Gene: a gene-centered information resource at NCBI

Compartment and hub definitions tune metabolic networks for metabolomic interpretations

BACKGROUND

Metabolic networks represent all chemical reactions that occur between molecular metabolites in an organism's cells. They offer biological context in which to integrate, analyze, and interpret omic measurements, but their large scale and extensive connectivity present unique challenges. While it is practical to simplify these networks by placing constraints on compartments and hubs, it is unclear how these simplifications alter the structure of metabolic networks and the interpretation of metabolomic experiments.

RESULTS

We curated and adapted the latest systemic model of human metabolism and developed customizable tools to define metabolic networks with and without compartmentalization in subcellular organelles and with or without inclusion of prolific metabolite hubs. Compartmentalization made networks larger, less dense, and more modular, whereas hubs made networks larger, more dense, and less modular. When present, these hubs also dominated shortest paths in the network, yet their exclusion exposed the subtler prominence of other metabolites that are typically more relevant to metabolomic experiments. We applied the non-compartmental network without metabolite hubs in a retrospective, exploratory analysis of metabolomic measurements from 5 studies on human tissues. Network clusters identified individual reactions that might experience differential regulation between experimental conditions, several of which were not apparent in the original publications.

CONCLUSIONS

Exclusion of specific metabolite hubs exposes modularity in both compartmental and non-compartmental metabolic networks, improving detection of relevant clusters in omic measurements. Better computational detection of metabolic network clusters in large data sets has potential to identify differential regulation of individual genes, transcripts, and proteins.

A network pharmacology approach to reveal the protective mechanism of Salvia miltiorrhiza-Dalbergia odorifera coupled-herbs on coronary heart disease

Salvia miltiorrhiza-Dalbergia odorifera coupled-herbs (SMDOCH) has been used to treat coronary heart disease (CHD) for thousands of years, but its unclear bioactive components and mechanisms greatly limit its clinical application. In this study, for the first time, we used network pharmacology to elucidate the mechanisms of action of SMDOCH on CHD. We collected 270 SMDOCH-related targets from 74 bioactive components and 375 CHD-related targets, with 58 overlapping common targets. Next, we performed enrichment analysis for common-target network and protein-protein interaction (PPI) network. The results showed that SMDOCH affected CHD mainly through 10 significant signaling pathways in three biological processes: 'vascular endothelial function regulation', 'inflammatory response', and 'lipid metabolism'. Six pathways belonged to the 'vascular endothelial function regulation' model, which primarily regulated hormone (renin, angiotensin, oestrogen) activity, and included three key upstream pathways that influence vascular endothelial function, namely KEGG:04933, KEGG:05418, and KEGG:04066. Three pathways, namely KEGG:04668, KEGG:04064, and KEGG:04620, belonged to the 'inflammatory response' model. One pathway (KEGG:04920) belonged to the 'lipid metabolism' model. To some extent, this study revealed the potential bioactive components and pharmacological mechanisms of SMDOCH on CHD, and provided a new direction for the development of new drugs for the treatment of CHD.

The JAX Synteny Browser for mouse-human comparative genomics

Visualizing regions of conserved synteny between two genomes is supported by numerous software applications. However, none of the current applications allow researchers to select genome features to display or highlight in blocks of synteny based on the annotated biological properties of the features (e.g., type, function, and/or phenotype association). To address this usability gap, we developed an interactive web-based conserved synteny browser, The Jackson Laboratory (JAX) Synteny Browser. The browser allows researchers to highlight or selectively display genome features in the reference and/or the comparison genome according to the biological attributes of the features. Although the current implementation for the browser is limited to the reference genomes for the laboratory mouse and human, the software platform is intentionally genome agnostic. The JAX Synteny Browser software can be deployed for any two genomes where genome coordinates for syntenic blocks are defined and for which biological attributes of the features in one or both genomes are available in widely used standard bioinformatics file formats. The JAX Synteny Browser is available at: http://syntenybrowser.jax.org/. The code base is available from GitHub: https://github.com/TheJacksonLaboratory/syntenybrowser and is distributed under the Creative Commons Attribution license (CC BY).

GEREDB: Gene expression regulation database curated by mining abstracts from literature

Understanding how genes are expressed and regulated in different biological processes are fundamental and challenging issues. Considerable progress has been made in studying the relationship between the expression and regulation of human genes. However, it is difficult to use these resources productively to analyze gene expression data. GEREDB (www.thua45.cn/geredb) has been developed to facilitate analyses that will provide insights into the regulation of genes that govern specific biological responses. GEREDB is a publicly available, manually curated biological database that stores the data regarding relationships between expression and regulation of human genes. To date, more than 39,000 Links have been contextually annotated by reviewing more than 53,000 abstracts. GEREDB can be searched using the official NCBI gene symbol as a query, and it can be downloaded along with the GEREA software package. GEREDB has the ability to analyze user-supplied gene expression data in a causal analysis oriented manner using the GEREA bioinformatics tool.

Progress in data interoperability to support computational toxicology and chemical safety evaluation

New approach methodologies (NAMs) in chemical safety evaluation are being explored to address the current public health implications of human environmental exposures to chemicals with limited or no data for assessment. For over a decade since a push toward "Toxicity Testing in the 21st Century," the field has focused on massive data generation efforts to inform computational approaches for preliminary hazard identification, adverse outcome pathways that link molecular initiating events and key events to apical outcomes, and high-throughput approaches to risk-based ratios of bioactivity and exposure to inform relative priority and safety assessment. Projects like the interagency Tox21 program and the US EPA ToxCast program have generated dose-response information on thousands of chemicals, identified and aggregated information from legacy systems, and created tools for access and analysis. The resulting information has been used to develop computational models as viable options for regulatory applications. This progress has introduced challenges in data management that are new, but not unique, to toxicology. Some of the key questions require critical thinking and solutions to promote semantic interoperability, including: (1) identification of bioactivity information from NAMs that might be related to a biological process; (2) identification of legacy hazard information that might be related to a key event or apical outcomes of interest; and, (3) integration of these NAM and traditional data for computational modeling and prediction of complex apical outcomes such as carcinogenesis. This work reviews a number of toxicology-related efforts specifically related to bioactivity and toxicological data interoperability based on the goals established by Findable, Accessible, Interoperable, and Reusable (FAIR) Data Principles. These efforts are essential to enable better integration of NAM and traditional toxicology information to support data-driven toxicology applications.

Hemimethylation Patterns in Breast Cancer Cell Lines

DNA methylation is an epigenetic event that involves adding a methyl group to the cytosine (C) site, especially the one that pairs with a guanine (G) site (ie, CG or CpG site), in a human genome. This event plays an important role in both cancerous and normal cell development. Previous studies often assume symmetric methylation on both DNA strands. However, asymmetric methylation, or hemimethylation (methylation that occurs only on 1 DNA strand), does exist and has been reported in several studies. Due to the limitation of previous DNA methylation sequencing technologies, researchers could only study hemimethylation on specific genes, but the overall genomic hemimethylation landscape remains relatively unexplored. With the development of advanced next-generation sequencing techniques, it is now possible to measure methylation levels on both forward and reverse strands at all CpG sites in an entire genome. Analyzing hemimethylation patterns may potentially reveal regions related to undergoing tumor growth. For our research, we first identify hemimethylated CpG sites in breast cancer cell lines using Wilcoxon signed rank tests. We then identify hemimethylation patterns by grouping consecutive hemimethylated CpG sites based on their methylation states, methylation "M" or unmethylation "U." These patterns include regular (or consecutive) hemimethylation clusters (eg, "MMM" on one strand and "UUU" on another strand) and polarity (or reverse) clusters (eg, "MU" on one strand and "UM" on another strand). Our results reveal that most hemimethylation clusters are the polarity type, and hemimethylation does occur across the entire genome with notably higher numbers in the breast cancer cell lines. The lengths or sizes of most hemimethylation clusters are very short, often less than 50 base pairs. After mapping hemimethylation clusters and sites to corresponding genes, we study the functions of these genes and find that several of the highly hemimethylated genes may influence tumor growth or suppression. These genes may also indicate a progressing transition to a new tumor stage.

