UniHI 7: an enhanced database for retrieval and interactive analysis of human molecular interaction networks

Uncovering domain motif interactions using high-throughput protein-protein interaction detection methods

Protein-protein interactions (PPIs) are often mediated by short linear motifs (SLiMs) in one protein and domain in another, known as domain-motif interactions (DMIs). During the past decade, SLiMs have been studied to find their role in cellular functions such as post-translational modifications, regulatory processes, protein scaffolding, cell cycle progression, cell adhesion, cell signalling and substrate selection for proteasomal degradation. This review provides a comprehensive overview of the current PPI detection techniques and resources, focusing on their relevance to capturing interactions mediated by SLiMs. We also address the challenges associated with capturing DMIs. Moreover, a case study analysing the BioGrid database as a source of DMI prediction revealed significant known DMI enrichment in different PPI detection methods. Overall, it can be said that current high-throughput PPI detection methods can be a reliable source for predicting DMIs.

Recent Advances and Techniques for Identifying Novel Antibacterial Targets

BACKGROUND

With the emergence of drug-resistant bacteria, the development of new antibiotics is urgently required. Target-based drug discovery is the most frequently employed approach for the drug development process. However, traditional drug target identification techniques are costly and time-consuming. As research continues, innovative approaches for antibacterial target identification have been developed which enabled us to discover drug targets more easily and quickly.

METHODS

In this review, methods for finding drug targets from omics databases have been discussed in detail including principles, procedures, advantages, and potential limitations. The role of phage-driven and bacterial cytological profiling approaches is also discussed. Moreover, current article demonstrates the advancements being made in the establishment of computational tools, machine learning algorithms, and databases for antibacterial target identification.

RESULTS

Bacterial drug targets successfully identified by employing these aforementioned techniques are described as well.

CONCLUSION

The goal of this review is to attract the interest of synthetic chemists, biologists, and computational researchers to discuss and improve these methods for easier and quicker development of new drugs.

Human endometrial cell-type-specific RNA sequencing provides new insights into the embryo-endometrium interplay

STUDY QUESTION

Which genes regulate receptivity in the epithelial and stromal cellular compartments of the human endometrium, and which molecules are interacting in the implantation process between the blastocyst and the endometrial cells?

SUMMARY ANSWER

A set of receptivity-specific genes in the endometrial epithelial and stromal cells was identified, and the role of galectins (LGALS1 and LGALS3), integrin β1 (ITGB1), basigin (BSG) and osteopontin (SPP1) in embryo-endometrium dialogue among many other protein-protein interactions were highlighted.

WHAT IS KNOWN ALREADY

The molecular dialogue taking place between the human embryo and the endometrium is poorly understood due to ethical and technical reasons, leaving human embryo implantation mostly uncharted.

STUDY DESIGN SIZE DURATION

Paired pre-receptive and receptive phase endometrial tissue samples from 16 healthy women were used for RNA sequencing. Trophectoderm RNA sequences were from blastocysts.

PARTICIPANTS/MATERIALS SETTING METHODS

Cell-type-specific RNA-seq analysis of freshly isolated endometrial epithelial and stromal cells using fluorescence-activated cell sorting (FACS) from 16 paired pre-receptive and receptive tissue samples was performed. Endometrial transcriptome data were further combined in silico with trophectodermal gene expression data from 466 single cells originating from 17 blastocysts to characterize the first steps of embryo implantation. We constructed a protein-protein interaction network between endometrial epithelial and embryonal trophectodermal cells, and between endometrial stromal and trophectodermal cells, thereby focusing on the very first phases of embryo implantation, and highlighting the molecules likely to be involved in the embryo apposition, attachment and invasion.

MAIN RESULTS AND THE ROLE OF CHANCE

In total, 499 epithelial and 581 stromal genes were up-regulated in the receptive phase endometria when compared to pre-receptive samples. The constructed protein-protein interactions identified a complex network of 558 prioritized protein-protein interactions between trophectodermal, epithelial and stromal cells, which were grouped into clusters based on the function of the involved molecules. The role of galectins (LGALS1 and LGALS3), integrin β1 (ITGB1), basigin (BSG) and osteopontin (SPP1) in the embryo implantation process were highlighted.

LARGE SCALE DATA

RNA-seq data are available at www.ncbi.nlm.nih.gov/geo under accession number GSE97929.

LIMITATIONS REASONS FOR CAUTION

Providing a static snap-shot of a dynamic process and the nature of prediction analysis is limited to the known interactions available in databases. Furthermore, the cell sorting technique used separated enriched epithelial cells and stromal cells but did not separate luminal from glandular epithelium. Also, the use of biopsies taken from non-pregnant women and using spare IVF embryos (due to ethical considerations) might miss some of the critical interactions characteristic of natural conception only.

WIDER IMPLICATIONS OF THE FINDINGS

The findings of our study provide new insights into the molecular embryo-endometrium interplay in the first steps of implantation process in humans. Knowledge about the endometrial cell-type-specific molecules that coordinate successful implantation is vital for understanding human reproduction and the underlying causes of implantation failure and infertility. Our study results provide a useful resource for future reproductive research, allowing the exploration of unknown mechanisms of implantation. We envision that those studies will help to improve the understanding of the complex embryo implantation process, and hopefully generate new prognostic and diagnostic biomarkers and therapeutic approaches to target both infertility and fertility, in the form of new contraceptives.

STUDY FUNDING/COMPETING INTERESTS

This research was funded by the Estonian Research Council (grant PRG1076); Horizon 2020 innovation grant (ERIN, grant no. EU952516); Enterprise Estonia (grant EU48695); the EU-FP7 Marie Curie Industry-Academia Partnerships and Pathways (IAPP, grant SARM, EU324509); Spanish Ministry of Economy, Industry and Competitiveness (MINECO) and European Regional Development Fund (FEDER) (grants RYC-2016-21199, ENDORE SAF2017-87526-R, and Endo-Map PID2021-127280OB-100); Programa Operativo FEDER Andalucía (B-CTS-500-UGR18; A-CTS-614-UGR20), Junta de Andalucía (PAIDI P20_00158); Margarita Salas program for the Requalification of the Spanish University system (UJAR01MS); the Knut and Alice Wallenberg Foundation (KAW 2015.0096); Swedish Research Council (2012-2844); and Sigrid Jusélius Foundation; Academy of Finland. A.S.-L. is funded by the Spanish Ministry of Science, Innovation and Universities (PRE2018-085440). K.G.-D. has received consulting fees and/or honoraria from RemovAid AS, Norway Bayer, MSD, Gedeon Richter, Mithra, Exeltis, MedinCell, Natural cycles, Exelgyn, Vifor, Organon, Campus Pharma and HRA-Pharma and NIH support to the institution; D.B. is an employee of IGENOMIX. The rest of the authors declare no conflict of interest.

A review of bioinformatics tools and web servers in different microarray platforms used in cancer research

Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.

