A directed protein interaction network for investigating intracellular signal transduction

Interactome Mapping Provides a Network of Neurodegenerative Disease Proteins and Uncovers Widespread Protein Aggregation in Affected Brains

Interactome maps are valuable resources to elucidate protein function and disease mechanisms. Here, we report on an interactome map that focuses on neurodegenerative disease (ND), connects ∼5,000 human proteins via ∼30,000 candidate interactions and is generated by systematic yeast two-hybrid interaction screening of ∼500 ND-related proteins and integration of literature interactions. This network reveals interconnectivity across diseases and links many known ND-causing proteins, such as α-synuclein, TDP-43, and ATXN1, to a host of proteins previously unrelated to NDs. It facilitates the identification of interacting proteins that significantly influence mutant TDP-43 and HTT toxicity in transgenic flies, as well as of ARF-GEP₁₀₀ that controls misfolding and aggregation of multiple ND-causing proteins in experimental model systems. Furthermore, it enables the prediction of ND-specific subnetworks and the identification of proteins, such as ATXN1 and MKL1, that are abnormally aggregated in postmortem brains of Alzheimer's disease patients, suggesting widespread protein aggregation in NDs.

Long non-coding RNAs and their targets as potential biomarkers in breast cancer

Breast cancer is among the lethal types of cancer with a high mortality rate, globally. Its high prevalence can be controlled through improved analysis and identification of disease-specific biomarkers. Recently, long non-coding RNAs (lncRNAs) have been reported as key contributors of carcinogenesis and regulate various cellular pathways through post-transcriptional regulatory mechanisms. The specific aim of this study was to identify the novel interactions of aberrantly expressed genetic components in breast cancer by applying integrative analysis of publicly available expression profiles of both lncRNAs and mRNAs. Differential expression patterns were identified by comparing the breast cancer expression profiles of samples with controls. Significant co-expression networks were identified through WGCNA analysis. WGCNA is a systems biology approach used to elucidate the pattern of correlation between genes across microarray samples. It is also used to identify the highly correlated modules. The results obtained from this study revealed significantly differentially expressed and co-expressed lncRNAs and their cis- and trans-regulating mRNA targets which include RP11-108F13.2 targeting TAF5L, RPL23AP2 targeting CYP4F3, CYP4F8 and AL022324.2 targeting LRP5L, AL022324.3, and Z99916.3, respectively. Moreover, pathway analysis revealed the involvement of identified mRNAs and lncRNAs in major cell signalling pathways, and target mRNAs expression is also validated through cohort data. Thus, the identified lncRNAs and their target mRNAs represent novel biomarkers that could serve as potential therapeutics for breast cancer and their roles could also be further validated through wet labs to employ them as potential therapeutic targets in future.

Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer's disease

BACKGROUND

Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective.

METHODS

In this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes.

RESULTS

We applied our computational framework to prioritize novel putative target genes for Alzheimer's disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib).

CONCLUSIONS

Our deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.

Performance assessment of sample-specific network control methods for bulk and single-cell biological data analysis

In the past few years, a wealth of sample-specific network construction methods and structural network control methods has been proposed to identify sample-specific driver nodes for supporting the Sample-Specific network Control (SSC) analysis of biological networked systems. However, there is no comprehensive evaluation for these state-of-the-art methods. Here, we conducted a performance assessment for 16 SSC analysis workflows by using the combination of 4 sample-specific network reconstruction methods and 4 representative structural control methods. This study includes simulation evaluation of representative biological networks, personalized driver genes prioritization on multiple cancer bulk expression datasets with matched patient samples from TCGA, and cell marker genes and key time point identification related to cell differentiation on single-cell RNA-seq datasets. By widely comparing analysis of existing SSC analysis workflows, we provided the following recommendations and banchmarking workflows. (i) The performance of a network control method is strongly dependent on the up-stream sample-specific network method, and Cell-Specific Network construction (CSN) method and Single-Sample Network (SSN) method are the preferred sample-specific network construction methods. (ii) After constructing the sample-specific networks, the undirected network-based control methods are more effective than the directed network-based control methods. In addition, these data and evaluation pipeline are freely available on https://github.com/WilfongGuo/Benchmark_control.

pDriver : A novel method for unravelling personalised coding and miRNA cancer drivers

MOTIVATION

Unravelling cancer driver genes is important in cancer research. Although computational methods have been developed to identify cancer drivers, most of them detect cancer drivers at population level. However, two patients who have the same cancer type and receive the same treatment may have different outcomes because each patient has a different genome and their disease might be driven by different driver genes. Therefore new methods are being developed for discovering cancer drivers at individual level, but existing personalised methods only focus on coding drivers while microRNAs (miRNAs) have been shown to drive cancer progression as well. Thus, novel methods are required to discover both coding and miRNA cancer drivers at individual level.

RESULTS

We propose the novel method, pDriver, to discover personalised cancer drivers. pDriver includes two stages: (1) Constructing gene networks for each cancer patient and (2) Discovering cancer drivers for each patient based on the constructed gene networks. To demonstrate the effectiveness of pDriver, we have applied it to five TCGA cancer datasets and compared it with the state-of-the-art methods. The result indicates that pDriver is more effective than other methods. Furthermore, pDriver can also detect miRNA cancer drivers and most of them have been confirmed to be associated with cancer by literature. We further analyse the predicted personalised drivers for breast cancer patients and the result shows that they are significantly enriched in many GO processes and KEGG pathways involved in breast cancer.

AVAILABILITY AND IMPLEMENTATION

pDriver is available at https://github.com/pvvhoang/pDriver.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Network controllability-based algorithm to target personalized driver genes for discovering combinatorial drugs of individual patients

Multiple driver genes in individual patient samples may cause resistance to individual drugs in precision medicine. However, current computational methods have not studied how to fill the gap between personalized driver gene identification and combinatorial drug discovery for individual patients. Here, we developed a novel structural network controllability-based personalized driver genes and combinatorial drug identification algorithm (CPGD), aiming to identify combinatorial drugs for an individual patient by targeting personalized driver genes from network controllability perspective. On two benchmark disease datasets (i.e. breast cancer and lung cancer datasets), performance of CPGD is superior to that of other state-of-the-art driver gene-focus methods in terms of discovery rate among prior-known clinical efficacious combinatorial drugs. Especially on breast cancer dataset, CPGD evaluated synergistic effect of pairwise drug combinations by measuring synergistic effect of their corresponding personalized driver gene modules, which are affected by a given targeting personalized driver gene set of drugs. The results showed that CPGD performs better than existing synergistic combinatorial strategies in identifying clinical efficacious paired combinatorial drugs. Furthermore, CPGD enhanced cancer subtyping by computationally providing personalized side effect signatures for individual patients. In addition, CPGD identified 90 drug combinations candidates from SARS-COV2 dataset as potential drug repurposing candidates for recently spreading COVID-19.