A network pharmacology approach to explore active compounds and pharmacological mechanisms of epimedium for treatment of premature ovarian insufficiency

Background and purpose

Premature ovarian insufficiency (POI) refers to a hypergonadotropic hypoestrogenism and the condition of pre-onset ovarian function failure. Epimedium is a common traditional Chinese herbal medicine that is widely used to relieve POI in China. To systematically explore the pharmacological mechanism of epimedium on POI therapy, a network pharmacology approach was conducted at the molecular level.

Methods

In this study, we adopt the network pharmacology method, which mainly includes active ingredients prescreening, target prediction, gene enrichment analysis and network analysis.

Results

The network analysis revealed that 6 targets (ESR1, AR, ESR2, KDR, CYP19A1 and ESRRG) might be the therapeutic targets of epimedium on POI. In addition, gene-enrichment analysis suggested that epimedium appeared to play a role in POI by modulating 6 molecular functions, 5 cellular components, 15 biological processes and striking 52 potential targets involved in 13 signaling pathways.

Conclusion

This study predicted the pharmacological and molecular mechanism of epimedium against POI from a holistic perspective, as well as provided a powerful tool for exploring pharmacological mechanisms and rational clinical application of traditional Chinese medicine.

Gene relevance based on multiple evidences in complex networks

MOTIVATION

Multi-omics approaches offer the opportunity to reconstruct a more complete picture of the molecular events associated with human diseases, but pose challenges in data analysis. Network-based methods for the analysis of multi-omics leverage the complex web of macromolecular interactions occurring within cells to extract significant patterns of molecular alterations. Existing network-based approaches typically address specific combinations of omics and are limited in terms of the number of layers that can be jointly analysed. In this study, we investigate the application of network diffusion to quantify gene relevance on the basis of multiple evidences (layers).

RESULTS

We introduce a gene score (mND) that quantifies the relevance of a gene in a biological process taking into account the network proximity of the gene and its first neighbours to other altered genes. We show that mND has a better performance over existing methods in finding altered genes in network proximity in one or more layers. We also report good performances in recovering known cancer genes. The pipeline described in this article is broadly applicable, because it can handle different types of inputs: in addition to multi-omics datasets, datasets that are stratified in many classes (e.g., cell clusters emerging from single cell analyses) or a combination of the two scenarios.

AVAILABILITY AND IMPLEMENTATION

The R package 'mND' is available at URL: https://www.itb.cnr.it/mnd.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

AAgMarker 1.0: a resource of serological autoantigen biomarkers for clinical diagnosis and prognosis of various human diseases

Autoantibodies are produced to target an individual's own antigens (e.g. proteins). They can trigger autoimmune responses and inflammation, and thus, cause many types of diseases. Many high-throughput autoantibody profiling projects have been reported for unbiased identification of serological autoantigen-based biomarkers. However, a lack of centralized data portal for these published assays has been a major obstacle to further data mining and cross-evaluate the quality of these datasets generated from different diseases. Here, we introduce a user-friendly database, AAgMarker 1.0, which collects many published raw datasets obtained from serum profiling assays on the proteome microarrays, and provides a toolbox for mining these data. The current version of AAgMarker 1.0 contains 854 serum samples, involving 136 092 proteins. A total of 7803 (4470 non-redundant) candidate autoantigen biomarkers were identified and collected for 12 diseases, such as Alzheimer's disease, Bechet's disease and Parkinson's disease. Seven statistical parameters are introduced to quantitatively assess these biomarkers. Users can retrieve, analyse and compare the datasets through basic search, advanced search and browse. These biomarkers are also downloadable by disease terms. The AAgMarker 1.0 is now freely accessible at http://bioinfo.wilmer.jhu.edu/AAgMarker/. We believe this database will be a valuable resource for the community of both biomedical and clinical research.

ADReCS-Target: target profiles for aiding drug safety research and application

Delivering safe and effective therapeutic treatment to patients is one of the grand challenges in modern medicine. However, drug safety research has been progressing slowly in recent years, compared to other fields such as biotechnologies and precision medicine, due to the mechanistic complexity of adverse drug reactions (ADRs). To fill up this gap, we develop a new database, the Adverse Drug Reaction Classification System-Target Profile (ADReCS-Target, http://bioinf.xmu.edu.cn/ADReCS-Target), which provides comprehensive information about ADRs caused by drug interaction with protein, gene and genetic variation. In total, ADReCS-Target includes 66,573 pairwise relations, among which 1710 are protein–ADR associations, 2613 are genetic variation–ADR associations, and 63,298 are gene–ADR associations. In a case study of exploring the mechanism of rash, we find that HLAs, C1QA and APOA1 are the key gene players and thus can be potential targets (or biomarkers) in monitoring or countermining rashes. In summary, ADReCS-Target can be a useful resource for the biomedical scientific community by serving researchers in the fields of drug development, clinical pharmacology, precision medicine, and from web lab to high-throughput computational platform. Particularly, it helps to identify drug with better ADR profile and design safer drug therapy regimen.

dbCoRC: a database of core transcriptional regulatory circuitries modeled by H3K27ac ChIP-seq signals

Core transcription regulatory circuitry (CRC) is comprised of a small group of self-regulated transcription factors (TFs) and their interconnected regulatory loops. Studies from embryonic stem cells and other cellular models have revealed the elementary roles of CRCs in transcriptional control of cell identity and cellular fate. Systematic identification and subsequent archiving of CRCs across diverse cell types and tissues are needed to explore both cell/tissue type-specific and disease-associated transcriptional networks. Here, we present a comprehensive and interactive database (dbCoRC, http://dbcorc.cam-su.org) of CRC models which are computationally inferred from mapping of super-enhancer and prediction of TF binding sites. The current version of dbCoRC contains CRC models for 188 human and 50 murine cell lines/tissue samples. In companion with CRC models, this database also provides: (i) super enhancer, typical enhancer, and H3K27ac landscape for individual samples, (ii) putative binding sites of each core TF across the super-enhancer regions within CRC and (iii) expression of each core TF in normal or cancer cells/tissues. The dbCoRC will serve as a valuable resource for the scientific community to explore transcriptional control and regulatory circuitries in biological processes related to, but not limited to lineage specification, tissue homeostasis and tumorigenesis.

SuCComBase: a manually curated repository of plant sulfur-containing compounds

Plants produce a wide range of secondary metabolites that play important roles in plant defense and immunity, their interaction with the environment and symbiotic associations. Sulfur-containing compounds (SCCs) are a group of important secondary metabolites produced in members of the Brassicales order. SCCs constitute various groups of phytochemicals, but not much is known about them. Findings from previous studies on SCCs were scattered in published literatures, hence SuCComBase was developed to store all molecular information related to the biosynthesis of SCCs. Information that includes genes, proteins and compounds that are involved in the SCC biosynthetic pathway was manually identified from databases and published scientific literatures. Sets of co-expression data was analyzed to search for other possible (previously unknown) genes that might be involved in the biosynthesis of SCC. These genes were named as potential SCC-related encoding genes. A total of 147 known and 92 putative Arabidopsis thaliana SCC-related genes from literatures were used to identify other potential SCC-related encoding genes. We identified 778 potential SCC-related encoding genes, 4026 homologs to the SCC-related encoding genes and 116 SCCs as shown on SuCComBase homepage. Data entries are searchable from the Main page, Search, Browse and Datasets tabs. Users can easily download all data stored in SuCComBase. All publications related to SCCs are also indexed in SuCComBase, which is currently the first and only database dedicated to plant SCCs. SuCComBase aims to become a manually curated and au fait knowledge-based repository for plant SCCs.

