interaction (PPI) network, and analysed biological pathways, cellular processes, and role of PAK1 in different diseases. We found PAK1 to have significant role in breast cancer pathways such as integrin signaling, axonal guidance signaling, signaling by Rho family GTPases, ERK5 signaling. Additionally, it has been found as hub gene in PPI network, suggesting its possible regulatory role in breast carcinogenesis. Moreover, PAK1 had role in progression of various diseases as neoplasia, tumorigenesis, lymphatic neoplasia. Thereby, PAK1 can be used as a therapeutic target in breast cancer. Further, we put our efforts in identification of potential small molecules inhibitors against PAK1 by developing a composite virtual screening protocol involving molecular dynamics (MD) and molecular docking. The chemical library of compounds from NCI diversity sets, Pubchem and eMolecules were screened against PAK1 protein and hits which showed good binding affinity were considered for MD simulation study. Moreover, to assess binding of selected hits, MMGBSA (Molecular Mechanics-Generalized Born Surface Area) analysis was performed using AMBER (Assisted Model Building with Energy Refinement) package. MMGBSA calculations exhibited that the identified ligands showed good binding affinity with PAK1. HighlightsThe PAK1 has been found to be upregulated in breast cancer samples and is a potential oncogene playing role in different cellular functions and processes.The molecular docking studies revealed ligands showed good binding affinity towards PAK1 protein.The residues Glu345, Leu347, Thr406, Asp299, Asp393 and Gly350 were found to make H-bond interactions with small molecule inhibitors.The residues Ile276, Val284, Ala297, Tyr346, Leu396 and Asp407 were found to make hydrophobic interactions.The RMSD analysis confirmed stability of complexes throughout 40 ns production period.The MD simulations studies revealed the binding site flexibility, binding free energy of complexes and per-residue contribution in ligand binding.Communicated by Ramaswamy H. Sarma.

UniProt-Related Documents (UniReD): assisting wet lab biologists in their quest on finding novel counterparts in a protein network

The in-depth study of protein–protein interactions (PPIs) is of key importance for understanding how cells operate. Therefore, in the past few years, many experimental as well as computational approaches have been developed for the identification and discovery of such interactions. Here, we present UniReD, a user-friendly, computational prediction tool which analyses biomedical literature in order to extract known protein associations and suggest undocumented ones. As a proof of concept, we demonstrate its usefulness by experimentally validating six predicted interactions and by benchmarking it against public databases of experimentally validated PPIs succeeding a high coverage. We believe that UniReD can become an important and intuitive resource for experimental biologists in their quest for finding novel associations within a protein network and a useful tool to complement experimental approaches (e.g. mass spectrometry) by producing sorted lists of candidate proteins for further experimental validation. UniReD is available at http://bioinformatics.med.uoc.gr/unired/

Nuclear inclusions of pathogenic ataxin-1 induce oxidative stress and perturb the protein synthesis machinery

Spinocerebellar ataxia type-1 (SCA1) is caused by an abnormally expanded polyglutamine (polyQ) tract in ataxin-1. These expansions are responsible for protein misfolding and self-assembly into intranuclear inclusion bodies (IIBs) that are somehow linked to neuronal death. However, owing to lack of a suitable cellular model, the downstream consequences of IIB formation are yet to be resolved. Here, we describe a nuclear protein aggregation model of pathogenic human ataxin-1 and characterize IIB effects. Using an inducible Sleeping Beauty transposon system, we overexpressed the ATXN1(Q82) gene in human mesenchymal stem cells that are resistant to the early cytotoxic effects caused by the expression of the mutant protein. We characterized the structure and the protein composition of insoluble polyQ IIBs which gradually occupy the nuclei and are responsible for the generation of reactive oxygen species. In response to their formation, our transcriptome analysis reveals a cerebellum-specific perturbed protein interaction network, primarily affecting protein synthesis. We propose that insoluble polyQ IIBs cause oxidative and nucleolar stress and affect the assembly of the ribosome by capturing or down-regulating essential components. The inducible cell system can be utilized to decipher the cellular consequences of polyQ protein aggregation. Our strategy provides a broadly applicable methodology for studying polyQ diseases.

IGF1-mediated human embryonic stem cell self-renewal recapitulates the embryonic niche

Our understanding of the signalling pathways regulating early human development is limited, despite their fundamental biological importance. Here, we mine transcriptomics datasets to investigate signalling in the human embryo and identify expression for the insulin and insulin growth factor 1 (IGF1) receptors, along with IGF1 ligand. Consequently, we generate a minimal chemically-defined culture medium in which IGF1 together with Activin maintain self-renewal in the absence of fibroblast growth factor (FGF) signalling. Under these conditions, we derive several pluripotent stem cell lines that express pluripotency-associated genes, retain high viability and a normal karyotype, and can be genetically modified or differentiated into multiple cell lineages. We also identify active phosphoinositide 3-kinase (PI3K)/AKT/mTOR signalling in early human embryos, and in both primed and naïve pluripotent culture conditions. This demonstrates that signalling insights from human blastocysts can be used to define culture conditions that more closely recapitulate the embryonic niche.

