CoMentG: comprehensive retrieval of generic relationships between biomedical concepts from the scientific literature.

The CoMentG resource contains millions of relationships between terms of biomedical interest obtained from the scientific literature. At the core of the system is a methodology for detecting significant co-mentions of concepts in the entire PubMed corpus. That method was applied to nine sets of terms covering the most important classes of biomedical concepts: diseases, symptoms/clinical signs, molecular functions, biological processes, cellular compartments, anatomic parts, cell types, bacteria and chemical compounds. We obtained more than 7 million relationships between more than 74 000 terms, and many types of relationships were not available in any other resource. As the terms were obtained from widely used resources and ontologies, the relationships are given using the standard identifiers provided by them and hence can be linked to other data. A web interface allows users to browse these associations, searching for relationships for a set of terms of interests provided as input, such as between a disease and their associated symptoms, underlying molecular processes or affected tissues. The results are presented in an interactive interface where the user can explore the reported relationships in different ways and follow links to other resources. Database URL: https://csbg.cnb.csic.es/CoMentG/.

Each Cellular Compartment Has a Characteristic Protein Reactive Cysteine Ratio Determining Its Sensitivity to Oxidation.

Signaling and detoxification of Reactive Oxygen Species (ROS) are important patho-physiologcal processes. Despite this, we lack comprehensive information on individual cells and cellular structures and functions affected by ROS, which is essential to build quantitative models of the effects of ROS. The thiol groups from cysteines (Cys) in proteins play a major role in redox defense, signaling, and protein function. In this study, we show that the proteins in each subcellular compartment contain a characteristic Cys amount. Using a fluorescent assay for -SH in thiolate form and amino groups in proteins, we show that the thiolate content correlates with ROS sensitivity and signaling properties of each compartment. The highest absolute thiolate concentration was found in the nucleolus, followed by the nucleoplasm and cytoplasm whereas protein thiolate groups per protein showed an inverse pattern. In the nucleoplasm, protein reactive thiols concentrated in SC35 speckles, SMN, and the IBODY that accumulated oxidized RNA. Our findings have important functional consequences, and explain differential sensitivity to ROS.

MBROLE3: improved functional enrichment of chemical compounds for metabolomics data analysis.

MBROLE (Metabolites Biological Role) facilitates the biological interpretation of metabolomics experiments. It performs enrichment analysis of a set of chemical compounds through statistical analysis of annotations from several databases. The original MBROLE server was released in 2011 and, since then, different groups worldwide have used it to analyze metabolomics experiments from a variety of organisms. Here we present the latest version of the system, MBROLE3, accessible at http://csbg.cnb.csic.es/mbrole3. This new version contains updated annotations from previously included databases as well as a wide variety of new functional annotations, such as additional pathway databases and Gene Ontology terms. Of special relevance is the inclusion of a new category of annotations, 'indirect annotations', extracted from the scientific literature and from curated chemical-protein associations. The latter allows to analyze enriched annotations of the proteins known to interact with the set of chemical compounds of interest. Results are provided in the form of interactive tables, formatted data to download, and graphical plots.

PMIDigest: Interactive Review of Large Collections of PubMed Entries to Distill Relevant Information.

Scientific knowledge is being accumulated in the biomedical literature at an unprecedented pace. The most widely used database with biomedicine-related article abstracts, PubMed, currently contains more than 36 million entries. Users performing searches in this database for a subject of interest face thousands of entries (articles) that are difficult to process manually. In this work, we present an interactive tool for automatically digesting large sets of PubMed articles: PMIDigest (PubMed IDs digester). The system allows for classification/sorting of articles according to different criteria, including the type of article and different citation-related figures. It also calculates the distribution of MeSH (medical subject headings) terms for categories of interest, providing in a picture of the themes addressed in the set. These MeSH terms are highlighted in the article abstracts in different colors depending on the category. An interactive representation of the interarticle citation network is also presented in order to easily locate article "clusters" related to particular subjects, as well as their corresponding "hub" articles. In addition to PubMed articles, the system can also process a set of Scopus or Web of Science entries. In summary, with this system, the user can have a "bird's eye view" of a large set of articles and their main thematic tendencies and obtain additional information not evident in a plain list of abstracts.