NIDM: network impulsive dynamics on multiplex biological network for disease-gene prediction

The prediction of genes related to diseases is important to the study of the diseases due to high cost and time consumption of biological experiments. Network propagation is a popular strategy for disease-gene prediction. However, existing methods focus on the stable solution of dynamics while ignoring the useful information hidden in the dynamical process, and it is still a challenge to make use of multiple types of physical/functional relationships between proteins/genes to effectively predict disease-related genes. Therefore, we proposed a framework of network impulsive dynamics on multiplex biological network (NIDM) to predict disease-related genes, along with four variants of NIDM models and four kinds of impulsive dynamical signatures (IDSs). NIDM is to identify disease-related genes by mining the dynamical responses of nodes to impulsive signals being exerted at specific nodes. By a series of experimental evaluations in various types of biological networks, we confirmed the advantage of multiplex network and the important roles of functional associations in disease-gene prediction, demonstrated superior performance of NIDM compared with four types of network-based algorithms and then gave the effective recommendations of NIDM models and IDS signatures. To facilitate the prioritization and analysis of (candidate) genes associated to specific diseases, we developed a user-friendly web server, which provides three kinds of filtering patterns for genes, network visualization, enrichment analysis and a wealth of external links (http://bioinformatics.csu.edu.cn/DGP/NID.jsp). NIDM is a protocol for disease-gene prediction integrating different types of biological networks, which may become a very useful computational tool for the study of disease-related genes.

Weighted minimum feedback vertex sets and implementation in human cancer genes detection

Background

Recently, many computational methods have been proposed to predict cancer genes. One typical kind of method is to find the differentially expressed genes between tumour and normal samples. However, there are also some genes, for example, ‘dark’ genes, that play important roles at the network level but are difficult to find by traditional differential gene expression analysis. In addition, network controllability methods, such as the minimum feedback vertex set (MFVS) method, have been used frequently in cancer gene prediction. However, the weights of vertices (or genes) are ignored in the traditional MFVS methods, leading to difficulty in finding the optimal solution because of the existence of many possible MFVSs.

Results

Here, we introduce a novel method, called weighted MFVS (WMFVS), which integrates the gene differential expression value with MFVS to select the maximum-weighted MFVS from all possible MFVSs in a protein interaction network. Our experimental results show that WMFVS achieves better performance than using traditional bio-data or network-data analyses alone.

Conclusion

This method balances the advantage of differential gene expression analyses and network analyses, improves the low accuracy of differential gene expression analyses and decreases the instability of pure network analyses. Furthermore, WMFVS can be easily applied to various kinds of networks, providing a useful framework for data analysis and prediction.

Supplementary Information

The online version supplementary material available at 10.1186/s12859-021-04062-2.

Computational methods for cancer driver discovery: A survey

Identifying the genes responsible for driving cancer is of critical importance for directing treatment. Accordingly, multiple computational tools have been developed to facilitate this task. Due to the different methods employed by these tools, different data considered by the tools, and the rapidly evolving nature of the field, the selection of an appropriate tool for cancer driver discovery is not straightforward. This survey seeks to provide a comprehensive review of the different computational methods for discovering cancer drivers. We categorise the methods into three groups; methods for single driver identification, methods for driver module identification, and methods for identifying personalised cancer drivers. In addition to providing a “one-stop” reference of these methods, by evaluating and comparing their performance, we also provide readers the information about the different capabilities of the methods in identifying biologically significant cancer drivers. The biologically relevant information identified by these tools can be seen through the enrichment of discovered cancer drivers in GO biological processes and KEGG pathways and through our identification of a small cancer-driver cohort that is capable of stratifying patient survival.

Identification of disease treatment mechanisms through the multiscale interactome

Most diseases disrupt multiple proteins, and drugs treat such diseases by restoring the functions of the disrupted proteins. How drugs restore these functions, however, is often unknown as a drug's therapeutic effects are not limited to the proteins that the drug directly targets. Here, we develop the multiscale interactome, a powerful approach to explain disease treatment. We integrate disease-perturbed proteins, drug targets, and biological functions into a multiscale interactome network. We then develop a random walk-based method that captures how drug effects propagate through a hierarchy of biological functions and physical protein-protein interactions. On three key pharmacological tasks, the multiscale interactome predicts drug-disease treatment, identifies proteins and biological functions related to treatment, and predicts genes that alter a treatment's efficacy and adverse reactions. Our results indicate that physical interactions between proteins alone cannot explain treatment since many drugs treat diseases by affecting the biological functions disrupted by the disease rather than directly targeting disease proteins or their regulators. We provide a general framework for explaining treatment, even when drugs seem unrelated to the diseases they are recommended for.

Neurodegenerative phosphoprotein signaling landscape in models of SCA3

Spinocerebellar ataxia type 3 (SCA3) is a rare neurodegenerative disorder resulting from an aberrant expansion of a polyglutamine stretch in the ataxin-3 protein and subsequent neuronal death. The underlying intracellular signaling pathways are currently unknown. We applied the Reverse-phase Protein MicroArray (RPMA) technology to assess the levels of 50 signaling proteins (in phosphorylated and total forms) using three in vitro and in vivo models expressing expanded ataxin-3: (i) human embryonic kidney (HEK293T) cells stably transfected with human ataxin-3 constructs, (ii) mouse embryonic fibroblasts (MEF) from SCA3 transgenic mice, and (iii) whole brains from SCA3 transgenic mice. All three models demonstrated a high degree of similarity sharing a subset of phosphorylated proteins involved in the PI3K/AKT/GSK3/mTOR pathway. Expanded ataxin-3 strongly interfered (by stimulation or suppression) with normal ataxin-3 signaling consistent with the pathogenic role of the polyglutamine expansion. In comparison with normal ataxin-3, expanded ataxin-3 caused a pro-survival stimulation of the ERK pathway along with reduced pro-apoptotic and transcriptional responses.

NOGEA: A Network-oriented Gene Entropy Approach for Dissecting Disease Comorbidity and Drug Repositioning

Rapid development of high-throughput technologies has permitted the identification of an increasing number of disease-associated genes (DAGs), which are important for understanding disease initiation and developing precision therapeutics. However, DAGs often contain large amounts of redundant or false positive information, leading to difficulties in quantifying and prioritizing potential relationships between these DAGs and human diseases. In this study, a network-oriented gene entropy approach (NOGEA) is proposed for accurately inferring master genes that contribute to specific diseases by quantitatively calculating their perturbation abilities on directed disease-specific gene networks. In addition, we confirmed that the master genes identified by NOGEA have a high reliability for predicting disease-specific initiation events and progression risk. Master genes may also be used to extract the underlying information of different diseases, thus revealing mechanisms of disease comorbidity. More importantly, approved therapeutic targets are topologically localized in a small neighborhood of master genes on the interactome network, which provides a new way for predicting drug-disease associations. Through this method, 11 old drugs were newly identified and predicted to be effective for treating pancreatic cancer and then validated by in vitro experiments. Collectively, the NOGEA was useful for identifying master genes that control disease initiation and co-occurrence, thus providing a valuable strategy for drug efficacy screening and repositioning. NOGEA codes are publicly available at https://github.com/guozihuaa/NOGEA.

DriverGroup: a novel method for identifying driver gene groups

MOTIVATION

Identifying cancer driver genes is a key task in cancer informatics. Most existing methods are focused on individual cancer drivers which regulate biological processes leading to cancer. However, the effect of a single gene may not be sufficient to drive cancer progression. Here, we hypothesize that there are driver gene groups that work in concert to regulate cancer, and we develop a novel computational method to detect those driver gene groups.