Integration of multi-omics data to mine cancer-related gene modules

The identification of cancer-related genes is a major research goal, with implications for determining the pathogenesis of cancer and identifying biomarkers for early diagnosis and treatment. In this study, by integrating multi-omics data, including gene expression, DNA copy number variation, DNA methylation, transcription factors, miRNA, and lncRNA data, we propose a method for mining cancer-related genes based on network models. First, using random forest-based feature selection method multi-omics data are integrated to identify key regulatory factors that affect gene expression, and then genome-wide regulatory networks are constructed. Next, by comparing the regulatory networks of key candidate genes in variant samples and non-variant samples, a differential expression regulatory network is generated. The differential network contains a collection of abnormal regulatory genes of key candidate genes. Then, by introducing the functional similarity as a distance metric for gene sets, a density-based clustering method is used to mine gene modules related to cancer. We applied this method to LUSC (lung squamous cell carcinoma) and mined cancer-related gene modules composed of 20 genes. GO function and KEGG pathway analyses indicated that the modules were closely related to cancer. A survival analysis was used to verify that the excavated gene modules can effectively distinguish between high- and low-risk groups. Overall, these results suggest that the proposed method can be used to identify cancer-related gene modules, providing a basis for the development of biomarkers for diagnosis and treatment.

A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder

Drug targets are biomacromolecules or biomolecular structures that bind to specific drugs and produce therapeutic effects. Therefore, the prediction of drug-target interactions (DTIs) is important for disease therapy. Incorporating multiple similarity measures for drugs and targets is of essence for improving the accuracy of prediction of DTIs. However, existing studies with multiple similarity measures ignored the global structure information of similarity measures, and required manual extraction features of drug-target pairs, ignoring the non-linear relationship among features. In this paper, we proposed a novel approach MDADTI for DTIs prediction based on MDA. MDADTI applied random walk with restart method and positive pointwise mutual information to calculate the topological similarity matrices of drugs and targets, capturing the global structure information of similarity measures. Then, MDADTI applied multimodal deep autoencoder to fuse multiple topological similarity matrices of drugs and targets, automatically learned the low-dimensional features of drugs and targets, and applied deep neural network to predict DTIs. The results of 5-repeats of 10-fold cross-validation under three different cross-validation settings indicated that MDADTI is superior to the other four baseline methods. In addition, we validated the predictions of the MDADTI in six drug-target interactions reference databases, and the results showed that MDADTI can effectively identify unknown DTIs.

Plasticity of the Cullin-RING Ligase Repertoire Shapes Sensitivity to Ligand-Induced Protein Degradation

Inducing protein degradation via small molecules is a transformative therapeutic paradigm. Although structural requirements of target degradation are emerging, mechanisms determining the cellular response to small-molecule degraders remain poorly understood. To systematically delineate effectors required for targeted protein degradation, we applied genome-scale CRISPR/Cas9 screens for five drugs that hijack different substrate receptors (SRs) of cullin RING ligases (CRLs) to induce target proteolysis. We found that sensitivity to small-molecule degraders is dictated by shared and drug-specific modulator networks, including the COP9 signalosome and the SR exchange factor CAND1. Genetic or pharmacologic perturbation of these effectors impairs CRL plasticity and arrests a wide array of ligases in a constitutively active state. Resulting defects in CRL decommissioning prompt widespread CRL auto-degradation that confers resistance to multiple degraders. Collectively, our study informs on regulation and architecture of CRLs amenable for targeted protein degradation and outlines biomarkers and putative resistance mechanisms for upcoming clinical investigation.

The articles.ELM resource: simplifying access to protein linear motif literature by annotation, text-mining and classification

Modern biology produces data at a staggering rate. Yet, much of these biological data is still isolated in the text, figures, tables and supplementary materials of articles. As a result, biological information created at great expense is significantly underutilised. The protein motif biology field does not have sufficient resources to curate the corpus of motif-related literature and, to date, only a fraction of the available articles have been curated. In this study, we develop a set of tools and a web resource, 'articles.ELM', to rapidly identify the motif literature articles pertinent to a researcher's interest. At the core of the resource is a manually curated set of about 8000 motif-related articles. These articles are automatically annotated with a range of relevant biological data allowing in-depth search functionality. Machine-learning article classification is used to group articles based on their similarity to manually curated motif classes in the Eukaryotic Linear Motif resource. Articles can also be manually classified within the resource. The 'articles.ELM' resource permits the rapid and accurate discovery of relevant motif articles thereby improving the visibility of motif literature and simplifying the recovery of valuable biological insights sequestered within scientific articles. Consequently, this web resource removes a critical bottleneck in scientific productivity for the motif biology field. Database URL: http://slim.icr.ac.uk/articles/.

Explore Protein-Protein Interactions for Cancer Target Discovery Using the OncoPPi Portal

Protein-protein interactions (PPIs) control all functions and physiological states of the cell. Identification and understanding of novel PPIs would facilitate the discovery of new biological models and therapeutic targets for clinical intervention. Numerous resources and PPI databases have been developed to define a global interactome through the PPI data mining, curation, and integration of different types of experimental evidence obtained with various methods in different model systems. On the other hand, the recent advances in cancer genomics and proteomics have revealed a critical role of genomic alterations in acquisition of cancer hallmarks through a dysregulated network of oncogenic PPIs. Deciphering of cancer-specific interactome would uncover new mechanisms of oncogenic signaling for therapeutic interrogation. Toward this goal our team has developed a high-throughput screening platform to detect PPIs between cancer-associated proteins in the context of cancer cells. The established network of oncogenic PPIs, termed the OncoPPi network, is available through the OncoPPi Portal, an interactive web resource that allows to access and interpret a high-quality cancer-focused network of PPIs experimentally detected in cancer cell lines integrated with the analysis of mutual exclusivity of genomic alterations, cellular co-localization of interacting proteins, domain-domain interactions, and therapeutic connectivity. This chapter presents a guide to explore the OncoPPi network using the OncoPPi Portal to facilitate cancer biology.

