Developing a Mammalian Behaviour Ontology

The use of the Entity + Quality (EQ) model in phenotypic descriptions is dependent on the use of specialised domain ontologies to define the entity under observation. A domain currently lacking a specialised ontology is mammalian behaviour, and so the Mammalian Behaviour Ontology is being constructed to address this. Top-level class distinctions are made between behavioural activities and behavioural functions of individuals, and those between two or more individuals. The ontology is manually developed and encourages contributions from domain experts

Genetic factors regulating lung vasculature and immune cell functions associate with resistance to pneumococcal infection

Streptococcus pneumoniae is an important human pathogen responsible for high mortality and morbidity worldwide. The susceptibility to pneumococcal infections is controlled by as yet unknown genetic factors. To elucidate these factors could help to develop new medical treatments and tools to identify those most at risk. In recent years genome wide association studies (GWAS) in mice and humans have proved successful in identification of causal genes involved in many complex diseases for example diabetes, systemic lupus or cholesterol metabolism. In this study a GWAS approach was used to map genetic loci associated with susceptibility to pneumococcal infection in 26 inbred mouse strains. As a result four candidate QTLs were identified on chromosomes 7, 13, 18 and 19. Interestingly, the QTL on chromosome 7 was located within S. pneumoniae resistance QTL (Spir1) identified previously in a linkage study of BALB/cOlaHsd and CBA/CaOlaHsd F2 intercrosses. We showed that only a limited number of genes encoded within the QTLs carried phenotype-associated polymorphisms (22 genes out of several hundred located within the QTLs). These candidate genes are known to regulate TGFb signalling, smooth muscle and immune cells functions. Interestingly, our pulmonary histopathology and gene expression data demonstrated, lung vasculature plays an important role in resistance to pneumococcal infection. Therefore we concluded that the cumulative effect of these candidate genes on vasculature and immune cells functions as contributory factors in the observed differences in susceptibility to pneumococcal infection. We also propose that TGFbmediated regulation of fibroblast differentiation plays an important role in development of invasive pneumococcal disease.This work was supported by the European Union-funded Pneumopath Project HEALTH-F3-2009-222983. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer-reviewedPublisher Versio

Anatomy ontologies and potential users: bridging the gap.

MOTIVATION: To evaluate how well current anatomical ontologies fit the way real-world users apply anatomy terms in their data annotations. METHODS: Annotations from three diverse multi-species public-domain datasets provided a set of use cases for matching anatomical terms in two major anatomical ontologies (the Foundational Model of Anatomy and Uberon), using two lexical-matching applications (Zooma and Ontology Mapper). RESULTS: Approximately 1500 terms were identified; Uberon/Zooma mappings provided 286 matches, compared to the control and Ontology Mapper returned 319 matches. For the Foundational Model of Anatomy, Zooma returned 312 matches, and Ontology Mapper returned 397. CONCLUSIONS: Our results indicate that for our datasets the anatomical entities or concepts are embedded in user-generated complex terms, and while lexical mapping works, anatomy ontologies do not provide the majority of terms users supply when annotating data. Provision of searchable cross-products for compositional terms is a key requirement for using ontologies.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Genetic background influences tumour development in heterozygous Men1 knockout mice

