genenames.org: the HGNC resources in 2011

Network pharmacology, molecular docking, and dynamics analyses to predict the antiviral activity of ginger constituents against coronavirus infection

COVID-19 is a global pandemic that caused a dramatic loss of human life worldwide, leading to accelerated research for antiviral drug discovery. Herbal medicine is one of the most commonly used alternative medicine for the prevention and treatment of many conditions including respiratory system diseases. In this study, a computational pipeline was employed, including network pharmacology, molecular docking simulations, and molecular dynamics simulations, to analyze the common phytochemicals of ginger rhizomes and identify candidate constituents as viral inhibitors. Furthermore, experimental assays were performed to analyze the volatile and non-volatile compounds of ginger and to assess the antiviral activity of ginger oil and hydroalcoholic extract. Network pharmacology analysis showed that ginger compounds target human genes that are involved in related cellular processes to the viral infection. Docking analysis highlighted five pungent compounds and zingiberenol as potential inhibitors for the main protease (Mpro), spike receptor-binding domain (RBD), and human angiotensin-converting enzyme 2 (ACE2). Then, (6)-gingerdiacetate was selected for molecular dynamics (MD) simulations as it exhibited the best binding interactions and free energies over the three target proteins. Trajectories analysis of the three complexes showed that RBD and ACE2 complexes with the ligand preserved similar patterns of root mean square deviation (RMSD) and radius of gyration (Rg) values to their respective native structures. Finally, experimental validation of the ginger hydroalcoholic extract confirmed the existence of (6)-gingerdiacetate and revealed the strong antiviral activity of the hydroalcoholic extract with IC50 of 2.727 μ g / ml . Our study provides insights into the potential antiviral activity of (6)-gingerdiacetate that may enhance the host immune response and block RBD binding to ACE2, thereby, inhibiting SARS-CoV-2 infection.

A disease similarity approach identifies short-lived Niemann-Pick type C disease mice with accelerated brain aging as a novel mouse model for Alzheimer's disease and aging research

Since its first description in 1906 by Dr. Alois Alzheimer, Alzheimer's disease (AD) has been the most common type of dementia. Initially thought to be caused by age-associated accumulation of plaques, in recent years, research has increasingly associated AD with lysosomal storage and metabolic disorders, and the explanation of its pathogenesis has shifted from amyloid and tau accumulation to oxidative stress and impaired lipid and glucose metabolism aggravated by hypoxic conditions. However, the underlying mechanisms linking those cellular processes and conditions to disease progression have yet to be defined. Here, we applied a disease similarity approach to identify unknown molecular targets of AD by using transcriptomic data from congenital diseases known to increase AD risk, namely Down Syndrome, Niemann Pick Disease Type C (NPC), and Mucopolysaccharidoses I. We uncovered common pathways, hub genes, and miRNAs across in vitro and in vivo models of these diseases as potential molecular targets for neuroprotection and amelioration of AD pathology, many of which have never been associated with AD. We then investigated common molecular alterations in brain samples from an NPC disease mouse model by juxtaposing them with brain samples of both human and mouse models of AD. Detailed phenotypic and molecular analyses revealed that the NPC mut mouse model can serve as a potential short-lived in vivo model for AD research and for understanding molecular factors affecting brain aging. This research represents the first comprehensive approach to congenital disease association with neurodegeneration and a new perspective on AD research while highlighting shortcomings and lack of correlation in diverse in vitro models. Considering the lack of an AD mouse model that recapitulates the physiological hallmarks of brain aging, the characterization of a short-lived NPC mouse model will further accelerate the research in these fields and offer a unique model for understanding the molecular mechanisms of AD from a perspective of accelerated brain aging.

Dysregulation of alternative splicing in spinocerebellar ataxia type 1

Spinocerebellar ataxia type 1 is caused by an expansion of the polyglutamine tract in ATAXIN-1. Ataxin-1 is broadly expressed throughout the brain and is involved in regulating gene expression. However, it is not yet known if mutant ataxin-1 can impact the regulation of alternative splicing events. We performed RNA sequencing in mouse models of spinocerebellar ataxia type 1 and identified that mutant ataxin-1 expression abnormally leads to diverse splicing events in the mouse cerebellum of spinocerebellar ataxia type 1. We found that the diverse splicing events occurred in a predominantly cell autonomous manner. A majority of the transcripts with misregulated alternative splicing events were previously unknown, thus allowing us to identify overall new biological pathways that are distinctive to those affected by differential gene expression in spinocerebellar ataxia type 1. We also provide evidence that the splicing factor Rbfox1 mediates the effect of mutant ataxin-1 on misregulated alternative splicing and that genetic manipulation of Rbfox1 expression modifies neurodegenerative phenotypes in a Drosophila model of spinocerebellar ataxia type 1 in vivo. Together, this study provides novel molecular mechanistic insight into the pathogenesis of spinocerebellar ataxia type 1 and identifies potential therapeutic strategies for spinocerebellar ataxia type 1.

ENLIGHTENMENT: A Scalable Annotated Database of Genomics and NGS-Based Nucleotide Level Profiles

The revolution in sequencing technologies has enabled human genomes to be sequenced at a very low cost and time leading to exponential growth in the availability of whole-genome sequences. However, the complete understanding of our genome and its association with cancer is a far way to go. Researchers are striving hard to detect new variants and find their association with diseases, which further gives rise to the need for aggregation of this Big Data into a common standard scalable platform. In this work, a database named Enlightenment has been implemented which makes the availability of genomic data integrated from eight public databases, and DNA sequencing profiles of H. sapiens in a single platform. Annotated results with respect to cancer specific biomarkers, pharmacogenetic biomarkers and its association with variability in drug response, and DNA profiles along with novel copy number variants are computed and stored, which are accessible through a web interface. In order to overcome the challenge of storage and processing of NGS technology-based whole-genome DNA sequences, Enlightenment has been extended and deployed to a flexible and horizontally scalable database HBase, which is distributed over a hadoop cluster, which would enable the integration of other omics data into the database for enlightening the path towards eradication of cancer.

Molecular phenotyping of single pancreatic islet leader beta cells by "Flash-Seq"

AIMS

Spatially-organized increases in cytosolic Ca²⁺ within pancreatic beta cells in the pancreatic islet underlie the stimulation of insulin secretion by high glucose. Recent data have revealed the existence of subpopulations of beta cells including "leaders" which initiate Ca²⁺ waves. Whether leader cells possess unique molecular features, or localisation, is unknown.

MAIN METHODS

High speed confocal Ca²⁺ imaging was used to identify leader cells and connectivity analysis, running under MATLAB and Python, to identify highly connected "hub" cells. To explore transcriptomic differences between beta cell sub-groups, individual leaders or followers were labelled by photo-activation of the cryptic fluorescent protein PA-mCherry and subjected to single cell RNA sequencing ("Flash-Seq").