Genome-Wide Comparative Analysis of HIF Binding Sites in Cyprinus Carpio for In Silico Identification of Functional Hypoxia Response Elements

Cyprinus carpio is world's most widely distributed freshwater species highly used in aquaculture. It is a hypoxia-tolerant species as it lives in oxygen-deficient environment for a long period. The tolerance potential of an animal against hypoxia relates it to induced gene expression, where a hypoxia-inducible factor (HIF) binds to a transcriptionally active site, hypoxia response element (HRE), a 5-base short motif that lies within the promoter/enhancer region of a certain gene, for inducing gene expression and preventing/minimizing hypoxia effects. HRE is functionally active when it contains another motif, the hypoxia ancillary sequence (HAS), which is typically adjacent to downstream of HRE within 7- to 15-nt space. Here, an attempt was made for mining HRE and identifying functional HIF binding sites (HBS) in a genome-wide analysis of C. carpio. For this, gene information along with the 5,000-nt upstream (-4,900 to +100) sequences of 31,466 protein coding genes was downloaded from "Gene" and "RefSeq" databases. Analysis was performed after filtration of the impracticable genes. A total of 116,148 HRE consensus sequences were mined from 29,545 genes in different promoter regions. HRE with HAS consensus motifs were found in the promoter region of 9,589 genes. Further, the already reported genes for hypoxia response in humans and zebrafish were reanalyzed for detecting HRE sites in their promoters and used for comparative analysis with gene promoters of C. carpio for providing support to identify functional HBS in the gene promoter of C. carpio. An interactive user interface HREExplorer was developed for presenting the results on the World Wide Web and visualizing possible HBS in protein coding genes in C. carpio and displaying the comparative results along with the reported hypoxia-responsive genes of zebrafish and reported hypoxia-inducible genes in humans. In this study, a set of Perl program was written for the compilation and analysis of information that might be used for a similar study in other species. This novel work may provide a workbench for analyzing the promoter regions of hypoxia-responsive genes.

Genome Sequences of Three Cluster C Mycobacteriophages, Bipolarisk, Bread, and FudgeTart

Three mycobacteriophages, Bipolarisk, Bread, and FudgeTart, were isolated from enriched soil samples found in Crete, NE. All three phages are lytic, belong to subcluster C1, and infect Mycobacterium smegmatis mc²155. The structures of the three genomes are similar, with slight variations in gene number and content.

Three mycobacteriophages, Bipolarisk, Bread, and FudgeTart, were isolated from enriched soil samples found in Crete, NE. All three phages are lytic, belong to subcluster C1, and infect Mycobacterium smegmatis mc²155. The structures of the three genomes are similar, with slight variations in gene number and content.

Network-Based Integrative Analysis of Genomics, Epigenomics and Transcriptomics in Autism Spectrum Disorders

Current studies suggest that autism spectrum disorders (ASDs) may be caused by many genetic factors. In fact, collectively considering multiple studies aimed at characterizing the basic pathophysiology of ASDs, a large number of genes has been proposed. Addressing the problem of molecular data interpretation using gene networks helps to explain genetic heterogeneity in terms of shared pathways. Besides, the integrative analysis of multiple omics has emerged as an approach to provide a more comprehensive view of a disease. In this work, we carry out a network-based meta-analysis of the genes reported as associated with ASDs by studies that involved genomics, epigenomics, and transcriptomics. Collectively, our analysis provides a prioritization of the large number of genes proposed to be associated with ASDs, based on genes' relevance within the intracellular circuits, the strength of the supporting evidence of association with ASDs, and the number of different molecular alterations affecting genes. We discuss the presence of the prioritized genes in the SFARI (Simons Foundation Autism Research Initiative) database and in gene networks associated with ASDs by other investigations. Lastly, we provide the full results of our analyses to encourage further studies on common targets amenable to therapy.

HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods

Recent data indicate that up-to 30–40% of cancers can be prevented by dietary and lifestyle measures alone. Herein, we introduce a unique network-based machine learning platform to identify putative food-based cancer-beating molecules. These have been identified through their molecular biological network commonality with clinically approved anti-cancer therapies. A machine-learning algorithm of random walks on graphs (operating within the supercomputing DreamLab platform) was used to simulate drug actions on human interactome networks to obtain genome-wide activity profiles of 1962 approved drugs (199 of which were classified as “anti-cancer” with their primary indications). A supervised approach was employed to predict cancer-beating molecules using these ‘learned’ interactome activity profiles. The validated model performance predicted anti-cancer therapeutics with classification accuracy of 84–90%. A comprehensive database of 7962 bioactive molecules within foods was fed into the model, which predicted 110 cancer-beating molecules (defined by anti-cancer drug likeness threshold of >70%) with expected capacity comparable to clinically approved anti-cancer drugs from a variety of chemical classes including flavonoids, terpenoids, and polyphenols. This in turn was used to construct a ‘food map’ with anti-cancer potential of each ingredient defined by the number of cancer-beating molecules found therein. Our analysis underpins the design of next-generation cancer preventative and therapeutic nutrition strategies.

BEERE: a web server for biomedical entity expansion, ranking and explorations

BEERE (Biomedical Entity Expansion, Ranking and Explorations) is a new web-based data analysis tool to help biomedical researchers characterize any input list of genes/proteins, biomedical terms or their combinations, i.e. 'biomedical entities', in the context of existing literature. Specifically, BEERE first aims to help users examine the credibility of known entity-to-entity associative or semantic relationships supported by database or literature references from the user input of a gene/term list. Then, it will help users uncover the relative importance of each entity-a gene or a term-within the user input by computing the ranking scores of all entities. At last, it will help users hypothesize new gene functions or genotype-phenotype associations by an interactive visual interface of constructed global entity relationship network. The output from BEERE includes: a list of the original entities matched with known relationships in databases; any expanded entities that may be generated from the analysis; the ranks and ranking scores reported with statistical significance for each entity; and an interactive graphical display of the gene or term network within data provenance annotations that link to external data sources. The web server is free and open to all users with no login requirement and can be accessed at http://discovery.informatics.uab.edu/beere/.

Computational Drug Screening Identifies Compounds Targeting Renal Age-associated Molecular Profiles

Aging is a major driver for chronic kidney disease (CKD) and the counterbalancing of aging processes holds promise to positively impact disease development and progression.

In this study we generated a signature of renal age-associated genes (RAAGs) based on six different data sources including transcriptomics data as well as data extracted from scientific literature and dedicated databases. Protein abundance in renal tissue of the 634 identified RAAGs was studied next to the analysis of affected molecular pathways. RAAG expression profiles were furthermore analysed in a cohort of 63 CKD patients with available follow-up data to determine association with CKD progression. 23 RAAGs were identified showing concordant regulation in renal aging and CKD progression. This set was used as input to computationally screen for compounds with the potential of reversing the RAAG/CKD signature on the transcriptional level. Among the top-ranked drugs we identified atorvastatin, captopril, valsartan, and rosiglitazone, which are widely used in clinical practice for the treatment of patients with renal and cardiovascular diseases. Their positive impact on the RAAG/CKD signature could be validated in an in-vitro model of renal aging.

In summary, we have (i) consolidated a set of RAAGs, (ii) determined a subset of RAAGs with concordant regulation in CKD progression, and (iii) identified a set of compounds capable of reversing the proposed RAAG/CKD signature.