Urinary Metabolomics Study on the Protective Role of Cocoa in Zucker Diabetic Rats via 1H-NMR-Based Approach.

Cocoa constitutes one of the richest sources of dietary flavonoids with demonstrated anti-diabetic potential. However, the metabolic impact of cocoa intake in a diabetic context remains unexplored. In this study, metabolomics tools have been used to investigate the potential metabolic changes induced by cocoa in type 2 diabetes (T2D). To this end, male Zucker diabetic fatty rats were fed on standard (ZDF) or 10% cocoa-rich diet (ZDF-C) from week 10 to 20 of life. Cocoa supplementation clearly decreased serum glucose levels, improved glucose metabolism and produced significant changes in the urine metabolome of ZDF animals. Fourteen differential urinary metabolites were identified, with eight of them significantly modified by cocoa. An analysis of pathways revealed that butanoate metabolism and the synthesis and degradation of branched-chain amino acids and ketone bodies are involved in the beneficial impact of cocoa on diabetes. Moreover, correlation analysis indicated major associations between some of these urine metabolites (mainly valine, leucine, and isoleucine) and body weight, glycemia, insulin sensitivity, and glycated hemoglobin levels. Overall, this untargeted metabolomics approach provides a clear metabolic fingerprint associated to chronic cocoa intake that can be used as a marker for the improvement of glucose homeostasis in a diabetic context.

CoMent: Relationships Between Biomedical Concepts Inferred From the Scientific Literature.

The mining of the massive amounts of biomedical information is hindered by the still scarce representation of these data using formal vocabularies and ontologies, which is necessary for cross-linking conceptual entities between different resources and, in general, representing the information in a computer-tractable way. Basic things such as retrieving a comprehensive list of associations between complex diseases and their reported symptoms or underlying biological processes, given in terms of formal identifiers, are not trivial and, in many cases, these have to be generated by manual curation or inferred/predicted from indirect evidences. In this work, using a text-mining approach based on detecting significant co-mentions in the scientific literature, we generated a resource with millions of relationships between thousands of terms representing diseases, symptoms, biological processes, molecular functions and cellular compartments, all given in terms of formal identifiers of these terms in the main resources dealing with them. We show some examples that highlight the differences between these relationships and those that are available in other resources. These relationships can be queried and inspected in an interactive web interface freely available at: https://sysbiol.cnb.csic.es/CoMent.

Network-Based Methods for Approaching Human Pathologies from a Phenotypic Point of View.

Network and systemic approaches to studying human pathologies are helping us to gain insight into the molecular mechanisms of and potential therapeutic interventions for human diseases, especially for complex diseases where large numbers of genes are involved. The complex human pathological landscape is traditionally partitioned into discrete "diseases"; however, that partition is sometimes problematic, as diseases are highly heterogeneous and can differ greatly from one patient to another. Moreover, for many pathological states, the set of symptoms (phenotypes) manifested by the patient is not enough to diagnose a particular disease. On the contrary, phenotypes, by definition, are directly observable and can be closer to the molecular basis of the pathology. These clinical phenotypes are also important for personalised medicine, as they can help stratify patients and design personalised interventions. For these reasons, network and systemic approaches to pathologies are gradually incorporating phenotypic information. This review covers the current landscape of phenotype-centred network approaches to study different aspects of human diseases.

Predicting biological pathways of chemical compounds with a profile-inspired approach.

BACKGROUND: Assignment of chemical compounds to biological pathways is a crucial step to understand the relationship between the chemical repertory of an organism and its biology. Protein sequence profiles are very successful in capturing the main structural and functional features of a protein family, and can be used to assign new members to it based on matching of their sequences against these profiles. In this work, we extend this idea to chemical compounds, constructing a profile-inspired model for a set of related metabolites (those in the same biological pathway), based on a fragment-based vectorial representation of their chemical structures. RESULTS: We use this representation to predict the biological pathway of a chemical compound with good overall accuracy (AUC 0.74-0.90 depending on the database tested), and analyzed some factors that affect performance. The approach, which is compared with equivalent methods, can in addition detect those molecular fragments characteristic of a pathway. CONCLUSIONS: The method is available as a graphical interactive web server http://csbg.cnb.csic.es/iFragMent .