Network assisted analysis of de novo variants using protein-protein interaction information identified 46 candidate genes for congenital heart disease

De novo variants (DNVs) with deleterious effects have proved informative in identifying risk genes for early-onset diseases such as congenital heart disease (CHD). A number of statistical methods have been proposed for family-based studies or case/control studies to identify risk genes by screening genes with more DNVs than expected by chance in Whole Exome Sequencing (WES) studies. However, the statistical power is still limited for cohorts with thousands of subjects. Under the hypothesis that connected genes in protein-protein interaction (PPI) networks are more likely to share similar disease association status, we developed a Markov Random Field model that can leverage information from publicly available PPI databases to increase power in identifying risk genes. We identified 46 candidate genes with at least 1 DNV in the CHD study cohort, including 18 known human CHD genes and 35 highly expressed genes in mouse developing heart. Our results may shed new insight on the shared protein functionality among risk genes for CHD.

The topologic information in a pathway may be informative to identify functionally interrelated genes and help improve statistical power in DNV studies. Under the hypothesis that connected genes in PPI networks are more likely to share similar disease association status, we developed a novel statistical model that can leverage information from publicly available PPI databases. Through simulation studies under multiple settings, we proved our method can increase statistical power in identifying additional risk genes compared to methods without using the PPI network information. We then applied our method to a real example for CHD DNV data, and then visualized the subnetwork of candidate genes to find potential functional gene clusters for CHD.

Reconstruction of human protein-coding gene functional association network based on machine learning

Networks consisting of molecular interactions are intrinsically dynamical systems of an organism. These interactions curated in molecular interaction databases are still not complete and contain false positives introduced by high-throughput screening experiments. In this study, we propose a framework to integrate interactions of functional associated protein-coding genes from 31 data sources to reconstruct a network with high coverage and quality. For each interaction, 369 features were constructed including properties of both the interaction and the involved genes. The training and validation sets were built on the pathway interactions as positives and the potential negative instances resulting from our proposed semi-supervised strategy. Random forest classification method was then applied to train and predict multiple times to give a score for each interaction. After setting a threshold estimated by a Binomial distribution, a Human protein-coding Gene Functional Association Network (HuGFAN) was reconstructed with 20 383 genes and 1185 429 high confidence interactions. Then, HuGFAN was compared with other networks from data sources with respect to network properties, suggesting that HuGFAN is more function and pathway related. Finally, HuGFAN was applied to identify cancer driver through two famous network-based methods (DriverNet and HotNet2) to show its outstanding performance compared with other networks. HuGFAN and other supplementary files are freely available at https://github.com/xthuang226/HuGFAN.

SIRT3 mediates mitofusin 2 ubiquitination and degradation to suppress ischemia reperfusion-induced acute kidney injury

Ischemia reperfusion-induced acute kidney injury (IR-induced AKI) is a life-threatening disease with many complications. Mitofusin 2 (Mfn2) ubiquitination is related to AKI. But the underlying molecular mechanisms remain unknown. This study aimed to probe the mechanism of Mfn2 ubiquitination in IR-induced AKI development. In IR-induced AKI mouse models, orbital blood and urine were collected for assessing kidney function. The kidney injury, ultrastructure of mitochondria, and histopathology in mice were evaluated after injection of G5, an ubiquitination inhibitor. Oxygen glucose deprivation/reoxygenation (OGD/R) models were established in HK-2 cells, and the mitochondria were extracted. Cell viability, apoptosis, oxidative stress, inflammatory reaction, mitochondrial membrane potential, and ATP production were measured. Mfn2 ubiquitination in mouse and cell models was evaluated. si-SIRT3 and pcDNA3.1-SIRT3 were transfected into cell models. Consequently, kidney function in mice was impaired by IR-induced AKI. Mfn2 ubiquitination and degradation promoted IR-induced AKI. OGD/R induced renal tubular epithelial cell injury and disrupted mitochondrial dynamics and functions through promoting Mfn2 ubiquitination. SIRT3 knockdown led to Mfn2 ubiquitination by binding to UBC; while its overexpression alleviated tubular epithelial cell injury. Briefly, SIRT3 mediates Mfn2 ubiquitination to relieve IR-induced AKI. This investigation may offer new insights for the treatment of IR-induced AKI injury.

Multi-resolution visualization and analysis of biomolecular networks through hierarchical community detection and web-based graphical tools

The visual exploration and analysis of biomolecular networks is of paramount importance for identifying hidden and complex interaction patterns among proteins. Although many tools have been proposed for this task, they are mainly focused on the query and visualization of a single protein with its neighborhood. The global exploration of the entire network and the interpretation of its underlying structure still remains difficult, mainly due to the excessively large size of the biomolecular networks. In this paper we propose a novel multi-resolution representation and exploration approach that exploits hierarchical community detection algorithms for the identification of communities occurring in biomolecular networks. The proposed graphical rendering combines two types of nodes (protein and communities) and three types of edges (protein-protein, community-community, protein-community), and displays communities at different resolutions, allowing the user to interactively zoom in and out from different levels of the hierarchy. Links among communities are shown in terms of relationships and functional correlations among the biomolecules they contain. This form of navigation can be also combined by the user with a vertex centric visualization for identifying the communities holding a target biomolecule. Since communities gather limited-size groups of correlated proteins, the visualization and exploration of complex and large networks becomes feasible on off-the-shelf computer machines. The proposed graphical exploration strategies have been implemented and integrated in UNIPred-Web, a web application that we recently introduced for combining the UNIPred algorithm, able to address both integration and protein function prediction in an imbalance-aware fashion, with an easy to use vertex-centric exploration of the integrated network. The tool has been deeply amended from different standpoints, including the prediction core algorithm. Several tests on networks of different size and connectivity have been conducted to show off the vast potential of our methodology; moreover, enrichment analyses have been performed to assess the biological meaningfulness of detected communities. Finally, a CoV-human network has been embedded in the system, and a corresponding case study presented, including the visualization and the prediction of human host proteins that potentially interact with SARS-CoV2 proteins.

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.

Defect of SLC38A3 promotes epithelial-mesenchymal transition and predicts poor prognosis in esophageal squamous cell carcinoma

Objective

Solute carrier family 38 (SLC38s) transporters play important roles in amino acid transportation and signaling transduction. However, their genetic alterations and biological roles in tumors are still largely unclear. This study aimed to elucidate the genetic signatures of SLC38s transporters and their implications in esophageal squamous cell carcinoma (ESCC).

Methods

Analyses on somatic mutation and copy number alterations (CNAs) of SLC38A3 were performed as described. Immunohistochemistry (IHC) assay and Western blot assay were used to detect the protein expression level. MTS assay, colony formation assay, transwell assay and wound healing assay were used to explore the malignant phenotypes of ESCC cells. Immunofluorescence assay was used to verify the colocalization of two indicated proteins and immunopreciptation assay was performed to confirm the interaction of proteins.

Results

Our findings revealed that SLC38s family was significantly disrupted in ESCC, with high frequent CNAs and few somatic mutations. SLC38A3 was the most frequent loss gene among them and was linked to poor survival and lymph node metastasis. The expression of SLC38A3 was lower in tumor tissues compared to that in normal tissues, which was also significantly associated with worse clinical outcome. Further experiments revealed that depletion of SLC38A3 could promote EMT in ESCC cell lines, and the interaction of SLC38A3 and SETDB1 might lead to the reduced transcription of Snail. Pharmacogenomic analyses demonstrated that fifteen inhibitors were showed significantly correlated with SLC38A3 expression.

Conclusions

Our investigations have provided insights that SLC38A3 could act as a suppressor in EMT pathway and serve as a prognostic factor and predictor of differential drug sensitivities in ESCC.