Informed Use of Protein-Protein Interaction Data: A Focus on the Integrated Interactions Database (IID)

Protein-protein interaction data is fundamental in molecular biology, and numerous online databases provide access to this data. However, the huge quantity, complexity, and variety of PPI data can be overwhelming, and rather than helping to address research problems, the data may add to their complexity and reduce interpretability. This protocol focuses on solutions for some of the main challenges of using PPI data, including accessing data, ensuring relevance by integrating useful annotations, and improving interpretability. While the issues are generic, we highlight how to perform such operations using Integrated Interactions Database (IID; http://ophid.utoronto.ca/iid ).

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.

Integrating protein localization with automated signaling pathway reconstruction

Background

Understanding cellular responses via signal transduction is a core focus in systems biology. Tools to automatically reconstruct signaling pathways from protein-protein interactions (PPIs) can help biologists generate testable hypotheses about signaling. However, automatic reconstruction of signaling pathways suffers from many interactions with the same confidence score leading to many equally good candidates. Further, some reconstructions are biologically misleading due to ignoring protein localization information.

Results

We propose LocPL, a method to improve the automatic reconstruction of signaling pathways from PPIs by incorporating information about protein localization in the reconstructions. The method relies on a dynamic program to ensure that the proteins in a reconstruction are localized in cellular compartments that are consistent with signal transduction from the membrane to the nucleus. LocPL and existing reconstruction algorithms are applied to two PPI networks and assessed using both global and local definitions of accuracy. LocPL produces more accurate and biologically meaningful reconstructions on a versatile set of signaling pathways.

Conclusion

LocPL is a powerful tool to automatically reconstruct signaling pathways from PPIs that leverages cellular localization information about proteins. The underlying dynamic program and signaling model are flexible enough to study cellular signaling under different settings of signaling flow across the cellular compartments.

Identification of gene co-expression modules and hub genes associated with lymph node metastasis of papillary thyroid cancer

Papillary thyroid cancer (PTC) is the most prevalent histological type among thyroid cancers, and some patients are at a high risk for recurrent disease or even death. Identification for the potential biomarkers of PTC may contribute to early discovery of recurrence and treatment. In The Cancer Genome Atlas (TCGA) database, we obtained the information of RNA sequence data and clinical characteristics of PTC. Weighted gene co-expression network analysis (WGCNA) was performed to construct gene co-expression networks and investigate the relationship between modules and clinical traits. Finally, we constructed 16 co-expression modules in 10,428 genes, and three key modules (darkturquoise, lightyellow, and red) associated with tumor N grade were identified. The results of functional annotation indicated that the darkturquoise module was primarily enriched in the regulation of the extracellular matrix (ECM), collagen metabolism, and cell adhesion, the lightyellow module was primarily enriched in the mitochondrial function regulation and energy synthesis, and the red module was primarily enriched in the process of cell junction, apoptosis, and inflammatory response, suggesting their significant role in the progression of PTC. In addition, the hub genes in the three modules were identified and screened for differentially expressed genes (DEGs). Relapse-free survival analyses found that 11 genes (KCNQ3, MET, FN1, ITGA3, RUNX1, ITGA2, PERP, GCSH, FAAH, NGFRAP1, and HSPA5) may play a pivotal role in PTC relapse. In general, our research revealed the key co-expression modules and identified several prognostic biomarkers, which provides some new insights into the lymph node metastasis of PTC.

Assessing the reliability of gene expression measurements in very-low-numbers of human monocyte-derived macrophages

Tumor-derived primary cells are essential for in vitro and in vivo studies of tumor biology. The scarcity of this cellular material limits the feasibility of experiments or analyses and hence hinders basic and clinical research progress. We set out to determine the minimum number of cells that can be analyzed with standard laboratory equipment and that leads to reliable results, unbiased by cell number. A proof-of-principle study was conducted with primary human monocyte-derived macrophages, seeded in decreasing number and constant cell density. Gene expression of cells stimulated to acquire opposite inflammatory states was analyzed by quantitative PCR. Statistical analysis indicated the lack of significant difference in the expression profile of cells cultured at the highest (100,000 cells) and lowest numbers (3,610 cells) tested. Gene Ontology, pathway enrichment and network analysis confirmed the reliability of the data obtained with the lowest cell number. This statistical and computational analysis of gene expression profiles indicates that low cell number analysis is as dependable and informative as the analysis of a larger cell number. Our work demonstrates that it is possible to employ samples with a scarce number of cells in experimental studies and encourages the application of this approach on other cell types.

Graph cluster approach in identifying novel proteins and significant pathways involved in polycystic ovary syndrome

RESEARCH QUESTION

Polycystic ovary syndrome (PCOS) is a complex endocrine disorder with diverse clinical implications, such as infertility, metabolic disorders, cardiovascular diseases and psychological problems among others. The heterogeneity of conditions found in PCOS contribute to its various phenotypes, leading to difficulties in identifying proteins involved in this abnormality. Several studies, however, have shown the feasibility in identifying molecular evidence underlying other diseases using graph cluster analysis. Therefore, is it possible to identify proteins and pathways related to PCOS using the same approach?

METHODS

Known PCOS-related proteins (PCOSrp) from PCOSBase and DisGeNET were integrated with protein-protein interactions (PPI) information from Human Integrated Protein-Protein Interaction reference to construct a PCOS PPI network. The network was clustered with DPClusO algorithm to generate clusters, which were evaluated using Fisher's exact test. Pathway enrichment analysis using gProfileR was conducted to identify significant pathways.