RESULTS

We develop a novel method named DriverGroup to detect driver gene groups by using gene expression and gene interaction data. The proposed method has three stages: (i) constructing the gene network, (ii) discovering critical nodes of the constructed network and (iii) identifying driver gene groups based on the discovered critical nodes. Before evaluating the performance of DriverGroup in detecting cancer driver groups, we firstly assess its performance in detecting the influence of gene groups, a key step of DriverGroup. The application of DriverGroup to DREAM4 data demonstrates that it is more effective than other methods in detecting the regulation of gene groups. We then apply DriverGroup to the BRCA dataset to identify driver groups for breast cancer. The identified driver groups are promising as several group members are confirmed to be related to cancer in literature. We further use the predicted driver groups in survival analysis and the results show that the survival curves of patient subpopulations classified using the predicted driver groups are significantly differentiated, indicating the usefulness of DriverGroup.

AVAILABILITY AND IMPLEMENTATION

DriverGroup is available at https://github.com/pvvhoang/DriverGroup.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

NIBNA: A network-based node importance approach for identifying breast cancer drivers

MOTIVATION

Identifying meaningful cancer driver genes in a cohort of tumors is a challenging task in cancer genomics. Although existing studies have identified known cancer drivers, most of them focus on detecting coding drivers with mutations. It is acknowledged that non-coding drivers can regulate driver mutations to promote cancer growth. In this work, we propose a novel node importance based network analysis (NIBNA) framework to detect coding and non-coding cancer drivers. We hypothesize that cancer drivers are crucial to the formation of community structures in cancer network, and removing them from the network greatly perturbs the network structure thereby critically affecting the functioning of the network. NIBNA detects cancer drivers using a three-step process; first, a condition-specific network is built by incorporating gene expression data and gene networks, second, the community structures in the network are estimated and third, a centrality-based metric is applied to compute node importance.

RESULTS

We apply NIBNA to the BRCA dataset and it outperforms existing state-of-art methods in detecting coding cancer drivers. NIBNA also predicts 265 miRNA drivers and majority of these drivers have been validated in literature. Further we apply NIBNA to detect cancer subtype-specific drivers and several predicted drivers have been validated to be associated with cancer subtypes. Lastly, we evaluate NIBNA's performance in detecting epithelial-mesenchymal transition (EMT) drivers, and we confirmed 8 coding and 13 miRNA drivers in the list of known genes.

AVAILABILITY

The source code can be accessed at: https://github.com/mandarsc/NIBNA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

TGF-β-induced DACT1 biomolecular condensates repress Wnt signalling to promote bone metastasis

The complexity of intracellular signalling requires both a diversity of molecular players and the sequestration of activity to unique compartments within the cell. Recent findings on the role of liquid-liquid phase separation provide a distinct mechanism for the spatial segregation of proteins to regulate signalling pathway crosstalk. Here, we discover that DACT1 is induced by TGFβ and forms protein condensates in the cytoplasm to repress Wnt signalling. These condensates do not localize to any known organelles but, rather, exist as phase-separated proteinaceous cytoplasmic bodies. The deletion of intrinsically disordered domains within the DACT1 protein eliminates its ability to both form protein condensates and suppress Wnt signalling. Isolation and mass spectrometry analysis of these particles revealed a complex of protein machinery that sequesters casein kinase 2-a Wnt pathway activator. We further demonstrate that DACT1 condensates are maintained in vivo and that DACT1 is critical to breast and prostate cancer bone metastasis.

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.

Long non-coding RNA (LncRNA) HOTAIR regulates BMP9-induced osteogenic differentiation by targeting the proliferation of mesenchymal stem cells (MSCs)

Long non-coding RNAs are important regulators of biological processes, but their roles in the osteogenic differentiation of mesenchymal stem cells (MSCs) remain unclear. Here we investigated the role of murine HOX transcript antisense RNA (mHotair) in BMP9-induced osteogenic differentiation of MSCs using immortalized mouse adipose-derived cells (iMADs). Touchdown quantitative polymerase chain reaction analysis found increased mHotair expression in bones in comparison with most other tissues. Moreover, the level of mHotair in femurs peaked at the age of week-4, a period of fast skeleton development. BMP9 could induce earlier peak expression of mHotair during in vitro iMAD osteogenesis. Silencing mHotair diminished BMP9-induced ALP activity, matrix mineralization, and expression of osteogenic, chondrogenic and adipogenic markers. Cell implantation experiments further confirmed that knockdown of mHotair attenuated BMP9-induced ectopic bone formation and mineralization of iMADs, leading to more undifferentiated cells. Crystal violet staining and cell cycle analysis revealed that silencing of mHotair promoted the proliferation of iMAD cells regardless of BMP9 induction. Moreover, ectopic bone masses developed from mHotair-knockdown iMAD cells exhibited higher expression of PCNA than the control group. Taken together, our results demonstrated that murine mHotair is an important regulator of BMP9-induced MSC osteogenesis by targeting cell cycle and proliferation.

NGPINT: a next-generation protein-protein interaction software

Mapping protein-protein interactions at a proteome scale is critical to understanding how cellular signaling networks respond to stimuli. Since eukaryotic genomes encode thousands of proteins, testing their interactions one-by-one is a challenging prospect. High-throughput yeast-two hybrid (Y2H) assays that employ next-generation sequencing to interrogate complementary DNA (cDNA) libraries represent an alternative approach that optimizes scale, cost and effort. We present NGPINT, a robust and scalable software to identify all putative interactors of a protein using Y2H in batch culture. NGPINT combines diverse tools to align sequence reads to target genomes, reconstruct prey fragments and compute gene enrichment under reporter selection. Central to this pipeline is the identification of fusion reads containing sequences derived from both the Y2H expression plasmid and the cDNA of interest. To reduce false positives, these fusion reads are evaluated as to whether the cDNA fragment forms an in-frame translational fusion with the Y2H transcription factor. NGPINT successfully recognized 95% of interactions in simulated test runs. As proof of concept, NGPINT was tested using published data sets and it recognized all validated interactions. NGPINT can process interaction data from any biosystem with an available genome or transcriptome reference, thus facilitating the discovery of protein-protein interactions in model and non-model organisms.

PWD/Ph-Encoded Genetic Variants Modulate the Cellular Wnt/β-Catenin Response to Suppress Apc Min-Triggered Intestinal Tumor Formation

Genetic predisposition affects the penetrance of tumor-initiating mutations, such as APC mutations that stabilize β-catenin and cause intestinal tumors in mice and humans. However, the mechanisms involved in genetically predisposed penetrance are not well understood. Here, we analyzed tumor multiplicity and gene expression in tumor-prone Apc ^Min/+ mice on highly variant C57BL/6J (B6) and PWD/Ph (PWD) genetic backgrounds. (B6 × PWD) F1 APC ^Min offspring mice were largely free of intestinal adenoma, and several chromosome substitution (consomic) strains carrying single PWD chromosomes on the B6 genetic background displayed reduced adenoma numbers. Multiple dosage-dependent modifier loci on PWD chromosome 5 each contributed to tumor suppression. Activation of β-catenin-driven and stem cell-specific gene expression in the presence of Apc ^Min or following APC loss remained moderate in intestines carrying PWD chromosome 5, suggesting that PWD variants restrict adenoma initiation by controlling stem cell homeostasis. Gene expression of modifier candidates and DNA methylation on chromosome 5 were predominantly cis controlled and largely reflected parental patterns, providing a genetic basis for inheritance of tumor susceptibility. Human SNP variants of several modifier candidates were depleted in colorectal cancer genomes, suggesting that similar mechanisms may also affect the penetrance of cancer driver mutations in humans. Overall, our analysis highlights the strong impact that multiple genetic variants acting in networks can exert on tumor development. SIGNIFICANCE: These findings in mice show that, in addition to accidental mutations, cancer risk is determined by networks of individual gene variants.