Analysis of Protein-Protein Interaction Networks through Computational Approaches

The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein-protein interaction prediction.

Integrated Analysis of Prognostic and Immune Associated Integrin Family in Ovarian Cancer

Human integrin receptors are important for cell-cell and cell-matrix adhesion in normal epithelial cells. Emerging evidences have indicated integrin members are involved in cancer development and progression as well. However, the expression patterns and clinical significance of the whole integrin family in ovarian cancer (OC) have not yet been well understood. In the present study, we utilized the public datasets including GEPIA, GEO, ONCOMINE, cBioPortal, Kaplan-Meier Plotter, TIMER databases, to analyze the expression and prognostic value of integrin members in OC. We found ITGA3/B4/B6/B7/B8 were abnormally overexpressed in OC; ITGA6 was good prognosis predictor in OC; ITGA3/ B4/B8 were poor prognosis predictor specially in advanced OC patients; elevated ITGA3/B4 might promote metastasis and elevated ITGA3/B8 might promote platinum resistance of OC; ITGA3 and ITGB4 might synergistically or independently regulate cell adhesion and proliferation; ITGA4/AL/AM/AX/B2/B7 showed strong correlations with various tumor immune infiltrates (TILs), especially with pro-tumor immunes cell types like monocyte, M2 macrophage and exhaustion T cells infiltration; ITGAL/AM/B2/B7 and residing memory CD8+ T cells marker ITGAE were specially associated with early OC patients outcome. Our results implied that ITGA3/B4 were important prognostic markers of advanced OC, ITGAL/AM/ B2/B7 were immune associated prognosis markers of early OC, together they might render important therapeutic targets for OC.

Advancing from protein interactomes and gene co-expression networks towards multi-omics-based composite networks: approaches for predicting and extracting biological knowledge

Prediction of biological interaction networks from single-omics data has been extensively implemented to understand various aspects of biological systems. However, more recently, there is a growing interest in integrating multi-omics datasets for the prediction of interactomes that provide a global view of biological systems with higher descriptive capability, as compared to single omics. In this review, we have discussed various computational approaches implemented to infer and analyze two of the most important and well studied interactomes: protein-protein interaction networks and gene co-expression networks. We have explicitly focused on recent methods and pipelines implemented to infer and extract biologically important information from these interactomes, starting from utilizing single-omics data and then progressing towards multi-omics data. Accordingly, recent examples and case studies are also briefly discussed. Overall, this review will provide a proper understanding of the latest developments in protein and gene network modelling and will also help in extracting practical knowledge from them.

SETDB1 promotes the progression of colorectal cancer via epigenetically silencing p21 expression

SETDB1, a histone H3K9 methyltransferase, has been reported to be upregulated in a variety of tumors and promotes cancer development. However, the exact pathogenesis of SETDB1 in human colorectal cancer (CRC) is hitherto unknown. Here, we showed that SETDB1 expression was highly amplified in CRC. Functionally, SETDB1 downregulation in SW480 and HCT116 cells reduced cell proliferation, migration, invasion, and increased CRC cells apoptosis. In contrast, SETDB1 overexpression promoted CRC cells proliferation, migration, and invasion. High expression of SETDB1 was associated with a more aggressive phenotype in vitro. Flow cytometry showed that cell cycle was arrested in G1 phase after SETDB1 silencing. Furthermore, depletion of SETDB1 in vivo suppressed CRC cells proliferation. Mechanistically, p21 was identified as the target of SETDB1. After transfected with siSETDB1, expression of p21 was distinctly increased. In contrast, expression of p21 was significantly decreased after overexpression SETDB1. We also showed that SETDB1 could be involved in the regulation of epithelial–mesenchymal transition (EMT) in HCT116 cells. Moreover, we confirmed that SETDB1 could regulate the activity of p21 promoter by dual-luciferase repoter assay, and proved that SETDB1 could bind to the promoter of p21 and regulate its H3K9me3 enrichment level by ChIP-PCR experiment. Finally, we verified that silencing of SETDB1 inhibited CRC tumorigenesis in vivo. In conclusion, our results indicate that SETDB1 is a major driver of CRC development and might provide a new therapeutic target for the clinical treatment of CRC.

LncRNA MALAT1 regulates diabetic cardiac fibroblasts through the Hippo-YAP signaling pathway

Diabetic cardiomyopathy (DCM) is a major diabetes-related microvascular disease. LncRNA MALAT1 is widely expressed in cardiomyocytes responding to hypoxia and high levels of glucose (high glucose). In this study, cardiac fibroblasts (CFs) were transfected with si-MALAT1 and exposed to high glucose. CFs in the high glucose groups were treated with 30 mmol/L glucose, and the control CFs were treated with 5.5 mmol/L glucose. The expression of MALAT1 in the nucleus and cytoplasm of CFs was detected. The biological behavior of CFs, as well as collagen production, activity of the Hippo-YAP pathway, and nuclear localization of YAP were measured. Mouse models of DCM were established to observe the pathological changes to myocardium and determine the levels of collagen I, Bax, and Bcl-2. The interaction between MALAT1 and YAP was analyzed, and CREB expression in the high-glucose treated CFs was detected. MALAT1 was upregulated in high-glucose CFs and located in the nucleus. High-glucose increased collagen production, inflammation, cell proliferation, cell invasiveness, and phosphorylation of MST1 and LATS1, and also promoted nuclear translocation of YAP. These trends in high-glucose treated CFs and the DCM mice were reversed by transfection with si-MALAT1. MALAT1 positively regulated the nuclear translocation of YAP by binding to CREB. CREB levels were increased in the high-glucose CFs, but decreased after silencing MALAT1. These results indicate that si-MALAT1 reduces inflammation and collagen accumulation in high-glucose CFs and DCM mice via the Hippo-YAP pathway and CREB.

Generation and Interpretation of Context-Specific Human Protein-Protein Interaction Networks with HIPPIE

High-throughput techniques for the detection of protein-protein interactions (PPIs) have enabled a systems approach for the study of the living cell. However, the increasing amount of protein interaction data, the varying quality of these measurements, and the lack of context information make it difficult to construct meaningful and reliable protein networks.The Human Integrated Protein-Protein Interaction rEference (HIPPIE) is a web tool that integrates and annotates experimentally supported human PPIs from a heterogeneous set of data sources. In HIPPIE, one can query for the interactors of one or more proteins and generate high-quality and context-specific networks. This chapter highlights HIPPIE's most important features and exemplifies its functionality through a proposed use case.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Interaction network analysis of YBX1 for identification of therapeutic targets in adenocarcinomas

Human Y-box binding protein-1 (YBX1) is a member of highly conserved cold-shock domain protein family, which is involved in transcriptional as well as translational regulation of many genes. Nuclear localization of YBX1 has been observed in various cancer types and it's overexpression has been linked to adverse clinical outcome and poor therapy response, but no diagnostic or therapeutic correlation has been established so far. This study aimed to identify differentially expressed novel genes among the interactors of YBX1 in different cancer types. Analysis of RNA-Seq data for colorectal, lung, prostate and stomach adenocarcinoma identified 39 unique genes, which are differentially expressed in the four adenocarcinoma types. Gene-enrichment analysis for the differentially expressed genes from individual adenocarcinoma with focus on unique genes resulted in a total of 57 gene sets specific to each adenocarcinoma. Gene ontology for commonly expressed genes suggested the pathways and possible mechanisms through which they affect each adenocarcinoma type considered in the study. Gene regulatory network constructed for the common genes and network topology was analyzed for the central nodes. Here 12 genes were found to play important roles in the network formation; among them, two genes FOXM1 and TOP2A were found to be in central network formation, which makes them a common target for therapeutics. Furthermore, five common differentially expressed genes in all adenocarcinomas were also identified.