RESULTS

The statistical significance of the identified clusters has successfully predicted 138 novel PCOSrp with 61.5% reliability and, based on Cronbach's alpha, this prediction is acceptable. Androgen signalling pathway and leptin signalling pathway were among the significant PCOS-related pathways corroborating the information obtained from the clinical observation, where androgen signalling pathway is responsible in producing male hormones in women with PCOS, whereas leptin signalling pathway is involved in insulin sensitivity.

CONCLUSIONS

These results show that graph cluster analysis can provide additional insight into the pathobiology of PCOS, as the pathways identified as statistically significant correspond to earlier biological studies. Therefore, integrative analysis can reveal unknown mechanisms, which may enable the development of accurate diagnosis and effective treatment in PCOS.

Genome-wide characterization of copy number variations in diffuse large B-cell lymphoma with implications in targeted therapy

Diffuse large B-cell lymphoma (DLBCL) is the aggressive form of haematological malignancies with relapse/refractory in ~ 40% of cases. It mostly develops due to accumulation of various genetic and epigenetic variations that contribute to its aggressiveness. Though large-scale structural alterations have been reported in DLBCL, their functional role in pathogenesis and as potential targets for therapy is not yet well understood. In this study we performed detection and analysis of copy number variations (CNVs) in 11 human DLBCL cell lines (4 activated B-cell–like [ABC] and 7 germinal-centre B-cell–like [GCB]), that serve as model systems for DLBCL cancer cell biology. Significant heterogeneity observed in CNV profiles of these cell lines and poor prognosis associated with ABC subtype indicates the importance of individualized screening for diagnostic and prognostic targets. Functional analysis of key cancer genes exhibiting copy alterations across the cell lines revealed activation/disruption of ten potentially targetable immuno-oncogenic pathways. Genome guided in silico therapy that putatively target these pathways is elucidated. Based on our analysis, five CNV-genes associated with worst survival prognosis are proposed as potential prognostic markers of DLBCL.

A network-based approach to identify deregulated pathways and drug effects in metabolic syndrome

Metabolic syndrome is a pathological condition characterized by obesity, hyperglycemia, hypertension, elevated levels of triglycerides and low levels of high-density lipoprotein cholesterol that increase cardiovascular disease risk and type 2 diabetes. Although numerous predisposing genetic risk factors have been identified, the biological mechanisms underlying this complex phenotype are not fully elucidated. Here we introduce a systems biology approach based on network analysis to investigate deregulated biological processes and subsequently identify drug repurposing candidates. A proximity score describing the interaction between drugs and pathways is defined by combining topological and functional similarities. The results of this computational framework highlight a prominent role of the immune system in metabolic syndrome and suggest a potential use of the BTK inhibitor ibrutinib as a novel pharmacological treatment. An experimental validation using a high fat diet-induced obesity model in zebrafish larvae shows the effectiveness of ibrutinib in lowering the inflammatory load due to macrophage accumulation.

Decoding the similarities and specific differences between latent and active tuberculosis infections based on consistently differential expression networks

Although intensive efforts have been devoted to investigating latent tuberculosis (LTB) and active tuberculosis (PTB) infections, the similarities and differences in the host responses to these two closely associated stages remain elusive, probably due to the difficulty in identifying informative genes related to LTB using traditional methods. Herein, we developed a framework known as the consistently differential expression network to identify tuberculosis (TB)-related gene pairs by combining microarray profiles and protein-protein interactions. We thus obtained 774 and 693 pairs corresponding to the PTB and LTB stages, respectively. The PTB-specific genes showed higher expression values and fold-changes than the LTB-specific genes. Furthermore, the PTB-related pairs generally had higher expression correlations and would be more activated compared to their LTB-related counterparts. The module analysis implied that the detected gene pairs tended to cluster in the topological and functional modules. Functional analysis indicated that the LTB- and PTB-specific genes were enriched in different pathways and had remarkably different locations in the NF-κB signaling pathway. Finally, we showed that the identified genes and gene pairs had the potential to distinguish TB patients in different disease stages and could be considered as drug targets for the specific treatment of patients with LTB or PTB.

Mapping the perturbome network of cellular perturbations

Drug combinations provide effective treatments for diverse diseases, but also represent a major cause of adverse reactions. Currently there is no systematic understanding of how the complex cellular perturbations induced by different drugs influence each other. Here, we introduce a mathematical framework for classifying any interaction between perturbations with high-dimensional effects into 12 interaction types. We apply our framework to a large-scale imaging screen of cell morphology changes induced by diverse drugs and their combination, resulting in a perturbome network of 242 drugs and 1832 interactions. Our analysis of the chemical and biological features of the drugs reveals distinct molecular fingerprints for each interaction type. We find a direct link between drug similarities on the cell morphology level and the distance of their respective protein targets within the cellular interactome of molecular interactions. The interactome distance is also predictive for different types of drug interactions.