Phosphorylation-Related Crosstalk Between Distant Regions of the Core Region of the Coat Protein Contributes to Virion Assembly of Plum Pox Virus.

Eukaryotic proteins are often targets of posttranslational modifications (PTMs). Capsid protein (CP) of plum pox virus (PPV), a member of genus Potyvirus, has been reported to be prone to phosphorylation in four serines at the N-terminal region. CP phosphorylation has been proposed to influence PPV infection by regulating CP accumulation in coordination with a second PTM, O-GlcNAcylation. In this study, a further proteomic characterization of PPV CP phosphorylation revealed additional phospho-targets, thus evidencing even greater complexity of the network of PTMs affecting this protein. In particular, two new phosphorylation targets, T254 and T313, at protein distal core, appear to be highly relevant for infection. Although abolishing phosphorylation at these positions does not have a severe effect on infectivity or viral accumulation, phospho-mimicking at either of these targets disrupts cell-to-cell movement. Strand-specific reverse transcription-quantitative PCR analysis and fractionation by centrifugation in a continuous sucrose gradient enabled us to conclude that such a deleterious effect is not related to failures in replication but is a consequence of inaccurate virion assembly. The analysis of spontaneous compensatory mutations at the CP core identified in a multiple phospho-mimicking mutant disclosed a functional dialogue between distant phospho-targets, which was further supported by an in silico PPV virion model, built on the watermelon mosaic virus atomic structure. Therefore, whereas joint and opposite action of O-GlcNAcylation and phosphorylation at the N-terminal disordered protrusion of CP appears to regulate protein stability, we propose that phosphorylations at the core region control assembly and disassembly of viral particles.

Complex genetic and epigenetic regulation deviates gene expression from a unifying global transcriptional program.

Environmental or genetic perturbations lead to gene expression changes. While most analyses of these changes emphasize the presence of qualitative differences on just a few genes, we now know that changes are widespread. This large-scale variation has been linked to the exclusive influence of a global transcriptional program determined by the new physiological state of the cell. However, given the sophistication of eukaryotic regulation, we expect to have a complex architecture of specific control affecting this program. Here, we examine this architecture. Using data of Saccharomyces cerevisiae expression in different nutrient conditions, we first propose a five-sector genome partition, which integrates earlier models of resource allocation, as a framework to examine the deviations from the global control. In this scheme, we recognize invariant genes, whose regulation is dominated by physiology, specific genes, which substantially depart from it, and two additional classes that contain the frequently assumed growth-dependent genes. Whereas the invariant class shows a considerable absence of specific regulation, the rest is enriched by regulation at the level of transcription factors (TFs) and epigenetic modulators. We nevertheless find markedly different strategies in how these classes deviate. On the one hand, there are TFs that act in a unique way between partition constituents, and on the other, the action of chromatin modifiers is significantly diverse. The balance between regulatory strategies ultimately modulates the action of the general transcription machinery and therefore limits the possibility of establishing a unifying program of expression change at a genomic scale.

The Opposing Roles of PIK3R1/p85α and PIK3R2/p85ß in Cancer.

Dysregulation of the PI3K/PTEN pathway is a frequent event in cancer, and PIK3CA and PTEN are the most commonly mutated genes after TP53. PIK3R1 is the predominant regulatory isoform of PI3K. PIK3R2 is an ubiquitous isoform that has been so far overlooked, but data from The Cancer Genome Atlas shows that increased expression of PIK3R2 is also frequent in cancer. In contrast to PIK3R1, which is a tumor-suppressor gene, PIK3R2 is an oncogene. We review here the opposing roles of PIK3R1 and PIK3R2 in cancer, the regulatory mechanisms that control PIK3R2 expression, and emerging therapeutic approaches targeting PIK3R2.