Inhibition of histone methyltransferase DOT1L silences ERα gene and blocks proliferation of antiestrogen-resistant breast cancer cells

Breast cancer (BC) resistance to endocrine therapy results from constitutively active or aberrant estrogen receptor α (ERα) signaling, and ways to block ERα pathway in these tumors are sought after. We identified the H3K79 methyltransferase DOT1L as a novel cofactor of ERα in BC cell chromatin, where the two proteins colocalize to regulate estrogen target gene transcription. DOT1L blockade reduces proliferation of hormone-responsive BC cells in vivo and in vitro, consequent to cell cycle arrest and apoptotic cell death, with widespread effects on ER-dependent gene transcription, including ERα and FOXA1 gene silencing. Antiestrogen-resistant BC cells respond to DOT1L inhibition also in mouse xenografts, with reduction in ERα levels, H3K79 methylation, and tumor growth. These results indicate that DOT1L is an exploitable epigenetic target for treatment of endocrine therapy-resistant ERα-positive BCs.

HEDD: Human Enhancer Disease Database

Enhancers, as specialized genomic cis-regulatory elements, activate transcription of their target genes and play an important role in pathogenesis of many human complex diseases. Despite recent systematic identification of them in the human genome, currently there is an urgent need for comprehensive annotation databases of human enhancers with a focus on their disease connections. In response, we built the Human Enhancer Disease Database (HEDD) to facilitate studies of enhancers and their potential roles in human complex diseases. HEDD currently provides comprehensive genomic information for ∼2.8 million human enhancers identified by ENCODE, FANTOM5 and RoadMap with disease association scores based on enhancer-gene and gene-disease connections. It also provides Web-based analytical tools to visualize enhancer networks and score enhancers given a set of selected genes in a specific gene network. HEDD is freely accessible at http://zdzlab.einstein.yu.edu/1/hedd.php.

A fast approach to detect gene-gene synergy

Selecting informative genes, including individually discriminant genes and synergic genes, from expression data has been useful for medical diagnosis and prognosis. Detecting synergic genes is more difficult than selecting individually discriminant genes. Several efforts have recently been made to detect gene-gene synergies, such as dendrogram-based I(X₁; X₂; Y) (mutual information), doublets (gene pairs) and MIC(X₁; X₂; Y) based on the maximal information coefficient. It is unclear whether dendrogram-based I(X₁; X₂; Y) and doublets can capture synergies efficiently. Although MIC(X₁; X₂; Y) can capture a wide range of interaction, it has a high computational cost triggered by its 3-D search. In this paper, we developed a simple and fast approach based on abs conversion type (i.e. Z = |X₁ − X₂|) and t-test, to detect interactions in simulation and real-world datasets. Our results showed that dendrogram-based I(X₁; X₂; Y) and doublets are helpless for discovering pair-wise gene interactions, our approach can discover typical pair-wise synergic genes efficiently. These synergic genes can reach comparable accuracy to the individually discriminant genes using the same number of genes. Classifier cannot learn well if synergic genes have not been converted properly. Combining individually discriminant and synergic genes can improve the prediction performance.

Discerning molecular interactions: A comprehensive review on biomolecular interaction databases and network analysis tools

Computational analysis of biomolecular interaction networks is now gaining a lot of importance to understand the functions of novel genes/proteins. Gene interaction (GI) network analysis and protein-protein interaction (PPI) network analysis play a major role in predicting the functionality of interacting genes or proteins and gives an insight into the functional relationships and evolutionary conservation of interactions among the genes. An interaction network is a graphical representation of gene/protein interactome, where each gene/protein is a node, and interaction between gene/protein is an edge. In this review, we discuss the popular open source databases that serve as data repositories to search and collect protein/gene interaction data, and also tools available for the generation of interaction network, visualization and network analysis. Also, various network analysis approaches like topological approach and clustering approach to study the network properties and functional enrichment server which illustrates the functions and pathway of the genes and proteins has been discussed. Hence the distinctive attribute mentioned in this review is not only to provide an overview of tools and web servers for gene and protein-protein interaction (PPI) network analysis but also to extract useful and meaningful information from the interaction networks.

PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology

It has been acknowledged that source databases recording experimentally supported human protein-protein interactions (PPIs) exhibit limited overlap. Thus, the reconstruction of a comprehensive PPI network requires appropriate integration of multiple heterogeneous primary datasets, presenting the PPIs at various genetic reference levels. Existing PPI meta-databases perform integration via normalization; namely, PPIs are merged after converted to a certain target level. Hence, the node set of the integrated network depends each time on the number and type of the combined datasets. Moreover, the irreversible a priori normalization process hinders the identification of normalization artifacts in the integrated network, which originate from the nonlinearity characterizing the genetic information flow. PICKLE (Protein InteraCtion KnowLedgebasE) 2.0 implements a new architecture for this recently introduced human PPI meta-database. Its main novel feature over the existing meta-databases is its approach to primary PPI dataset integration via genetic information ontology. Building upon the PICKLE principles of using the reviewed human complete proteome (RHCP) of UniProtKB/Swiss-Prot as the reference protein interactor set, and filtering out protein interactions with low probability of being direct based on the available evidence, PICKLE 2.0 first assembles the RHCP genetic information ontology network by connecting the corresponding genes, nucleotide sequences (mRNAs) and proteins (UniProt entries) and then integrates PPI datasets by superimposing them on the ontology network without any a priori transformations. Importantly, this process allows the resulting heterogeneous integrated network to be reversibly normalized to any level of genetic reference without loss of the original information, the latter being used for identification of normalization biases, and enables the appraisal of potential false positive interactions through PPI source database cross-checking. The PICKLE web-based interface (www.pickle.gr) allows for the simultaneous query of multiple entities and provides integrated human PPI networks at either the protein (UniProt) or the gene level, at three PPI filtering modes.

The nuclear receptor ERβ engages AGO2 in regulation of gene transcription, RNA splicing and RISC loading

BACKGROUND

The RNA-binding protein Argonaute 2 (AGO2) is a key effector of RNA-silencing pathways It exerts a pivotal role in microRNA maturation and activity and can modulate chromatin remodeling, transcriptional gene regulation and RNA splicing. Estrogen receptor beta (ERβ) is endowed with oncosuppressive activities, antagonizing hormone-induced carcinogenesis and inhibiting growth and oncogenic functions in luminal-like breast cancers (BCs), where its expression correlates with a better prognosis of the disease.

RESULTS

Applying interaction proteomics coupled to mass spectrometry to characterize nuclear factors cooperating with ERβ in gene regulation, we identify AGO2 as a novel partner of ERβ in human BC cells. ERβ-AGO2 association was confirmed in vitro and in vivo in both the nucleus and cytoplasm and is shown to be RNA-mediated. ChIP-Seq demonstrates AGO2 association with a large number of ERβ binding sites, and total and nascent RNA-Seq in ERβ + vs ERβ - cells, and before and after AGO2 knock-down in ERβ + cells, reveals a widespread involvement of this factor in ERβ-mediated regulation of gene transcription rate and RNA splicing. Moreover, isolation and sequencing by RIP-Seq of ERβ-associated long and small RNAs in the cytoplasm suggests involvement of the nuclear receptor in RISC loading, indicating that it may also be able to directly control mRNA translation efficiency and stability.

