The Environmental Conditions, Treatments, and Exposures Ontology (ECTO): connecting toxicology and exposure to human health and beyond.

BACKGROUND: Evaluating the impact of environmental exposures on organism health is a key goal of modern biomedicine and is critically important in an age of greater pollution and chemicals in our environment. Environmental health utilizes many different research methods and generates a variety of data types. However, to date, no comprehensive database represents the full spectrum of environmental health data. Due to a lack of interoperability between databases, tools for integrating these resources are needed. In this manuscript we present the Environmental Conditions, Treatments, and Exposures Ontology (ECTO), a species-agnostic ontology focused on exposure events that occur as a result of natural and experimental processes, such as diet, work, or research activities. ECTO is intended for use in harmonizing environmental health data resources to support cross-study integration and inference for mechanism discovery. METHODS AND FINDINGS: ECTO is an ontology designed for describing organismal exposures such as toxicological research, environmental variables, dietary features, and patient-reported data from surveys. ECTO utilizes the base model established within the Exposure Ontology (ExO). ECTO is developed using a combination of manual curation and Dead Simple OWL Design Patterns (DOSDP), and contains over 2700 environmental exposure terms, and incorporates chemical and environmental ontologies. ECTO is an Open Biological and Biomedical Ontology (OBO) Foundry ontology that is designed for interoperability, reuse, and axiomatization with other ontologies. ECTO terms have been utilized in axioms within the Mondo Disease Ontology to represent diseases caused or influenced by environmental factors, as well as for survey encoding for the Personalized Environment and Genes Study (PEGS). CONCLUSIONS: We constructed ECTO to meet Open Biological and Biomedical Ontology (OBO) Foundry principles to increase translation opportunities between environmental health and other areas of biology. ECTO has a growing community of contributors consisting of toxicologists, public health epidemiologists, and health care providers to provide the necessary expertise for areas that have been identified previously as gaps

Effect of spinal manipulation on sensorimotor functions in back pain patients: study protocol for a randomised controlled trial

<p>Abstract</p> <p>Background</p> <p>Low back pain (LBP) is a recognized public health problem, impacting up to 80% of US adults at some point in their lives. Patients with LBP are utilizing integrative health care such as spinal manipulation (SM). SM is the therapeutic application of a load to specific body tissues or structures and can be divided into two broad categories: SM with a high-velocity low-amplitude load, or an impulse "thrust", (HVLA-SM) and SM with a low-velocity variable-amplitude load (LVVA-SM). There is evidence that sensorimotor function in people with LBP is altered. This study evaluates the sensorimotor function in the lumbopelvic region, as measured by postural sway, response to sudden load and repositioning accuracy, following SM to the lumbar and pelvic region when compared to a sham treatment.</p> <p>Methods/Design</p> <p>A total of 219 participants with acute, subacute or chronic low back pain are being recruited from the Quad Cities area located in Iowa and Illinois. They are allocated through a minimization algorithm in a 1:1:1 ratio to receive either 13 HVLA-SM treatments over 6 weeks, 13 LVVA-SM treatments over 6 weeks or 2 weeks of a sham treatment followed by 4 weeks of full spine "doctor's choice" SM. Sensorimotor function tests are performed before and immediately after treatment at baseline, week 2 and week 6. Self-report outcome assessments are also collected. The primary aims of this study are to 1) determine immediate pre to post changes in sensorimotor function as measured by postural sway following delivery of a single HVLA-SM or LVVA-SM treatment when compared to a sham treatment and 2) to determine changes from baseline to 2 weeks (4 treatments) of HVLA-SM or LVVA-SM compared to a sham treatment. Secondary aims include changes in response to sudden loads and lumbar repositioning accuracy at these endpoints, estimating sensorimotor function in the SM groups after 6 weeks of treatment, and exploring if changes in sensorimotor function are associated with changes in self-report outcome assessments.</p> <p>Discussion</p> <p>This study may provide clues to the sensorimotor mechanisms that explain observed functional deficits associated with LBP, as well as the mechanism of action of SM.</p> <p>Trial registration</p> <p>This trial is registered in ClinicalTrials.gov, with the ID number of <a href="http://www.clinicaltrials.gov/ct2/show/NCT00830596">NCT00830596</a>, registered on January 27, 2009. The first participant was allocated on 30 January 2009 and the final participant was allocated on 17 March 2011.</p

Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database

<div><p>Strategies for discovering common molecular events among disparate diseases hold promise for improving understanding of disease etiology and expanding treatment options. One technique is to leverage curated datasets found in the public domain. The Comparative Toxicogenomics Database (CTD; <a href="http://ctdbase.org/" target="_blank">http://ctdbase.org/</a>) manually curates chemical-gene, chemical-disease, and gene-disease interactions from the scientific literature. The use of official gene symbols in CTD interactions enables this information to be combined with the Gene Ontology (GO) file from NCBI Gene. By integrating these GO-gene annotations with CTD’s gene-disease dataset, we produce 753,000 inferences between 15,700 GO terms and 4,200 diseases, providing opportunities to explore presumptive molecular underpinnings of diseases and identify biological similarities. Through a variety of applications, we demonstrate the utility of this novel resource. As a proof-of-concept, we first analyze known repositioned drugs (e.g., raloxifene and sildenafil) and see that their target diseases have a greater degree of similarity when comparing GO terms vs. genes. Next, a computational analysis predicts seemingly non-intuitive diseases (e.g., stomach ulcers and atherosclerosis) as being similar to bipolar disorder, and these are validated in the literature as reported co-diseases. Additionally, we leverage other CTD content to develop testable hypotheses about thalidomide-gene networks to treat seemingly disparate diseases. Finally, we illustrate how CTD tools can rank a series of drugs as potential candidates for repositioning against B-cell chronic lymphocytic leukemia and predict cisplatin and the small molecule inhibitor JQ1 as lead compounds. The CTD dataset is freely available for users to navigate pathologies within the context of extensive biological processes, molecular functions, and cellular components conferred by GO. This inference set should aid researchers, bioinformaticists, and pharmaceutical drug makers in finding commonalities in disease mechanisms, which in turn could help identify new therapeutics, new indications for existing pharmaceuticals, potential disease comorbidities, and alerts for side effects.</p></div

Document workflow.

<p>(1) Independent CTD-specific queries were made of PubMed to retrieve 14,904 articles for the seven heavy metals cadmium, cobalt, copper, lead, manganese, mercury, and nickel. (2) These articles were text mined and assigned a document relevancy score (DRS). (3) Of this preliminary corpus, 1,020 articles were found to have been previously reviewed in CTD and were used as a test set to evaluate the DRS and determine suitable cut-offs. (4) Articles with DRS ≥100 (high), DRS ≤20 (low), and a subset with DRS between 21–99 (medium) were combined to provide a final corpus of 3,583 documents which was then (5) sent to five CTD biocurators (who were kept blind to the DRS of each article) for review. (6) Biocurators timed themselves while reviewing all articles and ultimately rejected 1,381 (as non-curatable for CTD) and curated 2,202 of them (7) from whence 41,208 chemical-gene-disease interactions were extracted.</p

CTD text mining technical overview.

<p>(1) A triaged corpus is retrieved for a target chemical-of-interest by querying PubMed. (2) Using the PMID, an article's title and abstract are mined for gene, chemical, disease, and action term recognition in CTD's integrated text-mining pipeline (red box). (3) Each text-mined term is first validated against CTD's controlled vocabularies and ignored if a match is not secured. The CTD text-mining pipeline process is run on a Red Hat Enterprise Linux 6.2 operating system using primarily Java 1.6 within the context of asynchronous batch processes. (4) PMIDs are then assigned a document relevancy score (DRS) by the text-mining tool and (5) sent to biocurators. (6) All interactions are composed and entered in CTD's web-based Curation Tool with the client running HTML 5, CSS3, JavaScript 1.85, and Ajax; a server processes the interactions and stores them in the Curation Database using Tomcat 6.0, Java 1.6, Servlet 2.5, JSP/JSTL, and Spring 3.0 framework.</p

DRS reflects the number of interactions per curated article.

<p>Biocurators extracted 41,208 interactions from 2,202 curated articles (top row, c). The average number of interactions per curated article (log-scale, <i>y</i>-axis) is distributed by the assigned DRS (binned in 20-unit increments, <i>x</i>-axis), with the number of curated articles (c) in each bin indicated at the top. The average number of interactions per curated article increases with the DRS. The aberrant spike in bin 240–259 is due to a single article (amongst a total of nine curated documents in the bin) from whence 5,977 interactions were curated from a microarray experiment.</p

CTD manual curation metrics.

a<p>DRS  =  document relevancy score bins: high (≥100), medium (21–99), low (1–20).</p>b<p>Curation rate  =  minutes spent per curated article only. SD  =  standard deviation.</p>c<p>Rejection rate  =  minutes spent per rejected article only.</p

Disease category distribution for the seven heavy metals.

<p>The number of diseases curated for each metal is indicated for cadmium (Cd), cobalt (Co), copper (Cu), lead (Pb), manganese (Mn), mercury (Hg), and nickel (Ni). These specific disorders were then mapped and distributed across 21 generic disease categories (legend at top) using CTD's MEDIC-Slim disease mappings <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058201#pone.0058201-Davis1" target="_blank">[<b>2</b>]</a> to look for overrepresented disease classes for each individual heavy metal. For example, of the 70 specific diseases associated with copper (Cu), 23 of them (33%) are nervous system disorders and 12 of them (17%) are cardiovascular disorders.</p

DRS effectively ranks articles for productivity.

<p>(A) The number of total interactions (both novel and repeated) for each quartile is divided by (B) the time spent on curating them to produce (C) an averaged interaction yield rate (interactions per minute) for each quartile.</p