Involvement of NEK2 and its interaction with NDC80 and CEP250 in hepatocellular carcinoma

BACKGROUND

NEK2 has an established involvement in hepatocellular carcinoma (HCC) but the roles of NEK2 and its interacting proteins in HCC have not been systematically explored.

METHODS

This study examined NEK2 and its interacting proteins in HCC based on multiple databases.

RESULTS

NEK2 mRNA was highly expressed in HCC tissues compared with normal liver tissues. The survival of HCC patients with high NEK2 mRNA expression was shorter than those with low expression. MAD1L1, CEP250, MAPK1, NDC80, PPP1CA, PPP1R2 and NEK11 were the interacting proteins of NEK2. Among them, NDC80 and CEP250 were the key interacting proteins of NEK2. Mitotic prometaphase may be the key pathway that NEK2 and its interacting proteins contributed to HCC pathogenesis. NEK2, NDC80 and CEP250 mRNAs were highly expressed in HCC tissues compared with normal liver tissues. The mRNA levels of NEK2 were positively correlated with those of NDC80 or CEP250. Univariate regression showed that NEK2, NDC80 and CEP250 mRNA expressions were significantly associated with HCC patients' survival. Multivariate regression showed that NDC80 mRNA expression was an independent predictor for HCC patients' survival. Methylations and genetic alterations of NEK2, NDC80 and CEP250 were observed in HCC samples. The alterations of NEK2, NDC80 and CEP250 genes were co-occurrence. Patients with high mRNA expression and genetic alterations of NEK2, NDC80 and CEP250 had poor prognosis.

CONCLUSIONS

NEK2 and its interacting proteins NDC80 and CEP250 play important roles in HCC development and progression and thus may be potentially used as biomarkers and therapeutic targets of HCC.

TBC1D1 interacting proteins, VPS13A and VPS13C, regulate GLUT4 homeostasis in C2C12 myotubes

Proteins involved in the spaciotemporal regulation of GLUT4 trafficking represent potential therapeutic targets for the treatment of insulin resistance and type 2 diabetes. A key regulator of insulin- and exercise-stimulated glucose uptake and GLUT4 trafficking is TBC1D1. This study aimed to identify proteins that regulate GLUT4 trafficking and homeostasis via TBC1D1. Using an unbiased quantitative proteomics approach, we identified proteins that interact with TBC1D1 in C2C12 myotubes including VPS13A and VPS13C, the Rab binding proteins EHBP1L1 and MICAL1, and the calcium pump SERCA1. These proteins associate with TBC1D1 via its phosphotyrosine binding (PTB) domains and their interactions with TBC1D1 were unaffected by AMPK activation, distinguishing them from the AMPK regulated interaction between TBC1D1 and AMPKα1 complexes. Depletion of VPS13A or VPS13C caused a post-transcriptional increase in cellular GLUT4 protein and enhanced cell surface GLUT4 levels in response to AMPK activation. The phenomenon was specific to GLUT4 because other recycling proteins were unaffected. Our results provide further support for a role of the TBC1D1 PTB domains as a scaffold for a range of Rab regulators, and also the VPS13 family of proteins which have been previously linked to fasting glycaemic traits and insulin resistance in genome wide association studies.

Network Controllability-Based Prioritization of Candidates for SARS-CoV-2 Drug Repositioning

In a short time, the COVID-19 pandemic has left the world with over 25 million cases and staggering death tolls that are still rising. Treatments for SARS-CoV-2 infection are desperately needed as there are currently no approved drug therapies. With limited knowledge of viral mechanisms, a network controllability method of prioritizing existing drugs for repurposing efforts is optimal for quickly moving through the drug approval pipeline using limited, available, virus-specific data. Based on network topology and controllability, 16 proteins involved in translation, cellular transport, cellular stress, and host immune response are predicted as regulators of the SARS-CoV-2 infected cell. Of the 16, eight are prioritized as possible drug targets where two, PVR and SCARB1, are previously unexplored. Known compounds targeting these genes are suggested for viral inhibition study. Prioritized proteins in agreement with previous analysis and viral inhibition studies verify the ability of network controllability to predict biologically relevant candidates.

A novel subnetwork representation learning method for uncovering disease-disease relationships

Analyzing disease-disease relationships plays an important role for understanding disease mechanisms and finding alternative uses for a drug. A disease is usually the result of abnormal state of multiple molecular process. Since biological networks can model the interplay of multiple molecular processes, network-based methods have been proposed to uncover the disease-disease relationships recently. Given a disease and a network, the disease could be represented as a subnetwork constructed by the disease genes involved in the given network, named disease subnetwork. Because it is difficult to learn the feature representation of disease subnetworks, most existing methods are unsupervised ones without using labeled information. To fill this gap, we propose a novel method named SubNet2vec to learn the feature vectors of diseases from their corresponding subnetwork in the biological network. By utilizing the feature representation of disease subnetwork, we can analyze disease-disease relationships in a supervised fashion. The evaluation results show that the proposed framework outperforms some state-of-the-art approaches in a large margin on disease-disease/disease-drug association prediction. The source code and data are available athttps://github.com/MedicineBiology-AI/SubNet2vec.git.

Network Approaches for Dissecting the Immune System

The immune system is a complex biological network composed of hierarchically organized genes, proteins, and cellular components that combat external pathogens and monitor the onset of internal disease. To meet and ultimately defeat these challenges, the immune system orchestrates an exquisitely complex interplay of numerous cells, often with highly specialized functions, in a tissue-specific manner. One of the major methodologies of systems immunology is to measure quantitatively the components and interaction levels in the immunologic networks to construct a computational network and predict the response of the components to perturbations. The recent advances in high-throughput sequencing techniques have provided us with a powerful approach to dissecting the complexity of the immune system. Here we summarize the latest progress in integrating omics data and network approaches to construct networks and to infer the underlying signaling and transcriptional landscape, as well as cell-cell communication, in the immune system, with a focus on hematopoiesis, adaptive immunity, and tumor immunology. Understanding the network regulation of immune cells has provided new insights into immune homeostasis and disease, with important therapeutic implications for inflammation, cancer, and other immune-mediated disorders.

Orchestration of Intracellular Circuits by G Protein-Coupled Receptor 39 for Hepatitis B Virus Proliferation

Hepatitis B virus (HBV), a highly persistent pathogen causing hepatocellular carcinoma (HCC), takes full advantage of host machinery, presenting therapeutic targets. Here we aimed to identify novel druggable host cellular factors using the reporter HBV we have recently generated. In an RNAi screen of G protein-coupled receptors (GPCRs), GPCR39 (GPR39) appeared as the top hit to facilitate HBV proliferation. Lentiviral overexpression of active GPR39 proteins and an agonist enhanced HBV replication and transcriptional activities of viral promoters, inducing the expression of CCAAT/enhancer binding protein (CEBP)-β (CEBPB). Meanwhile, GPR39 was uncovered to activate the heat shock response, upregulating the expression of proviral heat shock proteins (HSPs). In addition, glioma-associated oncogene homologue signaling, a recently reported target of GPR39, was suggested to inhibit HBV replication and eventually suppress expression of CEBPB and HSPs. Thus, GPR39 provirally governed intracellular circuits simultaneously affecting the carcinopathogenetic gene functions. GPR39 and the regulated signaling networks would serve as antiviral targets, and strategies with selective inhibitors of GPR39 functions can develop host-targeted antiviral therapies preventing HCC.