Multiple endocrine neoplasia type 1 (MEN1), an autosomal dominant disorder caused by MEN1 germline mutations, is characterised by parathyroid, pancreatic and pituitary tumours. MEN1 mutations also cause familial isolated primary hyperparathyroidism (FIHP), a milder condition causing hyperparathyroidism only. Identical mutations can cause either MEN1 or FIHP in different families, thereby implicating a role for genetic modifiers in altering phenotypic expression of tumours. We therefore investigated the effects of genetic background and potential for genetic modifiers on tumour development in adult Men1+/- mice, which develop tumours of the parathyroids, pancreatic islets, anterior pituitary, adrenal cortex and gonads, that had been backcrossed to generate C57BL/6 and 129S6/SvEv congenic strains. A total of 275 Men1+/- mice, aged 5–26 months were macroscopically studied, and this revealed that genetic background significantly influenced the development of pituitary, adrenal and ovarian tumours, which occurred in mice over 12 months of age and more frequently in C57BL/6 females, 129S6/SvEv males and 129S6/SvEv females, respectively. Moreover, pituitary and adrenal tumours developed earlier, in C57BL/6 males and 129S6/SvEv females, respectively, and pancreatic and testicular tumours developed earlier in 129S6/SvEv males. Furthermore, glucagon-positive staining pancreatic tumours occurred more frequently in 129S6/SvEv Men1+/- mice. Whole genome sequence analysis of 129S6/SvEv and C57BL/6 Men1+/- mice revealed >54,000 different variants in >300 genes. These included, Coq7, Dmpk, Ccne2, Kras, Wnt2b, Il3ra and Tnfrsf10a, and qRT-PCR analysis revealed that Kras was significantly higher in pituitaries of male 129S6/SvEv mice. Thus, our results demonstrate that Kras and other genes could represent possible genetic modifiers of Men1

Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework.

Funder: Oxford University | Jesus College, University of OxfordFunder: Joint Biosecurity CentreGlobal and national surveillance of SARS-CoV-2 epidemiology is mostly based on targeted schemes focused on testing individuals with symptoms. These tested groups are often unrepresentative of the wider population and exhibit test positivity rates that are biased upwards compared with the true population prevalence. Such data are routinely used to infer infection prevalence and the effective reproduction number, Rt, which affects public health policy. Here, we describe a causal framework that provides debiased fine-scale spatiotemporal estimates by combining targeted test counts with data from a randomized surveillance study in the United Kingdom called REACT. Our probabilistic model includes a bias parameter that captures the increased probability of an infected individual being tested, relative to a non-infected individual, and transforms observed test counts to debiased estimates of the true underlying local prevalence and Rt. We validated our approach on held-out REACT data over a 7-month period. Furthermore, our local estimates of Rt are indicative of 1-week- and 2-week-ahead changes in SARS-CoV-2-positive case numbers. We also observed increases in estimated local prevalence and Rt that reflect the spread of the Alpha and Delta variants. Our results illustrate how randomized surveys can augment targeted testing to improve statistical accuracy in monitoring the spread of emerging and ongoing infectious disease

Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework.

Funder: Oxford University | Jesus College, University of OxfordFunder: Joint Biosecurity CentreGlobal and national surveillance of SARS-CoV-2 epidemiology is mostly based on targeted schemes focused on testing individuals with symptoms. These tested groups are often unrepresentative of the wider population and exhibit test positivity rates that are biased upwards compared with the true population prevalence. Such data are routinely used to infer infection prevalence and the effective reproduction number, Rt, which affects public health policy. Here, we describe a causal framework that provides debiased fine-scale spatiotemporal estimates by combining targeted test counts with data from a randomized surveillance study in the United Kingdom called REACT. Our probabilistic model includes a bias parameter that captures the increased probability of an infected individual being tested, relative to a non-infected individual, and transforms observed test counts to debiased estimates of the true underlying local prevalence and Rt. We validated our approach on held-out REACT data over a 7-month period. Furthermore, our local estimates of Rt are indicative of 1-week- and 2-week-ahead changes in SARS-CoV-2-positive case numbers. We also observed increases in estimated local prevalence and Rt that reflect the spread of the Alpha and Delta variants. Our results illustrate how randomized surveys can augment targeted testing to improve statistical accuracy in monitoring the spread of emerging and ongoing infectious disease

Interoperability of Statistical Models in Pandemic Preparedness: Principles and Reality

We present "interoperability" as a guiding framework for statistical modelling to assist policy makers asking multiple questions using diverse datasets in the face of an evolving pandemic response. Interoperability provides an important set of principles for future pandemic preparedness, through the joint design and deployment of adaptable systems of statistical models for disease surveillance using probabilistic reasoning. We illustrate this through case studies for inferring spatial-temporal coronavirus disease 2019 (COVID-19) prevalence and reproduction numbers in England