KEY FINDINGS

Distinct Ca²⁺ wave types were identified in individual islets, with leader cells present in 73 % (28 of 38 islets imaged). Scale-free, power law-adherent behaviour was also observed in 29 % of islets, though "hub" cells in these islets did not overlap with leaders. Transcripts differentially expressed (295; padj < 0.05) between leader and follower cells included genes involved in cilium biogenesis and transcriptional regulation. Providing some support for these findings, ADCY6 immunoreactivity tended to be higher in leader than follower cells, whereas cilia number and length tended to be lower in the former. Finally, leader cells were located significantly closer to delta, but not alpha, cells in Euclidian space than were follower cells.

SIGNIFICANCE

The existence of both a discrete transcriptome and unique localisation implies a role for these features in defining the specialized function of leaders. These data also raise the possibility that localised signalling between delta and leader cells contributes to the initiation and propagation of islet Ca²⁺ waves.

Cross-species high-resolution transcriptome profiling suggests biomarkers and therapeutic targets for ulcerative colitis

Background: Ulcerative colitis (UC) is a disorder with unknown etiology, and animal models play an essential role in studying its molecular pathophysiology. Here, we aim to identify common conserved pathological UC-related gene expression signatures between humans and mice that can be used as treatment targets and/or biomarker candidates. Methods: To identify differentially regulated protein-coding genes and non-coding RNAs, we sequenced total RNA from the colon and blood of the most widely used dextran sodium sulfate Ulcerative colitis mouse. By combining this with public human Ulcerative colitis data, we investigated conserved gene expression signatures and pathways/biological processes through which these genes may contribute to disease development/progression. Results: Cross-species integration of human and mouse Ulcerative colitis data resulted in the identification of 1442 genes that were significantly differentially regulated in the same direction in the colon and 157 in blood. Of these, 51 genes showed consistent differential regulation in the colon and blood. Less known genes with importance in disease pathogenesis, including SPI1, FPR2, TYROBP, CKAP4, MCEMP1, ADGRG3, SLC11A1, and SELPLG, were identified through network centrality ranking and validated in independent human and mouse cohorts. Conclusion: The identified Ulcerative colitis conserved transcriptional signatures aid in the disease phenotyping and future treatment decisions, drug discovery, and clinical trial design.

Central resources of variant discovery and annotation and its role in precision medicine

Rapid technological advancement in high-throughput genomics, microarray, and deep sequencing technologies has accelerated the possibility of more complex precision medicine research using large amounts of heterogeneous health-related data from patients, including genomic variants. Genomic variants can be identified and annotated based on the reference human genome either within the sequence as a whole or in a putative functional genomic element. The American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) mutually created standards and guidelines for the appraisal of proof to expand consistency and straightforwardness in clinical variation interpretations. Various efforts toward precision medicine have been facilitated by many national and international public databases that classify and annotate genomic variation. In the present study, several resources are highlighted with recognition and data spreading of clinically important genetic variations.

Large-scale prediction of key dynamic interacting proteins in multiple cancers

Tracking cancer dynamic protein-protein interactions (PPIs) and deciphering their pathogenesis remain a challenge. We presented a dynamic PPIs' hypothesis: permanent and transient interactions might achieve dynamic switchings from normal cells to malignancy, which could cause maintenance functions to be interrupted and transient functions to be sustained. Based on the hypothesis, we first predicted >1400 key cancer genes (KCG) by applying PPI-express we proposed to 18 cancer gene expression datasets. We then further screened out key dynamic interactions (KDI) of cancer based on KCG and transient and permanent interactions under both conditions. Two prominent functional characteristics, "Cell cycle-related" and "Immune-related", were presented for KCG, suggesting that these might be their general characteristics. We found that, compared to permanent to transient KDI pairs (P2T) in the network, transient to permanent (T2P) have significantly higher edge betweenness (EB), and P2T pairs tending to locate intra-functional modules may play roles in maintaining normal biological functions, while T2P KDI pairs tending to locate inter-modules may play roles in biological signal transduction. It was consistent with our hypothesis. Also, we analyzed network characteristics of KDI pairs and their functions. Our findings of KDI may serve to understand and explain a few hallmarks of cancer.

Drugs and Epigenetic Molecular Functions. A Pharmacological Data Scientometric Analysis

Interactions of drugs with the classical epigenetic mechanism of DNA methylation or histone modification are increasingly being elucidated mechanistically and used to develop novel classes of epigenetic therapeutics. A data science approach is used to synthesize current knowledge on the pharmacological implications of epigenetic regulation of gene expression. Computer-aided knowledge discovery for epigenetic implications of current approved or investigational drugs was performed by querying information from multiple publicly available gold-standard sources to (i) identify enzymes involved in classical epigenetic processes, (ii) screen original biomedical scientific publications including bibliometric analyses, (iii) identify drugs that interact with epigenetic enzymes, including their additional non-epigenetic targets, and (iv) analyze computational functional genomics of drugs with epigenetic interactions. PubMed database search yielded 3051 hits on epigenetics and drugs, starting in 1992 and peaking in 2016. Annual citations increased to a plateau in 2000 and show a downward trend since 2008. Approved and investigational drugs in the DrugBank database included 122 compounds that interacted with 68 unique epigenetic enzymes. Additional molecular functions modulated by these drugs included other enzyme interactions, whereas modulation of ion channels or G-protein-coupled receptors were underrepresented. Epigenetic interactions included (i) drug-induced modulation of DNA methylation, (ii) drug-induced modulation of histone conformations, and (iii) epigenetic modulation of drug effects by interference with pharmacokinetics or pharmacodynamics. Interactions of epigenetic molecular functions and drugs are mutual. Recent research activities on the discovery and development of novel epigenetic therapeutics have passed successfully, whereas epigenetic effects of non-epigenetic drugs or epigenetically induced changes in the targets of common drugs have not yet received the necessary systematic attention in the context of pharmacological plasticity.

Deconvoluting global cytokine signaling networks in natural killer cells

Cytokine signaling via signal transducer and activator of transcription (STAT) proteins is crucial for optimal antiviral responses of natural killer (NK) cells. However, the pleiotropic effects of both cytokine and STAT signaling preclude the ability to precisely attribute molecular changes to specific cytokine-STAT modules. Here, we employed a multi-omics approach to deconstruct and rebuild the complex interaction of multiple cytokine signaling pathways in NK cells. Proinflammatory cytokines and homeostatic cytokines formed a cooperative axis to commonly regulate global gene expression and to further repress expression induced by type I interferon signaling. These cytokines mediated distinct modes of epigenetic regulation via STAT proteins, and collective signaling best recapitulated global antiviral responses. The most dynamically responsive genes were conserved across humans and mice, which included a cytokine-STAT-induced cross-regulatory program. Thus, an intricate crosstalk exists between cytokine signaling pathways, which governs NK cell responses.