First Insights on the Presence of the Unfolded Protein Response in Human Spermatozoa

The unfolded protein response (UPR) is involved in protein quality control and is activated in response to several stressors. Although in testis the UPR mechanisms are well described, their presence in spermatozoa is contentious. We aimed to investigate the presence of UPR-related proteins in human sperm and the impact of oxidative stress induction in UPR activation. To identify UPR-related proteins in human sperm, a bioinformatic approach was adopted. To explore the activation of UPR, sperm were exposed to hydrogen peroxide (H₂O₂) and motility, vitality, and the levels of UPR-related proteins were assessed. We identified 97 UPR-related proteins in human sperm and showed, for the first time, the presence of HSF1, GADD34, and phosphorylated eIF2α. Additionally, the exposure of human sperm to H₂O₂ resulted in a significant decrease in sperm viability and motility and an increase in the levels of HSF1, HSP90, HSP60, HSP27, and eIF2α; all proteins involved in sensing and response to unfolded proteins. This study gave us a first insight into the presence of UPR mechanisms in the male gamete. However, the belief that sperm are devoid of transcription and translation highlight the need to clarify if these pathways are activated in sperm in the same way as in somatic cells.

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes

BACKGROUND

The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology.

OBJECTIVE

Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions.

METHODS

We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions.

RESULTS

We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs.

CONCLUSION

PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein-protein complexes for experimental studies.

7C: Computational Chromosome Conformation Capture by Correlation of ChIP-seq at CTCF motifs

BACKGROUND

Knowledge of the three-dimensional structure of the genome is necessary to understand how gene expression is regulated. Recent experimental techniques such as Hi-C or ChIA-PET measure long-range chromatin interactions genome-wide but are experimentally elaborate, have limited resolution and such data is only available for a limited number of cell types and tissues.

RESULTS

While ChIP-seq was not designed to detect chromatin interactions, the formaldehyde treatment in the ChIP-seq protocol cross-links proteins with each other and with DNA. Consequently, also regions that are not directly bound by the targeted TF but interact with the binding site via chromatin looping are co-immunoprecipitated and sequenced. This produces minor ChIP-seq signals at loop anchor regions close to the directly bound site. We use the position and shape of ChIP-seq signals around CTCF motif pairs to predict whether they interact or not. We implemented this approach in a prediction method, termed Computational Chromosome Conformation Capture by Correlation of ChIP-seq at CTCF motifs (7C). We applied 7C to all CTCF motif pairs within 1 Mb in the human genome and validated predicted interactions with high-resolution Hi-C and ChIA-PET. A single ChIP-seq experiment from known architectural proteins (CTCF, Rad21, Znf143) but also from other TFs (like TRIM22 or RUNX3) predicts loops accurately. Importantly, 7C predicts loops in cell types and for TF ChIP-seq datasets not used in training.

CONCLUSION

7C predicts chromatin loops which can help to associate TF binding sites to regulated genes. Furthermore, profiling of hundreds of ChIP-seq datasets results in novel candidate factors functionally involved in chromatin looping. Our method is available as an R/Bioconductor package: http://bioconductor.org/packages/sevenC .

Biological Network Approaches and Applications in Rare Disease Studies

Network biology has the capability to integrate, represent, interpret, and model complex biological systems by collectively accommodating biological omics data, biological interactions and associations, graph theory, statistical measures, and visualizations. Biological networks have recently been shown to be very useful for studies that decipher biological mechanisms and disease etiologies and for studies that predict therapeutic responses, at both the molecular and system levels. In this review, we briefly summarize the general framework of biological network studies, including data resources, network construction methods, statistical measures, network topological properties, and visualization tools. We also introduce several recent biological network applications and methods for the studies of rare diseases.

The pathobiology of perturbed mutant huntingtin protein-protein interactions in Huntington's disease

Mutations are at the root of many human diseases. Still, we largely do not exactly understand how they trigger pathogenesis. One, more recent, hypothesis has been that they comprehensively perturb protein-protein interaction (PPI) networks and significantly alter key biological processes. Under this premise, many rare genetic disorders with Mendelian inheritance, like Huntington's disease and several spinocerebellar ataxias, are likely to be caused by complex genotype-phenotype relationships involving abnormal PPIs. These altered PPI networks and their effects on cellular pathways are poorly understood at the molecular level. In this review, we focus on PPIs that are perturbed by the expanded pathogenic polyglutamine tract in huntingtin (HTT), the protein which, in its mutated form, leads to the autosomal dominant, neurodegenerative Huntington's disease. One aspect of perturbed mutant HTT interactions is the formation of abnormal protein species such as fibrils or large neuronal inclusions as a result of homotypic and heterotypic aberrant molecular interactions. This review focuses on abnormal PPIs that are associated with the assembly of mutant HTT aggregates in cells and their potential relevance in disease. Furthermore, the mechanisms and pathobiological processes that may contribute to phenotype development, neuronal dysfunction and toxicity in Huntington's disease brains are also discussed. This article is part of the Special Issue "Proteomics".

Identifying Pathophysiological Mechanisms in Heart Failure With Reduced Versus Preserved Ejection Fraction

BACKGROUND

Information on the pathophysiological differences between heart failure with reduced ejection fraction (HFrEF) versus heart failure with preserved ejection fraction (HFpEF) is needed OBJECTIVES: The purpose of this study was to establish biological pathways specifically related to HFrEF and HFpEF.

METHODS

The authors performed a network analysis to identify unique biomarker correlations in HFrEF and HFpEF using 92 biomarkers from different pathophysiological domains in a cohort of 1,544 heart failure (HF) patients. Data were independently validated in 804 patients with HF. Networks were enriched with existing knowledge on protein-protein interactions and translated into biological pathways uniquely related to HFrEF, HF with a midrange ejection fraction, and HFpEF.

