Lipoprotein ontology as a functional knowledge base

The advances of high throughput research in the biomedical domain have resulted in an onslaught of data being generated at an exponential rate. As a result, researchers face challenges in navigating through overwhelming amounts of information in order to derive relevant scientific insights. Ontologies address these issues by providing explicit description of biomedical entities and a platform for the integration of data, thereby enabling a more efficient retrieval of information. There have been major efforts in the development of biomedical ontologies in the recent years; however no such ontology exists for lipoproteins, which play a crucial role in various biological and cellular functions. Dysregulation in lipoprotein metabolism is significantly associated with an increased risk to cardiovascular disease, the leading cause of mortality in the world today. The aim of this paper is to propose a preliminary framework for Lipoprotein Ontology, with particular focus on the etiology and treatment of lipoprotein dysregulation. This may provide a novel and effective strategy for managing at risk individuals

The Proteome of Biologically Active Membrane Vesicles from Piscirickettsia salmonis LF-89 Type Strain Identifies Plasmid-Encoded Putative Toxins

Indexación: Scopus.Piscirickettsia salmonis is the predominant bacterial pathogen affecting the Chilean salmonid industry. This bacterium is the etiological agent of piscirickettsiosis, a significant fish disease. Membrane vesicles (MVs) released by P. salmonis deliver several virulence factors to host cells. To improve on existing knowledge for the pathogenicity-associated functions of P. salmonis MVs, we studied the proteome of purified MVs from the P. salmonis LF-89 type strain using multidimensional protein identification technology. Initially, the cytotoxicity of different MV concentration purified from P. salmonis LF-89 was confirmed in an in vivo adult zebrafish infection model. The cumulative mortality of zebrafish injected with MVs showed a dose-dependent pattern. Analyses identified 452 proteins of different subcellular origins; most of them were associated with the cytoplasmic compartment and were mainly related to key functions for pathogen survival. Interestingly, previously unidentified putative virulence-related proteins were identified in P. salmonis MVs, such as outer membrane porin F and hemolysin. Additionally, five amino acid sequences corresponding to the Bordetella pertussis toxin subunit 1 and two amino acid sequences corresponding to the heat-labile enterotoxin alpha chain of Escherichia coli were located in the P. salmonis MV proteome. Curiously, these putative toxins were located in a plasmid region of P. salmonis LF-89. Based on the identified proteins, we propose that the protein composition of P. salmonis LF-89 MVs could reflect total protein characteristics of this P. salmonis type strain. © 2017 Oliver, Hernández, Tandberg, Valenzuela, Lagos, Haro, Sánchez, Ruiz, Sanhueza-Oyarzún, Cortés, Villar, Artigues, Winther-Larsen, Avendaño-Herrera and Yáñez.https://www.frontiersin.org/articles/10.3389/fcimb.2017.00420/ful

GO-WORDS: An Entropic Approach to Semantic Decomposition of Gene Ontology Terms

The Gene Ontology (GO) has a large and growing number of terms that constitute its vocabulary. An entropy-based approach is presented to automate the characterization of the compositional semantics of GO terms. The motivation is to extend the machine-readability of GO and to offer insights for the continued maintenance and growth of GO. A proto-type implementation illustrates the benefits of the approach

Ontology-assisted database integration to support natural language processing and biomedical data-mining

Successful biomedical data mining and information extraction require a complete picture of biological phenomena such as genes, biological processes, and diseases; as these exist on different levels of granularity. To realize this goal, several freely available heterogeneous databases as well as proprietary structured datasets have to be integrated into a single global customizable scheme. We will present a tool to integrate different biological data sources by mapping them to a proprietary biomedical ontology that has been developed for the purposes of making computers understand medical natural language

Ontology-guided data preparation for discovering genotype-phenotype relationships

International audienceComplexity of post-genomic data and multiplicity of mining strategies are two limits to Knowledge Discovery in Databases (KDD) in life sciences. Because they provide a semantic frame to data and because they benefit from the progress of semantic web technologies, bio-ontologies should be considered for playing a key role in the KDD process. In the frame of a case study relative to the search of genotype-phenotype relationships, we demonstrate the capability of bio-ontologies to guide data selection during the preparation step of the KDD process. We propose three scenarios to illustrate how domain knowledge can be taken into account in order to select or aggregate data to mine, and consequently how it can facilitate result interpretation at the end of the process

Differential expression of skeletal muscle genes following administration of clenbuterol to exercised horses.