Analysis of Genes Involved in Ulcerative Colitis Activity and Tumorigenesis Through Systematic Mining of Gene Co-expression Networks

Ulcerative colitis (UC) is an idiopathic, chronic inflammatory disorder of the colon, characterized by continuous mucosal inflammation. Recently, some studies have considered it as part of an inflammatory bowel disease-based global network. Herein, with the aim of identifying the underlying potential genetic mechanisms involved in the development of UC, multiple algorithms for weighted correlation network analysis (WGCNA), principal component analysis (PCA), and linear models for microarray data algorithm (LIMMA) were used to identify the hub genes. The map of platelet activation, ligand-receptor interaction, calcium signaling pathway, and cAMP signaling pathway showed significant links with UC development, and the hub genes CCR7, CXCL10, CXCL9, IDO1, MMP9, and VCAM1, which are associated with immune dysregulation and tumorigenesis in biological function, were found by multiple powerful bioinformatics methods. Analysis of The Cancer Genome Atlas (TCGA) also showed that the low expression of CCR7, CXCL10, CXCL9, and MMP9 may be correlated with a poor prognosis of overall survival (OS) in colorectal cancer (CRC) patients (all p < 0.05), while no significance detected in both of IDO1 and VCAM1. In addition, low expression of CCR7, CXCL10, CXCL9, MMP9, and IDO1 may be associated with a poor prognosis in recurrence free survival (RFS) time (all p < 0.05), but no significant difference was identified in VCAM1. Moreover, the NFKB1, FLI1, and STAT1 with the highest enrichment score were detected as the master regulators of hub genes. In summary, these results indicated the central role of the hub genes of CCR7, CXCL10, CXCL9, IDO1, VCAM1, and MMP9, in response to UC progression, as well as the development of UC to CRC, thus shedding light on the molecular mechanisms involved and assisting with drug target validation.

The Plasmodium falciparum MESA erythrocyte cytoskeleton-binding (MEC) motif binds to erythrocyte ankyrin

The MESA erythrocyte cytoskeleton binding (MEC) motif is a 13-amino acid sequence found in 14 exported Plasmodium falciparum proteins. First identified in the P. falciparum Mature-parasite-infected Erythrocyte Surface Antigen (MESA), the MEC motif is sufficient to target proteins to the infected red blood cell cytoskeleton. To identify host cell targets, purified MESA MEC motif was incubated with a soluble extract from uninfected erythrocytes, precipitated and subjected to mass spectrometry. The most abundant co-purifying protein was erythrocyte ankyrin (ANK1). A direct interaction between the MEC motif and ANK1 was independently verified using co-purification experiments, the split-luciferase assay, and the yeast two-hybrid assay. A systematic mutational analysis of the core MEC motif demonstrated a critical role for the conserved aspartic acid residue at the C-terminus of the MEC motif for binding to both erythrocyte inside-out vesicles and to ANK1. Using a panel of ANK1 constructs, the MEC motif binding site was localized to the ZU5^C domain, which has no known function. The MEC motif had no impact on erythrocyte deformability when introduced into uninfected erythrocyte ghosts, suggesting the MEC motif's primary function is to target exported proteins to the cytoskeleton. Finally, we show that PF3D7_0402100 (PFD0095c) binds to ANK1 and band 4.1, likely through its MEC and PHIST motifs, respectively. In conclusion, we have provided multiple lines of evidence that the MEC motif binds to erythrocyte ANK1.

Improving Interpretation of Cardiac Phenotypes and Enhancing Discovery With Expanded Knowledge in the Gene Ontology

Supplemental Digital Content is available in the text.

Background:

A systems biology approach to cardiac physiology requires a comprehensive representation of how coordinated processes operate in the heart, as well as the ability to interpret relevant transcriptomic and proteomic experiments. The Gene Ontology (GO) Consortium provides structured, controlled vocabularies of biological terms that can be used to summarize and analyze functional knowledge for gene products.

Methods and Results:

In this study, we created a computational resource to facilitate genetic studies of cardiac physiology by integrating literature curation with attention to an improved and expanded ontological representation of heart processes in the Gene Ontology. As a result, the Gene Ontology now contains terms that comprehensively describe the roles of proteins in cardiac muscle cell action potential, electrical coupling, and the transmission of the electrical impulse from the sinoatrial node to the ventricles. Evaluating the effectiveness of this approach to inform data analysis demonstrated that Gene Ontology annotations, analyzed within an expanded ontological context of heart processes, can help to identify candidate genes associated with arrhythmic disease risk loci.

Conclusions:

We determined that a combination of curation and ontology development for heart-specific genes and processes supports the identification and downstream analysis of genes responsible for the spread of the cardiac action potential through the heart. Annotating these genes and processes in a structured format facilitates data analysis and supports effective retrieval of gene-centric information about cardiac defects.

HNCDB: An Integrated Gene and Drug Database for Head and Neck Cancer

Head and neck cancer (HNC) is the sixth most common cancer worldwide. Over the last decade, an enormous amount of well-annotated gene and drug data has accumulated for HNC. However, a comprehensive repository is not yet available. Here, we constructed the Head and Neck Cancer Database (HNCDB: http://hncdb.cancerbio.info) using text mining followed by manual curation of the literature to collect reliable information on the HNC-related genes and drugs. The high-throughput gene expression data for HNC were also integrated into HNCDB. HNCDB includes the following three separate but closely related components: “HNC GENE,” “Connectivity Map,” and “ANALYSIS.” The “HNC GENE” component contains comprehensive information for the 1,173 HNC-related genes manually curated from 2,564 publications. The “Connectivity Map” includes information on the potential connections between the 176 drugs manually curated from 2,032 publications and the 1,173 HNC-related genes. The “ANALYSIS” component allows users to conduct correlation, differential expression, and survival analyses in the 2,403 samples from 78 HNC gene expression datasets. Taken together, we believe that HNCDB will be of significant benefit for the HNC community and promote further advances for precision medicine research on HNC.

Engineered Functional Recovery of Microbial Rhodopsin Without Retinal-Binding Lysine

Definition of rhodopsin is the retinal-binding membrane protein with the Schiff base linkage at a lysine on the 7^th transmembrane helix. However, ~ 600 microbial rhodopsins lack retinal-binding lysine at the corresponding position (Rh-noK) among ~ 5500 known microbial rhodopsins, suggesting that Rh-noK has each functional role without chromophore. Here, we report successful functional recovery of Rh-noK. Two Rh-noKs from bacteria were heterologously expressed in Escherichia coli, which exhibited no color. When retinal-binding lysine was introduced, one of them gained visible color. Additional mutation of the Schiff base counterion further gained proton-pumping activity. Successful engineered functional recovery such as visible color and proton-pump activity suggests that the Rh-noK protein forms a characteristic structure of microbial rhodopsins.

Human granulosa cells function as innate immune cells executing an inflammatory reaction during ovulation: a microarray analysis

Ovulation has been compared to a local inflammatory reaction. We performed an in silico study on a unique, PCR validated, transcriptome microarray study to evaluate if known inflammatory mechanisms operate during ovulation. The granulosa cells were obtained in paired samples at two different time points during ovulation (just before and 36 hours after ovulation induction) from nine women receiving fertility treatment. A total of 259 genes related to inflammation became significantly upregulated during ovulation (2-80 fold, p<0.05), while specific leukocyte markers were absent. The genes and pathway analysis indicated NF-KB-, MAPK- and JAK/STAT signalling (p<1.0E-10) as the major pathways involved in danger recognition and cytokine signalling to initiate inflammation. Upregulated genes further encoded enzymes in eicosanoid production, chemo-attractants, coagulation factors, cell proliferation factors involved in tissue repair, and anti-inflammatory factors to resolve the inflammation again. We conclude that granulosa cells, without involvement from the innate immune system, can orchestrate ovulation as a complete sterile inflammatory reaction.