RESULTS

In the index cohort (mean age 74 years; 34% female), 718 (47%) patients had HFrEF (left ventricular ejection fraction [LVEF] <40%) and 431 (27%) patients had HFpEF (LVEF ≥50%). A total of 8 (12%) correlations were unique for HFrEF and 6 (9%) were unique to HFpEF. Central proteins in HFrEF were N-terminal B-type natriuretic peptide, growth differentiation factor-15, interleukin-1 receptor type 1, and activating transcription factor 2, while central proteins in HFpEF were integrin subunit beta-2 and catenin beta-1. Biological pathways in HFrEF were related to DNA binding transcription factor activity, cellular protein metabolism, and regulation of nitric oxide biosynthesis. Unique pathways in patients with HFpEF were related to cytokine response, extracellular matrix organization, and inflammation. Biological pathways of patients with HF with a midrange ejection fraction were in between HFrEF and HFpEF.

CONCLUSIONS

Network analysis showed that biomarker profiles specific for HFrEF are related to cellular proliferation and metabolism, whereas biomarker profiles specific for HFpEF are related to inflammation and extracellular matrix reorganization. (The BIOlogy Study to TAilored Treatment in Chronic Heart Failure [BIOSTAT-CHF]; EudraCT 2010-020808-29).

Measuring rank robustness in scored protein interaction networks

BACKGROUND

Protein interaction databases often provide confidence scores for each recorded interaction based on the available experimental evidence. Protein interaction networks (PINs) are then built by thresholding on these scores, so that only interactions of sufficiently high quality are included. These networks are used to identify biologically relevant motifs or nodes using metrics such as degree or betweenness centrality. This type of analysis can be sensitive to the choice of threshold. If a node metric is to be useful for extracting biological signal, it should induce similar node rankings across PINs obtained at different reasonable confidence score thresholds.

RESULTS

We propose three measures-rank continuity, identifiability, and instability-to evaluate how robust a node metric is to changes in the score threshold. We apply our measures to twenty-five metrics and identify four as the most robust: the number of edges in the step-1 ego network, as well as the leave-one-out differences in average redundancy, average number of edges in the step-1 ego network, and natural connectivity. Our measures show good agreement across PINs from different species and data sources. Analysis of synthetically generated scored networks shows that robustness results are context-specific, and depend both on network topology and on how scores are placed across network edges.

CONCLUSION

Due to the uncertainty associated with protein interaction detection, and therefore network structure, for PIN analysis to be reproducible, it should yield similar results across different confidence score thresholds. We demonstrate that while certain node metrics are robust with respect to threshold choice, this is not always the case. Promisingly, our results suggest that there are some metrics that are robust across networks constructed from different databases, and different scoring procedures.

Efficient pathway enrichment and network analysis of GWAS summary data using GSA-SNP2

Pathway-based analysis in genome-wide association study (GWAS) is being widely used to uncover novel multi-genic functional associations. Many of these pathway-based methods have been used to test the enrichment of the associated genes in the pathways, but exhibited low powers and were highly affected by free parameters. We present the novel method and software GSA-SNP2 for pathway enrichment analysis of GWAS P-value data. GSA-SNP2 provides high power, decent type I error control and fast computation by incorporating the random set model and SNP-count adjusted gene score. In a comparative study using simulated and real GWAS data, GSA-SNP2 exhibited high power and best prioritized gold standard positive pathways compared with six existing enrichment-based methods and two self-contained methods (alternative pathway analysis approach). Based on these results, the difference between pathway analysis approaches was investigated and the effects of the gene correlation structures on the pathway enrichment analysis were also discussed. In addition, GSA-SNP2 is able to visualize protein interaction networks within and across the significant pathways so that the user can prioritize the core subnetworks for further studies. GSA-SNP2 is freely available at https://sourceforge.net/projects/gsasnp2.

A paradigm shift in medicine: A comprehensive review of network-based approaches

Network medicine is a rapidly evolving new field of medical research, which combines principles and approaches of systems biology and network science, holding the promise to uncovering the causes and to revolutionize the diagnosis and treatments of human diseases. This new paradigm reflects the fact that human diseases are not caused by single molecular defects, but driven by complex interactions among a variety of molecular mediators. The complexity of these interactions embraces different types of information: from the cellular-molecular level of protein-protein interactions to correlational studies of gene expression and regulation, to metabolic and disease pathways up to drug-disease relationships. The analysis of these complex networks can reveal new disease genes and/or disease pathways and identify possible targets for new drug development, as well as new uses for existing drugs. In this review, we offer a comprehensive overview of network types and algorithms used in the framework of network medicine. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Membrane protein-regulated networks across human cancers

Alterations in membrane proteins (MPs) and their regulated pathways have been established as cancer hallmarks and extensively targeted in clinical applications. However, the analysis of MP-interacting proteins and downstream pathways across human malignancies remains challenging. Here, we present a systematically integrated method to generate a resource of cancer membrane protein-regulated networks (CaMPNets), containing 63,746 high-confidence protein-protein interactions (PPIs) for 1962 MPs, using expression profiles from 5922 tumors with overall survival outcomes across 15 human cancers. Comprehensive analysis of CaMPNets links MP partner communities and regulated pathways to provide MP-based gene sets for identifying prognostic biomarkers and druggable targets. For example, we identify CHRNA9 with 12 PPIs (e.g., ERBB2) can be a therapeutic target and find its anti-metastasis agent, bupropion, for treatment in nicotine-induced breast cancer. This resource is a study to systematically integrate MP interactions, genomics, and clinical outcomes for helping illuminate cancer-wide atlas and prognostic landscapes in tumor homo/heterogeneity.

