CORUM: the comprehensive resource of mammalian protein complexes--2009

Functional Precision Medicine Identifies Novel Druggable Targets and Therapeutic Options in Head and Neck Cancer

Purpose: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide, with high mortality and a lack of targeted therapies. To identify and prioritize druggable targets, we performed genome analysis together with genome-scale siRNA and oncology drug profiling using low-passage tumor cells derived from a patient with treatment-resistant HPV-negative HNSCC.Experimental Design: A tumor cell culture was established and subjected to whole-exome sequencing, RNA sequencing, comparative genome hybridization, and high-throughput phenotyping with a siRNA library covering the druggable genome and an oncology drug library. Secondary screens of candidate target genes were performed on the primary tumor cells and two nontumorigenic keratinocyte cell cultures for validation and to assess cancer specificity. siRNA screens of the kinome on two isogenic pairs of p53-mutated HNSCC cell lines were used to determine generalizability. Clinical utility was addressed by performing drug screens on two additional HNSCC cell cultures derived from patients enrolled in a clinical trial.Results: Many of the identified copy number aberrations and somatic mutations in the primary tumor were typical of HPV(-) HNSCC, but none pointed to obvious therapeutic choices. In contrast, siRNA profiling identified 391 candidate target genes, 35 of which were preferentially lethal to cancer cells, most of which were not genomically altered. Chemotherapies and targeted agents with strong tumor-specific activities corroborated the siRNA profiling results and included drugs that targeted the mitotic spindle, the proteasome, and G₂-M kinases WEE1 and CHK1 We also show the feasibility of ex vivo drug profiling for patients enrolled in a clinical trial.Conclusions: High-throughput phenotyping with siRNA and drug libraries using patient-derived tumor cells prioritizes mutated driver genes and identifies novel drug targets not revealed by genomic profiling. Functional profiling is a promising adjunct to DNA sequencing for precision oncology. Clin Cancer Res; 24(12); 2828-43. ©2018 AACR.

Systematic characterization of pan-cancer mutation clusters

Cancer genome sequencing has shown that driver genes can often be distinguished not only by the elevated mutation frequency but also by specific nucleotide positions that accumulate changes at a high rate. However, properties associated with a residue's potential to drive tumorigenesis when mutated have not yet been systematically investigated. Here, using a novel methodological approach, we identify and characterize a compendium of 180 hotspot residues within 160 human proteins which occur with a significant frequency and are likely to have functionally relevant impact. We find that such mutations (i) are more prominent in proteins that can exist in the on and off state, (ii) reflect the identity of a tumor of origin, and (iii) often localize within interfaces which mediate interactions with other proteins or ligands. Following, we further examine structural data for human protein complexes and identify a number of additional protein interfaces that accumulate cancer mutations at a high rate. Jointly, these analyses suggest that disruption and dysregulation of protein interactions can be instrumental in switching functions of cancer proteins and activating downstream changes.

Modeling gene-wise dependencies improves the identification of drug response biomarkers in cancer studies

Motivation

In recent years, vast advances in biomedical technologies and comprehensive sequencing have revealed the genomic landscape of common forms of human cancer in unprecedented detail. The broad heterogeneity of the disease calls for rapid development of personalized therapies. Translating the readily available genomic data into useful knowledge that can be applied in the clinic remains a challenge. Computational methods are needed to aid these efforts by robustly analyzing genome-scale data from distinct experimental platforms for prioritization of targets and treatments.

Results

We propose a novel, biologically motivated, Bayesian multitask approach, which explicitly models gene-centric dependencies across multiple and distinct genomic platforms. We introduce a gene-wise prior and present a fully Bayesian formulation of a group factor analysis model. In supervised prediction applications, our multitask approach leverages similarities in response profiles of groups of drugs that are more likely to be related to true biological signal, which leads to more robust performance and improved generalization ability. We evaluate the performance of our method on molecularly characterized collections of cell lines profiled against two compound panels, namely the Cancer Cell Line Encyclopedia and the Cancer Therapeutics Response Portal. We demonstrate that accounting for the gene-centric dependencies enables leveraging information from multi-omic input data and improves prediction and feature selection performance. We further demonstrate the applicability of our method in an unsupervised dimensionality reduction application by inferring genes essential to tumorigenesis in the pancreatic ductal adenocarcinoma and lung adenocarcinoma patient cohorts from The Cancer Genome Atlas.

Availability and Implementation

: The code for this work is available at https://github.com/olganikolova/gbgfa.

Contact

: nikolova@ohsu.edu or margolin@ohsu.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Defining Pharmacological Targets by Analysis of Virus-Host Protein Interactions

Viruses are obligate parasites that depend on cellular factors for replication. Pharmacological inhibition of essential viral proteins, mostly enzymes, is an effective therapeutic alternative in the absence of effective vaccines. However, this strategy commonly encounters drug resistance mechanisms that allow these pathogens to evade control. Due to the dependency on host factors for viral replication, pharmacological disruption of the host-pathogen protein-protein interactions (PPIs) is an important therapeutic alternative to block viral replication. In this review we discuss salient aspects of PPIs implicated in viral replication and advances in the development of small molecules that inhibit viral replication through antagonism of these interactions.

Toward an integrated map of genetic interactions in cancer cells

Cancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and create vulnerabilities for potential therapeutic exploitation. To identify genotype-dependent vulnerabilities, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework to integrate CRISPR/Cas9 screens originating from different libraries building on approaches pioneered for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cells combining functional data with information on genetic variants to explore more than 2.1 million gene-background relationships. In addition to known dependencies, we identified new genotype-specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities identified GANAB and PRKCSH as new positive regulators of Wnt/β-catenin signaling. By clustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data are included.

Systematic analysis of protein turnover in primary cells

A better understanding of proteostasis in health and disease requires robust methods to determine protein half-lives. Here we improve the precision and accuracy of peptide ion intensity-based quantification, enabling more accurate protein turnover determination in non-dividing cells by dynamic SILAC-based proteomics. This approach allows exact determination of protein half-lives ranging from 10 to >1000 h. We identified 4000-6000 proteins in several non-dividing cell types, corresponding to 9699 unique protein identifications over the entire data set. We observed similar protein half-lives in B-cells, natural killer cells and monocytes, whereas hepatocytes and mouse embryonic neurons show substantial differences. Our data set extends and statistically validates the previous observation that subunits of protein complexes tend to have coherent turnover. Moreover, analysis of different proteasome and nuclear pore complex assemblies suggests that their turnover rate is architecture dependent. These results illustrate that our approach allows investigating protein turnover and its implications in various cell types.