Phen2Gene: rapid phenotype-driven gene prioritization for rare diseases

Human Phenotype Ontology (HPO) terms are increasingly used in diagnostic settings to aid in the characterization of patient phenotypes. The HPO annotation database is updated frequently and can provide detailed phenotype knowledge on various human diseases, and many HPO terms are now mapped to candidate causal genes with binary relationships. To further improve the genetic diagnosis of rare diseases, we incorporated these HPO annotations, gene-disease databases and gene-gene databases in a probabilistic model to build a novel HPO-driven gene prioritization tool, Phen2Gene. Phen2Gene accesses a database built upon this information called the HPO2Gene Knowledgebase (H2GKB), which provides weighted and ranked gene lists for every HPO term. Phen2Gene is then able to access the H2GKB for patient-specific lists of HPO terms or PhenoPacket descriptions supported by GA4GH (http://phenopackets.org/), calculate a prioritized gene list based on a probabilistic model and output gene-disease relationships with great accuracy. Phen2Gene outperforms existing gene prioritization tools in speed and acts as a real-time phenotype-driven gene prioritization tool to aid the clinical diagnosis of rare undiagnosed diseases. In addition to a command line tool released under the MIT license (https://github.com/WGLab/Phen2Gene), we also developed a web server and web service (https://phen2gene.wglab.org/) for running the tool via web interface or RESTful API queries. Finally, we have curated a large amount of benchmarking data for phenotype-to-gene tools involving 197 patients across 76 scientific articles and 85 patients' de-identified HPO term data from the Children's Hospital of Philadelphia.

Transcriptional Basis of Mouse and Human Dendritic Cell Heterogeneity

Dendritic cells (DCs) play a critical role in orchestrating adaptive immune responses due to their unique ability to initiate T cell responses and direct their differentiation into effector lineages. Classical DCs have been divided into two subsets, cDC1 and cDC2, based on phenotypic markers and their distinct abilities to prime CD8 and CD4 T cells. While the transcriptional regulation of the cDC1 subset has been well characterized, cDC2 development and function remain poorly understood. By combining transcriptional and chromatin analyses with genetic reporter expression, we identified two principal cDC2 lineages defined by distinct developmental pathways and transcriptional regulators, including T-bet and RORγt, two key transcription factors known to define innate and adaptive lymphocyte subsets. These novel cDC2 lineages were characterized by distinct metabolic and functional programs. Extending our findings to humans revealed conserved DC heterogeneity and the presence of the newly defined cDC2 subsets in human cancer.

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Single-cell analyses reveal novel dendritic cell subsets

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Major cDC2 subsets differentially express T-bet and RORγt

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Distinct pro- and anti-inflammatory potential of T-bet⁺ and Tbet^– cDC2s

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Transcriptional basis for cDC2 heterogeneity conserved across mouse and human

SingleCellNet: A Computational Tool to Classify Single Cell RNA-Seq Data Across Platforms and Across Species

Single-cell RNA-seq has emerged as a powerful tool in diverse applications, from determining the cell-type composition of tissues to uncovering regulators of developmental programs. A near-universal step in the analysis of single-cell RNA-seq data is to hypothesize the identity of each cell. Often, this is achieved by searching for combinations of genes that have previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other single-cell RNA-seq studies. Here, we describe our tool, SingleCellNet, which addresses these issues and enables the classification of query single-cell RNA-seq data in comparison to reference single-cell RNA-seq data. SingleCellNet compares favorably to other methods in sensitivity and specificity, and it is able to classify across platforms and species. We highlight SingleCellNet's utility by classifying previously undetermined cells, and by assessing the outcome of a cell fate engineering experiment.

The integrated National NeuroAIDS Tissue Consortium database: a rich platform for neuroHIV research

Herein we present major updates to the National NeuroAIDS Tissue Consortium (NNTC) database. The NNTC's ongoing multisite clinical research study was established to facilitate access to ante-mortem and post-mortem data, tissues and biofluids for the neurohuman immunodeficiency virus (HIV) research community. Recently, the NNTC has expanded to include data from the central nervous system HIV Antiretroviral Therapy Effects Research (CHARTER) study. The data and biospecimens from CHARTER and NNTC cohorts are available to qualified researchers upon request. Data generated by requestors using NNTC biospecimens and tissues are returned to the NNTC upon the conclusion of requestors' work, and this external, experimental data are annotated and curated in the publically accessible NNTC database, thereby extending the utility of each case. A flexible and extensible database ontology allows the integration of disparate data sets, including external experimental data, clinical neuropsychological and neuromedical testing data, tissue pathology and neuroimaging data.

Computational functional genomics-based approaches in analgesic drug discovery and repurposing

Persistent pain is a major healthcare problem affecting a fifth of adults worldwide with still limited treatment options. The search for new analgesics increasingly includes the novel research area of functional genomics, which combines data derived from various processes related to DNA sequence, gene expression or protein function and uses advanced methods of data mining and knowledge discovery with the goal of understanding the relationship between the genome and the phenotype. Its use in drug discovery and repurposing for analgesic indications has so far been performed using knowledge discovery in gene function and drug target-related databases; next-generation sequencing; and functional proteomics-based approaches. Here, we discuss recent efforts in functional genomics-based approaches to analgesic drug discovery and repurposing and highlight the potential of computational functional genomics in this field including a demonstration of the workflow using a novel R library ‘dbtORA’.

A phenotype centric benchmark of variant prioritisation tools

Next generation sequencing is a standard tool used in clinical diagnostics. In Mendelian diseases the challenge is to discover the single etiological variant among thousands of benign or functionally unrelated variants. After calling variants from aligned sequencing reads, variant prioritisation tools are used to examine the conservation or potential functional consequences of variants. We hypothesised that the performance of variant prioritisation tools may vary by disease phenotype. To test this we created benchmark data sets for variants associated with different disease phenotypes. We found that performance of 24 tested tools is highly variable and differs by disease phenotype. The task of identifying a causative variant amongst a large number of benign variants is challenging for all tools, highlighting the need for further development in the field. Based on our observations, we recommend use of five top performers found in this study (FATHMM, M-CAP, MetaLR, MetaSVM and VEST3). In addition we provide tables indicating which analytical approach works best in which disease context. Variant prioritisation tools are best suited to investigate variants associated with well-studied genetic diseases, as these variants are more readily available during algorithm development than variants associated with rare diseases. We anticipate that further development into disease focussed tools will lead to significant improvements.

WDR5 high expression and its effect on tumorigenesis in leukemia

WD repeat domain 5 (WDR5) plays an important role in various biological functions through the epigenetic regulation of gene transcription. However, the oncogenic effect of WDR5 in leukemia remains largely unknown. Here, we found WDR5 expression is increased in leukemia patients. High expression of WDR5 is associated with high risk leukemia; Patients with WDR5 and MLL1 high expression have poor complete remission rate. We further identified the global genomic binding of WDR5 in leukemic cells and found the genomic co-localization of WDR5 binding with H3K4me3 enrichment. Moreover, WDR5 knockdown by shRNA suppresses cell proliferation, induces apoptosis, inhibits the expression of WDR5 targets, and blocks the H3K4me3 enrichment on the promoter of its targets. We also observed the positive correlation of WDR5 expression with these targets in the cohort study of leukemia patients. Our data reveal that WDR5 may have oncogenic effect and WDR5-mediated H3K4 methylation plays an important role in leukemogenesis.

