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

Construction of Gene Modules and Analysis of Prognostic Biomarkers for Cervical Cancer by Weighted Gene Co-Expression Network Analysis

Background

Despite advances in the understanding of neoplasm, patients with cervical cancer still have a poor prognosis. Identifying prognostic markers of cervical cancer may enable early detection of recurrence and more effective treatment.

Methods

Gene expression profiling data were acquired from the Gene Expression Omnibus database. After data normalization, genes with large variation were screened out. Next, we built co-expression modules by using weighted gene co-expression network analysis to investigate the relationship between the modules and clinical traits related to cervical cancer progression. Functional enrichment analysis was also applied on these co-expressed genes. We integrated the genes into a human protein-protein interaction (PPI) network to expand seed genes and build a co-expression network. For further analysis of the dataset, the Cancer Genome Atlas (TCGA) database was used to identify seed genes and their correlation to cervical cancer prognosis. Verification was further conducted by qPCR and the Human Protein Atlas (HPA) database to measure the expression of hub genes.

Results

Using WGCNA, we identified 25 co-expression modules from 10,016 genes in 128 human cervical cancer samples. After functional enrichment analysis, the magenta, brown, and darkred modules were selected as the three most correlated modules for cancer progression. Additionally, seed genes in the three modules were combined with a PPI network to identify 31 tumor-specific genes. Hierarchical clustering and Gepia results indicated that the expression quantity of hub genes NDC80, TIPIN, MCM3, MCM6, POLA1, and PRC1 may determine the prognosis of cervical cancer. Finally, TIPIN and POLA1 were further filtered by a LASSO model. In addition, their expression was identified by immunohistochemistry in HPA database as well as a biological experiment.

Conclusion

Our research provides a co-expression network of gene modules and identifies TIPIN and POLA1 as stable potential prognostic biomarkers for cervical cancer.

Identification of potential and novel target genes in pituitary prolactinoma by bioinformatics analysis

Pituitary prolactinoma is one of the most complicated and fatally pathogenic pituitary adenomas. Therefore, there is an urgent need to improve our understanding of the underlying molecular mechanism that drives the initiation, progression, and metastasis of pituitary prolactinoma. The aim of the present study was to identify the key genes and signaling pathways associated with pituitary prolactinoma using bioinformatics analysis. Transcriptome microarray dataset GSE119063 was downloaded from Gene Expression Omnibus (GEO) database. Limma package in R software was used to screen DEGs. Pathway and Gene ontology (GO) enrichment analysis were conducted to identify the biological role of DEGs. A protein-protein interaction (PPI) network was constructed and analyzed by using HIPPIE database and Cytoscape software. Module analyses was performed. In addition, a target gene-miRNA regulatory network and target gene-TF regulatory network were constructed by using NetworkAnalyst and Cytoscape software. Finally, validation of hub genes by receiver operating characteristic (ROC) curve analysis. A total of 989 DEGs were identified, including 461 up regulated genes and 528 down regulated genes. Pathway enrichment analysis showed that the DEGs were significantly enriched in the retinoate biosynthesis II, signaling pathways regulating pluripotency of stem cells, ALK2 signaling events, vitamin D3 biosynthesis, cell cycle and aurora B signaling. Gene Ontology (GO) enrichment analysis showed that the DEGs were significantly enriched in the sensory organ morphogenesis, extracellular matrix, hormone activity, nuclear division, condensed chromosome and microtubule binding. In the PPI network and modules, SOX2, PRSS45, CLTC, PLK1, B4GALT6, RUNX1 and GTSE1 were considered as hub genes. In the target gene-miRNA regulatory network and target gene-TF regulatory network, LINC00598, SOX4, IRX1 and UNC13A were considered as hub genes. Using integrated bioinformatics analysis, we identified candidate genes in pituitary prolactinoma, which might improve our understanding of the molecular mechanisms of pituitary prolactinoma.