Network-based modeling of drug effects on disease module in systemic sclerosis

The network-based proximity between drug targets and disease genes can provide novel insights regarding the repercussions, interplay, and repositioning of drugs in the context of disease. Current understanding and treatment for reversing of the fibrotic process is limited in systemic sclerosis (SSc). We have developed a network-based analysis for drug effects that takes into account the human interactome network, proximity measures between drug targets and disease-associated genes, genome-wide gene expression and disease modules that emerge through pertinent analysis. Currently used and potential drugs showed a wide variation in proximity to SSc-associated genes and distinctive proximity to the SSc-relevant pathways, depending on their class and targets. Tyrosine kinase inhibitors (TyKIs) approach disease gene through multiple pathways, including both inflammatory and fibrosing processes. The SSc disease module includes the emerging molecular targets and is in better accord with the current knowledge of the pathophysiology of the disease. In the disease-module network, the greatest perturbing activity was shown by nintedanib, followed by imatinib, dasatinib, and acetylcysteine. Suppression of the SSc-relevant pathways and alleviation of the skin fibrosis was remarkable in the inflammatory subsets of the SSc patients receiving TyKI therapy. Our results show that network-based drug-disease proximity offers a novel perspective into a drug’s therapeutic effect in the SSc disease module. This could be applied to drug combinations or drug repositioning, and be helpful guiding clinical trial design and subgroup analysis.

Network determinants of cardiovascular calcification and repositioned drug treatments

Ectopic cardiovascular calcification is a highly prevalent pathology for which there are no effective novel or repurposed pharmacotherapeutics to prevent disease progression. We created a human calcification endophenotype module (ie, the "calcificasome") by mapping vascular calcification genes (proteins) to the human vascular smooth muscle-specific protein-protein interactome (218 nodes and 632 edges, P < 10^-5 ). Network proximity analysis was used to demonstrate that the calcificasome overlapped significantly with endophenotype modules governing inflammation, thrombosis, and fibrosis in the human interactome (P < 0.001). A network-based drug repurposing analysis further revealed that everolimus, temsirolimus, and pomalidomide are predicted to target the calcificasome. The efficacy of these agents in limiting calcification was confirmed experimentally by treating human coronary artery smooth muscle cells in an in vitro calcification assay. Each of the drugs affected expression or activity of their predicted target in the network, and decreased calcification significantly (P < 0.009). An integrated network analytical approach identified novel mediators of ectopic cardiovascular calcification and biologically plausible candidate drugs that could be repurposed to target calcification. This methodological framework for drug repurposing has broad applicability to other diseases.

Investigation and Prediction of Human Interactome Based on Quantitative Features

Protein is one of the most significant components of all living creatures. All significant and essential biological structures and functions relies on proteins and their respective biological functions. However, proteins cannot perform their unique biological significance independently. They have to interact with each other to realize the complicated biological processes in all living creatures including human beings. In other words, proteins depend on interactions (protein-protein interactions) to realize their significant effects. Thus, the significance comparison and quantitative contribution of candidate PPI features must be determined urgently. According to previous studies, 258 physical and chemical characteristics of proteins have been reported and confirmed to definitively affect the interaction efficiency of the related proteins. Among such features, essential physiochemical features of proteins like stoichiometric balance, protein abundance, molecular weight and charge distribution have been validated to be quite significant and irreplaceable for protein-protein interactions (PPIs). Therefore, in this study, we, on one hand, presented a novel computational framework to identify the key factors affecting PPIs with Boruta feature selection (BFS), Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and on the other hand, built a quantitative decision-rule system to evaluate the potential PPIs under real conditions with random forest (RF) and RIPPER algorithms, thereby supplying several new insights into the detailed biological mechanisms of complicated PPIs. The main datasets and codes can be downloaded at https://github.com/xypan1232/Mass-PPI.

Spectrum of gene mutations identified by targeted next-generation sequencing in Chinese leukemia patients

BACKGROUND

Despite targeted sequencing have identified several mutations for leukemia, there is still a limit of mutation screening for Chinese leukemia. Here, we used targeted next-generation sequencing for testing the mutation patterns of Chinese leukemia patients.

METHODS

We performed targeted sequencing of 504 tumor-related genes in 109 leukemia samples to identify single-nucleotide variants (SNVs) and insertions and deletions (INDELs). Pathogenic variants were assessed based on the American College of Medical Genetics and Genomics (ACMG) guidelines. The functional impact of pathogenic genes was explored through gene ontology (GO), pathway analysis, and protein-protein interaction network in silico.

RESULTS

We identified a total of 4,655 SNVs and 614 INDELs in 419 genes, in which PDE4DIP, NOTCH2, FANCA, BCR, and ROS1 emerged as the highly mutated genes. Of note, we were the first to demonstrate an association of PDE4DIP mutation and leukemia. Based on ACMG guidelines, 39 pathogenic and likely pathogenic mutations in 27 genes were found. GO annotation showed that the biological process including gland development, leukocyte differentiation, respiratory system development, myeloid leukocyte differentiation, mesenchymal to epithelial transition, and so on were involved.

CONCLUSION

Our study provided a map of gene mutations in Chinese patients with leukemia and gave insights into the molecular pathogenesis of leukemia.

PrGeFNE: Predicting disease-related genes by fast network embedding

Identifying disease-related genes is of importance for understanding of molecule mechanisms of diseases, as well as diagnosis and treatment of diseases. Many computational methods have been proposed to predict disease-related genes, but how to make full use of multi-source biological data to enhance the ability of disease-gene prediction is still challenging. In this paper, we proposed a novel method for predicting disease-related genes by using fast network embedding (PrGeFNE), which can integrate multiple types of associations related to diseases and genes. Specifically, we first constructed a heterogeneous network by using phenotype-disease, disease-gene, protein-protein and gene-GO associations; and low-dimensional representation of nodes is extracted from the network by using a fast network embedding algorithm. Then, a dual-layer heterogeneous network was reconstructed by using the low-dimensional representation, and a network propagation was applied to the dual-layer heterogeneous network to predict disease-related genes. Through cross-validation and newly added-association validation, we displayed the important roles of different types of association data in enhancing the ability of disease-gene prediction, and confirmed the excellent performance of PrGeFNE by comparing to state-of-the-art algorithms. Furthermore, we developed a web tool that can facilitate researchers to search for candidate genes of different diseases predicted by PrGeFNE, along with the enrichment analysis of GO and pathway on candidate gene set. This may be useful for investigation of diseases' molecular mechanisms as well as their experimental validations. The web tool is available at http://bioinformatics.csu.edu.cn/prgefne/.