SNOMED2HL7: A tool to normalize and bind SNOMED CT concepts to the HL7 Reference Information Model

BACKGROUND

Current clinical research and practice requires interoperability among systems in a complex and highly dynamic domain. There has been a significant effort in recent years to develop integrative common data models and domain terminologies. Such efforts have not completely solved the challenges associated with clinical data that are distributed among different and heterogeneous institutions with different systems to encode the information. Currently, when providing homogeneous interfaces to exploit clinical data, certain transformations still involve manual and time-consuming processes that could be automated.

OBJECTIVES

There is a lack of tools to support data experts adopting clinical standards. This absence is especially significant when links between data model and vocabulary are required. The objective of this work is to present SNOMED2HL7, a novel tool to automatically link biomedical concepts from widely used terminologies, and the corresponding clinical context, to the HL7 Reference Information Model (RIM).

METHODS

Based on the recommendations of the International Health Terminology Standards Development Organisation (IHTSDO), the SNOMED Normal Form has been implemented within SNOMED2HL7 to decompose and provide a method to reduce the number of options to store the same information. The binding of clinical terminologies to HL7 RIM components is the core of SNOMED2HL7, where terminology concepts have been annotated with the corresponding options within the interoperability standard. A web-based tool has been developed to automatically provide information from the normalization mechanisms and the terminology binding.

RESULTS

SNOMED2HL7 binding coverage includes the majority of the concepts used to annotate legacy systems. It follows HL7 recommendations to solve binding overlaps and provides the binding of the normalized version of the concepts. The first version of the tool, available at http://kandel.dia.fi.upm.es:8078, has been validated in EU funded projects to integrate real world data for clinical research with an 88.47% of accuracy.

CONCLUSIONS

This paper presents the first initiative to automatically retrieve concept-centered information required to transform legacy data into widely adopted interoperability standards. Although additional functionality will extend capabilities to automate data transformations, SNOMED2HL7 already provides the functionality required for the clinical interoperability community.

Long noncoding RNAs are dynamically regulated during β-cell mass expansion in mouse pregnancy and control β-cell proliferation in vitro

Pregnancy is associated with increased β-cell proliferation driven by prolactin. Long noncoding RNAs (lncRNA) are the most abundant RNA species in the mammalian genome, yet, their functional importance is mainly elusive. Aims/hypothesis: This study tests the hypothesis that lncRNAs regulate β-cell proliferation in response to prolactin in the context of β-cell mass compensation in pregnancy. Methods: The expression profile of lncRNAs in mouse islets at day 14.5 of pregnancy was explored by a bioinformatics approach, further confirmed by quantitative PCR at different days of pregnancy, and islet specificity was evaluated by comparing expression in islets versus other tissues. In order to establish the role of the candidate lncRNAs we studied cell proliferation in mouse islets and the MIN6 β-cell line by EdU incorporation and cell count. Results: We found that a group of lncRNAs is differentially regulated in mouse islets at 14.5 days of pregnancy. At different stages of pregnancy, these lncRNAs are dynamically expressed, and expression is prolactin dependent in mouse islets and MIN6 cells. One of those lncRNAs, Gm16308 (Lnc03), is dynamically regulated during pregnancy, prolactin-dependent and islet-enriched. Silencing Lnc03 in primary β-cells and MIN6 cells inhibits, whereas over-expression stimulates, proliferation even in the absence of prolactin, demonstrating that Lnc03 regulates β-cell growth. Conclusions/interpretation: During pregnancy mouse islet proliferation is correlated with dynamic changes of lncRNA expression. In particular, Lnc03 regulates mouse β-cell proliferation and may be a crucial component of β-cell proliferation in β-cell mass adaptation in both health and disease.

Integrated Computational Analysis of Genes Associated with Human Hereditary Insensitivity to Pain. A Drug Repurposing Perspective

Genes causally involved in human insensitivity to pain provide a unique molecular source of studying the pathophysiology of pain and the development of novel analgesic drugs. The increasing availability of “big data” enables novel research approaches to chronic pain while also requiring novel techniques for data mining and knowledge discovery. We used machine learning to combine the knowledge about n = 20 genes causally involved in human hereditary insensitivity to pain with the knowledge about the functions of thousands of genes. An integrated computational analysis proposed that among the functions of this set of genes, the processes related to nervous system development and to ceramide and sphingosine signaling pathways are particularly important. This is in line with earlier suggestions to use these pathways as therapeutic target in pain. Following identification of the biological processes characterizing hereditary insensitivity to pain, the biological processes were used for a similarity analysis with the functions of n = 4,834 database-queried drugs. Using emergent self-organizing maps, a cluster of n = 22 drugs was identified sharing important functional features with hereditary insensitivity to pain. Several members of this cluster had been implicated in pain in preclinical experiments. Thus, the present concept of machine-learned knowledge discovery for pain research provides biologically plausible results and seems to be suitable for drug discovery by identifying a narrow choice of repurposing candidates, demonstrating that contemporary machine-learned methods offer innovative approaches to knowledge discovery from available evidence.

GEAR: A database of Genomic Elements Associated with drug Resistance

Drug resistance is becoming a serious problem that leads to the failure of standard treatments, which is generally developed because of genetic mutations of certain molecules. Here, we present GEAR (A database of Genomic Elements Associated with drug Resistance) that aims to provide comprehensive information about genomic elements (including genes, single-nucleotide polymorphisms and microRNAs) that are responsible for drug resistance. Right now, GEAR contains 1631 associations between 201 human drugs and 758 genes, 106 associations between 29 human drugs and 66 miRNAs, and 44 associations between 17 human drugs and 22 SNPs. These relationships are firstly extracted from primary literature with text mining and then manually curated. The drug resistome deposited in GEAR provides insights into the genetic factors underlying drug resistance. In addition, new indications and potential drug combinations can be identified based on the resistome. The GEAR database can be freely accessed through http://gear.comp-sysbio.org.

Systems Biology Approach in Hypertension Research

Systems biology is an approach to study all genes, gene transcripts, proteins, metabolites, and their interactions in specific cells, tissues, organs, or the whole organism. It is based on data derived from high-throughput analytical technologies and bioinformatics tools to analyze these data, and aims to understand the whole system rather than individual aspects of it. Systems biology can be applied to virtually all conditions and diseases and therefore also to hypertension and its underlying vascular disorders. Unlike other methods in this book there is no clear-cut protocol to explain a systems biology approach. We will instead outline some of the most important and common steps in the generation and analysis of systems biology data.

TRAP-seq Profiling and RNAi-Based Genetic Screens Identify Conserved Glial Genes Required for Adult Drosophila Behavior

Although, glial cells have well characterized functions in the developing and mature brain, it is only in the past decade that roles for these cells in behavior and plasticity have been delineated. Glial astrocytes and glia-neuron signaling, for example, are now known to have important modulatory functions in sleep, circadian behavior, memory and plasticity. To better understand mechanisms of glia-neuron signaling in the context of behavior, we have conducted cell-specific, genome-wide expression profiling of adult Drosophila astrocyte-like brain cells and performed RNA interference (RNAi)-based genetic screens to identify glial factors that regulate behavior. Importantly, our studies demonstrate that adult fly astrocyte-like cells and mouse astrocytes have similar molecular signatures; in contrast, fly astrocytes and surface glia-different classes of glial cells-have distinct expression profiles. Glial-specific expression of 653 RNAi constructs targeting 318 genes identified multiple factors associated with altered locomotor activity, circadian rhythmicity and/or responses to mechanical stress (bang sensitivity). Of interest, 1 of the relevant genes encodes a vesicle recycling factor, 4 encode secreted proteins and 3 encode membrane transporters. These results strongly support the idea that glia-neuron communication is vital for adult behavior.