Current status and future perspectives of computational studies on human-virus protein-protein interactions

The protein-protein interactions (PPIs) between human and viruses mediate viral infection and host immunity processes. Therefore, the study of human-virus PPIs can help us understand the principles of human-virus relationships and can thus guide the development of highly effective drugs to break the transmission of viral infectious diseases. Recent years have witnessed the rapid accumulation of experimentally identified human-virus PPI data, which provides an unprecedented opportunity for bioinformatics studies revolving around human-virus PPIs. In this article, we provide a comprehensive overview of computational studies on human-virus PPIs, especially focusing on the method development for human-virus PPI predictions. We briefly introduce the experimental detection methods and existing database resources of human-virus PPIs, and then discuss the research progress in the development of computational prediction methods. In particular, we elaborate the machine learning-based prediction methods and highlight the need to embrace state-of-the-art deep-learning algorithms and new feature engineering techniques (e.g. the protein embedding technique derived from natural language processing). To further advance the understanding in this research topic, we also outline the practical applications of the human-virus interactome in fundamental biological discovery and new antiviral therapy development.

A genome-wide association study identifies a gene network associated with paranoid schizophrenia and antipsychotics-induced tardive dyskinesia

In the present study we conducted a genome-wide association study (GWAS) in a cohort of 505 patients with paranoid schizophrenia (SCZ), of which 95 had tardive dyskinesia (TD), and 503 healthy controls. Using data generated by the PsychENCODE Consortium (PEC) and other bioinformatic databases, we revealed a gene network, implicated in neurodevelopment and brain function, associated with both these disorders. Almost all these genes are in gene or isoform co-expression PEC network modules important for the functioning of the brain; the activity of these networks is also altered in SCZ, bipolar disorder and autism spectrum disorders. The associated PEC network modules are enriched for gene ontology terms relevant to the brain development and function (CNS development, neuron development, axon ensheathment, synapse, synaptic vesicle cycle, and signaling receptor activity) and to the immune system (inflammatory response). Results of the present study suggest that orofacial and limbtruncal types of TD seem to share the molecular network with SCZ. Paranoid SCZ and abnormal involuntary movements that indicate the orofacial type of TD are associated with the same genomic loci on chromosomes 3p22.2, 8q21.13, and 13q14.2. The limbtruncal type of TD is associated with a locus on chromosome 3p13 where the best functional candidate is FOXP1, a high-confidence SCZ gene. The results of this study shed light on common pathogenic mechanisms for SCZ and TD, and indicate that the pathogenesis of the orofacial and limbtruncal types of TD might be driven by interacting genes implicated in neurodevelopment.

Computational Analysis Indicates That PARP1 Acts as a Histone Deacetylases Interactor Sharing Common Lysine Residues for Acetylation, Ubiquitination, and SUMOylation in Alzheimer's and Parkinson's Disease

Aim/Hypothesis : Lysine residues are known for the post-translational modifications (PTMs) such as acetylation, ubiquitination, and SUMOylation. In acetylation, histone deacetylase (HDAC) and its interactors cause transcriptional deregulation and cause mitochondrial dysfunction, apoptosis, inflammatory response, and cell-cycle impairment that cause brain homeostasis and neuronal cell death. Other regulatory PTMs involved in the pathogenesis of neurodegenerative diseases (NDDs) are ubiquitination and SUMOylation for the degradation of the misfolded proteins. Thus, we aim to investigate the potential acetylation/ubiquitination/SUMOylation crosstalk sites in the HDAC interactors, which cause NDDs. Furthermore, we aim to identify the influence of PTMs on the structural features of proteins and the impact of putative lysine mutation on disease susceptibility. Last, we aim to examine the impact of the putative mutation on acetylated lysine for ubiquitination and SUMOylation. Results : Herein, we integrate 1455 genes, 3094 genes, and 1940 genes related to HDAC interactors, Alzheimer's disease (AD), and Parkinson's disease (PD), respectively. Furthermore, the protein-protein interaction and PTM integrations from different databases identified 32 proteins that are associated with HDAC, AD, and PD with 1489 potential lysine-modified sites. HDAC interactors poly(ADP-ribose) polymerase 1 (PARP1), nucleophosmin (NPM1), and cyclin-dependent kinase 1 (CDK1) involved in the progression of NDDs and 64 and 75% of PTM sites in PARP1, NPM1, and CDK1 fall into coiled and ordered regions, respectively. Moreover, 15 putative lysine sites have been found in the crosstalk and K148, K249, K528, K637, K700, and K796 of PARP1 are crosstalk hotspots. Conclusion : The loss of acetylated hotspot sites results in the loss of ubiquitination and SUMOylation function on nearby sites, which is relatively higher when compared to the gain of function.