100 Years of evolving gene-disease complexities and scientific debutants

It's been over 100 years since the word `gene' is around and progressively evolving in several scientific directions. Time-to-time technological advancements have heavily revolutionized the field of genomics, especially when it's about, e.g. triple code development, gene number proposition, genetic mapping, data banks, gene-disease maps, catalogs of human genes and genetic disorders, CRISPR/Cas9, big data and next generation sequencing, etc. In this manuscript, we present the progress of genomics from pea plant genetics to the human genome project and highlight the molecular, technical and computational developments. Studying genome and epigenome led to the fundamentals of development and progression of human diseases, which includes chromosomal, monogenic, multifactorial and mitochondrial diseases. World Health Organization has classified, standardized and maintained all human diseases, when many academic and commercial online systems are sharing information about genes and linking to associated diseases. To efficiently fathom the wealth of this biological data, there is a crucial need to generate appropriate gene annotation repositories and resources. Our focus has been how many gene-disease databases are available worldwide and which sources are authentic, timely updated and recommended for research and clinical purposes. In this manuscript, we have discussed and compared 43 such databases and bioinformatics applications, which enable users to connect, explore and, if possible, download gene-disease data.

A Network-guided Association Mapping Approach from DNA Methylation to Disease

Aberrant DNA methylation may contribute to development of cancer. However, understanding the associations between DNA methylation and cancer remains a challenge because of the complex mechanisms involved in the associations and insufficient sample sizes. The unprecedented wealth of DNA methylation, gene expression and disease status data give us a new opportunity to design machine learning methods to investigate the underlying associated mechanisms. In this paper, we propose a network-guided association mapping approach from DNA methylation to disease (NAMDD). Compared with existing methods, NAMDD finds methylation-disease path associations by integrating analysis of multiple data combined with a stability selection strategy, thereby mining more information in the datasets and improving the quality of resultant methylation sites. The experimental results on both synthetic and real ovarian cancer data show that NAMDD substantially outperforms former disease-related methylation site research methods (including NsRRR and PCLOGIT) under false positive control. Furthermore, we applied NAMDD to ovarian cancer data, identified significant path associations and provided hypothetical biological path associations to explain our findings.

A Network Pharmacology Approach for the Identification of Common Mechanisms of Drug-Induced Peripheral Neuropathy

Drug-induced peripheral neuropathy is a side effect of a variety of therapeutic agents that can affect therapeutic adherence and lead to regimen modifications, impacting patient quality of life. The molecular mechanisms involved in the development of this condition have yet to be completely described in the literature. We used a computational network pharmacology approach to explore the Connectivity Map, a large collection of transcriptional profiles from drug perturbation experiments to identify common genes affected by peripheral neuropathy-inducing drugs. Consensus profiles for 98 of these drugs were used to construct a drug-gene perturbation network. We identified 27 genes significantly associated with neuropathy-inducing drugs. These genes may have a potential role in the action of neuropathy-inducing drugs. Our results suggest that molecular mechanisms, including alterations in mitochondrial function, microtubule and cytoskeleton function, ion channels, transcriptional regulation including epigenetic mechanisms, signal transduction, and wound healing, may play a critical role in drug-induced peripheral neuropathy.

Investigating the dysfunctional pathogenesis of Wilms' tumor through a multidimensional integration strategy

Background

Wilms' tumor (WT) is a common kidney tumor in early childhood which is characterized by multiple congenital anomalies and syndromes. With the continuous improvement of medical standards, the cure rate and survival period of WT have increased. However, its molecular mechanism is still elusive.

Methods

A comprehensive multidimensional integration strategy was used to comprehensively analyze the mechanisms of WT.

Results

By integrating the potential pathogenic genes of kidney cancer and performing co-expression analysis on the disease-related genes, 23 functional modules were obtained. All the genes were differentially expressed in WT, and were mainly involved in many biological processes and signaling pathways, such as Wnt/β-catenin, mTOR/ERK and calcineurin. Additionally, based on the relationship between transcriptional and post-transcriptional regulatory systems, in functional modules, transcription factors (TFs) including STAT3, HDAC1 and SP1 as well as non-coding RNAs (ncRNAs) such as miR-335-5p, miR-21-5p and TUG1 were identified. Finally, potential drugs for these multifactor regulated dysfunctional modules which may have certain pharmacological or toxicological effects on WT such as cisplatin, sorafenib, and zinc were predicted.

Conclusions

A multidimensional dysfunction mechanism, involving disease-related genes, TFs and ncRNAs was revealed in the pathogenesis of WT. Functional modules were used to predict potential drugs which can be used in personalized therapy and drug delivery. This study explored the pathogenesis of WT from a new perspective, and provides new candidate targets and therapeutic drugs for improving the cure rate of WT.

Autophagy accounts for approximately one-third of mitochondrial protein turnover and is protein selective

The destruction of mitochondria through macroautophagy (autophagy) has been recognised as a major route of mitochondrial protein degradation since its discovery more than 50 years ago, but fundamental questions remain unanswered. First, how much mitochondrial protein turnover occurs through auto-phagy? Mitochondrial proteins are also degraded by nonautophagic mechanisms, and the proportion of mitochondrial protein turnover that occurs through autophagy is still unknown. Second, does auto-phagy degrade mitochondrial proteins uniformly or selectively? Autophagy was originally thought to degrade all mitochondrial proteins at the same rate, but recent work suggests that mitochondrial autophagy may be protein selective. To investigate these questions, we used a proteomics-based approach in the fruit fly Drosophila melanogaster, comparing mitochondrial protein turnover rates in autophagy-deficient Atg7 mutants and controls. We found that ~35% of mitochondrial protein turnover occurred via autophagy. Similar analyses using parkin mutants revealed that parkin-dependent mitophagy accounted for ~25% of mitochondrial protein turnover, suggesting that most mitochondrial autophagy specifically eliminates dysfunctional mitochondria. We also found that our results were incompatible with uniform autophagic turnover of mitochondrial proteins and consistent with protein-selective autophagy. In particular, the autophagic turnover rates of individual mitochondrial proteins varied widely, and only a small amount of the variation could be attributed to tissue differences in mitochondrial composition and autophagy rate. Furthermore, analyses comparing autophagy-deficient and control human fibroblasts revealed diverse autophagy-dependent turnover rates even in homogeneous cells. In summary, our work indicates that autophagy acts selectively on mitochondrial proteins, and that most mitochondrial protein turnover occurs through non-autophagic processes. Abbreviations: Atg5: Autophagy-related 5 (Drosophila); ATG5: autophagy related 5 (human); Atg7: Autophagy-related 7 (Drosophila); ATG7: autophagy related 7 (human); DNA: deoxyribonucleic acid; ER: endoplasmic reticulum; GFP: green fluorescent protein; MS: mass spectrometry; park: parkin (Drosophila); Pink1: PTEN-induced putative kinase 1 (Drosophila); PINK1: PTEN-induced kinase 1 (human); PRKN: parkin RBR E3 ubiquitin protein ligase (human); RNA: ribonucleic acid; SD: standard deviation; Ub: ubiquitin/ubiquitinated; WT: wild-type; YME1L: YME1 like ATPase (Drosophila); YME1L1: YME1 like 1 ATPase (human).

Architecture and Evolution of Blade Assembly in β-propeller Lectins

Lectins with a β-propeller fold bind glycans on the cell surface through multivalent binding sites and appropriate directionality. These proteins are formed by repeats of short domains, raising questions about evolutionary duplication. However, these repeats are difficult to detect in translated genomes and seldom correctly annotated in sequence databases. To address these issues, we defined the blade signature of the five types of β-propellers using 3D-structural data. With these templates, we predicted 3,887 β-propeller lectins in 1,889 species and organized this information in a searchable online database. The data reveal a widespread distribution of β-propeller lectins across species. Prediction also emphasizes multiple architectures and led to the discovery of a β-propeller assembly scenario. This was confirmed by producing and characterizing a predicted protein coded in the genome of Kordia zhangzhouensis. The crystal structure uncovers an intermediate in the evolution of β-propeller assembly and demonstrates the power of our tools.