Comprehensive coverage of cardiovascular disease data in the disease portals at the Rat Genome Database

Cardiovascular diseases are complex diseases caused by a combination of genetic and environmental factors. To facilitate progress in complex disease research, the Rat Genome Database (RGD) provides the community with a disease portal where genome objects and biological data related to cardiovascular diseases are systematically organized. The purpose of this study is to present biocuration at RGD, including disease, genetic, and pathway data. The RGD curation team uses controlled vocabularies/ontologies to organize data curated from the published literature or imported from disease and pathway databases. These organized annotations are associated with genes, strains, and quantitative trait loci (QTLs), thus linking functional annotations to genome objects. Screen shots from the web pages are used to demonstrate the organization of annotations at RGD. The human cardiovascular disease genes identified by annotations were grouped according to data sources and their annotation profiles were compared by in-house tools and other enrichment tools available to the public. The analysis results show that the imported cardiovascular disease genes from ClinVar and OMIM are functionally different from the RGD manually curated genes in terms of pathway and Gene Ontology annotations. The inclusion of disease genes from other databases enriches the collection of disease genes not only in quantity but also in quality.

An inter-species protein-protein interaction network across vast evolutionary distance

In cellular systems, biophysical interactions between macromolecules underlie a complex web of functional interactions. How biophysical and functional networks are coordinated, whether all biophysical interactions correspond to functional interactions, and how such biophysical-versus-functional network coordination is shaped by evolutionary forces are all largely unanswered questions. Here, we investigate these questions using an "inter-interactome" approach. We systematically probed the yeast and human proteomes for interactions between proteins from these two species and functionally characterized the resulting inter-interactome network. After a billion years of evolutionary divergence, the yeast and human proteomes are still capable of forming a biophysical network with properties that resemble those of intra-species networks. Although substantially reduced relative to intra-species networks, the levels of functional overlap in the yeast-human inter-interactome network uncover significant remnants of co-functionality widely preserved in the two proteomes beyond human-yeast homologs. Our data support evolutionary selection against biophysical interactions between proteins with little or no co-functionality. Such non-functional interactions, however, represent a reservoir from which nascent functional interactions may arise.

Extracting genetic alteration information for personalized cancer therapy from ClinicalTrials.gov

OBJECTIVE

Clinical trials investigating drugs that target specific genetic alterations in tumors are important for promoting personalized cancer therapy. The goal of this project is to create a knowledge base of cancer treatment trials with annotations about genetic alterations from ClinicalTrials.gov.

METHODS

We developed a semi-automatic framework that combines advanced text-processing techniques with manual review to curate genetic alteration information in cancer trials. The framework consists of a document classification system to identify cancer treatment trials from ClinicalTrials.gov and an information extraction system to extract gene and alteration pairs from the Title and Eligibility Criteria sections of clinical trials. By applying the framework to trials at ClinicalTrials.gov, we created a knowledge base of cancer treatment trials with genetic alteration annotations. We then evaluated each component of the framework against manually reviewed sets of clinical trials and generated descriptive statistics of the knowledge base.

RESULTS AND DISCUSSION

The automated cancer treatment trial identification system achieved a high precision of 0.9944. Together with the manual review process, it identified 20 193 cancer treatment trials from ClinicalTrials.gov. The automated gene-alteration extraction system achieved a precision of 0.8300 and a recall of 0.6803. After validation by manual review, we generated a knowledge base of 2024 cancer trials that are labeled with specific genetic alteration information. Analysis of the knowledge base revealed the trend of increased use of targeted therapy for cancer, as well as top frequent gene-alteration pairs of interest. We expect this knowledge base to be a valuable resource for physicians and patients who are seeking information about personalized cancer therapy.

A computational functional genomics based self-limiting self-concentration mechanism of cell specialization as a biological role of jumping genes

LINE-1 retrotransposition may result in silencing of genes. This is more likely with genes not carrying active LINE-1 as those are about 10 times more frequent in the given set of genes. Over time this leads to self-specialization of the cell toward processes associated with gene carrying active LINE-1, which then functionally prevail in the chronified situation.

Sgo1 is a potential therapeutic target for hepatocellular carcinoma

Shugoshin-like protein 1 (Sgo1) is an essential protein in mitosis; it protects sister chromatid cohesion and thereby ensures the fidelity of chromosome separation. We found that the expression of Sgo1 mRNA was relatively low in normal tissues, but was upregulated in 82% of hepatocellular carcinoma (HCC), and correlated with elevated alpha-fetoprotein and early disease onset of HCC. The depletion of Sgo1 reduced cell viability of hepatoma cell lines including HuH7, HepG2, Hep3B, and HepaRG. Using time-lapse microscopy, we showed that hepatoma cells were delayed and ultimately die in mitosis in the absence of Sgo1. In contrast, cell viability and mitotic progression of immortalized cells were not significantly affected. Notably, mitotic cell death induced upon Sgo1 depletion was suppressed upon inhibitions of cyclin-dependent kinase-1 and Aurora kinase-B, or the depletion of mitotic arrest deficient-2. Thus, mitotic cell death induced upon Sgo1 depletion in hepatoma cells is mediated by persistent activation of the spindle assembly checkpoint. Together, these results highlight the essential role of Sgo1 in the maintenance of a proper mitotic progression in hepatoma cells and suggest that Sgo1 is a promising oncotarget for HCC.

The pig X and Y Chromosomes: structure, sequence, and evolution

We have generated an improved assembly and gene annotation of the pig X Chromosome, and a first draft assembly of the pig Y Chromosome, by sequencing BAC and fosmid clones from Duroc animals and incorporating information from optical mapping and fiber-FISH. The X Chromosome carries 1033 annotated genes, 690 of which are protein coding. Gene order closely matches that found in primates (including humans) and carnivores (including cats and dogs), which is inferred to be ancestral. Nevertheless, several protein-coding genes present on the human X Chromosome were absent from the pig, and 38 pig-specific X-chromosomal genes were annotated, 22 of which were olfactory receptors. The pig Y-specific Chromosome sequence generated here comprises 30 megabases (Mb). A 15-Mb subset of this sequence was assembled, revealing two clusters of male-specific low copy number genes, separated by an ampliconic region including the HSFY gene family, which together make up most of the short arm. Both clusters contain palindromes with high sequence identity, presumably maintained by gene conversion. Many of the ancestral X-related genes previously reported in at least one mammalian Y Chromosome are represented either as active genes or partial sequences. This sequencing project has allowed us to identify genes--both single copy and amplified--on the pig Y Chromosome, to compare the pig X and Y Chromosomes for homologous sequences, and thereby to reveal mechanisms underlying pig X and Y Chromosome evolution.