CONCLUSIONS

These results demonstrate that AGO2 can act as a pleiotropic functional partner of ERβ, indicating that both factors are endowed with multiple roles in the control of key cellular functions.

Silencing the cleavage factor CFIm25 as a new strategy to control Entamoeba histolytica parasite

The 25 kDa subunit of the Clevage Factor Im (CFIm25) is an essential factor for messenger RNA polyadenylation in human cells. Therefore, here we investigated whether the homologous protein of Entamoeba histolytica, the protozoan responsible for human amoebiasis, might be considered as a biochemical target for parasite control. Trophozoites were cultured with bacterial double-stranded RNA molecules targeting the EhCFIm25 gene, and inhibition of mRNA and protein expression was confirmed by RT-PCR and Western blot assays, respectively. EhCFIm25 silencing was associated with a significant acceleration of cell proliferation and cell death. Moreover, trophozoites appeared as larger and multinucleated cells. These morphological changes were accompanied by a reduced mobility, and erythrophagocytosis was significantly diminished. Lastly, the knockdown of EhCFIm25 affected the poly(A) site selection in two reporter genes and revealed that EhCFIm25 stimulates the utilization of downstream poly(A) sites in E. histolytica mRNA. Overall, our data confirm that targeting the polyadenylation process represents an interesting strategy for controlling parasites, including E. histolytica. To our best knowledge, the present study is the first to have revealed the relevance of the cleavage factor CFIm25 as a biochemical target in parasites.

HDNetDB: A Molecular Interaction Database for Network-Oriented Investigations into Huntington's Disease

Huntington’s disease (HD) is a progressive and fatal neurodegenerative disorder caused by an expanded CAG repeat in the huntingtin gene. Although HD is monogenic, its molecular manifestation appears highly complex and involves multiple cellular processes. The recent application of high throughput platforms such as microarrays and mass-spectrometry has indicated multiple pathogenic routes. The massive data generated by these techniques together with the complexity of the pathogenesis, however, pose considerable challenges to researchers. Network-based methods can provide valuable tools to consolidate newly generated data with existing knowledge, and to decipher the interwoven molecular mechanisms underlying HD. To facilitate research on HD in a network-oriented manner, we have developed HDNetDB, a database that integrates molecular interactions with many HD-relevant datasets. It allows users to obtain, visualize and prioritize molecular interaction networks using HD-relevant gene expression, phenotypic and other types of data obtained from human samples or model organisms. We illustrated several HDNetDB functionalities through a case study and identified proteins that constitute potential cross-talk between HD and the unfolded protein response (UPR). HDNetDB is publicly accessible at http://hdnetdb.sysbiolab.eu.

Membrane-enriched proteome changes and prion protein expression during neural differentiation and in neuroblastoma cells

Background

The function of the prion protein, involved in the so-called prion diseases, remains a subject of intense debate and the possibility that it works as a pleiotropic protein through the interaction with multiple membrane proteins is somehow supported by recent reports. Therefore, the use of proteomic and bioinformatics combined to uncover cellular processes occurring together with changes in the expression of the prion protein may provide further insight into the putative pleiotropic role of the prion protein.

Results

This study assessed the membrane-enriched proteome changes accompanying alterations in the expression of the prion protein. A 2D-DIGE approach was applied to two cell lines after prefractionation towards the membrane protein subset: an embryonic stem cell line and the PK1 subline of neuroblastoma cells which efficiently propagates prion infection. Several proteins were differentially abundant with the increased expression of the prion protein during neural differentiation of embryonic stem cells and with the knockdown of the prion protein in PK1 cells. The identity of around 20% of the differentially abundant proteins was obtained by tandem MS. The catalytic subunit A of succinate dehydrogenase, a key enzyme for the aerobic energy metabolism and redox homeostasis, showed a similar abundance trend as the prion protein in both proteomic experiments. A gene ontology analysis revealed “myelin sheath”, “organelle membrane” and “focal adhesion” associated proteins as the main cellular components, and “protein folding” and “ATPase activity” as the biological processes enriched in the first set of differentially abundant proteins. The known interactome of these differentially abundant proteins was customized to reveal four interactors with the prion protein, including two heat shock proteins and a protein disulfide isomerase.

Conclusions

Overall, our study shows that expression of the prion protein occurs concomitantly with changes in chaperone activity and cell-redox homeostasis, emphasizing the functional link between these cellular processes and the prion protein.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3694-6) contains supplementary material, which is available to authorized users.

MiR-34a-5p promotes multi-chemoresistance of osteosarcoma through down-regulation of the DLL1 gene

MiR-34a-5p has been implicated in the tumorigenesis and progression of several types of cancer. However, the role of miR-34a-5p in osteosarcoma (OS) remains largely unknown. This study was performed in two multi-chemosensitive (G-292 and MG63.2) and two resistant (SJSA-1 and MNNG/HOS) OS cell lines. MiR-34a-5p promotes OS multi-chemoresistance via its repression of the Delta-like ligand 1 (DLL1) gene, the ligand of the Notch pathway, and thus negatively correlates with OS chemoresistance. The siRNA-mediated repression of the DLL1 gene suppressed cell apoptosis and de-sensitized G-292 and MG63.2 cells, while overexpression of DLL1 sensitized SJSA-1 and MNNG/HOS cells to drug-induced cell death. In agreement with the changes in the drug-induced cell death, the activity of the ATF2/ATF3/ATF4 signaling pathway was significantly altered by a forced reversal of miR-34a-5p or DLL1 levels in OS cells. DLL1 is a target of miR-34a-5p and negatively regulates the multi-chemoresistance of OS. This study suggested that miR-34a-5p, DLL1 and the ATF2/ATF3/ATF4 signaling pathway-associated genes are the potential diagnostic and/or therapeutic targets for an effective chemotherapy of OS. Our results also provide novel insights into the effective chemotherapy for OS patients.

Study on material base and action mechanism of compound Danshen dripping pills for treatment of atherosclerosis based on modularity analysis

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese medicine (TCM) has been widely used in China and its surrounding countries in clinical treatments for centuries-long time. However, due to the complexity of TCM constituents, both action mechanism and material base of TCM remain nearly unknown.

AIM OF THE STUDY

The present study was designed to uncover the action mechanism and material base of TCM in a low-cost manner.

MATERIALS AND METHODS

Compound Danshen dripping pills (DSP) is a widely used TCM for treatment of atherosclerosis, and was researched here to demonstrate the effectiveness of our method. We constructed a heterogeneous network for DSP, identified the significant network module, and analyzed the primary pharmacological units by performing GO and pathways enrichment analysis.

RESULTS

Two significant network modules were identified from the heterogeneous network of DSP, and three compounds out of four hub nodes in the network were found to intervene in the process of atherosclerosis. Moreover, 13 out of 20 enriched pathways that were ranked in top 10 corresponding to both the two pharmacological units were found to be involved in the process of atherosclerosis.