Bacterial Feature Finder (BaFF)-a system for extracting features overrepresented in sets of prokaryotic organisms.

MOTIVATION: The results of some experimental and computational techniques are given in terms of large sets of organisms, especially prokaryotic. While their distinctive features can provide useful data regarding specific phenomenon, there are no automated tools for extracting them. RESULTS: We present here the Bacterial Feature Finder web server, a tool to automatically interrogate sets of prokaryotic organisms provided by the user to evaluate their specific biological features. At the core of the system is a searchable database of qualitative and quantitative features compiled for more than 23 000 prokaryotic organisms. Both the input set of organisms and the background set used to calculate the enriched features can be directly provided by the user, or they can be obtained by searching the database. The results are presented via an interactive graphical interface, with links to external resources. AVAILABILITY AND IMPLEMENTATION: The web server is freely available at http://csbg.cnb.csic.es/BaFF. It has been tested in the main web browsers and does not require any especial plug-ins or additional software. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Characteristics and evolution of the ecosystem of software tools supporting research in molecular biology.

Daily work in molecular biology presently depends on a large number of computational tools. An in-depth, large-scale study of that 'ecosystem' of Web tools, its characteristics, interconnectivity, patterns of usage/citation, temporal evolution and rate of decay is crucial for understanding the forces that shape it and for informing initiatives aimed at its funding, long-term maintenance and improvement. In particular, the long-term maintenance of these tools is compromised because of their specific development model. Hundreds of published studies become irreproducible de facto, as the software tools used to conduct them become unavailable. In this study, we present a large-scale survey of >5400 publications describing Web servers within the two main bibliographic resources for disseminating new software developments in molecular biology. For all these servers, we studied their citation patterns, the subjects they address, their citation networks and the temporal evolution of these factors. We also analysed how these factors affect the availability of these servers (whether they are alive). Our results show that this ecosystem of tools is highly interconnected and adapts to the 'trendy' subjects in every moment. The servers present characteristic temporal patterns of citation/usage, and there is a worrying rate of server 'death', which is influenced by factors such as the server popularity and the institutions that hosts it. These results can inform initiatives aimed at the long-term maintenance of these resources.

Applications of molecular networks in biomedicine.

Due to the large interdependence between the molecular components of living systems, many phenomena, including those related to pathologies, cannot be explained in terms of a single gene or a small number of genes. Molecular networks, representing different types of relationships between molecular entities, embody these large sets of interdependences in a framework that allow their mining from a systemic point of view to obtain information. These networks, often generated from high-throughput omics datasets, are used to study the complex phenomena of human pathologies from a systemic point of view. Complementing the reductionist approach of molecular biology, based on the detailed study of a small number of genes, systemic approaches to human diseases consider that these are better reflected in large and intricate networks of relationships between genes. These networks, and not the single genes, provide both better markers for diagnosing diseases and targets for treating them. Network approaches are being used to gain insight into the molecular basis of complex diseases and interpret the large datasets associated with them, such as genomic variants. Network formalism is also suitable for integrating large, heterogeneous and multilevel datasets associated with diseases from the molecular level to organismal and epidemiological scales. Many of these approaches are available to nonexpert users through standard software packages.

Factors affecting interactome-based prediction of human genes associated with clinical signs.

BACKGROUND: Clinical signs are a fundamental aspect of human pathologies. While disease diagnosis is problematic or impossible in many cases, signs are easier to perceive and categorize. Clinical signs are increasingly used, together with molecular networks, to prioritize detected variants in clinical genomics pipelines, even if the patient is still undiagnosed. Here we analyze the ability of these network-based methods to predict genes that underlie clinical signs from the human interactome. RESULTS: Our analysis reveals that these approaches can locate genes associated with clinical signs with variable performance that depends on the sign and associated disease. We analyzed several clinical and biological factors that explain these variable results, including number of genes involved (mono- vs. oligogenic diseases), mode of inheritance, type of clinical sign and gene product function. CONCLUSIONS: Our results indicate that the characteristics of the clinical signs and their related diseases should be considered for interpreting the results of network-prediction methods, such as those aimed at discovering disease-related genes and variants. These results are important due the increasing use of clinical signs as an alternative to diseases for studying the molecular basis of human pathologies.