A genome-wide association study identifies single nucleotide polymorphisms associated with time-to-metastasis in colorectal cancer

Background

Differentiating between cancer patients who will experience metastasis within a short time and who will be long-term survivors without metastasis is a critical aim in healthcare. The microsatellite instability (MSI)-high tumor phenotype is such a differentiator in colorectal cancer, as patients with these tumors are unlikely to experience metastasis. Our aim in this study was to determine if germline genetic variations could further differentiate colorectal cancer patients based on the long-term risk and timing of metastasis.

Methods

The patient cohort consisted of 379 stage I-III Caucasian colorectal cancer patients with microsatellite stable or MSI-low tumors. We performed univariable analysis on 810,622 common single nucleotide polymorphisms (SNPs) under different genetic models. Depending on the long-term metastasis-free survival probability estimates, we applied a mixture cure model, Cox proportional hazards regression model, or log-rank test. For SNPs reaching Bonferroni-corrected significance (p < 6.2 × 10^− 8) having valid genetic models, multivariable analysis adjusting for significant baseline characteristics was conducted.

Results

After adjusting for significant baseline characteristics, specific genotypes of ten polymorphisms were significantly associated with time-to-metastasis. These polymorphisms are three intergenic SNPs, rs5749032 (p = 1.28 × 10^− 10), rs2327990 (p = 9.59 × 10^− 10), rs1145724 (p = 3 × 10^− 8), and seven SNPs within the non-coding sequences of three genes: FHIT (p = 2.59 × 10^− 9), EPHB1 (p = 8.23 × 10^− 9), and MIR7515 (p = 4.87 × 10^− 8).

Conclusions

Our results suggest novel associations of specific genotypes of SNPs with early metastasis in Caucasian colorectal cancer patients. These associations, once replicated in other patient cohorts, could assist in the development of personalized treatment strategies for colorectal cancer patients.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5346-5) contains supplementary material, which is available to authorized users.

NeuroMuscleDB: a Database of Genes Associated with Muscle Development, Neuromuscular Diseases, Ageing, and Neurodegeneration

Skeletal muscle is a highly complex, heterogeneous tissue that serves a multitude of biological functions in living organisms. With the advent of methods, such as microarrays, transcriptome analysis, and proteomics, studies have been performed at the genome level to gain insight of changes in the expression profiles of genes during different stages of muscle development and of associated diseases. In the present study, a database was conceived for the straightforward retrieval of information on genes involved in skeletal muscle formation, neuromuscular diseases (NMDs), ageing, and neurodegenerative disorders (NDs). The resulting database named NeuroMuscleDB ( http://yu-mbl-muscledb.com/NeuroMuscleDB ) is the result of a wide literature survey, database searches, and data curation. NeuroMuscleDB contains information of genes in Homo sapiens, Mus musculus, and Bos Taurus, and their promoter sequences and specified roles at different stages of muscle development and in associated myopathies. The database contains information on ~ 1102 genes, 6030 mRNAs, and 5687 proteins, and embedded analytical tools that can be used to perform tasks related to gene sequence usage. The authors believe NeuroMuscleDB provides a platform for obtaining desired information on genes related to myogenesis and their associations with various diseases (NMDs, ageing, and NDs). NeuroMuscleDB is freely available on the web at http://yu-mbl-muscledb.com/NeuroMuscleDB and supports all major browsers.

Computational characterization of the peptidome in transporter associated with antigen processing (TAP)-deficient cells

The transporter associated with antigen processing (TAP) is a key element of the major histocompatibility complex (MHC) class I antigen processing and presentation pathway. Nonfunctional TAP complexes impair the translocation of cytosol-derived proteolytic peptides to the endoplasmic reticulum lumen. This drastic reduction in the available peptide repertoire leads to a significant decrease in MHC class I cell surface expression. Using mass spectrometry, different studies have analyzed the cellular MHC class I ligandome from TAP-deficient cells, but the analysis of the parental proteins, the source of these ligands, still deserves an in-depth analysis. In the present report, several bioinformatics protocols were applied to investigate the nature of parental proteins for the previously identified TAP-independent MHC class I ligands. Antigen processing in TAP-deficient cells mainly focused on small, abundant or highly integral transmembrane proteins of the cellular proteome. This process involved abundant proteins of the central RNA metabolism. In addition, TAP-independent ligands were preferentially cleaved from the N- and C-terminal ends with respect to the central regions of the parental proteins. The abundance of glycine, proline and aromatic residues in the C-terminal sequences from TAP-independently processed proteins allows the accessibility and specificity required for the proteolytic activities that generates the TAP-independent ligandome. This limited proteolytic activity towards a set of preferred proteins in a TAP-negative environment would therefore suffice to promote the survival of TAP-deficient individuals.

Global pathway view analysis of microRNA clusters in myasthenia gravis

The significant roles of microRNAs (miRNAs) in the pathogenesis of myasthenia gravis (MG) have been observed in numerous previous studies. The impact of miRNA clusters on immunity has been demonstrated in previous years; however, the regulation of miRNA clusters in MG remains to be elucidated. In the present study, 245 MG risk genes were collected and 99 MG risk pathways enriched by these genes were identified. A catalog of 126 MG risk miRNAs was then created; the MG risk miRNAs were located on each chromosome and a miRNA cluster was defined as a number of miRNAs with a relative distance of <6 kb on the same sub‑band, same band, same region and same chromosome. Furthermore, enrichment analyses were performed using the target genes of the MG risk miRNA clusters, and a number of risk pathways of each miRNA clusters were identified. As a result, 15 significant miRNA clusters associated with MG were identified. Additionally, the most significant pathways of the miRNA clusters were identified to be enriched on chromosomes 9, 19 and 22, characterized by immunity, infection and carcinoma, suggesting that the mechanism of MG may be associated with certain abnormalities of miRNA clusters on chromosomes 9, 19 and 22. The present study provides novel insight into a global pathway view of miRNA clusters in the pathogenesis of MG.

Biological Interpretation of Complex Genomic Data

Tumor genomic profiling involves analyzing many data types to produce a molecular profile of a tumor. Many of these analyses result in a prioritized list of genes or variants for further study. Interpretation of these lists relies upon annotating and extracting biological meaning through literature and manually curated knowledge bases. This chapter will describe several of these approaches including gene annotation, variant annotation, clinical annotation, functional enrichment analyses, and network analyses. Taken together or individually, these analyses will result in a biological understanding of complex genomic data to improve clinical decision making.

CircFunBase: a database for functional circular RNAs

Increasing evidence reveals that circular RNAs (circRNAs) are widespread in eukaryotes and play important roles in diverse biological processes. However, a comprehensive functionally annotated circRNA database is still lacking. CircFunBase is a web-accessible database that aims to provide a high-quality functional circRNA resource including experimentally validated and computationally predicted functions. The current version of CircFunBase documents more than 7000 manually curated functional circRNA entries, mainly including Homo sapiens, Mus musculus etc. CircFunBase provides visualized circRNA-miRNA interaction networks. In addition, a genome browser is provided to visualize the genome context of circRNAs. As a biological information platform for circRNAs, CircFunBase will contribute for circRNA studies and bridge the gap between circRNAs and their functions.

