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.
Cell-cell interactions are of fundamental importance for cellular function. In islets of Langerhans, which control blood glucose levels by secreting insulin in response to the blood glucose concentration, the secretory response of intact islets is higher than that of insulin-producing beta-cells not arranged in the islet architecture. The objective was to define mechanisms by which cellular performance is enhanced when cells are arranged in three-dimensional space. The task was addressed by making a comprehensive analysis based on protein expression patterns generated from insulin-secreting MIN6 cells grown as islet-like clusters, so-called pseudoislets, and in monolayers. After culture, glucose-stimulated insulin secretion (GSIS) was measured from monolayers and pseudoislets. GSIS rose 6-fold in pseudoislets but only 3-fold in monolayers when the glucose concentration was increased from 2 to 20 mmol/L. Proteins from pseudoislets and monolayers were extracted and analyzed by liquid-chromatography mass spectrometry, and differentially expressed proteins were mapped onto KEGG pathways. Protein profiling identified 1576 proteins, which were common to pseudoislets and monolayers. When mapped onto KEGG pathways, 11 highly enriched pathways were identified. On the basis of differences in expression of proteins belonging to the pathways in pseudoislets and monolayers, predictions of differential pathway activation were performed. Mechanisms enhancing insulin secretory capacity of the beta-cell, when situated in the islet, include pathways regulating glucose metabolism, cell interaction, and translational regulation.
Parkinsons disease (PD) is a major neurodegenerative chronic disease, most likely caused by a complex interplay of genetic and environmental factors. Information on various aspects of PD pathogenesis is rapidly increasing and needs to be efficiently organized, so that the resulting data is available for exploration and analysis. Here we introduce a computationally tractable, comprehensive molecular interaction map of PD. This map integrates pathways implicated in PD pathogenesis such as synaptic and mitochondrial dysfunction, impaired protein degradation, alpha-synuclein pathobiology and neuroinflammation. We also present bioinformatics tools for the analysis, enrichment and annotation of the map, allowing the research community to open new avenues in PD research. The PD map is accessible at http://minerva.uni.lu/pd_map .
Cancer pain remains a major challenge and there is an urgent demand for the development of specific mechanism-based therapies. Various diseases are associated with unique signatures of expression of microRNAs (miRNAs), which reveal deep insights into disease pathology. Using a comprehensive approach combining genome-wide miRNA screening, molecular and in silico analyses with behavioural approaches in a clinically relevant model of metastatic bone-cancer pain in mice, we now show that tumour-induced conditions are associated with a marked dysregulation of 57 miRNAs in sensory neurons corresponding to tumour-affected areas. By establishing protocols for interference with disease-induced miRNA dysregulation in peripheral sensory neurons in vivo, we functionally validate six dysregulated miRNAs as significant modulators of tumour-associated hypersensitivity. In silico analyses revealed that their predicted targets include key pain-related genes and we identified Clcn3, a gene encoding a chloride channel, as a key miRNA target in sensory neurons, which is functionally important in tumour-induced nociceptive hypersensitivity in vivo. Our results provide new insights into endogenous gene regulatory mechanisms in cancer pain and open up attractive and viable therapeutic options.
Cancer-associated pain is a major cause of poor quality of life in cancer patients and is frequently resistant to conventional therapy. Recent studies indicate that some hematopoietic growth factors, namely granulocyte macrophage colony stimulating factor (GMCSF) and granulocyte colony stimulating factor (GCSF), are abundantly released in the tumor microenvironment and play a key role in regulating tumor-nerve interactions and tumor-associated pain by activating receptors on dorsal root ganglion (DRG) neurons. Moreover, these hematopoietic factors have been highly implicated in postsurgical pain, inflammatory pain and osteoarthritic pain. However, the molecular mechanisms via which G-/GMCSF bring about nociceptive sensitization and elicit pain are not known.
BioHackathon 2010 was the third in a series of meetings hosted by the Database Center for Life Sciences (DBCLS) in Tokyo, Japan. The overall goal of the BioHackathon series is to improve the quality and accessibility of life science research data on the Web by bringing together representatives from public databases, analytical tool providers, and cyber-infrastructure researchers to jointly tackle important challenges in the area of in silico biological research.
Persistent pain induced by noxious stimuli is characterized by the transition from normosensitivity to hypersensitivity. Underlying mechanisms are not well understood, although gene expression is considered important. Here, we show that persistent nociceptive-like activity triggers calcium transients in neuronal nuclei within the superficial spinal dorsal horn, and that nuclear calcium is necessary for the development of long-term inflammatory hypersensitivity. Using a nucleus-specific calcium signal perturbation strategy in vivo complemented by gene profiling, bioinformatics, and functional analyses, we discovered a pain-associated, nuclear calcium-regulated gene program in spinal excitatory neurons. This includes C1q, a modulator of synaptic spine morphogenesis, which we found to contribute to activity-dependent spine remodelling on spinal neurons in a manner functionally associated with inflammatory hypersensitivity. Thus, nuclear calcium integrates synapse-to-nucleus communication following noxious stimulation and controls a spinal genomic response that mediates the transition between acute and long-term nociceptive sensitization by modulating functional and structural plasticity.