The interactome of intact mitochondria by cross-linking mass spectrometry provides evidence for coexisting respiratory supercomplexes

Mitochondria exert an immense amount of cytophysiological functions, but the structural basis of most of these processes is still poorly understood. Here we use cross-linking mass spectrometry to probe the organization of proteins in native mouse heart mitochondria. Our approach provides the largest survey of mitochondrial protein interactions reported so far. In total, we identify 3,322 unique residue-to-residue contacts involving half of the mitochondrial proteome detected by bottom-up proteomics. The obtained mitochondrial protein interactome gives insights in the architecture and submitochondrial localization of defined protein assemblies, and reveals the mitochondrial localization of four proteins not yet included in the MitoCarta database. As one of the highlights, we show that the oxidative phosphorylation complexes I-V exist in close spatial proximity, providing direct evidence for supercomplex assembly in intact mitochondria. The specificity of these contacts is demonstrated by comparative analysis of mitochondria after high salt treatment, which disrupts the native supercomplexes and substantially changes the mitochondrial interactome.

PhenoDis: a comprehensive database for phenotypic characterization of rare cardiac diseases

Background

Thoroughly annotated data resources are a key requirement in phenotype dependent analysis and diagnosis of diseases in the area of precision medicine. Recent work has shown that curation and systematic annotation of human phenome data can significantly improve the quality and selectivity for the interpretation of inherited diseases. We have therefore developed PhenoDis, a comprehensive, manually annotated database providing symptomatic, genetic and imprinting information about rare cardiac diseases.

Results

PhenoDis includes 214 rare cardiac diseases from Orphanet and 94 more from OMIM. For phenotypic characterization of the diseases, we performed manual annotation of diseases with articles from the biomedical literature. Detailed description of disease symptoms required the use of 2247 different terms from the Human Phenotype Ontology (HPO). Diseases listed in PhenoDis frequently cover a broad spectrum of symptoms with 28% from the branch of ‘cardiovascular abnormality’ and others from areas such as neurological (11.5%) and metabolism (6%). We collected extensive information on the frequency of symptoms in respective diseases as well as on disease-associated genes and imprinting data. The analysis of the abundance of symptoms in patient studies revealed that most of the annotated symptoms (71%) are found in less than half of the patients of a particular disease. Comprehensive and systematic characterization of symptoms including their frequency is a pivotal prerequisite for computer based prediction of diseases and disease causing genetic variants. To this end, PhenoDis provides in-depth annotation for a complete group of rare diseases, including information on pathogenic and likely pathogenic genetic variants for 206 diseases as listed in ClinVar. We integrated all results in an online database (http://mips.helmholtz-muenchen.de/phenodis/) with multiple search options and provide the complete dataset for download.

Conclusion

PhenoDis provides a comprehensive set of manually annotated rare cardiac diseases that enables computational approaches for disease prediction via decision support systems and phenotype-driven strategies for the identification of disease causing genes.

Protein complex scaffolding predicted as a prevalent function of long non-coding RNAs

The human transcriptome contains thousands of long non-coding RNAs (lncRNAs). Characterizing their function is a current challenge. An emerging concept is that lncRNAs serve as protein scaffolds, forming ribonucleoproteins and bringing proteins in proximity. However, only few scaffolding lncRNAs have been characterized and the prevalence of this function is unknown. Here, we propose the first computational approach aimed at predicting scaffolding lncRNAs at large scale. We predicted the largest human lncRNA-protein interaction network to date using the catRAPID omics algorithm. In combination with tissue expression and statistical approaches, we identified 847 lncRNAs (∼5% of the long non-coding transcriptome) predicted to scaffold half of the known protein complexes and network modules. Lastly, we show that the association of certain lncRNAs to disease may involve their scaffolding ability. Overall, our results suggest for the first time that RNA-mediated scaffolding of protein complexes and modules may be a common mechanism in human cells.

Landscape of the regulatory elements for lysine 2-hydroxyisobutyrylation pathway

Short-chain fatty acids and their corresponding acyl-CoAs sit at the crossroads of metabolic pathways and play important roles in diverse cellular processes. They are also precursors for protein post-translational lysine acylation modifications. A noteworthy example is the newly identified lysine 2-hydroxyisobutyrylation (Khib) that is derived from 2-hydroxyisobutyrate and 2-hydroxyisobutyryl-CoA. Histone Khib has been shown to be associated with active gene expression in spermatogenic cells. However, the key elements that regulate this post-translational lysine acylation pathway remain unknown. This has hindered characterization of the mechanisms by which this modification exerts its biological functions. Here we show that Esa1p in budding yeast and its homologue Tip60 in human could add Khib to substrate proteins both in vitro and in vivo. In addition, we have identified HDAC2 and HDAC3 as the major enzymes to remove Khib. Moreover, we report the first global profiling of Khib proteome in mammalian cells, identifying 6 548 Khib sites on 1 725 substrate proteins. Our study has thus discovered both the "writers" and "erasers" for histone Khib marks, and major Khib protein substrates. These results not only illustrate the landscape of this new lysine acylation pathway, but also open new avenues for studying diverse functions of cellular metabolites associated with this pathway.

Interactome INSIDER: a structural interactome browser for genomic studies

We present Interactome INSIDER, a tool to link genomic variant information with structural protein-protein interactomes. Underlying this tool is the application of machine learning to predict protein interaction interfaces for 185,957 protein interactions with previously unresolved interfaces, in human and 7 model organisms, including the entire experimentally determined human binary interactome. Predicted interfaces exhibit similar functional properties as known interfaces, including enrichment for disease mutations and recurrent cancer mutations. Through 2,164 de novo mutagenesis experiments, we show that mutations of predicted and known interface residues disrupt interactions at a similar rate, and much more frequently than mutations outside of predicted interfaces. To spur functional genomic studies, Interactome INSIDER (http://interactomeinsider.yulab.org) enables users to identify whether variants or disease mutations are enriched in known and predicted interaction interfaces at various resolutions. Users may explore known population variants, disease mutations, and somatic cancer mutations, or upload their own set of mutations for this purpose.