BackgroundClenbuterol, a beta2-adrenergic receptor agonist, is used therapeutically to treat respiratory conditions in the horse. However, by virtue of its mechanism of action it has been suggested that clenbuterol may also have repartitioning affects in horses and as such the potential to affect performance. Clenbuterol decreases the percent fat and increases fat-free mass following high dose administration in combination with intense exercise in horses. In the current study, microarray analysis and real-time PCR were used to study the temporal effects of low and high dose chronic clenbuterol administration on differential gene expression of several skeletal muscle myosin heavy chains, genes involved in lipid metabolism and the β2-adrenergic receptor. The effect of clenbuterol administration on differential gene expression has not been previously reported in the horse, therefore the primary objective of the current study was to describe clenbuterol-induced temporal changes in gene expression following chronic oral administration of clenbuterol at both high and low doses.ResultsSteady state clenbuterol concentrations were achieved at approximately 50 h post administration of the first dose for the low dose regimen and at approximately 18-19 days (10 days post administration of 3.2 μg/kg) for the escalating dosing regimen. Following chronic administration of the low dose (0.8 μg/kg BID) of clenbuterol, a total of 114 genes were differentially expressed, however, none of these changes were found to be significant following FDR adjustment of the p-values. A total of 7,093 genes were differentially expressed with 3,623 genes up regulated and 3,470 genes down regulated following chronic high dose administration. Of the genes selected for further study by real-time PCR, down-regulation of genes encoding myosin heavy chains 2 and 7, steroyl CoA desaturase and the β2-adrenergic receptor were noted. For most genes, expression levels returned towards baseline levels following cessation of drug administration.ConclusionThis study showed no evidence of modified gene expression following chronic low dose administration of clenbuterol to horses. However, following chronic administration of high doses of clenbuterol alterations were noted in transcripts encoding various myosin heavy chains, lipid metabolizing enzymes and the β2-adrenergic receptor

Generation and characterization of a mitotane-resistant adrenocortical cell line

Mitotane is the only drug approved for the therapy of adrenocortical carcinoma (ACC). Its clinical use is limited by the occurrence of relapse during therapy. To investigate the underlying mechanisms in vitro, we here generated mitotane-resistant cell lines. After long-term pulsed treatment of HAC-15 human adrenocortical carcinoma cells with 70 µM mitotane, we isolated monoclonal cell populations of treated cells and controls and assessed their respective mitotane sensitivities by MTT assay. We performed exome sequencing and electron microscopy, conducted gene expression microarray analysis and determined intracellular lipid concentrations in the presence and absence of mitotane. Clonal cell lines established after pulsed treatment were resistant to mitotane (IC50 of 102.2 ± 7.3 µM (n = 12) vs 39.4 ± 6.2 µM (n = 6) in controls (biological replicates, mean ± s.d., P = 0.0001)). Unlike nonresistant clones, resistant clones maintained normal mitochondrial and nucleolar morphology during mitotane treatment. Resistant clones largely shared structural and single nucleotide variants, suggesting a common cell of origin. Resistance depended, in part, on extracellular lipoproteins and was associated with alterations in intracellular lipid homeostasis, including levels of free cholesterol, as well as decreased steroid production. By gene expression analysis, resistant cells showed profound alterations in pathways including steroid metabolism and transport, apoptosis, cell growth and Wnt signaling. These studies establish an in vitro model of mitotane resistance in ACC and point to underlying molecular mechanisms. They may enable future studies to overcome resistance in vitro and improve ACC treatment in vivo

Semi-automated Ontology Generation for Biocuration and Semantic Search

Background: In the life sciences, the amount of literature and experimental data grows at a tremendous rate. In order to effectively access and integrate these data, biomedical ontologies – controlled, hierarchical vocabularies – are being developed. Creating and maintaining such ontologies is a difficult, labour-intensive, manual process. Many computational methods which can support ontology construction have been proposed in the past. However, good, validated systems are largely missing. Motivation: The biocuration community plays a central role in the development of ontologies. Any method that can support their efforts has the potential to have a huge impact in the life sciences. Recently, a number of semantic search engines were created that make use of biomedical ontologies for document retrieval. To transfer the technology to other knowledge domains, suitable ontologies need to be created. One area where ontologies may prove particularly useful is the search for alternative methods to animal testing, an area where comprehensive search is of special interest to determine the availability or unavailability of alternative methods. Results: The Dresden Ontology Generator for Directed Acyclic Graphs (DOG4DAG) developed in this thesis is a system which supports the creation and extension of ontologies by semi-automatically generating terms, definitions, and parent-child relations from text in PubMed, the web, and PDF repositories. The system is seamlessly integrated into OBO-Edit and Protégé, two widely used ontology editors in the life sciences. DOG4DAG generates terms by identifying statistically significant noun-phrases in text. For definitions and parent-child relations it employs pattern-based web searches. Each generation step has been systematically evaluated using manually validated benchmarks. The term generation leads to high quality terms also found in manually created ontologies. Definitions can be retrieved for up to 78% of terms, child ancestor relations for up to 54%. No other validated system exists that achieves comparable results. To improve the search for information on alternative methods to animal testing an ontology has been developed that contains 17,151 terms of which 10% were newly created and 90% were re-used from existing resources. This ontology is the core of Go3R, the first semantic search engine in this field. When a user performs a search query with Go3R, the search engine expands this request using the structure and terminology of the ontology. The machine classification employed in Go3R is capable of distinguishing documents related to alternative methods from those which are not with an F-measure of 90% on a manual benchmark. Approximately 200,000 of the 19 million documents listed in PubMed were identified as relevant, either because a specific term was contained or due to the automatic classification. The Go3R search engine is available on-line under www.Go3R.org