G-protein coupled receptors (GPCRs) are a major family of membrane receptors in eukaryotic cells. They play a crucial role in the communication of a cell with the environment. Ligands bind to GPCRs on the outside of the cell, activating them by causing a conformational change, and allowing them to bind to G-proteins. Through their interaction with G-proteins, several effector molecules are activated leading to many kinds of cellular and physiological responses. The great importance of GPCRs and their corresponding signal transduction pathways is indicated by the fact that they take part in many diverse disease processes and that a large part of efforts towards drug development today is focused on them. We present Human-gpDB, a database which currently holds information about 713 human GPCRs, 36 human G-proteins and 99 human effectors. The collection of information about the interactions between these molecules was done manually and the current version of Human-gpDB holds information for about 1663 connections between GPCRs and G-proteins and 1618 connections between G-proteins and effectors. Major advantages of Human-gpDB are the integration of several external data sources and the support of advanced visualization techniques. Human-gpDB is a simple, yet a powerful tool for researchers in the life sciences field as it integrates an up-to-date, carefully curated collection of human GPCRs, G-proteins, effectors and their interactions. The database may be a reference guide for medical and pharmaceutical research, especially in the areas of understanding human diseases and chemical and drug discovery. Database URLs: http://schneider.embl.de/human_gpdb; http://bioinformatics.biol.uoa.gr/human_gpdb/
Hutchinson-Gilford Progeria Syndrome (HGPS) is a rare premature aging disorder caused by a de novo heterozygous point mutation G608G (GGC>GGT) within exon 11 of LMNA gene encoding A-type nuclear lamins. This mutation elicits an internal deletion of 50 amino acids in the carboxyl-terminus of prelamin A. The truncated protein, progerin, retains a farnesylated cysteine at its carboxyl terminus, a modification involved in HGPS pathogenesis. Inhibition of protein farnesylation has been shown to improve abnormal nuclear morphology and phenotype in cellular and animal models of HGPS. We analyzed global gene expression changes in fibroblasts from human subjects with HGPS and found that a lamin A-Rb signaling network is a major defective regulatory axis. Treatment of fibroblasts with a protein farnesyltransferase inhibitor reversed the gene expression defects. Our study identifies Rb as a key factor in HGPS pathogenesis and suggests that its modulation could ameliorate premature aging and possibly complications of physiological aging.
Despite our rapidly growing knowledge about the human genome, we do not know all of the genes required for some of the most basic functions of life. To start to fill this gap we developed a high-throughput phenotypic screening platform combining potent gene silencing by RNA interference, time-lapse microscopy and computational image processing. We carried out a genome-wide phenotypic profiling of each of the approximately 21,000 human protein-coding genes by two-day live imaging of fluorescently labelled chromosomes. Phenotypes were scored quantitatively by computational image processing, which allowed us to identify hundreds of human genes involved in diverse biological functions including cell division, migration and survival. As part of the Mitocheck consortium, this study provides an in-depth analysis of cell division phenotypes and makes the entire high-content data set available as a resource to the community.
Life scientists are often interested to compare two gene sets to gain insight into differences between two distinct, but related, phenotypes or conditions. Several tools have been developed for comparing gene sets, most of which find Gene Ontology (GO) terms that are significantly over-represented in one gene set. However, such tools often return GO terms that are too generic or too few to be informative. Here, we present Martini, an easy-to-use tool for comparing gene sets. Martini is based, not on GO, but on keywords extracted from Medline abstracts; Martini also supports a much wider range of species than comparable tools. To evaluate Martini we created a benchmark based on the human cell cycle, and we tested several comparable tools (CoPub, FatiGO, Marmite and ProfCom). Martini had the best benchmark performance, delivering a more detailed and accurate description of function. Martini also gave best or equal performance with three other datasets (related to Arabidopsis, melanoma and ovarian cancer), suggesting that Martini represents an advance in the automated comparison of gene sets. In agreement with previous studies, our results further suggest that literature-derived keywords are a richer source of gene-function information than GO annotations. Martini is freely available at http://martini.embl.de.
Keeping up-to-date with bioscience literature is becoming increasingly challenging. Several recent methods help meet this challenge by allowing literature search to be launched based on lists of abstracts that the user judges to be interesting. Some methods go further by allowing the user to provide a second input set of uninteresting abstracts; these two input sets are then used to search and rank literature by relevance. In this work we present the service Caipirini (http://caipirini.org) that also allows two input sets, but takes the novel approach of allowing ranking of literature based on one or more sets of genes.
Related JoVE Video
Journal of Visualized Experiments
What is Visualize?
JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.
How does it work?
We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.
Video X seems to be unrelated to Abstract Y...
In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.