Spatial perspectives in the redox code-Mass spectrometric proteomics studies of moonlighting proteins

The Redox Code involves specific, reversible oxidative changes in proteins that modulate protein tertiary structure, interactions, trafficking, and activity, and hence couple the proteome to the metabolic/oxidative state of cells. It is currently a major focus of study in cell biology. Recent studies of dynamic cellular spatial reorganization with MS-based subcellular-spatial-razor proteomics reveal that protein constituents of many subcellular structures, including mitochondria, the endoplasmic reticulum, the plasma membrane, and the extracellular matrix, undergo changes in their subcellular abundance/distribution in response to oxidative stress. These proteins are components of a diverse variety of functional processes spatially distributed across cells. Many of the same proteins are involved in response to suppression of DNA replication indicate that oxidative stress is strongly intertwined with DNA replication/proliferation. Both are replete with networks of moonlighting proteins that show coordinated changes in subcellular location and that include primary protein actuators of the redox code involved in the processing of NAD⁺ /NADH, NADP⁺ /NADPH, Cys/CySS, and GSH/GSSG redox couples. Small groups of key proteins such as {KPNA2, KPNB1, PCNA, PTMA, SET} constitute "spatial switches" that modulate many nuclear processes. Much of the functional response involves subcellular protein trafficking, including nuclear import/export processes, vesicle-mediated trafficking, the endoplasmic reticulum/Golgi pathway, chaperone-assisted processes, and other transport systems. This is not visible to measurements of total protein abundance by transcriptomics or proteomics. Comprehensive pictures of cellular function will require collection of data on the subcellular transport and local functions of many moonlighting proteins, especially of those with critical roles in spatial coordination across cells. The proteome-wide analysis of coordinated changes in abundance and trafficking of proteins offered by MS-based proteomics has a unique, crucial role to play in deciphering the complex adaptive systems that underlie cellular function. © 2016 Wiley Periodicals, Inc. Mass Spec Rev.

CeFra-seq reveals broad asymmetric mRNA and noncoding RNA distribution profiles in Drosophila and human cells

Cells are highly asymmetrical, a feature that relies on the sorting of molecular constituents, including proteins, lipids, and nucleic acids, to distinct subcellular locales. The localization of RNA molecules is an important layer of gene regulation required to modulate localized cellular activities, although its global prevalence remains unclear. We combine biochemical cell fractionation with RNA-sequencing (CeFra-seq) analysis to assess the prevalence and conservation of RNA asymmetric distribution on a transcriptome-wide scale in Drosophila and human cells. This approach reveals that the majority (∼80%) of cellular RNA species are asymmetrically distributed, whether considering coding or noncoding transcript populations, in patterns that are broadly conserved evolutionarily. Notably, a large number of Drosophila and human long noncoding RNAs and circular RNAs display enriched levels within specific cytoplasmic compartments, suggesting that these RNAs fulfill extra-nuclear functions. Moreover, fraction-specific mRNA populations exhibit distinctive sequence characteristics. Comparative analysis of mRNA fractionation profiles with that of their encoded proteins reveals a general lack of correlation in subcellular distribution, marked by strong cases of asymmetry. However, coincident distribution profiles are observed for mRNA/protein pairs related to a variety of functional protein modules, suggesting complex regulatory inputs of RNA localization to cellular organization.

Identifying the dynamic gene regulatory network during latent HIV-1 reactivation using high-dimensional ordinary differential equations

Reactivation of latently infected cells has emerged as an important strategy for eradication of HIV. However, genetic mechanisms of regulation after reactivation remain unclear. We describe a five-step pipeline to study the dynamics of the gene regulatory network following a viral reactivation using high-dimensional ordinary differential equations. Our pipeline implements a combination of five different methods, by detecting temporally differentially expressed genes (step 1), clustering genes with similar temporal expression patterns into a small number of response modules (step2), performing a functional enrichment analysis within each gene response module (step 3), identifying a network structure based on the gene response modules using ordinary differential equations (ODE) and a high-dimensional variable selection technique (step 4), and obtaining a gene regulatory model based on refined parameter estimates using nonlinear least squares (step 5). We applied our pipeline to a time course gene expression data of latently infected T-cells following a latency-reversion.

Automated Computational Inference of Multi-protein Assemblies from Biochemical Co-purification Data

Biology has amassed a wealth of information about the function of a multitude of protein-coding genes across species. The challenge now is to understand how all these proteins work together to form a living organism, and a crucial step for gaining this knowledge is a complete description of the molecular "wiring circuits" that underlie cellular processes. In this chapter, we describe a general computational framework for predicting multi-protein assemblies from biochemical co-fractionation data.

Refine gene functional similarity network based on interaction networks

BACKGROUND

In recent years, biological interaction networks have become the basis of some essential study and achieved success in many applications. Some typical networks such as protein-protein interaction networks have already been investigated systematically. However, little work has been available for the construction of gene functional similarity networks so far. In this research, we will try to build a high reliable gene functional similarity network to promote its further application.

RESULTS

Here, we propose a novel method to construct and refine the gene functional similarity network. It mainly contains three steps. First, we establish an integrated gene functional similarity networks based on different functional similarity calculation methods. Then, we construct a referenced gene-gene association network based on the protein-protein interaction networks. At last, we refine the spurious edges in the integrated gene functional similarity network with the help of the referenced gene-gene association network. Experiment results indicate that the refined gene functional similarity network (RGFSN) exhibits a scale-free, small world and modular architecture, with its degrees fit best to power law distribution. In addition, we conduct protein complex prediction experiment for human based on RGFSN and achieve an outstanding result, which implies it has high reliability and wide application significance.

CONCLUSIONS

Our efforts are insightful for constructing and refining gene functional similarity networks, which can be applied to build other high quality biological networks.

CUFID-query: accurate network querying through random walk based network flow estimation

BACKGROUND

Functional modules in biological networks consist of numerous biomolecules and their complicated interactions. Recent studies have shown that biomolecules in a functional module tend to have similar interaction patterns and that such modules are often conserved across biological networks of different species. As a result, such conserved functional modules can be identified through comparative analysis of biological networks.

RESULTS

In this work, we propose a novel network querying algorithm based on the CUFID (Comparative network analysis Using the steady-state network Flow to IDentify orthologous proteins) framework combined with an efficient seed-and-extension approach. The proposed algorithm, CUFID-query, can accurately detect conserved functional modules as small subnetworks in the target network that are expected to perform similar functions to the given query functional module. The CUFID framework was recently developed for probabilistic pairwise global comparison of biological networks, and it has been applied to pairwise global network alignment, where the framework was shown to yield accurate network alignment results. In the proposed CUFID-query algorithm, we adopt the CUFID framework and extend it for local network alignment, specifically to solve network querying problems. First, in the seed selection phase, the proposed method utilizes the CUFID framework to compare the query and the target networks and to predict the probabilistic node-to-node correspondence between the networks. Next, the algorithm selects and greedily extends the seed in the target network by iteratively adding nodes that have frequent interactions with other nodes in the seed network, in a way that the conductance of the extended network is maximally reduced. Finally, CUFID-query removes irrelevant nodes from the querying results based on the personalized PageRank vector for the induced network that includes the fully extended network and its neighboring nodes.