Semi-automated Ontology Generation for Biocuration and Semantic Search

Background: In the life sciences, the amount of literature and experimental data grows at a tremendous rate. In order to effectively access and integrate these data, biomedical ontologies – controlled, hierarchical vocabularies – are being developed. Creating and maintaining such ontologies is a difficult, labour-intensive, manual process. Many computational methods which can support ontology construction have been proposed in the past. However, good, validated systems are largely missing. Motivation: The biocuration community plays a central role in the development of ontologies. Any method that can support their efforts has the potential to have a huge impact in the life sciences. Recently, a number of semantic search engines were created that make use of biomedical ontologies for document retrieval. To transfer the technology to other knowledge domains, suitable ontologies need to be created. One area where ontologies may prove particularly useful is the search for alternative methods to animal testing, an area where comprehensive search is of special interest to determine the availability or unavailability of alternative methods. Results: The Dresden Ontology Generator for Directed Acyclic Graphs (DOG4DAG) developed in this thesis is a system which supports the creation and extension of ontologies by semi-automatically generating terms, definitions, and parent-child relations from text in PubMed, the web, and PDF repositories. The system is seamlessly integrated into OBO-Edit and Protégé, two widely used ontology editors in the life sciences. DOG4DAG generates terms by identifying statistically significant noun-phrases in text. For definitions and parent-child relations it employs pattern-based web searches. Each generation step has been systematically evaluated using manually validated benchmarks. The term generation leads to high quality terms also found in manually created ontologies. Definitions can be retrieved for up to 78% of terms, child ancestor relations for up to 54%. No other validated system exists that achieves comparable results. To improve the search for information on alternative methods to animal testing an ontology has been developed that contains 17,151 terms of which 10% were newly created and 90% were re-used from existing resources. This ontology is the core of Go3R, the first semantic search engine in this field. When a user performs a search query with Go3R, the search engine expands this request using the structure and terminology of the ontology. The machine classification employed in Go3R is capable of distinguishing documents related to alternative methods from those which are not with an F-measure of 90% on a manual benchmark. Approximately 200,000 of the 19 million documents listed in PubMed were identified as relevant, either because a specific term was contained or due to the automatic classification. The Go3R search engine is available on-line under www.Go3R.org

A Systemic Receptor Network Triggered by Human cytomegalovirus Entry

Virus entry is a multistep process that triggers a variety of cellular pathways interconnecting into a complex network, yet the molecular complexity of this network remains largely unsolved. Here, by employing systems biology approach, we reveal a systemic virus-entry network initiated by human cytomegalovirus (HCMV), a widespread opportunistic pathogen. This network contains all known interactions and functional modules (i.e. groups of proteins) coordinately responding to HCMV entry. The number of both genes and functional modules activated in this network dramatically declines shortly, within 25 min post-infection. While modules annotated as receptor system, ion transport, and immune response are continuously activated during the entire process of HCMV entry, those for cell adhesion and skeletal movement are specifically activated during viral early attachment, and those for immune response during virus entry. HCMV entry requires a complex receptor network involving different cellular components, comprising not only cell surface receptors, but also pathway components in signal transduction, skeletal development, immune response, endocytosis, ion transport, macromolecule metabolism and chromatin remodeling. Interestingly, genes that function in chromatin remodeling are the most abundant in this receptor system, suggesting that global modulation of transcriptions is one of the most important events in HCMV entry. Results of in silico knock out further reveal that this entire receptor network is primarily controlled by multiple elements, such as EGFR (Epidermal Growth Factor) and SLC10A1 (sodium/bile acid cotransporter family, member 1). Thus, our results demonstrate that a complex systemic network, in which components coordinating efficiently in time and space contributes to virus entry.Comment: 26 page