HIR V2: a human interactome resource for the biological interpretation of differentially expressed genes via gene set linkage analysis

To facilitate biomedical studies of disease mechanisms, a high-quality interactome that connects functionally related genes is needed to help investigators formulate pathway hypotheses and to interpret the biological logic of a phenotype at the biological process level. Interactions in the updated version of the human interactome resource (HIR V2) were inferred from 36 mathematical characterizations of six types of data that suggest functional associations between genes. This update of the HIR consists of 88 069 pairs of genes (23.2% functional interactions of HIR V2 are in common with the previous version of HIR), representing functional associations that are of strengths similar to those between well-studied protein interactions. Among these functional interactions, 57% may represent protein interactions, which are expected to cover 32% of the true human protein interactome. The gene set linkage analysis (GSLA) tool is developed based on the high-quality HIR V2 to identify the potential functional impacts of the observed transcriptomic changes, helping to elucidate their biological significance and complementing the currently widely used enrichment-based gene set interpretation tools. A case study shows that the annotations reported by the HIR V2/GSLA system are more comprehensive and concise compared to those obtained by the widely used gene set annotation tools such as PANTHER and DAVID. The HIR V2 and GSLA are available at http://human.biomedtzc.cn.

Increasing brain palmitoylation rescues behavior and neuropathology in Huntington disease mice

Huntington disease (HD) damages the corticostriatal circuitry in large part by impairing transport of brain-derived neurotrophic factor (BDNF). We hypothesized that improving vesicular transport of BDNF could slow or prevent disease progression. We therefore performed selective proteomic analysis of vesicles transported within corticostriatal projecting neurons followed by in silico screening and identified palmitoylation as a pathway that could restore defective huntingtin-dependent trafficking. Using a synchronized trafficking assay and an HD network-on-a-chip, we found that increasing brain palmitoylation via ML348, which inhibits the palmitate-removing enzyme acyl-protein thioesterase 1 (APT1), restores axonal transport, synapse homeostasis, and survival signaling to wild-type levels without toxicity. In human HD induced pluripotent stem cell-derived cortical neurons, ML348 increased BDNF trafficking. In HD knock-in mice, it efficiently crossed the blood-brain barrier to restore palmitoylation levels and reverse neuropathology, locomotor deficits, and anxio-depressive behaviors. APT1 and its inhibitor ML348 thus hold therapeutic interest for HD.

ccNetViz: a WebGL-based JavaScript library for visualization of large networks

SUMMARY

Visualizing a network provides a concise and practical understanding of the information it represents. Open-source web-based libraries help accelerate the creation of biologically based networks and their use. ccNetViz is an open-source, high speed and lightweight JavaScript library for visualization of large and complex networks. It implements customization and analytical features for easy network interpretation. These features include edge and node animations, which illustrate the flow of information through a network as well as node statistics. Properties can be defined a priori or dynamically imported from models and simulations. ccNetViz is thus a network visualization library particularly suited for systems biology.

AVAILABILITY AND IMPLEMENTATION

The ccNetViz library, demos and documentation are freely available at http://helikarlab.github.io/ccNetViz/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Role of Low Complexity Regions in Protein Interaction Modes: An Illustration in Huntingtin