CONCLUSIONS

Through extensive performance evaluation based on biological networks with known functional modules, we show that CUFID-query outperforms the existing state-of-the-art algorithms in terms of prediction accuracy and biological significance of the predictions.

A High-Resolution Genome-Wide CRISPR/Cas9 Viability Screen Reveals Structural Features and Contextual Diversity of the Human Cell-Essential Proteome

To interrogate genes essential for cell growth, proliferation and survival in human cells, we carried out a genome-wide clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 screen in a B-cell lymphoma line using a custom extended-knockout (EKO) library of 278,754 single-guide RNAs (sgRNAs) that targeted 19,084 RefSeq genes, 20,852 alternatively spliced exons, and 3,872 hypothetical genes. A new statistical analysis tool called robust analytics and normalization for knockout screens (RANKS) identified 2,280 essential genes, 234 of which were unique. Individual essential genes were validated experimentally and linked to ribosome biogenesis and stress responses. Essential genes exhibited a bimodal distribution across 10 different cell lines, consistent with a continuous variation in essentiality as a function of cell type. Genes essential in more lines had more severe fitness defects and encoded the evolutionarily conserved structural cores of protein complexes, whereas genes essential in fewer lines formed context-specific modules and encoded subunits at the periphery of essential complexes. The essentiality of individual protein residues across the proteome correlated with evolutionary conservation, structural burial, modular domains, and protein interaction interfaces. Many alternatively spliced exons in essential genes were dispensable and were enriched for disordered regions. Fitness defects were observed for 44 newly evolved hypothetical reading frames. These results illuminate the contextual nature and evolution of essential gene functions in human cells.

RocSampler: regularizing overlapping protein complexes in protein-protein interaction networks

Background

In recent years, protein-protein interaction (PPI) networks have been well recognized as important resources to elucidate various biological processes and cellular mechanisms. In this paper, we address the problem of predicting protein complexes from a PPI network. This problem has two difficulties. One is related to small complexes, which contains two or three components. It is relatively difficult to identify them due to their simpler internal structure, but unfortunately complexes of such sizes are dominant in major protein complex databases, such as CYC2008. Another difficulty is how to model overlaps between predicted complexes, that is, how to evaluate different predicted complexes sharing common proteins because CYC2008 and other databases include such protein complexes. Thus, it is critical how to model overlaps between predicted complexes to identify them simultaneously.

Results

In this paper, we propose a sampling-based protein complex prediction method, RocSampler (Regularizing Overlapping Complexes), which exploits, as part of the whole scoring function, a regularization term for the overlaps of predicted complexes and that for the distribution of sizes of predicted complexes. We have implemented RocSampler in MATLAB and its executable file for Windows is available at the site, http://imi.kyushu-u.ac.jp/~om/software/RocSampler/.

Conclusions

We have applied RocSampler to five yeast PPI networks and shown that it is superior to other existing methods. This implies that the design of scoring functions including regularization terms is an effective approach for protein complex prediction.

Network analysis reveals a causal role of mitochondrial gene activity in atherosclerotic lesion formation

BACKGROUND AND AIMS

Mitochondrial damage and augmented production of reactive oxygen species (ROS) may represent an intermediate step by which hypercholesterolemia exacerbates atherosclerotic lesion formation.

METHODS

To test this hypothesis, in mice with severe but genetically reversible hypercholesterolemia (i.e. the so called Reversa mouse model), we performed time-resolved analyses of mitochondrial transcriptome in the aortic arch employing a systems-level network approach.

RESULTS

During hypercholesterolemia, we observed a massive down-regulation (>28%) of mitochondrial genes, specifically at the time of rapid atherosclerotic lesion expansion and foam cell formation, i.e. between 30 and 40 weeks of age. Both phenomena - down-regulation of mitochondrial genes and lesion expansion - were largely reversible by genetically lowering plasma cholesterol (by >80%, from 427 to 54 ± 31 mg/L) at 30 weeks. Co-expression network analysis revealed that both mitochondrial signature genes were highly connected in two modules, negatively correlating with lesion size and supported as causal for coronary artery disease (CAD) in humans, as expression-associated single nucleotide polymorphisms (eSNPs) representing their genes overlapped markedly with established disease risk loci. Within these modules, we identified the transcription factor estrogen related receptor (ERR)-α and its co-factors PGC1-α and -β, i.e. two members of the peroxisome proliferator-activated receptor γ co-activator 1 family of transcription regulators, as key regulatory genes. Together, these factors are known as major orchestrators of mitochondrial biogenesis and antioxidant responses.

CONCLUSIONS

Using a network approach, we demonstrate how hypercholesterolemia could hamper mitochondrial activity during atherosclerosis progression and pinpoint potential therapeutic targets to counteract these processes.

Top-down characterization of endogenous protein complexes with native proteomics

Protein complexes exhibit great diversity in protein membership, post-translational modifications and noncovalent cofactors, enabling them to function as the actuators of many important biological processes. The exposition of these molecular features using current methods lacks either throughput or molecular specificity, ultimately limiting the use of protein complexes as direct analytical targets in a wide range of applications. Here, we apply native proteomics, enabled by a multistage tandem MS approach, to characterize 125 intact endogenous complexes and 217 distinct proteoforms derived from mouse heart and human cancer cell lines in discovery mode. The native conditions preserved soluble protein-protein interactions, high-stoichiometry noncovalent cofactors, covalent modifications to cysteines, and, remarkably, superoxide ligands bound to the metal cofactor of superoxide dismutase 2. These data enable precise compositional analysis of protein complexes as they exist in the cell and demonstrate a new approach that uses MS as a bridge to structural biology.