Functional Analysis of Metabolomics Data.

Metabolomics aims at characterizing the repertory of small chemical compounds in a biological sample. As it becomes more massive and larger sets of compounds are detected, a functional analysis is required to convert these raw lists of compounds into biological knowledge. The most common way of performing such analysis is "annotation enrichment analysis," also used in transcriptomics and proteomics. This approach extracts the annotations overrepresented in the set of chemical compounds arisen in a given experiment. Here, we describe the protocols for performing such analysis as well as for visualizing a set of compounds in different representations of the metabolic networks, in both cases using free accessible web tools.

MBROLE 2.0-functional enrichment of chemical compounds.

Metabolites Biological Role (MBROLE) is a server that performs functional enrichment analysis of a list of chemical compounds derived from a metabolomics experiment, which allows this list to be interpreted in biological terms. Since its release in 2011, MBROLE has been used by different groups worldwide to analyse metabolomics experiments from a variety of organisms. Here we present the latest version of the system, MBROLE2, accessible at http://csbg.cnb.csic.es/mbrole2 MBROLE2 has been supplemented with 10 databases not available in the previous version, which allow analysis over a larger, richer set of vocabularies including metabolite-protein and drug-protein interactions. This new version performs automatic conversion of compound identifiers from different databases, thus simplifying usage. In addition, the user interface has been redesigned to generate an interactive, more intuitive representation of the results.

Rare disease relations through common genes and protein interactions.

ODCs (Orphan Disease Connections), available at http://csbg.cnb.csic.es/odcs, is a novel resource to explore potential molecular relations between rare diseases. These molecular relations have been established through the integration of disease susceptibility genes and human protein-protein interactions. The database currently contains 54,941 relations between 3032 diseases.

Characterization of clinical signs in the human interactome.

MOTIVATION: Many diseases are related by shared associated molecules and pathways, exhibiting comorbidities and common phenotypes, an indication of the continuous nature of the human pathological landscape. Although it is continuous, this landscape is always partitioned into discrete diseases when studied at the molecular level. Clinical signs are also important phenotypic descriptors that can reveal the molecular mechanisms that underlie pathological states, but have seldom been the subject of systemic research. Here, we quantify the modular nature of the clinical signs associated with genetic diseases in the human interactome. RESULTS: We found that clinical signs are reflected as modules at the molecular network level, to at least to the same extent as diseases. They can thus serve as a valid complementary partition of the human pathological landscape, with implications for etiology research, diagnosis and treatment. CONTACT: monica.chagoyen@cnb.csic.es SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Clinical, genetics and bioinformatics characterization of a campomelic dysplasia case report.

Campomelic dysplasia is a rare disorder characterized by skeletal and extraskeletal defects. Up to two-thirds of affected XY individuals have a gradation of genital defects or may develop as phenotypic females. This syndrome is caused by alterations in SRY-related HMG-Box Gene 9 (SOX9), a transcription factor essential in both chondrocyte differentiation and sex determination. We report a 27-week fetus with ambiguous genitalia and upper and lower extremities bone malformations. Gross photographs, radiologic and pathological studies led the clinical diagnosis to campomelic dysplasia. A new frameshift mutation (p.Pro415Serfs*163) was identified in the SOX9 gene by genetic analysis. This mutation not only alters almost the entire sequence of the C-terminal transactivation (TA) domain of SOX9, but also enlarges it. This altered sequence does not resemble any other existing sequence. Since TA domain is entirely affected, SOX9 could not establish its normal function. The comparison between p.Pro415Serfs*163 and other frameshift mutations that enlarges SOX9 showed the same nucleotides added. This new sequence is not conserved either. We speculate that the fact of adding a sequence downstream of the C-terminal domain alters SOX9 and leads to campomelic dysplasia. The clinical information is essential not only to achieve a correct diagnosis in fetuses with pathologic ultrasound findings, but also to offer a proper genetic counseling.