Low complexity regions (LCRs) are very frequent in protein sequences, generally having a lower propensity to form structured domains and tending to be much less evolutionarily conserved than globular domains. Their higher abundance in eukaryotes and in species with more cellular types agrees with a growing number of reports on their function in protein interactions regulated by post-translational modifications. LCRs facilitate the increase of regulatory and network complexity required with the emergence of organisms with more complex tissue distribution and development. Although the low conservation and structural flexibility of LCRs complicate their study, evolutionary studies of proteins across species have been used to evaluate their significance and function. To investigate how to apply this evolutionary approach to the study of LCR function in protein-protein interactions, we performed a detailed analysis for Huntingtin (HTT), a large protein that is a hub for interaction with hundreds of proteins, has a variety of LCRs, and for which partial structural information (in complex with HAP40) is available. We hypothesize that proteins RASA1, SYN2, and KAT2B may compete with HAP40 for their attachment to the core of HTT using similar LCRs. Our results illustrate how evolution might favor the interplay of LCRs with domains, and the possibility of detecting multiple modes of LCR-mediated protein-protein interactions with a large hub such as HTT when enough protein interaction data is available.

PINA 3.0: mining cancer interactome

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

Expression and affinity purification of recombinant mammalian Mitochondrial Ribosomal Small Subunit (MRPS) proteins and protein-protein interaction analysis indicate putative role in tumorigenic cellular processes

MRPS group of proteins are structural constituents of the small subunit of mitoribosomes involved in translation. Recent studies indicate role in tumorigenic process, however, unlike cytosolic ribosomal proteins, knowledge on the role of MRPS proteins in alternate cellular processes is very limited. Mapping protein-protein interactions (PPIs) onto known cellular processes can be a valuable tool to identify novel protein functions. In this study, to identify PPIs of MRPS proteins, we have constructed thirty-one GST/MRPS fusion clones. GST/MRPS fusion proteins were confirmed by MALDI-TOF analysis. GST pull-downs were performed using eight GST/MRPS proteins (MRPS9, MRPS10, MRPS11, MRPS18B, MRPS31, MRPS33, MRPS38, MRPS39), GST alone as pull-down control, and HEK293 cell lysate as the source for anchor proteins followed by nLC/MS/MS analysis and probable PPIs of eight MRPS proteins were identified. Three PPIs from GST pull-downs and interaction between six MRPS proteins and p53 previously reported in PPI database were validated. The PPI network analysis revealed putative role in cellular processes with implications for tumorigenesis. Gene expression screening of a cancer cell line panel indicated overexpression of MRPS10 and MRPS31 in breast cancer. Co-expression module identification tool analysis of breast cancer gene expression and MRPS10 and MRPS31 PPIs revealed putative role for PPI with ACADSB in fatty acid oxidation process regulated by brain-derived neurotrophic factor (BDNF) signaling pathway.

Contextualized Protein-Protein Interactions

Protein-protein interaction (PPI) databases are an important bioinformatics resource, yet existing literature-curated databases usually represent cell-type-agnostic interactions, which is at variance with our understanding that protein dynamics are context specific and highly dependent on their environment. Here, we provide a resource derived through data mining to infer disease- and tissue-relevant interactions by annotating existing PPI databases with cell-contextual information extracted from reporting studies. This resource is applicable to the reconstruction and analysis of disease-centric molecular interaction networks. We have made the data and method publicly available and plan to release scheduled updates in the future. We expect these resources to be of interest to a wide audience of researchers in the life sciences.

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We present PPI Context: contextualization of existing literature-curated PPIs

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A resource for filtering PPIs by cell-line information mined from reporting studies

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A fast and flexible pipeline implementing the presented data mining method

Existing literature-curated protein-protein interaction (PPI) databases usually aggregate cell-type-agnostic interactions, yet PPIs are dependent on environmental conditions. Thus, new methods and resources for inferring the context in which a PPI is reported will extend their application and use in disease-centric modeling. We expect the resource presented in this article to be of high interest to those querying known interactions of proteins of interest, reconstruction and analyses of molecular interaction networks, and multi-omics data integration approaches.

Complex Networks in Health and Disease

From protein interactions to signal transduction, from metabolism to the nervous system: Virtually all processes in health and disease rely on the careful orchestration of a large number of diverse individual components ranging from molecules to cells and entire organs. Networks provide a powerful framework for describing and understanding these complex systems in a wholistic fashion. They offer a unique combination of a highly intuitive, qualitative description, and a plethora of analytical, quantitative tools. Here we provide a brief introduction to the emerging field of network medicine. After an overview of the core concepts for connecting network characteristics to biological functions, we review commonly used networks, ranging from the molecular interaction networks that form the basis of all biological processes in the cell to the global transportation networks that govern the spread of global epidemics. Lastly, we highlight current conceptual and practical challenges.

