Towards Machine-FAIR: Representing software and datasets to facilitate reuse and scientific discovery by machines.

OBJECTIVE: To use software, datasets, and data formats in the domain of Infectious Disease Epidemiology as a test collection to evaluate a novel M1 use case, which we introduce in this paper. M1 is a machine that upon receipt of a new digital object of research exhaustively finds all valid compositions of it with existing objects. METHOD: We implemented a data-format-matching-only M1 using exhaustive search, which we refer to as M1DFM. We then ran M1DFM on the test collection and used error analysis to identify needed semantic constraints. RESULTS: Precision of M1DFM search was 61.7%. Error analysis identified needed semantic constraints and needed changes in handling of data services. Most semantic constraints were simple, but one data format was sufficiently complex to be practically impossible to represent semantic constraints over, from which we conclude limitatively that software developers will have to meet the machines halfway by engineering software whose inputs are sufficiently simple that their semantic constraints can be represented, akin to the simple APIs of services. We summarize these insights as M1-FAIR guiding principles for composability and suggest a roadmap for progressively capable devices in the service of reuse and accelerated scientific discovery. CONCLUSION: Algorithmic search of digital repositories for valid workflow compositions has potential to accelerate scientific discovery but requires a scalable solution to the problem of knowledge acquisition about semantic constraints on software inputs. Additionally, practical limitations on the logical complexity of semantic constraints must be respected, which has implications for the design of software.

An Automated Workflow Composition System for Liquid Chromatography-Mass Spectrometry Metabolomics Data Processing.

Liquid chromatography-mass spectrometry (LC-MS) metabolomics studies produce high-dimensional data that must be processed by a complex network of informatics tools to generate analysis-ready data sets. As the first computational step in metabolomics, data processing is increasingly becoming a challenge for researchers to develop customized computational workflows that are applicable for LC-MS metabolomics analysis. Ontology-based automated workflow composition (AWC) systems provide a feasible approach for developing computational workflows that consume high-dimensional molecular data. We used the Automated Pipeline Explorer (APE) to create an AWC for LC-MS metabolomics data processing across three use cases. Our results show that APE predicted 145 data processing workflows across all the three use cases. We identified six traditional workflows and six novel workflows. Through manual review, we found that one-third of novel workflows were executable whereby the data processing function could be completed without obtaining an error. When selecting the top six workflows from each use case, the computational viable rate of our predicted workflows reached 45%. Collectively, our study demonstrates the feasibility of developing an AWC system for LC-MS metabolomics data processing.

Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software.

BACKGROUND: Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a popular approach for metabolomics data acquisition and requires many data processing software tools. The FAIR Principles - Findability, Accessibility, Interoperability, and Reusability - were proposed to promote open science and reusable data management, and to maximize the benefit obtained from contemporary and formal scholarly digital publishing. More recently, the FAIR principles were extended to include Research Software (FAIR4RS). AIM OF REVIEW: This study facilitates open science in metabolomics by providing an implementation solution for adopting FAIR4RS in the LC-HRMS metabolomics data processing software. We believe our evaluation guidelines and results can help improve the FAIRness of research software. KEY SCIENTIFIC CONCEPTS OF REVIEW: We evaluated 124 LC-HRMS metabolomics data processing software obtained from a systematic review and selected 61 software for detailed evaluation using FAIR4RS-related criteria, which were extracted from the literature along with internal discussions. We assigned each criterion one or more FAIR4RS categories through discussion. The minimum, median, and maximum percentages of criteria fulfillment of software were 21.6%, 47.7%, and 71.8%. Statistical analysis revealed no significant improvement in FAIRness over time. We identified four criteria covering multiple FAIR4RS categories but had a low %fulfillment: (1) No software had semantic annotation of key information; (2) only 6.3% of evaluated software were registered to Zenodo and received DOIs; (3) only 14.5% of selected software had official software containerization or virtual machine; (4) only 16.7% of evaluated software had a fully documented functions in code. According to the results, we discussed improvement strategies and future directions.

Semantic standards of external exposome data.