Exome sequencing analysis reveals variants in primary immunodeficiency genes in patients with very early onset inflammatory bowel disease

BACKGROUND & AIMS

Very early onset inflammatory bowel disease (VEO-IBD), IBD diagnosed at 5 years of age or younger, frequently presents with a different and more severe phenotype than older-onset IBD. We investigated whether patients with VEO-IBD carry rare or novel variants in genes associated with immunodeficiencies that might contribute to disease development.

METHODS

Patients with VEO-IBD and parents (when available) were recruited from the Children's Hospital of Philadelphia from March 2013 through July 2014. We analyzed DNA from 125 patients with VEO-IBD (age, 3 wk to 4 y) and 19 parents, 4 of whom also had IBD. Exome capture was performed by Agilent SureSelect V4, and sequencing was performed using the Illumina HiSeq platform. Alignment to human genome GRCh37 was achieved followed by postprocessing and variant calling. After functional annotation, candidate variants were analyzed for change in protein function, minor allele frequency less than 0.1%, and scaled combined annotation-dependent depletion scores of 10 or less. We focused on genes associated with primary immunodeficiencies and related pathways. An additional 210 exome samples from patients with pediatric IBD (n = 45) or adult-onset Crohn's disease (n = 20) and healthy individuals (controls, n = 145) were obtained from the University of Kiel, Germany, and used as control groups.

RESULTS

Four hundred genes and regions associated with primary immunodeficiency, covering approximately 6500 coding exons totaling more than 1 Mbp of coding sequence, were selected from the whole-exome data. Our analysis showed novel and rare variants within these genes that could contribute to the development of VEO-IBD, including rare heterozygous missense variants in IL10RA and previously unidentified variants in MSH5 and CD19.

CONCLUSIONS

In an exome sequence analysis of patients with VEO-IBD and their parents, we identified variants in genes that regulate B- and T-cell functions and could contribute to pathogenesis. Our analysis could lead to the identification of previously unidentified IBD-associated variants.

Phenolyzer: phenotype-based prioritization of candidate genes for human diseases

Prior biological knowledge and phenotype information may help to identify disease genes from human whole-genome and whole-exome sequencing studies. We developed Phenolyzer (http://phenolyzer.usc.edu), a tool that uses prior information to implicate genes involved in diseases. Phenolyzer exhibits superior performance over competing methods for prioritizing Mendelian and complex disease genes, based on disease or phenotype terms entered as free text.

The National NeuroAIDS Tissue Consortium (NNTC) Database: an integrated database for HIV-related studies

We herein present the National NeuroAIDS Tissue Consortium-Data Coordinating Center (NNTC-DCC) database, which is the only available database for neuroAIDS studies that contains data in an integrated, standardized form. This database has been created in conjunction with the NNTC, which provides human tissue and biofluid samples to individual researchers to conduct studies focused on neuroAIDS. The database contains experimental datasets from 1206 subjects for the following categories (which are further broken down into subcategories): gene expression, genotype, proteins, endo-exo-chemicals, morphometrics and other (miscellaneous) data. The database also contains a wide variety of downloadable data and metadata for 95 HIV-related studies covering 170 assays from 61 principal investigators. The data represent 76 tissue types, 25 measurement types, and 38 technology types, and reaches a total of 33 017 407 data points. We used the ISA platform to create the database and develop a searchable web interface for querying the data. A gene search tool is also available, which searches for NCBI GEO datasets associated with selected genes. The database is manually curated with many user-friendly features, and is cross-linked to the NCBI, HUGO and PubMed databases. A free registration is required for qualified users to access the database.

Database URL: http://nntc-dcc.unmc.edu

An ensemble rank learning approach for gene prioritization

Several different computational approaches have been developed to solve the gene prioritization problem. We intend to use the ensemble boosting learning techniques to combine variant computational approaches for gene prioritization in order to improve the overall performance. In particular we add a heuristic weighting function to the Rankboost algorithm according to: 1) the absolute ranks generated by the adopted methods for a certain gene, and 2) the ranking relationship between all gene-pairs from each prioritization result. We select 13 known prostate cancer genes in OMIM database as training set and protein coding gene data in HGNC database as test set. We adopt the leave-one-out strategy for the ensemble rank boosting learning. The experimental results show that our ensemble learning approach outperforms the four gene-prioritization methods in ToppGene suite in the ranking results of the 13 known genes in terms of mean average precision, ROC and AUC measures.

A meta-analysis and systematic review of the prevalence of mitochondrially encoded 12S RNA in the general population: Is there a role for screening neonates requiring aminoglycosides?

BACKGROUND

This was a meta-analysis and systematic review to determine the global prevalence of the mitochondrially encoded 12S RNA (MT-RNR1) genetic mutation in order to assess the need for neonatal screening prior to aminoglycoside therapy.

MATERIALS AND METHODS

A comprehensive search of MEDLINE, EMBASE, Ovid, Database of Abstracts of Reviews of Effect, Cochrane Library, Clinical Evidence and Cochrane Central Register of Trials was performed including cross-referencing independently by 2 assessors. Selections were restricted to human studies in English. Meta-analysis was done with MetaXL 2013.

RESULTS

Forty-five papers out of 295 met the criteria. Pooled prevalence in the general population for MT-RNR1 gene mutations (A1555G, C1494T, A7445G) was 2% (1-4%) at 99%.

CONCLUSION

Routine screening for MT-RNR1 mutations in the general population prior to treatment with aminoglycosides appear desirable but poorly supported by the weak level of evidence available in the literature. Routine screening in high-risk (Chinese and Spanish) populations appear justified.

In-silico analysis of inflammatory bowel disease (IBD) GWAS loci to novel connections

Genome-wide association studies (GWASs) for many complex diseases, including inflammatory bowel disease (IBD), produced hundreds of disease-associated loci—the majority of which are noncoding. The number of GWAS loci is increasing very rapidly, but the process of translating single nucleotide polymorphisms (SNPs) from these loci to genomic medicine is lagging. In this study, we investigated 4,734 variants from 152 IBD associated GWAS loci (IBD associated 152 lead noncoding SNPs identified from pooled GWAS results + 4,582 variants in strong linkage-disequilibrium (LD) (r² ≥0.8) for EUR population of 1K Genomes Project) using four publicly available bioinformatics tools, e.g. dbPSHP, CADD, GWAVA, and RegulomeDB, to annotate and prioritize putative regulatory variants. Of the 152 lead noncoding SNPs, around 11% are under strong negative selection (GERP++ RS ≥2); and ~30% are under balancing selection (Tajima’s D score >2) in CEU population (1K Genomes Project)—though these regions are positively selected (GERP++ RS <0) in mammalian evolution. The analysis of 4,734 variants using three integrative annotation tools produced 929 putative functional SNPs, of which 18 SNPs (from 15 GWAS loci) are in concordance with all three classifiers. These prioritized noncoding SNPs may contribute to IBD pathogenesis by dysregulating the expression of nearby genes. This study showed the usefulness of integrative annotation for prioritizing fewer functional variants from a large number of GWAS markers.