Protein–protein interaction network: an emerging tool for understanding fish disease in aquaculture

Protein–protein interactions (PPIs) play integral roles in a wide range of biological processes that regulate the overall growth, development, physiology and disease in living organisms. With the advancement of high‐throughput sequencing technologies, increasing numbers of PPI networks are being predicted and annotated, and these contribute greatly towards the understanding of pathogenesis and the discovery of novel drug targets for the treatment of diseases. The use of this tool is gaining popularity in the identification, understanding and treatment of diseases in humans and plants. Due to the importance of aquaculture in tackling the global food crisis by producing cheap and high‐quality protein source, the maintenance of the overall health status of aquaculture species is essential. With the increasing omics data on aquaculture species, the PPI network is an emerging tool for fish health maintenance. In this review, we first introduce the concept of PPI network, how they are discovered and their general application. Then, the current status of aquaculture and disease in aquaculture are discussed. The different applications of PPI network in aquaculture fish disease management such as biomarker identification, mechanism prediction, understanding of host–pathogen interaction, understanding of pathogen co‐infection interaction, and potential development of vaccines and treatments are subsequently highlighted. It is hoped that this emerging tool – PPI network – would deepen our understanding of the pathogenesis of various diseases and hasten the prevention and treatment processes in aquaculture species.

Emerging Methods and Resources for Biological Interrogation of Neuropsychiatric Polygenic Signal

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

Towards a unified open access dataset of molecular interactions

The International Molecular Exchange (IMEx) Consortium provides scientists with a single body of experimentally verified protein interactions curated in rich contextual detail to an internationally agreed standard. In this update to the work of the IMEx Consortium, we discuss how this initiative has been working in practice, how it has ensured database sustainability, and how it is meeting emerging annotation challenges through the introduction of new interactor types and data formats. Additionally, we provide examples of how IMEx data are being used by biomedical researchers and integrated in other bioinformatic tools and resources.

LL-37-Induced Autophagy Contributed to the Elimination of Live Porphyromonas gingivalis Internalized in Keratinocytes

Porphyromonas gingivalis (P. gingivalis), one of the most important pathogens of periodontitis, is closely associated with the aggravation and recurrence of periodontitis and systemic diseases. Antibacterial peptide LL-37, transcribed from the cathelicidin antimicrobial peptide (CAMP) gene, exhibits a broad spectrum of antibacterial activity and regulates the immune system. In this study, we demonstrated that LL-37 reduced the number of live P. gingivalis (ATCC 33277) in HaCaT cells in a dose-dependent manner via an antibiotic-protection assay. LL-37 promoted autophagy of HaCaT cells internalized with P. gingivalis. Inhibition of autophagy with 3-methyladenine (3-MA) weakened the inhibitory effect of LL-37 on the number of intracellular P. gingivalis. A cluster of orthologous groups (COGs) and a gene ontology (GO) functional analysis were used to individually assign 65 (10%) differentially expressed genes (DEGs) to an "Intracellular trafficking, secretion, and vesicular transport" cluster and 306 (47.08%) DEGs to metabolic processes including autophagy. Autophagy-related genes, a tripartite motif-containing 22 (TRIM22), and lysosomal-associated membrane protein 3 (LAMP3) were identified as potentially involved in LL-37-induced autophagy. Finally, bioinformatics software was utilized to construct and predict the protein-protein interaction (PPI) network of CAMP-TRIM22/LAMP3-Autophagy. The findings indicated that LL-37 can reduce the quantity of live P. gingivalis internalized in HaCaT cells by promoting autophagy in these cells. The transcriptome sequencing and analysis also revealed the potential molecular pathway of LL-37-induced autophagy.