Two missense mutations in KCNQ1 cause pituitary hormone deficiency and maternally inherited gingival fibromatosis

Familial growth hormone deficiency provides an opportunity to identify new genetic causes of short stature. Here we combine linkage analysis with whole-genome resequencing in patients with growth hormone deficiency and maternally inherited gingival fibromatosis. We report that patients from three unrelated families harbor either of two missense mutations, c.347G>T p.(Arg116Leu) or c.1106C>T p.(Pro369Leu), in KCNQ1, a gene previously implicated in the long QT interval syndrome. Kcnq1 is expressed in hypothalamic GHRH neurons and pituitary somatotropes. Co-expressing KCNQ1 with the KCNE2 β-subunit shows that both KCNQ1 mutants increase current levels in patch clamp analyses and are associated with reduced pituitary hormone secretion from AtT-20 cells. In conclusion, our results reveal a role for the KCNQ1 potassium channel in the regulation of human growth, and show that growth hormone deficiency associated with maternally inherited gingival fibromatosis is an allelic disorder with cardiac arrhythmia syndromes caused by KCNQ1 mutations.

Molecular property diagnostic suite (MPDS): Development of disease-specific open source web portals for drug discovery

Molecular property diagnostic suite (MPDS) is a Galaxy-based open source drug discovery and development platform. MPDS web portals are designed for several diseases, such as tuberculosis, diabetes mellitus, and other metabolic disorders, specifically aimed to evaluate and estimate the drug-likeness of a given molecule. MPDS consists of three modules, namely data libraries, data processing, and data analysis tools which are configured and interconnected to assist drug discovery for specific diseases. The data library module encompasses vast information on chemical space, wherein the MPDS compound library comprises 110.31 million unique molecules generated from public domain databases. Every molecule is assigned with a unique ID and card, which provides complete information for the molecule. Some of the modules in the MPDS are specific to the diseases, while others are non-specific. Importantly, a suitably altered protocol can be effectively generated for another disease-specific MPDS web portal by modifying some of the modules. Thus, the MPDS suite of web portals shows great promise to emerge as disease-specific portals of great value, integrating chemoinformatics, bioinformatics, molecular modelling, and structure- and analogue-based drug discovery approaches.

Systematic proteome and proteostasis profiling in human Trisomy 21 fibroblast cells

Down syndrome (DS) is mostly caused by a trisomy of the entire Chromosome 21 (Trisomy 21, T21). Here, we use SWATH mass spectrometry to quantify protein abundance and protein turnover in fibroblasts from a monozygotic twin pair discordant for T21, and to profile protein expression in 11 unrelated DS individuals and matched controls. The integration of the steady-state and turnover proteomic data indicates that protein-specific degradation of members of stoichiometric complexes is a major determinant of T21 gene dosage outcome, both within and between individuals. This effect is not apparent from genomic and transcriptomic data. The data also reveal that T21 results in extensive proteome remodeling, affecting proteins encoded by all chromosomes. Finally, we find broad, organelle-specific post-transcriptional effects such as significant downregulation of the mitochondrial proteome contributing to T21 hallmarks. Overall, we provide a valuable proteomic resource to understand the origin of DS phenotypic manifestations.

Trisomy 21 (T21) is a major cause of Down syndrome but little is known about its impact on the cellular proteome. Here, the authors define the proteome of T21 fibroblasts and its turnover and also map proteomic differences in monozygotic T21-discordant twins, revealing extensive, organelle-specific changes caused by T21.

A Compendium of Co-regulated Protein Complexes in Breast Cancer Reveals Collateral Loss Events

Protein complexes are responsible for the bulk of activities within the cell, but how their behavior and abundance varies across tumors remains poorly understood. By combining proteomic profiles of breast tumors with a large-scale protein-protein interaction network, we have identified a set of 285 high-confidence protein complexes whose subunits have highly correlated protein abundance across tumor samples. We used this set to identify complexes that are reproducibly under- or overexpressed in specific breast cancer subtypes. We found that mutation or deletion of one subunit of a co-regulated complex was often associated with a collateral reduction in protein expression of additional complex members. This collateral loss phenomenon was typically evident from proteomic, but not transcriptomic, profiles, suggesting post-transcriptional control. Mutation of the tumor suppressor E-cadherin (CDH1) was associated with a collateral loss of members of the adherens junction complex, an effect we validated using an engineered model of E-cadherin loss.

Regulatory remodeling in the allo-tetraploid frog Xenopus laevis

BACKGROUND

Genome duplication has played a pivotal role in the evolution of many eukaryotic lineages, including the vertebrates. A relatively recent vertebrate genome duplication is that in Xenopus laevis, which resulted from the hybridization of two closely related species about 17 million years ago. However, little is known about the consequences of this duplication at the level of the genome, the epigenome, and gene expression.

RESULTS

The X. laevis genome consists of two subgenomes, referred to as L (long chromosomes) and S (short chromosomes), that originated from distinct diploid progenitors. Of the parental subgenomes, S chromosomes have degraded faster than L chromosomes from the point of genome duplication until the present day. Deletions appear to have the largest effect on pseudogene formation and loss of regulatory regions. Deleted regions are enriched for long DNA repeats and the flanking regions have high alignment scores, suggesting that non-allelic homologous recombination has played a significant role in the loss of DNA. To assess innovations in the X. laevis subgenomes we examined p300-bound enhancer peaks that are unique to one subgenome and absent from X. tropicalis. A large majority of new enhancers comprise transposable elements. Finally, to dissect early and late events following interspecific hybridization, we examined the epigenome and the enhancer landscape in X. tropicalis × X. laevis hybrid embryos. Strikingly, young X. tropicalis DNA transposons are derepressed and recruit p300 in hybrid embryos.

CONCLUSIONS

The results show that erosion of X. laevis genes and functional regulatory elements is associated with repeats and non-allelic homologous recombination and furthermore that young repeats have also contributed to the p300-bound regulatory landscape following hybridization and whole-genome duplication.

A rapid and accurate approach for prediction of interactomes from co-elution data (PrInCE)

Background

An organism’s protein interactome, or complete network of protein-protein interactions, defines the protein complexes that drive cellular processes. Techniques for studying protein complexes have traditionally applied targeted strategies such as yeast two-hybrid or affinity purification-mass spectrometry to assess protein interactions. However, given the vast number of protein complexes, more scalable methods are necessary to accelerate interaction discovery and to construct whole interactomes. We recently developed a complementary technique based on the use of protein correlation profiling (PCP) and stable isotope labeling in amino acids in cell culture (SILAC) to assess chromatographic co-elution as evidence of interacting proteins. Importantly, PCP-SILAC is also capable of measuring protein interactions simultaneously under multiple biological conditions, allowing the detection of treatment-specific changes to an interactome. Given the uniqueness and high dimensionality of co-elution data, new tools are needed to compare protein elution profiles, control false discovery rates, and construct an accurate interactome.