Enabling semantic interoperability in multi-centric clinical trials on breast cancer

BACKGROUND AND OBJECTIVES

Post-genomic clinical trials require the participation of multiple institutions, and collecting data from several hospitals, laboratories and research facilities. This paper presents a standard-based solution to provide a uniform access endpoint to patient data involved in current clinical research.

METHODS

The proposed approach exploits well-established standards such as HL7 v3 or SPARQL and medical vocabularies such as SNOMED CT, LOINC and HGNC. A novel mechanism to exploit semantic normalization among HL7-based data models and biomedical ontologies has been created by using Semantic Web technologies.

RESULTS

Different types of queries have been used for testing the semantic interoperability solution described in this paper. The execution times obtained in the tests enable the development of end user tools within a framework that requires efficient retrieval of integrated data.

CONCLUSIONS

The proposed approach has been successfully tested by applications within the INTEGRATE and EURECA EU projects. These applications have been deployed and tested for: (i) patient screening, (ii) trial recruitment, and (iii) retrospective analysis; exploiting semantically interoperable access to clinical patient data from heterogeneous data sources.

Prediction of miRNA targets

Computational methods for miRNA target prediction are currently undergoing extensive review and evaluation. There is still a great need for improvement of these tools and bioinformatics approaches are looking towards high-throughput experiments in order to validate predictions. The combination of large-scale techniques with computational tools will not only provide greater credence to computational predictions but also lead to the better understanding of specific biological questions. Current miRNA target prediction tools utilize probabilistic learning algorithms, machine learning methods and even empirical biologically defined rules in order to build models based on experimentally verified miRNA targets. Large-scale protein downregulation assays and next-generation sequencing (NGS) are now being used to validate methodologies and compare the performance of existing tools. Tools that exhibit greater correlation between computational predictions and protein downregulation or RNA downregulation are considered the state of the art. Moreover, efficiency in prediction of miRNA targets that are concurrently verified experimentally provides additional validity to computational predictions and further highlights the competitive advantage of specific tools and their efficacy in extracting biologically significant results. In this review paper, we discuss the computational methods for miRNA target prediction and provide a detailed comparison of methodologies and features utilized by each specific tool. Moreover, we provide an overview of current state-of-the-art high-throughput methods used in miRNA target prediction.

Zebrafish Rab5 proteins and a role for Rab5ab in nodal signalling

The RAB5 gene family is the best characterised of all human RAB families and is essential for in vitro homotypic fusion of early endosomes. In recent years, the disruption or activation of Rab5 family proteins has been used as a tool to understand growth factor signal transduction in whole animal systems such as Drosophila melanogaster and zebrafish. In this study we have examined the functions for four rab5 genes in zebrafish. Disruption of rab5ab expression by antisense morpholino oligonucleotide (MO) knockdown abolishes nodal signalling in early zebrafish embryos, whereas overexpression of rab5ab mRNA leads to ectopic expression of markers that are normally downstream of nodal signalling. By contrast MO disruption of other zebrafish rab5 genes shows little or no effect on expression of markers of dorsal organiser development. We conclude that rab5ab is essential for nodal signalling and organizer specification in the developing zebrafish embryo.

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We have examined the activities of each of the zebrafish Rab5 genes using morpholino knockdowns.

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Loss of one Rab5 isoform, Rab5ab, affects formation of the dorsal organizer.

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Rab5ab overexpression leads to ectopic expression of dorsal markers.

What do all the (human) micro-RNAs do?

Background

Micro-RNAs (miRNA) are attributed to the systems biological role of a regulatory mechanism of the expression of protein coding genes. Research has identified miRNAs dysregulations in several but distinct pathophysiological processes, which hints at distinct systems-biology functions of miRNAs. The present analysis approached the role of miRNAs from a genomics perspective and assessed the biological roles of 2954 genes and 788 human miRNAs, which can be considered to interact, based on empirical evidence and computational predictions of miRNA versus gene interactions.

Results

From a genomics perspective, the biological processes in which the genes that are influenced by miRNAs are involved comprise of six major topics comprising biological regulation, cellular metabolism, information processing, development, gene expression and tissue homeostasis. The usage of this knowledge as a guidance for further research is sketched for two genetically defined functional areas: cell death and gene expression. Results suggest that the latter points to a fundamental role of miRNAs consisting of hyper-regulation of gene expression, i.e., the control of the expression of such genes which control specifically the expression of genes.

Conclusions

Laboratory research identified contributions of miRNA regulation to several distinct biological processes. The present analysis transferred this knowledge to a systems-biology level. A comprehensible and precise description of the biological processes in which the genes that are influenced by miRNAs are notably involved could be made. This knowledge can be employed to guide future research concerning the biological role of miRNA (dys-) regulations. The analysis also suggests that miRNAs especially control the expression of genes that control the expression of genes.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-976) contains supplementary material, which is available to authorized users.

Chimera: a Bioconductor package for secondary analysis of fusion products

Summary:Chimera is a Bioconductor package that organizes, annotates, analyses and validates fusions reported by different fusion detection tools; current implementation can deal with output from bellerophontes, chimeraScan, deFuse, fusionCatcher, FusionFinder, FusionHunter, FusionMap, mapSplice, Rsubread, tophat-fusion and STAR. The core of Chimera is a fusion data structure that can store fusion events detected with any of the aforementioned tools. Fusions are then easily manipulated with standard R functions or through the set of functionalities specifically developed in Chimera with the aim of supporting the user in managing fusions and discriminating false-positive results.

Availability and implementation:Chimera is implemented as a Bioconductor package in R. The package and the vignette can be downloaded at bioconductor.org.

Contact:raffaele.calogero@unito.it

Supplementary information:Supplementary data are available at Bioinformatics online.

Semantic Normalization and Query Abstraction Based on SNOMED-CT and HL7: Supporting Multicentric Clinical Trials

Advances in the use of omic data and other biomarkers are increasing the number of variables in clinical research. Additional data have stratified the population of patients and require that current studies be performed among multiple institutions. Semantic interoperability and standardized data representation are a crucial task in the management of modern clinical trials. In the past few years, different efforts have focused on integrating biomedical information. Due to the complexity of this domain and the specific requirements of clinical research, the majority of data integration tasks are still performed manually. This paper presents a semantic normalization process and a query abstraction mechanism to facilitate data integration and retrieval. A process based on well-established standards from the biomedical domain and the latest semantic web technologies has been developed. Methods proposed in this paper have been tested within the EURECA EU research project, where clinical scenarios require the extraction of semantic knowledge from biomedical vocabularies. The aim of this paper is to provide a novel method to abstract from the data model and query syntax. The proposed approach has been compared with other initiatives in the field by storing the same dataset with each of those solutions. Results show an extended functionality and query capabilities at the cost of slightly worse performance in query execution. Implementations in real settings have shown that following this approach, usable interfaces can be developed to exploit clinical trial data outcomes.