Construction of Protein Expression Network

In this post-genomic era, protein network can be used as a complementary way to shed light on the growing amount of data generated from current high-throughput technologies. Protein network is a powerful approach to describe the molecular mechanisms of the biological events through protein-protein interactions. Here, we describe the computational methods used to construct the protein network using expression data. We provide a list of available tools and databases that can be used in constructing the network.

Network medicine in Cardiovascular Research

The ability to generate multi-omics data coupled with deeply characterizing the clinical phenotype of individual patients promises to improve understanding of complex cardiovascular pathobiology. There remains an important disconnection between the magnitude and granularity of these data and our ability to improve phenotype-genotype correlations for complex cardiovascular diseases. This shortcoming may be due to limitations associated with traditional reductionist analytical methods, which tend to emphasize a single molecular event in the pathogenesis of diseases more aptly characterized by crosstalk between overlapping molecular pathways. Network medicine is a rapidly growing discipline that considers diseases as the consequences of perturbed interactions between multiple interconnected biological components. This powerful integrative approach has enabled a number of important discoveries in complex disease mechanisms. In this review, we introduce the basic concepts of network medicine and highlight specific examples by which this approach has accelerated cardiovascular research. We also review how network medicine is well-positioned to promote rational drug design for patients with cardiovascular diseases, with particular emphasis on advancing precision medicine.

IHP-PING-generating integrated human protein-protein interaction networks on-the-fly

Advances in high-throughput sequencing technologies have resulted in an exponential growth of publicly accessible biological datasets. In the 'big data' driven 'post-genomic' context, much work is being done to explore human protein-protein interactions (PPIs) for a systems level based analysis to uncover useful signals and gain more insights to advance current knowledge and answer specific biological and health questions. These PPIs are experimentally or computationally predicted, stored in different online databases and some of PPI resources are updated regularly. As with many biological datasets, such regular updates continuously render older PPI datasets potentially outdated. Moreover, while many of these interactions are shared between these online resources, each resource includes its own identified PPIs and none of these databases exhaustively contains all existing human PPI maps. In this context, it is essential to enable the integration of or combining interaction datasets from different resources, to generate a PPI map with increased coverage and confidence. To allow researchers to produce an integrated human PPI datasets in real-time, we introduce the integrated human protein-protein interaction network generator (IHP-PING) tool. IHP-PING is a flexible python package which generates a human PPI network from freely available online resources. This tool extracts and integrates heterogeneous PPI datasets to generate a unified PPI network, which is stored locally for further applications.

Evolutionary Study of Disorder in Protein Sequences

Intrinsically disordered proteins (IDPs) contain regions lacking intrinsic globular structure (intrinsically disordered regions, IDRs). IDPs are present across the tree of life, with great variability of IDR type and frequency even between closely related taxa. To investigate the function of IDRs, we evaluated and compared the distribution of disorder content in 10,695 reference proteomes, confirming its high variability and finding certain correlation along the Euteleostomi (bony vertebrates) lineage to number of cell types. We used the comparison of orthologs to study the function of disorder related to increase in cell types, observing that multiple interacting subunits of protein complexes might gain IDRs in evolution, thus stressing the function of IDRs in modulating protein-protein interactions, particularly in the cell nucleus. Interestingly, the conservation of local compositional biases of IDPs follows residue-type specific patterns, with E- and K-rich regions being evolutionarily stable and Q- and A-rich regions being more dynamic. We provide a framework for targeted evolutionary studies of the emergence of IDRs. We believe that, given the large variability of IDR distributions in different species, studies using this evolutionary perspective are required.

Identification of putative master regulators in rheumatoid arthritis synovial fibroblasts using gene expression data and network inference