An individual's health and conditions are associated with a complex interplay between the individual's genetics and his or her exposures to both internal and external environments. Much attention has been placed on characterizing of the genome in the past; nevertheless, genetics only account for about 10% of an individual's health conditions, while the remaining appears to be determined by environmental factors and gene-environment interactions. To comprehensively understand the causes of diseases and prevent them, environmental exposures, especially the external exposome, need to be systematically explored. However, the heterogeneity of the external exposome data sources (e.g., same exposure variables using different nomenclature in different data sources, or vice versa, two variables have the same or similar name but measure different exposures in reality) increases the difficulty of analyzing and understanding the associations between environmental exposures and health outcomes. To solve the issue, the development of semantic standards using an ontology-driven approach is inevitable because ontologies can (1) provide a unambiguous and consistent understanding of the variables in heterogeneous data sources, and (2) explicitly express and model the context of the variables and relationships between those variables. We conducted a review of existing ontology for the external exposome and found only four relevant ontologies. Further, the four existing ontologies are limited: they (1) often ignored the spatiotemporal characteristics of external exposome data, and (2) were developed in isolation from other conceptual frameworks (e.g., the socioecological model and the social determinants of health). Moving forward, the combination of multi-domain and multi-scale data (i.e., genome, phenome and exposome at different granularity) and different conceptual frameworks is the basis of health outcomes research in the future.

A realism-based approach to an ontological representation of symbiotic interactions.

BACKGROUND: The symbiotic interactions that occur between humans and organisms in our environment have a tremendous impact on our health. Recently, there has been a surge in interest in understanding the complex relationships between the microbiome and human health and host immunity against microbial pathogens, among other things. To collect and manage data about these interactions and their complexity, scientists will need ontologies that represent symbiotic interactions as they occur in reality. METHODS: We began with two papers that reviewed the usage of 'symbiosis' and related terms in the biology and ecology literature and prominent textbooks. We then analyzed several prominent standard terminologies and ontologies that contain representations of symbiotic interactions, to determine if they appropriately defined 'symbiosis' and related terms according to current scientific usage as identified by the review papers. In the process, we identified several subtypes of symbiotic interactions, as well as the characteristics that differentiate them, which we used to propose textual and axiomatic definitions for each subtype of interaction. To both illustrate how to use the ontological representations and definitions we created and provide additional quality assurance on key definitions, we carried out a referent tracking analysis and representation of three scenarios involving symbiotic interactions among organisms. RESULTS: We found one definition of 'symbiosis' in an existing ontology that was consistent with the vast preponderance of scientific usage in biology and ecology. However, that ontology changed its definition during the course of our work, and discussions are ongoing. We present a new definition that we have proposed. We also define 34 subtypes of symbiosis. Our referent tracking analysis showed that it is necessary to define symbiotic interactions at the level of the individual, rather than at the species level, due to the complex nature in which organisms can go from participating in one type of symbiosis with one organism to participating in another type of symbiosis with a different organism. CONCLUSION: As a result of our efforts here, we have developed a robust representation of symbiotic interactions using a realism-based approach, which fills a gap in existing biomedical ontologies.

Temporal and age-dependent effects of haptoglobin deletion on intracerebral hemorrhage-induced brain damage and neurobehavioral outcomes.

Intracerebral hemorrhage (ICH) is a devastating stroke subtype and the presence of extracorpuscular hemoglobin (Hb) exacerbates brain damage. Haptoglobin (Hp) binds Hb, which prevents its oxidation and participation in neurotoxic reactions. Multiple studies have investigated the role of Hp under conditions of intravascular hemolysis, but little is known about its role in the brain and following ICH where extravascular hemolysis is rampant. Young and aged wildtype and Hp-/- mice underwent the autologous blood or collagenase ICH model. Early after ICH, Hp-/- mice display 58.0 ±â€¯5.6% and 36.7 ±â€¯6.9% less brain damage in the autologous blood and collagenase ICH models, respectively. In line with these findings, Hp-/- mice display less neurological deficits on several neurobehavioral tests. Hp-/- mice have less Perl's iron content, HO1 expression, and blood brain barrier dysfunction, but no difference in brain Hb content, astrogliosis and angiogenesis/neovascularization. At the later endpoint, the young cohort displays 27.8 ±â€¯9.3% less brain damage, while no difference is seen with the aged cohort. For both cohorts, no differences are seen in HO1 levels or iron accumulation, but young Hp-/- mice display less thalamic astrogliosis and striatal microgliosis. This study reveals that the presence or absence of Hp exerts important time- and age-dependent influences on ICH outcomes.