Exploring the Molecular Mechanism of the Drug-Treated Breast Cancer Based on Gene Expression Microarray

: Breast cancer (BRCA) remains the leading cause of cancer morbidity and mortality worldwide. In the present study, we identified novel biomarkers expressed during estradiol and tamoxifen treatment of BRCA. The microarray dataset of E-MTAB-4975 from Array Express database was downloaded, and the differential expressed genes (DEGs) between estradiol-treated BRCA sample and tamoxifen-treated BRCA sample were identified by limma package. The pathway and gene ontology (GO) enrichment analysis, construction of protein-protein interaction (PPI) network, module analysis, construction of target genes-miRNA interaction network and target genes-transcription factor (TF) interaction network were performed using bioinformatics tools. The expression, prognostic values, and mutation of hub genes were validated by SurvExpress database, cBioPortal, and human protein atlas (HPA) database. A total of 856 genes (421 up-regulated genes and 435 down-regulated genes) were identified in T47D (overexpressing Split Ends (SPEN) + estradiol) samples compared to T47D (overexpressing Split Ends (SPEN) + tamoxifen) samples. Pathway and GO enrichment analysis revealed that the DEGs were mainly enriched in response to lysine degradation II (pipecolate pathway), cholesterol biosynthesis pathway, cell cycle pathway, and response to cytokine pathway. DEGs (MCM2, TCF4, OLR1, HSPA5, MAP1LC3B, SQSTM1, NEU1, HIST1H1B, RAD51, RFC3, MCM10, ISG15, TNFRSF10B, GBP2, IGFBP5, SOD2, DHF and MT1H) , which were significantly up- and down-regulated in estradiol and tamoxifen-treated BRCA samples, were selected as hub genes according to the results of protein-protein interaction (PPI) network, module analysis, target genes-miRNA interaction network and target genes-TF interaction network analysis. The SurvExpress database, cBioPortal, and Human Protein Atlas (HPA) database further confirmed that patients with higher expression levels of these hub genes experienced a shorter overall survival. A comprehensive bioinformatics analysis was performed, and potential therapeutic applications of estradiol and tamoxifen were predicted in BRCA samples. The data may unravel the future molecular mechanisms of BRCA.

Disease gene prediction for molecularly uncharacterized diseases

Network medicine approaches have been largely successful at increasing our knowledge of molecularly characterized diseases. Given a set of disease genes associated with a disease, neighbourhood-based methods and random walkers exploit the interactome allowing the prediction of further genes for that disease. In general, however, diseases with no known molecular basis constitute a challenge. Here we present a novel network approach to prioritize gene-disease associations that is able to also predict genes for diseases with no known molecular basis. Our method, which we have called Cardigan (ChARting DIsease Gene AssociatioNs), uses semi-supervised learning and exploits a measure of similarity between disease phenotypes. We evaluated its performance at predicting genes for both molecularly characterized and uncharacterized diseases in OMIM, using both weighted and binary interactomes, and compared it with state-of-the-art methods. Our tests, which use datasets collected at different points in time to replicate the dynamics of the disease gene discovery process, prove that Cardigan is able to accurately predict disease genes for molecularly uncharacterized diseases. Additionally, standard leave-one-out cross validation tests show how our approach outperforms state-of-the-art methods at predicting genes for molecularly characterized diseases by 14%-65%. Cardigan can also be used for disease module prediction, where it outperforms state-of-the-art methods by 87%-299%.

Automated feature engineering improves prediction of protein-protein interactions

Over the last decade, various machine learning (ML) and statistical approaches for protein-protein interaction (PPI) predictions have been developed to help annotating functional interactions among proteins, essential for our system-level understanding of life. Efficient ML approaches require informative and non-redundant features. In this paper, we introduce novel types of expert-crafted sequence, evolutionary and graph features and apply automatic feature engineering to further expand feature space to improve predictive modeling. The two-step automatic feature-engineering process encompasses the hybrid method for feature generation and unsupervised feature selection, followed by supervised feature selection through a genetic algorithm (GA). The optimization of both steps allows the feature-engineering procedure to operate on a large transformed feature space with no considerable computational cost and to efficiently provide newly engineered features. Based on GA and correlation filtering, we developed a stacking algorithm GA-STACK for automatic ensembling of different ML algorithms to improve prediction performance. We introduced a unified method, HP-GAS, for the prediction of human PPIs, which incorporates GA-STACK and rests on both expert-crafted and 40% of newly engineered features. The extensive cross validation and comparison with the state-of-the-art methods showed that HP-GAS represents currently the most efficient method for proteome-wide forecasting of protein interactions, with prediction efficacy of 0.93 AUC and 0.85 accuracy. We implemented the HP-GAS method as a free standalone application which is a time-efficient and easy-to-use tool. HP-GAS software with supplementary data can be downloaded from: http://www.vinca.rs/180/tools/HP-GAS.php .

mTOR Signaling Pathway Regulates Sperm Quality in Older Men

Understanding how age affects fertility becomes increasingly relevant as couples delay childbearing toward later stages of their lives. While the influence of maternal age on fertility is well established, the impact of paternal age is poorly characterized. Thus, this study aimed to understand the molecular mechanisms responsible for age-dependent decline in spermatozoa quality. To attain it, we evaluated the impact of male age on the activity of signaling proteins in two distinct spermatozoa populations: total spermatozoa fraction and highly motile/viable fraction. In older men, we observed an inhibition of the mechanistic target of rapamycin complex 1 (mTORC1) in the highly viable spermatozoa population. On the contrary, when considering the entire spermatozoa population (including defective/immotile/apoptotic cells) our findings support an active mTORC1 signaling pathway in older men. Additionally, total spermatozoa fractions of older men presented increased levels of apoptotic/stress markers [e.g., cellular tumor antigen p53 (TP53)] and mitogen-activated protein kinases (MAPKs) activity. Moreover, we established that the levels of most signaling proteins analyzed were consistently and significantly altered in men older than 27 years of age. This study was the first to associate the mTOR signaling pathway with the age impact on spermatozoa quality. Additionally, we constructed a network of the sperm proteins associated with male aging, identifying TP53 as a central player in spermatozoa aging.