Rheumatoid arthritis (RA) is a systemic autoimmune disease that affects the synovial joints of the body. Rheumatoid arthritis fibroblast-like synoviocytes (RA FLS) are central players in the disease pathogenesis, as they are involved in the secretion of cytokines and proteolytic enzymes, exhibit invasive traits, high rate of self-proliferation and an apoptosis-resistant phenotype. We aim at characterizing transcription factors (TFs) that are master regulators in RA FLS and could potentially explain phenotypic traits. We make use of differentially expressed genes in synovial tissue from patients suffering from RA and osteoarthritis (OA) to infer a TF co-regulatory network, using dedicated software. The co-regulatory network serves as a reference to analyze microarray and single-cell RNA-seq data from isolated RA FLS. We identified five master regulators specific to RA FLS, namely BATF, POU2AF1, STAT1, LEF1 and IRF4. TF activity of the identified master regulators was also estimated with the use of two additional, independent software. The identified TFs contribute to the regulation of inflammation, proliferation and apoptosis, as indicated by the comparison of their differentially expressed target genes with hallmark molecular signatures derived from the Molecular Signatures Database (MSigDB). Our results show that TFs influence could be used to identify putative master regulators of phenotypic traits and suggest novel, druggable targets for experimental validation.

From classical to new generation approaches: An excursus of -omics methods for investigation of protein-protein interaction networks

Functional Proteomics aims to the identification of in vivo protein-protein interaction (PPI) in order to piece together protein complexes, and therefore, cell pathways involved in biological processes of interest. Over the years, proteomic approaches used for protein-protein interaction investigation have relied on classical biochemical protocols adapted to a global overview of protein-protein interactions, within so-called "interactomics" investigation. In particular, their coupling with advanced mass spectrometry instruments and innovative analytical methods led to make great strides in the PPIs investigation in proteomics. In this review, an overview of protein complexes purification strategies, from affinity purification approaches, including proximity-dependent labeling techniques and cross-linking strategy for the identification of transient interactions, to Blue Native Gel Electrophoresis (BN-PAGE) and Size Exclusion Chromatography (SEC) employed in the "complexome profiling", has been reported, giving a look to their developments, strengths and weakness and providing to readers several recent applications of each strategy. Moreover, a section dedicated to bioinformatic databases and platforms employed for protein networks analyses was also included.

Protein phosphatase 1 in tumorigenesis: is it worth a closer look?

Cancer cells take advantage of signaling cascades to meet their requirements for sustained growth and survival. Cell signaling is tightly controlled by reversible protein phosphorylation mechanisms, which require the counterbalanced action of protein kinases and protein phosphatases. Imbalances on this system are associated with cancer development and progression. Protein phosphatase 1 (PP1) is one of the most relevant protein phosphatases in eukaryotic cells. Despite the widely recognized involvement of PP1 in key biological processes, both in health and disease, its relevance in cancer has been largely neglected. Here, we provide compelling evidence that support major roles for PP1 in tumorigenesis.

Predicting synthetic lethal interactions using heterogeneous data sources

MOTIVATION

A synthetic lethal (SL) interaction is a relationship between two functional entities where the loss of either one of the entities is viable but the loss of both entities is lethal to the cell. Such pairs can be used as drug targets in targeted anticancer therapies, and so, many methods have been developed to identify potential candidate SL pairs. However, these methods use only a subset of available data from multiple platforms, at genomic, epigenomic and transcriptomic levels; and hence are limited in their ability to learn from complex associations in heterogeneous data sources.

RESULTS

In this article, we develop techniques that can seamlessly integrate multiple heterogeneous data sources to predict SL interactions. Our approach obtains latent representations by collective matrix factorization-based techniques, which in turn are used for prediction through matrix completion. Our experiments, on a variety of biological datasets, illustrate the efficacy and versatility of our approach, that outperforms state-of-the-art methods for predicting SL interactions and can be used with heterogeneous data sources with minimal feature engineering.

AVAILABILITY AND IMPLEMENTATION

Software available at https://github.com/lianyh.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction

Protein-protein interactions (PPIs) are the physical connections between two or more proteins via electrostatic forces or hydrophobic effects. Identification of the PPIs is pivotal, which contributes to many biological processes including protein function, disease incidence, and therapy design. The experimental identification of PPIs via high-throughput technology is time-consuming and expensive. Bioinformatics approaches are expected to solve such restrictions. In this review, our main goal is to provide an inclusive view of the existing sequence-based computational prediction of PPIs. Initially, we briefly introduce the currently available PPI databases and then review the state-of-the-art bioinformatics approaches, working principles, and their performances. Finally, we discuss the caveats and future perspective of the next generation algorithms for the prediction of PPIs.