Large-scale genome-wide meta-analysis of polycystic ovary syndrome suggests shared genetic architecture for different diagnosis criteria

Polycystic ovary syndrome (PCOS) is a disorder characterized by hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphology. Affected women frequently have metabolic disturbances including insulin resistance and dysregulation of glucose homeostasis. PCOS is diagnosed with two different sets of diagnostic criteria, resulting in a phenotypic spectrum of PCOS cases. The genetic similarities between cases diagnosed based on the two criteria have been largely unknown. Previous studies in Chinese and European subjects have identified 16 loci associated with risk of PCOS. We report a fixed-effect, inverse-weighted-variance meta-analysis from 10,074 PCOS cases and 103,164 controls of European ancestry and characterisation of PCOS related traits. We identified 3 novel loci (near PLGRKT, ZBTB16 and MAPRE1), and provide replication of 11 previously reported loci. Only one locus differed significantly in its association by diagnostic criteria; otherwise the genetic architecture was similar between PCOS diagnosed by self-report and PCOS diagnosed by NIH or non-NIH Rotterdam criteria across common variants at 13 loci. Identified variants were associated with hyperandrogenism, gonadotropin regulation and testosterone levels in affected women. Linkage disequilibrium score regression analysis revealed genetic correlations with obesity, fasting insulin, type 2 diabetes, lipid levels and coronary artery disease, indicating shared genetic architecture between metabolic traits and PCOS. Mendelian randomization analyses suggested variants associated with body mass index, fasting insulin, menopause timing, depression and male-pattern balding play a causal role in PCOS. The data thus demonstrate 3 novel loci associated with PCOS and similar genetic architecture for all diagnostic criteria. The data also provide the first genetic evidence for a male phenotype for PCOS and a causal link to depression, a previously hypothesized comorbid disease. Thus, the genetics provide a comprehensive view of PCOS that encompasses multiple diagnostic criteria, gender, reproductive potential and mental health.

We performed an international meta-analysis of genome-wide association studies combining over 10,000,000 genetic markers in more than 10,000 European women with polycystic ovary syndrome (PCOS) and 100,000 controls. We found three new risk variants associated with PCOS. Our data demonstrate that the genetic architecture does not differ based on the diagnostic criteria used for PCOS. We also demonstrate a genetic pathway shared with male pattern baldness, representing the first evidence for shared disease biology in men, and shared genetics with depression, previously postulated based only on observational studies.

Cell Wall Synthesis, Development of Hyphae and Metabolic Pathways Are Processes Potentially Regulated by MicroRNAs Produced Between the Morphological Stages of Paracoccidioides brasiliensis

MicroRNAs are molecules involved in post-transcriptional gene regulation. In pathogenic fungi, microRNAs have been described at different morphological stages by regulating targets involved in processes such as morphogenesis and energy production. Members of the Paracoccidioides complex are the main etiological agents of a systemic mycosis in Latin America. Fungi of the Paracoccidioides complex present a wide range of plasticity to colonize different niches. In response to environmental changes these fungi undergo a morphological switch, remodel their cellular metabolism and modulate structural cell wall components. However, the underlying mechanisms regulating the gene expression is not well understood. By using high performance sequencing and bioinformatics analyses, this work characterizes microRNAs produced by Paracoccidioides brasiliensis. Here, we demonstrated that the transcript encoding proteins involved in microRNA biogenesis were differentially expressed in each morphological stage. In addition, 49 microRNAs were identified in cDNA libraries with 44 differentially regulated among the libraries. Sixteen microRNAs were differentially regulated in comparison to the mycelium in the mycelium-to-yeast transition phase. The yeast parasitic phase revealed a complete remodeling of the expression of these small RNAs. Analyses of targets of the induced microRNAs, from the different libraries, revealed that these molecules may potentially regulate in the cell wall, by repressing genes involved in the synthesis and degradation of glucans and chitin. Furthermore, mRNAs involved in cellular metabolism and development were predicted to be regulated by microRNAs. Therefore, this work describes a putative post transcriptional regulation, mediated by microRNAs in P. brasiliensis and its influence on the adaptive processes of thermal dimorphic fungus.

Embracing the Dark Side: Computational Approaches to Unveil the Functionality of Genes Lacking Biological Annotation in Drug-Induced Liver Injury

In toxicogenomics, functional annotation is an important step to gain additional insights into genes with aberrant expression that drive pathophysiological mechanisms. Nevertheless, there exists a gap on annotation of these genes which often hampers the interpretation of results and limits their applicability in translational medicine. In this study, we evaluated the coverage of functional annotations of differentially expressed genes (DEGs) induced by 10 selected compounds from the TG-GATEs database identified as high- or no-risk in causing drug-induced liver injury (most-DILI or no-DILI, respectively) using in vitro human data. Functional roles of DEGs not present in the most common biological annotation databases – termed “dark genes” – were unveiled via literature mining and via the identification of shared regulatory transcription factors or signaling pathways. Our results demonstrated that there were approximately 13% of dark genes induced by these compounds in vitro and we were able to obtain additional relevant information for up to 76% of those. Using interactome data from several sources, we have uncovered genes such as LRBA, and WDR26 as highly connected in the protein network that play roles in drug response. Genes such as MALAT1, H19, and MIR29C – whose links to hepatotoxicity have been confirmed – were identified as markers for the most-DILI group and appeared as top hits across all literature-based mining methods. Furthermore, we investigated the potential impact of dark genes on liver toxicity by identifying their rat orthologs in combination with their correlation to drug-induced liver pathologies observed in vivo following chemical exposure. We identified a set of important regulatory transcription factors of dark genes for all most-DILI compounds including E2F1 and JUND with supporting evidences in literature and we found Magee1 correlated with chemically induced bile duct hyperplasia and adverse responses at 29 days in rats in vivo. In conclusion, in this study we show the potential role of these poorly annotated genes in mechanisms underlying hepatotoxicity and offer a number of computational approaches that may help to minimize current gaps in gene annotation and highlight their values as potential biomarkers in toxicological studies.

A hybrid approach for automated mutation annotation of the extended human mutation landscape in scientific literature

As the cost of DNA sequencing continues to fall, an increasing amount of information on human genetic variation is being produced that could help progress precision medicine. However, information about such mutations is typically first made available in the scientific literature, and is then later manually curated into more standardized genomic databases. This curation process is expensive, time-consuming and many variants do not end up being fully curated, if at all. Detecting mutations in the literature is the first key step towards automating this process. However, most of the current methods have focused on identifying mutations that follow existing nomenclatures. In this work, we show that there is a large number of mutations that are missed by using this standard approach. Furthermore, we implement the first mutation annotator to cover an extended mutation landscape, and we show that its F1 performance is the same performance as human annotation (F1 78.29 for manual annotation vs F1 79.56 for automatic annotation).

Improving the Gene Ontology Resource to Facilitate More Informative Analysis and Interpretation of Alzheimer's Disease Data

The analysis and interpretation of high-throughput datasets relies on access to high-quality bioinformatics resources, as well as processing pipelines and analysis tools. Gene Ontology (GO, geneontology.org) is a major resource for gene enrichment analysis. The aim of this project, funded by the Alzheimer's Research United Kingdom (ARUK) foundation and led by the University College London (UCL) biocuration team, was to enhance the GO resource by developing new neurological GO terms, and use GO terms to annotate gene products associated with dementia. Specifically, proteins and protein complexes relevant to processes involving amyloid-beta and tau have been annotated and the resulting annotations are denoted in GO databases as 'ARUK-UCL'. Biological knowledge presented in the scientific literature was captured through the association of GO terms with dementia-relevant protein records; GO itself was revised, and new GO terms were added. This literature biocuration increased the number of Alzheimer's-relevant gene products that were being associated with neurological GO terms, such as 'amyloid-beta clearance' or 'learning or memory', as well as neuronal structures and their compartments. Of the total 2055 annotations that we contributed for the prioritised gene products, 526 have associated proteins and complexes with neurological GO terms. To ensure that these descriptive annotations could be provided for Alzheimer's-relevant gene products, over 70 new GO terms were created. Here, we describe how the improvements in ontology development and biocuration resulting from this initiative can benefit the scientific community and enhance the interpretation of dementia data.