A Comparison of Pathophysiology in Humans and Rodent Models of Subarachnoid Hemorrhage.

Non-traumatic subarachnoid hemorrhage (SAH) affects an estimated 30,000 people each year in the United States, with an overall mortality of ~30%. Most cases of SAH result from a ruptured intracranial aneurysm, require long hospital stays, and result in significant disability and high fatality. Early brain injury (EBI) and delayed cerebral vasospasm (CV) have been implicated as leading causes of morbidity and mortality in these patients, necessitating intense focus on developing preclinical animal models that replicate clinical SAH complete with delayed CV. Despite the variety of animal models currently available, translation of findings from rodent models to clinical trials has proven especially difficult. While the explanation for this lack of translation is unclear, possibilities include the lack of standardized practices and poor replication of human pathophysiology, such as delayed cerebral vasospasm and ischemia, in rodent models of SAH. In this review, we summarize the different approaches to simulating SAH in rodents, in particular elucidating the key pathophysiology of the various methods and models. Ultimately, we suggest the development of standardized model of rodent SAH that better replicates human pathophysiology for moving forward with translational research.

Therapeutic indications and other use-case-driven updates in the drug ontology: anti-malarials, anti-hypertensives, opioid analgesics, and a large term request.

BACKGROUND: The Drug Ontology (DrOn) is an OWL2-based representation of drug products and their ingredients, mechanisms of action, strengths, and dose forms. We originally created DrOn for use cases in comparative effectiveness research, primarily to identify historically complete sets of United States National Drug Codes (NDCs) that represent packaged drug products, by the ingredient(s), mechanism(s) of action, and so on contained in those products. Although we had designed DrOn from the outset to carefully distinguish those entities that have a therapeutic indication from those entities that have a molecular mechanism of action, we had not previously represented in DrOn any particular therapeutic indication. RESULTS: In this work, we add therapeutic indications for three research use cases: resistant hypertension, malaria, and opioid abuse research. We also added mechanisms of action for opioid analgesics and added 108 classes representing drug products in response to a large term request from the Program for Resistance, Immunology, Surveillance and Modeling of Malaria in Uganda (PRISM) project. The net result is a new version of DrOn, current to May 2016, that represents three major therapeutic classes of drugs and six new mechanisms of action. CONCLUSIONS: A therapeutic indication of a drug product is represented as a therapeutic function in DrOn. Adverse effects of drug products, as well as other therapeutic uses for which the drug product was not designed are dispositions. Our work provides a framework for representing additional therapeutic indications, adverse effects, and uses of drug products beyond their design. Our work also validated our past modeling decisions for specific types of mechanisms of action, namely effects mediated via receptor and/or enzyme binding. DrOn is available at: http://purl.obolibrary.org/obo/dron.owl . A smaller version without NDCs is available at: http://purl.obolibrary.org/obo/dron/dron-lite.owl.

PGE2-EP3 signaling exacerbates intracerebral hemorrhage outcomes in 24-mo-old mice.

With the population aging at an accelerated rate, the prevalence of stroke and financial burden of stroke-related health care costs are expected to continue to increase. Intracerebral hemorrhage (ICH) is a devastating stroke subtype more commonly affecting the elderly population, who display increased mortality and worse functional outcomes compared with younger patients. This study aimed to investigate the contribution of the prostaglandin E2 (PGE2) E prostanoid (EP) receptor subtype 3 in modulating anatomical outcomes and functional recovery following ICH in 24-mo-old mice. EP3 is the most abundant EP receptor in the brain and we have previously shown that signaling through the PGE2-EP3 axis exacerbates ICH outcomes in young mice. Here, we show that EP3 receptor deletion results in 17.9 ± 6.1% less ICH-induced brain injury (P < 0.05) and improves neurological functional recovery (P < 0.01), as identified by lower neurological deficit scores, decreased resting time, and more gross and fine motor movements. Immunohistological staining was performed to investigate possible mechanisms of EP3-mediated neurotoxicity. Identified mechanisms include reduced blood accumulation and modulation of angiogenic and astroglial responses. Using this aged cohort of mice, we have confirmed and extended our previous results in young mice demonstrating the deleterious role of the PGE2-EP3 signaling axis in modulating brain injury and functional recovery after ICH, further supporting the notion of the EP3 receptor as a putative therapeutic avenue for the treatment of ICH.