Identification of a dinucleotide signature that discriminates coding from non-coding long RNAs

To date, the main criterion by which long ncRNAs (lncRNAs) are discriminated from mRNAs is based on the capacity of the transcripts to encode a protein. However, it becomes important to identify non-ORF-based sequence characteristics that can be used to parse between ncRNAs and mRNAs. In this study, we first established an extremely selective workflow to define a highly refined database of lncRNAs which was used for comparison with mRNAs. Then using this highly selective collection of lncRNAs, we found the CG dinucleotide frequencies were clearly distinct. In addition, we showed that the bias in CG dinucleotide frequency was conserved in human and mouse genomes. We propose that this sequence feature will serve as a useful classifier in transcript classification pipelines. We also suggest that our refined database of "bona fide" lncRNAs will be valuable for the discovery of other sequence characteristics distinct to lncRNAs.

DICER1 mutations in childhood cystic nephroma and its relationship to DICER1-renal sarcoma

The pathogenesis of cystic nephroma of the kidney has interested pathologists for over 50 years. Emerging from its initial designation as a type of unilateral multilocular cyst, cystic nephroma has been considered as either a developmental abnormality or a neoplasm or both. Many have viewed cystic nephroma as the benign end of the pathologic spectrum with cystic partially differentiated nephroblastoma and Wilms tumor, whereas others have considered it a mixed epithelial and stromal tumor. We hypothesize that cystic nephroma, like the pleuropulmonary blastoma in the lung, represents a spectrum of abnormal renal organogenesis with risk for malignant transformation. Here we studied DICER1 mutations in a cohort of 20 cystic nephromas and 6 cystic partially differentiated nephroblastomas, selected independently of a familial association with pleuropulmonary blastoma and describe four cases of sarcoma arising in cystic nephroma, which have a similarity to the solid areas of type II or III pleuropulmonary blastoma. The genetic analyses presented here confirm that DICER1 mutations are the major genetic event in the development of cystic nephroma. Further, cystic nephroma and pleuropulmonary blastoma have similar DICER1 loss of function and 'hotspot' missense mutation rates, which involve specific amino acids in the RNase IIIb domain. We propose an alternative pathway with the genetic pathogenesis of cystic nephroma and DICER1-renal sarcoma paralleling that of type I to type II/III malignant progression of pleuropulmonary blastoma.

BioTextQuest(+): a knowledge integration platform for literature mining and concept discovery

SUMMARY

The iterative process of finding relevant information in biomedical literature and performing bioinformatics analyses might result in an endless loop for an inexperienced user, considering the exponential growth of scientific corpora and the plethora of tools designed to mine PubMed(®) and related biological databases. Herein, we describe BioTextQuest(+), a web-based interactive knowledge exploration platform with significant advances to its predecessor (BioTextQuest), aiming to bridge processes such as bioentity recognition, functional annotation, document clustering and data integration towards literature mining and concept discovery. BioTextQuest(+) enables PubMed and OMIM querying, retrieval of abstracts related to a targeted request and optimal detection of genes, proteins, molecular functions, pathways and biological processes within the retrieved documents. The front-end interface facilitates the browsing of document clustering per subject, the analysis of term co-occurrence, the generation of tag clouds containing highly represented terms per cluster and at-a-glance popup windows with information about relevant genes and proteins. Moreover, to support experimental research, BioTextQuest(+) addresses integration of its primary functionality with biological repositories and software tools able to deliver further bioinformatics services. The Google-like interface extends beyond simple use by offering a range of advanced parameterization for expert users. We demonstrate the functionality of BioTextQuest(+) through several exemplary research scenarios including author disambiguation, functional term enrichment, knowledge acquisition and concept discovery linking major human diseases, such as obesity and ageing.

AVAILABILITY

The service is accessible at http://bioinformatics.med.uoc.gr/biotextquest.

CONTACT

g.pavlopoulos@gmail.com or georgios.pavlopoulos@esat.kuleuven.be

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Functional implications of long non-coding RNAs in the pancreatic islets of Langerhans

Type-2 diabetes (T2D) is a complex disease characterized by insulin resistance in target tissues and impaired insulin release from pancreatic beta cells. As central tissue of glucose homeostasis, the pancreatic islet continues to be an important focus of research to understand the pathophysiology of the disease. The increased access to human pancreatic islets has resulted in improved knowledge of islet function, and together with advances in RNA sequencing and related technologies, revealed the transcriptional and epigenetic landscape of human islet cells. The discovery of thousands of long non-coding RNA (lncRNA) transcripts highly enriched in the pancreatic islet and/or specifically expressed in the beta-cells, points to yet another layer of gene regulation of many hitherto unknown mechanistic principles governing islet cell functions. Here we review fundamental islet physiology and propose functional implications of the lncRNAs in islet development and endocrine cell functions. We also take into account important differences between rodent and human islets in terms of morphology and function, and suggest how species-specific lncRNAs may partly influence gene regulation to define the unique phenotypic identity of an organism and the functions of its constituent cells. The implication of primate-specific lncRNAs will be far-reaching in all aspects of diabetes research, but most importantly in the identification and development of novel targets to improve pancreatic islet cell functions as a therapeutic approach to treat T2D.

Reproducible probe-level analysis of the Affymetrix Exon 1.0 ST array with R/Bioconductor

The presence of different transcripts of a gene across samples can be analysed by whole-transcriptome microarrays. Reproducing results from published microarray data represents a challenge owing to the vast amounts of data and the large variety of preprocessing and filtering steps used before the actual analysis is carried out. To guarantee a firm basis for methodological development where results with new methods are compared with previous results, it is crucial to ensure that all analyses are completely reproducible for other researchers. We here give a detailed workflow on how to perform reproducible analysis of the GeneChip®Human Exon 1.0 ST Array at probe and probeset level solely in R/Bioconductor, choosing packages based on their simplicity of use. To exemplify the use of the proposed workflow, we analyse differential splicing and differential gene expression in a publicly available dataset using various statistical methods. We believe this study will provide other researchers with an easy way of accessing gene expression data at different annotation levels and with the sufficient details needed for developing their own tools for reproducible analysis of the GeneChip®Human Exon 1.0 ST Array.

Identification of gene ontologies linked to prefrontal-hippocampal functional coupling in the human brain

Functional interactions between the dorsolateral prefrontal cortex and hippocampus during working memory have been studied extensively as an intermediate phenotype for schizophrenia. Coupling abnormalities have been found in patients, their unaffected siblings, and carriers of common genetic variants associated with schizophrenia, but the global genetic architecture of this imaging phenotype is unclear. To achieve genome-wide hypothesis-free identification of genes and pathways associated with prefrontal-hippocampal interactions, we combined gene set enrichment analysis with whole-genome genotyping and functional magnetic resonance imaging data from 269 healthy German volunteers. We found significant enrichment of the synapse organization and biogenesis gene set. This gene set included known schizophrenia risk genes, such as neural cell adhesion molecule (NRCAM) and calcium channel, voltage-dependent, beta 2 subunit (CACNB2), as well as genes with well-defined roles in neurodevelopmental and plasticity processes that are dysfunctional in schizophrenia and have mechanistic links to prefrontal-hippocampal functional interactions. Our results demonstrate a readily generalizable approach that can be used to identify the neurogenetic basis of systems-level phenotypes. Moreover, our findings identify gene sets in which genetic variation may contribute to disease risk through altered prefrontal-hippocampal functional interactions and suggest a link to both ongoing and developmental synaptic plasticity.