MELK mediates the stability of EZH2 through site-specific phosphorylation in extranodal natural killer/T-cell lymphoma

Oncogenic EZH2 is overexpressed and extensively involved in the pathophysiology of different cancers including extranodal natural killer/T-cell lymphoma (NKTL). However, the mechanisms regarding EZH2 upregulation is poorly understood, and it still remains untargetable in NKTL. In this study, we examine EZH2 protein turnover in NKTL and identify MELK kinase as a regulator of EZH2 ubiquitination and turnover. Using quantitative mass spectrometry analysis, we observed a MELK-mediated increase of EZH2 S220 phosphorylation along with a concomitant loss of EZH2 K222 ubiquitination, suggesting a phosphorylation-dependent regulation of EZH2 ubiquitination. MELK inhibition through both chemical and genetic means led to ubiquitination and destabilization of EZH2 protein. Importantly, we determine that MELK is upregulated in NKTL, and its expression correlates with EZH2 protein expression as determined by tissue microarray derived from NKTL patients. FOXM1, which connected MELK to EZH2 signaling in glioma, was not involved in mediating EZH2 ubiquitination. Furthermore, we identify USP36 as the deubiquitinating enzyme that deubiquitinates EZH2 at K222. These findings uncover an important role of MELK and USP36 in mediating EZH2 stability in NKTL. Moreover, MELK overexpression led to decreased sensitivity to bortezomib treatment in NKTL based on deprivation of EZH2 ubiquitination. Therefore, modulation of EZH2 ubiquitination status by targeting MELK may be a new therapeutic strategy for NKTL patients with poor bortezomib response.

TMEM98 is a negative regulator of FRAT mediated Wnt/ß-catenin signalling

Wnt/ß-catenin signalling is crucial for maintaining the balance between cell proliferation and differentiation, both during tissue morphogenesis and in tissue maintenance throughout postnatal life. Whereas the signalling activities of the core Wnt/ß-catenin pathway components are understood in great detail, far less is known about the precise role and regulation of the many different modulators of Wnt/ß-catenin signalling that have been identified to date. Here we describe TMEM98, a putative transmembrane protein of unknown function, as an interaction partner and regulator of the GSK3-binding protein FRAT2. We show that TMEM98 reduces FRAT2 protein levels and, accordingly, inhibits the FRAT2-mediated induction of ß-catenin/TCF signalling. We also characterize the intracellular trafficking of TMEM98 in more detail and show that it is recycled between the plasma membrane and the Golgi. Together, our findings not only reveal a new layer of regulation for Wnt/ß-catenin signalling, but also a new biological activity for TMEM98.

CBNA: A control theory based method for identifying coding and non-coding cancer drivers

A key task in cancer genomics research is to identify cancer driver genes. As these genes initialise and progress cancer, understanding them is critical in designing effective cancer interventions. Although there are several methods developed to discover cancer drivers, most of them only identify coding drivers. However, non-coding RNAs can regulate driver mutations to develop cancer. Hence, novel methods are required to reveal both coding and non-coding cancer drivers. In this paper, we develop a novel framework named Controllability based Biological Network Analysis (CBNA) to uncover coding and non-coding cancer drivers (i.e. miRNA cancer drivers). CBNA integrates different genomic data types, including gene expression, gene network, mutation data, and contains a two-stage process: (1) Building a network for a condition (e.g. cancer condition) and (2) Identifying drivers. The application of CBNA to the BRCA dataset demonstrates that it is more effective than the existing methods in detecting coding cancer drivers. In addition, CBNA also predicts 17 miRNA drivers for breast cancer. Some of these predicted miRNA drivers have been validated by literature and the rest can be good candidates for wet-lab validation. We further use CBNA to detect subtype-specific cancer drivers and several predicted drivers have been confirmed to be related to breast cancer subtypes. Another application of CBNA is to discover epithelial-mesenchymal transition (EMT) drivers. Of the predicted EMT drivers, 7 coding and 6 miRNA drivers are in the known EMT gene lists.

Cancer is a disease of cells in human body and it causes a high rate of deaths worldwide. There has been evidence that coding and non-coding RNAs are key players in the initialisation and progression of cancer. These coding and non-coding RNAs are considered as cancer drivers. To design better diagnostic and therapeutic plans for cancer patients, we need to know the roles of cancer drivers in cancer development as well as their regulatory mechanisms in the human body. In this study, we propose a novel framework to identify coding and non-coding cancer drivers (i.e. miRNA cancer drivers). The proposed framework is applied to the breast cancer dataset for identifying drivers of breast cancer. Comparing our method with existing methods in predicting coding cancer drivers, our method shows a better performance. Several miRNA cancer drivers predicted by our method have already been validated by literature. The predicted cancer drivers by our method could be a potential source for further wet-lab experiments to discover the causes of cancer. In addition, the proposed method can be used to detect drivers of cancer subtypes and drivers of the epithelial-mesenchymal transition in cancer.

Leveraging User-Friendly Network Approaches to Extract Knowledge From High-Throughput Omics Datasets

Recent technological advances for the acquisition of multi-omics data have allowed an unprecedented understanding of the complex intricacies of biological systems. In parallel, a myriad of computational analysis techniques and bioinformatics tools have been developed, with many efforts directed towards the creation and interpretation of networks from this data. In this review, we begin by examining key network concepts and terminology. Then, computational tools that allow for their construction and analysis from high-throughput omics datasets are presented. We focus on the study of functional relationships such as co-expression, protein-protein interactions, and regulatory interactions that are particularly amenable to modeling using the framework of networks. We envisage that many potential users of these analytical strategies may not be completely literate in programming languages and code adaptation, and for this reason, emphasis is given to tools' user-friendliness, including plugins for the widely adopted Cytoscape software, an open-source, cross-platform tool for network analysis, visualization, and data integration.

Genetic mapping of distal femoral, stifle, and tibial radiographic morphology in dogs with cranial cruciate ligament disease

Cranial cruciate ligament disease (CCLD) is a complex trait. Ten measurements were made on orthogonal distal pelvic limb radiographs of 161 pure and mixed breed dogs with, and 55 without, cranial cruciate partial or complete ligament rupture. Dogs with CCLD had significantly smaller infrapatellar fat pad width, higher average tibial plateau angle, and were heavier than control dogs. The first PC weightings captured the overall size of the dog’s stifle and PC2 weightings reflected an increasing tibial plateau angle coupled with a smaller fat pad width. Of these dogs, 175 were genotyped, and 144,509 polymorphisms were used in a genome-wide association study with both a mixed linear and a multi-locus model. For both models, significant (p_genome <3.46×10⁻⁷ for the mixed and< 6.9x10^-8 for the multilocus model) associations were found for PC1, tibial diaphyseal length and width, fat pad base length, and femoral and tibial condyle width at LCORL, a known body size-regulating locus. Other body size loci with significant associations were growth hormone 1 (GH1), which was associated with the length of the fat pad base and the width of the tibial diaphysis, and a region on CFAX near IRS4 and ACSL4 in the multilocus model. The tibial plateau angle was associated significantly with a locus on CFA10 in the linear mixed model with nearest candidate genes BET1 and MYH9 and on CFA08 near candidate genes WDHD1 and GCH1. MYH9 has a major role in osteoclastogenesis. Our study indicated that tibial plateau slope is associated with CCLD and a compressed infrapatellar fat pad, a surrogate for stifle osteoarthritis. Because of the association between tibial plateau slope and CCLD, and pending independent validation, these candidate genes for tibial plateau slope may be tested in breeds susceptible to CCLD before they develop disease or are bred.