Results

Here we describe a freely available bioinformatics pipeline, PrInCE, for the analysis of co-elution data. PrInCE is a modular, open-source library that is computationally inexpensive, able to use label and label-free data, and capable of detecting tens of thousands of protein-protein interactions. Using a machine learning approach, PrInCE offers greatly reduced run time, more predicted interactions at the same stringency, prediction of protein complexes, and greater ease of use over previous bioinformatics tools for co-elution data. PrInCE is implemented in Matlab (version R2017a). Source code and standalone executable programs for Windows and Mac OSX are available at https://github.com/fosterlab/PrInCE, where usage instructions can be found. An example dataset and output are also provided for testing purposes.

Conclusions

PrInCE is the first fast and easy-to-use data analysis pipeline that predicts interactomes and protein complexes from co-elution data. PrInCE allows researchers without bioinformatics expertise to analyze high-throughput co-elution datasets.

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1865-8) contains supplementary material, which is available to authorized users.

Systematic Analyses and Prediction of Human Drug Side Effect Associated Proteins from the Perspective of Protein Evolution

Identification of various factors involved in adverse drug reactions in target proteins to develop therapeutic drugs with minimal/no side effect is very important. In this context, we have performed a comparative evolutionary rate analyses between the genes exhibiting drug side-effect(s) (SET) and genes showing no side effect (NSET) with an aim to increase the prediction accuracy of SET/NSET proteins using evolutionary rate determinants. We found that SET proteins are more conserved than the NSET proteins. The rates of evolution between SET and NSET protein primarily depend upon their noncomplex (protein complex association number = 0) forming nature, phylogenetic age, multifunctionality, membrane localization, and transmembrane helix content irrespective of their essentiality, total druggability (total number of drugs/target), m-RNA expression level, and tissue expression breadth. We also introduced two novel terms—killer druggability (number of drugs with killing side effect(s)/target), essential druggability (number of drugs targeting essential proteins/target) to explain the evolutionary rate variation between SET and NSET proteins. Interestingly, we noticed that SET proteins are younger than NSET proteins and multifunctional younger SET proteins are candidates of acquiring killing side effects. We provide evidence that higher killer druggability, multifunctionality, and transmembrane helices support the conservation of SET proteins over NSET proteins in spite of their recent origin. By employing all these entities, our Support Vector Machine model predicts human SET/NSET proteins to a high degree of accuracy (∼86%).

MCM2-regulated functional networks in lung cancer by multi-dimensional proteomic approach

DNA replication control is vital for maintaining genome stability and the cell cycle, perhaps most notably during cell division. Malignancies often exhibit defective minichromosome maintenance protein 2 (MCM2), a cancer proliferation biomarker that serves as a licensing factor in the initiation of DNA replication. MCM2 is also known to be one of the ATPase active sites that facilitates conformational changes and drives DNA unwinding at the origin of DNA replication. However, the biological networks of MCM2 in lung cancer cells via protein phosphorylation remain unmapped. The RNA-seq datasets from The Cancer Genome Atlas (TCGA) revealed that MCM2 overexpression is correlated with poor survival rate in lung cancer patients. To uncover MCM2-regulated functional networks in lung cancer, we performed multi-dimensional proteomic approach by integrating analysis of the phosphoproteome and proteome, and identified a total of 2361 phosphorylation sites on 753 phosphoproteins, and 4672 proteins. We found that the deregulation of MCM2 is involved in lung cancer cell proliferation, the cell cycle, and migration. Furthermore, HMGA1^S99 phosphorylation was found to be differentially expressed under MCM2 perturbation in opposite directions, and plays an important role in regulating lung cancer cell proliferation. This study therefore enhances our capacity to therapeutically target cancer-specific phosphoproteins.

Widespread Post-transcriptional Attenuation of Genomic Copy-Number Variation in Cancer

Copy-number variations (CNVs) are ubiquitous in cancer and often act as driver events, but the effects of CNVs on the proteome of tumors are poorly understood. Here, we analyze recently published genomics, transcriptomics, and proteomics datasets made available by CPTAC and TCGA consortia on 282 breast, ovarian, and colorectal tumor samples to investigate the impact of CNVs in the proteomes of these cells. We found that CNVs are buffered by post-transcriptional regulation in 23%-33% of proteins that are significantly enriched in protein complex members. Our analyses show that complex subunits are highly co-regulated, and some act as rate-limiting steps of complex assembly, as their depletion induces decreased abundance of other complex members. We identified 48 such rate-limiting interactions and experimentally confirmed our predictions on the interactions of AP3B1 with AP3M1 and GTF2E2 with GTF2E1. This study highlights the importance of post-transcriptional mechanisms in cancer that allow cells to cope with their altered genomes.

Learning causal networks with latent variables from multivariate information in genomic data

Learning causal networks from large-scale genomic data remains challenging in absence of time series or controlled perturbation experiments. We report an information- theoretic method which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables, commonly found in many genomic datasets. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. The approach and associated algorithm, miic, outperform earlier methods on a broad range of benchmark networks. Causal network reconstructions are presented at different biological size and time scales, from gene regulation in single cells to whole genome duplication in tumor development as well as long term evolution of vertebrates. Miic is publicly available at https://github.com/miicTeam/MIIC.

The reconstruction of causal networks from genomic data is an important but challenging problem. Predicting key regulatory interactions or genomic alterations at the origin of human diseases can guide experimental investigation and ultimately inspire innovative therapy. However, causal relationships are difficult to establish without the possibility to directly perturb the organisms’ genome for ethical or practical reasons. Besides, unmeasured (latent) variables may be hidden in many genomic datasets and lead to spurious causal relationships between observed variables. We propose in this paper an efficient computational approach, miic, that overcomes these limitations and learns causal networks from non-perturbative (observational) data in the presence of latent variables. In addition, we assess the confidence of each predicted interaction and demonstrate the enhanced robustness and accuracy of miic compared to alternative existing methods. This approach can be applied on a wide range of datasets and provide new biological insights on regulatory networks from single cell expression data or genomic alterations during tumor development. Miic is implemented in an R package freely available to the scientific community under a General Public License.

Proteomics of Eosinophil Activation

We recently identified and quantified >7,000 proteins in non-activated human peripheral blood eosinophils using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) and described phosphoproteomic changes that accompany acute activation of eosinophils by interleukin-5 (IL5) (1). These data comprise a treasure trove of information about eosinophils. We illustrate the power of label-free LC-MS/MS quantification by considering four examples: complexity of eosinophil STATs, contribution of immunoproteasome subunits to eosinophil proteasomes, complement of integrin subunits, and contribution of platelet proteins originating from platelet-eosinophil complexes to the overall proteome. We describe how isobaric labeling enables robust sample-to-sample comparisons and relate the 220 phosphosites that changed significantly upon treatment with IL5 to previous studies of eosinophil activation. Finally, we review previous attempts to leverage the power of mass spectrometry to discern differences between eosinophils of healthy subjects and those with eosinophil-associated conditions and point out features of label-free quantification and isobaric labeling that are important in planning future mass spectrometric studies.