CONCLUSIONS

Quercetin, luteolin and apigenin may be the main active compounds which modulate the signaling pathways, such as metabolism of xenobiotics by cytochrome P450, retinol metabolism, etc. The present method helps reveal the action mechanism and material base of DSP for treatment of atherosclerosis.

Interactome-transcriptome analysis discovers signatures complementary to GWAS Loci of Type 2 Diabetes

Protein interactions play significant roles in complex diseases. We analyzed peripheral blood mononuclear cells (PBMC) transcriptome using a multi-method strategy. We constructed a tissue-specific interactome (T2Di) and identified 420 molecular signatures associated with T2D-related comorbidity and symptoms, mainly implicated in inflammation, adipogenesis, protein phosphorylation and hormonal secretion. Apart from explaining the residual associations within the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) study, the T2Di signatures were enriched in pathogenic cell type-specific regulatory elements related to fetal development, immunity and expression quantitative trait loci (eQTL). The T2Di revealed a novel locus near a well-established GWAS loci AChE, in which SRRT interacts with JAZF1, a T2D-GWAS gene implicated in pancreatic function. The T2Di also included known anti-diabetic drug targets (e.g. PPARD, MAOB) and identified possible druggable targets (e.g. NCOR2, PDGFR). These T2Di signatures were validated by an independent computational method, and by expression data of pancreatic islet, muscle and liver with some of the signatures (CEBPB, SREBF1, MLST8, SRF, SRRT and SLC12A9) confirmed in PBMC from an independent cohort of 66 T2D and 66 control subjects. By combining prior knowledge and transcriptome analysis, we have constructed an interactome to explain the multi-layered regulatory pathways in T2D.

Global profiling of protein complexes: current approaches and their perspective in biomedical research

INTRODUCTION

Despite the rapid evolution of proteomic methods, protein interactions and their participation in protein complexes - an important aspect of their function - has rarely been investigated on the proteome-wide level. Disease states, such as muscular dystrophy or viral infection, are induced by interference in protein-protein interactions within complexes. The purpose of this review is to describe the current methods for global complexome analysis and to critically discuss the challenges and opportunities for the application of these methods in biomedical research. Areas covered: We discuss advancements in experimental techniques and computational tools that facilitate profiling of the complexome. The main focus is on the separation of native protein complexes via size exclusion chromatography and gel electrophoresis, which has recently been combined with quantitative mass spectrometry, for a global protein-complex profiling. The development of this approach has been supported by advanced bioinformatics strategies and fast and sensitive mass spectrometers that have allowed the analysis of whole cell lysates. The application of this technique to biomedical research is assessed, and future directions are anticipated. Expert commentary: The methodology is quite new, and has already shown great potential when combined with complementary methods for detection of protein complexes.

Using Whole Exome Sequencing to Identify Candidate Genes With Rare Variants In Nonsyndromic Cleft Lip and Palate

Studies suggest that nonsyndromic cleft lip and palate (NSCLP) is polygenic with variable penetrance, presenting a challenge in identifying all causal genetic variants. Despite relatively high prevalence of NSCLP among Amerindian populations, no large whole exome sequencing (WES) studies have been completed in this population. Our goal was to identify candidate genes with rare genetic variants for NSCLP in a Honduran population using WES. WES was performed on two to four members of 27 multiplex Honduran families. Genetic variants with a minor allele frequency > 1% in reference databases were removed. Heterozygous variants consistent with dominant disease with incomplete penetrance were ascertained, and variants with predicted functional consequence were prioritized for analysis. Pedigree-specific P-values were calculated as the probability of all affected members in the pedigree being carriers, given that at least one is a carrier. Preliminary results identified 3,727 heterozygous rare variants; 1,282 were predicted to be functionally consequential. Twenty-three genes had variants of interest in ≥3 families, where some genes had different variants in each family, giving a total of 50 variants. Variant validation via Sanger sequencing of the families and unrelated unaffected controls excluded variants that were sequencing errors or common variants not in databases, leaving four genes with candidate variants in ≥3 families. Of these, candidate variants in two genes consistently segregate with NSCLP as a dominant variant with incomplete penetrance: ACSS2 and PHYH. Rare variants found at the same gene in all affected individuals in several families are likely to be directly related to NSCLP.

Complementary Post Transcriptional Regulatory Information is Detected by PUNCH-P and Ribosome Profiling

Two novel approaches were recently suggested for genome-wide identification of protein aspects synthesized at a given time. Ribo-Seq is based on sequencing all the ribosome protected mRNA fragments in a cell, while PUNCH-P is based on mass-spectrometric analysis of only newly synthesized proteins. Here we describe the first Ribo-Seq/PUNCH-P comparison via the analysis of mammalian cells during the cell-cycle for detecting relevant differentially expressed genes between G1 and M phase. Our analyses suggest that the two approaches significantly overlap with each other. However, we demonstrate that there are biologically meaningful proteins/genes that can be detected to be post-transcriptionally regulated during the mammalian cell cycle only by each of the approaches, or their consolidation. Such gene sets are enriched with proteins known to be related to intra-cellular signalling pathways such as central cell cycle processes, central gene expression regulation processes, processes related to chromosome segregation, DNA damage, and replication, that are post-transcriptionally regulated during the mammalian cell cycle. Moreover, we show that combining the approaches better predicts steady state changes in protein abundance. The results reported here support the conjecture that for gaining a full post-transcriptional regulation picture one should integrate the two approaches.

LENS: web-based lens for enrichment and network studies of human proteins

Background

Network analysis is a common approach for the study of genetic view of diseases and biological pathways. Typically, when a set of genes are identified to be of interest in relation to a disease, say through a genome wide association study (GWAS) or a different gene expression study, these genes are typically analyzed in the context of their protein-protein interaction (PPI) networks. Further analysis is carried out to compute the enrichment of known pathways and disease-associations in the network. Having tools for such analysis at the fingertips of biologists without the requirement for computer programming or curation of data would accelerate the characterization of genes of interest. Currently available tools do not integrate network and enrichment analysis and their visualizations, and most of them present results in formats not most conducive to human cognition.

Results

We developed the tool Lens for Enrichment and Network Studies of human proteins (LENS) that performs network and pathway and diseases enrichment analyses on genes of interest to users. The tool creates a visualization of the network, provides easy to read statistics on network connectivity, and displays Venn diagrams with statistical significance values of the network's association with drugs, diseases, pathways, and GWASs. We used the tool to analyze gene sets related to craniofacial development, autism, and schizophrenia.

Conclusion

LENS is a web-based tool that does not require and download or plugins to use. The tool is free and does not require login for use, and is available at http://severus.dbmi.pitt.edu/LENS.

Transcriptomics of diapause in an isogenic self-fertilizing vertebrate

BACKGROUND

Many vertebrate species have the ability to undergo weeks or even months of diapause (a temporary arrest of development during early ontogeny). Identification of diapause genes has been challenging due in part to the genetic heterogeneity of most vertebrate animals.