Effect of LCORL gene polymorphism on body size traits in horse populations

The aim of this study was to determine polymorphism of LCORL gene in horse breeds and its association with body size. PCR-RFLP technique was performed using AluI for genotyping of 306 horses. Results showed that C is the rare allele in Iranian Breeds, because these horses have been used since ancient times as a courier and for war and archery, hence selection has done to benefit of spiky horses with medium body that need less food and are tireless. While, for foreign breeds; frequency of C allele was high that can be concluded these breeds used in fields, forests, and mines. A UPGMA dendrogram based on the Nei's standard genetic distance among studied breeds showed separate clusters for Iranian native and exotic breeds. Statistical association analysis of three observed genotypes with body size showed that there is an association between this polymorphism and body size criteria (p < 0.01). Overall, it can be concluded that studied mutation in LCORL gene can be used as candidate marker for improving body weight in horse.

Connecting signaling and metabolic pathways in EGF receptor-mediated oncogenesis of glioblastoma

As malignant transformation requires synchronization of growth-driving signaling (S) and metabolic (M) pathways, defining cancer-specific S-M interconnected networks (SMINs) could lead to better understanding of oncogenic processes. In a systems-biology approach, we developed a mathematical model for SMINs in mutated EGF receptor (EGFRvIII) compared to wild-type EGF receptor (EGFRwt) expressing glioblastoma multiforme (GBM). Starting with experimentally validated human protein-protein interactome data for S-M pathways, and incorporating proteomic data for EGFRvIII and EGFRwt GBM cells and patient transcriptomic data, we designed a dynamic model for EGFR-driven GBM-specific information flow. Key nodes and paths identified by in silico perturbation were validated experimentally when inhibition of signaling pathway proteins altered expression of metabolic proteins as predicted by the model. This demonstrated capacity of the model to identify unknown connections between signaling and metabolic pathways, explain the robustness of oncogenic SMINs, predict drug escape, and assist identification of drug targets and the development of combination therapies.

Complex and highly dynamic interconnected networks allow cancer to take different routes and circumvent chemotherapy. Therefore, understanding these context-specific networks and their dynamics of molecular interactions driven by different oncogenic signaling and metabolic pathways is very much needed to predict drug targets and the effect of therapeutics. We incorporated high-throughput transcriptome and proteome data into mathematical models to deduce properties of cancer cells through systems biology approach. Here we report the development, testing and validation of an integrated systems biology model of information flow between signaling and metabolic pathways to understand the regulation of the interconnection between them in cancer. Our model efficiently identified unique connections and key nodes important in signaling-metabolic information flow. We predicted some potential novel targets before performing actual drug tests. We have successfully applied this model to identify the interconnections altered in the constitutive signaling of the mutated EGFR by comparing EGF-dependent and wild-type EGFR signaling in glioblastoma multiforme.

Molecular Interaction Search Tool (MIST): an integrated resource for mining gene and protein interaction data

Model organism and human databases are rich with information about genetic and physical interactions. These data can be used to interpret and guide the analysis of results from new studies and develop new hypotheses. Here, we report the development of the Molecular Interaction Search Tool (MIST; http://fgrtools.hms.harvard.edu/MIST/). The MIST database integrates biological interaction data from yeast, nematode, fly, zebrafish, frog, rat and mouse model systems, as well as human. For individual or short gene lists, the MIST user interface can be used to identify interacting partners based on protein–protein and genetic interaction (GI) data from the species of interest as well as inferred interactions, known as interologs, and to view a corresponding network. The data, interologs and search tools at MIST are also useful for analyzing ‘omics datasets. In addition to describing the integrated database, we also demonstrate how MIST can be used to identify an appropriate cut-off value that balances false positive and negative discovery, and present use-cases for additional types of analysis. Altogether, the MIST database and search tools support visualization and navigation of existing protein and GI data, as well as comparison of new and existing data.

Protein interactions study through proximity-labeling

Introduction: The proteome is a dynamic system in which protein-protein interactions play a crucial part in shaping the cell phenotype. However, given the current limitations of available technologies to describe the dynamic nature of these interactions, the identification of protein-protein interactions has long been a major challenge in proteomics. In recent years, the development of BioID and APEX, two proximity-tagging technologies, have opened-up new perspectives and have already started to change our conception of protein-protein interactions, and more generally, of the proteome. With a broad range of application encompassing health, these new technologies are currently setting milestones crucial to understand fine cellular mechanisms. Area covered: In this article, we describe both the recent and the more conventional available tools to study protein-protein interactions, compare the advantages and the limitations of these techniques, and discuss the recent advancements led by the proximity tagging techniques to refine our conception of the proteome. Expert opinion: The recent development of proximity labeling techniques emphasizes the growing importance of such technologies to decipher cellular mechanism. Although several challenges still need to be addressed, many fields can benefit from these tools and notably the detection of new therapeutic targets for patient care.

A systematic approach to orient the human protein-protein interaction network

The protein-protein interaction (PPI) network of an organism serves as a skeleton for its signaling circuitry, which mediates cellular response to environmental and genetic cues. Understanding this circuitry could improve the prediction of gene function and cellular behavior in response to diverse signals. To realize this potential, one has to comprehensively map PPIs and their directions of signal flow. While the quality and the volume of identified human PPIs improved dramatically over the last decade, the directions of these interactions are still mostly unknown, thus precluding subsequent prediction and modeling efforts. Here we present a systematic approach to orient the human PPI network using drug response and cancer genomic data. We provide a diffusion-based method for the orientation task that significantly outperforms existing methods. The oriented network leads to improved prioritization of cancer driver genes and drug targets compared to the state-of-the-art unoriented network.

Discovering personalized driver mutation profiles of single samples in cancer by network control strategy

Motivation

It is a challenging task to discover personalized driver genes that provide crucial information on disease risk and drug sensitivity for individual patients. However, few methods have been proposed to identify the personalized-sample driver genes from the cancer omics data due to the lack of samples for each individual. To circumvent this problem, here we present a novel single-sample controller strategy (SCS) to identify personalized driver mutation profiles from network controllability perspective.

Results

SCS integrates mutation data and expression data into a reference molecular network for each patient to obtain the driver mutation profiles in a personalized-sample manner. This is the first such a computational framework, to bridge the personalized driver mutation discovery problem and the structural network controllability problem. The key idea of SCS is to detect those mutated genes which can achieve the transition from the normal state to the disease state based on each individual omics data from network controllability perspective. We widely validate the driver mutation profiles of our SCS from three aspects: (i) the improved precision for the predicted driver genes in the population compared with other driver-focus methods; (ii) the effectiveness for discovering the personalized driver genes and (iii) the application to the risk assessment through the integration of the driver mutation signature and expression data, respectively, across the five distinct benchmarks from The Cancer Genome Atlas. In conclusion, our SCS makes efficient and robust personalized driver mutation profiles predictions, opening new avenues in personalized medicine and targeted cancer therapy.

Availability and implementation

The MATLAB-package for our SCS is freely available from http://sysbio.sibcb.ac.cn/cb/chenlab/software.htm.

Contact

zhangsw@nwpu.edu.cn or zengtao@sibs.ac.cn or lnchen@sibs.ac.cn.