The mTOR Complex Controls HIV Latency

A population of CD4 T lymphocytes harboring latent HIV genomes can persist in patients on antiretroviral therapy, posing a barrier to HIV eradication. To examine cellular complexes controlling HIV latency, we conducted a genome-wide screen with a pooled ultracomplex shRNA library and in vitro system modeling HIV latency and identified the mTOR complex as a modulator of HIV latency. Knockdown of mTOR complex subunits or pharmacological inhibition of mTOR activity suppresses reversal of latency in various HIV-1 latency models and HIV-infected patient cells. mTOR inhibitors suppress HIV transcription both through the viral transactivator Tat and via Tat-independent mechanisms. This inhibition occurs at least in part via blocking the phosphorylation of CDK9, a p-TEFb complex member that serves as a cofactor for Tat-mediated transcription. The control of HIV latency by mTOR signaling identifies a pathway that may have significant therapeutic opportunities.

Protein complex prediction via dense subgraphs and false positive analysis

Many proteins work together with others in groups called complexes in order to achieve a specific function. Discovering protein complexes is important for understanding biological processes and predict protein functions in living organisms. Large-scale and throughput techniques have made possible to compile protein-protein interaction networks (PPI networks), which have been used in several computational approaches for detecting protein complexes. Those predictions might guide future biologic experimental research. Some approaches are topology-based, where highly connected proteins are predicted to be complexes; some propose different clustering algorithms using partitioning, overlaps among clusters for networks modeled with unweighted or weighted graphs; and others use density of clusters and information based on protein functionality. However, some schemes still require much processing time or the quality of their results can be improved. Furthermore, most of the results obtained with computational tools are not accompanied by an analysis of false positives. We propose an effective and efficient mining algorithm for discovering highly connected subgraphs, which is our base for defining protein complexes. Our representation is based on transforming the PPI network into a directed acyclic graph that reduces the number of represented edges and the search space for discovering subgraphs. Our approach considers weighted and unweighted PPI networks. We compare our best alternative using PPI networks from Saccharomyces cerevisiae (yeast) and Homo sapiens (human) with state-of-the-art approaches in terms of clustering, biological metrics and execution times, as well as three gold standards for yeast and two for human. Furthermore, we analyze false positive predicted complexes searching the PDBe (Protein Data Bank in Europe) database in order to identify matching protein complexes that have been purified and structurally characterized. Our analysis shows that more than 50 yeast protein complexes and more than 300 human protein complexes found to be false positives according to our prediction method, i.e., not described in the gold standard complex databases, in fact contain protein complexes that have been characterized structurally and documented in PDBe. We also found that some of these protein complexes have recently been classified as part of a Periodic Table of Protein Complexes. The latest version of our software is publicly available at http://doi.org/10.6084/m9.figshare.5297314.v1.

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

OVERVIEW

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

Rapamycin-induced miR-21 promotes mitochondrial homeostasis and adaptation in mTORC1 activated cells

mTORC1 hyperactivation drives the multi-organ hamartomatous disease tuberous sclerosis complex (TSC). Rapamycin inhibits mTORC1, inducing partial tumor responses; however, the tumors regrow following treatment cessation. We discovered that the oncogenic miRNA, miR-21, is increased in Tsc2-deficient cells and, surprisingly, further increased by rapamycin. To determine the impact of miR-21 in TSC, we inhibited miR-21 in vitro. miR-21 inhibition significantly repressed the tumorigenic potential of Tsc2-deficient cells and increased apoptosis sensitivity. Tsc2-deficient cells' clonogenic and anchorage independent growth were reduced by ∼50% (p<0.01) and ∼75% (p<0.0001), respectively, and combined rapamycin treatment decreased soft agar growth by ∼90% (p<0.0001). miR-21 inhibition also increased sensitivity to apoptosis. Through a network biology-driven integration of RNAseq data, we discovered that miR-21 promotes mitochondrial adaptation and homeostasis in Tsc2-deficient cells. miR-21 inhibition reduced mitochondrial polarization and function in Tsc2-deficient cells, with and without co-treatment with rapamycin. Importantly, miR-21 inhibition limited Tsc2-deficient tumor growth in vivo, reducing tumor size by approximately 3-fold (p<0.0001). When combined with rapamcyin, miR-21 inhibition showed even more striking efficacy, both during treatment and after treatment cessation, with a 4-fold increase in median survival following rapamycin cessation (p=0.0008). We conclude that miR-21 promotes mTORC1-driven tumorigenesis via a mechanism that involves the mitochondria, and that miR-21 is a potential therapeutic target for TSC-associated hamartomas and other mTORC1-driven tumors, with the potential for synergistic efficacy when combined with rapalogs.

Detection of dysregulated protein-association networks by high-throughput proteomics predicts cancer vulnerabilities

The formation of protein complexes and the co-regulation of the cellular concentrations of proteins are essential mechanisms for cellular signaling and for maintaining homeostasis. Here we use isobaric-labeling multiplexed proteomics to analyze protein co-regulation and show that this allows the identification of protein-protein associations with high accuracy. We apply this 'interactome mapping by high-throughput quantitative proteome analysis' (IMAHP) method to a panel of 41 breast cancer cell lines and show that deviations of the observed protein co-regulations in specific cell lines from the consensus network affects cellular fitness. Furthermore, these aberrant interactions serve as biomarkers that predict the drug sensitivity of cell lines in screens across 195 drugs. We expect that IMAHP can be broadly used to gain insight into how changing landscapes of protein-protein associations affect the phenotype of biological systems.

Proteomic Analysis of Human Angiogenin Interactions Reveals Cytoplasmic PCNA as a Putative Binding Partner

Human Angiogenin (hAng) is a member of the ribonuclease A superfamily and a potent inducer of neovascularization. Protein interactions of hAng in the nucleus and cytoplasm of the human umbilical vein cell line EA.hy926 have been investigated by mass spectroscopy. Data are available via ProteomeXchange with identifiers PXD006583 and PXD006584. The first gel-free analysis of hAng immunoprecipitates revealed many statistically significant potential hAng-interacting proteins involved in crucial biological pathways. Surprisingly, proliferating cell nuclear antigen (PCNA), was found to be immunoprecipitated with hAng only in the cytoplasm. The hAng-PCNA interaction and colocalization in the specific cellular compartment was validated with immunoprecipitation, immunoblotting, and immunocytochemistry. The results revealed that PCNA is predominantly localized in the cytoplasm, while hAng is distributed both in the nucleus and in the cytoplasm. hAng and PCNA colocalize in the cytoplasm, suggesting that they may interact in this compartment.