Prostaglandin E2 EP2 receptor deletion attenuates intracerebral hemorrhage-induced brain injury and improves functional recovery.

Intracerebral hemorrhage (ICH) is a devastating type of stroke characterized by bleeding into the brain parenchyma and secondary brain injury resulting from strong neuroinflammatory responses to blood components. Production of prostaglandin E2 (PGE2) is significantly upregulated following ICH and contributes to this inflammatory response in part through its E prostanoid receptor subtype 2 (EP2). Signaling through the EP2 receptor has been shown to affect outcomes of many acute and chronic neurological disorders; although, not yet explored in the context of ICH. Wildtype (WT) and EP2 receptor knockout (EP2(-/-)) mice were subjected to ICH, and various anatomical and functional outcomes were assessed by histology and neurobehavioral testing, respectively. When compared with age-matched WT controls, EP2(-/-) mice had 41.9 ± 4.7% smaller ICH-induced brain lesions and displayed significantly less ipsilateral hemispheric enlargement and incidence of intraventricular hemorrhage. Anatomical outcomes correlated with improved functional recovery as identified by neurological deficit scoring. Histological staining was performed to begin investigating the mechanisms involved in EP2-mediated neurotoxicity after ICH. EP2(-/-) mice exhibited 45.5 ± 5.8% and 41.4 ± 8.1% less blood and ferric iron accumulation, respectively. Furthermore, significantly less striatal and cortical microgliosis, striatal and cortical astrogliosis, blood-brain barrier breakdown, and peripheral neutrophil infiltration were seen in EP2(-/-) mice. This study is the first to suggest a deleterious role for the PGE2-EP2 signaling axis in modulating brain injury, inflammation, and functional recovery following ICH. Targeting the EP2 G protein-coupled receptor may represent a new therapeutic avenue for the treatment of hemorrhagic stroke.

Genetic deletion of the prostaglandin E2 E prostanoid receptor subtype 3 improves anatomical and functional outcomes after intracerebral hemorrhage.

Intracerebral hemorrhage (ICH) is a stroke subtype associated with high mortality and morbidity. Following ICH, excitotoxicity and inflammation significantly contribute to secondary brain injury and poor outcomes. Prostaglandin E2 (PGE2 ) levels rise locally with insult to the nervous system, and PGE2 is known to modulate these processes mainly through its E prostanoid (EP) receptors, EP1-4. EP receptor subtype 3 (EP3) is the most abundant EP receptor in the brain and we have previously shown that signaling through the PGE2 -EP3 axis exacerbates excitotoxicity and ischemic stroke outcomes. This study aimed to investigate the contribution of this pathway in modulating anatomical outcomes and functional recovery following ICH. Genetic deletion of EP3 resulted in 48.2 ± 7.3% less ICH-induced brain injury (P < 0.005) and improved functional recovery (P < 0.05), as identified by neurological deficit scoring. To start investigating the mechanisms involved in neuroprotection with impaired PGE2 -EP3 signaling, histological staining was performed to evaluate blood and ferric iron accumulation, neuroinflammation, blood-brain barrier dysfunction, and peripheral neutrophil infiltration. After ICH, EP3 knockout mice demonstrated 49.5 ± 8.8% and 42.8 ± 13.1% less blood (P < 0.01) and ferric iron (P < 0.05), respectively. Furthermore, EP3 knockout mice had significantly reduced astrogliosis, microglial activation, blood-brain barrier breakdown, and neutrophil infiltration. Collectively, these results suggest an injurious role for the PGE2 -EP3 signaling axis in modulating brain injury, inflammation, and neurological functional recovery after ICH. Modulation of the PGE2 -EP3 signaling axis may represent a putative therapeutic avenue for the treatment of ICH.