Protein-Protein Interaction Network Analysis Reveals Several Diseases Highly Associated with Polycystic Ovarian Syndrome

Based on clinical observations, women with polycystic ovarian syndrome (PCOS) are prone to developing several other diseases, such as metabolic and cardiovascular diseases. However, the molecular association between PCOS and these diseases remains poorly understood. Recent studies showed that the information from protein-protein interaction (PPI) network analysis are useful in understanding the disease association in detail. This study utilized this approach to deepen the knowledge on the association between PCOS and other diseases. A PPI network for PCOS was constructed using PCOS-related proteins (PCOSrp) obtained from PCOSBase. MCODE was used to identify highly connected regions in the PCOS network, known as subnetworks. These subnetworks represent protein families, where their molecular information is used to explain the association between PCOS and other diseases. Fisher's exact test and comorbidity data were used to identify PCOS-disease subnetworks. Pathway enrichment analysis was performed on the PCOS-disease subnetworks to identify significant pathways that are highly involved in the PCOS-disease associations. Migraine, schizophrenia, depressive disorder, obesity, and hypertension, along with twelve other diseases, were identified to be highly associated with PCOS. The identification of significant pathways, such as ribosome biogenesis, antigen processing and presentation, and mitophagy, suggest their involvement in the association between PCOS and migraine, schizophrenia, and hypertension.

GScluster: network-weighted gene-set clustering analysis

Background

Gene-set analysis (GSA) has been commonly used to identify significantly altered pathways or functions from omics data. However, GSA often yields a long list of gene-sets, necessitating efficient post-processing for improved interpretation. Existing methods cluster the gene-sets based on the extent of their overlap to summarize the GSA results without considering interactions between gene-sets.

Results

Here, we presented a novel network-weighted gene-set clustering that incorporates both the gene-set overlap and protein-protein interaction (PPI) networks. Three examples were demonstrated for microarray gene expression, GWAS summary, and RNA-sequencing data to which different GSA methods were applied. These examples as well as a global analysis show that the proposed method increases PPI densities and functional relevance of the resulting clusters. Additionally, distinct properties of gene-set distance measures were compared. The methods are implemented as an R/Shiny package GScluster that provides gene-set clustering and diverse functions for visualization of gene-sets and PPI networks.

Conclusions

Network-weighted gene-set clustering provides functionally more relevant gene-set clusters and related network analysis.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5738-6) contains supplementary material, which is available to authorized users.

HACE1 deficiency leads to structural and functional neurodevelopmental defects

Objective

We aim to characterize the causality and molecular and functional underpinnings of HACE1 deficiency in a mouse model of a recessive neurodevelopmental syndrome called spastic paraplegia and psychomotor retardation with or without seizures (SPPRS).

Methods

By exome sequencing, we identified 2 novel homozygous truncating mutations in HACE1 in 3 patients from 2 families, p.Q209* and p.R332*. Furthermore, we performed detailed molecular and phenotypic analyses of Hace1 knock-out (KO) mice and SPPRS patient fibroblasts.

Results

We show that Hace1 KO mice display many clinical features of SPPRS including enlarged ventricles, hypoplastic corpus callosum, as well as locomotion and learning deficiencies. Mechanistically, loss of HACE1 results in altered levels and activity of the small guanosine triphosphate (GTP)ase, RAC1. In addition, HACE1 deficiency results in reduction in synaptic puncta number and long-term potentiation in the hippocampus. Similarly, in SPPRS patient-derived fibroblasts, carrying a disruptive HACE1 mutation resembling loss of HACE1 in KO mice, we observed marked upregulation of the total and active, GTP-bound, form of RAC1, along with an induction of RAC1-regulated downstream pathways.

Conclusions

Our results provide a first animal model to dissect this complex human disease syndrome, establishing the first causal proof that a HACE1 deficiency results in decreased synapse number and structural and behavioral neuropathologic features that resemble SPPRS patients.

Pathway centrality in protein interaction networks identifies putative functional mediating pathways in pulmonary disease

Identification of functional pathways mediating molecular responses may lead to better understanding of disease processes and suggest new therapeutic approaches. We introduce a method to detect such mediating functions using topological properties of protein-protein interaction networks. We define the concept of pathway centrality, a measure of communication between disease genes and differentially expressed genes. Using pathway centrality, we identify mediating pathways in three pulmonary diseases (asthma; bronchopulmonary dysplasia (BPD); and chronic obstructive pulmonary disease (COPD)). We systematically evaluate the significance of all identified central pathways using genetic interactions. Mediating pathways shared by all three pulmonary disorders favor innate immune and inflammation-related processes, including toll-like receptor (TLR) signaling, PDGF- and angiotensin-regulated airway remodeling, the JAK-STAT signaling pathway, and interferon gamma. Disease-specific mediators, such as neurodevelopmental processes in BPD or adhesion molecules in COPD, are also highlighted. Some of our findings implicate pathways already in development as drug targets, while others may suggest new therapeutic approaches.