Multilayered Control of Alternative Splicing Regulatory Networks by Transcription Factors

Networks of coordinated alternative splicing (AS) events play critical roles in development and disease. However, a comprehensive knowledge of the factors that regulate these networks is lacking. We describe a high-throughput system for systematically linking trans-acting factors to endogenous RNA regulatory events. Using this system, we identify hundreds of factors associated with diverse regulatory layers that positively or negatively control AS events linked to cell fate. Remarkably, more than one-third of the regulators are transcription factors. Further analyses of the zinc finger protein Zfp871 and BTB/POZ domain transcription factor Nacc1, which regulate neural and stem cell AS programs, respectively, reveal roles in controlling the expression of specific splicing regulators. Surprisingly, these proteins also appear to regulate target AS programs via binding RNA. Our results thus uncover a large "missing cache" of splicing regulators among annotated transcription factors, some of which dually regulate AS through direct and indirect mechanisms.

PrePhyloPro: phylogenetic profile-based prediction of whole proteome linkages

Direct and indirect functional links between proteins as well as their interactions as part of larger protein complexes or common signaling pathways may be predicted by analyzing the correlation of their evolutionary patterns. Based on phylogenetic profiling, here we present a highly scalable and time-efficient computational framework for predicting linkages within the whole human proteome. We have validated this method through analysis of 3,697 human pathways and molecular complexes and a comparison of our results with the prediction outcomes of previously published co-occurrency model-based and normalization methods. Here we also introduce PrePhyloPro, a web-based software that uses our method for accurately predicting proteome-wide linkages. We present data on interactions of human mitochondrial proteins, verifying the performance of this software. PrePhyloPro is freely available at http://prephylopro.org/phyloprofile/.

eDGAR: a database of Disease-Gene Associations with annotated Relationships among genes

BACKGROUND

Genetic investigations, boosted by modern sequencing techniques, allow dissecting the genetic component of different phenotypic traits. These efforts result in the compilation of lists of genes related to diseases and show that an increasing number of diseases is associated with multiple genes. Investigating functional relations among genes associated with the same disease contributes to highlighting molecular mechanisms of the pathogenesis.

RESULTS

We present eDGAR, a database collecting and organizing the data on gene/disease associations as derived from OMIM, Humsavar and ClinVar. For each disease-associated gene, eDGAR collects information on its annotation. Specifically, for lists of genes, eDGAR provides information on: i) interactions retrieved from PDB, BIOGRID and STRING; ii) co-occurrence in stable and functional structural complexes; iii) shared Gene Ontology annotations; iv) shared KEGG and REACTOME pathways; v) enriched functional annotations computed with NET-GE; vi) regulatory interactions derived from TRRUST; vii) localization on chromosomes and/or co-localisation in neighboring loci. The present release of eDGAR includes 2672 diseases, related to 3658 different genes, for a total number of 5729 gene-disease associations. 71% of the genes are linked to 621 multigenic diseases and eDGAR highlights their common GO terms, KEGG/REACTOME pathways, physical and regulatory interactions. eDGAR includes a network based enrichment method for detecting statistically significant functional terms associated to groups of genes.

CONCLUSIONS

eDGAR offers a resource to analyze disease-gene associations. In multigenic diseases genes can share physical interactions and/or co-occurrence in the same functional processes. eDGAR is freely available at: edgar.biocomp.unibo.it.

Defining a Cancer Dependency Map

Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.

Identification of new key genes for type 1 diabetes through construction and analysis of protein-protein interaction networks based on blood and pancreatic islet transcriptomes

BACKGROUND

Type 1 diabetes (T1D) is an autoimmune disease in which pancreatic β-cells are destroyed by infiltrating immune cells. Bilateral cooperation of pancreatic β-cells and immune cells has been proposed in the progression of T1D, but as yet no systems study has investigated this possibility. The aims of the study were to elucidate the underlying molecular mechanisms and identify key genes associated with T1D risk using a network biology approach.

METHODS

Interactome (protein-protein interaction [PPI]) and transcriptome data were integrated to construct networks of differentially expressed genes in peripheral blood mononuclear cells (PBMCs) and pancreatic β-cells. Centrality, modularity, and clique analyses of networks were used to get more meaningful biological information.

RESULTS

Analysis of genes expression profiles revealed several cytokines and chemokines in β-cells and their receptors in PBMCs, which is supports the dialogue between these two tissues in terms of PPIs. Functional modules and complexes analysis unraveled most significant biological pathways such as immune response, apoptosis, spliceosome, proteasome, and pathways of protein synthesis in the tissues. Finally, Y-box binding protein 1 (YBX1), SRSF protein kinase 1 (SRPK1), proteasome subunit alpha1/ 3, (PSMA1/3), X-ray repair cross complementing 6 (XRCC6), Cbl proto-oncogene (CBL), SRC proto-oncogene, non-receptor tyrosine kinase (SRC), phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1), phospholipase C gamma 1 (PLCG1), SHC adaptor protein1 (SHC1) and ubiquitin conjugating enzyme E2 N (UBE2N) were identified as key markers that were hub-bottleneck genes involved in functional modules and complexes.

CONCLUSIONS

This study provide new insights into network biomarkers that may be considered potential therapeutic targets.

An integer programming framework for inferring disease complexes from network data

MOTIVATION

Unraveling the molecular mechanisms that underlie disease calls for methods that go beyond the identification of single causal genes to inferring larger protein assemblies that take part in the disease process.

RESULTS

Here, we develop an exact, integer-programming-based method for associating protein complexes with disease. Our approach scores proteins based on their proximity in a protein-protein interaction network to a prior set that is known to be relevant for the studied disease. These scores are combined with interaction information to infer densely interacting protein complexes that are potentially disease-associated. We show that our method outperforms previous ones and leads to predictions that are well supported by current experimental data and literature knowledge.

AVAILABILITY AND IMPLEMENTATION

The datasets we used, the executables and the results are available at www.cs.tau.ac.il/roded/disease_complexes.zip

CONTACT

roded@post.tau.ac.il.