RESULTS

Here we take the advantage of the mangrove rivulus fish (Kryptolebias marmoratus or Kmar)-the only vertebrate that is extremely inbred due to consistent self-fertilization-to generate isogenic lineages for transcriptomic dissection. Because the Kmar genome is not publicly available, we built de novo genomic (642 Mb) and transcriptomic assemblies to serve as references for global genetic profiling of diapause in Kmar, via RNA-Seq. Transcripts unique to diapause in Kmar proved to constitute only a miniscule fraction (0.1 %) of the total pool of transcribed products. Most genes displayed lower expression in diapause than in post-diapause. However, some genes (notably dusp27, klhl38 and sqstm1) were significantly up-regulated during diapause, whereas others (col9a1, dspp and fmnl1) were substantially down-regulated, compared to both pre-diapause and post-diapause.

CONCLUSION

Kmar offers a strong model for understanding patterns of gene expression during diapause. Our study highlights the importance of using a combination of genome and transcriptome assemblies as references for NGS-based RNA-Seq analyses. As for all identified diapause genes, in future studies it will be critical to link various levels of RNA expression with the functional roles of the coded products.

Mining for novel candidate clock genes in the circadian regulatory network

BACKGROUND

Most physiological processes in mammals are temporally regulated by means of a master circadian clock in the brain and peripheral oscillators in most other tissues. A transcriptional-translation feedback network of clock genes produces near 24 h oscillations in clock gene and protein expression. Here, we aim to identify novel additions to the clock network using a meta-analysis of public chromatin immunoprecipitation sequencing (ChIP-seq), proteomics and protein-protein interaction data starting from a published list of 1000 genes with robust transcriptional rhythms and circadian phenotypes of knockdowns.

RESULTS

We identified 20 candidate genes including nine known clock genes that received significantly high scores and were also robust to the relative weights assigned to different data types. Our scoring was consistent with the original ranking of the 1000 genes, but also provided novel complementary insights. Candidate genes were enriched for genes expressed in a circadian manner in multiple tissues with regulation driven mainly by transcription factors BMAL1 and REV-ERB α,β. Moreover, peak transcription of candidate genes was remarkably consistent across tissues. While peaks of the 1000 genes were distributed uniformly throughout the day, candidate gene peaks were strongly concentrated around dusk. Finally, we showed that binding of specific transcription factors to a gene promoter was predictive of peak transcription at a certain time of day and discuss combinatorial phase regulation.

CONCLUSIONS

Combining complementary publicly-available data targeting different levels of regulation within the circadian network, we filtered the original list and found 11 novel robust candidate clock genes. Using the criteria of circadian proteomic expression, circadian expression in multiple tissues and independent gene knockdown data, we propose six genes (Por, Mtss1, Dgat2, Pim3, Ppp1r3b, Upp2) involved in metabolism and cancer for further experimental investigation. The availability of public high-throughput databases makes such meta-analysis a promising approach to test consistency between sources and tap their entire potential.

Selective multifaceted E3 ubiquitin ligases barricade extreme defense: Potential therapeutic targets for neurodegeneration and ageing

Efficient and regular performance of Ubiquitin Proteasome System and Autophagy continuously eliminate deleterious accumulation of nonnative protiens. In cellular quality control system, E3 ubiquitin ligases are significant employees for defense mechanism against abnormal toxic proteins. Few findings indicate that lack of functions of E3 ubiquitin ligases can be a causative factor of neurodevelopmental disorders, neurodegeneration, cancer and ageing. However, the detailed molecular pathomechanism implying E3 ubiquitin ligases in cellular functions in multifactorial disease conditions are not well understood. This article systematically represents the unique characteristics, molecular nature, and recent developments in the knowledge of neurobiological functions of few crucial E3 ubiquitin ligases. Here, we review recent literature on the roles of E6-AP, HRD1 and ITCH E3 ubiquitin ligases in the neuro-pathobiological mechanisms, with precise focus on the processes of neurodegeneration, and thereby propose new lines of potential targets for therapeutic interventions.

Integrated web visualizations for protein-protein interaction databases

Background

Understanding living systems is crucial for curing diseases. To achieve this task we have to understand biological networks based on protein-protein interactions. Bioinformatics has come up with a great amount of databases and tools that support analysts in exploring protein-protein interactions on an integrated level for knowledge discovery. They provide predictions and correlations, indicate possibilities for future experimental research and fill the gaps to complete the picture of biochemical processes. There are numerous and huge databases of protein-protein interactions used to gain insights into answering some of the many questions of systems biology. Many computational resources integrate interaction data with additional information on molecular background. However, the vast number of diverse Bioinformatics resources poses an obstacle to the goal of understanding. We present a survey of databases that enable the visual analysis of protein networks.

Results

We selected M =10 out of N =53 resources supporting visualization, and we tested against the following set of criteria: interoperability, data integration, quantity of possible interactions, data visualization quality and data coverage. The study reveals differences in usability, visualization features and quality as well as the quantity of interactions. StringDB is the recommended first choice. CPDB presents a comprehensive dataset and IntAct lets the user change the network layout. A comprehensive comparison table is available via web. The supplementary table can be accessed on http://tinyurl.com/PPI-DB-Comparison-2015.

Conclusions

Only some web resources featuring graph visualization can be successfully applied to interactive visual analysis of protein-protein interaction. Study results underline the necessity for further enhancements of visualization integration in biochemical analysis tools. Identified challenges are data comprehensiveness, confidence, interactive feature and visualization maturing.

The unfolded protein response and its potential role in Huntington's disease elucidated by a systems biology approach

Huntington ´s disease (HD) is a progressive, neurodegenerative disease with a fatal outcome. Although the disease-causing gene (huntingtin) has been known for over 20 years, the exact mechanisms leading to neuronal cell death are still controversial. One potential mechanism contributing to the massive loss of neurons observed in the brain of HD patients could be the unfolded protein response (UPR) activated by accumulation of misfolded proteins in the endoplasmic reticulum (ER). As an adaptive response to counter-balance accumulation of un- or misfolded proteins, the UPR upregulates transcription of chaperones, temporarily attenuates new translation, and activates protein degradation via the proteasome. However, persistent ER stress and an activated UPR can also cause apoptotic cell death. Although different studies have indicated a role for the UPR in HD, the evidence remains inconclusive. Here, we present extensive bioinformatic analyses that revealed UPR activation in different experimental HD models based on transcriptomic data. Accordingly, we have identified 53 genes, including RAB5A, HMGB1, CTNNB1, DNM1, TUBB, TSG101, EEF2, DYNC1H1, SLC12A5, ATG5, AKT1, CASP7 and SYVN1 that provide a potential link between UPR and HD. To further elucidate the potential role of UPR as a disease-relevant process, we examined its connection to apoptosis based on molecular interaction data, and identified a set of 40 genes including ADD1, HSP90B1, IKBKB, IKBKG, RPS3A and LMNB1, which seem to be at the crossroads between these two important cellular processes. Remarkably, we also found strong correlation of UPR gene expression with the length of the polyglutamine tract of Huntingtin, which is a critical determinant of age of disease onset in human HD patients pointing to the UPR as a promising target for therapeutic intervention. The study is complemented by a newly developed web-portal called UPR-HD (http://uprhd.sysbiolab.eu) that enables visualization and interactive analysis of UPR-associated gene expression across various HD models.