Supplementary information

Supplementary data are available at Bioinformatics online.

Altered Levels of Proteins and Phosphoproteins, in the Absence of Early Causative Transcriptional Changes, Shape the Molecular Pathogenesis in the Brain of Young Presymptomatic Ki91 SCA3/MJD Mouse

Spinocerebellar ataxia type 3 (SCA3/MJD) is a polyQ neurodegenerative disease where the presymptomatic phase of pathogenesis is unknown. Therefore, we investigated the molecular network of transcriptomic and proteomic triggers in young presymptomatic SCA3/MJD brain from Ki91 knock-in mouse. We found that transcriptional dysregulations resulting from mutant ataxin-3 are not occurring in young Ki91 mice, while old Ki91 mice and also postmitotic patient SCA3 neurons demonstrate the late transcriptomic changes. Unlike the lack of early mRNA changes, we have identified numerous early changes of total proteins and phosphoproteins in 2-month-old Ki91 mouse cortex and cerebellum. We discovered the network of processes in presymptomatic SCA3 with three main groups of disturbed processes comprising altered proteins: (I) modulation of protein levels and DNA damage (Pabpc1, Ddb1, Nedd8), (II) formation of neuronal cellular structures (Tubb3, Nefh, p-Tau), and (III) neuronal function affected by processes following perturbed cytoskeletal formation (Mt-Co3, Stx1b, p-Syn1). Phosphoproteins downregulate in the young Ki91 mouse brain and their phosphosites are associated with kinases that interact with ATXN3 such as casein kinase, Camk2, and kinases controlled by another Atxn3 interactor p21 such as Gsk3, Pka, and Cdk kinases. We conclude that the onset of SCA3 pathology occurs without altered transcript level and is characterized by changed levels of proteins responsible for termination of translation, DNA damage, spliceosome, and protein phosphorylation. This disturbs global cellular processes such as cytoskeleton and transport of vesicles and mitochondria along axons causing energy deficit and neurodegeneration also manifesting in an altered level of transcripts at later ages.

An oncogenic gene, SNRPA1, regulates PIK3R1, VEGFC, MKI67, CDK1 and other genes in colorectal cancer

PURPOSE

Colorectal cancer (CRC) caused more than 65,000 mortalities worldwide per year. It is a result of one or a combination of chromosomal instability, CpG island methylator phenotype, and microsatellite instability. SNRPA1 (small nuclear ribonucleoprotein polypeptide A) is a subunit of spliceosome complex that is involved in the RNA processing. Overexpression of SNRPA1 has been implicated in a variety of cancers including CRC. Besides from its role in mediating the RNA processing, the other aspects regarding its function in the progression of colorectal cancer have not been revealed.

METHODS

Herein, we combined regular gene overexpression or knock down in vitro and in vivo and microarray gene profiling analysis to decipher the unknow regulatory role of SNRPA1 in CRC.

RESULTS

We found SNRPA1 widely expression in many representative CRC cell lines. Knocking down expression of SNRPA1 by shRNA lentivirus inhibited the cell proliferation in vitro and impaired tumor formation from implanted CRC cells transduced with SNRPA1 silencing shRNA lentivirus in nude mice. It also promoted the cell apoptosis by upregulating the caspase 3/7 activity. Additional microarray gene profiling analysis uncovered the gene interaction network of SNRPA1, special focus was placed on its association with tumor suppressor or oncogenes.

CONCLUSIONS

According to the results of gene interaction network as well as qRT-PCR verification, it revealed that SNPRA1 regulates PIK3R1, VEGFC, MKI67, CDK1 in CRC. These novel findings identified new roles played by SNRPA1 in the progression of CRC and it may become a potential therapeutic target in the treatment of CRC.

MOB (Mps one Binder) Proteins in the Hippo Pathway and Cancer

The family of MOBs (monopolar spindle-one-binder proteins) is highly conserved in the eukaryotic kingdom. MOBs represent globular scaffold proteins without any known enzymatic activities. They can act as signal transducers in essential intracellular pathways. MOBs have diverse cancer-associated cellular functions through regulatory interactions with members of the NDR/LATS kinase family. By forming additional complexes with serine/threonine protein kinases of the germinal centre kinase families, other enzymes and scaffolding factors, MOBs appear to be linked to an even broader disease spectrum. Here, we review our current understanding of this emerging protein family, with emphases on post-translational modifications, protein-protein interactions, and cellular processes that are possibly linked to cancer and other diseases. In particular, we summarise the roles of MOBs as core components of the Hippo tissue growth and regeneration pathway.

A dual controllability analysis of influenza virus-host protein-protein interaction networks for antiviral drug target discovery

BACKGROUND

Host factors of influenza virus replication are often found in key topological positions within protein-protein interaction networks. This work explores how protein states can be manipulated through controllability analysis: the determination of the minimum manipulation needed to drive the cell system to any desired state. Here, we complete a two-part controllability analysis of two protein networks: a host network representing the healthy cell state and an influenza A virus-host network representing the infected cell state. In this context, controllability analyses aim to identify key regulating host factors of the infected cell's progression. This knowledge can be utilized in further biological analysis to understand disease dynamics and isolate proteins for study as drug target candidates.

RESULTS

Both topological and controllability analyses provide evidence of wide-reaching network effects stemming from the addition of viral-host protein interactions. Virus interacting and driver host proteins are significant both topologically and in controllability, therefore playing important roles in cell behavior during infection. Functional analysis finds overlap of results with previous siRNA studies of host factors involved in influenza replication, NF-kB pathway and infection relevance, and roles as interferon regulating genes. 24 proteins are identified as holding regulatory roles specific to the infected cell by measures of topology, controllability, and functional role. These proteins are recommended for further study as potential antiviral drug targets.

CONCLUSIONS

Seasonal outbreaks of influenza A virus are a major cause of illness and death around the world each year with a constant threat of pandemic infection. This research aims to increase the efficiency of antiviral drug target discovery using existing protein-protein interaction data and network analysis methods. These results are beneficial to future studies of influenza virus, both experimental and computational, and provide evidence that the combination of topology and controllability analyses may be valuable for future efforts in drug target discovery.

Discovery of SCY45, a Natural Small-Molecule MDM2-p53 Interaction Inhibitor

The disruption of the MDM2-p53 interaction has been regarded as an attractive strategy for anticancer drug discovery. Here, the natural small-molecule SCY45 was identified as a potent MDM2-p53 interaction inhibitor based on fluorescence polarization and molecular modeling. SCY45 inhibited the MDM2-p53 interaction with an IC₅₀ value of 4.93±0.08 μm. The structural modeling results showed that SCY45 not only had high structural similarity with nutlin-3a, a well-reported MDM2-P53 interaction inhibitor, but also bound to the p53 binding pocket of MDM2 with a binding mode similar to that of nutlin-3a. Moreover, SCY45 reduced the cell viability in cancer cells with MDM2 gene amplification. SCY45 showed the highest inhibition for SJSA-1 cells, which exhibit excessive MDM2 gene amplification, with an IC₅₀ value of 7.54±0.29 μm, whereas SCY45 showed a weaker inhibition for 22Rv1 cells and A549 cells, which have a single copy of the MDM2 gene, with IC₅₀ values of 18.47±0.75 μm and 31.62±1.96 μm, respectively.