Abscopal Effect in Non-injected Tumors Achieved with Cytokine-Armed Oncolytic Adenovirus

Cancer treatment with local administration of armed oncolytic viruses could potentially induce systemic antitumor effects, or the abscopal effect, as they self-amplify in tumors, induce danger signaling, and promote tumor-associated antigen presentation. In this study, oncolytic adenovirus coding for human tumor necrosis factor alpha (TNF-α) and interleukin-2 (IL-2) Ad5/3-E2F-d24-hTNF-α-IRES-hIL-2 (also known as [a.k.a.] TILT-123) provoked antitumor efficacy in tumors that were injected with Ad5/3-E2F-d24-hTNF-α-IRES-hIL-2 and those that were left non-injected in the same animal. Importantly, the virus was able to travel to distant tumors. To dissect the effects of oncolysis and cytokines, we studied replication-incompetent viruses in mice. Systemic antitumor effects were similar in both models, highlighting the importance of the arming device. The cytokines induced positive changes in immune cell infiltrates and induced the expression of several immune-reaction-related genes in tumors. In addition, Ad5/3-E2F-d24-hTNF-α-IRES-hIL-2 was able to increase homing of adoptively transferred tumor-infiltrating lymphocytes into both injected and non-injected tumors, possibly mediated through chemokine expression. In summary, local treatment with Ad5/3-E2F-d24-hTNF-α-IRES-hIL-2 resulted in systemic antitumor efficacy by inducing immune cell infiltration and trafficking into both treated and untreated tumors. Moreover, the oncolytic adenovirus platform had superior systemic effects over replication-deficient vector through spreading into distant tumors.

LncCeRBase: a database of experimentally validated human competing endogenous long non-coding RNAs

Long non-coding RNAs (lncRNAs) are endogenous molecules longer than 200 nucleotides, and lack coding potential. LncRNAs that interact with microRNAs (miRNAs) are known as a competing endogenous RNAs (ceRNAs) and have the ability to regulate the expression of target genes. The ceRNAs play an important role in the initiation and progression of various cancers. However, until now, there is no a database including a collection of experimentally verified, human ceRNAs. We developed the LncCeRBase database, which encompasses 432 lncRNA-miRNA-mRNA interactions, including 130 lncRNAs, 214 miRNAs and 245 genes from 300 publications. In addition, we compiled the signaling pathways associated with the included lncRNA-miRNA-mRNA interactions as a tool to explore their functions. LncCeRBase is useful for understanding the regulatory mechanisms of lncRNA.Database URL: http://lnccerbase.it1004.com.

Reconstructing phosphorylation signalling networks from quantitative phosphoproteomic data

Cascades of phosphorylation between protein kinases comprise a core mechanism in the integration and propagation of intracellular signals. Although we have accumulated a wealth of knowledge around some such pathways, this is subject to study biases and much remains to be uncovered. Phosphoproteomics, the identification and quantification of phosphorylated proteins on a proteomic scale, provides a high-throughput means of interrogating the state of intracellular phosphorylation, both at the pathway level and at the whole-cell level. In this review, we discuss methods for using human quantitative phosphoproteomic data to reconstruct the underlying signalling networks that generated it. We address several challenges imposed by the data on such analyses and we consider promising advances towards reconstructing unbiased, kinome-scale signalling networks.

3D Network exploration and visualisation for lifespan data

BACKGROUND

The Ageing Factor Database AgeFactDB contains a large number of lifespan observations for ageing-related factors like genes, chemical compounds, and other factors such as dietary restriction in different organisms. These data provide quantitative information on the effect of ageing factors from genetic interventions or manipulations of lifespan. Analysis strategies beyond common static database queries are highly desirable for the inspection of complex relationships between AgeFactDB data sets. 3D visualisation can be extremely valuable for advanced data exploration.

RESULTS

Different types of networks and visualisation strategies are proposed, ranging from basic networks of individual ageing factors for a single species to complex multi-species networks. The augmentation of lifespan observation networks by annotation nodes, like gene ontology terms, is shown to facilitate and speed up data analysis. We developed a new Javascript 3D network viewer JANet that provides the proposed visualisation strategies and has a customised interface for AgeFactDB data. It enables the analysis of gene lists in combination with AgeFactDB data and the interactive visualisation of the results.

CONCLUSION

Interactive 3D network visualisation allows to supplement complex database queries by a visually guided exploration process. The JANet interface allows gaining deeper insights into lifespan data patterns not accessible by common database queries alone. These concepts can be utilised in many other research fields.

Polysaccharides for tissue engineering: Current landscape and future prospects

Biological studies on the importance of carbohydrate moieties in tissue engineering have incited a growing interest in the application of polysaccharides as scaffolds over the past two decades. This review provides a perspective of the recent approaches in developing polysaccharide scaffolds, with a focus on their chemical modification, structural versatility, and biological applicability. The current major limitations are assessed, including structural reproducibility, the narrow scope of polysaccharide modifications being applied, and the effective replication of the extracellular environment. Areas with opportunities for further development are addressed with an emphasis on the application of rationally designed polysaccharides and their importance in elucidating the molecular interactions necessary to properly design tissue engineering materials.

Integrated systems analysis reveals conserved gene networks underlying response to spinal cord injury

Spinal cord injury (SCI) is a devastating neurological condition for which there are currently no effective treatment options to restore function. A major obstacle to the development of new therapies is our fragmentary understanding of the coordinated pathophysiological processes triggered by damage to the human spinal cord. Here, we describe a systems biology approach to integrate decades of small-scale experiments with unbiased, genome-wide gene expression from the human spinal cord, revealing a gene regulatory network signature of the pathophysiological response to SCI. Our integrative analyses converge on an evolutionarily conserved gene subnetwork enriched for genes associated with the response to SCI by small-scale experiments, and whose expression is upregulated in a severity-dependent manner following injury and downregulated in functional recovery. We validate the severity-dependent upregulation of this subnetwork in rodents in primary transcriptomic and proteomic studies. Our analysis provides systems-level view of the coordinated molecular processes activated in response to SCI.

IRS-1 genetic polymorphism (r.2963G>A) in type 2 diabetes mellitus patients associated with insulin resistance

Background

Insulin receptor substrate (IRS) molecules are key mediators in insulin signaling. Several polymorphisms in the IRS genes have been identified, but only the Gly to Arg 972 substitution of IRS-1 seems to have a pathogenic role in the development of type 2 diabetes mellitus (T2DM). Many polymorphisms described in IRS-1 gene, especially Gly972Arg substitution, are shown to be associated with insulin resistance (IR) in T2DM.

Subjects and methods

This prospective case-control study was performed during the period from November 2014 to May 2015. All patients were selected from the Department of Internal Medicine and were screened for eligibility for this study. Subjects were divided into two groups: first group consisted of 100 T2DM patients; second group consisted of 120 nondiabetic controls. First group was further divided into two subgroups: 66 IR patients and 34 insulin-sensitive (IS) patients (homeostatic model assessment [HOMA] was performed). Restriction fragment length polymorphism (RFLP) was performed using specific primers for scanning single-nucleotide polymorphisms (SNPs) such as Gly972Arg (rs1801278 SNP).

Results

Taking GG genotype and G allele as references, GA, GA+AA genotypes and A allele showed significantly higher frequency in the T2DM group when compared to the control group, with higher risk to develop T2DM in healthy controls. Taking GG as a reference, rs1801278GA+AA genotype and A allele showed significantly higher proportion in IR when compared to IS, with higher risk to develop IR in T2DM patients. Logistic regression analysis showed that higher FBG, fasting plasma insulin (FPI), HOMA-IR, GA+AA genotypes were associated with higher risk to develop IR in univariable analysis.

Conclusion

IRS-1 genetic factor may be a significant genetic determinant for IR in T2DM patients during severe/acute-phase hyperglycemia.