The unfolded protein response and its potential role in Huntington's disease elucidated by a systems biology approach

Huntington ´s disease (HD) is a progressive, neurodegenerative disease with a fatal outcome. Although the disease-causing gene (huntingtin) has been known for over 20 years, the exact mechanisms leading to neuronal cell death are still controversial. One potential mechanism contributing to the massive loss of neurons observed in the brain of HD patients could be the unfolded protein response (UPR) activated by accumulation of misfolded proteins in the endoplasmic reticulum (ER). As an adaptive response to counter-balance accumulation of un- or misfolded proteins, the UPR upregulates transcription of chaperones, temporarily attenuates new translation, and activates protein degradation via the proteasome. However, persistent ER stress and an activated UPR can also cause apoptotic cell death. Although different studies have indicated a role for the UPR in HD, the evidence remains inconclusive. Here, we present extensive bioinformatic analyses that revealed UPR activation in different experimental HD models based on transcriptomic data. Accordingly, we have identified 53 genes, including RAB5A, HMGB1, CTNNB1, DNM1, TUBB, TSG101, EEF2, DYNC1H1, SLC12A5, ATG5, AKT1, CASP7 and SYVN1 that provide a potential link between UPR and HD. To further elucidate the potential role of UPR as a disease-relevant process, we examined its connection to apoptosis based on molecular interaction data, and identified a set of 40 genes including ADD1, HSP90B1, IKBKB, IKBKG, RPS3A and LMNB1, which seem to be at the crossroads between these two important cellular processes. Remarkably, we also found strong correlation of UPR gene expression with the length of the polyglutamine tract of Huntingtin, which is a critical determinant of age of disease onset in human HD patients pointing to the UPR as a promising target for therapeutic intervention. The study is complemented by a newly developed web-portal called UPR-HD (http://uprhd.sysbiolab.eu) that enables visualization and interactive analysis of UPR-associated gene expression across various HD models.

Lysosome and Cytoskeleton Pathways Are Robustly Enriched in the Blood of Septic Patients: A Meta-Analysis of Transcriptomic Data

Background. Sepsis is a leading cause of mortality in intensive care units worldwide. A better understanding of the blood systems response to sepsis should expedite the identification of biomarkers for early diagnosis and therapeutic interventions. Methods. We analyzed microarray studies whose data is available from the GEO repository and which were performed on the whole blood of septic patients and normal controls. Results. We identified 6 cohorts consisting of 450 individuals (sepsis = 323, control = 127) providing genome-wide messenger RNA (mRNA) expression data. Through meta-analysis we found the “Lysosome” and “Cytoskeleton” pathways were upregulated in human sepsis patients relative to controls, in addition to previously known signaling pathways (including MAPK, TLR). The key regulatory genes in the “Lysosome” pathway include lysosomal acid hydrolases (e.g., protease cathepsin A, D) as well as the major (LAMP1, 2) and minor (SORT1, LAPTM4B) membrane proteins. In contrast, pathways related to “Ribosome”, “Spliceosome” and “Cell adhesion molecules” were found to be downregulated, along with known pathways for immune dysfunction. Overall, our study revealed distinct mRNA activation profiles and protein-protein interaction networks in blood of human sepsis. Conclusions. Our findings suggest that aberrant mRNA expression in the lysosome and cytoskeleton pathways may play a pivotal role in the molecular pathobiology of human sepsis.

Evolutionary signatures amongst disease genes permit novel methods for gene prioritization and construction of informative gene-based networks

Genes involved in the same function tend to have similar evolutionary histories, in that their rates of evolution covary over time. This coevolutionary signature, termed Evolutionary Rate Covariation (ERC), is calculated using only gene sequences from a set of closely related species and has demonstrated potential as a computational tool for inferring functional relationships between genes. To further define applications of ERC, we first established that roughly 55% of genetic diseases posses an ERC signature between their contributing genes. At a false discovery rate of 5% we report 40 such diseases including cancers, developmental disorders and mitochondrial diseases. Given these coevolutionary signatures between disease genes, we then assessed ERC's ability to prioritize known disease genes out of a list of unrelated candidates. We found that in the presence of an ERC signature, the true disease gene is effectively prioritized to the top 6% of candidates on average. We then apply this strategy to a melanoma-associated region on chromosome 1 and identify MCL1 as a potential causative gene. Furthermore, to gain global insight into disease mechanisms, we used ERC to predict molecular connections between 310 nominally distinct diseases. The resulting “disease map” network associates several diseases with related pathogenic mechanisms and unveils many novel relationships between clinically distinct diseases, such as between Hirschsprung's disease and melanoma. Taken together, these results demonstrate the utility of molecular evolution as a gene discovery platform and show that evolutionary signatures can be used to build informative gene-based networks.

Molecular evolution has informed our understanding of gene function; however, classical methods have largely been static in their implementation, focusing on single genes. Here, we present and prove the utility of a dynamic, network-based understanding of molecular evolution to infer relationships between genes associated with human diseases. We have shown previously that groups of genes within functional niches tend to share similar evolutionary histories. Exploiting the availability of whole genomes from multiple species, these histories can be numerically scored and dynamically compared to one another using a sequence-based signature termed Evolutionary Rate Covariation (ERC). To explore potential applications, we characterized ERC amongst disease genes and found that many diseases contain significant ERC signatures between their contributing genes. We show that ERC can also prioritize “true” disease genes amongst unrelated gene candidates. Lastly, these signatures can serve as a foundation for creating instructive gene-based networks, unveiling novel relationships between diseases thought to be clinically distinct. Our hope is that this study will add to the increasing evidence that advancing our understanding of molecular evolution can be a crucial asset in large-scale gene discovery pursuits (Link to our webserver that provides intuitive ERC analysis tools: http://csb.pitt.edu/erc_analysis/).

Attenuated monocyte apoptosis, a new mechanism for osteoporosis suggested by a transcriptome-wide expression study of monocytes

BACKGROUND

Osteoporosis is caused by excessive bone resorption (by osteoclasts) over bone formation (by osteoblasts). Monocytes are important to osteoporosis by serving as progenitors of osteoclasts and produce cytokines for osteoclastogenesis.

AIM

To identify osteoporosis-related genes, we performed microarray analyses of monocytes using Affymetrix 1.0 ST arrays in 42 (including 16 pre- and 26 postmenopausal) high hip BMD (bone mineral density) vs. 31 (including 15 pre- and 16 postmenopausal) low hip BMD Caucasian female subjects. Here, high vs. low BMD is defined as belonging to top vs. bottom 30% of BMD values in population.

METHOD

Differential gene expression analysis in high vs. low BMD subjects was conducted in the total cohort as well as pre- and post-menopausal subjects. Focusing on the top differentially expressed genes identified in the total, the pre- and the postmenopausal subjects (with a p <5E-03), we performed replication of the findings in 3 independent datasets of microarray analyses of monocytes (total N = 125).

RESULTS

We identified (in the 73 subjects) and successfully replicated in all the 3 independent datasets 2 genes, DAXX and PLK3. Interestingly, both genes are apoptosis induction genes and both down-regulated in the low BMD subjects. Moreover, using the top 200 genes identified in the meta-analysis across all of the 4 microarray datasets, GO term enrichment analysis identified a number of terms related to induction of apoptosis, for which the majority of component genes are also down-regulated in the low BMD subjects. Overall, our result may suggest that there might be a decreased apoptosis activity of monocytes in the low BMD subjects.

CONCLUSION

Our study for the first time suggested a decreased apoptosis rate (hence an increased survival) of monocytes, an important osteoclastogenic cell, as a novel mechanism for osteoporosis.