Transcriptomic analysis delineates potential signature genes and miRNAs associated with the pathogenesis of asthma

Asthma is a multifarious disease affecting several million people around the world. It has a heterogeneous risk architecture inclusive of both genetic and environmental factors. This heterogeneity can be utilised to identify differentially expressed biomarkers of the disease, which may ultimately aid in the development of more localized and molecularly targeted therapies. In this respect, our study complies with meta-analysis of microarray datasets containing mRNA expression profiles of both asthmatic and control patients, to identify the critical Differentially Expressed Genes (DEGs) involved in the pathogenesis of asthma. We found a total of 30 DEGs out of which 13 were involved in the pathway and functional enrichment analysis. Moreover, 5 DEGs were identified as the hub genes by network centrality-based analysis. Most hub genes were involved in protease/antiprotease pathways. Also, 26 miRNAs and 20 TFs having an association with these hub genes were found to be intricated in a 3-node miRNA Feed-Forward Loop. Out of these, miR-34b and miR-449c were identified as the key miRNAs regulating the expression of SERPINB2 gene and SMAD4 transcription factor. Thus, our study is suggestive of certain miRNAs and unexplored pathways which may pave a way to unravel critical therapeutic targets in asthma.

Unbiased Profiling of the Human Proinsulin Biosynthetic Interaction Network Reveals a Role for Peroxiredoxin 4 in Proinsulin Folding

The β-cell protein synthetic machinery is dedicated to the production of mature insulin, which requires the proper folding and trafficking of its precursor, proinsulin. The complete network of proteins that mediate proinsulin folding and advancement through the secretory pathway, however, remains poorly defined. Here we used affinity purification and mass spectrometry to identify, for the first time, the proinsulin biosynthetic interaction network in human islets. Stringent analysis established a central node of proinsulin interactions with endoplasmic reticulum (ER) folding factors, including chaperones and oxidoreductases, that is remarkably conserved in both sexes and across three ethnicities. The ER-localized peroxiredoxin PRDX4 was identified as a prominent proinsulin-interacting protein. In β-cells, gene silencing of PRDX4 rendered proinsulin susceptible to misfolding, particularly in response to oxidative stress, while exogenous PRDX4 improved proinsulin folding. Moreover, proinsulin misfolding induced by oxidative stress or high glucose was accompanied by sulfonylation of PRDX4, a modification known to inactivate peroxiredoxins. Notably, islets from patients with type 2 diabetes (T2D) exhibited significantly higher levels of sulfonylated PRDX4 than islets from healthy individuals. In conclusion, we have generated the first reference map of the human proinsulin interactome to identify critical factors controlling insulin biosynthesis, β-cell function, and T2D.

The Disordered Cellular Multi-Tasker WIP and Its Protein-Protein Interactions: A Structural View

WASp-interacting protein (WIP), a regulator of actin cytoskeleton assembly and remodeling, is a cellular multi-tasker and a key member of a network of protein-protein interactions, with significant impact on health and disease. Here, we attempt to complement the well-established understanding of WIP function from cell biology studies, summarized in several reviews, with a structural description of WIP interactions, highlighting works that present a molecular view of WIP's protein-protein interactions. This provides a deeper understanding of the mechanisms by which WIP mediates its biological functions. The fully disordered WIP also serves as an intriguing example of how intrinsically disordered proteins (IDPs) exert their function. WIP consists of consecutive small functional domains and motifs that interact with a host of cellular partners, with a striking preponderance of proline-rich motif capable of interactions with several well-recognized binding partners; indeed, over 30% of the WIP primary structure are proline residues. We focus on the binding motifs and binding interfaces of three important WIP segments, the actin-binding N-terminal domain, the central domain that binds SH3 domains of various interaction partners, and the WASp-binding C-terminal domain. Beyond the obvious importance of a more fundamental understanding of the biology of this central cellular player, this approach carries an immediate and highly beneficial effect on drug-design efforts targeting WIP and its binding partners. These factors make the value of such structural studies, challenging as they are, readily apparent.

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.

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.

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.